more docstrings

Remove_Noise.py

@@ -7,13 +7,26 @@ from scipy.stats import multivariate_normal
 
 from skimage.restoration import wiener
 def setup_remote_sftpclient():
+    """
+    Setup the SFTP client
+    Returns:
+        sftp_client (paramiko.SFTPClient): the SFTP client
+    """
     client = paramiko.SSHClient()
     client.load_system_host_keys()
     client.connect("192.168.0.107", username="elphel")
     sftp_client = client.open_sftp()
     return sftp_client
 
-def remove_noise(images, which_sensor):
+def create_average(images, which_sensor):
+    """
+    Create an average image from a list of images
+    Parameters:
+        images (list): a list of images
+        which_sensor (int): the sensor number
+    Returns:   
+        average_image (np.array): the average image
+    """
     same_sensor_images = []
     which_sensor = str(which_sensor)
     average_image = np.zeros_like(np.array(Image.open(images[0]))[1:])
@@ -31,6 +44,14 @@ def remove_noise(images, which_sensor):
     return average_image/len(same_sensor_images)
 
 def remote_create_average(images, which_sensor):
+    """
+    Create an average image from a list of images
+    Parameters:
+        images (list): a list of images
+        which_sensor (int): the sensor number
+    Returns:
+        average_image (np.array): the average image
+    """
     sftp_client = setup_remote_sftpclient()
     averages = []
     
@@ -81,6 +102,12 @@ def remote_file_extractor(headname = "/media/elphel/NVME/lwir16-proc/te0607/scen
     return scenes
 
 def remote_image_extractor(scenes):
+    """Find all the files in the directory
+    Parameters:
+        dirname (str): the directory name
+    Returns:
+       image_folder (list): a list of all the file paths in the directory
+    """
     sftp_client = setup_remote_sftpclient()
     image_folder = []
     for scene in scenes:
@@ -93,6 +120,14 @@ def remote_image_extractor(scenes):
     sftp_client.close()
     return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor
 def find_only_in_channel(images, channel_name = "10"):
+    """
+    Find the images that are only in the specified channel
+    Parameters:
+        images (list): a list of images
+        channel_name (str): the channel name
+    Returns:
+        same_sensor_images (list): a list of images that are only in the specified channel
+    """
     same_sensor_images = []
     for i, image_name in enumerate(images):
         if int(channel_name) > 9:
@@ -103,6 +138,14 @@ def find_only_in_channel(images, channel_name = "10"):
                 same_sensor_images.append(image_name)
     return same_sensor_images
 def adjust_to_original(new_image, average_image):
+    """
+    Adjust the image to the original image
+    Parameters:
+        new_image (np.array): the new image
+        average_image (np.array): the average image
+    Returns:
+        adjusted_image (np.array): the adjusted image
+    """
     original_image_min = np.min(new_image)
     original_image_max = np.max(new_image)
     average_image = average_image - np.mean(average_image)
@@ -126,12 +169,26 @@ def adjust_to_original(new_image, average_image):
     return adjusted_image.astype(np.uint16)
 
 def color_adjust(visual_array):
+    """
+    Adjust the image to the original image
+    Parameters:
+        visual_array (np.array): the visual array
+    Returns:
+        adjusted_array (np.array): the adjusted image
+    """
     min_of_errors = np.min(visual_array)
     adjusted_array = visual_array - min_of_errors
     adjusted_array = adjusted_array/np.max(adjusted_array)
     return adjusted_array
 
 def save_new_average(quantity_of_images = 1500, channel = "10", head_dir = "/media/elphel/NVME/lwir16-proc/te0607/scenes/"):
+    """
+    Save the new average image
+    Parameters:
+        quantity_of_images (int): the number of images to use in the average
+        channel (str): the channel name
+        head_dir (str): the head directory
+    """
     scenes = remote_file_extractor(head_dir)
     images = find_only_in_channel(remote_image_extractor(scenes),channel)
     if quantity_of_images != "all":
@@ -142,7 +199,14 @@ def save_new_average(quantity_of_images = 1500, channel = "10", head_dir = "/med
     average_savable_image = Image.fromarray(average_image)
     average_savable_image.save("Average_On_Channel(" + channel + ").tiff")
 
-def create_testable_images(images, selected_channel, quantity_of_images):
+def save_testable_images(images, selected_channel, quantity_of_images):
+    """
+    Saves the testable images
+    Parameters:
+        images (list): a list of images
+        selected_channel (str): the channel name
+        quantity_of_images (int): the number of images to use in the average
+    """
     sftp_client = setup_remote_sftpclient()
     images = find_only_in_channel(images, selected_channel)
 
@@ -174,8 +238,8 @@ def create_testable_images(images, selected_channel, quantity_of_images):
     sftp_client.close()
 
 def save_new_gauss():
-    """\
-    creates gaussian kernel with side length `l` and a sigma of `sig`
+    """
+    Save the new gauss image
     """
     
     # x,y = np.mgrid[-1:1:.003125, -1:1:.003125]
@@ -206,7 +270,7 @@ if __name__ == "__main__":
     images = remote_image_extractor(scenes)
     images = find_only_in_channel(images, "11")
     # average_image = np.array(Image.open("Average_On_Channel(" + "11" + ").tiff"))
-    # create_testable_images(images,"11",3)
+    # save_testable_images(images,"11",3)
     # plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
     # plt.show()