我正在尝试从一组scatter中制作一个bivariate gaussian distribution和xy coordinates的动画。我将记录先调用分散和分布的特定代码，然后再记录如何测量分布。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
''' Below is a section of the script that generates the scatter and contour '''
fig, ax = plt.subplots(figsize = (10,4))
def plotmvs(df, xlim=None, ylim=None, fig=fig, ax=ax):
if xlim is None: xlim = datalimits(df['X'])
if ylim is None: ylim = datalimits(df['Y'])
PDFs = []
for (group,gdf),color in zip(df.groupby('group'), ('red', 'blue')):
ax.plot(*gdf[['X','Y']].values.T, '.', c=color, alpha = 0.5)
kwargs = {
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)
PDFs.append(PDF)
PDF = PDFs[0] - PDFs[1]
normPDF = PDF - PDF.min()
normPDF = normPDF/normPDF.max()
cfs = ax.contourf(X, Y, normPDF, levels=100, cmap='jet')
return fig, ax
n = 10
time = [1]
d = ({
'A1_Y' : [10,20,15,20,25,40,50,60,61,65],
'A1_X' : [15,10,15,20,25,25,30,40,60,61],
'A2_Y' : [10,13,17,10,20,24,29,30,33,40],
'A2_X' : [10,13,15,17,18,19,20,21,26,30],
'A3_Y' : [11,12,15,17,19,20,22,25,27,30],
'A3_X' : [15,18,20,21,22,28,30,32,35,40],
'A4_Y' : [15,20,15,20,25,40,50,60,61,65],
'A4_X' : [16,20,15,30,45,30,40,10,11,15],
'B1_Y' : [18,10,11,13,18,10,30,40,31,45],
'B1_X' : [17,20,15,10,25,20,10,12,14,25],
'B2_Y' : [13,10,14,20,21,12,30,20,11,35],
'B2_X' : [12,20,16,22,15,20,10,20,16,15],
'B3_Y' : [15,20,15,20,25,10,20,10,15,25],
'B3_X' : [18,15,13,20,21,10,20,10,11,15],
'B4_Y' : [19,12,15,18,14,19,13,12,11,18],
'B4_X' : [20,10,12,18,17,15,13,14,19,13],
})
tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1:]), k.split('_')[1]), v[i]) for k,v in d.items() for i,t in enumerate(time)]
df = pd.Series(dict(tuples)).unstack(-1)
df.index.names = ['time', 'group', 'id']
for time,tdf in df.groupby('time'):
plotmvs(tdf)
'''MY ATTEMPT AT ANIMATING THE PLOT '''
def animate(i) :
tdf.set_offsets([[tdf.iloc[0:,1][0+i][0], tdf.iloc[0:,0][0+i][0]], [tdf.iloc[0:,1][0+i][1], tdf.iloc[0:,0][0+i][1]], [tdf.iloc[0:,1][0+i][2], tdf.iloc[0:,0][0+i][2]], [tdf.iloc[0:,1][0+i][3], tdf.iloc[0:,0][0+i][3]], [tdf.iloc[0:,1][0+i][4], tdf.iloc[0:,0][0+i][4]]])
normPDF = n[i,:,0,:].T
cfs.set_data(X, Y, normPDF)
ani = animation.FuncAnimation(fig, animate, np.arange(0,10),# init_func = init,
interval = 10, blit = False)
有关如何使用单个框架生成和绘制分布的完整工作代码
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
def datalimits(*data, pad=.15):
dmin,dmax = min(d.min() for d in data), max(d.max() for d in data)
spad = pad*(dmax - dmin)
return dmin - spad, dmax + spad
def rot(theta):
theta = np.deg2rad(theta)
return np.array([
[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]
])
def getcov(radius=1, scale=1, theta=0):
cov = np.array([
[radius*(scale + 1), 0],
[0, radius/(scale + 1)]
])
r = rot(theta)
return r @ cov @ r.T
def mvpdf(x, y, xlim, ylim, radius=1, velocity=0, scale=0, theta=0):
X,Y = np.meshgrid(np.linspace(*xlim), np.linspace(*ylim))
XY = np.stack([X, Y], 2)
x,y = rot(theta) @ (velocity/2, 0) + (x, y)
cov = getcov(radius=radius, scale=scale, theta=theta)
PDF = sts.multivariate_normal([x, y], cov).pdf(XY)
return X, Y, PDF
def mvpdfs(xs, ys, xlim, ylim, radius=None, velocity=None, scale=None, theta=None):
PDFs = []
for i,(x,y) in enumerate(zip(xs,ys)):
kwargs = {
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdf(x, y,**kwargs)
PDFs.append(PDF)
return X, Y, np.sum(PDFs, axis=0)
fig, ax = plt.subplots(figsize = (10,4))
def plotmvs(df, xlim=None, ylim=None, fig=fig, ax=ax):
if xlim is None: xlim = datalimits(df['X'])
if ylim is None: ylim = datalimits(df['Y'])
PDFs = []
for (group,gdf),color in zip(df.groupby('group'), ('red', 'blue')):
#Animate this scatter
ax.plot(*gdf[['X','Y']].values.T, '.', c=color, alpha = 0.5)
kwargs = {
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)
PDFs.append(PDF)
PDF = PDFs[0] - PDFs[1]
normPDF = PDF - PDF.min()
normPDF = normPDF/normPDF.max()
#Animate this contour
cfs = ax.contourf(X, Y, normPDF, levels=100, cmap='jet')
return fig, ax
n = 10
time = [1]
d = ({
'A1_Y' : [10,20,15,20,25,40,50,60,61,65],
'A1_X' : [15,10,15,20,25,25,30,40,60,61],
'A2_Y' : [10,13,17,10,20,24,29,30,33,40],
'A2_X' : [10,13,15,17,18,19,20,21,26,30],
'A3_Y' : [11,12,15,17,19,20,22,25,27,30],
'A3_X' : [15,18,20,21,22,28,30,32,35,40],
'A4_Y' : [15,20,15,20,25,40,50,60,61,65],
'A4_X' : [16,20,15,30,45,30,40,10,11,15],
'B1_Y' : [18,10,11,13,18,10,30,40,31,45],
'B1_X' : [17,20,15,10,25,20,10,12,14,25],
'B2_Y' : [13,10,14,20,21,12,30,20,11,35],
'B2_X' : [12,20,16,22,15,20,10,20,16,15],
'B3_Y' : [15,20,15,20,25,10,20,10,15,25],
'B3_X' : [18,15,13,20,21,10,20,10,11,15],
'B4_Y' : [19,12,15,18,14,19,13,12,11,18],
'B4_X' : [20,10,12,18,17,15,13,14,19,13],
})
tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1:]), k.split('_')[1]), v[i]) for k,v in d.items() for i,t in enumerate(time)]
df = pd.Series(dict(tuples)).unstack(-1)
df.index.names = ['time', 'group', 'id']
for time,tdf in df.groupby('time'):
plotmvs(tdf)
我本质上想通过遍历xy坐标的每一行来使此代码动画化。
答案 0 :(得分：4)
这是对OP代码的非常快速且肮脏的修改，修复了分散动画并添加了（一种形式）轮廓动画。
基本上，您首先为动画创建artists（在本例中为Line2D对象，由plot()返回）。随后，您创建一个update函数（以及可选的初始化函数）。在该功能中，您将更新现有的艺术家。我认为example in the matplotlib docs可以解释一切。
在这种情况下，我修改了OP的plotmvs函数以用作更新函数（而不是OP提出的animate函数）。
QuadContourSet（即您的contourf）返回的cfs本身不能用作艺术家，但是您可以使用cfs.collections（{ {3}}）。但是，您仍然需要创建一个新的轮廓图并删除旧的轮廓图，而不仅仅是更新轮廓数据。就我个人而言，我更喜欢较低级别的方法：尝试在不调用contourf的情况下获取轮廓数据，然后像对散点图一样初始化和更新轮廓线。
尽管如此，以上方法还是在以下OP的代码中实现（只需复制，粘贴和运行）即可：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
from matplotlib.animation import FuncAnimation
# quick and dirty override of datalimits(), to get a fixed contour-plot size
DATA_LIMITS = [0, 70]
def datalimits(*data, pad=.15):
# dmin,dmax = min(d.min() for d in data), max(d.max() for d in data)
# spad = pad*(dmax - dmin)
return DATA_LIMITS # dmin - spad, dmax + spad
def rot(theta):
theta = np.deg2rad(theta)
return np.array([
[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]
])
def getcov(radius=1, scale=1, theta=0):
cov = np.array([
[radius*(scale + 1), 0],
[0, radius/(scale + 1)]
])
r = rot(theta)
return r @ cov @ r.T
def mvpdf(x, y, xlim, ylim, radius=1, velocity=0, scale=0, theta=0):
X,Y = np.meshgrid(np.linspace(*xlim), np.linspace(*ylim))
XY = np.stack([X, Y], 2)
x,y = rot(theta) @ (velocity/2, 0) + (x, y)
cov = getcov(radius=radius, scale=scale, theta=theta)
PDF = sts.multivariate_normal([x, y], cov).pdf(XY)
return X, Y, PDF
def mvpdfs(xs, ys, xlim, ylim, radius=None, velocity=None, scale=None, theta=None):
PDFs = []
for i,(x,y) in enumerate(zip(xs,ys)):
kwargs = {
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdf(x, y,**kwargs)
PDFs.append(PDF)
return X, Y, np.sum(PDFs, axis=0)
fig, ax = plt.subplots(figsize = (10,4))
ax.set_xlim(DATA_LIMITS)
ax.set_ylim(DATA_LIMITS)
# Initialize empty lines for the scatter (increased marker size to make them more visible)
line_a, = ax.plot([], [], '.', c='red', alpha = 0.5, markersize=20, animated=True)
line_b, = ax.plot([], [], '.', c='blue', alpha = 0.5, markersize=20, animated=True)
cfs = None
# Modify the plotmvs function so it updates the lines
# (might as well rename the function to "update")
def plotmvs(tdf, xlim=None, ylim=None):
global cfs # as noted: quick and dirty...
if cfs:
for tp in cfs.collections:
# Remove the existing contours
tp.remove()
# Get the data frame for time t
df = tdf[1]
if xlim is None: xlim = datalimits(df['X'])
if ylim is None: ylim = datalimits(df['Y'])
PDFs = []
for (group, gdf), group_line in zip(df.groupby('group'), (line_a, line_b)):
#Animate this scatter
#ax.plot(*gdf[['X','Y']].values.T, '.', c=color, alpha = 0.5)
# Update the scatter line data
group_line.set_data(*gdf[['X','Y']].values.T)
kwargs = {
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)
PDFs.append(PDF)
PDF = PDFs[0] - PDFs[1]
normPDF = PDF - PDF.min()
normPDF = normPDF / normPDF.max()
# Plot a new contour
cfs = ax.contourf(X, Y, normPDF, levels=100, cmap='jet')
# Return the artists (the trick is to return cfs.collections instead of cfs)
return cfs.collections + [line_a, line_b]
n = 10
time = range(n) # assuming n represents the length of the time vector...
d = ({
'A1_Y' : [10,20,15,20,25,40,50,60,61,65],
'A1_X' : [15,10,15,20,25,25,30,40,60,61],
'A2_Y' : [10,13,17,10,20,24,29,30,33,40],
'A2_X' : [10,13,15,17,18,19,20,21,26,30],
'A3_Y' : [11,12,15,17,19,20,22,25,27,30],
'A3_X' : [15,18,20,21,22,28,30,32,35,40],
'A4_Y' : [15,20,15,20,25,40,50,60,61,65],
'A4_X' : [16,20,15,30,45,30,40,10,11,15],
'B1_Y' : [18,10,11,13,18,10,30,40,31,45],
'B1_X' : [17,20,15,10,25,20,10,12,14,25],
'B2_Y' : [13,10,14,20,21,12,30,20,11,35],
'B2_X' : [12,20,16,22,15,20,10,20,16,15],
'B3_Y' : [15,20,15,20,25,10,20,10,15,25],
'B3_X' : [18,15,13,20,21,10,20,10,11,15],
'B4_Y' : [19,12,15,18,14,19,13,12,11,18],
'B4_X' : [20,10,12,18,17,15,13,14,19,13],
})
tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1:]), k.split('_')[1]), v[i])
for k,v in d.items() for i,t in enumerate(time)]
df = pd.Series(dict(tuples)).unstack(-1)
df.index.names = ['time', 'group', 'id']
# Use the modified plotmvs as the update function, and supply the data frames
interval_ms = 200
delay_ms = 1000
ani = FuncAnimation(fig, plotmvs, frames=df.groupby('time'),
blit=True, interval=interval_ms, repeat_delay=delay_ms)
# Start the animation
plt.show()