#!/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/backup_Anesthesia_CA1/meta_data/meta_recordings - anesthesia.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(0,189)]
#rec = [1,2]

# 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,'fenta':color_mmf,'awa':color_awake}


# In[2]:


from capipeline import *

# ### Load the result of the analysis

# In[8]:

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")

#df_estimators["multihue"] = df_estimators["animal"].astype("string") + df_estimators["condition"]

df_estimators["batch"] = (df_estimators["recording"] > 133).astype("int") + 1
df_estimators["batch"] =  "batch" + df_estimators["batch"].astype("string")

df_estimators["multihue"] = df_estimators["batch"] + df_estimators["condition"]

print(df_estimators["multihue"])

# ### 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 = [[0,31],[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=(2, 4.5)) # sharex=Truerex=True
    sns.despine(left=True)

    for i, param in enumerate(parameters):
        
        lw = 0.8

        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)], dodge=False, hue = "condition", palette=custom_palette, showfliers = False, ax=axes[i],linewidth=lw)
        #sns.swarmplot(x='multihue', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)], dodge=False, hue = "condition", palette=custom_palette, showfliers = False, ax=axes[i], color=".25")
        #if (i == 0):
        #    param = "animal_cat"
        #    print(np.unique(df_estimators[param]))
        #    axes[i].set_yticks(np.unique(df_estimators[param]))
        #
        #    sns.swarmplot(x='multihue', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)&(df_estimators['neuronID'] == 0)],dodge=False, hue = "condition", palette=custom_palette,  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=6, pad=20)
        axes[i].set_ylabel(labels[i], fontsize=8,labelpad=5)
        #axes[i].set_xlabel("Recording", fontsize=8,labelpad=5)
        #axes[i].axis('off')

        axes[i].xaxis.set_tick_params(labelsize=6)
        axes[i].yaxis.set_tick_params(labelsize=6)

        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=8)
    #plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.,fontsize=9)
    plt.xticks(rotation=45)
    plt.savefig("Validation_stability_check_figure2).png",dpi=300)
    plt.savefig("Validation_stability_check_figure2).svg")
    #plt.show()


# In[13]:
'''
sns.displot(data=df_estimators, x="median.stability", hue = 'multihue',palette=custom_palette, kind="kde")
plt.xlim([80,120])
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()


# In[62]:


df_estimators["median.stability"].describe()
'''