In[1]:

import pytesmo.io.sat.ascat as ascat
import os
import matplotlib.pyplot as plt

In[2]:

#I've downloaded my ASCAT data to a folder on my D drive
path_to_ascat_ssm_data = os.path.join('D:\\','small_projects','cpa_2013_07_userformat_reader',
                                      'data','ASCAT_SSM_25km_ts_WARP5.5_R0.1','data')
path_to_ascat_swi_data = os.path.join('D:\\','small_projects','cpa_2013_07_userformat_reader',
                                      'data','ASCAT_SWI_25km_ts_WARP5.5_R0.1','data')
#path to grid definition file, default name TUW_W54_01_lonlat-ld-land.txt
path_to_grid_definition = os.path.join('D:\\','small_projects','cpa_2013_07_userformat_reader',
                                      'data','auxiliary_data','grid_info')
#path to advisory flags from FTP Server
path_to_adv_flags = os.path.join('D:\\','small_projects','cpa_2013_07_userformat_reader',
                                      'data','auxiliary_data','advisory_flags')

In[3]:

#init the ASCAT_SSM reader with the paths
ascat_SSM_reader = ascat.Ascat_SSM(path_to_ascat_ssm_data,path_to_grid_definition,
                                   advisory_flags_path = path_to_adv_flags)

In[4]:

lon, lat = 16, 48

#reads ssm data nearest to this lon,lat coordinates
ssm_data_raw = ascat_SSM_reader.read_ssm(lon,lat)

#plot the data using pandas builtin plot functionality
ssm_data_raw.plot()
plt.show()

In[5]:

#read the same data but mask observations where the SSF shows frozen
#and where frozen and snow probabilty are greater than 20%
ssm_data_masked = ascat_SSM_reader.read_ssm(lon,lat,mask_ssf=True,mask_frozen_prob=20,mask_snow_prob=20)

#plot the data using pandas builtin plot functionality
#this time using a subplot for each variable in the DataFrame
ssm_data_masked.plot(subplots=True)
plt.show()

In[6]:

#plot raw and masked SSM data in one plot to compare them

ssm_data_raw.data['SSM'].plot(label='raw SSM data')
ssm_data_masked.data['SSM'].plot(label='masked SSM data')
plt.legend()
plt.show()

In[7]:

ascat_SWI_reader = ascat.Ascat_SWI(path_to_ascat_swi_data,path_to_grid_definition,
                                   advisory_flags_path = path_to_adv_flags)


#reads swi data nearest to this lon,lat coordinates
#without any additional keywords all unmasked T values and
#Quality flags will be read
swi_data_raw = ascat_SWI_reader.read_swi(lon,lat)

#plot the data using pandas builtin plot functionality
swi_data_raw.plot()
plt.show()

In[8]:

#read the same data but this time only SWI with a T value
#of 20 is returned
swi_data_T_20 = ascat_SWI_reader.read_swi(lon,lat,T=20)

#plot the data using pandas builtin plot functionality
#this time using a subplot for each variable in the DataFrame
swi_data_T_20.plot(subplots=True)
plt.show()

In[9]:

#you can also mask manually if you prefer
swi_data_T_20.data = swi_data_T_20.data[swi_data_T_20.data['frozen_prob'] < 10]

swi_data_T_20.plot(subplots=True)
plt.show()