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Cleaned up plotting documentation.

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Bill Ladwig 8 years ago
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2 changed files with 58 additions and 32 deletions
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  1. 21
      doc/source/new.rst
  2. 69
      doc/source/plot.rst

21
doc/source/new.rst
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@ -1,6 +1,23 @@ @@ -1,6 +1,23 @@
What's New
===========
Releases
-------------
v1.0.0
^^^^^^^^^^^^^
- Release 1.0.0.
- Fixed issue with not being able to set the thread-local coordinate cache to
0 to disable it. Also, the cache will now correctly resize itself when
the size is reduced to less than its current setting.
- Fixed an issue with the '0000-00-00 00:00:00' time used in geo_em files
causing crashes due to the invalid time. The time is now set to
numpy.datetime64('NaT').
- Fixed issue with wrf.cape_3d not working correctly with a single
column of data.
Beta Releases
--------------
@ -67,8 +84,8 @@ v1.0a3 @@ -67,8 +84,8 @@ v1.0a3
Known Issues
--------------
v1.0b3
^^^^^^^^^^^^^^^
v1.0.0
^^^^^^^^
- Currently unable to build on Windows with Python 3.5+ using open source
mingw compiler. The mingwpy project is working on resolving the

69
doc/source/plot.rst
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@ -69,8 +69,10 @@ Plotting a Two-dimensional Field @@ -69,8 +69,10 @@ Plotting a Two-dimensional Field
ax.coastlines('50m', linewidth=0.8)
# Make the contour outlines and filled contours for the smoothed sea level pressure.
plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black", transform=crs.PlateCarree())
plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree(), cmap=get_cmap("jet"))
plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black",
transform=crs.PlateCarree())
plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree(),
cmap=get_cmap("jet"))
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
@ -86,6 +88,7 @@ Plotting a Two-dimensional Field @@ -86,6 +88,7 @@ Plotting a Two-dimensional Field
plt.show()
Horizontal Interpolation to a Pressure Level
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@ -139,7 +142,7 @@ Horizontal Interpolation to a Pressure Level @@ -139,7 +142,7 @@ Horizontal Interpolation to a Pressure Level
# Add the 500 hPa geopotential height contours
levels = np.arange(520., 580., 6.)
contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500), levels=levels, colors="black",
transform=crs.PlateCarree())
transform=crs.PlateCarree())
plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
# Add the wind speed contours
@ -194,8 +197,8 @@ plot, see :ref:`cross_example`. @@ -194,8 +197,8 @@ plot, see :ref:`cross_example`.
slp = getvar(ncfile, "slp")
smooth_slp = smooth2d(slp, 3)
ctt = getvar(ncfile, "ctt")
z = getvar(ncfile, "z", timeidx=0)
dbz = getvar(ncfile, "dbz", timeidx=0)
z = getvar(ncfile, "z")
dbz = getvar(ncfile, "dbz")
Z = 10**(dbz/10.)
wspd = getvar(ncfile, "wspd_wdir", units="kt")[0,:]
@ -203,10 +206,12 @@ plot, see :ref:`cross_example`. @@ -203,10 +206,12 @@ plot, see :ref:`cross_example`.
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon points along the cross-section
# in the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
# Compute the vertical cross-section interpolation. Also, include the lat/lon
# points along the cross-section in the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
dbz_cross = 10.0 * np.log10(z_cross)
# Get the lat/lon points
@ -223,7 +228,7 @@ plot, see :ref:`cross_example`. @@ -223,7 +228,7 @@ plot, see :ref:`cross_example`.
# Download and create the states, land, and oceans using cartopy features
states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
name='admin_1_states_provinces_shp')
land = cfeature.NaturalEarthFeature(category='physical', name='land', scale='50m',
facecolor=cfeature.COLORS['land'])
ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale='50m',
@ -232,15 +237,15 @@ plot, see :ref:`cross_example`. @@ -232,15 +237,15 @@ plot, see :ref:`cross_example`.
# Make the pressure contours
contour_levels = [960, 965, 970, 975, 980, 990]
c1 = ax_ctt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels, colors="white",
transform=crs.PlateCarree(), zorder=3, linewidths=1.0)
transform=crs.PlateCarree(), zorder=3, linewidths=1.0)
# Create the filled cloud top temperature contours
contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]
ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt), contour_levels, cmap=get_cmap("Greys"),
transform=crs.PlateCarree(), zorder=2)
ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt), contour_levels,
cmap=get_cmap("Greys"), transform=crs.PlateCarree(), zorder=2)
ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat], color="yellow",
marker="o", transform=crs.PlateCarree(), zorder=3)
ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat],
color="yellow", marker="o", transform=crs.PlateCarree(), zorder=3)
# Create the color bar for cloud top temperature
cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)
@ -298,7 +303,8 @@ plot, see :ref:`cross_example`. @@ -298,7 +303,8 @@ plot, see :ref:`cross_example`.
ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7})
plt.show()
Matplotlib with Basemap
-----------------------
@ -306,8 +312,7 @@ Although basemap is in maintenance mode only and becoming deprecated, it is stil @@ -306,8 +312,7 @@ Although basemap is in maintenance mode only and becoming deprecated, it is stil
widely used by many programmers. Cartopy is becoming the preferred package for
mapping, however it suffers from growing pains in some areas
(can't use latitude/longitude labels for many map projections). If you
run in to these issues, basemap is likely to accomplish what you need, despite
slower performance.
run in to these issues, basemap is likely to accomplish what you need.
Plotting a Two-Dimensional Field
@ -418,7 +423,7 @@ Horizontal Interpolation to a Pressure Level @@ -418,7 +423,7 @@ Horizontal Interpolation to a Pressure Level
# Add the wind speed contours
levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
wspd_contours = bm.contourf(x, y, to_np(wspd_500), levels=levels,
cmap=get_cmap("rainbow"))
cmap=get_cmap("rainbow"))
plt.colorbar(wspd_contours, ax=ax, orientation="horizontal", pad=.05)
# Add the geographic boundaries
@ -428,7 +433,7 @@ Horizontal Interpolation to a Pressure Level @@ -428,7 +433,7 @@ Horizontal Interpolation to a Pressure Level
# Add the 500 hPa wind barbs, only plotting every 125th data point.
bm.barbs(x[::125,::125], y[::125,::125], to_np(u_500[::125, ::125]),
to_np(v_500[::125, ::125]), length=6)
to_np(v_500[::125, ::125]), length=6)
plt.title("500 MB Height (dm), Wind Speed (kt), Barbs (kt)")
@ -462,8 +467,8 @@ plot, see :ref:`cross_example`. @@ -462,8 +467,8 @@ plot, see :ref:`cross_example`.
slp = getvar(ncfile, "slp")
smooth_slp = smooth2d(slp, 3)
ctt = getvar(ncfile, "ctt")
z = getvar(ncfile, "z", timeidx=0)
dbz = getvar(ncfile, "dbz", timeidx=0)
z = getvar(ncfile, "z")
dbz = getvar(ncfile, "dbz")
Z = 10**(dbz/10.)
wspd = getvar(ncfile, "wspd_wdir", units="kt")[0,:]
@ -471,10 +476,12 @@ plot, see :ref:`cross_example`. @@ -471,10 +476,12 @@ plot, see :ref:`cross_example`.
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon points along the cross-section in
# the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
# Compute the vertical cross-section interpolation. Also, include the lat/lon points
# along the cross-section in the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
dbz_cross = 10.0 * np.log10(z_cross)
# Get the latitude and longitude points
@ -500,11 +507,11 @@ plot, see :ref:`cross_example`. @@ -500,11 +507,11 @@ plot, see :ref:`cross_example`.
# Create the filled cloud top temperature contours
contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]
ctt_contours = bm.contourf(x, y, to_np(ctt), contour_levels, cmap=get_cmap("Greys"),
zorder=2, ax=ax_ctt)
zorder=2, ax=ax_ctt)
point_x, point_y = bm([start_point.lon, end_point.lon], [start_point.lat, end_point.lat])
bm.plot([point_x[0], point_x[1]], [point_y[0], point_y[1]], color="yellow",
marker="o", zorder=3, ax=ax_ctt)
marker="o", zorder=3, ax=ax_ctt)
# Create the color bar for cloud top temperature
cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)
@ -609,8 +616,10 @@ plotted using the standard matplotlib API. @@ -609,8 +616,10 @@ plotted using the standard matplotlib API.
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon points along the cross-section.
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
# Compute the vertical cross-section interpolation. Also, include the lat/lon
# points along the cross-section.
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
# Create the figure
fig = plt.figure(figsize=(12,6))

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