#!/usr/bin/env python
# coding: utf-8

database_path = '/media/andrey/My Passport/GIN_new/Anesthesia_CA1/meta_data/meta_recordings_sleep.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(0,54)] 

# 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'] = [8, 8]


# In[2]:


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,
# - traces and neuropils median inntensities for each ROI
# - their standard deviation
# - skewness of the signal
# - estamation of their baseline (defined as a bottom quartile of signal intensities) 
# - their temporal stability (defined as the ratio between median signals of all ROIs in the first and the second parts of the recording)  

# In[5]:

#df_estimators = pd.DataFrame()

motion_index = np.zeros((len(rec),50000),dtype='float')

recording_length = np.zeros((len(rec)),dtype='int')

start_quiet_period = np.zeros((len(rec)),dtype='int')

stop_quiet_period = np.zeros((len(rec)),dtype='int')


for i, r in enumerate(rec):

    
    
    animal = get_animal_from_recording(r, database_path)
        
    #condition = get_condition(r, database_path)
    
    #print("#" +  str(r) + " " + str(animal) + " " + str(condition) + " ")

    meta_data = pd.read_excel(database_path)
 
    path_excel_rec = str(meta_data['Folder'][r]) + str(meta_data['Subfolder'][r]) + '/suite2p/'

    stat = np.load(path_excel_rec+ '/plane0/ops.npy', allow_pickle=True)

    #plt.plot(stat.item(0)['yoff'],alpha=0.5)
    #plt.plot(stat.item(0)['xoff'],alpha=0.5)

    motion_index[i,:len(stat.item(0)['yoff'])] = np.sqrt(stat.item(0)['yoff']**2 + stat.item(0)['xoff']**2) 

    recording_length[i] = len(stat.item(0)['yoff'])
    print(meta_data['Quiet periods'][r].split(','))
    start_quiet_period[i] = int(meta_data['Quiet periods'][r].split(',')[0])
    stop_quiet_period[i] = int(meta_data['Quiet periods'][r].split(',')[1])


#np.save("./xy-motion.npy",motion_index)

mi = motion_index

#mi = np.load("./xy-motion.npy")

print(np.max(mi))

print(np.min(mi))

mi_av = np.mean(mi[:,:30000].reshape(mi.shape[0],100,300), axis=2)
plt.rcParams["axes.grid"] = False

plt.figure(figsize = (10,10))

pos = plt.imshow(mi_av,cmap='Purples',vmin = 0, vmax = 10,aspect='equal')

plt.colorbar(pos)

plt.scatter(start_quiet_period[0:max(rec)]/300,np.arange(max(rec)),marker='>',color='k',label='start of quiet period')

plt.scatter(stop_quiet_period[0:max(rec)]/300,np.arange(max(rec)),marker='<',color='k',label='end of quiet period')

plt.scatter(recording_length[0:max(rec)]/300, np.arange(max(rec)) ,marker='|',color='k',label='end of the recording')

plt.legend()

plt.xlabel('x 300 frames')
plt.ylabel('recording')

plt.title('Sleep dataset motion validation')

#plt.gcf().set_facecolor("white")

plt.savefig("Validation_motion.png")
plt.savefig("Validation_motion.svg")


#import plotly.express as px
#import numpy as np

#fig = px.imshow(mi, color_continuous_scale='RdBu_r',zmin = -10, zmax = 10)
#fig.show()


plt.show()