I would like to make a legend entry in a matplotlib look something like this:
It has multiple colors for a given legend item. Code is shown below which outputs a red rectangle. I'm wondering what I need to do to overlay one color ontop of another? Or is there a better solution?
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
red_patch = mpatches.Patch(color='red', label='Foo')
plt.legend(handles=[red_patch])
plt.show()
The solution I am proposing is to combine two different proxy-artists for one entry legend, as described here: Combine two Pyplot patches for legend.
The strategy is then to set the fillstyle of the first square marker to left while the other one is set to right (see http://matplotlib.org/1.3.0/examples/pylab_examples/filledmarker_demo.html). Two different colours can then be attributed to each marker in order to produce the desired two-colour legend entry.
The code below show how this can be done. Note that the numpoints=1 argument in plt.legend is important in order to display only one marker for each entry.
import matplotlib.pyplot as plt
plt.close('all')
#---- Generate a Figure ----
fig = plt.figure(figsize=(4, 4))
ax = fig.add_axes([0.15, 0.15, 0.75, 0.75])
ax.axis([0, 1, 0, 1])
#---- Define First Legend Entry ----
m1, = ax.plot([], [], c='red' , marker='s', markersize=20,
fillstyle='left', linestyle='none')
m2, = ax.plot([], [], c='blue' , marker='s', markersize=20,
fillstyle='right', linestyle='none')
#---- Define Second Legend Entry ----
m3, = ax.plot([], [], c='cyan' , marker='s', markersize=20,
fillstyle='left', linestyle='none')
m4, = ax.plot([], [], c='magenta' , marker='s', markersize=20,
fillstyle='right', linestyle='none')
#---- Plot Legend ----
ax.legend(((m2, m1), (m3, m4)), ('Foo', 'Foo2'), numpoints=1, labelspacing=2,
loc='center', fontsize=16)
plt.show(block=False)
Which results in:
Disclaimer: This will only work for a two-colors legend entry. If more than two colours is desired, I cannot think of any other way to do this other than the approach described by #jwinterm (Python Matplotlib Multi-color Legend Entry)
Perhaps another hack to handle more than two patches. Make sure you order the handles/labels according to the number of columns:
from matplotlib.patches import Patch
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
pa1 = Patch(facecolor='red', edgecolor='black')
pa2 = Patch(facecolor='blue', edgecolor='black')
pa3 = Patch(facecolor='green', edgecolor='black')
#
pb1 = Patch(facecolor='pink', edgecolor='black')
pb2 = Patch(facecolor='orange', edgecolor='black')
pb3 = Patch(facecolor='purple', edgecolor='black')
ax.legend(handles=[pa1, pb1, pa2, pb2, pa3, pb3],
labels=['', '', '', '', 'First', 'Second'],
ncol=3, handletextpad=0.5, handlelength=1.0, columnspacing=-0.5,
loc='center', fontsize=16)
plt.show()
which results in:
I absolutely loved #raphael's answer.
Here is a version with circles. Furthermore, I've refactored and trimmed the code a bit to make it more modular.
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
class MulticolorCircles:
"""
For different shapes, override the ``get_patch`` method, and add the new
class to the handler map, e.g. via
ax_r.legend(ax_r_handles, ax_r_labels, handlelength=CONF.LEGEND_ICON_SIZE,
borderpad=1.2, labelspacing=1.2,
handler_map={MulticolorCircles: MulticolorHandler})
"""
def __init__(self, face_colors, edge_colors=None, face_alpha=1,
radius_factor=1):
"""
"""
assert 0 <= face_alpha <= 1, f"Invalid face_alpha: {face_alpha}"
assert radius_factor > 0, "radius_factor must be positive"
self.rad_factor = radius_factor
self.fc = [mcolors.colorConverter.to_rgba(fc, alpha=face_alpha)
for fc in face_colors]
self.ec = edge_colors
if edge_colors is None:
self.ec = ["none" for _ in self.fc]
self.N = len(self.fc)
def get_patch(self, width, height, idx, fc, ec):
"""
"""
w_chunk = width / self.N
radius = min(w_chunk / 2, height) * self.rad_factor
xy = (w_chunk * idx + radius, radius)
patch = plt.Circle(xy, radius, facecolor=fc, edgecolor=ec)
return patch
def __call__(self, width, height):
"""
"""
patches = []
for i, (fc, ec) in enumerate(zip(self.fc, self.ec)):
patch = self.get_patch(width, height, i, fc, ec)
patches.append(patch)
result = PatchCollection(patches, match_original=True)
#
return result
class MulticolorHandler:
"""
"""
#staticmethod
def legend_artist(legend, orig_handle, fontsize, handlebox):
"""
"""
width, height = handlebox.width, handlebox.height
patch = orig_handle(width, height)
handlebox.add_artist(patch)
return patch
Sample usage and image, note that some of the legend handles have radius_factor=0.5 because the true size would be too small.
ax_handles, ax_labels = ax.get_legend_handles_labels()
ax_labels.append(AUDIOSET_LABEL)
ax_handles.append(MulticolorCircles([AUDIOSET_COLOR],
face_alpha=LEGEND_SHADOW_ALPHA))
ax_labels.append(FRAUNHOFER_LABEL)
ax_handles.append(MulticolorCircles([FRAUNHOFER_COLOR],
face_alpha=LEGEND_SHADOW_ALPHA))
ax_labels.append(TRAIN_SOURCE_NORMAL_LABEL)
ax_handles.append(MulticolorCircles(SHADOW_COLORS["source"],
face_alpha=LEGEND_SHADOW_ALPHA))
ax_labels.append(TRAIN_TARGET_NORMAL_LABEL)
ax_handles.append(MulticolorCircles(SHADOW_COLORS["target"],
face_alpha=LEGEND_SHADOW_ALPHA))
ax_labels.append(TEST_SOURCE_ANOMALY_LABEL)
ax_handles.append(MulticolorCircles(DOT_COLORS["anomaly_source"],
radius_factor=LEGEND_DOT_RATIO))
ax_labels.append(TEST_TARGET_ANOMALY_LABEL)
ax_handles.append(MulticolorCircles(DOT_COLORS["anomaly_target"],
radius_factor=LEGEND_DOT_RATIO))
#
ax.legend(ax_handles, ax_labels, handlelength=LEGEND_ICON_SIZE,
borderpad=1.1, labelspacing=1.1,
handler_map={MulticolorCircles: MulticolorHandler})
There is in fact a proper way to do this by implementing a custom
legend handler as explained in the matplotlib-doc under "implementing a custom legend handler" (here):
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
# define an object that will be used by the legend
class MulticolorPatch(object):
def __init__(self, colors):
self.colors = colors
# define a handler for the MulticolorPatch object
class MulticolorPatchHandler(object):
def legend_artist(self, legend, orig_handle, fontsize, handlebox):
width, height = handlebox.width, handlebox.height
patches = []
for i, c in enumerate(orig_handle.colors):
patches.append(plt.Rectangle([width/len(orig_handle.colors) * i - handlebox.xdescent,
-handlebox.ydescent],
width / len(orig_handle.colors),
height,
facecolor=c,
edgecolor='none'))
patch = PatchCollection(patches,match_original=True)
handlebox.add_artist(patch)
return patch
# ------ choose some colors
colors1 = ['g', 'b', 'c', 'm', 'y']
colors2 = ['k', 'r', 'k', 'r', 'k', 'r']
# ------ create a dummy-plot (just to show that it works)
f, ax = plt.subplots()
ax.plot([1,2,3,4,5], [1,4.5,2,5.5,3], c='g', lw=0.5, ls='--',
label='... just a line')
ax.scatter(range(len(colors1)), range(len(colors1)), c=colors1)
ax.scatter([range(len(colors2))], [.5]*len(colors2), c=colors2, s=50)
# ------ get the legend-entries that are already attached to the axis
h, l = ax.get_legend_handles_labels()
# ------ append the multicolor legend patches
h.append(MulticolorPatch(colors1))
l.append("a nice multicolor legend patch")
h.append(MulticolorPatch(colors2))
l.append("and another one")
# ------ create the legend
f.legend(h, l, loc='upper left',
handler_map={MulticolorPatch: MulticolorPatchHandler()},
bbox_to_anchor=(.125,.875))
Probably not exactly what you're looking for, but you can do it (very) manually by placing patches and text yourself on the plot. For instance:
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
red_patch = mpatches.Patch(color='red', label='Foo')
plt.legend(handles=[red_patch])
r1 = mpatches.Rectangle((0.1, 0.1), 0.18, 0.1, fill=False)
r2 = mpatches.Rectangle((0.12, 0.12), 0.03, 0.06, fill=True, color='red')
r3 = mpatches.Rectangle((0.15, 0.12), 0.03, 0.06, fill=True, color='blue')
ax.add_patch(r1)
ax.add_patch(r2)
ax.add_patch(r3)
ax.annotate('Foo', (0.2, 0.13), fontsize='x-large')
plt.show()
Related
I need to create a plot as close to this picture as possible (given the generated dataframe code below):
And here's the output plot of my code:
What I am having problems with is:
The edge of fill_between is not sharp as in the picture. What I have is some kind of white shadow. How do I change the line between the fillings to match a target picture?
How do I aling legend color lines to the center, but not to the left border which my code does?
Here's my code:
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
import numpy as np
import pandas as pd
ncols = 10
figsize = (20, 5)
fontsize = 14
dti = pd.date_range('2013-01-01', '2020-12-31', freq='2W')
probabilities_in_time = np.random.random((ncols, len(dti)))
probabilities_in_time = probabilities_in_time / \
probabilities_in_time.sum(axis=0)
probabilities_in_time = pd.DataFrame(probabilities_in_time).T
probabilities_in_time.index = dti
cm_subsection = np.linspace(0, 1, ncols)
colors = [cm.coolwarm(x) for x in cm_subsection]
def plot_time_probabilities(probabilities_in_time, figsize):
plt.figure(figsize=figsize)
plt.yticks(np.arange(0, 1.2, 0.2), fontsize=fontsize)
plt.xticks(fontsize=fontsize)
draw_stack_plot(colors, probabilities_in_time)
set_grid()
set_legend()
plt.show()
def draw_stack_plot(colors, probabilities_in_time):
for i, color in enumerate(colors):
if i == 0:
plt.plot(probabilities_in_time[i], color=color)
plt.fill_between(probabilities_in_time.index,
probabilities_in_time[0], color=color)
else:
probabilities_in_time[i] += probabilities_in_time[i-1]
plt.fill_between(probabilities_in_time.index,
probabilities_in_time[i], probabilities_in_time[i-1],
color=color)
plt.plot(probabilities_in_time[i], label=' Probability: {}'.format(
i), color=color)
def set_grid():
ax = plt.gca()
ax.set_axisbelow(False)
ax.xaxis.grid(True, linestyle='-', lw=1)
def set_legend():
leg = plt.legend(loc='lower left', fontsize=14, handlelength=1.3)
for i in leg.legendHandles:
i.set_linewidth(12)
plot_time_probabilities(probabilities_in_time, figsize)
To set the legend in the center, you can set loc='center', or you can put the legend outside. To avoid that the legend handles grow to larger, you can leave out .set_linewidth(12) (this sets a very wide edge width of 12 points).
Shifting the colors by one position can help to show the fill borders more pronounced. To still have a correct legend, the label should then be added to fill_between.
The code below also tries to simplify part of the calls:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import pandas as pd
ncols = 10
figsize = (20, 5)
fontsize = 14
dti = pd.date_range('2013-01-01', '2020-12-31', freq='2W')
probabilities_in_time = np.random.random((ncols, len(dti)))
probabilities_in_time = probabilities_in_time / probabilities_in_time.sum(axis=0)
probabilities_in_time = pd.DataFrame(probabilities_in_time).T
probabilities_in_time.index = dti
cm_subsection = np.linspace(0, 1, ncols)
colors = cm.coolwarm(cm_subsection)
def plot_time_probabilities(probabilities_in_time, figsize):
plt.figure(figsize=figsize)
plt.yticks(np.arange(0, 1.2, 0.2), fontsize=fontsize)
plt.xticks(fontsize=fontsize)
draw_stack_plot(colors, probabilities_in_time)
set_grid()
set_legend()
# plt.margins(x=0, y=0)
plt.margins(x=0.02)
plt.tight_layout()
plt.show()
def draw_stack_plot(colors, probabilities_in_time):
current_probabilities = 0
for i, color in enumerate(colors):
plt.fill_between(probabilities_in_time.index,
probabilities_in_time[i] + current_probabilities, current_probabilities,
color=color, label=f' Probability: {i}')
current_probabilities += probabilities_in_time[i]
plt.plot(current_probabilities,
color=colors[0] if i <= 1 else colors[-1] if i >= 8 else colors[i - 1] if i < 5 else colors[i + 1])
def set_grid():
ax = plt.gca()
ax.set_axisbelow(False)
ax.xaxis.grid(True, linestyle='-', lw=1)
def set_legend():
leg = plt.legend(loc='lower left', fontsize=14, handlelength=1.3)
# leg = plt.legend(loc='upper left', bbox_to_anchor=(1.01, 1), fontsize=14, handlelength=1.3)
# for i in leg.legendHandles:
# i.set_linewidth(12)
plot_time_probabilities(probabilities_in_time, figsize)
Here is my attempt to change the legend of a barplot from rectangle to square:
import matplotlib.patches as patches
rect1 = patches.Rectangle((0,0),1,1,facecolor='#FF605E')
rect2 = patches.Rectangle((0,0),1,1,facecolor='#64B2DF')
plt.legend((rect1, rect2), ('2016', '2015'))
But when I plot this, I still see rectangles instead of squares:
Any suggestions on how can I do this?
I tried both solutions provided by #ImportanceOfBeingErnest and #furas, here are the results:
#ImportanceOfBeingErnest's solution is the easiest to do:
plt.rcParams['legend.handlelength'] = 1
plt.rcParams['legend.handleheight'] = 1.125
Here is the result:
My final code looks like this:
plt.legend((df.columns[1], df.columns[0]), handlelength=1, handleheight=1) # the df.columns = the legend text
#furas's solution produces this, I don't know why the texts are further away from the rectangles, but I am sure the gap can be changed somehow:
Matplotlib provides the rcParams
legend.handlelength : 2. # the length of the legend lines in fraction of fontsize
legend.handleheight : 0.7 # the height of the legend handle in fraction of fontsize
You can set those within the call to plt.legend()
plt.legend(handlelength=1, handleheight=1)
or using the rcParams at the beginning of your script
import matplotlib
matplotlib.rcParams['legend.handlelength'] = 1
matplotlib.rcParams['legend.handleheight'] = 1
Unfortunately providing equal handlelength=1, handleheight=1 will not give a perfect rectange. It seems handlelength=1, handleheight=1.125 will do the job, but this may depend on the font being used.
An alternative, if you want to use proxy artists may be to use the square markers from the plot/scatter methods.
bar1 = plt.plot([], marker="s", markersize=15, linestyle="", label="2015")
and supply it to the legend, legend(handles=[bar1]). Using this approach needs to have set matplotlib.rcParams['legend.numpoints'] = 1, otherwise two markers would appear in the legend.
Here is a full example of both methods
import matplotlib.pyplot as plt
plt.rcParams['legend.handlelength'] = 1
plt.rcParams['legend.handleheight'] = 1.125
plt.rcParams['legend.numpoints'] = 1
fig, ax = plt.subplots(ncols=2, figsize=(5,2.5))
# Method 1: Set the handlesizes already in the rcParams
ax[0].set_title("Setting handlesize")
ax[0].bar([0,2], [6,3], width=0.7, color="#a30e73", label="2015", align="center")
ax[0].bar([1,3], [3,2], width=0.7, color="#0943a8", label="2016", align="center" )
ax[0].legend()
# Method 2: use proxy markers. (Needs legend.numpoints to be 1)
ax[1].set_title("Proxy markers")
ax[1].bar([0,2], [6,3], width=0.7, color="#a30e73", align="center" )
ax[1].bar([1,3], [3,2], width=0.7, color="#0943a8", align="center" )
b1, =ax[1].plot([], marker="s", markersize=15, linestyle="", color="#a30e73", label="2015")
b2, =ax[1].plot([], marker="s", markersize=15, linestyle="", color="#0943a8", label="2016")
ax[1].legend(handles=[b1, b2])
[a.set_xticks([0,1,2,3]) for a in ax]
plt.show()
producing
It seems they change it long time ago - and now some elements can't be used directly in legend.
Now it needs handler:
Implementing a custom legend handler
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.legend_handler import HandlerPatch
# --- handlers ---
class HandlerRect(HandlerPatch):
def create_artists(self, legend, orig_handle,
xdescent, ydescent, width, height,
fontsize, trans):
x = width//2
y = 0
w = h = 10
# create
p = patches.Rectangle(xy=(x, y), width=w, height=h)
# update with data from oryginal object
self.update_prop(p, orig_handle, legend)
# move xy to legend
p.set_transform(trans)
return [p]
class HandlerCircle(HandlerPatch):
def create_artists(self, legend, orig_handle,
xdescent, ydescent, width, height,
fontsize, trans):
r = 5
x = r + width//2
y = height//2
# create
p = patches.Circle(xy=(x, y), radius=r)
# update with data from oryginal object
self.update_prop(p, orig_handle, legend)
# move xy to legend
p.set_transform(trans)
return [p]
# --- main ---
rect = patches.Rectangle((0,0), 1, 1, facecolor='#FF605E')
circ = patches.Circle((0,0), 1, facecolor='#64B2DF')
plt.legend((rect, circ), ('2016', '2015'),
handler_map={
patches.Rectangle: HandlerRect(),
patches.Circle: HandlerCircle(),
})
plt.show()
Legend reserves place for rectangle and this method doesn't change it so there is so many empty space.
What I want is like this:
What I get is this:
So how to merge the markers into one label?
also for the lines, for the lines, of course, u can realize it by not assigning label to the second line while using the same linetype, but for the markers, you can not, since they are of different shapes.
Note that in recent versions of matplotlib you can achieve this using class matplotlib.legend_handler.HandlerTuple as illustrated in this answer and also in this guide:
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerTuple
fig, ax1 = plt.subplots(1, 1)
# First plot: two legend keys for a single entry
p2, = ax1.plot([3, 4], [2, 3], 'o', mfc="white", mec="k")
p1, = ax1.plot([1, 2], [5, 6], 's', mfc="gray", mec="gray")
# `plot` returns a list, but we want the handle - thus the comma on the left
p3, = ax1.plot([1, 5], [4, 4], "-k")
p4, = ax1.plot([2, 6], [3, 2], "-k")
# Assign two of the handles to the same legend entry by putting them in a tuple
# and using a generic handler map (which would be used for any additional
# tuples of handles like (p1, p3)).
l = ax1.legend([(p1, p2), p3], ['data', 'models'],
handler_map={tuple: HandlerTuple(ndivide=None)})
plt.savefig("demo.png")
I think it's best to use a full legend - otherwise, how will your readers know the difference between the two models, or the two datasets? I would do it this way:
But, if you really want to do it your way, you can use a custom legend as shown in this guide. You'll need to create your own class, like they do, that defines the legend_artist method, which then adds squares and circles as appropriate. Here is the plot generated and the code used to generate it:
#!/usr/bin/env python
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
# ==================================
# Define the form of the function
# ==================================
def model(x, A=190, k=1):
return A * np.exp(-k*x/50)
# ==================================
# How many data points are generated
# ==================================
num_samples = 15
# ==================================
# Create data for plots
# ==================================
x_model = np.linspace(0, 130, 200)
x_data1 = np.random.rand(num_samples) * 130
x_data1.sort()
x_data2 = np.random.rand(num_samples) * 130
x_data2.sort()
data1 = model(x_data1, k=1) * (1 + np.random.randn(num_samples) * 0.2)
data2 = model(x_data2, k=2) * (1 + np.random.randn(num_samples) * 0.15)
model1 = model(x_model, k=1)
model2 = model(x_model, k=2)
# ==================================
# Plot everything normally
# ==================================
fig = plt.figure()
ax = fig.add_subplot('111')
ax.plot(x_data1, data1, 'ok', markerfacecolor='none', label='Data (k=1)')
ax.plot(x_data2, data2, 'sk', markeredgecolor='0.5', markerfacecolor='0.5', label='Data (k=2)')
ax.plot(x_model, model1, '-k', label='Model (k=1)')
ax.plot(x_model, model2, '--k', label='Model (k=2)')
# ==================================
# Format plot
# ==================================
ax.set_xlabel('Distance from heated face($10^{-2}$ m)')
ax.set_ylabel('Temperature ($^\circ$C)')
ax.set_xlim((0, 130))
ax.set_title('Normal way to plot')
ax.legend()
fig.tight_layout()
plt.show()
# ==================================
# ==================================
# Do it again, but with custom
# legend
# ==================================
# ==================================
class AnyObject(object):
pass
class data_handler(object):
def legend_artist(self, legend, orig_handle, fontsize, handlebox):
scale = fontsize / 22
x0, y0 = handlebox.xdescent, handlebox.ydescent
width, height = handlebox.width, handlebox.height
patch_sq = mpatches.Rectangle([x0, y0 + height/2 * (1 - scale) ], height * scale, height * scale, facecolor='0.5',
edgecolor='0.5', transform=handlebox.get_transform())
patch_circ = mpatches.Circle([x0 + width - height/2, y0 + height/2], height/2 * scale, facecolor='none',
edgecolor='black', transform=handlebox.get_transform())
handlebox.add_artist(patch_sq)
handlebox.add_artist(patch_circ)
return patch_sq
# ==================================
# Plot everything
# ==================================
fig = plt.figure()
ax = fig.add_subplot('111')
d1 = ax.plot(x_data1, data1, 'ok', markerfacecolor='none', label='Data (k=2)')
d2 = ax.plot(x_data2, data2, 'sk', markeredgecolor='0.5', markerfacecolor='0.5', label='Data (k=1)')
m1 = ax.plot(x_model, model1, '-k', label='Model (k=1)')
m2 = ax.plot(x_model, model2, '-k', label='Model (k=2)')
# ax.legend([d1], handler_map={ax.plot: data_handler()})
ax.legend([AnyObject(), m1[0]], ['Data', 'Model'], handler_map={AnyObject: data_handler()})
# ==================================
# Format plot
# ==================================
ax.set_xlabel('Distance from heated face($10^{-2}$ m)')
ax.set_ylabel('Temperature ($^\circ$C)')
ax.set_xlim((0, 130))
ax.set_title('Custom legend')
fig.tight_layout()
plt.show()
I also found this link very useful (code below), it's an easier way to handle this issue. It's basically using a list of legend handles to make one of the markers of the first handle invisible and overplot it with the marker of the second handle. This way, you have both markers next to each other with one label.
fig, ax = plt.subplots()
p1 = ax.scatter([0.1],[0.5],c='r',marker='s')
p2 = ax.scatter([0.3],[0.2],c='b',marker='o')
l = ax.legend([(p1,p2)],['points'],scatterpoints=2)
With the above code, a TupleHandler is used to create legend handles which
simply overplot two handles (there are red squares behind the blue
circles if you look carefylly. What you want to do is make the second
marker of first handle and the first marker of the second handle
invisible. Unfortunately, the TupleHandler is a rather recent addition
and you need a special function to get all the handles. Otherwise, you
can use the Legend.legendHandles attribute (it only show the first
handle for the TupleHandler).
def get_handle_lists(l):
"""returns a list of lists of handles.
"""
tree = l._legend_box.get_children()[1]
for column in tree.get_children():
for row in column.get_children():
yield row.get_children()[0].get_children()
handles_list = list(get_handle_lists(l))
handles = handles_list[0] # handles is a list of two PathCollection.
# The first one is for red squares, and the second
# is for blue circles.
handles[0].set_facecolors(["r", "none"]) # for the fist
# PathCollection, make the
# second marker invisible by
# setting their facecolor and
# edgecolor to "none."
handles[0].set_edgecolors(["k", "none"])
handles[1].set_facecolors(["none", "b"])
handles[1].set_edgecolors(["none", "k"])
fig
Here is a new solution that will plot any collection of markers with the same label. I have not figured out how to make it work with markers from a line plot, but you can probably do a scatter plot on top of a line plot if you need to.
from matplotlib import pyplot as plt
import matplotlib.collections as mcol
import matplotlib.transforms as mtransforms
import numpy as np
from matplotlib.legend_handler import HandlerPathCollection
from matplotlib import cm
class HandlerMultiPathCollection(HandlerPathCollection):
"""
Handler for PathCollections, which are used by scatter
"""
def create_collection(self, orig_handle, sizes, offsets, transOffset):
p = type(orig_handle)(orig_handle.get_paths(), sizes=sizes,
offsets=offsets,
transOffset=transOffset,
)
return p
fig, ax = plt.subplots()
#make some data to plot
x = np.arange(0, 100, 10)
models = [.05 * x, 8 * np.exp(- .1 * x), np.log(x + 1), .01 * x]
tests = [model + np.random.rand(len(model)) - .5 for model in models]
#make colors and markers
colors = cm.brg(np.linspace(0, 1, len(models)))
markers = ['o', 'D', '*', 's']
markersize = 50
plots = []
#plot points and lines
for i in xrange(len(models)):
line, = plt.plot(x, models[i], linestyle = 'dashed', color = 'black', label = 'Model')
plot = plt.scatter(x, tests[i], c = colors[i], s = markersize, marker = markers[i])
plots.append(plot)
#get attributes
paths = []
sizes = []
facecolors = []
edgecolors = []
for plot in plots:
paths.append(plot.get_paths()[0])
sizes.append(plot.get_sizes()[0])
edgecolors.append(plot.get_edgecolors()[0])
facecolors.append(plot.get_facecolors()[0])
#make proxy artist out of a collection of markers
PC = mcol.PathCollection(paths, sizes, transOffset = ax.transData, facecolors = colors, edgecolors = edgecolors)
PC.set_transform(mtransforms.IdentityTransform())
plt.legend([PC, line], ['Test', 'Model'], handler_map = {type(PC) : HandlerMultiPathCollection()}, scatterpoints = len(paths), scatteryoffsets = [.5], handlelength = len(paths))
plt.show()
I have a solution for you if you're willing to use all circles for markers and differentiate by color only. You can use a circle collection to represent the markers, and then have a legend label for the collection as a whole.
Example code:
import matplotlib.pyplot as plt
import matplotlib.collections as collections
from matplotlib import cm
import numpy as np
#make some data to plot
x = np.arange(0, 100, 10)
models = [.05 * x, 8 * np.exp(- .1 * x), np.log(x + 1), .01 * x]
tests = [model + np.random.rand(len(model)) - .5 for model in models]
#make colors
colors = cm.brg(np.linspace(0, 1, len(models)))
markersize = 50
#plot points and lines
for i in xrange(len(models)):
line, = plt.plot(x, models[i], linestyle = 'dashed', color = 'black', label = 'Model')
plt.scatter(x, tests[i], c = colors[i], s = markersize)
#create collection of circles corresponding to markers
circles = collections.CircleCollection([markersize] * len(models), facecolor = colors)
#make the legend -- scatterpoints needs to be the same as the number
#of markers so that all the markers show up in the legend
plt.legend([circles, line], ['Test', 'Model'], scatterpoints = len(models), scatteryoffsets = [.5], handlelength = len(models))
plt.show()
You can do this by plotting data without any label and then adding the label separately:
from matplotlib import pyplot as plt
from numpy import random
xs = range(10)
data = random.rand(10, 2)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
kwargs = {'color': 'r', 'linewidth': 2, 'linestyle': '--'}
ax.plot(xs, data, **kwargs)
ax.plot([], [], label='Model', **kwargs)
ax.legend()
plt.show()
I am using python to plot and my codes are:
import matplotlib.pyplot as plt
import numpy as np
# these are the data to be plot
x = [1,2,3,4,5,6,7,8,9,10,11,12,13,14]
x_test = ['grid50', 'grid100', 'grid150', 'grid250', 'grid500', 'grid750', 'NN5', 'NN10', 'NN15', 'NN20', 'NN50', 'NN100', 'CB', 'CBG']
clf = [0.58502, 0.60799, 0.60342, 0.59629, 0.56464, 0.53757, 0.62567, 0.63429, 0.63583, 0.63239, 0.63315, 0.63156, 0.60630, 0.52755]
hitrate = [0.80544, 0.89422, 0.94029, 0.98379, 0.99413, 0.99921, 0.99478, 0.99961, 0.99997, 0.99980, 0.99899, 0.99991, 0.88435, 1.0]
level = [23.04527, 9.90955, 4.35757, 1.46438, 0.51277, 0.15071, 1.30057, 0.00016, 0.00001, 0.00021, 0.00005, 0.00004, 6.38019, 0]
fig = plt.figure(figsize=(20,7))
ax = fig.add_subplot(111)
fig.subplots_adjust(right=0.8)
# this is the function to put annotation on bars
def autolabel(rects):
# attach some text labels
for ii,rect in enumerate(rects):
height = rect.get_height()
plt. text(rect.get_x()+rect.get_width()/2., 1.02*height, '%s'% (clf[ii]),ha='center', va='bottom')
plt.xticks(x,x_test)
# this part is to plot the red bar charts
ins1 = ax.bar(x,clf,color='Red', align='center',label='classification results')
ax.set_ylabel('classification results', color='Red')
ax.tick_params(axis='y',colors='Red')
ax.set_ylim(0,1.5)
autolabel(ins1)
# this part is to plot the green hitrate and the for-loop is to put annotation next to the line
ax2 = ax.twinx()
ins2, = ax2.plot(x,hitrate,marker='o',color='Green', linewidth=3.0, label='hitrate')
ax2.set_ylabel('hitrate', color='Green')
ax2.tick_params(axis='y',colors='Green')
ax2.set_ylim(0,1.5)
for i,j in zip(x, hitrate):
ax2.annotate(str(j),xy=(i,j+0.02))
# this part is to plot the blue level, forloop same as that of hitrate
ax3 = ax.twinx()
axes = [ax, ax2, ax3]
ax3.spines['right'].set_position(('axes', 1.1))
ax3.set_frame_on(True)
ax3.patch.set_visible(False)
ins3, = ax3.plot(x,level,marker='^', color='Blue', linewidth=3.0, label='obfuscation level')
ax3.set_ylabel('obfuscation level', color='Blue')
ax3.tick_params(axis='y',colors='Blue')
ax3.set_ylim(0,25)
for i,j in zip(x, level):
ax3.annotate(str(j),xy=(i,j+0.02))
ax.set_xlabel('Cell Configurations')
ax.set_xlim(0,15)
ax.set_title('benchmark')
ax.legend([ins1,ins2,ins3],['clf', 'hit', 'level'])
plt.grid()
plt.show()
And I got a figure like :
The problem is that, some numbers are not put in a good place so to be read clearly, but I don't know whether there is a method to put the annotation naturally at a blank area. Any ideas?
In this example the color is correlative to the radius of each bar. How would one add a colorbar to this plot?
My code mimics a "rose diagram" projection which is essentially a bar chart on a polar projection.
here is a part of it:
angle = radians(10.)
patches = radians(360.)/angle
theta = np.arange(0,radians(360.),angle)
count = [0]*patches
for i, item in enumerate(some_array_of_azimuth_directions):
temp = int((item - item%angle)/angle)
count[temp] += 1
width = angle * np.ones(patches)
# force square figure and square axes looks better for polar, IMO
fig = plt.figure(figsize=(8,8))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
rmax = max(count) + 1
ax.set_rlim(0,rmax)
ax.set_theta_offset(np.pi/2)
ax.set_thetagrids(np.arange(0,360,10))
ax.set_theta_direction(-1)
# project strike distribution as histogram bars
bars = ax.bar(theta, count, width=width)
r_values = []
colors = []
for r,bar in zip(count, bars):
r_values.append(r/float(max(count)))
colors.append(cm.jet(r_values[-1], alpha=0.5))
bar.set_facecolor(colors[-1])
bar.set_edgecolor('grey')
bar.set_alpha(0.5)
# Add colorbar, make sure to specify tick locations to match desired ticklabels
colorlist = []
r_values.sort()
values = []
for val in r_values:
if val not in values:
values.append(val*float(max(count)))
color = cm.jet(val, alpha=0.5)
if color not in colorlist:
colorlist.append(color)
cpt = mpl.colors.ListedColormap(colorlist)
bounds = range(max(count)+1)
norm = mpl.colors.BoundaryNorm(values, cpt.N-1)
cax = fig.add_axes([0.97, 0.3, 0.03, 0.4])
cb = mpl.colorbar.ColorbarBase(cax, cmap=cpt,
norm=norm,
boundaries=bounds,
# Make the length of each extension
# the same as the length of the
# interior colors:
extendfrac='auto',
ticks=[bounds[i] for i in range(0, len(bounds), 2)],
#ticks=bounds,
spacing='uniform')
and here is the resulting plot:
As you can see, the colorbar is not quite right. If you look closely, between 16 and 17, there is a color missing (darker orange) and according to the colorbar the yellows reach a value of 15, which is not true in the rose diagram (or the data).
I have played around with the code so much and I just can't figure out how to normalize the colorbar correctly.
The easiest way is to use a PatchCollection and pass in your "z" (i.e. the values you want to color by) as the array kwarg.
As a simple example:
import itertools
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
import numpy as np
def main():
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')
x = np.radians(np.arange(0, 360, 10))
y = np.random.random(x.size)
z = np.random.random(y.size)
cmap = plt.get_cmap('cool')
coll = colored_bar(x, y, z, ax=ax, width=np.radians(10), cmap=cmap)
fig.colorbar(coll)
ax.set_yticks([0.5, 1.0])
plt.show()
def colored_bar(left, height, z=None, width=0.8, bottom=0, ax=None, **kwargs):
if ax is None:
ax = plt.gca()
width = itertools.cycle(np.atleast_1d(width))
bottom = itertools.cycle(np.atleast_1d(bottom))
rects = []
for x, y, w, h in zip(left, bottom, width, height):
rects.append(Rectangle((x,y), w, h))
coll = PatchCollection(rects, array=z, **kwargs)
ax.add_collection(coll)
ax.autoscale()
return coll
if __name__ == '__main__':
main()
If you want a discrete color map, it's easiest to just specify the number of intervals you'd like when you call plt.get_cmap. For example, in the code above, if you replace the line cmap = plt.get_cmap('cool') with:
cmap = plt.get_cmap('cool', 5)
Then you'll get a discrete colormap with 5 intervals. (Alternately, you could pass in the ListedColormap that you created in your example.)
If you want a "full-featured" rose diagram function, you might do something like this:
import itertools
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
import numpy as np
def main():
azi = np.random.normal(20, 30, 100)
z = np.cos(np.radians(azi + 45))
plt.figure(figsize=(5,6))
plt.subplot(111, projection='polar')
coll = rose(azi, z=z, bidirectional=True)
plt.xticks(np.radians(range(0, 360, 45)),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
plt.colorbar(coll, orientation='horizontal')
plt.xlabel('A rose diagram colored by a second variable')
plt.rgrids(range(5, 20, 5), angle=290)
plt.show()
def rose(azimuths, z=None, ax=None, bins=30, bidirectional=False,
color_by=np.mean, **kwargs):
"""Create a "rose" diagram (a.k.a. circular histogram).
Parameters:
-----------
azimuths: sequence of numbers
The observed azimuths in degrees.
z: sequence of numbers (optional)
A second, co-located variable to color the plotted rectangles by.
ax: a matplotlib Axes (optional)
The axes to plot on. Defaults to the current axes.
bins: int or sequence of numbers (optional)
The number of bins or a sequence of bin edges to use.
bidirectional: boolean (optional)
Whether or not to treat the observed azimuths as bi-directional
measurements (i.e. if True, 0 and 180 are identical).
color_by: function or string (optional)
A function to reduce the binned z values with. Alternately, if the
string "count" is passed in, the displayed bars will be colored by
their y-value (the number of azimuths measurements in that bin).
Additional keyword arguments are passed on to PatchCollection.
Returns:
--------
A matplotlib PatchCollection
"""
azimuths = np.asanyarray(azimuths)
if color_by == 'count':
z = np.ones_like(azimuths)
color_by = np.sum
if ax is None:
ax = plt.gca()
ax.set_theta_direction(-1)
ax.set_theta_offset(np.radians(90))
if bidirectional:
other = azimuths + 180
azimuths = np.concatenate([azimuths, other])
if z is not None:
z = np.concatenate([z, z])
# Convert to 0-360, in case negative or >360 azimuths are passed in.
azimuths[azimuths > 360] -= 360
azimuths[azimuths < 0] += 360
counts, edges = np.histogram(azimuths, range=[0, 360], bins=bins)
if z is not None:
idx = np.digitize(azimuths, edges)
z = np.array([color_by(z[idx == i]) for i in range(1, idx.max() + 1)])
z = np.ma.masked_invalid(z)
edges = np.radians(edges)
coll = colored_bar(edges[:-1], counts, z=z, width=np.diff(edges),
ax=ax, **kwargs)
return coll
def colored_bar(left, height, z=None, width=0.8, bottom=0, ax=None, **kwargs):
"""A bar plot colored by a scalar sequence."""
if ax is None:
ax = plt.gca()
width = itertools.cycle(np.atleast_1d(width))
bottom = itertools.cycle(np.atleast_1d(bottom))
rects = []
for x, y, h, w in zip(left, bottom, height, width):
rects.append(Rectangle((x,y), w, h))
coll = PatchCollection(rects, array=z, **kwargs)
ax.add_collection(coll)
ax.autoscale()
return coll
if __name__ == '__main__':
main()