Replace Main.py

TE_filters/Main.py

@@ -53,7 +53,7 @@ results = {}
 
 def gaussian_kernel(scale, position):
     size=2*scale+1
-    x = np.linspace(-size // 2, size // 2, size)
+    x = np.linspace(-(size//2), size // 2, size)
     kernel = np.exp(-0.5 * (x / scale) ** 2)
     
     if position == 'causal':
@@ -68,6 +68,101 @@ def gaussian_kernel(scale, position):
     kernel /= kernel.sum()
     return kernel
 
+def gaussian_HP_kernel(scale, position):
+    size=2*scale+1
+    x = np.linspace(-(size//2), size // 2, size)
+    kernel = np.exp(-0.5 * (x / scale) ** 2)
+
+    if position == 'causal':
+        kernel[:scale] = 0  
+
+    elif position == 'anticausal':
+        kernel[scale + 1:] = 0  
+
+    elif position == 'symmetric':
+        pass
+    
+
+    kernel /= kernel.sum()
+    delta=np.zeros(size)
+    delta[scale]=1
+    kernel=delta-kernel
+    return kernel
+
+
+
+def box_HP_kernel(scale, position):
+    size=2*scale+1
+    kernel = np.ones(size)
+    
+    if position == 'causal':
+        kernel[:scale] = 0  
+
+    elif position == 'anticausal':
+        kernel[scale + 1:] = 0  
+
+    elif position == 'symmetric':
+        pass
+    
+
+    kernel /= kernel.sum()
+    delta=np.zeros(size)
+    delta[scale]=1
+    kernel=delta-kernel
+    return kernel
+
+
+def gaussian_BP_kernel(scale_high,position,scale_low=None):
+    if scale_low==None:
+        scale_low=2*scale_high
+    size=2*scale_low+1
+    x = np.linspace(-(size//2), size // 2, size)
+    kernel_low = np.exp(-0.5 * (x / scale_low) ** 2)
+    kernel_high = np.exp(-0.5 * (x / scale_high) ** 2)
+    if position == 'causal':
+        kernel_low[:size//2] = 0  
+        kernel_high[:size//2] = 0  
+
+    elif position == 'anticausal':
+        kernel_low[size//2 + 1:] = 0  
+        kernel_high[size//2 + 1:] = 0  
+
+    elif position == 'symmetric':
+        pass
+
+    kernel_low /= kernel_low.sum()
+    kernel_high /= kernel_high.sum()
+
+    kernel=kernel_low-kernel_high
+    return kernel
+
+def box_BP_kernel(scale_high,position,scale_low=None ):
+    if scale_low==None:
+        scale_low=2*scale_high
+    size=2*scale_low+1
+    x = np.linspace(-(size//2), size // 2, size)
+    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
+    if position == 'causal':
+        kernel_low[:size//2] = 0  
+        kernel_high[:size//2] = 0  
+
+    elif position == 'anticausal':
+        kernel_low[size//2 + 1:] = 0  
+        kernel_high[size//2 + 1:] = 0  
+
+    elif position == 'symmetric':
+        pass
+
+    kernel_low /= kernel_low.sum()
+    kernel_high /= kernel_high.sum()
+
+    kernel=kernel_low-kernel_high
+    return kernel
+
+
 def increment_kernel(scale,position):
     if position == 'causal':
         kernel = np.zeros(2 * scale + 1)
@@ -92,7 +187,7 @@ def gabor_kernel(scale, position,omega=None):
     if omega==None:
         omega=1/scale
     size=2*scale+1
-    x = np.linspace(-size // 2, size // 2, size)
+    x = np.linspace(-(size//2), size // 2, size)
     
     
     if position == 'causal':
@@ -128,7 +223,10 @@ kernel_functions = {
     'increment': increment_kernel,
     'gaussian': gaussian_kernel,
     'gabor': gabor_kernel,
-    'box': box_kernel
+    'box': box_kernel,
+    'box_HP': box_HP_kernel,
+    'gaussian_HP': gaussian_HP_kernel,
+    
 }
 
 kernel_types = kernel_functions.keys()
@@ -207,7 +305,12 @@ for kernel_type in kernel_types:
 
 
  # %% 
-# Plotting impulse response for each filter
+# Impulse Response Plotting
+
+#kernel_types=['increment','box','gaussian','gabor']
+kernel_types=['box_HP','gaussian_HP']
+#kernel_types=['box_BP','gaussian_BP']
+
 
 impulse = np.zeros(100)
 impulse[len(impulse) // 2] = 1
@@ -225,10 +328,11 @@ for i, kernel_type in enumerate(kernel_types):
             kernel = kernel_functions[kernel_type](scale, position)
             impulse_response = convolve(impulse, kernel, mode='same')
             ax.plot(impulse_response, label=f'Scale={scale}')
+        ax.axhline(0, color='black', linestyle='--')  # Add horizontal line at 0
         ax.legend(fontsize=24)
 
 plt.tight_layout(rect=[0, 0, 1, 0.95])
-plt.savefig('/users2/local/e22froge/codes/TE_Filter/Impulse_responses.pdf', bbox_inches='tight')
+plt.savefig('/users2/local/e22froge/codes/TE_Filter/Impulse_responses_HP.pdf', bbox_inches='tight')
 plt.show()
 
 #%%
@@ -245,10 +349,11 @@ for i, kernel_type in enumerate(kernel_types):
         for scale in scales:
             kernel = kernel_functions[kernel_type](scale, position)
             ax.plot(kernel, label=f'Scale={scale}')
+        ax.axhline(0, color='black', linestyle='--')  # Add horizontal line at 0
         ax.legend(fontsize=24)
 
 plt.tight_layout(rect=[0, 0, 1, 0.95])
-plt.savefig('/users2/local/e22froge/codes/TE_Filter/Kernels.pdf', bbox_inches='tight')
+plt.savefig('/users2/local/e22froge/codes/TE_Filter/Kernels_HP.pdf', bbox_inches='tight')
 plt.show()
 
 # %%