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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 covariance_matrix(self, data, background_rms=1, exposure_map=1, noise_map=None, verbose=False):
 """ returns a diagonal matrix for the covariance estimation which describes the error Notes: - the exposure map must be positive definite. Values that deviate too much from the mean exposure time will be given a lower limit to not under-predict the Poisson component of the noise. - the data must be positive semi-definite for the Poisson noise estimate. Values < 0 (Possible after mean subtraction) will not have a Poisson component in their noise estimate. :param data: data array, eg in units of photons/second :param background_rms: background noise rms, eg. in units (photons/second)^2 :param exposure_map: exposure time per pixel, e.g. in units of seconds :return: len(d) x len(d) matrix that give the error of background and Poisson components; (photons/second)^2 """

        if noise_map is not None:
            return noise_map**2
        if isinstance(exposure_map, int) or isinstance(exposure_map, float):
            if exposure_map <= 0:
                exposure_map = 1
        else:
            mean_exp_time = np.mean(exposure_map)
            exposure_map[exposure_map < mean_exp_time / 10] = mean_exp_time / 10
        if verbose:
            if background_rms * np.max(exposure_map) < 1:
                print("WARNING! sigma_b*f %s < 1 count may introduce unstable error estimates" % (background_rms * np.max(exposure_map)))
        d_pos = np.zeros_like(data)
        #threshold = 1.5*sigma_b
        d_pos[data >= 0] = data[data >= 0]
        #d_pos[d < threshold] = 0
        sigma = d_pos / exposure_map + background_rms ** 2
        return sigma