From 67fe0fe36e85065619a7c830bb8a841e6cab5ec4 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sun, 25 Dec 2022 14:53:24 -0500 Subject: [PATCH 001/101] aesthetics: vary num p figure size override --- ...ach Simulated Two Coupled Resonators.ipynb | 29 +++++++++++++-- sim_series_of_experiments.py | 7 +++- simulated_experiment.py | 37 +++++++++++++++++++ 3 files changed, 67 insertions(+), 6 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 3888861..f070f14 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -2015,7 +2015,8 @@ "outputs": [], "source": [ "\"\"\" Vary the number of measurement frequencies / vary num p / vary nump \"\"\"\n", - " \n", + "from sim_series_of_experiments import vary_num_p_with_fixed_freqdiff\n", + " \n", "W1 = approx_width(k = k1_set, m = m1_set, b=b1_set)\n", "if MONOMER:\n", " W = W1\n", @@ -2034,16 +2035,36 @@ "verbose = False # if False, still shows one graph for each dimension\n", "freqdiff = round(W/6,4)\n", "print('freqdiff:', freqdiff)\n", + "\n", + "overlay = False\n", + "figsizeoverride1 = None\n", + "figsizeoverride2 = None\n", + "\n", + "if resonatorsystem == 2: # Monomer: set width, height\n", + " # spectra amplitude & phase\n", + " figsizeoverride1 = (2.1258, 1.3)\n", + " # complex plot\n", + " figsizeoverride2 = (figwidth/2, 1.3)\n", + "elif resonatorsystem == 10: # dimer\n", + " # spectra amplitude & phase\n", + " figsizeoverride1 = (figwidth, 1.45) #1.864736842105263)\n", + " # complex plot\n", + " figsizeoverride2 = (figwidth, 1.48)\n", + "\n", + "if MONOMER:\n", + " overlay = True\n", + "\n", "before = time()\n", "for i in range(1): # don't do repeats at this level.\n", " thisres = vary_num_p_with_fixed_freqdiff( vals_set, noiselevel, \n", - " MONOMER, forceboth,reslist,\n", + " MONOMER, forceboth,reslist = reslist,\n", " minfreq=minfreq, maxfreq = maxfreq,\n", " verbose = verbose, just_res1 = True, \n", - " max_num_p=max_num_p, reslist = reslist,\n", + " max_num_p=max_num_p, \n", " freqdiff = freqdiff,\n", " n=n, # number of frequencies for R^2\n", - " noiselevel= 1, repeats = repeats,\n", + " repeats = repeats,\n", + " figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2,\n", " recalculate_randomness = False)\n", " verbose = False\n", " try:\n", diff --git a/sim_series_of_experiments.py b/sim_series_of_experiments.py index af0719e..d680309 100644 --- a/sim_series_of_experiments.py +++ b/sim_series_of_experiments.py @@ -23,7 +23,9 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, max_num_p = 10, n = 100, # number of frequencies for R^2 freqdiff = .1,just_res1 = False, repeats = 100, - verbose = False,recalculate_randomness=True ): + verbose = False,recalculate_randomness=True, + figsizeoverride1 = None, figsizeoverride2 = None + ): if verbose: print('Running vary_num_p_with_fixed_freqdiff()') @@ -86,7 +88,8 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, thisres = simulated_experiment(drive[p], drive=drive,vals_set = vals_set, noiselevel=noiselevel, MONOMER=MONOMER, repeats=1 , verbose = verbose, forceboth=forceboth,labelcounts = False, - noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra) + noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, + figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) try: # repeated experiments results resultsdf = pd.concat([resultsdf,thisres], ignore_index=True) diff --git a/simulated_experiment.py b/simulated_experiment.py index 2c913db..b0f634a 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -165,6 +165,43 @@ def assert_results_length(results, columns): print('Unequal!') print( "len(flatten(results))", len(flatten(results)) ) print( "len(flatten(columns))", len(flatten(columns)) ) + + +# unscaled_vector = vh[-1] has elements: m1, b1, k1, f1 +def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_set, absval = False ): + [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, True) + m_err = syserr(M1,m1_set, absval) + b_err = syserr(B1,b1_set, absval) + k_err = syserr(K1,k1_set, absval) + sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_set/m1_set), absval) + + print("The Z matrix is ", make_real_iff_real(Zmatrix), \ + ". Its smallest singular value, s_1=", smallest_s, \ + ", corresponds to singular vector\n p\\vec\\hat=(m\\hat, b\\hat, k\\hat, F)=α(", \ + unscaled_vector[0], " kg, ", #M + unscaled_vector[1], "N/(m/s),", #B + unscaled_vector[2], "N/m,", #K + unscaled_vector[3], "N), where α=F_set/", unscaled_vector[3], "=", \ + F_set, "/" , unscaled_vector[3], "=", F_set/unscaled_vector[3], \ + "is a normalization constant obtained from our knowledge of the force amplitude F for a 1D-SVD analysis.", + "Dividing by α allows us to scale the singular vector to yield the modeled parameters vector.", + "Therefore, we obtain m\\hat= ", + M1, " kg, b\\hat=", + B1, " N/(m/s) and k\\hat=", \ + K1, "N/m. The percent errors for each of these is", \ + m_err, "%,", \ + b_err, "%, and", \ + k_err, "%, respectively.", \ + "Each of these is within ", \ + max([abs(err) for err in [m_err, b_err, k_err]]), \ + "% of the correct values for m, b, and k.", \ + "We also see that the recovered value √(k ̂/m ̂ )=", + np.sqrt(K1/M1), "rad/s is more accurate than the individually recovered values for mass and spring stiffness;", + "this is generally true. ", + "The percent error for √(k ̂/m ̂ ) compared to √(k_set/m_set ) is", + sqrtkoverm_err, "%. This high accuracy likely arises because we choose frequency ω_a at the peak amplitude." + ) + """ demo indicates that the data should be plotted without ticks""" def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, forceboth, From ff8328c3d4ce851f56aab30340b7c971ed681b3e Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 26 Dec 2022 00:52:02 -0500 Subject: [PATCH 002/101] Aesthetics, resonatorsystem -3 --- ...ach Simulated Two Coupled Resonators.ipynb | 28 +++++++++++-------- sim_series_of_experiments.py | 4 ++- simulated_experiment.py | 10 +++++-- 3 files changed, 27 insertions(+), 15 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index d7ba7de..98fe4bd 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -199,7 +199,7 @@ "maxfreq = 1.8\n", "noiselevel = 200 # increased 2022-11-16 for demo Fig 1.\n", "\"\"\"\n", - "\"\"\"\n", + "\n", "### medium damped monomer -- use for Fig 4, picking frequencies\n", "resonatorsystem = -3\n", "m1_set = 4\n", @@ -210,7 +210,7 @@ "minfreq = 1.4\n", "maxfreq = 1.8\n", "noiselevel = 1\n", - "\"\"\"\n", + "\n", "\n", "\"\"\"## somewhat heavily damped monomer\n", "MONOMER = True\n", @@ -2134,7 +2134,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "print('Noiselevel: ' + str(noiselevel))\n", @@ -2144,26 +2146,28 @@ "co2 = 'C1'\n", "co3 = 'C2'\n", "\n", - "plt.figure()\n", + "figsize = (figwidth, 1.48)\n", + "\n", + "plt.figure(figsize=figsize)\n", "#plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_3D'], symb, alpha = .1, color = co3 )\n", "#plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_2D'], symb, alpha = .1, color = co2)\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_1D'], symb, alpha = .1, color = co1)\n", "#plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_3D'], label='3D', color = co3)\n", "#plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_2D'], label='2D', color = co2)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_1D'], label='1D', color = co1)\n", - "plt.legend()\n", + "text_color_legend()\n", "#plt.gca().set_yscale('log')\n", "plt.xlabel('num frequency points')\n", "plt.ylabel('Avg err (%)')\n", "\n", - "plt.figure()\n", + "plt.figure(figsize=figsize)\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_3D'], symb, alpha = .1, color = co3 )\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_2D'], symb, alpha = .1, color = co2)\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_1D'], symb, alpha = .1, color = co1)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_3D'], label='3D', color = co3)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_2D'], label='2D', color = co2)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_1D'], label='1D', color = co1)\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.gca().set_yscale('log')\n", "plt.xlabel('num frequency points')\n", "plt.ylabel('Avg err (%)')\n", @@ -2175,7 +2179,7 @@ "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['K1syserr%_3D'], label='3D', color = co3)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['K1syserr%_2D'], label='2D', color = co2)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['K1syserr%_1D'], label='1D', color = co1)\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.gca().set_yscale('log')\n", "plt.xlabel('num frequency points')\n", "plt.ylabel('k1 syserr (%)')\n", @@ -2183,7 +2187,7 @@ "plt.figure()\n", "#plt.plot(resultsvarynump['R1Ampsyserr%mean(priv)'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , label='2D')\n", "plt.plot(resultsvarynump['R1Ampsyserr%mean(priv)'],resultsvarynump['K1syserr%_1D'] , symb, alpha = .3, label='1D')\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.gca().set_yscale('log')\n", "plt.xlabel('R1 Amp syserr mean (priv) (%)')\n", "plt.ylabel('k1 syserr (%)')\n", @@ -2191,7 +2195,7 @@ "plt.figure()\n", "#plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , label='2D')\n", "plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['K1syserr%_1D'], symb, alpha = .3, label='1D')\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.gca().set_yscale('log')\n", "plt.xlabel('R1 phase diff mean (privileged)')\n", "plt.ylabel('k1 syserr (%)')\n", @@ -2207,7 +2211,7 @@ "plt.gca().set_xscale('log')\n", "plt.xlabel('meanSNR_R1')\n", "plt.ylabel('Avg err (%)')\n", - "plt.legend()\n", + "text_color_legend()\n", "\n", "if not MONOMER:\n", " plt.figure()\n", @@ -2403,7 +2407,7 @@ }, "outputs": [], "source": [ - "import matplotlib as mpl #$$$$$\n", + "import matplotlib as mpl \n", "\n", "alpha = .01\n", "plotlog = True\n", diff --git a/sim_series_of_experiments.py b/sim_series_of_experiments.py index d680309..f87f959 100644 --- a/sim_series_of_experiments.py +++ b/sim_series_of_experiments.py @@ -24,6 +24,7 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, n = 100, # number of frequencies for R^2 freqdiff = .1,just_res1 = False, repeats = 100, verbose = False,recalculate_randomness=True, + overlay = False, context = 'paper', resonatorsystem = None, figsizeoverride1 = None, figsizeoverride2 = None ): if verbose: @@ -88,7 +89,8 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, thisres = simulated_experiment(drive[p], drive=drive,vals_set = vals_set, noiselevel=noiselevel, MONOMER=MONOMER, repeats=1 , verbose = verbose, forceboth=forceboth,labelcounts = False, - noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, + noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, overlay=overlay, + context = context, resonatorsystem = resonatorsystem, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) try: # repeated experiments results diff --git a/simulated_experiment.py b/simulated_experiment.py index 58c7c8a..79e3f2f 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -168,13 +168,19 @@ def assert_results_length(results, columns): # unscaled_vector = vh[-1] has elements: m1, b1, k1, f1 -def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_set, absval = False ): +def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_set, freqs = None, absval = False ): [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, True) m_err = syserr(M1,m1_set, absval) b_err = syserr(B1,b1_set, absval) k_err = syserr(K1,k1_set, absval) sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_set/m1_set), absval) + if freqs: + print("Using", len(freqs), "frequencies for SVD analysis, namely", + freqs, + "rad/s." ) + + print("The Z matrix is ", make_real_iff_real(Zmatrix), \ ". Its smallest singular value, s_1=", smallest_s, \ ", corresponds to singular vector\n p\\vec\\hat=(m\\hat, b\\hat, k\\hat, F)=α(", \ @@ -375,7 +381,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force if verbose and first: print("1D:") if MONOMER: - describe_monomer_results(Zmatrix, s[-1], vh[-1], M1, B1, K1, vals_set) + describe_monomer_results(Zmatrix, s[-1], vh[-1], M1, B1, K1, vals_set, freqs = drive[p]) plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay = overlay, context = context, saving = saving, labelname = '1D', demo=demo, From 63e62c45e248ea9e0791529962765676b71e1453 Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 26 Dec 2022 00:57:01 -0500 Subject: [PATCH 003/101] fix --- simulated_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/simulated_experiment.py b/simulated_experiment.py index 79e3f2f..859e54b 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -175,7 +175,7 @@ def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, v k_err = syserr(K1,k1_set, absval) sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_set/m1_set), absval) - if freqs: + if freqs is not None: print("Using", len(freqs), "frequencies for SVD analysis, namely", freqs, "rad/s." ) From 07d45ae15b985939e83958791c7c60ef130de6fd Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 26 Dec 2022 01:06:42 -0500 Subject: [PATCH 004/101] aesthetics for resonatorsystem -3 --- ...braic Approach Simulated Two Coupled Resonators.ipynb | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 98fe4bd..090fd28 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -2069,7 +2069,12 @@ "figsizeoverride1 = None\n", "figsizeoverride2 = None\n", "\n", - "if resonatorsystem == 2: # Monomer: set width, height\n", + "if resonatorsystem == -3: # Monomer: set width, height\n", + " # spectra amplitude & phase\n", + " figsizeoverride1 = (2.1258, 1.3)\n", + " # complex plot\n", + " figsizeoverride2 = (figwidth/2, 1.3)\n", + "elif resonatorsystem == 2: # Monomer: set width, height\n", " # spectra amplitude & phase\n", " figsizeoverride1 = (2.1258, 1.3)\n", " # complex plot\n", @@ -2093,6 +2098,8 @@ " freqdiff = freqdiff,\n", " n=n, # number of frequencies for R^2\n", " repeats = repeats,\n", + " overlay = overlay, saving = saving,\n", + " context = 'paper', resonatorsystem = resonatorsystem,\n", " figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2,\n", " recalculate_randomness = False)\n", " verbose = False\n", From ae5e2abc609cc795ae4efa271d9572323237cbfc Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 29 Dec 2022 11:26:40 -0500 Subject: [PATCH 005/101] Use **kwargs --- sim_series_of_experiments.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/sim_series_of_experiments.py b/sim_series_of_experiments.py index f87f959..91351e0 100644 --- a/sim_series_of_experiments.py +++ b/sim_series_of_experiments.py @@ -24,8 +24,7 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, n = 100, # number of frequencies for R^2 freqdiff = .1,just_res1 = False, repeats = 100, verbose = False,recalculate_randomness=True, - overlay = False, context = 'paper', resonatorsystem = None, - figsizeoverride1 = None, figsizeoverride2 = None + **kwargs ): if verbose: print('Running vary_num_p_with_fixed_freqdiff()') @@ -89,9 +88,8 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, thisres = simulated_experiment(drive[p], drive=drive,vals_set = vals_set, noiselevel=noiselevel, MONOMER=MONOMER, repeats=1 , verbose = verbose, forceboth=forceboth,labelcounts = False, - noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, overlay=overlay, - context = context, resonatorsystem = resonatorsystem, - figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) + noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, **kwargs + ) try: # repeated experiments results resultsdf = pd.concat([resultsdf,thisres], ignore_index=True) From aa16f3847f18fb7d439b967d8fb0827cee9f4ab7 Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 29 Dec 2022 16:54:25 -0500 Subject: [PATCH 006/101] aesthetics: bigger datapoints in spectra --- resonator_plotting.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/resonator_plotting.py b/resonator_plotting.py index af16147..ecf2cc6 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -71,8 +71,9 @@ def set_format(): 'size' : 7} mpl.rc('font', **font) plt.rcParams.update({'font.size': 7}) ## Nature Physics wants font size 5 to 7. - - + #plt.rcParams.update({ + # "pdf.use14corefonts": True # source: https://github.com/matplotlib/matplotlib/issues/21893 + #}) # findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial #plt.rcParams["length"] = 3 plt.rcParams['axes.linewidth'] = 0.7 @@ -89,6 +90,7 @@ def set_format(): plt.rcParams['ytick.minor.visible'] = True plt.rcParams['xtick.minor.visible'] = True + plt.minorticks_on() plt.rcParams['axes.spines.top'] = True plt.rcParams['axes.spines.right'] = True # source: https://physicalmodelingwithpython.blogspot.com/2015/06/making-plots-for-publication.html @@ -297,7 +299,7 @@ def plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, measurementdf, K1, figsize = (figwidth*.6, figratio * figwidth*.8 ) else: figsize = (figwidth, figratio * figwidth ) - s = 3 + s = 25 # increased from 3, 2022-12-29 bigcircle = 30 amplabel = '$A\;$(m)' phaselabel = '$\delta\;(\pi)$' From 6b92baef37a76ab563ebc49381a41a63f05eea62 Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 29 Dec 2022 16:54:43 -0500 Subject: [PATCH 007/101] clean up: hide warnings --- resonatorstats.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/resonatorstats.py b/resonatorstats.py index 75cb6c0..2772cbd 100644 --- a/resonatorstats.py +++ b/resonatorstats.py @@ -7,9 +7,11 @@ import numpy as np import matplotlib.pyplot as plt +import warnings def syserr(x_found,x_set, absval = True): - se = 100*(x_found-x_set)/x_set + with warnings.simplefilter('ignore'): + se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: From e98b956595e646857e258edef66176c139a4a395 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 30 Dec 2022 14:25:08 -0500 Subject: [PATCH 008/101] Aesthetics: 2 frequency sweep --- ...ach Simulated Two Coupled Resonators.ipynb | 297 +++++++++++++++--- 1 file changed, 251 insertions(+), 46 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 090fd28..95bb4d5 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -2062,18 +2062,21 @@ "# Ran 100 times in 786.946 sec with verbose = False\n", "repeats = 100\n", "verbose = False # if False, still shows one graph for each dimension\n", - "freqdiff = round(W/6,4)\n", + "freqdiff = round(W/10,4)\n", "print('freqdiff:', freqdiff)\n", "\n", - "overlay = False\n", - "figsizeoverride1 = None\n", - "figsizeoverride2 = None\n", - "\n", + "if MONOMER:\n", + " overlay = True\n", + "else:\n", + " overlay = False\n", + " \n", + "figsizeoverride1 = None # default\n", + "figsizeoverride2 = None # default\n", "if resonatorsystem == -3: # Monomer: set width, height\n", " # spectra amplitude & phase\n", - " figsizeoverride1 = (2.1258, 1.3)\n", + " figsizeoverride1 = (2.1258, 1.4)\n", " # complex plot\n", - " figsizeoverride2 = (figwidth/2, 1.3)\n", + " figsizeoverride2 = (figwidth/2, 1.4)\n", "elif resonatorsystem == 2: # Monomer: set width, height\n", " # spectra amplitude & phase\n", " figsizeoverride1 = (2.1258, 1.3)\n", @@ -2085,9 +2088,6 @@ " # complex plot\n", " figsizeoverride2 = (figwidth, 1.48)\n", "\n", - "if MONOMER:\n", - " overlay = True\n", - "\n", "before = time()\n", "for i in range(1): # don't do repeats at this level.\n", " thisres = vary_num_p_with_fixed_freqdiff( vals_set, noiselevel, \n", @@ -2112,6 +2112,7 @@ "display(resultsvarynump.transpose())\n", "\n", "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean()\n", + "datestr = datestring()\n", "\n", "verbose = False\n", "\n", @@ -2126,7 +2127,16 @@ " n = 100, # number of frequencies for R^2\n", " freqdiff = .1,just_res1 = False, repeats = 100,\n", " verbose = False,recalculate_randomness=True ):\n", - "\"\"\"" + "\"\"\";" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resultsvarynumpmean.SNR_R1_f1[0]" ] }, { @@ -2153,7 +2163,11 @@ "co2 = 'C1'\n", "co3 = 'C2'\n", "\n", + "reps = int(len(resultsvarynump) / len(resultsvarynumpmean))\n", + "\n", "figsize = (figwidth, 1.48)\n", + "if resonatorsystem == -3: # Monomer:\n", + " figsize = (figwidth, 1.4)\n", "\n", "plt.figure(figsize=figsize)\n", "#plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_3D'], symb, alpha = .1, color = co3 )\n", @@ -2166,6 +2180,11 @@ "#plt.gca().set_yscale('log')\n", "plt.xlabel('num frequency points')\n", "plt.ylabel('Avg err (%)')\n", + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"numpvsE,1D,\" + datestr + ', noise'+ str(noiselevel)\n", + " savefigure(savename)\n", + "plt.show()\n", "\n", "plt.figure(figsize=figsize)\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_3D'], symb, alpha = .1, color = co3 )\n", @@ -2176,8 +2195,16 @@ "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_1D'], label='1D', color = co1)\n", "text_color_legend()\n", "plt.gca().set_yscale('log')\n", - "plt.xlabel('num frequency points')\n", + "#plt.xlabel('num frequency points')\n", + "plt.xlabel('number of frequency points')\n", "plt.ylabel('Avg err (%)')\n", + "plt.tight_layout()\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"numpvsE,log,\" + datestr + ', noise'+ str(noiselevel)\n", + " savefigure(savename)\n", + " resultsvarynump[['num frequency points','avgsyserr%_1D','avgsyserr%_2D','avgsyserr%_3D']].to_csv(savename + '.csv')\n", + "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['K1syserr%_3D'], symb, alpha = .3 , color = co3)\n", @@ -2343,8 +2370,7 @@ "printtime(repeats, before, after) \n", "display(resultsvarynoiselevel.transpose())\n", "\n", - "resultsvarynoiselevelmean = resultsvarynoiselevel.groupby(by=['noiselevel'], ).mean()\n", - "resultsvarynoiselevelmean[resultsvarynoiselevelmean.index.name] = resultsvarynoiselevelmean.index\n", + "resultsvarynoiselevelmean = resultsvarynoiselevel.groupby(by=['noiselevel'],as_index=False ).mean()\n", "\n", "# initialize 95% confidence interval columns\n", "for column in ['E_lower_1D', 'E_upper_1D','E_lower_2D', 'E_upper_2D','E_lower_3D', 'E_upper_3D']:\n", @@ -2353,7 +2379,7 @@ "dimensions = ['1D', '2D', '3D']\n", " \n", "for noise in noises:\n", - " for D in dimensions:\n", + " for D in dimensions: # ASE stands for average systematic err\n", " #plt.hist(resultsvarynoiselevel[resultsvarynoiselevel['noiselevel']== noise]['avgsyserr%_1D'])\n", " ASE = resultsvarynoiselevel[resultsvarynoiselevel['noiselevel']== noise]['avgsyserr%_' + D]\n", " ASE = np.sort(ASE)\n", @@ -4183,7 +4209,7 @@ "metadata": {}, "outputs": [], "source": [ - "stophere" + "stophere # Sweep TWO frequencies" ] }, { @@ -4270,38 +4296,46 @@ " # 30 frequencies\n", " # Ran 1 times in 15.68 sec\"\"\"\n", "\n", - "# 30 is small. 200 is big.\n", - "numfreq = 30\n", - "repeats = 6\n", - "noiselevel = 1\n", + "if False:\n", + " # 30 is small. 200 is big.\n", + " numfreq = 200\n", + " repeats = 6\n", + " noiselevel = 1\n", + "\n", + " thisdrive, _ = create_drive_arrays(vals_set, MONOMER, forceboth, n=numfreq, \n", + " morefrequencies = morefrequencies, includefreqs = reslist,\n", + " minfreq = minfreq, maxfreq = maxfreq, \n", + " staywithinlims = False,\n", + " callmakemore = False,\n", + " verbose = verbose)\n", "\n", - "thisdrive, _ = create_drive_arrays(vals_set, MONOMER, forceboth, n=numfreq, \n", - " morefrequencies = morefrequencies, includefreqs = reslist,\n", - " minfreq = minfreq, maxfreq = maxfreq, \n", - " staywithinlims = False,\n", - " callmakemore = False,\n", - " verbose = verbose)\n", + " before = time()\n", "\n", - "before = time()\n", + " for i in range(1):\n", + " thisres = sweep_freq_pair(drive=thisdrive, vals_set = vals_set, noiselevel = noiselevel, freq3 = None, \n", + " MONOMER = MONOMER, forceboth=forceboth, repeats = repeats)\n", + " try:\n", + " resultsdfsweep2freqorig = pd.concat([resultsdfsweep2freqorig,thisres], ignore_index=True)\n", + " except:\n", + " resultsdfsweep2freqorig = thisres\n", + " after = time()\n", + " print(len(thisdrive), 'frequencies')\n", + " printtime(repeats, before, after)\n", + " # Ran 1 times in 6.624 sec\n", + " # Ran 1 times in 4.699 sec\n", + " # 30 frequencies Ran 1 times in 15.898 sec\n", + " # 231 frequencies Ran 1 times in 273.113 sec\n", + " # 291 frequencies Ran 1 times in 493.772 sec\n", + " # 33 frequencies Ran 6 times in 250.501 sec\n", + " # 201 frequencies Ran 6 times in 4358.699 sec (72.644983333 hours)\n", "\n", - "for i in range(1):\n", - " thisres = sweep_freq_pair(drive=thisdrive, vals_set = vals_set, noiselevel = noiselevel, freq3 = None, \n", - " MONOMER = MONOMER, forceboth=forceboth, repeats = repeats)\n", - " try:\n", - " resultsdfsweep2freqorig = pd.concat([resultsdfsweep2freqorig,thisres], ignore_index=True)\n", - " except:\n", - " resultsdfsweep2freqorig = thisres\n", - "after = time()\n", - "print(len(thisdrive), 'frequencies')\n", - "printtime(repeats, before, after)\n", - "# Ran 1 times in 6.624 sec\n", - "# Ran 1 times in 4.699 sec\n", - "# 30 frequencies Ran 1 times in 15.898 sec\n", - "# 231 frequencies Ran 1 times in 273.113 sec\n", - "# 291 frequencies Ran 1 times in 493.772 sec\n", - "# 33 frequencies Ran 6 times in 250.501 sec\n", + " datestr = datestring()\n", + " resultsdfsweep2freqorig.to_csv(\"sys\" + str(resonatorsystem) + ',2freq,' + datestr + '.csv')\n", + "else:\n", + " saveddf = 'sys-3,2freq,2022-12-29 20;03;50.csv'\n", + " resultsdfsweep2freqorig = pd.read_csv(saveddf)\n", "\n", - "resultsdfsweep2freqorigmean = resultsdfsweep2freqorig.groupby(by=['Freq1', 'Freq2'],as_index=False).mean()\n", + "resultsdfsweep2freqorigmean = resultsdfsweep2freqorig.groupby(by=['Freq1', 'Freq2'],as_index=False).mean(numeric_only=True)\n", "\n", "\n", "## remove diagonal parameters from resultsdf for the following plots.\n", @@ -4309,6 +4343,13 @@ "resultsdfmean = resultsdfsweep2freqorigmean[resultsdfsweep2freqorig.Difference != 0]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -4318,11 +4359,29 @@ "list(resultsdf.columns)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "round(resultsdfmean.Freq1.min(),1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "round(resultsdfmean.Freq1.max(),1)" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [], "source": [ @@ -4404,7 +4463,13 @@ " plt.sca(ax)\n", " ax.axis('equal');\n", " #plt.xticks([res1, res2])\n", - " plt.yticks([res1, res2])\n", + " if res1 == res2:\n", + " plt.yticks([round(resultsdfmean.Freq1.min(),1), round(res1,2), round(resultsdfmean.Freq1.max(),1)])\n", + " else:\n", + " try:\n", + " plt.yticks([res1, res2])\n", + " except:\n", + " pass\n", "\n", "fig, ((ax1, ax2, ax7), (ax3, ax4, ax4b), (ax5, ax6, ax6b)) = plt.subplots(3, 3, figsize=figsize)\n", "\n", @@ -4535,6 +4600,8 @@ "plt.gca().set_xscale('log')\n", "plt.gca().set_yscale('log')\n", "plt.gca().axis('equal');\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "plt.figure()\n", "if MONOMER:\n", @@ -4547,9 +4614,12 @@ "plt.gca().set_xscale('log')\n", "plt.gca().set_yscale('log')\n", "plt.gca().axis('equal');\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "## I cut out f1 = f2\n", "plt.figure()\n", + "print('I cut out f1 = f2.')\n", "if MONOMER:\n", " plt.loglog(resultsdf.SNR_R1_f1, resultsdf['maxsyserr%_2D'], '.', alpha=.5)\n", " plt.xlabel('SNR_R1_f1')\n", @@ -4558,6 +4628,8 @@ " plt.xlabel('SNR_R2_f1') \n", "plt.ylabel('maxsyserr_2D (%)')\n", "#plt.ylim(ymin=0, ymax=maxsyserr_to_plot)\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "plt.figure()\n", "if MONOMER:\n", @@ -4567,6 +4639,8 @@ " plt.loglog(resultsdf.minSNR_R2, resultsdf['avgsyserr%_2D'], '.', alpha=.5)\n", " plt.xlabel('minSNR_R2') \n", "plt.ylabel('avgsyserr%_2D')\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "plt.figure()\n", "if MONOMER:\n", @@ -4576,6 +4650,8 @@ " plt.loglog(resultsdf.maxSNR_R2, resultsdf['avgsyserr%_2D'], '.', alpha=.5)\n", " plt.xlabel('maxSNR_R2') \n", "plt.ylabel('avgsyserr%_2D')\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "plt.figure()\n", "if MONOMER:\n", @@ -4585,6 +4661,8 @@ " plt.loglog(resultsdf.meanSNR_R2, resultsdf['avgsyserr%_2D'], '.', alpha=.5)\n", " plt.xlabel('meanSNR_R2') \n", "plt.ylabel('avgsyserr%_2D')\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "\n", "plt.figure()\n", @@ -4595,6 +4673,133 @@ "#plt.xticks([res1, res2]);\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(figwidth/2, 1.3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# *****\n", + "figsize = (figwidth/2, 1.3)\n", + "\n", + "plt.figure(figsize = (1.555,1.3) )\n", + "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", + "myheatmap(SSgrid, \"log average error\", vmax=1, cmap='magma_r'); \n", + "plt.title('1D-SVD')\n", + "plt.axis('equal')\n", + "plt.tight_layout()\n", + "\n", + "plt.figure(figsize = (1.555,1.3) )\n", + "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_2D').sort_index(axis = 0, ascending = False)\n", + "myheatmap(SSgrid, \"log average error\", vmax=1, cmap='magma_r'); \n", + "plt.title('2D-SVD')\n", + "plt.axis('equal')\n", + "plt.tight_layout()\n", + "\n", + "X = resultsdfmeanbyfreq1['Freq1'] \n", + "\n", + "plt.figure(figsize=figsize)\n", + "#plt.plot(resultsdf.Freq1, resultsdf['avgsyserr%_3D'] , '.', alpha=.008, color = co3)\n", + "plt.plot(resultsdf.Freq1, resultsdf['avgsyserr%_1D'] , '.', alpha=.008, color = co1)\n", + "plt.plot(resultsdf.Freq1, resultsdf['avgsyserr%_2D'] , '.', alpha=.008, color = co2)\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", + "#plt.ylim(ymin=0, ymax=maxsyserr_to_plot)\n", + "plt.xlabel('Freq1 (rad/s)')\n", + "plt.ylabel('avg err (%)')\n", + "plt.yscale('log')\n", + "#plt.xticks([res1, res2]);\n", + "plt.tight_layout()\n", + "\n", + "\n", + "\n", + "plt.figure(figsize=figsize)\n", + "axa=plt.gca()\n", + "colors = [co1, co2, co3]\n", + "dimensions = ['1D', '2D', '3D']\n", + "if resonatorsystem == -3:\n", + " dim = ['1D', '2D']\n", + "else:\n", + " dim = dimensions\n", + "resultsdfmeanbyfreq1 = resultsdf.groupby(by=['Freq1'], as_index=False).mean(numeric_only =True)\n", + "\n", + "# initialize 95% confidence interval columns\n", + "for column in ['E_lower_1D', 'E_upper_1D','E_95range_1D','E_log95range_1D'\\\n", + " 'E_lower_2D', 'E_upper_2D', 'E_95range_2D', 'E_log95range_2D'\\\n", + " 'E_lower_3D', 'E_upper_3D', 'E_95range_3D','E_log95range_3D']:\n", + " resultsdfmeanbyfreq1[column] = np.nan\n", + " \n", + "for f1 in resultsdfmeanbyfreq1['Freq1']:\n", + " for D in dimensions: # ASE stands for average systematic err\n", + " #plt.hist(resultsvaryFreq2[resultsvaryFreq2['Freq2']== f1]['avgsyserr%_1D'])\n", + " ASE = resultsdf[resultsdf['Freq1']== f1]['avgsyserr%_' + D]\n", + " ASE = np.sort(ASE)\n", + " halfalpha = (1 - .95)/2\n", + " ## literally select the 95% confidence interval by tossing out the top 2.5% and the bottom 2.5% \n", + " ## I could do a weighted average to work better with selecting the top 2.5% and bottom 2.5%\n", + " ## But perhaps this is good enough for an estimate. It's ideal if I do 80 measurements.\n", + " lowerbound = np.mean([ASE[int(np.floor(halfalpha*len(ASE)))], ASE[int(np.ceil(halfalpha*len(ASE)))]])\n", + " #print(lowerbound)\n", + " upperbound = np.mean([ASE[-int(np.floor(halfalpha*len(ASE))+1)],ASE[-int(np.ceil(halfalpha*len(ASE))+1)]])\n", + " resultsdfmeanbyfreq1.loc[resultsdfmeanbyfreq1['Freq1']== f1,'E_95range_'+ D] = upperbound - lowerbound\n", + " resultsdfmeanbyfreq1.loc[resultsdfmeanbyfreq1['Freq1']== f1,'E_log95range_'+ D] = np.log10(upperbound) - np.log10(lowerbound)\n", + " resultsdfmeanbyfreq1.loc[resultsdfmeanbyfreq1['Freq1']== f1,'E_lower_'+ D] = lowerbound\n", + " resultsdfmeanbyfreq1.loc[resultsdfmeanbyfreq1['Freq1']== f1,'E_upper_' + D] = upperbound\n", + "\n", + "\n", + "for i in range(len(dim)): \n", + " Yhigh = resultsdfmeanbyfreq1['E_upper_' + dim[i]]\n", + " Ylow = resultsdfmeanbyfreq1['E_lower_' + dim[i]] \n", + " plt.plot(X, Yhigh, color = colors[i], alpha = .3, linewidth=.3)\n", + " plt.plot(X, Ylow, color = colors[i], alpha = .3, linewidth=.3)\n", + " axa.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", + "#plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_3D'], color = co3, label='3D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", + "plt.yscale('log')\n", + "text_color_legend()\n", + "plt.xlabel('Freq1 (rad/s)')\n", + "plt.ylabel('avg err (%)')\n", + "plt.tight_layout()\n", + "\n", + "plt.figure(figsize = figsize)\n", + "plt.axvline(res1, color='gray', lw=0.5)\n", + "#plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_3D'], color = co3, label='3D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", + "text_color_legend()\n", + "#plt.yscale('log')\n", + "plt.ylim(ymin=0)\n", + "plt.xlabel('Freq1 (rad/s)')\n", + "plt.ylabel('avg err (%)');\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('The average error varies over two orders of magnitude.')\n", + "plt.plot(resultsdfmeanbyfreq1.Freq1,resultsdfmeanbyfreq1.E_log95range_1D)\n", + "plt.plot(resultsdfmeanbyfreq1.Freq1,resultsdfmeanbyfreq1.E_log95range_2D)\n", + "\n", + "plt.xlabel('Freq1 (rad/s)')\n", + "plt.ylabel('Number of orders of magnitude');" + ] + }, { "cell_type": "code", "execution_count": null, From c2bd84790a311902606c8cdc7062eadb335b6d54 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 30 Dec 2022 15:19:02 -0500 Subject: [PATCH 009/101] aesthetics: sweep 2 freq --- ...ach Simulated Two Coupled Resonators.ipynb | 81 ++++++++++++++----- resonator_plotting.py | 2 + 2 files changed, 61 insertions(+), 22 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 95bb4d5..496659c 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -4209,7 +4209,7 @@ "metadata": {}, "outputs": [], "source": [ - "stophere # Sweep TWO frequencies" + "stophere # Sweep TWO frequencies / Sweep 2 freq / 2freq" ] }, { @@ -4690,23 +4690,39 @@ "source": [ "# *****\n", "figsize = (figwidth/2, 1.3)\n", + "datestr = datestring()\n", "\n", - "plt.figure(figsize = (1.555,1.3) )\n", - "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", + "SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", - "myheatmap(SSgrid, \"log average error\", vmax=1, cmap='magma_r'); \n", + "SSgrid2D=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_2D').sort_index(axis = 0, ascending = False)\n", + "vmin = min(SSgrid1D.min().min(), SSgrid2D.min().min()) # use same scale for both\n", + "\n", + "plt.figure(figsize = (1.555,1.3) )\n", + "myheatmap(SSgrid1D, \"log average error\", vmin=vmin, vmax=1, cmap='magma_r'); \n", "plt.title('1D-SVD')\n", + "plt.ylabel('$\\omega_a$ (rad/s)')\n", + "plt.xlabel('$\\omega_b$ (rad/s)')\n", "plt.axis('equal')\n", "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"1D2freqheatmap,\" + datestr\n", + " savefigure(savename)\n", + "plt.show()\n", "\n", "plt.figure(figsize = (1.555,1.3) )\n", - "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", - " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_2D').sort_index(axis = 0, ascending = False)\n", - "myheatmap(SSgrid, \"log average error\", vmax=1, cmap='magma_r'); \n", + "myheatmap(SSgrid2D, \"log average error\", vmin=vmin, vmax=1, cmap='magma_r'); \n", "plt.title('2D-SVD')\n", + "plt.ylabel('$\\omega_a$ (rad/s)')\n", + "plt.xlabel('$\\omega_b$ (rad/s)')\n", "plt.axis('equal')\n", "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"2D2freqheatmap,\" + datestr\n", + " savefigure(savename)\n", + "plt.show()\n", "\n", + "resultsdfmeanbyfreq1 = resultsdf.groupby(by=['Freq1'], as_index=False).mean(numeric_only =True)\n", "X = resultsdfmeanbyfreq1['Freq1'] \n", "\n", "plt.figure(figsize=figsize)\n", @@ -4716,7 +4732,7 @@ "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", "#plt.ylim(ymin=0, ymax=maxsyserr_to_plot)\n", - "plt.xlabel('Freq1 (rad/s)')\n", + "plt.xlabel('$\\omega_a$ (rad/s)')\n", "plt.ylabel('avg err (%)')\n", "plt.yscale('log')\n", "#plt.xticks([res1, res2]);\n", @@ -4732,7 +4748,6 @@ " dim = ['1D', '2D']\n", "else:\n", " dim = dimensions\n", - "resultsdfmeanbyfreq1 = resultsdf.groupby(by=['Freq1'], as_index=False).mean(numeric_only =True)\n", "\n", "# initialize 95% confidence interval columns\n", "for column in ['E_lower_1D', 'E_upper_1D','E_95range_1D','E_log95range_1D'\\\n", @@ -4769,9 +4784,10 @@ "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", "plt.yscale('log')\n", "text_color_legend()\n", - "plt.xlabel('Freq1 (rad/s)')\n", + "plt.xlabel('$\\omega_a$ (rad/s)')\n", "plt.ylabel('avg err (%)')\n", "plt.tight_layout()\n", + "plt.show()\n", "\n", "plt.figure(figsize = figsize)\n", "plt.axvline(res1, color='gray', lw=0.5)\n", @@ -4781,9 +4797,17 @@ "text_color_legend()\n", "#plt.yscale('log')\n", "plt.ylim(ymin=0)\n", - "plt.xlabel('Freq1 (rad/s)')\n", + "plt.title('$\\omega_b = $' + str(round(resultsdfmean.Freq2.min(),1)) + ' to ' \n", + " + str(round(resultsdfmean.Freq2.max(),1)) + ' rad/s',\n", + " loc='right')\n", + "plt.xlabel('$\\omega_a$ (rad/s)')\n", "plt.ylabel('avg err (%)');\n", - "plt.tight_layout()" + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"2freqavgerr,\" + datestr\n", + " savefigure(savename)\n", + " resultsdfmeanbyfreq1[['Freq1','log avgsyserr%_1D','log avgsyserr%_2D', 'log avgsyserr%_3D']].to_csv(savename + '.csv')\n", + "plt.show()" ] }, { @@ -4831,12 +4855,12 @@ " bardisplaylabels = ['K1', 'K2', 'K12','B1','B2','FD','M1','M2','avg', 'rms']\n", "elemslist_2D = [el + '_2D' for el in elemslist]\n", "elemslist = [el+ '_1D' for el in elemslist]\n", - "llist1 = ['Freq1', 'Freq2', 'avgsyserr%_1D-avgsyserr%_2D', 'rmssyserr%_1D', 'rmssyserr%_2D'] + elemslist + elemslist_2D\n", + "llist1 = ['Freq1', 'Freq2', 'R1_phase_noiseless1', 'R1_phase_noiseless2', 'avgsyserr%_1D-avgsyserr%_2D', 'rmssyserr%_1D', 'rmssyserr%_2D'] + elemslist + elemslist_2D\n", "syserrlist = [w + 'syserr%' for w in bardisplaylabels]\n", "syserrlist_2D = [w + '_2D' for w in syserrlist]\n", "syserrlist = [w + '_1D' for w in syserrlist]\n", "\n", - "min_df = resultsdf.iloc[resultsdf['avgsyserr%_1D-avgsyserr%_2D'].argmin()] # most likely to be 1d nullspace\n", + "min_df = resultsdfsweep2freqorigmean.iloc[resultsdfsweep2freqorigmean['avgsyserr%_1D-avgsyserr%_2D'].argmin()] # most likely to be 1d nullspace\n", "display(min_df[llist1])\n", "#min_df[['M1_2D', 'M2_2D', 'B1_2D', 'B2_2D', 'K1_2D', 'K2_2D', 'K12_2D', 'FD_2D',]]\n", "\n", @@ -4848,16 +4872,16 @@ " ax.bar(X + 0.50, syserrdf[syserrlist_2D], color = 'r', width = 0.3)\n", " plt.title('syserrs: 1d blue, 2d red');\n", " \n", - "\"\"\"\n", + "\n", "grapherror_1D_2D(min_df, bardisplaylabels, syserrlist, syserrlist_2D )\n", "plt.show()\n", "\n", - "print('1D nullspace')\n", + "\"\"\"print('1D nullspace')\n", "plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase,convert_to_measurementdf(min_df), \n", " min_df.K1_1D, min_df.K2_1D, min_df.K12_1D, min_df.B1_1D, min_df.B2_1D, min_df.FD_1D, min_df.M1_1D, min_df.M2_1D,\n", " MONOMER=MONOMER, forceboth=forceboth,saving=savefig)\n", - "plt.show()\n", - "print('The above is likely to be quite a poor choice of frequencies, since it was selected for having poor 2d nullspace results')\"\"\";" + "plt.show()\"\"\"\n", + "print('The above is likely to be quite a poor choice of frequencies, since it was selected for having poor 2d nullspace results')" ] }, { @@ -4880,10 +4904,11 @@ }, "outputs": [], "source": [ - "best_df = resultsdf.loc[resultsdf['avgsyserr%_1D'].argmin()] # most likely to be good\n", + "best_df = resultsdfsweep2freqorigmean.iloc[resultsdfsweep2freqorigmean['avgsyserr%_1D'].argmin()] # most likely to be good\n", "display(best_df[llist1])\n", - "print('Best 1d results (lowest average syserr):')\n", + "print('Best 1d results (lowest average err):')\n", "grapherror_1D_2D(best_df, bardisplaylabels, syserrlist, syserrlist_2D )\n", + "plt.ylabel('Err (%)')\n", "plt.show()\n", "\n", "'''print('1D nullspace, best choice of two frequencies')\n", @@ -4892,7 +4917,17 @@ " best_df.FD_2D, best_df.M1_2D, best_df.M2_2D,\n", " MONOMER=MONOMER, forceboth=forceboth,saving=savefig)\n", "plt.show()'''\n", - "print('1D nullspace, best choice of two frequencies')" + "print('1D nullspace, best choice of two frequencies')\n", + "\n", + "# -0.4 pi \n", + "# and\n", + "# -.12 pi\n", + "\n", + "# or\n", + "\n", + "# -0.74 pi\n", + "# and\n", + "# -0.454 pi (-5/11 pi)\n" ] }, { @@ -4931,7 +4966,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "#Code that loops through frequency 2 points (of different spacing)\n", diff --git a/resonator_plotting.py b/resonator_plotting.py index ecf2cc6..1f7ba7a 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -96,6 +96,8 @@ def set_format(): # source: https://physicalmodelingwithpython.blogspot.com/2015/06/making-plots-for-publication.html plt.rcParams['pdf.fonttype'] = 42 # Don't outline text for NPhys plt.rcParams['svg.fonttype'] = 'none' + + plt.rcParams['axes.titlepad'] = -5 """ Plot amplitude or phase versus frequency with set values, simulated data, and SVD results. Demo: if true, plot without tick marks """ From 96c24aac16933ec4f0250b78bc20c3f60d8dc4d1 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 31 Dec 2022 15:24:36 -0500 Subject: [PATCH 010/101] FIX syserr --- Algebraic Approach Simulated Two Coupled Resonators.ipynb | 5 +++++ resonatorstats.py | 3 ++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 496659c..866ad4a 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -4980,6 +4980,11 @@ "def sweep_freq2(freq1,drive=drive, vals_set = vals_set, \n", " noiselevel = noiselevel, MONOMER=MONOMER, freq3=None, repeats=repeats):\n", "\n", + " print('Running sweep_freq2 with', repeats, 'repeats: Sweeping freq2 with', len(drive), 'frequencies from', min(drive), 'to', max(drive), \n", + " 'while holding freq1 fixed at', freq1)\n", + " if freq3 is not None:\n", + " print('Holding freq3 fixed at', freq3)\n", + " \n", " [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER)\n", " \n", " R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp, privilegedrsqrd = \\\n", diff --git a/resonatorstats.py b/resonatorstats.py index 2772cbd..e7c4f93 100644 --- a/resonatorstats.py +++ b/resonatorstats.py @@ -10,7 +10,8 @@ import warnings def syserr(x_found,x_set, absval = True): - with warnings.simplefilter('ignore'): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) From 74f2cfaef1cf0c37cb633cc708f0e57f1536fa4d Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 7 Jan 2023 14:18:22 -0500 Subject: [PATCH 011/101] define calc_error_interval --- helperfunctions.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/helperfunctions.py b/helperfunctions.py index 9fbc5cb..b5e146f 100644 --- a/helperfunctions.py +++ b/helperfunctions.py @@ -71,6 +71,26 @@ def savefigure(savename): print("Saved:\n", savename + '.png') +def calc_error_interval(resultsdf, resultsdfmean, groupby, fractionofdata = .95): + for column in ['E_lower_1D', 'E_upper_1D','E_lower_2D', 'E_upper_2D','E_lower_3D', 'E_upper_3D']: + resultsdfmean[column] = np.nan + dimensions = ['1D', '2D', '3D'] + items = resultsdfmean[groupby].unique() + + for item in items: + for D in dimensions: + avgerr = resultsdf[resultsdf[groupby]== item]['avgsyserr%_' + D] + avgerr = np.sort(avgerr) + halfalpha = (1 - fractionofdata)/2 + ## literally select the 95% confidence interval by tossing out the top 2.5% and the bottom 2.5% + ## I could do a weighted average to work better with selecting the top 2.5% and bottom 2.5% + ## But perhaps this is good enough for an estimate. It's ideal if I do 40*N measurements for some integer N. + lowerbound = np.mean([avgerr[int(np.floor(halfalpha*len(avgerr)))], avgerr[int(np.ceil(halfalpha*len(avgerr)))]]) + upperbound = np.mean([avgerr[-int(np.floor(halfalpha*len(avgerr))+1)],avgerr[-int(np.ceil(halfalpha*len(avgerr))+1)]]) + resultsdfmean.loc[resultsdfmean[groupby]== item,'E_lower_'+ D] = lowerbound + resultsdfmean.loc[resultsdfmean[groupby]== item,'E_upper_' + D] = upperbound + return resultsdf, resultsdfmean + def beep(): try: winsound.PlaySound(r'C:\Windows\Media\Speech Disambiguation.wav', flags = winsound.SND_ASYNC) From c324ddb08e25710dae32aacd215aa849a8dd8a83 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 7 Jan 2023 14:18:49 -0500 Subject: [PATCH 012/101] text_color_legend() using kwargs --- ...ach Simulated Two Coupled Resonators.ipynb | 596 +++++++++++++----- 1 file changed, 454 insertions(+), 142 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 866ad4a..592da10 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -70,7 +70,7 @@ "source": [ "from myheatmap import myheatmap\n", "from helperfunctions import flatten,listlength,printtime,make_real_iff_real, \\\n", - " store_params, read_params, savefigure, datestring, beep\n", + " store_params, read_params, savefigure, datestring, beep, calc_error_interval\n", "from resonatorsimulator import *\n", "from simulated_experiment import *\n", "from resonatorstats import *\n", @@ -88,8 +88,8 @@ "metadata": {}, "outputs": [], "source": [ - "def text_color_legend():\n", - " l = plt.legend()\n", + "def text_color_legend(**kwargs):\n", + " l = plt.legend(**kwargs)\n", " # set text color in legend\n", " for text in l.get_texts():\n", " if '1D' in str(text):\n", @@ -173,7 +173,7 @@ "MONOMER = False\"\"\"\n", "\n", "\n", - "\n", + "\"\"\"\n", "### lightly damped monomer ## this is my official lightly damped monomer\n", "MONOMER = True\n", "resonatorsystem = 2\n", @@ -185,7 +185,7 @@ "maxfreq = 2.01\n", "noiselevel= 10\n", "forceboth = False\n", - "\n", + "\"\"\"\n", "\n", "\"\"\"\n", "### medium damped monomer -- use for demo\n", @@ -199,7 +199,7 @@ "maxfreq = 1.8\n", "noiselevel = 200 # increased 2022-11-16 for demo Fig 1.\n", "\"\"\"\n", - "\n", + "\"\"\"\n", "### medium damped monomer -- use for Fig 4, picking frequencies\n", "resonatorsystem = -3\n", "m1_set = 4\n", @@ -210,9 +210,9 @@ "minfreq = 1.4\n", "maxfreq = 1.8\n", "noiselevel = 1\n", - "\n", - "\n", - "\"\"\"## somewhat heavily damped monomer\n", + "\"\"\"\n", + "\"\"\"\n", + "## somewhat heavily damped monomer\n", "MONOMER = True\n", "resonatorsystem = 4\n", "m1_set = 1\n", @@ -220,8 +220,8 @@ "k1_set = 1\n", "F_set = 1\n", "minfreq = .01\n", - "maxfreq = 5\"\"\"\n", - "\n", + "maxfreq = 5\n", + "\"\"\"\n", "\n", "\"\"\"### heavily damped monomer\n", "MONOMER = True\n", @@ -289,7 +289,7 @@ "forceboth= False\n", "MONOMER = False\n", "\"\"\"\n", - "\"\"\"\n", + "\n", "### 1D better # weakly coupled dimer #4\n", "#define set values\n", "## This is the weakly coupled dimer I am using\n", @@ -307,7 +307,7 @@ "MONOMER = False\n", "forceboth= False\n", "minfreq = .1\n", - "maxfreq = 2.2\"\"\"\n", + "maxfreq = 2.2\n", "\n", "\n", "\"\"\"\n", @@ -444,6 +444,13 @@ "beep()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -1263,10 +1270,10 @@ " # complex plot\n", " figsizeoverride2 = (figwidth, 1.48)\n", "\n", - "# Ran 1000 times in 20.438 sec\n", "# Ran 1000 times in 16.996 sec on desktop with verbose = True\n", - "repeats = 1\n", - "#repeats = 1000\n", + "# Ran 1000 times in 53.661 sec on laptop\n", + "#repeats = 1\n", + "repeats = 1000\n", "if demo:\n", " repeats = 1\n", " overlay = True\n", @@ -1286,11 +1293,21 @@ " repeatedexptsres = repeatedexptsres.append(thisres, ignore_index=True)\n", " except:\n", " repeatedexptsres = thisres\n", + " \n", + "repeatedexptsres['sqrtk1m1_set'] = np.sqrt(repeatedexptsres['k1_set']/repeatedexptsres['m1_set'])\n", + "if not MONOMER:\n", + " repeatedexptsres['sqrtk2m2_set'] = np.sqrt(repeatedexptsres['k2_set']/repeatedexptsres['m2_set'])\n", + "for D in ['1D', '2D', '3D']:\n", + " repeatedexptsres['SQRTK1M1_' + D] = np.sqrt(repeatedexptsres['K1_' + D]/repeatedexptsres['M1_' + D])\n", + " if not MONOMER:\n", + " repeatedexptsres['SQRTK2M2_' + D] = np.sqrt(repeatedexptsres['K2_' + D]/repeatedexptsres['M2_' + D])\n", + "\n", + "\n", + "repeatedexptsresmean = repeatedexptsres.mean(numeric_only=True) \n", + " \n", "after = time()\n", "printtime(repeats, before, after) \n", - "display(repeatedexptsres.transpose()) \n", - "\n", - "repeatedexptsresmean = repeatedexptsres.mean() " + "display(repeatedexptsres.transpose()) \n" ] }, { @@ -1302,24 +1319,6 @@ "list(repeatedexptsres.columns)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "repeatedexptsres['sqrtk1m1_set'] = np.sqrt(repeatedexptsres['k1_set']/repeatedexptsres['m1_set'])\n", - "if not MONOMER:\n", - " repeatedexptsres['sqrtk2m2_set'] = np.sqrt(repeatedexptsres['k2_set']/repeatedexptsres['m2_set'])\n", - "for D in ['1D', '2D', '3D']:\n", - " repeatedexptsres['SQRTK1M1_' + D] = np.sqrt(repeatedexptsres['K1_' + D]/repeatedexptsres['M1_' + D])\n", - " if not MONOMER:\n", - " repeatedexptsres['SQRTK2M2_' + D] = np.sqrt(repeatedexptsres['K2_' + D]/repeatedexptsres['M2_' + D])\n", - "\n", - "\n", - "repeatedexptsresmean = repeatedexptsres.mean() " - ] - }, { "cell_type": "code", "execution_count": null, @@ -1419,8 +1418,12 @@ " savefigure(savename)\n", "plt.show()\n", "\n", + "figsize = (figwidth/2,figheight)\n", + "if resonatorsystem == 10:\n", + " figsize = (figwidth/3, figheight)\n", + "\n", "with sns.axes_style(rc={'xtick.bottom': True,}):\n", - " fig, ax2 = plt.subplots(1,1, figsize = (figwidth/2,figheight), dpi=150)\n", + " fig, ax2 = plt.subplots(1,1, figsize = figsize, dpi=150)\n", " plt.sca(ax2)\n", " description = 'avgsyserr%'\n", " dimension = list_to_show\n", @@ -1436,27 +1439,16 @@ "\n", "plt.tight_layout()\n", "if saving:\n", - " datestr = datestring()\n", " savename = \"sys\" + str(resonatorsystem) + ','+ \"probdist,\" + datestr\n", " savefigure(savename)\n", + "plt.show()\n", "\n", - "#sns.set_context('talk')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ "print('noiselevel:', noiselevel)\n", "print('meanSNR_R1:', repeatedexptsres.meanSNR_R1[0])\n", "if not MONOMER:\n", " print('meanSNR_R2:', repeatedexptsres.meanSNR_R2[0])\n", "\n", - "\n", + "\"\"\"\n", "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize = (figwidth,figwidth))#, gridspec_kw={'hspace': 0}, sharex = 'all')\n", "\n", "if MONOMER:\n", @@ -1490,9 +1482,9 @@ "plt.legend()\n", "\n", "plt.tight_layout()\n", - "plt.show()\n", + "plt.show()\"\"\"\n", "\n", - "fig, ax = plt.subplots(1,1, figsize = (figwidth/2,figheight), gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", + "fig, ax = plt.subplots(1,1, figsize = figsize, gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", "for D in list_to_show:\n", " plt.loglog(repeatedexptsres[Xkey +D], repeatedexptsres['avgsyserr%_'+ D], symb, markersize=1, alpha = .08, label=D)\n", " #plt.loglog(repeatedexptsres[Xkey +D][::5], repeatedexptsres['avgsyserr%_'+ D][::5], symb, alpha = .08, label=D)\n", @@ -1519,16 +1511,29 @@ " \n", "\n", " \n", - "display(len(repeatedexptsres.columns)) # 200 -> 142 distributions" + "display('(len(repeatedexptsres.columns)', len(repeatedexptsres.columns)) # 200 -> 142 distributions\n", + "\n", + "#sns.set_context('talk')" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stophere # The following looks at distributions of the date developed in teh above section." + ] + }, { "cell_type": "code", "execution_count": null, @@ -1724,7 +1729,7 @@ " shortkeylist = flatten([[key.upper() + '_1D', key.upper() + '_2D'] for key in shortkeysummary])\n", " shortkeylistset = flatten([[key.lower() + '_set']*2 for key in shortkeysummary])\n", " \n", - "#***aiming to make a publishable box and whisker figure about the error.\n", + "# box and whisker figure about the error.\n", "\n", "# create dataframe of signed systematic errors\n", "signederr = syserr(x_found = (repeatedexptsres[shortkeylist]), \n", @@ -2060,7 +2065,7 @@ "# Ran 100 times in 7.121 sec\n", "# Ran 100 times in 78.661 sec with verbose = True (only counts the first repeat).\n", "# Ran 100 times in 786.946 sec with verbose = False\n", - "repeats = 100\n", + "repeats = 80*2\n", "verbose = False # if False, still shows one graph for each dimension\n", "freqdiff = round(W/10,4)\n", "print('freqdiff:', freqdiff)\n", @@ -2111,7 +2116,7 @@ "printtime(repeats, before, after) \n", "display(resultsvarynump.transpose())\n", "\n", - "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean()\n", + "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean(numeric_only=True)\n", "datestr = datestring()\n", "\n", "verbose = False\n", @@ -2148,16 +2153,34 @@ "describeresonator(vals_set=vals_set, MONOMER=MONOMER, forceboth=forceboth, noiselevel=noiselevel)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resultsvarynump, resultsvarynumpmean = calc_error_interval(resultsvarynump, resultsvarynumpmean, groupby='num frequency points', fractionofdata = .95)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.xticks(list(range(0,max_num_p+1,5)))\n" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [], "source": [ "print('Noiselevel: ' + str(noiselevel))\n", - "\n", "symb = '.' # plotting style\n", "co1 = 'C0'\n", "co2 = 'C1'\n", @@ -2167,7 +2190,7 @@ "\n", "figsize = (figwidth, 1.48)\n", "if resonatorsystem == -3: # Monomer:\n", - " figsize = (figwidth, 1.4)\n", + " figsize = (2.8, 1.4)\n", "\n", "plt.figure(figsize=figsize)\n", "#plt.plot(resultsvarynump['num frequency points'],resultsvarynump['avgsyserr%_3D'], symb, alpha = .1, color = co3 )\n", @@ -2194,6 +2217,7 @@ "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_2D'], label='2D', color = co2)\n", "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_1D'], label='1D', color = co1)\n", "text_color_legend()\n", + "plt.xlim(xmin=0)\n", "plt.gca().set_yscale('log')\n", "#plt.xlabel('num frequency points')\n", "plt.xlabel('number of frequency points')\n", @@ -2206,6 +2230,106 @@ " resultsvarynump[['num frequency points','avgsyserr%_1D','avgsyserr%_2D','avgsyserr%_3D']].to_csv(savename + '.csv')\n", "plt.show()\n", "\n", + "# ***\n", + "plt.figure(figsize=figsize)\n", + "dimensions = ['3D', '2D', '1D']\n", + "colors = [co3, co2, co1]\n", + "X = resultsvarynumpmean['num frequency points']\n", + "for i in range(3):\n", + " Yhigh = resultsvarynumpmean['E_upper_' + dimensions[i]]\n", + " Ylow = resultsvarynumpmean['E_lower_' + dimensions[i]] \n", + " plt.plot(X, Yhigh, color = colors[i], alpha = .3, linewidth=.3)\n", + " plt.plot(X, Ylow, color = colors[i], alpha = .3, linewidth=.3)\n", + " axa.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", + "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_3D'], label='3D', color = co3)\n", + "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_2D'], label='2D', color = co2)\n", + "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_1D'], label='1D', color = co1)\n", + "#text_color_legend()\n", + "plt.xlim(xmin=0)\n", + "plt.yscale('log')\n", + "#plt.xlabel('num frequency points')\n", + "plt.xlabel('number of frequency points')\n", + "plt.ylabel('Avg err (%)')\n", + "plt.tight_layout()\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"numpvsE,log,cleaned,\" + datestr + ', noise'+ str(noiselevel)\n", + " savefigure(savename)\n", + "plt.show()\n", + "\n", + "plt.figure(figsize = figsize)\n", + "x = resultsvarynump['num frequency points']\n", + "sns.violinplot(x=x, y=resultsvarynump['log avgsyserr%_3D'], \n", + " positions=x.unique(), \n", + " color = co3,\n", + " saturation = .5,\n", + " lw = 2,\n", + " inner = None,\n", + " label='2D', fontsize=7, rot=0 )\n", + "sns.violinplot(x=x, y=resultsvarynump['log avgsyserr%_2D'], \n", + " positions=x.unique(), \n", + " color = co2,\n", + " saturation = .5,\n", + " lw = 0.1,\n", + " inner = None,\n", + " label='2D', fontsize=7, rot=0 )\n", + "sns.violinplot(x=x, y=resultsvarynump['log avgsyserr%_1D'], \n", + " positions=x.unique(), \n", + " color = co1,\n", + " saturation = .5,\n", + " lw = 0.3,\n", + " inner = None,\n", + " label='1D', fontsize=7, rot=0)\n", + "ax = plt.gca()\n", + "plt.setp(ax.collections, alpha=.7)\n", + "for i in range(max_num_p*3-3):\n", + " ax.collections[i].set_linewidth(.1)\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_3D'], lw = lw, color = co3)\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_2D'], lw = lw, color = co2)\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_1D'], lw = lw, color = co1)\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_3D'], '.', ms = 2, color = 'w')\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_2D'], '.', ms = 2, color = 'w')\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_1D'], '.', ms = 2, color = 'w')\n", + "plt.ylabel('Avg err (%)')\n", + "plt.xlim(xmin=-2)\n", + "xt = list(range(-2,max_num_p-1,5))\n", + "xt = xt + [2-2]\n", + "plt.xticks(xt);\n", + "yt,_ = plt.yticks()\n", + "yt = yt[1:-1]\n", + "print(yt)\n", + "#plt.gca().Axes.set_ylabels([10**y for y in yt]) # undo the log.\n", + "plt.yticks(yt,[10**y for y in yt] );\n", + "#plt.ticklabel_format(axis='y', style='sci', ) # AttributeError: This method only works with the ScalarFormatter\n", + "#plt.legend()\n", + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"numpvsE,log,violin,\" + datestr + ', noise'+ str(noiselevel)\n", + " savefigure(savename)\n", + "plt.show()\n", + "\n", + "\"\"\"plt.figure(figsize = figsize)\n", + "resultsvarynump.boxplot(column = 'log avgsyserr%_1D', by = 'num frequency points', grid=False, fontsize=7, \n", + " #positions =resultsvarynoiselevel['log meanSNR_R1.unique(),widths=widths, \n", + " #color = 'k',\n", + " flierprops={'marker': '.', 'markersize': 1, 'markerfacecolor': 'k', 'alpha': .1},\n", + " showmeans = True,\n", + " manage_ticks = True,\n", + " figsize=figsize);\n", + "plt.xticks(list(range(-1,max_num_p,5)), rotation= 0)\n", + "#plt.yscale('log')\n", + "\n", + "resultsvarynump.boxplot(column = 'log avgsyserr%_2D', by = 'num frequency points', grid=False, fontsize=7, \n", + " #positions =resultsvarynoiselevel['log meanSNR_R1.unique(),widths=widths, \n", + " #color = 'k',\n", + " flierprops={'marker': '.', 'markersize': 1, 'markerfacecolor': 'k', 'alpha': .1},\n", + " showmeans = True,\n", + " manage_ticks = True,\n", + " figsize=figsize);\n", + "plt.xticks(list(range(-1,max_num_p,5)), rotation= 0)\n", + "#plt.yscale('log')\"\"\"\n", + "\n", + "\n", "plt.figure()\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['K1syserr%_3D'], symb, alpha = .3 , color = co3)\n", "plt.plot(resultsvarynump['num frequency points'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , color = co2)\n", @@ -2263,6 +2387,20 @@ "plt.ylabel('Avg err (%)');" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -2370,9 +2508,9 @@ "printtime(repeats, before, after) \n", "display(resultsvarynoiselevel.transpose())\n", "\n", - "resultsvarynoiselevelmean = resultsvarynoiselevel.groupby(by=['noiselevel'],as_index=False ).mean()\n", + "resultsvarynoiselevelmean = resultsvarynoiselevel.groupby(by=['noiselevel'],as_index=False ).mean(numeric_only=True)\n", "\n", - "# initialize 95% confidence interval columns\n", + "# initialize 95% of data columns\n", "for column in ['E_lower_1D', 'E_upper_1D','E_lower_2D', 'E_upper_2D','E_lower_3D', 'E_upper_3D']:\n", " resultsvarynoiselevelmean[column] = np.nan\n", "\n", @@ -2384,7 +2522,7 @@ " ASE = resultsvarynoiselevel[resultsvarynoiselevel['noiselevel']== noise]['avgsyserr%_' + D]\n", " ASE = np.sort(ASE)\n", " halfalpha = (1 - .95)/2\n", - " ## literally select the 95% confidence interval by tossing out the top 2.5% and the bottom 2.5% \n", + " ## literally select interval for the 95% of the data by tossing out the top 2.5% and the bottom 2.5% \n", " ## I could do a weighted average to work better with selecting the top 2.5% and bottom 2.5%\n", " ## But perhaps this is good enough for an estimate. It's ideal if I do 80 measurements.\n", " lowerbound = np.mean([ASE[int(np.floor(halfalpha*len(ASE)))], ASE[int(np.ceil(halfalpha*len(ASE)))]])\n", @@ -2393,6 +2531,13 @@ " resultsvarynoiselevelmean.loc[resultsvarynoiselevelmean['noiselevel']== noise,'E_upper_' + D] = upperbound" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -2472,10 +2617,12 @@ " #logmeanSNRticks = preventDivisionByZero(logmeanSNRticks)\n", " plt.axis('equal');\n", " plt.tight_layout()\n", + " plt.show()\n", " \n", " ## reduce number of ticks (vary as needed)\n", " logmeanSNRticks=logmeanSNRticks[::5] #Set so that the number of ticks looks good.\n", - " \n", + " \n", + " \"\"\" \n", " plt.figure()\n", " #plt.plot(1/resultsvarynoiselevel['meanSNR_' + R], resultsvarynoiselevel['avgsyserr%_3D'], '.', alpha=alpha, color = co3,)# label='1D SVD')\n", " #plt.plot(1/resultsvarynoiselevel['meanSNR_' + R], resultsvarynoiselevel['avgsyserr%_2D'], '.', alpha=alpha, color = co2)# label='2D SVD')\n", @@ -2535,7 +2682,7 @@ " ',' + datestr;\n", " savefigure(savename)\n", " plt.show()\n", - " #sns.set_context('talk')\n", + " #sns.set_context('talk')\"\"\"\n", " \n", " #sns.set_context('paper')\n", " ## cleaned figures\n", @@ -2544,6 +2691,8 @@ " figsize = (2.7,2.8)\n", " elif resonatorsystem == 2:\n", " figsize = (1.4,figwidth/2) # width, height \n", + " elif resonatorsystem == 10:\n", + " figsize = (figwidth/3, 1.4)\n", " plt.figure(figsize = figsize, dpi=150)\n", " #signal / resultsvarynoiselevelmean['stdev']\n", " axa = plt.gca()\n", @@ -2572,16 +2721,34 @@ " axa.set_xscale('log')\n", " axa.set_yscale('log')\n", " plt.axis('equal');\n", - " locmaj = mpl.ticker.LogLocator(numticks=3)\n", + " if resonatorsystem == 10:\n", + " numticks = 3\n", + " else:\n", + " numticks = 3\n", + " locmaj = mpl.ticker.LogLocator(numticks=numticks)\n", " axa.yaxis.set_major_locator(locmaj)\n", " axa.xaxis.set_major_locator(locmaj)\n", + " if resonatorsystem == 10:\n", + " print('Decreasing ylim')\n", + " plt.ylim(ymin = 5e-4,ymax = .1,)\n", + " plt.xlim(xmin = 1e-5, xmax = .1)\n", + " else:\n", + " plt.ylim(ymax=100, ymin=1e-8)\n", " stdevticks = axa.get_xticks()\n", " # https://stackoverflow.com/questions/68715304/dual-x-axis-in-python-same-data-different-scale\n", " axb = axa.secondary_xaxis('top', functions=(SNR_to_stdev, stdev_to_SNR))\n", " if plotlog:\n", - " axb.set_xticks(10**logmeanSNRticks)\n", + " if resonatorsystem == 10:\n", + " axb.set_xticks([1e3, 1e6])\n", + " axb.set_xticks([1e4,1e5,1e7],minor=True,labels = \"\" )\n", + " else:\n", + " axb.set_xticks(10**logmeanSNRticks)\n", " axb.set_xlabel('Mean SNR for ' + R)\n", - " plt.ylim(ymax=100, ymin=1e-8)\n", + " if resonatorsystem == 10:\n", + " axa.tick_params(pad =0.5)\n", + " axb.tick_params(pad =0.5)\n", + " \n", + " #axa.set_yticks([1e-2])\n", " plt.tight_layout()\n", " if saving:\n", " savename = 'sys' + str(resonatorsystem) + 'err_vs_SNR_' + R + ',cleaned,'+ \\\n", @@ -2601,6 +2768,7 @@ " plt.ylabel('log10 Avg syserr difference (%)');\n", " plt.xlabel('log10 Mean SNR for ' + R);\n", " plt.tight_layout()\n", + " plt.show()\n", " \n", " \n", " #ax2 = ax1.secondary_xaxis('top', functions=(EtoWL, WLtoE))\n", @@ -2616,20 +2784,24 @@ " plt.title('');\n", " plt.show()\"\"\"\n", "\n", - "\n", + " \"\"\"\n", " plt.figure()\n", " plt.plot(resultsvarynoiselevel['log maxSNR_' + R], resultsvarynoiselevel['log avgsyserr%_1D'], '.', alpha=alpha,color = co1)#, label='1D SVD')\n", " plt.plot(resultsvarynoiselevel['log maxSNR_' + R], resultsvarynoiselevel['log avgsyserr%_2D'], '.', alpha=alpha, color = co2)\n", " plt.plot(resultsvarynoiselevelmean['log maxSNR_' + R], resultsvarynoiselevelmean['log avgsyserr%_1D'],color = co1, label='1D')\n", " plt.plot(resultsvarynoiselevelmean['log maxSNR_' + R], resultsvarynoiselevelmean['log avgsyserr%_2D'], color = co2, label='2D')\n", " plt.xlabel('log10 Max SNR for ' + R)\n", - " plt.legend()\n", + " text_color_legend()\n", " plt.ylabel('log10 Avg err (%)');\n", + " plt.show()\n", "\n", " plt.figure()\n", " plt.loglog((resultsvarynoiselevel['noiselevel']), resultsvarynoiselevel['meanSNR_' + R], '.')\n", " plt.xlabel('Noiselevel')\n", - " plt.ylabel('mean SNR ' + R);" + " plt.ylabel('mean SNR ' + R);\n", + " plt.show()\"\"\"\n", + " \n", + "beep()" ] }, { @@ -2638,7 +2810,6 @@ "metadata": {}, "outputs": [], "source": [ - "beep()\n", "stophere # Next: vary any param" ] }, @@ -2820,7 +2991,7 @@ " \n", " variedkey = paramname + '_set'\n", " \n", - " resultsdfvaryparammean = resultsdfvaryparam.groupby(by=[variedkey],as_index=False).mean()" + " resultsdfvaryparammean = resultsdfvaryparam.groupby(by=[variedkey],as_index=False).mean(numeric_only=True)" ] }, { @@ -3312,7 +3483,7 @@ " after = time()\n", " printtime(numk1*repeats, before, after)\n", " \n", - " resultsdfk1mean = resultsdfk1.groupby(by=['k1_set'],as_index=False).mean()" + " resultsdfk1mean = resultsdfk1.groupby(by=['k1_set'],as_index=False).mean(numeric_only=True)" ] }, { @@ -3643,7 +3814,7 @@ " after = time()\n", " printtime(numk12*repeats, before, after)\n", " \n", - " resultsdfk12mean = resultsdfk12.groupby(by=['k12_set'],as_index=False).mean()" + " resultsdfk12mean = resultsdfk12.groupby(by=['k12_set'],as_index=False).mean(numeric_only=True)" ] }, { @@ -4040,7 +4211,7 @@ " printtime(numk2*repeats, before, after)\n", " resultsdfk2\n", "\n", - " resultsdfk2mean = resultsdfk2.groupby(by=['k2_set'],as_index=False).mean()\n", + " resultsdfk2mean = resultsdfk2.groupby(by=['k2_set'],as_index=False).mean(numeric_only=True)\n", " resultsdfk2median = resultsdfk2.groupby(by=['k2_set'],as_index=False).median()" ] }, @@ -4377,6 +4548,13 @@ "round(resultsdfmean.Freq1.max(),1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -4722,6 +4900,33 @@ " savefigure(savename)\n", "plt.show()\n", "\n", + "plt.figure(figsize = (2,1.3))\n", + "alpha = .01\n", + "ms = .3\n", + "resultsdfsweep2freqorigmean_resort_s = resultsdfsweep2freqorigmean.sort_values(by='second smallest singular value')\n", + "Xs = resultsdfsweep2freqorigmean_resort_s['second smallest singular value']\n", + "plt.plot(Xs, resultsdfsweep2freqorigmean_resort_s['avgsyserr%_3D'], symb, ms = ms, lw=lw, color = co3, alpha = alpha, label = '3D')\n", + "plt.plot(Xs, resultsdfsweep2freqorigmean_resort_s['avgsyserr%_2D'], symb, ms = ms, lw=lw, color = co2, alpha = alpha, label = '2D')\n", + "plt.plot(Xs, resultsdfsweep2freqorigmean_resort_s['avgsyserr%_1D'], symb, ms = ms, lw=lw, color = co1, alpha = alpha, label = '1D')\n", + "\"\"\"plt.plot(X, resultsdfsweep2freqorigmean_resort_s['log avgsyserr%_1D'] -resultsdfsweep2freqorigmean_resort_s['log avgsyserr%_2D']\n", + " , lw=lw, alpha = .5, color = 'k')\n", + "\"\"\"\n", + "plt.xlabel('Second smallest singular value')\n", + "plt.yscale('log')\n", + "if resonatorsystem == -3:\n", + " plt.yticks([1e4,1e2, 1e0, 1e-2])\n", + "#text_color_legend(ncol=3)\n", + "plt.ylabel('Avg err (%)');\n", + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"2freq,err_vs_s,\" + datestr\n", + " savefigure(savename)\n", + " resultsdfsweep2freqorigmean_resort_s[['second smallest singular value',\n", + " 'log avgsyserr%_1D',\n", + " 'log avgsyserr%_2D', \n", + " 'log avgsyserr%_3D']].to_csv(savename + '.csv')\n", + "plt.show()\n", + "\n", "resultsdfmeanbyfreq1 = resultsdf.groupby(by=['Freq1'], as_index=False).mean(numeric_only =True)\n", "X = resultsdfmeanbyfreq1['Freq1'] \n", "\n", @@ -4737,7 +4942,7 @@ "plt.yscale('log')\n", "#plt.xticks([res1, res2]);\n", "plt.tight_layout()\n", - "\n", + "plt.show()\n", "\n", "\n", "plt.figure(figsize=figsize)\n", @@ -4974,7 +5179,7 @@ "#Code that loops through frequency 2 points (of different spacing)\n", "\n", "verbose = True\n", - "repeats = 10\n", + "repeats = 55\n", "n = 200\n", "\n", "def sweep_freq2(freq1,drive=drive, vals_set = vals_set, \n", @@ -5068,7 +5273,7 @@ " #display(results_sweep_2freq.transpose())\n", "\n", " results_sweep_2freq= results_sweep_2freq.sort_values(by='Freq2')\n", - " results_sweep_2freqmean = results_sweep_2freq.groupby(by=['Freq2']).mean()\n", + " results_sweep_2freqmean = results_sweep_2freq.groupby(by=['Freq2']).mean(numeric_only=True)\n", " results_sweep_2freqmean['Freq2'] = results_sweep_2freqmean.index\n", " min1dsyserrFreq2_2freq = results_sweep_2freqmean[['avgsyserr%_1D']].idxmin()[0]\n", " min2dsyserrFreq2_2freq = results_sweep_2freqmean[['avgsyserr%_2D']].idxmin()[0]\n", @@ -5076,9 +5281,9 @@ " ase1 = results_sweep_2freq[results_sweep_2freq.Freq2 == min1dsyserrFreq2_2freq].avgsyserr_1D\n", " ase2 = results_sweep_2freq[results_sweep_2freq.Freq2 == min2dsyserrFreq2_2freq]['avgsyserr%_2D']\n", " print('Min syserr for 1D-SVD at freq2: ' + str(min1dsyserrFreq2_2freq) + \n", - " ' and syserr is (' + str(ase1.mean()) + ' ± ' + str(np.std(ase1, ddof=1)) + ')%.')\n", + " ' and syserr is (' + str(ase1.mean(numeric_only=True)) + ' ± ' + str(np.std(ase1, ddof=1)) + ')%.')\n", " print('Min syserr for 2D-SVD at freq2: ' + str(min2dsyserrFreq2_2freq) + \n", - " ' and syserr is (' + str(ase2.mean()) + ' ± ' + str(np.std(ase2, ddof=1)) + ')%, where unc is stdev.')\n", + " ' and syserr is (' + str(ase2.mean(numeric_only=True)) + ' ± ' + str(np.std(ase2, ddof=1)) + ')%, where unc is stdev.')\n", " display(results_sweep_2freqmean.loc[[min1dsyserrFreq2_2freq,min2dsyserrFreq2_2freq]])\n", " \n", " \"\"\"\n", @@ -5101,7 +5306,6 @@ "### Run second to figure out ideal 3-frequency or Run once to figure out ideal 2-frequency\n", "# Ran 50 times in 21.149 sec\n", "before = time()\n", - "repeats = 5\n", "print('Running with fixed freqs: ' + str(res1) + ', ' + str(freq3))\n", "for i in range(1):\n", " thisres = sweep_freq2(freq1 = res1,drive=chosendrive, vals_set = vals_set, noiselevel = noiselevel, MONOMER=MONOMER, \n", @@ -5115,7 +5319,7 @@ "display(results_sweep_1freq.transpose())\n", "\n", "results_sweep_1freq = results_sweep_1freq.sort_values(by='Freq2')\n", - "results_sweep_1freqmean = results_sweep_1freq.groupby(by=['Freq2']).mean()\n", + "results_sweep_1freqmean = results_sweep_1freq.groupby(by=['Freq2']).mean(numeric_only=True)\n", "results_sweep_1freqmean['Freq2'] = results_sweep_1freqmean.index\n", "min1dsyserrFreq2 = results_sweep_1freqmean[['avgsyserr%_1D']].idxmin()[0]\n", "min2dsyserrFreq2 = results_sweep_1freqmean[['avgsyserr%_2D']].idxmin()[0]\n", @@ -5123,9 +5327,9 @@ "ase1B = (results_sweep_1freq[results_sweep_1freq.Freq2 == min1dsyserrFreq2])['avgsyserr%_1D']\n", "ase2B = (results_sweep_1freq[results_sweep_1freq.Freq2 == min2dsyserrFreq2])['avgsyserr%_2D']\n", "print('Min syserr for 1D-SVD at freq2: ' + str(min1dsyserrFreq2) + \n", - " ' and syserr is (' + str(ase1B.mean()) + ' ± ' + str(np.std(ase1B, ddof=1)) + ')%')\n", + " ' and syserr is (' + str(ase1B.mean(numeric_only=True)) + ' ± ' + str(np.std(ase1B, ddof=1)) + ')%')\n", "print('Min syserr for 2D-SVD at freq2: ' + str(min2dsyserrFreq2) + \n", - " ' and syserr is (' + str(ase2B.mean()) + ' ± ' + str(np.std(ase2B, ddof=1)) + ')%, where unc is std.')\n", + " ' and syserr is (' + str(ase2B.mean(numeric_only=True)) + ' ± ' + str(np.std(ase2B, ddof=1)) + ')%, where unc is std.')\n", "display(results_sweep_1freqmean.loc[[min1dsyserrFreq2,min2dsyserrFreq2]])\n", "\n", "\"\"\"\n", @@ -5146,13 +5350,22 @@ "if freq3 is not None:\n", " freq_label.append(freq3)\n", "#freq_label = np.unique(np.array(freq_label))\n", - "\n", + "\"\"\"\n", "plotcomplex(complexZ = results_sweep_1freq.R1_amp_meas2, parameter = results_sweep_1freq.Freq2, ax = ax5, \n", " label_markers = freq_label)\n", "if not MONOMER:\n", " plotcomplex(complexZ = results_sweep_1freq.R2_amp_meas2, parameter = results_sweep_1freq.Freq2, ax = ax6)\n", " #ax6.plot(np.real(results_sweep_1freq.R2AmpCom2), np.imag(results_sweep_1freq.R2AmpCom2), '.')\n", - " " + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_, d1, _, d2, _, _, _, _, _ = calculate_spectra([res1], vals_set, noiselevel, MONOMER, forceboth)" ] }, { @@ -5164,7 +5377,27 @@ "# some thoughts about alpha transparency\n", "\"\"\"1000 -> .1\n", "5400 -> .03\"\"\";\n", - "len(results_sweep_1freq)" + "repeats = len(results_sweep_1freq)\n", + "repeats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeats % 80" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_sweep_1freq, results_sweep_1freqmean = \\\n", + " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', confidenceinterval = .95)\n" ] }, { @@ -5176,6 +5409,13 @@ "#list(results_sweep_1freq.columns)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -5186,16 +5426,39 @@ "source": [ "from matplotlib.ticker import AutoLocator\n", "widths=.03 # widths of boxplots\n", - "figsize=(15, 5)\n", + "if False:\n", + " lw=1\n", + " figsize=(15, 5)\n", + " if repeats > 100:\n", + " alpha=.03\n", + " elif repeats > 50:\n", + " alpha=.1\n", + " elif repeats > 25:\n", + " alpha=.3\n", + " else:\n", + " alpha=.5\n", + " ms = 10\n", + "else:\n", + " lw = 1\n", + " ms = 2\n", + " figsize = (3.2, 1.3)\n", + " if repeats > 20000:\n", + " alpha = .003\n", + " elif repeats > 100:\n", + " alpha=.03\n", + " elif repeats > 50:\n", + " alpha=.1\n", + " elif repeats > 25:\n", + " alpha=.3\n", + " else:\n", + " alpha=.5\n", "symb='.'\n", - "if repeats > 100:\n", - " alpha=.03\n", - "elif repeats > 50:\n", - " alpha=.1\n", - "elif repeats > 25:\n", - " alpha=.3\n", + "\n", + "if MONOMER:\n", + " Rnote = ''\n", "else:\n", - " alpha=.5\n", + " Rnote = ' at R1'\n", + "\n", "\n", "results_sweep_1freq.boxplot(column='log avgsyserr%_2D', by='Freq2', grid=False, #fontsize=7, rot=90, \n", " positions=results_sweep_1freq.Freq2.unique(), widths=widths, \n", @@ -5209,7 +5472,10 @@ "plt.gca().xaxis.set_major_locator(AutoLocator()) \n", "plt.show()\n", "\n", - "results_sweep_1freq.boxplot(column='log avgsyserr%_1D', by='Freq2', grid=False, #fontsize=7, rot=90, \n", + "results_sweep_1freq.boxplot(column='log avgsyserr%_1D', \n", + " #by='Freq2',\n", + " by = 'R1Phase2_wrap',\n", + " grid=False, #fontsize=7, rot=90, \n", " positions=results_sweep_1freq.Freq2.unique(), widths=widths, \n", " #color='k', \n", " showmeans=True, \n", @@ -5220,40 +5486,90 @@ "plt.title('');\n", "\n", "plt.figure(figsize=figsize) # remove this to overplot the boxplots\n", - "plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['log avgsyserr%_2D'], '.', color=co2, alpha=alpha )\n", - "plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['log avgsyserr%_1D'], '.', color=co1, alpha=alpha)\n", - "plt.plot(results_sweep_1freqmean.Freq2, results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, label='2D' )\n", - "plt.plot(results_sweep_1freqmean.Freq2, results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, label='1D')\n", + "plt.axvline(res1, color='grey')\n", + "plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_2D'], '.', \n", + " ms = ms, color=co2, alpha=alpha )\n", + "plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_1D'], '.', \n", + " ms = ms, color=co1, alpha=alpha)\n", + "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, lw=lw, label='2D' )\n", + "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, lw=lw, label='1D')\n", "plt.xlabel('Freq2')\n", "plt.legend()\n", - "plt.ylabel('log avgsyserr%_1D');\n", + "plt.ylabel('avgsyserr%_1D');\n", + "plt.yscale('log')\n", "plt.show()\n", "\n", "plt.figure(figsize=figsize) \n", + "# calculations\n", "results_sweep_1freq['R1Phase2_wrap']=results_sweep_1freq.R1_phase_noiseless2%(2*np.pi) - 2*np.pi\n", "results_sweep_1freq_resort1=results_sweep_1freq.sort_values(by='R1Phase2_wrap')\n", - "results_sweep_1freq_resort1mean=results_sweep_1freq_resort1.groupby(by=['Freq2']).mean()\n", - "\n", - "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['log avgsyserr%_3D'], \n", - " '.', color=co3, alpha=alpha )\n", - "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['log avgsyserr%_2D'], \n", - " '.', color=co2, alpha=alpha )\n", - "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['log avgsyserr%_1D'], \n", - " '.', color=co1, alpha=alpha)\n", - "\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", - " color=co3, label='3D' )\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", - " color=co2, label='2D' )\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", - " color=co1, label='1D')\n", + "results_sweep_1freq_resort1mean=results_sweep_1freq_resort1.groupby(by=['Freq2'], as_index=False).mean(numeric_only=True)\n", + "\n", + "results_sweep_1freq, results_sweep_1freq_resort1mean = \\\n", + " calc_error_interval(results_sweep_1freq, results_sweep_1freq_resort1mean, groupby='Freq2', confidenceinterval = .95)\n", + "\n", + "# plotting\n", + "plt.axvline(d1/np.pi, color='grey')\n", + "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['avgsyserr%_3D'], \n", + " '.', ms = ms,color=co3, alpha=alpha )\n", + "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['avgsyserr%_2D'], \n", + " '.', ms = ms,color=co2, alpha=alpha )\n", + "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['avgsyserr%_1D'], \n", + " '.', ms = ms,color=co1, alpha=alpha)\n", + "\n", + "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", + " lw=lw,color=co3, label='3D' )\n", + "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", + " lw=lw,color=co2, label='2D' )\n", + "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", + " lw=lw,color=co1, label='1D')\n", "\n", "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", - "plt.xlabel('Phase of Freq2 at R1 ($\\pi$)')\n", + "plt.xlabel('Phase of Freq2'+ Rnote+' ($\\pi$)')\n", + "plt.xticks([-1,-3/4, -1/2, -1/4, 0])\n", "plt.legend()\n", - "plt.ylabel('log avgsyserr (%)');\n", + "plt.ylabel('avgsyserr (%)');\n", + "plt.yscale('log')\n", "plt.show()\n", "\n", + "# Export figure\n", + "plt.figure(figsize=figsize) \n", + "axa = plt.gca()\n", + "plt.axvline(d1/np.pi, color='grey')\n", + "\n", + "dimensions = ['3D', '2D', '1D']\n", + "colors = [co3, co2, co1]\n", + "X = results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi \n", + "for i in range(3):\n", + " Yhigh = results_sweep_1freq_resort1mean['E_upper_' + dimensions[i]]\n", + " Ylow = results_sweep_1freq_resort1mean['E_lower_' + dimensions[i]] \n", + " plt.plot(X, Yhigh, color = colors[i], alpha = .3, linewidth=.3)\n", + " plt.plot(X, Ylow, color = colors[i], alpha = .3, linewidth=.3)\n", + " axa.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", + "\n", + "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", + " lw=lw,color=co3, label='3D' )\n", + "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", + " lw=lw,color=co2, label='2D' )\n", + "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", + " lw=lw,color=co1, label='1D')\n", + "\n", + "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", + "plt.xlabel('Phase of Freq2'+ Rnote+' ($\\pi$)')\n", + "plt.xticks([-1,-3/4, -1/2, -1/4, 0])\n", + "text_color_legend()\n", + "plt.ylabel('avgsyserr (%)');\n", + "plt.yscale('log')\n", + "plt.tight_layout()\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"sweepfreq2,\" + datestr\n", + " savefigure(savename)\n", + " results_sweep_1freq_resort1mean[['R1Phase2_wrap','log avgsyserr%_1D','log avgsyserr%_2D','log avgsyserr%_3D']].to_csv(\n", + " savename + '.csv')\n", + "plt.show()\n", + "\n", + "\n", "\"\"\"results_sweep_1freq_resort=results_sweep_1freq.sort_values(by='R1_phase_noiseless2')\n", "results_sweep_1freq_resort.boxplot(column='log avgsyserr%_1D', by='R1_phase_noiseless2', grid=False, fontsize=7, rot=90, \n", " positions=results_sweep_1freq_resort.R1_phase_noiseless2.unique(), \n", @@ -5267,39 +5583,39 @@ "fig, ax=plt.subplots(subplot_kw={'projection': 'polar'})\n", "ax.plot(results_sweep_1freq_resort1.R1Phase2_wrap, results_sweep_1freq_resort1['log avgsyserr%_1D'], '.', color=co1, alpha=alpha)\n", "ax.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap, results_sweep_1freq_resort1mean['log avgsyserr%_1D'], color=co1 )\n", - "\n", + "plt.title('Log Avg Err 1D (%)')\n", "plt.show()\n", "\n", "\n", "plt.figure(figsize=figsize)\n", - "plt.plot(results_sweep_1freq.arclength_R1, results_sweep_1freq['log avgsyserr%_1D'], '.', color=co1, alpha=alpha)\n", - "plt.plot(results_sweep_1freq.arclength_R1, results_sweep_1freq['log avgsyserr%_2D'], '.', color=co2, alpha=alpha )\n", - "plt.plot(results_sweep_1freqmean.arclength_R1, results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, label='1D')\n", - "plt.plot(results_sweep_1freqmean.arclength_R1, results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, label='2D' )\n", + "plt.plot(results_sweep_1freq.arclength_R1, results_sweep_1freq['log avgsyserr%_1D'], '.', ms = ms,color=co1, alpha=alpha)\n", + "plt.plot(results_sweep_1freq.arclength_R1, results_sweep_1freq['log avgsyserr%_2D'], '.', ms = ms,color=co2, alpha=alpha )\n", + "plt.plot(results_sweep_1freqmean.arclength_R1, results_sweep_1freqmean['log avgsyserr%_1D'], lw=lw,color=co1, label='1D')\n", + "plt.plot(results_sweep_1freqmean.arclength_R1, results_sweep_1freqmean['log avgsyserr%_2D'], lw=lw, color=co2, label='2D' )\n", "plt.xlabel('arclength_R1')\n", "plt.legend()\n", "plt.ylabel('log avgsyserr%_1D');\n", "plt.show()\n", "\n", "plt.figure(figsize=figsize)\n", - "plt.plot(np.degrees(results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_1D'], '.', color=co1, alpha=alpha)\n", - "plt.plot(np.degrees(results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_2D'], '.', color=co2, alpha=alpha )\n", - "plt.plot(np.degrees(results_sweep_1freqmean.modifiedangle_R1), results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, label='1D')\n", - "plt.plot(np.degrees(results_sweep_1freqmean.modifiedangle_R1), results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, label='2D')\n", + "plt.plot(np.degrees(results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_1D'], '.', ms = ms,color=co1, alpha=alpha)\n", + "plt.plot(np.degrees(results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_2D'], '.', ms = ms,color=co2, alpha=alpha )\n", + "plt.plot(np.degrees(results_sweep_1freqmean.modifiedangle_R1), results_sweep_1freqmean['log avgsyserr%_1D'], lw=lw,color=co1, label='1D')\n", + "plt.plot(np.degrees(results_sweep_1freqmean.modifiedangle_R1), results_sweep_1freqmean['log avgsyserr%_2D'], lw=lw,color=co2, label='2D')\n", "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", - "plt.xlabel('Twirly angle (deg) of Freq2 at R1')\n", + "plt.xlabel('Twirly angle (deg) of Freq2'+ Rnote)\n", "plt.legend()\n", "plt.ylabel('log avgsyserr%_1D');\n", "plt.show()\n", "\n", "## Benjamin likes this one\n", "fig, ax=plt.subplots(subplot_kw={'projection': 'polar'})\n", - "ax.plot((results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_3D'], '.', color=co3, alpha=alpha/5)\n", - "ax.plot((results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_2D'], '.', color=co2, alpha=alpha/5)\n", - "ax.plot((results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_1D'], '.', color=co1, alpha=alpha/5)\n", - "ax.plot(results_sweep_1freqmean.modifiedangle_R1, results_sweep_1freqmean['log avgsyserr%_3D'],color=co3,)\n", - "ax.plot(results_sweep_1freqmean.modifiedangle_R1, results_sweep_1freqmean['log avgsyserr%_2D'],color=co2,)\n", - "ax.plot(results_sweep_1freqmean.modifiedangle_R1, results_sweep_1freqmean['log avgsyserr%_1D'],color=co1,)\n", + "ax.plot((results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_3D'], '.', ms = ms,color=co3, alpha=alpha/5)\n", + "ax.plot((results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_2D'], '.', ms = ms,color=co2, alpha=alpha/5)\n", + "ax.plot((results_sweep_1freq.modifiedangle_R1), results_sweep_1freq['log avgsyserr%_1D'], '.', ms = ms,color=co1, alpha=alpha/5)\n", + "ax.plot(results_sweep_1freqmean.modifiedangle_R1, results_sweep_1freqmean['log avgsyserr%_3D'],lw=lw,color=co3,)\n", + "ax.plot(results_sweep_1freqmean.modifiedangle_R1, results_sweep_1freqmean['log avgsyserr%_2D'],lw=lw,color=co2,)\n", + "ax.plot(results_sweep_1freqmean.modifiedangle_R1, results_sweep_1freqmean['log avgsyserr%_1D'],lw=lw,color=co1,)\n", "ax.set_theta_zero_location(\"S\") #south\n", "plt.show()\n", "\n", @@ -5328,9 +5644,6 @@ "metadata": {}, "outputs": [], "source": [ - "def datestring():\n", - " return datetime.today().strftime('%Y-%m-%d %H;%M;%S')\n", - "datestr = datestring()\n", "results_sweep_1freq.to_csv(os.path.join(savefolder,\n", " datestr + \"results_sweep_1freq.csv\"));\n", "results_sweep_1freq.to_pickle(os.path.join(savefolder,\n", @@ -5527,7 +5840,7 @@ "variedkey1 = paramname1 + '_set'\n", "variedkey2 = paramname2 + '_set'\n", "\n", - "resultsvary2mean = resultsvary2.groupby(by=[variedkey1, variedkey2],as_index=False).mean()" + "resultsvary2mean = resultsvary2.groupby(by=[variedkey1, variedkey2],as_index=False).mean(numeric_only=True)" ] }, { @@ -6286,7 +6599,7 @@ "source": [ "symb ='.'\n", "\n", - "resultsdfmonomerdoemeannump = resultsdfmonomerdoe.groupby(by=['num frequency points'],as_index=False).mean()\n", + "resultsdfmonomerdoemeannump = resultsdfmonomerdoe.groupby(by=['num frequency points'],as_index=False).mean(numeric_only=True)\n", "plt.plot(resultsdfmonomerdoe['num frequency points'],resultsdfmonomerdoe['log avgsyserr%_3D'], symb, color = co3, alpha = alpha)\n", "plt.plot(resultsdfmonomerdoe['num frequency points'],resultsdfmonomerdoe['log avgsyserr%_2D'], symb, color = co2, alpha = alpha)\n", "plt.plot(resultsdfmonomerdoe['num frequency points'],resultsdfmonomerdoe['log avgsyserr%_1D'], symb, color = co1, alpha = alpha)\n", @@ -6473,7 +6786,6 @@ "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(1,2, figsize= (10,5), sharex = 'all', sharey = 'all')\n", - "#***\n", "plt.sca(ax1)\n", "dim = '1D'\n", "cc = 'log meanSNR_R1'\n", From e4639836ae209a0c41ffa2ff9962b27a7bac8dc7 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 7 Jan 2023 14:20:06 -0500 Subject: [PATCH 013/101] Making figures Figures for frequency-picking --- ...ach Simulated Two Coupled Resonators.ipynb | 316 ++++++++++-------- 1 file changed, 178 insertions(+), 138 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 592da10..3dd0ac0 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -174,7 +174,7 @@ "\n", "\n", "\"\"\"\n", - "### lightly damped monomer ## this is my official lightly damped monomer\n", + "### lightly damped monomer ## this is my official lightly damped monomer for Fig 2.\n", "MONOMER = True\n", "resonatorsystem = 2\n", "m1_set = 4\n", @@ -289,7 +289,7 @@ "forceboth= False\n", "MONOMER = False\n", "\"\"\"\n", - "\n", + "\"\"\"\n", "### 1D better # weakly coupled dimer #4\n", "#define set values\n", "## This is the weakly coupled dimer I am using\n", @@ -307,10 +307,10 @@ "MONOMER = False\n", "forceboth= False\n", "minfreq = .1\n", - "maxfreq = 2.2\n", + "maxfreq = 2.2\"\"\"\n", + "\n", "\n", "\n", - "\"\"\"\n", "## Well-separated dimer / Medium coupled dimer #1\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -326,7 +326,8 @@ "forceboth= False\n", "minfreq = 0.1\n", "maxfreq = 5\n", - "#(but this is 3D for forceboth)\"\"\"\n", + "#(but this is 3D for forceboth)\n", + "\n", "\"\"\"\n", "### Medium coupled dimer #2\n", "# This is my official medium coupled dimer.\n", @@ -340,7 +341,7 @@ "k12_set = 4\n", "F_set = 1\n", "MONOMER = False\n", - "noiselevel = 10\n", + "noiselevel = 1 # reduced from 10, 2023-01-07 because the results were so poor\n", "forceboth= False\n", "minfreq = .1\n", "maxfreq = 3\n", @@ -1161,6 +1162,24 @@ "stophere # next: do 1D, 2D, 3D with Repeats. simulated_experiment()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reslist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "measurementfreqs + reslist" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1175,15 +1194,16 @@ " n = 20\n", "else:\n", " demo = False\n", + " \n", "\n", "if resonatorsystem == 15:\n", " measurementfreqs = desiredfreqs # Brittany's expermental setup\n", "else:\n", " measurementfreqs, category = res_freq_numeric(vals_set, MONOMER, forceboth,\n", - " mode = 'amp', includefreqs = reslist,\n", + " mode = 'amp', includefreqs = reslist + measurementfreqs,\n", " minfreq=minfreq, maxfreq=maxfreq, morefrequencies=None,\n", " unique = True, veryunique = True, numtoreturn = 2, \n", - " verboseplot = False, plottitle = None, verbose=True, iterations = 3,\n", + " verboseplot = False, plottitle = None, verbose=True, iterations = 10,\n", " returnoptions = True)\n", "\n", "print(measurementfreqs)\n", @@ -1270,10 +1290,10 @@ " # complex plot\n", " figsizeoverride2 = (figwidth, 1.48)\n", "\n", + "# Ran 1000 times in 20.438 sec\n", "# Ran 1000 times in 16.996 sec on desktop with verbose = True\n", - "# Ran 1000 times in 53.661 sec on laptop\n", - "#repeats = 1\n", - "repeats = 1000\n", + "repeats = 1\n", + "#repeats = 999\n", "if demo:\n", " repeats = 1\n", " overlay = True\n", @@ -1287,27 +1307,17 @@ " noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth,\n", " overlay=overlay, demo = demo, \n", " figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2,\n", - " resonatorsystem=resonatorsystem, show_set=False,\n", + " resonatorsystem=resonatorsystem, show_set=True,\n", " repeats=repeats , verbose = verbose, context = 'paper', saving = saving)\n", " try: # repeated experiments results\n", " repeatedexptsres = repeatedexptsres.append(thisres, ignore_index=True)\n", " except:\n", " repeatedexptsres = thisres\n", - " \n", - "repeatedexptsres['sqrtk1m1_set'] = np.sqrt(repeatedexptsres['k1_set']/repeatedexptsres['m1_set'])\n", - "if not MONOMER:\n", - " repeatedexptsres['sqrtk2m2_set'] = np.sqrt(repeatedexptsres['k2_set']/repeatedexptsres['m2_set'])\n", - "for D in ['1D', '2D', '3D']:\n", - " repeatedexptsres['SQRTK1M1_' + D] = np.sqrt(repeatedexptsres['K1_' + D]/repeatedexptsres['M1_' + D])\n", - " if not MONOMER:\n", - " repeatedexptsres['SQRTK2M2_' + D] = np.sqrt(repeatedexptsres['K2_' + D]/repeatedexptsres['M2_' + D])\n", - "\n", - "\n", - "repeatedexptsresmean = repeatedexptsres.mean(numeric_only=True) \n", - " \n", "after = time()\n", "printtime(repeats, before, after) \n", - "display(repeatedexptsres.transpose()) \n" + "display(repeatedexptsres.transpose()) \n", + "\n", + "repeatedexptsresmean = repeatedexptsres.mean() " ] }, { @@ -1319,6 +1329,24 @@ "list(repeatedexptsres.columns)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['sqrtk1m1_set'] = np.sqrt(repeatedexptsres['k1_set']/repeatedexptsres['m1_set'])\n", + "if not MONOMER:\n", + " repeatedexptsres['sqrtk2m2_set'] = np.sqrt(repeatedexptsres['k2_set']/repeatedexptsres['m2_set'])\n", + "for D in ['1D', '2D', '3D']:\n", + " repeatedexptsres['SQRTK1M1_' + D] = np.sqrt(repeatedexptsres['K1_' + D]/repeatedexptsres['M1_' + D])\n", + " if not MONOMER:\n", + " repeatedexptsres['SQRTK2M2_' + D] = np.sqrt(repeatedexptsres['K2_' + D]/repeatedexptsres['M2_' + D])\n", + "\n", + "\n", + "repeatedexptsresmean = repeatedexptsres.mean(numeric_only=True) " + ] + }, { "cell_type": "code", "execution_count": null, @@ -1328,9 +1356,9 @@ "outputs": [], "source": [ "#sns.set_context('paper')\n", + "saving = True\n", "\n", "describeresonator(vals_set, MONOMER, forceboth, noiselevel)\n", - "saving = True\n", "figheight = 1.3\n", "\n", "if MONOMER:\n", @@ -1338,15 +1366,17 @@ "else:\n", " shortkeysummary = ['m1', 'k1', 'sqrtk1m1', 'b1', 'm2', 'k2', 'sqrtk2m2', 'b2', 'k12']\n", " \n", - "# choose manually\n", - "list_to_show = ['1D', '2D', '3D']\n", - "#list_to_show = ['1D', '3D'] \n", "\n", "if resonatorsystem == 12:\n", " list_to_show = ['1D', '3D']\n", - "if resonatorsystem == 2:\n", + " colors = [co1, co3]\n", + "elif resonatorsystem == 2:\n", " list_to_show = ['1D', '2D']\n", - "\n", + " colors = [co1, co2]\n", + "else:\n", + " list_to_show = ['1D', '2D', '3D']\n", + " colors = [co1, co2, co3]\n", + " \n", "shortkeylist = list([])\n", "shortkeylistset = list([])\n", "for key in shortkeysummary:\n", @@ -1418,37 +1448,44 @@ " savefigure(savename)\n", "plt.show()\n", "\n", - "figsize = (figwidth/2,figheight)\n", - "if resonatorsystem == 10:\n", - " figsize = (figwidth/3, figheight)\n", - "\n", "with sns.axes_style(rc={'xtick.bottom': True,}):\n", - " fig, ax2 = plt.subplots(1,1, figsize = figsize, dpi=150)\n", + " fig, ax2 = plt.subplots(1,1, figsize = (figwidth/2,figheight), dpi=150)\n", " plt.sca(ax2)\n", " description = 'avgsyserr%'\n", - " dimension = list_to_show\n", - " for D in dimension:\n", + " for i in range(len(list_to_show)):\n", + " D = list_to_show[i]\n", " key = description + '_' + D\n", " #sns.histplot(repeatedexptsres[key],kde=False, stat=\"density\", linewidth=0, label=key, )\n", - " plt.hist(repeatedexptsres[key], bins=20,histtype = 'step', label = D);\n", + " plt.hist(repeatedexptsres[key], bins=20,histtype = 'step', label = D, color = colors[i]);\n", " text_color_legend()\n", " plt.xlabel('Average err (%)')\n", " plt.ylabel('Occurrences')\n", " ax2.set_yticks([])\n", " sns.despine(ax=ax2, left = True)\n", - "\n", "plt.tight_layout()\n", "if saving:\n", + " datestr = datestring()\n", " savename = \"sys\" + str(resonatorsystem) + ','+ \"probdist,\" + datestr\n", " savefigure(savename)\n", "plt.show()\n", "\n", + "#sns.set_context('talk')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ "print('noiselevel:', noiselevel)\n", "print('meanSNR_R1:', repeatedexptsres.meanSNR_R1[0])\n", "if not MONOMER:\n", " print('meanSNR_R2:', repeatedexptsres.meanSNR_R2[0])\n", "\n", - "\"\"\"\n", + "\n", "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize = (figwidth,figwidth))#, gridspec_kw={'hspace': 0}, sharex = 'all')\n", "\n", "if MONOMER:\n", @@ -1482,9 +1519,9 @@ "plt.legend()\n", "\n", "plt.tight_layout()\n", - "plt.show()\"\"\"\n", + "plt.show()\n", "\n", - "fig, ax = plt.subplots(1,1, figsize = figsize, gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", + "fig, ax = plt.subplots(1,1, figsize = (figwidth/2,figheight), gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", "for D in list_to_show:\n", " plt.loglog(repeatedexptsres[Xkey +D], repeatedexptsres['avgsyserr%_'+ D], symb, markersize=1, alpha = .08, label=D)\n", " #plt.loglog(repeatedexptsres[Xkey +D][::5], repeatedexptsres['avgsyserr%_'+ D][::5], symb, alpha = .08, label=D)\n", @@ -1511,28 +1548,15 @@ " \n", "\n", " \n", - "display('(len(repeatedexptsres.columns)', len(repeatedexptsres.columns)) # 200 -> 142 distributions\n", - "\n", - "#sns.set_context('talk')" + "display('Number of items measured:', len(repeatedexptsres.columns)) # 200 -> 142 distributions" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "stophere # The following looks at distributions of the date developed in teh above section." - ] + "source": [] }, { "cell_type": "code", @@ -2112,11 +2136,20 @@ " resultsvarynump = resultsvarynump.append(thisres, ignore_index=True)\n", " except:\n", " resultsvarynump = thisres\n", + " \n", + "datestr = datestring()\n", + "resultsvarynump.to_csv(os.path.join(savefolder,\n", + " datestr + \"resultsvarynump.csv\"));\n", + "resultsvarynump.to_pickle(os.path.join(savefolder,\n", + " datestr + 'resultsvarynump.pkl'))\n", + "print('Saved: ' + os.path.join(savefolder,\n", + " datestr + 'resultsvarynump.csv'))\n", + " \n", "after = time()\n", "printtime(repeats, before, after) \n", "display(resultsvarynump.transpose())\n", "\n", - "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean(numeric_only=True)\n", + "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean()\n", "datestr = datestring()\n", "\n", "verbose = False\n", @@ -2387,33 +2420,13 @@ "plt.ylabel('Avg err (%)');" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "datestr = datestring()\n", - "resultsvarynump.to_csv(os.path.join(savefolder,\n", - " datestr + \"resultsvarynump.csv\"));\n", - "resultsvarynump.to_pickle(os.path.join(savefolder,\n", - " datestr + 'resultsvarynump.pkl'))\n", - "print('Saved: ' + os.path.join(savefolder,\n", - " datestr + 'resultsvarynump.csv'))" + "stophere # Next: varynoiselevel()" ] }, { @@ -2421,9 +2434,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "stophere # Next: varynoiselevel()" - ] + "source": [] }, { "cell_type": "code", @@ -2508,7 +2519,7 @@ "printtime(repeats, before, after) \n", "display(resultsvarynoiselevel.transpose())\n", "\n", - "resultsvarynoiselevelmean = resultsvarynoiselevel.groupby(by=['noiselevel'],as_index=False ).mean(numeric_only=True)\n", + "resultsvarynoiselevelmean = resultsvarynoiselevel.groupby(by=['noiselevel'],as_index=False ).mean()\n", "\n", "# initialize 95% of data columns\n", "for column in ['E_lower_1D', 'E_upper_1D','E_lower_2D', 'E_upper_2D','E_lower_3D', 'E_upper_3D']:\n", @@ -2617,12 +2628,10 @@ " #logmeanSNRticks = preventDivisionByZero(logmeanSNRticks)\n", " plt.axis('equal');\n", " plt.tight_layout()\n", - " plt.show()\n", " \n", " ## reduce number of ticks (vary as needed)\n", " logmeanSNRticks=logmeanSNRticks[::5] #Set so that the number of ticks looks good.\n", - " \n", - " \"\"\" \n", + " \n", " plt.figure()\n", " #plt.plot(1/resultsvarynoiselevel['meanSNR_' + R], resultsvarynoiselevel['avgsyserr%_3D'], '.', alpha=alpha, color = co3,)# label='1D SVD')\n", " #plt.plot(1/resultsvarynoiselevel['meanSNR_' + R], resultsvarynoiselevel['avgsyserr%_2D'], '.', alpha=alpha, color = co2)# label='2D SVD')\n", @@ -2682,7 +2691,7 @@ " ',' + datestr;\n", " savefigure(savename)\n", " plt.show()\n", - " #sns.set_context('talk')\"\"\"\n", + " #sns.set_context('talk')\n", " \n", " #sns.set_context('paper')\n", " ## cleaned figures\n", @@ -2691,8 +2700,6 @@ " figsize = (2.7,2.8)\n", " elif resonatorsystem == 2:\n", " figsize = (1.4,figwidth/2) # width, height \n", - " elif resonatorsystem == 10:\n", - " figsize = (figwidth/3, 1.4)\n", " plt.figure(figsize = figsize, dpi=150)\n", " #signal / resultsvarynoiselevelmean['stdev']\n", " axa = plt.gca()\n", @@ -2721,34 +2728,16 @@ " axa.set_xscale('log')\n", " axa.set_yscale('log')\n", " plt.axis('equal');\n", - " if resonatorsystem == 10:\n", - " numticks = 3\n", - " else:\n", - " numticks = 3\n", - " locmaj = mpl.ticker.LogLocator(numticks=numticks)\n", + " locmaj = mpl.ticker.LogLocator(numticks=3)\n", " axa.yaxis.set_major_locator(locmaj)\n", " axa.xaxis.set_major_locator(locmaj)\n", - " if resonatorsystem == 10:\n", - " print('Decreasing ylim')\n", - " plt.ylim(ymin = 5e-4,ymax = .1,)\n", - " plt.xlim(xmin = 1e-5, xmax = .1)\n", - " else:\n", - " plt.ylim(ymax=100, ymin=1e-8)\n", " stdevticks = axa.get_xticks()\n", " # https://stackoverflow.com/questions/68715304/dual-x-axis-in-python-same-data-different-scale\n", " axb = axa.secondary_xaxis('top', functions=(SNR_to_stdev, stdev_to_SNR))\n", " if plotlog:\n", - " if resonatorsystem == 10:\n", - " axb.set_xticks([1e3, 1e6])\n", - " axb.set_xticks([1e4,1e5,1e7],minor=True,labels = \"\" )\n", - " else:\n", - " axb.set_xticks(10**logmeanSNRticks)\n", + " axb.set_xticks(10**logmeanSNRticks)\n", " axb.set_xlabel('Mean SNR for ' + R)\n", - " if resonatorsystem == 10:\n", - " axa.tick_params(pad =0.5)\n", - " axb.tick_params(pad =0.5)\n", - " \n", - " #axa.set_yticks([1e-2])\n", + " plt.ylim(ymax=100, ymin=1e-8)\n", " plt.tight_layout()\n", " if saving:\n", " savename = 'sys' + str(resonatorsystem) + 'err_vs_SNR_' + R + ',cleaned,'+ \\\n", @@ -2768,7 +2757,6 @@ " plt.ylabel('log10 Avg syserr difference (%)');\n", " plt.xlabel('log10 Mean SNR for ' + R);\n", " plt.tight_layout()\n", - " plt.show()\n", " \n", " \n", " #ax2 = ax1.secondary_xaxis('top', functions=(EtoWL, WLtoE))\n", @@ -2784,24 +2772,20 @@ " plt.title('');\n", " plt.show()\"\"\"\n", "\n", - " \"\"\"\n", + "\n", " plt.figure()\n", " plt.plot(resultsvarynoiselevel['log maxSNR_' + R], resultsvarynoiselevel['log avgsyserr%_1D'], '.', alpha=alpha,color = co1)#, label='1D SVD')\n", " plt.plot(resultsvarynoiselevel['log maxSNR_' + R], resultsvarynoiselevel['log avgsyserr%_2D'], '.', alpha=alpha, color = co2)\n", " plt.plot(resultsvarynoiselevelmean['log maxSNR_' + R], resultsvarynoiselevelmean['log avgsyserr%_1D'],color = co1, label='1D')\n", " plt.plot(resultsvarynoiselevelmean['log maxSNR_' + R], resultsvarynoiselevelmean['log avgsyserr%_2D'], color = co2, label='2D')\n", " plt.xlabel('log10 Max SNR for ' + R)\n", - " text_color_legend()\n", + " plt.legend()\n", " plt.ylabel('log10 Avg err (%)');\n", - " plt.show()\n", "\n", " plt.figure()\n", " plt.loglog((resultsvarynoiselevel['noiselevel']), resultsvarynoiselevel['meanSNR_' + R], '.')\n", " plt.xlabel('Noiselevel')\n", - " plt.ylabel('mean SNR ' + R);\n", - " plt.show()\"\"\"\n", - " \n", - "beep()" + " plt.ylabel('mean SNR ' + R);" ] }, { @@ -2810,6 +2794,7 @@ "metadata": {}, "outputs": [], "source": [ + "beep()\n", "stophere # Next: vary any param" ] }, @@ -2991,7 +2976,7 @@ " \n", " variedkey = paramname + '_set'\n", " \n", - " resultsdfvaryparammean = resultsdfvaryparam.groupby(by=[variedkey],as_index=False).mean(numeric_only=True)" + " resultsdfvaryparammean = resultsdfvaryparam.groupby(by=[variedkey],as_index=False).mean()" ] }, { @@ -3483,7 +3468,7 @@ " after = time()\n", " printtime(numk1*repeats, before, after)\n", " \n", - " resultsdfk1mean = resultsdfk1.groupby(by=['k1_set'],as_index=False).mean(numeric_only=True)" + " resultsdfk1mean = resultsdfk1.groupby(by=['k1_set'],as_index=False).mean()" ] }, { @@ -3814,7 +3799,7 @@ " after = time()\n", " printtime(numk12*repeats, before, after)\n", " \n", - " resultsdfk12mean = resultsdfk12.groupby(by=['k12_set'],as_index=False).mean(numeric_only=True)" + " resultsdfk12mean = resultsdfk12.groupby(by=['k12_set'],as_index=False).mean()" ] }, { @@ -4211,7 +4196,7 @@ " printtime(numk2*repeats, before, after)\n", " resultsdfk2\n", "\n", - " resultsdfk2mean = resultsdfk2.groupby(by=['k2_set'],as_index=False).mean(numeric_only=True)\n", + " resultsdfk2mean = resultsdfk2.groupby(by=['k2_set'],as_index=False).mean()\n", " resultsdfk2median = resultsdfk2.groupby(by=['k2_set'],as_index=False).median()" ] }, @@ -4421,7 +4406,7 @@ "\n", "def sweep_freq_pair(drive=drive, vals_set = vals_set, \n", " noiselevel = noiselevel, freq3 = None, MONOMER=MONOMER, repeats = 1,\n", - " verbose = verbose, forceboth = forceboth):\n", + " forceboth = forceboth):\n", "\n", " [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER)\n", " \n", @@ -4443,7 +4428,7 @@ "\n", " thisres = simulated_experiment(freqs, drive=drive,vals_set = vals_set, \n", " noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth,\n", - " repeats=repeats , verbose = verbose, noiseless_spectra=noiseless_spectra)\n", + " repeats=repeats , verbose = False, noiseless_spectra=noiseless_spectra)\n", " \n", " try: # repeated experiments results\n", " resultsdf = pd.concat([resultsdf,thisres], ignore_index=True)\n", @@ -4469,6 +4454,11 @@ "\n", "if False:\n", " # 30 is small. 200 is big.\n", + " \"\"\"\n", + " 200 frequencies\n", + " Ran 6 times in 11300.511 sec = 3.13 hours.\n", + " \"\"\"\n", + " \n", " numfreq = 200\n", " repeats = 6\n", " noiselevel = 1\n", @@ -4503,7 +4493,12 @@ " datestr = datestring()\n", " resultsdfsweep2freqorig.to_csv(\"sys\" + str(resonatorsystem) + ',2freq,' + datestr + '.csv')\n", "else:\n", - " saveddf = 'sys-3,2freq,2022-12-29 20;03;50.csv'\n", + " if MONOMER:\n", + " saveddf = 'sys-3,2freq,2022-12-29 20;03;50.csv' # MONOMER\n", + " resonatorsystem = -30\n", + " else:\n", + " saveddf = 'sys11,2freq,2023-01-07 13;53;00.csv' # DIMER\n", + " resonatorsystem = 110 # the 0 means it was reloaded\n", " resultsdfsweep2freqorig = pd.read_csv(saveddf)\n", "\n", "resultsdfsweep2freqorigmean = resultsdfsweep2freqorig.groupby(by=['Freq1', 'Freq2'],as_index=False).mean(numeric_only=True)\n", @@ -4744,6 +4739,7 @@ " grid=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'R2_phase_noiseless1', columns = 'R2_phase_noiseless2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", " myheatmap(grid, \"log avgsyserr%_1D\", cmap = 'magma_r')#, vmax = 2); \n", + " plt.axis('equal')\n", "\n", "maxsyserr_to_plot = 1\n", "\n", @@ -4860,6 +4856,33 @@ "(figwidth/2, 1.3)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reslist" + ] + }, { "cell_type": "code", "execution_count": null, @@ -4875,9 +4898,10 @@ "SSgrid2D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_2D').sort_index(axis = 0, ascending = False)\n", "vmin = min(SSgrid1D.min().min(), SSgrid2D.min().min()) # use same scale for both\n", + "vmax = 2\n", "\n", "plt.figure(figsize = (1.555,1.3) )\n", - "myheatmap(SSgrid1D, \"log average error\", vmin=vmin, vmax=1, cmap='magma_r'); \n", + "myheatmap(SSgrid1D, \"log average error\", vmin=vmin, vmax=vmax, cmap='magma_r'); \n", "plt.title('1D-SVD')\n", "plt.ylabel('$\\omega_a$ (rad/s)')\n", "plt.xlabel('$\\omega_b$ (rad/s)')\n", @@ -4889,7 +4913,7 @@ "plt.show()\n", "\n", "plt.figure(figsize = (1.555,1.3) )\n", - "myheatmap(SSgrid2D, \"log average error\", vmin=vmin, vmax=1, cmap='magma_r'); \n", + "myheatmap(SSgrid2D, \"log average error\", vmin=vmin, vmax=vmax, cmap='magma_r'); \n", "plt.title('2D-SVD')\n", "plt.ylabel('$\\omega_a$ (rad/s)')\n", "plt.xlabel('$\\omega_b$ (rad/s)')\n", @@ -4900,6 +4924,15 @@ " savefigure(savename)\n", "plt.show()\n", "\n", + "if not MONOMER:\n", + " plt.figure(figsize = (1.555,1.3) )\n", + " grid=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'R2_phase_noiseless1', columns = 'R2_phase_noiseless2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", + " myheatmap(grid, \"log avgsyserr%_1D\", cmap = 'magma_r')#, vmax = 2); \n", + " plt.axis('equal')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", "plt.figure(figsize = (2,1.3))\n", "alpha = .01\n", "ms = .3\n", @@ -4946,6 +4979,11 @@ "\n", "\n", "plt.figure(figsize=figsize)\n", + "\n", + "plt.axvline(reslist[0], color='gray', lw=0.5)\n", + "if not MONOMER:\n", + " plt.axvline(reslist[1], color='gray', lw=0.5)\n", + "\n", "axa=plt.gca()\n", "colors = [co1, co2, co3]\n", "dimensions = ['1D', '2D', '3D']\n", @@ -4995,7 +5033,9 @@ "plt.show()\n", "\n", "plt.figure(figsize = figsize)\n", - "plt.axvline(res1, color='gray', lw=0.5)\n", + "plt.axvline(reslist[0], color='gray', lw=0.5)\n", + "if not MONOMER:\n", + " plt.axvline(reslist[1], color='gray', lw=0.5)\n", "#plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_3D'], color = co3, label='3D')\n", "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", @@ -5273,7 +5313,7 @@ " #display(results_sweep_2freq.transpose())\n", "\n", " results_sweep_2freq= results_sweep_2freq.sort_values(by='Freq2')\n", - " results_sweep_2freqmean = results_sweep_2freq.groupby(by=['Freq2']).mean(numeric_only=True)\n", + " results_sweep_2freqmean = results_sweep_2freq.groupby(by=['Freq2']).mean()\n", " results_sweep_2freqmean['Freq2'] = results_sweep_2freqmean.index\n", " min1dsyserrFreq2_2freq = results_sweep_2freqmean[['avgsyserr%_1D']].idxmin()[0]\n", " min2dsyserrFreq2_2freq = results_sweep_2freqmean[['avgsyserr%_2D']].idxmin()[0]\n", @@ -5281,9 +5321,9 @@ " ase1 = results_sweep_2freq[results_sweep_2freq.Freq2 == min1dsyserrFreq2_2freq].avgsyserr_1D\n", " ase2 = results_sweep_2freq[results_sweep_2freq.Freq2 == min2dsyserrFreq2_2freq]['avgsyserr%_2D']\n", " print('Min syserr for 1D-SVD at freq2: ' + str(min1dsyserrFreq2_2freq) + \n", - " ' and syserr is (' + str(ase1.mean(numeric_only=True)) + ' ± ' + str(np.std(ase1, ddof=1)) + ')%.')\n", + " ' and syserr is (' + str(ase1.mean()) + ' ± ' + str(np.std(ase1, ddof=1)) + ')%.')\n", " print('Min syserr for 2D-SVD at freq2: ' + str(min2dsyserrFreq2_2freq) + \n", - " ' and syserr is (' + str(ase2.mean(numeric_only=True)) + ' ± ' + str(np.std(ase2, ddof=1)) + ')%, where unc is stdev.')\n", + " ' and syserr is (' + str(ase2.mean()) + ' ± ' + str(np.std(ase2, ddof=1)) + ')%, where unc is stdev.')\n", " display(results_sweep_2freqmean.loc[[min1dsyserrFreq2_2freq,min2dsyserrFreq2_2freq]])\n", " \n", " \"\"\"\n", @@ -5319,7 +5359,7 @@ "display(results_sweep_1freq.transpose())\n", "\n", "results_sweep_1freq = results_sweep_1freq.sort_values(by='Freq2')\n", - "results_sweep_1freqmean = results_sweep_1freq.groupby(by=['Freq2']).mean(numeric_only=True)\n", + "results_sweep_1freqmean = results_sweep_1freq.groupby(by=['Freq2']).mean()\n", "results_sweep_1freqmean['Freq2'] = results_sweep_1freqmean.index\n", "min1dsyserrFreq2 = results_sweep_1freqmean[['avgsyserr%_1D']].idxmin()[0]\n", "min2dsyserrFreq2 = results_sweep_1freqmean[['avgsyserr%_2D']].idxmin()[0]\n", @@ -5327,9 +5367,9 @@ "ase1B = (results_sweep_1freq[results_sweep_1freq.Freq2 == min1dsyserrFreq2])['avgsyserr%_1D']\n", "ase2B = (results_sweep_1freq[results_sweep_1freq.Freq2 == min2dsyserrFreq2])['avgsyserr%_2D']\n", "print('Min syserr for 1D-SVD at freq2: ' + str(min1dsyserrFreq2) + \n", - " ' and syserr is (' + str(ase1B.mean(numeric_only=True)) + ' ± ' + str(np.std(ase1B, ddof=1)) + ')%')\n", + " ' and syserr is (' + str(ase1B.mean()) + ' ± ' + str(np.std(ase1B, ddof=1)) + ')%')\n", "print('Min syserr for 2D-SVD at freq2: ' + str(min2dsyserrFreq2) + \n", - " ' and syserr is (' + str(ase2B.mean(numeric_only=True)) + ' ± ' + str(np.std(ase2B, ddof=1)) + ')%, where unc is std.')\n", + " ' and syserr is (' + str(ase2B.mean()) + ' ± ' + str(np.std(ase2B, ddof=1)) + ')%, where unc is std.')\n", "display(results_sweep_1freqmean.loc[[min1dsyserrFreq2,min2dsyserrFreq2]])\n", "\n", "\"\"\"\n", @@ -5503,7 +5543,7 @@ "# calculations\n", "results_sweep_1freq['R1Phase2_wrap']=results_sweep_1freq.R1_phase_noiseless2%(2*np.pi) - 2*np.pi\n", "results_sweep_1freq_resort1=results_sweep_1freq.sort_values(by='R1Phase2_wrap')\n", - "results_sweep_1freq_resort1mean=results_sweep_1freq_resort1.groupby(by=['Freq2'], as_index=False).mean(numeric_only=True)\n", + "results_sweep_1freq_resort1mean=results_sweep_1freq_resort1.groupby(by=['Freq2'], as_index=False).mean()\n", "\n", "results_sweep_1freq, results_sweep_1freq_resort1mean = \\\n", " calc_error_interval(results_sweep_1freq, results_sweep_1freq_resort1mean, groupby='Freq2', confidenceinterval = .95)\n", @@ -5840,7 +5880,7 @@ "variedkey1 = paramname1 + '_set'\n", "variedkey2 = paramname2 + '_set'\n", "\n", - "resultsvary2mean = resultsvary2.groupby(by=[variedkey1, variedkey2],as_index=False).mean(numeric_only=True)" + "resultsvary2mean = resultsvary2.groupby(by=[variedkey1, variedkey2],as_index=False).mean()" ] }, { @@ -6599,7 +6639,7 @@ "source": [ "symb ='.'\n", "\n", - "resultsdfmonomerdoemeannump = resultsdfmonomerdoe.groupby(by=['num frequency points'],as_index=False).mean(numeric_only=True)\n", + "resultsdfmonomerdoemeannump = resultsdfmonomerdoe.groupby(by=['num frequency points'],as_index=False).mean()\n", "plt.plot(resultsdfmonomerdoe['num frequency points'],resultsdfmonomerdoe['log avgsyserr%_3D'], symb, color = co3, alpha = alpha)\n", "plt.plot(resultsdfmonomerdoe['num frequency points'],resultsdfmonomerdoe['log avgsyserr%_2D'], symb, color = co2, alpha = alpha)\n", "plt.plot(resultsdfmonomerdoe['num frequency points'],resultsdfmonomerdoe['log avgsyserr%_1D'], symb, color = co1, alpha = alpha)\n", From c4b8ba8a20c5f3708dd9de4efefd6e11d630cf2a Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 16 Jan 2023 12:53:49 -0500 Subject: [PATCH 014/101] FIX MAINT: move def text_color_legend to resonator_plotting.py FIX: measurementfreqs not defined. FIX: symb not defined Aesthetics: heatmaps for publication --- ...ach Simulated Two Coupled Resonators.ipynb | 74 +++++++++++++------ resonator_plotting.py | 12 +++ 2 files changed, 62 insertions(+), 24 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 3dd0ac0..2850d53 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -87,19 +87,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "def text_color_legend(**kwargs):\n", - " l = plt.legend(**kwargs)\n", - " # set text color in legend\n", - " for text in l.get_texts():\n", - " if '1D' in str(text):\n", - " text.set_color(co1)\n", - " elif '2D' in str(text):\n", - " text.set_color(co2)\n", - " elif '3D' in str(text):\n", - " text.set_color(co3)\n", - " return l" - ] + "source": [] }, { "cell_type": "code", @@ -1171,15 +1159,6 @@ "reslist" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "measurementfreqs + reslist" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1195,6 +1174,11 @@ "else:\n", " demo = False\n", " \n", + "try:\n", + " measurementfreqs\n", + "except NameError:\n", + " measurementfreqs = reslist\n", + " \n", "\n", "if resonatorsystem == 15:\n", " measurementfreqs = desiredfreqs # Brittany's expermental setup\n", @@ -4883,6 +4867,24 @@ "reslist" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax.get_xlim()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.linspace(min(ax.get_xlim())/np.pi, max(ax.get_xlim())/np.pi,3) * np.pi" + ] + }, { "cell_type": "code", "execution_count": null, @@ -4891,6 +4893,8 @@ "source": [ "# *****\n", "figsize = (figwidth/2, 1.3)\n", + "symb = '.'\n", + "lw = 1\n", "datestr = datestring()\n", "\n", "SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", @@ -4927,9 +4931,25 @@ "if not MONOMER:\n", " plt.figure(figsize = (1.555,1.3) )\n", " grid=resultsdfsweep2freqorigmean.pivot_table(\n", - " index = 'R2_phase_noiseless1', columns = 'R2_phase_noiseless2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", + " index = 'R2_phase_noiseless1', columns = 'R1_phase_noiseless2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", " myheatmap(grid, \"log avgsyserr%_1D\", cmap = 'magma_r')#, vmax = 2); \n", - " plt.axis('equal')\n", + " if resonatorsystem == 11 or resonatorsystem == 110:\n", + " plt.xticks([0, -np.pi/2, -np.pi])\n", + " plt.yticks([0, -np.pi, -2*np.pi])\n", + " \n", + " #plt.axis('equal')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " plt.figure(figsize = (1.555,1.3) )\n", + " grid=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'R2_phase_noiseless1', columns = 'R1_phase_noiseless2', values = 'Freq1').sort_index(axis = 0, ascending = False)\n", + " myheatmap(grid, \"Freq1\", cmap = 'magma_r')#, vmax = 2); \n", + " if resonatorsystem == 11 or resonatorsystem == 110:\n", + " plt.xticks([0, -np.pi/2, -np.pi])\n", + " plt.yticks([0, -np.pi, -2*np.pi])\n", + " \n", + " #plt.axis('equal')\n", " plt.tight_layout()\n", " plt.show()\n", " \n", @@ -4970,6 +4990,9 @@ "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", "#plt.ylim(ymin=0, ymax=maxsyserr_to_plot)\n", + "plt.title('$\\omega_b = $' + str(round(resultsdfmean.Freq2.min(),1)) + ' to ' \n", + " + str(round(resultsdfmean.Freq2.max(),1)) + ' rad/s',\n", + " loc='right')\n", "plt.xlabel('$\\omega_a$ (rad/s)')\n", "plt.ylabel('avg err (%)')\n", "plt.yscale('log')\n", @@ -5027,6 +5050,9 @@ "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", "plt.yscale('log')\n", "text_color_legend()\n", + "plt.title('$\\omega_b = $' + str(round(resultsdfmean.Freq2.min(),1)) + ' to ' \n", + " + str(round(resultsdfmean.Freq2.max(),1)) + ' rad/s',\n", + " loc='right')\n", "plt.xlabel('$\\omega_a$ (rad/s)')\n", "plt.ylabel('avg err (%)')\n", "plt.tight_layout()\n", diff --git a/resonator_plotting.py b/resonator_plotting.py index 1f7ba7a..ec68796 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -98,6 +98,18 @@ def set_format(): plt.rcParams['svg.fonttype'] = 'none' plt.rcParams['axes.titlepad'] = -5 + +def text_color_legend(**kwargs): + l = plt.legend(**kwargs) + # set text color in legend + for text in l.get_texts(): + if '1D' in str(text): + text.set_color(co1) + elif '2D' in str(text): + text.set_color(co2) + elif '3D' in str(text): + text.set_color(co3) + return l """ Plot amplitude or phase versus frequency with set values, simulated data, and SVD results. Demo: if true, plot without tick marks """ From 46ce02a003cb6f95c65bb18c0d8ef0d3cde24f10 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 20 Jan 2023 01:04:41 -0500 Subject: [PATCH 015/101] More plots 1) violin plot uses colored spots instead of white 2) Unfinished attempt to include log minor ticks 3) New plots showing that the phase error (privileged) is the most important thing 4) paper style graphs for varying number of frequencies measured 5) simulated_experiment now catches issues where the SVD does not converge. --- ...ach Simulated Two Coupled Resonators.ipynb | 198 +++++++++++++----- simulated_experiment.py | 6 +- 2 files changed, 148 insertions(+), 56 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 2850d53..1e0b8d9 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -46,7 +46,7 @@ "\n", "sns.set_context('paper')\n", "\n", - "savefolder = r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files'\n", + "savefolder = r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\simulation_export'\n", "saving = True\n", "os.chdir(savefolder)\n", "\n", @@ -349,7 +349,7 @@ "noiselevel = 5\n", "forceboth= False\"\"\"\n", "\n", - "\"\"\"\n", + "\n", "### Can I scale Brittany's experimental data this way? Or do I need to incorporate the x10^-17 bits?\n", "resonatorsystem = 14\n", "k1_set = 1\n", @@ -362,7 +362,7 @@ "F_set = 1.861\n", "noiselevel = 1\n", "forceboth = False\n", - "\"\"\"\n", + "\n", "\"\"\"\n", "### Does this make sense for Brittany's experimental data?\n", "resonatorsystem = 15\n", @@ -658,33 +658,6 @@ "print('resonant phase at:',res_freq_numeric(mode = 'phase',vals_set=vals_set, MONOMER=MONOMER,forceboth=forceboth, includefreqs=reslist,minfreq=minfreq, maxfreq=maxfreq))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.describe()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -711,7 +684,11 @@ " display(pd.DataFrame(np.array(Zmatrix, dtype = np.double), columns = parameternames))\n", "\n", "#SVD\n", - "u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True)\n", + "try:\n", + " u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True)\n", + "except:\n", + " print('Could not solve')\n", + " \n", "#u, s, vh = sc.linalg.svd(Zmatrix, full_matrices = False, lapack_driver = 'gesvd')\n", "#vh = make_real_iff_real(vh)\n", "\n", @@ -1407,7 +1384,7 @@ " boxwhiskerfigsize = (figwidth*1,figheight)\n", "print('Box and Whisker figsize:', boxwhiskerfigsize)\n", "\n", - "with sns.axes_style(rc={'xtick.bottom': False,}):\n", + "with sns.axes_style(rc={'xtick.bottom': False,})\n", " fig, ax1 = plt.subplots(1,1, figsize = boxwhiskerfigsize, dpi=150)\n", " # notch shows 95% confidence interval of the median\n", " ax = ax1\n", @@ -2106,7 +2083,7 @@ " thisres = vary_num_p_with_fixed_freqdiff( vals_set, noiselevel, \n", " MONOMER, forceboth,reslist = reslist,\n", " minfreq=minfreq, maxfreq = maxfreq,\n", - " verbose = verbose, just_res1 = True, \n", + " verbose = verbose, just_res1 = False, \n", " max_num_p=max_num_p, \n", " freqdiff = freqdiff,\n", " n=n, # number of frequencies for R^2\n", @@ -2179,30 +2156,23 @@ "resultsvarynump, resultsvarynumpmean = calc_error_interval(resultsvarynump, resultsvarynumpmean, groupby='num frequency points', fractionofdata = .95)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "plt.xticks(list(range(0,max_num_p+1,5)))\n" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [], "source": [ "print('Noiselevel: ' + str(noiselevel))\n", "symb = '.' # plotting style\n", + "lw = 0.5\n", "co1 = 'C0'\n", "co2 = 'C1'\n", "co3 = 'C2'\n", + "#saving = False\n", "\n", + "set_format()\n", "reps = int(len(resultsvarynump) / len(resultsvarynumpmean))\n", "\n", "figsize = (figwidth, 1.48)\n", @@ -2218,7 +2188,7 @@ "plt.plot(resultsvarynumpmean['num frequency points'],resultsvarynumpmean['avgsyserr%_1D'], label='1D', color = co1)\n", "text_color_legend()\n", "#plt.gca().set_yscale('log')\n", - "plt.xlabel('num frequency points')\n", + "plt.xlabel('num frequency points');\n", "plt.ylabel('Avg err (%)')\n", "plt.tight_layout()\n", "if saving:\n", @@ -2237,7 +2207,7 @@ "plt.xlim(xmin=0)\n", "plt.gca().set_yscale('log')\n", "#plt.xlabel('num frequency points')\n", - "plt.xlabel('number of frequency points')\n", + "plt.xlabel('number of frequency points');\n", "plt.ylabel('Avg err (%)')\n", "plt.tight_layout()\n", "if saving:\n", @@ -2249,6 +2219,7 @@ "\n", "# ***\n", "plt.figure(figsize=figsize)\n", + "axa = plt.gca()\n", "dimensions = ['3D', '2D', '1D']\n", "colors = [co3, co2, co1]\n", "X = resultsvarynumpmean['num frequency points']\n", @@ -2265,7 +2236,7 @@ "plt.xlim(xmin=0)\n", "plt.yscale('log')\n", "#plt.xlabel('num frequency points')\n", - "plt.xlabel('number of frequency points')\n", + "plt.xlabel('number of frequency points');\n", "plt.ylabel('Avg err (%)')\n", "plt.tight_layout()\n", "if saving:\n", @@ -2304,19 +2275,26 @@ "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_3D'], lw = lw, color = co3)\n", "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_2D'], lw = lw, color = co2)\n", "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_1D'], lw = lw, color = co1)\n", - "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_3D'], '.', ms = 2, color = 'w')\n", - "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_2D'], '.', ms = 2, color = 'w')\n", - "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_1D'], '.', ms = 2, color = 'w')\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_3D'], '.', ms = 2, color = co3)\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_2D'], '.', ms = 2, color = co2)\n", + "plt.plot(resultsvarynumpmean['num frequency points']-2,resultsvarynumpmean['log avgsyserr%_1D'], '.', ms = 2, color = co1)\n", "plt.ylabel('Avg err (%)')\n", "plt.xlim(xmin=-2)\n", "xt = list(range(-2,max_num_p-1,5))\n", "xt = xt + [2-2]\n", "plt.xticks(xt);\n", "yt,_ = plt.yticks()\n", - "yt = yt[1:-1]\n", + "if MONOMER:\n", + " yt = yt[1:-1]\n", + "elif resonatorsystem == 11:\n", + " yt = range(-3,5,1)\n", + " #ytminor = np.arange(-3,4,.1)\n", + " #plt.yticks(ytminor, [10**y for y in ytminor], axis = 'minor',)\n", "print(yt)\n", "#plt.gca().Axes.set_ylabels([10**y for y in yt]) # undo the log.\n", "plt.yticks(yt,[10**y for y in yt] );\n", + "plt.yticks([], minor=True)\n", + "\n", "#plt.ticklabel_format(axis='y', style='sci', ) # AttributeError: This method only works with the ScalarFormatter\n", "#plt.legend()\n", "plt.tight_layout()\n", @@ -2372,10 +2350,18 @@ "plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['K1syserr%_1D'], symb, alpha = .3, label='1D')\n", "text_color_legend()\n", "plt.gca().set_yscale('log')\n", - "plt.xlabel('R1 phase diff mean (privileged)')\n", + "plt.xlabel('R1 phase diff mean (privileged)');\n", "plt.ylabel('k1 syserr (%)')\n", "\n", "plt.figure()\n", + "#plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , label='2D')\n", + "plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['avgsyserr%_1D'], symb, alpha = .3, label='1D')\n", + "text_color_legend()\n", + "plt.gca().set_yscale('log')\n", + "plt.xlabel('R1 phase diff mean (privileged)');\n", + "plt.ylabel('avgsyserr (%)')\n", + "\n", + "plt.figure()\n", "plt.plot(resultsvarynump['meanSNR_R1'],resultsvarynump['avgsyserr%_3D'], symb, color = co3, alpha = .5)# , label = '3D')\n", "plt.plot(resultsvarynump['meanSNR_R1'],resultsvarynump['avgsyserr%_2D'], symb,color = co2, alpha = .5)# , label = '2D')\n", "plt.plot(resultsvarynump['meanSNR_R1'],resultsvarynump['avgsyserr%_1D'], symb,color = co1, alpha = .5)#, label = '1D' )\n", @@ -2400,10 +2386,83 @@ "plt.plot(resultsvarynump['minSNR_R1'],resultsvarynump['avgsyserr%_1D'], symb, alpha = .5 )\n", "plt.gca().set_yscale('log')\n", "plt.gca().set_xscale('log')\n", - "plt.xlabel('minSNR_R1')\n", + "plt.xlabel('minSNR_R1');\n", "plt.ylabel('Avg err (%)');" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "figsize = (2,2)\n", + "plt.figure(figsize = figsize)\n", + "#plt.plot(resultsvarynump['R1Ampsyserr%mean(priv)'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , label='2D')\n", + "plt.plot(resultsvarynump['R1Ampsyserr%mean(priv)'],resultsvarynump['avgsyserr%_1D'] , symb, alpha = .3, label='1D')\n", + "text_color_legend()\n", + "plt.gca().set_yscale('log')\n", + "plt.xlabel('R1 Amp syserr mean (priv) (%)')\n", + "plt.ylabel('avgsyserr (%)')\n", + "\n", + "plt.figure(figsize = figsize)\n", + "#plt.plot(resultsvarynump['R1Ampsyserr%mean(priv)'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , label='2D')\n", + "plt.plot(resultsvarynump['R2Ampsyserr%mean(priv)'],resultsvarynump['avgsyserr%_1D'] , symb, alpha = .3, label='1D')\n", + "text_color_legend()\n", + "plt.gca().set_yscale('log')\n", + "plt.xlabel('R2 Amp syserr mean (priv) (%)')\n", + "plt.ylabel('avgsyserr (%)')\n", + "\n", + "plt.figure(figsize = figsize)\n", + "plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['avgsyserr%_3D'], symb, color=co3, alpha = .3 , label='3D')\n", + "plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['avgsyserr%_2D'], symb, color=co2, alpha = .3 , label='2D')\n", + "plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['avgsyserr%_1D'], symb, color = co1, alpha = .3, label='1D')\n", + "text_color_legend()\n", + "plt.gca().set_yscale('log')\n", + "plt.xlabel('R1 phase diff mean (privileged)');\n", + "plt.ylabel('avgsyserr (%)')\n", + "\n", + "plt.figure(figsize = figsize)\n", + "#plt.plot(resultsvarynump['R1phasediffmean(priv)'],resultsvarynump['K1syserr%_2D'], symb, alpha = .3 , label='2D')\n", + "plt.plot(resultsvarynump['R2phasediffmean(priv)'],resultsvarynump['avgsyserr%_1D'], symb, alpha = .3, label='1D')\n", + "text_color_legend()\n", + "plt.gca().set_yscale('log')\n", + "plt.xlabel('R2 phase diff mean (privileged)');\n", + "plt.ylabel('avgsyserr (%)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#np.logspace(-3,-2,10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "#with rc.Params()\n", + "ytminor = np.logspace(start=np.log10(1),stop = np.log10(2),num = 10)\n", + "print(yt)\n", + "print(ytminor)\n", + "print([np.log10(y) for y in ytminor])\n", + "print([10**y for y in ytminor])\n", + "plt.figure()\n", + "#plt.yticks(yt,[10**y for y in yt] );\n", + "plt.yticks(yt)\n", + "\n", + "plt.yticks( ytminor, minor = True)\n", + "\"\"\"" + ] + }, { "cell_type": "code", "execution_count": null, @@ -4349,7 +4408,7 @@ "metadata": {}, "outputs": [], "source": [ - "stophere # Sweep TWO frequencies / Sweep 2 freq / 2freq" + "stophere # Sweep TWO frequencies / Sweep 2 freq / 2freq / Sweep two freq / sweep pair of frequencies" ] }, { @@ -4484,6 +4543,7 @@ " saveddf = 'sys11,2freq,2023-01-07 13;53;00.csv' # DIMER\n", " resonatorsystem = 110 # the 0 means it was reloaded\n", " resultsdfsweep2freqorig = pd.read_csv(saveddf)\n", + " print('Opened exiting file:', saveddf)\n", "\n", "resultsdfsweep2freqorigmean = resultsdfsweep2freqorig.groupby(by=['Freq1', 'Freq2'],as_index=False).mean(numeric_only=True)\n", "\n", @@ -4890,12 +4950,26 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "list(range(round(maxfreq)+1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], "source": [ "# *****\n", "figsize = (figwidth/2, 1.3)\n", "symb = '.'\n", "lw = 1\n", "datestr = datestring()\n", + "roundedres = [round(w,2) for w in reslist[:2]]\n", + "ticklist = [round(minfreq),round(maxfreq)] + roundedres\n", + "saving = True\n", "\n", "SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", @@ -4909,6 +4983,11 @@ "plt.title('1D-SVD')\n", "plt.ylabel('$\\omega_a$ (rad/s)')\n", "plt.xlabel('$\\omega_b$ (rad/s)')\n", + "if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " #plt.xticks(ticklist)\n", + " #plt.yticks(ticklist)\n", + " plt.xticks(range(round(maxfreq)+1))\n", + " plt.yticks(range(round(maxfreq)+1))\n", "plt.axis('equal')\n", "plt.tight_layout()\n", "if saving:\n", @@ -4921,6 +5000,11 @@ "plt.title('2D-SVD')\n", "plt.ylabel('$\\omega_a$ (rad/s)')\n", "plt.xlabel('$\\omega_b$ (rad/s)')\n", + "if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " #plt.xticks(ticklist)\n", + " #plt.yticks(ticklist)\n", + " plt.xticks(range(round(maxfreq)+1))\n", + " plt.yticks(range(round(maxfreq)+1))\n", "plt.axis('equal')\n", "plt.tight_layout()\n", "if saving:\n", @@ -7045,7 +7129,11 @@ " frequencycolumn = 'drive', complexamplitude1 = 'R1AmpCom', \n", " complexamplitude2 = 'R2AmpCom',\n", " MONOMER=MONOMER, forceboth=forceboth, dtype =complex)\n", - " u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True)\n", + " try:\n", + " u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True)\n", + " except LinAlgError:\n", + " print('Could not solve')\n", + " continue\n", " vh = make_real_iff_real(vh)\n", "\n", " ## 1D NULLSPACE\n", diff --git a/simulated_experiment.py b/simulated_experiment.py index 859e54b..b1395dd 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -341,7 +341,11 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force Zmatrix = Zmat(df, frequencycolumn = 'drive', complexamplitude1 = 'R1AmpCom', complexamplitude2 = 'R2AmpCom', MONOMER=MONOMER, forceboth=forceboth, dtype=complex) - u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True) + try: + u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True) + except: + print('Could not solve with noiselevel', noiselevel) + continue vh = make_real_iff_real(vh) theseresults.append(approx_Q(m = m1_set, k = k1_set, b = b1_set)) From 9cb24f81893e57f0057d309689baa9dc96bc8bc7 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sun, 29 Jan 2023 23:05:53 -0500 Subject: [PATCH 016/101] typo --- Algebraic Approach Simulated Two Coupled Resonators.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 1e0b8d9..86ec4b2 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -4543,7 +4543,7 @@ " saveddf = 'sys11,2freq,2023-01-07 13;53;00.csv' # DIMER\n", " resonatorsystem = 110 # the 0 means it was reloaded\n", " resultsdfsweep2freqorig = pd.read_csv(saveddf)\n", - " print('Opened exiting file:', saveddf)\n", + " print('Opened existing file:', saveddf)\n", "\n", "resultsdfsweep2freqorigmean = resultsdfsweep2freqorig.groupby(by=['Freq1', 'Freq2'],as_index=False).mean(numeric_only=True)\n", "\n", From fc8db27e15aeec4fe2a3d68ac0e67894ab663468 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 3 Feb 2023 15:39:43 -0500 Subject: [PATCH 017/101] FIX --- Algebraic Approach Simulated Two Coupled Resonators.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 86ec4b2..7bb5e9e 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -5547,7 +5547,7 @@ "outputs": [], "source": [ "results_sweep_1freq, results_sweep_1freqmean = \\\n", - " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', confidenceinterval = .95)\n" + " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', fractionofdata = .95)\n" ] }, { @@ -5656,7 +5656,7 @@ "results_sweep_1freq_resort1mean=results_sweep_1freq_resort1.groupby(by=['Freq2'], as_index=False).mean()\n", "\n", "results_sweep_1freq, results_sweep_1freq_resort1mean = \\\n", - " calc_error_interval(results_sweep_1freq, results_sweep_1freq_resort1mean, groupby='Freq2', confidenceinterval = .95)\n", + " calc_error_interval(results_sweep_1freq, results_sweep_1freq_resort1mean, groupby='Freq2', fractionofdata = .95)\n", "\n", "# plotting\n", "plt.axvline(d1/np.pi, color='grey')\n", From 4412a21f6c6b399748de93cade088ec58db23f55 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 3 Feb 2023 15:40:03 -0500 Subject: [PATCH 018/101] BUILD: 3D-SVD heatmap --- ...ach Simulated Two Coupled Resonators.ipynb | 83 +++++++++++++++++-- 1 file changed, 77 insertions(+), 6 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 7bb5e9e..263af72 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -38,6 +38,8 @@ "from time import time\n", "import pyDOE2\n", "import sys\n", + "import warnings\n", + "warnings.filterwarnings(action='once')\n", "\n", "#from decimal import Decimal\n", "#sys.path.append('..') # myheatmap is in parent directory\n", @@ -2545,6 +2547,8 @@ " return resultsdf\n", "\n", "noises = np.logspace(-5,3,100)\n", + "if resonatorsystem == 15:\n", + " noises = np.logspace(-9,3,100)\n", "\n", "# Ran 50 times in 33.037 sec\n", "# Ran 80 times in 90.253 sec on desktop 21214\n", @@ -2738,11 +2742,14 @@ " \n", " #sns.set_context('paper')\n", " ## cleaned figures\n", - " figsize = (figwidth/2,figwidth/2)\n", " if resonatorsystem == 9:\n", " figsize = (2.7,2.8)\n", " elif resonatorsystem == 2:\n", " figsize = (1.4,figwidth/2) # width, height \n", + " elif resonatorsystem == 15:\n", + " figsize = (4,4)\n", + " else:\n", + " figsize = (figwidth/2,figwidth/2)\n", " plt.figure(figsize = figsize, dpi=150)\n", " #signal / resultsvarynoiselevelmean['stdev']\n", " axa = plt.gca()\n", @@ -2780,7 +2787,8 @@ " if plotlog:\n", " axb.set_xticks(10**logmeanSNRticks)\n", " axb.set_xlabel('Mean SNR for ' + R)\n", - " plt.ylim(ymax=100, ymin=1e-8)\n", + " if resonatorsystem == 11 or resonatorsystem == 110:\n", + " plt.ylim(ymax=100, ymin=1e-8)\n", " plt.tight_layout()\n", " if saving:\n", " savename = 'sys' + str(resonatorsystem) + 'err_vs_SNR_' + R + ',cleaned,'+ \\\n", @@ -4408,7 +4416,7 @@ "metadata": {}, "outputs": [], "source": [ - "stophere # Sweep TWO frequencies / Sweep 2 freq / 2freq / Sweep two freq / sweep pair of frequencies" + "stophere # Sweep TWO frequencies / Sweep 2 freq / 2freq / Sweep two freq / sweep pair of frequencies / vary two freqs" ] }, { @@ -4687,6 +4695,8 @@ " plt.yticks([res1, res2])\n", " except:\n", " pass\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "fig, ((ax1, ax2, ax7), (ax3, ax4, ax4b), (ax5, ax6, ax6b)) = plt.subplots(3, 3, figsize=figsize)\n", "\n", @@ -4970,12 +4980,18 @@ "roundedres = [round(w,2) for w in reslist[:2]]\n", "ticklist = [round(minfreq),round(maxfreq)] + roundedres\n", "saving = True\n", + "do_3D = True\n", "\n", "SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", "SSgrid2D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_2D').sort_index(axis = 0, ascending = False)\n", - "vmin = min(SSgrid1D.min().min(), SSgrid2D.min().min()) # use same scale for both\n", + "if do_3D:\n", + " SSgrid3D=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_3D').sort_index(axis = 0, ascending = False)\n", + " vmin = min(SSgrid1D.min().min(), SSgrid2D.min().min(),SSgrid3D.min().min()) # use same scale for all 3\n", + "else:\n", + " vmin = min(SSgrid1D.min().min(), SSgrid2D.min().min()) # use same scale for both\n", "vmax = 2\n", "\n", "plt.figure(figsize = (1.555,1.3) )\n", @@ -5012,6 +5028,23 @@ " savefigure(savename)\n", "plt.show()\n", "\n", + "plt.figure(figsize = (1.555,1.3) )\n", + "myheatmap(SSgrid3D, \"log average error\", cmap='magma_r'); \n", + "plt.title('3D-SVD')\n", + "plt.ylabel('$\\omega_a$ (rad/s)')\n", + "plt.xlabel('$\\omega_b$ (rad/s)')\n", + "if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " #plt.xticks(ticklist)\n", + " #plt.yticks(ticklist)\n", + " plt.xticks(range(round(maxfreq)+1))\n", + " plt.yticks(range(round(maxfreq)+1))\n", + "plt.axis('equal')\n", + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"3D2freqheatmap,\" + datestr\n", + " savefigure(savename)\n", + "plt.show()\n", + "\n", "if not MONOMER:\n", " plt.figure(figsize = (1.555,1.3) )\n", " grid=resultsdfsweep2freqorigmean.pivot_table(\n", @@ -5151,7 +5184,37 @@ "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", "text_color_legend()\n", "#plt.yscale('log')\n", - "plt.ylim(ymin=0)\n", + "#plt.ylim(ymin=0)\n", + "plt.ylim(ymin=0, ymax=10)\n", + "plt.title('$\\omega_b = $' + str(round(resultsdfmean.Freq2.min(),1)) + ' to ' \n", + " + str(round(resultsdfmean.Freq2.max(),1)) + ' rad/s',\n", + " loc='right')\n", + "plt.xlabel('$\\omega_a$ (rad/s)')\n", + "plt.ylabel('avg err (%)');\n", + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"2freqavgerr,\" + datestr\n", + " savefigure(savename)\n", + " resultsdfmeanbyfreq1[['Freq1','log avgsyserr%_1D','log avgsyserr%_2D', 'log avgsyserr%_3D']].to_csv(savename + '.csv')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = figsize)\n", + "plt.axvline(reslist[0], color='gray', lw=0.5)\n", + "if not MONOMER:\n", + " plt.axvline(reslist[1], color='gray', lw=0.5)\n", + "#plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_3D'], color = co3, label='3D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_2D'], color = co2, label='2D')\n", + "plt.plot(X, 10**resultsdfmeanbyfreq1['log avgsyserr%_1D'], color = co1,label='1D')\n", + "text_color_legend()\n", + "#plt.yscale('log')\n", + "plt.ylim(ymin=0, ymax=100)\n", "plt.title('$\\omega_b = $' + str(round(resultsdfmean.Freq2.min(),1)) + ' to ' \n", " + str(round(resultsdfmean.Freq2.max(),1)) + ' rad/s',\n", " loc='right')\n", @@ -5647,7 +5710,15 @@ "plt.legend()\n", "plt.ylabel('avgsyserr%_1D');\n", "plt.yscale('log')\n", - "plt.show()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "\n", "plt.figure(figsize=figsize) \n", "# calculations\n", From 9c1016678eddc8909074b597eeba50a99c3ab34a Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 3 Feb 2023 15:48:01 -0500 Subject: [PATCH 019/101] BUILD: Sweep frequency: dimer --- ...ach Simulated Two Coupled Resonators.ipynb | 90 +++++++++++++++---- 1 file changed, 71 insertions(+), 19 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 263af72..e1eaae3 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -5639,7 +5639,7 @@ "source": [ "from matplotlib.ticker import AutoLocator\n", "widths=.03 # widths of boxplots\n", - "if False:\n", + "if True:\n", " lw=1\n", " figsize=(15, 5)\n", " if repeats > 100:\n", @@ -5686,8 +5686,8 @@ "plt.show()\n", "\n", "results_sweep_1freq.boxplot(column='log avgsyserr%_1D', \n", - " #by='Freq2',\n", - " by = 'R1Phase2_wrap',\n", + " by='Freq2',\n", + " #by = 'R1Phase2_wrap',\n", " grid=False, #fontsize=7, rot=90, \n", " positions=results_sweep_1freq.Freq2.unique(), widths=widths, \n", " #color='k', \n", @@ -5710,7 +5710,9 @@ "plt.legend()\n", "plt.ylabel('avgsyserr%_1D');\n", "plt.yscale('log')\n", - "plt.show()\n" + "plt.show()\n", + "\n", + "beep()" ] }, { @@ -5719,11 +5721,20 @@ "metadata": {}, "outputs": [], "source": [ + "figsize = (8,4)\n", + "\n", + "if MONOMER:\n", + " Rnote = ''\n", + " x_axis_phase = 'R1Phase2_wrap'\n", + "else:\n", + " x_axis_phase = 'R2Phase2_wrap'\n", + " Rnote = ' at R2'\n", + "\n", "\n", "plt.figure(figsize=figsize) \n", "# calculations\n", - "results_sweep_1freq['R1Phase2_wrap']=results_sweep_1freq.R1_phase_noiseless2%(2*np.pi) - 2*np.pi\n", - "results_sweep_1freq_resort1=results_sweep_1freq.sort_values(by='R1Phase2_wrap')\n", + "results_sweep_1freq[x_axis_phase]=results_sweep_1freq.R1_phase_noiseless2%(2*np.pi) - 2*np.pi\n", + "results_sweep_1freq_resort1=results_sweep_1freq.sort_values(by=x_axis_phase)\n", "results_sweep_1freq_resort1mean=results_sweep_1freq_resort1.groupby(by=['Freq2'], as_index=False).mean()\n", "\n", "results_sweep_1freq, results_sweep_1freq_resort1mean = \\\n", @@ -5731,18 +5742,18 @@ "\n", "# plotting\n", "plt.axvline(d1/np.pi, color='grey')\n", - "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['avgsyserr%_3D'], \n", + "plt.plot(results_sweep_1freq_resort1[x_axis_phase]/np.pi, results_sweep_1freq_resort1['avgsyserr%_3D'], \n", " '.', ms = ms,color=co3, alpha=alpha )\n", - "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['avgsyserr%_2D'], \n", + "plt.plot(results_sweep_1freq_resort1[x_axis_phase]/np.pi, results_sweep_1freq_resort1['avgsyserr%_2D'], \n", " '.', ms = ms,color=co2, alpha=alpha )\n", - "plt.plot(results_sweep_1freq_resort1.R1Phase2_wrap/np.pi, results_sweep_1freq_resort1['avgsyserr%_1D'], \n", + "plt.plot(results_sweep_1freq_resort1[x_axis_phase]/np.pi, results_sweep_1freq_resort1['avgsyserr%_1D'], \n", " '.', ms = ms,color=co1, alpha=alpha)\n", "\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", + "plt.plot(results_sweep_1freq_resort1mean[x_axis_phase]/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", " lw=lw,color=co3, label='3D' )\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", + "plt.plot(results_sweep_1freq_resort1mean[x_axis_phase]/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", " lw=lw,color=co2, label='2D' )\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", + "plt.plot(results_sweep_1freq_resort1mean[x_axis_phase]/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", " lw=lw,color=co1, label='1D')\n", "\n", "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", @@ -5760,7 +5771,7 @@ "\n", "dimensions = ['3D', '2D', '1D']\n", "colors = [co3, co2, co1]\n", - "X = results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi \n", + "X = results_sweep_1freq_resort1mean[x_axis_phase]/np.pi \n", "for i in range(3):\n", " Yhigh = results_sweep_1freq_resort1mean['E_upper_' + dimensions[i]]\n", " Ylow = results_sweep_1freq_resort1mean['E_lower_' + dimensions[i]] \n", @@ -5768,11 +5779,11 @@ " plt.plot(X, Ylow, color = colors[i], alpha = .3, linewidth=.3)\n", " axa.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", "\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", + "plt.plot(results_sweep_1freq_resort1mean[x_axis_phase]/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_3D'], \n", " lw=lw,color=co3, label='3D' )\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", + "plt.plot(results_sweep_1freq_resort1mean[x_axis_phase]/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_2D'], \n", " lw=lw,color=co2, label='2D' )\n", - "plt.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", + "plt.plot(results_sweep_1freq_resort1mean[x_axis_phase]/np.pi, 10**results_sweep_1freq_resort1mean['log avgsyserr%_1D'], \n", " lw=lw,color=co1, label='1D')\n", "\n", "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", @@ -5786,7 +5797,7 @@ " datestr = datestring()\n", " savename = \"sys\" + str(resonatorsystem) + ','+ \"sweepfreq2,\" + datestr\n", " savefigure(savename)\n", - " results_sweep_1freq_resort1mean[['R1Phase2_wrap','log avgsyserr%_1D','log avgsyserr%_2D','log avgsyserr%_3D']].to_csv(\n", + " results_sweep_1freq_resort1mean[[x_axis_phase,'log avgsyserr%_1D','log avgsyserr%_2D','log avgsyserr%_3D']].to_csv(\n", " savename + '.csv')\n", "plt.show()\n", "\n", @@ -5802,8 +5813,8 @@ "plt.title('');\"\"\"\n", "\n", "fig, ax=plt.subplots(subplot_kw={'projection': 'polar'})\n", - "ax.plot(results_sweep_1freq_resort1.R1Phase2_wrap, results_sweep_1freq_resort1['log avgsyserr%_1D'], '.', color=co1, alpha=alpha)\n", - "ax.plot(results_sweep_1freq_resort1mean.R1Phase2_wrap, results_sweep_1freq_resort1mean['log avgsyserr%_1D'], color=co1 )\n", + "ax.plot(results_sweep_1freq_resort1[x_axis_phase], results_sweep_1freq_resort1['log avgsyserr%_1D'], '.', color=co1, alpha=alpha)\n", + "ax.plot(results_sweep_1freq_resort1mean[x_axis_phase], results_sweep_1freq_resort1mean['log avgsyserr%_1D'], color=co1 )\n", "plt.title('Log Avg Err 1D (%)')\n", "plt.show()\n", "\n", @@ -5859,6 +5870,47 @@ "beep()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_sweep_1freq, results_sweep_1freqmean = \\\n", + " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', fractionofdata = .95)\n", + "\n", + "plt.figure(figsize=figsize) \n", + "ax = plt.gca()\n", + "lw = 1\n", + "#plt.axvline(res1, color='grey')\n", + "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_2D'], '.', \n", + "# ms = ms, color=co2, alpha=alpha )\n", + "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_1D'], '.', \n", + "# ms = ms, color=co1, alpha=alpha)\n", + "\n", + "dimensions = [ '2D', '1D']\n", + "colors = [ co2, co1]\n", + "X = results_sweep_1freqmean.Freq2 \n", + "for i in range(len(dimensions)):\n", + " Yhigh = results_sweep_1freq_resort1mean['E_upper_' + dimensions[i]]\n", + " Ylow = results_sweep_1freq_resort1mean['E_lower_' + dimensions[i]] \n", + " plt.plot(X, Yhigh, color = colors[i], alpha = .8, linewidth=.5)\n", + " plt.plot(X, Ylow, color = colors[i], alpha = .8, linewidth=.5)\n", + " ax.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", + "\n", + "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, lw=lw, label='2D' )\n", + "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, lw=lw, label='1D')\n", + "plt.xlabel('Freq2')\n", + "#W = approx_width(k2_set, m2_set, b2_set)\n", + "#plt.xlim(xmin = res2-1, xmax = res2+1) #****\n", + "plt.xlim(2.5,4.5)\n", + "plt.ylim(6e-2, 3e3)\n", + "text_color_legend()\n", + "plt.ylabel('Avg err (%)');\n", + "plt.yscale('log')\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, From e7964fca2fa883ce3e5ee82103342d98b84b18d4 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 3 Feb 2023 15:53:31 -0500 Subject: [PATCH 020/101] BUILD: sweep freq --- ...ach Simulated Two Coupled Resonators.ipynb | 67 ++++++++++++++++--- 1 file changed, 59 insertions(+), 8 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index e1eaae3..1206cc5 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -5707,8 +5707,51 @@ "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, lw=lw, label='2D' )\n", "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, lw=lw, label='1D')\n", "plt.xlabel('Freq2')\n", - "plt.legend()\n", - "plt.ylabel('avgsyserr%_1D');\n", + "text_color_legend()\n", + "plt.ylabel('Avg err (%)');\n", + "plt.yscale('log')\n", + "plt.show()\n", + "\n", + "beep()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_sweep_1freq, results_sweep_1freqmean = \\\n", + " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', fractionofdata = .95)\n", + "\n", + "plt.figure(figsize=figsize) # *** for dimer figure, in progress\n", + "ax = plt.gca()\n", + "lw = 1\n", + "#plt.axvline(res1, color='grey')\n", + "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_2D'], '.', \n", + "# ms = ms, color=co2, alpha=alpha )\n", + "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_1D'], '.', \n", + "# ms = ms, color=co1, alpha=alpha)\n", + "\n", + "dimensions = [ '2D', '1D']\n", + "colors = [ co2, co1]\n", + "X = results_sweep_1freqmean.Freq2 \n", + "for i in range(len(dimensions)):\n", + " Yhigh = results_sweep_1freq_resort1mean['E_upper_' + dimensions[i]]\n", + " Ylow = results_sweep_1freq_resort1mean['E_lower_' + dimensions[i]] \n", + " plt.plot(X, Yhigh, color = colors[i], alpha = .8, linewidth=.5)\n", + " plt.plot(X, Ylow, color = colors[i], alpha = .8, linewidth=.5)\n", + " ax.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", + "\n", + "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, lw=lw, label='2D' )\n", + "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, lw=lw, label='1D')\n", + "plt.xlabel('Freq2')\n", + "#W = approx_width(k2_set, m2_set, b2_set)\n", + "#plt.xlim(xmin = res2-1, xmax = res2+1) #****\n", + "plt.xlim(2.5,4.5)\n", + "plt.ylim(6e-2, 3e3)\n", + "text_color_legend()\n", + "plt.ylabel('Avg err (%)');\n", "plt.yscale('log')\n", "plt.show()\n", "\n", @@ -5741,7 +5784,11 @@ " calc_error_interval(results_sweep_1freq, results_sweep_1freq_resort1mean, groupby='Freq2', fractionofdata = .95)\n", "\n", "# plotting\n", - "plt.axvline(d1/np.pi, color='grey')\n", + "plt.figure(figsize=figsize) \n", + "try:\n", + " plt.axvline(d1/np.pi, color='grey')\n", + "except NameError:\n", + " print('Calculating phase is broken')\n", "plt.plot(results_sweep_1freq_resort1[x_axis_phase]/np.pi, results_sweep_1freq_resort1['avgsyserr%_3D'], \n", " '.', ms = ms,color=co3, alpha=alpha )\n", "plt.plot(results_sweep_1freq_resort1[x_axis_phase]/np.pi, results_sweep_1freq_resort1['avgsyserr%_2D'], \n", @@ -5759,7 +5806,7 @@ "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", "plt.xlabel('Phase of Freq2'+ Rnote+' ($\\pi$)')\n", "plt.xticks([-1,-3/4, -1/2, -1/4, 0])\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.ylabel('avgsyserr (%)');\n", "plt.yscale('log')\n", "plt.show()\n", @@ -5767,7 +5814,11 @@ "# Export figure\n", "plt.figure(figsize=figsize) \n", "axa = plt.gca()\n", - "plt.axvline(d1/np.pi, color='grey')\n", + "plt.figure(figsize=figsize) \n", + "try:\n", + " plt.axvline(d1/np.pi, color='grey')\n", + "except NameError:\n", + " print('Calculating phase is broken')\n", "\n", "dimensions = ['3D', '2D', '1D']\n", "colors = [co3, co2, co1]\n", @@ -5825,7 +5876,7 @@ "plt.plot(results_sweep_1freqmean.arclength_R1, results_sweep_1freqmean['log avgsyserr%_1D'], lw=lw,color=co1, label='1D')\n", "plt.plot(results_sweep_1freqmean.arclength_R1, results_sweep_1freqmean['log avgsyserr%_2D'], lw=lw, color=co2, label='2D' )\n", "plt.xlabel('arclength_R1')\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.ylabel('log avgsyserr%_1D');\n", "plt.show()\n", "\n", @@ -5836,7 +5887,7 @@ "plt.plot(np.degrees(results_sweep_1freqmean.modifiedangle_R1), results_sweep_1freqmean['log avgsyserr%_2D'], lw=lw,color=co2, label='2D')\n", "#plt.xlim(xmin=-np.pi, xmax=np.pi)\n", "plt.xlabel('Twirly angle (deg) of Freq2'+ Rnote)\n", - "plt.legend()\n", + "text_color_legend()\n", "plt.ylabel('log avgsyserr%_1D');\n", "plt.show()\n", "\n", @@ -5863,7 +5914,7 @@ " label='2D', fontsize=7, rot=90 )\n", "plt.xticks([minfreq, maxfreq] + [round(w,3) for w in reslist])\n", "plt.xlabel('Freq2');\n", - "#plt.legend()\n", + "#text_color_legend()\n", "#plt.ylabel('log avgsyserr%_1D');\n", "\"\"\";\n", "\n", From 98fa73fe547a88150801af4319780b021217db59 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 4 Feb 2023 11:50:42 -0500 Subject: [PATCH 021/101] BUILD: return plot_info_1D --- sim_series_of_experiments.py | 15 +++++++++------ simulated_experiment.py | 13 ++++++++++--- 2 files changed, 19 insertions(+), 9 deletions(-) diff --git a/sim_series_of_experiments.py b/sim_series_of_experiments.py index 91351e0..f81e901 100644 --- a/sim_series_of_experiments.py +++ b/sim_series_of_experiments.py @@ -26,8 +26,8 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, verbose = False,recalculate_randomness=True, **kwargs ): - if verbose: - print('Running vary_num_p_with_fixed_freqdiff()') + if True: + print('Running vary_num_p_with_fixed_freqdiff() with max of', max_num_p, 'freqs.' ) [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER) @@ -70,7 +70,7 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth) for this_num_p in range(2, max_num_p+1): - if this_num_p == max_num_p and y == 0: + if this_num_p == max_num_p and y == 0: # first time with all the frequencies verbose = True ## Do we recalculate the spectra every time or use the same datapoints as before? (This is slower.) @@ -86,10 +86,13 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, p = freqpoints(desiredfreqs = desiredfreqs, drive = drive) - thisres = simulated_experiment(drive[p], drive=drive,vals_set = vals_set, noiselevel=noiselevel, MONOMER=MONOMER, + thisres, plot_info_1D = simulated_experiment(drive[p], drive=drive,vals_set = vals_set, noiselevel=noiselevel, MONOMER=MONOMER, repeats=1 , verbose = verbose, forceboth=forceboth,labelcounts = False, - noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, **kwargs + noiseless_spectra=noiseless_spectra, noisy_spectra = noisy_spectra, + return_1D_plot_info = True, + **kwargs ) + try: # repeated experiments results resultsdf = pd.concat([resultsdf,thisres], ignore_index=True) @@ -97,4 +100,4 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, resultsdf = thisres - return resultsdf \ No newline at end of file + return resultsdf, plot_info_1D \ No newline at end of file diff --git a/simulated_experiment.py b/simulated_experiment.py index b1395dd..7fd2ec7 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -216,7 +216,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force noiseless_spectra = None, noisy_spectra = None, freqnoise = False, overlay=False, context = None, saving = False, demo = False, resonatorsystem = None, show_set = None, - figsizeoverride1 = None, figsizeoverride2 = None,): + figsizeoverride1 = None, figsizeoverride2 = None, return_1D_plot_info= False): if verbose: @@ -392,8 +392,12 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force resonatorsystem = resonatorsystem, show_set = show_set, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() + plot_info_1D = [drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, + MONOMER, forceboth, labelcounts, overlay, + context, saving, '1D', demo, + resonatorsystem, show_set, + figsizeoverride1, figsizeoverride2] - el = store_params(M1, M2, B1, B2, K1, K2, K12, FD, MONOMER) theseresults.append(any(x<0 for x in el)) @@ -642,4 +646,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force resultsdf = pd.DataFrame( data=results, columns = flatten(theseresults_cols)) - return resultsdf \ No newline at end of file + if return_1D_plot_info: + return resultsdf, plot_info_1D + else: + return resultsdf \ No newline at end of file From cff503c58e77bd7a046cac0e16b7e4d314d2a1a2 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sun, 5 Feb 2023 23:47:19 -0500 Subject: [PATCH 022/101] in process: zoom in on twirly plot --- ...ach Simulated Two Coupled Resonators.ipynb | 77 +++++++++++++++++-- simulated_experiment.py | 1 + 2 files changed, 73 insertions(+), 5 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 1206cc5..3ed75d2 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -351,7 +351,7 @@ "noiselevel = 5\n", "forceboth= False\"\"\"\n", "\n", - "\n", + "\"\"\"\n", "### Can I scale Brittany's experimental data this way? Or do I need to incorporate the x10^-17 bits?\n", "resonatorsystem = 14\n", "k1_set = 1\n", @@ -363,7 +363,7 @@ "b2_set = 5.864\n", "F_set = 1.861\n", "noiselevel = 1\n", - "forceboth = False\n", + "forceboth = False\"\"\"\n", "\n", "\"\"\"\n", "### Does this make sense for Brittany's experimental data?\n", @@ -1162,11 +1162,13 @@ "if resonatorsystem == 15:\n", " measurementfreqs = desiredfreqs # Brittany's expermental setup\n", "else:\n", - " measurementfreqs, category = res_freq_numeric(vals_set, MONOMER, forceboth,\n", + " for i in range(5):\n", + " measurementfreqs, category = res_freq_numeric(vals_set, MONOMER, forceboth,\n", " mode = 'amp', includefreqs = reslist + measurementfreqs,\n", " minfreq=minfreq, maxfreq=maxfreq, morefrequencies=None,\n", " unique = True, veryunique = True, numtoreturn = 2, \n", - " verboseplot = False, plottitle = None, verbose=True, iterations = 10,\n", + " verboseplot = False, plottitle = None, verbose=False, \n", + " iterations = 3,\n", " returnoptions = True)\n", "\n", "print(measurementfreqs)\n", @@ -2027,6 +2029,20 @@ " pass" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -2082,7 +2098,7 @@ "\n", "before = time()\n", "for i in range(1): # don't do repeats at this level.\n", - " thisres = vary_num_p_with_fixed_freqdiff( vals_set, noiselevel, \n", + " thisres, plot_info_1D = vary_num_p_with_fixed_freqdiff( vals_set, noiselevel, \n", " MONOMER, forceboth,reslist = reslist,\n", " minfreq=minfreq, maxfreq = maxfreq,\n", " verbose = verbose, just_res1 = False, \n", @@ -2112,6 +2128,12 @@ "printtime(repeats, before, after) \n", "display(resultsvarynump.transpose())\n", "\n", + "[plot_info_1D_drive,R1_amp,R1_phase,R2_amp,R2_phase, plot_info_1D_df, K1, K2, K12, B1, B2, FD, M1, M2, plot_info_1D_vals_set, \n", + " plot_info_1D_MONOMER, plot_info_1D_forceboth, plot_info_1D_labelcounts, plot_info_1D_overlay,\n", + " _, _, _, plot_info_1D_demo,\n", + " _, show_set,\n", + " figsizeoverride1, figsizeoverride2] = plot_info_1D\n", + "\n", "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean()\n", "datestr = datestring()\n", "\n", @@ -2131,6 +2153,51 @@ "\"\"\";" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## **** I'm working on making a zoom-in plot.\n", + "\n", + "plt.figure()\n", + "if not MONOMER:\n", + " Z2 = complexamp(R2_amp, R2_phase)\n", + " plt.xlabel('Re($Z_1$) (m)')\n", + " plt.ylabel('Im($Z_1$) (m)') \n", + " title2 = ''\n", + " cbar_label = ''\n", + " s=50\n", + " measurementdf = plot_info_1D_df\n", + " plotcomplex(Z2, drive,title2, cbar_label=cbar_label,s=s,\n", + " label_markers=[])\n", + " ax6 = plt.gca()\n", + " ax6.scatter(np.real(measurementdf.R2AmpCom), np.imag(measurementdf.R2AmpCom), \n", + " s=bigcircle, facecolors='none', edgecolors='k', label=\"points for analysis\") \n", + " if labelcounts:\n", + " for i in range(len(measurementdf)):\n", + " plt.annotate(text=str(i+1), \n", + " xy=(np.real(measurementdf.R2AmpCom), \n", + " np.imag(measurementdf.R2AmpCom)) )\n", + " plt.xlabel('Re($Z_2$) (m)')\n", + " plt.ylabel('Im($Z_2$) (m)')\n", + "\n", + " if show_set:\n", + " ax6.plot(realamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", + " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", + " imamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", + " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", + " color='gray', alpha = .5)\n", + "\n", + "\n", + "plt.tight_layout()\n", + "if saving:\n", + " filename = datestr + 'spectrumZ_1D_zoomin' \n", + " savefigure(filename)\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/simulated_experiment.py b/simulated_experiment.py index 7fd2ec7..f09d0ba 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -258,6 +258,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force first = True results = [] + plot_info_1D = [] for i in range(repeats): # repeat the same measurement with different gaussian noise theseresults = [] From f2c52262267e976059f2593d8b42efb9ebd4556b Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 6 Feb 2023 00:06:32 -0500 Subject: [PATCH 023/101] aesthetics: no transparency for axes also go all the way across using axvline and axhline instead of vlines and hlines --- resonator_plotting.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/resonator_plotting.py b/resonator_plotting.py index ec68796..86d8b0f 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -220,6 +220,8 @@ def plotcomplex(complexZ, parameter, title = 'Complex Amplitude', cbar_label='Fr set_format() assert len(complexZ) == len(parameter) plt.sca(ax) + plt.axvline(0, color = 'k', linestyle='solid', linewidth = .5) + plt.axhline(0, color = 'k', linestyle='solid', linewidth = .5) sc = ax.scatter(np.real(complexZ), np.imag(complexZ), s=s, c = parameter, cmap = cmap, label = 'simulated data' ) # s is marker size cbar = plt.colorbar(sc) @@ -229,11 +231,6 @@ def plotcomplex(complexZ, parameter, title = 'Complex Amplitude', cbar_label='Fr ax.set_ylabel('$\mathrm{Im}(Z)$ (m)') ax.axis('equal'); plt.title(title) - plt.gcf().canvas.draw() # draw so I can get xlim and ylim. - ymin, ymax = ax.get_ylim() - xmin, xmax = ax.get_xlim() - plt.vlines(0, ymin=ymin, ymax = ymax, colors = 'k', linestyle='solid', alpha = .5) - plt.hlines(0, xmin=xmin, xmax = xmax, colors = 'k', linestyle='solid', alpha = .5) #ax.plot([0,1],[0,0], lw=10,transform=ax.xaxis.get_transform() )#,transform=ax.xaxis.get_transform() ) #transform=ax.transAxes # label markers that are closest to the desired frequencies From c966eece7d8f7b8fe3858b4670174877237be723 Mon Sep 17 00:00:00 2001 From: vivarose Date: Tue, 7 Feb 2023 00:22:58 -0500 Subject: [PATCH 024/101] aesthetics: frequencypick_dimer figure --- ...ach Simulated Two Coupled Resonators.ipynb | 253 ++++++++++-------- helperfunctions.py | 10 +- 2 files changed, 149 insertions(+), 114 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 3ed75d2..2e34c47 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -410,6 +410,7 @@ " MONOMER = MONOMER, forceboth=forceboth,\n", " n=n)\n", "\n", + "print('resonatorsystem:', resonatorsystem)\n", "describeresonator(vals_set, MONOMER, forceboth, noiselevel)\n", "print('Drive length:', len(drive), '(for calculating R^2)')\n", "\n", @@ -435,13 +436,6 @@ "beep()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -2068,7 +2062,8 @@ "# Ran 100 times in 7.121 sec\n", "# Ran 100 times in 78.661 sec with verbose = True (only counts the first repeat).\n", "# Ran 100 times in 786.946 sec with verbose = False\n", - "repeats = 80*2\n", + "#repeats = 80*2\n", + "repeats = 1\n", "verbose = False # if False, still shows one graph for each dimension\n", "freqdiff = round(W/10,4)\n", "print('freqdiff:', freqdiff)\n", @@ -2159,22 +2154,58 @@ "metadata": {}, "outputs": [], "source": [ - "## **** I'm working on making a zoom-in plot.\n", - "\n", - "plt.figure()\n", + "plt.scatter?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plotcomplex(Z2, plot_info_1D_drive)\n", + "saving = False\n", + "show_set = True\n", "if not MONOMER:\n", - " Z2 = complexamp(R2_amp, R2_phase)\n", - " plt.xlabel('Re($Z_1$) (m)')\n", - " plt.ylabel('Im($Z_1$) (m)') \n", - " title2 = ''\n", - " cbar_label = ''\n", - " s=50\n", - " measurementdf = plot_info_1D_df\n", - " plotcomplex(Z2, drive,title2, cbar_label=cbar_label,s=s,\n", - " label_markers=[])\n", + " figsize = (2.1, 1.7715)\n", + "\n", + " plt.figure(figsize = figsize, dpi=600)\n", + " \n", + " if show_set:\n", + " plt.plot(realamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", + " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", + " imamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", + " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", + " color='gray', alpha = .5, lw = 0.5, zorder = 1)\n", + "\n", + " plt.axvline(0, color = 'k', linestyle='solid', linewidth = .5, zorder = 3)\n", + " plt.axhline(0, color = 'k', linestyle='solid', linewidth = .5, zorder = 4)\n", + " sc = plt.scatter(np.real(Z2), np.imag(Z2), c = plot_info_1D_drive, s=10, \n", + " cmap = 'rainbow', zorder = 2) # option 3: s=4.\n", + " cbar = plt.colorbar(sc)\n", + " cbar.outline.set_visible(False)\n", + " ax = plt.gca()\n", + "\n", + " ax.set_xlabel('$\\mathrm{Re}(Z)$ (m)')\n", + " ax.set_ylabel('$\\mathrm{Im}(Z)$ (m)')\n", + " ax.axis('equal');\n", + " \"\"\" plt.gcf().canvas.draw() # draw so I can get xlim and ylim.\n", + " ymin, ymax = ax.get_ylim()\n", + " xmin, xmax = ax.get_xlim()\"\"\"\n", " ax6 = plt.gca()\n", + " \n", + " \n", + "\n", + " \n", " ax6.scatter(np.real(measurementdf.R2AmpCom), np.imag(measurementdf.R2AmpCom), \n", - " s=bigcircle, facecolors='none', edgecolors='k', label=\"points for analysis\") \n", + " marker = '+', color = 'w', lw = 0.5, s = 5,\n", + " #s=5, facecolors='none', edgecolors='k', lw = 0.5, # option 3\n", + " #s=1, facecolors='w', edgecolors='k', lw = 0.5, \n", + " label=\"points for analysis\", zorder = 6) \n", + " \n", + " ax6.plot(realamp2(morefrequencies, K1, K2, K12, B1, B2, FD, M1, M2, 0,forceboth=forceboth,), \n", + " imamp2(morefrequencies, K1, K2, K12, B1, B2, FD, M1, M2, 0,forceboth=forceboth,), \n", + " '--', color='black', alpha = 1, lw = 0.7, zorder = 5)\n", " if labelcounts:\n", " for i in range(len(measurementdf)):\n", " plt.annotate(text=str(i+1), \n", @@ -2183,19 +2214,15 @@ " plt.xlabel('Re($Z_2$) (m)')\n", " plt.ylabel('Im($Z_2$) (m)')\n", "\n", - " if show_set:\n", - " ax6.plot(realamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", - " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", - " imamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", - " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", - " color='gray', alpha = .5)\n", - "\n", "\n", - "plt.tight_layout()\n", - "if saving:\n", + " #plt.xlim((-0.11, 0.10))\n", + " #plt.ylim((-.02, .18))\n", + " \n", + " plt.tight_layout()\n", + " if saving:\n", " filename = datestr + 'spectrumZ_1D_zoomin' \n", " savefigure(filename)\n", - "plt.show()" + " plt.show()" ] }, { @@ -4505,13 +4532,6 @@ " pass" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -5048,6 +5068,7 @@ "ticklist = [round(minfreq),round(maxfreq)] + roundedres\n", "saving = True\n", "do_3D = True\n", + "set_format()\n", "\n", "SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", @@ -5060,8 +5081,10 @@ "else:\n", " vmin = min(SSgrid1D.min().min(), SSgrid2D.min().min()) # use same scale for both\n", "vmax = 2\n", + "print('vmin:', vmin, ', corresponding to ', 10**vmin, '%')\n", + "\n", "\n", - "plt.figure(figsize = (1.555,1.3) )\n", + "plt.figure(figsize = (1.555,1.3), dpi= 300 )\n", "myheatmap(SSgrid1D, \"log average error\", vmin=vmin, vmax=vmax, cmap='magma_r'); \n", "plt.title('1D-SVD')\n", "plt.ylabel('$\\omega_a$ (rad/s)')\n", @@ -5078,7 +5101,7 @@ " savefigure(savename)\n", "plt.show()\n", "\n", - "plt.figure(figsize = (1.555,1.3) )\n", + "plt.figure(figsize = (1.555,1.3), dpi= 300 )\n", "myheatmap(SSgrid2D, \"log average error\", vmin=vmin, vmax=vmax, cmap='magma_r'); \n", "plt.title('2D-SVD')\n", "plt.ylabel('$\\omega_a$ (rad/s)')\n", @@ -5095,22 +5118,23 @@ " savefigure(savename)\n", "plt.show()\n", "\n", - "plt.figure(figsize = (1.555,1.3) )\n", - "myheatmap(SSgrid3D, \"log average error\", cmap='magma_r'); \n", - "plt.title('3D-SVD')\n", - "plt.ylabel('$\\omega_a$ (rad/s)')\n", - "plt.xlabel('$\\omega_b$ (rad/s)')\n", - "if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", - " #plt.xticks(ticklist)\n", - " #plt.yticks(ticklist)\n", - " plt.xticks(range(round(maxfreq)+1))\n", - " plt.yticks(range(round(maxfreq)+1))\n", - "plt.axis('equal')\n", - "plt.tight_layout()\n", - "if saving:\n", - " savename = \"sys\" + str(resonatorsystem) + ','+ \"3D2freqheatmap,\" + datestr\n", - " savefigure(savename)\n", - "plt.show()\n", + "if do_3D:\n", + " plt.figure(figsize = (1.555,1.3), dpi= 300 )\n", + " myheatmap(SSgrid3D, \"log average error\",vmin=vmin, vmax=vmax, cmap='magma_r'); \n", + " plt.title('3D-SVD')\n", + " plt.ylabel('$\\omega_a$ (rad/s)')\n", + " plt.xlabel('$\\omega_b$ (rad/s)')\n", + " if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " #plt.xticks(ticklist)\n", + " #plt.yticks(ticklist)\n", + " plt.xticks(range(round(maxfreq)+1))\n", + " plt.yticks(range(round(maxfreq)+1))\n", + " plt.axis('equal')\n", + " plt.tight_layout()\n", + " if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"3D2freqheatmap,\" + datestr\n", + " savefigure(savename)\n", + " plt.show()\n", "\n", "if not MONOMER:\n", " plt.figure(figsize = (1.555,1.3) )\n", @@ -5421,7 +5445,7 @@ "metadata": {}, "outputs": [], "source": [ - "stophere# next sweep one frequency (called freq2)" + "stophere# next sweep one frequency (called freq2) (vary one freq) / sweep freq2" ] }, { @@ -5448,18 +5472,27 @@ "reslist" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resonatorsystem" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [], "source": [ "#Code that loops through frequency 2 points (of different spacing)\n", "\n", "verbose = True\n", - "repeats = 55\n", + "repeats = 80\n", "n = 200\n", "\n", "def sweep_freq2(freq1,drive=drive, vals_set = vals_set, \n", @@ -5524,9 +5557,18 @@ " plt.title('R2 complex amplitude')\n", "for ax in twirlax:\n", " ax.axis('equal');\n", + " \n", + "if resonatorsystem == 11:\n", + " minfreq = 2.5\n", + " maxfreq = 4.5\n", + "else:\n", + " minfreq = None\n", + " maxfreq = None\n", + " \n", "\n", "## Choose driving frequencies\n", "chosendrive, morefrequencies = create_drive_arrays(vals_set = vals_set, forceboth=forceboth, includefreqs = reslist,\n", + " minfreq = minfreq, maxfreq = maxfreq,\n", " MONOMER = MONOMER, n=n, morefrequencies = morefrequencies)\n", "\n", "plt.figure()\n", @@ -5645,7 +5687,7 @@ "metadata": {}, "outputs": [], "source": [ - "_, d1, _, d2, _, _, _, _, _ = calculate_spectra([res1], vals_set, noiselevel, MONOMER, forceboth)" + "_, d1, _, d2, _, _, _, _, _ = calculate_spectra(np.array(res1), vals_set, noiselevel, MONOMER, forceboth)" ] }, { @@ -5667,7 +5709,7 @@ "metadata": {}, "outputs": [], "source": [ - "repeats % 80" + "repeats % 80 # want this to be 0" ] }, { @@ -5785,50 +5827,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results_sweep_1freq, results_sweep_1freqmean = \\\n", - " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', fractionofdata = .95)\n", - "\n", - "plt.figure(figsize=figsize) # *** for dimer figure, in progress\n", - "ax = plt.gca()\n", - "lw = 1\n", - "#plt.axvline(res1, color='grey')\n", - "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_2D'], '.', \n", - "# ms = ms, color=co2, alpha=alpha )\n", - "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_1D'], '.', \n", - "# ms = ms, color=co1, alpha=alpha)\n", - "\n", - "dimensions = [ '2D', '1D']\n", - "colors = [ co2, co1]\n", - "X = results_sweep_1freqmean.Freq2 \n", - "for i in range(len(dimensions)):\n", - " Yhigh = results_sweep_1freq_resort1mean['E_upper_' + dimensions[i]]\n", - " Ylow = results_sweep_1freq_resort1mean['E_lower_' + dimensions[i]] \n", - " plt.plot(X, Yhigh, color = colors[i], alpha = .8, linewidth=.5)\n", - " plt.plot(X, Ylow, color = colors[i], alpha = .8, linewidth=.5)\n", - " ax.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", - "\n", - "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, lw=lw, label='2D' )\n", - "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, lw=lw, label='1D')\n", - "plt.xlabel('Freq2')\n", - "#W = approx_width(k2_set, m2_set, b2_set)\n", - "#plt.xlim(xmin = res2-1, xmax = res2+1) #****\n", - "plt.xlim(2.5,4.5)\n", - "plt.ylim(6e-2, 3e3)\n", - "text_color_legend()\n", - "plt.ylabel('Avg err (%)');\n", - "plt.yscale('log')\n", - "plt.show()\n", - "\n", - "beep()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "figsize = (8,4)\n", @@ -5994,13 +5995,31 @@ "metadata": {}, "outputs": [], "source": [ + "res1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# saved:\n", + "# G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\simulation_export\\2023-02-06 23;30;37results_sweep_1freq.csv\n", + "# smaller file saved:\n", + "# sys11,2023-02-06 23;30;37results_sweep_1freq_limitedcolumns.csv\n", + "\n", + "## for publication figure\n", + "\n", "results_sweep_1freq, results_sweep_1freqmean = \\\n", " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', fractionofdata = .95)\n", "\n", - "plt.figure(figsize=figsize) \n", + "figsize = (4, 1.3)\n", + "\n", + "plt.figure(figsize=figsize, dpi = 600) # *** for dimer figure, in progress\n", "ax = plt.gca()\n", - "lw = 1\n", - "#plt.axvline(res1, color='grey')\n", + "lw = 1 # heavier line for the mean\n", + "plt.axvline(res1, color='grey', lw = 0.5)\n", "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_2D'], '.', \n", "# ms = ms, color=co2, alpha=alpha )\n", "#plt.plot(results_sweep_1freq.Freq2, results_sweep_1freq['avgsyserr%_1D'], '.', \n", @@ -6012,10 +6031,10 @@ "for i in range(len(dimensions)):\n", " Yhigh = results_sweep_1freq_resort1mean['E_upper_' + dimensions[i]]\n", " Ylow = results_sweep_1freq_resort1mean['E_lower_' + dimensions[i]] \n", - " plt.plot(X, Yhigh, color = colors[i], alpha = .8, linewidth=.5)\n", + " plt.plot(X, Yhigh, color = colors[i], alpha = .8, linewidth=.5) # thinner line for the extremes\n", " plt.plot(X, Ylow, color = colors[i], alpha = .8, linewidth=.5)\n", " ax.fill_between(X, Ylow, Yhigh, color = colors[i], alpha=.2)\n", - "\n", + " \n", "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_2D'], color=co2, lw=lw, label='2D' )\n", "plt.plot(results_sweep_1freqmean.Freq2, 10**results_sweep_1freqmean['log avgsyserr%_1D'], color=co1, lw=lw, label='1D')\n", "plt.xlabel('Freq2')\n", @@ -6023,10 +6042,20 @@ "#plt.xlim(xmin = res2-1, xmax = res2+1) #****\n", "plt.xlim(2.5,4.5)\n", "plt.ylim(6e-2, 3e3)\n", - "text_color_legend()\n", + "#text_color_legend()\n", "plt.ylabel('Avg err (%)');\n", "plt.yscale('log')\n", - "plt.show()" + "plt.yticks([10**-1,10**0, 10**1, 10**2, 10**3])\n", + "plt.xlabel('$\\omega_b$ (rad/s)')\n", + "plt.show()\n", + "\n", + "results_sweep_1freqmean[['Freq1','Freq2','log avgsyserr%_1D', 'log avgsyserr%_2D', 'log avgsyserr%_3D', \n", + " 'E_lower_1D', 'E_upper_1D' ,\n", + " 'E_lower_2D', 'E_upper_2D',\n", + " 'E_lower_3D', 'E_upper_3D']].to_csv(os.path.join(savefolder,\n", + " 'sys' + str(resonatorsystem) + ',' + datestr + \"results_sweep_1freq_limitedcolumns.csv\"));\n", + "\n", + "beep()" ] }, { diff --git a/helperfunctions.py b/helperfunctions.py index b5e146f..75181d3 100644 --- a/helperfunctions.py +++ b/helperfunctions.py @@ -65,8 +65,14 @@ def read_params(vect, MONOMER): return [M1, M2, B1, B2, K1, K2, K12, FD] def savefigure(savename): - plt.savefig(savename + '.svg', dpi = 600, bbox_inches='tight') - plt.savefig(savename + '.pdf', dpi = 600, bbox_inches='tight') + try: + plt.savefig(savename + '.svg', dpi = 600, bbox_inches='tight') + except: + print('Could not save svg') + try: + plt.savefig(savename + '.pdf', dpi = 600, bbox_inches='tight') + except: + print('Could not save pdf') plt.savefig(savename + '.png', dpi = 600, bbox_inches='tight',) print("Saved:\n", savename + '.png') From 8568924b364cbf532ca222fa0688d3d6f8ae098c Mon Sep 17 00:00:00 2001 From: vivarose Date: Wed, 8 Feb 2023 10:29:40 -0500 Subject: [PATCH 025/101] BUILD: plot phase heatmap --- ...ach Simulated Two Coupled Resonators.ipynb | 37 ++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 2e34c47..397aa7e 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -5066,7 +5066,7 @@ "datestr = datestring()\n", "roundedres = [round(w,2) for w in reslist[:2]]\n", "ticklist = [round(minfreq),round(maxfreq)] + roundedres\n", - "saving = True\n", + "saving = False\n", "do_3D = True\n", "set_format()\n", "\n", @@ -5137,6 +5137,8 @@ " plt.show()\n", "\n", "if not MONOMER:\n", + " \n", + " \n", " plt.figure(figsize = (1.555,1.3) )\n", " grid=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'R2_phase_noiseless1', columns = 'R1_phase_noiseless2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", @@ -5290,6 +5292,39 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if not MONOMER:\n", + " plt.figure(figsize = (1.8,1.3), dpi= 300 ) # *** new subfigure\n", + " grid=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'R2_phase_noiseless1', columns = 'R2_phase_noiseless2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", + " ax,cbar = myheatmap(grid, \"log avgsyserr%_1D\", vmax = 4, cmap = 'magma_r', return_cbar=True)#, vmax = 2); \n", + " cbarticks = [1,2,3,4]\n", + " cbarticklabels = ['$10^'+str(tick)+'$' for tick in cbarticks]\n", + " cbarticklabels[-1] = '>' + cbarticklabels[-1]\n", + " cbar.set_ticks(cbarticks, labels=cbarticklabels)\n", + " if resonatorsystem == 11 or resonatorsystem == 110:\n", + " plt.xlim(0, 2*np.pi)\n", + " plt.xticks([0, -np.pi, -2*np.pi], labels = ['0','$-\\pi$', '$-2\\pi$'])\n", + " plt.yticks([0, -np.pi, -2*np.pi], labels = ['0','$-\\pi$', '$-2\\pi$'])\n", + " plt.xlabel('$\\delta_{2,b}$')\n", + " plt.ylabel('$\\delta_{2,a}$')\n", + " \n", + " plt.axis('equal')\n", + " plt.tight_layout()\n", + " if True:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"1D_heatmap_by_phase,\" + datestr\n", + " savefigure(savename)\n", + " plt.show()\n", + " \n", + " \n", + " # *** Do I need to add '1D-SVD' title?" + ] + }, { "cell_type": "code", "execution_count": null, From faf7dd5f234e379162803add11d22b16b89e75d3 Mon Sep 17 00:00:00 2001 From: vivarose Date: Wed, 8 Feb 2023 10:29:59 -0500 Subject: [PATCH 026/101] MAINT: Cleanup --- ...ach Simulated Two Coupled Resonators.ipynb | 63 ------------------- 1 file changed, 63 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 397aa7e..f195624 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -4988,69 +4988,6 @@ "#plt.xticks([res1, res2]);\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(figwidth/2, 1.3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "reslist" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax.get_xlim()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.linspace(min(ax.get_xlim())/np.pi, max(ax.get_xlim())/np.pi,3) * np.pi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(range(round(maxfreq)+1))" - ] - }, { "cell_type": "code", "execution_count": null, From 839d6951495629de5c330def9a1e771293df704e Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 11 Feb 2023 15:38:44 -0500 Subject: [PATCH 027/101] BUILD: figures for varying parameters --- ...ach Simulated Two Coupled Resonators.ipynb | 207 +++++++++++++----- 1 file changed, 157 insertions(+), 50 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index f195624..1ffe64d 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -223,7 +223,7 @@ "noiselevel = 10\"\"\"\n", "\n", "\n", - "\"\"\"\n", + "\n", "# FORCEBOTH true or false?\n", "# doe8 experiment that minimizes 1d syserr\n", "# weakly coupled dimer #1\n", @@ -244,7 +244,7 @@ "forceboth = False\n", "resonatorsystem = 7\n", "minfreq = .3\n", - "maxfreq = 2.2\"\"\"\n", + "maxfreq = 2.2\n", "\n", "\n", "\"\"\"\n", @@ -279,6 +279,7 @@ "forceboth= False\n", "MONOMER = False\n", "\"\"\"\n", + "\n", "\"\"\"\n", "### 1D better # weakly coupled dimer #4\n", "#define set values\n", @@ -300,7 +301,7 @@ "maxfreq = 2.2\"\"\"\n", "\n", "\n", - "\n", + "\"\"\"\n", "## Well-separated dimer / Medium coupled dimer #1\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -316,7 +317,7 @@ "forceboth= False\n", "minfreq = 0.1\n", "maxfreq = 5\n", - "#(but this is 3D for forceboth)\n", + "#(but this is 3D for forceboth)\"\"\"\n", "\n", "\"\"\"\n", "### Medium coupled dimer #2\n", @@ -2958,10 +2959,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "# varying param / varyparam / vary param vary\n", + "# varying param / varyparam / vary param vary / vary one param / vary 1 param\n", "\n", "def vary_param(paramname = 'm2', param_list = np.linspace(0.1, 60, num = 100), move_peaks = True, \n", " verboseall = False, repeats = 1, vals_set = vals_set, \n", @@ -3038,7 +3041,7 @@ "\n", " ## find peaks and choose frequency locations that match\n", " if move_peaks: \n", - " for i in range(2): # this is actually not redundant, even with iterations.\n", + " for i in range(3): # this is actually not redundant, even with iterations.\n", " morefrequencies = makemorefrequencies(minfreq= minfreq,maxfreq= maxfreq,\n", " res1 = res1, res2 = res2, # use last res1,2\n", " vals_set = vals_set, MONOMER=MONOMER, \n", @@ -3053,6 +3056,7 @@ " morefrequencies=morefrequencies,\n", " unique = True, veryunique = True, iterations = 3, numtoreturn = 2, \n", " verboseplot = False, verbose=False,returnoptions=True ) \n", + " reslist - np.sort(reslist)\n", " # I turned off verbose for res_freq_numeric\n", " drive = np.sort(np.unique(np.append(drive1,reslist)))\n", " morefrequencies = np.sort(np.unique(np.append(morefrequencies, drive)))\n", @@ -3095,13 +3099,17 @@ " # Ran 250 times in 9.627 sec on laptop\n", " # Ran 1000 times in 66.84 sec on laptop\n", " # Ran 25*100= 2500 times in 341.961 sec\n", - " maxparamvalue = 20\n", - " num_variations = 10\n", - " repeats = 25\n", - " paramname = 'm2'\n", - " noiselevel = .1\n", + " # Ran 10*25 = 250 times in 84.978 sec\n", + " # Ran 25*20 = 500 times in 87.478 sec\n", + " num_variations = 25\n", + " repeats = 20\n", + " minparamvalue = .1\n", + " maxparamvalue = 10\n", + " paramname = 'F'\n", + " #noiselevel = .1\n", " \n", - " param_list = np.linspace(0.1, maxparamvalue, num = num_variations)\n", + " param_list = np.linspace(minparamvalue, maxparamvalue, num = num_variations)\n", + " #param_list = np.linspace(maxparamvalue+.5, maxparamvalue * 2, num = num_variations)\n", " numberverbose = 2\n", "\n", " verboseindex = [int(x) for x in np.linspace(0, num_variations-1, numberverbose)]\n", @@ -3131,13 +3139,30 @@ "outputs": [], "source": [ "variedkey = paramname + '_set'\n", - "variedkeylabel = paramname + '$_\\mathrm{set}}$'\n", + "datestr = datestring()\n", + "saving = True\n", + "\n", + "def variedkeylabel(paramname):\n", + " if paramname == 'k1':\n", + " return '$k_{1,\\mathrm{set}}$ (N/m)'\n", + " if paramname == 'k2':\n", + " return '$k_{2,\\mathrm{set}}$ (N/m)'\n", + " if paramname == 'k12':\n", + " return '$k_{12,\\mathrm{set}}$ (N/m)'\n", + " if paramname == 'm2':\n", + " return '$m_{2,\\mathrm{set}}$ (N/m)'\n", + " if paramname == 'F' and not forceboth:\n", + " return '$F_{1,\\mathrm{set}}$ (N)'\n", + " else:\n", + " return paramname + '$_\\mathrm{set}}$'\n", + "\n", "\n", "try:\n", - " plt.plot(resultsdfvaryparam['Freq Method'], resultsdfvaryparam['avgsyserr%_1D'], '.')\n", + " plt.figure(figsize=(2,2))\n", + " plt.plot(resultsdfvaryparam['Freq Method'], resultsdfvaryparam['avgsyserr%_1D'], '.', alpha = .3)\n", " plt.xlabel('Freq Method')\n", - " plt.ylabel('Avg Syserr (%), 1D')\n", - " plt.figure()\n", + " plt.ylabel('Avg err (%), 1D')\n", + " plt.figure(figsize = (2,2))\n", " plt.scatter(x=resultsdfvaryparam[variedkey], y=resultsdfvaryparam['Freq1'], marker='.', c = resultsdfvaryparam['Freq Method'], cmap='tab10' )\n", " sc=plt.scatter(x=resultsdfvaryparam[variedkey], y=resultsdfvaryparam['Freq2'], marker='.', c = resultsdfvaryparam['Freq Method'], cmap = 'tab10')\n", " plt.xlabel(variedkey)\n", @@ -3146,9 +3171,89 @@ " cbar.outline.set_visible(False)\n", " cbar.set_label('Freq Method')\n", "except ValueError as e:\n", - " print(e)" + " print(e)\n", + "plt.show()\n", + "\n", + "log_SNR = False\n", + "if True: #***\n", + " fig, axs = plt.subplots(2,1, figsize=(figwidth/2, figwidth/2), sharex = 'all',\n", + " gridspec_kw={'hspace': 0, \n", + " 'height_ratios': [1, 3]})\n", + " plt.sca(axs[0])\n", + " if log_SNR:\n", + " vmin = np.log10(min(resultsdfvaryparam['SNR_R2_f1'].min(), resultsdfvaryparam['SNR_R2_f2'].min()))\n", + " vmax = np.log10(max(resultsdfvaryparam['SNR_R2_f1'].max(), resultsdfvaryparam['SNR_R2_f2'].max()))\n", + " c1 = np.log10(resultsdfvaryparam['SNR_R2_f1'])\n", + " c2 = np.log10(resultsdfvaryparam['SNR_R2_f2'])\n", + " else:\n", + " vmin = 0 #min(resultsdfvaryparam['SNR_R2_f1'].min(), resultsdfvaryparam['SNR_R2_f2'].min())/1000\n", + " vmax = max(resultsdfvaryparam['SNR_R2_f1'].max(), resultsdfvaryparam['SNR_R2_f2'].max())/1000\n", + " c1 = resultsdfvaryparam['SNR_R2_f1']/1000\n", + " c2 = resultsdfvaryparam['SNR_R2_f2']/1000\n", + " plt.scatter(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq1'] , c=c1 , \n", + " vmin=vmin,vmax=vmax,\n", + " marker='o', s=3, cmap = 'copper')\n", + " sc = plt.scatter(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq2'] , c= c2, #****\n", + " vmin=vmin,vmax=vmax,\n", + " marker='o', s=3, cmap = 'copper')\n", + " \"\"\"plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq1'] , \n", + " symb, ms=1, color='k', label='$\\omega_a$', alpha=alpha)\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq2'] , \n", + " symb, ms=1, color = 'k', label='$\\omega_b$', alpha=alpha)\"\"\"\n", + " #plt.legend()\n", + " #plt.title('Resonance frequencies')\n", + " plt.ylabel('$\\omega_\\mathrm{res}$ (rad/s)');\n", + " plt.xlabel(variedkeylabel(paramname));\n", + " \n", + " plt.sca(axs[1])\n", + " if paramname == 'm2':\n", + " plt.axvline(m1_set, color='k', lw= 0.5, alpha = .5 )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['avgsyserr%_3D'], symb, ms = 1, color=co3, alpha=alpha)#, label='3D')\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['avgsyserr%_2D'], symb, ms=1, color=co2,alpha=alpha)#, label='2D')\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['avgsyserr%_1D'], symb, ms=1, color=co1,alpha=alpha)#, label='1D')\n", + " plt.plot(resultsdfvaryparammean[variedkey], 10**resultsdfvaryparammean['log avgsyserr%_3D'], color=co3, label='3D')\n", + " plt.plot(resultsdfvaryparammean[variedkey], 10**resultsdfvaryparammean['log avgsyserr%_2D'], color=co2, label='2D')\n", + " plt.plot(resultsdfvaryparammean[variedkey], 10**resultsdfvaryparammean['log avgsyserr%_1D'], color=co1, label='1D')\n", + "\n", + " #plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam['rmssyserr%_2D'])\n", + " #plt.title('2D nullspace normalized by ' + normalizationpair)\n", + " plt.xlabel(variedkeylabel(paramname))\n", + " plt.ylabel('Avg err (%)');\n", + " plt.gca().set_yscale('log')\n", + " text_color_legend()\n", + " #plt.ylim(0,ymax=maxsyserr_to_plot)\n", + " \n", + " fig.subplots_adjust(right=0.8)\n", + " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n", + " cbar = fig.colorbar(sc, cax=cbar_ax)\n", + " cbar.outline.set_visible(False)\n", + " if log_SNR:\n", + " cbar.set_label('log SNR R2')\n", + " else:\n", + " cbar.set_label('SNR R2 (x1000)')\n", + "\n", + " if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"vary \" + paramname + ',' + datestr\n", + " savefigure(savename)\n", + " resultsdfvaryparam[[variedkey,'avgsyserr%_1D','avgsyserr%_2D','avgsyserr%_3D',\n", + " 'log avgsyserr%_1D','log avgsyserr%_2D','log avgsyserr%_3D',\n", + " 'SNR_R2_f1', 'SNR_R2_f2',\n", + " 'SNR_R1_f1', 'SNR_R1_f2'\n", + " ]].to_csv(savename + '.csv')\n", + " plt.show()\n", + " \n", + "print(len(resultsdfvaryparam), 'simulated experiments')\n", + "print(len(resultsdfvaryparammean), 'different ', paramname)\n", + "print(len(resultsdfvaryparam)/len(resultsdfvaryparammean), 'simulated experiments per each', paramname)\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -3178,7 +3283,7 @@ " try:\n", " cbar = plt.colorbar(sc, ax = plt.gca())\n", " cbar.outline.set_visible(False)\n", - " cbar.set_label(variedkeylabel)\n", + " cbar.set_label(variedkeylabel(paramname))\n", " except AttributeError:\n", " pass\n", " except:\n", @@ -3195,30 +3300,30 @@ " try:\n", " cbar = plt.colorbar(sc,ax = plt.gca())\n", " cbar.outline.set_visible(False)\n", - " cbar.set_label(variedkeylabel)\n", + " cbar.set_label(variedkeylabel(paramname))\n", " except AttributeError:\n", " pass\n", " except:\n", " pass\n", "\n", " plt.figure()\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq1'] , symb, color='k', label='$\\omega_1$', alpha=alpha)\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq2'] , symb, color = 'k', label='$\\omega_2$', alpha=alpha)\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq1'] , symb, color='k', label='$\\omega_a$', alpha=alpha)\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['Freq2'] , symb, color = 'k', label='$\\omega_b$', alpha=alpha)\n", " #plt.legend()\n", " plt.title('Resonance frequencies')\n", " plt.ylabel('Frequency (rad/s)');\n", - " plt.xlabel(variedkeylabel);\n", + " plt.xlabel(variedkeylabel(paramname));\n", "\n", " plt.figure()\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless2']/np.pi, symb, label='$\\delta_1(\\omega_2)$', )\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless1']/np.pi, symb, label='$\\delta_1(\\omega_1)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless2']/np.pi, symb, label='$\\delta_1(\\omega_b)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless1']/np.pi, symb, label='$\\delta_1(\\omega_a)$', )\n", " if not MONOMER:\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless1']/np.pi, symb, label='$\\delta_2(\\omega_1)$', )\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless2']/np.pi, symb, label='$\\delta_2(\\omega_2)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless1']/np.pi, symb, label='$\\delta_2(\\omega_a)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless2']/np.pi, symb, label='$\\delta_2(\\omega_b)$', )\n", " plt.axhline(-1/4)\n", " plt.legend()\n", " plt.ylabel('$\\delta$ ($\\pi$)');\n", - " plt.xlabel(variedkeylabel);\n", + " plt.xlabel(variedkeylabel(paramname));\n", " \n", " plt.figure()\n", " sc = plt.scatter(x=resultsdfvaryparam['1-avg_expt_cartes_rsqrd_1D'],y=resultsdfvaryparam['avgsyserr%_1D'], \n", @@ -3236,13 +3341,13 @@ "\n", "\n", " plt.figure()\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_amp_noiseless2'], symb, label='$A_1(\\omega_2)$', )\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_amp_noiseless1'], symb, label='$A_1(\\omega_1)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_amp_noiseless2'], symb, label='$A_1(\\omega_b)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_amp_noiseless1'], symb, label='$A_1(\\omega_a)$', )\n", " if not MONOMER:\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_amp_noiseless2'], symb, label='$A_2(\\omega_2)$', )\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_amp_noiseless1'], symb, label='$A_2(\\omega_1)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_amp_noiseless2'], symb, label='$A_2(\\omega_b)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_amp_noiseless1'], symb, label='$A_2(\\omega_a)$', )\n", " plt.ylabel('$A$ (arb. units)');\n", - " plt.xlabel(variedkeylabel);\n", + " plt.xlabel(variedkeylabel(paramname));\n", " plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1.05), ncol=1,)\n", "\n", "\n", @@ -3270,7 +3375,7 @@ " #plt.ylim(0,25)\n", " plt.gca().set_yscale('log')\n", " plt.ylabel('Syserr (%)');\n", - " plt.xlabel(variedkeylabel);\n", + " plt.xlabel(variedkeylabel(paramname));\n", "\n", " plt.figure()\n", " if paramname == 'm2':\n", @@ -3284,7 +3389,7 @@ "\n", " #plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam['rmssyserr%_2D'])\n", " #plt.title('2D nullspace normalized by ' + normalizationpair)\n", - " plt.xlabel(variedkeylabel)\n", + " plt.xlabel(variedkeylabel(paramname))\n", " plt.ylabel('Average syserr (%)');\n", " plt.gca().set_yscale('log')\n", " plt.legend()\n", @@ -3300,7 +3405,7 @@ " #plt.ylim(ymin=0)\n", " plt.gca().set_yscale('log')\n", " plt.legend()\n", - " plt.xlabel(variedkeylabel)\n", + " plt.xlabel(variedkeylabel(paramname))\n", " plt.ylabel('SNR');\n", "\n", " plt.figure();\n", @@ -3310,7 +3415,7 @@ " plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam.R2_amp_meas1, symb, label=\"R2, f1\", alpha=alpha)\n", " plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam.R2_amp_meas2, symb, label=\"R2, f2\", alpha=alpha)\n", " plt.legend()\n", - " plt.xlabel(variedkeylabel)\n", + " plt.xlabel(variedkeylabel(paramname))\n", " plt.ylabel('amplitude (arb. units)');\n", "\n", " \"\"\"\n", @@ -3319,7 +3424,7 @@ " plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam.R1_amp_meas1, label=\"R1, f1, measured\" )\n", " plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam.A1f1avg, label='R1, f1,average measured')\n", " plt.legend()\n", - " plt.xlabel(variedkeylabel)\n", + " plt.xlabel(variedkeylabel(paramname))\n", " plt.ylabel('amplitude (arb. units)');\n", " \"\"\"\n", "\n", @@ -3362,7 +3467,7 @@ " try:\n", " cbar = plt.colorbar(sc);\n", " cbar.outline.set_visible(False);\n", - " cbar.set_label(variedkeylabel);\n", + " cbar.set_label(variedkeylabel(paramname));\n", " except AttributeError:\n", " pass\n", " plt.gca().axis('equal');\n", @@ -3413,7 +3518,8 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure()\n", + "figsize = (figwidth/2,1.3)\n", + "plt.figure(figsize = figsize)\n", "#plt.loglog(resultsdfvaryparam['1-avg_expt_cartes_rsqrd_3D'],\n", "# resultsdfvaryparam['avgsyserr%_3D'], symb , color=co3, alpha = alpha/2)\n", "#plt.loglog(resultsdfvaryparam['1-avg_expt_cartes_rsqrd_2D'],\n", @@ -3424,14 +3530,15 @@ "\n", "sc = plt.scatter(x=resultsdfvaryparam['1-avg_expt_cartes_rsqrd_1D'],y=resultsdfvaryparam['avgsyserr%_1D'], \n", " c = resultsdfvaryparam[variedkey],\n", - " marker = symb , alpha = alpha, cmap = 'rainbow')\n", + " marker = symb , s=1, alpha = alpha, cmap = 'rainbow')\n", "cbar = plt.colorbar(sc)\n", "cbar.outline.set_visible(False)\n", "cbar.set_label(variedkey)\n", "plt.gca().set_xscale('log')\n", "plt.gca().set_yscale('log')\n", "\n", - "plt.xlabel('1-avg_expt_cartes_rsqrd_1D')\n", + "#plt.xlabel('1-avg_expt_cartes_rsqrd_1D')\n", + "plt.xlabel('$1-R^2_\\mathrm{avg}$')\n", "plt.ylabel('avgsyserr%_1D')\n", "plt.axis('equal');\n", "plt.title('$1-R^2$ predicts syserr')\n", @@ -3455,23 +3562,23 @@ "ydata = resultsdfvaryparam['avgsyserr%_1D']\n", "\n", "\n", - "fitparampowone, covpowone = curve_fit(powlawslopeone, xdata = xdata, ydata = ydata, \n", + "\"\"\"fitparampowone, covpowone = curve_fit(powlawslopeone, xdata = xdata, ydata = ydata, \n", " p0 = 1)#(fitparampow[0]))\n", "powonefit = powlawslopeone(xdata,fitparampowone[0])\n", - "plt.plot(xdata,powonefit, label='power law slope 1', color='k');\n", + "plt.plot(xdata,powonefit, label='power law slope 1', color='grey');\n", "print ('\\nPower law with slope fixed at 1:')\n", "print ( 'C = ' + str(fitparampowone[0]) + ' ± ' + str(np.sqrt(covpowone[0,0])))\n", - "print ('logarithmic slope m = 1')\n", + "print ('logarithmic slope m = 1')\"\"\"\n", "\n", - "\"\"\"fitparampow, covpow = curve_fit(powlaw, xdata = xdata, ydata = ydata, p0 = (1, 1))\n", + "fitparampow, covpow = curve_fit(powlaw, xdata = xdata, ydata = ydata, p0 = (1, 1))\n", "print('fitparampow:', fitparampow)\n", "powlawfit = powlaw(xdata,fitparampow[0],fitparampow[1])\n", - "plt.plot(xdata,powlawfit, label='power law fit', color='grey');\n", + "plt.plot(xdata,powlawfit, label='power law fit', color='k');\n", "\n", "fitparamtrunc, covtrunc = curve_fit(truncpow, xdata = xdata, ydata = ydata, \n", " p0 = (fitparampow[0], fitparampow[1],1))\n", "trucpowfit = truncpow(xdata,fitparamtrunc[0],fitparamtrunc[1], fitparamtrunc[2])\n", - "plt.plot(xdata,trucpowfit, label='truncated power law fit', color='r');\n", + "#plt.plot(xdata,trucpowfit, label='truncated power law fit', color='r');\n", "print('fitparamtrunc:', fitparamtrunc)\n", "\n", "\n", @@ -3490,7 +3597,7 @@ "\n", "print(\"\\nIt's ok to use the uncertainties below as long as there aren't strong off-diagonal values.\")\n", "print('But there are, unfortunately.')\n", - "print ('\\nPower law:')\n", + "print ('\\nPower law, y=C*x^m:')\n", "print ( 'C = ' + str(fitparampow[0]) + ' ± ' + str(np.sqrt(covpow[0,0])))\n", "print ('logarithmic slope m = ' + str(fitparampow[1]) + ' ± ' + str(np.sqrt(covpow[1,1])))\n", "\n", @@ -3498,7 +3605,7 @@ "print ( 'C = ' + str(fitparamtrunc[0]) + ' ± ' + str(np.sqrt(covtrunc[0,0])))\n", "print ('logarithmic slope m = ' + str(fitparamtrunc[1]) + ' ± ' + str(np.sqrt(covtrunc[1,1])))\n", "print ('constant tau = ' + str(fitparamtrunc[2]) + ' ± ' + str(np.sqrt(covtrunc[2,2])))\n", - "\"\"\";\n", + "\n", "\n" ] }, From afb3cd08561500ccead5b806b66be692c3fadcf4 Mon Sep 17 00:00:00 2001 From: vivarose Date: Tue, 21 Feb 2023 12:38:02 -0500 Subject: [PATCH 028/101] DOC: minor edit --- Algebraic Approach Simulated Two Coupled Resonators.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 1ffe64d..a83cdc0 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -6178,7 +6178,7 @@ "metadata": {}, "outputs": [], "source": [ - "# varying 2 param / vary2param / vary 2param vary\n", + "# varying 2 param / vary2param / vary 2param vary / vary two params\n", "\n", "def vary2param(paramname1 = 'm2', param_list1 = np.linspace(0.1, 60, num = 100),\n", " paramname2 = 'F',param_list2 = np.linspace(0.1, 60, num = 100),\n", From 7eb85465f104fcd0d30d37e8442e648493269251 Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 13 Mar 2023 23:43:54 -0700 Subject: [PATCH 029/101] MAINT: import without * --- ...ach Simulated Two Coupled Resonators.ipynb | 28 +++++++++++++------ 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index a83cdc0..518c91b 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -73,15 +73,25 @@ "from myheatmap import myheatmap\n", "from helperfunctions import flatten,listlength,printtime,make_real_iff_real, \\\n", " store_params, read_params, savefigure, datestring, beep, calc_error_interval\n", - "from resonatorsimulator import *\n", - "from simulated_experiment import *\n", - "from resonatorstats import *\n", - "from resonatorphysics import *\n", - "from resonatorfrequencypicker import *\n", - "from resonatorSVDanalysis import *\n", - "from resonator_plotting import *\n", - "\n", - "# When this runs, an empty graph will appear below." + "from resonatorsimulator import curve1, theta1, curve2, theta2, realamp1, imamp1, realamp2, imamp2, \\\n", + " curvemono, thetamono, realampmono, imampmono, rsqrdlist, arclength_between_pair, \\\n", + " complex_noise, calculate_spectra, noisyR1ampphase, noisyR2ampphase, SNRknown, SNRs, SNRcalc\n", + "from simulated_experiment import describeresonator, measurementdfcalc, compile_rsqrd, \\\n", + " assert_results_length, describe_monomer_results, simulated_experiment\n", + "from resonatorstats import syserr, combinedsyserr, rsqrd\n", + "from resonatorphysics import complexamp, amp, A_from_Z, res_freq_weak_coupling, \\\n", + " approx_Q, approx_width, calcnarrowerW\n", + "from resonatorfrequencypicker import freqpoints, find_freq_from_angle, makemorefrequencies,\\\n", + " create_drive_arrays, find_special_freq, res_freq_numeric, \\\n", + " allmeasfreq_one_res, allmeasfreq_two_res, best_choice_freq_set\n", + "from resonatorSVDanalysis import Zmat, \\\n", + " normalize_parameters_1d_by_force, quadratic_formula, normalize_parameters_to_res1_and_F_2d, \\\n", + " normalize_parameters_to_m1_m2_assuming_2d, normalize_parameters_to_m1_set_k1_set_assuming_2d, \\\n", + " normalize_parameters_to_m1_F_set_assuming_2d, normalize_parameters_assuming_3d\n", + "from resonator_plotting import set_format, text_color_legend, spectrum_plot, plotcomplex, \\\n", + " plot_SVD_results, convert_to_measurementdf\n", + "\n", + "# When this runs, an empty graph will appear below (because plotcomplex calls canvas.draw)." ] }, { From f11c7b78762204f8862e3cbf65cc81bf106743d1 Mon Sep 17 00:00:00 2001 From: vivarose Date: Tue, 14 Mar 2023 19:22:25 -0700 Subject: [PATCH 030/101] cleanup Don't import functions I am not using --- ...ach Simulated Two Coupled Resonators.ipynb | 42 ++----------------- resonatorfrequencypicker.py | 4 +- resonatorsimulator.py | 7 +++- 3 files changed, 10 insertions(+), 43 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 518c91b..050edbe 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -74,8 +74,8 @@ "from helperfunctions import flatten,listlength,printtime,make_real_iff_real, \\\n", " store_params, read_params, savefigure, datestring, beep, calc_error_interval\n", "from resonatorsimulator import curve1, theta1, curve2, theta2, realamp1, imamp1, realamp2, imamp2, \\\n", - " curvemono, thetamono, realampmono, imampmono, rsqrdlist, arclength_between_pair, \\\n", - " complex_noise, calculate_spectra, noisyR1ampphase, noisyR2ampphase, SNRknown, SNRs, SNRcalc\n", + " curvemono, thetamono, realampmono, imampmono, rsqrdlist, \\\n", + " complex_noise, calculate_spectra, noisyR1ampphase, noisyR2ampphase, SNRknown, SNRs\n", "from simulated_experiment import describeresonator, measurementdfcalc, compile_rsqrd, \\\n", " assert_results_length, describe_monomer_results, simulated_experiment\n", "from resonatorstats import syserr, combinedsyserr, rsqrd\n", @@ -3428,15 +3428,7 @@ " plt.xlabel(variedkeylabel(paramname))\n", " plt.ylabel('amplitude (arb. units)');\n", "\n", - " \"\"\"\n", - " # Make sure that SNRcalc is being used correctly.\n", - " plt.figure()\n", - " plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam.R1_amp_meas1, label=\"R1, f1, measured\" )\n", - " plt.plot(resultsdfvaryparam[variedkey],resultsdfvaryparam.A1f1avg, label='R1, f1,average measured')\n", - " plt.legend()\n", - " plt.xlabel(variedkeylabel(paramname))\n", - " plt.ylabel('amplitude (arb. units)');\n", - " \"\"\"\n", + "\n", "\n", " plt.figure();\n", " if len(resultsdfvaryparam) >400:\n", @@ -3883,15 +3875,6 @@ "plt.xlabel(variedkeylabel)\n", "plt.ylabel('amplitude (arb. units)');\n", "\n", - "\"\"\"\n", - "# Make sure that SNRcalc is being used correctly.\n", - "plt.figure()\n", - "plt.plot(resultsdfk1[variedkey],resultsdfk1.R1_amp_meas1, label=\"R1, f1, measured\" )\n", - "plt.plot(resultsdfk1[variedkey],resultsdfk1.A1f1avg, label='R1, f1,average measured')\n", - "plt.legend()\n", - "plt.xlabel(variedkeylabel)\n", - "plt.ylabel('amplitude (arb. units)');\n", - "\"\"\"\n", "\n", "plt.figure()\n", "if len(resultsdfk1) >400:\n", @@ -4264,15 +4247,6 @@ "plt.xlabel('$k_{12, \\mathrm{set}}$ (N/m)')\n", "plt.ylabel('amplitude (arb. units)');\n", "\n", - "\"\"\"\n", - "# Make sure that SNRcalc is being used correctly.\n", - "plt.figure()\n", - "plt.plot(resultsdfk12.k12_set,resultsdfk12.R1_amp_meas1, label=\"R1, f1, measured\" )\n", - "plt.plot(resultsdfk12.k12_set,resultsdfk12.A1f1avg, label='R1, f1,average measured')\n", - "plt.legend()\n", - "plt.xlabel('$k_{12, \\mathrm{set}}$ (N/m)')\n", - "plt.ylabel('amplitude (arb. units)');\n", - "\"\"\"\n", "\n", "plt.figure()\n", "if len(resultsdfk12) >400:\n", @@ -4572,16 +4546,6 @@ " plt.xlabel('$k_{2, \\mathrm{set}}$ (N/m)')\n", " plt.ylabel('amplitude (arb. units)');\n", "\n", - " \"\"\"\n", - " # Make sure that SNRcalc is being used correctly.\n", - " plt.figure()\n", - " plt.plot(resultsdfk2['k2_set'],resultsdfk2['R1_amp_meas1, label=\"R1, f1, measured\" )\n", - " plt.plot(resultsdfk2['k2_set'],resultsdfk2['A1f1avg, label='R1, f1,average measured')\n", - " plt.legend()\n", - " plt.xlabel('$k_{2, \\mathrm{set}}$ (N/m)')\n", - " plt.ylabel('amplitude (arb. units)');\n", - " \"\"\"\n", - "\n", " plt.figure()\n", " if len(resultsdfk2) >400:\n", " alpha = .4\n", diff --git a/resonatorfrequencypicker.py b/resonatorfrequencypicker.py index f293b09..bafa37d 100644 --- a/resonatorfrequencypicker.py +++ b/resonatorfrequencypicker.py @@ -356,7 +356,7 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, while morefrequencies[-1] > maxfreq: - if False: + if False: # too verbose! print('Removing frequency', morefrequencies[-1]) morefrequencies = morefrequencies[:-1] while morefrequencies[0]< minfreq: @@ -417,7 +417,7 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, indexlistampR1 = np.append(indexlist1,index1) assert max(indexlistampR1) <= len(morefrequencies) - if False: + if False: # too verbose! print('indexlistampR1:', indexlistampR1) if MONOMER: indexlist = indexlistampR1 diff --git a/resonatorsimulator.py b/resonatorsimulator.py index ff67c5b..c8108e8 100644 --- a/resonatorsimulator.py +++ b/resonatorsimulator.py @@ -235,7 +235,9 @@ def imampmono(w, k_1, b1_, F_, m_1, e): """ calculate rsqrd in polar and cartesian - using either the vals_set (privileged rsqrd) or the parameters from SVD (experimental rsqrd) """ + using either the vals_set (privileged rsqrd) or the parameters from SVD (experimental rsqrd) + rsqrd is the Coefficient of Determination. + """ def rsqrdlist(R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp, drive, k1, k2, k12, b1, b2, F, m1, m2, MONOMER, forceboth): R1_amp_rsqrd = rsqrd(model = curve1(drive, k1, k2, k12, b1, b2, F, m1, m2,0 , MONOMER, forceboth = forceboth), @@ -268,6 +270,7 @@ def rsqrdlist(R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_rea """ maxamp is the maximum amplitude, probably the amplitude at the resonance peak. Returns arclength in same units as amplitude. +Not used. """ def arclength_between_pair(maxamp, Z1, Z2): radius = maxamp/2 # radius of twirl, approximating it as a circle @@ -538,7 +541,7 @@ def SNRs(freqs,vals_set, noiselevel, MONOMER, forceboth, use_complexnoise=use_co return max(SNR_R1_list),max(SNR_R2_list),min(SNR_R1_list),min(SNR_R2_list), \ np.mean(SNR_R1_list),np.mean(SNR_R2_list), SNR_R1_list, SNR_R2_list -""" Experimentalist style to determine SNR """ +""" Experimentalist style to determine SNR, not used because I have a priori privilege """ def SNRcalc(freq,vals_set, noiselevel, MONOMER, forceboth, plot = False, ax = None, detailed = False): n = 50 # number of randomized values to calculate amps1 = np.zeros(n) From 5e3faf31697316722ef28c90ecd526bbb21faa95 Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 16 Mar 2023 14:17:49 -0700 Subject: [PATCH 031/101] Aesthetics: delta -> phi Matching Brittany's manuscript symbols --- ...ach Simulated Two Coupled Resonators.ipynb | 67 +++++++++++-------- resonator_plotting.py | 4 +- 2 files changed, 40 insertions(+), 31 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 050edbe..ed1494c 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -173,7 +173,7 @@ "MONOMER = False\"\"\"\n", "\n", "\n", - "\"\"\"\n", + "\n", "### lightly damped monomer ## this is my official lightly damped monomer for Fig 2.\n", "MONOMER = True\n", "resonatorsystem = 2\n", @@ -185,7 +185,7 @@ "maxfreq = 2.01\n", "noiselevel= 10\n", "forceboth = False\n", - "\"\"\"\n", + "\n", "\n", "\"\"\"\n", "### medium damped monomer -- use for demo\n", @@ -233,7 +233,7 @@ "noiselevel = 10\"\"\"\n", "\n", "\n", - "\n", + "\"\"\"\n", "# FORCEBOTH true or false?\n", "# doe8 experiment that minimizes 1d syserr\n", "# weakly coupled dimer #1\n", @@ -255,7 +255,7 @@ "resonatorsystem = 7\n", "minfreq = .3\n", "maxfreq = 2.2\n", - "\n", + "\"\"\"\n", "\n", "\"\"\"\n", "### Weakly coupled dimer #2\n", @@ -290,8 +290,7 @@ "MONOMER = False\n", "\"\"\"\n", "\n", - "\"\"\"\n", - "### 1D better # weakly coupled dimer #4\n", + "\"\"\"### 1D better # weakly coupled dimer #4\n", "#define set values\n", "## This is the weakly coupled dimer I am using\n", "## 2022-11-15 switched back to what I had before.\n", @@ -308,11 +307,11 @@ "MONOMER = False\n", "forceboth= False\n", "minfreq = .1\n", - "maxfreq = 2.2\"\"\"\n", - "\n", + "maxfreq = 2.2\n", + "\"\"\"\n", "\n", "\"\"\"\n", - "## Well-separated dimer / Medium coupled dimer #1\n", + "## Well-separated dimer / Medium coupled dimer #1 / Used for Figure 5.\n", "MONOMER = False\n", "resonatorsystem = 11\n", "m1_set = 8\n", @@ -327,7 +326,8 @@ "forceboth= False\n", "minfreq = 0.1\n", "maxfreq = 5\n", - "#(but this is 3D for forceboth)\"\"\"\n", + "#(but this is 3D for forceboth)\n", + "\"\"\"\n", "\n", "\"\"\"\n", "### Medium coupled dimer #2\n", @@ -594,7 +594,7 @@ " MONOMER=MONOMER, forceboth=forceboth)/np.pi, # true curve\n", " color = 'gray', alpha = 0.2) \n", "ax2.plot(drive, R1_phase/np.pi, '.', color = datacolor) # noisy simulated data\n", - "ax2.set_ylabel('Phase $\\delta$ ($\\pi$)')\n", + "ax2.set_ylabel('Phase $\\phi$ ($\\pi$)')\n", "ax2.set_title('Simulated R1 Phase')\n", "\n", "#For loop to plot chosen values from table\n", @@ -618,7 +618,7 @@ " forceboth=forceboth)/np.pi, # true curve\n", " color = 'gray', alpha = 0.2)\n", "ax4.plot(drive, R2_phase/np.pi, '.', color = datacolor)\n", - "ax4.set_ylabel('Phase $\\delta_2$ ($\\pi$)')\n", + "ax4.set_ylabel('Phase $\\phi_2$ ($\\pi$)')\n", "ax4.set_title('Simulated R2 Phase')\n", "\n", "#For loop to plot R1 amplitude values from table\n", @@ -1299,6 +1299,15 @@ "list(repeatedexptsres.columns)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['avgsyserr%_1D']" + ] + }, { "cell_type": "code", "execution_count": null, @@ -3325,14 +3334,14 @@ " plt.xlabel(variedkeylabel(paramname));\n", "\n", " plt.figure()\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless2']/np.pi, symb, label='$\\delta_1(\\omega_b)$', )\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless1']/np.pi, symb, label='$\\delta_1(\\omega_a)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless2']/np.pi, symb, label='$\\phi_1(\\omega_b)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R1_phase_noiseless1']/np.pi, symb, label='$\\phi_1(\\omega_a)$', )\n", " if not MONOMER:\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless1']/np.pi, symb, label='$\\delta_2(\\omega_a)$', )\n", - " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless2']/np.pi, symb, label='$\\delta_2(\\omega_b)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless1']/np.pi, symb, label='$\\phi_2(\\omega_a)$', )\n", + " plt.plot(resultsdfvaryparam[variedkey], resultsdfvaryparam['R2_phase_noiseless2']/np.pi, symb, label='$\\phi_2(\\omega_b)$', )\n", " plt.axhline(-1/4)\n", " plt.legend()\n", - " plt.ylabel('$\\delta$ ($\\pi$)');\n", + " plt.ylabel('$\\phi$ ($\\pi$)');\n", " plt.xlabel(variedkeylabel(paramname));\n", " \n", " plt.figure()\n", @@ -3790,14 +3799,14 @@ "plt.xlabel(variedkeylabel);\n", "\n", "plt.figure()\n", - "plt.plot(resultsdfk1[variedkey], resultsdfk1['R1_phase_noiseless2']/np.pi, symb, label='$\\delta_1(\\omega_2)$', alpha=alpha)\n", - "plt.plot(resultsdfk1[variedkey], resultsdfk1['R1_phase_noiseless1']/np.pi, symb, label='$\\delta_1(\\omega_1)$', alpha=alpha)\n", + "plt.plot(resultsdfk1[variedkey], resultsdfk1['R1_phase_noiseless2']/np.pi, symb, label='$\\phi_1(\\omega_2)$', alpha=alpha)\n", + "plt.plot(resultsdfk1[variedkey], resultsdfk1['R1_phase_noiseless1']/np.pi, symb, label='$\\phi_1(\\omega_1)$', alpha=alpha)\n", "if not MONOMER:\n", - " plt.plot(resultsdfk1[variedkey], resultsdfk1['R2_phase_noiseless1']/np.pi, symb, label='$\\delta_2(\\omega_1)$', alpha=alpha)\n", - " plt.plot(resultsdfk1[variedkey], resultsdfk1['R2_phase_noiseless2']/np.pi, symb, label='$\\delta_2(\\omega_2)$', alpha=alpha)\n", + " plt.plot(resultsdfk1[variedkey], resultsdfk1['R2_phase_noiseless1']/np.pi, symb, label='$\\phi_2(\\omega_1)$', alpha=alpha)\n", + " plt.plot(resultsdfk1[variedkey], resultsdfk1['R2_phase_noiseless2']/np.pi, symb, label='$\\phi_2(\\omega_2)$', alpha=alpha)\n", "plt.axhline(-1/4)\n", "plt.legend()\n", - "plt.ylabel('$\\delta$ ($\\pi$)');\n", + "plt.ylabel('$\\phi$ ($\\pi$)');\n", "plt.xlabel(variedkeylabel);\n", "\n", "\n", @@ -4166,13 +4175,13 @@ "plt.xlabel('$k_{12, \\mathrm{set}}$ (N/m)');\n", "\n", "plt.figure()\n", - "plt.plot(resultsdfk12.k12_set, resultsdfk12['R1_phase_noiseless2']/np.pi, symb, label='$\\delta_1(\\omega_2)$', alpha=alpha)\n", - "plt.plot(resultsdfk12.k12_set, resultsdfk12['R1_phase_noiseless1']/np.pi, symb, label='$\\delta_1(\\omega_1)$', alpha=alpha)\n", - "plt.plot(resultsdfk12.k12_set, resultsdfk12['R2_phase_noiseless1']/np.pi, symb, label='$\\delta_2(\\omega_1)$', alpha=alpha)\n", - "plt.plot(resultsdfk12.k12_set, resultsdfk12['R2_phase_noiseless2']/np.pi, symb, label='$\\delta_2(\\omega_2)$', alpha=alpha)\n", + "plt.plot(resultsdfk12.k12_set, resultsdfk12['R1_phase_noiseless2']/np.pi, symb, label='$\\phi_1(\\omega_2)$', alpha=alpha)\n", + "plt.plot(resultsdfk12.k12_set, resultsdfk12['R1_phase_noiseless1']/np.pi, symb, label='$\\phi_1(\\omega_1)$', alpha=alpha)\n", + "plt.plot(resultsdfk12.k12_set, resultsdfk12['R2_phase_noiseless1']/np.pi, symb, label='$\\phi_2(\\omega_1)$', alpha=alpha)\n", + "plt.plot(resultsdfk12.k12_set, resultsdfk12['R2_phase_noiseless2']/np.pi, symb, label='$\\phi_2(\\omega_2)$', alpha=alpha)\n", "plt.axhline(-1/4)\n", "plt.legend()\n", - "plt.ylabel('$\\delta$ ($\\pi$)');\n", + "plt.ylabel('$\\phi$ ($\\pi$)');\n", "plt.xlabel('$k_{12, \\mathrm{set}}$ (N/m)');\n", "\n", "\n", @@ -5329,8 +5338,8 @@ " plt.xlim(0, 2*np.pi)\n", " plt.xticks([0, -np.pi, -2*np.pi], labels = ['0','$-\\pi$', '$-2\\pi$'])\n", " plt.yticks([0, -np.pi, -2*np.pi], labels = ['0','$-\\pi$', '$-2\\pi$'])\n", - " plt.xlabel('$\\delta_{2,b}$')\n", - " plt.ylabel('$\\delta_{2,a}$')\n", + " plt.xlabel('$\\phi_{2,b}$')\n", + " plt.ylabel('$\\phi_{2,a}$')\n", " \n", " plt.axis('equal')\n", " plt.tight_layout()\n", diff --git a/resonator_plotting.py b/resonator_plotting.py index 86d8b0f..bd067df 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -313,7 +313,7 @@ def plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, measurementdf, K1, s = 25 # increased from 3, 2022-12-29 bigcircle = 30 amplabel = '$A\;$(m)' - phaselabel = '$\delta\;(\pi)$' + phaselabel = '$\phi\;(\pi)$' titleR1 = '' titleR2 = '' else: @@ -324,7 +324,7 @@ def plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, measurementdf, K1, s=50 bigcircle = 150 amplabel = 'Amplitude $A$ (m)\n' - phaselabel = 'Phase $\delta$ ($\pi$)' + phaselabel = 'Phase $\phi$ ($\pi$)' titleR1= 'Simulated R1 Spectrum' titleR2 = 'Simulated R2 Spectrum' if demo: # overwrite all these From c2b91e7863c187b6d558553dc2b8cfe110661614 Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 23 Mar 2023 15:45:17 -0400 Subject: [PATCH 032/101] variable naming: set -> in --- simulated_experiment.py | 165 ++++++++++++++++++++-------------------- 1 file changed, 83 insertions(+), 82 deletions(-) diff --git a/simulated_experiment.py b/simulated_experiment.py index f09d0ba..927a97d 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -14,7 +14,7 @@ read_params, store_params, make_real_iff_real, flatten from resonatorSVDanalysis import Zmat, \ normalize_parameters_1d_by_force, normalize_parameters_assuming_3d, \ - normalize_parameters_to_m1_F_set_assuming_2d + normalize_parameters_to_m1_F_in_assuming_2d from resonatorstats import syserr, combinedsyserr from resonatorphysics import \ approx_Q, approx_width, res_freq_weak_coupling, complexamp @@ -29,8 +29,8 @@ global use_complexnoise use_complexnoise = True # this just works best. Don't use the other. -def describeresonator(vals_set, MONOMER, forceboth, noiselevel = None): - [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER) +def describeresonator(vals_in, MONOMER, forceboth, noiselevel = None): + [m1_in, m2_in, b1_in, b2_in, k1_in, k2_in, k12_in, F_in] = read_params(vals_in, MONOMER) if MONOMER: print('MONOMER') @@ -40,16 +40,16 @@ def describeresonator(vals_set, MONOMER, forceboth, noiselevel = None): print('Applying oscillating force to both masses.') else: print('Applying oscillating force to m1.') - print('Approximate Q1: ' + "{:.2f}".format(approx_Q(k = k1_set, m = m1_set, b=b1_set)) + - ' width: ' + "{:.2f}".format(approx_width(k = k1_set, m = m1_set, b=b1_set))) + print('Approximate Q1: ' + "{:.2f}".format(approx_Q(k = k1_in, m = m1_in, b=b1_in)) + + ' width: ' + "{:.2f}".format(approx_width(k = k1_in, m = m1_in, b=b1_in))) if not MONOMER: - print('Approximate Q2: ' + "{:.2f}".format(approx_Q(k = k2_set, m = m2_set, b=b2_set)) + - ' width: ' + "{:.2f}".format(approx_width(k = k2_set, m = m2_set, b=b2_set))) + print('Approximate Q2: ' + "{:.2f}".format(approx_Q(k = k2_in, m = m2_in, b=b2_in)) + + ' width: ' + "{:.2f}".format(approx_width(k = k2_in, m = m2_in, b=b2_in))) print('Q ~ sqrt(m*k)/b') print('Set values:') if MONOMER: - print('m: ' + str(m1_set) + ', b: ' + str(b1_set) + ', k: ' + str(k1_set) + ', F: ' + str(F_set)) - res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) + print('m: ' + str(m1_in) + ', b: ' + str(b1_in) + ', k: ' + str(k1_in) + ', F: ' + str(F_in)) + res1 = res_freq_weak_coupling(k1_in, m1_in, b1_in) print('res freq: ', res1) else: if forceboth: @@ -57,8 +57,8 @@ def describeresonator(vals_set, MONOMER, forceboth, noiselevel = None): else: forcestr = ', F1: ' - print('m1: ' + str(m1_set) + ', b1: ' + str(b1_set) + ', k1: ' + str(k1_set) + forcestr + str(F_set)) - print('m2: ' + str(m2_set) + ', b2: ' + str(b2_set) + ', k2: ' + str(k2_set) + ', k12: ' + str(k12_set)) + print('m1: ' + str(m1_in) + ', b1: ' + str(b1_in) + ', k1: ' + str(k1_in) + forcestr + str(F_in)) + print('m2: ' + str(m2_in) + ', b2: ' + str(b2_in) + ', k2: ' + str(k2_in) + ', k12: ' + str(k12_in)) if noiselevel is not None and use_complexnoise: print('noiselevel:', noiselevel) print('stdev sigma:', complexamplitudenoisefactor*noiselevel) @@ -68,7 +68,7 @@ def measurementdfcalc(drive, p, R1_amp,R2_amp,R1_phase, R2_phase, R1_amp_noiseless,R2_amp_noiseless, R1_phase_noiseless, R2_phase_noiseless, - vals_set, noiselevel, MONOMER, forceboth): + vals_in, noiselevel, MONOMER, forceboth): table = [] for i in range(len(p)): if False: @@ -78,7 +78,7 @@ def measurementdfcalc(drive, p, print('correct amplitude: ' + str(R1_amp_noiseless[p[i]])) print('Syserr: ', syserr(R1_amp[p[i]], R1_amp_noiseless[p[i]]), ' %') - SNR_R1, SNR_R2 = SNRknown(drive[p[i]],vals_set=vals_set, noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth) + SNR_R1, SNR_R2 = SNRknown(drive[p[i]],vals_in=vals_in, noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth) table.append([drive[p[i]], R1_amp[p[i]], R1_phase[p[i]], R2_amp[p[i]], R2_phase[p[i]], complexamp(R1_amp[p[i]],R1_phase[p[i]] ), complexamp(R2_amp[p[i]], R2_phase[p[i]]), @@ -168,12 +168,13 @@ def assert_results_length(results, columns): # unscaled_vector = vh[-1] has elements: m1, b1, k1, f1 -def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_set, freqs = None, absval = False ): - [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, True) - m_err = syserr(M1,m1_set, absval) - b_err = syserr(B1,b1_set, absval) - k_err = syserr(K1,k1_set, absval) - sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_set/m1_set), absval) +def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_in, freqs = None, absval = False ): + + [m1_in, m2_in, b1_in, b2_in, k1_in, k2_in, k12_in, F_in] = read_params(vals_in, True) + m_err = syserr(M1,m1_in, absval) + b_err = syserr(B1,b1_in, absval) + k_err = syserr(K1,k1_in, absval) + sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_in/m1_in), absval) if freqs is not None: print("Using", len(freqs), "frequencies for SVD analysis, namely", @@ -187,8 +188,8 @@ def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, v unscaled_vector[0], " kg, ", #M unscaled_vector[1], "N/(m/s),", #B unscaled_vector[2], "N/m,", #K - unscaled_vector[3], "N), where α=F_set/", unscaled_vector[3], "=", \ - F_set, "/" , unscaled_vector[3], "=", F_set/unscaled_vector[3], \ + unscaled_vector[3], "N), where α=F_in/", unscaled_vector[3], "=", \ + F_in, "/" , unscaled_vector[3], "=", F_in/unscaled_vector[3], \ "is a normalization constant obtained from our knowledge of the force amplitude F for a 1D-SVD analysis.", "Dividing by α allows us to scale the singular vector to yield the modeled parameters vector.", "Therefore, we obtain m\\hat= ", @@ -201,29 +202,29 @@ def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, v "Each of these is within ", \ max([abs(err) for err in [m_err, b_err, k_err]]), \ "% of the correct values for m, b, and k.", \ - "We also see that the recovered value √(k ̂/m ̂ )=", + "We also see that the recovered value √(k̂/m̂)=", np.sqrt(K1/M1), "rad/s is more accurate than the individually recovered values for mass and spring stiffness;", "this is generally true. ", - "The percent error for √(k ̂/m ̂ ) compared to √(k_set/m_set ) is", + "The percent error for √(k̂/m̂) compared to √(k_in/m_in ) is", sqrtkoverm_err, "%. This high accuracy likely arises because we choose frequency ω_a at the peak amplitude." ) """ demo indicates that the data should be plotted without ticks""" -def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, forceboth, +def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceboth, drive=None,#np.linspace(minfreq,maxfreq,n), verbose = False, repeats=1, labelcounts = False, noiseless_spectra = None, noisy_spectra = None, freqnoise = False, overlay=False, context = None, saving = False, demo = False, - resonatorsystem = None, show_set = None, + resonatorsystem = None, show_in = None, figsizeoverride1 = None, figsizeoverride2 = None, return_1D_plot_info= False): if verbose: print('Running simulated_experiment()', repeats, 'times.') - describeresonator(vals_set, MONOMER, forceboth, noiselevel) + describeresonator(vals_in, MONOMER, forceboth, noiselevel) - [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER) + [m1_in, m2_in, b1_in, b2_in, k1_in, k2_in, k12_in, F_in] = read_params(vals_in, MONOMER) if drive is None: drive = measurementfreqs # fastest way to do it, but R^2 isn't very accurate @@ -234,7 +235,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force p = freqpoints(desiredfreqs = measurementfreqs, drive = drive) if noiseless_spectra is None: # calculate noiseless spectra - noiseless_spectra = calculate_spectra(drive, vals_set, noiselevel = 0, MONOMER = MONOMER, forceboth = forceboth) + noiseless_spectra = calculate_spectra(drive, vals_in, noiselevel = 0, MONOMER = MONOMER, forceboth = forceboth) R1_amp_noiseless, R1_phase_noiseless, R2_amp_noiseless, R2_phase_noiseless, \ R1_real_amp_noiseless, R1_im_amp_noiseless, R2_real_amp_noiseless, R2_im_amp_noiseless, _ = noiseless_spectra @@ -246,7 +247,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force ## privileged SNR and amplitude maxSNR_R1,maxSNR_R2, minSNR_R1,minSNR_R2,meanSNR_R1,meanSNR_R2, SNR_R1_list, SNR_R2_list, \ A1, STD1, A2, STD2 = SNRs( \ - drive[p],vals_set, noiselevel=noiselevel, MONOMER=MONOMER,forceboth=forceboth, + drive[p],vals_in, noiselevel=noiselevel, MONOMER=MONOMER,forceboth=forceboth, use_complexnoise=use_complexnoise, detailed = True, privilege = True) @@ -267,11 +268,11 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force theseresults.append(len(p)) theseresults_cols.append([ 'num frequency points']) - theseresults.append(vals_set) # Store vals_set # same for every row + theseresults.append(vals_in) # Store vals_in # same for every row if MONOMER: - theseresults_cols.append(['m1_set', 'b1_set', 'k1_set', 'F_set']) + theseresults_cols.append(['m1_in', 'b1_in', 'k1_in', 'F_in']) else: - theseresults_cols.append(['m1_set', 'm2_set', 'b1_set', 'b2_set', 'k1_set', 'k2_set', 'k12_set', 'F_set']) + theseresults_cols.append(['m1_in', 'm2_in', 'b1_in', 'b2_in', 'k1_in', 'k2_in', 'k12_in', 'F_in']) theseresults.append([noiselevel, noiselevel * complexamplitudenoisefactor]) theseresults_cols.append(['noiselevel', 'stdev']) @@ -281,7 +282,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force if noisy_spectra is None or i > 0 or freqnoise: # recalculate noisy spectra - noisy_spectra = calculate_spectra(drive, vals_set=vals_set, + noisy_spectra = calculate_spectra(drive, vals_in=vals_in, noiselevel=noiselevel,MONOMER=MONOMER, forceboth=forceboth) R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp,_ = noisy_spectra @@ -336,7 +337,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force R1_amp=R1_amp,R2_amp=R2_amp,R1_phase=R1_phase, R2_phase=R2_phase, R1_amp_noiseless=R1_amp_noiseless,R2_amp_noiseless=R2_amp_noiseless, R1_phase_noiseless=R1_phase_noiseless, R2_phase_noiseless=R2_phase_noiseless, - MONOMER=MONOMER, vals_set=vals_set, forceboth=forceboth, + MONOMER=MONOMER, vals_in=vals_in, forceboth=forceboth, noiselevel = noiselevel ) Zmatrix = Zmat(df, frequencycolumn = 'drive', @@ -349,10 +350,10 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force continue vh = make_real_iff_real(vh) - theseresults.append(approx_Q(m = m1_set, k = k1_set, b = b1_set)) + theseresults.append(approx_Q(m = m1_in, k = k1_in, b = b1_in)) theseresults_cols.append('approxQ1') if not MONOMER: - theseresults.append(approx_Q(m = m2_set, k = k2_set, b = b2_set)) + theseresults.append(approx_Q(m = m2_in, k = k2_in, b = b2_in)) theseresults_cols.append('approxQ2') theseresults.append(df['R1Amp_syserr%'].mean()) theseresults_cols.append('R1Ampsyserr%mean(priv)') @@ -373,7 +374,7 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force M1, M2, B1, B2, K1, K2, K12, FD = read_params(vh[-1], MONOMER) # the 7th singular value is the smallest one (closest to zero) # normalize parameters vector to the force, assuming 1D nullspace - allparameters = normalize_parameters_1d_by_force([M1, M2, B1, B2, K1, K2, K12, FD], F_set) + allparameters = normalize_parameters_1d_by_force([M1, M2, B1, B2, K1, K2, K12, FD], F_in) M1, M2, B1, B2, K1, K2, K12, FD = allparameters @@ -386,17 +387,17 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force if verbose and first: print("1D:") if MONOMER: - describe_monomer_results(Zmatrix, s[-1], vh[-1], M1, B1, K1, vals_set, freqs = drive[p]) - plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, + describe_monomer_results(Zmatrix, s[-1], vh[-1], M1, B1, K1, vals_in, freqs = drive[p]) + plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_in, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay = overlay, context = context, saving = saving, labelname = '1D', demo=demo, - resonatorsystem = resonatorsystem, show_set = show_set, + resonatorsystem = resonatorsystem, show_in = show_in, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() - plot_info_1D = [drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, + plot_info_1D = [drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_in, MONOMER, forceboth, labelcounts, overlay, context, saving, '1D', demo, - resonatorsystem, show_set, + resonatorsystem, show_in, figsizeoverride1, figsizeoverride2] el = store_params(M1, M2, B1, B2, K1, K2, K12, FD, MONOMER) @@ -412,25 +413,25 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force drive, K1, K2, K12, B1, B2, FD, M1, M2, MONOMER = MONOMER, forceboth = forceboth, label="1D") # calculate how close the SVD-determined parameters are compared to the originally set parameters - syserrs = [syserr(el[i], vals_set[i]) for i in range(len(el))] + syserrs = [syserr(el[i], vals_in[i]) for i in range(len(el))] # Values to compare: - # Set values: k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set + # Set values: k1_in, k2_in, k12_in, b1_in, b2_in, F_in, m1_in, m2_in # SVD-determined values: M1, M2, B1, B2, K1, K2, K12, FD - K1syserr = syserr(K1,k1_set) - B1syserr = syserr(B1,b1_set) - FDsyserr = syserr(FD,F_set) - M1syserr = syserr(M1,m1_set) + K1syserr = syserr(K1,k1_in) + B1syserr = syserr(B1,b1_in) + FDsyserr = syserr(FD,F_in) + M1syserr = syserr(M1,m1_in) if MONOMER: K2syserr = 0 K12syserr = 0 B2syserr = 0 M2syserr = 0 else: - K2syserr = syserr(K2,k2_set) - K12syserr = syserr(K12,k12_set) - B2syserr = syserr(B2,b2_set) - M2syserr = syserr(M2,m2_set) + K2syserr = syserr(K2,k2_in) + K12syserr = syserr(K12,k12_in) + B2syserr = syserr(B2,b2_in) + M2syserr = syserr(M2,m2_in) avgsyserr, rmssyserr, maxsyserr, Lavgsyserr = combinedsyserr(syserrs,1) # subtract 1 degrees of freedom for 1D nullspace if MONOMER: @@ -449,15 +450,15 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force ### Normalize parameters in 2D nullspace """ # Problem: res1 formula only for weak coupling. [M1_2D, M2_2D, B1_2D, B2_2D, K1_2D, K2_2D, K12_2D, FD_2D] = \ - normalize_parameters_to_res1_and_F_2d(vh, vals_set = vals_set) + normalize_parameters_to_res1_and_F_2d(vh, vals_in = vals_in) coefa = np.nan coefb = np.nan""" #[M1_2D, M2_2D, B1_2D, B2_2D, K1_2D, K2_2D, K12_2D, FD_2D], coefa, coefb = \ - # normalize_parameters_to_m1_set_k1_set_assuming_2d(vh) + # normalize_parameters_to_m1_in_k1_in_assuming_2d(vh) #[M1_2D, M2_2D, B1_2D, B2_2D, K1_2D, K2_2D, K12_2D, FD_2D], coefa, coefb = \ - # normalize_parameters_to_m1_m2_assuming_2d(vh, verbose = False, m1_set = m1_set, m2_set = m2_set) + # normalize_parameters_to_m1_m2_assuming_2d(vh, verbose = False, m1_in = m1_in, m2_in = m2_in) el_2D, coefa, coefb = \ - normalize_parameters_to_m1_F_set_assuming_2d(vh, MONOMER,verbose = False, m1_set = m1_set, F_set = F_set) + normalize_parameters_to_m1_F_in_assuming_2d(vh, MONOMER,verbose = False, m1_in = m1_in, F_in = F_in) #normalizationpair = 'm1 and F' if MONOMER: @@ -471,10 +472,10 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force if verbose and first: print("2D:") plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, - K1_2D, K2_2D, K12_2D, B1_2D, B2_2D, FD_2D, M1_2D, M2_2D, vals_set, + K1_2D, K2_2D, K12_2D, B1_2D, B2_2D, FD_2D, M1_2D, M2_2D, vals_in, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay=overlay, context = context,saving = saving, labelname = '2D', demo=demo, - resonatorsystem = resonatorsystem, show_set = show_set, + resonatorsystem = resonatorsystem, show_in = show_in, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() @@ -487,16 +488,16 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force rsqrdresults2D, rsqrdcolumns2D = compile_rsqrd(R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp, drive, K1_2D, K2_2D, K12_2D, B1_2D, B2_2D, FD_2D, M1_2D, M2_2D, MONOMER = MONOMER, forceboth = forceboth, label="2D") - syserrs_2D = [syserr(el_2D[i], vals_set[i]) for i in range(len(el_2D))] + syserrs_2D = [syserr(el_2D[i], vals_in[i]) for i in range(len(el_2D))] # Values to compare: - # Set values: k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set + # Set values: k1_in, k2_in, k12_in, b1_in, b2_in, F_in, m1_in, m2_in # SVD-determined values: M1, M2, B1, B2, K1, K2, K12, FD - K1syserr_2D = syserr(K1_2D,k1_set) - B1syserr_2D = syserr(B1_2D,b1_set) - FDsyserr_2D = syserr(FD_2D,F_set) - M1syserr_2D = syserr(M1_2D,m1_set) + K1syserr_2D = syserr(K1_2D,k1_in) + B1syserr_2D = syserr(B1_2D,b1_in) + FDsyserr_2D = syserr(FD_2D,F_in) + M1syserr_2D = syserr(M1_2D,m1_in) if MONOMER: K2syserr_2D = 0 @@ -504,10 +505,10 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force B2syserr_2D = 0 M2syserr_2D = 0 else: - K2syserr_2D = syserr(K2_2D,k2_set) - K12syserr_2D = syserr(K12_2D,k12_set) - B2syserr_2D = syserr(B2_2D,b2_set) - M2syserr_2D = syserr(M2_2D,m2_set) + K2syserr_2D = syserr(K2_2D,k2_in) + K12syserr_2D = syserr(K12_2D,k12_in) + B2syserr_2D = syserr(B2_2D,b2_in) + M2syserr_2D = syserr(M2_2D,m2_in) if MONOMER: theseresults.append([K1syserr_2D,B1syserr_2D,FDsyserr_2D,M1syserr_2D]) theseresults_cols.append(['K1syserr%_2D','B1syserr%_2D','FDsyserr%_2D','M1syserr%_2D']) @@ -530,9 +531,9 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force ## 3D normalization. if MONOMER: - el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh,vals_set, MONOMER) + el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh,vals_in, MONOMER) else: - el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh, vals_set, MONOMER=MONOMER) + el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh, vals_in, MONOMER=MONOMER) el_3D = [parameter.real for parameter in el_3D if parameter.imag == 0 ] if MONOMER: @@ -546,10 +547,10 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force if verbose and first: print("3D:") plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, - K1_3D, K2_3D, K12_3D, B1_3D, B2_3D, FD_3D, M1_3D, M2_3D, vals_set, + K1_3D, K2_3D, K12_3D, B1_3D, B2_3D, FD_3D, M1_3D, M2_3D, vals_in, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay=overlay, context = context,saving = saving, labelname = '3D', demo=demo, - resonatorsystem = resonatorsystem, show_set = show_set, + resonatorsystem = resonatorsystem, show_in = show_in, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() @@ -565,16 +566,16 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force drive, K1_3D, K2_3D, K12_3D, B1_3D, B2_3D, FD_3D, M1_3D, M2_3D, MONOMER = MONOMER, forceboth = forceboth, label="3D") - syserrs_3D = [syserr(el_3D[i], vals_set[i]) for i in range(len(el_3D))] + syserrs_3D = [syserr(el_3D[i], vals_in[i]) for i in range(len(el_3D))] # Values to compare: - # Set values: k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set + # Set values: k1_in, k2_in, k12_in, b1_in, b2_in, F_in, m1_in, m2_in # SVD-determined values: M1, M2, B1, B2, K1, K2, K12, FD - K1syserr_3D = syserr(K1_3D,k1_set) - B1syserr_3D = syserr(B1_3D,b1_set) - FDsyserr_3D = syserr(FD_3D,F_set) - M1syserr_3D = syserr(M1_3D,m1_set) + K1syserr_3D = syserr(K1_3D,k1_in) + B1syserr_3D = syserr(B1_3D,b1_in) + FDsyserr_3D = syserr(FD_3D,F_in) + M1syserr_3D = syserr(M1_3D,m1_in) if MONOMER: K2syserr_3D = 0 @@ -582,10 +583,10 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force B2syserr_3D = 0 M2syserr_3D = 0 else: - K2syserr_3D = syserr(K2_3D,k2_set) - K12syserr_3D = syserr(K12_3D,k12_set) - B2syserr_3D = syserr(B2_3D,b2_set) - M2syserr_3D = syserr(M2_3D,m2_set) + K2syserr_3D = syserr(K2_3D,k2_in) + K12syserr_3D = syserr(K12_3D,k12_in) + B2syserr_3D = syserr(B2_3D,b2_in) + M2syserr_3D = syserr(M2_3D,m2_in) if MONOMER: theseresults.append([K1syserr_3D,B1syserr_3D,FDsyserr_3D,M1syserr_3D]) theseresults_cols.append(['K1syserr%_3D','B1syserr%_3D','FDsyserr%_3D','M1syserr%_3D']) From 70d45b6e281083646e4c1f46596d414ab48ec29d Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 23 Mar 2023 15:46:49 -0400 Subject: [PATCH 033/101] Revert "variable naming: set -> in" This reverts commit c2b91e7863c187b6d558553dc2b8cfe110661614. --- simulated_experiment.py | 165 ++++++++++++++++++++-------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/simulated_experiment.py b/simulated_experiment.py index 927a97d..f09d0ba 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -14,7 +14,7 @@ read_params, store_params, make_real_iff_real, flatten from resonatorSVDanalysis import Zmat, \ normalize_parameters_1d_by_force, normalize_parameters_assuming_3d, \ - normalize_parameters_to_m1_F_in_assuming_2d + normalize_parameters_to_m1_F_set_assuming_2d from resonatorstats import syserr, combinedsyserr from resonatorphysics import \ approx_Q, approx_width, res_freq_weak_coupling, complexamp @@ -29,8 +29,8 @@ global use_complexnoise use_complexnoise = True # this just works best. Don't use the other. -def describeresonator(vals_in, MONOMER, forceboth, noiselevel = None): - [m1_in, m2_in, b1_in, b2_in, k1_in, k2_in, k12_in, F_in] = read_params(vals_in, MONOMER) +def describeresonator(vals_set, MONOMER, forceboth, noiselevel = None): + [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER) if MONOMER: print('MONOMER') @@ -40,16 +40,16 @@ def describeresonator(vals_in, MONOMER, forceboth, noiselevel = None): print('Applying oscillating force to both masses.') else: print('Applying oscillating force to m1.') - print('Approximate Q1: ' + "{:.2f}".format(approx_Q(k = k1_in, m = m1_in, b=b1_in)) + - ' width: ' + "{:.2f}".format(approx_width(k = k1_in, m = m1_in, b=b1_in))) + print('Approximate Q1: ' + "{:.2f}".format(approx_Q(k = k1_set, m = m1_set, b=b1_set)) + + ' width: ' + "{:.2f}".format(approx_width(k = k1_set, m = m1_set, b=b1_set))) if not MONOMER: - print('Approximate Q2: ' + "{:.2f}".format(approx_Q(k = k2_in, m = m2_in, b=b2_in)) + - ' width: ' + "{:.2f}".format(approx_width(k = k2_in, m = m2_in, b=b2_in))) + print('Approximate Q2: ' + "{:.2f}".format(approx_Q(k = k2_set, m = m2_set, b=b2_set)) + + ' width: ' + "{:.2f}".format(approx_width(k = k2_set, m = m2_set, b=b2_set))) print('Q ~ sqrt(m*k)/b') print('Set values:') if MONOMER: - print('m: ' + str(m1_in) + ', b: ' + str(b1_in) + ', k: ' + str(k1_in) + ', F: ' + str(F_in)) - res1 = res_freq_weak_coupling(k1_in, m1_in, b1_in) + print('m: ' + str(m1_set) + ', b: ' + str(b1_set) + ', k: ' + str(k1_set) + ', F: ' + str(F_set)) + res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) print('res freq: ', res1) else: if forceboth: @@ -57,8 +57,8 @@ def describeresonator(vals_in, MONOMER, forceboth, noiselevel = None): else: forcestr = ', F1: ' - print('m1: ' + str(m1_in) + ', b1: ' + str(b1_in) + ', k1: ' + str(k1_in) + forcestr + str(F_in)) - print('m2: ' + str(m2_in) + ', b2: ' + str(b2_in) + ', k2: ' + str(k2_in) + ', k12: ' + str(k12_in)) + print('m1: ' + str(m1_set) + ', b1: ' + str(b1_set) + ', k1: ' + str(k1_set) + forcestr + str(F_set)) + print('m2: ' + str(m2_set) + ', b2: ' + str(b2_set) + ', k2: ' + str(k2_set) + ', k12: ' + str(k12_set)) if noiselevel is not None and use_complexnoise: print('noiselevel:', noiselevel) print('stdev sigma:', complexamplitudenoisefactor*noiselevel) @@ -68,7 +68,7 @@ def measurementdfcalc(drive, p, R1_amp,R2_amp,R1_phase, R2_phase, R1_amp_noiseless,R2_amp_noiseless, R1_phase_noiseless, R2_phase_noiseless, - vals_in, noiselevel, MONOMER, forceboth): + vals_set, noiselevel, MONOMER, forceboth): table = [] for i in range(len(p)): if False: @@ -78,7 +78,7 @@ def measurementdfcalc(drive, p, print('correct amplitude: ' + str(R1_amp_noiseless[p[i]])) print('Syserr: ', syserr(R1_amp[p[i]], R1_amp_noiseless[p[i]]), ' %') - SNR_R1, SNR_R2 = SNRknown(drive[p[i]],vals_in=vals_in, noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth) + SNR_R1, SNR_R2 = SNRknown(drive[p[i]],vals_set=vals_set, noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth) table.append([drive[p[i]], R1_amp[p[i]], R1_phase[p[i]], R2_amp[p[i]], R2_phase[p[i]], complexamp(R1_amp[p[i]],R1_phase[p[i]] ), complexamp(R2_amp[p[i]], R2_phase[p[i]]), @@ -168,13 +168,12 @@ def assert_results_length(results, columns): # unscaled_vector = vh[-1] has elements: m1, b1, k1, f1 -def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_in, freqs = None, absval = False ): - - [m1_in, m2_in, b1_in, b2_in, k1_in, k2_in, k12_in, F_in] = read_params(vals_in, True) - m_err = syserr(M1,m1_in, absval) - b_err = syserr(B1,b1_in, absval) - k_err = syserr(K1,k1_in, absval) - sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_in/m1_in), absval) +def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, vals_set, freqs = None, absval = False ): + [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, True) + m_err = syserr(M1,m1_set, absval) + b_err = syserr(B1,b1_set, absval) + k_err = syserr(K1,k1_set, absval) + sqrtkoverm_err = syserr(np.sqrt(K1/M1),np.sqrt(k1_set/m1_set), absval) if freqs is not None: print("Using", len(freqs), "frequencies for SVD analysis, namely", @@ -188,8 +187,8 @@ def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, v unscaled_vector[0], " kg, ", #M unscaled_vector[1], "N/(m/s),", #B unscaled_vector[2], "N/m,", #K - unscaled_vector[3], "N), where α=F_in/", unscaled_vector[3], "=", \ - F_in, "/" , unscaled_vector[3], "=", F_in/unscaled_vector[3], \ + unscaled_vector[3], "N), where α=F_set/", unscaled_vector[3], "=", \ + F_set, "/" , unscaled_vector[3], "=", F_set/unscaled_vector[3], \ "is a normalization constant obtained from our knowledge of the force amplitude F for a 1D-SVD analysis.", "Dividing by α allows us to scale the singular vector to yield the modeled parameters vector.", "Therefore, we obtain m\\hat= ", @@ -202,29 +201,29 @@ def describe_monomer_results(Zmatrix, smallest_s, unscaled_vector, M1, B1, K1, v "Each of these is within ", \ max([abs(err) for err in [m_err, b_err, k_err]]), \ "% of the correct values for m, b, and k.", \ - "We also see that the recovered value √(k̂/m̂)=", + "We also see that the recovered value √(k ̂/m ̂ )=", np.sqrt(K1/M1), "rad/s is more accurate than the individually recovered values for mass and spring stiffness;", "this is generally true. ", - "The percent error for √(k̂/m̂) compared to √(k_in/m_in ) is", + "The percent error for √(k ̂/m ̂ ) compared to √(k_set/m_set ) is", sqrtkoverm_err, "%. This high accuracy likely arises because we choose frequency ω_a at the peak amplitude." ) """ demo indicates that the data should be plotted without ticks""" -def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceboth, +def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, forceboth, drive=None,#np.linspace(minfreq,maxfreq,n), verbose = False, repeats=1, labelcounts = False, noiseless_spectra = None, noisy_spectra = None, freqnoise = False, overlay=False, context = None, saving = False, demo = False, - resonatorsystem = None, show_in = None, + resonatorsystem = None, show_set = None, figsizeoverride1 = None, figsizeoverride2 = None, return_1D_plot_info= False): if verbose: print('Running simulated_experiment()', repeats, 'times.') - describeresonator(vals_in, MONOMER, forceboth, noiselevel) + describeresonator(vals_set, MONOMER, forceboth, noiselevel) - [m1_in, m2_in, b1_in, b2_in, k1_in, k2_in, k12_in, F_in] = read_params(vals_in, MONOMER) + [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER) if drive is None: drive = measurementfreqs # fastest way to do it, but R^2 isn't very accurate @@ -235,7 +234,7 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb p = freqpoints(desiredfreqs = measurementfreqs, drive = drive) if noiseless_spectra is None: # calculate noiseless spectra - noiseless_spectra = calculate_spectra(drive, vals_in, noiselevel = 0, MONOMER = MONOMER, forceboth = forceboth) + noiseless_spectra = calculate_spectra(drive, vals_set, noiselevel = 0, MONOMER = MONOMER, forceboth = forceboth) R1_amp_noiseless, R1_phase_noiseless, R2_amp_noiseless, R2_phase_noiseless, \ R1_real_amp_noiseless, R1_im_amp_noiseless, R2_real_amp_noiseless, R2_im_amp_noiseless, _ = noiseless_spectra @@ -247,7 +246,7 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb ## privileged SNR and amplitude maxSNR_R1,maxSNR_R2, minSNR_R1,minSNR_R2,meanSNR_R1,meanSNR_R2, SNR_R1_list, SNR_R2_list, \ A1, STD1, A2, STD2 = SNRs( \ - drive[p],vals_in, noiselevel=noiselevel, MONOMER=MONOMER,forceboth=forceboth, + drive[p],vals_set, noiselevel=noiselevel, MONOMER=MONOMER,forceboth=forceboth, use_complexnoise=use_complexnoise, detailed = True, privilege = True) @@ -268,11 +267,11 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb theseresults.append(len(p)) theseresults_cols.append([ 'num frequency points']) - theseresults.append(vals_in) # Store vals_in # same for every row + theseresults.append(vals_set) # Store vals_set # same for every row if MONOMER: - theseresults_cols.append(['m1_in', 'b1_in', 'k1_in', 'F_in']) + theseresults_cols.append(['m1_set', 'b1_set', 'k1_set', 'F_set']) else: - theseresults_cols.append(['m1_in', 'm2_in', 'b1_in', 'b2_in', 'k1_in', 'k2_in', 'k12_in', 'F_in']) + theseresults_cols.append(['m1_set', 'm2_set', 'b1_set', 'b2_set', 'k1_set', 'k2_set', 'k12_set', 'F_set']) theseresults.append([noiselevel, noiselevel * complexamplitudenoisefactor]) theseresults_cols.append(['noiselevel', 'stdev']) @@ -282,7 +281,7 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb if noisy_spectra is None or i > 0 or freqnoise: # recalculate noisy spectra - noisy_spectra = calculate_spectra(drive, vals_in=vals_in, + noisy_spectra = calculate_spectra(drive, vals_set=vals_set, noiselevel=noiselevel,MONOMER=MONOMER, forceboth=forceboth) R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp,_ = noisy_spectra @@ -337,7 +336,7 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb R1_amp=R1_amp,R2_amp=R2_amp,R1_phase=R1_phase, R2_phase=R2_phase, R1_amp_noiseless=R1_amp_noiseless,R2_amp_noiseless=R2_amp_noiseless, R1_phase_noiseless=R1_phase_noiseless, R2_phase_noiseless=R2_phase_noiseless, - MONOMER=MONOMER, vals_in=vals_in, forceboth=forceboth, + MONOMER=MONOMER, vals_set=vals_set, forceboth=forceboth, noiselevel = noiselevel ) Zmatrix = Zmat(df, frequencycolumn = 'drive', @@ -350,10 +349,10 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb continue vh = make_real_iff_real(vh) - theseresults.append(approx_Q(m = m1_in, k = k1_in, b = b1_in)) + theseresults.append(approx_Q(m = m1_set, k = k1_set, b = b1_set)) theseresults_cols.append('approxQ1') if not MONOMER: - theseresults.append(approx_Q(m = m2_in, k = k2_in, b = b2_in)) + theseresults.append(approx_Q(m = m2_set, k = k2_set, b = b2_set)) theseresults_cols.append('approxQ2') theseresults.append(df['R1Amp_syserr%'].mean()) theseresults_cols.append('R1Ampsyserr%mean(priv)') @@ -374,7 +373,7 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb M1, M2, B1, B2, K1, K2, K12, FD = read_params(vh[-1], MONOMER) # the 7th singular value is the smallest one (closest to zero) # normalize parameters vector to the force, assuming 1D nullspace - allparameters = normalize_parameters_1d_by_force([M1, M2, B1, B2, K1, K2, K12, FD], F_in) + allparameters = normalize_parameters_1d_by_force([M1, M2, B1, B2, K1, K2, K12, FD], F_set) M1, M2, B1, B2, K1, K2, K12, FD = allparameters @@ -387,17 +386,17 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb if verbose and first: print("1D:") if MONOMER: - describe_monomer_results(Zmatrix, s[-1], vh[-1], M1, B1, K1, vals_in, freqs = drive[p]) - plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_in, + describe_monomer_results(Zmatrix, s[-1], vh[-1], M1, B1, K1, vals_set, freqs = drive[p]) + plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay = overlay, context = context, saving = saving, labelname = '1D', demo=demo, - resonatorsystem = resonatorsystem, show_in = show_in, + resonatorsystem = resonatorsystem, show_set = show_set, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() - plot_info_1D = [drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_in, + plot_info_1D = [drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, MONOMER, forceboth, labelcounts, overlay, context, saving, '1D', demo, - resonatorsystem, show_in, + resonatorsystem, show_set, figsizeoverride1, figsizeoverride2] el = store_params(M1, M2, B1, B2, K1, K2, K12, FD, MONOMER) @@ -413,25 +412,25 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb drive, K1, K2, K12, B1, B2, FD, M1, M2, MONOMER = MONOMER, forceboth = forceboth, label="1D") # calculate how close the SVD-determined parameters are compared to the originally set parameters - syserrs = [syserr(el[i], vals_in[i]) for i in range(len(el))] + syserrs = [syserr(el[i], vals_set[i]) for i in range(len(el))] # Values to compare: - # Set values: k1_in, k2_in, k12_in, b1_in, b2_in, F_in, m1_in, m2_in + # Set values: k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set # SVD-determined values: M1, M2, B1, B2, K1, K2, K12, FD - K1syserr = syserr(K1,k1_in) - B1syserr = syserr(B1,b1_in) - FDsyserr = syserr(FD,F_in) - M1syserr = syserr(M1,m1_in) + K1syserr = syserr(K1,k1_set) + B1syserr = syserr(B1,b1_set) + FDsyserr = syserr(FD,F_set) + M1syserr = syserr(M1,m1_set) if MONOMER: K2syserr = 0 K12syserr = 0 B2syserr = 0 M2syserr = 0 else: - K2syserr = syserr(K2,k2_in) - K12syserr = syserr(K12,k12_in) - B2syserr = syserr(B2,b2_in) - M2syserr = syserr(M2,m2_in) + K2syserr = syserr(K2,k2_set) + K12syserr = syserr(K12,k12_set) + B2syserr = syserr(B2,b2_set) + M2syserr = syserr(M2,m2_set) avgsyserr, rmssyserr, maxsyserr, Lavgsyserr = combinedsyserr(syserrs,1) # subtract 1 degrees of freedom for 1D nullspace if MONOMER: @@ -450,15 +449,15 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb ### Normalize parameters in 2D nullspace """ # Problem: res1 formula only for weak coupling. [M1_2D, M2_2D, B1_2D, B2_2D, K1_2D, K2_2D, K12_2D, FD_2D] = \ - normalize_parameters_to_res1_and_F_2d(vh, vals_in = vals_in) + normalize_parameters_to_res1_and_F_2d(vh, vals_set = vals_set) coefa = np.nan coefb = np.nan""" #[M1_2D, M2_2D, B1_2D, B2_2D, K1_2D, K2_2D, K12_2D, FD_2D], coefa, coefb = \ - # normalize_parameters_to_m1_in_k1_in_assuming_2d(vh) + # normalize_parameters_to_m1_set_k1_set_assuming_2d(vh) #[M1_2D, M2_2D, B1_2D, B2_2D, K1_2D, K2_2D, K12_2D, FD_2D], coefa, coefb = \ - # normalize_parameters_to_m1_m2_assuming_2d(vh, verbose = False, m1_in = m1_in, m2_in = m2_in) + # normalize_parameters_to_m1_m2_assuming_2d(vh, verbose = False, m1_set = m1_set, m2_set = m2_set) el_2D, coefa, coefb = \ - normalize_parameters_to_m1_F_in_assuming_2d(vh, MONOMER,verbose = False, m1_in = m1_in, F_in = F_in) + normalize_parameters_to_m1_F_set_assuming_2d(vh, MONOMER,verbose = False, m1_set = m1_set, F_set = F_set) #normalizationpair = 'm1 and F' if MONOMER: @@ -472,10 +471,10 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb if verbose and first: print("2D:") plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, - K1_2D, K2_2D, K12_2D, B1_2D, B2_2D, FD_2D, M1_2D, M2_2D, vals_in, + K1_2D, K2_2D, K12_2D, B1_2D, B2_2D, FD_2D, M1_2D, M2_2D, vals_set, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay=overlay, context = context,saving = saving, labelname = '2D', demo=demo, - resonatorsystem = resonatorsystem, show_in = show_in, + resonatorsystem = resonatorsystem, show_set = show_set, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() @@ -488,16 +487,16 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb rsqrdresults2D, rsqrdcolumns2D = compile_rsqrd(R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp, drive, K1_2D, K2_2D, K12_2D, B1_2D, B2_2D, FD_2D, M1_2D, M2_2D, MONOMER = MONOMER, forceboth = forceboth, label="2D") - syserrs_2D = [syserr(el_2D[i], vals_in[i]) for i in range(len(el_2D))] + syserrs_2D = [syserr(el_2D[i], vals_set[i]) for i in range(len(el_2D))] # Values to compare: - # Set values: k1_in, k2_in, k12_in, b1_in, b2_in, F_in, m1_in, m2_in + # Set values: k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set # SVD-determined values: M1, M2, B1, B2, K1, K2, K12, FD - K1syserr_2D = syserr(K1_2D,k1_in) - B1syserr_2D = syserr(B1_2D,b1_in) - FDsyserr_2D = syserr(FD_2D,F_in) - M1syserr_2D = syserr(M1_2D,m1_in) + K1syserr_2D = syserr(K1_2D,k1_set) + B1syserr_2D = syserr(B1_2D,b1_set) + FDsyserr_2D = syserr(FD_2D,F_set) + M1syserr_2D = syserr(M1_2D,m1_set) if MONOMER: K2syserr_2D = 0 @@ -505,10 +504,10 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb B2syserr_2D = 0 M2syserr_2D = 0 else: - K2syserr_2D = syserr(K2_2D,k2_in) - K12syserr_2D = syserr(K12_2D,k12_in) - B2syserr_2D = syserr(B2_2D,b2_in) - M2syserr_2D = syserr(M2_2D,m2_in) + K2syserr_2D = syserr(K2_2D,k2_set) + K12syserr_2D = syserr(K12_2D,k12_set) + B2syserr_2D = syserr(B2_2D,b2_set) + M2syserr_2D = syserr(M2_2D,m2_set) if MONOMER: theseresults.append([K1syserr_2D,B1syserr_2D,FDsyserr_2D,M1syserr_2D]) theseresults_cols.append(['K1syserr%_2D','B1syserr%_2D','FDsyserr%_2D','M1syserr%_2D']) @@ -531,9 +530,9 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb ## 3D normalization. if MONOMER: - el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh,vals_in, MONOMER) + el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh,vals_set, MONOMER) else: - el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh, vals_in, MONOMER=MONOMER) + el_3D, coefa, coefb, coefc = normalize_parameters_assuming_3d(vh, vals_set, MONOMER=MONOMER) el_3D = [parameter.real for parameter in el_3D if parameter.imag == 0 ] if MONOMER: @@ -547,10 +546,10 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb if verbose and first: print("3D:") plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase, df, - K1_3D, K2_3D, K12_3D, B1_3D, B2_3D, FD_3D, M1_3D, M2_3D, vals_in, + K1_3D, K2_3D, K12_3D, B1_3D, B2_3D, FD_3D, M1_3D, M2_3D, vals_set, MONOMER=MONOMER, forceboth=forceboth, labelcounts = labelcounts, overlay=overlay, context = context,saving = saving, labelname = '3D', demo=demo, - resonatorsystem = resonatorsystem, show_in = show_in, + resonatorsystem = resonatorsystem, show_set = show_set, figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2) plt.show() @@ -566,16 +565,16 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb drive, K1_3D, K2_3D, K12_3D, B1_3D, B2_3D, FD_3D, M1_3D, M2_3D, MONOMER = MONOMER, forceboth = forceboth, label="3D") - syserrs_3D = [syserr(el_3D[i], vals_in[i]) for i in range(len(el_3D))] + syserrs_3D = [syserr(el_3D[i], vals_set[i]) for i in range(len(el_3D))] # Values to compare: - # Set values: k1_in, k2_in, k12_in, b1_in, b2_in, F_in, m1_in, m2_in + # Set values: k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set # SVD-determined values: M1, M2, B1, B2, K1, K2, K12, FD - K1syserr_3D = syserr(K1_3D,k1_in) - B1syserr_3D = syserr(B1_3D,b1_in) - FDsyserr_3D = syserr(FD_3D,F_in) - M1syserr_3D = syserr(M1_3D,m1_in) + K1syserr_3D = syserr(K1_3D,k1_set) + B1syserr_3D = syserr(B1_3D,b1_set) + FDsyserr_3D = syserr(FD_3D,F_set) + M1syserr_3D = syserr(M1_3D,m1_set) if MONOMER: K2syserr_3D = 0 @@ -583,10 +582,10 @@ def simulated_experiment(measurementfreqs, vals_in, noiselevel, MONOMER, forceb B2syserr_3D = 0 M2syserr_3D = 0 else: - K2syserr_3D = syserr(K2_3D,k2_in) - K12syserr_3D = syserr(K12_3D,k12_in) - B2syserr_3D = syserr(B2_3D,b2_in) - M2syserr_3D = syserr(M2_3D,m2_in) + K2syserr_3D = syserr(K2_3D,k2_set) + K12syserr_3D = syserr(K12_3D,k12_set) + B2syserr_3D = syserr(B2_3D,b2_set) + M2syserr_3D = syserr(M2_3D,m2_set) if MONOMER: theseresults.append([K1syserr_3D,B1syserr_3D,FDsyserr_3D,M1syserr_3D]) theseresults_cols.append(['K1syserr%_3D','B1syserr%_3D','FDsyserr%_3D','M1syserr%_3D']) From f6bc112b11be6f5ac25261305749f1d3b7d0d4ab Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 23 Mar 2023 17:58:39 -0400 Subject: [PATCH 034/101] pdf export preferences According to Nature, the following resource is preferred: https://jonathansoma.com/lede/data-studio/matplotlib/exporting-from-matplotlib-to-open-in-adobe-illustrator/ --- helperfunctions.py | 12 ++++++------ resonator_plotting.py | 5 +++++ 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/helperfunctions.py b/helperfunctions.py index 75181d3..a95fe07 100644 --- a/helperfunctions.py +++ b/helperfunctions.py @@ -66,14 +66,15 @@ def read_params(vect, MONOMER): def savefigure(savename): try: - plt.savefig(savename + '.svg', dpi = 600, bbox_inches='tight') + plt.savefig(savename + '.svg', dpi = 600, bbox_inches='tight', transparent=True) except: print('Could not save svg') try: - plt.savefig(savename + '.pdf', dpi = 600, bbox_inches='tight') + plt.savefig(savename + '.pdf', dpi = 600, bbox_inches='tight', transparent=True) + # transparent true source: https://jonathansoma.com/lede/data-studio/matplotlib/exporting-from-matplotlib-to-open-in-adobe-illustrator/ except: print('Could not save pdf') - plt.savefig(savename + '.png', dpi = 600, bbox_inches='tight',) + plt.savefig(savename + '.png', dpi = 600, bbox_inches='tight', transparent=True) print("Saved:\n", savename + '.png') @@ -88,9 +89,8 @@ def calc_error_interval(resultsdf, resultsdfmean, groupby, fractionofdata = .95) avgerr = resultsdf[resultsdf[groupby]== item]['avgsyserr%_' + D] avgerr = np.sort(avgerr) halfalpha = (1 - fractionofdata)/2 - ## literally select the 95% confidence interval by tossing out the top 2.5% and the bottom 2.5% - ## I could do a weighted average to work better with selecting the top 2.5% and bottom 2.5% - ## But perhaps this is good enough for an estimate. It's ideal if I do 40*N measurements for some integer N. + ## literally select the 95% fraction by tossing out the top 2.5% and the bottom 2.5% + ## For 95%, It's ideal if I do 40*N measurements for some integer N. lowerbound = np.mean([avgerr[int(np.floor(halfalpha*len(avgerr)))], avgerr[int(np.ceil(halfalpha*len(avgerr)))]]) upperbound = np.mean([avgerr[-int(np.floor(halfalpha*len(avgerr))+1)],avgerr[-int(np.ceil(halfalpha*len(avgerr))+1)]]) resultsdfmean.loc[resultsdfmean[groupby]== item,'E_lower_'+ D] = lowerbound diff --git a/resonator_plotting.py b/resonator_plotting.py index bd067df..88ecf41 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -99,6 +99,11 @@ def set_format(): plt.rcParams['axes.titlepad'] = -5 + plt.rcParams['pdf.fonttype']=42 + # source: Nature https://drive.google.com/drive/folders/15m_c_ZfP2X4C9G7bOtQBdSlcLmJkUA7D + plt.rcParams['ps.fonttype'] = 42 + # source: https://jonathansoma.com/lede/data-studio/matplotlib/exporting-from-matplotlib-to-open-in-adobe-illustrator/ + def text_color_legend(**kwargs): l = plt.legend(**kwargs) # set text color in legend From 97f8445d0ea194ef4af0b77a719d0b618eaf7c64 Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 6 Apr 2023 21:41:10 -0400 Subject: [PATCH 035/101] backup! --- ...ach Simulated Two Coupled Resonators.ipynb | 777 +++++++++++++++++- 1 file changed, 738 insertions(+), 39 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index ed1494c..34bbb49 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,9 +14,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.1'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%matplotlib inline\n", "import sympy as sp\n", @@ -57,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -66,9 +77,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:190: DeprecationWarning: invalid escape sequence \\o\n", + " ax.set_xlabel('$\\omega$ (rad/s)')\n", + "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:235: DeprecationWarning: invalid escape sequence \\m\n", + " ax.set_xlabel('$\\mathrm{Re}(Z)$ (m)')\n", + "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:320: DeprecationWarning: invalid escape sequence \\;\n", + " amplabel = '$A\\;$(m)'\n", + "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:321: DeprecationWarning: invalid escape sequence \\p\n", + " phaselabel = '$\\phi\\;(\\pi)$'\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from myheatmap import myheatmap\n", "from helperfunctions import flatten,listlength,printtime,make_real_iff_real, \\\n", @@ -103,9 +141,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\"import matplotlib.font_manager # See list of fonts\\nmatplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\"" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"import matplotlib.font_manager # See list of fonts\n", "matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\"\"\"" @@ -113,15 +162,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# global variables that I promise not to vary\n", "from simulated_experiment import complexamplitudenoisefactor, use_complexnoise\n", - "from resonator_plotting import co1,co2,co3 # color scheme\n", + "from resonator_plotting import co1,co2,co3, figwidth # color scheme\n", "\n", "# Nature says: (https://www.nature.com/npp/authors-and-referees/artwork-figures-tables)\n", "#Figure width - single image\t86 mm (3.38 in) (should be able to fit into a single column of the printed journal)\n", @@ -138,9 +222,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "resonatorsystem: 11\n", + "DIMER\n", + "Applying oscillating force to m1.\n", + "Approximate Q1: 8.00 width: 0.06\n", + "Approximate Q2: 26.46 width: 0.10\n", + "Q ~ sqrt(m*k)/b\n", + "Set values:\n", + "m1: 8, b1: 0.5, k1: 2, F1: 1\n", + "m2: 1, b2: 0.1, k2: 7, k12: 5\n", + "noiselevel: 0.1\n", + "stdev sigma: 5e-05\n", + "Drive length: 100 (for calculating R^2)\n", + "Desired freqs: [0.773987235127223, 3.5328457422902457]\n", + "Index of freqs: [27, 81]\n" + ] + } + ], "source": [ "verbose = False\n", "#MONOMER = False\n", @@ -173,7 +278,7 @@ "MONOMER = False\"\"\"\n", "\n", "\n", - "\n", + "\"\"\"\n", "### lightly damped monomer ## this is my official lightly damped monomer for Fig 2.\n", "MONOMER = True\n", "resonatorsystem = 2\n", @@ -185,7 +290,7 @@ "maxfreq = 2.01\n", "noiselevel= 10\n", "forceboth = False\n", - "\n", + "\"\"\"\n", "\n", "\"\"\"\n", "### medium damped monomer -- use for demo\n", @@ -310,7 +415,7 @@ "maxfreq = 2.2\n", "\"\"\"\n", "\n", - "\"\"\"\n", + "\n", "## Well-separated dimer / Medium coupled dimer #1 / Used for Figure 5.\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -327,7 +432,7 @@ "minfreq = 0.1\n", "maxfreq = 5\n", "#(but this is 3D for forceboth)\n", - "\"\"\"\n", + "\n", "\n", "\"\"\"\n", "### Medium coupled dimer #2\n", @@ -449,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -498,9 +603,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'stophere' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mstophere\u001b[49m\n", + "\u001b[1;31mNameError\u001b[0m: name 'stophere' is not defined" + ] + } + ], "source": [ "stophere # finish initialization. Next: try 1D, 2D, and 3D SVD" ] @@ -4612,7 +4729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -4624,11 +4741,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened existing file: sys11,2freq,2023-01-07 13;53;00.csv\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhorowit\\AppData\\Local\\Temp\\ipykernel_71112\\1444816346.py:106: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " resultsdfmean = resultsdfsweep2freqorigmean[resultsdfsweep2freqorig.Difference != 0]\n" + ] + } + ], "source": [ "#Code that loops through frequency points of different spacing \n", "\n", @@ -4774,18 +4907,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "## reset resonances\n", + "for i in range(3):\n", + " res1,res2 = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER, forceboth=forceboth, includefreqs = reslist, \n", + " minfreq = minfreq, maxfreq = maxfreq,\n", + " verboseplot = False, verbose=False, iterations = 3,\n", + " numtoreturn=2)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DIMER\n", + "Applying oscillating force to m1.\n", + "Approximate Q1: 8.00 width: 0.06\n", + "Approximate Q2: 26.46 width: 0.10\n", + "Q ~ sqrt(m*k)/b\n", + "Set values:\n", + "m1: 8, b1: 0.5, k1: 2, F1: 1\n", + "m2: 1, b2: 0.1, k2: 7, k12: 5\n", + "noiselevel: 0.1\n", + "stdev sigma: 5e-05\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+ "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I cut out f1 = f2.\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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0cBUPCgkShBBCCHFPei2gX3hkkh95dopXruUOxKJ6bUvXehBLC1fxwJAgQQghhBD3pN8C+o8/e5IvvHRm36fnGGN4+doylxfqlLIeTxwrM1NpdVu47tfAR4jt2NeFy0qpglLqHyulZpVSS0qpf6mUmtrr6xJCCCEOuo0L6I88PLluBgKA5zp9F9HGGOJEj/SshEQbqq2IehAzNZ4n40kLV/Hg2O8nCf8A+EPAZ4AA+FngX6S3CSGEEGKXrF1AP3Gs3F1Av3ZjpbuA9tzNAcJ+yvF3HcVY3qeU85iptJgaz0sLV/HA2O9BwqeB/94Y8x0ApdTfB76slMoZY4K9vTQhhBDi4LrXBfR+yvFXSvHimUMsVAMuL9R57cYKpZwnLVzFA2G/BwmLwE8opf4F0AL+I+CCBAgCpF2dEELspkELaIA40eveg/djjn+nVWuvkw8hDrL9HiT8ZeBfAvPYmY7TwCcH/SWl1MPAQ+l/nt+1qxN7Yj8dZQshxH7WawH9wiOTGAy/8M1rm96D7zVFaS8ppTh/erx7/bLxJB4U+z1IeBq4CnweiLA1Cr+slPoDxphwi7/3BeBv34frE3tgPx1lCyHEftZrAb3Ve/C5U2P7NsdfKTVyAYwQu2nfdjdSSj0K/C/AXzHG/DtjzO8BnwWex9YqbOUXsCcOnwR+ajevU9xfg7ptjHIXDSGE2K/sAtouKQZ1PHrxzCEeP1qi3o557cYK9XYsOf77zH7oTCV2bj+fJHwMMMaYi50bjDFzSqnrwGNb/UVjzA3gBiBvSAfMfjzKFkKIg2I778GS479/STrvg2U/BwnTgKeUetIY8x6AUmocOA1c3tMrE3tG2tUJIcTe2c57sOT471+Szvtg2bfpRsC3gVeBf6qUekEp9SzwS8A14Nf28sLE3ul025CjbCGEuP/u5j24k6Ik78v7g6TzPnj27UmCMSZRSv0J4L8HfhVwgX8P/PCAomVxwMlRthBC7B15Dz6Y1qaSPX60hOcoTozleP3mqqTzHlD7NkgAW4MA/Pm9vg4xWuQoWwgh9o68Bx9MrqMo5zy0MXzj8iI53yWIEgoZl3LOk3TeA2hfBwlCbEXa1QkhxN6R9+CDRSnF4WKWMNbMVgPakSbrO5yeyHO4mJVA8ACSIEEIIYQQQmzJGMNyo03Wc5kay5H1XdpRQtZzWW60McZIoHDASJAghBBCCCG2lGhDNYhRCj75xBE81yFONK/dWKUaxFKTcADt5+5GQgghhBDiPljf3jYgTgwzlUBajB9gEiQIIYQQYkdkAu/BJy3GHzySbiSEEEKIeyITeB8s0t72wSJBghBCCCHuyd1O4DXGSFvUfUza2z5YJEgQB5Z8GAkhxO5ZO4G3mPV44liJmUrQncC79jRBThwOFmlv+2CQIEEcOPJhJIQQuy9ONJfnalyaq3O0nGWlEXKsnKUexJsm8N7tiYMQYu9JkCDF2weOfBgJIcTue2emxvWlJo0wpr4Y2a43qy0OFbPrut2sPXEoZT2eOFZmptLqeeIghBgdskCGz+31BYjh2fhh9JGHJyllve6HkXTeEEKInTPG8Mr1FWKtmcj7lHM+9SBhpRXhOIoXzkx2F/6JNlRbEfUgZmo8T8ZzmBrPrztxEEKMHgkS4It7fQFieOTDSAghdl/nvVYBzz80ycOHCjx6pEgx4/HI4QJPnyh3v3Z9f/0WYayZqbSkv/4BNiotcUflOvYrSTcCvdcXIIZn44fR1HhePoyEEGLIOu+15bxPrDUfnhpjutJiPO/xxPEynntnD7LTX3+haouaX7uxQinncfZIke97eHIPfwoxbKNSE3g/r+MgN0mRIEEcKBs/jL53Y5lyzpdhL0IIMUQb32tfv7lKKWfrDXq91547NYbB8J0PlqgFEc1Qkxj47ffm+O6NFWkucUCMSk3g/biOUQmIdpMECeLAeeZkmcvzY3yw2AAUjlI8eWKMZ06WB/5dIYQQ27PdwVqdxdQr11aoBTH1dsJqM6IaRNJc4gAZlQL1u7mOnZwCjEpAtJskSBAHzlvTNd6brZJoDcaQaM17s1Xemi4dmF9cIYTYa9sdrLV2MVVrRay0ItqR5tEjRT7y8KR0OlpjP6eurK0JfOJYuVsT+NqNlU0tcff6OlyHHZ0CjEpAtNskSBAHysZf3MePlZk9gL+4QggxKrYarLVu4FrG5dEjRX73/QUqrYgo0WQ8tScLyVFzEFJXRqUmcDvXsdNTgFEJiHabBAniQEm0odIMma0EjOV9ZqttXEdRbUVUmuGB+cUVQohRp7Wm2Y5ZbbS778kzlYB2rEm0ZrHeJog0c9XggW8ucRBSV/oVqN/vmsBB1wHs+BRgVAKi3SZBgjhQXEfRDBNqQcRivY2jFNoYsp5DM0wOzC+uEEKMKq01X359hq9dnGG10Wau1qbRTrrvye04AaAVJbyRFjw/yM0lDlLqynbrVPbyOoZxCjAqAdFukyBBHFBpURJm3X8LIYTYXb/y2jT/8+9c4fZqizBOMOn7bzHjUsi6eI7Cz/qcHM/z8ccOMZbP7MlCclQcpNSV7dap7OV1uA5DOQUYlYBoN0mQIA6URBuKWZdyzuNUPk+caDzXodqKKGbdffVmK4QQ+43Wml9+9SY3V5oYA76raMcGbeyO+UOTBXzXodKKePHRSX7qD50l67sHZuf1XhzE1JWt6lT2+jqGdQowKgHRbpIgQRworqMYy/kUMx61VkTGc2iFEcWMx1huf77ZCiHEftGOEqbTqcpjOY+s71JpRbQiTawNz54aY7ERkc+4jBeyD3yAAA9O6sooGeYpwKgERLthKEGCUsoBXgS+HziOnWI8A7xsjHl5GPchxHYopThUzNKOE2aqAe1Ik/UdTk/kOVTMyputEELsItdReI6DUooo0fieA8Z0Ez7fvF1lLC8DLjd6EFJXRskwTwH2c9vaQXYUJCilJoD/HPhLwFHgKrAAuOl/P6KUmgN+FvjHxpjVndyfEIMYY1hqtMl4DifGcmQ8hzDWZDyHpUYbY8yB+yUWQohR4Xsu50+NMVsJaEUJzSgEA1nP4bGjJT5x9vADX4PQy4OQujKKdnIK0G1be3WJ1VbERN7nxUcP76tC80HuOUhQSv0Z4O8DvwP8Z8BvGGNaG75mHPhDwH8MvKWU+mljzC/t4HqF2JItAAtZqockWhMmmozr0AoTqi1pgSqEELtJKcUf/NBRXruxykw1sAteV3GsnOVzP/AIP/aR07IA3sJBTl05aC7crvClb17jwnSFRjummPV4e7rKT750hmdPT+z15Q3FTk4Sfhj4hDFmpt8XGGMqwFeAryilHgb+DiBBgtg1rqO4sdxivtamHScYbF+jrOdyY7klNQlCCLGLjDGsNEPKeZ8w0ZC+C5fzPqut6J4ChIOcziH6G+Xn3RjDV16f5tXrK7TjBKWg2op49foKk8UM50+Nj9w134t7DhKMMX/hLr/+BvD5e72/XeTs9QWI4THGMFsJiLXGUQrXgURDrDWzlUDSjYQQYhcl2nBlvsFyIyTrO8SJPb1dboRcmW/c1WnuQZhCLO7eMJ/33Qo04kTz/lyNSiviaCnDeCFDpRmyUA95f65GnGh8zx3a/e2VoXc3UkodB17AbuB+1xgzPez7GLLP7fUFiOEJY41OA4HJoo/C7mOtNiO0MYSxJpeRuFAIIYbNGIPWmqVGSJRoXOVwtJRlsd4mSuztd3OYu9+mEI/yzvd+st3nfavHe7uBxlCes87fO4DP+VCDBKXUn8YWKV8CfOBJpdRfNsZ8aZj3M2RfBL6w1xchhiPjOUwUMhQyLg5wJP2AKmRcJgoZMp4ECEIIMUxrF2SrjZD5mg0KEm2oLDTwXIWrFIeKPtps7/h+P00hlhOP4dnO8w4MfLwHBRo7fc481+FDx8tcX2pQCyIa7RhtDON5jw8dL+O5B2OtsZPCZWWMMRtu/n8AH0lTi1BK/RDwvwOjHCTovb4AMTyO4/Cpc1PMVwNurjS5PF8n6zs8NFngU+emcJyD8YsrhBCjYu2CrNaKWKoFxInBGJv2GSeGbNbOr9luXdh+mkK83048Rtl2nvd3ZmoDA4BBgcZOnzOlFJ9+7iQrjZA3b6/SbCeUcx7Pnprg08+dPDDB4U5WTG+nJwdrNYGPKaUySqk88BGgvoP7EOKuffq5E/zQU8c5UsoyXvA5UsryQ08d59PPndjrSxNCiANl44LsIw9P4nsOUaKJE43BYPcTDdzFumnjFOIw1iM5hbjXz1/Ket0F6ea9VLGVQc+7oxj4eK8NNKbG891Aox7EVFsRcaK3/ZwZY+zruMfzeO7UGJ984ihnDhc5NZnnzOEin3zi6IFq7buTIOHPAP+hUuoNpdRn0tv+AvY0IcAGB/8po1msLA6wt2fqNMOYhw4VODWR56FDBZphzNszEq8KIcQwbVyQ+a5DxnXQ2qAchcKmZkRxQqUZEoTxthbOnSnEjx8tUW/HvHZjhXo7HrkhbIMWpImWIOFuDHretWHg4z0o0IDB38MYw4VbFX7hm9f4n/7dZX7hm9e4cKuy7rX71nSN92artn+XsvWP781WeWu6tjcP3i7YSXejN4BPK6W+H/i7Sqm/AfwNY8y5dMiaSVugCnHf7Kc8ViGE2O82LsiOl7OsNENQoAwUMi6NdkzTwG+/t8Bf/Off5cnjY3z6+ZMD20TuhynEG3/+qfH8SJ547CeDnvdBj3cn0FioBlxeqPPajRVKOa8baHiuM/B7bKem4UFYa+y4cNkY823gh5VSP8j6YOF3dnx1Qtyl/ZTHKoQQ+93GBdnMaosk3WzVxrDciNDGFv9VdMyFW1VuLLVYaYR87qVHt8z/3sspxL263vS6bdCC9CAsFO+3Qc/7dh7vrQKNQc9Z5+9tFQA8KGuNnRQue9j0onPAVeDnjTEvKaX+OPAPlVIrwP/NGPOd4VyqEINt2tUayzFXDWRXRwghdsnaBdlqo81yM7TdXrRBYehk3GQ8xdFShlo75sJ0hZevLm1rx/V+TSHu5J+/M1vjlWsr3cXlC49MgmLdbWsXnPvhxGM/6ve8b+fxHhRobPU9thMAPCgnSDs5SfhF4GngN4BPAT8OvGSM+SrwVaXUnwR+QSl11RjzJ3Z+qUIMppTihTOTvH27woXpCq9cW6aY9Th/cpwXzkzKro4QQgzZ2gVZEMa8O1tjejXAKEOc3Pm6jGPTPOphTKMds3ofd1y321P/0lyN60tNG+AoKOU83r5tM6crQdQz9WQvTzweRHfzePcLNLb6Hq6z85Smg/L87yRI+DTwfcaYy0opH2gopY4aYxYAjDH/Sin1r4H/aBgXKsS29asTk/oxIYTYFZ2F9rc/WOTKQoPE2IVXIaNohRoDtBNbvKy1oVzwmbgPO67b6YffyT+/NF/j8nyDRhgzmfd57qEJokRzYdoGCR+eGtsy9/x+nXgcFDsdZDaMx7vX99huALDdE6T9PGRvJ0HCe8BPp4HAx4FlYGntF6RzFP7ZDu5DiLtijOGV6ytUgoinT5Q5MZ5nttKiEkS8cn2lu+sjhBBieDoL7ffna9SCtEMMtuuLoyA2EMaGm6stChmP0xN5XrgPO653U4BazHocLWepL8aEWjNfa/P0VJlXr68AcHwst+u55/t5Qbld+2H43DBSmjo/53euLrLSiJgs+nz80SMj9XMOspMg4c8A/wD4GeAa8CeMMTKYTOypXrmEJycKB66YSAghRsXahXY56zE1nqcWRESJQRlQju10BKBQZD2HiYJ/V3MTdnpd2ylAffxoidVmRDnnUQsimmHCTCWgmLVLpblqsGu55/th4Tws+2H43DBSyN68tcrP/NYlLtyuEESanO/wrctL/Jc//ATPPTS5S1c+XDtpgfoB8KeGeC1C7NiDUkwkhBCjYuNC+8Zyi4znkugY11UkGsDgOYpjYxmyrsvtlYBXrq0MbIM6rOvaTgHqbDXgWDnH9GoLpRQLtTYTBZ/zJ+3CtRJEu5Z7vh8WzsOw7uQm43L2aInZEW4dulVK01aBHcAvfvMar15fIYgSjDG0Y8Wr11f4xW9e43/40xMj9XP2s5PuRn8Z+Lntnh6kdQs/ZYz5H+/1PoUY5EEpJhJCiFGxdqE9XWnhKHtIUMi4JNoQa7tM8BwwGtomYbYaUG2Fu3q6u51No42fGbVWxKFihqOlLA8fLvDE8bJti9mnu9EwjGLP/d1Ke0q0odIMma0EjOV9ZqttXEdRbUVUmsN/Pexm+tZWgd2Tx4u8eatCM0zIeYpSLkM9PZ1681aFKE7I+DueQrDrdnKFDwNvK6V+HvjXxpgbvb5IKfUwtnj5PwH+jx3cnxDb0nnj/s7VJSrNiPGCz8cfPSzt6IQQYhd0usrNVVpcWaizWA/xPYesq2iGCY20cLkZGdrVAFBMFHwKGXdXT3fvpQC10gxphgkGKPgOKv0+506Ncf7U+K4tnEel5/5upz25jn1N1IKIhVqAoxTaGHK+SzNM9k361qDA7uyRPHE6udn3PJQC33NpRRGxNvtmEvdO0o3+mlLql4C/Bfx3Sqn3gXeARcAFjgLPAQ8BXwH+lDHmezu/ZCG2R6X/J2cHQgixO7qLsavLVIMY13F46kSZSivk9kpAK45Q2OZyBojT1KNWGBNEetd3yO+2APWNm6t8/eIsV9buDtfagE372Y3F+v1Ok91qd/1+pD3Z1BtDox3b4nZAKQfb62Y4hvVz9HusBgV2nutwcjzHfC2gFcaEsUOiNb5nb8/67tB+1t20o7MOY8zrwI8rpR4BfgR4ARsUaGAG+HvAV40xt3d4nbvJ2esLEMPV881hzZu8EEKI4dj4flvMujx2pMiHjh/nV9+cphlGGG0IE9PtQt1Zal2crqK1xnF272P4bgtQv3tjlSu7mPazl1ObB3XbuR9pT4k21NsRQRiTGLqPRRDG1NvDOTUZxs8x6CRiUGDney4/8bGHWG6E3F5tESUa33U4NZHnJz720K6+5odpKAlRxpjrwM+l/9tvPrfXFyCGZxRzO4UQ4iDq9377wWIDbSDrORR8h1o7WRcgZFxFnBhmVlu0o4R8dvcXTNvpqb+baT+DFp33Y2rzoG47d/Pz32uuv6Pg+lKLINFgDL5rd9iDRHN9ydazbNe97vJv53kcdBKxncDuxz5yEqUUX7swTaUVMZ73+ZHzJ/nM81Pb/yH32OhXTey+LwJf2OuLEMMxSrmdQghxkPV7v708X2e1GeE4ikaYECV30kjsUDWDq6DWjnl/vr6tdpD3Y37ATtN+dpLGs9tTm40xA7vtbOfn32muf6IN7ViTJDYBLdF3RnK3Y02iDYM22Xe6y7/2eez1nG13s3FQYOc4Dp/96Ck+8/wUYazJeM6+OUHokCDBpkaJA2Ljm8PxsRxz1UBaoAohxJD1WoxNrzYJE02YaJ48VqQdlajNVIh6fNK6Cl69vsqzp/u3g7zf8wO+7+EJ5qsBV+4i7WfQNd7NCfduTW2O4mRb3XbW7o5/78Yy5Zy/7ue/cKsynJqFTqHK2v/epmHs8m/1nG13s3G7gZ3jOOQy+ys46JAgQRwonS4bb9+ucGG6wivXlilmPc6fHOeFM5OSaiSEEEPSazFWyLqUMi6VVsxiI2KxHqDXBAidtaHrQJSYgW1Q71chbWfB2GnDeeZwkWLWZSyfGZj2M+gaR+GE27aiHdxt55mTZS7Pj/HBYgNQOErx5IkxnjlZHsqMA9exw/Q8R2GMwnUg0XYyd9ZztnVacze7/P26HG71nJ07NSbzllISJIiDp1+DhP3RcUwIIfaNjSkX5ZzHxdsVXruxwgcLdZphQiehxHaxsdOXdVq0Wsp6fdM/Ot93t2vMei0Yzx4p8oNPHue5h7Ye9radResoDPnM+u62uu28NV3jvdkqibY1A4nWvDdb5a3pEk9PlXc840AbOHO4wOX5OokxxIkhk1G4SnHmcAFttu4mc9cBlzFoY2BN56TtPGfbKSR/EKZk7zhISIekvQQ8A4wBFeAN4FtmmP2shNgGYwyvXF+hEkQ8faLMifE8s5UWlSDilesr3aNIIYTYa/cjz363bUy5cBT8t197F7A/k+/aYlVt7D6NA5i0P3XGc3jx0f7pH9/38CTVVrirO/D9FoxXFht898YKzz209WnF9hatTnfReWm+xsyqDRCeOFbelMa0W68Jx3EGdttZ+1iUcz4fOj62bvH8zMlyd8bBYr3dnXGQ9ZxtzzhwHcXjx8t8sNigGSZkPYd2rClkXB4/Xl73PbTWm3L5txtwXbhV4UvfusaF6QqNdkwx6/HOTI2f/IEzfPjk2MDnbDuF5A/ClOwdBQlKqT8J/CPgJNAEVrCBQhm4qZT6L4wxX97pRQqxXb3esE9OFKRwWQgxMg7iDmQnlz5ONMWsSznncTqfpx3FvD/foBrE3a/Neg6+q3jyeJkPT41hjOk5n2C+0iIx7OoOfL9F/veuL7PaCIkTje/172m/3UXrujQepXAdp5vGA/fnNTGo2852Ah4r3Unf1NR2MKUUH3/0MIu1Npfma9SDmImxLE8cK/PxRw+jlEJrzZdfn+HrF2e61/mpc1N85vkpHMfZVr3BV96Y5tXry7RjjaPsacer15eZLGa2lU40qN7gbupM9vNmwD0HCUqpHwV+CfgZ4B8bY66t+bOzwE8B/1Ip9ceMMb+z0wsVYjv6vWEXsy6lrHdX7dWEEGI33M8dyPu9QLHvwRlOjOcoZlwgw9XFJm56mqDsRXG4mONjZyZ4Z7bGy1eX+d33F5itBpwcz/ORhyeYqQRcWWxw5nCRs0dLd1VIfPfXe+cz48RYjndnq6y0It64tco//f3rvJjmsve6v+3OOOik8Whju/poY7ppPOdPj9+X18SgbjuDAh5gXQAYa4PnKCqtiGLW3fYm3KBd+i+/PsOXvnWVW2mL3KzvMp8+Fp/96KmB9QZxonl/rkalFXO0nGU871NpRSzU2rw/VyPRZtsnO/0KydcHVCU8VzE1nuO1G6vdgMp12PebATs5SfhrwN8zxvytjX9gjLkC/LRSqg78V4AECeK+2PiG/b3ryxjAcxw+WKjzi9+6vu9+SYUQB8f9muWyV6cVSileeMQ2j3jz9irz1TaN9p1ThNhAEhtWmhGNdsLX3pzh/fkaVxbqNNoJBd9lpRFyfCzHzI0VChmXH3rqGN9NF1/Dnh+w8TPjG5cXqQURoFhuhPz+B0sDh3EOWvQOes6fOVm+r/N9+nXbGRTweK7TDQBLWa/bPTCfsQXe2z3Z6ezSf3iqRCtMyGdcXNee1mit+dpFGyzFiU1fqwcxlxfqfO3iNJ95fupOJ6j0/7a8107W+4bs90EnO4O4jmIs52EMfOPSIlnfpR0lFDIeYzlbZ3MQ0pF2EiQ8C/zFAV/zr4D/dAf3IcRdW/uGfWmuxvWlJok2XF1ssFBv77tfUiHEwXG/Ot3s6QJlzeUnWqON7TXeudkAtSDiN9+eY2oiTznr8cjhIlfma9xcaVJpRWQ8BwM0w4RnT4/z3EMTu3YisnZn+nffXyBMNCfH8zw9VWamEgxcrA9KTRn0nIexvq/dj7Y6Xdpql35jEDF7c/WeTna2SicKY82V+QaNNQP4AFQMV+YbhLHm0nxj82t7TSDnuQ4fOl7m+lKTWjumHsYYA+N5nw8dL+O5DhdvV7c82RlEKcWhYpZ2nDBdadGONFnf4aHJAoeKWeD+FN3vtp0ECUVgecDXLALHd3AfQty1zhv2MyfL/G/fuMpivU0p63WPTvfbL6kQ4uC4H51u9nLyvDGGV67daR4xkff43o0KpIXLLpBgU49urwYU0+vLZzwWa22uLTWot2Pyvsd43melEfLWdC1d/O3ONXc+M546UaLSigivLvP01NhdL9b7paYMes4znjPU10S/IKBzutQvAFj3s6T/t/Geu5twV5dYbUVM5P1uOtZ2rgE2phPZxXUnnehHnz3OSjPc1IzQACvNEFdt77X96edPstII7xQu52wr9E8/f9Je/w5/P4wxLNaDbgF4xnMxxhAlmsV6QJzogelI+6E+cidBgmLwIDLDXY3IEGJ4tIF6O5Hpy0KIkbHdHPad2Iu+/J1FoTGm2yaznPOpBxFrBi5326EawBhNKZsOvixnSYzBdx0KWZcPHSvjuw6VILpvmzqe6zBRyAw9gBv0nG+nGHetQUFArwV8ZxDapo4/01V+8gfO8OxDE0CfE6ge6VYm/b9Ni/kBaW5aa75+cYZbqy1ynt15X6gF3Fpt8fWLM/zRp4/0rR10FATR9k5dzp8a53MvPdrzsRjG70eiDVcWGiw3QrKeS5xoPM9luRFyZaEB0E1H+r1Li90uTsU16Uj7wU5boD6rlNrqNOHIDr+/EPdsFHpTCyHERttpr7gT9/O9T2vNGzdXeeX6Co12TCnr8eatVW6vtAiTBpj+e4lZz+WJ4yWuLDSYWW2hjWEs5/GJs0d46FCeMDb3dVNnNwO4Qc/5dl4TgxbgF25X+NI31wcBb09X+cmXznD+1DhfeeM237qySL0dY4BqK6LWipgs+t0AYNAOe68gYnFNEDEozS2MNZVWRDvSPDRZIOM5HC3nuDxfp5I+z+Wsx0ojQqk17XMNlLPetk9dtkoBcx22/fvRLyBzFCw3QiJtcJKEo+UcC7WASBuWGyGuo5gsZFhpbm45O1nI7Jsshp0GCb/O4JOCXZuVoJRygL8N/AVs69XfA37KGHN9t+5T7B/3Y8dOCCHu1qAc9mF8/91+7zPGcOFWhV/45lVevbZMM0zIeA6HCj7z9ZBmGKONWTdteaMo0fzRDx/n9ZsVVpsh787WqbRCtDGEsdmTTZ3dCuAGPefbeU0MmhL8ldenefX6Cu04QSnStp8rTBYzPHW8xHevr7LctAtYB3uqs9wM+e71VeJEo9JWof122ONE77gAO+M5jOd9sr7DQi3oLq6zvr29mPN57uFJFuptmqEmSTtiFTIOzz08Scb37uq13SsFbDu/H4NOZbSBQwXb8akZaa4s1PFcOzH6UMEn0Ya3pitUgwhHQcZzUEA1iHhrusJnP3pqX6xBdhIkPDq0q7h3fxdbGP0fAzex7Vj/f8BH9/KixOh45mSZRJ/gu9dXqAbx0HfshBDiXvXLYR+G3T6tuHi7yhe/eZXfeX+BWhChUOQ8RbMdE8QJoMhnXJpB0vd7NMKE33xrlsiAMYrDRZ96O2ZmNWC2EuzJps79CODupa5hUJ3Jk8eLadvPiKOlDOOFDJVmyEI95P25GlGcpDv14DtQSlPBQg2VVoQxJu1etHUL1J0XYDt86twU81WbYnR5vk7Wdzg9kedT56ZwXZc/+MQRXru+QpQE3Tarh4tZ/uATR1BKDWyBuh2Dfj+2OpV59vQErqMo5Tx8R9GObJF1nGhynkcp52GM4dJ8nSDSHB/LdduwLtZDLs3XB87fGBX3HCTs9W69UqoM/FXg88aYf5Pe9peA31RKPWyMubGX1yf21sZj2XLO4wefPMazp8fW9YUWQoiDaDcXu8YYvvPBIq9cW+m2N3WUoR0bEmPTQxxlTwO2SiWIteF3Ly3SCG1QUcq6FLIunuPwyOEiTxwvDy2wudt5EbsZwN2L7Q86Azo/34aTivG8b+dVAPUgQgOusl1/lFLbbIG6/QLsTovUjadBneFtX784w2ozZKKQ6XY3Msa2xz0xnqOc8+7k8mc9Vpo2mOn+TOn/3cuztNXvhzHmzqlMlIAyVJt3TmXOn+rUZigynksxfaiNgYznsvGKVHp/o/Nq2r6dDFNzsDMQ/jwwAfwb4G8ZY26v+ZqjwBVjzG5s2/4BbKOE7kRnY8z7wCOD/qJS6mHgofQ/z+/CtYk91i9v0nWUtD4VQjwwdmOxm2i7S7rcbJMYW72aGDsDofs1Bkj0lt1NHKAaxFRaEb6jmCgUaYWaertNMevxZ198aMcFy/ttunW/YGZjncnGBXjWd9O2nw1qQUSjbdO9xvMeHzpeJpfx+OgjE8xUWjTSmgQXGCv6fPSRCTzXbp5ttcO+nQLsF87YGRkXpiu8cm2ZYtZ2FXrhzOSd+QZK8fixEi+cmWS1ETFR9Hn8WAmlFIk21IIYRyleOnsY13FItObN21VqQUyiDe/M1LZVXH2v4kTz3mzVfk+jUY7CaAOBw3uz1W5q1trBcp2ag85gOaXUwDas+8FOh6n9NPAPsYHpXwQ+rZT6EWPMK+nXOEBpZ5fY1+PANPBHlFJ/GzgJfAP4P68NVPr4AraWQRxAe9n+Twgx+u73FOKDplO0CQpXAQqiNVlFnup0L7K7qP1OEzzXprqEiSZK4NZKk1LWZ7kRoVSdr745g6OcHS38hjkvYhivm4GdifoszpVSWy7AHcfh08+tb/s5lvNt28/nTuI4Dp957hSrjYg3b6/SbCcUsi7PnprgM8+dWreA3+oEamAam7nz82C4s/O/5kXQ6zlZqofd71/KutSCiK+8OdOtDzhczFDKujhqfXH12aMl5qqDZ1n0eh761RwAzFUDWlFnVoP9f0XCXPrascPUfIoZj0orIus5NMMo7V7k47nOnTastys0QpuydP6UbcO6X953dhIkfAH4gjHmywBKqZ8Bfhmb7vODxpjX0q/brcLlMWz3pL+LDVZqwH+b3v/zxphwi7/7C8Bvpv9+Hvi5XbpGsQfssWxILYh44lhJWp8KIYD9t6s8qrSBw6Used9BYWhFuvtB7wBHyhkabU2jHdsgwvTul+4oyPkuzXaCBhId2cLPkk8p4/HBYmNHGzs72TBau5gHtvW62SqIGPTa21Yw0281ld5+/rRt+9kvV3/Qn2/HoDSdl68tc3OlScZ1GBv3CaKEmytNXr62vO0OSq1QM19rs5p+XruOPWFohXYwX7VlW+yO531mKgGeo6i0IqqtcNuf71vVHDwzVabainrOaqi2bCHy2mFqs9WAdpSQ9V1OT+Q5VMza2omTY3zyiaNU2zGVZsh4IcMnnzjKuZP7pyZyJ0HCCeBC5z+MMU2l1GeBXwN+XSn1B4GVHV7fViLsQLe/YIx5GUAp9RPALPBHgK/2+4tpvcKN9O/s4iWK+80Yw9vTVd6ZqTFfa/ONS4s8eaJMrI20PhXiAbenU4gPENex6SJX5otMr7ZIdEiQ1h84CnRiiBMNyqYgrZucm/7TANo41AOb+mKAWEMQJhwuZnnyRJlbK60dbezcSz/8Xov5Q4UM785WudLndbOd4HNQZ6JBC2eAV67fGVJ3YjzPbKVFJYh45foK50+PD6WD0nYD6V5pbIk2XJ6vc3O5SdZ3WW1F+K7DciPk8ny9Wzex1XMSxQkXpytEiaHgu2R9h3akiRLDxekKCkOjnVALYhbr7W4tQNZzabSTbX2+r6s56NEJ6vEjjxPq3hFZqA1hrMk5Dkv1gDgx+I7Cz9rldJwYluoBxhjemq7Zqc5a28FiWt/VVOdRsJMg4S3gz2F38gEwxkRKqR8H/j3wW8Dnd3R1W5tO//n2mvtfUEotAmd28X7FCLt4u8rXLthiqCBM30gaIQ9PFvjIwxPS+lSIB5SkIQ6PUoqPP3qYhWrATMV2oOmIDcw3ovTr1v89B8j7ijAxJNo+J7Y1pOrWMxgFvusMZWPnXuZFbFzMF7MurVCTaMPURG7g7IBaEFHO+ZuCiK1ee0+dKG2rKHnj15ycKPQMeO61g1Kvn/9uAum1swPcRHNsw+yAzg78Vs9Joo39/I4SMp6iGSY4CtqRZrUZEsadM6lOCpDCdMPMzXqd7sSJ3rITlN7Qt3djypyj0oBoocFsNSDWCXFiH//Zqr19Y7vYx46WmL+HtKi9tpMg4W8Cv6qU+hHgL3fSi4wxDaXUHwN+gy1284fgG+k/XwB+G0ApdQI4ClzZxfsVI2rtG/HUeI6HDxV5d7ZKI4wZL/j8yLNT0vpUiAfUXkwhPsjOnRojTjS/dmHGdjMiPR1Y+0UGPAei9EYNNKM7Wd5hYgiTBFdB1nPIZ1yCWLPUaHO0nOWJYztrf3q38yJ6LeZvrzb5YLGC1vDRRyZ6zw64usRrN1ZphDFZz2G2ElBrxRwtZ7c14Re2N9xrtwfk7TSQ1gYOFzP4roPjwFy1hecqfNfhcDGDNuA5Wz8nWd+lGSZEiSZtnAXYhXkztCcFhYxLOedzKucTa43nOFSDiELG7f4eb3UisuFFsv6fgOM4lNPaGFgfIJSzPo7j4Ci4ttigEcYYY9Jp0AalDNcWG+nk8TZXFxp27keyQsZ1cJTiw1PlffN+s5MWqL+plPo+4D8Emhv+bFkp9UngbwB/ameX2Pf+ryqlfgn4eaXUX8CmNv0j7AnHb+3GfYrR1uuN+Egpw2s3VnjqRJkPT+2PyF0IMXwygX24lFJ8eMr2JdHGrrEUrFtRpQ2O1jE9/j02EEeaSGtynsuxcpYfOHt4XSHpvbqbeRG9PkNOjud542YFx4HpSsDJHrMDLs3XubJQJzG6u6PsKodL88XuLvZWrz3PdbYVzAxrQF6/2omdBtKuozh7tMiFWxWWGm1Qiig2HC5mOXu02P0d2+o5WdviFO60Fl17H2N5n0LGpdIKyfkujXZIMbv+93hQetfaTlD1tl3odzpBZX2XE+NZplebxPrONXgOnBjPdmskgighjG2dhMG+/h1lbwe4sdxivtamHSfdP896LjeWW/vm/WZHE5eNMW8Bf73PnwXYIOFvdG5TSv174M8ZY6Z7/Z178AXgHwD/Gshjg4M/b4yJt/xb4kDq9UY8m7aIK+d89snvpBBiF8gE9uF7e6ZOnJhuJ6NetmqBCmkNQ/p3owSynuGhQwU+/9KZ7kybnXQVupt5Ef0+Q46PZXEdh0Y73vS6cR21ZkcZXAdaoUEpzbXFRjfFZtBrbzvBzE4H5A2qNxjUZnXQwlYpxeFSDs9VRNrQjmKyvovn2ts3dlB65mSZMNZkPKf7XLejhLzv4LsOGc9BG4OjFGGsyfsOUWKDjjDWzNXatCNth7G5DofTguGNJyK9OiD96HNTfLBQ58LtCkEUk/Ndzh4t8aPPTWFQnDlc5Mp8ncTQbW/qKjhzuNh9vS43Qtvqt/P4Ylv/LjdCjDHMVgJirXGUwnVswBxrzWzF1izsh/ecHQUJ9+D7gNywvlkaiPyX6f/EPrfT9nIb34i/d30ZA3iOwwcLdX7xW9elk4kQD7DdnkL8IDHG8N0bK2Q8Rda1KUV9aj0HfJ/1Od+xhuV6O+0WA29N14bSjWo78yL6LeY/8tAET02Ns9wMN11HnGjase5OE1YKHId1tzvO4NfedoKZrRbX6x/T3p+lg+oNBrVZHfSYG2NYbrTJei5TYzmyvmufR89ludHuLoy3ClYynsNEMUvOd0gSGxgEkSbn29t9Vw28j06Hw606ICkUk4UM43k7NbmQdZksZFDYYOmJ42WuLjZotON1A92eOF62JwmJphpEPR+HamALsHX6804W/e5rfLUZ2fSjWJPLjP6shPsdJAixyTDbEq59I740V+P6UpNEG64uNliot6WTiRAPsN2cQnwv9vO8hs7Qq7GcT963O8/NMFm3s7odG788TjRXl5r87L+/QivSrDRCKkFErRVRzHnMV1rA7r2HD1rM93q+SlmXjOeS82z+fZRoAmUnSHcM47U36LNyUB7+tuYLDGizupVEG6pBjFLw0uN3BqG9frNCNR2E5rmD272eOznGq1eXaUSaeqhRCsZdl3MnxzCodffR+bnX3ofrqC07IHVmLdxcaZL1XMbzmU2tWl989BDvTFd58/YqK02bzvTUZIEXH7UnP0Gk+56emXTq+EQhQyHj2rbApSyL9TaFjMtEIUPGG/0AASRIEPfJVh+Gw2xLuHan5X/7xlUW621KWY8TYzmmKy0uzdf2VWcBIcTw7cYU4rtxEOY1dFJTTozniBNNnCTcXGnddZDQoQA3TT1qtBO+c3WJ1VZMECYcLWfJ+i63V1rcSu/jmZPldbvowwq4Bs0B2MhzHT50vMyl+Qa1IKQZxiilKOcyPSfr9nvt7bSN6vnT41v++dNT5YG7666zvTarYNt5bjzNcB1FOeehjeEbl5fI+S5BlKSFxh6uszkVaGNx9DMny1RaEa5jawC0ticzrmMH7zkKyjmXWjvmV16foVMNcLiUoZxzN6RE9e6AtK1WrebO89X535pvSc531i30He6k1mU8h0LW41PnppivBtxabXFloWHToibyfOrcVM8ToFEkQYLYVdvZ+diNtoTaQL2dUGtFTOQzvD1TpRkmLNTaHC3bDzXfcwd/IyGEGLKDMK+hk5ozt9rkN9+Z48by3QUICvAdu7CyBZ8Kx1HE2haQPvfQJL/7/gLLTVswm0n75TfDhNdurPDmrQrPPzy5awHX2sX8oPv48MkxfvPtOaLEEGuD50DGtbdv9xoGvSYG5dk/c7I8cPE9aL5Az8LtDW1WHWX48uszfP3iDJVWxHje51PnpvjM83bh26kXsAPGdHdh3KkXiBO95j5KeK5iajzHazdWqbYi2lHCpbkaQWy6k4vjRBPEhktzNRJt5yRMr9jgxaRF8+0oodFOuvfR7YCUtlV1HUW1ZTsg2bSo/q1aFYZXri1za6VFxlGUx3K0o4RbKy1e6QyFUw5nj5ZYaS4T6zsBgufA2aMlUA6feX4KoOdjtV9IkCB21aA3vt1qS9jZ5TLA6zdXiNOjcaXg+lKTd2ZrPHt6Yrg/rDiQ9nNKiBg9B2lew7lTY/y7d3wW6+FdnyAo7I5wPuOTcRUTBZ+bKwFBpDk9WSDvOxwqZri+3GS1FXEyk8PLuGhjqLVjvnt9hecemrgvAdegTjlvz9SIEk3Wc8ikeehRonl7psaPb6NAdTuviUF59mF8Z/H9+NESnqM4MZbj9Zur62YtbDVfYG3h8vRqs3uSsLZw+Vdem+ZL37rKrdVWNwiYTx+LH/vIyYH1Aq6jGMt5GAO/9/5C92uKWZ+x9LSh3k4I4wRtHJy0e1CcaOrtBK0137q8SDOKcZRCKYNB0YxivnV5kf/8hx7HdRTjhQwnxnMUM2735yhkXMYLGVtgnbZqdR3FfDVY16o1SuxJw42VJr4LcTPEcxVLzah70uA6ih966gg3V5os1kI0BgfFkXKGH3rqSPfz4rMfPcWnnztBK0zIZ1xcd39tTkqQIHbNdt74dqstoVKKFx6Z5N++PcdqK8IYO5gn4zhobXjl2grnT43vmw9jcf8dhJQQMXoO0rwGYwwXpitEicYBUNsvXtbASjMhiCHjOhgUk8UM7SjBc2wXG99VZF2HMNHUQ9uB5sRYjjjNfd84sGqrgOteg/1Bn2NPHi9yaa5GK9KcGMsxlveptiIW6iGX5mrbOrXezmtiUJ59xnPWpPosbkr1Abq76ydzXvd71oJ4zXwBhxcesYXLb95e7RYuP3tqghcesac2X784w63VFjnP4aHJAgs1m07z9Ysz/PHzx7v1Ap984kj3FOC1G6trahIcJgsZVpoht1db3c5BpybyTBYyeK5D1rOLd601Sil0GlxkPfv9Zqp20nE565LLeARhTK2dMFMNbHekrN8tQL80X+P1tAD9iWNlXjxzCM91OHusyJu3KyzVQ8CQxHC4lOHssSK+q1ist2mFMU0D+YxLpWl/rsV6u9ux6mi5wNFSlnakCeKEnOdytJTlaLmwrkbkOx8sstQIOVzM8PHHjuyrzxAJEsSu2d6H4fb6Q9+Lp06UeOhQnturTY6WcxQytv/2rZXWvvswFvffQUgJEaPnIM1rCGNNpWlPERxn80yEQTSQaE2Yfq+HDxdYaYTcWGlwbanBsVKGRw4XWW2F5H2Xw8UsGc/Bc1V3RsHgxTU7Cva38znWYTAYY9Id+u3b/mvCEMYJjXbc7bufhkDp7rhN9ZmptNLuUC4PTRY4XMziuU53vkAtsB17mqENItbeh8Gw0gxZbUYEUUKU2P822I48lVZEO9I8NFkg4zkcLee4PF+nkj4Wd36OIP051rdQNcbw1kyFahChsIGgwlANIt6aqfCjz01x5nCBd2ertKPEpgSlw/bOHLaLb89xUEqRmDttRzu3d36OZ06WuTw/xpWFOgabzvbkiTGeOVlGKcWhYpYo1lRbNu3IT084DhWzGFT3NEgbaDVsOlYnONAG1JpOTifGsj1PTd64ucLf/eq7vDNT7QZDT0/N8Tf++FM8//Chu3qN7JWhVE4opf5yOu14kN8EGsO4TzH6Nr7xdd68Nr7xnTs1xh9/9iSfOHuEjz92mE+cPcIff/bkPbclNMZw4VaFf/rtG8xUAjKeSzHj8eGpMWJt9uWHsbi/Nu4efuThSUpZr7t72KuAUYjt6OTyP360RD3tu19vx/tyXoPvKlqRRmtDrLfVAGeTsbxP1lNoDEF6WtApVvU9lz/89DE+9sgk2hiuLzeYrrQYz/m8cGayu/Dd6jOmE+z//pVFvvPBEr9/ZZGvvjnNxdvVLa/LGEOcaBzFlveR9V2eOFbEc+DmcosL01VuLrfwHOzt7uBl1nZeE4k21IOYqNNqFVs0HaW3x4lmsd6iGsSEsU3BCWN74rJYtx2h1tYLvD9XZ7YaEMZ63XyBX31jhisLdVxHUcrZx/DKQp1ffWMG31WM532yvsNCzf7dhVpA1nfS2911P8d3ry9v+jniRPP+bI1qK+omPhlssPf+bA1jDMWcT953cV0H31G4rkPedynmfHIZj2dPj5P3XdqxZrkR0o41ed/l2dPj3VObi7erfOPSAteXm9xebXF9uck3Li1w8XbVrhFurjBXteltcWIIIs1cNeDCzRWbiNVt1wq+o9LaB9WdrpxoQ6UV0QhjxgoZu94pZGiEMZV0Evc/+q3LvHlrlWaYECaGZpjw5q1V/tFvXd43nyHDOkn4u8CvD/oiY8yPD+n+xD6w3eFFw25LuHYHeLkREsYJ15YaLDXanBjP7csPY3F/HaSUkLsh9Rf3x0GZ1/DWdG3LVpDbYQyUch61IGG+HvLs6Qk++vAk05UWjXZMtRUBPV6PZvBnDNxp+1nMuDxyqMBivd23/qMTGLwzW+Plq8tUmhHjBT+dGFziSo/7cByHsXyGREOkTbeQNtEwls9s+/do0GvCUbDctH35C77D0UM21aedGJabNqX2lWurTK+2iLXdAXcUTK9qXrm2yud+QA+sF4gTzftzNSqtmKPlLON5n0orYqHW5v25GtqwrmPP5fn6po49H54q8dvvetxcblJv2wXzs6cmutO5AeZrbYJ487HTfK3dfXI7JyU4as0cDbtw//xLZ6i2onQQWkLOdzl/apzPv3Smu5D/yhvTvHp9mXZsh5lVWxGvXl9mspjhqRMlvnlliWoQdwNbA1SDmG9eWSKMdTcwixJN2JkorpzuSYLrKJphQi2IWKy3uz9D54QmSRIu3F4lTAwOdOc9hImxt0cx2Yy/rdfGXhpWkPBN4CeUUn/P7Jfw6I790Ydqn7qbD8NhtCXUWvPtDxa7Hwo/cPYw783WmKkGHCpm+MRjh3nx0cP77sNY3F8HKSVkO6T+4v4atXkN98IYw3c+WKLRjrrv24m2CyJjIGF7JwuOA75S5H2bF39yPEfGczk1UeB715e5NF+3aRp9WnJu9RmTaMNqo827M1UaYdJN+ShmXJ4+Ue4G+2tf/+/PVnl7pkalGZEYO0Dr3NQYn3ziKMfGcpvuQ2vNxekqBkMp63ZrAQyGi9NVtNbbanc56DWhDRwqZvAdhec6zNcCfNdBG82hYgZjDO/N1ggTe6TjKNs+NDSa92ZraK0H1gtseILX/zM1qGPPV96Y5d+9O8diOhAviBL+3btzPHSowGc/egpHQStKMGb960PRuf3OyUjnGjv/rKfX+ezpCf7qH3mSb19ZYLERcqSY4fvPHu2+FgYFO2EUM1dtd++/077UAHPVNhibhqTTx8RJx0fotHi88/avjWE13UzqBGWltC4kjDVRmn+nFISJnfdAOsE5jDXZzMCXxZ4bVpDgYU8T/qZS6iYQrP1DY8yzQ7qf3fC5vb6A/eBedxjv14ehMYYLtyv8/uVF/u27c9xYDjhWzjBTaeE69sjy2VPj/PlPPCKtT8VA2z0FOyik/mJv7PW8hp1ItOHKQp16OwYgTtsbRYC3doTyIAa725o+DO/M1HjqRInpSkAh65JoaLRjPnS8d0tOz3X6TiF2HXjzdoXZasCdjWu7+/vm7Uo32O+8/i/N13hrusZquhAsZDwSHfHdGyscKmX56z/yVHcXufMe0I4SKs2QMDFMpHUSpazDaiuyt9/lZN1+rwnXUZw9WuTCrQxLjRClFO1Yc7iY4ezRIok2tGO7+HYUKEehtEEbaMfJtuoFQPGh42WuLzWptWPqYYwxMJ73uzMfOh17PvP81KbHW2vN1y/OcG2piTGGQtajHWmuLTX5+sUZPvP8FFFi8PpssnhpG9aVZgRKUfAURybzNuBI7O2Osp/3l+frvHxtpRuoHCnnu/UG619fNnVobbCTaINe899rX6qdBX4Q2WnZhvSvpilGnduVgrenqzZwSb9BYqAexLw9XSXruxQyHtUgHTC45k4KGY9Cdn+UBA/rKn8//d9+9EXgC3t9EaNqWDuMu/lhaIzhV16b5pdfvclMpcliPSSIEpYabVxl33iKWY9WlGwrP1QIODgpIYMcpJacvUgK1WD38hg5CpYaod0V3dD/NL6LfILxvM9yIyRObMHvxdurvDtbTTsZZTkxnqfejvue6HU/o64usdqKmMj73dNirTXvzlTZmNkSa3h35s4uf+f1X8i4+K4iSQyOp8j5Nhd+oRbyftqjf+MmU8ZzcNIUl+VGiJsWcCtli2WHNVlXKcWRUg7fdYgTQzuOyXouvutwpJTDS/P2uzO/0kWwUpD33U1NQr53Y5lyzt+08fHp50+y0gi5MF2h0Y4p5jzOnxzn08+fXPfasJ/pzrrbwlhze6VJLbBpUbV2p7Aabq80CWON7yq8tAi4E9DoNJ3HcxSuozhUzICBZpTwwWIDz1W4yrbE1QZ+9Y0Z24Z1pUU7tilTC2mq0mc/egrPdXjieJlLczVmqwEzlRZKKfv+drxMLmNfP/V2suk0YyxvZzPUg2j969jY04Z6+rNhNJcX6pta/yYGLi/UcRRMjeeYrbbZaGo898ANU1sE/rUxZnZI3+9+ust+DA+W/bDDeOFWhf/vqze5NG+PVLUx6YeCIQZCZYiS2HY52EbPaiHgYKSEbMdBrb/YavF4EJ/He7GTTaBOdkp0NxFBD6vNiHasSbQtIK2bBNeBnOeSaE0+4zFZyFANop4nem/eWuVL37x2Z1Gb9Xh7uspPvnSGRw/luycdG9XbMc12TD7rd1//jx0Z441bFRxHobWx3ZoG/HhKKU6M53hr2rE79oldbGZdlxPjuU2P405asXZrCsY31xR4rsMjhwvcWmkRd2ojsAvvRw4X8D232/Hng8UGoNZ1/Ok4f2qcz730aM/fm8519HvN+K5iqdF7ZsZSI8R3FQbbWeiDxYbtbpXu0jsKDhWzOI5DMetigGY7QRuDEytKOXu7wvC1izNcW2qgtSGfcQlCW3f4tfS0wnEcPnyizG++5dCOdTrgTjFZsLdnfI+PnTnEb7091019ctJg6mNnDpH1Xebrmxf3APNpC9RWqAnCpOfXBGFCvW1PYVzFusfDTYOj/TLQ9b4WLov9ZdR3GDuFVt/5YImZagtj4KHJApfm692vsS3LINaay/N13ri5ykce2R+tx8Ro2M8pIdtxUOsvLtyu9F08jsogxb0+5djJJpDt8KJ3vMu20gy7036j9JvpBHzXBhBvT1f5/scO8YnHDlMN4nWLUmMMX3l9mlevr9COE5QiLVBdYbKY4f/yw2cx3Hlc1y7YDArfVeuGe33r8hLttFsTQCOwBcGddBvXsd151j5fiTY8fCjPsXLWpqokmoxrTxcePpTvWfdwz61Y05qClx4/0n3MXr95p6bgsaMlLt6uUGvHaG1wHEU56/HYUVs0/NZ0jfdmqyRagzEkWvPebJW3pkvd57uzOdIrfQu2fs08eby4ubZhzfV3HotyziPne8RxguMqdGLwPDvPwVGwVLdpWsZeEAZ7StG5/faKLYp2HUXQtLn+SWS6pxVZX/H2TDV9LlTaZtXWBbw9U+XHgc9/4mHen61xZaHeDSIePlTg8594uDu9uZdGOyEIbQvZsEfxNem1ZhzsRGjs78rauodK+rraD6RwWfQ1qjuMa99sVxshr99coRUmlHM2pWjtmibjOeQ8h3o7oRkmvHJ9hecfnpSdRCFSB7H+YtDica8HKY5CofhON4F0ulO90w/8RNs88LVrS4O9zWCDiBvLLf7Bj58j1qybWhvFSVqgGnG0lFk3yOz9uRoGxdFShuvLtgXo2h3do6UMrut2e+a344TZWptGuhBXCty0xeq5qTE+fHKMX/zW9U3Pl53um+XRo0WKGZdjYznmqwGNMGG8kN1U9zAoIOsXOK4NZr5xafOkYoByzuPEeJ4P5bxukXY1iCnnvHWD58o5nw8dH+v5fK8dALbSjJgs+N0BYMCWr5mzR/LkPOfO/Ib0BWKAnOfYwnYHDhcz5HyH2LFFvLmMTYc6XMwQxprFeptEazxHdYfGJdrerjA02nH3deMoe+pjjK1d8V3VLVyut5M0JUsRJYZ6275e4kTzwWLLrmHSlLVOPcQHiy3OHM73nSCeGIiSBKUUqrPy30A5NqVN6zuv67VfpvX+yWiQwuURt5c7TaO6w7j2zbbWilhJW+Ql2tA2hmjNb7cxhjAx5H2HjOekby77M31CiN0yyvUX9/IeeKe7iV08jhcyVJphd/G410f9o5DGudNNoE7Hl52yAUGvPzC4jkOQaG6tNPkrv/Q6tXayqZuOwS4gV1oR9TAmSuwOucHOEfjDTx3jl1+9RSNMum01ixmXP/zUMTzXwRjDUqNNxnM4Xs6Sncyz2oqIEs0jh4v8n548yuFijndnq1xZqFMLIso5f93ztTbIfvPW6qZc/+0EZMCW6XFKqYGTisfyGU6M59YFK4Wsx1jettHZzvP95q1Vfua3Lq1rL/qtK0v8lz/8BM+cHN/ye3iuw3ghw3QlWFes6yoYL2TI+jYoK2Y9oljblBwgiDSlrEcx6+E6inoQEcZ6U5pOPYjQBnK+253u3SlAVsre3nkt1YLYdlcK70SzStkJ01pr/s1bsyw3QybyHkfLORZqAcvNkH/z1ix/+MnJLV+zbvo+5KjeYbKjVNpdqvdviNaG/XJAK4XLI2oUdppGcYex15vtOzMVgrDe3bqYKHjU22n7MaXIuLZg6cR4jlLW3ze/nELcL6NYfzG098DO147Izt2opHHudBPItnzc+XX2CzTaiUEpu/Ncb8e8drOSppI4zKcL9B/7yEkOF+8sgDuynt2V9j2Xc6cn+MblJW6tNIkSO+H35ESec6cnusO9qq2IZpgwns8Qa82JsTyVVsjzpyf489//CP/s2zd4/cYqzTAm67vMVQJqaXvNc6fGBub6rw/ISniuYmo8Z9uPpgv0t2eqNj3udoVGGFPMrE+PWzup2FH2lFxBd1LxZznFC49M8vbtChemK7x6fYVi1hYdv/DI+sFz06vNbjvZjUXgv/jNa7xyzZ7AGaAda165tsIvfvMa//AnntvyNeO5DmePFflgsU4Q6m5QlvXt7Z3X9eWFOo0wWRcENMKEy+kQt1qQbCp+jw3UAjv5OO87m06xFHYWAdjXdpTYWgSz5kWmjJ17kGjDajOk2U7IFn0WGyHKcWi2IlabIYlReKp3Ab6nwHXd7uPfi2LrNFUvPdlwR78kYThBgjHm7wzj+4g77manaTdPG0Zth7HXm+1TJ8os1kMmCz5TE3mW6yG1IOLGcpNGmBBrQ6QN7di27PvFb12XHvBC9DBK9Rc72W33XCdt5digFkQ02jHaGMbzXreV414ZZhrnTt77d7oJ1DlJ2FiYOSzGAMYOwooSQ5gkjOV9osRwa7XF1y/O8OnnTnC4mKGY9YHozklB1udwOjtguREyUfDJeCWyni1kLWRclhshxphNQ7EcpdDGkPUcgnSo1uX5OjdWmmQ8RdwM8VzFYiPk8nydRBvemdk61399qtBiN1WokPEYS/Pwv/z6NN+6smjbjmpDNYioBRETaXpcnGguzdVptG0+fGd2QKMdc2muTpzobishY2z6TTf7W9nne6sgQilFGMW8eatCK0rIeYpC1qPZjmlFCW/eqhAnessOSdrAQxN5DhUyNLz1cykemrD1GVprLs/Vu7ULnYydRBsuz9VptSOaYbTp9QDQDG0ufzWdS7CWNnYYWqdWoxZEmwJQA9SCCNexgZzGsFhv4zkOsbbPtZN2QXIc7LCPDRzHBmhRYl8jzWhzvlE27WjV6vFnndv9EXmfHWRojVqVUh8Dfhp4CvhR4M8Cl40x/8ew7uNBsd2dpvtx2jBqO4z93myLGfvh7ziK5UaYHkvawi2M3Q1ZqAVcvG3fFEatQ5MQ4o6d7rYrpfj0c+tbOY7lfNvK8bmTe/8ets0d/H5BwLDe+3eyCaQNHC1lyXmKRjT8KMFzFZNFn6VGbCcIJybNObcTbSutiFaYUMp5HClleOxIoVuAWk2DyjDW1IIYRyk++fgRfNchSjSv31yllhb73nm87T9Nd2nZSSm50+rVVQ7H0tSUKNF2XgFmW7n+nbqHmWpAO9LdScWHilkSbfje9RWWmxGuAsexbViXmxHfu75iAwBsCk2YGBKd4CjbyS8x9nZjDC9fXeLduRq1IO5+/btzNV6+usT5U+NsufUN6YThxHZ2MjZ4SoxNj4kSO2uhc2pyZaGOTuOSzqmJUoogto9pxlXkPDtYzHXu3B5GmkZkTyk8BzKOItS2I2EjSmjHWxcNt+OE5UbY889tK12dppD1/pqlNDg8PpZN1waGdhoN5DzF8bFsmrLW+6FKtC1M9lyHiYLPaivedKIxUfDT6+3dWauT9vzAnCQopf4o8CvAvwSeBHxgDPiXSqnPG2P++TDu50Gx3Z2m+5nXOio7jP3ebE+N51hq2BOEONE0Ik29nQCasVym21VgPO9Tynoj06FJCLHZoPfAOLG7flttWpw/bVs5fufqEpVmxHjB5+MjMG19Ozv4g4KAYb33K6U4d2qMp07Y7jcb+95vxVFQzHq21ivqvajbiccO5zk5kefV66tEiR24lhi7gPQ9h/G8Tz7jdvPwS1mP42M55qoBhWzMWD5DxruTYjNbtQPEZqvrB4gl2lDM2s46p/P57u53pRVRzLpEielOOnYdm+rkuQ6+Y2+PksGf165Dt+7hxFiOjOvYzjuew1KjjdaaSvq1Gd+hkHFphglhZG83xg6Ny3j2tdFOTDfdxnUUGc8+Zy9fXeGDhTpRbLtOOUClFfHy1RV+8hM2bagSRHx4aqz7WFWCiFeurXD+1DhZ32U8n2F6NSCIdPc+HAXjeVtTcOF2hV994zZv3Fzp1izYzkqFNV3D0t9LBepOGTNgX2MF32EVW9wbrzkSKPi20UizT2vRZphgkmTLtrZRbP9urx3+zu3tOKEZxvbkZ41Ea5phTBhtPqno0AaSJCGf9Xn4UIGby611aUmugocPFQAI+vxeBFFCFCdk/NEfqDasK/x/Av+VMeafKKX+JIAx5m8opRaB/ysgQcJd2M5O06jktd5va4vMTozlyPkuQZSkb7YhK40QV8Fq0+78GGOoBTE538X3FLE2HB/LMXtzdU87NAkh+uv3HljMujTaMf/0969vaoe58f1u1E5B1xq0g79VEHDu1NhQ3vu3exrR7zTjrekaVxcbtHchQAD40fNT3Kq0cVUlzTE33aFoZc/hj374OK7rrgu4Zm+urgu4HMcZGJC5DozlfIoZj0orsikkYUQx4zGW88l4To9Jx0l30vHaQKRfrn8n6F1Kd7o7gUgrSqi2bDHuWN5HYQhiTZD+oAo7JVkphTZQTqf0doq9O09HOethjOG92RrtWKdFuqTTljXvzdoZQutTdZ1NdRGdqc6X5+u000BDAb5rHwOAX/jGVb79wXK3ZiGIY779wTLlb1zl//WnnqOQcfFchVJOt4bEdRSFjNsdRndiLMd0ZfMcghNjOdsVaIsFejPS6D67/Dbby6Y09euzaQzoOOa9uTob44hIw3tzdYza+mSs89fG8z6eq0hi001181zFeDp5e6vr3C+GFSQ8A3ytx+1fBv7ekO7jgbGdnaZOsdWotSfdbTbXcP3xcRgnvHZjxe6wxJpqK8RzHTxXkySQGDuu3nM9PEcxV904il4IMUr6vQdO5DOsNEKuLTW2vYM+Kqega20VwAzaAHrqRGko7/2DTiO2CiIAvnN1iZsrTZvymSRDrUtwgcPlLG/O1HEc240oMXYH1gAnJ/L8WNrd6NypMbTRfPvKUtp2NLPuOgcFZOtaoG44nR7PZ9ZNOo4S3f3zzqRjx3EG5vq7DtxYbrJQa9ufIR3qmfNdbizbx/CRQ3nen63SjtcU/HoOjxzKd7sw1YI4rS+wHXY0tnNPLYiJE02Q7oAr1rSoNRBENr2lk6r7e5cWyLguYZJQzNgWqp1gJp9xyfkOYFOZXIWdPJ1xCcKYV6+vdFuNu+nQuVaU8Or1FbTWNMKY1aatp+jOasj5NMK4u8HpuYqN3UMd7AIbo/sWs9uTE4PvKdo9qop9T+E4dqjdVhqxZqXPsLSVejs9/egv67nEiWalGeEoRd5X6wqpV5r29KdfLKBh36w9hhUkzAFPA1c33P4SMD2k+3igDHpjG9X2pLtt48/tuy7vzlZphDHHSrYndTs2eC54joPn2PxBpRRZzx4h5zPuvu4BL8SDYON7YDnnMVsJuLbUODCnp70CmEGpVsDAnetBNgYijx8tMV1pcWm+1n0stwoinp4qU2mGrDYjosT03bW9Vwb4tQuzNMMEhSKTcWi0IrKewhg4NZHHoNBa8+XXZ/j6xRlWmyFjeZ8fOTfVzY/vPMZbnShtPJ3Oeg6rzYiVZsSX37jNarPNTNX++dTY5knHpjPauJc1hcSzlYAw0Shs6lCiNWGima0EGGM4Usoyls9QC8JuEFHOZThSygL2dRGmaUaZ7iLbnjCEaYTWKQbuPB2df9rA0WEi7zNTaTFbDbr1GyfGckykpxWOMiw3bEvxrOd2C71BsdyI0iDgTitZ34HQHlzYJiGJLUqutMLuqQ+JodIKuTxnh5yGseb2arBpAa2B26sBMeCmcwY2ch3I+z5Zt3cgkHUdsr4LZuut+oJnW+v2EiQaVykyLvQqjci4dIfL1dsxkTb2+UJhsM9bvR0TJcmWwU4UJ+SyW17mSBhWkPAzwM8rpf4mNiD8fqXUTwD/DXZ+grhLg97YRrE96f2w9uf+3o0Vbi43ibTBdx20NmRch7G8R953yfku9bbNOzwxlueZU+OUsjaHdVR6wAshetv4HmiM4R//9pUDf3o6aAPIcwfvXA/SCURqrYiJfIa3pis0w4SFWpuj5RxRnGx5mvHMyTKN0E6erYfDz53QwHuzNSYLGZSCVpjguLajTMZ1qAYR78zWuDLf4Evfusqt1VZ3h3+x1kYpxWc/empb97XxdHqlGfLebI1rS00+WKjjOYpKM6TSirtFrWN5n2oQU0nrY+7k+pc5UsqyWG+vy/UPY02ibUGwwqZOdYaAJTqhFSYUsy7FjEvOy3anNruOQzHrdhf/5ZxHxnXJeurOyUZsKKcnAf0CRNvNB3730kI6qOxOULFYb/O7lxb48e873U3zCaKEWGtqbVuP4KWLYse5U09gT3bulHkXfHvacWm+RqzXx02xhkvzNaI4wVWG1ead7kUud5oIrTYjcq7DZCHDQn1z4fFkIYPj2OyBXsLYFlejnL5dt7q3b7GCj5KEPmURhAlorcn4Xlrg3BmYlnZrUnZWkzZsOi3pcICoX07ViBlWC9T/USlVB/4WkMfWIMwAf9MY8z8N4z4eVFsdlY9ae9L75dypMYwxfLDYYKHe5kjG48kTZWJtmF5tcSqbp5BxqQcxh4p5Hj9a4kefO8X50+Mjl5s8LHs5dE+I3dR5DzTG7Lgr0H6wrQ2gHj+SMYbEmO4u9FY6gYg2hm9/sGgX/FGCoxRvTVe5OF0dmNK03AgJ+7WAGYJCxmVqPMdi2lko57lMlH0yjoPW8J0Plnjl2gq3VlvkPIeHJgss1IJue9TPPD+F4zgDay82BmUL9ZDZakDOdzhzuMhYzuPyfI3VZsR8LVjTZtWjEdoC2koz5Opiw+72x7Yg2XUUH56yAW4mLcaNErNubaqwxbj5jEsz1MRaE0R2mm8rSsj50Ax1+tpWPHG8zKW5GvV23P26UtbjieNlDDbvv99j2WzHXLht05nswt/W6LVjzYXbVaI4wXMdVpttYq273X3sQluz2myT9V2eOFZmvtYmLX0AbJeiJ47Z05tmGjQ6QMZThLEhwf4ccaLRhm77T4UNEDqpOr6r0Dg8faLM0uWlTelIT58op4XbvV8z7QTCKMb33C2DBM91tjz9CbXZ8hSgHSe4rksYJz1bsYaxbW/rqN71FY6Ccs7vcw+jZWil1caYXwB+QSlVBDxjTGVY31v0NsqFebtJKcWHT47ZI+9WyEcfniTjuYSxZma1xcOHChSzXtrDGgx3PlT3sj/6bhiFoXtC3A/D6Aq0X2y1AWSMWdel5lg5y/tzNS5MV/iHv/E+f+jJI3z80SNb/sydnvlffu02i42QJLFDy5SCxVrAV9+c4eREvm9AZoxhsRb0bRM5DM0wIYhsrr3Wtje9qxwePlyg3o5ZbdjBV+1I89BkAd9VHClluTxfp9KyE3tzGaebNnVpvtZzWvLa19X78zWuLzUIIs2ZwwUePlRgPO/TDJdsrUA6f0ClcweW6iGuo7i+3GS2EhBESTpkzk7/vb7c7ObhVza0ygS74Ky0bKBha+psm9dOIKKU0511oJTimakxfu/9BSpBRJzYtNpyzueZqTEyntM9IdgoSSf8NsOYxNjFc7xmIWw7/djhdSuNKP3cXPv3SW83nD1W4vVbq9TTWQWdLldn02LoQsZhtWUX/601dQOFjJ3t4DqKw8X1rUM7/zxcTDtW5eycgrUFvo4DYzlvcBpPkthp6lsEAb6jMGZtFcGa72EUWQd6/6m93cE+X3PV3nUNc9U2BluL0uvQw3UAtT/WIsOck/DjwO8bY2aUUv+5Uuo/BF4G/poxpjWs+xGbjWJh3m6z8xJ8SlmfmUrQ/RAr533KOR/XsTsljXbM1cU6X3tzBoU6cHMR7mcbXCH22k66Au2n34etNoA2Nq1YabSZXg24tdJittJmttrinelad1JvP0+dKNnvi01jKWRdFIpaEHNpvs6feHaKhVp7XUB29kiR73t4snuNqt9Kagiqgd35jtPi2SjWJJ7mxlKTQ8UsE0WfiUKGjKe4vdLE9xyqrTsD1fz09Ok7HyzyjUuLLDXadnVvWsxV2hwpZbqBVOf1c+TqEnFimK0GTBQyTBR8bq000drOJFAou7YzNrd+sR4QxQlX5usEkS3eVtAtsr4yX8cYY9Oytmjb2Qgi6u2IoLOIT59z+/fWt1H1XUUp467bfV9qtNMZB72fjCgx3eLnjrVfaYzpBhkrzbBnMLPStJ2ZmmFsu/g4dAub7YlIjOM4nBjLM1NpbzoxOTGWx/dclFIcGytwZXHzsvDYWAGD4p2ZyqaahFiT3j74BWdnJfT+M2OgFsboPt9Ha3tSoRQ9v4dSNu1Ka00r6v2ctqKYMAq3PPGIY3viMeqGNSfhbwF/DfhhpdRZ4H8A/lfgj2KDrr8yjPvZJfsjnNvnhnn839ktnKkErDZDbq40uTxf58R4jrNHisTa8NoNm2Ob813mqm1qQcyRcnbf7Shu5UFtgyseXDvpCrQffx96bQCtTY+ZXm1ybanJrdWmbS/p2Pz6V6+vMJlO6gV6vvfawliPrOdyuJhhvOBTaUbU0j9/5uQYnuvy8rVlKs2wO1jrt9+b49XryxwqZihmvZ7Tb4chTgxJulDLZmxufi2IbLehco6PP3aYI6Uc1xYb3FhuEqWzMwoZl0LG5e2ZOk9PlXnl2gpXlxp28Fj6OFSDmFeurfC5l0zarvPO6+pjj0zy9YuzXFmo89qNVfIZOzsi0aCU6a6uk8SwUAtpRwkrjaCb2tItFjaw0gi6LWLX7vKvja06u/zXllo0o7RuQSkbFGnDtaUWjrJfd2muxu2VFkGiSRKN6zqEcYtLczbfv1/6V5jYVJ+s33tRmvVdtOFOB6UeakGMwnB1oUErutPNKjHQihKuLjTSomwbOGycHeC5d9qTLtZam3bqFbBYaxFFEdeWg57XcG05wBnQnrRbP9EngnWUQg3oPOQ7kHGdbivatTKuPRFpR8mWA9dW+zyOHc0wJr8PKpeHdZLwnwB/1hjz+0qpnwW+ZYz5z5RS349tgzrKQcLn9voCDrLdOP7v7BZeXazTTtvJZT2bP/qp8yf5F69c5+Zy085OqNudl4VqwvtzNeJE74vofTu2O3RPiPtp2PUAvb7fvXQFOii/D2vTY96bq3JtsUEYG8pZlxPjeeJEs1APeW+2yus3VvjezUrP917PdfjQ8TLXl5rU2jH1MMYY2/v9Q8fL+J7bXTi/cXO1u3DunNCMZT3OHi3y9kyNVp/BVff8MwLHyhlWWxFBbFtxVoN0YWo05azLU8dLfPhEiS+/fptbq018HPK+w/HxPI6yp01PnyhybalBmGg8RzGe96i37WL62pJd1G70zMkxHEfx8gdLrDQjxrIO78zUuiv7Tp65URBEGt9V6eDOzertJC0odihmfWrp162912LWT2sBQrtLni7WFXZXe7UZdl//b09XqQR30nQibVuyvj1dBe4skG2qkg2wTHp7og2usgXMsL6o1k1T9aI46btTH2tDEMWstiKieH1HJx0bVtMJ2PW2TQdKD1y6wUC9nRDGuts6tPdpRbQu4NooMfZEaW2x81ou4LounmtnV/Qqfh7L++R8f8vv4bkepZxH0OPvl3IeSikyntOdjL2R4yhKfQKyjsw+SX0eVpBwDHgt/fdPAT+f/vscUBzSfeyWLwJf2OuLOKiGffzf2S28NF+zfZ8LGRZrAdXA9iU+d7LEUj2kGnRa85l0Bwi+eWmRL33rGh9/bOt83f3iQW2DK0bTsDcE7vb7PUi/D530mENXMlxZaBImAaWcTzHrUmnZRUu9nfC1i7NcXew9U0IpxaefP8lKI+TCdIVGO6aYs12SPv38yXWP8XdvrHJlwwlNJYg4PVkgiBLe7zGYaicUNlixg8VigkinHa5sPvdsNeDduTpRnPDeXJUgsuklsdZkmm08B6qtiCCywYFNITG004tUaeFulBhcd/1rbbXR5uZKi5lKC2OgmPPs4jq9MJ3+fQWU0s5DW6X5OMrWxZ0/VWaxHqxLo/EcOH+qjDYQrdmW9hy6u9SdoaBxYri9GvRcXN9eDdJcf4+5mk31WZsqc7jokct4Nn1qVaHTvChlwHEVE4U7A9vcPgvfzhyFMB2itvb7K2yxrudAo510g49uQIW93XcVntN7hx4giDU9xh+sYwM+eu7ie05au+K6PHm8yGI93HRa8eTxIsWcv2XRQdZ3Gc86LNY3//F41p4sOY5DxlU9n/uMq8hlfHyHnr8XvsO+mLYMwwsSLgF/RCl1C3gE+LX09s8D7w7pPnbLPpp9tzt2qxPIbhz/d3YL56pt25ouiGhHmmaY8PrNVd64VaUWpB8q65IuYb7e5jfenmMx3R3YTznKvTyobXDFaBrWhkDn/ejtmSpfe3Nm29/vfv8+7GUHpU56zDMny8xWA/7tO3O0Y830aoA2hrGc7XF/dXHrmRLnT43zuZce5eWrS6y2IibyPi8+enhdh7w40aw2w00nNDOrLaLEcLScZbEeMl/bnMt+rzRwuxKQJLq7Yd1JywGH1VbEyx8s8buXFpivtrvpTrGG+WpInNjBYfm0209nodp9/NL/yHh2N3fta/fqQoP5WkCUaBylyHkO7fQ61u6Qu46ilPPSgt/eP0enfWsp73JqPIfvOMRrqnF9xw5t81w7nK1jbQtR37WL0jCKu92UNmqEtsA75/s903hyvo/vuTw0meet6ardQU+/yAUemrQD24o5n0MFn9na5h30QwWfUi5jg7UNf2YgvV3ZQWzp4935DFbGDmSzKU1g+owcNlpTzmy9w17I2HavvXLc3DWBn1JOz8dCKYcgPTXreQ3GzjDYqp7AdRRxYnoHGZBOvO68L2z+ojt/NvqGFST8LeBfAD7wr4wxF5VS/2/gLwOfHdJ9iHvU78Psbnbq7uUDcePxv+8qjo/lmNnB8b+joJhxCBPNYt12qvAyCm0M1SDilQ8WWWoEPXNkE21ohvG+zlHe6EFtgytGyzA2BNa+H1WaIe/O1qg0I6Ymctv+fvfj92GUOigppfjRZ6dYbUZcTE8DxtJuN1nf5dpiY8vUq0E1HhdvV/nO1SXeuLnKcjPknZkqT0+VmakEFHMecWKYrwa0ov5Tcu+VMSbt728XpZ0Jv2BohQmLtcAueNM73rhr/fxD9rSkM6V53ffGFhZ37qfz2i34Ni2nFSbprrr93OssjG2nIntf2fQxzfpumuazeeHrOU43EHn9ZoXWhh30Vqx5/WYlbcThdVuCdq7Rhe40ZHut/R4r29VntRV2F8Zr/7naConihNVWvKkDUqINq2mHJdd1+dMfe4j/zzeurkshy/sOf/pjD+F7Lo0+AwQaoZ3CnPPUuudCpc9NzrtTeL9VOpFm8+K+QwGu42z59xU2berKYqPnwLYriw0a0eZOUx0GaIR2mF4vK82IKLZpzlvVgIRRuOUJUxzH++I0YVhzEn5FKXUKeMgY83p68z8F/rEx5uow7kPcvUEfZtvZ+et+jx47TdvtxV3MurwzUyFMDEv1dvomHnM3p/9rf5bLCw2a7YRGO0Glb9ZjOZ9qEPFv31vYdMR453vYYrhaEB2YHOUHtQ2uGC13Uw/Qb8Nh7ftRrRUxX28TRJqHDuW3XV/Q6/fBGEM7StIc4p3nAY9CB6WNAdWhYoY/8PgRChmX8YIdFvnKtWUW6+1tpV71qvFY+3MuN2yB7tXFBov1NifGczx+pMhcrc3FW6t9i113YjznESZ2yrBtKeoRJ0l3unA563UXYb5DWlxs6xdcR/HE0aItKm5u3hUH262nHdl2mZ3X7qNHxli9tNhNedGadI6uXbA7ChJjB6HlfZfTEzkcx+FIKUulx2NwpJTF91zCKObduR65K8C7c3WCMO4Whq+VGNsKVhvbvttzVM8hXF76u9QM76T6dL5Kk7aTDWPbFrzHfVyet6lbGd/jB586ym+8M8/l+RqJtuldZw4X+cGnjtKOElpR7yChFdmJy532qZ276fy7/V6KGNvqtZd2bPAcyLoQ9LibrGuf535TDAx2iJlONNU+z3u1GZJzelWj3Lle3916YFunGN30CWcMipV2/8DZAI0ooZDv8wUjZFjdjX4b+BLwrzu3GWPeGMb3Fvduqw+zc6fGtrXzd+F2hS9989qdnNWsx9vT1YHt9eDO8f/btyu8en252w96PO+z0gh5a7q27Q/VtT9LtRnaYTCuIuu6jOd9lhu26Gt6pUm9zweWAVZbEacPFQ5cjvKD2Ab3ftnPg7nul+3UA2y1aQGsez96/GiJb1xepBYEvDtT5XApw2wl2HZ9gVJ2B/pXXpvmaxdnqDRDxgsZfuTcVHfI1r0YlQ5Kvd7bzx4p8oefPsFz6Q66UmpTC9Ptpl6t/TmLGZcfOHuY92ZrzFQDDhUzfOKxw0zkfd6du72ukHaYfNfBpHlBiYZaEIECB8XhUobvP3uYX/7eNLV2nOan3+lYc6SUpZjzieKEoE+xRBBpFKa7i28MfOO9+U0BT+dnSwATm25qTSNMuLnSQinFmUO2y9LaZaULnDmUQxtohdGWefitdpu5au+OPnPVAEync5Oz6TQC7AyCrOeuq2tYy6ZOpY9hD7UgSpt6GH71whyrzZCc76LTgGi1GfKrF+b4q0eLW7YOjbUmSmtH1jLGThhOtN1932rxHGlDOZ/pWTRczmfwHAfTJ0ncaNa8N/R7jSvMgNe/g9oyAOicDrkKeq02XAVH81svr8cfsGFqvw/8beCfKKW+gj1F+HVj+j2VYrcN+jB76kRp4M6f68BXXp/m1esrtGO7a19tReva6w36sHnmZJnJYoas73Ii43G4mMF3HSpBdFdpCBt/lndmKnyw2MBzFSvNkFaUMJn3OX0oz9KVpZ67LfbYU/HYkaLk7IuBRimtZNRtpx7gwq1K302Lp6fKm96PnjxeZrbSYroa8GtvzlDM2qLaFx6Z3Nbj/yuvTfM//84Vbq+2iBKN7zpcX2xgjOHHv+/0Pf2co9BBqd97+5XFBt+9scJzD9mNl52kXiXaUGmGzFYCxvI+s9U2rqPwHcX5k2N85KFx/sGvv8dr11b6pn7sVGJM2p88HWBm/5Wsb18bH3nkED/+0VP84jeurgtU8r7DS48fttOWsc97r+pR33Uw2GDqUDFLO06Yq4db/jyGO12DgijhStr2c65HPYYB5mrhtk7MAw3tPkFEO9YEkW1fWsh6LDU3L0sLWQ9t7Al9L53b+7Wq7RQsx4nme9dXWG6Gtlg7LVZeboZ87/oKSinyGY8g3hxs5DMenuNQC3p3LqqlzUXoU4/QoQycmsixVA83TVw+NZEDIOrzc0TG/n44jtP399BzFXG89clXnCT0ySBLhw7awuVSzqPd2PxYlHIenp8h5ymCHqcmOU+hnP3RZXFY6UZ/HfjrSqlPAn8OGyQkSqlfAv6ZMeZ7w7gfsX2DPsyAgTt/caJ5f65GpRVxtJRhvJCh0gxZqIfbbieqjZ3GeKiQ4bmHJsj5DmFs7upDtdfP8tSJMRbrIZMFn3asmau2efb0OPO1Nv2+XSHj8szJMf7EcyclZ18MNAppJfvJoCnBW21aPHOyvOn9aLHexnN7TJHdxqJLa80vv3qTmytNjLEDpxJtuLnS5JdfvcmPfeTkPZ0mjEIHpfXvhyU8VzE1nuO1G6vbrjcYxHVs6kotiFist3GUrfvKeg63KwFfuzjLOzM1wl0KEABmq20yjqKT3dK5cm3uJLP8lR88y3y1zdcuztBsxyjHPhalrMfF21U+PFUi2+cDIevaHWFjDEv1gDgxOAP2NTOunaKbGJtXXm1FNIKIlVaflp4tu0Of9dwt8+zHfKfnxhbYnfVsmuffDHtfXzPUxElM2CeNJ4yNnUTc5/Xpp68NOxnaTnM2DngYYp0Gja0I11GcHM/1zNc/mRZgr7Z6n1Z0bk8G/AIrx7Yv3Vjzq5Rds8QDgowgiilkM1t/zYDt60as+w7QciD9HQOvz4/iKTA62bKGJOlMhh5xQ62aMMZ8A/iGUuqvAv818N8A/4VS6h3gnwA/L6cL98egDzPPde6uE0jnv+9yB3XtdcxVg3v6UO31s8xWA06M5/j4o4cwwMtXl4m0oRkm3c4QG38/Hz1c4K/80OMD06SEGJW0klGzVerV3UwJ3rhpoQ3d96NL8zWmV5qstCKynsujR4o8eaLMXDWgEkS8cm1l4ClmO0qYrrQIY81YzrZ/DMKYahAzXWnRjhLy2bsPEkaho9i69JhLi2R9l3aUUMh464pcO2z7TI2jnHu4vrSOJH03NQZmKwGrzTZxv0lSQxJr1vXs7/xbK9a8cbPCm7cqPPfQBI8dLfLEsRKNdkzOdwnTPz8+luPJ40WcPp8xjmNbfpp0SNmt1SbN9tY/U6QN2tiACWPz9VHphN8NX2tIi3S1zZO39Qw9rkNBaOysgl5cpYg1mMTWFfQShDGtSHcLhnvdhzYQ9Mmz79yulLItYw3oxBAmd4Izz7EpOHm/93XmfWXTu/oEMkGoSZKEfL+VdSrnwlK9venn0MbeniR92g51vk7blrFhn5OZMNaU/K1/9ydyHr7b+xTAdxWuowhjveXpTz2MSfpECUl6fftgltpwg4T0JOHPAn8SyGNrFP4ZcBL4vwN/MP1zscu282E26Dj6zrCdBrUgotGO0cYwnvf40PEy3jaGgQzjQ3Wr7/H9jx0BYLkecmm+xkKt3f17nrKnhZ2dxJOTeT48JScIYrBRSCsZJWs73VSaEeMFn4/3aWAwaEpwvx34Z06WuTw/xgeLDUgn3Hqu4qkTZbK+e1ePv+vYfuxKKWJt0MYQp5NsPcfZ0Y7//ewo1m+QXCc9ZqYa0I40Wd/h9ESeQ8Vs9+u01nz59Rm+fnGGSitiPO/zqW3WZCTaUMy6lHMep/N5Ym3wHJubbjA02gnFjEtzyEPUtpJ1O88lLNRDXr22zDMnx7g8X+fmSouMp1htRXiuYrERdotxwz479KG20399z+WtmRq1IB6YOqVN5yTD7igfKmYpZn36rBWJtW2z2o6MPc3qt+hUCqdPpr6D6QYYWw06K/guGd8h7rFIz/gO2T6pL4AdWJckZDM+fqfBQPpnnQ5FdsYBvD/f6Pk93p9vEMTxlpOMW1EysOf8cjthIZ31sJYBFmrtbleqfjq/M6bPAt0YgzGKPtlEONhhanGfF0Oc2HQmtN7y8cy5zpZTnzt1DaNuWIXL/x3wp4FTwG8DPw38a2NMc83XtIH/dRj39yDZSdHkoA+zQcfRSik+/dz6YTtjOd8O23nu5LavZxgfqr2+xwuPTPLUiVL6gX+Sl68tc6jg8+tvzxMmse0I4YDrdn4h1YEqVj4IRrUoeBTSSkbJhVsVvvSt9Q0M3pmu8pM/cIZnH5oY+Pe3s1lw8XaV92ar3XQSz1W0I807M1Wemhpj9i4ef99zefb0OAu1Nu04IYhta8i8b2/fyTH//egoNqjIe6nRJuM5nBjLkfNdgrR701Kjbaf1KsWXX5/hS9+6yq3VVjeQmE/T5T770VNb3r99/Wc4kabuHC1lWai3yfmObUGpNcVMm4Ue+di7QWEX565jB4EZbCtKYwxLjZAo0SgDR8tZFmptIg1LDVv46q+dQsydbju+Y/v2x4lmqd5/yu/G6+hwHRsMx4neck5Cos2aNqmbF7ie45DzPVy397LVTeck+G7/oU4au6k3lvNphu1Nfz6W86n3qVfoaLQjPM/bMhCpt0IafQYINNpJ/6KHVN53B/7uTmTcnp2iACpBjDNoc1LZTYCtHiutIOspWj0W+VlP0Q6jnn8G0IoNOokxqC2LuG3zhN7X4CiG0mntfhjWScKPAD8L/HNjzO0+X/MGMtl424ZRNLndD7OtOuOcP22H7fTaQdyuYXyorv0ecaJ5Z7bGK9dW+Lfvzncfm8//wCN891qZb1xZphbYHY1I20Ij33U5VMzYY9+7uufeRnVxu1+MelHwKKSVjApjDF95Y5pXry/Tju2AKdvAYNk2MDg9uIEB3H3NwtvTq7w3V+ftmartsnMXhctKKT7/0hmqrYgLtysEUULOdzl/apzPv3RmKM/fbnYU26oe5umpMrUgxlGKTz5+BN91iBLN6zdtK1I7dMzw9Ysz3FptkfMcHpossFALuLXa4usXZwaeJiileOHMJG/frqwLDM+fHOelJ47w/myN1Uab6yubJwDvFjednOw5tr5ssmi7wxitqQcxsbYFxJ6ryHsuE3kP33Nt4JCmr3Su1VE2oOikavXL9d/Id+wiszN92Ri7O573nLSHP6i0aWqiIe853V1t3WdnW6fpYHnfYbW1+c/zaWqM7cbUm8JeR65PGk0nuNuK59r6jE4nJAfwPYco1vazNNG0td6y+Dk2/XoCpY1DbNUvLr3CJdsRyqC23MXPD/gAL/h3gsJeFFDwPXzXodUj/cp3HdpsOZCZlSCi6Ptb3kk7TrasEdEDaitGxbAKl8+t/W+llAN8CLhmjAnSr3kHeGcY9/cgGGbR5E4+zIa5azaMD1WlFO/O1ntOYjVmin/z1hzNdoy7JtUoTAwGw9mjhW4rxnv9WUZ9cbtf7IeiYBlUZ91pYBBztJxlPO9TaUUs1NrbbmAAd1+zcKSU463p2ubF1TZ/zZ49PcFf/SNP8u0PFliuRxwq+Xz/Y0dH/vm7myLv2bTOa7a6vj1sO0qotOw0+ocmC2Q8h6PlHJfn61Rakc2HHjDZtt/q/+yRIk8cKxNpzeX5GpX27ocJjqLbWz+fcTh/apyPP3qEd2frXF1sEqYLWQAdGxSadqLxXIezx0pcXawTpAPfFHbRfPZYKU0/g/Y26yvWxhIOhkY7ppzzOTVZYLERovWdugDfdzg1WSDruzSCaMtd5UgbMm7v36GM66YnCWrLFJqM69ivZfOU4YzrUs5lyLqKdo8FeNZVlHIZDHeGusUGojQwcZUd6lbObL1kzDhbDiEmjOwJQb+EoQSotaMtF/iLvQYorFFtx5Rz7pYtTBNttlzgT2a9LWtIJnM+juNseZ2OGvBYPEg1CekgtZ8D/g5wEfgd4EVgQSn1HxhjXhvG/TwoRrFoclT68G/12Hz7gyUuzdft8B1HYbTpBgrtSDOR93e8wN8Pi9tRN4qv714O4qC6HZ+AdRYp/dp2bMN2ahZOjOV4b64GCk6O53np7GHmau1tFy6vuz9sK0e13ehiSO71sR7UvWhtkffaU66zR4p838OTgM2DH8/7ZH2HhVrAkVKWxXqbrG9vH5QPbYzhlesrVIKIp0+UOTGeZ7bSohJEvHpjlc//wCN8+0o2fUx3N0hwlU1TaYa2DXfec3nu9DgfnirxC9+8xmIjTOcn2Esx2NSY2Yotrn7kUJ4TY/lusWjGc3CV4pFDeRJtB3v5jqL3lIL+NHZ333Ec/tTHTjO92mKmGhAl9jk/Xsrypz52GsdxKGQ9SrkM9XDzvZRyGUpZb8sBYY4CndbTRD12oD3HSYetbX42DJ20W5fHjhR4Z25zTcFjRwqg7LC2UtYjWXPqYrCL5VLWA+VsuXhu9OtNmmonycCXi9F9Oo8AKMiprb9Bp4Zjq4AqiGOCPpOjgzChrRWTRY/F+ua0p8mih+vZGRye0zvo8raRjrpPShKGlm70j4FJYBH4j4GngB8APgf8Q+AHh3Q/D4RhF03ul9SY7VznoMdGG4M2djfH8xy0Md2g4a2ZGpcXmly5xwX+flncjrr9VhQ8KgHyTmz3BKzX7+CdBgZNau2YehhjjB2KuN0GBoP0Su9qhDG+6/DUiTK5jMfUuHNXr5FegyDfmaltaxDkTuz0tHFt96Lfe3+hmxJTzPrd7kVrT7kqzbA7rfe335vjuzdWePHMIf7YM8e5ttjg9mqL+Vob33U4NZHnP3jmxLYKlzf+jp6cKHQf/zDWrDZtR537YaLg4bmKIEpoxQn/9t0F/uCHjrLaiEi0TYHLunaXOEyvqfNaHs9nOFLKUgvaTOQyhElCOZdlPJ/BdRQ53yHru9T65NpvJYxj2lHC2SNFjo1lWWq0CY0h4yqOjWU5e6QI2DSbDx0vsVBbX/vgKvjQ8RKJUbh9Xs+uq4gSOyuinHNp1Tc/5uWcS5SYvnUH9XZsU550759R66S7sL662OwZaFxdbJIkydazFgaUJfuOwlFbv/Zyvrd1Lv+AU0ulbK1Jv5nKCjt/o99LN9L2RKTfvXQmb9sTo/4pZGsDrY0M9G15O2qGFST8EPAJY8x1pdSPAl81xnxbKbUAvDmk+3hgDKtocr+kxtzNdW712IwXfCaLGRSKMNE4afFj0XfI+w4fLDTwXYdy7t4W+PttcXuvdjuolKLg3nbzcR90Atb9Hby6xGorYiLv8+Ka7kWffn59A4Nizuanf/r57TcwGGTjwvfd2RqVZkSsDUGUMLcmpUZhCMKYjOf0XPBqrfmV127vaBDkvdrpaaNSislChpVmyK3VJlFs8D3F6YkCk4VM97rPnx7nmZNlvndjhd98e44rC4119/fEsRKnJ/OsNEOCCHK+y+nJPGePFdfdnzGGKE66BbZO2v1pq9/RjOcwXvD6DpwapsTAXNVmiSsFcaS5vtTgV9+Y5uR4jozr0GwnNPWdVq0ehrGcR9Z3mSj4zFRazFRaxNru4E6NayYKvn0slcPZI0VWGlsPUusliO2G1K9dmGWh1qbgO5QydlLxQq3Nr12Y5bmHJ0m0YWosa+d+rGmF5LkOU2NZHAVxnxZJcazxXYVSDqWcz3y91/Au25Wo0upT8NuKCaOY26ubpxgD3F4NiRNNFCdbTn7u10K1Y9AkY20gl/HwlE1n2shTpAXc/Y8S8v7Wy9ZSLjO4KHhAKlCzHbLU6P1YLjXsY6lUvzDEfo9BS4F+LW9HzbCCBA2ESqkMNmD4S+nt40DvflmjY+QOfTbuqn3vxjLlnH/XRZP7JTXmbq5zq4LSI6UcHhV8V9GO03HpsaFc9Hj4UAGwkyc/dPzeFvgHfXF7v4JKKQpeb7cf9+2cgPXadX97utrddT9/yjYw6BVEDMvG9K63piv8029d58J0hVeuLVPMejxzokw9iPip//01VpshE4XMuraencfy968s8lvvzLFQa3OklOX4WJZKM+oOgozipLsY7vcY30vQtvGxPnu0xFz6Or+bCfMXpyssNdq2gFPZgs2lRpuL05VuZ6JOS9rffX+B2WrAyfE8H3l4gpmKnTXxwWIDx1H8wNnDHBvLMV8NaIQJr15f7Z6kvHlzlV/81jXevFUhTjRTE3n+9AsP8WPPn9zyd9RxHF589AjF3Ae0eixahy3S4CqDSosKOtOOf/TZKTK+synHXWvw0lz+33lvnvla284awLYlna+1+Z335vnxj57GdRRPHS/y+s1VkruMEtqx7cn/3myVpUZE1lO4joNONEuNiPdmq8SJ5v/P3p8HW3Ll52Hgd87JzJt3fUvtVVgKDaCBBhpLN9kEV1EayouoYTfJ8Tq2JNoxIclhD2VHaLR4ZjwT1lhhhUYWKY0lzYxMNq1R2JJmQuwm2ZQsUqSavQPdAAproapQ69uXu9/czjJ/nLz57nvv5vm9qnurUFWNL6JQhbvlnvlbvt/3Cc7w9lr/kHZ/KjXeXusDQKmc7Cizw8KCAUpND1yVsgPrZUZjUmtEWYZhyTKGmUaaSUitpwbvgA3qpTxsGjfGeN6DAuccjYCjM8WTohFwS6kqOQ6ZsrMXggPTxkgE3xvALmETIVVASsiotiPp3A/DNEPV951KUJxxZzL0IBipAfNLEr4K4K8D6MB2Y36TMfYSgL8NK4l6P+MXPuoVmIZ9muFg4IzhmdMtPH+2eaTvPyjUmDt5qJbJob56bRedKMVSzUOUWVM1CaATS9QCgadONnB9d3THAf7DHtzey6Ty46HgPdzt/U5x3KXS+PIbq3jtehtxJsEY0B2lh6ru98N8xs12hNdudLCby176guPa9hAGBj//2UeKfXlxo4fuKEOmDPqxzPnUdvCzH0t88ZvXMUzU1IRslqTN7usU690YC1Ufa90YHmfoRhl6UXqkYoRUGq/f6GAQS3jcVs+NAQaxxOs3OpBK4/31AX7rwio+2OzjytYAw0Sh5gt0Rpm9t3Wi3PjSTKULKW3w3lofv/Q7H+Db13btUK8BVrux/S6An/uMTUbKrtHnz9RtNH6PYAzA+N7fdl8pdKe4/4IBH271EScpvnujg1jaAVwOG8zGUuO7NzrIpHW9fWvVzrLdLqJUIZEK/VgiziSiiUI9Y0A/l/KUSmOlPZ3Gs9IeIUkzjEqoQqNEQikFqRi2pnDkAWBrYI27XJKchqK/KAVGzBpRKlBlZm9j2B6BQVyyr2NlIJV0rmc3zpwbMkokUuI3qI5IvcQwbgyf5RpWJQsxBmDMQHBg2qLmwNC8Z5hXkvBnYR2VPwPgTxhj2oyxvwzbRfjFOS3jbuGLuA+lWd9Z7ePieg9Ka8AYKK1xcb2Hd1YbRwocHpS5hjt5qE4LWKTS+J++cwMXbvXQibJ9beNMGby72sVnH1/GkycauLw5wHr3zgL8hzW4vddJ5f0SdH7UuBf7nXLo3auEpgg9Dt/jVjt+mBaV0HHV627OZ0wG55N0o/Hg7Gp7iD+4vItBYoNnX1iVkpvtEf7xqzfxhZfOFPuyFfp4/Hgdlzf7GKUSK50ItUCgGggorfGdq7ulCdksSZvgDMPEBo3bg6QI8CuewDBRRypGGGPQjSzNyhgD3xOQys4cdKMMWutiO5sVD48u1XBtZ4j1Xox6xRZIGqFnK9rGTC2KcAZ868NtvH6zizivLjPY+/FqN8Y/evUmfvblswWlaTzwO+7WvHWri699sInd0e3z+O8UBrZ6zACEnsCTJ+r48ltrU+citAGGqUI/ydCLsuL7k3+PE2RjDC5v9u9o/DrTALRGKhWUPqAqZIBU2mMeJRJRSfU6yhRGUpVSnZSxzzDBtLPbACWdFXhTMo8whtblDsJjjNWJylBWWR8jkQoskU5p0Hbk7kxlUjkTAM6si7YLgRC2MzPlY4IB9WrolEAd+0m4jlmijNNkT30/zSQYY9YA/PyBl/+iMcYwxpbnsYy7iPtOrHYycGiGPj55qnXbgcODMtdw1IdqmfPoOGB5b62Pq1sD7I6mt0O3hhJfev0W/vCzJ5Ep+8A7f6yOn37xzD33fLgf8VHNWzwMQ8Gz4F7s96M49A4ShVQqaGPApSpMpgZ3MMx5p5gMzvtRhs1BgjjTeHS5itAXOLVQRZRaWkWzIhAGHuJUohdLrHYjjBK5r2MSeBydYYrVToQolfA4cKxeQeiL0oQMwJySNnsXGmvm344CUCF3CRtMZKmyNA5mXW+1sZ2eq9tDKG1lOBOpIbXGtZ0hQl/g6ZNNPHO6hYvrvaldT22A3UGCYSIL7xghrD690gZrnQhxKnFlOzp07zcw+MqFNXz7yuY9fXhOBvmCM/z086fwl//pW6V7tuYLNAIvd73d/xuAdb0dy8UOy7gpR0BmDHrx4eeOAdCL7TXMmFu+tEqUlkOfoxdNnycYY3OUOSkyPeJa1gZkXBBWfOf7ATEvAIbiipgGA6te5UIz9BEIjnhKBB4IDt8TZEIUcncXwCcUlHzBAO1OmIQud/BWBoC570LPqZiXBOpXYTsI18ev5QnCz8GarJ2Zx3K+X3C7gUNZAD0Pasy8qBB0J2L6Q/UoScpYri9T5RUGALixG+GbH+6iVfFQDz0IZpd3J0H+LMHt3ejKzPqbD/u8xd3Gne7/e7HfrSttuUMvZ0DF44VZFeOWRSI4K16/2zjYUXnqRANfu7yNfhzj4nofJ5oVrHcjCMHAtIHU1pxK5gGYxzkCj+/rmKRSYXeYwjAbEDLGkGmDUapwuhXC4wynWyHeuNkp7qsAZkralDaoBcJq51f94pzoRRlqgTj0fa31vio9YPf7Ui2AVxwPK+fM89cDj+PG7ghb/QRRKqG1pbYIzlCvePiRJ5fxyhPH8fzZJt5ZbZR2PaNs736pAWg1vgdbXvf76338L+9u7rv3b3YjKAPrO1AWkd4DDFKJ37+4iS3HPMT54zX4vo+TzWCqg+/Jph1w9Xi5Yg8FDssv3+xPD+A3+9Y7IfAITX3O82Tm8IpwxiC1VQZyoU5oatZ9d/AdCA5R4tUwxnKt4jRCW6oFzu/XAjt476rSV32Bsnl4DqAa+DjRCHCzc3jA+kTDHtOIoI5tJW736rV+4kxkBnFqaU8OTFu/SQyTDLVq6PzM/YB50Y0yABcYY/+5MeZXGWPHYJODn4eVR/0Yt4GjBg5UAD0rNWYeVAhqHamH6oVbXfyzt9edSco4qYIxpTcXwGbv7WGKn3z6BNZ7Ma5sD+/pfMbd6MrM6zcf9nmLu4VZ9/+92O9KG6dDb6YMzh+v4/LmAMpoSGUQBAyCcZw/Xp+bS/kY0xKqaYWRZ0+3sDNIMEglvnejjUbFw9mFGta6IyRSI5E2MKv6HC8+soDA94qOyWo3Qi+yLryCAdWKgOAMnVGKOFP42uVtVDyOROr8/uMV99WjJm3TtkNwhoVagNMLIWo+x6mFKja6luq0UAuK72ut8aU31vDbb6+hG2VYqPrFALY2wOPHari82YfKOzqe4BDMvl5U+nPaBcuVWlJl0I/Sgmox7noepAuN113kiZU8UEX3OPDCuSZev9k9dO+/tDWAVAZJplAljLXuJjJpHaUzVV4x3unH4AxolFS/GxUfnuCIknItHQr1ikCqdOk8Q6oM0kzC8zxn0Cm1hiwxdJNKg8GQ9wLKG8+nhPmZlYJ1BfAGxhlcYzxgXULjCQPPJsp8vzHdGB63c4QeZ0inZG4et3Kwp1oV3Orsd/tmAE61KjZZJhKiE4SJmSDOhjiTqFC7k+g0SMe5ez9hXnSjn2KM/WcAfpkx9u8BeAnAdQA/9LGR2u3jqIEDVeU/KjWmrAo6DyoEtY6uh2qr6uO719tkkjJOqpq1wCobOaoI44rbRyFdejcGVPdRNOIMzdC/4998WOct7ibmcUzv9n6fLDpMc+gNPI6nTzZwdWuIYSqL4LkeeHj6ZGNunQRXQjW5jqudEU4vVJFKiUeXa1isBfjUmSZa1QA//WkPv/nWGt5e6SLOFEJf4IVzC/iPfvQ8ABQdk1PNSqEbLzhDoyJQ9QVutWMkUqMXZ5DKFN4ByxPSotS9l0oMP/f4Et5d6eKt1S6+e6ODesXKxX7u8aViGV96Yw2/9o2ruNWJCvrXZn7e/OxnzuLpU01c3R5imMii81OveHj6VBNK2+3SykAIBqUMdD6UujVI8Y9eu4X31wf4kz/6ODjjU9dTaYN6xcNiKKC1sY7Dxt4fzy1V8R++8ji+dmX30L1/ZXeIiu+hXhFY66T3wEqt5FwC0ImklT8t4fpf240RJRlWu9HU91e7UVHlv9NtOFb3QWn6JMrSZl1qO1JJZ/CttCG7HdvTBrgn0IsS5/u+YIhyaltZktCJ3fMA/Uw5aTyptEpPKOmagDH4vJyQZGDjlPYonUrvao/S/FpwH5OUuZMhn4gHan65+d0YIWWnTMm03ieYZyngHwD4w7DdAwXgL3ycINAoC9CpwOF2qvxl1BjqYTcrFeKo61j2UP2Bx5bw+x9s7XtQnW6FeP1GG93R3lDzOKlaaw/xnavtqWUMBluFMMZ2L+yQn+Um3wsmzd0YUDXG4DtXd/D6jQ5GqUTFF9joxuhHEiealTvqJjyM8xZ3C/M6pnd7v1OSylbO8hi2+kmRbC7VQ/v+E8fmti5UQjV5H3jteru4D/zJH3kcz59bKO43nzzdwrc+3EZnmGGx7uOHP3G82HfjjsmPPnkM/+ydDUSZVX3pRhJxqnNpQkuv4sKq5PTjDO+s9fBzxuzvwJbIvZKJoYtXAttF+O2313CrE6EiGB5ZrGOrH+NWJ8Jvv72GL7x8Bq88cQzb/QSXNvsYxBKnF0I8fbKJV544Zqv/OQ1pTA0aa+xLbfXsv3FlG9IYnG6FU80jP32uhVGq4AmBemDgZXYIM/A4Xn50CZ99fAkXVvvFvf9Us4LXrrdxfTeCxxgqPkfiGCC9FzAATtQ9dOIS51ypEGVZMdjMgEI60wCIMo1RYr02hJiuQkMhlRqhJ3JjrcPvcwaEnoeI+PGxzOk0aGPvNbRDLzU07P52pt2deA2gJgiuPrTz+1JKGCFKh3bHyVBJUyUfXDe41Z5O5bnVjmG0Ku3KTP6QO0lw72yWz7K4UCEUTlvEfMf9gnnNJPxvAfwNAD0APwngBwD8HcbYfwDgzxpjrs5jOQ8TqACdChzuRZV/VirE5Do+daIxlQPs2cGAqeAc+6qLvhB4f72HYSrx/nof7671CnnG58828f5aE9VAlCpA1CsejjUCvH6jnVvVc3y4NcCvfuP6XTeZuxsDqkobXN4c4EZ7hMBjkKMUnmDYHqaWOnKHHZLv92Hio2Lex/Ru7ndKUvmo3Yw7nb04SkJlYNAepWgPU8SZ5erbiuHeo9xFoREcaIYetDH45oe7GKWqCPxGqUIMG9QaZu9DraqPXmS9Ey5t9PepOAF5AGH2BxIHt+OJY3VsDZJiO54/28Sr19roxhmeO9PCqVaIjV6Mbpzh1WttvHBuIXcrTjFKFCp1H5v9BIwxjBKJzihFKrXzeChtcP5YHbfaETKp9tEyBLPn5c4wxWvXdvHs6SaaoX9IWnp83BmzzvSe8KGNQehxHG9UbOI4ce+/cKuN9V6CVJo8YC0fxL1XWKj6aFV9ANMr5B7n8LkoAj7BxxViqzrji5xupTSYubOeyNYgBeccrYqHzpS5h1bFQy0MoIbTuxlj+MS1xJh1m3ah6rl/41jdPS8gGIMmJG01UaHPCGKiNADT2q0KlKTORKMdJaVuxVneZaNuo4wz5zIIpVdkiqZ/UYlKKiXqzk/cH5hXJ+EfAPhbAP5LY0wE4A8YY78J4FcAvA08EPvinuKoNIWywOFeVflnoUIIzoqH9pgDHGdqHwfYGFP6UH3tegefO7+ErV6M791o4+buCJm2FIHuKMNXLqwV9/UvvbGCf/n+JvpRVloh+MSJOv7N50/iytYIN3ZHUNrg6vYQW4PkrpvM3Y0BVc6AnVwvXjCOk80QW/0YWS5f+fGs8d3FgzTsTUkqU0WJWWcvqIRKKo3feHMNV7aGEMzSg7Q2eHetj//2ty/ilU8so1UN8LnHlwAGvHqtPXU9jtUrSKXGei8uNOoZ7NCnJ4AizmL23oop6z6Nwrfdt4Hop8409ykLjb0aBGd47kwLqdRk4hh4PB9G1djoxvsGkzljdsjVcTwEBz73xBJudSJsdiPsjqzkMwNQDTgYGBKpMUwk1rsxokxhpR3B9xh6kUQvsolI1ecQzC5zlCqAAdoY9GN7PMaJ5eXNPrb6GeJMF10YqTQZSN1tJKnEuqNCHwqGRjXAp8+18PXL29Y3Jw8uPW4T48D3IFVKUkfKkGlbHV+oiqlJwkI1TzoZd1auy717x5+xJmIuVEP3EGwYuJME3xMYTjE4m0RZcD5GxRCqQsLuM9e+2J2yHycxilNn16UiGErqhAUMoSxUIdSNKoKBMXf4XGJ+XWB7mGHpAWDyzitJ+EljzNcmXzDGXAbwhxhj97tPwj3HPGgK86zyu6qgs1Ahxg/tJFO41R4hSjU4twoIo1yO7bDRE99n9PSp003AAB/mwfzxwMMzp5uQ2uDy1gBffnMVu8MUr13bwXovLn1wGQDXd4ZYbUdY7cbYGaQ4u1jFs6cbWM0dSu/mEPPdGFDVBliuB3kAxLHZj+ELDl8bLNeDuQ+cfoz9eFCGvW9HUrmsKDHr7AWVUBlj8MFGH90ow4lGgFbVx2YvxkY/KaRZW1Uf7650AVhDpWkUmt1hgooncKYVIhAp2qMMvrCqQOeWQnywMUCmDHqxxDCxv7tQ9fDJU83CqXWSwhd4fB+F7/mzzUJZKM4UdB7ch77Ajd2RVVia2M5x0eNg4hjmg5XKAEaZwugr9MW+82ba8WCM4ZUnjmO7n+LiRg/fudrGKLW8d5UrHXFmK+nrvRiXNgcwOZWqGfoY5BSbKNMYZcomabkng+AM76318N56HwwMF9d7SJVCImURmH20/YM9tCOJil9+h+vEEgYMP/3cSXz3WhuDiaGA0BP46edO5vu3XFnoKBhkCt2SiLAbSSSZQhh4qHhsqiJUxWMwBD89yn1NXMF1KJhTeYgTgrVaazRD37mMZsUdMnYy9z5sxxLHG1XnMharhMwqwbtS2oBoqpDGed3U/X6qAWMICVTm3t8NatL8PsG8Bpe/BgCMsdbEbzIAFQAfU40O4Mg0HLjb+7NW+W+nCnonVAhjDHaHCZS2D6zQRyFB+M0Pt/GHnz2BF84tOI2ePMHx3NmWreBFKT772BICTyCVGt+7vouL6z2sd+PcYIkjdbT4doYZvvLOBnzBMcirFd0og+AMm70YxxoVZFLRWs93iHkPqArO8NTJBj7cGmCUqmLA8WSzgqfmOHD6/Y67dQ3eK8xKi7qXRQ2lNTpRhn6SoR8ru98Fw2ceXcR6L8ZbqzZJeO5M69B6PHu6gV5sHaN//OnjeGetj5u7I/RGKY43ApxoVCCVQSwNEqkwTCRaoY8Xzi7g8y+dBWPWlPHS5gBXtvaUnjxhlZ4ubdYhlbbKQnmCwHKZ0jhThVPx587vzVa8em13b3D5vB1czqSC0hqcMXg8nxY2BpwxqFzlZkx7ombWjl+toD3McGV7CKXsb4LZ4oEnODZ68YShk0E3SnF5Y1D8dpxpa7SV86qUBjb7Cb794TYYY3j9RgfdUQyT03G0gXVnvt2T8C4gUW5KR6oM4jjGb72zecjILMoUfuudTfw7r5zPX7mzLWIA6j4v9VkYpgoMBr7noRn6iAeHpVKboY86kQAEgluKi+Mz/UxDeAxqSiIiPIZ2opzSolGm0BDugV5JBNcekYiEwiZlgQCmjZIEAmgQqlnUvSaRipolxyhyy5My5X7fZwZtouPhEXKyi/Wq8/37BfOaSfgpAH8fwGNT3k4A1OaxnIcFB2k44+DuIA2Hau/PWuW/F9KL3SjF9jCByqtUjdDDIJZY78X49oc7eOHcAmn0JDjQqgZohtaReZzQ1EMPqdTYHqQYpqrUkn4S/TjDy48s4oPNAW51RggEhzG2Df2Nyzv4a//8A/zsy+fuSkdh3gOqtqK4f8DxVKtSDDjeL5XsBxV3+xq8G+s7bR1mpUXNa/ZiHNh+++oOuqMMCzUfr0wMBC/XAwDWbV2bPTWXxdBHGAicXqjitettAMDJZuVQcQXYm2Fa7yU42ajg6tYAiTK4sjXEIFX49JkWfvzpE9gdJmiPMizVfLySDz4DtgJ/bduqPBljqT1RasCYxrXtYW4wJ6G1gcg7D1xwqPz1VO7Z7hpjij/2hb19MUgUDPJuLawaizamMK+7nZm1zz66gL/9e1dw4WbbKj55As+cauDdtT6kHtNZxlKbwKXNPuJUohbYgJAx6w6MPOEZJBIfbAzAAFxc72GUyn30jfshQQCsP4EwVjKzDBvDBG/caB/iwCsDvHGjjTSTOW3sztZhqeaBcUEqE0mlIeX0NZVSQnDh7DRUAh9a2078tLEBzm2y4pIeagXcyfOp+gKZVM5t6RM7yvPcIWWjGiJOpdP0rU34D2TUOpTso0mMiE7B7sj9AyOpSclZKmGi5j/uF8yrZPrXAbwD4M8C+P8C+A8BnAPwVwD86TktwwnG2F8A8F8ZYxr3Ynmz4CB3djIwPpYHxm/d6s5dLvMg7oX04iBRiFKFUaYQCMuVFQyIUoVBIiGVdho9jdvk0xKap080sNaNEGWKbB+OkUmDYWoVUFJpoLSyiQIMulGGP7i0BS8Pmu7WfMI8B1QfhEr2g4rbodh8lMPeRwkoZykIzHv2guX/OfitY/UA9YqArVgbSGWZ4hrG3itzaeREanzjys6h4oon9g/brnciyHwGwONsLzjKI2M+ZSZBaWPdi5UN7qVGLg1q712cYUJZyKBV9dCPJRhnkLn++3eu7eJm2xYgmgtVJJnCzfYI37m2W8g+B4LBmHwAcmImIRBWVW587l3a7DuljceJIWDAOAfn9m9j7HYAdpg5EAypMpDGKumYnL4l8vVgjBXHRWqD3UGC9W48lWc/3oUfdbKwUBWIEzWdX5OjFXgYljgNDxOFJFMQ7E4nEoBW6EEw5pT9BGzHoxNPDwo7sT0eoeCIp8xYhPnMi+3mlIPnx336ehgI7jYp45wjTdyuziER+LYCd/WcMyBTyqleVJZMFZ8hDlamDCqEa3PVd+/LuoPGBthjshu7V2Sj696X24MY9dr9302YV5LwHKzj8juMse8BiIwxf4cx1gHw5wH8/+a0nKlgjD0H4L+Gu6hw32BMwxlzZ8cUm4onsDtMoLU+Unt/HkZOd7sKygDwXKpPGYDDPqisO6rlHrqMnsZVymnB8OceX8Kvv3EL+jZUNpQB1rs2MRtzgAVjWKx4kDkF4PLmncuS3mvcT5XsBxXTKvB3Q7L2buEoycwsyeS8uo5T13NiILhe8XC8UcETx+tQ2iDKFNY6MbQGvnejjWbo49GlGm61o9Liynh7vvXhNv7VxS30Eol6ILBQ9ZEqjQ82BljrxahXvGIdxkPJ432ltd4zjdIAmL1v2ddZoSyktEY3kuDM0kHOH6tDauDyRh8fbg2h8kTH0pUYLm/0C+nHVtUHZ1ZpSWmTDxDb15U2+PaH2/japW3sDJOcjhRho5vgeMMO4hpj8KU31vCVt1bx1q0uunGGQHA0Kta06+r2sLgvKgPEyhQBaz0Q8D2Bz50/hn/47evYHexpzvM8+jcArjscYwVDaTX4XuGTp5p482bH+ZlAcJQJFxlmn0P9pFzsgkKaJ5NlVwCDTbKllE5/gTjLSqmyqbJKX1prlM3bGm0NvlzBd6rdMqvaABFh8JUw7kw0+sTEcHeUoBr4Y4bdITAGVHz3TAJVk2AwVtvYgUrgXkZIUZ44c4i9WmTK3RFR32edhATAMP/3BwBeBPC/APgagL87p2VMBbMj5r8G4Nuw0qv3PZQ2+7iznrBqEa/f6KAXyyMpZHiCzc2c625VQceGPcu1AFVfYJTaQUFjDI43KviBx5fgCT5BEThs9DSuUk4LhpU2+Odv8+l3Gwe6kYQnOKqBwELoI9MaocchDcOxeoBhIu+pydo88LFsaTnKaDiuJPtuSNbeDRw1mZk1mbydJONOkq7nzzYLU8V6IHCyFWKjG6Hqi8JMbUw3HCTW8O1gcWWyimqMlQHdHqQQDNjoJ/C4VTdaqPr43BPLU/eV4MwOE+d/GPJ/A4WPwFhZaGeQgjEDYxiONQJ87okl+ILh2s7oAF3JBj/XdkZ7AQ6zw85aG4SBQJwq8Nxkaqz4dnXHBvqFC30s8eq1Nn7hxwy+/OYq/t7vX8HN3SFiaSvhSR6gBR7HIJXwOIOXB/PjXeMx4KkTdfiewAvnmjjVDLHeTWBgCqlQjzNkUiJOy2tuR2zc3lWcagUgaOHox6mzym+MwUJoE7ajbJPH7B9lbAPDE3YOzpUAKG3Qc+xLAOjF6VSqEQD7ep4dsJIInXGb3DppT0nifN+oDFWiAt8gtP8Z8SzWxnb2XMlKSDkZE/ctwxiIhgbpPp1RUq+KVpuqE0PeITVdfZ9gXknCdwD8ImPs/wDgTQA/B+D/DuAzKBMxnh/+EoAdAP8zHpAkYX/7Pj6kgHFQIWNae//gQ/egDvb9UOkcuymfP15DJjXSvI0NxvDZx5bw4h14MUwGw1YXfeyUerSnloG9UQkGLFY9DDOFfpxhkEicbVXhC45qwO47CcuPcfugOm2uJPvT51oPhLzp7YggAHeeTLr8CcYo9vcUEzIq6dJm/8DvpJnan/iRx/Ds6RY4A/773/8QnDH82FPHIDiH0hpv3Oyil3cd313r4de+fg1v3mrjxm5U0BAnN5kzWPWjKYmfMXZ/ccbAmcmpOABg95s2KJSFLm8NivPqqRMNvPLEcWgDJNJWbAWz+40za1SWSA2lLX3yeD1ANfAQCAapDKp1gVQZHK8HMMbg2s4QqdLwOMNizUc/lkiVxrWdIYxW+Eev3sT1neE+d3kNoBdLhJ5NdFq+wEKNox9n0Dr3nQl9PHnSeiRoAyzWAvgeg8kzo3ogwIXA8Wbo5HXfBzkCru2MXEwjAEBqysVFDYBYajSrFSzVPGwPaSKCNHsdFMGBU43AOjYTichi6JYfrQWecz1TqVHxhbNTQE3rbkTuyvX6MMNxQlko0uXu1DaRdp8ZFcERlVlP5+iS2rruZQSCoxO5qT7dITGYrN1dgIADGRFfVYmOiEe8f79gXknCXwLwFQCrsAPMf5ExtgpgGcDfmdMyDoEx9iKA/xw2Gfmp2/jeYwAezf/3hfmvGbn84oF4YaWLV6/uolbx8OI5q4Bx0MhmWuAslUYvSrHejbFQtRU2j9uhv16U7gsO7tQAaS7bOeGiOogz++A/08LnXzpbBBl3SoVgjOF4IyDt6g8i04BQGr7HUQNDnCpobT0HFmo+PvvY0n0lYfkx7gxUEkBV4B8EedOjiCDcDu6k6zL+3FsrXfza16/hrdUuholEveLh3dUe/tSPnbcqZlTSZQ6vS3uU4jcurOP3Lm6jGXoYxFm+rYdnEjgDvvzGKl673kaUZPuEDMaxNIMNjlc7I5xZrGH9wDpIZdCo2P0m84DewJqHNfLKoOt+JZVGM7TBf+gLeHkSEGcKzdArjplVJRsWTuljRbenTjagja1QA9b8K0oVfJHPPAiOQSKLgexDxw+5c25OpVmuV/DMqWbh59CLJZqhB6UN3l7p4O2VDvqFzIxBIg0eWfbxyRP1Uhfh+wU7PUKhBkCLiHAq3IDB3FFnRGtguVGB4KKUfjVOFMOKj6rHEE35UNVjqFcIozNun/muAJ0ahF10LwLLFUH6IAjtTqRCohMBxiEzdwAPRSVr7nuaVJTlGyC1+xPdzP3+MFOIiN/Q3L0vQkL96H7BvCRQv8sYewJA3RjTYYz9EIB/H8AtAP9kHss4CMaYD0sz+i+NMTdv86H9HwP4v9yN9ToqjLYPwM4oRZwppCp3F80vUipwFpxhmCj0Y4ntQVJw/CqewDBRR1ZImus2TQsw2N57YzC2f2rxKFXKsuXtDlMIzsib20FkyqCfKPzwE8sQnOODjT6GqcRiLcBPv3jm48Hfe4S7lcBS9JZnTzdIOtGsFJt7sZ1HEUE4CmbpurzwyAKMMUWAnkgFxoBelOG1620s1QO8cG7BmXQBwKvXranip043cXqhivfXeriyNcCtToTlWoBG6EFrg84ow+4oPbStShtc3Ohjd5jAY4cDPzGmlBjbcRh3K148t4jPPb5UaOZ7nOc0H8Ag9zBgBh7n8AR33q88wfHJU01b5c+VjgRnWK4HhRcDYww/9MQxbPWTwrDt1EJo98UTx1DxBc4uVrHSHkFrg2pOR6oH9vXQK5fcBGzwyriV1jzVCtEMvaJbXatItKoBOAN++XevYL23v9GfaYPtfoKFeogZrAPuCajKf9VjiAmnmE6isMAkhhRvaQoMgHdXe+AMaJQ4LjcqHnxP5IHr9J3J8iEQF0/fExxR4q5uUxX6KpGINOtVxMTgMgj1IkPMLFR8gVWiin9QrvYgFDE3MUolasTgcY0o4teJyNhjwELFvYwm8T6ZydwnmJsgvDFmxCyWYSlGX8zfWgKwO6/lTOD/DGDbGPP/uoPv/gqAf5H/+wXc5bmJgzDG4DcurOHK1qC4wWhjcGVrgN+4sIYXH128DQ6xvatYrQ2Dybv6vGYWxutcth5lAcbzZ5v4zlWr9FHxBFqhj0Rqq/RxdRcvnFuYaQDbSqxmpHvi9O2xN9XTrRBh4OFks4LXb7Tx7OkmnjtTvtx5BHv3orNzv6/n3U5gKXoLALKyfZRrkNoOFwVnHts5KYJwuhWi4nEkUu/j6R/sCEzblkk1Harr8sSxOrYGyb6ui1S6MEM7XvfRqvroRRm2hxk+2OhDKu1Mug4eL1/YxL8bSZwOPLz06CLWuxHeXeshkRqnGsHUmYR+bJ2BtT6sWDOpRNoeZUgyhVTmxZn809Y7wAYhgqFQHgKAOFNFsePtld5UKVfGGH7mpTP4cGuAC7c6VpLUF3juTAs/89KZYp+75GAZY/hjz5/G9e0hbnVG6IwyeILhkWYVf+z50+CcF87B06ABCDD4nOGpk3Vc2Rpi7aYd/B4nZZlUuHCrva/DMv7FKFXY7o4OCj/ddxiRsqUGFWIbap6dbbvTIey1XoxUaizV/alJwlLdhyc4RnFa6sAbSSDKpJOypHJFLRcM8T7lQqy0mfDUmI4ATpVV1I7Asw+pZgPx/WndmEloA/QJU7eI2hlEdmyMARPu8Lk3cidDnf4Qrfr97w5wL3wSAGv4N2/8CQBnGGOD/P89AJX8//+MMeYfln3RGHMDwA2AHoK5G9h7oFoXz4Wqj26UYaufFA9Un2jbKW3yNruPc7kihuAMvShDLbCVi3mos0wGQd1Rimbo45UnlvFCPk8AlCcjSp/B5c0Bbu6OUPEF2iPb8t4ZJLi8OZh5AFtwhlud6M40rpkd0FvvxTi7WMN6L0az6mOhFkylaMwjqL0XnZ0HZT3nmcBOw+Tcz2pnhNML1X3UkoNymS46kYvHT22Hi4Lz4iOLM2+nFUHIMEwlFqo+MqWL+0kv3ptJcB1TwAar37vRLkz5NnoJ+rHE8WYFz55uoDtKcXV7mGvK2+tYcIbnzrQKxR5tDFKpsNq1nifjoGessuOqwFsvlL3jdbIVYmdgq9zL9QCBYDi9EOI7V3cQZVaFrB1Z5+BYJuiMbAU0k9q6B0/ZV+OwIFUaQtoh4URqvLvWw5ffWMWLjyxaFRljuwaLDa+YSeiMZL59ViHp175xDRdWOhglCrWKwHurPfypHz2PFx9dhDH2mIxShUzZ+YRenE2Vp8zHHQ4FRp84UUOr6oN1bPjOYM/nT5yoQSpNKqNk2iBKMzx9soGr2yMYA3DG8MzpFp4/28QwzgpjLAabEGnYAMsA6CUSvhBIiart/YxUAYwYLg38AIKZO6ZVSQ0YozBKpmcAo2Tsw6CcwTX1PgBS1nMpJMrjBM9+FMUQBAWGOhsGmXt4OpEKFcKstOK716Fi3NtRYRoesa/kFKnZSVA+CSkYTOZej/W2O0nYGGSlAfP9hHl1Ev4OgAsA/hMA0Zx+k8IfBjB5Vfw8LIXoZQAb92gdZkdR3jIHXnYHauOh4LEayDgIqgUCCzXbVpyHOsvbKz385psreP1mBxu9BJwDv/f+Jv7tH3wUP/uZswBQmoy8em0Hu8MUmTYQSuNkM8RWP0amLU2IMzct5NtXd/Ds6UbRoj+86wzWOhEhRDYdnAHnFqsYpupIfPN5BLV3OzB+UNbzXsiLHpyHmRyEHVNLZvWYOIpiD0XBmXU7j0I7BPYf04O6+5860yyS+cDj2B0m+5J5ALixO8JWPymq6SxX57mxO8qXwSCYHbCcLPR5zAagXm48Zivw22gPMyzVfbzyxPHieI+P14WVDr5zdQeZsoWPjV6Eb1xRlpYpDYaJRD+2kpIMQOhz3GyPAFizxLJga7ynlbYDlpzZAd/dYYrv3ehAKpu4LFR9BB7DMJEIfQ9xKhF4DAtVH75g+NKbK/j6lW0ME7sOvThDP97GYt3Hp8+18Ktfv4Z31/pIpOWQa6nx7lofv/r1a/ilR5cO0beK45FLsX76XAu/cWEdK22rhuQLK4+60h7hNy6s47/4X33CejwQ1c7VboKvXd7Gh5uWSrk7SPC1Sx6ePFnH82eaWKz56CU2OJ08ZoHHcaJZxdmFCi5tjY54Jt5/0Abwiesr8Oxg9yzoRQl2RtN/Y2eUkfQYAAQpyj6vFJHJlEmojrFLtF52E4kTNXdwnWRuWlZGBM4ctOFareqmRY3gToZ2E4NlItFwdeIAIOTu7VjwOdb67s8Y497fTcKr4X7BvJKERwB83hhzcU6/R8IYc33y/xljm/Zlc/lercOdYo+3OrIPmCSDMVaab8xbnXyIjAOYyUBtUhXo0mYfb4wNxk428UPnl/dJi96pOss4CHr9Zge7wzQPciT6cR//+LWbePJkHc+daTmSEYmlmg9fcHAObPQieILBFxzH6vnAsTlMCzndCvG1y9v46gdb6EYZFmvBvuBN5YN5r9/o4ur2wL0RZceAM/yJH3kM/UQfiW8+a1B7LwLje72ed0pHumfyomU/MR6XmVEWlNqOJFMFBedEI8BCLUB3lGJrkB65YzgJ9/4upx0aY/Cdqzt4/UYHwyRD4HGsdyL0807mc2ca2BkkiDKFUapQDQQ6owyMATuDBAwG690YqbJmYkIIKK2RKo31blxUyHeG6aGKrDb2dWMM3lrp4pd/5xLeWukizjRCn+Mbl3fw5/7o03jp0SUY2Dmt9jCzplLaehUMkww3dyNwZqANw4SpMQBLHbi8OYRSCkOHMkrNB0Yyr8dojbDiYZRIaMNy2qIB5wIvnFvEGzc76EQZ+kkCwRkWqz5eOLcIbYDvXe+gPUwhBAOHra62hym+d72DKLFJYJTapHA8/BulCq9dbyOTCr4nnMfjmVN1fO96G7ujDIJZnxmlDXZHGb53vQ3GbMKyNXDzxyNp8NVLWxhE0h4HlqITWWrTC+eew//ms4/i//0HVzCY2GcMwPljdfzwU8fxznofN9tRqTTn/Q7OgYQInKMkI51xKURZOU1HaiDJFCkNmhI8n0QqcMZdhskYUS11gpZb47Q6EWVaSg2AM8YQFi7jU96HNcBzzTUcr7pTqsUKR0g8P2qE2pQkEpFupskBbEqGtVGruD9wn2BeScI/A/BjAO5ZkvAgY5K3+uatjuXV+gJPnqjjZ146AwD49tVt/MHl7T0t7l2GtW6M482gCNSeP9vE5c0WPtweAoxBcF60k+dhgKS0QXeUYqNnK5ShL7BUC3Bjd4j1XoRvf7iDT58tl4pcqPloVQXeWelhexADjCHNNI43Qjx5ol4kKge///56D/3YmsukV3fRCD1sdiNc3hxgd5SiF2UYJhLbvQiDEjdNCqEv8PkXz6AS+GSAOI+gdt6B8bSA8V6tp+CYiY40bwffsv3z6jU7CPvcmVYxuNmNM7x6rb2vin+nsqC3tR3FIP/tL8dVgadoh+Pq4wcbfVxc7yFRuvhMRXArT6xsVT1T1uk4GtoOhJUBZZbjbww4GBbrew/XzigrKDiWYiMBBoQeL4aEU6nRi63m/q9+/VoeQFv+dZwBr11v41e/fg3/3b+zgC+/sYoLt7roRjbZGAccHJaOlClgGilDG2ArT3J8x3HkE5OhIwmMcmdXDoNGhRczJLUKx8lWCMH5Ph+EWoVDa41ulEEZ28auV3wMkwypAbpRhkwpDPPqvMdspTqVGpmxDr9SafBcKOHieg+xVMXxCD2B88ctnagbZVbNhjP4zFY+xzNYnuB45fwi/tm7mySHfHdyuNcA28MMX7u0Bak0/tM/8gm8s9rFv7y49zuBYDjVqsBogxPNCuoVH5lMSZrJfQkDSCLoHRGV8aOgQVSEjVEYEfSWiFgPYwwqvlvOtU5EpcuN0Pn+Qr2KjFhPRqgbZYmbYpNlGaTxi+T6IDiA3cSt4iSZO2z1fR/l9nYW1FxE3XOfN4ExYEQ3QhHJNZUY3i+YV5LwiwBeZ4z9WwA+xIEk0Bjzi3NaTimMMV/E3rD0fQ+jNda6MfqxhNQGqTJY68Yw2j7EX73axrXtIVTuksiZbaW/erWNX/hRG+y9s2ofNGODMm0MLq738M5qo6iQAndOp7Dyij44tx2EVkVgEKeoBRxaA4PEVqjKkpFXnjiGSxt9pEqjn6iCy9yqaixPKK9Mfv9713fRjjIADGcXqvjUmRbWuhFev9nBhZUe6hWBfpShHWWI86pn7HCzLANjDO+tD/CZx5cPBYgHA/B5BLXzCoxdNLSDyzjovzGv9ZyVjjSPBJbCPJOysgo+tR2+Jwqlm35sE1ttDBaqXtExPAou3OqUVuBffGTRSTscq5y9s9pDP79eGbPqXqnUeGe1B4/bh68vrDFhpSqQpMoae8HSecYUnH6UohoIRKmaoOZYM0iPc3BmDbzGwbNkzKoFaY03b3YwTCQEZwg866w+TCTevGkr8H9waQs7Q1udm7yeNQBu3HzoOFWo+gJLNf+QYs8Yw7R8ViHOFDzBobRBP1ZoVjz8xFPHisThjZtd9GMFnXd8GazvQSLtshjGdCSBekXYhEEDMtUFLapeEfAEB2fA2yvW22H8oMy0QSol3l7pQnAGX9h9L7XZR43wBQPnHE+eaMLjWyRtYhpW2hG01ri4McRaNwbPZyIYbCX57ZUuvvTmKkapgtIangAexNEEm8i5908j8MjAmMKQCAZjaSV0XXAltwAgGEOfUDfKiK5JlZgFYIyRnYQR4WGw684R0JMaZ6q89FpWAOpi+nUK2PtChdiXgceREOZ1I0LiNDJE4geAstVoj9ydhkzOnqDeC8wrSfjrABYAHANQPfDeg9mrvIswxuBv/u5lXNocFDf5VBlc2hzgb/7uZfwPf/IH9pvpVL19ZjoMZj+FILUOpOvduGhZj6u6dyItOoaV6lvCl16/hZV2hLdHKRisW+HZBVHoiZclI8+fbeJLb9xCP84K3W4Gm+y8u9bFz5lzh7jhnWGKN29ZetOnzuzRj16/0QHnwIuPLOLJEw38/sVNdPNqqcclqdxwEJnSeO36Ll5+bGkffaYsAJ81qJ1XYFwWoBtj8NzZFn7w8cWCh//qtd09Hv75pbmsJ1A+g3I7tKlZE1gK80jKjjLA7doOxhg+/9JZtIdpMbjcCn28cHYBn3/p7JGFA8YV+DhT0NogkayowP/Nf/flfcfrzZudQ+dVJhXaoz0q0LjVr419kKVS41g9QDUQCDyGTBos1H2k0uBYPQAYx6fPLuDVa7sYpBr91OqQL1Tt65xz+Pm9ZqMXY5QpDFPbfaz6Ai88Yj8TZRrKAMxY/rTJuwVRppEphfVecscPC8YAzstNrYDp9IUxbrRjKKUghEArtAPLX7+8s8/DoBV6qPgCjy1X8d5aD0nudMwAVDyGx5arqFasgtB6N7bOzfnvcwY8daIB3xPIpMJG/7D7rYZ1hjbGYLHqW/M2bQoKGc9pTwwGv3txi6R+lCFVBplU+OaVLVzaHGCsnGlgGSntnNYU+BzDRN32vfV+gYE1S3NBG4DNqFcviWCv6nNUCOOsRtVNPdGgfRCo0yEFnDSeTBlyID4ltjUkvHMXfU5W0CMiwNfEfZMxgR7V0Ujdo7OM6Oz0Uokm4TvBiZmEQTTbLMy9wryShC8A+OPGmN+Z0+891EgziTdudIoEYfzQltrgjZynaqt6trpnzXQ4pLJ/Z8rAg00qrmwNoIyGVLYyKhjHpc36XGghdt1sdUHqPQUIlWnsDjNEqS5+Zxq3O5MKH2wMEGUapxdCtEIfvTjDVj/FBxuDgpM9mcwkmcI//M4NfOvDXax1Y5xZqGK1G4Fzq/99diG0Mw2NCta7MaJMQTCG8nHF6cik5TFLpYtuAWW+BcwW1N6NQdnVzgjfu9HGh9tDfOpME4NYTq9g3MbuuR2pSleF3sWhn3UegMLtJGWULKirY0JtxwuPLOAXfuwJfOvDbXSGGRbrPn74E8ePfMytVGVegWdW+SOTCsNE4sKtDjKp9o7XFJlVIJ/hyX9PcLvOLPcBGL/+5Mk6Lqx0sd1PwJhBnDIcb1bw5ElrqtWN01whB0VgbMz4dTvI/BNPHcO3rmzv0zlvVAR+4imr/V/xOBgDdD5TNO5qVDwOwZizKk4FQFWfI80kNvvuIKUMaWbNKZebNSzXK0ikVWia9GKY7H5ytn9fTOaczVAc8hhgzL4OWHW7uIQ/HueyrOeP13BxvV/QkbwJOtIokbjVjqyrM8o7LKW8b2Y7Bt/6cOdQEG1g93VnZA05b9d/5n4DZd6bao3qbcwFTUOFufeRVhqcCCgDomjhcU4mM552B6VCZk7loUAAfcKVmTN3kdGruCU9M8PICnrZEPgYcey+xgMBMGL+IlHu7RgS5w03GpK5j0dKdKjU91knYQPA+px+66FHkql9D4nJW0ycKRhtcGYhxLWdEVKpITiDUtaW/cxCiMCzJ/i17SGGOb9XcCBKDRjTuLY9zFvas9FCjDH49tUdbPYTcOzRqQ0sl/PCShta66I74eR2m1xudsr99GDFdphILIQ+unGG12+0Ua8InGlVobQpEodAMCxUPWuqdAcVNakNrm0P8cWvX0V7lGEh9LA5zHB9Z1haIZ81qL0bg7K+ELi5O8LWIEFnlKATSSSZxvljNTx7poX1bmR5+Nfb+2Rrj7KelFRlWYX+diRU73Qe4CigkjJKFvR2OibUdnDGwAXLVWmODqUNRqkqAupUqqILYOkgphixM/l/Dl4NFd9Sj1a7MZRGMdrMGbBQCxAGHpZrFWRSY5DIgha4UPWxXLMmZa/f6CLKFAKPgYNBwyDKFF6/0bVUI8Hx3lofjDHUAlFIhzLG8N5aH194GXjmdBOb/TiXGd1LEJ453QTn3EnJcF3hggGnWiGyA9Sc24EB4Ofn7s4wKTqYY9+JwOPYGSbIpLLKbJxjsWrN1aTSSJRVSYpTifdWe4dmBaSGfV3Z+7koOQ8Es1SsesV2ElRqYIu7NvmuV/Iu2OS4y4H94zOrVlSWJLRCD4HgeONGp3R/DOLMadj2oKAVuIPBqicQzziXMDTuMKovDbLITT3Z6btVpJTMYAgH320iuL62666e9+MMPpGsNIj9WeHu6NoYBUHMC3jGfTyoMcReIlGruDs3rdC9HQuEmmw1EBgQx1RS4bVPZI73CeaVJPwXAP57xthfBnAFwL6z1RhzN8zUHlhUfFEqYccZQ7XiFwob7UwikTY4a4YeXji3CM45MqmQSJ1XbnnebrdVqkTqufgkKG3QHqQYJhLS7FXLjAGkNFjr2Ad+WHLjmFRx6icSgzyhmVRxAg4nM/WKwGI1wOPH6qgFAotVH8v1Ct5ftw6s48ThBx5bwjev7mKU3D5NQRng8mYfFzf6VokEBtXAw1I9wL/x/OnSCvk8gtp5DcqeboV4f72HTBscDzx85rEl/P5FqwgltUHoc5xdrN02D19rjQu3unjt2i66kTxk9ERV6N+61b3rUq9HAUW3cyXRnzrTnJuEcJnU5VH2ReBxVPKigNR7lWNLceEIPD51O7YnlsEYw5Mn6ri8OUAirY45g+1SPnmiDgB4Z62LXpyBwcAXluDSizO8s9bFH3/hVFFZZrDJjla2+9IZ2U6CVBqXNgeIM43TrbDwatgepLi0OYAxBj90fgmXNwfY6se2m8AZTjRD/ND5JfiewOlWxQ4/3yaMse7CrWqAWiDu6DcsjVLkMwnW46AVepDaoBV66MUS/dx3wsBeIyNlizLjTsr49dXu9ErnajcpiiqnWhV0p6znqVYFnHPsDGIMElkoOY1nwHYGMcLAwyOLIbqjbGoXYewjFQjbnZmsoQgGPH92wd7b4/Joqx8rpA92EwEA3UCVSpMOvxROECI1x0IBRXDchwRHfmMkcYLw3aIq0xSVyMDsb4lNQUQU5DpUFyBVYETnJiMEYStE4yfggKa8GKoHWfH7MSSYQL1RBkZ0VY65F4FT1btTHJs35pUkfBFAA8AfHHh9HAnfDTO1Bxae4KgGHNEUbl41sCZF1YDjZLNSVJuNMTlvmBeSg42KQOAJhB6H73FkUiNmDI2K2Fd1fupEAx5nON0K8cbNzpGDHMEZokxiPA+1r0iXK264Bq4YY/j8y/s52fXQcuQ///LZYrumUWhutq087LOnmzAAnjxRx5Mn63j1Wruo+n7mkQW8s9bDRj+5o8mX9kjuE4uMI4lBovCdq7v4oSeWsda9vaHfu41pAfowlfCFrcZWPIFjjQo2egl2hiniTN/W4PK4sv7rr9/E16/sYCeXWFyqB/vMolwV+oPH88kTDWzk6zsvqdejYk8V6LCr7Xj9XR4HLjO2I0sI53NDo1Si4gtsTJkbcv4GGJ4+UcdKJ7L6/sjNrzjw9Ik6tAG5v5U2CH0BTzBkCkWV3xPW6yDJFD5Y72OQKIS+Z92OlcEgsa8rbRB43Jo+aYNsgtYQeIc9TBhyStPka4xhuV6Bxxm0sb/jMwaP29d9T+BUK8TlzeFtC1JqAKudqJAGLRtcdsH37HpN+k5s9ePCdyL0PQwTq0InGEOqdN4tsHcPjxuI/H5WJruZKI1MKtSrHk61QnywOTz0mVMtq0Dz3toAyYGB2EQavLc2gDbAp8+28P5a3+kU7HG73mOBCc5gz5GTdWTEJPLDkCAAwM7AzU3vxaml9s6AiAhqI2VQD9yh1rJbeAhLPjAgOzvug3ac8ECo+R4kMfxM+SGX0ejGkAZYJPZ3g/A44ATtypYy3NtBqRsJyowNDK0qQf8K6873u5poV9wnmFeS8LNz+p3vC2TKYCH0sTs6nPkvhD7iTGOQKDRDHz/+1DFwxqGNxpu3ehgkqugePHO6hRu7EaI0g9YKnAHH6gGeOd1CxRdohh60Mfja5e2ibW7lEr2jB72cTy0uCMbw+LE6KTX2wjnLyf72h9vYGaY4Vg/wygQnez+FplH4KNzcHWErEOjl1dftfoI//uJZ/Ec/+nhRFX4jV0shBBlKMe02kmmDK1u24np6IZyr4s4Yd+ovAOyn0HRHKd5f76M7sp2DTJmChqWNmTrE6sJbK1188WtX8S8vbtpqrAECz2q0v3Z91xqA5ZXpMtqUVJbbvd6NsVD1sdaN4XGrQ9+L0kPJ6Sz7gsJbt7r4tW/sdzseJzvPnXX5e2TQBqQZ2yTKJGkvbw5woz1C4DHIUQpPMGwP032O4y4IzlCv+vA431cJ9DhHvWofMtT+5gy4vjNElhclxqueSY3rO5aaOEgUUqmgjQaXDNoYyDxREJxhseZDMFZU0lm+bos1v9hmV9dQcIZ3VvNuBQMCwQFmjcjeWe3iCy+fwe4wvePB5Z1hiv4oQY+gAJQhyB2kAXssE6kwSGQxdDAuaBhjsD1IcDCWUhr2dVN+R2TIHY2NsbLVU/DhtvV72Bns746O/70zSCClxKXN8gRBsHFBx65J4PHi3AQDRom6Z4n6Rw1JVL5V3imaBRGhOqSkRsLdwXNiiMDX8yBS97lNURlTQjp0LIHsQkjQiRYq7nXwOSzlwQGi6YI4ctOm4jhBTOQ6bSJ5XCS242TdRy9xLyTg7n3ZFA8GnW8uSYIx5l/N43e+X+CL8iG9MU1kmsJGveKjlQf4k14LF251EKcK4YTXAuccy/UAnVGGW51RMdj8yGINy/XgSIObShuMcslCPjG4zGArUj/4+OKRqtOXNwf49tVddKMMC1Ufxxph4eUgOCu29Q8ubSMQDKvdGHGmcbxRwWcfW8Ja1xrGfemNFXznaohuPrcwlhEs497eKbQxWKr5+JFPHNs3AHpwu243uL0drn4ZDgbo76718JULa1Y+9sZuflyWsVQPUK94Rx6ONsbgS2+s4uuXt9CL9qQZE2kAppAqK687aQA2jTZ1VAfgeewLanu+/OYqXru+i0RqcMZyt2Ob7Hz6XLm/R9EpGK+GOfA327+ct1d6U4eGObPBa6Y0BON7juNKYyd3HD/Kdqx3reKN4CiC1vHrnMFZ+RacQSorNDBWwxkH+aky2M376mPlsXTMb2F7/HhboQ+s2o7ZE1vwhH1dG2tOOL4fTUq1ju9HSht8sGG7FT63/P9M592KjT6GcYbtGZIEqTSk0YjuUIrHGj7ygm40iKWlZmkb07CcbjRKJNZ78aH1NADWe7F1lOUMakpw6nFm5WOTrKC1HcRWL8YwzUpVeWJp6THvrZebSPoCkAoAY7arYez+HvtWXNsZoeIJ+GyPmvSwYqHm5n0v1gKUC24eDQER9Aa+IJWJKKdilUmMtHs51JA2IwablVbkTM9AuZOZvnSvY6Y0JNHFiomOyebIvaE7kYJPJDM7fXeS0E3c+8FwD2niNnKNCCdvSWVD9wnm1Un4GLeBcbt9GjJtYMCwVAvQHqVY6UTFIOG5xSqWansB/tjAyEoIGhhY46JxxeudlS52himkMtDaQMIGLe+sdPFznzkHwK1+xJmVw/OFpRrIosVu1UR+6AlaJeZLb6zh175xFbc6UaEUMuZk/9xnrQTq5LZaRQAbfJ5oVRB4AmcWqri8OUBnlOFqLbCmWKMEuyN5V4brGBheemQRf/JHHj/kiDtLcDvrIPm+dcwD9E+fbeHK5jCvTFpDvR9/+gQ+/9JpGLAjJzFS6cLl9WAFM8k0tDC3aVw3DiYPOwAD890X0yCVzt2OLbVnzJHf6if4YMNSaCip11ev7uJWO0LgcTTDEInUuNWO8OrV3cKM7a2VLn7t6/u7Fe+u9vCnfuw8njvTwnI9gM8ZPMGx2Y/hCw5fGyznjuMU0SHNh4mVsRViw+weVcZy1K2RmcYwydDPdfc5gGZoYHKFD2MMBsnh4woAg8RSDxeqvnX2labwJeDC0nfGn694HIHgCH2OONMY6xCMkx2W37daoQePS9QCz96v8qyqF2WIEokhJlSB8tc5o6UPXaj4AvXAv2PqiDYGDNZU7p3VLobpnm2b1sAwVXhntQvBTCmHPcoUNKyKUTJFRL2Zc9NtQjN9PaQBoHQp7UMqDQHl1KvXhqEVcghhO7GCMbC80KOMQSI1DBjOLQS41rmzzsuDAAZggZAWDTyP9B+gEPgEh94TGERuChyRQwBCYEm4A9cGQWmikgilDTJiiNtX7u1oEgwawTk5AzIYuTsFNUJmdSHkGDnmbQAAkur+EJ4UWYaU6LpsD93r2RkSphL3CT5OEj4CaK1LtYJTqaGUKgYJJ/0FxoOEP2dsgP+rX7+O99Z6Vj/d2ErAe2s9/OrXr+Ov/1stfO9GG704s4N13FYBe3GG791oQyqN99cHzkBNG2C5HkAwY4eV980kWA1voDxwfu5MA7/99hpudSKEnpUR3OrHuNke4bffXsMXXj4DxvZoCONtlflQ5PWdIZ451cRqJ7Luy0rjkaUqRqGP1c4IyYwmOGWoBhytqjfV7OpOg9tpsxd34i9wEGNDPaU1YKzO9cX1Ht492bitYNsY6+aqNIpqdPEeAM6Zlaokyt9HcQAWfD5eC7excfv/zkFJvU5Shdoj24nbmaAKCQ58+Y1VvHa9jSRTYMwOtL12vW27FWdbeOpkAx9uDTDKO31xpnCyWcFTJxtHovz5ghUBoza2Sjx+xkqlwRlwY2eEXm7KOJ436MUSN3ZGheOy634zlvIsSwIAS2Os+nYGSiqNxZqHVCocy5MdZgy+dWXLqqH14jxZSZFIjWdO1fH82SZ2hvudew3sjMXOMLXiC/SRnAqWrx/nHMERBQH2EhsrYuBx6ywdeMCN3Whqp+DGboRMGfh8utq8zzk8zp3KRb5ggC8KCdip60UkOruRO5BjxmC5XgHnzNIxYYtEJl+HRm7qdnap+lAnCR5ze2MAlmKzVKvM1I2mOPKZNugTxayMCEprggHCHYFXq+6uSUhcG5wxdCkVpikU6UlQJsK2O+neX4qgXknm3s5q4E8VBZhEQlTxe0QA349T1IikrErMVjBCrep+wcdJAl3MmzvG8n/TYGUONS5tWKWQU80KWlUfvSjD9jDDpdxfwBjLEx/lNx/B7MNulCq8dn230AyXyljlI2MrhEYbbPatysY4UKsHAk+eaGD9QKAmuFVFsdWWBIKhoFpEqcJX3l7HZx5fLg2cU3ka3dwZebkWYHeYQmpgECusdiIkucvpWBXlVK6KstGLsdlP0B5m+O51W+FtVjx08krwte0BepF0Du3NgkRqbPRTvL3S2xewzhLoz9MBeIzJ9WmGPj55qnXHwfZ46FPw6Zr0FY/j/PEaWf0WnJEOwPfCDfkoylou9SPOTEEV4ozjeKOC7UGyjyoklcbFjT52hwkqHofvCUilMBxKXMy7Fa88cQzb/QSXNvsYxBKnWhU8fbKJV544dujYjIsHk+sx7ioCAygDyCy/npnJXwc+2Bwi0wYsH041eafyg01rvDg+vmXHXZvJJMD6sCzWRGGyxhibmuzUgr1kR2mDL19Yx0pnPxVnpRPjyxfW8R+88hjaJcon7VGGTCr4HofbV3kPVQ9gjBfKQqdaofUfoKKU8XbDqv9kysDjthgypgINSzpmw0QBRqNV9aYGIa2qpYKWVWxTnXtWcI7Q4xhM+WDocYSCl3LDtTGoENeG53EoYwfDtTGIJ26UoQcs1S2djqJ1POiQBsUcThnGwhuzPEoMZUetNTkoq4kwpC81GsadSAiCTkQNHUujwYkElRro1UTgK41BlVhGKNzHzCPuEVIbNDz3tlLD0ZKgdkkN1Cru8LlZdSd1lFzs/YKPkwTgF+71AgOPox542B3upwAwAPXAs0oiuYFZJ5IYZhqZ1Hml0H5DKktDKNr9bG8wzkrnWUdTAwAG0HnwYGD/VtqgO7IDj61cEWRc8e2OxgOmHD/w2BL+Pq4Wuts+BwJPQGmNDzYGyKTaSzYqHp4+2cBa11I4jt3YtUNhDLi2PQDywUfGrCbzB5sDPHemtW/7GWMIPY7AE1isBXjliWUs1AK8vdLF9o0OrmwN0IvSu5YgAECcaVzd6uM331wBsNchmCW4nYcD8EHMQ8FqDE9wfPaxRax2IuwOk31VTp8zVH2xp9HuwEEFpmnD00fxWqBA0b6OqqxV9hvaAEs1H5nUGCYSW317fQSCY6nmF/tnEFvKjzIGPKebKGUwyIPIoxjoaa3xpTfW8JW3Vou5nZ9+4Sy+8PKZXJFGIPQFpDJFQO4JWxG2kscq9yGx9wINe40nUhU+K763V6kfU30A654uOMOTx2t49apn5UlhE8ETzRBPHq/BE7xIdj7Y6GEQS5xsBvjkqVaR7BgtcXV7WNzPxkULA+Dq9hBxZhOuacjybaA02ichhHVX5pyjUbGJH4CiaEJBGyCSdsh4MfTxx188CyEEpD7shFx8B0BmDJ491cRGL8kpnmM5WYZnTzWRKYO0hLKRZrLwlKh4YmqSUPEEUm1pGdkUDorgHLUgQEWU68WPUkuLawRiqkJSe2BdthUZVD7YMADSzF0Zz6RCh6ACUehOM6+cQKJ0Qf0rg8/cxyLkHKt9Nw3nZsf9fkRQiaTSWCYC23MLbvrWUoWYz+AMmtgXlAQqNeStjIEk5EkZMVS8ELjXseYxp68LAHiED4IR30c+CYyxXyl5y8C6gd8C8E+MMR/MY3lzxhcB/Mf3coEGDCebAW60owOvAyebAYSwUpZjitH4QTSWuPSETSLG/Ftt9reufWFNthqVPdsSnQf5dujYtptHqUI/zrA9SMDzqlPFs68XA6Yw1sApf+CnClBGIZiYU7i80celjQFONCtoD1OcbFYwiK2c6LOn6vj9i1vIJuQCOWzb8bXrHbxwbmGv4htLm/jklc0/8swJ/OJPPQ3BGf7qV94vvk+qtM0IwYCbuxFutiMoAzx/1po9jYes65XbD27n4QB8aD05KxSs/uDSVlHhrVc8NCqimE05qoHaFz5zDu1hin/5fq5uxICK4KgFuVvuEfffOAAukx69nX1RhqPQvsbKWmVOxOPfGFf5Dzpsx5lCpmx131LFbVAZZyo3jbMdFsYt9Y5xDq012AQ1i/JqAIBff30Vf+9fXcGtzgiZNPA9hus7Ixhj8PmXz+J4M0SzMpYm1YXr+vFmWJiXjXdZMVvNgFoginU41axgq5/safqPK/BNq8sfZZaKOExVMf9QiTNEmf3c82ebuLzZwofbQzCu4QmBZ0638PxZG5wPU1UUMID99yNjrPIWK/GGYYyh4okjq5SNiyFSWQnmVujh+XML+ZA2/SMcQBjw/PscT56s4yc/eRyAVY1yoSIEHl2uouYL9JQs1qfm29cDjzvnDcb3CFWysSrvFJRJODJo1EMfP/HUMfzOxZ2pnzEAMo2pPggGwFurPTAYNKoBAHdg+aBjQIwbDDMJOaOZGnXGMQAj5b6npYZwMhYMnHjwmdS9sZroeHBjIDx3kmC4+/0OMQvQSzIsCHfYyYgbQQVER8UYcMpxWbqPx27mXsdYM4DY35Du5LNKJCr3C+bVSagA+PcArAJ4Ffa6+AyARwF8E8AfBvB/Yoz9tDHm9+a0zHnhnvd8OLO63tOw2omslGmjYgM9aBhtZwoaFZskALby2ww4tqf8RjPgqPgCS/UKAjGyD+j8PV8wLNUrEwHtXjIw+f+Afbj/1lvr+xRDDGyrjTE7fHlpa4jrOyMMU4nBdmarw50Iy/UKmqGHf3Vx59CQtjaWh9yLUmiD/aoo6Z4qyudfPptTODRqgeX7VkT50N+8MMo0VrsRpDb42uUtvHnzNF5+bAlvr/Sw1o3RGaW4sTvEpY0+Ti+EePpk80jB7SwOwNN+mzGG5ZpVsBoPuHuc4XijgrdXevh//N6V2xqsfv5ME48t1wtjPl9YhazHl2voxbKYKThqd4Ll/5n26aNU2MtwVNqXS6p17Cb+vRttjFKFisex0UvQjyWONyt47kwDG70k53FbmnguFGNfN1bt6/yxGi5v2RkFqTSCwFJOzh+rFVz9t1d6+NaH2+gMMyzWffxwLgHMGIPWGv/o1Ru4tjPMEw2GNNO4tjPEP3r1Br7w8pl9VJ9AcKTKShk/dbKBMPDw2HINt9qWL2/MXmX7seVaMXj/2ceXsd6N8yTcgDGORsXDZx9fBmfAhZUOokzBFxxh3q2IMoULKx1orYvZF0uDsVKJF9d7eGfVzr40Kh5qgcAov1dMXqK1QGChWsFS1cd6//CDc6nqwxP8yCIEgcfwxLGarbpLjXrFR3uY5vMXR7s5nF+qwvM4pDYQjOO71zt46VFr6hYIWww5tFxhDdd2R/mAONvznNBAIWlddnUUnRxjoEoCPpUPNZsSlrwBg9TAucUQHqc54NPQjWxH49Nnmvj6h93b/4EHCIuEs25FcDAiQKdADctXfQGt3aFWQMw1jFKFZsNtplCtuav8lKGb4QKSuH6oEe+y2adiHZRGjaAC1avu7Rwp9/7uJRk4QXsSzL2eS777XlT3GXYJ47i1nrvDRPte3B+YV5KQAfj/APjfGWOJc4wxAeDvAoAx5k8zxv6vAP4bAD86p2U+sEgzia0SS7+tYYYoyVAPOGqBV/gb2MokRz2wMn3GGOiSx5GG5dqeP1bDxbUeGFNFkBR6AueP1ZApg3rFeiY8Uq1CalPoq9dzMzZjDL53vY1kyoWvtcFi6OG1a21IrbFY9ZEpjX4uSXq8GeLlR1r4W78zvXk0TCRqfl7pzHnXizW/UIiZVEURnGGUWrpVZ5ROHfibJ4yxlCPAcqp/88IqhOD4yoU1fLjVR2eUoRdn8DhDM/T3VVRdcAWtwO0PRRtj8O5aF/2JoW+tDXaGCV6/2S6M1I6qGvSlN9bwW2+tYpAqa9ylNYZxhjhTOL0QFjMFFI7iMkzti8ltnOY/cDu0r2lSrUobXN7o48OtoW1P5xLBgjFczl24tdG5nKiANrZqnSoDbWx3oeILPH2qiavbIwzitJAqboQBns69AS7c7OCXf/eSlSnOLJ//m1d28Od+6mm89NgSkkzh+q5N5AW3M0NgdqDy+u4IqdT43PklfOPyNi5v9ovfePnRJXzuvPVreOJ4Da/f6EBpWXQdq77AE8drxfZ/4eWz6AxTXFjpYpRI1CoeXjy3gC+8fBaZMljvxkgyjWpgO1DC44hSlb+u9s0vPXG8js0Dhm2e5+HpUy1sf3i4uv30qRYqgY/l+vQkYbnuI1W6VPHtIJQ2aIY+pLJ/9xOJbmQFGlDSrZiEBvDehqVGcQC1isD547WcCsnQqvjYnhIAtCp2EH97kCCVdrCb5ZlCKjW2B0kxwD4NjKFYBiu5jhhngNZOiWytJH7vg507ShCAvCMsJbrJbBX0BwGUy7DPGUC481KoEN9PlDmkkncQHpmnGCwTScAikUQExDJ8DlSJ9TxBDFccqxPD1YFHujaHvvsZs0DsB20YWoF7PQOCCjQgLo1UGXiacLhO3Z2EhJC9vV8wryTh5wF8bpwgAIAxRjHG/gZsZ+FPA/gfAfz5OS3vgcYolYXqyEEobY18honCMJWFWdiAAfXKnvZ5muVmP1MwSKRtJ1esQo/OlFUjMrYD0ajYuYdW1Q6YNirWAXSjF6MaCLSqNhjMpLYP3ynLMMbKJ3aiDEZrLNV9XNkcIpGqSDAeX64U2uyHvg/gpUf2qsk32yMEgqO5UEWSKdxsj/Cda7sTgW0enB2BTnC74HlcMX6c7FE2GOJU4rvXOzi1UM0rxkAj9DBIMkSZws4wxfvrPTy1enQ1oWlB62R1vGyQ/GAQbWU+B4gyjVOtCpoVDzfbETqRRJQpvPTIAjb6yZEGmbXW+Mev3cTNTgQGU1TOu7HCh9sjfO6J5SN1S27XZXjavhj/TllXZR7zHZwB1/IOmDHWLTfKKTHXdkYIfZ77dhqMMp1LuWrwPKkd+wd87vwy3l3t4a3VFJ1+gnrFwyNLVXwul1H9H772Ib5xZRuJtL26YarxjSvbaFQEfvnf/yw4Q0Fnkjrn8ucn4lh56IO1Ht682SkcwqNM4s2bHXyw1sPzZxcQpZaCNJ5R8ASHLziiVBcJU2FqeHUb7WGGpbqPV56wHQ2ttZ0XYHY+oBl66Od0syxXUOqOElzNE6pUthF4VsXnuTPN3LDNoB5YdaHJazQQDPWA28FkwSGwfzRZIPcoQJ4gHQFSA9+93rYzCczOtwyTzAbfR+wkjD+lgdxlfcd2BhhDrSKAKUlCrSLAGMNWP0Gat5VMXvNXRhdzK5xNH8DmzFLQRK6kNA1xppHm82RT19tYCurmlGTrdiC1wVcvTutDP1wYELr9A6lRvYN5sEnEibtizIwulADLQFFojQGGhHQoBS9wB/CZMmDEMG2HMNagZgE8zgFF0KKIxK5VdYetx+s+Kj4xNOy5t2M4dNPw+lEKxt3rIYh1CIn37xfMK0noA3gOwMUDrz8HYJT/uznx7+9rVH3hbElXfYHtYYp+nCHOdFEd1MZge2hvSNrYi3oarHMisD1IEWU2aNf5AzXKFLYH6SFe+NrNNpqhv48XzhhDGbPEwKqSfOqMh51hhludaN8g3+WtPn7nPev0PE1xpOoLvPzoIlnRlWpMdehjlEjMeJ+cikbAsVT1cLObFvtpTK1JFdDNh7nXOhGGqUJnlMETdnCJM+Dy5uzSnZOD5M3Qw1o3huAM/VhODJLv/bbWGnEqoc24w2JnQIapyhVQ+G0NVieZwmo3RiZ14cg9SpUNZhjwr33q1JGoQPNwGQZKuhETHZHJc/d7N3YPnbtHWc9EqkIMQOWEO8bswK82QBgI6y8yMU/jwSAMxN4yxrMAOdOlCO6YHYr85oe7+9RlDIBYGnzzw11kUuWDt17Rup68pBu5esbf++pV7EwErQbAzijD3/vqVfzcZ89ZLxRtA5Fa4CHOFCQrM2xjuTPrBK0QDOeP1XGrHUFpjc4o70wJjvPH6uCc4/ruCCudaN+1HHr2dcEZkkxhPadnNQNeDD/HymC9ZyvvUaYPcTs1gCjTduZHcKCEN131bHd0vCuziYx+N1d9E5zl8qO3X0hY7cTIpJoqezwJj9t7aCEKkS+NGfu6yO8J08DZXuGhbHZCqgMDZiW/M+tcFofBZv/h7yQs+e59uVjxsEsMHlOg/Af6qSQ7CYZgPXscSIhkJCUejoKYt7HFR3cA3+/1nO9nsVs6VHCOzYE7wb21M92NfIwuEcBHqUJKUKsoszRKCaoiGKqEU3eTGOKmvBjuF8wrSfh7AP4+Y+wpAN+BfT58DsBfAPB3GWOnAfwtAP9iTst7oCGEQMXnUw1xKj6HAcONnRFSafbxXlNpcGNnZF1vBSv1CUikAoPBjd2hNc7JecpW8UTjxu4QUuliGPHK1qD4zCR1RnBWSMQdhIGtCrRCgdVuvK96aAB0Rxl+/Y01vPjIAr5+eefQLfDpUw0EeZt2r6Jrud9Rarf36vYQb93q4J+9s4GNvg007gaLr59oKG3pQ1Ib+HyvHim4lQYdZRr9OMPOMCu4/36uUjJM5G2pCU2j0Ihc03x7kOD6zrBItmz3SBbV8bESzm+/vZbPRkSQSqMnbXIwPo5aa8RSYaMbH6nCLvieWsMwlvA9jiRPUBcqAi8+snCk4Juz/S7DJ6ZIhx5l/1DdiMlB2nHgW0b7cg2CG20KNaCxVDBguzSWwnLwt2zSOK7Yjw3XKh5DsxIiVaowXPvEcohOSQDSGaXIpEK14qNZ0sJvhgJpJrHStQ9Fhv2qQSvdCMMkA4zBKFWQyqCfWjFFT9iMZSxZO43Ktp1Xoz99roXPPbGEW50IOxMP8GONCj73xJKdWbjZOZTsx1Ljws1OLqLAkCqbqAxz2eXivqU0Kh5DnB1mPBsAcWY7n4KVP7gjxyCSBnBhpWuNxgTP7YZvD6kyiJIMYSVAVMIVjlJlu0paT90Opq2Xi0tu1hiDVB3+/uTvSAdrijFL2Zil+C2YpaQ+/CkCkBLV3kwzSDnbnqAkO3WW5fK+5aCSvlgaKCJw1UQVv0ro+geeKBzYy7DRc7/fSYgN4QxCuZOdeOhOEq6u953vr3cjnFqsudcjcycqIcHN8nwf1cBNWaKGo7uU4dt9grkkCcaYv8IYGwH4RdhhZQC4CeC/BvC3AfzrANYA/Ll5LO9hQCjY1LZKKOyDpJNXj4tLztjqT2eU2geNVM5qVJz7JIxpTWP7H5X7JFg6Rx9fu7SFa9tDDFOJ9tDD1y5t4ckTdbyYV/ldPOH2yMpLplPucFIDa70Yf/YnzuONmx30J/T6OLN/3l7t4VOnm0hyeVdrBpZXYxlwY3eE/+5fXMJmP0Ez9BAer+PCqvsGcScwsAOJp+oVdKMsV3MyEIJhIfDw2ceX8gc/K/jGOlcOGqYSJ1uVI9FcXBQawAbXw0TljqjjWoalNI1ROFi3I8SZhNQo5kkCwWxF2QC7owz/07dvIvQ5XnxkET/42KIzyPc9gUeXqrixO0SmgWyCDjFINX7zrXX87MtnDynzHIQ2lmMOYxVvulsDeMJSU5br/iGfhbKZA6ob8d7adBO5dyZoXxRladz5GgezxgAmnwfgDNjsxUVlfxyzKWNfH6/3pc0BrmwNoHIeuccZBE9wabNedPDK9tN4W6WeGGrF3r+ltgH2vkRlIng0xiY5a9242BYgl+pU9nVbZKAHvX/oiWN4b7WPC1IVScSzp5r4oSeOFV2maVjtxohTicD3EAi+1wmZ+NsOW8NNsVQGvhC4Ux2JfiyRZFnpMigw2HkAwQy6JWZl3UgiFECvhObZSyQEY6iUFI0rwh73BqGvXvU5BMPUIF7kx1+Ms8U7QL0iEAiBkAHx/Nmb9xW4ohIAPfOMW5/YiRIcnFD0qRC6/YFgIKj6aBKOzNy4g1IGgzqRSLSImYQakTBVOCAJ6c+YCkuJ/eALBkUkfoPInewMRgSdT2XkMDojnpWUktr9grn5JBhj/gaAv8EYWwYgjTGTfal/nv/5GDmmDQOPX2eMYZTKqa35USrBmA0Gy54RyqCgH4w/Mnl70DnF4ktvruDrV7btbIOxLq39K9tYrPt44ZEFGGMwiMsvpiRTuLTen8qfNbCJwPvr/UNzBNoA76318aU3VvDsv/HM3hfG3zVjBaQk93wwONsM7vSZeCQkqcTzTx2H7wm8vdLFIMlQDzy8cG4BP/PSWfze+xvwBUcr9NGNMmhj7BCrNvjE8fqRaC4uyc1nTzewO0wBGCxUfXiC5cmesiZ0yurdf+XtNVzbGcIYS4nqRBLaGFQDgadONrE7iHFtJ0JmDBIojDKFd1a7uLTZx0uPLU3s48PB+ULNL7opk+hFKX7tG9fAwPBznz3n3EbBGWq+gAEwSnIVGKnRCP1iUH28/LIA/mA34mQzxFY/LroRDPtN5J462cTGgUFaxhi5vz3OwDkDH0fm+d9eru4zmfxO7pHx6zzvdvXjzEpa5t8XjOHq9hAeLye+jK+PsTwoZzYA9DwOKe21nSlLH1qq+QWVZ/K2sVTzbZeln0ytbG/0E+vKzDnpp6GUwrtrPVzfGUFqayTncQ6lFJRSSEsq+am032W+QHswPZFoD2L43GBYwsMfZho1n6MReqWKIZSSjzZWjz4pWQaFqs/RCAMM48w5NNyPM+d2CM7QCH1sDQ8HKY3QHq84lZYyNGUxnFn6FytRN2JgyIxBwDlGd5hQ1QIPjWqAJ0+EeGfTTQ950NF2PL8AIMsUWWEnQQywetygSpl7lWWWOXzBEREdsvW++1iutAmefarQIhLYxbp7aNgQnZt+orBUce+LJcKj4HjdvYwTrSp2Su5FY1DO0m3CWXp7KKGY+zdaVfcxffRY3fn+/YK5JQmMsecA/CUAzwPwGGPvAvhbxphvzmsZDwu01ohLBoDizMBoVWrQ0h3ZIKniCXgMU+VAPQaEnldq9jEOgr57rY3tfrKPbhGnCt+91obMAyGXOVGj4qEfTx9s5gx4/mwLb9zsTk2Iokzju9faMLk3gxAMOld4SXKpSa33FEGut+O7Kn0aKZvQ/Lc//2l85R0fF9f6YBxYblTAma2Kj1KJfpxZXwptNdZPtSr4X790luTrU5Kbz5yyNwzBORarfuGyvTURSaRSY7U9wiCvWA5TUyRUUml86nQdX3qjv7/6bexsyhe/cR0//9lHiuD52x9uoz3KsFTz8conjuOZU3W0RxkEsyoXk3GQBnB9e4ivvLVqDb6ICsjuyHZjtLFJqgaQSrUvAHQF8J8608RyPYDPGTzBsdmPrfeHtrK7mTLoRXZ+Y6HqY60bF8pcvSjNkx/s29/BlP3dqvrWuE/Y5UhlB+NbuaEQKylZjV9XudpWps1eomwAzezriTIIBNs3kzCG7foAnseKpFBwZqlCHgfTebLoeXjhbBPrvcOVrRfONmHA9pmUTYaWmdKQSqPqicJP46sfbBZ+Go3QRzO01JVf+p3LuLTZLwLxVBlc2uzjl37nMn75333Bebzt8dXolDx4O1GKYapK55sEs5KjrRKOL0PuR5FplM1NmvwzrlvEwaHpSfi5AzdzsMM1kAszTH9faSBOpXNWDEBBz1JTzgs/7yTzkiyCc4ZQ2PvlnaIVetAGWKiHAKYHUzU8HAOEHlF2TrRBszKbqRXzqDCKoz3FoXsSOwTNJ9O6NFEfoxe5g+uImGkQWpMyqRkRMpYZJo6hjUGm3cvoS8LVmVFdF44+kQSkRMK1QJjK1QOGHpGIbG53nO/HxPzG/YJ5man96wB+E8BXAXwJ9n784wC+yhj7N40xvzuP5TwscHHrFYBOnJW6aSbKVmjDwHMqYHi5nvrU5SsNBoOVTrQv8DbGPghXOlFhxBU5KnNPnWzg61emG/pUPI5f+OHH8L//n9+Yvo4AurGtYj6yWMGbN3N6xcTiQp/h7EIVK7tD9O8BfW+lPcK/+mAb7672sN6NrZJLZqu8VzaHGKYKqdpzvWYMONUK8cK5/Xz9UgqNY0CbMbZnKpdIDPIZjYWqdZP1BAdnVmJWaUAeUFIfphr/5NVbiPL9JJiV7kylQqZh6UmpxKWtIX75dy5ZT4pcTvMbV3bwn/6RJy3nnXOw3Im1+H0DDFOJtW6MVGonX1MqjZ2BpbmNXX5Nvv07g6Tg8rsSpufPNgtvgGEiEfoB4kzhZLOCp042EHgcw0ShH0ts95OCKlTxRaH+Nd7fF9f7SJUujkcgOD5xvA7GGJ4+1cAHG30ME4lEqWIG5OlTDXj54PfBei6DlZodH+cks27Hdig1pwAZ22Wr+gInmxXcaB9+GJxsVlCreOCc4wceX8R6NyoSbg5goRbgBx5fBGfArRJPlVu5p8qkTvvkuvqCw8uD36Wqj/VujLVeDKUsle5MK8RS1UcmFV6/0T5UqZcaeP1G2yYuBxLHMTwOhL4HYwzKirZxZgsXzcp0l/lmxYPHUcjkHoSBLSqQtBAH1x8oTxAAqwgXFQpJ0ztADHkyUbIUBgNptFN1Ls6HWJuhj3hwOJBphj6EEAgnPCcmEQYCGhwhrZlZrPPk2nLYgfNMKlzcGJR+r8qA0UNARVKpOxDzGS1PSoHS/feYgUdM06WEXOYozrBEVPEfW3SHc0s1wgGYc3hEdyow7uA7JChNPgcSyh+AMCGjqEAbgwQNQgK1Sc0cEA7YHmOkm/elHfeQ91urI3zqSedH7gvMq5PwVwH8DWPMX558kTH23wD4KwA+ThImwIgLCTJ10hSMUZCy/KGnAIzStDSrz5RGktnK+DSMclnITCpni/+PPnMMv/3OxtT3BGd45lTDScFphV5eKU6RyMPKJ3Fm0B4luYHT3cdIGvyDb9+w8wicWfrDMMHr19vYyWdBWqEH37MDpYm09BepNHxPkBSasgHtaztWIebzL59Fe1LLPrRa9p9/+aw13hpz5/P1ndwrxmBfYmnGL46RU1t+9evX8Oq1NhKpYGDpba9ea+N//OZ1PHWygfdWu8X8SMFxN3sDqGWD7JMYJNJW1/XEACtnRfBkZw72Eqaxi7BgDJc3+9AG+KHzy3hvtYcLtzpoj1LUAw/Pnq4VtC4Dq040SPYcycGAMcFuPPMySOReQVZZPfu3V3sQnOG50y38i3c2sCv35gmWahzPnW6Bc44TjQDr3Rh6j0kEzoATjaA4rwPPUqsmfbxY/rrnefixp45j7bu39gXYPgd+7KnjELmB0s+8eBYfbg3xZu6lUPEFPnWmhZ958ayVA96dHujc3I0hc8nSrakBp1e4s3/10hY2+0lRzdbKziZ99dIW/uizx0uNzIapVUfzBZs6n+TnXZhRnDrvRwDw2HIVN3ejfZ/j+evawGmmdhTe+HbJLMFRoAyOpG5UERyB4JBTZBoDwVERwklXAuz8zzOnG9i+vHsoYXrmdAP1io+6L7A7xbqq7luzt21CIWaMg2uiYU2ctNalsxcA7IF5MOYqnbg6JUGfhNRHO7dciMoqejliDXCqQk/sawUGQ9BZU7gD4zKKcwGtsRO5d8Ym0V7aJlSDBnFG0nIlEZYmRCeCG4OASBISwi15pesenu7EGl3iGuxsOd+GSN1JxP2CeU1OPA/gV6a8/kUAL81pGQ8N+gRvtk1caKnU6EbuE7QfpVOlRwGrSmJgoEtuXNrYSqwignNpgLjk7pZkCu+sDXCqVZ36PgPw6HINnAHvrPam1i+kATb7GabQe+8aepGVnT1WryD0BFKtsd6LrTMrZ1is+lioemDMDr6udiK8vdItEoTfurCKb17Zxrc+3MY3r2zjty6s4u2VXi65aauh40okgx1sTaStdH/6bAs//vQJnD9ew9nFKs4fr+HHnz6BT5/dc2TmzFJSBMO+RwJjwHNnGoUpjzaWI51pe5E/shhac69bXUSZQiAYlmo+AsEQZQpvrfTwzKkGwnyeYBLjIHip5sNMtO8LGdaJgGk8EKy0lavMtD2OSpvcMIwVXH4rLZthmCh0Rxm6UYar28Ocl23QHqVojzL0Yon2KEN7lMLA/nY/yhCnquDuK2Opcv2cY287GvEhxoYywM7Ayl2+u95HpjQCwVD17OB3pjTeXe8XXgwHpS6Nsbr8nrCa99YvYf8yGLPdBs6sVB4/8AHOGCo5rcT+rkEvzqx0n9SIUoVenMHAdq1c80tSa5SRbHi+rzKp8Oq1XcTS2i+O/8RS49Vru3ANb2oDaKOdFJokU0gJbfM0H6g/eLdQ2AvSdIn86VEhZohqDey5yzkvNbbyuFWQaZVIG7Yq3CZ+jgrPeEDVlCgk2dcZTremV41PtyoYJLK003wU9OIMUrud6wcPQYIAAKFDMQsAMqNnUooCgK2+O3JWaQJN0Fs8Qra3wi2VzYVO3z1zsEu8P8okOdxc5wRliRgUHyVZqczxGJzoJCwQbsitUGCQuvdnm9CtHUwpukzCZFFpMWAMQQgsofL9NZOwAuAZAJcOvP4sgM6clvHQoOkTUmVEq0trBUHZjnOOrCS4yKSGYAw+B6bdNnyePzCZKKUZAMA7Kz2UPRG1Ad681cVLjzTx1kr3ULBW9TlOtUIMExscluFed7yt/4TGzfYISzUfw0Rhqe7jVDMEA7A7SjHKdLFvd4cZ/sG3boBzhteutUtlO585VUe9IiwlQcMOXIBBcOt8DdjK99cubeHq1hCjRKI9ytWmTtbx4iNWnWixFmC1G4Hlcqdaj/nYDLWKj+Wqh80DWVXoC/zUsydz/rqtDEeZQSLTPAi0DqzvrHZLeeXa2CqQNYS1Mqy/dWGlmGv44y+ewxdePpMnAxOyu9ibjchy2g8ArHdt4rUvANfW+VcqjS+9sYrv3mhjEEtoY+Unv3ujjS+9sYpPn23hxm6EWGmYfDZEKo0YGjd2LQUn0wbtkkppO5J26H6jj0Fi6U6+4MiUxiCxr2fSSplOEw+41Y4KSpmfJwtiX9fGyuMmmcLvfbCN5MDJnyiD3/tgG//HnHr1K1+/hrdXuoXhmtQab6908Stfv4a/9nPPl56rgO1sdEqun07uQiyVRi/nRB+8nnqxLKUljiEJlSatNenU6jHgnRJlsndW+5Y25XnADHKUFXHntJFAjKWpBZZrPjYGh/fpcs1Ho+JhWDIYMcwMGAyUnn7/VlpBKg2mDd5cmb4v3lzpQynlpJiZkt8/KqxBpymlVQHAbM4B9w+4cZ9PIQNG0YzccCJYjBUHI6r4CXENCsHJWTBKyjWK3UmClAoh4R6dGvc1FmfudWAwiImuygjueYAOUUTtjDIMB+VUOgAwkVsl0feIJCI18BhBzSJsEJrE4PP9gnklCb8K4P/JGPvzAL6Vv/ajAP4agF+b0zIeGlDDQZF2X4iRAo5Tagi8XCFPGUAZg5PNAL3k8I3jZDMobkg1n6Nbon18rR2h6gn0p7xvDNCPM6x0kqnroY1BzRfQREXrXsPA7p8o04i6CTwOnGhW8G//4CP4xuUd/O77G0jzG34gbFX8u9fbWKz5GKWqVLaT5R2AcdV7vLSxJKjgDF96YxXfuLJt5xG0QT+V+MaVbSzWA7xwbgGe4PjMYwv4cGuwn0aDvDKcyn18aIGxrjpHP7HDu4HgxTZOft/nDN+62p56LMdY6dgA/stvruCvfuUi2qM9qs/3bnSgtMTnXzqHdFyxPsDTT6UuZl368Z5m/jhYMbBSlmkm8bvvbeyjQyhjJSh/970N/Pl/7enC2Rv5XABjADSK1zk00pLsNs00OLTtGkmJWDLAyEKc3naTZKmr7WY/meCWewg9joovCmWoJFNohh6UUlNpQACwNUihlJXa/eaVnemGa1d2kEjlnAcYJ3zTEGWmoM655pcEY061HcbKVQftcWZgnBcyy4d+AzZp65UMLdguSoaAoPpQoAyUXHhkqYowsAO9vKS0zDlDKnWpmMMoVRimYzrF4Z3JmP0+jMawpBUwTBT6oxib/ennzWY/JSuYFMq6QpO4j27JM6GTus+pTqKhKPovgVroDmrHnWcXKEfmUaKwSKj6lIzCFIgy4vpgh31kDmI4IhSShu6ES2sgJBK3JndH14pIuIaJwki5j/vQeJguMmzRc7ONEA8HgHDPiIyI6Ho7+/5yXP5rAB6BTQgE7LMjA/BLAP6rOS3joUFZpWmMFiG2cLwWIKFuKgrOtjdjDLykG8G5gCc4tNZOytFbtzoYlnD7NIDvXtvGO2vTM/pYGlzbHpDeAh81pLaUqvPLIV7zOEap2lc4YjDYHab4YL2H0BeIUoUoMagEAsPYBp5j2c5bnWgq/eVWJ0ImFb53fRfbwxRG75Xh40zje9d3i7mHhWoAzthehT7/nViafR4SgWD5QKvBKNX4YL1XKFZNg9YaGyVa+GMME4kkzfBLv3N5n1KRgVU0+qXfuYyfefEsglxl5jBP3w7RqnxWYfL7xf5kQKYUNqao+QDARi+xwTXsg3VSl1+wvc5EnxiOG+VzCFrbhJUx28ngzFbOOeDklhtj4AmeD5tb00IGqxxWrwf45CmrPFS2z6WyFB6uFXolnYBe3gmoeALRlCphxbPOzy6HX2vwx52f8YWAL/jUh68vOKq+D9/jkFMyFd/jdh5HK6cq0DR6zRgGVk2Jz6DYAwANokNbBgbg3ELVzroo6QzQB0ni7qoo7VQ/qgYC3dFhU7kxDIB2lLrlrZUuTPXuCGbfXw81TjTdgVzFs/M5s6Aeuh/Y9TBw3nsBoEJU8MFNKQ2uWA7h8LtYcxcWq74gXYDbFGWJuO/6Hsc68Zy5uetehpLuCrwPCR27OwWjgTtRIdRi0Us0Qs+9HUnb/Rs17e523C+Yy0yCMUYaY/4TAMcB/DDsHMKiMeYvGkOkjR897rmjBSUTRt2zOOdkdtfwiMCAM9zqTOdS3uqMoJSylWkHt29lN4JLue2NW330Hd9/a7V3x+ZH9xLbgwT/t698gK+8tbavohtLg52RLKRR7ZyCRqI02sMUidLIlA0eR4m7Mh0lGTZ61hRLGpucyJxvv5Gbd2lth24NDJoVDyeaAUKROywf2I2pMhimCsNUI86UHQ7PlFN5RRG8coBhlGZY702/Oa73YiRphqTEWTfJZMHjr5UMlo1fdw3d25kEOTXh6kd2GQuhj2rJMqqBQCPYLxE8uf+8nJtepiAT5oExYww/89IZPHmiAalN4enx5IkGfualM6gGopg7OAhjDMI8qHWZjAnGEJZsRxgIVDyBVtUvKv2Tf49nJ4wxpcP/49ebJfKjzdAjOxGc2aSs7EbKAaTaoIwezoxVOJolRQg4gDs0JzLY85ToEQF6kpX73jJYVbmyworKKWqUMlFIJEue4DhWv/MqpDQ0NeVhwbljDef7zbCyTx3sThBW3MciVQaaOLvL7lVjtCpBKa1wDOpZulB3k+Q9z8Nmyb19jPbAPX/BCR9vDoOEoCTtElShtV139N2PU2yO3Mf0BjFUPOi4388yiZ2eez2vE76v13ceDJHhO746GGOfPfgHwFOws2g+gE9NvH4/4xfu9QKpdnGPsOvuJSkdXBtT+szk3EpalhkPJZl9WKbEg2RIdGkp1/Gtvq0K3++IpcHbq130SigCythtMcbkKkRWKUdP/D+Dce5vA+OkMQD5wPooRaoMqj4H8mFQAxuoLZeYtxhYSofHbWdi6jpIUxrQjiE4g0Y5BVcbYJQpdEqMaDojCansYGaZaU+r4sEXwpngcgZsl2hUbw8sJcr3ffz4U8cP3eA4gB9/6jh838cok8X+A/YGtEeZhCc4zi2Ghx7tDMC5xXBPBSf/spISUqo9p888wSvrlI0HvCk1HcMYlusBRL7dPN8HggHL9QBgHD/4+BIqY1nW/HsVz77ue4J0ftawQ+nTsFTzEWXKmbSNEolWxTs0wD0GY8BCxXeqHxlYv4g7TRSkxr4B+tvFei+G1rpU3nSM6pRB9THGr1M+CtQMCHVHDDyB4/XZtP27s0w+P0CQBAUt9D1ElOoPtQzqeCpZ6hEyhvCJ48k5hoRYCVXlTzJ3ksFgSs/tMQLfnRBJ476f9SKJmuc+9wLPfQ3enK66XuDyyi5CokrfJ4r4G0QnQfDyZ9AY19w/gVs7D4ZPwiwp9GsAXs3/dv15dcZ1vNv44r1e4GH9mP3wiOaLAEg1kV6mUfYRrQFFtP+DvAJ5N5FKDTlT/fDegXqO9GOr8hOlEok0GGUaiTSIUgljrIu2q6rMUa4UFWeWu271+Q2SVGG9n2JzKAtfCW2AblR+8x0m1uQpKKli+uKw0/JB1CsCrYrvNOkLBXPKaWZSgcGgPZz+wGsPE+va7Og0KGOcnO4sVxL5M3/oPE4vhPveP70Q4s/8ofM20egflho2sK8DwKfPLWAh9BB6dsg/9BgWQg+fPrdgkwtj8Ouv38R3ru6iE2tEykrjfefqLn799Zv2mJVEB5Ovl83kSGOvw7MLYW6yhcL40BcMZxdCVHyBn3j6OBarfpFYcQYsVn38xNPHwZg1aTMlNwOjdS7pWU6t8oj5JgYDMO6ssE+TT51EphQWK1SIXg4NFMf9TjBK7CyLR3QjDONOU7jJYfxD3wXAmCHlFzURzNkkebahR01ovD8syIh9KTXA2GydhMGIoJ1kGoIoBvhT5G73vW8UqTY4zVdjEruEZGc/SrBYD52fObXoVuQhLCOgVIYhYZbWI5RBuwSNpz/sY33kvhfcdP8EiDwEcZIiFO7jQZ1VZrbc9J5hlpmEJ+a2Fh8t7vmh4oQKhyGUi1ROY3FBZ6kz+GhWfOegYbXiI07v8oOEMVTvOdnr7kAqjXfX+lMpMO+u9XPKx/TvamPVLVwVSKk0At/DZj8h9einIc5UHjxO19RfrHroxe7jXfU4uPBwohFgpXv4gXOiEQDcHXAaY70K2iUJTTtSyJRBM/TRndKKaoY+GJhzXwI2gP/NtzaQSoUg7+pwxpBKhd98awPPnGo6B2ml0nj6VAtXt0cYJhIVjyOROjdba0FwO2/w229vYDCRFBlYHfrffnsDf+mPfQrHmyF6yeG28vFmiDDw0B24J+TiNIXHOVK5XwkqlTagZYzhvbV+4fMwdk6X2uC9/LxjjMHzxFTpQS/vNOyUVMV2BjHZtWSMWU8Vx/1mRATGRhtc3p6tspbMQKGRBoDRpBa9MRqqRBdIobwQsPd9RhAygIjQgR8mknTopTD6PukkDAiOvGCGlP2kkBCKP9LQylv9zP3++kghIEKVFqHIo4iEaRBlOL3k7miUFT3G0JpSNwI87X7OUCZ+bcIiREPAI2YrqLCDCgqHmSKF2KjgukVJpN4nuOMkwRhzfZ4r8n0F8kHi/rpmdMWNaoGOUncSkUl116v8nBl0Cb3iBwVSYd8w7yS2B5a65aow7un7lH8iSTOsde4skBrv5bLTIkklSXPoxRKClbekWU6HKZNWHCvhGK1KJf8SpcGMQr8kgO/HGQQ3ziFZBiv7+bVL29ieDKaMwfYww9cubeM/+8nHS7tDY+rKK08cw3Y/wQcbPQxiicVWBZ881cIrTxwDYwxSSlK9qIzv7OdOyDuEAdjmIMHbK52pUqxvr3SQpBm+e6ONzmj/9dwZpfjujbaldxkDVnJTYcYglRKDElWrQUIXJACa6iOJKn+cxKXXz1GRlKg8HRVRplAL3I9ExnipEo3WBoJzVASm+hhUBBAGHpnMVH2iSKT1zD5n2QzUrAcJPpEADFOJiHIyI3Dcd5+3y1WBlPAPYJm7WBAiQ8e4z82YuYe0qW64khI9Qp3o2rq7xr7SdvN42qMMA4IWtbPmfBsNIiypF9TH8myiBcA1lkBFPnEvwRaRrBAjCZgxN71neEjquA8WRtTVSl0EgQdGtMXLBlTHWC/R4d77fooG8bCaFcwwgLh53k9w7Q2JcmnBTJUbYo0R+l5htHQQFV/A9wRGqbxz+UNj9fd3R9OD2t0RHTREmUIvTp1UH0XMwgjOkCjjpKZ0E4lRSfI4SjU6ceb8fiw1tLZeF9Nwsz2C1OWjhONB8OfPNvHM6RY8IcA4hycEnjndwvNnmwDs/nDRcLpRipXd6Q//ld0h0kxiOXRfY6HHsT2cfsy2hymGSYYb24NDCb80wI3tQdHBKmMiJDXBaAAAo2RJREFUZBr0zAKhxmYMbbxI8fA3hmpmtR05473EYwAYLx0cDnPFMFcqXws8VEuUaqq+dWuvE4nIomM2gwGkXv5RUOHfH0kCNReXSjXTLAsArCfu4xkZgQ6RAG8OiKKe4qgRuvqhIdylietDGmCDCPJXe4RxHHER1wOBzb57PTcJAkNCqTxVq4iJziUV2VBeyFf6AHEZk+g+GHPLHycJHwUaxMlVo04+Qzs0xoRBzICgOUAbkkc8K7QxCCuzDeDdKzQC7qzeufoAgNW0dwUXWmn4JcGJL1jO0/fuWIlDcAZtdGnyGB/hUBtjaSH9kt8Yv04F342K59zW0GNONR4fzNnNqPoCmVTOQXHCiBWBx/HOah8X13uQShV/Lq73ClMwSqWGMYOopF0XSeunEFbcHOBGxUdS8huJNBAwpZSKQWpnWQQzhbfHQaRSgzM3j56sQJqc+lTyPgNIV1vKQPIoqIfuSioFwxh84ZaUpa6+OJPO2aJMKvK+Gkn3vBgIKsVRMEwfjFmwWUHNJHDoQ47ot4vlgBh2Zxpx5I4IR/2O830mU4yMe2iYpOEQ550vONolBYniMwSR/kyduCdqhQpJuHODsFFAwDSi2L0zqJkD6gprASCYWWQiMmPj855hXj4JH+M20Cf4oLtD90U0yhTZSTDcfWi1ICT0OIO8yy3pVFue+4OAsiDrqKAM4waZhiwpw0hlg7ww8PHYchUfbBIJ3hRUfJ4PrN/2VwtwzhB63EnTgXbPVnAGMC7QCD3EU1xtG6GHalCBJxjUlJ3mCYZ6GFjn5ym7izPrnJvKzBloUYGa0gbfurKF37iwiq1ebE22GHBxY4BjdR+fPteijFbBjXF2GjxukyLXbFAZdWu8HVEmncdjHJS66FmDTDspYlQQJRgrvBjKDNl8z30/8udQLGiUKGYdFWOzQ1mSoEpjSCWcQSoRlxz0WBnEmSTnFiiJyE1iKPMokNmDoawyK6jOqwY/krmcCykxQxJLjUgRw7op4WQsAY/oJIyGboJLj3ifqQwmJmakCNWfla77+ljrJtDEI59KITaI969tdbFNqBdRSQIFFgAjakib+A3xgDTzHowI7SEDxfUvryNZeMyUuoKOsdxwJwFnW+6qW833EMyhrU1hl+BA3i+YZXTCABBEBabmAYOS5HGQqEKa8WTzzoIpP/cnmOVxyDlDlxhu7saZMyC1LskawxLN72FkpVprJcZYNT8PRh2JiNa6lLo1RsVzV76VMfinr69gpRMjzT0rUm1dp//p6ytQ+ggUG+L2qgxDnGnn/uoStMFhppxUIam19RBwYDEQTsUeSrdf51K8rgo8pRpUpph1VDAAPmVIRcCavimUzbqmCgAxmMmJ65wZWkGJrPjOwXooVd8fnQTyhmcMhslsnZnNna7z/e4oQejgxwNAYNydBmNUKQVzDGpIe4coPm70UmxG7h327qrzbVwj5gl0lmLgUOEDACoioEpk3URidJepPIEHxDN6ofnV+azL3cbHScJHgBNV98OsTkhrxWmGkHggNmruM9AL3MGmAbsnsk/tEo78w4ZYu2k4qXJTPtJMIpMKH27f2d2P507Hs4ADiIkHUUIoYkkpMUpkadKVaptolAW2AbeBsXNf5dVe12cop29uNN5fn/4UeH99AKMVKWUcKPe+EFBk0EkpmtSITpzHOdnFipV2ihh0iWM+zJRVe3J0NOpEld8jOg0UBJtNAhWwAXyZg/wYuyXSvWP0I/cxzxTNgU9SYsh1Dk9t7/tkJoER06Hd4QgNSreTwDbBsc8yhTbRKehm9Pk/GBJlfELWlqK/JGmKkLt/QxGUJorFLLWCR5B5qNPbLcIKLPu465qVHmhKEoXmA8Lj+ThJ+AgQEwYvG4RL2c4oI03IqICQkvoDcNd9EoDyivHDhoCXK1IZ2Ja0C6PMGpG171ABxlaVZzueUptS/4IxKKGQUe6T4Kp++zBol0wctmMFn3gCMKMQEcFeP3bTkbaHibPC3xnGZFflZt8d7O3EEh2iitkndig1oyI4gyYGFnsj95N/MCKCE62glDtxo6iLzYpPGk65II1NtGdBpg0Y0eWVBLUkIojGqTYYEclMnzivRiVKVLeDe3Brvy8wUu7rY5gC5TIIR0Mm3ddPphRCRihaEdwT4Xnopu6DRhn4RtQYYpZAElwgquBAKfpsdXpoE7eTbeI31qn3e8AUJutcsTsCKIYkdVbNaPR9z/CArObDBU08JLgkhoeg0SYs2ilea416IhsNPo+SFYHAu7sKSvcLKDoFRQmpBR44AyLCMKcMUinMRjaysxGlDn05PIJRKowG527X2lQbJ+Vji0iUeqkik2QqoIyJ4D2Riky6hHYHDw1uSEqfRyTzEXGdx6lEj6AsdYkkISMq21IbtAlDqZ2eO0LRSs4cuFLJI/l9wbBQc9MwTxN0vyXC+KXqcZKSJIhjruagndgmjLUeFgTG/Sw9VhOohrPNwwTCXRIOPEa2f3zCRyFghhwKpnT7P+y43++knExQiZ+Am3gFtCONAZHMUL1yKv7fGWCmgsNRoBWQi9yVYoH4jY0Hg2n9cZLwUSAirmbKTZnzcpm+MXwiKO3ERHUxzkoVaOaJuz0cfa/gsfIbk2D0TAMjAoPA80jvCxeGiZ55QE+DDtDbEUGl0Aae4ChrIPkcRzAKdAc4ITOk3v1iWK4PZmAHvV0QDFgkgotqzf0UicFJOcwq8T41NCmNtk80B6igFUQAo4HSofsx+kRMutJPZ2YI9GaUC8lk+XzHGFQRn0riM6kwIBJQTVSmg5nFYoEkeUAilBlR5rcyxiiTWAxn432QYw/wMCIS7V7iPuaJ1OT5vUPodhJ2DlioaIRER4O6RqkuwGgIlChTzw2NAAjvcujSqAFD5r7/U2n4yQdD2PHjJOGjwIioukXEjc1wTiqORMSDaEhRCIwiKTDzgJoxcL1fwIjBTUYEYlRVWmkzkwO2gvW+mBUe8ZjwuXs7pNTWiKwkbpXKmuy5oAl2bcY4GexpVk4ssVr07sCBc4aAmAs6QVSVFyseRgSdiEoMK8QQK9ManHBwD4j5pFboFkFoBB4aRCvsRM19v6qY2fkBYsYKey9KMYzcj/ZeiTP1GB2iQt+JM4yIe/P6FDfzSax1Zo+yhkSR6GHBds+9L7c6UalgxFGRpkTCJRNyBoqaN2j3R+gT8zBE2ABFvO8B6JSYQ47Rcf8ESbGphwDBvCJBBa3LC7Ty0KzLqHsAiOcpNZesPp5J+BhlSAhlCUl0GgKjMCDIgWVOsGOUaXmPIYSgK4zzwD1QULoXyOCuKJVbd1lQ8x/GGHSppwCB/hyGxCl6WKPm1v2vVXxkWVYqD6cARMTNlxOV8QoDIiKhSuLY2UnwOXF9ME4ahA2IBDhV9LBtTFQgdxP3MtqJJKluPiMSEaKj6AkOSTxKYqLj0SO24yigFN8oaMYwJOhbVKKdEcOjkAqM2FcJQQ/b7c4oqwIgnYPXwoOAOHHvK47Z1Y2G0n3eDaVVU3OCYN0OEo0q0d1suG+9oPwyP2wPcatDrIf7bVBOJZEEBkR9kgIh3o56hfaimnUZ8O08iwvUfAZx271v8HBEaA8YKLfLEVHZ2B5JLBFncctzP3QZEZR6jCG+y2ZqAFB5UK6UI6Asp9IG5DwApXiitUYyYycg9GfvwWZEAlshHnaNio8OkezsEtwUavAzMyB5+Ks9omIWu4+HYYAhxAMyIviQSpKUPkksg5oBWah4pEdBnzit1ohqbKwM6oTkLEVHStLZOfKjO5zXGWOx4iEjgude3/3opwJ8IzgE0W2jnFx7c5CNlnPY3w8CMqIgJzyOeMb7qk/MHnkMEEQ3D0SS7OmYpGEKoiFCTv+lGWb1NKTOTBkBhH8kCerM7faBJlHGp56EVJJQEUCdWMYy8RvVGWlu9wofJwkfAcpcWMfICDUSnxvsEO3iAdEpSImKWSdKEdyDmYT+HJQ67he4hnEpNSkqvkmUQhCQ9Q0nanMwrKK0urtEcK0MQ42QHCzXgbIgAzFjUCFOXUH03gUlTco5maxQ3PNMa9LULSGCa/JhJziaBF2oTjysBEH/CmDIroohuj9UInMUeMQyKKQaJOm6PXAvo0NQSQPH7NIYhhCu6FEyNUfAgHjGPCyoEzS4LEnI+T0KQ2JWQMmUtBynCGS7ETCK3OcW5bG5Sywj8A3OHnN/ZtaAsauAc40Zf4TAag8grBiOMEfiRuhzhMTOoKRaW+GDIdryYKQydxf3PFHSRBWf4jqHlYBUX6H07Cm6UZwpLM8oDXcU6DkoddwvKKWvGHqQXJCyngY1olpLgTrvjgIqgGeaCL6ZgSQkgCkZyoDYD77gZAWxzFV3733i3Oec1JEk5yK0IQPGGnGHptazF2dgRBWzSXgYNKvu5HIkNTlHQo03VQlvmKNgZ0Yeg8wyDAl6V5q6l0FRRfuJIiVpV7vuYJDSoj8K4rs8PHq/wBD3kn4CCGqilwBZ2U45OCdmVQgL4HjYhfHc1+E6sRlt99tojzQWqgIur2CB2SwIoi4gPjHDDxwBRgELNfdnAgCuVJzqLSnQdCNqiLtD/cB9go87CcAv3OsFepQjSep+CqgsJTmOAdHSrhDV3KrHEM2gpnNUeFQb9gFCacxoQFaMqWBPGTsnMgv6hNTlUeBTtBJiO0aZRJy5n2YpwetmRJUfWpMROuW7JQnaCTeaVKSirzGBgKiga2J/ZoQfBOeclnMlAuM4JuhfUUIay5VrSeXLMLPfB25uUgKMbrSjlKT0USo1ESEhqbIYXSI4GIzczO8ZG4oAgBk0EB4o9AipcAZFertQeJSQxT1RF1jbddPU3ttyL+PmVgRDXOtU3keIH0FltIzqrFVl5pH+kTPj+IKleLlA3W0IdVMksUadoO9Sg8vpPZCYnwcejLW8u/jivV7gTuI+Ra/33DeDrT79MOsSKhwR0WlgjJEP9nlgQLRQHwYYAJoIOhUROBtjyGotBUpV6ygIKD3vIzjEDIlzr0/wczupexndVGFElK418aQiRgHQSSTpduwTQ/mpNhQLAUOCWpUQiXwoGNk1aUfuZdwkXJp6UYIBETxT1Cs1B468J2aLnmWawSe6uL7nXsaQyD47IwWKzEDEgmSCexRQhlYPCxgxs9OqBWhVZztvuO8mltSrAUZEYNwhlrExAjqEihLVKSAhAEMMJcx62lQCYFYqPvX1RgCSh0k0GshuifI5GlX3cMUp4jcepdod9wk+ThLuuoH3YQjpvtQiwql1MIxI9aIOEWhRHPhhnMLcg0x3t/f98bSKtDu4jolLUXBOur1S2KGImkcA1UkICAWOCmcQxLB6yN1RUoMRPgmcllGlOh6USRlTEm1iLohyx5bakEkAVcVPY/f1E2cSKaGJHw/cFXhFBCdKG2TE4H136E40BtHspe3lymwlSnkEgYEaIQjhEbMuWmaoE/SuetX9/jxYCg8PydONgKDoxJlCOGNrZrvtDs83OwMse+7rmFIFCg0w6NzdUKXuA3e7Zud5AJFngxiLIIeKIwnExAlORR1LxPtPLVXBiC7wAjGUcHypRSzl/sDHScJHgIQI9ijZZqM1KVt4uuFexiLhLJooBv8e+IYPB5RQ2IODsiOiATSJ4OJc0308GmEw80zCUjD7Q4bSeDfMfePsZQqG8CCIjfsx0Jfu/ZBquqPBCMMqTXR2NBdkN08RtKmAA4qSviXoeENiX3SiDBt997Zc23WvZ2oIaqPHsUgMR0dEQnVlddP5/lGwEc92fcg0ASMKI0TTBV2CbhQlkuTJV6mZtDmYMIXfH3PLMJq4RqUm5acpXCNK+Nc3B7jSdu9wairi+DFgcJcfx4kEtjt3dxnNAPAJLhB1FVMJFQOwRHB9ThO/cYLIRLyghoDoFFNP2/4cinb3Ah8nCR8BNMFloCSspZKoEFlsSmg3K+kuGSwEQEg8rOaBrdHDYaZGgRRxIvjtxhjwGT0lvCqlt0AjIYKgNHafV57WWCC6DQuEIgk5jyMYBLGvhoSTTZ/YzsBIeETSpimVJqlI1R9BmIw1uXs9a8KgQTzwKoaQcCQCrdDjoDwRh8T+3J5VPB2AjmbzDxgmmjS12u26uy6bHfcydvsjbO+6f6Pbd19DJ+bAUug9PKNgTrSJynhmGNIZ+VuGWIbQGSTh10AFvguBwAKxmrMGc+0hsDm7BYcTogbUK+4bEjUPQNXflxcY/MBNBaIiG4LxjZ1eD4tNgmZGXKfzEBK5F/g4SfgIQLmoEopqyDTHiKApbBBa8+3UfZlI7mFEUB3mgQXv+8PUh6JTUNSVfprN5LgM5C6RMyIl5HsHBI8t0QaSCOB9Sm2H0J7zOCOD1kVCNogR3Y6NoURIzRwQp3Y3Skl39R1C6WYncSeXsRbwqIF34Q5RBsr9/UQDGzsd52faBCVDUiX6I2A0o3v7KElxlYjyP7jljtSIHAPGGFxZc4tRXl53/wZxaz8SWg9GfDIzKHffxZCT83sUIup5rQwUMSNFjdwPEoVtotA0a5+YqZzPPwOoAP/xFiOH+6k8hbpT1IRHSmBTgS/Fb+gkGnWi+HKSSBKOEeyB+wUfJwkfAXZ77lOQKMaiHnq0LjlRuWhIt9aBZxTWiIrWXBAu3P1l3Aeg1BYCgkMfco7dGR9mfYrHdgSkxIBpSGnqewIValqXeOhS8aQ0DJzghh8nkoQGoWGdJTF5DXLjXtHQE9ghjulax610lhAzCVxnGBKJHSfMHRuE+7RMM1zfcq/nFqEy05lD4DuKZutGDPp9jEbu31glpCq3CTH6RsCw1nPvq1sEfWVt5glVwHsw4pOZQcyWwq+EkDOahnqUUhoMaVhIKQ9tDIHgLtfTohR45IT7M5QPGsXlr9XraBP7grjESMO2kQa0715TqpFGRSXHK8DmwH1/3ybkptQ9MKudBz5OEj4CdAnuiSSOSqsWIiB0xSPiMrjYcX9/ZyQRzmq/eAScqz8YF8qsoAY7KdlPqSTSGW8qo2j2SGy7404+KfMvnzMMiPXo9N2BWkYkKh7TuLjuTsTfXXNvh08o5YRhiHbf/RTYJbZDa03SjShHZUOcV7EEROZ+rBpiSLtPGOglWUYmwSlRHmxTTk9HQH/Gid6NXoSdnntFiSI/KAuD3ZHEAlGtrRH7cp2QyzwKHiIPSye6RN4YxzG8Gce4bxERvowHoEydqTu7GtLa/bNio0evCBUwUuf/IElAsBtJdyYqwOda4wRR5l8mWh6Uf432q2CExxMnrvOMHMG+P/BxkvAR4EzDXcY5SVR5mJFIKXkiQpGkZigteoWU3f2TeHvGYcMHBZQW/VrbfefcHGRoBrPtK2ro+CjYGbq3oztwB85xKrFN6AF2KeoV8f7mIMGQoMpttTvO95PEvR0+FHqE78Q6wT0fxhk8oquSEAF+u+8OaqM4RpuQjN0aus+L7sAdBXWHGTwihOkS3Z+dOXChr6zN9n1lFAZEt40K1Cit+lRJNEP3DZ6Q3Ud1DjMJxx4M9cWZ0SJK34MU2GzP5k5H0V9WBhqVGSOtoZqP9K0LdQ+kZwQVwFPeADAcx5fcO4NoZmCReL9WraMWutfkBDGed4JoJdQCH7WK+zo+Syzj/LEHo533cZLwEcAQ6fqAyOY3hxqKOHSKUJmpEMOhjdDH6erdPz3iaA72oQ8AKMdZEE7FFWGgZjSeM5SL8BEQEPKkVOC81RuhJtw7o0rQWyKCuhJHCR5puRNcQRgavnvL7Ze53U9Ig7wOMW/QGSVQRF1snZBDvrLhTiKGUYqAu39DEpLMa9uE3HKvj0sb7ij/OhHAz2gBkq/HbN8PYUCwzMggiHrsyzhGPyWM5Yh7BcEOOxIeIqN7Jyg/0EaFYbU7m/30BvF+OqATCQoyAwi9hplx7gSwSlDZqKc1NZNQ8xgCPtvgMvX+Ys1HSojDUKf/GSJJePxECwtEt6JFKEaYGX1d7hU+ThI+AqwTkoQbxF2HpQPUiPNLEUHnLsGnS5TGZnT3e9JzoMk/EPAIyVqfUKsKPA9V4jcoBETwfRR4vjsMolydB1FKDt3vEJOZo8j9UGdGoW/c67kRuROm60TQu9kZYJGIKJuCaEfLDIJQDuKE38mQmLBr9wfIiCGnlRX3/v6QCPB3+iP0+u4VoSrwavb8lRw2pGAqdZJuQV1BZK7jV2CIezPR2EGtQS2Exo0ZE6oHBsQBEQCa/mz3VXLQVtEUGQonlzH7jxDINDBrs5m6PjLOoYiKAKWvsbxIfED4yAgKJXWNZdTgs+8jIhRmKJ+c3owzhvcKHycJHwEWiBYood6FfsoxIuhGxLwjhrH7kaqlQqdDaS7MDqZmq+I8KEiI4VFt3MfT40B7NBsrtTsHBRmm3L/BCclOI1P0CEJ0h1CCouhI7Uhjgeh4LP3/2/vzaMuy/K4P/Ox9xju/MebIiMihKufMStUgqRKQAUEjrCohDNiLBooyxojGbdzYapqmzWiWl5tu8DINQraRsNr0sixjjNBgYYyQShKlUqmqsrLGnCMz5je/O59h9x/73hvvxbv3/G7EfWPE/qwVGRl3Ovuee4bf+P3p4uh5XQgZx2FIIvRftIX6gHZquLFWfKJeE05BqTluZbvPt24UW4Rful78GdeFGuI8TxD6fcVa/rV9yCQIX0MkylqiMyPd1qUzrOIbtFDGKeZW96HJu/JoVHmK/X3dXInZPAkpu7SZz648VAuhOuNvJr5dwfm54peI5UQCvg4wQpRfWqdQuUtARioVRk3hPBaSG7H2qi94TL2Drh/bJ060k6CUOq2U+gml1C2l1KpS6h8rpR476nVJlIPim4SUhDJ5X0wXJ8KNZE2IJN3ZbLIhudv7wI2tg27HOh70hbIqSQpzu5uxJkzZlugKEfxpkMqJrq8VO5+r2x2qggEfS6aaEIkNyegJXWOr3eK7RF8wvhdiw22hifvtD4o/Y319gw+E6UV3BKkPTzjPA5WxulW8Dcm4lnqKN7YThMSkGOW/Jjw/DbNOWrjZysRQqHRFlG77vVRRFQZZSiVL3X24a8/tQzbiJDAvTK9ulEKMMKNGQpzem4EQVBbZaMva/dJhIT1fCuVpyNKxKbW6nK2FKK84CirFC6Sq2VYKtajYzD+zUPwZi4vFawzjWJyTsCRInJZn7DE8LE60kwD8FHAF+D7gt2PnbPyMUofQcTsDHWGirGTLmcxgMkE/XYhAXhUGnK5sdPD0wSsP9ffBcD0JvLsmNIcKij/9JCEWpjZLSLXn07AhlK6sCpvo5T7bafHxvylo/2fCNGRFTr9dbMCvCTUC70jnz3qfbSEzsybsi/VeTkdokJZyeevCIdHutMQwpnRUSOUUNzahfNLvJNi675Jw55BsveJOFmi2W/jCdVWSqt+HgcuiesvDghaaElTeJ5w5zl9Mv2pnEMxCMwVB1E38FlL0dKEWIAyqF50Eaf7qei/n8qnicWiSAZ8KJoPKMgKhcXle6Bd4cqH4/afrAWfrxXvjdFWQYZ2xx/CwOLGXdqXUh4FXgT9hjPmiMeYrwGeA54FXjnJtEsvCHbUsdMX3jObaVvGZclO4s0vGXLvTIhNkOfeDR0SJj0gYy5kKOdR+lhFI8wUEVvYhM5QKcpjSkKYz5QwvKT44MyGM//bN4jvuW9dXWRdG3ithV4TCjWhjcwtfyGiIPlmvyaZwnkpRfCFZwQfrOYKQ08xNlc0NuUTyJNikaR9WhVofMVMgPL/dNWRecbxVCgVIA8KmIdwPT+MEcFvoAn/zTpftAx4aqjYhnjFsWQtACn1KUXxpxsFiJRKPC6lp2JNkP5OMU3PFBs5ZwUmQ7CPlB5wRovin60IqLSp+PsNjVZBcvinMmWpJ0zaPCfswg/XIuAX8XuDbOx4bXhHmDn0190EuTHOVrifVwNBPig0pqXZQumD0jGK1ffA1czP24p4YeqY4atDqFFuLza0ttmZsJDeSWPcUSIP+esLBu9YPaQsXx28J9S/vCwoc1zf6+KEgHSp8D2HEAWmWik6XVEJzq5lyZ6P4NVImQeroaXdltZxZWevBorDQkzANpWNA6COfmTSFZV18rseawujJ+j78no1qjNxhcfIRWu9IO1usHPB9bjsASTNCUXyOXFiSqwMqFF8PIo9CL7dpfE4vVCnKHZ6twJsFjrQ048NXCb28OEgaCY7GonCPKXmZOJeoI6QjVoTozVarS0sImEm+fKBPRoz+xDoJxpgN4Gfvefg/wpa//lrRewd9CxcH/3xh3xcn0BS6/KSZQEmuaGXFB1guXHVERc5ui6guuOz7wK19mB56Eki6xSHKq2vF5t57q316/dnKhYTxBFPxgXDTFapnCEyXTOrPEG6ouRD4Wywn4uTnbeGGe0MI8nT6KdduFp+okjTinW0QRinMTJZAeNCGL/C29GVPAKYHwgibmWn34fVVYWaKcHGWsnXTMF8KeBSchDtSMCDRdA74HIwNorSuT3EpWycBoYJGzNY1hetqySScW5qjyElYqlLYWS8dUT0TYbLii6vQ10wmRDh7RFwTaj1Xm8U3kUwoU+v0M07Vir2Vy4s1inKL87WTMazkxDoJ96KU+iPAnwP+jDFGEnj7LPCXDn5V4+kLen8dKVqbpIRC4lvKRkhOwnZqSKTiv33gUXES7ghyZzcEmcntzRWiaDanbT/2tRGcBEnlptXL2RZkH3rCTbsuBEFTE7HRFQx44QSQmnlXN/pcnXF/9puyjvusKG3VVQ4SzcNhbnYzEGZMzkwYQkcQa5BCAa19cBLWWrP3J50EWhvFzwd5h/aMpUBSFmC+Ap5gaUmFJ7c34PTcbJ8h5Usy5ZMLMwxWJNtEEnwoaTGTIJ2CZWFfLtdCtLDD56ulwi01ysXpjFKg7MW1gGq52JupSg1Qx4STke8QUEr9EPDjwN80xvzdKd7yD7D9DK8CP3SASxtLLA2TEqJZnuchzHFCmNcjnojagKA0uS8IGbuHhg1hQp7QYsJmqqlXZiskFoSJpqIjXDEEtU26aYYU85IaZa8JN6p+0mVVKMSXogjSoX+jBcIsHZFEITYKzsrqGtw54HmFD4u5ubkh66NLSK2IOpdryyWEIOhUbAoyxA8L0v2llaQ8KQzOkpB8ttST78cS5RKU4+Lrv1RCLKKg3ys+m1Ph2JOmgcflmIpg/wjCQlxeLu6MOL1Y46IwUvzSYnHALRKarJTnsyUMeZLOsXwfhpseBifeSVBK/SXg7wJ/zRjzw9O8xxhz1RjzK8aYXwG+eqALHIfUkyD8KtVAkani25EU5JSchFqlROQf/EF8sC1jxwdpTwqZS85WI0oz/h6d/bDmJBk+wbpOO00iofNSchKuCs+vbqW8d6f4NRvCZ0gsB5DP6ES3W3DQQzdNePAX+XmgfuLvJLCSQTajQyVFa1fXZZUaqfplP66Z89Gj4SRsCM/f2MipVGdV/y/Gy+DyYvH9+kPCZ5xfCImEkctSU3FD8GDnKiV6qtg4joV+3yXB4apEEakgT40vGPBR8UUz8jRSm8maoEwXCkqC89WYRSHbsFgRvJ0T0pNwMlY5AaXUfwL8ZeDPGWP+8tGuZnqMkAaQHMyEgJKgqSYFLqSbWdVX5LNaQVNwMnzp2RGEcBAkl1lemOPG9mwNdhv78HNKFwzJgLnVyeiKOhzFSGn1zbbceDwrvRTWZ9xGEEP9gJ2EXufga0qrARzCcPYDpw+sH3DW5ea2ncBbhHSWC5WJU5Hui5Dq8UfK7EQKbq3MdiJLLkalCmFYfM2bE07S+UqNICr+DCnQtCAY+JXI40PLxWUML50p/oxzi8XPl+KISBdfLCqhZB8Vr7GZabK0eBtdIbXjBcXnRyUMqAnzTpYEidTKCSn2P7FOglLqaeBvAD8K/COl1Jkdfw64/Ww2EmFqrVQWkvQ6YnOn5CSINZCtPluHMMNAmsT6sPCBUN/SF1I7yiTQl4pkipEi9NOwKhiDkqKP6ieUsmJhT+mmK9nVGWAOWE7ng43Zje8IeSLsrGwwu8SpxFYGtZlrHY6eU8jH96zkwIyzu0RFq2noCFPNHxbEsIoHGzMqUUrXq7KC7Wbx1Vdq/wujgCVBOujifPFnVIXYTKuXidPUEmHARrVUfCE4PV8mrgiZgrj4+VgocKypBC0IVzSE61Vd0KxNgVwIcSbCHaJ/Qszvk7HK8fwg9j79J7HBlZ1/vv8I1yXSEXSZpV6AxOT0hXDUrNMX292E/gHrR8OjMyehKuxwQSGVtQ6szzhqdT+S6lKgVSqV2OzAV4WJU28InyHd06M+LB7wRNlWb/YsWKsDW7P5fSIV4KCH60Y+CLMdTwR+eX8GlRVxqiYbhBL7EYBcOWBFn+PChvB8mkAw4+8hHforCawIE96lieSxhkQL8zUEwzeVKhQyuLkhCD5sCtEXVXx0lsKA843is2xZmHGwkRQb8Ot9RU/IJKRCY5DnF9+QAwW5ILOqjSAuM2u04JA4sU6CMeZvGGPUhD8/ddTrK6IlqLtIRlCOTy4MOhMH8gjPBx54wcFPBHw04lmIjeZSnU5JJZSC2UJe+2EAzRrFbCXQPWBFq2td6OxHuLWAeiS2Fom8n0F2wBmPEvL1ZFYawcOhbnSmKpenzIrvgTBjSWQ/MkPbj8iFV1SK6sP2jCeIdKlZ8GFRiNBUhBtyK9Ms1YovOLUZpyVXY00lEIZlCo5InhbvzGa7R19wc5VXfBZ6kgRZnuALRn45EpwAIWOS5og9BUFcvLNCaTz1MeHEOgknmVwSeheYjwyrwo1GClBKToKnwBc85f3gZAwmn53rQqjohhDZW+8kaEGeTkKa3jsNs9bxhRr6BxxA8RK4ftDzB3ow4wBsDAdfCtTm4J2EzezhOI/n5uTa8FlJstmdy/2gLYWuHxKkY19lsi7/rASRR6VcL3yNNEfBy7qUAqFhVygnyqRZDVoT+MUHZ1lQVUkEy2K1nYi1oJ6g5tCoFbdoV8sxC0Iz+oLQBNgRArntXp+ysD8XhN6KSngyrprOSTgCwqDYw5RM81vNhFgwUKQA5Ybw/GYLtg9hbPjDEIGcBslmXRN+9Ha3h+/PVvi9H5ckyb2VLijlkAO3jBu1KWqRZ6TJ7H0PFWza+iCpqIMv6QsNNA5+7uKB00+gd8B3xGZXjhofBm8+IpkE6ZrnRSCIBokIrQA0E6gLmviecGnf7ivaSfGZXBYy/0tC3WHPeBhd/BkVwUBvVIo3UvI1CMHHwBTbHctCQ8F8NWZBkAuvCV6ZUK1EruSBtiv9YodLmhd0XHBOwhGwVC8+kaR4cafXRfAzxLpVKUXaNY/ODIPDQLKhJPk6DWIqWGI/ekulDJW0wl5+8JHt9c2Dv7D1kSegSjSYogxtRuLo4NWNGvMwP3fAGzkEcmC5OOA7M9XK/jQez8p+ZBVPAtK9tBGB0GsrIjki8yGkwgBV6cKptBEb3htCdPzMfPFdZj72mKsU54ovzAvzB5aKL4pz1YBc+CJ9LeSrhebqOAjYFhowtjNJhl7IAvhaVJOq+8XRsEBorj4uOCfhCFisFl+VFoVzpBoHlIXwhxTdeEx4fqkEquu8hP1Cah6VT8SUjRn7yPdD3WhW2i3QB+wlbCeyXvesNPTsw9RSDdHBSrTjBbKSx6zUPA7e8zsEarEsRTwrV5bsoMqjZvmoF3BISJnqbiJP8JW4JFwH4uoCWqiRXxCuV+cX64TC5EUjORpCrX9YivCED9HCfCZPkHoNgxAtZBIqQr66JswwMMawEBf/qBcE7emyoMDUR4tZl9wvNuTyE2J+n4xVPmQ0hbShFMH3tBajCrOWhTRqMbcflUlnh0BFSBVIJcJrrQQvna2Q+BjYJqw3YeuAa8yawNIBG3tLFVjdmO0zQg2nD3idaRcWD9hhutWB7YcgnnDnDkhztSSfTrI3M4VYy3wYSEGkhwUpQ+uHUJ7xHHxiqfj501WP0/Xi8pcnBK/tVL0k1jemwlwjLfQLlHzNrWbxZ9zeLjYKsqw4WtDPDO28eB0rknpRT5AezcETHCItzEGoCifyXKTxhDtqLNTVhifE+j4hy3y4aG4XF20IYxRQXsRmu/iuLOnVvys830/6CApejvsgE+x7aVfnqeHaxmw/yHEoQ1Yc/JTtEtA54Mh2UJP7SCTqJZixF12kY2Yvp5CoBqAfgmvF7RQWa8VugFSNJGUMAw+OwxSf/RjI9jBQCyEUDEoJT3AyytUqflh8Enql4iMnUQHbQvRwW7joSfOZ0hxx0Jkn1EWlglybMhkNoRHrTKX491isSoPOfDpJ8QVJKvNUwu/VN5pEmHrbzIs9jc5BS9vtE85JOALutIrNtVXh2PGzDgc9DHmzl+M9BNHB48L7wvNSY7MfQlnqVhc4DoImXnjwlSkK+Madg91GmMxe69/s2cnNB0nNh/SAvcM4hLqgrCLtq7P7sI7Z5njDooZYkD4UBsqKfT+VWLEkhLcXhM/YDynjR0UwQtpXlVKJWnk2L9oIUQ+Vp2SCVaoE6dCSn1MPi4/NWlT8bY1Q658pzeJcsbOyvFDsJvuCApPyfFIpoyHIky5Xi79HGPhEwnkslZjFgsxqYDLmSsX7+5zQtFANj4HM2RQ4J+EI0MIBKF3Ab2wndA7YgA+zR+dGchhIQ8ikE1HltrHspJMnB19uUTqE3bSRzy7SlGWyisaslOaY+VohGVqPn4voCp6f9BlX9iGjIhnXEovLsJ0WG3NSwkRyVNodQ1WQkTwlfMac8Pw0PAQDsqdCStpU4gAt1JZLbAoXgs2eoizIXXqC9Gg7ASM07JYFo/XDZ4pTHhfmStRKxXtsWRiUsFQRZjmEHn0hgr4uSIyt9sxEN0MBWW5IBKesKTSSr/SLf6/1BAxq4n1bY6VcJ7WO+hp84Tc/Ljgn4QjoJcUHqDQARgPLs4bNBHq5jUI69gfpN5WMKKVg7WHw2gycPeByi+W5g58y3G3JAgMSy3MgCJ3NjNeDyowW4RXh+YVShTmhDueM8BkzVnwAs5fTPbFcIxdqqmeN4ucaUkEHftZBmNOwsQ+fcRKQ9lVqFNXybOoBHSECVPFSUqHUp9eXav1TMcsVCXX2SpBE1EGAzgU3WGhcTlTxGtIcyn7x91gqF5ul1VBNPEcMdhKyEZyEklA2dbYsZBQjTbefTj7AFHT6ycS+g1DbBuuTgHMSjmAfhEHxwSHZ5p6SbySz2ver2yD0FznuA6kOX5JFTDPodQ969NbBkwHt/aiXKKBeBiFrPjMLDXj5QvFrpOB4vQxPny4+U4WeSBETy87+nPAZkm+62ulzZbnYAFkQnID1fXCAZ/UzlOdTmVFuSroua6AaFp8AUtbxUZEv3Q+kBm3P0ywIsp8SDaF8TEcxHUFyU/nFx10pCgmFCb6VSvFniPMgcsOtdrGT0BOyknNlwVHRSpw0vCjULlZjoWzKGHxBCSoVlF86RlCS0h6eVkxSMVXGftdJ2QLf9w9c/nq/cE4CfOawN7ghFIdLNdvaV/SEn25W0RSlQThPHPuI1C/QbEPvOHQez0jW48A7qPupfOOelaUypMINURzkFELfFH+G5E9JCcXlCmRR8YksOajT9JCUy7NJKO2H+zurrOdmLxOHXglS80h74VQttEo1BUj74iFQmz00hKA09dCjLBjXEgvCSVj24ZRwQ77QKDacK1HAolCLf1rQOs7S4iOr1UsoCfKiQs8wdUF6tBIGGMFAT5RHacLHlHx5iKWnFErYhqDCSpIU76s8zwkDH39Cdsf3FJUwIJ+w2NwYAiEzdFxwZiD8+GFv8OZ68fPSwKpAK+aEG79kI0mHp5dBehym/jgA2OrD9kNQbhRUQeivm5leZqcAHyRBCRa8Ym/nvPAZy3WfltC5LDkaQjKDc4sRlxrFhu9F4TOkNdRCj7rgMEWCkVTahzuRsIQp3u+L2uWSkbQkpX6CKh1h0qp06B6wou1DhdSPs97JiGccuVyuFr+/k2r6grvfV8UGfq4gFNapvGIDfVMIznSSjKVascfjBcWOymq7eCNZbgh18feItSKb8LtlOWKWQAufD+B5qrCvwRO2AaAVBBN+k8DXZLlBT9iIVpA4daMTw6Fr+NSFG6aU/NQKAuGCIJUbSRHISt2WKjiOB7UAbgvO5UngdHTw8qTdLrQO+Prb70BPF0cgpXKj+XKJ0CveGZKzL9nFvTSnWisuupgTPkNqpMXzKQfFrsQZIVh7Zh8yP9LsGIkPn18Qmyoz4Y4pVJJS9nPWO8WGlBTXlvo7puGA/fRjg5jN04q20KwuYfLirfgepILEWL8vRIByQ1dYpxHC4w3BsCh7EAjN0aFwnieC9GgnScRMQs5kQYc0t/bPpLYGX9nfVKr3L/sepQlSrKVAUY2E8jCl6Cb55LSGMfQneToDJjkQxw3nJBwBC8INUSoV6qWw1im+qNwWPkNKEvgGZlTcdOwj2oP+Q1Bu1MzsTfMgKZegecDjpTcTUHlxp4l0cTXaB0GWUFJqknZlO4FeVzBKhf6NmiAbFIYRPSEs4UuBkbni56ehMmMfih+EKGE4jDTDRvpBPD9EC1Ntpev/fvT9PyoTlwU1TeZLmniKqHERDWEq1lI1QjowtFf8qyrPIxIafsNIMPCj4tCgjiI8wXJdElQQGtXi5xUKTyo3yvKJkdsc6CQ50QRnJQo8DIpMcBIypXju7Hi1hefO1vGlIKzWRL5ikt+WGoh9zSQdJoNcEnVccE7CEbBYma1UyGQ5PSHwIAVrpdjJRhNm7Ody3Afi5SJ/OCRp2wmcO+BxryUfloRtSIe2pNs/X0IsTZF8unavjxIijNI6pfkEtcgjEW6Y0nn+mFDfsljxWBJGlJ6qFh/hC/sggXp2xuOq2csIBYUYoeeSilSfXipTqwolHcI2pJkq0yA1Rz8sCEqX9AipCNKhEiYsduuUHzJfKz7JLglNC/PlACNcb2JhRsHpueIc1VI5IhWi34ZCQR8asTDJOA7FKL8xxRKngZ7chO1rCDzZEQm15rnzdUr3fFDJt49L7web1fAnvM5XiiQ3qAnfVRnj1I0ck1kXwvjSTSDV8hTVWVVR2sDWw2CVnhCky8X1bbkM4SQQqYOfANzO4LxgMEq1/JLNWoqw3f0FSF+zWi5hBFnCTDDgF4Uo/9mFeXypBlj4sp5gQ/VzD8nNjQSd+NI+HNxnpHHIApUQauXinXFK2N/nGsXf81zDZ15ojhaEovZF3vchSEpOh3BhzdMe3iSJmik5XRfEBzxNXZg/MF8rdhJ836chfMaS0Ng8Xy5+vhyH9Kdobi6SH+1N0fArVXcFvkc0YZZIFGiCIJgYhVdK2ZIkrQtnGGiteXe1uyfjkBkz9vF7yYwZlH2PX8fw8fKOgWk7X1kOnbqRowAtKAhI/QSBQUyHSUg3moaGzQMu2XBMT9aRo8YngbCEeJOQYjjSbri0BFE4W7+A9LxWHqcbxVbpRcGgPF2PRTWQSSofQ4T7PvV6hXlhGEMoGKUtQb/XYNhsF+cuN4UO0u196FPZnlHTTSkPJQzWmhMcptNCw1nuR2wnxSdARzg/9sO2OPRGvCNCcqL9IObmjAff4lxx7r8UenLWURhPkKQZURgUGr5GOHbXezmTBHU8BZ1+JhqufWH644pwHVjvJlSEgMF8OeKZ0+P36TOna5QCj/YEOfB2N8VTVgI1nuBoxIHGGMPVtRZpbvMzvrb7MM3t47nQ3+Ephdaa5Vq0536lgOVaRCkMeGyhNOqfGO5aX8FjC6WJJVPHDeckHAGRcJmXSgw8D0ItpOyEz5B6EoIYzD6UADj2By/ioThblytQE3QJpYCwpBp05fQiUShMJxU+QypA8ANFxSuOmkmiKe1+xmlhCtmSUOozVyp2qc40YhYrxfuiLtyrqsJ1YK4a0+oWexLdbvFNd34fOmnn9Wzx8W6/TxwWe2WJX7zQGy2hQbXXxxeuzlJDo+tJmJ5lIRp2br5MzZ/NZdJCwE77Pr2k+LjoCg1nmYFOt1dYq98XNLJDbQrVeDytdkW+xyH08+ILM8ljDT0hStRLDS9dbFANvZEBroBq6PHSxQbdZHI2IjXQTXK01lyYL4814C/MlzEo2gOnyGBLhwyQG3tdNqhCZSKtNb6neWq5MnYbTy1XCAOfK0vlPb3NxsCVpfJUKkzHgZOxyoeMXGhSkgz8XGnwi8N/UqWQdFnMDJx6GOpbHhKClNkn5B0DgpKmERUf/9LXlBJcQRASC01+NcH4FkqIKYcxb28W3xA/2Cj+jF4GsVDrU5POQSEt3u6ntIWpiB1hh6eCExEAVeF7VIWJbmF5xlohIBPkGSWSXItRziAvNsSM0GOSK4+GkBJcFHbF4lzx89PwECQlp0JUowo0asbjxhOmdJc8RWqKz0EtjByPfY9VQRZupVN8PQp8b08N/miNvqYUBaD0xBZrD3v8FhEJ+talQXN0UUYEZQ19z1OEno28h56VLe0mOSbPCkueNDm+p3n1qSUWKwGhVvYztGKxEvDqU0uUI3906TTc87ex5UBF05KHMqxvrLT22FL58PE85wvvbexxmzLgC+9tiNmK44JzEo6AslBuJOlgV6OYpWqx9SBFW+eE5yvhwQ+kckzPShvOHoPfQzLgzwnPL1UCNnrFNzOpqVIqDlhvbtMRolVaUKl5XNCZXJqfo6IF+VLB9qjFHplgPEhqOd0JEz2HrHUUWlDsuSAIKVxeLL4i9XRALBgH81JD4z5kLeMZjb1apEmkIzwoLieS1lDyjdjceVpwEhr70JQgZY8eFqRp481ejmdmKzdqCgMRt5KcuuBES5OKfa1ZEEoTl4RhIz5MNEzzPMcYg6cVk6pgAg8qQolMXchEhL4mCrzCIWSlwOOd1RbtXkY/s9mBfgbtXsY7qy0SY4r7IjKDUopPvXiG5VpMbgypsQPMlmsxn3rxDAZFOGENoadA6YlZlXLoo7Wm1094Z2V8TcY7K202mm2ub44P117f7NLpnYyxiM5JOAJyb7Z66UocoJWgwSt8hnQ7DQObTXAcDxKDrHd5CEgN8UIJMJVSBSPoSEqfMSc8v5X4hILSh9RVLA3DPDcXMlcvNp6lUqFSGJCmxQb8pBvZkDOC4XCukjNXK7Yql08Vu3ZnFou90zNVj5JgPJQFnVUVzh7bPlWd7QTJjaE8oY55iGSolYUmkUz5dJJi40AJhu3S4uwXgg2hBv5hYVsIBoRaoQXxAIlTwqE7H/niADEjBAt6WU4YhoXzAUIhQ9vODa3+eLuh1c9p91LbEDxJkEFpAs8rVB4S2m3o9DP6aU4+ofkhzw2dJOPmZpckN7ui/EluuLnZJdS6sBRoeC36xW+t8P56e2THZAbeX2/zi99awRhDd0J/RTfNweQkE9aY5AaFodVLSCcYSWlm2Oplhd8zOyGdy85JOAJOV4rvAqcFKykIS2wJjYDS4Se1+Pm+hzAXxXGIRN7sDYv7UcssXTAElT36WUZDUHeRlCyXBAP+qYWA00JoelFYpzAAlZYJWZovDvnW54o/o5cpAsG4joXeo1joOu7rmHlhTPCiIF8qTVnNvUgspK/ExUfO6drsqYTNdLbGhs1uTihFQuPi/V0Rnq+WAjIlZBuE3/RSY3aHqnwyJNpnZlFwHMulkHBGdaOwVPx7KO2RSU6CUG40FPOpTziP6rEWHZGSnlxmnAOeMmRZNlEGNc1yelleqOiTCSU0ioH0Z4GnkWUZWzsak3e+dKtrHZmiKL/neWRZxv/85Wu0etkuR6PVs4+nabqrCXvnNvppTjtJJzZx58Y6O6EwX2Mu8ogmDAWKfI+y1OBxTHBOwhHgCUNNhHkkzJc0npT2E7yAs5K0Yr0qTmh0HB6nqlCf8ffYj0mti8Lzgsw8SZqz3Su+kQhiOiwIXySqzLElFCNL4mCXBFnD01WfC3OC9nm9+ESulTQVYQBSEhRH4DdzQR0p1PSEnoTtbnFkO0+KO5xCD/qCIL0SeqhK5dmUiQBKajbN5uVaSEmQmZSkXCvCYK1GHHCuVvwZC8IchZ6e3UlozL67TwTzQllu4HvilG0RITPa6nSoCc7nKUFcoDIQYuhOCNx1M2voF0XY+0ZNjjQZ27zbTfNC41gB0YS+hsi31zOp4dfTqjAbodTupuGdy9HKDiK7vDD+PL28EKGUot1LubPd2/N1DXBnu0ezn1IanMvDbzP8uxR6+EpPHCznaUXoa8LAL5zXEIYhlxbGH3+XFkqihPZx4WSs8iEjEqNVxe9Plc+FRrER8+Tp4s9YkIZNaY0SnBnH4RHEEAvRWImL+9DTILVaSVOhQ99j8jxNS034moLtTegb6oLxLdgOeJGgWx6ENPNiYy8ThizFUUxZqE//0FLxl33lfPE25kohQgUNnbT4BVup4FEpxVy5eF8sC8Xhc9Jo6SnIvdmuV41ambrgBIRCxHdzgjTjkFaSkQmDJ8qCoxKr2WuZz0qNbw8JrUTKuCsCaUKewFqr+Dff6CMbhJ5faDijNFmW0Z1QLtTt56RZNlHHwBgIFEwKfnva9gtUI7/wNZUoKMwkeNojnGA5h75VBDIoShNsoFLoE3ge5QnnYTm0EfiFSrynJNRTsFCJ8T1N4CnyCTsjN4Zq6HNpoUzoWYfE1/bv0FNcWihTLYUsT8jALldDAt8bZDQmr9PTitONmFKg8ZR1HDwFpUBzuhGLKmbHBeckHAEVr/giXxYsMT9PWe0JN3YhHCtIO1Ouz6GEwSqO6ZHuyZK2S6OBXKwsINhxUyHF3KQl1soxCzWhxn2u+DOWBAM/N7AgdA3PCUOzkqy41q7bSyh7xXtjUdhGKQ7whd6JhcXi3I0XF+/LZpJTErZxtlp8LTkVyf1PcSxEbIXIR7M/e31u2ZutPjIOfFLhlhiExQa+Ek6AfmJVWoooCxOAg3j24M2jMgLHCLr+gQdz0jASAU3xffJMxScTrie5cK9V2Br6onKhbpoXlvEopfAmlCR5WqOUwqCIJ1QpxIFHlpuJmZd+ZkuJahPKaGqRj1I2Cj9pn8+VfALf4/xceawTcH6uTG5grb13qJth8LgxaK0LHRHf9/lDH3uMy4sVwkEUJQw0lxcr/KGPPQZKc2WxjK/ZJcPqa7iyWB5lW4qchNzAci1mrhRwrhGzXAk517D/tg3VY9967DgZRVEPGXe6xTftt4SRy7c2Owhl3fQF+z4QHJHLc5obN49Bp+xDQp3iSdp1YKvg+cfmY7pCw6OE3oezfbFM4ZCN+QjYnvx8OdQszy0CGxNfc/4csDb5M9paU5TTyIxHJskMezFFOklSkMcoLRqUkhxgPQ7FaFIsDFvoJ8XRAI0hyYsNlL4qvpj0hH2ZYzXMi5gwIHVEpzv7DGDjzdbXkCuNEYy5WlT8e5wVpq3FvqEipXYETX2TdorfPwVST87DQrkUUyQI7vuhLcOZgYqQDfT8gLYww2CrmxYr9qTZRPnSIeXASouOO4I1YJQi9mFcsiv2bXAFYygFmu0xCnSlQNPP8sKMR27gwnyJ9Vaf3H4cStntX5gvjRyRc3Nlrm10d4mjeArOzZVRSlGLfcqhR5LlaGU/N/A0tdgnSTO2ugn3tjYYA1vdhF6S4Xua5WrIxj3OhMJmApRS/MBHzqGU4me/ep3NTkKjFPB9L5zj0y+fta/1PKqRT5blxIEeyLJqlOfZsielqJdCbm7192yjXgrxPc2Tp6q8fadJu58RBx7dJKMcejx5qjqxnOm44ZyEI8imnG0U38xC4djZ6nVJ8uIL03aBoQbQFLzYD7YNyg+B2W9IDnk4l/R8HEa0Uo08Bm8yrQd/64jLZ+CX3578fFgDViY/r8ISZwW1nPPzNYo8jbowA6Hs55wTzrFzjXLhNsRhbIEmE1wJSd60m2bihN+WIBeSm+LLl9KajjDONRWimIERIg65oSvI+bVbxdcRoQdwKiaVOUxLWcOqMAytJWjVS5N1Pd8TFXdudouPia3+7Lftx87MwZsbM3/OcacSF+9LnwxlZguGpcI5mKSTdf2H5FjjctzrFPYJSdY5ye2Mg3FnuweUAxvJH8cwwg+Tr1uZsQ3DceCNdSLiwCMOfV65NM+NzS7NXjpyEqqRfdz37LTjWsmnFHqkmRk5Ab6nqA0yDIuVED1Ya5JZaVatFIsVa+B3+uP3aaef4WmFpxXzlZDAa5PlZrRvh4972k5M/n2vnOfTL5+ln+aEvh4NOEuznKVKSCn0CT1FmhnKkaKfGZYqIbmxnzVXDvaUNmllH/c9zSeuLLKy3eON29s0uymn6xFPnarxiSuLE3+L44ZzEuAzh73BxWpxZO7KKQojqfPlCj2h2SqsMos9icozlvahTthhkfoEpcvFVj+jIcgvSpRmPCYAjCTPKPQLnKmHdFKhyVWIbFcrFYryLn5cFqPKi/M14NbE55caVWB14vMZk1PNQyT50jTNqQl9JpLoTyAMfNBZn1JYfL1pVIqf12Fx2LlvYGtSR+WA1a4wt0LqeJ8CybGTaGeGjmDwbQrJvI5QA59miqqQjXhqsfgkuyhkK6ZhuTab7OdJIRImZLeTHKG9T6QupPWVVvhCwKAW+US+ojvGEYh8RSkK6PWKs4aBMsSRR3/MuRhHHla4c/w6DGokyTlJoSjLc5RSnKpFrDT3Rs9P1SIC3+NTL57jnTstXru2SS/JiAKPZ8/W+dSLNnKf5YbFSkQt8gl9my0IPE0/zVisDBqP+ym9NKeXmsFEZAPktPupNeYnGNhaWQchN3BlscJbt1tkxpBmdsCapxRXFivk5m50WCmF7+ldRrun1SAL0KLdT4kCj16SUQ79URYgyw1Xlqp2G7mVTA20Lem6slQlN/D8eVtI/OvvrrHVSaiXAj5+eWH0+EnAOQnw48BnD3ODzbz4orK0OA+sT3z+QxcX6PUzioyYZ5bgX9yevI2zgtU6V/Fp9SoUeiv7QISsZvMwIDX8Sk6CylIiQWpPorQPLSZLlYCiMp35+QZFhVVGB6i8eCH9RKjPFaLvVd+QCSU2UoRRcuQ9z0P7xeex1BDpa8iF7yI93xRkPzcTnwu14tfUhDkKdWFfVQJNVSihkQzbZUHdZRrmKrMZvp6CC43iz7i8VHzhlOR9S6HCE5qfo3Jxpq1Umb0nQeivfmgwwvljjc/ZisNjIRjgKzsQrIjA8/jw6SpfubY3u/nh01XCwOdOszgbt9VL6E5wUrtJTiqoMA2lT3f2++7Mbhhj5UHPz5V443aTNDNWUQmbBTg/V7IKSFoxXw6ZLwe0eopK5DNfDlGD8pq7Brgtwwk9W8ZUDiOePFVFK3h7pU0yWI8erCHJct5eadNPcyapvWptMw9RoHnydI23V1q0+xmRr+mluS31OV3D0wpjDK9f2xprwCul+PiVRe5s93jzTpPtbsLpRsyTy1U+PsgCeBqeOlXlnTstWv10tI1K6PPUwJFQSvHChQbPn6+T5Wb02EnCOQmy/bbvNMLiTT55ukqRk3B6YY6uUOPYmC92NFK/RFEpUU7AUuXg7yQBj4aT4E/KAw84U4c3C5oS5hoLNNuztRu296EOeXmuTpFzenGpuPsiDn2MKT7+/Qna0kMmRZGGpDogVMWf0TdC47IwuS7wtFiHHwlOQmqUmGFCiEAuCIL3Z+vBSD5x4muEOQrzgl6m7/vUBDWpxfni1n0jRHynoVaezUnIc0NF0J8+PV9swC8I2tOh748aJSe/pvBpAmlQ4BT0hb6Hh4W6kH1tlAM8SQ9ZYCsv/j07mSGdJDs0IDGGxxYqvH59e0+d/mML9pgqCc5IZhTJhAqDJLONz5PERxU2mg5Qjf1Rj8TQSVCDx0Nfk8Mgmm/QGvLcOgm2ZMrwhXfX2ewmPHu2zql6zO2tLpvdhC+8u84L5xsopfjY5Xl+9c0V3r7TpJvYmv8XL8zxscvz9NOcrU4yKOm5u4Ash61OMpjlsKO8h7tGXJoZ22ys1J5Sn7l7Sn2++sEmP/Pa9dHz1djnzpbtXxka9jA5CzDOkZiv7HYkRvtXqYlTpo87zkk4AnLhxu8JN8wg8LizIZjWwqj5+bIwOTTyaJuDH5TwaNyqBo2CBTb+kmAjnWto3svKFLc3F/OhBvzzaw/8dgCq1QpFToIk6VkOFIIsPwu1EkUO7vJ8jaIMVynQ1AW9+gXp+I+FLIGGWFC6CYQaeZNn5II0oqSEs1wvjirPVSvihN8NIRbgCZHWauhPnF46xAhGkj/zqEDEwXQSnu/RFbJYRnBQQ6FZHe3hCQ6sZLRWpaa1KciPw/j2Q6BWK3baylE0Ufd/Ws4K15J65NMSyvE8DN+6udtBADsl+Fs3t0mznJLg7DdCVdj8XPJUYQR+WKf/ymNz3Nm+ST+7a3wHHrzy2Bye5+EphTFgTE6SDmYXGPt4khk2231ubnapxwHXN7oEnmarm7DZ7pPlBt9TvHW7xQfrHTpJTpLlGOCD9Q5v3W7xzJnaqPRpKNg0bFLOcoNBUYuDUcnTcI0KqMUBaW4N2yIj3xjD599Z5Tevro+aim9t9djupizVolE2QcoCPAzlRBLOSTgC1jrFF4wN6YKiNB3pxu5HFFmlS/UKRY2bYeCz3jr4GP954J0D38rRU65R6CRkgj+23vO4vDibcTC3sEiRgT8NS0LUGcEA2u6DFl5TF4ZJnRLW4CtoCmqYwnwxakIEUvu+qE4hyZsqL6AhOCNVITqeCI2yWW7oClJnSbd4CFkmGJSJUSghIauFEpv9EA0PtSZQIPR6T6Tia3oTps0OkeZuVYTsUtnXeEKXdijsi0zPnnWpSPJ4DwnzQmYo8DSCf1uIApQfUI08mmOaeauRh1Ge2EPYSTKuro9vGLu63ibPczKjdkXNd6KBvnCedgYSpeMwxtBPc0qRx+NLFXxtS4CG+Frz+FLFlulg91ugFVHo2dJnNXxc0eqn3Njs8u1b26MsRC0OaPVT2y+Q5/zc125ye7uLxtAo+XT7Gbe3u/zc127yqZfOEO84j3auOB7MSViuhby/1ibNzWgbvlYs18JRA/bQyH/uXG1sY/Kbt5u8v9YmCjzW230CT7Pa7PHm7ebImRl+zqQsQNE2Hhack3AEnBI03BcrxTeBNM+oCpHSqjDyWwmNgloratHBR5uWeTSchL4QPRck9yHvU64vAe8/8BqWpC7YKegIk4xzoZSo5NuSoyIWhfKWtiq+8W/1c5aEr9oTdPm3haJtX2vR+K4KJTinahGxEHmeKwsTgAO/UBUlN3aAXSFCNiNPi/N9gTKkwo0xUsXHRTzjQCuwdd+Br0mE5uFJbPYy8bopXHbxhH2tPE+sSU4EJ1oJuvzTsCwYzw8L0RTZpcnxdxkFmNwwXwrGOgnzJat+Ix3fntaF8weSNKMU+pQnOCPlyBMHAUZCVlIryPOcf/32GslA6nR4bUky+/j/KctZqISUAk0U2KbjUiWkl2QsDFR/3rrdYrubkGR3DfjtbsJbt1v2+6Q519bbNHspnlJ0O1ZMOktTrq236fQzapGPp3Y7RBrb4J0bOFOPCXxFntzdRuArztTj0flV1HOgFay1+iS5wctyTtVi7mx3SXLDWqu/e+KzMRMzCVJfw8OAcxKOgIYQHbTazpPJjGJOSKM2hGisEpqYPKVR+yGsL5AXy1g/NCzUKZQGPT2v4Prkm1W1FIs62RLVfWh4lMrYpNR9GARoIZIaz9gEe6YeYURl42JDUrq8a6VIBYeoKkRr56plklSYYSDJk5IXlhjEgSYTaqalEpq2oEbVTnNKgnHcEqZTJxMUVe6HyNcTp6xOg+/drcuehCR7u9OwGvdcHPh0BJWasle8L6IZa+gBzD6oSZ0EcukepjVmhmOvFOrB9N3xz6uBvGetFOGp8ZkoT9kBX5OGa+UG0jzHoFgoj3dGFsoBOZrQg3GKx6FnBR9qccBae+81pRYHaK3pJRnvrbVJc9sPMDyW0xz7eJaPmo5bvZQ4COkmGadqtuk4z3OurrXIckOgIfA9kjQjyw1X11okaUbgKZq9lCyHbKC3NFxRs5cSeHZOQhz6xL49J9Msp5saarFPlhtiXxNqjfEZGfCh1sS+HmUBXr+2xc+8dp037zT39Bw8c7bGYiUk8DRaw62tDr6nCDzN4sDZUVM4AEXbeOHCwzHW/OHKi5wQpEuSVMsce4qyEPGKBENLsE1Is4xYiITuBwuPRkCLC/PF+/L0fLHCTK1SojRjzbXvz25chIKiz5agI2+UVfIoQipvKQl9Dwu1qji8KxSyGWXh2A88LU4yDsOQ5fL483C5rMmMYluYLyCVHm4KGY9mty/u73khaLEgTI6OPS06GnXhUlKdUb4UbE11NoOTMBcHGKFfLBfGpHpKMWmMh6/A9zQtIdPRk3T79+E8rh9Clvg4IB13tSggkkrhCpgv26FZnWT8edpJMowxdg5AwXHhab1nwvAQb8c187HFMrGvCLR9X6Ah9hWPLZbxtCKe4KzHvkfo6cIJwXYWAaMeATPIzBlzN5vgacXHLy9wcb5MP8u5udmln+VcnC/z8csLA4dm8JlRYIeiDa6laW4N+tzYRumhrlQ++NtmBGxfxIfP1FkczDMA6wQsVkI+fKZO6GvWOym+r1koBzxzpsZCOcAfPG57JAy//u4ab95pUo18PvLYPNXI5807TX793TW0gieWKyyWQ5LU9jkkqWGxHPLEsi2rGjoAv/bWCp9/e5Vfe2uFn3ntOq9fs32B0jakPqyTgnMSjgBJ3SUQ0oJh6BMINwo/KpayEcuNgoBlQTpxP1iYO/BNHAvCUnHmp1YtbnRarMe0ZqwyKO1DYkgLkoKSok/s+0gDZz1VfHHtCfvB1oZKqiZCNk9wIhSGOJz8XQJtJyq/+NgipXteVAo0Lz62SOhrcaLygjBHQfKGWv1UjI43yhHVYPznVANFXZAW9TxrgBRNYvUHZVGTnldazXwzavVThEtrIdt9q+k+6SfxtY24Fn2PzJiJfQuZgU4voSEEeOYEydnGPpzH/j44ZSeB7f7k3I/Cqv48aA25p2CpGpFmOf0JGqf91PYBJGk2sfchza1zOTehAXqubKPqvqd5+kyD0/WY+UrIUjVkvhJyuh7z9JkGYeBzdq60p71HKzg7V8KgaE8YrNju22i/1pqlamQNbaDTtw6DHnxXrfUozaqUAsXdshply7vONWICX9NNUra7Kd0kJfA15xoxUeBhjMHT9jMV1tlRg20ML1WfevkcH700T6Mc4mlNoxzy0UvzfOrlcxjUKAvg+5qVVh/f17uyAFlu2OokNLspZxslQl9ztlGi2U1HykmL1RjfUyS5odlNSAYZiMVBKd7QAaiEHi9dnKMSerscAGkbmRBQOCm4cqMjQDK0Jg08GZJPoXRwXpgQ8+Sp4tKTxZIvykDuB0vzPrz78It2h8KNaL5SfCrWyiVUe7a6rEBwHKehIjT0nmoUbyP0NV3BkJPmD0QD43xcQDbQ9gZRCoPCJr9GKcLXjL1x+xoSoU6/lRjizKa9k/7eD4l9myn4vS+e4/21DlfXmqSZlcK9OF/m9754Dq01vlQ6IgyFk7JL9SgY6Z9PIs1y6qWA5hhZzHopwAg3O2MMBo2eUE6hlR0cV4QGwgnDpKZHMUvwTpvcGkEF9VvlKCwsJ1JKT8wU50AvzcT6dC0cE9tC+dhUPCQGjESSFf6cpHlO4HkTrxX3slQJ8LQmy++Wv+QGSqHHZjcdHAOMou+l0CM3A3WvggMnB1463+BffXt1l1K2N3jcYOvhP/XSOdZbfV67tkG7l1GLPF48P8enXjpH4Hu8+tQSK80+292E3Bi0skpArz61hFZ3e630jnXm2MfzPCcMfF59conV4WcMXluLA159cglPq10Sp6frMbfukTj9gx+9yFqrz7WNzmhQ2vm5En/woxfRWqNyQz0OBvKmdqf4SoGyjyuleOF8g8988gqff2eF9VbCfCXgE1eWRqpBYwed1e4OOgN77arGPjc2O5xtlLix2aEa+9RLAVrBasv2IPhaE8QKY6zDsNrqkmb5XZWmUsDNrR6eVmx17qo0eVoVbkMStjgpOCfhCCgJNwnJSWglGUpIkfYEA18FxdFB5fmUDqHxZrlSokhl6WEhEspXckFuJ88yIkleUeDeiPYDfYawBkly0Pd9KkKm7Ew1LDTwF8qhbZobU5sbBR6VOCA3UI29sZOAq7E30Pb3WR8jE1aLfLEcoxZ6ttFvwrmaY1P3TyxVuDBfYr3dp5tYqb0L8yWeGAzl8rQqNDq10FQZh37hvirFEW1hpkqzn0wsW9rspjSF1E2a22mmRcYY2g4fGucreNrWS59vxLy1WjwwqoiyrycbYtOgtdVgn/D+1EC72y10ArTUuB94U/RNmIkDuHwFsZBpm4oZSmxOEvVAKA/DEHgK31cTswE7sWVF2Uix58NnalTigMcWSqw0ewOHwBrgdsZBiSjw6Bq7nWzMNgJPEXgetVJA4O9+TeArajsMzufP13n1qWUrKdpJaJQCXn1qeVQn/6mX7k47Hl5vXjzf4FMv2Qj8UPknB5S5e6qEvrZlQErx6ZfPs9FOeO3aBq1eSiXyefH8HJ9++Ty5YRQ9f+pUbRQ9/9LV9VH0/Ac+Yicr/+xXr4/W+H0vnOPTL58FbMnd5aUyb9zeppvkgzIjG3C5vFS+J/OpBnNxds8c2DmfYKuzd9AZwMcvL3Bnq8ubd5p86eo61di3rxmURb11u8Vaq0/kKxvA8RVrrf6owbrdz9juJqw0e2ilyI0h8jXtfjZqYi7ahmtcdjww/iA1P8kwkBQZfG3HixehJS9WiJSame6206P8R8NJaFSKI+zbgm5jM8mtETQDkVhCI//ioeDgzguSnqXAIzOKQNuozb0EWtHJreTcOLUP31N0k3yiTOTwcYWZOHRNK1sRO6kMx/c0oRDNlfYD2Ojhb1zdQGvFJy7PsdwocWezQzeD37i6wYsX5wa63xPeD3iejdCPC/xqBaUgoFHyWB8jq9wo2TrjwPMKP8PXalcGYKfTkRnESa1ZlpF6vv3NxhhBvqeIPJ+Sr9kek3Up+ZpGOeK58w2ubXYfKJsQe7b5UZIoLSLyPLppXthgertd3COy2i52yNIsF40HX2uqsc/GGAe2GvuitO40zAnn6cPCdl58nnYyA6mh5Gv6UqMe1gFIc0PgaWpxwLNnG3iexyefXOaNWy3WBz1ZxkAjDvjkk8tobZWAGuWA7tbe46NRDgh9za2tPkopYp/R+aqU4tZWH2MMSim+dn2bb93csob1oCToWze3+Nr1Ki9caKC4O+243dOUI89OO0YRBR5L1Yjb2wNnZrD9YSnR0O544cIwir/KZjuhUQ74xJXFURRfip4rpfh9r5zn0y+fnSgLuliNqccBWqeY3KC0ohr6o1KfcQ3BK9u90fqeO1fjzdt13l5poZR1JD58ps5z5+4OO5TmJKy2+rbPQmlO1QfqRlnO6i51o4FS0mhv7T53p52TUKSQdNxxTsIRkOZWbWBMIJTQQywlqschYeAX3viXq3GhI3K2UZxJaJRCbm8/eFRvWnwlaIM+JCw0JO1/qZZfy1rzApMk9oZM4yTUSsXlFh0hZ99LDaGviHwYN/Q18iEaXUj3bkUNVIUmldCkWU5nUHfrT3AkfK3oDm5e4wh9TZJPdpOHtczBIFo4Dk/Z12y0erxzp0VmDP33twh96+APBwZlWbFhEihFI57gBMQeWutB/8Xe50Pfp5/mRIFH6I0v5Qk9RTUKqUU+3cEPsnPP1iJfLH/JUJRCj8Abb2gFnqYcWSNiu79X2adesiUGy7WYShSQ5X3SfPe+l47N4fVwFpSyhoqnFdmYc8XTinPVoPC4kJzHTpqJjbIKxblGzEZn72CVcw1rXM1KGB38oMzjwGlh0FkjDgY9NdMdPJcWSpSjgG6SUYl81tp98jynHGgWKiFZnpMbew9eqISUAz0ox1MsVUJujXESliohvdRgsI7AQiUcZSPW2wkGMziP1a5G2adO1bix2RnVyT93rsYX3rtbCnSmUeLmZseWAr23zvPn6zxxqsLbK016owi+FTl54lRlZLxK2v87o+e/eXWNWhyMjZ5rrYnHaAZnuaEaeSxWI66UqqSZzURudRKqkUea5YXf8/nz9ZGzlOU5GEOW57ucpZ3fY9wwtCw3LFRCAm3Vk25v26FvweDxJDNUIo9a7HOhVCLNDb5WbHYSKpE3UlCSBq49DBKpzkk4AsqRTzUO6LX2GsjVOGBeaBSMBk5EoMc7GlZ6LKARe2PVURqxRxRGhTc7O1lxii8zI+vtR8NJKAnlRvV6sbpR6GlxvoDEJHWNaQmUVTcqqj2XpHXJU5SKBpmsMQev0nieRynQ9MbUppQCTcn3GBOQBqCfQ+RbhYxyFKBayR5jsxwFlEOfSuiN4kLDGybYgViR7xFoxm4n0NaR8D1VWG4UB5qra21ubHX23JSvrrXxtCItLlUmQfHUqSq//t7mnuefOlVFa00l8tBN+yGjwIGCSmQzCUlmCLSiO2YrgVagNN/9xCL/69du0Ut3rNO3jy9Xiw3KuVKIQVEJNK0xF6RKoOkm+ajHeuceG0ZE272UVj8ly/PBwCUzUj1RMIruT3IU0izHzJBG8PUge6qssXdza68zs1AJieOYONB0xjTExIHmlBCcqce2sbIoW+H5HufnS3z7dnNXeZav4fx8CaU9Yh8EYauJzMU+cw/JMLWaD9sT9oPG9nc0Yn9sOV0j9lHazq1QQmZ9yCefWCDwA7I857VrW2x3U/ppzlsrbdr9lPmBgRl4inY/5a2VNlluUBiurY8Pul1b7xD5ikYpJPQ0252EeHDOhJ62jw+kPe+W+lTxPcXZRsyXrm6w1Unop/meUqBzc+VRKVA/zXlsvszZeoksN/SzjNCz14jH5ssjw1cybHdG8Y1hbBS/CFvLH3KmEVON/FFfQzn0qJdsOWtRSdNOJ6IWB3zodH2PE7HTAB83DM3TaiTlOpy4vFPKNfT12DWWBmu8t99g0sC1h0Ei1TkJR4BSamLkIhx4p+EEA2XomOcGqsH4CaHVQKOUohyOdxLKoUeeF+urJ2lG5RAkULV+OG5WEqmgd68F+yY3sw+cksoUxMY9bTNdRU5CV5CQ3E5ywtBMlJIcPl6LvLHlFrXIlitNagrztCLNbebl8aUyNzbao6i0whpajy+VCXybhg+8NllmRk18nmfT9WHgc7oe8f7GXmPxdD3C8+wgoWDCDxdoG/17606LXpKPMn4G6CX2cWMMtTgYrHl85LoRBxjYU6PuD0oNosDjsYUyH6x3SDIzcjoCrXhswX5Pk6STJxpruy8/++oVtrvpnlrmz756BbTPXOyzMcbQmot9jLKKJZOGUtnSL5vZ8HWf0INK5NPqpfQz+3jgKd5daQ8cQ0UUQGdHCZ4ac9DtNMYNkDDZ+JZolIJRL8tTp6qstfqkO4ZB+Z511tIspzTBSSgF1sFdLPusjNGiXyz7RGGAMYZK6LE1zqEKPcqhz2Yn3eN+KmBzIPFYDj26U5THjONUPaI3U4P48WGCWA9gD/l+llMONZtjNB/KoY3yd/sZLWEw4pCf/uot9GC68GIlpBp5BJ66O5grN5yqRaw0e7sGc3V66dj5BgDNXkY/zXnubJ1ff2eVZj9jq5ehFdRjxXNn67bh1xjqsY8x8Lk3Vu427IY+9dgfGLaTS4FC36oELVZDmt2E+SCgl2RU42CgImSPOMmwff36Fp974w7vrNhZCevtPp974w5PnKrw4oU5cR/eW8t/8/2NXbX8vlf8PUDui5g0IXnnGj5xZZGV7R5v3N6m2U05XY946lSNT1xZRGtduMZpsgD3SqSOy4ichGyCcxKOgF6SsTkhgr7ZTugkGdXYHzv0pDpQU4gCD8/3gb3pS8+3qfe1CdtYayd0s6yw4TEzhrLQpLpcgjszViQ9fqoCrM/2ISeArjCQSlL88T1NX1CpKSJQtnRkFpIMVlr9Qa3/XiNDK0UoFCzVQ5uqLbBZrcGp9/btKEBpja9tNL41xkKIgoHKTm54/lydN283afWzkcpGJfR4/lydJDNcXirzrZvbdFU2urHEvsflpTJaaxar8VgnwUrnWQMjDnwYUwoUBz5pnrPVTazzoXaoiRjY6ib0kgzf0yxVQm5u793OUiUkM4qtbmYbDbnr7OTAVteu+4nlCq9f27QKJQzVSHyeWLbN0bbO2WN7TOYm9m0k9cULc/xH3/vhsWoixhheudTgl95Y3RPZfuVSg9C3w4562fgftZcpAt/jhfMNbm116SYZnSRBK4gD+7jWNnM0/B200njKNhEbGERj7w6n4p5joxr5VMOAcqjZ7t3/eXJpsYLWGg18+Eydq2ttumk20HM3xL7Hh8/YOuOioVf9NOfyUoWVq3szP5eXKqPG1lIw3kkoBR5JZtjsJHuavNMcNodG0Ay2xe3tnthnclLoFVxuPE/ZMk01/lqiB9mDYb34NNxp9kelQL0kp92348AWKiEYaPVSNjoJgVZ4Wo+mEKdZXtjwnqQZm90ETyt8re5m0LRis5uMehIWKhG9NOPGVpdekhMFmgtzJRYq0S7D9o3b29zYsIb1U6dqfPzyAlprFsohG+2ED9bbo2vihfkyC+UQpZRo2D53rsY//fJ1fuO9dXpphlJWGek33ltnvhLywvnGVIZvUS2/1BAsORHTqgpJ/QTT9htMYnfm58GcmeOAcxKOYFaEyTO6E8Jd3cwQKEM1Dlhvp3subNXYRgTyPKc5IYzS7Ge0+/3CMe8M1BZ6Y14zHCOvlKLkjbWBANu3cKdT3KgnMT8/B3ww02ecBEoTdOiHSOl/af6ARDnyiMOQeFaZSZPZpvgxx43WijgKC5VZPN9exBvlkFa/u+f4bpRt6Up1oB4Ed9VCAKqhzSQsVyM22sme55cHWt6eVsxXIs7Pldlqd0dRt3o5Zr4SEfqaSujjeZq8b8+zPDd4nn1cYbixNV5y9sZWl3yg6LNYjbg1aAQc7QcFi9WIyPdGjclD431YQjOsXwV44lSZlVZvjwH+xKmy1VjPzCgLMppgamzPQ5rlVKKAM40ST57yRvW9zV42qO+3xsVcyedOc++5OlfyB+UWk2trlVK8fHGRr17bZq3VH9Vcz5dDXr5oo24mtY7YOIaP/9YPLfGlq+vc3Opi8rsDkn7rh5ZsP0DkEfoekW8HkiVZTpra7x77atQLkprdwY3QU3znlQUqpYgnT9X40vt7DfR78dQOFRqtKPmKNMsJfG8kM/nV65sDdZeAF841RjKTk4wgpRSBp9jqpqOMxvA39xRsdVMUtn66aPhWlmVsdfp73G0DbA2ut5Mcsmlo9rKxmauHjYVySI62ss1jDolKbI/9wPdsuYmQ7YVBQm6w69v9lF97a4U/8288QTn0SPKc7W46Oq5qsT8aUmbnb0wQYxhk89683STL4dJCmVocsN1NuNPs8+bt5ui8Xm31CH3NmXo8KpEJffu4MWZXKRDKOirDUiBjDK9f32St1RsNHcyMYa3V4/Xrm/y+V86LJU29JOPbt7bZ7CQsV0Ma5ZDNdp87zT7fvrU9OoeguFlX6nu4HyeiqC+iCKmfQHpe4mGRSHVOAnzmsDfYy0xh03E3h3ONEtfWO7tVR5R93KDY7vYLmzfbwhCZ2LcH6jjDoV4KCHxrcJRCj84EL6GdGgJsmv9BqTwiKhuxkJVplItrmUuhbe6cpO0vUYnsMJ7TtYj31vcav+eqHjebmVhyVIlCGqWATrI38t0oBcSBTyXy2BxT5lYZpOY9z+Ojlxb439q36CbZyOiMA/t4JQ6olwL8QRnO8LrsDy665ahYy9v3bLndfDlkvd3n2kbvrl432qp9KMVaO7GNtkqNzpV+mrHWTmj30onTo7c69vlSFHC2EfOtm9u7ZC09ZW+sWttt3dzskg8MW7PDwB6uc7lWohxu0+mno31RCn2WayUC36NRCvA9NZBLNfi+1Rq3j9uoWjn0aPYyIt86CLa+196IjIFJSagsZ9fNalxtrTGGUqg5VYtGN0pjDIuVkNKgZGNYdz0OhXVm1tsJZxoxtUFpRD/NqUQ+620bQf3Q6SrfuLHFVjcly8wo7xF6tl47yXKSTkLg254Fu3Y4U4/5nqdPoZTiwlzM165vFpahKHb4uAaUMbR3lA8VqbvkeV5YKmcnzg6nyu7I/AweTzJbajfJSE9zQ2ZM4eAtk2eiLG0xg3TGQ86HTlUohR6Nkr+nRFIraJT8wbGvOD83vlEcdswUwGZjh8b5Vjfl+maXXpLx5i1bsjLKchlodlPevGWV+zzP40wj5ura3tT7mUaMd08z+zh1wiw3bHdTtFK8+uQSgadJspwvv7/Bdjclyw3fuGEbeu31yJAbM2roffpM1Rr73RTfUwSeJjeGrW7Kl65ujByRopKmXYbtqMlo7/VCataVXiMZ6LucIcarG03LpH6CaZ8vet/DIJHqnAT4ceCzh7nBWhxQjQI2xhgh1SigEdtx5qXQI83MKCrle4pa7A+kD73Cso9GHFKbIKNXi30b/ZtwozDGjByQSQoxAK1+xlxZc6f94GUwoSD3+rAQSRNOtXUAxg0I8/XwQqUph+O1/4tQ2GPOlqn5Y1PvpShkziSsFYx1VtgymscWStzZ7u266Q41wQ2KxWrEVre9ZxuL1Yg0t3rUn331MtvdhNeubdJL7KRbWwN/Ga01ZxolQn8bk2YjQyv0Pc40SmitC7W8h0bs125sstVNBvXwtuRgq5vwtRub/JvpGVabvbvlLVgjPssNq80emLzQkPMHE0O3B5/va0YW4fBxgOfO1nh31fYlZMYqZESB5rmztVED61I1pBJ6AxUU+10rocdS1ToS33FpjpubHZo9m/HwsLKJ33FpjsD3WKxE9NOcm/eUICxWIpRSJGnG1gSjcquXkmb5aNhfnud7IntZbmj2MmpxwCefWBwNk3rt2hbN3rBUS1MKfZr9vUGHUmgjtludhHY/o1EKSfOcUuCz1U1GE1DrpZAsHxjZO96fG8N2NxlNM4615tJSicj36GcZ1ciWUCRpxkY3I/I9yoGh2ctHGS0FRB50xwzXSnPYaPVHBlCRgdJLMpIJilRJlpHn+cghG/6Ww7+z3GZpU6yBNu5kDzyrfhVMMEoCT7HVz2aahRZ4eioJ3+NOgJU+mHT3efHCHCjNcBb4zt8CQKNG+7ES7b0uAlRDzUcvzfG1G03W28no2EwHGTpf26z+129s7UmuZga+fmOLJM0IfI/vfeY0P/XFD+x5PMg2VCOf733mNHHo86HTNd5bbbPdS2n27WsapYAPna6N5JqHkembW13ONkrc3OruGhBWVCr0oVNlNgfnmqcU5cin3UvpG1veNk1JU+B7g3W22O4mtHopuTE0Sv5ondM0607b0DvJQJ9G3eg4MGvJ0nHg5F8pZufBLdwHxPd9vu+FM/xPX/xgV7lP5Cm+74UzaM9nsRpRi2zEbaiW0E9zFqsRubFSe0vVkA829kaFl6ohlVLEq08s8PNfv72njOHVJ6wXW1QeMEwbnmmUuN2aMMfAwJakeSlwr37ySWVSFmBI2S+ejeFpVVinn+WG3OS2lvk+nAT72fZmowclHFpZ49T3bC15ZuwN7dkzdT731trEz4p9yIziypKddpnmZhR98rV93Pc0y9WI99c6u4xsX9sSoaHsaFENfJYbHlsocaoWkRtDP7MqH1opHluwyhySlnea5bxxq0k3yTlTj6mXArY6Nn3/xi2bvm/2MjJj7NhBpcHYfdHsZaC0naY8xiCMfQ3Kqo1Y6ULrhAwH7qSZGTWGVgcBAUwfY9TAMAioDhqWs9xQDu35fmV5txxgObT9R5966TzrrWRH+Ys/KH85D8Baq0fke5ytx6MIfeR7rA1KENIspzuhvKWbZINt5vwvX77Bz71+g412n7lyyO95/iyffvksnlaDybKGX317bRRJLYdWItAeu5rT9ZjVQTnSzmP3dD0mCrzC4UQKw+vXtzAYe93zYK2d2jkNOYQo8txeawzWSUozO9F2q5uy1U1GfQsMBtmpYRPIgKLg+fpANUWajA7FPQk5VuVopHO/Yz/EgR1YpbXmdCNmq9scfZ/h604Posq1OEBv7S1jq8UBtSmGIpZ820sxTgFpsWJVdE46pxoha62EzoTyyVJgz+mNToJSCg+D70GaAUrZxwdX5JtbvbHX5rlywI/+ke/gh//x1/gX37hNL83opn0bWAk8XhwYpEWlv0maEQb+KLDx1WsbNHsp1cjnhUFgQ2vNp16+p8wtHpznL5+bekBYUQ18PnA6vMFU+lYvtSWWg/vDMLhSVNIE7CnHq8d3y/Gg2FHZaTQ/aEPvzr6JadSNjpJZS5aOA85JOCL+6qeeRaH4ha/fpJNklAKP3/XsGf7Kp56ZKM9VDr3R2PEsNzx9psbNze4e1ZOnz9QwKL7n6dP8xnub3NrujW5ES5WI73n6tFhb63sa39O88tgcX72+PfYCGvuKzb1VJyMiVdxYFjBFhP2EIAX2+txtXL0Xpaw2uu959MYYpf5Apk4pRSXy8Zp9PKUwuZmq1MuqZPgYFHPlkOsbHbS2ZSCep1A5zJVDXrrQ4M1bW9xsjo86R2FAHGieOl3jnZUWzW4yOjarccBTg0jS2bkS8Y0tK6c5iJhFvn38Xi3ucRdPT0OjHHFluUIl9DhVj7m91aXVz2iU75a83M/F997nPK2IRoY9KGXIjTUuI9/q+i9WArbHNJcuVgKiwBvpfYe+R+xrAl+TpDldlVMdTGxWShH5ChP5Owzjnd/V9mecacRUQm+kbV4OvZHiSJFDlOWGrW5Cq59SLwWkWU5t4BANDWdPq13DF3c6q56yZUz/5MvX+ZFffItrG51Rada7Ky0Mhh985YKYrRg2in+w3qabZKPtxoFtFL9r7A7KDe4ZTjSUb8xyeGK5YiPut5usd1J7rA5KsLr9bFSqYBvc7WyDZs9moxYqIUrZpu70HgO7qEyvvcOYKyqFmDRbY0jo2cFM76+37Xa5G4U6VbfS1r6nefXJRVabvT017K8+uUgUeMyVbbndzjI2rRRz5QCjPHzPStuOY67kUw5sKUo/TXdFwTyskzCuF+2k8QMvneWnvnSdzvbeq6ACrq536Sb5KBvuaXuue3qQmR/IAxtjxkr3glXYyozij3/yMludhK/uUP964XyDP/7Jy6OG93HYUiW7fSmw8cJ5+/yvv7PKRidhrhTw8R3PgxyZLqqBjwKPVy7N26xk3w4x8zxFIwx45dI8vqenKmkSr0dCsy7Mpk50EhuCH7Rk6TjgnIQjwvd9/rMffIG/8qlnRlEF37/7cxTJc9kJg1YBphr7ZHlO7Gs7LVTrgTKM4Zs3tweRS29U66wUfPPmNj/w8u5a5J14g0YqpRSf+sgF/tlXb7Jyz0yH0FN8+FyD1TfXxjapAixWPK43i4uDyw9BRGvIpH4BXysqoV/YuDZsehxHMJDFDXyPFy/McWe7Z5VghPUMK2CMsWU2oa955dIcNzY7tAalKz5QKfm8cmmOhWrM0+fn2H5nfY9ykMI2AmbG1ll+4/oWr32wwUanSyX0uThfHkWzLs6XOFWLyYwZGZyeUlycL00tT7czYvbVDzYn1nJOuvj6ni5M32utubxUts2Cg+h/GFpj+vJS2RoTE4ZeDR/3PT1QwunQTVKMyfG0NcI+fKY+kiGuxQHnBzrknlaDLMFwIM9uqb2vTJDaK3KoWr2M7W7KSrM3ckQj36PVywY9CZozg8i1gV3m+ZnBUMWf/ML7vL/exhh7vGW54f31Nj/5hff59Etnd2UrhnXKO7MVnlY8ebrK+etlW641CEssViOePG1ngBQNJ/K0olEKiALNSrPHUjWy+0dbBaYPnarSTTJubfXY7qV31WgyAyYdbBMWq+FI4rmT5KMSEK0UkW9oTvCqdx5BRaUQTywNHd2957EaOJwfPl3j6mqLXpqPHLLI13x44EQrpfj0y+d5d6XNV95fHxmdL12wUWWD4spShbdutwbHps0ueco+HvqacqDZnFD2FPm2cT8zmsBXpNndwYC+Zx232NcECiYNeq940HowhdVDwVPwyScW+V++cnPia1ZafeJA22yXYtSsa9+vRr0xSZphzN0L985f1xibeR1lPt9eYb2dMF8O+MTjS6M+lXopoD2mT2tonIPcrDtN4EN6TVGmwZZpnmOj1R+UaWZUIm9QpmmzFZ4uLmmSAjQ731/UrDtLQ+/D0hB8UnBOwhHjeR7VWO85sKWIQW6sMVIKPEI/JM1yypGdeLpYCemnOd++1aTZS0fKCmluaPZSvn2rSXtQFzkOY2xkr+R5eEpxph6z2UlJBjdmX8NyLeKTjy/yrZtNbm2PVziar0SsttoTswmJgfaDdOEeU6yU5d59caYeUYkiGhMaxRulgMDzKAfjZwOUd9xkPvNdl/hgvc03bmyT58W1yUPZzMxYffXcwKdfOs9Gy9byt3sZ5cFN4gdevoBSilubHb76waadWjyor9dKEfoeTyxbA8VgWG/32egkdJOcJLP/NtgbxlzFZgHKgeZ0o8StzQ7tJGeuEo2Oc6lxbdZaTqVUYfre9zRPnqrx9p0W7b5t+O2l+SBbVyPNcluahXWsh+UjxtiSrV6SUYqCvduIdm9jmCXYuS92Zglg+rrV4miUPRCGkp07jVgbuZ7c6J3lhuubHfppTj2+tzGzY5u4uylKwatPLY3K1IZNkENnZ7ESEwyuM8M+k0ArFivxoMF6OJzIY6lqteSHw4kC3+P3PH+W21tdPtjo8NadllWoKocs1yICT1Mv+Wx2EjqJraceRjm7Sc5qs0cvyaiEPkvViCuLFT7YaLPRTuklKXNln/mSx1urvbG9JkOxhp2lDJXQ44nlKjd3lDI8vnhuV1ZmJ96g4XKvOpI/KscYGXTDXonBe3Y+bjPJk49NrTVz5ZDN7nj96XY/Q4UeUeChOukgSzYIEmEzV0rbbMWdMQM9AebKPu3tVMyQHhWZgZ99/dZEg1ArWCgHGNSov6k3pr9JKTt4sRaHI8nxnd+5Fod2RkGBYWxQvHi+wVrrDmluRpkhX9vHh0Vw007fnSbqPOk1oqznuTqvPrXMdi8dlRW++tQyz5+rjz532mbbcWuY9v2zNPQ+LA3BJwXnJBwRs3b3Ty5JikYlSdvdlH5myPIUrQeKIIZBOnG3KMHOIURDo8gYw29c3cBgo4DdxErnKaxiynKjzB945Tz/n3/1ztibSTuF8w2ftzcmN8Om+cPhJIQaPvn4PD/79Tv00nx0U458zXddmcfzPL7r8QV+/mu3dmUTQk/xXY8v2OFdjXigw333RqOU4nQjvlumoxX12Jb9GJOjMqvystsstAT6rmHbT3OMkWv54QK3m31+7a0VtroJGBsNv7hQ5vtesEbOT3/lxsiIq8YexsBbd1r89Fdu8OKFuamyAFLj2n7Uchal78cN05nbka2zfRYarZWd8hx6dPoZnSTH13edemkbH7s0z9evbfLV65t88erGyGD82KX5qUqvdjJOUtD2NAyyFaVgYraiqNF7+F3BGpj9LCfNhgEBfc+Qpu4gcrc7unhvLfPQsN1Zy/zRS3P8yht3+Nybq7ua1T96aW4QXT8LwM+9foPNTkI9tjXb5Uiz3c2ohB6bnYxWP2cu9kfyiyvZoOZ8T+lWzBfeXWPdGLqpIVceZ+oRt7d7tswE7k6WfnKRYCBZu9nuc3OzSz0OuLHZxdearW7CZruP72miwBtbgx4F1jCXzjFjDD/92g3eutPE8zQ13yM3hrfuNPnp127w4sW5wmNTKcXFhRK3tnqjnqLhua8Hxmk/zdGeJg40GE0UaHpJPrroB57i8mKZO63NsefOpaUKd9rFKlFHzVeubTFXsX15I1WhwXNxYEtzgV39Tb00H5UZDvubPK04U4+4Phi8OMzG+doGeCT1L08rPnZlgWsbHVZbd4NAi5WQj11ZmHpI2X4gZSt2NvwqGNvwO2uAZpr3H8Y2HPuDcxKOiFm7+8cZOafvMXLsxXDQQGfMLsM18G00b711V2t+eClZGmjND2+Y292UUugT+la7fqubgIKV7S7//m+9wn/3+ffZGnTIKQYOBnZAzNlKsZNQ9vXE6dIniY9cnOPZCwt8sJns0eV/+twcvqf5bR86xZff3+T6hpW29ZSVb/xtHzpF4Nt60Rub3VEpkMKqbgzrRY0x/NOvXOcrHwwGZw2cPoN1CHwN3fTujXLki6iBus8O53PSTeSFCw3+5r/1An/nX77Fz71+c1QK99ufPs2nXjpDmuWiTrZ0Ad/PSZTTaHFPMr6ldb5wocGtrQ7tfkZrICkcB5oXLjRGWuCigT/pa4x5fNK5XhRQmKanYfhdigzXxxZKvL/Wpp8ZeqO5DNaQCgNfjNylWc5216oX1WOfNLfTYbe6qVUmyg1v3mry5u0mWx2rEtNLc9683eTNW01eujiP1prf98p5Pv3y2V3H5vA31soOAltr9Wn2M1pJZ1cJWeB7u6ekbtjm+dDXBIPf5cnlCmcaMe+ttke9YC+cb/DZ774yKrdo9W3p1rurrdFvUI18Wv0Upexsh7XW7jkGCmsUDpVjis6xu+dQynLNZhg3Owl3tntTn0NPn2nw/lqXdq/PZjcbzdJYKAfEgUcnyQbSyd5g2J0hDj36qZWuTXN4fLnKl97f3FMuqrFKU6Hv0z/GQ9eavZTv+dAy19Y7bHYTsuxuBP/x5Qrf+YTNejVKIUvViGbPlgn10tyqCJbCUX/flaUKb620yPIdJZLalnflO+6N41BK8YnHF/nmjW2+8sH6yAl/+kydTzy+OGoIPozpu0XXCmCqht9ZAzT7UTa1H9tw7A/OSTgC9uuCUXQjyXLD5cUJNdeLdqLsdz+xwM3N7khaUWFvht/9xMJIcq0WB3ieotXPuDhfot1PCX2Fwk68jcKAxxbKfOPG1iiaM3RGGuWQzTGSiDtRnm387B/nkJVA5CuePVfjY1cWuLPd4807Tba7CUv1kjWkriwCsN7uU48D+rW7HlE9Dlhv2300qRTo0y9ZWc8kzfjcGytstPt7ImeegmfP1nnjdoutXjZy1uxzivny3YivlPL+xs0W3STn/Fw8cmDb/ZSv32jy9Jnq3S8+PEbv8wYwTeOZpylc57Sp++F6JjnaRev8rU8u8aX37PCvNB8O/4r4rU8uTbUNYwxfeHedzW7Cs2frnK7H3NrqstlN+MK761NPJ5UCCtP0NBSWSxjD40sVvnpt02q9Y42iauzz+JKd2iwZrZ5Whb0RCsP/+MUPuN3soTDEvpVRvd3s8T9+8QN+3yvnR4a01po43F2rPdy3kgLMcD2ff2eVX/r2Hfq54Vw95qnTVe4Myq1evljndz97mo12ylwl4DsHteVDVrZtYKSTZKOMnjH2cYBTtZCra3b9QzytOVULR/tz2mNzVPN5T+2ndGwO98NrH2zQ6nfIckMceAxFJ4IsZ7kWEXqaTj+7q3df83nyVJXQ1zy+XCEKNOmOCI3G9i1sdvoT514cB3wNc+WI73/pHBvthF99e43NgaT4fCXkdz5zmufP2f09lPS8td0bNd0HnmZh0HTvaQZiDG1avWS0ryqRFWOYqsZ92Pui9ejPzscPq9m26FrxzNnafa1h1mbbWcqm9nMbjtlwTsIRsF8XDKmB6MnTNd5eGVPXOmige+H8PL/y5hr99RZpZrd5uh7zwvm7pRCfuLLAv/zmbba727y/3qYa+0Sex0Iloj6IxCxWwl3D4ZSCKNBcXizxK2+2Ji2fSqiJ/LuTdU8yX/5gm//b91VRL56b6LS9eafFaqtHtEPWdrXV4807ralkPfM8Z6XZH2UhQs9OTzZYadKXH5tjpZXQSbsjidPAU3hKc3mpSm7gG9eLDc6dDmy9FPLhM41dDuxz52qiTvaQSRfwaRrPJMN4P1P3kwz89c7d4V/D86cS+azv0BQvYj/O82kCCveTeh/3XbPcTng/2yhRPeWNriXNnlWtGpYsTRe5G98b0Usyrm92SVLb5BkHmm5iFY2GA6lKkR5950nbkBRghtfEp89U2Wz3WftWnyQ3vHZta9Qo/fzgM4YlSju3kWY5V9ds0zEDBwEDvdQ+Dnbeh9aKJLsrXxpo26Q9jU681FQ/zTk0rC3f7CZ0k4ztQcbvdD2ml+acqkV2W1rz1iBocboRj4IWdtBftKenyWB7kJSymehx6l5HjafsvvqOS3O8/Ng87611ubnd48ZGh9wYztRLtPsZX7u+zfPn6+KUYqUUH7+yuCvAM1+5G+CRznNjDF94zwYDnjlTG2XzNrsJX3hvnRcuNA6l2Va6Vjx3ruYafh33jXMSjoD9vmBMaiAqqmsFG9meL4eEvr5rBIW+bUIdXDxfuNDgD3z0Ij/5G+9zc6tDnsNCJeLlx2zt+WvXtri52QE1lPK0JRnz5ZCL8+VR09Y4PnJxDt/zbenGMbwZTUuWm1EUa5IhpZVhtdWnk2S2hyT0aLVtM+hqqz/SdS8yxHJjByGBfW06yNhkA2PmKx9sEfqapUpIOfKYK4Wj3/SpU1Vx2M40Ena5KdbJniYyLjWeQfE6nztXO/DU/U4pwN/y1PJYKUDJwN+P83w6R2NaA37yOseVLFX72a6SJZhstEq9EXBXxStJbdAiSTP7eQM1tWki8NOWGfieppPkEzMbQ5WhezHGcHvbDtlTaiBhquz3u71tjUrU3Qndo94hu7ips8TTaOIXMawtN8ZO3laDGs9uknGmEfPUqRrf98JZlFJj96cxhm/c2LyrEjX8PoPfcqESMl8OWG0lxyKf4CvwfT2Y82Ib2v/NF+33W2v3qUQ+Lz02z7lGzI3NLm8N9vfTZ6qipKfvzSaUMO4cPTdX3nOOHnSz7TTXbtfw67hfnJNwBBxWd75UjjRULPktExVL7A34Bz5yjidOVfj826s0eyn1UsjHLy/w3Lka/9nPfIPbzT4KRaCNvanmBq1tiYs1avc6AB7wZ3/HE0SBRyXyWJ2ccDj2aKVGw2hgvCE1rGtNMjsdspNkdqjZQGN7Z93rJEMsCjwuLZRYaXbJcwMDw2fY2/DdTyzR7mest/psdpNBROxu9DA3sj71NIat3PwsIx6bBescauofZOp+536YJAUosR/n+f04Gg+aer93nZNKlmBylF/qjYhDnxcvNFhp9ugmGavNHkrZpvgXBz0ekyLwxhiePVfftc3pvquhn2a7enwGBVbT7ResUIDK7051SLOc9VZ/YKgGo+tmLzWst/r0kmyqY3MaTfyJ3+qeYVJPnarxzZtbXN/ssliN+O4nlnY1z49zqJI04zevboyVZDYYlmsx7662J+4pXzFR+vogiAM7t8cDUmP3oTGyYQyypCfMVuM+7Tk6uuY9wG++X+twDb+O+8U5CUfEYZysUjmSpFiy83NevDDHC+cbuz4nSTO+fbtJb2Dw5sY2IA8Nie+4vMhPfvH6nhkLYBvqPM8O+Dpbj7m23tkz1v64EXnQz9jTsFiLfT5ysbGrTOBehs3cgWebKKPQo9e3nXYGpiq5sn0kS3xr0PRozN3t/+BHzvNnfsdTaGWjjA8ybGf4u05j2M7aNDb9sbl3nbvVdg4mbb5fjvx+SLkedUABplNjK3I0tNb3DKTKbRP4YCDVcNs7I/DXN9r85tV13l5p8czZ2ig4MU2mKMsNza6VbR4a8r6nSQaPT3IklbKTwW9vdxl64VorNIblarRju0O3gx3/nt5gnMUoHWcYP3O2Qaef8dKFOf7od10aNdYPtzWunO5Oc/IkzFAb3ltpTny+HHpsHVL2VwHtJCdIc5SyIgxZbvjNqxu8dHFutL+vb3ZGmYTh/r7fCP7OfVVU9nbve+5nG2bwn/2+3R3WtdvxaHGinQSllA/8F8D/EYiAnwT+rDHm2MelD7M7f1I50v0aH+M+p9lNSdIcpSHL7/Yl+Frx7JkK47IIADmGL763zosXGtRKAaXQI0nt4JrOYYaopkQBF+bLvL/e3iULGHi2mfX5C3OFv19uYKESUgqshGKS5ZQqIb0kY2GgiiKNlTPGUI48zs+ViHwbiTXGsFSNqMS+OOgGpks3749u/3Q8yLGp9cGn7mF/HPn9OM+POqAA06mxSescDaR6Z4X1VsJ8JeATV5YmZo8Cz+P9tTZ3mj02O31qcTB134lWsNZOSDODyXNKA6fcGM1aO0Fhh5Td+z19T/PRy/Pc2uqy3U2wgs9WL/+jl+eJQ5/5SkhuDOvtZOQqxIFmvhLuUVi6H6N0WiY5IrVSwFwlLAxWTINWipLn05w0aQ1oH4KDMJxWbbAZ0+G8nV5me7M2B5mZhXJIq5fx1p0mX75qs6rDctidzezTnj/31Xg+YJptjDuHVrato7ZfEqiHee2WmNbJchxvTrSTAPx14AeB34+9nvwY8F8Cf+IoF3U/HGV3/qzGh6cVka9RniLLzK7ISD/N+drNFp0JN5o0szfZJLNGbi3yCco24hf1UjZ72bGohR1yth7yQ9/zBD/6y+9wfaODUrBYiahGHpUwYL11t49jHJPmWpyq3Z1rIZHlhmYvGw3B8rUmzXO+8sEmzV62Kzo6seFxit/8MB3YSYhDgY6B4Xy/n/Wg5/lRBxSmrbOX1nk/2aMz9Zhv3twiyQ1Loc8rj1l54Gn7TgbVeCRZTm4MnXZit5fbcqB/8Cvv0uxlYzMin3rpPOutZMwwNKsyxmCOye59NPzPwR+b+5FdUkpxqh5zY7O39zqrYK6sJg7bBDgMYdRJqtgGWzb53mqLb9zc5ps3NsnynDwHre219ukzdxv67/f8eRBRBGkbhyWBehyu3Q/iZDmOLyfWSVBKxcCfAT5rjPnlwWP/PvALSqkfNsasHekCTwCzXlByA5cWSnz12iZ5nqMGE3fN4LN/450V+hMmKg8VcUJf7zKeI1/zwXqbClDyFZlRo4jdURH5ij/7O5/iBz5ygatrHf7126u88tgcge+N7eMYh1K7G8m3u8muuRb3W59+a7vH2UaJW9u9+66Tn/Y3P0oHdhaD8yDWchxk9o5qHfer0iStc9rsUaufEniaD5+pEfreffWd2PJHqyBmjKJUssPwMmO4sdnh8++sTTQAi3pu0ixnrdUfTD32Ro2wvdSw1uqTZjmB7x34sTmrI+J7movzJV57f3PPtVUD317toJhcElP03H4x7CHJd/ybwXZzA9c22vz6O6u8tdLibCPm5Ytz3Ni0c03W2nult6c5f6ZRCMoNE3/TSds4LAnU+/muB8VhDI1zHB4n1kkAXgYqwL/a8djnsNe47wJ+ZtIblVKPARcH/3zhgNZ3YnjQC4qNjteYK62y3rblALmxpUaBp2j2rNzhuJ4ErRSNcoDWerfx3EkohR5ZDsvVgOtbvUO5IRWxUAl5Yrk6as6slwJubvUK+zjG8dy5Gm/ervP2SgulNJ7WfPhMnefO1aZax37Wpx8Xw1fiQQxOx/5yGPKNsNvw3Wz3+ebNbTbbdvBaP83va5u5sVKlpcAOE0uynEbJZ2OQvazMmBHxtWK+FNyd+txK9qWUaFr2x0k2Y639LId3brcIfUXvCEs/Q22XNhzj4OvBgMgd0rRrzcF07ME1eaiotdnuP5DhPcmY/8331njj1jb/7efeGZuBkjisc+ioOayMiePwOMlOwnkgM8bcGj5gjEmUUivcdQAm8VngLx3k4h4FlLKTJv/lt+7Qy3LS3FCNfUJtB9XMVUIeXyrzwVqbfn73fqTVoPFtoDd/r3Hw+bdXaXabXF3v0EnyiWnnw0AD3X7Oz33tFq9cXpzJSB/KFmZ5DsaqHH3r5hZfu16dOsLi1Ckch81hNU/fa/h+/cYWP/vajQfa5rjyvk4/Jc3tsLBzjfiBMiI7ZxyMm/p8v/0As9ZtP6gjkmY5a83+LgdheH02GLTn8eRyla/f2B4boDkM18HztL1OGjtZPhncQzwFcegxX4noDSZ9rzR7aKXIjSHyNe1+9kCG9yRj3gDvrbZZafYeKDp+WOfQUXPYGRPHwXOSnYQyME6eoQfEwnv/AfDPB///AvD39nFdjxRFcxS+8/El5kohv3l1k42BTrOnoBZ5LFYjmr18j877cJjR129s0UvzI1U80tjG5F6W8e1bTdIsf2Aj/V7Zwg+drj9QhOU41Jw6Hj0O0zkdGr4vnG+gGK/zP81n3Dsn5nQ9olEKyXKzQ9Ht/qK5Ss0+4wCOR912a2fk5u5faKX40KkaZ+diNjrvcWerSzKI1HiDAE8vzQ50tM1wWYGvWagGbHYSOkmOwU6WfmK5wu95/ixfv7HFXWFbs+PdD7jdMcZ8JfLwtVVUmiU6/igEeB6VjMmjxEl2EjpAOObxCChUNzLGXAWuAs7AmhGlJs9RsIZsxmIlpDmYzBtoRRT4LFbCsVKrAKutu/rWrX5GNzmaXILW9maem9lr4fc7wuLKbByHyVE4p7Nuc5xRtlAO+ebNLd6aIZo7y4yDIUddt+1pRRx4+BrSbJhBsOZ1PQ749EfO4WmPtVbCt25t8c2b23T6GY/Nl9juZdzY7HCQ+QQDJGlOOQg4P1fi3/qO87z2wRbNXspcOeT3PH+W3/vCad5ba1GLfS6USqS5GU3VrkTeA0et7z1uqpHP23eavLPS4myj9MDX7kchwPOoZEweJU6yk/AB4Cullo0xdwCUUgGwBFw70pU9Yig1fo6CMYYvXt1kvhLa2tIsp9VLyYzB05qPXZ4f3/yFHTI2F/v005zuIX2PoeTekDy3/y75tnRhZynB/RrpLsLieBg4Cuf0Qbc5zigDeP1a9UhlbY9D3XZu4PJSmTdubdPPDb0kQynwlOKVS3M8d+7u3JeldyLSzPDeWpvtXsqdZp/uYfQqKADDnWafr99o8ty5GtUo5DsfX+DFi3MA1Et2gF818jldj7m11aUUetRL4QNfU+/9fbWCH/vV97jT7O3LtfthD/A8ChmTR4mT7CR8BZsx+C3APx489ipWmP9fH9WiHmXuvfgNo+cKePniPLe3u7T7GXe2e1xaLPPMmd0Nu2aguffUoOZ3u5fQG6gj7VS22Nc1Yxut09ygtXUMhnWvQ4ehHvt86qWzM924XYTF4Tga7r0uHbWs7XGo2x6KTrx9p0W7nxF6il6aUw49Pn5lEd/Tu4zluVLAT/zr93h/rUXrkIao2YnKcGurQy/JRrMy1ttWXeqFC41d19SbBZPCH4Sdv6+7dk/Po5AxeZQ4sU6CMaajlPpR4G8rpdaxvQh/H/gxJ396PBhGz2ulgCTLePqMjZg1Sj5P7Wjyu7c+FwxPLFd4f61Nu5+T5ZmdLqzY9x6Fx+ZjWv2cZi8ZKDMZIk+Dsus3xg6Beu7c7CUAw0jKOGlFh8NxeBxlNPc4ZBXH9WzMV8KJksybnYTY9zhbj+n0W4cy8NJT0EkyMmOv+x+5OM/Nrd2zMg4rau2i4/fPw54xeVQ4sU7CgD+PbVL+n7GB358C/sMjXZFjhFKKj12a5+vXNvnq9U1+47310WCij126W2p0b31uJfKYL4dcWa6y3enzjZvbXF/vstXtj6Zv7tctKjdwZbmMpxTd1GqgB55mruTTS3LKoc/HryzMPMV0J2rwH3f5dDgePY5LVnFawzfLDVvdFKXg1aeWuNXs02nunUOw3yQ5KGWv9HNln2BsxkUfStTaRccdjyon2kkwxvSBPz344ziOTLqODh6fVJ+72U145lyd/+vv/hDfuLnN3/nf3+Rr17fIDdQjzY2tHq1eNpM8qq/hTKPE9z57mu+8skiW5/z8127x1p0m292E+Upkb9xTDjuT2OkMbXcTanHAnW0r0OWGzDgcjw7HITI9reG7M/NxfbPL/oVLpsPTCk8r0iyfOJfmsKLWLjrueNQ40U6C43hjjOEL766z2U149mx91Fi22U34wrvro0bnSfW5290UrTXPnWvw7Lk6W92Ej1ycx/cUv/neOq9f20Rh2Ow9mKtQ8jWRr9DYi/9LF+fwPe9AbtzGGH79nVW+dHWDdj8lCjxubXbZ7qQs1yI3ZMbheIQ4TpHpaQYWDjMf37q1Rat/OD0JYGNJpcCjEvp86er+9hw4HA4Z5yQ4DoxpGvSmrc+tl0JqccCNzQ6B53Frq0duDLOUxnq+ZqOT8q/fWWOl2QfOHdiNO8sNb95ucnW9Tegr0nYf31OstPq8ebvphsw4HI8gJyUyPQyULL4d8u1bTXppjsntxOZZ+8S0smWfO1HYGTWRr3n2bJ3f/swpWvdMOnY4HAePcxIcB8Y0DsC09bnD1/zm1XXeX2vTz3J8T5OnZo906TSEnuLxxSqfeHyBG5vdPfKD+33j1gpWW32SLMdTmlO1mDvbXZIsZ7XVxymgOhyO48ow8/H0mSpv3NrmF752i26a0d/hIUSeVSPKBkMztYZpkg47HQRPQRRosjSnGntcWazwvc+e5o9/8rIdxul6ARyOQ8U5CY4DY1oHYJr63OfP1zHG8PZKizvNHotBiOdpbmx2iANNFHi0Oj22+6awqdlKnkIt9vn4lXlC3zsU+cHcwEIlJNAK39Pc3u4SeJogNyxUQnLDodf6OhwOx/3ge5p6KaAUemTGoJQhzXKUUtRLPhfmYvqZYauTstHpE3m2AbmX5rvmIU+6Roe+Ig40rdyQ5+B7HguVyAZuCiIpxpgjL9tyOB5GnJPgOFCmcQCmqc9VSvHsuTrPnK2x2enz8sU5vvz+Jr7WeNpwYS7m3OV5/rev36Kd5IWOQhR4BJ7mmzebPHO2fijyg1aXvMrbd5q0+xlx4NFNMk7VIp48VXXD1BwOxwlBEfoeFcCYnE5f0U0zmt2UN++00YMSpDSDONDk5HiDxzytqIaKbmodB6V2q9UlmcFLDQpFPzNc3+jyy2/c4YnlymiA2k7ulc/eeX9xzoLDMTvOSXAcKPfToCeV+djyJdubcHOzi6eUjWYZCH0PpRRPn63y3mqH9bade2DYHbkKPUUl8AC4vtmhm2SH0gw3Tpf8dD2aqEvucDgcx4GdUfosN1Qij1rsc6FUYrOTcGurS6uX0WfgGQxQQJrn+FrbzENugJy5OGCrb2zppVb4GvqZIc1Ba5sxiDyPMND0s4wvvrfGQiXkhQuNPdfJe+Wzq7HPna0u4BTjHI79wDkJjkNhP+r8h+VLtzc7fOn9Da6udej0MzytuLnVJQ40H7k4TzkM+MJ76/SSfJeD4CuolwKiUNNP4Uw95qWLc8yVw0NphjsOsocOh8MxDeOi9B+7NE89DjjTiKkMDP93++nYnjADpLnNNoSeJg48eincaSVorUcOgdaKEAOD1wEsVEMapYDNTsKd7R7fvrVNmuUEvrdrfePks+/tL3M4HA+OcxIcJ4rnz9d583aT165tEQcK8Am0IvAUzW7K1bUOV9faYMwoxT3E8xQL5ZCVVo96KeDVJxf545+8gu/pQ7mZHCfZQ4fD4ShiUpT+w2fqPLlc5du3t7m62qIrlHdqpdBKk2Q5SWbQCgKlCDw9qDUyeJ5iMQrwfY9WL7OPw92/xzCNet5JUI5yOI4zzklw/aInjrV2n0rk8dLFec41Yr5+fYt3Vlu0ehnvrjRZ7yQkmVUU0kqhsM3MvtastHoopThTL/GdTyztikwdFidF9tDhcDyaFEXpl2oR3/fiWZbeiVhr9llt9XZWGY0YikSUQ003y0kzQ+gpLs6XqUQ+1zc7pLkh8jTlyOeFc3Uy4MtXN9jupTT7KcZAoxTwodO1PVPvp5XPdjgcD45zEuAzR70Ax/TcGz0KPEWSGzbbCVHgoZQGFEoZK5mnbOQqHDQr10sBZ2oxv/+VCzx/zpX5OBwOx71IQy6fPVvn+XN1rm90uLHZYbXVJ72n5sjX8NhCmWfPN/jK+xtsthMuLVZ49lyduZLP597ImCuHvHSxwUIl4uNXFslNzn+n3uOr1zdp9VIqsc8L5xp86uVzY8UsplHPczgcD45zEuDHgc8e9SIc03Fv9Oh0PWa12QNsU/J2NyXwNFme4Xu20U5pxWI14hOX56nGAUop3l9v8WO/+p5TwnA4HI57mCZKn+WGauSzVI2oxQHXNjr005zc2EFoHz5d44+/eoVPv3SW//ZX3uXzb69SiwPmSgHfvLlFPzcoBXOlkI/t6M36zCc1v/7OKhudhLlSwMevLE7s23J9Xg7HweKchPufw+U4Qu6NHt24uo7BpqR9T4GCfpoRBxqFwgsVWim+88o8f+A7LvLPv3Gbt+6psTXG8Oy5uusRcDgcDqaL0nsaGuWQs3MlyoHmhfMNvnVri1Yv5cnTdf7Pv/1JXrwwh1KK73p8ibVmnzfvNPncmytsdxNAsd5O+Py7a6y2+qOerfvp23J9Xg7HweKcBMeJ497oUauXst7qc3Wtxcp2D5QiMzAXe1Qjn4VKxFOn63zlg03e2lFje32jzW9eXeftlRbPnK1RL4Uus+BwOBzIUfp7HYlmt8/ZRoknlqv8my+e2zXXYPiez7+zyi99+w79LOdco8QzZ2tjJ97fb9+W6/NyOA4G5yQ4Thz3Ro+0gq9d3+bz76wyX4n49q1tkjTH9xQLlYiXHpvjOx6b5xe/fWdXjW3geby/1uZOs8dmp08tDpzGtsPhcDBdlH7acp/hZz19pspmJ6H/zhrPnK07RSKH45jjnATHiWVn9Gh4M/tj33WJr9/Y4vNvr/L2Sou1lm2+++J7a7R66ajG9kw95ps3t0hyw1Lo88pj82MjWg6Hw/EoUxSlv99yH9/TzJVDp0jkcJwQnJPgeGhQShH4VhpVKcVKs89Ks887Ky3uNHs04oBGHLDZTfjS1XVafdvk/OEzNULfcxEth8PheACmLfeZ1OvwxFKF73hs/hBW6nA47gfnJDgeOowxfOFd22uwU+N7o9PnylKVZ8/V2ewkfPPmNpvthDQ39NPcRbQcDofjgNlZorTZ7tPuZ2QG/uW3bvHFq+uuL8zhOEY4J8Hx0LFb47uK7ynONmJubnapRD5/9LsuoZTi6ze2+NnXbjiNbYfD4TgkdpYofeX9DX7u9Zt7FOfA9YU5HMcB5yQ4Hjo8rajHPsbA595YIQo8eklGOfSpxz6+p+2N6nwDhXIa2w6Hw3EEfPHqxi7FueFUZ9cX5nAcD5yT4HjoUMqqGvXSjBtbXXpJThRoLsyVWKhEoxuP09h2OByOo6FoqrPrC3M4jgfOSXCcKIwxokFvjGG11SP0NWfqMXHg0U0yQl+z2uphjNn1Xqex7XA4HIfLNFOdHQ7H0eKcBMeJwBjD69e2xpYG3essZLlhu5uileLVJ5cIPE2S5Xz5/Q22u6mLUDkcDschc2+AZ5qpzg6H42hxToLjRPD6tS1+5rXrg8mexQ1uOyNUN7e6nG2UuLnVdREqh8PhOGSKAjzTDmNzOBxHg3MSHMceYwy//u4ab07Z4OYiVA6Hw3E8kAI8ri/M4Ti+OCfBcex5kAY3F6FyOByOo2XaAI/rC3M4jifOSXAcex6kwc0pFzkcDsfR4hSMHI6TjT7qBRwD3D445gzLh55crtLspXzp6jrNXjpV+ZCNUGnnIDgcDschc2+Ax022dzhOFi6TAJ856gU4ZFz5kMPhcJwsXH+Yw3GycU4C/Djw2aNehKMYVz7kcDgcJw8X4HE4Ti7OSYD8qBfgmB7X4OZwOBwnBxfgcThOLs5JcDgcDofDcaC4AI/DcfJwTbsOh8PhcDgcDodjF85JcDgcDofD4XA4HLtwToLD4XA4HA6Hw+HYhXMSHA6Hw+FwOBwOxy6ck+BwOBwOh8PhcDh24ZwEh8PhcDgcDofDsQvnJDgcDofD4XA4HI5dOCfB4XA4HA6Hw+Fw7MI5CQ6Hw+FwOBwOh2MXzklwOBwOh8PhcDgcu3BOgsPhcDgcDofD4diFcxIcDofD4XA4HA7HLpyT4PaBw+FwOBwOh8OxC/+oF3AM+PcAXnvttaNeh8PhcDgcDofDsW/ssG/L9/teZYzZ39WcMJRSPwT83aNeh8PhcDgcDofDcUD8kDHmR+7nDS6TAP8UMMBbQHvGz3oB+HvADwFfnfGzitDAZ4AfB3K3nWOzDXDHgNuOxR0Hx3Mbh7kddwy47cDhHAcP2z572LZz1NeCMvAE8NP3+4GPfCZhP1FKfRL4HPCqMeZXjno9jsPHHQMOcMeBwx0DDos7Dhwn+RhwTbsOh8PhcDgcDodjF85J2F/eB/7K4G/Ho4k7BhzgjgOHOwYcFnccOE7sMeDKjRwOh8PhcDgcDscuXCbB4XA4HA6Hw+Fw7MI5CQ6Hw+FwOBwOh2MXzklwOBwOh8PhcDgcu3BOgsPhcDgcDofD4diFcxIcDofD4XA4HA7HLpyT4HA4HA6Hw+FwOHbhnIR9QinlK6X+30qp20qpTaXUf62Uqhz1uhyHj7L8vFLqPz7qtTgOF6XUaaXUTyilbimlVpVS/1gp9dhRr8txuCilLiml/olSakMptaKU+hGlVO2o1+U4GpRSP6yUah71OhyHj1Lq+5RS5p4/3aNe17Q4J2H/+OvADwK/H/g+4LcB/+WRrshx6CilfODvA7/7qNfiOBJ+CriCvQb8dqAO/IxSKjjSVTkODaWUAn4G8IHvBn4v8EnsdcHxiKGUehb4q0e9DseR8TzwOeDsjj+XjnRF94F/1At4GFBKxcCfAT5rjPnlwWP/PvALSqkfNsasHekCHYfC4Gbw48AysHGki3EcOkqpDwOvAs8YY745eOwz2CmbrwCfP7rVOQ6R08DXgD9rjLkBoJT6r4H/+5GuynHoDIJG/xB77n/HES/HcTQ8B7xujLl51At5EFwmYX94GagA/2rHY5/D7t/vOooFOY6E3wb8BvARYPOI1+I4fG5ho8bf3vFYPvh77tBX4zgSjDE3jTF/aIeD8CTwR4BfONqVOY6APw+sAj921AtxHBnPA9886kU8KM5J2B/OA5kx5tbwAWNMAqwAF49sVY5DxRjz94wxf9oYs3HUa3EcPsaYDWPMzxpj8h0P/0fANvBrR7QsxxGilPpXwBvAEtZgdDwiKKVeBP4s8O8d8VIcR4RSSgPPAN+tlHpdKfWBUuofKaXOHfXapsU5CftDGeiNebwHxIe8FofDcQxQSv0R4M8Bf94Ys3XU63EcCf8htgTtBvC/D0pTHQ85gx6kfwj8BWPM+0e9HseRcRkoAR7wx7AZxceB/00pFR3huqbG9STsDx0gHPN4BLQOeS0Oh+OIUUr9EPB3gL9pjPm7R70ex9FgjPkygFLq9wMfYMvR/qejXJPjUPh/ACvGmB896oU4jg5jzNtKqUVgY5hhVkr9AHAN+F7gnx3h8qbCZRL2hw8AXym1PHxgEElYwh4MDofjEUEp9ZeAvwv8NWPMDx/1ehyHy0AG9w/ufGzQn7CKFTVwPPz8EeC3KKWaA+nTHwEqg3//4SNem+MQMcas7SxBHTQwrwIXjm5V0+OchP3hK9iMwW/Z8dirQAb86yNZkcPhOHSUUv8J8JeBP2eM+ctHuxrHEXEZ+B+UUs8NH1BKXcY6CK8f0Zoch8v3YBtWXx78+U+B9uD//+kRrclxyCilvl8ptaWUWtjx2GPYa8HXj25l0+PKjfYBY0xHKfWjwN9WSq1jexH+PvBjTv7U4Xg0UEo9DfwN4EeBf6SUOrPj6XVjzLi+JcfDxxeAXwH+waDsLAD+K+CfG2M+d6QrcxwKxpj3dv5bKXXbPmzePKIlOY6GXwa2gH+olPrzQA07P+sXjTG/dKQrmxKXSdg//jy2vux/Hvz9i9imNYfD8Wjwg9jAy5/ENqru/PP9R7guxyEyKC34QeBN4J8DP4/NIPzBovc5HI6Hi4HS4fdiG5d/Bfg5bAbhB49wWfeFMsYc9RocDofD4XA4HA7HMcJlEhwOh8PhcDgcDscunJPgcDgcDofD4XA4duGcBIfD4XA4HA6Hw7EL5yQ4HA6Hw+FwOByOXTgnweFwOBwOh8PhcOzCOQkOh8PhcDgcDodjF85JcDgcDofD4XA4HLtwToLD4XA4HA6Hw+HYhXMSHA6Hw+FwOBwOxy6ck+BwOBwOh8PhcDh24ZwEh8PhcDgcDofDsQvnJDgcDsdDhFLqF5VSZsKf7zmkNfyoUurvTPna/0Ip9R/s8/a/rJT6y/c89l8rpf6M8L45pdTrSqn5/VyPw+FwnESck+BwOBwPHz8GnB3z51cPesNKqf8U+PemfO3LwA8AP3KASxryu4H/tegFxpgN4B8A/89DWI/D4XAca/yjXoDD4XA49p22MebmYW5QKXUea2B/B/D+lG/7S8DfN8YkB7YwQCn1DJAZY96Y4uV/H7iplPrPjDHvHOS6HA6H4zjjMgkOh8PxCKGUujwoPfqLSqlVpdTnBo8/rpT6aaVUUyl1TSn1I0qp+o73Pa6U+gWlVEsp9TWl1B8efM7S4CWvAOvAi8Db06wD+H7gf9rx2C8qpf7eoORnRSn1caXUslLqJ5RSt5VSiVLqXaXUf7jjPVop9deUUjeVUhuDMiN1z+Z+F4MsglKqqpT6/w4+v6WU+hdKqReGLzTGtIB/ARSWJjkcDsfDjnMSHA6H49Hk+4HvAn5IKRVijejrwEeB3w+8APwjAKVUBPwCkAKfAP4i8Dd3fpgx5qeNMf+2Meb6lNv/PuANY8y79zz+7wL/MfB7gN8E/iFwHvhe4Bngvwf+llLqycHr/yLwp7AlTp8EXsY6Kjv53YP1A/xV4AngtwMfAbaA/989r/957P5xOByORxZXbuRwOBwPH39SKfWZex77q8aY/2LHv/8rY8y3AZRSfwwIgT9ljDGDx/4w8I5S6lngceAS8EljzC3gdaXUJeBvzbDGjwHfGPP4Lxljfn74D6XUzwE/PywVUkr9VeAvAM8rpd4C/jTwnxtjfnrHd/lgx/sjrPPw7wweegJoAu8YY7aVUn8KePqeNbwOPKWUWjDGrM3wHR0Oh+PE4pwEh8PhePj4H4C/cs9jq/f8+90d//88cAHYVureSh2ewRrW1wcOwpBfnnGNp9lhzE9YF8DfA/6tgQLSh7CZArD3r6XB5/zm8MXGmE2l1E7n41XgdWPM5uDffxP4aWBFKfVLwD/DZit2cmfHGp2T4HA4Hkmck+BwOBwPH5vGmDeF13R3/H+ANbT/nTGvuwX8MfaWp/YefHkA5IBXtC6llMaW/jyGLQn6b4DPA1cHLzGDv+9dW3/H/4/6EQCMMb+slLqALXf6P2DLlf4DpdTHjDHrg5cN15Xf53dyOByOhwbXk+BwOByOr2OzBTeNMW8OHAyNLSc6BXwVOKeUemzHez464zZvAMvCaz4C/A7g08aYv2SM+SmgNnhOGWNWgGvAdw7foJQqY7MfQ3b2I6CU+gvAdxpjftIY81ls/8IT2P6MIcN13bjvb+VwOBwPCc5JcDgcDsd/D7SAf6SUekkp9dHBY49ha/f/FfBrwE8opV5WSv1O4K/PuM3fAF4SXnMTyIB/Wyl1SSn1b3C3yTga/P3/An5YKfUHBlKn/w3QAFBKnQYuAl/Y8ZlXgL+rlPotSqkrwJ/EZh5e2/Gal4FvGWO2HvTLORwOx0nHOQkOh8PxiDOQ/fxd2BLUX8FG3t8Bfo8xZlhy8/uxfQ2/iu0TuLeO/375GeDiQAp10rquAX8C+KPANwfb/YnBGr5j8Jq/BfwN4G9jnYF14JcGH/G7gP/dGJPt+Nj/y+D5/xHbOP0p4PuNMTv7I34btm/B4XA4HlnUQMjC4XA4HI6pUUp9D/AvgeVB2c+DfMY/AX7DGDNrVmLfUEotYIfBvWiMeeuo1+NwOBxHhcskOBwOh+Oo+GvAvzuQKT0u/EngJ52D4HA4HnWck+BwOByOI8EY80XgHwM/dNRrAVBKzQOfwQ5zczgcjkcaV27kcDgcDofD4XA4duEyCQ6Hw+FwOBwOh2MXzklwOBwOh8PhcDgcu3BOgsPhcDgcDofD4diFcxIcDofD4XA4HA7HLpyT4HA4HA6Hw+FwOHbhnASHw+FwOBwOh8OxC+ckOBwOh8PhcDgcjl04J8HhcDgcDofD4XDswjkJDofD4XA4HA6HYxfOSXA4HA6Hw+FwOBy7cE6Cw+FwOBwOh8Ph2IVzEhwOh8PhcDgcDscunJPgcDgcDofD4XA4dvH/B9W3KNlgSzr7AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "describeresonator(vals_set = vals_set, MONOMER=MONOMER, noiselevel = noiselevel, forceboth = forceboth)\n", "#figsize = (8*3/2,7.7)\n", @@ -5080,11 +5412,308 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[ 0.773987235127223, \\ 3.50126378407557, \\ 3.50443407234539, \\ 3.53284574229025, \\ 3.53383897316219\\right]$" + ], + "text/plain": [ + "[0.773987235127223, 3.501263784075566, 3.504434072345391, 3.5328457422902457, \n", + "3.533838973162194]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reslist" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 110$" + ], + "text/plain": [ + "110" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resonatorsystem" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vmin: -0.4709607173913786 , corresponding to 0.338095416077065 %\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,1D2freqheatmap,2023-03-31 14;23;36.png\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,2D2freqheatmap,2023-03-31 14;23;36.png\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,3D2freqheatmap,2023-03-31 14;23;36.png\n" + ] + }, + { + "data": { + "image/png": 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ghn3Wy4A/BjZjaV7fxGrH1xWR8J05AuF7ExZMMRXHLd12orh+uKjq6Kk6RkREXSH1U67AmZZfggndx1atKmCWsX9U1ftmcUoPYgKpRURWquq+KcaePW67ukJE/hB4HObb/Qzw1xNEiD8MXCci/4RFZ78VeKqIPFtVfzarEz4OkfCdOQLhe4uq3jOH8wiiGutTHYiImGGsg2Td8HLgP6re92AlET+vqofnYD7XYrWmcc9fn2iQ65F7hXu7U1VrSU2abV7hnn+nqn851UAnlN/u2pReDbwWqCvhWz+3jPMYEVmEFdkHK2AfERExa8RqfMwKwU3vQ5ifd5Wqvm+OBC9Yo4+AP5ti3MsJI8H/e4pxc8klmNb7xWlsE/Svrju3YCR8Z4bqf+w9czUJRxO1BVdERJwWiHg1PWaJh7Gazueo6r9Np7vVqcCVf/yxe/sEVx7yKFwpyY+6t1nMpFuPdLjnndPYZpd7ns2SnjVRV/aaeUwgfAvYHe+cEfl6IxYaUkc6hKrecvxRtSEiZwJXjltc3U7wpSJSHcU7oqoTaa1/iRXy6AC+ICIXAd/DXFSXYYVHutzYd6vq/pmY/ylgEOhkeoI0iC6vu+jtSPjODBe65y3AhSIS1HJdhjXBvhX413pz+EdEnA7Umc93JrkSa084GZ8Y9343E5iMVXW7iDwHK3W5HEt5esO4YT7woVNZHnIGuBfrAf4q4H9r3OZV7vn+UzKjk6B+bhnnN4HmeyYmaF+Nhe0nsAbVzwV+KiJfF5Fakt0jIiJqpM7MznWJ08jPAd4L3IkpBTnMhPtV4HJXxaqe+R7mU3+xiLz5eINF5I1Y5LlSh37sqKvRSeKE6Qhhabp9mM/kNuyffiXwdkwIA/y7qv7JbM8zIuJ0JZNeU9NFLJvbHWUAzGPctfZuLH9XgV8AXybM8wW7zl6GRTc/CxPWjwDnqeoprWA2XSLhe5K4UPZ73NtbgWerat+4McuB64Az3KKnqer/neRxj1Mk3muYKcNGQ3o1Y7l6zbmfPWKxFgDK5SEAmjJWlncku+OoMcH6iBNHtVizoGzMnFHTRWw0+2gkfOc5InIWVuFqBSaApxwOHAKeqKrbTvXcpsvCtsXMDA9g5uZnAC8cL3gBVPUA8KdVi94yA8dNYKkBkzyim6qIhUFkdl44qOrDmGb7TcxPLZM8fODbwEX1KHgh0nxnFRF5FOtiMgy0nkxpudnUfCOMWKwRgHLZAsqDQJ90cinZ/FSFg6amujyiRjdNwPQ03+aGTTWdtJHsI0227ygj4HTA1cx+EubL7sSEbh8WXHWNqh6aw+kdl0j4ziIi8kPghe5t50Ra8swdq+Y2axE1Egnf2WM6wrel8eyaTtrw2ENu3xqZn+chIvI2oB/47ilqRDGrRKrR7FLdzqzuo56rTXXtTefM2H4DITbXiPurlXJ5tCJ4AVRLqJaOEryppKVhepLAk9raw2rVX/W8quc2mdnU81ITLl9IxCRe0yNi3vNGLDL7Y3M9kZkg+kaeJCJyPrABi7L74nFMyUHEcxkzj0RERJwk0/DnRpXf5jcr3fP/zOUkZopI8z153orlkP0rsHGyQa7byaXu7f2qWjgVkxGRxqCz0clwdvsfoOpX3vePTNzkZDLNsSmzvhINDKYRBo9q7XEuqdY2xxOLtVSim6dDvnAIEQ9fi/jjMhtE4jUVhBivCdu2E2vRvl+XfcJnlVoDrlR1NPL3zmsG3XPdWw1rIRK+J891Va9fOcW4VwHt7vV3T910GCHsbBQRcdoTmPiP94iY9/w7FlT1HteFaV4TmZ1Pnh9gfSMXYS2sfqCqd1cPcKbpT7q33UyvK0dERMQU1FNt54hTyvsw192fAltE5OvALcBeYIjj5Feqal0VK4iinWcAEXk5lncGpnV+Cvg/zLLwdOBtQCPm632xqtZal/RE5uJMzvEZ0X4zqZWU/RyFotVvTyYWVV4vBKpNz7UUz5irIhuCnFaR0tOJdl7SdmVNH/zwwA1RlPM8RkSCajarmX6/clXVulI262oy8xVV/S8RaQM+jQV1/J17VDMMvO5UCl43l1GY3EcYEXG64UWa70Jh7VxPYCaJhO8Moar/IiK/wQKwngKswjTd3cBPgc+r6t45nOK0aG7YwPDYtqPSaAQhEW86rTXfhvRqgEo5zVq12PGlJ090zIlyOmm90yXy5y4YPshpVLovEr4ziKpuxXLRIiIiZolI810YzIOuS9MiEr6nGVU+35Paz/BYWA5VJI5qCfGSjGZ3kYh3UCxNnKbc2Xwho4Uj5PMHbVvPsgImS4kJ9j0XBGlS1VpjZ2YDEGq+1elS1Q0UICxw4fv5o7RZkXhlXXVa1XQ03vFzSya6KBSPTLXJgkUkNtdTiJgFRORrmDXxC6p621zP52SJhO/pR5RmFLGgiEWXsYXCU4BlwDasjeC8JvrWRkREzGuESPNdICxyz9fO6SxmiEj4nn4EJfRmTANWLSEI8VgrBf8IxVJfpcfvopaL6Rm6qzK2d/geIKxxnC9M3VhkNk3OQRnCoHJXMrnkmDk0SPtR2yxKb6i8LpTMbBwEnAW9uZsy6ymU7HSXSv34WqRctn22NG46Zh6F0jAAufyBSedaqfNcad7QPqHZeaGlfk1ElOe7YNiNtW9dC9w0t1M5eaJv7WlGVEIvYqHhSaymR8S854NYfu/HRGTmOr3MEVGRjdOUmWwpGBRwOK/9Vdzf//Vj1r+g4538avSbKP4x2tx4bbMe6Gy+EAi19I7m8yrr+obvB8J5J+IdlXWBFhpo84tbrFR3z/Bdlc/X0XwefcP3T9htSH0r5y1eGgDft65ogZUg2Hd1EFoQ8BUEe3leCt/PE4s1olrC9/NkUisplkcolQamfS7qlekU2Tir8w9r+q5v7ft21M93HiMiq7EyvR9wi25iehWu/vOUTnCaRGbniIiIeY2nNRvwAldMVOlqfrKz6rUAj3ePWlCgroRvpPmeZsx0eUkwzUy1RKHYQzzedoyGFYu10Nawnmyxj7JfOMrPO75oxVzSmFkLwGh2FwAr264+ZkzB9eguu9SovpEHKutSyS4AmlKmqY4VzNfamOwi5Zmrfd/ANXS1Xl7ZfmAsTE9qTq8AoOg03sBXGfiS7bjZSbs+zVXpyrlgOprvOR2vqukitqX/G27fGgnfeYiInIz5TFW1rnwPkeZ7+hGlGkUsKGLUXOEq6uc7v3nSXE9gJomEb8RxqdZky+VQtovEObftD3mg/xsV/2ksdnQr4VI5B1i5yoDqAh6zScKzLmRBBPJA3rTx0SqtvCmzzp6dllsdCb0oY+2aR0vd7r3tp3vsIUqJVsD844PZ3XQ1WjxIIRVqsUPOb5tJLQOOLdoREIu1EHN+4aCYiWqJcnmoEl0+VaGThUatFa4iX+/8RlVPixSjgEj4nn7MeKpRREQ9E5WXjJiP1L3wFZFVwKXAemA51ppPMeFyAKt2cruqHp6zSdYRUVejiIXGNAKuIk4jROSxwNWYbGgD3qGqu0VkIxaI9W1VF8RRh9Sl8BWRC4A/Bp5HjW2kRGQb8H3gv1T1wVM3u+khIn8DfBzmZ6BHIt7B+U0v4u7B/8TXIqol1rc/lx39P0G1xL7iXUfVRh4fLBQUhljTHAYlnkqzc5AalIibAaBUHqusK7kgqHVpm8uO3PUAdDSdWxnTEl8OwEY9G4Cmjq5wnW/7HkpaIQ7F4j/isQyN8cUAlBs3UvbzNNEJwOHivZXt47FmIAz4WtZm88gWQ/Px4NhWF1RVtgVaPurz9Y9udZ9rqPJ5F7r5OSovubAQkYuBLwEXjlv1Iff8GODLwN+LyJtV9XuzOL2aqatbRhF5nojcDNwFvBlYh4WU1/LYCLwLuE9EfiYiT5j9T3A0InIG8P65nkdExOmMh9T0iJj/iMhTgBswwVt9/a8mkBtdwLdF5A2zOcdaqYtUIxG5DPgcZl4OTuQB4LfAvcADwKPAoHv4QAOwFFgDnA9cBjwZnMphpumbgb9S1Vtn5YOMQ0T+DysGbhOaBc13plONUsmllMpjJOJNNCa7KPpZGuIdDOX3k060Mji2A09SFEt9eF6Glsxaxgq9lMqDwOTdjGaSoE8uwLktLwLg3v6vAdYNKGBj09MAGKUfgDJWHvJcvbAy5u5xVeuqtdKB0YeAsEhHkE40lN1FIm4BV42pxQyO7SDmgrua0suqtn8ECIt1ZFx5y7Fc2OZZKSOSqhTgSKdME9/Q8BTu7/86sVijrVM9rXv4TifV6HFtb63pRNw08JlIAs9jRKQd2A60A4eA9wK/xspOKnCRqt4nIkuwvupvA9JAAbjAtXytG+bUXiMiDcAngddhWvgh4BvA11X1/uNsPuQej2D/gGCfjwP+EHg58DjgBhH5LPAeVc3N+IeYBBH5E6oE7ywSBVpFLCiigKsFw1swwdsHXKGqewBEjr6ncvE/7xaR/8VkQwNmSf2LWZ3tcZhrZ8l9mLP8UeDDwDf1JCvtq+pNwE0i8tdYKbJ3AW/H/McbT266teHuvD7p3vYQduOYd+QLh/AkgdCCJ3F8v4gnCboym9nV/0te2fUevnHkI7Q1bqYjeQZjfi+j+cOVdJrAv3myBMU64NiCHW0NYc/dHXnTXNsaNwOwMnlxZV1KLX1nf2k3ABfFng7AfXJ7ZUxQAOMcuRKAdCJZWfe7hKUYZWJttp9hy3wQL0lHwxmVcb6fr6QopWJhY4WYZ0aJwFecLw4AkEh0VsbkC4dIJzvxPPtpBufvgcI3SSWXki8cmrCZwkJusBCPhO9C4bmYhvuZQPBOhareIiKfw2TAXChCUzLX39pm7G5ks6p+7WQFbzWqmlPVL2EC9/XuWLPF57A7tN8CP53F44KlGkXFBCIWDLV5fOf6UhcxA5zpnqeT7/sb97x6ylFzwFx/I89U1X+ZSaE7HlUtVwnhU46IPA/4PSAHzLqjP+pqFLHQiAKuFgxBt5LppA8FY+suQGJOzc6qOnw6HUtEWoB/cW8/rKrbxvsj5htNmfWMZHewsvEx7B+7k2SskTP0AnZyP1e3vo3v9n+RrtbLuUSewK+Hv0w60cHS5ovY2283nOvanw3Azv6fndQ88sXeY5atbrcAqlbCrkAtngU++c60u8wLg7EOlAcAiLmOQ72eBVO1s6oyJufi1A5gQVCBiRhgadNFAJyjFwAQbw87Fx0es+w2z4vTlFlHvmgBZ31jj1bGBJ2Sii6Iq73JqmBli/2VMZ6XIeYlj6l+1Zhei+LjxzuOMi8H9Z4XqskZICZzrUNEzBIHsdTTzcDtUw+t8NiqbeuK6Fs7s3wMWAk8CPzDHM8lImJBEGm+C4ZrsWyY19cyWEQWA3+Fab03nMJ5nRBzHXB1wojIeuDpWCj5nap6/RzP5/GYmVmB16lqcS7nM1MEGlhCTctrSCzmnsIvuDDxTHKSY3Xz49ne9yPuaYuTSS5GtUxZi5UUn+lqvON77QZc2fSnldeHYtYzuMk3LbckodciL5b+k3HBVbvL3ZV1Y2LGj6sSFmjVV7Lg92rtdlRsuyCCdh8PVda1imnYvx75sr13XZI8ibOxweI5+thHttxPY8Ji7IIALggDtYaKdhMeaMLlchiEv7zlcg4M3Vzp8VsoHK7MMZvbj69FPC9FMtFJLn8A1TwicTwvNWk3pNOdWjWIIA0vcsvMW/4Z+CPgChH5PPC2yVyWInIJ8DUsHVWBf5u1WdZIXQtfEXkp8KfA11T121XL345pll7VshuAl6pq9zE7OvXzTGIVVwT4gou4PtXHfA/w7slHeESGjYiFgNSu1Ub9fOcxqnqniHwKeAfw58CLROTGqiF/ISJlrObDhVXL/11Vb569mdZG3QpfFyL+Rvf2LuDbbvljsDSe8T+gK4Gfi8hjdPYrh7wH80McxMLaZ4MElr82CTN7Crb0f5urW9/GkkQDt8k9rEm0cE3hRrpHHubc9ldwuLyVzcknc8/oDzmQvW5a+17edhUABwauozluhSdSblmHmD/22v7PVsZf0mZa8CKszGNbPEwHGi25G2H37Uj44Ve8Vc3/uxPTJvPesXEbXb5pnFuw32pLLPQnN/gWML+25YkAHMpaKvqyzAVsz1oAZntqLZlYO3nfrvOFKm3Uc8U1kq7zU8/QXW55WIe7WbrIpJZV/NLFkvmOs7n9IB4tDZsYGt1KvnCEWKyRZLzd+cPrqlXprBKPfL4LBlX9G7FAmr8ElgEvIbzY/WnV0EA+fIM5CHythbr81orIlcCbsBM4yNHO8r90yxX4f8BFuNrJ7vUfzd5MQUTOJhS4b1HVwVk6dBEYm/wR3dxHLAxEansQpeGdFqjqX2OBVN/D5MP4UsM54JfAs1X11acym+ZkqIvykuMRka8CrwF2Alep6n63PIUVrWgAfqOqT6/a5ptYZatfqOqzZ2meHubIfyzwE1V93gRj/gN3QzCbjRVEEjP+j31s6xvZ520lLS14xOgodzHo9bHcX04JnyYvySHt546BLx13X0HRjE2ZpzCIeQrSNNGvFmXc6xoILG+6BIDXdV1W2faOHnOnr24yjfHugfB+p9n1wd3v9pnSMCK51V13y2o+3ra4rUvGwn9LtmTrtrAdOLpjTqtaY4Xt/m0ArPVsbr2ynzZMY9+ev4FieZRlDRcCUCIsr3l4xDTloCDHQM6KfSSqeiAXSsOsaLyUPcMWHxL4lQvlUbKFblKJNrL5w6gWScQ7KJUHEUmRTnYwmt1FY2btjBU2mUumU17yxZ3vq+m7/oPeD0V3pKchIrIOKyscx6pf7ZwPMTf1anZ+AqbZ/kMgeB1PJGwp+INx23wXE74XzMoMjddjgrcMfElELpxgTEfwomr9iKpuP+Wzi4hYAMTmeTpfxMmhqjsxRW1eUa/CN3C0ja/v/Iyq178aty4wTXcye1zunmPAj2oYf7d7vhbrQzmv6Io1c1DjdPpLWB1vZ9AvMkgfQzJKUQr043PvwNeOu5/GzFrWpK8AoNNvp8UVH9vtbaXd+XhXNVqbv0NYtPVdPaHlqCNlX9uerGmpsSp/Z79vLQQ3Jq28ZXch9Ot67hodExuf861d3/5SWA67LLas17fjdsbD0pWH3O/7Eu/JAOwRq3A3Uj7CcrFxa1KXcbD0IOpaAhb9sKVhzDPfdLPzI5dTdnPeUxXZvbT1cvaP3kHMafBB1HfSRU+vSz+ehwo/BnxK5WHUL+CTZzRrLQZPB613uniR7I2Yh9Sr8A2upuVxy5/pnnep6o5x65a451lrnlCPVHU1mtuJRETMEvO9kE3EwqRer9B7gA3A2cCtUOmNuxkzOU9ULznw/+6ahfkBoKqvwXzTkzIHPt9T1tVov99Hg7SzMtZOUyLGkWKWVawkJTH2aw8tOnn57KCNHlh0bxbT1A55MdJqQduLdDVLxSKYe9U+xmsWWYGagULo1uvLmcbbXTB/6vrG0Gd6JGvaZCZmvtpVmTAgfHvWfMPL4jbPfWWrLNXlqmIB7OUIAOeKRVsPVsXPFTAtdr+YkaXocooVn8Mx81XHSNCeWMNQ+RAAq+X8yvallI0/nN8CUMkFbm3YUBmTkXY6GzZW8owPuMperZm1DOcPsGXwB3heikS8E8GjWB6hVBrA8zKVNoQLjbqMGo2IOA71Knx/h9VifpeI/BI4jFWPCvh+9WAReTbwZ5hg/g0Rp4Q7Br7ESzv/H2ua46xqgIPZOJtbMzw8mCMvOe4c+Mox2wQ5mI9pDbMAemQ/r+y0bkM/7dlPo1rv21WpZnqdQH3jujYAbuwxodtY9U1d0mCXWx8z4/bmQpN0Uc1YknbpR9nS8WNx0rHw8p1zqUG7/TsAOFMeV1m3Wq2u+0N2P1gRrOVYkRXlNTYXrxcEUp4Fdx2puhdsii0GYKBsQjLjbjSO5ELvSqE8StkvsKLxUgDWu/Kch7L3E/fSFMXD97MUtYhqCc/1DV6oghcin2/E/KRebxr/BTM5n4lpst3AizHheq+qXguW5iMivwX+F0hiJufPz8WE64gonSJiQTGNVKOIiLqhLjVfVb1fRN6ACeEE0OZWHcJ69AbECAOX8sCrXeTbgiUonSdVhRtmkv/u/XvekHkft3aXuaSzgWxZWZxO8rND35xw/Is63wlAvOrqt1LbGXUa6bnppWTidg+4rgn2jJom1104ej/FsAIkcberrowLnCqHAVejRXvd7D7+4bEwbODiFtM0Hx22sIBFYkU39hZD03LeM9PyxZ41bfCrUvGGxEzhXWIacN6FF4z6vWxz69pYzmB5f6VwxuGqYKozW80zkklUAuDtfTJs99yeXsfhkftJuDKXvSULbWhOLSNb7KM5vZqh7C5SiU7a0mtplaU81P/dSoOFhUgUcBUxH6lL4Qugql8Rkeux9KElwFbg66raVzXsEUwr/inwcVXdOvszjYiImEsis3PEfGROha+IeKrqT7ZeVR8BPjDF+jxhlHNdUktQ1nxDVUnFPM5tLfLAYIJf5a6ZdOyli47VwIeLQjpmGmVPDtpcdciD2dC3u99l6Fy12Py59w0e+1Xd0m/qcWsyXNecMC16yAVoJarUov1jFox1RrNplUNOnW7z2ytjckXznbY4n/E9/gPhOjXN8lLXpWzEt+M3SmOlocMg3TTHljLqW6DUipbLK9sPli1lPVcaACCTtOM2JcOvcElztGZW01+2AK6hrKUzNadXkkl0UPLzbGh7DkcKDzGQ28XB/A1kUisplk9ZnF3dE2m+EfORudZ8e0TkN8CvgV+p6q45ns+852RSjUQ8FjVfTMprYt/ANZOO++L+D/PmVe8j4SkffvSDE4752Kb3ATBSsitjXz4037YklUFnVr6gIww7WJws8+CQmY03NptgPJS3z+FXxU0Nudo1j+syARmvilw44IT2g4NmEm6Nh8I/CKwKhHBQIevOgYHKmDPiloN7sGQdkFZzZmVdR9yEdmCJbo7Z8Yf8EdK+mcvTrGbMG2XAN+HZEA8F+wosqnl3wgS65/KN/aquSp2yisP6EGN5E95Bha+h0gEK5VH6Ru6DZmhLrKG7/DCp5FKy+X3MNEFXqkLxyIzve6aJZO/CRUSWAesx1+R1qjosIg0Aqjo21bZzzVwL3zasMPaLAUTkUawm56+A30atv06I46pAMVfOcHwLOlWf7qE72Nz+suMeZF0z3NGXnHDd8rarOOCCb+NikmpNVQjYtiHlrFa7ZHYmyzwybF/D5WllRcbGl1xWVtkJutGq6qwrXPZQdz6Yd7humVu33mm3WwfDS3PGfdsfHjA/8PYh20G7F6Yj9ZRs4iuT5g9e2Rj6k68dsPShhJrQ7o6ZJtvKIpLup9Tn9ZPUJJfHrBDHrqoCbYe8XUBYsnJJ2QqKlL2wEp7gkSsOssG1KAzKbeZLw3SkzyDRcjkJr4F9w7dwbvML6GEPe/MHqAURj2pDU+CX9ieoxDcfhG5ApPkuLEQkhtX+fxNU3R1bbf/7gKcB/+XKFP+tqvbP/iyPz1wL32VYfu7TsRN2pnu8ESiJyC2YIP6Vqt4+Z7M8zZis7+va9mewe+DX7M7ewur2p+Hhsav/lxOObUv4XNSW4+/HlTr53iXv4ra+BBe2mYC7s8+E14amMILqso4SYyVb7omyKGmS9ebeNGsbbbuUZxI179u4i9pDofHoiC0Lgqky8fDqO+xipzrTtqzaHfjwgM0h7q7WjXH7+peq1Or+gu2z7Jbd0R8GY62NW2BU2Ul79W1Oq+PtbCmbeXiQQ6xgQyVQ65L02sr2D+XsDuSQ2Enb4Qqelcph/ef92TtY2nQBK9VM0QfKlobUklpBWYss8tZzTnwNP0/t497Bb+P7WQRBJ+liFfQFBsgX7OYhnVoOQK4Goe15YW3soFxuIMCDqlsAhWLPcfd1qoh8vgsHEekAfgxcwdFGj+ofwDogg8mR54vIU1V12+zNsjbqqrGCq338dKyS1eOw9KFggn1YDm8gjGfe1nYaEJidWxrPGQEYChoUuBZ9YK37AJ7W9lcA/HrgUwAsbX0sm3gMrS53dNDP8uSuVi7vyPHMWz9x3GP/xwXvYWm6wNKGMQ6NmTbZnTftqjkRRh3vyyZYnbEL+ZF8nGw5/A3lfHvdHLd/+7AzW5erIgOK47Th6otvzqnKgcbckgjX3dxjNx2PX9zotrNBvzscGguCohy7smZ2XhwPC3jEnNAO/Mq3Z00rba/yGZcp0+t1k8CE1gZveWXdjWU77+v1IgC2YfeTrbFwzKjfS7PXRVfZymPuFBO+bbKCouTpK+2iObaUwdI+zvOu5prBT1NNU2Y9Y7m9KGVSyaWVloQA6hcQL1m5+QoLn4QnNxYzjb/k/NLTJdjnyTaSmU5jhb9Y/cGaLmL/vPfvmmzfkUVtvuJSS692b2/ELKUfxOTERap6n4g8B/hnYLUbtx24QFXrKhm+roRvNSKSwU7yMzCBfJZbFUx4K6GJ+pp6O7FzzeXtf6EAu/QeAJq8xZV1y/y1AGzVmwDY5ApJFKVIVsZo89tYm25hf26MF6xs4u5e5fUbhnns9Z+p7MP/9z8HwPvjf7V9Pf31LFs5xI6dHbRmQk1uYMzMvwfGqky7hXhF2902kqpop5e2D7NlyIRdIDMDsTBcCh27BbdwsGCDqs2ObUn7ejzQb8/LGsLtgipZfTm7ERgrl92xwjGBVtvjBJRfdUN9blOb24+rKe2O+0ihm8uanK94rEhRfR6WYwPvF5dXANAkdnPzoBO+Qc9igH2Fu1iTvIyi64YUVAIbKh0gW+yj7BdY3ngxR3JbGMvtYXy8YizWSLk8Sjq1HN8vHWU+TqeWk8sfqBQ+Wdv+LAD2DF0bfv5xVpFMamXltbr/xnQ0Zt/PH2fkxExH+L6lRuH7ub1/5/Y9e93FImYOEfk94DuYDHiDqn7JLfepEr5uWQz4FPAWt+6vVPXTczHvyahb4TseEVlJKIifCgTqhgIF4CZCrfjuCXeygPC8lAJc2fImAIa8MAc00NTaYyYYd6k1l78ovYI1TcIt3VkuX9yAr9CcUM5qznPpysPs721h92gjL7njY/jfegv+/kGK+/IURyA/HCeR9rl72zLaknnEaZWPDlspx4ZYqPlmYmXScXs/UkzQVzDteMdInBbXCXGl04wP5GzdYNW1ONCU8+MrfxMK321D9lwdqBWYp7vsY3NgzFZ2V1XIWtloxwsEfHVlrXv7TTAtTZnwLLrfzlCxUBFobckE/YUCAy7WY0U8LF25q2ym2UCIpV1lrwyh73y79yAFzbKcTQAckV2VbXLlQdKxVnLlQbqH7qhs43kZEvFWCoXDJBKdgIdqiab0MkZyYSvssp8FfDSoAuZM0rUGbI03V5+o2bkWwTwd4fv2tbUJ30/vjoTvfEZE/hd4LvBtVX151fJjhG/Vup9hltQbVfUJsznf4zHXPt+acWbmrwBfEauk/hhMED8D6y70JPf4CPPoc50qEnEr5HBP8RcAnJN8SmVdTiwSeKuaD3C9s87050u0JBN0JlMMF5WkUym3jaRI7O+irxCnK+38fiN5JBkjsTwJBwrkhyHV7rOxq5fb9y+pCL0NLWa+7c6mK8cfKCZodcLqSC5R0VwvbMsxVjYtNDAJn9VsQuyegdD8O+zig85uMaG5PxsGRQXm5tak7TRT1au329WEDiKig893bnso/G7tsZUpF4mciYf7DrTgnXkz5a5JmYl2SHOUxebSRgc7vF0sKptgu6V8c2X7Nlnh9mPCb8izlPVEVc/hpbqOfTxM3PUWOTRyr23bsI7lsXO4f/C7JKoEukic1ob1DGV3cUbH8ynoGE10sq9wN9liP4l4GOm2KvM49gzfQNmZogOhG4+3VcaMNzcHUc9wrMZ7on7eE9WGJ2MaZfqiym/zm0sxIfudaWzzFUz4nnNKZnQSzEshpaau3+YeHxaRZuDJ2El+2lzOrV4ILoxrmu1m7+F8aFocGH3oqLF7MtYO74Lks3lCYzseHnf2D/OkrmaWpsr0FWPkfI+X3/33lW3k4g3ozQ8R/5v/BODIy15Fz44M+UKcK8/YTy7ron+HXWR1lbIxWoqR8kwQtidLFc23qFLRdAPN945+275UZV1dmrE320bsGGc0hZproNT05u2SPFZV23lx2pb1O/Nzf96Zj73w8v3kpaaN3nKk6OYa7rstnnJzCepGh5Nqcs0h7iw9QlIzFWHsTVBpLPAHD2s3AHtGH6ysS8abaU4u465Ra1ddKtvNS9/oVo6UrKZ0vqpNokiCQnkU1RKN2sbh/BZ6/K1k8wc4v+0VFfM1wJb+b9PcsIFy3G4aJtJ4G9J2IzaWswCyWqOeA81/ssCvajqbLwTCdoknS63xVgvZ1ysiL8H6oV8InA+0Ateq6tVzOK3pEpSGm068z1733DjlqDlgXgrf8ajqMNZPt5aeuqc14/N8t/X98JgxQfBVSkwRGCnbBfbmoS9yXvO72D48yv7Ybh4e2AzEaIzDC2//+FH78C55B02ZsNft5+9ax8vXHSGVKHGwu4WDoyaMAq222i8bE2XrsGnCbQmfNuf/zZU9muMm0PoKNv9NzSY8Hh0JtcPtw7azIKL6oaFQOw2EwFKXsrSrKvEq53KN0274skYTunf2hYNGi3b6Gp3GO1wlvLNlm2cvJhC7MA3Ux6czaZ8nWVzHfjlS0ZIbK94RGMFuiNb6mwHYrxZM1dUY3pQ3s5gj5UcolpxW7LTSZLyFkXFa6ar2p9AztpX16SfQndxOH/tIxZvJxFaTTS6inwP05R6tjE8ll5Ir9OK5vsKBv9iv8vM2NZq5OxC+UzFds3OgRc+U0A2IUo1q4n3ABcAoVi+/dcrR9ckAsBjoOs64ata6576pBs0Fc13havXxR00fVT3+leP05bh5vn3Z7UBoRlzaahWblrRexv+N3UfeG6FVlyICf7ftQxPuI5NaycNPv4qVP7K0mQ9u/yDLM+/l0o5BRKDoH20+rtaHlqQLJLxwSUfSUoDu6m+i0QnfIOAqEMLVzYnObbUxN3bboBWN4dU3UEYfdAFXqVAuV3y+e0ZMiG5qM620NRYK9t68rUs4bbgzFWque1xxjmHP0gZTTgMe80bZXrSbhDWxxazXZfjuc2er8vxLamN2e48AsBQTwu1+WOt5u9zFSP5Q5SaiMWUpRwMjWxhPWYu0plcxyCE8SZCWFtQrM1bqJealODh8J20N4Q1SU3IJo8Ue2lNrAWsVBkdrt+NzflucMIYwcj7opDRds/Opyh2ORcK3Ft4O7Mcif88G7p96eF1yLxbv8zzg5zVu83rs8nPf8QbONnOt+Z6KJgjK3H+uuqZYOlo+D+R2A9CcWs6B3N0symyi3e9gd37yQv3/fNYfcceBo9NJ3nD/h/nS+e9hc8tYxXe7ssFMpPvGMpVxPflERfg2xHx2jJiWvKk5x5hrknD/oAm2rpTvtgmPk3ZX25VO6DbEQ8kcpCQFUc75crguaOawsil+1PtyVdDhIidst44NAGG5SYALGiz9pzG/ztaJad5LvOWVYKo95V52+XeScALqTL24sn3KNUsoYjcbB8pmbj4i4R1Co3SSyx+omH/7R0KT9Hh8LeLjU9I8A7ldxDMpxsq9bPaeyFa9iSVN5zNa6q6M7x17kHRyMd1j5nYI8n6D+ACAnqG7ACpWjfIE/tmJ2hc2u57Ew2Ozn04pUY2r46Kqvwtey/zNi/4u5lb8UxH5tqpeN9lAFxf0cSwOSBnXhrYemGshNW+/BXVMEFQyqQYcdL8JcjJ9V6M4iKBdkXou1w9+btIDvHnV+0hImd1jST5+1vt458Ohdvxn932Ej5/1PlZlTDBvdz7fewZCAbOxWXEWXLwU7BqzdQdyaVZkXOEMV/t5tHx0patqghKVWvU1GnZJwEMuXLlQFe7cnjKBHBTnCITu+pYw4OrBQdNUG8S04XXJ0DrX77TiQFbvETOwLCotoRETthsaW0iMXkHZBVUlqn5iW10gZlDhKjjf1QFPR5xpeSqzr4hHQ3o17d4q9uXvZlFqA2dknsi+4l00xhezmwfIF4cpSZa8K5MJ0JRZzWhu3zHpSfFYmAaWSthcCu4GrVQKiwMFZuaJNN5A6I7P8632eY/XqqdaNx3q0ewsIi1YxaVL3eMSrIBQMNt10ymnKyJtWNGIF2HlFDPAAaz3+RdU9c6Zmnsd8+/AX2Dm81+IyL8Av61av9qlqF4G/Anm2wbT9r86mxOthbkWvn88xbozgf+HBTNuA74O3IFZy4pYqtEFwMuxaiejwJuZn+aUGWM6LQVjMZPTwcW0Ib2axmQXNw/+y5TbJTzoLsQYK0Fr4lip2JOHJU7QjTnhubklvOAPlTwuaDUhty+bos3tY6QEd/e7KOPY0QE8nVWVLAuuEEfQLXBoNNx3cGXLVrTcMJjqll4TROsb7HMHwvdAVe3KIMq51zfhU86H+y5i44K0rZKYwEgQ54hnJtWRsVF8z6/Ueg6WA6TcfVETnQAsbrfKePf3f53pkIh3EPNSHC49ZFHsCj3eQTLxThbLOgbpZlXqEvr83SxKbaxsN1I+AmlIxSz9a2DMXAbVTRnyznx+duuLAXigP2wVGfPCiPVJ0aPzv6YqtiFe1T+1fOLCt07NztdiwU0njYhchmluK8etOsM9/lhEPqCqE/uIThNU1ReR5wHXYDcgb3eP4CI0PuZHgCPA8/Vkq76cAuZU+Krq1yZaLiLtmBAVLFH6Xao6QVYn1wOfF5G/BD4JfAK725wTROQc7MvwFKx05hDwMPBN4N9VtTDF5rNOkFayoeNFgAVn1RJoc2azsjhV4pbeBNmyx3vP+NujGix8YueHeO8ZfwvA+W12Ud2XDb9quTLc1GvaVldKK4U0FEg6WRmYiwPZ11T1TR3J29V2daOrglUMBWygBR3O2obVGvOqdFDZyt4H/l2vSnNOOl9vl5iAGq0SCkHlr34Xu3FpzHy2j/q9pF2087DXT6vfScF1ORol1ByzZXs9XDZf+3AN57oaEY9MaiVlv0ChNExHw3mMMkrGHbvBa2dQu2mglT2FO2hKLCGv1cFkPTQnlzHkopwDf/Jo/nDVMeym7UDpWBdZEB09UWRzoL2XqypqjR8znsnKnE6XetR8OdqqNwjcjRUKWjrx8El2IrIea5m6CPuJ/BvwPcyydTnwLuxa80ERGVDVyU1WpwGquk9ELsJMyq8BJrsjLGM3LG9TdTmVdcZca76T8W5gOfBTVf3r4w1W1X8UkUuw3r9/C7zuFM/vGETkz7CSZtUq52L3eALw5yLyIlU9FX7uiIgFi9SQ3jQHfBXrNX4HsF1VVUSuYZrCF/hHTPCCVXX6t6p1t4rID4E7sevMx0Tk+6paW6eNeYrLbnmjiLwHS5+6COjE5FkfsAVrzFPX56Fehe+LCe/yauU/MeH7zFMyoykQkWcCX8TudgeBjwG3AM3YZ3kNZiL/kYhcfipLYZ5IS8GJ0pEm4+rWt/HQoHB3Oc5Isczli2MUFd606n38897Q6hX06y06E/FwlVWx2kzYXxC2D9vYxWmPI05jvagzKMJhz/vGwgvsUME01kbXS3BjVdLE9YfMuNCUMPPxee2hr/nOPjN1B/Wbh8s2NlUV8JR11qnANH1/Lgx4utA7FwDfNyPMtqIFMxWlwJiYKbpBWzjA1kqbwEKVSbdUtn/7oPOPelXdlCYjiDYeGt3KkpbL6R3dytLmi+jLPUpPbhu5pB13pX8uu/27aYx1MkIPGxNXsUfvo9HrrOyr7OcZyu+jULJtys6M3JxZURkz6EzRuaJpsGH9ZwiKfU7UgjtYFmi6gT9XvDDQLog1mGnqUfNV1c+e7D5E5GzgBe7tDeMEb3CcvSLybuBLQAPwVuCdJ3vs+YDrVvQ/7jHvqFfhu8w9T8dcENi7Fk05aoZxUXWfxgTvKHCZqj5SNeTHIrIF+AfgPODVmKA+VZzSrurLUw0VM/Hvry1z3ZEYQ0UlExP+aOl7+NqhjwCwYzjIqTXBNlLlccmXYbW7RTiSg6ZEaMZMu3SgrBt/cMyO9tjFofDdNmz7DHoE91YF5JY0MDfbmO1V1/sm52PMOKEdvM9XeTQGPfsabclaFbBVGqbq7HZFMZaqfT17XYWqC1Mr+cHQdwFoTZogC3KofQnvOrK+je9oMiE+UENkcGfyDMCE70jhMIpPggwb00+iW3dS0CytseX06B7WeZeAwqPlW2mPL6dcznO4FKYoJWON5EqDtGbW2nyKNp+RXGh2DkzBeVdhS6puTHzf/ilBJHYuX1W6cpxgrQRQnYQvt1bq1Oc7E7y06vWXphj3TeAzmPB9KQtE+M536lX4HsSSo88Dam0l+Fj3PNs5vo8FgmTIT44TvAGfwoLH2rH+xadS+EZELCimUV5yvvHEqte/nWyQqmZd+9UnA+tFZJWq7p1s/HxFRK46/qhjUCCPKSUH66m3b70K39uxnozvdj6MwakGi8gqTLgpFmU4m8Sw/pIXAv870QAXpfcIFiAxPmJxpjluqtGJ8voV7+XsNuHGwyXyvs9d/SkOZUu0JWOsbIT+gsfHNr2PHx3q5aDTHC/tNNPmWClUT7rSMOTq5jfGFRdETH9eKzWZb+02jSnQUq89FG4f1EYeLdlzrhR+jQNNO+vW7R6paujgqlZtHzbt7rDnNNmqgjlBlHJQW3lFKiwHvNMFaB309gOQdOlF9+UPsDx9AQDdxa10Js6kQa2E44HsXZXtA9PsaHYXtdKTN+14bfszODR2Pxubn8kIvfSyjzJF2mOr8CkzVDrA0tg6SpQ5y7uSg+yiMb6YnB9qpCV/jJb0qkpjhbaUabBDXqjBxqojkJm66cJE6UHVZnI4uh9wUNN5ptoOBszftNXjEpQ+G6qhheoWTPiCFdE47YQvFuV8Ug5+ETmIRUX/narOXRNq6lf4/hPwMkwAXy8ib1LV68cPcibf5wOfxczNRSzqedZw8zpmbtW4eQbVvE5p5N10Uo0iIk4HTkezs4ikgCXubS2CtHrMmgn290Lghe5tm3s+S0T+Ixijqq+Z3iznhJP9by8H3gC8RESeqep6rs4BdSl8VfVWEfkoFvV8DnCNiBzCyosFNToXE0a5Bf+Q16vq7JfYOT5/TujHrrtKK7Vy72gPLcnFtCRjxCTGz3oOsjrWyZGscn57jMNZ4UgZWqSBQ9ILQFnNB1pdaWqoKJWykz05JemunklP2DdqWtmyBrt5SDqXY2eoQPGoaxeYdHlJQ8VQgxp11TsWJ22DYlX1qoRTkRa7OswNJWuRt5/wBnh1zPoehx2MBirrAm047Wq0H1arm5wt9bI2cZmN8Ro4lHuAVNxSlaoLUpyMpjdcOsyyhgvp83ezgUs5GNtDRlrx1MPDIxVrYb9sY7R0hKb4UkZL3TTHlxylnca9BorlEbqSliK1c/ga+6xV80onrNpVQ9LOQ77YWzUL09yDdDSp6oPseWYhyBWqx0/cwWimUy5rDbhy0bHvnmLIR1X1IzMxpxmguep1LVas4arXzROsvxD4o3HLloxb9ppaJjaHLAOuwmo+JIEHgP/AGuwECfWdmFx4BeYSVCwFdQvm9nsiprB1Ad8XkQtd9PSsU5fCF0BV3ysiA1hB8GbsxI8P0w9+doeAt6jqf8/eDCfHabptwLlYVZrfd6tuBL48R9OKiDgtmYbPN4EFJU21vl7IVL2upT5A9V1OZvxKVX0/8P6Tm9Kck8QCzxJYAOu7daLQe7gZ+BcReRuWqvUnwLmqegT4jIg8DTM9rwVeCfzrqZ/6sdR1rIKqfhLYALwN+AWwG8gCY1hd6J8ArwU21YvgdbwT09CvA/4Au0n4CvAcVZ3ZZqYREQsckdoemFtqbIrHqQ/Nrp3qdMTkpKNCqmxDnLJUxjnmPUAL8BNVfdckgreCqn4a+C/MJfnequW/xkpVCqFiNOvUreYb4O5WPuse84VjfC5YN47XYCkBJ83xTWgeM3lvtazt8aQ1zW19g2xubmVdE2xuXcoNh0uc3Z7gjl6fgXwJT6AtkaTRN3fVvS62sD0V2gYPjfk0J1zXoLRQdD+hg2NlNrXaV3LE1WjuztnKpqqm9j15u3+JuStqSyL8GgdW5sDcnCuHAVeeC8Z6uGQBRhvj5gmIFcPtR3y7/vZjgUqJKmUoqIQ1IgMArPEtZehwIgywH87t5+zMM3go92s3n+mZWFsaNzE0upW2RjMNL0lZzE1Bxxj1zaS739vJ2Xo2RfU5IIe5JLWGa/KDdOpKdtFLUhqIxZezRFfzKKEZuMFrY1TLbB+0hjCXNZnF8fbR/6qMyTizc1Bysr2qq1GQmhSYnauvfUGgVnWp0uqxEAZfTWSKPhkCF8bxcCblejErH49qU2jTpKMmHjMnZtRZ4FmYGXmqtKvxfB0rQfx84C1Vy3+BuQPXT7TRbFD3wnee8gPg25i56HLgHZhA/rSIbFDVv5iBYxzHhFaXVX8iImacujbfnSCqmheRI5hvclUNm1S3Zz1dW6oGbsfp9KYMUouWjFseJLdPpzfwjFL3wlcsqqMTM6tM9DuLY2aZFmAz8DJVfc7szfBYnFkj4GYR+SZwA7AReJOI/FhVf3mShwlMaJMgxy+fNA1e2vxkdo7kOai9PDQMqi0MF332lwYp9DazsjHJQClPniJpSXJRux1+sGA3Abf3hTfjGxqbOJQ1DbM8ppWeucPFIr/oMU3t/AYL+Ak02UcGQ4vg6kbToHpyQeeccJ7tqaBd4LEWqSAlpcG3gKngy3RIdlTGxHxrjRdovCrhfnaKteJrUfu93jZipcmXNT+GXSWrGprN7+Pu4jempd1Vd/dJx9vIJRYRc1rifpeqtDRzHl3emZQpMkIvj7KLopcnoSluy2/nbD2fIwywOn4R23M30JpcQb/XQ4e3trLvfdk76EifQdoFU92Ts8y4lkx4bc/E2gFoj5nGva90b2Xd0cFXRwdcBRpvMmHnZqIa4SKBZXRmNd96rHA1QzyICYcWEVl5nHSjs8dtdzrSh52Pi7Egq1q4xD33jlselH6bMxN93d40isgKEfkvzIRyCPP37pzgsQ37st2M1VOd9fKSx0NVu7FWWAGvnoF9fkRVGyd71PG/NiJiRomJ1vSYh1TXLHjSZINcG70r3Nudp2OBDccNmJ/2PSKy+HiDRaQDazyhwE3jVj/VPW+d0RlOg7rUfEWkCQtWWsv087rG3+HUC7/BNNUGrHLXvOJIrszqxhRbx/rYGFvCwyNDxIgx6PXxihUd/PZgkY3NjfTny6xsirHNaapnt5svsD0VZj/c0jvM2c32frjoV7TUJy1L80C/je9zBS1WuJSj60bCm/7+EasgujRlKUPduTAYNOG6EgX+4JQX3oRsd2kwj2sz7ex3Q6adbdYLws/pHf31Cfy7AF3+WgAOYdlsFzb/gc1VDlIshzfQ0/VpdrVcWnndFOtCM2WWuo5JPTHTypOaodlvZb+3jRhxsgyywT+XVZkM92UP84j3CDESFDXHE1Mv4lF20ey30yP7K/tOx9vIlQdJuw5Eq+PWAKzJD92FNw5aMP5AxqyYxVLYecj37TPGYmY5qO5K1JQx19lodvK+IaeqtvPpq/jy34QRyn+G+S8n4uWELqh6CjydaT6LVQhcDtwkIn8xmQVRRJ4CfA4z2fvudWBJfRHwekwo/2wW5j0hdSl8MUf4Ouzk7Ad+jmm/73bL/h4Lp18NPB1odcv/Epi6Ge0MIyKtWET2YlX9+WTjXJWrQexHUkv0YkRERA2crmZnVX1QRH4MPA94goi8bnxzBVfd76PubZYZCuisR1T1ehF5J9ZOcD3wM+cXvwfowcx9i4DzMfN08M34SFWRpg9gckQwM/Y/z9oHGEe9Ct/nuuc9wPlBErTrHnQJ8OvgZIpIJxZO/jSsl+5Xmd2UgS9i4eolEelU1Qlv7123ocBUsn+iMTPBiXQ1morntv8NeS1ze+k+GkqtPK3xXEZLPgVKdHppEqQ4mPVYmkmwrAF8YuwcKrGqyTTW7UOmwa5uCqOVl6caGHYhzp1prxLZfEdPmeGi/evObDGtts9FOz+pJYwnGXJ+5P2uhOWKTLpqnes45Fs3sZSG6xZhJR/vG7BI3k4ssveQKzMJ0OkHyywiur+0u7KuFDONtsXFfdzZ/xXAtL6RbOg3Ph7JhGnugZ+0VJV9djB7L8syF1Ti5drEipSc7a3jsD9CttzPOXIlLV6K/fRwMHeIs2Kryfnt+Kocpo9t3g6a/XbykmO4HBZU2xC7gt3cT0rM3TXgYk625n9TGZNK2DrPuS3CbsvQ2mCRz9mixbDEvMbKurHcXjf+aPPuROUlZ5p6dLCIyJnAleMWV9cpeKmIVJc3HJkkXfIvgccDHcAXXC/boJ/vZZggCYKG3q2qp+zaUg+o6idEpBtTwJa4x9PHDQuEbj/wDlX996p1G9z6I8DvqeqcWUrrVfiehV1+/mlc9ZGbMeH7RFxJR1XtFZGXYtVOVmF5v/80i3O9ERO+ccz884VJxr2c8Hz/ZpIxM8Ep7WoUEVFv1Kk/90osl3QyPjHu/W4mMBmr6nYReQ5WGS8ojfiGccN84EMur/W0R1X/Q0T+G/g94DmYprsEC8rtwSoh/hT4xgTK0E+BH2K94uf0WlmvwrfNPY+P2rsfu2u5uHqhqg6LyL8BH8L6X86m8P0WljvYDHxARH6hqruqB4jIBYQ/tiGm16d4TulMJTiU82llMTGNs3Mkyz45TErSbGxJERteyrf67uIx8fPZMjxGkRLL4s38csQig1/Qar7Le/tDn+hFHRn6XTvAPSNFNrWaFT4moGpfyQOjR0cy3z0Q3oMFzQ7SXuyoMQA9JTtOOxa1G6/Sizw3cHXaNLatWctbHa4KE/Ccj3iFK4u5VJZV1m3nATuGv9Md17T76Wi9EGq86ZSVtwwijIPXvYXtXBJ7hs3Rs2jjh/0EA7KfJd4mcppjWIdJawbB42BpmBFvGE88mvwmRhgkToyM38JoPOzV+0jxRpriofI1VDLrQDoRNkReH7O4nV3+nTbXqs9WKJiFYKKGCp7r2xtzz4F/91Rpu0cd+zQ1Oweo6i0icg7wJsxfeQaQxrq//Q74V1W9Yw6nOOs4wfnvTH2DM9F2k/nNZ516Fb5jWOrQ6LjlQd3mszmWW93zWadqUhOhqj0i8g7M/NwF3CMiH8e0dIBnAG8GGjFt/s9OcTeNGe1qtDXXS7/XQ5PfSpM2MkSOKxrWcO9YN7tGiixrSLLYv5DWpJCQRg7ls7QkYzzJBQwdGDWzZWs8TKfZMVSi5HKI1jYluMc1uW9PJukr2sU6qL/cXTDT8rpMS2X7NlewY+ewmZirr71rM/bxby48DMCFsY2VdY8WTXg0l836P+x69y5zgVQAmxraALgvaybZUS9sqNWlNu6Owenk+B/Lue2vsPnnbjxqv2ApTn78TLZg19JOV69lsb+IsldE8GjQDEUSbGps5Ui2yJDm6dadnMNj6JchipojpUlixGjUtvDAifUkNcOjo9fYeUib8C+XQwH5qFpQ6HDOrJfVpuWybz9HT0zABgFYAHEXhFVdy9rGhv/3iYT2TCB1mNOuqv+B1R2eqf0NYDf586VISMRxqFfhexgTvquAW6qWb3fPZ4hIRlWrc7QCQd126qd3NKr6byLSAnwMC/766ATDxjDB+91TPJeoq1HEgiJej07fiDnFRTWPr//wprmd1dHUq/C9CStI8SosuAAAVd0vIqNYxPATgF9VbRP0vpzZlik1oqqfFJFfAG/F+mquwAK/dmDR2p9R1YNT7KIuGZVRNukZLMokSMeEW0cOcDjbSEqTPH15nB/tzdIcT3D32BHOTCzhXr0Of+wJdMRNcw06FsWqmq4Ol0qVwhrbhwoknfl4RWOMkm8BOk2u/GRryjSqR4ZDRb4n78y9vqUYHRwbqKwLgqouSZrGm6iySa4oWzDVAyULDlrvWivf791fGZMZM8NJm2sMs7VwTTjvsek3zApMywBNKTP5HvHtHnJZxqU4VdUD6fUO0+ov4gyX/pRwZvhur4dLkxvozRfY7x2h0+9g1+gYeYq0ehnO9i+lSJnF0kpcY4zIKGMyTFFCrTavIwyW93N1wysBuNsFgHYkwzkOlc0UXSoNACBVdoUlrWaSPjxkRqZYLLRGpBKB6fxoSVgqV4VsLCDNN+LUICLNWO/2l2LX2BS1Z5tFwrcGvoPVQX6Oqw71EVXd4tbdhEU2/52I3KCqYyKyHGtmoMAjczFhAFV9AMvHi4iImCWm0VKwEULrUMT8QkSSwG8JY36m4+2vp6YZQJ0KX1X9pYj8Cgsh/wMsyCBIIv9XTPheAewRkUcxrbcBE74/mP0Z1w8znWo0Sj878HkoP0arv4gECbJaYm26hYeHhDEtcEFLhqbsMnaNjbLOu4Ru6eGQa1DwmMQZADyS7a/s89zGDvrzYcODgm+vh4sQd1fSbaOusH/MNOghCTXflFqA0EqnFd9SCgs7lH1n+CiYlhuvKoHY7ZsWNuIKaexwKudZGoYQ3CtWtS4tLe4YF1XWPXQCmm9jMiwd2zNkpSLjrshFU7P5nps1LEAivkdOwqqhnpp/dZUu5c7Co3gSY7Uupyg+TV6Crnia3fkhOr1GilpmRPMIHiWvRKt2sJ9wzm0sZzgW527fNN7AB9udD4v8BJ93JGUpSolY6PMddf7cVHLpMevyxcA3bue0MG4sQL4weSW/4JwEGvd0mIbmG3yJTvMQrdOWP8GyXRRz492M1X94JVAGvonF1qx24zw39v1YTE5dUZfC1/FSLJLtJUClXJqq/o/Thl+B5b61E/6YHgA+NcvzrDdmNHz+WU0X8b2hX7FWLqTBCYIjXg+tpeXcNzDGZR2t3NuXZXVjmrTEaZI2YtJeqaNcKNuFcbEXCpgHRvvY3NDh1nsMO3nZEIOsE74psa9mo+tm1F4ITZwBe/MmTEterrKs3QnmoqvJ3FeVqbYuacfcFLMb53vzFlR0t95YGbPIs0pNDWqBW3ePVLwe06LFdQMqVFWB6mq9HIBlnpm2gxzkMQmF0pgMs0ZXVsy9ezwzAzezgjW6sjJukBE6Yp3cX9xLUpLs1yy9sg+RGG0soY99KGX8qs5DA3KAbLm/Uj1rhGPj/mLjWtqm42EkdK5kAjZfMMG8qPWqyrrDrnpYJmU3FIHwDcYej+N0h5uSeCRKFwovcs+9wCVBGU0RuRBTwD4fRH2LyCYsE+VCLCXp47M92eNRt6EKqjqiqr8HXAD87bjVr8Z8q1uwzkEHsPJhV40LwoqIiDjNEdGaHlgmQC3t+SLqk/MxTfYfx9WvDuo2V+pfq+pWLAe4BxPMr5qtSdZKXWq+IpJU1QKAqt6P5fdWUFXFhO3n5mB69c6MphpdN7KL9tgqRnSQJm0gITEe27ia1Y2wdbCBa/uPsDLWjjhVd8wvsq6xgfvHTPNZ55m2efWysKLm9/d79ObN5KkKvS6FhWHYoabpLVHToB4uWPewxYQaWIC4e8eLJDQN97lGT0sTdhqGi6Hm+UjR0odanRbd4oKqVnmhBrffZYEdcPXWi6W+2k5UFZ6XoSFun3u4EMbYBWbeLrUKV8MuQL9Lws+WiXdyoDBMVkybzzsT9GA5S1qSZJxFIOUn6S3kWOMtoz2V4L7CPrp0LQ2aISExilJgTAaPSqM6KLtIxZsQtfOWdzUGOuNhS9PDJfvcgUl5jRee2+1yg61zn23fwDWVdYGmXozV3kwrVmW2Ppm6z7VqEJGvd94TRPXdPW75vZj189Lqhap6UES+jDVX+H3gK6d8htOgXjXf74vI70TkuccfGlGNqo5GF5mIhcRp3NUo4miCDir945YHQbabJ9gm6Ax1zgTr5pS61HyBx2B1kBd08NRc09a4mXPjq7ij/BDPatnMAwOjjGme5kSGLx+5m8clLqCdZgZKeYZHinQkUmRLHisaPUZLph31F+z38vWDYRWpsdgwqbL1kB2UIS5osCpSPbkiGzyryJRwyZsdaqlHj5bC/tmL3A3worj5oA+XQiW/w2lehwumMUrV/WUTQR/fwElo/uRDOlAZ0yemqe7rv6bm8yQSr+RVP7v1zdzhX0+DZzWSWzNhhamYSxva50IYlviW4jNY7SkpZRiTMVZ6ph33uew0HyWOMOhnafUybGpsrdTH3p8bY423lH4do8VLsUUeJqEpztBNjFS1K23XJTT4DTysVv+lKWbBYEUNfea+mgM+k7D/332jP6ysW938eADGEmaVGMqHnaZGc7tsX+XaDS7VXZFOBi8SrAuFbiygatm45UH9h00iklA9KqdtwD13nOK5TZt61XwDO9yCKpkWERExfTyp7REx77nNPb943PK9QB5TJi8ft+4M91x3d2j1qvk+jDnXzycs0xhRAzOVanRu+yto9lvZVxhmg5zJTYNHGPEGeXrLBtqT0KHLuKl4Lyt0HRsaWrk9t5tFrKLXH+WrPXdxoUvFW91oEb1rJIzUvW50G2nnuxzCI1c2Dc5HOVgadrM3rTThPsfK2KLK9mVXmjIds3tHvxT+rgZcX909nt0Md2qoeY6J87E6n+vNWSs2Vl3buH9kfDnxYwkKZ+TyB9wSn9YG85s+wL2kvRYa3P1j4F8FS9sCWO2fCcDBmGmOZ3NmZUxMhHQpQ9G3c7KIcP5pL474QlHL7BotUnT1ZC5pa2XncJFFsUZ6yqOsYA1ZyXNIjpAi7OqU1gwj3gjNrrnOEt86Re2WBypj1sUeA8Aevc/eN19dWbc/a6lSuaL5wctVWm4QrRz4bjuarWV13/BR4RqnhKjIxoLhB5jv9pUish/4hKr2u3att2Pdn94pIjeqqrpr4V+5bSdvND1H1Kvm+04sYfCjIvKsuZ7MPGOEqLNRxAIi8vkuGP4buAsLrnon1gkq4Ktu+bOBB0XkO5gSdxGm9f5kdqd6fOpV881hHYr+FviJiOzDGifsxboCTflLUtUPnvIZnuY0aTOHvT08IXUBO7JDFL0Ca3UVO4cLjBYTdNLKE1uXEhP40eBDnKHr8IHL29rpGr6EotOEunOmna1oCPNHz/bW0RiUjywv5lDetNWYeGzMmD/XKcNsd/mjhDU5KLs3XTGLoF6TCnOAe52PeY1v5SUfrSodeZZeCMAN2e8A0JkxjbM6arcWGpLmz93Q8BT7jP52Ml7YmahTV9Aje9zrUONfJxbzcUgGAFhadhq0hBVRm+MJzsy00p2zz1EUe96UWsT2XD+dXhPtySQNcWHXaI6uVIrhopKOefhAqpzg0s5GdgyVOFxMHeXzXpJsoD3Vzq9GrcHEoGcabJrw/A2Laede2bbrLoYFONalzed7f9YawwS5zADFklkV8gWLKF8Vd0WIwvRuBkas2cVMN1iIfL4LA6fNPgf4MRbZXJ2o/jWs9sNTgE3uEbAf+IfZmmet1KvwvYZQwAqw0j1qZSEL3xlNNYqIqHcif+7CQVUPA5e5TJgNVctVRJ6HdX16LXbbV8Lq6r9ZVXsn2t9cUq/CF44uATedn9eCvg2OuhpFLDRicuLVsSLmDyKyLGhOo6rHmJFVNQf8lYj8NZYtM+iW1SX1KnzXzfUEThQRORPr3/sUYA2QwJpeX4+VP7ttis3rhrzkOUM3sTXXy4Z0JyW/mT2FIVLlBG1+nMXJFAkPru3t5Rmtm+nOllnbHOd3fUc4M9lJb8FMqXGxwKkHh0JFfJe3jWclrHjDrblDeK7+8hPbu7in31KEmmN281B09dBbY22V7YNORXe7nrOtfhgwNeKCqlJYilLcPQNcn/0WAL7rhjRdc/OyNjO7HnFBROc1WUGdfgnNuxfIuWxjL2t9SzkcrOoHvCRjn2ls1GLilqYsVSooJAKQ9jvpz+UYdIaLDbGwNvLj2jvJlSFfVkaLigc0JzyGiz6tqRgHxwokxOPG3gGWxJtIEK8EZQEMF4vsLvaxWs3cHqRc7YntqIwZUzM7513gVKqqc1GP2rgzO14AwFA5LB3puc+fL5qC8eDwj4ATq9U8XSTSfBcK3xCRtcCHXL/kCVGL/js8W5M6UepS+Krq7uOPqj9E5HVY1a3kuFVr3eNVIvJRVX3PLE8tIuK0xVvYxq6FxPlYvm69BgpPi7oUvvMREXkh8AXMRN4L/COm7ZaAy4C/xvpPvltEelX1H0/RPGYk1eh5nav4XXc/Z2UWcVt+O5tkLXE8fJQ1TTG60j7XHSrRJBmGCkrcE77TfxeXJ87nQH6M1RmbRoOrer84E5bU9YfC1JrNqS625i1uYt9IuaKN9ZZMA96UWgKETRQAGoKev675QY/XXVl3qPwQALniAACXJMMiabsLv7bj1xDw09xg7qTqHr5xsbSdJzW9FoAdnhXWWeefR9rdb+3VXpbqEmJOm/ermvU+MmraZNb12C3lbV2mKh1oUTpOl8Tpzdn+GlxjiXzZ5+7+ETY2N3F2m3DD4RLtSRszWipzMJdlcTJN0fe5qLWNQ2MlGrwE+apUpz6GWSQt+E5YdaXtPA7mwjSuAy6GpTNp/6PB0v7KuoRYAZP+ot0bj1Zp7EH3piDlqj1hxqsd/ZMHmQZlKuHEyngGRAFXC4agHumWKUfNE+b0DkJEXj6Lx/qDU7jvGPBpTPB2A49R1Y+q6vWqerOqfgbrQfmo2+SDIrL4FE0nSjWKWFDUmmokIo3hzWnEPCSo6fyEOZ3FDDHXmu83ROQtwDtV9drjjj4BRORJWNrSY4Fvn4pjAE/E/LsA71fVYxK6VfWIiLwD+CF2B/cC4MunaD4nzY96d9NAI9uyA7TSwTmLktzVW+bc9jR39ebY3Jaip5ijNZaiMy0UysLl/vk0JjzOSTexe8T8qn2m5FGqahlXpMR1I7sAOFNWkFLT4MqqJJzGuMOzEoxDxTYAVseq+uK6soQtThMdqLqHTHqmDa9IW5GHmB+rrKtF4405H2fJFevY0PGiyrqu8go3N9N4V/im3flo2PCAdjyxMpAA672wEp4fMw2tz/lVq7XigGUN7fys5yDr46aNNidcw4qS0uAlGCqU2TcaY7hcZKDkAxk6UnEOl0YQSdOcSNCf99lbHOQxrZ0MFUKtMJctMKbFila+J2vn8aCE2v0qzgVg2M3xDC+sVZ92LRDv4zfA0aU7+0ZcoQ61NLCxpPl+PS8Tfn7/6IZjJ6PtViO1a75RP9/5zZuxTJgPisgI8FVVzc/tlE6cuRa+nwLeDvxWRH7n3v/CdS06YUQkjvUBfgtwBVaw41TmeT2u6vVUydy/qXp97imaS5RqFLGgiFKNFgznAv+MufA+D/yTiNxHbfUfVFX/9NRPsXbmVPiq6l+LyI8wX+mTsX6MB0Xk28AvgetqvbNx5qSrgedjtT87sDvc+4E/UdU7Z/4TVLgZ+Bjm091/nLEBqeMPmT5RqlHEQmMamm/Uy3d+8x8cLWCTwCXuUQuR8K1GVW8QkQuANwL/D1iOacNvB0oi8gjwANa5YgC7wykDDcBSzNx7PtZOKrAxCrAD+ATwFVUN8y1OzWf4DUdrtZNxVdXrvZOOqgOa/GbObmzj5uxO0trA//UeoUtaublvgAZJsXWgwFVdzfyi+wi5gSZaEklUlQfGehiSAZ7UZAFLjwxb4FROC5V9L4o1Mua6Hg6W84y5nrX7i8qZGUsburRsqTp3uACqPVU/uUEXYBX3LbinRcNUo4OuR+1Y3IKbdpeml9kV1CZuc7WJW/wwKKgrZtfue7JmYl2VtONvl7vI+FbPfW9sD+3+Ioa8AQCyVWlQG5NmSt7sW72YuFPZRkth+a6HB4qs8jpJep5bZx88JsKyjJnnu3M+l3U2UvCVRwYLFMpKk6TJlsyM7eGRIsnekWKl/jVAt3eEspTYpFZrPh6z41/sX1EZk/Ptp5J1/5N9PFxZl4lZFS9P7cYuSCsCWNJiNaEPDVop9nwhTEM61cS82vJ8o1abpwXj7Rzz1u4x58IXQFXLwOdE5N+wu5M3YCaGBNaH8ezj7KL6H3AXZr7+jqrWW/b9O6pe/2rOZhERcRpxWuSdRBwXVT2t/tV1IXwDnIn5X4B/EZGLMBPy1VikcPMkmw1grQevAX6oqg+d8omeAC4H+Gr39mZVrdt2iWd2vIBl0sy20RFWyDL2y0HOjq2mv1QgLUnyWuLctgw/7t7PmthiLl+c4OcHh3lQb+AP257FjuEW/m/U/g2Pz5wFwEAhNIOf0ZJgcT7s1rPD6SMJibHbBQGNiQXndLmuRH1emNZyReJ8AG4v2TEGywcq64ayVlO5ucmKUwyMHvt1iMUs4DXoJ+tVmejPanuJHde37avTmPrKpg2uTl8GQNZ5RDbrZZViFmdxBme0Jvn5kAUTHZVqVLA0nrPTFuj+aHbQHSOsB/DYxEZWNnpsG7T9lZ3GHxSSyMSF5oTgK+wZKbM4naApISS8OP15ZSBfpjUVIxNvIlvyaU+FAWcNxWYSfoJuLG1rH1a3Oah5DVSCsQ6WrbtTXELvyEDJjDVBoFW1a2O4cJC5Yhpm54iIuqGuhG81qno3Flr+AQAR6cKaKDdidv8R4EA91uwcj4u4/qx7W8JM6ie7z/cA7558hEekE0QsBGo1O0dE1BN1K3zHo6pHgCPHHVhniMiVwI8IA6zeo6q3zsCuE5jfexJOTBsQhBgJ0jGPJV4DhwojnB1fzWCpSFl9nrW8iYcGlN3DJS7ILGO05LNjWPEQnpR8GrcP9nJuYwfNResqtCVr2t4iCcsU5kowkA/9nClXgjIhsUpZxRWuS9B2tfi1koRpQgMFe50T8882eG2VdaNx88vu7P/ZpJ9xvDdCqtJhstg+j7j7lnY/LEBxKGYa9oryKgA64pZ6c7A0XPEH7/V7GRxoZJnrwzvifKcALWoa96ArvdnktMoGXX3UfPrzWqXp2kTaY3YrVVLIleFQtsRjF8c4lDMNuFBWiuoTE6EzJdzfV2BDa5KRYvg9aKeFPbHdlc90tlqJz7GqmMZep+lf5FnpzO0SWg52D/7Gnb/iMedxNDvEXBEV2Vh4OGXsTzFr4nqgFXiSqj4oIo8DXg98rp4tjPNG+M5HRORpwP8QCsl/VdWZSnkqAmOTr5YpBHNExOlDVNt5YSEib8JSR4PScIJpG4GPZTPwKuCVIvKvwFvqMP4nEr6nChF5FfAVTEMF+BLwppnav6p+BGufNcnxEyekDqxqfyoryqvYVx5lTLKkSNJXzNORSLEkk+HWIyUe9B/lg+vX89P9sLopRksCjmRTiAiXt3Xy0GCWQ2Ia1NkJ0wAHi6HmeiRXrjRH6CnmK00UcuUyDWpaaN43zbhDTQMe8sKvatlp9YswjfGe/v8Iz0sNGv+SFus1u0gsWjnwbwI0Ysdb6ltZS79qf4HG2O2ZNp8tWRjCsngLZZea3qgZ1mQauStvvudNXtgJM+hhHPTqbY7b5/arptybL1L04xVfb5DyPlKEloSQisHSDGxqjfGL/Vm60inSMY/OlDBU9GlKeGRiyqJ0gv68MlIMLQwNXpxl5ZUsioeaPkCbF5YiTxTsPO9yzRb2Dd0SDnTXr1TS/OHFUphKHkSJzwXiRZrvQsEVKvo4YZDtDkzzrSaoHijAn2PKz5/MygSnQeQUPAWIyN9gzZ0DwftZ4PUnWzwkIiLiWGKeX9MjYn4jIpuAv3dvfwtsUNUzx49T1Y8Bz8TK+QrwRyLylFmbaI1EwneGEZGPcPSd2ftV9a2R4I2IODWIaE2PiHnPWzHT8sPAc1T10ckGquqvsOqGQT2F15366U2PyOw8g4yLQPaBN6rqF2d5DifV1WiNfxarMg08lOuhQTNc3NbMUNG6Fh3JlVnfEuepTRu57oi5WfaPlrmnkKc9niTpCUvTcPdAgTVipsmgkMTyhjBlZcfoKKsy5pJeEctwa97VStaV5F3/3i1q5s4mV9O5oKF7e59v/XS7h6YXS7Gu/dkAtDir1GK/E4Ckd1FlzBVNywH45eh9ACzyw9rMy11P4YMlu2ddl7L3WwuHWeTM1a1ehtGiT5NvJuliVaP3Xhdk1hCz/01wblqSYTpQX65EwhPSrgBGxnWFGsib8Cj70BxXYgIrG9Jk4kLBh/aUAB5DRZ9tzsLfGPfIxMPvQXe2xPJ4Q6WAx/05Sw86LxF+xuD87xo6ttR6UBs7lz9wzLq5JPL5LhiejPl2P1tL5UNV7RWRTwKfwWr71xWR8J0hROR5wIfd2xLwKlU9VY0cpiKq6RyxoIjFIpPyAmGVe75nGtvc5Z67phw1B0TCdwYQkQ6O7lD0pjkSvCfN8mQz+7NZ8l6OczOLOZL1aU157B7J0xyPo8C2YaEpIRwcK9GY8FjbkCEZE3pyZfI+rM00syjtijWMmbYnVUXI2uIpjuTsxnV1Y5qVar+pPbHdlTGXqVXivMu3rKz+/K7KulR8snorx7K2/RmV10HBCM8Vyun1rHPPsITddQ6MmlZ8lm6y4xMGHMVKVtyj0QVZBj2GV8cWs9e3dPOLMkvYNZalLPa5C34Y8JR3HX/Snmm6BRdVNVIIhUdDPMa6ZmH7kC1rc9pxQ1wYK0Nj3M7/igbY2ApFH/aMQnNC8UTYN1qmMx2nIS6MFJWWZHjeS6r4Zdift+CojUkLKrs3H5Yj7/EsUMx3FVnnMpCqViLNd8EQlAmOTTnqaAKTW3bKUXNAJHxnhrcS3lndCtwmIhceZ5s+Vd1zCuYSdTWKWFDUGu0cuGSiGs/zll1Y2eHLgZtq3OY57nn3lKPmgEj4zgzV3TIuJ2z6PBVfA14z0xM52a5Gw8UiDV6CtbKUoUKZ5Y1xGp3f8WBhjFS2iXPbhd48tKVixMR8l6NFpTHusWWgTK5cpjFu6SuL0naT2p0NNcBcuUyjSy96YMQaNQCcJxvIOU3xiFrpxTaxVKVcItTAeofvm3T+K9uuBmCdWmOErX7YWOEy7wIAHmUXAFc1WCGQ/WOLK2P2uz62S6UNgIslbDowrDkg7Oe71rfmEUfKI4x6Nt/mxFJSEiPj22da2ZiubN/vfL5BmlVQ+vFQNkzDapQYR7JCzKlzwbn3VVGFZRlIx5SRouADR3IQE+jNQ1zgMYsTFHxhS3+RjnScbCkUTAnPY7RUYkXK7s/uLm634xfDVKumhPnq54PGGxD1810w/AI4D3iriHxZVYenGiwil2MNexT49SzMb1rM+2hnEWkSkZUiMh1TxEwefxGw4rgDIyIiTgleTGt6RMx7Pg2MYr7fX4nIhD3RRaRVRP4KE7gpoAB8brYmWSvzVvMVkTTw78BLsZuIoog8iDVZuNM9369BLbxThKr2EN1JR0TMGVE/34WBqh4UkT8FvgVcBtwrItW1/f9DRDLAmZhMCK7Lbz9FLr6TYt4KX8zP+vvAQ+7xYuAi93itG1MQkftV9bK5meLsc7KpRssbUqRiQrakNCWEsZKyNAOb21I80O/znBUlbulNUChrJVVmcVpIx+AxHSW+sbNMZyrBXSNW4eqCRqsKFZifAXYVBuhwvWHb/QY89xs5WBpm0AVBjYhVkcr5Zv7sGbqLyehsvrDyeqxsv8WeuHUK6mRNZd02sapNQT/bI1mL3+hKhxWeyFn/3YfETMsb/DCHvyPmKnaWraBOg2em84PsZ7Nny24YPEKDZkiL7bNcJReSLn2ov2AVrlpTVmlqtBy2m97clmT7UKlikg5KYBfK0JUx03LBN5NzZ0LZPSJc2umzP+vRHLeDDRSUTNyjIQbV94UxMTdBUPUqi5nK+0dCs3M/9np856d6Rmq030W+3vmPqn5XRLJY9cBF7hH8yi5wz8GXfgwrLfnV2Z1lbcxn4fsKzAF/sarmRcQHPoGFlr8duzMaAC6ZqwnOEVGgVcSCIjIpLyxU9ccishZ4NVbJ6iKgE5NnfcAWzOT85Xruejefhe9a4Ovjkq1HVfU7IvJ94KuYL3bTXExuPiIISzMeO4ZLDBVLjJbiFMo+O0bznN/WxIqGNN/YWeTMFiiqpcqsbvLozSnZsnI4H+fMljiFMjyu1YKYgiSa5DjtZLhk2t6YXySL/QtVfIZc46q+nBWvaUguZjxtjZsBKPm23Rmxyyvr7hn9PgAtrljGZbGLK+uOFC3boNEVtdhTcEFF+dAaGaQDXRq3Y+wrhzEdzTFLcUqU7WfT67sOTKwk7rTazWnT9A+5VKpyVWGzZlfbuS1pGu9yp0h7hLWW94/6rGyMMVSw7YIsJBFoSyjDJSHhQVfKZ6Tk0ZoUtg3HaIwrwyXIlYWWhDJUtGOd2Rwe/7Ye6C0UKrW0U3psytbq9qfZuemfmfiUWCzsZnWqgrhq1XwjTh9UdQz4gnvMS+bz17aMabbV7xMAqloC3gBsxO6OFhJNRL6tiIWEp7U9IuY1IvJMkdPnVms+a74HgGVV7weoqmKiqmMi8issnefzszqzOeREUo0Wt1wKwDlyJSMlpSHu0RhP0p4SFqdjdOcSJD2hPQlrm5K0JpSBojBW8ij58JSlBXyFbSNJPCylJkiV2Zc3N1u/8+UCLJfFNLuyhynPY2/RtMQjso8VLn1nT9Y0r5HsjmPm25a0bkZ5Nc2zS1or665seJXtV512WwqPuyRm4wKte33a3t9d2FUZc37SfMT787bvZYnwPqbR9dYt+ubPjZXtfWs8wfoW+zzbB0v0lXIkXPB9a5XK35czNbbougP15EwgBNsCXNShHMoJLQk7f4Gr/ILWHLf0ZdjQVKI5Xmak5HEk71FWpTkByzM+Q0WPfBkSHizNCPky3NoTFvAolJUGL86+sp2TQdcrOfDvwsxpvAGzkbIUFdlYMPwMOCwi3wG+qaq3z/WETob5fBdxK5ZwHfAw8JhxY3qJzM4REac1Eq/tEXFasAR4M3CLiGwVkfeJyBlzPakTYT5/JX8M/L6IrFDV/e7934vIs1X1ZyKSAp4NTJmIHQEva30WAOua4HAOMjFBBDyBXSNW0rCkylDelvUXhGUZeNayfv57bxv3DyaJCTTEoDvns6whxvYhi+h9bKf5/LYMhI0V2lJx9matUcKgDFViE3f0/4T9rldswJkdLwDg0f6fVpYFGm9KTCs9syXc9+0Dtt+y891mvbCqXEvCGimUXAPdwYJpwGd5qytjel0k8qNihTz2lBsq6873rXBHOmbqaNpp10eKWZIjpj0mPCElMR7fZX7cg1VF7VqSgebsmiQ4S2h1P9+kp+TKQlvS9Qd2wURbR9JkYspg0WNxqsD20SQXt+Uoq9BXiPPgUIwzmnx8PI7koDOlFH05qqznA6U9nJdYxVBxwI7vWxbeXEU0J+IdldfFUt8UI6fm9DFERhyH3wP+ALuuZ4ANwPuB94vIbcA3gO+49M+6Z95+bVX1h0ALEJzof8VM0T8SkZuwXo6bgZ/MzQwjIiJmA5HaHhHzG1X9vqr+Hqb9vgr4KVbvWbDKgp8FDojIT0TkD13Ob90yb4UvgKqWg2hnV2rs6cC9WB/H5cAvgb+ZuxnOPiLSGOb6RkSc/kRm54WFqo6o6jdV9XmYIP4z4DdYckUceBamBR8Wkf8UkWfUY6DWafWVVNWHgEtFZCmQU9WBOZ7SXFBznm9gkgy669x4uMTKxgRlhbKvdKasr2xLQin6Vld4+xB0ZmCgIHz0gUYe12XpMENFWJr2Wd0Uo+grFy8yU/Ct3WYGzsTCr1pvvkjM/RbuHvoGZ7RZn932pnOOKvgA0KhtAFzZ8ueVZXtdAQzPgtu5fjDsL7s6ZqlJ/WWrw7xCw05iR/IW2NXg2VzSMZtDUCwEoMcFI52pFwKh+Rqg4DoV7VFLh9rs+uCmvAy+y/M/UBghLUkWpez9/f1hwNOaJjNT391vtujzWs2kHQReAdyQ91B8Eq7nbsaZnVXtPJ/ZlONgLsnm5hxjZY/2ZInufILzWsssz+Q5mE0CcfaPgSdKvOqSk5UhegsFduetU9NodhdzyUSmZjkRKVl3l9WI2cJd478CfEVEujDT9MuAx2NZH69wj8OYQlY3nFbCN0BVD831HOYDytHpF5m4x77RIutbEhTKwlDRBPPaBp8tQx5daauyJFgLu3TMY9+YXfvWNyt39HrERDmjGe6rEjpARZgA9JbGSLpobN/PU3YVQPtHHqQhbf7XsZxVgxvGcuQPaCiUX5h5JgC7xkyIJaquvtt0LwDNWCTzWFUnsU0py8ENKjwNluy4va7SE0DM/SRaPLt56PbDkIE1TWbFkhHbT67su+cyrQn7PKtSzRR8n1/uN39yUyKs7HVrnx3n/BabWyAYz24Lhf/hHLQnodFVq+ov2LrOpNKehK3DaTY15xgrxziST6BYtauxsvDgUIb9Yx7pmJLwzKd8YCxMg1+ha7hfbmNN2ppFbJlj4TsRqqXjDxpH/ek0EXOBqh4B/hn4ZxG5AstyuQi7ZC2Zy7lNRN0KXxE5E6tStRG7Y0liRbX3YJHON48rsBFh1NxS8F/OfS8Aj47YBb4n57MobUUy0nEo+/CErhK/OhijPWXddJakfBKeciAX56Wry3x9h09TIoaIR0vSLvh7Ri0wC2BZxrr6/Dz7u8pxy5TwXbDP6vansaM/dMt3ZizVaH3mCUAoDNfLxsqYW7LWHWy5+z01xcOvcWfRgqoSbrt8VWnvewom0M+OW//gNc0mTI9kw/KSQceh7VkTlGek2ivrjrjuQ+d32Ge6r8+0a8XKOYIJ9lzZpyXhhHhVqtFZ8bDghO3PBGxHVc/d4SKkPKEzaYJ9QG1dX0EoK6xvLLNnLMWydJGWeJnBYoyCL3QmS6Q8pSvlsWUoQVNcGSlBcyJMOWslwUhxA7vLtTTdmkdEwjcCcG1cXwY8H4v3gbDU5P1zMaepqEvhKyL7gaXHGZYXkR9jJcTqrl3UXHGyLQUjIuYbEqstmirq53v6ISLnYQL3ZVhDBQgF7j6sCcM3VDUSvjWy7PhDSGMdjV4qItcBf6yqu07prKaJiJwF3AMcUtW1czGHpowV/H//2ldUlh02hY27+kzzaneZOud1JGmIKXf3ldnQEkc9OJyLsSgtNMVhqCiMlmJsbi7QnRPWNAgvXu2xN+uxc1i5qsvnwaEYjUlhz4iZDwfLlrqzSNZXjt+t22mM2b3Vzv6f4bkbhcbMWrrcabqowb4C3TnTNosSmrGXOj/uOa2mud4/OFZZ1+AaGgSj98vOyroLXAeyjPP19rqiFwfy4fYp53Ps9CxmrSNdVSTDpU9d22uFIwpihpd1sS5c5UhEhNZknN68jY17YRpUgO9KTgZm5+4q+81Vi0vsGI3zwIBdP85pc2NFKamZmDe3jFL0PRanc2wfbiQmQm8hTku8zP5cnI1NRW7ri7N1MEt/lQGkS1pJkJiyScW8pHbNN+rnexogIpuxpjovI6zjEPxPB4HvYwFX16qqHruH+qBehW8fcBtwH7AX6MY6VzQDa7BmCVdiqUYAVwF3ichTVbUurixVLQ+PvfpGRETMGOJFsnQhICLvwwTu2cEi91wAfo4J3J/MF3dkXQpfVV10vDFidtVnYh2MrgbagB+LyFku7WjOEAvZ/B6W8jTbx24EeOPKDwBhecK7esOo3aUZWxhY64LnoaLSGIfnr4Rsucy+bIzmuLKyAZalS+wYTfCY9lG680nakkpPPs65rSPcM9DMue3C7X0xPIFcWVneaF+tWNbtvBhGHd8/+PWq+XqsbLsagFX+JtYk7X4q0Hj7yqaVrk2H/tK9uVE3xjTXgxLG120Q8+cGvtsrvPMr63bnTWPtz7rSlk7LDUpBAoyomQWKatr4LUMDlXWL3L1e4Ade1mgq1y19AzSWbd3mtjj39RUouCjp5kRVJHXOlqm7ZuzO2ud4WUdYwnLLUAJP4KIOG7skZefhoeEUi1NlCr7QnU/RHC9x30AzCU85p3WYW3pbSXjK0lSZ7+1WhsujlPBZ5IXn7SF5hG39PyTlCpnkCycXl1hdwCMWtyCyUmngpPY1PgiwJuI1C9+o5vn85gOYEibu+Qbgm8B3VbV/qg3rkboUvrWgqkWsqtWPReR1wL9gfuK3AB+Zq3mJyGLgu9gNwVwwArBrxITIk5ZZcFBrMhQwZRfEk3EXrZxLNVrdCAlRbu2N0RA38+hYWRgtCd35OBuaCmwZamBpukhMYHGqyO7RDC0JaIj5dKRi7B/1Ob8dfnfIzM4bW02I/aYnDLiqJhHv4IqY1ZZ+gJ2sc1HKnSnbbmDMBUDlwt/WY9ssqKo3b8J3o7eqsq4xbp/zgYKlH/WW91XWXZW6zH1e350HF1Hs0pIAFrk6x1uwdKazCQO9ir5/9Pa+Cd+0JDmQs30MFxP0lsYqEc2NVa73g2N2vCAI65J26yq0vepWMROHgq+V6lcDxfAn+tBQjCs6C7QmShR94cL2IfaMNlD0PVKe0hQv8+hIgnPa49zSU6AjniZXDm+6EqRY3HIpKc9k0L6TFL7VgvJEhe5E+5ouUT/f2hCRZVhFqGdjdfCPYBrj36nqwTmc2nR4GBO431TV3XM9mZNh3grfalT135x/9W3Ai5gj4Ssiz8VC3YN6hT5RLGZExKklMjsfF9f/9iYsnuanWPTveViBiueKyOPqLWZmAi5R1dMmVP+0EL6O72LCd8NcHNx12niZe6vYDcATgCfO8lSaAB7blR4BuKPHNJ+VjaHmO+SaxK5psotWm8u06csLz1o2TM5vpiWu7Bnz6C1YH9nmuM+2kSRJD+4ZSHHlojEGignWNGa5Z6CZbcMeG5vLdOc8DueExWn7av33gNVIrg7yicfbaM2sBeBc7yoWubFXsYEHhwcAOL+lDYBnLTUN8oGqvOF7Bixu5txW0+CCIiEAPa6QRgL7UH/QHlr+dw2bNn6nfy8AZ6m5js5rCfva3uPMzGtYB8BBGaisOzNpGne/q//84KCZhJu8ROUz3DvWTZfXyvZhyy9e3xxWuOvK2JhDLmWpMW5qcXtVVEBXyqcp7rMva/+vxozNeVHSZ11jme58nIFCnLNaR2hN51lSjrGkaRQRuKmnhV2jZr0YJUfaj9NXVdr80dFrTsrU7LngMd8/1qU21brxnJSJecKJRcK3Bv4FE7zvUNVPBQtF5B3AJzCl4TlzNLeaOJ0EL5xewjdIop6rHJugo/su4LWq+hsRuWa2JxGY1j686YOzfeiIiDmh1lSjhYqIrMNKLu4G/mnc6n/CugQ9W0TWzgPtFwARWQ6swAJaJ7IuxrHaEC1Yzu9LVfW82Zvh8ZlXwldE/hzowOz++zD/ZhMm+N6NaZwPzNH0DmB3j5+th2i74HJ0ZZd9L6vrTe10Pt9AYejL24uYwM8PtlDwlQHP48ldo/zf4cZK0FbSg5WZIq3xGFddtY9fX7OaHSMNXNFZYMdokt6Cx4Zm2J9V7hw9DMD2gR8BYcoTwFWpl1QKVTw0UKDJ3S51pqA/b1po0HnousPHnsrWmGlZQX/c6s+W9GyyVzZbgNdoVcGkoMfw0xsuBkILQG8+9Iue1djqltmGiXL4uw6SFja7FKeHB83PO1jOkxuz8Stj7ewp99JFGwC3DoeaZhyb25M6bG6DBXXHOnqOvYUYLQlbN1yybc5pHeVANs2GpizdedPqdw620JnO84u9S/EEir51oEp6QlesmXQsxk+6v3DM+RtPPN5WeT3ed1vd63eq7ke1aLwBM6bxBtSh5isiLVh1pUvd4xIsDzWY7LrpCDoRaQPeiLnV1mNdfQ4AvwO+oKp3TrH5k93zr1T1qNJzqlp2fc9f68Z9tdY5zQUur/eLhMrOvGVeCV8spehlk6wToIxFxM0FV47/YkdERMwCdSh8gWuBC2diRyJyGZa7unLcqjPc449F5AOq+qFJdhHkwj4yyfpHxo2rS0RkEfBbTAGb7j89KrJxkjwKZIGGqmU+cD0WTPBdVb13LiYWCd6IiLlB4nUZ01gtHAaBu4GzOH7lvqN3IrIeC5BahFn2/g1LYxzBtL93Yb7cD4rIgKp+boLdtLrngUkOEyxvm87c5oA3A53YebgbuyE5hLWT9YA3YW7H1cBLMAuBAq9T1a/MxYSnYl4JX1V9r4j8LWbCeTbwh1jt5yuB7ZhwXhCIyHswU/uEvHrlVfzhisfzo/1m3t3cGq7Llszsl3a+st0jdt+wvllIxyBbgrinDBbjnNFUZqjkMVzyWJUpMFCMU/CFO29aypahFL152NAsHM5BJiZ8sfcuNuhZ3DHwJQDO7HgBAFenQ3dLQ1zoz7u0m0ScxTZFir7lyQJ0u04/SzNmYu7KhNeyIL7qW/3XA3CGhrm8SxJmJg1Mwue2pyvrgsbPyxqcKd7tZ1FVFavbey2YqyNhxx3QsPpVd9HqPWcKR9dob/ASJFy+S8rzeFzjUu4btECnRsKAqw2NlnObdR/g4FjRHT/8Ga5q97m7P8aGJjNj9xVs3ZFckmxZaEoUiXvKooYxGuJJ2hqzbC4k2TmaxhNhc4vP9/bk+eXAJ6mVhlT4ebpaLD5w56Clhvl+WCErMEEH5ufqDkSLWy6xeQ7eWvNxq/OEvVjDUfueFvWp+X4VKw50B7BdVdXFgExL+AL/iAlegDeo6r9VrbtVRH4I3AksBj4mIt9X1QPjd3Ka8Ez3vAW4TNWS6UXkNVgXo12q+iu37H3Al7C+vx8XkR+7xgt1Q13eMk6FqvqqeruqfkBVz8KKaO8C/gT4rasstRBIYBaACR/lSBGPWCh4UttjFlHVz6rqt1R124mWOBSRs4EXuLc3jBO8wXH2Et6ENwBvnWBXQduutkkOFSwfOJF5ziKBJvvZQPA6bnPPTwgWqGoB82M/DLQDb5itSdZKXWq+InKFqt5Sy1hV/YmIXIv5WC4B/hqYzPdxOlEExiZbOVRMNuwcTfH0pZYWc0tv2LmnkmKUMAG9udXuwXJlaEuUKfoW5PPoaJIV6RIjJY+SQrYcw1frKVvwY5zfaq3tbulNEBPYPlRik57Fzwc+SXvTOTaPsgUcLclcUDl+Oqa0uMpP1xws8WC/a5uXFlxhrEow1GrXA3fPSPhbSzmN/c1L7bd24+FCZV2H0yJTRftMWwbCQKCiuyG5tdvGPyxbAHhB4pLKmJIL31rbbPvJDYRpSC5eiztz1rawUzvsmLF0Zd8HC2PEpJH2mN0Dlquuu4eydtyLG2zdsgaLNNszGs7xVff8g71w5QNeu9w6T61pSvLCVb08MthCY6xMzFOWdw0yNJhGUFY35Bkrp3ntfbWnuF/U/scA3N3/75VlSafdBhpvtTclGbfKXl7SeiZX9wMedJ2mYjHT7v2yaf4TBVdNlGp0QhpvQH1qvjPBS6tef2mKcd8EPoMJ35cC7xy3fqt73sjEbBw3rl4J7Hfjfdf3Y2b+i6oXqmpRRL6IRXQ/C6irFJC6FL7ADSLyFeDdqtp7vMGqOiwi7wV+ArycBSB8VfUjTFFM5O1rP6iPDEPZlUnsqrIHLEubILtnwARbYH7tTMGu0RiXdeQYLsU5nIvRkSyRjvmUVRguxUh4ypqWIe7usZzX3WNxutKwf8widX/uzJ1Pz9gN+9ltdozefHihTXhSaTJ/YWeCg67l7r7RMi9YaePakvbV3DVi7zurTMMPDdoGRd9MwxcvChNlxV3Qfz5iX5vWqoqCG13O7XUj1lpwo8vzvWkwtEadnTHBsn3ITMJBa0CAnXlTIH5vidVQ+d8jtt1lrU38pH+Hfe629QwUlLI6M3QsnHdQanL7kJmUA7l8aIrKeF8+8OHK6ycufjtnNA8T85SOrlF6DzfSn83QW0jSGCuzdbA2IXRFmykBh7HzsLztqsq6ofw+Nzf7/yQTYaXXXH4/AE0NZzIecWb31oY1APQNTx7fEgjdRLyjsqxU6j9q3bSIzTsDXq1U1wj47WSDVDUrIrdg0crrRWSV04jHb/t0EfGq41PE/nFPd28nLkNXPwxjWvr4ps/b3PNmjiWIATr2SzvH1Kvw9TCTwR+IyGeAf6qhdueAe153Kic2X3jCYhMe13eb8F1ZFaK2pjEQXrZwwGmJXakS+3Nx9mWTNMZ8zm7Jsi+bYnPLCFuHmji3dRgfYedgCy3xErf0ZViZKXPfgMe1w3u4t/9rvPeMv+WevhxrnMbqqkvSXJV97aGsXOT8njmhxa0bLQoHcmExEJi4bG+Q6hP4rm/sDrWmxUkTxE1qn+3314aC+b93mx/4Ka0mPG8a7Aag3wuF766s7Tvj/JmHS6HPs98zr/Ft3aYddondiP+0fxdpd7w9IyUOl0YY8AYAOJew9GWXq6k9Omo3P4d9E+ZdVdbAV3a9h98WrufAwHXHfO6rbvwnvn/pu9jYOsTWHYtZu7Sfsu+xHuWiaz5/7Ikax4s63sWh8hC73PXosZ5ZDv6n/zOVMRe3vBKAsbSV4tzS/+3KupZGC4YtlbPH7DsZNwtBIHQDX39/MawA2Dt8z1HbFEt9x51zLZzGjRXOcc9DqrpvypHmBw1Sis7GGtIAoKo7ReTnmPb3duBTVdv9JRag9HNV3Ul9cwATvuuAG6uWB7E+a0WkRVWHqtYFgvroZtp1QL0K368Af4p1MXoP8Fci8t/Ad4BrVPUoc6trZPCX7u2Crt8acDhn/9qnLTGT5taRUAj95rBpg4HGeWG7CbFD+ThHctCZhP25ODtG4zx+0RjbhptoS5bYPdpATJRNbUMMF5JsbIpzW1+ca4f38GjuejKplaxpLDNYSNHhrNzpmO27rxBeIFsSWhGqgmm8AI/r8miI2U35tmETVF3pQBMONaK42OudozZmuBh+thWukle2ZBP4yb7QXN3qGssfytqyK1pNy82VF1fGdKZsYj/sM8tW0QtNwmt9y1U+4lxoG1Km/S+SNeRdvef/y/+KP+96Njf32PHPaAl/Yvf3276CGtGrnRm3PRXecKxqFNaWr+bDEwhfgJfc8TFetuj/8dWX7GS0N0l7+xiH9zVOOBZCszXA9bmHyXqDbPTNzH67fzsA57SG1k3PaexZ7PpVnQM8PGbXuNYGKyJX/SMcGj3aYvlo/0+Bo4OqThmnofAVkRRh4aC9U42dYMyaCda/EcsI+aSIPImwvORzsIjhN574bGeN67EbkteJyLcCv6+qHhaRQUzAPhX4QdU2gU8pR51Rl8JXVf9MRP4LCyHfiCWUv9I9SiKyFbvb6ccE9OOwKEIF6qKl4FwRdDX64nnvB+D+QRNMG5qLlTEPDJoQevISuyk84Lr8DBfhnBaf5kSZlKesbxplrBRncarAIyNpOhJlzlk0wPaBVtqSBR634ghf3dHKvYNf48Wd76ItGWe4JKwL3aQMl8TtO7xAbmgqVopH9BdgU6u9vrXb5/FdRxcAmYi7+kxANCaCiOhQeO12JSQv7LTPeGdP6A9e67Tx2wcGAHikYL/Hs5Jhx6VRd5qe2mwCpjpUZtRp2rvGTHhmS/ZswtPmtM67hPv7CjyxyzTh+/tDC9m5ro7kNT2m8T1rSRsA24bDD1vwhWxZ+fDG9wHw3keO9aB8t+fv+dDO17N8zSCF0RhX3jC+aBGsa38253Eeu8dCLTUhKRpYSZ9nRqSz9EI7voR1aeKyHIC0M9dXF90I/Pj9Iw8CkEmFaadZZ672PLMc+L4dd1aaqdaYanS8DAHgo86dUw9U/YqqmjJPTnUnt+bxK1V1l4hcijVWeA5mau7GfMnvnycR0v+JBU49Hguu/Ziq/tytuwYLTvuwiNymqvtE5CIsFUsxy0BdUZfCF0BVfyci52CFv99NmGCewO5+zhm3SdBm6lMsbEYADuWCoBZ7vn8wDLg6p8UE0r0DtuzsFpM4i1NC0Rd2jCZ4ydqDHBxuojufpDsfIx1TCr7HdYcWsaYhT64c47XXt3LIO8yHNr6PfFkYKSlFHzY0FWhJBH5luxg3xMPL8GgpRnPc1m9sFlxsFZcvFs5rtevMdYdNeK13gU939IRC7GnLTYje5ayWj+kM992RirvPbWxqCz/3ff0mEM5pbgOsYhfA7pFQQC9rsPFXLrbjfXtXuO+LnEDPle0z7c3b9e6ZK1r43F4TPktZzB6/m3u6rUnMUzMXV7bf4Xy9Qeekm7vtgwd+cYC+vJKJC09fOuCWmBBenvH5k3tDubDpV1/E/85byewdgP+qLOb3F7+b9c1x8mXl4FiZ7kJ4TV7GUtrjSfJO8y67szRUCq+7qYRdtw/kjk2XD4RuQCBwqwmE7nTxxM6tr8XjjJxo45p9vkGGwFTr64VM1evCpKNCqkuMZSYa4ATs605mUnOJqt4iIv8JvBpLL/0eYZvIT2PCdxOwQ0T6sRStQC781zE7nGPqVviClT4DviAiX8JO7MuBpxBGvVXTjRUN/8UsTrFuCXytvztkv8mLOlPHjHn8IrtQtqdcRHRPCz15YWnG5+7uRRzOxelIlkl40JEsU1ZBykImVuZ7exvp9fbx12vWsKlliIcHmxgseTx75WEe7GvnYNaE2KXt5gXYPhJeDwaKHisbbF59hTjrG+3420ZSDBdt4pudQAoCqIL+wAAPOY/OwTHb7oZyeM08y30zfnpoAIAl8TDgarWLMn50xAymObXtmyQ8N+e22fF+tt+Ov6k1vLD/7rAJMt/N6VBsnztv57BelgEw6OfxPZ8npU3otqVCrfY3WWsycXX6QgAa48ENUshVXQW+vyfO57aaEAwi07vzHt+86N2c1z7I2c8fw+/NQyyGvOoZFLMltKB843/XsWs0RlnhgvYyoyWPi9OdlX1vHRol7/vcWjaT9mqx/OjqiObdozcBcEn6xbZNVdMJcdp999AdjCcIzCoUe45ZVwsnJHQDajc7T5kh4NbXC9V3MclJR4VU/8BP7A5ofvBa7Fr/Fio5AaCq14rI3wP/D5Nri6u2+S1W+reuqGvhG+CE8A+AH4iIAGux7kVtbsge4PZxuV8LlSaAPaOmAa9wmlwmFl7i92dNWC1N20Ur7/rS/u5Qgc1tSXryHiU/QdJT0jEfijH6CjFiAkNFj2/v8tglW3nzio20Joo0Joo8a8Nefrh1NQ/3tzFairHYNYEfcML03LbQclYoexTdMXO+0OMKSfQVhLsGTEC2Ox9vruwEVJWE2j1i+376Ctfzt8qf3O3u/5+02Pyp9/eFSsOSjB2zJWnKz85h235xVZGLrDtesKg5ER641zNVO6U2x01qpum9YzmGXBiCh0en317ZZl9VilQgdIPiJoHIu6PKNN6VSrCmyWO3225RKihG4rN9JMYr7v7nSuyq/523Ij+7nsT7vgnAW1a/j86U0hCDm3tiDBfLR523zmSamEATZmZ/tGgFMS6IPa0y5uGYLbtx0AK4lrY+trLu8ODR2X+dzRdWXo/XimeVGoXv8TIE6oxqM3LTpKMmHjM86ah5jqqWgL92gvbcceve46K+X4+V3ezFqmB9vh4rEM4L4VuNS1jf6R4R4wi6Gr1qyfsB2FCVwxuQcMrcNUfs33+mU24e12U+2IGCkvZgVUOJvkKcR0eEgTw0JoQj2TJb5WFe2nYRZVViotzf10pqsIX1jTkO55LsGotVNNautAnK7lx4Y74/myTmgqYKPmwbdi0FF49xa59pyP2Foy+o1dkk7c60/Kv9tu+3bw5N0l/fadsnvUDAVefZ+ketC9KZqvoqVAT5Uqeo39odWvPOS5jnY7hkx9vmenlfkTmTFy0ygXzjEeWGwt0kspbG9MQlocb/8KDNJXBR/mwk0ITD6l85X/CESrT427ZMnpro/f5njnr/2T2hf/hz57yXn+wvEKsKeNrYmmbncIHL4uaxSafsuL/N3V4Zc2TwHgDObX8FAI+M/LqybnwaUN9I6Cu2a2JI0EhjJLtj0vnPGLHY8cfMM1Q1LyJHsKb3q443nrCHOOByyE5jVLUPOCYqUVV/DPx49mc0fead8I2ojXPb7QofdCTaUxUDvnfULqKBEDrs4gDjIuTKyupG2DkM24fjzmGiPDQ6wM1DX8TXIj+49J3szcITl/RTKHukvBRLG7KMleIMFBP84fpDPNRn2l9fIeGej75AdibtYt2dT1a0uzv6M3S7ufS4u4WLO227M5rCC/9vD9q2a5pMq//OntAq9+iYRSIHZu8XrAoTnNNekJpk71uSrrzmcGhtXJyx+f6619KPqvOE+/Km3fZ6toNO3yxbSzIejwzZvlWVq1IX0Z6y839ffyjZG53U/b9hS0vMTJD9sGcUVjUob9ty/FT1n172Nzzntn+YcN2bH7T84Devel9l2bIM/HT4ETYXrabCr3O/AiDhhW7Q1a2W87tl0AJGWxv+f3v3HSdXWe9x/PPbXpJsEtJJTNDQuyJNOgooFmygWC4WsMBFLnr1qtd6vfbCxWsBUbkqKIqioiIgTaSIYhAEhFACqYSUzWY327Lzu388z8mcTGZmZ2Y3s+37zuu8zpk5zznnmZnN/OZ5zlOys1H19Yf3ZPfprwZg6YZrCuatKkE3MQ5bO0cPEoLvFDObP0h3o31yjpNRTsF3J3L340bq2g+2h1Je8r3UlJrz9MBYK/p0vPvVHPc93ZlhWXcnfQOTWd87QPfWAW7p/QUnNL2GuzouoalxLi9rfRP3bqxnbnOGDT2NPNbZzPSGrazsasGA1toB/rh6Fk90hqCZlPJeMC3b0n9NTz1bY5eWOU0Z7nw2XH+vNts2GEjSgvmRWFpM+vRCdqSqZIzk9lTRdd/J4abv453httf9G7Ov+9HN4QX3x65/LRYC9MZUY9J1G0LwrY9tb9rqUrfb4mX2rwsB6YGtIcDcuKGHRg8l+7l1k2nA6OqP0wX2ZauUn41TCB7UEI6f0hBe47dXlD8mzG8P/SD7z17H/SecywE3F76d9fXl25973tRjWG4hiB5ScxyQHekLYLdM6Mu7y5TcCXTgqSnhNS7vClXTycAaAHPbjgLI2z95pxu/wfc24Pi4fTzww3yJzKwZODw+fDJngA0ZpazCYUdllEq6Gp0155OdkK1iHkh9zBvjXLVrBkLLpTlxSMDZLfXsNikEvPW9/dzc8wumNj6HlZvuJJPpZnLL7ry+7QyWd3fzpoVNdA4Ysxu3stukLnoHalnZ3UzvgDG/pZfO2JWoPlYvr+jOBrHuAdvW0nj5lpptfYFnN2V4KvbdXdEVAmzSfzZ9w6Y51kH3xVa7e6daCyf3T2tidWtTqr46mc/3wF3Cc0vWh2i6cFL2N+h9G7Zsd73eVHXq/MbW7a6btBpuqa3lvq2PAdDm05nkLUyujffaU91gVsWS8xYLPwzuab+ESj3x0ndy7dNz2XtyNwcvXMPMq35U9jkOnfouADbVZAeRW9EdGlPNjcOBzhzYddu+fgs1BMmkGfkkw0sODHQUTNPSFGpIt/QUrh117y85ovpv/7OkLzE79TMjGqXjxArJqFWDzucbe3skdfu3u/sxBdK9A7gsPvySu39w6LmVnU0l3/GnE+CZnlDS7I7BY0FTdiCGzkz4En1eYygC98cfYNd33cukrl2Y77N5TmsTmzcsZfOWpTTUz+C06Rfwj4EnqTE4dV4LuzT20t9TT1NthiUbpzDgRmtthhmN/dz0TCuH7RKu/0BHKMrOa8qWTptrnQc2hYA5td7pjZHuwU017JI0MIpVwi11Id26nuz3a1vc19EXAluq1pieTLjO+jgQxt712T68LbF18S2rQ96WWxh3unPjvG1p9oyjZyUl179tWb1t3+yWcHP8p7Hh0fTY++0FdQs4qj5U5TbUGjObjE8//iWg8q43xfzPvh/jGw+H2admN9ZzxQOVDeqWL/hPjgNorO39JwDLem/bti/d5zedFqCrJ9SIJkE3dwaktGJBtyLjtOTr7g+a2bXAK4Cjzeyc3MkVzGwB8Nn4sJswxrOMAQq+49Rz4zjG924O9yfX9mY/6iWZMITr4r4whOAaC23XmmnjyZ47eKD7iW2Ddc5qO4wZNbtxyq41tD27mGmNxvIt8GB7Ay/YBfozxoLmPpZtaWTT1hr6vYFDpvdy0zOhivJVu4bS3j0bsvcVu7Yae0wOQfLRzTX0x+DbvdXpj0X1LXEAC/fwxdqYqjZPNvvioNQ3tq/ctu+U6aFtyoMbY9DuzTaY2tgbqpcXN8TuN31hdrcnLdvPde2mEDSSe73dlg0eX1m2fRVuUre3Y4/YnWPBtBN5XmZ/VmwJo4LtN62WVT113LiqmwsWfozL111Ne9fDQ7rG5i1LB09UQtohTZRQrlT192hhZosJfVHT0tMJvs7M0v2yOt396jynupAwqMR0QrfLg8nO53soYQyE5BfmR9x9ZZ5zyCik4Dv+JC2EShkVR2TsG50l36OA7xfZ/6Wcx08BOwRfd3/MzE4ldJmZRxjhKXd6vAzwX+5+UcW5lapT8B1nkq5GB0x/OwCrBsJA9/Pqst1ZTp9yKgDfXRvu3U1pCtWnmdoMXT3LOHzqu7m7/ds0NsxhVs1iWnwSj26uYX1vPwNexzGznN6MUWvOlPoBegdqmN4wwF5tHdz17DQ29NVtm8jhrxvDxoLm7L3TVT11/GltMo1g9r70jKaabTMsJV+nSfXzlFR/26QRVn9MvF9Dtto4mUIw6WLUn+rel9nWvSmWqmOa51r2+HWZUGJbVfMMAE9sGYEGRAU83w6h3zJs7HWeM6mW/owx4M4Jc5v50zN9fHjB6/jQP0f/hF6l3BcuS93462qUFkd22hc4F3g1oQ9rE7CaMBPRt9x9x5FPZFRTg6txarfpL3eAybFGKhkYAmCKh8LxzZt2HBMYwpfjwEAHDfWzOKD1Vdy76bu8sO1s9mueyYA7CyfV0pdx5jTBzMYB6mscw5lcN8BTWxpZ02PbZjFaE295burL/p1NazQ29iZ9Xm3b3L5PdW6lL7YM22daOEFyr7ch9f2aTAHYb3GEqky2O1B9/D2ZNHiqT1VJXr3+c4O9baPWgmknsnrzvbQ0zqatYT4nNh7But4+bun5KbOb9mPZpps4ZMpbWWNPsLz9lh363Y41ZTW4uvW/S2twddxHR2URWSYmlXzHqYHYnWbV1lDy3a8m21Dyfr9tu7T5SiLTJu2Le4Zmb+Kgqf/C3o0z6OwfYI+2evacvJW/bKhlnyndLGlvpqkWptYP0N5fy+Odxrxm2BLbV+3bFjbW9qZbJA+w/7TweFmns7w3WzpNWgc/3Rme27U1PH5yczaYzK2ZCkBrLPGs7M3WsD+3NdyzXdYV7jX/YdNXS3m7Rq2prWGK0lUdd7O47aVkGGDOwHx+tulHfGXxm5m85kzqaoy1jQ9xT/slvLjtQtY1PjL8jZpGs9FZ7SxSlILvOJN0Ncr9aB9uy44wta4jTPx08LS3AbBkY/bWVEvTczCroav3GV43LYzBflf/4zTXzWdGU+gWlHGY0wRzW7fwj45mZjZsZXl3HX0ZmNkE0xoG2DVODfhgRyiBZnLKJs/EEvGebcbyrqT1srOpLwTZpA/sTXGwi7vbv13S679tU0nJRr2amvB5ZUj6azfyyIaraaifxZTWV3BM4+u4ZnkPj9cs5Zi6/enqDo3mbum8bPiqc8eKcTjClYx/Cr7jTydAQ+xiMxDvYW7uyTaCbGoM9ziToJueBi4pMTU2zOHqjZdyfOtZHFCzPx19A8xpruOZbmdes7HV4aplM1jUOkDXQA0HtPVwy9omnu2BWqtlTSZOHB8Hx5ifbezMjKZa9mwLz9+91pneFEq3jm8r+SajbyVz3m4/qvD4l8mEe9fpeXLr6qayS+vu7FY7i5v7fsOeNUfT4M08vqUDq2nAM70MDHQwb+oxIzPYxUgpfVYjGcPMrNKx+3sJ34urCfMY/9rdfzpsGauQgu84lQwFmOjO0/WjtXkRAF3dy7Y9t2DaidTTTBOTmJmZxcP2d17WcjjrureyZavTPeCs76thXnOG57Z2s7SzmQ19NcxrCqNTNdU6A27EMTJojEG01rJVy7OajIfaw/PHzXGWhOll+b81Y2XM+53HsB3GUE4MDHSwuv0O7pgK7V2P0tN2CPvXPZc/9PyOTKaX2tpWBga6JlbghZKDb1IrlDRKlDGn0vsLTXGZQZiM4Y1mdg5whruvL3rkTqTgO/6oq5FMLKXf803+T+gm8dj0RmAu8HnCNIv9wPXAPUBS2tgFOBh4KdBKmLHzZ4SpJKcRhuGcTRiu8ydmdpKPUKtjBd9xJvlVbzaa5gUX2Yl0z3ei+DVwL1BPGPf6LYUmmzCzqYQhN18D7AYc5e79Fr4YPw58FDgBOA0oPEPITqTgO4ziAOfvA04H9iD86nqS0Hn+YndvH7ncZSVj63b3bD8YzuLpr2J199+Z3DCXHuugjem8fcYR3Lq2gz0nTWLJxi28dF4L+0zuYUNfHXeub6W9D7ZmoKm2kfY+o6k2zEH7bE8oXOzaEn5Uru7OVg021Pi2CRfufNb43urR3ze1WgpVOUN20vuere0cPflddHoX1235OZ3xtkFVR5UaTUq/51vKvLgyen0A2Isw3vUp7t5bKKG7t5vZ6cAdhJHALiCMe90PfMzMDgReDvwLIxR81VJhmJjZPOCvwOcI1R6thP/s+wOfAu6LHeVFZDjV1JS0uHuX7veOaWcQCjRfLBZ4Ex5+rX6NcJvhLTm7k5lI9hvWHJZBwXcYmFkd8CvCnJoOfJNQpXEccDFh+LeFwLVm1raT89Ka7W6Uej71r7t3Fd29q8h4PxnPzkqwsf8paqyefTic82Yfy5aaLpZtzjC7oYXGWuPYWS1s7oeb1zbxYEc9x8zsZJ8pGWY3w4yGAWY2OVvdaO8zJteH5alOeKoTdmsd2LZ0D4RxnLu3+rYRrKR0Gzsf5E+dl9PgjexbdyKN9TNHOksjq8ZKW2SsSyaXfqRoqu09GdfPy3k+6Qg/d0g5GgJVOw+Ps4FD4vZ57v7N1L7bzOwu4MeEew/vJ9xz2FnU0Eomljp9jU0QvUAjsAj4S4nHLIrrLTnPJ50fM4wQ/dUOj/Pj+h/At3J3uvtPzOxNhHsM55nZf8V7D1Wz3b3EOPSgxUafDQ2zAejsXcOJLW/hnsxN/H1VhgNrjmdtTy8zmxpY2tGNezMd/RkyDkfMNK5ZMYnjZvViVseK7lpWd0N9jTNnktMU28DsOyVc67pV2UqW1npoilMT/bzjzp390seltpaF9NR0s7TnNrp787Y5mThMpdoJYgmhNvECM/u5uxcNnGZmhDY4Tujfm/b8uB6xoeBU7TxEZrYXoREAwJVFmq0nw0hNI/wB7SyTUMMSmUC8pqakRca85Dv0cOBqM5teKGG8vXclcGR86vLUvvlkg/ItOyWnJVDJd+iOTG3fVjAV/Cm1fSxw487IjLoayYSjaueJ4keEWZ1OA14FvMTMfgf8DVhHKEzOAA4i288X4AZ3/wGAmb2D0A6nCRggtM8ZEfqrHbq9U9uPFUrk7mvNrJNQKt27ULpqSqqbZ7dmpxu8vuMSTp3yHnp9gA7fwiY6aOmfxcJJzTTVwdyWGppqoaEmw/OnZWipHaCmASbXDbCiK4zjvGJLDau2hBqhxVPCn1i6hmh+CzywMYwUt3zjTVV5rePNhs0P0DR1Grs3HcuS7idoqJ9BX/+6wQ8cj9SYakJwdzezM4DvEFovtwKvi0uu5I/ih8A5qeePApoJpd4PuPtDOy/HxakuZuiSyWB73X1t0ZSQdKydVzSViJSuxK5GMva5e7+7nwUcQQis6wmBNr10EcZWONbd/yWnW9I64BLgSHe/uJp5z6X5fIcoVnu8FFjv7jMGSXsv4Ub/w+6+TzXyJzLeZVZcWdKXWM38M1VEHmdio6q5hCEjGwnB9fGRGjKyHKp2Hrpkrr6eEtJ25xxTMTP7KPCRIkk+6+6aqUDGP93znbBikF0VlzFFf7VDl0xzVc4vreH4VVZPtq9aof0i45/u+U5IZjaXMJjRPsB0wvfqBsIgHLcUGvd5tFDwHbpkuLqmEtI2x3UppeTB9LNjx/Hc/SLjn+7nTihmNgO4iDCGfqFZNdzMfgG8191HZUtEBd+h2xzXOwzpmEeSZuNQLxqrlFWtLBOe15b2Nab5fMc+M3secCuh0WqxKg8DXgu8yMyOcvcni6QdEfrJOHRPxXWzmU0bJO2ucT3m7k+IjFqlt3buRMOvjllxOsBrCd+jBtxACLALCO1oWgjDSZ5OGEchaYz1szj+/qii4Dt0D6e2cwfv3sbMZpEdeWrE+paJjDvqajRRvIMwmqADF7j7Ke5+jbuvjF2Qetz9aXe/2t1PBi6Mxx1MCNKjiv4ih+6e1PaLiqQ7OrW90wY0LjSrkci4VXrw1dCrY9vrCYH3mlL66Lr7RYS5eg04a6fmrAIKvkPk7o+RHbT7rUWSnhXXG4A/7sQsqWpNJpba2pIWzec75iVD8f2gjGMuj+tRN66Cgu/wSGYyer6ZvT93ZxwS7eXx4aWlTAQtIiVStfNEkcyFvrqMY9bE9axhzsuQ6S9yeFxKmO4K4MtmdrmZvdjMjjGzrwFXxH3LgM/v5Lyoak0mFgXfiWJ9XO9WxjFJ2iH3MBluGl5ymJjZPOAmstML5loJnDSSA3kPpzjCVj3Qr5G0Ro4+B5kozOzXhBrEm939xSUe8wfgeOA6d3/5YOmrScF3GJlZE3A+oan7HkAD8ATwS+DL7r5h5HI3vMysi9C0f4u7q4HXCNHnIBNFvH33Y0Kjq4sIsxLlDWBxzOcvAB+I6d/q7lfkSztSFHylIvrSHx30OchEEQPq7YQ51B14EPg+ocdJMqPcTOBQ4G3AfoSWzn929yOqnuFBKPhKRfSlPzroc5CJxMzmAL8HDmDwMfKNMKbCie7+zM7OW7nUCkFERMYEd18DHEUYWreDHefyTZZ2QuPWF47GwAsq+UqFVOIaHfQ5yERlZrXAC4F9gV0IQXcDYdyFe919VE8uo+ArFdGX/uigz0FkbFK1s4iISJWNupkeZMz4LLF/6UhnZILT5yDjipl9fGec190/vTPOWylVO4uIyKhhZhkGb8lcNnevHe5zDoVKviIiMtrYMJ9v1JUyFXxFRGQ0KWfs5jFL1c4iIiJVptbOIiIiVabgKyIiUmUKviIiIlWm4CsiIlJlCr4iIiJVpuArIiJSZQq+IiIiVabgKyIiUmUKviIiIlWm4CsiIlJlCr5jmJk1m9l/mNnfzKzTzDab2f1m9nEzm1rB+c4yM69gWRSPX1Th8WeVkcffxmNuLff17Sxmtq+ZXWZmT5pZj5mtNbM/mtm7zKxhCOedM9T30MzqzezdZna7mbWbWbeZLTWzb5vZASXm4xQz+5WZPWNmvWa23MyuMbNTK31tIhOdJlYYo8xsHnAjsE/Orv3j8nYzO9XdH6xCdoY6l2xfKYnM7EzgZUO81rAys7OBbxDm1E3MjMvRwHvM7NXu/mQFpz9wiHmbAVwHHJKza3Fc3mFmH3X3LxY4vgb4DvD2nF3z43Kamf0MeIu79w4lryITjYLvGGRmdcCvCIHXgW8BVwMZ4DXAecBC4FozO9jdN5V46l8DB5eQ7h3xGgCfd/eVcXtViccfBHyPMG3Y7cDPBjvAzHYBLirh3FVjZqcAlxBexybg88DdwGTC53AWIYD+yswOc/fuMi+RBN9e4AiKT4v2dE7eaoBfkg281xPe81XAfsBHgAXAF8xspbtfkeec/0028D4KfBH4ZzzuPOBFwOsJr/3sMl6XiLi7ljG2AO8hfBE78N48+9+Q2v/pYb72gUBPPPftQE2Zx7cCS+Px64C5JR73g9RrcuDWEf4MjBCIHOgE9siT5t9T+X1XBde4Ih57bwXHvjF17Uvz7J8GLI/7nwbqcvYvINRoOHA/0JTn9V8b92eAvUby89CiZawtuuc7Np0f1/8glHq34+4/AX4TH55nZvW5aSoRS9yXA43AFuCt7p4p8zRfIFR5QvjhsLqE654EvIVQAuws83o7yxHAnnH7y+7+aJ40XwE2xu3XVnCNpOT79wqOfVtcdwEX5u50942E/EEItIfmJHkL2Zqx8929J+d4Bz4WHxqhpC8iJVLwHWPMbC9gr/jwyvglmM/343oacNwwXf58QpUxwKe8zPuYZnYIodQOcJ27/7SEY1qAb8eHnwXWl3PNnaiWUPJbTqiu30H8YZIE5fnlnNzMGskG9/sqyN9DwF+A37p7oR8s/0xt5+bvWeAW4HFCDUe5x4tIEbrnO/Ycmdq+rUi6P6W2jyU0zqpYbLzz8fjwMeBrFZzmfwg/+PqB95V4zGeA3YCHCfdUcxv/jAh3v53CQQkAMzPgOfHhmjIvsS/Z/59ll3zd/YISki1MbW+XP3f/DqGxVUXHi0hxKvmOPXunth8rlMjd15Ktot27ULoyfBhoS7bdvawWzmb2CrI/HC5196UlHPNCQmnbgXPcvaRW0aPIe4C5cfvnZR57UGp7vZl9wcwejF2F2s3sDjM7t9JbCmY2jWx19CrgrjKPrwE+lXrqF5XkQ2SiUsl37JkX170xwBazklB1OW+QdEXFlsZJdfFDlB9IAP4zrvsJJdjBrlkHXEao3v2Ou/9pkENGXCzpTiW0Jn4vcEbcdQfhtZQjud87APwZaEntayL8kDmS0F3o5e6+qoT81RNK4qcAH4zbGcK990F/TMWAOwc4LB5/eNz1FXf/RykvSkQCBd+xZ1pcl9LwqCuu24qmGtw7gea4/ZUi95nzMrPDyDbo+Ym7ryjhsA8CBwDPxO2x4EPA53Ke+y7wfi+/H2wSfGvj+mvA74EOQpX0+YT352Dg+tiVaUuhk8UfBh2EwJ1YCbzD3a8vMU+/A05OPe4l1IhcVOLxIhKp2nnsaYzrnqKpgqRfaWPRVEWYWS2hFAfhvt6PKjjNv6a2v1zCNfcg25L2fe7eXsE1R8LCPM+9mNDft1zJ6FPrgSPc/UJ3v8Hd73b37wIvJNvQK+m3W8wstg+8EEqx74rvdylyX18joXR/cp60IlKEgu/YMxDX5ZQ+yyqp5ngF2UZDXy/3vquZzSIMxABwo7vfP0h6Ay4lBIrr3P2qMvM7kn5BaFl+JPBvhJLlQuAiM/vfMs+1P3AMcHy+9yx+Dm8lDHAB8N74Q6mQAcLncBjwSuAnhFL1q4HbzWzfEvL0aUIXqxMIA250x/P9xszeUMqLEpFopDsaaylvAa4hBNNnS0h7b0z7jyFc78dkB1JYUMHx7yI72MObS0h/NtmBKxbm2b+MUTDIRomvfSbwSOr1n7wTrvHd1PkPKfPY81PH3lXBtQ8j9Pd2YDOwy0i/51q0jJVFJd+xZ3Nct5aQNkmzsWiqAmKjp1Piwz+7+/IKTvPKuO6lQH/Y1PXmEkpUAJ9w96cquN6o4e7Pkh2GE0JJdbilS8XPKZgqD3e/GLgzPjzczHYv8/g/k+1yNgkNtCFSMgXfsScJSM2xu0gxu8b1oC1hCziW0HoXShh/OZeZtRKqKAFucPeOQQ65KF5vBXCrmR2UuwDJLEGTUs/PKTdvVXQToXQIoSp5uKUbWVUyg9K1qe1K8jfU40UmJLV2HnseTm0/D/hrvkTxXuuk+PChCq+VnkGoku5FJ5Bt5FPK8YfF9XwKvK6UFwBL4vangE+Wm7mhMLM2YHdgprtfVyidu2fMbBOhq1BJwdHM5hNaMc8Gfu/FW4fPTG2vjcfXAIsIw3g+5u5PFDl+Q2p7W/5iLcRioC+WcMs6XkSKU8l37Lkntf2iIumOTm3fWTBVcUfF9fIKq4CPSm0XHQ1qDLqEMHzjr81sSqFEsfSfBMiVhdLlOJ5QRf8dYLA5c5OBSxz4W9zegzAs5PWEe+7F7JbaTudvCfBH4OIKjxeRIhR8xxh3fwx4ID4sdg/xrLjeQPgSLUsckOGg+PBvRZIWk/TtbR+k9AWAuy9ydyu2kK12vy31/CcrzN9Q3BHXdcCZRdKdSbaG6aYSz53+ofKmQoniPdrknvyNnu2StZQwNjPAG80sb4k0Pp+0Uu5k+x92yes71MyKjZCW/hss9fWJTHgKvmNTMpPR883s/bk7zewM4OXx4aVe2UTne5GtRlxSLGERyT3A+yo8fjT7MdnGb58ys0W5CczsQOBL8WEHoQvVoNx9GaHUCnC0mZ2T59xTyXYXcsIY2MnxA2THZV5A6CKUe3wN4e8oyffXc/5OLkltf8vMcvsIY2Znkf3hcae7V1rDIjLxjHRzay3lL4Qv3L+R7SZyOWEwh2MIrU+3xuefBNpyjl2UOu7WItd4TSrdORXkcXrq+CuH8bUvGyzvVfwczkm9xnbCaE/HxeVzhNJk0k3r9HI+B8KPn02p478HnEQY0vFcQg1AcvwX8xw/me27Od0AnE64r34m4VZEsu8vQHOec1yZSvMoYaSzIwg/7K6I+XJC7coO8xlr0aKl8DLiGdBS4QcXxmt+OPXlmLusAPbJc1ypwfdfU+lOryB/+6eO/+Ywvu5RE3xjfj6Q+rGTb+kCzqzkcyDc019T5NwZQsnaChy/kDAjUqHjHfgDBfrnEkawumqQ458EDhrpz0GLlrG2qNp5jPIwkP7BhPGE7yVUgfYSAvLngAPcvdJWzgDpRkSbCqbaecePCe7+ZcK98cuAJwifQSeh/+0XgMXufmWF574D2Af4BKH1d/IZLyPUdhzh7v/u7l7g+KcI993fQ7jvv5EwscUaQoOu1wIvcfe8cyS7e6+7n0Fo9PXLeFw/oaR7O3AB4QfefZW8PpGJzAr8vxUREZGdRCVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfmfDM7Etm5mb25pHOSz5mdlbMn5vZokHSzjGzjJm1m1ndEK45xczWmtkTZjap0vOISH4KvjKhmdlRwIXAX4ErRjg7w+EkwICb3X1rpSdx9w7gU8BuwJeHKW8iEin4yoQVS4bfJvw/+JC7+whnaTicFNc3DMO5LgUeB84xs8OH4XwiEin4ykT2HmBf4FZ3v3mkMzNUZmbAS+LDIQdfd+8HPkMoSX89nl9EhoGCr0xIZtYEfCQ+/MpI5mUYHQTMAh539yeG6ZxXAGuAQ4BXDtM5RSY8BV+ZqN4OzAFWA9eNcF6GS1LlfP1wnTCWfn8UH36kWFoRKZ2CrwwLM/tObI2bMbMjyzhuXTxu9c7MXx7vieufuPtATp4WpVoXH2dmJ5nZEjPrMbNnzOy3seScPuYUM/u+mT0SWxr3xdbCfzSzD5rZ5GKZMbPDzOyq2Lq4O66/bGbTy3hNJ8f1DlXOZnaAmX075q/bzDrN7LGY56MHOe8P4/pQMzu4jPyISCHurkXLkBbgeMDjclmZx16XOnaPKuX3+alrnpxn/6LU/k8AW1OPHbgtlXYy8Puc/fmWJ4AFBfLzmSLHrczZv6jAOVqAXqAfmJyz7x15XkPu8g3AirxnK2O6i0f6702LlvGwqOQrQxJbDH8rPuwEPlzmKe5PbR86LJkaXHLvcivwx0HSfhzYDJwLHAVcwPb3iL9HtsT5O+ANwIsIVcD/Bjwd9+0GfCn35Gb2AeCj8eGTwDnA4cBp8XzzKO09PQ5oAO52982p8+9J+HxqgceAs4EjgaOB9wLLYtL3Aq8vcv6kQdorSsiLiAyi4k74ItE7gT3j9sXu/myZxy9Pbe8xPFka1HFx/ai7dw+StgZ4i7v/Jj6+I9lhZgcAr4sPr3L3N+Qce6OZfQ94CNgVeJWZ1Xnsf2tmswnBnZjmaHffkDr+V2b2FUI/5MEUqnJ+I1APDAAnuHv6/f6TmV0LPABMJZSQf1rg/H8H3gwsMrOF7v5UCXkSkQJU8pWKxVJvUmrrAb5awWnSwXrekDNVmufH9T9KSLsiFXhz7UMoTfYRqoZ34GGwiqRBVxOwS2r3GwjV1gDn5gTexIeApSXks1D/3tlx3UlotZybvxWEqvXPA/9X5Pzp9+qQEvIjIkUo+MpQvAqYH7evdvf1FZwjPbBFQ+5OMzszNnwalpa2sbSZBLzHSjjkz4V2uPtP3H13oNndiwXydNBrTG2fkux391sLXGMr2QZPeZnZAmAvYAPwl5zdj8R1G/BjM1uc5xoXu/uH3f3KIpd5PLX93GL5EZHBqdpZhuLM1PZV+RKY2WeAxcDt7v6NPEnSrYC78ux/QVz/taIc7mhuaru9hPTLB0vg7hnYNsjFroTXuzuwP3AE2ZI2bP+Dd6+4/vsglxjstSdVzjcleUn5IaH0PAd4LfBaM3uY0EjsekLjsZ5Bzg+wKbVdrRoKkXFLwVcqYmY1wInx4Vbg1jxpagmNjlqABwucamFq++k8+5MqzuEKvq2p7Y4S0g+axsxOIzRYOjLn/IncgJiYFdf5qpvTnhlkf8EhJd19vZmdRAjCB8an947LvwFdZvYb4Kvufk+Ra6SDb77XKCJlULWzVGoRoSoT4BF378yT5gWEwAvwzwLneUFqe7sq3liSPBhYVuB+aCXS1dwDBVPlT78dM6sxsx8C1xCGdWwlBNqlwK8I91KPAz5b7rlz9BfLA/Di+DDvkJLu/gDhfTyB0KVoWWp3K3AGcLeZvb9IHtI/IMbDGNgiI0rBVyq1e2r78QJpXpbaXpG708waCN13IPRRvSsnyZ6Eaum/mtliM/uBma2Jg138xcxOoXzpHwktBVOV5lxCC2AIPxxeBkxx9z3c/TR3/7S73wY0Fzg+KdHOHOQ604rse2Hc/4i756s5AMCDW9z9PHffjfD5nQvcFJMY8MV894Sj9HuV74eWiJRBwVcqNTW1vbFAmjemtvN9YZ8MTInbv8nT7ScpFU8BlgAzCC1ybyBUR//WzI4tI88Aq1Lbc8o8Nte743oj8BJ3v87d8923fk6B4x+K64NjFX0hBxbZV3RISTNriqNb7Zt+3t0fc/dvuvuLyfYjrkmdL1f6vVpVII2IlEjBV4bDDkMnmtnLCf12kyrKfBOyp/uvfifP/uR+71HAae7+Mnf/kLu/EvgPwt9vWYN6uPs6sg2tFhZJWornxfXj6YEt0sxsBtsHtHQ7i1/G9S4Un7TgzCL7Cg4pGT1FaNCVr7Fb4vep7aYCadLvVSldn0SkCAVfqVS6ivPIWIUMgJlNAb5GuE94e3z6sPTBZnYO2cEubnf3fCW3pOT7aXe/KWffxYTAXslYw0njrQMqODYt6Vq1r5nNz90Z34cryN4bh+27U/2MbDeki81shxKymV1IGPFqB/H8hxH6Gd9aII+/i+tj4g+ifM5Ibd9bIE36vcrtziQiZVLwlUr9hewAGXMIfUhfZGYvIwSCxcDlwJ0xzUfM7DQzO8LMvkp2SMpNwNtyTx4bEh0MrAP+N3d/rKLeROGWxMXcGtd7DzbhwSB+HtfNwK1mdraZHWlmLzGzjxIGpsitxk2q2ZMBOM6PD+cD95rZhfE9OsXMriAMZbmlwPVPIJSk7yxQ3Q3wBcL9dAN+bmb/a2avjBM5nBav8aGY9o54jzqfZOjPpe5e7UkwRMafkR5cWsvYXYC3Unig/nsJgeYgCg/qvwZ4YYFz7x3T/LDA/mmEwHtXBfneJ5WHV+TZvyi1/5NFzjOVMDRjsQkLlhO69CSP35bnPG8ntGjOd/yGuH+HiRUIP2Ac+PAgr/d1QPcg+VwCzC5wvBF+BDnw+ZH+u9OiZTwsKvlKxdz9B8CphEH32wkB5FHgU4Rxijvc/T7g1YRg3EtoeLWE0A1nT3cvVIWZ3O9dVmD/awhB4fcF9hfL90PA3+LDiieId/d2QpXwJwj3VbsJ78GzhOr2fwf2I9xvTbpKnZ7nPN+L6S4j3E/tJcwi9D3Cj5f7c4+JCvbvzTn/1fH8FxF+LGyO+VxDaKj1TuAQdy/Un/hIssNiXlHsWiJSGnNXlz0ZfczsIuB9wDfd/dycffWEYRPnAIvdvezWt2Z2JiGQbADmunvfkDM9TpnZ14HzgBvc/eTB0ovI4FTyldEqKfm+1sy29XONkzl8gzBF36crCbzRVYQ5dqcThl2UPMysGXhTfPi5kcyLyHiikq+MOrGxVQehRXU3YRCKnxPu8SZdmC5z97OHeJ13E+6b3u3uRwwp0+OUmZ0NXIreI5FhpZKvjEZ7EYY9vI/Qj/WPwFmEQS3WAWcONfBG3yXcAz3czE4cLPFEE2sZ/oPwo6fY0JMiUiaVfGVCM7NDgLuBe9z9yJHOz2iSKvX+j7tfMMLZERlXFHxlwjOzzxP6ur7Ji89pO2HEATweJUzzuL+7F+prLCIVUPAVERGpMt3zFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqTIFXxERkSpT8BUREakyBV8REZEqU/AVERGpMgVfERGRKlPwFRERqbL/B2SbuGHO2/2WAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,3D-2D-2freqheatmap,2023-03-31 14;23;36.png\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,2freq,err_vs_s,2023-03-31 14;23;36.png\n" + ] + }, + { + "data": { + "image/png": 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dW4HBhiaqQKAuK8YZDAZrzM+HZFlGmqZ0u1263S6dToeiKOh0Olsmjftwuq5xW5fdTt5LVf2sqgmbcTk2it5uuxgmSSRu1PSk/e7288bUkXS2q891KRPUvZYdK7dPrmF9EkG2rzVJCrNjZsd/0j1up8pygfa2k5ou+8pFrq3qKpWofgV4o4h8t/n+KBH5IuDngF+bUhsHF5HwWVa5kwfJ6fAs4MwpSHpa2up1oJvC+jrkBdx7nyatpUPQ6VR0u9pOlec5q6urLCwssLCwQFVV2iDvJDe7b1Fr35okFVzIeN0mE2sPs9/jON4yebe7Tht2f13XjQppScHd3+5jG5OkpnYdL/fcSaTa9gxOIjWX/K0NyD1/O5ugax9yicl6DycZ6dt9av9mbQ/vTsi83a9LOX5XELnqJarXmL+/j14V5gNACfw/wI9NqY0Di7xUrLCGLjZcUQIrZ2A5gXEJ4xw2BvDwSR0Jq8xnfQOGQwiCDTY2IIo0gVVVRRzHLC4uNhPe5gy6koFLMnme0+nolezdN+0kVceSj3sNS4juA28n0naSi0t27vc2EU6SyLaTkCzsfbv36sLutxO+7Sl1++ESqLvdvaZrn7NtbqfSTSJZt6z0xXLv2kTcti9eSJqzx9k+tiW35niX9FpjMel+LgorTW1jQ9tvTCuOqgb+rYj8FPAYIAb+VunVaa551DU8wApaA1bchf5XUigLGIw0Aa2jFz0szKcawsYX9O++sQFJAnEM8/Obi5NaAnFXs7ET1Ea1W5uW+7YHXZt9u0mjlGq8jGVZkmUZcRw3E00pRSCil58313OlDduO+5DbIFb7f2qqR9g+u5LHdnCJySWWIAgoy3ILcVpVzVVd3f0u0dp97UnpqntWGrJ/XTJoS3Nu/6yaHUXRREdIe9zd8+04utdwj90Jtj3uAtKPS+o7lsCukDQFU16FRimVocMUrjhEj/4vAF9kNv1rpdT/3o+21AA0Sd0LLPJZhih6DE7ppZxLs/fjwMg5bxWYB4Z3w6zA3BykKaysZMTxaZIkodPp0Ol0mokYhiGFyRssiqL53xJDmqbN5HNtJq6R2JUk7Lk2ZMKemyQJCiirijRJzjMyu2RgJTL7v6syukGsFzN2tyeu7ae9D0uidl1E1y7Uvi83HcklKttvt8qqK0m1r+Oe1x6ztmpot7t9tee7sO259jB7ju27S+aT1DpXmpxUdcOcuKVv9q8lYve+J57fxhWSpuDaXi7rS4EblFLPEZGbgfegjftTR35uBS1g10DASTYo6FGhPQlj4J4J590LPGTOulFBsQpHD2mpyiUcSxwWdoJEUURRFI3kNMnw3JYiXOnEkl1uarxbo7zNReykKYFMzoFzJ7s9136s5DQej+l2u2RZ1hBVkiTEcXyeVOVOWDtp7bWseuumFLlexbbU6O5zx8NVeduqtCtVuWNm229Lq3bbxsZGo3ra4F17fXu9JEkmeihdUnQl2fZYu8e7pHshu1b73MAZV/fe3BdQ+/ew4QgiolUCvWOTsOznMuCaJSql1B+JyAfN15vQQs2+IB+NoAnVWwUe4BxH+OgOzi3QrtLjwJEU5uchjoXRaNRUYChMjmDbYGttWTbz35UYXPvHJFtG+01a1zWDwYAkSZiZmdFLejkPY5vsrDRT1zVZljUT0z7wLhEGQdBUM3VVtkmwZOCWaXalHSu52QluVV47ye29tonAXstVz2zlCks6diwsWbljGZkMA0twTepNEFBXFaPhsHlp2DCTNE1RSjXLo9nrY9RqBWDackm/bWNsq7LuWLm/tfsM2GfFJaDKjpvjPHHHpnkeTLsBoILg/Mj0K1Aq+5olKgClVCkirwO+HXjVfrUzGmZo6xPo/OxLi0Wy8sDcnCaq5eVlOp0OCwsLjdrU9sRZCcvamFwppb1MvDtpXanEqnr2ge92u1odqCpUXRPG8ZZz29KLO5ltoKoljSzLABgOh3S73WaCu55L23/710oJVqV11SdLJrHpk7XlWDXJSjLuRBYgCsMt9b6KotiSFeCqpNs5DFyVzvYxiiJUXVMZ8hqNRqRKNVKk9Z66Uphr6JYgQDlqorvfJZG2WtcmUJfcXLJxQ1YaFdT53QFCq3qC7o/9nW1flEKqShOTHRvbtzC8rGrgVIhKRG7YZpdCz9xTxuB+2aGU+kEReQ3wpyLyfqXUndNuo6BtsL4TnfLY29H5h4EF4OhROHq0z/z8fPOgh84bFzYfWPsA27e1+wa2+YIuqbj2GHt8EATEcdxIPVmW6W1RRGakjsBMcnstdzK1DeSuhAI6GVtE6Pf7DbE2E8x9+5t7dCWANEkacqvZapNpT1TbL0ukVqqqyhJV11DXhCIEJqey3+83aU12QrvGeJfwrE3HjqlVb+u6JgpDev1+I0FZW58ltDzPmxeMKxk15OCoY+3l1CzadjbXNmbv3z3XlbLtvYBexbuuaypzP3meExhpOXTaaZ4VpTalqSAAQ2CEoSary6j2wXTrUV3IHZCJyDuBVyqlRhc4bmoQka8FnqWUejXaTFRyqaLODlFXwJYsrY+itc1b2QlZHQaOLcF11/U4vLzM/JxeIrHT7TbSg5UYLBHZh96uwGwnuv1rt7VVNvtA24llJ6iVLBpCaklQrvHVThBXpbFkZsnKSj52n53wzTUdqaItVYkIqq4JzHGh6a9N5nZtKm7huPPIwG4z13MnuZVK3WtatKXR5neuWwGsatOzt7C42KzfaF8S1jZl1dXGo2cmeV3XWwjble7axnZXEnbVO0u41pkieiDBeU5cI73rCGir/5a4q7qmKgp9rbomMr9lIAJVhYShJlrXNrjPpDUtZfNfAmeA7waeBjwd+E7gJPATwMvRCcs/s5uLi8Z7ReRVre2RiLxORE6KyKqI/JKI9M3u9wDXichHgA8B/1Epdddu2r8Y0plZIHW2PAj8JfAXFz33+cALjsNtt8LC/DwL8/MEYdhUWkjTdAv5WKKxKoz9WBXQko/1trnGYdeb1C6F7EoWZVWh0DYN2CQh+6C7k91NpLblbHq9HnEcN9H2SZJM9GRVRjKxth87oeM41tcyEplrk3KlBBdbVBxDTEAj+VjCT5KEJEno9XpbiMqOVZu03LCBRuUzJCFBQBTHBOa8TqfD7Ows/X6/ySqwxG0JwpKGlZBtG2EYNup76EiY9h7a92q32ftxQyoC89tbcrGqvOstDcOQWqkmps/9XV3J1UqJ48GA8XCoA5OdaqhXmzH9VcC3KaXe7Wz7mIg8ALxWKfUkEbkfeCfw/ZdyYRGJgDeiy8b8YWv3TwJfA3wtWlp6Gzok4duUDpX4pl3cy6Uj7NEqaooORqiAv7vtaY8Bbp2B5WU4fuIEh48cYbbfB9ELlxaGPGDTyAs0k9rGWFVlSSdNGQwG9Pr95vi2bcSd8Pb/IAjo9XpNjaw8zxuVLYljOsYg7JKMq37ZyW1zFe01O53OFqO0a+gOggDlSFtuqACwqbKaSRebSWwnn5Uii6LQJGQIt5ZNL5adRDU0b/+2oRm22vMmxXi1jdoNScmmN9JKT24+ZtsIDppU7PnnhSdgpFi2Rqm7qqhbO8vtgysxKaO2iQhVWWrJqq5J45jQGP8rOT9kxf1dq6qiKksq0JJVXaPKEgkC0k4HleeNje5yGdanRVQ3AJ+dsP3zwM3m/y+gw4Z2DBF5EvB2tHa00trXQUtwL1dKfdhs+w7gD0Tkh5VSu84xNDa3683XJ1/s+LJM0P47F2eAv8KGLLTxHOC5R+BpT++zvLzM/Pw887OzxOZhLssSgUZFcR98+4a3YQVWrbEPeVu0d21JsKliJUnCaDRqpIVeT6upVgqKjJ0nnDCBlVJbyDIIAtI0bdQdK2m5ZNZIKkq7vpV5o7tG/jYpuvFULpk1DgWlqI16aK9vJUR7HSsptqUyV31sq3ltuKpkGIbUVaVd/kGwxeZh79fa/ew21x7mGr1dtdWquu2+uBJxOyWprfqKkfTcvigTawd6DcooDFHOdZUyXkjzNzCSYl3XlEWhycoY1vMwJLB20Mtop5oWHf4ZOjK90X8Mkfwom/rP30MT16XgBcCfo9XJ1da+pwJ94IPOto+g7+m5l9hOGy831/oI8KaLHi2gTWBtDJiUk/14tKn92CGg0Kb4wLwBs/FYu7zrulF9YNMV7S4PbydNmiQNoVhbiGv8bkdW20lTGmNzHEV0DMnYTxzHRGZi2IfYJR4R7enrdruNS961ibjq0qRQBAkCAnN9Ky3ZiQibEqG9hiVAa/9y1Slha5K22Akk0kTVAxOlEDc0oQ37wnAN6K6KBDQGe/tbuC8Tey9W4nKJxh7jjo+qJydft1VBC5fA3d/F/a0qI1mKcZKExoZmP1teXg5hR3GMAqIkQYJAOzTMiyUMQ01c+sSJYzdtTEui+m7gfcB9IvJxNFncig7E/goReSG65Mu3XMpFlVINSUx4210HVEqph53jCxE5zaY0tFv8CvB+8/+TuQhZzcwCZ7ez038WWDb/r7NMxGPpMh+CDCBSiigIyFdXiY3IHS8vaympKLSKZMRs+0hY1SpJEq16KL0MV2COtRKE+2a2k9S1ZVgywZCOBEETQxWbB7Xx+rDViGslKmsXS9O0sYvZyW8N4K7B1hp6Xc+RnYTuca5aBFuJyPahuc9gaxE8V820y5Up4/GyEoCqawonJacda+a2Zds5j4SUojb3rIqiOc6NeXMJ1ZWwXNILjPs/aEXTu6TVVtXsNbYLF7Ekbz141nAf2jAO5+XVqIzoF0jj9DDPY2BeJEEQEIVho05b9f1yYFq5fp8QkScA3wA8BS1evB34DaXUWERuBJ6ilJpmek0PyCZsz9CJ0buGUuoeTDD5hdQBi+4iWrGdiI+hOXUNzdsBq6zzYDVL5555yvsFdcuIMK058tjrkSjSD0scI0VBlKaUxnMVGRXQtVcEQUBo7Qp13cTGuB7A9mS0Bus8z/WD6Ez00Hh0AmOIlWAzhkZZkjH7rdG7qipCa/htTei2dwnz5tYX0e5vgS0TrvVbbLEnuTYeqx43H2cyJ0nSTD6MWmWDLBv7kDnvQhO+LUHZPilDtnVVUVoicO6zPaa2z244he2P/dsmJrcvrgrs2tnaz6drE9vyojLqqiUq1w6mlNryG9t9SZKQlSVxklANhzpcwTyHUZrujwt9G0wrjupVwK8rpd46ab9SattpvAeMgGTC9hStc102qEm9aPBhYBa99OFdnEb4CIob0BHpR6sOw8+8hF4orHxqEaHH8qNvZvnxt9Cdn6e3uAhJQpgkKBN8ZyWXxihrHjxRCjGxPrCZlgFbM/OtCiIiiKm44HqjQmPDaEjCSjbORGpsHsZGpmBTsmFz8jdR1kohSulwBIdAtkhbnG8/ciUlex9WEmykBWWiqB1pQqyEwmaohXv/lVLErZCPLb+pI7W1J7U9PhCdWiJBQGIkRzfw1BJJQ6qOOqiMlGfH2VWjVEt9c8dEHEJx0R4/dxxEtOfP2pzEEiPnE6KqawJjGgjCkCgICKzkniQ6YtBWqrAvHNuXfZSwpqX6fSvwM6JTVn4N+G2l1MaUrr0d7gMiETmslDoFICIxWs+6f5/b3oLqopa+33f+1z+qFdm6jLmv/k0OC9ymbuSQ3Ii6c0xdlXQPH2bh2DG6hw/TnZ/XNgbHY2Q9RfZhD4OtS2y144tgcwI2D7O1r5gJFwSBfsPXNco89IH7ABrSsSoD0LjnlfMmd13rgW54S5CgnSxYgp0wAbd4xZzPZleMtIATyOq0q+pak6Pdb9sGnbriXseRhOz5tj/tIFO7vTJjY6O5LVG5UomLdoDsloltpGZaY+iS0xYSDrYmUrfhEp01jEeGsBDH8D7h/EblryqkLJG6buyoqih0gK5SSBzr/l4Gz99UWlBKPRFdpeDP0bWpHhaR/yYi/0hELlycZ/f4GFpycv3/z0fHBOwkzW5qUPHFj9kOI+DTwJ9XcEf5Bf66/hCn67tYveeTsL5OORpRFwV1WWqXcVHohw0zGc3fIs+bCggWbe8ZnB8YqoxEYr1iQRBog2sca0OqVf/YfPhrR1WYZCxXLYlCRHRwoFGXVFlq6dBIRFtP1ioR2xCUO9EjYxy2xl6XkK1E1fzFUYWs6iRCaEIbXKJsOx7aycKuxGbHyhr7bRtuepI95zx1zRKkfZk46UyWILeDS6RtuOqn+70JSXEIcsv5RrITQ/BS18RJosNDgoAwywjynKCqoCw3f7+WNLofmFqun1Lqr9D++FeLyHOAfwK8A20zOjqtdpz2RiLyFuD1InLOtPNm4G1qD6EJu4GN49ktbH2qCJirYTE4gtBHej1qpZAwpBYTF2NtK8Fm0mltAjRrY7x2o6Db9gobpOlGikfBZjVOcKSIlu0Ga3dybGHnSTgimwm3OMGXjrHWlWCaaeLagJz+tqUJe0/udsFR+5xx3XYS24+ZmFa6aUts9hou+cFmvJJVPZHzqxJMSmy2KuMk6VAZ4mv/Xs13p31pjX27j+17AMfjacfRqOLIpn2sUeHNy0LGY+rRSPdZKYIkgSyjFu20kTjW1R6dF9d+YepJySLyXODrga9GBxH9zrTbcPBqtOH8Xaat/w583z62NxFRml78oItAgBlgXj2OpHcd84cX6c3Oki4uInGsycpKI+g6UaDJqRyNmjy2rvHYTUqPAP1AWs+ciHbf18Zdb13PkXmD1k5bwQQy3jIprLRlPYqO5NDkjLmkZSaalc7EqLJN4OMEIoTzV40xHdEEbonDftrXc4/XF9v2mi4pubYla7OzUpklvjZBWEnKNaRP+j3s93b0vbuvUatlU+22Y27/2vFq99+Fa3O0BCUuCdtz6hqyTKt9VUWgFGo00tuqCjG1yrC/pTj2y30irGkZ05+FJqevR0tP/xP4QeA9Sql8Gm0opW6asC0HXmk+VwwrK3u33T8beBQ38bj4WVx365PoLM0zc911JDMzRGna2HNCk1+XOwGFNWj7g2xNtbFw3+BK6bCB3EQX2zgmdyJVVYUSoShLYhHKqiJxJqr7preGYFVVzSSyD3EjVVijd1nqCWKuT1WhjGOgNlJas9++3Z2cQX3ZTXvRFomrZQMDmoBZgqAJ0LTj0RiujS3O9S5OkkrcHEiU0p4+Q+6R+U3a0e5tYnIlqqbOl9rMRZRwcn35hoCMLcySllI6dMRKQ5PIyX0G3OtZKbTZ5qqZ5reSqkKtrGgJytbKjiJUGCImHxWlLouNaloS1Z8Cf4LO5XunUuocgIg8VUS+TSn13Rc8+yrHNELebuI4x3k6xx79XGavP0HU6ZDOzekH0nwKk87g2jDG4zF1UZCkKaFIU/fJjWWyycP2rW1tUUmSUBaFnqx1Ta20S5qqQuqaOs8pgbwsdZxWEGwpoCbGeG89VmJsUGGSaCIykocyKmtZFNreVRQQhoSiAxKVMfDaqWQnYEN8LRWukdTs21xvbPqljD3P/jZxHGtXurGvWaKATZWqcmxQF5rwYAIflQ55yIuCoN6awD2RZAxs+Ic4fVat49sG9Ob+LBnbRGbzcrCq9o5lGUtyVv21KrMlvKLQJGXCPwJABgNqk/SslNLe4ixrpNmmj/uEaRHVY5RJ+BWRORF5BdoT+HR0qfBrmqiGe6wH8STgZr6UPidIDx1CxTFZGFKurZGXJd1+vzEM90yJEjuJwygiiWPiNN186Fr2Civ9FCYo0UZZl2VJKNqgHIVho66VVUUNFHVNaRJZUYo6y6iLAhXoXD0rs4m+eBMEWRniCYwnUQFVllGNRjpNRERLV3GMCsPNQEecwE5zjXYgpns/1uOmTNCntd0hgsSxdkAopYMxjYdKnLYsWUyKlYLJ9in3GBuiYIl6C3E657i/iX0pNGquUgRR1IR0uHatiTDqliV3q8JdCkk0R1ryM9cUQIoCTOUElecwGFCNxwSjEfXqKpKm2vO3sKAN6tZJ0vZiThnTCvi8S0S+GE1OX4te0v0u4F8BE2OrriVcf8OC9kHuAos8kTkey1meRhScoDeGs/feSx1F9GdnKeqahSzTQaDWJqJUs+hCbNSauigIoojSqITWU2XVKjecIc8yZmZnKfJcl1ApCiqTdBoZQ7GqKqSqiJJElyIB/VCigzoDQzBgJrQIRV1rm4aTCmMnsKprKqUo85woCIg6HQITlGlVESuRBWFIhZF0qs2l7ZuUFEuc1ksWBNpzae6hNEX7qGttP2xJZk2wp2tbMecD1I69p/FqOgRlU0kCO/ZmnOxYtMnGqouBUc/FjrFLzrDFAXEhNB5U02+gIfYthOEe05bM2udYSc46Bcx9qTBETE0tlAlJyHN9DZP03Fx3H7EnohKRo+i0mJcDt6ATh38L+EbgJWq6kegHFkcPH971uee4nod5Dh/mBEs1zP2lzsBOeiWz8+foduH40VVuvPl6ummqPUNlqcvfbmygioJKTJR4t0sQx01EeTuWR9BSSBKGWrUrCkojYXV6PS3ZVBV5UWj7EUClC7EJ6OOrCgxJWjc8oAlGNo3aNmUlMB7LIAgIjGRjPYp1lhH3es1bua5rTVC2bcfmY6WNoigQG7cFYLZR10hdU1qCSRJUWep+sik9uZ5DG9LQ9N/2w1EDm1xAo+raOCSAMs91eAQ01QaAJmm5dsMDlLNEl7FJKfMCUVa6jWO9mEaablbStFKTJXxD5tZh4ZKS69hoJDzHcbHFltRWJS3pjMeawIZD7fVbXaVeWYGVFS2lArKwoH+zJNms9OmS4T5g10QlIu8CXoxOFn4P2nj+fqXz7b5xSv27KnDdddft4WzhLuaabyE6UbEzhMcN4cZFOH4IhisrRFVF2ekwXF/XBvHRiGw4RAGd2VnCTofy4YfpdjrEScL8wgKIDkKsioLAqFQ2MDQAgromFtF2naqiznMitNifmmoKYRyjRJAk0Z5ApSPBrX3HVoxUIk1wZ610XJAYUgyDgCCOtS2oqhpCtbYsEROXVOsA0cLYX0oj3dV1rSU2Ng33mIkeiDRJs3meE6Ij7FMbIGsIQmwwqykOBxDYPEpHcrNePKVUUzSuKgodkmHUTMpSxxOJoILNnD7rYbTeOkvAVprCSFSINPFroXHxB+b6GOnT9gMxgZrWyG22B+Y8McREEDRSb6POVRUqijalJ8c2BWipCPT+qtJG8/FYE1VdE6YpKk1RR4+i1tcJFheh30clyWbhvANuTP9KdMbt64E/VEq1l3N/xCBphSekxuY8mlRQ4Ty8D50J9DjgFirmuNtsOQzMnYN8BdbLdaqNDc7c9wBVCVUIZx7WSZUKCNIhR06kTaZ+URQcHg6Jk4SlpSVC9Js+zzKt4oShfoiDgAqdHB0kCXlRUA6HUBTEVUXS7erJoRT1aERoI6/NW9+mxQRVRWQnsjEYV2VJiTbGhkBiCEUlCXVRaDd3WepMfeMJVCZUomazuF/jNTPtNVHlomPLamOzoyx1grYhVls9MxTRgbNKaVUmCLSdzRiMUzPp4iBowj6U6X9V15Sm7SBNGwnIqrJ1lun4IkOiUahL9UoQ6KRyaMihNiRX1bUm/KpqKpw294fxVDpGckQQZQojGimuLkskSZC6JoiiJjvASlvVaKTHOssIk4TAxEIRhpvqoYgmtDCEwQDyHHX6tO7v6qqulz4aoc6eRXo9fV5fx/c1i1Buk6M5beyFqJ6KLkz3Y8AvisgngN8EfnsK/bqqUFUVHef3siaETgjjHQXt/i7arHczugjMYXKO8RBzLLLE3X/bQVGzLAEDpUv0PYz2UmTo5MZDwOc+mZHEMNcfcvg4lCsDDh1domMiqJVSZHlONh5TxzHj8ZheFFHVerWYUysrDNbG9ALo9RLmFxfpLCyQVBVxGFIPh1qlyjLqKCI05VasB6o00kogovMP7ZtbKYrhUBNQWYJZREICp7JAXSMmXMLmLzYqF0Z1C4ImGFFZYjL7ShEIwyZxO7ZR4KAnvSlrYskjN4Z9BdTh5go2to3IGPpx+lEVBaEIUpbExlsaxrFWiYwdz6pzYRAgbhiBTYqudTqKtavVShEatVJM+1Yqq8y9qaLQ5YHHY52nmGXaCWKIX5TShu8goLQr7AyHxJ3O5grOQGhCDhgO9UMax/r3SRK9bTyGwQApS9TJk6iFBT3+R44gMzOaoA4f1otPhuFWQ/o+Y9dEpZS6A/ghEfkR4MuAb0YHYP64OeSficjrlVJn9t7Ng426KIhbaTRWwo6qyZWqzscIvXbrpllvjeOc4ya6PI4NUgbqBh4mp+YQn0A4xxyKlBC9OEQKPL2AB1eAIZQzMKvOMqwqZhYXOX36NOM8Z309J5qJOXu2IC3h3DqsrMHpczAEjneg08+56ZaHSdbXWVxYoJemyHhMp9vVhnBTdUEVBUVdM1pd1WEJdU0xGum3Ojo0QMqSejwmAl0yxPwtzOQOXPtGVVEau4qy+9ASjlhVyxifqXWpliDPiTodfYyZPKJUk90vYaiva4rAhXFMqDa9eZUhYksWlSGnQG3GhLmSna3aYNU6lWXNfYRB0CRI1ybNxEqg4qhvpSGNuq5RcaxJwNrBTLxZ48U16qzNiRQTJlKZNQUxhIUhsSgMqbOMwnhpxdigyrNnCcoSLGF1u0ivh8zO6pCRPAdbJWFpCUwtKuJYx03Nz8PCwqYklVwwG3+q2LPXT+nVZd4HvE90vfJ/gjam/2vgVSLy20qpa9pmVY7bZYg3Jes00QRiPdc2xc2qbNtBgEUe5GEeJOZBCmbocBcFywx4gPs5S8UKWrGaY5VZYJ0Oj+UoXciv58Gz0PurDiuLqyzfsMp9fwtJD86dgnS+4MHTsIFeCPV+KgpCVoAnjuHGMfTuhBseo9++qq4ps4w6z6nX1+kMh4SdDvl4TCDC2soK1DXj4ZDMeORUGNLtdlmam6PIMuKqgk6H3twcHRHiXk/bbsxqM9S19jblOYEhDBsFbSuGFkbFJAyJraHYqjJBQD0eExiiauKaggCxC2SYESuMekRVEaSpJo5uFzUaEfX7TZR+4/Gsaz3BwxDJc8QQpApDnerkxB4p68SIIm2DC0NUlmnpTXSZFGvkD0CX+l1Z0f1XTixanuuAV2M7E4DBoJFWgzzXzoORjo9RaI9skSRaAqsqwromHww0EZ86peOhhkOd9rW0pD2lItpb2+0iy8t6LPMcFUWoJIFOB2Zm9Gd2dnMlmsuIaS/pPkDXoXq7iDwKTVjXNEkBW1zXbVipuP13FhgUUNWayI6i1bh5NldKnTPfH8OYNWqWWKTPSdZZ4TpOcg/3bWkrJaDHU0hYouY2UgJSeQzj0zGr3VnuXVmhXIkZUNA/PcdDnOVTfJ4KxQPkHGKBijUGHKViicHJJQYpsLJO1l1no4KgB2UB851TUMLph6GTwoP3gMpBEihHsKGgT0kh65x4zDprQ5ibgV4fjj66QqJI21uAbDgksGEURUExHGp1sap0VL7xdAZVxXh1VducgN7MDHEY6kmtFGGno+1kYahreBn1kuFQOxWSRBNSnpOtrmpj/XiMzMxQnztHMj9Pludw+LCOODdGb6syVVVFVlWaQIZDlAjlmTMEaUp9dkDVCVHnhshiDymhiKAWoNdFoojO0hKVCMHMDKyu6jCMjQ2Co0eRoiBIEuTsWSSO9XFKQb9PmGU6JKPTQfJcq2irq5RrG9pGF0UgiqDT0SrreEzS71NvbCDdLvVwhMQR5cqKtomtr8ORI9pAn+dafU1Tom5XS1HW6D87qx/YNNVR6VblM+S73yEJLvZtAVKl1H3Az5rPNY0qO79+3xOBm5nlXtYZoBW7OeC4aGlqBv0gI3CuhCWBvIReAOdqeLRAX6CqF1ngKRwi4Rg3cpbTrFGT8zFmgM9A4xa/mZrr+RzLHGWOE5wm41y2SE1KL5tFGCAkxKxQskqHU2ScoeYswr0sIZwAFnk0wnWkPI5odZHu4YhxCedWoXgIwgjOVVANYWNNMWbMA6yQUrFWRnTIiJgnQNFTi8Sf02k+Q6Dbh/HGSW64raZ36BCRCKOzZ8nHY7LRiHJcUK7rBM5OCkUM60MoMhjXUGeQZxBn0Ott0O1BZxEk1fsjIIpDesbDOB5nWvWOQnomzqybJIzuGlPmUJ4b0j/aQ1LoXFewMRgxE4ZIp6PDKcoSxmNdh6koyAcDbT86U8DKOsN7HyCjS84XEJbJOUlBn5oRJfPMMkPaP0S4OM/Mo9ZR/YRydpYgy3RKVF3DygrVyZOEaUq9skGwNEdpjOKMxwRxTJgkyNqaNm6vriIPPEQ1ylHjMdXyEZCQKo61VNfpaLuUMfSLQBWGqLSrA3WHGdKdRS0voubnkTRFOh2qNNUeQkANh1riC0PCbldvt55NR21upwvtF67plZIvFyZl6X/fY3+RmYUFTj74IAyHjNfXiZRi5vBhxmfOEPZ6nF25hxEPI9EGsxJSRQELHGdFnWauPkyNYiY6QnxkmVxiopk5unfex3w+wzynOcLHWUZLYiFwAxGP5TEc5UaWWGKJmOPPeCzD9Yq5w7C2vkyxMWBIRUxOyimewoiYNToo5gkYcZoaCBESbmAwGtEdzbKRQZTCIANRkI4hX8vIGJGxQcZZhigCInooFik4xwaH2GCDoySEPEDG/CAiP5uwMBySK0WQ5wxW1jn3IBQjGJwE6pIxgZYQexDEmrTGOaQ5nBlCQsXJcyAERCJ05kGFcOgoMFdxz50VQQyjUzC7CFVeEQUVo3MQRGPuWV9DMSJiwNL6PAmzzD0EpDCWB5BQ24Hy1ZJIQVRAOYRsANX6mLVhyZCMAevMssEqZ8nIOMNZFMIqJV3OscRxjgx6LAw6LNdd+tfn1NUKUlYMHoZOD9TqBuQgUUF+FsLxGuF8SKaU9sZlNb1FLT3q/LuM+vSY8aCmzGrKIKTKFd2w1DbyDjpHc3FRG/cXF7Un8fAh8o2K/Lp5qhgky4jynG4cE9a1VmmVospzbbhXijrPSU0YiY1jA5p6+XbFonbu5bThiWoKUNX5rr3bX/xi4iThxOnT2ng6HjfFv/IsI+l0GJ49S3bmDJlRA3pmCfcgDJlZWCArCqo4JgeSXo8wihg/+gQrdz7Auc8sMKdu5gYe4iwrhHR5FE9hOeiycPwm6rSDkNA/1CVdrDl07Bhx9yFkLWXlng5yboON7PHcTE7OTcScYo77yZmn5GbGzDPfnyE9MUv3mE6xGa1n2kMdQrUEZZIyeybkbFWRssCIiC5DuigOkzJLwrF4iQ0lBGlJNVAksVCL9pTWUaTtMWlESEm8AGtnYZxHBEAXRYQgxiFVZFB3tV2nYsQAIQdGSlha6REmWqrqrMOZeyEN4YGqZH49IgbSCKpSe03PMgJWWCdmTEKPhDPrkAgcHkJvRhHEMRujkjKE1S9Atq4IEDaoGbLOiDVWSAiJ+ByH6FPxIMt0qVgn4DARfeZ5HCEZXTZOwXIfqrIiSGC0Ab0SwrF+0YxXgFTbqGW9oqw1SY/HMBNv0IsiiqqiKOH+k8cZooNMo/sjwhkIT0MsMJ9ukA6HenyrimJcMR5qh8nag5CEECUwe6JmXlbp5Dmyvk5WFJw9O0DV8PApHWJ18jT0Z0ECbZ6am4MjR3qcOHGC+fl5ZmZmmsVA9hOeqKaAuHN+ifbe7CyIMH/0qBbDbfS0SVkJoohkYYH6hhu0+1wpkl5PBzGijendQJeBrTBlXUS0HWZ2lrnrl8kGT4OsYJxpj9j8kT5RktA/coQoTRmVJf3FRcq6JklTpN+H4ZDesVVYXSVbr4lLRZiNCXg8eVUwOrdOOTtHHSTEN8zQW1wk6fcJej2q8VgHXWaZDng8vkE2HDMeHqLIDzEaQacLSQdmShjlkHaAxZgsCFB3Z6iunnzR3ByduTmyM2eI+n16R1YJQ1gY6wm8fg42esJ8Hxbnte0rrKBOoViH4XiG4X004QnJPCwdgXhGX39Nh4KxsBbROwJpAQRQjWA0hqo+yipHiVAM0PFKS4f0BA4TKI3tq5wb6Qquy1ClwvoZOKtSTnGE+znCEMX1lAwJiCgZUpBQ0UGMQ0QH8xYzUHQhelTEcKWkGMPpDKoNLS1KAkRarZ0rYHYGSgE1hjCFtQzWspKVc5Ctad9wTESOtmP2xtBdh7QHQQVDpYspFgUM1mE4gLPnYJjDGQW9IRwrYGal4viJDVY2tEr/8EkYj+DOda2ufwKYyfSSuo86qav/X58Mee5zP89NN93EzMxMszTZdnXvpwFPVFNA3O1u+X4r6Ghj0Qm/qq6h26UcjQiThCjPCZOETr+vpQMTANhUogzDxrhsYSOruzMzqMOHKTY2yLMMsoyk29WxNdboCaAU/SAg7nSaYM+5TgfqmtH8PFJVpGtrzSIIsQlgPFQUFN2uNt52uzp4MoqIkoR8NGo8czYZt7ZR3kFAnefEQaA9X3XNvMlBVGlKHYaEc6uIWUG4v7BAEscUpsxMcWhA1OuxfMuILMsYnD7N3Nwc5DlRkhCYhShUFFHkOZKmHHrsGqsrFZVAfz6k1+83MVdRssYoB8lBJdCPgDSiHpYcj2HjlLaxB7UgC9DpQ9rV3sTFo0d1CZw0JZmZIRuPGQ7PUOfQvw6SCtJTcKKEFYQeMX0gIORmUrrogN0KOHocihqO3qRV2yoMSeYVY6lIl6EuYPkIDMbQ62mS6M8H9Hs9HUYhwjDLKItCPzPRBudKfe11tKdvaRHoQv8IKK23U1RoYla6KGMOnBloL+8YHc4S9yHqQdIT4lKhajh3Ds4WOk5vDf3XLvP0WbSdcTbX5g67ClHXxsUdxBQaj03EJtXE4tV82/keERN0COhIYaf2UBhFBDbxsznciZmBzUL6UaTjXPp90n5fLygZxxDoRSNt/I2b5GorBBR5rovg9Xo6LsgsvV5ZMgCiKCINAmLjQg/CsKkNHnY6m1UbbP9MKkgYBE1SsDJ5YyrPIYp0Okgckxw+3FQ/iJKE0PQ5CAI6c3MESULHENJofl6TaFU1S17VZgxrdD2ufllyKMt0knMYNnW7KqVYMlJrkec63zDLSLtd7fULArLRiMyUaEEp4jSl0+83idmVMRT3ZmZAKY4eOcLq6ioKyPKc1ZUVsrwkG0CvA6mCQqDTT8jrmvmZGYajEYtLS6yPRswvLFBVla5+kef01tcpq4rVc+eYXVqik+eknQ5FUWjbT5rq3yJNWTQBtWfPnSNOU4bZGZ7Q0SphnMKjrterUi8vLzfnZ1lGVVWMx2Pmh0OqsmR2rqLIYX0D+n2Ym4XlZb2eY9rTS74/+jEFh9ehZ1YduBFNVDbD4Bbg5qOQpilzc3NNCeb9hieqKUDimB8lISBllXVu+ubv2rrf/JBhK0BuUkXJbdsw+0MTEdwUabO5ZW4CsmxWzLRw64zXRhWNkkRLfmGovUMm2CsInCXX2fTu2IdFbPqJIa3AeIRs7lcQxzrlw6SAhEaFDR3DK+gEabvIqSVFMelIMwsLCOiATpMqg0nPqZWib5OvZbOUbm6z+mVzRWO7lFfz1+S2LRw50qTC2HsMTdxTwGbhwcocE0UREkXESQJBwPWmTbuysE396XS7DEYjBBhsbBAnCd261lKH6HphVVXpPEygvv56+jMzOjo/1Css2/bcIohKKZJOhyAIWDp0iOFwqCtKmGj+brfbXNs1egNsbGwQRxFHjgxQSjEYDpmbmyNNUzpGwhUR8jyn232Q8XjM4cM5adpEQmDCwjh6FA4dWuDEiRMsLi42i8d6r99Vgi/+++/S0dbr68wcO7bv7TWlRAxJ2Qnr1j6aBJsOktiqoUCRZYzH4yYvLbJeIBFCNqU7V0q0OWjIZvG7yko/6BgkSVPtRXIqBbgVQqu6booC2uTmuqo2qwTYNBsnRy0wwYaNRCqbZXwjx6BrrxMa6TCWzURjK1lacopMBYnARKcDTcBbZO657nTozMwANPfZ2GYMEdpyzqkh27wo9PVF6Bg7pl2MomPMBbZ8TW5SX+wSY/baIkJqwgI6SjVL2S8sLm4JFbCpR7ZwYhAEZCZsxm635Xfm5uebEsmWZLIsI0kS5ubmWF5eZmlpSGoCYeu6birCdrtdZmdnSY3E115ifr/giWpKWHriE3WeVllqFWmf0ZaYLC5EUkEQkKTpZqE4Qx6xCTAcj8c68dW80e1bvZGcnP9rkyRrC/DFcdy8xcfjcZNc2zGpLe0E4yanz1zTTobcFuqDzbX7zH0FJqFXGRe6ndyJI6nahS2spFc6FThtukxZVU2ke1mWzXLrcRCAIaHYUcUtEdr2qqpqVCvr7Vq3FS3MMfbTDTcXbHCXy7L379ZiT9OULMsaQt+ydqK5f7u6ja3TZVEUBVVVMRjosti9Xq8ZX0uq9h4GgwEzMzPN6tZWeiyKQj8LVcVoNCLPc4qiYGNjg42NAZ1Oh04n5fjx48zPzwM0Y+clqqsEifnhwLEnXWbs9GFxq1UWRcF4PG7sGbaIW1VVzcRLbdUAm9tmyMqSii2JLCKMRiOKoiCKIoqypBwMGimrYwIRJ5EA0LRnr9/pdBoyKMzCmePxuFnl2b7N8zwnjmPSNMXWkBqNRs3ELqvNhVht/+0924kvRi2zUox7fFEUZFm2mUAdBM1YbWxskCQJ6+vrrK+vc/LkSZIkodPp0O12SdOU+fl5VlZWmvGJ45iesWvahUlzs9zZeDxu7D7uUmRKKbIsYzQaUZYla2trRlXrblnxJggC+v3+FkkniiI2NjZYXV1lY2ODtbU1FkwozNLS0pYSypZMwzAkyzLuu+8+TlenOclJ+qM+s8yyvr7OzTffzKFDhxiNRqRp2sRT7Rc8UU0Je10y63LDqnNRFDUSiSUI++BaYnHPcd/q9ni7qk2SJIxM3lmWZY30YInFEpZbohc475p28tq2bQUAd/JmWUaapuR53kxMK9FZNdjWfLLkBVtXeB4OhwyHwyYWyJbImbQwhpViLDnked5IVmtra6ysrJDneUNgKysrHDt2rFGxiqKg0+k0Uqo9Ls/zxk5kV8C217ULb7jjZe1P6+vrDdFa4rMkmud6PZU0TZu+13XNcDhkbW2N8XjMeDxmdnZ2i3Rnf7/xeMypU6fIsoyHq4cZMuQsZznNaW7kRvI8Z2QSz91n44IllPeIq2t2eUwFlkCshAD6QbMSiVWdmhLGIufZIqwaZ4nNkolVm1yjrns8cF4ddEseVkWx/bDbbH+Hw2FDRtYQbGHVTAt3xeLAGtPNhLRqbZqmpEYV7na7DYm4qpfth1Xxut0uIkKv12MwGDS2mqIoGtXNkn+n02kI0Npz3OWz7Bjb+7MkbfuapumWhTms5GUDLO15Vqqyhu1+v98QriW+NE05dOhQM042qjwIAjrGSF9VFf1+HxHh2LFjnD59mpnhDDk5SyxRUpKQMM7HW4zwPoXGY99gyQPYrMXEJom4a925BOWSjZ14LpG5UpMtiWJJw7VluNKU/e7G4tR13UxGO0l7vR6zs7ONHQfYstyXq8JYadBt35KoJQ63vZmZmUYtde99y3p+aJuMjR3q9XqNBGJVnyNHjjR2O3vdngkHSZKksbsNh8NmrCzZieiS0taQ7hIt0PR/OBxuUZGXlpa0sd2RzNwy1HZ8bP/tuPV6vYakrGqZpmmzCEhd1xw6dIhz586hlGI0GjE7O0tiijFawttvkgJPVB5slhNuewy3e1O6ROYeC2yRtKz04l7/Qn2wZNJeENRdwdmSiSUf2werwtg+22tYqaFZucYhRldas9drE6lt3712s8iEkXrKsmxUXStd2v52Op3mXFeNAxpycckxiqItarK75qI7jtbOVxQF/X6/Ud3a92C39/v9LRKb/a3suFljvh0zEWkM88eMF9uqe3EcMzc315Dcfgd7gieqRzQm2Rja23d6/naEdqH97X1uCsYkknS9exeD66G0kkn7ftvtb9dHu327fLYkSZqQBJcorM1tElFbUnHbsIRknRfuYqttgrVtWbuXe8/2OHtNu8+STLuf9q/tkyVBKy25LzDXC2sJzktUHpcN+/Gw7Ybwpt3+xchnWphEyhfLfWv3oR3hvd351sbWfrlMut/tosbbxu/2ea7a7/5tL01/ueCJysNjCrick9Ztc7ft7uS8dkxek9Z1Be7VE5WHxyMUO1HHd3Ls5cCViUz08PDwuAR4ieri6AHccccdV7ofHh5XLZz507vQcdtBLpQb5gEi8p3Am650Pzw8rhG8Qin1ny/1JC9RXRy/a/7eia4bNglPRpPZK4C/2WU7AfBS9Co+u1mLaK/nH5Rr+LGc3jUO0lj+JnqF3d+94NHbwEtUU4CIPA/4CPB8pdQfX+n+XM3wYzk9XEtj6Y3pHh4eBx6eqKaDe9FL2d97pTtyDcCP5fRwzYylV/08PDwOPLxE5eHhceDhicrDw+PAwxOVh4fHgYcnKg8PjwMPT1QeHh4HHp6oPDw8Djw8UW0DEYlE5HUiclJEVkXkl0Skf4HjXyYid4rIUETeLyKPae3/ChH5hIiMROSjIvKM/b+Lg4F9GMuTIqJan3+6/3dy5XGpY+mc93+JyHsmbL86nku3XKv/bH6AnwXuBv4u8Dzgs8Avb3PsPwRGwDcBtwH/A/g0EJn9twNj4AeAJwJvAU4Bi1f6Pq/CsTyCXqj56cAx59O50vd50MbSOeflQAW8p7X9qnkur3gHDuIH6AAbwNc7274EKIClCcf/EfBG5/usOf8l5vtbgd939gfAXcD3Xul7vQrH8kuAASZY+ZH02cVYzgH/FZ1M/+kJRHXVPJde9ZuMpwJ94IPOto+gf8jnugeKSAA8xz1WKbUO/CX6rQf6zefur4EPO/uvZTyV6Y7lbcBnlZlZjzA8lR2OpcHNwBJa+vzohP1XzXPpiWoyrgMqpdTDdoNSqgBOA9e3jl1EFwN7oLX9QefY6y6y/1rGtMfyVkBE5L0i8pCI/B8RefH+dP3A4VLGEqXUx5RSL1JKffoC17sqnktPVJPRA7IJ2zO0+N0+FrSuv92xvYvsv5Yx7bF8ErAMvAFtz/oA8B4RecE0OnvAcSljudPrXRXPpS+cNxkjYNIicinaPtI+1u7b7tjRRfZfy5j2WH4ZkBiVEOCvROR24HvYqhJdi7iUsdzp9a6K59JLVJNxHxCJyGG7QURi9Jv8/taxZ9E/+PHW9uPOsfddZP+1jKmOpVIqc0jK4hPAo6bZ6QOKSxnLnV7vqnguPVFNxsfQbxXXqPh8tIt3i1HSGCD/j3usiMyiDZgfNpv+uLU/MN8/zLWPqY2liAQicreIfE+rjWcAn9yHvh807Hgsd4ir57m80m7Hg/oBXgfcg3b//h10vMqbzb4Z4Jhz7FeidfuXob1Sv4OeOKHZ/3S0C/mH0fEqbwYeBuav9H1ehWP5BuAk8GLgFuCn0XaWJ1zp+zxoY9k67+2cH55w1TyXV7wDB/WDtgW8EVhBqyRvwQQVAq/RHL/l+FeaB2gAvA+4ubX/68xDNQL+BHjalb7Hq3Es0TaUnzH7x2hJ4ouv9D0e1LF0zjuPqMz2q+K59BU+PTw8Djy8jcrDw+PAwxOVh4fHgYcnKg8PjwMPT1QeHh4HHp6oPDw8Djw8UXl4eBx4eKLy8PA48PBE5eHhceDhicrDw+PAwxOVh4fHgYcnql1CREIR+SER+aSIjEXkjIj8DxF52pXu24UgIq8XkQ9c6X4AiMjb7cooIvJSEdmY0nU7IvKKXZ77QrOqzfI0+rKD9qZ239PAQeuPhSeq3ePfA98FvBqdef5l6CL6H2ov7+Rx2fEdwI/s8tw/QddkOjO97njsFZ6odo/vAH5KKfVupdRdSqm/BL4RndH+0ivaMw/Z7YlKqVwp9ZDy2foHCp6odo8a+BJTYREApVQFvBD4BbtNRJ4mIh8wCzzeJSI/LSKJs/86Efkts5jkKRH5ZbugpIjEIvJvzGKcYxH5CxH5cufct4vIfxaRN5vzz4nIfxKR0Dnmn4rIZ0z7v4VefmpbiMiPisg9IpKJyB0i8jXOvrtF5DtF5I/M9T4hIs8UkR80Cy2cFZGfd47vicgbROR+ESlE5AER+ZmdDK6ILIvIO0RkxSy2+d9E5Jiz/6tE5G/MuNwjIv9ONF4K/EfgRqPC3TTh2idE5N3m2msi8jsicr3Zt0X1M/f8/SLyB+ae7xGRb3WuJSLyY+beNkTkV0Xkv4rI283+14jIx1vtNyrvhL49Q0T+0PQrE5G/EpG/3/oNXmv+PiAij2qd/3JzX+4zdkJEKhH5IvP9e0Xk0+b6K+b5W5rQl5vMWDzT2dYen56I/KJ5ds+JyO+JyOMm3duecKXrzFytH7RqodALNr4D+Dbg+tYxh9AS1o8DjwO+FF0E7hfN/th8/9/oImbPQpfV/WWz/03oVUG+Eng8evHJArhNbdYYyoHXAo9FLzRZA19n9r8AXf3x+9BF5l5j+vyBbe7pJWiV50uAm4AfM9c/YfbfDZwD/om53p+Y+3sXWv39dnP9LzHHvxH4G+DZ6KWbvtvs/1Kn/+8x/78U2HD68kF00bynmM/voCtchujSuxnwL4Ebga9C11P6OqBr+n0vemHScMJ9/r/ohU0fjy7O98dOP15o+rjs3PM68M3Ao81vUgDHzf5/Zcbk68wY/BfzG7zd7H8N8PFW+xPvG70U1kn0i+4xwJOB3wO+AE1JprvRtai+CPiiCfc2Z8biHzvbfgD4jPn/6839vMSM3YvRq9j8xIT+3GTG4pnOtdrj81/QS3Y9G73wxpvRK9vMTXW+XekJfzV/0ATyXnQBN2Ue0P8GzJj9/w74UOucF6LJYxa9ikqJIQKz/3lou9e8mRDf2Dr/j4B3OA/8XfYhNtv+Cvh58/87gd9tnf8htieqHzAP2aPN9wD4cud+7gbe6hz/SnPfboXOh4HvMf9/E/CsVhsPAt/v9H/ShH0Bmoi6znldtA3wxcDTTLsvao3bdeb/7wfuvsDv9jHgV4HUfL/Z9nPCRLwbQzrm+7zZ/4/N93uAVzv7E3Qt8reb769h50R1BF1tM3aO/XLT3oLTn4utjPxO+4yY738K/FtnbL+2dfxvAL8zoT83cQGiMvtr+7yY/QJ8HnjlNOeaV/32AKXU/1BKvQi9Ht0/AN6GfmO9wRxyG/BcoxJsiPam/B6aAG4x++9XSj3gXPOPlVI/i37bR+ga4i4+hF7bzuJzyjwhBqtsrlRyG3rxThcXqq396+b8z4nIXwA/AdyplHK9QJ9z/h8CI6XUQ842d2WTdwBHROTnReQ9ImKlnIutfnQbWto85YzbKfQyTk8E/hr4beB/GhXoTUCglNrpogQ/if6dzojIu4G/j5b8tsPf2n+UUqvm30REDqHXwPtTZ38O/H877McWKKVOAr8EfKdoE8CH0PcJW8fs7otc6teAl4j2fj4aLam/w7TxQeDzIvITIvKbRi39ena3ItWtaGK6w/md1oEb0L/T1OCJahcQkdtF5I2ii+GjlBoppd6vlPpW4OeAF5lDY+A96BVu7ecpaDXwk2i1ajvDb3u9NYuQrQ/VpHXe7DUV5//G+TbXtRPlNvTE/V9oFe+vReTvOIcVrdPq7a4H/Ap64hVoEnwBWtq4GGLgIbaO21PR5P42pfF16LF8C1pF+qCIfN8Oro1S6rfQq9Z8F7rc8c+jvbXbTdbtxrg0/7fH+GLG/IntiMgJNGF+Dbo88E+jHTRtbPdsWLwX3ed/CHwD8FGl1OdNGy9Fv/wOA+8HvgWtBewUbt9j9O//TLb+Tk9Ae8WnBk9Uu0MAvAItRbWxgrYzgCajJwCfV0p9Tin1OeAE8B/QUs9ngBMtI/FXiMidaLtEgS7g7+L5wKd22M+/Ri+R7uKZE46zbX8d8B1Kqf+llPoh9FvxXrQN6JJgpI2XAt+qlHq1Uuo30Paso1x8In/SHJc543YGbSS/RUSeKiKvU0rdoZT6aaXU89GE+C/M+dt67ESvZPNatD3xV5VS/wytXj0LTYQ7hpGu7jXn2utHaNXUIud8B8Z2xuZ/hiaCL1VK/Qel1HvRzwtcgidTKVWiyeer0C+bdzi7/xXwC0qpVyilfkkp9RemP5Oub19qbv/dvn8SPReWnN/pbnRN+2fvtL87gSeqXUAp9dfAbwG/LiLfJSKPE5FbRcTG7/y0OfQN6Df3m0XkiaJX830begHNVfQb7ZPAr4rIU0TkOWjD+P8y+18P/N8i8hIRuUVEfgotlfynHXb19WjP5L8x5/8QWlraDinwcyLyDSJijdQ3sTtVZg2tBny1iNwsIs9GG7Fjzl/0so0/RNva3ikizxaR24D/inY4fMpc+7tE5N+LyKPNuL3Q6ecGsGh+ly3Si9JLcj0VeJPxsD0GLVWc5uIq1ST8PPDDIvI1IvIEtAPhBjbJ8qPADSLy7WYc/g1w+zbXug9YAr5SRG4UkX+OVlPh4mPWxjvQktmtaJuV28YLReQ280y+AU20k67/IPqF+YNmLL8M+EG7Uyn1WbQj5VdE5EtF5BbgrWji/8Ql9vfCmKbB65H0QUtErwbuQKsPA7T36CWt456LtiuN0Ibmt+B4RNCG3Heb80+iya1n9sVoT9+D5vyPAv/AOfftnL8E0geANzjf/xHwcbS68AdokvvABe7rR9DG0Mz8/QFn393Aq5zvL8Xx1LWPQRu+P2X6/nk0gf8m8K52/9vXQksS70RLqGvA/8RZEgv4CrT9bYi2X70J6Jt9x9Aq1JjJnrHj6BfNadO3DwLPMPteyPnG9Fe1zldselYD4KfMb7cB/DLaG2qXsBK0re8kmrh/xYzDJGN6gH65nDbH/ilaIhoDX7ldfy7wW34WeHdr2+PM/Q7Qz9VvoL2kJ9HSXPt3eAHa+ZABf45+ebnjM49+pk+Za34YeM6055tfhcbDYw8QkRcBH1NKPehs+zTw60qpqdppHsnYjaXfw8NjEy8DjovI96Ilv29GS8m/dUV7dY3B26g8PPaG7wHuR9vVPo72+H6FUurTV7RX1xi86ufh4XHg4SUqDw+PAw9PVB4eHgcenqg8PDwOPDxReXh4HHh4ovLw8Djw8ETl4eFx4OGJysPD48DDE5WHh8eBhycqDw+PAw9PVB4eHgcenqg8PDwOPDxReXh4HHh4ovLw8Djw8ETl4eFx4PH/A1uMyM5Q7H/1AAAAAElFTkSuQmCC\n", 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\n", 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q/1gdcS+Iw5iqzCEwKCKPADdgql+vC5ZYpNwUg5VBtuS2bDiH1GXB89967mHCTT+ypt23whbfGfgOjuXgOR7tyfYLOv9c0bRbclt4bOQxPNvDsRyqrSr/duzf6Ex3knST7MjvoC/Td0kjcldiv/w68BJVvRdjzv/8THCUqg6o6i8C90X71oOfxEy7Esl1K0aZrSs96R4eHX6UPWN74twDlyEPDT5kLIVoKnM1STgJdrXvwhKL3kwv17RdQ3e2m2bQZLw+ztdOfI3R2ui8cybqE3xn4DtrllZgJQqiCtwkIg5wHdFMwdwDVPWYqv6nFfQBgBg+LyJvXbDdEZF3i8iwiEyJyAdEJBPt/t9AQ0S+BXwe+HVVHVmpLCulM9VJ1styunyaqh+X+rxcKDfKfPHoF2fLHHSlu9akn2KySHe6G8dysC2b7nQ3Wwtb2Z7fDgJ7RvfMWx28Z3QPoYZUW2vzW1qJgngz8EdAA/gs8JYo/mFViRTQ+4CXLbL794BXAj8KfD9m5eifAqiqr6r/SVXvjR7/dAF9bhWR+0TkPkyOi1XDsRx2FnYyVBliurHxUtLFLM7e8b3YYtMMm3Sluy6589ASi2vbruXw1GGOTh1FVRmpjlBtVam1amvW70qmOT8rIr2YZd2j0Vh/VRGR3cBHoj4mF+xLAm8BXq+qX4+2vQH4VxF5m6qOr6Dr1wPvWMH556Qt2YZivuDeTG+8ynODU21VmaxPghg/wXp9Xyk3xdb8Vh4aeohKq8JEfQLbslHWbqh60WpQRG5Rw/BylYOI3HaB3dwPPIRZIr5w2vR2IAN8dc62b2De03MusJ+FfAh4bvR40wrbWpTeTC8nSieotNZrkidmuewb34dne+S83CVVDqEqkxWfRuvMGsfeTC+bsps4MHEAP/TnVXVbC1YyzfkXInIIeI+qPnauA0XkXuCXgK3AvcvtQFX/Yk4bC3dvAoIognPm+JaIjAJbltvHEv3O5LJYsx9Ed7qbPSN7mG5Ob6jAmJj5NPwGo1XjGLwU041BqJyeaDIw2aTeCgjCED9U7tqRo5jxsMSiM9VJvZ5icHKC7R1t+Lp2vqyVKIjnAb8AfE5EqhhH4D5glDMZpW4Dng8kMf6K16xI2vmkMf6PhTSi/jY0SSdJQMB4bfyST13FLJ+Hhx/Gsz1c271ov0OzFVJu+LSnPc7VxPBUiwODVSwrAEIsG9IJm8lqwHcOldjSnqDpYxSHhrTCFN8Zr5HP1tnVtjaZFFbig1Dgf4rI+4GfAF4B/L/MT1r7EPCbwN+r6monZqxhVowuJAFcFnb79vx2nhx9kp5Mz5p5xWMunoMTB6m1agQaXLT1EKry2PEKE9UmbSmHG/vT5NMuqsrgVIsTYw1sAbGFWiMAq4nnOGQTCRzb3DTyKZeBqRoTtQbF8hHaqgNYThLNFBn22ploNZmu+bAG+YhWvNxbVZvAX0ePS8lJwBGRrpnpSxFxgU7g1CWW5aLoTnczXB1m7+henrPpOSTsxHqLdElQVWq+8bzPjUAMNcQPfRzLWdbdOoxcXxd7Zz/X1OBkfZIjk0dQUbrT3Rfdx6GhOqH6ZBLKVKPJt5/xSbg2rm0hVgDqUwvBblm4QZmdpT1IMsN08TpCOx+9P+gvpHAnjpMORwhTLqJN7Npxcv5BDuMhrbUJN7qcS+89jrEUngf8Q7TtuUAAPLBeQl0IIsK1bdfy+Mjj7Gjbwbb8tvUWac1RVap+lSAMaIUt/NAn42awLZtKq0IQBoSEJG0TviwIfugTYpSBI0Z51IM6zaBJEAb88scO8Nxru3jD/dcA8Or3fZvvHDkzieXZFn1tSX7g1j5+8UW7SLomgnVGSdX9+lmL5w5NHcKyLDJe5rwL61SVqUrISLlFR9ammHFBYLzsMzTVACugM5dAgFLDp9HyCVVxsCikE0ZZVCcoTu+l4bm49TE6T36T0MsS2h6h7SGqSLNC0xL8RBtqJ5CwSasxjV8Zh+baGM2XrYJQ1Vo0vPkTEZnA+B7eB3x4hVOcl5SUm8KxHEaqI2zObr6iw6/90Kfu1wk1RFESdoJm2GS6OY1gFiaJCA4ONb9GM2jO+mZmok4VnT3WD5Tf/8xhvn5wjGftyNPwG7O5Nl5112Z+7WXXg0C9GbLn9BS/95mn2DdQ4oM/ffc8n0+lVYE5LqC6X2e0OoqiZN1zO5AbzZCHj5bx1UdQDgwqvXkPy7KpNFpYlk826eBYpoO2lAsp16x1jvpMVofJTuyn6jio7VErbKPZmMYKGhA0IGwCCmLhe3nUMS42tRMEThZhbBW+ncW5bBVExNsxDsl/xPg9/g6z/PyyYmfbTp4ae4reTC+bc5vXW5xVJwxDKn6FUEMEQVFcy0VESEiCQANmpvJnttuWTSs4M4XnWOanqiiqyqHhCr/1qYOMV1rkkg5hZJmor6iGOHZIKtki7aSxczZbO9Js78jwA3/+db60b4i7txdp+MEZvRD1r6o8NPQQCTuBbdnnHFq0AuWRYxUsp4UXGodiOqGMlRvkUhZYSjbpkPYWucwEUCU7dRSvfIqaCOokCaPl4kGySGAEwvy0I40il/YGsioKQkS8yBexZqjq9kW2NTERnW9ey77XmmKyyIAzwP6J/XQkO0i5qfUWacUEYYCihBpS82tIdClaYmGLPXv3tMRa9CKcKYasKBbWvDs8wKPHy9y0Kc+vfO9O/sP/ehDbsvFsDz/0CTQgRGkEDQQh7aaxxGJ3f567txf5lycHuHVzgSA8cwHMBBtVWhXqjrFy+tOLL98GqDVCHj9RwbJ8UKEj683KmEk42KVhOqrHsKabNBMF6ulemumiucA1JFUZJD11lJZAUyBIFFBnke9dBDMpuD6slgXxpIi8+nzxEDFLs6OwgwdOP0AzaHJP7z20e6u7EOhSUvfr1P0o6l7AwgwdXHHPutDnogp+oPihYlvgWGYoEahiIfPKWfzks+dbWjO5EzzLQyKF49kejaBBoAEZN4NjOVzfk+OR4xME2iIIFYczVokf+kw2J9Gk0pXuOmvqeWSqxWi5hSqMV1o4TkCoSmc2cUbh+U26Rx4jDJvUxcLSAKs+Qr5yGlFQNwOhT8uyqVsWiEWQ7EA3aL7K1VIQeRaPSYiJCEKlXPPJpVysRazWtJvmmvZr2De2D8dyeE7vSoNBL4zf+fRenjq9/LUhM76A+dsgCMPZ2YXZvSJYMv/oUKN7tsKunixvfdm1c1qBVmAUhm0JoSoiQtq1sO3zxIsICIItFq7lmvUTgfFzZN0s2YTDdL1FSH22t6avKFXj51CzpHuhY3JwsskzQ1WUwPRhKUlb6GyNkhoaJLA9/ESB9PQx6raLCoRensBJgvo0/Tq2X0dCH3Uc1EoQuml0g89crZaCeD/waRH5IHAMmLdoS1X/YdGzrhLKtZAnTpRpBAG3bclQzC7uFd+W34aFxb6xfSSCTi7lavC9p6f57pH18+0ahWMsaksEG2NNKIolRqGUGwF25OzzHMGzzx56LMQSi4SdoBE0mKyXGKlUSHvGoglV8FVwWj6h5aIoSSdJKj3f1C/XQg4O1kiF43QEE6iTINcYw/GrtERoWhZ2a4pEdYiam0bFIkj2nKngJQ7qZvHP4/DciKyWgvjN6Pn3F9mnrOcgah0JQuXISJ3ByQa2HTBZ8vHDFKOlFq4NhfR8s/LkeIMg7KDSrPPlp0ep41CqtyikjcmxVLRlqdbisZOTXNOdob+Q5omTk3RmE/QVkrPntIKQ4+NVRksN2tIuk1XjMkq5No5tsas7SxgaU5vobj/jaFdmLAIr8isEwJlj5rrk5Rxy6uwfoju94freLN4iloHrCGHIrMUVRPKpQrWpSAJc+/zxCSKCjUcj9Dk4VGFXTxrBwrECXFo4YYiExhfiWi6ee0aBNxo+Tx0exnMt+qefwbV9rEZA0/YILCF0koROCt8qIn4DxJ6dZbgSWC0FkVqDSMnLhiBQHjtWoemHbOvwSHsOw6UWg9MtbPHBCkh5DkHoE4bwzEidqWqTbZ0Jko6N51oEofLkiSrVZsDxmsumZIORMMM/PfkUB8qPULC3cGNxN925NLdsLpD2HFSVwyMV9g1ME6J88akKt/QXOD5e4dHjE3TnkhSzHhnP4fRkjSBU8qHOWiaKmZe3Rfj177seMEMBPzwzRGgETVphEyQgZWdBAkL1zUUXedRnhhbG4bh6n6sAc69/Z07jTV+pNkJc2yiNRiukXD9jYcz7fkKl4SuHh6s8caLEG56/nZSbwKKFLQGKEIb2PIstCJWDA1Wsow/RHYzhagsvV6AlFoGbN1aBnZhX51Pdy31S8GxW6x09cbU6KUs1nydPNOnJNik3Wjx6vEHStXAdUAIsy6YrlcC2BD9scHikQStQHCfg8eM1NhWF4ekQC5veNhgZCEhJF+WmifL79sADJBJNcukyNb/GvgGfU5M1thUzjJTrqBoTPJ/0EBUePzlJfz5FuuBQawWcnqhh2YJrC4KQcCwsCzzHXHlBqAShsQBmQnu96JatqjS1hSPghxDQYOYqsuXMT0cAZzHHyhriOkLTV8LZq1oBpemHBKFSbQaMlBqEoTJR9dk/WOLP/+0I91/fwb3XFgGwQh+ieRLxfTQMZ5XEkeE69omHCaSCegkk2UYLn8DLo/aVYyGcj9V0Uq7pNOdG5dRkHcupoyibi2mqTZ+WH+I6FknXnXfX62uzUG1h29CR9LCkSahQSEPSDQlV2N5pc3jSoVJrkvcGKGsPPjUaTNFbcEm05zk9VePkZBXXtnAsi/a0R9K1aUu5pBM2KHTnzY+4FYS0AmMRJF2bcGq+I9K2BFtk0bF8PaiawKXI9K4HVdxolmC9l5YJxg8xc0HbllGCMxbEpx4d5FOPmvSjGc+mvy3Jj9/Tz2uevWleG6EonqUILcIwZHS6ztOjVbKVUxSDErmEEGZ7COTKsw6Ww2o6Kf/5anRSZpMWfqh0ZMy4NZtwzHKxRegrpAhVscVM2WWTUfBP5NF3LAGFpniMJ6rULJ/NhXYK6T6qwQRNrdHuFbmmM0utFeDa1rwxuIjQnZt/d1t4zKKJOxZe7Qq1oGLuyarYloMArpUAwnVPxT7DjFPzc//5e2a3WQIf/H9up94KzedsAQruorMfQghYKLaALSF2UMW1lY7gNIGApjvgKlUOEDspLylW5KGf+xqYN45FoCuTxrbLHAd6cwXaku0cmp6k1JyiO9lvpvwWi85bAapKM2wQajAn4hEcy53VHzPRjOttPZwPY11YhKGJn1gset0KGthhixoOvm0hYpRgLihxU3kPmsjgq03L2hgFjlQhVAhCKLeEiZpFMxAIXcrVNFO1VU/oBqySglDVjXFLuUKwLJtAQ0JCLMC1E/Sk+jlRPkxvejNZd/XqS84oBj9swZx5CTAKYa4y2OiKYS62cM6YCQnNTAyImeK0HFRsQtvBd5MoAX6qOF95ryKqxq9TbQmDZZvpupBLKClXOT5pc3TSZrJuUWoIpYZFqSn44WKyZIFOgmSNe9egouOq3oZE5AbgRuBfMWXvjqxm+1cLltgm2EggIMTCopjsZnD8JKPVQbKFlSsIVaUe1Ag1mBMGLVhyeVgJK8UKmmA50SADZuZdAzdNy4vCnleoHCZqwp5hl6eGHaYbxm9T84XJmlBpre49NeGszTe2WmsxcsDHMEljFNiFWWW5BXj5elazuhyxkNnYA8VEEQpCT7qfZ0r7aEt20pYoXnwHCjW/MnsBmClLZ+MpBQ2RMEAvsBq65ddBQ8IlLnJjPSi22NgqOPMWQAk6p/jucgkVTk9bnC7ZnJq2eWrY4cT0xV9eaTekMx2SSyj5hHlOOsZXknKV9lRI0lGqjSajpVHu2brsTI4XxGpZEH8MtAE7gSejbW/F1KZ4D6aITcwyEbHPLG+ekw+4mOyh4pd5bPTb3NF5L4XE8tZr+KHPSG2AnNeGpwFBFNekqvN8DBsJK2iau7wGBCSXDknWECtsgUJou9jRORK2CNVb1MEo6hPMvu9oMRhcVOTqsUmbbx7zeHzQZaqxuFWQ80L68yYlnGdDezIkm1BcS0k4SncmpD0VUmpYlJtCfy6gNxcuK6ak0Wwy4k7Ql18bR+pqtfqDwCtV9eic9fsHROTNmFyVMReAIARzLIgZbLHZnNnJycoRHhr5Gn2ZLfSkNtOe6FxyZmG0PsihqadpBg1C9dlhdbNZuqlXa2QyuY2jHDRAolBL268TimCG3Ba2X4OgSWi5hLYHYoEqtl8FVVRAVHGCBqFlQxS27fg1QsshtJM4rQoqoJaLBC0zQ2M5zMzOB0GA7/uzltv5eHrE4dP7kxwaX0QBoWwpBNzc43NLT4ttbcEyA8jWxtG4ElZLQRSAiUW2N1k8b2TMObDEgiUqCTiWw+bMDibqI4zVhjlRPsRNxbvZnNlx1rGVVplnJp/CEoukk8IWh6pCqVlDB0aYciexLWvNHHFLooposMAlai5yFTjjAZHZvAnRiWZFpMiCc2V2/1mvVWffns5GhMvsMb4fMl2y8H2fWqNCMyyfU/RKU/j7vUm+deKMRWOJckOnz229LXYWA3qyAd4lmLeT0Kdz4gA7T38XZ+fGHmJ8BfgVzuRlUBFJA78LfG2V+rggxKz5/UtM9XEPeL+qfmC1+7GaVfLVIXLjLSy/jhDSSLRRy28+27ydk0XoXNhi0wgbtGTxmgeO5dCV7iMIfYbrAxyY2EPBK5Jzz2QtDTVg38Qj2JZDwk6QnbNvQqHlN7DrjbW3IBSssIUVNJDQR9Qs0wwXqgcRVCyz2jGaVViIqELYxAp9VGzUss+7TFo0xIryUYSWjYpZYq1iAsrGh8sUu7OohDTD8pIKQhUePu3yt0+mKDWNtZawlRfsaPCCHQ3aU2u7ss4KmnjNMo5fpzB1hK7RPXSOPY0TleE7ffI7cMvLV73f1VIQvwx8QUSOACng48A1mJyRL12lPi6UVwFJVb0vqsL1lIj8o6qOnu/EC8GbPkaqegqxfQIUBDKlk6SnjzHVdQt+sgiq5Cf241RHmOq6lSCxdPrh/Nh+qulO2k5/i9DvxWuWSZQHaGR6z7rT25ZDT2oT+ycf53jpEP3pbYzUT1HxyzSCOq7loRrOUw4AocC0u3Jz1mmWSdTGCC3bmP+Wi1ougW3G97mpI7jVEQKUpliEIuY9qOJ7OZMbAePgKzeEqbpFxgtpTypD0xZBCF3pkMm6RWfmzJhctAVhiJJYplXuY9enCN3MvIVUYag88sgh7nnxNVjnGANM1YW/ejjDwbEzl8tN3S1ec2uVjvQaKAZVEs1p8tPHKUwdpWN8P4Wpo1hLWJUhgltem3mA1YqDOCIiN2HqXtwEuMBfAf9HdQ2repybz3DG/zETrLXq4eC+l6MJNJNtYFmoWPhegUR1hOKpb9NKdYDlEoRNkpVTWAqtRI5S+7UUR/Yy0XE9QaJttr382F7yY8pBlIJ1ihuGQHL9Jt9AquOs/l2/xpbMDo6VDjDdmsDCMY5NUQL1aU90gSpuY5JWIjdr1TiNSZxmmXq2d3mRgqpYQQ23UcbxqySqI0hQxcesaRA1d2s0pNS0GWhkeCrIUmm2U0hDPmUzWEtyfNpltGozXrOotoSmL5SbQqBnLlDXUloL5vyTjrKz3efaDp9rizbFtJJxQ1LLmeAQh2CRz245HBq3ef+DmVkHZMYL+fGbazxrU2tVRmYSBmSqQ+SnT9A+eYjC1FHS1ZFZy2Apmm6Wkc7dDLRdxz4nzZ23v5q1KJywaq5PVW0BH12t9laKqlYARCSBmYL9kKquWbXcuXcmdR3q+c00myW8+hShkzArJ5NdhLaQKp2gozqC26rQ5leZ6r4DO2jg1SeRMECCepR1CGqtabpKSjXdi59sBxUTgqlK2+he3OoQRZRkqogfjpFVIdusYKE0E23UrCy5yUO4lQHUSuAnc1hBC8IWVqtMeuook923YaF4tQkStTHjFEQJo0ApCX0Im6gqoVhUA5vRMIkfpDhQbuPRoTTDVbMastKyqLRWNgBfqBwA6r7w1IjLUyPzNUJPNuDOvhZ39rfYnA9WzZ2iCl896vHJPalZ5XXvlgY/dnOdtLsyq8EKWnSN7mHzyW/SMb4fS89d9KaU7Wes4wbKmT58O0Et3clUfiuIRaNZozF1bEXynIsrOshcRHowiWw/q6p/cGk7twkTbdS9HOLXonyDAoSU3Ty2X0ZsB8dv0HXiK4SWS2B7TGR7cJslyuEobfUiE5l2eipDdI48Qb182szv20nUdmgh+E4C0ZCu6ihqJwlFaFk2Ega45ZOkpo5QT7bjO0mT56FZoWXZYFmEmV6S1RE6Tn2DwE4SWA5NyzbBQyIQGm9/qWnzTKmD/RNZDkymODHtRvMEF44tSmc6pJgOyXqKZysZT2lLhmRc5diUzXTdoj8fMFq1sAU25QOOT9ocHHcYqcxXPkNlm88dtPncwSRdmTPKYmvh4pVFEML/eTw164i0RXn1LTWet615QW1K6OM1y7itMu2Th+kcfYp86STJ+kQUyD4f3/KYKmynnO2jmu6ilNvEdG4r/jrmKL1iFYSIFDHO099U1b9fP0FsdF4mIQt1HfwoGKelAc36BJZfA8tFLZta2zVssR2OinJttpORTBd2o0TgmPwDlt+C0HjzW+lOEIeWXwf1TVLUyMnnh02sVhUJffyZoYSGQDg7rCjZaUbGqzwzmmKgliTExlczBJioWYxVLRrBua+KnBdybYePY0HCUXqzIT3ZgPZkSMpVJmqW8SOkQzblA9xzGBjP3nLuYrRTdeHYpE2paTFZE54Ycjk2ad7LSMXmC8/YfOGZJD2ZgJdfV+eu/tY5+1uIKvzN42keOGEm3wrJkDfcXWFncRml7TQkXzpJx9g+Oseepm3yEHboL3l4y0kx0Hs3k4UdlHP9lLKb0A1W9uCyUBBigis+B3xJVd81Z7uDCdL6Kcwayk8A/zkaXvw60A38ooj8YnTKz6rqM5dU+PMhNkGqk4U/v67cTo5yiNAr4FtinJ1odJErEjaNBz+Kf1gsi5FaHkHCoxnAVHUmTsLi0LjNw6c9jk+ZNQDK8srKWaJsbwvY1eHTnQ1xLKUjFbKzeO55/o50AGe9w4ujkFRu7T1z0b3i+gZjVeHRAY9HTrscnjA/6aGKzUcezfD3e0NeuLPB/dubZLz5d+1kfYIuHcX40w2f2pecVQ5bCz6/8OwKheS5hxReo8S2419my8lv4rWWniZteHnGi7uopHsoZfsZ6brZxHVsYDa8goiUwHuBlwFfWrD794BXAj+K8Wd/GPhTjCL4dYySuJg+t3KmQvgtSx33vq8e4p8faVKu3krqSIZcQumITGfXMvPvrdC4DDKuMaXTrtIKIJtQUo4Jn51xtEXZ3gBj5s6L7BObICqPEKpQaiap+0LDF+rRo+HDkQmHvcMOCaNHqLWEybosa0jg2kZuW6AtGdKeNgqgPRWytRCws91nlReRrgodaeUl1zR4yTUNJmrCg6c8vvhMglLTotS0+OenU3zhYJIf2V3jhTuMn9r2azzv27/Hi6jz9fJmKvnNPHTK5QvPGEXblQl4y/dUyCfOVg5W0GTzqW/SPbKHRGOKdHUEO5xv+VTS3YwVr6eU20TLTVNNdzOd28w5q/duQFZrLcZXYJFBVZQ0GFNH83+r6lcvsN3dwEcwlcInF+xLAm8BXq+qX4+2vQH4VxF52wqra70eeMf5Djo0UmbvkALtUFpBb0tyNx/5jJlvT7vKVONMgpSL9QHMkPFCdnf5dKZDOtIhO9v9ZYf3bmTaU8pLr23wwh0NHhlw+dIzCU5MOzQC4W+fTHN43OF1t1XZMroPNzBpS3Yd+hc+0P0W/vpRM+xLuyG/tFA5aEjv0KMUxw/QO/ToopbCRGEHpzbdy2jHDdQvctZko7Fa94PHgV/EVPP+JsYbdw9wL/ApYDPwRRF5rap+8gLavT9q878Ajy3YdzuQAeYqnW8AFvAc4F8u8D3M5UPAF6P/bwH+YrGDdnRmubFbKFUncd0MU3WLidry7tYXQiOQ8/oB5rI5H9CeCrHEWCfFlFn4Y4kSqtCeCrmuw2cZ+V4vW1wbnr25xbM2tXhqxOGjj6aZblg8eMqj4Qu/vfXMsX9buosPnjAxGZYor7m1RlfGxBxkKoP0Dj5C79Aj5Mqn5/VRTXZQym2ikSgw2HsX4+27zhmVGgQQorjW4hm8FlJpwvEJxZKQzQUl6TgMV5Vmy1ijrmMxUYF6c+1qaqyWgtgOvCsy62cRkXcCu1X15dG6jP8KLFtBqOrshblIpuRNQKCqQ3OOb4nIKGeGBxeFqh4Hji/R7yxvesE13NVxhIcPPMaOvtsAMzRoBphkHoBjQaBm3X+1KVRaZljQ8IXTJYvhik3dF9KuuXsPlW1Ol+Y7qvKJkEIy5MYun1YgZjVf5ABMRMOUpEM0XFGy3hoE71ymiMBN3T5vf16JP/5Gjsm6xRNDLp9N9HMn0FSb90y+ZPb4n7+7wu19PtnSKa499Fl6hx+d115guUwWtnNiy/MZ7Llj0SFDwzdZt4NQKbfMhR4GykxUexACAnlPSLpmWXgzCGd1hiVifncasKUNxLI5Mam0goBiWsklhUpTKDVDUm5IxgqXyJi1clZLQXwv8GuLbP9r4Ino/38B3rXIMRdLmsWL9TQw9TrXBduClGWW5M5lsbHsuQhD5cF/O3+U32WDKqnaGG1TRwjFpunlsMMmicbU7CPZmMIKfQLbI7ATuM0yoJSzmwgt24Rao2aKMFrYNbOtnmxnsrCDarpzdsp47sVbTCu/9cIS7/yKURIfObGLH3Cv4Ueavzt7zLVFnxcl9rL5iW/RN/jQvKnIUrafoe7bObL9JaYYTkQQwpEJI0fGU6bqFn4YkPEExzLf++a8RdpzsW2T/9MPlVozYLIeUmsp29qUbMLGdWyCMKTpK60gxHMc2jMeKdeiL+/T8EOSrkXStQkCxQ9DNIRSNUVhWRFjF85qKYhB4HnAwQXbnw+MRP/3sfiCroulxuILwRKYEO+YNUJCn0RzGq9ZNvP8zRJeqxytFaghqthhi3R1hGTdfOVWdM7F0D2694LPaTkpRjpvYqTrFkY6b8J306Rd5efvrvCub2bx1Z6nHAD+OvXf6X/oydnXinC6/9kc3vFSKpnes/oIFQ6MQDEdkPKEckPoLwS0JR2yKWe2DEDCsRZNx98fmCzctm2yjc9Fo1CUGbOibUENFXPl2rRaPrU6a8ZqKYg/At4rIncB38X4Ae4BfgZ4m4jsAD4I/NMq9QfG8emISJeqjgCIiAt0AqdWsZ+leehD3PCdvyIVguVPU8r2U872zcY4XHZoSKY6Qi4K5knWJ0k2Jkg0pgAhFJtkY5JUbXTJdQEXS9NJ00gUopwODZPXIeozXTuzfEajq2b2OcqeuXAWwfVr9A8+RP/gQwCEYjHevoudxes5ufl7+NiJ7fOO/6z3dvrHjps+EAZ77uDI9u9lurBtUXkrTTg2Dh0Zny1tHh1ZzwwTBFKOvSwfg2MLjr143MNyJzusVh23uXb3w9Vai/F+ERnDLNp6HeBjEsf8hKr+s4g8H/g08Nur0V/E4xhL4XnATNbs52Im3B9YxX6W5sSD5Ef2mnnQsTN3uXqibVZZlLP9lLL9VDK9JtDpEiNhQGH6KKnaGCCkamPmTh/UcFp18+zXcfw6icYUrl+76L5CsfCjiNHQsqmmOqmlOmZ/7dO5TUy0XYtaNl5jmsD2aCTaaCTyK4sHUCVVH6cwdRSvWcIOmuSnj9M1+hRONFNhaUjn+H46x/fzu/pp1H49/zd4MQC/5nyc3dZxQrEY7LmLZ655BdVM96JdtXw4OKbYErC5DTozLl25BJbFkhf7SrEaZbyJYwRi0eq8BmwP8Zt440cYqo4wlu1g0cy8q8BqTXP2RNGKi0YsqurXWOVl36paE5H3Y1LbTWB8D+8DPrzCKc7lU9xBuf0aEpPHcPVM8E6yMUmyMUnX2FNn5EWoprsoZ/uwQp9yppfp/BYGeu8+63Zh+zW+78u/is39BHOCeM6FhAG50kmKEwcpTB/DbZZJ1idJ1cfPursuF9/yqCfbaSQKqAh26NPwslQyvdSTRRpejqaXpTXz7KSWf+vLnb0pNOkbsAROl5RSPWB7O4g4WKJLO+JEqKU6jDKauzn0KU4cpHvkSdomDpGqj+O1Ktii/IH7QX7X+yh/Er6el+1o8FTyVZzqf848/8JcWj6MVQOGyrCtXenOeuSSDmlvedbCsgh9KkP7sN0UiVwfrl/DmjzN6dYUx5JJvFaL6048RC7TSakyyuOug7b3s6vnDhK5Tedv/yJYrSHGSRH5N+BvgH9Q1Yu/DV0Yb8c4JP8REyj1dxgr5tJw/9t4qngnD+//ArvbNpEtD5ArnyZbPk2ufJpMZWh2IY6gZKrDZKrDAHRF4+rbnvwIDS9HPdFGoXRiXvMv46vwpa/ynbt/hanCNlQsEo0psuUBMtUhUrUJkvUxUvUJMpXh2bvl+fDtJC0nie+kCJwkvpOk5aRouWlKuS1MFbZRTXUaa2AFq59UoVQHzzHOtUoTTk0pfqh4NmQSSt4zS8AnqspEjdlKWSbnosczYy0Es67CD81Kz86M4DmmXGHG46zx+2z/lsNYx42MddwYbQgpjh+ka3QPDS/P4a0vpvLlwxy49ofO6QguN4TD4z7tKdjSpvTlE3RmvdVTDIFPWBnj8NDD1FNFCMqEE/sJnRSNVJ5i+y52F29AbZd9R75Mqz6El+9hS+eN9HXcQF9+M5lo6fxqs1oK4nnAT2DCnv9SRP4Jk4/yX1VXZ7CqqtsX2dbEJKl581knXEpEqKW7qKW7GOm+9czmMCBdHY4UxykK08fJVAZJ18bmnZ5olkg0l460evZD77kgcWqJNhrJduqJAvVkO9O5LVQzXfh2klqqY8m75GrSaMHhCWOKB2rRbPkkHOgvQMq1qPlKpSGcnAYhoD0pXN8FGc+l2vLJJZM0AhiaFrqzNtmEgx8qkzWfkbJSbprx/mDJQjWgL6e0p51z6zOxGO+4nvGO683r8PwzS4MlGKv47OqAYsbDc4Wc516UcqiXhhgefQpVn0BDGn6LUEIUi6bj0ttzC7t77iSf38RUeYCGXyOT6yOd6qAt0YZruWwubGd08iiFfD+FVCd5L3/OqfiVslo+iAeAB0TkV4AXAK/GLP0OReTjqvorq9HP5YZaNpVsH5VsH0PcOW9f+/gBrj/wj0y17cAKWmSqwxQnFk4CnZvAcqmlitSTRWqpDiYLOxgvXneWqb2WqELdh2rLzOP7IYxXhSAI2dyudGVcEq5NveUDQj7pkvZsfFX8QGn6Aa1QSTg2SdfCsYQwNGP6MIR8whQJmqkb2pnx2FQI8KPiPkGoTFR8Dk0oE7UWW9vtZVX8Ph9BCIfGFQefG7ptunJJMomLHOdryNDpRxmuDtLVfSuel8GxXDwvS8JN4zopbNslm+mhu30nllj0tO9ctKm+/Bb68isK87kgVjWyXk0q5q+IyDhmevOXMDMZV6WCOBcTxet44HvmLxWx/UaUFs0mDENG/u2r3HMdpBoTpOrjlLL9NL08lUwPpWwfLTd7SfJJhlFgz4wVHipMVpXhCtF8vZJyBdTEgWxrUzIJm3zKJZdworvt/Gk6RwTHEpLu2RfzTB1gy4L8gvl9sTD1R+cUa2tLuXTkWhwagQOjIUEQkPHCFSmLwZKSdQO2tLt05ZLnD0RSpTzwCCP1KUqJFIGbJuVloTZJUDqNl+pg9zXfx6a+u8jm+hGxTNr9DbZ6cyGrpiCijFKvBn4c2IHJ5vRzwD+vVh9XOvNmOcTiqGyla+v6BEqpwlQdhkpKMwiZSSEZZYwjn1C2tplapEnPxrOtKPrPhAAv5RdYEwTySZeb+hy2NVoEIewZDKg2WxRSFy5HqDBRg2s6TK1TZxlRitXRAzzTnGBb981sqZax/RLV6hAks6S2vYS24g66u28hkVjEO7uBWa1ZjL3ADcCDwJ8DH1fVsXOfdWVQbqgpibbMhLQbiUoTTk8ptWh4IBakHMFzjHJIOgGbC0Iu6ZBwbfwgoBWYupcJ2yLl2SQWsQDWC88Rio6ZLu2vNjk0GpAoB3RllELKOXshmupZxTDqLXhmTCmmfbIJd1Hl0PDrVEvDtOrj5FJduI7HwYmDXLv9heza8WLydgK71aDl1wnUJ5nbhCTWxom41qyWBfF3mNWaFzaIvgKwbaHUyvLEQEBXBrpzi0fNrQd+AKdLIY2WKdiCQMMX/BCCMMS1lL4CtKccHEto+Eq5GVBvQXcntKWSZJPOosOAC5Yl9KPIwsXWLjQ5Pr4fmmW257aScFI0bJMI13NTF+WE29np0p5SJqo+gyWLwZLPjiIk3TM/ea8xQRvTWM1pSJrEvkcnlK1tAT25JO2Z+cObarPC8dMPo40pkskCdqKd0ckD1BpTbOq7i639d1PM9Z9p/4Kl3nislpPyHQAikp/TpmDCnu9S1U+vRj8bkS3tCW7o8clmbE5OKHuHQgpJ6Mpg5sjXgKYPJ6eVpK2kPOMYbM5JXNQKodESWkFIT07pSJvksIrSnTHxBJ5j4zk2ac8mM2cuPwhCgto0jvq4UkeqLSRs4icKaKoAIgTVSSYmD1OpTxBqQLptB/lMD0weo1odZcyv0tCApNi02ynGgxpN9VEEC0jZHuJm8EVotqpIUKcn1UszkWfP+H7QAFcVbdXo6b6F7vYdlCeOcGJsH/n8Vvq6dmNZNoQhEpgYj1ZtnKGJZxDLpqv7FtxEju58gs5sgk3tAfuHWhwYabEp79OeMdaE5dewCbCbZYJkgWMTStLxaUu5dOY8JmrjDI88RbM2gWU7BH6DLR030rv9RRTat+Ml2qgHNSZLp/HcDL0dN6zJ972erNYQ40WYUOqti+xuYBZWXZEkKkN0NSfpSQ3Rk3eZToScrjkcG8vgz2SAESh4guso+SRkXHNBhlEyGT801ainG+CHAQdHTfKX8SDHA8dCEi60J5VMQnAt4dQ09OWVQGG6Bo7NvEItaU9JZoWUa1NIOaRdC6lXCDTADRo46hvfZiBQU6QaIEETaTWo1UtM2ULDEgJVyoQ0RGkbP8g2N0+VgMONMdry28h034wtFpWx/Rwf3Y/mekh230Bfto+8l6XUKDNSHabLy9GV6SHtJJmsT1GujxPUxrFVSSZypBPtdLTvIJntYbQyDBriiDA89CQHjn2V8ZE9+F6WLX13Mzq8l4O1b5C0HUrVMUIxn6W6SToK2wlbNfYf/TIpO0lDfQI3RX/nzezu7aA7p+wbhlAbJvox9HEIQODbx4WkU2FXN+TSHkdG99IaP0Jf+/W0b7oXP2jSCJps6t5Nb++dYJtLpwD0zEybXoGs1hDjXcBe4I2Y4cZPYZZj/y7w86vUx4ZkMJ3neKZILajiBAGKT7fbYmvYJAxCKhrQkgT1oIvJ0jgDmqNi9yGOSysQLAnxQwvHDqk0bcDCEsGxFR8L27bZ1RlQ94WWL5QasKUtpL8tQdZzaAUmzMS2TYFfaVYRBFdbJBpDWOMlNAxQL4WKhTopmiIMNaaZ9Cu0NCBACCwb37bx2jppz/aScLN4lkOHlybt5JioDvHY4CPYGnL9llewuesm8tk+XDtJtXyaWnkQy8vi5HpJeVlSToogDCi3ythik/VMXs4tQCtsEbbqqN/A8zJYUUJdgOwcE727eB35wjbGKoMU0h30d91CY/N97DvwKUIsNm16NoX8FpOJy7LIZXvxnAzHT32b0fFD5DPdSGWEQ6e/y2gqRzrTS2dbG6MTabIJn3yzjI3PdN1My2rhOM1qg8HpKZwwZPvW57Nz0/dQKF4Llk0Y+lgXWEj4cme1FMRu4HWquldEHgFqqvpeEZnEFPFdv6Sxa0xP526qdoJKY5Ja0MRSwQ8bDNQnCIMmaRWCRola9RR1q0nOqpOu7KPd24Kb3gZeCjds4lsOYglhqLTCFk3fpba/xl2blHzaI+XYKBD4LfB9Co6P2g4J10GaNZzKGHZlmLqCY7sMh02qiTSp9s2URBgLa1hi00JBbLrarmdTsohneziWjWMnSbgZEoksaSdNwk5giYVjObiWSytscU3PHdQaU/S175wXuVdo30GhfcdZn41t2RQWKRLkWi4kXDiPR9/zMly7/X62hS1ssY3/IttDJtNFq1kmW9iG7Zw90t997SuoVkdIpjqQoEnPye8wfuxrBNNj7PePkXF38/DpIt9bO01dszw56nHzlhqTlRHuKW4l1/1cSLWRym2i0HHtbLtXm3KA1VMQDc4ssT4A3Ar8KybD06LZmK4UNuc205XuohW0CDRARFBVmkGTVtjCtVwkDDkxcYDJ6gjdmR6C6jj7T3yD0bEvsDNR5LQG7PAKJMOA49pivDLAdWGGu8SjvwXFwSHquV5It5lFO24aaorVMpWsQ9tj1EtyyEvQcBP4lkXO7aGY7mI4DEgnstyY6kJRUk6KrJcl62bJeTk821uy8O9cPNtjU2GxEeTa4y4or5fO9Z3zeLEsMtke88J26NvxAjo7b8BqVZh+7MNYpUfZ0XQYqGdRDbm58TTtU+C25chvejY9W559XuV1tbBaCuK7wC+JyK9hVln+B8yw4w4WT+pyRZGwEySWKk8fUcx0UWqWyLgZLLHo67mVr+79vzzTnGZH2w6eHNtPPaiTmx4n42WQ0FwUxdJJxHVI1MZpaUCl+0aOaYsRv8zWdC/dbo4j1dNMhz7XdNxId24z9aCOLTZ92T4CDQg1pOCZO7ljOWsamrshEcHNG6Vy8y2vZeqpj5FsDOE7PdSHxtnWGVIJJ0i03YjXc3OsHOawWgri7cBngdOYknu/LiKngSImI/VVjyXWPHO7q7CN63a+jGbQoL+wlZ7eO5mqDOPXp3CmB5gGqnsf4ROZBNgWbV6GtJehHpTpzm3mpuwtHJ46zKHqSTrTndzRfj1b81vJeTlUFUWXZRlcbbR178Yfu4lafis9jQyHDimNndcx2TxOW7KIl4yVw1xWa5rz4SgpTEZVJ0XkWcBPYpK6XEiS2quK67t2E4QBaTdNd7qbul9nojHB2PQpJicO891Umpe3XUeQ70JEyDpZtuS3UEgU6Ex1srNtJ8enjpNP5Nma34oX5VQQMQ7LmMVJpopoqkhfuh94hsKWm/nWkcO8sGMXnnUlRC+sHqtZm7MKVKP/B4B3r1bbVyoJOzG7pMCxHOMb8LLYYnOkMgBAT+9tFNNFFKU90U5nqnM2fr8z1Ulbog1b7Ktv2LACPMujFszJSCBQSHWSdNM41gYs/LGOxJ/GBiTn5WgGpsBL2klzfXHpefb4B33hFJNFHh95nJRlal7W/BoJO0EhUYgV7QLiQeoGJO2kKTVL2GpjWfFXtNrsat+FH/qcKJ2gZtf4xqlvsC2/jZSzfkVyNyrxr28DYls2juXQ1mxbb1GuSNJumpu7bqbUKjHhTdCT7qE7000xWVxv0TYcsYLYoNzVcxeJMMGutl3rLcoVyV3dd3F3190A3NRxE9ty2zZ8bob1IFYQG5TdHbsBcK/C6L1LgW3Z3NFzB7213nmh4DHziRVEzFWLbdlYWFxXvG69RdmwxAoi5qonnglamviTOT9pgCeeeOJ8x60qQRBw/PhxvvnNb2KvUUGWq52r4TOe87u9qJQLonr+1N9XMyLyRq7wBWcxVwVvUtW/vNCTYgvi/MxkwzqEiRS9BaMw3oQpL3gxWJhs3x/BFPy51OevRhsr/Rw2wntY789gNWQ43/lp4BrO/I4viNiCuEBE5D7MMvbnquo311ue9SL+HK6OzyB2UsbExCxJrCAunBPA70TPVzPx53AVfAbxECMmJmZJYgsiJiZmSWIFERMTsySxgoiJiVmSWEHExMQsSawgYmJiliRWEDExMUsSK4gLQEQcEXm3iAyLyJSIfEBELs+67heJiPSIyN+IyJCIjInIP4jI+lTU2QCIyNtEpLzecqwVcRzEBSAifwj8BPA6TNz7h4GvqerPrqtglxAR+TqmFvgvAz7wP4Ae4E5Vba2nbJcaEdkNPAL4qnpFZpyJFcQyEZEkMAq8XlU/EW17IabEYI+qjq+nfJcCEbkeeBq4UVWfjrZtxkQSfo+qfmc95buUiIgDfBuzgO+uK1VBxEOM5XM7kAG+OmfbNzCf4XPWQ6B1YAh4Bab+6gwzKwjbLrk068vbgTGMFXnFEiuI5bMJCFR1aGZDZFKPYqraX/Go6qSqflZV5y4r/hWghLmbXhWIyK3AfwZ+bp1FWXPifBDLJ83ihYgbQPISy7IhEJHXAb8KvEVVp9dbnkuBiLjAR4H/oqonrvRCO7EFsXxqwGKFGxNA5RLLsu6IyJswSUrepapXU4Hm3wRGVfX96y3IpSBWEMvnJOCISNfMhuhu0gmcWjep1gEReQemavvvqurb1lueS8zrgOeJSDma3vxLIBO9fu06y7bqxEOM5fM4xlJ4HvAP0bbnAgHwwHoJdakRkV8Dfhv4VVW9Ggs0vwCYW6zklcA7ME7soUWOv6yJFcQyUdWaiLwf+BMRmcD4Ht4HfPhqmOIEEJEbgP8GvB/4mIj0ztk9oaqL+WiuKFT12NzXIjJsNusz6yTSmhIriAvj7RiH5D9ipvf+DhMwdLXwSsxv5uejx1xehfk8Yq4g4kCpmJiYJYmdlDExMUsSK4iYmJgliRVETEzMksQKIiYmZkliBRETE7MksYKIiYlZklhBxMTELEmsIGJiYpYkVhAxMTFLEiuImJiYJYkVRExMzJLECiImJmZJYgURc0kQkbSIPLlgifhK2rtdRFREti/YvimqW7JkLriovskjInLNashyJRMriJhLxTuBT6nq4Br38zLgi3qOZcqq6gO/h8kGFXMOYgURM4uIvFlEJkSkuMi+nxeRQERSF9FuN/Am4M9XQ87z8DJMrZLz8Y/ALhF5/hrLc1kTK4gYAKISgr8DvHuJDFkHMb+X3RfR/BuB76rqcNSXisg7RWRQRA6KSFJE7hKRL4nItIg0RORREXnJHPm2i8jnRaQiIvuBexd5DxbwYiIFISI/F7XfiJ7fOHNsZGF8iqsr4c8FEyuImBleD+QxyWgXYyYx786LaPsHgM8v2PZTwEuAVwM28DlgL3AHcDdwGvigGNzofAWejalJ8RuL9HM3cFpVB0TkNuB/Am8FrgP+EHiviNw95/jPAy+L2o9ZhFhBxMzwWszYfQxARG6K6l7MkI6emxfSqIjYwJ3AvgW7PqSqe1T1EUzFsncBb1XVQ6r6JPBnwFaggFEk1wA/E53zORZXEC8FvhD9vxOjUI6p6jFV/WDUzpE5x++J+r7lQt7T1USsIGIQkQTm7vutOZtfFz1m6I6exy6w+Q6MhTC6YPvRmX+ioccHgDeKyF+JyNeAv492O8DNGMtgbtboxTKJz/U/fB54CHhURPaJyP8AxmcUYMRI9Nxzge/pqiFWEDFg7tQ2MDdj8wsxZv4M92EyeT8CICI/JCIPiMhj0ZThDUu0PVOmz16wvT7zj4j0A09ikuIewGTO/qk5xypn/1bnWTIikgduBb4OJgu5qj4XUzf1k8CLgAdF5EfnnDYj09xSgjFziBVEDJy5UBIAInIj5mJzo9cOxpr4jKpWo2O/CTxHVW/HzE68YYm2x4AW0LXEfoCfxFgKL1LVP1bVzwP90T4BHgP6RWTrnHPunt8ELwK+rar1SOYXishvqOoDqvpbqnoH8GWMz2OGGZkGziHbVU2c9j4GjLlfBt4U1Xl4B8bk/wkR+SHM3bwf+KE557wGeG007dmBqVd5FqqqIvIwcBtnCg4t5CRQBH5YRB7FWCu/F+1LAF/BKIm/EZFfjo79bwvamOt/AKOUfjuqYfIvGB/GHcB/n3PM7ZjCw08vIVeMqsaP+AHGSTkITAC/j7EqPo2pSfo45u4+c+xPYuII8tHrjwGvPkfb/xX49zmvFfixOa8t4E8wfooS8B1MnY068MPRMb1Rn2WMQntT1M72aP8h4KYF/f4Mxjlax8zC/D5gz9n/buCT6/3Zb+RHXBcj5oIRkT8AJlX1j0TkBRjH4C2qun+J43sxF/CNqnr8kgl6DqJh0wngJ1T1q+stz0Yl9kHEXAx/Dfw/IvIgxrSfwgRSLYqa8OoPAL9wacRbFj8G7I+Vw7mJLYiYS4KIZIHvAi9W1XV1CkbWw8OYYc6Sii0mVhAxMTHnIB5ixMTELEmsIGJiYpYkVhAxMTFLEiuImJiYJYkVRExMzJLECiImJmZJYgURExOzJLGCiImJWZJYQcTExCxJrCBiYmKWJFYQMTExSxIriJiYmCWJFURMTMySxAoiJiZmSWIFERMTsySxgoiJiVmS/x8U13Q5nsyzWwAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,2freqavgerr,2023-03-31 14;23;36.png\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# *****\n", "figsize = (figwidth/2, 1.3)\n", @@ -5096,6 +5725,7 @@ "saving = False\n", "do_3D = True\n", "set_format()\n", + "saving = True\n", "\n", "SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", @@ -5119,8 +5749,10 @@ "if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", " #plt.xticks(ticklist)\n", " #plt.yticks(ticklist)\n", - " plt.xticks(range(round(maxfreq)+1))\n", - " plt.yticks(range(round(maxfreq)+1))\n", + " #plt.xticks(range(round(maxfreq)+1))\n", + " plt.xticks([res1, res2])\n", + " plt.xticks([], minor = True)\n", + " plt.yticks(range(round(maxfreq)+1)) \n", "plt.axis('equal')\n", "plt.tight_layout()\n", "if saving:\n", @@ -5136,7 +5768,9 @@ "if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", " #plt.xticks(ticklist)\n", " #plt.yticks(ticklist)\n", - " plt.xticks(range(round(maxfreq)+1))\n", + " #plt.xticks(range(round(maxfreq)+1))\n", + " plt.xticks([res1, res2])\n", + " plt.xticks([], minor = True)\n", " plt.yticks(range(round(maxfreq)+1))\n", "plt.axis('equal')\n", "plt.tight_layout()\n", @@ -5147,14 +5781,21 @@ "\n", "if do_3D:\n", " plt.figure(figsize = (1.555,1.3), dpi= 300 )\n", - " myheatmap(SSgrid3D, \"log average error\",vmin=vmin, vmax=vmax, cmap='magma_r'); \n", + " ax,cbar = myheatmap(SSgrid3D, \"log average error\",vmin=vmin, vmax=vmax, cmap='magma_r',return_cbar=True); \n", + " if resonatorsystem == 110:\n", + " cbarticks = [0,1,2]\n", + " cbarticklabels = ['$10^'+str(tick)+'$' for tick in cbarticks]\n", + " cbarticklabels[-1] = '>' + cbarticklabels[-1]\n", + " cbar.set_ticks(cbarticks, labels=cbarticklabels)\n", " plt.title('3D-SVD')\n", " plt.ylabel('$\\omega_a$ (rad/s)')\n", " plt.xlabel('$\\omega_b$ (rad/s)')\n", " if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", " #plt.xticks(ticklist)\n", " #plt.yticks(ticklist)\n", - " plt.xticks(range(round(maxfreq)+1))\n", + " #plt.xticks(range(round(maxfreq)+1))\n", + " plt.xticks([res1, res2])\n", + " plt.xticks([], minor = True)\n", " plt.yticks(range(round(maxfreq)+1))\n", " plt.axis('equal')\n", " plt.tight_layout()\n", @@ -5162,6 +5803,31 @@ " savename = \"sys\" + str(resonatorsystem) + ','+ \"3D2freqheatmap,\" + datestr\n", " savefigure(savename)\n", " plt.show()\n", + " \n", + " ## 3D minus 2D\n", + " plt.figure(figsize = (1.555,1.3), dpi= 300 )\n", + " ax,cbar = myheatmap(SSgrid3D-SSgrid2D, \"log average error\",vmin=vmin, vmax=vmax, cmap='magma_r',return_cbar=True); \n", + " if resonatorsystem == 110:\n", + " cbarticks = [0,1,2]\n", + " cbarticklabels = ['$10^'+str(tick)+'$' for tick in cbarticks]\n", + " cbarticklabels[-1] = '>' + cbarticklabels[-1]\n", + " cbar.set_ticks(cbarticks, labels=cbarticklabels)\n", + " plt.title('3D-2D-SVD')\n", + " plt.ylabel('$\\omega_a$ (rad/s)')\n", + " plt.xlabel('$\\omega_b$ (rad/s)')\n", + " if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " #plt.xticks(ticklist)\n", + " #plt.yticks(ticklist)\n", + " #plt.xticks(range(round(maxfreq)+1))\n", + " plt.xticks([res1, res2])\n", + " plt.xticks([], minor = True)\n", + " plt.yticks(range(round(maxfreq)+1))\n", + " plt.axis('equal')\n", + " plt.tight_layout()\n", + " if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"3D-2D-2freqheatmap,\" + datestr\n", + " savefigure(savename)\n", + " plt.show()\n", "\n", "if not MONOMER:\n", " \n", @@ -5321,9 +5987,42 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:12: DeprecationWarning: invalid escape sequence \\p\n", + "C:\\Users\\vhorowit\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py:2954: RuntimeWarning: invalid value encountered in log10\n", + " majorstep_no_exponent = 10 ** (np.log10(majorstep) % 1)\n", + "C:\\Users\\vhorowit\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py:2954: RuntimeWarning: invalid value encountered in double_scalars\n", + " majorstep_no_exponent = 10 ** (np.log10(majorstep) % 1)\n", + "meta NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved:\n", + " sys110,1D_heatmap_by_phase,2023-03-31 14;15;18.png\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "if not MONOMER:\n", " plt.figure(figsize = (1.8,1.3), dpi= 300 ) # *** new subfigure\n", From d3a2b57b1850f67640f5fb53d3b6a5d62ed39a2b Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 10 Apr 2023 10:51:41 -0400 Subject: [PATCH 036/101] cleared outputs --- ...ach Simulated Two Coupled Resonators.ipynb | 701 +----------------- 1 file changed, 32 insertions(+), 669 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 34bbb49..6fd3cf7 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14,20 +14,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'3.6.1'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import sympy as sp\n", @@ -68,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -77,36 +66,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:190: DeprecationWarning: invalid escape sequence \\o\n", - " ax.set_xlabel('$\\omega$ (rad/s)')\n", - "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:235: DeprecationWarning: invalid escape sequence \\m\n", - " ax.set_xlabel('$\\mathrm{Re}(Z)$ (m)')\n", - "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:320: DeprecationWarning: invalid escape sequence \\;\n", - " amplabel = '$A\\;$(m)'\n", - "C:\\Users\\vhorowit\\Documents\\GitHub\\SimulatedResonator\\resonator_plotting.py:321: DeprecationWarning: invalid escape sequence \\p\n", - " phaselabel = '$\\phi\\;(\\pi)$'\n" - ] - }, - { - "data": { - "image/png": "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\n", - 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from myheatmap import myheatmap\n", "from helperfunctions import flatten,listlength,printtime,make_real_iff_real, \\\n", @@ -141,20 +103,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"import matplotlib.font_manager # See list of fonts\\nmatplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\"" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\"\"\"import matplotlib.font_manager # See list of fonts\n", "matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\"\"\"" @@ -162,46 +113,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# global variables that I promise not to vary\n", "from simulated_experiment import complexamplitudenoisefactor, use_complexnoise\n", @@ -222,30 +138,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "resonatorsystem: 11\n", - "DIMER\n", - "Applying oscillating force to m1.\n", - "Approximate Q1: 8.00 width: 0.06\n", - "Approximate Q2: 26.46 width: 0.10\n", - "Q ~ sqrt(m*k)/b\n", - "Set values:\n", - "m1: 8, b1: 0.5, k1: 2, F1: 1\n", - "m2: 1, b2: 0.1, k2: 7, k12: 5\n", - "noiselevel: 0.1\n", - "stdev sigma: 5e-05\n", - "Drive length: 100 (for calculating R^2)\n", - "Desired freqs: [0.773987235127223, 3.5328457422902457]\n", - "Index of freqs: [27, 81]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "verbose = False\n", "#MONOMER = False\n", @@ -554,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -603,21 +498,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'stophere' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mstophere\u001b[49m\n", - "\u001b[1;31mNameError\u001b[0m: name 'stophere' is not defined" - ] - } - ], + "outputs": [], "source": [ "stophere # finish initialization. Next: try 1D, 2D, and 3D SVD" ] @@ -4729,7 +4612,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4741,27 +4624,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opened existing file: sys11,2freq,2023-01-07 13;53;00.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhorowit\\AppData\\Local\\Temp\\ipykernel_71112\\1444816346.py:106: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", - " resultsdfmean = resultsdfsweep2freqorigmean[resultsdfsweep2freqorig.Difference != 0]\n" - ] - } - ], + "outputs": [], "source": [ "#Code that loops through frequency points of different spacing \n", "\n", @@ -4907,7 +4774,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4921,203 +4788,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DIMER\n", - "Applying oscillating force to m1.\n", - "Approximate Q1: 8.00 width: 0.06\n", - "Approximate Q2: 26.46 width: 0.10\n", - "Q ~ sqrt(m*k)/b\n", - "Set values:\n", - "m1: 8, b1: 0.5, k1: 2, F1: 1\n", - "m2: 1, b2: 0.1, k2: 7, k12: 5\n", - "noiselevel: 0.1\n", - "stdev sigma: 5e-05\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I cut out f1 = f2.\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "describeresonator(vals_set = vals_set, MONOMER=MONOMER, noiselevel = noiselevel, forceboth = forceboth)\n", "#figsize = (8*3/2,7.7)\n", @@ -5412,308 +5087,29 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left[ 0.773987235127223, \\ 3.50126378407557, \\ 3.50443407234539, \\ 3.53284574229025, \\ 3.53383897316219\\right]$" - ], - "text/plain": [ - "[0.773987235127223, 3.501263784075566, 3.504434072345391, 3.5328457422902457, \n", - "3.533838973162194]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "reslist" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle 110$" - ], - "text/plain": [ - "110" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resonatorsystem" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "vmin: -0.4709607173913786 , corresponding to 0.338095416077065 %\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "meta NOT subset; don't know how to subset; dropped\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved:\n", - " sys110,1D2freqheatmap,2023-03-31 14;23;36.png\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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sys110,1D_heatmap_by_phase,2023-03-31 14;15;18.png\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "if not MONOMER:\n", " plt.figure(figsize = (1.8,1.3), dpi= 300 ) # *** new subfigure\n", From 70769285bf08f2d0d20dceccad11da85de11afed Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 15 May 2023 17:14:09 -0400 Subject: [PATCH 037/101] update describeresonator() Make describe resonator more like written text. --- simulated_experiment.py | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/simulated_experiment.py b/simulated_experiment.py index f09d0ba..6d96f16 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -40,25 +40,28 @@ def describeresonator(vals_set, MONOMER, forceboth, noiselevel = None): print('Applying oscillating force to both masses.') else: print('Applying oscillating force to m1.') - print('Approximate Q1: ' + "{:.2f}".format(approx_Q(k = k1_set, m = m1_set, b=b1_set)) + - ' width: ' + "{:.2f}".format(approx_width(k = k1_set, m = m1_set, b=b1_set))) + print('Q1 ~ ' + "{:.0f}".format(approx_Q(k = k1_set, m = m1_set, b=b1_set)) + + ' and peak width ~ ' + "{:.2f}".format(approx_width(k = k1_set, m = m1_set, b=b1_set)) + ' rad/s') if not MONOMER: - print('Approximate Q2: ' + "{:.2f}".format(approx_Q(k = k2_set, m = m2_set, b=b2_set)) + - ' width: ' + "{:.2f}".format(approx_width(k = k2_set, m = m2_set, b=b2_set))) + print(' Q2 ~ ' + "{:.0f}".format(approx_Q(k = k2_set, m = m2_set, b=b2_set)) + + ' and second peak width: ' + "{:.2f}".format(approx_width(k = k2_set, m = m2_set, b=b2_set))) print('Q ~ sqrt(m*k)/b') - print('Set values:') + print('We set the input values to:') if MONOMER: - print('m: ' + str(m1_set) + ', b: ' + str(b1_set) + ', k: ' + str(k1_set) + ', F: ' + str(F_set)) + print('m = ' + str(m1_set) + ' kg, b = ' + str(b1_set) + ' N s/m, k = ' + str(k1_set) + ' N/m, f = ' + str(F_set), ' N') res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) - print('res freq: ', res1) + print('res freq ~ ', res1, 'rad/s') else: if forceboth: - forcestr = ', F1=F2: ' + forcestr = ', f1=f2: ' else: - forcestr = ', F1: ' - - print('m1: ' + str(m1_set) + ', b1: ' + str(b1_set) + ', k1: ' + str(k1_set) + forcestr + str(F_set)) - print('m2: ' + str(m2_set) + ', b2: ' + str(b2_set) + ', k2: ' + str(k2_set) + ', k12: ' + str(k12_set)) + forcestr = ', f1 = ' + + print('m_1= ' + str(m1_set) + 'kg, b_1 = ' + str(b1_set) + + 'N s/m, k_1 = ' + str(k1_set) + forcestr + str(F_set)) + print('m_2= ' + str(m2_set) + 'kg, b_2 = ' + str(b2_set) + + 'N s/m, k_2 = ' + str(k2_set) + ', k_{12} = ' + str(k12_set)) + if noiselevel is not None and use_complexnoise: print('noiselevel:', noiselevel) print('stdev sigma:', complexamplitudenoisefactor*noiselevel) From 8b630cda0a3c4cf30c4ca2efaeb1677efaadba31 Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 15 May 2023 17:17:28 -0400 Subject: [PATCH 038/101] BUILD and FIX Calculate discrepancy and fractional discrepancy. FIX (add colon) Use Delta p_j for discrepancy notation. Save datasets Fitting (vary noise) Scatter plot of s1 vs s2 --- ...ach Simulated Two Coupled Resonators.ipynb | 445 ++++++++++++++++-- 1 file changed, 416 insertions(+), 29 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 6fd3cf7..f14ba63 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -199,8 +199,8 @@ "maxfreq = 1.8\n", "noiselevel = 200 # increased 2022-11-16 for demo Fig 1.\n", "\"\"\"\n", - "\"\"\"\n", - "### medium damped monomer -- use for Fig 4, picking frequencies\n", + "\n", + "\"\"\"### medium damped monomer -- use for Fig 4, picking frequencies\n", "resonatorsystem = -3\n", "m1_set = 4\n", "b1_set = .4\n", @@ -292,7 +292,7 @@ "\n", "\"\"\"### 1D better # weakly coupled dimer #4\n", "#define set values\n", - "## This is the weakly coupled dimer I am using\n", + "## This is the weakly coupled dimer I am using (Figure 3)\n", "## 2022-11-15 switched back to what I had before.\n", "resonatorsystem = 10\n", "m1_set = 1\n", @@ -311,6 +311,7 @@ "\"\"\"\n", "\n", "\n", + "\n", "## Well-separated dimer / Medium coupled dimer #1 / Used for Figure 5.\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -1262,7 +1263,7 @@ "\n", "# Ran 1000 times in 20.438 sec\n", "# Ran 1000 times in 16.996 sec on desktop with verbose = True\n", - "repeats = 1\n", + "repeats = 1000\n", "#repeats = 999\n", "if demo:\n", " repeats = 1\n", @@ -1287,7 +1288,124 @@ "printtime(repeats, before, after) \n", "display(repeatedexptsres.transpose()) \n", "\n", - "repeatedexptsresmean = repeatedexptsres.mean() " + "repeatedexptsresmean = repeatedexptsres.mean() \n", + "\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ str(repeats) + \"simulations,\" + datestr + ', noise'+ str(noiselevel)\n", + " repeatedexptsres.to_csv(savename + '.csv')\n", + " print(\"Saved:\", savename + '.csv')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['M1_1Ddiscrep']=(repeatedexptsres['M1_1D'] - repeatedexptsres['m1_set'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['B1_1Ddiscrep']=(repeatedexptsres['B1_1D'] - repeatedexptsres['b1_set'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['K1_1Ddiscrep']=(repeatedexptsres['K1_1D'] - repeatedexptsres['k1_set'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Ds= [ '1D', '2D', '3D']\n", + "if MONOMER:\n", + " Rs = ['1']\n", + "else:\n", + " Rs = ['1','2']\n", + "\n", + "for D in Ds:\n", + " for R in Rs:\n", + " repeatedexptsres['M'+ R +'_' + D +'discrep']=(repeatedexptsres['M'+ R +'_' + D] - repeatedexptsres['m'+ R +'_set'])\n", + " repeatedexptsres['B'+ R +'_' + D +'discrep']=(repeatedexptsres['B'+ R +'_' + D] - repeatedexptsres['b'+ R +'_set'])\n", + " repeatedexptsres['K'+ R +'_' + D +'discrep']=(repeatedexptsres['K'+ R +'_' + D] - repeatedexptsres['k'+ R +'_set'])\n", + " repeatedexptsres['M'+ R +'_' + D +'fract_discrep'] = repeatedexptsres['M'+ R +'_' + D +'discrep'] / \\\n", + " repeatedexptsres['m'+ R +'_set']\n", + " repeatedexptsres['B'+ R +'_' + D +'fract_discrep'] = repeatedexptsres['B'+ R +'_' + D +'discrep'] / \\\n", + " repeatedexptsres['b'+ R +'_set']\n", + " repeatedexptsres['K'+ R +'_' + D +'fract_discrep'] = repeatedexptsres['K'+ R +'_' + D +'discrep'] / \\\n", + " repeatedexptsres['k'+ R +'_set']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if saving:\n", + " datestr = datestring()\n", + " #savename = \"sys\" + str(resonatorsystem) + ','+ str(repeats) + \"simulations,\" + datestr + ', noise'+ str(noiselevel)\n", + " repeatedexptsres.to_csv(savename + '.csv')\n", + " print(\"Saved:\", savename + '.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['1-avg_expt_cartes_rsqrd_1D'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['1-expt_A1_rsqrd_1D'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['1-expt_realZ1_rsqrd_1D'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['1-expt_imZ1_rsqrd_1D'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres['1-expt_phase1_rsqrd_1D'][0]" ] }, { @@ -1335,7 +1453,7 @@ "outputs": [], "source": [ "#sns.set_context('paper')\n", - "saving = True\n", + "saving = True \n", "\n", "describeresonator(vals_set, MONOMER, forceboth, noiselevel)\n", "figheight = 1.3\n", @@ -1402,7 +1520,7 @@ " boxwhiskerfigsize = (figwidth*1,figheight)\n", "print('Box and Whisker figsize:', boxwhiskerfigsize)\n", "\n", - "with sns.axes_style(rc={'xtick.bottom': False,})\n", + "with sns.axes_style(rc={'xtick.bottom': False,}):\n", " fig, ax1 = plt.subplots(1,1, figsize = boxwhiskerfigsize, dpi=150)\n", " # notch shows 95% confidence interval of the median\n", " ax = ax1\n", @@ -1417,7 +1535,7 @@ " plt.xticks(rotation=60, ha='right');\n", " ax1.tick_params(axis = \"x\", left=True, bottom=False, pad = -2)\n", " ax1.tick_params(axis='y',length=3)\n", - " plt.ylabel('$({p_i}-{p_{i,set}})/{p_{i,set}}$ (%)');\n", + " plt.ylabel('$\\Delta p_j/p_{j,\\mathrm{in}}$ (%)');\n", " #plt.ylabel(r'$\\frac{{p_i}-{p_{i,set}}}{p_{i,set}} \\cdot 100\\%$');\n", " sns.despine(ax = ax1, bottom = True)\n", " plt.tight_layout()\n", @@ -1441,9 +1559,10 @@ " plt.ylabel('Occurrences')\n", " ax2.set_yticks([])\n", " sns.despine(ax=ax2, left = True)\n", + " #plt.xlim(xmax = 0.06)\n", "plt.tight_layout()\n", "if saving:\n", - " datestr = datestring()\n", + " #datestr = datestring()\n", " savename = \"sys\" + str(resonatorsystem) + ','+ \"probdist,\" + datestr\n", " savefigure(savename)\n", "plt.show()\n", @@ -1451,6 +1570,33 @@ "#sns.set_context('talk')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsresmean[Xkey +'1D']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedexptsres[Xkey +'1D']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(repeatedexptsres[Xkey +'1D']).mean()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1468,7 +1614,10 @@ "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize = (figwidth,figwidth))#, gridspec_kw={'hspace': 0}, sharex = 'all')\n", "\n", "if MONOMER:\n", - " Xkey = '1-expt_A1_rsqrd_'\n", + " #Xkey = '1-expt_A1_rsqrd_'\n", + " #xlab = '$1-R_A^2$'\n", + " Xkey = '1-avg_expt_cartes_rsqrd_'\n", + " xlab = '$1-R_\\mathrm{cart}^2$'\n", "else:\n", " Xkey = '1-expt_ampavg_rsqrd_'\n", " \n", @@ -1477,21 +1626,21 @@ "plt.sca(ax1)\n", "plt.loglog(repeatedexptsres[Xkey + '1D'], repeatedexptsres['avgsyserr%_1D'], symb, alpha = .08, label='1D')\n", "#plt.title('1D');\n", - "plt.xlabel('$1-R^2$')\n", + "plt.xlabel(xlab)\n", "plt.ylabel('syserr (%)');\n", "plt.legend()\n", " \n", "plt.sca(ax2)\n", "plt.loglog(repeatedexptsres[Xkey + '2D'], repeatedexptsres['avgsyserr%_2D'], symb, alpha = .08, label='2D')\n", "#plt.title(' 2D');\n", - "plt.xlabel('$1-R^2$')\n", + "plt.xlabel(xlab)\n", "plt.ylabel('syserr (%)');\n", "plt.legend()\n", "\n", "plt.sca(ax3)\n", "plt.loglog(repeatedexptsres[Xkey + '3D'], repeatedexptsres['avgsyserr%_3D'], symb, alpha = .08, label='3D')\n", "#plt.title('3D');\n", - "plt.xlabel('$1-R^2$')\n", + "plt.xlabel(xlab)\n", "plt.ylabel('syserr (%)');\n", "\n", "plt.suptitle('$R^2$ is useful for predicting syserr\\nbut not dimension')\n", @@ -1502,14 +1651,14 @@ "\n", "fig, ax = plt.subplots(1,1, figsize = (figwidth/2,figheight), gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", "for D in list_to_show:\n", - " plt.loglog(repeatedexptsres[Xkey +D], repeatedexptsres['avgsyserr%_'+ D], symb, markersize=1, alpha = .08, label=D)\n", + " plt.loglog(repeatedexptsres[Xkey +D], repeatedexptsres['avgsyserr%_'+ D], symb, markersize=2, alpha = .08, label=D)\n", " #plt.loglog(repeatedexptsres[Xkey +D][::5], repeatedexptsres['avgsyserr%_'+ D][::5], symb, alpha = .08, label=D)\n", " #plt.loglog(repeatedexptsresmean[Xkey +D], repeatedexptsresmean['avgsyserr%_'+ D] )\n", "#plt.title('1D');\n", - "plt.xlabel('$1-R^2$')\n", + "plt.xlabel(xlab)\n", "#plt.xlim(xmax = 10**-6)\n", "#plt.legend()\n", - "plt.ylabel('err (%)');\n", + "plt.ylabel('Avg err (%)');\n", "if resonatorsystem == 2:\n", " plt.xticks([1e-6,1e-7])\n", "if False:\n", @@ -1530,6 +1679,66 @@ "display('Number of items measured:', len(repeatedexptsres.columns)) # 200 -> 142 distributions" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "saving = False\n", + "\n", + "fig, ax = plt.subplots(1,1, figsize = (figwidth/2,figheight), gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", + "for D in list_to_show:\n", + " plt.loglog(repeatedexptsres[Xkey +D], (repeatedexptsres['avgsyserr%_'+ D])**2, symb, markersize=2, alpha = .08, label=D)\n", + " #plt.loglog(repeatedexptsres[Xkey +D][::5], repeatedexptsres['avgsyserr%_'+ D][::5], symb, alpha = .08, label=D)\n", + " #plt.loglog(repeatedexptsresmean[Xkey +D], repeatedexptsresmean['avgsyserr%_'+ D] )\n", + "#plt.title('1D');\n", + "plt.xlabel(xlab)\n", + "#plt.xlim(xmax = 10**-6)\n", + "#plt.legend()\n", + "plt.ylabel('[Avg err (%)]$^2$');\n", + "if resonatorsystem == 2:\n", + " plt.xticks([1e-6,1e-7])\n", + "if False:\n", + " locmaj = mpl.ticker.LogLocator(numticks=2)\n", + " #ax.yaxis.set_major_locator(locmaj)\n", + " ax.xaxis.set_major_locator(locmaj)\n", + "ax.tick_params(axis='x', which='minor', bottom=True)\n", + "ax.tick_params(axis='y', which='minor', left=True)\n", + "#plt.axis('equal');\n", + "plt.tight_layout()\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"esqd,Rsqd,manypts,\" + datestr\n", + " savefigure(savename)\n", + " \n", + " \n", + "fig, ax = plt.subplots(1,1, figsize = (figwidth/2,figheight), gridspec_kw={'hspace': 0}, sharex = 'all', dpi=150)\n", + "for D in list_to_show:\n", + " plt.loglog((repeatedexptsres[Xkey +D])**(1/2), (repeatedexptsres['avgsyserr%_'+ D]), symb, markersize=2, alpha = .08, label=D)\n", + " #plt.loglog(repeatedexptsres[Xkey +D][::5], repeatedexptsres['avgsyserr%_'+ D][::5], symb, alpha = .08, label=D)\n", + " #plt.loglog(repeatedexptsresmean[Xkey +D], repeatedexptsresmean['avgsyserr%_'+ D] )\n", + "#plt.title('1D');\n", + "plt.xlabel('sqrt' + xlab)\n", + "#plt.xlim(xmax = 10**-6)\n", + "#plt.legend()\n", + "plt.ylabel('Avg err (%)');\n", + "#if resonatorsystem == 2:\n", + "# plt.xticks([1e-6,1e-7])\n", + "if False:\n", + " locmaj = mpl.ticker.LogLocator(numticks=2)\n", + " #ax.yaxis.set_major_locator(locmaj)\n", + " ax.xaxis.set_major_locator(locmaj)\n", + "ax.tick_params(axis='x', which='minor', bottom=True)\n", + "ax.tick_params(axis='y', which='minor', left=True)\n", + "#plt.axis('equal');\n", + "plt.tight_layout()\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"R,manypts,\" + datestr\n", + " savefigure(savename)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -2700,16 +2909,18 @@ " lowerbound = np.mean([ASE[int(np.floor(halfalpha*len(ASE)))], ASE[int(np.ceil(halfalpha*len(ASE)))]])\n", " upperbound = np.mean([ASE[-int(np.floor(halfalpha*len(ASE))+1)],ASE[-int(np.ceil(halfalpha*len(ASE))+1)]])\n", " resultsvarynoiselevelmean.loc[resultsvarynoiselevelmean['noiselevel']== noise,'E_lower_'+ D] = lowerbound\n", - " resultsvarynoiselevelmean.loc[resultsvarynoiselevelmean['noiselevel']== noise,'E_upper_' + D] = upperbound" + " resultsvarynoiselevelmean.loc[resultsvarynoiselevelmean['noiselevel']== noise,'E_upper_' + D] = upperbound\n", + "\n", + "if saving:\n", + " datestr = datestring()\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ str(repeats) + \"sims_per_sigma,\" + datestr + ', varynoise'\n", + " resultsvarynoiselevel.to_csv(savename + '.csv')\n", + " print(\"Saved:\", savename + '.csv')\n", + " savename = \"sys\" + str(resonatorsystem) + ',logmean_of_'+ str(repeats) + \"sims_per_sigma,\" + datestr + ', varynoise'\n", + " resultsvarynoiselevelmean.to_csv(savename + '.csv')\n", + " print(\"Saved:\", savename + '.csv')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -2749,6 +2960,124 @@ " resultsvarynoiselevel[['log meanSNR_R2','log meanSNR_R1' ]]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "def powlaw(x, C, m): #****\n", + " return C * x**(m)\n", + "\n", + "# perhaps a power law with slope 1 is better called a \n", + "# \"linear fit\" or a \"proportional fit\"\n", + "def linear(x, b,m):\n", + " return m * x + b\n", + "\n", + "# truncated power law from https://www.nature.com/articles/srep08898\n", + "def truncpow(t,C,m,tau):\n", + " return(C * np.exp(t/(-tau)) * t**(m))\n", + "\n", + "Ds = ['1D', '2D', '3D']\n", + "if MONOMER:\n", + " Rs = ['R1']\n", + "else:\n", + " Rs = ['R1', 'R2']\n", + "\n", + "for D in Ds:\n", + " for R in Rs:\n", + " xdata = resultsvarynoiselevelmean['log meanSNR_' + R]\n", + " ydata = resultsvarynoiselevelmean['log avgsyserr%_' + D]\n", + "\n", + "\n", + " \"\"\"fitparampowone, covpowone = curve_fit(powlawslopeone, xdata = xdata, ydata = ydata, \n", + " p0 = 1)#(fitparampow[0]))\n", + " powonefit = powlawslopeone(xdata,fitparampowone[0])\n", + " plt.plot(xdata,powonefit, label='power law slope 1', color='grey');\n", + " print ('\\nPower law with slope fixed at 1:')\n", + " print ( 'C = ' + str(fitparampowone[0]) + ' ± ' + str(np.sqrt(covpowone[0,0])))\n", + " print ('logarithmic slope m = 1')\"\"\"\n", + "\n", + " plt.figure()\n", + " plt.scatter(xdata,ydata, label= D + ',' + R)\n", + " \n", + " fitparampow, covpow = curve_fit(linear, xdata = xdata, ydata = ydata, p0 = (1, 1))\n", + " print('fitparampow:', fitparampow)\n", + " linearfit = linear(xdata,fitparampow[0],fitparampow[1])\n", + " plt.plot(xdata,linearfit, label='log-log linear fit', color='k');\n", + "\n", + " \"\"\"\n", + " fitparamtrunc, covtrunc = curve_fit(truncpow, xdata = xdata, ydata = ydata, \n", + " p0 = (fitparampow[0], fitparampow[1],1))\n", + " trucpowfit = truncpow(xdata,fitparamtrunc[0],fitparamtrunc[1], fitparamtrunc[2])\n", + " #plt.plot(xdata,trucpowfit, label='truncated power law fit', color='r');\n", + " print('fitparamtrunc:', fitparamtrunc)\n", + " \"\"\"\n", + " plt.legend()\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " plt.figure(figsize=(1,6))\n", + " plt.imshow(abs(covpow), cmap=\"gray\", interpolation=\"nearest\", vmin=0)\n", + " plt.colorbar()\n", + " plt.title('Covariance matrix, power law fit, absolute values')\n", + " \n", + " \"\"\"\n", + " plt.figure(figsize=(1,6))\n", + " plt.imshow(abs(covtrunc), cmap=\"gray\", interpolation=\"nearest\", vmin=0)\n", + " plt.colorbar()\n", + " plt.title('Covariance matrix, truncated powlaw fit, absolute values')\n", + " \"\"\"\n", + " \n", + " print(\"\\nIt's ok to use the uncertainties below as long as there aren't strong off-diagonal values.\")\n", + " print('But there are, unfortunately.')\n", + " print ('\\nPower law, y=C*x^m:')\n", + " print ( 'C = ' + str(fitparampow[0]) + ' ± ' + str(np.sqrt(covpow[0,0])))\n", + " print ('logarithmic slope m = ' + str(fitparampow[1]) + ' ± ' + str(np.sqrt(covpow[1,1])))\n", + "\n", + " \"\"\"\n", + " print ('\\nTruncated Power law:')\n", + " print ( 'C = ' + str(fitparamtrunc[0]) + ' ± ' + str(np.sqrt(covtrunc[0,0])))\n", + " print ('logarithmic slope m = ' + str(fitparamtrunc[1]) + ' ± ' + str(np.sqrt(covtrunc[1,1])))\n", + " print ('constant tau = ' + str(fitparamtrunc[2]) + ' ± ' + str(np.sqrt(covtrunc[2,2])))\n", + " \"\"\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(1.5,1.5))\n", + "\n", + "for D in Ds:\n", + " for R in Rs:\n", + " xdata = 1/resultsvarynoiselevelmean['meanSNR_' + R]\n", + " ydata = (resultsvarynoiselevelmean['avgsyserr%_' + D])\n", + " #plt.figure()\n", + " #plt.scatter(x=resultsvarynoiselevel['meanSNR_' + R], y=100/resultsvarynoiselevel['avgsyserr%_' + D], \n", + " # marker = '.' , alpha = .05)\n", + " plt.plot(xdata,ydata, label = R + ',' + D )\n", + " plt.xlabel('1/SNR for resonator')\n", + " plt.ylabel(\"Avg err (%)\")\n", + " #plt.title(R + ',' + D)\n", + "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "\n", + "if resonatorsystem == 2:\n", + " plt.ylim(ymin = 0, ymax = 2)\n", + "\n", + "print('resonatorsystem:', resonatorsystem)\n", + "describeresonator(vals_set, MONOMER, forceboth, noiselevel)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5211,7 +5540,7 @@ " plt.title('3D-2D-SVD')\n", " plt.ylabel('$\\omega_a$ (rad/s)')\n", " plt.xlabel('$\\omega_b$ (rad/s)')\n", - " if True: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " if not MONOMER: #resonatorsystem == 11 or resonatorsystem == 110:\n", " #plt.xticks(ticklist)\n", " #plt.yticks(ticklist)\n", " #plt.xticks(range(round(maxfreq)+1))\n", @@ -5251,7 +5580,57 @@ " #plt.axis('equal')\n", " plt.tight_layout()\n", " plt.show()\n", - " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "plt.figure(figsize = (3,3), dpi= 300 )\n", + "\"\"\"SSgrid1D=resultsdfsweep2freqorigmean.pivot_table(\n", + " index = 'smallest singular value', columns = 'second smallest singular value', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", + "myheatmap(SSgrid1D, \"log average error (%)\", cmap='rainbow'); \"\"\"\n", + "plt.scatter(x=resultsdfsweep2freqorigmean['smallest singular value'], \n", + " y=resultsdfsweep2freqorigmean['second smallest singular value'],\n", + " c=resultsdfsweep2freqorigmean['log avgsyserr%_1D'],\n", + " vmax = 1,\n", + " s=.5,\n", + " #alpha = .8,\n", + " marker = '.'\n", + " )\n", + "cbar = plt.colorbar()\n", + "cbar.outline.set_visible(False)\n", + "cbar.set_label('log error (%)')\n", + "plt.title('1D-SVD')\n", + "plt.xlabel('$\\lambda_1$')\n", + "plt.ylabel('$\\lambda_2$')\n", + "if False: #resonatorsystem == 11 or resonatorsystem == 110:\n", + " #plt.xticks(ticklist)\n", + " #plt.yticks(ticklist)\n", + " #plt.xticks(range(round(maxfreq)+1))\n", + " plt.xticks([res1, res2])\n", + " plt.xticks([], minor = True)\n", + " plt.yticks(range(round(maxfreq)+1)) \n", + "#plt.axis('equal')\n", + "plt.tight_layout()\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"1D2freqheatmap,\" + datestr\n", + " savefigure(savename)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "saving = False\n", + "\n", "plt.figure(figsize = (2,1.3))\n", "alpha = .01\n", "ms = .3\n", @@ -5279,6 +5658,11 @@ " 'log avgsyserr%_3D']].to_csv(savename + '.csv')\n", "plt.show()\n", "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "resultsdfmeanbyfreq1 = resultsdf.groupby(by=['Freq1'], as_index=False).mean(numeric_only =True)\n", "X = resultsdfmeanbyfreq1['Freq1'] \n", "\n", @@ -5569,7 +5953,7 @@ "metadata": {}, "outputs": [], "source": [ - "stophere# next sweep one frequency (called freq2) (vary one freq) / sweep freq2" + "stophere# next sweep one frequency (called freq2) (vary one freq) / sweep freq2 /sweep 1 freq" ] }, { @@ -5685,13 +6069,16 @@ "if resonatorsystem == 11:\n", " minfreq = 2.5\n", " maxfreq = 4.5\n", + " includefreqs = reslist[1:]\n", "else:\n", " minfreq = None\n", " maxfreq = None\n", + " includefreqs = reslist\n", " \n", - "\n", + "print('Choosing drive frequencies, which must include', includefreqs)\n", "## Choose driving frequencies\n", - "chosendrive, morefrequencies = create_drive_arrays(vals_set = vals_set, forceboth=forceboth, includefreqs = reslist,\n", + "chosendrive, morefrequencies = create_drive_arrays(vals_set = vals_set, forceboth=forceboth, \n", + " includefreqs = includefreqs,\n", " minfreq = minfreq, maxfreq = maxfreq,\n", " MONOMER = MONOMER, n=n, morefrequencies = morefrequencies)\n", "\n", From 13d715b4d07b7a85116f37c599d7e23dc4c85331 Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 15 May 2023 17:18:32 -0400 Subject: [PATCH 039/101] FIX indent --- simulated_experiment.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/simulated_experiment.py b/simulated_experiment.py index 6d96f16..91b1645 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -43,8 +43,8 @@ def describeresonator(vals_set, MONOMER, forceboth, noiselevel = None): print('Q1 ~ ' + "{:.0f}".format(approx_Q(k = k1_set, m = m1_set, b=b1_set)) + ' and peak width ~ ' + "{:.2f}".format(approx_width(k = k1_set, m = m1_set, b=b1_set)) + ' rad/s') if not MONOMER: - print(' Q2 ~ ' + "{:.0f}".format(approx_Q(k = k2_set, m = m2_set, b=b2_set)) + - ' and second peak width: ' + "{:.2f}".format(approx_width(k = k2_set, m = m2_set, b=b2_set))) + print(' Q2 ~ ' + "{:.0f}".format(approx_Q(k = k2_set, m = m2_set, b=b2_set)) + + ' and second peak width: ' + "{:.2f}".format(approx_width(k = k2_set, m = m2_set, b=b2_set))) print('Q ~ sqrt(m*k)/b') print('We set the input values to:') if MONOMER: From aeae0916c8f38e418c154ccc7a78d35c380bb66b Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 20 May 2023 20:06:42 -0600 Subject: [PATCH 040/101] minor edits Minor edits, mostly DOC (documentation) --- ...ach Simulated Two Coupled Resonators.ipynb | 37 +++++++++---------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index f14ba63..2aad4aa 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -129,11 +129,10 @@ "#Text size\t8 point (should be readable after reduction - avoid large type or thick lines)\n", "#Line width\tBetween 0.5 and 1 point\n", "\n", + "set_format() # displays an empty graph\n", "\n", - "set_format()\n", - "\n", - "plt.figure(figsize = (3.82/2,1))\n", - "plt.plot(1)" + "#plt.figure(figsize = (3.82/2,1))\n", + "#plt.plot(1)" ] }, { @@ -200,7 +199,7 @@ "noiselevel = 200 # increased 2022-11-16 for demo Fig 1.\n", "\"\"\"\n", "\n", - "\"\"\"### medium damped monomer -- use for Fig 4, picking frequencies\n", + "### medium damped monomer -- use for Fig 4, picking frequencies\n", "resonatorsystem = -3\n", "m1_set = 4\n", "b1_set = .4\n", @@ -210,7 +209,7 @@ "minfreq = 1.4\n", "maxfreq = 1.8\n", "noiselevel = 1\n", - "\"\"\"\n", + "\n", "\"\"\"\n", "## somewhat heavily damped monomer\n", "MONOMER = True\n", @@ -248,13 +247,13 @@ "MONOMER = False\n", "noiselevel = 10\n", "\n", - "#forceboth=True\n", - "#resonatorsystem = 6\n", + "forceboth=True # for SI\n", + "resonatorsystem = 6\n", "\n", - "forceboth = False\n", - "resonatorsystem = 7\n", - "minfreq = .3\n", - "maxfreq = 2.2\n", + "#forceboth = False\n", + "#resonatorsystem = 7\n", + "#minfreq = .3\n", + "#maxfreq = 2.2\n", "\"\"\"\n", "\n", "\"\"\"\n", @@ -311,7 +310,7 @@ "\"\"\"\n", "\n", "\n", - "\n", + "\"\"\"\n", "## Well-separated dimer / Medium coupled dimer #1 / Used for Figure 5.\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -328,11 +327,11 @@ "minfreq = 0.1\n", "maxfreq = 5\n", "#(but this is 3D for forceboth)\n", - "\n", + "\"\"\"\n", "\n", "\"\"\"\n", "### Medium coupled dimer #2\n", - "# This is my official medium coupled dimer.\n", + "# This is my official medium coupled dimer, in SI only\n", "resonatorsystem = 12\n", "m1_set = 11\n", "m2_set = 5\n", @@ -346,10 +345,10 @@ "noiselevel = 1 # reduced from 10, 2023-01-07 because the results were so poor\n", "forceboth= False\n", "minfreq = .1\n", - "maxfreq = 3\n", - "\"\"\"\n", + "maxfreq = 3\"\"\"\n", "\n", - "\"\"\"## strongly coupled dimer\n", + "\"\"\"\n", + "## strongly coupled dimer in SI only\n", "MONOMER = False\n", "resonatorsystem = 13\n", "m1_set = 8\n", @@ -7000,7 +6999,7 @@ " print('Saved: ' + os.path.join(savefolder,\n", " datestr + name + '.csv'))\n", "else:\n", - " resultsdoedf=pd.read_pickle(r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\2022-07-23 01;43;31resultsdoe, movepeaks.pkl')" + " resultsdoedf=pd.read_pickle(r'2022-07-23 01;43;31resultsdoe, movepeaks.pkl')" ] }, { From f5f680e9e5ccce0c4a2ce3a4b2ab4987b9a6fef8 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 26 May 2023 00:14:55 -0400 Subject: [PATCH 041/101] BUILD: frequency pick plots Figure 5. --- ...ach Simulated Two Coupled Resonators.ipynb | 179 ++++++++++++++---- resonator_plotting.py | 1 + 2 files changed, 142 insertions(+), 38 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 2aad4aa..16ac186 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -199,7 +199,7 @@ "noiselevel = 200 # increased 2022-11-16 for demo Fig 1.\n", "\"\"\"\n", "\n", - "### medium damped monomer -- use for Fig 4, picking frequencies\n", + "\"\"\"### medium damped monomer -- use for Fig 4, picking frequencies\n", "resonatorsystem = -3\n", "m1_set = 4\n", "b1_set = .4\n", @@ -208,7 +208,7 @@ "MONOMER = True\n", "minfreq = 1.4\n", "maxfreq = 1.8\n", - "noiselevel = 1\n", + "noiselevel = 1\"\"\"\n", "\n", "\"\"\"\n", "## somewhat heavily damped monomer\n", @@ -310,7 +310,7 @@ "\"\"\"\n", "\n", "\n", - "\"\"\"\n", + "\n", "## Well-separated dimer / Medium coupled dimer #1 / Used for Figure 5.\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -327,7 +327,7 @@ "minfreq = 0.1\n", "maxfreq = 5\n", "#(but this is 3D for forceboth)\n", - "\"\"\"\n", + "\n", "\n", "\"\"\"\n", "### Medium coupled dimer #2\n", @@ -429,9 +429,11 @@ "if resonatorsystem == 15: # 22.1208 MHz and 23.3554 MHz\n", " desiredfreqs = [22.1208*2 * np.pi * 1e6, 23.3554*2 * np.pi * 1e6]\n", "else:\n", - " desiredfreqs = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER, forceboth=forceboth, includefreqs = reslist,\n", + " for i in range(7):\n", + " reslist = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER, forceboth=forceboth, includefreqs = reslist,\n", " minfreq=minfreq, maxfreq = maxfreq,\n", " verboseplot = False, verbose=False, iterations = 3, numtoreturn=2)\n", + " desiredfreqs = reslist\n", "\n", "drive = np.sort(np.unique(np.append(drive, desiredfreqs)))\n", "print('Desired freqs:', desiredfreqs)\n", @@ -2251,20 +2253,6 @@ " pass" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -2290,8 +2278,8 @@ "# Ran 100 times in 7.121 sec\n", "# Ran 100 times in 78.661 sec with verbose = True (only counts the first repeat).\n", "# Ran 100 times in 786.946 sec with verbose = False\n", - "#repeats = 80*2\n", - "repeats = 1\n", + "repeats = 80*2\n", + "#repeats = 1\n", "verbose = False # if False, still shows one graph for each dimension\n", "freqdiff = round(W/10,4)\n", "print('freqdiff:', freqdiff)\n", @@ -2356,6 +2344,18 @@ " _, _, _, plot_info_1D_demo,\n", " _, show_set,\n", " figsizeoverride1, figsizeoverride2] = plot_info_1D\n", + "Z1 = R1_amp * np.exp(R1_phase *1j)\n", + "if not MONOMER:\n", + " Z2 = R2_amp * np.exp(R2_phase *1j)\n", + "\n", + "\"\"\"\n", + "in simulated_experiment.py:\n", + " plot_info_1D = [drive,R1_amp,R1_phase,R2_amp,R2_phase, df, K1, K2, K12, B1, B2, FD, M1, M2, vals_set, \n", + " MONOMER, forceboth, labelcounts, overlay,\n", + " context, saving, '1D', demo,\n", + " resonatorsystem, show_set,\n", + " figsizeoverride1, figsizeoverride2]\n", + "\"\"\"\n", "\n", "resultsvarynumpmean = resultsvarynump.groupby(by=['num frequency points'],as_index=False).mean()\n", "datestr = datestring()\n", @@ -2382,7 +2382,16 @@ "metadata": {}, "outputs": [], "source": [ - "plt.scatter?" + "desiredfreqs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_info_1D_df[0:2 ]" ] }, { @@ -2392,28 +2401,116 @@ "outputs": [], "source": [ "#plotcomplex(Z2, plot_info_1D_drive)\n", - "saving = False\n", + "saving = True\n", "show_set = True\n", + "labelcounts = True\n", + "bigcircle = 23\n", "if not MONOMER:\n", - " figsize = (2.1, 1.7715)\n", + " #figsize = (1.5, 1.3)\n", + " #figsize = (1.7, 1.4)\n", + " figsize = (1.76, 1.5)\n", "\n", " plt.figure(figsize = figsize, dpi=600)\n", " \n", " if show_set:\n", + " # subtle grey line\n", + " plt.plot(realamp1(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", + " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", + " imamp1(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", + " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", + " color='gray', alpha = .5, lw = 0.5, zorder = 1)\n", + "\n", + " # axes\n", + " plt.axvline(0, color = 'k', linestyle='solid', linewidth = .5, zorder = 3)\n", + " plt.axhline(0, color = 'k', linestyle='solid', linewidth = .5, zorder = 4)\n", + " sc = plt.scatter(np.real(Z1), np.imag(Z1), c = plot_info_1D_drive, s=10, \n", + " cmap = 'rainbow', vmin=0, zorder = 2) # option 3: s=4.\n", + " cbar = plt.colorbar(sc)\n", + " cbar.outline.set_visible(False)\n", + " ax = plt.gca()\n", + "\n", + " # axes labels\n", + " ax.set_xlabel('$\\mathrm{Re}(Z)$ (m)')\n", + " ax.set_ylabel('$\\mathrm{Im}(Z)$ (m)')\n", + " ax.axis('equal');\n", + " \"\"\" plt.gcf().canvas.draw() # draw so I can get xlim and ylim.\n", + " ymin, ymax = ax.get_ylim()\n", + " xmin, xmax = ax.get_xlim()\"\"\"\n", + " ax6 = plt.gca()\n", + "\n", + " # plus signs\n", + " ax6.scatter(np.real(plot_info_1D_df.R1AmpCom), np.imag(plot_info_1D_df.R1AmpCom), \n", + " marker = '+', color = 'w', lw = 0.5, s = 5,\n", + " #s=5, facecolors='none', edgecolors='k', lw = 0.5, # option 3\n", + " #s=1, facecolors='w', edgecolors='k', lw = 0.5, \n", + " label=\"points for analysis\", zorder = 7) \n", + " \n", + " \n", + " # black circles\n", + " ax6.scatter(np.real(plot_info_1D_df.R1AmpCom[0:2 ]), np.imag(plot_info_1D_df.R1AmpCom[0: 2]), \n", + " s=bigcircle, facecolors='none', edgecolors='k', label=\"points for analysis\", zorder = 6)\n", + " \n", + " # black dashed line\n", + " ax6.plot(realamp1(morefrequencies, K1, K2, K12, B1, B2, FD, M1, M2, 0,forceboth=forceboth, MONOMER=MONOMER), \n", + " imamp1(morefrequencies, K1, K2, K12, B1, B2, FD, M1, M2, 0,forceboth=forceboth, MONOMER=MONOMER), \n", + " '--', color='black', alpha = 1, lw = 0.7, zorder = 5)\n", + " if labelcounts: # this doesn't work\n", + " for i in range(0,len(plot_info_1D_df)//4,2):\n", + " plt.annotate(text=str(i+1), \n", + " xy=(np.real(plot_info_1D_df.R1AmpCom[i]), \n", + " np.imag(plot_info_1D_df.R1AmpCom[i])),\n", + " xytext = (np.real(plot_info_1D_df.R1AmpCom[i])+.07,\n", + " np.imag(plot_info_1D_df.R1AmpCom[i]) - .07) )\n", + " \n", + " plt.xlabel('Re($Z_1$) (m)')\n", + " plt.ylabel('Im($Z_1$) (m)')\n", + " #plt.xlim((-0.11, 0.10))\n", + " #plt.ylim((-.02, .18))\n", + " \n", + " plt.tight_layout()\n", + " if saving:\n", + " datestr = datestring()\n", + " filename = 'sys' + str(resonatorsystem) + ',' + datestr + 'spectrumZ1_1D_zoomin' \n", + " savefigure(filename)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## plotcomplex(Z2, plot_info_1D_drive)\n", + "saving = True\n", + "show_set = True\n", + "labelcounts = True\n", + "bigcircle = 23\n", + "if not MONOMER:\n", + " #figsize = (2.1, 1.7715)\n", + " #figsize = (1.8, 1.4)\n", + " figsize = (1.76, 1.5)\n", + " \n", + " plt.figure(figsize = figsize, dpi=600)\n", + " \n", + " if show_set:\n", + " # subtle grey line\n", " plt.plot(realamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", " imamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, \n", " 0,MONOMER=MONOMER, forceboth=forceboth,), \n", " color='gray', alpha = .5, lw = 0.5, zorder = 1)\n", "\n", + " # axes\n", " plt.axvline(0, color = 'k', linestyle='solid', linewidth = .5, zorder = 3)\n", " plt.axhline(0, color = 'k', linestyle='solid', linewidth = .5, zorder = 4)\n", " sc = plt.scatter(np.real(Z2), np.imag(Z2), c = plot_info_1D_drive, s=10, \n", - " cmap = 'rainbow', zorder = 2) # option 3: s=4.\n", + " cmap = 'rainbow', zorder = 2, vmin=0) # option 3: s=4.\n", " cbar = plt.colorbar(sc)\n", " cbar.outline.set_visible(False)\n", " ax = plt.gca()\n", "\n", + " # axes labels\n", " ax.set_xlabel('$\\mathrm{Re}(Z)$ (m)')\n", " ax.set_ylabel('$\\mathrm{Im}(Z)$ (m)')\n", " ax.axis('equal');\n", @@ -2421,34 +2518,39 @@ " ymin, ymax = ax.get_ylim()\n", " xmin, xmax = ax.get_xlim()\"\"\"\n", " ax6 = plt.gca()\n", - " \n", - " \n", "\n", - " \n", - " ax6.scatter(np.real(measurementdf.R2AmpCom), np.imag(measurementdf.R2AmpCom), \n", + " # plus signs\n", + " ax6.scatter(np.real(plot_info_1D_df.R2AmpCom), np.imag(plot_info_1D_df.R2AmpCom), \n", " marker = '+', color = 'w', lw = 0.5, s = 5,\n", " #s=5, facecolors='none', edgecolors='k', lw = 0.5, # option 3\n", " #s=1, facecolors='w', edgecolors='k', lw = 0.5, \n", - " label=\"points for analysis\", zorder = 6) \n", + " label=\"points for analysis\", zorder = 7) \n", + " \n", + " \n", + " # black circles\n", + " ax6.scatter(np.real(plot_info_1D_df.R2AmpCom[0:2 ]), np.imag(plot_info_1D_df.R2AmpCom[0: 2]), \n", + " s=bigcircle, facecolors='none', edgecolors='k', label=\"points for analysis\", zorder = 6)\n", " \n", + " # black dashed line\n", " ax6.plot(realamp2(morefrequencies, K1, K2, K12, B1, B2, FD, M1, M2, 0,forceboth=forceboth,), \n", " imamp2(morefrequencies, K1, K2, K12, B1, B2, FD, M1, M2, 0,forceboth=forceboth,), \n", " '--', color='black', alpha = 1, lw = 0.7, zorder = 5)\n", - " if labelcounts:\n", - " for i in range(len(measurementdf)):\n", + " if labelcounts: # this doesn't work\n", + " for i in range(0,len(plot_info_1D_df)//4,2):\n", " plt.annotate(text=str(i+1), \n", - " xy=(np.real(measurementdf.R2AmpCom), \n", - " np.imag(measurementdf.R2AmpCom)) )\n", + " xy=(np.real(plot_info_1D_df.R2AmpCom[i]), \n", + " np.imag(plot_info_1D_df.R2AmpCom[i])),\n", + " xytext = (np.real(plot_info_1D_df.R2AmpCom[i])+.05,\n", + " np.imag(plot_info_1D_df.R2AmpCom[i]) - .02) )\n", + " \n", " plt.xlabel('Re($Z_2$) (m)')\n", " plt.ylabel('Im($Z_2$) (m)')\n", - "\n", - "\n", " #plt.xlim((-0.11, 0.10))\n", " #plt.ylim((-.02, .18))\n", - " \n", + " \n", " plt.tight_layout()\n", " if saving:\n", - " filename = datestr + 'spectrumZ_1D_zoomin' \n", + " filename = 'sys' + str(resonatorsystem) + ',' + datestr + 'spectrumZ2_1D_zoomin' \n", " savefigure(filename)\n", " plt.show()" ] @@ -6086,6 +6188,7 @@ " e=0, MONOMER=MONOMER, forceboth=forceboth), '.')\n", "plt.xlabel('Freq2')\n", "plt.ylabel('R1 phase')\n", + "plt.show()\n", "\n", "reset_ideal_freq3 = False\n", "if reset_ideal_freq3:\n", diff --git a/resonator_plotting.py b/resonator_plotting.py index 88ecf41..d056311 100644 --- a/resonator_plotting.py +++ b/resonator_plotting.py @@ -227,6 +227,7 @@ def plotcomplex(complexZ, parameter, title = 'Complex Amplitude', cbar_label='Fr plt.sca(ax) plt.axvline(0, color = 'k', linestyle='solid', linewidth = .5) plt.axhline(0, color = 'k', linestyle='solid', linewidth = .5) + # colorful circles sc = ax.scatter(np.real(complexZ), np.imag(complexZ), s=s, c = parameter, cmap = cmap, label = 'simulated data' ) # s is marker size cbar = plt.colorbar(sc) From 2bf646e17c007a8442355f5dbf79c7563feda184 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 26 May 2023 01:22:01 -0400 Subject: [PATCH 042/101] FIX creating list of frequencies Not a bug fix per se. But I had an issue where I wanted to select a region of frequencies that did not encompass all phases. So I needed to remove any frequencies outside the region. --- resonatorfrequencypicker.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/resonatorfrequencypicker.py b/resonatorfrequencypicker.py index bafa37d..2a18127 100644 --- a/resonatorfrequencypicker.py +++ b/resonatorfrequencypicker.py @@ -169,6 +169,16 @@ def create_drive_arrays(vals_set, MONOMER, forceboth, n=n, else: m = int((n-3-(fracevenfreq*n))/2) + morefrequencies = list(np.sort(morefrequencies)) + while morefrequencies[-1] > maxfreq: + if False: # too verbose! + print('Removing frequency', morefrequencies[-1]) + morefrequencies = morefrequencies[:-1] + while morefrequencies[0]< minfreq: + if False: + print('Removing frequency', morefrequencies[0]) + morefrequencies = morefrequencies[1:] + phaseR1 = theta1(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, MONOMER, forceboth=forceboth) From 8e9ed6f6583f9a7310c046c84e83f71bf30d50c7 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 26 May 2023 01:22:29 -0400 Subject: [PATCH 043/101] FIX error if there's only one frequency --- resonatorsimulator.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/resonatorsimulator.py b/resonatorsimulator.py index c8108e8..623e504 100644 --- a/resonatorsimulator.py +++ b/resonatorsimulator.py @@ -338,8 +338,11 @@ def complex_noise(n, noiselevel): ## Calculate the amplitude and phase as spectra, possibly adding noise def calculate_spectra(drive, vals_set, noiselevel, MONOMER, forceboth): [m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set] = read_params(vals_set, MONOMER) - - n = len(drive) + + try: + n = len(drive) + except TypeError: + n = drive.size if usenoise: # add a random vector of positive and negative numbers to the curve. From 0a3198d270f4fe84c43b9882b714091291f133c8 Mon Sep 17 00:00:00 2001 From: vivarose Date: Sat, 27 May 2023 13:56:19 -0400 Subject: [PATCH 044/101] updating sweep 2freq and 1freq 1) Use full path for opening 2freq files 2) not sys error, just call it error. 3) staywithinlims = True for create_drive_arrays 4) save sweep1freq figure --- ...ach Simulated Two Coupled Resonators.ipynb | 63 ++++++++++++++++--- 1 file changed, 53 insertions(+), 10 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 16ac186..c796fc3 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -5152,10 +5152,12 @@ " resultsdfsweep2freqorig.to_csv(\"sys\" + str(resonatorsystem) + ',2freq,' + datestr + '.csv')\n", "else:\n", " if MONOMER:\n", - " saveddf = 'sys-3,2freq,2022-12-29 20;03;50.csv' # MONOMER\n", + " saveddf = os.path.join(r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\simulation_export',\n", + " 'sys-3,2freq,2022-12-29 20;03;50.csv') # MONOMER\n", " resonatorsystem = -30\n", " else:\n", - " saveddf = 'sys11,2freq,2023-01-07 13;53;00.csv' # DIMER\n", + " saveddf = os.path.join(r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\simulation_export',\n", + " 'sys11,2freq,2023-01-07 13;53;00.csv') # DIMER\n", " resonatorsystem = 110 # the 0 means it was reloaded\n", " resultsdfsweep2freqorig = pd.read_csv(saveddf)\n", " print('Opened existing file:', saveddf)\n", @@ -5257,13 +5259,13 @@ "plt.sca(ax4)\n", "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_1D').sort_index(axis = 0, ascending = False)\n", - "myheatmap(SSgrid, \"log average sys error\", vmax=vmax, cmap='magma_r'); \n", + "myheatmap(SSgrid, \"log average error, 1D (%)\", vmax=vmax, cmap='magma_r'); \n", "plt.title('1d')\n", "\n", "plt.sca(ax4b)\n", "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log maxsyserr%_1D').sort_index(axis = 0, ascending = False)\n", - "myheatmap(SSgrid, \"log max sys error\", vmax=vmax, cmap='magma_r'); \n", + "myheatmap(SSgrid, \"log max error, 1D (%)\", vmax=vmax, cmap='magma_r'); \n", "plt.title('1d')\n", "\n", "plt.sca(ax5)\n", @@ -5276,13 +5278,13 @@ "plt.sca(ax6)\n", "SSgrid=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log avgsyserr%_2D').sort_index(axis = 0, ascending = False)\n", - "myheatmap(SSgrid, \"log average sys error\", vmax=vmax, cmap='magma_r'); \n", + "myheatmap(SSgrid, \"log average error, 2D (%)\", vmax=vmax, cmap='magma_r'); \n", "plt.title('2d')\n", "\n", "plt.sca(ax6b)\n", "grid=resultsdfsweep2freqorigmean.pivot_table(\n", " index = 'Freq1', columns = 'Freq2', values = 'log maxsyserr%_2D').sort_index(axis = 0, ascending = False)\n", - "myheatmap(grid, \"log max sys error\", vmax=vmax, cmap='magma_r'); \n", + "myheatmap(grid, \"log max error, 2D (%)\", vmax=vmax, cmap='magma_r'); \n", "plt.title('2d')\n", "\n", " \n", @@ -6101,7 +6103,7 @@ "#Code that loops through frequency 2 points (of different spacing)\n", "\n", "verbose = True\n", - "repeats = 80\n", + "repeats = 80*20\n", "n = 200\n", "\n", "def sweep_freq2(freq1,drive=drive, vals_set = vals_set, \n", @@ -6180,7 +6182,7 @@ "## Choose driving frequencies\n", "chosendrive, morefrequencies = create_drive_arrays(vals_set = vals_set, forceboth=forceboth, \n", " includefreqs = includefreqs,\n", - " minfreq = minfreq, maxfreq = maxfreq,\n", + " minfreq = minfreq, maxfreq = maxfreq, staywithinlims = True,\n", " MONOMER = MONOMER, n=n, morefrequencies = morefrequencies)\n", "\n", "plt.figure()\n", @@ -6325,6 +6327,42 @@ "repeats % 80 # want this to be 0" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "repeats / 80" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(results_sweep_1freq.Freq2.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(results_sweep_1freq)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(results_sweep_1freq) / len(results_sweep_1freq.Freq2.unique())" + ] + }, { "cell_type": "code", "execution_count": null, @@ -6627,7 +6665,7 @@ "results_sweep_1freq, results_sweep_1freqmean = \\\n", " calc_error_interval(results_sweep_1freq, results_sweep_1freqmean, groupby='Freq2', fractionofdata = .95)\n", "\n", - "figsize = (4, 1.3)\n", + "figsize = (2.3, .9)\n", "\n", "plt.figure(figsize=figsize, dpi = 600) # *** for dimer figure, in progress\n", "ax = plt.gca()\n", @@ -6660,14 +6698,19 @@ "plt.yscale('log')\n", "plt.yticks([10**-1,10**0, 10**1, 10**2, 10**3])\n", "plt.xlabel('$\\omega_b$ (rad/s)')\n", - "plt.show()\n", "\n", + "datestr = datestring()\n", "results_sweep_1freqmean[['Freq1','Freq2','log avgsyserr%_1D', 'log avgsyserr%_2D', 'log avgsyserr%_3D', \n", " 'E_lower_1D', 'E_upper_1D' ,\n", " 'E_lower_2D', 'E_upper_2D',\n", " 'E_lower_3D', 'E_upper_3D']].to_csv(os.path.join(savefolder,\n", " 'sys' + str(resonatorsystem) + ',' + datestr + \"results_sweep_1freq_limitedcolumns.csv\"));\n", "\n", + "if saving:\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ \"sweep1freq,\" + datestr\n", + " savefigure(savename)\n", + "plt.show()\n", + "\n", "beep()" ] }, From bc585398399517ce911795819bf2e6a9648bc084 Mon Sep 17 00:00:00 2001 From: vivarose Date: Tue, 13 Jun 2023 23:34:58 -0400 Subject: [PATCH 045/101] fix frequency peakfinding use_R2_only implemented --- ...ach Simulated Two Coupled Resonators.ipynb | 192 ++++++++++++++---- resonatorfrequencypicker.py | 45 ++-- sim_series_of_experiments.py | 16 +- 3 files changed, 186 insertions(+), 67 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index c796fc3..d37cd12 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -516,17 +516,28 @@ "if resonatorsystem == 15: # 22.1208 MHz and 23.3554 MHz\n", " desiredfreqs = [22.1208*2 * np.pi * 1e6, 23.3554*2 * np.pi * 1e6]\n", "else:\n", - " desiredfreqs = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER, forceboth=forceboth, includefreqs = reslist,\n", - " minfreq=minfreq, maxfreq=maxfreq,\n", - " numtoreturn = 2, iterations = 3, verbose=False)\n", + " desiredfreqs = reslist\n", + " for i in range(5):\n", + " desiredfreqs, method = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER, forceboth=forceboth, \n", + " includefreqs = desiredfreqs,\n", + " minfreq=minfreq, maxfreq=maxfreq, returnoptions=True, numtoreturn=2, \n", + " use_R2_only=True, # for consideration !!!\n", + " iterations = 3, verbose=False)\n", "drive = np.unique(np.sort(np.append(drive, desiredfreqs)))\n", "p = freqpoints(desiredfreqs = desiredfreqs, drive = drive)\n", "print(\"p:\",p)\n", - "assert len(np.unique(p)) == 2\n", + "#assert len(np.unique(p)) == 2\n", "print(len(drive))\n", - "\n" + "print(method)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -631,7 +642,9 @@ " plt.sca(ax)\n", " #plt.xticks([res1, res2])\n", " ax.set_xlabel('Freq (rad/s)')\n", - "\n", + " \n", + "for ax in [ax1, ax3]:\n", + " ax.set_yscale('log') # It's an option!\n", " \n", "plt.tight_layout()\n", "\n", @@ -659,7 +672,8 @@ "\n", "plotcomplex(Z2, drive, 'Complex Amplitude $Z_2$', ax=ax6, label_markers=label_markers)\n", "ax6.scatter(np.real(df.R2AmpCom), np.imag(df.R2AmpCom), s=150, facecolors='none', edgecolors='k', label=\"data for SVD\") \n", - "plt.legend() \n", + "plt.legend() \n", + "\n", " \n", "plt.tight_layout()\n", "\n", @@ -1169,13 +1183,17 @@ "if resonatorsystem == 15:\n", " measurementfreqs = desiredfreqs # Brittany's expermental setup\n", "else:\n", + " if resonatorsystem == 11:\n", + " use_R2_only=True\n", + " else:\n", + " use_R2_only=False\n", " for i in range(5):\n", " measurementfreqs, category = res_freq_numeric(vals_set, MONOMER, forceboth,\n", " mode = 'amp', includefreqs = reslist + measurementfreqs,\n", " minfreq=minfreq, maxfreq=maxfreq, morefrequencies=None,\n", " unique = True, veryunique = True, numtoreturn = 2, \n", " verboseplot = False, plottitle = None, verbose=False, \n", - " iterations = 3,\n", + " iterations = 3, use_R2_only=use_R2_only,\n", " returnoptions = True)\n", "\n", "print(measurementfreqs)\n", @@ -1359,7 +1377,7 @@ "source": [ "if saving:\n", " datestr = datestring()\n", - " #savename = \"sys\" + str(resonatorsystem) + ','+ str(repeats) + \"simulations,\" + datestr + ', noise'+ str(noiselevel)\n", + " savename = \"sys\" + str(resonatorsystem) + ','+ str(repeats) + \"simulations,\" + datestr + ', noise'+ str(noiselevel)\n", " repeatedexptsres.to_csv(savename + '.csv')\n", " print(\"Saved:\", savename + '.csv')" ] @@ -1571,33 +1589,6 @@ "#sns.set_context('talk')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "repeatedexptsresmean[Xkey +'1D']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "repeatedexptsres[Xkey +'1D']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(repeatedexptsres[Xkey +'1D']).mean()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1621,6 +1612,7 @@ " xlab = '$1-R_\\mathrm{cart}^2$'\n", "else:\n", " Xkey = '1-expt_ampavg_rsqrd_'\n", + " xlab = '$1-R_\\mathrm{A}^2$'\n", " \n", "symb = '.'\n", "\n", @@ -1968,6 +1960,7 @@ "ax.boxplot(abs(signederr), notch=True, \n", " vert=None, patch_artist=None, widths=None, meanline = True,\n", " labels=signederr.columns); \n", + "plt.xticks(rotation=90);\n", "plt.ylabel('abs$(({p_i}-{p_{i,set}})/{p_{i,set}}) \\cdot 100\\%$');\n", "\n", "\n", @@ -2039,7 +2032,8 @@ "print('Showing ', count, ' plots')\n", "printtime(count, before, after)\n", "print('Some of these are the folded normal (half normal) distribution')\n", - "plt.tight_layout()" + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -2253,6 +2247,20 @@ " pass" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import importlib\n", + "import sim_series_of_experiments\n", + "\n", + "# Make changes to the module code\n", + "\n", + "importlib.reload(sim_series_of_experiments)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -2278,9 +2286,9 @@ "# Ran 100 times in 7.121 sec\n", "# Ran 100 times in 78.661 sec with verbose = True (only counts the first repeat).\n", "# Ran 100 times in 786.946 sec with verbose = False\n", - "repeats = 80*2\n", + "repeats = 99\n", "#repeats = 1\n", - "verbose = False # if False, still shows one graph for each dimension\n", + "verbose = False # if False, still shows 2 graphs for each dimension\n", "freqdiff = round(W/10,4)\n", "print('freqdiff:', freqdiff)\n", "\n", @@ -2307,8 +2315,14 @@ " # complex plot\n", " figsizeoverride2 = (figwidth, 1.48)\n", "\n", + "\n", + "if resonatorsystem == 11:\n", + " use_R2_only=True\n", + "else:\n", + " use_R2_only=False\n", + "\n", "before = time()\n", - "for i in range(1): # don't do repeats at this level.\n", + "for i in range(1): # don't do repeats at this level. ***\n", " thisres, plot_info_1D = vary_num_p_with_fixed_freqdiff( vals_set, noiselevel, \n", " MONOMER, forceboth,reslist = reslist,\n", " minfreq=minfreq, maxfreq = maxfreq,\n", @@ -2316,9 +2330,10 @@ " max_num_p=max_num_p, \n", " freqdiff = freqdiff,\n", " n=n, # number of frequencies for R^2\n", - " repeats = repeats,\n", + " repeats = repeats, \n", " overlay = overlay, saving = saving,\n", " context = 'paper', resonatorsystem = resonatorsystem,\n", + " use_R2_only=use_R2_only,\n", " figsizeoverride1 = figsizeoverride1, figsizeoverride2 = figsizeoverride2,\n", " recalculate_randomness = False)\n", " verbose = False\n", @@ -2376,6 +2391,97 @@ "\"\"\";" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resultsvarynump['num frequency points'].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(resultsvarynump)/ resultsvarynump['num frequency points'].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(resultsvarynump)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print('standard deviation')\n", + "resultsvarynump.groupby(by=['num frequency points'],as_index=False)['avgsyserr%_3D'].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print('standard error')\n", + "resultsvarynump.groupby(by=['num frequency points'],as_index=False)['avgsyserr%_3D'].std() / np.sqrt(len(resultsvarynump)/ resultsvarynump['num frequency points'].nunique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "resultsvarynumpmean[['num frequency points','avgsyserr%_1D', 'avgsyserr%_2D','avgsyserr%_3D']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resultsvarynumpmean[['avgsyserr%_2D']].min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(resultsvarynumpmean['num frequency points'][0:11],resultsvarynumpmean[['avgsyserr%_1D', 'avgsyserr%_2D','avgsyserr%_3D']][0:11])\n", + "#plt.gca().ylims(ymax=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resultsvarynumporig = resultsvarynump\n", + "resultsvarynump = resultsvarynumporig.copy()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -2713,7 +2819,7 @@ "if MONOMER:\n", " yt = yt[1:-1]\n", "elif resonatorsystem == 11:\n", - " yt = range(-3,5,1)\n", + " yt = range(-3,7,1)\n", " #ytminor = np.arange(-3,4,.1)\n", " #plt.yticks(ytminor, [10**y for y in ytminor], axis = 'minor',)\n", "print(yt)\n", @@ -9143,7 +9249,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/resonatorfrequencypicker.py b/resonatorfrequencypicker.py index 2a18127..3a13f2e 100644 --- a/resonatorfrequencypicker.py +++ b/resonatorfrequencypicker.py @@ -289,6 +289,7 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, minfreq=.1, maxfreq=5, morefrequencies=None, includefreqs = [], unique = True, veryunique = True, numtoreturn = None, verboseplot = False, plottitle = None, verbose=verbose, iterations = 1, + use_R2_only = False, returnoptions = False): if verbose: @@ -341,6 +342,8 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, print('indexlist:', indexlist) if max(indexlist) > len(morefrequencies): print('len(morefrequencies):', len(morefrequencies)) + print('morefrequencies:', morefrequencies) + print('indexlist:', indexlist) print('Repeating with finer frequency mesh around frequencies:', morefrequencies[np.sort(indexlist)]) assert min(morefrequencies) >= minfreq @@ -355,6 +358,9 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, try: spacing = abs(morefrequenciesprev[index] - morefrequenciesprev[index-1]) except: + if verbose: + print('morefrequenciesprev:',morefrequenciesprev) + print('index:', index) spacing = abs(morefrequenciesprev[index+1] - morefrequenciesprev[index]) finerlist = np.linspace(max(minfreq,morefrequenciesprev[index]-spacing), min(maxfreq,morefrequenciesprev[index] + spacing), @@ -394,23 +400,26 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, ## find maxima index1 = np.argmax(R1_amp_noiseless) - indexlist1, heights = find_peaks(R1_amp_noiseless, height=.015, distance = 5) - if debug: - print('index1:', index1) - print('indexlist1:',indexlist1) - print('heights', heights) - plt.axvline(morefrequencies[index1]) - for i in indexlist1: - plt.axvline(morefrequencies[i]) - assert index1 <= len(morefrequencies) - if len(indexlist1)>0: - assert max(indexlist1) <= len(morefrequencies) + if not MONOMER and not use_R2_only: + indexlist1, heights = find_peaks(R1_amp_noiseless, height=.015, distance = 5) + if debug: + print('index1:', index1) + print('indexlist1:',indexlist1) + print('heights', heights) + plt.axvline(morefrequencies[index1]) + for i in indexlist1: + plt.axvline(morefrequencies[i]) + assert index1 <= len(morefrequencies) + if len(indexlist1)>0: + assert max(indexlist1) <= len(morefrequencies) + else: + print('Warning: find_peaks on R1_amp returned indexlist:', indexlist1) + plt.figure() + plt.plot(R1_amp_noiseless) + plt.xlabel(R1_amp_noiseless) + plt.figure() else: - print('Warning: find_peaks on R1_amp returned indexlist:', indexlist1) - plt.figure() - plt.plot(R1_amp_noiseless) - plt.xlabel(R1_amp_noiseless) - plt.figure() + indexlist1 = [] if MONOMER: indexlist2 = [] else: @@ -444,7 +453,7 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, assert max(indexlist) <= len(morefrequencies) indexlist = list(np.unique(indexlist)) - + indexlist = [int(index) for index in indexlist] first = False ## Check to see if findpeaks just worked @@ -458,7 +467,7 @@ def res_freq_numeric(vals_set, MONOMER, forceboth, if returnoptions: return opt2freqlist, 2 return opt2freqlist - if len(indexlist1) == 2: + if len(indexlist1) == 2 and not use_R2_only: opt3freqlist = list(np.sort(morefrequencies[indexlist1])) if abs(opt3freqlist[1]-opt3freqlist[0]) > thresh: if verbose: diff --git a/sim_series_of_experiments.py b/sim_series_of_experiments.py index f81e901..71f616f 100644 --- a/sim_series_of_experiments.py +++ b/sim_series_of_experiments.py @@ -23,7 +23,7 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, max_num_p = 10, n = 100, # number of frequencies for R^2 freqdiff = .1,just_res1 = False, repeats = 100, - verbose = False,recalculate_randomness=True, + verbose = False,recalculate_randomness=True, use_R2_only = False, **kwargs ): if True: @@ -36,10 +36,12 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, else: numtoreturn = 2 - ## To be fair for each, I use 3 iterations to really nail down the highest amplitudes. - reslist = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER,forceboth=forceboth, - mode = 'amp', iterations = 3, includefreqs = reslist, - unique = True, veryunique = True, numtoreturn = numtoreturn, verboseplot = False, verbose=verbose) + for i in range(5):## To be fair for each, I use iterations to really nail down the highest amplitudes. + reslist = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER,forceboth=forceboth, + mode = 'amp', iterations = 3, includefreqs = reslist, + unique = True, veryunique = True, numtoreturn = numtoreturn, + use_R2_only = use_R2_only, + verboseplot = False, verbose=verbose) ## measure the top two resonant frequencies res1 = reslist[0] if not MONOMER: @@ -70,8 +72,10 @@ def vary_num_p_with_fixed_freqdiff(vals_set, noiselevel, noiselevel=noiselevel, MONOMER=MONOMER, forceboth=forceboth) for this_num_p in range(2, max_num_p+1): - if this_num_p == max_num_p and y == 0: # first time with all the frequencies + if y == 0 and (this_num_p == max_num_p or this_num_p == 2): # first time with 2 or all the frequencies verbose = True + else: + verbose = False ## Do we recalculate the spectra every time or use the same datapoints as before? (This is slower.) if recalculate_randomness: From 3b09d5b34ac8e54a364c5d8f7e2dcbd2d4e239fa Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 26 Jun 2023 16:13:14 -0400 Subject: [PATCH 046/101] Minor clean up --- ...ach Simulated Two Coupled Resonators.ipynb | 135 ++---------------- 1 file changed, 14 insertions(+), 121 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index d37cd12..e545df8 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -6,10 +6,10 @@ "metadata": {}, "outputs": [], "source": [ - "## Update matplotlib to a version that can label bar graphs.\n", - "#!pip install -U matplotlib --user\n", + "## Update matplotlib\n", + "#%pip install -U matplotlib --user\n", "## Install pydoe2\n", - "#!pip install pyDOE2" + "#%pip install pyDOE2" ] }, { @@ -70,6 +70,8 @@ "metadata": {}, "outputs": [], "source": [ + "## Imports from my py files.\n", + "\n", "from myheatmap import myheatmap\n", "from helperfunctions import flatten,listlength,printtime,make_real_iff_real, \\\n", " store_params, read_params, savefigure, datestring, beep, calc_error_interval\n", @@ -94,23 +96,6 @@ "# When this runs, an empty graph will appear below (because plotcomplex calls canvas.draw)." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"import matplotlib.font_manager # See list of fonts\n", - "matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\"\"\"" - ] - }, { "cell_type": "code", "execution_count": null, @@ -123,16 +108,7 @@ "from simulated_experiment import complexamplitudenoisefactor, use_complexnoise\n", "from resonator_plotting import co1,co2,co3, figwidth # color scheme\n", "\n", - "# Nature says: (https://www.nature.com/npp/authors-and-referees/artwork-figures-tables)\n", - "#Figure width - single image\t86 mm (3.38 in) (should be able to fit into a single column of the printed journal)\n", - "#Figure width - multi-part\t178 mm (7 in) (should be able to fit into a double column of the printed journal)\n", - "#Text size\t8 point (should be readable after reduction - avoid large type or thick lines)\n", - "#Line width\tBetween 0.5 and 1 point\n", - "\n", - "set_format() # displays an empty graph\n", - "\n", - "#plt.figure(figsize = (3.82/2,1))\n", - "#plt.plot(1)" + "set_format() # displays an empty graph" ] }, { @@ -141,6 +117,9 @@ "metadata": {}, "outputs": [], "source": [ + "## Set parameters. We will assume these are in SI units for the purpose of these simultions.\n", + "\n", + "\n", "verbose = False\n", "#MONOMER = False\n", "#forceboth = False\n", @@ -394,6 +373,9 @@ "maxfreq = 150796447 # 21 MHz * (2 * pi) \n", "\"\"\"\n", "\n", + "\n", + "## Make calculations for this resonator system\n", + "\n", "res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set)\n", "\n", "\n", @@ -446,16 +428,7 @@ "#p = range(len(drive))\n", "print('Index of freqs:', p)\n", "\n", - "beep()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# redo in case it was updated\n", + "\n", "vals_set = store_params(m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set, MONOMER=MONOMER)\n", "\n", "R1_amp_noiseless = curve1(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, MONOMER, forceboth=forceboth)\n", @@ -475,27 +448,7 @@ "R1_amp, R1_phase, R2_amp, R2_phase, R1_real_amp, R1_im_amp, R2_real_amp, R2_im_amp, _ = noisyspectra\n", "\n", "\n", - "\n", - "'''def vh_from_vals_set(drive, vals_set, MONOMER, forceboth):\n", - " vals_set = store_params(m1_set, m2_set, b1_set, b2_set, k1_set, k2_set, k12_set, F_set, MONOMER=MONOMER)\n", - " \n", - " R1_amp_noiseless = curve1(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, MONOMER, forceboth = forceboth)\n", - " R1_phase_noiseless = theta1(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, MONOMER, forceboth = forceboth)\n", - " R2_amp_noiseless = curve2(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth = forceboth)\n", - " R2_phase_noiseless = theta2(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth = forceboth)\n", - " R1_real_amp_noiseless = realamp1(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, MONOMER, forceboth = forceboth)\n", - " R1_im_amp_noiseless = imamp1(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, MONOMER, forceboth = forceboth)\n", - " R2_real_amp_noiseless = realamp2(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth = forceboth)\n", - " R2_im_amp_noiseless = imamp2(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth = forceboth)\n", - "\n", - " df = measurementdfcalc(drive, p,R1_amp=R1_amp,R2_amp=R2_amp,R1_phase=R1_phase, R2_phase=R2_phase, \n", - " R1_amp_noiseless=R1_amp_noiseless,R2_amp_noiseless=R2_amp_noiseless,\n", - " R1_phase_noiseless=R1_phase_noiseless, R2_phase_noiseless=R2_phase_noiseless\n", - " )\n", - " Zmatrix = Zmat(df, frequencycolumn = 'drive', complexamplitude1 = 'R1AmpCom', complexamplitude2 = 'R2AmpCom',MONOMER=MONOMER)\n", - " u, s, vh = np.linalg.svd(Zmatrix, full_matrices = True)\n", - " vh = make_real_iff_real(vh)\n", - " return u,s,vh''';\n" + "beep()" ] }, { @@ -531,13 +484,6 @@ "print(method)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -748,13 +694,6 @@ "display(vh)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1238,16 +1177,6 @@ "set_format()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams\n", - "# display settings" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1732,13 +1661,6 @@ " savefigure(savename)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1879,21 +1801,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"syserrlist = [key for key in keylist if 'syserr' in key]\n", - "\n", - "syserrresults = repeatedexptsres[syserrlist] # Do I want violin plots?\n", - "\n", - "sns.violinplot(x=syserrlist,y=syserrresults ,\n", - " fontsize=7, rot=90)\n", - " \"\"\";" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1997,13 +1904,6 @@ "lims" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -9023,13 +8923,6 @@ "sns.heatmap(corr, vmax = 1, vmin=-1, cmap ='PiYG'); # correlation can run from -1 to 1." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, From 7a8e7bc326e6a71d63f93aa25bf2b5499417e8da Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 18 Jan 2024 15:21:54 -0500 Subject: [PATCH 047/101] Create README.md --- README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..6ec6911 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +The related publication is: https://www.nature.com/articles/s41598-023-50089-1. Please cite it if you use this code! From c77983971a00de18960554498d4f63c02abfc839 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 16 Feb 2024 23:36:42 -0500 Subject: [PATCH 048/101] DOC: rename resonatorSVDanalysis.py to NetMAP.py --- resonatorSVDanalysis.py => NetMAP.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) rename resonatorSVDanalysis.py => NetMAP.py (99%) diff --git a/resonatorSVDanalysis.py b/NetMAP.py similarity index 99% rename from resonatorSVDanalysis.py rename to NetMAP.py index dbafb82..ebd0cdd 100644 --- a/resonatorSVDanalysis.py +++ b/NetMAP.py @@ -2,6 +2,8 @@ """ Created on Tue Aug 9 16:50:38 2022 +NetMAP: Create script-Z matrix and find its kernel, or null-space. + @author: vhorowit """ @@ -350,4 +352,4 @@ def normalize_parameters_assuming_3d(vh, vals_set, MONOMER, known1 = None, known if verbose: print('Parameters 3D: ') print(parameters) - return parameters, coefa, coefb, coefc \ No newline at end of file + return parameters, coefa, coefb, coefc From dae6c4770a6219b4a8dac03a79c7a2322a35e239 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 16 Feb 2024 23:37:30 -0500 Subject: [PATCH 049/101] Rename SVD algebraic approach.nb to NetMAP 4-mass.nb --- SVD algebraic approach.nb => NetMAP 4-mass.nb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename SVD algebraic approach.nb => NetMAP 4-mass.nb (100%) diff --git a/SVD algebraic approach.nb b/NetMAP 4-mass.nb similarity index 100% rename from SVD algebraic approach.nb rename to NetMAP 4-mass.nb From 75826cb6fb42695099d4c6355f1b19616d958dbc Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 16 Feb 2024 23:38:24 -0500 Subject: [PATCH 050/101] DOC: comment for resonatorsimulator.py --- resonatorsimulator.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/resonatorsimulator.py b/resonatorsimulator.py index 623e504..7ca4e31 100644 --- a/resonatorsimulator.py +++ b/resonatorsimulator.py @@ -2,6 +2,8 @@ """ Created on Tue Aug 9 16:42:36 2022 +Solve equations of motion using Cramer's rule in order to obtain amplitude and phase of each resonator in the network. + @author: vhorowit """ @@ -575,4 +577,4 @@ def SNRcalc(freq,vals_set, noiselevel, MONOMER, forceboth, plot = False, ax = No # SNR, SNR, signal, noise, signal, noise return SNR_R1,SNR_R2, np.mean(amps1), np.std(amps1), np.mean(amps2), np.std(amps2) else: - return SNR_R1,SNR_R2 \ No newline at end of file + return SNR_R1,SNR_R2 From c6ab954747ff883ae4e67a4aab2fa7715b9ba960 Mon Sep 17 00:00:00 2001 From: vivarose Date: Fri, 16 Feb 2024 23:39:51 -0500 Subject: [PATCH 051/101] DOC: simulated_experiment.py --- simulated_experiment.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/simulated_experiment.py b/simulated_experiment.py index 91b1645..908d971 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -2,7 +2,9 @@ """ Created on Tue Aug 9 16:08:21 2022 -Simulated spectra + SVD recovery +Validating NetMAP: + +Simulated spectra + NetMAP recovery @author: vhorowit """ @@ -653,4 +655,4 @@ def simulated_experiment(measurementfreqs, vals_set, noiselevel, MONOMER, force if return_1D_plot_info: return resultsdf, plot_info_1D else: - return resultsdf \ No newline at end of file + return resultsdf From 558e5912d56b48ed275fdc53e4801af8944638ad Mon Sep 17 00:00:00 2001 From: sjfeldma Date: Tue, 26 Mar 2024 15:17:16 -0400 Subject: [PATCH 052/101] BUILD Initial commit by Sam --- Trimer_NetMAP.py | 47 +++++++ Trimer_curvefit.py | 68 +++++++++++ Trimer_simulator.py | 290 ++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 405 insertions(+) create mode 100644 Trimer_NetMAP.py create mode 100644 Trimer_curvefit.py create mode 100644 Trimer_simulator.py diff --git a/Trimer_NetMAP.py b/Trimer_NetMAP.py new file mode 100644 index 0000000..8a833c1 --- /dev/null +++ b/Trimer_NetMAP.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 22 15:57:10 2024 + +@author: samfeldman +""" +import numpy as np + +def Zmatrix(array, force_all, freq, complexamp1, complexamp2, complexamp3): + result = [] + for rowindex in range(len(array)): + w = freq[rowindex] + Z1 = complexamp1[rowindex] + Z2 = complexamp2[rowindex] + Z3 = complexamp3[rowindex] + + Z1R = np.array([-w**2*np.real(Z1), 0, 0, -w*np.imag(Z1), 0, 0, np.real(Z1), + 0, np.real(Z1)-np.real(Z2), 0, np.real(Z1)-np.real(Z3), -1]) + Z1I = np.array([-w**2*np.imag(Z1), 0, 0, w*np.real(Z1), 0, 0, np.imag(Z1), + np.imag(Z1) - np.imag(Z2), 0, np.imag(Z1)-np.imag(Z3), 0]) + + if force_all: + Z2R = np.array([0, -w**2*np.real(Z2), 0, 0, -w*np.imag(Z2), 0, 0, + np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0, -1]) + else: + Z2R = np.array([0, -w**2*np.real(Z1), 0, 0, -w*np.imag(Z2), 0, 0, + np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0, 0]) + Z2I = np.array([0, -w**2*np.imag(Z2), 0, 0, w*np.real(Z2), 0, 0, + np.imag(Z2)-np.imag(Z1), np.imag(Z2) - np.imag(Z3), 0, 0]) + + if force_all: + Z3R = np.array([0, 0, -w**2*np.real(Z3), 0, 0, -w*np.imag(Z3), 0, 0, + np.real(Z3)-np.real(Z2), np.real(Z3) - np.real(Z1), -1]) + else: + Z3R = np.array([0, 0, -w**2*np.real(Z3), 0, 0, -w*np.imag(Z3), 0, 0, + np.real(Z3)-np.real(Z2), np.real(Z3) - np.real(Z1), 0]) + Z3I = np.array([0, 0, -w**2*np.imag(Z3), 0, 0, w*np.real(Z3), 0, 0, + np.imag(Z3)-np.imag(Z2), np.imag(Z3) - np.imag(Z1), -1]) + + result.append(np.concatenate([Z1R, Z1I, Z2R, Z2I, Z3R, Z3I])) + + Zmatrix = np.array(result) + return Zmatrix + + + diff --git a/Trimer_curvefit.py b/Trimer_curvefit.py new file mode 100644 index 0000000..9dd527c --- /dev/null +++ b/Trimer_curvefit.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 22 15:53:26 2024 + +@author: samfeldman +""" + +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit +from sklearn.metrics import r2_score +from Trimer_simulator import c1 + +freq = np.linspace(.01, 5, 500) +A_squared = c1(freq, 5, 5, 3, 5, .1, .1, .1, 1, 5, 2, 1) + +def curve_func(freq, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return c1(freq, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) + +initial_guess = [5, 5, 3, 5, .1, .1, .1, 1, 5, 2, 1] +# Perform curve fitting +popt, pcov = curve_fit(curve_func, freq, A_squared, p0=initial_guess) + +# Extract fitting constants +k_1_fit, k_2_fit, k_3_fit, k_4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit = popt + +# Print the fitting parameters +print("Fitting Parameters:") +print("k1:", k_1_fit) +print("k2:", k_2_fit) +print("k3:", k_3_fit) +print("k4:", k_4_fit) +print("b1:", b1_fit) +print("b2:", b2_fit) +print("b3:", b3_fit) +print("F:", F_fit) +print("m1:", m1_fit) +print("m2:", m2_fit) +print("m3:", m3_fit) + +# Plotting +plt.figure(figsize=(8, 6)) +plt.scatter(freq, A_squared, label='Original Data') +plt.xlabel('Frequency (f)') +plt.ylabel('A_squared') +plt.title('Curve Fitting with Three Peaks') +plt.grid(True) + +# Generate points for the fitted curve +freq_fit = np.linspace(min(freq), max(freq), 500) +A_squared_fit = curve_func(freq_fit, *popt) + +# Plot the fitted curve +plt.plot(freq_fit, A_squared_fit, color='red', label='Fitted Curve') + +# Add legend +plt.legend() + +# Show plot +plt.show() + +# Calculate R-squared +r_squared = r2_score(A_squared, curve_func(freq, *popt)) + +# Print R-squared value +print("R-squared:", r_squared) + \ No newline at end of file diff --git a/Trimer_simulator.py b/Trimer_simulator.py new file mode 100644 index 0000000..a3bafc4 --- /dev/null +++ b/Trimer_simulator.py @@ -0,0 +1,290 @@ +# -*- coding: utf-8 -*- +""" +Spyder Editor + +This is a temporary script file. +""" + +# Create code that simulates spectrum response for trimer +# See if we can recover the parameters + +import numpy as np +import sympy as sp +# from helperfunctions import read_params, listlength +# from resonatorphysics import amp, complexamp +# import matplotlib.pyplot as plt +# from resonatorstats import rsqrd +# from resonatorphysics import A_from_Z + +# defaults +usenoise = True +use_complexnoise = True + +#Define all variables for sympy + +#individual springs that correspond to individual masses +k1 = sp.symbols('k_1', real = True) + +#springs that connect two masses +k2 = sp.symbols('k_2', real = True) +k3 = sp.symbols('k_3', real = True) +k4 = sp.symbols('k_4', real = True) + +#damping coefficients +b1 = sp.symbols('b1', real = True) +b2 = sp.symbols('b2', real = True) +b3 = sp.symbols('b3', real = True) + +#masses +m1 = sp.symbols('m1', real = True) +m2 = sp.symbols('m2', real = True) +m3 = sp.symbols('m3', real = True) + +#Driving force amplitude +F = sp.symbols('F', real = True) + +#driving frequency (leave as variable) +wd = sp.symbols('\omega_d', real = True) + +#symbolically Solve for driving amplitudes and phase using sympy + +### Dimer +#Matrix for complex equations of motion, Matrix . Zvec = Fvec +unknownsmatrix = sp.Matrix([[-wd**2*m1 + 1j*wd*b1 + k1 + k2 + k4, -k2, -k4], + [-k2, -wd**2*m2 + 1j*wd*b2 + k2 + k3, -k3], + [-k4, -k3, -wd**2*m3 + 1j*wd*b3 + k3 + k4]]) + +#Matrices for Cramer's Rule: substitute force vector Fvec=[F,0] for each column in turn (m1 is driven, m2 is not) +unknownsmatrix1 = sp.Matrix([[F, -k2, -k4], + [0, -wd**2*m2 + 1j*wd*b2 + k2 + k3, -k3], + [0, -k3, -wd**2*m3 + 1j*wd*b3 + k3 + k4]]) +unknownsmatrix2 = sp.Matrix([[-wd**2*m1 + 1j*wd*b1 + k1 + k2 + k4, F, -k4], + [-k2, 0, -k3], + [-k4, 0, -wd**2*m3 + 1j*wd*b3 + k3 + k4]]) +unknownsmatrix3 = sp.Matrix([[-wd**2*m1 + 1j*wd*b1 + k1 + k2 + k4, -k3, F], + [-k2, -wd**2*m2 + 1j*wd*b2 + k2 + k3, 0], + [-k4, -k3, 0]]) + +#Apply Cramer's Rule to solve for Zvec +complexamp1, complexamp2, complexamp3 = (unknownsmatrix1.det()/unknownsmatrix.det(), + unknownsmatrix2.det()/unknownsmatrix.det(), + unknownsmatrix3.det()/unknownsmatrix.det()) + +#Solve for phases for each mass +delta1 = sp.arg(complexamp1) # Returns the argument (phase angle in radians) of a complex number. +delta2 = sp.arg(complexamp2) # sp.re(complexamp2)/sp.cos(delta2) (this is the same thing) +delta3 = sp.arg(complexamp3) + + +### What if we apply the same force to all three masses of dimer? +#Matrices for Cramer's Rule: substitute force vector Fvec=[F,0] for each column in turn (m1 is driven, m2 is not) +unknownsmatrix1FFF = sp.Matrix([[F, -k2, -k4], + [F, -wd**2*m2 + 1j*wd*b2 + k2 + k3, -k3], + [F, -k3, -wd**2*m3 + 1j*wd*b3 + k3 + k4]]) +unknownsmatrix2FFF = sp.Matrix([[-wd**2*m1 + 1j*wd*b1 + k1 + k2 + k4, F, -k4], + [-k2, F, -k3], + [-k4, F, -wd**2*m3 + 1j*wd*b3 + k3 + k4]]) +unknownsmatrix3FFF = sp.Matrix([[-wd**2*m1 + 1j*wd*b1 + k1 + k2 + k4, -k3, F], + [-k2, -wd**2*m2 + 1j*wd*b2 + k2 + k3,F], + [-k4, -k3, F]]) +#Apply Cramer's Rule to solve for Zvec +complexamp1FFF, complexamp2FFF, complexamp3FFF = (unknownsmatrix1FFF.det()/unknownsmatrix.det(), + unknownsmatrix2FFF.det()/unknownsmatrix.det(), + unknownsmatrix3FFF.det()/unknownsmatrix.det()) +#Solve for phases for each mass +delta1FFF = sp.arg(complexamp1FFF) # Returns the argument (phase angle in radians) of a complex number. +delta2FFF = sp.arg(complexamp2FFF) # sp.re(complexamp2)/sp.cos(delta2) (this is the same thing) +delta3FFF = sp.arg(complexamp3FFF) + +### Ampolitude and phase +#Wrap phases for plots + +wrap1 = (delta1)%(2*sp.pi) +wrap2 = (delta2)%(2*sp.pi) +wrap3 = (delta3)%(2*sp.pi) +wrap1FFF = (delta1FFF)%(2*sp.pi) +wrap2FFF = (delta2FFF)%(2*sp.pi) +wrap3FFF = (delta3FFF)%(2*sp.pi) + +#Solve for amplitude coefficients +amp1 = sp.Abs(complexamp1) +amp2 = sp.Abs(complexamp2) +amp3 = sp.Abs(complexamp3) +amp1FFF = sp.Abs(complexamp1FFF) +amp2FFF = sp.Abs(complexamp2FFF) +amp3FFF = sp.Abs(complexamp3FFF) + +#lambdify curves using sympy + +c1 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), amp1) +t1 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), wrap1) + +c2 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), amp2) +t2 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), wrap2) + +c3 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), amp3) +t3 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), wrap3) + +re1 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.re(complexamp1)) +im1 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.im(complexamp1)) +re2 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.re(complexamp2)) +im2 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.im(complexamp2)) +re3 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.re(complexamp3)) +im3 = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.im(complexamp3)) + + +c1FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), amp1FFF) +t1FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), wrap1FFF) + +c2FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), amp2FFF) +t2FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), wrap2FFF) + +c3FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), amp3FFF) +t3FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), wrap3FFF) + +re1FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.re(complexamp1FFF)) +im1FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.im(complexamp1FFF)) +re2FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.re(complexamp2FFF)) +im2FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.im(complexamp2FFF)) +re3FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.re(complexamp3FFF)) +im3FFF = sp.lambdify((wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3), sp.im(complexamp3FFF)) + +#define functions + +#curve = amplitude, theta = phase, e = err (i.e. noise) +def curve1(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return c1FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + else: #force just m1 + return c1(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + +def theta1(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return t1FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + else: #force just m1 + return t1(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + +def curve2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return c2FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + else: #force just m1 + return c2(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + +def theta2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return t2FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + else: #force just m1 + return t2(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + +def curve3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return c3FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + else: #force just m1 + return c3(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + +def theta3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return t3FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + else: #force just m1 + return t3(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + +def realamp1(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return re1FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + else: #force just m1 + return re1(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + +def imamp1(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return im1FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + else: #force just m1 + return im1(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + +def realamp2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return re2FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + else: #force just m1 + return re2(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + +def imamp2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return im2FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + else: #force just m1 + return im2(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + +def realamp3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return re3FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + else: #force just m1 + return re3(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e + +def imamp3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): + with np.errstate(divide='ignore'): + if force_all: + return im3FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + else: #force just m1 + return im3(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + + +def complexamp(A,phi): + return A * np.exp(1j*phi) + +def amp(a,b): + return np.sqrt(a**2 + b**2) + +def A_from_Z(Z): # calculate amplitude of complex number + return amp(Z.real, Z.imag) + +freq = np.linspace(.01,5,500) +Z1 = (complexamp(curve1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False), theta1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False))) +Z2 = (complexamp(curve2(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False), theta2(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False))) +Z3 = (complexamp(curve3(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False), theta3(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False))) + +def Zmatrix(res_list, force_all, freq, complexamp1, complexamp2, complexamp3): + for i in range(res_list): + for rowindex in range(len(array)): + w = freq[rowindex] + Z1 = complexamp1[rowindex] + Z2 = complexamp2[rowindex] + Z3 = complexamp3[rowindex] + + Z1R = np.array([-w**2*np.real(Z1), 0, 0, -w*np.imag(Z1), 0, 0, np.real(Z1), + 0, np.real(Z1)-np.real(Z2), 0, np.real(Z1)-np.real(Z3), -1]) + Z1I = np.array([-w**2*np.imag(Z1), 0, 0, w*np.real(Z1), 0, 0, np.imag(Z1), + np.imag(Z1) - np.imag(Z2), 0, np.imag(Z1)-np.imag(Z3), 0]) + + if force_all: + Z2R = np.array([0, -w**2*np.real(Z2), 0, 0, -w*np.imag(Z2), 0, 0, + np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0, -1]) + else: + Z2R = np.array([0, -w**2*np.real(Z1), 0, 0, -w*np.imag(Z2), 0, 0, + np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0, 0]) + Z2I = np.array([0, -w**2*np.imag(Z2), 0, 0, w*np.real(Z2), 0, 0, + np.imag(Z2)-np.imag(Z1), np.imag(Z2) - np.imag(Z3), 0, 0]) + + if force_all: + Z3R = np.array([0, 0, -w**2*np.real(Z3), 0, 0, -w*np.imag(Z3), 0, 0, + np.real(Z3)-np.real(Z2), np.real(Z3) - np.real(Z1), -1]) + else: + Z3R = np.array([0, 0, -w**2*np.real(Z3), 0, 0, -w*np.imag(Z3), 0, 0, + np.real(Z3)-np.real(Z2), np.real(Z3) - np.real(Z1), 0]) + Z3I = np.array([0, 0, -w**2*np.imag(Z3), 0, 0, w*np.real(Z3), 0, 0, + np.imag(Z3)-np.imag(Z2), np.imag(Z3) - np.imag(Z1), -1]) + + result.append(np.concatenate([Z1R, Z1I, Z2R, Z2I, Z3R, Z3I])) + + Zmatrix = np.array(result) + return Zmatrix + + From f7a9ebf0132f5cfc75687d9659c0f4dfbf5b9471 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 15 Jul 2024 11:13:26 -0400 Subject: [PATCH 053/101] Cleaning Up NetMAP and Writing New Curve Fit Testing Trimer_NetMAP: I fixed the Z-matrix function. The trimer system has three masses and four springs. At the bottom, I used data to find the normalized parameters of the trimer Z-matrix. Trimer_simulator: I practiced simulating data and creating graphs. Trimer_curvefit_lmfit: I created a new file that did trimer curve fitting with lmfit instead of scipy. Finally, I wrote multiple files that continued Sam's curve fit analysis. I analyzed not just the amplitudes (like he did), but also the phase and the real and imaginary parts of each mass. I both randomly changed the initial guess parameters for curve fitting and only changed one parameter at a time. Again, I used lmfit instead of scipy. --- .DS_Store | Bin 0 -> 6148 bytes Curve Fit Testing/.DS_Store | Bin 0 -> 6148 bytes .../Changing One Param - Curve Fit/.DS_Store | Bin 0 -> 6148 bytes .../Changing_k1_M2-Amplitude.xlsx | Bin 0 -> 19957 bytes .../Mass 2 plots - amp/.DS_Store | Bin 0 -> 8196 bytes .../.DS_Store | Bin 0 -> 10244 bytes ...nerating_Random_Params_Imaginary_Part.xlsx | Bin 0 -> 37821 bytes .../Generating_Random_Params_Phase.xlsx | Bin 0 -> 33801 bytes .../Generating_Random_Params_Real_Part.xlsx | Bin 0 -> 37664 bytes Curve Fit Testing/Imaginary_vs_freq_random.py | 167 +++++++++ Curve Fit Testing/Phase_vs_freq_random.py | 169 ++++++++++ Curve Fit Testing/Real_vs_freq_random.py | 168 ++++++++++ Curve Fit Testing/Trimer_simulator.py | 316 ++++++++++++++++++ Curve Fit Testing/Vary_one_initial_guess.py | 238 +++++++++++++ NetMAP.py | 77 ++++- Trimer_NetMAP.py | 98 ++++-- Trimer_curvefit.py | 18 +- Trimer_curvefit_lmfit.py | 96 ++++++ Trimer_simulator.py | 166 +++++---- resonatorsimulator.py | 141 +++++++- 20 files changed, 1535 insertions(+), 119 deletions(-) create mode 100644 .DS_Store create mode 100644 Curve Fit Testing/.DS_Store create mode 100644 Curve Fit Testing/Changing One Param - Curve Fit/.DS_Store create mode 100644 Curve Fit Testing/Changing One Param - Curve Fit/Changing_k1_M2-Amplitude.xlsx create mode 100644 Curve Fit Testing/Changing One Param - Curve Fit/Mass 2 plots - amp/.DS_Store create mode 100644 Curve Fit Testing/Generating Random Params - Curve Fit/.DS_Store create mode 100644 Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Imaginary_Part.xlsx create mode 100644 Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Phase.xlsx create mode 100644 Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Real_Part.xlsx create mode 100644 Curve Fit Testing/Imaginary_vs_freq_random.py create mode 100644 Curve Fit Testing/Phase_vs_freq_random.py create 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(q)jX<)> z;w?l1R+IMG~s}I$5B&qkLMp#@L#HeV1d*PG$3tN)HDi>as}n@H;X_s$>R%j++aYjIpq- zZ#^{3R>8UKrG+Z_-}htg4HqhDs$RqtGs(ojed2W)=^~)6j~+Vlo)g6`ogZO>06#t2 zb-IwFI4-{WDivnfJbxDqe_-)gwf3jR)vKFivgoYEgmUo4Pp0?iT!C0B@JBx{?M`v# zwn5bd?w1y4X<6XXqt&$#x(HB;>em%277nCw4l}Bvgc;Kz6x=Dm**ASm+DJOFfQ)-FjfG=VuKByvXbK6*vYoD6)xRcws$sRPLWts#I>#r@sjaW8v z;z%Opp6aGh(68zh3!`^_(0U;3+u$XKBVrD8sKID&*etF{y6#QOh;59YyRF|Dn4fLk zs5uB~BGWNj{Nh&DHcqZ%A|&*4n1FG;wl6@fcloCX$&{6ddd=aPKLdO&1XXyMkOn{q z`4JrEftgGe>SHvP$?cZ6?vFiN!voVT2>9u`0BtqzWn z4Qiz%W)K$j2NZZ-%45*O(b)%?$^b~)`%%Q4vV5$~%r1b!{qcti?~sD5zEobNwr(CP z4IHs%L-29y*KPZxL8a!C5eE82G=-Sp1wGd;&es;EW|&1@t}?h>2_{O4zEz)lL&l|0 zB`$y`i6xk8CM)$*CtmZd@`FU?FW~GKVPI>;LWz4lLlKEm2h2G)sCT4R&tCu0ctEZx|G$> z7iRRpsaqn}0^O)Xd!h2CvyRJz?9B#N-PVQv;gaykwmMXAfpmA1J&dhJWVn0z{#r6z zy(_iLmz+$7xFWTlE(1%VEFNr(bTxYYlwVj{%}goZ_30?+fgm@#O3@>~<>D^8TKsMU zk(z(jTn)!L(y}ll{TU@sq-pg!y5Pl-nVkPPr_x$7<}Sscor=Bsj1;pSi>|Da?miR@ z1L-v1V~Rz#>Qw0eWyPM5w?8w(J_ray(OA>8kBUj&n$2UrO2mIyHv3?((yvBP)Pt|Wb>Nz%|yus;Q>c@!0!0C!rje|{dXF$maQ>5PZ+96 zc!sEcsR{SskSGEchJ-u0i-3-=r-h8Q9vxeRfnNv{h3yYv1_9;mB=Wb`%LVE9t8V;P zPUx9#8B;=+iGhE*O#DIxhjbCh1kN$R-3|`-a2Gkbog{9j|IO)M6~8H_pns*Dbgno} zIlW==H|3epzfw-NFP^5HuAYBW3{n53{Ibbu@#&iHw|G4CMErjh;Az6?;^;SFGwg4| ze@ms)($g99Zz)H_iS%^-d>U{%BL4;)Jo=aTd|G}w`23bb Date: Mon, 15 Jul 2024 15:19:40 -0400 Subject: [PATCH 054/101] Editing some comments --- Trimer_curvefit_lmfit.py | 2 +- Trimer_simulator.py | 24 ++++++++++++------------ resonatorsimulator.py | 4 ++-- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/Trimer_curvefit_lmfit.py b/Trimer_curvefit_lmfit.py index ae8b351..1d3d54b 100644 --- a/Trimer_curvefit_lmfit.py +++ b/Trimer_curvefit_lmfit.py @@ -22,7 +22,7 @@ def c3_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): #create data for all three amplitudes freq = np.linspace(0, 5, 300) A_c1 = c1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) -# A_c2 = c2_function(frwq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +# A_c2 = c2_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) # A_c3 = c3_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) model1 = Model(c1_function) diff --git a/Trimer_simulator.py b/Trimer_simulator.py index 9cc633d..83af7f1 100644 --- a/Trimer_simulator.py +++ b/Trimer_simulator.py @@ -240,18 +240,18 @@ def imamp3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all ''' Let's create some graphs ''' #Amplitude and phase vs frequency -freq = np.linspace(.01,5,500) -amps1 = curve1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False) -phase1 = theta1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False) -fig, ax1 = plt.subplots() -ax1.plot(freq, amps1,'r-', label='Amplitude') -ax1.set_xlabel('Frequency') -ax1.set_ylabel('Amplitude') -ax2 = ax1.twinx() -ax2.plot(freq, phase1,'b-', label='Phase') -ax2.set_ylabel('Phase') -ax1.legend(loc='upper right') -ax2.legend(loc='center right') +# freq = np.linspace(.01,5,500) +# amps1 = curve1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False) +# phase1 = theta1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False) +# fig, ax1 = plt.subplots() +# ax1.plot(freq, amps1,'r-', label='Amplitude') +# ax1.set_xlabel('Frequency') +# ax1.set_ylabel('Amplitude') +# ax2 = ax1.twinx() +# ax2.plot(freq, phase1,'b-', label='Phase') +# ax2.set_ylabel('Phase') +# ax1.legend(loc='upper right') +# ax2.legend(loc='center right') # #Z_1 - complex plane # realpart1 = realamp1(freq, 1,2,3,4,.5,.5,.5, 1, 2, 3, 4, 0 , False) diff --git a/resonatorsimulator.py b/resonatorsimulator.py index f6db5b9..3e2be9e 100644 --- a/resonatorsimulator.py +++ b/resonatorsimulator.py @@ -485,7 +485,7 @@ def noisyR2ampphase(drive, vals_set, noiselevel, MONOMER, forceboth): return a,p, complexamp(a,p) -""" Simulator privilege to determine SNR (signal to noise ratio?). +""" Simulator privilege to determine SNR. Only one (first) frequency will be used. """ def SNRknown(freq,vals_set, noiselevel, MONOMER, forceboth, use_complexnoise=use_complexnoise, @@ -599,7 +599,7 @@ def SNRcalc(freq,vals_set, noiselevel, MONOMER, forceboth, plot = False, ax = No """ Below, I (Lydia) am practicing using the data to make graphs. - This is a helpful teaching tool that can be used in the tutorial later on. + This is a helpful teaching tool. Comment and uncomment sections to see the graph produced """ From e8c0517077a5a5df50f99e321f88f3c1c4ea6ea1 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 15 Jul 2024 15:54:03 -0400 Subject: [PATCH 055/101] Updated Trimer_curvefit_lmfit Curve fitted amplitude and phase for mass 1 at the same time. --- Trimer_curvefit_lmfit_mass_1.py | 87 +++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 Trimer_curvefit_lmfit_mass_1.py diff --git a/Trimer_curvefit_lmfit_mass_1.py b/Trimer_curvefit_lmfit_mass_1.py new file mode 100644 index 0000000..46dddc7 --- /dev/null +++ b/Trimer_curvefit_lmfit_mass_1.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 8 15:13:48 2024 + +@author: lydiabullock +""" + +import numpy as np +import matplotlib.pyplot as plt +from lmfit import Model +from Trimer_simulator import c1, t1 + +#type of function to fit for all three amplitude curves +def c1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return c1(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) +def t1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return t1(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) + +#create data for all three amplitudes +freq = np.linspace(0.001, 5, 300) +A_c1 = c1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +phase_1 = t1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) + +model1 = Model(c1_function) +model2 = Model(t1_function) + +#make parameters/initial guesses +#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] +initial_guesses = { + 'k_1': 3, + 'k_2': 3, + 'k_3': 3.53, + 'k_4': 0, + 'b1': 1, + 'b2': 0.9, + 'b3': 0.1, + 'F': 1, + 'm1': 5, + 'm2': 5, + 'm3': 4.5 +} + +params1 = model1.make_params(**initial_guesses) +params2 = model2.make_params(**initial_guesses) + +graph1 = model1.fit(A_c1, params1, w=freq) +graph2 = model2.fit(phase_1, params2, w=freq) + +#print(graph1.fit_report()) +#print(graph2.fit_report()) + +##Graph it! + +#generate points for fitted curve +freq_fit = np.linspace(min(freq),max(freq), 500) #more w-values than before +A_c1_fit = graph1.model.func(freq_fit, **graph1.best_values) +phase_1_fit = graph2.model.func(freq_fit, **graph2.best_values) + +#generate points for guessed parameters curve +freq_guess = np.linspace(min(freq),max(freq), 500) +A_c1_guess = c1_function(freq_guess, **initial_guesses) +phase_1_guess = t1_function(freq_guess, **initial_guesses) + +plt.figure(figsize=(8,6)) +fig, ax1 = plt.subplots() +ax2 = ax1.twinx() + +#original data +ax1.plot(freq, A_c1,'ro', label='Amplitude') +ax2.plot(freq, phase_1,'bo', label='Phase') + +#fitted curve +ax1.plot(freq_fit, A_c1_fit, 'm-', label='Fitted Curve Amp 1') +ax2.plot(freq_fit, phase_1_fit, 'g-', label='Fitted Curve Phase 1') + +#guessed parameters curve +ax1.plot(freq_guess, A_c1_guess, linestyle='dashed', color='magenta', label='Guessed Parameters Amp 1') +ax2.plot(freq_guess, phase_1_guess, linestyle='dashed', color='green', label='Guessed Parameters Phase 1') + +#Graph parts +ax1.set_title('Mass 1') +ax1.set_xlabel('Frequency') +ax1.set_ylabel('Amplitude') +ax2.set_ylabel('Phase') +ax1.legend(loc='upper right') +ax2.legend(loc='center right') From 1fe9db5fab8f9212a7667b8d72ecb86bbbf565ec Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 15 Jul 2024 16:10:39 -0400 Subject: [PATCH 056/101] Amplitude and Phase Curve Fit for Trimer Curve fitted amplitude and phase for each mass of the trimer at the same time, on the same graph --- Trimer_curvefit_lmfit_mass_1.py | 32 ++++++------ Trimer_curvefit_lmfit_mass_2.py | 87 +++++++++++++++++++++++++++++++++ Trimer_curvefit_lmfit_mass_3.py | 87 +++++++++++++++++++++++++++++++++ 3 files changed, 190 insertions(+), 16 deletions(-) create mode 100644 Trimer_curvefit_lmfit_mass_2.py create mode 100644 Trimer_curvefit_lmfit_mass_3.py diff --git a/Trimer_curvefit_lmfit_mass_1.py b/Trimer_curvefit_lmfit_mass_1.py index 46dddc7..eb15d0c 100644 --- a/Trimer_curvefit_lmfit_mass_1.py +++ b/Trimer_curvefit_lmfit_mass_1.py @@ -19,8 +19,8 @@ def t1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): #create data for all three amplitudes freq = np.linspace(0.001, 5, 300) -A_c1 = c1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) -phase_1 = t1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +Amp = c1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +Phase = t1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) model1 = Model(c1_function) model2 = Model(t1_function) @@ -44,8 +44,8 @@ def t1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): params1 = model1.make_params(**initial_guesses) params2 = model2.make_params(**initial_guesses) -graph1 = model1.fit(A_c1, params1, w=freq) -graph2 = model2.fit(phase_1, params2, w=freq) +graph1 = model1.fit(Amp, params1, w=freq) +graph2 = model2.fit(Phase, params2, w=freq) #print(graph1.fit_report()) #print(graph2.fit_report()) @@ -54,34 +54,34 @@ def t1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): #generate points for fitted curve freq_fit = np.linspace(min(freq),max(freq), 500) #more w-values than before -A_c1_fit = graph1.model.func(freq_fit, **graph1.best_values) -phase_1_fit = graph2.model.func(freq_fit, **graph2.best_values) +Amp_fit = graph1.model.func(freq_fit, **graph1.best_values) +Phase_fit = graph2.model.func(freq_fit, **graph2.best_values) #generate points for guessed parameters curve freq_guess = np.linspace(min(freq),max(freq), 500) -A_c1_guess = c1_function(freq_guess, **initial_guesses) -phase_1_guess = t1_function(freq_guess, **initial_guesses) +Amp_guess = c1_function(freq_guess, **initial_guesses) +Phase_guess = t1_function(freq_guess, **initial_guesses) plt.figure(figsize=(8,6)) fig, ax1 = plt.subplots() ax2 = ax1.twinx() #original data -ax1.plot(freq, A_c1,'ro', label='Amplitude') -ax2.plot(freq, phase_1,'bo', label='Phase') +ax1.plot(freq, Amp,'ro', label='Amplitude') +ax2.plot(freq, Phase,'bo', label='Phase') #fitted curve -ax1.plot(freq_fit, A_c1_fit, 'm-', label='Fitted Curve Amp 1') -ax2.plot(freq_fit, phase_1_fit, 'g-', label='Fitted Curve Phase 1') +ax1.plot(freq_fit, Amp_fit, 'm-', label='Fitted Curve Amp 1') +ax2.plot(freq_fit, Phase_fit, 'g-', label='Fitted Curve Phase 1') #guessed parameters curve -ax1.plot(freq_guess, A_c1_guess, linestyle='dashed', color='magenta', label='Guessed Parameters Amp 1') -ax2.plot(freq_guess, phase_1_guess, linestyle='dashed', color='green', label='Guessed Parameters Phase 1') +ax1.plot(freq_guess, Amp_guess, linestyle='dashed', color='magenta', label='Guessed Parameters Amp 1') +ax2.plot(freq_guess, Phase_guess, linestyle='dashed', color='green', label='Guessed Parameters Phase 1') #Graph parts ax1.set_title('Mass 1') ax1.set_xlabel('Frequency') ax1.set_ylabel('Amplitude') ax2.set_ylabel('Phase') -ax1.legend(loc='upper right') -ax2.legend(loc='center right') +ax1.legend(loc='center right') +ax2.legend(loc='upper right') diff --git a/Trimer_curvefit_lmfit_mass_2.py b/Trimer_curvefit_lmfit_mass_2.py new file mode 100644 index 0000000..7b81dbc --- /dev/null +++ b/Trimer_curvefit_lmfit_mass_2.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 8 15:13:48 2024 + +@author: lydiabullock +""" + +import numpy as np +import matplotlib.pyplot as plt +from lmfit import Model +from Trimer_simulator import c2, t2 + +#type of function to fit for all three amplitude curves +def c2_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return c2(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) +def t2_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return t2(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) + +#create data for all three amplitudes +freq = np.linspace(0.001, 5, 300) +Amp = c2_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +Phase = t2_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) + +model1 = Model(c2_function) +model2 = Model(t2_function) + +#make parameters/initial guesses +#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] +initial_guesses = { + 'k_1': 3, + 'k_2': 3, + 'k_3': 3.53, + 'k_4': 0, + 'b1': 1, + 'b2': 0.9, + 'b3': 0.1, + 'F': 1, + 'm1': 5, + 'm2': 5, + 'm3': 4.5 +} + +params1 = model1.make_params(**initial_guesses) +params2 = model2.make_params(**initial_guesses) + +graph1 = model1.fit(Amp, params1, w=freq) +graph2 = model2.fit(Phase, params2, w=freq) + +#print(graph1.fit_report()) +#print(graph2.fit_report()) + +##Graph it! + +#generate points for fitted curve +freq_fit = np.linspace(min(freq),max(freq), 500) #more w-values than before +Amp_fit = graph1.model.func(freq_fit, **graph1.best_values) +Phase_fit = graph2.model.func(freq_fit, **graph2.best_values) + +#generate points for guessed parameters curve +freq_guess = np.linspace(min(freq),max(freq), 500) +Amp_guess = c2_function(freq_guess, **initial_guesses) +Phase_guess = t2_function(freq_guess, **initial_guesses) + +plt.figure(figsize=(8,6)) +fig, ax1 = plt.subplots() +ax2 = ax1.twinx() + +#original data +ax1.plot(freq, Amp,'ro', label='Amplitude') +ax2.plot(freq, Phase,'bo', label='Phase') + +#fitted curve +ax1.plot(freq_fit, Amp_fit, 'm-', label='Fitted Curve Amp 2') +ax2.plot(freq_fit, Phase_fit, 'g-', label='Fitted Curve Phase 2') + +#guessed parameters curve +ax1.plot(freq_guess, Amp_guess, linestyle='dashed', color='magenta', label='Guessed Parameters Amp 2') +ax2.plot(freq_guess, Phase_guess, linestyle='dashed', color='green', label='Guessed Parameters Phase 2') + +#Graph parts +ax1.set_title('Mass 2') +ax1.set_xlabel('Frequency') +ax1.set_ylabel('Amplitude') +ax2.set_ylabel('Phase') +ax1.legend(loc='center right') +ax2.legend(loc='upper right') diff --git a/Trimer_curvefit_lmfit_mass_3.py b/Trimer_curvefit_lmfit_mass_3.py new file mode 100644 index 0000000..f56605e --- /dev/null +++ b/Trimer_curvefit_lmfit_mass_3.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 8 15:13:48 2024 + +@author: lydiabullock +""" + +import numpy as np +import matplotlib.pyplot as plt +from lmfit import Model +from Trimer_simulator import c3, t3 + +#type of function to fit for all three amplitude curves +def c3_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return c3(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) +def t3_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): + return t3(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) + +#create data for all three amplitudes +freq = np.linspace(0.001, 5, 300) +Amp = c3_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +Phase = t3_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) + +model1 = Model(c3_function) +model2 = Model(t3_function) + +#make parameters/initial guesses +#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] +initial_guesses = { + 'k_1': 3, + 'k_2': 3, + 'k_3': 3.53, + 'k_4': 0, + 'b1': 1, + 'b2': 0.9, + 'b3': 0.1, + 'F': 1, + 'm1': 5, + 'm2': 5, + 'm3': 4.5 +} + +params1 = model1.make_params(**initial_guesses) +params2 = model2.make_params(**initial_guesses) + +graph1 = model1.fit(Amp, params1, w=freq) +graph2 = model2.fit(Phase, params2, w=freq) + +#print(graph1.fit_report()) +#print(graph2.fit_report()) + +##Graph it! + +#generate points for fitted curve +freq_fit = np.linspace(min(freq),max(freq), 500) #more w-values than before +Amp_fit = graph1.model.func(freq_fit, **graph1.best_values) +Phase_fit = graph2.model.func(freq_fit, **graph2.best_values) + +#generate points for guessed parameters curve +freq_guess = np.linspace(min(freq),max(freq), 500) +Amp_guess = c3_function(freq_guess, **initial_guesses) +Phase_guess = t3_function(freq_guess, **initial_guesses) + +plt.figure(figsize=(8,6)) +fig, ax1 = plt.subplots() +ax2 = ax1.twinx() + +#original data +ax1.plot(freq, Amp,'ro', label='Amplitude') +ax2.plot(freq, Phase,'bo', label='Phase') + +#fitted curve +ax1.plot(freq_fit, Amp_fit, 'm-', label='Fitted Curve Amp') +ax2.plot(freq_fit, Phase_fit, 'g-', label='Fitted Curve Phase') + +#guessed parameters curve +ax1.plot(freq_guess, Amp_guess, linestyle='dashed', color='magenta', label='Guessed Parameters Amp 1') +ax2.plot(freq_guess, Phase_guess, linestyle='dashed', color='green', label='Guessed Parameters Phase 1') + +#Graph parts +ax1.set_title('Mass 3') +ax1.set_xlabel('Frequency') +ax1.set_ylabel('Amplitude') +ax2.set_ylabel('Phase') +ax1.legend(loc='upper right') +ax2.legend(loc='center right') From c87db4c2939217962cc5bc12276078b5a4d43bbe Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 15 Jul 2024 16:59:33 -0400 Subject: [PATCH 057/101] Adding error to data Added error to original data points. Also cleaned up resonatorsimulator error section. --- Trimer_curvefit_lmfit_mass_1.py | 18 +++++++++++++----- Trimer_curvefit_lmfit_mass_2.py | 15 ++++++++++----- Trimer_curvefit_lmfit_mass_3.py | 15 ++++++++++----- resonatorsimulator.py | 21 --------------------- 4 files changed, 33 insertions(+), 36 deletions(-) diff --git a/Trimer_curvefit_lmfit_mass_1.py b/Trimer_curvefit_lmfit_mass_1.py index eb15d0c..0575b0c 100644 --- a/Trimer_curvefit_lmfit_mass_1.py +++ b/Trimer_curvefit_lmfit_mass_1.py @@ -9,19 +9,27 @@ import numpy as np import matplotlib.pyplot as plt from lmfit import Model -from Trimer_simulator import c1, t1 +from Trimer_simulator import c1, t1, curve1, theta1 +from resonatorsimulator import complex_noise -#type of function to fit for all three amplitude curves +#type of function to fit for curves def c1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): return c1(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) def t1_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): return t1(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) -#create data for all three amplitudes +##Create data - functions from simulator code freq = np.linspace(0.001, 5, 300) -Amp = c1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) -Phase = t1_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +force_all = False +#noise +e = complex_noise(300, 2) + +Amp = curve1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase = theta1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + + 2 * np.pi + +#model functions model1 = Model(c1_function) model2 = Model(t1_function) diff --git a/Trimer_curvefit_lmfit_mass_2.py b/Trimer_curvefit_lmfit_mass_2.py index 7b81dbc..ab4f4aa 100644 --- a/Trimer_curvefit_lmfit_mass_2.py +++ b/Trimer_curvefit_lmfit_mass_2.py @@ -9,18 +9,23 @@ import numpy as np import matplotlib.pyplot as plt from lmfit import Model -from Trimer_simulator import c2, t2 +from Trimer_simulator import c2, t2, curve2, theta2 +from resonatorsimulator import complex_noise -#type of function to fit for all three amplitude curves +#type of function to fit for curves def c2_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): return c2(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) def t2_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): return t2(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) -#create data for all three amplitudes +#create data freq = np.linspace(0.001, 5, 300) -Amp = c2_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) -Phase = t2_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +force_all = False +#noise +e = complex_noise(300, 2) + +Amp = curve2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) model1 = Model(c2_function) model2 = Model(t2_function) diff --git a/Trimer_curvefit_lmfit_mass_3.py b/Trimer_curvefit_lmfit_mass_3.py index f56605e..66d99fe 100644 --- a/Trimer_curvefit_lmfit_mass_3.py +++ b/Trimer_curvefit_lmfit_mass_3.py @@ -9,18 +9,23 @@ import numpy as np import matplotlib.pyplot as plt from lmfit import Model -from Trimer_simulator import c3, t3 +from Trimer_simulator import c3, t3, curve3, theta3 +from resonatorsimulator import complex_noise -#type of function to fit for all three amplitude curves +#type of function to fit for curves def c3_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): return c3(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) def t3_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): return t3(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3) -#create data for all three amplitudes +#create data freq = np.linspace(0.001, 5, 300) -Amp = c3_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) -Phase = t3_function(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5) +force_all = False +#noise +e = complex_noise(300, 2) + +Amp = curve3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase = theta3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2 * np.pi model1 = Model(c3_function) model2 = Model(t3_function) diff --git a/resonatorsimulator.py b/resonatorsimulator.py index 3e2be9e..b86b199 100644 --- a/resonatorsimulator.py +++ b/resonatorsimulator.py @@ -326,27 +326,6 @@ def arclength_between_pair(maxamp, Z1, Z2): # calculate signed arclength s = r*theta return s, theta, r - -#define noise (randn(n,) gives a array of normally-distributed random numbers of size n) -# legacy values from before I implemented use_complexnoise. Hold on to them; Brittany was thoughtful about choosing these. -def amp1_noise(n, noiselevel): - global amplitudenoisefactor1 - amplitudenoisefactor1 = 0.005 - return noiselevel* amplitudenoisefactor1 * np.random.randn(n,) -def phase1_noise(n, noiselevel): - global phasenoisefactor1 - phasenoisefactor1 = 0.1 - return noiselevel* phasenoisefactor1 * np.random.randn(n,) -def amp2_noise(n, noiselevel): - global amplitudenoisefactor2 - amplitudenoisefactor2 = 0.0005 - return noiselevel* amplitudenoisefactor2 * np.random.randn(n,) -def phase2_noise(n, noiselevel): - global phasenoisefactor2 - phasenoisefactor2 = 0.2 - return noiselevel* phasenoisefactor2 * np.random.randn(n,) - -""" This is the one I'm actually using """ def complex_noise(n, noiselevel): global complexamplitudenoisefactor From 7e2dc64d85702ac47ce500d70ba1f2d3a6121bc0 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 15 Jul 2024 17:00:56 -0400 Subject: [PATCH 058/101] Update Trimer_curvefit_mass_2 Forgot to add 2*np.pi --- Trimer_curvefit_lmfit_mass_2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Trimer_curvefit_lmfit_mass_2.py b/Trimer_curvefit_lmfit_mass_2.py index ab4f4aa..f6d3618 100644 --- a/Trimer_curvefit_lmfit_mass_2.py +++ b/Trimer_curvefit_lmfit_mass_2.py @@ -25,7 +25,7 @@ def t2_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): e = complex_noise(300, 2) Amp = curve2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Phase = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi model1 = Model(c2_function) model2 = Model(t2_function) From 553ab3367dbc29ca0df7a8a19d90b2ff8b946c9b Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 16 Jul 2024 14:12:25 -0400 Subject: [PATCH 059/101] Created curve_fitting_amp_phase_all I made stack plots for all three masses with amplitude and phase versus frequency. This curve fits all 6 functions at once and shows the resulting parameters. --- .DS_Store | Bin 6148 -> 6148 bytes curve_fitting_amp_phase_all.py | 163 +++++++++++++++++++++++++++++++++ 2 files changed, 163 insertions(+) create mode 100644 curve_fitting_amp_phase_all.py diff --git a/.DS_Store b/.DS_Store index 1f61f003208c913137ea0550b1b8f2aa5f4848d1..0a1115d0210d60d03043449d7610e6cbf7da213f 100644 GIT binary patch delta 32 ocmZoMXfc@J&nU1lU^g?Pz-As6KgP|StaVHi3-mX$bNuB80HfRq`Tzg` delta 43 zcmZoMXfc@J&nUPtU^g?P;AS2cKSs{9l;Y&1{QMlo$@f^*Hb=46F>Pk&_{$FfBu@>G diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py new file mode 100644 index 0000000..8dfec6b --- /dev/null +++ b/curve_fitting_amp_phase_all.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jul 16 11:31:59 2024 + +@author: lydiabullock +""" + +import numpy as np +import matplotlib.pyplot as plt +import lmfit +from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 +from resonatorsimulator import complex_noise + +##Create data - functions from simulator code +freq = np.linspace(0.001, 5, 300) +force_all = False + +#noise +e = complex_noise(300, 2) + +Amp1 = curve1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase1 = theta1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + + 2 * np.pi +Amp2 = curve2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase2 = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + + 2 * np.pi +Amp3 = curve3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Phase3 = theta3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + + 2 * np.pi + +#make parameters/initial guesses +#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] + +params = lmfit.Parameters() +params.add('k1', value = 3) +params.add('k2', value = 3) +params.add('k3', value = 3.009) +params.add('k4', value = 0) +params.add('b1', value = 2) +params.add('b2', value = 1.99) +params.add('b3', value = 2.0076) +params.add('F', value = 1) +params.add('m1', value = 5) +params.add('m2', value = 5) +params.add('m3', value = 4.739) + +#get residuals +def residuals(params, wd, Amp1_data, Phase1_data): #, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): + k1 = params['k1'].value + k2 = params['k2'].value + k3 = params['k3'].value + k4 = params['k4'].value + b1 = params['b1'].value + b2 = params['b2'].value + b3 = params['b3'].value + F = params['F'].value + m1 = params['m1'].value + m2 = params['m2'].value + m3 = params['m3'].value + + modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + # modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + # modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + # modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + # modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + + residc1 = Amp1_data - modelc1 + # residc2 = Amp2_data - modelc2 + # residc3 = Amp3_data - modelc3 + residt1 = Phase1_data - modelt1 + # residt2 = Phase2_data - modelt2 + # residt3 = Phase3_data - modelt3 + + return np.concatenate((residc1, residt1)) #residc3, residt1, residt2, residt3) + + +result = lmfit.minimize(residuals, params, args = (freq, Amp1, Phase1)) +print(lmfit.fit_report(result)) + +#Create fitted y-values and intial guessed y-values +k_1 = result.params['k1'].value +k_2 = result.params['k2'].value +k_3 = result.params['k3'].value +k_4 = result.params['k4'].value +b_1 = result.params['b1'].value +b_2 = result.params['b2'].value +b_3 = result.params['b3'].value +F_ = result.params['F'].value +m_1 = result.params['m1'].value +m_2 = result.params['m2'].value +m_3 = result.params['m3'].value + +c1_fitted = c1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +c2_fitted = c2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +c3_fitted = c3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +t1_fitted = t1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +t2_fitted = t2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +t3_fitted = t3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) + +k1 = params['k1'].value +k2 = params['k2'].value +k3 = params['k3'].value +k4 = params['k4'].value +b1 = params['b1'].value +b2 = params['b2'].value +b3 = params['b3'].value +F = params['F'].value +m1 = params['m1'].value +m2 = params['m2'].value +m3 = params['m3'].value + +c1_guess = c1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +c2_guess = c2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +c3_guess = c3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +t1_guess = t1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +t2_guess = t2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +t3_guess = t3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + +## Begin graphing +fig = plt.figure(figsize=(10,8)) +gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) +((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') + +#original data +ax1.plot(freq, Amp1,'ro',) +ax2.plot(freq, Amp2,'bo') +ax3.plot(freq, Amp3,'go') +ax4.plot(freq, Phase1,'ro') +ax5.plot(freq, Phase2,'bo') +ax6.plot(freq, Phase3,'go') + +#fitted curves +ax1.plot(freq, c1_fitted,'g-', label='Best Fit') +ax2.plot(freq, c2_fitted,'r-', label='Best Fit') +ax3.plot(freq, c3_fitted,'b-', label='Best Fit') +ax4.plot(freq, t1_fitted,'g-', label='Best Fit') +ax5.plot(freq, t2_fitted,'r-', label='Best Fit') +ax6.plot(freq, t3_fitted,'b-', label='Best Fit') + +#inital guess curves +ax1.plot(freq, c1_guess, linestyle='dashed', label='Initial Guess') +ax2.plot(freq, c2_guess, linestyle='dashed', label='Initial Guess') +ax3.plot(freq, c3_guess, linestyle='dashed', label='Initial Guess') +ax4.plot(freq, t1_guess, linestyle='dashed', label='Initial Guess') +ax5.plot(freq, t2_guess, linestyle='dashed', label='Initial Guess') +ax6.plot(freq, t3_guess, linestyle='dashed', label='Initial Guess') + + +#Graph parts +fig.suptitle('Trimer Resonator: Amplitude and Phase') +ax1.set_title('Mass 1') +ax2.set_title('Mass 2') +ax3.set_title('Mass 2') +ax1.set_ylabel('Amplitude') +ax4.set_ylabel('Phase') + +for ax in fig.get_axes(): + ax.set(xlabel='Frequency') + ax.label_outer() +plt.show() + From 23aaf749593907989833b560d653d09669a850c7 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 16 Jul 2024 15:52:33 -0400 Subject: [PATCH 060/101] Created curve_fitting_X_Y_all and Modified curve_fitting_amp_phase_all I attempted to do the same thing with Real and Imaginary parts as I did with Amplitude and Phase. But it's still not quite right. I also modified the Amplitude and Phase curve fit so that the parameters cannot be fit to something below 0. --- Curve Fit Testing/.DS_Store | Bin 6148 -> 6148 bytes curve_fitting_X_Y_all.py | 162 +++++++++++++++++++++++++++++++++ curve_fitting_amp_phase_all.py | 50 +++++----- 3 files changed, 187 insertions(+), 25 deletions(-) create mode 100644 curve_fitting_X_Y_all.py diff --git a/Curve Fit Testing/.DS_Store b/Curve Fit Testing/.DS_Store index c9a881cb414ac99ddd55a03ed3b6a8bd0ce809cc..c00734aac3611c69da7ae4426c5d22f49e72bf3f 100644 GIT binary patch delta 40 wcmZoMXfc@J&&aVcU^g=($7Ekthskzqyqg!XxHE3HV>`(>v0>h3c84xDKz#mPze`8fLDS$#GyU_HsW JnVsV=KLEm|74`rC diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py new file mode 100644 index 0000000..4a46915 --- /dev/null +++ b/curve_fitting_X_Y_all.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jul 16 11:31:59 2024 + +@author: lydiabullock +""" + +import numpy as np +import matplotlib.pyplot as plt +import lmfit +from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 +from resonatorsimulator import complex_noise + +##Create data - functions from simulator code +freq = np.linspace(0.001, 5, 300) +force_all = False + +#noise +e = complex_noise(300, 2) + +X1 = realamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Y1 = imamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi + +X2 = realamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Y2 = imamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi + +X3 = realamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Y3 = imamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi + +#make parameters/initial guesses +#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] + +params = lmfit.Parameters() +params.add('k1', value = 3, min=0) +params.add('k2', value = 3, min=0) +params.add('k3', value = 3.009, min=0) +params.add('k4', value = 0, min=0) +params.add('b1', value = 2, min=0) +params.add('b2', value = 1.99, min=0) +params.add('b3', value = 2.006, min=0) +params.add('F', value = 1, min=0) +params.add('m1', value = 5, min=0) +params.add('m2', value = 5.1568, min=0) +params.add('m3', value = 4.739, min=0) + +#get residuals +def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): + k1 = params['k1'].value + k2 = params['k2'].value + k3 = params['k3'].value + k4 = params['k4'].value + b1 = params['b1'].value + b2 = params['b2'].value + b3 = params['b3'].value + F = params['F'].value + m1 = params['m1'].value + m2 = params['m2'].value + m3 = params['m3'].value + + modelre1 = re1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelre2 = re2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelre3 = re3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelim1 = im1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelim2 = im2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelim3 = im3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + + residX1 = X1_data - modelre1 + residX2 = X2_data - modelre2 + residX3 = X3_data - modelre3 + residY1 = Y1_data - modelim1 + residY2 = Y2_data - modelim2 + residY3 = Y3_data - modelim3 + + return np.concatenate((residX1, residX2, residX3, residY1, residY2, residY3)) + + +result = lmfit.minimize(residuals, params, args = (freq, X1, X2, X3, Y1, Y2, Y3)) +print(lmfit.fit_report(result)) + +#Create fitted y-values and intial guessed y-values +k_1 = result.params['k1'].value +k_2 = result.params['k2'].value +k_3 = result.params['k3'].value +k_4 = result.params['k4'].value +b_1 = result.params['b1'].value +b_2 = result.params['b2'].value +b_3 = result.params['b3'].value +F_ = result.params['F'].value +m_1 = result.params['m1'].value +m_2 = result.params['m2'].value +m_3 = result.params['m3'].value + +re1_fitted = re1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +re2_fitted = re2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +re3_fitted = re3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +im1_fitted = im1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +im2_fitted = im2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) +im3_fitted = im3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) + +k1 = params['k1'].value +k2 = params['k2'].value +k3 = params['k3'].value +k4 = params['k4'].value +b1 = params['b1'].value +b2 = params['b2'].value +b3 = params['b3'].value +F = params['F'].value +m1 = params['m1'].value +m2 = params['m2'].value +m3 = params['m3'].value + +re1_guess = re1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +re2_guess = re2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +re3_guess = re3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +im1_guess = im1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +im2_guess = im2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +im3_guess = im3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + +## Begin graphing +fig = plt.figure(figsize=(10,6)) +gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) +((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') + +#original data +ax1.plot(freq, X1,'ro',) +ax2.plot(freq, X2,'bo') +ax3.plot(freq, X3,'go') +ax4.plot(freq, Y1,'ro') +ax5.plot(freq, Y2,'bo') +ax6.plot(freq, Y3,'go') + +#fitted curves +ax1.plot(freq, re1_fitted,'g-', label='Best Fit') +ax2.plot(freq, re2_fitted,'r-', label='Best Fit') +ax3.plot(freq, re3_fitted,'b-', label='Best Fit') +ax4.plot(freq, im1_fitted,'g-', label='Best Fit') +ax5.plot(freq, im2_fitted,'r-', label='Best Fit') +ax6.plot(freq, im3_fitted,'b-', label='Best Fit') + +#inital guess curves +ax1.plot(freq, re1_guess, linestyle='dashed', label='Initial Guess') +ax2.plot(freq, re2_guess, linestyle='dashed', label='Initial Guess') +ax3.plot(freq, re3_guess, linestyle='dashed', label='Initial Guess') +ax4.plot(freq, im1_guess, linestyle='dashed', label='Initial Guess') +ax5.plot(freq, im2_guess, linestyle='dashed', label='Initial Guess') +ax6.plot(freq, im3_guess, linestyle='dashed', label='Initial Guess') + + +#Graph parts +fig.suptitle('Trimer Resonator: Real and Imaginary') +ax1.set_title('Mass 1') +ax2.set_title('Mass 2') +ax3.set_title('Mass 2') +ax1.set_ylabel('Real') +ax4.set_ylabel('Imaginary') + +for ax in fig.get_axes(): + ax.set(xlabel='Frequency') + ax.label_outer() +plt.show() + diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 8dfec6b..33fe75c 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -13,7 +13,7 @@ from resonatorsimulator import complex_noise ##Create data - functions from simulator code -freq = np.linspace(0.001, 5, 300) +freq = np.linspace(0.001, 4, 300) force_all = False #noise @@ -33,20 +33,20 @@ #true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] params = lmfit.Parameters() -params.add('k1', value = 3) -params.add('k2', value = 3) -params.add('k3', value = 3.009) -params.add('k4', value = 0) -params.add('b1', value = 2) -params.add('b2', value = 1.99) -params.add('b3', value = 2.0076) -params.add('F', value = 1) -params.add('m1', value = 5) -params.add('m2', value = 5) -params.add('m3', value = 4.739) +params.add('k1', value = 3, min=0) +params.add('k2', value = 3, min=0) +params.add('k3', value = 3.109, min=0) +params.add('k4', value = 0, min=0) +params.add('b1', value = 2, min=0) +params.add('b2', value = 1.99, min=0) +params.add('b3', value = 2.76, min=0) +params.add('F', value = 1, min=0) +params.add('m1', value = 5, min=0) +params.add('m2', value = 5.1568, min=0) +params.add('m3', value = 4.739, min=0) #get residuals -def residuals(params, wd, Amp1_data, Phase1_data): #, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): +def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value @@ -60,23 +60,23 @@ def residuals(params, wd, Amp1_data, Phase1_data): #, Amp2_data, Amp3_data, Phas m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - # modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - # modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - # modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - # modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 - # residc2 = Amp2_data - modelc2 - # residc3 = Amp3_data - modelc3 + residc2 = Amp2_data - modelc2 + residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 - # residt2 = Phase2_data - modelt2 - # residt3 = Phase3_data - modelt3 + residt2 = Phase2_data - modelt2 + residt3 = Phase3_data - modelt3 - return np.concatenate((residc1, residt1)) #residc3, residt1, residt2, residt3) + return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) -result = lmfit.minimize(residuals, params, args = (freq, Amp1, Phase1)) +result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create fitted y-values and intial guessed y-values @@ -119,7 +119,7 @@ def residuals(params, wd, Amp1_data, Phase1_data): #, Amp2_data, Amp3_data, Phas t3_guess = t3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) ## Begin graphing -fig = plt.figure(figsize=(10,8)) +fig = plt.figure(figsize=(10,6)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') @@ -152,7 +152,7 @@ def residuals(params, wd, Amp1_data, Phase1_data): #, Amp2_data, Amp3_data, Phas fig.suptitle('Trimer Resonator: Amplitude and Phase') ax1.set_title('Mass 1') ax2.set_title('Mass 2') -ax3.set_title('Mass 2') +ax3.set_title('Mass 3') ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') From 1547feb1f699f6cf6a9a7e5dcd775ef9c8e86eab Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 16 Jul 2024 17:27:25 -0400 Subject: [PATCH 061/101] Update: Fixing bug There was a bug in Trimer_simulator that we found. Now the curve fitting should work. There are still flaws in curve_fitting_X_Y_all because I'm still messing around with the plots. --- Trimer_simulator.py | 12 +-- curve_fitting_X_Y_all.py | 37 +++++--- curve_fitting_amp_phase_all.py | 164 +-------------------------------- 3 files changed, 33 insertions(+), 180 deletions(-) diff --git a/Trimer_simulator.py b/Trimer_simulator.py index 83af7f1..3e7c248 100644 --- a/Trimer_simulator.py +++ b/Trimer_simulator.py @@ -204,9 +204,9 @@ def realamp1(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_a def imamp1(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): with np.errstate(divide='ignore'): if force_all: - return im1FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + return im1FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e else: #force just m1 - return im1(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + return im1(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e def realamp2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): with np.errstate(divide='ignore'): @@ -218,9 +218,9 @@ def realamp2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_a def imamp2(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): with np.errstate(divide='ignore'): if force_all: - return im2FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + return im2FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e else: #force just m1 - return im2(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + return im2(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e def realamp3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): with np.errstate(divide='ignore'): @@ -232,9 +232,9 @@ def realamp3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_a def imamp3(w, k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3, e, force_all): with np.errstate(divide='ignore'): if force_all: - return im3FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + return im3FFF(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e else: #force just m1 - return im3(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) - 2*np.pi + e + return im3(np.array(w), k_1, k_2, k_3, k_4, b1_, b2_, b_3, F_, m_1, m_2, m_3) + e ''' Let's create some graphs ''' diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index 4a46915..90ad065 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -20,29 +20,29 @@ e = complex_noise(300, 2) X1 = realamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y1 = imamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi +Y1 = imamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) X2 = realamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y2 = imamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi +Y2 = imamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) X3 = realamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y3 = imamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) + 2*np.pi +Y3 = imamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) #make parameters/initial guesses #true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] params = lmfit.Parameters() params.add('k1', value = 3, min=0) -params.add('k2', value = 3, min=0) +params.add('k2', value = 3.864, min=0) params.add('k3', value = 3.009, min=0) -params.add('k4', value = 0, min=0) +params.add('k4', value = 0.008, min=0) params.add('b1', value = 2, min=0) params.add('b2', value = 1.99, min=0) params.add('b3', value = 2.006, min=0) -params.add('F', value = 1, min=0) +params.add('F', value = 1.0021, min=0) params.add('m1', value = 5, min=0) params.add('m2', value = 5.1568, min=0) -params.add('m3', value = 4.739, min=0) +params.add('m3', value = 4.0639, min=0) #get residuals def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): @@ -119,8 +119,16 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): ## Begin graphing fig = plt.figure(figsize=(10,6)) -gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) -((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') +gs = fig.add_gridspec(3, 3, hspace=0.1, wspace=0.1) +ax1 = fig.add_subplot(gs[0, 0], sharey = 'row') +ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey = 'row') +ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey = 'row') +ax4 = fig.add_subplot(gs[1, 0], sharex=ax1, sharey = 'row') +ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey = 'row') +ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey = 'row') +ax7 = fig.add_subplot(gs[2, 0], sharey = 'row') +ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey = 'row') +ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey = 'row') #original data ax1.plot(freq, X1,'ro',) @@ -129,6 +137,9 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): ax4.plot(freq, Y1,'ro') ax5.plot(freq, Y2,'bo') ax6.plot(freq, Y3,'go') +ax7.plot(X1,Y1,'ro') +ax8.plot(X2,Y2,'bo') +ax9.plot(X3,Y3,'go') #fitted curves ax1.plot(freq, re1_fitted,'g-', label='Best Fit') @@ -138,6 +149,7 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): ax5.plot(freq, im2_fitted,'r-', label='Best Fit') ax6.plot(freq, im3_fitted,'b-', label='Best Fit') + #inital guess curves ax1.plot(freq, re1_guess, linestyle='dashed', label='Initial Guess') ax2.plot(freq, re2_guess, linestyle='dashed', label='Initial Guess') @@ -151,12 +163,15 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): fig.suptitle('Trimer Resonator: Real and Imaginary') ax1.set_title('Mass 1') ax2.set_title('Mass 2') -ax3.set_title('Mass 2') +ax3.set_title('Mass 3') ax1.set_ylabel('Real') ax4.set_ylabel('Imaginary') for ax in fig.get_axes(): - ax.set(xlabel='Frequency') ax.label_outer() + +ax4.set_xlabel('Frequency') +ax5.set_xlabel('Frequency') +ax6.set_xlabel('Frequency') plt.show() diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 33fe75c..1c83ff0 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1,163 +1 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jul 16 11:31:59 2024 - -@author: lydiabullock -""" - -import numpy as np -import matplotlib.pyplot as plt -import lmfit -from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 -from resonatorsimulator import complex_noise - -##Create data - functions from simulator code -freq = np.linspace(0.001, 4, 300) -force_all = False - -#noise -e = complex_noise(300, 2) - -Amp1 = curve1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Phase1 = theta1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ - + 2 * np.pi -Amp2 = curve2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Phase2 = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ - + 2 * np.pi -Amp3 = curve3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Phase3 = theta3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ - + 2 * np.pi - -#make parameters/initial guesses -#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] - -params = lmfit.Parameters() -params.add('k1', value = 3, min=0) -params.add('k2', value = 3, min=0) -params.add('k3', value = 3.109, min=0) -params.add('k4', value = 0, min=0) -params.add('b1', value = 2, min=0) -params.add('b2', value = 1.99, min=0) -params.add('b3', value = 2.76, min=0) -params.add('F', value = 1, min=0) -params.add('m1', value = 5, min=0) -params.add('m2', value = 5.1568, min=0) -params.add('m3', value = 4.739, min=0) - -#get residuals -def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): - k1 = params['k1'].value - k2 = params['k2'].value - k3 = params['k3'].value - k4 = params['k4'].value - b1 = params['b1'].value - b2 = params['b2'].value - b3 = params['b3'].value - F = params['F'].value - m1 = params['m1'].value - m2 = params['m2'].value - m3 = params['m3'].value - - modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - - residc1 = Amp1_data - modelc1 - residc2 = Amp2_data - modelc2 - residc3 = Amp3_data - modelc3 - residt1 = Phase1_data - modelt1 - residt2 = Phase2_data - modelt2 - residt3 = Phase3_data - modelt3 - - return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) - - -result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) -print(lmfit.fit_report(result)) - -#Create fitted y-values and intial guessed y-values -k_1 = result.params['k1'].value -k_2 = result.params['k2'].value -k_3 = result.params['k3'].value -k_4 = result.params['k4'].value -b_1 = result.params['b1'].value -b_2 = result.params['b2'].value -b_3 = result.params['b3'].value -F_ = result.params['F'].value -m_1 = result.params['m1'].value -m_2 = result.params['m2'].value -m_3 = result.params['m3'].value - -c1_fitted = c1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -c2_fitted = c2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -c3_fitted = c3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -t1_fitted = t1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -t2_fitted = t2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -t3_fitted = t3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) - -k1 = params['k1'].value -k2 = params['k2'].value -k3 = params['k3'].value -k4 = params['k4'].value -b1 = params['b1'].value -b2 = params['b2'].value -b3 = params['b3'].value -F = params['F'].value -m1 = params['m1'].value -m2 = params['m2'].value -m3 = params['m3'].value - -c1_guess = c1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -c2_guess = c2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -c3_guess = c3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -t1_guess = t1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -t2_guess = t2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -t3_guess = t3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) - -## Begin graphing -fig = plt.figure(figsize=(10,6)) -gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) -((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') - -#original data -ax1.plot(freq, Amp1,'ro',) -ax2.plot(freq, Amp2,'bo') -ax3.plot(freq, Amp3,'go') -ax4.plot(freq, Phase1,'ro') -ax5.plot(freq, Phase2,'bo') -ax6.plot(freq, Phase3,'go') - -#fitted curves -ax1.plot(freq, c1_fitted,'g-', label='Best Fit') -ax2.plot(freq, c2_fitted,'r-', label='Best Fit') -ax3.plot(freq, c3_fitted,'b-', label='Best Fit') -ax4.plot(freq, t1_fitted,'g-', label='Best Fit') -ax5.plot(freq, t2_fitted,'r-', label='Best Fit') -ax6.plot(freq, t3_fitted,'b-', label='Best Fit') - -#inital guess curves -ax1.plot(freq, c1_guess, linestyle='dashed', label='Initial Guess') -ax2.plot(freq, c2_guess, linestyle='dashed', label='Initial Guess') -ax3.plot(freq, c3_guess, linestyle='dashed', label='Initial Guess') -ax4.plot(freq, t1_guess, linestyle='dashed', label='Initial Guess') -ax5.plot(freq, t2_guess, linestyle='dashed', label='Initial Guess') -ax6.plot(freq, t3_guess, linestyle='dashed', label='Initial Guess') - - -#Graph parts -fig.suptitle('Trimer Resonator: Amplitude and Phase') -ax1.set_title('Mass 1') -ax2.set_title('Mass 2') -ax3.set_title('Mass 3') -ax1.set_ylabel('Amplitude') -ax4.set_ylabel('Phase') - -for ax in fig.get_axes(): - ax.set(xlabel='Frequency') - ax.label_outer() -plt.show() - +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorsimulator import complex_noise from resonatorstats import rsqrd ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) force_all = False #noise e = complex_noise(300, 2) Amp1 = curve1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase1 = theta1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase2 = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase3 = theta3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi #make parameters/initial guesses #true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] params = lmfit.Parameters() params.add('k1', value = 3, min=0) params.add('k2', value = 3, min=0) params.add('k3', value = 3.109, min=0) params.add('k4', value = 0, min=0) params.add('b1', value = 2, min=0) params.add('b2', value = 1.99, min=0) params.add('b3', value = 2.76, min=0) params.add('F', value = 1, min=0) params.add('m1', value = 5, min=0) params.add('m2', value = 5.1568, min=0) params.add('m3', value = 4.739, min=0) #get residuals def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create fitted y-values and intial guessed y-values k_1 = result.params['k1'].value k_2 = result.params['k2'].value k_3 = result.params['k3'].value k_4 = result.params['k4'].value b_1 = result.params['b1'].value b_2 = result.params['b2'].value b_3 = result.params['b3'].value F_ = result.params['F'].value m_1 = result.params['m1'].value m_2 = result.params['m2'].value m_3 = result.params['m3'].value c1_fitted = c1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) c2_fitted = c2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) c3_fitted = c3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) t1_fitted = t1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) t2_fitted = t2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) t3_fitted = t3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value c1_guess = c1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) c2_guess = c2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) c3_guess = c3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) t1_guess = t1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) t2_guess = t2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) t3_guess = t3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) ## Begin graphing fig = plt.figure(figsize=(10,6)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro',) ax2.plot(freq, Amp2,'bo') ax3.plot(freq, Amp3,'go') ax4.plot(freq, Phase1,'ro') ax5.plot(freq, Phase2,'bo') ax6.plot(freq, Phase3,'go') #fitted curves ax1.plot(freq, c1_fitted,'g-', label='Best Fit') ax2.plot(freq, c2_fitted,'r-', label='Best Fit') ax3.plot(freq, c3_fitted,'b-', label='Best Fit') ax4.plot(freq, t1_fitted,'g-', label='Best Fit') ax5.plot(freq, t2_fitted,'r-', label='Best Fit') ax6.plot(freq, t3_fitted,'b-', label='Best Fit') #inital guess curves ax1.plot(freq, c1_guess, linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase') ax1.set_title('Mass 1') ax2.set_title('Mass 2') ax3.set_title('Mass 3') ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() plt.show() #Find R^2 \ No newline at end of file From d39eb2196b589f64e5181cbdec8f5748e682254f Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 17 Jul 2024 11:18:59 -0400 Subject: [PATCH 062/101] Calculated Systematic Error for Multiple Curve Fitting I created data frames to store guessed parameters, recovered parameters, and systematic error of each recovered parameter. Can now move on to running multiple times and storing in spreadsheet. --- .DS_Store | Bin 6148 -> 6148 bytes curve_fitting_X_Y_all.py | 170 ++++++++++++++++++++------------- curve_fitting_amp_phase_all.py | 2 +- 3 files changed, 107 insertions(+), 65 deletions(-) diff --git a/.DS_Store b/.DS_Store index 0a1115d0210d60d03043449d7610e6cbf7da213f..1f61f003208c913137ea0550b1b8f2aa5f4848d1 100644 GIT binary patch delta 43 zcmZoMXfc@J&nUPtU^g?P;AS2cKSs{9l;Y&1{QMlo$@f^*Hb=46F>Pk&_{$FfBu@>G delta 32 ocmZoMXfc@J&nU1lU^g?Pz-As6KgP|StaVHi3-mX$bNuB80HfRq`Tzg` diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index 90ad065..807497b 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -11,6 +11,7 @@ import lmfit from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 from resonatorsimulator import complex_noise +from resonatorstats import rsqrd, syserr ##Create data - functions from simulator code freq = np.linspace(0.001, 5, 300) @@ -19,30 +20,32 @@ #noise e = complex_noise(300, 2) -X1 = realamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y1 = imamp1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +X1 = realamp1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Y1 = imamp1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) -X2 = realamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y2 = imamp2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +X2 = realamp2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Y2 = imamp2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) -X3 = realamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y3 = imamp3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) +X3 = realamp3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) +Y3 = imamp3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) #make parameters/initial guesses -#true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] +true_params = {'k1': 3, 'k2': 3, 'k3': 3, 'k4': 0.5, + 'b1': 2, 'b2': 2, 'b3': 2, 'F': 1, + 'm1': 5, 'm2': 5, 'm3': 5} params = lmfit.Parameters() params.add('k1', value = 3, min=0) -params.add('k2', value = 3.864, min=0) -params.add('k3', value = 3.009, min=0) -params.add('k4', value = 0.008, min=0) +params.add('k2', value = 3, min=0) +params.add('k3', value = 3.109, min=0) +params.add('k4', value = 0.47, min=0) params.add('b1', value = 2, min=0) params.add('b2', value = 1.99, min=0) -params.add('b3', value = 2.006, min=0) -params.add('F', value = 1.0021, min=0) +params.add('b3', value = 2.76, min=0) +params.add('F', value = 1, min=0) params.add('m1', value = 5, min=0) params.add('m2', value = 5.1568, min=0) -params.add('m3', value = 4.0639, min=0) +params.add('m3', value = 4.739, min=0) #get residuals def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): @@ -79,56 +82,57 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): print(lmfit.fit_report(result)) #Create fitted y-values and intial guessed y-values -k_1 = result.params['k1'].value -k_2 = result.params['k2'].value -k_3 = result.params['k3'].value -k_4 = result.params['k4'].value -b_1 = result.params['b1'].value -b_2 = result.params['b2'].value -b_3 = result.params['b3'].value -F_ = result.params['F'].value -m_1 = result.params['m1'].value -m_2 = result.params['m2'].value -m_3 = result.params['m3'].value - -re1_fitted = re1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -re2_fitted = re2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -re3_fitted = re3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -im1_fitted = im1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -im2_fitted = im2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) -im3_fitted = im3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) - -k1 = params['k1'].value -k2 = params['k2'].value -k3 = params['k3'].value -k4 = params['k4'].value -b1 = params['b1'].value -b2 = params['b2'].value -b3 = params['b3'].value -F = params['F'].value -m1 = params['m1'].value -m2 = params['m2'].value -m3 = params['m3'].value - -re1_guess = re1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -re2_guess = re2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -re3_guess = re3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -im1_guess = im1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -im2_guess = im2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) -im3_guess = im3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) +k1_fit = result.params['k1'].value +k2_fit = result.params['k2'].value +k3_fit = result.params['k3'].value +k4_fit = result.params['k4'].value +b1_fit = result.params['b1'].value +b2_fit = result.params['b2'].value +b3_fit = result.params['b3'].value +F_fit = result.params['F'].value +m1_fit = result.params['m1'].value +m2_fit = result.params['m2'].value +m3_fit= result.params['m3'].value + +re1_fitted = re1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) +re2_fitted = re2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) +re3_fitted = re3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) +im1_fitted = im1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) +im2_fitted = im2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) +im3_fitted = im3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + +k1_guess = params['k1'].value +k2_guess = params['k2'].value +k3_guess = params['k3'].value +k4_guess = params['k4'].value +b1_guess = params['b1'].value +b2_guess = params['b2'].value +b3_guess = params['b3'].value +F_guess = params['F'].value +m1_guess = params['m1'].value +m2_guess = params['m2'].value +m3_guess = params['m3'].value + +re1_guess = re1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) +re2_guess = re2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) +re3_guess = re3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) +im1_guess = im1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) +im2_guess = im2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) +im3_guess = im3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing -fig = plt.figure(figsize=(10,6)) -gs = fig.add_gridspec(3, 3, hspace=0.1, wspace=0.1) -ax1 = fig.add_subplot(gs[0, 0], sharey = 'row') -ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey = 'row') -ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey = 'row') -ax4 = fig.add_subplot(gs[1, 0], sharex=ax1, sharey = 'row') -ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey = 'row') -ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey = 'row') -ax7 = fig.add_subplot(gs[2, 0], sharey = 'row') -ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey = 'row') -ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey = 'row') +fig = plt.figure(figsize=(11,7)) +gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.05) + +ax1 = fig.add_subplot(gs[0, 0]) +ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) +ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) +ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) +ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) +ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) +ax7 = fig.add_subplot(gs[2, 0]) +ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7) +ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7) #original data ax1.plot(freq, X1,'ro',) @@ -166,12 +170,50 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): ax3.set_title('Mass 3') ax1.set_ylabel('Real') ax4.set_ylabel('Imaginary') - -for ax in fig.get_axes(): - ax.label_outer() +ax7.set_ylabel('Imaginary') + +ax1.label_outer() +ax2.label_outer() +ax3.label_outer() +ax5.tick_params(labelleft=False) +ax6.tick_params(labelleft=False) +ax7.label_outer() +ax8.label_outer() +ax9.label_outer() ax4.set_xlabel('Frequency') ax5.set_xlabel('Frequency') ax6.set_xlabel('Frequency') +ax7.set_xlabel('Real') +ax8.set_xlabel('Real') +ax9.set_xlabel('Real') + plt.show() +#create dictionary for storing data +data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], + 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], + 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], + 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], + 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], + 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], + 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], + 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], + 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} + +for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: + #Add guessed parameters to dataframe + param_guess = params[param_name].value + data[f'{param_name}_guess'].append(param_guess) + + #Add fitted parameters to dataframe + param_fit = result.params[param_name].value + data[f'{param_name}_recovered'].append(param_fit) + + #Calculate systematic error and add to dataframe + param_true = true_params[param_name] + systematic_error = syserr(param_fit, param_true) + data[f'syserr_{param_name}'].append(systematic_error) + +print(data) + diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 1c83ff0..cbbec94 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorsimulator import complex_noise from resonatorstats import rsqrd ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) force_all = False #noise e = complex_noise(300, 2) Amp1 = curve1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase1 = theta1(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase2 = theta2(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase3 = theta3(freq, 3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi #make parameters/initial guesses #true parameters = [3, 3, 3, 0, 2, 2, 2, 1, 5, 5, 5] params = lmfit.Parameters() params.add('k1', value = 3, min=0) params.add('k2', value = 3, min=0) params.add('k3', value = 3.109, min=0) params.add('k4', value = 0, min=0) params.add('b1', value = 2, min=0) params.add('b2', value = 1.99, min=0) params.add('b3', value = 2.76, min=0) params.add('F', value = 1, min=0) params.add('m1', value = 5, min=0) params.add('m2', value = 5.1568, min=0) params.add('m3', value = 4.739, min=0) #get residuals def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create fitted y-values and intial guessed y-values k_1 = result.params['k1'].value k_2 = result.params['k2'].value k_3 = result.params['k3'].value k_4 = result.params['k4'].value b_1 = result.params['b1'].value b_2 = result.params['b2'].value b_3 = result.params['b3'].value F_ = result.params['F'].value m_1 = result.params['m1'].value m_2 = result.params['m2'].value m_3 = result.params['m3'].value c1_fitted = c1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) c2_fitted = c2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) c3_fitted = c3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) t1_fitted = t1(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) t2_fitted = t2(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) t3_fitted = t3(freq, k_1, k_2, k_3, k_4, b_1, b_2, b_3, F_, m_1, m_2, m_3) k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value c1_guess = c1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) c2_guess = c2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) c3_guess = c3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) t1_guess = t1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) t2_guess = t2(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) t3_guess = t3(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) ## Begin graphing fig = plt.figure(figsize=(10,6)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro',) ax2.plot(freq, Amp2,'bo') ax3.plot(freq, Amp3,'go') ax4.plot(freq, Phase1,'ro') ax5.plot(freq, Phase2,'bo') ax6.plot(freq, Phase3,'go') #fitted curves ax1.plot(freq, c1_fitted,'g-', label='Best Fit') ax2.plot(freq, c2_fitted,'r-', label='Best Fit') ax3.plot(freq, c3_fitted,'b-', label='Best Fit') ax4.plot(freq, t1_fitted,'g-', label='Best Fit') ax5.plot(freq, t2_fitted,'r-', label='Best Fit') ax6.plot(freq, t3_fitted,'b-', label='Best Fit') #inital guess curves ax1.plot(freq, c1_guess, linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase') ax1.set_title('Mass 1') ax2.set_title('Mass 2') ax3.set_title('Mass 3') ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() plt.show() #Find R^2 \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorsimulator import complex_noise from resonatorstats import rsqrd, syserr ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) force_all = False #noise e = complex_noise(300, 2) Amp1 = curve1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase1 = theta1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase2 = theta2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase3 = theta3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi #make parameters/initial guesses true_params = {'k1': 3, 'k2': 3, 'k3': 3, 'k4': 0.5, 'b1': 2, 'b2': 2, 'b3': 2, 'F': 1, 'm1': 5, 'm2': 5, 'm3': 5} params = lmfit.Parameters() params.add('k1', value = 3, min=0) params.add('k2', value = 3, min=0) params.add('k3', value = 3.109, min=0) params.add('k4', value = 0.47, min=0) params.add('b1', value = 2, min=0) params.add('b2', value = 1.99, min=0) params.add('b3', value = 2.76, min=0) params.add('F', value = 1, min=0) params.add('m1', value = 5, min=0) params.add('m2', value = 5.1568, min=0) params.add('m3', value = 4.739, min=0) #get residuals def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create fitted y-values and intial guessed y-values k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(10,6)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro',) ax2.plot(freq, Amp2,'bo') ax3.plot(freq, Amp3,'go') ax4.plot(freq, Phase1,'ro') ax5.plot(freq, Phase2,'bo') ax6.plot(freq, Phase3,'go') #fitted curves ax1.plot(freq, c1_fitted,'g-', label='Best Fit') ax2.plot(freq, c2_fitted,'r-', label='Best Fit') ax3.plot(freq, c3_fitted,'b-', label='Best Fit') ax4.plot(freq, t1_fitted,'g-', label='Best Fit') ax5.plot(freq, t2_fitted,'r-', label='Best Fit') ax6.plot(freq, t3_fitted,'b-', label='Best Fit') #inital guess curves ax1.plot(freq, c1_guess, linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase') ax1.set_title('Mass 1') ax2.set_title('Mass 2') ax3.set_title('Mass 3') ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() plt.show() #create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dataframe param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Add fitted parameters to dataframe param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dataframe param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'syserr_{param_name}'].append(systematic_error) print(data) #Calculate R^2 for each curve and add to dataframe # rsqrd_c1 = rsqrd(c1_fitted,Amp1) \ No newline at end of file From 494a2e510947c1aab4489ebf58a8abbea3d766a9 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 17 Jul 2024 15:09:12 -0400 Subject: [PATCH 063/101] Finalized Multiple Curve Fitting Turned everything in curve_fitting_amp_phase.py and curve_fitting_X_Y.py into functions so they can be used in other files (makes everything cleaner). --- .DS_Store | Bin 6148 -> 10244 bytes Trimer_curvefit_lmfit_mass_2.py | 3 +- curve_fitting_X_Y_all.py | 361 +++++++++++++++++--------------- curve_fitting_amp_phase_all.py | 2 +- 4 files changed, 192 insertions(+), 174 deletions(-) diff --git a/.DS_Store b/.DS_Store index 1f61f003208c913137ea0550b1b8f2aa5f4848d1..29cb7ef1ae5d18163b0ba035af64884bad53fcdb 100644 GIT binary patch literal 10244 zcmeHMJ8u&~5S|SdBuWHMq6nn$IEjYF76BR>+b=DV%JR%y$ z$uc>Gi>2{%K8M?x_tN$8gkp+^?_4MoV&f$vFm5*a1k)iK~07-oRq-4m3i3T;r^e1A`T+1WIo z`9U^YtOZ5%jeq>Gy}dL4X)*kKedO~-b?|K<@%1xIX_E3d8q}pVD&VYAiFf;Bx7 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a/Trimer_curvefit_lmfit_mass_2.py +++ b/Trimer_curvefit_lmfit_mass_2.py @@ -52,7 +52,8 @@ def t2_function(w, k_1, k_2, k_3, k_4, b1, b2, b3, F, m1, m2, m3): graph1 = model1.fit(Amp, params1, w=freq) graph2 = model2.fit(Phase, params2, w=freq) -#print(graph1.fit_report()) +print(graph1.fit_report()) + #print(graph2.fit_report()) ##Graph it! diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index 807497b..2b9331e 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -7,45 +7,11 @@ """ import numpy as np +import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 -from resonatorsimulator import complex_noise -from resonatorstats import rsqrd, syserr - -##Create data - functions from simulator code -freq = np.linspace(0.001, 5, 300) -force_all = False - -#noise -e = complex_noise(300, 2) - -X1 = realamp1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y1 = imamp1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) - -X2 = realamp2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y2 = imamp2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) - -X3 = realamp3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) -Y3 = imamp3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) - -#make parameters/initial guesses -true_params = {'k1': 3, 'k2': 3, 'k3': 3, 'k4': 0.5, - 'b1': 2, 'b2': 2, 'b3': 2, 'F': 1, - 'm1': 5, 'm2': 5, 'm3': 5} - -params = lmfit.Parameters() -params.add('k1', value = 3, min=0) -params.add('k2', value = 3, min=0) -params.add('k3', value = 3.109, min=0) -params.add('k4', value = 0.47, min=0) -params.add('b1', value = 2, min=0) -params.add('b2', value = 1.99, min=0) -params.add('b3', value = 2.76, min=0) -params.add('F', value = 1, min=0) -params.add('m1', value = 5, min=0) -params.add('m2', value = 5.1568, min=0) -params.add('m3', value = 4.739, min=0) +from resonatorstats import syserr #get residuals def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): @@ -77,143 +43,194 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): return np.concatenate((residX1, residX2, residX3, residY1, residY2, residY3)) +def multiple_fit_X_Y(params_guess, params_correct, e, force_all): -result = lmfit.minimize(residuals, params, args = (freq, X1, X2, X3, Y1, Y2, Y3)) -print(lmfit.fit_report(result)) - -#Create fitted y-values and intial guessed y-values -k1_fit = result.params['k1'].value -k2_fit = result.params['k2'].value -k3_fit = result.params['k3'].value -k4_fit = result.params['k4'].value -b1_fit = result.params['b1'].value -b2_fit = result.params['b2'].value -b3_fit = result.params['b3'].value -F_fit = result.params['F'].value -m1_fit = result.params['m1'].value -m2_fit = result.params['m2'].value -m3_fit= result.params['m3'].value - -re1_fitted = re1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) -re2_fitted = re2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) -re3_fitted = re3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) -im1_fitted = im1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) -im2_fitted = im2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) -im3_fitted = im3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) - -k1_guess = params['k1'].value -k2_guess = params['k2'].value -k3_guess = params['k3'].value -k4_guess = params['k4'].value -b1_guess = params['b1'].value -b2_guess = params['b2'].value -b3_guess = params['b3'].value -F_guess = params['F'].value -m1_guess = params['m1'].value -m2_guess = params['m2'].value -m3_guess = params['m3'].value - -re1_guess = re1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) -re2_guess = re2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) -re3_guess = re3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) -im1_guess = im1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) -im2_guess = im2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) -im3_guess = im3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - -## Begin graphing -fig = plt.figure(figsize=(11,7)) -gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.05) - -ax1 = fig.add_subplot(gs[0, 0]) -ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) -ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) -ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) -ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) -ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) -ax7 = fig.add_subplot(gs[2, 0]) -ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7) -ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7) - -#original data -ax1.plot(freq, X1,'ro',) -ax2.plot(freq, X2,'bo') -ax3.plot(freq, X3,'go') -ax4.plot(freq, Y1,'ro') -ax5.plot(freq, Y2,'bo') -ax6.plot(freq, Y3,'go') -ax7.plot(X1,Y1,'ro') -ax8.plot(X2,Y2,'bo') -ax9.plot(X3,Y3,'go') - -#fitted curves -ax1.plot(freq, re1_fitted,'g-', label='Best Fit') -ax2.plot(freq, re2_fitted,'r-', label='Best Fit') -ax3.plot(freq, re3_fitted,'b-', label='Best Fit') -ax4.plot(freq, im1_fitted,'g-', label='Best Fit') -ax5.plot(freq, im2_fitted,'r-', label='Best Fit') -ax6.plot(freq, im3_fitted,'b-', label='Best Fit') - - -#inital guess curves -ax1.plot(freq, re1_guess, linestyle='dashed', label='Initial Guess') -ax2.plot(freq, re2_guess, linestyle='dashed', label='Initial Guess') -ax3.plot(freq, re3_guess, linestyle='dashed', label='Initial Guess') -ax4.plot(freq, im1_guess, linestyle='dashed', label='Initial Guess') -ax5.plot(freq, im2_guess, linestyle='dashed', label='Initial Guess') -ax6.plot(freq, im3_guess, linestyle='dashed', label='Initial Guess') - - -#Graph parts -fig.suptitle('Trimer Resonator: Real and Imaginary') -ax1.set_title('Mass 1') -ax2.set_title('Mass 2') -ax3.set_title('Mass 3') -ax1.set_ylabel('Real') -ax4.set_ylabel('Imaginary') -ax7.set_ylabel('Imaginary') - -ax1.label_outer() -ax2.label_outer() -ax3.label_outer() -ax5.tick_params(labelleft=False) -ax6.tick_params(labelleft=False) -ax7.label_outer() -ax8.label_outer() -ax9.label_outer() - -ax4.set_xlabel('Frequency') -ax5.set_xlabel('Frequency') -ax6.set_xlabel('Frequency') -ax7.set_xlabel('Real') -ax8.set_xlabel('Real') -ax9.set_xlabel('Real') - -plt.show() - -#create dictionary for storing data -data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], - 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], - 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], - 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], - 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], - 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], - 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], - 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], - 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} - -for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: - #Add guessed parameters to dataframe - param_guess = params[param_name].value - data[f'{param_name}_guess'].append(param_guess) + ##Create data - functions from simulator code + freq = np.linspace(0.001, 5, 300) - #Add fitted parameters to dataframe - param_fit = result.params[param_name].value - data[f'{param_name}_recovered'].append(param_fit) + X1 = realamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y1 = imamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - #Calculate systematic error and add to dataframe - param_true = true_params[param_name] - systematic_error = syserr(param_fit, param_true) - data[f'syserr_{param_name}'].append(systematic_error) + X2 = realamp2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y2 = imamp2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) -print(data) - + X3 = realamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y3 = imamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + #make parameters/initial guesses + true_params = {'k1': 3, 'k2': 3, 'k3': 3, 'k4': 0.5, + 'b1': 2, 'b2': 2, 'b3': 2, 'F': 1, + 'm1': 5, 'm2': 5, 'm3': 5} + + #Create intial parameters + params = lmfit.Parameters() + params.add('k1', value = params_guess[0], min=0) + params.add('k2', value = params_guess[1], min=0) + params.add('k3', value = params_guess[2], min=0) + params.add('k4', value = params_guess[3], min=0) + params.add('b1', value = params_guess[4], min=0) + params.add('b2', value = params_guess[5], min=0) + params.add('b3', value = params_guess[6], min=0) + params.add('F', value = params_guess[7], min=0) + params.add('m1', value = params_guess[8], min=0) + params.add('m2', value = params_guess[9], min=0) + params.add('m3', value = params_guess[10], min=0) + + #Create dictionary for storing data + data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], + 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], + 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], + 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], + 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], + 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], + 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], + 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], + 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} + + + #get resulting data and fit parameters by minimizing the residuals + result = lmfit.minimize(residuals, params, args = (freq, X1, X2, X3, Y1, Y2, Y3)) + # print(lmfit.fit_report(result)) + + #Create dictionary of true parameters from list provided (need for compliting data) + true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], + 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], + 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} + + #Compling the Data + for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: + #Add guessed parameters to dictionary + param_guess = params[param_name].value + data[f'{param_name}_guess'].append(param_guess) + + #Add fitted parameters to dictionary + param_fit = result.params[param_name].value + data[f'{param_name}_recovered'].append(param_fit) + + #Calculate systematic error and add to dictionary + param_true = true_params[param_name] + systematic_error = syserr(param_fit, param_true) + data[f'syserr_{param_name}'].append(systematic_error) + + #Create fitted y-values (for graphing) + k1_fit = result.params['k1'].value + k2_fit = result.params['k2'].value + k3_fit = result.params['k3'].value + k4_fit = result.params['k4'].value + b1_fit = result.params['b1'].value + b2_fit = result.params['b2'].value + b3_fit = result.params['b3'].value + F_fit = result.params['F'].value + m1_fit = result.params['m1'].value + m2_fit = result.params['m2'].value + m3_fit= result.params['m3'].value + + re1_fitted = re1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + re2_fitted = re2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + re3_fitted = re3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + im1_fitted = im1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + im2_fitted = im2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + im3_fitted = im3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) + + #Create intial guessed y-values (for graphing) + k1_guess = params['k1'].value + k2_guess = params['k2'].value + k3_guess = params['k3'].value + k4_guess = params['k4'].value + b1_guess = params['b1'].value + b2_guess = params['b2'].value + b3_guess = params['b3'].value + F_guess = params['F'].value + m1_guess = params['m1'].value + m2_guess = params['m2'].value + m3_guess = params['m3'].value + + re1_guess = re1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + re2_guess = re2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + re3_guess = re3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + im1_guess = im1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + im2_guess = im2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + im3_guess = im3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + + ## Begin graphing + fig = plt.figure(figsize=(16,11)) + gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.05) + + ax1 = fig.add_subplot(gs[0, 0]) + ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) + ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) + ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) + ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) + ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) + ax7 = fig.add_subplot(gs[2, 0]) + ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7) + ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7) + + #original data + ax1.plot(freq, X1,'ro', alpha=0.5, markersize=5.5) + ax2.plot(freq, X2,'bo', alpha=0.5, markersize=5.5) + ax3.plot(freq, X3,'go', alpha=0.5, markersize=5.5) + ax4.plot(freq, Y1,'ro', alpha=0.5, markersize=5.5) + ax5.plot(freq, Y2,'bo', alpha=0.5, markersize=5.5) + ax6.plot(freq, Y3,'go', alpha=0.5, markersize=5.5) + ax7.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5) + ax8.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5) + ax9.plot(X3,Y3,'go', alpha=0.5, markersize=5.5) + + #fitted curves + ax1.plot(freq, re1_fitted,'c-', label='Best Fit', lw=2.5) + ax2.plot(freq, re2_fitted,'r-', label='Best Fit', lw=2.5) + ax3.plot(freq, re3_fitted,'m-', label='Best Fit', lw=2.5) + ax4.plot(freq, im1_fitted,'c-', label='Best Fit', lw=2.5) + ax5.plot(freq, im2_fitted,'r-', label='Best Fit', lw=2.5) + ax6.plot(freq, im3_fitted,'m-', label='Best Fit', lw=2.5) + + + #inital guess curves + ax1.plot(freq, re1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax2.plot(freq, re2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax3.plot(freq, re3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax4.plot(freq, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax5.plot(freq, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax6.plot(freq, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + + + #Graph parts + fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=16) + ax1.set_title('Mass 1', fontsize=14) + ax2.set_title('Mass 2', fontsize=14) + ax3.set_title('Mass 3', fontsize=14) + ax1.set_ylabel('Real') + ax4.set_ylabel('Imaginary') + ax7.set_ylabel('Imaginary') + + ax1.label_outer() + ax2.label_outer() + ax3.label_outer() + ax5.tick_params(labelleft=False) + ax6.tick_params(labelleft=False) + ax7.label_outer() + ax8.label_outer() + ax9.label_outer() + + ax4.set_xlabel('Frequency') + ax5.set_xlabel('Frequency') + ax6.set_xlabel('Frequency') + ax7.set_xlabel('Real') + ax8.set_xlabel('Real') + ax9.set_xlabel('Real') + + ax1.legend() + ax2.legend() + ax3.legend() + ax4.legend() + ax5.legend() + ax6.legend() + + plt.show() + + df = pd.DataFrame(data) + return df + +#with pd.ExcelWriter('Real_Imaginary_Curve_Fit_Simultaneously.xlsx', engine='xlsxwriter') as writer: + # df.to_excel(writer, sheet_name='', index=False) diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index cbbec94..3bd64ba 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorsimulator import complex_noise from resonatorstats import rsqrd, syserr ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) force_all = False #noise e = complex_noise(300, 2) Amp1 = curve1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase1 = theta1(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase2 = theta2(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) Phase3 = theta3(freq, 3, 3, 3, 0.5, 2, 2, 2, 1, 5, 5, 5, e, force_all) \ + 2 * np.pi #make parameters/initial guesses true_params = {'k1': 3, 'k2': 3, 'k3': 3, 'k4': 0.5, 'b1': 2, 'b2': 2, 'b3': 2, 'F': 1, 'm1': 5, 'm2': 5, 'm3': 5} params = lmfit.Parameters() params.add('k1', value = 3, min=0) params.add('k2', value = 3, min=0) params.add('k3', value = 3.109, min=0) params.add('k4', value = 0.47, min=0) params.add('b1', value = 2, min=0) params.add('b2', value = 1.99, min=0) params.add('b3', value = 2.76, min=0) params.add('F', value = 1, min=0) params.add('m1', value = 5, min=0) params.add('m2', value = 5.1568, min=0) params.add('m3', value = 4.739, min=0) #get residuals def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create fitted y-values and intial guessed y-values k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(10,6)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro',) ax2.plot(freq, Amp2,'bo') ax3.plot(freq, Amp3,'go') ax4.plot(freq, Phase1,'ro') ax5.plot(freq, Phase2,'bo') ax6.plot(freq, Phase3,'go') #fitted curves ax1.plot(freq, c1_fitted,'g-', label='Best Fit') ax2.plot(freq, c2_fitted,'r-', label='Best Fit') ax3.plot(freq, c3_fitted,'b-', label='Best Fit') ax4.plot(freq, t1_fitted,'g-', label='Best Fit') ax5.plot(freq, t2_fitted,'r-', label='Best Fit') ax6.plot(freq, t3_fitted,'b-', label='Best Fit') #inital guess curves ax1.plot(freq, c1_guess, linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase') ax1.set_title('Mass 1') ax2.set_title('Mass 2') ax3.set_title('Mass 3') ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() plt.show() #create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dataframe param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Add fitted parameters to dataframe param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dataframe param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'syserr_{param_name}'].append(systematic_error) print(data) #Calculate R^2 for each curve and add to dataframe # rsqrd_c1 = rsqrd(c1_fitted,Amp1) \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and a boolean (whether you want to apply force to one or all masses) #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #Create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) # print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'syserr_{param_name}'].append(systematic_error) #Create fitted y-values (for graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5) ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5) ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5) ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5) ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5) ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5) #fitted curves ax1.plot(freq, c1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, c2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, c3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, t1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, t2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, t3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df \ No newline at end of file From 6ac4dc0775050ee8be6319b79afb56e01ff05129 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 17 Jul 2024 16:26:32 -0400 Subject: [PATCH 064/101] Update: Multiple Curve FIt --- curve_fitting_X_Y_all.py | 34 +++++++++++++++++++--------------- curve_fitting_amp_phase_all.py | 2 +- 2 files changed, 20 insertions(+), 16 deletions(-) diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index 2b9331e..c19ae9c 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -90,7 +90,7 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, X1, X2, X3, Y1, Y2, Y3)) - # print(lmfit.fit_report(result)) + print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], @@ -167,15 +167,15 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7) #original data - ax1.plot(freq, X1,'ro', alpha=0.5, markersize=5.5) - ax2.plot(freq, X2,'bo', alpha=0.5, markersize=5.5) - ax3.plot(freq, X3,'go', alpha=0.5, markersize=5.5) - ax4.plot(freq, Y1,'ro', alpha=0.5, markersize=5.5) - ax5.plot(freq, Y2,'bo', alpha=0.5, markersize=5.5) - ax6.plot(freq, Y3,'go', alpha=0.5, markersize=5.5) - ax7.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5) - ax8.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5) - ax9.plot(X3,Y3,'go', alpha=0.5, markersize=5.5) + ax1.plot(freq, X1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax2.plot(freq, X2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax3.plot(freq, X3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax4.plot(freq, Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax5.plot(freq, Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax6.plot(freq, Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax7.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax8.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax9.plot(X3,Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, re1_fitted,'c-', label='Best Fit', lw=2.5) @@ -184,7 +184,9 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): ax4.plot(freq, im1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, im2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, im3_fitted,'m-', label='Best Fit', lw=2.5) - + ax7.plot(re1_fitted, im1_fitted, 'c-', label='Best Fit', lw=2.5) + ax8.plot(re2_fitted, im2_fitted, 'r-', label='Best Fit', lw=2.5) + ax9.plot(re3_fitted, im3_fitted, 'm-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, re1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') @@ -193,7 +195,9 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): ax4.plot(freq, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - + ax7.plot(re1_guess, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax8.plot(re2_guess, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax9.plot(re3_guess, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=16) @@ -226,11 +230,11 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): ax4.legend() ax5.legend() ax6.legend() + ax7.legend() + ax8.legend() + ax9.legend() plt.show() df = pd.DataFrame(data) return df - -#with pd.ExcelWriter('Real_Imaginary_Curve_Fit_Simultaneously.xlsx', engine='xlsxwriter') as writer: - # df.to_excel(writer, sheet_name='', index=False) diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 3bd64ba..49fd003 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and a boolean (whether you want to apply force to one or all masses) #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #Create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) # print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'syserr_{param_name}'].append(systematic_error) #Create fitted y-values (for graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5) ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5) ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5) ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5) ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5) ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5) #fitted curves ax1.plot(freq, c1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, c2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, c3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, t1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, t2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, t3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and a boolean (whether you want to apply force to one or all masses) #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #Create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'syserr_{param_name}'].append(systematic_error) #Create fitted y-values (for graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, c1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, c2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, c3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, t1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, t2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, t3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df \ No newline at end of file From aa9fce986cedd91333f465a56f95fbd6243aad8d Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 17 Jul 2024 16:49:33 -0400 Subject: [PATCH 065/101] Update: Scaled by F for Multiple Curve Fitting --- curve_fitting_X_Y_all.py | 18 +++++++++++------- curve_fitting_amp_phase_all.py | 2 +- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index c19ae9c..82c1c68 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -83,9 +83,9 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], - 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], - 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], - 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} + 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], + 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], + 'e_m1': [], 'e_m2': [], 'e_m3': []} #get resulting data and fit parameters by minimizing the residuals @@ -103,14 +103,18 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all): param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) - #Add fitted parameters to dictionary + #Scale fitted parameters by force param_fit = result.params[param_name].value - data[f'{param_name}_recovered'].append(param_fit) + scaling_factor = (true_params['F'])/(result.params['F'].value) + scaled_param_fit = param_fit*scaling_factor + + #Add fitted parameters to dictionary + data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] - systematic_error = syserr(param_fit, param_true) - data[f'syserr_{param_name}'].append(systematic_error) + systematic_error = syserr(scaled_param_fit, param_true) + data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for graphing) k1_fit = result.params['k1'].value diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 49fd003..c9241f5 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and a boolean (whether you want to apply force to one or all masses) #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #Create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'syserr_k1': [], 'syserr_k2': [], 'syserr_k3': [], 'syserr_k4': [], 'syserr_b1': [], 'syserr_b2': [], 'syserr_b3': [], 'syserr_F': [], 'syserr_m1': [], 'syserr_m2': [], 'syserr_m3': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'syserr_{param_name}'].append(systematic_error) #Create fitted y-values (for graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, c1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, c2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, c3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, t1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, t2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, t3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and a boolean (whether you want to apply force to one or all masses) #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #Create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'F_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value c1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) c3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) t3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, c1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, c2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, c3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, t1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, t2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, t3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df \ No newline at end of file From 32e28163ce213fb009ca7899b54c91e49250b9dd Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 18 Jul 2024 15:06:59 -0400 Subject: [PATCH 066/101] Created: Curvefit_compare_scale_vs_fix, Deleted: Incorrect multiple curve fit files Curvefit_compare_scale_vs_fix: Graphs and compiles data for the same system but with different noise a choice amount of times. It compares the curve fit method of scaling F to fixing F. 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From 110ff6d2b151f5ea056b6d7f27e7dd6424597ab6 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Fri, 19 Jul 2024 11:01:09 -0400 Subject: [PATCH 068/101] Update: Curve Fit Code --- Curvefit_compare_scale_vs_fix_F.py | 8 ++++---- curve_fitting_X_Y_all.py | 14 ++++++++++---- curve_fitting_amp_phase_all.py | 2 +- 3 files changed, 15 insertions(+), 9 deletions(-) diff --git a/Curvefit_compare_scale_vs_fix_F.py b/Curvefit_compare_scale_vs_fix_F.py index 7a29123..45a56f9 100644 --- a/Curvefit_compare_scale_vs_fix_F.py +++ b/Curvefit_compare_scale_vs_fix_F.py @@ -3,7 +3,7 @@ """ Created on Wed Jul 17 14:25:50 2024 -@author: Student +@author: lydiabullock """ from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y @@ -17,8 +17,8 @@ starting_row = 0 -with pd.ExcelWriter('Curve_Fit_Simultaneously.xlsx', engine='xlsxwriter') as writer: - for i in range(3): +with pd.ExcelWriter('Curve_Fit_Simultaneously_Scale_vs_Fix.xlsx', engine='xlsxwriter') as writer: + for i in range(5): #Create noise e = complex_noise(300, 2) @@ -32,7 +32,7 @@ #Add to excel spreadsheet dataframe1.to_excel(writer, sheet_name='Amp & Phase - Scaled vs Fixed F', startrow=starting_row, index=False) - dataframe1.to_excel(writer, sheet_name='Amp & Phase - Scaled vs Fixed F', startrow=starting_row+2, index=False, header=False) + dataframe2.to_excel(writer, sheet_name='Amp & Phase - Scaled vs Fixed F', startrow=starting_row+2, index=False, header=False) dataframe3.to_excel(writer, sheet_name='X & Y - Scaled vs Fixed F', startrow=starting_row, index=False) dataframe4.to_excel(writer, sheet_name='X & Y - Scaled vs Fixed F', startrow=starting_row+2, index=False, header=False) diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index ab0704e..7f15cd6 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -81,7 +81,10 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): params['F'].vary = False #Create dictionary for storing data - data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], + data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], + 'b1_true': [], 'b2_true': [], 'b3_true': [], + 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], + 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], @@ -98,17 +101,21 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): result = lmfit.minimize(residuals, params, args = (freq, X1, X2, X3, Y1, Y2, Y3)) #print(lmfit.fit_report(result)) - #Create dictionary of true parameters from list provided (need for compliting data) + #Create dictionary of true parameters from list provided (need for compiling data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: + #Add true parameters to dictionary + param_true = true_params[param_name] + data[f'{param_name}_true'].append(param_true) + #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) - + #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary @@ -116,7 +123,6 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary - param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 3543758..83c9708 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df print('') print('') \ No newline at end of file From 94b945e99014c7c1535728eed163f20a1056a42f Mon Sep 17 00:00:00 2001 From: lydiabull Date: Fri, 19 Jul 2024 11:12:21 -0400 Subject: [PATCH 069/101] Created: comparing_curvefit_types This code runs 50 trials of the same system with different noise. It curves fit with Amplitude and Phase simultaneously and with X and Y simultaneously. This code is used to determine which curve fitting produces lower error. --- .DS_Store | Bin 10244 -> 10244 bytes Curve Fit Testing/.DS_Store | Bin 6148 -> 8196 bytes .../Changing One Param - Curve Fit/.DS_Store | Bin 6148 -> 6148 bytes comparing_curvefit_types.py | 40 ++++++++++++++++++ 4 files changed, 40 insertions(+) create mode 100644 comparing_curvefit_types.py diff --git a/.DS_Store b/.DS_Store index 2c0ef8ce5878f6958b02b5901cfea640393d1c14..64f15c4bf6e559860fc8719749acbec04d1fad51 100644 GIT binary patch delta 70 zcmZn(XbG6$jIU^hRb_GTV|2qw<7l;Y&1{QMlo$#z1ICp!u$^OOY_<>ln(r86)v aFm9F;c4XT;SJItrGrPhsmdy)9n3(|=ycLB2 delta 74 zcmZn(XbG6$LAU^hRb)@B}o2&T!Ch4yZ461HO#=Vu6EC}PND$Yn?c((w#AKvo(< c=Hz|}>CIatgV`n)C~sz0_{Fk0NtBrx05S9xO#lD@ diff --git a/Curve Fit Testing/.DS_Store b/Curve Fit Testing/.DS_Store index f00e6fde8bbdbfa84544dd88be08ca264579ca38..22d1fe499bcf0ed328d780e8af0bd75bdfe46ff3 100644 GIT binary patch delta 144 zcmZoMXmOBWU|?W$DortDU;r^WfEYvza8E20o2aMA$hR?IH}hr%jz7$c**Q2SHn1@A zP3B?wz?qg(oSc-OpTjUYfz@Gh02}XSPF8nDWMMX+%>itdjGNVYo-=bva03l>1(~~9 bkmEb^WPTCP$^JYX9E=bv88*lB%wYxqDwrUV delta 116 zcmZp1XfcprU|?W$DortDU=RQ@Ie-{Mvv5r;6q~50$jGxXU^g=(&tx8f50l>s@oo+h zbZ6ZBPRNpRVtw6Wb`B0fW}s>y5a0$9t{{yY3%@f@=9lpV8N*S408k Date: Fri, 19 Jul 2024 16:34:52 -0400 Subject: [PATCH 070/101] Created: curvefit_automated_random_pguesses I have automated the guess parameters by creating a function that takes the true parameters, picks a random number within a threshold of the true value that you define, and rounds it to an interval that you pick. The code then creates noise for the data and then does curve fit many times with different guesses. --- comparing_curvefit_types.py | 4 +-- curvefit_automated_random_pguesses.py | 50 +++++++++++++++++++++++++++ simulated_experiment.py | 2 +- 3 files changed, 53 insertions(+), 3 deletions(-) create mode 100644 curvefit_automated_random_pguesses.py diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 9bf0c48..1b6fc43 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -17,8 +17,8 @@ #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses -true_params = [4, 3, 2, 1, 1, 2, 3, 1, 1, 1, 1] -guessed_params = [4.023, 3, 1.909, 0.80911, 1.2985, 2, 2.891, 1, 1, 1.11, 1] +true_params = [5, 5, 1, 1, 2, 2, 2, 1, 1.5, 1.5, 6.5] +guessed_params = [5.23, 4.5, 1.39, 0.47, 1.983, 2.01, 2.76, 1, 2.025, 1.7, 5.739] starting_row = 0 diff --git a/curvefit_automated_random_pguesses.py b/curvefit_automated_random_pguesses.py new file mode 100644 index 0000000..d02f113 --- /dev/null +++ b/curvefit_automated_random_pguesses.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Jul 19 14:48:10 2024 + +@author: lydiabullock +""" +''' Function that automates guess parameters. + Seeing success of curve fit with random guesses? ''' + +import random +from curve_fitting_X_Y_all import multiple_fit_X_Y +import pandas as pd +from resonatorsimulator import complex_noise + +def automate_guess(true_params, threshold, interval): + params_guess = [] + for index, value in enumerate(true_params): + if index == 7: #Doing this because we must know what Force is going in + params_guess.append(value) + else: + num = random.uniform(value-threshold, value+threshold) + rounded_num = round(num / interval) * interval # Round the number to the nearest interval + formatted_num = round(rounded_num, 4) # Just in case, format to 4 decimal places + params_guess.append(formatted_num) + return params_guess + +#Create noise +e = complex_noise(300, 2) + +true_params = [5, 5, 1, 1, 2, 2, 2, 1, 1.5, 1.5, 6.5] + +starting_row = 0 + +with pd.ExcelWriter('Curve_Fit_Simultaneously_Auto_Random_Guess.xlsx', engine='xlsxwriter') as writer: + + for i in range(10): + + #Created different guess parameters + guessed_params = automate_guess(true_params, 2, 0.001) + + #Get the data! + dataframe1 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Fixed + + #Add to excel spreadsheet + dataframe1.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) + + starting_row += len(dataframe1) + (1 if i==0 else 0) + + \ No newline at end of file diff --git a/simulated_experiment.py b/simulated_experiment.py index 908d971..42d65dc 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -14,7 +14,7 @@ import matplotlib.pyplot as plt from helperfunctions import \ read_params, store_params, make_real_iff_real, flatten -from resonatorSVDanalysis import Zmat, \ +from NetMAP import Zmat, \ normalize_parameters_1d_by_force, normalize_parameters_assuming_3d, \ normalize_parameters_to_m1_F_set_assuming_2d from resonatorstats import syserr, combinedsyserr From 15e52ebefd4ab01579705ad5ff1a59650eae91c8 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Fri, 19 Jul 2024 17:01:53 -0400 Subject: [PATCH 071/101] Added a comment to resonatorstats --- .DS_Store | Bin 10244 -> 10244 bytes resonatorstats.py | 4 ++++ 2 files changed, 4 insertions(+) diff --git a/.DS_Store b/.DS_Store index 64f15c4bf6e559860fc8719749acbec04d1fad51..a416a638f558845ba6f5e6b822bc3cedd8fe6cac 100644 GIT binary patch delta 16 XcmZn(XbISmBQ)7oNPY8Up+ZprHB$w? delta 18 ZcmZn(XbISmBgDu!*-l7p^JJkyQ2;v_1;79R diff --git a/resonatorstats.py b/resonatorstats.py index e7c4f93..13ee085 100644 --- a/resonatorstats.py +++ b/resonatorstats.py @@ -45,6 +45,10 @@ def combinedsyserr(syserrs, notdof): # notdof = not degrees of freedom, meaning return avg, rms, max(abssyserrs), Lavg +""" +This definition of R^2 can come out negative. +Negative means that a flat line would fit the data better than the curve. +""" def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) From 5f6abefde6a7b50a122278e5f9e21b4be7d357d5 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 22 Jul 2024 14:54:40 -0400 Subject: [PATCH 072/101] Almost Final comparing_curvefit_types I was able to get a histogram that shows X and Y is better. However, I need to be more fair when finding my guessed parameters. This is the next step. --- comparing_curvefit_types.py | 121 +++++++++++++++++++++----- curve_fitting_X_Y_all.py | 31 ++++--- curve_fitting_amp_phase_all.py | 2 +- curvefit_automated_random_pguesses.py | 5 +- 4 files changed, 125 insertions(+), 34 deletions(-) diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 1b6fc43..243eecb 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -7,34 +7,115 @@ """ ''' Which has more accurated recovered parameters: Amp & Phase or X & Y? - Same system, different noise - this replicates doing experiment many times - Using method of fixing F ''' + Same system, different noise - this replicates doing experiment many times. + Using method of fixing F. ''' +import pandas as pd +import math +import matplotlib.pyplot as plt from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y -import pandas as pd from resonatorsimulator import complex_noise -#Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] -#Note that right now we only scale/fix by F, so make sure to keep F correct in guesses -true_params = [5, 5, 1, 1, 2, 2, 2, 1, 1.5, 1.5, 6.5] -guessed_params = [5.23, 4.5, 1.39, 0.47, 1.983, 2.01, 2.76, 1, 2.025, 1.7, 5.739] +''' Functions contained: + find_avg_e - calculates average across systematic error for each parameter + for one trial of the same system + artithmetic_then_logarithmic - calculates arithmetic average across parameters first, + then logarithmic average across trials + run_trials - Runs a set number of trials for one system, graphs curvefit result, + puts data and averages into spreadsheet, returns _bar for both types of curves + - Must include number of trials to run and name of excel sheet +''' -starting_row = 0 +#Calculate for one trial of the same system +def find_avg_e(dictionary): + sum_e = dictionary['e_k1'][0] + \ + dictionary['e_k2'][0] + \ + dictionary['e_k3'][0] + \ + dictionary['e_k4'][0] + \ + dictionary['e_b1'][0] + \ + dictionary['e_b2'][0] + \ + dictionary['e_b3'][0] + \ + dictionary['e_F'][0] + \ + dictionary['e_m1'][0] + \ + dictionary['e_m2'][0] + \ + dictionary['e_m3'][0] + avg_e = sum_e/10 + return avg_e -with pd.ExcelWriter('Curve_Fit_Simultaneously_Which_More_Accurate.xlsx', engine='xlsxwriter') as writer: - for i in range(50): - - #Create noise - e = complex_noise(300, 2) +#Calculate _bar +def arithmetic_then_logarithmic(avg_e_list): + ln_avg_e = [] + for item in avg_e_list: + ln_avg_e.append(math.log(item)) + sum_ln_avg_e = sum(ln_avg_e) + e_raised_to_sum = math.exp(sum_ln_avg_e) + return e_raised_to_sum + +#Runs a set number of trials for one system, graphs curvefit result, +# puts data and averages into spreadsheet, returns _bar for both types of curves +def run_trials(true_params, guessed_params, num_trials, file_name): - #Get the data! - dataframe1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True) #Fixed - dataframe2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Fixed + starting_row = 0 + avg_e1_list = [] + avg_e2_list = [] - #Add to excel spreadsheet - dataframe1.to_excel(writer, sheet_name='Amp & Phase', startrow=starting_row, index=False, header=(i==0)) - dataframe2.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) + #Put data into excel spreadsheet + with pd.ExcelWriter(file_name, engine='xlsxwriter') as writer: + for i in range(num_trials): + + #Create noise + e = complex_noise(300, 2) + + #Get the data! + dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True) #Polar, Fixed force + dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Cartesian, Fixed force + + #Find (average across parameters) for each trial and add to dictionary + avg_e1 = find_avg_e(dictionary1) + dictionary1[''] = avg_e1 + + avg_e2 = find_avg_e(dictionary2) + dictionary2[''] = avg_e2 + + #Append to list for later graphing + avg_e1_list.append(avg_e1) + avg_e2_list.append(avg_e2) + + #Turn data into dataframe for excel + dataframe1 = pd.DataFrame(dictionary1) + dataframe2 = pd.DataFrame(dictionary2) + + #Add to excel spreadsheet + dataframe1.to_excel(writer, sheet_name='Amp & Phase', startrow=starting_row, index=False, header=(i==0)) + dataframe2.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) + + starting_row += len(dataframe1) + (1 if i==0 else 0) + + avg_e1_bar = arithmetic_then_logarithmic(avg_e1_list) + avg_e2_bar = arithmetic_then_logarithmic(avg_e2_list) + + dataframe1.at[0,'_bar'] = avg_e1_bar + dataframe2.at[0,'_bar'] = avg_e2_bar + + dataframe1.to_excel(writer, sheet_name='Amp & Phase', index=False) + dataframe2.to_excel(writer, sheet_name='X & Y', index=False) + + return avg_e1_list, avg_e2_list, arithmetic_then_logarithmic(avg_e1_list), arithmetic_then_logarithmic(avg_e2_list) - starting_row += len(dataframe1) + (1 if i==0 else 0) + +''' Begin work here. ''' + +#Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] +#Note that right now we only scale/fix by F, so make sure to keep F correct in guesses +sys5_true_params = [5, 5, 1, 1, 2, 2, 2, 1, 1.5, 1.5, 6.5] +sys5_guessed_params = [5.23, 4.5, 1.39, 0.47, 1.983, 2.01, 2.76, 1, 2.025, 1.7, 5.739] + +sys5_avg_e1_list, sys5_avg_e2_list, sys5_avg_e1_bar, sys5_avg_e2_bar = run_trials(sys5_true_params, sys5_guessed_params, 50, 'System_5.xlsx') + +plt.hist(sys5_avg_e1_list, bins=30, alpha=0.75, color='blue', edgecolor='black') +plt.hist(sys5_avg_e2_list, bins=30, alpha=0.75, color='green', edgecolor='black') + + + diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index 7f15cd6..44053f3 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -7,13 +7,21 @@ """ import numpy as np -import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 from resonatorstats import syserr, rsqrd -#get residuals +''' 2 functions contained: + multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once + - Calculates systematic error and returns a dictionary of info + - Graphs curve fit analysis + residuals - calculates residuals of multiple data sets and concatenates them + - used in multiple_fit function to minimize the residuals of + multiple graphs at the same time to find the best fit curve +''' + +#Get residuals def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): k1 = params['k1'].value k2 = params['k2'].value @@ -43,8 +51,14 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): return np.concatenate((residX1, residX2, residX3, residY1, residY2, residY3)) +#Takes in a *list* of correct parameters and a *list* of the guessed parameters, +#as well as error and three booleans (whether you want to apply force to one or all masses, +#scale by force, or fix the force) +# +#Returns a dataframe containing guessed parameters, recovered parameters, +#and systematic error def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): - + ##Create data - functions from simulator code freq = np.linspace(0.001, 5, 300) @@ -57,11 +71,6 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): X3 = realamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Y3 = imamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - #make parameters/initial guesses - true_params = {'k1': 3, 'k2': 3, 'k3': 3, 'k4': 0.5, - 'b1': 2, 'b2': 2, 'b3': 2, 'F': 1, - 'm1': 5, 'm2': 5, 'm3': 5} - #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) @@ -141,7 +150,6 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) - #Create fitted y-values (for rsrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value @@ -279,6 +287,5 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): ax9.legend(fontsize='10') plt.show() - - df = pd.DataFrame(data) - return df + + return data diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 83c9708..77e955c 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a data frame residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() df = pd.DataFrame(data) return df print('') print('') \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() return data \ No newline at end of file diff --git a/curvefit_automated_random_pguesses.py b/curvefit_automated_random_pguesses.py index d02f113..db90c66 100644 --- a/curvefit_automated_random_pguesses.py +++ b/curvefit_automated_random_pguesses.py @@ -40,7 +40,10 @@ def automate_guess(true_params, threshold, interval): guessed_params = automate_guess(true_params, 2, 0.001) #Get the data! - dataframe1 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Fixed + dictionary1 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Fixed + + #Convert to dataframe + dataframe1 = pd.DataFrame(dictionary1) #Add to excel spreadsheet dataframe1.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) From 854c5b9eeb9c832c50fb91abd1bb3ba8b9cc9606 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 22 Jul 2024 15:06:41 -0400 Subject: [PATCH 073/101] Update comparing_curvefit_types.py --- comparing_curvefit_types.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 243eecb..6cc07c9 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -113,9 +113,12 @@ def run_trials(true_params, guessed_params, num_trials, file_name): sys5_avg_e1_list, sys5_avg_e2_list, sys5_avg_e1_bar, sys5_avg_e2_bar = run_trials(sys5_true_params, sys5_guessed_params, 50, 'System_5.xlsx') -plt.hist(sys5_avg_e1_list, bins=30, alpha=0.75, color='blue', edgecolor='black') -plt.hist(sys5_avg_e2_list, bins=30, alpha=0.75, color='green', edgecolor='black') - +plt.title('Average Across Parameters - Comparing Cartesian and Polar') +plt.xlabel('') +plt.ylabel('Counts') +plt.hist(sys5_avg_e1_list, bins=10, alpha=0.75, color='blue') +plt.hist(sys5_avg_e2_list, bins=10, alpha=0.75, color='green') +plt.show() From a1581d5b8e81bf3aceaeb82627bb0d3bad13b3b9 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 22 Jul 2024 17:02:40 -0400 Subject: [PATCH 074/101] Update comparing_curvefit_types Made a while loop that allows you to guess the parameters until the guess looks good. Then it performs the curve fit, stores the data, and produces a histogram that shows error for Cartesian and Polar methods of curve fitting. --- comparing_curvefit_types.py | 218 +++++++++++++++++++++++++++++++++++- curve_fitting_X_Y_all.py | 2 +- 2 files changed, 214 insertions(+), 6 deletions(-) diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 6cc07c9..1128270 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -12,10 +12,13 @@ import pandas as pd import math +import random +import numpy as np import matplotlib.pyplot as plt from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y from resonatorsimulator import complex_noise +from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 ''' Functions contained: find_avg_e - calculates average across systematic error for each parameter @@ -103,21 +106,226 @@ def run_trials(true_params, guessed_params, num_trials, file_name): return avg_e1_list, avg_e2_list, arithmetic_then_logarithmic(avg_e1_list), arithmetic_then_logarithmic(avg_e2_list) +def generate_random_system(): + system_params = [] + for i in range(11): + if i==7: + system_params.append(1) + else: + param = random.uniform(0.1,10) + round_param = round(param, 3) + system_params.append(round_param) + return system_params + +def make_guess(params_guess, params_correct): + ##Create data - this is the same as what I use in the curve fit functions + freq = np.linspace(0.001, 4, 300) + + #Create noise + e = complex_noise(300, 2) + force_all = False + + #Original Data + X1 = realamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y1 = imamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + X2 = realamp2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y2 = imamp2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + X3 = realamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y3 = imamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + + #Guessed Curve + re1_guess = re1(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + re2_guess = re2(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + re3_guess = re3(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + im1_guess = im1(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + im2_guess = im2(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + im3_guess = im3(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + c1_guess = c1(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + c2_guess = c2(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + c3_guess = c3(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + t1_guess = t1(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + t2_guess = t2(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + t3_guess = t3(freq, params_guess[0], params_guess[1], params_guess[2], params_guess[3], params_guess[4], params_guess[5], params_guess[6], params_guess[7], params_guess[8], params_guess[9], params_guess[10]) + + ## Begin graphing + fig = plt.figure(figsize=(16,11)) + gs = fig.add_gridspec(3, 3, hspace=0.25, wspace=0.05) + + ax1 = fig.add_subplot(gs[0, 0]) + ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) + ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) + ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) + ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) + ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) + ax7 = fig.add_subplot(gs[2, 0], aspect='equal') + ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7, aspect='equal') + ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7, aspect='equal') + + #original data + ax1.plot(freq, X1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax2.plot(freq, X2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax3.plot(freq, X3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax4.plot(freq, Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax5.plot(freq, Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax6.plot(freq, Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax7.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax8.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax9.plot(X3,Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') + + #inital guess curves + ax1.plot(freq, re1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax2.plot(freq, re2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax3.plot(freq, re3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax4.plot(freq, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax5.plot(freq, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax6.plot(freq, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax7.plot(re1_guess, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax8.plot(re2_guess, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax9.plot(re3_guess, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + + #Graph parts + fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=16) + ax1.set_title('Mass 1', fontsize=14) + ax2.set_title('Mass 2', fontsize=14) + ax3.set_title('Mass 3', fontsize=14) + ax1.set_ylabel('Real') + ax4.set_ylabel('Imaginary') + ax7.set_ylabel('Imaginary') + + ax1.label_outer() + ax2.label_outer() + ax3.label_outer() + ax5.tick_params(labelleft=False) + ax6.tick_params(labelleft=False) + ax7.label_outer() + ax8.label_outer() + ax9.label_outer() + + ax4.set_xlabel('Frequency') + ax5.set_xlabel('Frequency') + ax6.set_xlabel('Frequency') + ax7.set_xlabel('Real') + ax8.set_xlabel('Real') + ax9.set_xlabel('Real') + + ax1.legend() + ax2.legend() + ax3.legend() + ax4.legend() + ax5.legend() + ax6.legend() + ax7.legend(fontsize='10') + ax8.legend(fontsize='10') + ax9.legend(fontsize='10') + + plt.show() + + ## Begin graphing + fig = plt.figure(figsize=(16,8)) + gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) + ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') + + #original data + ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') + + #inital guess curves + ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + + + #Graph parts + fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) + ax1.set_title('Mass 1', fontsize=14) + ax2.set_title('Mass 2', fontsize=14) + ax3.set_title('Mass 3', fontsize=14) + ax1.set_ylabel('Amplitude') + ax4.set_ylabel('Phase') + + for ax in fig.get_axes(): + ax.set(xlabel='Frequency') + ax.label_outer() + ax.legend() + + print(f"Graphing guessed curve with guessed parameters: {params_guess}") + + plt.show() ''' Begin work here. ''' #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses -sys5_true_params = [5, 5, 1, 1, 2, 2, 2, 1, 1.5, 1.5, 6.5] -sys5_guessed_params = [5.23, 4.5, 1.39, 0.47, 1.983, 2.01, 2.76, 1, 2.025, 1.7, 5.739] +true_params = generate_random_system() +guessed_params = [1,1,1,1,1,1,1,1,1,1,1] + +# Start the loop +while True: + # Graph + make_guess(guessed_params, true_params) + + # Ask the user for the new list of guessed parameters + print(f'Current list of parameter guesses is {guessed_params}') + indices = input("Enter the indices of the elements you want to update (comma-separated, or 'q' to quit): ") + + # Check if the user wants to quit + if indices.lower() == 'q': + break + + # Parse and validate the indices + try: + index_list = [int(idx.strip()) for idx in indices.split(',')] + if any(index < 0 or index >= len(guessed_params) for index in index_list): + print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") + continue + except ValueError: + print("Invalid input. Please enter valid indices or 'q' to quit.") + continue + + # Ask the user for the new values + values = input(f"Enter the new values for indices {index_list} (comma-separated): ") + + # Parse and validate the new values + try: + value_list = [float(value.strip()) for value in values.split(',')] + if len(value_list) != len(index_list): + print("The number of values must match the number of indices.") + continue + except ValueError: + print("Invalid input. Please enter valid numbers.") + continue + + # Update the list with the new values + for index, new_value in zip(index_list, value_list): + guessed_params[index] = new_value + -sys5_avg_e1_list, sys5_avg_e2_list, sys5_avg_e1_bar, sys5_avg_e2_bar = run_trials(sys5_true_params, sys5_guessed_params, 50, 'System_5.xlsx') +avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, 'System_5.xlsx') plt.title('Average Across Parameters - Comparing Cartesian and Polar') plt.xlabel('') plt.ylabel('Counts') -plt.hist(sys5_avg_e1_list, bins=10, alpha=0.75, color='blue') -plt.hist(sys5_avg_e2_list, bins=10, alpha=0.75, color='green') +plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue') +plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green') plt.show() diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index 44053f3..e856706 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -60,7 +60,7 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): ##Create data - functions from simulator code - freq = np.linspace(0.001, 5, 300) + freq = np.linspace(0.001, 4, 300) X1 = realamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Y1 = imamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) From 47dd043d219e0523e4ffb884849a49a2f399f01a Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 23 Jul 2024 10:29:51 -0400 Subject: [PATCH 075/101] Update comparing_curvefit_types.py Made the histogram better --- comparing_curvefit_types.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 1128270..39ad1f1 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -15,6 +15,7 @@ import random import numpy as np import matplotlib.pyplot as plt +from brokenaxes import brokenaxes from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y from resonatorsimulator import complex_noise @@ -271,7 +272,7 @@ def make_guess(params_guess, params_correct): plt.show() -''' Begin work here. ''' +''' Begin work here. Case Study. ''' #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses @@ -319,14 +320,16 @@ def make_guess(params_guess, params_correct): guessed_params[index] = new_value -avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, 'System_5.xlsx') +avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx') -plt.title('Average Across Parameters - Comparing Cartesian and Polar') -plt.xlabel('') -plt.ylabel('Counts') -plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue') -plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green') -plt.show() +bax = brokenaxes(xlims=((0, max(avg_e2_list)+0.5), (min(avg_e1_list)-0.5, max(avg_e1_list)+0.5))) +bax.set_title('Average Systematic Error Across Parameters') +bax.set_xlabel('') +bax.set_ylabel('Counts') +bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)') +bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)') +bax.legend(loc='upper center') +plt.show() From 4e67020f79656b377aa0152dc949e71e22d496fa Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 23 Jul 2024 17:43:28 -0400 Subject: [PATCH 076/101] Updated comparing_curvefit_types Can now run a case study or multiple case studies. Deleted curvefit_automated_random_pguesses because I just moved that function to the main code. --- comparing_curvefit_types.py | 256 +++++++++++++++++++------- curve_fitting_X_Y_all.py | 18 +- curve_fitting_amp_phase_all.py | 2 +- curvefit_automated_random_pguesses.py | 53 ------ 4 files changed, 207 insertions(+), 122 deletions(-) delete mode 100644 curvefit_automated_random_pguesses.py diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 39ad1f1..7757b41 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -7,28 +7,36 @@ """ ''' Which has more accurated recovered parameters: Amp & Phase or X & Y? - Same system, different noise - this replicates doing experiment many times. Using method of fixing F. ''' +import os import pandas as pd import math import random import numpy as np import matplotlib.pyplot as plt from brokenaxes import brokenaxes +import matplotlib.ticker as ticker from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y from resonatorsimulator import complex_noise from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 ''' Functions contained: - find_avg_e - calculates average across systematic error for each parameter + find_avg_e - Calculates average across systematic error for each parameter for one trial of the same system - artithmetic_then_logarithmic - calculates arithmetic average across parameters first, + artithmetic_then_logarithmic - Calculates arithmetic average across parameters first, then logarithmic average across trials run_trials - Runs a set number of trials for one system, graphs curvefit result, puts data and averages into spreadsheet, returns _bar for both types of curves - Must include number of trials to run and name of excel sheet + generate_random_system - Randomly generates parameters for system. All parameter values btw 0.1 and 10 + plot_guess - Used for the Case Study. Plots just the data and the guessed parameters curve. No curve fitting. + automate_guess - Randomly generates guess parameters within a certain percent of the true parameters + save_figure - Saves figures to a folder of your naming choice. Also allows you to name the figure whatever. + + This file also imports multiple_fit_amp_phase, which performs curve fitting on Amp vs Freq and Phase vs Freq curves for all 3 masses simultaneously, + and multiple_fit_X_Y, which performs curve fitting on X vs Freq and Y vs Freq curves for all 3 masses simulatenously. ''' #Calculate for one trial of the same system @@ -48,32 +56,32 @@ def find_avg_e(dictionary): return avg_e #Calculate _bar -def arithmetic_then_logarithmic(avg_e_list): +def arithmetic_then_logarithmic(avg_e_list, num_trials): ln_avg_e = [] for item in avg_e_list: ln_avg_e.append(math.log(item)) - sum_ln_avg_e = sum(ln_avg_e) - e_raised_to_sum = math.exp(sum_ln_avg_e) + avg_ln_avg_e = sum(ln_avg_e)/num_trials + e_raised_to_sum = math.exp(avg_ln_avg_e) return e_raised_to_sum #Runs a set number of trials for one system, graphs curvefit result, # puts data and averages into spreadsheet, returns _bar for both types of curves -def run_trials(true_params, guessed_params, num_trials, file_name): +def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_folder_name): starting_row = 0 avg_e1_list = [] avg_e2_list = [] #Put data into excel spreadsheet - with pd.ExcelWriter(file_name, engine='xlsxwriter') as writer: + with pd.ExcelWriter(excel_file_name, engine='xlsxwriter') as writer: for i in range(num_trials): #Create noise e = complex_noise(300, 2) #Get the data! - dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True) #Polar, Fixed force - dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Cartesian, Fixed force + dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force + dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force #Find (average across parameters) for each trial and add to dictionary avg_e1 = find_avg_e(dictionary1) @@ -96,8 +104,8 @@ def run_trials(true_params, guessed_params, num_trials, file_name): starting_row += len(dataframe1) + (1 if i==0 else 0) - avg_e1_bar = arithmetic_then_logarithmic(avg_e1_list) - avg_e2_bar = arithmetic_then_logarithmic(avg_e2_list) + avg_e1_bar = arithmetic_then_logarithmic(avg_e1_list, num_trials) + avg_e2_bar = arithmetic_then_logarithmic(avg_e2_list, num_trials) dataframe1.at[0,'_bar'] = avg_e1_bar dataframe2.at[0,'_bar'] = avg_e2_bar @@ -105,8 +113,9 @@ def run_trials(true_params, guessed_params, num_trials, file_name): dataframe1.to_excel(writer, sheet_name='Amp & Phase', index=False) dataframe2.to_excel(writer, sheet_name='X & Y', index=False) - return avg_e1_list, avg_e2_list, arithmetic_then_logarithmic(avg_e1_list), arithmetic_then_logarithmic(avg_e2_list) + return avg_e1_list, avg_e2_list, arithmetic_then_logarithmic(avg_e1_list, num_trials), arithmetic_then_logarithmic(avg_e2_list, num_trials) +#Randomly generates parameters of a system. All parameters between 0.1 and 10 def generate_random_system(): system_params = [] for i in range(11): @@ -118,7 +127,8 @@ def generate_random_system(): system_params.append(round_param) return system_params -def make_guess(params_guess, params_correct): +#Plots data and guessed parameters curve +def plot_guess(params_guess, params_correct): ##Create data - this is the same as what I use in the curve fit functions freq = np.linspace(0.001, 4, 300) @@ -272,64 +282,180 @@ def make_guess(params_guess, params_correct): plt.show() +#Generates random guess parameters that are within a certain percent of the true parameters +def automate_guess(true_params, percent_threshold): + params_guess = [] + for index, value in enumerate(true_params): + if index == 7: #Doing this because we must know what Force is going in + params_guess.append(value) + else: + threshold = value * (percent_threshold / 100) + num = random.uniform(value-threshold, value+threshold) + rounded_num = round(num, 4) # Round to 4 decimal places + params_guess.append(rounded_num) + return params_guess + +#Saves graphs +def save_figure(figure, folder_name, file_name): + # Create the folder if it does not exist + if not os.path.exists(folder_name): + os.makedirs(folder_name) + + # Save the figure to the folder + file_path = os.path.join(folder_name, file_name) + figure.savefig(file_path) + plt.close(figure) + ''' Begin work here. Case Study. ''' -#Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] -#Note that right now we only scale/fix by F, so make sure to keep F correct in guesses -true_params = generate_random_system() -guessed_params = [1,1,1,1,1,1,1,1,1,1,1] +# #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] +# #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses +# true_params = generate_random_system() +# guessed_params = [1,1,1,1,1,1,1,1,1,1,1] -# Start the loop -while True: - # Graph - make_guess(guessed_params, true_params) - - # Ask the user for the new list of guessed parameters - print(f'Current list of parameter guesses is {guessed_params}') - indices = input("Enter the indices of the elements you want to update (comma-separated, or 'q' to quit): ") - - # Check if the user wants to quit - if indices.lower() == 'q': - break - - # Parse and validate the indices - try: - index_list = [int(idx.strip()) for idx in indices.split(',')] - if any(index < 0 or index >= len(guessed_params) for index in index_list): - print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") - continue - except ValueError: - print("Invalid input. Please enter valid indices or 'q' to quit.") - continue - - # Ask the user for the new values - values = input(f"Enter the new values for indices {index_list} (comma-separated): ") - - # Parse and validate the new values - try: - value_list = [float(value.strip()) for value in values.split(',')] - if len(value_list) != len(index_list): - print("The number of values must match the number of indices.") - continue - except ValueError: - print("Invalid input. Please enter valid numbers.") - continue - - # Update the list with the new values - for index, new_value in zip(index_list, value_list): - guessed_params[index] = new_value +# # Start the loop +# while True: +# # Graph +# plot_guess(guessed_params, true_params) + +# # Ask the user for the new list of guessed parameters +# print(f'Current list of parameter guesses is {guessed_params}') +# indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") + +# # Check if the user wants to quit +# if indices.lower() == 'c': +# break + +# # Parse and validate the indices +# try: +# index_list = [int(idx.strip()) for idx in indices.split(',')] +# if any(index < 0 or index >= len(guessed_params) for index in index_list): +# print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") +# continue +# except ValueError: +# print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") +# continue + +# # Ask the user for the new values +# values = input(f"Enter the new values for indices {index_list} (comma-separated): ") + +# # Parse and validate the new values +# try: +# value_list = [float(value.strip()) for value in values.split(',')] +# if len(value_list) != len(index_list): +# print("The number of values must match the number of indices.") +# continue +# except ValueError: +# print("Invalid input. Please enter valid numbers.") +# continue +# # Update the list with the new values +# for index, new_value in zip(index_list, value_list): +# guessed_params[index] = new_value + +# #Curve fit with the guess made above +# avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx') + +# #Graph histogram of for both curve fits +# bax = brokenaxes(xlims=((0, max(avg_e2_list)+0.5), (min(avg_e1_list)-0.5, max(avg_e1_list)+0.5))) +# bax.set_title('Average Systematic Error Across Parameters') +# bax.set_xlabel('') +# bax.set_ylabel('Counts') +# bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)') +# bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)') +# bax.legend(loc='upper center') -avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx') +# plt.show() +''' Begin work here. Automated guesses. ''' -bax = brokenaxes(xlims=((0, max(avg_e2_list)+0.5), (min(avg_e1_list)-0.5, max(avg_e1_list)+0.5))) -bax.set_title('Average Systematic Error Across Parameters') -bax.set_xlabel('') -bax.set_ylabel('Counts') -bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)') -bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)') -bax.legend(loc='upper center') +avg_e1_bar_list = [] +avg_e2_bar_list = [] + +for i in range(15): + + #Generate system and guess parameters + true_params = generate_random_system() + guessed_params = automate_guess(true_params, 20) + + #Curve fit with the guess made above + avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') + + #Add _bar to lists to make one graph at the end + avg_e1_bar_list.append(avg_e1_bar) #Polar + avg_e2_bar_list.append(avg_e2_bar) #Cartesian + + #Graph histogram of for both curve fits + fig = plt.figure(figsize=(10, 6)) + + if max(avg_e2_list) >= min(avg_e1_list): + plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + plt.title('Average Systematic Error Across Parameters') + plt.xlabel(' (%)') + plt.ylabel('Counts') + plt.legend(loc='upper center') + + else: + spread1 = (max(avg_e1_list)-min(avg_e1_list)) #Polar + spread2 = (max(avg_e2_list)-min(avg_e2_list)) #Cartesian + + bax = brokenaxes(xlims=((min(avg_e2_list)-min(avg_e2_list)*0.1, max(avg_e2_list)+max(avg_e2_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) + bax.set_title('Average Systematic Error Across Parameters') + bax.set_xlabel(' (%)') + bax.set_ylabel('Counts') + bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + bax.legend(loc='upper center') + + # Adjust the scales + + bax.axs[0].set_xlim(min(avg_e2_list)-spread2*0.1, max(avg_e2_list)+spread2*0.1) #Cartesian + bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #Polar + + bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + + plt.show() + save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) + +#Graph histogram of _bar for both curve fits +fig = plt.figure(figsize=(10, 6)) + +if max(avg_e2_bar_list) >= min(avg_e1_bar_list): + plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + plt.title('Average Systematic Error Across Parameters') + plt.xlabel(' (%)') + plt.ylabel('Counts') + plt.legend(loc='upper center') + +else: + spread1 = (max(avg_e1_bar_list)-min(avg_e1_bar_list)) #Polar + spread2 = (max(avg_e2_bar_list)-min(avg_e2_bar_list)) #Cartesian + + bax = brokenaxes(xlims=((min(avg_e2_bar_list)-min(avg_e2_list)*0.1, max(avg_e2_bar_list)+max(avg_e2_bar_list)*0.1), (min(avg_e1_bar_list)-min(avg_e1_bar_list)*0.1, max(avg_e1_bar_list)+max(avg_e1_bar_list)*0.1)), hspace=.05) + bax.set_title('Average Systematic Error Across Parameters') + bax.set_xlabel(' (%)') + bax.set_ylabel('Counts') + bax.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + bax.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + bax.legend(loc='upper center') + + # Adjust the scales + + bax.axs[0].set_xlim(min(avg_e2_bar_list)-spread2*0.1, max(avg_e2_bar_list)+spread2*0.1) #Cartesian + bax.axs[1].set_xlim(min(avg_e1_bar_list)-spread1*0.1, max(avg_e1_bar_list)+spread1*0.1) #Polar + + bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) plt.show() +fig.savefig('Histogram__bar.png') + + diff --git a/curve_fitting_X_Y_all.py b/curve_fitting_X_Y_all.py index e856706..b8a0439 100644 --- a/curve_fitting_X_Y_all.py +++ b/curve_fitting_X_Y_all.py @@ -5,20 +5,21 @@ @author: lydiabullock """ - +import os import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 from resonatorstats import syserr, rsqrd -''' 2 functions contained: +''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve + save_figure - saves the curve fit graph created to a named folder ''' #Get residuals @@ -51,13 +52,23 @@ def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): return np.concatenate((residX1, residX2, residX3, residY1, residY2, residY3)) +def save_figure(figure, folder_name, file_name): + # Create the folder if it does not exist + if not os.path.exists(folder_name): + os.makedirs(folder_name) + + # Save the figure to the folder + file_path = os.path.join(folder_name, file_name) + figure.savefig(file_path) + plt.close(figure) + #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error -def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): +def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) @@ -287,5 +298,6 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F): ax9.legend(fontsize='10') plt.show() + save_figure(fig, graph_folder_name, graph_name) return data diff --git a/curve_fitting_amp_phase_all.py b/curve_fitting_amp_phase_all.py index 77e955c..2c1a204 100644 --- a/curve_fitting_amp_phase_all.py +++ b/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 2 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() return data \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data \ No newline at end of file diff --git a/curvefit_automated_random_pguesses.py b/curvefit_automated_random_pguesses.py deleted file mode 100644 index db90c66..0000000 --- a/curvefit_automated_random_pguesses.py +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Jul 19 14:48:10 2024 - -@author: lydiabullock -""" -''' Function that automates guess parameters. - Seeing success of curve fit with random guesses? ''' - -import random -from curve_fitting_X_Y_all import multiple_fit_X_Y -import pandas as pd -from resonatorsimulator import complex_noise - -def automate_guess(true_params, threshold, interval): - params_guess = [] - for index, value in enumerate(true_params): - if index == 7: #Doing this because we must know what Force is going in - params_guess.append(value) - else: - num = random.uniform(value-threshold, value+threshold) - rounded_num = round(num / interval) * interval # Round the number to the nearest interval - formatted_num = round(rounded_num, 4) # Just in case, format to 4 decimal places - params_guess.append(formatted_num) - return params_guess - -#Create noise -e = complex_noise(300, 2) - -true_params = [5, 5, 1, 1, 2, 2, 2, 1, 1.5, 1.5, 6.5] - -starting_row = 0 - -with pd.ExcelWriter('Curve_Fit_Simultaneously_Auto_Random_Guess.xlsx', engine='xlsxwriter') as writer: - - for i in range(10): - - #Created different guess parameters - guessed_params = automate_guess(true_params, 2, 0.001) - - #Get the data! - dictionary1 = multiple_fit_X_Y(guessed_params, true_params, e, False, True) #Fixed - - #Convert to dataframe - dataframe1 = pd.DataFrame(dictionary1) - - #Add to excel spreadsheet - dataframe1.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) - - starting_row += len(dataframe1) + (1 if i==0 else 0) - - \ No newline at end of file From a1f505729f42e02589c2349773291ec2ac6f0fad Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 24 Jul 2024 16:54:38 -0400 Subject: [PATCH 077/101] Started adding NetMAP to comparing_curvefit_types Using error posed a problem. Was able to fix this but I'm still only using 2 frequencies because I'm getting an "SVD did not converge" error. --- Trimer_NetMAP.py | 46 ++-- Trimer_simulator.py | 6 +- comparing_curvefit_types.py | 505 +++++++++++++++++++++++------------- 3 files changed, 348 insertions(+), 209 deletions(-) diff --git a/Trimer_NetMAP.py b/Trimer_NetMAP.py index 00ca766..0670fba 100644 --- a/Trimer_NetMAP.py +++ b/Trimer_NetMAP.py @@ -3,16 +3,15 @@ """ Created on Fri Mar 22 15:57:10 2024 -@author: samfeldman +@author: samfeldman & lydiabullock """ import numpy as np # return amp(Z.real, Z.imag) from Trimer_simulator import calculate_spectra -''' THIS IS THE NETMAP PART - Note that we do not deal with noise in any way ''' +''' THIS IS THE NETMAP PART ''' -def Zmatrix(force_all, freq, complexamp1, complexamp2, complexamp3): +def Zmatrix(freq, complexamp1, complexamp2, complexamp3, force_all): Zmatrix = [] for rowindex in range(len(freq)): w = freq[rowindex] @@ -21,29 +20,29 @@ def Zmatrix(force_all, freq, complexamp1, complexamp2, complexamp3): Z3 = complexamp3[rowindex] Zmatrix.append([-w**2*np.real(Z1), 0, 0, -w*np.imag(Z1), 0, 0, np.real(Z1), - np.real(Z1)-np.real(Z2), 0, -1]) + np.real(Z1)-np.real(Z2), 0, 0, -1]) Zmatrix.append([-w**2*np.imag(Z1), 0, 0, w*np.real(Z1), 0, 0, np.imag(Z1), - np.imag(Z1) - np.imag(Z2), 0, 0]) + np.imag(Z1) - np.imag(Z2), 0, 0, 0]) if force_all: Zmatrix.append([0, -w**2*np.real(Z2), 0, 0, -w*np.imag(Z2), 0, 0, - np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), -1]) + np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0, -1]) else: Zmatrix.append([0, -w**2*np.real(Z2), 0, 0, -w*np.imag(Z2), 0, 0, - np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0]) + np.real(Z2)-np.real(Z1), np.real(Z2) - np.real(Z3), 0, 0]) Zmatrix.append([0, -w**2*np.imag(Z2), 0, 0, w*np.real(Z2), 0, 0, - np.imag(Z2)-np.imag(Z1), np.imag(Z2) - np.imag(Z3), 0]) + np.imag(Z2)-np.imag(Z1), np.imag(Z2) - np.imag(Z3), 0, 0]) if force_all: Zmatrix.append([0, 0, -w**2*np.real(Z3), 0, 0, -w*np.imag(Z3), 0, 0, - np.real(Z3)-np.real(Z2), -1]) + np.real(Z3)-np.real(Z2), np.real(Z3), -1]) else: Zmatrix.append([0, 0, -w**2*np.real(Z3), 0, 0, -w*np.imag(Z3), 0, 0, - np.real(Z3)-np.real(Z2), 0]) + np.real(Z3)-np.real(Z2), np.real(Z3), 0]) Zmatrix.append([0, 0, -w**2*np.imag(Z3), 0, 0, w*np.real(Z3), 0, 0, - np.imag(Z3)-np.imag(Z2), 0]) + np.imag(Z3)-np.imag(Z2), np.imag(Z3), 0]) return np.array(Zmatrix) @@ -53,13 +52,15 @@ def unnormalizedparameters(Zmatrix): return V[:,-1] #Will it always be the last column of V?? def normalize_parameters_1d_by_force(unnormalizedparameters, F_set): - # parameters vector: 'm1', 'm2', 'b1', 'b2', 'k1', 'k2','k12', 'Driving Force' + # parameters vector: 'm1', 'm2', 'm3', 'b1', 'b2', 'b3', 'k1', 'k2', 'k3', 'k4', 'Driving Force' c = F_set / unnormalizedparameters[-1] parameters = [c*unnormalizedparameters[k] for k in range(len(unnormalizedparameters)) ] return parameters -#This is the data for NetMAP to work with. Using the same data as Sam in thesis +''' Example work begins here. ''' + +#This is the data for NetMAP to work with. Using the same data as Sam in thesis f1 = 1.7 f2 = 2.3 m1 = 3 @@ -71,21 +72,20 @@ def normalize_parameters_1d_by_force(unnormalizedparameters, F_set): k1 = 5 k2 = 5 k3 = 5 -k4 = 0 #no fourth spring connecting mass 4 to wall in this +k4 = 1 #no fourth spring connecting mass 4 to wall in this F = 1 +#create some noise +from resonatorsimulator import complex_noise +e = complex_noise(2, 2) #number of frequencies, noise level +frequencies = [f1, f2] + # getting the complex amplitudes with a function from Trimer_simulator -Z11, Z21, Z31 = calculate_spectra(f1, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3, 0, False) -Z12, Z22, Z32 = calculate_spectra(f2, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3, 0, False) +comamps1, comamps2, comamps3 = calculate_spectra(frequencies, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3, e, False) -frequencies = [f1, f2] -comamps1 = [Z11, Z12] -comamps2 = [Z21, Z22] -comamps3 = [Z31, Z32] -#these are good #Create the Zmatrix: -trizmatrix = Zmatrix(False, frequencies, comamps1, comamps2, comamps3) +trizmatrix = Zmatrix(frequencies, comamps1, comamps2, comamps3, False) #Get the unnormalized parameters: notnormparam_tri = unnormalizedparameters(trizmatrix) diff --git a/Trimer_simulator.py b/Trimer_simulator.py index 3e7c248..54f4d6d 100644 --- a/Trimer_simulator.py +++ b/Trimer_simulator.py @@ -307,9 +307,9 @@ def A_from_Z(Z): # calculate amplitude of complex number #Complex amps at a frequency #Can call this function in other code :) def calculate_spectra(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all): - Z1 = (complexamp(curve1(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all), theta1(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all))) - Z2 = (complexamp(curve2(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all), theta2(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all))) - Z3 = (complexamp(curve3(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all), theta3(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all))) + Z1 = list(complexamp(curve1(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all), theta1(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all))) + Z2 = list(complexamp(curve2(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all), theta2(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all))) + Z3 = list(complexamp(curve3(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all), theta3(drive, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, e, force_all))) return Z1, Z2, Z3 diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index 7757b41..c1fcc8c 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -20,7 +20,10 @@ from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y from resonatorsimulator import complex_noise -from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 +from Trimer_simulator import calculate_spectra, curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 +from Trimer_NetMAP import Zmatrix, unnormalizedparameters, normalize_parameters_1d_by_force +from scipy.signal import find_peaks +from resonatorstats import syserr ''' Functions contained: find_avg_e - Calculates average across systematic error for each parameter @@ -64,62 +67,11 @@ def arithmetic_then_logarithmic(avg_e_list, num_trials): e_raised_to_sum = math.exp(avg_ln_avg_e) return e_raised_to_sum -#Runs a set number of trials for one system, graphs curvefit result, -# puts data and averages into spreadsheet, returns _bar for both types of curves -def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_folder_name): - - starting_row = 0 - avg_e1_list = [] - avg_e2_list = [] - - #Put data into excel spreadsheet - with pd.ExcelWriter(excel_file_name, engine='xlsxwriter') as writer: - for i in range(num_trials): - - #Create noise - e = complex_noise(300, 2) - - #Get the data! - dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force - dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force - - #Find (average across parameters) for each trial and add to dictionary - avg_e1 = find_avg_e(dictionary1) - dictionary1[''] = avg_e1 - - avg_e2 = find_avg_e(dictionary2) - dictionary2[''] = avg_e2 - - #Append to list for later graphing - avg_e1_list.append(avg_e1) - avg_e2_list.append(avg_e2) - - #Turn data into dataframe for excel - dataframe1 = pd.DataFrame(dictionary1) - dataframe2 = pd.DataFrame(dictionary2) - - #Add to excel spreadsheet - dataframe1.to_excel(writer, sheet_name='Amp & Phase', startrow=starting_row, index=False, header=(i==0)) - dataframe2.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) - - starting_row += len(dataframe1) + (1 if i==0 else 0) - - avg_e1_bar = arithmetic_then_logarithmic(avg_e1_list, num_trials) - avg_e2_bar = arithmetic_then_logarithmic(avg_e2_list, num_trials) - - dataframe1.at[0,'_bar'] = avg_e1_bar - dataframe2.at[0,'_bar'] = avg_e2_bar - - dataframe1.to_excel(writer, sheet_name='Amp & Phase', index=False) - dataframe2.to_excel(writer, sheet_name='X & Y', index=False) - - return avg_e1_list, avg_e2_list, arithmetic_then_logarithmic(avg_e1_list, num_trials), arithmetic_then_logarithmic(avg_e2_list, num_trials) - #Randomly generates parameters of a system. All parameters between 0.1 and 10 def generate_random_system(): system_params = [] for i in range(11): - if i==7: + if i==7: #Doing this because we must keep force the same throughout system_params.append(1) else: param = random.uniform(0.1,10) @@ -306,156 +258,343 @@ def save_figure(figure, folder_name, file_name): figure.savefig(file_path) plt.close(figure) -''' Begin work here. Case Study. ''' +def get_parameters_NetMAP(params_guess, params_correct, e, force_all): -# #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] -# #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses -# true_params = generate_random_system() -# guessed_params = [1,1,1,1,1,1,1,1,1,1,1] - -# # Start the loop -# while True: -# # Graph -# plot_guess(guessed_params, true_params) - -# # Ask the user for the new list of guessed parameters -# print(f'Current list of parameter guesses is {guessed_params}') -# indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") - -# # Check if the user wants to quit -# if indices.lower() == 'c': -# break - -# # Parse and validate the indices -# try: -# index_list = [int(idx.strip()) for idx in indices.split(',')] -# if any(index < 0 or index >= len(guessed_params) for index in index_list): -# print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") -# continue -# except ValueError: -# print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") -# continue - -# # Ask the user for the new values -# values = input(f"Enter the new values for indices {index_list} (comma-separated): ") - -# # Parse and validate the new values -# try: -# value_list = [float(value.strip()) for value in values.split(',')] -# if len(value_list) != len(index_list): -# print("The number of values must match the number of indices.") -# continue -# except ValueError: -# print("Invalid input. Please enter valid numbers.") -# continue - -# # Update the list with the new values -# for index, new_value in zip(index_list, value_list): -# guessed_params[index] = new_value - -# #Curve fit with the guess made above -# avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx') - -# #Graph histogram of for both curve fits -# bax = brokenaxes(xlims=((0, max(avg_e2_list)+0.5), (min(avg_e1_list)-0.5, max(avg_e1_list)+0.5))) -# bax.set_title('Average Systematic Error Across Parameters') -# bax.set_xlabel('') -# bax.set_ylabel('Counts') -# bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)') -# bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)') -# bax.legend(loc='upper center') + # #Get frequencies + # freq = np.linspace(0.001, 4, 300) + + # # getting the complex amplitudes with a function from Trimer_simulator + # complex_amps1 = [] + # complex_amps2 = [] + # complex_amps3 = [] + # for f in range(len(freq)): + # Z1, Z2, Z3 = calculate_spectra(f, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + # complex_amps1.append(Z1) + # complex_amps2.append(Z2) + # complex_amps3.append(Z3) -# plt.show() + #Choosing 2 frequencies for now + freq = [1, 3] + + # getting the complex amplitudes with a function from Trimer_simulator + Z1, Z2, Z3 = calculate_spectra(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + #Create the Zmatrix: + trizmatrix = Zmatrix(freq, Z1, Z2, Z3, False) -''' Begin work here. Automated guesses. ''' + #Get the unnormalized parameters: + notnormparam_tri = unnormalizedparameters(trizmatrix) -avg_e1_bar_list = [] -avg_e2_bar_list = [] + #Normalize the parameters + final_tri = normalize_parameters_1d_by_force(notnormparam_tri, 1) + # parameters vector: 'm1', 'm2', 'm3', 'b1', 'b2', 'b3', 'k1', 'k2', 'k3', 'k4', 'Driving Force' -for i in range(15): - - #Generate system and guess parameters - true_params = generate_random_system() - guessed_params = automate_guess(true_params, 20) - - #Curve fit with the guess made above - avg_e1_list, avg_e2_list, avg_e1_bar, avg_e2_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') - - #Add _bar to lists to make one graph at the end - avg_e1_bar_list.append(avg_e1_bar) #Polar - avg_e2_bar_list.append(avg_e2_bar) #Cartesian + #Put everything into dictionary + data = {'k1_true': [params_correct[0]], 'k2_true': [params_correct[1]], 'k3_true': [params_correct[2]], 'k4_true': [params_correct[3]], + 'b1_true': [params_correct[4]], 'b2_true': [params_correct[5]], 'b3_true': [params_correct[6]], + 'm1_true': [params_correct[8]], 'm2_true': [params_correct[9]], 'm3_true': [params_correct[10]], 'F_true': [params_correct[7]], + 'k1_guess': [params_guess[0]], 'k2_guess': [params_guess[1]], 'k3_guess': [params_guess[2]], 'k4_guess': [params_guess[3]], + 'b1_guess': [params_guess[4]], 'b2_guess': [params_guess[5]], 'b3_guess': [params_guess[6]], + 'm1_guess': [params_guess[8]], 'm2_guess': [params_guess[9]], 'm3_guess': [params_guess[10]], 'F_guess': [params_guess[7]], + 'k1_recovered': [final_tri[6]], 'k2_recovered': [final_tri[7]], 'k3_recovered': [final_tri[8]], 'k4_recovered': [final_tri[9]], + 'b1_recovered': [final_tri[3]], 'b2_recovered': [final_tri[4]], 'b3_recovered': [final_tri[5]], + 'm1_recovered': [final_tri[0]], 'm2_recovered': [final_tri[1]], 'm3_recovered': [final_tri[2]], 'F_recovered': [final_tri[10]], + 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], + 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], + 'e_m1': [], 'e_m2': [], 'e_m3': []} + + #Calculate systematic error and add to data dictionary + for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: + param_true = data[f'{param_name}_true'][0] + param_fit = data[f'{param_name}_recovered'][0] + systematic_error = syserr(param_fit, param_true) + data[f'e_{param_name}'].append(systematic_error) + + return data + +#Runs a set number of trials for one system, graphs curvefit result, +# puts data and averages into spreadsheet, returns _bar for both types of curves +def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_folder_name): - #Graph histogram of for both curve fits - fig = plt.figure(figsize=(10, 6)) + starting_row = 0 + avg_e1_list = [] #Polar + avg_e2_list = [] #Cartesian + avg_e3_list = [] #NetMAP - if max(avg_e2_list) >= min(avg_e1_list): - plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - plt.title('Average Systematic Error Across Parameters') - plt.xlabel(' (%)') - plt.ylabel('Counts') - plt.legend(loc='upper center') + #Put data into excel spreadsheet + with pd.ExcelWriter(excel_file_name, engine='xlsxwriter') as writer: + for i in range(num_trials): + + #Create noise + e = complex_noise(300, 2) + e_special = complex_noise(2,2) + + #Get the data! + dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force + dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force + dictionary3 = get_parameters_NetMAP(guessed_params, true_params, e_special, False) #NetMAP + + #Find (average across parameters) for each trial and add to dictionary + avg_e1 = find_avg_e(dictionary1) #Polar + dictionary1[''] = avg_e1 + + avg_e2 = find_avg_e(dictionary2) #Cartesian + dictionary2[''] = avg_e2 + + avg_e3 = find_avg_e(dictionary3) #NetMAP + dictionary3[''] = avg_e3 + + #Append to list for later graphing + avg_e1_list.append(avg_e1) + avg_e2_list.append(avg_e2) + avg_e3_list.append(avg_e3) + + #Turn data into dataframe for excel + dataframe1 = pd.DataFrame(dictionary1) + dataframe2 = pd.DataFrame(dictionary2) + dataframe3 = pd.DataFrame(dictionary3) + + #Add to excel spreadsheet + dataframe1.to_excel(writer, sheet_name='Amp & Phase', startrow=starting_row, index=False, header=(i==0)) + dataframe2.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) + dataframe3.to_excel(writer, sheet_name='NetMAP', startrow=starting_row, index=False, header=(i==0)) + + starting_row += len(dataframe1) + (1 if i==0 else 0) + + avg_e1_bar = arithmetic_then_logarithmic(avg_e1_list, num_trials) + avg_e2_bar = arithmetic_then_logarithmic(avg_e2_list, num_trials) + avg_e3_bar = arithmetic_then_logarithmic(avg_e3_list, num_trials) + + dataframe1.at[0,'_bar'] = avg_e1_bar + dataframe2.at[0,'_bar'] = avg_e2_bar + dataframe3.at[0,'_bar'] = avg_e3_bar + + dataframe1.to_excel(writer, sheet_name='Amp & Phase', index=False) + dataframe2.to_excel(writer, sheet_name='X & Y', index=False) + dataframe3.to_excel(writer, sheet_name='NetMAP', index=False) + + return avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar - else: - spread1 = (max(avg_e1_list)-min(avg_e1_list)) #Polar - spread2 = (max(avg_e2_list)-min(avg_e2_list)) #Cartesian + + +''' Begin work here. Case Study. ''' + +#Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] +#Note that right now we only scale/fix by F, so make sure to keep F correct in guesses +true_params = generate_random_system() +guessed_params = [1,1,1,1,1,1,1,1,1,1,1] + +# Start the loop +while True: + # Graph + plot_guess(guessed_params, true_params) + + # Ask the user for the new list of guessed parameters + print(f'Current list of parameter guesses is {guessed_params}') + indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") + + # Check if the user wants to quit + if indices.lower() == 'c': + break + + # Parse and validate the indices + try: + index_list = [int(idx.strip()) for idx in indices.split(',')] + if any(index < 0 or index >= len(guessed_params) for index in index_list): + print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") + continue + except ValueError: + print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") + continue + + # Ask the user for the new values + values = input(f"Enter the new values for indices {index_list} (comma-separated): ") + + # Parse and validate the new values + try: + value_list = [float(value.strip()) for value in values.split(',')] + if len(value_list) != len(index_list): + print("The number of values must match the number of indices.") + continue + except ValueError: + print("Invalid input. Please enter valid numbers.") + continue + + # Update the list with the new values + for index, new_value in zip(index_list, value_list): + guessed_params[index] = new_value + # Graph + plot_guess(guessed_params, true_params) + + # Ask the user for the new list of guessed parameters + print(f'Current list of parameter guesses is {guessed_params}') + indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") + + # Check if the user wants to quit + if indices.lower() == 'c': + break + + # Parse and validate the indices + try: + index_list = [int(idx.strip()) for idx in indices.split(',')] + if any(index < 0 or index >= len(guessed_params) for index in index_list): + print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") + continue + except ValueError: + print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") + continue + + # Ask the user for the new values + values = input(f"Enter the new values for indices {index_list} (comma-separated): ") + + # Parse and validate the new values + try: + value_list = [float(value.strip()) for value in values.split(',')] + if len(value_list) != len(index_list): + print("The number of values must match the number of indices.") + continue + except ValueError: + print("Invalid input. Please enter valid numbers.") + continue + + # Update the list with the new values + for index, new_value in zip(index_list, value_list): + guessed_params[index] = new_value - bax = brokenaxes(xlims=((min(avg_e2_list)-min(avg_e2_list)*0.1, max(avg_e2_list)+max(avg_e2_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) - bax.set_title('Average Systematic Error Across Parameters') - bax.set_xlabel(' (%)') - bax.set_ylabel('Counts') - bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - bax.legend(loc='upper center') +#Curve fit with the guess made above +avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx', 'Case Study Plots') + +#Graph histogram of for both curve fits +bax = brokenaxes(xlims=((0, max(avg_e2_list)+0.5), (min(avg_e1_list)-0.5, max(avg_e1_list)+0.5))) +bax.set_title('Average Systematic Error Across Parameters') +bax.set_xlabel('') +bax.set_ylabel('Counts') +bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)') +bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)') +bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP') +bax.legend(loc='upper center') + +plt.show() + +''' Begin work here. Automated guesses. ''' + +# avg_e1_bar_list = [] +# avg_e2_bar_list = [] +# avg_e3_bar_list = [] + +# for i in range(1): - # Adjust the scales +# #Generate system and guess parameters +# true_params = generate_random_system() +# guessed_params = automate_guess(true_params, 20) - bax.axs[0].set_xlim(min(avg_e2_list)-spread2*0.1, max(avg_e2_list)+spread2*0.1) #Cartesian - bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #Polar +# #Curve fit with the guess made above +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') - bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) +# #Add _bar to lists to make one graph at the end +# avg_e1_bar_list.append(avg_e1_bar) #Polar +# avg_e2_bar_list.append(avg_e2_bar) #Cartesian +# avg_e3_bar_list.append(avg_e3_bar) #NetMAP - plt.show() - save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) +# #Graph histogram of for both curve fits +# fig = plt.figure(figsize=(10, 6)) +# spread1 = (max(avg_e1_list)-min(avg_e1_list)) #Polar +# spread2 = (max(avg_e2_list)-min(avg_e2_list)) #Cartesian +# spread3 = (max(avg_e3_list)-min(avg_e3_list)) #NetMAP + +# #If NetMAP and X&Y overlap but no overlap with Polar +# if max(avg_e2_list) < min(avg_e1_list) and max(avg_e3_list) <= min(avg_e1_list) and (max(avg_e2_list) >= min(avg_e3_list) or max(avg_e3_list) >= min(avg_e2_list)): + +# #If NetMAP is greater than X&Y +# if max(avg_e2_list) >= min(avg_e3_list): +# bax = brokenaxes(xlims=((min(avg_e2_list)-min(avg_e2_list)*0.1, max(avg_e3_list)+max(avg_e3_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) +# bax.set_title('Average Systematic Error Across Parameters') +# bax.set_xlabel(' (%)') +# bax.set_ylabel('Counts') +# bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') +# bax.legend(loc='upper center') + +# # Adjust the scales +# bax.axs[0].set_xlim(min(avg_e2_list)-spread2*0.1, max(avg_e3_list)+spread3*0.1) #left +# bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right + +# bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) +# bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + +# #If X&Y is greater than NetMAP +# else: +# bax = brokenaxes(xlims=((min(avg_e3_list)-min(avg_e3_list)*0.1, max(avg_e2_list)+max(avg_e2_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) +# bax.set_title('Average Systematic Error Across Parameters') +# bax.set_xlabel(' (%)') +# bax.set_ylabel('Counts') +# bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') +# bax.legend(loc='upper center') + +# # Adjust the scales +# bax.axs[0].set_xlim(min(avg_e3_list)-spread3*0.1, max(avg_e2_list)+spread2*0.1) #left +# bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right + +# bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) +# bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + +# #If Polar overlaps with either X&Y or NetMAP +# #Or, for now, there is no overlap +# else: +# plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') +# plt.title('Average Systematic Error Across Parameters') +# plt.xlabel(' (%)') +# plt.ylabel('Counts') +# plt.legend(loc='upper center') + +# plt.show() +# save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) -#Graph histogram of _bar for both curve fits -fig = plt.figure(figsize=(10, 6)) +# #Graph histogram of _bar for both curve fits +# fig = plt.figure(figsize=(10, 6)) -if max(avg_e2_bar_list) >= min(avg_e1_bar_list): - plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - plt.title('Average Systematic Error Across Parameters') - plt.xlabel(' (%)') - plt.ylabel('Counts') - plt.legend(loc='upper center') +# # if max(avg_e2_bar_list) >= min(avg_e1_bar_list): +# plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_bar_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') +# plt.title('Average Systematic Error Across Parameters') +# plt.xlabel(' (%)') +# plt.ylabel('Counts') +# plt.legend(loc='upper center') -else: - spread1 = (max(avg_e1_bar_list)-min(avg_e1_bar_list)) #Polar - spread2 = (max(avg_e2_bar_list)-min(avg_e2_bar_list)) #Cartesian - - bax = brokenaxes(xlims=((min(avg_e2_bar_list)-min(avg_e2_list)*0.1, max(avg_e2_bar_list)+max(avg_e2_bar_list)*0.1), (min(avg_e1_bar_list)-min(avg_e1_bar_list)*0.1, max(avg_e1_bar_list)+max(avg_e1_bar_list)*0.1)), hspace=.05) - bax.set_title('Average Systematic Error Across Parameters') - bax.set_xlabel(' (%)') - bax.set_ylabel('Counts') - bax.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - bax.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - bax.legend(loc='upper center') +# # else: +# # spread1 = (max(avg_e1_bar_list)-min(avg_e1_bar_list)) #Polar +# # spread2 = (max(avg_e2_bar_list)-min(avg_e2_bar_list)) #Cartesian + +# # bax = brokenaxes(xlims=((min(avg_e2_bar_list)-min(avg_e2_list)*0.1, max(avg_e2_bar_list)+max(avg_e2_bar_list)*0.1), (min(avg_e1_bar_list)-min(avg_e1_bar_list)*0.1, max(avg_e1_bar_list)+max(avg_e1_bar_list)*0.1)), hspace=.05) +# # bax.set_title('Average Systematic Error Across Parameters, Then Average Logarithmic Error Across Trials') +# # bax.set_xlabel(' (%)') +# # bax.set_ylabel('Counts') +# # bax.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# # bax.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# # bax.legend(loc='upper center') - # Adjust the scales +# # # Adjust the scales - bax.axs[0].set_xlim(min(avg_e2_bar_list)-spread2*0.1, max(avg_e2_bar_list)+spread2*0.1) #Cartesian - bax.axs[1].set_xlim(min(avg_e1_bar_list)-spread1*0.1, max(avg_e1_bar_list)+spread1*0.1) #Polar +# # bax.axs[0].set_xlim(min(avg_e2_bar_list)-spread2*0.1, max(avg_e2_bar_list)+spread2*0.1) #Cartesian +# # bax.axs[1].set_xlim(min(avg_e1_bar_list)-spread1*0.1, max(avg_e1_bar_list)+spread1*0.1) #Polar - bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) +# # bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# # bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) +# # bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# # bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -plt.show() -fig.savefig('Histogram__bar.png') +# plt.show() +# fig.savefig('Histogram__bar.png') From 4aa018b4286059acef5b0f7c0b2ea0ba542145de Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 25 Jul 2024 16:24:21 -0400 Subject: [PATCH 078/101] Update comparing_curvefit_types.py Added NetMAP. Changed some plotting things because now I have three sets of data. --- comparing_curvefit_types.py | 502 ++++++++++++++++++++---------------- 1 file changed, 282 insertions(+), 220 deletions(-) diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index c1fcc8c..c88ea46 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -22,7 +22,6 @@ from resonatorsimulator import complex_noise from Trimer_simulator import calculate_spectra, curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 from Trimer_NetMAP import Zmatrix, unnormalizedparameters, normalize_parameters_1d_by_force -from scipy.signal import find_peaks from resonatorstats import syserr ''' Functions contained: @@ -30,13 +29,15 @@ for one trial of the same system artithmetic_then_logarithmic - Calculates arithmetic average across parameters first, then logarithmic average across trials - run_trials - Runs a set number of trials for one system, graphs curvefit result, - puts data and averages into spreadsheet, returns _bar for both types of curves - - Must include number of trials to run and name of excel sheet generate_random_system - Randomly generates parameters for system. All parameter values btw 0.1 and 10 plot_guess - Used for the Case Study. Plots just the data and the guessed parameters curve. No curve fitting. automate_guess - Randomly generates guess parameters within a certain percent of the true parameters save_figure - Saves figures to a folder of your naming choice. Also allows you to name the figure whatever. + get_parameters_NetMAP - + run_trials - Runs a set number of trials for one system, graphs curvefit result, + puts data and averages into spreadsheet, returns _bar for both types of curves + - Must include number of trials to run and name of excel sheet + histogram_3_data_sets - This file also imports multiple_fit_amp_phase, which performs curve fitting on Amp vs Freq and Phase vs Freq curves for all 3 masses simultaneously, and multiple_fit_X_Y, which performs curve fitting on X vs Freq and Y vs Freq curves for all 3 masses simulatenously. @@ -258,23 +259,13 @@ def save_figure(figure, folder_name, file_name): figure.savefig(file_path) plt.close(figure) -def get_parameters_NetMAP(params_guess, params_correct, e, force_all): +def get_parameters_NetMAP(params_guess, params_correct, force_all): - # #Get frequencies - # freq = np.linspace(0.001, 4, 300) + #creat error + e = complex_noise(10,2) - # # getting the complex amplitudes with a function from Trimer_simulator - # complex_amps1 = [] - # complex_amps2 = [] - # complex_amps3 = [] - # for f in range(len(freq)): - # Z1, Z2, Z3 = calculate_spectra(f, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - # complex_amps1.append(Z1) - # complex_amps2.append(Z2) - # complex_amps3.append(Z3) - - #Choosing 2 frequencies for now - freq = [1, 3] + #Get frequencies + freq = np.linspace(0.001, 4, 10) # getting the complex amplitudes with a function from Trimer_simulator Z1, Z2, Z3 = calculate_spectra(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) @@ -327,12 +318,11 @@ def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_f #Create noise e = complex_noise(300, 2) - e_special = complex_noise(2,2) #Get the data! dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force - dictionary3 = get_parameters_NetMAP(guessed_params, true_params, e_special, False) #NetMAP + dictionary3 = get_parameters_NetMAP(guessed_params, true_params, False) #NetMAP #Find (average across parameters) for each trial and add to dictionary avg_e1 = find_avg_e(dictionary1) #Polar @@ -375,226 +365,298 @@ def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_f return avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar - +#Incomplete +def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_name, graph_title, x_label): + #Data 1 = polar, Data 2 = X&Y, Data 3 = NetMAP + + fig = plt.figure(figsize=(10, 6)) + spread1 = (max(data1)-min(data1)) + spread2 = (max(data2)-min(data2)) + spread3 = (max(data3)-min(data3)) + + #If 1 and 2 overlap but no overlap with 3 + #3 can be above or below 1 and 2 + if (max(data1)>=min(data2) or max(data2)>=min(data1)) and ((max(data1)min(data3) and max(data2)>min(data3))): + + #If 2 is greater than 1 + if max(data1) >= min(data2) and (max(data1)= min(data2) and (max(data1)>min(data3) and max(data2)>min(data3)): + bax = brokenaxes(xlims=((min(data2)-min(data2)*0.1, max(data1)+max(data1)*0.1), (min(data3)-min(data3)*0.1, max(data3)+max(data3)*0.1)), hspace=.05) + bax.set_title(graph_title) + bax.set_xlabel(x_label) + bax.set_ylabel('Counts') + bax.hist(data1, bins=10, alpha=0.75, color='blue', label=data1_name, edgecolor='black') + bax.hist(data2, bins=10, alpha=0.75, color='green', label=data2_name, edgecolor='black') + bax.hist(data3, bins=10, alpha=0.75, color='red', label=data3_name, edgecolor='black') + bax.legend(loc='upper center') + + # Adjust the scales + bax.axs[0].set_xlim(min(data2)-spread2*0.1, max(data1)+spread1*0.1) #left + bax.axs[1].set_xlim(min(data3)-spread3*0.1, max(data3)+spread3*0.1) #right + + bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + + #If 2 and 3 overlap but no overlap with 1 + elif (max(data2)>=min(data3) or max(data3)>=min(data2)) and max(data1)= min(data2): + bax = brokenaxes(xlims=((min(data1)-min(data1)*0.1, max(data2)+max(data2)*0.1), (min(data3)-min(data3)*0.1, max(data3)+max(data3)*0.1)), hspace=.05) + bax.set_title(graph_title) + bax.set_xlabel(x_label) + bax.set_ylabel('Counts') + bax.hist(data1, bins=10, alpha=0.75, color='blue', label=data1_name, edgecolor='black') + bax.hist(data2, bins=10, alpha=0.75, color='green', label=data2_name, edgecolor='black') + bax.hist(data3, bins=10, alpha=0.75, color='red', label=data3_name, edgecolor='black') + bax.legend(loc='upper center') + + # Adjust the scales + bax.axs[0].set_xlim(min(data1)-spread1*0.1, max(data2)+spread2*0.1) #left + bax.axs[1].set_xlim(min(data3)-spread3*0.1, max(data3)+spread3*0.1) #right + + bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + + #If 1 is greater than 2 + else: + bax = brokenaxes(xlims=((min(data2)-min(data2)*0.1, max(data1)+max(data1)*0.1), (min(data3)-min(data3)*0.1, max(data3)+max(data3)*0.1)), hspace=.05) + bax.set_title(graph_title) + bax.set_xlabel(x_label) + bax.set_ylabel('Counts') + bax.hist(data1, bins=10, alpha=0.75, color='blue', label=data1_name, edgecolor='black') + bax.hist(data2, bins=10, alpha=0.75, color='green', label=data2_name, edgecolor='black') + bax.hist(data3, bins=10, alpha=0.75, color='red', label=data3_name, edgecolor='black') + bax.legend(loc='upper center') + + # Adjust the scales + bax.axs[0].set_xlim(min(data2)-spread2*0.1, max(data1)+spread1*0.1) #left + bax.axs[1].set_xlim(min(data3)-spread3*0.1, max(data3)+spread3*0.1) #right + + bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + + plt.show() ''' Begin work here. Case Study. ''' -#Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] -#Note that right now we only scale/fix by F, so make sure to keep F correct in guesses -true_params = generate_random_system() -guessed_params = [1,1,1,1,1,1,1,1,1,1,1] - -# Start the loop -while True: - # Graph - plot_guess(guessed_params, true_params) - - # Ask the user for the new list of guessed parameters - print(f'Current list of parameter guesses is {guessed_params}') - indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") - - # Check if the user wants to quit - if indices.lower() == 'c': - break - - # Parse and validate the indices - try: - index_list = [int(idx.strip()) for idx in indices.split(',')] - if any(index < 0 or index >= len(guessed_params) for index in index_list): - print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") - continue - except ValueError: - print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") - continue - - # Ask the user for the new values - values = input(f"Enter the new values for indices {index_list} (comma-separated): ") - - # Parse and validate the new values - try: - value_list = [float(value.strip()) for value in values.split(',')] - if len(value_list) != len(index_list): - print("The number of values must match the number of indices.") - continue - except ValueError: - print("Invalid input. Please enter valid numbers.") - continue - - # Update the list with the new values - for index, new_value in zip(index_list, value_list): - guessed_params[index] = new_value - # Graph - plot_guess(guessed_params, true_params) - - # Ask the user for the new list of guessed parameters - print(f'Current list of parameter guesses is {guessed_params}') - indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") - - # Check if the user wants to quit - if indices.lower() == 'c': - break - - # Parse and validate the indices - try: - index_list = [int(idx.strip()) for idx in indices.split(',')] - if any(index < 0 or index >= len(guessed_params) for index in index_list): - print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") - continue - except ValueError: - print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") - continue - - # Ask the user for the new values - values = input(f"Enter the new values for indices {index_list} (comma-separated): ") - - # Parse and validate the new values - try: - value_list = [float(value.strip()) for value in values.split(',')] - if len(value_list) != len(index_list): - print("The number of values must match the number of indices.") - continue - except ValueError: - print("Invalid input. Please enter valid numbers.") - continue - - # Update the list with the new values - for index, new_value in zip(index_list, value_list): - guessed_params[index] = new_value +# #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] +# #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses +# true_params = generate_random_system() +# guessed_params = [1,1,1,1,1,1,1,1,1,1,1] + +# # Start the loop +# while True: +# # Graph +# plot_guess(guessed_params, true_params) + +# # Ask the user for the new list of guessed parameters +# print(f'Current list of parameter guesses is {guessed_params}') +# indices = input("Enter the indices of the elements you want to update (comma-separated, or 'c' to continue to curve fit): ") + +# # Check if the user wants to quit +# if indices.lower() == 'c': +# break + +# # Parse and validate the indices +# try: +# index_list = [int(idx.strip()) for idx in indices.split(',')] +# if any(index < 0 or index >= len(guessed_params) for index in index_list): +# print(f"Invalid indices. Please enter values between 0 and {len(guessed_params)-1}.") +# continue +# except ValueError: +# print("Invalid input. Please enter valid indices or 'c' to continue to curve fit.") +# continue + +# # Ask the user for the new values +# values = input(f"Enter the new values for indices {index_list} (comma-separated): ") + +# # Parse and validate the new values +# try: +# value_list = [float(value.strip()) for value in values.split(',')] +# if len(value_list) != len(index_list): +# print("The number of values must match the number of indices.") +# continue +# except ValueError: +# print("Invalid input. Please enter valid numbers.") +# continue + +# # Update the list with the new values +# for index, new_value in zip(index_list, value_list): +# guessed_params[index] = new_value -#Curve fit with the guess made above -avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx', 'Case Study Plots') - -#Graph histogram of for both curve fits -bax = brokenaxes(xlims=((0, max(avg_e2_list)+0.5), (min(avg_e1_list)-0.5, max(avg_e1_list)+0.5))) -bax.set_title('Average Systematic Error Across Parameters') -bax.set_xlabel('') -bax.set_ylabel('Counts') -bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)') -bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)') -bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP') -bax.legend(loc='upper center') +# #Curve fit with the guess made above and get average lists +# #Will not do anything with _bar for a single case study +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx', 'Case Study Plots') -plt.show() +# #Graph histogram of for curve fits + +# plt.title('Average Systematic Error Across Parameters') +# plt.xlabel('') +# plt.ylabel('Counts') +# plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') +# plt.legend(loc='upper center') + +# plt.show() ''' Begin work here. Automated guesses. ''' -# avg_e1_bar_list = [] -# avg_e2_bar_list = [] -# avg_e3_bar_list = [] +avg_e1_bar_list = [] +avg_e2_bar_list = [] +avg_e3_bar_list = [] -# for i in range(1): +for i in range(15): -# #Generate system and guess parameters -# true_params = generate_random_system() -# guessed_params = automate_guess(true_params, 20) + #Generate system and guess parameters + true_params = generate_random_system() + guessed_params = automate_guess(true_params, 20) -# #Curve fit with the guess made above -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') + #Curve fit with the guess made above + avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') -# #Add _bar to lists to make one graph at the end -# avg_e1_bar_list.append(avg_e1_bar) #Polar -# avg_e2_bar_list.append(avg_e2_bar) #Cartesian -# avg_e3_bar_list.append(avg_e3_bar) #NetMAP + #Add _bar to lists to make one graph at the end + avg_e1_bar_list.append(avg_e1_bar) #Polar + avg_e2_bar_list.append(avg_e2_bar) #Cartesian + avg_e3_bar_list.append(avg_e3_bar) #NetMAP -# #Graph histogram of for both curve fits -# fig = plt.figure(figsize=(10, 6)) -# spread1 = (max(avg_e1_list)-min(avg_e1_list)) #Polar -# spread2 = (max(avg_e2_list)-min(avg_e2_list)) #Cartesian -# spread3 = (max(avg_e3_list)-min(avg_e3_list)) #NetMAP + #Graph histogram of for curve fits + fig = plt.figure(figsize=(10, 6)) + plt.title('Average Systematic Error Across Parameters') + plt.xlabel('') + plt.ylabel('Counts') + plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') + plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') + plt.legend(loc='upper center') + + plt.show() + save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) -# #If NetMAP and X&Y overlap but no overlap with Polar -# if max(avg_e2_list) < min(avg_e1_list) and max(avg_e3_list) <= min(avg_e1_list) and (max(avg_e2_list) >= min(avg_e3_list) or max(avg_e3_list) >= min(avg_e2_list)): + # fig = plt.figure(figsize=(10, 6)) + # spread1 = (max(avg_e1_list)-min(avg_e1_list)) #Polar + # spread2 = (max(avg_e2_list)-min(avg_e2_list)) #Cartesian + # spread3 = (max(avg_e3_list)-min(avg_e3_list)) #NetMAP + + # #If NetMAP and X&Y overlap but no overlap with Polar + # if max(avg_e2_list) < min(avg_e1_list) and max(avg_e3_list) <= min(avg_e1_list) and (max(avg_e2_list) >= min(avg_e3_list) or max(avg_e3_list) >= min(avg_e2_list)): -# #If NetMAP is greater than X&Y -# if max(avg_e2_list) >= min(avg_e3_list): -# bax = brokenaxes(xlims=((min(avg_e2_list)-min(avg_e2_list)*0.1, max(avg_e3_list)+max(avg_e3_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) -# bax.set_title('Average Systematic Error Across Parameters') -# bax.set_xlabel(' (%)') -# bax.set_ylabel('Counts') -# bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -# bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') -# bax.legend(loc='upper center') + # #If NetMAP is greater than X&Y + # if max(avg_e2_list) >= min(avg_e3_list): + # bax = brokenaxes(xlims=((min(avg_e2_list)-min(avg_e2_list)*0.1, max(avg_e3_list)+max(avg_e3_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) + # bax.set_title('Average Systematic Error Across Parameters') + # bax.set_xlabel(' (%)') + # bax.set_ylabel('Counts') + # bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + # bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + # bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') + # bax.legend(loc='upper center') -# # Adjust the scales -# bax.axs[0].set_xlim(min(avg_e2_list)-spread2*0.1, max(avg_e3_list)+spread3*0.1) #left -# bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right + # # Adjust the scales + # bax.axs[0].set_xlim(min(avg_e2_list)-spread2*0.1, max(avg_e3_list)+spread3*0.1) #left + # bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right -# bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -# bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + # bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + # bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + # bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + # bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -# #If X&Y is greater than NetMAP -# else: -# bax = brokenaxes(xlims=((min(avg_e3_list)-min(avg_e3_list)*0.1, max(avg_e2_list)+max(avg_e2_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) -# bax.set_title('Average Systematic Error Across Parameters') -# bax.set_xlabel(' (%)') -# bax.set_ylabel('Counts') -# bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -# bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') -# bax.legend(loc='upper center') + # #If X&Y is greater than NetMAP + # else: + # bax = brokenaxes(xlims=((min(avg_e3_list)-min(avg_e3_list)*0.1, max(avg_e2_list)+max(avg_e2_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) + # bax.set_title('Average Systematic Error Across Parameters') + # bax.set_xlabel(' (%)') + # bax.set_ylabel('Counts') + # bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + # bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + # bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') + # bax.legend(loc='upper center') -# # Adjust the scales -# bax.axs[0].set_xlim(min(avg_e3_list)-spread3*0.1, max(avg_e2_list)+spread2*0.1) #left -# bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right + # # Adjust the scales + # bax.axs[0].set_xlim(min(avg_e3_list)-spread3*0.1, max(avg_e2_list)+spread2*0.1) #left + # bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right -# bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -# bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - -# #If Polar overlaps with either X&Y or NetMAP -# #Or, for now, there is no overlap -# else: -# plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -# plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# plt.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') -# plt.title('Average Systematic Error Across Parameters') -# plt.xlabel(' (%)') -# plt.ylabel('Counts') -# plt.legend(loc='upper center') - -# plt.show() -# save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) - -# #Graph histogram of _bar for both curve fits -# fig = plt.figure(figsize=(10, 6)) - -# # if max(avg_e2_bar_list) >= min(avg_e1_bar_list): -# plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -# plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# plt.hist(avg_e3_bar_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') -# plt.title('Average Systematic Error Across Parameters') -# plt.xlabel(' (%)') -# plt.ylabel('Counts') -# plt.legend(loc='upper center') - -# # else: -# # spread1 = (max(avg_e1_bar_list)-min(avg_e1_bar_list)) #Polar -# # spread2 = (max(avg_e2_bar_list)-min(avg_e2_bar_list)) #Cartesian - -# # bax = brokenaxes(xlims=((min(avg_e2_bar_list)-min(avg_e2_list)*0.1, max(avg_e2_bar_list)+max(avg_e2_bar_list)*0.1), (min(avg_e1_bar_list)-min(avg_e1_bar_list)*0.1, max(avg_e1_bar_list)+max(avg_e1_bar_list)*0.1)), hspace=.05) -# # bax.set_title('Average Systematic Error Across Parameters, Then Average Logarithmic Error Across Trials') -# # bax.set_xlabel(' (%)') -# # bax.set_ylabel('Counts') -# # bax.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -# # bax.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# # bax.legend(loc='upper center') + # bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) + # bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + # bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) + # bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) + + # #If Polar overlaps with either X&Y or NetMAP + # #Or, for now, there is no overlap + # else: + # plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') + # plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + # plt.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') + # plt.title('Average Systematic Error Across Parameters') + # plt.xlabel(' (%)') + # plt.ylabel('Counts') + # plt.legend(loc='upper center') + + # plt.show() + # save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) + +#Graph histogram of _bar for both curve fits +fig = plt.figure(figsize=(10, 6)) + +# if max(avg_e2_bar_list) >= min(avg_e1_bar_list): +plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +plt.hist(avg_e3_bar_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') +plt.title('Average Systematic Error Across Parameters') +plt.xlabel(' (%)') +plt.ylabel('Counts') +plt.legend(loc='upper center') + +# else: +# spread1 = (max(avg_e1_bar_list)-min(avg_e1_bar_list)) #Polar +# spread2 = (max(avg_e2_bar_list)-min(avg_e2_bar_list)) #Cartesian + +# bax = brokenaxes(xlims=((min(avg_e2_bar_list)-min(avg_e2_list)*0.1, max(avg_e2_bar_list)+max(avg_e2_bar_list)*0.1), (min(avg_e1_bar_list)-min(avg_e1_bar_list)*0.1, max(avg_e1_bar_list)+max(avg_e1_bar_list)*0.1)), hspace=.05) +# bax.set_title('Average Systematic Error Across Parameters, Then Average Logarithmic Error Across Trials') +# bax.set_xlabel(' (%)') +# bax.set_ylabel('Counts') +# bax.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# bax.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# bax.legend(loc='upper center') + +# # Adjust the scales + +# bax.axs[0].set_xlim(min(avg_e2_bar_list)-spread2*0.1, max(avg_e2_bar_list)+spread2*0.1) #Cartesian +# bax.axs[1].set_xlim(min(avg_e1_bar_list)-spread1*0.1, max(avg_e1_bar_list)+spread1*0.1) #Polar + +# bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) +# bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) +# bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -# # # Adjust the scales - -# # bax.axs[0].set_xlim(min(avg_e2_bar_list)-spread2*0.1, max(avg_e2_bar_list)+spread2*0.1) #Cartesian -# # bax.axs[1].set_xlim(min(avg_e1_bar_list)-spread1*0.1, max(avg_e1_bar_list)+spread1*0.1) #Polar - -# # bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# # bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -# # bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# # bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - -# plt.show() -# fig.savefig('Histogram__bar.png') +plt.show() +fig.savefig('_bar_Histogram.png') From a2ed185d96548dd6d00235e2a5ff02f2222e3bf2 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 29 Jul 2024 14:03:49 -0400 Subject: [PATCH 079/101] Created trimer_case_study_frequency_picker Began a case study using Viva's code to pick the best frequencies for NetMAP. --- Trimer_NetMAP.py | 3 +- comparing_curvefit_types.py | 209 ++++----- trimer_case_study_frequency_picker.py | 584 ++++++++++++++++++++++++++ 3 files changed, 659 insertions(+), 137 deletions(-) create mode 100644 trimer_case_study_frequency_picker.py diff --git a/Trimer_NetMAP.py b/Trimer_NetMAP.py index 0670fba..1b86874 100644 --- a/Trimer_NetMAP.py +++ b/Trimer_NetMAP.py @@ -83,8 +83,7 @@ def normalize_parameters_1d_by_force(unnormalizedparameters, F_set): # getting the complex amplitudes with a function from Trimer_simulator comamps1, comamps2, comamps3 = calculate_spectra(frequencies, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3, e, False) - -#Create the Zmatrix: +#Now that we have the data, create the Zmatrix: trizmatrix = Zmatrix(frequencies, comamps1, comamps2, comamps3, False) #Get the unnormalized parameters: diff --git a/comparing_curvefit_types.py b/comparing_curvefit_types.py index c88ea46..5922a96 100644 --- a/comparing_curvefit_types.py +++ b/comparing_curvefit_types.py @@ -33,11 +33,11 @@ plot_guess - Used for the Case Study. Plots just the data and the guessed parameters curve. No curve fitting. automate_guess - Randomly generates guess parameters within a certain percent of the true parameters save_figure - Saves figures to a folder of your naming choice. Also allows you to name the figure whatever. - get_parameters_NetMAP - + get_parameters_NetMAP - Recovers parameters for a system given the guessed parameters run_trials - Runs a set number of trials for one system, graphs curvefit result, puts data and averages into spreadsheet, returns _bar for both types of curves - Must include number of trials to run and name of excel sheet - histogram_3_data_sets - + histogram_3_data_sets - incomplete but tries to graph histograms better This file also imports multiple_fit_amp_phase, which performs curve fitting on Amp vs Freq and Phase vs Freq curves for all 3 masses simultaneously, and multiple_fit_X_Y, which performs curve fitting on X vs Freq and Y vs Freq curves for all 3 masses simulatenously. @@ -259,19 +259,15 @@ def save_figure(figure, folder_name, file_name): figure.savefig(file_path) plt.close(figure) -def get_parameters_NetMAP(params_guess, params_correct, force_all): - - #creat error - e = complex_noise(10,2) - - #Get frequencies - freq = np.linspace(0.001, 4, 10) +def get_parameters_NetMAP(frequencies, params_guess, params_correct, e, force_all): - # getting the complex amplitudes with a function from Trimer_simulator - Z1, Z2, Z3 = calculate_spectra(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + #Getting the complex amplitudes (data) with a function from Trimer_simulator + #Still part of the simulation + Z1, Z2, Z3 = calculate_spectra(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) #Create the Zmatrix: - trizmatrix = Zmatrix(freq, Z1, Z2, Z3, False) + #This is where we begin NetMAP + trizmatrix = Zmatrix(frequencies, Z1, Z2, Z3, False) #Get the unnormalized parameters: notnormparam_tri = unnormalizedparameters(trizmatrix) @@ -318,11 +314,17 @@ def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_f #Create noise e = complex_noise(300, 2) + + ##For NetMAP + #Get frequencies + freqs_NetMAP = np.linspace(0.001, 4, 10) + #create error + e_NetMAP = complex_noise(10,2) #Get the data! dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force - dictionary3 = get_parameters_NetMAP(guessed_params, true_params, False) #NetMAP + dictionary3 = get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False) #NetMAP #Find (average across parameters) for each trial and add to dictionary avg_e1 = find_avg_e(dictionary1) #Polar @@ -528,135 +530,72 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam ''' Begin work here. Automated guesses. ''' -avg_e1_bar_list = [] -avg_e2_bar_list = [] -avg_e3_bar_list = [] +# avg_e1_bar_list = [] +# avg_e2_bar_list = [] +# avg_e3_bar_list = [] -for i in range(15): +# for i in range(15): - #Generate system and guess parameters - true_params = generate_random_system() - guessed_params = automate_guess(true_params, 20) +# #Generate system and guess parameters +# true_params = generate_random_system() +# guessed_params = automate_guess(true_params, 20) - #Curve fit with the guess made above - avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') +# #Curve fit with the guess made above +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') - #Add _bar to lists to make one graph at the end - avg_e1_bar_list.append(avg_e1_bar) #Polar - avg_e2_bar_list.append(avg_e2_bar) #Cartesian - avg_e3_bar_list.append(avg_e3_bar) #NetMAP +# #Add _bar to lists to make one graph at the end +# avg_e1_bar_list.append(avg_e1_bar) #Polar +# avg_e2_bar_list.append(avg_e2_bar) #Cartesian +# avg_e3_bar_list.append(avg_e3_bar) #NetMAP - #Graph histogram of for curve fits - fig = plt.figure(figsize=(10, 6)) - plt.title('Average Systematic Error Across Parameters') - plt.xlabel('') - plt.ylabel('Counts') - plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') - plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') - plt.legend(loc='upper center') +# #Graph histogram of for curve fits +# fig = plt.figure(figsize=(10, 6)) +# plt.title('Average Systematic Error Across Parameters') +# plt.xlabel('') +# plt.ylabel('Counts') +# plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') +# plt.legend(loc='upper center') - plt.show() - save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) - - # fig = plt.figure(figsize=(10, 6)) - # spread1 = (max(avg_e1_list)-min(avg_e1_list)) #Polar - # spread2 = (max(avg_e2_list)-min(avg_e2_list)) #Cartesian - # spread3 = (max(avg_e3_list)-min(avg_e3_list)) #NetMAP +# plt.show() +# save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) - # #If NetMAP and X&Y overlap but no overlap with Polar - # if max(avg_e2_list) < min(avg_e1_list) and max(avg_e3_list) <= min(avg_e1_list) and (max(avg_e2_list) >= min(avg_e3_list) or max(avg_e3_list) >= min(avg_e2_list)): - - # #If NetMAP is greater than X&Y - # if max(avg_e2_list) >= min(avg_e3_list): - # bax = brokenaxes(xlims=((min(avg_e2_list)-min(avg_e2_list)*0.1, max(avg_e3_list)+max(avg_e3_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) - # bax.set_title('Average Systematic Error Across Parameters') - # bax.set_xlabel(' (%)') - # bax.set_ylabel('Counts') - # bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - # bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - # bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') - # bax.legend(loc='upper center') - - # # Adjust the scales - # bax.axs[0].set_xlim(min(avg_e2_list)-spread2*0.1, max(avg_e3_list)+spread3*0.1) #left - # bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right - - # bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - # bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - # bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - # bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - - # #If X&Y is greater than NetMAP - # else: - # bax = brokenaxes(xlims=((min(avg_e3_list)-min(avg_e3_list)*0.1, max(avg_e2_list)+max(avg_e2_list)*0.1), (min(avg_e1_list)-min(avg_e1_list)*0.1, max(avg_e1_list)+max(avg_e1_list)*0.1)), hspace=.05) - # bax.set_title('Average Systematic Error Across Parameters') - # bax.set_xlabel(' (%)') - # bax.set_ylabel('Counts') - # bax.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - # bax.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - # bax.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') - # bax.legend(loc='upper center') - - # # Adjust the scales - # bax.axs[0].set_xlim(min(avg_e3_list)-spread3*0.1, max(avg_e2_list)+spread2*0.1) #left - # bax.axs[1].set_xlim(min(avg_e1_list)-spread1*0.1, max(avg_e1_list)+spread1*0.1) #right - - # bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - # bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - # bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - # bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - - # #If Polar overlaps with either X&Y or NetMAP - # #Or, for now, there is no overlap - # else: - # plt.hist(avg_e2_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') - # plt.hist(avg_e1_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - # plt.hist(avg_e3_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') - # plt.title('Average Systematic Error Across Parameters') - # plt.xlabel(' (%)') - # plt.ylabel('Counts') - # plt.legend(loc='upper center') - - # plt.show() - # save_figure(fig, f'Sys {i} - Rand Auto Guess Plots', ' Histogram ' ) - -#Graph histogram of _bar for both curve fits -fig = plt.figure(figsize=(10, 6)) - -# if max(avg_e2_bar_list) >= min(avg_e1_bar_list): -plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -plt.hist(avg_e3_bar_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') -plt.title('Average Systematic Error Across Parameters') -plt.xlabel(' (%)') -plt.ylabel('Counts') -plt.legend(loc='upper center') - -# else: -# spread1 = (max(avg_e1_bar_list)-min(avg_e1_bar_list)) #Polar -# spread2 = (max(avg_e2_bar_list)-min(avg_e2_bar_list)) #Cartesian - -# bax = brokenaxes(xlims=((min(avg_e2_bar_list)-min(avg_e2_list)*0.1, max(avg_e2_bar_list)+max(avg_e2_bar_list)*0.1), (min(avg_e1_bar_list)-min(avg_e1_bar_list)*0.1, max(avg_e1_bar_list)+max(avg_e1_bar_list)*0.1)), hspace=.05) -# bax.set_title('Average Systematic Error Across Parameters, Then Average Logarithmic Error Across Trials') -# bax.set_xlabel(' (%)') -# bax.set_ylabel('Counts') -# bax.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') -# bax.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# bax.legend(loc='upper center') - -# # Adjust the scales - -# bax.axs[0].set_xlim(min(avg_e2_bar_list)-spread2*0.1, max(avg_e2_bar_list)+spread2*0.1) #Cartesian -# bax.axs[1].set_xlim(min(avg_e1_bar_list)-spread1*0.1, max(avg_e1_bar_list)+spread1*0.1) #Polar - -# bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) -# bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) -# bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - -plt.show() -fig.savefig('_bar_Histogram.png') +# #Graph histogram of _bar for both curve fits +# fig = plt.figure(figsize=(10, 6)) +# # if max(avg_e2_bar_list) >= min(avg_e1_bar_list): +# plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_bar_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') +# plt.title('Average Systematic Error Across Parameters') +# plt.xlabel(' (%)') +# plt.ylabel('Counts') +# plt.legend(loc='upper center') + +# plt.show() +# fig.savefig('_bar_Histogram.png') + +''' Begin work here. Checking Worst System. ''' + +## System 0 from 15 Systems - 10 Freqs NetMAP +## Expecting there to be no error in recovery for everything +# true_parameters = [1.045, 0.179, 3.852, 1.877, 5.542, 1.956, 3.71, 1, 3.976, 0.656, 3.198] +# guessed_parameters = [1.2379, 0.1764, 3.7327, 1.8628, 5.93, 2.1793, 4.2198, 1, 4.3335, 0.7016, 3.0719] + +# #Run the trials with 0 error +# # MUST CHANGE ERROR IN run_trials AND IN get_parameters_NetMAP +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, 50, 'Sys0_No_Error.xlsx', 'Sys0_No_Error - Plots') + +# #Plot histogram +# plt.title('Average Systematic Error Across Parameters') +# plt.xlabel('') +# plt.ylabel('Counts') +# plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') +# plt.legend(loc='upper center') +# plt.show() +# plt.savefig('_Histogram_Sys0_no_error.png') diff --git a/trimer_case_study_frequency_picker.py b/trimer_case_study_frequency_picker.py new file mode 100644 index 0000000..42b7311 --- /dev/null +++ b/trimer_case_study_frequency_picker.py @@ -0,0 +1,584 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 29 10:55:10 2024 + +@author: lydiabullock +""" +''' Case Study for System 0 from '15 Systems - 10 Freqs NetMAP' + Using "ideal" frequencies to test NetMAP. ''' + +from comparing_curvefit_types import run_trials +import numpy as np +from resonatorphysics import res_freq_weak_coupling, calcnarrowerW +from Trimer_simulator import curve1, theta1, curve2, theta2 +import matplotlib.pyplot as plt +from scipy.signal import find_peaks +import resonatorphysics + +## Copy of Viva's code from resonatorfrequency picker but adding information so I can run it with a Trimer + +# default settings +verbose = False +n=100 +debug = False + +## Uses privilege +## Not guaranteed to find all resonance peaks but should work ok for dimer +## Returns list of peak frequencies. +## If numtoreturn is None, then any number of frequencies could be returned. +## You can also set numtoreturn to 1 or 2 to return that number of frequencies. +def res_freq_numeric(vals_set, MONOMER, forceall, + mode = 'all', + minfreq=.1, maxfreq=5, morefrequencies=None, includefreqs = [], + unique = True, veryunique = True, numtoreturn = None, + verboseplot = False, plottitle = None, verbose=verbose, iterations = 1, + use_R2_only = False, + returnoptions = False): + + if verbose: + print('\nRunning res_freq_numeric() with mode ' + mode) + if plottitle is not None: + print(plottitle) + k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set = read_params(vals_set, MONOMER) + + # Never Monomer in this case + if MONOMER and numtoreturn != 2: # 2 is a tricky case... just use the rest of the algorithm + if numtoreturn is not None and numtoreturn != 1: + print('Cannot return ' + str(numtoreturn) + ' res freqs for Monomer.') + if verbose: + print('option 1') + + freqlist = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] # just compute it directly for Monomer + if returnoptions: + return freqlist, 1 + return freqlist + + approx_res_freqs = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] + if not MONOMER: + approx_res_freqs.append(res_freq_weak_coupling(k2_set, m2_set, b2_set)) + + for f in approx_res_freqs: + if f > maxfreq or f < minfreq: + print('Warning! Check minfreq and maxfreq') + print('minfreq', minfreq) + print('maxfreq', maxfreq) + print('Approx resonant freq', f) + + if morefrequencies is None: + morefrequencies = makemorefrequencies(vals_set=vals_set, minfreq=minfreq, maxfreq=maxfreq, + forceall=forceall, includefreqs = approx_res_freqs, + MONOMER=MONOMER, n=n) + else: + morefrequencies = np.append(morefrequencies, approx_res_freqs) + morefrequencies = np.sort(np.unique(morefrequencies)) + + # init + indexlist = [] + + # Never Monomer in this case + if MONOMER: + freqlist = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] + resfreqs_from_amp = freqlist + else: + first = True + for i in range(iterations): + if not first: # not first. This is a repeated iteration. indexlist has been defined. + if verbose: + print('indexlist:', indexlist) + if max(indexlist) > len(morefrequencies): + print('len(morefrequencies):', len(morefrequencies)) + print('morefrequencies:', morefrequencies) + print('indexlist:', indexlist) + print('Repeating with finer frequency mesh around frequencies:', morefrequencies[np.sort(indexlist)]) + + assert min(morefrequencies) >= minfreq + assert max(morefrequencies) <= maxfreq + if debug: + print('minfreq', minfreq) + print('Actual min freq', min(morefrequencies)) + print('maxfreq', maxfreq) + print('Actual max freq', max(morefrequencies)) + morefrequenciesprev = morefrequencies.copy() + for index in indexlist: + try: + spacing = abs(morefrequenciesprev[index] - morefrequenciesprev[index-1]) + except: + if verbose: + print('morefrequenciesprev:',morefrequenciesprev) + print('index:', index) + spacing = abs(morefrequenciesprev[index+1] - morefrequenciesprev[index]) + finerlist = np.linspace(max(minfreq,morefrequenciesprev[index]-spacing), + min(maxfreq,morefrequenciesprev[index] + spacing), + num = n) + assert min(finerlist) >= minfreq + assert max(finerlist) <= maxfreq + morefrequencies = np.append(morefrequencies,finerlist) + morefrequencies = np.sort(np.unique(morefrequencies)) + + + while morefrequencies[-1] > maxfreq: + if False: # too verbose! + print('Removing frequency', morefrequencies[-1]) + morefrequencies = morefrequencies[:-1] + while morefrequencies[0]< minfreq: + if False: + print('Removing frequency', morefrequencies[0]) + morefrequencies = morefrequencies[1:] + R1_amp_noiseless = curve1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = np.unwrap(R1_phase_noiseless) + if debug: + plt.figure() + plt.plot(morefrequencies, R1_amp_noiseless, label = 'R1_amp') + plt.plot(morefrequencies, R1_phase_noiseless, label = 'R1_phase') + if not MONOMER: + R2_amp_noiseless = curve2(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R2_phase_noiseless = theta2(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R2_phase_noiseless = np.unwrap(R2_phase_noiseless) + if debug: + plt.plot(morefrequencies, R2_amp_noiseless, label = 'R2_amp') + plt.plot(morefrequencies, R2_phase_noiseless, label = 'R2_phase') + + ## find maxima + index1 = np.argmax(R1_amp_noiseless) + if not MONOMER and not use_R2_only: + indexlist1, heights = find_peaks(R1_amp_noiseless, height=.015, distance = 5) + if debug: + print('index1:', index1) + print('indexlist1:',indexlist1) + print('heights', heights) + plt.axvline(morefrequencies[index1]) + for i in indexlist1: + plt.axvline(morefrequencies[i]) + assert index1 <= len(morefrequencies) + if len(indexlist1)>0: + assert max(indexlist1) <= len(morefrequencies) + else: + print('Warning: find_peaks on R1_amp returned indexlist:', indexlist1) + plt.figure() + plt.plot(R1_amp_noiseless) + plt.xlabel(R1_amp_noiseless) + plt.figure() + else: + indexlist1 = [] + if MONOMER: + indexlist2 = [] + else: + index2 = np.argmax(R2_amp_noiseless) + indexlist2, heights2 = find_peaks(R2_amp_noiseless, height=.015, distance = 5) + assert index2 <= len(morefrequencies) + if len(indexlist2) >0: + assert max(indexlist2) <= len(morefrequencies) + + if verbose: + print('Maximum amplitude for R1 is ', R1_amp_noiseless[index1], 'at', morefrequencies[index1]) + if not MONOMER: + print('Maximum amplitude for R2 is ', R2_amp_noiseless[index2], 'at', morefrequencies[index2]) + + indexlistampR1 = np.append(indexlist1,index1) + assert max(indexlistampR1) <= len(morefrequencies) + if False: # too verbose! + print('indexlistampR1:', indexlistampR1) + if MONOMER: + indexlist = indexlistampR1 + assert max(indexlist) <= len(morefrequencies) + indexlistampR2 = [] + else: + indexlistampR2 = np.append(indexlist2, index2) + if False: + print('indexlistampR2:',indexlistampR2) + assert max(indexlistampR2) <= len(morefrequencies) + indexlist = np.append(indexlistampR1, indexlistampR2) + if False: + print('indexlist:', indexlist) + + assert max(indexlist) <= len(morefrequencies) + indexlist = list(np.unique(indexlist)) + indexlist = [int(index) for index in indexlist] + first = False + + ## Check to see if findpeaks just worked + if (numtoreturn == 2) and (mode != 'phase'): + thresh = .006 + if len(indexlist2) == 2: + if verbose: + print("Used findpeaks on R2 amplitude (option 2)") + opt2freqlist = list(np.sort(morefrequencies[indexlist2])) + if abs(opt2freqlist[1]-opt2freqlist[0]) > thresh: + if returnoptions: + return opt2freqlist, 2 + return opt2freqlist + if len(indexlist1) == 2 and not use_R2_only: + opt3freqlist = list(np.sort(morefrequencies[indexlist1])) + if abs(opt3freqlist[1]-opt3freqlist[0]) > thresh: + if verbose: + print("Used findpeaks on R1 amplitude (option 3)") + if returnoptions: + return opt3freqlist, 3 + return opt3freqlist + if verbose: + print('indexlist1 from R1 amp find_peaks is', indexlist1) + print('indexlist2 from R2 amp find_peaks is', indexlist2) + + if verbose: + print('indexlist:',indexlist) + resfreqs_from_amp = morefrequencies[indexlist] + + if not MONOMER or mode == 'phase': + ## find where angles are resonant angles + angleswanted = [np.pi/2, -np.pi/2] # the function will wrap angles so don't worry about mod 2 pi. + R1_flist,indexlistphaseR1 = find_freq_from_angle(morefrequencies, R1_phase_noiseless, angleswanted=angleswanted, returnindex=True) + if MONOMER: + assert mode == 'phase' + resfreqs_from_phase = R1_flist + else: + R2_flist,indexlistphaseR2 = find_freq_from_angle(morefrequencies, R2_phase_noiseless, angleswanted=angleswanted, + returnindex=True) + resfreqs_from_phase = np.append(R1_flist, R2_flist) + else: + assert MONOMER + resfreqs_from_phase = [] # don't bother with this for the MONOMER + indexlistphaseR1 = [] + indexlistphaseR2 = [] + R1_flist = [] + + if verboseplot: + #Never Monomer in this case + if MONOMER: # still need to calculate the curves + R1_amp_noiseless = curve1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = np.unwrap(R1_phase_noiseless) + indexlistampR1 = [np.argmin(abs(w - morefrequencies )) for w in resfreqs_from_amp] + print('Plotting!') + fig, (ampax, phaseax) = plt.subplots(2,1,gridspec_kw={'hspace': 0}, sharex = 'all') + plt.sca(ampax) + plt.title(plottitle) + plt.plot(morefrequencies, R1_amp_noiseless, color='gray') + if not MONOMER: + plt.plot(morefrequencies, R2_amp_noiseless, color='lightblue') + + plt.plot(morefrequencies[indexlistampR1],R1_amp_noiseless[indexlistampR1], '.') + if not MONOMER: + plt.plot(morefrequencies[indexlistampR2],R2_amp_noiseless[indexlistampR2], '.') + + plt.sca(phaseax) + plt.plot(morefrequencies,R1_phase_noiseless, color='gray' ) + if not MONOMER: + plt.plot(morefrequencies,R2_phase_noiseless, color = 'lightblue') + plt.plot(R1_flist, theta1(np.array(R1_flist), k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), '.') + if not MONOMER: + plt.plot(R2_flist, theta2(np.array(R2_flist), k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), '.') + + if mode == 'maxamp' or mode == 'amp' or mode == 'amplitude': + freqlist = resfreqs_from_amp + elif mode == 'phase': + freqlist = resfreqs_from_phase + else: + if mode != 'all': + print("Set mode to any of 'all', 'maxamp', or 'phase'. Recovering to 'all'.") + # mode is 'all' + freqlist = np.sort(np.append(resfreqs_from_amp, resfreqs_from_phase)) + + + if veryunique: # Don't return both close frequencies; just pick the higher amplitude frequency of the two. + ## I obtained indexlists four ways: indexlistampR1, indexlistampR2, indexlistphaseR1, indexlistphaseR2 + indexlist = indexlist + indexlistphaseR1 + if not MONOMER: + indexlist = indexlist + indexlistphaseR2 + indexlist = list(np.sort(np.unique(indexlist))) + if verbose: + print('indexlist:', indexlist) + + narrowerW = calcnarrowerW(vals_set, MONOMER) + + """ a and b are indices of morefrequencies """ + def veryclose(a,b): + ## option 1: veryclose if indices are within 2. + #return abs(b-a) <= 2 + + ## option 2: very close if frequencies are closer than .01 rad/s + #veryclose = abs(morefrequencies[a]-morefrequencies[b]) <= .1 + + ## option 3: very close if freqeuencies are closer than W/20 + veryclose = abs(morefrequencies[a]-morefrequencies[b]) <= narrowerW/20 + + return veryclose + + if len(freqlist) > 1: + ## if two elements of indexlist are veryclose to each other, want to remove the smaller amplitude. + removeindex = [] # create a list of indices to remove + try: + tempfreqlist = morefrequencies[indexlist] # indexlist is indicies of morefrequencies. + # if the 10th element of indexlist is indexlist[10]=200, then tempfreqlist[10] = morefrequencies[200] + except: + print('indexlist:', indexlist) + A2 = curve2(tempfreqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set,) + # and then A2[10] is the amplitude of R2 at the frequency morefrequencies[200] + # and then the number 10 is the sort of number we will add to a removeindex list + for i in range(len(indexlist)-1): + if veryclose(indexlist[i], indexlist[i-1]): + if A2[i] < A2[i-1]: # remove the smaller amplitude + removeindex.append(i) + else: + removeindex.append(i-1) + numtoremove = len(removeindex) + if verbose and numtoremove > 0: + print('Removing', numtoremove, 'frequencies') + + removeindex = list(np.unique(removeindex)) + indexlist = list(indexlist) + ## Need to work on removal from the end of the list + ## in order to avoid changing index numbers while working with the list + while removeindex != []: + i = removeindex.pop(-1) # work backwards through indexes to remove + el = indexlist.pop(i) # remove it from indexlist + if numtoremove < 5 and verbose: + print('Removed frequency', morefrequencies[el]) + + freqlist = morefrequencies[indexlist] + + freqlist = np.sort(freqlist) + + if unique or veryunique or (numtoreturn is not None): ## Don't return multiple copies of the same number. + freqlist = np.unique(np.array(freqlist)) + + if verbose: + print('Possible frequencies are:', freqlist) + + if numtoreturn is not None: + if len(freqlist) == numtoreturn: + if verbose: + print ('option 4') + if returnoptions: + return list(freqlist), 4 + return list(freqlist) + if len(freqlist) < numtoreturn: + if verbose: + print('Warning: I do not have as many resonant frequencies as was requested.') + freqlist = list(freqlist) + # instead I should add another frequency corresponding to some desireable phase. + if verbose: + print('Returning instead a freq2 at phase -3pi/4.') + goodphase = -3*np.pi/4 + for i in range(iterations): + f2, ind2 = find_freq_from_angle(drive = morefrequencies, + phase = theta1(morefrequencies, + k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), + angleswanted = [goodphase], returnindex = True) + ind2 = ind2[0] + try: + spacing = abs(morefrequencies[ind2] - morefrequencies[ind2-1]) + except IndexError: + spacing = abs(morefrequencies[ind2+1] - morefrequencies[ind2]) + finermesh = np.linspace(morefrequencies[ind2] - spacing,morefrequencies[ind2] + spacing, num=n) + morefrequencies = np.append(morefrequencies, finermesh) + f2 = f2[0] + freqlist.append(f2) + if verboseplot: + plt.sca(phaseax) + plt.plot(f2, theta1(f2, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), '.') + print('Appending: ', f2) + for i in range(numtoreturn - len(freqlist)): + # This is currently unlikely to be true, but I'm future-proofing + # for a future when I want to set the number to an integer greater than 2. + freqlist.append(np.nan) # increase list to requested length with nan + if verboseplot: + plt.sca(ampax) + plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), 'x') + if verbose: + print ('option 5') + if returnoptions: + return freqlist, 5 + return freqlist + + R1_amp_noiseless = curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R2_amp_noiseless = curve2(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + + topR1index = np.argmax(R1_amp_noiseless) + + if numtoreturn == 1: + # just return the one max amp frequency. + if verbose: + print('option 6') + if returnoptions: + return [freqlist[topR1index]],6 + return [freqlist[topR1index]] + + if numtoreturn != 2: + print('Warning: returning ' + str(numtoreturn) + ' frequencies is not implemented. Returning 2 frequencies.') + + # Choose a second frequency to return. + topR2index = np.argmax(R2_amp_noiseless) + threshold = .2 # rad/s + if abs(freqlist[topR1index] - freqlist[topR2index]) > threshold: + freqlist = list([freqlist[topR1index], freqlist[topR2index]]) + if verboseplot: + plt.sca(ampax) + plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), 'x') + if verbose: + print('option 7') + if returnoptions: + return freqlist, 7 + return freqlist + else: + R1_amp_noiseless = list(R1_amp_noiseless) + freqlist = list(freqlist) + f1 = freqlist.pop(topR1index) + R1_amp_noiseless.pop(topR1index) + secondR1index = np.argmax(R1_amp_noiseless) + f2 = freqlist.pop(secondR1index) + if abs(f2-f1) > threshold: + freqlist = list([f1, f2]) # overwrite freqlist + if verboseplot: + plt.sca(ampax) + plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), 'x') + if verbose: + print('option 8') + if returnoptions: + return freqlist, 8 + return freqlist + else: # return whatever element of the freqlist is furthest + freqlist.append(f2) + # is f1 closer to top or bottom of freqlist? + if abs(f1 - min(freqlist)) > abs(f1 - max(freqlist)): + if verbose: + print('option 9') + if returnoptions: + return [f1, min(freqlist)], 9 + return [f1, min(freqlist)] + else: + if verbose: + print('option 10') + if returnoptions: + return [f1, max(freqlist)], 10 + return [f1, max(freqlist)] + + + else: + if verbose: + print('option 11') + if returnoptions: + return list(freqlist),11 + return list(freqlist) + +#Function needed in res_freq_numeric +def makemorefrequencies(vals_set, minfreq, maxfreq,MONOMER,forceall, + res1 = None, res2 = None, + includefreqs = None, n=n, staywithinlims = False): + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + + if res1 is None: + res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) + if not MONOMER and res2 is None: + res2 = res_freq_weak_coupling(k2_set, m2_set, b2_set) + + morefrequencies = np.linspace(minfreq, maxfreq, num = n*60) + if MONOMER: + morefrequencies = np.append(morefrequencies, [res1]) + else: + morefrequencies = np.append(morefrequencies, [res1,res2]) + + if includefreqs is not None: + morefrequencies = np.append(morefrequencies, np.array(includefreqs)) + + try: + W1 = resonatorphysics.approx_width(k = k1_set, m = m1_set, b=b1_set) + except ZeroDivisionError: + print('k1_set:', k1_set) + print('m1_set:', m1_set) + print('b1_set:', b1_set) + W1 = (maxfreq - minfreq)/5 + morefrequencies = np.append(morefrequencies, np.linspace(res1-W1, res1+W1, num = 7*n)) + morefrequencies = np.append(morefrequencies, np.linspace(res1-2*W1, res1+2*W1, num = 10*n)) + if not MONOMER: + W2 = resonatorphysics.approx_width(k = k2_set, m = m2_set, b=b2_set) + morefrequencies = np.append(morefrequencies, np.linspace(res2-W2, res2+W2, num = 7*n)) + morefrequencies = np.append(morefrequencies, np.linspace(res2-2*W2, res2+2*W2, num = 10*n)) + morefrequencies = list(np.sort(np.unique(morefrequencies))) + + while morefrequencies[0] < 0: + morefrequencies.pop(0) + + if staywithinlims: + while morefrequencies[0] < minfreq: + morefrequencies.pop(0) + while morefrequencies[-1] > maxfreq: + morefrequencies.pop(-1) + + return np.array(morefrequencies) + +#Function needed in res_freq_numeric +def find_freq_from_angle(drive, phase, angleswanted = [-np.pi/4], returnindex = False, verbose = False): + assert len(drive) == len(phase) + + #specialanglefreq = [drive[np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi)))] \ + # for anglewanted in angleswanted ] + + threshold = np.pi/30 # small angle threshold + specialanglefreq = [] # initialize list + indexlist = [] + for anglewanted in angleswanted: + index = np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi))) # find where phase is closest + + if index == 0 or index >= len(drive)-1: # edges of dataset require additional scrutiny + ## check to see if it's actually close after all + nearness = abs(phase[index]%(2*np.pi)-anglewanted%(2*np.pi)) + if nearness > threshold: + continue # don't include this index + specialanglefreq.append(drive[index]) + indexlist.append(index) + + if False: + plt.figure() + plt.plot(specialanglefreq,phase[indexlist]/np.pi) + plt.xlabel('Freq') + plt.ylabel('Angle (pi)') + + if returnindex: + return specialanglefreq, indexlist + else: + return specialanglefreq + +#Function needed in res_freq_numeric +def read_params(vect, MONOMER): + #Will never need to use the Monomer part in this case + if MONOMER: + [M1, B1, K1, FD] = vect + K12 = 0 + M2 = 0 + B2 = 0 + K2= 0 + else: + [K1, K2, K3, K4, B1, B2, B3, FD, M1, M2, M3] = vect + return [K1, K2, K3, K4, B1, B2, B3, FD, M1, M2, M3] + +''' Begin Work Here. ''' + +## System 0 from '15 Systems - 10 Freqs NetMAP' +true_parameters = [1.045, 0.179, 3.852, 1.877, 5.542, 1.956, 3.71, 1, 3.976, 0.656, 3.198] +guessed_parameters = [1.2379, 0.1764, 3.7327, 1.8628, 5.93, 2.1793, 4.2198, 1, 4.3335, 0.7016, 3.0719] + +MONOMER = False +forceall = False + +best_frequencies_list = res_freq_numeric(true_parameters, MONOMER, forceall) +print(best_frequencies_list) + + +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, 50, 'Sys0_Freq_Pick.xlsx', 'Sys0_Freq_Pick - Plots') \ No newline at end of file From 214e9703282fed19cc751297c29ca38be49577a1 Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 29 Jul 2024 14:06:29 -0400 Subject: [PATCH 080/101] FIX: Update name I used to call it resonatorSVDanalysis.py but I renamed it NetMAP.py --- Algebraic Approach Simulated Two Coupled Resonators.ipynb | 2 +- simulated_experiment.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index e545df8..b56284c 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -86,7 +86,7 @@ "from resonatorfrequencypicker import freqpoints, find_freq_from_angle, makemorefrequencies,\\\n", " create_drive_arrays, find_special_freq, res_freq_numeric, \\\n", " allmeasfreq_one_res, allmeasfreq_two_res, best_choice_freq_set\n", - "from resonatorSVDanalysis import Zmat, \\\n", + "from NetMAP import Zmat, \\\n", " normalize_parameters_1d_by_force, quadratic_formula, normalize_parameters_to_res1_and_F_2d, \\\n", " normalize_parameters_to_m1_m2_assuming_2d, normalize_parameters_to_m1_set_k1_set_assuming_2d, \\\n", " normalize_parameters_to_m1_F_set_assuming_2d, normalize_parameters_assuming_3d\n", diff --git a/simulated_experiment.py b/simulated_experiment.py index 908d971..42d65dc 100644 --- a/simulated_experiment.py +++ b/simulated_experiment.py @@ -14,7 +14,7 @@ import matplotlib.pyplot as plt from helperfunctions import \ read_params, store_params, make_real_iff_real, flatten -from resonatorSVDanalysis import Zmat, \ +from NetMAP import Zmat, \ normalize_parameters_1d_by_force, normalize_parameters_assuming_3d, \ normalize_parameters_to_m1_F_set_assuming_2d from resonatorstats import syserr, combinedsyserr From cf0db3450fbae312301d6b4c4cf8174e4a082739 Mon Sep 17 00:00:00 2001 From: vivarose Date: Mon, 29 Jul 2024 14:07:22 -0400 Subject: [PATCH 081/101] FIX: monomer case --- ...c Approach Simulated Two Coupled Resonators.ipynb | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index b56284c..3c233e2 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -609,15 +609,17 @@ " imamp1(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, \n", " MONOMER, forceboth=forceboth), \n", " color='gray', alpha = .5)\n", - "ax6.plot(realamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth=forceboth), \n", - " imamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth=forceboth), \n", - " color='gray', alpha = .5)\n", + "if not MONOMER:\n", + " ax6.plot(realamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth=forceboth), \n", + " imamp2(morefrequencies, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth=forceboth), \n", + " color='gray', alpha = .5)\n", "\n", "plotcomplex(Z1, drive, 'Complex Amplitude $Z_1$', ax=ax5, label_markers=label_markers)\n", "ax5.scatter(np.real(df.R1AmpCom), np.imag(df.R1AmpCom), s=150, facecolors='none', edgecolors='k', label=\"data for SVD\") \n", "\n", - "plotcomplex(Z2, drive, 'Complex Amplitude $Z_2$', ax=ax6, label_markers=label_markers)\n", - "ax6.scatter(np.real(df.R2AmpCom), np.imag(df.R2AmpCom), s=150, facecolors='none', edgecolors='k', label=\"data for SVD\") \n", + "if not MONOMER:\n", + " plotcomplex(Z2, drive, 'Complex Amplitude $Z_2$', ax=ax6, label_markers=label_markers)\n", + " ax6.scatter(np.real(df.R2AmpCom), np.imag(df.R2AmpCom), s=150, facecolors='none', edgecolors='k', label=\"data for SVD\") \n", "plt.legend() \n", "\n", " \n", From 866b124f13fb31f4f3ac745832f98c190ade57d5 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 29 Jul 2024 14:21:17 -0400 Subject: [PATCH 082/101] Creating trimer directory --- .gitignore | 4 +++- .../Curve Fit Testing}/.DS_Store | Bin .../Changing One Param - Curve Fit/.DS_Store | Bin .../Changing_k1_M2-Amplitude.xlsx | Bin .../Mass 2 plots - amp/.DS_Store | Bin .../Generating Random Params - Curve Fit/.DS_Store | Bin .../Generating_Random_Params_Imaginary_Part.xlsx | Bin .../Generating_Random_Params_Phase.xlsx | Bin .../Generating_Random_Params_Real_Part.xlsx | Bin .../Curve Fit Testing}/Imaginary_vs_freq_random.py | 0 .../Curve Fit Testing}/Phase_vs_freq_random.py | 0 .../Curve Fit Testing}/Real_vs_freq_random.py | 0 .../Curve Fit Testing}/Trimer_simulator.py | 0 .../Curve Fit Testing}/Vary_one_initial_guess.py | 0 .../Curvefit_compare_scale_vs_fix_F.py | 0 .../Three Coupled Resonator Model.ipynb | 0 Trimer_NetMAP.py => trimer/Trimer_NetMAP.py | 0 Trimer_curvefit.py => trimer/Trimer_curvefit.py | 0 .../Trimer_curvefit_lmfit.py | 0 Trimer_simulator.py => trimer/Trimer_simulator.py | 0 .../comparing_curvefit_types.py | 0 .../curve_fitting_X_Y_all.py | 0 .../curve_fitting_amp_phase_all.py | 0 .../trimer_case_study_frequency_picker.py | 0 24 files changed, 3 insertions(+), 1 deletion(-) rename {Curve Fit Testing => trimer/Curve Fit Testing}/.DS_Store (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Changing One Param - Curve Fit/.DS_Store (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Changing One Param - Curve Fit/Changing_k1_M2-Amplitude.xlsx (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Changing One Param - Curve Fit/Mass 2 plots - amp/.DS_Store (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Generating Random Params - Curve Fit/.DS_Store (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Generating Random Params - Curve Fit/Generating_Random_Params_Imaginary_Part.xlsx (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Generating Random Params - Curve Fit/Generating_Random_Params_Phase.xlsx (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Generating Random Params - Curve Fit/Generating_Random_Params_Real_Part.xlsx (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Imaginary_vs_freq_random.py (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Phase_vs_freq_random.py (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Real_vs_freq_random.py (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Trimer_simulator.py (100%) rename {Curve Fit Testing => trimer/Curve Fit Testing}/Vary_one_initial_guess.py (100%) rename Curvefit_compare_scale_vs_fix_F.py => trimer/Curvefit_compare_scale_vs_fix_F.py (100%) rename Three Coupled Resonator Model.ipynb => trimer/Three Coupled Resonator Model.ipynb (100%) rename Trimer_NetMAP.py => trimer/Trimer_NetMAP.py (100%) rename Trimer_curvefit.py => trimer/Trimer_curvefit.py (100%) rename Trimer_curvefit_lmfit.py => trimer/Trimer_curvefit_lmfit.py (100%) rename Trimer_simulator.py => trimer/Trimer_simulator.py (100%) rename comparing_curvefit_types.py => trimer/comparing_curvefit_types.py (100%) rename curve_fitting_X_Y_all.py => trimer/curve_fitting_X_Y_all.py (100%) rename curve_fitting_amp_phase_all.py => trimer/curve_fitting_amp_phase_all.py (100%) rename trimer_case_study_frequency_picker.py => trimer/trimer_case_study_frequency_picker.py (100%) diff --git a/.gitignore b/.gitignore index 526dc88..3512864 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +~$* +*.xlsx *~ myheatmap.py~ *.pyc @@ -11,4 +13,4 @@ myheatmap.py~ *.swp main.py *.svg -*.png \ No newline at end of file +*.png diff --git a/Curve Fit Testing/.DS_Store b/trimer/Curve Fit Testing/.DS_Store similarity index 100% rename from Curve Fit Testing/.DS_Store rename to trimer/Curve Fit Testing/.DS_Store diff --git a/Curve Fit Testing/Changing One Param - Curve Fit/.DS_Store b/trimer/Curve Fit Testing/Changing One Param - Curve Fit/.DS_Store similarity index 100% rename from Curve Fit Testing/Changing One Param - Curve Fit/.DS_Store rename to trimer/Curve Fit Testing/Changing One Param - Curve Fit/.DS_Store diff --git a/Curve Fit Testing/Changing One Param - Curve Fit/Changing_k1_M2-Amplitude.xlsx b/trimer/Curve Fit Testing/Changing One Param - Curve Fit/Changing_k1_M2-Amplitude.xlsx similarity index 100% rename from Curve Fit Testing/Changing One Param - Curve Fit/Changing_k1_M2-Amplitude.xlsx rename to trimer/Curve Fit Testing/Changing One Param - Curve Fit/Changing_k1_M2-Amplitude.xlsx diff --git a/Curve Fit Testing/Changing One Param - Curve Fit/Mass 2 plots - amp/.DS_Store b/trimer/Curve Fit Testing/Changing One Param - Curve Fit/Mass 2 plots - amp/.DS_Store similarity index 100% rename from Curve Fit Testing/Changing One Param - Curve Fit/Mass 2 plots - amp/.DS_Store rename to trimer/Curve Fit Testing/Changing One Param - Curve Fit/Mass 2 plots - amp/.DS_Store diff --git a/Curve Fit Testing/Generating Random Params - Curve Fit/.DS_Store b/trimer/Curve Fit Testing/Generating Random Params - Curve Fit/.DS_Store similarity index 100% rename from Curve Fit Testing/Generating Random Params - Curve Fit/.DS_Store rename to trimer/Curve Fit Testing/Generating Random Params - Curve Fit/.DS_Store diff --git a/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Imaginary_Part.xlsx b/trimer/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Imaginary_Part.xlsx similarity index 100% rename from Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Imaginary_Part.xlsx rename to trimer/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Imaginary_Part.xlsx diff --git a/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Phase.xlsx b/trimer/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Phase.xlsx similarity index 100% rename from Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Phase.xlsx rename to trimer/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Phase.xlsx diff --git a/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Real_Part.xlsx b/trimer/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Real_Part.xlsx similarity index 100% rename from Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Real_Part.xlsx rename to trimer/Curve Fit Testing/Generating Random Params - Curve Fit/Generating_Random_Params_Real_Part.xlsx diff --git a/Curve Fit Testing/Imaginary_vs_freq_random.py b/trimer/Curve Fit Testing/Imaginary_vs_freq_random.py similarity index 100% rename from Curve Fit Testing/Imaginary_vs_freq_random.py rename to trimer/Curve Fit Testing/Imaginary_vs_freq_random.py diff --git a/Curve Fit Testing/Phase_vs_freq_random.py b/trimer/Curve Fit Testing/Phase_vs_freq_random.py similarity index 100% rename from Curve Fit Testing/Phase_vs_freq_random.py rename to trimer/Curve Fit Testing/Phase_vs_freq_random.py diff --git a/Curve Fit Testing/Real_vs_freq_random.py b/trimer/Curve Fit Testing/Real_vs_freq_random.py similarity index 100% rename from Curve Fit Testing/Real_vs_freq_random.py rename to trimer/Curve Fit Testing/Real_vs_freq_random.py diff --git a/Curve Fit Testing/Trimer_simulator.py b/trimer/Curve Fit Testing/Trimer_simulator.py similarity index 100% rename from Curve Fit Testing/Trimer_simulator.py rename to trimer/Curve Fit Testing/Trimer_simulator.py diff --git a/Curve Fit Testing/Vary_one_initial_guess.py b/trimer/Curve Fit Testing/Vary_one_initial_guess.py similarity index 100% rename from Curve Fit Testing/Vary_one_initial_guess.py rename to trimer/Curve Fit Testing/Vary_one_initial_guess.py diff --git a/Curvefit_compare_scale_vs_fix_F.py b/trimer/Curvefit_compare_scale_vs_fix_F.py similarity index 100% rename from Curvefit_compare_scale_vs_fix_F.py rename to trimer/Curvefit_compare_scale_vs_fix_F.py diff --git a/Three Coupled Resonator Model.ipynb b/trimer/Three Coupled Resonator Model.ipynb similarity index 100% rename from Three Coupled Resonator Model.ipynb rename to trimer/Three Coupled Resonator Model.ipynb diff --git a/Trimer_NetMAP.py b/trimer/Trimer_NetMAP.py similarity index 100% rename from Trimer_NetMAP.py rename to trimer/Trimer_NetMAP.py diff --git a/Trimer_curvefit.py b/trimer/Trimer_curvefit.py similarity index 100% rename from Trimer_curvefit.py rename to trimer/Trimer_curvefit.py diff --git a/Trimer_curvefit_lmfit.py b/trimer/Trimer_curvefit_lmfit.py similarity index 100% rename from Trimer_curvefit_lmfit.py rename to trimer/Trimer_curvefit_lmfit.py diff --git a/Trimer_simulator.py b/trimer/Trimer_simulator.py similarity index 100% rename from Trimer_simulator.py rename to trimer/Trimer_simulator.py diff --git a/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py similarity index 100% rename from comparing_curvefit_types.py rename to trimer/comparing_curvefit_types.py diff --git a/curve_fitting_X_Y_all.py b/trimer/curve_fitting_X_Y_all.py similarity index 100% rename from curve_fitting_X_Y_all.py rename to trimer/curve_fitting_X_Y_all.py diff --git a/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py similarity index 100% rename from curve_fitting_amp_phase_all.py rename to trimer/curve_fitting_amp_phase_all.py diff --git a/trimer_case_study_frequency_picker.py b/trimer/trimer_case_study_frequency_picker.py similarity index 100% rename from trimer_case_study_frequency_picker.py rename to trimer/trimer_case_study_frequency_picker.py From 0c87da7d63549d6b6c0b3422f42d9c822fbcb179 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 29 Jul 2024 14:22:05 -0400 Subject: [PATCH 083/101] Ignore .DS_Store on mac --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 3512864..77982d8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +.DS_Store ~$* *.xlsx *~ From b86e93739be737c3d1b148899f109861a1131af9 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 29 Jul 2024 15:06:33 -0400 Subject: [PATCH 084/101] Copying files to work for Trimer and updating since files moved directory --- .DS_Store | Bin 10244 -> 10244 bytes trimer/Curvefit_compare_scale_vs_fix_F.py | 7 +- trimer/comparing_curvefit_types.py | 19 +- trimer/curve_fitting_X_Y_all.py | 31 +- trimer/curve_fitting_amp_phase_all.py | 2 +- ...kind_of_trimer_resonatorfrequencypicker.py | 791 ++++++++++++++++++ trimer/trimer_case_study_frequency_picker.py | 558 +----------- trimer/trimer_helperfunctions.py | 105 +++ trimer/trimer_resonatorphysics.py | 59 ++ 9 files changed, 1009 insertions(+), 563 deletions(-) create mode 100644 trimer/kind_of_trimer_resonatorfrequencypicker.py create mode 100644 trimer/trimer_helperfunctions.py create mode 100644 trimer/trimer_resonatorphysics.py diff --git a/.DS_Store b/.DS_Store index a416a638f558845ba6f5e6b822bc3cedd8fe6cac..0ae500052cff8811c73afa00e5d1c02b8bcb1a4f 100644 GIT binary patch delta 284 zcmZn(XbIS$DiHs|_&fsx0}F#5LpnnyLrHGFi%U{YeiBfOgMp#pVWH|VM^yO~yz&JZ zhQZ1CxdlKy3@q;w7$!RiN==?8;KFpw7tH)8z|R~RqdHkYP#a3iOpX%dV-%R2Dfn9; zCmo@iVdELI$#z0&lTQlqu`vMk9CHM5MRN0zU4W37Y%9#i*f7~vNPTm$um&SbgA34_ z`65bS#^$3U+gZfe7)lt5fWFLSNM$HWODRrH%FoYX+*~Qy$2PG*Y%{yUFP6;)qRh+y DzQ#=B delta 254 zcmZn(XbIS$DiHs8ZW#ju0}F#5LpnnyLrHGFi%U{YeiBfOWAC1>uDR0398u*{@X8lt z7zQWj=N16!VnaNRte2ff}GX;Nh zq@@%mC*|koOtuv=WjbOud7Y5Pa#lpc*0`yxxL*8UPak0%GM4mDM0E%@@ AhX4Qo diff --git a/trimer/Curvefit_compare_scale_vs_fix_F.py b/trimer/Curvefit_compare_scale_vs_fix_F.py index 45a56f9..8ffe986 100644 --- a/trimer/Curvefit_compare_scale_vs_fix_F.py +++ b/trimer/Curvefit_compare_scale_vs_fix_F.py @@ -8,7 +8,12 @@ from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y import pandas as pd -from resonatorsimulator import complex_noise +import numpy as np + +def complex_noise(n, noiselevel): + global complexamplitudenoisefactor + complexamplitudenoisefactor = 0.0005 + return noiselevel* complexamplitudenoisefactor * np.random.randn(n,) #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses diff --git a/trimer/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py index 5922a96..c9d2f91 100644 --- a/trimer/comparing_curvefit_types.py +++ b/trimer/comparing_curvefit_types.py @@ -19,12 +19,13 @@ import matplotlib.ticker as ticker from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y -from resonatorsimulator import complex_noise from Trimer_simulator import calculate_spectra, curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 from Trimer_NetMAP import Zmatrix, unnormalizedparameters, normalize_parameters_1d_by_force -from resonatorstats import syserr +import warnings ''' Functions contained: + complex_noise - creates noise, e + syserr - Calculates systematic error find_avg_e - Calculates average across systematic error for each parameter for one trial of the same system artithmetic_then_logarithmic - Calculates arithmetic average across parameters first, @@ -43,6 +44,20 @@ and multiple_fit_X_Y, which performs curve fitting on X vs Freq and Y vs Freq curves for all 3 masses simulatenously. ''' +def complex_noise(n, noiselevel): + global complexamplitudenoisefactor + complexamplitudenoisefactor = 0.0005 + return noiselevel* complexamplitudenoisefactor * np.random.randn(n,) + +def syserr(x_found,x_set, absval = True): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + se = 100*(x_found-x_set)/x_set + if absval: + return abs(se) + else: + return se + #Calculate for one trial of the same system def find_avg_e(dictionary): sum_e = dictionary['e_k1'][0] + \ diff --git a/trimer/curve_fitting_X_Y_all.py b/trimer/curve_fitting_X_Y_all.py index b8a0439..09aa762 100644 --- a/trimer/curve_fitting_X_Y_all.py +++ b/trimer/curve_fitting_X_Y_all.py @@ -9,9 +9,8 @@ import numpy as np import matplotlib.pyplot as plt import lmfit +import warnings from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 -from resonatorstats import syserr, rsqrd - ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info @@ -20,8 +19,36 @@ - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder + syserr - calculates systematic error + rsqrd - calculates R^2 ''' +def syserr(x_found,x_set, absval = True): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + se = 100*(x_found-x_set)/x_set + if absval: + return abs(se) + else: + return se + +""" +This definition of R^2 can come out negative. +Negative means that a flat line would fit the data better than the curve. +""" +def rsqrd(model, data, plot=False, x=None, newfigure = True): + SSres = sum((data - model)**2) + SStot = sum((data - np.mean(data))**2) + rsqrd = 1 - (SSres/ SStot) + + if plot: + if newfigure: + plt.figure() + plt.plot(x,data, 'o') + plt.plot(x, model, '--') + + return rsqrd + #Get residuals def residuals(params, wd, X1_data, X2_data, X3_data, Y1_data, Y2_data, Y3_data): k1 = params['k1'].value diff --git a/trimer/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py index 2c1a204..c771b6e 100644 --- a/trimer/curve_fitting_amp_phase_all.py +++ b/trimer/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 from resonatorstats import syserr, rsqrd ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder ''' #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data \ No newline at end of file diff --git a/trimer/kind_of_trimer_resonatorfrequencypicker.py b/trimer/kind_of_trimer_resonatorfrequencypicker.py new file mode 100644 index 0000000..775ac4d --- /dev/null +++ b/trimer/kind_of_trimer_resonatorfrequencypicker.py @@ -0,0 +1,791 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 9 16:07:55 2022 + +@author: vhorowit +""" + +import numpy as np +import trimer_resonatorphysics +from trimer_resonatorphysics import res_freq_weak_coupling, calcnarrowerW +from trimer_helperfunctions import read_params +import matplotlib.pyplot as plt +from Trimer_simulator import curve1, theta1, curve2, theta2 +from scipy.signal import find_peaks + +# default settings +verbose = False +n=100 +debug = False + +""" Given a limited set of available frequencies called "drive", +find those indices that most closely correspond to the desired frequencies. +This will not throw an err if two are the same; that could be added by checking if nunique is a shorter length. """ +def freqpoints(desiredfreqs, drive): + p = [] # p stands for frequency points; these are the indicies of frequencies that we will be measuring. + for f in desiredfreqs: + absolute_val_array = np.abs(drive - f) + f_index = absolute_val_array.argmin() + p.append(f_index) + return p + + +""" drive and phase are two lists of the same length + This will only return one frequency for each requested angle, even if there are additional solutions. + It's helpful if drive is morefrequencies. +""" +def find_freq_from_angle(drive, phase, angleswanted = [-np.pi/4], returnindex = False, verbose = False): + assert len(drive) == len(phase) + + #specialanglefreq = [drive[np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi)))] \ + # for anglewanted in angleswanted ] + + threshold = np.pi/30 # small angle threshold + specialanglefreq = [] # initialize list + indexlist = [] + for anglewanted in angleswanted: + index = np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi))) # find where phase is closest + + if index == 0 or index >= len(drive)-1: # edges of dataset require additional scrutiny + ## check to see if it's actually close after all + nearness = abs(phase[index]%(2*np.pi)-anglewanted%(2*np.pi)) + if nearness > threshold: + continue # don't include this index + specialanglefreq.append(drive[index]) + indexlist.append(index) + + if False: + plt.figure() + plt.plot(specialanglefreq,phase[indexlist]/np.pi) + plt.xlabel('Freq') + plt.ylabel('Angle (pi)') + + if returnindex: + return specialanglefreq, indexlist + else: + return specialanglefreq + +""" n is the number of frequencies is the drive; we'll have more for more frequencies. + Can you improve this by calling create_drive_arrays afterward? """ +def makemorefrequencies(vals_set, minfreq, maxfreq, MONOMER, forceall, + res1 = None, res2 = None, + includefreqs = None, n=n, staywithinlims = False): + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + + if res1 is None: + res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) + if not MONOMER and res2 is None: + res2 = res_freq_weak_coupling(k2_set, m2_set, b2_set) + + morefrequencies = np.linspace(minfreq, maxfreq, num = n*60) + if MONOMER: + morefrequencies = np.append(morefrequencies, [res1]) + else: + morefrequencies = np.append(morefrequencies, [res1,res2]) + + if includefreqs is not None: + morefrequencies = np.append(morefrequencies, np.array(includefreqs)) + + try: + W1 = trimer_resonatorphysics.approx_width(k = k1_set, m = m1_set, b=b1_set) + except ZeroDivisionError: + print('k1_set:', k1_set) + print('m1_set:', m1_set) + print('b1_set:', b1_set) + W1 = (maxfreq - minfreq)/5 + morefrequencies = np.append(morefrequencies, np.linspace(res1-W1, res1+W1, num = 7*n)) + morefrequencies = np.append(morefrequencies, np.linspace(res1-2*W1, res1+2*W1, num = 10*n)) + if not MONOMER: + W2 = trimer_resonatorphysics.approx_width(k = k2_set, m = m2_set, b=b2_set) + morefrequencies = np.append(morefrequencies, np.linspace(res2-W2, res2+W2, num = 7*n)) + morefrequencies = np.append(morefrequencies, np.linspace(res2-2*W2, res2+2*W2, num = 10*n)) + morefrequencies = list(np.sort(np.unique(morefrequencies))) + + while morefrequencies[0] < 0: + morefrequencies.pop(0) + + if staywithinlims: + while morefrequencies[0] < minfreq: + morefrequencies.pop(0) + while morefrequencies[-1] > maxfreq: + morefrequencies.pop(-1) + + return np.array(morefrequencies) + + +def create_drive_arrays(vals_set, MONOMER, forceboth, n=n, + morefrequencies = None, + minfreq = None, maxfreq = None, + staywithinlims = False, + includefreqs = [], + callmakemore = False, + verbose = verbose): + + if verbose: + print('Running create_drive_arrays()') + + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + + if morefrequencies is None: + if minfreq is None: + minfreq = 0.1 + if maxfreq is None: + maxfreq = 5 + morefrequencies=np.linspace(minfreq,maxfreq,50*n) + if minfreq is None: + minfreq = min(morefrequencies) + if maxfreq is None: + maxfreq = max(morefrequencies) + + if minfreq <= 0: + minfreq = 1e-6 + + if callmakemore: + evenmore = makemorefrequencies(vals_set=vals_set, minfreq=minfreq, maxfreq=maxfreq,MONOMER=MONOMER,forceboth=forceboth, + res1 = None, res2 = None, + includefreqs = None, n=n, staywithinlims = staywithinlims) + morefrequencies = np.sort(np.unique(np.append(morefrequencies, evenmore))) + + Q1 = trimer_resonatorphysics.approx_Q(k1_set, m1_set, b1_set) + + # set the fraction of points that are spread evenly in frequency (versus evenly in phase) + if MONOMER: + if Q1 >= 30: + fracevenfreq = .2 + elif Q1 >=10: + fracevenfreq = .4 + else: + fracevenfreq = .5 # such a broad peak that we might as well spread evenly in frequency + else: + Q2 = trimer_resonatorphysics.approx_Q(k2_set, m2_set, b2_set) + if Q1 >=30 and Q2 >= 30: + fracevenfreq = .2 + else: + fracevenfreq = .4 + if MONOMER: + # choose length of anglelist + m = n-3-int(fracevenfreq*n) # 3 are special angles; 20% are evenly spaced freqs + else: + m = int((n-3-(fracevenfreq*n))/2) + + morefrequencies = list(np.sort(morefrequencies)) + while morefrequencies[-1] > maxfreq: + if False: # too verbose! + print('Removing frequency', morefrequencies[-1]) + morefrequencies = morefrequencies[:-1] + while morefrequencies[0]< minfreq: + if False: + print('Removing frequency', morefrequencies[0]) + morefrequencies = morefrequencies[1:] + + phaseR1 = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, + b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceboth=forceboth) + + anglelist = np.linspace(min(phaseR1), max(phaseR1), m) ## most of the points are evenly spaced in phase + #anglelist = np.append(anglelist, -np.pi/3) + #anglelist = np.append(anglelist, -2*np.pi/3) + anglelist = np.append(anglelist, -np.pi/4) # special angle 1 + anglelist = np.append(anglelist, -3*np.pi/4) # special angle 2 + anglelist = np.unique(np.sort(np.append(anglelist, -np.pi/2))) # special angle 3 + + freqlist = find_freq_from_angle(morefrequencies, + phase = phaseR1, + angleswanted = anglelist, verbose = verbose) + if False: + print('anglelist/pi:', anglelist/np.pi, 'corresponds to frequency list:', freqlist, '. But still adding to chosendrive.') + + if not MONOMER: + phaseR2 = theta2(morefrequencies, k1_set, k2_set, k3_set, k4_set, + b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, 0, forceboth=forceboth) + + del anglelist + anglelist = np.linspace(min(phaseR2), max(phaseR2), m) + #anglelist = np.append(anglelist, -np.pi/3) + #anglelist = np.append(anglelist, -2*np.pi/3) + anglelist = np.append(anglelist, -np.pi/4) + anglelist = np.append(anglelist, -3*np.pi/4) + anglelist = np.unique(np.sort(np.append(anglelist, -np.pi/2))) + + freqlist2 = find_freq_from_angle(morefrequencies, + phase = phaseR2, + angleswanted = anglelist) + if False: + print('anglelist/pi: ', anglelist/np.pi) + print('freqlist2: ', freqlist2) + freqlist.extend(freqlist2) + res2 = res_freq_weak_coupling(k2_set, m2_set, b2_set) + freqlist.append(res2) + morefrequencies = np.append(morefrequencies,res2) + + freqlist.extend(includefreqs) + morefrequencies = np.append(morefrequencies, includefreqs) + res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) + freqlist.append(res1) + try: + reslist = res_freq_numeric(vals_set=vals_set, MONOMER=MONOMER, mode = 'all', forceboth=forceboth, + minfreq=minfreq, maxfreq=maxfreq, morefrequencies=morefrequencies, + unique = True, veryunique = True, verboseplot = False, verbose=verbose, iterations = 3) + freqlist.extend(reslist) + except NameError: + pass + + freqlist = list(np.sort(np.unique(freqlist))) + + while freqlist[0] < 0: + freqlist.pop(0) # drop negative frequencies + + numwanted = n-len(freqlist) # how many more frequencies are wanted? + evenlyspacedfreqlist = np.linspace(minfreq, maxfreq, + num = max(numwanted + 2,3)) # I added 2 for the endpoints + freqlist.extend(evenlyspacedfreqlist) + #print(freqlist) + chosendrive = list(np.sort(np.unique(np.array(freqlist)))) + + if staywithinlims: + while chosendrive[0] < minfreq or chosendrive[0] < 0: + f = chosendrive.pop(0) + if verbose: + print('Warning: Unexpected frequency', f) + while chosendrive[-1] > maxfreq: + f = chosendrive.pop(-1) + if verbose: + print('Warning: Unexpected frequency', f) + else: + while chosendrive[0] < 0: + f = chosendrive.pop(0) + print('Warning: Unexpected negative frequency', f) + chosendrive = np.array(chosendrive) + + #morefrequencies.extend(chosendrive) + morefrequencies = np.concatenate((morefrequencies, chosendrive)) + morefrequencies = list(np.sort(np.unique(morefrequencies))) + + if staywithinlims: + while morefrequencies[0] < minfreq: + f = morefrequencies.pop(0) + print('Warning: Unexpected frequency', f) + while morefrequencies[-1] > maxfreq: + f = morefrequencies.pop(-1) + print('warning: Unexpected frequency', f) + + return chosendrive, np.array(morefrequencies) + +def find_special_freq(drive, amp, phase, anglewanted = np.radians(225)): + maxampfreq = drive[np.argmax(amp)] + specialanglefreq = drive[np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi)))] + return maxampfreq, specialanglefreq + + +### res_freq_numeric() +## Uses privilege +## Not guaranteed to find all resonance peaks but should work ok for dimer +## Returns list of peak frequencies. +## If numtoreturn is None, then any number of frequencies could be returned. +## You can also set numtoreturn to 1 or 2 to return that number of frequencies. +def res_freq_numeric(vals_set, MONOMER, forceall, + mode = 'all', + minfreq=.1, maxfreq=5, morefrequencies=None, includefreqs = [], + unique = True, veryunique = True, numtoreturn = None, + verboseplot = False, plottitle = None, verbose=verbose, iterations = 1, + use_R2_only = False, + returnoptions = False): + + if verbose: + print('\nRunning res_freq_numeric() with mode ' + mode) + if plottitle is not None: + print(plottitle) + k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set = read_params(vals_set, MONOMER) + + # Never Monomer in this case + if MONOMER and numtoreturn != 2: # 2 is a tricky case... just use the rest of the algorithm + if numtoreturn is not None and numtoreturn != 1: + print('Cannot return ' + str(numtoreturn) + ' res freqs for Monomer.') + if verbose: + print('option 1') + + freqlist = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] # just compute it directly for Monomer + if returnoptions: + return freqlist, 1 + return freqlist + + approx_res_freqs = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] + if not MONOMER: + approx_res_freqs.append(res_freq_weak_coupling(k2_set, m2_set, b2_set)) + + for f in approx_res_freqs: + if f > maxfreq or f < minfreq: + print('Warning! Check minfreq and maxfreq') + print('minfreq', minfreq) + print('maxfreq', maxfreq) + print('Approx resonant freq', f) + + if morefrequencies is None: + morefrequencies = makemorefrequencies(vals_set=vals_set, minfreq=minfreq, maxfreq=maxfreq, + forceall=forceall, includefreqs = approx_res_freqs, + MONOMER=MONOMER, n=n) + else: + morefrequencies = np.append(morefrequencies, approx_res_freqs) + morefrequencies = np.sort(np.unique(morefrequencies)) + + # init + indexlist = [] + + # Never Monomer in this case + if MONOMER: + freqlist = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] + resfreqs_from_amp = freqlist + else: + first = True + for i in range(iterations): + if not first: # not first. This is a repeated iteration. indexlist has been defined. + if verbose: + print('indexlist:', indexlist) + if max(indexlist) > len(morefrequencies): + print('len(morefrequencies):', len(morefrequencies)) + print('morefrequencies:', morefrequencies) + print('indexlist:', indexlist) + print('Repeating with finer frequency mesh around frequencies:', morefrequencies[np.sort(indexlist)]) + + assert min(morefrequencies) >= minfreq + assert max(morefrequencies) <= maxfreq + if debug: + print('minfreq', minfreq) + print('Actual min freq', min(morefrequencies)) + print('maxfreq', maxfreq) + print('Actual max freq', max(morefrequencies)) + morefrequenciesprev = morefrequencies.copy() + for index in indexlist: + try: + spacing = abs(morefrequenciesprev[index] - morefrequenciesprev[index-1]) + except: + if verbose: + print('morefrequenciesprev:',morefrequenciesprev) + print('index:', index) + spacing = abs(morefrequenciesprev[index+1] - morefrequenciesprev[index]) + finerlist = np.linspace(max(minfreq,morefrequenciesprev[index]-spacing), + min(maxfreq,morefrequenciesprev[index] + spacing), + num = n) + assert min(finerlist) >= minfreq + assert max(finerlist) <= maxfreq + morefrequencies = np.append(morefrequencies,finerlist) + morefrequencies = np.sort(np.unique(morefrequencies)) + + + while morefrequencies[-1] > maxfreq: + if False: # too verbose! + print('Removing frequency', morefrequencies[-1]) + morefrequencies = morefrequencies[:-1] + while morefrequencies[0]< minfreq: + if False: + print('Removing frequency', morefrequencies[0]) + morefrequencies = morefrequencies[1:] + R1_amp_noiseless = curve1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = np.unwrap(R1_phase_noiseless) + if debug: + plt.figure() + plt.plot(morefrequencies, R1_amp_noiseless, label = 'R1_amp') + plt.plot(morefrequencies, R1_phase_noiseless, label = 'R1_phase') + if not MONOMER: + R2_amp_noiseless = curve2(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R2_phase_noiseless = theta2(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R2_phase_noiseless = np.unwrap(R2_phase_noiseless) + if debug: + plt.plot(morefrequencies, R2_amp_noiseless, label = 'R2_amp') + plt.plot(morefrequencies, R2_phase_noiseless, label = 'R2_phase') + + ## find maxima + index1 = np.argmax(R1_amp_noiseless) + if not MONOMER and not use_R2_only: + indexlist1, heights = find_peaks(R1_amp_noiseless, height=.015, distance = 5) + if debug: + print('index1:', index1) + print('indexlist1:',indexlist1) + print('heights', heights) + plt.axvline(morefrequencies[index1]) + for i in indexlist1: + plt.axvline(morefrequencies[i]) + assert index1 <= len(morefrequencies) + if len(indexlist1)>0: + assert max(indexlist1) <= len(morefrequencies) + else: + print('Warning: find_peaks on R1_amp returned indexlist:', indexlist1) + plt.figure() + plt.plot(R1_amp_noiseless) + plt.xlabel(R1_amp_noiseless) + plt.figure() + else: + indexlist1 = [] + if MONOMER: + indexlist2 = [] + else: + index2 = np.argmax(R2_amp_noiseless) + indexlist2, heights2 = find_peaks(R2_amp_noiseless, height=.015, distance = 5) + assert index2 <= len(morefrequencies) + if len(indexlist2) >0: + assert max(indexlist2) <= len(morefrequencies) + + if verbose: + print('Maximum amplitude for R1 is ', R1_amp_noiseless[index1], 'at', morefrequencies[index1]) + if not MONOMER: + print('Maximum amplitude for R2 is ', R2_amp_noiseless[index2], 'at', morefrequencies[index2]) + + indexlistampR1 = np.append(indexlist1,index1) + assert max(indexlistampR1) <= len(morefrequencies) + if False: # too verbose! + print('indexlistampR1:', indexlistampR1) + if MONOMER: + indexlist = indexlistampR1 + assert max(indexlist) <= len(morefrequencies) + indexlistampR2 = [] + else: + indexlistampR2 = np.append(indexlist2, index2) + if False: + print('indexlistampR2:',indexlistampR2) + assert max(indexlistampR2) <= len(morefrequencies) + indexlist = np.append(indexlistampR1, indexlistampR2) + if False: + print('indexlist:', indexlist) + + assert max(indexlist) <= len(morefrequencies) + indexlist = list(np.unique(indexlist)) + indexlist = [int(index) for index in indexlist] + first = False + + ## Check to see if findpeaks just worked + if (numtoreturn == 2) and (mode != 'phase'): + thresh = .006 + if len(indexlist2) == 2: + if verbose: + print("Used findpeaks on R2 amplitude (option 2)") + opt2freqlist = list(np.sort(morefrequencies[indexlist2])) + if abs(opt2freqlist[1]-opt2freqlist[0]) > thresh: + if returnoptions: + return opt2freqlist, 2 + return opt2freqlist + if len(indexlist1) == 2 and not use_R2_only: + opt3freqlist = list(np.sort(morefrequencies[indexlist1])) + if abs(opt3freqlist[1]-opt3freqlist[0]) > thresh: + if verbose: + print("Used findpeaks on R1 amplitude (option 3)") + if returnoptions: + return opt3freqlist, 3 + return opt3freqlist + if verbose: + print('indexlist1 from R1 amp find_peaks is', indexlist1) + print('indexlist2 from R2 amp find_peaks is', indexlist2) + + if verbose: + print('indexlist:',indexlist) + resfreqs_from_amp = morefrequencies[indexlist] + + if not MONOMER or mode == 'phase': + ## find where angles are resonant angles + angleswanted = [np.pi/2, -np.pi/2] # the function will wrap angles so don't worry about mod 2 pi. + R1_flist,indexlistphaseR1 = find_freq_from_angle(morefrequencies, R1_phase_noiseless, angleswanted=angleswanted, returnindex=True) + if MONOMER: + assert mode == 'phase' + resfreqs_from_phase = R1_flist + else: + R2_flist,indexlistphaseR2 = find_freq_from_angle(morefrequencies, R2_phase_noiseless, angleswanted=angleswanted, + returnindex=True) + resfreqs_from_phase = np.append(R1_flist, R2_flist) + else: + assert MONOMER + resfreqs_from_phase = [] # don't bother with this for the MONOMER + indexlistphaseR1 = [] + indexlistphaseR2 = [] + R1_flist = [] + + if verboseplot: + #Never Monomer in this case + if MONOMER: # still need to calculate the curves + R1_amp_noiseless = curve1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R1_phase_noiseless = np.unwrap(R1_phase_noiseless) + indexlistampR1 = [np.argmin(abs(w - morefrequencies )) for w in resfreqs_from_amp] + print('Plotting!') + fig, (ampax, phaseax) = plt.subplots(2,1,gridspec_kw={'hspace': 0}, sharex = 'all') + plt.sca(ampax) + plt.title(plottitle) + plt.plot(morefrequencies, R1_amp_noiseless, color='gray') + if not MONOMER: + plt.plot(morefrequencies, R2_amp_noiseless, color='lightblue') + + plt.plot(morefrequencies[indexlistampR1],R1_amp_noiseless[indexlistampR1], '.') + if not MONOMER: + plt.plot(morefrequencies[indexlistampR2],R2_amp_noiseless[indexlistampR2], '.') + + plt.sca(phaseax) + plt.plot(morefrequencies,R1_phase_noiseless, color='gray' ) + if not MONOMER: + plt.plot(morefrequencies,R2_phase_noiseless, color = 'lightblue') + plt.plot(R1_flist, theta1(np.array(R1_flist), k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), '.') + if not MONOMER: + plt.plot(R2_flist, theta2(np.array(R2_flist), k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), '.') + + if mode == 'maxamp' or mode == 'amp' or mode == 'amplitude': + freqlist = resfreqs_from_amp + elif mode == 'phase': + freqlist = resfreqs_from_phase + else: + if mode != 'all': + print("Set mode to any of 'all', 'maxamp', or 'phase'. Recovering to 'all'.") + # mode is 'all' + freqlist = np.sort(np.append(resfreqs_from_amp, resfreqs_from_phase)) + + + if veryunique: # Don't return both close frequencies; just pick the higher amplitude frequency of the two. + ## I obtained indexlists four ways: indexlistampR1, indexlistampR2, indexlistphaseR1, indexlistphaseR2 + indexlist = indexlist + indexlistphaseR1 + if not MONOMER: + indexlist = indexlist + indexlistphaseR2 + indexlist = list(np.sort(np.unique(indexlist))) + if verbose: + print('indexlist:', indexlist) + + narrowerW = calcnarrowerW(vals_set, MONOMER) + + """ a and b are indices of morefrequencies """ + def veryclose(a,b): + ## option 1: veryclose if indices are within 2. + #return abs(b-a) <= 2 + + ## option 2: very close if frequencies are closer than .01 rad/s + #veryclose = abs(morefrequencies[a]-morefrequencies[b]) <= .1 + + ## option 3: very close if freqeuencies are closer than W/20 + veryclose = abs(morefrequencies[a]-morefrequencies[b]) <= narrowerW/20 + + return veryclose + + if len(freqlist) > 1: + ## if two elements of indexlist are veryclose to each other, want to remove the smaller amplitude. + removeindex = [] # create a list of indices to remove + try: + tempfreqlist = morefrequencies[indexlist] # indexlist is indicies of morefrequencies. + # if the 10th element of indexlist is indexlist[10]=200, then tempfreqlist[10] = morefrequencies[200] + except: + print('indexlist:', indexlist) + A2 = curve2(tempfreqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set,) + # and then A2[10] is the amplitude of R2 at the frequency morefrequencies[200] + # and then the number 10 is the sort of number we will add to a removeindex list + for i in range(len(indexlist)-1): + if veryclose(indexlist[i], indexlist[i-1]): + if A2[i] < A2[i-1]: # remove the smaller amplitude + removeindex.append(i) + else: + removeindex.append(i-1) + numtoremove = len(removeindex) + if verbose and numtoremove > 0: + print('Removing', numtoremove, 'frequencies') + + removeindex = list(np.unique(removeindex)) + indexlist = list(indexlist) + ## Need to work on removal from the end of the list + ## in order to avoid changing index numbers while working with the list + while removeindex != []: + i = removeindex.pop(-1) # work backwards through indexes to remove + el = indexlist.pop(i) # remove it from indexlist + if numtoremove < 5 and verbose: + print('Removed frequency', morefrequencies[el]) + + freqlist = morefrequencies[indexlist] + + freqlist = np.sort(freqlist) + + if unique or veryunique or (numtoreturn is not None): ## Don't return multiple copies of the same number. + freqlist = np.unique(np.array(freqlist)) + + if verbose: + print('Possible frequencies are:', freqlist) + + if numtoreturn is not None: + if len(freqlist) == numtoreturn: + if verbose: + print ('option 4') + if returnoptions: + return list(freqlist), 4 + return list(freqlist) + if len(freqlist) < numtoreturn: + if verbose: + print('Warning: I do not have as many resonant frequencies as was requested.') + freqlist = list(freqlist) + # instead I should add another frequency corresponding to some desireable phase. + if verbose: + print('Returning instead a freq2 at phase -3pi/4.') + goodphase = -3*np.pi/4 + for i in range(iterations): + f2, ind2 = find_freq_from_angle(drive = morefrequencies, + phase = theta1(morefrequencies, + k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), + angleswanted = [goodphase], returnindex = True) + ind2 = ind2[0] + try: + spacing = abs(morefrequencies[ind2] - morefrequencies[ind2-1]) + except IndexError: + spacing = abs(morefrequencies[ind2+1] - morefrequencies[ind2]) + finermesh = np.linspace(morefrequencies[ind2] - spacing,morefrequencies[ind2] + spacing, num=n) + morefrequencies = np.append(morefrequencies, finermesh) + f2 = f2[0] + freqlist.append(f2) + if verboseplot: + plt.sca(phaseax) + plt.plot(f2, theta1(f2, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), '.') + print('Appending: ', f2) + for i in range(numtoreturn - len(freqlist)): + # This is currently unlikely to be true, but I'm future-proofing + # for a future when I want to set the number to an integer greater than 2. + freqlist.append(np.nan) # increase list to requested length with nan + if verboseplot: + plt.sca(ampax) + plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), 'x') + if verbose: + print ('option 5') + if returnoptions: + return freqlist, 5 + return freqlist + + R1_amp_noiseless = curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + R2_amp_noiseless = curve2(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall) + + topR1index = np.argmax(R1_amp_noiseless) + + if numtoreturn == 1: + # just return the one max amp frequency. + if verbose: + print('option 6') + if returnoptions: + return [freqlist[topR1index]],6 + return [freqlist[topR1index]] + + if numtoreturn != 2: + print('Warning: returning ' + str(numtoreturn) + ' frequencies is not implemented. Returning 2 frequencies.') + + # Choose a second frequency to return. + topR2index = np.argmax(R2_amp_noiseless) + threshold = .2 # rad/s + if abs(freqlist[topR1index] - freqlist[topR2index]) > threshold: + freqlist = list([freqlist[topR1index], freqlist[topR2index]]) + if verboseplot: + plt.sca(ampax) + plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), 'x') + if verbose: + print('option 7') + if returnoptions: + return freqlist, 7 + return freqlist + else: + R1_amp_noiseless = list(R1_amp_noiseless) + freqlist = list(freqlist) + f1 = freqlist.pop(topR1index) + R1_amp_noiseless.pop(topR1index) + secondR1index = np.argmax(R1_amp_noiseless) + f2 = freqlist.pop(secondR1index) + if abs(f2-f1) > threshold: + freqlist = list([f1, f2]) # overwrite freqlist + if verboseplot: + plt.sca(ampax) + plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, + 0, forceall), 'x') + if verbose: + print('option 8') + if returnoptions: + return freqlist, 8 + return freqlist + else: # return whatever element of the freqlist is furthest + freqlist.append(f2) + # is f1 closer to top or bottom of freqlist? + if abs(f1 - min(freqlist)) > abs(f1 - max(freqlist)): + if verbose: + print('option 9') + if returnoptions: + return [f1, min(freqlist)], 9 + return [f1, min(freqlist)] + else: + if verbose: + print('option 10') + if returnoptions: + return [f1, max(freqlist)], 10 + return [f1, max(freqlist)] + + + else: + if verbose: + print('option 11') + if returnoptions: + return list(freqlist),11 + return list(freqlist) + + +# create list of all measured frequencies, centered around res (the resonance frequency), and spaced out by freqdiff +def allmeasfreq_one_res(res, max_num_p, freqdiff): + newfreqplus = res + newfreqminus = res + freqlist = [res] + while len(freqlist) < max_num_p: + newfreqplus = newfreqplus + freqdiff + newfreqminus = newfreqminus - freqdiff + freqlist.append(newfreqplus) + freqlist.append(newfreqminus) + if min(freqlist) < 0: + print('Value less than zero!') + print('min(freqlist):', min(freqlist)) + return freqlist + +# create list of all measured frequencies, centered around res1 and res2, respectively, and spaced out by freqdiff +def allmeasfreq_two_res(res1, res2, max_num_p, freqdiff): + newfreq1plus = res1 + newfreq1minus = res1 + newfreq2plus = res2 + newfreq2minus = res2 + freqlist = [res1, res2] + while len(freqlist) < max_num_p: + newfreq1plus = newfreq1plus + freqdiff + newfreq1minus = newfreq1minus - freqdiff + newfreq2plus = newfreq2plus + freqdiff + newfreq2minus = newfreq2minus - freqdiff + freqlist.append(newfreq1plus) ## this order might matter + freqlist.append(newfreq2plus) + freqlist.append(newfreq1minus) + freqlist.append(newfreq2minus) + if min(freqlist) < 0: + print('Value less than zero!') + print('min(freqlist):', min(freqlist)) + return freqlist + + + +def best_choice_freq_set(vals_set, MONOMER, forceboth, reslist, num_p = 10): + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + narrowerW = calcnarrowerW(vals_set, MONOMER) + freqdiff = round(narrowerW/6,4) + if MONOMER: + measurementfreqs = allmeasfreq_one_res(reslist[0], num_p, freqdiff) + else: + measurementfreqs = allmeasfreq_two_res(reslist[0], reslist[1], num_p, freqdiff) + + return measurementfreqs[:num_p] + + + + + + + + diff --git a/trimer/trimer_case_study_frequency_picker.py b/trimer/trimer_case_study_frequency_picker.py index 42b7311..e5d7826 100644 --- a/trimer/trimer_case_study_frequency_picker.py +++ b/trimer/trimer_case_study_frequency_picker.py @@ -9,564 +9,8 @@ Using "ideal" frequencies to test NetMAP. ''' from comparing_curvefit_types import run_trials -import numpy as np -from resonatorphysics import res_freq_weak_coupling, calcnarrowerW -from Trimer_simulator import curve1, theta1, curve2, theta2 -import matplotlib.pyplot as plt -from scipy.signal import find_peaks -import resonatorphysics +from kind_of_trimer_resonatorfrequencypicker import res_freq_numeric -## Copy of Viva's code from resonatorfrequency picker but adding information so I can run it with a Trimer - -# default settings -verbose = False -n=100 -debug = False - -## Uses privilege -## Not guaranteed to find all resonance peaks but should work ok for dimer -## Returns list of peak frequencies. -## If numtoreturn is None, then any number of frequencies could be returned. -## You can also set numtoreturn to 1 or 2 to return that number of frequencies. -def res_freq_numeric(vals_set, MONOMER, forceall, - mode = 'all', - minfreq=.1, maxfreq=5, morefrequencies=None, includefreqs = [], - unique = True, veryunique = True, numtoreturn = None, - verboseplot = False, plottitle = None, verbose=verbose, iterations = 1, - use_R2_only = False, - returnoptions = False): - - if verbose: - print('\nRunning res_freq_numeric() with mode ' + mode) - if plottitle is not None: - print(plottitle) - k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set = read_params(vals_set, MONOMER) - - # Never Monomer in this case - if MONOMER and numtoreturn != 2: # 2 is a tricky case... just use the rest of the algorithm - if numtoreturn is not None and numtoreturn != 1: - print('Cannot return ' + str(numtoreturn) + ' res freqs for Monomer.') - if verbose: - print('option 1') - - freqlist = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] # just compute it directly for Monomer - if returnoptions: - return freqlist, 1 - return freqlist - - approx_res_freqs = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] - if not MONOMER: - approx_res_freqs.append(res_freq_weak_coupling(k2_set, m2_set, b2_set)) - - for f in approx_res_freqs: - if f > maxfreq or f < minfreq: - print('Warning! Check minfreq and maxfreq') - print('minfreq', minfreq) - print('maxfreq', maxfreq) - print('Approx resonant freq', f) - - if morefrequencies is None: - morefrequencies = makemorefrequencies(vals_set=vals_set, minfreq=minfreq, maxfreq=maxfreq, - forceall=forceall, includefreqs = approx_res_freqs, - MONOMER=MONOMER, n=n) - else: - morefrequencies = np.append(morefrequencies, approx_res_freqs) - morefrequencies = np.sort(np.unique(morefrequencies)) - - # init - indexlist = [] - - # Never Monomer in this case - if MONOMER: - freqlist = [res_freq_weak_coupling(k1_set, m1_set, b1_set)] - resfreqs_from_amp = freqlist - else: - first = True - for i in range(iterations): - if not first: # not first. This is a repeated iteration. indexlist has been defined. - if verbose: - print('indexlist:', indexlist) - if max(indexlist) > len(morefrequencies): - print('len(morefrequencies):', len(morefrequencies)) - print('morefrequencies:', morefrequencies) - print('indexlist:', indexlist) - print('Repeating with finer frequency mesh around frequencies:', morefrequencies[np.sort(indexlist)]) - - assert min(morefrequencies) >= minfreq - assert max(morefrequencies) <= maxfreq - if debug: - print('minfreq', minfreq) - print('Actual min freq', min(morefrequencies)) - print('maxfreq', maxfreq) - print('Actual max freq', max(morefrequencies)) - morefrequenciesprev = morefrequencies.copy() - for index in indexlist: - try: - spacing = abs(morefrequenciesprev[index] - morefrequenciesprev[index-1]) - except: - if verbose: - print('morefrequenciesprev:',morefrequenciesprev) - print('index:', index) - spacing = abs(morefrequenciesprev[index+1] - morefrequenciesprev[index]) - finerlist = np.linspace(max(minfreq,morefrequenciesprev[index]-spacing), - min(maxfreq,morefrequenciesprev[index] + spacing), - num = n) - assert min(finerlist) >= minfreq - assert max(finerlist) <= maxfreq - morefrequencies = np.append(morefrequencies,finerlist) - morefrequencies = np.sort(np.unique(morefrequencies)) - - - while morefrequencies[-1] > maxfreq: - if False: # too verbose! - print('Removing frequency', morefrequencies[-1]) - morefrequencies = morefrequencies[:-1] - while morefrequencies[0]< minfreq: - if False: - print('Removing frequency', morefrequencies[0]) - morefrequencies = morefrequencies[1:] - R1_amp_noiseless = curve1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R1_phase_noiseless = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R1_phase_noiseless = np.unwrap(R1_phase_noiseless) - if debug: - plt.figure() - plt.plot(morefrequencies, R1_amp_noiseless, label = 'R1_amp') - plt.plot(morefrequencies, R1_phase_noiseless, label = 'R1_phase') - if not MONOMER: - R2_amp_noiseless = curve2(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R2_phase_noiseless = theta2(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R2_phase_noiseless = np.unwrap(R2_phase_noiseless) - if debug: - plt.plot(morefrequencies, R2_amp_noiseless, label = 'R2_amp') - plt.plot(morefrequencies, R2_phase_noiseless, label = 'R2_phase') - - ## find maxima - index1 = np.argmax(R1_amp_noiseless) - if not MONOMER and not use_R2_only: - indexlist1, heights = find_peaks(R1_amp_noiseless, height=.015, distance = 5) - if debug: - print('index1:', index1) - print('indexlist1:',indexlist1) - print('heights', heights) - plt.axvline(morefrequencies[index1]) - for i in indexlist1: - plt.axvline(morefrequencies[i]) - assert index1 <= len(morefrequencies) - if len(indexlist1)>0: - assert max(indexlist1) <= len(morefrequencies) - else: - print('Warning: find_peaks on R1_amp returned indexlist:', indexlist1) - plt.figure() - plt.plot(R1_amp_noiseless) - plt.xlabel(R1_amp_noiseless) - plt.figure() - else: - indexlist1 = [] - if MONOMER: - indexlist2 = [] - else: - index2 = np.argmax(R2_amp_noiseless) - indexlist2, heights2 = find_peaks(R2_amp_noiseless, height=.015, distance = 5) - assert index2 <= len(morefrequencies) - if len(indexlist2) >0: - assert max(indexlist2) <= len(morefrequencies) - - if verbose: - print('Maximum amplitude for R1 is ', R1_amp_noiseless[index1], 'at', morefrequencies[index1]) - if not MONOMER: - print('Maximum amplitude for R2 is ', R2_amp_noiseless[index2], 'at', morefrequencies[index2]) - - indexlistampR1 = np.append(indexlist1,index1) - assert max(indexlistampR1) <= len(morefrequencies) - if False: # too verbose! - print('indexlistampR1:', indexlistampR1) - if MONOMER: - indexlist = indexlistampR1 - assert max(indexlist) <= len(morefrequencies) - indexlistampR2 = [] - else: - indexlistampR2 = np.append(indexlist2, index2) - if False: - print('indexlistampR2:',indexlistampR2) - assert max(indexlistampR2) <= len(morefrequencies) - indexlist = np.append(indexlistampR1, indexlistampR2) - if False: - print('indexlist:', indexlist) - - assert max(indexlist) <= len(morefrequencies) - indexlist = list(np.unique(indexlist)) - indexlist = [int(index) for index in indexlist] - first = False - - ## Check to see if findpeaks just worked - if (numtoreturn == 2) and (mode != 'phase'): - thresh = .006 - if len(indexlist2) == 2: - if verbose: - print("Used findpeaks on R2 amplitude (option 2)") - opt2freqlist = list(np.sort(morefrequencies[indexlist2])) - if abs(opt2freqlist[1]-opt2freqlist[0]) > thresh: - if returnoptions: - return opt2freqlist, 2 - return opt2freqlist - if len(indexlist1) == 2 and not use_R2_only: - opt3freqlist = list(np.sort(morefrequencies[indexlist1])) - if abs(opt3freqlist[1]-opt3freqlist[0]) > thresh: - if verbose: - print("Used findpeaks on R1 amplitude (option 3)") - if returnoptions: - return opt3freqlist, 3 - return opt3freqlist - if verbose: - print('indexlist1 from R1 amp find_peaks is', indexlist1) - print('indexlist2 from R2 amp find_peaks is', indexlist2) - - if verbose: - print('indexlist:',indexlist) - resfreqs_from_amp = morefrequencies[indexlist] - - if not MONOMER or mode == 'phase': - ## find where angles are resonant angles - angleswanted = [np.pi/2, -np.pi/2] # the function will wrap angles so don't worry about mod 2 pi. - R1_flist,indexlistphaseR1 = find_freq_from_angle(morefrequencies, R1_phase_noiseless, angleswanted=angleswanted, returnindex=True) - if MONOMER: - assert mode == 'phase' - resfreqs_from_phase = R1_flist - else: - R2_flist,indexlistphaseR2 = find_freq_from_angle(morefrequencies, R2_phase_noiseless, angleswanted=angleswanted, - returnindex=True) - resfreqs_from_phase = np.append(R1_flist, R2_flist) - else: - assert MONOMER - resfreqs_from_phase = [] # don't bother with this for the MONOMER - indexlistphaseR1 = [] - indexlistphaseR2 = [] - R1_flist = [] - - if verboseplot: - #Never Monomer in this case - if MONOMER: # still need to calculate the curves - R1_amp_noiseless = curve1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R1_phase_noiseless = theta1(morefrequencies, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R1_phase_noiseless = np.unwrap(R1_phase_noiseless) - indexlistampR1 = [np.argmin(abs(w - morefrequencies )) for w in resfreqs_from_amp] - print('Plotting!') - fig, (ampax, phaseax) = plt.subplots(2,1,gridspec_kw={'hspace': 0}, sharex = 'all') - plt.sca(ampax) - plt.title(plottitle) - plt.plot(morefrequencies, R1_amp_noiseless, color='gray') - if not MONOMER: - plt.plot(morefrequencies, R2_amp_noiseless, color='lightblue') - - plt.plot(morefrequencies[indexlistampR1],R1_amp_noiseless[indexlistampR1], '.') - if not MONOMER: - plt.plot(morefrequencies[indexlistampR2],R2_amp_noiseless[indexlistampR2], '.') - - plt.sca(phaseax) - plt.plot(morefrequencies,R1_phase_noiseless, color='gray' ) - if not MONOMER: - plt.plot(morefrequencies,R2_phase_noiseless, color = 'lightblue') - plt.plot(R1_flist, theta1(np.array(R1_flist), k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), '.') - if not MONOMER: - plt.plot(R2_flist, theta2(np.array(R2_flist), k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), '.') - - if mode == 'maxamp' or mode == 'amp' or mode == 'amplitude': - freqlist = resfreqs_from_amp - elif mode == 'phase': - freqlist = resfreqs_from_phase - else: - if mode != 'all': - print("Set mode to any of 'all', 'maxamp', or 'phase'. Recovering to 'all'.") - # mode is 'all' - freqlist = np.sort(np.append(resfreqs_from_amp, resfreqs_from_phase)) - - - if veryunique: # Don't return both close frequencies; just pick the higher amplitude frequency of the two. - ## I obtained indexlists four ways: indexlistampR1, indexlistampR2, indexlistphaseR1, indexlistphaseR2 - indexlist = indexlist + indexlistphaseR1 - if not MONOMER: - indexlist = indexlist + indexlistphaseR2 - indexlist = list(np.sort(np.unique(indexlist))) - if verbose: - print('indexlist:', indexlist) - - narrowerW = calcnarrowerW(vals_set, MONOMER) - - """ a and b are indices of morefrequencies """ - def veryclose(a,b): - ## option 1: veryclose if indices are within 2. - #return abs(b-a) <= 2 - - ## option 2: very close if frequencies are closer than .01 rad/s - #veryclose = abs(morefrequencies[a]-morefrequencies[b]) <= .1 - - ## option 3: very close if freqeuencies are closer than W/20 - veryclose = abs(morefrequencies[a]-morefrequencies[b]) <= narrowerW/20 - - return veryclose - - if len(freqlist) > 1: - ## if two elements of indexlist are veryclose to each other, want to remove the smaller amplitude. - removeindex = [] # create a list of indices to remove - try: - tempfreqlist = morefrequencies[indexlist] # indexlist is indicies of morefrequencies. - # if the 10th element of indexlist is indexlist[10]=200, then tempfreqlist[10] = morefrequencies[200] - except: - print('indexlist:', indexlist) - A2 = curve2(tempfreqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set,) - # and then A2[10] is the amplitude of R2 at the frequency morefrequencies[200] - # and then the number 10 is the sort of number we will add to a removeindex list - for i in range(len(indexlist)-1): - if veryclose(indexlist[i], indexlist[i-1]): - if A2[i] < A2[i-1]: # remove the smaller amplitude - removeindex.append(i) - else: - removeindex.append(i-1) - numtoremove = len(removeindex) - if verbose and numtoremove > 0: - print('Removing', numtoremove, 'frequencies') - - removeindex = list(np.unique(removeindex)) - indexlist = list(indexlist) - ## Need to work on removal from the end of the list - ## in order to avoid changing index numbers while working with the list - while removeindex != []: - i = removeindex.pop(-1) # work backwards through indexes to remove - el = indexlist.pop(i) # remove it from indexlist - if numtoremove < 5 and verbose: - print('Removed frequency', morefrequencies[el]) - - freqlist = morefrequencies[indexlist] - - freqlist = np.sort(freqlist) - - if unique or veryunique or (numtoreturn is not None): ## Don't return multiple copies of the same number. - freqlist = np.unique(np.array(freqlist)) - - if verbose: - print('Possible frequencies are:', freqlist) - - if numtoreturn is not None: - if len(freqlist) == numtoreturn: - if verbose: - print ('option 4') - if returnoptions: - return list(freqlist), 4 - return list(freqlist) - if len(freqlist) < numtoreturn: - if verbose: - print('Warning: I do not have as many resonant frequencies as was requested.') - freqlist = list(freqlist) - # instead I should add another frequency corresponding to some desireable phase. - if verbose: - print('Returning instead a freq2 at phase -3pi/4.') - goodphase = -3*np.pi/4 - for i in range(iterations): - f2, ind2 = find_freq_from_angle(drive = morefrequencies, - phase = theta1(morefrequencies, - k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), - angleswanted = [goodphase], returnindex = True) - ind2 = ind2[0] - try: - spacing = abs(morefrequencies[ind2] - morefrequencies[ind2-1]) - except IndexError: - spacing = abs(morefrequencies[ind2+1] - morefrequencies[ind2]) - finermesh = np.linspace(morefrequencies[ind2] - spacing,morefrequencies[ind2] + spacing, num=n) - morefrequencies = np.append(morefrequencies, finermesh) - f2 = f2[0] - freqlist.append(f2) - if verboseplot: - plt.sca(phaseax) - plt.plot(f2, theta1(f2, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), '.') - print('Appending: ', f2) - for i in range(numtoreturn - len(freqlist)): - # This is currently unlikely to be true, but I'm future-proofing - # for a future when I want to set the number to an integer greater than 2. - freqlist.append(np.nan) # increase list to requested length with nan - if verboseplot: - plt.sca(ampax) - plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), 'x') - if verbose: - print ('option 5') - if returnoptions: - return freqlist, 5 - return freqlist - - R1_amp_noiseless = curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - R2_amp_noiseless = curve2(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall) - - topR1index = np.argmax(R1_amp_noiseless) - - if numtoreturn == 1: - # just return the one max amp frequency. - if verbose: - print('option 6') - if returnoptions: - return [freqlist[topR1index]],6 - return [freqlist[topR1index]] - - if numtoreturn != 2: - print('Warning: returning ' + str(numtoreturn) + ' frequencies is not implemented. Returning 2 frequencies.') - - # Choose a second frequency to return. - topR2index = np.argmax(R2_amp_noiseless) - threshold = .2 # rad/s - if abs(freqlist[topR1index] - freqlist[topR2index]) > threshold: - freqlist = list([freqlist[topR1index], freqlist[topR2index]]) - if verboseplot: - plt.sca(ampax) - plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), 'x') - if verbose: - print('option 7') - if returnoptions: - return freqlist, 7 - return freqlist - else: - R1_amp_noiseless = list(R1_amp_noiseless) - freqlist = list(freqlist) - f1 = freqlist.pop(topR1index) - R1_amp_noiseless.pop(topR1index) - secondR1index = np.argmax(R1_amp_noiseless) - f2 = freqlist.pop(secondR1index) - if abs(f2-f1) > threshold: - freqlist = list([f1, f2]) # overwrite freqlist - if verboseplot: - plt.sca(ampax) - plt.plot(freqlist, curve1(freqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, - 0, forceall), 'x') - if verbose: - print('option 8') - if returnoptions: - return freqlist, 8 - return freqlist - else: # return whatever element of the freqlist is furthest - freqlist.append(f2) - # is f1 closer to top or bottom of freqlist? - if abs(f1 - min(freqlist)) > abs(f1 - max(freqlist)): - if verbose: - print('option 9') - if returnoptions: - return [f1, min(freqlist)], 9 - return [f1, min(freqlist)] - else: - if verbose: - print('option 10') - if returnoptions: - return [f1, max(freqlist)], 10 - return [f1, max(freqlist)] - - - else: - if verbose: - print('option 11') - if returnoptions: - return list(freqlist),11 - return list(freqlist) - -#Function needed in res_freq_numeric -def makemorefrequencies(vals_set, minfreq, maxfreq,MONOMER,forceall, - res1 = None, res2 = None, - includefreqs = None, n=n, staywithinlims = False): - [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) - - if res1 is None: - res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) - if not MONOMER and res2 is None: - res2 = res_freq_weak_coupling(k2_set, m2_set, b2_set) - - morefrequencies = np.linspace(minfreq, maxfreq, num = n*60) - if MONOMER: - morefrequencies = np.append(morefrequencies, [res1]) - else: - morefrequencies = np.append(morefrequencies, [res1,res2]) - - if includefreqs is not None: - morefrequencies = np.append(morefrequencies, np.array(includefreqs)) - - try: - W1 = resonatorphysics.approx_width(k = k1_set, m = m1_set, b=b1_set) - except ZeroDivisionError: - print('k1_set:', k1_set) - print('m1_set:', m1_set) - print('b1_set:', b1_set) - W1 = (maxfreq - minfreq)/5 - morefrequencies = np.append(morefrequencies, np.linspace(res1-W1, res1+W1, num = 7*n)) - morefrequencies = np.append(morefrequencies, np.linspace(res1-2*W1, res1+2*W1, num = 10*n)) - if not MONOMER: - W2 = resonatorphysics.approx_width(k = k2_set, m = m2_set, b=b2_set) - morefrequencies = np.append(morefrequencies, np.linspace(res2-W2, res2+W2, num = 7*n)) - morefrequencies = np.append(morefrequencies, np.linspace(res2-2*W2, res2+2*W2, num = 10*n)) - morefrequencies = list(np.sort(np.unique(morefrequencies))) - - while morefrequencies[0] < 0: - morefrequencies.pop(0) - - if staywithinlims: - while morefrequencies[0] < minfreq: - morefrequencies.pop(0) - while morefrequencies[-1] > maxfreq: - morefrequencies.pop(-1) - - return np.array(morefrequencies) - -#Function needed in res_freq_numeric -def find_freq_from_angle(drive, phase, angleswanted = [-np.pi/4], returnindex = False, verbose = False): - assert len(drive) == len(phase) - - #specialanglefreq = [drive[np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi)))] \ - # for anglewanted in angleswanted ] - - threshold = np.pi/30 # small angle threshold - specialanglefreq = [] # initialize list - indexlist = [] - for anglewanted in angleswanted: - index = np.argmin(abs(phase%(2*np.pi) - anglewanted%(2*np.pi))) # find where phase is closest - - if index == 0 or index >= len(drive)-1: # edges of dataset require additional scrutiny - ## check to see if it's actually close after all - nearness = abs(phase[index]%(2*np.pi)-anglewanted%(2*np.pi)) - if nearness > threshold: - continue # don't include this index - specialanglefreq.append(drive[index]) - indexlist.append(index) - - if False: - plt.figure() - plt.plot(specialanglefreq,phase[indexlist]/np.pi) - plt.xlabel('Freq') - plt.ylabel('Angle (pi)') - - if returnindex: - return specialanglefreq, indexlist - else: - return specialanglefreq - -#Function needed in res_freq_numeric -def read_params(vect, MONOMER): - #Will never need to use the Monomer part in this case - if MONOMER: - [M1, B1, K1, FD] = vect - K12 = 0 - M2 = 0 - B2 = 0 - K2= 0 - else: - [K1, K2, K3, K4, B1, B2, B3, FD, M1, M2, M3] = vect - return [K1, K2, K3, K4, B1, B2, B3, FD, M1, M2, M3] ''' Begin Work Here. ''' diff --git a/trimer/trimer_helperfunctions.py b/trimer/trimer_helperfunctions.py new file mode 100644 index 0000000..8d80070 --- /dev/null +++ b/trimer/trimer_helperfunctions.py @@ -0,0 +1,105 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 9 16:08:31 2022 + +@author: vhorowit +""" + +import os +import datetime +import matplotlib.pyplot as plt +import numpy as np +try: + import winsound +except: + pass + +def datestring(): + return datetime.datetime.today().strftime('%Y-%m-%d %H;%M;%S') + +## source: https://stackabuse.com/python-how-to-flatten-list-of-lists/ +def flatten(list_of_lists): + if len(list_of_lists) == 0: + return list_of_lists + if isinstance(list_of_lists[0], list): + return flatten(list_of_lists[0]) + flatten(list_of_lists[1:]) + return list_of_lists[:1] + flatten(list_of_lists[1:]) + + +def listlength(list1): + try: + length = len(list1) + except TypeError: + length = 1 + return length + +def printtime(repeats, before, after, dobeep = True): + print('Ran ' + str(repeats) + ' times in ' + str(round(after-before,3)) + ' sec') + if dobeep: + beep() + +""" vh is often complex but its imaginary part is actually zero, so let's store it as a real list of vectors instead """ +def make_real_iff_real(vh): + vhr = [] # real list of vectors + for vect in vh: + vhr.append([v.real for v in vect if v.imag == 0]) # make real if and only if real + return (np.array(vhr)) + +""" Store parameters extracted from SVD """ +def store_params(M1, M2, M3, B1, B2, B3, K1, K2, K3, K4, FD): + params = [M1, M2, M3, B1, B2, B3, K1, K2, K3, K4, FD] + return params + +def read_params(vect): + [M1, M2, M3, B1, B2, B3, K1, K2, K3, K4, FD] = vect + return [M1, M2, M3, B1, B2, B3, K1, K2, K3, K4, FD] + +def savefigure(savename): + try: + plt.savefig(savename + '.svg', dpi = 600, bbox_inches='tight', transparent=True) + except: + print('Could not save svg') + try: + plt.savefig(savename + '.pdf', dpi = 600, bbox_inches='tight', transparent=True) + # transparent true source: https://jonathansoma.com/lede/data-studio/matplotlib/exporting-from-matplotlib-to-open-in-adobe-illustrator/ + except: + print('Could not save pdf') + plt.savefig(savename + '.png', dpi = 600, bbox_inches='tight', transparent=True) + print("Saved:\n", savename + '.png') + + +def calc_error_interval(resultsdf, resultsdfmean, groupby, fractionofdata = .95): + for column in ['E_lower_1D', 'E_upper_1D','E_lower_2D', 'E_upper_2D','E_lower_3D', 'E_upper_3D']: + resultsdfmean[column] = np.nan + dimensions = ['1D', '2D', '3D'] + items = resultsdfmean[groupby].unique() + + for item in items: + for D in dimensions: + avgerr = resultsdf[resultsdf[groupby]== item]['avgsyserr%_' + D] + avgerr = np.sort(avgerr) + halfalpha = (1 - fractionofdata)/2 + ## literally select the 95% fraction by tossing out the top 2.5% and the bottom 2.5% + ## For 95%, It's ideal if I do 40*N measurements for some integer N. + lowerbound = np.mean([avgerr[int(np.floor(halfalpha*len(avgerr)))], avgerr[int(np.ceil(halfalpha*len(avgerr)))]]) + upperbound = np.mean([avgerr[-int(np.floor(halfalpha*len(avgerr))+1)],avgerr[-int(np.ceil(halfalpha*len(avgerr))+1)]]) + resultsdfmean.loc[resultsdfmean[groupby]== item,'E_lower_'+ D] = lowerbound + resultsdfmean.loc[resultsdfmean[groupby]== item,'E_upper_' + D] = upperbound + return resultsdf, resultsdfmean + +def beep(): + try: + winsound.PlaySound(r'C:\Windows\Media\Speech Disambiguation.wav', flags = winsound.SND_ASYNC) + return + except: + pass + try: + winsound.PlaySound("SystemHand", winsound.SND_ALIAS) + return + except: + pass + try: + winsound.Beep(450,150) + return + except: + pass \ No newline at end of file diff --git a/trimer/trimer_resonatorphysics.py b/trimer/trimer_resonatorphysics.py new file mode 100644 index 0000000..ebe901d --- /dev/null +++ b/trimer/trimer_resonatorphysics.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 9 16:07:09 2022 + +@author: vhorowit +""" + +''' Changes by lydiabullock. Adapting resonatorphysics to work for the Trimer functions and parameters. + I believe I only changed line 49 for now. ''' + +from helperfunctions import read_params +import numpy as np +import math + + +def complexamp(A,phi): + return A * np.exp(1j*phi) + +def amp(a,b): + return np.sqrt(a**2 + b**2) + +def A_from_Z(Z): # calculate amplitude of complex number + return amp(Z.real, Z.imag) + +# For driven, damped oscillator: res_freq = sqrt(k/m - b^2/(2m^2)) +# Note: Requires b < sqrt(2mk) to be significantly underdamped +# Otherwise there is no resonant frequency and we get an err from the negative number under the square root +# This works for monomer and for weak coupling. It does not work for strong coupling. +# Uses privilege. See also res_freq_numeric() +def res_freq_weak_coupling(k, m, b): + try: + w = math.sqrt(k/m - (b*b)/(2*m*m)) + except: + w = np.nan + print('no resonance frequency for k=', k, ', m=', m, ' b=', b) + return w + +## source: https://en.wikipedia.org/wiki/Q_factor#Mechanical_systems +# Does not work for strong coupling. +def approx_Q(k, m, b): + return math.sqrt(m*k)/b + +# Approximate width of Lorentzian peak. +# Does not work for strong coupling. +def approx_width(k, m, b): + return res_freq_weak_coupling(k, m, b) / approx_Q(k, m, b) + +def calcnarrowerW(vals_set, MONOMER): + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + W1=approx_width(k1_set, m1_set, b1_set) + if MONOMER: + narrowerW = W1 + else: + W2=approx_width(k2_set, m2_set, b2_set) + narrowerW = min(W1,W2) + return narrowerW + + + From ee4d2b7535f25300534355c3ef61b3f7cee16515 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Mon, 29 Jul 2024 15:16:48 -0400 Subject: [PATCH 085/101] Changed read_params function to work for Trimer --- trimer/Trimer_NetMAP.py | 6 ++++-- trimer/kind_of_trimer_resonatorfrequencypicker.py | 8 ++++---- trimer/trimer_resonatorphysics.py | 4 ++-- 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/trimer/Trimer_NetMAP.py b/trimer/Trimer_NetMAP.py index 1b86874..3ef4b69 100644 --- a/trimer/Trimer_NetMAP.py +++ b/trimer/Trimer_NetMAP.py @@ -6,7 +6,6 @@ @author: samfeldman & lydiabullock """ import numpy as np -# return amp(Z.real, Z.imag) from Trimer_simulator import calculate_spectra ''' THIS IS THE NETMAP PART ''' @@ -57,6 +56,10 @@ def normalize_parameters_1d_by_force(unnormalizedparameters, F_set): parameters = [c*unnormalizedparameters[k] for k in range(len(unnormalizedparameters)) ] return parameters +def complex_noise(n, noiselevel): + global complexamplitudenoisefactor + complexamplitudenoisefactor = 0.0005 + return noiselevel* complexamplitudenoisefactor * np.random.randn(n,) ''' Example work begins here. ''' @@ -76,7 +79,6 @@ def normalize_parameters_1d_by_force(unnormalizedparameters, F_set): F = 1 #create some noise -from resonatorsimulator import complex_noise e = complex_noise(2, 2) #number of frequencies, noise level frequencies = [f1, f2] diff --git a/trimer/kind_of_trimer_resonatorfrequencypicker.py b/trimer/kind_of_trimer_resonatorfrequencypicker.py index 775ac4d..5b676ee 100644 --- a/trimer/kind_of_trimer_resonatorfrequencypicker.py +++ b/trimer/kind_of_trimer_resonatorfrequencypicker.py @@ -70,7 +70,7 @@ def find_freq_from_angle(drive, phase, angleswanted = [-np.pi/4], returnindex = def makemorefrequencies(vals_set, minfreq, maxfreq, MONOMER, forceall, res1 = None, res2 = None, includefreqs = None, n=n, staywithinlims = False): - [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set) if res1 is None: res1 = res_freq_weak_coupling(k1_set, m1_set, b1_set) @@ -124,7 +124,7 @@ def create_drive_arrays(vals_set, MONOMER, forceboth, n=n, if verbose: print('Running create_drive_arrays()') - [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set) if morefrequencies is None: if minfreq is None: @@ -295,7 +295,7 @@ def res_freq_numeric(vals_set, MONOMER, forceall, print('\nRunning res_freq_numeric() with mode ' + mode) if plottitle is not None: print(plottitle) - k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set = read_params(vals_set, MONOMER) + k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set = read_params(vals_set) # Never Monomer in this case if MONOMER and numtoreturn != 2: # 2 is a tricky case... just use the rest of the algorithm @@ -772,7 +772,7 @@ def allmeasfreq_two_res(res1, res2, max_num_p, freqdiff): def best_choice_freq_set(vals_set, MONOMER, forceboth, reslist, num_p = 10): - [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set) narrowerW = calcnarrowerW(vals_set, MONOMER) freqdiff = round(narrowerW/6,4) if MONOMER: diff --git a/trimer/trimer_resonatorphysics.py b/trimer/trimer_resonatorphysics.py index ebe901d..ccff283 100644 --- a/trimer/trimer_resonatorphysics.py +++ b/trimer/trimer_resonatorphysics.py @@ -8,7 +8,7 @@ ''' Changes by lydiabullock. Adapting resonatorphysics to work for the Trimer functions and parameters. I believe I only changed line 49 for now. ''' -from helperfunctions import read_params +from trimer_helperfunctions import read_params import numpy as np import math @@ -46,7 +46,7 @@ def approx_width(k, m, b): return res_freq_weak_coupling(k, m, b) / approx_Q(k, m, b) def calcnarrowerW(vals_set, MONOMER): - [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set, MONOMER) + [k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set] = read_params(vals_set) W1=approx_width(k1_set, m1_set, b1_set) if MONOMER: narrowerW = W1 From 9a9d11f0952ab72551a73a3eb3ecae337a2f0dda Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 30 Jul 2024 14:27:16 -0400 Subject: [PATCH 086/101] Case studies of Trimer frequency picker for NetMAP Did multiple case studies with selected frequencies instead of 10 frequencies between 0.001 and 4. Also fixed the damping coefficients so that they will be smaller and guarantee a resonant system. --- .DS_Store | Bin 10244 -> 8196 bytes trimer/Trimer_NetMAP.py | 2 +- trimer/comparing_curvefit_types.py | 26 ++++++---- ...kind_of_trimer_resonatorfrequencypicker.py | 4 +- trimer/trimer_case_study_frequency_picker.py | 47 ++++++++++++++---- 5 files changed, 57 insertions(+), 22 deletions(-) diff --git a/.DS_Store b/.DS_Store index 0ae500052cff8811c73afa00e5d1c02b8bcb1a4f..ff04b24053a3dd2f42f4d4d1066bb618d0c67f38 100644 GIT binary patch delta 275 zcmZn(XmOBWU|?W$DortDU;r^WfEYvza8FDWo2aMAD8DgaH$S8NWF7&@_{Ve0fKn_B zdJO3dnG7Yl`7SO=Ir&K-ZF~20b*vE!!S5GKeqs=hk@nE1BS^Cf>M*` z3AivFIt^z26X0i_+@w5NKu{Y>%S?_E=Ydiz z40;Ud44Diix%nA3zQI+z-w9rP;(@*0ZfcPCw~yEVQFvy z#(IH}5{x0Td6keY3%4>bb~Au6p30Cqd7Yptw*rGNLq0376Gk5cnavH$q8a&3XDI29C-rvk^Cta0O+hjfvlxC-ch$iZDSkm_bar for both types of curves -def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_folder_name): +def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, num_trials, excel_file_name, graph_folder_name): starting_row = 0 avg_e1_list = [] #Polar @@ -331,10 +335,8 @@ def run_trials(true_params, guessed_params, num_trials, excel_file_name, graph_f e = complex_noise(300, 2) ##For NetMAP - #Get frequencies - freqs_NetMAP = np.linspace(0.001, 4, 10) #create error - e_NetMAP = complex_noise(10,2) + e_NetMAP = complex_noise(length_noise_NetMAP,2) #Get the data! dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force @@ -529,7 +531,9 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # #Curve fit with the guess made above and get average lists # #Will not do anything with _bar for a single case study -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, 'Case_Study.xlsx', 'Case Study Plots') +# freqs_NetMAP = np.linspace(0.001, 4, 10) +# length_noise = 10 +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 50, 'Case_Study.xlsx', 'Case Study Plots') # #Graph histogram of for curve fits @@ -556,7 +560,9 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # guessed_params = automate_guess(true_params, 20) # #Curve fit with the guess made above -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') +# freqs_NetMAP = np.linspace(0.001, 4, 10) +# length_noise = 10 +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') # #Add _bar to lists to make one graph at the end # avg_e1_bar_list.append(avg_e1_bar) #Polar @@ -583,7 +589,7 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # plt.hist(avg_e2_bar_list, bins=10, alpha=0.75, color='green', label='Cartesian (X & Y)', edgecolor='black') # plt.hist(avg_e1_bar_list, bins=10, alpha=0.75, color='blue', label='Polar (Amp & Phase)', edgecolor='black') # plt.hist(avg_e3_bar_list, bins=10, alpha=0.75, color='red', label='NetMAP', edgecolor='black') -# plt.title('Average Systematic Error Across Parameters') +# plt.title('Average Error Across Parameters Then Across Trials') # plt.xlabel(' (%)') # plt.ylabel('Counts') # plt.legend(loc='upper center') @@ -600,7 +606,9 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # #Run the trials with 0 error # # MUST CHANGE ERROR IN run_trials AND IN get_parameters_NetMAP -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, 50, 'Sys0_No_Error.xlsx', 'Sys0_No_Error - Plots') +# freqs_NetMAP = np.linspace(0.001, 4, 10) +# length_noise = 0 +# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, freqs_NetMAP, length_noise, 50, 'Sys0_No_Error.xlsx', 'Sys0_No_Error - Plots') # #Plot histogram # plt.title('Average Systematic Error Across Parameters') diff --git a/trimer/kind_of_trimer_resonatorfrequencypicker.py b/trimer/kind_of_trimer_resonatorfrequencypicker.py index 5b676ee..f7584ce 100644 --- a/trimer/kind_of_trimer_resonatorfrequencypicker.py +++ b/trimer/kind_of_trimer_resonatorfrequencypicker.py @@ -287,7 +287,7 @@ def res_freq_numeric(vals_set, MONOMER, forceall, mode = 'all', minfreq=.1, maxfreq=5, morefrequencies=None, includefreqs = [], unique = True, veryunique = True, numtoreturn = None, - verboseplot = False, plottitle = None, verbose=verbose, iterations = 1, + verboseplot = True, plottitle = None, verbose=verbose, iterations = 1, use_R2_only = False, returnoptions = False): @@ -576,7 +576,7 @@ def veryclose(a,b): # if the 10th element of indexlist is indexlist[10]=200, then tempfreqlist[10] = morefrequencies[200] except: print('indexlist:', indexlist) - A2 = curve2(tempfreqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set,) + A2 = curve2(tempfreqlist, k1_set, k2_set, k3_set, k4_set, b1_set, b2_set, b3_set, F_set, m1_set, m2_set, m3_set, 0, forceall) # and then A2[10] is the amplitude of R2 at the frequency morefrequencies[200] # and then the number 10 is the sort of number we will add to a removeindex list for i in range(len(indexlist)-1): diff --git a/trimer/trimer_case_study_frequency_picker.py b/trimer/trimer_case_study_frequency_picker.py index e5d7826..2336ae6 100644 --- a/trimer/trimer_case_study_frequency_picker.py +++ b/trimer/trimer_case_study_frequency_picker.py @@ -5,24 +5,51 @@ @author: lydiabullock """ -''' Case Study for System 0 from '15 Systems - 10 Freqs NetMAP' - Using "ideal" frequencies to test NetMAP. ''' +''' Case Study for System 0/2 from '15 Systems - 10 Freqs NetMAP' + Using "ideal" frequencies to test NetMAP. + The frequencies picked are only based off of the first two resonators but use the trimer information.''' from comparing_curvefit_types import run_trials from kind_of_trimer_resonatorfrequencypicker import res_freq_numeric - +import math +import matplotlib.pyplot as plt ''' Begin Work Here. ''' -## System 0 from '15 Systems - 10 Freqs NetMAP' -true_parameters = [1.045, 0.179, 3.852, 1.877, 5.542, 1.956, 3.71, 1, 3.976, 0.656, 3.198] -guessed_parameters = [1.2379, 0.1764, 3.7327, 1.8628, 5.93, 2.1793, 4.2198, 1, 4.3335, 0.7016, 3.0719] - MONOMER = False forceall = False -best_frequencies_list = res_freq_numeric(true_parameters, MONOMER, forceall) -print(best_frequencies_list) +## System 0 from '15 Systems - 10 Freqs NetMAP' +# true_parameters = [1.045, 0.179, 3.852, 1.877, 5.542, 1.956, 3.71, 1, 3.976, 0.656, 3.198] +# guessed_parameters = [1.2379, 0.1764, 3.7327, 1.8628, 5.93, 2.1793, 4.2198, 1, 4.3335, 0.7016, 3.0719] + +## System 2 from '15 Systems - 10 Freqs NetMAP' +# true_parameters = [3.264, 7.71, 6.281, 3.564, 5.859, 0.723, 3.087, 1, 3.391, 3.059, 7.796] +# guessed_parameters = [3.1169, 7.0514, 6.9721, 3.6863, 4.9006, 0.707, 3.2658, 1, 2.9289, 2.7856, 6.8323] +## System 8 from '15 systems - 10 Freqs NetMAP & Better Parameters' +true_parameters = [7.731, 1.693, 2.051, 8.091, 0.427, 0.363, 0.349, 1, 7.07, 7.195, 4.814] +guessed_parameters = [7.2806, 1.8748, 1.8077, 8.7478, 0.3767, 0.2974, 0.3744, 1, 7.4933, 6.7781, 4.2136] + +best_frequencies_list = res_freq_numeric(true_parameters, MONOMER, forceall) +best_frequencies_list = [x for x in best_frequencies_list if not math.isnan(x)] +length_noise_NetMAP = len(best_frequencies_list) + +#Run Trials +if length_noise_NetMAP == 0: + print('No Possible Frequencies.') +else: + print(f'Best frequencies to use are: {best_frequencies_list}') + avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, best_frequencies_list, length_noise_NetMAP, 50, 'Sys8_Better_Params_Freq_Pick.xlsx', 'Sys8_Better_Params_Freq_Pick - Plots') + + #Create histogram + plt.title('Average Systematic Error Across Parameters') + plt.xlabel('') + plt.ylabel('Counts') + plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') + plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') + plt.legend(loc='upper center') + + plt.savefig('_Histogram_Sys8_Better_Params_Freq_Pick.png') -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, 50, 'Sys0_Freq_Pick.xlsx', 'Sys0_Freq_Pick - Plots') \ No newline at end of file From c418466aa6f71621cda4be6bb830b3dbdda3491e Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 30 Jul 2024 14:28:22 -0400 Subject: [PATCH 087/101] Created: trimer_frequency_study --- trimer/trimer_frequency_study.py | 68 ++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 trimer/trimer_frequency_study.py diff --git a/trimer/trimer_frequency_study.py b/trimer/trimer_frequency_study.py new file mode 100644 index 0000000..8933328 --- /dev/null +++ b/trimer/trimer_frequency_study.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jul 30 12:35:48 2024 + +@author: lydiabullock +""" +from comparing_curvefit_types import complex_noise, get_parameters_NetMAP, find_avg_e +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from Trimer_simulator import realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 + +#Code that loops through frequency points of different spacing + +def sweep_freq_pair(frequencies, params_guess, params_correct, e, force_all): + + #Graph Real vs Imaginary for the trimer + X1 = realamp1(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y1 = imamp1(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + plt.plot(X1, Y1) + plt.xlabel('Re(Z) (m)') + plt.ylabel('Im(Z) (m)') + plt.title('Resonator 1') + plt.show() + + # X2 = realamp2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + # Y2 = imamp2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + # X3 = realamp3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + # Y3 = imamp3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + # Loop over possible combinations of frequency indices, i1 and i2 + for i1 in range(len(frequencies)): + freq1 = frequencies[i1] + + + for i2 in range(len(frequencies)): + freq2 = frequencies[i2] + freqs = [freq1, freq2] + + NetMAP_info = get_parameters_NetMAP(freqs, params_guess, params_correct, e, force_all) + + #Find (average across parameters) for the trial and add to dictionary + avg_e1 = find_avg_e(NetMAP_info) + NetMAP_info[''] = avg_e1 + + + try: # repeated experiments results + resultsdf = pd.concat([resultsdf, NetMAP_info], ignore_index=True) + except: + resultsdf = NetMAP_info + + return resultsdf + + +''' Begin work here. ''' + +e = complex_noise(5,2) +frequencies = np.linspace(0.001, 4, 5) + +params_guess = +params_correct = +force_all = False + +result = sweep_freq_pair(frequencies, params_guess, params_correct, e, force_all) + + From 9bc039c0a5d3b041b12d5a2f77971ad98025dec4 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 31 Jul 2024 14:27:33 -0400 Subject: [PATCH 088/101] Completed data taking - trimer_frequency_study Successfully tested frequencies and NetMAP recovery abilities on two systems and created heat maps. --- trimer/trimer_frequency_study.py | 157 ++++++++++++++++++++++++++----- 1 file changed, 134 insertions(+), 23 deletions(-) diff --git a/trimer/trimer_frequency_study.py b/trimer/trimer_frequency_study.py index 8933328..b9c5ee6 100644 --- a/trimer/trimer_frequency_study.py +++ b/trimer/trimer_frequency_study.py @@ -5,30 +5,99 @@ @author: lydiabullock """ -from comparing_curvefit_types import complex_noise, get_parameters_NetMAP, find_avg_e +from comparing_curvefit_types import complex_noise, get_parameters_NetMAP, find_avg_e, automate_guess import numpy as np import pandas as pd import matplotlib.pyplot as plt -from Trimer_simulator import realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 +from Trimer_simulator import realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, curve1, theta1, curve2, theta2, curve3, theta3 +import sys +import os +myheatmap = os.path.abspath('..') +sys.path.append(myheatmap) +from myheatmap import myheatmap +import matplotlib.colors as mcolors -#Code that loops through frequency points of different spacing -def sweep_freq_pair(frequencies, params_guess, params_correct, e, force_all): - - #Graph Real vs Imaginary for the trimer +def plot_data(frequencies, params_guess, params_correct, e, force_all): + #Original Data X1 = realamp1(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Y1 = imamp1(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - plt.plot(X1, Y1) - plt.xlabel('Re(Z) (m)') - plt.ylabel('Im(Z) (m)') - plt.title('Resonator 1') + + X2 = realamp2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y2 = imamp2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + X3 = realamp3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Y3 = imamp3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + + Amp1 = curve1(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Phase1 = theta1(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + Amp2 = curve2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Phase2 = theta2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + Amp3 = curve3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + Phase3 = theta3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + + ## Begin graphing - Re vs Im + fig = plt.figure(figsize=(10,6)) + gs = fig.add_gridspec(1, 3, width_ratios=[1,1,1], hspace=0.25, wspace=0.05) + + ax1 = fig.add_subplot(gs[0, 0], aspect='equal') + ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1, aspect='equal') + ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1, aspect='equal') + + #Original Data + ax1.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax2.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax3.plot(X3,Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') + + fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=16) + ax1.set_title('Resonator 1', fontsize=14) + ax2.set_title('Resonator 2', fontsize=14) + ax3.set_title('Resonator 3', fontsize=14) + ax1.set_ylabel('Im(Z) (m)') + ax1.set_xlabel('Re(Z) (m)') + ax2.set_xlabel('Re(Z) (m)') + ax3.set_xlabel('Re(Z) (m)') + ax1.label_outer() + ax2.label_outer() + ax3.label_outer() plt.show() - # X2 = realamp2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - # Y2 = imamp2(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + ## Begin graphing - Amp and Phase + fig = plt.figure(figsize=(16,8)) + gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) + ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') + + #original data + ax1.plot(frequencies, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax2.plot(frequencies, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax3.plot(frequencies, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax4.plot(frequencies, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax5.plot(frequencies, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax6.plot(frequencies, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') - # X3 = realamp3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - # Y3 = imamp3(frequencies, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + #Graph parts + fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) + ax1.set_title('Resonator 1', fontsize=14) + ax2.set_title('Resonator 2', fontsize=14) + ax3.set_title('Resonator 3', fontsize=14) + ax1.set_ylabel('Amplitude') + ax4.set_ylabel('Phase') + + for ax in fig.get_axes(): + ax.set(xlabel='Frequency') + ax.label_outer() + + plt.show() + +#Code that loops through frequency points of different spacing + +def sweep_freq_pair(frequencies, params_guess, params_correct, e, force_all): + + #Graph Real vs Imaginary for the trimer + plot_data(frequencies, params_guess, params_correct, e, force_all) # Loop over possible combinations of frequency indices, i1 and i2 for i1 in range(len(frequencies)): @@ -38,31 +107,73 @@ def sweep_freq_pair(frequencies, params_guess, params_correct, e, force_all): for i2 in range(len(frequencies)): freq2 = frequencies[i2] freqs = [freq1, freq2] - - NetMAP_info = get_parameters_NetMAP(freqs, params_guess, params_correct, e, force_all) + e_2freqs = complex_noise(2,2) + + NetMAP_info = get_parameters_NetMAP(freqs, params_guess, params_correct, e_2freqs, force_all) #Find (average across parameters) for the trial and add to dictionary avg_e1 = find_avg_e(NetMAP_info) NetMAP_info[''] = avg_e1 + NetMAP_info['freq1'] = freq1 + NetMAP_info['freq2'] = freq2 + + # Convert lists to scalars when they contain only one item + for key in NetMAP_info: + if isinstance(NetMAP_info[key], list) and len(NetMAP_info[key]) == 1: + NetMAP_info[key] = NetMAP_info[key][0] + + NetMAP_df = pd.DataFrame([NetMAP_info]) - try: # repeated experiments results - resultsdf = pd.concat([resultsdf, NetMAP_info], ignore_index=True) + resultsdf = pd.concat([resultsdf, NetMAP_df], ignore_index=True) except: - resultsdf = NetMAP_info + resultsdf = NetMAP_df return resultsdf ''' Begin work here. ''' -e = complex_noise(5,2) -frequencies = np.linspace(0.001, 4, 5) +##Create the System +#Randomly chosen one that "looks easy" +# params_correct = [3, 3, 3, 3, 0.5, 0.5, 0.1, 1, 2, 5, 5] +# params_guess = automate_guess(params_correct, 20) + +#Worst system - System 8 from ‘15 systems - 10 Freqs NetMAP & Better Parameters’ +params_correct = [7.731, 1.693, 2.051, 8.091, 0.427, 0.363, 0.349, 1, 7.07, 7.195, 4.814] +params_guess = [7.2806, 1.8748, 1.8077, 8.7478, 0.3767, 0.2974, 0.3744, 1, 7.4933, 6.7781, 4.2136] -params_guess = -params_correct = force_all = False +e = complex_noise(200,2) +frequencies = np.linspace(0.001, 4, 200) +#Test each pair of frequencies result = sweep_freq_pair(frequencies, params_guess, params_correct, e, force_all) +result.to_excel('Frequency_Study.xlsx', index=False) + +#Recall the data if I need to +# result = pd.read_excel('/Users/Student/Desktop/Summer Research 2024/Multiple Curve Fit - Which Type/Frequency Study/Frequency_Study_200.xlsx') + +#Pivot the DataFrame for the heatmap +heatmap_data = result.pivot_table(index='freq2', columns='freq1', values='') + +#Create heatmap +#For log scale! +colors = [(1, 0.439, 0), 'yellow','green', 'blue', (0.533, 0.353, 0.537)] +n_bins = 100 # Number of bins for interpolation + +cmap_name = 'custom_cmap' +custom_cmap = mcolors.LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bins) + +norm = mcolors.LogNorm(vmin=heatmap_data.min().min(), vmax=heatmap_data.max().max()) +ax = myheatmap(heatmap_data, cmap=custom_cmap, norm=norm, colorbarlabel='Average Error (%)') + +#For regular +# ax = myheatmap(heatmap_data, cmap=custom_cmap, vmax=10, colorbarlabel='Average Error (%)') + +ax.set_title('NetMAP Recovery of Trimer Parameters') +ax.set_xlabel('Frequency 1 (rad/s)') +ax.set_ylabel('Frequency 2 (rad/s)') + From 68041962ead37951a6084845dd9b76a8e81a7bb1 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Sat, 5 Oct 2024 18:08:07 -0400 Subject: [PATCH 089/101] Can graph each iteration of the curve fit for amplitude and phase I only graphed the first amplitude as an example. --- trimer/Graphing_each_iteration.py | 133 ++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 trimer/Graphing_each_iteration.py diff --git a/trimer/Graphing_each_iteration.py b/trimer/Graphing_each_iteration.py new file mode 100644 index 0000000..e7409a6 --- /dev/null +++ b/trimer/Graphing_each_iteration.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Sep 30 22:51:27 2024 + +@author: Lydia Bullock +""" + +import numpy as np +import matplotlib.pyplot as plt +from lmfit import minimize, Parameters +from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 +from comparing_curvefit_types import complex_noise +import seaborn as sns + +# Example model function +def model(params, x): + a = params['a'] + b = params['b'] + return a * np.exp(-b * x) + +# Objective function to minimize +def objective(params, x, Amp1): + model_vals = model(params, x) + return model_vals - Amp1 + +def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): + k1 = params['k1'].value + k2 = params['k2'].value + k3 = params['k3'].value + k4 = params['k4'].value + b1 = params['b1'].value + b2 = params['b2'].value + b3 = params['b3'].value + F = params['F'].value + m1 = params['m1'].value + m2 = params['m2'].value + m3 = params['m3'].value + + modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + + residc1 = Amp1_data - modelc1 + residc2 = Amp2_data - modelc2 + residc3 = Amp3_data - modelc3 + residt1 = Phase1_data - modelt1 + residt2 = Phase2_data - modelt2 + residt3 = Phase3_data - modelt3 + + return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) + +#Callback function to plot each iteration +def plot_callback(params, iter, resid, *args, **kws): + plt.clf() + if iter % 5 == 0: + + freq = args[0] + Amp1 = args[1] + + #Recall parameters + k1 = params['k1'].value + k2 = params['k2'].value + k3 = params['k3'].value + k4 = params['k4'].value + b1 = params['b1'].value + b2 = params['b2'].value + b3 = params['b3'].value + F = params['F'].value + m1 = params['m1'].value + m2 = params['m2'].value + m3 = params['m3'].value + + #Get model data to plot + modelc1 = c1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) + + # sns.reset_defaults() + # sns.set_context("talk") + # sns.scatterplot(freq, Amp1, 'bo', label='Data') + # sns.scatterplot(freq, modelc1, 'r-', label='Model') + plt.plot(freq, Amp1, 'bo', label='Data') #Plot the data + plt.plot(freq, modelc1, 'r-', label='Model') #Plot the model + plt.title(f"Trimer System - Iteration: {iter}", fontsize=18) + plt.ylabel('Amplitude (m)', fontsize=16) + plt.xlabel('Frequency (Hz)', fontsize=16) + plt.legend(fontsize=14) + plt.pause(0.1) + +'''Begin Work Here''' +#this is using System 10 of 15 Systems - 10 Freqs NetMAP Better Params +##Create data and system parameters +#x data +freq = np.linspace(0.001, 4, 300) + +e = complex_noise(300, 2) +force_all = False +params_correct = [5.385, 7.276, 5.271, 4.382, 0.984, 0.646, 0.775, 1, 3.345, 9.26, 7.439] +#[k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] + +#y data +Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) +Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi +Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) +Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi +Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) +Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + + 2 * np.pi + +#Create parameter guesses +# params_guess = automate_guess(params_correct, 0.8) +params_guess = [4.6455, 7.1909, 4.9103, 3.4398, 1.0832, 0.596, 0.6245, 1, 3.4532, 8.7681, 8.7575] +params = Parameters() +params.add('k1', value = params_guess[0], min=0) +params.add('k2', value = params_guess[1], min=0) +params.add('k3', value = params_guess[2], min=0) +params.add('k4', value = params_guess[3], min=0) +params.add('b1', value = params_guess[4], min=0) +params.add('b2', value = params_guess[5], min=0) +params.add('b3', value = params_guess[6], min=0) +params.add('F', value = params_guess[7], min=0) +params.add('m1', value = params_guess[8], min=0) +params.add('m2', value = params_guess[9], min=0) +params.add('m3', value = params_guess[10], min=0) + +# Perform minimization and plot each step! +result = minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3), iter_cb=plot_callback) + + From 8aed0bb0691e694abdd41c4b1334a45ca1d97a2f Mon Sep 17 00:00:00 2001 From: lydiabull Date: Sun, 6 Oct 2024 12:22:55 -0400 Subject: [PATCH 090/101] Updated the graphing so y-axis is fixed --- trimer/Graphing_each_iteration.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/trimer/Graphing_each_iteration.py b/trimer/Graphing_each_iteration.py index e7409a6..d124595 100644 --- a/trimer/Graphing_each_iteration.py +++ b/trimer/Graphing_each_iteration.py @@ -81,9 +81,10 @@ def plot_callback(params, iter, resid, *args, **kws): # sns.set_context("talk") # sns.scatterplot(freq, Amp1, 'bo', label='Data') # sns.scatterplot(freq, modelc1, 'r-', label='Model') - plt.plot(freq, Amp1, 'bo', label='Data') #Plot the data - plt.plot(freq, modelc1, 'r-', label='Model') #Plot the model - plt.title(f"Trimer System - Iteration: {iter}", fontsize=18) + plt.plot(freq, Amp1, 'bo', label='Data') + plt.plot(freq, modelc1, 'r-', label='Model') + plt.ylim(ymax=0.6) + plt.title(f"Trimer Resonator System - Iteration: {iter}", fontsize=18) plt.ylabel('Amplitude (m)', fontsize=16) plt.xlabel('Frequency (Hz)', fontsize=16) plt.legend(fontsize=14) From 6860bbb9d842814a06d5498d07750b6de6994aef Mon Sep 17 00:00:00 2001 From: lydiabull Date: Sun, 6 Oct 2024 13:49:15 -0400 Subject: [PATCH 091/101] Forgot to fix the force and now there are two example systems --- trimer/Graphing_each_iteration.py | 73 +++++++++++++++++++-------- trimer/curve_fitting_amp_phase_all.py | 2 +- 2 files changed, 52 insertions(+), 23 deletions(-) diff --git a/trimer/Graphing_each_iteration.py b/trimer/Graphing_each_iteration.py index d124595..7608a48 100644 --- a/trimer/Graphing_each_iteration.py +++ b/trimer/Graphing_each_iteration.py @@ -10,20 +10,9 @@ import matplotlib.pyplot as plt from lmfit import minimize, Parameters from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 -from comparing_curvefit_types import complex_noise +from comparing_curvefit_types import complex_noise, syserr import seaborn as sns -# Example model function -def model(params, x): - a = params['a'] - b = params['b'] - return a * np.exp(-b * x) - -# Objective function to minimize -def objective(params, x, Amp1): - model_vals = model(params, x) - return model_vals - Amp1 - def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value @@ -56,7 +45,7 @@ def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_d #Callback function to plot each iteration def plot_callback(params, iter, resid, *args, **kws): plt.clf() - if iter % 5 == 0: + if iter % 2 == 0: freq = args[0] Amp1 = args[1] @@ -73,7 +62,7 @@ def plot_callback(params, iter, resid, *args, **kws): m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value - + #Get model data to plot modelc1 = c1(freq, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) @@ -83,7 +72,7 @@ def plot_callback(params, iter, resid, *args, **kws): # sns.scatterplot(freq, modelc1, 'r-', label='Model') plt.plot(freq, Amp1, 'bo', label='Data') plt.plot(freq, modelc1, 'r-', label='Model') - plt.ylim(ymax=0.6) + plt.ylim(ymax=1.6) plt.title(f"Trimer Resonator System - Iteration: {iter}", fontsize=18) plt.ylabel('Amplitude (m)', fontsize=16) plt.xlabel('Frequency (Hz)', fontsize=16) @@ -91,17 +80,22 @@ def plot_callback(params, iter, resid, *args, **kws): plt.pause(0.1) '''Begin Work Here''' -#this is using System 10 of 15 Systems - 10 Freqs NetMAP Better Params + ##Create data and system parameters -#x data freq = np.linspace(0.001, 4, 300) e = complex_noise(300, 2) force_all = False -params_correct = [5.385, 7.276, 5.271, 4.382, 0.984, 0.646, 0.775, 1, 3.345, 9.26, 7.439] +#this is using System 10 of 15 Systems - 10 Freqs NetMAP Better Params +# params_correct = [5.385, 7.276, 5.271, 4.382, 0.984, 0.646, 0.775, 1, 3.345, 9.26, 7.439] +# params_guess = [4.6455, 7.1909, 4.9103, 3.4398, 1.0832, 0.596, 0.6245, 1, 3.4532, 8.7681, 8.7575] + +#this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params +params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] +params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] + #[k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] -#y data Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi @@ -113,8 +107,6 @@ def plot_callback(params, iter, resid, *args, **kws): + 2 * np.pi #Create parameter guesses -# params_guess = automate_guess(params_correct, 0.8) -params_guess = [4.6455, 7.1909, 4.9103, 3.4398, 1.0832, 0.596, 0.6245, 1, 3.4532, 8.7681, 8.7575] params = Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) @@ -128,7 +120,44 @@ def plot_callback(params, iter, resid, *args, **kws): params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) -# Perform minimization and plot each step! +params['F'].vary = False + +#Perform minimization and plot each step! result = minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3), iter_cb=plot_callback) +#Put information into dictionary +data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], + 'b1_true': [], 'b2_true': [], 'b3_true': [], + 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], + 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], + 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], + 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], + 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], + 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], + 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], + 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], + 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], + 'e_m1': [], 'e_m2': [], 'e_m3': []} + +#Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) +true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], + 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], + 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} + +for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: + #Add true parameters to dictionary + param_true = true_params[param_name] + data[f'{param_name}_true'].append(param_true) + + #Add guessed parameters to dictionary + param_guess = params[param_name].value + data[f'{param_name}_guess'].append(param_guess) + + #Add fitted parameters to dictionary + param_fit = result.params[param_name].value + data[f'{param_name}_recovered'].append(param_fit) + + #Calculate systematic error and add to dictionary + systematic_error = syserr(param_fit, param_true) + data[f'e_{param_name}'].append(systematic_error) diff --git a/trimer/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py index c771b6e..a002be6 100644 --- a/trimer/curve_fitting_amp_phase_all.py +++ b/trimer/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data ''' Graph each iteration of the curve fit! ''' #Must modify the fit function - in minimize(), use iter_cb= __ and define a callback function \ No newline at end of file From 8a723b16b3d4a8b912eef6619223825a295a8b3d Mon Sep 17 00:00:00 2001 From: lydiabull Date: Tue, 8 Oct 2024 12:34:32 -0400 Subject: [PATCH 092/101] Implementing the ability to scale the residuals for amp and phase The scaling part is not working the way I think it is supposed to yet. But can still not scale the residuals in all functions, so we can still take data the same way as before if needed. --- trimer/comparing_curvefit_types.py | 2 +- trimer/curve_fitting_amp_phase_all.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/trimer/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py index 6829ac6..8376f7a 100644 --- a/trimer/comparing_curvefit_types.py +++ b/trimer/comparing_curvefit_types.py @@ -339,7 +339,7 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n e_NetMAP = complex_noise(length_noise_NetMAP,2) #Get the data! - dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force + dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, False, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force dictionary3 = get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False) #NetMAP diff --git a/trimer/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py index a002be6..94bdadd 100644 --- a/trimer/curve_fitting_amp_phase_all.py +++ b/trimer/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data ''' Graph each iteration of the curve fit! ''' #Must modify the fit function - in minimize(), use iter_cb= __ and define a callback function \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/amp_max) scaled_residt2.append(phase2/amp_max) scaled_residt3.append(phase3/amp_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, scaled, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data import pandas as pd e = 0 force_all = False fix_F = True #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] #Get the data (and the graphs) scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') #Turn dictionaries into data frames dfscaled = pd.DataFrame(scaled_dict) dfnotscaled = pd.DataFrame(not_scaled_dict) #Add to excel spreadsheet writer = pd.ExcelWriter('Scalinf_Amp_Phase_Residuals.xlsx') dfscaled.to_excel(writer, sheet_name='Scaled', index=False) dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file From d0e55bcdcbf10239e6efbe35d8820913bdd8a023 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 10 Oct 2024 15:53:44 -0400 Subject: [PATCH 093/101] Can scale the amplitude and phase by setting the "scaled" argument to True or False --- trimer/curve_fitting_amp_phase_all.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trimer/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py index 94bdadd..72a22c7 100644 --- a/trimer/curve_fitting_amp_phase_all.py +++ b/trimer/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/amp_max) scaled_residt2.append(phase2/amp_max) scaled_residt3.append(phase3/amp_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, scaled, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data import pandas as pd e = 0 force_all = False fix_F = True #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] #Get the data (and the graphs) scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') #Turn dictionaries into data frames dfscaled = pd.DataFrame(scaled_dict) dfnotscaled = pd.DataFrame(not_scaled_dict) #Add to excel spreadsheet writer = pd.ExcelWriter('Scalinf_Amp_Phase_Residuals.xlsx') dfscaled.to_excel(writer, sheet_name='Scaled', index=False) dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/phase_max) scaled_residt2.append(phase2/phase_max) scaled_residt3.append(phase3/phase_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, scaled, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts if scaled: fig.suptitle('Trimer Resonator: Amplitude and Phase (Scaled)', fontsize=16) else: fig.suptitle('Trimer Resonator: Amplitude and Phase (Not Scaled)', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data import pandas as pd e = 0 force_all = False fix_F = True #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] #Get the data (and the graphs) scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') with pd.ExcelWriter('Scaling_Amp_Phase_Residuals.xlsx', engine='xlsxwriter') as writer: dfscaled = pd.DataFrame(scaled_dict) dfnotscaled = pd.DataFrame(not_scaled_dict) dfscaled.to_excel(writer, sheet_name='Scaled', index=False) dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file From 2d0e4267596892d45f5c87dcda515bf2492bffa9 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 5 Dec 2024 00:15:25 -0500 Subject: [PATCH 094/101] Attempting to do 100 systems with more trials in comparing_curvefit_types Currently, the run time is far too long. I believe something is wrong with the polar fitting because the error obtained is in the thousands. I'm not sure why the curve fitting isn't working now when it has in the past. --- trimer/comparing_curvefit_types.py | 420 +++++++++++++++-------------- 1 file changed, 217 insertions(+), 203 deletions(-) diff --git a/trimer/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py index 8376f7a..8002dae 100644 --- a/trimer/comparing_curvefit_types.py +++ b/trimer/comparing_curvefit_types.py @@ -15,21 +15,16 @@ import random import numpy as np import matplotlib.pyplot as plt -from brokenaxes import brokenaxes -import matplotlib.ticker as ticker from curve_fitting_amp_phase_all import multiple_fit_amp_phase from curve_fitting_X_Y_all import multiple_fit_X_Y from Trimer_simulator import calculate_spectra, curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3, re1, re2, re3, im1, im2, im3 from Trimer_NetMAP import Zmatrix, unnormalizedparameters, normalize_parameters_1d_by_force import warnings +import time ''' Functions contained: complex_noise - creates noise, e syserr - Calculates systematic error - find_avg_e - Calculates average across systematic error for each parameter - for one trial of the same system - artithmetic_then_logarithmic - Calculates arithmetic average across parameters first, - then logarithmic average across trials generate_random_system - Randomly generates parameters for system. All parameter values btw 0.1 and 10 plot_guess - Used for the Case Study. Plots just the data and the guessed parameters curve. No curve fitting. automate_guess - Randomly generates guess parameters within a certain percent of the true parameters @@ -38,8 +33,7 @@ run_trials - Runs a set number of trials for one system, graphs curvefit result, puts data and averages into spreadsheet, returns _bar for both types of curves - Must include number of trials to run and name of excel sheet - histogram_3_data_sets - incomplete but tries to graph histograms better - + This file also imports multiple_fit_amp_phase, which performs curve fitting on Amp vs Freq and Phase vs Freq curves for all 3 masses simultaneously, and multiple_fit_X_Y, which performs curve fitting on X vs Freq and Y vs Freq curves for all 3 masses simulatenously. ''' @@ -58,31 +52,6 @@ def syserr(x_found,x_set, absval = True): else: return se -#Calculate for one trial of the same system -def find_avg_e(dictionary): - sum_e = dictionary['e_k1'][0] + \ - dictionary['e_k2'][0] + \ - dictionary['e_k3'][0] + \ - dictionary['e_k4'][0] + \ - dictionary['e_b1'][0] + \ - dictionary['e_b2'][0] + \ - dictionary['e_b3'][0] + \ - dictionary['e_F'][0] + \ - dictionary['e_m1'][0] + \ - dictionary['e_m2'][0] + \ - dictionary['e_m3'][0] - avg_e = sum_e/10 - return avg_e - -#Calculate _bar -def arithmetic_then_logarithmic(avg_e_list, num_trials): - ln_avg_e = [] - for item in avg_e_list: - ln_avg_e.append(math.log(item)) - avg_ln_avg_e = sum(ln_avg_e)/num_trials - e_raised_to_sum = math.exp(avg_ln_avg_e) - return e_raised_to_sum - #Randomly generates parameters of a system. All parameters between 0.1 and 10 def generate_random_system(): system_params = [] @@ -275,7 +244,7 @@ def save_figure(figure, folder_name, file_name): # Save the figure to the folder file_path = os.path.join(folder_name, file_name) - figure.savefig(file_path) + figure.savefig(file_path, bbox_inches = 'tight') plt.close(figure) def get_parameters_NetMAP(frequencies, params_guess, params_correct, e, force_all): @@ -294,40 +263,38 @@ def get_parameters_NetMAP(frequencies, params_guess, params_correct, e, force_al #Normalize the parameters final_tri = normalize_parameters_1d_by_force(notnormparam_tri, 1) # parameters vector: 'm1', 'm2', 'm3', 'b1', 'b2', 'b3', 'k1', 'k2', 'k3', 'k4', 'Driving Force' - - #Put everything into dictionary - data = {'k1_true': [params_correct[0]], 'k2_true': [params_correct[1]], 'k3_true': [params_correct[2]], 'k4_true': [params_correct[3]], - 'b1_true': [params_correct[4]], 'b2_true': [params_correct[5]], 'b3_true': [params_correct[6]], - 'm1_true': [params_correct[8]], 'm2_true': [params_correct[9]], 'm3_true': [params_correct[10]], 'F_true': [params_correct[7]], - 'k1_guess': [params_guess[0]], 'k2_guess': [params_guess[1]], 'k3_guess': [params_guess[2]], 'k4_guess': [params_guess[3]], - 'b1_guess': [params_guess[4]], 'b2_guess': [params_guess[5]], 'b3_guess': [params_guess[6]], - 'm1_guess': [params_guess[8]], 'm2_guess': [params_guess[9]], 'm3_guess': [params_guess[10]], 'F_guess': [params_guess[7]], - 'k1_recovered': [final_tri[6]], 'k2_recovered': [final_tri[7]], 'k3_recovered': [final_tri[8]], 'k4_recovered': [final_tri[9]], - 'b1_recovered': [final_tri[3]], 'b2_recovered': [final_tri[4]], 'b3_recovered': [final_tri[5]], - 'm1_recovered': [final_tri[0]], 'm2_recovered': [final_tri[1]], 'm3_recovered': [final_tri[2]], 'F_recovered': [final_tri[10]], - 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], - 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], - 'e_m1': [], 'e_m2': [], 'e_m3': []} - - #Calculate systematic error and add to data dictionary - for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: - param_true = data[f'{param_name}_true'][0] - param_fit = data[f'{param_name}_recovered'][0] - systematic_error = syserr(param_fit, param_true) - data[f'e_{param_name}'].append(systematic_error) - - return data + + #Put everything into a np array + #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 + data_array = np.zeros(45) + data_array[:11] += np.array(params_correct) + data_array[11:22] += np.array(params_guess) + #Adding the recovered parameters and fixing the order + data_array[22:26] += np.array(final_tri[6:10]) + data_array[26:29] += np.array(final_tri[3:6]) + data_array[29] += np.array(final_tri[-1]) + data_array[30:33] += np.array(final_tri[:3]) + #adding systematic error calculations + syserr_result = syserr(data_array[22:33], data_array[:11]) + data_array[33:44] += np.array(syserr_result) + data_array[-1] += np.sum(data_array[33:44]/10) #dividing by 10 because we aren't counting the error in Force because it is 0 + + return data_array #Runs a set number of trials for one system, graphs curvefit result, -# puts data and averages into spreadsheet, returns _bar for both types of curves +# puts data and averages into spreadsheet, returns avg_e arrays and _bar for all types of curves def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, num_trials, excel_file_name, graph_folder_name): + + #Needed for calculating e_bar and for graphing - also these are things that will be returned + avg_e1_array = np.zeros(num_trials) #Polar + avg_e2_array = np.zeros(num_trials) #Cartesian + avg_e3_array = np.zeros(num_trials) #NetMAP - starting_row = 0 - avg_e1_list = [] #Polar - avg_e2_list = [] #Cartesian - avg_e3_list = [] #NetMAP + #Needed to add all the data to a spreadsheet at the end + all_data1 = np.empty((0, 51)) #Polar + all_data2 = np.empty((0, 51)) #Cartesian + all_data3 = np.empty((0, 45)) #NetMAP - #Put data into excel spreadsheet with pd.ExcelWriter(excel_file_name, engine='xlsxwriter') as writer: for i in range(num_trials): @@ -339,41 +306,48 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n e_NetMAP = complex_noise(length_noise_NetMAP,2) #Get the data! - dictionary1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, False, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force - dictionary2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force - dictionary3 = get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False) #NetMAP - - #Find (average across parameters) for each trial and add to dictionary - avg_e1 = find_avg_e(dictionary1) #Polar - dictionary1[''] = avg_e1 + array1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, False, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force + array2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force + array3 = get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False) #NetMAP - avg_e2 = find_avg_e(dictionary2) #Cartesian - dictionary2[''] = avg_e2 + #Find (average across parameters) for each trial and add to arrays + avg_e1_array[i] += array1[-1] + avg_e2_array[i] += array2[-1] + avg_e3_array[i] += array3[-1] - avg_e3 = find_avg_e(dictionary3) #NetMAP - dictionary3[''] = avg_e3 + #Stack to the larger array + all_data1 = np.vstack((all_data1, array1)) + all_data2 = np.vstack((all_data2, array2)) + all_data3 = np.vstack((all_data3, array3)) - #Append to list for later graphing - avg_e1_list.append(avg_e1) - avg_e2_list.append(avg_e2) - avg_e3_list.append(avg_e3) - - #Turn data into dataframe for excel - dataframe1 = pd.DataFrame(dictionary1) - dataframe2 = pd.DataFrame(dictionary2) - dataframe3 = pd.DataFrame(dictionary3) - - #Add to excel spreadsheet - dataframe1.to_excel(writer, sheet_name='Amp & Phase', startrow=starting_row, index=False, header=(i==0)) - dataframe2.to_excel(writer, sheet_name='X & Y', startrow=starting_row, index=False, header=(i==0)) - dataframe3.to_excel(writer, sheet_name='NetMAP', startrow=starting_row, index=False, header=(i==0)) - starting_row += len(dataframe1) + (1 if i==0 else 0) + avg_e1_bar = math.exp(sum(np.log(avg_e1_array))/num_trials) + avg_e2_bar = math.exp(sum(np.log(avg_e2_array))/num_trials) + avg_e3_bar = math.exp(sum(np.log(avg_e3_array))/num_trials) + - avg_e1_bar = arithmetic_then_logarithmic(avg_e1_list, num_trials) - avg_e2_bar = arithmetic_then_logarithmic(avg_e2_list, num_trials) - avg_e3_bar = arithmetic_then_logarithmic(avg_e3_list, num_trials) + #For labeling the excel sheet + param_names = ['k1_true', 'k2_true', 'k3_true', 'k4_true', + 'b1_true', 'b2_true', 'b3_true', + 'F_true', 'm1_true', 'm2_true', 'm3_true', + 'k1_guess', 'k2_guess', 'k3_guess', 'k4_guess', + 'b1_guess', 'b2_guess', 'b3_guess', + 'F_guess', 'm1_guess', 'm2_guess', 'm3_guess', + 'k1_recovered', 'k2_recovered', 'k3_recovered', 'k4_recovered', + 'b1_recovered', 'b2_recovered', 'b3_recovered', + 'F_recovered', 'm1_recovered', 'm2_recovered', 'm3_recovered', + 'e_k1', 'e_k2', 'e_k3', 'e_k4', + 'e_b1', 'e_b2', 'e_b3', 'e_F', + 'e_m1', 'e_m2', 'e_m3', + 'Amp1_rsqrd', 'Amp2_rsqrd', 'Amp3_rsqrd', + 'Phase1_rsqrd', 'Phase2_rsqrd', 'Phase3_rsqrd', ''] + #Turn the final data arrays into a dataframe so they can be written to excel + dataframe1 = pd.DataFrame(all_data1, columns=param_names) + dataframe2 = pd.DataFrame(all_data2, columns=param_names) + dataframe3 = pd.DataFrame(all_data3, columns=param_names[:44]+[param_names[-1]]) + + #Add _bar values to data frame dataframe1.at[0,'_bar'] = avg_e1_bar dataframe2.at[0,'_bar'] = avg_e2_bar dataframe3.at[0,'_bar'] = avg_e3_bar @@ -382,105 +356,9 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n dataframe2.to_excel(writer, sheet_name='X & Y', index=False) dataframe3.to_excel(writer, sheet_name='NetMAP', index=False) - return avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar - -#Incomplete -def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_name, graph_title, x_label): - #Data 1 = polar, Data 2 = X&Y, Data 3 = NetMAP - - fig = plt.figure(figsize=(10, 6)) - spread1 = (max(data1)-min(data1)) - spread2 = (max(data2)-min(data2)) - spread3 = (max(data3)-min(data3)) - - #If 1 and 2 overlap but no overlap with 3 - #3 can be above or below 1 and 2 - if (max(data1)>=min(data2) or max(data2)>=min(data1)) and ((max(data1)min(data3) and max(data2)>min(data3))): - - #If 2 is greater than 1 - if max(data1) >= min(data2) and (max(data1)= min(data2) and (max(data1)>min(data3) and max(data2)>min(data3)): - bax = brokenaxes(xlims=((min(data2)-min(data2)*0.1, max(data1)+max(data1)*0.1), (min(data3)-min(data3)*0.1, max(data3)+max(data3)*0.1)), hspace=.05) - bax.set_title(graph_title) - bax.set_xlabel(x_label) - bax.set_ylabel('Counts') - bax.hist(data1, bins=10, alpha=0.75, color='blue', label=data1_name, edgecolor='black') - bax.hist(data2, bins=10, alpha=0.75, color='green', label=data2_name, edgecolor='black') - bax.hist(data3, bins=10, alpha=0.75, color='red', label=data3_name, edgecolor='black') - bax.legend(loc='upper center') - - # Adjust the scales - bax.axs[0].set_xlim(min(data2)-spread2*0.1, max(data1)+spread1*0.1) #left - bax.axs[1].set_xlim(min(data3)-spread3*0.1, max(data3)+spread3*0.1) #right - - bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - - #If 2 and 3 overlap but no overlap with 1 - elif (max(data2)>=min(data3) or max(data3)>=min(data2)) and max(data1)= min(data2): - bax = brokenaxes(xlims=((min(data1)-min(data1)*0.1, max(data2)+max(data2)*0.1), (min(data3)-min(data3)*0.1, max(data3)+max(data3)*0.1)), hspace=.05) - bax.set_title(graph_title) - bax.set_xlabel(x_label) - bax.set_ylabel('Counts') - bax.hist(data1, bins=10, alpha=0.75, color='blue', label=data1_name, edgecolor='black') - bax.hist(data2, bins=10, alpha=0.75, color='green', label=data2_name, edgecolor='black') - bax.hist(data3, bins=10, alpha=0.75, color='red', label=data3_name, edgecolor='black') - bax.legend(loc='upper center') - - # Adjust the scales - bax.axs[0].set_xlim(min(data1)-spread1*0.1, max(data2)+spread2*0.1) #left - bax.axs[1].set_xlim(min(data3)-spread3*0.1, max(data3)+spread3*0.1) #right - - bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - - #If 1 is greater than 2 - else: - bax = brokenaxes(xlims=((min(data2)-min(data2)*0.1, max(data1)+max(data1)*0.1), (min(data3)-min(data3)*0.1, max(data3)+max(data3)*0.1)), hspace=.05) - bax.set_title(graph_title) - bax.set_xlabel(x_label) - bax.set_ylabel('Counts') - bax.hist(data1, bins=10, alpha=0.75, color='blue', label=data1_name, edgecolor='black') - bax.hist(data2, bins=10, alpha=0.75, color='green', label=data2_name, edgecolor='black') - bax.hist(data3, bins=10, alpha=0.75, color='red', label=data3_name, edgecolor='black') - bax.legend(loc='upper center') - - # Adjust the scales - bax.axs[0].set_xlim(min(data2)-spread2*0.1, max(data1)+spread1*0.1) #left - bax.axs[1].set_xlim(min(data3)-spread3*0.1, max(data3)+spread3*0.1) #right - - bax.axs[0].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[0].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - bax.axs[1].xaxis.set_major_locator(ticker.MaxNLocator(5)) - bax.axs[1].xaxis.set_major_formatter(ticker.FormatStrFormatter('%1.3f')) - - plt.show() + # return avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar + return avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar + ''' Begin work here. Case Study. ''' @@ -533,16 +411,16 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # #Will not do anything with _bar for a single case study # freqs_NetMAP = np.linspace(0.001, 4, 10) # length_noise = 10 -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 50, 'Case_Study.xlsx', 'Case Study Plots') +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 10, 'Case_Study.xlsx', 'Case Study Plots') # #Graph histogram of for curve fits # plt.title('Average Systematic Error Across Parameters') # plt.xlabel('') # plt.ylabel('Counts') -# plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') -# plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') +# plt.hist(avg_e2_array, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_array, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_array, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') # plt.legend(loc='upper center') # plt.show() @@ -562,7 +440,7 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # #Curve fit with the guess made above # freqs_NetMAP = np.linspace(0.001, 4, 10) # length_noise = 10 -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 50, f'Random_Automated_Guess_{i}.xlsx', f'Sys {i} - Rand Auto Guess Plots') # #Add _bar to lists to make one graph at the end # avg_e1_bar_list.append(avg_e1_bar) #Polar @@ -574,9 +452,9 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # plt.title('Average Systematic Error Across Parameters') # plt.xlabel('') # plt.ylabel('Counts') -# plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') -# plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') +# plt.hist(avg_e2_array, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_array, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_array, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') # plt.legend(loc='upper center') # plt.show() @@ -608,17 +486,153 @@ def histogram_3_data_sets(data1, data2, data3, data1_name, data2_name, data3_nam # # MUST CHANGE ERROR IN run_trials AND IN get_parameters_NetMAP # freqs_NetMAP = np.linspace(0.001, 4, 10) # length_noise = 0 -# avg_e1_list, avg_e2_list, avg_e3_list, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, freqs_NetMAP, length_noise, 50, 'Sys0_No_Error.xlsx', 'Sys0_No_Error - Plots') +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_parameters, guessed_parameters, freqs_NetMAP, length_noise, 50, 'Sys0_No_Error.xlsx', 'Sys0_No_Error - Plots') # #Plot histogram # plt.title('Average Systematic Error Across Parameters') # plt.xlabel('') # plt.ylabel('Counts') -# plt.hist(avg_e2_list, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') -# plt.hist(avg_e1_list, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') -# plt.hist(avg_e3_list, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') +# plt.hist(avg_e2_array, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') +# plt.hist(avg_e1_array, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') +# plt.hist(avg_e3_array, bins=50, alpha=0.5, color='red', label='NetMAP', edgecolor='black') # plt.legend(loc='upper center') # plt.show() # plt.savefig('_Histogram_Sys0_no_error.png') +'''Begin work here. Redoing Case Study - 10 Freqs Better Params with 1000 trials instead of 50 ''' + +# file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP & Better Parameters/Case_Study_10_Freqs_Better_Parameters.xlsx' +# array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() +# array_X_Y = pd.read_excel(file_path, sheet_name = 'X & Y').to_numpy() + +# true_params = np.concatenate((array_amp_phase[1,:7], [array_amp_phase[1,10]], array_amp_phase[1,7:10])) +# guessed_params = np.concatenate((array_amp_phase[1,11:18], [array_amp_phase[1,21]], array_amp_phase[1,18:21])) + +# freq = np.linspace(0.001, 4, 300) +# freqs_NetMAP = np.linspace(0.001, 4, 10) +# length_noise = 10 + +# run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 1000, 'Case_Study_1000_Trials.xlsx', 'Case Study 1000 Trials Plots') + +'''Begin work here. Redoing 15 systems data. Still using 10 Freqs and Better Params. + I want to do many more systems and 500 trials per system. Seeing how many systems it can do in 3 hours.''' + + +## 1. Make sure I save the error used for each trial. NOT DONE +## 2. Set a runtime limit of 2-3 hours perhaps. NOT DONE +## 3. Don't graph all the curvefits. DONE +## 4. Guesses are automated to within 20% of generated parameters, 10 evenly spaced frequencies for NetMAP, noise level 2 and n=300. + +# Set the time limit in seconds +time_limit = 10800 # 3 hours + +# Record the start time +start_time = time.time() + +avg_e_bar_list_polar = [] +avg_e_bar_list_cartesian = [] +avg_e_bar_list_NetMAP = [] + +for i in range(100): + + # Check if the time limit has been exceeded + elapsed_time = time.time() - start_time + if elapsed_time > time_limit: + print("Time limit exceeded. Exiting loop.") + break + + loop_start_time = time.time() + + #Generate system and guess parameters + true_params = generate_random_system() + guessed_params = automate_guess(true_params, 20) + print(true_params) + print(guessed_params) + + #Curve fit with the guess made above + freqs_NetMAP = np.linspace(0.001, 4, 10) + length_noise = 10 + avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 250, f'System_{i}_500.xlsx', f'Sys {i} - Rand Auto Guess Plots') + + #Add _bar to lists to make one graph at the end + avg_e_bar_list_polar.append(avg_e1_bar) #Polar + avg_e_bar_list_cartesian.append(avg_e2_bar) #Cartesian + avg_e_bar_list_NetMAP.append(avg_e3_bar) #NetMAP + + linearbins = np.linspace(0,15,50) + #Graph histogram of for curve fits + fig = plt.figure(figsize=(5, 4)) + # plt.title('Average Systematic Error Across Parameters') + plt.xlabel(' (%)', fontsize = 16) + plt.ylabel('Counts', fontsize = 16) + plt.yticks(fontsize=14) + plt.xticks(fontsize=14) + plt.hist(avg_e2_array, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') + plt.hist(avg_e1_array, bins = linearbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + plt.hist(avg_e3_array, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='black') + plt.legend(loc='upper right', fontsize = 13) + + plt.show() + save_figure(fig, 'More Systems 250 - Histograms', f' Lin Hist System {i}') + + # logbins = np.logspace(-2,1.5,50) + # #Graph histogram of for curve fits + # fig = plt.figure(figsize=(5, 4)) + # # plt.title('Average Systematic Error Across Parameters') + # plt.xlabel(' (%)', fontsize = 16) + # plt.ylabel('Counts', fontsize = 16) + # plt.yticks(fontsize=14) + # plt.xticks(fontsize=14) + # plt.hist(avg_e2_array, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') + # plt.hist(avg_e1_array, bins = logbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + # plt.hist(avg_e3_array, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='black') + # plt.legend(loc='upper right', fontsize = 13) + + # plt.show() + # save_figure(fig, 'More Systems 500 - Histograms', f' Log Hist System {i}') + + loop_end_time = time.time() + loop_time = loop_end_time - loop_start_time + + print(f"Iteration {i + 1} completed. Loop time: {loop_time}") + + +#Graph histogram of _bar for both curve fits + +linearbins = np.linspace(0,15,50) +#Graph! +fig = plt.figure(figsize=(5, 4)) +plt.xlabel(' Bar (%)', fontsize = 16) +plt.ylabel('Counts', fontsize = 16) +plt.xlim(0.02, 15) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.hist(avg_e_bar_list_cartesian, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') +plt.hist(avg_e_bar_list_polar, bins = linearbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') +plt.hist(avg_e_bar_list_NetMAP, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +plt.legend(loc='upper right', fontsize = 13) +plt.show() +save_figure(fig, 'More Systems 250 - Histograms', ' Bar Lin Hist' ) + + +logbins = np.logspace(-2,1.5,50) +#Graph! +fig = plt.figure(figsize=(5, 4)) +plt.xlabel(' Bar (%)', fontsize = 16) +plt.ylabel('Counts', fontsize = 16) +plt.xscale('log') +plt.xlim(0.02, 15) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.hist(avg_e_bar_list_cartesian, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') +plt.hist(avg_e_bar_list_polar, bins = logbins, alpha=0.4, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') +plt.hist(avg_e_bar_list_NetMAP, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +plt.legend(loc='upper right', fontsize = 13) +plt.show() +save_figure(fig, 'More Systems 250 - Histograms', ' Bar Log Hist' ) + + +# End time +end_time = time.time() +print("Time Elapsed:", end_time - start_time) From be668017e055deb912d351ac80c8e3c723e3c9ed Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 5 Dec 2024 00:53:34 -0500 Subject: [PATCH 095/101] Issue with comparing_curvefit_types fixed Not yet good to run 100 systems but almost there. --- trimer/Trimer_simulator.py | 1 + trimer/comparing_curvefit_types.py | 51 ++-- trimer/curve_fitting_X_Y_all.py | 360 ++++++++++++-------------- trimer/curve_fitting_amp_phase_all.py | 2 +- 4 files changed, 188 insertions(+), 226 deletions(-) diff --git a/trimer/Trimer_simulator.py b/trimer/Trimer_simulator.py index 54f4d6d..25a2bcb 100644 --- a/trimer/Trimer_simulator.py +++ b/trimer/Trimer_simulator.py @@ -46,6 +46,7 @@ unknownsmatrix = sp.Matrix([[-wd**2*m1 + 1j*wd*b1 + k1 + k2, -k2, 0], [-k2, -wd**2*m2 + 1j*wd*b2 + k2 + k3, -k3], [0, -k3, -wd**2*m3 + 1j*wd*b3 + k3 + k4]]) + ''' Lydia - I'm pretty sure he had a mistake in the unknowns matrix. There were some k4's showing up where they weren't supposed to be (-k4 where the zeros are now and one +k4 in the first entry) ''' diff --git a/trimer/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py index 8002dae..d48803a 100644 --- a/trimer/comparing_curvefit_types.py +++ b/trimer/comparing_curvefit_types.py @@ -533,7 +533,7 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n avg_e_bar_list_cartesian = [] avg_e_bar_list_NetMAP = [] -for i in range(100): +for i in range(1): # Check if the time limit has been exceeded elapsed_time = time.time() - start_time @@ -546,13 +546,11 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n #Generate system and guess parameters true_params = generate_random_system() guessed_params = automate_guess(true_params, 20) - print(true_params) - print(guessed_params) #Curve fit with the guess made above freqs_NetMAP = np.linspace(0.001, 4, 10) length_noise = 10 - avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 250, f'System_{i}_500.xlsx', f'Sys {i} - Rand Auto Guess Plots') + avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 10, f'System_{i}_500.xlsx', f'Sys {i} - Rand Auto Guess Plots') #Add _bar to lists to make one graph at the end avg_e_bar_list_polar.append(avg_e1_bar) #Polar @@ -573,28 +571,29 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n plt.legend(loc='upper right', fontsize = 13) plt.show() - save_figure(fig, 'More Systems 250 - Histograms', f' Lin Hist System {i}') - - # logbins = np.logspace(-2,1.5,50) - # #Graph histogram of for curve fits - # fig = plt.figure(figsize=(5, 4)) - # # plt.title('Average Systematic Error Across Parameters') - # plt.xlabel(' (%)', fontsize = 16) - # plt.ylabel('Counts', fontsize = 16) - # plt.yticks(fontsize=14) - # plt.xticks(fontsize=14) - # plt.hist(avg_e2_array, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') - # plt.hist(avg_e1_array, bins = logbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - # plt.hist(avg_e3_array, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='black') - # plt.legend(loc='upper right', fontsize = 13) - - # plt.show() - # save_figure(fig, 'More Systems 500 - Histograms', f' Log Hist System {i}') + save_figure(fig, 'More Systems 500 - Histograms', f' Lin Hist System {i}') + + logbins = np.logspace(-2,1.5,50) + #Graph histogram of for curve fits + fig = plt.figure(figsize=(5, 4)) + # plt.title('Average Systematic Error Across Parameters') + plt.xlabel(' (%)', fontsize = 16) + plt.ylabel('Counts', fontsize = 16) + plt.xscale('log') + plt.yticks(fontsize=14) + plt.xticks(fontsize=14) + plt.hist(avg_e2_array, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') + plt.hist(avg_e1_array, bins = logbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') + plt.hist(avg_e3_array, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='black') + plt.legend(loc='upper right', fontsize = 13) + + plt.show() + save_figure(fig, 'More Systems 500 - Histograms', f' Log Hist System {i}') loop_end_time = time.time() loop_time = loop_end_time - loop_start_time - print(f"Iteration {i + 1} completed. Loop time: {loop_time}") + print(f"Iteration {i + 1} completed. Loop time: {loop_time} secs ") #Graph histogram of _bar for both curve fits @@ -604,7 +603,6 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n fig = plt.figure(figsize=(5, 4)) plt.xlabel(' Bar (%)', fontsize = 16) plt.ylabel('Counts', fontsize = 16) -plt.xlim(0.02, 15) plt.yticks(fontsize=14) plt.xticks(fontsize=14) plt.hist(avg_e_bar_list_cartesian, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') @@ -612,7 +610,7 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n plt.hist(avg_e_bar_list_NetMAP, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') plt.legend(loc='upper right', fontsize = 13) plt.show() -save_figure(fig, 'More Systems 250 - Histograms', ' Bar Lin Hist' ) +save_figure(fig, 'More Systems 500 - Histograms', ' Bar Lin Hist' ) logbins = np.logspace(-2,1.5,50) @@ -621,7 +619,6 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n plt.xlabel(' Bar (%)', fontsize = 16) plt.ylabel('Counts', fontsize = 16) plt.xscale('log') -plt.xlim(0.02, 15) plt.yticks(fontsize=14) plt.xticks(fontsize=14) plt.hist(avg_e_bar_list_cartesian, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') @@ -629,10 +626,10 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n plt.hist(avg_e_bar_list_NetMAP, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') plt.legend(loc='upper right', fontsize = 13) plt.show() -save_figure(fig, 'More Systems 250 - Histograms', ' Bar Log Hist' ) +save_figure(fig, 'More Systems 500 - Histograms', ' Bar Log Hist' ) # End time end_time = time.time() -print("Time Elapsed:", end_time - start_time) +print("Time Elapsed:", end_time - start_time, " secs", (end_time - start_time)/3600, " hrs") diff --git a/trimer/curve_fitting_X_Y_all.py b/trimer/curve_fitting_X_Y_all.py index 09aa762..d5dabe3 100644 --- a/trimer/curve_fitting_X_Y_all.py +++ b/trimer/curve_fitting_X_Y_all.py @@ -11,8 +11,9 @@ import lmfit import warnings from Trimer_simulator import re1, re2, re3, im1, im2, im3, realamp1, realamp2, realamp3, imamp1, imamp2, imamp3 + ''' 3 functions contained: - multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once + multiple_fit - Curve fits to multiple Real and Imaginary Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them @@ -23,7 +24,7 @@ rsqrd - calculates R^2 ''' -def syserr(x_found,x_set, absval = True): +def syserr(x_found, x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set @@ -95,111 +96,77 @@ def save_figure(figure, folder_name, file_name): # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error -def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name): +def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name, show_curvefit_graphs = False): + + ##Put params_guess and params_correct into np array + #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 + data_array = np.zeros(51) + data_array[:11] += np.array(params_correct) + data_array[11:22] += np.array(params_guess) + ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) - X1 = realamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - Y1 = imamp1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + X1 = realamp1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) + Y1 = imamp1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) - X2 = realamp2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - Y2 = imamp2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + X2 = realamp2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) + Y2 = imamp2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) - X3 = realamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) - Y3 = imamp3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) + X3 = realamp3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) + Y3 = imamp3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) #Create intial parameters params = lmfit.Parameters() - params.add('k1', value = params_guess[0], min=0) - params.add('k2', value = params_guess[1], min=0) - params.add('k3', value = params_guess[2], min=0) - params.add('k4', value = params_guess[3], min=0) - params.add('b1', value = params_guess[4], min=0) - params.add('b2', value = params_guess[5], min=0) - params.add('b3', value = params_guess[6], min=0) - params.add('F', value = params_guess[7], min=0) - params.add('m1', value = params_guess[8], min=0) - params.add('m2', value = params_guess[9], min=0) - params.add('m3', value = params_guess[10], min=0) + params.add('k1', value = data_array[11], min=0) + params.add('k2', value = data_array[12], min=0) + params.add('k3', value = data_array[13], min=0) + params.add('k4', value = data_array[14], min=0) + params.add('b1', value = data_array[15], min=0) + params.add('b2', value = data_array[16], min=0) + params.add('b3', value = data_array[17], min=0) + params.add('F', value = data_array[18], min=0) + params.add('m1', value = data_array[19], min=0) + params.add('m2', value = data_array[20], min=0) + params.add('m3', value = data_array[21], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False - #Create dictionary for storing data - data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], - 'b1_true': [], 'b2_true': [], 'b3_true': [], - 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], - 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], - 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], - 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], - 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], - 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], - 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], - 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], - 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], - 'e_m1': [], 'e_m2': [], 'e_m3': [], - 'X1_rsqrd': [], 'X2_rsqrd': [], 'X3_rsqrd': [], - 'Y1_rsqrd': [], 'Y2_rsqrd': [], 'Y3_rsqrd': []} - #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, X1, X2, X3, Y1, Y2, Y3)) #print(lmfit.fit_report(result)) - #Create dictionary of true parameters from list provided (need for compiling data bc I can't do it with a list) - true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], - 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], - 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} - - #Compling the Data - for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: - #Add true parameters to dictionary - param_true = true_params[param_name] - data[f'{param_name}_true'].append(param_true) - - #Add guessed parameters to dictionary - param_guess = params[param_name].value - data[f'{param_name}_guess'].append(param_guess) - - #If you planned on fixing F so it cannot be changed - if fix_F: - #Add fitted parameters to dictionary - param_fit = result.params[param_name].value - data[f'{param_name}_recovered'].append(param_fit) - - #Calculate systematic error and add to dictionary - systematic_error = syserr(param_fit, param_true) - data[f'e_{param_name}'].append(systematic_error) - - else: - #If you included F in parameters to be varied, you must scale by F - #Scale fitted parameters by force - param_fit = result.params[param_name].value - scaling_factor = (true_params['F'])/(result.params['F'].value) - scaled_param_fit = param_fit*scaling_factor - - #Add fitted parameters to dictionary - data[f'{param_name}_recovered'].append(scaled_param_fit) - - #Calculate systematic error and add to dictionary - param_true = true_params[param_name] - systematic_error = syserr(scaled_param_fit, param_true) - data[f'e_{param_name}'].append(systematic_error) + ##Add recovered parameters and systematic error + #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 + param_values = np.array([result.params[param].value for param in result.params]) + data_array[22:33] += param_values - #Create fitted y-values (for rsrd and graphing) - k1_fit = result.params['k1'].value - k2_fit = result.params['k2'].value - k3_fit = result.params['k3'].value - k4_fit = result.params['k4'].value - b1_fit = result.params['b1'].value - b2_fit = result.params['b2'].value - b3_fit = result.params['b3'].value - F_fit = result.params['F'].value - m1_fit = result.params['m1'].value - m2_fit = result.params['m2'].value - m3_fit= result.params['m3'].value + if fix_F == False: + scaling_factor = (data_array[7])/(result.params['F'].value) + data_array[22:33] *= scaling_factor + + syserr_result = syserr(data_array[22:33], data_array[:11]) + data_array[33:44] += np.array(syserr_result) + + #average error + data_array[-1] += np.sum(data_array[33:44]/10) #dividing by 10 because we aren't counting the error in Force because it is 0 + + #Create fitted y-values (for rsqrd and graphing) + k1_fit = data_array[22] + k2_fit = data_array[23] + k3_fit = data_array[24] + k4_fit = data_array[25] + b1_fit = data_array[26] + b2_fit = data_array[27] + b3_fit = data_array[28] + F_fit = data_array[29] + m1_fit = data_array[30] + m2_fit = data_array[31] + m3_fit= data_array[32] X1_fitted = re1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) X2_fitted = re2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) @@ -208,123 +175,120 @@ def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F, graph_fo Y2_fitted = im2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Y3_fitted = im3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) - #Calculate R^2 + #Calculate R^2 and add to data_array X1_rsqrd = rsqrd(X1_fitted, X1) X2_rsqrd = rsqrd(X2_fitted, X2) X3_rsqrd = rsqrd(X3_fitted, X3) Y1_rsqrd = rsqrd(Y1_fitted, Y1) Y2_rsqrd = rsqrd(Y2_fitted, Y2) Y3_rsqrd = rsqrd(Y3_fitted, Y3) - data['X1_rsqrd'].append(X1_rsqrd) - data['X2_rsqrd'].append(X2_rsqrd) - data['X3_rsqrd'].append(X3_rsqrd) - data['Y1_rsqrd'].append(Y1_rsqrd) - data['Y2_rsqrd'].append(Y2_rsqrd) - data['Y3_rsqrd'].append(Y3_rsqrd) - - #Create intial guessed y-values (for graphing) - k1_guess = params['k1'].value - k2_guess = params['k2'].value - k3_guess = params['k3'].value - k4_guess = params['k4'].value - b1_guess = params['b1'].value - b2_guess = params['b2'].value - b3_guess = params['b3'].value - F_guess = params['F'].value - m1_guess = params['m1'].value - m2_guess = params['m2'].value - m3_guess = params['m3'].value - - re1_guess = re1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - re2_guess = re2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - re3_guess = re3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - im1_guess = im1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - im2_guess = im2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - im3_guess = im3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) - - ## Begin graphing - fig = plt.figure(figsize=(16,11)) - gs = fig.add_gridspec(3, 3, hspace=0.25, wspace=0.05) - - ax1 = fig.add_subplot(gs[0, 0]) - ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) - ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) - ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) - ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) - ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) - ax7 = fig.add_subplot(gs[2, 0], aspect='equal') - ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7, aspect='equal') - ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7, aspect='equal') - - #original data - ax1.plot(freq, X1,'ro', alpha=0.5, markersize=5.5, label = 'Data') - ax2.plot(freq, X2,'bo', alpha=0.5, markersize=5.5, label = 'Data') - ax3.plot(freq, X3,'go', alpha=0.5, markersize=5.5, label = 'Data') - ax4.plot(freq, Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') - ax5.plot(freq, Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') - ax6.plot(freq, Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') - ax7.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') - ax8.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') - ax9.plot(X3,Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') - #fitted curves - ax1.plot(freq, X1_fitted,'c-', label='Best Fit', lw=2.5) - ax2.plot(freq, X2_fitted,'r-', label='Best Fit', lw=2.5) - ax3.plot(freq, X3_fitted,'m-', label='Best Fit', lw=2.5) - ax4.plot(freq, Y1_fitted,'c-', label='Best Fit', lw=2.5) - ax5.plot(freq, Y2_fitted,'r-', label='Best Fit', lw=2.5) - ax6.plot(freq, Y3_fitted,'m-', label='Best Fit', lw=2.5) - ax7.plot(X1_fitted, Y1_fitted, 'c-', label='Best Fit', lw=2.5) - ax8.plot(X2_fitted, Y2_fitted, 'r-', label='Best Fit', lw=2.5) - ax9.plot(X3_fitted, Y3_fitted, 'm-', label='Best Fit', lw=2.5) - - #inital guess curves - ax1.plot(freq, re1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax2.plot(freq, re2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax3.plot(freq, re3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax4.plot(freq, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax5.plot(freq, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax6.plot(freq, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax7.plot(re1_guess, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax8.plot(re2_guess, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - ax9.plot(re3_guess, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') - - #Graph parts - fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=16) - ax1.set_title('Mass 1', fontsize=14) - ax2.set_title('Mass 2', fontsize=14) - ax3.set_title('Mass 3', fontsize=14) - ax1.set_ylabel('Real') - ax4.set_ylabel('Imaginary') - ax7.set_ylabel('Imaginary') - - ax1.label_outer() - ax2.label_outer() - ax3.label_outer() - ax5.tick_params(labelleft=False) - ax6.tick_params(labelleft=False) - ax7.label_outer() - ax8.label_outer() - ax9.label_outer() + data_array[44:50] += np.array([X1_rsqrd, X2_rsqrd, X3_rsqrd, Y1_rsqrd, Y2_rsqrd, Y3_rsqrd]) + + if show_curvefit_graphs == True: + #Create intial guessed y-values (for graphing) + k1_guess = data_array[11] + k2_guess = data_array[12] + k3_guess = data_array[13] + k4_guess = data_array[14] + b1_guess = data_array[15] + b2_guess = data_array[16] + b3_guess = data_array[17] + F_guess = data_array[18] + m1_guess = data_array[19] + m2_guess = data_array[20] + m3_guess = data_array[21] - ax4.set_xlabel('Frequency') - ax5.set_xlabel('Frequency') - ax6.set_xlabel('Frequency') - ax7.set_xlabel('Real') - ax8.set_xlabel('Real') - ax9.set_xlabel('Real') - - ax1.legend() - ax2.legend() - ax3.legend() - ax4.legend() - ax5.legend() - ax6.legend() - ax7.legend(fontsize='10') - ax8.legend(fontsize='10') - ax9.legend(fontsize='10') - - plt.show() - save_figure(fig, graph_folder_name, graph_name) + re1_guess = re1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + re2_guess = re2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + re3_guess = re3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + im1_guess = im1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + im2_guess = im2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + im3_guess = im3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) + + ## Begin graphing + fig = plt.figure(figsize=(16,11)) + gs = fig.add_gridspec(3, 3, hspace=0.25, wspace=0.05) + + ax1 = fig.add_subplot(gs[0, 0]) + ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) + ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) + ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) + ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) + ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) + ax7 = fig.add_subplot(gs[2, 0], aspect='equal') + ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7, aspect='equal') + ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7, aspect='equal') + + #original data + ax1.plot(freq, X1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax2.plot(freq, X2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax3.plot(freq, X3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax4.plot(freq, Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax5.plot(freq, Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax6.plot(freq, Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') + ax7.plot(X1,Y1,'ro', alpha=0.5, markersize=5.5, label = 'Data') + ax8.plot(X2,Y2,'bo', alpha=0.5, markersize=5.5, label = 'Data') + ax9.plot(X3,Y3,'go', alpha=0.5, markersize=5.5, label = 'Data') + + #fitted curves + ax1.plot(freq, X1_fitted,'c-', label='Best Fit', lw=2.5) + ax2.plot(freq, X2_fitted,'r-', label='Best Fit', lw=2.5) + ax3.plot(freq, X3_fitted,'m-', label='Best Fit', lw=2.5) + ax4.plot(freq, Y1_fitted,'c-', label='Best Fit', lw=2.5) + ax5.plot(freq, Y2_fitted,'r-', label='Best Fit', lw=2.5) + ax6.plot(freq, Y3_fitted,'m-', label='Best Fit', lw=2.5) + ax7.plot(X1_fitted, Y1_fitted, 'c-', label='Best Fit', lw=2.5) + ax8.plot(X2_fitted, Y2_fitted, 'r-', label='Best Fit', lw=2.5) + ax9.plot(X3_fitted, Y3_fitted, 'm-', label='Best Fit', lw=2.5) + + #inital guess curves + ax1.plot(freq, re1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax2.plot(freq, re2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax3.plot(freq, re3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax4.plot(freq, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax5.plot(freq, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax6.plot(freq, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax7.plot(re1_guess, im1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax8.plot(re2_guess, im2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + ax9.plot(re3_guess, im3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') + + #Graph parts + fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=16) + ax1.set_title('Mass 1', fontsize=14) + ax2.set_title('Mass 2', fontsize=14) + ax3.set_title('Mass 3', fontsize=14) + ax1.set_ylabel('Real') + ax4.set_ylabel('Imaginary') + ax7.set_ylabel('Imaginary') + + ax1.label_outer() + ax2.label_outer() + ax3.label_outer() + ax5.tick_params(labelleft=False) + ax6.tick_params(labelleft=False) + ax7.label_outer() + ax8.label_outer() + ax9.label_outer() + + ax4.set_xlabel('Frequency') + ax5.set_xlabel('Frequency') + ax6.set_xlabel('Frequency') + ax7.set_xlabel('Real') + ax8.set_xlabel('Real') + ax9.set_xlabel('Real') + + ax1.legend() + ax2.legend() + ax3.legend() + ax4.legend() + ax5.legend() + ax6.legend() + ax7.legend(fontsize='10') + ax8.legend(fontsize='10') + ax9.legend(fontsize='10') + + plt.show() + save_figure(fig, graph_folder_name, graph_name) - return data + return data_array \ No newline at end of file diff --git a/trimer/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py index 72a22c7..071c04d 100644 --- a/trimer/curve_fitting_amp_phase_all.py +++ b/trimer/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/phase_max) scaled_residt2.append(phase2/phase_max) scaled_residt3.append(phase3/phase_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, scaled, graph_folder_name, graph_name): ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase1 = theta1(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase2 = theta2(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) Phase3 = theta3(freq, params_correct[0], params_correct[1], params_correct[2], params_correct[3], params_correct[4], params_correct[5], params_correct[6], params_correct[7], params_correct[8], params_correct[9], params_correct[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = params_guess[0], min=0) params.add('k2', value = params_guess[1], min=0) params.add('k3', value = params_guess[2], min=0) params.add('k4', value = params_guess[3], min=0) params.add('b1', value = params_guess[4], min=0) params.add('b2', value = params_guess[5], min=0) params.add('b3', value = params_guess[6], min=0) params.add('F', value = params_guess[7], min=0) params.add('m1', value = params_guess[8], min=0) params.add('m2', value = params_guess[9], min=0) params.add('m3', value = params_guess[10], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #Create dictionary for storing data data = {'k1_true': [], 'k2_true': [], 'k3_true': [], 'k4_true': [], 'b1_true': [], 'b2_true': [], 'b3_true': [], 'm1_true': [], 'm2_true': [], 'm3_true': [], 'F_true': [], 'k1_guess': [], 'k2_guess': [], 'k3_guess': [], 'k4_guess': [], 'b1_guess': [], 'b2_guess': [], 'b3_guess': [], 'm1_guess': [], 'm2_guess': [], 'm3_guess': [], 'F_guess': [], 'k1_recovered': [], 'k2_recovered': [], 'k3_recovered': [], 'k4_recovered': [], 'b1_recovered': [], 'b2_recovered': [], 'b3_recovered': [], 'm1_recovered': [], 'm2_recovered': [], 'm3_recovered': [], 'F_recovered': [], 'e_k1': [], 'e_k2': [], 'e_k3': [], 'e_k4': [], 'e_b1': [], 'e_b2': [], 'e_b3': [], 'e_F': [], 'e_m1': [], 'e_m2': [], 'e_m3': [], 'Amp1_rsqrd': [], 'Amp2_rsqrd': [], 'Amp3_rsqrd': [], 'Phase1_rsqrd': [], 'Phase2_rsqrd': [], 'Phase3_rsqrd': []} #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) #Create dictionary of true parameters from list provided (need for compliting data bc I can't do it with a list) true_params = {'k1': params_correct[0], 'k2': params_correct[1], 'k3': params_correct[2], 'k4': params_correct[3], 'b1': params_correct[4], 'b2': params_correct[5], 'b3': params_correct[6], 'F': params_correct[7], 'm1': params_correct[8], 'm2': params_correct[9], 'm3': params_correct[10]} #Compling the Data for param_name in ['k1','k2','k3','k4','b1','b2','b3','F','m1','m2','m3']: #Add true parameters to dictionary param_true = true_params[param_name] data[f'{param_name}_true'].append(param_true) #Add guessed parameters to dictionary param_guess = params[param_name].value data[f'{param_name}_guess'].append(param_guess) #If you planned on fixing F so it cannot be changed if fix_F: #Add fitted parameters to dictionary param_fit = result.params[param_name].value data[f'{param_name}_recovered'].append(param_fit) #Calculate systematic error and add to dictionary systematic_error = syserr(param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) else: #If you included F in parameters to be varied, you must scale by F #Scale fitted parameters by force param_fit = result.params[param_name].value scaling_factor = (true_params['F'])/(result.params['F'].value) scaled_param_fit = param_fit*scaling_factor #Add fitted parameters to dictionary data[f'{param_name}_recovered'].append(scaled_param_fit) #Calculate systematic error and add to dictionary param_true = true_params[param_name] systematic_error = syserr(scaled_param_fit, param_true) data[f'e_{param_name}'].append(systematic_error) #Create fitted y-values (for rsqrd and graphing) k1_fit = result.params['k1'].value k2_fit = result.params['k2'].value k3_fit = result.params['k3'].value k4_fit = result.params['k4'].value b1_fit = result.params['b1'].value b2_fit = result.params['b2'].value b3_fit = result.params['b3'].value F_fit = result.params['F'].value m1_fit = result.params['m1'].value m2_fit = result.params['m2'].value m3_fit= result.params['m3'].value Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data['Amp1_rsqrd'].append(Amp1_rsqrd) data['Amp2_rsqrd'].append(Amp2_rsqrd) data['Amp3_rsqrd'].append(Amp3_rsqrd) data['Phase1_rsqrd'].append(Phase1_rsqrd) data['Phase2_rsqrd'].append(Phase2_rsqrd) data['Phase3_rsqrd'].append(Phase3_rsqrd) #Create intial guessed y-values (for graphing) k1_guess = params['k1'].value k2_guess = params['k2'].value k3_guess = params['k3'].value k4_guess = params['k4'].value b1_guess = params['b1'].value b2_guess = params['b2'].value b3_guess = params['b3'].value F_guess = params['F'].value m1_guess = params['m1'].value m2_guess = params['m2'].value m3_guess = params['m3'].value c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts if scaled: fig.suptitle('Trimer Resonator: Amplitude and Phase (Scaled)', fontsize=16) else: fig.suptitle('Trimer Resonator: Amplitude and Phase (Not Scaled)', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data import pandas as pd e = 0 force_all = False fix_F = True #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] #Get the data (and the graphs) scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') with pd.ExcelWriter('Scaling_Amp_Phase_Residuals.xlsx', engine='xlsxwriter') as writer: dfscaled = pd.DataFrame(scaled_dict) dfnotscaled = pd.DataFrame(not_scaled_dict) dfscaled.to_excel(writer, sheet_name='Scaled', index=False) dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found, x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/phase_max) scaled_residt2.append(phase2/phase_max) scaled_residt3.append(phase3/phase_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, scaled, graph_folder_name, graph_name, show_curvefit_graphs = False): ##Put params_guess and params_correct into np array #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 data_array = np.zeros(51) data_array[:11] += np.array(params_correct) data_array[11:22] += np.array(params_guess) ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase1 = theta1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase2 = theta2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase3 = theta3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = data_array[11], min=0) params.add('k2', value = data_array[12], min=0) params.add('k3', value = data_array[13], min=0) params.add('k4', value = data_array[14], min=0) params.add('b1', value = data_array[15], min=0) params.add('b2', value = data_array[16], min=0) params.add('b3', value = data_array[17], min=0) params.add('F', value = data_array[18], min=0) params.add('m1', value = data_array[19], min=0) params.add('m2', value = data_array[20], min=0) params.add('m3', value = data_array[21], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) ##Add recovered parameters and systematic error #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 param_values = np.array([result.params[param].value for param in result.params]) data_array[22:33] += param_values if fix_F == False: scaling_factor = (data_array[7])/(result.params['F'].value) data_array[22:33] *= scaling_factor syserr_result = syserr(data_array[22:33], data_array[:11]) data_array[33:44] += np.array(syserr_result) #average error data_array[-1] += np.sum(data_array[33:44]/10) #dividing by 10 because we aren't counting the error in Force because it is 0 if show_curvefit_graphs == True: #Create fitted y-values (for rsqrd and graphing) k1_fit = data_array[22] k2_fit = data_array[23] k3_fit = data_array[24] k4_fit = data_array[25] b1_fit = data_array[26] b2_fit = data_array[27] b3_fit = data_array[28] F_fit = data_array[29] m1_fit = data_array[30] m2_fit = data_array[31] m3_fit= data_array[32] Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 and add to data_array Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data_array[44:50] += np.array([Amp1_rsqrd, Amp2_rsqrd, Amp3_rsqrd, Phase1_rsqrd, Phase2_rsqrd, Phase3_rsqrd]) #Create intial guessed y-values (for graphing) k1_guess = data_array[11] k2_guess = data_array[12] k3_guess = data_array[13] k4_guess = data_array[14] b1_guess = data_array[15] b2_guess = data_array[16] b3_guess = data_array[17] F_guess = data_array[18] m1_guess = data_array[19] m2_guess = data_array[20] m3_guess = data_array[21] c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts if scaled: fig.suptitle('Trimer Resonator: Amplitude and Phase (Scaled)', fontsize=16) else: fig.suptitle('Trimer Resonator: Amplitude and Phase (Not Scaled)', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data_array '''Begin Work - Does scaling the residuals change anything?''' # import pandas as pd # e = 0 # force_all = False # fix_F = False #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params # params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] # params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] # #Get the data (and the graphs) # scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') # not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') # with pd.ExcelWriter('Scaling_Amp_Phase_Residuals.xlsx', engine='xlsxwriter') as writer: # dfscaled = pd.DataFrame(scaled_dict) # dfnotscaled = pd.DataFrame(not_scaled_dict) # dfscaled.to_excel(writer, sheet_name='Scaled', index=False) # dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file From af58188c950a882f535394a4476da52176198caa Mon Sep 17 00:00:00 2001 From: vivarose Date: Tue, 25 Feb 2025 23:02:03 -0500 Subject: [PATCH 096/101] update name of folder to save files to --- Algebraic Approach Simulated Two Coupled Resonators.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 3c233e2..3a8d382 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -48,7 +48,7 @@ "\n", "sns.set_context('paper')\n", "\n", - "savefolder = r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\simulation_export'\n", + "savefolder = r'G:\\Shared drives\\Horowitz Lab Notes\\Horowitz, Viva - notes and files\\Validating NetMAP simulated data (public share)'\n", "saving = True\n", "os.chdir(savefolder)\n", "\n", From c207dd311988dd5d2ebd731ae20de4e26bf3dcf7 Mon Sep 17 00:00:00 2001 From: vivarose Date: Tue, 25 Feb 2025 23:03:21 -0500 Subject: [PATCH 097/101] Add resonatorsystem 16: weakly coupled dimer --- ...ach Simulated Two Coupled Resonators.ipynb | 171 +++++++----------- 1 file changed, 64 insertions(+), 107 deletions(-) diff --git a/Algebraic Approach Simulated Two Coupled Resonators.ipynb b/Algebraic Approach Simulated Two Coupled Resonators.ipynb index 3a8d382..eac7168 100644 --- a/Algebraic Approach Simulated Two Coupled Resonators.ipynb +++ b/Algebraic Approach Simulated Two Coupled Resonators.ipynb @@ -133,6 +133,7 @@ "b2_set = np.nan\n", "forceboth = False\n", "\n", + "\n", "\"\"\"#Use functions to make matrices of amplitude and phase for each resonator (with noise)\n", "#define set values (sandbox version.)\n", "resonatorsystem = 1\n", @@ -201,15 +202,18 @@ "maxfreq = 5\n", "\"\"\"\n", "\n", - "\"\"\"### heavily damped monomer\n", + "\"\"\"\n", + "### heavily damped monomer\n", "MONOMER = True\n", "resonatorsystem = 5\n", "m1_set = 4\n", "b1_set = 8\n", "k1_set = 9\n", "F_set = 1\n", - "noiselevel = 10\"\"\"\n", - "\n", + "noiselevel = 10\n", + "minfreq = .01\n", + "maxfreq = 5\n", + "\"\"\"\n", "\n", "\"\"\"\n", "# FORCEBOTH true or false?\n", @@ -249,6 +253,7 @@ "MONOMER = False\n", "forceboth= False\n", "\"\"\"\n", + "\n", "\"\"\"\n", "## well-separated dimer, 1D then 2D, then 3D. Weakly coupled dimer #3\n", "## But not very accurate.\n", @@ -289,7 +294,7 @@ "\"\"\"\n", "\n", "\n", - "\n", + "\"\"\"\n", "## Well-separated dimer / Medium coupled dimer #1 / Used for Figure 5.\n", "MONOMER = False\n", "resonatorsystem = 11\n", @@ -305,7 +310,7 @@ "forceboth= False\n", "minfreq = 0.1\n", "maxfreq = 5\n", - "#(but this is 3D for forceboth)\n", + "#(but this is 3D for forceboth)\"\"\"\n", "\n", "\n", "\"\"\"\n", @@ -373,6 +378,20 @@ "maxfreq = 150796447 # 21 MHz * (2 * pi) \n", "\"\"\"\n", "\n", + "# creating this in 2025-02 to try to get overlapping resonance peaks. Everyone would say this is weak coupling.\n", + "resonatorsystem = 16 \n", + "m1_set = 11\n", + "m2_set = 5\n", + "b1_set = 0.5\n", + "b2_set = 0.1\n", + "k1_set = 21\n", + "k2_set = 10\n", + "k12_set = .1\n", + "F_set = 1\n", + "MONOMER = False\n", + "forceboth= False\n", + "minfreq = 1.3843945877020478 - .4\n", + "maxfreq = 1.3843945877020478 + .4\n", "\n", "## Make calculations for this resonator system\n", "\n", @@ -441,6 +460,8 @@ "R2_real_amp_noiseless = realamp2(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth=forceboth)\n", "R2_im_amp_noiseless = imamp2(drive, k1_set, k2_set, k12_set, b1_set, b2_set, F_set, m1_set, m2_set, 0, forceboth=forceboth)\n", "\n", + "plt.plot(drive,R2_amp_noiseless)\n", + "\n", "usenoise = True\n", "\n", "## actually calculate the spectra\n", @@ -779,9 +800,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "#View vh array and assign variables to proper row vector\n", @@ -869,7 +888,7 @@ "\n", "plot_SVD_results(drive,R1_amp,R1_phase,R2_amp,R2_phase,df, K1, K2, K12, B1, B2, FD, M1, M2, \n", " vals_set = vals_set, MONOMER=MONOMER, forceboth=forceboth, labelfreqs=drive[p], overlay = False,\n", - " saving=saving) " + " saving=saving);" ] }, { @@ -893,9 +912,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "## what if the null-space is 2D?\n", @@ -983,7 +1000,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "print(\"2D nullspace\")\n", @@ -994,9 +1013,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "## What if it's 3D nullspace?\n", @@ -1189,12 +1206,8 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ "overlay = False\n", "figsizeoverride1 = None\n", @@ -1397,9 +1410,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "#sns.set_context('paper')\n", @@ -1523,9 +1534,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "print('noiselevel:', noiselevel)\n", @@ -1708,9 +1717,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "plotgrid = ((int(math.ceil((len(keylist)+1)/5))),5)\n", @@ -1909,9 +1916,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "fig, axs = plt.subplots(plotgrid[0], plotgrid[1], figsize = figsizefull)\n", @@ -1969,9 +1974,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "if MONOMER:\n", @@ -2593,9 +2596,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "print('Noiselevel: ' + str(noiselevel))\n", @@ -2827,9 +2828,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "figsize = (2,2)\n", @@ -3072,9 +3071,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "def powlaw(x, C, m): #****\n", @@ -3160,9 +3157,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(1.5,1.5))\n", @@ -3190,9 +3185,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib as mpl \n", @@ -3714,9 +3707,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "describeresonator(vals_set, MONOMER,forceboth=forceboth, noiselevel=noiselevel)\n", @@ -4175,9 +4166,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "maxsyserr_to_plot = 10\n", @@ -4567,9 +4556,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "maxsyserr_to_plot = 10\n", @@ -4915,9 +4902,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "symb = '.'\n", @@ -5229,9 +5214,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "describeresonator(vals_set = vals_set, MONOMER=MONOMER, noiselevel = noiselevel, forceboth = forceboth)\n", @@ -5546,9 +5529,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "# *****\n", @@ -5967,9 +5948,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "\"\"\"print('The most likely frequency pair to be 1d nullspace:')\"\"\"\n", @@ -6027,9 +6006,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "best_df = resultsdfsweep2freqorigmean.iloc[resultsdfsweep2freqorigmean['avgsyserr%_1D'].argmin()] # most likely to be good\n", @@ -6400,9 +6377,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "from matplotlib.ticker import AutoLocator\n", @@ -6486,9 +6461,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "figsize = (8,4)\n", @@ -6939,9 +6912,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "symb = '.'\n", @@ -7498,9 +7469,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "with pd.option_context('display.max_rows', None,):\n", @@ -7962,9 +7931,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "\"\"\" DOE experiment: vary the noiselevel, driving force, and number of frequencies.\n", @@ -8177,9 +8144,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "#display(resultsdoethreedf.transpose())\n", @@ -8252,9 +8217,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "doe5 = pyDOE2.fullfact([2,2,2,2,2]) \n", @@ -8819,9 +8782,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "# clearchoice is so named because it's the df of those experiments for which you better know 1D or 2D.\n", @@ -8837,9 +8798,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "#display(resultsdoechooseSNR.transpose())\n", @@ -8913,9 +8872,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "corr = resultsdoechooseSNR[llist3].corr()\n", @@ -9144,7 +9101,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.14" } }, "nbformat": 4, From 79196d8a1844bb338ef40b64057115234d2774f9 Mon Sep 17 00:00:00 2001 From: vivarose Date: Thu, 27 Feb 2025 01:20:55 -0500 Subject: [PATCH 098/101] MAINT: Clean up building dimer matrix in Zmatrix2resonators() --- NetMAP.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/NetMAP.py b/NetMAP.py index a19975c..e8e32cb 100644 --- a/NetMAP.py +++ b/NetMAP.py @@ -22,7 +22,14 @@ def Zmatrix2resonators(measurementdf, forceboth, frequencycolumn = 'drive', complexamplitude1 = 'R1AmpCom', complexamplitude2 = 'R2AmpCom', dtype=complex): - Zmatrix = [] + + ## Are both masses being pushed? or just the first? + if forceboth: + ff = -1 + else: + ff = 0 + + Zmatrix = [] # this would likely be more efficient as a numpy array. for rowindex in measurementdf.index: w = measurementdf[frequencycolumn][rowindex] #print(w) @@ -31,10 +38,7 @@ def Zmatrix2resonators(measurementdf, forceboth, # Matrix columns: m1, m2, b1, b2, k1, k2, k12, F1 Zmatrix.append([-w**2*np.real(ZZ1), 0, -w*np.imag(ZZ1), 0, np.real(ZZ1), 0, np.real(ZZ1)-np.real(ZZ2), -1]) Zmatrix.append([-w**2*np.imag(ZZ1), 0, w*np.real(ZZ1), 0, np.imag(ZZ1), 0, np.imag(ZZ1)-np.imag(ZZ2), 0]) - if forceboth: - Zmatrix.append([0, -w**2*np.real(ZZ2), 0, -w*np.imag(ZZ2), 0, np.real(ZZ2), np.real(ZZ2)-np.real(ZZ1), -1]) - else: - Zmatrix.append([0, -w**2*np.real(ZZ2), 0, -w*np.imag(ZZ2), 0, np.real(ZZ2), np.real(ZZ2)-np.real(ZZ1), 0]) + Zmatrix.append([0, -w**2*np.real(ZZ2), 0, -w*np.imag(ZZ2), 0, np.real(ZZ2), np.real(ZZ2)-np.real(ZZ1), ff]) Zmatrix.append([0, -w**2*np.imag(ZZ2), 0, w*np.real(ZZ2), 0, np.imag(ZZ2), np.imag(ZZ2)-np.imag(ZZ1), 0]) #display(Zmatrix) return np.array(Zmatrix, dtype=dtype) From ee3699ea7b832ae47174615af6cb54e39948ef06 Mon Sep 17 00:00:00 2001 From: lydiabull Date: Wed, 14 May 2025 15:29:11 -0400 Subject: [PATCH 099/101] Testing Run time with different frequencies I have edited the code to use timeit.timeit for timing the three functions called multiple_fit_amp_phase, multiple_fit_X_Y, and get_parameters_NetMAP. I have begun to carry out that testing as well. --- .DS_Store | Bin 8196 -> 8196 bytes trimer/Creating_graphs_with_data.py | 606 +++++++++++++++++++++++++ trimer/comparing_curvefit_types.py | 613 ++++++++++++++++++-------- trimer/curve_fitting_X_Y_all.py | 5 +- trimer/curve_fitting_amp_phase_all.py | 2 +- 5 files changed, 1042 insertions(+), 184 deletions(-) create mode 100644 trimer/Creating_graphs_with_data.py diff --git a/.DS_Store b/.DS_Store index ff04b24053a3dd2f42f4d4d1066bb618d0c67f38..3a1005914e54e0a4e269bc25e5169ae2cb8dbffd 100644 GIT binary patch delta 54 ycmZp1XmOa}¥U^hRb!e$Yp;c#A5=&IkbY(-Is2 delta 38 pcmZp1XmOa}&nUk!U^hRb{AM137i^Px#eYpKklf5J@fOM!0{{V>4Tt~$ diff --git a/trimer/Creating_graphs_with_data.py b/trimer/Creating_graphs_with_data.py new file mode 100644 index 0000000..fec83ce --- /dev/null +++ b/trimer/Creating_graphs_with_data.py @@ -0,0 +1,606 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Oct 22 11:09:41 2024 + +@author: Lydia Bullock +""" + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from Trimer_simulator import re1, re2, re3, im1, im2, im3, c1, t1, c2, t2, c3, t3 +import os +import math + +#Saves graphs +def save_figure(figure, folder_name, file_name): + # Create the folder if it does not exist + if not os.path.exists(folder_name): + os.makedirs(folder_name) + + # Save the figure to the folder + file_path = os.path.join(folder_name, file_name) + figure.savefig(file_path, bbox_inches = 'tight') + plt.close(figure) + +''' Redoing the histogram for 2269 systems with 1 trial ''' + +# #Recall the data from first sheet +# file_path1 = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/More Systems 1 Trial - Histograms/All_Systems_1_Trial_1.xlsx' +# array_amp_phase1 = pd.read_excel(file_path1, sheet_name = 'Polar').to_numpy() +# array_X_Y1 = pd.read_excel(file_path1, sheet_name = 'Cartesian').to_numpy() +# array_NetMAP1 = pd.read_excel(file_path1, sheet_name = 'NetMAP').to_numpy() + +# #Recall the data from second sheet +# file_path2 = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/More Systems 1 Trial - Histograms/All_Systems_1_Trial_2.xlsx' +# array_amp_phase2 = pd.read_excel(file_path2, sheet_name = 'Polar').to_numpy() +# array_X_Y2 = pd.read_excel(file_path2, sheet_name = 'Cartesian').to_numpy() +# array_NetMAP2 = pd.read_excel(file_path2, sheet_name = 'NetMAP').to_numpy() + +# #Pull out _bar for each type from first sheet +# amp_phase_error1 = array_amp_phase1[:,50] +# X_Y_error1 = array_X_Y1[:, 50] +# NetMAP_error1 = array_NetMAP1[:,44] + +# #Pull out _bar for each type from first sheet +# amp_phase_error2 = array_amp_phase2[:,50] +# X_Y_error2 = array_X_Y2[:, 50] +# NetMAP_error2 = array_NetMAP2[:,44] + +# #Concatenate +# all_polar_error = np.concatenate((amp_phase_error1, amp_phase_error2)) +# all_NetMAP_error = np.concatenate((NetMAP_error1, NetMAP_error2)) +# almost_all_cartesian_error = np.concatenate((X_Y_error1, X_Y_error2)) +# all_cartesian_error = almost_all_cartesian_error[almost_all_cartesian_error != np.max(almost_all_cartesian_error)] + + +# #Graph histogram of _bar for both curve fits + +# # Compute max of data and set the bin limits so all data is included on graph +# data_max = np.max(np.concatenate((all_cartesian_error, all_polar_error, all_NetMAP_error))) +# if data_max > 39: +# linearbins = np.linspace(0, data_max + 2,50) +# else: +# linearbins = np.linspace(0, 40, 50) + +# #Graph linear! +# fig = plt.figure(figsize=(5, 4)) +# plt.xlabel(r'$\overline{\langle e \rangle}$ (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.yticks(fontsize=14) +# plt.xticks(fontsize=14) +# plt.hist(all_cartesian_error , bins = linearbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green', histtype= 'step') +# plt.hist(all_polar_error , bins = linearbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue', histtype= 'step') +# plt.hist(all_NetMAP_error , bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red', histtype= 'step') +# plt.legend(loc='best', fontsize = 13) + +# plt.show() +# save_figure(fig, 'Final', ' Bar Lin Hist Total without Largest Value.pdf' ) + +# # Set the bin limits so all data is included on graph +# if data_max > 100: +# logbins = np.logspace(-2, math.log10(data_max)+0.1, 50) +# else: +# logbins = np.logspace(-2, 1.8, 50) + +# #Graph log! +# fig = plt.figure(figsize=(5, 4)) +# plt.xlabel(r'$\overline{\langle e \rangle}$ (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.xscale('log') +# plt.yticks(fontsize=14) +# plt.xticks(fontsize=14) +# plt.hist(all_cartesian_error , bins = logbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green', histtype= 'step', lw = 2) +# plt.hist(all_polar_error , bins = logbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue', histtype= 'step', lw = 2) +# plt.hist(all_NetMAP_error , bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red', histtype= 'step', lw = 2) +# plt.legend(loc='best', fontsize = 13) + +# plt.show() +# save_figure(fig, 'Final', ' Bar Log Hist Total without Largest Value.pdf' ) + +''' Redoing the histogram for 15 Systems - 10 freqs, better params ''' + +# #Recall the data +# amp_phase_e_bar = np.zeros(15) +# X_Y_e_bar = np.zeros(15) +# NetMAP_e_bar = np.zeros(15) + +# for i in range(15): +# file_path = f'/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/15 systems - 10 Freqs NetMAP & Better Parameters/Random_Automated_Guess_{i}.xlsx' +# array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() +# array_X_Y = pd.read_excel(file_path, sheet_name = 'X & Y').to_numpy() +# array_NetMAP = pd.read_excel(file_path, sheet_name = 'NetMAP').to_numpy() + +# #Pull out _bar for each trial and add to list +# amp_phase_e_bar[i] = array_amp_phase[0, 51] +# X_Y_e_bar[i] = array_X_Y[0, 51] +# NetMAP_e_bar[i] = array_NetMAP[0, 45] + +# #Graph! +# fig = plt.figure(figsize=(10, 6)) +# linearbins = np.linspace(0,48,50) +# plt.title('Average Systematic Error Across Parameters Then Trials', fontsize = 18) +# plt.xlabel(' (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.xticks(fontsize=14) +# plt.yticks(fontsize=14) +# plt.hist(X_Y_e_bar, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') +# plt.hist(amp_phase_e_bar, bins =linearbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') +# plt.hist(NetMAP_e_bar, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# plt.legend(loc='upper right', fontsize = 14) +# plt.show() + +# fig = plt.figure(figsize=(10, 6)) +# logbins = np.logspace(-2,1.5,50) +# plt.title('Average Systematic Error Across Parameters Then Trials', fontsize = 18) +# plt.xlabel(' (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.xscale('log') +# plt.xticks(fontsize=14) +# plt.yticks(fontsize=14) +# plt.hist(X_Y_e_bar, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') +# plt.hist(amp_phase_e_bar, bins = logbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') +# plt.hist(NetMAP_e_bar, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# plt.legend(loc='upper right', fontsize = 14) +# plt.show() + +''' Redoing the histogram for Case Study''' + +#Recall the data +file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP Better Params 1000 Trials/Case_Study_1000_Trials.xlsx' +array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() +array_X_Y = pd.read_excel(file_path, sheet_name = 'X & Y').to_numpy() +array_NetMAP = pd.read_excel(file_path, sheet_name = 'NetMAP').to_numpy() + +#Pull out for each type +amp_phase_error = array_amp_phase[:,50] +X_Y_error = array_X_Y[:, 50] +NetMAP_error = array_NetMAP[:,44] + +#Graph histograms! +linearbins = np.linspace(0,15,50) +fig = plt.figure(figsize=(5, 4)) +plt.xlabel(r'$\langle e \rangle$ (%)', fontsize = 16) +plt.ylabel('Counts', fontsize = 16) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.hist(X_Y_error, bins = linearbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green') +plt.hist(amp_phase_error, bins=linearbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue') +plt.hist(NetMAP_error, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +plt.legend(loc='upper right', fontsize = 13) +plt.show() +save_figure(fig, 'Final', 'Case Study 1000 Lin Err Hist.pdf' ) + +logbins = np.logspace(-2,1.5,50) +fig = plt.figure(figsize=(5, 4)) +plt.xlabel(r'$\langle e \rangle$ (%)', fontsize = 16) +plt.ylabel('Counts', fontsize = 16) +plt.xscale('log') +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.hist(X_Y_error, bins = logbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green')#, histtype= 'step', lw = 2) +plt.hist(amp_phase_error, bins = logbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue')#, histtype= 'step', lw = 2) +plt.hist(NetMAP_error, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red')#, histtype= 'step', lw = 2) +plt.legend(loc='upper right', fontsize = 13) +plt.show() +save_figure(fig, 'Final', 'Case Study 1000 Log Err Hist.pdf' ) + +def nonlinearhistc(X, bins, thresh=3, verbose=False): + map_to_bins = np.digitize(X, bins) - 1 # Adjusting to match zero-indexing + r = np.zeros(len(bins) - 1) # Adjusted to match the number of intervals + + # Populate counts for each bin + for i in map_to_bins: + if 0 <= i < len(r): + r[i] += 1 # count for bin i. + + if verbose: + print(f"Counts per bin: {r}") + + # Normalize by bin width + probabilitydensity = np.zeros(len(bins) - 1) + area = 0 + thinbincount = 0 + + for i in range(len(bins) - 1): # iterate through bins + if r[i] <= 1: + thinbincount += 1 + thisbinwidth = bins[i + 1] - bins[i] + probabilitydensity[i] = r[i] / thisbinwidth + area += probabilitydensity[i] * thisbinwidth # calculate total area + + print('Divide by area to make P dens. Area:', area) + + if thinbincount > thresh: + print(f"Warning: too many bins for data, thinbincount={thinbincount}") + elif verbose: + print(f"thinbincount={thinbincount}") + + # Normalize area + normedprobabilitydensity = [eachPdens / area + for eachPdens in probabilitydensity] + return normedprobabilitydensity, map_to_bins + +# # Graph probability densities +# bins_i_want = np.logspace(-2, 1.5, 100) + +# normprobXY, map_to_bins = nonlinearhistc(X_Y_error, bins_i_want) +# normprobampphase, map_to_bins = nonlinearhistc(amp_phase_error, bins_i_want) +# normprobNetMAP, map_to_bins = nonlinearhistc(NetMAP_error, bins_i_want) + +# plt.figure(figsize=(10, 6)) + +# plt.loglog(bins_i_want[:-1], normprobXY , '.', color='green', alpha = 0.5, label = 'Cartesian') +# plt.loglog(bins_i_want[:-1], normprobampphase ,'.', color='blue', alpha = 0.5, label = 'Polar') +# plt.loglog(bins_i_want[:-1], normprobNetMAP , '.', color='red', alpha = 0.5, label = 'NetMAP') + +# plt.xlabel(' (%)', fontsize=16) +# plt.ylabel('Normalized Probability Density', fontsize=16) +# plt.title('Normalized Probability Density of Average Systematic Error Across Parameters') +# plt.legend(loc='upper center', fontsize = 14) +# plt.show() + + + +'''Creating graphs - one example + Using the case study data (10 freq/better params 1000 trials), trial 1. + ''' + +# #Recall the data +# file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP Better Params 1000 Trials/Case_Study_1000_Trials.xlsx' +# array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() +# array_X_Y = pd.read_excel(file_path, sheet_name = 'X & Y').to_numpy() + +# #True and guessed parameters +# true_params = array_amp_phase[1,:11] +# guess_params = array_amp_phase[1,11:22] +# freq = np.linspace(0.001, 4, 800) +# freq1 = np.linspace(0.001, 4, 700) + +# #The recovered parameters +# recovered_params_amp_phase = array_amp_phase[1,22:33] +# recovered_params_X_Y = array_X_Y[1,22:33] + +# #Error for each parameter from Amp/Phase Plots +# e_k1_amp = array_amp_phase[:, 33] +# e_k2_amp = array_amp_phase[:, 34] +# e_k3_amp = array_amp_phase[:, 35] +# e_k4_amp = array_amp_phase[:, 36] +# e_b1_amp = array_amp_phase[:, 37] +# e_b2_amp = array_amp_phase[:, 38] +# e_b3_amp = array_amp_phase[:, 39] +# e_m1_amp = array_amp_phase[:, 41] +# e_m2_amp = array_amp_phase[:, 42] +# e_m3_amp = array_amp_phase[:, 43] + +# #Error for each parameter from X/Y Plots +# e_k1_XY = array_X_Y[:, 33] +# e_k2_XY = array_X_Y[:, 34] +# e_k3_XY = array_X_Y[:, 35] +# e_k4_XY = array_X_Y[:, 36] +# e_b1_XY = array_X_Y[:, 37] +# e_b2_XY = array_X_Y[:, 38] +# e_b3_XY = array_X_Y[:, 39] +# e_m1_XY = array_X_Y[:, 41] +# e_m2_XY = array_X_Y[:, 42] +# e_m3_XY = array_X_Y[:, 43] + +# #Total error +# err_amp_phase = array_amp_phase[:,50] +# err_X_Y = array_X_Y[:,50] + +# #1 - R^2 values +# amp1_1minusR2 = 1 - array_amp_phase[:,44] +# amp2_1minusR2 = 1 - array_amp_phase[:,45] +# amp3_1minusR2 = 1 - array_amp_phase[:,46] +# phase1_1minusR2 = 1 - array_amp_phase[:,47] +# phase2_1minusR2 = 1 - array_amp_phase[:,48] +# phase3_1minusR2 = 1 - array_amp_phase[:,49] + +# X1_1minusR2 = 1 - array_X_Y[:,44] +# X2_1minusR2 = 1 - array_X_Y[:,45] +# X3_1minusR2 = 1 - array_X_Y[:,46] +# Y1_1minusR2 = 1 - array_X_Y[:,47] +# Y2_1minusR2 = 1 - array_X_Y[:,48] +# Y3_1minusR2 = 1 - array_X_Y[:,49] + +'''Box plots of recovered parameter spread - only 50 trials''' +# plt.boxplot([e_k1_amp, e_k2_amp, e_k3_amp, e_k4_amp, e_b1_amp, e_b2_amp, e_b3_amp, e_m1_amp, e_m2_amp, e_m3_amp], positions=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) +# plt.xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], ['k1', 'k2', 'k3', 'k4', 'b1', 'b2', 'b3', 'm1', 'm2', 'm3']) +# plt.xlabel('Parameters') +# plt.ylabel('Error (%)') +# plt.title('Amplitude and Phase') +# plt.savefig('parameter_box_plot.pdf') +# plt.show() + +''' How does error compare to 1-R^2?''' +#Amp, Phase +# fig = plt.figure(figsize=(16,8)) +# gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) +# ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=False, sharey='row') + +# ax1.plot(amp1_1minusR2, err_amp_phase,'ro', alpha=0.5, markersize=5.5) +# ax2.plot(amp2_1minusR2, err_amp_phase,'bo', alpha=0.5, markersize=5.5) +# ax3.plot(amp3_1minusR2, err_amp_phase,'go', alpha=0.5, markersize=5.5) +# ax4.plot(phase1_1minusR2, err_amp_phase,'ro', alpha=0.5, markersize=5.5) +# ax5.plot(phase2_1minusR2, err_amp_phase,'bo', alpha=0.5, markersize=5.5) +# ax6.plot(phase3_1minusR2, err_amp_phase,'go', alpha=0.5, markersize=5.5) + +# ax1.set_title('Amp 1', fontsize=18) +# ax2.set_title('Amp 2', fontsize=18) +# ax3.set_title('Amp 3', fontsize=18) +# ax4.set_title('Phase 1', fontsize=18) +# ax5.set_title('Phase 2', fontsize=18) +# ax6.set_title('Phase 3', fontsize=18) +# ax1.set_ylabel(' (%)', fontsize=16) +# ax4.set_ylabel(' (%)', fontsize=16) +# ax4.set_xlabel('1-R^2', fontsize=16) +# ax5.set_xlabel('1-R^2', fontsize=16) +# ax6.set_xlabel('1-R^2', fontsize=16) + +# for ax in [ax1, ax2, ax3, ax4, ax5, ax6]: +# ax.set_xscale('log') +# ax.set_yscale('log') + +# plt.savefig('err_vs_rsquared_amp_phase.pdf') +# plt.show() + +#X and Y +# fig = plt.figure(figsize=(16,8)) +# gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) +# ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=False, sharey='row') + +# ax1.plot(X1_1minusR2, err_X_Y,'ro', alpha=0.2, markersize=5.5) +# ax2.plot(X2_1minusR2, err_X_Y,'bo', alpha=0.2, markersize=5.5) +# ax3.plot(X3_1minusR2, err_X_Y,'go', alpha=0.2, markersize=5.5) +# ax4.plot(Y1_1minusR2, err_X_Y,'ro', alpha=0.2, markersize=5.5) +# ax5.plot(Y2_1minusR2, err_X_Y,'bo', alpha=0.2, markersize=5.5) +# ax6.plot(Y3_1minusR2, err_X_Y,'go', alpha=0.2, markersize=5.5) + +# ax1.set_title('X 1', fontsize=18) +# ax2.set_title('X 2', fontsize=18) +# ax3.set_title('X 3', fontsize=18) +# ax4.set_title('Y 1', fontsize=18) +# ax5.set_title('Y 2', fontsize=18) +# ax6.set_title('Y 3', fontsize=18) +# ax1.set_ylabel(' (%)', fontsize=16) +# ax4.set_ylabel(' (%)', fontsize=16) +# ax4.set_xlabel('1-R^2', fontsize=16) +# ax5.set_xlabel('1-R^2', fontsize=16) +# ax6.set_xlabel('1-R^2', fontsize=16) + +# for ax in [ax1, ax2, ax3, ax4, ax5, ax6]: +# ax.set_xscale('log') +# ax.set_yscale('log') + +# plt.savefig('err_vs_rsquared_XY.pdf') +# plt.show() + +# '''Graphing Amp/Phase with addition of complex plots''' +# #Create the true data - not including complex noise (so not using curve1, etc functions) because I didn't save the exact noise +# #for each trial and also this is just for visualization so it doesn't matter so much because I have the recovered parameters regardless and the noise is not noticable on the graph +# Amp1 = c1(freq1, *true_params) +# Phase1 = t1(freq1, *true_params) +# Amp2 = c2(freq1, *true_params) +# Phase2 = t2(freq1, *true_params) +# Amp3 = c3(freq1, *true_params) +# Phase3 = t3(freq1, *true_params) +# X1 = re1(freq1, *true_params) +# Y1 = im1(freq1, *true_params) +# X2 = re2(freq1, *true_params) +# Y2 = im2(freq1, *true_params) +# X3 = re3(freq1, *true_params) +# Y3 = im3(freq1, *true_params) + +# #Create the initial guesses +# Amp1_guess = c1(freq, *guess_params) +# Phase1_guess = t1(freq, *guess_params) +# Amp2_guess = c2(freq, *guess_params) +# Phase2_guess = t2(freq, *guess_params) +# Amp3_guess = c3(freq, *guess_params) +# Phase3_guess = t3(freq, *guess_params) +# X1_guess = re1(freq, *guess_params) +# Y1_guess = im1(freq, *guess_params) +# X2_guess = re2(freq, *guess_params) +# Y2_guess = im2(freq, *guess_params) +# X3_guess = re3(freq, *guess_params) +# Y3_guess = im3(freq, *guess_params) + +# #Create the final fit! +# Amp1_fitted = c1(freq, *recovered_params_amp_phase) +# Phase1_fitted = t1(freq, *recovered_params_amp_phase) +# Amp2_fitted = c2(freq, *recovered_params_amp_phase) +# Phase2_fitted = t2(freq, *recovered_params_amp_phase) +# Amp3_fitted = c3(freq, *recovered_params_amp_phase) +# Phase3_fitted = t3(freq, *recovered_params_amp_phase) +# X1_fitted = re1(freq, *recovered_params_X_Y) +# Y1_fitted = im1(freq, *recovered_params_X_Y) +# X2_fitted = re2(freq, *recovered_params_X_Y) +# Y2_fitted = im2(freq, *recovered_params_X_Y) +# X3_fitted = re3(freq, *recovered_params_X_Y) +# Y3_fitted = im3(freq, *recovered_params_X_Y) + +# # Begin graphing for Amp and Phase +# fig = plt.figure(figsize=(16,11)) +# gs = fig.add_gridspec(3, 3, hspace=0.4, wspace=0.05) + +# ax1 = fig.add_subplot(gs[0, 0]) +# ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) +# ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) +# ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) +# ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) +# ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) +# ax7 = fig.add_subplot(gs[2, 0], aspect='equal') +# ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7, aspect='equal') +# ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7, aspect='equal') + +# #original data +# ax1.plot(freq1, Amp1,'ro-', alpha=0.5, markersize=5.5, label = 'Data') +# ax2.plot(freq1, Amp2,'bo-', alpha=0.5, markersize=5.5, label = 'Data') +# ax3.plot(freq1, Amp3,'go-', alpha=0.5, markersize=5.5, label = 'Data') +# ax4.plot(freq1, Phase1,'ro-', alpha=0.5, markersize=5.5, label = 'Data') +# ax5.plot(freq1, Phase2,'bo-', alpha=0.5, markersize=5.5, label = 'Data') +# ax6.plot(freq1, Phase3,'go-', alpha=0.5, markersize=5.5, label = 'Data') +# ax7.plot(X1,Y1,'ro-', alpha=0.5, markersize=5.5, label = 'Data') +# ax8.plot(X2,Y2,'bo-', alpha=0.5, markersize=5.5, label = 'Data') +# ax9.plot(X3,Y3,'go-', alpha=0.5, markersize=5.5, label = 'Data') + +# #fitted curves +# ax1.plot(freq, Amp1_fitted,'c-', label='Fit', lw=2.5) +# ax2.plot(freq, Amp2_fitted,'r-', label='Fit', lw=2.5) +# ax3.plot(freq, Amp3_fitted,'m-', label='Fit', lw=2.5) +# ax4.plot(freq, Phase1_fitted,'c-', label='Fit', lw=2.5) +# ax5.plot(freq, Phase2_fitted,'r-', label='Fit', lw=2.5) +# ax6.plot(freq, Phase3_fitted,'m-', label='Fit', lw=2.5) +# ax7.plot(X1_fitted, Y1_fitted, 'c-', label='Fit', lw=2.5) +# ax8.plot(X2_fitted, Y2_fitted, 'r-', label='Fit', lw=2.5) +# ax9.plot(X3_fitted, Y3_fitted, 'm-', label='Fit', lw=2.5) + +# #inital guess curves +# ax1.plot(freq, Amp1_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax2.plot(freq, Amp2_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax3.plot(freq, Amp3_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax4.plot(freq, Phase1_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax5.plot(freq, Phase2_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax6.plot(freq, Phase3_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax7.plot(X1_guess, Y1_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax8.plot(X2_guess, Y2_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax9.plot(X3_guess, Y3_guess, color='#4682B4', linestyle='dashed', label='Guess') + + +# #Graph parts +# fig.suptitle('Trimer Resonator: Amplitude and Phase', fontsize=32) +# ax1.set_title('Mass 1', fontsize=26) +# ax2.set_title('Mass 2', fontsize=26) +# ax3.set_title('Mass 3', fontsize=26) +# ax1.set_ylabel('Amplitude', fontsize=26) +# ax4.set_ylabel('Phase', fontsize=26) +# ax7.set_ylabel('Imaginary', fontsize=26) + +# ax1.label_outer() +# ax2.label_outer() +# ax3.label_outer() +# ax5.tick_params(labelleft=False) +# ax6.tick_params(labelleft=False) +# ax7.label_outer() +# ax8.label_outer() +# ax9.label_outer() + +# ax4.set_xlabel('Frequency', fontsize=26) +# ax5.set_xlabel('Frequency', fontsize=26) +# ax6.set_xlabel('Frequency', fontsize=26) +# ax7.set_xlabel('Real', fontsize=26) +# ax8.set_xlabel('Real', fontsize=26) +# ax9.set_xlabel('Real', fontsize=26) + +# ax1.legend(fontsize=20) +# ax2.legend(fontsize=20) +# ax3.legend(fontsize=20) +# # ax4.legend(fontsize=20) +# # ax5.legend(fontsize=20) +# # ax6.legend(fontsize=20, loc = 'upper right') +# # ax7.legend(fontsize=20, bbox_to_anchor=(1, 1)) +# # ax8.legend(fontsize=20, bbox_to_anchor=(1, 1)) +# # ax9.legend(fontsize=20, bbox_to_anchor=(1, 1)) + +# axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9] +# for ax in axes: +# ax.tick_params(axis='both', labelsize=18) # Change tick font size + +# plt.show() + +# # Begin graphing for X and Y +# fig = plt.figure(figsize=(16,11)) +# gs = fig.add_gridspec(3, 3, hspace=0.5, wspace=0.05) + +# ax1 = fig.add_subplot(gs[0, 0]) +# ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) +# ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1) +# ax4 = fig.add_subplot(gs[1, 0], sharex=ax1) +# ax5 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax4) +# ax6 = fig.add_subplot(gs[1, 2], sharex=ax1, sharey=ax4) +# ax7 = fig.add_subplot(gs[2, 0], aspect='equal') +# ax8 = fig.add_subplot(gs[2, 1], sharex=ax7, sharey=ax7, aspect='equal') +# ax9 = fig.add_subplot(gs[2, 2], sharex=ax7, sharey=ax7, aspect='equal') + +# #original data +# ax1.plot(freq1, X1,'ro-', alpha=0.5, markersize=5.5, label = 'Data') +# ax2.plot(freq1, X2,'bo-', alpha=0.5, markersize=5.5, label = 'Data') +# ax3.plot(freq1, X3,'go-', alpha=0.5, markersize=5.5, label = 'Data') +# ax4.plot(freq1, Y1,'ro-', alpha=0.5, markersize=5.5, label = 'Data') +# ax5.plot(freq1, Y2,'bo-', alpha=0.5, markersize=5.5, label = 'Data') +# ax6.plot(freq1, Y3,'go-', alpha=0.5, markersize=5.5, label = 'Data') +# ax7.plot(X1,Y1,'ro-', alpha=0.5, markersize=5.5, label = 'Data') +# ax8.plot(X2,Y2,'bo-', alpha=0.5, markersize=5.5, label = 'Data') +# ax9.plot(X3,Y3,'go-', alpha=0.5, markersize=5.5, label = 'Data') + +# #fitted curves +# ax1.plot(freq, X1_fitted,'c-', label='Fit', lw=2.5) +# ax2.plot(freq, X2_fitted,'r-', label='Fit', lw=2.5) +# ax3.plot(freq, X3_fitted,'m-', label='Fit', lw=2.5) +# ax4.plot(freq, Y1_fitted,'c-', label='Fit', lw=2.5) +# ax5.plot(freq, Y2_fitted,'r-', label='Fit', lw=2.5) +# ax6.plot(freq, Y3_fitted,'m-', label='Fit', lw=2.5) +# ax7.plot(X1_fitted, Y1_fitted, 'c-', label='Fit', lw=2.5) +# ax8.plot(X2_fitted, Y2_fitted, 'r-', label='Fit', lw=2.5) +# ax9.plot(X3_fitted, Y3_fitted, 'm-', label='Fit', lw=2.5) + +# #inital guess curves +# ax1.plot(freq, X1_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax2.plot(freq, X2_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax3.plot(freq, X3_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax4.plot(freq, Y1_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax5.plot(freq, Y2_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax6.plot(freq, Y3_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax7.plot(X1_guess, Y1_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax8.plot(X2_guess, Y2_guess, color='#4682B4', linestyle='dashed', label='Guess') +# ax9.plot(X3_guess, Y3_guess, color='#4682B4', linestyle='dashed', label='Guess') + +# #Graph parts +# fig.suptitle('Trimer Resonator: Real and Imaginary', fontsize=24) +# ax1.set_title('Mass 1', fontsize=26) +# ax2.set_title('Mass 2', fontsize=26) +# ax3.set_title('Mass 3', fontsize=26) +# ax1.set_ylabel('Real', fontsize=26) +# ax4.set_ylabel('Imaginary', fontsize=26) +# ax7.set_ylabel('Imaginary', fontsize=26) + +# ax1.label_outer() +# ax2.label_outer() +# ax3.label_outer() +# ax5.tick_params(labelleft=False) +# ax6.tick_params(labelleft=False) +# ax7.label_outer() +# ax8.label_outer() +# ax9.label_outer() + +# ax4.set_xlabel('Frequency', fontsize=26) +# ax5.set_xlabel('Frequency', fontsize=26) +# ax6.set_xlabel('Frequency', fontsize=26) +# ax7.set_xlabel('Real', fontsize=26) +# ax8.set_xlabel('Real', fontsize=26) +# ax9.set_xlabel('Real', fontsize=26) + +# ax1.legend(fontsize=20) +# ax2.legend(fontsize=20) +# ax3.legend(fontsize=20) +# # ax4.legend(fontsize=13) +# # ax5.legend(fontsize=13) +# # ax6.legend(fontsize=13) +# # ax7.legend(fontsize=13, loc='upper left', bbox_to_anchor=(1, 1)) +# # ax8.legend(fontsize=13, loc='upper left', bbox_to_anchor=(1, 1)) +# # ax9.legend(fontsize=13, loc='upper left', bbox_to_anchor=(1, 1)) + +# axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9] +# for ax in axes: +# ax.tick_params(axis='both', labelsize=18) # Change tick font size + + +# plt.show() + + + + + + diff --git a/trimer/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py index d48803a..48fcb85 100644 --- a/trimer/comparing_curvefit_types.py +++ b/trimer/comparing_curvefit_types.py @@ -21,14 +21,18 @@ from Trimer_NetMAP import Zmatrix, unnormalizedparameters, normalize_parameters_1d_by_force import warnings import time +import timeit +import statistics ''' Functions contained: complex_noise - creates noise, e syserr - Calculates systematic error - generate_random_system - Randomly generates parameters for system. All parameter values btw 0.1 and 10 + generate_random_system - Randomly generates parameters for system. Parameter values btw 0.1 and 10 for all but the coefficients of friction which is between 0.1 and 1. plot_guess - Used for the Case Study. Plots just the data and the guessed parameters curve. No curve fitting. automate_guess - Randomly generates guess parameters within a certain percent of the true parameters save_figure - Saves figures to a folder of your naming choice. Also allows you to name the figure whatever. + timeit_function - Uses the timeit package to time how long a function takes to run. + - Runs it multiple times (number of your choosing) and returns the average time and std dev for more accurate results. get_parameters_NetMAP - Recovers parameters for a system given the guessed parameters run_trials - Runs a set number of trials for one system, graphs curvefit result, puts data and averages into spreadsheet, returns _bar for both types of curves @@ -41,7 +45,10 @@ def complex_noise(n, noiselevel): global complexamplitudenoisefactor complexamplitudenoisefactor = 0.0005 - return noiselevel* complexamplitudenoisefactor * np.random.randn(n,) + return noiselevel* complexamplitudenoisefactor * np.random.randn(n,) +# np.random.radn returns a number from a gaussian distribution with variance 1 and mean 0 +# noiselevel* complexamplitudenoisefactor is standard deviation + def syserr(x_found,x_set, absval = True): with warnings.catch_warnings(): @@ -52,7 +59,7 @@ def syserr(x_found,x_set, absval = True): else: return se -#Randomly generates parameters of a system. All parameters between 0.1 and 10 +#Randomly generates parameters of a system. k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 def generate_random_system(): system_params = [] for i in range(11): @@ -247,6 +254,20 @@ def save_figure(figure, folder_name, file_name): figure.savefig(file_path, bbox_inches = 'tight') plt.close(figure) +# runs > 1 if you want to run one function several times to get the average time +def timeit_function(func, args=None, kwargs=None, runs=7): + args = args or () + kwargs = kwargs or {} + + times = [] + for _ in range(runs): + t = timeit.timeit(lambda: func(*args, **kwargs), number=1) + times.append(t) + + mean_time = statistics.mean(times) + std_dev = statistics.stdev(times) if runs > 1 else 0.0 + return mean_time, std_dev, times + def get_parameters_NetMAP(frequencies, params_guess, params_correct, e, force_all): #Getting the complex amplitudes (data) with a function from Trimer_simulator @@ -266,7 +287,7 @@ def get_parameters_NetMAP(frequencies, params_guess, params_correct, e, force_al #Put everything into a np array #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 - data_array = np.zeros(45) + data_array = np.zeros(46) #44 elements are generated in this code, but I leave the last entry empty because I want to time how long it takes the function to run in other code, so I'm giving the array space to add the time if necessary data_array[:11] += np.array(params_correct) data_array[11:22] += np.array(params_guess) #Adding the recovered parameters and fixing the order @@ -276,14 +297,14 @@ def get_parameters_NetMAP(frequencies, params_guess, params_correct, e, force_al data_array[30:33] += np.array(final_tri[:3]) #adding systematic error calculations syserr_result = syserr(data_array[22:33], data_array[:11]) - data_array[33:44] += np.array(syserr_result) - data_array[-1] += np.sum(data_array[33:44]/10) #dividing by 10 because we aren't counting the error in Force because it is 0 + data_array[33:44] += np.array(syserr_result) #individual errors for each parameter + data_array[-2] += np.sum(data_array[33:44]/10) #this is average error ... dividing by 10 (not 11) because we aren't counting the error in Force because the error is 0 return data_array #Runs a set number of trials for one system, graphs curvefit result, # puts data and averages into spreadsheet, returns avg_e arrays and _bar for all types of curves -def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, num_trials, excel_file_name, graph_folder_name): +def run_trials(true_params, guessed_params, freqs_NetMAP, freqs_curvefit, length_noise_NetMAP, length_noise_curvefit, num_trials, excel_file_name, graph_folder_name): #Needed for calculating e_bar and for graphing - also these are things that will be returned avg_e1_array = np.zeros(num_trials) #Polar @@ -291,76 +312,123 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n avg_e3_array = np.zeros(num_trials) #NetMAP #Needed to add all the data to a spreadsheet at the end - all_data1 = np.empty((0, 51)) #Polar - all_data2 = np.empty((0, 51)) #Cartesian - all_data3 = np.empty((0, 45)) #NetMAP - - with pd.ExcelWriter(excel_file_name, engine='xlsxwriter') as writer: - for i in range(num_trials): - - #Create noise - e = complex_noise(300, 2) - - ##For NetMAP - #create error - e_NetMAP = complex_noise(length_noise_NetMAP,2) + all_data1 = np.empty((0, 52)) #Polar + all_data2 = np.empty((0, 52)) #Cartesian + all_data3 = np.empty((0, 46)) #NetMAP + + #FOR ONLY when I'm running 1 trial per system: + # with pd.ExcelWriter(excel_file_name, engine='xlsxwriter') as writer: + + #Creating arrays to store the time it takes each curvefit/NetMAP function to run - will average them at the end + times_polar = np.empty(num_trials) + times_cartesian = np.empty(num_trials) + times_NetMAP = np.empty(num_trials) + + #For more than 1 trial per system: + for i in range(num_trials): - #Get the data! - array1 = multiple_fit_amp_phase(guessed_params, true_params, e, False, True, False, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force - array2 = multiple_fit_X_Y(guessed_params, true_params, e, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force - array3 = get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False) #NetMAP - - #Find (average across parameters) for each trial and add to arrays - avg_e1_array[i] += array1[-1] - avg_e2_array[i] += array2[-1] - avg_e3_array[i] += array3[-1] - - #Stack to the larger array - all_data1 = np.vstack((all_data1, array1)) - all_data2 = np.vstack((all_data2, array2)) - all_data3 = np.vstack((all_data3, array3)) - - - avg_e1_bar = math.exp(sum(np.log(avg_e1_array))/num_trials) - avg_e2_bar = math.exp(sum(np.log(avg_e2_array))/num_trials) - avg_e3_bar = math.exp(sum(np.log(avg_e3_array))/num_trials) + #Create noise - noise level 2 + e = complex_noise(length_noise_curvefit, 2) + ##For NetMAP + #create noise - noise level 2 + e_NetMAP = complex_noise(length_noise_NetMAP,2) - #For labeling the excel sheet - param_names = ['k1_true', 'k2_true', 'k3_true', 'k4_true', - 'b1_true', 'b2_true', 'b3_true', - 'F_true', 'm1_true', 'm2_true', 'm3_true', - 'k1_guess', 'k2_guess', 'k3_guess', 'k4_guess', - 'b1_guess', 'b2_guess', 'b3_guess', - 'F_guess', 'm1_guess', 'm2_guess', 'm3_guess', - 'k1_recovered', 'k2_recovered', 'k3_recovered', 'k4_recovered', - 'b1_recovered', 'b2_recovered', 'b3_recovered', - 'F_recovered', 'm1_recovered', 'm2_recovered', 'm3_recovered', - 'e_k1', 'e_k2', 'e_k3', 'e_k4', - 'e_b1', 'e_b2', 'e_b3', 'e_F', - 'e_m1', 'e_m2', 'e_m3', - 'Amp1_rsqrd', 'Amp2_rsqrd', 'Amp3_rsqrd', - 'Phase1_rsqrd', 'Phase2_rsqrd', 'Phase3_rsqrd', ''] + #Get the data! + array1 = multiple_fit_amp_phase(guessed_params, true_params, e, freqs_curvefit, False, True, False, graph_folder_name, f'Polar_fig_{i}') #Polar, Fixed force + array2 = multiple_fit_X_Y(guessed_params, true_params, e, freqs_curvefit, False, True, graph_folder_name, f'Cartesian_fig_{i}') #Cartesian, Fixed force + array3 = get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False) #NetMAP - #Turn the final data arrays into a dataframe so they can be written to excel - dataframe1 = pd.DataFrame(all_data1, columns=param_names) - dataframe2 = pd.DataFrame(all_data2, columns=param_names) - dataframe3 = pd.DataFrame(all_data3, columns=param_names[:44]+[param_names[-1]]) + #Time how long it takes to get the data and add the time to the larger array: + #NOTE THAT - if you are outputting graphs within the curve fitting functions, the run time will be longer than it takes to get the actual data + #that is, only use the timeit functions below when show_curvefit_graphs = False + t_polar = timeit.timeit(lambda: multiple_fit_amp_phase(guessed_params, true_params, e, freqs_curvefit, False, True, False, graph_folder_name, f'Polar_fig_{i}'), number=1) + times_polar[i] = t_polar + t_cartesian = timeit.timeit(lambda: multiple_fit_X_Y(guessed_params, true_params, e, freqs_curvefit, False, True, graph_folder_name, f'Cartesian_fig_{i}'), number=1) + times_cartesian[i] = t_cartesian + t_NetMAP = timeit.timeit(lambda: get_parameters_NetMAP(freqs_NetMAP, guessed_params, true_params, e_NetMAP, False), number=1) + times_NetMAP[i] = t_NetMAP - #Add _bar values to data frame - dataframe1.at[0,'_bar'] = avg_e1_bar - dataframe2.at[0,'_bar'] = avg_e2_bar - dataframe3.at[0,'_bar'] = avg_e3_bar + #add each individual time to the array for each method so it can be stored with the data for each trial + #array1, array2, array3 to be stacked into the larger all_data arrays + array1[-1] = t_polar + array2[-1] = t_cartesian + array3[-1] = t_NetMAP - dataframe1.to_excel(writer, sheet_name='Amp & Phase', index=False) - dataframe2.to_excel(writer, sheet_name='X & Y', index=False) - dataframe3.to_excel(writer, sheet_name='NetMAP', index=False) + #Pull out (average across parameters) for each trial and add to arrays for e_bar calculation later + #it is the second the last entry in the array (times is the last) + avg_e1_array[i] += array1[-2] + avg_e2_array[i] += array2[-2] + avg_e3_array[i] += array3[-2] - # return avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar - return avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar - - -''' Begin work here. Case Study. ''' + #Stack each trial's data to the larger array + all_data1 = np.vstack((all_data1, array1)) + all_data2 = np.vstack((all_data2, array2)) + all_data3 = np.vstack((all_data3, array3)) + + #Calculate average time it took for each method to recover parameters, along with standard deviation + mean_time_polar = statistics.mean(times_polar) + std_dev_polar = statistics.stdev(times_polar) + mean_time_cartesian = statistics.mean(times_cartesian) + std_dev_cartesian = statistics.stdev(times_cartesian) + mean_time_NetMAP = statistics.mean(times_NetMAP) + std_dev_NetMAP = statistics.stdev(times_NetMAP) + + #Calculate average error across parameters + avg_e1_bar = math.exp(sum(np.log(avg_e1_array))/num_trials) + avg_e2_bar = math.exp(sum(np.log(avg_e2_array))/num_trials) + avg_e3_bar = math.exp(sum(np.log(avg_e3_array))/num_trials) + + + #For labeling the excel sheet + param_names = ['k1_true', 'k2_true', 'k3_true', 'k4_true', + 'b1_true', 'b2_true', 'b3_true', + 'F_true', 'm1_true', 'm2_true', 'm3_true', + 'k1_guess', 'k2_guess', 'k3_guess', 'k4_guess', + 'b1_guess', 'b2_guess', 'b3_guess', + 'F_guess', 'm1_guess', 'm2_guess', 'm3_guess', + 'k1_recovered', 'k2_recovered', 'k3_recovered', 'k4_recovered', + 'b1_recovered', 'b2_recovered', 'b3_recovered', + 'F_recovered', 'm1_recovered', 'm2_recovered', 'm3_recovered', + 'e_k1', 'e_k2', 'e_k3', 'e_k4', + 'e_b1', 'e_b2', 'e_b3', 'e_F', + 'e_m1', 'e_m2', 'e_m3', + 'Amp1_rsqrd', 'Amp2_rsqrd', 'Amp3_rsqrd', + 'Phase1_rsqrd', 'Phase2_rsqrd', 'Phase3_rsqrd', '', 'trial time'] + + #Turn the final data arrays into a dataframe so they can be written to excel + dataframe_polar = pd.DataFrame(all_data1, columns=param_names) + dataframe_cart = pd.DataFrame(all_data2, columns=param_names) + dataframe_net = pd.DataFrame(all_data3, columns=param_names[:44] + param_names[-2:]) #cutting out the 6 r-squared columns because those values can only be found for the curvefits + + #Add _bar values to data frame (one value for the whole system) + dataframe_polar.at[0,'_bar'] = avg_e1_bar + dataframe_cart.at[0,'_bar'] = avg_e2_bar + dataframe_net.at[0,'_bar'] = avg_e3_bar + + #Add the mean time and std dev to the data frame (one value each for the whole system) + dataframe_polar.at[0,'mean trial time'] = mean_time_polar + dataframe_polar.at[0,'std dev trial time'] = std_dev_polar + dataframe_cart.at[0,'mean trial time'] = mean_time_cartesian + dataframe_cart.at[0,'std dev trial time'] = std_dev_cartesian + dataframe_net.at[0,'mean trial time'] = mean_time_NetMAP + dataframe_net.at[0,'std dev trial time'] = std_dev_NetMAP + + + #FOR ONLY when I'm running 1 trial per system: + # dataframe_polar.to_excel(writer, sheet_name='Amp & Phase', index=False) + # dataframe_cart.to_excel(writer, sheet_name='X & Y', index=False) + # dataframe_net.to_excel(writer, sheet_name='NetMAP', index=False) + + # return avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar + + #For more than 1 trial per system: + return avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar, dataframe_polar, dataframe_cart, dataframe_net + + +''' Begin work here. Case Study. +Randomly generate a system, then graph the data (no noise) and make a guess of parameters based on visual accuracy of the curve. +Use this guess to curvefit to the data. NetMAP does not require this initial guess to function.''' # #Make parameters/initial guesses - [k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3] # #Note that right now we only scale/fix by F, so make sure to keep F correct in guesses @@ -410,8 +478,10 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n # #Curve fit with the guess made above and get average lists # #Will not do anything with _bar for a single case study # freqs_NetMAP = np.linspace(0.001, 4, 10) -# length_noise = 10 -# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 10, 'Case_Study.xlsx', 'Case Study Plots') +# freqs_curvefit = np.linspace(0.001, 4, 10) +# length_noise_NetMAP = 10 +# length_noise_curvefit = 10 +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, freqs_curvefit, length_noise_NetMAP, length_noise_curvefit 10, 'Case_Study.xlsx', 'Case Study Plots') # #Graph histogram of for curve fits @@ -425,7 +495,9 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n # plt.show() -''' Begin work here. Automated guesses. ''' +''' Begin work here. Automated guesses. Multiple systems. +Instead of manually guessing the intial parameters, guess is generated to be within a certain percentage of the true parameters. +Error across trials and across parameters is calculated. Error across parameters is graphed (e_bar) at the end to visualize error for all the systems on one graph.''' # avg_e1_bar_list = [] # avg_e2_bar_list = [] @@ -475,7 +547,9 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n # plt.show() # fig.savefig('_bar_Histogram.png') -''' Begin work here. Checking Worst System. ''' +''' Begin work here. Checking Worst System - System 0 from 15 Systems - 10 Freqs NetMAP. +Running the system with no noise to understand why recovered error was so bad. +''' ## System 0 from 15 Systems - 10 Freqs NetMAP ## Expecting there to be no error in recovery for everything @@ -500,136 +574,315 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, length_noise_NetMAP, n # plt.savefig('_Histogram_Sys0_no_error.png') '''Begin work here. Redoing Case Study - 10 Freqs Better Params with 1000 trials instead of 50 ''' +'''Additionally, I am going to use the same frequencies for all three methods of parameter recovery: + 300 or 10 evenly spaced frequencies from 0.001 to 4.''' +''' Note that all information saves to the same folder that this code is located in.''' +# #Recover the system information from a file on my computer # file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP & Better Parameters/Case_Study_10_Freqs_Better_Parameters.xlsx' # array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() # array_X_Y = pd.read_excel(file_path, sheet_name = 'X & Y').to_numpy() +# #These are the true and the guessed parameters for the system +# #Guessed parameters were the same ones guesssed by hand the first time we ran this case study # true_params = np.concatenate((array_amp_phase[1,:7], [array_amp_phase[1,10]], array_amp_phase[1,7:10])) # guessed_params = np.concatenate((array_amp_phase[1,11:18], [array_amp_phase[1,21]], array_amp_phase[1,18:21])) -# freq = np.linspace(0.001, 4, 300) +# #Create the frequencies that both NetMAP and the Curvefitting functions require +# #Note that if the number of frequencies are not the same, the noise must be adjusted +# # freq_curvefit = np.linspace(0.001, 4, 300) +# freq_curvefit = np.linspace(0.001, 4, 10) # freqs_NetMAP = np.linspace(0.001, 4, 10) -# length_noise = 10 +# length_noise_curvefit = 10 +# length_noise_NetMAP = 10 + +# #Run the trials (1000 in this case) +# #Currently saves saves all plots to a folder called "Case Study 1000 Trials Same Frequencies Plots" +# #(the excel name is not used here - it is only required when doing multiple systems with one trial per system) +# #returns average error across trials (e_bar) and parameters (e), and dataframes for all three methods that include all the information +# #there is only one e_bar for each when doing a case study, so it will not be used +# #NOTE: error is different every time, to simulate a real experiment +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar, dataframe_polar, dataframe_cart, dataframe_net = run_trials(true_params, guessed_params, freqs_NetMAP, freq_curvefit, length_noise_NetMAP, length_noise_curvefit, 1000, 'Second_Case_Study_1000_Trials_10_Frequencies.xlsx', 'Second Case Study 1000 Trials 10 Frequencies Plots') + +# #Save the new data to a new excel spreadsheet: +# with pd.ExcelWriter('Second_Case_Study_1000_Trials_10_Frequencies.xlsx', engine='xlsxwriter') as writer: +# dataframe_polar.to_excel(writer, sheet_name='Amp & Phase', index=False) +# dataframe_cart.to_excel(writer, sheet_name='X & Y', index=False) +# dataframe_net.to_excel(writer, sheet_name='NetMAP', index=False) + +# #Graph lin and log histograms of for both curve fits: + +# #Compute max of data and set the bin limits so all data is seen/included on graph +# data_max = max(avg_e1_array + avg_e2_array + avg_e3_array) +# if data_max > 39: +# linearbins = np.linspace(0, data_max + 2,50) +# else: +# linearbins = np.linspace(0, 40, 50) + +# #Graph linear plots +# fig = plt.figure(figsize=(5, 4)) +# plt.xlabel(' Bar (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.yticks(fontsize=14) +# plt.xticks(fontsize=14) +# plt.hist(avg_e1_array, bins = linearbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue') +# plt.hist(avg_e2_array, bins = linearbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green') +# plt.hist(avg_e3_array, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# plt.legend(loc='best', fontsize = 13) + +# plt.show() +# save_figure(fig, 'Second Case Study 1000 Trials 10 Frequencies', 'Linear Histogram') + +# # Set the bin limits so all data is seen/included on graph +# if data_max > 100: +# logbins = np.logspace(-2, math.log10(data_max)+0.25, 50) +# else: +# logbins = np.logspace(-2, 1.8, 50) + +# #Graph log! +# fig = plt.figure(figsize=(5, 4)) +# plt.xlabel(' Bar (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.xscale('log') +# plt.yticks(fontsize=14) +# plt.xticks(fontsize=14) +# plt.hist(avg_e1_array, bins = logbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue') +# plt.hist(avg_e2_array, bins = logbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green') +# plt.hist(avg_e3_array, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# plt.legend(loc='best', fontsize = 13) -# run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 1000, 'Case_Study_1000_Trials.xlsx', 'Case Study 1000 Trials Plots') +# plt.show() +# save_figure(fig, 'Second Case Study 1000 Trials 10 Frequencies', 'Logarithmic Histogram') + + +'''Begin work here. Case Study - 10 Freqs Better Params with 1000 trials + GOAL: graph runtime versus number of frequencies given to each method. + Create a for loop that varies frequencies from 2 to 300. (2 because that is the minimum required by NetMAP. 300 because that produces a very nice graph for curvefitting (and is what I have been using as a standard up until now.''' + +#Recover the system information from a file on my computer +file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP & Better Parameters/Case_Study_10_Freqs_Better_Parameters.xlsx' +array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() + +#These are the true and the guessed parameters for the system +#Guessed parameters were the same ones guesssed by hand the first time we ran this case study +true_params = np.concatenate((array_amp_phase[1,:7], [array_amp_phase[1,10]], array_amp_phase[1,7:10])) +guessed_params = np.concatenate((array_amp_phase[1,11:18], [array_amp_phase[1,21]], array_amp_phase[1,18:21])) + +#create array to store the run times for the given number of frequencies +#there will be a total of 98 different times since we start with 2 frequencies and end with 100 +run_times_polar = np.zeros(98) +run_times_cartesian = np.zeros(98) +run_times_NetMAP = np.zeros(98) + +#used for graphing (below for loop) +num_freq = np.arange(2,101,1) #arange does not include the "stop" number, so the array goes from 2 to 100 + +#loop to change which frequency is used to recover parameters +for i in range(0,99): #range does not include the "stop" number, so the index actually goes up to 98 + #Create the frequencies that both NetMAP and the Curvefitting functions require + #Frequencies are values between 0.001 and 4, evenly spaced depending on how many frequencies we use + #Note that the number of frequencies must match the length of the noise + #minimum 2 frequencies required - max of 300 because that how high I was going before (gives a very good curve for curvefit) + freq_curvefit = np.linspace(0.001, 4, i+2) + freqs_NetMAP = np.linspace(0.001, 4, i+2) + length_noise_curvefit = i+2 + length_noise_NetMAP = i+2 + + #Run the trials (1000 in this case) + #Currently saves saves all plots to a folder called "Case Study 1000 Trials Varying Frequencies Plots" + #(the excel name is not used here - it is only required when doing multiple systems with one trial per system) + #returns average error across trials (e_bar) and parameters (e), and dataframes for all three methods that include all the information + #there is only one e_bar for each when doing a case study, so those arrays will not be used in any graphing moving forward + #NOTE: error is different every time, to simulate a real experiment + avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar, dataframe_polar, dataframe_cart, dataframe_net = run_trials(true_params, guessed_params, freqs_NetMAP, freq_curvefit, length_noise_NetMAP, length_noise_curvefit, 50, f'Second_Case_Study_50_Trials_{i+2}_Frequencies.xlsx', f'Second Case Study 50 Trials {i+2} Frequencies Plots') + + #Save the new data to a new excel spreadsheet: + with pd.ExcelWriter(f'Case_Study_50_Trials_{i+2}_Frequencies.xlsx', engine='xlsxwriter') as writer: + dataframe_polar.to_excel(writer, sheet_name='Amp & Phase', index=False) + dataframe_cart.to_excel(writer, sheet_name='X & Y', index=False) + dataframe_net.to_excel(writer, sheet_name='NetMAP', index=False) + + #The run times are stored in the dataframes, so we extract the mean here and add it to the run_times arrays so we can graph it later + run_times_polar[i] = dataframe_polar.at[0,'mean trial time'] + run_times_cartesian[i] = dataframe_cart.at[0,'mean trial time'] + run_times_NetMAP[i] = dataframe_net .at[0,'mean trial time'] + + print(f"Frequency {i+2} Complete") + +#Plot number of frequencies versus run time: +fig = plt.figure(figsize=(5, 4)) +plt.xlabel('Number of Frequencies', fontsize = 16) +plt.ylabel('Mean Time to Run (s)', fontsize = 16) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.plot(num_freq, run_times_polar, 'o-', color='blue', label='Polar') +plt.plot(num_freq, run_times_cartesian, 'o-', color='green', label='Cartesian') +plt.plot(num_freq, run_times_NetMAP, 'o-', color='red', label='NetMAP') +plt.legend(loc='best', fontsize = 13) + +plt.show() '''Begin work here. Redoing 15 systems data. Still using 10 Freqs and Better Params. - I want to do many more systems and 500 trials per system. Seeing how many systems it can do in 3 hours.''' + I want to run parameter recovery for many more systems but only 1 trial per system. + Seeing how many systems it can do in 2 hours or 2000 systems.''' -## 1. Make sure I save the error used for each trial. NOT DONE -## 2. Set a runtime limit of 2-3 hours perhaps. NOT DONE +## 1. What am I doing for error? + ## 300 frequencies (n=300 -- so 300 different noises for each frequency used) and noise level 2 + ## 10 evenly spaced frequencies for NetMAP (n=10) and noise level 2. +## 2. Set a runtime limit of 2-3 hours. DONE ## 3. Don't graph all the curvefits. DONE -## 4. Guesses are automated to within 20% of generated parameters, 10 evenly spaced frequencies for NetMAP, noise level 2 and n=300. - -# Set the time limit in seconds -time_limit = 10800 # 3 hours - -# Record the start time -start_time = time.time() - -avg_e_bar_list_polar = [] -avg_e_bar_list_cartesian = [] -avg_e_bar_list_NetMAP = [] - -for i in range(1): - - # Check if the time limit has been exceeded - elapsed_time = time.time() - start_time - if elapsed_time > time_limit: - print("Time limit exceeded. Exiting loop.") - break - - loop_start_time = time.time() - - #Generate system and guess parameters - true_params = generate_random_system() - guessed_params = automate_guess(true_params, 20) - - #Curve fit with the guess made above - freqs_NetMAP = np.linspace(0.001, 4, 10) - length_noise = 10 - avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 10, f'System_{i}_500.xlsx', f'Sys {i} - Rand Auto Guess Plots') - - #Add _bar to lists to make one graph at the end - avg_e_bar_list_polar.append(avg_e1_bar) #Polar - avg_e_bar_list_cartesian.append(avg_e2_bar) #Cartesian - avg_e_bar_list_NetMAP.append(avg_e3_bar) #NetMAP - - linearbins = np.linspace(0,15,50) - #Graph histogram of for curve fits - fig = plt.figure(figsize=(5, 4)) - # plt.title('Average Systematic Error Across Parameters') - plt.xlabel(' (%)', fontsize = 16) - plt.ylabel('Counts', fontsize = 16) - plt.yticks(fontsize=14) - plt.xticks(fontsize=14) - plt.hist(avg_e2_array, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') - plt.hist(avg_e1_array, bins = linearbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - plt.hist(avg_e3_array, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='black') - plt.legend(loc='upper right', fontsize = 13) +## 4. Guesses are automated to within 20% of generated parameters, 10 evenly spaced frequencies for NetMAP - plt.show() - save_figure(fig, 'More Systems 500 - Histograms', f' Lin Hist System {i}') - - logbins = np.logspace(-2,1.5,50) - #Graph histogram of for curve fits - fig = plt.figure(figsize=(5, 4)) - # plt.title('Average Systematic Error Across Parameters') - plt.xlabel(' (%)', fontsize = 16) - plt.ylabel('Counts', fontsize = 16) - plt.xscale('log') - plt.yticks(fontsize=14) - plt.xticks(fontsize=14) - plt.hist(avg_e2_array, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='black') - plt.hist(avg_e1_array, bins = logbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='black') - plt.hist(avg_e3_array, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='black') - plt.legend(loc='upper right', fontsize = 13) - plt.show() - save_figure(fig, 'More Systems 500 - Histograms', f' Log Hist System {i}') +# # Set the time limit in seconds +# time_limit = 14400 # 4 hours + +# # Record the start time +# start_time = time.time() + +# # Compile a list of all the e bars so we can graph at the end +# avg_e_bar_list_polar = [] +# avg_e_bar_list_cartesian = [] +# avg_e_bar_list_NetMAP = [] + +# # Initialize an array so I can put each system into one spreadsheet since I'm only doing one trial per system +# all_data1 = pd.DataFrame() #Polar +# all_data2 = pd.DataFrame() #Cartesian +# all_data3 = pd.DataFrame() #NetMAP + +# for i in range(2000): + +# # Check if the time limit has been exceeded +# elapsed_time = time.time() - start_time +# if elapsed_time > time_limit: +# print("Time limit exceeded. Exiting loop.") +# break - loop_end_time = time.time() - loop_time = loop_end_time - loop_start_time +# loop_start_time = time.time() - print(f"Iteration {i + 1} completed. Loop time: {loop_time} secs ") +# #Generate system and guess parameters +# true_params = generate_random_system() +# guessed_params = automate_guess(true_params, 20) +# #Curve fit with the guess made above +# freqs_NetMAP = np.linspace(0.001, 4, 10) +# length_noise = 10 +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar, dataframe_polar, dataframe_cart, dataframe_net = run_trials(true_params, guessed_params, freqs_NetMAP, length_noise, 1, f'System_{i+1}_1.xlsx', f'Sys {i+1} - Rand Auto Guess Plots') + +# #Add each system data to one big dataframe so I can store everything in the same spreadsheet -#Graph histogram of _bar for both curve fits +# all_data1 = pd.concat([all_data1, dataframe_polar], ignore_index=True) +# all_data2 = pd.concat([all_data2, dataframe_cart], ignore_index=True) +# all_data3 = pd.concat([all_data3, dataframe_net], ignore_index=True) + +# #Add _bar to lists to make one graph at the end +# avg_e_bar_list_polar.append(avg_e1_bar) #Polar +# avg_e_bar_list_cartesian.append(avg_e2_bar) #Cartesian +# avg_e_bar_list_NetMAP.append(avg_e3_bar) #NetMAP + +# ## FOR NOW - don't need this either + +# # # Compute max of data and set the bin limits so all data is included on graph +# # data_max1 = max(avg_e2_array + avg_e1_array + avg_e3_array) +# # if data_max1 > 39: +# # linearbins = np.linspace(0, data_max1 + 2,50) +# # else: +# # linearbins = np.linspace(0, 40, 50) + +# # #Graph histogram of for curve fits - linear +# # fig = plt.figure(figsize=(5, 4)) +# # # plt.title('Average Systematic Error Across Parameters') +# # plt.xlabel(' (%)', fontsize = 16) +# # plt.ylabel('Counts', fontsize = 16) +# # plt.yticks(fontsize=14) +# # plt.xticks(fontsize=14) +# # plt.hist(avg_e2_array, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') +# # plt.hist(avg_e1_array, bins = linearbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') +# # plt.hist(avg_e3_array, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# # plt.legend(loc='best', fontsize = 13) + +# # # plt.show() +# # save_figure(fig, 'More Systems 1 Trial - Histograms', f' Lin Hist System {i+1}') + +# # # Set the bin limits so all data is included on graph +# # if data_max > 100: +# # logbins = np.logspace(-2, math.log10(data_max), 50) +# # else: +# # logbins = np.logspace(-2, 1.8, 50) +# # #Graph histogram of for curve fits - log +# # fig = plt.figure(figsize=(5, 4)) +# # # plt.title('Average Systematic Error Across Parameters') +# # plt.xlabel(' (%)', fontsize = 16) +# # plt.ylabel('Counts', fontsize = 16) +# # plt.xscale('log') +# # plt.yticks(fontsize=14) +# # plt.xticks(fontsize=14) +# # plt.hist(avg_e2_array, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') +# # plt.hist(avg_e1_array, bins = logbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') +# # plt.hist(avg_e3_array, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# # plt.legend(loc='best', fontsize = 13) + +# # # plt.show() +# # save_figure(fig, 'More Systems 1 Trial - Histograms', f' Log Hist System {i+1}') + +# loop_end_time = time.time() +# loop_time = loop_end_time - loop_start_time + +# print(f"Iteration {i + 1} completed. Loop time: {loop_time} secs ") + +# #Write the data for each system (which is now in one big dataframe) to excel +# with pd.ExcelWriter('All_Systems_1_Trial_2.xlsx') as writer: +# all_data1.to_excel(writer, sheet_name='Polar', index=False) +# all_data2.to_excel(writer, sheet_name='Cartesian', index=False) +# all_data3.to_excel(writer, sheet_name='NetMAP', index=False) -linearbins = np.linspace(0,15,50) -#Graph! -fig = plt.figure(figsize=(5, 4)) -plt.xlabel(' Bar (%)', fontsize = 16) -plt.ylabel('Counts', fontsize = 16) -plt.yticks(fontsize=14) -plt.xticks(fontsize=14) -plt.hist(avg_e_bar_list_cartesian, bins = linearbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') -plt.hist(avg_e_bar_list_polar, bins = linearbins, alpha=0.5, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') -plt.hist(avg_e_bar_list_NetMAP, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') -plt.legend(loc='upper right', fontsize = 13) -plt.show() -save_figure(fig, 'More Systems 500 - Histograms', ' Bar Lin Hist' ) +# #Graph histogram of _bar for both curve fits -logbins = np.logspace(-2,1.5,50) -#Graph! -fig = plt.figure(figsize=(5, 4)) -plt.xlabel(' Bar (%)', fontsize = 16) -plt.ylabel('Counts', fontsize = 16) -plt.xscale('log') -plt.yticks(fontsize=14) -plt.xticks(fontsize=14) -plt.hist(avg_e_bar_list_cartesian, bins = logbins, alpha=0.5, color='green', label='Cartesian (X & Y)', edgecolor='green') -plt.hist(avg_e_bar_list_polar, bins = logbins, alpha=0.4, color='blue', label='Polar (Amp & Phase)', edgecolor='blue') -plt.hist(avg_e_bar_list_NetMAP, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') -plt.legend(loc='upper right', fontsize = 13) -plt.show() -save_figure(fig, 'More Systems 500 - Histograms', ' Bar Log Hist' ) +# # Compute max of data and set the bin limits so all data is included on graph +# data_max = max(avg_e_bar_list_cartesian + avg_e_bar_list_polar + avg_e_bar_list_NetMAP) +# if data_max > 39: +# linearbins = np.linspace(0, data_max + 2,50) +# else: +# linearbins = np.linspace(0, 40, 50) + +# #Graph linear! +# fig = plt.figure(figsize=(5, 4)) +# plt.xlabel(' Bar (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.yticks(fontsize=14) +# plt.xticks(fontsize=14) +# plt.hist(avg_e_bar_list_cartesian, bins = linearbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green') +# plt.hist(avg_e_bar_list_polar, bins = linearbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue') +# plt.hist(avg_e_bar_list_NetMAP, bins = linearbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# plt.legend(loc='best', fontsize = 13) +# plt.show() +# save_figure(fig, 'More Systems 1 Trial - Histograms', ' Bar Lin Hist 2' ) + +# # Set the bin limits so all data is included on graph +# if data_max > 100: +# logbins = np.logspace(-2, math.log10(data_max)+0.25, 50) +# else: +# logbins = np.logspace(-2, 1.8, 50) + +# #Graph log! +# fig = plt.figure(figsize=(5, 4)) +# plt.xlabel(' Bar (%)', fontsize = 16) +# plt.ylabel('Counts', fontsize = 16) +# plt.xscale('log') +# plt.yticks(fontsize=14) +# plt.xticks(fontsize=14) +# plt.hist(avg_e_bar_list_cartesian, bins = logbins, alpha=0.5, color='green', label='Cartesian', edgecolor='green') +# plt.hist(avg_e_bar_list_polar, bins = logbins, alpha=0.5, color='blue', label='Polar', edgecolor='blue') +# plt.hist(avg_e_bar_list_NetMAP, bins = logbins, alpha=0.5, color='red', label='NetMAP', edgecolor='red') +# plt.legend(loc='best', fontsize = 13) + +# plt.show() +# save_figure(fig, 'More Systems 1 Trial - Histograms', ' Bar Log Hist 2' ) -# End time -end_time = time.time() -print("Time Elapsed:", end_time - start_time, " secs", (end_time - start_time)/3600, " hrs") +# # End time +# end_time = time.time() +# print(f"Time Elapsed: {end_time - start_time} secs -- {(end_time - start_time)/3600} hrs") diff --git a/trimer/curve_fitting_X_Y_all.py b/trimer/curve_fitting_X_Y_all.py index d5dabe3..d902c25 100644 --- a/trimer/curve_fitting_X_Y_all.py +++ b/trimer/curve_fitting_X_Y_all.py @@ -96,17 +96,16 @@ def save_figure(figure, folder_name, file_name): # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error -def multiple_fit_X_Y(params_guess, params_correct, e, force_all, fix_F, graph_folder_name, graph_name, show_curvefit_graphs = False): +def multiple_fit_X_Y(params_guess, params_correct, e, freq, force_all, fix_F, graph_folder_name, graph_name, show_curvefit_graphs = False): ##Put params_guess and params_correct into np array #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 - data_array = np.zeros(51) + data_array = np.zeros(52) #50 elements are generated in this code, but I leave the last entry empty because I want to time how long it takes the function to run in other code, so I'm giving the array space to add the time if necessary data_array[:11] += np.array(params_correct) data_array[11:22] += np.array(params_guess) ##Create data - functions from simulator code - freq = np.linspace(0.001, 4, 300) X1 = realamp1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Y1 = imamp1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) diff --git a/trimer/curve_fitting_amp_phase_all.py b/trimer/curve_fitting_amp_phase_all.py index 071c04d..37158fe 100644 --- a/trimer/curve_fitting_amp_phase_all.py +++ b/trimer/curve_fitting_amp_phase_all.py @@ -1 +1 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found, x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/phase_max) scaled_residt2.append(phase2/phase_max) scaled_residt3.append(phase3/phase_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, scaled, graph_folder_name, graph_name, show_curvefit_graphs = False): ##Put params_guess and params_correct into np array #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 data_array = np.zeros(51) data_array[:11] += np.array(params_correct) data_array[11:22] += np.array(params_guess) ##Create data - functions from simulator code freq = np.linspace(0.001, 4, 300) Amp1 = curve1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase1 = theta1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase2 = theta2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase3 = theta3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = data_array[11], min=0) params.add('k2', value = data_array[12], min=0) params.add('k3', value = data_array[13], min=0) params.add('k4', value = data_array[14], min=0) params.add('b1', value = data_array[15], min=0) params.add('b2', value = data_array[16], min=0) params.add('b3', value = data_array[17], min=0) params.add('F', value = data_array[18], min=0) params.add('m1', value = data_array[19], min=0) params.add('m2', value = data_array[20], min=0) params.add('m3', value = data_array[21], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) ##Add recovered parameters and systematic error #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 param_values = np.array([result.params[param].value for param in result.params]) data_array[22:33] += param_values if fix_F == False: scaling_factor = (data_array[7])/(result.params['F'].value) data_array[22:33] *= scaling_factor syserr_result = syserr(data_array[22:33], data_array[:11]) data_array[33:44] += np.array(syserr_result) #average error data_array[-1] += np.sum(data_array[33:44]/10) #dividing by 10 because we aren't counting the error in Force because it is 0 if show_curvefit_graphs == True: #Create fitted y-values (for rsqrd and graphing) k1_fit = data_array[22] k2_fit = data_array[23] k3_fit = data_array[24] k4_fit = data_array[25] b1_fit = data_array[26] b2_fit = data_array[27] b3_fit = data_array[28] F_fit = data_array[29] m1_fit = data_array[30] m2_fit = data_array[31] m3_fit= data_array[32] Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 and add to data_array Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data_array[44:50] += np.array([Amp1_rsqrd, Amp2_rsqrd, Amp3_rsqrd, Phase1_rsqrd, Phase2_rsqrd, Phase3_rsqrd]) #Create intial guessed y-values (for graphing) k1_guess = data_array[11] k2_guess = data_array[12] k3_guess = data_array[13] k4_guess = data_array[14] b1_guess = data_array[15] b2_guess = data_array[16] b3_guess = data_array[17] F_guess = data_array[18] m1_guess = data_array[19] m2_guess = data_array[20] m3_guess = data_array[21] c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts if scaled: fig.suptitle('Trimer Resonator: Amplitude and Phase (Scaled)', fontsize=16) else: fig.suptitle('Trimer Resonator: Amplitude and Phase (Not Scaled)', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data_array '''Begin Work - Does scaling the residuals change anything?''' # import pandas as pd # e = 0 # force_all = False # fix_F = False #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params # params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] # params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] # #Get the data (and the graphs) # scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') # not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') # with pd.ExcelWriter('Scaling_Amp_Phase_Residuals.xlsx', engine='xlsxwriter') as writer: # dfscaled = pd.DataFrame(scaled_dict) # dfnotscaled = pd.DataFrame(not_scaled_dict) # dfscaled.to_excel(writer, sheet_name='Scaled', index=False) # dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file +#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 16 11:31:59 2024 @author: lydiabullock """ import os import numpy as np import matplotlib.pyplot as plt import lmfit import warnings from Trimer_simulator import curve1, theta1, curve2, theta2, curve3, theta3, c1, t1, c2, t2, c3, t3 ''' 3 functions contained: multiple_fit - Curve fits to multiple Amplitude and Phase Curves at once - Calculates systematic error and returns a dictionary of info - Graphs curve fit analysis residuals - calculates residuals of multiple data sets and concatenates them - used in multiple_fit function to minimize the residuals of multiple graphs at the same time to find the best fit curve save_figure - saves the curve fit graph created to a named folder syserr - calculates systematic error rsqrd - calculates R^2 ''' def syserr(x_found, x_set, absval = True): with warnings.catch_warnings(): warnings.simplefilter('ignore') se = 100*(x_found-x_set)/x_set if absval: return abs(se) else: return se """ This definition of R^2 can come out negative. Negative means that a flat line would fit the data better than the curve. """ def rsqrd(model, data, plot=False, x=None, newfigure = True): SSres = sum((data - model)**2) SStot = sum((data - np.mean(data))**2) rsqrd = 1 - (SSres/ SStot) if plot: if newfigure: plt.figure() plt.plot(x,data, 'o') plt.plot(x, model, '--') return rsqrd #Calculates and concatenates residuals given multiple data sets #Takes in parameters, frequency, and dependent variables def residuals(params, wd, Amp1_data, Amp2_data, Amp3_data, Phase1_data, Phase2_data, Phase3_data, scaled): k1 = params['k1'].value k2 = params['k2'].value k3 = params['k3'].value k4 = params['k4'].value b1 = params['b1'].value b2 = params['b2'].value b3 = params['b3'].value F = params['F'].value m1 = params['m1'].value m2 = params['m2'].value m3 = params['m3'].value modelc1 = c1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc2 = c2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelc3 = c3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt1 = t1(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt2 = t2(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) modelt3 = t3(wd, k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3) residc1 = Amp1_data - modelc1 residc2 = Amp2_data - modelc2 residc3 = Amp3_data - modelc3 residt1 = Phase1_data - modelt1 residt2 = Phase2_data - modelt2 residt3 = Phase3_data - modelt3 #Trying to scale Amp and Phase because their units are different amp_max = max([max(residc1), max(residc2), max(residc3)]) phase_max = max([max(residt1), max(residt2), max(residt3)]) scaled_residc1 = [] scaled_residc2 = [] scaled_residc3 = [] scaled_residt1 = [] scaled_residt2 = [] scaled_residt3 = [] for amp1, amp2, amp3 in zip(residc1, residc2, residc3): scaled_residc1.append(amp1/amp_max) scaled_residc2.append(amp2/amp_max) scaled_residc3.append(amp3/amp_max) for phase1, phase2, phase3 in zip(residt1, residt2, residt3): scaled_residt1.append(phase1/phase_max) scaled_residt2.append(phase2/phase_max) scaled_residt3.append(phase3/phase_max) if scaled: return np.concatenate((scaled_residc1, scaled_residc2, scaled_residc3, scaled_residt1, scaled_residt2, scaled_residt3)) else: return np.concatenate((residc1, residc2, residc3, residt1, residt2, residt3)) def save_figure(figure, folder_name, file_name): # Create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) # Save the figure to the folder file_path = os.path.join(folder_name, file_name) figure.savefig(file_path) #Takes in a *list* of correct parameters and a *list* of the guessed parameters, #as well as error and three booleans (whether you want to apply force to one or all masses, #scale by force, or fix the force) # #Returns a dataframe containing guessed parameters, recovered parameters, #and systematic error def multiple_fit_amp_phase(params_guess, params_correct, e, freq, force_all, fix_F, scaled, graph_folder_name, graph_name, show_curvefit_graphs = False): ##Put params_guess and params_correct into np array #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 data_array = np.zeros(52) #50 elements are generated in this code, but I leave the last entry empty because I want to time how long it takes the function to run in other code, so I'm giving the array space to add the time if necessary data_array[:11] += np.array(params_correct) data_array[11:22] += np.array(params_guess) ##Create data - functions from simulator code Amp1 = curve1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase1 = theta1(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi Amp2 = curve2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase2 = theta2(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi Amp3 = curve3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) Phase3 = theta3(freq, data_array[0], data_array[1], data_array[2], data_array[3], data_array[4], data_array[5], data_array[6], data_array[7], data_array[8], data_array[9], data_array[10], e, force_all) \ + 2 * np.pi #Create intial parameters params = lmfit.Parameters() params.add('k1', value = data_array[11], min=0) params.add('k2', value = data_array[12], min=0) params.add('k3', value = data_array[13], min=0) params.add('k4', value = data_array[14], min=0) params.add('b1', value = data_array[15], min=0) params.add('b2', value = data_array[16], min=0) params.add('b3', value = data_array[17], min=0) params.add('F', value = data_array[18], min=0) params.add('m1', value = data_array[19], min=0) params.add('m2', value = data_array[20], min=0) params.add('m3', value = data_array[21], min=0) #If you plan on fixing F so it cannot be changed if fix_F: params['F'].vary = False #get resulting data and fit parameters by minimizing the residuals result = lmfit.minimize(residuals, params, args = (freq, Amp1, Amp2, Amp3, Phase1, Phase2, Phase3, scaled)) #print(lmfit.fit_report(result)) ##Add recovered parameters and systematic error #Order added: k1, k2, k3, k4, b1, b2, b3, F, m1, m2, m3 param_values = np.array([result.params[param].value for param in result.params]) data_array[22:33] += param_values if fix_F == False: scaling_factor = (data_array[7])/(result.params['F'].value) data_array[22:33] *= scaling_factor syserr_result = syserr(data_array[22:33], data_array[:11]) data_array[33:44] += np.array(syserr_result) #average error data_array[-1] += np.sum(data_array[33:44]/10) #dividing by 10 because we aren't counting the error in Force because it is 0 #Create fitted y-values (for rsqrd and graphing) k1_fit = data_array[22] k2_fit = data_array[23] k3_fit = data_array[24] k4_fit = data_array[25] b1_fit = data_array[26] b2_fit = data_array[27] b3_fit = data_array[28] F_fit = data_array[29] m1_fit = data_array[30] m2_fit = data_array[31] m3_fit= data_array[32] Amp1_fitted = c1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp2_fitted = c2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Amp3_fitted = c3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase1_fitted = t1(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase2_fitted = t2(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) Phase3_fitted = t3(freq, k1_fit, k2_fit, k3_fit, k4_fit, b1_fit, b2_fit, b3_fit, F_fit, m1_fit, m2_fit, m3_fit) #Calculate R^2 and add to data_array Amp1_rsqrd = rsqrd(Amp1_fitted, Amp1) Amp2_rsqrd = rsqrd(Amp2_fitted, Amp2) Amp3_rsqrd = rsqrd(Amp3_fitted, Amp3) Phase1_rsqrd = rsqrd(Phase1_fitted, Phase1) Phase2_rsqrd = rsqrd(Phase2_fitted, Phase2) Phase3_rsqrd = rsqrd(Phase3_fitted, Phase3) data_array[44:50] += np.array([Amp1_rsqrd, Amp2_rsqrd, Amp3_rsqrd, Phase1_rsqrd, Phase2_rsqrd, Phase3_rsqrd]) if show_curvefit_graphs == True: #Create intial guessed y-values (for graphing) k1_guess = data_array[11] k2_guess = data_array[12] k3_guess = data_array[13] k4_guess = data_array[14] b1_guess = data_array[15] b2_guess = data_array[16] b3_guess = data_array[17] F_guess = data_array[18] m1_guess = data_array[19] m2_guess = data_array[20] m3_guess = data_array[21] c1_guess = c1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c2_guess = c2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) c3_guess = c3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t1_guess = t1(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t2_guess = t2(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) t3_guess = t3(freq, k1_guess, k2_guess, k3_guess, k4_guess, b1_guess, b2_guess, b3_guess, F_guess, m1_guess, m2_guess, m3_guess) ## Begin graphing fig = plt.figure(figsize=(16,8)) gs = fig.add_gridspec(2, 3, hspace=0.1, wspace=0.1) ((ax1, ax2, ax3), (ax4, ax5, ax6)) = gs.subplots(sharex=True, sharey='row') #original data ax1.plot(freq, Amp1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax2.plot(freq, Amp2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax3.plot(freq, Amp3,'go', alpha=0.5, markersize=5.5, label = 'Data') ax4.plot(freq, Phase1,'ro', alpha=0.5, markersize=5.5, label = 'Data') ax5.plot(freq, Phase2,'bo', alpha=0.5, markersize=5.5, label = 'Data') ax6.plot(freq, Phase3,'go', alpha=0.5, markersize=5.5, label = 'Data') #fitted curves ax1.plot(freq, Amp1_fitted,'c-', label='Best Fit', lw=2.5) ax2.plot(freq, Amp2_fitted,'r-', label='Best Fit', lw=2.5) ax3.plot(freq, Amp3_fitted,'m-', label='Best Fit', lw=2.5) ax4.plot(freq, Phase1_fitted,'c-', label='Best Fit', lw=2.5) ax5.plot(freq, Phase2_fitted,'r-', label='Best Fit', lw=2.5) ax6.plot(freq, Phase3_fitted,'m-', label='Best Fit', lw=2.5) #inital guess curves ax1.plot(freq, c1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax2.plot(freq, c2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax3.plot(freq, c3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax4.plot(freq, t1_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax5.plot(freq, t2_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') ax6.plot(freq, t3_guess, color='#4682B4', linestyle='dashed', label='Initial Guess') #Graph parts if scaled: fig.suptitle('Trimer Resonator: Amplitude and Phase (Scaled)', fontsize=16) else: fig.suptitle('Trimer Resonator: Amplitude and Phase (Not Scaled)', fontsize=16) ax1.set_title('Mass 1', fontsize=14) ax2.set_title('Mass 2', fontsize=14) ax3.set_title('Mass 3', fontsize=14) ax1.set_ylabel('Amplitude') ax4.set_ylabel('Phase') for ax in fig.get_axes(): ax.set(xlabel='Frequency') ax.label_outer() ax.legend() plt.show() save_figure(fig, graph_folder_name, graph_name) return data_array '''Begin Work - Does scaling the residuals change anything?''' #import pandas as pd # e = 0 # force_all = False # fix_F = False # freq = np.linspace(0.001, 4, 10) #this is using System 7 of 15 Systems - 10 Freqs NetMAP Better Params # params_correct = [1.427, 6.472, 3.945, 3.024, 0.675, 0.801, 0.191, 1, 7.665, 9.161, 7.139] # params_guess = [1.1942, 5.4801, 3.2698, 3.3004, 0.7682, 0.8185, 0.1765, 1, 7.4923, 8.9932, 8.1035] #Get the data (and the graphs) params_guess, params_correct, e, freq, force_all, fix_F, scaled, graph_folder_name, graph_name, show_curvefit_graphs = False # scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, freq, force_all, fix_F, True, 'Scaling Amp_Phase Residuals', 'Scaled') # not_scaled_dict = multiple_fit_amp_phase(params_guess, params_correct, e, force_all, fix_F, False, 'Scaling Amp_Phase Residuals', 'Not_Scaled') # with pd.ExcelWriter('Scaling_Amp_Phase_Residuals.xlsx', engine='xlsxwriter') as writer: # dfscaled = pd.DataFrame(scaled_dict) # dfnotscaled = pd.DataFrame(not_scaled_dict) # dfscaled.to_excel(writer, sheet_name='Scaled', index=False) # dfnotscaled.to_excel(writer, sheet_name='Not Sclaed', index=False) \ No newline at end of file From b101a0239b3e1f9135ad397acaf263d972dc8ddc Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 19 Jun 2025 22:05:40 -0400 Subject: [PATCH 100/101] Code for Number of Frequencies vs Average Run TIme --- trimer/comparing_curvefit_types.py | 174 ++++++++++++++++++++--------- 1 file changed, 124 insertions(+), 50 deletions(-) diff --git a/trimer/comparing_curvefit_types.py b/trimer/comparing_curvefit_types.py index 48fcb85..ac00949 100644 --- a/trimer/comparing_curvefit_types.py +++ b/trimer/comparing_curvefit_types.py @@ -659,69 +659,143 @@ def run_trials(true_params, guessed_params, freqs_NetMAP, freqs_curvefit, length GOAL: graph runtime versus number of frequencies given to each method. Create a for loop that varies frequencies from 2 to 300. (2 because that is the minimum required by NetMAP. 300 because that produces a very nice graph for curvefitting (and is what I have been using as a standard up until now.''' -#Recover the system information from a file on my computer -file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP & Better Parameters/Case_Study_10_Freqs_Better_Parameters.xlsx' -array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() - -#These are the true and the guessed parameters for the system -#Guessed parameters were the same ones guesssed by hand the first time we ran this case study -true_params = np.concatenate((array_amp_phase[1,:7], [array_amp_phase[1,10]], array_amp_phase[1,7:10])) -guessed_params = np.concatenate((array_amp_phase[1,11:18], [array_amp_phase[1,21]], array_amp_phase[1,18:21])) - -#create array to store the run times for the given number of frequencies -#there will be a total of 98 different times since we start with 2 frequencies and end with 100 -run_times_polar = np.zeros(98) -run_times_cartesian = np.zeros(98) -run_times_NetMAP = np.zeros(98) - -#used for graphing (below for loop) -num_freq = np.arange(2,101,1) #arange does not include the "stop" number, so the array goes from 2 to 100 - -#loop to change which frequency is used to recover parameters -for i in range(0,99): #range does not include the "stop" number, so the index actually goes up to 98 - #Create the frequencies that both NetMAP and the Curvefitting functions require - #Frequencies are values between 0.001 and 4, evenly spaced depending on how many frequencies we use - #Note that the number of frequencies must match the length of the noise - #minimum 2 frequencies required - max of 300 because that how high I was going before (gives a very good curve for curvefit) - freq_curvefit = np.linspace(0.001, 4, i+2) - freqs_NetMAP = np.linspace(0.001, 4, i+2) - length_noise_curvefit = i+2 - length_noise_NetMAP = i+2 - - #Run the trials (1000 in this case) - #Currently saves saves all plots to a folder called "Case Study 1000 Trials Varying Frequencies Plots" - #(the excel name is not used here - it is only required when doing multiple systems with one trial per system) - #returns average error across trials (e_bar) and parameters (e), and dataframes for all three methods that include all the information - #there is only one e_bar for each when doing a case study, so those arrays will not be used in any graphing moving forward - #NOTE: error is different every time, to simulate a real experiment - avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar, dataframe_polar, dataframe_cart, dataframe_net = run_trials(true_params, guessed_params, freqs_NetMAP, freq_curvefit, length_noise_NetMAP, length_noise_curvefit, 50, f'Second_Case_Study_50_Trials_{i+2}_Frequencies.xlsx', f'Second Case Study 50 Trials {i+2} Frequencies Plots') - - #Save the new data to a new excel spreadsheet: - with pd.ExcelWriter(f'Case_Study_50_Trials_{i+2}_Frequencies.xlsx', engine='xlsxwriter') as writer: - dataframe_polar.to_excel(writer, sheet_name='Amp & Phase', index=False) - dataframe_cart.to_excel(writer, sheet_name='X & Y', index=False) - dataframe_net.to_excel(writer, sheet_name='NetMAP', index=False) +# #Recover the system information from a file on my computer +# file_path = '/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - 10 Freqs NetMAP & Better Parameters/Case_Study_10_Freqs_Better_Parameters.xlsx' +# array_amp_phase = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() + +# #These are the true and the guessed parameters for the system +# #Guessed parameters were the same ones guesssed by hand the first time we ran this case study +# true_params = np.concatenate((array_amp_phase[1,:7], [array_amp_phase[1,10]], array_amp_phase[1,7:10])) +# guessed_params = np.concatenate((array_amp_phase[1,11:18], [array_amp_phase[1,21]], array_amp_phase[1,18:21])) + +# #create array to store the run times for the given number of frequencies +# #there will be a total of 98 different times since we start with 2 frequencies and end with 100 +# run_times_polar = np.zeros(99) +# run_times_cartesian = np.zeros(99) +# run_times_NetMAP = np.zeros(99) + +# #used for graphing (below for loop) +# num_freq = np.arange(2,101,1) #arange does not include the "stop" number, so the array goes from 2 to 100 + +# #loop to change which frequency is used to recover parameters +# for i in range(0,99): #range does not include the "stop" number, so the index actually goes up to 98 +# #Create the frequencies that both NetMAP and the Curvefitting functions require +# #Frequencies are values between 0.001 and 4, evenly spaced depending on how many frequencies we use +# #Note that the number of frequencies must match the length of the noise +# #minimum 2 frequencies required - max of 300 because that how high I was going before (gives a very good curve for curvefit) +# freq_curvefit = np.linspace(0.001, 4, i+2) +# freqs_NetMAP = np.linspace(0.001, 4, i+2) +# length_noise_curvefit = i+2 +# length_noise_NetMAP = i+2 + +# #Run the trials (1000 in this case) +# #Currently saves saves all plots to a folder called "Case Study 1000 Trials Varying Frequencies Plots" +# #(the excel name is not used here - it is only required when doing multiple systems with one trial per system) +# #returns average error across trials (e_bar) and parameters (e), and dataframes for all three methods that include all the information +# #there is only one e_bar for each when doing a case study, so those arrays will not be used in any graphing moving forward +# #NOTE: error is different every time, to simulate a real experiment +# avg_e1_array, avg_e2_array, avg_e3_array, avg_e1_bar, avg_e2_bar, avg_e3_bar, dataframe_polar, dataframe_cart, dataframe_net = run_trials(true_params, guessed_params, freqs_NetMAP, freq_curvefit, length_noise_NetMAP, length_noise_curvefit, 50, f'Second_Case_Study_50_Trials_{i+2}_Frequencies.xlsx', f'Second Case Study 50 Trials {i+2} Frequencies Plots') + +# #Save the new data to a new excel spreadsheet: +# with pd.ExcelWriter(f'Case_Study_50_Trials_{i+2}_Frequencies.xlsx', engine='xlsxwriter') as writer: +# dataframe_polar.to_excel(writer, sheet_name='Amp & Phase', index=False) +# dataframe_cart.to_excel(writer, sheet_name='X & Y', index=False) +# dataframe_net.to_excel(writer, sheet_name='NetMAP', index=False) - #The run times are stored in the dataframes, so we extract the mean here and add it to the run_times arrays so we can graph it later - run_times_polar[i] = dataframe_polar.at[0,'mean trial time'] - run_times_cartesian[i] = dataframe_cart.at[0,'mean trial time'] - run_times_NetMAP[i] = dataframe_net .at[0,'mean trial time'] +# #The run times are stored in the dataframes, so we extract the mean here and add it to the run_times arrays so we can graph it later +# run_times_polar[i] = dataframe_polar.at[0,'mean trial time'] +# run_times_cartesian[i] = dataframe_cart.at[0,'mean trial time'] +# run_times_NetMAP[i] = dataframe_net .at[0,'mean trial time'] - print(f"Frequency {i+2} Complete") +# print(f"Frequency {i+2} Complete") +''' Graphing the above didn't work, so I'm doing it again below ''' + +run_times_polar = np.zeros(99) +run_times_cartesian = np.zeros(99) +run_times_NetMAP = np.zeros(99) +std_dev_time_polar = np.zeros(99) +std_dev_time_cartesian = np.zeros(99) +std_dev_time_NetMAP = np.zeros(99) +num_freq = np.arange(2,101,1) + +for i in range(99): + file_path = f'/Users/Student/Desktop/Summer Research 2024/Curve Fit vs NetMAP/Case Study - Number of Frequencies vs Average Run Time/50 Trials/Case_Study_50_Trials_{i+2}_Frequencies.xlsx' + polar = pd.read_excel(file_path, sheet_name = 'Amp & Phase').to_numpy() + cartesian = pd.read_excel(file_path, sheet_name = 'X & Y').to_numpy() + NetMAP = pd.read_excel(file_path, sheet_name = 'NetMAP').to_numpy() + + run_times_polar[i] = polar[0,53] + run_times_cartesian[i] = cartesian[0,53] + run_times_NetMAP[i] = NetMAP[0,47] + std_dev_time_polar[i] = polar[0,54] + std_dev_time_cartesian[i] = cartesian[0,54] + std_dev_time_NetMAP[i] = NetMAP[0,48] + + #Plot number of frequencies versus run time: fig = plt.figure(figsize=(5, 4)) plt.xlabel('Number of Frequencies', fontsize = 16) plt.ylabel('Mean Time to Run (s)', fontsize = 16) plt.yticks(fontsize=14) plt.xticks(fontsize=14) -plt.plot(num_freq, run_times_polar, 'o-', color='blue', label='Polar') -plt.plot(num_freq, run_times_cartesian, 'o-', color='green', label='Cartesian') -plt.plot(num_freq, run_times_NetMAP, 'o-', color='red', label='NetMAP') +plt.yscale('log') +plt.plot(num_freq, run_times_polar, 'o', color='blue', label='Polar') +plt.plot(num_freq, run_times_cartesian, 'o', color='green', label='Cartesian') +plt.plot(num_freq, run_times_NetMAP, 'o', color='red', label='NetMAP') plt.legend(loc='best', fontsize = 13) +plt.show() + +fig = plt.figure(figsize=(5, 4)) +plt.xlabel('Number of Frequencies', fontsize = 16) +plt.ylabel('Mean Time to Run (s)', fontsize = 16) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.yscale('log') +plt.plot(num_freq, run_times_polar, 'o', color='blue', label='Polar') +plt.legend(loc='best', fontsize = 13) plt.show() +fig = plt.figure(figsize=(5, 4)) +plt.xlabel('Number of Frequencies', fontsize = 16) +plt.ylabel('Mean Time to Run (s)', fontsize = 16) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.yscale('log') +plt.plot(num_freq, run_times_cartesian, 'o', color='green', label='Cartesian') +plt.legend(loc='best', fontsize = 13) +plt.show() + +fig = plt.figure(figsize=(5, 4)) +plt.xlabel('Number of Frequencies', fontsize = 16) +plt.ylabel('Mean Time to Run (s)', fontsize = 16) +plt.yticks(fontsize=14) +plt.xticks(fontsize=14) +plt.plot(num_freq, run_times_NetMAP, 'o', color='red', label='NetMAP') +plt.legend(loc='best', fontsize = 13) +plt.show() + + +# polar_outliers = run_times_polar[run_times_polar > 20] +# cartesian_outliers = run_times_cartesian[run_times_cartesian > 20] +# polar_outlier_indices = np.nonzero(run_times_polar > 20) +# cartesian_outlier_indices = np.nonzero(run_times_cartesian > 20) + +# no_outliers_polar_times = np.empty +# no_outliers_cartesian_times = np.empty +# new_freq_polar = np.empty + +# for i in range(len(run_times_polar)): +# if run_times_polar[i] not in polar_outliers: +# no_outliers_polar_times[i] = run_times_polar[i] +# if run_times_cartesian[i] not in cartesian_outliers: +# no_outliers_cartesian_times[i] = run_times_cartesian[i] +# if i not in polar_outlier_indices: + + + + '''Begin work here. Redoing 15 systems data. Still using 10 Freqs and Better Params. I want to run parameter recovery for many more systems but only 1 trial per system. Seeing how many systems it can do in 2 hours or 2000 systems.''' From ab4b1765c66309ece484fce714113fe53f4b612a Mon Sep 17 00:00:00 2001 From: lydiabull Date: Thu, 19 Jun 2025 22:06:18 -0400 Subject: [PATCH 101/101] Update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 77982d8..a154876 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,4 @@ myheatmap.py~ main.py *.svg *.png +.DS_Store