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 map_close_up(ax):
ax=map_china(ax)
ax.set_extent([70,140,25,60], crs=ccrs.PlateCarree())
ax.set_xticks([70,80,90,100,110,120,130, 140], crs=ccrs.PlateCarree())
ax.set_yticks([25,30,35,40,45,50,55, 60], crs=ccrs.PlateCarree())
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
def map_large_scale(ax):
ax.set_extent([0,180,0,90], crs=ccrs.PlateCarree())
ax.set_xticks([0,20,40,60,80,100,120,140,160,180], crs=ccrs.PlateCarree())
ax.set_yticks([0,30,60,90], crs=ccrs.PlateCarree())
lon_formatter = LongitudeFormatter(zero_direction_label=True, auto_hide=False)
lat_formatter = LatitudeFormatter(auto_hide=False)
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.yaxis.set_minor_locator( AutoMinorLocator(3))
ax.xaxis.set_minor_locator( AutoMinorLocator(2))
ax.coastlines()
def plot_german_data(lon,
lat,
adata,
pos=[],
plottitle='',
mycmap='RdBu_r',
axlims=[]):
"""
plot map of annual mean temperature
Parameters
----------
lon : 2D array of longitude
lat : 2D array of latitute
adata : 2D array of temperature
pos : position for a single marker, optional
plottitle : title of figure, optional
mycmap : colormap, optional
axlims : 1x2 array of colobar axis limits, optional
Returns
-------
fig : figure handle
"""
states_provinces = cfeature.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='10m',
facecolor='none')
fig = plt.figure(figsize=[10.5, 8])
ax = plt.subplot(projection=ccrs.PlateCarree())
da = plt.scatter(lon, lat, c=adata, marker='.', cmap=mycmap)
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_aspect('equal', 'box')
if len(pos) != 0:
plt.plot(pos[1], pos[0], marker="o")
if len(plottitle) != 0:
ax.set_title(plottitle, fontsize=12)
lonLabels = np.arange(0, 18, 3)
latLabels = np.arange(45, 60, 3)
gl = ax.gridlines(draw_labels=True, color='k', linewidth=1.5)
gl.xlocator = mticker.FixedLocator(lonLabels)
gl.ylocator = mticker.FixedLocator(latLabels)
gl.xformatter = LongitudeFormatter()
gl.yformatter = LatitudeFormatter()
cbar = plt.colorbar(da, pad=0.07)
cbar.set_label('°C', rotation=90)
if len(axlims) != 0:
da.set_clim(axlims)
plt.show()
return fig
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,
righttitle=V.units,
lefttitlefontsize=12,
righttitlefontsize=12)
# Import an NCL colormap
cmap = gvcmaps.wgne15
# Specify which contour levels to draw
contour_lev = np.arange(-20, 28, 4)
# Plot filled contour
def Plot(color, ext, xext, yext, npts, title, subt, style, pt):
"""
Helper function to create two similar plots where color, extent, title,
line style, and marker style can all be customized on the same style
map projection.
Args:
color (:class: 'str'):
color for line on map in format 'color'
ext (:class: 'list'):
extent of the projection view in format [minlon, maxlon, minlat, maxlat]
xext (:class: 'list'):
start and stop points for curve in format [startlon, stoplon]
yext (:class: 'list'):
start and stop points for curve in format [startlat, stoplat]
title (:class: 'str'):
title of graph in format "Title"
style (:class: 'str'):
line style in format 'style'
pt (:class: 'str'):
marker type in format 'type'
"""
plt.figure(figsize=(8, 8))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(ext, ccrs.PlateCarree())
ax.add_feature(cfeature.LAND, color="lightgrey")
# This gets geodesic between the two points
# WGS84 ellipsoid is used
# yext and xext refer to the start and stop points for the curve
# [0] being start, [1] being stop
gl = Geodesic.WGS84.InverseLine(yext[0], xext[0], yext[1], xext[1])
npoints = npts
# Compute points on the geodesic, and plot them
# gl.s13 is the total length of the geodesic
# the points are equally spaced by 'true distance', but visually
# there is a slight distortion due to curvature/projection style
lons = []
lats = []
for ea in np.linspace(0, gl.s13, npoints):
g = gl.Position(ea, Geodesic.STANDARD | Geodesic.LONG_UNROLL)
lon2 = g["lon2"]
lat2 = g["lat2"]
lons.append(lon2)
lats.append(lat2)
plt.plot(lons, lats, style, color=color, transform=ccrs.Geodetic())
ax.plot(lons, lats, pt, transform=ccrs.PlateCarree())
plt.suptitle(title, y=0.90, fontsize=16)
# 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=(-125, -60),
ylim=(15, 65),
xticks=np.linspace(-180, 180, 13),
yticks=np.linspace(0, 80, 5),
)
# 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, 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=subt, maintitlefontsize=12, xlabel="", ylabel=""
)
plt.show()
def plot_regression(varin,lon,lat,cint=[0],ax=None,bbox=[-90,40,0,80],latlonlab=True,cbar=True,cbloc='horizontal'):
"""
# Create regression plots for NAO and related EOFs (SLP)
Inputs
1) N (int) - PC index
2) e (int) - ensemble index
3) m (int) - month index
4) varin (array: ens x mon x lat x lon x pc) - Regression Pattern
5) lon (array: lon) - longitudes
6) lat (array: lat) - latitudes
7) varexp (array: ens x mon x pc) - Variance explained
-OPTIONAL-
8) cint (array: levels) - contour intervals
9) ax (geoaxes object) - axis to plot on
10) bbox (array: lonW,lonE,latS,latN) - plot extent
11) latlonlab (bool) - True to include lat/lon labels
12) cbar (bool) - True to include colorbar
"""
# Get current axis if none is assigned
if ax is None:
ax = plt.gca()
# Set colormap
cmap = cmocean.cm.balance
cmap.set_under(color='yellow')
#Set Plotting boundaries
ax.set_extent(bbox)
# Add cyclic point to remove blank meridian
var1,lon1 = add_cyclic_point(varin,lon)
# Add filled coastline
ax.add_feature(cfeature.COASTLINE,linewidth=0.75,linestyle=":")
# Add contours
if len(cint) == 1:
# Plot without specificing range
cs = ax.contourf(lon1,lat,var1,cmap=cmap,extend="both",transform=ccrs.PlateCarree())
else:
cs = ax.contourf(lon1,lat,var1,cint,cmap=cmap,extend="both",transform=ccrs.PlateCarree())
# Negative contours
cln = ax.contour(lon1,lat,var1,
cint[cint<0],
linestyles='dashed',
colors='k',
linewidths=1,
transform=ccrs.PlateCarree())
# Positive Contours
clp = ax.contour(lon1,lat,var1,
cint[cint>=0],
colors='k',
linewidths=1,
transform=ccrs.PlateCarree())
# Add Label
plt.clabel(cln,fmt='%.1f',fontsize=8)
plt.clabel(clp,fmt='%.1f',fontsize=8)
# Add Gridlines
gl = ax.gridlines(draw_labels=True,linewidth=0.5,color='black',linestyle=':')
if latlonlab is True:
gl.top_labels = gl.right_labels = False
gl.xformatter = LongitudeFormatter(degree_symbol='')
gl.yformatter = LatitudeFormatter(degree_symbol='')
else:
gl.top_labels = gl.right_labels = gl.left_labels = gl_bottom_labels=False
# Create colorbar
if cbar == True:
plt.colorbar(cs,ax=ax,fraction=0.046,pad=0.10,orientation=cbloc)
#ax.set_title("PC%i Regression ENS %s Mon%i \n Variance Explained: %03d" %(N+1,estring,m+1,varexp*100)+r"%",fontsize=16)
return ax
ds = (snm2 - snm1) * filter_coeff
# Love numbers
kn = pgfl.LoveNumbers(deg_max)
# Legendre functions
lf = pgfl.LegendreFunctions(deg_max)
if __name__ == '__main__':
# calculations
w = ewh_calc()
# Plot of result
fig = plt.figure(figsize=[15, 8])
ax = plt.subplot(projection=ccrs.PlateCarree())
imgextend = [-180, 180, -90, 90]
t = ax.imshow(w,
origin='upper',
extent=imgextend,
transform=ccrs.PlateCarree(),
cmap='RdBu')
ax.coastlines('110m') # 50m is also available
lonLabels = np.arange(-180, 181, 60)
latLabels = np.arange(-90, 91, 30)
gl = ax.gridlines(draw_labels=True, color='k', linewidth=1.5)
gl.xlocator = mticker.FixedLocator(lonLabels)
gl.ylocator = mticker.FixedLocator(latLabels)
gl.xformatter = LongitudeFormatter()
gl.yformatter = LatitudeFormatter()
cbar = plt.colorbar(t)
cbar.set_label('EWH / m', rotation=90)
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