added functions to downsample images

design_matrix_creation_module.py

@@ -0,0 +1,19 @@
+import numpy as np
+import pandas as pd
+
+
+def get_frame_at_time(time, stim_table):
+    starts = stim_table.start.values
+    idx = np.searchsorted(starts, time)-1
+    return(stim_table.iloc[idx].values[2])
+
+
+def get_sampled_stimulus_time_array(stim_table, sampling_time, flattened_image_list):
+    ns_time_start = stim_table.iloc[0].values[0]
+    ns_time_end = stim_table.iloc[stim_table.last_valid_index()][1]
+    time_array = np.linspace(ns_time_start,ns_time_end,int((ns_time_end-ns_time_start)/sampling_time))
+    stim_array = []
+    for idx, time_point in enumerate(time_array):
+        stim_index = get_frame_at_time(time_point, stim_table)
+        stim_array.append(flattened_image_list[int(stim_index)])
+    return(stim_array)

downsampling_module.py

@@ -0,0 +1,25 @@
+import numpy as np
+
+def downsample(arr, new_shape):
+    shape = (new_shape[0], arr.shape[0] // new_shape[0],
+             new_shape[1], arr.shape[1] // new_shape[1])
+    return arr.reshape(shape).mean(-1).mean(1)    
+
+def downsample_images(images, x_frac, y_frac):
+    """ Takes the 3d array of images from natural scenes and downsaples it by your chosen fractions
+
+        If the fractions do not divide the original image dimensions, this function removes pixels until
+        the fractions divide the original image dimensions
+
+    """
+    (current_image_size_x, current_image_size_y) = np.shape(images[0])
+    (new_image_size_x, new_image_size_y) = (current_image_size_x//x_frac, current_image_size_y//y_frac)
+    rem0 = current_image_size_x % new_image_size_x
+    rem1 = current_image_size_y % new_image_size_y
+    new_image_size = (new_image_size_x, new_image_size_y)
+    downsampled_image_list = []
+
+    for idx in np.arange(images.shape[0]):
+        downsampled_image_list.append(downsample(images[idx,0:current_image_size_x-rem0,0:current_image_size_y-rem1], new_image_size))
+
+    return(downsampled_image_list)