Python numpy.ma.expand_dims() Examples
The following are 8
code examples of numpy.ma.expand_dims().
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Example #1
| Source File: mstats_basic.py From Computable with MIT License | 6 votes |
|
def moment(a, moment=1, axis=0):
a, axis = _chk_asarray(a, axis)
if moment == 1:
# By definition the first moment about the mean is 0.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return np.zeros(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return np.float64(0.0)
else:
mn = ma.expand_dims(a.mean(axis=axis), axis)
s = ma.power((a-mn), moment)
return s.mean(axis=axis)
Example #2
| Source File: gtiff.py From mapchete with MIT License | 6 votes |
|
def read(self, indexes=None, **kwargs):
"""
Read reprojected & resampled input data.
Parameters
----------
indexes : integer or list
band number or list of band numbers
Returns
-------
data : array
"""
band_indexes = self._get_band_indexes(indexes)
arr = self.process.get_raw_output(self.tile)
return (
arr[band_indexes[0] - 1]
if len(band_indexes) == 1
else ma.concatenate([ma.expand_dims(arr[i - 1], 0) for i in band_indexes])
)
Example #3
| Source File: UncertMath.py From westpa with MIT License | 5 votes |
|
def concatenate(self,value,axis=0):
""" Concatentate UncertContainer value to self.
Assumes that if dimensions of self and value do not match, to
add a np.newaxis along axis of value
"""
if isinstance(value,UncertContainer):
if value.vals.ndim == self.vals.ndim:
vals = value.vals
dmin = value.dmin
dmax = value.dmax
wt = value.wt
uncert = value.uncert
mask = value.mask
elif (value.vals.ndim + 1) == self.vals.ndim:
vals = ma.expand_dims(value.vals,axis)
dmin = ma.expand_dims(value.dmin,axis)
dmax = ma.expand_dims(value.dmax,axis)
wt = ma.expand_dims(value.wt,axis)
uncert = ma.expand_dims(value.uncert,axis)
mask = np.expand_dims(value.mask,axis)
else:
raise ValueError('Could not propery match dimensionality')
self.vals = ma.concatenate((self.vals,vals),axis=axis)
self.dmin = ma.concatenate((self.dmin,dmin),axis=axis)
self.dmax = ma.concatenate((self.dmax,dmax),axis=axis)
self.wt = ma.concatenate((self.wt,wt),axis=axis)
self.uncert = ma.concatenate((self.uncert,uncert),axis=axis)
self.mask = np.concatenate((self.mask,mask),axis=axis)
else:
raise ValueError('Can only concatenate with an UncertContainer object')
Example #4
| Source File: UncertMath.py From westpa with MIT License | 4 votes |
|
def weighted_average(self,axis=0,expaxis=None):
""" Calculate weighted average of data along axis
after optionally inserting a new dimension into the
shape array at position expaxis
"""
if expaxis is not None:
vals = ma.expand_dims(self.vals,expaxis)
dmin = ma.expand_dims(self.dmin,expaxis)
dmax = ma.expand_dims(self.dmax,expaxis)
wt = ma.expand_dims(self.wt,expaxis)
else:
vals = self.vals
wt = self.wt
dmin = self.dmin
dmax = self.dmax
# Get average value
avg,norm = ma.average(vals,axis=axis,weights=wt,returned=True)
avg_ex = ma.expand_dims(avg,0)
# Calculate weighted uncertainty
wtmax = ma.max(wt,axis=axis)
neff = norm/wtmax # Effective number of samples based on uncertainties
# Seeking max deviation from the average; if above avg use max, if below use min
term = np.empty_like(vals)
indices = np.where(vals > avg_ex)
i0 = indices[0]
irest = indices[1:]
ii = tuple(x for x in itertools.chain([i0],irest))
jj = tuple(x for x in itertools.chain([np.zeros_like(i0)],irest))
term[ii] = (dmax[ii] - avg_ex[jj])**2
indices = np.where(vals <= avg_ex)
i0 = indices[0]
irest = indices[1:]
ii = tuple(x for x in itertools.chain([i0],irest))
jj = tuple(x for x in itertools.chain([np.zeros_like(i0)],irest))
term[ii] = (avg_ex[jj] - dmin[ii])**2
dsum = ma.sum(term*wt,axis=0) # Sum for weighted average of deviations
dev = 0.5*np.sqrt(dsum/(norm*neff))
if isinstance(avg,(float,np.float)):
avg = avg_ex
tmp_min = avg - dev
ii = np.where(tmp_min < 0)
tmp_min[ii] = TOL*avg[ii]
return UncertContainer(avg,tmp_min,avg+dev)
Example #5
| Source File: mstats_basic.py From lambda-packs with MIT License | 4 votes |
|
def moment(a, moment=1, axis=0):
"""
Calculates the nth moment about the mean for a sample.
Parameters
----------
a : array_like
data
moment : int, optional
order of central moment that is returned
axis : int or None, optional
Axis along which the central moment is computed. Default is 0.
If None, compute over the whole array `a`.
Returns
-------
n-th central moment : ndarray or float
The appropriate moment along the given axis or over all values if axis
is None. The denominator for the moment calculation is the number of
observations, no degrees of freedom correction is done.
Notes
-----
For more details about `moment`, see `stats.moment`.
"""
a, axis = _chk_asarray(a, axis)
if moment == 1:
# By definition the first moment about the mean is 0.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return np.zeros(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return np.float64(0.0)
else:
# Exponentiation by squares: form exponent sequence
n_list = [moment]
current_n = moment
while current_n > 2:
if current_n % 2:
current_n = (current_n-1)/2
else:
current_n /= 2
n_list.append(current_n)
# Starting point for exponentiation by squares
a_zero_mean = a - ma.expand_dims(a.mean(axis), axis)
if n_list[-1] == 1:
s = a_zero_mean.copy()
else:
s = a_zero_mean**2
# Perform multiplications
for n in n_list[-2::-1]:
s = s**2
if n % 2:
s *= a_zero_mean
return s.mean(axis)
Example #6
| Source File: mstats_basic.py From GraphicDesignPatternByPython with MIT License | 4 votes |
|
def moment(a, moment=1, axis=0):
"""
Calculates the nth moment about the mean for a sample.
Parameters
----------
a : array_like
data
moment : int, optional
order of central moment that is returned
axis : int or None, optional
Axis along which the central moment is computed. Default is 0.
If None, compute over the whole array `a`.
Returns
-------
n-th central moment : ndarray or float
The appropriate moment along the given axis or over all values if axis
is None. The denominator for the moment calculation is the number of
observations, no degrees of freedom correction is done.
Notes
-----
For more details about `moment`, see `stats.moment`.
"""
a, axis = _chk_asarray(a, axis)
if moment == 1:
# By definition the first moment about the mean is 0.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return np.zeros(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return np.float64(0.0)
else:
# Exponentiation by squares: form exponent sequence
n_list = [moment]
current_n = moment
while current_n > 2:
if current_n % 2:
current_n = (current_n-1)/2
else:
current_n /= 2
n_list.append(current_n)
# Starting point for exponentiation by squares
a_zero_mean = a - ma.expand_dims(a.mean(axis), axis)
if n_list[-1] == 1:
s = a_zero_mean.copy()
else:
s = a_zero_mean**2
# Perform multiplications
for n in n_list[-2::-1]:
s = s**2
if n % 2:
s *= a_zero_mean
return s.mean(axis)
Example #7
| Source File: mstats_basic.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def moment(a, moment=1, axis=0):
"""
Calculates the nth moment about the mean for a sample.
Parameters
----------
a : array_like
data
moment : int, optional
order of central moment that is returned
axis : int or None, optional
Axis along which the central moment is computed. Default is 0.
If None, compute over the whole array `a`.
Returns
-------
n-th central moment : ndarray or float
The appropriate moment along the given axis or over all values if axis
is None. The denominator for the moment calculation is the number of
observations, no degrees of freedom correction is done.
Notes
-----
For more details about `moment`, see `stats.moment`.
"""
a, axis = _chk_asarray(a, axis)
if moment == 1:
# By definition the first moment about the mean is 0.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return np.zeros(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return np.float64(0.0)
else:
# Exponentiation by squares: form exponent sequence
n_list = [moment]
current_n = moment
while current_n > 2:
if current_n % 2:
current_n = (current_n-1)/2
else:
current_n /= 2
n_list.append(current_n)
# Starting point for exponentiation by squares
a_zero_mean = a - ma.expand_dims(a.mean(axis), axis)
if n_list[-1] == 1:
s = a_zero_mean.copy()
else:
s = a_zero_mean**2
# Perform multiplications
for n in n_list[-2::-1]:
s = s**2
if n % 2:
s *= a_zero_mean
return s.mean(axis)
Example #8
| Source File: raster.py From mapchete with MIT License | 4 votes |
|
def prepare_array(data, masked=True, nodata=0, dtype="int16"):
"""
Turn input data into a proper array for further usage.
Output array is always 3-dimensional with the given data type. If the output
is masked, the fill_value corresponds to the given nodata value and the
nodata value will be burned into the data array.
Parameters
----------
data : array or iterable
array (masked or normal) or iterable containing arrays
nodata : integer or float
nodata value (default: 0) used if input is not a masked array and
for output array
masked : bool
return a NumPy Array or a NumPy MaskedArray (default: True)
dtype : string
data type of output array (default: "int16")
Returns
-------
array : array
"""
# input is iterable
if isinstance(data, (list, tuple)):
return _prepare_iterable(data, masked, nodata, dtype)
# special case if a 2D single band is provided
elif isinstance(data, np.ndarray) and data.ndim == 2:
data = ma.expand_dims(data, axis=0)
# input is a masked array
if isinstance(data, ma.MaskedArray):
return _prepare_masked(data, masked, nodata, dtype)
# input is a NumPy array
elif isinstance(data, np.ndarray):
if masked:
return ma.masked_values(data.astype(dtype, copy=False), nodata, copy=False)
else:
return data.astype(dtype, copy=False)
else:
raise ValueError(
"Data must be array, masked array or iterable containing arrays. "
"Current data: %s (%s)" % (data, type(data))
)
