How to use the pytesmo.io.sat.ascat.AscatH25_SSM function in pytesmo

def setUp(self):
        self.ascat_folder = os.path.join(os.path.dirname(__file__),
                                         '..', 'test-data', 'sat',
                                         'ascat', 'netcdf', '55R12')

        self.ascat_grid_folder = os.path.join(os.path.dirname(__file__),
                                              '..', 'test-data', 'sat',
                                              'ascat', 'netcdf', 'grid')

        # init the ASCAT_SSM reader with the paths
        self.ascat_SSM_reader = ascat.AscatH25_SSM(self.ascat_folder,
                                                   self.ascat_grid_folder)

def setUp(self):

        self.ascat_folder = os.path.join(os.path.dirname(__file__),
                                         '..', 'test-data', 'sat',
                                         'ascat', 'netcdf', '55R22')

        self.ascat_grid_folder = os.path.join(os.path.dirname(__file__),
                                              '..', 'test-data', 'sat',
                                              'ascat', 'netcdf', 'grid')
        # init the ASCAT_SSM reader with the paths
        self.ascat_SSM_reader = ascat.AscatH25_SSM(self.ascat_folder,
                                                   self.ascat_grid_folder)

# 

import pytesmo.io.sat.ascat as ascat
import pytesmo.time_series as ts

import os
import matplotlib.pyplot as plt

# 

ascat_folder = os.path.join('R:\\','Datapool_processed','WARP','WARP5.5',
                                         'ASCAT_WARP5.5_R1.2','080_ssm','netcdf')
ascat_grid_folder = os.path.join('R:\\','Datapool_processed','WARP','ancillary','warp5_grid')
#init the ASCAT_SSM reader with the paths

ascat_SSM_reader = ascat.AscatH25_SSM(ascat_folder,ascat_grid_folder)

# 

ascat_ts = ascat_SSM_reader.read_ssm(45,0)
#plot soil moisture
ascat_ts.data['sm'].plot()

# 

#calculate anomaly based on moving +- 17 day window
anomaly = ts.anomaly.calc_anomaly(ascat_ts.data['sm'], window_size=35)
anomaly.plot()

# 

#calculate climatology

import pytesmo.scaling as scaling
import pytesmo.df_metrics as df_metrics
import pytesmo.metrics as metrics

import os
import matplotlib.pyplot as plt


ascat_folder = os.path.join('R:\\', 'Datapool_processed', 'WARP', 'WARP5.5',
                                         'ASCAT_WARP5.5_R1.2', '080_ssm', 'netcdf')
ascat_grid_folder = os.path.join('R:\\', 'Datapool_processed', 'WARP', 'ancillary', 'warp5_grid')
# init the ASCAT_SSM reader with the paths

# let's not include the orbit direction since it is saved as 'A'
# or 'D' it can not be plotted
ascat_SSM_reader = ascat.AscatH25_SSM(ascat_folder, ascat_grid_folder,
                                      include_in_df=['sm', 'sm_noise', 'ssf', 'proc_flag'])


# set path to ISMN data
path_to_ismn_data = os.path.join('D:\\', 'small_projects', 'cpa_2013_07_ISMN_userformat_reader', 'header_values_parser_test')
# Initialize reader
ISMN_reader = ismn.ISMN_Interface(path_to_ismn_data)

i = 0

label_ascat = 'sm'
label_insitu = 'insitu_sm'

# this loops through all stations that measure soil moisture
for station in ISMN_reader.stations_that_measure('soil moisture'):

if included in kwargs then all observations taken when
            snow probability > mask_snow_prob are removed from the result
        absolute_values : boolean, optional
            if True soil porosities from HWSD and GLDAS will be used to
            derive absolute values which will be available in the
            pandas.DataFrame in the columns
            'sm_por_gldas','sm_noise_por_gldas',
            'sm_por_hwsd','sm_noise_por_hwsd'

        Returns
        -------
        ASCATTimeSeries : object
            :class:`pytesmo.io.sat.ascat.ASCATTimeSeries` instance
        """
        df, gpi, lon, lat, cell, topo, wetland, porosity = super(
            AscatH25_SSM, self)._read_ts(*args, **kwargs)
        if 'mask_ssf' in kwargs:
            mask_ssf = kwargs['mask_ssf']
            if mask_ssf:
                df = df[df['ssf'] == 1]

        return ASCATTimeSeries(gpi, lon, lat, cell, df,
                               topo_complex=topo, wetland_frac=wetland,
                               porosity_gldas=porosity['gldas'],
                               porosity_hwsd=porosity['hwsd'])