def update_alpha(self, p_y_given_x, theta, Xm, tcs):
"""A rule for non-tree CorEx structure.
"""
sample = np.random.choice(np.arange(Xm.shape[0]), min(self.max_samples, Xm.shape[0]), replace=False)
p_y_given_x = p_y_given_x[:, sample, :]
not_missing = np.logical_not(ma.getmaskarray(Xm[sample]))
alpha = np.empty((self.n_hidden, self.n_visible))
n_samples, n_visible = Xm.shape
memory_size = float(self.max_samples * n_visible * self.n_hidden * self.dim_hidden * 64) / 1000**3 # GB
batch_size = np.clip(int(self.ram * n_visible / memory_size), 1, n_visible)
for i in range(0, n_visible, batch_size):
log_marg_x = self.calculate_marginals_on_samples(theta[i:i+batch_size], Xm[sample, i:i+batch_size])
correct_predictions = np.argmax(p_y_given_x, axis=2)[:, :, np.newaxis] == np.argmax(log_marg_x, axis=3)
for ip in range(i, min(i + batch_size, n_visible)):
alpha[:, ip] = self.unique_info(correct_predictions[:, not_missing[:, ip], ip - i].T)
for j in np.where(np.abs(tcs) < self.tc_min)[0]: # Priming for un-used hidden units
amax = np.clip(np.max(alpha[j, :]), 0.01, 0.99)
alpha[j, :] = alpha[j, :]**(np.log(0.99)/np.log(amax)) + 0.001 * np.random.random(self.n_visible)
self.alpha = alpha[:, :, np.newaxis] # TODO: This is the "correct" update but it is quite noisy. Add smoothing?
python类getmaskarray()的实例源码
def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
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)
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
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)
def calculate_p_xi_given_y(self, xi, thetai):
not_missing = np.logical_not(ma.getmaskarray(xi))
z = np.zeros((self.n_hidden, len(xi), self.dim_hidden))
z[:, not_missing, :] = self.marginal_p(xi[not_missing], thetai)
return z # n_hidden, n_samples, dim_hidden
def calculate_mis(self, p_y_given_x, theta, Xm):
mis = np.zeros((self.n_hidden, self.n_visible))
sample = np.random.choice(np.arange(Xm.shape[0]), min(self.max_samples, Xm.shape[0]), replace=False)
n_observed = np.sum(np.logical_not(ma.getmaskarray(Xm[sample])), axis=0)
n_samples, n_visible = Xm.shape
memory_size = float(n_samples * n_visible * self.n_hidden * self.dim_hidden * 64) / 1000**3 # GB
batch_size = np.clip(int(self.ram * n_visible / memory_size), 1, n_visible)
for i in range(0, n_visible, batch_size):
log_marg_x = self.calculate_marginals_on_samples(theta[i:i+batch_size, ...], Xm[sample, i:i+batch_size]) # n_hidden, n_samples, n_visible, dim_hidden
mis[:, i:i+batch_size] = np.einsum('ijl,ijkl->ik', p_y_given_x[:, sample, :], log_marg_x) / n_observed[i:i+batch_size][np.newaxis, :]
return mis # MI in nats
def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
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)
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
frame.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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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
test_mrecords.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
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def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
mrecords.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
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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)
mrecords.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
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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 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
def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
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)
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
def test_no_data_available():
""" This is a position without valid data """
db = WOA()
out = db['sea_water_temperature'].extract(
doy=155, lat=48.1953, lon=-69.5855,
depth=[2.0, 5.0, 6.0, 21.0, 44.0, 79.0, 5000])
assert sorted(out.keys()) == [u't_dd', u't_mn', u't_sd', u't_se']
for v in out:
ma.getmaskarray(out[v]).all()
def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
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)
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
def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
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)
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
def test_set_mask(self):
base = self.base.copy()
mbase = base.view(mrecarray)
# Set the mask to True .......................
mbase.mask = masked
assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
assert_equal(mbase['a']._mask, mbase['b']._mask)
assert_equal(mbase['a']._mask, mbase['c']._mask)
assert_equal(mbase._mask.tolist(),
np.array([(1, 1, 1)]*5, dtype=bool))
# Delete the mask ............................
mbase.mask = nomask
assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0)]*5, dtype=bool))
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)
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
def test_set_fields(self):
# Tests setting fields.
base = self.base.copy()
mbase = base.view(mrecarray)
mbase = mbase.copy()
mbase.fill_value = (999999, 1e20, 'N/A')
# Change the data, the mask should be conserved
mbase.a._data[:] = 5
assert_equal(mbase['a']._data, [5, 5, 5, 5, 5])
assert_equal(mbase['a']._mask, [0, 1, 0, 0, 1])
# Change the elements, and the mask will follow
mbase.a = 1
assert_equal(mbase['a']._data, [1]*5)
assert_equal(ma.getmaskarray(mbase['a']), [0]*5)
# Use to be _mask, now it's recordmask
assert_equal(mbase.recordmask, [False]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0),
(0, 1, 1),
(0, 0, 0),
(0, 0, 0),
(0, 1, 1)],
dtype=bool))
# Set a field to mask ........................
mbase.c = masked
# Use to be mask, and now it's still mask !
assert_equal(mbase.c.mask, [1]*5)
assert_equal(mbase.c.recordmask, [1]*5)
assert_equal(ma.getmaskarray(mbase['c']), [1]*5)
assert_equal(ma.getdata(mbase['c']), [asbytes('N/A')]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 1),
(0, 1, 1),
(0, 0, 1),
(0, 0, 1),
(0, 1, 1)],
dtype=bool))
# Set fields by slices .......................
mbase = base.view(mrecarray).copy()
mbase.a[3:] = 5
assert_equal(mbase.a, [1, 2, 3, 5, 5])
assert_equal(mbase.a._mask, [0, 1, 0, 0, 0])
mbase.b[3:] = masked
assert_equal(mbase.b, base['b'])
assert_equal(mbase.b._mask, [0, 1, 0, 1, 1])
# Set fields globally..........................
ndtype = [('alpha', '|S1'), ('num', int)]
data = ma.array([('a', 1), ('b', 2), ('c', 3)], dtype=ndtype)
rdata = data.view(MaskedRecords)
val = ma.array([10, 20, 30], mask=[1, 0, 0])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rdata['num'] = val
assert_equal(rdata.num, val)
assert_equal(rdata.num.mask, [1, 0, 0])
def __setattr__(self, attr, val):
"""
Sets the attribute attr to the value val.
"""
# Should we call __setmask__ first ?
if attr in ['mask', 'fieldmask']:
self.__setmask__(val)
return
# Create a shortcut (so that we don't have to call getattr all the time)
_localdict = object.__getattribute__(self, '__dict__')
# Check whether we're creating a new field
newattr = attr not in _localdict
try:
# Is attr a generic attribute ?
ret = object.__setattr__(self, attr, val)
except:
# Not a generic attribute: exit if it's not a valid field
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
optinfo = ndarray.__getattribute__(self, '_optinfo') or {}
if not (attr in fielddict or attr in optinfo):
exctype, value = sys.exc_info()[:2]
raise exctype(value)
else:
# Get the list of names
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
# Check the attribute
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except:
return ret
# Let's try to set the field
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("record array has no attribute %s" % attr)
if val is masked:
_fill_value = _localdict['_fill_value']
if _fill_value is not None:
dval = _localdict['_fill_value'][attr]
else:
dval = val
mval = True
else:
dval = filled(val)
mval = getmaskarray(val)
obj = ndarray.__getattribute__(self, '_data').setfield(dval, *res)
_localdict['_mask'].__setitem__(attr, mval)
return obj
def test_set_fields(self):
# Tests setting fields.
base = self.base.copy()
mbase = base.view(mrecarray)
mbase = mbase.copy()
mbase.fill_value = (999999, 1e20, 'N/A')
# Change the data, the mask should be conserved
mbase.a._data[:] = 5
assert_equal(mbase['a']._data, [5, 5, 5, 5, 5])
assert_equal(mbase['a']._mask, [0, 1, 0, 0, 1])
# Change the elements, and the mask will follow
mbase.a = 1
assert_equal(mbase['a']._data, [1]*5)
assert_equal(ma.getmaskarray(mbase['a']), [0]*5)
# Use to be _mask, now it's recordmask
assert_equal(mbase.recordmask, [False]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 0),
(0, 1, 1),
(0, 0, 0),
(0, 0, 0),
(0, 1, 1)],
dtype=bool))
# Set a field to mask ........................
mbase.c = masked
# Use to be mask, and now it's still mask !
assert_equal(mbase.c.mask, [1]*5)
assert_equal(mbase.c.recordmask, [1]*5)
assert_equal(ma.getmaskarray(mbase['c']), [1]*5)
assert_equal(ma.getdata(mbase['c']), [asbytes('N/A')]*5)
assert_equal(mbase._mask.tolist(),
np.array([(0, 0, 1),
(0, 1, 1),
(0, 0, 1),
(0, 0, 1),
(0, 1, 1)],
dtype=bool))
# Set fields by slices .......................
mbase = base.view(mrecarray).copy()
mbase.a[3:] = 5
assert_equal(mbase.a, [1, 2, 3, 5, 5])
assert_equal(mbase.a._mask, [0, 1, 0, 0, 0])
mbase.b[3:] = masked
assert_equal(mbase.b, base['b'])
assert_equal(mbase.b._mask, [0, 1, 0, 1, 1])
# Set fields globally..........................
ndtype = [('alpha', '|S1'), ('num', int)]
data = ma.array([('a', 1), ('b', 2), ('c', 3)], dtype=ndtype)
rdata = data.view(MaskedRecords)
val = ma.array([10, 20, 30], mask=[1, 0, 0])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rdata['num'] = val
assert_equal(rdata.num, val)
assert_equal(rdata.num.mask, [1, 0, 0])
