'''
  Regroup results into one file and a plot.

  TODO: Mettre en valeur les valeurs maximales
  TODO: Sauvegarder les valeurs quelques part pour qu'on puisse facilement les retrouver.

  '''
  
  import numpy as np
  import matplotlib.pyplot as plt
  import argparse
  import os
  import json

  def plot_values_clusters(values, title, xlabel, ylabel):
      values = np.asarray(values)
      x = np.arange(len(values)) + 2
      x_ticks = np.arange(len(values), step=10) + 2
      y = values
      plt.scatter(x, y, s=1)
      plt.xticks(x_ticks)
      plt.title(title)
      plt.xlabel(xlabel)
      plt.ylabel(ylabel)
  
  
  def save_plot(filepath):
      plt.savefig(filepath)
      plt.close()
  
  
  def save_results(outfile, measures, titles):
      with open(outfile, "w") as f:
          f.write(",".join(titles) + "
  ")
          n = len(measures[0])
          for i in range(n):
              f.write(",".join([str(ms[i]) for ms in measures]) + "
  ")

  
  # -- PARSER
  parser = argparse.ArgumentParser(description="")
  parser.add_argument("expdir", type=str, help="Directory of experiment")

  parser.add_argument("--nkfold", type=int, default=4, help="number of kfold")
  parser.add_argument("--nkfoldmin", type=int, default=1, help="Begin with this numero of kfold")

  parser.add_argument("--measurefile", type=str, default="measures.json", 
                      help="Measure file it searchs in folders")
  parser.add_argument("--suffix", type=str, default="", 
                      help="suffix of saved files")

  
  args = parser.parse_args()
  EXP_DIR = args.expdir

  MEASURE_FILE = args.measurefile

  SUFFIX = args.suffix

  MAX_KFOLD = args.nkfold
  MIN_KFOLD = args.nkfoldmin

  # EXP_DIR="exp/kmeans_teacher_1/pvector-1"
  RESULTS_DIR = os.path.join(EXP_DIR, "res")

  
  # -- CONFIG
  kmin = 2
  kmax = 100
  
  
  # -- CREATE FOLDER
  if not os.path.exists(RESULTS_DIR):

      os.makedirs(RESULTS_DIR)

  
  # -- BEGIN REGROUPMENT
  
  subsets = ["train", "val"]
  
  disequilibriums = []

  def init_measures():

      measures = {}
  
      for subset in subsets:
          measures[subset] = {}
          measures[subset]["entropy"] = []
          measures[subset]["vscore"] = []
          measures[subset]["homogeneity"] = []
          measures[subset]["completeness"] = []
      return measures

  
  measures = init_measures()

  for kfold in range(MIN_KFOLD, MAX_KFOLD + 1):

      print("Regrouping on kfold: " + str(kfold))
      # -- REGROUP MEASURES INTO LISTS
      for k in range(kmin, kmax+1):
          measures_file = os.path.join(EXP_DIR, str(kfold), str(k), MEASURE_FILE)
          with open(measures_file, 'r') as f:
              meas_data = json.load(f)
          disequilibriums.append(meas_data["disequilibrium"])
          for subset in subsets:
              measures[subset]["entropy"].append(
                  meas_data[subset]["entropy"])
              measures[subset]["vscore"].append(
                  meas_data[subset]["vscore"])
              measures[subset]["homogeneity"].append(
                  meas_data[subset]["homogeneity"])
              measures[subset]["completeness"].append(
                  meas_data[subset]["completeness"])
  
      # -- PLOT AND SAVE MEASURES FOR A SPECIFIC SUBSET
      for subset in subsets:
          # Plot all measures
          outf = "measures_" + str(subset) + "_" + str(kfold) + str(SUFFIX) + ".pdf"
  
          fig = plt.figure(1)
          for i, measure in enumerate(measures[subset]):
  
              plt.subplot(220 + i + 1)
  
              plot_values_clusters(
                  measures[subset][measure],
                  measure.capitalize() + " " + str(subset) + " set " + str(kfold),
                  "N clusters",
                  measure.capitalize())
          plt.subplots_adjust(hspace=0.5, wspace=0.3)
          save_plot(os.path.join(RESULTS_DIR, outf))
  
          # Save all measures on a csv file
          save_results(
              os.path.join(RESULTS_DIR, "measures_" + str(subset) + "_" + str(kfold) + str(SUFFIX) + ".csv"), 
              [
                  measures[subset]["entropy"],
                  measures[subset]["homogeneity"],
                  measures[subset]["completeness"],
                  measures[subset]["vscore"]
              ],
              [
                  "entropy",
                  "homogeneity",
                  "completeness",
                  "vscore"
              ]
          )
  
      # PLOT AND SAVE FOR DISEQUILIBRIUM
      plot_values_clusters(
          disequilibriums,
          "Disequilibrium set " + str(kfold),
          "N clusters",
          "Disequilibrium")
      save_plot(os.path.join(RESULTS_DIR, "disequilibrium_" + str(kfold) + str(SUFFIX) + ".pdf"))
  
      save_results(
          os.path.join(RESULTS_DIR, "disequilibrium_" + str(kfold) + str(SUFFIX) + ".csv"), 
          [disequilibriums],
          ["disequilibrium"])
  
      measures = init_measures()
      disequilibriums = []