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 plot(network,
margin=0.05,
ax=None,
geomap=True,
projection=None,
bus_colors='b',
line_colors='g',
bus_sizes=10,
line_widths=2,
title="",
line_cmap=None,
bus_cmap=None,
boundaries=None,
geometry=False,
branch_components=['Line', 'Link'],
jitter=None,
basemap=None):
"""
Plot the network buses and lines using matplotlib and Basemap.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
geomap: bool/str, default True
Switch to use Basemap or Cartopy (depends on what is installed).
If string is passed, it will be used as a resolution argument.
For Basemap users 'c' (crude), 'l' (low), 'i' (intermediate),
'h' (high), 'f' (full) are valid resolutions options.
For Cartopy users '10m', '50m', '110m' are valid resolutions options.
projection: cartopy.crs.Projection, defaults to None
Define the projection of your geomap, only valid if cartopy is
installed. If None (default) is passed the projection for cartropy
is set to cartopy.crs.PlateCarree
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = {
'Link': dict(color="cyan", width=2),
'Line': dict(color="b", width=2),
'Transformer': dict(color='green', width=2)
}
if not plt_present:
logger.error("Matplotlib is not present, so plotting won't work.")
return
if basemap is not None:
logger.warning("argument `basemap` is deprecated, "
"use `geomap` instead.")
geomap = basemap
if cartopy_present and geomap:
if projection is None:
projection = get_projection_from_crs(network.srid)
if ax is None:
ax = plt.gca(projection=projection)
else:
assert isinstance(ax, cartopy.mpl.geoaxes.GeoAxesSubplot), (
'The passed axis is not a GeoAxesSubplot. You can '
'create one with: \nimport cartopy.crs as ccrs \n'
'fig, ax = plt.subplots('
'subplot_kw={"projection":ccrs.PlateCarree()})')
elif ax is None:
ax = plt.gca()
x, y = network.buses["x"], network.buses["y"]
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
if geomap:
transform = draw_map(network, x, y, ax, boundaries, margin, geomap)
else:
transform = ax.transData
if isinstance(bus_sizes, pd.Series) and isinstance(bus_sizes.index,
pd.MultiIndex):
# We are drawing pies to show all the different shares
assert len(bus_sizes.index.levels[0].difference(network.buses.index)) == 0, \
"The first MultiIndex level of bus_sizes must contain buses"
assert (isinstance(bus_colors, dict) and
set(bus_colors).issuperset(bus_sizes.index.levels[1])), \
"bus_colors must be a dictionary defining a color for each element " \
"in the second MultiIndex level of bus_sizes"
bus_sizes = bus_sizes.sort_index(level=0, sort_remaining=False)\
* projected_area_factor(ax, network.srid)**2
patches = []
for b_i in bus_sizes.index.levels[0]:
s = bus_sizes.loc[b_i]
radius = s.sum()**0.5
if radius == 0.0:
ratios = s
else:
ratios = s / s.sum()
start = 0.25
for i, ratio in ratios.iteritems():
patches.append(
Wedge((x.at[b_i], y.at[b_i]),
radius,
360 * start,
360 * (start + ratio),
facecolor=bus_colors[i]))
start += ratio
bus_collection = PatchCollection(patches,
match_original=True,
transform=transform)
ax.add_collection(bus_collection)
else:
c = pd.Series(bus_colors, index=network.buses.index)
s = pd.Series(bus_sizes, index=network.buses.index,
dtype="float").fillna(10)
bus_collection = ax.scatter(x,
y,
c=c,
s=s,
cmap=bus_cmap,
edgecolor='face',
transform=transform)
def as_branch_series(ser):
if isinstance(ser, dict) and set(ser).issubset(branch_components):
return pd.Series(ser)
elif isinstance(ser, pd.Series):
if isinstance(ser.index, pd.MultiIndex):
return ser
index = ser.index
ser = ser.values
else:
index = network.lines.index
return pd.Series(ser,
index=pd.MultiIndex(levels=(["Line"], index),
codes=(np.zeros(len(index)),
np.arange(len(index)))))
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
for c in network.iterate_components(branch_components):
l_defaults = defaults_for_branches[c.name]
l_widths = line_widths.get(c.name, l_defaults['width'])
l_nums = None
l_colors = line_colors.get(c.name, l_defaults['color'])
if isinstance(l_colors, pd.Series):
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
if not geometry:
segments = (np.asarray(
((c.df.bus0.map(x), c.df.bus0.map(y)),
(c.df.bus1.map(x), c.df.bus1.map(y)))).transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = c.df.geometry.map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), (
"The WKT-encoded geometry in the 'geometry' column must be "
"composed of LineStrings")
segments = np.asarray(list(linestrings.map(np.asarray)))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1, ),
colors=l_colors,
transOffset=ax.transData,
transform=transform)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(c.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(1)
branch_collections.append(l_collection)
bus_collection.set_zorder(2)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
if geomap:
if cartopy_present:
ax.outline_patch.set_visible(False)
ax.axis('off')
ax.set_title(title)
return (bus_collection, ) + tuple(branch_collections)
def plot(network,
margin=0.05,
ax=None,
basemap=True,
bus_colors='b',
line_colors='g',
bus_sizes=10,
line_widths=2,
title="",
line_cmap=None,
bus_cmap=None,
boundaries=None,
geometry=False,
branch_components=['Line', 'Link'],
jitter=None):
"""
Plot the network buses and lines using matplotlib and Basemap.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
basemap : bool, default True
Switch to use Basemap
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = {
'Link': dict(color="cyan", width=2),
'Line': dict(color="b", width=2),
'Transformer': dict(color='green', width=2)
}
if not plt_present:
logger.error("Matplotlib is not present, so plotting won't work.")
return
if ax is None:
ax = plt.gca()
def compute_bbox_with_margins(margin, x, y):
#set margins
pos = np.asarray((x, y))
minxy, maxxy = pos.min(axis=1), pos.max(axis=1)
xy1 = minxy - margin * (maxxy - minxy)
xy2 = maxxy + margin * (maxxy - minxy)
return tuple(xy1), tuple(xy2)
x = network.buses["x"]
y = network.buses["y"]
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
if basemap and basemap_present:
if boundaries is None:
(x1, y1), (x2, y2) = compute_bbox_with_margins(margin, x, y)
else:
x1, x2, y1, y2 = boundaries
bmap = Basemap(resolution='l',
epsg=network.srid,
llcrnrlat=y1,
urcrnrlat=y2,
llcrnrlon=x1,
urcrnrlon=x2,
ax=ax)
bmap.drawcountries()
bmap.drawcoastlines()
x, y = bmap(x.values, y.values)
x = pd.Series(x, network.buses.index)
y = pd.Series(y, network.buses.index)
if isinstance(bus_sizes, pd.Series) and isinstance(bus_sizes.index,
pd.MultiIndex):
# We are drawing pies to show all the different shares
assert len(bus_sizes.index.levels[0].difference(network.buses.index)) == 0, \
"The first MultiIndex level of bus_sizes must contain buses"
assert isinstance(bus_colors, dict) and set(bus_colors).issuperset(bus_sizes.index.levels[1]), \
"bus_colors must be a dictionary defining a color for each element " \
"in the second MultiIndex level of bus_sizes"
bus_sizes = bus_sizes.sort_index(level=0, sort_remaining=False)
patches = []
for b_i in bus_sizes.index.levels[0]:
s = bus_sizes.loc[b_i]
radius = s.sum()**0.5
ratios = s / s.sum()
start = 0.25
for i, ratio in ratios.iteritems():
patches.append(
Wedge((x.at[b_i], y.at[b_i]),
radius,
360 * start,
360 * (start + ratio),
facecolor=bus_colors[i]))
start += ratio
bus_collection = PatchCollection(patches, match_original=True)
ax.add_collection(bus_collection)
else:
c = pd.Series(bus_colors, index=network.buses.index)
if c.dtype == np.dtype('O'):
c.fillna("b", inplace=True)
c = list(c.values)
s = pd.Series(bus_sizes, index=network.buses.index,
dtype="float").fillna(10)
bus_collection = ax.scatter(x, y, c=c, s=s, cmap=bus_cmap)
def as_branch_series(ser):
if isinstance(ser, dict) and set(ser).issubset(branch_components):
return pd.Series(ser)
elif isinstance(ser, pd.Series):
if isinstance(ser.index, pd.MultiIndex):
return ser
index = ser.index
ser = ser.values
else:
index = network.lines.index
return pd.Series(ser,
index=pd.MultiIndex(levels=(["Line"], index),
labels=(np.zeros(len(index)),
np.arange(len(index)))))
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
for c in network.iterate_components(branch_components):
l_defaults = defaults_for_branches[c.name]
l_widths = line_widths.get(c.name, l_defaults['width'])
l_nums = None
l_colors = line_colors.get(c.name, l_defaults['color'])
if isinstance(l_colors, pd.Series):
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
if not geometry:
segments = (np.asarray(
((c.df.bus0.map(x), c.df.bus0.map(y)),
(c.df.bus1.map(x), c.df.bus1.map(y)))).transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = c.df.geometry.map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), \
"The WKT-encoded geometry in the 'geometry' column must be composed of LineStrings"
segments = np.asarray(list(linestrings.map(np.asarray)))
if basemap and basemap_present:
segments = np.transpose(
bmap(*np.transpose(segments, (2, 0, 1))), (1, 2, 0))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1, ),
colors=l_colors,
transOffset=ax.transData)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(c.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(1)
branch_collections.append(l_collection)
bus_collection.set_zorder(2)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
ax.set_title(title)
return (bus_collection, ) + tuple(branch_collections)
def plot(n, margin=None, ax=None, geomap=True, projection=None,
bus_colors='cadetblue', bus_alpha=1, bus_sizes=2e-2, bus_cmap=None,
line_colors='rosybrown', link_colors='darkseagreen',
transformer_colors='orange',
line_widths=1.5, link_widths=1.5, transformer_widths=1.5,
line_cmap=None, link_cmap=None, transformer_cmap=None,
flow=None, branch_components=None, layouter=None, title="",
boundaries=None, geometry=False, jitter=None, color_geomap=None):
"""
Plot the network buses and lines using matplotlib and cartopy.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
geomap: bool/str, default True
Switch to use Cartopy and draw geographical features.
If string is passed, it will be used as a resolution argument,
valid options are '10m', '50m' and '110m'.
projection: cartopy.crs.Projection, defaults to None
Define the projection of your geomap, only valid if cartopy is
installed. If None (default) is passed the projection for cartopy
is set to cartopy.crs.PlateCarree
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "cadetblue". If bus_sizes is a
pandas.Series with a Multiindex, bus_colors defaults to the
n.carriers['color'] column.
bus_alpha : float
Adds alpha channel to buses, defaults to 1.
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 1e-2. If a multiindexed Series is passed,
the function will draw pies for each bus (first index level) with
segments of different color (second index level). Such a Series is ob-
tained by e.g. n.generators.groupby(['bus', 'carrier']).p_nom.sum()
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
line_colors : str/pandas.Series
Colors for the lines, defaults to 'rosybrown'.
link_colors : str/pandas.Series
Colors for the links, defaults to 'darkseagreen'.
transfomer_colors : str/pandas.Series
Colors for the transfomer, defaults to 'orange'.
line_widths : dict/pandas.Series
Widths of lines, defaults to 1.5
link_widths : dict/pandas.Series
Widths of links, defaults to 1.5
transformer_widths : dict/pandas.Series
Widths of transformer, defaults to 1.5
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
link_cmap : plt.cm.ColorMap/str|dict
If link_colors are floats, this color map will assign the colors.
transformer_cmap : plt.cm.ColorMap/str|dict
If transformer_colors are floats, this color map will assign the colors.
flow : snapshot/pandas.Series/function/string
Flow to be displayed in the plot, defaults to None. If an element of
n.snapshots is given, the flow at this timestamp will be
displayed. If an aggregation function is given, is will be applied
to the total network flow via pandas.DataFrame.agg (accepts also
function names). Otherwise flows can be specified by passing a pandas
Series with MultiIndex including all necessary branch components.
Use the line_widths argument to additionally adjust the size of the
flow arrows.
layouter : networkx.drawing.layout function, default None
Layouting function from `networkx <https://networkx.github.io/>`_ which
overrules coordinates given in ``n.buses[['x','y']]``. See
`list <https://networkx.github.io/documentation/stable/reference/drawing.html#module-networkx.drawing.layout>`_
of available options.
title : string
Graph title
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
color_geomap : dict or bool
Specify colors to paint land and sea areas in.
If True, it defaults to `{'ocean': 'lightblue', 'land': 'whitesmoke'}`.
If no dictionary is provided, colors are white.
If False, no geographical features are plotted.
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
x, y = _get_coordinates(n, layouter=layouter)
if boundaries is None and margin:
boundaries = sum(zip(*compute_bbox_with_margins(margin, x, y)), ())
if geomap and not cartopy_present:
logger.warning("Cartopy needs to be installed to use `geomap=True`.")
geomap = False
if geomap:
transform = get_projection_from_crs(n.srid)
if projection is None:
projection = transform
else:
assert isinstance(projection, cartopy.crs.Projection), (
'The passed projection is not a cartopy.crs.Projection')
if ax is None:
ax = plt.gca(projection=projection)
else:
assert isinstance(ax, cartopy.mpl.geoaxes.GeoAxesSubplot), (
'The passed axis is not a GeoAxesSubplot. You can '
'create one with: \nimport cartopy.crs as ccrs \n'
'fig, ax = plt.subplots('
'subplot_kw={"projection":ccrs.PlateCarree()})')
x, y, z = ax.projection.transform_points(transform, x.values, y.values).T
x, y = pd.Series(x, n.buses.index), pd.Series(y, n.buses.index)
if color_geomap is not False:
draw_map_cartopy(ax, geomap, color_geomap)
if boundaries is not None:
ax.set_extent(boundaries, crs=transform)
elif ax is None:
ax = plt.gca()
if not geomap and boundaries:
ax.axis(boundaries)
ax.set_aspect('equal')
ax.axis('off')
ax.set_title(title)
# Plot buses:
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
if isinstance(bus_sizes, pd.Series) and isinstance(bus_sizes.index, pd.MultiIndex):
# We are drawing pies to show all the different shares
assert len(bus_sizes.index.levels[0].difference(n.buses.index)) == 0, \
"The first MultiIndex level of bus_sizes must contain buses"
if isinstance(bus_colors, dict):
bus_colors = pd.Series(bus_colors)
# case bus_colors isn't a series or dict: look in n.carriers for existent colors
if not isinstance(bus_colors, pd.Series):
bus_colors = n.carriers.color.dropna()
assert bus_sizes.index.levels[1].isin(bus_colors.index).all(), (
"Colors not defined for all elements in the second MultiIndex "
"level of bus_sizes, please make sure that all the elements are "
"included in bus_colors or in n.carriers.color")
bus_sizes = bus_sizes.sort_index(level=0, sort_remaining=False)
if geomap:
bus_sizes = bus_sizes * projected_area_factor(ax, n.srid)**2
patches = []
for b_i in bus_sizes.index.levels[0]:
s = bus_sizes.loc[b_i]
radius = s.sum()**0.5
if radius == 0.0:
ratios = s
else:
ratios = s/s.sum()
start = 0.25
for i, ratio in ratios.iteritems():
patches.append(Wedge((x.at[b_i], y.at[b_i]), radius,
360*start, 360*(start+ratio),
facecolor=bus_colors[i], alpha=bus_alpha))
start += ratio
bus_collection = PatchCollection(patches, match_original=True, zorder=5)
ax.add_collection(bus_collection)
else:
c = pd.Series(bus_colors, index=n.buses.index)
s = pd.Series(bus_sizes, index=n.buses.index, dtype="float")
if geomap:
s = s * projected_area_factor(ax, n.srid)**2
if bus_cmap is not None and c.dtype is np.dtype('float'):
if isinstance(bus_cmap, str):
bus_cmap = plt.cm.get_cmap(bus_cmap)
norm = plt.Normalize(vmin=c.min(), vmax=c.max())
c = c.apply(lambda cval: bus_cmap(norm(cval)))
patches = []
for b_i in s.index:
radius = s.at[b_i]**0.5
patches.append(Circle((x.at[b_i], y.at[b_i]), radius,
facecolor=c.at[b_i], alpha=bus_alpha))
bus_collection = PatchCollection(patches, match_original=True, zorder=5)
ax.add_collection(bus_collection)
# Plot branches:
if isinstance(line_widths, pd.Series):
if isinstance(line_widths.index, pd.MultiIndex):
raise TypeError("Index of argument 'line_widths' is a Multiindex, "
"this is not support since pypsa v0.17. "
"Set differing widths with arguments 'line_widths', "
"'link_widths' and 'transformer_widths'.")
if isinstance(line_colors, pd.Series):
if isinstance(line_colors.index, pd.MultiIndex):
raise TypeError("Index of argument 'line_colors' is a Multiindex, "
"this is not support since pypsa v0.17. "
"Set differing colors with arguments 'line_colors', "
"'link_colors' and 'transformer_colors'.")
if branch_components is None:
branch_components = n.branch_components
branch_colors = {'Line': line_colors, 'Link': link_colors,
'Transformer': transformer_colors}
branch_widths = {'Line': line_widths, 'Link': link_widths,
'Transformer': transformer_widths}
branch_cmap = {'Line': line_cmap, 'Link': link_cmap,
'Transformer': transformer_cmap}
branch_collections = []
arrow_collections = []
if flow is not None:
rough_scale = sum(len(n.df(c)) for c in branch_components) + 100
flow = _flow_ds_from_arg(flow, n, branch_components) / rough_scale
for c in n.iterate_components(branch_components):
b_widths = as_branch_series(branch_widths[c.name], 'width', c.name, n)
b_colors = as_branch_series(branch_colors[c.name], 'color', c.name, n)
b_nums = None
b_cmap = branch_cmap[c.name]
b_flow = flow.get(c.name, None) if flow is not None else None
if issubclass(b_colors.dtype.type, np.number):
b_nums = b_colors
b_colors = None
if not geometry:
segments = (np.asarray(((c.df.bus0.map(x), c.df.bus0.map(y)),
(c.df.bus1.map(x), c.df.bus1.map(y))))
.transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = c.df.geometry[lambda ds: ds != ''].map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), (
"The WKT-encoded geometry in the 'geometry' column must be "
"composed of LineStrings")
segments = np.asarray(list(linestrings.map(np.asarray)))
if b_flow is not None:
coords = pd.DataFrame({'x1': c.df.bus0.map(x), 'y1': c.df.bus0.map(y),
'x2': c.df.bus1.map(x), 'y2': c.df.bus1.map(y)})
b_flow = b_flow.mul(b_widths[b_flow.index], fill_value=0)
# update the line width, allows to set line widths separately from flows
b_widths.update((5 * b_flow.abs()).pipe(np.sqrt))
area_factor = projected_area_factor(ax, n.srid)
f_collection = directed_flow(coords, b_flow, b_colors, area_factor,
b_cmap)
if b_nums is not None:
f_collection.set_array(np.asarray(b_nums))
f_collection.set_cmap(b_cmap)
f_collection.autoscale()
arrow_collections.append(f_collection)
ax.add_collection(f_collection)
b_collection = LineCollection(segments, linewidths=b_widths,
antialiaseds=(1,), colors=b_colors,
transOffset=ax.transData)
if b_nums is not None:
b_collection.set_array(np.asarray(b_nums))
b_collection.set_cmap(b_cmap)
b_collection.autoscale()
ax.add_collection(b_collection)
b_collection.set_zorder(3)
branch_collections.append(b_collection)
if boundaries is None:
ax.autoscale()
return (bus_collection,) + tuple(branch_collections) + tuple(arrow_collections)
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
**kwds):
"""Draw the edges of the graph G
This draws only the edges of the graph G.
pos is a dictionary keyed by vertex with a two-tuple
of x-y positions as the value.
See networkx.layout for functions that compute node positions.
edgelist is an optional list of the edges in G to be drawn.
If provided, only the edges in edgelist will be drawn.
edgecolor can be a list of matplotlib color letters such as 'k' or
'b' that lists the color of each edge; the list must be ordered in
the same way as the edge list. Alternatively, this list can contain
numbers and those number are mapped to a color scale using the color
map edge_cmap. Finally, it can also be a list of (r,g,b) or (r,g,b,a)
tuples, in which case these will be used directly to color the edges. If
the latter mode is used, you should not provide a value for alpha, as it
would be applied globally to all lines.
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
See draw_networkx for the list of other optional parameters.
"""
try:
import matplotlib
import matplotlib.pylab as pylab
import numpy as np
from matplotlib.colors import colorConverter,Colormap
from matplotlib.collections import LineCollection
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
pass # unable to open display
if ax is None:
ax=pylab.gca()
if edgelist is None:
edgelist=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)
alpha=None
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 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,
)
# 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)
# need 0.87.7 or greater for edge colormaps. No checks done, this will
# just not work with an older mpl
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()
pylab.sci(edge_collection)
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 = ( colorConverter.to_rgba('k', alpha), )
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,
)
# 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()
edge_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(edge_collection)
if arrow_collection:
arrow_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(arrow_collection)
return edge_collection
def plot(network, margin=0.05, ax=None, basemap=True, bus_colors='b',
line_colors='g', bus_sizes=10, line_widths=2, title="",
line_cmap=None, bus_cmap=None, boundaries=None,
geometry=False, branch_components=['Line', 'Link'], jitter=None):
"""
Plot the network buses and lines using matplotlib and Basemap.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
basemap : bool, default True
Switch to use Basemap
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = {
'Link': dict(color="cyan", width=2),
'Line': dict(color="b", width=2),
'Transformer': dict(color='green', width=2)
}
if not plt_present:
logger.error("Matplotlib is not present, so plotting won't work.")
return
if ax is None:
ax = plt.gca()
def compute_bbox_with_margins(margin, x, y):
#set margins
pos = np.asarray((x, y))
minxy, maxxy = pos.min(axis=1), pos.max(axis=1)
xy1 = minxy - margin*(maxxy - minxy)
xy2 = maxxy + margin*(maxxy - minxy)
return tuple(xy1), tuple(xy2)
x = network.buses["x"]
y = network.buses["y"]
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
if basemap and basemap_present:
if boundaries is None:
(x1, y1), (x2, y2) = compute_bbox_with_margins(margin, x, y)
else:
x1, x2, y1, y2 = boundaries
bmap = Basemap(resolution='l', epsg=network.srid,
llcrnrlat=y1, urcrnrlat=y2, llcrnrlon=x1,
urcrnrlon=x2, ax=ax)
bmap.drawcountries()
bmap.drawcoastlines()
x, y = bmap(x.values, y.values)
x = pd.Series(x, network.buses.index)
y = pd.Series(y, network.buses.index)
if isinstance(bus_sizes, pd.Series) and isinstance(bus_sizes.index, pd.MultiIndex):
# We are drawing pies to show all the different shares
assert len(network.buses.index.difference(bus_sizes.index.levels[0])) == 0, \
"The first MultiIndex level of bus_sizes must contain buses"
assert isinstance(bus_colors, dict) and set(bus_colors).issuperset(bus_sizes.index.levels[1]), \
"bus_colors must be a dictionary defining a color for each element " \
"in the second MultiIndex level of bus_sizes"
bus_sizes = bus_sizes.sort_index(level=0, sort_remaining=False)
patches = []
for b_i in bus_sizes.index.levels[0]:
s = bus_sizes.loc[b_i]
radius = s.sum()**0.5
ratios = s/s.sum()
start = 0.25
for i, ratio in ratios.iteritems():
patches.append(Wedge((x.at[b_i], y.at[b_i]), radius,
360*start, 360*(start+ratio),
facecolor=bus_colors[i]))
start += ratio
bus_collection = PatchCollection(patches, match_original=True)
ax.add_collection(bus_collection)
else:
c = pd.Series(bus_colors, index=network.buses.index)
if c.dtype == np.dtype('O'):
c.fillna("b", inplace=True)
c = list(c.values)
s = pd.Series(bus_sizes, index=network.buses.index, dtype="float").fillna(10)
bus_collection = ax.scatter(x, y, c=c, s=s, cmap=bus_cmap)
def as_branch_series(ser):
if isinstance(ser, dict) and set(ser).issubset(branch_components):
return pd.Series(ser)
elif isinstance(ser, pd.Series):
if isinstance(ser.index, pd.MultiIndex):
return ser
index = ser.index
ser = ser.values
else:
index = network.lines.index
return pd.Series(ser,
index=pd.MultiIndex(levels=(["Line"], index),
labels=(np.zeros(len(index)),
np.arange(len(index)))))
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
for c in network.iterate_components(branch_components):
l_defaults = defaults_for_branches[c.name]
l_widths = line_widths.get(c.name, l_defaults['width'])
l_nums = None
l_colors = line_colors.get(c.name, l_defaults['color'])
if isinstance(l_colors, pd.Series):
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
if not geometry:
segments = (np.asarray(((c.df.bus0.map(x),
c.df.bus0.map(y)),
(c.df.bus1.map(x),
c.df.bus1.map(y))))
.transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = c.df.geometry.map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), \
"The WKT-encoded geometry in the 'geometry' column must be composed of LineStrings"
segments = np.asarray(list(linestrings.map(np.asarray)))
if basemap and basemap_present:
segments = np.transpose(bmap(*np.transpose(segments, (2, 0, 1))), (1, 2, 0))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1,),
colors=l_colors,
transOffset=ax.transData)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(c.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(1)
branch_collections.append(l_collection)
bus_collection.set_zorder(2)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
ax.set_title(title)
return (bus_collection,) + tuple(branch_collections)
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
**kwds):
"""Draw the edges of the graph G
This draws only the edges of the graph G.
pos is a dictionary keyed by vertex with a two-tuple
of x-y positions as the value.
See networkx.layout for functions that compute node positions.
edgelist is an optional list of the edges in G to be drawn.
If provided, only the edges in edgelist will be drawn.
edgecolor can be a list of matplotlib color letters such as 'k' or
'b' that lists the color of each edge; the list must be ordered in
the same way as the edge list. Alternatively, this list can contain
numbers and those number are mapped to a color scale using the color
map edge_cmap. Finally, it can also be a list of (r,g,b) or (r,g,b,a)
tuples, in which case these will be used directly to color the edges. If
the latter mode is used, you should not provide a value for alpha, as it
would be applied globally to all lines.
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
See draw_networkx for the list of other optional parameters.
"""
try:
import matplotlib
import matplotlib.pylab as pylab
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:
pass # unable to open display
if ax is None:
ax=pylab.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])
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 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,
)
# 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)
# need 0.87.7 or greater for edge colormaps
mpl_version=matplotlib.__version__
if mpl_version.endswith('svn'):
mpl_version=matplotlib.__version__[0:-3]
if mpl_version.endswith('pre'):
mpl_version=matplotlib.__version__[0:-3]
if map(int,mpl_version.split('.'))>=[0,87,7]:
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()
pylab.sci(edge_collection)
# else:
# sys.stderr.write(\
# """matplotlib version >= 0.87.7 required for colormapped edges.
# (version %s detected)."""%matplotlib.__version__)
# raise UserWarning(\
# """matplotlib version >= 0.87.7 required for colormapped edges.
# (version %s detected)."""%matplotlib.__version__)
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 = ( colorConverter.to_rgba('k', alpha), )
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,
)
# 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()
edge_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(edge_collection)
if arrow_collection:
arrow_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(arrow_collection)
return edge_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 plot(network, margin=0.05, ax=None, basemap=True, bus_colors='b',
line_colors='g', bus_sizes=10, line_widths=2, title="",
line_cmap=None, bus_cmap=None, boundaries=None,
geometry=False, branch_types=['Line', 'TransportLink', 'Link']):
"""
Plot the network buses and lines using matplotlib and Basemap.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
basemap : bool, default True
Switch to use Basemap
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
TransportLinks and Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_types : list of str or pypsa.component
Branch types to be plotted, defaults to Line, TransportLink and Link.
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = {
'TransportLink': dict(color="cyan", width=2),
'Link': dict(color="cyan", width=2),
'Line': dict(color="b", width=2)
}
if not plt_present:
print("Matplotlib is not present, so plotting won't work.")
return
if ax is None:
ax = plt.gca()
def compute_bbox_with_margins(margin, x, y):
#set margins
pos = np.asarray((x, y))
minxy, maxxy = pos.min(axis=1), pos.max(axis=1)
xy1 = minxy - margin*(maxxy - minxy)
xy2 = maxxy + margin*(maxxy - minxy)
return tuple(xy1), tuple(xy2)
x = network.buses["x"]
y = network.buses["y"]
if basemap and basemap_present:
if boundaries is None:
(x1, y1), (x2, y2) = compute_bbox_with_margins(margin, x, y)
else:
x1, x2, y1, y2 = boundaries
bmap = Basemap(resolution='l', epsg=network.srid,
llcrnrlat=y1, urcrnrlat=y2, llcrnrlon=x1,
urcrnrlon=x2, ax=ax)
bmap.drawcountries()
bmap.drawcoastlines()
x, y = bmap(x.values, y.values)
x = pd.Series(x, network.buses.index)
y = pd.Series(y, network.buses.index)
c = pd.Series(bus_colors, index=network.buses.index)
if c.dtype == np.dtype('O'):
c.fillna("b", inplace=True)
s = pd.Series(bus_sizes, index=network.buses.index, dtype="float").fillna(10)
bus_collection = ax.scatter(x, y, c=c, s=s, cmap=bus_cmap)
def as_branch_series(ser):
if isinstance(ser, pd.Series):
if isinstance(ser.index, pd.MultiIndex):
return ser
index = ser.index
ser = ser.values
else:
index = network.lines.index
return pd.Series(ser,
index=pd.MultiIndex(levels=(["Line"], index),
labels=(np.zeros(len(index)),
np.arange(len(index)))))
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
for t in network.iterate_components(branch_types):
l_defaults = defaults_for_branches[t.name]
l_widths = line_widths.get(t.name, l_defaults['width'])
l_nums = None
if t.name in line_colors:
l_colors = line_colors[t.name]
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
else:
l_colors = l_defaults['color']
if not geometry:
segments = (np.asarray(((t.df.bus0.map(x),
t.df.bus0.map(y)),
(t.df.bus1.map(x),
t.df.bus1.map(y))))
.transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = t.df.geometry.map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), \
"The WKT-encoded geometry in the 'geometry' column must be composed of LineStrings"
segments = np.asarray(list(linestrings.map(np.asarray)))
if basemap and basemap_present:
segments = np.transpose(bmap(*np.transpose(segments, (2, 0, 1))), (1, 2, 0))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1,),
colors=l_colors,
transOffset=ax.transData)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(t.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(1)
branch_collections.append(l_collection)
bus_collection.set_zorder(2)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
ax.set_title(title)
return (bus_collection,) + tuple(branch_collections)
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,
**kwds):
"""Draw the edges of the graph G
This draws only the edges of the graph G.
pos is a dictionary keyed by vertex with a two-tuple
of x-y positions as the value.
See networkx_v099.layout for functions that compute node positions.
edgelist is an optional list of the edges in G to be drawn.
If provided, only the edges in edgelist will be drawn.
edgecolor can be a list of matplotlib color letters such as 'k' or
'b' that lists the color of each edge; the list must be ordered in
the same way as the edge list. Alternatively, this list can contain
numbers and those number are mapped to a color scale using the color
map edge_cmap.
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
See draw_networkx_v099 for the list of other optional parameters.
"""
if ax is None:
ax=matplotlib.pylab.gca()
if edgelist is None:
edgelist=G.edges()
if not edgelist or len(edgelist)==0: # no edges!
return None
# set edge positions
edge_pos=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 matplotlib.numerix.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 matplotlib.numerix.alltrue([not cb.is_string_like(c)
for c in edge_color]):
# 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 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_alpha(alpha)
# need 0.87.7 or greater for edge colormaps
mpl_version=matplotlib.__version__
if mpl_version.endswith('svn'):
mpl_version=matplotlib.__version__[0:-3]
if mpl_version.endswith('pre'):
mpl_version=matplotlib.__version__[0:-3]
if map(int,mpl_version.split('.'))>=[0,87,7]:
if edge_colors is None:
if edge_cmap is not None: assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(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()
matplotlib.pylab.sci(edge_collection)
# else:
# sys.stderr.write(\
# """matplotlib version >= 0.87.7 required for colormapped edges.
# (version %s detected)."""%matplotlib.__version__)
# raise UserWarning(\
# """matplotlib version >= 0.87.7 required for colormapped edges.
# (version %s detected)."""%matplotlib.__version__)
arrow_collection=None
if G.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 = ( colorConverter.to_rgba('k', alpha), )
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=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=arctan2(dy,dx)
xa=p*d*cos(theta)+x1
ya=p*d*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,
)
# update view
minx = amin(ravel(edge_pos[:,:,0]))
maxx = amax(ravel(edge_pos[:,:,0]))
miny = amin(ravel(edge_pos[:,:,1]))
maxy = amax(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()
edge_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(edge_collection)
if arrow_collection:
arrow_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(arrow_collection)
return edge_collection
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 plot(n,
margin=0.05,
ax=None,
geomap=True,
projection=None,
bus_colors='b',
bus_alpha=1,
line_colors={
'Line': 'g',
'Link': 'cyan'
},
bus_sizes=1e-2,
line_widths={
'Line': 2,
'Link': 2
},
flow=None,
layouter=None,
title="",
line_cmap=None,
bus_cmap=None,
boundaries=None,
geometry=False,
branch_components=['Line', 'Link'],
jitter=None,
color_geomap=None):
"""
Plot the network buses and lines using matplotlib and cartopy.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
geomap: bool/str, default True
Switch to use Cartopy and draw geographical features.
If string is passed, it will be used as a resolution argument,
valid options are '10m', '50m' and '110m'.
projection: cartopy.crs.Projection, defaults to None
Define the projection of your geomap, only valid if cartopy is
installed. If None (default) is passed the projection for cartopy
is set to cartopy.crs.PlateCarree
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b". If bus_sizes is a pandas.Series
with a Multiindex, bus_colors defaults to the n.carriers['color']
column.
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 1e-2. If a multiindexed Series is passed,
the function will draw pies for each bus (first index level) with
segments of different color (second index level). Such a Series is ob-
tained by e.g. n.generators.groupby(['bus', 'carrier']).p_nom.sum()
bus_alpha : float
Adds alpha channel to buses, defaults to 1.
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
flow : snapshot/pandas.Series/function/string
Flow to be displayed in the plot, defaults to None. If an element of
n.snapshots is given, the flow at this timestamp will be
displayed. If an aggregation function is given, is will be applied
to the total network flow via pandas.DataFrame.agg (accepts also
function names). Otherwise flows can be specified by passing a pandas
Series with MultiIndex including all necessary branch components.
Use the line_widths argument to additionally adjust the size of the
flow arrows.
layouter : networkx.drawing.layout function, default None
Layouting function from `networkx <https://networkx.github.io/>`_ which
overrules coordinates given in ``n.buses[['x','y']]``. See
`list <https://networkx.github.io/documentation/stable/reference/drawing.html#module-networkx.drawing.layout>`_
of available options.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
color_geomap : dict or bool
Specify colors to paint land and sea areas in.
If True, it defaults to `{'ocean': 'lightblue', 'land': 'whitesmoke'}`.
If no dictionary is provided, colors are white.
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = pd.Series({
'Link':
dict(color="cyan", width=2),
'Line':
dict(color="b", width=2),
'Transformer':
dict(color='green', width=2)
}).rename_axis('component')
x, y = _get_coordinates(n, layouter=layouter)
if geomap:
if not cartopy_present:
logger.warning(
"Cartopy needs to be installed to use `geomap=True`.")
geomap = False
if projection is None:
projection = get_projection_from_crs(n.srid)
if ax is None:
ax = plt.gca(projection=projection)
else:
assert isinstance(ax, cartopy.mpl.geoaxes.GeoAxesSubplot), (
'The passed axis is not a GeoAxesSubplot. You can '
'create one with: \nimport cartopy.crs as ccrs \n'
'fig, ax = plt.subplots('
'subplot_kw={"projection":ccrs.PlateCarree()})')
transform = draw_map_cartopy(n, x, y, ax, boundaries, margin, geomap,
color_geomap)
x, y, z = ax.projection.transform_points(transform, x.values,
y.values).T
x, y = pd.Series(x, n.buses.index), pd.Series(y, n.buses.index)
elif ax is None:
ax = plt.gca()
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
if isinstance(bus_sizes, pd.Series) and isinstance(bus_sizes.index,
pd.MultiIndex):
# We are drawing pies to show all the different shares
assert len(bus_sizes.index.levels[0].difference(n.buses.index)) == 0, \
"The first MultiIndex level of bus_sizes must contain buses"
if isinstance(bus_colors, dict):
bus_colors = pd.Series(bus_colors)
# case bus_colors isn't a series or dict: look in n.carriers for existent colors
if not isinstance(bus_colors, pd.Series):
bus_colors = n.carriers.color.dropna()
assert bus_sizes.index.levels[1].isin(bus_colors.index).all(), (
"Colors not defined for all elements in the second MultiIndex "
"level of bus_sizes, please make sure that all the elements are "
"included in bus_colors or in n.carriers.color")
bus_sizes = bus_sizes.sort_index(level=0, sort_remaining=False)
if geomap:
bus_sizes *= projected_area_factor(ax, n.srid)**2
patches = []
for b_i in bus_sizes.index.levels[0]:
s = bus_sizes.loc[b_i]
radius = s.sum()**0.5
if radius == 0.0:
ratios = s
else:
ratios = s / s.sum()
start = 0.25
for i, ratio in ratios.iteritems():
patches.append(
Wedge((x.at[b_i], y.at[b_i]),
radius,
360 * start,
360 * (start + ratio),
facecolor=bus_colors[i],
alpha=bus_alpha))
start += ratio
bus_collection = PatchCollection(patches, match_original=True)
ax.add_collection(bus_collection)
else:
c = pd.Series(bus_colors, index=n.buses.index)
s = pd.Series(bus_sizes, index=n.buses.index, dtype="float")
if geomap:
s *= projected_area_factor(ax, n.srid)**2
if bus_cmap is not None and c.dtype is np.dtype('float'):
if isinstance(bus_cmap, str):
bus_cmap = plt.cm.get_cmap(bus_cmap)
norm = plt.Normalize(vmin=c.min(), vmax=c.max())
c = c.apply(lambda cval: bus_cmap(norm(cval)))
patches = []
for b_i in s.index:
radius = s.at[b_i]**0.5
patches.append(
Circle((x.at[b_i], y.at[b_i]),
radius,
facecolor=c.at[b_i],
alpha=bus_alpha))
bus_collection = PatchCollection(patches, match_original=True)
ax.add_collection(bus_collection)
def as_branch_series(ser):
# ensure that this function always return a multiindexed series
if isinstance(ser, dict) and set(ser).issubset(branch_components):
return pd.concat(
{
c.name: pd.Series(s, index=c.df.index)
for c, s in zip(n.iterate_components(ser.keys()),
ser.values())
},
names=['component', 'name'])
elif isinstance(ser, pd.Series) and isinstance(ser.index,
pd.MultiIndex):
return ser.rename_axis(index=['component', 'name'])
else:
ser = pd.Series(ser, n.lines.index)
return pd.concat([ser],
axis=0,
keys=['Line'],
names=['component', 'name']).fillna(0)
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
if flow is not None:
flow = (_flow_ds_from_arg(
flow, n, branch_components).pipe(as_branch_series).div(
sum(
len(t.df)
for t in n.iterate_components(branch_components)) + 100))
flow = flow.mul(line_widths[flow.index], fill_value=1)
# update the line width, allows to set line widths separately from flows
line_widths.update((5 * flow.abs()).pipe(np.sqrt))
arrows = directed_flow(n,
flow,
x=x,
y=y,
ax=ax,
geomap=geomap,
branch_colors=line_colors,
branch_comps=branch_components,
cmap=line_cmap['Line'])
branch_collections.append(arrows)
for c in n.iterate_components(branch_components):
l_defaults = defaults_for_branches[c.name]
l_widths = line_widths.get(c.name, l_defaults['width'])
l_nums = None
l_colors = line_colors.get(c.name, l_defaults['color'])
if isinstance(l_colors, pd.Series):
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
if not geometry:
segments = (np.asarray(
((c.df.bus0.map(x), c.df.bus0.map(y)),
(c.df.bus1.map(x), c.df.bus1.map(y)))).transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = c.df.geometry[lambda ds: ds != ''].map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), (
"The WKT-encoded geometry in the 'geometry' column must be "
"composed of LineStrings")
segments = np.asarray(list(linestrings.map(np.asarray)))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1, ),
colors=l_colors,
transOffset=ax.transData)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(c.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(3)
branch_collections.append(l_collection)
bus_collection.set_zorder(4)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
if geomap:
ax.outline_patch.set_visible(False)
ax.axis('off')
else:
ax.set_aspect('equal')
ax.set_title(title)
return (bus_collection, ) + tuple(branch_collections)
def plot(network,
margin=0.05,
ax=None,
basemap=True,
bus_colors='b',
line_colors='g',
bus_sizes=10,
line_widths=2,
title="",
line_cmap=None,
bus_cmap=None,
boundaries=None,
geometry=False,
branch_types=['Line', 'Link']):
"""
Plot the network buses and lines using matplotlib and Basemap.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
basemap : bool, default True
Switch to use Basemap
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_types : list of str or pypsa.component
Branch types to be plotted, defaults to Line and Link.
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = {
'Link': dict(color="cyan", width=2),
'Line': dict(color="b", width=2)
}
if not plt_present:
logger.error("Matplotlib is not present, so plotting won't work.")
return
if ax is None:
ax = plt.gca()
def compute_bbox_with_margins(margin, x, y):
#set margins
pos = np.asarray((x, y))
minxy, maxxy = pos.min(axis=1), pos.max(axis=1)
xy1 = minxy - margin * (maxxy - minxy)
xy2 = maxxy + margin * (maxxy - minxy)
return tuple(xy1), tuple(xy2)
x = network.buses["x"]
y = network.buses["y"]
if basemap and basemap_present:
if boundaries is None:
(x1, y1), (x2, y2) = compute_bbox_with_margins(margin, x, y)
else:
x1, x2, y1, y2 = boundaries
bmap = Basemap(resolution='l',
epsg=network.srid,
llcrnrlat=y1,
urcrnrlat=y2,
llcrnrlon=x1,
urcrnrlon=x2,
ax=ax)
bmap.drawcountries()
bmap.drawcoastlines()
x, y = bmap(x.values, y.values)
x = pd.Series(x, network.buses.index)
y = pd.Series(y, network.buses.index)
c = pd.Series(bus_colors, index=network.buses.index)
if c.dtype == np.dtype('O'):
c.fillna("b", inplace=True)
c = list(c.values)
s = pd.Series(bus_sizes, index=network.buses.index,
dtype="float").fillna(10)
bus_collection = ax.scatter(x, y, c=c, s=s, cmap=bus_cmap)
def as_branch_series(ser):
if isinstance(ser, pd.Series):
if isinstance(ser.index, pd.MultiIndex):
return ser
index = ser.index
ser = ser.values
else:
index = network.lines.index
return pd.Series(ser,
index=pd.MultiIndex(levels=(["Line"], index),
labels=(np.zeros(len(index)),
np.arange(len(index)))))
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
for t in network.iterate_components(branch_types):
l_defaults = defaults_for_branches[t.name]
l_widths = line_widths.get(t.name, l_defaults['width'])
l_nums = None
if t.name in line_colors:
l_colors = line_colors[t.name]
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
else:
l_colors = l_defaults['color']
if not geometry:
segments = (np.asarray(
((t.df.bus0.map(x), t.df.bus0.map(y)),
(t.df.bus1.map(x), t.df.bus1.map(y)))).transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = t.df.geometry.map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), \
"The WKT-encoded geometry in the 'geometry' column must be composed of LineStrings"
segments = np.asarray(list(linestrings.map(np.asarray)))
if basemap and basemap_present:
segments = np.transpose(
bmap(*np.transpose(segments, (2, 0, 1))), (1, 2, 0))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1, ),
colors=l_colors,
transOffset=ax.transData)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(t.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(1)
branch_collections.append(l_collection)
bus_collection.set_zorder(2)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
ax.set_title(title)
return (bus_collection, ) + tuple(branch_collections)
def plot(network, margin=0.05, ax=None, geomap=True, projection=None,
bus_colors='b', line_colors={'Line':'g', 'Link':'cyan'}, bus_sizes=10,
line_widths={'Line':2, 'Link':2},
flow=None, title="", line_cmap=None, bus_cmap=None, boundaries=None,
geometry=False, branch_components=['Line', 'Link'], jitter=None,
basemap=None, basemap_parameters=None, color_geomap=None):
"""
Plot the network buses and lines using matplotlib and Basemap.
Parameters
----------
margin : float
Margin at the sides as proportion of distance between max/min x,y
ax : matplotlib ax, defaults to plt.gca()
Axis to which to plot the network
geomap: bool/str, default True
Switch to use Basemap or Cartopy (depends on what is installed).
If string is passed, it will be used as a resolution argument.
For Basemap users 'c' (crude), 'l' (low), 'i' (intermediate),
'h' (high), 'f' (full) are valid resolutions options.
For Cartopy users '10m', '50m', '110m' are valid resolutions options.
projection: cartopy.crs.Projection, defaults to None
Define the projection of your geomap, only valid if cartopy is
installed. If None (default) is passed the projection for cartopy
is set to cartopy.crs.PlateCarree
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
flow : snapshot/pandas.Series/function/string
Flow to be displayed in the plot, defaults to None. If an element of
network.snapshots is given, the flow at this timestamp will be
displayed. If an aggregation function is given, is will be applied
to the total network flow via pandas.DataFrame.agg (accepts also
function names). Otherwise flows can be specified by passing a pandas
Series with MultiIndex including all necessary branch components.
Use the line_widths argument to additionally adjust the size of the
flow arrows.
title : string
Graph title
line_cmap : plt.cm.ColorMap/str|dict
If line_colors are floats, this color map will assign the colors.
Use a dict to specify colormaps for more than one branch type.
bus_cmap : plt.cm.ColorMap/str
If bus_colors are floats, this color map will assign the colors
boundaries : list of four floats
Boundaries of the plot in format [x1,x2,y1,y2]
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
basemap_parameters : dict
Specify a dict with additional constructor parameters for the
Basemap. Will disable Cartopy.
Use this feature to set a custom projection.
(e.g. `{'projection': 'tmerc', 'lon_0':10.0, 'lat_0':50.0}`)
color_geomap : dict or bool
Specify colors to paint land and sea areas in.
If True, it defaults to `{'ocean': 'lightblue', 'land': 'whitesmoke'}`.
If no dictionary is provided, colors are white.
Returns
-------
bus_collection, branch_collection1, ... : tuple of Collections
Collections for buses and branches.
"""
defaults_for_branches = pd.Series({
'Link': dict(color="cyan", width=2),
'Line': dict(color="b", width=2),
'Transformer': dict(color='green', width=2)
}).rename_axis('component')
if not plt_present:
logger.error("Matplotlib is not present, so plotting won't work.")
return
if basemap is not None:
logger.warning("argument `basemap` is deprecated, "
"use `geomap` instead.")
geomap = basemap
if geomap:
if not (cartopy_present or basemap_present):
# Not suggesting Basemap since it is being deprecated
logger.warning("Cartopy needs to be installed to use `geomap=True`.")
geomap = False
# Use cartopy by default, fall back on basemap
use_basemap = False
use_cartopy = cartopy_present
if not use_cartopy:
use_basemap = basemap_present
# If the user specifies basemap parameters, they prefer
# basemap over cartopy.
# (This means that you can force the use of basemap by
# setting `basemap_parameters={}`)
if basemap_present:
if basemap_parameters is not None:
logger.warning("Basemap is being deprecated, consider "
"switching to Cartopy.")
use_basemap = True
use_cartopy = False
if use_cartopy:
if projection is None:
projection = get_projection_from_crs(network.srid)
if ax is None:
ax = plt.gca(projection=projection)
else:
assert isinstance(ax, cartopy.mpl.geoaxes.GeoAxesSubplot), (
'The passed axis is not a GeoAxesSubplot. You can '
'create one with: \nimport cartopy.crs as ccrs \n'
'fig, ax = plt.subplots('
'subplot_kw={"projection":ccrs.PlateCarree()})')
elif ax is None:
ax = plt.gca()
x, y = network.buses["x"], network.buses["y"]
axis_transform = ax.transData
if geomap:
if use_cartopy:
axis_transform = draw_map_cartopy(network, x, y, ax,
boundaries, margin, geomap, color_geomap)
new_coords = pd.DataFrame(
ax.projection.transform_points(axis_transform,
x.values, y.values),
index=network.buses.index, columns=['x', 'y', 'z'])
x, y = new_coords['x'], new_coords['y']
elif use_basemap:
basemap_transform = draw_map_basemap(network, x, y, ax,
boundaries, margin, geomap, basemap_parameters, color_geomap)
# A non-standard projection might be used; the easiest way to
# support this is to tranform the bus coordinates.
x, y = basemap_transform(x.values, y.values)
x = pd.Series(x, network.buses.index)
y = pd.Series(y, network.buses.index)
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
if isinstance(bus_sizes, pd.Series) and isinstance(bus_sizes.index, pd.MultiIndex):
# We are drawing pies to show all the different shares
assert len(bus_sizes.index.levels[0].difference(network.buses.index)) == 0, \
"The first MultiIndex level of bus_sizes must contain buses"
assert (isinstance(bus_colors, dict) and
set(bus_colors).issuperset(bus_sizes.index.levels[1])), \
"bus_colors must be a dictionary defining a color for each element " \
"in the second MultiIndex level of bus_sizes"
bus_sizes = bus_sizes.sort_index(level=0, sort_remaining=False)
if geomap:
bus_sizes *= projected_area_factor(ax, network.srid)**2
patches = []
for b_i in bus_sizes.index.levels[0]:
s = bus_sizes.loc[b_i]
radius = s.sum()**0.5
if radius == 0.0:
ratios = s
else:
ratios = s/s.sum()
start = 0.25
for i, ratio in ratios.iteritems():
patches.append(Wedge((x.at[b_i], y.at[b_i]), radius,
360*start, 360*(start+ratio),
facecolor=bus_colors[i]))
start += ratio
bus_collection = PatchCollection(patches, match_original=True)
ax.add_collection(bus_collection)
else:
c = pd.Series(bus_colors, index=network.buses.index)
s = pd.Series(bus_sizes, index=network.buses.index, dtype="float").fillna(10)
bus_collection = ax.scatter(x, y, c=c, s=s, cmap=bus_cmap, edgecolor='face')
def as_branch_series(ser):
# ensure that this function always return a multiindexed series
if isinstance(ser, dict) and set(ser).issubset(branch_components):
return pd.concat(
{c.name: pd.Series(s, index=c.df.index) for c, s in
zip(network.iterate_components(ser.keys()), ser.values())},
names=['component', 'name'])
elif isinstance(ser, pd.Series) and isinstance(ser.index, pd.MultiIndex):
return ser.rename_axis(index=['component', 'name'])
else:
ser = pd.Series(ser, network.lines.index)
return pd.concat([ser], axis=0, keys=['Line'],
names=['component', 'name']).fillna(0)
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if not isinstance(line_cmap, dict):
line_cmap = {'Line': line_cmap}
branch_collections = []
if flow is not None:
flow = (_flow_ds_from_arg(flow, network, branch_components)
.pipe(as_branch_series)
.div(sum(len(t.df) for t in
network.iterate_components(branch_components)) + 100))
flow = flow.mul(line_widths[flow.index], fill_value=1)
# update the line width, allows to set line widths separately from flows
line_widths.update((5 * flow.abs()).pipe(np.sqrt))
arrows = directed_flow(network, flow, x=x, y=y, ax=ax, geomap=geomap,
branch_colors=line_colors,
branch_comps=branch_components,
cmap=line_cmap['Line'])
branch_collections.append(arrows)
for c in network.iterate_components(branch_components):
l_defaults = defaults_for_branches[c.name]
l_widths = line_widths.get(c.name, l_defaults['width'])
l_nums = None
l_colors = line_colors.get(c.name, l_defaults['color'])
if isinstance(l_colors, pd.Series):
if issubclass(l_colors.dtype.type, np.number):
l_nums = l_colors
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
if not geometry:
segments = (np.asarray(((c.df.bus0.map(x),
c.df.bus0.map(y)),
(c.df.bus1.map(x),
c.df.bus1.map(y))))
.transpose(2, 0, 1))
else:
from shapely.wkt import loads
from shapely.geometry import LineString
linestrings = c.df.geometry[lambda ds: ds != ''].map(loads)
assert all(isinstance(ls, LineString) for ls in linestrings), (
"The WKT-encoded geometry in the 'geometry' column must be "
"composed of LineStrings")
segments = np.asarray(list(linestrings.map(np.asarray)))
l_collection = LineCollection(segments,
linewidths=l_widths,
antialiaseds=(1,),
colors=l_colors,
transOffset=ax.transData)
if l_nums is not None:
l_collection.set_array(np.asarray(l_nums))
l_collection.set_cmap(line_cmap.get(c.name, None))
l_collection.autoscale()
ax.add_collection(l_collection)
l_collection.set_zorder(3)
branch_collections.append(l_collection)
bus_collection.set_zorder(4)
ax.update_datalim(compute_bbox_with_margins(margin, x, y))
ax.autoscale_view()
if geomap:
if use_cartopy:
ax.outline_patch.set_visible(False)
ax.axis('off')
ax.set_title(title)
return (bus_collection,) + tuple(branch_collections)
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 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 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=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
