cleanup

From Tomasz Balawajder:
"Since we are using a Java service to launch the Python process, its behavior differs from running the script directly on the cluster.

By default, Dask uses fork() to create worker processes. However, when running under the JVM, the start method defaults to spawn, which does not share memory between processes. This caused the slowdown and unexpected behavior.

I’ve forced Python to use fork() in the configuration, and now the application completes in the same time as when executed with sbatch."

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 +45,18 @@ 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
@@ -72,6 +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')
 
@@ -84,15 +81,14 @@ 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)
     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
@@ -105,25 +101,13 @@ verbose: {verbose}")
 
     # print key package version numbers
     logger.debug(f"Python version {sys.version}")
-    logger.debug(f"Numpy version {np.__version__}")
+    logger.debug(f"Numpy version {version('numpy')}")
-    logger.debug(f"Scipy version {scipy.__version__}")
+    logger.debug(f"Scipy version {version('scipy')}")
-    logger.debug(f"Obspy version {obspy.__version__}")
+    logger.debug(f"Obspy version {version('obspy')}")
-    logger.debug(f"Openquake version {openquake.engine.__version__}")
+    logger.debug(f"Openquake version {version('openquake.engine')}")
-    logger.debug(f"Igfash version {igfash.__version__}")
+    logger.debug(f"Igfash version {version('igfash')}")
-
+    logger.debug(f"Rbeast version {version('rbeast')}")
-    # print number of cpu cores available
+    logger.debug(f"Dask version {version('dask')}")
-    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)}")
 
     dask.config.set(scheduler='processes')
 
@@ -139,18 +123,20 @@ 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
+        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")
@@ -166,9 +152,10 @@ 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()
 
@@ -233,8 +220,6 @@ 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)
@@ -267,7 +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))
@@ -328,10 +313,10 @@ 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 = [custom_rate]
+        lambdas = np.array([custom_rate], dtype='d')
-        lambdas_perc = [1]
+        lambdas_perc = np.array([1], dtype='d')
 
     elif rate_select:
         logger.info(f"Activity rate modeling selected")
@@ -349,6 +334,12 @@ 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]]
@@ -365,10 +356,12 @@ 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)
+        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}")
@@ -377,18 +370,29 @@ verbose: {verbose}")
 
     if forecast_select:
         products = products_string.split()
-        logger.info(
+        logger.info(f"Ground motion forecasting selected with ground motion model {model} and IMT products {products_string}")
-            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)
@@ -426,7 +430,10 @@ 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:
@@ -439,25 +446,56 @@ verbose: {verbose}")
 
         PGA = np.zeros(shape=(nx * ny))
 
-        # use dask parallel computing
         start = timer()
-        pbar = ProgressBar()
+
-        pbar.register()
+        use_pp = True
-        # iter = range(0,len(distances))
+
+        if use_pp:         # use dask parallel computing
+            mp.set_start_method("fork", force=True)
             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,
+                                                            log_level=logging.DEBUG, imt=imt, IMT_min=0.0, IMT_max=IMT_max, rx_label=i,
-                                                         rx_label=i) for i in iter]
+                                                            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:
+
+                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 = 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)
+
         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
@@ -480,12 +518,21 @@ 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)
+                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
             east, west = lon.max(), lon.min()  # Longitude range
@@ -494,8 +541,10 @@ 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, interpolation='bilinear')
             ax.axis('off')
 
             # Save the figure
@@ -513,7 +562,6 @@ verbose: {verbose}")
             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
 
@@ -521,16 +569,20 @@ 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),
+            ax.imshow(gradient, aspect='auto', cmap=cmap.reversed(),
-                      extent=[0, 1, vmin, vmax])  # Note: extent order is different for vertical
+                      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)