我正在尝试生成 animated scatter plot在功能内animated contour plot 。我可以让两者单独工作，但不能一起工作。
下面的代码生成 contour来自坐标A和B在 df 。我试图包含一个单独的动画 scatter样本内plot使用C坐标。此尝试目前已被注释掉。
所以我基本上想包括另一个 animated scatter使用C_X和C_Y 。我尝试将它们应用于 line_c 。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
import matplotlib.transforms as transforms
''' Section 1 '''
DATA_LIMITS = [-85, 85]
def datalimits(*data):
return DATA_LIMITS # dmin - spad, dmax + spad
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)
PDF = sts.multivariate_normal([x, y]).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)):
X, Y, PDF = mvpdf(x, y, xlim, ylim)
PDFs.append(PDF)
return X, Y, np.sum(PDFs, axis=0)
''' Animate Plot '''
fig, ax = plt.subplots(figsize = (10,6))
ax.set_xlim(DATA_LIMITS)
ax.set_ylim(DATA_LIMITS)
#Animated coordinates for group A,B
line_a, = ax.plot([], [], '.', c='red', alpha = 0.5, markersize=5, animated=True)
line_b, = ax.plot([], [], '.', c='blue', alpha = 0.5, markersize=5, animated=True)
#Attempt to incorporate scatter for C
line_c, = ax.plot([], [], '.', c='white', alpha = 0.5, markersize=2.5, animated=True)
cfs = None
def plotmvs(tdf, xlim=None, ylim=None, fig=fig, ax=ax):
global cfs
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)):
group_line.set_data(*gdf[['X','Y']].values.T)
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, xlim, ylim)
PDFs.append(PDF)
normPDF = PDF - PDF.min()
normPDF = normPDF / normPDF.max()
cfs = ax.contourf(X, Y, normPDF, cmap='viridis', alpha = 1, levels=np.linspace(-1,1,10))
#Create offset scatter for Group C
# for (group, g2df), group_line in zip(df.groupby('group'), (line_c)):
# group_line.set_data(*g2df[['XX','YY']].values.T)
# offset = lambda p: transforms.ScaledTranslation(p/82.,0, plt.gcf().dpi_scale_trans)
# trans = plt.gca().transData
# ax.scatter(line_c,transform=trans+offset(+2))
return cfs.collections + [line_a, line_b]#, line_c]
n = 10
time = range(n)
d = ({
'A1_X' : [13.3,13.16,12.99,12.9,12.79,12.56,12.32,12.15,11.93,11.72],
'A1_Y' : [26.12,26.44,26.81,27.18,27.48,27.82,28.13,28.37,28.63,28.93],
'A2_X' : [6.97,6.96,7.03,6.98,6.86,6.76,6.55,6.26,6.09,5.9],
'A2_Y' : [10.92,10.83,10.71,10.52,10.22,10.02,9.86,9.7,9.54,9.37],
'B1_X' : [38.35,38.1,37.78,37.55,37.36,37.02,36.78,36.46,36.21,35.79],
'B1_Y' : [12.55,12.58,12.58,12.55,12.5,12.47,12.43,12.48,12.44,12.44],
'B2_X' : [14.6,14.38,14.16,13.8,13.45,13.11,12.71,12.3,12.06,11.61],
'B2_Y' : [4.66,4.44,4.24,4.1,4.01,3.84,3.67,3.56,3.44,3.47],
# 'C_X' : [10,15,18,20,30,33,35,42,34,20],
# 'C_Y' : [10,16,20,10,20,13,15,12,14,10],
})
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']
interval_ms = 200
delay_ms = 1000
ani = animation.FuncAnimation(fig, plotmvs, frames=df.groupby('time'),
blit=True, interval=interval_ms, repeat_delay=delay_ms)
plt.show()
最佳答案
好吧，所以我必须改变一些事情:
- 最重要的是，我关闭了
blit=True(主要是因为我使用的是 Mac，这并不完全支持，因此您的情况可能会有所不同)和animated=True在line_a和line_b的定义中。 - 您在代码注释中创建的组“C”缺少一个整数，无法让
tuples=[…]代码正常工作(它期望找到一个int(k. split('_')[0][1:]) - 散点图应该是
.scatter()，因为您没有在FuncAnimation中使用init_func=init调用，我们可以简单地在动画函数本身内“即时”创建它。 - 我添加了第二个
for循环，其中包含if group=='C'情况 - 尽管您可以更优雅地解决这个问题 - 创建散点图分散。 请注意，我包含了转换，尽管我不确定这最终会为您实现什么 - 我将
ax.contourf()中的级别设置为区间 [0,1]，但这在我看来完全是迂腐的;-) - 我在所有绘图项中添加了
zorder=，以便控制在哪个 z 平面上绘制它们(可选，但很有帮助)，并稍微调整了绘制线的外观(可选) ，仅用于强调) - 最后，您需要确保返回一个可迭代列表，因此可能“看起来很奇怪”
返回 cfs.collections + [scat] + [line_a,line_b ]
绘图结果:
完整代码:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
import matplotlib.transforms as transforms
from matplotlib.lines import Line2D
''' Section 1 '''
DATA_LIMITS = [-85, 85]
def datalimits(*data):
return DATA_LIMITS # dmin - spad, dmax + spad
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)
PDF = sts.multivariate_normal([x, y]).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)):
X, Y, PDF = mvpdf(x, y, xlim, ylim)
PDFs.append(PDF)
return X, Y, np.sum(PDFs, axis=0)
''' Animate Plot '''
fig, ax = plt.subplots(figsize = (10,6))
ax.set_xlim(DATA_LIMITS)
ax.set_ylim(DATA_LIMITS)
#Animated coordinates for group A,B
line_a, = ax.plot([], [], '-o', c='red', alpha = 0.5, markersize=5,zorder=3)
line_b, = ax.plot([], [], '-o', c='blue', alpha = 0.5, markersize=5,zorder=3)
cfs = None
def plotmvs(tdf, xlim=None, ylim=None, fig=fig, ax=ax):
global cfs
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)):
group_line.set_data(*gdf[['X','Y']].values.T)
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, xlim, ylim)
PDFs.append(PDF)
for (group, gdf) in df.groupby('group'):
if group=='C':
offset = lambda p: transforms.ScaledTranslation(p/82.,0, plt.gcf().dpi_scale_trans)
trans = plt.gca().transData
scat=ax.scatter(gdf['X'].values, gdf['Y'].values,
marker='o', c='white', alpha = 0.5,zorder=3,
transform=trans+offset(+2) )#markersize=2,zorder=3)
normPDF = PDF - PDF.min()
normPDF = normPDF / normPDF.max()
cfs = ax.contourf(X, Y, normPDF, cmap='viridis', alpha = 1, levels=np.linspace(0,1,10),zorder=1)
return cfs.collections + [scat] + [line_a,line_b] # make sure that these are iterable!
n = 10
time = range(n)
d = ({
'A1_X' : [13.3,13.16,12.99,12.9,12.79,12.56,12.32,12.15,11.93,11.72],
'A1_Y' : [26.12,26.44,26.81,27.18,27.48,27.82,28.13,28.37,28.63,28.93],
'A2_X' : [6.97,6.96,7.03,6.98,6.86,6.76,6.55,6.26,6.09,5.9],
'A2_Y' : [10.92,10.83,10.71,10.52,10.22,10.02,9.86,9.7,9.54,9.37],
'B1_X' : [38.35,38.1,37.78,37.55,37.36,37.02,36.78,36.46,36.21,35.79],
'B1_Y' : [12.55,12.58,12.58,12.55,12.5,12.47,12.43,12.48,12.44,12.44],
'B2_X' : [14.6,14.38,14.16,13.8,13.45,13.11,12.71,12.3,12.06,11.61],
'B2_Y' : [4.66,4.44,4.24,4.1,4.01,3.84,3.67,3.56,3.44,3.47],
'C1_X' : [10.,15.,18.,20.,30.,33.,35.,42.,34.,20.],## name contains an int so that tuples=... list works!
'C1_Y' : [10.,16.,20.,10.,20.,13.,15.,12.,14.,10.],
})
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']
interval_ms = 200
delay_ms = 1000
ani = animation.FuncAnimation(fig, plotmvs, frames=df.groupby('time'), interval=interval_ms, repeat_delay=delay_ms,)
plt.show()
更新
如果您希望有一个单个移动点，同时最小化/简化代码行，您还可以使用以下方式启动绘图元素:
line_a, = ax.plot([], [], '-o', c='red', alpha = 0.5, markersize=5,zorder=3)
line_b, = ax.plot([], [], '-o', c='blue', alpha = 0.5, markersize=5,zorder=3)
lines=[line_a,line_b] ## this is iterable!
offset = lambda p: transforms.ScaledTranslation(p/82.,0, plt.gcf().dpi_scale_trans)
trans = plt.gca().transData
scat = ax.scatter([], [], s=5**2,marker='o', c='white', alpha = 0.5,zorder=3,transform=trans+offset(+2) )
scats=[scat] ## this is iterable, too!
然后在plotmvs()中:
for (group, gdf), group_line in zip(df.groupby('group'), lines+scats):
if group in ['A','B']:
group_line.set_data(*gdf[['X','Y']].values.T)
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, xlim, ylim)
PDFs.append(PDF)
elif group in ['C']:
x,y=(gdf['X'].values, gdf['Y'].values)
scat.set_offsets( gdf[['X','Y']].values )
请注意，更新散点图和线图使用不同的函数和坐标列表!
最后更新返回值:
return cfs.collections + scats + lines # make sure that these are iterable!
这会产生以下动画:
关于python - 动画散点图和等高线图，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/55624400/