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mgrover1 · Dec 16, 2024 · 28ec47d · 28ec47d
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diff --git a/_downloads/023767e9a98f895b96f37ef3bdd0b559/plot_rhi_cfradial.zip b/_downloads/023767e9a98f895b96f37ef3bdd0b559/plot_rhi_cfradial.zip
diff --git a/_downloads/113656d11b78abf152bbfd5d68a9e2e6/plot_cloud_mask.zip b/_downloads/113656d11b78abf152bbfd5d68a9e2e6/plot_cloud_mask.zip
diff --git a/_downloads/1408fef76e21bdfaab1f27d6100ca0e2/plot_read_cfradial2.zip b/_downloads/1408fef76e21bdfaab1f27d6100ca0e2/plot_read_cfradial2.zip
diff --git a/_downloads/141be1b30509b41f6f0e2893989a2d22/plot_convective_stratiform.zip b/_downloads/141be1b30509b41f6f0e2893989a2d22/plot_convective_stratiform.zip
diff --git a/_downloads/182481e6c5738d915b8ec732baf8739c/plot_zdr_check.ipynb b/_downloads/182481e6c5738d915b8ec732baf8739c/plot_zdr_check.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# ZDR Bias Calculation\n\nThis example shows how to calculate the ZDR bias from VPT/Birdbath scans.\nThe technique here uses a vertically pointing scan in regions of light rain.\nIn these regions, raindrops should be approximately spherical and therefore their\nZDR near zero. Therefore, we want the average ZDR in these regions.\nThis code applies reflectivity and cross correlation ratio-based thresholds to the ZDR\nbias calculation to ensure that we are taking the average ZDR in light rain.\n"
+        "\n# ZDR Bias Calculation\n\nThis example shows how to calculate the ZDR bias from VPT/Birdbath scans.\nThe technique here uses a vertically pointing scan in regions of light rain.\nIn these regions, raindrops should be approximately spherical and therefore their\nZDR near zero. Therefore, we want the average ZDR in these regions.\nThis code applies reflectivity and cross correlation ratio-based threshold to the ZDR\nbias calculation to ensure that we are taking the average ZDR in light rain.\n"
       ]
     },
     {
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom open_radar_data import DATASETS\n\nimport pyart\n\n# Read in example data\nfilename = DATASETS.fetch(\"sgpxsaprcfrvptI4.a1.20200205.100827.nc\")\nds = pyart.io.read(filename)\n\n# Set up a typical filter for ZDR bias calculation in birdbath scan\n# Light rain and RhoHV near 1 ensures that raindrops are close to spherical\n# Therefore ZDR should be zero in these regions\ngatefilter = pyart.filters.GateFilter(ds)\ngatefilter.exclude_below(\"cross_correlation_ratio_hv\", 0.995)\ngatefilter.exclude_above(\"cross_correlation_ratio_hv\", 1)\ngatefilter.exclude_below(\"reflectivity\", 10)\ngatefilter.exclude_above(\"reflectivity\", 30)\n\nresults = pyart.correct.calc_zdr_offset(\n    ds,\n    zdr_var=\"differential_reflectivity\",\n    gatefilter=gatefilter,\n    height_range=(1000, 3000),\n)\n\nprint(\"Zdr Bias: \" + \"{:.2f}\".format(results[\"bias\"]))\n\nfig, ax = plt.subplots(1, 3, figsize=(8, 5))\nax[0].plot(results[\"profile_zdr\"], results[\"range\"])\nax[0].set_ylabel(\"Range (m)\")\nax[0].set_xlabel(\"Zdr (dB)\")\nax[1].plot(results[\"profile_reflectivity\"], results[\"range\"])\nax[1].set_xlabel(\"Zh (dBZ)\")\nax[2].plot(results[\"profile_cross_correlation_ratio_hv\"], results[\"range\"])\nax[2].set_xlabel(\"RhoHV ()\")\nfig.tight_layout()\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport xradar as xd\nfrom open_radar_data import DATASETS\n\nimport pyart\n\n# Read in example data\nfilename = DATASETS.fetch(\"sgpxsaprcfrvptI4.a1.20200205.100827.nc\")\n\n# Read in the data\ntree = xd.io.open_cfradial1_datatree(filename)\nradar = tree.pyart.to_radar()\n\n# Set up a typical filter for ZDR bias calculation in birdbath scan\n# Light rain and RhoHV near 1 ensures that raindrops are close to spherical\n# Therefore ZDR should be zero in these regions\ngatefilter = pyart.filters.GateFilter(radar)\ngatefilter.exclude_below(\"cross_correlation_ratio_hv\", 0.995)\ngatefilter.exclude_above(\"cross_correlation_ratio_hv\", 1)\ngatefilter.exclude_below(\"reflectivity\", 10)\ngatefilter.exclude_above(\"reflectivity\", 30)\n\nresults = pyart.correct.calc_zdr_offset(\n    radar,\n    zdr_var=\"differential_reflectivity\",\n    gatefilter=gatefilter,\n    height_range=(1000, 3000),\n)\n\nprint(\"Zdr Bias: \" + \"{:.2f}\".format(results[\"bias\"]))\n\nfig, ax = plt.subplots(1, 3, figsize=(8, 5))\nax[0].plot(results[\"profile_zdr\"], results[\"range\"])\nax[0].set_ylabel(\"Range (m)\")\nax[0].set_xlabel(\"Zdr (dB)\")\nax[1].plot(results[\"profile_reflectivity\"], results[\"range\"])\nax[1].set_xlabel(\"Zh (dBZ)\")\nax[2].plot(results[\"profile_cross_correlation_ratio_hv\"], results[\"range\"])\nax[2].set_xlabel(\"RhoHV ()\")\nfig.tight_layout()\nplt.show()"
       ]
     }
   ],

diff --git a/_downloads/186aee502f3fe8da716eddb0555d1711/plot_rhi_mdv.zip b/_downloads/186aee502f3fe8da716eddb0555d1711/plot_rhi_mdv.zip
diff --git a/_downloads/1b5e02b764264317a8a38a5f4d087c94/plot_attenuation.ipynb b/_downloads/1b5e02b764264317a8a38a5f4d087c94/plot_attenuation.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Jonathan J. Helmus ([email protected])\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\n\nimport pyart\n\nfile = pyart.testing.get_test_data(\"sgpcsaprsurcmacI7.c0.20110520.095101.nc\")\n\n# read in the data\nradar = pyart.io.read_cfradial(file)\n\n# remove existing corrections\nradar.fields.pop(\"specific_attenuation\")\nradar.fields.pop(\"corrected_reflectivity_horizontal\")\n\n# perform attenuation correction\nspec_at, cor_z = pyart.correct.calculate_attenuation(\n    radar,\n    0,\n    refl_field=\"reflectivity_horizontal\",\n    ncp_field=\"norm_coherent_power\",\n    rhv_field=\"copol_coeff\",\n    phidp_field=\"proc_dp_phase_shift\",\n)\nradar.add_field(\"specific_attenuation\", spec_at)\nradar.add_field(\"corrected_reflectivity_horizontal\", cor_z)\n\n# create the plot\nfig = plt.figure(figsize=(15, 5))\nax1 = fig.add_subplot(131)\ndisplay = pyart.graph.RadarDisplay(radar)\ndisplay.plot(\n    \"reflectivity_horizontal\",\n    0,\n    ax=ax1,\n    vmin=0,\n    vmax=60.0,\n    colorbar_label=\"\",\n    title=\"Raw Reflectivity\",\n)\n\nax2 = fig.add_subplot(132)\ndisplay.plot(\n    \"specific_attenuation\",\n    0,\n    vmin=0,\n    vmax=1.0,\n    colorbar_label=\"\",\n    ax=ax2,\n    title=\"Specific Attenuation\",\n)\n\nax3 = fig.add_subplot(133)\ndisplay = pyart.graph.RadarDisplay(radar)\ndisplay.plot(\n    \"corrected_reflectivity_horizontal\",\n    0,\n    vmin=0,\n    vmax=60.0,\n    colorbar_label=\"\",\n    ax=ax3,\n    title=\"Corrected Reflectivity\",\n)\n\nplt.suptitle(\"Attenuation correction using Py-ART\", fontsize=16)\nplt.show()"
+        "print(__doc__)\n\n# Author: Jonathan J. Helmus ([email protected])\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport xradar as xd\n\nimport pyart\n\nfile = pyart.testing.get_test_data(\"sgpcsaprsurcmacI7.c0.20110520.095101.nc\")\n\n# read in the data\ntree = xd.io.open_cfradial1_datatree(file)\nradar = tree.pyart.to_radar()\n\n# remove existing corrections\nradar.fields.pop(\"specific_attenuation\")\nradar.fields.pop(\"corrected_reflectivity_horizontal\")\n\n# perform attenuation correction\nspec_at, cor_z = pyart.correct.calculate_attenuation(\n    radar,\n    0,\n    refl_field=\"reflectivity_horizontal\",\n    ncp_field=\"norm_coherent_power\",\n    rhv_field=\"copol_coeff\",\n    phidp_field=\"proc_dp_phase_shift\",\n)\nradar.add_field(\"specific_attenuation\", spec_at)\nradar.add_field(\"corrected_reflectivity_horizontal\", cor_z)\n\n# create the plot\nfig = plt.figure(figsize=(15, 5))\nax1 = fig.add_subplot(131)\ndisplay = pyart.graph.RadarDisplay(radar)\ndisplay.plot(\n    \"reflectivity_horizontal\",\n    0,\n    ax=ax1,\n    vmin=0,\n    vmax=60.0,\n    colorbar_label=\"\",\n    title=\"Raw Reflectivity\",\n)\n\nax2 = fig.add_subplot(132)\ndisplay.plot(\n    \"specific_attenuation\",\n    0,\n    vmin=0,\n    vmax=1.0,\n    colorbar_label=\"\",\n    ax=ax2,\n    title=\"Specific Attenuation\",\n)\n\nax3 = fig.add_subplot(133)\ndisplay = pyart.graph.RadarDisplay(radar)\ndisplay.plot(\n    \"corrected_reflectivity_horizontal\",\n    0,\n    vmin=0,\n    vmax=60.0,\n    colorbar_label=\"\",\n    ax=ax3,\n    title=\"Corrected Reflectivity\",\n)\n\nplt.suptitle(\"Attenuation correction using Py-ART\", fontsize=16)\nplt.show()"
       ]
     }
   ],

diff --git a/_downloads/1c09047e492c2d0e92111f0865c74e47/plot_feature_detection.zip b/_downloads/1c09047e492c2d0e92111f0865c74e47/plot_feature_detection.zip
diff --git a/_downloads/215077bfd9708e2acf1fb59cb789c743/plot_grid_single_sweep_ppi.zip b/_downloads/215077bfd9708e2acf1fb59cb789c743/plot_grid_single_sweep_ppi.zip
diff --git a/_downloads/23bd85a5b713018e66c4c093ca137678/plot_nexrad_data_aws.zip b/_downloads/23bd85a5b713018e66c4c093ca137678/plot_nexrad_data_aws.zip
diff --git a/_downloads/2423382f77a49592b3c807cc29f058f9/plot_dealias_xradar.zip b/_downloads/2423382f77a49592b3c807cc29f058f9/plot_dealias_xradar.zip
diff --git a/_downloads/24ca58c28ab7b9027ec0b3c0d59ba94b/plot_ppi_with_rings.zip b/_downloads/24ca58c28ab7b9027ec0b3c0d59ba94b/plot_ppi_with_rings.zip
diff --git a/_downloads/3d9a790c335b161c1cd717cff023f996/plot_hydrometeor_class_x_band.zip b/_downloads/3d9a790c335b161c1cd717cff023f996/plot_hydrometeor_class_x_band.zip
diff --git a/_downloads/3e4e352c88f759a6a25973ce5452d39c/plot_max_cappi.zip b/_downloads/3e4e352c88f759a6a25973ce5452d39c/plot_max_cappi.zip
diff --git a/_downloads/3f5c1cee3a69527eff990f0ea5648943/plot_corner_reflector.zip b/_downloads/3f5c1cee3a69527eff990f0ea5648943/plot_corner_reflector.zip
diff --git a/_downloads/3f9a930125aeb4cc2b8a9f85ebb0c3cf/plot_attenuation.py b/_downloads/3f9a930125aeb4cc2b8a9f85ebb0c3cf/plot_attenuation.py
@@ -14,13 +14,15 @@
 # License: BSD 3 clause
 
 import matplotlib.pyplot as plt
+import xradar as xd
 
 import pyart
 
 file = pyart.testing.get_test_data("sgpcsaprsurcmacI7.c0.20110520.095101.nc")
 
 # read in the data
-radar = pyart.io.read_cfradial(file)
+tree = xd.io.open_cfradial1_datatree(file)
+radar = tree.pyart.to_radar()
 
 # remove existing corrections
 radar.fields.pop("specific_attenuation")

diff --git a/_downloads/422f684956c90b77f98e307629348f8d/plot_cfad.zip b/_downloads/422f684956c90b77f98e307629348f8d/plot_cfad.zip
diff --git a/_downloads/4689bb2540b3ff219a562f2271a21415/plot_map_two_radars_to_grid.zip b/_downloads/4689bb2540b3ff219a562f2271a21415/plot_map_two_radars_to_grid.zip
diff --git a/_downloads/4c163f9c524ea7f3f69d0be8edde0139/plot_hydrometeor.zip b/_downloads/4c163f9c524ea7f3f69d0be8edde0139/plot_hydrometeor.zip
diff --git a/_downloads/5127dcd9ccbf91c6d83d2225640c4f53/plot_rhi_contours_differential_reflectivity.zip b/_downloads/5127dcd9ccbf91c6d83d2225640c4f53/plot_rhi_contours_differential_reflectivity.zip
diff --git a/_downloads/518555feb558ed1324ebefeebd9a02b4/plot_composite_reflectivity.zip b/_downloads/518555feb558ed1324ebefeebd9a02b4/plot_composite_reflectivity.zip
diff --git a/_downloads/51d282e4b686b82578300459c770ae93/plot_xradar.zip b/_downloads/51d282e4b686b82578300459c770ae93/plot_xradar.zip
diff --git a/_downloads/5929e6031866d5126600c627740360cf/plot_rhi_two_panel.zip b/_downloads/5929e6031866d5126600c627740360cf/plot_rhi_two_panel.zip
diff --git a/_downloads/5cf0faabe5ec18661325da2f683e0e80/plot_modify_colorbar.zip b/_downloads/5cf0faabe5ec18661325da2f683e0e80/plot_modify_colorbar.zip
diff --git a/_downloads/6991b3bdc0b3954388ede67eab3fbc60/plot_rhi_cfradial_singlescan.zip b/_downloads/6991b3bdc0b3954388ede67eab3fbc60/plot_rhi_cfradial_singlescan.zip
diff --git a/_downloads/6c3ba476aee885971ff79561f8c13f99/plot_column_subset.zip b/_downloads/6c3ba476aee885971ff79561f8c13f99/plot_column_subset.zip
diff --git a/_downloads/803a29460e27c86c5df8332fb7f53174/plot_xsect.zip b/_downloads/803a29460e27c86c5df8332fb7f53174/plot_xsect.zip
diff --git a/_downloads/95cf67c80061e49ed3454acbd239b37a/plot_zdr_check.py b/_downloads/95cf67c80061e49ed3454acbd239b37a/plot_zdr_check.py
@@ -6,31 +6,35 @@
 The technique here uses a vertically pointing scan in regions of light rain.
 In these regions, raindrops should be approximately spherical and therefore their
 ZDR near zero. Therefore, we want the average ZDR in these regions.
-This code applies reflectivity and cross correlation ratio-based thresholds to the ZDR
+This code applies reflectivity and cross correlation ratio-based threshold to the ZDR
 bias calculation to ensure that we are taking the average ZDR in light rain.
 
 """
 
 import matplotlib.pyplot as plt
+import xradar as xd
 from open_radar_data import DATASETS
 
 import pyart
 
 # Read in example data
 filename = DATASETS.fetch("sgpxsaprcfrvptI4.a1.20200205.100827.nc")
-ds = pyart.io.read(filename)
+
+# Read in the data
+tree = xd.io.open_cfradial1_datatree(filename)
+radar = tree.pyart.to_radar()
 
 # Set up a typical filter for ZDR bias calculation in birdbath scan
 # Light rain and RhoHV near 1 ensures that raindrops are close to spherical
 # Therefore ZDR should be zero in these regions
-gatefilter = pyart.filters.GateFilter(ds)
+gatefilter = pyart.filters.GateFilter(radar)
 gatefilter.exclude_below("cross_correlation_ratio_hv", 0.995)
 gatefilter.exclude_above("cross_correlation_ratio_hv", 1)
 gatefilter.exclude_below("reflectivity", 10)
 gatefilter.exclude_above("reflectivity", 30)
 
 results = pyart.correct.calc_zdr_offset(
-    ds,
+    radar,
     zdr_var="differential_reflectivity",
     gatefilter=gatefilter,
     height_range=(1000, 3000),

diff --git a/_downloads/964bf233d4ae9125de452f56cf6a7e9c/plot_nexrad_reflectivity.zip b/_downloads/964bf233d4ae9125de452f56cf6a7e9c/plot_nexrad_reflectivity.zip
diff --git a/_downloads/98d8d042e77984251532ea82bc58a668/plot_vad.zip b/_downloads/98d8d042e77984251532ea82bc58a668/plot_vad.zip
diff --git a/_downloads/a5d5ec1b7037dbde171e1e4d5cff0fc0/plot_attenuation.zip b/_downloads/a5d5ec1b7037dbde171e1e4d5cff0fc0/plot_attenuation.zip
diff --git a/_downloads/ac02bf53c8a37d4117cb5ad6d9dbe6bd/plot_map_one_radar_to_grid.zip b/_downloads/ac02bf53c8a37d4117cb5ad6d9dbe6bd/plot_map_one_radar_to_grid.zip
diff --git a/_downloads/ac4288e4990aabba891b3265f51cb5d0/plot_nexrad_multiple_moments.zip b/_downloads/ac4288e4990aabba891b3265f51cb5d0/plot_nexrad_multiple_moments.zip
diff --git a/_downloads/b26799b06b17fa6d7709065b673ae55e/plot_rhi_data_overlay.zip b/_downloads/b26799b06b17fa6d7709065b673ae55e/plot_rhi_data_overlay.zip
diff --git a/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
diff --git a/_downloads/c7988a06f7aec2f48108bdf5d5fc3f22/plot_zdr_check.zip b/_downloads/c7988a06f7aec2f48108bdf5d5fc3f22/plot_zdr_check.zip
diff --git a/_downloads/d0514770b9e92f45836a6c18a4aa71cd/plot_ppi_mdv.zip b/_downloads/d0514770b9e92f45836a6c18a4aa71cd/plot_ppi_mdv.zip
diff --git a/_downloads/d5501074893e5d83167c803c3a5ba7b1/plot_dealias.zip b/_downloads/d5501074893e5d83167c803c3a5ba7b1/plot_dealias.zip
diff --git a/_downloads/db26ed973b058b8a2188a63f3838ed77/plot_cappi.zip b/_downloads/db26ed973b058b8a2188a63f3838ed77/plot_cappi.zip
diff --git a/_downloads/e32824c3275081f0341f53eaef7cd20e/plot_choose_a_colormap.zip b/_downloads/e32824c3275081f0341f53eaef7cd20e/plot_choose_a_colormap.zip
diff --git a/_downloads/e3f168b4fe115073e588c0990542f402/plot_cross_section.zip b/_downloads/e3f168b4fe115073e588c0990542f402/plot_cross_section.zip
diff --git a/_downloads/e77b71366e0f7d41c9880a842ac1b65c/plot_grid_xradar.zip b/_downloads/e77b71366e0f7d41c9880a842ac1b65c/plot_grid_xradar.zip
diff --git a/_downloads/edf86a28b509f3556204e7cc675b031a/plot_older_nexrad_data_aws.zip b/_downloads/edf86a28b509f3556204e7cc675b031a/plot_older_nexrad_data_aws.zip
diff --git a/_downloads/f0fc8285a5c2126c46a44511234996d6/plot_compare_two_radars_gatemapper.zip b/_downloads/f0fc8285a5c2126c46a44511234996d6/plot_compare_two_radars_gatemapper.zip
diff --git a/_downloads/f5d4400874dc8ca4e9e68b4caffe4200/plot_ppi_cfradial.zip b/_downloads/f5d4400874dc8ca4e9e68b4caffe4200/plot_ppi_cfradial.zip
diff --git a/_downloads/f7fd65b3b92066fefb2dbb738f80ec40/plot_nexrad_image_muted_reflectivity.zip b/_downloads/f7fd65b3b92066fefb2dbb738f80ec40/plot_nexrad_image_muted_reflectivity.zip
diff --git a/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
diff --git a/_downloads/fcbe9b2ac9709aa42af54a3733f7b9eb/plot_three_panel_gridmapdisplay.zip b/_downloads/fcbe9b2ac9709aa42af54a3733f7b9eb/plot_three_panel_gridmapdisplay.zip
diff --git a/_images/sphx_glr_plot_attenuation_001.png b/_images/sphx_glr_plot_attenuation_001.png
diff --git a/_images/sphx_glr_plot_attenuation_thumb.png b/_images/sphx_glr_plot_attenuation_thumb.png
diff --git a/_images/sphx_glr_plot_dealias_001.png b/_images/sphx_glr_plot_dealias_001.png
diff --git a/_images/sphx_glr_plot_dealias_thumb.png b/_images/sphx_glr_plot_dealias_thumb.png
diff --git a/_images/sphx_glr_plot_rhi_contours_differential_reflectivity_001.png b/_images/sphx_glr_plot_rhi_contours_differential_reflectivity_001.png
diff --git a/_images/sphx_glr_plot_rhi_contours_differential_reflectivity_thumb.png b/_images/sphx_glr_plot_rhi_contours_differential_reflectivity_thumb.png
diff --git a/_images/sphx_glr_plot_rhi_data_overlay_001.png b/_images/sphx_glr_plot_rhi_data_overlay_001.png
diff --git a/_images/sphx_glr_plot_rhi_data_overlay_thumb.png b/_images/sphx_glr_plot_rhi_data_overlay_thumb.png
diff --git a/_modules/pyart/correct/attenuation.html b/_modules/pyart/correct/attenuation.html
@@ -463,6 +463,7 @@ <h1>Source code for pyart.correct.attenuation</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy.ma</span> <span class="k">as</span> <span class="nn">ma</span>
 <span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">cumulative_trapezoid</span>
 
 <span class="kn">from</span> <span class="nn">..config</span> <span class="kn">import</span> <span class="n">get_field_name</span><span class="p">,</span> <span class="n">get_fillvalue</span><span class="p">,</span> <span class="n">get_metadata</span>
@@ -1511,6 +1512,10 @@ <h1>Source code for pyart.correct.attenuation</h1><div class="highlight"><pre>
 
     <span class="n">cor_z</span> <span class="o">=</span> <span class="n">get_metadata</span><span class="p">(</span><span class="n">corr_refl_field</span><span class="p">)</span>
     <span class="n">cor_z</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">atten</span> <span class="o">+</span> <span class="n">reflectivity_horizontal</span> <span class="o">+</span> <span class="n">z_offset</span>
+
+    <span class="c1"># If the numpy arrays are not masked arrays, convert it before returning</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cor_z</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">cor_z</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked_invalid</span><span class="p">(</span><span class="n">cor_z</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">])</span>
     <span class="n">cor_z</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">init_refl_correct</span><span class="o">.</span><span class="n">mask</span>
     <span class="n">cor_z</span><span class="p">[</span><span class="s2">&quot;_FillValue&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_fillvalue</span><span class="p">()</span>
 

diff --git a/_sources/examples/correct/index.rst.txt b/_sources/examples/correct/index.rst.txt
@@ -19,7 +19,7 @@ Performing radar moment corrections in antenna (radial) coordinates.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to calculate the ZDR bias from VPT/Birdbath scans. The technique here uses a vertically pointing scan in regions of light rain. In these regions, raindrops should be approximately spherical and therefore their ZDR near zero. Therefore, we want the average ZDR in these regions. This code applies reflectivity and cross correlation ratio-based thresholds to the ZDR bias calculation to ensure that we are taking the average ZDR in light rain.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to calculate the ZDR bias from VPT/Birdbath scans. The technique here uses a vertically pointing scan in regions of light rain. In these regions, raindrops should be approximately spherical and therefore their ZDR near zero. Therefore, we want the average ZDR in these regions. This code applies reflectivity and cross correlation ratio-based threshold to the ZDR bias calculation to ensure that we are taking the average ZDR in light rain.">
 
 .. only:: html
 

diff --git a/_sources/examples/correct/plot_attenuation.rst.txt b/_sources/examples/correct/plot_attenuation.rst.txt
@@ -25,7 +25,7 @@ Correct reflectivity attenuation
 In this example the reflectivity attenuation is calculated and then corrected
 for a polarimetric radar using a Z-PHI method implemented in Py-ART.
 
-.. GENERATED FROM PYTHON SOURCE LINES 10-80
+.. GENERATED FROM PYTHON SOURCE LINES 10-82
 
 
 
@@ -47,13 +47,15 @@ for a polarimetric radar using a Z-PHI method implemented in Py-ART.
     # License: BSD 3 clause
 
     import matplotlib.pyplot as plt
+    import xradar as xd
 
     import pyart
 
     file = pyart.testing.get_test_data("sgpcsaprsurcmacI7.c0.20110520.095101.nc")
 
     # read in the data
-    radar = pyart.io.read_cfradial(file)
+    tree = xd.io.open_cfradial1_datatree(file)
+    radar = tree.pyart.to_radar()
 
     # remove existing corrections
     radar.fields.pop("specific_attenuation")
@@ -114,7 +116,7 @@ for a polarimetric radar using a Z-PHI method implemented in Py-ART.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.024 seconds)
+   **Total running time of the script:** (0 minutes 9.411 seconds)
 
 
 .. _sphx_glr_download_examples_correct_plot_attenuation.py:

diff --git a/_sources/examples/correct/plot_cloud_mask.rst.txt b/_sources/examples/correct/plot_cloud_mask.rst.txt
@@ -154,7 +154,7 @@ to calculate the mask.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.200 seconds)
+   **Total running time of the script:** (0 minutes 0.706 seconds)
 
 
 .. _sphx_glr_download_examples_correct_plot_cloud_mask.py:

diff --git a/_sources/examples/correct/plot_dealias.rst.txt b/_sources/examples/correct/plot_dealias.rst.txt
@@ -124,7 +124,7 @@ using the region-based dealiasing algorithm in Py-ART.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 5.400 seconds)
+   **Total running time of the script:** (0 minutes 58.326 seconds)
 
 
 .. _sphx_glr_download_examples_correct_plot_dealias.py:

diff --git a/_sources/examples/correct/plot_zdr_check.rst.txt b/_sources/examples/correct/plot_zdr_check.rst.txt
@@ -25,10 +25,10 @@ This example shows how to calculate the ZDR bias from VPT/Birdbath scans.
 The technique here uses a vertically pointing scan in regions of light rain.
 In these regions, raindrops should be approximately spherical and therefore their
 ZDR near zero. Therefore, we want the average ZDR in these regions.
-This code applies reflectivity and cross correlation ratio-based thresholds to the ZDR
+This code applies reflectivity and cross correlation ratio-based threshold to the ZDR
 bias calculation to ensure that we are taking the average ZDR in light rain.
 
-.. GENERATED FROM PYTHON SOURCE LINES 13-51
+.. GENERATED FROM PYTHON SOURCE LINES 13-55
 
 
 
@@ -55,25 +55,29 @@ bias calculation to ensure that we are taking the average ZDR in light rain.
 
 
     import matplotlib.pyplot as plt
+    import xradar as xd
     from open_radar_data import DATASETS
 
     import pyart
 
     # Read in example data
     filename = DATASETS.fetch("sgpxsaprcfrvptI4.a1.20200205.100827.nc")
-    ds = pyart.io.read(filename)
+
+    # Read in the data
+    tree = xd.io.open_cfradial1_datatree(filename)
+    radar = tree.pyart.to_radar()
 
     # Set up a typical filter for ZDR bias calculation in birdbath scan
     # Light rain and RhoHV near 1 ensures that raindrops are close to spherical
     # Therefore ZDR should be zero in these regions
-    gatefilter = pyart.filters.GateFilter(ds)
+    gatefilter = pyart.filters.GateFilter(radar)
     gatefilter.exclude_below("cross_correlation_ratio_hv", 0.995)
     gatefilter.exclude_above("cross_correlation_ratio_hv", 1)
     gatefilter.exclude_below("reflectivity", 10)
     gatefilter.exclude_above("reflectivity", 30)
 
     results = pyart.correct.calc_zdr_offset(
-        ds,
+        radar,
         zdr_var="differential_reflectivity",
         gatefilter=gatefilter,
         height_range=(1000, 3000),
@@ -95,7 +99,7 @@ bias calculation to ensure that we are taking the average ZDR in light rain.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.916 seconds)
+   **Total running time of the script:** (3 minutes 28.955 seconds)
 
 
 .. _sphx_glr_download_examples_correct_plot_zdr_check.py:

diff --git a/_sources/examples/correct/sg_execution_times.rst.txt b/_sources/examples/correct/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**01:19.539** total execution time for 4 files **from examples/correct**:
+**04:37.398** total execution time for 4 files **from examples/correct**:
 
 .. container::
 
@@ -32,15 +32,15 @@ Computation times
    * - Example
      - Time
      - Mem (MB)
+   * - :ref:`sphx_glr_examples_correct_plot_zdr_check.py` (``plot_zdr_check.py``)
+     - 03:28.955
+     - 0.0
    * - :ref:`sphx_glr_examples_correct_plot_dealias.py` (``plot_dealias.py``)
-     - 01:05.400
+     - 00:58.326
      - 0.0
    * - :ref:`sphx_glr_examples_correct_plot_attenuation.py` (``plot_attenuation.py``)
-     - 00:12.024
+     - 00:09.411
      - 0.0
    * - :ref:`sphx_glr_examples_correct_plot_cloud_mask.py` (``plot_cloud_mask.py``)
-     - 00:01.200
-     - 0.0
-   * - :ref:`sphx_glr_examples_correct_plot_zdr_check.py` (``plot_zdr_check.py``)
-     - 00:00.916
+     - 00:00.706
      - 0.0
diff --git a/_sources/examples/index.rst.txt b/_sources/examples/index.rst.txt
@@ -37,7 +37,7 @@ Performing radar moment corrections in antenna (radial) coordinates.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to calculate the ZDR bias from VPT/Birdbath scans. The technique here uses a vertically pointing scan in regions of light rain. In these regions, raindrops should be approximately spherical and therefore their ZDR near zero. Therefore, we want the average ZDR in these regions. This code applies reflectivity and cross correlation ratio-based thresholds to the ZDR bias calculation to ensure that we are taking the average ZDR in light rain.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to calculate the ZDR bias from VPT/Birdbath scans. The technique here uses a vertically pointing scan in regions of light rain. In these regions, raindrops should be approximately spherical and therefore their ZDR near zero. Therefore, we want the average ZDR in these regions. This code applies reflectivity and cross correlation ratio-based threshold to the ZDR bias calculation to ensure that we are taking the average ZDR in light rain.">
 
 .. only:: html
 

diff --git a/_sources/examples/io/plot_nexrad_data_aws.rst.txt b/_sources/examples/io/plot_nexrad_data_aws.rst.txt
@@ -116,7 +116,7 @@ Let's take a look at a summary of what fields are available.
  .. code-block:: none
 
 
-    ['differential_reflectivity', 'cross_correlation_ratio', 'reflectivity', 'spectrum_width', 'clutter_filter_power_removed', 'differential_phase', 'velocity']
+    ['differential_reflectivity', 'differential_phase', 'clutter_filter_power_removed', 'spectrum_width', 'reflectivity', 'velocity', 'cross_correlation_ratio']
 
 
 
@@ -314,7 +314,7 @@ Note: the reflectivity and velocity fields are in different radars, so we need t
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 15.287 seconds)
+   **Total running time of the script:** (0 minutes 14.238 seconds)
 
 
 .. _sphx_glr_download_examples_io_plot_nexrad_data_aws.py:

diff --git a/_sources/examples/io/plot_older_nexrad_data_aws.rst.txt b/_sources/examples/io/plot_older_nexrad_data_aws.rst.txt
@@ -227,7 +227,7 @@ Everything now looks correct as this is in Handford CA!
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.138 seconds)
+   **Total running time of the script:** (0 minutes 8.325 seconds)
 
 
 .. _sphx_glr_download_examples_io_plot_older_nexrad_data_aws.py: