How to use the pymedphys._imports.numpy.zeros function in pymedphys

def merge_view_horz(volume, dx, dy):
    junctions = []

    # creating merged volume
    merge_vol = np.zeros((volume.shape[0], volume.shape[1]))

    # creating vector for processing along cols (one row)
    amplitude = np.zeros(
        (volume.shape[0], volume.shape[2])
    )  # 1 if it is vertical 0 if the bars are horizontal

    y = np.linspace(
        0, 0 + (volume.shape[0] * dy), volume.shape[0], endpoint=False
    )  # definition of the distance axis
    # x = np.arange(0,) #definition of the distance axis

    # merging the two images together
    ampl_resamp = np.zeros(((volume.shape[0]) * 10, volume.shape[2]))
    # amp_peak = np.zeros((volume.shape[0]) * 10)

    for item in tqdm(range(0, volume.shape[2])):

diag_stack[3] = (
                diag_stack[3]
                + volume[
                    int(np.shape(volume)[0] / 2) + j,
                    int(np.shape(volume)[1] / 2) - j,
                    i,
                ]
            )

        volume_resort[:, :, np.argmax(diag_stack)] = volume[:, :, i]

    # creating merged volumes
    merge_vol = np.zeros((volume_resort.shape[0], volume_resort.shape[1]))

    # creating vector for processing (1 horizontal & 1 vertical)
    amplitude_horz = np.zeros(
        (volume_resort.shape[1], volume_resort.shape[2])
    )  # 1 if it is vertical 0 if the bars are horizontal
    amplitude_vert = np.zeros((volume_resort.shape[0], volume_resort.shape[2]))

    y = np.linspace(
        0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0], endpoint=False
    )  # definition of the distance axis
    x = np.linspace(
        0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1], endpoint=False
    )  # definition of the distance axis

    ampl_resamp_y1 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_y2 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))

junctions = []

    # creating merged volume
    merge_vol = np.zeros((volume.shape[0], volume.shape[1]))

    # creating vector for processing along cols (one row)
    amplitude = np.zeros(
        (volume.shape[0], volume.shape[2])
    )  # 1 if it is vertical 0 if the bars are horizontal

    y = np.linspace(
        0, 0 + (volume.shape[0] * dy), volume.shape[0], endpoint=False
    )  # definition of the distance axis

    # merging the two images together
    ampl_resamp = np.zeros(((volume.shape[0]) * 10, volume.shape[2]))

    for slice in tqdm(range(0, volume.shape[2])):
        merge_vol = merge_vol + volume[:, :, slice]
        amplitude[:, slice] = volume[:, int(volume.shape[1] / 2), slice]
        ampl_resamp[:, slice] = signal.resample(
            amplitude[:, slice], int(len(amplitude)) * 10
        )  # resampling the amplitude vector

    fig, ax = plt.subplots(nrows=2, squeeze=True, figsize=(6, 8))

    extent = (0, 0 + (volume.shape[1] * dx), 0, 0 + (volume.shape[0] * dy))

    ax[0].imshow(merge_vol, extent=extent, aspect="auto")
    ax[0].set_xlabel("x distance [mm]")
    ax[0].set_ylabel("y distance [mm]")

)  # 1 if it is vertical 0 if the bars are horizontal
    amplitude_vert = np.zeros((volume_resort.shape[0], volume_resort.shape[2]))

    # y = np.linspace(0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0],
    #                 endpoint=False)  # definition of the distance axis
    # x = np.linspace(0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1],
    #                 endpoint=False)  # definition of the distance axis

    ampl_resamp_y1 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_y2 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )

    ampl_resamp_x1 = np.zeros(
        ((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_x2 = np.zeros(
        ((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
    )

    amplitude_horz[:, 0] = volume_resort[
        int(volume_resort.shape[0] / 3.25), :, 0
    ]  # for profile 1
    amplitude_horz[:, 1] = volume_resort[
        int(volume_resort.shape[0] / 3.25), :, 1
    ]  # for profile 1
    amplitude_horz[:, 3] = volume_resort[
        int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 2
    ]  # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error  #for profile 2
    amplitude_horz[:, 2] = volume_resort[

# y = np.linspace(0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0],
    #                 endpoint=False)  # definition of the distance axis
    # x = np.linspace(0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1],
    #                 endpoint=False)  # definition of the distance axis

    ampl_resamp_y1 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_y2 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )

    ampl_resamp_x1 = np.zeros(
        ((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_x2 = np.zeros(
        ((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
    )

    amplitude_horz[:, 0] = volume_resort[
        int(volume_resort.shape[0] / 3.25), :, 0
    ]  # for profile 1
    amplitude_horz[:, 1] = volume_resort[
        int(volume_resort.shape[0] / 3.25), :, 1
    ]  # for profile 1
    amplitude_horz[:, 3] = volume_resort[
        int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 2
    ]  # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error  #for profile 2
    amplitude_horz[:, 2] = volume_resort[
        int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 3
    ]

int(np.shape(volume)[0] / 2) + j,
                    int(np.shape(volume)[1] / 2) - j,
                    i,
                ]
            )

        volume_resort[:, :, np.argmax(diag_stack)] = volume[:, :, i]

    # creating merged volumes
    merge_vol = np.zeros((volume_resort.shape[0], volume_resort.shape[1]))

    # creating vector for processing (1 horizontal & 1 vertical)
    amplitude_horz = np.zeros(
        (volume_resort.shape[1], volume_resort.shape[2])
    )  # 1 if it is vertical 0 if the bars are horizontal
    amplitude_vert = np.zeros((volume_resort.shape[0], volume_resort.shape[2]))

    y = np.linspace(
        0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0], endpoint=False
    )  # definition of the distance axis
    x = np.linspace(
        0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1], endpoint=False
    )  # definition of the distance axis

    ampl_resamp_y1 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_y2 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )

    ampl_resamp_x1 = np.zeros(

def merge_view_horz(volume, dx, dy):
    junctions = []

    # creating merged volume
    merge_vol = np.zeros((volume.shape[0], volume.shape[1]))

    # creating vector for processing along cols (one row)
    amplitude = np.zeros(
        (volume.shape[0], volume.shape[2])
    )  # 1 if it is vertical 0 if the bars are horizontal

    y = np.linspace(
        0, 0 + (volume.shape[0] * dy), volume.shape[0], endpoint=False
    )  # definition of the distance axis

    # merging the two images together
    ampl_resamp = np.zeros(((volume.shape[0]) * 10, volume.shape[2]))

    for slice in tqdm(range(0, volume.shape[2])):
        merge_vol = merge_vol + volume[:, :, slice]
        amplitude[:, slice] = volume[:, int(volume.shape[1] / 2), slice]
        ampl_resamp[:, slice] = signal.resample(
            amplitude[:, slice], int(len(amplitude)) * 10
        )  # resampling the amplitude vector

y = np.linspace(
        0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0], endpoint=False
    )  # definition of the distance axis
    x = np.linspace(
        0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1], endpoint=False
    )  # definition of the distance axis

    ampl_resamp_y1 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_y2 = np.zeros(
        ((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
    )

    ampl_resamp_x1 = np.zeros(
        ((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
    )
    ampl_resamp_x2 = np.zeros(
        ((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
    )

    amplitude_horz[:, 0] = volume_resort[
        int(volume_resort.shape[0] / 3.25), :, 0
    ]  # for profile 1
    amplitude_horz[:, 1] = volume_resort[
        int(volume_resort.shape[0] / 3.25), :, 1
    ]  # for profile 1
    amplitude_horz[:, 3] = volume_resort[
        int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 2
    ]  # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error  #for profile 2
    amplitude_horz[:, 2] = volume_resort[

def merge_view_vert(volume, dx, dy):
    junctions = []

    # creating merged volume
    merge_vol = np.zeros((volume.shape[0], volume.shape[1]))

    # creating vector for processing along cols (one row)
    amplitude = np.zeros(
        (volume.shape[1], volume.shape[2])
    )  # 1 if it is vertical 0 if the bars are horizontal

    x = np.linspace(
        0, 0 + (volume.shape[1] * dx), volume.shape[1], endpoint=False
    )  # definition of the distance axis
    # x = np.arange(0,)#definition of the distance axis

    # merging the two images together
    ampl_resamp = np.zeros(((volume.shape[1]) * 10, volume.shape[2]))
    # amp_peak = np.zeros((volume.shape[1]) * 10)

    for item in tqdm(range(0, volume.shape[2])):

(dataset.Rows, dataset.Columns),
                            dtype=dataset.pixel_array.dtype,
                        )
                        ArrayDicom = np.dstack((ArrayDicom, dataset.pixel_array))
                        # print("slice thickness [mm]=",dataset.SliceThickness)
                        SID = dataset.RTImageSID
                        dx = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[0]) / 1000)
                        dy = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[1]) / 1000)
                        print("pixel spacing row [mm]=", dx)
                        print("pixel spacing col [mm]=", dy)
                    else:
                        ArrayDicom = np.dstack((ArrayDicom, dataset.pixel_array))
                elif poption.startswith(("n", "no", "nope")):
                    dataset = pydicom.dcmread(dirname + file)
                    if k == 0:
                        ArrayDicom = np.zeros(
                            (dataset.Rows, dataset.Columns, 0),
                            dtype=dataset.pixel_array.dtype,
                        )
                        ArrayDicom = np.dstack((ArrayDicom, dataset.pixel_array))
                        # print("slice thickness [mm]=", dataset.SliceThickness)
                        SID = dataset.RTImageSID
                        dx = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[0]) / 1000)
                        dy = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[1]) / 1000)
                        print("pixel spacing row [mm]=", dx)
                        print("pixel spacing col [mm]=", dy)
                    else:
                        ArrayDicom = np.dstack((ArrayDicom, dataset.pixel_array))
                print(k)
                k = k + 1

    xfield, yfield, rotfield = image_analyze(ArrayDicom, ioption)