small testing changes

FPNprojstart.py

@@ -13,7 +13,7 @@ from sklearn.metrics import mean_squared_error
 
 scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
 images = remote_image_extractor(scenes)
-# images = find_only_in_channel(images, "11")
+images = find_only_in_channel(images, "5")
 sftp_client = setup_remote_sftpclient()
 image_array = np.array(Image.open(sftp_client.open(images[250])))
 # image_array = np.array(Image.open(images[250])).astype(np.uint16)
@@ -56,14 +56,14 @@ information_array = np.zeros((len(images),3))
 def celcius_to_kelvin(celcius):
     return celcius + 273.15
 
-for i in range(len(images)):
+for i in range(1000):
     image_array = np.array(Image.open(sftp_client.open(images[i]))).astype(np.uint16)
     current_frame_count = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[84:88])
     frame_count_at_FFC = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[88:92])
     # signaltonoise.append(1/np.std(image_array[1:]))
     information_array[i,0] = current_frame_count - frame_count_at_FFC
     # information_array[i,-1] = 1/np.std(image_array[1:])
-    information_array[i,-1] = mean_squared_error(image_array[1:],gaussian_filter(image_array[1:],sigma=.3))
+    information_array[i,-1] = mean_squared_error(gaussian_filter(image_array[1:],sigma=.2),image_array[1:])
     # print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[76:77])
     # print("start")
     information_array[i,1] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[94:96]) - \
@@ -84,7 +84,7 @@ for i in range(len(images)):
 mask = information_array[:,0] > 0
 information_df = pd.DataFrame(information_array[mask],columns=["Frame_Spacing","Curr_Temp_Diff","Signal_to_Noise"])
 information_df.to_csv("information_weird_array.csv")
-# information_df = pd.read_csv("information_array.csv")
+information_df = pd.read_csv("information_weird_array.csv")
 information_array = information_df.values
 plt.scatter(information_array[:,0],information_array[:,-1],s=1)
 plt.legend()