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dopplershift committed Oct 3, 2023
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The `StationPlotLayout` class is used to standardize the plotting various parameters
(i.e. temperature), keeping track of the location, formatting, and even the units for use in
the station plot. This makes it easy (if using standardized names) to re-use a given layout
the station plot. This makes it easy (if using standardized names) to reuse a given layout
of a station plot.
"""
import cartopy.crs as ccrs
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4 changes: 2 additions & 2 deletions dev/_downloads/ca19cf4d9fe41266757c83e7aff770db/Vorticity.py
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ds = example_data()

# Calculate the vertical vorticity of the flow
vor = mpcalc.vorticity(ds.uwind, ds.vwind)
vort = mpcalc.vorticity(ds.uwind, ds.vwind)

# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))

# scale vorticity by 1e5 for plotting purposes
cf = ax.contourf(ds.lon, ds.lat, vor * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)
cf = ax.contourf(ds.lon, ds.lat, vort * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)
plt.colorbar(cf, pad=0, aspect=50)
ax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)
ax.set(xlim=(260, 270), ylim=(30, 40))
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Expand Up @@ -162,11 +162,11 @@ def draw_circle(ax, x, y, r, m, label):
# spatial data structure that we use here simply to show areal ratios.
# Notice that the two natural neighbor triangle circumcenters are also vertices
# in the Voronoi plot (green dots), and the observations are in the polygons (blue dots).
vor = Voronoi(list(zip(xp, yp)))
vort = Voronoi(list(zip(xp, yp)))

fig, ax = plt.subplots(1, 1, figsize=(15, 10))
ax.ishold = lambda: True # Work-around for Matplotlib 3.0.0 incompatibility
voronoi_plot_2d(vor, ax=ax)
voronoi_plot_2d(vort, ax=ax)

nn_ind = np.array([0, 5, 7, 8])
z_0 = zp[nn_ind]
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Station Plot with Layout\n\nMake a station plot, complete with sky cover and weather symbols, using a\nstation plot layout built into MetPy.\n\nThe station plot itself is straightforward, but there is a bit of code to perform the\ndata-wrangling (hopefully that situation will improve in the future). Certainly, if you have\nexisting point data in a format you can work with trivially, the station plot will be simple.\n\nThe `StationPlotLayout` class is used to standardize the plotting various parameters\n(i.e. temperature), keeping track of the location, formatting, and even the units for use in\nthe station plot. This makes it easy (if using standardized names) to re-use a given layout\nof a station plot.\n"
"\n# Station Plot with Layout\n\nMake a station plot, complete with sky cover and weather symbols, using a\nstation plot layout built into MetPy.\n\nThe station plot itself is straightforward, but there is a bit of code to perform the\ndata-wrangling (hopefully that situation will improve in the future). Certainly, if you have\nexisting point data in a format you can work with trivially, the station plot will be simple.\n\nThe `StationPlotLayout` class is used to standardize the plotting various parameters\n(i.e. temperature), keeping track of the location, formatting, and even the units for use in\nthe station plot. This makes it easy (if using standardized names) to reuse a given layout\nof a station plot.\n"
]
},
{
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},
"outputs": [],
"source": [
"vor = Voronoi(list(zip(xp, yp)))\n\nfig, ax = plt.subplots(1, 1, figsize=(15, 10))\nax.ishold = lambda: True # Work-around for Matplotlib 3.0.0 incompatibility\nvoronoi_plot_2d(vor, ax=ax)\n\nnn_ind = np.array([0, 5, 7, 8])\nz_0 = zp[nn_ind]\nx_0 = xp[nn_ind]\ny_0 = yp[nn_ind]\n\nfor x, y, z in zip(x_0, y_0, z_0):\n ax.annotate(f'{x}, {y}: {z:.3f} F', xy=(x, y))\n\nax.plot(sim_gridx[0], sim_gridy[0], 'k+', markersize=10)\nax.annotate(f'{sim_gridx[0]}, {sim_gridy[0]}', xy=(sim_gridx[0] + 2, sim_gridy[0]))\nax.plot(cc[:, 0], cc[:, 1], 'ks', markersize=15, fillstyle='none',\n label='natural neighbor\\ncircumcenters')\n\nfor center in cc:\n ax.annotate(f'{center[0]:.3f}, {center[1]:.3f}', xy=(center[0] + 1, center[1] + 1))\n\ntris = tri.points[tri.simplices[members[0]]]\nfor triangle in tris:\n x = [triangle[0, 0], triangle[1, 0], triangle[2, 0], triangle[0, 0]]\n y = [triangle[0, 1], triangle[1, 1], triangle[2, 1], triangle[0, 1]]\n ax.plot(x, y, ':', linewidth=2)\n\nax.legend()\nax.set_aspect('equal', 'datalim')\n\n\ndef draw_polygon_with_info(ax, polygon, off_x=0, off_y=0):\n \"\"\"Draw one of the natural neighbor polygons with some information.\"\"\"\n pts = np.array(polygon)[ConvexHull(polygon).vertices]\n for i, pt in enumerate(pts):\n ax.plot([pt[0], pts[(i + 1) % len(pts)][0]],\n [pt[1], pts[(i + 1) % len(pts)][1]], 'k-')\n\n avex, avey = np.mean(pts, axis=0)\n ax.annotate(f'area: {geometry.area(pts):.3f}', xy=(avex + off_x, avey + off_y),\n fontsize=12)\n\n\ncc1 = geometry.circumcenter((53, 66), (15, 60), (30, 30))\ncc2 = geometry.circumcenter((34, 24), (53, 66), (30, 30))\ndraw_polygon_with_info(ax, [cc[0], cc1, cc2])\n\ncc1 = geometry.circumcenter((53, 66), (15, 60), (30, 30))\ncc2 = geometry.circumcenter((15, 60), (8, 24), (30, 30))\ndraw_polygon_with_info(ax, [cc[0], cc[1], cc1, cc2], off_x=-9, off_y=3)\n\ncc1 = geometry.circumcenter((8, 24), (34, 24), (30, 30))\ncc2 = geometry.circumcenter((15, 60), (8, 24), (30, 30))\ndraw_polygon_with_info(ax, [cc[1], cc1, cc2], off_x=-15)\n\ncc1 = geometry.circumcenter((8, 24), (34, 24), (30, 30))\ncc2 = geometry.circumcenter((34, 24), (53, 66), (30, 30))\ndraw_polygon_with_info(ax, [cc[0], cc[1], cc1, cc2])"
"vort = Voronoi(list(zip(xp, yp)))\n\nfig, ax = plt.subplots(1, 1, figsize=(15, 10))\nax.ishold = lambda: True # Work-around for Matplotlib 3.0.0 incompatibility\nvoronoi_plot_2d(vort, ax=ax)\n\nnn_ind = np.array([0, 5, 7, 8])\nz_0 = zp[nn_ind]\nx_0 = xp[nn_ind]\ny_0 = yp[nn_ind]\n\nfor x, y, z in zip(x_0, y_0, z_0):\n ax.annotate(f'{x}, {y}: {z:.3f} F', xy=(x, y))\n\nax.plot(sim_gridx[0], sim_gridy[0], 'k+', markersize=10)\nax.annotate(f'{sim_gridx[0]}, {sim_gridy[0]}', xy=(sim_gridx[0] + 2, sim_gridy[0]))\nax.plot(cc[:, 0], cc[:, 1], 'ks', markersize=15, fillstyle='none',\n label='natural neighbor\\ncircumcenters')\n\nfor center in cc:\n ax.annotate(f'{center[0]:.3f}, {center[1]:.3f}', xy=(center[0] + 1, center[1] + 1))\n\ntris = tri.points[tri.simplices[members[0]]]\nfor triangle in tris:\n x = [triangle[0, 0], triangle[1, 0], triangle[2, 0], triangle[0, 0]]\n y = [triangle[0, 1], triangle[1, 1], triangle[2, 1], triangle[0, 1]]\n ax.plot(x, y, ':', linewidth=2)\n\nax.legend()\nax.set_aspect('equal', 'datalim')\n\n\ndef draw_polygon_with_info(ax, polygon, off_x=0, off_y=0):\n \"\"\"Draw one of the natural neighbor polygons with some information.\"\"\"\n pts = np.array(polygon)[ConvexHull(polygon).vertices]\n for i, pt in enumerate(pts):\n ax.plot([pt[0], pts[(i + 1) % len(pts)][0]],\n [pt[1], pts[(i + 1) % len(pts)][1]], 'k-')\n\n avex, avey = np.mean(pts, axis=0)\n ax.annotate(f'area: {geometry.area(pts):.3f}', xy=(avex + off_x, avey + off_y),\n fontsize=12)\n\n\ncc1 = geometry.circumcenter((53, 66), (15, 60), (30, 30))\ncc2 = geometry.circumcenter((34, 24), (53, 66), (30, 30))\ndraw_polygon_with_info(ax, [cc[0], cc1, cc2])\n\ncc1 = geometry.circumcenter((53, 66), (15, 60), (30, 30))\ncc2 = geometry.circumcenter((15, 60), (8, 24), (30, 30))\ndraw_polygon_with_info(ax, [cc[0], cc[1], cc1, cc2], off_x=-9, off_y=3)\n\ncc1 = geometry.circumcenter((8, 24), (34, 24), (30, 30))\ncc2 = geometry.circumcenter((15, 60), (8, 24), (30, 30))\ndraw_polygon_with_info(ax, [cc[1], cc1, cc2], off_x=-15)\n\ncc1 = geometry.circumcenter((8, 24), (34, 24), (30, 30))\ncc2 = geometry.circumcenter((34, 24), (53, 66), (30, 30))\ndraw_polygon_with_info(ax, [cc[0], cc[1], cc1, cc2])"
]
},
{
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},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nimport metpy.calc as mpcalc\nfrom metpy.cbook import example_data\n\n# load example data\nds = example_data()\n\n# Calculate the vertical vorticity of the flow\nvor = mpcalc.vorticity(ds.uwind, ds.vwind)\n\n# start figure and set axis\nfig, ax = plt.subplots(figsize=(5, 5))\n\n# scale vorticity by 1e5 for plotting purposes\ncf = ax.contourf(ds.lon, ds.lat, vor * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)\nplt.colorbar(cf, pad=0, aspect=50)\nax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)\nax.set(xlim=(260, 270), ylim=(30, 40))\nax.set_title('Relative Vorticity Calculation')\n\nplt.show()"
"import matplotlib.pyplot as plt\n\nimport metpy.calc as mpcalc\nfrom metpy.cbook import example_data\n\n# load example data\nds = example_data()\n\n# Calculate the vertical vorticity of the flow\nvort = mpcalc.vorticity(ds.uwind, ds.vwind)\n\n# start figure and set axis\nfig, ax = plt.subplots(figsize=(5, 5))\n\n# scale vorticity by 1e5 for plotting purposes\ncf = ax.contourf(ds.lon, ds.lat, vort * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)\nplt.colorbar(cf, pad=0, aspect=50)\nax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)\nax.set(xlim=(260, 270), ylim=(30, 40))\nax.set_title('Relative Vorticity Calculation')\n\nplt.show()"
]
}
],
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2 changes: 1 addition & 1 deletion dev/_modules/index.html
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
<link rel="canonical" href="https://unidata.github.io/MetPy/latest/_modules/index.html" />

</head>
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
<link rel="canonical" href="https://unidata.github.io/MetPy/latest/_modules/metpy/io/gempak.html" />

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<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
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<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="docsearch:language" content="en"/>
<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="docsearch:language" content="en"/>
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<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="docsearch:language" content="en"/>
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<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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<meta name="docbuild:last-update" content="Oct 02, 2023 at 22:56:05"/>
<meta name="docbuild:last-update" content="Oct 03, 2023 at 17:29:54"/>
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.. rst-class:: sphx-glr-timing

**Total running time of the script:** (0 minutes 0.224 seconds)
**Total running time of the script:** (0 minutes 0.320 seconds)


.. _sphx_glr_download_examples_Advanced_Sounding.py:
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.. code-block:: none
[<matplotlib.lines.Line2D object at 0x7efccbd20250>]
[<matplotlib.lines.Line2D object at 0x7facb1565e50>]
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.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7efccbe0a690>
<matplotlib.collections.PolyCollection object at 0x7facb1f1b450>
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.. code-block:: none
<matplotlib.patches.FancyArrow object at 0x7efccbed9350>
<matplotlib.patches.FancyArrow object at 0x7facb1532c50>
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.. _sphx_glr_download_examples_Complex-Sounding-Plot.py:
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Expand Up @@ -259,7 +259,7 @@ By reading model output data from a netCDF file, we can create a four panel plot

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.. _sphx_glr_download_examples_Four_Panel_Map.py:
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Expand Up @@ -68,7 +68,7 @@ data on a map using CartoPy.
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.. _sphx_glr_download_examples_XArray_Projections.py:
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Expand Up @@ -68,7 +68,7 @@ Dataset and plotting using Matplotlib.
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.. _sphx_glr_download_examples_calculations_Absolute_Vorticity.py:
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Expand Up @@ -91,7 +91,7 @@ the example xarray Dataset and plotting using Matplotlib.
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.. _sphx_glr_download_examples_calculations_Advection.py:
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Expand Up @@ -143,7 +143,7 @@ If you want the unabbrieviated version, input full=True
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.. _sphx_glr_download_examples_calculations_Angle_to_Direction.py:
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2 changes: 1 addition & 1 deletion dev/_sources/examples/calculations/Bulk_Shear.rst.txt
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Expand Up @@ -148,7 +148,7 @@ Compute the bulk shear for the lowest three km
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.. _sphx_glr_download_examples_calculations_Bulk_Shear.py:
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Expand Up @@ -212,7 +212,7 @@ And print the corresponding dewpoint
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.. _sphx_glr_download_examples_calculations_Dewpoint_and_Mixing_Ratio.py:
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Expand Up @@ -68,7 +68,7 @@ Dataset and plotting using Matplotlib.
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.. _sphx_glr_download_examples_calculations_Divergence.py:
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Expand Up @@ -168,7 +168,7 @@ Use the layer defined above and plot the equivalent potential temperature

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.. _sphx_glr_download_examples_calculations_Equivalent_Potential_Temperature.py:
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Expand Up @@ -166,7 +166,7 @@ Finally, the deltas can be arrays for unevenly spaced data.
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.. _sphx_glr_download_examples_calculations_Gradient.py:
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Expand Up @@ -130,7 +130,7 @@ Calculate the mean pressure weighted temperature over a depth of 500 hPa
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.. _sphx_glr_download_examples_calculations_Mean_Pressure_Weighted.py:
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.. _sphx_glr_download_examples_calculations_Mountain_Problem.py:
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Expand Up @@ -97,7 +97,7 @@ Dataset and plotting using Matplotlib.
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.. _sphx_glr_download_examples_calculations_QVector.py:
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.. _sphx_glr_download_examples_calculations_Shearing_Deformation.py:
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Expand Up @@ -140,7 +140,7 @@ Now, create a grid showing different smoothing options

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.. _sphx_glr_download_examples_calculations_Smoothing.py:
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Expand Up @@ -461,7 +461,7 @@ Print Important Sounding Parameters
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.. _sphx_glr_download_examples_calculations_Sounding_Calculations.py:
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