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