Convert strings in *value* to floats using mapping information stored in the *unit* object. Parameters ---------- value : str or iterable Value or list of values to be converted. unit : `.UnitData` An object mapping strings to
(value, unit, axis)
| 26 | class StrCategoryConverter(units.ConversionInterface): |
| 27 | @staticmethod |
| 28 | def convert(value, unit, axis): |
| 29 | """ |
| 30 | Convert strings in *value* to floats using mapping information stored |
| 31 | in the *unit* object. |
| 32 | |
| 33 | Parameters |
| 34 | ---------- |
| 35 | value : str or iterable |
| 36 | Value or list of values to be converted. |
| 37 | unit : `.UnitData` |
| 38 | An object mapping strings to integers. |
| 39 | axis : `~matplotlib.axis.Axis` |
| 40 | The axis on which the converted value is plotted. |
| 41 | |
| 42 | .. note:: *axis* is unused. |
| 43 | |
| 44 | Returns |
| 45 | ------- |
| 46 | float or `~numpy.ndarray` of float |
| 47 | """ |
| 48 | if unit is None: |
| 49 | raise ValueError( |
| 50 | 'Missing category information for StrCategoryConverter; ' |
| 51 | 'this might be caused by unintendedly mixing categorical and ' |
| 52 | 'numeric data') |
| 53 | StrCategoryConverter._validate_unit(unit) |
| 54 | # dtype = object preserves numerical pass throughs |
| 55 | values = np.atleast_1d(np.array(value, dtype=object)) |
| 56 | # force an update so it also does type checking |
| 57 | unit.update(values) |
| 58 | s = np.vectorize(unit._mapping.__getitem__, otypes=[float])(values) |
| 59 | return s if not cbook.is_scalar_or_string(value) else s[0] |
| 60 | |
| 61 | @staticmethod |
| 62 | def axisinfo(unit, axis): |
nothing calls this directly
no test coverage detected