def draw_edges(tree, steiner_pos, diams=None, fig=None, ax=None, lim=None,
colorbar=True):
'''Draw edges with given positions.'''
if fig is None:
fig, ax = new_fig()
if lim is None:
lim = diam_min, diam_max
pos = merge_pos(tree, steiner_pos)
nodes = tree.get_nodes()
arcs = tree.get_arcs()
x = np.array([pos[n][0] for n in nodes])
y = np.array([pos[n][1] for n in nodes])
segments = [(pos[u], pos[v]) for (u,v) in arcs]
if diams is None:
lines = LineCollection(segments, colors='k', zorder=1)
else:
diams = np.array([diams[a] for a in arcs])
lw = 7*diams + 1
lines = LineCollection(segments, linewidths=lw, zorder=1)
# set colors
lines.set_array(diams)
lines.set_cmap(_diam_cmap)
lines.set_clim(*lim)
if colorbar:
plt.colorbar(lines, orientation='horizontal')
ax.add_collection(lines)
def __plot_all(self, spectrum):
total = len(spectrum)
count = 0.0
for timeStamp in spectrum:
if self.settings.fadeScans:
alpha = (total - count) / total
else:
alpha = 1
data = spectrum[timeStamp].items()
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.__create_segments(data)
if segments is not None:
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.settings.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
return peakF, peakL
def __plot_all(self):
total = len(self.data)
count = 0.0
for timeStamp in self.data:
if len(self.data[timeStamp]) < 2:
self.parent.threadPlot = None
return None, None
if self.fade:
alpha = (total - count) / total
else:
alpha = 1
data = self.data[timeStamp].items()
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.__create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
return peakF, peakL
def plot_linestring_collection(ax, geoms, values=None, color=None,
cmap=None, vmin=None, vmax=None, **kwargs):
"""
Plots a collection of LineString and MultiLineString geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be
mixed)
values : a sequence of `N` values, optional
Values will be mapped to colors using vmin/vmax/cmap. They should
have 1:1 correspondence with the geometries (not their components).
color : single color or sequence of `N` colors
Cannot be used together with `values`.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
from matplotlib.collections import LineCollection
geoms, values = _flatten_multi_geoms(geoms, values)
if None in values:
values = None
# LineCollection does not accept some kwargs.
if 'markersize' in kwargs:
del kwargs['markersize']
# color=None gives black instead of default color cycle
if color is not None:
kwargs['color'] = color
segments = [np.array(linestring)[:, :2] for linestring in geoms]
collection = LineCollection(segments, **kwargs)
if values is not None:
collection.set_array(np.asarray(values))
collection.set_cmap(cmap)
collection.set_clim(vmin, vmax)
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
def __plot_single(self, points):
data = points.items()
peakF, peakL = max(data, key=lambda item: item[1])
segments, levels = self.__create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
self.axes.add_collection(lc)
return peakF, peakL
def plot(self, time, beam=None,
maxground=2000, maxalt=500, step=1,
showrefract=False, nr_cmap='jet_r', nr_lim=[0.8, 1.],
raycolor='0.3', title=False, zorder=2, alpha=1,
fig=None, rect=111, ax=None, aax=None):
"""Plot ray paths
Parameters
----------
time : datetime.datetime
time of rays
beam: Optional[ ]
beam number
maxground : Optional[int]
maximum ground range [km]
maxalt : Optional[int]
highest altitude limit [km]
step : Optional[int]
step between each plotted ray (in number of ray steps)
showrefract : Optional[bool]
show refractive index along ray paths (supersedes raycolor)
nr_cmap : Optional[str]
color map name for refractive index coloring
nr_lim : Optional[list, float]
refractive index plotting limits
raycolor : Optional[float]
color of ray paths
title : Optional[bool]
Show default title
zorder : Optional[int]
alpha : Optional[int]
fig : Optional[pylab.figure]
object (default to gcf)
rect : Optional[int]
subplot spcification
ax : Optional[ ]
Existing main axes
aax : Optional[ ]
Existing auxialary axes
Returns
-------
ax : matplotlib.axes
object containing formatting
aax : matplotlib.axes
object containing data
cbax : matplotlib.axes
object containing colorbar
Example
-------
# Show ray paths with colored refractive index along path
import datetime as dt
from davitpy.models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12, title=True)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=10, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
import datetime as dt
from davitpy.utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
from types import MethodType
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'time'):
time = ax.time
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
# Allow a 60 second difference between the requested time and the time
# available.
keys = np.array(self.paths.keys())
diffs = np.abs(keys-time)
if diffs.min() < dt.timedelta(minutes=1):
time = keys[diffs.argmin()]
assert (time in self.paths.keys()), logging.error('Unkown time %s' % time)
if beam:
assert (beam in self.paths[time].keys()), logging.error('Unkown beam %s' % beam)
else:
beam = self.paths[time].keys()[0]
for ir, (el, rays) in enumerate( sorted(self.paths[time][beam].items()) ):
if not ir % step:
if not showrefract:
aax.plot(rays['th'], rays['r']*1e-3, c=raycolor,
zorder=zorder, alpha=alpha)
else:
points = np.array([rays['th'], rays['r']*1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lcol = LineCollection( segments, zorder=zorder, alpha=alpha)
_ = lcol.set_cmap( nr_cmap )
_ = lcol.set_norm( plt.Normalize(*nr_lim) )
_ = lcol.set_array( rays['nr'] )
_ = aax.add_collection( lcol )
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, self.name)
ax.set_title( stitle )
# Add a colorbar when plotting refractive index
if showrefract:
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("refractive index")
else: cbax = None
# Declare a new method to show range markers
# This method is only available after rays have been plotted
# This ensures that the markers match the plotted rays
def showRange(self, markers=None,
color='.8', s=2, zorder=3,
**kwargs):
"""Plot ray paths
Parameters
----------
markers : Optional[ ]
range markers. Defaults to every 250 km
color : Optional[float]
s : Optional[int]
zorder : Optional[int]
**kwargs :
Returns
-------
coll :
a collection of range markers
Notes
-----
Parameters other than markers are borrowed from matplotlib.pyplot.scatter
Example
-------
# Add range markers to an existing ray plot
ax, aax, cbax = rto.rays.plot(sTime, step=10)
rto.rays.showRange()
written by Sebastien, 2013-04
"""
if not markers:
markers = np.arange(0, 5000, 250)
x, y = [], []
for el, rays in self.paths[time][beam].items():
for rm in markers:
inds = (rays['gran']*1e-3 >= rm)
if inds.any():
x.append( rays['th'][inds][0] )
y.append( rays['r'][inds][0]*1e-3 )
coll = aax.scatter(x, y,
color=color, s=s, zorder=zorder, **kwargs)
return coll
# End of new method
# Assign new method
self.showRange = MethodType(showRange, self)
ax.beam = beam
return ax, aax, cbax
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.
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:
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, 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)
if boundaries:
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)
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)
for p in ps:
gm = cmpy.machines.GoldenMean(bias=p)
data = gm.cmech_quantities(['HX0L', 'hmu'], maxL)
lines1.append(zip(range(maxL + 1), data[0]))
data[1].mask[0] = False
lines2.append(zip(range(maxL + 1), data[1, :]))
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
fig.subplots_adjust(left=0.09, right=0.86)
ax1 = fig.add_subplot(1, 2, 1)
lc1 = LineCollection(lines1)
lc1.set_array(ps)
lc1.set_cmap(plt.cm.jet)
ax1.add_collection(lc1)
ax1.set_xlabel("$L$ [symbols]")
ax1.set_ylabel("$H[X_0^L]$ [bits]")
ax1.axis('auto')
ax1.set_xlim((0, maxL))
ax2 = fig.add_subplot(1, 2, 2)
lc2 = LineCollection(lines2)
lc2.set_array(ps)
lc2.set_cmap(plt.cm.jet)
ax2.add_collection(lc2)
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 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
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)
# need 0.87.7 or greater for edge colormaps
mpl_version = matplotlib.__version__
if mpl_version.endswith('.svn'):
mpl_version = matplotlib.__version__[0:-4]
elif mpl_version.endswith('svn'):
mpl_version = matplotlib.__version__[0:-3]
elif 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.axes(ax)
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 = 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
ax.add_collection(arrow_collection)
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:, :, 0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:, :, 0]))
miny = numpy.amin(numpy.ravel(edge_pos[:, :, 1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def draw_networkx_edges(G,
pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=None,
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.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float
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
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.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
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:
print("Matplotlib unable to open display")
raise
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 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()
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 = 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 create_line_collection(net,
lines=None,
line_geodata=None,
use_bus_geodata=False,
infofunc=None,
cmap=None,
norm=None,
picker=False,
z=None,
cbar_title="Line Loading [%]",
clim=None,
**kwargs):
"""
Creates a matplotlib line collection of pandapower lines.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**lines** (list, None) - The lines for which the collections are created. If None, all lines
in the network are considered.
**line_geodata** (DataFrame, None) - coordinates to use for plotting If None,
net["line_geodata"] is used
**infofunc** (function, None) - infofunction for the patch element
**kwargs - key word arguments are passed to the patch function
"""
lines = net.line.index.tolist() if lines is None else list(lines)
if len(lines) == 0:
return None
if line_geodata is None:
line_geodata = net["line_geodata"]
if len(lines) == 0:
return None
if use_bus_geodata:
data = [
([(net.bus_geodata.at[a, "x"], net.bus_geodata.at[a, "y"]),
(net.bus_geodata.at[b, "x"], net.bus_geodata.at[b, "y"])],
infofunc(line) if infofunc else [])
for line, (a,
b) in net.line.loc[lines,
["from_bus", "to_bus"]].iterrows()
if a in net.bus_geodata.index.values
and b in net.bus_geodata.index.values
]
else:
data = [(line_geodata.loc[line, "coords"],
infofunc(line) if infofunc else []) for line in lines
if line in line_geodata.index.values]
if len(data) == 0:
return None
data, info = list(zip(*data))
# This would be done anyways by matplotlib - doing it explicitly makes it a) clear and
# b) prevents unexpected behavior when observing colors being "none"
lc = LineCollection(data, picker=picker, **kwargs)
lc.line_indices = np.array(lines)
if cmap:
if z is None:
z = net.res_line.loading_percent.loc[lines]
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.array(z))
lc.has_colormap = True
lc.cbar_title = cbar_title
lc.info = info
return lc
def plot_dft_angles(snap_times,
angles,
amps,
sim_time,
logx=False,
logy=False,
plot_wheel=False,
lw=1.5,
cmap='hsv',
wheel_axis=None,
label_size=10):
"""
Plot 2D-DFT amplitudes at a specific frequency as a function of time and angle
"""
color_steps = 2056
if logy: pl.gca().set_yscale('log')
if logx: pl.gca().set_xscale('log')
ax = pl.gca()
custom_axes()
ax.set_xlim(snap_times[1] if pl.gca().get_xscale() == 'log' else 0,
sim_time)
det_lines = LineCollection(
[list(zip(snap_times[1:], amp[1:])) for amp in amps],
linewidths=lw,
linestyles='-')
ax.set_ylim((np.amin(amps), np.amax(amps)))
det_lines.set_array(angles)
det_lines.set_clim(0, 180)
det_lines.set_cmap(cmap)
ax.add_collection(det_lines)
pl.sci(det_lines)
pos = ax.get_position()
wheel_pos = [
pos.x0 + pos.width / 20., pos.y0 + pos.height * 0.7, pos.width / 4.0,
pos.height / 4.0
]
if plot_wheel is True:
if wheel_axis is None:
wheel_axis = pl.gcf().add_axes(wheel_pos, projection='polar')
else:
wheel_axis.projection = 'polar'
wheel_axis._direction = 2 * np.pi
norm = pl.colors.Normalize(0.0, 180.)
cb = pl.colorbar.ColorbarBase(wheel_axis,
cmap=cm.get_cmap(cmap, color_steps),
norm=norm,
orientation='horizontal',
ticks=[0, 30, 60, 90, 120, 150])
cb.ax.tick_params(labelsize=label_size)
cb.outline.set_visible(False)
return ax
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 numpy.iterable(width):
lw = width
else:
#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, )
if not is_string_like(edge_color) and numpy.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(
[numpy.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 None and edge_vmax is None:
edge_collection.autoscale()
else:
edge_collection.set_clim(edge_vmin, edge_vmax)
# 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 create_line_collection(net, lines=None, line_geodata=None, bus_geodata=None,
use_bus_geodata=False, infofunc=None,
cmap=None, norm=None, picker=False, z=None,
cbar_title="Line Loading [%]", clim=None, **kwargs):
"""
Creates a matplotlib line collection of pandapower lines.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**lines** (list, None) - The lines for which the collections are created. If None, all lines
in the network are considered.
**line_geodata** (DataFrame, None) - coordinates to use for plotting. If None,
net["line_geodata"] is used
**bus_geodata** (DataFrame, None) - coordinates to use for plotting
If None, net["bus_geodata"] is used
**use_bus_geodata** (bool, False) - Defines whether bus or line geodata are used.
**infofunc** (function, None) - infofunction for the patch element
**cmap** - colormap for the patch colors
**norm** (matplotlib norm object, None) - matplotlib norm object
**picker** (bool, False) - picker argument passed to the patch collection
**z** (array, None) - array of bus voltage magnitudes for colormap. Used in case of given
cmap. If None net.res_bus.vm_pu is used.
**cbar_title** (str, "Bus Voltage [pu]") - colormap bar title in case of given cmap
**clim** (tuple of floats, None) - setting the norm limits for image scaling
**kwargs - key word arguments are passed to the patch function
OUTPUT:
**lc** - line collection
"""
if use_bus_geodata:
linetab = net.line if lines is None else net.line.loc[lines]
lines = net.line.index.tolist() if lines is None else list(lines)
if len(lines) == 0:
return None
if line_geodata is None:
line_geodata = net["line_geodata"]
if bus_geodata is None:
bus_geodata = net["bus_geodata"]
if len(lines) == 0:
return None
lines_with_geo = []
if use_bus_geodata:
data = []
buses_with_geodata = bus_geodata.index.values
bg_dict = bus_geodata.to_dict() #transforming to dict to make lookup faster
for line, fb, tb in zip(linetab.index, linetab.from_bus.values, linetab.to_bus.values):
if fb in buses_with_geodata and tb in buses_with_geodata:
lines_with_geo.append(line)
data.append(([(bg_dict["x"][fb], bg_dict["y"][fb]),
(bg_dict["x"][tb], bg_dict["y"][tb])],
infofunc(line) if infofunc else[]))
lines_without_geo = set(lines)-set(lines_with_geo)
if lines_without_geo:
logger.warning("Could not plot lines %s. Bus geodata is missing for those lines!"
% lines_without_geo)
else:
data = []
for line in lines:
if line in line_geodata.index.values:
lines_with_geo.append(line)
data.append((line_geodata.loc[line, "coords"], infofunc(line) if infofunc else []))
lines_without_geo = set(lines)-set(lines_with_geo)
if len(lines_without_geo) > 0:
logger.warning("Could not plot lines %s. Line geodata is missing for those lines!"
% lines_without_geo)
if len(data) == 0:
return None
data, info = list(zip(*data))
# This would be done anyways by matplotlib - doing it explicitly makes it a) clear and
# b) prevents unexpected behavior when observing colors being "none"
lc = LineCollection(data, picker=picker, **kwargs)
lc.line_indices = np.array(lines_with_geo)
if cmap is not None:
if z is None:
z = net.res_line.loading_percent.loc[lines_with_geo]
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.array(z))
lc.has_colormap = True
lc.cbar_title = cbar_title
lc.info = info
return lc
def create_line_collection(net,
lines=None,
line_geodata=None,
bus_geodata=None,
use_bus_geodata=False,
infofunc=None,
cmap=None,
norm=None,
picker=False,
z=None,
cbar_title="Line Loading [%]",
clim=None,
**kwargs):
"""
Creates a matplotlib line collection of pandapower lines.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**lines** (list, None) - The lines for which the collections are created. If None, all lines
in the network are considered.
**line_geodata** (DataFrame, None) - coordinates to use for plotting. If None,
net["line_geodata"] is used
**bus_geodata** (DataFrame, None) - coordinates to use for plotting
If None, net["bus_geodata"] is used
**use_bus_geodata** (bool, False) - Defines whether bus or line geodata are used.
**infofunc** (function, None) - infofunction for the patch element
**cmap** - colormap for the patch colors
**norm** (matplotlib norm object, None) - matplotlib norm object
**picker** (bool, False) - picker argument passed to the patch collection
**z** (array, None) - array of bus voltage magnitudes for colormap. Used in case of given
cmap. If None net.res_bus.vm_pu is used.
**cbar_title** (str, "Bus Voltage [pu]") - colormap bar title in case of given cmap
**clim** (tuple of floats, None) - setting the norm limits for image scaling
**kwargs - key word arguments are passed to the patch function
OUTPUT:
**lc** - line collection
"""
lines = net.line.index.tolist() if lines is None else list(lines)
if len(lines) == 0:
return None
if line_geodata is None:
line_geodata = net["line_geodata"]
if bus_geodata is None:
bus_geodata = net["bus_geodata"]
if len(lines) == 0:
return None
if use_bus_geodata:
data = [
([(bus_geodata.at[a, "x"], bus_geodata.at[a, "y"]),
(bus_geodata.at[b, "x"], bus_geodata.at[b, "y"])],
infofunc(line) if infofunc else [])
for line, (a,
b) in net.line.loc[lines,
["from_bus", "to_bus"]].iterrows()
if a in bus_geodata.index.values and b in bus_geodata.index.values
]
else:
data = [(line_geodata.loc[line, "coords"],
infofunc(line) if infofunc else []) for line in lines
if line in line_geodata.index.values]
if len(data) == 0:
return None
data, info = list(zip(*data))
# This would be done anyways by matplotlib - doing it explicitly makes it a) clear and
# b) prevents unexpected behavior when observing colors being "none"
lc = LineCollection(data, picker=picker, **kwargs)
lc.line_indices = np.array(lines)
if cmap:
if z is None:
z = net.res_line.loading_percent.loc[lines]
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.array(z))
lc.has_colormap = True
lc.cbar_title = cbar_title
lc.info = info
return lc
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 plot(self, time, beam=None,
maxground=2000, maxalt=500, step=1,
showrefract=False, nr_cmap='jet_r', nr_lim=[0.8, 1.],
raycolor='0.3', title=False, zorder=2, alpha=1,
fig=None, rect=111, ax=None, aax=None):
"""Plot ray paths
**Args**:
* **time** (datetime.datetime): time of rays
* [**beam**]: beam number
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**step**]: step between each plotted ray (in number of ray steps)
* [**showrefract**]: show refractive index along ray paths (supersedes raycolor)
* [**nr_cmap**]: color map name for refractive index coloring
* [**nr_lim**]: refractive index plotting limits
* [**raycolor**]: color of ray paths
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
* [**title**]: Show default title
* [**ax**]: Existing main axes
* [**aax**]: Existing auxialary axes
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ray paths with colored refractive index along path
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12, title=True)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=10, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
from utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
from types import MethodType
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'time'):
time = ax.time
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.paths.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.paths[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.paths[time].keys()[0]
for ir, (el, rays) in enumerate( sorted(self.paths[time][beam].items()) ):
if not ir % step:
if not showrefract:
aax.plot(rays['th'], rays['r']*1e-3, c=raycolor,
zorder=zorder, alpha=alpha)
else:
points = np.array([rays['th'], rays['r']*1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lcol = LineCollection( segments, zorder=zorder, alpha=alpha)
_ = lcol.set_cmap( nr_cmap )
_ = lcol.set_norm( plt.Normalize(*nr_lim) )
_ = lcol.set_array( rays['nr'] )
_ = aax.add_collection( lcol )
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, self.name)
ax.set_title( stitle )
# Add a colorbar when plotting refractive index
if showrefract:
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("refractive index")
else: cbax = None
# Declare a new method to show range markers
# This method is only available after rays have been plotted
# This ensures that the markers match the plotted rays
def showRange(self, markers=None,
color='.8', s=2, zorder=3,
**kwargs):
"""Plot ray paths
**Args**:
* [**markers**]: range markers. Defaults to every 250 km
* All other keywords are borrowed from :func:`matplotlib.pyplot.scatter`
**Returns**:
* **coll**: a collection of range markers
**Example**:
::
# Add range markers to an existing ray plot
ax, aax, cbax = rto.rays.plot(sTime, step=10)
rto.rays.showRange()
written by Sebastien, 2013-04
"""
if not markers:
markers = np.arange(0, 5000, 250)
x, y = [], []
for el, rays in self.paths[time][beam].items():
for rm in markers:
inds = (rays['gran']*1e-3 >= rm)
if inds.any():
x.append( rays['th'][inds][0] )
y.append( rays['r'][inds][0]*1e-3 )
coll = aax.scatter(x, y,
color=color, s=s, zorder=zorder, **kwargs)
return coll
# End of new method
# Assign new method
self.showRange = MethodType(showRange, self)
ax.beam = beam
return ax, aax, cbax
ax.axvline(0, lw=0.5, color='lightgray')
ax.axhline(1, lw=0.5, color='lightgray')
fpr, tpr, t = roc_curve(df['label'],
df['probabilities'],
drop_intermediate=False)
nth = 5000
fpr, tpr, t = fpr[::nth], tpr[::nth], t[::nth]
points = np.array([fpr, tpr]).T.reshape((-1, 1, 2))
segments = np.concatenate([points[:-1], points[1:]], axis=1)
col = LineCollection(segments, zorder=3)
col.set_array(t)
col.set_clim(0, 1)
col.set_cmap('inferno')
ax.add_collection(col)
rocs = np.array([
roc_auc_score(fold['label'], fold['probabilities'])
for _, fold in df.groupby('cv_fold')
])
ax.set_title(f'ROC-AUC: {rocs.mean():.4f} ± {rocs.std():.4f}')
ax.set_ylim(0, 1.025)
ax.set_xlim(-0.025, 1)
ax.set_aspect(1)
ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
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
for p in ps:
gm = cmpy.machines.BiasedCoin(bias = p)
data = gm.cmech_quantities(['HX0L', 'hmu'], maxL)
lines1.append(zip(range(maxL+1),data[0]))
data[1].mask[0] = False
lines2.append(zip(range(maxL+1),data[1,:]))
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
fig.subplots_adjust(left = 0.09, right = 0.86)
ax1 = fig.add_subplot(1,2,1)
lc1 = LineCollection(lines1)
lc1.set_array(ps)
lc1.set_cmap(plt.cm.jet)
ax1.add_collection(lc1)
ax1.set_xlabel("$L$ [symbols]")
ax1.set_ylabel("$H[X_0^L]$ [bits]")
ax1.axis('auto')
ax1.set_xlim((0,maxL))
ax2 = fig.add_subplot(1,2,2)
lc2 = LineCollection(lines2)
lc2.set_array(ps)
lc2.set_cmap(plt.cm.jet)
ax2.add_collection(lc2)
class SunPlotPy(wx.Frame, Spatial, Grid ):
"""
The main frame of the application
"""
title = 'sunplot(py)'
# Plotting options
autoclim=True
showedges=False
bgcolor='k'
textcolor='w'
cmap='RdBu'
particlesize = 1.8
particlecolor = 'm'
# other flags
collectiontype='cells'
oldcollectiontype='cells'
#
tindex=0
depthlevs = [0., 10., 100., 200., 300., 400., 500.,\
1000.,2000.,3000.,4000.,5000]
_FillValue=999999
def __init__(self):
wx.Frame.__init__(self, None, -1, self.title)
self.create_menu()
self.create_status_bar()
self.create_main_panel()
#self.draw_figure()
def create_menu(self):
self.menubar = wx.MenuBar()
###
# File Menu
###
menu_file = wx.Menu()
# Load a hydro output file
m_expt = menu_file.Append(-1, "&Open file\tCtrl-O", "Open netcdf file")
self.Bind(wx.EVT_MENU, self.on_open_file, m_expt)
# Load a grid file
m_grid = menu_file.Append(-1, "&Load grid\tCtrl-G", "Load SUNTANS grid from folder")
self.Bind(wx.EVT_MENU, self.on_load_grid, m_grid)
# Load a particle file
m_part = menu_file.Append(-1, "&Load PTM file\tCtrl-Shift-P", "Load a PTM file")
self.Bind(wx.EVT_MENU, self.on_load_ptm, m_part)
# Save current scene as an animation
m_anim = menu_file.Append(-1,"&Save animation of current scene\tCtrl-S","Save animation")
self.Bind(wx.EVT_MENU, self.on_save_anim, m_anim)
# Save the current figure
m_prin = menu_file.Append(-1,"&Print current scene\tCtrl-P","Save figure")
self.Bind(wx.EVT_MENU, self.on_save_fig, m_prin)
menu_file.AppendSeparator()
# Exit
m_exit = menu_file.Append(-1, "E&xit\tCtrl-X", "Exit")
self.Bind(wx.EVT_MENU, self.on_exit, m_exit)
###
# Tools menu
###
menu_tools = wx.Menu()
m_gridstat = menu_tools.Append(-1, "&Plot grid size statistics", "SUNTANS grid size")
self.Bind(wx.EVT_MENU, self.on_plot_gridstat, m_gridstat)
m_countcells = menu_tools.Append(-1, "&Count # grid cells", "Grid cell count")
self.Bind(wx.EVT_MENU, self.on_count_cells, m_countcells)
m_overlaybathy = menu_tools.Append(-1, "&Overlay depth contours", "Depth overlay")
self.Bind(wx.EVT_MENU, self.on_overlay_bathy, m_overlaybathy)
###
# Help Menu
###
menu_help = wx.Menu()
m_about = menu_help.Append(-1, "&About\tF1", "About the demo")
self.Bind(wx.EVT_MENU, self.on_about, m_about)
# Add all of the menu bars
self.menubar.Append(menu_file, "&File")
self.menubar.Append(menu_tools, "&Tools")
self.menubar.Append(menu_help, "&Help")
self.SetMenuBar(self.menubar)
def create_main_panel(self):
""" Creates the main panel with all the controls on it:
* mpl canvas
* mpl navigation toolbar
* Control panel for interaction
"""
self.panel = wx.Panel(self)
# Create the mpl Figure and FigCanvas objects.
# 5x4 inches, 100 dots-per-inch
#
self.dpi = 100
#self.fig = Figure((7.0, 6.0), dpi=self.dpi,facecolor=self.bgcolor)
self.fig = Figure((7.0, 6.0), dpi=self.dpi)
self.canvas = FigCanvas(self.panel, -1, self.fig)
# Since we have only one plot, we can use add_axes
# instead of add_subplot, but then the subplot
# configuration tool in the navigation toolbar wouldn't
# work.
#
self.axes = self.fig.add_subplot(111)
#SetAxColor(self.axes,self.textcolor,self.bgcolor)
# Bind the 'pick' event for clicking on one of the bars
#
#self.canvas.mpl_connect('pick_event', self.on_pick)
########
# Create widgets
########
self.variable_list = wx.ComboBox(
self.panel,
size=(200,-1),
choices=['Select a variable...'],
style=wx.CB_READONLY)
self.variable_list.Bind(wx.EVT_COMBOBOX, self.on_select_variable)
self.time_list = wx.ComboBox(
self.panel,
size=(200,-1),
choices=['Select a time step...'],
style=wx.CB_READONLY)
self.time_list.Bind(wx.EVT_COMBOBOX, self.on_select_time)
self.depthlayer_list = wx.ComboBox(
self.panel,
size=(200,-1),
choices=['Select a vertical layer...'],
style=wx.CB_READONLY)
self.depthlayer_list.Bind(wx.EVT_COMBOBOX, self.on_select_depth)
self.show_edge_check = wx.CheckBox(self.panel, -1,
"Show Edges",
style=wx.ALIGN_RIGHT)
self.show_edge_check.Bind(wx.EVT_CHECKBOX, self.on_show_edges)
if USECMOCEAN:
cmaps=[]
for cmap in cm.cmapnames:
cmaps.append(cmap)
cmaps.append(cmap+'_r') # Add all reverse map options
else:
# Use matplotlib standard
cmaps = list(matplotlib.cm.datad.keys())
cmaps.sort()
self.colormap_list = wx.ComboBox(
self.panel,
size=(100,-1),
choices=cmaps,
style=wx.CB_READONLY)
self.colormap_list.Bind(wx.EVT_COMBOBOX, self.on_select_cmap)
self.colormap_label = wx.StaticText(self.panel, -1,"Colormap:")
self.clim_check = wx.CheckBox(self.panel, -1,
"Manual color limits ",
style=wx.ALIGN_RIGHT)
self.clim_check.Bind(wx.EVT_CHECKBOX, self.on_clim_check)
self.climlow = wx.TextCtrl(
self.panel,
size=(100,-1),
style=wx.TE_PROCESS_ENTER)
self.climlow.Bind(wx.EVT_TEXT_ENTER, self.on_climlow)
self.climhigh = wx.TextCtrl(
self.panel,
size=(100,-1),
style=wx.TE_PROCESS_ENTER)
self.climhigh.Bind(wx.EVT_TEXT_ENTER, self.on_climhigh)
# Labels
self.variable_label = wx.StaticText(self.panel, -1,"Variable:",size=(200,-1))
self.time_label = wx.StaticText(self.panel, -1,"Time step:",size=(200,-1))
self.depth_label = wx.StaticText(self.panel, -1,"Vertical level:",size=(200,-1))
# Create the navigation toolbar, tied to the canvas
#
self.toolbar = NavigationToolbar(self.canvas)
#self.toolbar.toolitems[8][3]='my_save_fig'
#def my_save_fig(self,*args):
# print 'saving figure'
# return "break"
#########
# Layout with box sizers
#########
self.vbox = wx.BoxSizer(wx.VERTICAL)
self.vbox.Add(self.canvas, 1, wx.LEFT | wx.TOP | wx.GROW)
self.vbox.Add(self.toolbar, 0, wx.EXPAND)
self.vbox.AddSpacer(10)
#self.vbox.Add((-1,25))
flags = wx.ALIGN_LEFT | wx.ALL | wx.ALIGN_CENTER_VERTICAL
self.hbox0 = wx.BoxSizer(wx.HORIZONTAL)
self.hbox0.Add(self.show_edge_check, 0, border=10, flag=flags)
self.hbox0.Add(self.colormap_label, 0, border=10, flag=flags)
self.hbox0.Add(self.colormap_list, 0, border=10, flag=flags)
self.hbox0.Add(self.clim_check, 0, border=10, flag=flags)
self.hbox0.Add(self.climlow, 0, border=10, flag=flags)
self.hbox0.Add(self.climhigh, 0, border=10, flag=flags)
self.vbox.AddSpacer(5)
self.hbox1 = wx.BoxSizer(wx.HORIZONTAL)
self.hbox1.Add(self.variable_label, 0, border=10, flag=flags)
self.hbox1.Add(self.time_label, 0, border=10, flag=flags)
self.hbox1.Add(self.depth_label, 0, border=10, flag=flags)
self.vbox.AddSpacer(5)
self.hbox2 = wx.BoxSizer(wx.HORIZONTAL)
self.hbox2.Add(self.variable_list, 0, border=10, flag=flags)
self.hbox2.Add(self.time_list, 0, border=10, flag=flags)
self.hbox2.Add(self.depthlayer_list, 0, border=10, flag=flags)
self.vbox.Add(self.hbox1, 0, flag = wx.ALIGN_LEFT | wx.TOP)
self.vbox.Add(self.hbox2, 0, flag = wx.ALIGN_LEFT | wx.TOP)
self.vbox.Add(self.hbox0, 0, flag = wx.ALIGN_LEFT | wx.TOP)
self.panel.SetSizer(self.vbox)
self.vbox.Fit(self)
##########
# Event functions
##########
def create_figure(self):
"""
Creates the figure
"""
# Find the colorbar limits if unspecified
if self.autoclim:
self.clim = [self.data.min(),self.data.max()]
self.climlow.SetValue('%3.1f'%self.clim[0])
self.climhigh.SetValue('%3.1f'%self.clim[1])
if 'collection' in self.__dict__:
#self.collection.remove()
self.axes.collections.remove(self.collection)
else:
# First call - set the axes limits
self.axes.set_aspect('equal')
self.axes.set_xlim(self.xlims)
self.axes.set_ylim(self.ylims)
if self.collectiontype=='cells':
self.collection = PolyCollection(self.xy,cmap=self.cmap)
self.collection.set_array(np.array(self.data[:]))
if not self.showedges:
self.collection.set_edgecolors(self.collection.to_rgba(np.array((self.data[:]))))
elif self.collectiontype=='edges':
xylines = [self.xp[self.edges],self.yp[self.edges]]
linesc = [list(zip(xylines[0][ii,:],xylines[1][ii,:])) for ii in range(self.Ne)]
self.collection = LineCollection(linesc,array=np.array(self.data[:]),cmap=self.cmap)
self.collection.set_clim(vmin=self.clim[0],vmax=self.clim[1])
self.axes.add_collection(self.collection)
self.title=self.axes.set_title(self.genTitle(),color=self.textcolor)
self.axes.set_xlabel('Easting [m]')
self.axes.set_ylabel('Northing [m]')
# create a colorbar
if 'cbar' not in self.__dict__:
self.cbar = self.fig.colorbar(self.collection)
#SetAxColor(self.cbar.ax.axes,self.textcolor,self.bgcolor)
else:
#pass
print('Updating colorbar...')
#self.cbar.check_update(self.collection)
self.cbar.on_mappable_changed(self.collection)
self.canvas.draw()
def update_figure(self):
if self.autoclim:
self.clim = [self.data.min(),self.data.max()]
self.climlow.SetValue('%3.1f'%self.clim[0])
self.climhigh.SetValue('%3.1f'%self.clim[1])
else:
self.clim = [float(self.climlow.GetValue()),\
float(self.climhigh.GetValue())]
# check whether it is cell or edge type
if self.hasDim(self.variable,self.griddims['Ne']):
self.collectiontype='edges'
elif self.hasDim(self.variable,self.griddims['Nc']):
self.collectiontype='cells'
# Create a new figure if the variable has gone from cell to edge of vice
# versa
if not self.collectiontype==self.oldcollectiontype:
self.create_figure()
self.oldcollectiontype=self.collectiontype
self.collection.set_array(np.array(self.data[:]))
self.collection.set_clim(vmin=self.clim[0],vmax=self.clim[1])
# Cells only
if self.collectiontype=='cells':
if not self.showedges:
self.collection.set_edgecolors(self.collection.to_rgba(np.array((self.data[:]))))
else:
self.collection.set_edgecolors('k')
self.collection.set_linewidths(0.2)
# Update the title
self.title=self.axes.set_title(self.genTitle(),color=self.textcolor)
#Update the colorbar
self.cbar.update_normal(self.collection)
# redraw the figure
self.canvas.draw()
def on_pick(self, event):
# The event received here is of the type
# matplotlib.backend_bases.PickEvent
#
# It carries lots of information, of which we're using
# only a small amount here.
#
box_points = event.artist.get_bbox().get_points()
msg = "You've clicked on a bar with coords:\n %s" % box_points
dlg = wx.MessageDialog(
self,
msg,
"Click!",
wx.OK | wx.ICON_INFORMATION)
dlg.ShowModal()
dlg.Destroy()
def on_select_variable(self, event):
vname = event.GetString()
self.flash_status_message("Selecting variable: %s"%vname)
# update the spatial object and load the data
self.variable = vname
self.loadData(variable=self.variable)
# Check if the variable has a depth coordinate
depthstr = ['']
# If so populate the vertical layer box
if self.hasDim(self.variable,self.griddims['Nk']):
depthstr = ['%3.1f'%self.z_r[k] for k in range(self.Nkmax)]
depthstr += ['surface','seabed']
elif self.hasDim(self.variable,'Nkw'):
depthstr = ['%3.1f'%self.z_w[k] for k in range(self.Nkmax+1)]
self.depthlayer_list.SetItems(depthstr)
# Update the plot
self.update_figure()
def on_select_time(self, event):
self.tindex = event.GetSelection()
# Update the object time index and reload the data
if self.plot_type=='hydro':
if not self.tstep==self.tindex:
self.tstep=self.tindex
self.loadData()
self.flash_status_message("Selecting variable: %s..."%event.GetString())
# Update the plot
self.update_figure()
elif self.plot_type=='particles':
self.PTM.plot(self.tindex,ax=self.axes,\
xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
self.canvas.draw()
def on_select_depth(self, event):
kindex = event.GetSelection()
if not self.klayer[0]==kindex:
# Check if its the seabed or surface value
if kindex>=self.Nkmax:
kindex=event.GetString()
self.klayer = [kindex]
self.loadData()
self.flash_status_message("Selecting depth: %s..."%event.GetString())
# Update the plot
self.update_figure()
def on_open_file(self, event):
file_choices = "SUNTANS NetCDF (*.nc)|*.nc*|UnTRIM NetCDF (*.nc)|*.nc*|All Files (*.*)|*.*"
dlg = wx.FileDialog(
self,
message="Open SUNTANS file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_MULTIPLE)
if dlg.ShowModal() == wx.ID_OK:
self.plot_type='hydro'
path = dlg.GetPaths()
# Initialise the class
if dlg.GetFilterIndex() == 0 or dlg.GetFilterIndex() > 1: #SUNTANS
self.flash_status_message("Opening SUNTANS file: %s" % path)
try:
Spatial.__init__(self, path, _FillValue=self._FillValue)
except:
Spatial.__init__(self, path, _FillValue=-999999)
startvar='dv'
# Populate the drop down menus
vnames = self.listCoordVars()
self.variable_list.SetItems(vnames)
# Update the time drop down list
if 'time' in self.__dict__:
self.timestr = [datetime.strftime(tt,'%d-%b-%Y %H:%M:%S') for tt in self.time]
else:
# Assume that it is a harmonic-type file
self.timestr = self.nc.Constituent_Names.split()
self.time_list.SetItems(self.timestr)
# Draw the depth
if startvar in vnames:
self.variable=startvar
self.loadData()
self.create_figure()
def on_load_grid(self, event):
dlg = wx.DirDialog(
self,
message="Open SUNTANS grid from folder...",
defaultPath=os.getcwd(),
style= wx.DD_DEFAULT_STYLE)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
# Initialise the class
self.flash_status_message("Opening SUNTANS grid from folder: %s" % path)
Grid.__init__(self,path)
# Plot the Grid
if 'collection' in self.__dict__:
self.axes.collections.remove(self.collection)
self.axes,self.collection = self.plotmesh(ax=self.axes,edgecolors='y')
# redraw the figure
self.canvas.draw()
def on_load_ptm(self, event):
file_choices = "PTM NetCDF (*.nc)|*.nc|PTM Binary (*_bin.out)|*_bin.out|All Files (*.*)|*.*"
dlg = wx.FileDialog(
self,
message="Open PTM file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_MULTIPLE)
if dlg.ShowModal() == wx.ID_OK:
self.plot_type = 'particles'
path = dlg.GetPath()
# Initialise the class
if dlg.GetFilterIndex() == 0: #SUNTANS
self.flash_status_message("Opening PTM netcdf file: %s" % path)
self.PTM = PtmNC(path)
elif dlg.GetFilterIndex() == 1: #PTM
self.flash_status_message("Opening PTM binary file: %s" % path)
self.PTM = PtmBin(path)
self.Nt = self.PTM.nt
# Update the time drop down list
self.timestr = [datetime.strftime(tt,'%d-%b-%Y %H:%M:%S') for tt in self.PTM.time]
self.time_list.SetItems(self.timestr)
# Plot the first time step
if 'xlims' in self.__dict__:
self.PTM.plot(self.PTM.nt-1,ax=self.axes,xlims=self.xlims,\
ylims=self.ylims,color=self.particlecolor,\
fontcolor='w',markersize=self.particlesize)
else:
self.PTM.plot(self.PTM.nt-1,ax=self.axes,fontcolor='w',\
color=self.particlecolor,markersize=self.particlesize)
# redraw the figure
self.canvas.draw()
def on_show_edges(self,event):
sender=event.GetEventObject()
self.showedges = sender.GetValue()
# Update the figure
self.update_figure()
def on_clim_check(self,event):
sender=event.GetEventObject()
if sender.GetValue() == True:
self.autoclim=False
self.update_figure()
else:
self.autoclim=True
def on_climlow(self,event):
self.clim[0] = event.GetString()
#self.update_figure()
def on_climhigh(self,event):
self.clim[1] = event.GetString()
#self.update_figure()
def on_select_cmap(self,event):
self.cmap=event.GetString()
if USECMOCEAN:
self.collection.set_cmap(getattr(cm,self.cmap))
else:
self.collection.set_cmap(self.cmap)
# Update the figure
self.update_figure()
def on_save_fig(self,event):
"""
Save a figure of the current scene to a file
"""
file_choices = " (*.png)|*.png| (*.pdf)|*.pdf |(*.jpg)|*.jpg |(*.eps)|*eps "
filters=['.png','.pdf','.png','.png']
dlg = wx.FileDialog(
self,
message="Save figure to file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
ext = filters[dlg.GetFilterIndex()]
if ext in path:
outfile=path
else:
outfile = path+ext
self.fig.savefig(outfile)
def on_save_anim(self,event):
"""
Save an animation of the current scene to a file
"""
file_choices = "Quicktime (*.mov)|*.mov| (*.gif)|*.gif| (*.avi)|*.avi |(*.mp4)|*.mp4 "
filters=['.mov','.gif','.avi','.mp4']
dlg = wx.FileDialog(
self,
message="Output animation file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
ext = filters[dlg.GetFilterIndex()]
if ext in path:
outfile=path
else:
outfile = path+ext
self.flash_status_message("Saving figure to file: %s" %outfile)
self.flash_status_message("Saving animation to file: %s" %outfile)
# Create the animation
#self.tstep = range(self.Nt) # Use all time steps for animation
#self.animate(cbar=self.cbar,cmap=self.cmap,\
# xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
def initanim():
if not self.plot_type=='particles':
return (self.title, self.collection)
else:
return (self.PTM.title,self.PTM.p_handle)
def updateScalar(i):
if not self.plot_type=='particles':
self.tstep=[i]
self.loadData()
self.update_figure()
return (self.title,self.collection)
elif self.plot_type=='particles':
self.PTM.plot(i,ax=self.axes,\
xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
return (self.PTM.title,self.PTM.p_handle)
self.anim = animation.FuncAnimation(self.fig, \
updateScalar, init_func = initanim, frames=self.Nt, interval=50, blit=True)
if ext=='.gif':
self.anim.save(outfile,writer='imagemagick',fps=6)
elif ext=='.mp4':
print('Saving html5 video...')
# Ensures html5 compatibility
self.anim.save(outfile,fps=6,\
writer='ffmpeg',\
bitrate=3600,extra_args=['-vcodec','libx264'])
#writer='mencoder',
#bitrate=3600,extra_args=['-ovc','x264']) # mencoder options
else:
self.anim.save(outfile,writer='mencoder',fps=6,bitrate=3600)
# Return the figure back to its status
del self.anim
self.tstep=self.tindex
if not self.plot_type=='particles':
self.loadData()
self.update_figure()
# Bring up a dialog box
dlg2= wx.MessageDialog(self, 'Animation complete.', "Done", wx.OK)
dlg2.ShowModal()
dlg2.Destroy()
def on_exit(self, event):
self.Destroy()
def on_about(self, event):
msg = """ SUNTANS NetCDF visualization tool
*Author: Matt Rayson
*Institution: Stanford University
*Created: October 2013
"""
dlg = wx.MessageDialog(self, msg, "About", wx.OK)
dlg.ShowModal()
dlg.Destroy()
def on_count_cells(self,eveny):
msg = "Total 3-D grid cells = %d"%(self.count_cells())
dlg = wx.MessageDialog(self, msg, "No. cells", wx.OK)
dlg.ShowModal()
dlg.Destroy()
def on_overlay_bathy(self,event):
# Plot depth contours
print('Plotting contours...')
self.contourf(z=self.dv, clevs=self.depthlevs,\
ax=self.axes,\
filled=False, colors='0.5', linewidths=0.5, zorder=1e6)
print('Done')
def on_plot_gridstat(self, event):
"""
Plot the grid size histogram in a new figure
"""
matplotlib.pyplot.figure()
self.plothist()
matplotlib.pyplot.show()
def create_status_bar(self):
self.statusbar = self.CreateStatusBar()
def flash_status_message(self, msg, flash_len_ms=1500):
self.statusbar.SetStatusText(msg)
self.timeroff = wx.Timer(self)
self.Bind(
wx.EVT_TIMER,
self.on_flash_status_off,
self.timeroff)
self.timeroff.Start(flash_len_ms, oneShot=True)
def on_flash_status_off(self, event):
self.statusbar.SetStatusText('')
class SunPlotPy(Spatial, QMainWindow):
"""
The main frame of the application
"""
title = 'sunplot(py)'
# Plotting options
autoclim = True
showedges = False
bgcolor = 'k'
textcolor = 'w'
cmap = 'RdBu'
particlesize = 1.8
particlecolor = 'm'
# other flags
collectiontype = 'cells'
oldcollectiontype = 'cells'
#
tindex = 0
depthlevs = [0., 10., 100., 200., 300., 400., 500.,\
1000.,2000.,3000.,4000.,5000]
_FillValue = 999999
def __init__(self, parent=None):
#wx.Frame.__init__(self, None, -1, self.title)
QMainWindow.__init__(self, parent)
#super(SunPlotPy, self).__init__(parent)
self.create_menu()
#self.create_status_bar()
self.create_main_panel()
#self.draw_figure()
def create_menu(self):
self.file_menu = self.menuBar().addMenu("&File")
###
# File Menu
###
# Load a hydro output file
load_file_action = self.create_action("&Open file",\
shortcut="ctrl-o", slot=self.on_open_file,
tip="open netcdf file")
load_grid_action = self.create_action("&Open grid",\
shortcut="ctrl-g", slot=self.on_load_grid,
tip="open suntans grid")
save_anim_action = self.create_action("&Save animation",\
shortcut="ctrl-a", slot=self.on_save_anim,
tip="animate current scene")
quit_action = self.create_action("&Exit",\
shortcut="ctrl-x", slot=self.close,
tip="Close the application")
self.add_actions(self.file_menu,
(load_file_action, load_grid_action,\
save_anim_action, None, quit_action))
# self.Bind(wx.EVT_MENU, self.on_open_file, m_expt)
###
# Tools menu
###
self.tools_menu = self.menuBar().addMenu("&Tools")
###
# File Menu
###
# Load a hydro output file
load_stat_action = self.create_action("&Plot grid size statistics",\
slot=self.on_plot_gridstat,
tip="grid stats")
self.add_actions(self.tools_menu, (load_stat_action, ))
# # Load a grid file
# m_grid = menu_file.Append(-1, "&Load grid\tCtrl-G", "Load SUNTANS grid from folder")
# self.Bind(wx.EVT_MENU, self.on_load_grid, m_grid)
# # Load a particle file
# m_part = menu_file.Append(-1, "&Load PTM file\tCtrl-Shift-P", "Load a PTM file")
# self.Bind(wx.EVT_MENU, self.on_load_ptm, m_part)
# # Save current scene as an animation
# m_anim = menu_file.Append(-1,"&Save animation of current scene\tCtrl-S","Save animation")
# self.Bind(wx.EVT_MENU, self.on_save_anim, m_anim)
# # Save the current figure
# m_prin = menu_file.Append(-1,"&Print current scene\tCtrl-P","Save figure")
# self.Bind(wx.EVT_MENU, self.on_save_fig, m_prin)
# menu_file.AppendSeparator()
# # Exit
# m_exit = menu_file.Append(-1, "E&xit\tCtrl-X", "Exit")
# self.Bind(wx.EVT_MENU, self.on_exit, m_exit)
# ###
# # Tools menu
# ###
# menu_tools = wx.Menu()
# m_gridstat = menu_tools.Append(-1, "&Plot grid size statistics", "SUNTANS grid size")
# self.Bind(wx.EVT_MENU, self.on_plot_gridstat, m_gridstat)
# m_countcells = menu_tools.Append(-1, "&Count # grid cells", "Grid cell count")
# self.Bind(wx.EVT_MENU, self.on_count_cells, m_countcells)
# m_overlaybathy = menu_tools.Append(-1, "&Overlay depth contours", "Depth overlay")
# self.Bind(wx.EVT_MENU, self.on_overlay_bathy, m_overlaybathy)
#
# ###
# # Help Menu
# ###
# menu_help = wx.Menu()
# m_about = menu_help.Append(-1, "&About\tF1", "About the demo")
# self.Bind(wx.EVT_MENU, self.on_about, m_about)
#
#
# # Add all of the menu bars
# self.menubar.Append(menu_file, "&File")
# self.menubar.Append(menu_tools, "&Tools")
# self.menubar.Append(menu_help, "&Help")
# self.SetMenuBar(self.menubar)
def add_actions(self, target, actions):
for action in actions:
if action is None:
target.addSeparator()
else:
target.addAction(action)
def create_action(
self,
text,
slot=None,
shortcut=None,
icon=None,
tip=None,
checkable=False,
):
#signal="triggered()"):
action = QAction(text, self)
if icon is not None:
action.setIcon(QIcon(":/%s.png" % icon))
if shortcut is not None:
action.setShortcut(shortcut)
if tip is not None:
action.setToolTip(tip)
action.setStatusTip(tip)
if slot is not None:
#self.connect(action, SIGNAL(signal), slot)
# Qt5 style
action.triggered.connect(slot)
if checkable:
action.setCheckable(True)
return action
def create_main_panel(self):
""" Creates the main panel with all the controls on it:
* mpl canvas
* mpl navigation toolbar
* Control panel for interaction
"""
self.panel = QWidget()
# Create the mpl Figure and FigCanvas objects.
# 5x4 inches, 100 dots-per-inch
#
self.dpi = 100
#self.fig = Figure((7.0, 6.0), dpi=self.dpi,facecolor=self.bgcolor)
self.fig = Figure((7.0, 6.0), dpi=self.dpi)
#self.canvas = FigCanvas(self.panel, -1, self.fig)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.panel)
# Since we have only one plot, we can use add_axes
# instead of add_subplot, but then the subplot
# configuration tool in the navigation toolbar wouldn't
# work.
#
self.axes = self.fig.add_subplot(111)
#SetAxColor(self.axes,self.textcolor,self.bgcolor)
# Bind the 'pick' event for clicking on one of the bars
#
#self.canvas.mpl_connect('pick_event', self.on_pick)
#########
## Create widgets
#########
self.variable_list = QComboBox()
self.variable_list.addItem("Select a variable...")
self.variable_list.activated[str].connect(self.on_select_variable)
self.time_list = QComboBox()
self.time_list.addItem("Select a time...")
self.time_list.activated[int].connect(self.on_select_time)
self.depthlayer_list = QComboBox()
self.depthlayer_list.addItem("Select a vertical layer...")
self.depthlayer_list.activated[int].connect(self.on_select_depth)
self.show_edge_check = QCheckBox('Show Edges', self)
#self.show_edge_check.toggle()
self.show_edge_check.stateChanged.connect(self.on_show_edges)
cmaps = list(matplotlib.cm.datad.keys())
cmaps.sort()
self.colormap_list = QComboBox()
self.colormap_list.clear()
self.colormap_list.addItems(cmaps)
self.colormap_list.activated[str].connect(self.on_select_cmap)
self.clim_check = QCheckBox('Manual color limits', self)
#self.show_edge_check.toggle()
self.clim_check.stateChanged.connect(self.on_clim_check)
#self.clim_check = wx.CheckBox(self.panel, -1,
# "Manual color limits ",
# style=wx.ALIGN_RIGHT)
#self.clim_check.Bind(wx.EVT_CHECKBOX, self.on_clim_check)
self.climlow = QLineEdit()
self.climlow.textChanged[str].connect(self.on_climlow)
self.climhigh = QLineEdit()
self.climhigh.textChanged[str].connect(self.on_climhigh)
## Labels
#self.variable_label = wx.StaticText(self.panel, -1,"Variable:",size=(200,-1))
#self.time_label = wx.StaticText(self.panel, -1,"Time step:",size=(200,-1))
#self.depth_label = wx.StaticText(self.panel, -1,"Vertical level:",size=(200,-1))
# Create the navigation toolbar, tied to the canvas
#
self.toolbar = NavigationToolbar(self.canvas, self)
#self.toolbar.toolitems[8][3]='my_save_fig'
#def my_save_fig(self,*args):
# print 'saving figure'
## return "break"
#########
# Layout with box sizers
#########
hbox = QHBoxLayout()
for w in [self.variable_list, self.time_list, self.depthlayer_list]:
hbox.addWidget(w)
hbox.setAlignment(w, Qt.AlignVCenter)
hbox1 = QHBoxLayout()
for w in [
self.show_edge_check, self.colormap_list, self.clim_check,
self.climlow, self.climhigh
]:
hbox1.addWidget(w)
hbox1.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.canvas)
self.vbox.addWidget(self.toolbar)
self.vbox.addLayout(hbox)
self.vbox.addLayout(hbox1)
self.panel.setLayout(self.vbox)
self.setCentralWidget(self.panel)
#
###########
## Event functions
###########
def create_figure(self):
"""
Creates the figure
"""
# Find the colorbar limits if unspecified
if self.autoclim:
self.clim = [self.data.min(), self.data.max()]
self.climlow.setText('%3.1f' % self.clim[0])
self.climhigh.setText('%3.1f' % self.clim[1])
if 'collection' in self.__dict__:
#self.collection.remove()
self.axes.collections.remove(self.collection)
else:
# First call - set the axes limits
self.axes.set_aspect('equal')
self.axes.set_xlim(self.xlims)
self.axes.set_ylim(self.ylims)
if self.collectiontype == 'cells':
self.collection = PolyCollection(self.xy, cmap=self.cmap)
self.collection.set_array(np.array(self.data[:]))
if not self.showedges:
self.collection.set_edgecolors(
self.collection.to_rgba(np.array((self.data[:]))))
elif self.collectiontype == 'edges':
xylines = [self.xp[self.edges], self.yp[self.edges]]
linesc = [
list(zip(xylines[0][ii, :], xylines[1][ii, :]))
for ii in range(self.Ne)
]
self.collection = LineCollection(linesc,
array=np.array(self.data[:]),
cmap=self.cmap)
self.collection.set_clim(vmin=self.clim[0], vmax=self.clim[1])
self.axes.add_collection(self.collection)
self.title = self.axes.set_title(self.genTitle(), color=self.textcolor)
self.axes.set_xlabel('Easting [m]')
self.axes.set_ylabel('Northing [m]')
# create a colorbar
if 'cbar' not in self.__dict__:
self.cbar = self.fig.colorbar(self.collection)
#SetAxColor(self.cbar.ax.axes,self.textcolor,self.bgcolor)
else:
#pass
print('Updating colorbar...')
#self.cbar.check_update(self.collection)
self.cbar.on_mappable_changed(self.collection)
self.canvas.draw()
def update_figure(self):
if self.autoclim:
self.clim = [self.data.min(), self.data.max()]
self.climlow.setText('%3.1f' % self.clim[0])
self.climhigh.setText('%3.1f' % self.clim[1])
else:
self.clim = [float(self.climlow.text()),\
float(self.climhigh.text())]
# check whether it is cell or edge type
if self.hasDim(self.variable, self.griddims['Ne']):
self.collectiontype = 'edges'
elif self.hasDim(self.variable, self.griddims['Nc']):
self.collectiontype = 'cells'
# Create a new figure if the variable has gone from cell to edge of vice
# versa
if not self.collectiontype == self.oldcollectiontype:
self.create_figure()
self.oldcollectiontype = self.collectiontype
self.collection.set_array(np.array(self.data[:]))
self.collection.set_clim(vmin=self.clim[0], vmax=self.clim[1])
# Cells only
if self.collectiontype == 'cells':
if not self.showedges:
self.collection.set_edgecolors(
self.collection.to_rgba(np.array((self.data[:]))))
else:
self.collection.set_edgecolors('k')
self.collection.set_linewidths(0.2)
# Update the title
self.title = self.axes.set_title(self.genTitle(), color=self.textcolor)
#Update the colorbar
self.cbar.update_normal(self.collection)
# redraw the figure
self.canvas.draw()
#def on_pick(self, event):
# # The event received here is of the type
# # matplotlib.backend_bases.PickEvent
# #
# # It carries lots of information, of which we're using
# # only a small amount here.
# #
# box_points = event.artist.get_bbox().get_points()
# msg = "You've clicked on a bar with coords:\n %s" % box_points
#
# dlg = wx.MessageDialog(
# self,
# msg,
# "Click!",
# wx.OK | wx.ICON_INFORMATION)
# dlg.ShowModal()
# dlg.Destroy()
#
def on_select_variable(self, event):
#vname = event.GetString()
vname = event
#self.flash_status_message("Selecting variable: %s"%vname)
# update the spatial object and load the data
self.variable = vname
self.loadData(variable=self.variable)
# Check if the variable has a depth coordinate
depthstr = ['']
# If so populate the vertical layer box
if self.hasDim(self.variable, self.griddims['Nk']):
depthstr = ['%3.1f' % self.z_r[k] for k in range(self.Nkmax)]
depthstr += ['surface', 'seabed']
elif self.hasDim(self.variable, 'Nkw'):
depthstr = ['%3.1f' % self.z_w[k] for k in range(self.Nkmax + 1)]
self.depthlayer_list.clear()
self.depthlayer_list.addItems(depthstr)
# Update the plot
self.update_figure()
def on_select_time(self, event):
self.tindex = event #
# Update the object time index and reload the data
#if self.plot_type=='hydro':
if not self.tstep == self.tindex:
self.tstep = self.tindex
self.loadData()
#self.flash_status_message("Selecting variable: %s..."%event.GetString())
# Update the plot
self.update_figure()
#elif self.plot_type=='particles':
# self.PTM.plot(self.tindex,ax=self.axes,\
# xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
#
# self.canvas.draw()
def on_select_depth(self, event):
print(event)
kindex = event
if not self.klayer[0] == kindex:
# Check if its the seabed or surface value
if kindex >= self.Nkmax:
kindex = event.GetString()
self.klayer = [kindex]
self.loadData()
#self.flash_status_message("Selecting depth: %s..."%event.GetString())
# Update the plot
self.update_figure()
def on_open_file(self, event):
file_choices = "SUNTANS NetCDF (*.nc);;All Files (*.*)"
dlg = QFileDialog.getOpenFileNames(self, "Open SUNTANS file...", "",
file_choices)
path = dlg[0]
if len(path) == 0:
return
self.statusBar().showMessage("Opening SUNTANS file: %s" % path)
try:
Spatial.__init__(self, path, _FillValue=self._FillValue)
except:
Spatial.__init__(self, path, _FillValue=-999999)
startvar = 'dv'
self.statusBar().clearMessage()
# Populate the drop down menus
vnames = self.listCoordVars()
self.variable_list.clear()
self.variable_list.addItems(vnames)
# Update the time drop down list
if 'time' in self.__dict__:
self.timestr = [
datetime.strftime(tt, '%d-%b-%Y %H:%M:%S') for tt in self.time
]
else:
# Assume that it is a harmonic-type file
self.timestr = self.nc.Constituent_Names.split()
self.time_list.clear()
self.time_list.addItems(self.timestr)
# Draw the depth
if startvar in vnames:
self.variable = startvar
self.loadData()
self.create_figure()
def on_load_grid(self, event):
dir_ = QFileDialog.getExistingDirectory(None, 'Select a SUNTANS grid folder:',\
'~/', QFileDialog.ShowDirsOnly)
print(dir_)
if dir_ is not None:
path = dir_
# Initialise the class
#self.flash_status_message("Opening SUNTANS grid from folder: %s" % path)
Grid.__init__(self, path)
# Plot the Grid
if 'collection' in self.__dict__:
self.axes.collections.remove(self.collection)
self.axes, self.collection = self.plotmesh(ax=self.axes,
edgecolors='y')
# redraw the figure
self.canvas.draw()
#def on_load_ptm(self, event):
# file_choices = "PTM NetCDF (*.nc)|*.nc|PTM Binary (*_bin.out)|*_bin.out|All Files (*.*)|*.*"
#
# dlg = wx.FileDialog(
# self,
# message="Open PTM file...",
# defaultDir=os.getcwd(),
# defaultFile="",
# wildcard=file_choices,
# style= wx.FD_MULTIPLE)
#
# if dlg.ShowModal() == wx.ID_OK:
# self.plot_type = 'particles'
# path = dlg.GetPath()
# # Initialise the class
# if dlg.GetFilterIndex() == 0: #SUNTANS
# self.flash_status_message("Opening PTM netcdf file: %s" % path)
# self.PTM = PtmNC(path)
# elif dlg.GetFilterIndex() == 1: #PTM
# self.flash_status_message("Opening PTM binary file: %s" % path)
# self.PTM = PtmBin(path)
# self.Nt = self.PTM.nt
#
# # Update the time drop down list
# self.timestr = [datetime.strftime(tt,'%d-%b-%Y %H:%M:%S') for tt in self.PTM.time]
# self.time_list.SetItems(self.timestr)
# # Plot the first time step
# if self.__dict__.has_key('xlims'):
# self.PTM.plot(self.PTM.nt-1,ax=self.axes,xlims=self.xlims,\
# ylims=self.ylims,color=self.particlecolor,\
# fontcolor='w',markersize=self.particlesize)
# else:
# self.PTM.plot(self.PTM.nt-1,ax=self.axes,fontcolor='w',\
# color=self.particlecolor,markersize=self.particlesize)
# # redraw the figure
# self.canvas.draw()
#
def on_show_edges(self, event):
if event > 0:
self.showedges = True
else:
self.showedges = False
# Update the figure
self.update_figure()
def on_clim_check(self, event):
if event > 0:
self.autoclim = False
self.update_figure()
else:
self.autoclim = True
def on_climlow(self, event):
try:
self.clim[0] = float(event)
except:
return # do nothing
# self.clim[0] = event.GetString()
# #self.update_figure()
def on_climhigh(self, event):
try:
self.clim[1] = float(event)
except:
return # do nothing
# print event
# self.clim[1] = event.GetString()
# #self.update_figure()
def on_select_cmap(self, event):
self.cmap = event
self.collection.set_cmap(self.cmap)
# Update the figure
self.update_figure()
#def on_save_fig(self,event):
# """
# Save a figure of the current scene to a file
# """
# file_choices = " (*.png)|*.png| (*.pdf)|*.pdf |(*.jpg)|*.jpg |(*.eps)|*eps "
# filters=['.png','.pdf','.png','.png']
#
# dlg = wx.FileDialog(
# self,
# message="Save figure to file...",
# defaultDir=os.getcwd(),
# defaultFile="",
# wildcard=file_choices,
# style= wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)
# if dlg.ShowModal() == wx.ID_OK:
# path = dlg.GetPath()
# ext = filters[dlg.GetFilterIndex()]
# if ext in path:
# outfile=path
# else:
# outfile = path+ext
# self.fig.savefig(outfile)
#
def on_save_anim(self, event):
"""
Save an animation of the current scene to a file
"""
#file_choices = "Quicktime (*.mov)|*.mov| (*.gif)|*.gif| (*.avi)|*.avi |(*.mp4)|*.mp4 "
#filters=['.mov','.gif','.avi','.mp4']
filters = "Movie formats (*.mp4 *.avi *.gif);;All files (*.*)"
dir_ = QFileDialog.getSaveFileName(None, 'Save animation to file:',\
'~/', filters)
print(dir_)
#dlg = wx.FileDialog(
# self,
# message="Output animation file...",
# defaultDir=os.getcwd(),
# defaultFile="",
# wildcard=file_choices,
# style= wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)
if dir_ is not None:
outfile = dir_[0]
ext = outfile[-4::]
# Create the animation
#self.tstep = range(self.Nt) # Use all time steps for animation
#self.animate(cbar=self.cbar,cmap=self.cmap,\
# xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
def initanim():
return (self.title, self.collection)
#if not self.plot_type=='particles':
# return (self.title, self.collection)
#else:
# return (self.PTM.title,self.PTM.p_handle)
def updateScalar(i):
self.tstep = [i]
self.loadData()
self.update_figure()
return (self.title, self.collection)
#if not self.plot_type=='particles':
# self.tstep=[i]
# self.loadData()
# self.update_figure()
# return (self.title,self.collection)
#elif self.plot_type=='particles':
# self.PTM.plot(i,ax=self.axes,\
# xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
# return (self.PTM.title,self.PTM.p_handle)
self.anim = animation.FuncAnimation(self.fig, \
updateScalar, init_func = initanim, frames=self.Nt, interval=50, blit=True)
if ext == '.gif':
self.anim.save(outfile, writer='imagemagick', fps=6)
elif ext == '.mp4':
print('Saving html5 video...')
# Ensures html5 compatibility
self.anim.save(outfile,fps=6,\
writer='ffmpeg',\
bitrate=3600,extra_args=['-vcodec','libx264'])
#writer='mencoder',
#bitrate=3600,extra_args=['-ovc','x264']) # mencoder options
else:
self.anim.save(outfile, writer='mencoder', fps=6, bitrate=3600)
# Return the figure back to its status
del self.anim
self.tstep = self.tindex
self.loadData()
self.update_figure()
print('Finished saving animation to %s' % outfile)
print(72 * '#')
#if not self.plot_type=='particles':
# self.loadData()
# self.update_figure()
# Bring up a dialog box
#dlg2= wx.MessageDialog(self, 'Animation complete.', "Done", wx.OK)
#dlg2.ShowModal()
#dlg2.Destroy()
#def on_exit(self, event):
# self.Destroy()
#
#def on_about(self, event):
# msg = """ SUNTANS NetCDF visualization tool
#
# *Author: Matt Rayson
# *Institution: Stanford University
# *Created: October 2013
# """
# dlg = wx.MessageDialog(self, msg, "About", wx.OK)
# dlg.ShowModal()
# dlg.Destroy()
#def on_count_cells(self,eveny):
# msg = "Total 3-D grid cells = %d"%(self.count_cells())
# dlg = wx.MessageDialog(self, msg, "No. cells", wx.OK)
# dlg.ShowModal()
# dlg.Destroy()
#def on_overlay_bathy(self,event):
# # Plot depth contours
# print 'Plotting contours...'
# self.contourf(z=self.dv, clevs=self.depthlevs,\
# ax=self.axes,\
# filled=False, colors='0.5', linewidths=0.5, zorder=1e6)
# print 'Done'
def on_plot_gridstat(self, event):
"""
Plot the grid size histogram in a new figure
"""
matplotlib.pyplot.figure()
self.plothist()
matplotlib.pyplot.show()
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 draw_networkx_edges(
G,
pos,
edgelist=None,
width=1.0,
edge_color="k",
style="solid",
alpha=None,
arrowstyle="-|>",
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
min_source_margin=0,
min_target_margin=0,
):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color or array of colors (default='k')
Edge color. Can be a single color or a sequence of colors with the same
length as edgelist. Color can be string, or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=None)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : str, optional (default=None)
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See :py:class: `matplotlib.patches.ConnectionStyle` and
:py:class: `matplotlib.patches.FancyArrowPatch` for more info.
label : [None| string]
Label for legend
min_source_margin : int, optional (default=0)
The minimum margin (gap) at the begining of the edge at the source.
min_target_margin : int, optional (default=0)
The minimum margin (gap) at the end of the edge at the target.
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if len(edgelist) == 0: # no edges!
if not G.is_directed() or not arrows:
return LineCollection(None)
else:
return []
if nodelist is None:
nodelist = list(G.nodes())
# FancyArrowPatch handles color=None different from LineCollection
if edge_color is None:
edge_color = "k"
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# Check if edge_color is an array of floats and map to edge_cmap.
# This is the only case handled differently from matplotlib
if (
np.iterable(edge_color)
and (len(edge_color) == len(edge_pos))
and np.alltrue([isinstance(c, Number) for c in edge_color])
):
if edge_cmap is not None:
assert isinstance(edge_cmap, Colormap)
else:
edge_cmap = plt.get_cmap()
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
if not G.is_directed() or not arrows:
edge_collection = LineCollection(
edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
alpha=alpha,
)
edge_collection.set_cmap(edge_cmap)
edge_collection.set_clim(edge_vmin, edge_vmax)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
# FancyArrowPatch doesn't handle color strings
arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if np.iterable(node_size): # many node sizes
source, target = edgelist[i][:2]
source_node_size = node_size[nodelist.index(source)]
target_node_size = node_size[nodelist.index(target)]
shrink_source = to_marker_edge(source_node_size, node_shape)
shrink_target = to_marker_edge(target_node_size, node_shape)
else:
shrink_source = shrink_target = to_marker_edge(node_size, node_shape)
if shrink_source < min_source_margin:
shrink_source = min_source_margin
if shrink_target < min_target_margin:
shrink_target = min_target_margin
if len(arrow_colors) == len(edge_pos):
arrow_color = arrow_colors[i]
elif len(arrow_colors) == 1:
arrow_color = arrow_colors[0]
else: # Cycle through colors
arrow_color = arrow_colors[i % len(arrow_colors)]
if np.iterable(width):
if len(width) == len(edge_pos):
line_width = width[i]
else:
line_width = width[i % len(width)]
else:
line_width = width
arrow = FancyArrowPatch(
(x1, y1),
(x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
linestyle=style,
zorder=1,
) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return arrow_collection
lines1.append(zip(range(maxL + 1), data[0]))
data[1].mask[0] = False
er = gm.entropy_rate()
data[1] -= er
data[1] /= er
lines2.append(zip(range(maxL + 1), data[1, :]))
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
fig.subplots_adjust(left=0.09, right=0.86)
ax1 = fig.add_subplot(1, 2, 1)
lc1 = LineCollection(lines1)
lc1.set_array(ps)
lc1.set_cmap(cmap)
ax1.add_collection(lc1)
ax1.set_xlabel(r"$L$ [symbols]")
ax1.set_ylabel(r"$1-(\mathbf{E}(L) / \mathbf{E})$")
ax1.axis('auto')
ax1.set_xlim((0, maxL))
ax2 = fig.add_subplot(1, 2, 2)
lc2 = LineCollection(lines2)
lc2.set_array(ps)
lc2.set_cmap(cmap)
ax2.add_collection(lc2)
def draw_networkx_edges(
G,
pos,
edgelist=None,
width=1.0,
edge_color="k",
style="solid",
alpha=None,
arrowstyle="-|>",
arrowsize=3,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
min_source_margin=0,
min_target_margin=0,
):
"""Draw the edges of the graph G. Adjusted from networkx."""
try:
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
from numbers import Number
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# FancyArrowPatch handles color=None different from LineCollection
if edge_color is None:
edge_color = "k"
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# Check if edge_color is an array of floats and map to edge_cmap.
# This is the only case handled differently from matplotlib
if (
np.iterable(edge_color)
and (len(edge_color) == len(edge_pos))
and np.alltrue([isinstance(c, Number) for c in edge_color])
):
if edge_cmap is not None:
assert isinstance(edge_cmap, Colormap)
else:
edge_cmap = plt.get_cmap()
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
if not G.is_directed() or not arrows:
edge_collection = LineCollection(
edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
alpha=alpha,
)
edge_collection.set_cmap(edge_cmap)
edge_collection.set_clim(edge_vmin, edge_vmax)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
# FancyArrowPatch doesn't handle color strings
arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if np.iterable(node_size): # many node sizes
source, target = edgelist[i][:2]
source_node_size = node_size[nodelist.index(source)]
target_node_size = node_size[nodelist.index(target)]
shrink_source = to_marker_edge(source_node_size, node_shape)
shrink_target = to_marker_edge(target_node_size, node_shape)
else:
shrink_source = shrink_target = to_marker_edge(node_size, node_shape)
if shrink_source < min_source_margin:
shrink_source = min_source_margin
if shrink_target < min_target_margin:
shrink_target = min_target_margin
if len(arrow_colors) == len(edge_pos):
arrow_color = arrow_colors[i]
elif len(arrow_colors) == 1:
arrow_color = arrow_colors[0]
else: # Cycle through colors
arrow_color = arrow_colors[i % len(arrow_colors)]
if np.iterable(width):
if len(width) == len(edge_pos):
line_width = width[i]
else:
line_width = width[i % len(width)]
else:
line_width = width
arrow = FancyArrowPatch(
(x1, y1),
(x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
linestyle=style,
zorder=1,
) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return arrow_collection
def plot(self,
time,
beam=None,
maxground=2000,
maxalt=500,
iscat=True,
gscat=True,
title=False,
weighted=False,
cmap='hot_r',
fig=None,
rect=111,
ax=None,
aax=None,
zorder=4):
"""Plot scatter on ground/altitude profile
**Args**:
* **time** (datetime.datetime): time of profile
* [**beam**]: beam number
* [**iscat**] (bool): show ionospheric scatter
* [**gscat**] (bool): show ground scatter
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
* [**ax**]: Existing main axes
* [**aax**]: Existing auxialary axes
* [**title**]: Show default title
* [**weighted**] (bool): plot ionospheric scatter relative strength (based on background density and range)
* [**cmap**]: colormap used for weighted ionospheric scatter
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ionospheric scatter
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, title=True)
rto.readScatter() # read scatter into memory
rto.scatter.plot(sTime, ax=ax, aax=aax)
ax.grid()
written by Sebastien, 2013-04
"""
from utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig,
rect=rect,
maxground=maxground,
maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.isc.keys()
or time in self.gsc.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.isc[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.isc[time].keys()[0]
if gscat and time in self.gsc.keys():
for ir, (el,
rays) in enumerate(sorted(self.gsc[time][beam].items())):
if len(rays['r']) == 0: continue
_ = aax.scatter(rays['th'],
ax.Re * np.ones(rays['th'].shape),
color='0',
zorder=zorder)
if iscat and time in self.isc.keys():
if weighted:
wmin = np.min([
r['w'].min() for r in self.isc[time][beam].values()
if r['nstp'] > 0
])
wmax = np.max([
r['w'].max() for r in self.isc[time][beam].values()
if r['nstp'] > 0
])
for ir, (el,
rays) in enumerate(sorted(self.isc[time][beam].items())):
if rays['nstp'] == 0: continue
t = rays['th']
r = rays['r'] * 1e-3
spts = np.array([t, r]).T.reshape(-1, 1, 2)
h = rays['h'] * 1e-3
rel = np.radians(rays['rel'])
r = np.sqrt(r**2 + h**2 + 2 * r * h * np.sin(rel))
t = t + np.arcsin(h / r * np.cos(rel))
epts = np.array([t, r]).T.reshape(-1, 1, 2)
segments = np.concatenate([spts, epts], axis=1)
lcol = LineCollection(segments, zorder=zorder)
if weighted:
_ = lcol.set_cmap(cmap)
_ = lcol.set_norm(plt.Normalize(0, 1))
_ = lcol.set_array((rays['w'] - wmin) / wmax)
else:
_ = lcol.set_color('0')
_ = aax.add_collection(lcol)
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, None)
ax.set_title(stitle)
# If weighted, plot ionospheric scatter with colormap
if weighted:
# Add a colorbar
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("Ionospheric Scatter")
else:
cbax = None
ax.beam = beam
return ax, aax, cbax
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(self,
time,
beam=None,
maxground=2000,
maxalt=500,
step=1,
showrefract=False,
nr_cmap='jet_r',
nr_lim=[0.8, 1.],
raycolor='0.3',
title=False,
zorder=2,
alpha=1,
fig=None,
rect=111,
ax=None,
aax=None):
"""Plot ray paths
**Args**:
* **time** (datetime.datetime): time of rays
* [**beam**]: beam number
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**step**]: step between each plotted ray (in number of ray steps)
* [**showrefract**]: show refractive index along ray paths (supersedes raycolor)
* [**nr_cmap**]: color map name for refractive index coloring
* [**nr_lim**]: refractive index plotting limits
* [**raycolor**]: color of ray paths
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
* [**title**]: Show default title
* [**ax**]: Existing main axes
* [**aax**]: Existing auxialary axes
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ray paths with colored refractive index along path
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12, title=True)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=10, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
from utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
from types import MethodType
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig,
rect=rect,
maxground=maxground,
maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'time'):
time = ax.time
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.paths.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.paths[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.paths[time].keys()[0]
for ir, (el,
rays) in enumerate(sorted(self.paths[time][beam].items())):
if not ir % step:
if not showrefract:
aax.plot(rays['th'],
rays['r'] * 1e-3,
c=raycolor,
zorder=zorder,
alpha=alpha)
else:
points = np.array([rays['th'],
rays['r'] * 1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]],
axis=1)
lcol = LineCollection(segments, zorder=zorder, alpha=alpha)
_ = lcol.set_cmap(nr_cmap)
_ = lcol.set_norm(plt.Normalize(*nr_lim))
_ = lcol.set_array(rays['nr'])
_ = aax.add_collection(lcol)
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, self.name)
ax.set_title(stitle)
# Add a colorbar when plotting refractive index
if showrefract:
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("refractive index")
else:
cbax = None
# Declare a new method to show range markers
# This method is only available after rays have been plotted
# This ensures that the markers match the plotted rays
def showRange(self, markers=None, color='.8', s=2, zorder=3, **kwargs):
"""Plot ray paths
**Args**:
* [**markers**]: range markers. Defaults to every 250 km
* All other keywords are borrowed from :func:`matplotlib.pyplot.scatter`
**Returns**:
* **coll**: a collection of range markers
**Example**:
::
# Add range markers to an existing ray plot
ax, aax, cbax = rto.rays.plot(sTime, step=10)
rto.rays.showRange()
written by Sebastien, 2013-04
"""
if not markers:
markers = np.arange(0, 5000, 250)
x, y = [], []
for el, rays in self.paths[time][beam].items():
for rm in markers:
inds = (rays['gran'] * 1e-3 >= rm)
if inds.any():
x.append(rays['th'][inds][0])
y.append(rays['r'][inds][0] * 1e-3)
coll = aax.scatter(x, y, color=color, s=s, zorder=zorder, **kwargs)
return coll
# End of new method
# Assign new method
self.showRange = MethodType(showRange, self)
ax.beam = beam
return ax, aax, cbax
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 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, 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.
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 = {
'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 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)\
* 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):
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[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 plot(self, time, beam=None, maxground=2000, maxalt=500, step=1,
showrefract=False, nr_cmap='jet_r', nr_lim=[0.8, 1.],
raycolor='0.4',
fig=None, rect=111):
"""Plot ray paths
**Args**:
* **time** (datetime.datetime): time of rays
* [**beam**]: beam number
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**step**]: step between each plotted ray (in number of ray steps)
* [**showrefract**]: show refractive index along ray paths (supersedes raycolor)
* [**nr_cmap**]: color map name for refractive index coloring
* [**nr_lim**]: refractive index plotting limits
* [**raycolor**]: color of ray paths
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ray paths with colored refractive index along path
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.rtRun(sTime, rCode='bks', beam=12)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=2, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
from utils import plotUtils
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
# make sure that the required time and beam are present
assert (time in self.paths.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.paths[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.paths[time].keys()[0]
for ir, (el, rays) in enumerate( sorted(self.paths[time][beam].items()) ):
if not ir % step:
if not showrefract:
aax.plot(rays['th'], rays['r']*1e-3, c=raycolor, zorder=2)
else:
points = np.array([rays['th'], rays['r']*1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lcol = LineCollection( segments )
lcol.set_cmap( nr_cmap )
lcol.set_norm( plt.Normalize(*nr_lim) )
lcol.set_array( rays['nr'] )
aax.add_collection( lcol )
# Add a colorbar when plotting refractive index
if showrefract:
from mpl_toolkits.axes_grid1 import SubplotDivider, LocatableAxes, Size
fig1 = ax.get_figure()
divider = SubplotDivider(fig1, *ax.get_geometry(), aspect=True)
# axes for colorbar
cbax = LocatableAxes(fig1, divider.get_position())
h = [Size.AxesX(ax), # main axes
Size.Fixed(0.1), # padding
Size.Fixed(0.2)] # colorbar
v = [Size.AxesY(ax)]
divider.set_horizontal(h)
divider.set_vertical(v)
ax.set_axes_locator(divider.new_locator(nx=0, ny=0))
cbax.set_axes_locator(divider.new_locator(nx=2, ny=0))
fig1.add_axes(cbax)
cbax.axis["left"].toggle(all=False)
cbax.axis["top"].toggle(all=False)
cbax.axis["bottom"].toggle(all=False)
cbax.axis["right"].toggle(ticklabels=True, label=True)
plt.colorbar(lcol, cax=cbax)
cbax.set_ylabel("refractive index")
return ax, aax, cbax
def plot_linestring_collection(ax,
geoms,
colors_or_values,
plot_values,
vmin=None,
vmax=None,
cmap=None,
linewidth=1.0,
**kwargs):
"""
Plots a collection of LineString and MultiLineString geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed)
colors_or_values : a sequence of `N` values or RGBA tuples
It should have 1:1 correspondence with the geometries (not their components).
plot_values : bool
If True, `colors_or_values` is interpreted as a list of values, and will
be mapped to colors using vmin/vmax/cmap (which become required).
Otherwise `colors_or_values` is interpreted as a list of colors.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
from matplotlib.collections import LineCollection
components, component_colors_or_values = _flatten_multi_geoms(
geoms, colors_or_values)
# LineCollection does not accept some kwargs.
if 'markersize' in kwargs:
del kwargs['markersize']
segments = [np.array(linestring)[:, :2] for linestring in components]
collection = LineCollection(segments, linewidth=linewidth, **kwargs)
if plot_values:
collection.set_array(np.array(component_colors_or_values))
collection.set_cmap(cmap)
collection.set_clim(vmin, vmax)
else:
# set_color magically sets the correct combination of facecolor and
# edgecolor, based on collection type.
collection.set_color(component_colors_or_values)
# If the user set facecolor and/or edgecolor explicitly, the previous
# call to set_color might have overridden it (remember, the 'color' may
# have come from plot_series, not from the user). The user should be
# able to override matplotlib's default behavior, by setting them again
# after set_color.
if 'facecolor' in kwargs:
collection.set_facecolor(kwargs['facecolor'])
if 'edgecolor' in kwargs:
collection.set_edgecolor(kwargs['edgecolor'])
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
def plot(self, time, beam=None, maxground=2000, maxalt=500,
iscat=True, gscat=True, title=False, weighted=False, cmap='hot_r',
fig=None, rect=111, ax=None, aax=None, zorder=4):
"""Plot scatter on ground/altitude profile
Parameters
----------
time : datetime.datetime
time of profile
beam : Optional[ ]
beam number
maxground : Optional[int]
maximum ground range [km]
maxalt : Optional[int]
highest altitude limit [km]
iscat : Optional[bool]
show ionospheric scatter
gscat : Optional[bool]
show ground scatter
title : Optional[bool]
Show default title
weighted : Optional[bool]
plot ionospheric scatter relative strength (based on background density and range)
cmap : Optional[str]
colormap used for weighted ionospheric scatter
fig : Optional[pylab.figure]
object (default to gcf)
rect : Optional[int]
subplot spcification
ax : Optional[ ]
Existing main axes
aax : Optional[ ]
Existing auxialary axes
zorder : Optional[int]
Returns
-------
ax : matplotlib.axes
object containing formatting
aax : matplotlib.axes
object containing data
cbax : matplotlib.axes
object containing colorbar
Example
-------
# Show ionospheric scatter
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, title=True)
rto.readScatter() # read scatter into memory
rto.scatter.plot(sTime, ax=ax, aax=aax)
ax.grid()
written by Sebastien, 2013-04
"""
from davitpy.utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.isc.keys() or time in self.gsc.keys()), logging.error('Unkown time %s' % time)
if beam:
assert (beam in self.isc[time].keys()), logging.error('Unkown beam %s' % beam)
else:
beam = self.isc[time].keys()[0]
if gscat and time in self.gsc.keys():
for ir, (el, rays) in enumerate( sorted(self.gsc[time][beam].items()) ):
if len(rays['r']) == 0: continue
_ = aax.scatter(rays['th'], ax.Re*np.ones(rays['th'].shape),
color='0', zorder=zorder)
if iscat and time in self.isc.keys():
if weighted:
wmin = np.min( [ r['w'].min() for r in self.isc[time][beam].values() if r['nstp'] > 0] )
wmax = np.max( [ r['w'].max() for r in self.isc[time][beam].values() if r['nstp'] > 0] )
for ir, (el, rays) in enumerate( sorted(self.isc[time][beam].items()) ):
if rays['nstp'] == 0: continue
t = rays['th']
r = rays['r']*1e-3
spts = np.array([t, r]).T.reshape(-1, 1, 2)
h = rays['h']*1e-3
rel = np.radians( rays['rel'] )
r = np.sqrt( r**2 + h**2 + 2*r*h*np.sin( rel ) )
t = t + np.arcsin( h/r * np.cos( rel ) )
epts = np.array([t, r]).T.reshape(-1, 1, 2)
segments = np.concatenate([spts, epts], axis=1)
lcol = LineCollection( segments, zorder=zorder )
if weighted:
_ = lcol.set_cmap( cmap )
_ = lcol.set_norm( plt.Normalize(0, 1) )
_ = lcol.set_array( ( rays['w'] - wmin ) / wmax )
else:
_ = lcol.set_color('0')
_ = aax.add_collection( lcol )
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, None)
ax.set_title( stitle )
# If weighted, plot ionospheric scatter with colormap
if weighted:
# Add a colorbar
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("Ionospheric Scatter")
else: cbax = None
ax.beam = beam
return ax, aax, cbax
def draw_networkx_edges(G,
pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
arrowstyle='thick',
label=None,
**kwds):
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
import matplotlib.patches as patches
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
from matplotlib.path import Path
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
# print "drawing_edges"
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = G.edges()
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = numpy.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# for e in edge_pos:
# print e
if not cb.iterable(width):
lw = (width, )
else:
lw = width
if not cb.is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if cb.is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
edge_collection = LineCollection(
edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
# print type(edge_collection)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
arrow_collection = None
if G.is_directed() and arrows:
# a directed graph hack-fix
# draws arrows at each
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos = []
p = .1 # make arrows 10% of total length
angle = 2.7 #angle for arrows
for src, dst in edge_pos:
x1, y1 = src
x2, y2 = dst
dx = x2 - x1 # x offset
dy = y2 - y1 # y offset
d = numpy.sqrt(float(dx**2 + dy**2)) # length of edge
theta = numpy.arctan2(dy, dx)
if d == 0: # source and target at same position
continue
if dx == 0: # vertical edge
xa = x2
ya = dy + y1
if dy == 0: # horizontal edge
ya = y2
xa = dx + x1
else:
# xa = p*d*numpy.cos(theta)+x1
# ya = p*d*numpy.sin(theta)+y1
#corrects the endpoints to better draw
x2 -= .04 * numpy.cos(theta)
y2 -= .04 * numpy.sin(theta)
lx1 = p * d * numpy.cos(theta + angle) + (x2)
lx2 = p * d * numpy.cos(theta - angle) + (x2)
ly1 = p * d * numpy.sin(theta + angle) + (y2)
ly2 = p * d * numpy.sin(theta - angle) + (y2)
a_pos.append(((lx1, ly1), (x2, y2)))
a_pos.append(((lx2, ly2), (x2, y2)))
arrow_collection = LineCollection(
a_pos,
colors=arrow_colors,
linewidths=[1 * ww for ww in lw],
antialiaseds=(1, ),
transOffset=ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
arrow_collection.set_label(label)
# print type(ax)
ax.add_collection(arrow_collection)
#drawing self loops
d = 1
c = 0.0707
selfedges = []
verts = [
(0.1 * d - 0.1 * d, 0.0), # P0
(c * d - 0.1 * d, c * d), # P0
(0.0 - 0.1 * d, 0.1 * d), # P0
(-c * d - 0.1 * d, c * d), # P0
(-0.1 * d - 0.1 * d, 0.0), # P0
(-c * d - 0.1 * d, -c * d), # P0
(0.0 - 0.1 * d, -0.1 * d), # P0
(c * d - 0.1 * d, -c * d), # P0
(0.1 * d - 0.1 * d, 0.0)
]
# print verts
codes = [
Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
]
for e in edge_pos:
if (numpy.array_equal(e[0], e[1])):
nodes = verts[:]
for i in range(len(nodes)):
nodes[i] += e[0]
# print nodes
path = Path(nodes, codes)
patch = patches.PathPatch(path,
color=None,
facecolor=None,
edgecolor=edge_colors[0],
fill=False,
lw=4)
ax.add_patch(patch)
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:, :, 0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:, :, 0]))
miny = numpy.amin(numpy.ravel(edge_pos[:, :, 1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
# print ax
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def 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,
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_linestring_collection(ax,
geoms,
values=None,
color=None,
cmap=None,
vmin=None,
vmax=None,
**kwargs):
"""
Plots a collection of LineString and MultiLineString geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be
mixed)
values : a sequence of `N` values, optional
Values will be mapped to colors using vmin/vmax/cmap. They should
have 1:1 correspondence with the geometries (not their components).
color : single color or sequence of `N` colors
Cannot be used together with `values`.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
from matplotlib.collections import LineCollection
geoms, multiindex = _flatten_multi_geoms(geoms, range(len(geoms)))
if values is not None:
values = np.take(values, multiindex)
# LineCollection does not accept some kwargs.
if "markersize" in kwargs:
del kwargs["markersize"]
# color=None gives black instead of default color cycle
if color is not None:
if pd.api.types.is_list_like(color):
kwargs["color"] = np.take(color, multiindex)
else:
kwargs["color"] = color
segments = [np.array(linestring)[:, :2] for linestring in geoms]
collection = LineCollection(segments, **kwargs)
if values is not None:
collection.set_array(np.asarray(values))
collection.set_cmap(cmap)
if "norm" not in kwargs:
collection.set_clim(vmin, vmax)
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
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,
layouter=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 cartopy/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". If bus_sizes is a pandas.Series
with a Multiindex, bus_colors defaults to the network.carriers['color']
column.
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10. 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. network.generators.groupby(['bus', 'carrier']).p_nom.sum()
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.
layouter : networkx.drawing.layout function, default None
Layouting function from `networkx <https://networkx.github.io/>`_ which
overrules coordinates given in ``network.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
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 = _get_coordinates(network, layouter=layouter)
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"
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 = network.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 network.carrier.color")
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 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 run(self):
if self.data is None:
self.parent.threadPlot = None
return
peakF = None
peakL = None
total = len(self.data)
if total > 0:
self.parent.clear_plots()
lc = None
if self.average:
avg = OrderedDict()
count = len(self.data)
for timeStamp in self.data:
if len(self.data[timeStamp]) < 2:
return
for x, y in self.data[timeStamp].items():
if x in avg:
avg[x] = (avg[x] + y) / 2
else:
avg[x] = y
data = avg.items()
peakF, peakL = max(data, key=lambda item: item[1])
segments, levels = self.create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
self.axes.add_collection(lc)
self.parent.lc = lc
else:
count = 0.0
for timeStamp in self.data:
if len(self.data[timeStamp]) < 2:
self.parent.threadPlot = None
return
if self.fade:
alpha = (total - count) / total
else:
alpha = 1
data = self.data[timeStamp].items()
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
if self.annotate:
self.annotate_plot(peakF, peakL)
if total > 0:
self.parent.scale_plot()
self.parent.redraw_plot()
self.parent.threadPlot = None
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
class SunPlotPy(wx.Frame, Spatial, Grid ):
"""
The main frame of the application
"""
title = 'sunplot(py)'
# Plotting options
autoclim=True
showedges=False
bgcolor='k'
textcolor='w'
cmap='RdBu'
particlesize = 1.8
particlecolor = 'm'
# other flags
collectiontype='cells'
oldcollectiontype='cells'
#
tindex=0
depthlevs = [0., 10., 100., 200., 300., 400., 500.,\
1000.,2000.,3000.,4000.,5000]
_FillValue=999999
def __init__(self):
wx.Frame.__init__(self, None, -1, self.title)
self.create_menu()
self.create_status_bar()
self.create_main_panel()
#self.draw_figure()
def create_menu(self):
self.menubar = wx.MenuBar()
###
# File Menu
###
menu_file = wx.Menu()
# Load a hydro output file
m_expt = menu_file.Append(-1, "&Open file\tCtrl-O", "Open netcdf file")
self.Bind(wx.EVT_MENU, self.on_open_file, m_expt)
# Load a grid file
m_grid = menu_file.Append(-1, "&Load grid\tCtrl-G", "Load SUNTANS grid from folder")
self.Bind(wx.EVT_MENU, self.on_load_grid, m_grid)
# Load a particle file
m_part = menu_file.Append(-1, "&Load PTM file\tCtrl-Shift-P", "Load a PTM file")
self.Bind(wx.EVT_MENU, self.on_load_ptm, m_part)
# Save current scene as an animation
m_anim = menu_file.Append(-1,"&Save animation of current scene\tCtrl-S","Save animation")
self.Bind(wx.EVT_MENU, self.on_save_anim, m_anim)
# Save the current figure
m_prin = menu_file.Append(-1,"&Print current scene\tCtrl-P","Save figure")
self.Bind(wx.EVT_MENU, self.on_save_fig, m_prin)
menu_file.AppendSeparator()
# Exit
m_exit = menu_file.Append(-1, "E&xit\tCtrl-X", "Exit")
self.Bind(wx.EVT_MENU, self.on_exit, m_exit)
###
# Tools menu
###
menu_tools = wx.Menu()
m_gridstat = menu_tools.Append(-1, "&Plot grid size statistics", "SUNTANS grid size")
self.Bind(wx.EVT_MENU, self.on_plot_gridstat, m_gridstat)
m_countcells = menu_tools.Append(-1, "&Count # grid cells", "Grid cell count")
self.Bind(wx.EVT_MENU, self.on_count_cells, m_countcells)
m_overlaybathy = menu_tools.Append(-1, "&Overlay depth contours", "Depth overlay")
self.Bind(wx.EVT_MENU, self.on_overlay_bathy, m_overlaybathy)
###
# Help Menu
###
menu_help = wx.Menu()
m_about = menu_help.Append(-1, "&About\tF1", "About the demo")
self.Bind(wx.EVT_MENU, self.on_about, m_about)
# Add all of the menu bars
self.menubar.Append(menu_file, "&File")
self.menubar.Append(menu_tools, "&Tools")
self.menubar.Append(menu_help, "&Help")
self.SetMenuBar(self.menubar)
def create_main_panel(self):
""" Creates the main panel with all the controls on it:
* mpl canvas
* mpl navigation toolbar
* Control panel for interaction
"""
self.panel = wx.Panel(self)
# Create the mpl Figure and FigCanvas objects.
# 5x4 inches, 100 dots-per-inch
#
self.dpi = 100
#self.fig = Figure((7.0, 6.0), dpi=self.dpi,facecolor=self.bgcolor)
self.fig = Figure((7.0, 6.0), dpi=self.dpi)
self.canvas = FigCanvas(self.panel, -1, self.fig)
# Since we have only one plot, we can use add_axes
# instead of add_subplot, but then the subplot
# configuration tool in the navigation toolbar wouldn't
# work.
#
self.axes = self.fig.add_subplot(111)
#SetAxColor(self.axes,self.textcolor,self.bgcolor)
# Bind the 'pick' event for clicking on one of the bars
#
#self.canvas.mpl_connect('pick_event', self.on_pick)
########
# Create widgets
########
self.variable_list = wx.ComboBox(
self.panel,
size=(200,-1),
choices=['Select a variable...'],
style=wx.CB_READONLY)
self.variable_list.Bind(wx.EVT_COMBOBOX, self.on_select_variable)
self.time_list = wx.ComboBox(
self.panel,
size=(200,-1),
choices=['Select a time step...'],
style=wx.CB_READONLY)
self.time_list.Bind(wx.EVT_COMBOBOX, self.on_select_time)
self.depthlayer_list = wx.ComboBox(
self.panel,
size=(200,-1),
choices=['Select a vertical layer...'],
style=wx.CB_READONLY)
self.depthlayer_list.Bind(wx.EVT_COMBOBOX, self.on_select_depth)
self.show_edge_check = wx.CheckBox(self.panel, -1,
"Show Edges",
style=wx.ALIGN_RIGHT)
self.show_edge_check.Bind(wx.EVT_CHECKBOX, self.on_show_edges)
if USECMOCEAN:
cmaps=[]
for cmap in cm.cmapnames:
cmaps.append(cmap)
cmaps.append(cmap+'_r') # Add all reverse map options
else:
# Use matplotlib standard
cmaps = matplotlib.cm.datad.keys()
cmaps.sort()
self.colormap_list = wx.ComboBox(
self.panel,
size=(100,-1),
choices=cmaps,
style=wx.CB_READONLY)
self.colormap_list.Bind(wx.EVT_COMBOBOX, self.on_select_cmap)
self.colormap_label = wx.StaticText(self.panel, -1,"Colormap:")
self.clim_check = wx.CheckBox(self.panel, -1,
"Manual color limits ",
style=wx.ALIGN_RIGHT)
self.clim_check.Bind(wx.EVT_CHECKBOX, self.on_clim_check)
self.climlow = wx.TextCtrl(
self.panel,
size=(100,-1),
style=wx.TE_PROCESS_ENTER)
self.climlow.Bind(wx.EVT_TEXT_ENTER, self.on_climlow)
self.climhigh = wx.TextCtrl(
self.panel,
size=(100,-1),
style=wx.TE_PROCESS_ENTER)
self.climhigh.Bind(wx.EVT_TEXT_ENTER, self.on_climhigh)
# Labels
self.variable_label = wx.StaticText(self.panel, -1,"Variable:",size=(200,-1))
self.time_label = wx.StaticText(self.panel, -1,"Time step:",size=(200,-1))
self.depth_label = wx.StaticText(self.panel, -1,"Vertical level:",size=(200,-1))
# Create the navigation toolbar, tied to the canvas
#
self.toolbar = NavigationToolbar(self.canvas)
#self.toolbar.toolitems[8][3]='my_save_fig'
#def my_save_fig(self,*args):
# print 'saving figure'
# return "break"
#########
# Layout with box sizers
#########
self.vbox = wx.BoxSizer(wx.VERTICAL)
self.vbox.Add(self.canvas, 1, wx.LEFT | wx.TOP | wx.GROW)
self.vbox.Add(self.toolbar, 0, wx.EXPAND)
self.vbox.AddSpacer(10)
#self.vbox.Add((-1,25))
flags = wx.ALIGN_LEFT | wx.ALL | wx.ALIGN_CENTER_VERTICAL
self.hbox0 = wx.BoxSizer(wx.HORIZONTAL)
self.hbox0.Add(self.show_edge_check, 0, border=10, flag=flags)
self.hbox0.Add(self.colormap_label, 0, border=10, flag=flags)
self.hbox0.Add(self.colormap_list, 0, border=10, flag=flags)
self.hbox0.Add(self.clim_check, 0, border=10, flag=flags)
self.hbox0.Add(self.climlow, 0, border=10, flag=flags)
self.hbox0.Add(self.climhigh, 0, border=10, flag=flags)
self.vbox.AddSpacer(5)
self.hbox1 = wx.BoxSizer(wx.HORIZONTAL)
self.hbox1.Add(self.variable_label, 0, border=10, flag=flags)
self.hbox1.Add(self.time_label, 0, border=10, flag=flags)
self.hbox1.Add(self.depth_label, 0, border=10, flag=flags)
self.vbox.AddSpacer(5)
self.hbox2 = wx.BoxSizer(wx.HORIZONTAL)
self.hbox2.Add(self.variable_list, 0, border=10, flag=flags)
self.hbox2.Add(self.time_list, 0, border=10, flag=flags)
self.hbox2.Add(self.depthlayer_list, 0, border=10, flag=flags)
self.vbox.Add(self.hbox1, 0, flag = wx.ALIGN_LEFT | wx.TOP)
self.vbox.Add(self.hbox2, 0, flag = wx.ALIGN_LEFT | wx.TOP)
self.vbox.Add(self.hbox0, 0, flag = wx.ALIGN_LEFT | wx.TOP)
self.panel.SetSizer(self.vbox)
self.vbox.Fit(self)
##########
# Event functions
##########
def create_figure(self):
"""
Creates the figure
"""
# Find the colorbar limits if unspecified
if self.autoclim:
self.clim = [self.data.min(),self.data.max()]
self.climlow.SetValue('%3.1f'%self.clim[0])
self.climhigh.SetValue('%3.1f'%self.clim[1])
if self.__dict__.has_key('collection'):
#self.collection.remove()
self.axes.collections.remove(self.collection)
else:
# First call - set the axes limits
self.axes.set_aspect('equal')
self.axes.set_xlim(self.xlims)
self.axes.set_ylim(self.ylims)
if self.collectiontype=='cells':
self.collection = PolyCollection(self.xy,cmap=self.cmap)
self.collection.set_array(np.array(self.data[:]))
if not self.showedges:
self.collection.set_edgecolors(self.collection.to_rgba(np.array((self.data[:]))))
elif self.collectiontype=='edges':
xylines = [self.xp[self.edges],self.yp[self.edges]]
linesc = [zip(xylines[0][ii,:],xylines[1][ii,:]) for ii in range(self.Ne)]
self.collection = LineCollection(linesc,array=np.array(self.data[:]),cmap=self.cmap)
self.collection.set_clim(vmin=self.clim[0],vmax=self.clim[1])
self.axes.add_collection(self.collection)
self.title=self.axes.set_title(self.genTitle(),color=self.textcolor)
self.axes.set_xlabel('Easting [m]')
self.axes.set_ylabel('Northing [m]')
# create a colorbar
if not self.__dict__.has_key('cbar'):
self.cbar = self.fig.colorbar(self.collection)
#SetAxColor(self.cbar.ax.axes,self.textcolor,self.bgcolor)
else:
#pass
print 'Updating colorbar...'
#self.cbar.check_update(self.collection)
self.cbar.on_mappable_changed(self.collection)
self.canvas.draw()
def update_figure(self):
if self.autoclim:
self.clim = [self.data.min(),self.data.max()]
self.climlow.SetValue('%3.1f'%self.clim[0])
self.climhigh.SetValue('%3.1f'%self.clim[1])
else:
self.clim = [float(self.climlow.GetValue()),\
float(self.climhigh.GetValue())]
# check whether it is cell or edge type
if self.hasDim(self.variable,self.griddims['Ne']):
self.collectiontype='edges'
elif self.hasDim(self.variable,self.griddims['Nc']):
self.collectiontype='cells'
# Create a new figure if the variable has gone from cell to edge of vice
# versa
if not self.collectiontype==self.oldcollectiontype:
self.create_figure()
self.oldcollectiontype=self.collectiontype
self.collection.set_array(np.array(self.data[:]))
self.collection.set_clim(vmin=self.clim[0],vmax=self.clim[1])
# Cells only
if self.collectiontype=='cells':
if not self.showedges:
self.collection.set_edgecolors(self.collection.to_rgba(np.array((self.data[:]))))
else:
self.collection.set_edgecolors('k')
self.collection.set_linewidths(0.2)
# Update the title
self.title=self.axes.set_title(self.genTitle(),color=self.textcolor)
#Update the colorbar
self.cbar.update_normal(self.collection)
# redraw the figure
self.canvas.draw()
def on_pick(self, event):
# The event received here is of the type
# matplotlib.backend_bases.PickEvent
#
# It carries lots of information, of which we're using
# only a small amount here.
#
box_points = event.artist.get_bbox().get_points()
msg = "You've clicked on a bar with coords:\n %s" % box_points
dlg = wx.MessageDialog(
self,
msg,
"Click!",
wx.OK | wx.ICON_INFORMATION)
dlg.ShowModal()
dlg.Destroy()
def on_select_variable(self, event):
vname = event.GetString()
self.flash_status_message("Selecting variable: %s"%vname)
# update the spatial object and load the data
self.variable = vname
self.loadData(variable=self.variable)
# Check if the variable has a depth coordinate
depthstr = ['']
# If so populate the vertical layer box
if self.hasDim(self.variable,self.griddims['Nk']):
depthstr = ['%3.1f'%self.z_r[k] for k in range(self.Nkmax)]
depthstr += ['surface','seabed']
elif self.hasDim(self.variable,'Nkw'):
depthstr = ['%3.1f'%self.z_w[k] for k in range(self.Nkmax+1)]
self.depthlayer_list.SetItems(depthstr)
# Update the plot
self.update_figure()
def on_select_time(self, event):
self.tindex = event.GetSelection()
# Update the object time index and reload the data
if self.plot_type=='hydro':
if not self.tstep==self.tindex:
self.tstep=self.tindex
self.loadData()
self.flash_status_message("Selecting variable: %s..."%event.GetString())
# Update the plot
self.update_figure()
elif self.plot_type=='particles':
self.PTM.plot(self.tindex,ax=self.axes,\
xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
self.canvas.draw()
def on_select_depth(self, event):
kindex = event.GetSelection()
if not self.klayer[0]==kindex:
# Check if its the seabed or surface value
if kindex>=self.Nkmax:
kindex=event.GetString()
self.klayer = [kindex]
self.loadData()
self.flash_status_message("Selecting depth: %s..."%event.GetString())
# Update the plot
self.update_figure()
def on_open_file(self, event):
file_choices = "SUNTANS NetCDF (*.nc)|*.nc*|UnTRIM NetCDF (*.nc)|*.nc*|All Files (*.*)|*.*"
dlg = wx.FileDialog(
self,
message="Open SUNTANS file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_MULTIPLE)
if dlg.ShowModal() == wx.ID_OK:
self.plot_type='hydro'
path = dlg.GetPaths()
# Initialise the class
if dlg.GetFilterIndex() == 0 or dlg.GetFilterIndex() > 1: #SUNTANS
self.flash_status_message("Opening SUNTANS file: %s" % path)
try:
Spatial.__init__(self, path, _FillValue=self._FillValue)
except:
Spatial.__init__(self, path, _FillValue=-999999)
startvar='dv'
if dlg.GetFilterIndex()==1: #UnTRIM
self.flash_status_message("Opening UnTRIMS file: %s" % path)
#Spatial.__init__(self,path,gridvars=untrim_gridvars,griddims=untrim_griddims)
UNTRIMSpatial.__init__(self,path)
startvar='Mesh2_face_depth'
# Populate the drop down menus
vnames = self.listCoordVars()
self.variable_list.SetItems(vnames)
# Update the time drop down list
if self.__dict__.has_key('time'):
self.timestr = [datetime.strftime(tt,'%d-%b-%Y %H:%M:%S') for tt in self.time]
else:
# Assume that it is a harmonic-type file
self.timestr = self.nc.Constituent_Names.split()
self.time_list.SetItems(self.timestr)
# Draw the depth
if startvar in vnames:
self.variable=startvar
self.loadData()
self.create_figure()
def on_load_grid(self, event):
dlg = wx.DirDialog(
self,
message="Open SUNTANS grid from folder...",
defaultPath=os.getcwd(),
style= wx.DD_DEFAULT_STYLE)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
# Initialise the class
self.flash_status_message("Opening SUNTANS grid from folder: %s" % path)
Grid.__init__(self,path)
# Plot the Grid
if self.__dict__.has_key('collection'):
self.axes.collections.remove(self.collection)
self.axes,self.collection = self.plotmesh(ax=self.axes,edgecolors='y')
# redraw the figure
self.canvas.draw()
def on_load_ptm(self, event):
file_choices = "PTM NetCDF (*.nc)|*.nc|PTM Binary (*_bin.out)|*_bin.out|All Files (*.*)|*.*"
dlg = wx.FileDialog(
self,
message="Open PTM file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_MULTIPLE)
if dlg.ShowModal() == wx.ID_OK:
self.plot_type = 'particles'
path = dlg.GetPath()
# Initialise the class
if dlg.GetFilterIndex() == 0: #SUNTANS
self.flash_status_message("Opening PTM netcdf file: %s" % path)
self.PTM = PtmNC(path)
elif dlg.GetFilterIndex() == 1: #PTM
self.flash_status_message("Opening PTM binary file: %s" % path)
self.PTM = PtmBin(path)
self.Nt = self.PTM.nt
# Update the time drop down list
self.timestr = [datetime.strftime(tt,'%d-%b-%Y %H:%M:%S') for tt in self.PTM.time]
self.time_list.SetItems(self.timestr)
# Plot the first time step
if self.__dict__.has_key('xlims'):
self.PTM.plot(self.PTM.nt-1,ax=self.axes,xlims=self.xlims,\
ylims=self.ylims,color=self.particlecolor,\
fontcolor='w',markersize=self.particlesize)
else:
self.PTM.plot(self.PTM.nt-1,ax=self.axes,fontcolor='w',\
color=self.particlecolor,markersize=self.particlesize)
# redraw the figure
self.canvas.draw()
def on_show_edges(self,event):
sender=event.GetEventObject()
self.showedges = sender.GetValue()
# Update the figure
self.update_figure()
def on_clim_check(self,event):
sender=event.GetEventObject()
if sender.GetValue() == True:
self.autoclim=False
self.update_figure()
else:
self.autoclim=True
def on_climlow(self,event):
self.clim[0] = event.GetString()
#self.update_figure()
def on_climhigh(self,event):
self.clim[1] = event.GetString()
#self.update_figure()
def on_select_cmap(self,event):
self.cmap=event.GetString()
if USECMOCEAN:
self.collection.set_cmap(getattr(cm,self.cmap))
else:
self.collection.set_cmap(self.cmap)
# Update the figure
self.update_figure()
def on_save_fig(self,event):
"""
Save a figure of the current scene to a file
"""
file_choices = " (*.png)|*.png| (*.pdf)|*.pdf |(*.jpg)|*.jpg |(*.eps)|*eps "
filters=['.png','.pdf','.png','.png']
dlg = wx.FileDialog(
self,
message="Save figure to file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
ext = filters[dlg.GetFilterIndex()]
if ext in path:
outfile=path
else:
outfile = path+ext
self.fig.savefig(outfile)
def on_save_anim(self,event):
"""
Save an animation of the current scene to a file
"""
file_choices = "Quicktime (*.mov)|*.mov| (*.gif)|*.gif| (*.avi)|*.avi |(*.mp4)|*.mp4 "
filters=['.mov','.gif','.avi','.mp4']
dlg = wx.FileDialog(
self,
message="Output animation file...",
defaultDir=os.getcwd(),
defaultFile="",
wildcard=file_choices,
style= wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)
if dlg.ShowModal() == wx.ID_OK:
path = dlg.GetPath()
ext = filters[dlg.GetFilterIndex()]
if ext in path:
outfile=path
else:
outfile = path+ext
self.flash_status_message("Saving figure to file: %s" %outfile)
self.flash_status_message("Saving animation to file: %s" %outfile)
# Create the animation
#self.tstep = range(self.Nt) # Use all time steps for animation
#self.animate(cbar=self.cbar,cmap=self.cmap,\
# xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
def initanim():
if not self.plot_type=='particles':
return (self.title, self.collection)
else:
return (self.PTM.title,self.PTM.p_handle)
def updateScalar(i):
if not self.plot_type=='particles':
self.tstep=[i]
self.loadData()
self.update_figure()
return (self.title,self.collection)
elif self.plot_type=='particles':
self.PTM.plot(i,ax=self.axes,\
xlims=self.axes.get_xlim(),ylims=self.axes.get_ylim())
return (self.PTM.title,self.PTM.p_handle)
self.anim = animation.FuncAnimation(self.fig, \
updateScalar, init_func = initanim, frames=self.Nt, interval=50, blit=True)
if ext=='.gif':
self.anim.save(outfile,writer='imagemagick',fps=6)
elif ext=='.mp4':
print 'Saving html5 video...'
# Ensures html5 compatibility
self.anim.save(outfile,writer='mencoder',fps=6,\
bitrate=3600,extra_args=['-ovc','x264']) # mencoder options
#bitrate=3600,extra_args=['-vcodec','libx264'])
else:
self.anim.save(outfile,writer='mencoder',fps=6,bitrate=3600)
# Return the figure back to its status
del self.anim
self.tstep=self.tindex
if not self.plot_type=='particles':
self.loadData()
self.update_figure()
# Bring up a dialog box
dlg2= wx.MessageDialog(self, 'Animation complete.', "Done", wx.OK)
dlg2.ShowModal()
dlg2.Destroy()
def on_exit(self, event):
self.Destroy()
def on_about(self, event):
msg = """ SUNTANS NetCDF visualization tool
*Author: Matt Rayson
*Institution: Stanford University
*Created: October 2013
"""
dlg = wx.MessageDialog(self, msg, "About", wx.OK)
dlg.ShowModal()
dlg.Destroy()
def on_count_cells(self,eveny):
msg = "Total 3-D grid cells = %d"%(self.count_cells())
dlg = wx.MessageDialog(self, msg, "No. cells", wx.OK)
dlg.ShowModal()
dlg.Destroy()
def on_overlay_bathy(self,event):
# Plot depth contours
print 'Plotting contours...'
self.contourf(z=self.dv, clevs=self.depthlevs,\
ax=self.axes,\
filled=False, colors='0.5', linewidths=0.5, zorder=1e6)
print 'Done'
def on_plot_gridstat(self, event):
"""
Plot the grid size histogram in a new figure
"""
matplotlib.pyplot.figure()
self.plothist()
matplotlib.pyplot.show()
def create_status_bar(self):
self.statusbar = self.CreateStatusBar()
def flash_status_message(self, msg, flash_len_ms=1500):
self.statusbar.SetStatusText(msg)
self.timeroff = wx.Timer(self)
self.Bind(
wx.EVT_TIMER,
self.on_flash_status_off,
self.timeroff)
self.timeroff.Start(flash_len_ms, oneShot=True)
def on_flash_status_off(self, event):
self.statusbar.SetStatusText('')
data = m.cmech_quantities(['El','hmu'], maxL)
lines1.append(zip(range(maxL+1),data[0]))
#data[1].mask[0] = False
#er = m.entropy_rate()
#data[1] -= er
#lines2.append(zip(range(maxL+1),data[1,:]))
fig = plt.figure()
#fig.set_figheight(5)
#fig.set_figwidth(10)
#fig.subplots_adjust(left = 0.09, right = 0.86)
ax1 = fig.add_subplot(1,1,1)
lc1 = LineCollection(lines1)
lc1.set_array(ps)
lc1.set_cmap(cmap)
ax1.add_collection(lc1)
ax1.set_xlabel(r"$L$ [symbols]")
ax1.set_ylabel(r"$\mathbf{E}(L)$ [bits]")
ax1.axis('auto')
ax1.set_xlim((0,maxL))
#ax2 = fig.add_subplot(1,2,2)
#lc2 = LineCollection(lines2)
#lc2.set_array(ps)
#lc2.set_cmap(cmap)
#ax2.add_collection(lc2)
def plot_linestring_collection(ax, geoms, colors_or_values, plot_values,
vmin=None, vmax=None, cmap=None,
linewidth=1.0, **kwargs):
"""
Plots a collection of LineString and MultiLineString geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed)
colors_or_values : a sequence of `N` values or RGBA tuples
It should have 1:1 correspondence with the geometries (not their components).
plot_values : bool
If True, `colors_or_values` is interpreted as a list of values, and will
be mapped to colors using vmin/vmax/cmap (which become required).
Otherwise `colors_or_values` is interpreted as a list of colors.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
from matplotlib.collections import LineCollection
components, component_colors_or_values = _flatten_multi_geoms(
geoms, colors_or_values)
# LineCollection does not accept some kwargs.
if 'markersize' in kwargs:
del kwargs['markersize']
segments = [np.array(linestring)[:, :2] for linestring in components]
collection = LineCollection(segments,
linewidth=linewidth, **kwargs)
if plot_values:
collection.set_array(np.array(component_colors_or_values))
collection.set_cmap(cmap)
collection.set_clim(vmin, vmax)
else:
# set_color magically sets the correct combination of facecolor and
# edgecolor, based on collection type.
collection.set_color(component_colors_or_values)
# If the user set facecolor and/or edgecolor explicitly, the previous
# call to set_color might have overridden it (remember, the 'color' may
# have come from plot_series, not from the user). The user should be
# able to override matplotlib's default behavior, by setting them again
# after set_color.
if 'facecolor' in kwargs:
collection.set_facecolor(kwargs['facecolor'])
if 'edgecolor' in kwargs:
collection.set_edgecolor(kwargs['edgecolor'])
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
