from __future__ import print_function
from pyiron_base import state
from vaspparser.vasp.output import (
GenericOutput as _GenericOutput,
)
from vaspparser.vasp.output import (
Output as _Output,
)
from pyiron_atomistics.atomistics.structure.atoms import (
Atoms,
dict_group_to_hdf,
structure_dict_to_hdf,
)
from pyiron_atomistics.dft.waves.electronic import (
ElectronicStructure,
electronic_structure_dict_to_hdf,
)
from pyiron_atomistics.vasp.volumetric_data import (
VaspVolumetricData,
volumetric_data_dict_to_hdf,
)
[docs]
class Output(_Output):
[docs]
def __init__(self):
super(Output, self).__init__()
self.generic_output = GenericOutput()
self.charge_density = VaspVolumetricData()
self.electrostatic_potential = VaspVolumetricData()
self.dft_output = DFTOutput()
self.electronic_structure = ElectronicStructure()
[docs]
def to_hdf(self, hdf):
"""
Save the object in a HDF5 file
Args:
hdf (pyiron_base.generic.hdfio.ProjectHDFio): HDF path to which the object is to be saved
"""
output_dict_to_hdf(data_dict=self.to_dict(), hdf=hdf, group_name="output")
[docs]
def from_hdf(self, hdf):
"""
Reads the attributes and reconstructs the object from a hdf file
Args:
hdf: The hdf5 instance
"""
with hdf.open("output") as hdf5_output:
# self.description = hdf5_output["description"]
if self.structure is None:
self.structure = Atoms()
self.structure.from_hdf(hdf5_output)
self.generic_output.from_hdf(hdf5_output)
try:
if "electrostatic_potential" in hdf5_output.list_groups():
self.electrostatic_potential.from_hdf(
hdf5_output, group_name="electrostatic_potential"
)
if "charge_density" in hdf5_output.list_groups():
self.charge_density.from_hdf(
hdf5_output, group_name="charge_density"
)
if "electronic_structure" in hdf5_output.list_groups():
self.electronic_structure.from_hdf(hdf=hdf5_output)
if "outcar" in hdf5_output.list_groups():
with hdf.open("outcar") as hdf5_output:
for key in hdf5_output.list_nodes():
self.outcar.parse_dict[key]: hdf5_output[key]
except (TypeError, IOError, ValueError):
state.logger.warning("Routine from_hdf() not completely successful")
[docs]
class GenericOutput(_GenericOutput):
"""
This class stores the generic output like different structures, energies and forces from a simulation in a highly
generic format. Usually the user does not have to access this class.
Attributes:
log_dict (dict): A dictionary of all tags and values of generic data (positions, forces, etc)
"""
[docs]
def __init__(self):
super(GenericOutput, self).__init__()
self._bands = ElectronicStructure()
[docs]
def to_hdf(self, hdf):
"""
Save the object in a HDF5 file
Args:
hdf (pyiron_base.generic.hdfio.ProjectHDFio): HDF path to which the object is to be saved
"""
generic_output_dict_to_hdf(
data_dict=self.to_dict(), hdf=hdf, group_name="generic"
)
[docs]
def from_hdf(self, hdf):
"""
Reads the attributes and reconstructs the object from a hdf file
Args:
hdf: The hdf5 instance
"""
with hdf.open("generic") as hdf_go:
for node in hdf_go.list_nodes():
if node == "description":
# self.description = hdf_go[node]
pass
else:
self.log_dict[node] = hdf_go[node]
if "dft" in hdf_go.list_groups():
with hdf_go.open("dft") as hdf_dft:
for node in hdf_dft.list_nodes():
self.dft_log_dict[node] = hdf_dft[node]
if "bands" in hdf_dft.list_groups():
self.bands.from_hdf(hdf=hdf_dft, group_name="bands")
[docs]
class DFTOutput:
"""
This class stores the DFT specific output
Attributes:
log_dict (dict): A dictionary of all tags and values of DFT data
"""
[docs]
def __init__(self):
self.log_dict = dict()
self.description = "contains DFT specific output"
[docs]
def to_hdf(self, hdf):
"""
Save the object in a HDF5 file
Args:
hdf (pyiron_base.generic.hdfio.ProjectHDFio): HDF path to which the object is to be saved
"""
with hdf.open("dft") as hdf_dft:
# hdf_go["description"] = self.description
for key, val in self.log_dict.items():
hdf_dft[key] = val
[docs]
def from_hdf(self, hdf):
"""
Reads the attributes and reconstructs the object from a hdf file
Args:
hdf: The hdf5 instance
"""
with hdf.open("dft") as hdf_dft:
for node in hdf_dft.list_nodes():
if node == "description":
# self.description = hdf_go[node]
pass
else:
self.log_dict[node] = hdf_dft[node]
[docs]
def generic_output_dict_to_hdf(data_dict, hdf, group_name="generic"):
with hdf.open(group_name) as hdf_go:
for k, v in data_dict.items():
if k not in ["dft"]:
hdf_go[k] = v
with hdf_go.open("dft") as hdf_dft:
for k, v in data_dict["dft"].items():
if k not in ["bands"]:
hdf_dft[k] = v
if "bands" in data_dict["dft"].keys():
electronic_structure_dict_to_hdf(
data_dict=data_dict["dft"]["bands"],
hdf=hdf_dft,
group_name="bands",
)
[docs]
def output_dict_to_hdf(data_dict, hdf, group_name="output"):
with hdf.open(group_name) as hdf5_output:
for k, v in data_dict.items():
if k not in [
"generic",
"structure",
"electrostatic_potential",
"charge_density",
"electronic_structure",
"outcar",
]:
hdf5_output[k] = v
if "generic" in data_dict.keys():
generic_output_dict_to_hdf(
data_dict=data_dict["generic"],
hdf=hdf5_output,
group_name="generic",
)
if "structure" in data_dict.keys():
structure_dict_to_hdf(
data_dict=data_dict["structure"],
hdf=hdf5_output,
group_name="structure",
)
if "electrostatic_potential" in data_dict.keys():
volumetric_data_dict_to_hdf(
data_dict=data_dict["electrostatic_potential"],
hdf=hdf5_output,
group_name="electrostatic_potential",
)
if "charge_density" in data_dict.keys():
volumetric_data_dict_to_hdf(
data_dict=data_dict["charge_density"],
hdf=hdf5_output,
group_name="charge_density",
)
if "electronic_structure" in data_dict.keys():
electronic_structure_dict_to_hdf(
data_dict=data_dict["electronic_structure"],
hdf=hdf5_output,
group_name="electronic_structure",
)
dict_group_to_hdf(data_dict=data_dict, hdf=hdf5_output, group="outcar")
