def saveNetcdf(fileName,data):
# NetCDF package
try:
from ase.io.pupynere import NetCDFFile
except ImportError:
print "ase.io.pupynere not installed on this computer."
# get input data shape
nx,ny,nz = data.shape
print 'data type '+str(data.dtype)
# open file
f = NetCDFFile(fileName,'w')
# create netcdf dimensions
f.createDimension('x',nx)
f.createDimension('y',ny)
f.createDimension('z',nz)
rho = f.createVariable('rho','d',('x','y','z'))
rho[:] = np.ones((nx,ny,nz))
E = f.createVariable('E','d',('x','y','z'))
E[:] = np.zeros((nx,ny,nz))
rho_vx = f.createVariable('rho_vx','d',('x','y','z'))
rho_vx[:] = data
rho_vy = f.createVariable('rho_vy','d',('x','y','z'))
rho_vy[:] = np.zeros((nx,ny,nz))
rho_vz = f.createVariable('rho_vz','d',('x','y','z'))
rho_vz[:] = np.zeros((nx,ny,nz))
Bx = f.createVariable('Bx','d',('x','y','z'))
Bx[:] = np.zeros((nx,ny,nz))
By = f.createVariable('By','d',('x','y','z'))
By[:] = np.zeros((nx,ny,nz))
Bz = f.createVariable('Bz','d',('x','y','z'))
Bz[:] = np.zeros((nx,ny,nz))
f.sync()
f.close()
def saveNetcdf(fileName, data):
# NetCDF package
try:
from ase.io.pupynere import NetCDFFile
except ImportError:
print "ase.io.pupynere not installed on this computer."
# get input data shape
nx, ny, nz = data.shape
print 'data type ' + str(data.dtype)
# open file
f = NetCDFFile(fileName, 'w')
# create netcdf dimensions
f.createDimension('x', nx)
f.createDimension('y', ny)
f.createDimension('z', nz)
rho = f.createVariable('rho', 'd', ('x', 'y', 'z'))
rho[:] = np.ones((nx, ny, nz))
E = f.createVariable('E', 'd', ('x', 'y', 'z'))
E[:] = np.zeros((nx, ny, nz))
rho_vx = f.createVariable('rho_vx', 'd', ('x', 'y', 'z'))
rho_vx[:] = data
rho_vy = f.createVariable('rho_vy', 'd', ('x', 'y', 'z'))
rho_vy[:] = np.zeros((nx, ny, nz))
rho_vz = f.createVariable('rho_vz', 'd', ('x', 'y', 'z'))
rho_vz[:] = np.zeros((nx, ny, nz))
Bx = f.createVariable('Bx', 'd', ('x', 'y', 'z'))
Bx[:] = np.zeros((nx, ny, nz))
By = f.createVariable('By', 'd', ('x', 'y', 'z'))
By[:] = np.zeros((nx, ny, nz))
Bz = f.createVariable('Bz', 'd', ('x', 'y', 'z'))
Bz[:] = np.zeros((nx, ny, nz))
f.sync()
f.close()
import numpy as np
from ase.io.pupynere import NetCDFFile
# Write array
a1 = np.random.rand(5, 5)
a2 = a1 * 2 - 5
nc = NetCDFFile('test.nc', 'w')
nc.createDimension('dimx', a1.shape[0])
nc.createDimension('dimy', a1.shape[1])
nc.createVariable('matrix1', 'd', ('dimx', 'dimy'))[:] = a1
nc.createVariable('matrix2', 'd', ('dimx', 'dimy'))[:] = a2
nc.sync()
nc.close()
# Read array
nc = NetCDFFile('test.nc', 'r')
b1 = nc.variables['matrix1'][:]
b2 = nc.variables['matrix2'][:]
assert np.all(a1 == b1) and np.all(a2 == b2)
class ETSFWriter:
def __init__(self, filename):
from Scientific.IO.NetCDF import NetCDFFile
self.nc = NetCDFFile(filename, 'w')
self.nc.file_format = 'ETSF Nanoquanta'
self.nc.file_format_version = np.array([3.3], dtype=np.float32)
self.nc.Conventions = 'http://www.etsf.eu/fileformats/'
self.nc.history = 'File generated by ASE'
def write_atoms(self, atoms):
specie_a = np.empty(len(atoms), np.int32)
nspecies = 0
species = {}
numbers = []
for a, Z in enumerate(atoms.get_atomic_numbers()):
if Z not in species:
species[Z] = nspecies
nspecies += 1
numbers.append(Z)
specie_a[a] = species[Z]
dimensions = [
('character_string_length', 80),
('number_of_atoms', len(atoms)),
('number_of_atom_species', nspecies),
('number_of_cartesian_directions', 3),
('number_of_reduced_dimensions', 3),
('number_of_vectors', 3)]
for name, size in dimensions:
self.nc.createDimension(name, size)
var = self.add_variable
var('primitive_vectors',
('number_of_vectors', 'number_of_cartesian_directions'),
atoms.cell / Bohr, units='atomic units')
var('atom_species', ('number_of_atoms',), specie_a + 1)
var('reduced_atom_positions',
('number_of_atoms', 'number_of_reduced_dimensions'),
atoms.get_scaled_positions())
var('atomic_numbers', ('number_of_atom_species',),
np.array(numbers, dtype=float))
def close(self):
self.nc.close()
def add_variable(self, name, dims, data=None, **kwargs):
if data is None:
char = 'd'
else:
if isinstance(data, np.ndarray):
char = data.dtype.char
elif isinstance(data, float):
char = 'd'
elif isinstance(data, int):
char = 'i'
else:
char = 'c'
var = self.nc.createVariable(name, char, dims)
for attr, value in kwargs.items():
setattr(var, attr, value)
if data is not None:
if len(dims) == 0:
var.assignValue(data)
else:
if char == 'c':
if len(dims) == 1:
var[:len(data)] = data
else:
for i, x in enumerate(data):
var[i, :len(x)] = x
else:
var[:] = data
return var
