774 lines
26 KiB
Python
774 lines
26 KiB
Python
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""":mod:`numpy.ma..mrecords`
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Defines the equivalent of :class:`numpy.recarrays` for masked arrays,
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where fields can be accessed as attributes.
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Note that :class:`numpy.ma.MaskedArray` already supports structured datatypes
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and the masking of individual fields.
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.. moduleauthor:: Pierre Gerard-Marchant
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"""
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# We should make sure that no field is called '_mask','mask','_fieldmask',
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# or whatever restricted keywords. An idea would be to no bother in the
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# first place, and then rename the invalid fields with a trailing
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# underscore. Maybe we could just overload the parser function ?
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import warnings
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import numpy as np
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import numpy.ma as ma
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_byteorderconv = np._core.records._byteorderconv
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_check_fill_value = ma.core._check_fill_value
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__all__ = [
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'MaskedRecords', 'mrecarray', 'fromarrays', 'fromrecords',
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'fromtextfile', 'addfield',
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]
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reserved_fields = ['_data', '_mask', '_fieldmask', 'dtype']
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def _checknames(descr, names=None):
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"""
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Checks that field names ``descr`` are not reserved keywords.
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If this is the case, a default 'f%i' is substituted. If the argument
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`names` is not None, updates the field names to valid names.
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"""
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ndescr = len(descr)
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default_names = [f'f{i}' for i in range(ndescr)]
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if names is None:
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new_names = default_names
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else:
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if isinstance(names, (tuple, list)):
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new_names = names
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elif isinstance(names, str):
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new_names = names.split(',')
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else:
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raise NameError(f'illegal input names {names!r}')
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nnames = len(new_names)
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if nnames < ndescr:
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new_names += default_names[nnames:]
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ndescr = []
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for (n, d, t) in zip(new_names, default_names, descr.descr):
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if n in reserved_fields:
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if t[0] in reserved_fields:
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ndescr.append((d, t[1]))
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else:
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ndescr.append(t)
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else:
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ndescr.append((n, t[1]))
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return np.dtype(ndescr)
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def _get_fieldmask(self):
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mdescr = [(n, '|b1') for n in self.dtype.names]
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fdmask = np.empty(self.shape, dtype=mdescr)
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fdmask.flat = tuple([False] * len(mdescr))
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return fdmask
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class MaskedRecords(ma.MaskedArray):
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"""
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Attributes
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----------
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_data : recarray
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Underlying data, as a record array.
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_mask : boolean array
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Mask of the records. A record is masked when all its fields are
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masked.
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_fieldmask : boolean recarray
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Record array of booleans, setting the mask of each individual field
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of each record.
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_fill_value : record
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Filling values for each field.
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"""
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def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None,
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formats=None, names=None, titles=None,
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byteorder=None, aligned=False,
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mask=ma.nomask, hard_mask=False, fill_value=None, keep_mask=True,
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copy=False,
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**options):
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self = np.recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset,
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strides=strides, formats=formats, names=names,
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titles=titles, byteorder=byteorder,
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aligned=aligned,)
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mdtype = ma.make_mask_descr(self.dtype)
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if mask is ma.nomask or not np.size(mask):
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if not keep_mask:
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self._mask = tuple([False] * len(mdtype))
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else:
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mask = np.array(mask, copy=copy)
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if mask.shape != self.shape:
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(nd, nm) = (self.size, mask.size)
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if nm == 1:
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mask = np.resize(mask, self.shape)
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elif nm == nd:
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mask = np.reshape(mask, self.shape)
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else:
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msg = (f"Mask and data not compatible: data size is {nd},"
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" mask size is {nm}.")
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raise ma.MAError(msg)
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if not keep_mask:
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self.__setmask__(mask)
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self._sharedmask = True
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else:
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if mask.dtype == mdtype:
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_mask = mask
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else:
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_mask = np.array([tuple([m] * len(mdtype)) for m in mask],
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dtype=mdtype)
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self._mask = _mask
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return self
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def __array_finalize__(self, obj):
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# Make sure we have a _fieldmask by default
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_mask = getattr(obj, '_mask', None)
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if _mask is None:
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objmask = getattr(obj, '_mask', ma.nomask)
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_dtype = np.ndarray.__getattribute__(self, 'dtype')
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if objmask is ma.nomask:
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_mask = ma.make_mask_none(self.shape, dtype=_dtype)
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else:
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mdescr = ma.make_mask_descr(_dtype)
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_mask = np.array([tuple([m] * len(mdescr)) for m in objmask],
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dtype=mdescr).view(np.recarray)
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# Update some of the attributes
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_dict = self.__dict__
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_dict.update(_mask=_mask)
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self._update_from(obj)
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if _dict['_baseclass'] == np.ndarray:
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_dict['_baseclass'] = np.recarray
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@property
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def _data(self):
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"""
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Returns the data as a recarray.
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"""
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return np.ndarray.view(self, np.recarray)
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@property
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def _fieldmask(self):
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"""
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Alias to mask.
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"""
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return self._mask
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def __len__(self):
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"""
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Returns the length
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"""
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# We have more than one record
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if self.ndim:
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return len(self._data)
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# We have only one record: return the nb of fields
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return len(self.dtype)
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def __getattribute__(self, attr):
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try:
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return object.__getattribute__(self, attr)
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except AttributeError:
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# attr must be a fieldname
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pass
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fielddict = np.ndarray.__getattribute__(self, 'dtype').fields
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try:
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res = fielddict[attr][:2]
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except (TypeError, KeyError) as e:
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raise AttributeError(
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f'record array has no attribute {attr}') from e
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# So far, so good
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_localdict = np.ndarray.__getattribute__(self, '__dict__')
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_data = np.ndarray.view(self, _localdict['_baseclass'])
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obj = _data.getfield(*res)
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if obj.dtype.names is not None:
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raise NotImplementedError("MaskedRecords is currently limited to"
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"simple records.")
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# Get some special attributes
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# Reset the object's mask
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hasmasked = False
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_mask = _localdict.get('_mask', None)
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if _mask is not None:
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try:
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_mask = _mask[attr]
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except IndexError:
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# Couldn't find a mask: use the default (nomask)
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pass
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tp_len = len(_mask.dtype)
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hasmasked = _mask.view((bool, ((tp_len,) if tp_len else ()))).any()
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if (obj.shape or hasmasked):
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obj = obj.view(ma.MaskedArray)
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obj._baseclass = np.ndarray
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obj._isfield = True
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obj._mask = _mask
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# Reset the field values
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_fill_value = _localdict.get('_fill_value', None)
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if _fill_value is not None:
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try:
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obj._fill_value = _fill_value[attr]
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except ValueError:
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obj._fill_value = None
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else:
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obj = obj.item()
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return obj
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def __setattr__(self, attr, val):
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"""
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Sets the attribute attr to the value val.
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"""
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# Should we call __setmask__ first ?
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if attr in ['mask', 'fieldmask']:
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self.__setmask__(val)
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return
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# Create a shortcut (so that we don't have to call getattr all the time)
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_localdict = object.__getattribute__(self, '__dict__')
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# Check whether we're creating a new field
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newattr = attr not in _localdict
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try:
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# Is attr a generic attribute ?
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ret = object.__setattr__(self, attr, val)
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except Exception:
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# Not a generic attribute: exit if it's not a valid field
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fielddict = np.ndarray.__getattribute__(self, 'dtype').fields or {}
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optinfo = np.ndarray.__getattribute__(self, '_optinfo') or {}
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if not (attr in fielddict or attr in optinfo):
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raise
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else:
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# Get the list of names
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fielddict = np.ndarray.__getattribute__(self, 'dtype').fields or {}
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# Check the attribute
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if attr not in fielddict:
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return ret
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if newattr:
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# We just added this one or this setattr worked on an
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# internal attribute.
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try:
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object.__delattr__(self, attr)
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except Exception:
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return ret
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# Let's try to set the field
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try:
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res = fielddict[attr][:2]
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except (TypeError, KeyError) as e:
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raise AttributeError(
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f'record array has no attribute {attr}') from e
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if val is ma.masked:
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_fill_value = _localdict['_fill_value']
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if _fill_value is not None:
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dval = _localdict['_fill_value'][attr]
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else:
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dval = val
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mval = True
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else:
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dval = ma.filled(val)
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mval = ma.getmaskarray(val)
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obj = np.ndarray.__getattribute__(self, '_data').setfield(dval, *res)
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_localdict['_mask'].__setitem__(attr, mval)
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return obj
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def __getitem__(self, indx):
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"""
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Returns all the fields sharing the same fieldname base.
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The fieldname base is either `_data` or `_mask`.
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"""
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_localdict = self.__dict__
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_mask = np.ndarray.__getattribute__(self, '_mask')
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_data = np.ndarray.view(self, _localdict['_baseclass'])
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# We want a field
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if isinstance(indx, str):
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# Make sure _sharedmask is True to propagate back to _fieldmask
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# Don't use _set_mask, there are some copies being made that
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# break propagation Don't force the mask to nomask, that wreaks
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# easy masking
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obj = _data[indx].view(ma.MaskedArray)
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obj._mask = _mask[indx]
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obj._sharedmask = True
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fval = _localdict['_fill_value']
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if fval is not None:
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obj._fill_value = fval[indx]
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# Force to masked if the mask is True
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if not obj.ndim and obj._mask:
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return ma.masked
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return obj
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# We want some elements.
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# First, the data.
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obj = np.asarray(_data[indx]).view(mrecarray)
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obj._mask = np.asarray(_mask[indx]).view(np.recarray)
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return obj
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def __setitem__(self, indx, value):
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"""
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Sets the given record to value.
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"""
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ma.MaskedArray.__setitem__(self, indx, value)
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if isinstance(indx, str):
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self._mask[indx] = ma.getmaskarray(value)
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def __str__(self):
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"""
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Calculates the string representation.
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"""
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if self.size > 1:
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mstr = [f"({','.join([str(i) for i in s])})"
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for s in zip(*[getattr(self, f) for f in self.dtype.names])]
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return f"[{', '.join(mstr)}]"
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else:
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mstr = [f"{','.join([str(i) for i in s])}"
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for s in zip([getattr(self, f) for f in self.dtype.names])]
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return f"({', '.join(mstr)})"
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def __repr__(self):
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"""
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Calculates the repr representation.
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"""
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_names = self.dtype.names
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fmt = f"%{max(len(n) for n in _names) + 4}s : %s"
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reprstr = [fmt % (f, getattr(self, f)) for f in self.dtype.names]
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reprstr.insert(0, 'masked_records(')
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reprstr.extend([fmt % (' fill_value', self.fill_value),
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' )'])
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return str("\n".join(reprstr))
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def view(self, dtype=None, type=None):
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"""
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Returns a view of the mrecarray.
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"""
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# OK, basic copy-paste from MaskedArray.view.
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if dtype is None:
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if type is None:
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output = np.ndarray.view(self)
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else:
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output = np.ndarray.view(self, type)
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# Here again.
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elif type is None:
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try:
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if issubclass(dtype, np.ndarray):
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output = np.ndarray.view(self, dtype)
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else:
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output = np.ndarray.view(self, dtype)
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# OK, there's the change
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except TypeError:
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dtype = np.dtype(dtype)
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# we need to revert to MaskedArray, but keeping the possibility
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# of subclasses (eg, TimeSeriesRecords), so we'll force a type
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# set to the first parent
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if dtype.fields is None:
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basetype = self.__class__.__bases__[0]
|
||
|
|
output = self.__array__().view(dtype, basetype)
|
||
|
|
output._update_from(self)
|
||
|
|
else:
|
||
|
|
output = np.ndarray.view(self, dtype)
|
||
|
|
output._fill_value = None
|
||
|
|
else:
|
||
|
|
output = np.ndarray.view(self, dtype, type)
|
||
|
|
# Update the mask, just like in MaskedArray.view
|
||
|
|
if (getattr(output, '_mask', ma.nomask) is not ma.nomask):
|
||
|
|
mdtype = ma.make_mask_descr(output.dtype)
|
||
|
|
output._mask = self._mask.view(mdtype, np.ndarray)
|
||
|
|
output._mask.shape = output.shape
|
||
|
|
return output
|
||
|
|
|
||
|
|
def harden_mask(self):
|
||
|
|
"""
|
||
|
|
Forces the mask to hard.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self._hardmask = True
|
||
|
|
|
||
|
|
def soften_mask(self):
|
||
|
|
"""
|
||
|
|
Forces the mask to soft
|
||
|
|
|
||
|
|
"""
|
||
|
|
self._hardmask = False
|
||
|
|
|
||
|
|
def copy(self):
|
||
|
|
"""
|
||
|
|
Returns a copy of the masked record.
|
||
|
|
|
||
|
|
"""
|
||
|
|
copied = self._data.copy().view(type(self))
|
||
|
|
copied._mask = self._mask.copy()
|
||
|
|
return copied
|
||
|
|
|
||
|
|
def tolist(self, fill_value=None):
|
||
|
|
"""
|
||
|
|
Return the data portion of the array as a list.
|
||
|
|
|
||
|
|
Data items are converted to the nearest compatible Python type.
|
||
|
|
Masked values are converted to fill_value. If fill_value is None,
|
||
|
|
the corresponding entries in the output list will be ``None``.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if fill_value is not None:
|
||
|
|
return self.filled(fill_value).tolist()
|
||
|
|
result = np.array(self.filled().tolist(), dtype=object)
|
||
|
|
mask = np.array(self._mask.tolist())
|
||
|
|
result[mask] = None
|
||
|
|
return result.tolist()
|
||
|
|
|
||
|
|
def __getstate__(self):
|
||
|
|
"""Return the internal state of the masked array.
|
||
|
|
|
||
|
|
This is for pickling.
|
||
|
|
|
||
|
|
"""
|
||
|
|
state = (1,
|
||
|
|
self.shape,
|
||
|
|
self.dtype,
|
||
|
|
self.flags.fnc,
|
||
|
|
self._data.tobytes(),
|
||
|
|
self._mask.tobytes(),
|
||
|
|
self._fill_value,
|
||
|
|
)
|
||
|
|
return state
|
||
|
|
|
||
|
|
def __setstate__(self, state):
|
||
|
|
"""
|
||
|
|
Restore the internal state of the masked array.
|
||
|
|
|
||
|
|
This is for pickling. ``state`` is typically the output of the
|
||
|
|
``__getstate__`` output, and is a 5-tuple:
|
||
|
|
|
||
|
|
- class name
|
||
|
|
- a tuple giving the shape of the data
|
||
|
|
- a typecode for the data
|
||
|
|
- a binary string for the data
|
||
|
|
- a binary string for the mask.
|
||
|
|
|
||
|
|
"""
|
||
|
|
(ver, shp, typ, isf, raw, msk, flv) = state
|
||
|
|
np.ndarray.__setstate__(self, (shp, typ, isf, raw))
|
||
|
|
mdtype = np.dtype([(k, np.bool) for (k, _) in self.dtype.descr])
|
||
|
|
self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk))
|
||
|
|
self.fill_value = flv
|
||
|
|
|
||
|
|
def __reduce__(self):
|
||
|
|
"""
|
||
|
|
Return a 3-tuple for pickling a MaskedArray.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return (_mrreconstruct,
|
||
|
|
(self.__class__, self._baseclass, (0,), 'b',),
|
||
|
|
self.__getstate__())
|
||
|
|
|
||
|
|
|
||
|
|
def _mrreconstruct(subtype, baseclass, baseshape, basetype,):
|
||
|
|
"""
|
||
|
|
Build a new MaskedArray from the information stored in a pickle.
|
||
|
|
|
||
|
|
"""
|
||
|
|
_data = np.ndarray.__new__(baseclass, baseshape, basetype).view(subtype)
|
||
|
|
_mask = np.ndarray.__new__(np.ndarray, baseshape, 'b1')
|
||
|
|
return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,)
|
||
|
|
|
||
|
|
|
||
|
|
mrecarray = MaskedRecords
|
||
|
|
|
||
|
|
|
||
|
|
###############################################################################
|
||
|
|
# Constructors #
|
||
|
|
###############################################################################
|
||
|
|
|
||
|
|
|
||
|
|
def fromarrays(arraylist, dtype=None, shape=None, formats=None,
|
||
|
|
names=None, titles=None, aligned=False, byteorder=None,
|
||
|
|
fill_value=None):
|
||
|
|
"""
|
||
|
|
Creates a mrecarray from a (flat) list of masked arrays.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
arraylist : sequence
|
||
|
|
A list of (masked) arrays. Each element of the sequence is first converted
|
||
|
|
to a masked array if needed. If a 2D array is passed as argument, it is
|
||
|
|
processed line by line
|
||
|
|
dtype : {None, dtype}, optional
|
||
|
|
Data type descriptor.
|
||
|
|
shape : {None, integer}, optional
|
||
|
|
Number of records. If None, shape is defined from the shape of the
|
||
|
|
first array in the list.
|
||
|
|
formats : {None, sequence}, optional
|
||
|
|
Sequence of formats for each individual field. If None, the formats will
|
||
|
|
be autodetected by inspecting the fields and selecting the highest dtype
|
||
|
|
possible.
|
||
|
|
names : {None, sequence}, optional
|
||
|
|
Sequence of the names of each field.
|
||
|
|
fill_value : {None, sequence}, optional
|
||
|
|
Sequence of data to be used as filling values.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Lists of tuples should be preferred over lists of lists for faster processing.
|
||
|
|
|
||
|
|
"""
|
||
|
|
datalist = [ma.getdata(x) for x in arraylist]
|
||
|
|
masklist = [np.atleast_1d(ma.getmaskarray(x)) for x in arraylist]
|
||
|
|
_array = np.rec.fromarrays(datalist,
|
||
|
|
dtype=dtype, shape=shape, formats=formats,
|
||
|
|
names=names, titles=titles, aligned=aligned,
|
||
|
|
byteorder=byteorder).view(mrecarray)
|
||
|
|
_array._mask.flat = list(zip(*masklist))
|
||
|
|
if fill_value is not None:
|
||
|
|
_array.fill_value = fill_value
|
||
|
|
return _array
|
||
|
|
|
||
|
|
|
||
|
|
def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,
|
||
|
|
titles=None, aligned=False, byteorder=None,
|
||
|
|
fill_value=None, mask=ma.nomask):
|
||
|
|
"""
|
||
|
|
Creates a MaskedRecords from a list of records.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
reclist : sequence
|
||
|
|
A list of records. Each element of the sequence is first converted
|
||
|
|
to a masked array if needed. If a 2D array is passed as argument, it is
|
||
|
|
processed line by line
|
||
|
|
dtype : {None, dtype}, optional
|
||
|
|
Data type descriptor.
|
||
|
|
shape : {None,int}, optional
|
||
|
|
Number of records. If None, ``shape`` is defined from the shape of the
|
||
|
|
first array in the list.
|
||
|
|
formats : {None, sequence}, optional
|
||
|
|
Sequence of formats for each individual field. If None, the formats will
|
||
|
|
be autodetected by inspecting the fields and selecting the highest dtype
|
||
|
|
possible.
|
||
|
|
names : {None, sequence}, optional
|
||
|
|
Sequence of the names of each field.
|
||
|
|
fill_value : {None, sequence}, optional
|
||
|
|
Sequence of data to be used as filling values.
|
||
|
|
mask : {nomask, sequence}, optional.
|
||
|
|
External mask to apply on the data.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Lists of tuples should be preferred over lists of lists for faster processing.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# Grab the initial _fieldmask, if needed:
|
||
|
|
_mask = getattr(reclist, '_mask', None)
|
||
|
|
# Get the list of records.
|
||
|
|
if isinstance(reclist, np.ndarray):
|
||
|
|
# Make sure we don't have some hidden mask
|
||
|
|
if isinstance(reclist, ma.MaskedArray):
|
||
|
|
reclist = reclist.filled().view(np.ndarray)
|
||
|
|
# Grab the initial dtype, just in case
|
||
|
|
if dtype is None:
|
||
|
|
dtype = reclist.dtype
|
||
|
|
reclist = reclist.tolist()
|
||
|
|
mrec = np.rec.fromrecords(reclist, dtype=dtype, shape=shape, formats=formats,
|
||
|
|
names=names, titles=titles,
|
||
|
|
aligned=aligned, byteorder=byteorder).view(mrecarray)
|
||
|
|
# Set the fill_value if needed
|
||
|
|
if fill_value is not None:
|
||
|
|
mrec.fill_value = fill_value
|
||
|
|
# Now, let's deal w/ the mask
|
||
|
|
if mask is not ma.nomask:
|
||
|
|
mask = np.asarray(mask)
|
||
|
|
maskrecordlength = len(mask.dtype)
|
||
|
|
if maskrecordlength:
|
||
|
|
mrec._mask.flat = mask
|
||
|
|
elif mask.ndim == 2:
|
||
|
|
mrec._mask.flat = [tuple(m) for m in mask]
|
||
|
|
else:
|
||
|
|
mrec.__setmask__(mask)
|
||
|
|
if _mask is not None:
|
||
|
|
mrec._mask[:] = _mask
|
||
|
|
return mrec
|
||
|
|
|
||
|
|
|
||
|
|
def _guessvartypes(arr):
|
||
|
|
"""
|
||
|
|
Tries to guess the dtypes of the str_ ndarray `arr`.
|
||
|
|
|
||
|
|
Guesses by testing element-wise conversion. Returns a list of dtypes.
|
||
|
|
The array is first converted to ndarray. If the array is 2D, the test
|
||
|
|
is performed on the first line. An exception is raised if the file is
|
||
|
|
3D or more.
|
||
|
|
|
||
|
|
"""
|
||
|
|
vartypes = []
|
||
|
|
arr = np.asarray(arr)
|
||
|
|
if arr.ndim == 2:
|
||
|
|
arr = arr[0]
|
||
|
|
elif arr.ndim > 2:
|
||
|
|
raise ValueError("The array should be 2D at most!")
|
||
|
|
# Start the conversion loop.
|
||
|
|
for f in arr:
|
||
|
|
try:
|
||
|
|
int(f)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
try:
|
||
|
|
float(f)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
try:
|
||
|
|
complex(f)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
vartypes.append(arr.dtype)
|
||
|
|
else:
|
||
|
|
vartypes.append(np.dtype(complex))
|
||
|
|
else:
|
||
|
|
vartypes.append(np.dtype(float))
|
||
|
|
else:
|
||
|
|
vartypes.append(np.dtype(int))
|
||
|
|
return vartypes
|
||
|
|
|
||
|
|
|
||
|
|
def openfile(fname):
|
||
|
|
"""
|
||
|
|
Opens the file handle of file `fname`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# A file handle
|
||
|
|
if hasattr(fname, 'readline'):
|
||
|
|
return fname
|
||
|
|
# Try to open the file and guess its type
|
||
|
|
try:
|
||
|
|
f = open(fname)
|
||
|
|
except FileNotFoundError as e:
|
||
|
|
raise FileNotFoundError(f"No such file: '{fname}'") from e
|
||
|
|
if f.readline()[:2] != "\\x":
|
||
|
|
f.seek(0, 0)
|
||
|
|
return f
|
||
|
|
f.close()
|
||
|
|
raise NotImplementedError("Wow, binary file")
|
||
|
|
|
||
|
|
|
||
|
|
def fromtextfile(fname, delimiter=None, commentchar='#', missingchar='',
|
||
|
|
varnames=None, vartypes=None,
|
||
|
|
*, delimitor=np._NoValue): # backwards compatibility
|
||
|
|
"""
|
||
|
|
Creates a mrecarray from data stored in the file `filename`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : {file name/handle}
|
||
|
|
Handle of an opened file.
|
||
|
|
delimiter : {None, string}, optional
|
||
|
|
Alphanumeric character used to separate columns in the file.
|
||
|
|
If None, any (group of) white spacestring(s) will be used.
|
||
|
|
commentchar : {'#', string}, optional
|
||
|
|
Alphanumeric character used to mark the start of a comment.
|
||
|
|
missingchar : {'', string}, optional
|
||
|
|
String indicating missing data, and used to create the masks.
|
||
|
|
varnames : {None, sequence}, optional
|
||
|
|
Sequence of the variable names. If None, a list will be created from
|
||
|
|
the first non empty line of the file.
|
||
|
|
vartypes : {None, sequence}, optional
|
||
|
|
Sequence of the variables dtypes. If None, it will be estimated from
|
||
|
|
the first non-commented line.
|
||
|
|
|
||
|
|
|
||
|
|
Ultra simple: the varnames are in the header, one line"""
|
||
|
|
if delimitor is not np._NoValue:
|
||
|
|
if delimiter is not None:
|
||
|
|
raise TypeError("fromtextfile() got multiple values for argument "
|
||
|
|
"'delimiter'")
|
||
|
|
# NumPy 1.22.0, 2021-09-23
|
||
|
|
warnings.warn("The 'delimitor' keyword argument of "
|
||
|
|
"numpy.ma.mrecords.fromtextfile() is deprecated "
|
||
|
|
"since NumPy 1.22.0, use 'delimiter' instead.",
|
||
|
|
DeprecationWarning, stacklevel=2)
|
||
|
|
delimiter = delimitor
|
||
|
|
|
||
|
|
# Try to open the file.
|
||
|
|
ftext = openfile(fname)
|
||
|
|
|
||
|
|
# Get the first non-empty line as the varnames
|
||
|
|
while True:
|
||
|
|
line = ftext.readline()
|
||
|
|
firstline = line[:line.find(commentchar)].strip()
|
||
|
|
_varnames = firstline.split(delimiter)
|
||
|
|
if len(_varnames) > 1:
|
||
|
|
break
|
||
|
|
if varnames is None:
|
||
|
|
varnames = _varnames
|
||
|
|
|
||
|
|
# Get the data.
|
||
|
|
_variables = ma.masked_array([line.strip().split(delimiter) for line in ftext
|
||
|
|
if line[0] != commentchar and len(line) > 1])
|
||
|
|
(_, nfields) = _variables.shape
|
||
|
|
ftext.close()
|
||
|
|
|
||
|
|
# Try to guess the dtype.
|
||
|
|
if vartypes is None:
|
||
|
|
vartypes = _guessvartypes(_variables[0])
|
||
|
|
else:
|
||
|
|
vartypes = [np.dtype(v) for v in vartypes]
|
||
|
|
if len(vartypes) != nfields:
|
||
|
|
msg = f"Attempting to {len(vartypes)} dtypes for {nfields} fields!"
|
||
|
|
msg += " Reverting to default."
|
||
|
|
warnings.warn(msg, stacklevel=2)
|
||
|
|
vartypes = _guessvartypes(_variables[0])
|
||
|
|
|
||
|
|
# Construct the descriptor.
|
||
|
|
mdescr = list(zip(varnames, vartypes))
|
||
|
|
mfillv = [ma.default_fill_value(f) for f in vartypes]
|
||
|
|
|
||
|
|
# Get the data and the mask.
|
||
|
|
# We just need a list of masked_arrays. It's easier to create it like that:
|
||
|
|
_mask = (_variables.T == missingchar)
|
||
|
|
_datalist = [ma.masked_array(a, mask=m, dtype=t, fill_value=f)
|
||
|
|
for (a, m, t, f) in zip(_variables.T, _mask, vartypes, mfillv)]
|
||
|
|
|
||
|
|
return fromarrays(_datalist, dtype=mdescr)
|
||
|
|
|
||
|
|
|
||
|
|
def addfield(mrecord, newfield, newfieldname=None):
|
||
|
|
"""Adds a new field to the masked record array
|
||
|
|
|
||
|
|
Uses `newfield` as data and `newfieldname` as name. If `newfieldname`
|
||
|
|
is None, the new field name is set to 'fi', where `i` is the number of
|
||
|
|
existing fields.
|
||
|
|
|
||
|
|
"""
|
||
|
|
_data = mrecord._data
|
||
|
|
_mask = mrecord._mask
|
||
|
|
if newfieldname is None or newfieldname in reserved_fields:
|
||
|
|
newfieldname = f'f{len(_data.dtype)}'
|
||
|
|
newfield = ma.array(newfield)
|
||
|
|
# Get the new data.
|
||
|
|
# Create a new empty recarray
|
||
|
|
newdtype = np.dtype(_data.dtype.descr + [(newfieldname, newfield.dtype)])
|
||
|
|
newdata = np.recarray(_data.shape, newdtype)
|
||
|
|
# Add the existing field
|
||
|
|
[newdata.setfield(_data.getfield(*f), *f)
|
||
|
|
for f in _data.dtype.fields.values()]
|
||
|
|
# Add the new field
|
||
|
|
newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname])
|
||
|
|
newdata = newdata.view(MaskedRecords)
|
||
|
|
# Get the new mask
|
||
|
|
# Create a new empty recarray
|
||
|
|
newmdtype = np.dtype([(n, np.bool) for n in newdtype.names])
|
||
|
|
newmask = np.recarray(_data.shape, newmdtype)
|
||
|
|
# Add the old masks
|
||
|
|
[newmask.setfield(_mask.getfield(*f), *f)
|
||
|
|
for f in _mask.dtype.fields.values()]
|
||
|
|
# Add the mask of the new field
|
||
|
|
newmask.setfield(ma.getmaskarray(newfield),
|
||
|
|
*newmask.dtype.fields[newfieldname])
|
||
|
|
newdata._mask = newmask
|
||
|
|
return newdata
|