def plot_labelled_filled_contours(data, ax=None):
"""
A utility function for convenience that plots labelled, filled contours with black contours
marking each level.It will return a dictionary containing three objects corresponding to the
filled contours, the black contours, and the contour labels.
"""
# Import an NCL colormap, truncating it by using geocat.viz.util convenience function
newcmp = gvutil.truncate_colormap(gvcmaps.gui_default,
minval=0.03,
maxval=0.9)
handles = dict()
handles["filled"] = data.plot.contourf(
ax=ax, # this is the axes we want to plot to
cmap=newcmp, # our special colormap
levels=levels, # contour levels specified outside this function
xticks=np.arange(-180, 181, 30), # nice x ticks
yticks=np.arange(-90, 91, 30), # nice y ticks
transform=projection, # data projection
add_colorbar=False, # don't add individual colorbars for each plot call
add_labels=False, # turn off xarray's automatic Lat, lon labels
)
# matplotlib's contourf doesn't let you specify the "edgecolors" (MATLAB terminology)
# instead we plot black contours on top of the filled contours
handles["contour"] = data.plot.contour(
ax=ax,
levels=levels,
colors="black", # note plurals in this and following kwargs
linestyles="-",
linewidths=0.5,
add_labels=False, # again turn off automatic labels
)
# Label the contours
ax.clabel(
handles["contour"],
fontsize=8,
fmt="%.0f", # Turn off decimal points
)
# Add coastlines
ax.coastlines(linewidth=0.5)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax)
# Use geocat.viz.util convenience function to make plots look like NCL plots by using latitude, longitude tick labels
gvutil.add_lat_lon_ticklabels(ax)
# Use geocat.viz.util convenience function to add main title as well as titles to left and right of the plot axes.
gvutil.set_titles_and_labels(ax,
lefttitle=data.attrs['long_name'],
lefttitlefontsize=10,
righttitle=data.attrs['units'],
righttitlefontsize=10)
return handles
def plot_labelled_filled_contours(data, ax=None):
"""
A utility function for convenience that plots labelled, filled contours with black contours
marking each level.It will return a dictionary containing three objects corresponding to the
filled contours, the black contours, and the contour labels.
"""
cmap = truncate_colormap(mpl.cm.rainbow, 0.2, 0.9)
handles = dict()
handles["filled"] = data.plot.contourf(
ax=ax, # this is the axes we want to plot to
cmap=cmap, # our special colormap
levels=levels, # contour levels specified outside this function
xticks=np.arange(-180, 181, 30), # nice x ticks
yticks=np.arange(-90, 91, 30), # nice y ticks
transform=ccrs.PlateCarree(), # data projection
add_colorbar=False, # don't add individual colorbars for each plot call
add_labels=False, # turn off xarray's automatic Lat, lon labels
)
# matplotlib's contourf doesn't let you specify the "edgecolors" (MATLAB terminology)
# instead we plot black contours on top of the filled contours
handles["contour"] = data.plot.contour(
ax=ax,
levels=levels,
colors="k", # note plurals in this and following kwargs
linestyles="-",
linewidths=0.5,
add_labels=False, # again turn off automatic labels
)
# Label the contours
ax.clabel(
handles["contour"],
fontsize="small",
fmt="%.0f", # Turn off decimal points
)
# make a nice title
title = f"{data.attrs['long_name']} [{data.attrs['units']}]"
ax.set_title(title, loc="left", y=1.05)
return handles
###############################################################################
# Plot:
# -----
# Generate figure (set its size (width, height) in inches) and axes using Cartopy
fig = plt.figure(figsize=(10, 10))
ax = plt.axes(projection=projection)
ax.set_extent([100, 145, 15, 55], crs=projection)
# Define the contour levels
clevs = np.arange(228, 273, 4, dtype=float)
# Import an NCL colormap, truncating it by using geocat.viz.util convenience function
newcmp = gvutil.truncate_colormap(gvcmaps.BkBlAqGrYeOrReViWh200,
minval=0.1,
maxval=0.6,
n=len(clevs))
# Draw the temperature contour plot with the subselected colormap
# (Place the zorder of the contour plot at the lowest level)
cf = ax.contourf(lon, lat, T, levels=clevs, cmap=newcmp, zorder=1)
# Draw horizontal color bar
cax = plt.axes((0.14, 0.08, 0.74, 0.02))
cbar = plt.colorbar(cf,
ax=ax,
cax=cax,
ticks=clevs[1:-1],
drawedges=True,
orientation='horizontal')
cbar.ax.tick_params(labelsize=12)
# Plot contour lines lines = percentage.plot.contour(ax = ax, levels = levels, colors = 'black', linewidths = 0.5, linestyles = 'solid', add_labels = False) # Draw contour labels and set their backgrounds to be white ax.clabel(lines, fmt = '%d', levels = levels) [ txt.set_bbox(dict(facecolor = 'white', edgecolor = 'none', pad = 1)) for txt in lines.labelTexts ] # Import Color map cmap = gvcmap.BlueRed # Truncate colormap to only use the light blue colors and the reds cmap = gvutil.truncate_colormap(cmap, minval = 0.31, maxval = 1, n = 10) # on ncl 78-253 # Plot filled contours colors = percentage.plot.contourf(ax = ax, levels = levels, cmap = cmap, add_labels = False, add_colorbar = False) # Add colorbar plt.colorbar(colors, ax = ax, orientation = 'vertical', ticks = levels[1::2], drawedges = True, aspect = 12, shrink = 0.7, pad = 0.1) # Look up slice notation on levels[1::2] # Use geocat.viz.util convenience function to set axes tick values # Set y-lim in order for y-axis to have descending values gvutil.set_axes_limits_and_ticks(ax, xlim = (0, 90), xticks = np.arange(0, 80, 20), ylim = (0, 1000), yticks = np.arange(0, 1000, 200)) # Force the plot to be square by setting the aspect ratio to 1 ax.set_box_aspect(1) plt.tight_layout() # automatically adjust subplot parameters to give specified padding
# Set up figure and axes
fig, ax = plt.subplots(figsize=(10, 6.25))
ax = plt.axes(projection=ccrs.PlateCarree())
fig.suptitle('Vectors colored by a scalar map', fontsize=14, y=.9)
nclize_axis(ax)
add_lat_lon_ticklabels(ax)
# Set major and minor ticks
plt.xticks(range(-180, 181, 30))
plt.yticks(range(-90, 91, 30))
# Draw vector plot
# (there is no matplotlib equivalent to "CurlyVector" yet)
plt.cm.register_cmap(
'BlAqGrYeOrReVi200',
truncate_colormap(cmaps.BlAqGrYeOrReVi200, minval=0.03, maxval=0.95, n=16))
cmap = plt.cm.get_cmap('BlAqGrYeOrReVi200', 16)
Q = plt.quiver(ds['lon'],
ds['lat'],
ds['U'].data,
ds['V'].data,
ds['T'].data,
cmap=cmap,
zorder=1,
pivot="middle",
width=0.001)
plt.clim(228, 292)
# Draw legend for vector plot
ax.add_patch(
plt.Rectangle((150, -140),
y = 1.05
ax1.set_title('Time(0)', fontsize=size, y=y)
ax1.set_title(TS_0.long_name, fontsize=size, loc='left', y=y)
ax1.set_title(TS_0.units, fontsize=size, loc='right', y=y)
# Plot zonal mean temperature
ax2.plot(mean.data, mean.lat, color='black', linewidth=0.5)
# Plot vertical reference line in zonal mean plot
ax2.axvline(273.15, color='black', linewidth=0.5)
# Import color map
cmap = gvcmaps.BlWhRe
# Truncate colormap to only use paler colors in the center of the colormap
cmap = gvutil.truncate_colormap(cmap, minval=0.22, maxval=0.74, n=14)
# Plot filled contour for deviation from time avg plot
deviations = time_dev.plot.contourf(ax=ax3,
transform=proj,
vmin=-14,
vmax=18,
levels=np.arange(-14, 20, 2),
cmap=cmap,
add_colorbar=False,
add_labels=False)
# Draw contour lines for deviation from time avg plot
time_dev.plot.contour(ax=ax3,
transform=proj,
vmin=-14,
ocean_only = gvutil.xr_add_cyclic_longitudes(ocean_only, "lon")
###############################################################################
# Plot Ocean Only:
# Generate figure (set its size (width, height) in inches)
plt.figure(figsize=(10, 6))
# Generate axes using Cartopy and draw coastlines
projection = ccrs.PlateCarree()
ax = plt.axes(projection=projection)
ax.coastlines(linewidth=0.5, resolution="110m")
# Import an NCL colormap, truncating it by using geocat.viz.util convenience function
newcmp = gvutil.truncate_colormap(gvcmaps.BlAqGrYeOrRe,
minval=0.1,
maxval=1.0,
n=22)
# Contourf-plot ocean-only data (for filled contours)
filled = ocean_only.plot.contourf(ax=ax,
cmap=newcmp,
levels=np.arange(260, 305, 2),
xticks=np.arange(-180, 181, 30),
yticks=np.arange(-90, 91, 30),
transform=ccrs.PlateCarree(),
add_colorbar=False,
add_labels=False,
vmin=260,
vmax=304)
# Add horizontal colorbar
# Draw legend for vector plot
qk = ax.quiverkey(Q,
94,
26,
4,
r'4 $m/s$',
labelpos='N',
zorder=2,
coordinates='data',
color='black')
# Draw SST contours
truncate_colormap(cmaps.BlAqGrYeOrReVi200,
minval=0.08,
maxval=0.96,
n=len(levels),
name='BlAqGrYeOrReVi200')
cf = sst.plot.contourf('lon',
'lat',
extend='both',
levels=levels,
cmap='BlAqGrYeOrReVi200',
zorder=0,
xlabel='',
add_labels=False,
cbar_kwargs={
'shrink': 0.75,
'ticks': np.linspace(24, 28.8, 17),
'drawedges': True,
'label': '$^\circ$C'
)
# make a nice title
ax.set_title(f"{data.attrs['long_name']}", loc="left", y=1.05)
ax.set_title(f"{data.attrs['units']}", loc="right", y=1.05)
return filled
###############################################################################
# Plot Ocean Only
# ===============
levels = np.arange(260, 305, 2)
plt.register_cmap("BlAqGrYeOrRe",
truncate_colormap(cmaps.BlAqGrYeOrRe, 0.1, 1.0))
cmap = plt.cm.get_cmap("BlAqGrYeOrRe", 22)
f, ax = plt.subplots(1, 1, subplot_kw={"projection": ccrs.PlateCarree()})
filled = plot_filled_contours(ocean_only, vmin=260, vmax=304, ax=ax, cmap=cmap)
cbar = f.colorbar(
filled, # make colorbar appropriate for this object
ax=
ax, # a list of *two* axes, matplotlib will steal space from both these to fit the colorbar
orientation=
"horizontal", # horizontal colorbars are on the bottom by default
aspect=30, # aspect ratio of colorbar, just because we can.
drawedges=True,
)
