All Samples(4746) | Call(4565) | Derive(0) | Import(181)
Sum of array elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : integer, optional
Axis over which the sum is taken. By default `axis` is None,
and all elements are summed.
dtype : dtype, optional
The type of the returned array and of the accumulator in which
the elements are summed. By default, the dtype of `a` is used.
An exception is when `a` has an integer type with less precision
than the default platform integer. In that case, the default
platform integer is used instead.
out : ndarray, optional
Array into which the output is placed. By default, a new array is
created. If `out` is given, it must be of the appropriate shape
(the shape of `a` with `axis` removed, i.e.,
``numpy.delete(a.shape, axis)``). Its type is preserved. See
`doc.ufuncs` (Section "Output arguments") for more details.
Returns
-------
sum_along_axis : ndarray
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
ndarray.sum : Equivalent method.
cumsum : Cumulative sum of array elements.
trapz : Integration of array values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
Examples
--------
>>> np.sum([0.5, 1.5])
2.0
>>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
1
>>> np.sum([[0, 1], [0, 5]])
6
>>> np.sum([[0, 1], [0, 5]], axis=0)
array([0, 6])
>>> np.sum([[0, 1], [0, 5]], axis=1)
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
-128
def sum(a, axis=None, dtype=None, out=None):
"""
Sum of array elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : integer, optional
Axis over which the sum is taken. By default `axis` is None,
and all elements are summed.
dtype : dtype, optional
The type of the returned array and of the accumulator in which
the elements are summed. By default, the dtype of `a` is used.
An exception is when `a` has an integer type with less precision
than the default platform integer. In that case, the default
platform integer is used instead.
out : ndarray, optional
Array into which the output is placed. By default, a new array is
created. If `out` is given, it must be of the appropriate shape
(the shape of `a` with `axis` removed, i.e.,
``numpy.delete(a.shape, axis)``). Its type is preserved. See
`doc.ufuncs` (Section "Output arguments") for more details.
Returns
-------
sum_along_axis : ndarray
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
ndarray.sum : Equivalent method.
cumsum : Cumulative sum of array elements.
trapz : Integration of array values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
Examples
--------
>>> np.sum([0.5, 1.5])
2.0
>>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
1
>>> np.sum([[0, 1], [0, 5]])
6
>>> np.sum([[0, 1], [0, 5]], axis=0)
array([0, 6])
>>> np.sum([[0, 1], [0, 5]], axis=1)
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
-128
"""
if isinstance(a, _gentype):
res = _sum_(a)
if out is not None:
out[...] = res
return out
return res
try:
sum = a.sum
except AttributeError:
return _wrapit(a, 'sum', axis, dtype, out)
return sum(axis, dtype, out)
total_cells += na.prod(b.my_data[0].shape)
LE = na.array(LE) - self.back_center
RE = na.array(RE) - self.back_center
LE = na.sum(LE * self.unit_vectors[2], axis=1)
RE = na.sum(RE * self.unit_vectors[2], axis=1)
dist = na.minimum(LE, RE)
ind = na.argsort(dist)
src/a/l/algopy-HEAD/documentation/ICCS2010/odoe_example.py algopy(Download)
# accumulate over the different directions qbar = UTPM(numpy.zeros((D,1))) qbar.data[:,0] = numpy.sum( qbars.data[:,:], axis=1) ############################################# # compare with analytical solution
src/p/y/PyMVPA-HEAD/doc/examples/pylab_2d.py PyMVPA(Download)
res = np.asarray(pre)
elif 'Nearest-Ne' in c:
# Use the votes
res = clf.ca.estimates[:, 1] / np.sum(clf.ca.estimates, axis=1)
elif c == 'Logistic Regression':
# get out the values used for the prediction
res = np.asarray(clf.ca.estimates)
src/p/y/PyPWDG-HEAD/examples/3D/test.py PyPWDG(Download)
t.append(time.time())
A2 = [A,A,A,A]
t.append(time.time())
B2 = numpy.sum(A2, axis=0)
t.append(time.time())
l = len(A2)
B3 = numpy.reshape(numpy.dot(numpy.ones(l), numpy.reshape(A2, (l, -1))), (n,n))
src/n/i/NiPy-OLD-HEAD/examples/neurospin/demo_dmtx.py NiPy-OLD(Download)
import matplotlib.pylab as mp
mp.figure()
mp.imshow(x1/np.sqrt(np.sum(x1**2,0)),interpolation='Nearest', aspect='auto')
mp.xlabel('conditions')
mp.ylabel('scan number')
if name1!=None:
mp.xticks(np.arange(len(name1)),name1,rotation=60,ha='right')
mp.subplots_adjust(top=0.95,bottom=0.25)
mp.title('Example of event-related design matrix')
mp.figure()
mp.imshow(x2/np.sqrt(np.sum(x2**2,0)),interpolation='Nearest', aspect='auto')
mp.title('Example of block design matrix')
mp.figure()
mp.imshow(x3/np.sqrt(np.sum(x3**2,0)),interpolation='Nearest', aspect='auto')
mp.xlabel('conditions')
mp.ylabel('scan number')
if name3!=None:
src/n/i/nipy-HEAD/examples/formula/parametric_design.py nipy(Download)
# the columns or d/d_b0 and d/dl tt = tval.view(np.float) v1 = np.sum([hrf.glovert(tt - s)*np.exp(-4.5*a) for s,a in zip(t, dt)], 0) v2 = np.sum([-3.5*a*hrf.glovert(tt - s)*np.exp(-4.5*a) for s,a in zip(t, dt)], 0) V = np.array([v1,v2]).T
src/n/i/NiPy-OLD-HEAD/examples/formula/parametric_design.py NiPy-OLD(Download)
# the columns or d/d_b0 and d/dl tt = tval.view(np.float) v1 = np.sum([hrf.glovert(tt - s)*np.exp(-4.5*a) for s,a in zip(t, dt)], 0) v2 = np.sum([-3.5*a*hrf.glovert(tt - s)*np.exp(-4.5*a) for s,a in zip(t, dt)], 0) V = np.array([v1,v2]).T
src/s/c/scikits.statsmodels-0.2.0/scikits/statsmodels/sandbox/regression/example_kernridge.py scikits.statsmodels(Download)
#xs1 /= np.std(xs1[::k,:],0) # normalize scale, could use cov to normalize ##y1true = np.sum(np.sin(xs1)+np.sqrt(xs1),1)[:,np.newaxis] xs1 = np.sin(xs)#[:,np.newaxis] y1true = np.sum(xs1 + 0.01*np.sqrt(np.abs(xs1)),1)[:,np.newaxis] y1 = y1true + 0.10 * np.random.randn(m,1) stride = 3 #use only some points as trainig points e.g 2 means every 2nd
src/i/p/ipython-py3k-HEAD/docs/examples/kernel/phistogram.py ipython-py3k(Download)
lower_edges = rc.pull('lower_edges', targets=0)
hist_array = rc.gather('hist')
hist_array.shape = (nengines,-1)
total_hist = numpy.sum(hist_array, 0)
if normed:
total_hist = total_hist/numpy.sum(total_hist,dtype=float)
return total_hist, lower_edges
src/n/i/NiPy-OLD-HEAD/examples/neurospin/neurospy/DesignMatrix.py NiPy-OLD(Download)
def show(self):
"""Vizualization of self
"""
x = self._design
import matplotlib.pylab as mp
mp.figure()
mp.imshow(x/np.sqrt(np.sum(x**2,0)),interpolation='Nearest', aspect='auto')
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