import psycopg2
import matplotlib.pyplot as plt 
from scipy import signal
import numpy as np

# data_spin_histogram
def histogram_slope(x_data, y_data, slope =0, bins =0, plot =True):
  if len(x_data)!=len(y_data):
    print('Scale of x_data and y_data are different.')
    return False
  elif bins == 0:
    bins = round(len(x_data)/2)
  arr_list = [y_data[i] - slope*x_data[i] for i in range(len(x_data))]
  cons_max = max(arr_list)
  cons_min = min(arr_list)
  hist_range = np.linspace(cons_min, cons_max, bins+1)
  hist_range_mid = [(hist_range[i]+hist_range[i+1])/2 for i in range(len(hist_range)-1)]
  hist_range[-1] = hist_range[-1]+1
  hist_list = [len([j for j in arr_list if j>=hist_range[i] and j<hist_range[i+1]]) for i in range(len(hist_range)-1)]
  peek_temp_list,_ = signal.find_peaks(hist_list)
  peek_temp_list = list(peek_temp_list)
  print(peek_temp_list)
  # peek_temp_list[0] = list(peek_temp_list[0])
  if np.sign(np.diff(hist_list))[0] < 0:
    # print( np.sign(np.diff(hist_list))[0])
    peek_temp_list = [0] + peek_temp_list
  if np.sign(np.diff(hist_list))[-1] > 0:
    # print(np.sign(np.diff(hist_list))[-1])
    peek_temp_list = peek_temp_list+[len(hist_list)-1]
    print(peek_temp_list)
  
  peek_list = [i for i in peek_temp_list if hist_list[i] >= len(x_data)/bins*3]
  print(peek_list)
  results_half = signal.peak_widths(hist_list,peek_list, rel_height=0.8)
  peak_min,peak_max = [cons_min + (cons_max-cons_min)/bins*j for j in results_half[2]],[cons_min + (cons_max-cons_min)/bins*j for j in results_half[3]]
  print(peak_min,peak_max)
  peak_80=[]
  for i in range(len(peak_min)):
    peak_filt_list = [j for j in arr_list if j>=peak_min[i] and j<=peak_max[i]]
    print(np.mean(peak_filt_list))
    print(len(peak_filt_list))
    peak_80.append(np.mean(peak_filt_list))
  
  print(results_half[2])
 
  print(results_half)
  plt.plot(peak_80, [hist_list[j] for j in peek_list], "y")
  if plot:
    plt.hist(arr_list, bins)
    plt.plot([hist_range_mid[i] for i in peek_list], [hist_list[j] for j in peek_list], "xb")
    plt.plot(peak_80, [hist_list[j] for j in peek_list], "xr")
    # plt.hlines(results_half[1],[ hist_list[int(j)] for j in results_half[2]],[ hist_list[int(j)] for j in results_half[2]], color="g")
    plt.savefig("histogrim.png")
  return hist_list, hist_range_mid, peek_list

def run():
    conn = psycopg2.connect(database="postgres", user="biopro", password="BioProControlBox", host="127.0.0.1", port="5432")
    cur = conn.cursor()
    id = 14304

    sql_str = f'select "raw_data" from "recording_data_metas" WHERE id = {id}'
    cur.execute(sql_str)
    data = cur.fetchall()[0][0]
    channel_data = []
    for i in ['1','2']:
        channel_data_temp = []
        for j in data[i]:
            # print(j)
            sql_str = f'select "data" from "{i}_recording_data_raws" WHERE id = {j}'
            cur.execute(sql_str)
            raw_data = cur.fetchall()[0][0]
            raw_data = raw_data.replace('"***"',' ').split(" ")
            channel_data_temp =channel_data_temp + [int(raw_data[k]) for k in range(len(raw_data)) if k%2]
            # print(len(channel_data_temp))
        channel_data.append(channel_data_temp)
    # print(channel_data[1][-1])
    # print(channel_data[0][:500])
    plt.plot(channel_data[0],channel_data[1],'o',markersize=2)
    plt.hlines(1e6,2.4e6,2.6e6,color='g')
    plt.savefig('test.png')
    plt.close()

    x_data, y_data ,peek_list = histogram_slope(channel_data[0],channel_data[1],bins = 200)
    print([y_data[i] for i in peek_list])
    print(peek_list, channel_data)
    return channel_data, peek_list
    # print([y_data])