def format_contour_axes(ax):
"""
Format the axes limits, tick marks, and tick labels for the contour plots
Args:
ax (:class: 'matplotlib.Axes'):
The set of axes to be manipulated
"""
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax=ax,
xlim=(-180, 180),
ylim=(-90, 90),
xticks=np.arange(-180, 181, 30),
yticks=np.arange(-90, 91, 30))
# Use geocat.viz.util convenience function to add minor and major ticks
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 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 set titles and labels
gvutil.set_titles_and_labels(ax, maintitle='300mb', maintitlefontsize=8)
def make_bar_plot(ax, dataset):
years = list(dataset.time.dt.year)
values = list(dataset.values)
colors = ['blue' if val < 0 else 'red' for val in values]
ax.bar(years,
values,
color=colors,
width=1.0,
edgecolor='black',
linewidth=0.5)
ax.set_ylabel('Pa')
# 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=8)
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xticks=np.linspace(1980, 2000, 6),
xlim=[1978.5, 2003.5])
return ax
def __init__(self, *args, **kwargs):
# Set class defaults
self._default_height = 8
self._default_width = 10
# TODO: address all input arguments and run checks
# Pull out title arguments
self.maintitle = kwargs.get('maintitle')
self.lefttitle = kwargs.get('lefttitle')
self.righttitle = kwargs.get('righttitle')
# Pull out axes info
self.xlim = kwargs.get('xlim')
self.ylim = kwargs.get('ylim')
# Make sure x and y limits specified (for now)
#TODO: make x and y limits self-determinable somehow
if self.xlim is None or self.ylim is None:
raise AttributeError(
"For now, xlim and ylim must be specified as kwargs")
# Pull out tick arguments if specified
self.xticks = kwargs.get('xticks')
self.yticks = kwargs.get('yticks')
# Pull out axes label arguments
self.xlabel = kwargs.get('xlabel')
self.ylabel = kwargs.get('ylabel')
# pull out colorbar arguments
self.colorbar = kwargs.get('colorbar')
# Set up figure
self._set_up_fig()
# Set up axes with projection if specified
if kwargs.get('projection') is not None:
self.projection = kwargs.get('projection')
self.ax = plt.axes(projection=self.projection)
self.ax.coastlines(linewidths=0.5, alpha=0.6)
else:
self.ax = plt.axes()
# TODO: un-hardcode
set_axes_limits_and_ticks(self.ax,
xlim=self.xlim,
ylim=self.ylim,
xticks=self.xticks,
yticks=self.yticks)
# Set specified features
if kwargs.get('show_land') is True:
self.show_land()
if kwargs.get('show_coastline') is True:
self.show_coastline()
if kwargs.get('show_lakes') is True:
self.show_lakes()
def radar_plot(X, Y, values, bg_color=None):
# Create a figure and axes using subplots
fig, ax = plt.subplots(figsize=(6, 8))
# Choose default colormap
cmap = gvcmaps.gui_default
# Plot using contourf
p = plt.contourf(X,
Y,
values,
cmap=cmap,
levels=np.arange(-20, 70, 5) * 100,
zorder=3)
# Change orientation and tick marks of colorbar
plt.colorbar(p,
orientation="horizontal",
ticks=np.arange(-15, 65, 15) * 100,
drawedges=True,
aspect=12)
# 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=ds.DZ.long_name,
lefttitlefontsize=16,
righttitle=ds.DZ.units,
righttitlefontsize=16,
xlabel="",
ylabel="")
# Use geocat.viz.util convenience function to set axes limits & tick values
gvutil.set_axes_limits_and_ticks(ax,
xlim=(-240, 240),
ylim=(-240, 240),
xticks=np.arange(-200, 201, 100),
yticks=np.arange(-200, 201, 100))
# Use geocat.viz.util convenience function to set tick placements
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=5,
labelsize=14)
# Set aspect ratio
ax.set_aspect('equal')
# Allow optional background circle to be set
if (bg_color is not None):
circle_bg = plt.Circle((0, 0), 240, color=bg_color, zorder=1)
ax.add_artist(circle_bg)
# Show plot
plt.show()
def Plot(color, row, col, pos, title):
# Generate axes, using Cartopy, drawing coastlines, and adding features
projection = ccrs.PlateCarree()
ax1 = plt.subplot(row, col, pos, projection=projection)
ax1.coastlines(linewidths=0.5)
ax1.add_feature(cfeature.LAND, facecolor="lightgray")
# Import an NCL colormap
newcmp = color
# Contourf-plot data
hgt = t.plot.contourf(ax=ax1,
transform=projection,
levels=40,
vmin=100,
vmax=1600,
cmap=newcmp,
add_colorbar=False)
# Add color bar
cbar_ticks = np.arange(100, 1600, 100)
cbar = plt.colorbar(hgt,
orientation='vertical',
shrink=0.8,
pad=0.05,
extendrect=True,
ticks=cbar_ticks)
cbar.ax.tick_params(labelsize=10)
# Use 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(ax1,
xlim=(0, 90),
ylim=(0, 90),
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(ax1)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax1, labelsize=12)
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax1,
maintitle=title,
maintitlefontsize=16,
righttitlefontsize=14,
xlabel="",
ylabel="")
def make_contour_plot(ax, dataset):
lat = dataset['lat']
lon = dataset['lon']
values = dataset.data
# Import an NCL colormap
cmap = gvcmaps.BlWhRe
# Specify contour levelstamam
v = np.linspace(-0.08, 0.08, 9, endpoint=True)
# The function contourf() produces fill colors, and contour() calculates contour label locations.
cplot = ax.contourf(lon,
lat,
values,
levels=v,
cmap=cmap,
extend="both",
transform=ccrs.PlateCarree())
p = ax.contour(lon,
lat,
values,
levels=v,
linewidths=0.0,
transform=ccrs.PlateCarree())
# Label the contours
ax.clabel(p, fontsize=8, fmt="%0.2f", colors="black")
# 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,
x_minor_per_major=3,
y_minor_per_major=4,
labelsize=10)
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xticks=[-60, -30, 0, 30],
yticks=[40, 60, 80])
# Use geocat.viz.util convenience function to make plots look like NCL plots, using latitude & longitude tick labels
gvutil.add_lat_lon_ticklabels(ax)
return cplot, ax
def Plot(color, row, col, pos, title):
# Generate axes, using Cartopy, drawing coastlines, and adding features
projection = ccrs.PlateCarree()
ax1 = plt.subplot(row, col, pos, projection=projection)
ax1.coastlines(linewidths=0.5)
ax1.add_feature(cfeature.LAND, facecolor="lightgray")
# Import an NCL colormap
newcmp = color #gvcmaps.BlAqGrYeOrRe
# Contourf-plot data
t.plot.contourf(ax=ax1,
transform=projection,
levels=40,
vmin=100,
vmax=1600,
cmap=newcmp,
cbar_kwargs={
"orientation": "vertical",
"ticks": np.arange(100, 1600, 100),
"label": "",
"shrink": 0.8
})
# Use 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(ax1,
xlim=(0, 90),
ylim=(0, 90),
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(ax1)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax1, labelsize=12)
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax1,
maintitle=title,
maintitlefontsize=16,
righttitlefontsize=14,
xlabel="",
ylabel="")
def Plot(color, row, col, pos, title):
# Generate axes, using Cartopy, drawing coastlines, and adding features
projection = ccrs.PlateCarree()
ax1 = plt.subplot(row, col, pos, projection=projection)
ax1.coastlines(linewidths=0.5)
ax1.add_feature(cfeature.LAND, facecolor="lightgray")
# Import an NCL colormap
newcmp = color
# Contourf-plot data
t.plot.contourf(
ax=ax1,
transform=projection,
levels=40,
vmin=0,
vmax=32,
cmap=newcmp,
cbar_kwargs={
"orientation": "vertical",
"extendrect": True,
"ticks": np.arange(0, 32, 2),
"label": "",
"shrink": 0.8})
# Use 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(
ax1,
xlim=(30, 120),
ylim=(-60, 30))
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(
ax1,
maintitle=title,
maintitlefontsize=14,
xlabel="",
ylabel="")
def Plot(color, row, col, pos, title):
# Generate axes, using Cartopy, drawing coastlines, and adding features
projection = ccrs.PlateCarree()
ax1 = plt.subplot(row, col, pos, projection=projection)
ax1.coastlines(linewidths=0.5)
ax1.add_feature(cfeature.LAND, facecolor="lightgray")
# Import an NCL colormap
newcmp = color
# Contourf-plot data
temp = t.plot.contourf(ax=ax1,
transform=projection,
levels=40,
vmin=0,
vmax=32,
cmap=newcmp,
add_colorbar=False)
# Add color bar
cbar_ticks = np.arange(0, 32, 2)
cbar = plt.colorbar(temp,
orientation='vertical',
shrink=0.8, pad=0.05,
extendrect=True,
ticks=cbar_ticks)
cbar.ax.tick_params(labelsize=10)
# Use 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(ax1, xlim=(30, 120), ylim=(-60, 30))
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax1,
maintitle=title,
maintitlefontsize=14,
xlabel="",
ylabel="")
def format_linegraph_axes(ax):
"""
Format the axes limits, tick marks, and tick labels for the line graphs
Args:
ax (:class: 'matplotlib.Axes'):
The set of axes to be manipulated
"""
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(
ax=ax,
xlim=(-90, 90),
ylim=(-20, 50),
xticks=np.arange(-90, 91, 30),
yticks=np.arange(-20, 51, 10),
xticklabels=['90S', '60S', '30S', '0', '30N', '60N', '90N'])
# Use geocat.viz.util convenience function to add minor and major ticks
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=3,
y_minor_per_major=5,
labelsize=12)
def make_shared_plot(title):
# Generate figure (set its size (width, height) in inches) and axes using Cartopy projection
plt.figure(figsize=(10, 5.5))
ax = plt.axes(projection=ccrs.PlateCarree())
# 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=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 set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
xlim=(-125, -70),
ylim=(25, 50),
xticks=range(-120, -75, 20),
yticks=range(30, 51, 10))
# Turn on continent shading
ax.add_feature(cartopy.feature.LAND,
edgecolor='lightgray',
facecolor='lightgray',
zorder=0)
ax.add_feature(cartopy.feature.LAKES,
edgecolor='white',
facecolor='white',
zorder=0)
# Scatter-plot the location data on the map
scatter = plt.scatter(lon, lat, c=dummy_data, cmap=cmap, zorder=1)
plt.title(title, fontsize=16, y=1.04)
return scatter, ax
# Add a text box to indicate the pressure level
props = dict(facecolor='white', edgecolor='none', alpha=0.8)
ax.text(105,
52.7,
'500hPa',
transform=projection,
fontsize=18,
ha='center',
va='center',
color='mediumorchid',
bbox=props)
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xticks=[100, 120, 140],
yticks=[20, 30, 40, 50])
# 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,
x_minor_per_major=4,
y_minor_per_major=5,
labelsize=18)
# 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="Temp",
lefttitlefontsize=20,
# Plot with linear y axis:
# Generate figure (set its size (width, height) in inches) and axes
plt.figure(figsize=(8, 8))
ax = plt.axes()
# 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 axes parameters
gvutil.set_axes_limits_and_ticks(ax,
xlim=(-20, 40),
ylim=(1000, 0),
xticks=np.arange(-20, 60, 10),
yticks=np.arange(0, 1200, 200))
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax,
maintitle='Profile Plot',
xlabel=U.long_name,
ylabel=U['lev'].long_name)
# Add reference line x=0
ax.axvline(x=0, color='black', linewidth=0.5)
# Plot data
plt.plot(U20.data,
U20.lev,
# Plot curves that bound the region to be colored
plt.plot(TS.lat, top, color='SlateBlue')
plt.plot(TS.lat, bottom, color='SlateBlue')
# Fill the area between the bounds
ax.fill_between(TS.lat, top, bottom, color='SlateBlue')
# 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=4,
labelsize=14)
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(
ax,
ylim=(220, 320),
xlim=(-90, 90),
xticks=np.arange(-90, 91, 30),
xticklabels=['90S', '60S', '30S', '0', '30N', '60N', '90N'])
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(
ax,
maintitle="A Title with $\\eta\epsilon\lambda\\alpha\sigma$ Characters",
ylabel=TS.long_name)
# Draw sigma on axes
ax.text(0.15, 0.15, "$\sigma$", fontsize=40, transform=ax.transAxes)
plt.show()
data2 = generate_2d_array((ny, nx), 10, 10, -28., 15., 1)
data3 = generate_2d_array((ny, nx), 10, 10, -25., 18., 2)
###############################################################################
# Create figure and axes using gvutil
fig, axs = plt.subplots(1,
3,
figsize=(12, 6),
sharex='all',
sharey='all',
gridspec_kw={'wspace': 0})
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(axs[0],
xticks=np.arange(0, 120, 20),
yticks=np.arange(0, 120, 20),
xticklabels=np.arange(0, 100, 20),
yticklabels=np.arange(0, 100, 20))
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(axs[0], x_minor_per_major=4, y_minor_per_major=4)
# Specify which edges of the subplot should have tick lines
axs[0].tick_params(axis='both', which='both', left=True, right=False)
# Force subplot to be square
axs[0].set_aspect(aspect='equal')
# Repeat for other subplots with a few changes
gvutil.set_axes_limits_and_ticks(axs[1],
xticks=np.arange(0, 120, 20),
yticks=np.arange(0, 120, 20),
xticklabels=np.arange(0, 100, 20),
yticklabels=np.arange(0, 100, 20))
edgecolor='lightgray',
facecolor='lightgray',
zorder=0)
# Draw the key for the quiver plot as a rectangle patch
ax.add_patch(
plt.Rectangle((155, 65),
25,
25,
facecolor='white',
edgecolor='black',
zorder=1))
# Use geocat.viz.util convenience function to set axes tick values
gvutil.set_axes_limits_and_ticks(ax,
xticks=range(-180, 181, 30),
yticks=range(-90, 91, 30))
# 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 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=ds['U'].long_name,
righttitle=ds['U'].units)
# Add timestamp
ax.text(-200, -115, f'Created: {datetime.now()}')
xdata, ydata = np.meshgrid(xlist, ylist)
zdata = np.random.normal(0, 3.0, size=(31, 31))
###############################################################################
# Create figure
plt.figure(figsize=(10, 10))
# Create axes
ax = plt.axes()
# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
xlim=(0, 30),
ylim=(0, 30),
xticks=None,
yticks=None,
xticklabels=None,
yticklabels=None)
# Use geocat.viz.util to add major and minor tics
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=5,
y_minor_per_major=5,
labelsize=18)
# Use geocat.viz.util convenience function to add titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax, ylabel="wave number", labelfontsize=24)
# Set ticks and labels only on left and top of plot
ax.xaxis.tick_top()
# Set 0 level contour line to a thicker linewidth
# If you try to access the "levels" attribute of cs (cs.levels),
# the list of levels is: [-6, -4, -2, 0, 2, 4, 6, 8, 10]
# level 0 is at the 3rd index of that list, so those contour lines
# can be accessed at cs.collections[3]
cs.collections[3].set_linewidth(1)
# Label the contour levels -4, 0, and 4
cl = ax.clabel(cs, fmt='%d', levels=[-4, 0, 4])
# Use geocat.viz.util convenience function to set axes limits & tick values
gvutil.set_axes_limits_and_ticks(ax,
xlim=[100, 220],
ylim=[0, 1.55 * 1e16],
xticks=[135, 180],
yticks=np.linspace(0, 1.55 * 1e16, 7),
xticklabels=['135E', '180'],
yticklabels=np.linspace(0, 180, 7,
dtype='int'))
# 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=16)
# Use geocat.viz.util convenience function to add titles
gvutil.set_titles_and_labels(ax,
maintitle="Pacific Region",
maintitlefontsize=20,
lefttitle="Velocity Potential",
'Nov', 'Dec'
]
###############################################################################
# Plot:
fig, axs = plt.subplots(2, 2, figsize=(12, 8), gridspec_kw=dict(wspace=0.25))
x = np.arange(len(months)) # where to draw x ticks
width = 0.2 # width of each bar within the groups
# Create the subplots using a loop
panel = 0
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)
U = ds.U
###############################################################################
# Plot:
# Generate figure (set its size (width, height) in inches) and axes
plt.figure(figsize=(7, 6.5))
ax = plt.gca()
# Plot slices of data
U.isel(time=0).sel(lon=82, method='nearest').plot(x="lat", marker='', color='#C0C2EA', linewidth=1.1)
U.isel(time=0).sel(lon=-69, method='nearest').plot(x="lat", marker='', color='#E28D90', linewidth=1.1, linestyle='--',
dashes=[6.5, 3.7])
# 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=16)
# Use geocat.viz.util convenience function to set axes parameters without calling several matplotlib functions
# Set axes limits, tick values, and tick labels to show latitude & longitude (i.e. North (N) - South (S))
gvutil.set_axes_limits_and_ticks(ax, xlim=(-90,90), ylim=(-20,50),
xticks=np.linspace(-90, 90, 7), yticks=np.linspace(-20, 50, 8),
xticklabels=['90S', '60S', '30S', '0', '30N', '60N', '90N'])
# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax, maintitle="Two Curve XY Plot", xlabel="", ylabel="Zonal Wind")
# Show the plot
plt.tight_layout()
plt.show()
widths=[w, w, w],
showfliers=False)
# Set whiskers style to dashed
plt.setp(boxplots['whiskers'], linestyle='--')
# Set median line to black
plt.setp(boxplots['medians'], color='black')
# Remove axis lines on top and right sides
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# 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='Default Box Plot')
# Make both major and minor ticks point inwards towards the plot
ax.tick_params(direction="in", which='both')
# Set ticks only at left and bottom sides of plot
# 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
plt.tight_layout()
plt.show()
# 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)
# Make axis invisible
axin.patch.set_alpha(0.0)
# Make ticks point inwards
axin.tick_params(axis="x", direction="in", length=8)
axin.tick_params(axis="y", direction="in", length=8, labelsize=9)
# Rotate the labels on the x axis so they are vertical
for tick in axin.get_xticklabels():
tick.set_rotation(90)
vmax=32,
cmap=newcmp,
add_colorbar=False)
# Add colorbar
cbar = plt.colorbar(heatmap, ticks=np.arange(0, 32, 2))
cbar.ax.set_yticklabels([str(i) for i in np.arange(0, 32, 2)])
# Adjust tick label size
ax.tick_params(labelsize=12)
# Use 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, 25),
yticks=np.linspace(-90, 90, 13))
# 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 set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax,
maintitle="15-degree major but no minor ticks",
maintitlefontsize=16,
lefttitle="Potential Temperature",
lefttitlefontsize=14,
righttitle="Celsius",
righttitlefontsize=14,
xlabel="",
plt.tick_params(which='major', length=10.0, width=0.5)
plt.tick_params(which='minor', length=5.0, width=0.25)
# Set the minor tick spacing for X and Y axes.
ax1.xaxis.set_minor_locator(MultipleLocator(1.25))
ax1.yaxis.set_minor_locator(MultipleLocator(0.5))
# Add a descriptive string to the top left corner of the plot.
ax1.text(0.01, 1.1, spacingString, transform=ax1.transAxes, fontWeight='bold')
# Plot data and set the X axis limits.
plt.plot(x_data, y_data, color='black', linewidth=0.5)
# Usa geocat.viz.util convenience function to set axes parameters without calling several matplotlib functions
# Set axes limits
gvutil.set_axes_limits_and_ticks(ax1, xlim=(1949, 2006), ylim=(-4.2, 4.2))
# Make a subplot with major ticks that are set to explicit values and minor ticks that are multiples of 1.
# Set the current plot context to the bottom subplot.
ax2 = plt.subplot(2, 1, 2)
# Format the tick labels.
# For the minor ticks, use no labels; defaults to NullFormatter.
ax2.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
# Draw ticks on all sides of the plot.
plt.tick_params(which='both', top=True, right=True)
# Increase the length of the tick marks.
V = gvutil.xr_add_cyclic_longitudes(V, "lon")
###############################################################################
# Plot:
# Generate figure (set its size (width, height) in inches)
fig = plt.figure(figsize=(10, 6))
# Generate axes using Cartopy and draw coastlines
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines(linewidths=0.5, alpha=0.6)
# Use geocat.viz.util convenience function to set axes limits & tick values
gvutil.set_axes_limits_and_ticks(ax,
xlim=(-180, 180),
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 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 titles
gvutil.set_titles_and_labels(ax,
lefttitle=V.long_name,
linewidths=0.5,
colors='k',
add_colorbar=False,
transform=projection,
extend='neither',
add_labels=False)
# Add horizontal colorbar
cbar = plt.colorbar(p, orientation='horizontal', shrink=0.5)
cbar.ax.tick_params(labelsize=16)
cbar.set_ticks(np.linspace(0, 9, 10))
# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
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,
################################################################################
# Plot:
# Generate figure (set its size (width, height) in inches) and axes (with two different y-axes)
fig, ax1 = plt.subplots(figsize=(7, 6.5))
# Plot data
ax1.plot(ds.time, ds.T, color="blue", linestyle="-", linewidth=0.9)
# Usa geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax1, x_minor_per_major=5, labelsize=14)
# 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(ax1,
xlim=(1970, 1973),
ylim=(0.0, 16.0),
yticks=np.arange(0, 17, 3))
# Usa geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax1,
maintitle="Curves Offset",
xlabel=ds.time.long_name,
ylabel=f"{ds.T.long_name} [solid]")
# Create second y-axis
ax2 = ax1.twinx()
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax2, x_minor_per_major=5, labelsize=14)
# Line-plot data
# `ncol` being equal to the number of labels makes it appear horizontal
legend = ax.legend(bbox_to_anchor=(-0.075, -0.2),
ncol=numBins,
loc='lower left',
columnspacing=0.5,
frameon=False)
for txt in legend.get_texts():
txt.set_ha("center") # horizontal alignment of text item
txt.set_va("center") # vertical alignment of text item
# Move label text so it is centered under the marker
txt.set_x(-25) # x-position
txt.set_y(-20) # y-position
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(ax,
xlim=(0, 300),
ylim=(0, 21),
xticks=range(0, 301, 50),
yticks=range(0, 22, 3))
# 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=3,
labelsize=14)
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax, maintitle="Scatter plot with grouped markers")
plt.show()
gavav_avg,
color='red',
label='Anthropogenic + Natural',
zorder=2)
ax.legend(loc='upper left', frameon=False, fontsize=18)
# 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=3,
labelsize=20)
# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
xlim=(1890, 2000),
ylim=(-0.4, 1),
xticks=np.arange(1900, 2001, step=20),
yticks=np.arange(-0.3, 1, step=0.3))
# Set three titles on top of each other using axes title and texts
ax.set_title('Parallel Climate Model Ensembles', fontsize=24, pad=60.0)
ax.text(0.5,
1.125,
'Global Temperature Anomalies',
fontsize=18,
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.5,
1.06,
'from 1890-1919 average',
