Add text outline and axis call to plot_maxmin_points

HiLo.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+
+
+def plot_maxmin_points(ax,lon, lat, data, extrema, nsize, symbol,color='k', outline_color='k',
+                       outline_width=2.5, press_spacing=0.66,plotValue=True, transform=None):
+
+    """
+    ax argument allows for sending current axis to the HiLo plot
+
+    Path effects on the symbols and pressure readings - outline them in black (default) with linewidth
+    2.5 (default) to make them pop a bit more. The press_spacing (0.66 default) is based off latitude and helps
+    serarate the pressure reading and the symbol with the outline effects making them overlap.
+
+
+    This function will find and plot relative maximum and minimum for a 2D grid. The function
+    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum
+    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale
+    max/min value. The symbol text can be set to a string value and optionally the color of the
+    symbol and any plotted value can be set with the parameter color
+    lon = plotting longitude values (2D)
+    lat = plotting latitude values (2D)
+    data = 2D data that you wish to plot the max/min symbol placement
+    extrema = Either a value of max for Maximum Values or min for Minimum Values
+    nsize = Size of the grid box to filter the max and min values to plot a reasonable number
+    symbol = String to be placed at location of max/min value
+    color = String matplotlib colorname to plot the symbol (and numerica value, if plotted)
+    plot_value = Boolean (True/False) of whether to plot the numeric value of max/min point
+    The max/min symbol will be plotted on the current axes within the bounding frame
+    (e.g., clip_on=True)
+    """
+    import numpy as np
+    from matplotlib import patheffects
+    from scipy.ndimage.filters import maximum_filter, minimum_filter
+    outline_effect = [patheffects.withStroke(linewidth=outline_width, foreground=outline_color)]
+
+    if (extrema == 'max'):
+        data_ext = maximum_filter(data, nsize, mode='nearest')
+    elif (extrema == 'min'):
+        data_ext = minimum_filter(data, nsize, mode='nearest')
+    else:
+        raise ValueError('Value for hilo must be either max or min')
+
+    mxy, mxx = np.where(data_ext == data)
+
+    for i in range(len(mxy)):
+        A = ax.text(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]], symbol, color=color, size=24,
+                    clip_on=True, horizontalalignment='center', verticalalignment='center',
+                    transform=transform)
+        A.set_path_effects(outline_effect)
+        B = ax.text(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]]-float(press_spacing),
+                    str(np.int(data[mxy[i], mxx[i]])),
+                    color=color, size=12, clip_on=True, fontweight='bold',
+                    horizontalalignment='center', verticalalignment='top', transform=transform)
+        B.set_path_effects(outline_effect)

examples/HILO_Symbol_Plot.py

@@ -0,0 +1,113 @@
+ # # MSLP and 1000-500 hPa Thickness with High and Low Symbols
+#
+#
+# Plot MSLP, calculate and plot 1000-500 hPa thickness, and plot H and L markers.
+# Beyond just plotting a few variables, in the example we use functionality
+# from the scipy module to find local maximum and minimimum values within the
+# MSLP field in order to plot symbols at those locations.
+#
+
+# Imports
+from datetime import datetime
+
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+from metpy.units import units
+from netCDF4 import num2date
+import numpy as np
+from scipy.ndimage import gaussian_filter
+from siphon.ncss import NCSS
+
+# Function for finding and plotting max/min points
+# import HiLo module for plot_maxmin_points function
+from HiLo import plot_maxmin_points as plot_maxmin_points
+
+# Get NARR data
+dattim = datetime(1999, 1, 3, 0)
+
+ncss = NCSS('https://www.ncei.noaa.gov/thredds/ncss/grid/narr-a-files/{0:%Y%m}/{0:%Y%m%d}/'
+            'narr-a_221_{0:%Y%m%d}_{0:%H}00_000.grb'.format(dattim))
+query = ncss.query()
+query.all_times().variables('Pressure_reduced_to_MSL_msl',
+                            'Geopotential_height_isobaric').add_lonlat().accept('netcdf')
+data = ncss.get_data(query)
+
+
+# Extract data into variables
+# Grab pressure levels
+plev = list(data.variables['isobaric1'][:])
+
+# Grab lat/lons and make all lons 0-360
+lats = data.variables['lat'][:]
+lons = data.variables['lon'][:]
+lons[lons < 0] = 360 + lons[lons < 0]
+
+# Grab valid time and get into datetime format
+time = data['time2']
+vtime = num2date(time[:], units=time.units)
+
+# Grab MSLP and smooth, use MetPy Units module for conversion
+EMSL = data.variables['Pressure_reduced_to_MSL_msl'][:] * units.Pa
+EMSL.ito('hPa')
+mslp = gaussian_filter(EMSL[0], sigma=3.0)
+
+# Grab pressure level data
+hght_1000 = data.variables['Geopotential_height_isobaric'][0, plev.index(1000)]
+hght_500 = data.variables['Geopotential_height_isobaric'][0, plev.index(500)]
+
+# Calculate and smooth 1000-500 hPa thickness
+thickness_1000_500 = gaussian_filter(hght_500 - hght_1000, sigma=3.0)
+
+
+# Set map and data projections for use in mapping
+# Set projection of map display
+mapproj = ccrs.LambertConformal(central_latitude=45., central_longitude=-100.)
+
+# Set projection of data
+dataproj = ccrs.PlateCarree()
+
+# Grab data for plotting state boundaries
+states_provinces = cfeature.NaturalEarthFeature(
+        category='cultural',
+        name='admin_1_states_provinces_lakes',
+        scale='50m',
+        facecolor='none')
+
+
+# Create figure and plot data
+fig = plt.figure(1, figsize=(17., 11.))
+ax = plt.subplot(111, projection=mapproj)
+
+# Set extent and plot map lines
+ax.set_extent([-145., -70, 20., 60.], ccrs.PlateCarree())
+ax.coastlines('50m', edgecolor='black', linewidth=0.75)
+ax.add_feature(states_provinces, edgecolor='black', linewidth=0.5)
+
+# Plot thickness with multiple colors
+clevs = (np.arange(0, 5400, 60),
+         np.array([5400]),
+         np.arange(5460, 7000, 60))
+colors = ('tab:blue', 'b', 'tab:red')
+kw_clabels = {'fontsize': 11, 'inline': True, 'inline_spacing': 5, 'fmt': '%i',
+              'rightside_up': True, 'use_clabeltext': True}
+for clevthick, color in zip(clevs, colors):
+    cs = ax.contour(lons, lats, thickness_1000_500, levels=clevthick, colors=color,
+                    linewidths=1.0, linestyles='dashed', transform=dataproj)
+    plt.clabel(cs, **kw_clabels)
+
+# Plot MSLP
+clevmslp = np.arange(800., 1120., 4)
+cs2 = ax.contour(lons, lats, mslp, clevmslp, colors='k', linewidths=1.25,
+                 linestyles='solid', transform=dataproj)
+plt.clabel(cs2, **kw_clabels)
+
+# Use definition to plot H/L symbols
+plot_maxmin_points(ax,lons, lats, mslp, 'max', 50, symbol='H', color='b',  transform=dataproj)
+plot_maxmin_points(ax,lons, lats, mslp, 'min', 25, symbol='L', color='r', transform=dataproj)
+
+# Put on some titles
+plt.title('MSLP (hPa) with Highs and Lows, 1000-500 hPa Thickness (m)', loc='left')
+plt.title('VALID: {}'.format(vtime[0]), loc='right')
+
+plt.show()

examples/HiLo.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+
+
+def plot_maxmin_points(ax,lon, lat, data, extrema, nsize, symbol,color='k', outline_color='k',
+                       outline_width=2.5, press_spacing=0.66,plotValue=True, transform=None):
+
+    """
+    ax argument allows for sending current axis to the HiLo plot 
+
+    Path effects on the symbols and pressure readings - outline them in black (default) with linewidth 
+    2.5 (default) to make them pop a bit more. The press_spacing (0.66 default) is based off latitude and helps 
+    serarate the pressure reading and the symbol with the outline effects making them overlap.
+
+
+    This function will find and plot relative maximum and minimum for a 2D grid. The function
+    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum
+    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale
+    max/min value. The symbol text can be set to a string value and optionally the color of the
+    symbol and any plotted value can be set with the parameter color
+    lon = plotting longitude values (2D)
+    lat = plotting latitude values (2D)
+    data = 2D data that you wish to plot the max/min symbol placement
+    extrema = Either a value of max for Maximum Values or min for Minimum Values
+    nsize = Size of the grid box to filter the max and min values to plot a reasonable number
+    symbol = String to be placed at location of max/min value
+    color = String matplotlib colorname to plot the symbol (and numerica value, if plotted)
+    plot_value = Boolean (True/False) of whether to plot the numeric value of max/min point
+    The max/min symbol will be plotted on the current axes within the bounding frame
+    (e.g., clip_on=True)
+    """
+    import numpy as np
+    from matplotlib import patheffects
+    from scipy.ndimage.filters import maximum_filter, minimum_filter
+    outline_effect = [patheffects.withStroke(linewidth=outline_width, foreground=outline_color)]
+
+    if (extrema == 'max'):
+        data_ext = maximum_filter(data, nsize, mode='nearest')
+    elif (extrema == 'min'):
+        data_ext = minimum_filter(data, nsize, mode='nearest')
+    else:
+        raise ValueError('Value for hilo must be either max or min')
+
+    mxy, mxx = np.where(data_ext == data)
+    #print(mxy,mxx)
+
+    for i in range(len(mxy)):
+        A = ax.text(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]], symbol, color=color, size=24,
+                    clip_on=True, horizontalalignment='center', verticalalignment='center',
+                    transform=transform)
+        A.set_path_effects(outline_effect)
+        B = ax.text(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]]-float(press_spacing),
+                    str(np.int(data[mxy[i], mxx[i]])),
+                    color=color, size=12, clip_on=True, fontweight='bold',
+                    horizontalalignment='center', verticalalignment='top', transform=transform)
+        B.set_path_effects(outline_effect)
+
+