As the title says, I am trying to put my matplotlib animation into a GUI however, im not too sure where to start. I am very much new to python, especially using it to make GUIs. Right now this is what I have for my animation:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation
points = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)
fig, ax = plt.subplots()
xfixdata, yfixdata = 15,10
xdata, ydata = 5, None
ln, = plt.plot([], [], 'ro-', animated=True)
plt.plot([xfixdata], [yfixdata], 'bo', ms=10)
def init():
ax.set_xlim(0, 20)
ax.set_ylim(0, 20)
return ln,
def update(frame):
ydata = points[frame]
ln.set_data([xfixdata,xdata], [yfixdata,ydata])
return ln,
ani = FuncAnimation(fig, update, interval=80, frames=range(len(points)),
init_func=init, blit=True)
plt.show()
Right now, I've been attempting to transfer this code into a canvas using pysimpleGUI however, I am not making any progress. Is there any chance that one of you could somewhat walk me through the process of converting this? Thank you very much.
Here's one demo code for you about the matplotlib animation in PySimpleGUI Graph element, of course, you can use Canvas element
import math
from matplotlib import use as use_agg
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
import PySimpleGUI as sg
def pack_figure(graph, figure):
canvas = FigureCanvasTkAgg(figure, graph.Widget)
plot_widget = canvas.get_tk_widget()
plot_widget.pack(side='top', fill='both', expand=1)
return plot_widget
def plot_figure(index, theta):
fig = plt.figure(index) # Active an existing figure
ax = plt.gca() # Get the current axes
x = [degree for degree in range(1080)]
y = [math.sin((degree+theta)/180*math.pi) for degree in range(1080)]
ax.cla() # Clear the current axes
ax.set_title(f"Sensor Data {index}")
ax.set_xlabel("X axis")
ax.set_ylabel("Y axis")
ax.set_xscale('log')
ax.grid()
plt.plot(x, y) # Plot y versus x as lines and/or markers
fig.canvas.draw() # Rendor figure into canvas
# Use Tkinter Agg
use_agg('TkAgg')
layout = [[sg.Graph((640, 480), (0, 0), (640, 480), key='Graph1'), sg.Graph((640, 480), (0, 0), (640, 480), key='Graph2')]]
window = sg.Window('Matplotlib', layout, finalize=True)
# Initial
graph1 = window['Graph1']
graph2 = window['Graph2']
plt.ioff() # Turn the interactive mode off
fig1 = plt.figure(1) # Create a new figure
ax1 = plt.subplot(111) # Add a subplot to the current figure.
fig2 = plt.figure(2) # Create a new figure
ax2 = plt.subplot(111) # Add a subplot to the current figure.
pack_figure(graph1, fig1) # Pack figure under graph
pack_figure(graph2, fig2)
theta1 = 0 # theta for fig1
theta2 = 90 # theta for fig2
plot_figure(1, theta1)
plot_figure(2, theta2)
while True:
event, values = window.read(timeout=10)
if event == sg.WINDOW_CLOSED:
break
elif event == sg.TIMEOUT_EVENT:
theta1 = (theta1 + 40) % 360
plot_figure(1, theta1)
theta2 = (theta2 + 40) % 260
plot_figure(2, theta2)
window.close()
I am desperately trying to add a "dark" border around this pie chart. I have tried the solutions described in plenty of questions here, but none turned out to add anything. You can find part of the attempts in the code:
import matplotlib.pyplot as plt
from cycler import cycler
plt.rc("axes", prop_cycle=cycler("color", ["darkgray", "gray", "lightgray"])
)
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.25
labels = ["lab1", "lab2"]
sizes = [2000, 3000]
def make_autopct(values):
def my_autopct(pct):
total = sum(values)
val = int(round(pct*total/100.0))
s = '{p:.2f}%({v:d}%)'.format(p=pct,v=val)
s = f"${val}_{{\\ {pct:.2f}\%}}$"
return s
return my_autopct
fig, ax = plt.subplots(figsize=(10, 3))
ax.pie(sizes, explode=(0,0.02), labels=labels, autopct=make_autopct(sizes))
ax.set_title("title")
ax.patch.set_edgecolor('black')
ax.patch.set_linewidth('1')
plt.savefig("title.png")
If I've understood your question right possible solution is the following:
# pip install matplotlib
import matplotlib.pyplot as plt
import numpy as np
# set chart style
plt.style.use('_mpl-gallery-nogrid')
# set data
x = [5, 2, 3, 4]
# set colors of segments
colors = plt.get_cmap('GnBu')(np.linspace(0.2, 0.7, len(x)))
# plot
fig, ax = plt.subplots()
ax.pie(x, colors=colors, radius=2,
wedgeprops={"linewidth": 2, "edgecolor": "black", 'antialiased': True}, # << HERE
frame=False, startangle=0, autopct='%.1f%%', pctdistance=0.6)
plt.show()
Below, three possibilities:
add a frame around pie patch:
ax.pie(sizes,
explode=(0,0.02),
labels=labels,
autopct=make_autopct(sizes),
frame=True)
add a border using axes coordinates (0, 0) to (1, 1) with fig.add_artist which draw on the fig object:
rect = pt.Rectangle((-0.1, -0.1), 1.2, 1.2,
fill=False, color="blue", lw=3, zorder=-1
transform=ax.transAxes)
fig.add_artist(rect)
add a border using fig coordinates (0, 0) to (1, 1) with fig.add_artist which draw on the fig object:
rect = pt.Rectangle((0.05, 0.05), .9, .9,
fill=False, ec="red", lw=1, zorder=-1,
transform=fig.transFigure)
fig.add_artist(rect)
Result:
Edit This matplotlib's transformations page explains the different coordinate systems
Having the geographic points with values, I would like to encode the values with colormap and customize the legend position and colormap range.
Using geopandas, I have written the following function:
def plot_continuous(df, column_values, title):
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
df.plot(ax=ax, column=column_values, cmap='OrRd', legend=True);
ax.title.set_text(title)
The colorbar by default is vertical, but I would like to make it horizontal.
In order to have a horizontal colorbar, I have written the following function:
def plot_continuous(df, column_values, title, legend_title=None):
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
x = np.array(df.geometry.apply(lambda x: x.x))
y = np.array(df.geometry.apply(lambda x: x.y))
vals = np.array(df[column_values])
sc = ax.scatter(x, y, c=vals, cmap='OrRd')
cbar = plt.colorbar(sc, orientation="horizontal")
if legend_title is not None:
cbar.ax.set_xlabel(legend_title)
ax.title.set_text(title)
The image width and height in the latter case, however, is not proportional so the output looks distorted.
Does anyone know how to customize the geographic plot and keep the width-height ratio undistorted?
This gets far simpler if you use geopandas customisation of plot()
This is documented: https://geopandas.org/en/stable/docs/user_guide/mapping.html
Below I show MWE using your function and then using geopandas. Later has scaled data correctly.
MWE of your code
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
def plot_continuous(df, column_values, title, legend_title=None):
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
x = np.array(df.geometry.apply(lambda x: x.x))
y = np.array(df.geometry.apply(lambda x: x.y))
vals = np.array(df[column_values])
sc = ax.scatter(x, y, c=vals, cmap='OrRd')
cbar = plt.colorbar(sc, orientation="horizontal")
if legend_title is not None:
cbar.ax.set_xlabel(legend_title)
ax.title.set_text(title)
cities = gpd.read_file(gpd.datasets.get_path("naturalearth_cities"))
cities["color"] = np.random.randint(1,10, len(cities))
plot_continuous(cities, "color", "my title", "Color")
use geopandas
ax = cities.plot(
column="color",
cmap="OrRd",
legend=True,
legend_kwds={"label": "Color", "orientation": "horizontal"},
)
ax.set_title("my title")
I have seen Possible to make labels appear when hovering over a point in matplotlib? - but unfortunately, it does not help me with this specific case.
Consider this example:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib
print("matplotlib.__version__ {}".format(matplotlib.__version__))
import matplotlib.pyplot as plt
import numpy as np
def onhover(event, fig, axes):
print(event)
def main():
xdata = np.arange(0, 101, 1) # 0 to 100, both included
ydata1 = np.sin(0.01*xdata*np.pi/2)
fig, ax1 = plt.subplots(1, 1, figsize=(9, 6), dpi=120)
fig.subplots_adjust(hspace=0)
pl11, = ax1.plot(xdata, ydata1, color="Red", label="My plot")
leg = ax1.legend(ncol=1, bbox_to_anchor=(0,1.01), loc="lower left", borderaxespad=0, prop={'size': 8})
fig.canvas.mpl_connect('motion_notify_event', lambda event: onhover(event, fig, (ax1,) ))
plt.show()
# ENTRY POINT
if __name__ == '__main__':
main()
This results with the following plot:
The thing is: if I move the mouse pointer, so it hovers over the legend entry, all I get as the printout in the event is:
....
motion_notify_event: xy=(175, 652) xydata=(None, None) button=None dblclick=False inaxes=None
motion_notify_event: xy=(174, 652) xydata=(None, None) button=None dblclick=False inaxes=None
motion_notify_event: xy=(173, 652) xydata=(None, None) button=None dblclick=False inaxes=None
motion_notify_event: xy=(172, 652) xydata=(None, None) button=None dblclick=False inaxes=None
... which makes sense, as I've deliberately placed the legend outside of the plot.
However, now I do not know how I can get a reference to the legend entry? What I would like to do, is basically get the reference to the legend entry, so that I could write the same text as the legend label (here "My plot") in a "tooltip" (that is, in this case, Matplotlib annotation) followed by some other text; and then, once the mouse leaves the region of the legend entry, the tooltip/annotation should disappear.
Can I achieve this with Matplotlib - and if so, how?
You could do something like that:
def onhover(event):
if leg.get_window_extent().contains(event.x,event.y):
print(event, "In legend! do something!")
xdata = np.arange(0, 101, 1) # 0 to 100, both included
ydata1 = np.sin(0.01*xdata*np.pi/2)
fig, ax1 = plt.subplots(1, 1, figsize=(9, 6), dpi=120)
pl11, = ax1.plot(xdata, ydata1, color="Red", label="My plot")
leg = ax1.legend(ncol=1, bbox_to_anchor=(0,1.01), loc="lower left", borderaxespad=0, prop={'size': 8})
fig.canvas.mpl_connect('motion_notify_event', onhover)
plt.show()
Here is a complete rework of the OP, with two plots and two legend entries, with actual hovering - a little complicated to achieve, as there are coordinate systems transformations and z order to take into account as well:
Here's the code - including comments:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib
print("matplotlib.__version__ {}".format(matplotlib.__version__))
import matplotlib.pyplot as plt
import numpy as np
def onhover(event, fig, axes, leg, tooltip):
if leg.get_window_extent().contains(event.x,event.y):
print(event, "In legend! do something!")
ax1 = axes[0]
hand, labl = ax1.get_legend_handles_labels()
#print(hand)
#print(labl)
r = plt.gcf().canvas.get_renderer()
#for ilidx, ilabl in enumerate(labl): # labl contents are just str! so no geometry data
# print(ilabl.get_window_extent())
#for ihidx, ihand in enumerate(hand):
# # NOTE: just ihand.get_window_extent() causes: TypeError: get_window_extent() missing 1 required positional argument: 'renderer'
# print(ihand.get_window_extent(r)) # apparently, this is the original line, not just the legend line, as here it prints: Bbox(x0=173.04545454545456, y0=104.10999999999999, x1=933.9545454545455, y1=606.71)
# if ihand.get_window_extent(r).contains(event.x,event.y):
# print("EEEE")
hoveredtxt = None
for itxt in leg.get_texts():
#print(itxt)
#print(itxt.get_window_extent().contains(event.x,event.y))
if itxt.get_window_extent().contains(event.x,event.y):
#print("Legend hover on: {}".format(itxt))
hoveredtxt = itxt
break
if hoveredtxt is not None:
tooltip.set_text("'{}' is hovered!".format(hoveredtxt.get_text()))
tooltip.set_visible(True)
# see: https://matplotlib.org/3.1.1/gallery/recipes/placing_text_boxes.html
# https://matplotlib.org/3.1.1/tutorials/advanced/transforms_tutorial.html - None for "display" coord system
tooltip.set_transform( None )
tooltip.set_position( (event.x, event.y) ) # is by default in data coords!
else:
tooltip.set_visible(False)
fig.canvas.draw_idle()
def main():
xdata = np.arange(0, 101, 1) # 0 to 100, both included
ydata1 = np.sin(0.01*xdata*np.pi/2)
ydata2 = 10*np.sin(0.01*xdata*np.pi/4)
fig, ax1 = plt.subplots(1, 1, figsize=(9, 6), dpi=120)
fig.subplots_adjust(hspace=0)
pl11, = ax1.plot(xdata, ydata1, color="Red", label="My plot")
pl12, = ax1.plot(xdata, ydata2, color="Blue", label="Other stuff")
leg = ax1.legend(ncol=2, bbox_to_anchor=(0,1.01), loc="lower left", borderaxespad=0, prop={'size': 8})
# NOTE: ax.annotate without an arrow is basically ax.text;
#tooltip = ax.annotate("", xy=(0, 0), xytext=(-20, 20), textcoords="offset points",
# bbox=dict(boxstyle="round", fc="w"),
# arrowprops=dict(arrowstyle="->"))
# however, no `textcoords` in .text; "The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords", via transform=ax.transAxes
# must add zorder of high number too, else the tooltip comes under the legend!
tooltip = ax1.text(0, 0, 'TEST', bbox=dict(boxstyle="round", fc="w"), zorder=10)
tooltip.set_visible(False)
fig.canvas.mpl_connect('motion_notify_event', lambda event: onhover(event, fig, (ax1,), leg, tooltip ))
plt.show()
# ENTRY POINT
if __name__ == '__main__':
main()
I am using matplotlib to make scatter plots. Each point on the scatter plot is associated with a named object. I would like to be able to see the name of an object when I hover my cursor over the point on the scatter plot associated with that object. In particular, it would be nice to be able to quickly see the names of the points that are outliers. The closest thing I have been able to find while searching here is the annotate command, but that appears to create a fixed label on the plot. Unfortunately, with the number of points that I have, the scatter plot would be unreadable if I labeled each point. Does anyone know of a way to create labels that only appear when the cursor hovers in the vicinity of that point?
It seems none of the other answers here actually answer the question. So here is a code that uses a scatter and shows an annotation upon hovering over the scatter points.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
x = np.random.rand(15)
y = np.random.rand(15)
names = np.array(list("ABCDEFGHIJKLMNO"))
c = np.random.randint(1,5,size=15)
norm = plt.Normalize(1,4)
cmap = plt.cm.RdYlGn
fig,ax = plt.subplots()
sc = plt.scatter(x,y,c=c, s=100, cmap=cmap, norm=norm)
annot = ax.annotate("", xy=(0,0), xytext=(20,20),textcoords="offset points",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
def update_annot(ind):
pos = sc.get_offsets()[ind["ind"][0]]
annot.xy = pos
text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))),
" ".join([names[n] for n in ind["ind"]]))
annot.set_text(text)
annot.get_bbox_patch().set_facecolor(cmap(norm(c[ind["ind"][0]])))
annot.get_bbox_patch().set_alpha(0.4)
def hover(event):
vis = annot.get_visible()
if event.inaxes == ax:
cont, ind = sc.contains(event)
if cont:
update_annot(ind)
annot.set_visible(True)
fig.canvas.draw_idle()
else:
if vis:
annot.set_visible(False)
fig.canvas.draw_idle()
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.show()
Because people also want to use this solution for a line plot instead of a scatter, the following would be the same solution for plot (which works slightly differently).
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
x = np.sort(np.random.rand(15))
y = np.sort(np.random.rand(15))
names = np.array(list("ABCDEFGHIJKLMNO"))
norm = plt.Normalize(1,4)
cmap = plt.cm.RdYlGn
fig,ax = plt.subplots()
line, = plt.plot(x,y, marker="o")
annot = ax.annotate("", xy=(0,0), xytext=(-20,20),textcoords="offset points",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
def update_annot(ind):
x,y = line.get_data()
annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))),
" ".join([names[n] for n in ind["ind"]]))
annot.set_text(text)
annot.get_bbox_patch().set_alpha(0.4)
def hover(event):
vis = annot.get_visible()
if event.inaxes == ax:
cont, ind = line.contains(event)
if cont:
update_annot(ind)
annot.set_visible(True)
fig.canvas.draw_idle()
else:
if vis:
annot.set_visible(False)
fig.canvas.draw_idle()
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.show()
In case someone is looking for a solution for lines in twin axes, refer to How to make labels appear when hovering over a point in multiple axis?
In case someone is looking for a solution for bar plots, please refer to e.g. this answer.
This solution works when hovering a line without the need to click it:
import matplotlib.pyplot as plt
# Need to create as global variable so our callback(on_plot_hover) can access
fig = plt.figure()
plot = fig.add_subplot(111)
# create some curves
for i in range(4):
# Giving unique ids to each data member
plot.plot(
[i*1,i*2,i*3,i*4],
gid=i)
def on_plot_hover(event):
# Iterating over each data member plotted
for curve in plot.get_lines():
# Searching which data member corresponds to current mouse position
if curve.contains(event)[0]:
print("over %s" % curve.get_gid())
fig.canvas.mpl_connect('motion_notify_event', on_plot_hover)
plt.show()
From http://matplotlib.sourceforge.net/examples/event_handling/pick_event_demo.html :
from matplotlib.pyplot import figure, show
import numpy as npy
from numpy.random import rand
if 1: # picking on a scatter plot (matplotlib.collections.RegularPolyCollection)
x, y, c, s = rand(4, 100)
def onpick3(event):
ind = event.ind
print('onpick3 scatter:', ind, npy.take(x, ind), npy.take(y, ind))
fig = figure()
ax1 = fig.add_subplot(111)
col = ax1.scatter(x, y, 100*s, c, picker=True)
#fig.savefig('pscoll.eps')
fig.canvas.mpl_connect('pick_event', onpick3)
show()
This recipe draws an annotation on picking a data point: http://scipy-cookbook.readthedocs.io/items/Matplotlib_Interactive_Plotting.html .
This recipe draws a tooltip, but it requires wxPython:
Point and line tooltips in matplotlib?
The easiest option is to use the mplcursors package.
mplcursors: read the docs
mplcursors: github
If using Anaconda, install with these instructions, otherwise use these instructions for pip.
This must be plotted in an interactive window, not inline.
For jupyter, executing something like %matplotlib qt in a cell will turn on interactive plotting. See How can I open the interactive matplotlib window in IPython notebook?
Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
import matplotlib.pyplot as plt
import pandas_datareader as web # only for test data; must be installed with conda or pip
from mplcursors import cursor # separate package must be installed
# reproducible sample data as a pandas dataframe
df = web.DataReader('aapl', data_source='yahoo', start='2021-03-09', end='2022-06-13')
plt.figure(figsize=(12, 7))
plt.plot(df.index, df.Close)
cursor(hover=True)
plt.show()
Pandas
ax = df.plot(y='Close', figsize=(10, 7))
cursor(hover=True)
plt.show()
Seaborn
Works with axes-level plots like sns.lineplot, and figure-level plots like sns.relplot.
import seaborn as sns
# load sample data
tips = sns.load_dataset('tips')
sns.relplot(data=tips, x="total_bill", y="tip", hue="day", col="time")
cursor(hover=True)
plt.show()
The other answers did not address my need for properly showing tooltips in a recent version of Jupyter inline matplotlib figure. This one works though:
import matplotlib.pyplot as plt
import numpy as np
import mplcursors
np.random.seed(42)
fig, ax = plt.subplots()
ax.scatter(*np.random.random((2, 26)))
ax.set_title("Mouse over a point")
crs = mplcursors.cursor(ax,hover=True)
crs.connect("add", lambda sel: sel.annotation.set_text(
'Point {},{}'.format(sel.target[0], sel.target[1])))
plt.show()
Leading to something like the following picture when going over a point with mouse:
A slight edit on an example provided in http://matplotlib.org/users/shell.html:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('click on points')
line, = ax.plot(np.random.rand(100), '-', picker=5) # 5 points tolerance
def onpick(event):
thisline = event.artist
xdata = thisline.get_xdata()
ydata = thisline.get_ydata()
ind = event.ind
print('onpick points:', *zip(xdata[ind], ydata[ind]))
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
This plots a straight line plot, as Sohaib was asking
mpld3 solve it for me.
EDIT (CODE ADDED):
import matplotlib.pyplot as plt
import numpy as np
import mpld3
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
N = 100
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s=1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.grid(color='white', linestyle='solid')
ax.set_title("Scatter Plot (with tooltips!)", size=20)
labels = ['point {0}'.format(i + 1) for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
You can check this example
mplcursors worked for me. mplcursors provides clickable annotation for matplotlib. It is heavily inspired from mpldatacursor (https://github.com/joferkington/mpldatacursor), with a much simplified API
import matplotlib.pyplot as plt
import numpy as np
import mplcursors
data = np.outer(range(10), range(1, 5))
fig, ax = plt.subplots()
lines = ax.plot(data)
ax.set_title("Click somewhere on a line.\nRight-click to deselect.\n"
"Annotations can be dragged.")
mplcursors.cursor(lines) # or just mplcursors.cursor()
plt.show()
showing object information in matplotlib statusbar
Features
no extra libraries needed
clean plot
no overlap of labels and artists
supports multi artist labeling
can handle artists from different plotting calls (like scatter, plot, add_patch)
code in library style
Code
### imports
import matplotlib as mpl
import matplotlib.pylab as plt
import numpy as np
# https://stackoverflow.com/a/47166787/7128154
# https://matplotlib.org/3.3.3/api/collections_api.html#matplotlib.collections.PathCollection
# https://matplotlib.org/3.3.3/api/path_api.html#matplotlib.path.Path
# https://stackoverflow.com/questions/15876011/add-information-to-matplotlib-navigation-toolbar-status-bar
# https://stackoverflow.com/questions/36730261/matplotlib-path-contains-point
# https://stackoverflow.com/a/36335048/7128154
class StatusbarHoverManager:
"""
Manage hover information for mpl.axes.Axes object based on appearing
artists.
Attributes
----------
ax : mpl.axes.Axes
subplot to show status information
artists : list of mpl.artist.Artist
elements on the subplot, which react to mouse over
labels : list (list of strings) or strings
each element on the top level corresponds to an artist.
if the artist has items
(i.e. second return value of contains() has key 'ind'),
the element has to be of type list.
otherwise the element if of type string
cid : to reconnect motion_notify_event
"""
def __init__(self, ax):
assert isinstance(ax, mpl.axes.Axes)
def hover(event):
if event.inaxes != ax:
return
info = 'x={:.2f}, y={:.2f}'.format(event.xdata, event.ydata)
ax.format_coord = lambda x, y: info
cid = ax.figure.canvas.mpl_connect("motion_notify_event", hover)
self.ax = ax
self.cid = cid
self.artists = []
self.labels = []
def add_artist_labels(self, artist, label):
if isinstance(artist, list):
assert len(artist) == 1
artist = artist[0]
self.artists += [artist]
self.labels += [label]
def hover(event):
if event.inaxes != self.ax:
return
info = 'x={:.2f}, y={:.2f}'.format(event.xdata, event.ydata)
for aa, artist in enumerate(self.artists):
cont, dct = artist.contains(event)
if not cont:
continue
inds = dct.get('ind')
if inds is not None: # artist contains items
for ii in inds:
lbl = self.labels[aa][ii]
info += '; artist [{:d}, {:d}]: {:}'.format(
aa, ii, lbl)
else:
lbl = self.labels[aa]
info += '; artist [{:d}]: {:}'.format(aa, lbl)
self.ax.format_coord = lambda x, y: info
self.ax.figure.canvas.mpl_disconnect(self.cid)
self.cid = self.ax.figure.canvas.mpl_connect(
"motion_notify_event", hover)
def demo_StatusbarHoverManager():
fig, ax = plt.subplots()
shm = StatusbarHoverManager(ax)
poly = mpl.patches.Polygon(
[[0,0], [3, 5], [5, 4], [6,1]], closed=True, color='green', zorder=0)
artist = ax.add_patch(poly)
shm.add_artist_labels(artist, 'polygon')
artist = ax.scatter([2.5, 1, 2, 3], [6, 1, 1, 7], c='blue', s=10**2)
lbls = ['point ' + str(ii) for ii in range(4)]
shm.add_artist_labels(artist, lbls)
artist = ax.plot(
[0, 0, 1, 5, 3], [0, 1, 1, 0, 2], marker='o', color='red')
lbls = ['segment ' + str(ii) for ii in range(5)]
shm.add_artist_labels(artist, lbls)
plt.show()
# --- main
if __name__== "__main__":
demo_StatusbarHoverManager()
I have made a multi-line annotation system to add to: https://stackoverflow.com/a/47166787/10302020.
for the most up to date version:
https://github.com/AidenBurgess/MultiAnnotationLineGraph
Simply change the data in the bottom section.
import matplotlib.pyplot as plt
def update_annot(ind, line, annot, ydata):
x, y = line.get_data()
annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
# Get x and y values, then format them to be displayed
x_values = " ".join(list(map(str, ind["ind"])))
y_values = " ".join(str(ydata[n]) for n in ind["ind"])
text = "{}, {}".format(x_values, y_values)
annot.set_text(text)
annot.get_bbox_patch().set_alpha(0.4)
def hover(event, line_info):
line, annot, ydata = line_info
vis = annot.get_visible()
if event.inaxes == ax:
# Draw annotations if cursor in right position
cont, ind = line.contains(event)
if cont:
update_annot(ind, line, annot, ydata)
annot.set_visible(True)
fig.canvas.draw_idle()
else:
# Don't draw annotations
if vis:
annot.set_visible(False)
fig.canvas.draw_idle()
def plot_line(x, y):
line, = plt.plot(x, y, marker="o")
# Annotation style may be changed here
annot = ax.annotate("", xy=(0, 0), xytext=(-20, 20), textcoords="offset points",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
line_info = [line, annot, y]
fig.canvas.mpl_connect("motion_notify_event",
lambda event: hover(event, line_info))
# Your data values to plot
x1 = range(21)
y1 = range(0, 21)
x2 = range(21)
y2 = range(0, 42, 2)
# Plot line graphs
fig, ax = plt.subplots()
plot_line(x1, y1)
plot_line(x2, y2)
plt.show()
Based off Markus Dutschke" and "ImportanceOfBeingErnest", I (imo) simplified the code and made it more modular.
Also this doesn't require additional packages to be installed.
import matplotlib.pylab as plt
import numpy as np
plt.close('all')
fh, ax = plt.subplots()
#Generate some data
y,x = np.histogram(np.random.randn(10000), bins=500)
x = x[:-1]
colors = ['#0000ff', '#00ff00','#ff0000']
x2, y2 = x,y/10
x3, y3 = x, np.random.randn(500)*10+40
#Plot
h1 = ax.plot(x, y, color=colors[0])
h2 = ax.plot(x2, y2, color=colors[1])
h3 = ax.scatter(x3, y3, color=colors[2], s=1)
artists = h1 + h2 + [h3] #concatenating lists
labels = [list('ABCDE'*100),list('FGHIJ'*100),list('klmno'*100)] #define labels shown
#___ Initialize annotation arrow
annot = ax.annotate("", xy=(0,0), xytext=(20,20),textcoords="offset points",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
def on_plot_hover(event):
if event.inaxes != ax: #exit if mouse is not on figure
return
is_vis = annot.get_visible() #check if an annotation is visible
# x,y = event.xdata,event.ydata #coordinates of mouse in graph
for ii, artist in enumerate(artists):
is_contained, dct = artist.contains(event)
if(is_contained):
if('get_data' in dir(artist)): #for plot
data = list(zip(*artist.get_data()))
elif('get_offsets' in dir(artist)): #for scatter
data = artist.get_offsets().data
inds = dct['ind'] #get which data-index is under the mouse
#___ Set Annotation settings
xy = data[inds[0]] #get 1st position only
annot.xy = xy
annot.set_text(f'pos={xy},text={labels[ii][inds[0]]}')
annot.get_bbox_patch().set_edgecolor(colors[ii])
annot.get_bbox_patch().set_alpha(0.7)
annot.set_visible(True)
fh.canvas.draw_idle()
else:
if is_vis:
annot.set_visible(False) #disable when not hovering
fh.canvas.draw_idle()
fh.canvas.mpl_connect('motion_notify_event', on_plot_hover)
Giving the following result:
Maybe this helps anybody, but I have adapted the #ImportanceOfBeingErnest's answer to work with patches and classes. Features:
The entire framework is contained inside of a single class, so all of the used variables are only available within their relevant scopes.
Can create multiple distinct sets of patches
Hovering over a patch prints patch collection name and patch subname
Hovering over a patch highlights all patches of that collection by changing their edge color to black
Note: For my applications, the overlap is not relevant, thus only one object's name is displayed at a time. Feel free to extend to multiple objects if you wish, it is not too hard.
Usage
fig, ax = plt.subplots(tight_layout=True)
ap = annotated_patches(fig, ax)
ap.add_patches('Azure', 'circle', 'blue', np.random.uniform(0, 1, (4,2)), 'ABCD', 0.1)
ap.add_patches('Lava', 'rect', 'red', np.random.uniform(0, 1, (3,2)), 'EFG', 0.1, 0.05)
ap.add_patches('Emerald', 'rect', 'green', np.random.uniform(0, 1, (3,2)), 'HIJ', 0.05, 0.1)
plt.axis('equal')
plt.axis('off')
plt.show()
Implementation
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection
np.random.seed(1)
class annotated_patches:
def __init__(self, fig, ax):
self.fig = fig
self.ax = ax
self.annot = self.ax.annotate("", xy=(0,0),
xytext=(20,20),
textcoords="offset points",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
self.annot.set_visible(False)
self.collectionsDict = {}
self.coordsDict = {}
self.namesDict = {}
self.isActiveDict = {}
self.motionCallbackID = self.fig.canvas.mpl_connect("motion_notify_event", self.hover)
def add_patches(self, groupName, kind, color, xyCoords, names, *params):
if kind=='circle':
circles = [mpatches.Circle(xy, *params, ec="none") for xy in xyCoords]
thisCollection = PatchCollection(circles, facecolor=color, alpha=0.5, edgecolor=None)
ax.add_collection(thisCollection)
elif kind == 'rect':
rectangles = [mpatches.Rectangle(xy, *params, ec="none") for xy in xyCoords]
thisCollection = PatchCollection(rectangles, facecolor=color, alpha=0.5, edgecolor=None)
ax.add_collection(thisCollection)
else:
raise ValueError('Unexpected kind', kind)
self.collectionsDict[groupName] = thisCollection
self.coordsDict[groupName] = xyCoords
self.namesDict[groupName] = names
self.isActiveDict[groupName] = False
def update_annot(self, groupName, patchIdxs):
self.annot.xy = self.coordsDict[groupName][patchIdxs[0]]
self.annot.set_text(groupName + ': ' + self.namesDict[groupName][patchIdxs[0]])
# Set edge color
self.collectionsDict[groupName].set_edgecolor('black')
self.isActiveDict[groupName] = True
def hover(self, event):
vis = self.annot.get_visible()
updatedAny = False
if event.inaxes == self.ax:
for groupName, collection in self.collectionsDict.items():
cont, ind = collection.contains(event)
if cont:
self.update_annot(groupName, ind["ind"])
self.annot.set_visible(True)
self.fig.canvas.draw_idle()
updatedAny = True
else:
if self.isActiveDict[groupName]:
collection.set_edgecolor(None)
self.isActiveDict[groupName] = True
if (not updatedAny) and vis:
self.annot.set_visible(False)
self.fig.canvas.draw_idle()