def setup_sfr_reach_parameters(nam_file,model_ws='.', par_cols=['strhc1']):
    """Setup multiplier paramters for reach data, when reachinput option is specififed in sfr.
    Similare to setup_sfr_seg_parameters() method will apply params to sfr reachdata
    Can load the dis, bas, and sfr files with flopy using model_ws. Or can pass a model object (SFR loading can be slow)
    However, expects that apply_sfr_reach_parameters() will be called
    from within model_ws at runtime.

    Parameters
    ----------
        nam_file : str
            MODFLOw name file.  DIS, BAS, and SFR must be available as pathed in the nam_file
        model_ws : str
            model workspace for flopy to load the MODFLOW model from
        OR
        nam_file : flopy.modflow.mf.Modflow
            flopy modflow model object

        par_cols : list(str)
            segment data entires to parameterize

    Returns
    -------
        df : pandas.DataFrame
            a dataframe with useful parameter setup information

    Note
    ----
        skips values = 0.0 since multipliers don't work for these

    """
    try:
        import flopy
    except Exception as e:
        return
    if par_cols is None:
        par_cols = ['strhc1']
    if isinstance(nam_file,flopy.modflow.mf.Modflow) and nam_file.sfr is not None:
        # flopy MODFLOW model has been passed and has SFR loaded
        m = nam_file
        nam_file = m.namefile
        model_ws = m.model_ws
    else:
        # if model has not been passed or SFR not loaded # load MODFLOW model
        m = flopy.modflow.Modflow.load(nam_file, load_only=["sfr"], model_ws=model_ws, check=False, forgive=False)
    # get reachdata as dataframe
    reach_data = pd.DataFrame.from_records(m.sfr.reach_data)
    # write inital reach_data as csv
    reach_data_orig = reach_data.copy()
    reach_data.to_csv(os.path.join(m.model_ws, "sfr_reach_pars.dat"), sep=',')

    # generate template file with pars in par_cols
    #process par cols
    tpl_str,pvals = [],[]
    # par_cols=["strhc1"]
    idx_cols=["node", "k", "i", "j", "iseg", "ireach", "reachID", "outreach"]
    #the data cols not to parameterize
    notpar_cols = [c for c in reach_data.columns if c not in par_cols+idx_cols]
    # make sure all par cols are found and search of any data in kpers
    missing = []
    cols = par_cols.copy()
    for par_col in par_cols:
        if par_col not in reach_data.columns:
            missing.append(par_col)
            cols.remove(par_col)
    if len(missing) > 0:
        warnings.warn("the following par_cols were not found in reach data: {0}".format(','.join(missing)),
                      PyemuWarning)
        if len(missing) >= len(par_cols):
            warnings.warn("None of the passed par_cols ({0}) were found in reach data.".
                          format(','.join(par_cols)), PyemuWarning)
    for par_col in cols:
        if par_col == "strhc1":
            prefix = 'strk'  # shorten par
        else:
            prefix = par_col
        reach_data.loc[:, par_col] = reach_data.apply(
            lambda x: "~    {0}_{1:04d}   ~".format(prefix, int(x.reachID))
            if float(x[par_col]) != 0.0 else "1.0", axis=1)
        org_vals = reach_data_orig.loc[reach_data_orig.loc[:, par_col] != 0.0, par_col]
        pnames = reach_data.loc[org_vals.index, par_col]
        pvals.extend(list(org_vals.values))
        tpl_str.extend(list(pnames.values))
    pnames = [t.replace('~','').strip() for t in tpl_str]
    df = pd.DataFrame({"parnme":pnames,"org_value":pvals,"tpl_str":tpl_str},index=pnames)
    df.drop_duplicates(inplace=True)
    if df.empty:
        warnings.warn("No sfr reach parameters have been set up, either none of {0} were found or all were zero.".
                      format(','.join(par_cols)), PyemuWarning)
    else:
        # set not par cols to 1.0
        reach_data.loc[:, notpar_cols] = "1.0"

        # write the template file
        write_df_tpl(os.path.join(model_ws, "sfr_reach_pars.dat.tpl"),reach_data,sep=',')

        # write the config file used by apply_sfr_pars()
        with open(os.path.join(model_ws, "sfr_reach_pars.config"), 'w') as f:
            f.write("nam_file {0}\n".format(nam_file))
            f.write("model_ws {0}\n".format(model_ws))
            f.write("mult_file sfr_reach_pars.dat\n")
            f.write("sfr_filename {0}".format(m.sfr.file_name[0]))

        # make sure the tpl file exists and has the same num of pars
        parnme = parse_tpl_file(os.path.join(model_ws, "sfr_reach_pars.dat.tpl"))
        assert len(parnme) == df.shape[0]

        # set some useful par info
        df.loc[:, "pargp"] = df.parnme.apply(lambda x: x.split('_')[0])
        df.loc[:, "parubnd"] = 1.25
        df.loc[:, "parlbnd"] = 0.75
        hpars = df.loc[df.pargp.apply(lambda x: x.startswith("strk")), "parnme"]
        df.loc[hpars, "parubnd"] = 100.0
        df.loc[hpars, "parlbnd"] = 0.01
    return df
Setup multiplier paramters for reach data, when reachinput option is specififed in sfr.
    Similare to setup_sfr_seg_parameters() method will apply params to sfr reachdata
    Can load the dis, bas, and sfr files with flopy using model_ws. Or can pass a model object (SFR loading can be slow)
    However, expects that apply_sfr_reach_parameters() will be called
    from within model_ws at runtime.

    Parameters
    ----------
        nam_file : str
            MODFLOw name file.  DIS, BAS, and SFR must be available as pathed in the nam_file
        model_ws : str
            model workspace for flopy to load the MODFLOW model from
        OR
        nam_file : flopy.modflow.mf.Modflow
            flopy modflow model object

        par_cols : list(str)
            segment data entires to parameterize

    Returns
    -------
        df : pandas.DataFrame
            a dataframe with useful parameter setup information

    Note
    ----
        skips values = 0.0 since multipliers don't work for these