text: '''This module plots average of GDD for couple of years, and also its percentile of 5-95 and 25-75 as a band. It also
  shows scatter plot of GDD for last year involved in calculations'''
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  import glob
  
  Sum = [np.nan] * 365
  average = []
  T = []  # Will keep a list of GDDs for  calculating percentile
  for fname in glob.glob("./input/Montreal*"):  # For loop for .csv files in given input folder
      D = pd.read_csv(fname, header=0)  # skipped rows will change if data frame's shape change
      df = pd.DataFrame(D)
      print("Accessing headers of " + fname)
  
      year = list(df['Year'])[1]
      df = df[df["Date/Time"] != str(year) + "-02-29"]  # Deletes February 29th from leap year's data
      t = list(df["GDD"])
      T.append(t)
      average = np.nanmean(np.array(T), axis=0)
  x = df["Date/Time"]
  year = list(df['Year'])[1]
  
  plt.plot(x, average, color="red", label="Average",linewidth=1)
  plt.scatter(x, t, label=str(year), s=8)
  
  U = np.nanpercentile(T, 95, axis=0)
  D = np.nanpercentile(T, 5, axis=0)
  Uu = np.nanpercentile(T, 75, axis=0)
  Dd = np.nanpercentile(T, 25, axis=0)
  
  plt.fill_between(x, U, D, alpha=0.15, color='blue', label="5-95 percentile")
  plt.fill_between(x, Uu, Dd, alpha=0.15, color='red', label="25-75 percentile")
  
  plt.xlabel('time')
  plt.ylabel('Daily Accumulation (Celicius)')
  plt.title('2014-2017 daily Growing degree days of Montreal')
  plt.grid(True)
  plt.tight_layout()
  plt.legend()
  plt.xticks([0, 30, 58, 89, 119, 150, 180, 211, 242, 272, 303, 333],
  ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], rotation='vertical')
  fig = plt.gcf()
  fig.set_size_inches(18.5, 10.5)
  fig.savefig('test2png.png', dpi=100)
  plt.savefig('./docs/task1.png')
  