main.py

import pandas as pd
import numpy as np
import preprocessing
import community_detection
import network_utils
import modularity_analysis
import variance_analysis
import centrality_analysis
import blansky_analysis
import chart_generator
import time

def wait_for(duration):
  for wait in range(duration):
      print("continuing in ", duration - wait, "sec")
      time.sleep(1)


output_file = 'cc9_rel_deduced_directed.csv'
output_file_undirected = 'cc9_rel_deduced_undirected.csv'

def convert():
  rel_df = pd.read_csv("cc9_rel.csv")
  aux_df = pd.read_csv("cc9_auxiliary.csv")

  rel_df = preprocessing.preprocess(rel_df, aux_df)
  # rel_df = network_utils.to_undirected(rel_df)
  # rel_df = preprocessing.remove_null_links(rel_df)

  rel_df = rel_df.sort_values(['Source', 'Target'])

  rel_df.to_csv(output_file, index=None)


def analyze():
  original_rel_df = pd.read_csv(output_file)
  aux_df = pd.read_csv("cc9_auxiliary.csv")

  aux_df = preprocessing.fill_missing_scores(original_rel_df, aux_df)
  
  null_results = []

  rel_df = original_rel_df.copy(deep=True)

  # apply the null model transformation
  # rel_df = network_utils.apply_null_transformation(rel_df)

  undirected_df = network_utils.to_undirected(rel_df, save_to=None)

  mode = 'scale'
  write = False

  # for i in range(10 + 1):
  for i in range(7, 8):
    threshold = i / 2.0
    exponent = i

    print("Exponent: ", i)
    modularity_result = None
    if mode == 'threshold':
      modularity_result = modularity_analysis.analyze_threshold(undirected_df, threshold)
    else:
      modularity_result = modularity_analysis.analyze_scale(undirected_df, exponent)

    wait_for(4)

    # time.sleep(20)
    # # Prepare things for the merge
    modularity_result = modularity_result.rename(columns={'Label': 'docid'})

    # Merge the auxiliary DataFrame with the detected communities
    merged = pd.merge(aux_df, modularity_result, on='docid')
    print(merged)

    if write == True:
      if mode == 'threshold':
        merged.to_csv("cc9_auxiliary_merged_threshold_%.2f.csv" % threshold, index=None)
      else:
        merged.to_csv("cc9_auxiliary_merged_w^%d.csv" % exponent, index=None)

    # # time.sleep(10)
    # variance_analysis.analyze(merged)

    # # centrality_df = centrality_analysis.analyze(rel_df, undirected_df, merged)

    result = blansky_analysis.analyze(rel_df, merged)
    wait_for(10)


# convert()
analyze()