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

Probably small rerun

parent 2aee9566
...@@ -13,7 +13,7 @@ from sklearn.metrics import mean_squared_error ...@@ -13,7 +13,7 @@ from sklearn.metrics import mean_squared_error
scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/") scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
images = remote_image_extractor(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() sftp_client = setup_remote_sftpclient()
image_array = np.array(Image.open(sftp_client.open(images[250]))) image_array = np.array(Image.open(sftp_client.open(images[250])))
# image_array = np.array(Image.open(images[250])).astype(np.uint16) # image_array = np.array(Image.open(images[250])).astype(np.uint16)
...@@ -56,7 +56,7 @@ information_array = np.zeros((len(images),5)) ...@@ -56,7 +56,7 @@ information_array = np.zeros((len(images),5))
def celcius_to_kelvin(celcius): def celcius_to_kelvin(celcius):
return celcius + 273.15 return celcius + 273.15
last_image = np.array(Image.open(sftp_client.open(images[0]))).astype(np.uint16) 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) 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]) 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]) 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): ...@@ -64,7 +64,8 @@ for i in range(1,3000):
information_array[i,0] = current_frame_count - frame_count_at_FFC information_array[i,0] = current_frame_count - frame_count_at_FFC
information_array[i,-1] = 4096/np.std(image_array[1:]) 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:]) 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(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[76:77])
# print("start") # print("start")
# information_array[i,1] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[94:96]) - \ # 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): ...@@ -91,38 +92,38 @@ for i in range(1,3000):
# print("middle") # 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 = 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.to_csv("smaller_information_weird_array.csv")
information_df = pd.read_csv("smaller_information_weird_array.csv",index_col=0) # information_df = pd.read_csv("smaller_information_weird_array.csv",index_col=0)
information_array = information_df.values # information_array = information_df.values
print(information_array[0,2]) # print(information_array[0,2])
plt.scatter(information_array[:,0],information_array[:,-1],s=1) # plt.scatter(information_array[:,0],information_array[:,-1],s=1)
plt.legend() # plt.legend()
plt.xlabel("Frame_Spacing") # plt.xlabel("Frame_Spacing")
plt.ylabel("Signal_to_Noise") # plt.ylabel("Signal_to_Noise")
plt.show() # 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"] # 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 # secondmask = information_array[:,1] < 30000
print(np.unique(information_array[:,2])) # print(np.unique(information_array[:,2]))
for g in np.unique(information_array[:,2]): # for g in np.unique(information_array[:,2]):
i = np.where(information_array[:,2] == g) # i = np.where(information_array[:,2] == g)
print(g) # print(g)
plt.scatter(information_array[:,1][secondmask][i],information_array[:,4][secondmask][i],s=1,label=g,color = colors[int(g)]) # plt.scatter(information_array[:,1][secondmask][i],information_array[:,4][secondmask][i],s=1,label=g,color = colors[int(g)])
plt.legend() # plt.legend()
plt.xlabel("Frame_Spacing") # plt.xlabel("Frame_Spacing")
plt.ylabel("Signal_to_Noise") # plt.ylabel("Signal_to_Noise")
plt.show() # plt.show()
# print(np.max(information_array[:,0])) # # print(np.max(information_array[:,0]))
# print(information_array[-5:-1,0]) # # 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() # end_result = sm.OLS(y,X).fit()
print(end_result.summary()) # print(end_result.summary())
sftp_client.close() sftp_client.close()
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