I have written a code that uses griddata interpolation to interpolate points on an irregular grid onto a regular grid. I also creating contour plots for two dependent variables after the interpolation. I am able to generate the line contour and filled contour plots and they also match the images qualitatively that were directly generated from my simulation. But the images are shown in a horizontal orientation and also the quality is not as per my expectation. I want the images to be shown as vertical i.e (x-axis; width =30.48(always fixed),y-axis; width varies according to a condition). In the plots x and y are interchanged. I did not understand the reason for this. I think it has to do with how the rows and columns are handled by contour function.
My second issue is that I am importing multiple text files and I want to save the generated contour plots separately for each of the imported text files. I am not sure of the best way to do this.
from __future__ import print_function
import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import griddata
###############################################################################
path = 'location of the files'
FT_init = 5.4311
delt = 0.15
TS_init = 140
dj_length = 2.4384
###############################################################################
def streamfunction2d(y,x,Si_f):
plt.figure(3)
Stf= plt.contour(y,x,Si_f,50)
Stf1 = plt.colorbar(Stf)
plt.savefig('location of the saved image',format='png',dpi=1200)
plt.close(3)
###############################################################################
def tracer2d(y,x,Ti_f):
plt.figure(1)
trc= plt.contour(y,x,Ti_f,25)
trc1 = plt.colorbar(trc)
plt.clabel(trc,fmt='%.0f',inline=True)
plt.savefig('location of the saved image',format='png',dpi=1200)
plt.close(1)
plt.figure(2)
trp = plt.pcolormesh(y,x,Ti_f,linestyle='dashed') ## Alternate method of creating filled-contour plots.
plt.savefig('location of the saved image',format='png',dpi=1200)
plt.close(2)
###############################################################################
flowtime =np.linspace(500,600,2) ## Here I would include multiple flow times. So I want to save the plots for each flow time in the location provided.
timestep = np.zeros(len(flowtime))
for n in range(len(flowtime)):
timestep = (flowtime-FT_init)/delt + TS_init
timestep = np.array(np.round(timestep,-2),dtype = 'int')
for p in range(len(timestep)):
timestepstring=str(timestep[p]).zfill(4)
fname = path+"ddn150AE-"+timestepstring+".txt"
f = open(fname,'r')
data = np.loadtxt(f,skiprows=1)
data = data[data[:, 1].argsort()]
data = data[np.logical_not(data[:,11]== 0)]
Y = data[:,2]
limit = np.nonzero(Y==dj_length)[0][0]
Y = Y[limit:]
Vf = data[:,11]
Vf = Vf[limit:]
Tr = data[:,9]
Tr = Tr[limit:]
X = data[:,1]
X = X[limit:]
Y = data[:,2]
Y = Y[limit:]
U = data[:,3]
U = U[limit:]
V = data[:,4]
V = V[limit:]
St = data[:,5]
St = St[limit:]
###############################################################################
### The following code will do the interpolation for the dependent variable
## Using griddata for interpolation from Unstructured to Structured data
# resample onto a 100x100 grid
nx, ny = 100,100
# (N, 2) arrays of input x,y coords and dependent values
pts = np.vstack((X,Y )).T
vals = np.vstack((Tr))
vals1 = np.vstack((St))
# The new x and y coordinates for the grid
x = np.linspace(X.min(), X.max(), nx)
y = np.linspace(Y.min(), Y.max(), ny)
r = np.meshgrid(y,x)[::-1]
# An (nx * ny, 2) array of x,y coordinates to interpolate at
ipts = np.vstack(a.ravel() for a in r).T
# An (nx * ny, 2) array of interpolated Tr values
Ti = griddata(pts, vals, ipts, method='nearest')
Ti_f = np.reshape(Ti,(len(y),len(x)))
tracer2d(y,x,Ti_f)
Si = griddata(pts, vals1, ipts, method='nearest')
Si_f = np.reshape(Si,(len(y),len(x)))
streamfunction2d(y,x,Si_f)
Location for the text files Sample Text Files. This is the image that I have from simulation corresponding to 3400 text file.
I would appreciate any advice or help in this.
Thank you
SOLVED:
