I'd like to update a matrix text in dynamic by using animation function of matplotlib. But I found that if the data array is too large , the animation will become very very slow. Is there any way to improve it ?
from matplotlib import animation
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10,20))
def updatefig(i):
plt.cla()
ax.grid()
data = np.random.rand(50,50)
ax.set_xticks(np.arange(data.shape[1]+1))
ax.set_yticks(np.arange(data.shape[0]+1))
for y in range(data.shape[0]):
for x in range(data.shape[1]):
plt.text(x + 0.5 , y + 0.5, '%.1f' % data[y, x],horizontalalignment='center',verticalalignment='center',color='b',size = 6)
plt.draw()
anim = animation.FuncAnimation(fig, updatefig,interval=50)
plt.show()
Actually, I wants to create a heatmmap plot with data values like below link. But use annotations is the only way i could figure out.
Heamap with values
Find a workaround by import seaborn module.
But how to avoid the graph keep flashing
from matplotlib import animation
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
fig, ax = plt.subplots(figsize=(10,20))
sns.set()
def updatefig(i):
plt.clf()
data = np.random.rand(10,10)
sns.heatmap(data, annot=True, linewidths=.5,cbar=False)
anim = animation.FuncAnimation(fig, updatefig,interval=50)
plt.show()
Related
I want to do an animated scatter plot with one only pair of x,y data for each frame.
The code I wrote creates an animated scatter plot but the old dots appear in the plot, that means that new dots are added on the plot, keeping the old ones.
For the code below I want a dot per frame like a moving dot on x axis and not adding one more value.
I tried with plt.clf() but then all data disappear.
%matplotlib notebook
from bokeh.plotting import figure, output_file, show
import pandas
import numpy as np
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation, PillowWriter
import matplotlib.pyplot as plt
writer = PillowWriter(fps=10)
list_x=[1,2,3,4,5,6,7,8,9]
list_y=[5,5,5,5,5,5,5,5,5]
def plot(listax, listay):
plt.scatter(listax, listay, c='blue', alpha=0.5)
plt.show()
fig2 = plt.figure()
plt.xlim([0, 10])
plt.ylim([0, 10])
with writer.saving(fig2, "plotvideo.gif", 100):
for i in range(0, len(list_x)):
x_value = list_x[i]
y_value = list_y[i]
writer.grab_frame()
plot(x_value, y_value)
Use the .remove() method on the point objects to remove them from the figure.
I would try this:
from bokeh.plotting import figure, output_file, show
import pandas
import numpy as np
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation, PillowWriter
import matplotlib.pyplot as plt
import time
writer = PillowWriter(fps=10)
list_x=[1,2,3,4,5,6,7,8,9]
list_y=[5,5,5,5,5,5,5,5,5]
points = []
def plot(listax, listay, j):
points.append(plt.scatter(listax[j], listay[j], c='blue', alpha=0.5))
if len(points) == 2:
points[0].remove()
points.pop(0)
plt.show(block=False)
fig2 = plt.figure()
plt.xlim([0, 10])
plt.ylim([0, 10])
with writer.saving(fig2, "plotvideo.gif", 100):
for i in range(0, len(list_x)):
x_value = list_x
y_value = list_y
writer.grab_frame()
print(points)
plot(x_value, y_value, i)
See this link for a better explanation (albeit with a different implementation):
How to remove points from a plot?
So I am trying to create histograms for each specific variable in my dataset and then save it as a PNG file.
My code is as follows:
import pandas as pd
import matplotlib.pyplot as plt
x=combined_databook.groupby('x_1').hist()
x.figure.savefig("x.png")
I keep getting "AttributeError: 'Series' object has no attribute 'figure'"
Use matplotlib to create a figure and axis objects, then tell pandas which axes to plot on using the ax argument. Finally, use matplotlib (or the fig) to save the figure.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Sample Data (3 groups, normally distributed)
df = pd.DataFrame({'gp': np.random.choice(list('abc'), 1000),
'data': np.random.normal(0, 1, 1000)})
fig, ax = plt.subplots()
df.groupby('gp').hist(ax=ax, ec='k', grid=False, bins=20, alpha=0.5)
fig.savefig('your_fig.png', dpi=200)
your_fig.png
Instead of using *.hist() I would use matplotlib.pyplot.hist().
Example :
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
y =[10, 20,30,40,100,200,300,400,1000,2000]
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(x, y, label='$y = Values')
plt.title('my plot')
ax.legend()
plt.show()
fig.savefig('tada.png')
I'm trying to have my matplotlib plot update in real-time as data is added to a CSV file. The plot is of a small geographic location, axes given by longitude and latitude. This is what I have so far:
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
df = pd.read_csv("cayugacoords.txt")
BoundaryBox = [-76.5119, -76.5013, 42.4596, 42.4642]
ruh_m = plt.imread('map.png')
fig, ax = plt.subplots(figsize=(8, 7))
ax.scatter(df.longitude, df.latitude, zorder=1, alpha=1, c='r', s=10)
ax.set_title('Cayuga Lake Shore')
ax.set_xlim(BoundaryBox[0], BoundaryBox[1])
ax.set_ylim(BoundaryBox[2], BoundaryBox[3])
ax.imshow(ruh_m, zorder=0, extent=BoundaryBox, aspect='equal')
plt.show()
And this is what shows when I run the code (the three points on the bottom left are already in the CSV file):
Current plot
And here's the background image on its own: Cayuga Lake
I want the map to be regularly updated as new coordinates are added to the CSV file. How can this be done? I've looked into animation tools but I'm having trouble retaining the background image of the map while updating the plot. For reference, the CSV file "cayugacoords.txt" looks like this:
longitude,latitude
-76.51,42.46
-76.511,42.46
-76.5105,42.46
Thank you!
An alternative solution which updates only the points on the background image is provided by using ax.collections = [] which clears ALL lines plotted on the image. For the sake of demonstration I plot each coordinate per frame.
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as animation
df = pd.read_csv("cayugacoords.txt")
BoundaryBox = [-76.5119, -76.5013, 42.4596, 42.4642]
ruh_m = plt.imread('map.png')
fig, ax = plt.subplots(figsize=(8, 7))
ax.set_title('Cayuga Lake Shore')
ax.set_xlim(BoundaryBox[0], BoundaryBox[1])
ax.set_ylim(BoundaryBox[2], BoundaryBox[3])
ax.imshow(ruh_m, zorder=0, extent=BoundaryBox, aspect='equal')
def animate(nframe):
ax.collections = []
points = ax.scatter(df.longitude[nframe], df.latitude[nframe], zorder=1,
alpha=1, c='r', s=10)
return
anim = animation.FuncAnimation(fig, animate, frames=3)
This code worked for me. It seems quite hacky but it works. You can adjust the time.sleep to your liking.
from matplotlib import pyplot as plt
from IPython.display import clear_output
import pandas as pd
import numpy as np
import time
%matplotlib inline
ruh_m = plt.imread('map.png')
BoundaryBox = [-76.5119, -76.5013, 42.4596, 42.4642]
while True:
clear_output(wait=True)
df = pd.read_csv("cayugacoords.txt")
fig, ax = plt.subplots(figsize=(10, 10))
ax.scatter(df.longitude, df.latitude, zorder=1, alpha=1, c='r', s=10)
ax.set_title('Cayuga Lake Shore')
ax.set_xlim(BoundaryBox[0], BoundaryBox[1])
ax.set_ylim(BoundaryBox[2], BoundaryBox[3])
ax.imshow(ruh_m, zorder=0, extent=BoundaryBox, aspect='equal')
plt.show()
time.sleep(1E-3)
I am trying to make an animation in 3D using Matplotlib and mpl_toolkits. For starter, I am trying to make an animation of a shifting cos wave. But when I run the program, the plot is completely empty. I have just started learning matplotlib animations, so I don't have in-depth knowledge of it. Here is my code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import math
import matplotlib.animation as animation
fig = plt.figure()
ax = Axes3D(fig)
line, = ax.plot([],[])
print(line)
X = np.linspace(0, 6*math.pi, 100)
def animate(frame):
line.set_data(X-frame, np.cos(X-frame))
return line
anim = animation.FuncAnimation(fig, animate, frames = 100, interval = 50)
plt.show()
Here is the output:
What is wrong with my code? Why am I not getting any output?
There are two issues with your code:
use set_data_3d to update the data of a Line3D object instead of set_data
initialize the Axes3D scales before starting the animation
This should work:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import math
import matplotlib.animation as animation
fig = plt.figure()
ax = Axes3D(fig)
# initialize scales
ax.set_xlim3d(0, 6 * math.pi)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(0, 100)
X = np.linspace(0, 6 * math.pi, 100)
line, = ax.plot([], [], [])
def animate(frame):
# update Line3D data
line.set_data_3d(X, np.cos(X - frame), frame)
return line,
anim = animation.FuncAnimation(fig, animate, frames = 20, interval = 50)
plt.show()
and yield an animation like this (I have truncated the number of frames to reduce image file size).
I am trying to animate a violinplot, so I have started off with something I think should be very basic, but it is not working. I think the problem is that violinplot doesn't accept set_data, but I don't otherwise know how to pass the changing data to violinplot. For this example I would like a plot where the mean slowly shifts to higher values. If I am barking up the wrong tree, please advise on a code which does work to animate violinplot.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
data = np.random.rand(100)
def animate(i):
v.set_data(data+i) # update the data
return v
v = ax.violinplot([])
ax.set_ylim(0,200)
v_ani = animation.FuncAnimation(fig, animate, np.arange(1, 200),
interval=50, blit=True)
Indeed, there is no set_data method for the violinplot. The reason is probably, that there is a lot of calculations going on in the background when creating such a plot and it consists of a lot of different elements, which are hard to update.
The easiest option would be to simply redraw the violin plot and not use blitting.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
data = np.random.normal(loc=25, scale=20, size=100)
def animate(i, data):
ax.clear()
ax.set_xlim(0,2)
ax.set_ylim(0,200)
data[:20] = np.random.normal(loc=25+i, scale=20, size=20)
np.random.shuffle(data)
ax.violinplot(data)
animate(0)
v_ani = animation.FuncAnimation(fig, animate, np.arange(1, 200),
fargs=(data,), interval=50, blit=False)
plt.show()