<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def daily_pr_intensity(pr, thresh='1 mm/day', freq='YS'):
 r"""Average daily precipitation intensity Return the average precipitation over wet days. Parameters pr : xarray.DataArray Daily precipitation [mm/d or kg/m²/s] thresh : str precipitation value over which a day is considered wet. Default : '1 mm/day' freq : str, optional Resampling frequency defining the periods defined in http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling. Default : '1 mm/day' Returns ------- xarray.DataArray The average precipitation over wet days for each period Notes ----- Let :math:`\mathbf{p} = p_0, p_1, \ldots, p_n` be the daily precipitation and :math:`thresh` be the precipitation threshold defining wet days. Then the daily precipitation intensity is defined as .. math:: \frac{\sum_{i=0}^n p_i [p_i \leq thresh]}{\sum_{i=0}^n [p_i \leq thresh]} where :math:`[P]` is 1 if :math:`P` is true, and 0 if false. Examples -------- The following would compute for each grid cell of file `pr.day.nc` the average precipitation fallen over days with precipitation >= 5 mm at seasonal frequency, ie DJF, MAM, JJA, SON, DJF, etc.: """

    t = utils.convert_units_to(thresh, pr, 'hydro')

    # put pr=0 for non wet-days
    pr_wd = xr.where(pr >= t, pr, 0)
    pr_wd.attrs['units'] = pr.units

    # sum over wanted period
    s = pr_wd.resample(time=freq).sum(dim='time', keep_attrs=True)
    sd = utils.pint_multiply(s, 1 * units.day, 'mm')

    # get number of wetdays over period
    wd = wetdays(pr, thresh=thresh, freq=freq)
    return sd / wd