Python numpy.ma.getmaskarray() Examples
The following are 30
code examples of numpy.ma.getmaskarray().
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Example #1
| Source File: corex.py From discrete_sieve with Apache License 2.0 | 6 votes |
|
def calculate_marginals_on_samples(self, theta, Xm, return_ratio=True):
"""Calculate the value of the marginal distribution for each variable, for each hidden variable and each sample.
theta: array parametrizing the marginals
Xm: the data
returns log p(y_j|x_i)/p(y_j) for each j,sample,i,y_j. [n_hidden, n_samples, n_visible, dim_hidden]
"""
n_samples = Xm.shape[0]
log_marg_x = np.zeros((self.n_hidden, n_samples, self.n_visible, self.dim_hidden))
for i in range(self.n_visible):
not_missing = np.logical_not(ma.getmaskarray(Xm)[:, i])
log_marg_x[:, not_missing, i, :] = self.marginal_p(Xm[not_missing,i], theta[i, :, :, :])
if return_ratio:
# Again, I use the same p(y) here for each x_i, but for missing variables, p(y) on obs. sample may be different.
log_p_xi = logsumexp(log_marg_x + self.log_p_y.reshape((self.n_hidden, 1, 1, self.dim_hidden)), axis=3)
log_marg_x -= log_p_xi[:, :, :, np.newaxis]
return log_marg_x
Example #2
| Source File: colors.py From Computable with MIT License | 6 votes |
|
def __call__(self, x, clip=None):
if clip is None:
clip = self.clip
x = ma.asarray(x)
mask = ma.getmaskarray(x)
xx = x.filled(self.vmax + 1)
if clip:
np.clip(xx, self.vmin, self.vmax)
iret = np.zeros(x.shape, dtype=np.int16)
for i, b in enumerate(self.boundaries):
iret[xx >= b] = i
if self._interp:
scalefac = float(self.Ncmap - 1) / (self.N - 2)
iret = (iret * scalefac).astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = self.Ncmap
ret = ma.array(iret, mask=mask)
if ret.shape == () and not mask:
ret = int(ret) # assume python scalar
return ret
Example #3
| Source File: colors.py From matplotlib-4-abaqus with MIT License | 6 votes |
|
def __call__(self, x, clip=None):
if clip is None:
clip = self.clip
x = ma.asarray(x)
mask = ma.getmaskarray(x)
xx = x.filled(self.vmax + 1)
if clip:
np.clip(xx, self.vmin, self.vmax)
iret = np.zeros(x.shape, dtype=np.int16)
for i, b in enumerate(self.boundaries):
iret[xx >= b] = i
if self._interp:
scalefac = float(self.Ncmap - 1) / (self.N - 2)
iret = (iret * scalefac).astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = self.Ncmap
ret = ma.array(iret, mask=mask)
if ret.shape == () and not mask:
ret = int(ret) # assume python scalar
return ret
Example #4
| Source File: colors.py From neural-network-animation with MIT License | 6 votes |
|
def __call__(self, x, clip=None):
if clip is None:
clip = self.clip
x = ma.asarray(x)
mask = ma.getmaskarray(x)
xx = x.filled(self.vmax + 1)
if clip:
np.clip(xx, self.vmin, self.vmax)
iret = np.zeros(x.shape, dtype=np.int16)
for i, b in enumerate(self.boundaries):
iret[xx >= b] = i
if self._interp:
scalefac = float(self.Ncmap - 1) / (self.N - 2)
iret = (iret * scalefac).astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = self.Ncmap
ret = ma.array(iret, mask=mask)
if ret.shape == () and not mask:
ret = int(ret) # assume python scalar
return ret
Example #5
| Source File: mrecords.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
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%i' % 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 = 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #6
| Source File: mrecords.py From lambda-packs with MIT License | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #7
| Source File: mrecords.py From Computable with MIT License | 5 votes |
|
def addfield(mrecord, newfield, newfieldname=None):
"""Adds a new field to the masked record array, using `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%i' % 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 = recarray(_data.shape, newdtype)
# Add the exisintg 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #8
| Source File: frame.py From Computable with MIT License | 5 votes |
|
def _masked_rec_array_to_mgr(data, index, columns, dtype, copy):
""" extract from a masked rec array and create the manager """
# essentially process a record array then fill it
fill_value = data.fill_value
fdata = ma.getdata(data)
if index is None:
index = _get_names_from_index(fdata)
if index is None:
index = _default_index(len(data))
index = _ensure_index(index)
if columns is not None:
columns = _ensure_index(columns)
arrays, arr_columns = _to_arrays(fdata, columns)
# fill if needed
new_arrays = []
for fv, arr, col in zip(fill_value, arrays, arr_columns):
mask = ma.getmaskarray(data[col])
if mask.any():
arr, fv = _maybe_upcast(arr, fill_value=fv, copy=True)
arr[mask] = fv
new_arrays.append(arr)
# create the manager
arrays, arr_columns = _reorder_arrays(new_arrays, arr_columns, columns)
if columns is None:
columns = arr_columns
mgr = _arrays_to_mgr(arrays, arr_columns, index, columns)
if copy:
mgr = mgr.copy()
return mgr
Example #9
| Source File: mrecords.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def __setitem__(self, indx, value):
"""
Sets the given record to value.
"""
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #10
| Source File: mrecords.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #11
| Source File: mrecords.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
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%i' % 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 = 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #12
| Source File: mrecords.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def __setitem__(self, indx, value):
"""
Sets the given record to value.
"""
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #13
| Source File: mrecords.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #14
| Source File: mrecords.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
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%i' % 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 = 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #15
| Source File: mrecords.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def __setitem__(self, indx, value):
"""
Sets the given record to value.
"""
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #16
| Source File: mrecords.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #17
| Source File: mrecords.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #18
| Source File: construction.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def masked_rec_array_to_mgr(data, index, columns, dtype, copy):
"""
Extract from a masked rec array and create the manager.
"""
# essentially process a record array then fill it
fill_value = data.fill_value
fdata = ma.getdata(data)
if index is None:
index = get_names_from_index(fdata)
if index is None:
index = ibase.default_index(len(data))
index = ensure_index(index)
if columns is not None:
columns = ensure_index(columns)
arrays, arr_columns = to_arrays(fdata, columns)
# fill if needed
new_arrays = []
for fv, arr, col in zip(fill_value, arrays, arr_columns):
mask = ma.getmaskarray(data[col])
if mask.any():
arr, fv = maybe_upcast(arr, fill_value=fv, copy=True)
arr[mask] = fv
new_arrays.append(arr)
# create the manager
arrays, arr_columns = reorder_arrays(new_arrays, arr_columns, columns)
if columns is None:
columns = arr_columns
mgr = arrays_to_mgr(arrays, arr_columns, index, columns, dtype)
if copy:
mgr = mgr.copy()
return mgr
# ---------------------------------------------------------------------
# DataFrame Constructor Interface
Example #19
| Source File: mrecords.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def __setitem__(self, indx, value):
"Sets the given record to value."
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #20
| Source File: mrecords.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def addfield(mrecord, newfield, newfieldname=None):
"""Adds a new field to the masked record array, using `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%i' % 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 = recarray(_data.shape, newdtype)
# Add the exisintg 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #21
| Source File: mrecords.py From pySINDy with MIT License | 5 votes |
|
def __setitem__(self, indx, value):
"""
Sets the given record to value.
"""
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #22
| Source File: mrecords.py From pySINDy with MIT License | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #23
| Source File: mrecords.py From pySINDy with MIT License | 5 votes |
|
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%i' % 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 = 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #24
| Source File: mrecords.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def __setitem__(self, indx, value):
"""
Sets the given record to value.
"""
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #25
| Source File: mrecords.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
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 = [getdata(x) for x in arraylist]
masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]
_array = recfromarrays(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
Example #26
| Source File: mrecords.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
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%i' % 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 = 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #27
| Source File: mrecords.py From ImageFusion with MIT License | 5 votes |
|
def __setitem__(self, indx, value):
"Sets the given record to value."
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
Example #28
| Source File: mrecords.py From ImageFusion with MIT License | 5 votes |
|
def addfield(mrecord, newfield, newfieldname=None):
"""Adds a new field to the masked record array, using `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%i' % 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 = recarray(_data.shape, newdtype)
# Add the exisintg 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, bool_) for n in newdtype.names])
newmask = 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(getmaskarray(newfield),
*newmask.dtype.fields[newfieldname])
newdata._mask = newmask
return newdata
Example #29
| Source File: corex.py From discrete_sieve with Apache License 2.0 | 5 votes |
|
def calculate_theta(self, Xm, p_y_given_x):
"""Estimate marginal parameters from data and expected latent labels."""
theta = []
for i in range(self.n_visible):
not_missing = np.logical_not(ma.getmaskarray(Xm)[:, i])
theta.append(self.estimate_parameters(Xm.data[not_missing, i], p_y_given_x[:, not_missing]))
return np.array(theta)
Example #30
| Source File: mrecords.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def __setitem__(self, indx, value):
"""
Sets the given record to value.
"""
MaskedArray.__setitem__(self, indx, value)
if isinstance(indx, basestring):
self._mask[indx] = ma.getmaskarray(value)
