Update src/seismic_hazard_forecasting.py

temporary global variables since GUI not ready

src/seismic_hazard_forecasting.py

@@ -5,6 +5,14 @@ from scipy.stats import bootstrap
 from matplotlib.dates import DateFormatter, AutoDateLocator
 import numpy as np
 
+global ncp_choice, tcp_max, torder_min, torder_max, AOI_lat, AOI_lon
+ncp_choice = 'default'
+tcp_max = 5
+torder_min = 0
+torder_max = 1
+AOI_lat = np.array([51.48, 51.54])
+AOI_lon = np.array([16.15, 16.24])
+
 def plot_results(act_rate, bin_edges, bin_edges_dt, rt, boundaries,
                   bin_dur, unit, multiplicator,
                   rate_forecast, rate_unc_high, rate_unc_low,
@@ -130,7 +138,7 @@ def calc_rates(act_rate, cps):
           for i in range(len(boundaries)-1)]
     return rt, boundaries
 
-def apply_beast(act_rate, *args):
+def apply_beast(act_rate):
     """
     Applies BEAST to the smmothed rate data using different smoothing windows.
     Input
@@ -183,7 +191,7 @@ def apply_beast(act_rate, *args):
 
     return beast_result, np.sort(cps)
 
-def bins_and_beast(dates, unit, bin_dur, multiplicator, *args):
+def bins_and_beast(dates, unit, bin_dur, multiplicator):
     start_date = dates.min()
     end_date = dates.max()
 
@@ -224,7 +232,7 @@ def bins_and_beast(dates, unit, bin_dur, multiplicator, *args):
     act_rate = [count / ((bin_edges[i + 1] - bin_edges[i]) * multiplicator / bin_dur)
                 for i, count in enumerate(bin_counts)]
 
-    out, cps = apply_beast(act_rate, *args)
+    out, cps = apply_beast(act_rate)
 
     if len(cps) > 0:
         rt, boundaries = calc_rates(act_rate, cps)
@@ -334,9 +342,6 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
 
 #    AOI_lat = np.array(AOI_extent[:2]) 
 #    AOI_lon = np.array(AOI_extent[2:])
-    AOI_lat = np.array([51.48, 51.54])
-    AOI_lon = np.array([16.15, 16.24])
-
     
     # log user selections
     logger.debug(f"User input files\n Catalog: {catalog_file}\n Mc: {mc_file}\n Mag_PDF: {pdf_file}\n Mag: {m_file}")
@@ -586,12 +591,7 @@ verbose: {verbose}")
 
     elif rate_select:
         logger.info(f"Activity rate modeling selected")
-        
+                
-        ncp_choice = 'default'
-        tcp_max = 5
-        torder_min = 0
-        torder_max = 1
-        
         time, mag_data, lat_dummy, lon_dummy, depth_dummy = read_mat_cat(catalog_file, mag_label=mag_label, output_datenum=True)
 
         datenum_data = time  # REMEMBER THE DECIMAL DENOTES DAYS
@@ -617,7 +617,7 @@ verbose: {verbose}")
 
         #-------split the data into bins and apply BEAST for changepoint detection--------------------
         act_rate, bin_counts, bin_edges, bin_edges_dt, out, cps, rt, boundaries, bin_dur, time_unit = bins_and_beast(
-            np.array(datenum_data), time_unit, time_win_duration, multiplicator, tcp_max, torder_min, torder_max)
+            np.array(datenum_data), time_unit, time_win_duration, multiplicator)
         
         #------Forecasted rate is taken from BEAST or is equal to last value if no changepoints detected-----
         if len(cps) > 0: