def add_china_map_2basemap(ax,name ="province", facecolor='none',
edgecolor='c', lw=2, encoding='utf-8', **kwargs):
"""
Add china province boundary to basemap instance.
:param mp: basemap instance.
:param ax: matplotlib axes instance.
:param name: map name.
:param facecolor: fill color, default is none.
:param edgecolor: edge color.
:param lw: line width.
:param kwargs: keywords passing to Polygon.
:return: None.
"""
# map name
names = {'nation': "bou1_4p", 'province': "bou2_4p",
'county': "BOUNT_poly", 'river': "hyd1_4p",
'river_high': "hyd2_4p"}
# get shape file and information
shpfile = pkg_resources.resource_filename(
'meteva', "resources/maps/" + names[name])
shp1 = readshapefile(shpfile, default_encoding=encoding)
lines = LineCollection(shp1,antialiaseds=(1,))
lines.set_color(edgecolor)
lines.set_linewidth(lw)
lines.set_label('_nolabel_')
ax.add_collection(lines)
def plot_multicolor_line(splot,
x,
y,
z,
cmap=None,
vmin=None,
vmax=None,
lw=None,
label=None,
off_x=0,
off_y=0):
"""
Adds a line to a plot that changes color based on the z-value and a cmap (default=viridis)
"""
# Based on the matplotlib example: https://matplotlib.org/gallery/lines_bars_and_markers/multicolored_line.html
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
xo = np.ones(len(segments)) * off_x
yo = np.ones(len(segments)) * off_y
# xyo = list(zip(xo, yo))
xyo = np.array([xo, yo]).T
lc = LineCollection(segments,
cmap=cmap,
offsets=xyo,
transOffset=matplotlib.transforms.IdentityTransform())
lc.set_array(z)
lc.set_clim(vmin=vmin, vmax=vmax)
if lw is not None:
lc.set_linewidth(lw)
if label is not None:
lc.set_label(label)
return splot.add_collection(lc)
def loadlines(self,
name,
curdir='beijingJson',
zorder=None,
linewidth=0.5,
color='k',
antialiased=1,
ax=None,
default_encoding='utf-8',
linestyle='-',
linesalpha=1):
# get current axes instance (if none specified).
filename = curdir + '/' + name + '.json'
coords = json.load(codecs.open(filename, 'r', 'utf-8'))
coords = self.projectcoords(coords)
ax = ax or self._check_ax()
# make LineCollections for each polygon.
lines = LineCollection(coords, antialiaseds=(1, ))
lines.set_color(color)
lines.set_linewidth(linewidth)
lines.set_linestyle(linestyle)
lines.set_alpha(linesalpha)
lines.set_label('_nolabel_')
if zorder is not None:
lines.set_zorder(zorder)
ax.add_collection(lines)
# set axes limits to fit map region.
self.set_axes_limits(ax=ax)
# clip boundaries to map limbs
lines, c = self._cliplimb(ax, lines)
self.__dict__[name] = coords
return lines
def draw_swap_gate(axes, gate_pos, wire_pos1, wire_pos2, plot_params):
"""
Draws the symbol for a SWAP gate.
Args:
axes (matplotlib.axes.Axes): axes object
x (float): x coordinate [data units]
y1 (float): y coordinate of the 1st qubit wire
y2 (float): y coordinate of the 2nd qubit wire
plot_params (dict): plot parameters
"""
delta = plot_params['swap_delta']
lines = []
for wire_pos in (wire_pos1, wire_pos2):
lines.append([(gate_pos - delta, wire_pos - delta),
(gate_pos + delta, wire_pos + delta)])
lines.append([(gate_pos - delta, wire_pos + delta),
(gate_pos + delta, wire_pos - delta)])
lines.append([(gate_pos, wire_pos1), (gate_pos, wire_pos2)])
gate = LineCollection(lines,
colors='k',
linewidths=plot_params['linewidth'])
gate.set_label('SWAP')
axes.add_collection(gate)
def add_trajectory(fig_lung, name, color_map, line_label, show_predicted, data):
plt.figure(fig_lung.number) # set fig as current figure
if show_predicted:
T = len(data.dx_predicted)
for i in range(T):
if i % 50 == 0:
plt.plot(data.dx_predicted[i][:, 1],
data.dx_predicted[i][:, 0],
'.-', color = purple, alpha = 0.1,
label = 'predicted trajectory')
y = data.x_sensed[:,1] - data.x_sensed[0,1]
x = data.x_sensed[:,0] - data.x_sensed[0,0]
t = np.linspace(2, 10, len(y))
points = np.array([y, x]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments, cmap=color_map,
norm=plt.Normalize(0, 10))
lc.set_array(t)
lc.set_linewidth(3)
lc.set_label(line_label)
plt.gca().add_collection(lc)
plt.plot(data.x_desired[:,1] - data.x_sensed[0,1],
data.x_desired[:,0] - data.x_sensed[0,0],
'g.', label = 'desired')
point_count = 0
last_xd = np.array([])
offset_y = 0.75
offset_x = 1
for i, xd in enumerate(data.x_desired):
if not np.array_equal(xd, last_xd):
plt.text(xd[1] + offset_y, xd[0] + offset_x,
str(point_count), color = green)
last_xd = xd.copy()
point_count += 1
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
newLabels, newHandles = [], []
for handle, label in zip(handles, labels):
if label not in newLabels:
newLabels.append(label)
newHandles.append(handle)
plt.legend(newHandles, newLabels, loc = 'upper left', bbox_to_anchor = [1,1])
legend = ax.get_legend()
for i, name in enumerate(newLabels):
if name.lower() == 'ukf + mpc':
legend.legendHandles[i].set_color(blue)
elif name.lower() == 'mlc':
legend.legendHandles[i].set_color(red)
elif name.lower() == 'tip trajectory':
legend.legendHandles[i].set_color(purple)
def plot_magnetic_trace(self, fig, x, y):
plt.figure(fig.number)
t = np.linspace(2, 10, len(x))
points = np.array([x,y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments, cmap=blues_cmap,
norm=plt.Normalize(0, 10))
lc.set_array(t)
lc.set_linewidth(3)
lc.set_label('sensed position')
plt.gca().add_collection(lc)
return fig
def plot(self, plot, col):
# Set the scale of the plot
if self.log_x:
plot.xscale('log')
if self.log_y:
plot.yscale('log')
# Process the values
values = []
for x in self.get_x_data_gen().get_values():
for y in self.get_y_data_gen().get_values():
values.append(zip(x,y))
lines = LineCollection(values)
lines.set_color(col)
lines.set_label(str(self.get_name()))
# Plot the values
plot.add_collection(lines)
def draw_wires(axes, n_labels, gate_grid, wire_grid, plot_params):
"""
Draws all the circuit qubit wires.
Args:
axes (matplotlib.axes.Axes): axes object
n_labels (int): number of qubit
gate_grid (ndarray): array with the ref. x positions of the gates
wire_grid (ndarray): array with the ref. y positions of the qubit
wires
plot_params (dict): plot parameters
"""
# pylint: disable=invalid-name
lines = []
for i in range(n_labels):
lines.append(((gate_grid[0] - plot_params['column_spacing'],
wire_grid[i]), (gate_grid[-1], wire_grid[i])))
all_lines = LineCollection(lines,
linewidths=plot_params['linewidth'],
edgecolor='k')
all_lines.set_label('qubit_wires')
axes.add_collection(all_lines)
class DecayLine(object):
def __init__(self, n_points, tail_length, rgb_color, zorder=2, label=None):
self.n_points = int(n_points)
self.tail_length = int(tail_length)
self.rgb_color = rgb_color
self._zorder = zorder
self.lc = None
self._label = label
self.dbg = True
def __str__(self):
if not hasattr(self, 'points') or not hasattr(self, 'segments'):
return ""
res = "DecayLine:" + "\n\t{}".format(self.segments)
return res
def set_data(self, x=None, y=None):
if x is None or y is None:
self.lc = LineCollection([], linewidths=1.0, zorder=self._zorder)
else:
# ensure we don't start with more points than we want
x = x[-self.n_points:]
y = y[-self.n_points:]
# create a list of points with shape (len(x), 1, 2)
# array([[[ x0 , y0 ]],
# [[ x1 , y1 ]],
# ...,
# [[ xn , yn ]]])
#self.points = np.array([x, y]).T.reshape(-1, 1, 2)
if not hasattr(self, 'points'):
self.points = deque([[x[i], y[i]] for i in range(len(x))])
else:
for i in range(len(x)):
self.points.append([x[i], y[i]])
# group each point with the one following it (shape (len(x)-1, 2, 2)):
# array([[[ x0 , y0 ],
# [ x1 , y1 ]],
# [[ x1 , y1 ],
# [ x2 , y2 ]],
# ...
# self.segments = np.concatenate([self.points[:-1], self.points[1:]], axis=1)
if len(self.points) > 1:
pts = np.array(self.points).reshape(-1, 1, 2)
self.segments = np.concatenate([pts[:-1], pts[1:]], axis=1)
if hasattr(self, 'alphas'):
del self.alphas
if hasattr(self, 'rgba_colors'):
del self.rgba_colors
if self.lc is None:
self.lc = LineCollection(
self.segments,
colors=self.get_colors(),
linewidths=1.0,
zorder=self._zorder,
label="" if self._label is None else self._label)
self.lc.set_segments(self.segments)
colours = self.get_colors()
if self.dbg:
print(colours)
self.dbg = False
self.lc.set_color(colours)
self.lc.set_edgecolors(colours)
# self.lc.set_facecolors(colours)
else:
if self.lc is None:
self.lc = LineCollection(
[],
linewidths=1.0,
colors=(self.rgb_color[0], self.rgb_color[1],
self.rgb_color[2], 1.0),
zorder=self._zorder,
label="" if self._label is None else self._label)
def get_LineCollection(self):
if not hasattr(self, 'lc'):
self.set_data()
return self.lc
def get_label(self):
"""Return the label used for this artist in the legend."""
if self.lc is None:
return "" if self._label is None else self._label
return self.lc.get_label()
def set_label(self, s):
self._label = s
if self.lc is not None:
self.lc.set_label(self._label)
def add_point(self, x, y):
if not hasattr(self, 'points') or not hasattr(self, 'segments'):
self.set_data([x], [y])
else:
# TODO: could use a circular buffer to reduce memory operations...
# self.segments = np.concatenate((self.segments,[[self.points[-1][0],[x,y]]]))
# self.points = np.concatenate((self.points, [[[x,y]]]))
self.points.append([x, y])
# remove points if necessary:
while len(self.points) > self.n_points:
self.points.popleft()
pts = np.array(self.points).reshape(-1, 1, 2)
self.segments = np.concatenate([pts[:-1], pts[1:]], axis=1)
self.lc.set_segments(self.segments)
colours = self.get_colors()
self.lc.set_color(colours)
self.lc.set_edgecolors(colours)
# self.lc.set_facecolors(colours)
@property
def npoints(self):
if not hasattr(self, 'points'):
return 0
else:
#return self.points.shape[0]
return len(self.points)
def get_alphas(self):
n_segments = self.n_points - 1
n = len(self.segments)
# n = self.points.shape[0]
if n < n_segments:
rest_length = n_segments - self.tail_length
if n <= rest_length:
return np.ones(n)
else:
tail_length = n - rest_length
tail = np.linspace(1. / tail_length, 1., tail_length)
rest = np.ones(rest_length)
return np.concatenate((tail, rest), axis=None)
else: # n == n_segments
if not hasattr(self, 'alphas'):
tail = np.linspace(1. / self.tail_length, 1., self.tail_length)
rest = np.ones(n_segments - self.tail_length)
self.alphas = np.concatenate((tail, rest), axis=None)
return self.alphas
def get_colors(self):
n_segments = self.n_points - 1
n = len(self.segments)
if n < 2:
# return [self.rgb_color+[1.] for i in range(n)]
colour = None
if isinstance(self.rgb_color, np.ndarray):
colour = np.concatenate([self.rgb_color, [
1.0,
]])
elif type(self.rgb_color) in [list, tuple]:
colour = self.rgb_color + [1.]
return [colour for i in range(n)]
if n < n_segments:
alphas = self.get_alphas()
rgba_colors = np.zeros((n, 4), dtype=np.float32)
# first place the rgb color in the first three columns
rgba_colors[:, 0:3] = self.rgb_color[:]
# and the fourth column needs to be your alphas
rgba_colors[:, 3] = alphas
# self.rgba_colors = rgba_colors
# return self.rgba_colors
return rgba_colors
else:
if hasattr(self, 'rgba_colors'):
pass
else:
alphas = self.get_alphas()
rgba_colors = np.zeros((n, 4), dtype=np.float32)
# first place the rgb color in the first three columns
rgba_colors[:, 0:3] = self.rgb_color[:]
# and the fourth column needs to be your alphas
rgba_colors[:, 3] = alphas
self.rgba_colors = rgba_colors
return self.rgba_colors
get_color = get_colors # for compatibility with old versions
G = nx.read_gpickle(args['graph'])
graph_data = nx.get_node_attributes(G, 'pos')
data = np.array(tuple(graph_data.values()))
try:
labels = nx.get_node_attributes(G, 'label')
labels = np.array(list(labels.values()))
except:
labels = np.zeros(len(data))
instances = np.zeros(len(data))
edgelist = list(G.edges())
edge_pos = np.asarray([(graph_data[e[0]], graph_data[e[1]])
for e in edgelist])
edge_collection = LineCollection(edge_pos)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(None)
fig, ax = plt.subplots()
pts = ax.scatter(data[:, 0], data[:, 1], s=80, c=labels)
ax.add_collection(edge_collection)
selector = SelectFromCollection(ax, pts)
fc = pts.get_facecolors()
mode = 'SELECT'
instance = 1
fig.suptitle("Mode: " + mode + " | Instance: " + str(instance),
x=0.5,
y=0.05)
ann_list = []
def draw_networkx_edges(
G,
pos,
edgelist=None,
width=1.0,
edge_color="k",
style="solid",
alpha=None,
arrowstyle="-|>",
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
min_source_margin=0,
min_target_margin=0,
):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color or array of colors (default='k')
Edge color. Can be a single color or a sequence of colors with the same
length as edgelist. Color can be string, or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=None)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : str, optional (default=None)
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See :py:class: `matplotlib.patches.ConnectionStyle` and
:py:class: `matplotlib.patches.FancyArrowPatch` for more info.
label : [None| string]
Label for legend
min_source_margin : int, optional (default=0)
The minimum margin (gap) at the begining of the edge at the source.
min_target_margin : int, optional (default=0)
The minimum margin (gap) at the end of the edge at the target.
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if len(edgelist) == 0: # no edges!
if not G.is_directed() or not arrows:
return LineCollection(None)
else:
return []
if nodelist is None:
nodelist = list(G.nodes())
# FancyArrowPatch handles color=None different from LineCollection
if edge_color is None:
edge_color = "k"
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# Check if edge_color is an array of floats and map to edge_cmap.
# This is the only case handled differently from matplotlib
if (np.iterable(edge_color) and (len(edge_color) == len(edge_pos))
and np.alltrue([isinstance(c, Number) for c in edge_color])):
if edge_cmap is not None:
assert isinstance(edge_cmap, Colormap)
else:
edge_cmap = plt.get_cmap()
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
if not G.is_directed() or not arrows:
edge_collection = LineCollection(
edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
alpha=alpha,
)
edge_collection.set_cmap(edge_cmap)
edge_collection.set_clim(edge_vmin, edge_vmax)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
# FancyArrowPatch doesn't handle color strings
arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if np.iterable(node_size): # many node sizes
source, target = edgelist[i][:2]
source_node_size = node_size[nodelist.index(source)]
target_node_size = node_size[nodelist.index(target)]
shrink_source = to_marker_edge(source_node_size, node_shape)
shrink_target = to_marker_edge(target_node_size, node_shape)
else:
shrink_source = shrink_target = to_marker_edge(
node_size, node_shape)
if shrink_source < min_source_margin:
shrink_source = min_source_margin
if shrink_target < min_target_margin:
shrink_target = min_target_margin
if len(arrow_colors) == len(edge_pos):
arrow_color = arrow_colors[i]
elif len(arrow_colors) == 1:
arrow_color = arrow_colors[0]
else: # Cycle through colors
arrow_color = arrow_colors[i % len(arrow_colors)]
if np.iterable(width):
if len(width) == len(edge_pos):
line_width = width[i]
else:
line_width = width[i % len(width)]
else:
line_width = width
arrow = FancyArrowPatch(
(x1, y1),
(x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
linestyle=style,
zorder=1,
) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return arrow_collection
def draw_edges(G,
pos,
ax,
edgelist=None,
width=1.0,
width_adjuster=50,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
traversal_weight=1.0,
edge_delengthify=0.15,
arrows=True,
label=None,
zorder=1,
**kwds):
"""
Code cleaned-up version of networkx.draw_networkx_edges
New args:
width_adjuster - the line width is generated from the weight if present, use this adjuster to thicken the lines (multiply)
"""
if edgelist is None:
edgelist = G.edges()
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = [(pos[e[0]], pos[e[1]]) for e in edgelist]
new_ep = []
for e in edge_pos:
x, y = e[0]
dx, dy = e[1]
# Get edge length
elx = (dx - x) * edge_delengthify
ely = (dy - y) * edge_delengthify
x += elx
y += ely
dx -= elx
dy -= ely
new_ep.append(((x, y), (dx, dy)))
edge_pos = numpy.asarray(new_ep)
if not cb.iterable(width):
#print [G.get_edge_data(n[0], n[1])['weight'] for n in edgelist]
# see if I can find an edge attribute:
if 'weight' in G.get_edge_data(edgelist[0][0],
edgelist[0][1]): # Test an edge
lw = [
0.5 +
((G.get_edge_data(n[0], n[1])['weight'] - traversal_weight) *
width_adjuster) for n in edgelist
]
else:
lw = (width, )
else:
lw = width
if not is_string_like(edge_color) and cb.iterable(edge_color) and len(
edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
zorder=zorder)
edge_collection.set_label(label)
ax.add_collection(edge_collection)
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
# update view
'''
minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim(corners)
ax.autoscale_view()
'''
return (edge_collection)
def animate_convolution(x, h, y, t, tau, td, taud, interval=75):
"""Plot animation of graphical representation of linear convolution.
Parameters
----------
x : sympy function
First function to be convolved.
h: sympy function
Second function to be convolved.
t: sympy variable
Independent variable for functions (e.g. time).
tau: sympy variable
Integration variable for convolution.
td: array like
Discrete values of independent variable evaluated for plot.
taud:
Discrete values of integration variable evaluated for animation.
interval:
Interval in ms between frames of animation.
Returns
-------
matplotlib.animation.FuncAnimation object.
"""
def animate(ti):
p = sym.plot(x.subs(t, tau), (tau, taud[0], taud[-1]), show=False)
line_x.set_segments(p[0].get_segments())
p = sym.plot(h.subs(t, t - tau).subs(t, ti), (tau, taud[0], taud[-1]),
show=False)
line_h.set_segments(p[0].get_segments())
p = sym.plot(y, (t, taud[0], taud[-1]), show=False)
line_y.set_segments(p[0].get_segments())
p = sym.plot(x.subs(t, tau) * h.subs(t, ti - tau), (tau, -5, 5),
show=False)
points = p[0].get_points()
verts = [[(xi[0], xi[1]) for xi in np.transpose(np.array(points))]]
fill.set_verts(verts)
dot.set_data(ti, y.subs(t, ti))
# define line/fill collections and setup plot
default_figsize = plt.rcParams.get('figure.figsize')
fig, ax = plt.subplots(2,
1,
figsize=(default_figsize[0],
1.5 * default_figsize[1]))
fig.subplots_adjust(hspace=0.2)
plt.close() # suppresses empty plot in notebook
fill = PolyCollection([], facecolors='r')
fill.set_alpha(0.3)
ax[0].add_collection(fill)
line_x = LineCollection([], colors='C0')
line_x.set_label(r'$x(\tau)$')
ax[0].add_collection(line_x)
line_h = LineCollection([], colors='C1')
line_h.set_label(r'$h(t - \tau)$')
ax[0].add_collection(line_h)
line_y = LineCollection([], colors='C2')
line_y.set_label(r'$y(t)$')
ax[1].add_collection(line_y)
dot, = ax[1].plot([], 'ro')
for axi in ax:
axi.spines['left'].set_position('zero')
axi.spines['bottom'].set_position('zero')
axi.spines['right'].set_color('none')
axi.spines['top'].set_color('none')
axi.xaxis.set_ticks_position('bottom')
axi.yaxis.set_ticks_position('left')
axi.set_xlim((-3, 4))
axi.set_ylim((-.1, 1.2))
ax[0].set_xlabel(r'$\tau$', horizontalalignment='right', x=1.0)
ax[0].legend(loc='upper right')
ax[1].set_xlabel(r'$t$', horizontalalignment='right', x=1.0)
ax[1].legend(loc='upper right')
return FuncAnimation(fig, animate, td, interval=interval)
def draw_networkx_edges(G,
pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
arrowstyle='thick',
label=None,
**kwds):
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
import matplotlib.patches as patches
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
from matplotlib.path import Path
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
# print "drawing_edges"
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = G.edges()
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = numpy.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# for e in edge_pos:
# print e
if not cb.iterable(width):
lw = (width, )
else:
lw = width
if not cb.is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if cb.is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
edge_collection = LineCollection(
edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
# print type(edge_collection)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
arrow_collection = None
if G.is_directed() and arrows:
# a directed graph hack-fix
# draws arrows at each
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos = []
p = .1 # make arrows 10% of total length
angle = 2.7 #angle for arrows
for src, dst in edge_pos:
x1, y1 = src
x2, y2 = dst
dx = x2 - x1 # x offset
dy = y2 - y1 # y offset
d = numpy.sqrt(float(dx**2 + dy**2)) # length of edge
theta = numpy.arctan2(dy, dx)
if d == 0: # source and target at same position
continue
if dx == 0: # vertical edge
xa = x2
ya = dy + y1
if dy == 0: # horizontal edge
ya = y2
xa = dx + x1
else:
# xa = p*d*numpy.cos(theta)+x1
# ya = p*d*numpy.sin(theta)+y1
#corrects the endpoints to better draw
x2 -= .04 * numpy.cos(theta)
y2 -= .04 * numpy.sin(theta)
lx1 = p * d * numpy.cos(theta + angle) + (x2)
lx2 = p * d * numpy.cos(theta - angle) + (x2)
ly1 = p * d * numpy.sin(theta + angle) + (y2)
ly2 = p * d * numpy.sin(theta - angle) + (y2)
a_pos.append(((lx1, ly1), (x2, y2)))
a_pos.append(((lx2, ly2), (x2, y2)))
arrow_collection = LineCollection(
a_pos,
colors=arrow_colors,
linewidths=[1 * ww for ww in lw],
antialiaseds=(1, ),
transOffset=ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
arrow_collection.set_label(label)
# print type(ax)
ax.add_collection(arrow_collection)
#drawing self loops
d = 1
c = 0.0707
selfedges = []
verts = [
(0.1 * d - 0.1 * d, 0.0), # P0
(c * d - 0.1 * d, c * d), # P0
(0.0 - 0.1 * d, 0.1 * d), # P0
(-c * d - 0.1 * d, c * d), # P0
(-0.1 * d - 0.1 * d, 0.0), # P0
(-c * d - 0.1 * d, -c * d), # P0
(0.0 - 0.1 * d, -0.1 * d), # P0
(c * d - 0.1 * d, -c * d), # P0
(0.1 * d - 0.1 * d, 0.0)
]
# print verts
codes = [
Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
]
for e in edge_pos:
if (numpy.array_equal(e[0], e[1])):
nodes = verts[:]
for i in range(len(nodes)):
nodes[i] += e[0]
# print nodes
path = Path(nodes, codes)
patch = patches.PathPatch(path,
color=None,
facecolor=None,
edgecolor=edge_colors[0],
fill=False,
lw=4)
ax.add_patch(patch)
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:, :, 0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:, :, 0]))
miny = numpy.amin(numpy.ravel(edge_pos[:, :, 1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
# print ax
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def plot2d(x: Union[core.NeuronObject, core.Volume, np.ndarray,
List[Union[core.NeuronObject, np.ndarray, core.Volume]]],
method: Union[Literal['2d'], Literal['3d'],
Literal['3d_complex']] = '2d',
**kwargs) -> Tuple[mpl.figure.Figure, mpl.axes.Axes]:
""" Generate 2D plots of neurons and neuropils.
The main advantage of this is that you can save plot as vector graphics.
Important
---------
This function uses matplotlib which "fakes" 3D as it has only very limited
control over layers. Therefore neurites aren't necessarily plotted in the
right Z order which becomes especially troublesome when plotting a complex
scene with lots of neurons criss-crossing. See the ``method`` parameter
for details. All methods use orthogonal projection.
Parameters
----------
x : skeleton IDs | TreeNeuron | NeuronList | Volume | Dotprops | np.ndarray
Objects to plot::
- int is intepreted as skeleton ID(s)
- str is intepreted as volume name(s)
- multiple objects can be passed as list (see examples)
- numpy array of shape (n,3) is intepreted as scatter
method : '2d' | '3d' | '3d_complex'
Method used to generate plot. Comes in three flavours:
1. '2d' uses normal matplotlib. Neurons are plotted in
the order their are provided. Well behaved when
plotting neuropils and connectors. Always gives
frontal view.
2. '3d' uses matplotlib's 3D axis. Here, matplotlib
decide the order of plotting. Can chance perspective
either interacively or by code (see examples).
3. '3d_complex' same as 3d but each neuron segment is
added individually. This allows for more complex
crossing patterns to be rendered correctly. Slows
down rendering though.
**kwargs
See Notes for permissible keyword arguments.
Examples
--------
>>> import navis
>>> import matplotlib.pyplot as plt
Plot list of neurons as simple 2d
>>> nl = navis.example_neurons()
>>> fig, ax = navis.plot2d(nl)
>>> plt.show()
Add a volume
>>> v = navis.example_volume('LH')
>>> fig, ax = navis.plot2d([nl, vol])
>>> plt.show()
Change neuron colors
>>> fig, ax = navis.plot2d(nl, color=['r', 'g', 'b', 'm', 'c', 'y'])
>>> plt.show()
Plot in "fake" 3D
>>> fig, ax = navis.plot2d(nl, method='3d')
>>> plt.show()
>>> # Try dragging the window
Plot in "fake" 3D and change perspective
>>> fig, ax = navis.plot2d(nl, method='3d')
>>> # Change view to lateral
>>> ax.azim = 0
>>> ax.elev = 0
>>> # Change view to top
>>> ax.azim = -90
>>> ax.elev = 90
>>> # Tilted top view
>>> ax.azim = -135
>>> ax.elev = 45
>>> # Move camera closer (will make image bigger)
>>> ax.dist = 5
>>> plt.show()
Plot using depth-coloring
>>> fig, ax = navis.plot2d(nl, method='3d', depth_coloring=True)
>>> plt.show()
Returns
--------
fig, ax : matplotlib figure and axis object
Notes
-----
Optional keyword arguments:
``soma`` (bool, default = True)
Plot soma if one exists.
``connectors`` (boolean, default = True)
Plot connectors (synapses, gap junctions, abutting)
``connectors_only`` (boolean, default = False)
Plot only connectors, not the neuron.
``cn_size`` (int | float, default = 1)
Size of connectors.
``linewidth``/``lw`` (int | float, default = .5)
Width of neurites.
``linestyle``/``ls`` (str, default = '-')
Line style of neurites.
``autoscale`` (bool, default=True)
If True, will scale the axes to fit the data.
``scalebar`` (int | float, default=False)
Adds scale bar. Provide integer/float to set size of scalebar.
For methods '3d' and '3d_complex', this will create an axis object.
``ax`` (matplotlib ax, default=None)
Pass an ax object if you want to plot on an existing canvas.
``figsize`` (tuple, default = (8, 8))
Size of figure.
``color`` (tuple | list | str | dict)
Tuples/lists (r,g,b) and str (color name) are interpreted as a single
colors that will be applied to all neurons. Dicts will be mapped onto
neurons by skeleton ID.
``alpha`` (float [0-1], default = .9)
Alpha value for neurons. Overriden if alpha is provided as fourth value
in ``color``.
``use_neuron_color`` (bool, default = False)
If True, will attempt to use ``.color`` attribute of neurons.
``depth_coloring`` (bool, default = False)
If True, will color encode depth (Z). Overrides ``color``. Does not work
with ``method = '3d_complex'``.
``depth_scale`` (bool, default = True)
If True and ``depth_coloring=True`` will plot a scale.
``cn_mesh_colors`` (bool, default = False)
If True, will use the neuron's color for its connectors too.
``group_neurons`` (bool, default = False)
If True, neurons will be grouped. Works with SVG export (not PDF).
Does NOT work with ``method='3d_complex'``.
``scatter_kws`` (dict, default = {})
Parameters to be used when plotting points. Accepted keywords are:
``size`` and ``color``.
``view`` (tuple, default = ("x", "y"))
Sets view for ``method='2d'``.
See Also
--------
:func:`navis.plot3d`
Use this if you want interactive, perspectively correct renders
and if you don't need vector graphics as outputs.
:func:`navis.plot1d`
A nifty way to visualise neurons in a single dimension.
"""
# Filter kwargs
_ACCEPTED_KWARGS = [
'soma', 'connectors', 'connectors_only', 'ax', 'color', 'colors', 'c',
'view', 'scalebar', 'cn_mesh_colors', 'linewidth', 'cn_size',
'group_neurons', 'scatter_kws', 'figsize', 'linestyle', 'alpha',
'depth_coloring', 'autoscale', 'depth_scale', 'use_neuron_color', 'ls',
'lw'
]
wrong_kwargs = [a for a in kwargs if a not in _ACCEPTED_KWARGS]
if wrong_kwargs:
raise KeyError(f'Unknown kwarg(s): {",".join(wrong_kwargs)}. '
f'Currently accepted: {",".join(_ACCEPTED_KWARGS)}')
_METHOD_OPTIONS = ['2d', '3d', '3d_complex']
if method not in _METHOD_OPTIONS:
raise ValueError(f'Unknown method "{method}". Please use either: '
f'{",".join(_METHOD_OPTIONS)}')
# Set axis to plot for method '2d'
axis1, axis2 = kwargs.get('view', ('x', 'y'))
plot_soma = kwargs.get('soma', True)
connectors = kwargs.get('connectors', False)
connectors_only = kwargs.get('connectors_only', False)
cn_mesh_colors = kwargs.get('cn_mesh_colors', False)
use_neuron_color = kwargs.get('use_neuron_color', False)
ax = kwargs.get('ax', None)
color = kwargs.get('color', kwargs.get('c', kwargs.get('colors', None)))
scalebar = kwargs.get('scalebar', None)
group_neurons = kwargs.get('group_neurons', False)
# This is overwritten if color specifies alphas
alpha = kwargs.get('alpha', .9)
# Depth coloring
depth_coloring = kwargs.get('depth_coloring', False)
depth_scale = kwargs.get('depth_scale', True)
scatter_kws = kwargs.get('scatter_kws', {})
linewidth = kwargs.get('linewidth', kwargs.get('lw', .5))
cn_size = kwargs.get('cn_size', 1)
linestyle = kwargs.get('linestyle', kwargs.get('ls', '-'))
autoscale = kwargs.get('autoscale', True)
# Keep track of limits if necessary
lim = []
# Parse objects
skdata, dotprops, volumes, points, visuals = utils.parse_objects(x)
# Generate the colormaps
(neuron_cmap, dotprop_cmap,
volumes_cmap) = prepare_colormap(color,
skdata,
dotprops,
volumes,
use_neuron_color=use_neuron_color,
color_range=1)
# Make sure axes are projected orthogonally
if method in ['3d', '3d_complex']:
proj3d.persp_transformation = _orthogonal_proj
# Generate axes
if not ax:
if method == '2d':
fig, ax = plt.subplots(figsize=kwargs.get('figsize', (8, 8)))
ax.set_aspect('equal')
elif method in ['3d', '3d_complex']:
fig = plt.figure(figsize=kwargs.get('figsize',
plt.figaspect(1) * 1.5))
ax = fig.gca(projection='3d')
# This sets front view
ax.azim = -90
ax.elev = 0
ax.dist = 7
# Disallowed for 3D in matplotlib 3.1.0
# ax.set_aspect('equal')
# Check if correct axis were provided
else:
if not isinstance(ax, mpl.axes.Axes):
raise TypeError('Ax must be of type "mpl.axes.Axes", '
f'not "{type(ax)}"')
fig = ax.get_figure()
if method in ['3d', '3d_complex']:
if ax.name != '3d':
raise TypeError('Axis must be 3d.')
# Add existing limits to ax
lim += np.array((ax.get_xlim3d(), ax.get_zlim3d(),
ax.get_ylim3d())).T.tolist()
elif method == '2d':
if ax.name == '3d':
raise TypeError('Axis must be 2d.')
# Prepare some stuff for depth coloring
if depth_coloring and method == '3d_complex':
raise Exception(f'Depth coloring unavailable for method "{method}"')
elif depth_coloring and method == '2d':
all_co = skdata.nodes[['x', 'y', 'z']]
norm = plt.Normalize(vmin=all_co.z.min(), vmax=all_co.z.max())
# Plot volumes first
if volumes:
for i, v in enumerate(volumes):
c = volumes_cmap[i]
if method == '2d':
vpatch = mpatches.Polygon(v.to_2d(view=f'{axis1}{axis2}',
invert_y=True),
closed=True,
lw=0,
fill=True,
fc=c,
alpha=c[3] if len(c) == 4 else 1)
ax.add_patch(vpatch)
elif method in ['3d', '3d_complex']:
verts = np.vstack(v.vertices)
# Invert y-axis
verts[:, 1] *= -1
# Add alpha
if len(c) == 3:
c = (c[0], c[1], c[2], .1)
ts = ax.plot_trisurf(verts[:, 0],
verts[:, 2],
v.faces,
verts[:, 1],
label=v.name,
color=c)
ts.set_gid(v.name)
# Keep track of limits
lim.append(verts.max(axis=0))
lim.append(verts.min(axis=0))
# Create lines from segments
line3D_collections = []
surf3D_collections = []
for i, neuron in enumerate(
config.tqdm(skdata.itertuples(),
desc='Plot neurons',
total=skdata.shape[0],
leave=False,
disable=config.pbar_hide | len(dotprops) == 0)):
this_color = neuron_cmap[i]
if neuron.nodes.empty:
logger.warning(f'Skipping neuron w/o nodes: {neuron.uuid}')
continue
if not connectors_only:
# Now make traces (invert y axis)
coords = segments_to_coords(neuron,
neuron.segments,
modifier=(1, -1, 1))
if method == '2d':
if not depth_coloring:
# We have to add (None, None, None) to the end of each
# slab to make that line discontinuous there
coords = np.vstack(
[np.append(t, [[None] * 3], axis=0) for t in coords])
this_line = mlines.Line2D(
coords[:, 0],
coords[:, 1],
lw=linewidth,
ls=linestyle,
alpha=alpha,
color=this_color,
label=
f'{getattr(neuron, "name", "NA")} - #{neuron.uuid}')
ax.add_line(this_line)
else:
coords = tn_pairs_to_coords(neuron, modifier=(1, -1, 1))
lc = LineCollection(coords[:, :, [0, 1]],
cmap='jet',
norm=norm)
lc.set_array(neuron.nodes.loc[neuron.nodes.parent_id >= 0,
'z'].values)
lc.set_linewidth(linewidth)
lc.set_alpha(alpha)
lc.set_linestyle(linestyle)
lc.set_label(
f'{getattr(neuron, "name", "NA")} - #{neuron.uuid}')
line = ax.add_collection(lc)
if plot_soma and not isinstance(neuron.soma, type(None)):
soma = utils.make_iterable(neuron.soma)
for s in soma:
n = neuron.nodes.set_index('node_id').loc[s]
r = getattr(n, neuron.soma_radius) if isinstance(
neuron.soma_radius, str) else neuron.soma_radius
if depth_coloring:
this_color = mpl.cm.jet(norm(n.z))
s = mpatches.Circle((int(n.x), int(-n.y)),
radius=r,
alpha=alpha,
fill=True,
fc=this_color,
zorder=4,
edgecolor='none')
ax.add_patch(s)
elif method in ['3d', '3d_complex']:
cmap = mpl.cm.jet if depth_coloring else None
# For simple scenes, add whole neurons at a time -> will speed
# up rendering
if method == '3d':
if depth_coloring:
this_coords = tn_pairs_to_coords(
neuron, modifier=(1, -1, 1))[:, :, [0, 2, 1]]
else:
this_coords = [c[:, [0, 2, 1]] for c in coords]
lc = Line3DCollection(this_coords,
color=this_color,
label=neuron.uuid,
alpha=alpha,
cmap=cmap,
lw=linewidth,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.uuid)
ax.add_collection3d(lc)
line3D_collections.append(lc)
# For complex scenes, add each segment as a single collection
# -> help preventing Z-order errors
elif method == '3d_complex':
for c in coords:
lc = Line3DCollection([c[:, [0, 2, 1]]],
color=this_color,
lw=linewidth,
alpha=alpha,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.uuid)
ax.add_collection3d(lc)
coords = np.vstack(coords)
lim.append(coords.max(axis=0))
lim.append(coords.min(axis=0))
surf3D_collections.append([])
if plot_soma and not isinstance(neuron.soma, type(None)):
soma = utils.make_iterable(neuron.soma)
for s in soma:
n = neuron.nodes.set_index('node_id').loc[s]
r = getattr(n, neuron.soma_radius) if isinstance(
neuron.soma_radius, str) else neuron.soma_radius
resolution = 20
u = np.linspace(0, 2 * np.pi, resolution)
v = np.linspace(0, np.pi, resolution)
x = r * np.outer(np.cos(u), np.sin(v)) + n.x
y = r * np.outer(np.sin(u), np.sin(v)) - n.y
z = r * np.outer(np.ones(np.size(u)), np.cos(v)) + n.z
surf = ax.plot_surface(x,
z,
y,
color=this_color,
shade=False,
alpha=alpha)
if group_neurons:
surf.set_gid(neuron.uuid)
surf3D_collections[-1].append(surf)
if (connectors or connectors_only) and neuron.has_connectors:
if not cn_mesh_colors:
cn_types = {0: 'red', 1: 'blue', 2: 'green', 3: 'magenta'}
else:
cn_types = {
0: this_color,
1: this_color,
2: this_color,
3: this_color
}
if method == '2d':
for c in cn_types:
this_cn = neuron.connectors[neuron.connectors.relation ==
c]
ax.scatter(this_cn.x.values, (-this_cn.y).values,
c=cn_types[c],
alpha=alpha,
zorder=4,
edgecolor='none',
s=cn_size)
ax.get_children()[-1].set_gid(f'CN_{neuron.uuid}')
elif method in ['3d', '3d_complex']:
all_cn = neuron.connectors
c = [cn_types[i] for i in all_cn.relation.values]
ax.scatter(all_cn.x.values,
all_cn.z.values,
-all_cn.y.values,
c=c,
s=cn_size,
depthshade=False,
edgecolor='none',
alpha=alpha)
ax.get_children()[-1].set_gid(f'CN_{neuron.uuid}')
coords = neuron.connectors[['x', 'y', 'z']].values
coords[:, 1] *= -1
lim.append(coords.max(axis=0))
lim.append(coords.min(axis=0))
for i, neuron in enumerate(
config.tqdm(dotprops.itertuples(),
desc='Plt dotprops',
total=dotprops.shape[0],
leave=False,
disable=config.pbar_hide | len(dotprops) == 0)):
# Prepare lines - this is based on nat:::plot3d.dotprops
halfvect = neuron.points[['x_vec', 'y_vec', 'z_vec']] / 2
starts = neuron.points[['x', 'y', 'z']].values - halfvect.values
ends = neuron.points[['x', 'y', 'z']].values + halfvect.values
try:
this_color = dotprop_cmap[i]
except BaseException:
this_color = (.1, .1, .1)
if method == '2d':
# Add None between segments
x_coords = [
n for sublist in zip(starts[:, 0], ends[:, 0], [None] *
starts.shape[0]) for n in sublist
]
y_coords = [
n for sublist in zip(starts[:, 1] * -1, ends[:, 1] *
-1, [None] * starts.shape[0])
for n in sublist
]
this_line = mlines.Line2D(x_coords,
y_coords,
lw=linewidth,
ls=linestyle,
alpha=alpha,
color=this_color,
label='%s' % (neuron.gene_name))
ax.add_line(this_line)
# Add soma
if plot_soma:
s = mpatches.Circle((neuron.X, -neuron.Y),
radius=2,
alpha=alpha,
fill=True,
fc=this_color,
zorder=4,
edgecolor='none')
ax.add_patch(s)
elif method in ['3d', '3d_complex']:
# Combine coords by weaving starts and ends together
coords = np.empty((starts.shape[0] * 2, 3), dtype=starts.dtype)
coords[0::2] = starts
coords[1::2] = ends
# Invert y-axis
coords[:, 1] *= -1
# For simple scenes, add whole neurons at a time
# -> will speed up rendering
if method == '3d':
lc = Line3DCollection(np.split(coords[:, [0, 2, 1]],
starts.shape[0]),
color=this_color,
label=neuron.gene_name,
lw=linewidth,
alpha=alpha,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.gene_name)
ax.add_collection3d(lc)
# For complex scenes, add each segment as a single collection
# -> help preventing Z-order errors
elif method == '3d_complex':
for c in np.split(coords[:, [0, 2, 1]], starts.shape[0]):
lc = Line3DCollection([c],
color=this_color,
lw=linewidth,
alpha=alpha,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.gene_name)
ax.add_collection3d(lc)
lim.append(coords.max(axis=0))
lim.append(coords.min(axis=0))
resolution = 20
u = np.linspace(0, 2 * np.pi, resolution)
v = np.linspace(0, np.pi, resolution)
x = 2 * np.outer(np.cos(u), np.sin(v)) + neuron.X
y = 2 * np.outer(np.sin(u), np.sin(v)) - neuron.Y
z = 2 * np.outer(np.ones(np.size(u)), np.cos(v)) + neuron.Z
surf = ax.plot_surface(x,
z,
y,
color=this_color,
shade=False,
alpha=alpha)
if group_neurons:
surf.set_gid(neuron.gene_name)
if points:
for p in points:
if method == '2d':
default_settings = dict(c='black',
zorder=4,
edgecolor='none',
s=1)
default_settings.update(scatter_kws)
default_settings = _fix_default_dict(default_settings)
ax.scatter(p[:, 0], p[:, 1] * -1, **default_settings)
elif method in ['3d', '3d_complex']:
default_settings = dict(c='black',
s=1,
depthshade=False,
edgecolor='none')
default_settings.update(scatter_kws)
default_settings = _fix_default_dict(default_settings)
ax.scatter(p[:, 0], p[:, 2], p[:, 1] * -1, **default_settings)
coords = p
coords[:, 1] *= -1
lim.append(coords.max(axis=0))
lim.append(coords.min(axis=0))
if autoscale:
if method == '2d':
ax.autoscale()
elif method in ['3d', '3d_complex']:
lim = np.vstack(lim)
lim_min = lim.min(axis=0)
lim_max = lim.max(axis=0)
center = lim_min + (lim_max - lim_min) / 2
max_dim = (lim_max - lim_min).max()
new_min = center - max_dim / 2
new_max = center + max_dim / 2
ax.set_xlim(new_min[0], new_max[0])
ax.set_ylim(new_min[2], new_max[2])
ax.set_zlim(new_min[1], new_max[1])
if scalebar is not None:
# Convert sc size to nm
sc_size = scalebar * 1000
# Hard-coded offset from figure boundaries
ax_offset = 1000
if method == '2d':
xlim = ax.get_xlim()
ylim = ax.get_ylim()
coords = np.array(
[[xlim[0] + ax_offset, ylim[0] + ax_offset],
[xlim[0] + ax_offset + sc_size, ylim[0] + ax_offset]])
sbar = mlines.Line2D(coords[:, 0],
coords[:, 1],
lw=3,
alpha=.9,
color='black')
sbar.set_gid(f'{scalebar}_um')
ax.add_line(sbar)
elif method in ['3d', '3d_complex']:
left = lim_min[0] + ax_offset
bottom = lim_min[1] + ax_offset
front = lim_min[2] + ax_offset
sbar = [
np.array([[left, front, bottom], [left, front, bottom]]),
np.array([[left, front, bottom], [left, front, bottom]]),
np.array([[left, front, bottom], [left, front, bottom]])
]
sbar[0][1][0] += sc_size
sbar[1][1][1] += sc_size
sbar[2][1][2] += sc_size
lc = Line3DCollection(sbar, color='black', lw=1)
lc.set_gid(f'{scalebar}_um')
ax.add_collection3d(lc)
def set_depth():
"""Sets depth information for neurons according to camera position."""
# Modifier for soma coordinates
modifier = np.array([1, 1, -1])
# Get all coordinates
all_co = np.concatenate(
[lc._segments3d[:, 0, :] for lc in line3D_collections], axis=0)
# Get projected coordinates
proj_co = mpl_toolkits.mplot3d.proj3d.proj_points(
all_co, ax.get_proj())
# Get min and max of z coordinates
z_min, z_max = min(proj_co[:, 2]), max(proj_co[:, 2])
# Generate a new normaliser
norm = plt.Normalize(vmin=z_min, vmax=z_max)
# Go over all neurons and update Z information
for neuron, lc, surf in zip(skdata, line3D_collections,
surf3D_collections):
# Get this neurons coordinates
this_co = lc._segments3d[:, 0, :]
# Get projected coordinates
this_proj = mpl_toolkits.mplot3d.proj3d.proj_points(
this_co, ax.get_proj())
# Normalise z coordinates
ns = norm(this_proj[:, 2]).data
# Set array
lc.set_array(ns)
# No need for normaliser - already happened
lc.set_norm(None)
if not isinstance(neuron.soma, type(None)):
# Get depth of soma(s)
soma = utils.make_iterable(neuron.soma)
soma_co = neuron.nodes.set_index('node_id').loc[soma][[
'x', 'z', 'y'
]].values
soma_proj = mpl_toolkits.mplot3d.proj3d.proj_points(
soma_co * modifier, ax.get_proj())
soma_cs = norm(soma_proj[:, 2]).data
# Set soma color
for cs, s in zip(soma_cs, surf):
s.set_color(cmap(cs))
def Update(event):
set_depth()
if depth_coloring:
if method == '2d' and depth_scale:
fig.colorbar(line, ax=ax, fraction=.075, shrink=.5, label='Depth')
elif method == '3d':
fig.canvas.mpl_connect('draw_event', Update)
set_depth()
plt.axis('off')
logger.debug('Done. Use matplotlib.pyplot.show() to show plot.')
return fig, ax
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
Notes
-----
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
Yes, it is ugly but drawing proper arrows with Matplotlib this
way is tricky.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> edges=nx.draw_networkx_edges(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.github.io/documentation/latest/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = numpy.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not cb.is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_string_like(c)
for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c)
for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must consist of either color names or numbers')
else:
if cb.is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError('edge_color must be a single color or list of exactly m colors where m is the number or edges')
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset = ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
arrow_collection = None
if G.is_directed() and arrows:
# a directed graph hack
# draw thick line segments at head end of edge
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos = []
p = 1.0-0.25 # make head segment 25 percent of edge length
for src, dst in edge_pos:
x1, y1 = src
x2, y2 = dst
dx = x2-x1 # x offset
dy = y2-y1 # y offset
d = numpy.sqrt(float(dx**2 + dy**2)) # length of edge
if d == 0: # source and target at same position
continue
if dx == 0: # vertical edge
xa = x2
ya = dy*p+y1
if dy == 0: # horizontal edge
ya = y2
xa = dx*p+x1
else:
theta = numpy.arctan2(dy, dx)
xa = p*d*numpy.cos(theta)+x1
ya = p*d*numpy.sin(theta)+y1
a_pos.append(((xa, ya), (x2, y2)))
arrow_collection = LineCollection(a_pos,
colors=arrow_colors,
linewidths=[4*ww for ww in lw],
antialiaseds=(1,),
transOffset = ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
arrow_collection.set_label(label)
ax.add_collection(arrow_collection)
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:, :, 0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:, :, 0]))
miny = numpy.amin(numpy.ravel(edge_pos[:, :, 1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:, :, 1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def draw_networkx_edges(
G,
pos,
edgelist=None,
width=1.0,
edge_color="k",
style="solid",
alpha=None,
arrowstyle="-|>",
arrowsize=3,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
min_source_margin=0,
min_target_margin=0,
):
"""Draw the edges of the graph G. Adjusted from networkx."""
try:
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
from numbers import Number
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# FancyArrowPatch handles color=None different from LineCollection
if edge_color is None:
edge_color = "k"
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# Check if edge_color is an array of floats and map to edge_cmap.
# This is the only case handled differently from matplotlib
if (
np.iterable(edge_color)
and (len(edge_color) == len(edge_pos))
and np.alltrue([isinstance(c, Number) for c in edge_color])
):
if edge_cmap is not None:
assert isinstance(edge_cmap, Colormap)
else:
edge_cmap = plt.get_cmap()
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
if not G.is_directed() or not arrows:
edge_collection = LineCollection(
edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
alpha=alpha,
)
edge_collection.set_cmap(edge_cmap)
edge_collection.set_clim(edge_vmin, edge_vmax)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
# FancyArrowPatch doesn't handle color strings
arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if np.iterable(node_size): # many node sizes
source, target = edgelist[i][:2]
source_node_size = node_size[nodelist.index(source)]
target_node_size = node_size[nodelist.index(target)]
shrink_source = to_marker_edge(source_node_size, node_shape)
shrink_target = to_marker_edge(target_node_size, node_shape)
else:
shrink_source = shrink_target = to_marker_edge(node_size, node_shape)
if shrink_source < min_source_margin:
shrink_source = min_source_margin
if shrink_target < min_target_margin:
shrink_target = min_target_margin
if len(arrow_colors) == len(edge_pos):
arrow_color = arrow_colors[i]
elif len(arrow_colors) == 1:
arrow_color = arrow_colors[0]
else: # Cycle through colors
arrow_color = arrow_colors[i % len(arrow_colors)]
if np.iterable(width):
if len(width) == len(edge_pos):
line_width = width[i]
else:
line_width = width[i % len(width)]
else:
line_width = width
arrow = FancyArrowPatch(
(x1, y1),
(x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
linestyle=style,
zorder=1,
) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return arrow_collection
def QuickPlot(conf=None,
bodies=None,
xaxis=None,
yaxis=None,
aaxis=None,
interactive=True,
nolog=False):
'''
'''
# Assign defaults
if conf is None:
conf = GetConf()
if bodies is None:
bodies = conf.bodies
if xaxis is None:
xaxis = conf.xaxis
if yaxis is None:
yaxis = conf.yaxis
if aaxis is None:
aaxis = conf.aaxis
if nolog:
conf.xlog = False
conf.ylog = False
# Output object
output = GetArrays(bodies=bodies)
# Names of all available params
param_names = list(
set([param.name for body in output.bodies for param in body.params]))
if len(param_names) == 0:
raise Exception("There don't seem to be any parameters to be plotted.")
# The x-axis parameter
x = param_names[np.argmax(
np.array(
[name.lower().startswith(xaxis.lower()) for name in param_names]))]
if not x.lower().startswith(xaxis.lower()):
raise Exception('Parameter not understood: %s.' % xaxis)
# The array of y-axis parameters
# Ensure it's a list
if type(yaxis) is str:
yaxis = [yaxis]
if len(yaxis) == 0:
# User didn't specify any; let's plot all of them
yarr = list(sorted(set(param_names) - set([x])))
else:
yarr = []
for yn in yaxis:
y = param_names[np.argmax(
np.array([
name.lower().startswith(yn.lower()) for name in param_names
]))]
if not y.lower().startswith(yn.lower()):
raise Exception('Parameter not found: %s.' % yn)
yarr.append(y)
# The alpha parameter
if aaxis is not None and aaxis != 'None':
a = param_names[np.argmax(
np.array([
name.lower().startswith(aaxis.lower()) for name in param_names
]))]
if not a.lower().startswith(aaxis.lower()):
raise Exception('Parameter not found: %s.' % aaxis)
# We will only plot up to a maximum of ``conf.maxrows`` if columns are off
if (not conf.columns) and len(yarr) > conf.maxrows:
yarr = yarr[:conf.maxrows]
rows = conf.maxrows
elif conf.columns and len(yarr) > conf.maxrows:
columns = int(np.ceil(float(len(yarr)) / conf.maxrows))
rows = conf.maxrows
else:
columns = 1
rows = len(yarr)
# Correct for border cases
if len(yarr) <= (columns * (rows - 1)):
rows -= 1
elif len(yarr) <= ((columns - 1) * rows):
columns -= 1
# Set up the plot
# See http://stackoverflow.com/a/19627237
mpl.rcParams['figure.autolayout'] = False
fig = pl.figure(1, figsize=(conf.figwidth, conf.figheight))
gs = gridspec.GridSpec(rows, columns)
for i, ss in enumerate(gs):
if i == 0:
ax = [fig.add_subplot(gs[0])]
else:
ax.append(fig.add_subplot(ss, sharex=ax[0]))
# Hide empty subplots
empty = range(len(yarr), rows * columns)
for i in empty:
ax[i].set_visible(False)
# Loop over all parameters (one subplot per each)
for i, y in enumerate(yarr):
# Axes limits
ymin = np.inf
ymax = -np.inf
xmin = np.inf
xmax = -np.inf
# Loop over all bodies
for b, body in enumerate(output.bodies):
# Get indices of the parameters
if len(body.params):
xi = np.where(
np.array([param.name for param in body.params]) == x)[0]
yi = np.where(
np.array([param.name for param in body.params]) == y)[0]
else:
xi = np.array([], dtype=int)
yi = np.array([], dtype=int)
if aaxis is not None and aaxis != 'None':
ai = np.where(
a == np.array([param.name for param in body.params]))[0]
else:
ai = []
# Are both x and y arrays preset for this body?
if len(xi) and len(yi):
xi = xi[0]
yi = yi[0]
else:
continue
if len(ai):
ai = ai[0]
else:
ai = None
# Get the arrays
xpts = body.params[xi].array
ypts = body.params[yi].array
# Decide whether to plot individual legends
if conf.legend_all:
label = body.name
else:
label = None
# Should we plot the first point if x = 0?
if xpts[0] == 0. and conf.xlog and conf.skip_xzero_log:
xpts = xpts[1:]
ypts = ypts[1:]
# Plot the curve
if ai is None:
# A simple solid color line
ax[i].plot(xpts,
ypts,
conf.line_styles[b],
label=label,
lw=conf.linewidth)
else:
# Let's get fancy: make the curve opacity proportional
# to the `aaxis` parameter
apts = body.params[ai].array
# Make logarithmic if necessary
if conf.alog:
if apts[0] == 0.:
apts = apts[1:]
apts = np.log10(apts)
points = np.array([xpts, ypts]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(
segments,
cmap=AlphaMap(
*ColorConverter().to_rgb(conf.line_styles[b][0])),
norm=pl.Normalize(np.nanmin(apts), np.nanmax(apts)))
lc.set_array(apts)
lc.set_linestyle(conf.line_styles[b][1:])
lc.set_linewidth(conf.linewidth)
lc.set_label(label)
ax[i].add_collection(lc)
# Limits
if np.nanmin(xpts) < xmin:
xmin = np.nanmin(xpts)
if np.nanmin(ypts) < ymin:
ymin = np.nanmin(ypts)
if np.nanmax(xpts) > xmax:
xmax = np.nanmax(xpts)
if np.nanmax(ypts) > ymax:
ymax = np.nanmax(ypts)
# Labels
ax[i].set_xlabel(body.params[xi].label(conf.short_labels),
fontsize=conf.xlabel_fontsize)
ax[i].set_ylabel(body.params[yi].label(conf.short_labels,
conf.maxylabelsize),
fontsize=conf.ylabel_fontsize)
# Tick sizes
for tick in ax[i].xaxis.get_major_ticks():
tick.label.set_fontsize(conf.xticklabel_fontsize)
for tick in ax[i].yaxis.get_major_ticks():
tick.label.set_fontsize(conf.yticklabel_fontsize)
# Log scale?
if conf.xlog:
if np.log(xmax) - np.log(xmin) > conf.xlog:
if xmin > 0:
ax[i].set_xscale('log')
if conf.ylog:
if np.log(ymax) - np.log(ymin) > conf.ylog:
if ymin > 0:
ax[i].set_yscale('log')
# Tighten things up a bit
if i < len(yarr) - columns:
if conf.tight_layout:
ax[i].set_xticklabels([])
ax[i].set_xlabel('')
# Add y-axis margins (doesn't work well with log)
ax[i].margins(0., conf.ymargin)
# Add a legend
if conf.legend_all:
ax[i].legend(loc=conf.legend_loc, fontsize=conf.legend_fontsize)
elif i == 0:
xlim = ax[i].get_xlim()
ylim = ax[i].get_ylim()
for b, body in enumerate(output.bodies):
# HACK: Plot a line of length zero in the center
# of the plot so that it will show up on the legend
foo = ax[i].plot([(xlim[0] + xlim[1]) / 2.],
[(ylim[0] + ylim[1]) / 2.],
conf.line_styles[b],
label=body.name)
ax[i].legend(loc=conf.legend_loc, fontsize=conf.legend_fontsize)
# Add title
if conf.title:
if len(yarr) == 1:
pl.title('VPLANET: %s' % output.sysname, fontsize=24)
else:
pl.suptitle('VPLANET: %s' % output.sysname, fontsize=24)
try:
gs.tight_layout(fig, rect=[0, 0.03, 1, 0.95])
except:
# No biggie
pass
# Show or save?
if interactive and conf.interactive:
pl.show()
else:
fig.savefig(conf.figname, bbox_inches='tight')
return fig, ax
def _plot_skeleton(neuron, color, method, ax, **kwargs):
"""Plot skeleton."""
depth_coloring = kwargs.get('depth_coloring', False)
linewidth = kwargs.get('linewidth', kwargs.get('lw', .5))
linestyle = kwargs.get('linestyle', kwargs.get('ls', '-'))
alpha = kwargs.get('alpha', .9)
norm = kwargs.get('norm')
plot_soma = kwargs.get('soma', True)
group_neurons = kwargs.get('group_neurons', False)
# Generate by-segment coordinates
coords = segments_to_coords(neuron,
neuron.segments,
modifier=(1, 1, 1))
if method == '2d':
if not depth_coloring:
# We have to add (None, None, None) to the end of each
# slab to make that line discontinuous there
coords = np.vstack(
[np.append(t, [[None] * 3], axis=0) for t in coords])
this_line = mlines.Line2D(coords[:, 0], coords[:, 1],
lw=linewidth, ls=linestyle,
alpha=alpha, color=color,
label=f'{getattr(neuron, "name", "NA")} - #{neuron.id}')
ax.add_line(this_line)
else:
coords = tn_pairs_to_coords(neuron, modifier=(1, 1, 1))
lc = LineCollection(coords[:, :, [0, 1]],
cmap='jet',
norm=norm)
lc.set_array(neuron.nodes.loc[neuron.nodes.parent_id >= 0, 'z'].values)
lc.set_linewidth(linewidth)
lc.set_alpha(alpha)
lc.set_linestyle(linestyle)
lc.set_label(f'{getattr(neuron, "name", "NA")} - #{neuron.id}')
ax.add_collection(lc)
if plot_soma and not isinstance(neuron.soma, type(None)):
soma = utils.make_iterable(neuron.soma)
# If soma detection is messed up we might end up producing
# dozens of soma which will freeze the kernel
if len(soma) >= 10:
logger.warning(f'{neuron.id} - {len(soma)} somas found.')
for s in soma:
n = neuron.nodes.set_index('node_id').loc[s]
r = getattr(n, neuron.soma_radius) if isinstance(neuron.soma_radius, str) else neuron.soma_radius
if depth_coloring:
color = mpl.cm.jet(norm(n.z))
s = mpatches.Circle((int(n.x), int(n.y)), radius=r,
alpha=alpha, fill=True, fc=color,
zorder=4, edgecolor='none')
ax.add_patch(s)
return None, None
elif method in ['3d', '3d_complex']:
# For simple scenes, add whole neurons at a time -> will speed
# up rendering
if method == '3d':
if depth_coloring:
this_coords = tn_pairs_to_coords(neuron,
modifier=(1, 1, 1))[:, :, [0, 1, 2]]
else:
this_coords = [c[:, [0, 1, 2]] for c in coords]
lc = Line3DCollection(this_coords,
color=color,
label=neuron.id,
alpha=alpha,
cmap=mpl.cm.jet if depth_coloring else None,
lw=linewidth,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.id)
# Need to get this before adding data
line3D_collection = ax.add_collection3d(lc)
# For complex scenes, add each segment as a single collection
# -> helps preventing Z-order errors
elif method == '3d_complex':
for c in coords:
lc = Line3DCollection([c],
color=color,
lw=linewidth,
alpha=alpha,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.id)
ax.add_collection3d(lc)
line3D_collection = None
surf3D_collections = []
if plot_soma and not isinstance(neuron.soma, type(None)):
soma = utils.make_iterable(neuron.soma)
# If soma detection is messed up we might end up producing
# dozens of soma which will freeze the kernel
if len(soma) >= 5:
logger.warning(f'Neuron {neuron.id} appears to have {len(soma)}'
' somas. Skipping plotting its somas.')
else:
for s in soma:
n = neuron.nodes.set_index('node_id').loc[s]
r = getattr(n, neuron.soma_radius) if isinstance(neuron.soma_radius, str) else neuron.soma_radius
resolution = 20
u = np.linspace(0, 2 * np.pi, resolution)
v = np.linspace(0, np.pi, resolution)
x = r * np.outer(np.cos(u), np.sin(v)) + n.x
y = r * np.outer(np.sin(u), np.sin(v)) + n.y
z = r * np.outer(np.ones(np.size(u)), np.cos(v)) + n.z
surf = ax.plot_surface(x, y, z,
color=color,
shade=False,
alpha=alpha)
if group_neurons:
surf.set_gid(neuron.id)
surf3D_collections.append(surf)
return line3D_collection, surf3D_collections
def readshapefileext(self,
shapefile,
name,
drawbounds=True,
zorder=None,
linewidth=0.5,
color='k',
antialiased=1,
ax=None,
default_encoding='utf-8',
linestyle='-'):
"""
Read in shape file, optionally draw boundaries on map.
.. note::
- Assumes shapes are 2D
- only works for Point, MultiPoint, Polyline and Polygon shapes.
- vertices/points must be in geographic (lat/lon) coordinates.
Mandatory Arguments:
.. tabularcolumns:: |l|L|
============== ====================================================
Argument Description
============== ====================================================
shapefile path to shapefile components. Example:
shapefile='/home/jeff/esri/world_borders' assumes
that world_borders.shp, world_borders.shx and
world_borders.dbf live in /home/jeff/esri.
name name for Basemap attribute to hold the shapefile
vertices or points in map projection
coordinates. Class attribute name+'_info' is a list
of dictionaries, one for each shape, containing
attributes of each shape from dbf file, For
example, if name='counties', self.counties
will be a list of x,y vertices for each shape in
map projection coordinates and self.counties_info
will be a list of dictionaries with shape
attributes. Rings in individual Polygon
shapes are split out into separate polygons, and
additional keys 'RINGNUM' and 'SHAPENUM' are added
to the shape attribute dictionary.
============== ====================================================
The following optional keyword arguments are only relevant for Polyline
and Polygon shape types, for Point and MultiPoint shapes they are
ignored.
.. tabularcolumns:: |l|L|
============== ====================================================
Keyword Description
============== ====================================================
drawbounds draw boundaries of shapes (default True).
zorder shape boundary zorder (if not specified,
default for mathplotlib.lines.LineCollection
is used).
linewidth shape boundary line width (default 0.5)
color shape boundary line color (default black)
antialiased antialiasing switch for shape boundaries
(default True).
ax axes instance (overrides default axes instance)
============== ====================================================
A tuple (num_shapes, type, min, max) containing shape file info
is returned.
num_shapes is the number of shapes, type is the type code (one of
the SHPT* constants defined in the shapelib module, see
http://shapelib.maptools.org/shp_api.html) and min and
max are 4-element lists with the minimum and maximum values of the
vertices. If ``drawbounds=True`` a
matplotlib.patches.LineCollection object is appended to the tuple.
"""
import shapefile as shp
from shapefile import Reader
shp.default_encoding = default_encoding
if not os.path.exists('%s.shp' % shapefile):
raise IOError('cannot locate %s.shp' % shapefile)
if not os.path.exists('%s.shx' % shapefile):
raise IOError('cannot locate %s.shx' % shapefile)
if not os.path.exists('%s.dbf' % shapefile):
raise IOError('cannot locate %s.dbf' % shapefile)
# open shapefile, read vertices for each object, convert
# to map projection coordinates (only works for 2D shape types).
try:
shf = Reader(shapefile)
except:
raise IOError('error reading shapefile %s.shp' % shapefile)
fields = shf.fields
coords = []
attributes = []
msg = dedent("""
shapefile must have lat/lon vertices - it looks like this one has vertices
in map projection coordinates. You can convert the shapefile to geographic
coordinates using the shpproj utility from the shapelib tools
(http://shapelib.maptools.org/shapelib-tools.html)""")
shptype = shf.shapes()[0].shapeType
bbox = shf.bbox.tolist()
info = (shf.numRecords, shptype, bbox[0:2] + [0., 0.],
bbox[2:] + [0., 0.])
npoly = 0
for shprec in shf.shapeRecords():
shp = shprec.shape
rec = shprec.record
npoly = npoly + 1
if shptype != shp.shapeType:
raise ValueError(
'readshapefile can only handle a single shape type per file'
)
if shptype not in [1, 3, 5, 8]:
raise ValueError(
'readshapefile can only handle 2D shape types')
verts = shp.points
if shptype in [1, 8]: # a Point or MultiPoint shape.
lons, lats = list(zip(*verts))
if max(lons) > 721. or min(lons) < -721. or max(
lats) > 90.01 or min(lats) < -90.01:
raise ValueError(msg)
# if latitude is slightly greater than 90, truncate to 90
lats = [max(min(lat, 90.0), -90.0) for lat in lats]
if len(verts) > 1: # MultiPoint
x, y = self(lons, lats)
coords.append(list(zip(x, y)))
else: # single Point
x, y = self(lons[0], lats[0])
coords.append((x, y))
attdict = {}
for r, key in zip(rec, fields[1:]):
attdict[key[0]] = r
attributes.append(attdict)
else: # a Polyline or Polygon shape.
parts = shp.parts.tolist()
ringnum = 0
for indx1, indx2 in zip(parts, parts[1:] + [len(verts)]):
ringnum = ringnum + 1
lons, lats = list(zip(*verts[indx1:indx2]))
if max(lons) > 721. or min(lons) < -721. or max(
lats) > 90.01 or min(lats) < -90.01:
raise ValueError(msg)
# if latitude is slightly greater than 90, truncate to 90
lats = [max(min(lat, 90.0), -90.0) for lat in lats]
x, y = self(lons, lats)
coords.append(list(zip(x, y)))
attdict = {}
for r, key in zip(rec, fields[1:]):
attdict[key[0]] = r
# add information about ring number to dictionary.
attdict['RINGNUM'] = ringnum
attdict['SHAPENUM'] = npoly
attributes.append(attdict)
# draw shape boundaries for polylines, polygons using LineCollection.
if shptype not in [1, 8] and drawbounds:
# get current axes instance (if none specified).
ax = ax or self._check_ax()
# make LineCollections for each polygon.
lines = LineCollection(coords, antialiaseds=(1, ))
lines.set_color(color)
lines.set_linewidth(linewidth)
lines.set_linestyle(linestyle)
lines.set_label('_nolabel_')
if zorder is not None:
lines.set_zorder(zorder)
ax.add_collection(lines)
# set axes limits to fit map region.
self.set_axes_limits(ax=ax)
# clip boundaries to map limbs
lines, c = self._cliplimb(ax, lines)
info = info + (lines, )
self.__dict__[name] = coords
self.__dict__[name + '_info'] = attributes
return info
def _plot_skeleton(neuron, color, method, ax, **kwargs):
"""Plot skeleton."""
depth_coloring = kwargs.get('depth_coloring', False)
linewidth = kwargs.get('linewidth', kwargs.get('lw', .5))
linestyle = kwargs.get('linestyle', kwargs.get('ls', '-'))
alpha = kwargs.get('alpha', .9)
norm = kwargs.get('norm')
plot_soma = kwargs.get('soma', True)
group_neurons = kwargs.get('group_neurons', False)
view = kwargs.get('view', ('x', 'y'))
if method == '2d':
if not depth_coloring and not (isinstance(color, np.ndarray)
and color.ndim == 2):
# Generate by-segment coordinates
coords = segments_to_coords(neuron,
neuron.segments,
modifier=(1, 1, 1))
# We have to add (None, None, None) to the end of each
# slab to make that line discontinuous there
coords = np.vstack(
[np.append(t, [[None] * 3], axis=0) for t in coords])
x, y = _parse_view2d(coords, view)
this_line = mlines.Line2D(
x,
y,
lw=linewidth,
ls=linestyle,
alpha=alpha,
color=color,
label=f'{getattr(neuron, "name", "NA")} - #{neuron.id}')
ax.add_line(this_line)
else:
coords = tn_pairs_to_coords(neuron, modifier=(1, 1, 1))
xy = _parse_view2d(coords, view)
lc = LineCollection(xy,
cmap='jet' if depth_coloring else None,
norm=norm if depth_coloring else None,
joinstyle='round')
lc.set_linewidth(linewidth)
lc.set_linestyle(linestyle)
lc.set_label(f'{getattr(neuron, "name", "NA")} - #{neuron.id}')
if depth_coloring:
lc.set_alpha(alpha)
lc.set_array(neuron.nodes.loc[neuron.nodes.parent_id >= 0,
'z'].values)
elif (isinstance(color, np.ndarray) and color.ndim == 2):
# If we have a color for each node, we need to drop the roots
if color.shape[1] != coords.shape[0]:
lc.set_color(color[neuron.nodes.parent_id.values >= 0])
else:
lc.set_color(color)
ax.add_collection(lc)
if plot_soma and np.any(neuron.soma):
soma = utils.make_iterable(neuron.soma)
# If soma detection is messed up we might end up producing
# dozens of soma which will freeze the kernel
if len(soma) >= 10:
logger.warning(f'{neuron.id} - {len(soma)} somas found.')
for s in soma:
if isinstance(color, np.ndarray) and color.ndim > 1:
s_ix = np.where(neuron.nodes.node_id == s)[0][0]
soma_color = color[s_ix]
else:
soma_color = color
n = neuron.nodes.set_index('node_id').loc[s]
r = getattr(n, neuron.soma_radius) if isinstance(
neuron.soma_radius, str) else neuron.soma_radius
if depth_coloring:
d = [n.x, n.y, n.z][_get_depth_axis(view)]
soma_color = mpl.cm.jet(norm(d))
sx, sy = _parse_view2d(np.array([[n.x, n.y, n.z]]), view)
c = mpatches.Circle((sx[0], sy[0]),
radius=r,
alpha=alpha,
fill=True,
fc=soma_color,
zorder=4,
edgecolor='none')
ax.add_patch(c)
return None, None
elif method in ['3d', '3d_complex']:
# For simple scenes, add whole neurons at a time to speed up rendering
if method == '3d':
if (isinstance(color, np.ndarray)
and color.ndim == 2) or depth_coloring:
coords = tn_pairs_to_coords(neuron, modifier=(1, 1, 1))
# If we have a color for each node, we need to drop the roots
if isinstance(
color,
np.ndarray) and color.shape[1] != coords.shape[0]:
line_color = color[neuron.nodes.parent_id.values >= 0]
else:
line_color = color
else:
# Generate by-segment coordinates
coords = segments_to_coords(neuron,
neuron.segments,
modifier=(1, 1, 1))
line_color = color
lc = Line3DCollection(
coords,
color=line_color,
label=neuron.id,
alpha=alpha if not kwargs.get('shade_by', False) else None,
cmap=mpl.cm.jet if depth_coloring else None,
lw=linewidth,
joinstyle='round',
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.id)
# Need to get this before adding data
line3D_collection = lc
ax.add_collection3d(lc)
# For complex scenes, add each segment as a single collection
# -> helps reducing Z-order errors
elif method == '3d_complex':
# Generate by-segment coordinates
coords = segments_to_coords(neuron,
neuron.segments,
modifier=(1, 1, 1))
for c in coords:
lc = Line3DCollection([c],
color=color,
lw=linewidth,
alpha=alpha,
linestyle=linestyle)
if group_neurons:
lc.set_gid(neuron.id)
ax.add_collection3d(lc)
line3D_collection = None
surf3D_collections = []
if plot_soma and not isinstance(getattr(neuron, 'soma', None),
type(None)):
soma = utils.make_iterable(neuron.soma)
# If soma detection is messed up we might end up producing
# dozens of soma which will freeze the kernel
if len(soma) >= 5:
logger.warning(
f'Neuron {neuron.id} appears to have {len(soma)}'
' somas. Skipping plotting its somas.')
else:
for s in soma:
if isinstance(color, np.ndarray) and color.ndim > 1:
s_ix = np.where(neuron.nodes.node_id == s)[0][0]
soma_color = color[s_ix]
else:
soma_color = color
n = neuron.nodes.set_index('node_id').loc[s]
r = getattr(n, neuron.soma_radius) if isinstance(
neuron.soma_radius, str) else neuron.soma_radius
resolution = 20
u = np.linspace(0, 2 * np.pi, resolution)
v = np.linspace(0, np.pi, resolution)
x = r * np.outer(np.cos(u), np.sin(v)) + n.x
y = r * np.outer(np.sin(u), np.sin(v)) + n.y
z = r * np.outer(np.ones(np.size(u)), np.cos(v)) + n.z
surf = ax.plot_surface(x,
y,
z,
color=soma_color,
shade=False,
alpha=alpha)
if group_neurons:
surf.set_gid(neuron.id)
surf3D_collections.append(surf)
return line3D_collection, surf3D_collections
def drawMap(filename,names,totalCount):
countries = json.loads(open(filename).read())["features"]
for country in countries:
name = country["properties"]["name"]
polygonList = country["geometry"]["coordinates"]
polygonType = country["geometry"]["type"]
shpsegs = []
print "Processing %s [%d/%s] ..." % (unicode(name).encode("utf-8"), len(polygonList), polygonType)
maxverts = 0
maxindex = 0
counter = 0
for polygon in polygonList:
if polygonType == "MultiPolygon":
for p in polygon:
if len(p) == 1:
p = p[0]
if p[0] != p[-1]:
p.append(p[0])
lons, lats = zip(*p)
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if len(x) > maxverts:
maxverts = len(x)
maxindex = counter
counter+=1
else:
if len(polygon) == 1:
polygon = polygon[0]
if polygon[0] != polygon[-1]:
polygon.append(polygon[0])
lons, lats = zip(*polygon)
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if len(x) > maxverts:
maxverts = len(x)
maxindex = counter
counter+=1
# Compute centroid
centroid = Polygon(shpsegs[maxindex]).centroid
# Create country shape
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_edgecolors('k')
lines.set_linewidth(0.5)
#import brewer2mpl
#colormap = brewer2mpl.get_map('Paired', 'qualitative', 12).mpl_colors
colormap = [(0.6509803921568628, 0.807843137254902, 0.8901960784313725), (0.12156862745098039, 0.47058823529411764, 0.7058823529411765), (0.6980392156862745, 0.8745098039215686, 0.5411764705882353), (0.2, 0.6274509803921569, 0.17254901960784313), (0.984313725490196, 0.6039215686274509, 0.6), (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), (0.9921568627450981, 0.7490196078431373, 0.43529411764705883), (1.0, 0.4980392156862745, 0.0), (0.792156862745098, 0.6980392156862745, 0.8392156862745098), (0.41568627450980394, 0.23921568627450981, 0.6039215686274509), (1.0, 1.0, 0.6), (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)]
# Add color and label if covered by Safecast
if name in names.keys():
color = colormap[(int((float(names[name][0])/totalCount)*12)+1)]
lines.set_label("%s - %0.1fK (%d)" % (name, names[name][0]/1000.0, names[name][1]) )
#lines.set_label(name)
lines.set_edgecolors(color)
lines.set_facecolors(color)
label = plt.text(centroid.x, centroid.y, "%d" % names[name][1], fontsize=5, ha='center', va='center', color='k', fontweight='bold')
plt.setp(label, path_effects=[PathEffects.withStroke(linewidth=2, foreground="w")])
ax.add_collection(lines)
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
arrowstyle='-|>',
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size' as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must contain color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
msg = 'edge_color must be a color or list of one color per edge'
raise ValueError(msg)
if (not G.is_directed() or not arrows):
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values
# for each line in a LineCollection. It was fixed in matplotlib by
# r7184 and r7189 (June 6 2009). We should then not set the alpha
# value globally, since the user can instead provide per-edge alphas
# now. Only set it globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
arrow_colors = edge_colors
if arrow_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
else:
edge_cmap = plt.get_cmap() # default matplotlib colormap
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
arrow_color = None
line_width = None
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if cb.iterable(node_size): # many node sizes
src_node, dst_node = edgelist[i]
index_node = nodelist.index(dst_node)
marker_size = node_size[index_node]
shrink_target = to_marker_edge(marker_size, node_shape)
else:
shrink_target = to_marker_edge(node_size, node_shape)
if arrow_colors is None:
arrow_color = edge_cmap(color_normal(edge_color[i]))
elif len(arrow_colors) > 1:
arrow_color = arrow_colors[i]
else:
arrow_color = arrow_colors[0]
if len(lw) > 1:
line_width = lw[i]
else:
line_width = lw[0]
arrow = FancyArrowPatch((x1, y1), (x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
zorder=1) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
return arrow_collection
def draw_animation_edges(G,
pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
**kwds):
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
box_pos = numpy.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
p = 0.25
edge_pos = []
for edge in edgelist:
src, dst = np.array(pos[edge[0]]), np.array(pos[edge[1]])
s = dst - src
# src = src + p * s # Box at beginning
# dst = src + (1-p) * s # Box at the end
dst = src # No edge at all
edge_pos.append((src, dst))
edge_pos = numpy.asarray(edge_pos)
if not cb.iterable(width):
lw = (width, )
else:
lw = width
if not cb.is_scalar_or_string(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_scalar_or_string(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif numpy.alltrue(
[not cb.is_scalar_or_string(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if cb.is_scalar_or_string(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
'''
modEdgeColors = list(edge_colors)
modEdgeColors = tuple(modEdgeColors + [colorConverter.to_rgba('w', alpha)
for c in edge_color])
#print(modEdgeColors)
edge_collection = LineCollection(np.asarray(list(edge_pos)*2),
colors=modEdgeColors,
linewidths=[6]*len(list(edge_colors))+[4]*len(list(edge_colors)),
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
'''
edge_collection = LineCollection(
edge_pos,
colors=edge_colors,
linewidths=6,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
tube_collection = LineCollection(
edge_pos,
colors=tuple([
colorConverter.to_rgba('lightgrey', alpha) for c in edge_color
]),
linewidths=4,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
tube_collection.set_zorder(1) # edges go behind nodes
tube_collection.set_label(label)
ax.add_collection(tube_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
box_collection = Utilities.get_boxes(edge_colors=edge_colors,
edge_pos=box_pos)
box_collection.set_zorder(1) # edges go behind nodes
box_collection.set_label(label)
ax.add_collection(box_collection)
arrow_collection = Utilities.get_arrows_on_edges(
edge_colors=edge_colors, edge_pos=box_pos)
arrow_collection.set_zorder(0)
if arrows:
# Visualize them only if wanted
ax.add_collection(arrow_collection)
return edge_collection, box_collection, tube_collection, arrow_collection
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
Notes
-----
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
Yes, it is ugly but drawing proper arrows with Matplotlib this
way is tricky.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not cb.is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([cb.is_string_like(c)
for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not cb.is_string_like(c)
for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must consist of either color names or numbers')
else:
if cb.is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges')
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
arrow_collection = None
if G.is_directed() and arrows:
# a directed graph hack
# draw thick line segments at head end of edge
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos = []
p = 1.0 - 0.25 # make head segment 25 percent of edge length
for src, dst in edge_pos:
x1, y1 = src
x2, y2 = dst
dx = x2 - x1 # x offset
dy = y2 - y1 # y offset
d = np.sqrt(float(dx**2 + dy**2)) # length of edge
if d == 0: # source and target at same position
continue
if dx == 0: # vertical edge
xa = x2
ya = dy * p + y1
if dy == 0: # horizontal edge
ya = y2
xa = dx * p + x1
else:
theta = np.arctan2(dy, dx)
xa = p * d * np.cos(theta) + x1
ya = p * d * np.sin(theta) + y1
a_pos.append(((xa, ya), (x2, y2)))
arrow_collection = LineCollection(a_pos,
colors=arrow_colors,
linewidths=[4 * ww for ww in lw],
antialiaseds=(1,),
transOffset=ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
arrow_collection.set_label(label)
ax.add_collection(arrow_collection)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def plot_tree(tree, figure_name, color_by_trait, initial_branch_width,
tip_size, start_date, end_date, include_color_bar):
"""Plot a BioPython Phylo tree in the BALTIC-style.
"""
# Plot H3N2 tree in BALTIC style from Bio.Phylo tree.
mpl.rcParams['savefig.dpi'] = 120
mpl.rcParams['figure.dpi'] = 100
mpl.rcParams['font.weight'] = 300
mpl.rcParams['axes.labelweight'] = 300
mpl.rcParams['font.size'] = 14
yvalues = [node.yvalue for node in tree.find_clades()]
y_span = max(yvalues)
y_unit = y_span / float(len(yvalues))
# Setup colors.
trait_name = color_by_trait
traits = [k.attr[trait_name] for k in tree.find_clades()]
norm = mpl.colors.Normalize(min(traits), max(traits))
cmap = mpl.cm.viridis
#
# Setup the figure grid.
#
if include_color_bar:
fig = plt.figure(figsize=(8, 6), facecolor='w')
gs = gridspec.GridSpec(2,
1,
height_ratios=[14, 1],
width_ratios=[1],
hspace=0.1,
wspace=0.1)
ax = fig.add_subplot(gs[0])
colorbar_ax = fig.add_subplot(gs[1])
else:
fig = plt.figure(figsize=(8, 4), facecolor='w')
gs = gridspec.GridSpec(1, 1)
ax = fig.add_subplot(gs[0])
L = len([k for k in tree.find_clades() if k.is_terminal()])
# Setup arrays for tip and internal node coordinates.
tip_circles_x = []
tip_circles_y = []
tip_circles_color = []
tip_circle_sizes = []
node_circles_x = []
node_circles_y = []
node_circles_color = []
node_line_widths = []
node_line_segments = []
node_line_colors = []
branch_line_segments = []
branch_line_widths = []
branch_line_colors = []
branch_line_labels = []
for k in tree.find_clades(): ## iterate over objects in tree
x = k.attr["num_date"] ## or from x position determined earlier
y = k.yvalue ## get y position from .drawTree that was run earlier, but could be anything else
if k.parent is None:
xp = None
else:
xp = k.parent.attr[
"num_date"] ## get x position of current object's parent
if x == None: ## matplotlib won't plot Nones, like root
x = 0.0
if xp == None:
xp = x
c = 'k'
if trait_name in k.attr:
c = cmap(norm(k.attr[trait_name]))
branchWidth = 2
if k.is_terminal(): ## if leaf...
s = tip_size ## tip size can be fixed
tip_circle_sizes.append(s)
tip_circles_x.append(x)
tip_circles_y.append(y)
tip_circles_color.append(c)
else: ## if node...
k_leaves = [
child for child in k.find_clades() if child.is_terminal()
]
# Scale branch widths by the number of tips.
branchWidth += initial_branch_width * len(k_leaves) / float(L)
if len(k.clades) == 1:
node_circles_x.append(x)
node_circles_y.append(y)
node_circles_color.append(c)
ax.plot([x, x], [k.clades[-1].yvalue, k.clades[0].yvalue],
lw=branchWidth,
color=c,
ls='-',
zorder=9,
solid_capstyle='round')
branch_line_segments.append([(xp, y), (x, y)])
branch_line_widths.append(branchWidth)
branch_line_colors.append(c)
branch_lc = LineCollection(branch_line_segments, zorder=9)
branch_lc.set_color(branch_line_colors)
branch_lc.set_linewidth(branch_line_widths)
branch_lc.set_label(branch_line_labels)
branch_lc.set_linestyle("-")
ax.add_collection(branch_lc)
# Add circles for tips and internal nodes.
tip_circle_sizes = np.array(tip_circle_sizes)
ax.scatter(tip_circles_x,
tip_circles_y,
s=tip_circle_sizes,
facecolor=tip_circles_color,
edgecolor='none',
zorder=11) ## plot circle for every tip
ax.scatter(tip_circles_x,
tip_circles_y,
s=tip_circle_sizes * 2,
facecolor='k',
edgecolor='none',
zorder=10) ## plot black circle underneath
ax.scatter(
node_circles_x,
node_circles_y,
facecolor=node_circles_color,
s=50,
edgecolor='none',
zorder=10,
lw=2,
marker='|'
) ## mark every node in the tree to highlight that it's a multitype tree
#ax.set_ylim(-10, y_span - 300)
ax.spines['top'].set_visible(False) ## no axes
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.grid(axis='x', ls='-', color='grey')
ax.tick_params(axis='y', size=0)
ax.set_yticklabels([])
if start_date:
# Always add a buffer to the left edge of the plot so data up to the
# given end date can be clearly seen.
ax.set_xlim(left=timestamp_to_float(pd.to_datetime(start_date)) - 2.0)
if end_date:
# Always add a buffer of 3 months to the right edge of the plot so data
# up to the given end date can be clearly seen.
ax.set_xlim(right=timestamp_to_float(pd.to_datetime(end_date)) + 0.25)
if include_color_bar:
cb1 = mpl.colorbar.ColorbarBase(colorbar_ax,
cmap=cmap,
norm=norm,
orientation='horizontal')
cb1.set_label(color_by_trait)
gs.tight_layout(fig)
plt.savefig(figure_name)
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
arrowstyle='-|>',
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : str, optional (default=None)
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See :py:class: `matplotlib.patches.ConnectionStyle` and
:py:class: `matplotlib.patches.FancyArrowPatch` for more info.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must contain color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
msg = 'edge_color must be a color or list of one color per edge'
raise ValueError(msg)
if (not G.is_directed() or not arrows):
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values
# for each line in a LineCollection. It was fixed in matplotlib by
# r7184 and r7189 (June 6 2009). We should then not set the alpha
# value globally, since the user can instead provide per-edge alphas
# now. Only set it globally if provided as a scalar.
if isinstance(alpha, Number):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
arrow_colors = edge_colors
if arrow_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
else:
edge_cmap = plt.get_cmap() # default matplotlib colormap
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
arrow_color = None
line_width = None
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if cb.iterable(node_size): # many node sizes
src_node, dst_node = edgelist[i][:2]
index_node = nodelist.index(dst_node)
marker_size = node_size[index_node]
shrink_target = to_marker_edge(marker_size, node_shape)
else:
shrink_target = to_marker_edge(node_size, node_shape)
if arrow_colors is None:
arrow_color = edge_cmap(color_normal(edge_color[i]))
elif len(arrow_colors) > 1:
arrow_color = arrow_colors[i]
else:
arrow_color = arrow_colors[0]
if len(lw) > 1:
line_width = lw[i]
else:
line_width = lw[0]
arrow = FancyArrowPatch((x1, y1), (x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
zorder=1) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
plt.tick_params(
axis='both',
which='both',
bottom=False,
left=False,
labelbottom=False,
labelleft=False)
return arrow_collection
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=None,
arrowstyle='-|>',
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color or array of colors (default='k')
Edge color. Can be a single color or a sequence of colors with the same
length as edgelist. Color can be string, or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=None)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : str, optional (default=None)
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See :py:class: `matplotlib.patches.ConnectionStyle` and
:py:class: `matplotlib.patches.FancyArrowPatch` for more info.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# FancyArrowPatch handles color=None different from LineCollection
if edge_color is None:
edge_color = 'k'
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# Check if edge_color is an array of floats and map to edge_cmap.
# This is the only case handled differently from matplotlib
if cb.iterable(edge_color) and (len(edge_color) == len(edge_pos)) \
and np.alltrue([isinstance(c,Number) for c in edge_color]):
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
else:
edge_cmap = plt.get_cmap()
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
if (not G.is_directed() or not arrows):
edge_collection = LineCollection(edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
alpha=alpha
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
# FancyArrowPatch doesn't handle color strings
arrow_colors = colorConverter.to_rgba_array(edge_color,alpha)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if cb.iterable(node_size): # many node sizes
src_node, dst_node = edgelist[i][:2]
index_node = nodelist.index(dst_node)
marker_size = node_size[index_node]
shrink_target = to_marker_edge(marker_size, node_shape)
else:
shrink_target = to_marker_edge(node_size, node_shape)
if cb.iterable(arrow_colors):
if len(arrow_colors) == len(edge_pos):
arrow_color = arrow_colors[i]
elif len(arrow_colors)==1:
arrow_color = arrow_colors[0]
else: # Cycle through colors
arrow_color = arrow_colors[i%len(arrow_colors)]
else:
arrow_color = edge_color
if cb.iterable(width):
if len(width) == len(edge_pos):
line_width = width[i]
else:
line_width = width[i%len(width)]
else:
line_width = width
arrow = FancyArrowPatch((x1, y1), (x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
zorder=1) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
plt.tick_params(
axis='both',
which='both',
bottom=False,
left=False,
labelbottom=False,
labelleft=False)
return arrow_collection