本文整理汇总了Python中matplotlib.collections.LineCollection.autoscale方法的典型用法代码示例。如果您正苦于以下问题：Python LineCollection.autoscale方法的具体用法？Python LineCollection.autoscale怎么用？Python LineCollection.autoscale使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类matplotlib.collections.LineCollection的用法示例。
在下文中一共展示了LineCollection.autoscale方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: draw_networkx_edges
# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import autoscale [as 别名]
#.........这里部分代码省略.........
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示例2: draw_networkx_edges
# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import autoscale [as 别名]
#.........这里部分代码省略.........
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示例3: plot
# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import autoscale [as 别名]
#.........这里部分代码省略.........
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)示例4: draw_networkx_edges
# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import autoscale [as 别名]
#.........这里部分代码省略.........
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示例5: draw_networkx_edges
# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import autoscale [as 别名]
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:
#.........这里部分代码省略.........
示例6: plot
# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import autoscale [as 别名]
#.........这里部分代码省略.........
"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)