Deploy dev to GitHub Pages

dev/_downloads/111f8f913d47b49227ba6ea3c06fe84c/NEXRAD_Level_3_File.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "spec = gridspec.GridSpec(1, 2)\nfig = plt.figure(figsize=(15, 8))\nadd_metpy_logo(fig, 190, 85, size='large')\nctables = (('NWSStormClearReflectivity', -20, 0.5),  # dBZ\n           ('NWS8bitVel', -100, 1.0))  # m/s\n\nfor v, ctable, ax_rect in zip(('N0Q', 'N0U'), ctables, spec):\n    # Open the file\n    name = get_test_data(f'nids/KOUN_SDUS54_{v}TLX_201305202016', as_file_obj=False)\n    f = Level3File(name)\n\n    # Pull the data out of the file object\n    datadict = f.sym_block[0][0]\n\n    # Turn into an array using the scale specified by the file\n    data = f.map_data(datadict['data'])\n\n    # Grab azimuths and calculate a range based on number of gates,\n    # both with their respective units\n    az = units.Quantity(np.array(datadict['start_az'] + [datadict['end_az'][-1]]), 'degrees')\n    rng = units.Quantity(np.linspace(0, f.max_range, data.shape[-1] + 1), 'kilometers')\n\n    # Extract central latitude and longitude from the file\n    cent_lon = f.lon\n    cent_lat = f.lat\n\n    # Convert az,range to x,y\n    xlocs, ylocs = azimuth_range_to_lat_lon(az, rng, cent_lon, cent_lat)\n\n    # Plot the data\n    crs = ccrs.LambertConformal()\n    ax = fig.add_subplot(ax_rect, projection=crs)\n    ax.add_feature(USCOUNTIES, linewidth=0.5)\n    norm, cmap = colortables.get_with_steps(*ctable)\n    ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap, transform=ccrs.PlateCarree())\n    ax.set_extent([cent_lon - 0.5, cent_lon + 0.5, cent_lat - 0.5, cent_lat + 0.5])\n    ax.set_aspect('equal', 'datalim')\n    add_timestamp(ax, f.metadata['prod_time'], y=0.02, high_contrast=True)\n\nplt.show()"
+        "spec = gridspec.GridSpec(1, 2)\nfig = plt.figure(figsize=(15, 8))\nadd_metpy_logo(fig, 190, 85, size='large')\nctables = (('NWSStormClearReflectivity', -20, 0.5),  # dBZ\n           ('NWS8bitVel', -100, 1.0))  # m/s\n\nfor v, ctable, ax_rect in zip(('N0Q', 'N0U'), ctables, spec, strict=False):\n    # Open the file\n    name = get_test_data(f'nids/KOUN_SDUS54_{v}TLX_201305202016', as_file_obj=False)\n    f = Level3File(name)\n\n    # Pull the data out of the file object\n    datadict = f.sym_block[0][0]\n\n    # Turn into an array using the scale specified by the file\n    data = f.map_data(datadict['data'])\n\n    # Grab azimuths and calculate a range based on number of gates,\n    # both with their respective units\n    az = units.Quantity(np.array(datadict['start_az'] + [datadict['end_az'][-1]]), 'degrees')\n    rng = units.Quantity(np.linspace(0, f.max_range, data.shape[-1] + 1), 'kilometers')\n\n    # Extract central latitude and longitude from the file\n    cent_lon = f.lon\n    cent_lat = f.lat\n\n    # Convert az,range to x,y\n    xlocs, ylocs = azimuth_range_to_lat_lon(az, rng, cent_lon, cent_lat)\n\n    # Plot the data\n    crs = ccrs.LambertConformal()\n    ax = fig.add_subplot(ax_rect, projection=crs)\n    ax.add_feature(USCOUNTIES, linewidth=0.5)\n    norm, cmap = colortables.get_with_steps(*ctable)\n    ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap, transform=ccrs.PlateCarree())\n    ax.set_extent([cent_lon - 0.5, cent_lon + 0.5, cent_lat - 0.5, cent_lat + 0.5])\n    ax.set_aspect('equal', 'datalim')\n    add_timestamp(ax, f.metadata['prod_time'], y=0.02, high_contrast=True)\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/37fc469596f0b44f565b299efd380e76/US_Counties.py

@@ -22,6 +22,6 @@
 ax2 = fig.add_subplot(1, 3, 2, projection=proj)
 ax3 = fig.add_subplot(1, 3, 3, projection=proj)
 
-for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
+for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3], strict=False):
     axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
     axis.add_feature(USCOUNTIES.with_scale(scale))

dev/_downloads/579b4b69b0858bf8ead929de64ab8135/Inverse_Distance_Verification.py

@@ -61,10 +61,10 @@ def draw_circle(ax, x, y, r, m, label):
 #
 # The variable ``indices`` represents the index of each matched coordinate within the
 # cKDTree's ``data`` list.
-grid_points = np.array(list(zip(sim_gridx, sim_gridy)))
+grid_points = np.array(list(zip(sim_gridx, sim_gridy, strict=False)))
 
 radius = 40
-obs_tree = cKDTree(list(zip(xp, yp)))
+obs_tree = cKDTree(list(zip(xp, yp, strict=False)))
 indices = obs_tree.query_ball_point(grid_points, r=radius)
 
 ###########################################
@@ -83,7 +83,7 @@ def draw_circle(ax, x, y, r, m, label):
 barnes_dist = dist_2(sim_gridx[1], sim_gridy[1], x2, y2)
 barnes_obs = zp[indices[1]]
 
-kappa = calc_kappa(average_spacing(list(zip(xp, yp))))
+kappa = calc_kappa(average_spacing(list(zip(xp, yp, strict=False))))
 
 barnes_val = barnes_point(barnes_dist, barnes_obs, kappa)
 
@@ -121,7 +121,7 @@ def draw_circle(ax, x, y, r, m, label):
 mx, my = obs_tree.data[indices[0]].T
 mz = zp[indices[0]]
 
-for x, y, z in zip(mx, my, mz):
+for x, y, z in zip(mx, my, mz, strict=False):
     d = np.sqrt((sim_gridx[0] - x)**2 + (y - sim_gridy[0])**2)
     ax.plot([sim_gridx[0], x], [sim_gridy[0], y], '--')
 
@@ -160,7 +160,7 @@ def draw_circle(ax, x, y, r, m, label):
 mx, my = obs_tree.data[indices[1]].T
 mz = zp[indices[1]]
 
-for x, y, z in zip(mx, my, mz):
+for x, y, z in zip(mx, my, mz, strict=False):
     d = np.sqrt((sim_gridx[1] - x)**2 + (y - sim_gridy[1])**2)
     ax.plot([sim_gridx[1], x], [sim_gridy[1], y], '--')
 

dev/_downloads/6405360ec40d7796ed64eb783f2ffe55/NEXRAD_Level_2_File.py

@@ -73,7 +73,8 @@
 fig = plt.figure(figsize=(15, 8))
 add_metpy_logo(fig, 190, 85, size='large')
 
-for var_data, var_range, ax_rect in zip((ref, rho), (ref_range, rho_range), spec):
+for var_data, var_range, ax_rect in zip((ref, rho), (ref_range, rho_range), spec,
+                                        strict=False):
     # Turn into an array, then mask
     data = np.ma.array(var_data)
     data[np.isnan(data)] = np.ma.masked

dev/_downloads/8391eb2491209a8dddd4b7a4b8976f0b/NEXRAD_Level_3_File.py

@@ -26,7 +26,7 @@
 ctables = (('NWSStormClearReflectivity', -20, 0.5),  # dBZ
            ('NWS8bitVel', -100, 1.0))  # m/s
 
-for v, ctable, ax_rect in zip(('N0Q', 'N0U'), ctables, spec):
+for v, ctable, ax_rect in zip(('N0Q', 'N0U'), ctables, spec, strict=False):
     # Open the file
     name = get_test_data(f'nids/KOUN_SDUS54_{v}TLX_201305202016', as_file_obj=False)
     f = Level3File(name)

dev/_downloads/c8d69f5ef125a6cf9e9562b609816681/NEXRAD_Level_2_File.ipynb

@@ -91,7 +91,7 @@
       },
       "outputs": [],
       "source": [
-        "spec = gridspec.GridSpec(1, 2)\nfig = plt.figure(figsize=(15, 8))\nadd_metpy_logo(fig, 190, 85, size='large')\n\nfor var_data, var_range, ax_rect in zip((ref, rho), (ref_range, rho_range), spec):\n    # Turn into an array, then mask\n    data = np.ma.array(var_data)\n    data[np.isnan(data)] = np.ma.masked\n\n    # Convert az,range to x,y\n    xlocs, ylocs = azimuth_range_to_lat_lon(az, var_range, cent_lon, cent_lat)\n\n    # Plot the data\n    crs = ccrs.LambertConformal(central_longitude=cent_lon, central_latitude=cent_lat)\n    ax = fig.add_subplot(ax_rect, projection=crs)\n    ax.add_feature(USCOUNTIES, linewidth=0.5)\n    ax.pcolormesh(xlocs, ylocs, data, cmap='viridis', transform=ccrs.PlateCarree())\n    ax.set_extent([cent_lon - 0.5, cent_lon + 0.5, cent_lat - 0.5, cent_lat + 0.5])\n    ax.set_aspect('equal', 'datalim')\n    add_timestamp(ax, f.dt, y=0.02, high_contrast=True)\n\nplt.show()"
+        "spec = gridspec.GridSpec(1, 2)\nfig = plt.figure(figsize=(15, 8))\nadd_metpy_logo(fig, 190, 85, size='large')\n\nfor var_data, var_range, ax_rect in zip((ref, rho), (ref_range, rho_range), spec,\n                                        strict=False):\n    # Turn into an array, then mask\n    data = np.ma.array(var_data)\n    data[np.isnan(data)] = np.ma.masked\n\n    # Convert az,range to x,y\n    xlocs, ylocs = azimuth_range_to_lat_lon(az, var_range, cent_lon, cent_lat)\n\n    # Plot the data\n    crs = ccrs.LambertConformal(central_longitude=cent_lon, central_latitude=cent_lat)\n    ax = fig.add_subplot(ax_rect, projection=crs)\n    ax.add_feature(USCOUNTIES, linewidth=0.5)\n    ax.pcolormesh(xlocs, ylocs, data, cmap='viridis', transform=ccrs.PlateCarree())\n    ax.set_extent([cent_lon - 0.5, cent_lon + 0.5, cent_lat - 0.5, cent_lat + 0.5])\n    ax.set_aspect('equal', 'datalim')\n    add_timestamp(ax, f.dt, y=0.02, high_contrast=True)\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/cb6e8f9f9881e6360cc13e545e8433e9/US_Counties.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)\n\nfig = plt.figure(figsize=(12, 9))\nax1 = fig.add_subplot(1, 3, 1, projection=proj)\nax2 = fig.add_subplot(1, 3, 2, projection=proj)\nax3 = fig.add_subplot(1, 3, 3, projection=proj)\n\nfor scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):\n    axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())\n    axis.add_feature(USCOUNTIES.with_scale(scale))"
+        "proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)\n\nfig = plt.figure(figsize=(12, 9))\nax1 = fig.add_subplot(1, 3, 1, projection=proj)\nax2 = fig.add_subplot(1, 3, 2, projection=proj)\nax3 = fig.add_subplot(1, 3, 3, projection=proj)\n\nfor scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3], strict=False):\n    axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())\n    axis.add_feature(USCOUNTIES.with_scale(scale))"
       ]
     }
   ],

dev/_downloads/d04a9494716f000a1433c75d1fe171ba/Natural_Neighbor_Verification.py

@@ -93,7 +93,8 @@
 ax.set_title('Triangulation of observations and test grid cell '
              'natural neighbor interpolation values')
 
-members, circumcenters = geometry.find_natural_neighbors(tri, list(zip(sim_gridx, sim_gridy)))
+members, circumcenters = geometry.find_natural_neighbors(tri, list(zip(sim_gridx, sim_gridy,
+                                                                       strict=False)))
 
 val = natural_neighbor_point(xp, yp, zp, (sim_gridx[0], sim_gridy[0]), tri, members[0],
                              circumcenters)
@@ -164,7 +165,7 @@ 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).
-vort = Voronoi(list(zip(xp, yp)))
+vort = Voronoi(list(zip(xp, yp, strict=False)))
 
 fig, ax = plt.subplots(1, 1, figsize=(15, 10))
 ax.ishold = lambda: True  # Work-around for Matplotlib 3.0.0 incompatibility
@@ -175,7 +176,7 @@ def draw_circle(ax, x, y, r, m, label):
 x_0 = xp[nn_ind]
 y_0 = yp[nn_ind]
 
-for x, y, z in zip(x_0, y_0, z_0):
+for x, y, z in zip(x_0, y_0, z_0, strict=False):
     ax.annotate(f'{x}, {y}: {z:.3f} F', xy=(x, y))
 
 ax.plot(sim_gridx[0], sim_gridy[0], 'k+', markersize=10)

dev/_downloads/d0fb0bcc036dab38c2a49ad11eb0832b/Plotting_Surface_Analysis.ipynb

@@ -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))\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        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')"
       ]
     },
     {

dev/_downloads/d7d718be94d68f64fd8796450bbb0ff2/Plotting_Surface_Analysis.py

@@ -44,7 +44,7 @@ def plot_bulletin(ax, data):
     # Handle H/L points using MetPy's StationPlot class
     for field in ('HIGH', 'LOW'):
         rows = data[data.feature == field]
-        x, y = zip(*((pt.x, pt.y) for pt in rows.geometry))
+        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])