numpy.asarray

All Samples(4284) | Call(4040) | Derive(0) | Import(244)

Convert the input to an array.

Parameters
----------
a : array_like
    Input data, in any form that can be converted to an array.  This
    includes lists, lists of tuples, tuples, tuples of tuples, tuples
    of lists and ndarrays.
dtype : data-type, optional
    By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
    Whether to use row-major ('C') or column-major ('F' for FORTRAN)
    memory representation.  Defaults to 'C'.

Returns
-------
out : ndarray
    Array interpretation of `a`.  No copy is performed if the input
    is already an ndarray.  If `a` is a subclass of ndarray, a base
    class ndarray is returned.

See Also
--------
asanyarray : Similar function which passes through subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
                 memory order.
asarray_chkfinite : Similar function which checks input for NaNs and Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
               positions.

Examples
--------
Convert a list into an array:

>>> a = [1, 2]
>>> np.asarray(a)
array([1, 2])

Existing arrays are not copied:

>>> a = np.array([1, 2])
>>> np.asarray(a) is a
True

If `dtype` is set, array is copied only if dtype does not match:

>>> a = np.array([1, 2], dtype=np.float32)
>>> np.asarray(a, dtype=np.float32) is a
True
>>> np.asarray(a, dtype=np.float64) is a
False

Contrary to `asanyarray`, ndarray subclasses are not passed through:

>>> issubclass(np.matrix, np.ndarray)
True
>>> a = np.matrix([[1, 2]])
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True

Show Implementation

        def asarray(a, dtype=None, order=None):
    """
    Convert the input to an array.

    Parameters
    ----------
    a : array_like
        Input data, in any form that can be converted to an array.  This
        includes lists, lists of tuples, tuples, tuples of tuples, tuples
        of lists and ndarrays.
    dtype : data-type, optional
        By default, the data-type is inferred from the input data.
    order : {'C', 'F'}, optional
        Whether to use row-major ('C') or column-major ('F' for FORTRAN)
        memory representation.  Defaults to 'C'.

    Returns
    -------
    out : ndarray
        Array interpretation of `a`.  No copy is performed if the input
        is already an ndarray.  If `a` is a subclass of ndarray, a base
        class ndarray is returned.

    See Also
    --------
    asanyarray : Similar function which passes through subclasses.
    ascontiguousarray : Convert input to a contiguous array.
    asfarray : Convert input to a floating point ndarray.
    asfortranarray : Convert input to an ndarray with column-major
                     memory order.
    asarray_chkfinite : Similar function which checks input for NaNs and Infs.
    fromiter : Create an array from an iterator.
    fromfunction : Construct an array by executing a function on grid
                   positions.

    Examples
    --------
    Convert a list into an array:

    >>> a = [1, 2]
    >>> np.asarray(a)
    array([1, 2])

    Existing arrays are not copied:

    >>> a = np.array([1, 2])
    >>> np.asarray(a) is a
    True

    If `dtype` is set, array is copied only if dtype does not match:

    >>> a = np.array([1, 2], dtype=np.float32)
    >>> np.asarray(a, dtype=np.float32) is a
    True
    >>> np.asarray(a, dtype=np.float64) is a
    False

    Contrary to `asanyarray`, ndarray subclasses are not passed through:

    >>> issubclass(np.matrix, np.ndarray)
    True
    >>> a = np.matrix([[1, 2]])
    >>> np.asarray(a) is a
    False
    >>> np.asanyarray(a) is a
    True

    """
    return array(a, dtype, copy=False, order=order)

src/n/i/nipy-HEAD/nipy/algorithms/tests/test_resample.py nipy(Download)

def test_resample_img2img():
    fimg = load_image(funcfile)
    aimg = load_image(anatfile)
    resimg = resample_img2img(fimg, fimg)
    yield assert_true, np.allclose(np.asarray(resimg), np.asarray(fimg))
    yield assert_raises, ValueError, resample_img2img, fimg, aimg

                  [0,0,0,1.]])
 
    ir = resample(i, g2, a, (100,80,90))
    yield assert_array_almost_equal, np.transpose(np.asarray(ir), (0,2,1)), i
 
 
def test_resample2d():

        pylab.gca().set_ylim([0,99])
        pylab.gca().set_xlim([0,89])
        pylab.figure(num=4)
        pylab.plot(np.asarray(ir))
 
 
def test_2d_from_3d():

    a = np.identity(4)
    g2 = ArrayCoordMap.from_shape(g, shape)[10]
    ir = resample(i, g2.coordmap, a, g2.shape)
    yield assert_array_almost_equal, np.asarray(ir), np.asarray(i[10])
 
 
@parametric

                        ((0,39.5), 80),
                        i.reference)
    ir = resample(i, zsl, a, (90, 80))
    yield assert_true(np.allclose(np.asarray(ir), np.asarray(i[53])))
    ysl = slices.yslice(22, (0,49.5), (0,39.5), i.reference, (100,80))
    ir = resample(i, ysl.coordmap, a, ysl.shape)
    yield assert_true(np.allclose(np.asarray(ir), np.asarray(i[:,45])))
    xsl = slices.xslice(15.5, (0,49.5), (0,44.5), i.reference, (100,90))
    ir = resample(i, xsl.coordmap, a, xsl.shape)
    yield assert_true(np.allclose(np.asarray(ir), np.asarray(i[:,:,32])))

src/n/i/NiPy-OLD-HEAD/nipy/algorithms/tests/test_resample.py NiPy-OLD(Download)

def test_resample_img2img():
    fimg = load_image(funcfile)
    aimg = load_image(anatfile)
    resimg = resample_img2img(fimg, fimg)
    yield assert_true, np.allclose(np.asarray(resimg), np.asarray(fimg))
    yield assert_raises, ValueError, resample_img2img, fimg, aimg

                  [0,0,0,1.]])
 
    ir = resample(i, g2, a, (100,80,90))
    yield assert_array_almost_equal, np.transpose(np.asarray(ir), (0,2,1)), i
 
 
def test_resample2d():

        pylab.gca().set_ylim([0,99])
        pylab.gca().set_xlim([0,89])
        pylab.figure(num=4)
        pylab.plot(np.asarray(ir))
 
 
def test_2d_from_3d():

    a = np.identity(4)
    g2 = ArrayCoordMap.from_shape(g, shape)[10]
    ir = resample(i, g2.coordmap, a, g2.shape)
    yield assert_array_almost_equal, np.asarray(ir), np.asarray(i[10])
 
 
def test_slice_from_3d():

                        i.coordmap.output_coords,
                        (90,80))
    ir = resample(i, zsl.coordmap, a, zsl.shape)
    yield assert_true, np.allclose(np.asarray(ir), np.asarray(i[53]))
    ysl = slices.yslice(22, (0,49.5), (0,39.5), i.coordmap.output_coords, (100,80))
    ir = resample(i, ysl.coordmap, a, ysl.shape)
    yield assert_true, np.allclose(np.asarray(ir), np.asarray(i[:,45]))
    xsl = slices.xslice(15.5, (0,49.5), (0,44.5), i.coordmap.output_coords, (100,90))
    ir = resample(i, xsl.coordmap, a, xsl.shape)
    yield assert_true, np.allclose(np.asarray(ir), np.asarray(i[:,:,32]))

src/m/a/matplotlib-HEAD/matplotlib/examples/pylab_examples/demo_agg_filter.py matplotlib(Download)

def smooth2d(A, sigma=3):
 
    window_len = max(int(sigma), 3)*2+1
    A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
    A2 = np.transpose(A1)
    A3 = np.array([smooth1d(x, window_len) for x in A2])
    A4 = np.transpose(A3)

src/m/a/matplotlib-HEAD/examples/pylab_examples/demo_agg_filter.py matplotlib(Download)

def smooth2d(A, sigma=3):
 
    window_len = max(int(sigma), 3)*2+1
    A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
    A2 = np.transpose(A1)
    A3 = np.array([smooth1d(x, window_len) for x in A2])
    A4 = np.transpose(A3)

src/n/i/nipy-HEAD/examples/neurospin/need_data/erp.py nipy(Download)

nsubjects = Y.shape[2]
nconditions = Y.shape[3]
ndata = nsubjects*nconditions
conditions = np.asarray(range(nconditions))*50 + 50
saturation = np.array([0,0,1,1,1,1])
 
# Regressors
baseline = np.ones(ndata)
conditions = np.tile(conditions, nsubjects)
saturation = np.tile(saturation, nsubjects)
subject_factor = np.repeat(np.asarray(range(nsubjects)),6)

src/m/a/matplotlib-HEAD/matplotlib/examples/event_handling/poly_editor.py matplotlib(Download)

"""
from matplotlib.artist import Artist
from matplotlib.patches import Polygon, CirclePolygon
from numpy import sqrt, nonzero, equal, array, asarray, dot, amin, cos, sin
from matplotlib.mlab import dist_point_to_segment

    def get_ind_under_point(self, event):
        'get the index of the vertex under point if within epsilon tolerance'
 
        # display coords
        xy = asarray(self.poly.xy)
        xyt = self.poly.get_transform().transform(xy)
        xt, yt = xyt[:, 0], xyt[:, 1]

src/n/i/NiPy-OLD-HEAD/examples/neurospin/erp.py NiPy-OLD(Download)

nsubjects = Y.shape[2]
nconditions = Y.shape[3]
ndata = nsubjects*nconditions
conditions = np.asarray(range(nconditions))*50 + 50
saturation = np.array([0,0,1,1,1,1])
 
# Regressors
baseline = np.ones(ndata)
conditions = np.tile(conditions, nsubjects)
saturation = np.tile(saturation, nsubjects)
subject_factor = np.repeat(np.asarray(range(nsubjects)),6)

src/m/a/matplotlib-HEAD/examples/event_handling/poly_editor.py matplotlib(Download)

"""
from matplotlib.artist import Artist
from matplotlib.patches import Polygon, CirclePolygon
from numpy import sqrt, nonzero, equal, array, asarray, dot, amin, cos, sin
from matplotlib.mlab import dist_point_to_segment

    def get_ind_under_point(self, event):
        'get the index of the vertex under point if within epsilon tolerance'
 
        # display coords
        xy = asarray(self.poly.xy)
        xyt = self.poly.get_transform().transform(xy)
        xt, yt = xyt[:, 0], xyt[:, 1]

src/p/y/PyMVPA-HEAD/doc/examples/pylab_2d.py PyMVPA(Download)

        # if ridge, use the prediction, otherwise use the values
        if c == 'Ridge Regression':
            # use the prediction
            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)
        elif c in ['SMLR']:
            res = np.asarray(clf.ca.estimates[:, 1])

            # visualization of trade-off just plot relative
            # "trade-off" which determines decision boundaries if an
            # alternative log-odd value was chosen for a cutoff
            res = np.asarray(clf.ca.estimates[:, 1]
                             - clf.ca.estimates[:, 0])
            # Scale and position around 0.5
            res = 0.5 + res/max(np.abs(res))
        else:
            # get the probabilities from the svm
            res = np.asarray([(q[1][1] - q[1][0] + 1) / 2

src/m/a/matplotlib-HEAD/matplotlib/examples/pylab_examples/ginput_manual_clabel.py matplotlib(Download)

    pts = []
    while len(pts) < 3:
        tellme('Select 3 corners with mouse')
        pts = np.asarray( plt.ginput(3,timeout=-1) )
        if len(pts) < 3:
            tellme('Too few points, starting over')
            time.sleep(1) # Wait a second

happy = False
while not happy:
    tellme( 'Select two corners of zoom, middle mouse button to finish' )
    pts = np.asarray( plt.ginput(2,timeout=-1) )
 
    happy = len(pts) < 2
    if happy: break

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