In[1]:

import pytesmo.io.ismn.interface as ismn
import pytesmo.io.sat.ascat as ascat
import pytesmo.temporal_matching as temp_match
import pytesmo.scaling as scaling
import pytesmo.metrics as metrics

import os
import matplotlib.pyplot as plt

In[2]:

#set paths for ASCAT SSM
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_grid_definition = os.path.join('D:\\','small_projects',
                                      'cpa_2013_07_userformat_reader','data','auxiliary_data','grid_info')

path_to_adv_flags = os.path.join('D:\\','small_projects',
                                      'cpa_2013_07_userformat_reader','data','auxiliary_data','advisory_flags')

In[3]:

#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')

In[4]:

#Initialize readers
ascat_SSM_reader = ascat.Ascat_SSM(path_to_ascat_ssm_data,path_to_grid_definition,
                                   advisory_flags_path = path_to_adv_flags)

ISMN_reader = ismn.ISMN_Interface(path_to_ismn_data)

In[5]:

i = 0

label_ascat='SSM'
label_insitu='insitu_sm'

In[6]:

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

    #this loops through all time series of this station that measure soil moisture
    #between 0 and 0.1 meters
    for ISMN_time_series in station.data_for_variable('soil moisture',min_depth=0,max_depth=0.1):

        ascat_time_series = ascat_SSM_reader.read_ssm(ISMN_time_series.longitude,
                                                      ISMN_time_series.latitude,
                                                      mask_ssf=True,
                                                      mask_frozen_prob = 5,
                                                      mask_snow_prob = 5)


        #drop nan values before doing any matching
        ascat_time_series.data = ascat_time_series.data.dropna()
        ISMN_time_series.data = ISMN_time_series.data.dropna()

        #rename the soil moisture column in ISMN_time_series.data to insitu_sm
        #to clearly differentiate the time series when they are plotted together
        ISMN_time_series.data.rename(columns={'soil moisture':label_insitu},inplace=True)

        #get ISMN data that was observerd within +- 1 hour(1/24. day) of the ASCAT observation
        #do not include those indexes where no observation was found
        matched_ISMN_data = temp_match.df_match(ascat_time_series.data,ISMN_time_series.data,
                                                window=1/24.,dropna=True)
        #matched ISMN data is now a dataframe with the same datetime index
        #as ascat_time_series.data and the nearest insitu observation

        #temporal matching also includes distance information
        #but we are not interested in it right now so let's drop it
        matched_ISMN_data = matched_ISMN_data.drop(['distance'],axis=1)

        #this joins the SSM column of the ASCAT data to the matched ISMN data
        matched_data = matched_ISMN_data.join(ascat_time_series.data[label_ascat])

        #continue only with relevant columns
        matched_data = matched_data[[label_ascat,label_insitu]]

        #the plot shows that ISMN and ASCAT are observed in different units
        matched_data.plot(figsize=(15,5),secondary_y=[label_ascat])
        plt.show()

        #this takes the matched_data DataFrame and adds a column
        scaled_data = scaling.add_scaled(matched_data, method='lin_cdf_match',
                                         label_in=label_ascat,label_scale=label_insitu)
        #the label of the scaled data is construced as label_in+'_scaled_'+method
        scaled_ascat_label = label_ascat+'_scaled_'+'lin_cdf_match'
        #now the scaled ascat data and insitu_sm are in the same space
        scaled_data.plot(figsize=(15,5),secondary_y=[label_ascat])
        plt.show()

        plt.scatter(scaled_data[scaled_ascat_label].values,scaled_data[label_insitu].values)
        plt.xlabel(scaled_ascat_label)
        plt.ylabel(label_insitu)
        plt.show()

        #calculate correlation coefficients, RMSD, bias, Nash Sutcliffe
        x, y = scaled_data[scaled_ascat_label].values, scaled_data[label_insitu].values

        print "ISMN time series:",ISMN_time_series
        print "compared to"
        print ascat_time_series
        print "Results:"
        print "Pearson's (R,p_value)", metrics.pearsonr(x, y)
        print "Spearman's (rho,p_value)", metrics.spearmanr(x, y)
        print "Kendalls's (tau,p_value)", metrics.kendalltau(x, y)
        print "RMSD", metrics.rmsd(x, y)
        print "Bias", metrics.bias(x, y)
        print "Nash Sutcliffe", metrics.nash_sutcliffe(x, y)


    i += 1

    #only show the first 2 stations, otherwise this program would run a long time
    #and produce a lot of plots
    if i >= 2:
        break

ISMN time series: OZNET Alabama 0.00 m - 0.05 m soil moisture measured with Stevens-Hydra-Probe
compared to
ASCAT time series gpi:1884359 lat:-35.342 lon:147.541
Results:
Pearson's (R,p_value) (0.59736953256517777, 1.4810058830429653e-60)
Spearman's (rho,p_value) (0.63684906343988457, 4.8971200217989799e-71)
Kendalls's (tau,p_value) (0.45994629380576146, 4.6771942474849024e-65)
RMSD 0.0807313501609
Bias 0.00258302466701
Nash Sutcliffe 0.221824420266

ISMN time series: OZNET Balranald-Bolton_Park 0.00 m - 0.08 m soil moisture measured with CS615
compared to
ASCAT time series gpi:1821003 lat:-33.990 lon:146.381
Results:
Pearson's (R,p_value) (0.65811087356086551, 9.1620935528699124e-126)
Spearman's (rho,p_value) (0.65874491635978671, 4.3707663858540222e-126)
Kendalls's (tau,p_value) (0.48451720923430946, 4.6613967263363183e-117)
RMSD 0.0283269899964
Bias -0.000181669876467
Nash Sutcliffe 0.314284186192