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.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'):

    #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_data = temp_match.matching(ascat_time_series.data,ISMN_time_series.data,
                                                window=1/24.)
        #matched ISMN data is now a dataframe with the same datetime index
        #as ascat_time_series.data and the nearest insitu observation

        #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],
                          title='temporally merged data')
        plt.show()

        #this takes the matched_data DataFrame and scales all columns to the
        #column with the given reference_index, in this case in situ
        scaled_data = scaling.scale(matched_data, method='lin_cdf_match',
                                         reference_index=1)

        #now the scaled ascat data and insitu_sm are in the same space
        scaled_data.plot(figsize=(15,5), title='scaled data')
        plt.show()

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

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

        print "ISMN time series:",ISMN_time_series
        print "compared to"
        print ascat_time_series
        print "Results:"

        #df_metrics takes a DataFrame as input and automatically
        #calculates the metric on all combinations of columns
        #returns a named tuple for easy printing
        print df_metrics.pearsonr(scaled_data)
        print "Spearman's (rho,p_value)", metrics.spearmanr(x, y)
        print "Kendalls's (tau,p_value)", metrics.kendalltau(x, y)
        print df_metrics.kendalltau(scaled_data)
        print df_metrics.rmsd(scaled_data)
        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