In[1]:

import matplotlib.pyplot as plt
import pytesmo.io.sat.ascat as ascat
import os
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_folder is the path in which the cell files are located e.g. TUW_METOP_ASCAT_WARP55R12_0600.nc
#ascat_grid_folder is the path in which the file TUW_WARP5_grid_info_2_1.nc is located

#let's not include the orbit direction since it is saved as 'A'
#or 'D' it can not be plotted

# the AscatH25_SSM class automatically detects the version of data that you have in your
# ascat_folder. Please do not mix files of different versions in one folder
ascat_SSM_reader = ascat.AscatH25_SSM(ascat_folder,ascat_grid_folder,
                                      include_in_df=['sm', 'sm_noise', 'ssf', 'proc_flag'])

gpi = 2329253
ascat_ts = ascat_SSM_reader.read_ssm(gpi)

ascat_ts.plot()
plt.show()

In[2]:

#the ASCATTimeSeries object also contains metadata

print "Topographic complexity", ascat_ts.topo_complex
print "Wetland fraction", ascat_ts.wetland_frac
print "Porosity from GLDAS model", ascat_ts.porosity_gldas
print "Porosity from Harmonized World Soil Database", ascat_ts.porosity_hwsd

Topographic complexity 14
Wetland fraction 0
Porosity from GLDAS model 0.542222
Porosity from Harmonized World Soil Database 0.430234

In[3]:

#It is also possible to automatically convert the soil moisture to absolute values using

ascat_ts_absolute = ascat_SSM_reader.read_ssm(gpi, absolute_values=True)
#this introduces 4 new columns in the returned data
#scaled sm and sm_noise with porosity_gldas
#scaled sm and sm_noise with porosity_hwsd
print ascat_ts_absolute.data

#select relevant columns and plot
ascat_ts_absolute.data = ascat_ts_absolute.data[['sm_por_gldas','sm_por_hwsd']]
ascat_ts_absolute.plot()
plt.show()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2292 entries, 2007-01-01 09:39:39 to 2013-07-12 20:39:08
Data columns (total 10 columns):
proc_flag             2292  non-null values
sm                    2285  non-null values
sm_noise              2285  non-null values
ssf                   2292  non-null values
sm_por_gldas          2285  non-null values
sm_noise_por_gldas    2285  non-null values
sm_por_hwsd           2285  non-null values
sm_noise_por_hwsd     2285  non-null values
frozen_prob           2285  non-null values
snow_prob             2285  non-null values
dtypes: float64(8), int16(1), int8(1)

In[4]:

#We can also automatically mask the data during reading
#In this example all measurements where the Surface State Flag
#shows frozen or where the frozen or snow probabilities are more
#than 10 percent are removed from the time series
ascat_ts = ascat_SSM_reader.read_ssm(gpi, mask_ssf=True,
                                     mask_frozen_prob=10,
                                     mask_snow_pro=10)

ascat_ts.plot()
plt.show()