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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,53 +1,45 @@ | ||
| import numpy.ma as ma | ||
| from astropy.table import MaskedColumn | ||
| import numpy as np | ||
| from astropy.table.pprint import TableFormatter | ||
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| # * Special table formatting for bounded (val, min, max) values | ||
| def fmt_func(fmt): | ||
| def _fmt(v): | ||
| if ma.is_masked(v[0]): | ||
| return " <n/a> " | ||
| if ma.is_masked(v[1]): | ||
| return f"{v[0]:{fmt}} (Fixed)" | ||
| return f"{v[0]:{fmt}} ({v[1]:{fmt}}, {v[2]:{fmt}})" | ||
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| def fmt_func(fmt: str): | ||
| """Format bounded variables specially.""" | ||
| if fmt.startswith('%'): | ||
| fmt = fmt[1:] | ||
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| def _fmt(x): | ||
| ret = f"{x['val']:{fmt}}" | ||
| if np.isnan(x['min']) and np.isnan(x['max']): | ||
| return ret + " (fixed)" | ||
| else: | ||
| mn = ("-∞" if np.isnan(x['min']) or x['min'] == -np.inf | ||
| else f"{x['min']:{fmt}}") | ||
| mx = ("∞" if np.isnan(x['max']) or x['max'] == np.inf | ||
| else f"{x['max']:{fmt}}") | ||
| return f"{ret} ({mn}, {mx})" | ||
| return _fmt | ||
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| class BoundedMaskedColumn(MaskedColumn): | ||
| """Masked column which can be toggled to group rows into one item | ||
| for formatting. To be set as Table's `MaskedColumn'. | ||
| """ | ||
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| _omit_shape = False | ||
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| @property | ||
| def shape(self): | ||
| sh = super().shape | ||
| return sh[0:-1] if self._omit_shape and len(sh) > 1 else sh | ||
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| def is_fixed(self): | ||
| return ma.getmask(self)[:, 1:].all(1) | ||
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| class BoundedParTableFormatter(TableFormatter): | ||
| """Format bounded parameters. | ||
| Bounded parameters are 3-field structured arrays, with fields | ||
| 'var', 'min', and 'max'. To be set as Table's `TableFormatter'. | ||
| 'val', 'min', and 'max'. To be set as Table's `TableFormatter'. | ||
| """ | ||
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| def _pformat_table(self, table, *args, **kwargs): | ||
| bpcols = [] | ||
| tlfmt = table.meta.get('pahfit_format') | ||
| try: | ||
| colsh = [(col, col.shape) for col in table.columns.values()] | ||
| BoundedMaskedColumn._omit_shape = True | ||
| for col, sh in colsh: | ||
| if len(sh) == 2 and sh[1] == 3: | ||
| for col in table.columns.values(): | ||
| if len(col.dtype) == 3: # bounded! | ||
| bpcols.append((col, col.info.format)) | ||
| col.info.format = fmt_func(col.info.format or "g") | ||
| fmt = col.meta.get('pahfit_format') or tlfmt or "g" | ||
| col.info.format = fmt_func(fmt) | ||
| return super()._pformat_table(table, *args, **kwargs) | ||
| finally: | ||
| BoundedMaskedColumn._omit_shape = False | ||
| for col, fmt in bpcols: | ||
| col.info.format = fmt | ||
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| def _name_and_structure(self, name, *args): | ||
| "Simplified column name: no val, min, max needed." | ||
| return name |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,29 +1,28 @@ | ||
| """pahfit.util General pahfit.features utility functions.""" | ||
| import numpy as np | ||
| import numpy.ma as ma | ||
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| def bounded_is_missing(val): | ||
| """Return a mask array indicating which of the bounded values | ||
| are missing. A missing bounded value has a masked value.""" | ||
| return ma.getmask(val)[..., 0] | ||
| return getattr(val['val'], 'mask', None) or np.zeros_like(val['val'], dtype=bool) | ||
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| def bounded_is_fixed(val): | ||
| """Return a mask array indicating which of the bounded values | ||
| are fixed. A fixed bounded value has masked bounds.""" | ||
| return ma.getmask(val)[..., -2:].all(-1) | ||
| return np.isnan(val['min']) & np.isnan(val['max']) | ||
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| def bounded_min(val): | ||
| """Return the minimum of each bounded value passed. | ||
| Either the lower bound, or, if no such bound is set, the value itself.""" | ||
| lower = val[..., 1] | ||
| return np.where(lower, lower, val[..., 0]) | ||
| lower = val['min'] | ||
| return np.where(lower, lower, val['val']) | ||
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| def bounded_max(val): | ||
| """Return the maximum of each bounded value passed. | ||
| Either the upper bound, or, if no such bound is set, the value itself.""" | ||
| upper = val[..., 2] | ||
| return np.where(upper, upper, val[..., 0]) | ||
| upper = val['max'] | ||
| return np.where(upper, upper, val['val']) |