def test_mpl_ax_with_ax():
fig, ax = pyplot.subplots()
fig1, ax1 = validate.mpl_ax(ax)
assert isinstance(ax1, pyplot.Axes)
assert isinstance(fig1, pyplot.Figure)
assert ax1 is ax
assert fig1 is fig
def _plot_boundaries(extent_x=None, extent_y=None, extent=None, islands_x=None,
islands_y=None, islands_name=None, islands=None, ax=None):
"""
"""
# setup the figure
fig, ax = validate.mpl_ax(ax)
if extent is not None:
extent_x, extent_y = extent[extent_x], extent[extent_y]
if extent_x is not None:
ax.plot(extent_x, extent_y, 'k-', label='extent')
if islands is not None:
islands_x, islands_y = islands[islands_x], islands[islands_y]
if islands_name is not None:
islands_name = islands[islands_name]
if islands_x is not None and islands_y is not None:
for name in np.unique(islands_name):
subset = islands_name == name
coords = list(zip(islands_x[subset], islands_y[subset]))
patch = pyplot.Polygon(coords, facecolor='0.25')
ax.add_patch(patch)
ax.margins(0.1, 0.1)
return fig
def _plot_cells(x, y, mask=None, ax=None, **plot_opts):
fig, ax = validate.mpl_ax(ax)
ec = plot_opts.pop('edgecolor', None) or plot_opts.pop('ec', '0.125')
fc = plot_opts.pop('facecolor', None) or plot_opts.pop('fc', '0.875')
lw = plot_opts.pop('linewidth', None) or plot_opts.pop('lw', 0.75)
rows, cols = x.shape
if mask is None:
if hasattr(x, 'mask'):
mask = x.mask
else:
mask = np.zeros(x.shape)
for jj in range(rows - 1):
for ii in range(cols - 1):
if mask[jj, ii]:
coords = None
else:
coords = misc.make_poly_coords(
x[jj:jj + 2, ii:ii + 2],
y[jj:jj + 2, ii:ii + 2],
)
if coords is not None:
rect = pyplot.Polygon(coords, edgecolor=ec, facecolor=fc,
linewidth=lw, **plot_opts)
ax.add_patch(rect)
ax.margins(0.1, 0.1)
return fig
def test_without_ax(self):
fig, ax = validate.mpl_ax(None)
nt.assert_not_equals(self.fig, fig)
nt.assert_not_equals(self.ax, ax)
nt.assert_true(isinstance(fig, plt.Figure))
nt.assert_true(isinstance(ax, plt.Axes))
def _plot_points(x, y, ax=None, **plot_opts):
"""
"""
fig, ax = validate.mpl_ax(ax)
dots, = ax.plot(x.flatten(), y.flatten(), 'ko', **plot_opts)
ax.margins(0.1)
return fig, {'points': dots}
def _plot_points(x, y, ax=None, **plot_opts):
"""
"""
fig, ax = validate.mpl_ax(ax)
ax.plot(x, y, 'ko', **plot_opts)
ax.margins(0.1, 0.1)
return fig
def _plot_domain(domain_x=None, domain_y=None, beta=None, data=None, ax=None):
""" Plot a model grid's domain.
Parameters
----------
x, y, beta : str or array-like, optional
Either column labels or sequences representing the x- and
y-coordinates and beta values of each point defining the domain
of the model grid.
data : pandas.DataFrame, optional
If ``x``, ``y``, and ``beta`` are strings (column labels),
``data`` must be a pandas.DataFrame containing those labels.
ax : matplotib.Axes, optional
The Axes on which the domain will be drawn. If omitted, a new
one will be created.
"""
# setup the figure
fig, ax = validate.mpl_ax(ax)
# coerce values into a dataframe if necessary
if data is not None:
domain_x, domain_y = data[domain_x], data[domain_y]
if beta is not None:
beta = data[beta]
# plot the boundary as a line
ax.plot(domain_x, domain_y, 'k-', label='__nolegend__')
if beta is not None:
# plot negative turns
beta_neg1 = beta == -1
ax.plot(domain_x[beta_neg1], domain_y[beta_neg1], 's',
linestyle='none', label="negative")
# plot non-turns
beta_zero = beta == 0
ax.plot(domain_x[beta_zero], domain_y[beta_zero], 'o',
linestyle='none', label="neutral")
# plot positive turns
beta_pos1 = beta == 1
ax.plot(domain_x[beta_pos1], domain_y[beta_pos1], '^',
linestyle='none', label="positive")
ax.legend()
ax.margins(0.1, 0.1)
return fig
def _plot_domain(domain, betacol='beta', ax=None, show_legend=True):
""" Plot a model grid's domain.
Parameters
----------
domain : GeoDataFrame
Defines the boundary of the model area. Needs to have a column of
Point geometries and a a column of beta (turning point) values.
betacol : str
Label of the column in *domain* that contains the beta (i.e., turning
point) values of domain. This sum of this column must be 4.
ax : matplotib.Axes, optional
The Axes on which the domain will be drawn. If omitted, a new
one will be created.
show_legend : bool, default = True
"""
leg = None
# setup the figure
fig, ax = validate.mpl_ax(ax)
# plot the boundary as a line
line_artist, = ax.plot(domain.geometry.x,
domain.geometry.y,
'k-',
label='__nolegend__')
beta_artists = []
beta_selectors = [
domain[betacol] < 0, domain[betacol] == 0, domain[betacol] > 0
]
beta_markers = ['s', 'o', '^']
beta_labels = ['negative', 'neutral', 'positive']
beta_artists = [
ax.plot(domain.geometry.x[idx],
domain.geometry.y[idx],
marker,
ls='none',
label=label)[0] for idx, marker, label in zip(
beta_selectors, beta_markers, beta_labels)
]
if show_legend:
leg = ax.legend()
ax.autoscale()
ax.margins(0.1)
return fig, {'domain': line_artist, 'beta': beta_artists, 'legend': leg}
def _plot_domain(domain_x=None, domain_y=None, beta=None, data=None, ax=None):
""" Plot a model grid's domain.
Parameters
----------
x, y, beta : str or array-like, optional
Either column labels or sequences representing the x- and
y-coordinates and beta values of each point defining the domain
of the model grid.
data : pandas.DataFrame, optional
If ``x``, ``y``, and ``beta`` are strings (column labels),
``data`` must be a pandas.DataFrame containing those labels.
ax : matplotib.Axes, optional
The Axes on which the domain will be drawn. If omitted, a new
one will be created.
"""
# setup the figure
fig, ax = validate.mpl_ax(ax)
# coerce values into a dataframe if necessary
if data is not None:
domain_x, domain_y = data[domain_x], data[domain_y]
if beta is not None:
beta = data[beta]
# plot the boundary as a line
line_artist, = ax.plot(domain_x, domain_y, 'k-', label='__nolegend__')
beta_artists = []
if beta is not None:
beta_selectors = [beta < 0, beta == 0, beta > 0]
beta_markers = ['s', 'o', '^']
beta_labels = ['negative', 'neutral', 'positive']
beta_artists = [
ax.plot(domain_x[idx],
domain_y[idx],
marker,
ls='none',
label=label)[0] for idx, marker, label in zip(
beta_selectors, beta_markers, beta_labels)
]
ax.legend()
ax.autoscale()
ax.margins(0.1)
return fig, {'domain': line_artist, 'beta': beta_artists}
def _plot_boundaries(extent_x=None,
extent_y=None,
extent=None,
islands_x=None,
islands_y=None,
islands_name=None,
islands=None,
ax=None):
"""
"""
# setup the figure
fig, ax = validate.mpl_ax(ax)
if extent is not None:
extent_x, extent_y = extent[extent_x], extent[extent_y]
if extent_x is not None:
extent, = ax.plot(extent_x, extent_y, 'k-', label='extent')
if islands is not None:
islands_x, islands_y = islands[islands_x], islands[islands_y]
if islands_name is not None:
islands_name = islands[islands_name]
polys = []
if islands_x is not None and islands_y is not None:
for name in numpy.unique(islands_name):
_subset = islands_name == name
_coords = list(zip(islands_x[_subset], islands_y[_subset]))
_island = patches.Polygon(_coords, facecolor='0.25')
polys.append(_island)
ax.add_patch(_island)
ax.margins(0.1)
return fig, {'extent': extent, 'islands': polys}
def _plot_cells(x,
y,
mask=None,
colors=None,
ax=None,
sticky_edges=False,
**plot_opts):
fig, ax = validate.mpl_ax(ax)
ax.use_sticky_edges = sticky_edges
if mask is None:
mask = numpy.zeros(x.shape)[1:, 1:]
if colors is None:
colors = numpy.ma.masked_array(mask + 1, mask)
vmin, vmax = 0, 2
else:
vmin = plot_opts.pop('vmin', None)
vmax = plot_opts.pop('vmax', None)
cell_colors = numpy.ma.masked_array(data=colors, mask=mask)
ec = plot_opts.pop('edgecolor', None) or plot_opts.pop('ec', '0.125')
lw = plot_opts.pop('linewidth', None) or plot_opts.pop('lw', 0.75)
fc = plot_opts.pop('facecolor', None) or plot_opts.pop('fc', '0.875')
cmap = plot_opts.pop('cmap', 'Greys')
cells = ax.pcolor(x,
y,
cell_colors,
edgecolors=ec,
lw=lw,
vmin=vmin,
vmax=vmax,
cmap=cmap,
**plot_opts)
return fig, {'cells': cells}
def test_mpl_ax_with_None():
fig1, ax1 = validate.mpl_ax(None)
assert isinstance(ax1, pyplot.Axes)
assert isinstance(fig1, pyplot.Figure)
def test_mpl_ax_invalid():
with utils.raises(ValueError):
validate.mpl_ax('junk')
def test_bad_ax(self):
validate.mpl_ax('junk')
def test_with_ax(self):
fig, ax = validate.mpl_ax(self.ax)
nt.assert_equal(self.fig, fig)
nt.assert_equal(self.ax, ax)
