def process(
self,
dataIn: Optional[DataDictBase] = None
) -> Optional[Dict[str, Optional[DataDictBase]]]:
if super().process(dataIn=dataIn) is None:
return None
assert dataIn is not None
data = dataIn.copy()
if self.scale_unit_option != ScaleUnitsOption.never:
for name, data_item in data.data_items():
prefix, selected_scale = find_scale_and_prefix(
data_item['values'], data_item["unit"])
data_item["unit"] = prefix + data_item["unit"]
data_item['values'] = data_item['values'] * 10**(
-selected_scale)
return dict(dataOut=data)
def _make_rescaled_ticks_and_units(
data_dict: DSPlotData,
) -> Tuple[matplotlib.ticker.FuncFormatter, str]:
"""
Create a ticks formatter and a new label for the data that is to be used
on the axes where the data is plotted.
For example, if values of data are all "nano" in units of volts "V",
then the plot might be more readable if the tick formatter would show
values like "1" instead of "0.000000001" while the units in the axis label
are changed from "V" to "nV" ('n' is for 'nano').
The units for which unit prefixes are added can be found in
`qcodes.utils.plotting._UNITS_FOR_RESCALING`. For all other units
an exponential scaling factor is added to the label i.e.
`(10^3 x e^2/hbar)`.
Args:
data_dict: A dictionary of the following structure
{
'data': <1D numpy array of points>,
'name': <name of the parameter>,
'label': <label of the parameter or ''>,
'unit': <unit of the parameter or ''>
}
Returns:
A tuple with the ticks formatter (matlplotlib.ticker.FuncFormatter) and
the new label.
"""
unit = data_dict['unit']
maxval = np.nanmax(np.abs(data_dict['data']))
prefix, selected_scale = find_scale_and_prefix(maxval, unit)
new_unit = prefix + unit
label = _get_label_of_data(data_dict)
new_label = _make_axis_label(label, new_unit)
scale_factor = 10**(-selected_scale)
ticks_formatter = FuncFormatter(
partial(_scale_formatter, factor=scale_factor))
return ticks_formatter, new_label