Python numpy.resize函数代码示例

    def genImg(self,xml,compressionLevel):
        xml = re.split('\n',xml)
        img_data = re.split('"',xml[1])

        type = img_data[1]
        size = [int(x) for x in img_data[3].split(',')]
        compressed = img_data[5]
        pixels = xml[2]

        pixels = base64.b64decode(pixels)       # decode base 64 encoding

        if compressed == 'True':
            pixels = zlib.decompress(pixels)    # if data was compressed, decompress it

        pixels = list(pixels)                   # converting byte data into a list, which will give us the actual numbers of ndarray(i.e. the image)

        if type == 'Gray':                      # Based on image type, reconstruct numpy.ndarray
            return numpy.resize(pixels,tuple(size))

        else:
            r = pixels[:size[0]*size[1]]
            g = pixels[size[0]*size[1]:2*size[0]*size[1]]
            b = pixels[2*size[0]*size[1]:3*size[0]*size[1]]
            image = []
            for i in range(size[0] * size[1]):
                image.append(r[i])
                image.append(g[i])
                image.append(b[i])

            size.append(3)
            return numpy.resize(image,tuple(size))

def batchsd(trace, batches=5):
    """
    Calculates the simulation standard error, accounting for non-independent
    samples. The trace is divided into batches, and the standard deviation of
    the batch means is calculated.
    """

    if len(np.shape(trace)) > 1:

        dims = np.shape(trace)
        # ttrace = np.transpose(np.reshape(trace, (dims[0], sum(dims[1:]))))
        ttrace = np.transpose([t.ravel() for t in trace])

        return np.reshape([batchsd(t, batches) for t in ttrace], dims[1:])

    else:
        if batches == 1:
            return np.std(trace) / np.sqrt(len(trace))

        try:
            batched_traces = np.resize(trace, (batches, len(trace) / batches))
        except ValueError:
            # If batches do not divide evenly, trim excess samples
            resid = len(trace) % batches
            batched_traces = np.resize(trace[:-resid], 
                (batches, len(trace[:-resid]) / batches))

        means = np.mean(batched_traces, 1)

        return np.std(means) / np.sqrt(batches)

 def aabut (source, *args):
    """
Like the |Stat abut command.  It concatenates two arrays column-wise
and returns the result.  CAUTION:  If one array is shorter, it will be
repeated until it is as long as the other.

Usage:   aabut (source, args)    where args=any # of arrays
Returns: an array as long as the LONGEST array past, source appearing on the
         'left', arrays in <args> attached on the 'right'.
"""
    if len(source.shape)==1:
        width = 1
        source = N.resize(source,[source.shape[0],width])
    else:
        width = source.shape[1]
    for addon in args:
        if len(addon.shape)==1:
            width = 1
            addon = N.resize(addon,[source.shape[0],width])
        else:
            width = source.shape[1]
        if len(addon) < len(source):
            addon = N.resize(addon,[source.shape[0],addon.shape[1]])
        elif len(source) < len(addon):
            source = N.resize(source,[addon.shape[0],source.shape[1]])
        source = N.concatenate((source,addon),1)
    return source

def mc_error(x, batches=5):
    """
    Calculates the simulation standard error, accounting for non-independent
    samples. The trace is divided into batches, and the standard deviation of
    the batch means is calculated.

    :Arguments:
      x : Numpy array
          An array containing MCMC samples
      batches : integer
          Number of batchas
    """

    if x.ndim > 1:

        dims = np.shape(x)
        #ttrace = np.transpose(np.reshape(trace, (dims[0], sum(dims[1:]))))
        trace = np.transpose([t.ravel() for t in x])

        return np.reshape([mc_error(t, batches) for t in trace], dims[1:])

    else:
        if batches == 1: return np.std(x)/np.sqrt(len(x))

        try:
            batched_traces = np.resize(x, (batches, len(x)/batches))
        except ValueError:
            # If batches do not divide evenly, trim excess samples
            resid = len(x) % batches
            batched_traces = np.resize(x[:-resid], (batches, len(x)/batches))

        means = np.mean(batched_traces, 1)

        return np.std(means)/np.sqrt(batches)

def draw_lnm_samples(**kwargs):
    ''' Draw samples for uniform-in-log model

        Parameters
        ----------
        **kwargs: string
           Keyword arguments as model parameters and number of samples

        Returns
        -------
        array
           The first mass
        array
           The second mass
    '''

    #PDF doesnt match with sampler
    nsamples = kwargs.get('nsamples', 1)
    min_mass = kwargs.get('min_mass', 5.)
    max_mass = kwargs.get('max_mass', 95.)
    max_mtotal = min_mass + max_mass
    lnmmin = log(min_mass)
    lnmmax = log(max_mass)

    k = nsamples * int(1.5 + log(1 + 100./nsamples))
    aa = np.exp(np.random.uniform(lnmmin, lnmmax, k))
    bb = np.exp(np.random.uniform(lnmmin, lnmmax, k))

    idx = np.where(aa + bb < max_mtotal)
    m1, m2 = (np.maximum(aa, bb))[idx], (np.minimum(aa, bb))[idx]

    return np.resize(m1, nsamples), np.resize(m2, nsamples)

def test(npoints):
    xx = numpy.arange(npoints)
    xx=numpy.resize(xx,(npoints,1))
    #yy = 1000.0 * exp (- 0.5 * (xx * xx) /15)+ 2.0 * xx + 10.5
    yy = gauss([10.5,2,1000.0,20.,15],xx)
    yy=numpy.resize(yy,(npoints,1))
    sy = numpy.sqrt(abs(yy))
    sy=numpy.resize(sy,(npoints,1))
    data = numpy.concatenate((xx, yy, sy),1)
    parameters = [0.0,1.0,900.0, 25., 10]
    stime = time.time()
    if 0:
        #old fashion
        fittedpar, chisq, sigmapar = LeastSquaresFit(gauss,parameters,data)
    else:
        #easier to handle
        fittedpar, chisq, sigmapar = LeastSquaresFit(gauss,parameters,
                                                     xdata=xx.reshape((-1,)),
                                                     ydata=yy.reshape((-1,)),
                                                     sigmadata=sy.reshape((-1,)))
    etime = time.time()
    print("Took ",etime - stime, "seconds")
    print("chi square  = ",chisq)
    print("Fitted pars = ",fittedpar)
    print("Sigma pars  = ",sigmapar)

    def __init__(self, name, geometry, order, init_context=True):
        LagrangeSimplexPolySpace.__init__(self, name, geometry, order,
                                          init_context=False)

        nodes, nts, node_coors = self.nodes, self.nts, self.node_coors

        shape = [nts.shape[0] + 1, 2]
        nts = nm.resize(nts, shape)
        nts[-1,:] = [3, 0]

        shape = [nodes.shape[0] + 1, nodes.shape[1]]
        nodes = nm.resize(nodes, shape)
        # Make a 'hypercubic' (cubic in 2D) node.
        nodes[-1,:] = 1

        n_v = self.geometry.n_vertex
        tmp = nm.ones((n_v,), nm.int32)

        node_coors = nm.vstack((node_coors,
                                nm.dot(tmp, self.geometry.coors) / n_v))

        self.nodes, self.nts = nodes, nts
        self.node_coors = nm.ascontiguousarray(node_coors)

        self.bnode = nodes[-1:,:]

        self.n_nod = self.nodes.shape[0]

        if init_context:
            self.eval_ctx = self.create_context(None, 0, 1e-15, 100, 1e-8,
                                                tdim=n_v - 1)

        else:
            self.eval_ctx = None

 def test_QuaternionClass(self):        
     v1 = np.array([0.2, 0.2, 0.4])
     v2 = np.array([1, 0, 0])         
     q1 = Quaternion.q_exp(v1)
     q2 = Quaternion.q_exp(v2)
     v=np.array([1, 2, 3])
     # Testing Mult and rotate
     np.testing.assert_almost_equal(Quaternion.q_rotate(Quaternion.q_mult(q1,q2),v), Quaternion.q_rotate(q1,Quaternion.q_rotate(q2,v)), decimal=7)
     np.testing.assert_almost_equal(Quaternion.q_rotate(q1,v2), np.resize(Quaternion.q_toRotMat(q1),(3,3)).dot(v2), decimal=7)
     # Testing Boxplus, Boxminus, Log and Exp
     np.testing.assert_almost_equal(Quaternion.q_boxPlus(q1,Quaternion.q_boxMinus(q2,q1)), q2, decimal=7)
     np.testing.assert_almost_equal(Quaternion.q_log(q1), v1, decimal=7)
     # Testing Lmat and Rmat
     np.testing.assert_almost_equal(Quaternion.q_mult(q1,q2), Quaternion.q_Lmat(q1).dot(q2), decimal=7)
     np.testing.assert_almost_equal(Quaternion.q_mult(q1,q2), Quaternion.q_Rmat(q2).dot(q1), decimal=7)
     # Testing ypr and quat
     roll = 0.2
     pitch = -0.5
     yaw = 2.5
     q_test = Quaternion.q_mult(np.array([np.cos(0.5*pitch), 0, np.sin(0.5*pitch), 0]),np.array([np.cos(0.5*yaw), 0, 0, np.sin(0.5*yaw)]))
     q_test = Quaternion.q_mult(np.array([np.cos(0.5*roll), np.sin(0.5*roll), 0, 0]),q_test)
     np.testing.assert_almost_equal(Quaternion.q_toYpr(q_test), np.array([roll, pitch, yaw]), decimal=7)
     # Testing Jacobian of Ypr
     for i in np.arange(0,3):
         dv1 = np.array([0.0, 0.0, 0.0])
         dv1[i] = 1.0
         epsilon = 1e-6
         ypr1 = Quaternion.q_toYpr(q1)
         ypr1_dist = Quaternion.q_toYpr(Quaternion.q_boxPlus(q1,dv1*epsilon))
         dypr1_1 = (ypr1_dist-ypr1)/epsilon
         J = np.resize(Quaternion.q_toYprJac(q1),(3,3))
         dypr1_2 = J.dot(dv1)
         np.testing.assert_almost_equal(dypr1_1,dypr1_2, decimal=5)

def explicitmidpoint(ode, vardict, soln, h, relerr):
    """
    Implementation of the Explicit Midpoint method.
    """
    eqnum = len(ode)
    dim = [eqnum, 2]
    dim.extend(soln[0][0].shape)
    dim = tuple(dim)
    if numpy.iscomplexobj(soln[0]):
        aux = numpy.resize([0. + 0j], dim)
    else:
        aux = numpy.resize([0.], dim)
    dim = soln[0][0].shape
    for vari in range(eqnum):
        vardict.update({'y_{}'.format(vari): soln[vari][-1]})
    for vari in range(eqnum):
        aux[vari][0] = numpy.resize([seval(ode[vari], **vardict) * h[0] + soln[vari][-1]], dim)
    for vari in range(eqnum):
        vardict.update({"y_{}".format(vari): aux[vari][0]})
    vardict.update({'t': vardict['t'] + 0.5 * h[0]})
    for vari in range(eqnum):
        aux[vari][0] = numpy.resize([seval(ode[vari], **vardict)], dim)
    for vari in range(eqnum):
        vardict.update({"y_{}".format(vari): numpy.array(soln[vari][-1] + h[0] * aux[vari][0])})
        pt = soln[vari]
        kt = numpy.array([vardict['y_{}'.format(vari)]])
        soln[vari] = numpy.concatenate((pt, kt))
    vardict.update({'t': vardict['t'] + 0.5 * h[0]})

 def append(self, value):
     # convert the appended object to an array if it starts as something else
     if type(value) is not np.ndarray:
         value = np.array(value)
     # add the data
     if value.ndim == 1:
         # adding a single row of data
         n = self.__n
         if n + 1 > self.__N:
             # need to allocate more memory
             self.__N += self.__n_grow
             self.__data = np.resize(self.__data, (self.__N, self.__cols))
         self.__data[n] = value
         self.__n = n + 1
     elif value.ndim == 2:
         # adding multiple rows of data
         # avoid loops for appending large arrays
         n = self.__n
         L = value.shape[0]
         N_needed = n + L - self.__N
         if N_needed > 0:
             # need to allocate more memory
             self.__N += (N_needed / self.__n_grow + 1) * self.__n_grow
             self.__data = np.resize(self.__data, (self.__N, self.__cols))
         self.__data[n:n+L] = value
         self.__n += L

def initialize(dx, x_shore, eta_shore, eta_toe, S_d, S_b, S, Q_w, B0):
    """Initialize the variables for the simulation.

    Args:

    Returns: 
        eta_b : array of z-coordinate of the basement at every node. Has the
                same size as dx. [ L ] 

    Comments: 
    """
    # First thing we should do is specify our computational domain.
    N, dx_shore = nodes_in_domain(x_shore, dx)
    N_old = N.copy()
    dx = init_domain(N, dx_shore, dx)
    x_shore = x(dx)[-1]
    # Then we compute the basement elevation and the location of the delta toe.
    eta_b = np.resize(eta_toe, N)
    eta_b, x_toe = init_basement(dx, eta_shore, eta_toe, eta_b, S_d, S_b)
    # Next, we compute the initial fluvial profile
    eta, S = init_flumen(dx, eta_shore, S)
    # Instantiate a water-depth array for the domain.
    H = np.zeros_like(dx)
    # Compute unit flow rate
    qw = unit_flowrate(Q_w, B0) # [ L**2 / T ]
    # Compute critical depth for the section.
    Hc = critical_flow(qw)
    # Redefine B0 as a vector, to have that information on every node.    
    B0 = np.resize(B0, N)
    # Instantiate a sediment transport capacity array for the domain
    qt = np.zeros_like(dx)
    return (N, N_old, dx_shore, dx, x_shore, eta_b, x_toe, eta, S, H, Hc, qw,
            B0, qt)

def _wrap(vector, pad_tuple, iaxis, kwargs):
    '''
    Private function to calculate the before/after vectors for pad_wrap.

    Parameters
    ----------
    vector : ndarray
        Input vector that already includes empty padded values.
    pad_tuple : tuple
        This tuple represents the (before, after) width of the padding
        along this particular iaxis.
    iaxis : int
        The axis currently being looped across.  Not used in _wrap.
    kwargs : keyword arguments
        Keyword arguments. Not used in _wrap.

    Return
    ------
    _wrap : ndarray
        Padded vector
    '''
    if pad_tuple[1] == 0:
        after_vector = vector[pad_tuple[0]:None]
    else:
        after_vector = vector[pad_tuple[0]:-pad_tuple[1]]

    before_vector = np.resize(after_vector[::-1], pad_tuple[0])[::-1]
    after_vector = np.resize(after_vector, pad_tuple[1])

    return _create_vector(vector, pad_tuple, before_vector, after_vector)

def cube():
    """
    Build vertices for a colored cube.

    V  is the vertices
    I1 is the indices for a filled cube (use with GL_TRIANGLES)
    I2 is the indices for an outline cube (use with GL_LINES)
    """
    vtype = [('a_position', np.float32, 3),
             ('a_normal'  , np.float32, 3),
             ('a_color',    np.float32, 4)] 
    # Vertices positions
    v = [ [ 1, 1, 1],  [-1, 1, 1],  [-1,-1, 1], [ 1,-1, 1],
          [ 1,-1,-1],  [ 1, 1,-1],  [-1, 1,-1], [-1,-1,-1] ]
    # Face Normals
    n = [ [ 0, 0, 1],  [ 1, 0, 0],  [ 0, 1, 0] ,
          [-1, 0, 1],  [ 0,-1, 0],  [ 0, 0,-1] ]
    # Vertice colors
    c = [ [0, 1, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 1, 0, 1],
          [1, 1, 0, 1], [1, 1, 1, 1], [1, 0, 1, 1], [1, 0, 0, 1] ];

    V =  np.array([(v[0],n[0],c[0]), (v[1],n[0],c[1]), (v[2],n[0],c[2]), (v[3],n[0],c[3]),
                   (v[0],n[1],c[0]), (v[3],n[1],c[3]), (v[4],n[1],c[4]), (v[5],n[1],c[5]),
                   (v[0],n[2],c[0]), (v[5],n[2],c[5]), (v[6],n[2],c[6]), (v[1],n[2],c[1]),
                   (v[1],n[3],c[1]), (v[6],n[3],c[6]), (v[7],n[3],c[7]), (v[2],n[3],c[2]),
                   (v[7],n[4],c[7]), (v[4],n[4],c[4]), (v[3],n[4],c[3]), (v[2],n[4],c[2]),
                   (v[4],n[5],c[4]), (v[7],n[5],c[7]), (v[6],n[5],c[6]), (v[5],n[5],c[5]) ],
                  dtype = vtype)
    I1 = np.resize( np.array([0,1,2,0,2,3], dtype=np.uint32), 6*(2*3))
    I1 += np.repeat( 4*np.arange(2*3), 6)

    I2 = np.resize( np.array([0,1,1,2,2,3,3,0], dtype=np.uint32), 6*(2*4))
    I2 += np.repeat( 4*np.arange(6), 8)

    return V, I1, I2

    def plot_image(self, image, nb_repeat=40, show_plot=True):
        """Plot augmented variations of an image.

        This method takes an image and plots it by default in 40 differently
        augmented versions.

        This method is intended to visualize the strength of your chosen
        augmentations (so for debugging).

        Args:
            image: The image to plot.
            nb_repeat: How often to plot the image. Each time it is plotted,
                the chosen augmentation will be different. (Default: 40).
            show_plot: Whether to show the plot. False makes sense if you
                don't have a graphical user interface on the machine.
                (Default: True)

        Returns:
            The figure of the plot.
            Use figure.savefig() to save the image.
        """
        if len(image.shape) == 2:
            images = np.resize(image, (nb_repeat, image.shape[0], image.shape[1]))
        else:
            images = np.resize(image, (nb_repeat, image.shape[0], image.shape[1],
                               image.shape[2]))
        return self.plot_images(images, True, show_plot=show_plot)

def _assignInitialPoints(cvmat,S):
    h,w,c = cvmat.shape
    
    # Compute the max grid assignment
    nx = w/S
    ny = h/S

    # Compute the super pixel x,y grid
    xgrid = np.arange(nx).reshape(1,nx)*np.ones(ny,dtype=np.int).reshape(ny,1)
    ygrid = np.arange(ny).reshape(ny,1)*np.ones(nx,dtype=np.int).reshape(1,nx)
    
    # compute an x,y lookup to a label look up
    label_map = nx*ygrid + xgrid    
    
    # Compute the x groups in pixel space
    tmp = np.arange(nx)
    tmp = np.resize(tmp,(w,))
    xgroups = tmp[tmp.argsort()]

    # Compute the y groups in pixel space
    tmp = np.arange(ny)
    tmp = np.resize(tmp,(h,))
    ygroups = tmp[tmp.argsort()]

    labels = np.zeros((h,w),dtype=np.int)
    
    for x in range(w):
        for y in range(h):
            labels[y,x] = label_map[ygroups[y],xgroups[x]]

    return label_map,xgroups,ygroups,labels