def test_scatter_longitude(self):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
iplt.scatter(self.lat_lon_cube,
self.lat_lon_cube.coord('longitude'), axes=ax)
plt.close(fig)
def main():
# Enable a future option, to ensure that the netcdf load works the same way
# as in future Iris versions.
iris.FUTURE.netcdf_promote = True
# Load the gridded temperature and salinity data.
fname = iris.sample_data_path('atlantic_profiles.nc')
cubes = iris.load(fname)
theta, = cubes.extract('sea_water_potential_temperature')
salinity, = cubes.extract('sea_water_practical_salinity')
# Extract profiles of temperature and salinity from a particular point in
# the southern portion of the domain, and limit the depth of the profile
# to 1000m.
lon_cons = iris.Constraint(longitude=330.5)
lat_cons = iris.Constraint(latitude=lambda l: -10 < l < -9)
depth_cons = iris.Constraint(depth=lambda d: d <= 1000)
theta_1000m = theta.extract(depth_cons & lon_cons & lat_cons)
salinity_1000m = salinity.extract(depth_cons & lon_cons & lat_cons)
# Plot these profiles on the same set of axes. In each case we call plot
# with two arguments, the cube followed by the depth coordinate. Putting
# them in this order places the depth coordinate on the y-axis.
# The first plot is in the default axes. We'll use the same color for the
# curve and its axes/tick labels.
plt.figure(figsize=(5, 6))
temperature_color = (.3, .4, .5)
ax1 = plt.gca()
iplt.plot(theta_1000m, theta_1000m.coord('depth'), linewidth=2,
color=temperature_color, alpha=.75)
ax1.set_xlabel('Potential Temperature / K', color=temperature_color)
ax1.set_ylabel('Depth / m')
for ticklabel in ax1.get_xticklabels():
ticklabel.set_color(temperature_color)
# To plot salinity in the same axes we use twiny(). We'll use a different
# color to identify salinity.
salinity_color = (.6, .1, .15)
ax2 = plt.gca().twiny()
iplt.plot(salinity_1000m, salinity_1000m.coord('depth'), linewidth=2,
color=salinity_color, alpha=.75)
ax2.set_xlabel('Salinity / PSU', color=salinity_color)
for ticklabel in ax2.get_xticklabels():
ticklabel.set_color(salinity_color)
plt.tight_layout()
iplt.show()
# Now plot a T-S diagram using scatter. We'll use all the profiles here,
# and each point will be coloured according to its depth.
plt.figure(figsize=(6, 6))
depth_values = theta.coord('depth').points
for s, t in iris.iterate.izip(salinity, theta, coords='depth'):
iplt.scatter(s, t, c=depth_values, marker='+', cmap='RdYlBu_r')
ax = plt.gca()
ax.set_xlabel('Salinity / PSU')
ax.set_ylabel('Potential Temperature / K')
cb = plt.colorbar(orientation='horizontal')
cb.set_label('Depth / m')
plt.tight_layout()
iplt.show()
def test_scatter_longitude(self):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
iplt.scatter(self.lat_lon_cube,
self.lat_lon_cube.coord('longitude'),
axes=ax)
plt.close(fig)
def test_yaxis_labels_with_axes(self):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_ylim(0, 3)
iplt.scatter(self.cube, self.cube.coord('str_coord'), axes=ax)
plt.close(fig)
self.assertPointsTickLabels('yaxis', ax)
def test_yaxis_labels_with_axes(self):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_ylim(0, 3)
iplt.scatter(self.cube, self.cube.coord('str_coord'), axes=ax)
plt.close(fig)
self.assertPointsTickLabels('yaxis', ax)
def test_scatter_with_c_kwarg_specified_mappable(self):
mappable_initial = scatter(self.traj_lon, self.traj_lat,
c=self.traj_lon.points)
mappable = scatter(self.traj_lon, self.traj_lat,
c=self.traj_lon.points,
cmap='cool')
cbar = plt.colorbar(mappable_initial)
self.assertIs(cbar.mappable, mappable_initial)
def test_scatter_with_c_kwarg_specified_mappable(self):
mappable_initial = scatter(self.traj_lon, self.traj_lat,
c=self.traj_lon.points)
mappable = scatter(self.traj_lon, self.traj_lat,
c=self.traj_lon.points,
cmap='cool')
cbar = plt.colorbar(mappable_initial)
self.assertIs(cbar.mappable, mappable_initial)
def scatter(x, y, *args, **kwargs):
"""
Draws a labelled scatter plot based on the given cubes or
coordinates.
See :func:`iris.plot.scatter` for details of valid arguments and
keyword arguments.
"""
result = iplt.scatter(x, y, *args, **kwargs)
_label_1d_plot(x, y)
return result
def main():
# Load the gridded temperature and salinity data.
fname = iris.sample_data_path('atlantic_profiles.nc')
cubes = iris.load(fname)
theta, = cubes.extract('sea_water_potential_temperature')
salinity, = cubes.extract('sea_water_practical_salinity')
# Extract profiles of temperature and salinity from a particular point in
# the southern portion of the domain, and limit the depth of the profile
# to 1000m.
lon_cons = iris.Constraint(longitude=330.5)
lat_cons = iris.Constraint(latitude=lambda l: -10 < l < -9)
depth_cons = iris.Constraint(depth=lambda d: d <= 1000)
theta_1000m = theta.extract(depth_cons & lon_cons & lat_cons)
salinity_1000m = salinity.extract(depth_cons & lon_cons & lat_cons)
# Plot these profiles on the same set of axes. In each case we call plot
# with two arguments, the cube followed by the depth coordinate. Putting
# them in this order places the depth coordinate on the y-axis.
# The first plot is in the default axes. We'll use the same color for the
# curve and its axes/tick labels.
fig = plt.figure(figsize=(5, 6))
temperature_color = (.3, .4, .5)
ax1 = plt.gca()
iplt.plot(theta_1000m,
theta_1000m.coord('depth'),
linewidth=2,
color=temperature_color,
alpha=.75)
ax1.set_xlabel('Potential Temperature / K', color=temperature_color)
ax1.set_ylabel('Depth / m')
for ticklabel in ax1.get_xticklabels():
ticklabel.set_color(temperature_color)
# To plot salinity in the same axes we use twiny(). We'll use a different
# color to identify salinity.
salinity_color = (.6, .1, .15)
ax2 = plt.gca().twiny()
iplt.plot(salinity_1000m,
salinity_1000m.coord('depth'),
linewidth=2,
color=salinity_color,
alpha=.75)
ax2.set_xlabel('Salinity / PSU', color=salinity_color)
for ticklabel in ax2.get_xticklabels():
ticklabel.set_color(salinity_color)
plt.tight_layout()
iplt.show()
# Now plot a T-S diagram using scatter. We'll use all the profiles here,
# and each point will be coloured according to its depth.
plt.figure(figsize=(6, 6))
depth_values = theta.coord('depth').points
for s, t in iris.iterate.izip(salinity, theta, coords='depth'):
iplt.scatter(s, t, c=depth_values, marker='+', cmap='RdYlBu_r')
ax = plt.gca()
ax.set_xlabel('Salinity / PSU')
ax.set_ylabel('Potential Temperature / K')
cb = plt.colorbar(orientation='horizontal')
cb.set_label('Depth / m')
plt.tight_layout()
iplt.show()
def test_yaxis_labels(self):
iplt.scatter(self.cube, self.cube.coord('str_coord'))
self.assertBoundsTickLabels('yaxis')
def test_scatter_with_c_kwarg(self):
mappable = scatter(self.traj_lon, self.traj_lat, c=self.traj_lon.points)
cbar = plt.colorbar()
self.assertIs(cbar.mappable, mappable)
def main():
# Load the gridded temperature and salinity data.
fname = iris.sample_data_path("atlantic_profiles.nc")
cubes = iris.load(fname)
(theta, ) = cubes.extract("sea_water_potential_temperature")
(salinity, ) = cubes.extract("sea_water_practical_salinity")
# Extract profiles of temperature and salinity from a particular point in
# the southern portion of the domain, and limit the depth of the profile
# to 1000m.
lon_cons = iris.Constraint(longitude=330.5)
lat_cons = iris.Constraint(latitude=lambda l: -10 < l < -9)
depth_cons = iris.Constraint(depth=lambda d: d <= 1000)
theta_1000m = theta.extract(depth_cons & lon_cons & lat_cons)
salinity_1000m = salinity.extract(depth_cons & lon_cons & lat_cons)
# Plot these profiles on the same set of axes. Depth is automatically
# recognised as a vertical coordinate and placed on the y-axis.
# The first plot is in the default axes. We'll use the same color for the
# curve and its axes/tick labels.
plt.figure(figsize=(5, 6))
temperature_color = (0.3, 0.4, 0.5)
ax1 = plt.gca()
iplt.plot(
theta_1000m,
linewidth=2,
color=temperature_color,
alpha=0.75,
)
ax1.set_xlabel("Potential Temperature / K", color=temperature_color)
ax1.set_ylabel("Depth / m")
for ticklabel in ax1.get_xticklabels():
ticklabel.set_color(temperature_color)
# To plot salinity in the same axes we use twiny(). We'll use a different
# color to identify salinity.
salinity_color = (0.6, 0.1, 0.15)
ax2 = plt.gca().twiny()
iplt.plot(
salinity_1000m,
linewidth=2,
color=salinity_color,
alpha=0.75,
)
ax2.set_xlabel("Salinity / PSU", color=salinity_color)
for ticklabel in ax2.get_xticklabels():
ticklabel.set_color(salinity_color)
plt.tight_layout()
iplt.show()
# Now plot a T-S diagram using scatter. We'll use all the profiles here,
# and each point will be coloured according to its depth.
plt.figure(figsize=(6, 6))
depth_values = theta.coord("depth").points
for s, t in iris.iterate.izip(salinity, theta, coords="depth"):
iplt.scatter(s, t, c=depth_values, marker="+", cmap="RdYlBu_r")
ax = plt.gca()
ax.set_xlabel("Salinity / PSU")
ax.set_ylabel("Potential Temperature / K")
cb = plt.colorbar(orientation="horizontal")
cb.set_label("Depth / m")
plt.tight_layout()
iplt.show()
def plot(self):
"""
Produce trajectory plot.
Returns fig object for further plotting if needed.
"""
if not self.lines:
raise ValueError("TrajectoryPlot: no lines have been added")
if self.fig is None:
if self.rsmc:
self.fig = plt.figure(figsize=[12, 6])
ax = plt.subplot2grid(
(3, 3), (0, 0),
rowspan=2,
colspan=2,
projection=ccrs.PlateCarree(central_longitude=self.clon))
elif self.annote:
self.fig = plt.figure(figsize=[12, 6])
ax = plt.subplot2grid(
(3, 3), (0, 0),
rowspan=2,
colspan=2,
projection=ccrs.PlateCarree(central_longitude=self.clon))
else:
self.fig = plt.figure(figsize=[7, 9])
ax = plt.subplot2grid(
(3, 1), (0, 0),
rowspan=2,
projection=ccrs.PlateCarree(central_longitude=self.clon))
ax = plt.gca()
for line in self.lines:
add_settings = {}
if 'add_settings' in line:
add_settings = line['add_settings']
style = {
'label': line['label'],
'color': line['colour'],
'linestyle': line['linestyle'],
'linewidth': line['linewidth'],
'marker': line['marker']
}
style2 = style.copy()
style2.update(add_settings)
iplt.plot(line['x'], line['y'], **style2)
# Add a black square at the trajectory start point
iplt.scatter(line['x'][0], line['y'][0], color='k', marker='s')
#Add title and axis labels
if self.title is not None:
ax.set_title(self.title)
# Set the extent
# Bug in Cartopy Dec'17 - Global extent will not be plotted with
# extent[0] = 0, extent[1] = 360 So the longitudinal extents are
# deliberately taken in by 0.1
if abs(self.extent[1] - self.extent[0]) > 330:
self.extent[0] = -179.9
self.extent[1] = 179.9
ax.set_extent(self.extent, crs=ccrs.PlateCarree())
# Determine extent of plotting region and use this to
# select an appropriate mapping zoom
if abs(self.extent[1] - self.extent[0]) < 15.0:
res = '10m'
elif abs(self.extent[1] - self.extent[0]) < 50.0:
res = '50m'
else:
res = '110m'
if self.mapping == 'countries' or self.mapping == 'states':
countries = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_0_countries_lakes',
scale=res,
facecolor='none')
if self.mapping == 'states':
states = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_shp',
scale=res,
facecolor='none')
if self.mapping == 'coastlines':
ax.coastlines(res)
elif self.mapping == 'countries':
ax.coastlines(res, zorder=3)
ax.add_feature(countries,
edgecolor='gray',
zorder=2,
linewidth=0.5)
elif self.mapping == 'states':
ax.coastlines(res, zorder=3)
ax.add_feature(countries, edgecolor='gray', zorder=2, linewidth=1)
ax.add_feature(states,
edgecolor='lightgray',
zorder=2,
linewidth=0.5)
elif self.mapping == 'wms':
# NOTE WMS mapping does not appear to work for extents
# greater than 130 degrees in either direction for a
# typical 6x6 sized map. For smaller maps, the useable
# WMS extents are smaller.
# It should also be noted that if the WMS map
# crosses 180E/W, if the northern or southern
# edge of the map is on the equator, this will
# result in the size of page and the placing of
# the map on the page being altered.
num_layers = np.linspace(0, 40, 41)
layers = ['{:.0f}'.format(x) for x in num_layers]
ax.add_wms(wms='http://exxdmmsprd01:6080/arcgis/services/DMMS/' +
'Global_NE_HC_Hybrid_Greyscale/MapServer/WMSServer',
layers=layers)
#Add gridlines
if self.gridlines:
try:
if self.extent[0] < 180 and self.extent[1] > 180:
xlocs, xlocs_extend = compute_grid_line_locs(self.extent)
ax.gridlines(xlocs=xlocs_extend)
gl = ax.gridlines(draw_labels=True,
xlocs=xlocs,
linewidth=0.001)
else:
gl = ax.gridlines(draw_labels=True,
linewidth=0.8,
alpha=0.9,
zorder=9)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
except:
gl = ax.gridlines()
# Add information about the release location and time if provided
if self.release_info is not None:
release_text = 'Release location: {}, {},\n'.format(
self.release_info[0], self.release_info[1])
release_text += 'Release time: {}'.format(self.release_info[2])
ax.annotate(release_text,
xy=(0.5, 0.34),
xycoords=('axes fraction', 'figure fraction'),
xytext=(0, 10),
textcoords='offset points',
size=12,
ha='center',
va='bottom')
# Apply branding
if self.mobrand:
insert_logo()
return self.fig
def plot_layer(self, layer, mapping):
"""
Method for plotting each individual layer using the information
provided in the layer class.
:param layer: field layer to plot.
:param mapping: map type to use for plot background.
"""
# First make sure matplotlib knows which figure to draw on:
fig = self.fig
alpha = 1.0
if mapping in ['grey_os', 'wms', 'jam']:
alpha = 0.5
# Quick plot on a linear scale if only a colormap is given
if layer.levels is None and layer.colorscale == 'linear':
if layer.plottype == 'pcolormesh':
fig.cf1 = iplt.pcolormesh(layer.cube,
cmap=layer.cmap,
alpha=alpha)
elif layer.plottype == 'contour':
fig.cf1 = iplt.contour(layer.cube,
cmap=layer.cmap,
alpha=alpha)
elif layer.plottype == 'contourf':
fig.cf1 = iplt.contourf(layer.cube,
cmap=layer.cmap,
alpha=alpha)
elif layer.plottype == 'contourf_edge':
fig.cf1 = iplt.contourf(layer.cube,
cmap=layer.cmap,
alpha=alpha)
iplt.contour(layer.cube, levels=fig.cf1.levels, colors='k')
elif layer.plottype == 'scatter_latlon':
fig.cf1 = iplt.scatter(layer.cube.coord('longitude'),
layer.cube.coord('latitude'),
c=np.atleast_1d(layer.cube.data),
marker=layer.marker,
s=layer.markersize,
edgecolors="k",
cmap=layer.cmap)
else:
if layer.levels[0] > np.max(layer.cube.data):
# If max data point is less than lowest level don't
# plot and don't add a colorbar
layer.cbar = False
elif layer.plottype == 'pcolormesh':
fig.cf1 = iplt.pcolormesh(layer.cube,
cmap=layer.cmap,
norm=layer.norm,
alpha=alpha)
elif layer.plottype == 'contour':
fig.cf1 = iplt.contour(layer.cube,
levels=layer.levels,
cmap=layer.cmap,
norm=layer.norm,
alpha=alpha)
elif layer.plottype == 'contourf':
fig.cf1 = iplt.contourf(layer.cube,
levels=layer.levels,
cmap=layer.cmap,
norm=layer.norm,
alpha=alpha)
elif layer.plottype == 'contourf_edge':
fig.cf1 = iplt.contourf(layer.cube,
levels=layer.levels,
cmap=layer.cmap,
norm=layer.norm,
alpha=alpha)
iplt.contour(layer.cube,
levels=layer.levels,
colors='k')
elif layer.plottype == 'scatter_latlon':
fig.cf1 = iplt.scatter(layer.cube.coord('longitude'),
layer.cube.coord('latitude'),
c=np.atleast_1d(layer.cube.data),
marker=layer.marker,
s=layer.markersize,
edgecolors="k",
label=layer.label,
cmap=layer.cmap,
norm=layer.norm)
# Set an axes extent (assuming extent is given in WGS84)
# Needs to follow plot in order that axes have become geoaxes
if self.extent is not None:
plt.gca().set_extent(self.extent, crs=ccrs.PlateCarree())
# Add a colorbar if required
# First extract or determine required orientation:
if layer.cbar_orientation:
layer.cbar_orientation = layer.cbar_orientation
elif layer.colorscale == 'linear':
layer.cbar_orientation = 'horizontal'
else:
layer.cbar_orientation = 'vertical'
# Please note that at this point, unless the 'layer.cbar' variable is
# explicitly set as False, a colorbar will be constructed anyway.
if layer.cbar is not False:
layer.construct_cbar(fig.cf1,
position=None,
orientation=layer.cbar_orientation,
title=None,
title_fontsize=None,
label_fontsize=None,
tickmark_size=None)
# Tell the object which fig to hold on to:
self.fig = fig
def test_yaxis_labels(self):
iplt.scatter(self.cube, self.cube.coord("str_coord"))
self.assertBoundsTickLabels("yaxis")
def plot(self, legendcols=None):
"""
Produce plot.
:param legendcols: Number of columns to include in legend.
"""
if not self.lines:
raise ValueError("SoccerPlot: no lines have been added")
if self.fig is None:
self.fig = plt.figure()
ax = self.fig.add_subplot(111)
#Scatter Plot
for line in self.lines:
if line['marker'] is None:
line['marker'] = 'o' #Default value for a scatter plot
#Check has coordinates
if line['cube'].coords(self.stat_xaxis) and \
line['cube'].coords(self.stat_yaxis):
#Check that data are not all NaNs:
xaxispts = line['cube'].coord(self.stat_xaxis).points
yaxispts = line['cube'].coord(self.stat_yaxis).points
if not np.isnan(np.nanmax(xaxispts)) and \
not np.isnan(np.nanmax(yaxispts)):
iplt.scatter(line['cube'].coord(self.stat_xaxis),
line['cube'].coord(self.stat_yaxis),
color=np.atleast_1d(line['colour']),
label=line['label'],
marker=line['marker'],
edgecolor='k',
s=30)
else:
raise ValueError(
"Cube does not have statistics coordinates \n" +
"May need to run get_stats() first")
#Set x & y axis limits
range_x = self.get_range(self.stat_xaxis)
if range_x is not None:
ax.set_xlim(range_x)
range_y = self.get_range(self.stat_yaxis)
if range_y is not None:
ax.set_ylim(range_y)
#Plot goal regions
if self.stat_xgoal is None:
#Get goal if not already set
self.stat_xgoal = self.get_goals(self.stat_xaxis)
if self.stat_ygoal is None:
#Get goal if not already set
self.stat_ygoal = self.get_goals(self.stat_yaxis)
if self.stat_xgoal is not None and self.stat_ygoal is not None:
#Can plot goals - plot as a square
for xgoal, ygoal in zip(self.stat_xgoal, self.stat_ygoal):
xpoints = [xgoal, -xgoal, -xgoal, xgoal, xgoal]
ypoints = [ygoal, ygoal, -ygoal, -ygoal, ygoal]
ax.plot(xpoints, ypoints, 'k--')
#Add lines through zero
ax.plot(ax.get_xlim(), [0, 0], 'k')
ax.plot([0, 0], ax.get_ylim(), 'k')
#Add legend
if self.legend:
if legendcols is None:
plotting_functions.add_legend_belowaxes(scatterpoints=1)
else:
plotting_functions.add_legend_belowaxes(scatterpoints=1,
ncol=legendcols)
#Add title
if self.title is None:
self.gen_title()
ax.set_title(self.title)
#Add x and y labels
if self.xlabel is None:
if self.stat_xaxis in timeseries_stats.STATS_INFO:
self.xlabel = timeseries_stats.STATS_INFO[
self.stat_xaxis]['long_name']
else:
self.xlabel = self.stat_xaxis
ax.set_xlabel(self.xlabel)
if self.ylabel is None:
if self.stat_yaxis in timeseries_stats.STATS_INFO:
self.ylabel = timeseries_stats.STATS_INFO[
self.stat_yaxis]['long_name']
else:
self.ylabel = self.stat_yaxis
ax.set_ylabel(self.ylabel)
#Add gridlines
if self.gridlines:
plt.grid()
# Apply branding
if self.mobrand:
line_plot.add_mobranding()
return self.fig
lon_points = [lp + 360 if lp < 0 else lp for lp in lon_points]
cube.coord('longitude').points = lon_points
clon = 180
# Set up figure
fig = plt.figure(figsize=[11, 7])
# Scatter plot the plume showing altitude by colour
ax1 = plt.subplot2grid((3, 3), (0, 0),
colspan=2,
rowspan=2,
projection=ccrs.PlateCarree(central_longitude=clon))
cf = iplt.scatter(cube.coord('longitude'),
cube.coord('latitude'),
s=20,
c=cube.coord('height').points,
edgecolor='',
cmap=cmap,
norm=norm)
ax1.coastlines('10m')
ax1.gridlines()
ax1.set_extent(extent)
gl = ax1.gridlines(draw_labels=True, linewidth=0.8, alpha=0.9, zorder=9)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Scatter plot a cross-section through the plume
ax2 = plt.subplot2grid((3, 3), (2, 0), colspan=2)
def test_scatter_with_c_kwarg(self):
mappable = scatter(self.traj_lon,
self.traj_lat,
c=self.traj_lon.points)
cbar = plt.colorbar()
self.assertIs(cbar.mappable, mappable)
def test_yaxis_labels(self):
iplt.scatter(self.cube, self.cube.coord('str_coord'))
self.assertBoundsTickLabels('yaxis')
