Exemplo n.º 1
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def toepsol(a, b, w=None):
    """..."""

    m = _import_module(v.dtype)
    x = vector(a.dtype, a.length())
    if not w:
        w = vector(a.dtype, a.length())
    m.covsol(a.block, b.block, w.block, x.block)
    return x
Exemplo n.º 2
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def toepsol(a, b, w=None):
    """Solve a real symmetric or complex Hermitian positive definite Toeplitz linear system."""

    m = _import_module(v.dtype)
    x = vector(a.dtype, a.length())
    if not w:
        w = vector(a.dtype, a.length())
    m.covsol(a.block, b.block, w.block, x.block)
    return x
Exemplo n.º 3
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def toepsol(a, b, w=None):
    """Solve a real symmetric or complex Hermitian positive definite Toeplitz linear system."""

    m = _import_module(v.dtype)
    x = vector(a.dtype, a.length())
    if not w:
        w = vector(a.dtype, a.length())
    m.covsol(a.block, b.block, w.block, x.block)
    return x
Exemplo n.º 4
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def toepsol(a, b, w=None):
    """..."""

    m = _import_module(v.dtype)
    x = vector(a.dtype, a.length())
    if not w:
        w = vector(a.dtype, a.length())
    m.covsol(a.block, b.block, w.block, x.block)
    return x
Exemplo n.º 5
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    def decompose(self, m):

        x = vector(m.dtype, min(m.rows(), m.cols()))
        if not self.impl_.decompose(m.block, x.block):
            raise ValueError, "something went wrong"
        else:
            return x
Exemplo n.º 6
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    def decompose(self, m):

        x = vector(m.dtype, min(m.rows(), m.cols()))
        if not self.impl_.decompose(m.block, x.block):
            raise ValueError, "something went wrong"
        else:
            return x
Exemplo n.º 7
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def freqswap(v):

    m = _import_module(v.dtype)
    if isinstance(v, vector):
        vout = vector(v.dtype, v.length())
    else:
        vout = matrix(v.dtype, v.rows(), v.cols())
    m.freqswap(v.block, vout.block)
    return vout
Exemplo n.º 8
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def clip(v, lt, ut, lc, uc):

    m = import_module('vsip.selgen.clip', v.dtype)
    if isinstance(v, vector):
        vout = vector(v.dtype, v.length())
    else:
        vout = matrix(v.dtype, v.rows(), v.cols())
    m.clip(v.block, vout.block, lt, ut, lc, uc)
    return vout
Exemplo n.º 9
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def freqswap(v):

    m = _import_module(v.dtype)
    if isinstance(v, vector):
        vout = vector(v.dtype, v.length())
    else:
        vout = matrix(v.dtype, v.rows(), v.cols())
    m.freqswap(v.block, vout.block)
    return vout
Exemplo n.º 10
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def invclip(v, lt, mt, ut, lc, uc):

    m = _import_module(v.dtype)
    if isinstance(v, vector):
        vout = vector(v.dtype, v.length())
    else:
        vout = matrix(v.dtype, v.rows(), v.cols())
    m.invclip(v.block, vout.block, lt, mt, ut, lc, uc)
    return vout
Exemplo n.º 11
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def clip(v, lt, ut, lc, uc):

    m = import_module('vsip.selgen.clip', v.dtype)
    if isinstance(v, vector):
        vout = vector(v.dtype, v.length())
    else:
        vout = matrix(v.dtype, v.rows(), v.cols())
    m.clip(v.block, vout.block, lt, ut, lc, uc)
    return vout
Exemplo n.º 12
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def invclip(v, lt, mt, ut, lc, uc):

    m = _import_module(v.dtype)
    if isinstance(v, vector):
        vout = vector(v.dtype, v.length())
    else:
        vout = matrix(v.dtype, v.rows(), v.cols())
    m.invclip(v.block, vout.block, lt, mt, ut, lc, uc)
    return vout
Exemplo n.º 13
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def prod(v, w):
    """Return the matrix product of v and w."""

    b1 = v.block
    b2 = w.block
    if b1.dtype == b2.dtype:
        mod = import_module('vsip.math.matvec', b1.dtype)
        b3 = mod.prod(b1, b2)
        if len(b3.shape) == 2:
            return matrix(block=b3)
        else:
            return vector(block=b3)
    else:
        import numpy
        if len(b1.shape) == len(b2.shape):
            return matrix(array=numpy.tensordot(array(b1), array(b2), (1,0)))
        elif len(b1.shape) == 1:
            return vector(array=numpy.tensordot(array(b1), array(b2), (0,0)))
        else:
            return vector(array=numpy.tensordot(array(b1), array(b2), (1,0)))
Exemplo n.º 14
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def prod(v, w):
    """Return the matrix product of v and w."""

    b1 = v.block
    b2 = w.block
    if b1.dtype == b2.dtype:
        mod = _import_module(b1.dtype)
        b3 = mod.prod(b1, b2)
        if len(b3.shape) == 2:
            return matrix(block=b3)
        else:
            return vector(block=b3)
    else:
        import numpy
        if len(b1.shape) == len(b2.shape):
            return matrix(array=numpy.tensordot(array(b1), array(b2), (1, 0)))
        elif len(b1.shape) == 1:
            return vector(array=numpy.tensordot(array(b1), array(b2), (0, 0)))
        else:
            return vector(array=numpy.tensordot(array(b1), array(b2), (1, 0)))
Exemplo n.º 15
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def kaiser(dtype, N, beta):
    """Create a vector with Kaiser window weights.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector

    """

    m = _import_module(dtype)
    return vector(block=m.kaiser(N, beta))
Exemplo n.º 16
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def kaiser(dtype, N, beta):
    """Create a vector with Kaiser window weights.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector

    """

    m = _import_module(dtype)
    return vector(block=m.kaiser(N, beta))
Exemplo n.º 17
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def cheby(dtype, N, ripple):
    """Create a vector with a Dolph-Chebyshev window of length N.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector
      :ripple: 

"""

    m = _import_module(dtype)
    return vector(block=m.cheby(N, ripple))
Exemplo n.º 18
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def cheby(dtype, N, ripple):
    """Create a vector with a Dolph-Chebyshev window of length N.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector
      :ripple: 

"""

    m = _import_module(dtype)
    return vector(block=m.cheby(N, ripple))
Exemplo n.º 19
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def blackman(dtype, N):
    """Create a vector with blackman window weights.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector

    .. math::

       y_i = 0.42 * 0.5 * cos(\\frac{2*\pi*i}{N-1}) + 0.08 * cos(\\frac{4*\pi*i}{N-1})"""

    m = _import_module(dtype)
    return vector(block=m.blackman(N))
Exemplo n.º 20
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def hanning(dtype, N):
    """Create a vector with Hanning window weights.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector

    .. math::

       y_i = \\frac{1}{2}(1 - cos(\\frac{2\pi(i+1)}{N+1}))"""

    m = _import_module(dtype)
    return vector(block=m.hanning(N))
Exemplo n.º 21
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def blackman(dtype, N):
    """Create a vector with blackman window weights.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector

    .. math::

       y_i = 0.42 * 0.5 * cos(\\frac{2*\pi*i}{N-1}) + 0.08 * cos(\\frac{4*\pi*i}{N-1})"""

    m = _import_module(dtype)
    return vector(block=m.blackman(N))
Exemplo n.º 22
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def hanning(dtype, N):
    """Create a vector with Hanning window weights.

    arguments:

      :dtype: data type of the vector (e.g. float)
      :N: length of the vector

    .. math::

       y_i = \\frac{1}{2}(1 - cos(\\frac{2\pi(i+1)}{N+1}))"""


    m = _import_module(dtype)
    return vector(block=m.hanning(N))
Exemplo n.º 23
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    def randn(self, *shape):
        """Produce normal-distributed numbers from the open interval :math:`(0,1)`

        arguments:

          :shape: the shape of the object to be returned:

                    - no argument: return a single number
                    - a single number: return a vector of the given length
                    - two numbers: return a matrix of the given number of rows and columns
        """
        
        if len(shape) == 0:
            return self._rand.randn()
        elif len(shape) == 1:
            return vector(block=self._rand.randn(shape[0]))
        else:
            return matrix(block=self._rand.randn(shape[0], shape[1]))
Exemplo n.º 24
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    def randn(self, *shape):
        """Produce normal-distributed numbers from the open interval :math:`(0,1)`

        arguments:

          :shape: the shape of the object to be returned:

                    - no argument: return a single number
                    - a single number: return a vector of the given length
                    - two numbers: return a matrix of the given number of rows and columns
        """

        if len(shape) == 0:
            return self._rand.randn()
        elif len(shape) == 1:
            return vector(block=self._rand.randn(shape[0]))
        else:
            return matrix(block=self._rand.randn(shape[0], shape[1]))
Exemplo n.º 25
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#
# Copyright (c) 2014 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

import numpy as np
from numpy import array
from vsip import vector, matrix
from vsip.math import reductions as red

a = np.arange(16, dtype=float)
v = vector(array=a)

assert array(red.meanval(v) == np.mean(a)).all()
assert array(red.maxval(v)[0] == np.max(a)).all()
assert array(red.minval(v)[0] == np.min(a)).all()
assert array(red.sumval(v) == np.sum(a)).all()
Exemplo n.º 26
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# ... and feed it some input ...
input = "Hello, World !"

inbuf = cl.buffer(cxt, 32)
outbuf = cl.buffer(cxt, 32)
queue = cl.default_queue()
queue.write(input, inbuf)
kernel(queue, 32, inbuf, outbuf)
output = queue.read_string(outbuf, len(input))

# ...finally test that the output is equal to the input
assert input == output
print 'copying string PASSED'

# Now try a copy operation on vectors
v1 = vector(array=numpy.arange(16, dtype=numpy.float32))
v2 = vector(length=16, dtype=numpy.float32)
v1_data = dda.dda(v1.block, dda.sync_in)
v2_data = dda.dda(v2.block, dda.sync_out)
kernel(queue, 32, v1_data.buf(), v2_data.buf())
# force the data back to the host
v2_data.sync_out();
assert v1 == v2
print 'copying buffers PASSED'

# now do the same using the wrapper
kernel = wrap(program.create_kernel("copy"), (1,0,1))
v2 = vector(length=16, dtype=numpy.float32)
v3 = vector(length=16, dtype=numpy.float32)
v1[:] = 1.
logging.info('copying v1->v2')
Exemplo n.º 27
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def ramp(dtype, start, increment, length):
    """Produce a vector whose values are :math:`v_i = start + increment * i`."""

    import numpy
    a = start + increment * numpy.arange(length, dtype=dtype)
    return vector(array=a)
Exemplo n.º 28
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#
# Copyright (c) 2013 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

from vsip import vector 
from vsip import matrix
from vsip.selgen.generation import ramp
from vsip.math import elementwise as elm
from vsip.signal import *
from vsip.signal.fft import *
import numpy as np
#from matplotlib.pyplot import *

v1 = ramp(float, 0, 0.1, 1024)
v1 = elm.sin(v1)
fwd_fft = fft(float, fwd, 1024, 1., 1, alg_hint.time)
inv_fft = fft(float, inv, 1024, 1./1024, 1, alg_hint.time)
v2 = vector(complex, 513)
fwd_fft(v1, v2)
v3 = vector(float, 1024)
inv_fft(v2, v3)

assert np.isclose(v1, v3).all()

#plot(v1)
#plot(v3)
#show()
Exemplo n.º 29
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#!/usr/bin/env python

""" Description
      Define simple step-function and display its fourier transform."""

from vsip import vector
from vsip.signal import *
from vsip.signal.fft import fft
from matplotlib.pyplot import *
import numpy

fft_fwd = fft(numpy.float64, fwd, 1000, 1., 0, alg_hint.time)
input = vector(numpy.float64, 1000)
input[100:] = 0
input[:100] = 1

output = vector(complex, 501)
fft_fwd(input, output)
plot(output)
show()
Exemplo n.º 30
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# ... and feed it some input ...
input = "Hello, World !"

inbuf = cl.buffer(cxt, 32)
outbuf = cl.buffer(cxt, 32)
queue = cl.default_queue()
queue.write(input, inbuf)
kernel(queue, 32, inbuf, outbuf)
output = queue.read_string(outbuf, len(input))

# ...finally test that the output is equal to the input
assert input == output
print 'copying string PASSED'

# Now try a copy operation on vectors
v1 = vector(array=numpy.arange(16, dtype=numpy.float32))
v2 = vector(length=16, dtype=numpy.float32)
v1_data = dda.dda(v1.block, dda.sync_in)
v2_data = dda.dda(v2.block, dda.sync_out)
kernel(queue, 32, v1_data.buf(), v2_data.buf())
# force the data back to the host
v2_data.sync_out()
assert v1 == v2
print 'copying buffers PASSED'

# now do the same using the wrapper
kernel = wrap(program.create_kernel("copy"), (1, 0, 1))
v2 = vector(length=16, dtype=numpy.float32)
v3 = vector(length=16, dtype=numpy.float32)
v1[:] = 1.
logging.info('copying v1->v2')
Exemplo n.º 31
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#
# Copyright (c) 2014 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

import numpy as np
from numpy import array
from vsip import vector, matrix
from vsip.math import elementwise as elm

a1 = np.arange(16, dtype=float)
v1 = vector(array=a1)


# Unary functions

assert array(elm.cos(v1) == np.cos(a1)).all()
assert array(elm.sin(v1) == np.sin(a1)).all()

a2 = np.arange(16, dtype=float)
v2 = vector(array=a2)

# Binary functions

assert array(elm.mul(v1, v2) == a1*a2).all()

a3 = np.arange(16, dtype=float)
v3 = vector(array=a3)
Exemplo n.º 32
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#! /usr/bin/env python

from numpy import array, arange
from vsip import vector
from vsip.cuda.compiler import SourceModule

# Create 1D float array
a = arange(8, dtype=float)
# Wrap it in a vector
input = vector(a)
print input.array()
output = vector(float, 8)

mod = SourceModule("""
    __global__ void doublify(float *in, float *out)
    {
      int idx = threadIdx.x + threadIdx.y*4;
      out[idx] = 2 * in[idx];
    }
    """)

func = mod.get_function("doublify")
func(input, output, block=(4,4,1))

print "original array:"
print input.array()
print "doubled with kernel:"
print output.array()
Exemplo n.º 33
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#! /usr/bin/env python

from numpy import array, arange
from vsip import vector
from vsip.cuda.compiler import SourceModule

# Create 1D float array
a = arange(8, dtype=float)
# Wrap it in a vector
input = vector(a)
print input.array()
output = vector(float, 8)

mod = SourceModule("""
    __global__ void doublify(float *in, float *out)
    {
      int idx = threadIdx.x + threadIdx.y*4;
      out[idx] = 2 * in[idx];
    }
    """)

func = mod.get_function("doublify")
func(input, output, block=(4, 4, 1))

print "original array:"
print input.array()
print "doubled with kernel:"
print output.array()
Exemplo n.º 34
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from numpy import array, arange
from vsip import vector

# Create vector from scratch
v = vector(float, 8)
# Access array
a = v.array()
v[0] = 3
# Make sure v and a are referring to the same memory.
assert v[0] == a[0] == 3

# Create 1D float array
a = arange(8, dtype=float)
# Wrap it in a vector
v = vector(a)
v[0] = 3
assert v[0] == a[0] == 3

# Test slicing
assert (v[1:3] == a[1:3]).all()
assert (v[1:5:2] == a[1:5:2]).all()
assert (v[5:1:-2] == a[5:1:-2]).all()
assert (v[1::2] == a[1::2]).all()
assert (v[:5:2] == a[:5:2]).all()
assert (v[1:-2:2] == a[1:-2:2]).all()
assert (v[-1::-2] == a[-1::-2]).all()
Exemplo n.º 35
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#!/usr/bin/env python

""" Description
      Define simple difference operator and convolve simple input with it."""

from vsip import vector
from vsip.signal import *
from vsip.signal.conv import convolution
from matplotlib.pyplot import *
import numpy

# define differentiation operator
K = vector(numpy.array([-1., 0., 1.]))
# define tics for the X axis
X = vector(numpy.arange(1024, dtype=numpy.float64))
# set up input array
input = vector(numpy.sin(X/numpy.float64(100.)))
# set up output array
output = vector(numpy.float64, 1022)
# create convolution object
conv = convolution(K, symmetry.none, 1024, 1, support_region.min, 0, alg_hint.time)
# run convolution
conv(input, output)
# scale
output *= numpy.float64(50.)
# plot input and output
plot(X, input)
plot(X[:1022], output)
show()
Exemplo n.º 36
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#
# Copyright (c) 2014 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

import numpy as np
from numpy import array
from vsip import vector, matrix
from vsip.math import reductions as red

a = np.arange(16, dtype=float)
v = vector(array=a)


assert array(red.meanval(v) == np.mean(a)).all()
assert array(red.maxval(v)[0] == np.max(a)).all()
assert array(red.minval(v)[0] == np.min(a)).all()
assert array(red.sumval(v) == np.sum(a)).all()

Exemplo n.º 37
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# Copyright (c) 2013 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

from vsip import vector
from vsip import matrix
from vsip.selgen.generation import ramp
from vsip.math.matvec import dot, prod
import numpy as np
from numpy import array as A

v1 = ramp(float, 0, 1, 8)
v2 = ramp(float, 1, 2, 8)
d = dot(v1, v2)
assert d == np.dot(A(v1), A(v2))

v1 = vector(array=np.arange(4, dtype=float))
m1 = matrix(array=np.arange(16, dtype=float).reshape(4, 4))
m2 = matrix(array=np.arange(16, dtype=float).reshape(4, 4))
# vector * matrix
v3 = prod(v1, m1)
assert (A(v3) == np.tensordot(A(v1), A(m1), (0, 0))).all()
# matrix * vector
v3 = prod(m1, v1)
assert (A(v3) == np.tensordot(A(m1), A(v1), (1, 0))).all()
# matrix * matrix
m3 = prod(m1, m2)
assert (A(m3) == np.tensordot(A(m1), A(m2), (1, 0))).all()
Exemplo n.º 38
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#!/usr/bin/env python
""" Description
      Define simple difference operator and convolve simple input with it."""

from vsip import vector
from vsip.signal import *
from vsip.signal.conv import convolution
from matplotlib.pyplot import *
import numpy

# define differentiation operator
K = vector(numpy.array([-1., 0., 1.]))
# define tics for the X axis
X = vector(numpy.arange(1024, dtype=numpy.float64))
# set up input array
input = vector(numpy.sin(X / numpy.float64(100.)))
# set up output array
output = vector(numpy.float64, 1022)
# create convolution object
conv = convolution(K, symmetry.none, 1024, 1, support_region.min, 0,
                   alg_hint.time)
# run convolution
conv(input, output)
# scale
output *= numpy.float64(50.)
# plot input and output
plot(X, input)
plot(X[:1022], output)
show()
Exemplo n.º 39
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#
# Copyright (c) 2014 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

import numpy as np
from numpy import array
from vsip import vector, matrix
from vsip.math import elementwise as elm

a1 = np.arange(16, dtype=float)
v1 = vector(array=a1)

# Unary functions

assert array(elm.cos(v1) == np.cos(a1)).all()
assert array(elm.sin(v1) == np.sin(a1)).all()

a2 = np.arange(16, dtype=float)
v2 = vector(array=a2)

# Binary functions

assert array(elm.mul(v1, v2) == a1 * a2).all()

a3 = np.arange(16, dtype=float)
v3 = vector(array=a3)

# Ternary functions
Exemplo n.º 40
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#
# Copyright (c) 2013 Stefan Seefeld
# All rights reserved.
#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

from numpy import array, arange
from vsip import vector

# Create vector from scratch
v = vector(float, 8)
# Access as array (by-reference)
a = array(v, copy=False)
v[0] = 3
# Make sure v and a are referring to the same memory.
assert v[0] == a[0] == 3

# Create 1D float array
a = arange(8, dtype=float)
# Wrap it in a vector
v = vector(array=a)
v[0] = 3
a[0] = 3
assert v[0] == 3

# Test slicing access
assert array(v[1:3] == a[1:3]).all()
assert array(v[1:5:2] == a[1:5:2]).all()
assert array(v[5:1:-2] == a[5:1:-2]).all()
assert array(v[1::2] == a[1::2]).all()
Exemplo n.º 41
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def ramp(dtype, start, increment, length):
    """Produce a vector whose values are :math:`v_i = start + increment * i`."""

    import numpy
    a = start + increment * numpy.arange(length, dtype=dtype)
    return vector(array=a)
Exemplo n.º 42
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#
# This file is part of OpenVSIP. It is made available under the
# license contained in the accompanying LICENSE.BSD file.

from vsip import vector 
from vsip import matrix
from vsip.selgen.generation import ramp
from vsip.math.matvec import dot, prod
import numpy as np
from numpy import array as A


v1 = ramp(float, 0, 1, 8)
v2 = ramp(float, 1, 2, 8)
d = dot(v1, v2)
assert d == np.dot(A(v1), A(v2))

v1 = vector(array=np.arange(4, dtype=float))
m1 = matrix(array=np.arange(16, dtype=float).reshape(4,4))
m2 = matrix(array=np.arange(16, dtype=float).reshape(4,4))
# vector * matrix
v3 = prod(v1, m1)
assert (A(v3) == np.tensordot(A(v1), A(m1), (0, 0))).all()
# matrix * vector
v3 = prod(m1, v1)
assert (A(v3) == np.tensordot(A(m1), A(v1), (1, 0))).all()
# matrix * matrix
m3 = prod(m1, m2)
assert (A(m3) == np.tensordot(A(m1), A(m2), (1, 0))).all()