gin commit from andrey-UX510UXK

New files: 365
Modified files: 10
Deleted files: 2

validation/calcium_imaging_transition_state/Figure2_compact.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# ### Link to the file with meta information on recordings
+
+# 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_transition_state.xlsx'
+
+
+# ### Select the range of recordings for the analysis (see "Number" row in the meta data file)
+
+# In[4]:
+
+
+#rec = [x for x in range(0,198+1)]
+rec = [127,128]
+
+# In[1]:
+
+
+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
+import os
+
+sns.set()
+sns.set_style("whitegrid")
+
+from scipy.signal import medfilt 
+
+from scipy.stats import skew, kurtosis, zscore
+
+from scipy import signal
+
+from sklearn.linear_model import LinearRegression, TheilSenRegressor
+
+plt.rcParams['figure.figsize'] = [16, 8]
+
+color_awake = (0,191/255,255/255)
+color_mmf = (245/255,143/255,32/255)
+color_keta = (181./255,34./255,48./255)
+color_iso = (143./255,39./255,143./255)
+
+custom_palette ={'keta':color_keta, 'iso':color_iso,'mmf':color_mmf,'awake':color_awake}
+
+
+# In[2]:
+
+
+from capipeline import *
+
+
+
+
+df_estimators = pd.read_pickle("./transition_state_calcium_imaging_stability_validation.pkl") 
+df_estimators['neuronID'] = df_estimators.index
+
+#df_estimators["animal"] = df_estimators["animal"]
+
+print(np.unique(df_estimators["condition"]))
+
+df_estimators["CONDITION"] = df_estimators["condition"]
+
+df_estimators.loc[:,"CONDITION"] = 'awake'
+
+df_estimators.loc[(df_estimators.condition == 'iso'),"CONDITION"] = 'iso'
+df_estimators.loc[(df_estimators.condition == 'keta'),"CONDITION"] = 'keta'
+df_estimators.loc[(df_estimators.condition == 'mmf'),"CONDITION"] = 'mmf'
+
+print(np.unique(df_estimators["CONDITION"]))
+
+#df_estimators["multihue"] = df_estimators["animal"].astype("string") + df_estimators["CONDITION"]
+
+df_estimators["batch"] = (df_estimators["recording"] > 101).astype("int") + 1
+
+#df_estimators["multihue"] = df_estimators["animal"].astype("string") + df_estimators["CONDITION"]
+
+df_estimators["multihue"] = "batch" + df_estimators["batch"].astype("string") + df_estimators["CONDITION"]
+
+#print(np.unique(df_estimators["batch"]))
+
+#print()
+
+
+# ### Plot 
+
+# In[9]:
+
+parameters = ['number.neurons','traces.median','traces.skewness','decay','median.stability']
+labels = ['Extracted \n ROIs','Median, \n A.U.','Skewness','Decay time, \n s','1st/2nd \n ratio, %']
+number_subplots = len(parameters)
+
+recordings_ranges = [[0,198]]
+
+#sns.stripplot(x='multihue', y='number.neurons', data=df_estimators, jitter=True)
+
+#sns.scatterplot(x='multihue', y='number.neurons', data=df_estimators)
+
+#plt.show()
+
+#sns.swarmplot(x='multihue', y='number.neurons', data=df_estimators)
+
+
+for rmin,rmax in recordings_ranges:
+
+    f, axes = plt.subplots(number_subplots, 1, figsize=(2, 5)) # sharex=Truerex=True
+    #plt.subplots_adjust(left=None, bottom=0.1, right=None, top=0.9, wspace=None, hspace=0.2)
+    #f.tight_layout()
+    sns.despine(left=True)
+
+    for i, param in enumerate(parameters):
+        
+        lw = 0.8
+    
+        #else:
+ 
+        df_nneurons = df_estimators.groupby(['recording','multihue','CONDITION'], as_index=False)['number.neurons'].median()
+
+        if (i == 0):
+            sns.scatterplot(x='multihue', y='number.neurons', data=df_nneurons[(df_nneurons.recording>=rmin)&(df_nneurons.recording<=rmax)], hue = "CONDITION", palette = custom_palette,  ax=axes[i], marker = '.',edgecolor="black", linewidth = lw*0.5)  #
+        else:
+            sns.boxplot(x='multihue', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)],  hue = "CONDITION", palette = custom_palette, dodge=False, showfliers = False,ax=axes[i],linewidth=lw)
+         #   param = "animal"
+         #   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, ax=axes[i])
+            #ax.set(ylabel="")
+        if i == 0:
+            axes[i].set_ylim([0.0,1200.0])    
+        if i > 0:
+            axes[i].set_ylim([0.0,1000.0])
+        if i > 1:
+            axes[i].set_ylim([0.0,10.0])
+        if i > 2:
+            axes[i].set_ylim([0.0,1.0])
+        if i > 3:
+            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=9, pad=30) #45
+        axes[i].set_ylabel(labels[i], fontsize=9,labelpad=5) #40
+        #axes[i].set_xlabel("Recording", fontsize=5,labelpad=5) #40
+        #axes[i].axis('off')
+
+        axes[i].xaxis.set_tick_params(labelsize=9) #35
+        axes[i].yaxis.set_tick_params(labelsize=9) #30
+
+        axes[i].get_legend().remove()
+        #axes[i].xaxis.set_major_locator(ticker.MultipleLocator(10))
+        #axes[i].xaxis.set_major_formatter(ticker.ScalarFormatter())
+
+    plt.xticks(rotation=90)
+    #plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.,fontsize=25)
+    plt.savefig("Validation_stability_check_figure2.png",dpi=400)
+    plt.savefig("Validation_stability_check_figure2.svg")
+    #plt.show()
+
+
+# In[13]:
+
+
+sns.displot(data=df_estimators, x="median.stability", kind="kde", hue = "animal")
+plt.xlim([80,120])
+plt.xlabel("Stability, %", fontsize = 15)
+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()
+
+
+# In[62]:

validation/calcium_imaging_transition_state/ROI_adjustment_check.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# ### Define paths and animals for the analysis
+
+# In[1]:
+
+
+path = '/media/andrey/My Passport/GIN/Anesthesia_CA1/meta_data/meta_recordings_transition_state.xlsx'
+path4results = '/media/andrey/My Passport/GIN/Anesthesia_CA1/validation/calcium_imaging_transition_state/' #To store transformation matrix
+save_plots_path = '/media/andrey/My Passport/GIN/Anesthesia_CA1/validation/calcium_imaging_transition_state/'
+log_file_path = save_plots_path + 'registration_logs.txt'
+
+#animals_for_analysis = [8235,8237,8238] 
+animals_for_analysis = ['F0','M0','M3']
+
+# ### Align FOV's for all recordings
+
+# In[2]:
+
+
+repeat_calc = 1
+silent_mode = False
+
+#######################
+
+import pandas as pd
+import numpy as np
+import os
+
+import matplotlib.pyplot as plt
+
+import sys
+np.set_printoptions(threshold=sys.maxsize)
+
+from pystackreg import StackReg
+
+# Sobel filter (not used)
+#from scipy import ndimage #https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.sobel.html
+
+meta_data = pd.read_excel(path, engine='openpyxl')
+
+#%% compute transformations matrices between recordings
+
+recordings = meta_data['Number']
+
+animals = animals_for_analysis
+
+#print("Recordings: ", recordings)
+
+#ALIGNMENT ALGORITHM
+
+sr = StackReg(StackReg.AFFINE)
+
+
+# ROI adjustment check for all recordings
+
+from matplotlib import colors
+Amap = colors.ListedColormap(['white','tab:blue', 'tab:red','purple'])
+
+'''  
+if not os.path.exists(save_plots_path + 'ROIAdjustmentCheck/'):
+    os.makedirs(save_plots_path + 'ROIAdjustmentCheck/')
+ 
+for animal in animals:
+    if not os.path.exists(save_plots_path + 'ROIAdjustmentCheck/' + str(animal) + "/"):
+        os.makedirs(save_plots_path + 'ROIAdjustmentCheck/' + str(animal) + "/")  
+    
+    tmats_loaded = np.load(path4results + 'StackReg/' + str(animal) + "_best_tmats" + '.npy')
+    meta_animal = meta_data[meta_data['Mouse_all'] == animal]
+    recordings = meta_animal['Number'] #,'blue', 'green','cyan'])
+    print(recordings)
+    images = np.zeros((512, 512, np.shape(meta_animal)[0]))
+
+    for idx, recording in enumerate(recordings):
+        stats = np.load(meta_data['Folder'][recording] +
+                        str(meta_data['Subfolder'][recording]) +
+                        str(int(meta_data['Recording idx'][recording])) +
+                        '/suite2p/plane0/stat.npy',
+                        allow_pickle=True)
+        iscell = np.load(meta_data['Folder'][recording] +
+                         str(meta_data['Subfolder'][recording]) +
+                         str(int(meta_data['Recording idx'][recording])) +
+                         '/suite2p/plane0/iscell.npy',
+                         allow_pickle=True)
+        stats = stats[iscell[:, 0].astype(bool)]
+
+        for stats_neuron in stats:
+                images[stats_neuron['ypix'], stats_neuron['xpix'], idx] += 1
+        images[:,:,idx][images[:,:,idx]>1] = 1
+        
+    roi_corresp = np.zeros((512, 512))
+ 
+    #for idx0 in range(np.shape(images)[2]):
+        #for idx1 in range(idx0, np.shape(images)[2]):
+    for idx0, recording0 in enumerate(recordings):
+        for idx1, recording1 in enumerate(recordings):
+           # roi_corresp = images[:,:,idx0] + 2*images[:,:,idx1]
+            roi_corresp = images[:,:,idx0] + 2*sr.transform(images[:, :, idx1], tmats_loaded[idx0, idx1, :, :])
+            file_title = str(idx0) + "_" + str(idx1) + "_" + meta_data['Condition'][recording0] + "_" + meta_data['Condition'][recording1]
+            print(file_title)
+            plt.imshow(roi_corresp[:,:],cmap=Amap) #'tab10'
+            #plt.annotate('blue - reference / green - corrected / cyan - intersection', xy=(0, 0), xytext=(.8, 0), fontsize=12)
+            plt.savefig(save_plots_path + "ROIAdjustmentCheck/" + str(animal) + '/' + file_title + "_ROI_correspondence.svg")
+            #plt.show()
+'''
+
+if not os.path.exists(save_plots_path + 'ROIs/'):
+    os.makedirs(save_plots_path + 'ROIs/')
+   
+for animal in animals:
+    if not os.path.exists(save_plots_path + 'ROIs/' + str(animal) + "/"):
+        os.makedirs(save_plots_path + 'ROIs/' + str(animal) + "/")  
+    
+    #tmats_loaded = np.load(path4results + 'StackReg/' + str(animal) + "_best_tmats" + '.npy')
+    meta_animal = meta_data[meta_data['Mouse_all'] == animal]
+    recordings = meta_animal['Number'] #,'blue', 'green','cyan'])
+    print(recordings)
+    images = np.zeros((512, 512, np.shape(meta_animal)[0]))
+
+    for idx, recording in enumerate(recordings):
+        stats = np.load(meta_data['Folder'][recording] +
+                        str(meta_data['Subfolder'][recording]) +
+                        str(int(meta_data['Recording idx'][recording])) +
+                        '/suite2p/plane0/stat.npy',
+                        allow_pickle=True)
+        iscell = np.load(meta_data['Folder'][recording] +
+                         str(meta_data['Subfolder'][recording]) +
+                         str(int(meta_data['Recording idx'][recording])) +
+                         '/suite2p/plane0/iscell.npy',
+                         allow_pickle=True)
+        #stats = stats[iscell[:, 0].astype(bool)]
+
+        for stats_neuron in stats:
+                images[stats_neuron['ypix'], stats_neuron['xpix'], idx] += 1
+        images[:,:,idx][images[:,:,idx]>1] = 1
+        
+    roi_corresp = np.zeros((512, 512))
+
+    for idx0, recording0 in enumerate(recordings):
+    
+            #roi_corresp = sr.transform(images[:, :, idx0], tmats_loaded[0, idx0, :, :])
+            roi_corresp = images[:, :, idx0]
+            #roi_corresp [roi_corresp > 0.5] = 1
+            file_title = str(idx0) + "_" + meta_data['Condition'][recording0]
+            print(file_title)
+            plt.imshow(roi_corresp[:,:],cmap='binary') #'tab10'
+
+            plt.savefig(save_plots_path + "ROIs/" + str(animal) + '/' + file_title + "_ROIs.svg")
+
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