![]() Number of rows and columns is the same for each call. Limitations # Incompatible functions #Ĭonstrained_layout will work with pyplot.subplot, but only if the This prevents theĪxes from changing position during zooming and panning. GUI events for the backends that use the toolbar. Note that constrained_layout is turned off for ZOOM and PAN This is potentially useful for animations where the tick labels may If you want to get the spacing provided byĬonstrained_layout but not have it update, then do the initialĭraw and then call fig.set_layout_engine(None). suptitle ( "fixed-aspect plots, layout='compressed'" ) Manually turning off constrained_layout #Ĭonstrained_layout usually adjusts the axes positions on each draw subplots ( 2, 2, figsize = ( 5, 3 ), sharex = True, sharey = True, layout = 'compressed' ) for ax in axs. Line, = ot(,, 'b') # Add some dataĪxes.legend((line,), ('Test',), loc='center') # Create legend on bottommost axisĪxes.Fig, axs = plt. Gspec = gridspec.GridSpec(ncols=4, nrows=rows)Īxes.append(fig.add_subplot(gspec)) Rows = 17 # The larger the number here, the smaller the spacing around the legend The 'faux' axis is then turned off so only the legend shows. A 'faux' axis is created at the bottom to place the legend in a fixed spot. In the example below, the aim is to have four subplots arranged in a 2x2 fashion with the legend shown at the bottom. Using Matplotlib 2.2.2, this can be achieved using the gridspec feature. Plt.subplots_adjust(right=0.85) # Adjust the subplot to the right for the legend Magenta_patch = mpatches.Patch(facecolor='m', edgecolor='#000000')Ĭyan_patch = mpatches.Patch(facecolor='c', edgecolor='#000000')įig.legend(handles =, labels=labels, Red_patch = mpatches.Patch(facecolor='r', edgecolor='#000000') # This will create a red bar with black borders, you can leave out edgecolor if you do not want the bordersīlack_patch = mpatches.Patch(facecolor='k', edgecolor='#000000') Say you have four bars with different colours as r, m, c, and k, you can set the legend as follows: import matplotlib.patches as mpatches If you are using subplots with bar charts, with a different colour for each bar, it may be faster to create the artefacts yourself using mpatches. I recognize that sum(list_of_lists, ) is a really inefficient method to flatten a list of lists, but ① I love its compactness, ② usually is a few curves in a few subplots and ③ Matplotlib and efficiency? -) Return fig.legend(handles, labels, **kwdargs) # Call fig.legend with the keyword arguments, return the legend object Handles, labels = (sum(list_of_lists, ) for list_of_lists in tolohs) # a possible solution is to sum the sublists - we use unpacking # Finally, we need to concatenate the individual lists in the two ![]() # generating two tuples of lists of homogeneous stuff(tolohs), i.e., # so our first step is to transpose our data, # The legend needs a list of handles and a list of labels, # E.g., a figure with two axes, ax0 with two curves, ax1 with one curve Tuples_lohand_lolbl = (ax.get_legend_handles_labels() for ax in fig.axes) # Generate a sequence of tuples, each contains I have encapsulated the two tricky lines in a function, just four lines of code, but heavily commented def fig_legend(fig, **kwdargs): it's really much much much easier from matplotlib.legend import _get_legend_handles_labelsįig.legend(*_get_legend_handles_and_labels(fig.axes). If you don't mind using a private method of the matplotlib.legend module. I tried the method proposed by the most up-voted and accepted answer, # fig.legend(lines, labels, loc='upper center', ncol=4)įig.legend(*a2.get_legend_handles_labels(), The two lines lines_labels = ĭeserve an explanation, see note 2 below. Perfect, otherwise see note no.1 below (there is a private If you want to stick with the official Matplotlib API, this is # Finally, the legend (that maybe you'll customize differently)įig.legend(lines, labels, loc='upper center', ncol=4) # So far, nothing special except the managed prop_cycle. ![]() # Note: we CALL the axes.prop_cycle to get an itertoools.cycleĬolor_cycle = plt.rcParams() # colored curves in different Axes, we need our own prop_cycle # each Axes has a brand new prop_cycle, so to have differently Now, if I've teased you enough, here it is the code from numpy import linspace I have noticed that none of the other answers displays an image with a single legend referencing many curves in different subplots, so I have to show you one. Lines, labels = ) for lol in zip(*lines_labels)] ![]()
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