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Sliding window changes #33

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Merged
mtorabi59 merged 8 commits into from
Mar 4, 2025
Merged

Sliding window changes #33

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1af1a72
added std parameter to SW methods and limit the width to the window size
mibur1 Feb 14, 2025
fd8db39
vectorized pearson correlation
mibur1 Feb 14, 2025
cd270e3
Set diagonal to 1
mibur1 Feb 18, 2025
0702f25
readded accidentally removed lines for GraphLasso
mibur1 Feb 18, 2025
40d2031
readded convolution to follow Allen et al.
mibur1 Feb 18, 2025
9f10bf7
minor changes
mtorabi59 Mar 4, 2025
c3b7579
minor changes
mtorabi59 Mar 4, 2025
6ba60e6
minor changes
mtorabi59 Mar 4, 2025
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1 change: 1 addition & 0 deletions pydfc/dfc_methods/discrete_hmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,6 +65,7 @@ def __init__(self, **params):
"backend",
"n_subj_clstrs",
"W",
"window_std",
"n_overlap",
"n_states",
"normalization",
Expand Down
51 changes: 22 additions & 29 deletions pydfc/dfc_methods/sliding_window.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@ def __init__(self, **params):
"is_state_based",
"sw_method",
"tapered_window",
"window_std",
"W",
"n_overlap",
"normalization",
Expand Down Expand Up @@ -95,7 +96,7 @@ def calc_MI(self, X, Y):
return MI

def FC(self, time_series):

# Graphical Lasso
if self.params["sw_method"] == "GraphLasso":
# Standardize the data (zero mean, unit variance for each feature)
mean = np.mean(time_series, axis=1, keepdims=True)
Expand All @@ -105,29 +106,28 @@ def FC(self, time_series):
model.fit(time_series_standardized.T)
# the covariance matrix will equal the correlation matrix
C = model.covariance_
else:

# Mutual information
elif self.params["sw_method"] == "MI":
C = np.zeros((time_series.shape[0], time_series.shape[0]))

for i in range(time_series.shape[0]):
for j in range(i, time_series.shape[0]):

X = time_series[i, :]
Y = time_series[j, :]
C[j, i] = self.calc_MI(X, Y)

if self.params["sw_method"] == "MI":
########### Mutual Information ##############
C[j, i] = self.calc_MI(X, Y)
else:
########### Pearson Correlation ##############
if np.var(X) == 0 or np.var(Y) == 0:
C[j, i] = 0
else:
C[j, i] = np.corrcoef(X, Y)[0, 1]

C[i, j] = C[j, i]

# Pearson correlation
else:
C = np.corrcoef(time_series)
C[np.isnan(C)] = 0
# make the diagonal elements 1 (for nan values on the diagonal)
C[np.diag_indices_from(C)] = 1
return C

def dFC(self, time_series, W=None, n_overlap=None, tapered_window=False):
def dFC(
self, time_series, W=None, n_overlap=None, tapered_window=False, window_std=None
):
# W is in time samples

L = time_series.shape[1]
Expand All @@ -141,34 +141,26 @@ def dFC(self, time_series, W=None, n_overlap=None, tapered_window=False):
model.fit(time_series.T)
self.graphical_lasso_alpha_ = model.alpha_

window_taper = signal.windows.gaussian(W, std=3 * W / 22)
# C = DFC(measure=self)
FCSs = list()
TR_array = list()
for l in range(0, L - W + 1, step):

######### creating a rectangel window ############
# Create rectangular window
window = np.zeros((L))
window[l : l + W] = 1

########### tapering the window ##############
# Taper the window
if tapered_window:
std = window_std if window_std is not None else 3 * W / 22
window_taper = signal.windows.gaussian(W, std=std)
window = signal.convolve(window, window_taper, mode="same") / sum(
window_taper
)

window = np.repeat(
np.expand_dims(window, axis=0), time_series.shape[0], axis=0
)

# int(l-W/2):int(l+3*W/2) is the nonzero interval after tapering
FCSs.append(
self.FC(
np.multiply(time_series, window)[
:, max(int(l - W / 2), 0) : min(int(l + 3 * W / 2), L)
]
)
)
FCSs.append(self.FC(np.multiply(time_series, window)[:, l : l + W]))
TR_array.append(int((l + (l + W)) / 2))

return np.array(FCSs), np.array(TR_array)
Expand Down Expand Up @@ -200,6 +192,7 @@ def estimate_dFC(self, time_series):
W=int(self.params["W"] * time_series.Fs),
n_overlap=self.params["n_overlap"],
tapered_window=self.params["tapered_window"],
window_std=self.params["window_std"],
)

# record time
Expand Down
1 change: 1 addition & 0 deletions pydfc/dfc_methods/sliding_window_clustr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ def __init__(self, clstr_distance="euclidean", **params):
"coi_correction",
"n_subj_clstrs",
"W",
"window_std",
"n_overlap",
"n_states",
"normalization",
Expand Down
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