def test_integer_levels(self):
data = self.data + 1
cmap_params = _determine_cmap_params(data, levels=5, vmin=0, vmax=5, cmap="Blues")
self.assertEqual(cmap_params["vmin"], cmap_params["levels"][0])
self.assertEqual(cmap_params["vmax"], cmap_params["levels"][-1])
self.assertEqual(cmap_params["cmap"].name, "Blues")
self.assertEqual(cmap_params["extend"], "neither")
self.assertEqual(cmap_params["cmap"].N, 5)
self.assertEqual(cmap_params["norm"].N, 6)
cmap_params = _determine_cmap_params(data, levels=5, vmin=0.5, vmax=1.5)
self.assertEqual(cmap_params["cmap"].name, "viridis")
self.assertEqual(cmap_params["extend"], "max")
def test_list_levels(self):
data = self.data + 1
orig_levels = [0, 1, 2, 3, 4, 5]
# vmin and vmax should be ignored if levels are explicitly provided
cmap_params = _determine_cmap_params(data, levels=orig_levels, vmin=0, vmax=3)
self.assertEqual(cmap_params["vmin"], 0)
self.assertEqual(cmap_params["vmax"], 5)
self.assertEqual(cmap_params["cmap"].N, 5)
self.assertEqual(cmap_params["norm"].N, 6)
for wrap_levels in [list, np.array, pd.Index, DataArray]:
cmap_params = _determine_cmap_params(data, levels=wrap_levels(orig_levels))
self.assertArrayEqual(cmap_params["levels"], orig_levels)
def test_integer_levels(self):
data = self.data + 1
cmap_params = _determine_cmap_params(data, levels=5, vmin=0, vmax=5,
cmap='Blues')
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['cmap'].name, 'Blues')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertEqual(cmap_params['cmap'].N, 5)
self.assertEqual(cmap_params['norm'].N, 6)
cmap_params = _determine_cmap_params(data, levels=5,
vmin=0.5, vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'max')
def test_integer_levels(self):
data = self.data + 1
cmap_params = _determine_cmap_params(data, levels=5, vmin=0, vmax=5,
cmap='Blues')
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['cmap'].name, 'Blues')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertEqual(cmap_params['cmap'].N, 5)
self.assertEqual(cmap_params['norm'].N, 6)
cmap_params = _determine_cmap_params(data, levels=5,
vmin=0.5, vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'max')
def test_center(self):
cmap_params = _determine_cmap_params(self.data, center=0.5)
self.assertEqual(cmap_params['vmax'] - 0.5, 0.5 - cmap_params['vmin'])
self.assertEqual(cmap_params['cmap'], 'RdBu_r')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['norm'])
def test_center(self):
cmap_params = _determine_cmap_params(self.data, center=0.5)
self.assertEqual(cmap_params["vmax"] - 0.5, 0.5 - cmap_params["vmin"])
self.assertEqual(cmap_params["cmap"], "RdBu_r")
self.assertEqual(cmap_params["extend"], "neither")
self.assertIsNone(cmap_params["levels"])
self.assertIsNone(cmap_params["norm"])
def test_center(self):
cmap_params = _determine_cmap_params(self.data, center=0.5)
self.assertEqual(cmap_params['vmax'] - 0.5, 0.5 - cmap_params['vmin'])
self.assertEqual(cmap_params['cmap'], 'RdBu_r')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['norm'])
def test_list_levels(self):
data = self.data + 1
orig_levels = [0, 1, 2, 3, 4, 5]
# vmin and vmax should be ignored if levels are explicitly provided
cmap_params = _determine_cmap_params(data, levels=orig_levels,
vmin=0, vmax=3)
self.assertEqual(cmap_params['vmin'], 0)
self.assertEqual(cmap_params['vmax'], 5)
self.assertEqual(cmap_params['cmap'].N, 5)
self.assertEqual(cmap_params['norm'].N, 6)
for wrap_levels in [list, np.array, pd.Index, DataArray]:
cmap_params = _determine_cmap_params(
data, levels=wrap_levels(orig_levels))
self.assertArrayEqual(cmap_params['levels'], orig_levels)
def test_center(self):
cmap_params = _determine_cmap_params(self.data, center=0.5)
assert cmap_params['vmax'] - 0.5 == 0.5 - cmap_params['vmin']
assert cmap_params['cmap'] == 'RdBu_r'
assert cmap_params['extend'] == 'neither'
assert cmap_params['levels'] is None
assert cmap_params['norm'] is None
def test_robust(self):
cmap_params = _determine_cmap_params(self.data, robust=True)
self.assertEqual(cmap_params["vmin"], np.percentile(self.data, 2))
self.assertEqual(cmap_params["vmax"], np.percentile(self.data, 98))
self.assertEqual(cmap_params["cmap"].name, "viridis")
self.assertEqual(cmap_params["extend"], "both")
self.assertIsNone(cmap_params["levels"])
self.assertIsNone(cmap_params["norm"])
def test_robust(self):
cmap_params = _determine_cmap_params(self.data, robust=True)
self.assertEqual(cmap_params['vmin'], np.percentile(self.data, 2))
self.assertEqual(cmap_params['vmax'], np.percentile(self.data, 98))
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'both')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['norm'])
def test_robust(self):
cmap_params = _determine_cmap_params(self.data, robust=True)
self.assertEqual(cmap_params['vmin'], np.percentile(self.data, 2))
self.assertEqual(cmap_params['vmax'], np.percentile(self.data, 98))
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'both')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['norm'])
def test_robust(self):
cmap_params = _determine_cmap_params(self.data, robust=True)
assert cmap_params['vmin'] == np.percentile(self.data, 2)
assert cmap_params['vmax'] == np.percentile(self.data, 98)
assert cmap_params['cmap'].name == 'viridis'
assert cmap_params['extend'] == 'both'
assert cmap_params['levels'] is None
assert cmap_params['norm'] is None
def test_integer_levels(self):
data = self.data + 1
# default is to cover full data range but with no guarantee on Nlevels
for level in np.arange(2, 10, dtype=int):
cmap_params = _determine_cmap_params(data, levels=level)
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['extend'], 'neither')
# with min max we are more strict
cmap_params = _determine_cmap_params(data,
levels=5,
vmin=0,
vmax=5,
cmap='Blues')
self.assertEqual(cmap_params['vmin'], 0)
self.assertEqual(cmap_params['vmax'], 5)
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['cmap'].name, 'Blues')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertEqual(cmap_params['cmap'].N, 4)
self.assertEqual(cmap_params['norm'].N, 5)
cmap_params = _determine_cmap_params(data,
levels=5,
vmin=0.5,
vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'max')
cmap_params = _determine_cmap_params(data, levels=5, vmin=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'min')
cmap_params = _determine_cmap_params(data,
levels=5,
vmin=1.3,
vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'both')
def test_integer_levels(self):
data = self.data + 1
# default is to cover full data range but with no guarantee on Nlevels
for level in np.arange(2, 10, dtype=int):
cmap_params = _determine_cmap_params(data, levels=level)
assert cmap_params['vmin'] == cmap_params['levels'][0]
assert cmap_params['vmax'] == cmap_params['levels'][-1]
assert cmap_params['extend'] == 'neither'
# with min max we are more strict
cmap_params = _determine_cmap_params(data,
levels=5,
vmin=0,
vmax=5,
cmap='Blues')
assert cmap_params['vmin'] == 0
assert cmap_params['vmax'] == 5
assert cmap_params['vmin'] == cmap_params['levels'][0]
assert cmap_params['vmax'] == cmap_params['levels'][-1]
assert cmap_params['cmap'].name == 'Blues'
assert cmap_params['extend'] == 'neither'
assert cmap_params['cmap'].N == 4
assert cmap_params['norm'].N == 5
cmap_params = _determine_cmap_params(data,
levels=5,
vmin=0.5,
vmax=1.5)
assert cmap_params['cmap'].name == 'viridis'
assert cmap_params['extend'] == 'max'
cmap_params = _determine_cmap_params(data, levels=5, vmin=1.5)
assert cmap_params['cmap'].name == 'viridis'
assert cmap_params['extend'] == 'min'
cmap_params = _determine_cmap_params(data,
levels=5,
vmin=1.3,
vmax=1.5)
assert cmap_params['cmap'].name == 'viridis'
assert cmap_params['extend'] == 'both'
def test_integer_levels(self):
data = self.data + 1
# default is to cover full data range but with no guarantee on Nlevels
for level in np.arange(2, 10, dtype=int):
cmap_params = _determine_cmap_params(data, levels=level)
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['extend'], 'neither')
# with min max we are more strict
cmap_params = _determine_cmap_params(data, levels=5, vmin=0, vmax=5,
cmap='Blues')
self.assertEqual(cmap_params['vmin'], 0)
self.assertEqual(cmap_params['vmax'], 5)
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['cmap'].name, 'Blues')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertEqual(cmap_params['cmap'].N, 4)
self.assertEqual(cmap_params['norm'].N, 5)
cmap_params = _determine_cmap_params(data, levels=5,
vmin=0.5, vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'max')
cmap_params = _determine_cmap_params(data, levels=5,
vmin=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'min')
cmap_params = _determine_cmap_params(data, levels=5,
vmin=1.3, vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'both')
def geo_plot(darray, ax=None, method='contourf',
projection='PlateCarree', grid=False, **kwargs):
""" Create a global plot of a given variable.
Parameters:
-----------
darray : xray.DataArray
The darray to be plotted.
ax : axis
An existing axis instance, else one will be created.
method : str
String to use for looking up name of plotting function via iris
projection : str or tuple
Name of the cartopy projection to use and any args
necessary for initializing it passed as a dictionary;
see func:`make_geoaxes` for more information
grid : bool
Include lat-lon grid overlay
**kwargs : dict
Any additional keyword arguments to pass to the plotter,
including colormap params. If 'vmin' is not in this
set of optional keyword arguments, the plot colormap will be
automatically inferred.
"""
# Set up plotting function
if method in _PLOTTYPE_ARGS:
extra_args = _PLOTTYPE_ARGS[method].copy()
else:
raise ValueError("Don't know how to deal with '%s' method" % method)
extra_args.update(**kwargs)
# Alias a plot function based on the requested method and the
# datatype being plotted
plot_func = plt.__dict__[method]
# `transform` should be the ORIGINAL coordinate system -
# which is always a simple lat-lon coordinate system in CESM
# output
extra_args['transform'] = ccrs.PlateCarree()
# Was an axis passed to plot on?
new_axis = ax is None
if new_axis: # Create a new cartopy axis object for plotting
if isinstance(projection, (list, tuple)):
if len(projection) != 2:
raise ValueError("Expected 'projection' to only have 2 values")
projection, proj_kwargs = projection[0], projection[1]
else:
proj_kwargs = {}
# hack to look up the name of the projection in the cartopy
# reference system namespace; makes life a bit easier, so you
# can just pass a string with the name of the projection wanted.
proj = ccrs.__dict__[projection](**proj_kwargs)
ax = plt.axes(projection=proj)
else: # Set current axis to one passed as argument
if not hasattr(ax, 'projection'):
raise ValueError("Expected `ax` to be a GeoAxes instance")
plt.sca(ax)
# Setup map
ax.set_global()
ax.coastlines()
try:
gl = ax.gridlines(crs=extra_args['transform'], draw_labels=True,
linewidth=0.5, color='grey', alpha=0.8)
LON_TICKS = [ -180, -90, 0, 90, 180 ]
LAT_TICKS = [ -90, -60, -30, 0, 30, 60, 90 ]
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlines = grid
gl.ylines = grid
gl.xlocator = mticker.FixedLocator(LON_TICKS)
gl.ylocator = mticker.FixedLocator(LAT_TICKS)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
except TypeError:
warnings.warn("Could not label the given map projection.")
# Infer colormap settings if not provided
if not ('vmin' in kwargs):
warnings.warn("Re-inferring color parameters...")
cmap_kws = _determine_cmap_params(darray.data)
extra_args.update(cmap_kws)
gp = plot_func(darray.lon.values, darray.lat.values, darray.data,
**extra_args)
return ax, gp
def TS_diagram(od, display=True, FuncAnimation_kwargs=None, **kwargs):
"""
Animate TS diagrams.
Parameters
----------
od: OceanDataset
oceandataset used to plot.
display: bool
If True, display the animation.
FuncAnimation_kwargs: dict
Keyword arguments from :py:func:`matplotlib.animation.FuncAnimation`
**kwargs:
Keyword arguments from :py:func:`oceanspy.plot.TS_diagram`
Returns
-------
anim: matplotlib.animation.FuncAnimation
Animation object
See also
--------
oceanspy.plot.TS_diagram
"""
# Check parameters
_check_instance(
{
"od": od,
"display": display,
"FuncAnimation_kwargs": FuncAnimation_kwargs
},
{
"od": "oceanspy.OceanDataset",
"display": "bool",
"FuncAnimation_kwargs": ["type(None)", "dict"],
},
)
# Handle kwargs
if FuncAnimation_kwargs is None:
FuncAnimation_kwargs = {}
FuncAnimation_kwargs = dict(FuncAnimation_kwargs)
# Name of the plot_functions
plot_func = eval("_plot.TS_diagram")
# First cutout and get time
cutout_kwargs = kwargs.pop("cutout_kwargs", None)
if cutout_kwargs is not None:
od = od.subsample.cutout(**cutout_kwargs)
time = od._ds["time"]
# Check Temp and S
varList = ["Temp", "S"]
od = _compute._add_missing_variables(od, varList)
# Fix T and S axes
Tlim = kwargs.pop("Tlim", None)
Slim = kwargs.pop("Slim", None)
if Tlim is None:
cmap_params = _determine_cmap_params(od._ds["Temp"].values,
center=False)
Tlim = [cmap_params["vmin"], cmap_params["vmax"]]
if Slim is None:
cmap_params = _determine_cmap_params(od._ds["S"].values, center=False)
Slim = [cmap_params["vmin"], cmap_params["vmax"]]
kwargs["Tlim"] = Tlim
kwargs["Slim"] = Slim
# Fix density
dens = kwargs.pop("dens", None)
if dens is None:
t, s = _xr.broadcast(
_xr.DataArray(_np.linspace(Tlim[0], Tlim[-1], 100), dims=("t")),
_xr.DataArray(_np.linspace(Slim[0], Slim[-1], 100), dims=("s")),
)
odSigma0 = _ospy.OceanDataset(_xr.Dataset({"Temp": t, "S": s}))
odSigma0 = odSigma0.set_parameters(od.parameters)
odSigma0 = odSigma0.compute.potential_density_anomaly()
odSigma0._ds = odSigma0._ds.set_coords(["Temp", "S"])
# Freezing point
paramsList = ["tempFrz0", "dTempFrz_dS"]
params2use = {
par: od.parameters[par]
for par in od.parameters if par in paramsList
}
tempFrz0 = params2use["tempFrz0"]
dTempFrz_dS = params2use["dTempFrz_dS"]
freez_point = tempFrz0 + odSigma0._ds["S"] * dTempFrz_dS
# Extract Density
dens = odSigma0._ds["Sigma0"]
dens = dens.where(odSigma0._ds["Temp"] > freez_point)
kwargs["dens"] = dens
# Fix colorbar
colorName = kwargs.pop("colorName", None)
if colorName is not None:
# Add missing variables (use private)
_colorName = _rename_aliased(od, colorName)
od = _compute._add_missing_variables(od, _colorName)
# Extract color (use public)
color = od.dataset[colorName]
# Create colorbar (stolen from xarray)
cmap_kwargs = kwargs.pop("cmap_kwargs", None)
if cmap_kwargs is None:
cmap_kwargs = {}
cmap_kwargs = dict(cmap_kwargs)
cmap_kwargs["plot_data"] = color.values
kwargs["cmap_kwargs"] = _determine_cmap_params(**cmap_kwargs)
kwargs["colorName"] = colorName
# Remove ax
_ax_warning(kwargs)
# Animation
anim = _create_animation(od=od,
time=time,
plot_func=plot_func,
func_kwargs=kwargs,
display=display,
**FuncAnimation_kwargs)
return anim
def TS_diagram(od, display=True, FuncAnimation_kwargs=None,
**kwargs):
"""
Animate TS diagrams.
Parameters
----------
od: OceanDataset
oceandataset used to plot.
display: bool
If True, display the animation.
FuncAnimation_kwargs: dict
Keyword arguments from :py:func:`matplotlib.animation.FuncAnimation`
**kwargs:
Keyword arguments from :py:func:`oceanspy.plot.TS_diagram`
Returns
-------
anim: matplotlib.animation.FuncAnimation
Animation object
See also
--------
oceanspy.plot.TS_diagram
"""
# Check parameters
_check_instance({'od': od,
'display': display,
'FuncAnimation_kwargs': FuncAnimation_kwargs},
{'od': 'oceanspy.OceanDataset',
'display': 'bool',
'FuncAnimation_kwargs': ['type(None)', 'dict']})
# Handle kwargs
if FuncAnimation_kwargs is None:
FuncAnimation_kwargs = {}
FuncAnimation_kwargs = dict(FuncAnimation_kwargs)
# Name of the plot_functions
plot_func = eval('_plot.TS_diagram')
# First cutout and get time
cutout_kwargs = kwargs.pop('cutout_kwargs', None)
if cutout_kwargs is not None:
od = od.subsample.cutout(**cutout_kwargs)
time = od._ds['time']
# Check Temp and S
varList = ['Temp', 'S']
od = _compute._add_missing_variables(od, varList)
# Fix T and S axes
Tlim = kwargs.pop('Tlim', None)
Slim = kwargs.pop('Slim', None)
if Tlim is None:
cmap_params = _determine_cmap_params(od._ds['Temp'].values,
center=False)
Tlim = [cmap_params['vmin'], cmap_params['vmax']]
if Slim is None:
cmap_params = _determine_cmap_params(od._ds['S'].values,
center=False)
Slim = [cmap_params['vmin'], cmap_params['vmax']]
kwargs['Tlim'] = Tlim
kwargs['Slim'] = Slim
# Fix density
dens = kwargs.pop('dens', None)
if dens is None:
t, s = _xr.broadcast(_xr.DataArray(_np.linspace(Tlim[0],
Tlim[-1],
100), dims=('t')),
_xr.DataArray(_np.linspace(Slim[0],
Slim[-1],
100), dims=('s')))
odSigma0 = _ospy.OceanDataset(_xr.Dataset({'Temp': t,
'S': s}))
odSigma0 = odSigma0.set_parameters(od.parameters)
odSigma0 = odSigma0.compute.potential_density_anomaly()
odSigma0._ds = odSigma0._ds.set_coords(['Temp', 'S'])
# Freezing point
paramsList = ['tempFrz0', 'dTempFrz_dS']
params2use = {par: od.parameters[par]
for par in od.parameters
if par in paramsList}
tempFrz0 = params2use['tempFrz0']
dTempFrz_dS = params2use['dTempFrz_dS']
freez_point = tempFrz0 + odSigma0._ds['S']*dTempFrz_dS
# Extract Density
dens = odSigma0._ds['Sigma0']
dens = dens.where(odSigma0._ds['Temp'] > freez_point)
kwargs['dens'] = dens
# Fix colorbar
colorName = kwargs.pop('colorName', None)
if colorName is not None:
# Add missing variables (use private)
_colorName = _rename_aliased(od, colorName)
od = _compute._add_missing_variables(od, _colorName)
# Extract color (use public)
color = od.dataset[colorName]
# Create colorbar (stolen from xarray)
cmap_kwargs = kwargs.pop('cmap_kwargs', None)
if cmap_kwargs is None:
cmap_kwargs = {}
cmap_kwargs = dict(cmap_kwargs)
cmap_kwargs['plot_data'] = color.values
kwargs['cmap_kwargs'] = _determine_cmap_params(**cmap_kwargs)
kwargs['colorName'] = colorName
# Remove ax
_ax_warning(kwargs)
# Animation
anim = _create_animation(od=od,
time=time,
plot_func=plot_func,
func_kwargs=kwargs,
display=display,
**FuncAnimation_kwargs)
return anim
def geo_plot(darray,
ax=None,
method='contourf',
projection='PlateCarree',
grid=False,
**kwargs):
""" Create a global plot of a given variable.
Parameters:
-----------
darray : xray.DataArray
The darray to be plotted.
ax : axis
An existing axis instance, else one will be created.
method : str
String to use for looking up name of plotting function via iris
projection : str or tuple
Name of the cartopy projection to use and any args
necessary for initializing it passed as a dictionary;
see func:`make_geoaxes` for more information
grid : bool
Include lat-lon grid overlay
**kwargs : dict
Any additional keyword arguments to pass to the plotter,
including colormap params. If 'vmin' is not in this
set of optional keyword arguments, the plot colormap will be
automatically inferred.
"""
# Set up plotting function
if method in _PLOTTYPE_ARGS:
extra_args = _PLOTTYPE_ARGS[method].copy()
else:
raise ValueError("Don't know how to deal with '%s' method" % method)
extra_args.update(**kwargs)
# Alias a plot function based on the requested method and the
# datatype being plotted
plot_func = plt.__dict__[method]
# `transform` should be the ORIGINAL coordinate system -
# which is always a simple lat-lon coordinate system in CESM
# output
extra_args['transform'] = ccrs.PlateCarree()
# Was an axis passed to plot on?
new_axis = ax is None
if new_axis: # Create a new cartopy axis object for plotting
if isinstance(projection, (list, tuple)):
if len(projection) != 2:
raise ValueError("Expected 'projection' to only have 2 values")
projection, proj_kwargs = projection[0], projection[1]
else:
proj_kwargs = {}
# hack to look up the name of the projection in the cartopy
# reference system namespace; makes life a bit easier, so you
# can just pass a string with the name of the projection wanted.
proj = ccrs.__dict__[projection](**proj_kwargs)
ax = plt.axes(projection=proj)
else: # Set current axis to one passed as argument
if not hasattr(ax, 'projection'):
raise ValueError("Expected `ax` to be a GeoAxes instance")
plt.sca(ax)
# Setup map
ax.set_global()
ax.coastlines()
try:
gl = ax.gridlines(crs=extra_args['transform'],
draw_labels=True,
linewidth=0.5,
color='grey',
alpha=0.8)
LON_TICKS = [-180, -90, 0, 90, 180]
LAT_TICKS = [-90, -60, -30, 0, 30, 60, 90]
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlines = grid
gl.ylines = grid
gl.xlocator = mticker.FixedLocator(LON_TICKS)
gl.ylocator = mticker.FixedLocator(LAT_TICKS)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
except TypeError:
warnings.warn("Could not label the given map projection.")
# Infer colormap settings if not provided
if not ('vmin' in kwargs):
warnings.warn("Re-inferring color parameters...")
cmap_kws = _determine_cmap_params(darray.data)
extra_args.update(cmap_kws)
gp = plot_func(darray.lon.values, darray.lat.values, darray.data,
**extra_args)
return ax, gp
# Read colorfile arguments; else, infer the color parameters
if args.colorfile is not None:
colorfile = args.colorfile
try:
with open(colorfile, 'rb') as f:
color_data = pickle.load(f)
except (FileNotFoundError, IOError):
print("Could not open colorfile '%s'" % args.colorfile)
sys.exit(1)
# Warn about variables where color will be freshly inferred
for v in dataset.variables:
if v in dataset.dims: continue
if not (v in color_data):
warnings.warn("Couldn't find color data for %s" % v)
color_data[v] = _determine_cmap_params(dataset[v].data)
else:
print("Inferring new colormaps")
color_data = {}
for v in dataset.variables:
if v in dataset.dims: continue
print(" " + v)
color_data[v] = _determine_cmap_params(dataset[v].data,
levels=21,
robust=True,
extend='both')
# Save/update the new colorfile
print("Saving colormap data")
fn_basename, _ = os.path.splitext(args.nc_file)
def test_divergentcontrol(self):
neg = self.data - 0.1
pos = self.data
# Default with positive data will be a normal cmap
cmap_params = _determine_cmap_params(pos)
self.assertEqual(cmap_params["vmin"], 0)
self.assertEqual(cmap_params["vmax"], 1)
self.assertEqual(cmap_params["cmap"].name, "viridis")
# Default with negative data will be a divergent cmap
cmap_params = _determine_cmap_params(neg)
self.assertEqual(cmap_params["vmin"], -0.9)
self.assertEqual(cmap_params["vmax"], 0.9)
self.assertEqual(cmap_params["cmap"], "RdBu_r")
# Setting vmin or vmax should prevent this only if center is false
cmap_params = _determine_cmap_params(neg, vmin=-0.1, center=False)
self.assertEqual(cmap_params["vmin"], -0.1)
self.assertEqual(cmap_params["vmax"], 0.9)
self.assertEqual(cmap_params["cmap"].name, "viridis")
cmap_params = _determine_cmap_params(neg, vmax=0.5, center=False)
self.assertEqual(cmap_params["vmin"], -0.1)
self.assertEqual(cmap_params["vmax"], 0.5)
self.assertEqual(cmap_params["cmap"].name, "viridis")
# Setting center=False too
cmap_params = _determine_cmap_params(neg, center=False)
self.assertEqual(cmap_params["vmin"], -0.1)
self.assertEqual(cmap_params["vmax"], 0.9)
self.assertEqual(cmap_params["cmap"].name, "viridis")
# However, I should still be able to set center and have a div cmap
cmap_params = _determine_cmap_params(neg, center=0)
self.assertEqual(cmap_params["vmin"], -0.9)
self.assertEqual(cmap_params["vmax"], 0.9)
self.assertEqual(cmap_params["cmap"], "RdBu_r")
# Setting vmin or vmax alone will force symetric bounds around center
cmap_params = _determine_cmap_params(neg, vmin=-0.1)
self.assertEqual(cmap_params["vmin"], -0.1)
self.assertEqual(cmap_params["vmax"], 0.1)
self.assertEqual(cmap_params["cmap"], "RdBu_r")
cmap_params = _determine_cmap_params(neg, vmax=0.5)
self.assertEqual(cmap_params["vmin"], -0.5)
self.assertEqual(cmap_params["vmax"], 0.5)
self.assertEqual(cmap_params["cmap"], "RdBu_r")
cmap_params = _determine_cmap_params(neg, vmax=0.6, center=0.1)
self.assertEqual(cmap_params["vmin"], -0.4)
self.assertEqual(cmap_params["vmax"], 0.6)
self.assertEqual(cmap_params["cmap"], "RdBu_r")
# But this is only true if vmin or vmax are negative
cmap_params = _determine_cmap_params(pos, vmin=-0.1)
self.assertEqual(cmap_params["vmin"], -0.1)
self.assertEqual(cmap_params["vmax"], 0.1)
self.assertEqual(cmap_params["cmap"], "RdBu_r")
cmap_params = _determine_cmap_params(pos, vmin=0.1)
self.assertEqual(cmap_params["vmin"], 0.1)
self.assertEqual(cmap_params["vmax"], 1)
self.assertEqual(cmap_params["cmap"].name, "viridis")
cmap_params = _determine_cmap_params(pos, vmax=0.5)
self.assertEqual(cmap_params["vmin"], 0)
self.assertEqual(cmap_params["vmax"], 0.5)
self.assertEqual(cmap_params["cmap"].name, "viridis")
# If both vmin and vmax are provided, output is non-divergent
cmap_params = _determine_cmap_params(neg, vmin=-0.2, vmax=0.6)
self.assertEqual(cmap_params["vmin"], -0.2)
self.assertEqual(cmap_params["vmax"], 0.6)
self.assertEqual(cmap_params["cmap"].name, "viridis")
def test_divergentcontrol(self):
neg = self.data - 0.1
pos = self.data
# Default with positive data will be a normal cmap
cmap_params = _determine_cmap_params(pos)
self.assertEqual(cmap_params['vmin'], 0)
self.assertEqual(cmap_params['vmax'], 1)
self.assertEqual(cmap_params['cmap'].name, "viridis")
# Default with negative data will be a divergent cmap
cmap_params = _determine_cmap_params(neg)
self.assertEqual(cmap_params['vmin'], -0.9)
self.assertEqual(cmap_params['vmax'], 0.9)
self.assertEqual(cmap_params['cmap'], "RdBu_r")
# Setting vmin or vmax should prevent this only if center is false
cmap_params = _determine_cmap_params(neg, vmin=-0.1, center=False)
self.assertEqual(cmap_params['vmin'], -0.1)
self.assertEqual(cmap_params['vmax'], 0.9)
self.assertEqual(cmap_params['cmap'].name, "viridis")
cmap_params = _determine_cmap_params(neg, vmax=0.5, center=False)
self.assertEqual(cmap_params['vmin'], -0.1)
self.assertEqual(cmap_params['vmax'], 0.5)
self.assertEqual(cmap_params['cmap'].name, "viridis")
# Setting center=False too
cmap_params = _determine_cmap_params(neg, center=False)
self.assertEqual(cmap_params['vmin'], -0.1)
self.assertEqual(cmap_params['vmax'], 0.9)
self.assertEqual(cmap_params['cmap'].name, "viridis")
# However, I should still be able to set center and have a div cmap
cmap_params = _determine_cmap_params(neg, center=0)
self.assertEqual(cmap_params['vmin'], -0.9)
self.assertEqual(cmap_params['vmax'], 0.9)
self.assertEqual(cmap_params['cmap'], "RdBu_r")
# Setting vmin or vmax alone will force symmetric bounds around center
cmap_params = _determine_cmap_params(neg, vmin=-0.1)
self.assertEqual(cmap_params['vmin'], -0.1)
self.assertEqual(cmap_params['vmax'], 0.1)
self.assertEqual(cmap_params['cmap'], "RdBu_r")
cmap_params = _determine_cmap_params(neg, vmax=0.5)
self.assertEqual(cmap_params['vmin'], -0.5)
self.assertEqual(cmap_params['vmax'], 0.5)
self.assertEqual(cmap_params['cmap'], "RdBu_r")
cmap_params = _determine_cmap_params(neg, vmax=0.6, center=0.1)
self.assertEqual(cmap_params['vmin'], -0.4)
self.assertEqual(cmap_params['vmax'], 0.6)
self.assertEqual(cmap_params['cmap'], "RdBu_r")
# But this is only true if vmin or vmax are negative
cmap_params = _determine_cmap_params(pos, vmin=-0.1)
self.assertEqual(cmap_params['vmin'], -0.1)
self.assertEqual(cmap_params['vmax'], 0.1)
self.assertEqual(cmap_params['cmap'], "RdBu_r")
cmap_params = _determine_cmap_params(pos, vmin=0.1)
self.assertEqual(cmap_params['vmin'], 0.1)
self.assertEqual(cmap_params['vmax'], 1)
self.assertEqual(cmap_params['cmap'].name, "viridis")
cmap_params = _determine_cmap_params(pos, vmax=0.5)
self.assertEqual(cmap_params['vmin'], 0)
self.assertEqual(cmap_params['vmax'], 0.5)
self.assertEqual(cmap_params['cmap'].name, "viridis")
# If both vmin and vmax are provided, output is non-divergent
cmap_params = _determine_cmap_params(neg, vmin=-0.2, vmax=0.6)
self.assertEqual(cmap_params['vmin'], -0.2)
self.assertEqual(cmap_params['vmax'], 0.6)
self.assertEqual(cmap_params['cmap'].name, "viridis")
# Read colorfile arguments; else, infer the color parameters
if args.colorfile is not None:
colorfile = args.colorfile
try:
with open(colorfile, 'rb') as f:
color_data = pickle.load(f)
except (FileNotFoundError, IOError):
print("Could not open colorfile '%s'" % args.colorfile)
sys.exit(1)
# Warn about variables where color will be freshly inferred
for v in dataset.variables:
if v in dataset.dims: continue
if not (v in color_data):
warnings.warn("Couldn't find color data for %s" % v)
color_data[v] = _determine_cmap_params(dataset[v].data)
else:
print("Inferring new colormaps")
color_data = {}
for v in dataset.variables:
if v in dataset.dims: continue
print(" " + v)
color_data[v] = _determine_cmap_params(dataset[v].data,
levels=21,
robust=True,
extend='both')
# Save/update the new colorfile
print("Saving colormap data")
fn_basename, _ = os.path.splitext(args.nc_file)