end of day, no major findings

FPNprojstart.py

-from functools import singledispatchmethod
-from cv2 import mean
 import numpy as np
 from WorkingPyDemo import *
 from Remove_Noise import *
@@ -13,7 +11,7 @@ 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)
@@ -52,22 +50,30 @@ def intarray_to_uint32(int_array):
 # print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[0:6])
 frame_spacings = []
 signaltonoise = []
-information_array = np.zeros((len(images),3))
+information_array = np.zeros((len(images),5))
 def celcius_to_kelvin(celcius):
     return celcius + 273.15
-
-for i in range(len(images)):
+last_image = np.array(Image.open(sftp_client.open(images[0]))).astype(np.uint16)
+for i in range(1,3000):
     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(gaussian_filter(image_array[1:],sigma=.4),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:])
     # 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]) - \
-    celcius_to_kelvin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[162:166]))
+    # information_array[i,1] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[94:96]) - \
+    # celcius_to_kelvin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[162:166]))
+    information_array[i,1] = mean_squared_error(gaussian_filter(last_image[1:],sigma=.4),last_image[1:])
+    # information_array[i,2] = 20*np.log10(np.std(image_array[1:]))
+    if images[i][-7] == "1":
+        information_array[i,2] = images[i][-7:-5]
+    else:
+        information_array[i,2] = images[i][-6:-5]
+    # print(information_array[i,2])
+    # information_array[i,3] = np.std(image_array[1:])
     # information_array[i,2] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[96:98])
     # information_array[i,2] = celcius_to_kelvin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[162:166]))
     # information_array[i,4] = information_array[i,1] - information_array[i,2]
@@ -75,6 +81,7 @@ for i in range(len(images)):
     # information_array[i,4] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[170:174])
     # information_array[i,5] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[174:178])
     # frame_spacings.append(current_frame_count - frame_count_at_FFC)
+    last_image = np.array(Image.open(sftp_client.open(images[i]))).astype(np.uint16)
 # print(np.argmin(signaltonoise))
 # print(np.argmax(signaltonoise))
 # print(np.min(abs(np.array(frame_spacings))))
@@ -82,11 +89,25 @@ 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_weird_array.csv")
+
+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")