[英]Using matplotlib.animate to animate a contour plot in python
I have a 3D array of data (2 spatial dimensions and 1 time dimension) and I'm trying to produce an animated contour plot using matplotlib.animate. 我有一个3D数据阵列(2个空间维度和1个时间维度),我正在尝试使用matplotlib.animate生成动画轮廓图。 I'm using this link as a basis:
我使用此链接作为基础:
http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/ http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/
And here's my attempt: 这是我的尝试:
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
from numpy import array, zeros, linspace, meshgrid
from boutdata import collect
# First collect data from files
n = collect("n") # This is a routine to collect data
Nx = n.shape[1]
Nz = n.shape[2]
Ny = n.shape[3]
Nt = n.shape[0]
fig = plt.figure()
ax = plt.axes(xlim=(0, 200), ylim=(0, 100))
cont, = ax.contourf([], [], [], 500)
# initialisation function
def init():
cont.set_data([],[],[])
return cont,
# animation function
def animate(i):
x = linspace(0, 200, Nx)
y = linspace(0, 100, Ny)
x,y = meshgrid(x,y)
z = n[i,:,0,:].T
cont.set_data(x,y,z)
return cont,
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=200, interval=20, blit=True)
plt.show()
But when I do this, I get the following error: 但是当我这样做时,我收到以下错误:
Traceback (most recent call last):
File "showdata.py", line 16, in <module>
cont, = ax.contourf([], [], [], 500)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 7387, in contourf
return mcontour.QuadContourSet(self, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1112, in __init__
ContourSet.__init__(self, ax, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 703, in __init__
self._process_args(*args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1125, in _process_args
x, y, z = self._contour_args(args, kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1172, in _contour_args
x,y,z = self._check_xyz(args[:3], kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1204, in _check_xyz
raise TypeError("Input z must be a 2D array.")
TypeError: Input z must be a 2D array.
So I've tried replacing all the [] by [[],[]] but this then produces: 所以我尝试用[[],[]]替换所有[],但这会产生:
Traceback (most recent call last):
File "showdata.py", line 16, in <module>
cont, = ax.contourf([[],[]], [[],[]], [[],[]],500)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 7387, in contourf
return mcontour.QuadContourSet(self, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1112, in __init__
ContourSet.__init__(self, ax, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 703, in __init__
self._process_args(*args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1125, in _process_args
x, y, z = self._contour_args(args, kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/contour.py", line 1177, in _contour_args
self.zmax = ma.maximum(z)
File "/usr/lib/python2.7/dist-packages/numpy/ma/core.py", line 5806, in __call__
return self.reduce(a)
File "/usr/lib/python2.7/dist-packages/numpy/ma/core.py", line 5824, in reduce
t = self.ufunc.reduce(target, **kargs)
ValueError: zero-size array to maximum.reduce without identity
Thanks in advance! 提前致谢!
This is what I got to work: 这就是我的工作:
# Generate grid for plotting
x = linspace(0, Lx, Nx)
y = linspace(0, Ly, Ny)
x,y = meshgrid(x,y)
fig = plt.figure()
ax = plt.axes(xlim=(0, Lx), ylim=(0, Ly))
plt.xlabel(r'x')
plt.ylabel(r'y')
# animation function
def animate(i):
z = var[i,:,0,:].T
cont = plt.contourf(x, y, z, 25)
if (tslice == 0):
plt.title(r't = %1.2e' % t[i] )
else:
plt.title(r't = %i' % i)
return cont
anim = animation.FuncAnimation(fig, animate, frames=Nt)
anim.save('animation.mp4')
I found that removing the blit=0 argument in the FuncAnimation call also helped... 我发现在FuncAnimation调用中删除blit = 0参数也有帮助......
Felix Schneider is correct about the animation becoming very slow. Felix Schneider对于动画变得非常慢很正确。 His solution of setting
ax.collections = []
removes all old (and superseded) "artist"s. 他设置
ax.collections = []
解决方案删除了所有旧的(和被取代的)“艺术家”。 A more surgical approach is to only remove the artists involved in the drawing the contours: 更外科的方法是只删除参与绘制轮廓的艺术家:
for c in cont.collections:
c.remove()
which is useful in more complicated cases, in lieu of reconstructing the entire figure for each frame. 这在更复杂的情况下是有用的,而不是重建每个帧的整个图形。 This also works in Rehman Ali's example;
这也适用于Rehman Ali的例子; instead of clearing the entire figure with
clf()
the value returned by contourf()
is saved and used in the next iteration. 而不是用
clf()
清除整个图形,而是保存contourf()
返回的值,并在下一次迭代中使用。
Here is an example code similar to Luke's from Jun 7 '13, demonstrating removing the contours only: 这是一个类似于2013年6月7日的Luke的示例代码,演示了仅删除轮廓:
import pylab as plt
import numpy
import matplotlib.animation as animation
#plt.rcParams['animation.ffmpeg_path'] = r"C:\some_path\ffmpeg.exe" # if necessary
# Generate data for plotting
Lx = Ly = 3
Nx = Ny = 11
Nt = 20
x = numpy.linspace(0, Lx, Nx)
y = numpy.linspace(0, Ly, Ny)
x,y = numpy.meshgrid(x,y)
z0 = numpy.exp(-(x-Lx/2)**2-(y-Ly/2)**2) # 2 dimensional Gaussian
def some_data(i): # function returns a 2D data array
return z0 * (i/Nt)
fig = plt.figure()
ax = plt.axes(xlim=(0, Lx), ylim=(0, Ly), xlabel='x', ylabel='y')
cvals = numpy.linspace(0,1,Nt+1) # set contour values
cont = plt.contourf(x, y, some_data(0), cvals) # first image on screen
plt.colorbar()
# animation function
def animate(i):
global cont
z = some_data(i)
for c in cont.collections:
c.remove() # removes only the contours, leaves the rest intact
cont = plt.contourf(x, y, z, cvals)
plt.title('t = %i: %.2f' % (i,z[5,5]))
return cont
anim = animation.FuncAnimation(fig, animate, frames=Nt, repeat=False)
anim.save('animation.mp4', writer=animation.FFMpegWriter())
This is the line: 这是一行:
cont, = ax.contourf([], [], [], 500)
change to: 改成:
x = linspace(0, 200, Nx)
y = linspace(0, 100, Ny)
x, y = meshgrid(x, y)
z = n[i,:,0,:].T
cont, = ax.contourf(x, y, z, 500)
You need to intilize with sized arrays. 您需要使用大小的数组进行插入。
Here is another way of doing the same thing if matplotlib.animation don't work for you. 如果matplotlib.animation不适合你,这是另一种做同样事情的方法。 If you want to continuously update the colorbar and everything else in the figure, use plt.ion() at the very beginning to enable interactive plotting and use a combo of plt.draw() and plt.clf() to continuously update the plot.
如果要连续更新颜色栏和图中的其他所有内容,请在开头使用plt.ion()启用交互式绘图,并使用plt.draw()和plt.clf()的组合来连续更新绘图。
import matplotlib.pyplot as plt
import numpy as np
plt.ion(); plt.figure(1);
for k in range(10):
plt.clf(); plt.subplot(121);
plt.contourf(np.random.randn(10,10)); plt.colorbar();
plt.subplot(122,polar=True)
plt.contourf(np.random.randn(10,10)); plt.colorbar();
plt.draw();
Note that this works with figures containing different subplots and various types of plots (ie polar or cartesian) 请注意,这适用于包含不同子图和各种类型图(即极地或笛卡儿)的图形
I have been looking at this a while ago. 我不久前一直在看这个。 I my situation I had a few subplots with contours which I wanted to animate.
我的情况我有一些带有轮廓的子图,我想要制作动画。 I did not want to use the plt.clf() solution as Rehman ali suggest as I used some special setup of my axis (with pi symbols etc) which would be cleaned as well, so I preferred the 'remove()' approach suggest be Felix.
我不想使用plh.clf()解决方案,正如Rehman ali建议的那样,我使用了一些特殊的轴设置(带有pi符号等)也会被清理,所以我更喜欢'remove()'方法建议菲利克斯。 The thing is that only using 'remove' does not clean up memory and will clog your computer eventually, so you need to explicitly delete of the contours by setting it to an empty list as well.
问题是只使用'删除'不会清理内存并最终堵塞您的计算机,因此您需要通过将轮廓设置为空列表来明确删除轮廓。
In order to have a generic remove routine which is able to take away contours as well as text, I wrote the routine 'clean_up_artists' which you should use on every time step on all the axis. 为了有一个能够带走轮廓和文本的通用删除例程,我写了例程'clean_up_artists',你应该在所有轴上的每个时间步上使用它。
This routine cleans up the artists which are passed in a list called 'artist_list' in a given axis 'axis'. 此例程清除在给定轴“轴”中名为“artist_list”的列表中传递的艺术家。 This means that for animating multiple subplots, we need to store the lists of artists for each axis which we need to clean every time step.
这意味着,为了动画化多个子图,我们需要存储每个轴的艺术家列表,我们需要每个时间步清洁它们。
Below the full code to animate a number of subplots of random data. 在完整代码下方为一些随机数据的子图设置动画。 It is pretty self-explanatory, so hopefully it becomes clear what happens.
这是非常不言自明的,所以希望它变得清晰。 Anyhow, I just thought to post it, as it combines several ideas I found on stack overflow which I just to come up with this working example.
无论如何,我只是想发布它,因为它结合了我在堆栈溢出时发现的几个想法,我只是想出这个工作示例。
Anybody with suggestions to improve the code, please shoot-) 有人提出改进代码的建议,请拍 - )
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.animation as animation
import string
import numpy as np
def clean_up_artists(axis, artist_list):
"""
try to remove the artists stored in the artist list belonging to the 'axis'.
:param axis: clean artists belonging to these axis
:param artist_list: list of artist to remove
:return: nothing
"""
for artist in artist_list:
try:
# fist attempt: try to remove collection of contours for instance
while artist.collections:
for col in artist.collections:
artist.collections.remove(col)
try:
axis.collections.remove(col)
except ValueError:
pass
artist.collections = []
axis.collections = []
except AttributeError:
pass
# second attempt, try to remove the text
try:
artist.remove()
except (AttributeError, ValueError):
pass
def update_plot(frame_index, data_list, fig, axis, n_cols, n_rows, number_of_contour_levels, v_min, v_max,
changed_artists):
"""
Update the the contour plots of the time step 'frame_index'
:param frame_index: integer required by animation running from 0 to n_frames -1. For initialisation of the plot,
call 'update_plot' with frame_index = -1
:param data_list: list with the 3D data (time x 2D data) per subplot
:param fig: reference to the figure
:param axis: reference to the list of axis with the axes per subplot
:param n_cols: number of subplot in horizontal direction
:param n_rows: number of subplot in vertical direction
:param number_of_contour_levels: number of contour levels
:param v_min: minimum global data value. If None, take the smallest data value in the 2d data set
:param v_max: maximum global data value. If None, take the largest value in the 2d data set
:param changed_artists: list of lists of artists which need to be updated between the time steps
:return: the changed_artists list
"""
nr_subplot = 0 # keep the index of the current subplot (nr_subplot = 0,1, n_cols x n_rows -1)
# loop over the subplots
for j_col in range(n_cols):
for i_row in range(n_rows):
# set a short reference to the current axis
ax = axis[i_row][j_col]
# for the first setup call, add and empty list which can hold the artists belonging to the current axis
if frame_index < 0:
# initialise the changed artist list
changed_artists.append(list())
else:
# for the next calls of update_plot, remove all artists in the list stored in changed_artists[nr_subplot]
clean_up_artists(ax, changed_artists[nr_subplot])
# get a reference to 2d data of the current time and subplot
data_2d = data_list[nr_subplot][frame_index]
# manually set the levels for better contour range control
if v_min is None:
data_min = np.nanmin(data_2d)
else:
data_min = v_min
if v_max is None:
data_max = np.nanmax(data_2d)
else:
data_max = v_max
# set the contour levels belonging to this subplot
levels = np.linspace(data_min, data_max, number_of_contour_levels + 1, endpoint=True)
# create the contour plot
cs = ax.contourf(data_2d, levels=levels, cmap=cm.rainbow, zorder=0)
cs.cmap.set_under("k")
cs.cmap.set_over("k")
cs.set_clim(v_min, v_max)
# store the contours artists to the list of artists belonging to the current axis
changed_artists[nr_subplot].append(cs)
# set some grid lines on top of the contours
ax.xaxis.grid(True, zorder=0, color="black", linewidth=0.5, linestyle='--')
ax.yaxis.grid(True, zorder=0, color="black", linewidth=0.5, linestyle='--')
# set the x and y label on the bottom row and left column respectively
if i_row == n_rows - 1:
ax.set_xlabel(r"Index i ")
if j_col == 0:
ax.set_ylabel(r"Index j")
# set the changing time counter in the top left subplot
if i_row == 0 and j_col == 1:
# set a label to show the current time
time_text = ax.text(0.6, 1.15, "{}".format("Time index : {:4d}".format(frame_index)),
transform=ax.transAxes, fontdict=dict(color="black", size=14))
# store the artist of this label in the changed artist list
changed_artists[nr_subplot].append(time_text)
# for the initialisation call only, set of a contour bar
if frame_index < 0:
# the first time we add this (make sure to pass -1 for the frame_index
cbar = fig.colorbar(cs, ax=ax)
cbar.ax.set_ylabel("Random number {}".format(nr_subplot))
ax.text(0.0, 1.02, "{}) {}".format(string.ascii_lowercase[nr_subplot],
"Random noise {}/{}".format(i_row, j_col)),
transform=ax.transAxes, fontdict=dict(color="blue", size=12))
nr_subplot += 1
return changed_artists
def main():
n_pixels_x = 50
n_pixels_y = 30
number_of_time_steps = 100
number_of_contour_levels = 10
delay_of_frames = 1000
n_rows = 3 # number of subplot rows
n_cols = 2 # number of subplot columns
min_data_value = 0.0
max_data_value = 1.0
# list containing the random plot per sub plot. Insert you own data here
data_list = list()
for j_col in range(n_cols):
for i_row in range(n_rows):
data_list.append(np.random.random_sample((number_of_time_steps, n_pixels_x, n_pixels_y)))
# set up the figure with the axis
fig, axis = plt.subplots(nrows=n_rows, ncols=n_cols, sharex=True, sharey=True, figsize=(12,8))
fig.subplots_adjust(wspace=0.05, left=0.08, right=0.98)
# a list used to store the reference to the axis of each subplot with a list of artists which belong to this subplot
# this list will be returned and will be updated every time plot which new artists
changed_artists = list()
# create first image by calling update_plot with frame_index = -1
changed_artists = update_plot(-1, data_list, fig, axis, n_cols, n_rows, number_of_contour_levels,
min_data_value, max_data_value, changed_artists)
# call the animation function. The fargs argument equals the parameter list of update_plot, except the
# 'frame_index' parameter.
ani = animation.FuncAnimation(fig, update_plot, frames=number_of_time_steps,
fargs=(data_list, fig, axis, n_cols, n_rows, number_of_contour_levels, min_data_value,
max_data_value, changed_artists),
interval=delay_of_frames, blit=False, repeat=True)
plt.show()
if __name__ == "__main__":
main()
删除blit = 0或blit = FuncAnimation调用中的True参数也很有用!!!
I used Lukes approach (from Jun 7 '13 at 8:08 ), but added 我使用了Lukes方法(从2013年6月7日8:08开始),但补充道
ax.collections = []
right before 就在之前
cont = plt.contourf(x, y, z, 25).
Otherwise I experienced that creating the animation will become very slow for large frame numbers. 否则我经历过,对于大帧数,创建动画会变得非常慢。
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