gin commit from andrey-UX510UXK

Modified files: 1

validation/calcium_imaging/Validation_stability.py

@@ -5,6 +5,9 @@
 
 # In[14]:
 
+#import matplotlib.pyplot as plt
+#plt.rcParams["figure.figsize"] = (20,3)
+
 
 database_path = '/media/andrey/My Passport/GIN/Anesthesia_CA1/meta_data/meta_recordings - anesthesia.xlsx'
 
@@ -25,6 +28,8 @@ import numpy as np
 import numpy.ma as ma
 
 import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
 import pandas as pd
 import seaborn as sns
 import pickle
@@ -57,9 +62,8 @@ custom_palette ={'keta':color_keta, 'iso':color_iso,'fenta':color_mmf,'awa':colo
 from capipeline import *
 
 
-
 # ### Run the analysis
-# 
+# /media/andrey/My Passport/GIN/Anesthesia_CA1/validation/calcium_imaging
 # It creates a data frame *df_estimators* that contains basic information regarding stability of the recordings, such as 
 # 
 # - total number of identified neurons,
@@ -73,6 +77,7 @@ from capipeline import *
 
 
 df_estimators = pd.DataFrame()
+'''
 
 for r in rec:
     
@@ -95,7 +100,7 @@ for r in rec:
     
     baseline = np.quantile(Traces,0.25,axis=1)
     
-        recording_length = int(Traces.shape[1])
+    recording_length = int(Traces.shape[1])
     
     half = int(recording_length/2)
     
@@ -133,8 +138,8 @@ for r in rec:
 
     print("*****")
 
-    
-#df_estimators['neuronID'] = df_estimators.index
+'''    
+
 
 
 # ### Save the result of the analysis
@@ -142,7 +147,7 @@ for r in rec:
 # In[7]:
 
 
-df_estimators.to_pickle("./calcium_imaging_stability_validation.pkl") 
+#df_estimators.to_pickle("./calcium_imaging_stability_validation.pkl") 
 
 
 # ### Load the result of the analysis
@@ -151,6 +156,9 @@ df_estimators.to_pickle("./calcium_imaging_stability_validation.pkl")
 
 
 df_estimators = pd.read_pickle("./calcium_imaging_stability_validation.pkl") 
+df_estimators['neuronID'] = df_estimators.index
+
+df_estimators["animal_cat"] = df_estimators["animal"].astype("category")
 
 
 # ### Plot 
@@ -159,42 +167,66 @@ df_estimators = pd.read_pickle("./calcium_imaging_stability_validation.pkl")
 
 
 parameters = ["animal",'number.neurons','baseline.quantile.25','traces.median','traces.skewness','median.stability']
-labels = ["Animal ID",'Extracted ROIs','Baseline, A.U.','Traces Median, A.U.','Traces Skewness','Stability, %']
+labels = ["Animal \n ID",'Extracted \n ROIs','Baseline, \n A.U.','Median, \n A.U.','Skewness','Stability, \n %']
 number_subplots = len(parameters)
-recordings_ranges = [[0,31],[32,55],[56,95],[96,133],[134,160],[161,188]]
-#recordings_ranges = [[1,2]] #test
+#recordings_ranges = [[0,31],[32,55],[56,95],[96,133],[134,160],[161,188]]
+#recordings_ranges = [[0,55],[56,133],[134,188]]
+#recordings_ranges = [[0,133],[134,188]] #test
+recordings_ranges = [[0,188]]
 
 for rmin,rmax in recordings_ranges:
 
-    f, axes = plt.subplots(number_subplots, 1, figsize=(32, 24)) # sharex=Truerex=True
+    f, axes = plt.subplots(number_subplots, 1, figsize=(8, 6)) # sharex=Truerex=True
     sns.despine(left=True)
 
     for i, param in enumerate(parameters):
         
         lw = 3
-        if i > 4:
-            lw = 10
-        
-        sns.boxplot(x='recording', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)], hue='condition',                     palette=custom_palette,ax=axes[i],linewidth=lw)
-        
+        if (i == 5)|(i == 0)|(i == 1):
+            lw = 1
+        else:
+            lw = 1
+
+
+        #else:
+        sns.boxplot(x='recording', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)], width=0.9, dodge=False, showfliers = False,hue='condition', palette=custom_palette,ax=axes[i],linewidth=lw)
+        if (i == 0):
+            param = "animal_cat"
+            print(np.unique(df_estimators[param]))
+            axes[i].set_yticks(np.unique(df_estimators[param]))
+ 
+            sns.swarmplot(x='recording', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)&(df_estimators['neuronID'] == 0)],dodge=False,  s=1,  edgecolor='black', linewidth=1, hue='condition', palette=custom_palette, ax=axes[i])
+            #ax.set(ylabel="")
+      
         if i > 1:
-            axes[i].set_ylim([0.0,3000.0])
+            axes[i].set_ylim([0.0,2000.0])
         if i > 3:
             axes[i].set_ylim([0.0,10.0])
         if i > 4:
             axes[i].set_ylim([80,120])
+            axes[i].get_xaxis().set_visible(True)
+        else:
+            axes[i].get_xaxis().set_visible(False)
+           
         if i < number_subplots-1:
             axes[i].xaxis.label.set_visible(False)
         if i==0:
-            axes[i].set_title("Validation: stability check (recordings #%d-#%d)" % (rmin,rmax), fontsize=35)
-        axes[i].set_ylabel(labels[i], fontsize=20,labelpad=55)
-        axes[i].set_xlabel("Recording", fontsize=25,labelpad=55)
-        axes[i].xaxis.set_tick_params(labelsize=20)
-        axes[i].yaxis.set_tick_params(labelsize=20)
+            axes[i].set_title("Validation: stability check (recordings #%d-#%d)" % (rmin,rmax), fontsize=9, pad=20)
+        axes[i].set_ylabel(labels[i], fontsize=9,labelpad=5)
+        axes[i].set_xlabel("Recording", fontsize=9,labelpad=5)
+        #axes[i].axis('off')
+
+        axes[i].xaxis.set_tick_params(labelsize=9)
+        axes[i].yaxis.set_tick_params(labelsize=9)
+
         axes[i].get_legend().remove()
+        axes[i].xaxis.set_major_locator(ticker.MultipleLocator(20))
+        axes[i].xaxis.set_major_formatter(ticker.ScalarFormatter())
 
-    plt.legend(loc='upper right',fontsize=25)
-    plt.savefig("Validation_stability_check_rec_#%d-#%d).png" % (rmin,rmax))
+    #plt.legend(loc='upper right',fontsize=8)
+    #plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.,fontsize=9)
+    plt.savefig("Validation_stability_check_rec_#%d-#%d).png" % (rmin,rmax),dpi=300)
+    plt.savefig("Validation_stability_check_rec_#%d-#%d).svg" % (rmin,rmax))
     #plt.show()
 
 
@@ -203,8 +235,8 @@ for rmin,rmax in recordings_ranges:
 
 sns.displot(data=df_estimators, x="median.stability", hue = 'condition',palette=custom_palette, kind="kde")
 plt.xlim([80,120])
-plt.xlabel("Stability, %", fontsize = 15)
-plt.title("Validation: summary on stability (recordings #%d-#%d)" % (min(rec),max(rec)), fontsize = 10)
+plt.xlabel("Stability, %", fontsize = 25)
+plt.title("Validation: summary on stability (recordings #%d-#%d)" % (min(rec),max(rec)), fontsize = 20, pad=20)
 plt.grid(False)
 plt.savefig("Validation_summary_stability_recordings_#%d-#%d)" % (min(rec),max(rec)))
 #plt.show()