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()
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