Probably small rerun

MetadataAnalyzer.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, "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()