def main_output(data,
geo_info,
geo_spec,
outputname='new',
otype='h5',
drv=None,
omit=[],
**kwargs):
'''Creates the requested output.
**Parameters:**
data : numpy.ndarray, shape=N or shape=((NDRV,) + N)
Contains the output data. The shape (N) depends on the grid and the data, i.e.,
3d for regular grid, 1d for vector grid.
geo_info, geo_spec :
See :ref:`Central Variables` for details.
outputname : str
Contains the base name of the output file.
otype : str or list of str
Contains the output file type. Possible options:
'h5', 'cb', 'am', 'hx', 'vmd', 'mayavi'
drv : None or list of str, optional
If not None, a 4d(regular)/2d(vector) input data array will be expected
with NDRV = len(drv).
omit : list of str, optional
If not empty, the input file types specified here are omitted.
**Note:**
All additional keyword arguments are forwarded to the output functions.
'''
print_waring = False
output_written = []
if isinstance(otype, str):
otype = [otype]
if 'vmd' in otype and not 'cb' in otype:
otype.append('cb')
otype = [i for i in otype if i not in omit]
if otype is None or otype == []:
return output_written
# Convert the data to a regular grid, if possible
output_not_possible = (grid.is_vector and not grid.is_regular)
is_regular_vector = (grid.is_vector and grid.is_regular)
if is_regular_vector:
display(
'\nConverting the regular 1d vector grid to a 3d regular grid.')
grid.vector2grid(*grid.N_)
data = grid.mv2g(data=data)
if 'mayavi' in otype:
if output_not_possible: print_waring = True
else:
view_with_mayavi(grid.x,
grid.y,
grid.z,
data,
geo_spec=geo_spec,
**kwargs)
if drv is not None:
fid = '%(f)s_d%(d)s'
it = enumerate(drv)
else:
fid = '%(f)s'
it = [(0, None)]
data = [data]
f = {'f': outputname}
for i, j in it:
f['d'] = j
d = data[i]
if 'h5' in otype:
display('\nSaving to Hierarchical Data Format file (HDF5)...' +
'\n\t%(o)s.h5' % {'o': fid % f})
HDF5_creator(d, (fid % f), geo_info, geo_spec, **kwargs)
output_written.append('%s.h5' % (fid % f))
if 'am' in otype or 'hx' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
display('\nSaving to ZIBAmiraMesh file...' +
'\n\t%(o)s.am' % {'o': fid % f})
amira_creator(d, (fid % f))
output_written.append('%s.am' % (fid % f))
if 'hx' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
# Create Amira network incl. Alphamap
display('\nCreating ZIBAmira network file...')
hx_network_creator(data, (fid % f))
output_written.append('%s.hx' % (fid % f))
if 'cb' in otype or 'vmd' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
display('\nSaving to .cb file...' +
'\n\t%(o)s.cb' % {'o': fid % f})
cube_creator(d, (fid % f), geo_info, geo_spec, **kwargs)
output_written.append('%s.cb' % (fid % f))
#else: output_creator(d,(fid % f),geo_info,geo_spec) # Axel's cube files
if 'vmd' in otype and not print_waring:
if output_not_possible: print_waring = True
else:
# Create VMD network
display('\nCreating VMD network file...' +
'\n\t%(o)s.vmd' % {'o': fid % f})
vmd_network_creator((fid % f),
cube_files=['%s.cb' % (fid % f)],
**kwargs)
output_written.append('%s.vmd' % (fid % f))
if print_waring:
display(
'For a non-regular vector grid (`if grid.is_vector and not grid.is_regular`)'
)
display('only HDF5 is available as output format...')
display('Skipping all other formats...')
if is_regular_vector:
# Convert the data back to a regular vector grid
grid.grid2vector()
return output_written
def main_output(data,
qc=None,
outputname='data',
otype='auto',
gname='',
drv=None,
omit=[],
datalabels='',
mode='w',
**kwargs):
'''Creates the requested output.
**Parameters:**
data : numpy.ndarray, shape=N, shape=((NDRV,) + N), shape=(n, (NDRV,) + N) or list of numpy.ndarrays
Contains the output data. The shape (N) depends on the grid and the data, i.e.,
3d for regular grid, 1d for vector grid.
qc : class or dict
QCinfo class or dictionary containing the following attributes/keys.
See :ref:`Central Variables` for details.
outputname : str or list of str
Contains the base name of the output file. If outputname contains @, string will be split and first
part interpreted as outputname and second as gname (cf. Parameters:gname).
otype : str or list of str, optional
Contains the output file type. Possible options:
'auto', 'h5', 'cb', 'am', 'hx', 'vmd', 'mayavi'
If otype='native', a native input file will be written, the type of which may be specifie by
ftype='numpy'.
gname : str, optional
For native, HDF5, or npz output, specifies the group, where the data will be stored.
drv : None, list of str or list of list of str, optional
If not None, a 4d(regular)/2d(vector) input data array will be expected
with NDRV = len(drv). Specifies the file labels, i.e. e.g., data_d{drv}.cube for 4d array.
For 5d arrays i.e., data_0_d{drv}.cube
datalabels : list of str, optional
If not empty, the output file types specified here are omitted.
omit : list of str, optional
If not empty, the output file types specified here are omitted.
mode : str={'r', 'w', 'a'}, optional
Specifies the mode used to open the file (native, HDF5, or npz).
**Note:**
All additional keyword arguments are forwarded to the output functions.
'''
if otype is None or otype == []:
return []
if isinstance(outputname, str):
if '@' in outputname:
outputname, gname = outputname.split('@')
if isinstance(otype, str):
if otype == 'auto':
outputname, otype = path.splitext(outputname)
otype = otype[1:]
otype = [otype]
elif isinstance(otype, list) and len(otype) == 1:
if otype[0] == 'auto':
outputname, otype = path.splitext(outputname)
otype = [otype[1:]]
else:
for iot in range(len(otype)):
if otype[iot] == 'auto':
outputname, tmp = path.splitext(outputname)
if tmp != '':
otype[iot] = tmp[1:]
# Catch our native format before all else
# We can't figure this out by the file ending alone
# as we support hdf5 for output of both grid-based data
# as well as our internal format
output_written = []
internals = [i for i in range(len(otype)) if otype[i] == 'native']
if len(internals) > 0:
if not isinstance(data, list):
data = [data]
if isinstance(outputname, str):
outputname = [outputname for _ in data]
if 'ftype' in kwargs.keys():
if isinstance(kwargs['ftype'], str):
ftype = [kwargs['ftype'] for _ in data]
else:
ftype = ['numpy' for _ in data]
if 'group' in kwargs.keys():
if isinstance(kwargs['group'], str):
group = [kwargs['group'] for _ in data]
else:
group = [i.__class__.__name__.lower() for i in data]
display('Writing native input file...')
for i, oname in enumerate(outputname):
output_written.append(
write_native(data[i],
oname,
ftype[i],
mode=mode,
gname=path.join(gname, group[i])))
display('\n'.join(['\t' + i for i in output_written]))
else:
print_waring = False
output_not_possible = (grid.is_vector and not grid.is_regular)
# Shape shall be (Ndrv,Ndata,Nx,Ny,Nz) or (Ndrv,Ndata,Nxyz)
data = numpy.array(data)
dims = 1 if grid.is_vector else 3
shape = data.shape
if drv is not None and isinstance(drv, str):
drv = [drv]
if data.ndim < dims:
output_not_possible = True
display('data.ndim < ndim of grid')
elif data.ndim == dims: # 3d data set
data = data[numpy.newaxis, numpy.newaxis]
elif data.ndim == dims + 1: # 4d data set
if drv is not None:
data = data[:, numpy.newaxis]
else:
data = data[numpy.newaxis]
elif data.ndim == dims + 2: # 5d data set check if drv matches Ndrv
if drv is None or len(drv) != data.shape[0]:
drv = list(range(data.shape[0]))
elif data.ndim > dims + 2:
output_not_possible = True
display('data.ndim > (ndim of grid) +2')
if 'vmd' in otype and not ('cb' in otype or 'cube' in otype):
otype.append('cube')
if 'hx' in otype and not 'am' in otype:
otype.append('am')
otype = [i for i in otype if i not in omit]
otype_synonyms = [synonyms[i] for i in otype]
otype_ext = dict(zip(otype_synonyms, otype))
# Convert the data to a regular grid, if possible
is_regular_vector = (grid.is_vector and grid.is_regular)
if is_regular_vector:
display(
'\nConverting the regular 1d vector grid to a 3d regular grid.'
)
grid.vector2grid(*grid.N_)
data = numpy.array(grid.mv2g(data))
isstr = isinstance(outputname, str)
if isinstance(datalabels, str):
if data.shape[1] > 1:
datalabels = numpy.array([
str(idata) + ',' + datalabels
for idata in range(data.shape[1])
])
else:
datalabels = numpy.array([datalabels])
elif isinstance(datalabels, list):
datalabels = numpy.array(datalabels)
if drv is not None:
fid = '%(f)s_d%(d)s.'
datalabel_id = 'd/d%(d)s %(f)s'
contents = {
'axis:0':
numpy.array(
['d/d%s' % i if i is not None else str(i) for i in drv]),
'axis:1':
datalabels
}
it = enumerate(drv)
elif data.shape[0] > 1:
fid = '%(f)s_%(d)s.'
datalabel_id = '%(d)s %(f)s'
it = enumerate(data.shape[0])
contents = {
'axis:0': numpy.arange(data.shape[0]).astype(str),
'axis:1': datalabels
}
else:
fid = '%(f)s.'
datalabel_id = '%(f)s'
it = [(0, None)]
if data.shape[1] > 1:
contents = {'axis:0': datalabels}
else:
contents = datalabels
cube_files = []
all_datalabels = []
for idrv, jdrv in it:
datasetlabels = []
for idata in range(data.shape[1]):
if isstr:
f = {
'f':
outputname + '_' +
str(idata) if data.shape[1] > 1 else outputname,
'd':
jdrv
}
else:
f = {'f': outputname[idata], 'd': jdrv}
c = {'f': datalabels[idata], 'd': jdrv}
datalabel = datalabel_id % c
datasetlabels.append(datalabel)
if 'am' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
else:
filename = fid % f + otype_ext['am']
display('\nSaving to ZIBAmiraMesh file...\n\t' +
filename)
amira_creator(data[idrv, idata], filename)
output_written.append(filename)
if 'hx' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
else:
filename = fid % f + otype_ext['hx']
display('\nCreating ZIBAmira network file...\n\t' +
filename)
hx_network_creator(data[idrv, idata], filename)
output_written.append(filename)
if 'cube' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
elif qc is None:
display(
'\nFor cube file output `qc` is a required keyword parameter in `main_output`.'
)
else:
filename = fid % f + otype_ext['cube']
display('\nSaving to cube file...\n\t' + filename)
cube_creator(data[idrv, idata],
filename,
qc.geo_info,
qc.geo_spec,
comments=datalabel,
**kwargs)
output_written.append(filename)
cube_files.append(filename)
all_datalabels.extend(datasetlabels)
if 'vmd' in otype_synonyms and not print_waring:
if output_not_possible: print_waring = True
else:
filename = (outputname if isstr else
outputname[-1]) + '.' + otype_ext['vmd']
display('\nCreating VMD network file...\n\t' + filename)
vmd_network_creator(filename, cube_files=cube_files, **kwargs)
output_written.append(filename)
if 'h5' in otype_synonyms:
filename = (outputname
if isstr else outputname[-1]) + '.' + otype_ext['h5']
display('\nSaving to Hierarchical Data Format file (HDF5)...\n\t' +
filename)
hdf5_creator(data.reshape(shape),
filename,
qcinfo=qc,
gname=gname,
ftype='hdf5',
contents=contents,
mode=mode,
**kwargs)
output_written.append(filename)
if 'npz' in otype_synonyms:
filename = (outputname if isstr else outputname[-1])
display('\nSaving to a compressed .npz archive...\n\t' + filename +
'.npz')
hdf5_creator(data.reshape(shape),
filename,
qcinfo=qc,
gname=gname,
ftype='numpy',
contents=contents,
mode=mode,
**kwargs)
output_written.append(filename)
if 'mayavi' in otype_synonyms:
if output_not_possible: print_waring = True
else:
display('\nDepicting the results with MayaVi...\n\t')
if drv == ['x', 'y', 'z'] or drv == [0, 1, 2]:
is_vectorfield = True
data = numpy.swapaxes(data, 0, 1)
datalabels = datalabels
else:
is_vectorfield = False
data = data.reshape((-1, ) + grid.get_shape())
datalabels = all_datalabels
view_with_mayavi(grid.x,
grid.y,
grid.z,
data,
is_vectorfield=is_vectorfield,
geo_spec=qc.geo_spec,
datalabels=datalabels,
**kwargs)
if print_waring:
display(
'For a non-regular vector grid (`if grid.is_vector and not grid.is_regular`)'
)
display('only HDF5 is available as output format...')
display('Skipping all other formats...')
if is_regular_vector:
# Convert the data back to a regular vector grid
grid.grid2vector()
return output_written
filepath = os.path.join(folder, 'h2o_rhf_sph.molden')
qc = read.main_read(filepath, all_mo=True)
grid.adjust_to_geo(qc, extend=2.0, step=1)
grid.grid_init(is_vector=False, force=True)
drv = [None, 'x', 'y', 'z', 'xx', 'xy', 'xz', 'yy', 'yz', 'zz']
data = []
for i in range(2):
if i: grid.grid2vector()
data.append([
rho_compute(qc, slice_length=0),
rho_compute(qc, numproc=options.numproc),
rho_compute(qc, laplacian=True, slice_length=0)[-1],
rho_compute(qc, laplacian=True, numproc=options.numproc)[-1],
rho_compute(qc, calc_mo=True, drv=drv, slice_length=0),
rho_compute(qc, calc_mo=True, drv=drv, numproc=options.numproc)
])
data[1] = [grid.mv2g(d=i) for i in data[1]]
for i in range(len(data[0])):
equal(data[0][i], data[1][i])
filepath = os.path.join(tests_home, 'refdata_rho_compute.npz')
#numpy.savez(filepath, data=data[0])
refdata = numpy.load(filepath)
in_dic = {0: 'zero', 1: 'one', 2: 'two', 3: 'three', 4: 'four'}
for i in range(5):
equal(data[0][i], refdata[in_dic[i]])