Merge pull request 'upload Lasocki2013 ground motion model' (!20) from lasocki2013_gmm into master

Reviewed-on: official-apps/SeismicHazardForecasting#20

LICENCE.txt

@@ -0,0 +1,13 @@
+Copyright 2025 Institute of Geophysics, Polish Academy of Sciences
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

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

@@ -17,7 +17,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
                     then the program looks for a label of 'Mw' for magnitude in the catalog
         mc: The magnitude of completeness (Mc) of the catalog
         m_max:M_max. The magnitude distribution is estimated for the range from Mc to M_max. If no value is provided,
-                    then the program sets M_max to be 3 magnitude units above the maximum magnitude value in the catalog.
+                    then the program sets M_max to be 1 magnitude units above the maximum magnitude value in the catalog.
         m_kde_method: The kernel density estimator to use.
         xy_select: If True, perform an estimation of the magnitude distribution using KDE with the chosen KDE method
         grid_dim: The grid cell size (in metres) of the final ground motion product map. A smaller cell size will
@@ -45,22 +45,19 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     """
 
     import sys
+    from importlib.metadata import version
     import logging
     from base_logger import getDefaultLogger
     from timeit import default_timer as timer
-    from math import ceil, floor
+    from math import ceil, floor, isnan
     import numpy as np
-    import scipy
-    import obspy
     import dask
     from dask.diagnostics import ProgressBar  # use Dask progress bar
     import kalepy as kale
     import utm
     from skimage.transform import resize
-    import psutil
-    import openquake.engine
     import igfash
-    from igfash.io import read_mat_cat, read_mat_m, read_mat_pdf, read_csv
+    from igfash.io import read_mat_cat, read_mat_m, read_mat_mc, read_mat_pdf, read_csv
     from igfash.window import win_CTL, win_CNE
     import igfash.kde as kde
     from igfash.gm import compute_IMT_exceedance
@@ -70,6 +67,8 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     import matplotlib.pyplot as plt
     from matplotlib.ticker import MultipleLocator
     from matplotlib.contour import ContourSet
+    import xml.etree.ElementTree as ET
+    import json
 
     logger = getDefaultLogger('igfash')
 
@@ -82,6 +81,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
         m_range = [None]
     else:
         m_range = read_mat_m(m_file)
+        m_max = m_range[-1] # take m_max from the m_file
 
     if verbose:
         logger.setLevel(logging.DEBUG)
@@ -103,25 +103,13 @@ verbose: {verbose}")
 
     # print key package version numbers
     logger.debug(f"Python version {sys.version}")
-    logger.debug(f"Numpy version {np.__version__}")
-    logger.debug(f"Scipy version {scipy.__version__}")
-    logger.debug(f"Obspy version {obspy.__version__}")
-    logger.debug(f"Openquake version {openquake.engine.__version__}")
-    logger.debug(f"Igfash version {igfash.__version__}")
-
-    # print number of cpu cores available
-    ncpu = psutil.cpu_count(logical=False)
-    logger.debug(f"Number of cpu cores available: {ncpu}")
-    for process in psutil.process_iter():
-        with process.oneshot():
-
-            # cpu = process.cpu_percent()
-            cpu = process.cpu_percent() / ncpu
-
-            if cpu > 1:
-                logger.debug(f"{process.name()}, {cpu}")
-
-    logger.debug(f"BASELINE CPU LOAD% {psutil.cpu_percent(interval=None, percpu=True)}")
+    logger.debug(f"Numpy version {version('numpy')}")
+    logger.debug(f"Scipy version {version('scipy')}")
+    logger.debug(f"Obspy version {version('obspy')}")
+    logger.debug(f"Openquake version {version('openquake.engine')}")
+    logger.debug(f"Igfash version {version('igfash')}")
+    logger.debug(f"Rbeast version {version('rbeast')}")
+    logger.debug(f"Dask version {version('dask')}")
 
     dask.config.set(scheduler='processes')
 
@@ -143,12 +131,18 @@ verbose: {verbose}")
 
         time, mag, lat, lon, depth = read_mat_cat(catalog_file, mag_label=mag_label, catalog_label='Catalog')
 
+        # check for null magnitude values
+        m_null_idx = np.where(np.isnan(mag))[0]
+        if len(m_null_idx) > 0:
+            msg = f"There are null values in the magnitude column of the catalog at indices {m_null_idx}"
+            logger.error(msg)
+            raise Exception(msg)
         if mc != None:
             logger.info("Mc value provided by user")
             trim_to_mc = True
         elif mc_file != None:
             logger.info("Mc estimation output file provided; selecting largest Mc from the list")
-            mc = read_mc(mc_file)
+            mc = read_mat_mc(mc_file)
             trim_to_mc = True
         else:
             logger.info("No Mc provided; using all magnitudes from the catalog")
@@ -164,9 +158,10 @@ verbose: {verbose}")
             lat = np.delete(lat, indices)
             lon = np.delete(lon, indices)
 
-        # if user does not provide a m_max, set m_max to 3 magnitude units above max magnitude in catalog
+        # if user does not provide a m_max, set m_max to 1 magnitude unit above max magnitude in catalog
         if m_max == None:
-            m_max = mag.max() + 3.0
+            m_max = mag.max() + 1.0
+            logger.info(f"No m_max was given. Therefore m_max is automatically set to: {m_max}")
 
         start = timer()
 
@@ -231,14 +226,15 @@ verbose: {verbose}")
         y_min = y.min()
         x_max = x.max()
         y_max = y.max()
-        z_min = depth.min()
-        z_max = depth.max()
 
         grid_x_max = int(ceil(x_max / grid_dim) * grid_dim)
         grid_x_min = int(floor(x_min / grid_dim) * grid_dim)
         grid_y_max = int(ceil(y_max / grid_dim) * grid_dim)
         grid_y_min = int(floor(y_min / grid_dim) * grid_dim)
 
+        grid_lat_max, grid_lon_max = utm.to_latlon(grid_x_max, grid_y_max, utm_zone_number, utm_zone_letter)
+        grid_lat_min, grid_lon_min = utm.to_latlon(grid_x_min, grid_y_min, utm_zone_number, utm_zone_letter)
+
         # rectangular grid
         nx = int((grid_x_max - grid_x_min) / grid_dim) + 1
         ny = int((grid_y_max - grid_y_min) / grid_dim) + 1
@@ -325,8 +321,8 @@ verbose: {verbose}")
     lambdas = [None]
     if custom_rate != None and forecast_select:
         logger.info(f"Using activity rate specified by user: {custom_rate} per {time_unit}")
-        lambdas = [custom_rate]
-        lambdas_perc = [1]
+        lambdas = np.array([custom_rate], dtype='d')
+        lambdas_perc = np.array([1], dtype='d')
 
     elif rate_select:
         logger.info(f"Activity rate modeling selected")
@@ -344,6 +340,12 @@ verbose: {verbose}")
         elif time_unit == 'years':
             multiplicator = 1 / 365
 
+        # Raise an exception when time_win_duration from the user is too large relative to the catalog
+        if time_win_duration/multiplicator > 0.5*(time[-1] - time[0]):
+            msg = "Activity rate estimation time window must be less than half the catalog length. Use a shorter time window."
+            logger.error(msg)
+            raise Exception(msg)
+
         # Selects dates in datenum format and procceeds to forecast value
         start_date = datenum_data[-1] - (2 * time_win_duration / multiplicator)
         dates_calc = [date for date in datenum_data if start_date <= date <= datenum_data[-1]]
@@ -360,7 +362,7 @@ verbose: {verbose}")
         act_rate, bin_counts, bin_edges, out, pprs, rt, idx, u_e = calc_bins(np.array(datenum_data), time_unit,
                                                                              time_win_duration, dates_calc,
                                                                              rate_forecast, rate_unc_high, rate_unc_low,
-                                                                             multiplicator, quiet=True)
+                                                                             multiplicator, quiet=True, figsize=(14,9))
 
         # Assign probabilities 
         lambdas, lambdas_perc = lambda_probs(act_rate, dates_calc, bin_edges)
@@ -372,18 +374,29 @@ verbose: {verbose}")
 
     if forecast_select:
         products = products_string.split()
-        logger.info(
-            f"Ground motion forecasting selected with ground motion model {model} and IMT products {products_string}")
+        logger.info(f"Ground motion forecasting selected with ground motion model {model} and IMT products {products_string}")
+
+        # validate m_max against the grond motion model
+        models_anthro_limited = ['Lasocki2013', 'Atkinson2015', 'ConvertitoEtAl2012Geysers'] # these models require that m_max<=4.5
+        if m_max > 4.5 and model in models_anthro_limited:
+            if m_file is None: 
+                msg = f"The selected ground motion model {model} is only valid for magnitudes up to 4.5. Please select a lower maximum magnitude."
+            else:
+                msg = f"The selected ground motion model {model} is only valid for magnitudes up to 4.5, but the provided magnitude file includes values up to {m_max}. Please adjust the magnitude range in the file accordingly."
+            logger.error(msg)
+            raise Exception(msg)
 
         if not xy_select:
             msg = "Event location distribution modeling was not selected; cannot continue..."
             logger.error(msg)
             raise Exception(msg)
-        elif m_pdf[0] == None:
+
+        if m_pdf[0] == None:
             msg = "Magnitude distribution modeling was not selected and magnitude PDF file was not provided; cannot continue..."
             logger.error(msg)
             raise Exception(msg)
-        elif lambdas[0] == None:
+        
+        if lambdas[0] == None:
             msg = "Activity rate modeling was not selected and custom activity rate was not provided; cannot continue..."
             logger.error(msg)
             raise Exception(msg)
@@ -421,7 +434,10 @@ verbose: {verbose}")
             rx_lat[i], rx_lon[i] = utm.to_latlon(x_rx[i], y_rx[i], utm_zone_number,
                                                  utm_zone_letter)  # get receiver location as lat,lon
 
-        # experimental - compute ground motion only at grid points that have minimum probability density of thresh_fxy
+        # convert distances from m to km because openquake ground motion models take input distances in kilometres
+        distances = distances/1000.0
+
+        # compute ground motion only at grid points that have minimum probability density of thresh_fxy
         if exclude_low_fxy:
             indices = list(np.where(fxy.flatten() > thresh_fxy)[0])
         else:
@@ -434,25 +450,47 @@ verbose: {verbose}")
 
         PGA = np.zeros(shape=(nx * ny))
 
-        # use dask parallel computing
         start = timer()
-        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}")
 
-            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) for i in iter]
-            iml = dask.compute(*imls)
-            iml_grid_raw.append(list(iml))
+        use_pp = False
+
+        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}")
+
+                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,
+                                                            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
+            for imt in products:
+                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)
+                    iml.append(iml_i)
+                    logger.info(f"Estimated {imt} at rx {i} is {iml_i}")
+                iml_grid_raw.append(iml)
+
         end = timer()
         logger.info(f"Ground motion exceedance computation time: {round(end - start, 1)} seconds")
 
+        if np.isnan(iml_grid_raw).all():
+            msg = "No valid ground motion intensity measures were forecasted. Try a different ground motion model."
+            logger.error(msg)
+            raise Exception(msg)
+
         # create list of one empty list for each imt
         iml_grid = [[] for _ in range(len(products))]  # final ground motion grids
         iml_grid_prep = iml_grid.copy()  # temp ground motion grids
@@ -481,6 +519,12 @@ verbose: {verbose}")
                                  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
+
             # generate image overlay
             north, south = lat.max(), lat.min()  # Latitude range
             east, west = lon.max(), lon.min()  # Longitude range
@@ -489,17 +533,27 @@ verbose: {verbose}")
             map_center = [np.mean([north, south]), np.mean([east, west])]
 
             # Create an image from the grid
+            cmap_name = 'YlOrRd'
+            cmap = plt.get_cmap(cmap_name)
             fig, ax = plt.subplots(figsize=(6, 6))
-            ax.imshow(iml_grid_hd, origin='lower', cmap='viridis')
+            ax.imshow(iml_grid_hd, origin='lower', cmap=cmap, vmin=vmin, vmax=vmax)
             ax.axis('off')
 
             # Save the figure
             fig.canvas.draw()
-            plt.savefig("overlay_" + str(j) + ".svg", bbox_inches="tight", pad_inches=0, transparent=True)
+            overlay_filename = f"overlay_{j}.svg"
+            plt.savefig(overlay_filename, bbox_inches="tight", pad_inches=0, transparent=True)
             plt.close(fig)
 
+            # Embed geographic bounding box into the SVG
+            map_bounds = dict(zip(("south", "west", "north", "east"),
+                            map(float, (grid_lat_min, grid_lon_min, grid_lat_max, grid_lon_max))))
+            tree = ET.parse(overlay_filename)
+            tree.getroot().set("data-map-bounds", json.dumps(map_bounds))
+            tree.write(overlay_filename, encoding="utf-8", xml_declaration=True)
+            logger.info(f"Saved geographic bounds to SVG metadata (data-map-bounds): {overlay_filename} → {map_bounds}")
+
             # Make the color bar
-            cmap_name = 'viridis'
             width = 50
             height = 500
 
@@ -507,16 +561,20 @@ verbose: {verbose}")
             gradient = np.vstack((gradient, gradient)).T
             gradient = np.tile(gradient, (1, width))
 
+            colorbar_title = products[j]
+            if "PGA" in colorbar_title or "SA" in colorbar_title:
+                colorbar_title = colorbar_title + "  (g)"
+
             fig, ax = plt.subplots(figsize=((width + 40) / 100.0, (height + 20) / 100.0),
                                    dpi=100)  # Increase fig size for labels
-            ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(cmap_name),
-                      extent=[0, 1, vmin, vmax])  # Note: extent order is different for vertical
+            ax.imshow(gradient, aspect='auto', cmap=cmap.reversed(),
+                      extent=[0, 1, vmin, vmax_hd])  # Note: extent order is different for vertical
             ax.set_xticks([])  # Remove x-ticks for vertical colorbar
             num_ticks = 11  # Show more ticks
-            tick_positions = np.linspace(vmin, vmax, num_ticks)
+            tick_positions = np.linspace(vmin, vmax_hd, num_ticks)
             ax.set_yticks(tick_positions)
             ax.set_yticklabels([f"{tick:.2f}" for tick in tick_positions])  # format tick labels
-            ax.set_title(imt, pad=15)
+            ax.set_title(colorbar_title, loc='right', pad=15) 
             fig.subplots_adjust(left=0.25, right=0.75, bottom=0.05, top=0.95)  # Adjust Layout
             fig.savefig("colorbar_" + str(j) + ".svg", bbox_inches='tight')
             plt.close(fig)