Hi,

I'm not sure I fully understood what you're trying to do but the following code does the steps you've requested. You may however adapt some of them for your intended purpose. The code 1) creates a 512*512 array, 2) selects a bit of it, 3) rescales it to have more array elements, 4) Passes the gaussian filter over it, 5) and finally views all results (with matplotlib).

from __future__ import division

import numpy as np

import scipy.ndimage as im

import matplotlib.pyplot as plt

# 1) CREATING SOME TEST DATA

X = np.linspace(-5, 5,512)

Y = np.linspace(-5, 5,512)

X, Y = np.meshgrid(X, Y)

R = np.sqrt(X**2 + Y**2)

Z = np.sin(R)

# 2) SELECTING A BIT OF MY DATA

x0,x1 = 200,400 # X COORDINATES OF PARCEL

y0,y1 = 400,512 # Y COORDINATES OF PARCEL

Z2 = Z[x0:x1,y0:y1].copy() # CREATING TE PARCEL FROM COORDINATES

# 3) RESCALING THE BIT OF DATA

nx = int(1/((x1-x0)/512)) # APROX. NUMBER OF REPEATABLES FOR X

ny = int(1/((y1-y0)/512)) # APROX. NUMBER OF REPEATABLES FOR Y

Z3 = Z2.repeat(nx,axis=1).repeat(ny,axis=0) # BUILDING A RESCALE FOR Z2

# 4) PASSING A GAUSSIAN FILTER OVER IT.

Z4 = im.filters.gaussian_filter(Z3,3) # SIGMA 3 (PUT WHATEVER YOU WANT)

# VIEW ALL RESULTS

plt.imshow(Z.T,origin='lower',interpolation='nearest')

plt.title('Original')

plt.show()

plt.imshow(Z2.T,origin='lower',interpolation='nearest')

plt.title('Parcel')

plt.show()

plt.imshow(Z3.T,origin='lower',interpolation='nearest')

plt.title('Rescaled parcel')

plt.show()

plt.imshow(Z4.T,origin='lower',interpolation='nearest')

plt.title('Gaussian convoluted rescaled parcel')

plt.show()

Grid computing system is a widely distributed resource for a common goal. It is Brother of Cloud Computing and Sister of Supercomputer. We can think the grid is a distributed system connected to a single network. This types of computing work with the large volume of files. Basically, it is a cluster types system. So people call it cluster computing.

Source: https://www.cloudwebhostingtips.com/grid-computing-vs-cloud-computing-supercomputer/

Grid computer tends to be more geographically disperse and heterogeneous by nature. Grid network also has various types. A single grid is like dedicated connection but a common grid perform multiple tasks.

The size of the grid is large. So grid computing is like supercomputing. It consists of many network, computer, and middleware. Grid computer is dedicated to some specific function of the large volume of data. In the grid process, each task divided into a various process. All the process starts execution simultaneously on a different computer. As a result, very few seconds needs to execute and enjoy the flavor of supercomputing.

For more:https://www.cloudwebhostingtips.com/