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Commit ccd27a30 authored by FROGE Ewen's avatar FROGE Ewen
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Replace Main.py

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......@@ -43,11 +43,17 @@ Modane = np.fromfile(fid, dtype='>f')[:N]
fbm_signal,_ = synthmrw(dim=1, N=N, H=1/3, lambda2=0) # Placeholder for FBM
fbm_signal=fbm_signal[:,0]
fgn_signal = np.diff(fbm_signal, axis=0)
scales = [10, 20] # Define your scales
positions = ['causal', 'symmetric', 'anticausal']
signals=['fgn_signal', 'fbm_signal', 'Modane']
max_lag_factor = 2 # We will vary lag up to 10 times the largest scale
# fbm_signal_flip=np.flip(fbm_signal)
# fgn_signal_flip=np.flip(fgn_signal)
# Modane_flip=np.flip(Modane)
# flipped_signals=['fgn_signal_flip','fbm_signal_flip','Modane_flip']
# Prepare results dictionary
results = {}
......@@ -144,7 +150,7 @@ def box_BP_kernel(scale_high,position,scale_low=None ):
kernel_low = np.zeros(size)
kernel_low[np.where(np.abs(x)<=scale_low)]=1
kernel_high = np.zeros(size)
kernel_high[np.where(np.abs(x)<=scale_low)]=1
kernel_high[np.where(np.abs(x)<=scale_high)]=1
if position == 'causal':
kernel_low[:size//2] = 0
kernel_high[:size//2] = 0
......@@ -220,12 +226,14 @@ def box_kernel(scale, position):
kernel_functions = {
'increment': increment_kernel,
'gaussian': gaussian_kernel,
'gabor': gabor_kernel,
'box': box_kernel,
'box_HP': box_HP_kernel,
'gaussian_HP': gaussian_HP_kernel,
#'increment': increment_kernel,
#'gaussian': gaussian_kernel,
#'gabor': gabor_kernel,
#'box': box_kernel,
#'box_HP': box_HP_kernel,
#'gaussian_HP': gaussian_HP_kernel,
'box_BP': box_BP_kernel,
'gaussian_BP': gaussian_BP_kernel,
}
......@@ -281,7 +289,7 @@ for kernel_type in kernel_types:
fig, axes = plt.subplots(3, 3, figsize=(20, 22))
fig.suptitle(f'Transfer Entropy Analysis - {kernel_type.capitalize()}', fontsize=30)
for i, sgn in enumerate(['fbm_signal', 'fgn_signal', 'Modane']):
for i, sgn in enumerate(signals):
for j, position in enumerate(positions):
ax = axes[i, j]
ax.set_title(f'{sgn.capitalize()} - {position.capitalize()}', fontsize=26)
......@@ -307,9 +315,9 @@ for kernel_type in kernel_types:
# %%
# Impulse Response Plotting
#kernel_types=['increment','box','gaussian','gabor']
kernel_types=['box_HP','gaussian_HP']
#kernel_types=['box_BP','gaussian_BP']
#kernel_types=['box','gaussian','gabor']
#kernel_types=['increment','box_HP','gaussian_HP']
kernel_types=['box_BP','gaussian_BP']
impulse = np.zeros(100)
......@@ -332,7 +340,7 @@ for i, kernel_type in enumerate(kernel_types):
ax.legend(fontsize=24)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig('/users2/local/e22froge/codes/TE_Filter/Impulse_responses_HP.pdf', bbox_inches='tight')
plt.savefig('/users2/local/e22froge/codes/TE_Filter/Impulse_responses_BP.pdf', bbox_inches='tight')
plt.show()
#%%
......@@ -353,7 +361,7 @@ for i, kernel_type in enumerate(kernel_types):
ax.legend(fontsize=24)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig('/users2/local/e22froge/codes/TE_Filter/Kernels_HP.pdf', bbox_inches='tight')
plt.savefig('/users2/local/e22froge/codes/TE_Filter/Kernels_BP.pdf', bbox_inches='tight')
plt.show()
# %%
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