numpy.ones

All Samples(5228) | Call(4776) | Derive(0) | Import(452)

Return a new array of given shape and type, filled with ones.

Please refer to the documentation for `zeros` for further details.

See Also
--------
zeros, ones_like

Examples
--------
>>> np.ones(5)
array([ 1.,  1.,  1.,  1.,  1.])

>>> np.ones((5,), dtype=np.int)
array([1, 1, 1, 1, 1])

>>> np.ones((2, 1))
array([[ 1.],
       [ 1.]])

>>> s = (2,2)
>>> np.ones(s)
array([[ 1.,  1.],
       [ 1.,  1.]])

Show Implementation

        def ones(shape, dtype=None, order='C'):
    """
    Return a new array of given shape and type, filled with ones.

    Please refer to the documentation for `zeros` for further details.

    See Also
    --------
    zeros, ones_like

    Examples
    --------
    >>> np.ones(5)
    array([ 1.,  1.,  1.,  1.,  1.])

    >>> np.ones((5,), dtype=np.int)
    array([1, 1, 1, 1, 1])

    >>> np.ones((2, 1))
    array([[ 1.],
           [ 1.]])

    >>> s = (2,2)
    >>> np.ones(s)
    array([[ 1.,  1.],
           [ 1.,  1.]])

    """
    a = empty(shape, dtype, order)
    try:
        a.fill(1)
        # Above is faster now after addition of fast loops.
        #a = zeros(shape, dtype, order)
        #a+=1
    except TypeError:
        obj = _maketup(dtype, 1)
        a.fill(obj)
    return a

src/p/y/pymc-HEAD/pymc/examples/gelman_bioassay.py pymc(Download)

from pymc import *
from numpy import ones, array
 
n = 5*ones(4,dtype=int)
dose=array([-.86,-.3,-.05,.73])
 
@stochastic

src/p/y/pymc-2.1beta/pymc/examples/gelman_bioassay.py pymc(Download)

from pymc import *
from numpy import ones, array
 
n = 5*ones(4,dtype=int)
dose=array([-.86,-.3,-.05,.73])
 
@stochastic

src/a/q/aqsis-HEAD/trunk/testing/prototypes/texfilt/downsample_test.py aqsis(Download)

import matplotlib.pylab as pylab
 
import numpy
from numpy import r_, size, zeros, ones, ceil, floor, array, linspace, meshgrid
 
import scipy
from scipy.signal import boxcar, convolve2d as conv2

def boxKer(width, scale):
	'''
	Trivial box filter kernel
	'''
	return ones(width+1)

def imConv(im, ker, conv2Func=conv2):
	'''
	Convolve an image with the given filter kernel
	'''
	res = zeros(im.shape)
	normalisation = conv2Func(ones(im.shape[0:2],'d'), ker, 'full')
	# compute how much of the full convolution to trim from top left and bottom right

	mipSize = list(mipmap[0].shape)
	mipSize[dim] = len
 
	mipmapImg = ones(mipSize)*0.7
	pos = 0
	for mipLevel in mipmap:
		if dim == 0:

src/a/q/aqsis-HEAD/testing/prototypes/texfilt/downsample_test.py aqsis(Download)

import matplotlib.pylab as pylab
 
import numpy
from numpy import r_, size, zeros, ones, ceil, floor, array, linspace, meshgrid
 
import scipy
from scipy.signal import boxcar, convolve2d as conv2

def boxKer(width, scale):
	'''
	Trivial box filter kernel
	'''
	return ones(width+1)

def imConv(im, ker, conv2Func=conv2):
	'''
	Convolve an image with the given filter kernel
	'''
	res = zeros(im.shape)
	normalisation = conv2Func(ones(im.shape[0:2],'d'), ker, 'full')
	# compute how much of the full convolution to trim from top left and bottom right

	mipSize = list(mipmap[0].shape)
	mipSize[dim] = len
 
	mipmapImg = ones(mipSize)*0.7
	pos = 0
	for mipLevel in mipmap:
		if dim == 0:

src/p/y/pyfusion-HEAD/examples/Boyds/wid_specgram.py pyfusion(Download)

"""
from matplotlib.widgets import RadioButtons, Button
import pylab as pl
from numpy import sin, pi, ones, hanning, hamming, bartlett, kaiser, arange, blackman, cos, sqrt, log10, fft
 
import pyfusion

def local_none(vec):
    return(ones(len(vec)))
 
def local_hanning(vec):
    return(hanning(len(vec)))
 
def local_hamming(vec):

src/b/i/BIP-0.5.2/BIP/Bayes/Samplers/MCMC.py BIP(Download)

 
import numpy as np
from liveplots.xmlrpcserver import rpc_plot
from numpy import array, mean,isnan,  nan_to_num, var, sqrt, inf, exp, greater, less, identity, ones, zeros, floor, log, recarray, nan
from numpy.random import random,  multivariate_normal,  multinomial,  rand
from scipy.stats import cov,  uniform, norm, scoreatpercentile

        end = relevantHistoryEnd
        N = end - start
        if N==0:
            self._R = np.inf*np.ones(self.nchains)
            return
        N = min(min([len(self.seqhist[c]) for c in range(self.nchains)]), N)
        seq = [self.seqhist[c][-N:] for c in range(self.nchains)]

        #self.sequenceHistories = np.zeros((self.nchains, self.dimensions, self.maxChainDraws))
        # initialize the temporary storage vectors
        self.currentVectors = zeros((self.nchains, self.dimensions))
        self.currentLiks = ones(self.nchains)*-inf
        self.scaling_factor = 2.38/sqrt(2*DEpairs*self.dimensions)
        self.setup_xmlrpc_plotserver()

                for d in range(delta):
                    d1, d2 = sample(others, 2)
                    dif+=array(d1)-array(d2)
                zi = array(proptheta[c])+(ones(self.dimensions)+e)*gam*dif+eps
                #revert offlimits proposals
                for i in xrange(len(zi)):
                    if zi[i]<= self.parlimits[i][0] or zi[i]>= self.parlimits[i][1]:# or isnan(zi):

src/s/c/scipy-HEAD/scipy/weave/size_check.py scipy(Download)

from numpy import ones, ndarray, array, asarray, concatenate, zeros, shape, \
         alltrue, equal, divide, arccos, arcsin, arctan, cos, cosh, \
         sin, sinh, exp, ceil, floor, fabs, log, log10, sqrt, argmin, \
         argmax, argsort, around, absolute, sign, negative, float32
 
import sys

def time_it():
    import time
 
    expr = "ex[:,1:,1:] =   ca_x[:,1:,1:] * ex[:,1:,1:]" \
                         "+ cb_y_x[:,1:,1:] * (hz[:,1:,1:] - hz[:,:-1,1:])" \
                         "- cb_z_x[:,1:,1:] * (hy[:,1:,1:] - hy[:,1:,:-1])"
    ex = ones((10,10,10),dtype=float32)
    ca_x = ones((10,10,10),dtype=float32)
    cb_y_x = ones((10,10,10),dtype=float32)
    cb_z_x = ones((10,10,10),dtype=float32)
    hz = ones((10,10,10),dtype=float32)
    hy = ones((10,10,10),dtype=float32)

        return asarray(x),asarray(y)
    else:
        diff = abs(Nx - Ny)
        front = ones(diff, int)
        if Nx > Ny:
            return asarray(x), concatenate((front,y))
        elif Ny > Nx:

src/p/y/pyfusion-HEAD/examples/Boyds/plot_bar_fs_list.py pyfusion(Download)

def plot_bar_fs_list(fs_list, orientation='horizontal', width=None, hold=1):
    """ accept a flucstruc list and plot as an overlapped bar
    width is the bar width relative to unity.
    """ 
    import pylab as pl
    from numpy import zeros, ones, arange, array
#data=exp(-0.5*arange(10))

    for (i,fs) in enumerate(fs_list):
        if fs.svd.id!=fs0.svd.id: ec0[i]='gray'
 
    wid0=width*ones(len(data))
    if orientation=='horizontal': (wid0,data)=(data,wid0)
    pl.bar(left,data,wid0, bottom, color='c', hold=hold, edgecolor=ec0, **com_kw)

src/p/y/pymc-HEAD/pymc/examples/model_4.py pymc(Download)

"""
 
from pymc import *
from numpy import array, ones, append
from numpy.random import randint
 
__all__ = ['disasters_array', 'switchpoint', 'early_mean', 'late_mean', 'disasters']

# Define data and stochastics
 
switchpoint = DiscreteUniform('switchpoint',lower=0,upper=110)
means = Exponential('means',beta=ones(2))
 
@stochastic(observed=True, dtype=int)
def disasters(  value = disasters_array,

src/s/c/scipy-0.8.0/scipy/weave/size_check.py scipy(Download)

from numpy import ones, ndarray, array, asarray, concatenate, zeros, shape, \
         alltrue, equal, divide, arccos, arcsin, arctan, cos, cosh, \
         sin, sinh, exp, ceil, floor, fabs, log, log10, sqrt, argmin, \
         argmax, argsort, around, absolute, sign, negative, float32
 
import sys

def time_it():
    import time
 
    expr = "ex[:,1:,1:] =   ca_x[:,1:,1:] * ex[:,1:,1:]" \
                         "+ cb_y_x[:,1:,1:] * (hz[:,1:,1:] - hz[:,:-1,1:])" \
                         "- cb_z_x[:,1:,1:] * (hy[:,1:,1:] - hy[:,1:,:-1])"
    ex = ones((10,10,10),dtype=float32)
    ca_x = ones((10,10,10),dtype=float32)
    cb_y_x = ones((10,10,10),dtype=float32)
    cb_z_x = ones((10,10,10),dtype=float32)
    hz = ones((10,10,10),dtype=float32)
    hy = ones((10,10,10),dtype=float32)

        return asarray(x),asarray(y)
    else:
        diff = abs(Nx - Ny)
        front = ones(diff, int)
        if Nx > Ny:
            return asarray(x), concatenate((front,y))
        elif Ny > Nx:

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