Problem returning interpolated z-value from contour

I am trying to return the z-value of a contour. The specific point I want to use to return the z-value is called from ['C1_X'],['C1_Y'] in the df. The code works when these coordinates do not change. This is found when using the dict labelled d below. But when the values do change it returns an error. This is expressed when using the dict d1 below.

Error:

raise ValueError("Error code returned by bispev: %s" % ier)
ValueError: Error code returned by bispev: 10

The code used to return the z-value is:

    # Return z-value for C coordinate
    f = RectBivariateSpline(X[0, :], Y[:, 0], normPDF.T)
    z = f(d['C1_X'], d['C1_Y']) 
    print(z)

Are there any suggestions why?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.interpolate import RectBivariateSpline
import matplotlib.transforms as transforms

DATA_LIMITS = [-85, 85]

def datalimits(*data):
    return DATA_LIMITS  

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 = {
            'radius': radius[i] if radius is not None else 0.5,
            'velocity': velocity[i] if velocity is not None else 0,
            'scale': scale[i] if scale is not None else 0,
            'theta': theta[i] if theta is not None else 0,
            '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 = (10,6))

ax.set_xlim(DATA_LIMITS)
ax.set_ylim(DATA_LIMITS)

line_a, = ax.plot([], [], 'o', c='red', alpha = 0.5, markersize=3,zorder=3)
line_b, = ax.plot([], [], 'o', c='blue', alpha = 0.5, markersize=3,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,marker='o', c='white', alpha = 1,zorder=3,transform=trans+offset(+2) )
scats=[scat] 


cfs = None

def plotmvs(tdf, xlim=None, ylim=None, fig=fig, ax=ax):
    global cfs  # as noted: quick and dirty...
    if cfs:
        for tp in cfs.collections:
            tp.remove()

    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'), lines+scats):
        if group in ['A','B']:
            group_line.set_data(*gdf[['X','Y']].values.T)
            kwargs = {
            'radius': gdf['Radius'].values if 'Radius' in gdf else None,
            'velocity': gdf['Velocity'].values if 'Velocity' in gdf else None,
            'scale': gdf['Scaling'].values if 'Scaling' in gdf else None,
            'theta': gdf['Rotation'].values if 'Rotation' in gdf else None,
            'xlim': xlim,
            'ylim': ylim
            }
            X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)
            PDFs.append(PDF)
        elif group in ['C']:
            gdf['X'].values, gdf['Y'].values
            scat.set_offsets(gdf[['X','Y']].values)

    normPDF = (PDFs[0]-PDFs[1])/max(PDFs[0].max(),PDFs[1].max())

    # Return z-value for C coordinate
    f = RectBivariateSpline(X[0, :], Y[:, 0], normPDF.T)
    z = f(d['C1_X'], d['C1_Y']) 
    print(z)


    cfs = ax.contourf(X, Y, normPDF, cmap='viridis', alpha = 1, levels=np.linspace(-1,1,10),zorder=1)

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.1)
    cbar = fig.colorbar(cfs, ax=ax, cax=cax)
    cbar.set_ticks([-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1])

    return  cfs.collections + [scat] + [line_a,line_b] 

''' Sample Dataframe '''

n = 9
time = range(n)  

d = ({
     'A1_X' :    [13,13,13,13,12,12,12,12,11,10],
     'A1_Y' :    [26,26,27,27,27,28,28,28,29,33],
     'A2_X' :    [7,7,7,7,7,7,7,6,6,6],
     'A2_Y' :    [11,11,11,11,10,10,10,10,10,9],
     'B1_X' :    [38,38,38,38,37,37,37,36,36,36],
     'B1_Y' :    [13,13,13,13,12,12,12,12,12,12],
     'B2_X' :    [15,14,14,14,13,13,13,12,12,12],
     'B2_Y' :    [5,4,4,4,4,4,4,4,3,3],
     'C1_X' :   [5,5,5,5,5,5,5,5,5,5],
     'C1_Y' :   [10,10,10,10,10,10,10,10,10,10],
    })


d1 = ({
     'A1_X' :    [13,13,13,13,12,12,12,12,11,10],
     'A1_Y' :    [26,26,27,27,27,28,28,28,29,33],
     'A2_X' :    [7,7,7,7,7,7,7,6,6,6],
     'A2_Y' :    [11,11,11,11,10,10,10,10,10,9],
     'B1_X' :    [38,38,38,38,37,37,37,36,36,36],
     'B1_Y' :    [13,13,13,13,12,12,12,12,12,12],
     'B2_X' :    [15,14,14,14,13,13,13,12,12,12],
     'B2_Y' :    [5,4,4,4,4,4,4,4,3,3],
     'C1_X' :   [5,5,5,5,5,5,5,5,5,5],
     'C1_Y' :   [10,10,10,10,10,10,10,10,10,9],
    })

# a list of tuples of the form ((time, group_id, point_id, value_label), value)
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 = 1000
delay_ms = 2000
ani = animation.FuncAnimation(fig, plotmvs, frames=df.groupby('time'), interval=interval_ms, repeat_delay=delay_ms,)

plt.show()

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