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Commitaf5bb74

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legend-for-scatter
1 parent0984694 commitaf5bb74

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4 files changed

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4 files changed

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:orphan:
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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.Axes.scatter`
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plot, one could either plot several scatters, each with an individual label,
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or create 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_elements` 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_elements())
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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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Original file line numberDiff line numberDiff line change
@@ -3,24 +3,116 @@
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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_elements` 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_elements(),
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loc="lower left",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_elements(prop="sizes",alpha=0.6)
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legend2=ax.legend(handles,labels,loc="upper right",title="Sizes")
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plt.show()
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##############################################################################
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# Further arguments to the :meth:`~.PathCollection.legend_elements` method
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# can be used to steer how many legend entries are to be created and how they
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# should be labeled. The following shows how to use some of them.
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#
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volume=np.random.rayleigh(27,size=40)
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amount=np.random.poisson(10,size=40)
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ranking=np.random.normal(size=40)
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price=np.random.uniform(1,10,size=40)
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fig,ax=plt.subplots()
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# Because the price is much too small when being provided as size for ``s``,
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# we normalize it to some useful point sizes, s=0.3*(price*3)**2
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scatter=ax.scatter(volume,amount,c=ranking,s=0.3*(price*3)**2,
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vmin=-3,vmax=3,cmap="Spectral")
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# Produce a legend for the ranking (colors). Even though there are 40 different
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# rankings, we only want to show 5 of them in the legend.
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legend1=ax.legend(*scatter.legend_elements(num=5),
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loc="upper left",title="Ranking")
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ax.add_artist(legend1)
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# Produce a legend for the price (sizes). Because we want to show the prices
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# in dollars, we use the *func* argument to supply the inverse of the function
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# used to calculate the sizes from above. The *fmt* ensures to show the price
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# in dollars. Note how we target at 5 elements here, but obtain only 4 in the
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# created legend due to the automatic round prices that are chosen for us.
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kw=dict(prop="sizes",num=5,color=scatter.cmap(0.7),fmt="\$ {x:.2f}",
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func=lambdas:np.sqrt(s/.3)/3)
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legend2=ax.legend(*scatter.legend_elements(**kw),
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loc="lower right",title="Price")
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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_elements

‎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.
880881
"""
881882
@docstring.dedent_interpd
882883
def__init__(self,paths,sizes=None,**kwargs):
@@ -899,6 +900,125 @@ def set_paths(self, paths):
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defget_paths(self):
900901
returnself._paths
901902

903+
deflegend_elements(self,prop="colors",num="auto",
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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_elements())
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Also see the :ref:`automatedlegendcreation` example.
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Parameters
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----------
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prop : 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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num : int, None, "auto" (default), or `~.ticker.Locator`, optional
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Target number of elements to create.
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If None, use all unique elements of the mappable array. If an
923+
integer, target to use *num* elements in the normed range.
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If *"auto"*, try to determine which option better suits the nature
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of the data.
926+
The number of created elements may slightly deviate from *num* due
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to a `~.ticker.Locator` being used to find useful locations.
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Finally, a `~.ticker.Locator` can be provided.
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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 labels. Often the size (or color)
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argument to :meth:`~.Axes.scatter` will have been pre-processed
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by the user using a function *s = f(x)* to make the markers
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visible; e.g. *size = np.log10(x)*. Providing the inverse of this
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function here allows that pre-processing to be inverted, so that
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the legend labels have the correct values;
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e.g. *func = np.exp(x, 10)*.
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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 *prop="sizes"* is used; similarly
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to set the size of the markers if *prop="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)
952+
with *handles* being a list of `.Line2D` objects
953+
and *labels* a matching list of strings.
954+
"""
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handles= []
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labels= []
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hasarray=self.get_array()isnotNone
958+
iffmtisNone:
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fmt=mpl.ticker.ScalarFormatter(useOffset=False,useMathText=True)
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elifisinstance(fmt,str):
961+
fmt=mpl.ticker.StrMethodFormatter(fmt)
962+
fmt.create_dummy_axis()
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964+
ifprop=="colors"andhasarray:
965+
u=np.unique(self.get_array())
966+
size=kwargs.pop("size",mpl.rcParams["lines.markersize"])
967+
elifprop=="sizes":
968+
u=np.unique(self.get_sizes())
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color=kwargs.pop("color","k")
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else:
971+
warnings.warn("Invalid prop provided, or collection without "
972+
"array used.")
973+
returnhandles,labels
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975+
fmt.set_bounds(func(u).min(),func(u).max())
976+
ifnum=="auto":
977+
num=9
978+
iflen(u)<=num:
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num=None
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ifnumisNone:
981+
values=u
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label_values=func(values)
983+
else:
984+
ifprop=="colors":
985+
arr=self.get_array()
986+
elifprop=="sizes":
987+
arr=self.get_sizes()
988+
ifisinstance(num,mpl.ticker.Locator):
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loc=num
990+
else:
991+
num=int(num)
992+
loc=mpl.ticker.MaxNLocator(nbins=num,min_n_ticks=num-1,
993+
steps=[1,2,2.5,3,5,6,8,10])
994+
label_values=loc.tick_values(func(arr).min(),func(arr).max())
995+
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],
1002+
alpha=self.get_alpha())
1003+
kw.update(kwargs)
1004+
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forval,labinzip(values,label_values):
1006+
ifprop=="colors":
1007+
color=self.cmap(self.norm(val))
1008+
elifprop=="sizes":
1009+
size=np.sqrt(val)
1010+
ifnp.isclose(size,0.0):
1011+
continue
1012+
h=mlines.Line2D([0], [0],ls="",color=color,ms=size,
1013+
marker=self.get_paths()[0],**kw)
1014+
handles.append(h)
1015+
ifhasattr(fmt,"set_locs"):
1016+
fmt.set_locs(label_values)
1017+
l=fmt(lab)
1018+
labels.append(l)
1019+
1020+
returnhandles,labels
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9021022

9031023
classPolyCollection(_CollectionWithSizes):
9041024
@docstring.dedent_interpd

‎lib/matplotlib/tests/test_collections.py

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Original file line numberDiff line numberDiff line change
@@ -669,3 +669,51 @@ def test_scatter_post_alpha():
669669
# this needs to be here to update internal state
670670
fig.canvas.draw()
671671
sc.set_alpha(.1)
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deftest_pathcollection_legend_elements():
675+
np.random.seed(19680801)
676+
x,y=np.random.rand(2,10)
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y=np.random.rand(10)
678+
c=np.random.randint(0,5,size=10)
679+
s=np.random.randint(10,300,size=10)
680+
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fig,ax=plt.subplots()
682+
sc=ax.scatter(x,y,c=c,s=s,cmap="jet",marker="o",linewidths=0)
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h,l=sc.legend_elements(fmt="{x:g}")
685+
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])
688+
colors2=sc.cmap(np.arange(5)/4)
689+
assert_array_equal(colors,colors2)
690+
l1=ax.legend(h,l,loc=1)
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h2,lab2=sc.legend_elements(num=9)
693+
assertlen(h2)==9
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l2=ax.legend(h2,lab2,loc=2)
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h,l=sc.legend_elements(prop="sizes",alpha=0.5,color="red")
697+
alpha=np.array([line.get_alpha()forlineinh])
698+
assert_array_equal(alpha,0.5)
699+
color=np.array([line.get_markerfacecolor()forlineinh])
700+
assert_array_equal(color,"red")
701+
l3=ax.legend(h,l,loc=4)
702+
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h,l=sc.legend_elements(prop="sizes",num=4,fmt="{x:.2f}",
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func=lambdax:2*x)
705+
actsizes= [line.get_markersize()forlineinh]
706+
labeledsizes=np.sqrt(np.array(l).astype(float)/2)
707+
assert_array_almost_equal(actsizes,labeledsizes)
708+
l4=ax.legend(h,l,loc=3)
709+
710+
importmatplotlib.tickerasmticker
711+
loc=mticker.MaxNLocator(nbins=9,min_n_ticks=9-1,
712+
steps=[1,2,2.5,3,5,6,8,10])
713+
h5,lab5=sc.legend_elements(num=loc)
714+
assertlen(h2)==len(h5)
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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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