我试图在一个函数animated
scatter
plot
{contour
plot
。我可以分开工作,但不能一起工作。在
下面的代码从A
和{contour
生成{C
坐标在示例plot
中包含一个分离的动画scatter
。此尝试当前被注释掉。在
所以我基本上想包括另一个animated
scatter
,使用C_X
和{line_c
。在
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()
好吧,所以我不得不改变一些事情:
line_a
和{blit=True
(主要是因为我使用的是Mac电脑,在Mac上不完全支持,所以你的里程数可能会有所不同)。在tuples=[…]
代码可以工作(它期望找到一个int(k.split('_')[0][1:]
).scatter()
,由于您没有在FuncAnimation
中使用init_func=init
调用,我们可以在动画函数本身中“动态”创建它。在for
循环,其中有一个if group=='C'
大小写——尽管你可以更优雅地解决这个问题——创建散点图scat
。请注意,我包含了转换,尽管我不确定这将为您实现什么,最终ax.contourf()
中的级别设置为间隔[0,1],但从我的角度来看,这完全是迂腐;-)zorder=
,以便控制它们在哪个z平面上绘制(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)return cfs.collections + [scat] + [line_a,line_b]
绘图结果:
完整代码:
^{1}$更新
如果您希望使用一个单个移动点,同时最小化/简化代码行,您还可以使用以下命令启动绘图元素:
^{pr2}$然后在plotmvs()中:
注意,更新散点图和折线图使用不同的函数和坐标列表!
最后更新返回值:
生成以下动画:
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