Adapting post_proc script in this branch for single node operation fo…

…r now.

jb/src/post_proc.py

@@ -35,7 +35,6 @@ def coi_mask(b, T, min_period, max_period):
  def get_cases(node_id=0):
  df = pd.read_csv('simulation_output.csv')
 
- # Filter rows where Node is 507
  df_filtered = df[df['Node'] == node_id]
 
  # Calculate the week number based on Timestep
@@ -61,7 +60,7 @@ def log_transform(x, debug=1):
  widths = np.logspace(np.log10(1), np.log10(MAX_PERIOD), int(MAX_PERIOD))
  y = widths / 52
 
- cases = get_cases(507)
+ cases = get_cases(0)
  log_cases = pad_data(log_transform(cases))
  cwt = signal.cwt(log_cases, wavelet, widths) # (M x N)
  # Number of time steps in padded time series
@@ -181,15 +180,17 @@ def analyze():
  average_new_infections_per_year = total_new_infections / num_years
 
  # Filter the DataFrame to include only rows where Node is 507 (London)
- df_london = df[df["Node"] == 507]
+ df_london = df[df["Node"] == 0]
 
  # Calculate the total number of new infections in London
  total_new_infections_london = df_london["New Infections"].sum()
 
  # Calculate the average number of new infections in London per year
  average_new_infections_per_year_london = total_new_infections_london / num_years
 
+ """
  ccs_bigcity_mean, ccs_median, sig_slope = analyze_ccs()
+ """
 
  # Create a DataFrame with the metric and its value
  data = {
@@ -204,9 +205,9 @@ def analyze():
  "value": [
  average_new_infections_per_year,
  average_new_infections_per_year_london,
- ccs_bigcity_mean,
- ccs_median,
- sig_slope,
+ 0,
+ 0,
+ 0,
  get_wavelet_power_peak()
  ]
  }