def save_to_igor_itx(file_path: str,
xs: List[np.ndarray],
datas: List[np.ndarray],
names: List[str],
ys: Optional[List[np.ndarray]] = None,
x_labels: Optional[Union[str, List[str]]] = None,
y_labels: Optional[Union[str, List[str]]] = None):
"""Save data to a .itx file which can be dropped into Igor"""
def check_axis_linear(arr: np.ndarray, axis: str, name: str,
current_waves: list) -> bool:
if arr.shape[-1] > 1 and not np.all(
np.isclose(np.diff(arr),
np.diff(arr)[0])):
logger.warning(
f"{file_path}: Igor doesn't support a non-linear {axis}-axis. Saving as separate wave"
)
axis_wave = IgorWave(arr, name=name + f'_{axis}')
current_waves.append(axis_wave)
return False
else:
return True
if x_labels is None or isinstance(x_labels, str):
x_labels = [x_labels] * len(datas)
if y_labels is None or isinstance(y_labels, str):
y_labels = [y_labels] * len(datas)
if ys is None:
ys = [None] * len(datas)
assert all([
len(datas) == len(list_) for list_ in [xs, names, x_labels, y_labels]
])
waves = []
for x, y, data, name, x_label, y_label in zip(xs, ys, datas, names,
x_labels, y_labels):
wave = IgorWave(data, name=name)
if x is not None:
if check_axis_linear(x, 'x', name, waves):
wave.set_dimscale('x',
x[0],
np.mean(np.diff(x)),
units=x_label)
if y is not None:
if check_axis_linear(y, 'y', name, waves):
wave.set_dimscale('y',
y[0],
np.mean(np.diff(y)),
units=y_label)
elif y_label is not None:
wave.set_datascale(y_label)
waves.append(wave)
with open(file_path, 'w') as fp:
for wave in waves:
wave.save_itx(
fp, image=True
) # Image = True hopefully makes np and igor match in x/y
def test_data_units(self):
wavename = 'wave0'
unitslist = ('', 'sec', 'second')
descs = ('empty units', 'short units', 'long units')
for desc, units in zip(descs, unitslist):
with self.subTest(desc):
com = 'X SetScale d,.*"%s",.*\'%s\'' % (units, wavename)
array = np.random.randint(0, 100, 10, dtype=np.int32)
wave = IgorWave(array)
wave.set_datascale(units)
with TemporaryFile('wb') as fp:
wave.save(fp)
with TemporaryFile('w+t') as fp:
wave.save_itx(fp)
fp.seek(0)
content = fp.read()
self.assertRegex(content, com)
def fig_to_igor_itx(f: go.Figure, filepath: str):
d = data_from_plotly_fig(f)
waves = []
for k in d:
if not k.endswith('_x') and not k.endswith('_y'):
wave = IgorWave(d[k], name=k)
wave.set_datascale(f.layout.yaxis.title.text)
for dim in ['x', 'y']:
if f'{k}_{dim}' in d:
dim_arr = d[f'{k}_{dim}']
wave.set_dimscale('x',
dim_arr[0],
np.mean(np.diff(dim_arr)),
units=f.layout.xaxis.title.text)
waves.append(wave)
with open(filepath, 'w') as fp:
for wave in waves:
wave.save_itx(
fp, image=True
) # Image = True hopefully makes np and igor match in x/y