Anesthesia_CA1/validation/calcium_imaging_transition_state/Figure2_super_compact.py

#!/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)

color_post = (142/255,226/255,255/255)

custom_palette ={'keta':color_keta, 'iso':color_iso,'mmf':color_mmf,'awake':color_awake,'post':color_post}


# 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[(df_estimators.condition == 'awake1'),"condition"] = 'awake'

df_estimators.loc[:,"CONDITION"] = 'post'

df_estimators.loc[(df_estimators.condition == 'awake'),"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["animal"] = df_estimators["animal"].astype("string")

#df_estimators = df_estimators[df_estimators.animal == 'F1']

print(np.unique(df_estimators["animal"].astype("string")))

df_estimators["multihue"] = "batch" + df_estimators["batch"].astype("string") + df_estimators["CONDITION"]

#print(np.unique(df_estimators["batch"]))

print(np.unique(df_estimators["CONDITION"]))

#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=(1.25, 4.5)) # 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.stripplot(x='CONDITION', y='number.neurons', data=df_nneurons[(df_nneurons.recording>=rmin)&(df_nneurons.recording<=rmax)], hue = "CONDITION", palette = custom_palette,  order = ['awake', 'iso', 'mmf', 'keta', 'post'], ax=axes[i], marker = '.',edgecolor="black", linewidth = lw*0.5)  #
            sns.swarmplot(x='CONDITION', y='number.neurons', data=df_nneurons[(df_nneurons.recording>=rmin)&(df_nneurons.recording<=rmax)], hue = "CONDITION", palette = custom_palette, order=['awake', 'iso', 'mmf', 'keta', 'post'], ax=axes[i], marker = '.',size=1,edgecolor="black", linewidth = lw*0.5)
        else:
            sns.boxplot(x='CONDITION', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)],  hue = "CONDITION", palette = custom_palette, dodge=False, order = ['awake', 'iso', 'mmf','keta', 'post' ]
, 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([-100.0,1500.0])
        if i > 1:
            axes[i].set_ylim([-3.0,15.0])
        if i > 2:
            axes[i].set_ylim([-0.5,1.5])
        if i > 3:
            axes[i].set_ylim([90,110])
            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_super_compact.png",dpi=400)
    plt.savefig("Validation_stability_check_figure2_super_compact.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]:


sns.displot(data=df_estimators, x="number.neurons", kind="kde", hue = "multihue")
#plt.xlim([80,120])
plt.xlabel("Number of neurons", fontsize = 15)

plt.grid(False)
plt.savefig("Number of neurons multihue.png")
#plt.show()