#Createmesh grid with a spacing of 0.5 x 0.5
stepx = 0.5
stepy = 0.5
xx = np.arange(min(x), max(x), stepx)
yy = np.arange(min(y), max(y), stepy)
xgrid, ygrid = np.meshgrid(xx, yy)
grid_z1 = interpolate.griddata((x,y), Arrival_Time, (xgrid, ygrid), method='linear') #Interpolating the Time values

#Formatdata
X = np.ravel(xgrid)
Y= np.ravel(ygrid)
zs = np.ravel(grid_z1)
Z = zs.reshape(X.shape)

#Calculate Gradient
(dy,dx) = np.gradient(grid_z1) #Find gradient for points on meshgrid

Velocity_dx= dx/stepx #velocity ms/m
Velocity_dy= dy/stepx #velocity ms/m

Resultant = (Velocity_dx**2 + Velocity_dy**2)**0.5 #Resultant scalar value ms/m

average_BR = np.nanmean(Resultant)
Min_BR = np.nanmin(Resultant)
Max_BR = np.nanmax(Resultant)
Median_BR =np.nanmedian(Resultant)
Resultant = np.ravel(Resultant)


#Integrate to compare the original data input
dxintegral = np.nancumsum(Velocity_dx, axis=1)*stepx
dyintegral = np.nancumsum(Velocity_dy, axis=0)*stepy

valintegral = np.ma.zeros(dxintegral.shape) #Makes an array filled with 0's the same shape as dx integral
for i in range(len(yy)):
    for j in range(len(xx)):
        valintegral[i, j] = np.ma.sum([dxintegral[0, len(xx) // 2],  dyintegral[i, len(xx)  // 2], dxintegral[i, j], - dxintegral[i, len(xx) // 2]])
valintegral[np.isnan(dx)] = np.nan
min_value = np.nanmin(valintegral)


valintegral=valintegral+(min_value*-1)

## convert your array into a dataframe

print(valintegral)
fig = pyplot.figure()
ax = fig.add_subplot()
ax.scatter(x,y,color='black',s=7,zorder=3)
ax.set_xlabel('X-Coordinates')
ax.set_ylabel('Y-Coordinates')
ax.contour(xgrid, ygrid, valintegral,levels=100,colors='red',zorder=2)
ax.contour(xgrid,ygrid, grid_z1 ,levels=100,colors='blue',zorder=1)
ax.set_aspect('equal')

pyplot.show()