diff --git a/lifecycle_anslysis/comparison.py b/lifecycle_anslysis/comparison.py index 52ce94b..6c2d7af 100644 --- a/lifecycle_anslysis/comparison.py +++ b/lifecycle_anslysis/comparison.py @@ -1,8 +1,12 @@ import numpy as np +from scipy.optimize import curve_fit from lifecycle_anslysis.constants import NEW_SYSTEM, OLD_SYSTEM from lifecycle_anslysis.system import System +# Define an exponential decay function for fitting +def exponential_decay(x, A, k): + return A * np.exp(-k * x) def generate_systems_comparison(new_system: System, old_system: System, time_horizon: int, country: str, utilization: int, opex_calculation: str): @@ -23,4 +27,47 @@ def generate_systems_comparison(new_system: System, old_system: System, time_hor relative_savings = 1 - (old_system_opex / new_system_opex) ratio = new_system_opex / old_system_opex + if not np.any(ratio < 1): + stop = False + # x_target = ratio.shape[0] + ###### Estimate when we will hit the break-even point with an exponential decay model + + # Create an x array as the index of y + x = np.arange(ratio.shape[0]) + + # Fit the curve to get optimal A and k values + params, covariance = curve_fit(exponential_decay, x, ratio, p0=(ratio[0], 0.1)) + + # Extract parameters + A_fit, k_fit = params + + # Calculate the x-value where y is approximately 1 --> Break-even + target_y = 1 + x_target = int(np.ceil(-np.log(target_y / A_fit) / k_fit)) + if x_target == ratio.shape[0]: + x_target += 1 + while not stop: + + new_system_opex = new_system.generate_accumm_projected_opex_emissions( + x_target, system_id=NEW_SYSTEM, country=country, utilization=utilization, opex_calculation=opex_calculation) + new_system_capex = new_system.calculate_capex_emissions() + old_system_opex = old_system.generate_accumm_projected_opex_emissions( + x_target, system_id=OLD_SYSTEM, country=country, utilization=utilization, opex_calculation=opex_calculation) + + performance_factor = old_system.performance_indicator / new_system.performance_indicator ##### --> Assumption: Better performance leads to lower utilization, hence less power draw. + + new_system_opex = new_system_opex * performance_factor + + # new_system_opex[0] = new_system_opex[0] + new_system_capex ###### --> Add the CAPEX at time 0 + new_system_opex = np.array([opex + new_system_capex for opex in new_system_opex]) + + abs_savings = new_system_opex - old_system_opex + relative_savings = 1 - (old_system_opex / new_system_opex) + ratio = new_system_opex / old_system_opex + + x_target += 1 + + if np.any(np.round(ratio, 1) <= 1): + stop = True + return new_system_opex, old_system_opex, abs_savings, relative_savings, ratio diff --git a/lifecycle_anslysis/plotting.py b/lifecycle_anslysis/plotting.py index 116dc12..137889d 100644 --- a/lifecycle_anslysis/plotting.py +++ b/lifecycle_anslysis/plotting.py @@ -1,3 +1,5 @@ +import os.path + import numpy as np from matplotlib import pyplot as plt @@ -5,10 +7,11 @@ BAR2 = "#abd9e9" BAR1 = "#fdae61" LINE = "#d7191c" +LINE2 = "#2c7bb6" def create_projections_plot(system_a_projected_emissions, system_b_projected_emissions, ratio, save_path, step_size=1, - fig_size=None, break_even_label_pos=0): + y_top_lim=None, fig_size=None, break_even_label_pos=15, separate_legend=False): plt.rcParams.update({'text.usetex': True , 'pgf.rcfonts': False , 'text.latex.preamble': r"""\usepackage{iftex} @@ -21,54 +24,165 @@ def create_projections_plot(system_a_projected_emissions, system_b_projected_emi \fi""" }) + if system_a_projected_emissions.shape[0] > 10 and step_size == 1: + step_size += 1 + bar_width = 0.25 * step_size font_size = 26 - fig, ax1 = plt.subplots(figsize=(10,6)) - ax2 = ax1.twinx() + fig, ax1 = plt.subplots(figsize=(10, 6)) + # ax2 = ax1.twinx() x_values = list(range(1, system_a_projected_emissions.shape[0] + 1, step_size)) - system_a_projected_emissions = [system_a_projected_emissions[i - 1] for i in x_values] - system_b_projected_emissions = [system_b_projected_emissions[i - 1] for i in x_values] - ratio = [ratio[i - 1] for i in x_values] + system_a_projected_emissions = [system_a_projected_emissions[i - 1] / 1000 for i in + x_values] ### Divinding by 1000 to get thousands of kgs to be consistent with the units in the model + system_b_projected_emissions = [system_b_projected_emissions[i - 1] / 1000 for i in + x_values] ### Divinding by 1000 to get thousands of kgs to be consistent with the units in the model + + # Fit lines (find slope and intercept) for both curves + m1, b1 = np.polyfit(x_values, system_a_projected_emissions, 1) # Line 1: y = m1*x + b1 + m2, b2 = np.polyfit(x_values, system_b_projected_emissions, 1) # Line 2: y = m2*x + b2 + + # Find the intersection point analytically + # Intersection occurs when m1*x + b1 = m2*x + b2 + # Solve for x: x = (b2 - b1) / (m1 - m2) + intersection_x = (b2 - b1) / (m1 - m2) + intersection_y = m1 * intersection_x + b1 # Substitute into either line equation + + x_values.insert(0, 0) + system_b_projected_emissions.insert(0, b2) + system_a_projected_emissions.insert(0, b1) time_horizon_array = np.array(x_values) - ax1.bar(time_horizon_array - bar_width / 2, system_b_projected_emissions, color=BAR2, label='Current HW', - width=bar_width) + ratio = [ratio[i - 1] for i in x_values] + + # ax1.bar(time_horizon_array - bar_width / 2, system_b_projected_emissions, color=BAR2, label='Current HW', + # width=bar_width) + + # ax1.bar(time_horizon_array + bar_width / 2, system_a_projected_emissions, color=BAR1, label='New HW', + # width=bar_width) + + ax1.plot(time_horizon_array, system_b_projected_emissions, color=BAR2, label='Current HW', linewidth=4) + + ax1.plot(time_horizon_array, system_a_projected_emissions, color=BAR1, label='New HW', linewidth=4) + + # Mark the intersection + # ax1.axvline(x=intersection_x, color=LINE, linestyle="dashed", alpha=0.7, label='Break-even') # Vertical line + ax1.scatter(intersection_x, intersection_y, color=LINE, zorder=5) # Mark the point + + ax1.axhline(y=system_a_projected_emissions[0], color=LINE, linestyle="dashed", alpha=0.5) # Horizonatal line - ax1.bar(time_horizon_array + bar_width / 2, system_a_projected_emissions, color=BAR1, label='New HW', - width=bar_width) + ax1.annotate( + "New HW's embodied carbon", + xy=(time_horizon_array[-5], system_a_projected_emissions[0]), + xytext=(time_horizon_array[-5], system_a_projected_emissions[0] + (system_a_projected_emissions[0] + system_a_projected_emissions[1])/15), + # arrowprops=dict(arrowstyle="->", color=LINE), + fontsize=font_size-3, + color=LINE + ) + + if intersection_x > time_horizon_array[-1]: + y_top_lim = intersection_y + abs(min(system_a_projected_emissions) - min(system_b_projected_emissions))/2 + if intersection_x > 1 and intersection_x < 2: + x_text_coord = intersection_x + 0.3 + y_text_coord = intersection_y - abs(min(system_a_projected_emissions) - min(system_b_projected_emissions)) + else: + x_text_coord = intersection_x - 3.1 + y_text_coord = intersection_y + abs(min(system_a_projected_emissions) - min(system_b_projected_emissions))/25 - ax2.plot(time_horizon_array, ratio, linestyle='-', color=LINE, marker='^', linewidth=2, markersize=10) - ax2.set_ylabel('Ratio', color=LINE, fontsize=font_size, fontweight='bold') - ax2.tick_params(axis='y', colors=LINE) + ax1.annotate( + f"{intersection_x:.1f} years", + xy=(intersection_x, intersection_y), + xytext=(x_text_coord, y_text_coord), + # arrowprops=dict(arrowstyle="->", color=LINE), + fontsize=font_size-3, + color=LINE + ) - line = ax2.lines[0] - for x_value, y_value in zip(line.get_xdata(), line.get_ydata()): - label = "{:.2f}".format(y_value) - ax2.annotate(label, (x_value, y_value), xytext=(0, 5), - textcoords="offset points", ha='center', va='bottom', color=LINE, fontsize=20) - ax2.axhline(1, color=LINE, linestyle='dashed', linewidth=2) - ax2.annotate('Break-even', (1.5, 1), xytext=(break_even_label_pos, 5), - textcoords="offset points", ha='center', va='bottom', color=LINE, fontsize=20, fontweight='bold') + for idx, i in enumerate(time_horizon_array): - ax1.set_ylabel('Accumulated CO$_2$ Kg.', fontsize=font_size) - ax1.set_xlabel('Year', fontsize=font_size) + if i < round(intersection_x) - 1 and idx != 0 and ratio[idx]>1: + + # Annotate with arrow and ratio text + + ax1.annotate( + '', + xy=(i, system_a_projected_emissions[idx]), # Arrow tip (upper line) + xytext=(i, system_b_projected_emissions[idx]), # Arrow base (bottom line) + arrowprops=dict(arrowstyle='<->', color=LINE), + fontsize=font_size - 5, + color=LINE, + ha='center' + ) + + # Add text next to the arrow at the midpoint + ax1.text( + i + 0.1, (system_b_projected_emissions[idx] + system_a_projected_emissions[idx])/2, + f'{ratio[idx]:.1f}x', + fontsize=font_size - 3, + ha='left', + va='center', + color=LINE + ) + + + + # ax2.plot(time_horizon_array, ratio, linestyle='-', color=LINE, marker='.', linewidth=2, markersize=20) + # ax2.set_ylabel('Ratio', color=LINE, fontsize=font_size, fontweight='bold') + # ax2.tick_params(axis='y', colors=LINE) + + # line = ax2.lines[0] + # for x_value, y_value in zip(line.get_xdata(), line.get_ydata()): + # label = "{:.2f}".format(y_value) + # ax2.annotate(label, (x_value, y_value), xytext=(15, 5), + # textcoords="offset points", ha='center', va='bottom', color=LINE, fontsize=font_size) + # ax2.axhline(1, color=LINE, linestyle='dashed', linewidth=2, label="Break-even") + + ax1.set_ylabel('Acc. CO$_2$ [Tons]', fontsize=font_size) + ax1.set_xlabel('Duration [Years]', fontsize=font_size) ax1.set_xticks(time_horizon_array) ax1.tick_params(axis='x', labelsize=font_size) ax1.tick_params(axis='y', labelsize=font_size) - ax2.tick_params(axis='y', labelsize=font_size) - ax2.set_ylim(bottom=0) - ax2.set_ylim(top=np.max(ratio) + np.std(ratio)) + if y_top_lim is not None: + ax1.set_ylim(top=y_top_lim) + ax1.set_xlim(left=0) + ax1.set_ylim(bottom=0) + x_tick_labels = ax1.get_xticklabels() + x_tick_labels[0].set_visible(False) + # ax2.tick_params(axis='y', labelsize=font_size) + # ax2.set_ylim(bottom=0) + # ax2.set_ylim(top=np.max(ratio) + np.std(ratio)) + + # Get handles and labels from both axes + handles1, labels1 = ax1.get_legend_handles_labels() + # handles2, labels2 = ax2.get_legend_handles_labels() - ax1.set_title('Projected CO$_2$ Emissions', fontsize=font_size) + # Combine them + handles = handles1 + labels = labels1 - # Move the legend above the plot - ax1.legend(loc='upper center', ncol=2, fontsize=font_size, frameon=False, - bbox_to_anchor=(0.5, 1.3)) # Adjust vertical position + if not separate_legend: + # Move the legend above the plot + # Add a combined legend to the figure + fig.legend(handles, labels, loc="upper center", ncol=3, fontsize=font_size, bbox_to_anchor=(0.53, 1.14)) + # ax1.legend(loc='upper center', ncol=3, fontsize=font_size, + # bbox_to_anchor=(0.5, 1.2)) # Adjust vertical position + plt.tight_layout() + print(save_path) plt.savefig(f"{save_path}.png", bbox_inches='tight') plt.savefig(f"{save_path}.svg", bbox_inches='tight') - plt.show() + if separate_legend: + # Separate legend figure + legend_fig = plt.figure(figsize=(8, 1)) + legend_ax = legend_fig.add_subplot(111) + legend_ax.axis("off") + separate_legend = legend_ax.legend(handles, labels, loc="center", ncol=3, fontsize=font_size, frameon=True) + + # Save the separate legend figure + legend_fig.savefig(os.path.join(os.path.dirname(save_path), "legend.png"), bbox_inches="tight") + legend_fig.savefig(os.path.join(os.path.dirname(save_path), "legend.svg"), bbox_inches="tight") + + # plt.show() diff --git a/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.png b/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.png index 4a47f35..5995fe9 100644 Binary files a/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.png and b/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.png differ diff --git a/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.svg b/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.svg index 45c76b1..dae9ca1 100644 --- a/lifecycle_anslysis/scenarios/sorting_intel_model/plots/country-germany-utilization-30-workload-sorting.svg +++ 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--- a/lifecycle_anslysis/scenarios/sorting_intel_model/sorting_intel.py +++ b/lifecycle_anslysis/scenarios/sorting_intel_model/sorting_intel.py @@ -6,14 +6,14 @@ from lifecycle_anslysis.system import System # assumptions -time_horizon = 20 +time_horizon = 17 ############################## # Systems ############################## # shared specs -lifetime = 20 +lifetime = 10 dram_capacity = 8 * 64 ssd_capacity = 2 * 1600 hdd_capacity = 0 @@ -58,4 +58,4 @@ opex_calculation=GUPTA_MODEL) fig_size = (10, 5) - create_projections_plot(new_system_opex, old_system_opex, ratio, save_path, step_size=2, fig_size=fig_size) + create_projections_plot(new_system_opex, old_system_opex, ratio, save_path, step_size=1, fig_size=fig_size) diff --git a/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/plots/HPE-country-germany-utilization-30-workload-specint.png b/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/plots/HPE-country-germany-utilization-30-workload-specint.png index 2b39e7d..0f84685 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+ Matplotlib v3.9.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd.py b/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd.py index 4caa165..e4bdf06 100644 --- a/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd.py +++ b/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd.py @@ -6,7 +6,7 @@ from lifecycle_anslysis.system import System # assumptions -time_horizon = 10 +time_horizon = 17 ############################## # Systems @@ -22,8 +22,8 @@ old_system = System( # cm^2; # Die size from: https://www.techpowerup.com/cpu-specs/epyc-7502p.c2260 die_size=74 / 100, - # according to https://www.spec.org/cpu2006/results/ and https://www.spec.org/cpu2017/results/ - performance_indicator=285.44, + # according to https://www.spec.org/cpu2017/results/res2020q2/cpu2017-20200413-21933.pdf + performance_indicator=217, cpu_tdp=180, lifetime=lifetime, dram_capacity=dram_capacity, @@ -34,8 +34,8 @@ # AMD 9334, 2.7 GHz new_system = System( die_size=4 * 72 / 100, # cm^2, - # according to https://www.spec.org/cpu2006/results/ and https://www.spec.org/cpu2017/results/ - performance_indicator=470.4, + # according to https://www.spec.org/cpu2017/results/res2023q1/cpu2017-20230130-33834.pdf + performance_indicator=363, cpu_tdp=210, lifetime=lifetime, dram_capacity=dram_capacity, diff --git a/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd_no_legend.py b/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd_no_legend.py new file mode 100644 index 0000000..ae38395 --- /dev/null +++ b/lifecycle_anslysis/scenarios/specint_amd_model_vs_hpe/specint_amd_no_legend.py @@ -0,0 +1,95 @@ +import os + +from lifecycle_anslysis.comparison import generate_systems_comparison +from lifecycle_anslysis.constants import GERMANY, SWEDEN, HPE_POWER_ADVISOR, GUPTA_MODEL +from lifecycle_anslysis.plotting import create_projections_plot +from lifecycle_anslysis.system import System + +# assumptions +time_horizon = 17 + +############################## +# Systems +############################## + +# shared specs +lifetime = 10 +dram_capacity = 8 * 64 +ssd_capacity = 2 * 1600 +hdd_capacity = 0 + +# AMD EPYC 7502P, 2.5GHz +old_system = System( + # cm^2; # Die size from: https://www.techpowerup.com/cpu-specs/epyc-7502p.c2260 + die_size=74 / 100, + # according to https://www.spec.org/cpu2017/results/res2020q2/cpu2017-20200413-21933.pdf + performance_indicator=217, + cpu_tdp=180, + lifetime=lifetime, + dram_capacity=dram_capacity, + ssd_capacity=ssd_capacity, + hdd_capacity=hdd_capacity, +) + +# AMD 9334, 2.7 GHz +new_system = System( + die_size=4 * 72 / 100, # cm^2, + # according to https://www.spec.org/cpu2017/results/res2023q1/cpu2017-20230130-33834.pdf + performance_indicator=363, + cpu_tdp=210, + lifetime=lifetime, + dram_capacity=dram_capacity, + ssd_capacity=ssd_capacity, + hdd_capacity=hdd_capacity, +) + +fixed_y_axis_lim = { + (SWEDEN, 30): 0.7 +} + +if __name__ == '__main__': + # plot comparison plots + save_root_path = "./plots_no_legend" + os.makedirs(save_root_path, exist_ok=True) + for country in [GERMANY, SWEDEN]: + for utilization in [30, 60, 90]: + fig_size = (10, 5) + + save_path = os.path.join(save_root_path, + f"HPE-country-{country}-utilization-{utilization}-workload-specint") + hpe_new_system_opex, hpe_old_system_opex, hpe_abs_savings, hpe_relative_savings, hpe_ratio = \ + generate_systems_comparison( + new_system=new_system, + old_system=old_system, + time_horizon=time_horizon, + country=country, + utilization=utilization, + opex_calculation=HPE_POWER_ADVISOR) + create_projections_plot(hpe_new_system_opex, hpe_old_system_opex, hpe_ratio, save_path, fig_size=fig_size) + + if (country, utilization) in fixed_y_axis_lim: + create_projections_plot(hpe_new_system_opex, hpe_old_system_opex, hpe_ratio, save_path, + fig_size=fig_size, y_top_lim=fixed_y_axis_lim[(country, utilization)], + separate_legend=True) + else: + create_projections_plot(hpe_new_system_opex, hpe_old_system_opex, hpe_ratio, save_path, + fig_size=fig_size, separate_legend=True) + + save_path = os.path.join(save_root_path, + f"MODEL-country-{country}-utilization-{utilization}-workload-specint") + model_new_system_opex, model_old_system_opex, model_abs_savings, model_relative_savings, model_ratio = \ + generate_systems_comparison( + new_system=new_system, + old_system=old_system, + time_horizon=time_horizon, + country=country, + utilization=utilization, + opex_calculation=GUPTA_MODEL) + + if (country, utilization) in fixed_y_axis_lim: + create_projections_plot(model_new_system_opex, model_old_system_opex, model_ratio, save_path, + fig_size=fig_size, y_top_lim=fixed_y_axis_lim[(country, utilization)], + separate_legend=True) + else: + create_projections_plot(model_new_system_opex, model_old_system_opex, model_ratio, save_path, + fig_size=fig_size, separate_legend=True) diff --git a/lifecycle_anslysis/scenarios/specint_intel_model/plots/country-germany-utilization-30-workload-specint.png b/lifecycle_anslysis/scenarios/specint_intel_model/plots/country-germany-utilization-30-workload-specint.png index 80fe4b7..a0cbf06 100644 Binary files a/lifecycle_anslysis/scenarios/specint_intel_model/plots/country-germany-utilization-30-workload-specint.png and b/lifecycle_anslysis/scenarios/specint_intel_model/plots/country-germany-utilization-30-workload-specint.png differ diff --git a/lifecycle_anslysis/scenarios/specint_intel_model/plots/country-germany-utilization-30-workload-specint.svg b/lifecycle_anslysis/scenarios/specint_intel_model/plots/country-germany-utilization-30-workload-specint.svg index 73c8ed8..1df989f 100644 --- 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new_system = System( die_size=(4 * 477) / 100, # cm^2, - performance_indicator=1649.2, + performance_indicator=445, cpu_tdp=350, lifetime=lifetime, dram_capacity=dram_capacity, diff --git a/lifecycle_anslysis/scenarios/utilization/power_utilization.py b/lifecycle_anslysis/scenarios/utilization/power_utilization.py new file mode 100644 index 0000000..12f2449 --- /dev/null +++ b/lifecycle_anslysis/scenarios/utilization/power_utilization.py @@ -0,0 +1,93 @@ +import os.path + +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.axes import Axes + +plt.rcParams.update({'text.usetex': True + , 'pgf.rcfonts': False + , 'text.latex.preamble': r"""\usepackage{iftex} + \ifxetex + \usepackage[libertine]{newtxmath} + \usepackage[tt=false]{libertine} + \setmonofont[StylisticSet=3]{inconsolata} + \else + \RequirePackage[tt=false, type1=true]{libertine} + \fi""" + }) + +def create_utilization_plots(intercept:float, ax:Axes, color): + + def generate_normalized_power_usage(intercept): + + #### Source: https://ieeexplore.ieee.org/document/4404806/?arnumber=4404806&tag=1 + ### See Fig 2 + power_slope = (100 - intercept) / (100 - 0) + utilization_range = list(range(0, 110, 25)) + + return [(intercept + utilization * power_slope) for utilization in utilization_range] + + npc = generate_normalized_power_usage(intercept) + utilization_range = list(range(0, 110, 25)) + + ax.plot(utilization_range, npc, color=color, label=f'$\\textsf{{P}}_0$: {intercept}', linewidth=1.5) + +# Define the capped exponential growth function +def capped_exponential(x, L, a, k): + return L - (L - a) * np.exp(-k * x) + +fig, ax = plt.subplots(figsize=(2, 1)) + +intercept_range = [i for i in range(0, 110, 25)] +colors = ['#fd8d3c','#fc4e2a','#e31a1c','#bd0026','#800026'] + +for id, intercept in enumerate(intercept_range): + color = colors[id] + create_utilization_plots(intercept, ax, color) + +save_root_path = "lifecycle_anslysis/scenarios/utilization/plots/" +os.makedirs(save_root_path, exist_ok=True) +# save_path = os.path.join(save_root_path, f"utilization_power_usage") + +# ax.set_title('A) Linear Model', fontsize = 25) +ax.set_xlabel('Utilization [\%]', fontsize = 10) +ax.set_ylabel('NPC [\%]', fontsize = 10) +# Place legend outside the Axes on the right +ax.legend(loc='upper right', bbox_to_anchor=(1.7, 1.175), fontsize=10, frameon=True) +ax.tick_params(axis='x', labelsize=10) # Set x-axis tick label size +ax.tick_params(axis='y', labelsize=10) # Set y-axis tick label size +fig.savefig(os.path.join(os.path.dirname(save_root_path), "utilization_power.png"), bbox_inches="tight") +fig.savefig(os.path.join(os.path.dirname(save_root_path), "utilization_power.svg"), bbox_inches="tight") + +# # Set parameters +# L = 100 # Cap (upper bound) +# a = 50 # Initial value +# k_values = np.arange(0.03, 1, round((1-0.045)/4, 2)) # Different values of k +# colors = ['#fd8d3c','#fc4e2a','#e31a1c','#bd0026','#800026'] + +# # Generate x values +# x = np.linspace(0, 110, 25) + +# # Plot the curves for different k values +# # plt.figure(figsize=(10, 6)) +# for idk, k in enumerate(k_values): +# y = capped_exponential(x, L, a, k) +# ax[1].plot(x, y, label=f'$\\textsf{{k}}= {round(k, 2)}$', color=colors[idk], linewidth=3) + +# # Customize the plot +# ax[1].set_title('B) Exponential Growth Model', fontsize = 25) +# ax[1].set_xlabel('Utilization [\%]', fontsize = 25) +# # ax[1].set_ylabel('Normalized Power Usage [\%]', fontsize = 20) +# ax[1].legend(fontsize = 19) +# ax[0].set_xlim(-1,105) +# ax[1].set_xlim(-1,105) +# ax[0].set_ylim(-1,105) +# ax[1].set_ylim(-1,105) +# ax[0].tick_params(axis='x', labelsize=25) # Set x-axis tick label size +# ax[0].tick_params(axis='y', labelsize=25) # Set y-axis tick label size +# ax[1].tick_params(axis='x', labelsize=25) # Set x-axis tick label size +# ax[1].tick_params(axis='y', labelsize=25) # Set y-axis tick label size +# plt.tight_layout() +# # plt.grid(True) +# plt.savefig(os.path.join(os.path.dirname(save_root_path), "power_models.png"), bbox_inches="tight") +# plt.savefig(os.path.join(os.path.dirname(save_root_path), "power_models.svg"), bbox_inches="tight") \ No newline at end of file