Dear Wannier90 community,
I am using Wannier90/3.1.0 to plot a slice of my data. The postw90.x creates
the necessary python code but I get the following error when running this
python script:
File
"/gpfs/scratch/vaskarpo/Mn3Ge_hex/kslice/wannier90-kslice-curv_x+fermi_lines.py",
line 64, in <module>
valint = ml.griddata(points_x,points_y, val_log, xint,yint)
AttributeError: module 'matplotlib.mlab' has no attribute ‘griddata'
Does any one know how to remedy this? I tried replacing the ml.griddata with
scipy.interpolate.griddata but then I get the following error:
File
"/gpfs/scratch/vaskarpo/Mn3Ge_hex/kslice/wannier90-kslice-curv_x+fermi_lines.py",
line 64, in <module>
valint = interpolate.griddata(points_x,points_y, val_log, grid_x, grid_y)
File
"/cvmfs/soft.computecanada.ca/easybuild/software/2020/avx512/Core/scipy-stack/2023b/lib/python3.10/site-packages/scipy/interpolate/_ndgriddata.py",
line 253, in griddata
if ndim == 1 and method in ('nearest', 'linear', 'cubic'):
ValueError: The truth value of an array with more than one element is
ambiguous. Use a.any() or a.all()
I am attaching the python script.
Any suggestion is appreciated.
Thanks,
Vahid
import pylab as pl
import numpy as np
import matplotlib.mlab as ml
from scipy import interpolate
from collections import OrderedDict
points = np.loadtxt('wannier90-kslice-coord.dat')
# Avoid numerical noise
points_x=np.around(points[:,0],decimals=10)
points_y=np.around(points[:,1],decimals=10)
num_pt=len(points)
area= 0.660576
square= False
if square:
x_coord=list(OrderedDict.fromkeys(points_x))
y_coord=list(OrderedDict.fromkeys(points_y))
dimx=len(x_coord)
dimy=len(y_coord)
else:
xmin=np.min(points_x)
ymin=np.min(points_y)
xmax=np.max(points_x)
ymax=np.max(points_y)
a=np.max(np.array([xmax-xmin,ymax-ymin]))
num_int=int(round(np.sqrt(num_pt*a**2/area)))
xint = np.linspace(xmin,xmin+a,num_int)
yint = np.linspace(ymin,ymin+a,num_int)
# Energy level for isocontours (typically the Fermi level)
ef= 11.879500
bands=np.loadtxt('wannier90-kslice-bands.dat')
numbands=bands.size//num_pt
if square:
bbands=bands.reshape((dimy,dimx,numbands))
for i in range(numbands):
Z=bbands[:,:,i]
pl.contour(x_coord,y_coord,Z,[ef],colors='black')
else:
bbands=bands.reshape((num_pt,numbands))
bandint=[]
grid_x, grid_y = np.meshgrid(xint,yint)
for i in range(numbands):
bandint.append(interpolate.griddata((points_x,points_y), bbands[:,i], (grid_x,grid_y), method='nearest'))
pl.contour(grid_x,grid_y,bandint[i],[ef],colors='black')
outfile = 'wannier90-kslice-curv_x.pdf'
val = np.loadtxt('wannier90-kslice-curv.dat', usecols=(0,))
val_log=np.array([np.log10(abs(elem))*np.sign(elem) if abs(elem)>10 else elem/10.0 for elem in val])
if square:
Z=val_log.reshape(dimy,dimx)
mn=int(np.floor(Z.min()))
mx=int(np.ceil(Z.max()))
ticks=range(mn,mx+1)
pl.contourf(x_coord,y_coord,Z,ticks,origin='lower')
#pl.imshow(Z,origin='lower',extent=(min(x_coord),max(x_coord),min(y_coord),max(y_coord)))
else:
valint = ml.griddata(points_x,points_y, val_log, xint,yint)
mn=int(np.floor(valint.min()))
mx=int(np.ceil(valint.max()))
ticks=range(mn,mx+1)
pl.contourf(xint,yint,valint,ticks)
#pl.imshow(valint,origin='lower',extent=(min(xint),max(xint),min(yint),max(yint)))
ticklabels=[]
for n in ticks:
if n<0:
ticklabels.append('-$10^{%d}$' % abs(n))
elif n==0:
ticklabels.append(' $%d$' % n)
else:
ticklabels.append(' $10^{%d}$' % n)
cbar=pl.colorbar()
cbar.set_ticks(ticks)
cbar.set_ticklabels(ticklabels)
ax = pl.gca()
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
pl.savefig(outfile,bbox_inches='tight')
pl.show()
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