Commit 5d150231 authored by Bryce Hepner's avatar Bryce Hepner
Browse files

small testing changes

parent b0b364b9

FPNprojstart.py

+4 −4
Original line number Diff line number Diff line
@@ -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()