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
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)
""" 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/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/m/a/Matplotlib--JJ-s-dev-HEAD/examples/event_handling/poly_editor.py Matplotlib--JJ-s-dev(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/s/c/scipy-HEAD/scipy/stats/stats.py scipy(Download)
pysum = sum # save it before it gets overwritten # Scipy imports. from numpy import array, asarray, dot, ma, zeros, sum import scipy.special as special import scipy.linalg as linalg import numpy as np
if isinstance(a,np.ma.MaskedArray):
log_a=np.log(np.ma.asarray(a, dtype=dtype))
else:
log_a=np.log(np.asarray(a, dtype=dtype))
else:
log_a = np.log(a)
return np.exp(log_a.mean(axis=axis))
tmean : float
"""
a = asarray(a)
# Cast to a float if this is an integer array. If it is already a float
# array, leave it as is to preserve its precision.
tvar : float
"""
a = asarray(a)
a = a.astype(float).ravel()
if limits is None:
n = len(a)
Returns: trimmed version of array a
"""
a = asarray(a)
lowercut = int(proportiontocut*len(a))
uppercut = len(a) - lowercut
if (lowercut >= uppercut):
Returns: trimmed version of array a
"""
a = asarray(a)
if tail.lower() == 'right':
lowercut = 0
uppercut = len(a) - int(proportiontocut*len(a))
- numpy.cov rowvar argument defaults to true, not false
- numpy.cov bias argument defaults to false, not true
""", DeprecationWarning)
m = asarray(m)
if y is None:
y = m
else:
y = asarray(y)
http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation
"""
# x and y should have same length.
x = np.asarray(x)
y = np.asarray(y)
n = len(x)
mx = x.mean()
# 0.1]
# rpb = 0.36149
x = np.asarray(x, dtype=bool)
y = np.asarray(y, dtype=float)
n = len(x)
"""
TINY = 1.0e-20
if y is None: # x is a (2, N) or (N, 2) shaped array_like
x = asarray(x)
if x.shape[0] == 2:
x, y = x
elif x.shape[1] == 2:
x, y = x.T
else:
msg = "If only `x` is given as input, it has to be of shape (2, N) \
or (N, 2), provided shape was %s" % str(x.shape)
raise ValueError(msg)
else:
x = asarray(x)
raise ValueError(msg)
else:
x = asarray(x)
y = asarray(y)
n = len(x)
xmean = np.mean(x,None)
ymean = np.mean(y,None)
"""
f_obs = asarray(f_obs)
k = len(f_obs)
if f_exp is None:
f_exp = array([np.sum(f_obs,axis=0)/float(k)] * len(f_obs),float)
p-value multiply the returned p-value by 2.
"""
x = asarray(x)
y = asarray(y)
n1 = len(x)
n2 = len(y)
Returns: T correction factor for U or H
"""
sorted,posn = fastsort(asarray(rankvals))
n = len(sorted)
T = 0.0
i = 0
-------
"""
x = np.asarray(x)
x = np.where(x < 1.0, x, 1.0) # if x > 1 then return 1.0
return special.betainc(a, b, x)
src/s/h/shapely-3k-HEAD/examples/geoms.py shapely-3k(Download)
from numpy import asarray import pylab from shapely.geometry import Point, LineString, Polygon polygon = Polygon(((-1.0, -1.0), (-1.0, 1.0), (1.0, 1.0), (1.0, -1.0))) point_r = Point(-1.5, 1.2)
def plot_line(g, o):
a = asarray(g)
pylab.plot(a[:,0], a[:,1], o)
def fill_polygon(g, o):
a = asarray(g.exterior)
pylab.fill(a[:,0], a[:,1], o, alpha=0.5)
def fill_multipolygon(g, o):
for g in g.geoms:
fill_polygon(g, o)
if __name__ == "__main__":
from numpy import asarray
#pylab.axis([-2.0, 2.0, -1.5, 1.5])
pylab.axis('tight')
a = asarray(polygon.exterior)
pylab.fill(a[:,0], a[:,1], 'c')
plot_point(point_r, 'ro', 'b')
src/s/h/shapely-HEAD/examples/geoms.py shapely(Download)
from numpy import asarray import pylab from shapely.geometry import Point, LineString, Polygon polygon = Polygon(((-1.0, -1.0), (-1.0, 1.0), (1.0, 1.0), (1.0, -1.0))) point_r = Point(-1.5, 1.2)
def plot_line(g, o):
a = asarray(g)
pylab.plot(a[:,0], a[:,1], o)
def fill_polygon(g, o):
a = asarray(g.exterior)
pylab.fill(a[:,0], a[:,1], o, alpha=0.5)
def fill_multipolygon(g, o):
for g in g.geoms:
fill_polygon(g, o)
if __name__ == "__main__":
from numpy import asarray
#pylab.axis([-2.0, 2.0, -1.5, 1.5])
pylab.axis('tight')
a = asarray(polygon.exterior)
pylab.fill(a[:,0], a[:,1], 'c')
plot_point(point_r, 'ro', 'b')
src/s/c/scipy-0.8.0/scipy/stats/stats.py scipy(Download)
pysum = sum # save it before it gets overwritten # Scipy imports. from numpy import array, asarray, dot, ma, zeros, sum import scipy.special as special import scipy.linalg as linalg import numpy as np
def _chk_asarray(a, axis):
if axis is None:
a = np.ravel(a)
outaxis = 0
else:
a = np.asarray(a)
outaxis = axis
def _chk2_asarray(a, b, axis):
if axis is None:
a = np.ravel(a)
b = np.ravel(b)
outaxis = 0
else:
a = np.asarray(a)
b = np.asarray(b)
if isinstance(a,np.ma.MaskedArray):
log_a=np.log(np.ma.asarray(a, dtype=dtype))
else:
log_a=np.log(np.asarray(a, dtype=dtype))
else:
log_a = np.log(a)
return np.exp(log_a.mean(axis=axis))
tmean : float
"""
a = asarray(a)
# Cast to a float if this is an integer array. If it is already a float
# array, leave it as is to preserve its precision.
tvar : float
"""
a = asarray(a)
a = a.astype(float).ravel()
if limits is None:
n = len(a)
Returns: trimmed version of array a
"""
a = asarray(a)
lowercut = int(proportiontocut*len(a))
uppercut = len(a) - lowercut
if (lowercut >= uppercut):
Returns: trimmed version of array a
"""
a = asarray(a)
if tail.lower() == 'right':
lowercut = 0
uppercut = len(a) - int(proportiontocut*len(a))
- numpy.cov rowvar argument defaults to true, not false
- numpy.cov bias argument defaults to false, not true
""", DeprecationWarning)
m = asarray(m)
if y is None:
y = m
else:
y = asarray(y)
http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation
"""
# x and y should have same length.
x = np.asarray(x)
y = np.asarray(y)
n = len(x)
mx = x.mean()
# 0.1]
# rpb = 0.36149
x = np.asarray(x, dtype=bool)
y = np.asarray(y, dtype=float)
n = len(x)
"""
TINY = 1.0e-20
if len(args) == 1: # more than 1D array?
args = asarray(args[0])
if len(args) == 2:
x = args[0]
y = args[1]
else:
x = args[:,0]
y = args[:,1]
else:
x = asarray(args[0])
y = asarray(args[1])
"""
f_obs = asarray(f_obs)
k = len(f_obs)
if f_exp is None:
f_exp = array([np.sum(f_obs,axis=0)/float(k)] * len(f_obs),float)
p-value multiply the returned p-value by 2.
"""
x = asarray(x)
y = asarray(y)
n1 = len(x)
n2 = len(y)
Returns: T correction factor for U or H
"""
sorted,posn = fastsort(asarray(rankvals))
n = len(sorted)
T = 0.0
i = 0
-------
"""
x = np.asarray(x)
x = np.where(x < 1.0, x, 1.0) # if x > 1 then return 1.0
return special.betainc(a, b, x)
src/s/c/scipy-HEAD/scipy/optimize/nonlin.py scipy(Download)
import sys import numpy as np from scipy.linalg import norm, solve, inv, qr, svd, lstsq, LinAlgError from numpy import asarray, dot, vdot import scipy.sparse.linalg import scipy.sparse import scipy.lib.blas as blas
def _as_inexact(x):
"""Return `x` as an array, of either floats or complex floats"""
x = asarray(x)
if not np.issubdtype(x.dtype, np.inexact):
return asarray(x, dtype=np.float_)
return x
elif isinstance(J, np.ndarray):
if J.ndim > 2:
raise ValueError('array must have rank <= 2')
J = np.atleast_2d(np.asarray(J))
if J.shape[0] != J.shape[1]:
raise ValueError('array must be square')
src/n/u/numpy-refactor-HEAD/numpy/oldnumeric/arrayfns.py numpy-refactor(Download)
'to_corners', 'zmin_zmax']
import numpy as np
from numpy import asarray
class error(Exception):
pass
def array_set(vals1, indices, vals2):
indices = asarray(indices)
def array_set(vals1, indices, vals2):
indices = asarray(indices)
if indices.ndim != 1:
raise ValueError, "index array must be 1-d"
if not isinstance(vals1, np.ndarray):
raise TypeError, "vals1 must be an ndarray"
vals1 = asarray(vals1)
vals2 = asarray(vals2)
def nz(x):
x = asarray(x,dtype=np.ubyte)
if x.ndim != 1:
raise TypeError, "intput must have 1 dimension."
indxs = np.flatnonzero(x != 0)
return indxs[-1].item()+1
def reverse(x, n):
x = asarray(x,dtype='d')
def zmin_zmax(z, ireg):
z = asarray(z, dtype=float)
ireg = asarray(ireg, dtype=int)
if z.shape != ireg.shape or z.ndim != 2:
raise ValueError, "z and ireg must be the same shape and 2-d"
ix, iy = np.nonzero(ireg)
# Now, add more indices
src/w/a/wafo-0.11/src/wafo/misc.py wafo(Download)
from numpy import arange from numpy import arctan2 from numpy import array from numpy import asarray from numpy import atleast_1d from numpy import broadcast_arrays from numpy import ceil
#y = x0.copy()
xu = (n - 1) * (x0 - xo[0]) / (xo[-1] - xo[0])
fi = asarray(floor(xu), dtype=int)
fi = where(fi == n - 1, fi - 1, fi)
xu = xu - fi
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