I previously asked the question "How to zoom subplots together?", and have been using the excellent answer since then.
I'm now plotting just two sets of time-series data, and I need to continue to zoom as above, but now I need to also pan one plot relative to the other (I'm doing eyeball correlation). The data comes from 2 independent instruments with different start times and different clock settings.
In use, I zoom using the 'Zoom to Rectangle' toolbar button, and I scroll using the "Pan/Zoom" button.
How may I best scroll one plot in X relative to the other? Ideally, I'd also like to capture and display the time difference. I do not need to scroll vertically in Y.
I suspect I may need to stop using the simple "sharex=" "sharey=" method, but am not certain how best to proceed.
Thanks, in advance, to the great StackOverflow community!
-BobC
I hacked the above solution until it did want I think I want.
# File: ScrollTest.py
# coding: ASCII
"""
Interatively zoom plots together, but permit them to scroll independently.
"""
from matplotlib import pyplot
import sys
def _get_limits( ax ):
""" Return X and Y limits for the passed axis as [[xlow,xhigh],[ylow,yhigh]]
"""
return [list(ax.get_xlim()), list(ax.get_ylim())]
def _set_limits( ax, lims ):
""" Set X and Y limits for the passed axis
"""
ax.set_xlim(*(lims[0]))
ax.set_ylim(*(lims[1]))
return
def pre_zoom( fig ):
""" Initialize history used by the re_zoom() event handler.
Call this after plots are configured and before pyplot.show().
"""
global oxy
oxy = [_get_limits(ax) for ax in fig.axes]
# :TODO: Intercept the toolbar Home, Back and Forward buttons.
return
def re_zoom(event):
""" Pyplot event handler to zoom all plots together, but permit them to
scroll independently. Created to support eyeball correlation.
Use with 'motion_notify_event' and 'button_release_event'.
"""
global oxy
for ax in event.canvas.figure.axes:
navmode = ax.get_navigate_mode()
if navmode is not None:
break
scrolling = (event.button == 1) and (navmode == "PAN")
if scrolling: # Update history (independent of event type)
oxy = [_get_limits(ax) for ax in event.canvas.figure.axes]
return
if event.name != 'button_release_event': # Nothing to do!
return
# We have a non-scroll 'button_release_event': Were we zooming?
zooming = (navmode == "ZOOM") or ((event.button == 3) and (navmode == "PAN"))
if not zooming: # Nothing to do!
oxy = [_get_limits(ax) for ax in event.canvas.figure.axes] # To be safe
return
# We were zooming, but did anything change? Check for zoom activity.
changed = None
zoom = [[0.0,0.0],[0.0,0.0]] # Zoom from each end of axis (2 values per axis)
for i, ax in enumerate(event.canvas.figure.axes): # Get the axes
# Find the plot that changed
nxy = _get_limits(ax)
if (oxy[i] != nxy): # This plot has changed
changed = i
# Calculate zoom factors
for j in [0,1]: # Iterate over x and y for each axis
# Indexing: nxy[x/y axis][lo/hi limit]
# oxy[plot #][x/y axis][lo/hi limit]
width = oxy[i][j][1] - oxy[i][j][0]
# Determine new axis scale factors in a way that correctly
# handles simultaneous zoom + scroll: Zoom from each end.
zoom[j] = [(nxy[j][0] - oxy[i][j][0]) / width, # lo-end zoom
(oxy[i][j][1] - nxy[j][1]) / width] # hi-end zoom
break # No need to look at other axes
if changed is not None:
for i, ax in enumerate(event.canvas.figure.axes): # change the scale
if i == changed:
continue
for j in [0,1]:
width = oxy[i][j][1] - oxy[i][j][0]
nxy[j] = [oxy[i][j][0] + (width*zoom[j][0]),
oxy[i][j][1] - (width*zoom[j][1])]
_set_limits(ax, nxy)
event.canvas.draw() # re-draw the canvas (if required)
pre_zoom(event.canvas.figure) # Update history
return
# End re_zoom()
def main(argv):
""" Test/demo code for re_zoom() event handler.
"""
import numpy
x = numpy.linspace(0,100,1000) # Create test data
y = numpy.sin(x)*(1+x)
fig = pyplot.figure() # Create plot
ax1 = pyplot.subplot(211)
ax1.plot(x,y)
ax2 = pyplot.subplot(212)
ax2.plot(x,y)
pre_zoom( fig ) # Prepare plot event handler
pyplot.connect('motion_notify_event', re_zoom) # for right-click pan/zoom
pyplot.connect('button_release_event',re_zoom) # for rectangle-select zoom
pyplot.show() # Show plot and interact with user
# End main()
if __name__ == "__main__":
# Script is being executed from the command line (not imported)
main(sys.argv)
# End of file ScrollTest.py
Ok, here's my stab at it. This works, but there might be a simpler approach. This solution uses some matplotlib event-handling to trigger a new set_xlim() every time it notices the mouse in motion. The trigger event 'motion_notify_event' could be eliminated if dynamic synchronous zooming isn't required.
Bonus: this works for any number of subplots.
from matplotlib import pyplot
import numpy
x = numpy.linspace(0,10,100)
y = numpy.sin(x)*(1+x)
fig = pyplot.figure()
ax1 = pyplot.subplot(121)
ax1.plot(x,y)
ax2 = pyplot.subplot(122)
ax2.plot(x,y)
ax1.old_xlim = ax1.get_xlim() # store old values so changes
ax2.old_xlim = ax2.get_xlim() # can be detected
def re_zoom(event):
zoom = 1.0
for ax in event.canvas.figure.axes: # get the change in scale
nx = ax.get_xlim()
ox = ax.old_xlim
if ox != nx: # of axes that have changed scale
zoom = (nx[1]-nx[0])/(ox[1]-ox[0])
for ax in event.canvas.figure.axes: # change the scale
nx = ax.get_xlim()
ox = ax.old_xlim
if ox == nx: # of axes that need an update
mid = (ox[0] + ox[1])/2.0
dif = zoom*(ox[1] - ox[0])/2.0
nx = (mid - dif, mid + dif)
ax.set_xlim(*nx)
ax.old_xlim = nx
if zoom != 1.0:
event.canvas.draw() # re-draw the canvas (if required)
pyplot.connect('motion_notify_event', re_zoom) # for right-click pan/zoom
pyplot.connect('button_release_event', re_zoom) # for rectangle-select zoom
pyplot.show()
Related
I am using Multicursor to get a cursor on every graph.
I want to show the value of the datapoint, which is hit by the cursor, inside a legend during hovering over the graphs, like this
Actually I have thought that this is a standard feature of matplotlib respectively Multicursor, but it seems not. Did someone already something like this or do I have to implement it by my own.
I already found this post matplotlib multiple values under cursor, but this could be just the beginning for the implementation I want.
I have developed a solution.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import MultiCursor
from bisect import bisect_left
fig = plt.figure(figsize=(15, 8))
# create random graph with 60 datapoints, 0 till 59
x = list(range(0,60))
axes_list = []
def createRandomGraph(ax,x):
y = np.random.randint(low=0, high=15, size=60)
data.append(y)
ax.plot(x,y, marker='.')
def take_closest(myList, myNumber):
"""
Assumes myList is sorted. Returns closest value to myNumber.
If two numbers are equally close, return the smallest number.
"""
pos = bisect_left(myList, myNumber)
if pos == 0:
return myList[0]
if pos == len(myList):
return myList[-1]
before = myList[pos - 1]
after = myList[pos]
if after - myNumber < myNumber - before:
return after, pos
else:
return before, pos-1
def show_Legend(event):
#get mouse coordinates
mouseXdata = event.xdata
# the value of the closest data point to the current mouse position shall be shown
closestXValue, posClosestXvalue = take_closest(data[0], mouseXdata)
i = 1
for ax in axes_list:
datalegend = ax.text(1.05, 0.5, data[i][posClosestXvalue], fontsize=7,
verticalalignment='top', bbox=props, transform=ax.transAxes)
ax.draw_artist(datalegend)
# this remove is required because otherwise after a resizing of the window there is
# an artifact of the last label, which lies behind the new one
datalegend.remove()
i +=1
fig.canvas.update()
# store the x value of the graph in the first element of the list
data = [x]
# properties of the legend labels
props = dict(boxstyle='round', edgecolor='black', facecolor='wheat', alpha=1.5)
for i in range(5):
if(i>0):
# all plots share the same x axes, thus during zooming and panning
# we will see always the same x section of each graph
ax = plt.subplot(5, 1, i+1, sharex=ax)
else:
ax = plt.subplot(5, 1, i+1)
axes_list.append(ax)
createRandomGraph(ax,x)
multi = MultiCursor(fig.canvas, axes_list, color='r', lw=1)
# function show_Legend is called while hovering over the graphs
fig.canvas.mpl_connect('motion_notify_event', show_Legend)
plt.show()
The output looks like this
Maybe you like it and find it useful
I am stuck again with interactive plotting with matplotlib.
Everything else works like a charm (hovering and clicking of objects in a figure) but if I zoom the shown figure and it will be updated, zooming rectangle will remain in the new figure. Probably I have to reset zooming settings somehow but I couldn't find out the correct method to do it from other StackOverflow questions (clearing the figure is not obviously enough).
I built a toy example to illustrate the problem. Four points are attached to four images and they are plotted to the figure. With interactive-mode by inserting cursor on top of chosen point, it shows related image in a imagebox. After one point is clicked, program waits 2 seconds and updates the view by rotating all the samples 15 degrees.
The problem occurs when current view is zoomed and then its updated. Zoom-to-rectangle will start automatically and after clicking once anywhere in the figure, the rectangle is gone without doing anything. This is shown in below image. I just want to have normal cursor after figure is updated.
Here is the code for the toy example:
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
import copy
def initialize_figure(fignum):
plt.figure(fignum)
plt.clf()
def draw_interactive_figures(new_samples, images):
global new_samples_tmp, images_tmp, offset_image_tmp, image_box_tmp, fig_tmp, x_tmp, y_tmp
initialize_figure(1)
plt.ion()
fig_tmp = plt.gcf()
images_tmp = copy.deepcopy(images)
offset_image_tmp = OffsetImage(images_tmp[0,:,:,:])
image_box_tmp = (40., 40.)
x_tmp = new_samples[:,0]
y_tmp = new_samples[:,1]
new_samples_tmp = copy.deepcopy(new_samples)
update_plot()
fig_tmp.canvas.mpl_connect('motion_notify_event', hover)
fig_tmp.canvas.mpl_connect('button_press_event', click)
plt.show()
fig_tmp.canvas.start_event_loop()
plt.ioff()
def update_plot():
global points_tmp, annotationbox_tmp
ax = plt.gca()
points_tmp = plt.scatter(*new_samples_tmp.T, s=14, c='b', edgecolor='k')
annotationbox_tmp = AnnotationBbox(offset_image_tmp, (0,0), xybox=image_box_tmp, xycoords='data', boxcoords='offset points', pad=0.3, arrowprops=dict(arrowstyle='->'))
ax.add_artist(annotationbox_tmp)
annotationbox_tmp.set_visible(False)
def hover(event):
if points_tmp.contains(event)[0]:
inds = points_tmp.contains(event)[1]['ind']
ind = inds[0]
w,h = fig_tmp.get_size_inches()*fig_tmp.dpi
ws = (event.x > w/2.)*-1 + (event.x <= w/2.)
hs = (event.y > h/2.)*-1 + (event.y <= h/2.)
annotationbox_tmp.xybox = (image_box_tmp[0]*ws, image_box_tmp[1]*hs)
annotationbox_tmp.set_visible(True)
annotationbox_tmp.xy =(x_tmp[ind], y_tmp[ind])
offset_image_tmp.set_data(images_tmp[ind,:,:])
else:
annotationbox_tmp.set_visible(False)
fig_tmp.canvas.draw_idle()
def click(event):
if points_tmp.contains(event)[0]:
inds = points_tmp.contains(event)[1]['ind']
ind = inds[0]
initialize_figure(1)
update_plot()
plt.scatter(x_tmp[ind], y_tmp[ind], s=20, marker='*', c='y')
plt.pause(2)
fig_tmp.canvas.stop_event_loop()
fig_tmp.canvas.draw_idle()
def main():
fig, ax = plt.subplots(1, figsize=(7, 7))
points = np.array([[1,1],[1,-1],[-1,1],[-1,-1]])
zero_layer = np.zeros([28,28])
one_layer = np.ones([28,28])*255
images = np.array([np.array([zero_layer, zero_layer, one_layer]).astype(np.uint8),np.array([zero_layer, one_layer, zero_layer]).astype(np.uint8),np.array([one_layer, zero_layer, zero_layer]).astype(np.uint8),np.array([one_layer, zero_layer, one_layer]).astype(np.uint8)])
images = np.transpose(images, (0,3,2,1))
theta = 0
delta = 15 * (np.pi/180)
rotation_matrix = np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]])
while True:
rotated_points = np.matmul(points, rotation_matrix)
draw_interactive_figures(rotated_points, images)
theta += delta
rotation_matrix = np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]])
if __name__== "__main__":
main()
Thanks in advance!
I'm providing you with a starting point here. The following is a script that creates a plot and allows you to add new points by clicking on the axes. For each point one may mouse hover and show a respective image.
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
class MyInteractivePlotter():
def __init__(self):
self.fig, self.ax = plt.subplots()
self.ax.set(xlim=(0,1), ylim=(0,1))
self.points = np.array([[0.5,0.5]]) # will become N x 2 array
self.images = [np.random.rand(10,10)]
self.scatter = self.ax.scatter(*self.points.T)
self.im = OffsetImage(self.images[0], zoom=5)
self.ab = AnnotationBbox(self.im, (0,0), xybox=(50., 50.), xycoords='data',
boxcoords="offset points", pad=0.3,
arrowprops=dict(arrowstyle="->"))
# add it to the axes and make it invisible
self.ax.add_artist(self.ab)
self.ab.set_visible(False)
self.cid = self.fig.canvas.mpl_connect("button_press_event", self.onclick)
self.hid = self.fig.canvas.mpl_connect("motion_notify_event", self.onhover)
def add_point(self):
# Update points (here, we just add a new random point)
self.points = np.concatenate((self.points, np.random.rand(1,2)), axis=0)
# For each points there is an image. (Here, we just add a random one)
self.images.append(np.random.rand(10,10))
# Update the scatter plot to show the new point
self.scatter.set_offsets(self.points)
def onclick(self, event):
self.add_point()
self.fig.canvas.draw_idle()
def onhover(self, event):
# if the mouse is over the scatter points
if self.scatter.contains(event)[0]:
# find out the index within the array from the event
ind, = self.scatter.contains(event)[1]["ind"]
# make annotation box visible
self.ab.set_visible(True)
# place it at the position of the hovered scatter point
self.ab.xy = self.points[ind,:]
# set the image corresponding to that point
self.im.set_data(self.images[ind])
else:
#if the mouse is not over a scatter point
self.ab.set_visible(False)
self.fig.canvas.draw_idle()
m = MyInteractivePlotter()
plt.show()
I would suggest you take this and add your functionality into it. Once you stumble upon a problem you can use it to ask for clarifications.
I am working on a project that involves plotting data on a map with Cartopy.
Everything has been working so far, but I have been refactoring the code to make different functions callable by other parts of the program. So to that end, I have one function which adds my background to the map, and another which adds a placemarker at a specified lat/lon. Obviously, I want the placemarker above the background, but I can't seem to make it work.
For the background, I want to be able to use Cartopy stock images or web map tiles. The problem is the same either way, so I am using the Cartopy background for current testing purpose. Here is that function:
def custom_background(self, source_point):
cartmap = self.plot
source_point = source_point.split(" ")
source_point = (float(source_point[0]), float(source_point[1]))
dx = 2.5
dy = 5
pad = 0.5
lon_min, lon_max = source_point[0]-dx, source_point[0]+dx
lat_min, lat_max = source_point[1]-dy, source_point[1]+dy
area = 4*dx*dy
zoom = self.get_zoom(area) ##only relevant when using a map tile
cartmap.set_extent([lat_min-pad, lat_max+pad, lon_min-pad, lon_max+pad])
#~ cartmap.add_image(self.tile, zoom)
cartmap.add_feature(cartopy.feature.LAND, zorder=1)
return cartmap
Here is the placemark function:
def add_point_icon(self, x, y, cartmap):
src_point = np.array(Image.open('icons/icon63.png'))
im = OffsetImage(src_point, zoom=1, alpha=1.0, zorder=3)
ab = AnnotationBbox(im, (x,y), xycoords='data', frameon=False)
cartmap.add_artist(ab)
Both of these are called one after the other like so:
cartmap = self.custom_background(mysrc)
#~ cartmap=self.plot
self.add_point_icon(x1, y1, cartmap)
Results:
If I run the code as it is, this is how the map looks:
If I change it to (i.e. bypassing the function which draws the background):
#~ cartmap = self.custom_background(mysrc)
cartmap=self.plot
self.add_point_icon(x1, y1, cartmap)
Then I get:
Why can't I get the red "plus" sign to show up on top of the map? I've tried setting the "zorder" parameter of the different objects and it doesn't seem to do anything. I'm at a complete loss right now. Any help would be hugely appreciated, thanks.
Edit: perhaps I should also include the lines which create the subplot:
def __init__(self, mylevs):
self.fig, self.header, self.footer, self.plot, self.legend =
self.create_spec()
def create_spec(self):
"""Define layout of figure"""
#left column: header, footer, plt
fig = plt.figure(figsize=(12,10))
layout = 1
if layout == 1: #Default
widths = [8,1]
heights = [2, 10, 3]
column_border = 0.75
pad = 0.1
colorbar_width = 0.05
spec = gridspec.GridSpec(ncols=1, nrows=3, width_ratios = [1], height_ratios=heights, left=0.1, right = column_border)
#right column: colorbar
spec2 = gridspec.GridSpec(ncols=1, nrows=1, width_ratios = [1], height_ratios=[1], left=column_border+pad, right=column_border+pad+colorbar_width)
header = plt.subplot(spec[0,0])
footer = plt.subplot(spec[2,0])
plot = plt.subplot(spec[1,0], projection=cimgt.OSM().crs)
legend = plt.subplot(spec2[0,0])
return fig, header, footer, plot, legend
The problem was that in switching back to the Cartopy stock background from the map tiles, I forgot to switch the projection from ccrs.OSM() back to ccrs.PlateCarree(). In the map tile projection, the placemark was being plotted outside the viewing window.
I'm trying to create a plot that updates when given a set of points ([x,y]) but the figure gets stuck on the first plot points and won't plot the rest of the data. I looped a function call but it gets stuck on the first call. I need to be able to give the function multiple sets of single x and y values, and have them plot in a graph.
This is the code I have so far.
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
from numpy import *
from time import sleep
import random as rd
class graphUpdater():
def __init__(self):
# Initialize arrays to be plotted
self.xs = []
self.ys = []
style.use('fivethirtyeight') # Figure Style
self.fig = plt.figure() # Initialize figure
self.ax1 = self.fig.add_subplot(111) # Create a subplot
# Ensure the figure auto-scales to fit all points. Might be overkill
self.ax1.set_autoscalex_on(True)
self.ax1.set_autoscaley_on(True)
self.ax1.set_autoscale_on(True)
self.ax1.autoscale(enable = True, axis = 'both', tight = False)
self.ax1.autoscale_view(False, True, True)
# Function that plots the arrays xs and ys. Also plots a linear regression of the data
def plotPoint(self):
self.ax1.clear() # Clears previous values to save memory
xp = linspace(min(self.xs), max(self.xs)) # x-range for regression
if(len(self.xs) > 1): # Conditional for regression, can't linearise 1 point
p1 = polyfit(self.xs, self.ys, 1) # Get the coefficients of the polynomial (slope of line)
self.ax1.plot(xp, polyval(p1, xp)) # Plot the line
self.ax1.plot(self.xs, self.ys, "+") # Plot the raw data points
self.ax1.set_xlabel('(L/A)*I') # Axis and title labels
self.ax1.set_ylabel('V')
self.ax1.set_title('DC Potential Drop')
def appendPlot(self, x, y):
self.xs.append(float(x)) # Append xs with x value
self.ys.append(float(y)) # Append ys with y value
self.plotPoint() # Call the plotPoint function to plot new array values
plt.show(block=False) # Plot and release so graphs can be over written
# Call the function
plsWork = graphUpdater() # I'm very hopeful
i = 0
while(i < 50):
plsWork.appendPlot(i, rd.randint(0, 20))
i += 1
sleep(0.1)
quit_case = input("Hit 'Enter' to Quit") # Conditional so the plot won't disappear
It doesn't work fully. If you put a breakpoint on the quit_case line and run it on debugger on pycharm it plots the graph "properly".
Don't use plt.show(block=False) and don't use time.sleep. Instead, matplotlib provides an animation module, which can be used to avoid such problems as here.
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
from numpy import *
from time import sleep
import random as rd
#%matplotlib notebook use in case of running this in a Jupyter notebook
class graphUpdater():
def __init__(self):
# Initialize arrays to be plotted
self.xs = []
self.ys = []
style.use('fivethirtyeight') # Figure Style
self.fig = plt.figure() # Initialize figure
self.ax1 = self.fig.add_subplot(111) # Create a subplot
# Ensure the figure auto-scales to fit all points. Might be overkill
self.ax1.set_autoscalex_on(True)
self.ax1.set_autoscaley_on(True)
self.ax1.set_autoscale_on(True)
self.ax1.autoscale(enable = True, axis = 'both', tight = False)
self.ax1.autoscale_view(False, True, True)
# Function that plots the arrays xs and ys. Also plots a linear regression of the data
def plotPoint(self):
self.ax1.clear() # Clears previous values to save memory
xp = linspace(min(self.xs), max(self.xs)) # x-range for regression
if(len(self.xs) > 1): # Conditional for regression, can't linearise 1 point
p1 = polyfit(self.xs, self.ys, 1) # Get the coefficients of the polynomial (slope of line)
self.ax1.plot(xp, polyval(p1, xp)) # Plot the line
self.ax1.plot(self.xs, self.ys, "+") # Plot the raw data points
self.ax1.set_xlabel('(L/A)*I') # Axis and title labels
self.ax1.set_ylabel('V')
self.ax1.set_title('DC Potential Drop')
def appendPlot(self, x, y):
self.xs.append(float(x)) # Append xs with x value
self.ys.append(float(y)) # Append ys with y value
self.plotPoint() # Call the plotPoint function to plot new array values
# Call the function
plsWork = graphUpdater() # I'm very hopeful
f = lambda i: plsWork.appendPlot(i, rd.randint(0, 20))
ani = animation.FuncAnimation(plsWork.fig, f, frames=50, interval=100, repeat=False)
plt.show()
I'm trying to monitor real-time data with matplotlib.
I found that I can update plot dynamically with interactive mode in Pyplot.
And it worked well, but one problem is 'I cannot manipulate the figure window at all'. For example, move or re-size the figure window.
Here is my code.
This is cons of interactive mode? or I'm using it incorrectly?
import matplotlib.pyplot as plt
import time
import math
# generate data
x = [0.1*_a for _a in range(1000)]
y = map(lambda x : math.sin(x), x)
# interactive mode
plt.ion() # identical plt.interactive(True)
fig, ax = plt.subplots()
# ax = plt.gca()
lines, = ax.plot([], [])
# ax.set_ylim(-1, 1)
ax.grid()
MAX_N_DATA = 100
x_data = []
y_data = []
for i in range(len(x)):
# New data received
x_data.append(x[i])
y_data.append(y[i])
# limit data length
if x_data.__len__() > MAX_N_DATA:
x_data.pop(0)
y_data.pop(0)
# Set Data
lines.set_xdata(x_data)
lines.set_ydata(y_data)
# The data limits are not updated automatically.
ax.relim()
# with tight True, graph flows smoothly.
ax.autoscale_view(tight=True, scalex=True, scaley=True)
# draw
plt.draw()
time.sleep(0.01)
Thank you.
As shown in this answer to another question, replace plt.draw() with plt.pause(0.05). This solved the problem for me.
Although I still think you should use bokeh, I'll tell you how to do it with matplotlib.
The problem why it won't work ist that matplotlib's event loop is not active and therefore it cannot digest window events (like close or resize). Unfortunately it is not possible to trigger this digestion from the outside. What you have to do is to use matplotlib's animation system.
Your code is actually quite well prepared for it so you can use FuncAnimation.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import math
# generate data
x = [0.1*_a for _a in range(1000)]
y = map(lambda x : math.sin(x), x)
# don't need ion, we're using block=True (see end of code)
fig, ax = plt.subplots()
fig.show()
# ax = plt.gca()
lines, = ax.plot([], [])
# ax.set_ylim(-1, 1)
ax.grid()
MAX_N_DATA = 100
x_data = []
y_data = []
def showdata(i):
# New data received
x_data.append(x[i])
y_data.append(y[i])
# limit data length
if x_data.__len__() > MAX_N_DATA:
x_data.pop(0)
y_data.pop(0)
# Set Data
lines.set_xdata(x_data)
lines.set_ydata(y_data)
# The data limits are not updated automatically.
ax.relim()
# with tight True, graph flows smoothly.
ax.autoscale_view(tight=True, scalex=True, scaley=True)
# draw will be called by the animation system
# instead of time.sleep(0.01) we use an update interval of 10ms
# which has the same effect
anim = FuncAnimation(fig, showdata, range(len(x)), interval=10, repeat=False)
# start eventloop
plt.show(block=True)