python类resize()的实例源码

def readFlow(fn):
    """ Read .flo file in Middlebury format"""
    # Code adapted from:
    # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy

    # WARNING: this will work on little-endian architectures (eg Intel x86) only!
    with open(fn, 'rb') as f:
        magic = np.fromfile(f, np.float32, count=1)
        if 202021.25 != magic:
            print 'Magic number incorrect. Invalid .flo file'
            return None
        else:
            w = np.fromfile(f, np.int32, count=1)
            h = np.fromfile(f, np.int32, count=1)
            #print 'Reading %d x %d flo file' % (w, h)
            data = np.fromfile(f, np.float32, count=2*w*h)
            # Reshape data into 3D array (columns, rows, bands)
            return np.resize(data, (h, w, 2))

def readFlow(fn):
    """ Read .flo file in Middlebury format"""
    # Code adapted from:
    # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy

    # WARNING: this will work on little-endian architectures (eg Intel x86) only!
    with open(fn, 'rb') as f:
        magic = np.fromfile(f, np.float32, count=1)
        if 202021.25 != magic:
            print 'Magic number incorrect. Invalid .flo file'
            return None
        else:
            w = np.fromfile(f, np.int32, count=1)
            h = np.fromfile(f, np.int32, count=1)
            #print 'Reading %d x %d flo file' % (w, h)
            data = np.fromfile(f, np.float32, count=2*w*h)
            # Reshape data into 3D array (columns, rows, bands)
            # The reshape here is for visualization, the original code is (w,h,2)
            return np.resize(data, (h, w, 2))

def read_flow(filename):
    """
    read optical flow from Middlebury .flo file
    :param filename: name of the flow file
    :return: optical flow data in matrix
    """
    f = open(filename, 'rb')
    magic = np.fromfile(f, np.float32, count=1)
    data2d = None

    if 202021.25 != magic:
        print 'Magic number incorrect. Invalid .flo file'
    else:
        w = np.fromfile(f, np.int32, count=1)
        h = np.fromfile(f, np.int32, count=1)
        print "Reading %d x %d flo file" % (h, w)
        data2d = np.fromfile(f, np.float32, count=2 * w * h)
        # reshape data into 3D array (columns, rows, channels)
        data2d = np.resize(data2d, (h[0], w[0], 2))
    f.close()
    return data2d

def label_preproc(label_string):
    """
    This function is supposed to prepare the label so that it fits the standard of the rnn_ctc network.
    It computes following steps:
    1. make list of integers out of string    e.g. [hallo] --> [8,1,12,12,15]
    :param label_string: a string of the label
    :return: label_int: the string represented in integers
    """
    chars = char_alpha.chars

    label_int = []

    for letter in label_string:
        label_int.append(chars.index(letter))

    label_int_arr = np.resize(np.asarray(label_int), (1, len(label_int)))

    # print(label_int_arr.shape)

    return label_int_arr

def squeeze(image, width_max, border):
    """
    This function squeezes images in the width.
    :param image: Numpy array of image
    :param width_max: max image width
    :param border: border left and right of squeezed image
    :return: Squeezed Image
    """
    image_wd = image.shape[1]
    image_ht = image.shape[0]
    basewidth = width_max - border

    wpercent = basewidth / image_wd
    dim = (int(wpercent*image_wd), image_ht)
    img_squeeze = cv.resize(image, dim, interpolation=cv.INTER_NEAREST)
    return img_squeeze

def string_to_array(label_string):
    """
    This function converts string into integers. e.g. [hallo] --> [8,1,12,12,15]
    :param label_string: a string of the label
    :return: label_int: the string represented in integers
    """
    chars = char_alpha.chars

    label_int = []

    for letter in label_string:
        label_int.append(chars.index(letter))

    label_int_arr = np.resize(np.asarray(label_int), (1, len(label_int)))

    # print(label_int_arr.shape)

    return label_int_arr

def plot_Reynolds_number(Fr, ReyNum, edge):

    # figr = Fr.plot_fracture("complete", "footPrint")
    # ax = figr.axes[0]
    figr = plt.figure()
    ax = figr.add_subplot(111)
    ReMesh = np.resize(ReyNum[edge, :], (Fr.mesh.ny, Fr.mesh.nx))
    x = np.linspace(-Fr.mesh.Lx, Fr.mesh.Lx, Fr.mesh.nx)
    y = np.linspace(-Fr.mesh.Ly, Fr.mesh.Ly, Fr.mesh.ny)
    xv, yv = np.meshgrid(x, y)
    # cax = ax.contourf(xv, yv, ReMesh, levels=[0, 100, 2100, 10000])
    cax = ax.matshow(ReMesh)
    figr.colorbar(cax)
    plt.title("Reynolds number")
    plt.show()

    return figr

#-----------------------------------------------------------------------------------------------------------------------

def MakeVisual( X_src, X_tar): 
    #LAB pair
    #pdb.set_trace()
    #X_rst = np.zeros( X_src.shape, np.float32)
    #for i in range( X_src.shape[0]):
    #    X_rst[i,:,:,:] = np.concatenate(
    #                    (np.resize( X_src[i,:,:,:], (1,nc,npx,npx/2)),
    #                      np.resize( X_tar[i,:,:,:], (1,nc,npx,npx/2))), axis =3 )


    X_src = np.resize(X_src,(X_src.shape[0],nc,npx,npx/2))
    X_tar = np.resize(X_tar,(X_tar.shape[0],nc,npx,npx/2))

    return X_tar
    #return np.concatenate( (X_src,X_tar), axis = 2) 


# SET PARAMETERS.

def MakeVisual( X_src, X_tar): 
    #LAB pair
    #pdb.set_trace()
    #X_rst = np.zeros( X_src.shape, np.float32)
    #for i in range( X_src.shape[0]):
    #    X_rst[i,:,:,:] = np.concatenate(
    #                    (np.resize( X_src[i,:,:,:], (1,nc,npx,npx/2)),
    #                      np.resize( X_tar[i,:,:,:], (1,nc,npx,npx/2))), axis =3 )


    X_src = np.resize(X_src,(X_src.shape[0],nc,npx,npx/2))
    X_tar = np.resize(X_tar,(X_tar.shape[0],nc,npx,npx/2))

    return X_tar
    #return np.concatenate( (X_src,X_tar), axis = 2) 


# SET PARAMETERS.

def MakeVisual( X_src, X_tar): 
    #LAB pair
    #pdb.set_trace()
    #X_rst = np.zeros( X_src.shape, np.float32)
    #for i in range( X_src.shape[0]):
    #    X_rst[i,:,:,:] = np.concatenate(
    #                    (np.resize( X_src[i,:,:,:], (1,nc,npx,npx/2)),
    #                      np.resize( X_tar[i,:,:,:], (1,nc,npx,npx/2))), axis =3 )


    X_src = np.resize(X_src,(X_src.shape[0],nc,npx,npx/2))
    X_tar = np.resize(X_tar,(X_tar.shape[0],nc,npx,npx/2))

    return X_tar
    #return np.concatenate( (X_src,X_tar), axis = 2) 


# SET PARAMETERS.

def test_mask_broadcast(self):
        # GH 8801
        # copied from test_where_broadcast
        for size in range(2, 6):
            for selection in [
                    # First element should be set
                    np.resize([True, False, False, False, False], size),
                    # Set alternating elements]
                    np.resize([True, False], size),
                    # No element should be set
                    np.resize([False], size)]:
                for item in [2.0, np.nan, np.finfo(np.float).max,
                             np.finfo(np.float).min]:
                    for arr in [np.array([item]), [item], (item, )]:
                        data = np.arange(size, dtype=float)
                        s = Series(data)
                        result = s.mask(selection, arr)
                        expected = Series([item if use_item else data[
                            i] for i, use_item in enumerate(selection)])
                        assert_series_equal(result, expected)

def resize(self, newshape, refcheck=True, order=False):
        """
        .. warning::

            This method does nothing, except raise a ValueError exception. A
            masked array does not own its data and therefore cannot safely be
            resized in place. Use the `numpy.ma.resize` function instead.

        This method is difficult to implement safely and may be deprecated in
        future releases of NumPy.

        """
        # Note : the 'order' keyword looks broken, let's just drop it
        errmsg = "A masked array does not own its data "\
                 "and therefore cannot be resized.\n" \
                 "Use the numpy.ma.resize function instead."
        raise ValueError(errmsg)

def test_wahba():
    lat = 45
    Cnb = dcm.from_hpr(45, -30, 60)

    dt = 1e-1
    n = 1000

    gyro = np.array([0, 1 / np.sqrt(2), 1 / np.sqrt(2)]) * earth.RATE * dt
    gyro = Cnb.T.dot(gyro)
    gyro = np.resize(gyro, (n, 3))

    accel = np.array([0, 0, earth.gravity(1 / np.sqrt(2))]) * dt
    accel = Cnb.T.dot(accel)
    accel = np.resize(accel, (n, 3))

    np.random.seed(0)
    gyro += 1e-6 * np.random.randn(*gyro.shape) * dt
    accel += 1e-4 * np.random.randn(*accel.shape) * dt

    phi, dv = coning_sculling(gyro, accel)
    hpr, P = align.align_wahba(dt, phi, dv, lat)

    assert_allclose(hpr, [45, -30, 60], rtol=1e-3)

def _verify_param(param, name, only_positive=False):
        if param is None:
            return None

        param = np.asarray(param)
        if param.ndim == 0:
            param = np.resize(param, 3)
        if param.shape != (3,):
            raise ValueError("`{}` might be float or array with "
                             "3 elements.".format(name))
        if only_positive and np.any(param <= 0):
            raise ValueError("`{}` must contain positive values.".format(name))
        elif np.any(param < 0):
            raise ValueError("`{}` must contain non-negative values."
                             .format(name))

        return param

def __init__(self, doc=None, lda=None, max_doc_len=None, num_topics=None, gamma=None, lhood=None):

        self.doc = doc
        self.lda = lda
        self.gamma = gamma
        self.lhood = lhood
        if self.gamma is None:
            self.gamma = np.zeros(num_topics)
        if self.lhood is None:
            self.lhood = np.zeros(num_topics + 1)

        if max_doc_len is not None and num_topics is not None:
            self.phi = np.resize(np.zeros(max_doc_len * num_topics), (max_doc_len, num_topics))
            self.log_phi = np.resize(np.zeros(max_doc_len * num_topics), (max_doc_len, num_topics))

        # the following are class variables which are to be integrated during Document Influence Model

        self.doc_weight = None
        self.renormalized_doc_weight = None

def resize(self, newshape, refcheck=True, order=False):
        """
        .. warning::

            This method does nothing, except raise a ValueError exception. A
            masked array does not own its data and therefore cannot safely be
            resized in place. Use the `numpy.ma.resize` function instead.

        This method is difficult to implement safely and may be deprecated in
        future releases of NumPy.

        """
        # Note : the 'order' keyword looks broken, let's just drop it
        errmsg = "A masked array does not own its data "\
                 "and therefore cannot be resized.\n" \
                 "Use the numpy.ma.resize function instead."
        raise ValueError(errmsg)

def PostprocessImage(img):
    """
    Postprocess target style image    
    1. add the images dataset mean to optimized image
    2. swap axis (b,g,r) to (r,g,b) and save it

    Parameters
    --------
    img: ndarray (1x3xMxN), optimized image

    Returns
    out : ndarray (3xMxN), Postprocessed image   
    """

    img = np.resize(img, (3, img.shape[2], img.shape[3]))
    img[0, :] += 123.68
    img[1, :] += 116.779
    img[2, :] += 103.939
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 0, 1)
    img = np.clip(img, 0, 255)
    return img.astype('uint8')

def read_lines(self, track_id, offset, count):
        lines = []
        max_num_values = 0
        for i in range(0, count - 1):
            num_values = self.RA.get_values_count(track_id, i) #????? ????? ????? ? ??????
            if num_values > max_num_values:
                max_num_values = num_values
            buffer_output = (c_float * num_values)() #??????????? ????? ? ??????
            num_values = self.RA.get_values(track_id, offset + i, byref(buffer_output), num_values) #?????? ?????? ?? ??
            lines.append(buffer_output)
        retval = np.zeros((count, max_num_values), dtype=np.uint16)
        for i in range(count-1):
            nparr = np.asarray(lines[i], dtype=np.float16)
            nparr = np.multiply(nparr, 65535)
            nparr = np.asarray(nparr, dtype=np.uint16)
            a = np.resize(nparr, retval[i].shape)
            retval[i] += a
        return retval

    #< gidroGraf_DBreader.c_float_Array_6252     object    at    0x7fcb40a2df28 >
    #< gidroGraf_DBreader.c_float_Array_20837    object    at    0x7f142f2eef28 >

def add_col_mult(self, vec, mult, target = None):
        """
        Add a multiple of vector vec to every column of the matrix. If a target
        is provided, it is used to store the result instead of self.
        """

        a, b = self.shape
        a_, b_ = vec.shape

        if not (b_ == 1 and a_ == a):
            raise IncompatibleDimensionsException


        if target is None:
            target = self

        target.resize(self.shape)

        target.numpy_array[:] = self.numpy_array + vec.numpy_array * mult

        return target

def add_row_vec(self, vec, target = None):
        """
        Add vector vec to every row of the matrix. If a target is provided,
        it is used to store the result instead of self.
        """

        a, b = self.shape
        a_, b_ = vec.shape

        if not (a_ == 1 and b_ == b):
            raise IncompatibleDimensionsException


        if target is None:
            target = self

        target.resize(self.shape)

        target.numpy_array[:] = vec.numpy_array + self.numpy_array

        return target