Feature 518 tcrmw

docs/Users_Guide/tcrmw_cross_section.rst

@@ -85,44 +85,19 @@ Save and close the *test_plot_cross_section.sh* script.
 
 To generate the plot, perform the following:
 
-* Make sure you have the following Python packages installed:
-   * **Python 3.7** or above
 
-   * METcalcpy (use the same version number as this METplotpy)
-
-   * matplotlib 3.4.3
-
-   * metpy 1.1.0
-
-   * netcdf4 1.5.7 or above
-
-   * numpy
-
-   * pandas
-
-   * pint 0.17
-
-   * xarray
-
-   * yaml
-
-
-
-*Specific version numbers are specified when necessary.  If versions are not specified, use a
-compatible version number for your operating system and existing packages.*
-
-* If you are running in a conda environment, verify that you are running the conda environment that has the above Python packages installed.
+* If  you are running in a conda environment, verify that your conda environment that has the Python packages specified in the requirements.txt file located in the $METPLOTPY_BASE directory.
 
 * cd to the $METPLOTPY_BASE/metplotpy/contributed/tc_rmw directory
 
-``cd $METPLOTPY_BASE/metplotpy/contributed/tc_rmw``
+   ``cd $METPLOTPY_BASE/metplotpy/contributed/tc_rmw``
 
 replacing the $METPLOTPY_BASE with the directory where you saved the METplotpy
 source code.
 
 * run the test_plot_cross_section.sh script:
 
-``sh test_plot_cross_section.sh``
+   ``sh test_plot_cross_section.sh``
 
 Two files will be created, *example.png* and *example.pdf* (if you used the *plot_filename* value
 of 'example' in the *plot_cross_section.yaml* configuration file).  They will be located in

metplotpy/contributed/tc_rmw/plot_cross_section.py

@@ -1,13 +1,12 @@
 # ============================*
- # ** Copyright UCAR (c) 2020
- # ** University Corporation for Atmospheric Research (UCAR)
- # ** National Center for Atmospheric Research (NCAR)
- # ** Research Applications Lab (RAL)
- # ** P.O.Box 3000, Boulder, Colorado, 80307-3000, USA
- # ============================*
-
-
-
+# ** Copyright UCAR (c) 2025
+# ** University Corporation for Atmospheric Research (UCAR)
+# ** National Science Foundation National Center for Atmospheric Research (NSF NCAR)
+# ** Research Applications Lab (RAL)
+# ** P.O.Box 3000, Boulder, Colorado, 80307-3000, USA
+# ============================*
+
+
 """
 plot_cross_section
 """
@@ -26,6 +25,7 @@
 matplotlib.use('Agg')
 from metplotpy.plots import util
 
+
 def plot_cross_section(config, data_set, args):
     """
         Generate the cross-section plot of the field specified in the YAML config file
@@ -57,7 +57,7 @@ def plot_cross_section(config, data_set, args):
     # Create the log file using the same name as the plot name with '_log' and in the
     # same location.
     try:
-       os.makedirs(plot_outdir, exist_ok=True)
+        os.makedirs(plot_outdir, exist_ok=True)
     except FileExistsError:
         pass
     log_filename = os.path.join(plot_outdir, config['plot_filename'] + '_log' + '.txt')
@@ -69,6 +69,7 @@ def plot_cross_section(config, data_set, args):
     fig, ax = plt.subplots(figsize=(plot_width, plot_height))
 
     field = data_set[config['field']]
+
     itime = config['index_time_slice']
 
     # originally, axis=1 but the order of dimensions was modified
@@ -78,24 +79,47 @@ def plot_cross_section(config, data_set, args):
     # originally, the transpose of the field_azi_mean was used, but this is no
     # longer necessary.  If the transpose is used, the dimensions are incorrect
     # and a TypeError will be raised by the contour plot.
-    scalar_contour = ax.contour(data_set['range'],
-                                data_set[config['vertical_coord_name']],
-                                field_azi_mean,
-                                levels=np.arange(config['contour_level_start'],
-                                                 config['contour_level_end'],
+    if config['colormap'] != 'custom':
+        colormap = config['colormap']
+    else:
+        if config['colormap_by_rgb']:
+            methodology = "by_rgb"
+            colormap_triplet_list = config['colormap_colors_rgb']
+        else:
+            methodology = "by_hex"
+            colormap_triplet_list = config['colormap_colors_hexadecimal']
+
+        colormap = util.customize_colormap(colormap_triplet_list, methodology)
+
+    if config['filled_contour_on']:
+        scalar_contour = ax.contourf(data_set['range'], data_set[config['vertical_coord_name']], field_azi_mean,
+                                     levels=np.arange(config['contour_level_start'], config['contour_level_end'],
+                                                      config['contour_level_stepsize']), cmap=colormap)
+        # Add colorbar legend
+        cbar = plt.colorbar(scalar_contour, label=config['filled_contour_colorbar_label'])
+        # cbar.set_label(config['filled_contour_colorbar_label'])
+
+        # scalar_contour = ax.contour(data_set['range'], data_set[config['vertical_coord_name']], field_azi_mean,
+        #                         levels=np.arange(config['contour_level_start'], config['contour_level_end'],
+        #                                          config['contour_level_stepsize']),
+        #                         colors=config['contour_line_colors'], linewidths=(config['line_width'],),
+        #                             linestyles=[config['contour_line_style']])
+
+
+
+    if config['line_contour_on']:
+        scalar_contour = ax.contour(data_set['range'], data_set[config['vertical_coord_name']], field_azi_mean,
+                                levels=np.arange(config['contour_level_start'], config['contour_level_end'],
                                                  config['contour_level_stepsize']),
-                                                 colors=config['contour_line_colors'],
-                                                 linewidths=(config['line_width'])
-                                )
+                                colors=config['contour_line_colors'], linewidths=(config['line_width'],),
+                                    linestyles=[config['contour_line_style']])
 
     plt.title(config['plot_title'])
     ax.clabel(scalar_contour, colors=config['contour_label_color'], fmt=config['contour_label_fmt'])
     ax.set_xlabel(config['x_label'])
     ax.set_ylabel(config['y_label'])
     ax.set_xticks(np.arange(config['x_tick_start'], config['x_tick_end']))
-    ax.set_yticks(np.arange(config['y_tick_start'],
-                            config['y_tick_end'],
-                            config['y_tick_stepsize']))
+    ax.set_yticks(np.arange(config['y_tick_start'], config['y_tick_end'], config['y_tick_stepsize']))
     ax.set_yscale(config['y_scale'])
     ax.set_ylim(config['y_lim_start'], config['y_lim_end'])
 
@@ -107,32 +131,21 @@ def plot_cross_section(config, data_set, args):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='Plot Tropical Cyclone Cross Section')
-
-    parser.add_argument(
-        '--datadir', type=str, dest='datadir',
-        required=True)
-    parser.add_argument(
-        '--plotdir', type=str, dest='plotdir',
-        required=True)
-    parser.add_argument(
-        '--filename', type=str, dest='filename',
-        required=True)
-    parser.add_argument('--config', type=str,
-                        required=True,
-                        help='configuration file')
-    parser.add_argument('--loglevel', type=str,
-                        required=False)
+    parser = argparse.ArgumentParser(description='Plot Tropical Cyclone Cross Section')
+
+    parser.add_argument('--datadir', type=str, dest='datadir', required=True)
+    parser.add_argument('--plotdir', type=str, dest='plotdir', required=True)
+    parser.add_argument('--filename', type=str, dest='filename', required=True)
+    parser.add_argument('--config', type=str, required=True, help='configuration file')
+    parser.add_argument('--loglevel', type=str, required=False)
 
     input_args = parser.parse_args()
 
     """
     Read YAML configuration file
     """
     try:
-        plotting_config = yaml.load(
-            open(input_args.config), Loader=yaml.FullLoader)
+        plotting_config = yaml.load(open(input_args.config), Loader=yaml.FullLoader)
     except yaml.YAMLError as exc:
         sys.exit(1)
 
@@ -144,4 +157,3 @@ def plot_cross_section(config, data_set, args):
     except (ValueError, FileNotFoundError, PermissionError):
         sys.exit(1)
     plot_cross_section(plotting_config, input_data, input_args)
-

metplotpy/contributed/tc_rmw/plot_cross_section.yaml

@@ -9,8 +9,10 @@ vertical_coord_name:
 field:
     'TMP'
 
+filled_contour_colorbar_label: ' Temperature (K) '
+
 contour_level_start: 0
-contour_level_end: 500
+contour_level_end: 450
 contour_level_stepsize: 5
 
 # time slice of interest
@@ -19,10 +21,77 @@ index_time_slice: 0
 #
 # contour plot appearance customizations
 #
-line_width: 2
+line_width: 1
 contour_line_colors: 'darkblue'
-contour_label_color: 'orange'
+contour_label_color: 'darkblue'
 contour_label_fmt: '%1.0f'
+# contour line style: solid, dashed, dotted
+contour_line_style: 'dotted'
+line_contour_on:  True
+
+
+#
+# filled contour
+#
+filled_contour_on: True
+
+#  for a list of available colormaps:
+# https://matplotlib.org/stable/users/explain/colors/colormaps.html
+#  e.g. gist_stern, jet, gist_ncar
+
+colormap: 'custom'
+
+# for custom colormap, set value to 'custom' and then provide values for
+# rgb (red, green, blue) triplets as either
+#colormap: 'custom'
+
+# Method to define a custom colormap
+# If True, then set the colormap by a list of RGB triplets.
+# If False, then set colormap by a list of hex triplets
+colormap_by_rgb: False
+
+colormap_colors_rgb:
+    - [ 110, 28, 255 ]  # purples
+    - [ 56, 109, 249 ] # blues
+    - [ 44, 126, 247 ]
+    - [ 23, 203, 228 ]
+    - [ 36, 216, 212 ] # aqua blues
+    - [ 49, 225, 218 ]
+    - [ 74, 242, 207 ] # blue greens
+    - [ 100, 251, 195 ]
+    - [ 193, 235, 141 ] # greens
+    - [ 207, 225, 132 ]
+    - [ 219, 216, 123 ] # yellows
+    - [ 246, 190, 104 ] # oranges
+    - [ 255, 176, 84 ]
+    - [ 255, 160, 85 ]
+    - [ 255, 89, 45 ]
+    - [ 255, 68, 34 ] # reds
+    - [ 255, 50, 25 ]
+    - [ 255, 28, 14 ]
+
+
+# case-insensitive hexadecimals, enclose each value with ' ' or " "
+colormap_colors_hexadecimal:
+  - '#6e1cff' # purples
+  - '#386df9' # blues
+  - '#2c7ef7'
+  - '#17cbe4'
+  - '#24d8d4' # aqua blues
+  - '#31e1da'
+  - '#4af2cf' # blue greens
+  - '#64fbc3'
+  - '#c1eb8d' # greens
+  - '#cfe184'
+  - '#dbd87b' # yellows
+  - '#f6be68' # oranges
+  - '#ffb054'
+  - '#ffa055'
+  - '#ff592d'
+  - '#ff4422' # reds
+  - '#ff3219'
+  - '#ff1c0e'
+
 
 # axis labels
 # x-axis is in units of RMW

metplotpy/contributed/tc_rmw/tc_utils.py

@@ -1,7 +1,7 @@
 # ============================*
- # ** Copyright UCAR (c) 2020
+ # ** Copyright UCAR (c) 2025
  # ** University Corporation for Atmospheric Research (UCAR)
- # ** National Center for Atmospheric Research (NCAR)
+ # ** National Science Foundation National Center for Atmospheric Research (NSF NCAR)
  # ** Research Applications Lab (RAL)
  # ** P.O.Box 3000, Boulder, Colorado, 80307-3000, USA
  # ============================*
@@ -188,7 +188,6 @@ def read_tcrmw_levels(filename, levels=['L0']):
     file_id.close()
 
     return valid_time, lat_grid, lon_grid, wind_data, scalar_data
-
-
+
 if __name__ == '__main__':
     print(format_valid_time(2019050123))

metplotpy/contributed/tc_rmw/test_plot_cross_section.sh

@@ -1,15 +1,16 @@
 # ============================*
-# ** Copyright UCAR (c) 2020
+# ** Copyright UCAR (c) 2025
 # ** University Corporation for Atmospheric Research (UCAR)
-# ** National Center for Atmospheric Research (NCAR)
+# ** National Science Foundation National Center for Atmospheric Research (NSF NCAR)
 # ** Research Applications Lab (RAL)
 # ** P.O.Box 3000, Boulder, Colorado, 80307-3000, USA
 # ============================*
+export datadir=/path/to/
+export plotdir=/path/to/plot/dir
+#export filename=vertically_interpolated.nc
+export filename=tc_rmw_vertical_interp.nc
+export configfile=/path/to/METplotpy/metplotpy/contributed/tc_rmw/plot_cross_section.yaml
 
-export datadir=/Users/minnawin/AF_STIG/feature_57_METplotpy_logging/METplotpy/metplotpy/contributed/tc_rmw/Data
-export plotdir=/Users/minnawin/AF_STIG/feature_57_METplotpy_logging/METplotpy/metplotpy/contributed/tc_rmw/plots
-export filename=vertically_interpolated.nc
-export configfile=/Users/minnawin/AF_STIG/feature_57_METplotpy_logging/METplotpy/metplotpy/contributed/tc_rmw/plot_cross_section.yaml
 # Default is set to INFO in code, set to any other value here and add to the arguments
 # in the call to the plot_cross_section.py below
 export loglevel="ERROR"