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



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:
(Pearsons_r(sm_and_insitu_sm=0.61607679781575175), p_value(sm_and_insitu_sm=3.1170801211098453e-65))
Spearman's (rho,p_value) (0.64651747115098912, 1.0057610194056589e-73)
Kendalls's (tau,p_value) (0.4685441550995097, 2.4676437876515864e-67)
(Kendall_tau(sm_and_insitu_sm=0.4685441550995097), p_value(sm_and_insitu_sm=2.4676437876515864e-67))
rmsd(sm_and_insitu_sm=0.078018684719599857)
Bias 0.00168114697282
Nash Sutcliffe 0.246416864767



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:
(Pearsons_r(sm_and_insitu_sm=0.66000287576696759), p_value(sm_and_insitu_sm=1.3332742454781072e-126))
Spearman's (rho,p_value) (0.65889275747696652, 4.890533231776912e-126)
Kendalls's (tau,p_value) (0.48653686844813893, 6.6517671082477896e-118)
(Kendall_tau(sm_and_insitu_sm=0.48653686844813893), p_value(sm_and_insitu_sm=6.6517671082477896e-118))
rmsd(sm_and_insitu_sm=0.028314835540753237)
Bias 4.56170862568e-05
Nash Sutcliffe 0.316925662899