# Import an NCL colormap
newcmp = gvcmaps.wgne15
# Contourf-plot data (for filled contours)
a = wrap_v.plot.contourf(levels = 14, cmap = newcmp, add_colorbar=False, add_labels=False)
# Contour-plot data (for borderlines)
wrap_v.plot.contour(levels = 14, linewidths=0.5, cmap='k', add_labels=False)
# Add vertical colorbar
clabels = ["-70","-50","-30","-10","10","30","50","70","90","110","130","150"]
cbar = fig.colorbar(a, label='', ticks=np.linspace(-24, 24, 12), shrink=0.4)
cbar.ax.set_yticklabels(clabels)
# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax, ylim=(-90,90),
xticks=np.linspace(-180, 180, 13), yticks=np.linspace(-90, 90, 7))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=10)
# 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 titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax, lefttitle="meridional wind component", lefttitlefontsize=14,
righttitle="m/s", righttitlefontsize=14, xlabel="", ylabel="")
# Show the plot
plt.show()
# Set boxplot edge colors
setBoxColor(boxplots, ['blue', 'red', 'green'])
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
ylim=(-6.0, 9.0),
yticks=[-3.0, 0.0, 3.0, 6.0])
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
y_minor_per_major=3,
x_minor_per_major=1,
labelsize=14)
# Use geocat.viz.util convenience function to add title to the plot axis.
gvutil.set_titles_and_labels(ax, maintitle='Box Plot with Polymarkers')
# Make both major and minor ticks point inwards towards the plot
ax.tick_params(direction="in", which='both')
# Get rid of right and top axis spines
removeSpines(ax)
# Set ticks only at left and bottom sides of plot
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
# Add another partially transparent axis on top of the first one
ax2 = ax.inset_axes([0, 0, 1, 1])
ax2.patch.set_alpha(0.2)
def make_base_plot():
# Generate axes using Cartopy projection
ax = plt.axes(projection=ccrs.PlateCarree())
# Add continents
continents = cartopy.feature.NaturalEarthFeature(
name="coastline",
category="physical",
scale="50m",
edgecolor="None",
facecolor="lightgray",
)
ax.add_feature(continents)
# Set map extent
ax.set_extent([-130, 0, -20, 40], crs=ccrs.PlateCarree())
# Define the contour levels. The top range value of 44 is not included in the levels.
levels = np.arange(-12, 44, 4)
# Using a dictionary prevents repeating the same keyword arguments twice for the contours.
kwargs = dict(
levels=levels, # contour levels specified outside this function
xticks=[-120, -90, -60, -30, 0], # nice x ticks
yticks=[-20, 0, 20, 40], # nice y ticks
transform=ccrs.PlateCarree(), # ds projection
add_colorbar=False, # don't add individual colorbars for each plot call
add_labels=False, # turn off xarray's automatic Lat, lon labels
colors="gray", # note plurals in this and following kwargs
linestyles="-",
linewidths=0.5,
)
# Contourf-plot data (for filled contours)
hdl = ds.U.plot.contour(x="lon", y="lat", ax=ax, **kwargs)
# Add contour labels. Default contour labels are sparsely placed, so we specify label locations manually.
# Label locations only need to be approximate; the nearest contour will be selected.
label_locations = [
(-123, 35),
(-116, 17),
(-94, 4),
(-85, -6),
(-95, -10),
(-85, -15),
(-70, 35),
(-42, 28),
(-54, 7),
(-53, -5),
(-39, -11),
(-28, 11),
(-16, -1),
(-8, -9), # Python allows trailing list separators.
]
ax.clabel(
hdl,
np.arange(-8, 24,
8), # Only label these contour levels: [-8, 0, 8, 16]
fontsize="small",
colors="black",
fmt="%.0f", # Turn off decimal points
manual=label_locations, # Manual label locations
inline=False
) # Don't remove the contour line where labels are located.
# Create a rectangle patch, to color the border of the rectangle a different color.
# Specify the rectangle as a corner point with width and height, to help place border text more easily.
left, width = -90, 45
bottom, height = 0, 30
right = left + width
top = bottom + height
# Draw rectangle patch on the plot
p = plt.Rectangle(
(left, bottom),
width,
height,
fill=False,
zorder=3, # Plot on top of the purple box border.
edgecolor='red',
alpha=0.5) # Lower color intensity.
ax.add_patch(p)
# Draw text labels around the box.
# Change the default padding around a text box to zero, making it a "tight" box.
# Create "text_args" to keep from repeating code when drawing text.
text_shared_args = dict(
fontsize=8,
bbox=dict(boxstyle='square, pad=0',
facecolor='white',
edgecolor='white'),
)
# Draw top text
ax.text(left + 0.6 * width,
top,
'test',
horizontalalignment='right',
verticalalignment='center',
**text_shared_args)
# Draw bottom text. Change text background to match the map.
ax.text(
left + 0.5 * width,
bottom,
'test',
horizontalalignment='right',
verticalalignment='center',
fontsize=8,
bbox=dict(boxstyle='square, pad=0',
facecolor='lightgrey',
edgecolor='lightgrey'),
)
# Draw left text
ax.text(left,
top,
'test',
horizontalalignment='center',
verticalalignment='top',
rotation=90,
**text_shared_args)
# Draw right text
ax.text(right,
bottom,
'test',
horizontalalignment='center',
verticalalignment='bottom',
rotation=-90,
**text_shared_args)
# Add lower text box. Box appears off-center, but this is to leave room
# for lower-case letters that drop lower.
ax.text(1.0,
-0.20,
"CONTOUR FROM -12 TO 40 BY 4",
fontname='Helvetica',
horizontalalignment='right',
transform=ax.transAxes,
bbox=dict(boxstyle='square, pad=0.15',
facecolor='white',
edgecolor='black'))
# 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="Zonal Wind",
lefttitlefontsize=12,
righttitle="m/s",
righttitlefontsize=12)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, y_minor_per_major=4)
# 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)
return ax
# Determine the labels for each tick on the x and y axes
yticklabels = np.array(levels, dtype=np.int)
xticklabels = [
'12z', '15z', '18z', '21z', 'Apr29', '03z', '06z', '09z', '12z', '15z',
'18z', '21z', 'Apr30', '03z', '06z', '09z', '12z', '15z', '18z', '21z',
'May01', '03z', '06z', '09z', '12z'
]
# Make an axis to overlay on top of the contour plot
axin = fig.add_subplot(spec[0, 0])
# Use the geocat.viz function to set the main title of the plot
gvutil.set_titles_and_labels(axin,
maintitle='Meteogram for LGSA, 28/12Z',
maintitlefontsize=18,
ylabel='Pressure (mb)',
labelfontsize=12)
# Add a pad between the y axis label and the axis spine
axin.yaxis.labelpad = 5
# Use the geocat.viz function to set axes limits and ticks
gvutil.set_axes_limits_and_ticks(axin,
xlim=[taus[0], taus[-1]],
ylim=[levels[0], levels[-1]],
xticks=np.array(taus),
yticks=np.linspace(1000, 400, 8),
xticklabels=xticklabels,
yticklabels=yticklabels)
# Generate figure (set its size (width, height) in inches) and axes
plt.figure(figsize=(6.2, 6))
ax = plt.gca()
# Scatter-plot the data
plt.scatter(ts_rolled.time, ts_rolled.values, c='r', s=3)
# Plot a regression line
plt.plot(ts.time, regline_vals, 'k')
# specify X and Y axis limits
plt.xlim([6000, 9500])
plt.ylim([268.0, 271.5])
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=5,
labelsize=12)
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax,
maintitle="Output from regline",
xlabel="simulation time",
ylabel="Surface temperature")
# Show the plot
plt.tight_layout()
plt.show()
for row in range(0, 2):
for col in range(0, 2):
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(axs[row][col],
ylim=(0.4, 1.2),
xticks=x,
yticks=np.arange(0.4, 1.4, 0.2),
xticklabels=months)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(axs[row][col],
x_minor_per_major=1,
y_minor_per_major=4,
labelsize=12)
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(axs[row][col],
ylabel='(\u00B0C)',
labelfontsize=14)
# Add overall figure title
fig.suptitle('Paneling bar plots, dummy data', size=20, y=0.94)
# Add data to subplot
axs[row][col].bar(x - width * 3 / 2,
data[panel][0][:],
width,
edgecolor='black',
linewidth=0.25,
color='red',
label='first')
axs[row][col].bar(x - width / 2,
data[panel][1][:],
npts = 50
random = np.random.default_rng(seed=1)
data = random.chisquare(2.0, npts)
###############################################################################
# Plot
fig = plt.figure(figsize=(8, 8))
ax = plt.axes()
plt.plot(data, marker='o', linewidth=0, color='darkblue')
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(ax,
xlim=(0, 50),
ylim=(0, 10),
xticks=range(0, 51, 10),
yticks=range(0, 11, 2))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=4,
labelsize=14)
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax, maintitle="Scatter Plot")
plt.show()
ax=ax,
orientation="horizontal",
aspect=30,
drawedges=True)
cbar.set_ticks(np.arange(262, 304, 4))
# 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,
maintitle="Ocean Only",
lefttitle=ocean_only.attrs['long_name'],
lefttitlefontsize=14,
righttitle=ocean_only.attrs['units'],
righttitlefontsize=14)
# Show the plot
plt.show()
###############################################################################
# Plot Land Only:
# Generate figure (set its size (width, height) in inches)
plt.figure(figsize=(10, 6))
# Generate axes using Cartopy and draw coastlines
ax = plt.axes(projection=projection)
ax.coastlines(linewidth=0.5, resolution="110m")
xlim=(0, 49),
ylim=(0, 29),
xticks=np.linspace(0, 40, 5),
yticks=np.linspace(0, 25, 6))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=5,
labelsize=16)
# Use geocat.viz.util convenience function to add titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax,
lefttitle="Cone amplitude",
lefttitlefontsize=18,
righttitle="ndim",
righttitlefontsize=18,
xlabel="X",
ylabel="Y",
labelfontsize=18)
plt.show()
###############################################################################
# Plot With Enhancements:
# Make this figure the thumbnail image on the HTML page.
# sphinx_gallery_thumbnail_number = 2
#create figure
plt.figure(figsize=(10, 10))
vmax=10,
levels=[-12, -10, -8, -6, -4, -2, -1, 1, 2, 4, 6, 8, 10, 12],
cmap=newcmp,
add_colorbar=False,
transform=projection,
add_labels=False)
# Add horizontal colorbar
cbar = plt.colorbar(p, orientation='horizontal', shrink=0.5)
cbar.ax.tick_params(labelsize=11)
cbar.set_ticks([-12, -10, -8, -6, -4, -2, -1, 1, 2, 4, 6, 8, 10, 12])
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xticks=np.linspace(-180, 180, 13),
yticks=np.linspace(-90, 90, 7))
# 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 minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=12)
# Use geocat.viz.util convenience function to add titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax,
lefttitle='Anomalies: Surface Temperature',
righttitle='K')
# Show the plot
plt.show()
gvutil.add_lat_lon_ticklabels(ax)
# Remove the degree symbol from tick labels
ax.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
ax.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=3,
y_minor_per_major=3,
labelsize=10)
gvutil.set_titles_and_labels(ax,
maintitle=olr.long_name,
maintitlefontsize=14,
lefttitle='degC',
lefttitlefontsize=12,
righttitle='(W m s$^{-2}$)',
righttitlefontsize=12)
# Add center title
ax.text(0.35, 1.06, 'December 1982', fontsize=12, transform=ax.transAxes)
# Add lower text box
ax.text(1,
-0.2,
"CONTOUR FROM -80 TO 40 BY 10",
horizontalalignment='right',
transform=ax.transAxes,
bbox=dict(boxstyle='square, pad=0.25',
facecolor='white',
edgecolor='black'))
align='center')
# Add the legend
plt.legend(['OBS', 'CCSM2 (T42)', 'CCSM3 (T42)', 'CCSM3 (T85)'],
loc='lower center',
bbox_to_anchor=(0.5, -0.30),
ncol=2)
# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
ylim=(0.4, plot_y_max),
xticks=x,
xticklabels=labels,
yticks=np.linspace(0.4, plot_y_max, 5))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=1,
y_minor_per_major=4,
labelsize=12)
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax,
maintitle='Nino3.4 Monthly Standard Deviation',
maintitlefontsize=16,
ylabel="(" + u'\N{DEGREE SIGN}' + "C)")
# Show the plot
plt.tight_layout()
plt.show()
def plot_labelled_filled_contours(data, ax=None, label=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
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 and make them semitransparent for plot legibility
ax.coastlines(linewidth=0.5, alpha=0.75)
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xticks=np.arange(-180, 181, 30),
yticks=np.arange(-90, 91, 30))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=8)
# 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)
# Remove degree symbol from tick labels
ax.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
ax.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
# 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)
# Add a label in the upper left of the axes
ax.text(0.025,
0.9,
label,
transform=ax.transAxes,
bbox=dict(boxstyle='square, pad=0.25', facecolor='white'))
return handles
gvutil.add_major_minor_ticks(axs[1])
# Use geocat.viz.util convenience function to make plots look like NCL plots by
# using latitude, longitude tick labels
gvutil.add_lat_lon_ticklabels(axs[0])
gvutil.add_lat_lon_ticklabels(axs[1])
# Remove the degree symbol from tick labels
axs[0].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
axs[0].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
axs[1].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
axs[1].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(axs[0],
lefttitle='Speed',
lefttitlefontsize=10,
righttitle=U.units,
righttitlefontsize=10)
gvutil.set_titles_and_labels(axs[1],
lefttitle='Wind',
lefttitlefontsize=10,
righttitle=U.units,
righttitlefontsize=10)
# Load in colormap
newcmap = gvcmaps.gui_default
# Specify contour levels and contour ticks
speed_levels = np.arange(0, 40, 2.5)
speed_ticks = np.arange(2.5, 37.5, 2.5)
wind_levels = np.arange(-16, 44, 4)
xticks=np.linspace(-180, 180, 13),
yticks=np.linspace(-60, 60, 5))
# Use geocat.viz.util convenience function to make latitude, longitude tick labels
gvutil.add_lat_lon_ticklabels(ax)
# Removing degree symbol from tick labels to more closely resemble NCL example
ax.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
ax.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=12)
# Use geocat.viz.util convenience function to add titles
gvutil.set_titles_and_labels(ax,
lefttitle=temp.long_name,
righttitle=temp.units,
lefttitlefontsize=14,
righttitlefontsize=14)
# Add lower text box
ax.text(1,
-0.15,
"CONTOUR FROM -5 TO 30 BY 5",
horizontalalignment='right',
transform=ax.transAxes,
bbox=dict(boxstyle='square, pad=0.25',
facecolor='white',
edgecolor='black'))
# Specify which contour levels to draw
contour_lev = np.arange(-5, 35, 5)
plt.figure(figsize=(6, 6))
ax = plt.gca()
# Plot original data
# Note that the s parameter sets the size of the markers in pts
plt.scatter(x, y, color='red', s=4)
# Plot regression
plt.plot(x_regress, y_regress, color='black', linewidth=0.5)
# specify X and Y axis limits
plt.xlim([6000, 9000])
plt.ylim([266, 274])
# Use geocat.viz utility functions to add a main title
gvutil.set_titles_and_labels(ax=ax, maintitle="Regression 1")
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=4,
labelsize=12)
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(ax,
xlim=(6000, 9000),
xticks=np.arange(6000, 9001, 500),
ylim=(266, 274),
yticks=np.arange(266, 275, 2))
# Show plot
cbar.ax.set_yticklabels([str(i) for i in np.arange(0, 32, 2)])
# Usa geocat.viz.util convenience function to set axes parameters without calling several matplotlib functions
# Set axes limits, and tick values
gvutil.set_axes_limits_and_ticks(ax,
xlim=(30, 120),
ylim=(-60, 30),
xticks=np.linspace(-180, 180, 13),
yticks=np.linspace(-90, 90, 7))
# 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 minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=12)
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(
ax,
maintitle="30-degree major and 10-degree minor ticks",
maintitlefontsize=16,
lefttitle="Potential Temperature",
lefttitlefontsize=14,
righttitle="Celsius",
righttitlefontsize=14,
xlabel="",
ylabel="")
# Show the plot
plt.show()
# 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)
# Remove the degree symbol from tick labels
ax.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
ax.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, x_minor_per_major=3, y_minor_per_major=5,
labelsize=12)
# Remove ticks on right side
ax.tick_params(which='both', right=False)
# Use geocat.viz.util convenience function to add title
gvutil.set_titles_and_labels(ax, maintitle="Explanation of Python contour levels")
# Create labels by colorbar
size = 8
y = 1 / num_lev / 2 # Offset from x axis in axes coordinates
ax.text(0.949, y, 'T < 248', fontsize=size, horizontalalignment='center',
verticalalignment='center', transform=ax.transAxes,
bbox=dict(boxstyle='square, pad=0.25',
facecolor='papayawhip',
edgecolor='papayawhip'))
text = '{} <= T < {}'
for i in range(0, 14):
y = y + 1 / num_lev # Vertical spacing between the labels
ax.text(0.904, y, text.format(cbar_ticks[i], cbar_ticks[i + 1]),
fontsize=size, horizontalalignment='center',
verticalalignment='center', transform=ax.transAxes,
# labels
gvutil.add_lat_lon_ticklabels(ax)
# Removing degree symbol from tick labels to more closely resemble NCL example
ax.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
ax.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=4,
y_minor_per_major=5,
labelsize=12)
# Use geocat.viz.util convenience function to add titles
gvutil.set_titles_and_labels(
ax,
maintitlefontsize=16,
maintitle=
"Dummy station data colored and\nsized according to range of values")
# Plot markers with values less than first bin value
masked_lon = np.where(r < bins[0], lon, np.nan)
masked_lat = np.where(r < bins[0], lat, np.nan)
label = "x < " + str(bins[0])
plt.scatter(masked_lon,
masked_lat,
label=label,
s=sizes[0],
color=colors[0],
zorder=1)
# Plot all other markers but those in the last bin
ax = plt.gca()
# Plot data
plt.plot(ds.data, ds.lev)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, x_minor_per_major=5, y_minor_per_major=4)
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(ax,
ylim=(1000, 0),
xticks=np.arange(-10, 30, 5))
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax,
maintitle="Profile Plot",
xlabel=ds.long_name,
ylabel=ds['lev'].long_name)
plt.show()
###############################################################################
# Plot:
# Generate figure (set its size (width, height) in inches) and axes
plt.figure(figsize=(8, 8))
ax = plt.gca()
# Plot data with custom line characterisitcs
# Use keyword `color` to change the line color
# Use keyword `linewidth` to change the line thickness
# Use keyword `dashes` to create a custom dash pattern
verticalalignment='center',
transform=ccrs.Geodetic())
# Contour-plot U-data
p = wrap_U.plot.contour(ax=ax,
vmin=-8,
vmax=16,
transform=ccrs.PlateCarree(),
levels=np.arange(-12, 44, 4),
linewidths=0.5,
cmap='black',
add_labels=False)
ax.clabel(p, np.arange(-8, 17, 8), fmt='%d', inline=1, fontsize=14)
# Use geocat.viz.util convenience function to add left and right titles
gvutil.set_titles_and_labels(ax, lefttitle="Zonal Wind", righttitle="m/s")
# Add lower text box
ax.text(1.0,
-.10,
"CONTOUR FROM -12 TO 40 BY 4",
horizontalalignment='right',
transform=ax.transAxes,
bbox=dict(boxstyle='square, pad=0.25',
facecolor='white',
edgecolor='black'))
# Show the plot
plt.show()
'4',
labelpos='N',
color='black',
coordinates='axes',
fontproperties={'size': 14},
labelsep=0.1)
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xlim=(65, 95),
ylim=(5, 25),
xticks=range(70, 95, 10),
yticks=range(5, 27, 5))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=5,
labelsize=14)
# 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 titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax,
lefttitle='Sea Surface Temperature',
righttitle='C')
# Show the plot
plt.show()
ax.set_global()
ax.coastlines(linewidths=0.5)
# Plot data and add a colorbar
temp = wrap_t.plot.contourf(
ax=ax,
transform=ccrs.PlateCarree(),
levels=11,
cmap='coolwarm',
add_colorbar=False)
cbar_ticks = np.arange(210, 311, 10)
cbar = plt.colorbar(temp,
orientation='horizontal',
shrink=0.75,
pad=0.05,
extendrect=True,
ticks=cbar_ticks)
cbar.ax.tick_params(labelsize=10)
# Use geocat.viz.util convenience function to add titles to left and right
# of the plot axis.
gvutil.set_titles_and_labels(ax,
maintitle="Example of Orthogonal Projection",
lefttitle="Surface Temperature",
righttitle="K")
# Show the plot
plt.show()
patch = mpatches.Polygon(shape.points,
facecolor=color,
edgecolor='black',
linewidth=0.5,
transform=ccrs.PlateCarree(),
zorder=2)
ax.add_patch(patch)
# Create colorbar
plt.colorbar(cm.ScalarMappable(cmap=colormap, norm=norm),
ax=ax,
boundaries=colorbounds,
orientation='horizontal',
shrink=0.75,
ticks=[1, 2, 3, 4],
label='percent',
aspect=30,
pad=0.075)
# Add latitude and longitude labels
gl = ax.gridlines(draw_labels=True, x_inline=False, y_inline=False)
gl.xlocator = mticker.FixedLocator(np.linspace(-120, -80, 5))
gl.ylocator = mticker.FixedLocator(np.linspace(25, 45, 5))
gl.xlabel_style = {'rotation': 0}
gl.ylabel_style = {'rotation': 0}
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax, maintitle='Percentage unemployment, by state')
plt.show()
###############################################################################
# Plot
# Generate figure (set its size (width, height) in inches) and axes
plt.figure(figsize=(12, 6))
ax = plt.axes()
# Create a list of colors based on the color bar values
colors = ['red' if (value > 0) else 'blue' for value in dsoik[::8]]
plt.bar(date_frac[::8], dsoik[::8], align='edge', edgecolor='black',
color=colors, width=8/12, linewidth=.6)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, x_minor_per_major=4, y_minor_per_major=5,
labelsize=20)
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(ax, ylim=(-3, 3),
yticks=np.linspace(-3, 3, 7),
yticklabels=np.linspace(-3, 3, 7),
xlim=(date_frac[40], date_frac[-16]),
xticks=np.linspace(1900, 1980, 5))
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax, maintitle="Darwin Southern Oscillation Index",
ylabel='Anomalies', maintitlefontsize=28,
labelfontsize=20)
plt.show()
