f_vtk = vtkWritePointDataStructured2D( f_vtk, Ftmp , Xt, 'fp90' )
# ======= Write 100% F_km ================
f_vtk = vtkWritePointDataStructured2D( f_vtk, F_km, Xt, 'fp_km' )
# ======= Write 00% F_km ================
Ftmp[:,:] = 0.; Ftmp += F_km*id90_km
f_vtk = vtkWritePointDataStructured2D( f_vtk, Ftmp , Xt, 'fp90_km' )
# Close the file at the end.
f_vtk.close(); Ftmp = None
if( printOn ):
CfD = dict()
CfD['title']='F(x,y)'; CfD['label']=fileout; CfD['N']=16
Cfp = addContourf( Xt, Yt, Ft , CfD )
CfD['title']='F_km(x,y), Ana'; CfD['label']=fileout+'_km'
Cfa = addContourf( Xt, Yt, F_km, CfD )
Fym = writeCrossWindSum( Ft, Xt, None, None )
pfig = plt.figure(num=3, figsize=(12.,9.))
varLabel = '$fp_y(x) = \sum_y fp(x,y)$'
axLabels = ['Cross Wind Integrated Footprint', 'x', 'sum_y fp(x,y) ']
pfig = addToPlot(pfig, Xt[0,:], Fym, varLabel, axLabels, False )
Ft = Zt = Xt = Yt = None
F_km = None
plt.show()
Q, X, Y, resDict = quadrantAnalysis(v1, v2, qaDict)
# Extract the results
nQ = resDict['nQ'] # Number of quadrant hits (nQ[0] := Ntotal)
SQ = resDict['SQ'] # Quadrant contributions (e.g. Reynolds stress)
#klims = resDict['klims']
# === Plot quadrant analysis output === #
cDict = dict()
cDict['cmap'] = plt.cm.gist_yarg # Include the colormap info within a dict
cDict['N'] = 12 # Number of levels in contour plot
cDict['title'] = "Quadrant Analysis\n{}: z={}-{} m".format(
filename, z[ijk1[2]], z[ijk2[2]])
cDict['label'] = "JPDF"
CO = addContourf(X, Y, Q, cDict)
CO.ax.spines['left'].set_position('zero')
CO.ax.spines['bottom'].set_position('zero')
#CO.ax.set_ylabel(r"$w'/\sigma_w$")
#CO.ax.set_xlabel(r"$u'/sigma_u$")
#plt.clabel(CO, CO.levels[:-2:3], inline=False, fontsize=10)
cn = 100. / nQ[0]
print(' Ejections (%) = {}, Sweeps (%) = {} '.format(cn * nQ[2], cn * nQ[4]))
print(' Outward Interactions (%) = {}, Inward Interactions (%) = {} '.format(
cn * nQ[1], cn * nQ[3]))
#plt.legend(loc=0)
if (saveFig):
plt.savefig(saveFig, format='jpg', dpi=300)
# Retain unused keys from original raster
PRdict = Rdict.copy()
Rdict = None
PRdict['R'] = PR
PRdict['GlobOrig'] = PROrig
PRdict['gridRot'] = rotation
PRdict['dPx'] = np.array([dxG[0], dxG[1]])
if (not args.printOnly):
saveTileAsNumpyZ(fileout, PRdict)
# Print the raster map, first, in a coordinate system where x-axis is aligned with the windDir
# and, second, in its original orientation.
if (printOn or printOnly):
figDims = 13. * (Xdims[::-1].astype(float) / np.max(Xdims))
fig = plt.figure(num=1, figsize=figDims)
fig = addImagePlot(fig, PR, args.fileout)
CfD = dict()
CfD['title'] = ' Z(x,y) '
CfD['label'] = "PALM DOMAIN ON MAP"
CfD['N'] = 16
CO = addContourf(XTRM, YTRM, PR[::-1, :], CfD)
plt.show()
XTRM = None
YTRM = None
PR = None
PRDict = None
Rdims = np.array(np.shape(R))
ROrig = Rdict['GlobOrig']
dPx = entry2Int(Rdict['dPx'])
Rdict = None
if (Ry[-1] < Ry[0]):
Ry *= -1. # Make values run into positive direction.
R = R[::-1, :]
dPx = entry2Int(dPx)
print ' Rdims = {} '.format(Rdims)
print ' ROrig = {} '.format(ROrig)
X, Y = np.meshgrid(Rx, Ry)
if (ROrig[0] == 0):
print ' Resetting y-origin. '
ROrig[0] = Rdims[0] * dPx
if (not printOnly):
vtkWriteDataStructured2d(None, X, Y, R, fileout, 'Z')
if (printOnly or printOn):
CfD = dict()
CfD['title'] = 'R(X,Y)'
CfD['label'] = fileout + ' raster data.'
CfD['N'] = 16
C = addContourf(X, Y, R, CfD)
plt.show()