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Commita801e21

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legend-for-scatter
1 parent0984694 commita801e21

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Legend for scatter
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------------------
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A new method for creating legends for scatter plots has been introduced.
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Previously, in order to obtain a legend for a:meth:`~Axes.scatter` plot, one
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could either plot several scatters, each with an individual label, or create
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proxy artists to show in the legend manually.
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Now,:class:`~.collections.PathCollection` provides a method
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:meth:`~.collections.PathCollection.legend_items` to obtain the handles and labels
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for a scatter plot in an automated way. This makes creating a legend for a
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scatter plot as easy as::
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scatter = plt.scatter([1,2,3], [4,5,6], c=[7,2,3])
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plt.legend(*scatter.legend_items())
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An example can be found in
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:ref:`automatedlegendcreation`.

‎examples/lines_bars_and_markers/scatter_with_legend.py

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Scatter plots with a legend
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===========================
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Also demonstrates how transparency of the markers
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can be adjusted by giving ``alpha`` a value between
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0 and 1.
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To create a scatter plot with a legend one may use a loop and create one
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`~.Axes.scatter` plot per item to appear in the legend and set the ``label``
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accordingly.
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The following also demonstrates how transparency of the markers
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can be adjusted by giving ``alpha`` a value between 0 and 1.
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"""
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importnumpyasnp
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np.random.seed(19680801)
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importmatplotlib.pyplotasplt
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fromnumpy.randomimportrand
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fig,ax=plt.subplots()
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forcolorin ['red','green','blue']:
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n=750
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x,y=rand(2,n)
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scale=200.0*rand(n)
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x,y=np.random.rand(2,n)
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scale=200.0*np.random.rand(n)
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ax.scatter(x,y,c=color,s=scale,label=color,
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alpha=0.3,edgecolors='none')
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ax.legend()
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ax.grid(True)
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plt.show()
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##############################################################################
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# .. _automatedlegendcreation:
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#
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# Automated legend creation
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# -------------------------
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#
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# Another option for creating a legend for a scatter is to use the
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# :class:`~matplotlib.collections.PathCollection`'s
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# :meth:`~.PathCollection.legend_items` method.
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# It will automatically try to determine a useful number of legend entries
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# to be shown and return a tuple of handles and labels. Those can be passed
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# to the call to :meth:`~.axes.Axes.legend`.
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N=45
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x,y=np.random.rand(2,N)
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c=np.random.randint(1,5,size=N)
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s=np.random.randint(10,220,size=N)
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fig,ax=plt.subplots()
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scatter=ax.scatter(x,y,c=c,s=s)
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# produce a legend with the unique colors from the scatter
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legend1=ax.legend(*scatter.legend_items(),loc=3,title="Classes")
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ax.add_artist(legend1)
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# produce a legend with a cross section of sizes from the scatter
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handles,labels=scatter.legend_items(mode="sizes",alpha=0.6)
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legend2=ax.legend(handles,labels,loc=1,title="Sizes")
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The usage of the following functions and methods is shown in this example:
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importmatplotlib
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matplotlib.axes.Axes.scatter
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matplotlib.pyplot.scatter
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matplotlib.axes.Axes.legend
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matplotlib.pyplot.legend
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matplotlib.collections.PathCollection.legend_items

‎lib/matplotlib/collections.py

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Original file line numberDiff line numberDiff line change
@@ -877,6 +877,7 @@ def draw(self, renderer):
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classPathCollection(_CollectionWithSizes):
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"""
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This is the most basic :class:`Collection` subclass.
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A :class:`PathCollection` is e.g. created by a :meth:`~.Axes.scatter` plot.
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"""
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@docstring.dedent_interpd
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def__init__(self,paths,sizes=None,**kwargs):
@@ -899,6 +900,118 @@ def set_paths(self, paths):
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defget_paths(self):
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returnself._paths
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deflegend_items(self,mode="colors",useall="auto",num=10,
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fmt=None,func=lambdax:x,**kwargs):
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"""
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Creates legend handles and labels for a PathCollection. This is useful
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for obtaining a legend for a :meth:`~.Axes.scatter` plot. E.g.::
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scatter = plt.scatter([1,2,3], [4,5,6], c=[7,2,3])
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plt.legend(*scatter.legend_items())
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Also see the :ref:`automatedlegendcreation` example.
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Parameters
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----------
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mode : string, optional, default *"colors"*
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Can be *"colors"* or *"sizes"*. In case of *"colors"*, the legend
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handles will show the different colors of the collection. In case
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of "sizes", the legend will show the different sizes.
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useall : bool or string "auto", optional (default "auto")
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If True, use all unique elements of the mappable array. If False,
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target to use *num* elements in the normed range. If *"auto"*, try
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to determine which option better suits the nature of the data.
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num : int or None, optional (default 10)
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Target number of elements to create in case of *useall=False*.
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The number of created elements may slightly deviate from that due
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to a `~.ticker.Locator` being used to find useful locations.
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fmt : string, `~matplotlib.ticker.Formatter`, or None (default)
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The format or formatter to use for the labels. If a string must be
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a valid input for a `~.StrMethodFormatter`. If None (the default),
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use a `~.ScalarFormatter`.
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func : function, default *lambda x: x*
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Function to calculate the shown labels. This converts the initial
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values for color or size and needs to take a numpy array as input.
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Note that if e.g. the :meth:`~.Axes.scatter`'s *s* parameter has
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been calculated from some values *x* via a function
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*f* as *s = f(x)*, you need to supply the inverse of that function
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*f_inv* here, *func = f_inv*.
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kwargs : further parameters
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Allowed kwargs are *color* and *size*. E.g. it may be useful to
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set the color of the markers if *mode="sizes"* is used; similarly
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to set the size of the markers if *mode="colors"* is used.
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Any further parameters are passed onto the `.Line2D` instance.
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This may be useful to e.g. specify a different *markeredgecolor* or
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*alpha* for the legend handles.
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Returns
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-------
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tuple (handles, labels)
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with *handles* being a list of `.Line2D` objects
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and *labels* a list of strings of the same length.
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"""
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handles= []
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labels= []
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hasarray=self.get_array()isnotNone
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iffmtisNone:
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fmt=mpl.ticker.ScalarFormatter(useOffset=False,useMathText=True)
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eliftype(fmt)==str:
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fmt=mpl.ticker.StrMethodFormatter(fmt)
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fmt.create_dummy_axis()
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ifmode=="colors"andhasarray:
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u=np.unique(self.get_array())
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size=kwargs.pop("size",mpl.rcParams["lines.markersize"])
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elifmode=="sizes":
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u=np.unique(self.get_sizes())
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color=kwargs.pop("color","k")
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else:
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warnings.warn("Invalid mode provided, or collections without "
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"array used.")
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fmt.set_bounds(func(u).min(),func(u).max())
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ifuseall=="auto":
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useall=False
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iflen(u)<=num:
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useall=True
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ifuseall:
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values=u
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label_values=func(values)
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else:
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ifmode=="colors"andhasarray:
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arr=self.get_array()
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elifmode=="sizes":
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arr=self.get_sizes()
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loc=mpl.ticker.MaxNLocator(nbins=num,min_n_ticks=num-1,
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steps=[1,2,2.5,3,5,6,8,10])
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label_values=loc.tick_values(func(arr).min(),func(arr).max())
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cond= (label_values>=func(arr).min())& \
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(label_values<=func(arr).max())
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label_values=label_values[cond]
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xarr=np.linspace(arr.min(),arr.max(),256)
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values=np.interp(label_values,func(xarr),xarr)
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kw=dict(markeredgewidth=self.get_linewidths()[0],
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alpha=self.get_alpha())
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kw.update(kwargs)
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forval,labinzip(values,label_values):
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ifmode=="colors"andhasarray:
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color=self.cmap(self.norm(val))
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elifmode=="sizes":
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size=np.sqrt(val)
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ifnp.isclose(size,0.0):
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continue
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h=mlines.Line2D([0], [0],ls="",color=color,ms=size,
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marker=self.get_paths()[0],**kw)
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handles.append(h)
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ifhasattr(fmt,"set_locs"):
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fmt.set_locs(label_values)
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l=fmt(lab)
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labels.append(l)
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returnhandles,labels
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classPolyCollection(_CollectionWithSizes):
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@docstring.dedent_interpd

‎lib/matplotlib/tests/test_collections.py

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@@ -669,3 +669,45 @@ def test_scatter_post_alpha():
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# this needs to be here to update internal state
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fig.canvas.draw()
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sc.set_alpha(.1)
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deftest_pathcollection_legend_items():
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np.random.seed(19680801)
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x,y=np.random.rand(2,10)
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y=np.random.rand(10)
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c=np.random.randint(0,5,size=10)
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s=np.random.randint(10,300,size=10)
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fig,ax=plt.subplots()
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sc=ax.scatter(x,y,c=c,s=s,cmap="jet",marker="o",linewidths=0)
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h,l=sc.legend_items(fmt="{x:g}")
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assertlen(h)==5
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assert_array_equal(np.array(l).astype(float),np.arange(5))
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colors=np.array([line.get_color()forlineinh])
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colors2=sc.cmap(np.arange(5)/4)
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assert_array_equal(colors,colors2)
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l1=ax.legend(h,l,loc=1)
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h,l=sc.legend_items(useall=False)
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assertlen(h)==9
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l2=ax.legend(h,l,loc=2)
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h,l=sc.legend_items(mode="sizes",useall=False,alpha=0.5,color="red")
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alpha=np.array([line.get_alpha()forlineinh])
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assert_array_equal(alpha,0.5)
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color=np.array([line.get_markerfacecolor()forlineinh])
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assert_array_equal(color,"red")
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l3=ax.legend(h,l,loc=4)
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h,l=sc.legend_items(mode="sizes",useall=False,num=4,fmt="{x:.2f}",
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func=lambdax:2*x)
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actsizes= [line.get_markersize()forlineinh]
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labeledsizes=np.sqrt(np.array(l).astype(float)/2)
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assert_array_almost_equal(actsizes,labeledsizes)
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l4=ax.legend(h,l,loc=3)
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forlin [l1,l2,l3,l4]:
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ax.add_artist(l)
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fig.canvas.draw()

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