Develop Reference

How to use python matplot animation to draw the change of grid color

I want to create a simple animation to show my data changes.
create a 3 * 3 grid.
I have an array which is 20 * 9. The data is read into the animation line by line.
Color = [[0,0,0,0,0,0,0,0,0],
[100,0,0,0,100,0,0,0,0,0],
[80,0,80,0,80,100,0,0,0]
......]
I hope the list for the grid is read line by line and each line works for 100ms. If the number is more than 0, the color of the grid change into red. For example, in the first 100ms, all color is black, then, in the second 100ms, grid number 0 and grid number 4 change to red and last for 100ms. In the third 100ms, grid number 0,2,4,5 change to red and last for 100ms.
My current version of the code looks like this. I don't know how to draw like what I describe above.
%matplotlib notebook ## show in jupyter
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(7, 6.5)
ax = plt.axes(xlim=(0, 10), ylim=(0, 10))
patch = plt.Circle((5, -5), 0.75, fc='y')
def init():
patch.center = (5, 5)
ax.add_patch(patch)
return patch,
def animate(i):
x, y = patch.center
x = 5 + 3 * np.sin(np.radians(i))
y = 5 + 3 * np.cos(np.radians(i))
patch.center = (x, y)
return patch,
anim = animation.FuncAnimation(fig, animate,
init_func=init,
frames=360,
interval=20,
blit=True)
plt.show()
I would appreciate it a lot if anyone could help me!!!

Cool question! I have an answer that works but it changes the structure of your Colors list. Here's the answer with an explanation below.
%matplotlib notebook
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(7, 6.5)
ax = plt.axes(xlim=(0, 10), ylim=(0, 10))
patch = plt.Circle((5, -5), 0.75, fc='y')
colors = [
[["black", None, None], ["black", None, "black"], [None, None, "red"]],
[["black", None, None], ["black", None, "red"], ["black", None, "red"]],
[["red", None, None], [None, None, None], [None, None, "red"]],
]
def get_coords(colors):
y = 0
x = 0
coords = []
for row in colors:
x = 0
for entry in row:
if entry:
coords.append([entry, x, y])
x += 3.33
y += 3.33
return coords
def get_grids(coord):
return [plt.Rectangle((x[1], x[2]), 3.33, 3.33, fc=x[0]) for x in coord]
coords = [get_coords(color) for color in colors]
grids = [get_grids(coord) for coord in coords]
def init():
patch.center = (5, 5)
ax.add_patch(patch)
return patch,
def animate(i):
patches = []
if (i % 100 == 0):
ax.patches = []
next_grid = grids.pop(0)
for rectangle in next_grid:
patches.append(ax.add_patch(rectangle))
x, y = patch.center
x = 5 + 3 * np.sin(np.radians(i))
y = 5 + 3 * np.cos(np.radians(i))
patch.center = (x, y)
patches.append(ax.add_patch(patch))
return patches
anim = animation.FuncAnimation(fig, animate,
init_func=init,
frames=360,
interval=20,
blit=True)
plt.show()
The key idea is adding and removing plt.Rectangles to give the appearance of a grid. Because your graph is 10x10, these rectangles are squares of side length 10/3 =~ 3.33.
I think it's easier to use 20 * 3 * 3 instead of a 20 * 9 list for the colors. I use the following:
colors = [
[["black", None, None], ["black", None, "black"], [None, None, "red"]],
[["black", None, None], ["black", None, "red"], ["black", None, "red"]],
[["red", None, None], [None, None, None], [None, None, "red"]],
]
Each entry in this list, as yours, is a grid. Within these grids, however, are rows, each with a desired color entry. colors[0][0] == ["black", None, None] means at the first frame of the animation, the bottom left corner of the grid will be black and the rest of the bottom transparent. colors[0][1] == ["black", None, "black"] means the middle of the grid will have the left and right thirds black with the middle transparent.
The get_coords and get_grids functions are pretty hairy and clearly hard-coded to support a 3x3 grid with dimensions 10x10 - it'd be cool to parameterize that out down the line.
Only other important idea is that to change the animation every 100ms, we just check if i in the animate function is divisible by 100. If it is, we clear the existing patches (so we're not just adding rectangles ad nausem) and plot our new ones. When the grid list runs out, the grid will be transparent for the rest of the animation.
Hope this helps - happy plotting!

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
Color = [[0,0,0,0,0,0,0,0,0],
[100,0,0,0,100,0,0,0,0,0],
[80,0,80,0,80,100,0,0,0]]
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(7, 6.5)
fig.set_tight_layout(True)
ax = plt.axes(xlim=(0, 6), ylim=(0, 6))
patch = []
patch.append(plt.Rectangle(xy = (0,0),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (2,0),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (4,0),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (0,2),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (2,2),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (4,2),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (0,4),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (2,4),width = 2,height = 2,fill = True,color = 'k'))
patch.append(plt.Rectangle(xy = (4,4),width = 2,height = 2,fill = True,color = 'k'))
def init():
for i in range(9):
patch[i].set_color('k')
ax.add_patch(patch[i])
return patch
def animate(i):
value = (np.array(Color[i]) == 0)
for j in range(9):
patch[j].set_color('k' if value[j] else 'r')
return patch
anim = animation.FuncAnimation(fig, animate,
init_func=init,
frames=3,
interval=1000,
blit=True)
plt.show()

Can't get my program to animate multiple patches in python matplotlib

I am attempting to animate two different particles in matplotlib (python). I just figured out a way to animate one particle in matplotlib, but I am havign difficulties trying to get the program to work with multiple particles. Does anyone know what is wrong and how to fix it?
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(5, 4.5)
ax = plt.axes(xlim=(0, 100), ylim=(0, 100))
enemy = plt.Circle((10, -10), 0.75, fc='r')
agent = plt.Circle((10, -10), 0.75, fc='b')
def init():
#enemy.center = (5, 5)
#agent.center = (5, 5)
ax.add_patch(agent)
ax.add_patch(enemy)
return []
def animationManage(i,agent,enemy):
patches = []
enemy.center = (5, 5)
agent.center = (5, 5)
enemy_patches = animateCos(i,agent)
agent_patches = animateLine(i,enemy)
patches[enemy_patches, agent_patches]
#patches.append(ax.add_patch(enemy_patches))
#patches.append(ax.add_patch(agent_patches))
return enemy_patches
def animateCirc(i, patch):
# It seems that i represents time step
x, y = patch.center
# 1st constant = position and 2nd constant = trajectory
x = 50 + 30 * np.sin(np.radians(i))
y = 50 + 30 * np.cos(np.radians(i))
patch.center = (x, y)
return patch,
def animateLine(i, patch):
x, y = patch.center
x = x + 1
y = x+ 1
patch.center = (x, y)
return patch,
def animateCos(i, patch):
x, y = patch.center
x = x + 0.2
y = 50 + 30 * np.cos(np.radians(i))
patch.center = (x, y)
return patch,
def animateSin(i, patch):
x, y = patch.center
x = x + 0.2
y = 50 + 30 * np.sin(np.radians(i))
patch.center = (x, y)
return patch,
anim = animation.FuncAnimation(fig, animationManage,
init_func=init,
frames=360,
fargs=(agent,enemy,),
interval=20,
blit=True)
plt.show()
Working code for animating one particle
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(5, 4.5)
ax = plt.axes(xlim=(0, 100), ylim=(0, 100))
enemy = plt.Circle((10, -10), 0.75, fc='r')
agent = plt.Circle((10, -10), 0.75, fc='b')
def init():
enemy.center = (5, 5)
agent.center = (5, 5)
ax.add_patch(enemy)
ax.add_patch(agent)
return enemy,
def animateCirc(i, patch):
# It seems that i represents time step
x, y = patch.center
# 1st constant = position and 2nd constant = trajectory
x = 50 + 30 * np.sin(np.radians(i))
y = 50 + 30 * np.cos(np.radians(i))
patch.center = (x, y)
return patch,
def animateLine(i, patch):
x, y = patch.center
x = x + 1
y = x+ 1
patch.center = (x, y)
return patch,
def animateCos(i, patch):
x, y = patch.center
x = x + 0.2
y = 50 + 30 * np.cos(np.radians(i))
patch.center = (x, y)
return patch,
def animateSin(i, patch):
x, y = patch.center
x = x + 0.2
y = 50 + 30 * np.sin(np.radians(i))
patch.center = (x, y)
return patch,
anim = animation.FuncAnimation(fig, animateCos,
init_func=init,
frames=360,
fargs=(enemy,),
interval=20,
blit=True)
plt.show()

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(5, 4.5)
ax = plt.axes(xlim=(0, 100), ylim=(0, 100))
enemy = plt.Circle((10, -10), 0.75, fc='r')
agent = plt.Circle((10, -10), 0.75, fc='b')
def init():
enemy.center = (5, 5)
agent.center = (5, 5)
ax.add_patch(agent)
ax.add_patch(enemy)
return []
def animationManage(i,agent,enemy):
animateCos(i,enemy)
animateLine(i,agent)
return []
def animateLine(i, patch):
x, y = patch.center
x += 0.25
y += 0.25
patch.center = (x, y)
return patch,
def animateCos(i, patch):
x, y = patch.center
x += 0.2
y = 50 + 30 * np.cos(np.radians(i))
patch.center = (x, y)
return patch,
anim = animation.FuncAnimation(fig, animationManage,
init_func=init,
frames=360,
fargs=(agent,enemy,),
interval=20,
blit=True,
repeat=True)
plt.show()

Get data from Python plot with matplotlib then save to array

The first block of code in this answer allows the user to generate a matplotlib figure and by clicking on the graph, it is possible to display the x- and y- coordinates of the graph after each click. How can I save these coordinates to 5 decimal places, say, into a numpy array (X for the x-coordinates and Y for the y-coordinates)? I'm not really sure how to start this (and it's probably trivial), but here is the code:
import numpy as np
import matplotlib.pyplot as plt
X = []
Y = []
class DataCursor(object):
text_template = 'x: %0.2f\ny: %0.2f'
x, y = 0.0, 0.0
xoffset, yoffset = -20, 20
text_template = 'x: %0.2f\ny: %0.2f'
def __init__(self, ax):
self.ax = ax
self.annotation = ax.annotate(self.text_template,
xy=(self.x, self.y), xytext=(self.xoffset, self.yoffset),
textcoords='offset points', ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')
)
self.annotation.set_visible(False)
def __call__(self, event):
self.event = event
self.x, self.y = event.mouseevent.xdata, event.mouseevent.ydata
if self.x is not None:
self.annotation.xy = self.x, self.y
self.annotation.set_text(self.text_template % (self.x, self.y))
self.annotation.set_visible(True)
event.canvas.draw()
fig = plt.figure()
line, = plt.plot(range(10), 'ro-')
fig.canvas.mpl_connect('pick_event', DataCursor(plt.gca()))
line.set_picker(5) # Tolerance in points
plt.show()

It sounds like you want plt.ginput().
As a quick example:
fig, ax = plt.subplots()
ax.plot(range(10), 'ro-')
points = plt.ginput(n=4)
print points
np.savetxt('yourfilename', points)
plt.show()

I think you can do this by using list members in DataCursor:
def __init__(self, ax):
...
self.mouseX = []
self.mouseY = []
In your call, you would then store the X and Y for each event into these members:
def __call__(self, event):
...
self.mouseX.append(self.x)
self.mouseY.append(self.y)
You would then pass this to mpl_connect like this:
DC = DataCursor(plt.gca())
fig.canvas.mpl_connect('pick_event', DC)
...
print DC.mouseX, DC.mouseY
I have illustrated the principle here, but I don't see why this couldn't be applied to numpy arrays as well.

Possible to make labels appear when hovering over a point in matplotlib in stem plot?

I am new to matplotlib and I am looking to label stems in a stem plot with x,y co-od when mouse hovers over that point. When I searched everything was meant for scatter plot (Possible to make labels appear when hovering over a point in matplotlib? present code is like this:
def plot_matching(mzs,ints,matching,scan_num):
fig=p1.gcf()
fig.canvas.set_window_title('MS/MS Viewer')
rel_ints=relative_intensity(ints)
p1.xlim(min(mzs)-100,max(mzs)+100)
p1.ylim(min(rel_ints),max(rel_ints)+5)
p1.title('Scan Number:'+scan_num)
p1.xlabel('m/z')
p1.ylabel('Relative intensity')
mzs_rel=zip(mzs,rel_ints)
for x,y in mzs_rel:
x1=[]
y1=[]
x1.append(x)
y1.append(y)
markerline, stemlines, baseline=p1.stem(x1,y1)
p1.setp(markerline, 'Marker', '')
for m in matching:
if x==m[1] and y>3.0:
p1.setp(stemlines, linewidth=2, color='r')
p1.text(x,y,m[0],fontsize=12)
break
else:
p1.setp(stemlines,linewidth=2, color='g')
return p1
Will the scatter plot link for stem plot too?

To make a hovering label, you need to hook up a function to handle motion_notify_events:
plt.connect('motion_notify_event', some_function)
Below is some code showing one way to do it. The hovering label behavior is produced by
cursor = FollowDotCursor(ax, x, y)
where ax is the axis, x and y are lists of coordinates. Since you supply x and y, it does not matter if you are making a line plot or a stem plot or whatever. The labels appear when the mouse is moved near any point (xi, yi).
The code below uses scipy.spatial.cKDTree to locate the nearest point. Here is an older version of this code which does not require scipy.
import matplotlib.pyplot as plt
import scipy.spatial as spatial
import numpy as np
pi = np.pi
cos = np.cos
def fmt(x, y):
return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x=x, y=y)
class FollowDotCursor(object):
"""Display the x,y location of the nearest data point.
https://stackoverflow.com/a/4674445/190597 (Joe Kington)
https://stackoverflow.com/a/13306887/190597 (unutbu)
https://stackoverflow.com/a/15454427/190597 (unutbu)
"""
def __init__(self, ax, x, y, tolerance=5, formatter=fmt, offsets=(-20, 20)):
try:
x = np.asarray(x, dtype='float')
except (TypeError, ValueError):
x = np.asarray(mdates.date2num(x), dtype='float')
y = np.asarray(y, dtype='float')
mask = ~(np.isnan(x) | np.isnan(y))
x = x[mask]
y = y[mask]
self._points = np.column_stack((x, y))
self.offsets = offsets
y = y[np.abs(y-y.mean()) <= 3*y.std()]
self.scale = x.ptp()
self.scale = y.ptp() / self.scale if self.scale else 1
self.tree = spatial.cKDTree(self.scaled(self._points))
self.formatter = formatter
self.tolerance = tolerance
self.ax = ax
self.fig = ax.figure
self.ax.xaxis.set_label_position('top')
self.dot = ax.scatter(
[x.min()], [y.min()], s=130, color='green', alpha=0.7)
self.annotation = self.setup_annotation()
plt.connect('motion_notify_event', self)
def scaled(self, points):
points = np.asarray(points)
return points * (self.scale, 1)
def __call__(self, event):
ax = self.ax
# event.inaxes is always the current axis. If you use twinx, ax could be
# a different axis.
if event.inaxes == ax:
x, y = event.xdata, event.ydata
elif event.inaxes is None:
return
else:
inv = ax.transData.inverted()
x, y = inv.transform([(event.x, event.y)]).ravel()
annotation = self.annotation
x, y = self.snap(x, y)
annotation.xy = x, y
annotation.set_text(self.formatter(x, y))
self.dot.set_offsets(np.column_stack((x, y)))
bbox = self.annotation.get_window_extent()
self.fig.canvas.blit(bbox)
self.fig.canvas.draw_idle()
def setup_annotation(self):
"""Draw and hide the annotation box."""
annotation = self.ax.annotate(
'', xy=(0, 0), ha = 'right',
xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
bbox = dict(
boxstyle='round,pad=0.5', fc='yellow', alpha=0.75),
arrowprops = dict(
arrowstyle='->', connectionstyle='arc3,rad=0'))
return annotation
def snap(self, x, y):
"""Return the value in self.tree closest to x, y."""
dist, idx = self.tree.query(self.scaled((x, y)), k=1, p=1)
try:
return self._points[idx]
except IndexError:
# IndexError: index out of bounds
return self._points[0]
fig, ax = plt.subplots()
x = np.linspace(0.1, 2*pi, 10)
y = cos(x)
markerline, stemlines, baseline = ax.stem(x, y, '-.')
plt.setp(markerline, 'markerfacecolor', 'b')
plt.setp(baseline, 'color','r', 'linewidth', 2)
cursor = FollowDotCursor(ax, x, y, tolerance=20)
plt.show()

There is a great plugin: https://github.com/anntzer/mplcursors
very simple to install. No neet to implement on your own.

Get data from plot with matplotlib

I'm using matplotlib in python to build a scatter plot.
suppose I have the following 2 data lists.
X=[1,2,3,4,5]
Y=[6,7,8,9,10]
then I use X as the X-axis value and Y as the Y-axis value to make a scatter plot. So I will have a picture with 5 scattering points on it, right?
Now the question: is it possible to build connection for these 5 points with the actual data. For example, when I click on one of these 5 points, it can tell me what original data I have used to make this point?
thanks in advance

Using a slightly modified version of Joe Kington's DataCursor:
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
import numpy as np
def fmt(x, y):
return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x = x, y = y)
class DataCursor(object):
# https://stackoverflow.com/a/4674445/190597
"""A simple data cursor widget that displays the x,y location of a
matplotlib artist when it is selected."""
def __init__(self, artists, x = [], y = [], tolerance = 5, offsets = (-20, 20),
formatter = fmt, display_all = False):
"""Create the data cursor and connect it to the relevant figure.
"artists" is the matplotlib artist or sequence of artists that will be
selected.
"tolerance" is the radius (in points) that the mouse click must be
within to select the artist.
"offsets" is a tuple of (x,y) offsets in points from the selected
point to the displayed annotation box
"formatter" is a callback function which takes 2 numeric arguments and
returns a string
"display_all" controls whether more than one annotation box will
be shown if there are multiple axes. Only one will be shown
per-axis, regardless.
"""
self._points = np.column_stack((x,y))
self.formatter = formatter
self.offsets = offsets
self.display_all = display_all
if not cbook.iterable(artists):
artists = [artists]
self.artists = artists
self.axes = tuple(set(art.axes for art in self.artists))
self.figures = tuple(set(ax.figure for ax in self.axes))
self.annotations = {}
for ax in self.axes:
self.annotations[ax] = self.annotate(ax)
for artist in self.artists:
artist.set_picker(tolerance)
for fig in self.figures:
fig.canvas.mpl_connect('pick_event', self)
def annotate(self, ax):
"""Draws and hides the annotation box for the given axis "ax"."""
annotation = ax.annotate(self.formatter, xy = (0, 0), ha = 'right',
xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0')
)
annotation.set_visible(False)
return annotation
def snap(self, x, y):
"""Return the value in self._points closest to (x, y).
"""
idx = np.nanargmin(((self._points - (x,y))**2).sum(axis = -1))
return self._points[idx]
def __call__(self, event):
"""Intended to be called through "mpl_connect"."""
# Rather than trying to interpolate, just display the clicked coords
# This will only be called if it's within "tolerance", anyway.
x, y = event.mouseevent.xdata, event.mouseevent.ydata
annotation = self.annotations[event.artist.axes]
if x is not None:
if not self.display_all:
# Hide any other annotation boxes...
for ann in self.annotations.values():
ann.set_visible(False)
# Update the annotation in the current axis..
x, y = self.snap(x, y)
annotation.xy = x, y
annotation.set_text(self.formatter(x, y))
annotation.set_visible(True)
event.canvas.draw()
x=[1,2,3,4,5]
y=[6,7,8,9,10]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
scat = ax.scatter(x, y)
DataCursor(scat, x, y)
plt.show()
yields
You can click on any of the points and the balloon will show the underlying data values.
My slight modification to the DataCursor was to add the snap method, which ensures that the data point displayed came from the original data set, rather than the location where the mouse actually clicked.
If you have scipy installed, you might prefer this version of the Cursor, which makes the balloon follow the mouse (without clicking):
import datetime as DT
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import scipy.spatial as spatial
def fmt(x, y, is_date):
if is_date:
x = mdates.num2date(x).strftime("%Y-%m-%d")
return 'x: {x}\ny: {y}'.format(x=x, y=y)
else:
return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x=x, y=y)
class FollowDotCursor(object):
"""Display the x,y location of the nearest data point."""
def __init__(self, ax, x, y, tolerance=5, formatter=fmt, offsets=(-20, 20)):
try:
x = np.asarray(x, dtype='float')
self.is_date = False
except (TypeError, ValueError):
x = np.asarray(mdates.date2num(x), dtype='float')
self.is_date = True
y = np.asarray(y, dtype='float')
self._points = np.column_stack((x, y))
self.offsets = offsets
self.scale = x.ptp()
self.scale = y.ptp() / self.scale if self.scale else 1
self.tree = spatial.cKDTree(self.scaled(self._points))
self.formatter = formatter
self.tolerance = tolerance
self.ax = ax
self.fig = ax.figure
self.ax.xaxis.set_label_position('top')
self.dot = ax.scatter(
[x.min()], [y.min()], s=130, color='green', alpha=0.7)
self.annotation = self.setup_annotation()
plt.connect('motion_notify_event', self)
def scaled(self, points):
points = np.asarray(points)
return points * (self.scale, 1)
def __call__(self, event):
ax = self.ax
# event.inaxes is always the current axis. If you use twinx, ax could be
# a different axis.
if event.inaxes == ax:
x, y = event.xdata, event.ydata
elif event.inaxes is None:
return
else:
inv = ax.transData.inverted()
x, y = inv.transform([(event.x, event.y)]).ravel()
annotation = self.annotation
x, y = self.snap(x, y)
annotation.xy = x, y
annotation.set_text(self.formatter(x, y, self.is_date))
self.dot.set_offsets((x, y))
bbox = ax.viewLim
event.canvas.draw()
def setup_annotation(self):
"""Draw and hide the annotation box."""
annotation = self.ax.annotate(
'', xy=(0, 0), ha = 'right',
xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
bbox = dict(
boxstyle='round,pad=0.5', fc='yellow', alpha=0.75),
arrowprops = dict(
arrowstyle='->', connectionstyle='arc3,rad=0'))
return annotation
def snap(self, x, y):
"""Return the value in self.tree closest to x, y."""
dist, idx = self.tree.query(self.scaled((x, y)), k=1, p=1)
try:
return self._points[idx]
except IndexError:
# IndexError: index out of bounds
return self._points[0]
x = [DT.date.today()+DT.timedelta(days=i) for i in [10,20,30,40,50]]
y = [6,7,8,9,10]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x, y)
cursor = FollowDotCursor(ax, x, y)
fig.autofmt_xdate()
plt.show()