Python: Compute Bin-Mean Value of Scatter Plot Bullets - python-3.x

Question

I have three 1D arrays (A, B, C) of equal length/size. I plot a scatter plot of B vs. A where I color each scatter plot bullet by the corresponding value in the C array (see the code below).
# Imports
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
# Create the Arrays
A = 10 * np.random.random_sample((20, 20))
B = 10 * np.random.random_sample((20, 20))
C = 100 * np.random.random_sample((20, 20))
A = A.reshape(20*20)
B = B.reshape(20*20)
C = C.reshape(20*20)
# Create the Colormap and Define Boundaries
cmap_C = cm.jet
cmap_C.set_bad(color='white')
bounds_C = np.arange(0, 110, 10)
norm_C = mpl.colors.BoundaryNorm(bounds_C, cmap_C.N)
# Plot the Figure
plt.figure()
plt.scatter(A, B, c=C, marker='o', s=100, cmap=cmap_C, norm=norm_C)
plt.xlim([-1, 11])
plt.ylim([-1, 11])
plt.xticks(np.arange(0, 11, 1))
plt.yticks(np.arange(0, 11, 1))
plt.xlabel('A')
plt.ylabel('B')
plt.grid()
plt.colorbar(label='Value of C')
plt.show()
Some bullets overlap in the figure so we cannot see them clearly. Therfore, next I now want to compute and plot the mean C value of all scatter plot bullets within each 1 integer x 1 integer bin in the figure so that each square grid point is colored by one single color (these bins are illustrated by the figure gridding). How can I do this?

Answer 1

It's not totally clear what you are trying to do, but I think there is an analytic result to your question before you work too hard. The expected mean value of color (C vector) is 50 because you have generated a uniformly distributed sample [0, 100]. The coordinates are also uniformly distributed, but that is irrelevant. Of course, there will be some variance in each of the grid squares.
If you need to go forward as an exercise, I'd construct a dictionary of coordinate:color mappings to help set up a screen...
color_map = {(x, y): color for x, y, color in zip(A,B,C)}
Then you could set up a dictionary to gather results for each grid and probably by taking the int() value of the coordinates put the data into the correct data field for the grid

Answer 2

Below is a solution that works for my purposes.
# Imports
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from zipfile import ZipFile
# Create the Arrays
xx = 5
yy = 5
A = 10 * np.random.random_sample((xx, yy))
B = 10 * np.random.random_sample((xx, yy))
C = 100 * np.random.random_sample((xx, yy))
A = A.reshape(xx*yy)
B = B.reshape(xx*yy)
C = C.reshape(xx*yy)
color_map = {(x, y): color for x, y, color in zip(A,B,C)}
xedges = np.arange(11)
yedges = np.arange(11)
H, xedges, yedges = np.histogram2d(A, B, bins=(xedges, yedges))
HT = H.T
ca = np.asarray(list(color_map))
print(ca)
cai = ca.astype(int)
print(cai)
# Extracting all dictionary values using loop + keys()
res = []
for key in color_map.keys() :
res.append(color_map[key])
res = np.asarray(res)
resi = res.astype(int)
print(resi)
BMC = np.zeros([10, 10])
for i in np.arange(len(resi)):
BMC[cai[i,1],cai[i,0]] = BMC[cai[i,1],cai[i,0]] + resi[i]
print(cai[i])
print(resi[i])
print(BMC[cai[i,1],cai[i,0]])
print(HT)
print(BMC)
BMC = BMC/HT
print(BMC)
# Create the Colormap and Define Boundaries
cmap_C = cm.jet
cmap_C.set_bad(color='white')
bounds_C = np.arange(-5, 115, 10)
norm_C = mpl.colors.BoundaryNorm(bounds_C, cmap_C.N)
cmap_hist2d = cm.CMRmap_r
cmap_hist2d.set_bad(color='white')
bounds_hist2d = np.arange(-0.5, 4.5, 1)
norm_hist2d = mpl.colors.BoundaryNorm(bounds_hist2d, cmap_hist2d.N)
cmap_C = cm.jet
cmap_C.set_bad(color='white')
BMC_plot = np.ma.array ( BMC, mask=np.isnan(BMC)) # Mask NaN
bounds_C = np.arange(-5, 115, 10)
norm_C = mpl.colors.BoundaryNorm(bounds_C, cmap_C.N)
plt.subplot(311)
plt.scatter(A, B, c=C, marker='o', s=100, cmap=cmap_C, norm=norm_C)
plt.xlim([-1, 11])
plt.ylim([-1, 11])
plt.xticks(np.arange(0, 11, 1))
plt.yticks(np.arange(0, 11, 1))
plt.ylabel('B')
plt.grid()
plt.colorbar(label='Value of C', ticks=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
plt.subplot(312)
x, y = np.meshgrid(xedges, yedges)
plt.pcolor(x, y, HT, cmap=cmap_hist2d, norm=norm_hist2d)
plt.xlim([-1, 11])
plt.ylim([-1, 11])
plt.xticks(np.arange(0, 11, 1))
plt.yticks(np.arange(0, 11, 1))
plt.ylabel('B')
plt.grid()
plt.colorbar(label='Number of Data in Bin', ticks=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
plt.subplot(313)
plt.pcolor(x, y, BMC_plot, cmap=cmap_C, norm=norm_C)
plt.xlim([-1, 11])
plt.ylim([-1, 11])
plt.xticks(np.arange(0, 11, 1))
plt.yticks(np.arange(0, 11, 1))
plt.xlabel('A')
plt.ylabel('B')
plt.grid()
plt.colorbar(label='Bin-Mean C Value', ticks=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
plt.show()