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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def detect_threshold(data, snr, background=None, error=None, mask=None, mask_value=None, sigclip_sigma=3.0, sigclip_iters=None):
 """ Calculate a pixel-wise threshold image that can be used to detect sources. Parameters data : array_like The 2D array of the image. snr : float The signal-to-noise ratio per pixel above the ``background`` for which to consider a pixel as possibly being part of a source. background : float or array_like, optional The background value(s) of the input ``data``. ``background`` may either be a scalar value or a 2D image with the same shape as the input ``data``. If the input ``data`` has been background-subtracted, then set ``background`` to ``0.0``. If `None`, then a scalar background value will be estimated using sigma-clipped statistics. error : float or array_like, optional The Gaussian 1-sigma standard deviation of the background noise in ``data``. ``error`` should include all sources of "background" error, but *exclude* the Poisson error of the sources. If ``error`` is a 2D image, then it should represent the 1-sigma background error in each pixel of ``data``. If `None`, then a scalar background rms value will be estimated using sigma-clipped statistics. mask : array_like, bool, optional A boolean mask with the same shape as ``data``, where a `True` value indicates the corresponding element of ``data`` is masked. Masked pixels are ignored when computing the image background statistics. mask_value : float, optional An image data value (e.g., ``0.0``) that is ignored when computing the image background statistics. ``mask_value`` will be ignored if ``mask`` is input. sigclip_sigma : float, optional The number of standard deviations to use as the clipping limit when calculating the image background statistics. sigclip_iters : int, optional The number of iterations to perform sigma clipping, or `None` to clip until convergence is achieved (i.e., continue until the last iteration clips nothing) when calculating the image background statistics. Returns ------- threshold : 2D `~numpy.ndarray` A 2D image with the same shape as ``data`` containing the pixel-wise threshold values. See Also -------- :func:`photutils.segmentation.detect_sources` Notes ----- The ``mask``, ``mask_value``, ``sigclip_sigma``, and ``sigclip_iters`` inputs are used only if it is necessary to estimate ``background`` or ``error`` using sigma-clipped background statistics. If ``background`` and ``error`` are both input, then ``mask``, ``mask_value``, ``sigclip_sigma``, and ``sigclip_iters`` are ignored. """


    if background is None or error is None:
        if astropy_version < '3.1':
            data_mean, data_median, data_std = sigma_clipped_stats(
                data, mask=mask, mask_value=mask_value, sigma=sigclip_sigma,
                iters=sigclip_iters)
        else:
            data_mean, data_median, data_std = sigma_clipped_stats(
                data, mask=mask, mask_value=mask_value, sigma=sigclip_sigma,
                maxiters=sigclip_iters)

        bkgrd_image = np.zeros_like(data) + data_mean
        bkgrdrms_image = np.zeros_like(data) + data_std

    if background is None:
        background = bkgrd_image
    else:
        if np.isscalar(background):
            background = np.zeros_like(data) + background
        else:
            if background.shape != data.shape:
                raise ValueError('If input background is 2D, then it '
                                 'must have the same shape as the input '
                                 'data.')

    if error is None:
        error = bkgrdrms_image
    else:
        if np.isscalar(error):
            error = np.zeros_like(data) + error
        else:
            if error.shape != data.shape:
                raise ValueError('If input error is 2D, then it '
                                 'must have the same shape as the input '
                                 'data.')

    return background + (error * snr)