Python numpy.flipud() Examples

def convert_image(self, filename):
        pic = img.imread(filename)
        # Set FFT size to be double the image size so that the edge of the spectrum stays clear
        # preventing some bandfilter artifacts
        self.NFFT = 2*pic.shape[1]

        # Repeat image lines until each one comes often enough to reach the desired line time
        ffts = (np.flipud(np.repeat(pic[:, :, 0], self.repetitions, axis=0) / 16.)**2.) / 256.

        # Embed image in center bins of the FFT
        fftall = np.zeros((ffts.shape[0], self.NFFT))
        startbin = int(self.NFFT/4)
        fftall[:, startbin:(startbin+pic.shape[1])] = ffts

        # Generate random phase vectors for the FFT bins, this is important to prevent high peaks in the output
        # The phases won't be visible in the spectrum
        phases = 2*np.pi*np.random.rand(*fftall.shape)
        rffts = fftall * np.exp(1j*phases)

        # Perform the FFT per image line, then concatenate them to form the final signal
        timedata = np.fft.ifft(np.fft.ifftshift(rffts, axes=1), axis=1) / np.sqrt(float(self.NFFT))
        linear = timedata.flatten()
        linear = linear / np.max(np.abs(linear))
        return linear 

def save_movie_to_frame(images, filename, idx=0, cmap='Blues'):
    # Collect to single image
    image = movie_to_frame(images[idx])

    # Flip it
    # image = np.fliplr(image)
    # image = np.flipud(image)

    f = plt.figure(figsize=[12, 12])
    plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1)

    plt.axis('image')
    plt.xticks([])
    plt.yticks([])
    plt.savefig(filename, format='png', bbox_inches='tight', dpi=80)
    plt.close(f) 

def quadrature_cc_1D(N):
    """ Computes the Clenshaw Curtis nodes and weights """
    N = np.int(N)        
    if N == 1:
        knots = 0
        weights = 2
    else:
        n = N - 1
        C = np.zeros((N,2))
        k = 2*(1+np.arange(np.floor(n/2)))
        C[::2,0] = 2/np.hstack((1, 1-k*k))
        C[1,1] = -n
        V = np.vstack((C,np.flipud(C[1:n,:])))
        F = np.real(ifft(V, n=None, axis=0))
        knots = F[0:N,1]
        weights = np.hstack((F[0,0],2*F[1:n,0],F[n,0]))
            
    return knots, weights 

def test_flip_axis():
    a = np.arange(24).reshape((2,3,4))
    assert_array_equal(
        flip_axis(a),
        np.flipud(a))
    assert_array_equal(
        flip_axis(a, axis=0),
        np.flipud(a))
    assert_array_equal(
        flip_axis(a, axis=1),
        np.fliplr(a))
    # check accepts array-like
    assert_array_equal(
        flip_axis(a.tolist(), axis=0),
        np.flipud(a))
    # third dimension
    b = a.transpose()
    b = np.flipud(b)
    b = b.transpose()
    assert_array_equal(flip_axis(a, axis=2), b) 

def test_closest_canonical():
    arr = np.arange(24).reshape((2,3,4,1))
    # no funky stuff, returns same thing
    img = Nifti1Image(arr, np.eye(4))
    xyz_img = as_closest_canonical(img)
    assert_true(img is xyz_img)
    # a axis flip
    img = Nifti1Image(arr, np.diag([-1,1,1,1]))
    xyz_img = as_closest_canonical(img)
    assert_false(img is xyz_img)
    out_arr = xyz_img.get_data()
    assert_array_equal(out_arr, np.flipud(arr))
    # no error for enforce_diag in this case
    xyz_img = as_closest_canonical(img, True)
    # but there is if the affine is not diagonal
    aff = np.eye(4)
    aff[0,1] = 0.1
    # although it's more or less canonical already
    img = Nifti1Image(arr, aff)
    xyz_img = as_closest_canonical(img)
    assert_true(img is xyz_img)
    # it's still not diagnonal
    assert_raises(OrientationError, as_closest_canonical, img, True) 

def decompose_projection_matrix(P, return_t=True):
  if P.shape[0] != 3 or P.shape[1] != 4:
    raise Exception('P has to be 3x4')
  M = P[:, :3]
  C = -np.linalg.inv(M) @ P[:, 3:]

  R,K = np.linalg.qr(np.flipud(M).T)
  K = np.flipud(K.T)
  K = np.fliplr(K)
  R = np.flipud(R.T)

  T = np.diag(np.sign(np.diag(K)))
  K = K @ T
  R = T @ R

  if np.linalg.det(R) < 0:
    R *= -1

  K /= K[2,2]
  if return_t:
    return K, R, cameracenter_to_translation(R, C)
  else:
    return K, R, C 

def draw_lane_fit(undist, warped ,Minv, left_fitx, right_fitx, ploty):
	# Drawing
	# Create an image to draw the lines on
	warp_zero = np.zeros_like(warped).astype(np.uint8)
	color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

	# Recast the x and y points into usable format for cv2.fillPoly()
	pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
	pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
	pts = np.hstack((pts_left, pts_right))

	# Draw the lane onto the warped blank image
	cv2.fillPoly(color_warp, np.int_([pts]), (0,255,0))

	# Warp the blank back to original image space using inverse perspective matrix(Minv)
	newwarp = cv2.warpPerspective(color_warp, Minv, (undist.shape[1], undist.shape[0]))
	# Combine the result with the original image
	result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)

	return result 

def calc_axon_contribution(self, axons):
        xyret = np.column_stack((self.grid.xret.ravel(),
                                 self.grid.yret.ravel()))
        # Only include axon segments that are < `max_d2` from the soma. These
        # axon segments will have `sensitivity` > `self.min_ax_sensitivity`:
        max_d2 = -2.0 * self.axlambda ** 2 * np.log(self.min_ax_sensitivity)
        axon_contrib = []
        for xy, bundle in zip(xyret, axons):
            idx = np.argmin((bundle[:, 0] - xy[0]) ** 2 +
                            (bundle[:, 1] - xy[1]) ** 2)
            # Cut off the part of the fiber that goes beyond the soma:
            axon = np.flipud(bundle[0: idx + 1, :])
            # Add the exact location of the soma:
            axon = np.insert(axon, 0, xy, axis=0)
            # For every axon segment, calculate distance from soma by
            # summing up the individual distances between neighboring axon
            # segments (by "walking along the axon"):
            d2 = np.cumsum(np.diff(axon[:, 0], axis=0) ** 2 +
                           np.diff(axon[:, 1], axis=0) ** 2)
            idx_d2 = d2 < max_d2
            sensitivity = np.exp(-d2[idx_d2] / (2.0 * self.axlambda ** 2))
            idx_d2 = np.insert(idx_d2, 0, False)
            contrib = np.column_stack((axon[idx_d2, :], sensitivity))
            axon_contrib.append(contrib)
        return axon_contrib 

def rank_sites_by_record_count(database, threshold=0):
    """
    Function to determine count the number of records per site and return
    the list ranked in descending order
    """
    name_id_list = [(rec.site.id, rec.site.name) for rec in database.records]
    name_id = dict([])
    for name_id_pair in name_id_list:
        if name_id_pair[0] in name_id:
            name_id[name_id_pair[0]]["Count"] += 1
        else:
            name_id[name_id_pair[0]] = {"Count": 1, "Name": name_id_pair[1]}
    counts = np.array([name_id[key]["Count"] for key in name_id])
    sort_id = np.flipud(np.argsort(counts))

    key_vals = list(name_id)
    output_list = []
    for idx in sort_id:
        if name_id[key_vals[idx]]["Count"] >= threshold:
            output_list.append((key_vals[idx], name_id[key_vals[idx]]))
    return OrderedDict(output_list) 

def save_pfm(fname, image, scale=1):
    file = open(fname, 'w') 
    color = None
     
    if image.dtype.name != 'float32':
        raise Exception('Image dtype must be float32.')
     
    if len(image.shape) == 3 and image.shape[2] == 3: # color image
        color = True
    elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1: # greyscale
        color = False
    else:
        raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.')
     
    file.write('PF\n' if color else 'Pf\n')
    file.write('%d %d\n' % (image.shape[1], image.shape[0]))
     
    endian = image.dtype.byteorder
     
    if endian == '<' or endian == '=' and sys.byteorder == 'little':
        scale = -scale
     
    file.write('%f\n' % scale)
     
    np.flipud(image).tofile(file) 

def augment_img(img, mode=0):
    '''Kai Zhang (github: https://github.com/cszn)
    '''
    if mode == 0:
        return img
    elif mode == 1:
        return np.flipud(np.rot90(img))
    elif mode == 2:
        return np.flipud(img)
    elif mode == 3:
        return np.rot90(img, k=3)
    elif mode == 4:
        return np.flipud(np.rot90(img, k=2))
    elif mode == 5:
        return np.rot90(img)
    elif mode == 6:
        return np.rot90(img, k=2)
    elif mode == 7:
        return np.flipud(np.rot90(img, k=3)) 

def update(self):
        if self.inky_colour is None:
            raise RuntimeError("You must specify which colour of Inky pHAT you're using: inkyphat.set_colour('red', 'black' or 'yellow')")

        self._display_init()

        x1, x2 = self.update_x1, self.update_x2
        y1, y2 = self.update_y1, self.update_y2

        region = self.buffer[y1:y2, x1:x2]

        if self.v_flip:
            region = numpy.fliplr(region)

        if self.h_flip:
            region = numpy.flipud(region)

        buf_red = numpy.packbits(numpy.where(region == RED, 1, 0)).tolist()
        if self.inky_version == 1:
            buf_black = numpy.packbits(numpy.where(region == 0, 0, 1)).tolist()
        else:
            buf_black = numpy.packbits(numpy.where(region == BLACK, 0, 1)).tolist()

        self._display_update(buf_black, buf_red)
        self._display_fini() 

def matrix_visualization(matrix,title=None):
    """ Visualize 2D matrices like spectrograms or feature maps.
    """
    plt.figure()
    plt.imshow(np.flipud(matrix.T),interpolation=None)
    plt.colorbar()
    if title!=None:
        plt.title(title)
    plt.show() 

def readPFM(file):
    file = open(file, 'rb')

    color = None
    width = None
    height = None
    scale = None
    endian = None

    header = file.readline().rstrip()
    if header == 'PF':
        color = True
    elif header == 'Pf':
        color = False
    else:
        raise Exception('Not a PFM file.')

    dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline())
    if dim_match:
        width, height = map(int, dim_match.groups())
    else:
        raise Exception('Malformed PFM header.')

    scale = float(file.readline().rstrip())
    if scale < 0: # little-endian
        endian = '<'
        scale = -scale
    else:
        endian = '>' # big-endian

    data = np.fromfile(file, endian + 'f')
    shape = (height, width, 3) if color else (height, width)

    data = np.reshape(data, shape)
    data = np.flipud(data)
    return data, scale 

def readPFM(file):
    file = open(file, 'rb')

    color = None
    width = None
    height = None
    scale = None
    endian = None

    header = file.readline().rstrip()
    encode_type = chardet.detect(header)  
    header = header.decode(encode_type['encoding'])
    if header == 'PF':
        color = True
    elif header == 'Pf':
        color = False
    else:
        raise Exception('Not a PFM file.')

    dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode(encode_type['encoding']))
    if dim_match:
        width, height = map(int, dim_match.groups())
    else:
        raise Exception('Malformed PFM header.')

    scale = float(file.readline().rstrip().decode(encode_type['encoding']))
    if scale < 0: # little-endian
        endian = '<'
        scale = -scale
    else:
        endian = '>' # big-endian

    data = np.fromfile(file, endian + 'f')
    shape = (height, width, 3) if color else (height, width)

    data = np.reshape(data, shape)
    data = np.flipud(data)
    return data, scale 

def _ecg_simulate_rrprocess(
    flo=0.1, fhi=0.25, flostd=0.01, fhistd=0.01, lfhfratio=0.5, hrmean=60, hrstd=1, sfrr=1, n=256
):
    w1 = 2 * np.pi * flo
    w2 = 2 * np.pi * fhi
    c1 = 2 * np.pi * flostd
    c2 = 2 * np.pi * fhistd
    sig2 = 1
    sig1 = lfhfratio
    rrmean = 60 / hrmean
    rrstd = 60 * hrstd / (hrmean * hrmean)

    df = sfrr / n
    w = np.arange(n) * 2 * np.pi * df
    dw1 = w - w1
    dw2 = w - w2

    Hw1 = sig1 * np.exp(-0.5 * (dw1 / c1) ** 2) / np.sqrt(2 * np.pi * c1 ** 2)
    Hw2 = sig2 * np.exp(-0.5 * (dw2 / c2) ** 2) / np.sqrt(2 * np.pi * c2 ** 2)
    Hw = Hw1 + Hw2
    Hw0 = np.concatenate((Hw[0 : int(n / 2)], Hw[int(n / 2) - 1 :: -1]))
    Sw = (sfrr / 2) * np.sqrt(Hw0)

    ph0 = 2 * np.pi * np.random.uniform(size=int(n / 2 - 1))
    ph = np.concatenate([[0], ph0, [0], -np.flipud(ph0)])
    SwC = Sw * np.exp(1j * ph)
    x = (1 / n) * np.real(np.fft.ifft(SwC))

    xstd = np.std(x)
    ratio = rrstd / xstd
    return rrmean + x * ratio  # Return RR 

def apply_orientation(arr, ornt):
    ''' Apply transformations implied by `ornt` to the first
    n axes of the array `arr`

    Parameters
    ----------
    arr : array-like of data with ndim >= n
    ornt : (n,2) orientation array
       orientation transform. ``ornt[N,1]` is flip of axis N of the
       array implied by `shape`, where 1 means no flip and -1 means
       flip.  For example, if ``N==0`` and ``ornt[0,1] == -1``, and
       there's an array ``arr`` of shape `shape`, the flip would
       correspond to the effect of ``np.flipud(arr)``.  ``ornt[:,0]`` is
       the transpose that needs to be done to the implied array, as in
       ``arr.transpose(ornt[:,0])``

    Returns
    -------
    t_arr : ndarray
       data array `arr` transformed according to ornt
    '''
    t_arr = np.asarray(arr)
    ornt = np.asarray(ornt)
    n = ornt.shape[0]
    if t_arr.ndim < n:
        raise OrientationError('Data array has fewer dimensions than '
                               'orientation')
    # no coordinates can be dropped for applying the orientations
    if np.any(np.isnan(ornt[:, 0])):
        raise OrientationError('Cannot drop coordinates when '
                               'applying orientation to data')
    # apply ornt transformations
    for ax, flip in enumerate(ornt[:, 1]):
        if flip == -1:
            t_arr = flip_axis(t_arr, axis=ax)
    full_transpose = np.arange(t_arr.ndim)
    # ornt indicates the transpose that has occurred - we reverse it
    full_transpose[:n] = np.argsort(ornt[:, 0])
    t_arr = t_arr.transpose(full_transpose)
    return t_arr 

def flip_axis(arr, axis=0):
    ''' Flip contents of `axis` in array `arr`

    ``flip_axis`` is the same transform as ``np.flipud``, but for any
    axis.  For example ``flip_axis(arr, axis=0)`` is the same transform
    as ``np.flipud(arr)``, and ``flip_axis(arr, axis=1)`` is the same
    transform as ``np.fliplr(arr)``

    Parameters
    ----------
    arr : array-like
    axis : int, optional
       axis to flip.  Default `axis` == 0

    Returns
    -------
    farr : array
       Array with axis `axis` flipped

    Examples
    --------
    >>> a = np.arange(6).reshape((2,3))
    >>> a
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> flip_axis(a, axis=0)
    array([[3, 4, 5],
           [0, 1, 2]])
    >>> flip_axis(a, axis=1)
    array([[2, 1, 0],
           [5, 4, 3]])
    '''
    arr = np.asanyarray(arr)
    arr = arr.swapaxes(0, axis)
    arr = np.flipud(arr)
    return arr.swapaxes(axis, 0) 

def generateBoundingBox(imap, reg, scale, t):
    # use heatmap to generate bounding boxes
    stride=2
    cellsize=12

    imap = np.transpose(imap)
    dx1 = np.transpose(reg[:,:,0])
    dy1 = np.transpose(reg[:,:,1])
    dx2 = np.transpose(reg[:,:,2])
    dy2 = np.transpose(reg[:,:,3])
    y, x = np.where(imap >= t)
    if y.shape[0]==1:
        dx1 = np.flipud(dx1)
        dy1 = np.flipud(dy1)
        dx2 = np.flipud(dx2)
        dy2 = np.flipud(dy2)
    score = imap[(y,x)]
    reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ]))
    if reg.size==0:
        reg = np.empty((0,3))
    bb = np.transpose(np.vstack([y,x]))
    q1 = np.fix((stride*bb+1)/scale)
    q2 = np.fix((stride*bb+cellsize-1+1)/scale)
    boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])
    return boundingbox, reg
 
# function pick = nms(boxes,threshold,type) 

def snap(image):
    return np.flipud(image) 

def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be flipped.
        Returns:
            PIL.Image: Randomly flipped image.
        """
        if self.p < 0.5:
            if isinstance(img, np.ndarray):
                return np.flipud(img).copy()
            else:
                return img.transpose(Image.FLIP_TOP_BOTTOM)
        return img 

def test_4d(self):
        a = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
        for i in range(a.ndim):
            assert_equal(np.flip(a, i),
                         np.flipud(a.swapaxes(0, i)).swapaxes(i, 0)) 

def test_hanning(self):
        # check symmetry
        w = hanning(10)
        assert_array_almost_equal(w, flipud(w), 7)
        # check known value
        assert_almost_equal(np.sum(w, axis=0), 4.500, 4) 

def test_hamming(self):
        # check symmetry
        w = hamming(10)
        assert_array_almost_equal(w, flipud(w), 7)
        # check known value
        assert_almost_equal(np.sum(w, axis=0), 4.9400, 4) 

def test_bartlett(self):
        # check symmetry
        w = bartlett(10)
        assert_array_almost_equal(w, flipud(w), 7)
        # check known value
        assert_almost_equal(np.sum(w, axis=0), 4.4444, 4) 

def test_blackman(self):
        # check symmetry
        w = blackman(10)
        assert_array_almost_equal(w, flipud(w), 7)
        # check known value
        assert_almost_equal(np.sum(w, axis=0), 3.7800, 4) 

def load_pfm(fname):
    assert os.path.isfile(fname)
    color = None
    width = None
    height = None
    scale = None
    endian = None
    
    file = open(fname)
    header = file.readline().rstrip()
    if header == 'PF':
        color = True    
    elif header == 'Pf':
        color = False
    else:
        raise Exception('Not a PFM file.')
     
    dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline())
    if dim_match:
        width, height = map(int, dim_match.groups())
    else:
        raise Exception('Malformed PFM header.')
     
    scale = float(file.readline().rstrip())
    if scale < 0: # little-endian
        endian = '<'
        scale = -scale
    else:
        endian = '>' # big-endian
     
    data = np.fromfile(file, endian + 'f')
    shape = (height, width, 3) if color else (height, width)
    return np.flipud(np.reshape(data, shape)).copy(), scale 

def flip(self, direction="lr"):
    """ Flips the image
          Parameters:
              direction - "lr" denotes left-right fliplr
                          "ud" denotes up-down flip
    """
    if direction == "lr":
      return np.fliplr(self.Image)
    elif direction == "ud":
      return np.flipud(self.Image)
    else:
      raise ValueError(
          "Invalid flip command : Enter either lr (for left to right flip) or ud (for up to down flip)"
      ) 

def test_flipping(self):
    # Check flip
    dt = DataTransforms(self.d)
    flip_lr = dt.flip(direction="lr")
    flip_ud = dt.flip(direction="ud")
    check_lr = np.fliplr(self.d)
    check_ud = np.flipud(self.d)
    assert np.allclose(flip_ud, check_ud)
    assert np.allclose(flip_lr, check_lr) 

def cmap_from_act(file, name=None):
    """Import colormap from Adobe Color Table file.

    Parameters:
        file (str): Path to act file.
        name (str): Colormap name. Defaults to filename without extension.

    Returns:
        LinearSegmentedColormap.
    """
    # Extract colormap name from filename.
    if name is None:
        name = os.path.splitext(os.path.basename(file))[0]

    # Read binary file and determine number of colors
    rgb = np.fromfile(file, dtype=np.uint8)
    if rgb.shape[0] >= 770:
        ncolors = rgb[768] * 2**8 + rgb[769]
    else:
        ncolors = 256

    colors = rgb[:ncolors*3].reshape(ncolors, 3) / 255

    # Create and register colormap...
    cmap = LinearSegmentedColormap.from_list(name, colors, N=ncolors)
    plt.register_cmap(cmap=cmap)  # Register colormap.

    # ... and the reversed colormap.
    cmap_r = LinearSegmentedColormap.from_list(
            name + '_r', np.flipud(colors), N=ncolors)
    plt.register_cmap(cmap=cmap_r)

    return cmap