still in progess, added a ton of code for remote

Remove_Noise.py

 from matplotlib.image import composite_images
 from WorkingPyDemo import *
-
+import paramiko
+def setup_remote_sftpclient():
+    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):
     same_sensor_images = []
     which_sensor = str(which_sensor)
@@ -19,18 +25,127 @@ def remove_noise(images, which_sensor):
         # print(np.array(image_object)[1:] + average_image)
         average_image = np.array(image_object)[1:] + average_image
     return average_image/len(same_sensor_images)
-scenes = file_extractor(folder_name)
-images = image_extractor(scenes)
-average_image = remove_noise(images,"7")
+
+def remote_remove_noise(images, which_sensor):
+    sftp_client = setup_remote_sftpclient()
+    averages = []
+    same_sensor_images = []
+    which_sensor = str(which_sensor)
+    first_image = sftp_client.open(images[0])
+    average_image = np.array(Image.open(first_image))[1:]
+    for i, image_name in enumerate(images):
+        if int(which_sensor) > 9:
+            if image_name[-7:-5] == which_sensor:
+                same_sensor_images.append(image_name)
+        else:
+            if image_name[-7:-5] == "_" + which_sensor:
+                same_sensor_images.append(image_name)
+    images = []
+    for i, image_name in enumerate(same_sensor_images):
+        # print(image_name)
+        image_object = sftp_client.open(image_name)
+        image_object = Image.open(image_object)
+        images.append(np.array(image_object)[1:])
+        # print(np.array(image_object).shape)
+        # print(np.array(image_object)[1:] + average_image)
+        if (i % 100 == 0) and i!=0:
+
+            image_object = np.mean(np.array(images),axis = 0)
+            # print(image_object.shape)
+            averages.append(image_object)
+            # print(average_image.shape)
+            images = []
+    image_object = np.mean(np.array(images))
+    averages.append(image_object)
+    sftp_client.close()
+    return np.mean(averages,axis=0)
+
+def remote_file_extractor(headname = "/media/elphel/NVME/lwir16-proc/te0607/scenes/"):
+    """Find all the files in the directory
+    
+    Parameters:
+        dirname (str): the directory name
+        
+    Returns:
+        files (list): a list of all the files in the directory
+    """
+    client = paramiko.SSHClient()
+    client.load_system_host_keys()
+    client.connect("192.168.0.107", username="elphel")
+    sftp_client = client.open_sftp()
+    # sftp_client.listdir("media/elphel/NVME/lwir16-proc/te0607/scenes/")
+    dirs_in_scenes = sftp_client.listdir("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
+    scenes = []
+    for i, curr_folder in enumerate(dirs_in_scenes):
+        if "." not in curr_folder:
+            smaller_dirs = sftp_client.listdir(headname + curr_folder)
+            for small_folder in smaller_dirs:
+                scenes.append(headname + curr_folder + "/" + small_folder)
+    return scenes
+
+def remote_image_extractor(scenes):
+    sftp_client = setup_remote_sftpclient()
+    image_folder = []
+    for scene in scenes:
+        files = sftp_client.listdir(scene)
+        for file in files:
+            if file[-5:] != ".tiff" or file[-7:] == "_6.tiff":
+                continue
+            else:
+                image_folder.append(os.path.join(scene, file))
+    sftp_client.close()
+    return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor
+
+def remove_the_noise(new_image, average_image):
+    original_image_min = np.min(newimage)
+    original_image_max = np.max(new_image)
+    adjusted_image = new_image - average_image
+    adjusted_image = adjusted_image - np.min(adjusted_image)
+    adjusted_image = adjusted_image*original_image_max/np.max(adjusted_image)
+    adjusted_image = adjusted_image + original_image_min
+    return adjusted_image
+
 def color_adjust(visual_array):
     min_of_errors = np.min(visual_array)
     adjusted_array = visual_array - min_of_errors
-    adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))
+    adjusted_array = adjusted_array/np.max(adjusted_array)
+    # print(adjusted_array)
+    # print(np.max(adjusted_array))
     return adjusted_array
-print(np.max(average_image))
-print(np.min(average_image))
-plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=255)
-plt.show()
+if __name__ == "__main__":
+    scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
+    # images = remote_image_extractor(np.random.choice(scenes,10000,replace = False))
+    images = remote_image_extractor(scenes)
+    # average_image = remote_remove_noise(images,"10")
+
+    average_image = np.array(Image.open("hopefullyaverage.tiff"))
+
+    # average_savable_image = Image.fromarray(average_image)
+    # average_savable_image.save("hopefullyaverage.tiff")
+    # print(np.max(average_image))
+    # print(np.min(average_image))
+    # average_image = color_adjust(average_image)
+    plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
+    plt.show()
+
+    # print(len(images))
+    sftp_client = setup_remote_sftpclient()
+    print(len(images))
+    for i, item in enumerate(images[22000:22030]):
+        if item[-7:-5] == "10":
+            print(i)
+            print(item)
+    print(images[22016])
+    test_image = sftp_client.open(images[22016])
+    test_image = Image.open(test_image)
+    test_image = np.array(test_image)[1:]
+    newimage = Image.fromarray(test_image - average_image)
+    newimage.save("NoInterference.tiff")
+    plt.subplot(121)
+    plt.imshow(color_adjust(test_image),cmap='gray',vmin = 0, vmax=1)
+    plt.subplot(122)
+    plt.imshow(color_adjust(test_image-average_image),cmap='gray',vmin = 0, vmax=1)
+    plt.show()
 
-# print(np.linalg.inv(np.array([[3,0,-1],[0,3,3],[1,-3,-4]])))
-print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))
+    # print(np.linalg.inv(np.array([[3,0,-1],[0,3,3],[1,-3,-4]])))
+    # print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))
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