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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 _gti_dirint_lt_90(poa_global, aoi, aoi_lt_90, solar_zenith, solar_azimuth, times, surface_tilt, surface_azimuth, pressure=101325., use_delta_kt_prime=True, temp_dew=None, albedo=.25, model='perez', model_perez='allsitescomposite1990', max_iterations=30):
 """ GTI-DIRINT model for AOI < 90 degrees. See Marion 2015 Section 2.1. See gti_dirint signature for parameter details. """

    I0 = get_extra_radiation(times, 1370, 'spencer')
    cos_zenith = tools.cosd(solar_zenith)
    # I0h as in Marion 2015 eqns 1, 3
    I0h = I0 * np.maximum(0.065, cos_zenith)

    airmass = atmosphere.get_relative_airmass(solar_zenith, model='kasten1966')
    airmass = atmosphere.get_absolute_airmass(airmass, pressure)

    # these coeffs and diff variables and the loop below
    # implement figure 1 of Marion 2015

    # make coeffs that is at least 30 elements long so that all
    # coeffs can be assigned as specified in Marion 2015.
    # slice below will limit iterations if necessary
    coeffs = np.empty(max(30, max_iterations))
    coeffs[0:3] = 1
    coeffs[3:10] = 0.5
    coeffs[10:20] = 0.25
    coeffs[20:] = 0.125
    coeffs = coeffs[:max_iterations]  # covers case where max_iterations < 30

    # initialize diff
    diff = pd.Series(9999, index=times)
    best_diff = diff

    # initialize poa_global_i
    poa_global_i = poa_global

    for iteration, coeff in enumerate(coeffs):

        # test if difference between modeled GTI and
        # measured GTI (poa_global) is less than 1 W/m^2
        # only test for aoi less than 90 deg
        best_diff_lte_1 = best_diff <= 1
        best_diff_lte_1_lt_90 = best_diff_lte_1[aoi_lt_90]
        if best_diff_lte_1_lt_90.all():
            # all aoi < 90 points have a difference <= 1, so break loop
            break

        # calculate kt and DNI from GTI
        kt = clearness_index(poa_global_i, aoi, I0)  # kt from Marion eqn 2
        disc_dni = np.maximum(_disc_kn(kt, airmass)[0] * I0, 0)
        kt_prime = clearness_index_zenith_independent(kt, airmass)
        # dirint DNI in Marion eqn 3
        dni = _dirint_from_dni_ktprime(disc_dni, kt_prime, solar_zenith,
                                       use_delta_kt_prime, temp_dew)

        # calculate DHI using Marion eqn 3 (identify 1st term on RHS as GHI)
        # I0h has a minimum zenith projection, but multiplier of DNI does not
        ghi = kt * I0h                  # Kt * I0 * max(0.065, cos(zen))
        dhi = ghi - dni * cos_zenith    # no cos(zen) restriction here

        # following SSC code
        dni = np.maximum(dni, 0)
        ghi = np.maximum(ghi, 0)
        dhi = np.maximum(dhi, 0)

        # use DNI and DHI to model GTI
        # GTI-DIRINT uses perez transposition model, but we allow for
        # any model here
        all_irrad = get_total_irradiance(
            surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
            dni, ghi, dhi, dni_extra=I0, airmass=airmass,
            albedo=albedo, model=model, model_perez=model_perez)

        gti_model = all_irrad['poa_global']

        # calculate new diff
        diff = gti_model - poa_global

        # determine if the new diff is smaller in magnitude
        # than the old diff
        diff_abs = diff.abs()
        smallest_diff = diff_abs < best_diff

        # save the best differences
        best_diff = diff_abs.where(smallest_diff, best_diff)

        # on first iteration, the best values are the only values
        if iteration == 0:
            best_ghi = ghi
            best_dni = dni
            best_dhi = dhi
            best_kt_prime = kt_prime
        else:
            # save new DNI, DHI, DHI if they provide the best consistency
            # otherwise use the older values.
            best_ghi = ghi.where(smallest_diff, best_ghi)
            best_dni = dni.where(smallest_diff, best_dni)
            best_dhi = dhi.where(smallest_diff, best_dhi)
            best_kt_prime = kt_prime.where(smallest_diff, best_kt_prime)

        # calculate adjusted inputs for next iteration. Marion eqn 4
        poa_global_i = np.maximum(1.0, poa_global_i - coeff * diff)
    else:
        # we are here because we ran out of coeffs to loop over and
        # therefore we have exceeded max_iterations
        import warnings
        failed_points = best_diff[aoi_lt_90][~best_diff_lte_1_lt_90]
        warnings.warn(
            ('%s points failed to converge after %s iterations. best_diff:\n%s'
             % (len(failed_points), max_iterations, failed_points)),
            RuntimeWarning)

    # return the best data, whether or not the solution converged
    return best_ghi, best_dni, best_dhi, best_kt_prime