Deploy dev to GitHub Pages

dev/_downloads/0b6818769d40534115552a3c9056e9ee/High_Low_Analysis.py

@@ -0,0 +1,62 @@
+# Copyright (c) 2025 MetPy Developers.
+# Distributed under the terms of the BSD 3-Clause License.
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+=================
+High/Low Analysis
+=================
+
+This uses MetPy's `find_peaks` function to automatically identify locations of high and low
+centers, and then plots them on a map.
+"""
+
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+import xarray as xr
+
+from metpy.calc import find_peaks
+from metpy.cbook import get_test_data
+from metpy.plots import add_metpy_logo, scattertext
+from metpy.units import units
+
+###########################################
+# Start by loading some data from our sample GFS model dataset. Pull out the geopotential
+# heights field for the 850 hPa layer, as well as grab the projection metadata.
+data = xr.open_dataset(get_test_data('GFS_test.nc', as_file_obj=False)).metpy.parse_cf()
+mslp = data.Geopotential_height_isobaric.metpy.sel(vertical=850 * units.hPa).squeeze()
+dataproj = mslp.metpy.cartopy_crs
+
+
+###########################################
+# Here we use `find_peaks` to find the locations of the highs and then the lows
+h_y, h_x = find_peaks(mslp.values)
+l_y, l_x = find_peaks(mslp.values, maxima=False)
+
+###########################################
+# Plot the analyzed locations on top of the contours of height on a map
+fig = plt.figure(figsize=(11., 8.))
+ax = fig.add_subplot(1, 1, 1, projection=ccrs.LambertConformal(central_longitude=-95))
+ax.contour(mslp.metpy.x, mslp.metpy.y, mslp, range(0, 2000, 30),
+           colors='k', linewidths=1.25, linestyles='solid', transform=dataproj)
+
+# Using scattertext() plot the high centers using a red 'H' and put the height value
+# below the 'H' using a smaller font.
+scattertext(ax, mslp.metpy.x[h_x], mslp.metpy.y[h_y], 'H', size=20, color='red',
+            fontweight='bold', transform=dataproj)
+scattertext(ax, mslp.metpy.x[h_x], mslp.metpy.y[h_y], mslp.values[h_y, h_x], formatter='.0f',
+            size=12, color='red', loc=(0, -15), fontweight='bold', transform=dataproj)
+
+# Now do the same for the lows using a blue 'L'
+scattertext(ax, mslp.metpy.x[l_x], mslp.metpy.y[l_y], 'L', size=20, color='blue',
+            fontweight='bold', transform=dataproj)
+scattertext(ax, mslp.metpy.x[l_x], mslp.metpy.y[l_y], mslp.values[l_y, l_x], formatter='.0f',
+            size=12, color='blue', loc=(0, -15), fontweight='bold', transform=dataproj)
+
+ax.add_feature(cfeature.OCEAN)
+ax.add_feature(cfeature.LAND)
+ax.add_feature(cfeature.COASTLINE)
+
+ax.set_title('Automated 850hPa High and Low Locations')
+add_metpy_logo(fig, 275, 295, size='large')
+plt.show()

dev/_downloads/c4e42ef7eb3e1a680ce140554e20e981/High_Low_Analysis.ipynb

@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# High/Low Analysis\n\nThis uses MetPy's `find_peaks` function to automatically identify locations of high and low\ncenters, and then plots them on a map.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport xarray as xr\n\nfrom metpy.calc import find_peaks\nfrom metpy.cbook import get_test_data\nfrom metpy.plots import add_metpy_logo, scattertext\nfrom metpy.units import units"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Start by loading some data from our sample GFS model dataset. Pull out the geopotential\nheights field for the 850 hPa layer, as well as grab the projection metadata.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "data = xr.open_dataset(get_test_data('GFS_test.nc', as_file_obj=False)).metpy.parse_cf()\nmslp = data.Geopotential_height_isobaric.metpy.sel(vertical=850 * units.hPa).squeeze()\ndataproj = mslp.metpy.cartopy_crs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we use `find_peaks` to find the locations of the highs and then the lows\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "h_y, h_x = find_peaks(mslp.values)\nl_y, l_x = find_peaks(mslp.values, maxima=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the analyzed locations on top of the contours of height on a map\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(figsize=(11., 8.))\nax = fig.add_subplot(1, 1, 1, projection=ccrs.LambertConformal(central_longitude=-95))\nax.contour(mslp.metpy.x, mslp.metpy.y, mslp, range(0, 2000, 30),\n           colors='k', linewidths=1.25, linestyles='solid', transform=dataproj)\n\n# Using scattertext() plot the high centers using a red 'H' and put the height value\n# below the 'H' using a smaller font.\nscattertext(ax, mslp.metpy.x[h_x], mslp.metpy.y[h_y], 'H', size=20, color='red',\n            fontweight='bold', transform=dataproj)\nscattertext(ax, mslp.metpy.x[h_x], mslp.metpy.y[h_y], mslp.values[h_y, h_x], formatter='.0f',\n            size=12, color='red', loc=(0, -15), fontweight='bold', transform=dataproj)\n\n# Now do the same for the lows using a blue 'L'\nscattertext(ax, mslp.metpy.x[l_x], mslp.metpy.y[l_y], 'L', size=20, color='blue',\n            fontweight='bold', transform=dataproj)\nscattertext(ax, mslp.metpy.x[l_x], mslp.metpy.y[l_y], mslp.values[l_y, l_x], formatter='.0f',\n            size=12, color='blue', loc=(0, -15), fontweight='bold', transform=dataproj)\n\nax.add_feature(cfeature.OCEAN)\nax.add_feature(cfeature.LAND)\nax.add_feature(cfeature.COASTLINE)\n\nax.set_title('Automated 850hPa High and Low Locations')\nadd_metpy_logo(fig, 275, 295, size='large')\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.13.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/d0fb0bcc036dab38c2a49ad11eb0832b/Plotting_Surface_Analysis.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\n\nfrom metpy.cbook import get_test_data\nfrom metpy.io import parse_wpc_surface_bulletin\nfrom metpy.plots import (add_metpy_logo, ColdFront, OccludedFront, StationaryFront,\n                         StationPlot, WarmFront)"
+        "import cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\n\nfrom metpy.cbook import get_test_data\nfrom metpy.io import parse_wpc_surface_bulletin\nfrom metpy.plots import (add_metpy_logo, ColdFront, OccludedFront, scattertext,\n                         StationaryFront, WarmFront)"
       ]
     },
     {
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "def plot_bulletin(ax, data):\n    \"\"\"Plot a dataframe of surface features on a map.\"\"\"\n    # Set some default visual styling\n    size = 4\n    fontsize = 9\n    complete_style = {'HIGH': {'color': 'blue', 'fontsize': fontsize},\n                      'LOW': {'color': 'red', 'fontsize': fontsize},\n                      'WARM': {'linewidth': 1, 'path_effects': [WarmFront(size=size)]},\n                      'COLD': {'linewidth': 1, 'path_effects': [ColdFront(size=size)]},\n                      'OCFNT': {'linewidth': 1, 'path_effects': [OccludedFront(size=size)]},\n                      'STNRY': {'linewidth': 1, 'path_effects': [StationaryFront(size=size)]},\n                      'TROF': {'linewidth': 2, 'linestyle': 'dashed',\n                               'edgecolor': 'darkorange'}}\n\n    # Handle H/L points using MetPy's StationPlot class\n    for field in ('HIGH', 'LOW'):\n        rows = data[data.feature == field]\n        x, y = zip(*((pt.x, pt.y) for pt in rows.geometry), strict=False)\n        sp = StationPlot(ax, x, y, transform=ccrs.PlateCarree(), clip_on=True)\n        sp.plot_text('C', [field[0]] * len(x), **complete_style[field])\n        sp.plot_parameter('S', rows.strength, **complete_style[field])\n\n    # Handle all the boundary types\n    for field in ('WARM', 'COLD', 'STNRY', 'OCFNT', 'TROF'):\n        rows = data[data.feature == field]\n        ax.add_geometries(rows.geometry, crs=ccrs.PlateCarree(), **complete_style[field],\n                          facecolor='none')"
+        "def plot_bulletin(ax, data):\n    \"\"\"Plot a dataframe of surface features on a map.\"\"\"\n    # Set some default visual styling\n    size = 4\n    fontsize = 9\n    complete_style = {'HIGH': {'color': 'blue', 'fontsize': fontsize},\n                      'LOW': {'color': 'red', 'fontsize': fontsize},\n                      'WARM': {'linewidth': 1, 'path_effects': [WarmFront(size=size)]},\n                      'COLD': {'linewidth': 1, 'path_effects': [ColdFront(size=size)]},\n                      'OCFNT': {'linewidth': 1, 'path_effects': [OccludedFront(size=size)]},\n                      'STNRY': {'linewidth': 1, 'path_effects': [StationaryFront(size=size)]},\n                      'TROF': {'linewidth': 2, 'linestyle': 'dashed',\n                               'edgecolor': 'darkorange'}}\n\n    # Handle H/L points using MetPy's StationPlot class\n    for field in ('HIGH', 'LOW'):\n        rows = data[data.feature == field]\n        x, y = zip(*((pt.x, pt.y) for pt in rows.geometry), strict=False)\n        scattertext(ax, x, y, field[0],\n                    **complete_style[field], transform=ccrs.PlateCarree(), clip_on=True)\n        scattertext(ax, x, y, rows.strength, formatter='.0f', loc=(0, -10),\n                    **complete_style[field], transform=ccrs.PlateCarree(), clip_on=True)\n\n    # Handle all the boundary types\n    for field in ('WARM', 'COLD', 'STNRY', 'OCFNT', 'TROF'):\n        rows = data[data.feature == field]\n        ax.add_geometries(rows.geometry, crs=ccrs.PlateCarree(), **complete_style[field],\n                          facecolor='none')"
       ]
     },
     {

dev/_downloads/d7d718be94d68f64fd8796450bbb0ff2/Plotting_Surface_Analysis.py

@@ -17,8 +17,8 @@
 
 from metpy.cbook import get_test_data
 from metpy.io import parse_wpc_surface_bulletin
-from metpy.plots import (add_metpy_logo, ColdFront, OccludedFront, StationaryFront,
-                         StationPlot, WarmFront)
+from metpy.plots import (add_metpy_logo, ColdFront, OccludedFront, scattertext,
+                         StationaryFront, WarmFront)
 
 ###########################################
 # Define a function that can be used to readily plot a bulletin that has been parsed into a
@@ -45,9 +45,10 @@ def plot_bulletin(ax, data):
     for field in ('HIGH', 'LOW'):
         rows = data[data.feature == field]
         x, y = zip(*((pt.x, pt.y) for pt in rows.geometry), strict=False)
-        sp = StationPlot(ax, x, y, transform=ccrs.PlateCarree(), clip_on=True)
-        sp.plot_text('C', [field[0]] * len(x), **complete_style[field])
-        sp.plot_parameter('S', rows.strength, **complete_style[field])
+        scattertext(ax, x, y, field[0],
+                    **complete_style[field], transform=ccrs.PlateCarree(), clip_on=True)
+        scattertext(ax, x, y, rows.strength, formatter='.0f', loc=(0, -10),
+                    **complete_style[field], transform=ccrs.PlateCarree(), clip_on=True)
 
     # Handle all the boundary types
     for field in ('WARM', 'COLD', 'STNRY', 'OCFNT', 'TROF'):

dev/_sources/api/generated/metpy.calc.find_peaks.rst.txt

@@ -0,0 +1,12 @@
+find_peaks
+=====================
+
+.. currentmodule:: metpy.calc
+
+.. autofunction:: find_peaks
+
+.. include:: metpy.calc.find_peaks.examples
+
+.. raw:: html
+
+      <div style='clear:both'></div>

dev/_sources/api/generated/metpy.calc.peak_persistence.rst.txt

@@ -0,0 +1,12 @@
+peak_persistence
+===========================
+
+.. currentmodule:: metpy.calc
+
+.. autofunction:: peak_persistence
+
+.. include:: metpy.calc.peak_persistence.examples
+
+.. raw:: html
+
+      <div style='clear:both'></div>

dev/_sources/api/generated/metpy.calc.rst.txt

@@ -217,13 +217,15 @@ Other
       azimuth_range_to_lat_lon
       find_bounding_indices
       find_intersections
+      find_peaks
       get_layer
       get_layer_heights
       get_perturbation
       isentropic_interpolation
       isentropic_interpolation_as_dataset
       nearest_intersection_idx
       parse_angle
+      peak_persistence
       reduce_point_density
       resample_nn_1d
 

dev/_sources/api/generated/metpy.plots.rst.txt

@@ -20,6 +20,7 @@ Plots ``(metpy.plots)``
       :toctree: ./
 
       read_colortable
+      scattertext
       wx_code_to_numeric
       add_metpy_logo
       add_timestamp

dev/_sources/api/generated/metpy.plots.scattertext.rst.txt

@@ -0,0 +1,12 @@
+scattertext
+=======================
+
+.. currentmodule:: metpy.plots
+
+.. autofunction:: scattertext
+
+.. include:: metpy.plots.scattertext.examples
+
+.. raw:: html
+
+      <div style='clear:both'></div>

dev/_sources/api/references.rst.txt

@@ -114,6 +114,11 @@ References
 .. [Holton2004] Holton, J. R., 2004: *An Introduction to Dynamic Meteorology*. 4th ed.
            Academic Press, 535 pp.
 
+.. [Huber2021] Huber, S., 2020: Persistent Homology in Data Science.
+           *Proc. Int. Data Sci. Conf.*, doi:`10.1007/978-3-658-32182-6_13
+           <https://doi.org/10.1007/978-3-658-32182-6_13>`_.
+           `[PDF] <https://www.sthu.org/research/publications/files/Hub20.pdf>`_
+
 .. [IAPWS1995] The International Association for the Properties of Water and Steam, 1995:
            `Revised Release on the IAPWS Formulation 1995 for the Thermodynamic Properties
            of Ordinary Water Substance for General and Scientific Use (updated

dev/_sources/examples/Advanced_Sounding.rst.txt

@@ -165,7 +165,7 @@ Create a new figure. The dimensions here give a good aspect ratio.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.189 seconds)
+   **Total running time of the script:** (0 minutes 0.174 seconds)
 
 
 .. _sphx_glr_download_examples_Advanced_Sounding.py:

dev/_sources/examples/Advanced_Sounding_With_Complex_Layout.rst.txt

@@ -143,7 +143,7 @@ Create a new figure. The dimensions here give a good aspect ratio
  .. code-block:: none
 
 
-    [<matplotlib.lines.Line2D object at 0x7f7de14b3750>]
+    [<matplotlib.lines.Line2D object at 0x7f6a6c4dae90>]
 
 
 
@@ -429,7 +429,7 @@ try another Skew-T with a few more advanced features:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.649 seconds)
+   **Total running time of the script:** (0 minutes 0.682 seconds)
 
 
 .. _sphx_glr_download_examples_Advanced_Sounding_With_Complex_Layout.py:

dev/_sources/examples/Four_Panel_Map.rst.txt

@@ -259,7 +259,7 @@ By reading model output data from a netCDF file, we can create a four panel plot
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.264 seconds)
+   **Total running time of the script:** (0 minutes 7.308 seconds)
 
 
 .. _sphx_glr_download_examples_Four_Panel_Map.py:

dev/_sources/examples/XArray_Projections.rst.txt

@@ -68,7 +68,7 @@ data on a map using CartoPy.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.982 seconds)
+   **Total running time of the script:** (0 minutes 8.073 seconds)
 
 
 .. _sphx_glr_download_examples_XArray_Projections.py:

dev/_sources/examples/calculations/Absolute_Vorticity.rst.txt

@@ -68,7 +68,7 @@ Dataset and plotting using Matplotlib.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.131 seconds)
+   **Total running time of the script:** (0 minutes 0.139 seconds)
 
 
 .. _sphx_glr_download_examples_calculations_Absolute_Vorticity.py:

dev/_sources/examples/calculations/Advection.rst.txt

@@ -91,7 +91,7 @@ the example xarray Dataset and plotting using Matplotlib.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.116 seconds)
+   **Total running time of the script:** (0 minutes 0.115 seconds)
 
 
 .. _sphx_glr_download_examples_calculations_Advection.py:

dev/_sources/examples/calculations/Divergence.rst.txt

@@ -68,7 +68,7 @@ Dataset and plotting using Matplotlib.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.115 seconds)
+   **Total running time of the script:** (0 minutes 0.126 seconds)
 
 
 .. _sphx_glr_download_examples_calculations_Divergence.py: