def create_artists(self, legend, orig_handle, xdescent, ydescent, width,
height, fontsize, trans):
handler_map = legend.get_legend_handler_map()
if self._ndivide is None:
ndivide = len(orig_handle)
else:
ndivide = self._ndivide
if self._pad is None:
pad = legend.borderpad * fontsize
else:
pad = self._pad * fontsize
if ndivide > 1:
width = (width - pad * (ndivide - 1)) / ndivide
xds = [xdescent - (width + pad) * i for i in range(ndivide)]
xds_cycle = cycle(xds)
a_list = []
for handle1 in orig_handle:
handler = legend.get_legend_handler(handler_map, handle1)
_a_list = handler.create_artists(legend, handle1,
six.next(xds_cycle), ydescent,
width, height, fontsize, trans)
a_list.extend(_a_list)
return a_list
def create_artists(self, legend, orig_handle,
xdescent, ydescent, width, height, fontsize,
trans):
handler_map = legend.get_legend_handler_map()
if self._ndivide is None:
ndivide = len(orig_handle)
else:
ndivide = self._ndivide
if self._pad is None:
pad = legend.borderpad * fontsize
else:
pad = self._pad * fontsize
if ndivide > 1:
width = (width - pad*(ndivide - 1)) / ndivide
xds = [xdescent - (width + pad) * i for i in range(ndivide)]
xds_cycle = cycle(xds)
a_list = []
for handle1 in orig_handle:
handler = legend.get_legend_handler(handler_map, handle1)
_a_list = handler.create_artists(legend, handle1,
six.next(xds_cycle),
ydescent,
width, height,
fontsize,
trans)
a_list.extend(_a_list)
return a_list
def streamplot(axes,
x,
y,
u,
v,
density=1,
linewidth=None,
color=None,
cmap=None,
norm=None,
arrowsize=1,
arrowstyle='-|>',
minlength=0.1,
transform=None,
zorder=1):
"""Draws streamlines of a vector flow.
*x*, *y* : 1d arrays
an *evenly spaced* grid.
*u*, *v* : 2d arrays
x and y-velocities. Number of rows should match length of y, and
the number of columns should match x.
*density* : float or 2-tuple
Controls the closeness of streamlines. When `density = 1`, the domain
is divided into a 30x30 grid---*density* linearly scales this grid.
Each cell in the grid can have, at most, one traversing streamline.
For different densities in each direction, use [density_x, density_y].
*linewidth* : numeric or 2d array
vary linewidth when given a 2d array with the same shape as velocities.
*color* : matplotlib color code, or 2d array
Streamline color. When given an array with the same shape as
velocities, *color* values are converted to colors using *cmap*.
*cmap* : :class:`~matplotlib.colors.Colormap`
Colormap used to plot streamlines and arrows. Only necessary when using
an array input for *color*.
*norm* : :class:`~matplotlib.colors.Normalize`
Normalize object used to scale luminance data to 0, 1. If None, stretch
(min, max) to (0, 1). Only necessary when *color* is an array.
*arrowsize* : float
Factor scale arrow size.
*arrowstyle* : str
Arrow style specification.
See :class:`~matplotlib.patches.FancyArrowPatch`.
*minlength* : float
Minimum length of streamline in axes coordinates.
*zorder* : int
any number
Returns:
*stream_container* : StreamplotSet
Container object with attributes
- lines: `matplotlib.collections.LineCollection` of streamlines
- arrows: collection of `matplotlib.patches.FancyArrowPatch`
objects representing arrows half-way along stream
lines.
This container will probably change in the future to allow changes
to the colormap, alpha, etc. for both lines and arrows, but these
changes should be backward compatible.
"""
grid = Grid(x, y)
mask = StreamMask(density)
dmap = DomainMap(grid, mask)
# default to data coordinates
if transform is None:
transform = axes.transData
if color is None:
color = six.next(axes._get_lines.color_cycle)
if linewidth is None:
linewidth = matplotlib.rcParams['lines.linewidth']
line_kw = {}
arrow_kw = dict(arrowstyle=arrowstyle, mutation_scale=10 * arrowsize)
use_multicolor_lines = isinstance(color, np.ndarray)
if use_multicolor_lines:
if color.shape != grid.shape:
msg = "If 'color' is given, must have the shape of 'Grid(x,y)'"
raise ValueError(msg)
line_colors = []
color = np.ma.masked_invalid(color)
else:
line_kw['color'] = color
arrow_kw['color'] = color
if isinstance(linewidth, np.ndarray):
if linewidth.shape != grid.shape:
msg = "If 'linewidth' is given, must have the shape of 'Grid(x,y)'"
raise ValueError(msg)
line_kw['linewidth'] = []
else:
line_kw['linewidth'] = linewidth
arrow_kw['linewidth'] = linewidth
line_kw['zorder'] = zorder
arrow_kw['zorder'] = zorder
## Sanity checks.
if (u.shape != grid.shape) or (v.shape != grid.shape):
msg = "'u' and 'v' must be of shape 'Grid(x,y)'"
raise ValueError(msg)
u = np.ma.masked_invalid(u)
v = np.ma.masked_invalid(v)
integrate = get_integrator(u, v, dmap, minlength)
trajectories = []
for xm, ym in _gen_starting_points(mask.shape):
if mask[ym, xm] == 0:
xg, yg = dmap.mask2grid(xm, ym)
t = integrate(xg, yg)
if t is not None:
trajectories.append(t)
if use_multicolor_lines:
if norm is None:
norm = mcolors.Normalize(color.min(), color.max())
if cmap is None:
cmap = cm.get_cmap(matplotlib.rcParams['image.cmap'])
else:
cmap = cm.get_cmap(cmap)
streamlines = []
arrows = []
for t in trajectories:
tgx = np.array(t[0])
tgy = np.array(t[1])
# Rescale from grid-coordinates to data-coordinates.
tx = np.array(t[0]) * grid.dx + grid.x_origin
ty = np.array(t[1]) * grid.dy + grid.y_origin
points = np.transpose([tx, ty]).reshape(-1, 1, 2)
streamlines.extend(np.hstack([points[:-1], points[1:]]))
# Add arrows half way along each trajectory.
s = np.cumsum(np.sqrt(np.diff(tx)**2 + np.diff(ty)**2))
n = np.searchsorted(s, s[-1] / 2.)
arrow_tail = (tx[n], ty[n])
arrow_head = (np.mean(tx[n:n + 2]), np.mean(ty[n:n + 2]))
if isinstance(linewidth, np.ndarray):
line_widths = interpgrid(linewidth, tgx, tgy)[:-1]
line_kw['linewidth'].extend(line_widths)
arrow_kw['linewidth'] = line_widths[n]
if use_multicolor_lines:
color_values = interpgrid(color, tgx, tgy)[:-1]
line_colors.append(color_values)
arrow_kw['color'] = cmap(norm(color_values[n]))
p = patches.FancyArrowPatch(arrow_tail,
arrow_head,
transform=transform,
**arrow_kw)
axes.add_patch(p)
arrows.append(p)
lc = mcollections.LineCollection(streamlines,
transform=transform,
**line_kw)
if use_multicolor_lines:
lc.set_array(np.ma.hstack(line_colors))
lc.set_cmap(cmap)
lc.set_norm(norm)
axes.add_collection(lc)
axes.autoscale_view()
ac = matplotlib.collections.PatchCollection(arrows)
stream_container = StreamplotSet(lc, ac)
return stream_container
def stackplot(axes, x, *args, **kwargs):
"""Draws a stacked area plot.
*x* : 1d array of dimension N
*y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension
1xN. The data is assumed to be unstacked. Each of the following
calls is legal::
stackplot(x, y) # where y is MxN
stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm
Keyword arguments:
*baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle']
Method used to calculate the baseline. 'zero' is just a
simple stacked plot. 'sym' is symmetric around zero and
is sometimes called `ThemeRiver`. 'wiggle' minimizes the
sum of the squared slopes. 'weighted_wiggle' does the
same but weights to account for size of each layer.
It is also called `Streamgraph`-layout. More details
can be found at http://www.leebyron.com/else/streamgraph/.
*labels* : A list or tuple of labels to assign to each data series.
*colors* : A list or tuple of colors. These will be cycled through and
used to colour the stacked areas.
All other keyword arguments are passed to
:func:`~matplotlib.Axes.fill_between`
Returns *r* : A list of
:class:`~matplotlib.collections.PolyCollection`, one for each
element in the stacked area plot.
"""
if len(args) == 1:
y = np.atleast_2d(*args)
elif len(args) > 1:
y = np.row_stack(args)
labels = iter(kwargs.pop('labels', []))
colors = kwargs.pop('colors', None)
if colors is not None:
axes.set_prop_cycle(cycler('color', colors))
baseline = kwargs.pop('baseline', 'zero')
# Assume data passed has not been 'stacked', so stack it here.
stack = np.cumsum(y, axis=0)
r = []
margins = {}
if baseline == 'zero':
first_line = 0.
margins['bottom'] = False
elif baseline == 'sym':
first_line = -np.sum(y, 0) * 0.5
stack += first_line[None, :]
elif baseline == 'wiggle':
m = y.shape[0]
first_line = (y * (m - 0.5 - np.arange(0, m)[:, None])).sum(0)
first_line /= -m
stack += first_line
margins['bottom'] = False
elif baseline == 'weighted_wiggle':
m, n = y.shape
center = np.zeros(n)
total = np.sum(y, 0)
increase = np.hstack((y[:, 0:1], np.diff(y)))
below_size = total - stack
below_size += 0.5 * y
move_up = below_size / total
move_up[:, 0] = 0.5
center = (move_up - 0.5) * increase
center = np.cumsum(center.sum(0))
first_line = center - 0.5 * total
stack += first_line
margins['bottom'] = False
else:
errstr = "Baseline method %s not recognised. " % baseline
errstr += "Expected 'zero', 'sym', 'wiggle' or 'weighted_wiggle'"
raise ValueError(errstr)
# Color between x = 0 and the first array.
color = axes._get_lines.get_next_color()
r.append(
axes.fill_between(x,
first_line,
stack[0, :],
facecolor=color,
label=six.next(labels, None),
margins=margins,
**kwargs))
# Color between array i-1 and array i
for i in xrange(len(y) - 1):
color = axes._get_lines.get_next_color()
r.append(
axes.fill_between(x,
stack[i, :],
stack[i + 1, :],
facecolor=color,
label=six.next(labels, None),
margins=margins,
**kwargs))
return r
def streamplot(axes, x, y, u, v, density=1, linewidth=None, color=None,
cmap=None, norm=None, arrowsize=1, arrowstyle='-|>',
minlength=0.1, transform=None, zorder=1, start_points=None):
"""Draws streamlines of a vector flow.
*x*, *y* : 1d arrays
an *evenly spaced* grid.
*u*, *v* : 2d arrays
x and y-velocities. Number of rows should match length of y, and
the number of columns should match x.
*density* : float or 2-tuple
Controls the closeness of streamlines. When `density = 1`, the domain
is divided into a 30x30 grid---*density* linearly scales this grid.
Each cell in the grid can have, at most, one traversing streamline.
For different densities in each direction, use [density_x, density_y].
*linewidth* : numeric or 2d array
vary linewidth when given a 2d array with the same shape as velocities.
*color* : matplotlib color code, or 2d array
Streamline color. When given an array with the same shape as
velocities, *color* values are converted to colors using *cmap*.
*cmap* : :class:`~matplotlib.colors.Colormap`
Colormap used to plot streamlines and arrows. Only necessary when using
an array input for *color*.
*norm* : :class:`~matplotlib.colors.Normalize`
Normalize object used to scale luminance data to 0, 1. If None, stretch
(min, max) to (0, 1). Only necessary when *color* is an array.
*arrowsize* : float
Factor scale arrow size.
*arrowstyle* : str
Arrow style specification.
See :class:`~matplotlib.patches.FancyArrowPatch`.
*minlength* : float
Minimum length of streamline in axes coordinates.
*start_points*: Nx2 array
Coordinates of starting points for the streamlines.
In data coordinates, the same as the ``x`` and ``y`` arrays.
*zorder* : int
any number
Returns:
*stream_container* : StreamplotSet
Container object with attributes
- lines: `matplotlib.collections.LineCollection` of streamlines
- arrows: collection of `matplotlib.patches.FancyArrowPatch`
objects representing arrows half-way along stream
lines.
This container will probably change in the future to allow changes
to the colormap, alpha, etc. for both lines and arrows, but these
changes should be backward compatible.
"""
grid = Grid(x, y)
mask = StreamMask(density)
dmap = DomainMap(grid, mask)
# default to data coordinates
if transform is None:
transform = axes.transData
if color is None:
color = six.next(axes._get_lines.color_cycle)
if linewidth is None:
linewidth = matplotlib.rcParams['lines.linewidth']
line_kw = {}
arrow_kw = dict(arrowstyle=arrowstyle, mutation_scale=10 * arrowsize)
use_multicolor_lines = isinstance(color, np.ndarray)
if use_multicolor_lines:
if color.shape != grid.shape:
msg = "If 'color' is given, must have the shape of 'Grid(x,y)'"
raise ValueError(msg)
line_colors = []
color = np.ma.masked_invalid(color)
else:
line_kw['color'] = color
arrow_kw['color'] = color
if isinstance(linewidth, np.ndarray):
if linewidth.shape != grid.shape:
msg = "If 'linewidth' is given, must have the shape of 'Grid(x,y)'"
raise ValueError(msg)
line_kw['linewidth'] = []
else:
line_kw['linewidth'] = linewidth
arrow_kw['linewidth'] = linewidth
line_kw['zorder'] = zorder
arrow_kw['zorder'] = zorder
## Sanity checks.
if (u.shape != grid.shape) or (v.shape != grid.shape):
msg = "'u' and 'v' must be of shape 'Grid(x,y)'"
raise ValueError(msg)
u = np.ma.masked_invalid(u)
v = np.ma.masked_invalid(v)
integrate = get_integrator(u, v, dmap, minlength)
trajectories = []
if start_points is None:
for xm, ym in _gen_starting_points(mask.shape):
if mask[ym, xm] == 0:
xg, yg = dmap.mask2grid(xm, ym)
t = integrate(xg, yg)
if t is not None:
trajectories.append(t)
else:
# Convert start_points from data to array coords
# Shift the seed points from the bottom left of the data so that
# data2grid works properly.
sp2 = np.asanyarray(start_points).copy()
sp2[:, 0] += np.abs(x[0])
sp2[:, 1] += np.abs(y[0])
for xs, ys in sp2:
xg, yg = dmap.data2grid(xs, ys)
t = integrate(xg, yg)
if t is not None:
trajectories.append(t)
if use_multicolor_lines:
if norm is None:
norm = mcolors.Normalize(color.min(), color.max())
if cmap is None:
cmap = cm.get_cmap(matplotlib.rcParams['image.cmap'])
else:
cmap = cm.get_cmap(cmap)
streamlines = []
arrows = []
for t in trajectories:
tgx = np.array(t[0])
tgy = np.array(t[1])
# Rescale from grid-coordinates to data-coordinates.
tx = np.array(t[0]) * grid.dx + grid.x_origin
ty = np.array(t[1]) * grid.dy + grid.y_origin
points = np.transpose([tx, ty]).reshape(-1, 1, 2)
streamlines.extend(np.hstack([points[:-1], points[1:]]))
# Add arrows half way along each trajectory.
s = np.cumsum(np.sqrt(np.diff(tx) ** 2 + np.diff(ty) ** 2))
n = np.searchsorted(s, s[-1] / 2.)
arrow_tail = (tx[n], ty[n])
arrow_head = (np.mean(tx[n:n + 2]), np.mean(ty[n:n + 2]))
if isinstance(linewidth, np.ndarray):
line_widths = interpgrid(linewidth, tgx, tgy)[:-1]
line_kw['linewidth'].extend(line_widths)
arrow_kw['linewidth'] = line_widths[n]
if use_multicolor_lines:
color_values = interpgrid(color, tgx, tgy)[:-1]
line_colors.append(color_values)
arrow_kw['color'] = cmap(norm(color_values[n]))
p = patches.FancyArrowPatch(arrow_tail,
arrow_head,
transform=transform,
**arrow_kw)
axes.add_patch(p)
arrows.append(p)
lc = mcollections.LineCollection(streamlines,
transform=transform,
**line_kw)
if use_multicolor_lines:
lc.set_array(np.ma.hstack(line_colors))
lc.set_cmap(cmap)
lc.set_norm(norm)
axes.add_collection(lc)
axes.autoscale_view()
ac = matplotlib.collections.PatchCollection(arrows)
stream_container = StreamplotSet(lc, ac)
return stream_container
def stackplot(axes, x, *args, **kwargs):
"""Draws a stacked area plot.
*x* : 1d array of dimension N
*y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension
1xN. The data is assumed to be unstacked. Each of the following
calls is legal::
stackplot(x, y) # where y is MxN
stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm
Keyword arguments:
*baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle']
Method used to calculate the baseline. 'zero' is just a
simple stacked plot. 'sym' is symmetric around zero and
is sometimes called `ThemeRiver`. 'wiggle' minimizes the
sum of the squared slopes. 'weighted_wiggle' does the
same but weights to account for size of each layer.
It is also called `Streamgraph`-layout. More details
can be found at http://www.leebyron.com/else/streamgraph/.
*labels* : A list or tuple of labels to assign to each data series.
*colors* : A list or tuple of colors. These will be cycled through and
used to colour the stacked areas.
All other keyword arguments are passed to
:func:`~matplotlib.Axes.fill_between`
Returns *r* : A list of
:class:`~matplotlib.collections.PolyCollection`, one for each
element in the stacked area plot.
"""
if len(args) == 1:
y = np.atleast_2d(*args)
elif len(args) > 1:
y = np.row_stack(args)
labels = iter(kwargs.pop('labels', []))
colors = kwargs.pop('colors', None)
if colors is not None:
axes.set_prop_cycle(cycler('color', colors))
baseline = kwargs.pop('baseline', 'zero')
# Assume data passed has not been 'stacked', so stack it here.
stack = np.cumsum(y, axis=0)
r = []
if baseline == 'zero':
first_line = 0.
elif baseline == 'sym':
first_line = -np.sum(y, 0) * 0.5
stack += first_line[None, :]
elif baseline == 'wiggle':
m = y.shape[0]
first_line = (y * (m - 0.5 - np.arange(0, m)[:, None])).sum(0)
first_line /= -m
stack += first_line
elif baseline == 'weighted_wiggle':
m, n = y.shape
center = np.zeros(n)
total = np.sum(y, 0)
increase = np.hstack((y[:, 0:1], np.diff(y)))
below_size = total - stack
below_size += 0.5 * y
move_up = below_size / total
move_up[:, 0] = 0.5
center = (move_up - 0.5) * increase
center = np.cumsum(center.sum(0))
first_line = center - 0.5 * total
stack += first_line
else:
errstr = "Baseline method %s not recognised. " % baseline
errstr += "Expected 'zero', 'sym', 'wiggle' or 'weighted_wiggle'"
raise ValueError(errstr)
# Color between x = 0 and the first array.
if 'color' in axes._get_lines._prop_keys:
color = six.next(axes._get_lines.prop_cycler)['color']
else:
color = None
r.append(axes.fill_between(x, first_line, stack[0, :],
facecolor=color,
label= six.next(labels, None),
**kwargs))
# Color between array i-1 and array i
for i in xrange(len(y) - 1):
if 'color' in axes._get_lines._prop_keys:
color = six.next(axes._get_lines.prop_cycler)['color']
else:
color = None
r.append(axes.fill_between(x, stack[i, :], stack[i + 1, :],
facecolor=color,
label= six.next(labels, None),
**kwargs))
return r
c_top = [((x, y), (90 - a)%180+180) for (y, x, a) in c_top_ \
if bbox.containsx(x)]
return list(zip(lx4, ly4)), [c_left, c_bottom, c_right, c_top]
if __name__ == "__main__":
import matplotlib.pyplot as plt
x = np.array([-3, -2, -1, 0., 1, 2, 3, 2, 1, 0, -1, -2, -3, 5])
#x = np.array([-3, -2, -1, 0., 1, 2, 3])
y = np.arange(len(x))
#x0 = 2
plt.plot(x, y, lw=1)
from matplotlib.transforms import Bbox
bb = Bbox.from_extents(-2, 3, 2, 12.5)
lxy, ticks = clip_line_to_rect(x, y, bb)
for xx, yy in lxy:
plt.plot(xx, yy, lw=1, color="g")
ccc = iter(["ro", "go", "rx", "bx"])
for ttt in ticks:
cc = six.next(ccc)
for (xx, yy), aa in ttt:
plt.plot([xx], [yy], cc)
#xlim(
def add(self, patchlabel='', flows=None, orientations=None, labels='',
trunklength=1.0, pathlengths=0.25, prior=None, connect=(0, 0),
rotation=0, **kwargs):
"""
Add a simple Sankey diagram with flows at the same hierarchical level.
Return value is the instance of :class:`Sankey`.
Optional keyword arguments:
=============== ===================================================
Keyword Description
=============== ===================================================
*patchlabel* label to be placed at the center of the diagram
Note: *label* (not *patchlabel*) will be passed to
the patch through ``**kwargs`` and can be used to
create an entry in the legend.
*flows* array of flow values
By convention, inputs are positive and outputs are
negative.
*orientations* list of orientations of the paths
Valid values are 1 (from/to the top), 0 (from/to
the left or right), or -1 (from/to the bottom). If
*orientations* == 0, inputs will break in from the
left and outputs will break away to the right.
*labels* list of specifications of the labels for the flows
Each value may be *None* (no labels), '' (just
label the quantities), or a labeling string. If a
single value is provided, it will be applied to all
flows. If an entry is a non-empty string, then the
quantity for the corresponding flow will be shown
below the string. However, if the *unit* of the
main diagram is None, then quantities are never
shown, regardless of the value of this argument.
*trunklength* length between the bases of the input and output
groups
*pathlengths* list of lengths of the arrows before break-in or
after break-away
If a single value is given, then it will be applied
to the first (inside) paths on the top and bottom,
and the length of all other arrows will be
justified accordingly. The *pathlengths* are not
applied to the horizontal inputs and outputs.
*prior* index of the prior diagram to which this diagram
should be connected
*connect* a (prior, this) tuple indexing the flow of the
prior diagram and the flow of this diagram which
should be connected
If this is the first diagram or *prior* is *None*,
*connect* will be ignored.
*rotation* angle of rotation of the diagram [deg]
*rotation* is ignored if this diagram is connected
to an existing one (using *prior* and *connect*).
The interpretation of the *orientations* argument
will be rotated accordingly (e.g., if *rotation*
== 90, an *orientations* entry of 1 means to/from
the left).
=============== ===================================================
Valid kwargs are :meth:`matplotlib.patches.PathPatch` arguments:
%(Patch)s
As examples, ``fill=False`` and ``label='A legend entry'``.
By default, ``facecolor='#bfd1d4'`` (light blue) and
``linewidth=0.5``.
The indexing parameters (*prior* and *connect*) are zero-based.
The flows are placed along the top of the diagram from the inside out
in order of their index within the *flows* list or array. They are
placed along the sides of the diagram from the top down and along the
bottom from the outside in.
If the sum of the inputs and outputs is nonzero, the discrepancy
will appear as a cubic Bezier curve along the top and bottom edges of
the trunk.
.. seealso::
:meth:`finish`
"""
# Check and preprocess the arguments.
if flows is None:
flows = np.array([1.0, -1.0])
else:
flows = np.array(flows)
n = flows.shape[0] # Number of flows
if rotation is None:
rotation = 0
else:
# In the code below, angles are expressed in deg/90.
rotation /= 90.0
if orientations is None:
orientations = [0, 0]
if len(orientations) != n:
raise ValueError(
"orientations and flows must have the same length.\n"
"orientations has length %d, but flows has length %d."
% (len(orientations), n))
if labels != '' and getattr(labels, '__iter__', False):
# iterable() isn't used because it would give True if labels is a
# string
if len(labels) != n:
raise ValueError(
"If labels is a list, then labels and flows must have the "
"same length.\nlabels has length %d, but flows has length %d."
% (len(labels), n))
else:
labels = [labels] * n
if trunklength < 0:
raise ValueError(
"trunklength is negative.\nThis isn't allowed, because it would "
"cause poor layout.")
if np.absolute(np.sum(flows)) > self.tolerance:
verbose.report(
"The sum of the flows is nonzero (%f).\nIs the "
"system not at steady state?" % np.sum(flows), 'helpful')
scaled_flows = self.scale * flows
gain = sum(max(flow, 0) for flow in scaled_flows)
loss = sum(min(flow, 0) for flow in scaled_flows)
if not (0.5 <= gain <= 2.0):
verbose.report(
"The scaled sum of the inputs is %f.\nThis may "
"cause poor layout.\nConsider changing the scale so"
" that the scaled sum is approximately 1.0." % gain, 'helpful')
if not (-2.0 <= loss <= -0.5):
verbose.report(
"The scaled sum of the outputs is %f.\nThis may "
"cause poor layout.\nConsider changing the scale so"
" that the scaled sum is approximately 1.0." % gain, 'helpful')
if prior is not None:
if prior < 0:
raise ValueError("The index of the prior diagram is negative.")
if min(connect) < 0:
raise ValueError(
"At least one of the connection indices is negative.")
if prior >= len(self.diagrams):
raise ValueError(
"The index of the prior diagram is %d, but there are "
"only %d other diagrams.\nThe index is zero-based."
% (prior, len(self.diagrams)))
if connect[0] >= len(self.diagrams[prior].flows):
raise ValueError(
"The connection index to the source diagram is %d, but "
"that diagram has only %d flows.\nThe index is zero-based."
% (connect[0], len(self.diagrams[prior].flows)))
if connect[1] >= n:
raise ValueError(
"The connection index to this diagram is %d, but this diagram"
"has only %d flows.\n The index is zero-based."
% (connect[1], n))
if self.diagrams[prior].angles[connect[0]] is None:
raise ValueError(
"The connection cannot be made. Check that the magnitude "
"of flow %d of diagram %d is greater than or equal to the "
"specified tolerance." % (connect[0], prior))
flow_error = (self.diagrams[prior].flows[connect[0]] +
flows[connect[1]])
if abs(flow_error) >= self.tolerance:
raise ValueError(
"The scaled sum of the connected flows is %f, which is not "
"within the tolerance (%f)." % (flow_error, self.tolerance))
# Determine if the flows are inputs.
are_inputs = [None] * n
for i, flow in enumerate(flows):
if flow >= self.tolerance:
are_inputs[i] = True
elif flow <= -self.tolerance:
are_inputs[i] = False
else:
verbose.report(
"The magnitude of flow %d (%f) is below the "
"tolerance (%f).\nIt will not be shown, and it "
"cannot be used in a connection."
% (i, flow, self.tolerance), 'helpful')
# Determine the angles of the arrows (before rotation).
angles = [None] * n
for i, (orient, is_input) in enumerate(zip(orientations, are_inputs)):
if orient == 1:
if is_input:
angles[i] = DOWN
elif not is_input:
# Be specific since is_input can be None.
angles[i] = UP
elif orient == 0:
if is_input is not None:
angles[i] = RIGHT
else:
if orient != -1:
raise ValueError(
"The value of orientations[%d] is %d, "
"but it must be [ -1 | 0 | 1 ]." % (i, orient))
if is_input:
angles[i] = UP
elif not is_input:
angles[i] = DOWN
# Justify the lengths of the paths.
if iterable(pathlengths):
if len(pathlengths) != n:
raise ValueError(
"If pathlengths is a list, then pathlengths and flows must "
"have the same length.\npathlengths has length %d, but flows "
"has length %d." % (len(pathlengths), n))
else: # Make pathlengths into a list.
urlength = pathlengths
ullength = pathlengths
lrlength = pathlengths
lllength = pathlengths
d = dict(RIGHT=pathlengths)
pathlengths = [d.get(angle, 0) for angle in angles]
# Determine the lengths of the top-side arrows
# from the middle outwards.
for i, (angle, is_input, flow) in enumerate(zip(angles, are_inputs,
scaled_flows)):
if angle == DOWN and is_input:
pathlengths[i] = ullength
ullength += flow
elif angle == UP and not is_input:
pathlengths[i] = urlength
urlength -= flow # Flow is negative for outputs.
# Determine the lengths of the bottom-side arrows
# from the middle outwards.
for i, (angle, is_input, flow) in enumerate(reversed(list(zip(
angles, are_inputs, scaled_flows)))):
if angle == UP and is_input:
pathlengths[n - i - 1] = lllength
lllength += flow
elif angle == DOWN and not is_input:
pathlengths[n - i - 1] = lrlength
lrlength -= flow
# Determine the lengths of the left-side arrows
# from the bottom upwards.
has_left_input = False
for i, (angle, is_input, spec) in enumerate(reversed(list(zip(
angles, are_inputs, zip(scaled_flows, pathlengths))))):
if angle == RIGHT:
if is_input:
if has_left_input:
pathlengths[n - i - 1] = 0
else:
has_left_input = True
# Determine the lengths of the right-side arrows
# from the top downwards.
has_right_output = False
for i, (angle, is_input, spec) in enumerate(zip(
angles, are_inputs, list(zip(scaled_flows, pathlengths)))):
if angle == RIGHT:
if not is_input:
if has_right_output:
pathlengths[i] = 0
else:
has_right_output = True
# Begin the subpaths, and smooth the transition if the sum of the flows
# is nonzero.
urpath = [(Path.MOVETO, [(self.gap - trunklength / 2.0), # Upper right
gain / 2.0]),
(Path.LINETO, [(self.gap - trunklength / 2.0) / 2.0,
gain / 2.0]),
(Path.CURVE4, [(self.gap - trunklength / 2.0) / 8.0,
gain / 2.0]),
(Path.CURVE4, [(trunklength / 2.0 - self.gap) / 8.0,
-loss / 2.0]),
(Path.LINETO, [(trunklength / 2.0 - self.gap) / 2.0,
-loss / 2.0]),
(Path.LINETO, [(trunklength / 2.0 - self.gap),
-loss / 2.0])]
llpath = [(Path.LINETO, [(trunklength / 2.0 - self.gap), # Lower left
loss / 2.0]),
(Path.LINETO, [(trunklength / 2.0 - self.gap) / 2.0,
loss / 2.0]),
(Path.CURVE4, [(trunklength / 2.0 - self.gap) / 8.0,
loss / 2.0]),
(Path.CURVE4, [(self.gap - trunklength / 2.0) / 8.0,
-gain / 2.0]),
(Path.LINETO, [(self.gap - trunklength / 2.0) / 2.0,
-gain / 2.0]),
(Path.LINETO, [(self.gap - trunklength / 2.0),
-gain / 2.0])]
lrpath = [(Path.LINETO, [(trunklength / 2.0 - self.gap), # Lower right
loss / 2.0])]
ulpath = [(Path.LINETO, [self.gap - trunklength / 2.0, # Upper left
gain / 2.0])]
# Add the subpaths and assign the locations of the tips and labels.
tips = np.zeros((n, 2))
label_locations = np.zeros((n, 2))
# Add the top-side inputs and outputs from the middle outwards.
for i, (angle, is_input, spec) in enumerate(zip(
angles, are_inputs, list(zip(scaled_flows, pathlengths)))):
if angle == DOWN and is_input:
tips[i, :], label_locations[i, :] = self._add_input(
ulpath, angle, *spec)
elif angle == UP and not is_input:
tips[i, :], label_locations[i, :] = self._add_output(
urpath, angle, *spec)
# Add the bottom-side inputs and outputs from the middle outwards.
for i, (angle, is_input, spec) in enumerate(reversed(list(zip(
angles, are_inputs, list(zip(scaled_flows, pathlengths)))))):
if angle == UP and is_input:
tip, label_location = self._add_input(llpath, angle, *spec)
tips[n - i - 1, :] = tip
label_locations[n - i - 1, :] = label_location
elif angle == DOWN and not is_input:
tip, label_location = self._add_output(lrpath, angle, *spec)
tips[n - i - 1, :] = tip
label_locations[n - i - 1, :] = label_location
# Add the left-side inputs from the bottom upwards.
has_left_input = False
for i, (angle, is_input, spec) in enumerate(reversed(list(zip(
angles, are_inputs, list(zip(scaled_flows, pathlengths)))))):
if angle == RIGHT and is_input:
if not has_left_input:
# Make sure the lower path extends
# at least as far as the upper one.
if llpath[-1][1][0] > ulpath[-1][1][0]:
llpath.append((Path.LINETO, [ulpath[-1][1][0],
llpath[-1][1][1]]))
has_left_input = True
tip, label_location = self._add_input(llpath, angle, *spec)
tips[n - i - 1, :] = tip
label_locations[n - i - 1, :] = label_location
# Add the right-side outputs from the top downwards.
has_right_output = False
for i, (angle, is_input, spec) in enumerate(zip(
angles, are_inputs, list(zip(scaled_flows, pathlengths)))):
if angle == RIGHT and not is_input:
if not has_right_output:
# Make sure the upper path extends
# at least as far as the lower one.
if urpath[-1][1][0] < lrpath[-1][1][0]:
urpath.append((Path.LINETO, [lrpath[-1][1][0],
urpath[-1][1][1]]))
has_right_output = True
tips[i, :], label_locations[i, :] = self._add_output(
urpath, angle, *spec)
# Trim any hanging vertices.
if not has_left_input:
ulpath.pop()
llpath.pop()
if not has_right_output:
lrpath.pop()
urpath.pop()
# Concatenate the subpaths in the correct order (clockwise from top).
path = (urpath + self._revert(lrpath) + llpath + self._revert(ulpath) +
[(Path.CLOSEPOLY, urpath[0][1])])
# Create a patch with the Sankey outline.
codes, vertices = list(zip(*path))
vertices = np.array(vertices)
def _get_angle(a, r):
if a is None:
return None
else:
return a + r
if prior is None:
if rotation != 0: # By default, none of this is needed.
angles = [_get_angle(angle, rotation) for angle in angles]
rotate = Affine2D().rotate_deg(rotation * 90).transform_affine
tips = rotate(tips)
label_locations = rotate(label_locations)
vertices = rotate(vertices)
text = self.ax.text(0, 0, s=patchlabel, ha='center', va='center')
else:
rotation = (self.diagrams[prior].angles[connect[0]] -
angles[connect[1]])
angles = [_get_angle(angle, rotation) for angle in angles]
rotate = Affine2D().rotate_deg(rotation * 90).transform_affine
tips = rotate(tips)
offset = self.diagrams[prior].tips[connect[0]] - tips[connect[1]]
translate = Affine2D().translate(*offset).transform_affine
tips = translate(tips)
label_locations = translate(rotate(label_locations))
vertices = translate(rotate(vertices))
kwds = dict(s=patchlabel, ha='center', va='center')
text = self.ax.text(*offset, **kwds)
if False: # Debug
print("llpath\n", llpath)
print("ulpath\n", self._revert(ulpath))
print("urpath\n", urpath)
print("lrpath\n", self._revert(lrpath))
xs, ys = list(zip(*vertices))
self.ax.plot(xs, ys, 'go-')
if rcParams['_internal.classic_mode']:
fc = kwargs.pop('fc', kwargs.pop('facecolor', '#bfd1d4'))
lw = kwargs.pop('lw', kwargs.pop('linewidth', 0.5))
else:
fc = kwargs.pop('fc', kwargs.pop('facecolor', None))
lw = kwargs.pop('lw', kwargs.pop('linewidth', None))
if fc is None:
fc = six.next(self.ax._get_patches_for_fill.prop_cycler)['color']
patch = PathPatch(Path(vertices, codes), fc=fc, lw=lw, **kwargs)
self.ax.add_patch(patch)
# Add the path labels.
texts = []
for number, angle, label, location in zip(flows, angles, labels,
label_locations):
if label is None or angle is None:
label = ''
elif self.unit is not None:
quantity = self.format % abs(number) + self.unit
if label != '':
label += "\n"
label += quantity
texts.append(self.ax.text(x=location[0], y=location[1],
s=label,
ha='center', va='center'))
# Text objects are placed even they are empty (as long as the magnitude
# of the corresponding flow is larger than the tolerance) in case the
# user wants to provide labels later.
# Expand the size of the diagram if necessary.
self.extent = (min(np.min(vertices[:, 0]),
np.min(label_locations[:, 0]),
self.extent[0]),
max(np.max(vertices[:, 0]),
np.max(label_locations[:, 0]),
self.extent[1]),
min(np.min(vertices[:, 1]),
np.min(label_locations[:, 1]),
self.extent[2]),
max(np.max(vertices[:, 1]),
np.max(label_locations[:, 1]),
self.extent[3]))
# Include both vertices _and_ label locations in the extents; there are
# where either could determine the margins (e.g., arrow shoulders).
# Add this diagram as a subdiagram.
self.diagrams.append(Bunch(patch=patch, flows=flows, angles=angles,
tips=tips, text=text, texts=texts))
# Allow a daisy-chained call structure (see docstring for the class).
return self
c_top = [((x, y), (90 - a)%180+180) for (y, x, a) in c_top_ \
if bbox.containsx(x)]
return list(zip(lx4, ly4)), [c_left, c_bottom, c_right, c_top]
if __name__ == "__main__":
import matplotlib.pyplot as plt
x = np.array([-3, -2, -1, 0., 1, 2, 3, 2, 1, 0, -1, -2, -3, 5])
#x = np.array([-3, -2, -1, 0., 1, 2, 3])
y = np.arange(len(x))
#x0 = 2
plt.plot(x, y, lw=1)
from matplotlib.transforms import Bbox
bb = Bbox.from_extents(-2, 3, 2, 12.5)
lxy, ticks = clip_line_to_rect(x, y, bb)
for xx, yy in lxy:
plt.plot(xx, yy, lw=1, color="g")
ccc = iter(["ro", "go", "rx", "bx"])
for ttt in ticks:
cc = six.next(ccc)
for (xx, yy), aa in ttt:
plt.plot([xx], [yy], cc)
#xlim(
