Merge pull request 'Changes made in September 2025' (!22) from Sept2025 into master

Reviewed-on: #22
Reviewed-by: tomekbalawajder <tomekbalawajder@noreply.example.org>

src/seismic_hazard_forecasting.py

@@ -52,7 +52,6 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     from math import ceil, floor, isnan
     import numpy as np
     import dask
-    from dask.diagnostics import ProgressBar  # use Dask progress bar
     import kalepy as kale
     import utm
     from skimage.transform import resize
@@ -69,6 +68,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     from matplotlib.contour import ContourSet
     import xml.etree.ElementTree as ET
     import json
+    import multiprocessing as mp
 
     logger = getDefaultLogger('igfash')
 
@@ -88,9 +88,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     else:
         logger.setLevel(logging.INFO)
 
-    # temporary hard-coded configuration
+    exclude_low_fxy = True # skip low probability areas of the map
-    # exclude_low_fxy = False
-    exclude_low_fxy = True
     thresh_fxy = 1e-3  # minimum fxy value (location PDF) needed to do PGA estimation (to skip low probability areas); also should scale according to number of grid points
 
     # log user selections
@@ -125,10 +123,6 @@ verbose: {verbose}")
             logger.info("No magnitude label of catalog specified, therefore try Mw by default")
             mag_label = 'Mw'
 
-        # if cat_label == None:
-        #   print("No magnitude label of catalog specified, therefore try 'Catalog' by default")
-        #   cat_label='Catalog'
-
         time, mag, lat, lon, depth = read_mat_cat(catalog_file, mag_label=mag_label, catalog_label='Catalog')
 
         # check for null magnitude values
@@ -258,7 +252,7 @@ verbose: {verbose}")
         # %% compute KDE and extract PDF
         start = timer()
 
-        if xy_win_method == "TW":
+        if xy_win_method:
             logger.info("Time weighting function selected")
 
             x_weights = np.linspace(0, 15, len(t_windowed))
@@ -319,7 +313,7 @@ verbose: {verbose}")
 
     # run activity rate modeling
     lambdas = [None]
-    if custom_rate != None and forecast_select:
+    if custom_rate != None and forecast_select and not rate_select:
         logger.info(f"Using activity rate specified by user: {custom_rate} per {time_unit}")
         lambdas = np.array([custom_rate], dtype='d')
         lambdas_perc = np.array([1], dtype='d')
@@ -454,20 +448,23 @@ verbose: {verbose}")
 
         start = timer()
 
-        use_pp = False
+        use_pp = True
 
         if use_pp:         # use dask parallel computing
-            pbar = ProgressBar()
+            mp.set_start_method("fork", force=True)
-            pbar.register()
-            # iter = range(0,len(distances))
             iter = indices
             iml_grid_raw = []  # raw ground motion grids
             for imt in products:
                 logger.info(f"Estimating {imt}")
 
+                if imt == "PGV":
+                    IMT_max = 200 # search interval max for velocity (cm/s)
+                else:
+                    IMT_max = 2.0 # search interval max for acceleration (g)
+
                 imls = [dask.delayed(compute_IMT_exceedance)(rx_lat[i], rx_lon[i], distances[i].flatten(), fr, p, lambdas,
                                                             forecast_len, lambdas_perc, m_range, m_pdf, m_cdf, model,
-                                                            log_level=logging.DEBUG, imt=imt, IMT_min=0.0, IMT_max=2.0, rx_label=i,
+                                                            log_level=logging.DEBUG, imt=imt, IMT_min=0.0, IMT_max=IMT_max, rx_label=i,
                                                             rtol=0.1, use_cython=True) for i in iter]
                 iml = dask.compute(*imls)
                 iml_grid_raw.append(list(iml))
@@ -476,11 +473,17 @@ verbose: {verbose}")
             iml_grid_raw = []
             iter = indices
             for imt in products:
+
+                if imt == "PGV":
+                    IMT_max = 200 # search interval max for velocity (cm/s)
+                else:
+                    IMT_max = 2.0 # search interval max for acceleration (g)
+
                 iml = []
                 for i in iter:
                     iml_i = compute_IMT_exceedance(rx_lat[i], rx_lon[i], distances[i].flatten(), fr, p, lambdas, forecast_len, 
                                                     lambdas_perc, m_range, m_pdf, m_cdf, model, imt=imt, IMT_min = 0.0,
-                                                    IMT_max = 2.0, rx_label = i, rtol = 0.1, use_cython=True)
+                                                    IMT_max = IMT_max, rx_label = i, rtol = 0.1, use_cython=True)
                     iml.append(iml_i)
                     logger.info(f"Estimated {imt} at rx {i} is {iml_i}")
                 iml_grid_raw.append(iml)
@@ -515,17 +518,20 @@ verbose: {verbose}")
                 dtype=np.float64)  # this reduces values to 8 decimal places
             iml_grid_tmp = np.nan_to_num(iml_grid[j])  # change nans to zeroes
 
-            # upscale the grid
+            # upscale the grid, trim, and interpolate if there are at least 10 grid values with range greater than 0.1
-            up_factor = 4
+            if np.count_nonzero(iml_grid_tmp) >= 10 and vmax-vmin > 0.1:
-            iml_grid_hd = resize(iml_grid_tmp, (up_factor * len(iml_grid_tmp), up_factor * len(iml_grid_tmp)),
+                up_factor = 4
-                                 mode='reflect', anti_aliasing=False)
+                iml_grid_hd = resize(iml_grid_tmp, (up_factor * len(iml_grid_tmp), up_factor * len(iml_grid_tmp)),
+                                    mode='reflect', anti_aliasing=False)
+                trim_thresh = vmin
+                iml_grid_hd[iml_grid_hd < trim_thresh] = np.nan
+            else:
+                iml_grid_hd = iml_grid_tmp
+
             iml_grid_hd[iml_grid_hd == 0.0] = np.nan  # change zeroes back to nan
 
-            # trim edges so the grid is not so blocky
+            #vmin_hd = min(x for x in iml_grid_hd.flatten() if not isnan(x))
-            vmin_hd = min(x for x in iml_grid_hd.flatten() if not isnan(x))
             vmax_hd = max(x for x in iml_grid_hd.flatten() if not isnan(x))
-            trim_thresh = vmin
-            iml_grid_hd[iml_grid_hd < trim_thresh] = np.nan
 
             # generate image overlay
             north, south = lat.max(), lat.min()  # Latitude range
@@ -538,7 +544,7 @@ verbose: {verbose}")
             cmap_name = 'YlOrRd'
             cmap = plt.get_cmap(cmap_name)
             fig, ax = plt.subplots(figsize=(6, 6))
-            ax.imshow(iml_grid_hd, origin='lower', cmap=cmap, vmin=vmin, vmax=vmax)
+            ax.imshow(iml_grid_hd, origin='lower', cmap=cmap, vmin=vmin, vmax=vmax, interpolation='bilinear')
             ax.axis('off')
 
             # Save the figure