Merge pull request #127 from jukent/snake_case

Consistent function naming convention

docs/conf.py

@@ -12,6 +12,8 @@
 #
 # -- Project information -----------------------------------------------------
 
+import datetime
+import geocat.viz as gv
 import os
 import sys
 import pathlib
@@ -22,8 +24,6 @@
 
 print("sys.path:", sys.path)
 
-import geocat.viz as gv
-
 LOGGER = logging.getLogger("conf")
 
 try:
@@ -128,7 +128,7 @@ def __getattr__(cls, name):
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
-# source_suffix = ['.rst', '.md']
+#source_suffix = ['.rst', '.md']
 source_suffix = {
     '.rst': 'restructuredtext',
     '.ipynb': 'myst-nb',
@@ -144,8 +144,6 @@ def __getattr__(cls, name):
 # General information about the project.
 project = u'GeoCAT-viz'
 
-import datetime
-
 current_year = datetime.datetime.now().year
 copyright = u'{}, University Corporation for Atmospheric Research'.format(
     current_year)
@@ -326,7 +324,7 @@ def __getattr__(cls, name):
 nb_execution_mode = "off"
 
 # generate warning for all invalid links
-# nitpicky = True
+#nitpicky = True
 
 
 # Allow for changes to be made to the css in the theme_overrides file

docs/user_api/index.rst

@@ -42,12 +42,25 @@ GeoCAT-viz Utility Functions
 
    set_map_boundary
 
-   findLocalExtrema
+   find_local_extrema
 
-   plotCLabels
+   plot_contour_labels
 
-   plotELabels
+   plot_extrema_labels
 
    set_vector_density
 
    get_skewt_vars
+
+Deprecated Functions
+--------------------
+Util
+^^^^^^^^^^^^^
+.. currentmodule:: geocat.viz.util
+.. autosummary::
+   :nosignatures:
+   :toctree: ./generated/
+
+   findLocalExtrema
+   plotCLabels
+   plotELabels

gallery/util/add_height_from_pressure_axis.ipynb

@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import Packages:\n",
+    "\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import ScalarFormatter\n",
+    "import cmaps\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "\n",
+    "import scipy\n",
+    "\n",
+    "#import geocat.datafiles as gdf\n",
+    "import geocat.viz as gv"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# Open a netCDF data file using xarray default engine and load the data into xarrays\n",
+    "ds = xr.open_dataset(gdf.get(\"netcdf_files/mxclim.nc\"))\n",
+    "\n",
+    "# Extract variables\n",
+    "U = ds.U[0, :, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate figure (set its size (width, height) in inches) and axes\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "ax = plt.axes()\n",
+    "\n",
+    "# Set y-axis to have log-scale\n",
+    "plt.yscale('log')\n",
+    "\n",
+    "# Specify which contours should be drawn\n",
+    "levels = np.linspace(-55, 55, 23)\n",
+    "\n",
+    "# Plot contour lines\n",
+    "lines = U.plot.contour(ax=ax,\n",
+    "                       levels=levels,\n",
+    "                       colors='black',\n",
+    "                       linewidths=0.5,\n",
+    "                       linestyles='solid',\n",
+    "                       add_labels=False)\n",
+    "\n",
+    "# Create second y-axis to show geo-potential height.\n",
+    "axRHS = gv.add_height_from_pressure_axis(ax, heights=[4, 8])\n",
+    "\n",
+    "plt.show();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geocat_viz_build",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

src/geocat/viz/taylor.py

@@ -159,20 +159,20 @@ def __init__(self,
         self.ax = ax.get_aux_axes(tr)  # Polar coordinates
 
         # Add reference point stddev contour
-        t = np.linspace(0, np.pi / 2)
-        r = np.zeros_like(t) + self.refstd
-        h, = self.ax.plot(t,
-                          r,
-                          linewidth=1,
-                          linestyle=(0, (9, 5)),
-                          color='black',
-                          zorder=1)
+        t_array = np.linspace(0, np.pi / 2)
+        r_array = np.zeros_like(t_array) + self.refstd
+        h_plot, = self.ax.plot(t_array,
+                               r_array,
+                               linewidth=1,
+                               linestyle=(0, (9, 5)),
+                               color='black',
+                               zorder=1)
 
         # Set aspect ratio
         self.ax.set_aspect('equal')
 
         # Store the reference line
-        self.referenceLine = h
+        self.referenceLine = h_plot
 
         # Collect sample points for latter use (e.g. legend)
         self.modelMarkerSet = []
@@ -317,8 +317,8 @@ def add_model_set(self,
         else:
             label = kwargs.get('label')
             if percent_bias_on:
-                handle = plt.scatter(1, 2, color=color, label=label)
-                self.modelMarkerSet.append(handle)
+                plot_handle = plt.scatter(1, 2, color=color, label=label)
+                self.modelMarkerSet.append(plot_handle)
 
         # Annotate model markers if annotate_on is True
         if annotate_on:
@@ -461,15 +461,15 @@ def add_ygrid(self,
          - `NCL_taylor_2.py <https://geocat-examples.readthedocs.io/en/latest/gallery/TaylorDiagrams/NCL_taylor_2.html?highlight=add_ygrid>`_
         """
 
-        t = np.linspace(0, np.pi / 2)
+        t_array = np.linspace(0, np.pi / 2)
         for value in arr:
-            r = np.zeros_like(t) + value
-            h, = self.ax.plot(t,
-                              r,
-                              color=color,
-                              linestyle=linestyle,
-                              linewidth=linewidth,
-                              **kwargs)
+            r_array = np.zeros_like(t_array) + value
+            h_plot, = self.ax.plot(t_array,
+                                   r_array,
+                                   color=color,
+                                   linestyle=linestyle,
+                                   linewidth=linewidth,
+                                   **kwargs)
 
     def add_grid(self, *args, **kwargs):
         """Add a grid.