动画散点图和等高线图

Question

I am trying to generate an animated scatter plot within a functioning animated contour plot . I can get both working separately but not together.

The code below generates the contour from coordinates A and B in the df . I've attempted to include a separated animated scatter within the sample plot using the C coordinates. This attempt is currently commented out.

So I basically want to include another animated scatter using C_X and C_Y . I've trying by applying them to 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()

Answer 1

Ok, so I had to change a few things:

• Most importanty, I turned off blit=True (mostly because I'm on a Mac, where this isn't fully supported, so your mileage may vary) and animated=True in the definitions of line_a and line_b .
• The group "C" that you had created in your code comments was missing an integer for the tuples=[…] code to work (it expects to find an int(k.split('_')[0][1:] )
• The scatter plot should be a .scatter() , and since you're not using an init_func=init call within the FuncAnimation , we can simply create it "on the fly", within the animation function itself.
• I've added a second for loop with an if group=='C' case — though you could solve this more elegantly — that creates the scatterplot scat . Note that I included the transform, although I'm not sure what this will achieve for you, in the end
• I set the levels in ax.contourf() to the interval [0,1], but that's completely pedantic from my side ;-)
• I've added zorder= in all plot items in order to control in which z-plane they are plotted (optional, but helpful), and slightly adjusted the look of the plotted lines (optional, only for emphasis)
• In the end, you need to make sure you're returning an iterable list , hence the perhaps "odd looking" return cfs.collections + [scat] + [line_a,line_b]

Plot result:

Complete code:

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()

---

Update

If you'd rather have a single moving point , at the same time minimising/streamlining the lines of code, you could also initiate the plot elements with:

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!

and then inside plotmvs():

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 )

Note that updating scatter plots and line plots uses different functions and coordinate lists!

And finally update the return values:

return  cfs.collections + scats + lines # make sure that these are iterable!

Which yields the following animation: