__author__ = 'github.com/arm61'
from typing import TextIO
from typing import Union
import numpy as np
import scipp as sc
from orsopy.fileio import Header
from orsopy.fileio import orso
from easyreflectometry.data import DataSet1D
[docs]
def load(fname: Union[TextIO, str]) -> sc.DataGroup:
"""Load data from an ORSO .ort file.
:param fname: The file to be read.
"""
try:
return _load_orso(fname)
except (IndexError, ValueError):
return _load_txt(fname)
[docs]
def load_as_dataset(fname: Union[TextIO, str]) -> DataSet1D:
"""Load data from an ORSO .ort file as a DataSet1D."""
data_group = load(fname)
return DataSet1D(
x=data_group['coords']['Qz_0'].values,
y=data_group['data']['R_0'].values,
ye=data_group['data']['R_0'].variances,
xe=data_group['coords']['Qz_0'].variances,
)
def _load_orso(fname: Union[TextIO, str]) -> sc.DataGroup:
"""Load from an ORSO compatible file.
:param fname: The path for the file to be read.
"""
data = {}
coords = {}
attrs = {}
f_data = orso.load_orso(fname)
for i, o in enumerate(f_data):
name = i
if o.info.data_set is not None:
name = o.info.data_set
coords[f'Qz_{name}'] = sc.array(
dims=[f'{o.info.columns[0].name}_{name}'],
values=o.data[:, 0],
variances=np.square(o.data[:, 3]),
unit=sc.Unit(o.info.columns[0].unit),
)
try:
data[f'R_{name}'] = sc.array(
dims=[f'{o.info.columns[0].name}_{name}'],
values=o.data[:, 1],
variances=np.square(o.data[:, 2]),
unit=sc.Unit(o.info.columns[1].unit),
)
except TypeError:
data[f'R_{name}'] = sc.array(
dims=[f'{o.info.columns[0].name}_{name}'],
values=o.data[:, 1],
variances=np.square(o.data[:, 2]),
)
attrs[f'R_{name}'] = {'orso_header': sc.scalar(Header.asdict(o.info))}
return sc.DataGroup(data=data, coords=coords, attrs=attrs)
def _load_txt(fname: Union[TextIO, str]) -> sc.DataGroup:
"""Load data from a simple txt file.
:param fname: The path for the file to be read.
"""
# fname can have either a space or a comma as delimiter
# Determine the delimiter used in the file
delimiter = None
with open(fname, 'r') as f:
# find first non-comment and non-empty line
for line in f:
if line.strip() and not line.startswith('#'):
break
first_line = line
if ',' in first_line:
delimiter = ','
try:
# First load only the data to check column count
data = np.loadtxt(fname, delimiter=delimiter, comments='#')
if data.ndim == 1:
# Handle single row case
num_columns = len(data)
else:
num_columns = data.shape[1]
# Verify minimum column requirement
if num_columns < 3:
raise ValueError(f"File must contain at least 3 columns (found {num_columns})")
# Now unpack the data based on column count
if num_columns >= 4:
x, y, e, xe = np.loadtxt(fname, delimiter=delimiter, comments='#', unpack=True)
else: # 3 columns
x, y, e = np.loadtxt(fname, delimiter=delimiter, comments='#', unpack=True)
xe = np.zeros_like(x)
except (ValueError, IOError) as error:
# Re-raise with more descriptive message
raise ValueError(f"Failed to load data from {fname}: {str(error)}") from error
data = {'R_0': sc.array(dims=['Qz_0'], values=y, variances=np.square(e))}
coords = {
data['R_0'].dims[0]: sc.array(
dims=['Qz_0'],
values=x,
variances=np.square(xe),
unit=sc.Unit('1/angstrom'),
)
}
return sc.DataGroup(data=data, coords=coords)
