Commit 5981345f authored by Bryce Hepner's avatar Bryce Hepner
Browse files

Probably small rerun

parent 2aee9566

MetadataAnalyzer.py

+32 −31
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, "5")

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)
@@ -56,7 +56,7 @@ information_array = np.zeros((len(images),5)) 
def celcius_to_kelvin(celcius):

    return celcius + 273.15

last_image = np.array(Image.open(sftp_client.open(images[0]))).astype(np.uint16)

for i in range(1,3000):

for i in range(20,40):

    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])
@@ -64,7 +64,8 @@ for i in range(1,3000): 
    information_array[i,0] = current_frame_count - frame_count_at_FFC

    information_array[i,-1] = 4096/np.std(image_array[1:])

    information_array[i,-2] = mean_squared_error(gaussian_filter(image_array[1:],sigma=.4),image_array[1:])

    print(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[110:111]))

    # print(bin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[110:112]))[2:].zfill(8))

    print(bin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[110:114]))[2:].zfill(8))

    # 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]) - \
@@ -91,38 +92,38 @@ for i in range(1,3000): 
# print("middle")





mask = information_array[:,0] > 0

# mask = information_array[:,0] > 0



information_df = pd.DataFrame(information_array[mask],columns=["Frame_Spacing","Last_MSE","ImageName","Signal_to_Noise","MSE"])

# information_df.to_csv("smaller_information_weird_array.csv")

information_df = pd.read_csv("smaller_information_weird_array.csv",index_col=0)

information_array = information_df.values

print(information_array[0,2])

plt.scatter(information_array[:,0],information_array[:,-1],s=1)

plt.legend()

plt.xlabel("Frame_Spacing")

plt.ylabel("Signal_to_Noise")

plt.show()

colors = ["red","blue","green","orange","purple","brown","pink","black","grey","cyan","magenta","yellow","white","lime","teal","olive","maroon","navy","silver","gold","indigo","violet"]

secondmask = information_array[:,1] < 30000

print(np.unique(information_array[:,2]))

for g in np.unique(information_array[:,2]):

    i = np.where(information_array[:,2] == g)

    print(g)

    plt.scatter(information_array[:,1][secondmask][i],information_array[:,4][secondmask][i],s=1,label=g,color = colors[int(g)])

# information_df = pd.DataFrame(information_array[mask],columns=["Frame_Spacing","Last_MSE","ImageName","Signal_to_Noise","MSE"])

# # information_df.to_csv("smaller_information_weird_array.csv")

# information_df = pd.read_csv("smaller_information_weird_array.csv",index_col=0)

# information_array = information_df.values

# print(information_array[0,2])

# plt.scatter(information_array[:,0],information_array[:,-1],s=1)

# plt.legend()

# plt.xlabel("Frame_Spacing")

# plt.ylabel("Signal_to_Noise")

# plt.show()

# colors = ["red","blue","green","orange","purple","brown","pink","black","grey","cyan","magenta","yellow","white","lime","teal","olive","maroon","navy","silver","gold","indigo","violet"]

# secondmask = information_array[:,1] < 30000

# print(np.unique(information_array[:,2]))

# for g in np.unique(information_array[:,2]):

#     i = np.where(information_array[:,2] == g)

#     print(g)

#     plt.scatter(information_array[:,1][secondmask][i],information_array[:,4][secondmask][i],s=1,label=g,color = colors[int(g)])



plt.legend()

plt.xlabel("Frame_Spacing")

plt.ylabel("Signal_to_Noise")

plt.show()

# print(np.max(information_array[:,0]))

# print(information_array[-5:-1,0])

# plt.legend()

# plt.xlabel("Frame_Spacing")

# plt.ylabel("Signal_to_Noise")

# plt.show()

# # print(np.max(information_array[:,0]))

# # print(information_array[-5:-1,0])



X = sm.add_constant(information_df.drop(["Signal_to_Noise"],axis=1))

# X = sm.add_constant(information_df.drop(["Signal_to_Noise"],axis=1))



y = information_df["Signal_to_Noise"]

# y = information_df["Signal_to_Noise"]





end_result = sm.OLS(y,X).fit()

print(end_result.summary())

# end_result = sm.OLS(y,X).fit()

# print(end_result.summary())

sftp_client.close()