*Bounty: 100*

I have a set of xy cooridnates that generate a contour. For the code below, these cooridnates are from groups `A`

and `B`

. I have also created a separate xy cooridnate that is called from `C1_X`

and `C1_Y`

. However this isn’t used creating the contour itself. It is a separate xy coordinate.

**Question: Is it possible to return the z-value at the **`C1_X`

`C1_Y`

cooridnate?

It is similar to another question: enter link description here. This figure displays what I’m hoping to return.

The `contour`

below is normalised so values fall between `-1`

and `1`

. I’m hoping to return the z-value for `C1_X`

and `C1_Y`

, which is the white scatter point seen in the figure beneath the code.

I have attempted to return the z-value for this point using:

```
# Attempt at returning the z-value for C1
f = RectBivariateSpline(X, Y, normPDF)
z = f(d['C1_X'], d['C1_Y'])
print(z)
```

But I’m returning an error: `raise TypeError('x must be strictly increasing')`

TypeError: x must be strictly increasing

```
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 mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.interpolate import RectBivariateSpline
DATA_LIMITS = [0, 15]
def datalimits(*data):
return DATA_LIMITS
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)
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]
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 = 1,zorder=3,transform=trans+offset(+2) )
scats=[scat]
cfs = None
def plotmvs(tdf, xlim=None, ylim=None, fig=fig, ax=ax):
global cfs
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'), (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), 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)
#plot white scatter point
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())
# Attempt at returning the z-value for C1
f = RectBivariateSpline(X, Y, normPDF)
z = f(d['C1_X'], d['C1_Y'])
print(z)
cfs = ax.contourf(X, Y, normPDF, cmap='jet', 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]
n = 1
time = range(n)
d = ({
'A1_X' : [3],
'A1_Y' : [6],
'A2_X' : [6],
'A2_Y' : [10],
'B1_X' : [12],
'B1_Y' : [2],
'B2_X' : [14],
'B2_Y' : [4],
'C1_X' : [4],
'C1_Y' : [6],
'A1_Radius' : [107],
'A2_Radius' : [95],
'B1_Radius' : [250],
'B2_Radius' : [213],
'A1_Scaling' : [7],
'A2_Scaling' : [5],
'B1_Scaling' : [2],
'B2_Scaling' : [4],
'A1_Rotation' : [0],
'A2_Rotation' : [0],
'B1_Rotation' : [0],
'B2_Rotation' : [0],
})
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()
```

I am hoping to return the `contour`

value for this point. Intended Output will be either a `list`

or `df`

that displays the normalised `z`

value `(-1,1)`

for `C`

.

Upon visual inspection this would be approx `0.6`

or `0.7`

Get this bounty!!!