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动画轮廓和散点图

[英]Animate contour and scatter plot

 原文  2019-01-03 03:27:08  5   1    python/ pandas/ matplotlib/ animation

问题描述

我试图从一组xy coordinates动画scatter和bivariate gaussian distribution 。 我将首先记录调用分散和分布的特定代码，然后记录我之后如何测量分布。 

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)

关于如何使用单个帧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)

我基本上想通过遍历xy坐标的每一行来设置此代码的动画。

1 个解决方案

解决方案1
 4 已采纳  2019-01-08 12:11:12

这是对OP代码的一种非常快速和肮脏的修改，修复了散射动画并添加（一种形式）轮廓动画。

基本上，您首先要为动画创建艺术家 （在本例中为Line2D对象，由plot()返回）。 随后，您将创建一个update函数（以及可选的初始化函数）。 在该功能中，您将更新现有艺术家。 我认为matplotlib文档中的示例解释了这一切。

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）。

该QuadContourSet通过返回contourf （即你的cfs ）不能用作本身的艺术家，但你可以把它工作中使用cfs.collections （道具， 这个苏答案 ）。 但是，您仍然需要创建新的轮廓图并删除旧的轮廓图，而不是仅更新轮廓数据。 我个人更喜欢低级方法：尝试获取轮廓数据而不调用contourf ，然后初始化和更新轮廓线，就像你对散射一样。

不过，上面的方法是在下面的OP代码中实现的（只需复制，粘贴和运行）： 

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()

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