"""Implements loading the text file format for MB Scientific analyzers."""
import warnings
from pathlib import Path
import numpy as np
import xarray as xr
from arpes.endstations import HemisphericalEndstation
from arpes.utilities import clean_keys
__all__ = ("MBSEndstation",)
[docs]class MBSEndstation(HemisphericalEndstation):
"""Implements loading text files from the MB Scientific text file format.
There's not too much metadata here except what comes with the analyzer settings.
"""
PRINCIPAL_NAME = "MBS"
ALIASES = [
"MB Scientific",
]
_TOLERATED_EXTENSIONS = {
".txt",
}
RENAME_KEYS = {
"deflx": "psi",
}
def resolve_frame_locations(self, scan_desc: dict = None):
"""There is only a single file for the MBS loader, so this is simple."""
return [scan_desc.get("path", scan_desc.get("file"))]
def postprocess_final(self, data: xr.Dataset, scan_desc: dict = None):
"""Performs final data normalization.
Because the MBS format does not come from a proper ARPES DAQ setup,
we have to attach a bunch of missing coordinates with blank values
in order to fit the data model.
"""
warnings.warn(
"Loading from text format misses metadata. You will need to supply "
"missing coordinates as appropriate."
)
data.attrs["psi"] = float(data.attrs["psi"])
for s in data.S.spectra:
s.attrs["psi"] = float(s.attrs["psi"])
defaults = {
"x": np.nan,
"y": np.nan,
"z": np.nan,
"theta": 0,
"beta": 0,
"chi": 0,
"alpha": np.nan,
"hv": np.nan,
}
for k, v in defaults.items():
data.attrs[k] = v
for s in data.S.spectra:
s.attrs[k] = v
return super().postprocess_final(data, scan_desc)
def load_single_frame(self, frame_path: str = None, scan_desc: dict = None, **kwargs):
"""Load a single frame from an MBS spectrometer.
Most of the complexity here is in header handling and building
the resultant coordinates. Namely, coordinates are stored in an indirect
format using start/stop/step whch needs to be hydrated.
"""
p = Path(frame_path)
with open(str(p)) as f:
lines = f.readlines()
lines = [l.strip() for l in lines]
data_index = lines.index("DATA:")
header = lines[:data_index]
data = lines[data_index + 1 :]
data = [d.split() for d in data]
data = np.array([[float(f) for f in d] for d in data])
header = [h.split("\t") for h in header]
alt = [h for h in header if len(h) == 1]
header = [h for h in header if len(h) == 2]
header.append(["alt", str(alt)])
attrs = clean_keys(dict(header))
eV_axis = np.linspace(
float(attrs["start_k_e_"]),
float(attrs["end_k_e_"]),
num=int(attrs["no_steps"]),
endpoint=False,
)
n_eV = int(attrs["no_steps"])
idx_eV = data.shape.index(n_eV)
if len(data.shape) == 2:
phi_axis = np.linspace(
float(attrs["xscalemin"]),
float(attrs["xscalemax"]),
num=data.shape[1 if idx_eV == 0 else 0],
endpoint=False,
)
coords = {"phi": phi_axis * np.pi / 180, "eV": eV_axis}
dims = ["eV", "phi"] if idx_eV == 0 else ["phi", "eV"]
else:
coords = {"eV": eV_axis}
dims = ["eV"]
return xr.Dataset(
{"spectrum": xr.DataArray(data, coords=coords, dims=dims, attrs=attrs)}, attrs=attrs
)