interpolation is always used on the final grid

Previously, user could enter a value enter the  custom rate box, enable "activity rate estimation" and the custom rate box would disappear but the program would still see the value previously entered and use it even though it was no longer visible in the user interface

gsim/lasocki_2013.py

@@ -0,0 +1,142 @@
+# -*- coding: utf-8 -*-
+# vim: tabstop=4 shiftwidth=4 softtabstop=4
+#
+# Copyright (C) 2013-2023 GEM Foundation
+#
+# OpenQuake is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# OpenQuake is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
+
+"""
+Module exports :class:`Lasocki2013`.
+"""
+import numpy as np
+from scipy.constants import g
+
+from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
+from openquake.hazardlib import const
+from openquake.hazardlib.imt import PGA, PGV, SA
+
+def get_magnitude_energy(mag):
+    """
+    Converts magnitude to energy-based magnitude term.
+    """
+    return 1.15 + 1.96 * mag
+
+def get_distance_term(coeffs, repi):
+    """
+    Computes the distance term using the given GMPE coefficients.
+    """
+    R_h = np.sqrt(repi ** 2 + coeffs["c7"] ** 2)
+    return np.log10(R_h)
+
+def get_standard_deviation(coeffs, coeffs_cov, magE, repi):
+    """
+    Computes the standard deviation term.
+    """
+    Cb = np.array(list(coeffs_cov)).reshape(3,3)
+    R_h = np.sqrt(repi ** 2 + coeffs["c7"] ** 2)
+    X0 = np.array([1, magE[0], np.log10(R_h[0]**2)])
+    variance_term = np.sqrt(X0 @ Cb @ X0 + coeffs["sigma"]**2)
+    return variance_term
+
+class Lasocki2013(GMPE):
+    """
+    Implement equation developed by Lasocki in "REPORT ON THE ATTENUATION 
+    RELATIONS OF PEAK GROUND ACCELERATION AND SPECTRAL ORDINATES OF GROUND 
+    MOTION FOR MINING-INDUCED SEISMIC EVENTS IN THE REGION OF THE ŻELAZNY 
+    MOST REPOSITORY", 2009.
+    Equation coefficients provided for the random horizontal component
+    """
+    #: Supported tectonic region type is induced, given
+    #: that the equations have been derived for the LGCD mining area
+    DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.INDUCED
+
+    #: Supported intensity measure types are spectral acceleration,
+    #: and peak ground acceleration
+    DEFINED_FOR_INTENSITY_MEASURE_TYPES = {PGA, SA}
+
+    #: Supported intensity measure component is random horizontal
+    #: :attr:`~openquake.hazardlib.const.IMC.RANDOM_HORIZONTAL`,
+    DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.RANDOM_HORIZONTAL
+    # DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.VERTICAL
+
+    #: Supported standard deviation type is total
+    DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL}
+
+    #: site params are not required
+    REQUIRES_SITES_PARAMETERS = set()
+
+    #: Required rupture parameter is magnitude
+    REQUIRES_RUPTURE_PARAMETERS = {'mag'}
+
+    #: Required distance measure is epicentral distance
+    #: see paragraph 'Predictor Variables', page 6.
+    REQUIRES_DISTANCES = {'repi'}
+
+    def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
+        """
+        Computes mean ground motion values and standard deviations for Lasocki (2013) GMPE.
+        
+        Parameters:
+            repi (float): Epicentral distance (m)
+            mag (float): Earthquake magnitude
+            imts (list of str): List of intensity measure types (e.g., 'PHA', 'PVA')
+            mean (np.array): Array to store computed mean values
+            sig (np.array): Array to store computed standard deviations
+        """
+        # Loop through each IMT and compute values
+        for i, imt in enumerate(imts):            
+            C = self.COEFFS[imt]
+            C_cov = self.COEFFS_COV[imt]
+            mag = ctx.mag
+            repi = ctx.repi*1000.0 # Convert distance from km to m
+            # Compute magnitude energy term
+            magE = get_magnitude_energy(mag)
+
+            # Compute GMPE terms
+            mag_term = C['c1'] + C['c2'] * magE
+            dist_term = C['c5'] * get_distance_term(C, repi)
+            
+            # Compute mean ground motion
+            imean = mag_term + dist_term
+            mean_value = np.log((10 ** imean) / g)  # Convert to natural log scale and divide by g
+            mean[i] = mean_value
+
+            # Compute standard deviation
+            sigma = get_standard_deviation(C, C_cov, magE, repi)
+            sig[i] = sigma
+
+    #: coefficient table provided by report
+    COEFFS = CoeffsTable(sa_damping=5, table="""\
+    IMT  c1     c2      c5      c7    sigma     N        
+    pga  1.25   0.31    -1.34   558   0.196     1196
+    0.6  -3.86  0.55    -0.65   183   0.242     1194
+    1.0  -3.94  0.62    -0.66   308   0.262     1197
+    2.0  -1.14  0.47    -1.02   741   0.235     1195
+    5.0  0.99   0.31    -1.15   690   0.234     1206
+    10.0 3.06   0.27    -1.65   906   0.203     1192
+    20.0 2.62   0.27    -1.60   435   0.196     1191
+    50.0 2.09   0.27    -1.48   375   0.204     1191
+    """)
+    
+    COEFFS_COV = CoeffsTable(sa_damping=5, table="""\
+    IMT  Cb00      Cb01      Cb02      Cb10      Cb11      Cb12      Cb20      Cb21      Cb22        
+    pga  0.005586  -0.000376 -0.000752 -0.000376 0.000103  -0.000111 -0.000752 -0.000111 0.000440
+    0.6  0.007509  -0.000662 -0.000688 -0.000662 0.000161  -0.000154 -0.000688 -0.000154 0.000516
+    1.0  0.009119  -0.00075  -0.000948 -0.00075  0.000189  -0.000187 -0.000948 -0.000187 0.000657                 
+    2.0  0.008563  -0.000514 -0.001282 -0.000514 0.000147  -0.000164 -0.001282 -0.000164 0.000696         
+    5.0  0.008283  -0.000516 -0.001202 -0.000516 0.000145  -0.000161 -0.001202 -0.000161 0.000668
+   10.0  0.006904  -0.00036  -0.001145 -0.00036  0.000108  -0.000126 -0.001145 -0.000126 0.000578
+   20.0  0.005389  -0.000396 -0.000658 -0.000396 0.000104  -0.000107 -0.000658 -0.000107 0.000408
+   50.0  0.005874  -0.000449 -0.000678 -0.000449 0.000114  -0.000114 -0.000678 -0.000114 0.000428
+    """)

src/seismic_hazard_forecasting.py

@@ -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 = False
-    exclude_low_fxy = True
+    exclude_low_fxy = True # 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
 
     # 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')
@@ -366,6 +360,8 @@ verbose: {verbose}")
 
         # Assign probabilities 
         lambdas, lambdas_perc = lambda_probs(act_rate, dates_calc, bin_edges)
+        lambdas = np.array(lambdas, dtype='d')
+        lambdas_perc = np.array(lambdas_perc, dtype='d')
         
         # print("Forecasted activity rates: ", lambdas, "events per", time_unit[:-1])
         logger.info(f"Forecasted activity rates: {lambdas} events per {time_unit} with percentages {lambdas_perc}")
@@ -457,15 +453,19 @@ verbose: {verbose}")
         if use_pp:         # use dask parallel computing
             pbar = ProgressBar()
             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))
@@ -474,11 +474,17 @@ 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)
@@ -513,17 +519,20 @@ 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
@@ -536,7 +545,7 @@ 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