from __future__ import division

import matplotlib

matplotlib.use('Agg')

import matplotlib.pyplot as plt

import ssnt_mete_comparison as smc

import working_functions as wk

import mete_distributions as medis

import numpy as np

import multiprocessing

dat_list = ['ACA', 'BCI', 'BVSF', 'CSIRO', 'FERP', 'Lahei', 'LaSelva', 'NC', 'Oosting', 'Serimbu',

'WesternGhats', 'Cocoli', 'Luquillo', 'Sherman', 'Shirakami']

# Obtain predicted-observed values for the three patterns from the four models

dat_list_keep = []

dat_site_list = []

model_list = ['ssnt_0', 'ssnt_1', 'asne', 'agsne']

for dat_name in dat_list:

dat = wk.import_raw_data('./data/' + dat_name + '.csv')

for site in np.unique(dat['site']):

dat_site = dat[dat['site'] == site]

dat_clean = smc.clean_data_agsne(dat_site)

if dat_clean is not None:

dat_list_keep.append(dat_name)

dat_site_list.append([dat_name, site])

smc.get_lik_sp_abd_dbh_four_models(dat_clean, dat_name)

for model in model_list:

if model is 'ssnt_0': smc.get_obs_pred_sad(dat_clean, dat_name, 'ssnt')

elif model in ['asne', 'agsne']: smc.get_obs_pred_sad(dat_clean, dat_name, model)

smc.get_obs_pred_isd(dat_clean, dat_name, model)

smc.get_obs_pred_sdr(dat_clean, dat_name, model)

# Obtain and plot the log-likelihood comparisons (Fig.1)

smc.plot_likelihood_comp()

# Plot the predicted versus observed values (Fig. 2)

smc.plot_obs_pred_four_models(dat_list_keep)

# Plot the R^2 comparisons (Fig. 3)

smc.plot_r2_comp(dat_site_list)

# Boostrap analyses

# Caution: the bootstrap analyses can take days, depending on the size of the data sets

for model in model_list:

def model_boot_sad(name_site_combo):

smc.bootstrap_SAD(name_site_combo, model, Niter = 500)

pool = multiprocessing.Pool(8) # Assuming that there are 8 cores

pool.map(model_boot_sad, dat_site_list)

pool.close()

pool.join()

for model in model_list:

def model_boot_isd(name_site_combo):

smc.bootstrap_ISD(name_site_combo, model, Niter = 500)

pool = multiprocessing.Pool(8) # Assuming that there are 8 cores

pool.map(model_boot_isd, dat_site_list)

pool.close()

pool.join()

for model in model_list:

def model_boot_sdr(name_site_combo):

smc.bootstrap_SDR(name_site_combo, model, Niter = 500)

pool = multiprocessing.Pool(8) # Assuming that there are 8 cores

pool.map(model_boot_sdr, dat_site_list)

pool.close()

pool.join()

# Plot results from bootstrap analysis for four models (Figs B1 - B4)

for model in model_list:

smc.plot_bootstrap(dat_list, model)