Update src/seismic_hazard_forecasting.py

when the 4 values specifying the lat and lon range of the area of interest (AOI) are provided, only do forecasting for grid points within the AOI

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')
 
@@ -88,11 +88,12 @@ 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 = False
-    exclude_low_fxy = True
+    exclude_low_fxy = False # skip low probability areas of the map
     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
 
+    AOI_lat = np.array([51.48, 51.54]) # temporary hard-coding to area of Zelazny Most. To be replaced with user-defined lat and lon range 
+    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}")
     logger.debug(
@@ -125,10 +126,6 @@ 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
@@ -221,6 +218,25 @@ verbose: {verbose}")
         utm_zone_letter = u[3]
         logger.debug(f"Latitude / Longitude coordinates correspond to UTM zone {utm_zone_number}{utm_zone_letter}")
 
+        if (None not in AOI_lat) and (None not in AOI_lon):
+            use_AOI = True
+            
+            #convert AOI to UTM
+            u_AOI = utm.from_latlon(AOI_lat, AOI_lon) 
+            x_AOI = u_AOI[0]
+            y_AOI = u_AOI[1]
+            
+            # make sure grid contains the user's AOI
+            x_min = np.concatenate((x, x_AOI)).min()
+            y_min = np.concatenate((y, y_AOI)).min()
+            x_max = np.concatenate((x, x_AOI)).max()
+            y_max = np.concatenate((y, y_AOI)).max()
+            
+            exclude_low_fxy = False # don't exclude any points because we need to analyze all grid points in the AOI
+            
+        else:
+            use_AOI = False
+        
             # define corners of grid based on global dataset
             x_min = x.min()
             y_min = y.min()
@@ -258,7 +274,7 @@ 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 +335,7 @@ 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')
@@ -445,42 +461,60 @@ verbose: {verbose}")
         else:
             indices = range(0, len(distances))
 
+        if use_AOI:
+            # Filter out receivers outside the AOI; Find indices where values are OUTSIDE the AOI
+            indices_outside_x = np.where((x_rx < x_AOI[0]) | (x_rx > x_AOI[1]))[0]
+            indices_outside_y = np.where((y_rx < y_AOI[0]) | (y_rx > y_AOI[1]))[0]      
+            indices_outside_AOI = np.unique(np.concatenate((indices_outside_x, indices_outside_y)))
+            indices_filtered = np.setdiff1d(indices, indices_outside_AOI)
+        else:
+            indices_filtered = indices
+        
         fr = fxy.flatten()
 
         # For each receiver compute estimated ground motion values
-        logger.info(f"Estimating ground motion intensity at {len(indices)} grid points...")
+        logger.info(f"Estimating ground motion intensity at {len(indices_filtered)} grid points...")
 
         PGA = np.zeros(shape=(nx * ny))
 
         start = timer()
 
-        use_pp = False
+        use_pp = True
 
         if use_pp:         # use dask parallel computing
-            pbar = ProgressBar()
-            pbar.register()
-            # iter = range(0,len(distances))
-            iter = indices
+            mp.set_start_method("fork", force=True)
+            iter = indices_filtered
             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))
 
         else:
             iml_grid_raw = []
-            iter = indices
+            iter = indices_filtered
             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)
@@ -497,7 +531,15 @@ verbose: {verbose}")
         iml_grid = [[] for _ in range(len(products))]  # final ground motion grids
         iml_grid_prep = iml_grid.copy()  # temp ground motion grids
 
-        if exclude_low_fxy:
+        if use_AOI:
+            for i in indices:
+                if i in indices_filtered:
+                    for j in range(0, len(products)):
+                        iml_grid_prep[j].append(iml_grid_raw[j].pop(0))
+                else:
+                    list(map(lambda lst: lst.append(np.nan),
+                             iml_grid_prep))  # use np.nan to indicate grid point excluded
+        elif exclude_low_fxy:
             for i in range(0, len(distances)):
                 if i in indices:
                     for j in range(0, len(products)):
@@ -515,17 +557,20 @@ 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
+            if np.count_nonzero(iml_grid_tmp) >= 10 and vmax-vmin > 0.1:
                 up_factor = 4
                 iml_grid_hd = resize(iml_grid_tmp, (up_factor * len(iml_grid_tmp), up_factor * len(iml_grid_tmp)),
                                     mode='reflect', anti_aliasing=False)
-            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))
-            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
+            else:
+                iml_grid_hd = iml_grid_tmp
+
+            iml_grid_hd[iml_grid_hd == 0.0] = np.nan  # change zeroes back to nan
+
+            #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))
 
             # generate image overlay
             north, south = lat.max(), lat.min()  # Latitude range
@@ -538,7 +583,7 @@ 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