Animate scatter and contour plot

I am trying to incorporate a separate animated scatter plot within a functioning animated contour plot. I can get both working separated but looking to amalgamate the new scatter within the functioning contour.

The code below roughly animates the contour from coordinates A and B in the df. I've attempted to animate the C coordinates in the code that is currently commented out.

Section 1 is used to create the contour. I'm trying to get line_c working that animates a single set of XY data relating to C_X, C_Y in the df

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()

from Animate scatter and contour plot