Deploying to gh-pages from @ 1fbd577 🚀

build/html/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: cd593a3de90e666855bfac108913297a
+config: bbce5a3dbb2622559a7f3ef30ea5187e
 tags: 645f666f9bcd5a90fca523b33c5a78b7

build/html/API/index.html

@@ -100,7 +100,7 @@ <h1>API Reference<a class="headerlink" href="#api-reference" title="Link to this
 <dd class="field-odd"><p>0.0.post1</p>
 </dd>
 <dt class="field-even">Date<span class="colon">:</span></dt>
-<dd class="field-even"><p>Jan 04, 2024</p>
+<dd class="field-even"><p>Jan 05, 2024</p>
 </dd>
 </dl>
 <p>This shows the details of the API of the RADar TRAcking and Quality (RadTraq) Toolkit. Documentation

build/html/_downloads/3f519d3e8a5042ebdd24b69a599a91d3/plot_cloud_mask.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import act\nimport matplotlib.pyplot as plt\n\nimport radtraq\n\n# Read in sample data using ACT\nds = act.io.armfiles.read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)\n\n# Resample data for ease of processing\nds = ds.resample(time='1min').nearest()\n\n# Calculate and apply cloud mask\nds = radtraq.proc.cloud_mask.calc_cloud_mask(ds, 'reflectivity_copol')\nds = ds.where(ds['cloud_mask_2'] == 1)\n\n# Plot data using ACT\ndisplay = act.plotting.TimeSeriesDisplay(ds)\ndisplay.plot('reflectivity_copol', cmap='jet')\ndisplay.axes[0].set_ylim([0, 20000])\nplt.show()\n\nds.close()"
+        "import act\nimport matplotlib.pyplot as plt\nfrom open_radar_data import DATASETS\n\nimport radtraq\n\n# Read in sample data using ACT\nfilename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')\nds = act.io.armfiles.read_netcdf(filename)\n\n# Resample data for ease of processing\nds = ds.resample(time='1min').nearest()\n\n# Calculate and apply cloud mask\nds = radtraq.proc.cloud_mask.calc_cloud_mask(ds, 'reflectivity_copol')\nds = ds.where(ds['cloud_mask_2'] == 1)\n\n# Plot data using ACT\ndisplay = act.plotting.TimeSeriesDisplay(ds)\ndisplay.plot('reflectivity_copol', cmap='jet')\ndisplay.axes[0].set_ylim([0, 20000])\nplt.show()\n\nds.close()"
       ]
     }
   ],

build/html/_downloads/527e86cba148e1f21443a08d18448c9d/plot_cloud_mask.py

@@ -9,11 +9,13 @@
 
 import act
 import matplotlib.pyplot as plt
+from open_radar_data import DATASETS
 
 import radtraq
 
 # Read in sample data using ACT
-ds = act.io.armfiles.read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+filename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')
+ds = act.io.armfiles.read_netcdf(filename)
 
 # Resample data for ease of processing
 ds = ds.resample(time='1min').nearest()

build/html/_downloads/54db3605a180105eb70a910f4e9676df/plot_zdr_check.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\n\nimport radtraq\n\n# Read in example data\nds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_XSAPR)\nthresh = {'cross_correlation_ratio_hv': [0.995, 1], 'reflectivity': [10, 30], 'range': [1000, 3000]}\n# Call RadTraQ function\nresults = radtraq.proc.calc_zdr_offset(ds, zdr_var='differential_reflectivity', thresh=thresh)\n\nprint('Zdr Bias: ' + '%.2f' % results['bias'])\n\nfig, ax = plt.subplots(1, 3, figsize=(10, 8))\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 ()')\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\nfrom open_radar_data import DATASETS\n\nimport radtraq\n\n# Read in example data\nfilename = DATASETS.fetch('sgpxsaprcfrvptI4.a1.20200205.100827.nc')\nds = read_netcdf(filename)\nthresh = {'cross_correlation_ratio_hv': [0.995, 1], 'reflectivity': [10, 30], 'range': [1000, 3000]}\n# Call RadTraQ function\nresults = radtraq.proc.calc_zdr_offset(ds, zdr_var='differential_reflectivity', thresh=thresh)\n\nprint('Zdr Bias: ' + '%.2f' % results['bias'])\n\nfig, ax = plt.subplots(1, 3, figsize=(10, 8))\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 ()')\nplt.show()"
       ]
     }
   ],

build/html/_downloads/6daea51d4c650160c3c07199260666f3/plot_mask_vpt.py

@@ -11,17 +11,19 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from act.io.armfiles import read_netcdf
+from open_radar_data import DATASETS
 
 import radtraq
 
 # Read in Example KAZR File using ACT
-obj = read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+filename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')
+ds = read_netcdf(filename)
 
 # Resample to 1-minute to simplify processing
-obj = obj.resample(time='1min').nearest()
+ds = ds.resample(time='1min').nearest()
 
 # Process cloud mask in order to properly produce average VPT profiles through cloud
-obj = radtraq.proc.cloud_mask.calc_cloud_mask(obj, 'reflectivity_copol')
+ds = radtraq.proc.cloud_mask.calc_cloud_mask(ds, 'reflectivity_copol')
 
 # Variables to calculate average profiles
 variable = ['reflectivity_copol', 'mean_doppler_velocity_copol', 'reflectivity_xpol']
@@ -31,17 +33,17 @@
 ygrid = np.arange(first_height, 15000, 50)
 
 # Calculate average profiles
-obj = radtraq.proc.profile.calc_avg_profile(
-    obj, variable=variable, first_height=first_height, ygrid=ygrid
+ds = radtraq.proc.profile.calc_avg_profile(
+    ds, variable=variable, first_height=first_height, ygrid=ygrid
 )
 
 # Showing how to do this for multiple radars
 # Set up dictionary for profile comparison plotting
 rad_dict = {
-    'sgpkazrgeC1.b1': {'object': obj, 'variable': variable[0]},
-    'sgpkazrge2C1.b1': {'object': obj, 'variable': variable[0]},
-    'sgpkazrmdC1.b1': {'object': obj, 'variable': 'reflectivity_xpol'},
-    'sgpkazrmd2C1.b1': {'object': obj, 'variable': 'reflectivity_xpol'},
+    'sgpkazrgeC1.b1': {'object': ds, 'variable': variable[0]},
+    'sgpkazrge2C1.b1': {'object': ds, 'variable': variable[0]},
+    'sgpkazrmdC1.b1': {'object': ds, 'variable': 'reflectivity_xpol'},
+    'sgpkazrmd2C1.b1': {'object': ds, 'variable': 'reflectivity_xpol'},
 }
 
 # Plot up profiles and perform comparisons from data in dictionary
@@ -51,4 +53,4 @@
 plt.show()
 
 # Close out object
-obj.close()
+ds.close()

build/html/_downloads/8a469cd81953feb73dad7f4d20b89339/plot_self_consistency.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\n\nimport radtraq\n\n# Read in example data\nds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_CSAPR)\n\n# Set thresholds of RhoHv > 0.99\nthresh = {'copol_correlation_coeff': 0.99}\n\n# Set up dictionary of variables to plot\nvar_dict = {\n    'differential_reflectivity': {\n        'variable': 'reflectivity',\n        'bin_width': [1, 0.25],\n        'linreg': True,\n    },\n    'specific_differential_phase': {'variable': 'reflectivity'},\n    'differential_phase': {'variable': 'reflectivity', 'bin_width': [1, 2.0]},\n    'mean_doppler_velocity': {'variable': 'reflectivity', 'bin_width': [1, 0.5]},\n}\n\n# Call RadTraQ function\ndisplay = radtraq.plotting.plot_self_consistency(ds, variables=var_dict, thresh=thresh)\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\nfrom open_radar_data import DATASETS\n\nimport radtraq\n\n# Read in example data\nfilename = DATASETS.fetch('csapr.nc')\nds = read_netcdf(filename)\n\n# Set thresholds of RhoHv > 0.99\nthresh = {'copol_correlation_coeff': 0.99}\n\n# Set up dictionary of variables to plot\nvar_dict = {\n    'differential_reflectivity': {\n        'variable': 'reflectivity',\n        'bin_width': [1, 0.25],\n        'linreg': True,\n    },\n    'specific_differential_phase': {'variable': 'reflectivity'},\n    'differential_phase': {'variable': 'reflectivity', 'bin_width': [1, 2.0]},\n    'mean_doppler_velocity': {'variable': 'reflectivity', 'bin_width': [1, 0.5]},\n}\n\n# Call RadTraQ function\ndisplay = radtraq.plotting.plot_self_consistency(ds, variables=var_dict, thresh=thresh)\nplt.show()"
       ]
     }
   ],

build/html/_downloads/91b9af102333dd3cb5d310e28542871c/plot_zdr_check.py

@@ -8,11 +8,13 @@
 
 import matplotlib.pyplot as plt
 from act.io.armfiles import read_netcdf
+from open_radar_data import DATASETS
 
 import radtraq
 
 # Read in example data
-ds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_XSAPR)
+filename = DATASETS.fetch('sgpxsaprcfrvptI4.a1.20200205.100827.nc')
+ds = read_netcdf(filename)
 thresh = {'cross_correlation_ratio_hv': [0.995, 1], 'reflectivity': [10, 30], 'range': [1000, 3000]}
 # Call RadTraQ function
 results = radtraq.proc.calc_zdr_offset(ds, zdr_var='differential_reflectivity', thresh=thresh)

build/html/_downloads/a75f5f6c5daade3fcda891b4d8e1f089/plot_corner_reflector_raster.py

@@ -8,14 +8,15 @@
 
 """
 
-
 import matplotlib.pyplot as plt
 from act.io.armfiles import read_netcdf
+from open_radar_data import DATASETS
 
 import radtraq
 
 # Read in sample data using ACT
-ds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_RASTER)
+filename = DATASETS.fetch('sgpkasacrcrrasterC1.a1.20130419.012153.nc')
+ds = read_netcdf(filename)
 
 # Process and plot raster file
 data = radtraq.plotting.corner_reflector.plot_cr_raster(

build/html/_downloads/a8dcd1d5262e69b1171d2fdfcca66307/plot_corner_reflector_raster.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\n\nimport radtraq\n\n# Read in sample data using ACT\nds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_RASTER)\n\n# Process and plot raster file\ndata = radtraq.plotting.corner_reflector.plot_cr_raster(\n    ds, target_range=478.0, el_limits=[-0.5, 2.5], noplot=False\n)\nplt.show()\nds.close()"
+        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\nfrom open_radar_data import DATASETS\n\nimport radtraq\n\n# Read in sample data using ACT\nfilename = DATASETS.fetch('sgpkasacrcrrasterC1.a1.20130419.012153.nc')\nds = read_netcdf(filename)\n\n# Process and plot raster file\ndata = radtraq.plotting.corner_reflector.plot_cr_raster(\n    ds, target_range=478.0, el_limits=[-0.5, 2.5], noplot=False\n)\nplt.show()\nds.close()"
       ]
     }
   ],

build/html/_downloads/ba743c325059d26943adc184e85fc1d1/plot_cfad.py

@@ -8,11 +8,13 @@
 
 import matplotlib.pyplot as plt
 from act.io.armfiles import read_netcdf
+from open_radar_data import DATASETS
 
 import radtraq
 
 # Read in example data
-ds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+filename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')
+ds = read_netcdf(filename)
 
 # Calculate CFAD histogram
 data_array = radtraq.plotting.cfad.calc_cfad(ds, 'reflectivity_copol')

build/html/_downloads/c33baaf2f9d9dd83c493b92f76565b37/plot_self_consistency.py

@@ -8,11 +8,13 @@
 
 import matplotlib.pyplot as plt
 from act.io.armfiles import read_netcdf
+from open_radar_data import DATASETS
 
 import radtraq
 
 # Read in example data
-ds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_CSAPR)
+filename = DATASETS.fetch('csapr.nc')
+ds = read_netcdf(filename)
 
 # Set thresholds of RhoHv > 0.99
 thresh = {'copol_correlation_coeff': 0.99}

build/html/_downloads/e6d60e55857d6e2f394552fd406b547d/plot_cfad.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\n\nimport radtraq\n\n# Read in example data\nds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)\n\n# Calculate CFAD histogram\ndata_array = radtraq.plotting.cfad.calc_cfad(ds, 'reflectivity_copol')\n\n# Plot CFAD histogram\ndims = data_array.dims\ndisplay = radtraq.plotting.cfad.plot_cfad(data_array, data_array[dims[1]], data_array[dims[0]])\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom act.io.armfiles import read_netcdf\nfrom open_radar_data import DATASETS\n\nimport radtraq\n\n# Read in example data\nfilename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')\nds = read_netcdf(filename)\n\n# Calculate CFAD histogram\ndata_array = radtraq.plotting.cfad.calc_cfad(ds, 'reflectivity_copol')\n\n# Plot CFAD histogram\ndims = data_array.dims\ndisplay = radtraq.plotting.cfad.plot_cfad(data_array, data_array[dims[1]], data_array[dims[0]])\nplt.show()"
       ]
     }
   ],

build/html/_downloads/f5b24d53af896e7e04ca6f33a95b3801/plot_mask_vpt.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom act.io.armfiles import read_netcdf\n\nimport radtraq\n\n# Read in Example KAZR File using ACT\nobj = read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)\n\n# Resample to 1-minute to simplify processing\nobj = obj.resample(time='1min').nearest()\n\n# Process cloud mask in order to properly produce average VPT profiles through cloud\nobj = radtraq.proc.cloud_mask.calc_cloud_mask(obj, 'reflectivity_copol')\n\n# Variables to calculate average profiles\nvariable = ['reflectivity_copol', 'mean_doppler_velocity_copol', 'reflectivity_xpol']\n\n# Create a grid to interpolate data onto - Needed for different radars\nfirst_height = 1500.0\nygrid = np.arange(first_height, 15000, 50)\n\n# Calculate average profiles\nobj = radtraq.proc.profile.calc_avg_profile(\n    obj, variable=variable, first_height=first_height, ygrid=ygrid\n)\n\n# Showing how to do this for multiple radars\n# Set up dictionary for profile comparison plotting\nrad_dict = {\n    'sgpkazrgeC1.b1': {'object': obj, 'variable': variable[0]},\n    'sgpkazrge2C1.b1': {'object': obj, 'variable': variable[0]},\n    'sgpkazrmdC1.b1': {'object': obj, 'variable': 'reflectivity_xpol'},\n    'sgpkazrmd2C1.b1': {'object': obj, 'variable': 'reflectivity_xpol'},\n}\n\n# Plot up profiles and perform comparisons from data in dictionary\ndisplay = radtraq.plotting.plot_avg_profile(rad_dict)\n\n# Show plot\nplt.show()\n\n# Close out object\nobj.close()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom act.io.armfiles import read_netcdf\nfrom open_radar_data import DATASETS\n\nimport radtraq\n\n# Read in Example KAZR File using ACT\nfilename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')\nds = read_netcdf(filename)\n\n# Resample to 1-minute to simplify processing\nds = ds.resample(time='1min').nearest()\n\n# Process cloud mask in order to properly produce average VPT profiles through cloud\nds = radtraq.proc.cloud_mask.calc_cloud_mask(ds, 'reflectivity_copol')\n\n# Variables to calculate average profiles\nvariable = ['reflectivity_copol', 'mean_doppler_velocity_copol', 'reflectivity_xpol']\n\n# Create a grid to interpolate data onto - Needed for different radars\nfirst_height = 1500.0\nygrid = np.arange(first_height, 15000, 50)\n\n# Calculate average profiles\nds = radtraq.proc.profile.calc_avg_profile(\n    ds, variable=variable, first_height=first_height, ygrid=ygrid\n)\n\n# Showing how to do this for multiple radars\n# Set up dictionary for profile comparison plotting\nrad_dict = {\n    'sgpkazrgeC1.b1': {'object': ds, 'variable': variable[0]},\n    'sgpkazrge2C1.b1': {'object': ds, 'variable': variable[0]},\n    'sgpkazrmdC1.b1': {'object': ds, 'variable': 'reflectivity_xpol'},\n    'sgpkazrmd2C1.b1': {'object': ds, 'variable': 'reflectivity_xpol'},\n}\n\n# Plot up profiles and perform comparisons from data in dictionary\ndisplay = radtraq.plotting.plot_avg_profile(rad_dict)\n\n# Show plot\nplt.show()\n\n# Close out object\nds.close()"
       ]
     }
   ],

build/html/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:04.272** total execution time for 7 files **from all galleries**:
+**00:04.131** total execution time for 7 files **from all galleries**:
 
 .. container::
 
@@ -33,23 +33,23 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_source_auto_examples_plot_corner_reflector_raster.py` (``../../examples/plot_corner_reflector_raster.py``)
-     - 00:01.039
+     - 00:00.960
      - 0.0
    * - :ref:`sphx_glr_source_auto_examples_plot_cfad.py` (``../../examples/plot_cfad.py``)
-     - 00:00.823
+     - 00:00.815
      - 0.0
    * - :ref:`sphx_glr_source_auto_examples_plot_self_consistency.py` (``../../examples/plot_self_consistency.py``)
-     - 00:00.775
+     - 00:00.778
      - 0.0
    * - :ref:`sphx_glr_source_auto_examples_plot_mask_vpt.py` (``../../examples/plot_mask_vpt.py``)
-     - 00:00.627
+     - 00:00.604
      - 0.0
    * - :ref:`sphx_glr_source_auto_examples_plot_cloud_mask.py` (``../../examples/plot_cloud_mask.py``)
-     - 00:00.570
+     - 00:00.538
      - 0.0
    * - :ref:`sphx_glr_source_auto_examples_plot_zdr_check.py` (``../../examples/plot_zdr_check.py``)
-     - 00:00.371
+     - 00:00.369
      - 0.0
    * - :ref:`sphx_glr_source_auto_examples_plot_dual_doppler_lobes.py` (``../../examples/plot_dual_doppler_lobes.py``)
-     - 00:00.068
+     - 00:00.067
      - 0.0

build/html/_sources/source/auto_examples/plot_cfad.rst.txt

@@ -23,7 +23,7 @@ Example on how to calculate and plot cfad
 
 This example shows how to calculate and plot a cfad
 
-.. GENERATED FROM PYTHON SOURCE LINES 8-24
+.. GENERATED FROM PYTHON SOURCE LINES 8-26
 
 
 
@@ -37,8 +37,8 @@ This example shows how to calculate and plot a cfad
 
  .. code-block:: none
 
-    /home/runner/work/RadTraQ/RadTraQ/examples/plot_cfad.py:15: DeprecationWarning: act.io.armfiles.read_netcdf will be replaced in version 2.0.0 by act.io.arm.read_arm_netcdf()
-      ds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+    /home/runner/work/RadTraQ/RadTraQ/examples/plot_cfad.py:17: DeprecationWarning: act.io.armfiles.read_netcdf will be replaced in version 2.0.0 by act.io.arm.read_arm_netcdf()
+      ds = read_netcdf(filename)
 
 
 
@@ -52,11 +52,13 @@ This example shows how to calculate and plot a cfad
 
     import matplotlib.pyplot as plt
     from act.io.armfiles import read_netcdf
+    from open_radar_data import DATASETS
 
     import radtraq
 
     # Read in example data
-    ds = read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+    filename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')
+    ds = read_netcdf(filename)
 
     # Calculate CFAD histogram
     data_array = radtraq.plotting.cfad.calc_cfad(ds, 'reflectivity_copol')
@@ -69,7 +71,7 @@ This example shows how to calculate and plot a cfad
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.823 seconds)
+   **Total running time of the script:** (0 minutes 0.815 seconds)
 
 
 .. _sphx_glr_download_source_auto_examples_plot_cfad.py:

build/html/_sources/source/auto_examples/plot_cloud_mask.rst.txt

@@ -23,7 +23,7 @@ Example on how to calculate and plot cloud masks
 
 This example shows how to calculate a cloud mask and plot data
 
-.. GENERATED FROM PYTHON SOURCE LINES 8-32
+.. GENERATED FROM PYTHON SOURCE LINES 8-34
 
 
 
@@ -37,8 +37,8 @@ This example shows how to calculate a cloud mask and plot data
 
  .. code-block:: none
 
-    /home/runner/work/RadTraQ/RadTraQ/examples/plot_cloud_mask.py:16: DeprecationWarning: act.io.armfiles.read_netcdf will be replaced in version 2.0.0 by act.io.arm.read_arm_netcdf()
-      ds = act.io.armfiles.read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+    /home/runner/work/RadTraQ/RadTraQ/examples/plot_cloud_mask.py:18: DeprecationWarning: act.io.armfiles.read_netcdf will be replaced in version 2.0.0 by act.io.arm.read_arm_netcdf()
+      ds = act.io.armfiles.read_netcdf(filename)
 
 
 
@@ -53,11 +53,13 @@ This example shows how to calculate a cloud mask and plot data
 
     import act
     import matplotlib.pyplot as plt
+    from open_radar_data import DATASETS
 
     import radtraq
 
     # Read in sample data using ACT
-    ds = act.io.armfiles.read_netcdf(radtraq.tests.sample_files.EXAMPLE_KAZR)
+    filename = DATASETS.fetch('sgpkazrgeC1.a1.20190529.000002.cdf')
+    ds = act.io.armfiles.read_netcdf(filename)
 
     # Resample data for ease of processing
     ds = ds.resample(time='1min').nearest()
@@ -77,7 +79,7 @@ This example shows how to calculate a cloud mask and plot data
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.570 seconds)
+   **Total running time of the script:** (0 minutes 0.538 seconds)
 
 
 .. _sphx_glr_download_source_auto_examples_plot_cloud_mask.py: