def get_isd_lik_three_models(dat_list, out_dir = './out_files/', cutoff = 9):
"""Function to obtain the community-level log-likelihood (standardized by the number of individuals)
as well as AICc values for METE, SSNT on D, and SSNT on D**(2/3) and write to files.
"""
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]
S0 = len(np.unique(dat_site['sp']))
if S0 > cutoff:
N0 = len(dat_site)
dbh_scaled = dat_site['dbh'] / min(dat_site['dbh'])
psi = mete_distributions.psi_epsilon(S0, N0, sum(dbh_scaled ** 2))
ssnt_isd = ssnt_isd_bounded(1, N0 / (sum(dbh_scaled) - N0))
ssnt_isd_transform = ssnt_isd_bounded(2/3, N0 / (sum(dbh_scaled ** (2/3)) - N0))
lik_mete, lik_ssnt, lik_ssnt_transform = 0, 0, 0
for dbh in dbh_scaled:
lik_mete += np.log(psi.pdf(dbh ** 2) * 2 * dbh) # psi is on dbh**2
lik_ssnt += np.log(ssnt_isd.pdf(dbh))
lik_ssnt_transform += np.log(ssnt_isd_transform.pdf(dbh))
out1 = open(out_dir + 'isd_lik_three_models.txt', 'a')
print>>out1, dat_name, site, str(lik_mete / N0), str(lik_ssnt / N0), str(lik_ssnt_transform / N0)
out1.close()
out2 = open(out_dir + 'isd_aicc_three_models.txt', 'a')
# METE has three parameters (S0, N0, E0) for ISD, while SSNT has two (N0 and sum(dbh**alpha))
print>>out2, dat_name, site, str(mtools.AICc(lik_mete, 3, N0)), str(mtools.AICc(lik_ssnt, 2, N0)), \
str(mtools.AICc(lik_ssnt_transform, 2, N0))
out2.close()
def bootstrap_SDR(name_site_combo, model, in_dir = './data/', out_dir = './out_files/', Niter = 200):
"""A general function of bootstrapping for ISD applying to all four models.
Inputs:
name_site_combo: a list with dat_name and site
model - takes one of four values 'ssnt_0', 'ssnt_1', 'asne', or 'agsne'
in_dir - directory of raw data
out_dir - directory used both in input (obs_pred.csv file) and output
Niter - number of bootstrap samples
Output:
Writes to one file on disk for R^2.
"""
dat_name, site = name_site_combo
dat = wk.import_raw_data(in_dir + dat_name + '.csv')
dat_site = dat[dat['site'] == site]
dat_clean = clean_data_agsne(dat_site)
G, S, N, E = get_GSNE(dat_clean)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
par_list = []
for sp in np.unique(dat_clean['sp']):
dat_sp = dat_clean[dat_clean['sp'] == sp]
n = len(dat_sp)
genus_sp = dat_sp['genus'][0]
m = len(np.unique(dat_clean[dat_clean['genus'] == genus_sp]['sp']))
par_list.append([m, n])
pred_obs = wk.import_obs_pred_data(out_dir + dat_name + '_obs_pred_sdr_' + model + '.csv')
pred = pred_obs[pred_obs['site'] == site]['pred']
obs = pred_obs[pred_obs['site'] == site]['obs']
out_list_rsquare = [dat_name, site, str(mtools.obs_pred_rsquare(np.log10(obs), np.log10(pred)))]
iisd_agsne = mete_distributions.theta_agsne([G, S, N, E], [lambda1, beta, lambda3, agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3])
iisd_asne = mete_distributions.theta_epsilon(S, N, E)
dbh_scaled = np.array(dat_clean['dbh'] / min(dat_clean['dbh']))
iisd_ssnt_0 = ssnt_isd_bounded(1, N / (sum(dbh_scaled ** 1) - N))
iisd_ssnt_1 = ssnt_isd_bounded(2/3, N / (sum(dbh_scaled ** (2/3)) - N))
dist_for_model = {'ssnt_0': iisd_ssnt_0, 'ssnt_1': iisd_ssnt_1, 'asne': iisd_asne, 'agsne': iisd_agsne}
dist = dist_for_model[model]
for i in range(Niter):
if model in ['ssnt_0', 'ssnt_1']: obs_boot = np.array([np.mean((dist.rvs(par[1])) ** 2) for par in par_list]) # Here par[1] is n for each species
elif model == 'asne':
obs_boot = np.array([np.mean(np.array(dist.rvs(par[1], par[1]))) for par in par_list])
else:
obs_boot = np.array([np.mean(np.array(dist.rvs(par[1], par[1], par[0]))) for par in par_list])
out_list_rsquare.append(str(mtools.obs_pred_rsquare(np.log10(obs_boot), np.log10(pred))))
wk.write_to_file(out_dir + 'SDR_bootstrap_' + model + '_rsquare.txt', ",".join(str(x) for x in out_list_rsquare))
def bootstrap_SAD(name_site_combo, model, in_dir = './data/', out_dir = './out_files/', Niter = 200):
"""A general function of bootstrapping for SAD applying to all four models.
Inputs:
name_site_combo: a list with dat_name and site
model - takes one of four values 'ssnt_0', 'ssnt_1', 'asne', or 'agsne'
in_dir - directory of raw data
out_dir - directory used both in input (obs_pred.csv file) and output
Niter - number of bootstrap samples
Output:
Writes to disk, with one file for R^2 and one for KS statistic.
"""
dat_name, site = name_site_combo
dat = wk.import_raw_data(in_dir + dat_name + '.csv')
dat_site = dat[dat['site'] == site]
dat_clean = clean_data_agsne(dat_site)
G, S, N, E = get_GSNE(dat_clean)
beta_ssnt = mete.get_beta(S, N, version = 'untruncated')
beta_asne = mete.get_beta(S, N)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
sad_agsne = mete_distributions.sad_agsne([G, S, N, E], [lambda1, beta, lambda3, agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3])
dist_for_model = {'ssnt_0': stats.logser(np.exp(-beta_ssnt)),
'ssnt_1': stats.logser(np.exp(-beta_ssnt)),
'asne': md.trunc_logser(np.exp(-beta_asne), N),
'agsne': sad_agsne}
dist = dist_for_model[model]
pred_obs = wk.import_obs_pred_data(out_dir + dat_name + '_obs_pred_rad_' + model + '.csv')
pred = pred_obs[pred_obs['site'] == site]['pred'][::-1]
obs = pred_obs[pred_obs['site'] == site]['obs'][::-1]
out_list_rsquare = [dat_name, site, str(mtools.obs_pred_rsquare(np.log10(obs), np.log10(pred)))]
emp_cdf = mtools.get_emp_cdf(obs)
out_list_ks = [dat_name, site, str(max(abs(emp_cdf - np.array([dist.cdf(x) for x in obs]))))]
for i in range(Niter):
obs_boot = np.array(sorted(dist.rvs(S)))
cdf_boot = np.array([dist.cdf(x) for x in obs_boot])
emp_cdf_boot = mtools.get_emp_cdf(obs_boot)
out_list_rsquare.append(str(mtools.obs_pred_rsquare(np.log10(obs_boot), np.log10(pred))))
out_list_ks.append(str(max(abs(emp_cdf_boot - np.array(cdf_boot)))))
wk.write_to_file(out_dir + 'SAD_bootstrap_' + model + '_rsquare.txt', ",".join(str(x) for x in out_list_rsquare))
wk.write_to_file(out_dir + 'SAD_bootstrap_' + model + '_ks.txt', ",".join(str(x) for x in out_list_ks))
def get_isd_lik_three_models(dat_list, out_dir='./out_files/', cutoff=9):
"""Function to obtain the community-level log-likelihood (standardized by the number of individuals)
as well as AICc values for METE, SSNT on D, and SSNT on D**(2/3) and write to files.
"""
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]
S0 = len(np.unique(dat_site['sp']))
if S0 > cutoff:
N0 = len(dat_site)
dbh_scaled = dat_site['dbh'] / min(dat_site['dbh'])
psi = mete_distributions.psi_epsilon(S0, N0,
sum(dbh_scaled**2))
ssnt_isd = ssnt_isd_bounded(1, N0 / (sum(dbh_scaled) - N0))
ssnt_isd_transform = ssnt_isd_bounded(
2 / 3, N0 / (sum(dbh_scaled**(2 / 3)) - N0))
lik_mete, lik_ssnt, lik_ssnt_transform = 0, 0, 0
for dbh in dbh_scaled:
lik_mete += np.log(psi.pdf(dbh**2) * 2 *
dbh) # psi is on dbh**2
lik_ssnt += np.log(ssnt_isd.pdf(dbh))
lik_ssnt_transform += np.log(ssnt_isd_transform.pdf(dbh))
out1 = open(out_dir + 'isd_lik_three_models.txt', 'a')
print >> out1, dat_name, site, str(lik_mete / N0), str(
lik_ssnt / N0), str(lik_ssnt_transform / N0)
out1.close()
out2 = open(out_dir + 'isd_aicc_three_models.txt', 'a')
# METE has three parameters (S0, N0, E0) for ISD, while SSNT has two (N0 and sum(dbh**alpha))
print>>out2, dat_name, site, str(mtools.AICc(lik_mete, 3, N0)), str(mtools.AICc(lik_ssnt, 2, N0)), \
str(mtools.AICc(lik_ssnt_transform, 2, N0))
out2.close()
"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)
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)
def bootstrap_ISD(name_site_combo, model, in_dir = './data/', out_dir = './out_files/', Niter = 200):
"""A general function of bootstrapping for ISD applying to all four models.
Inputs:
name_site_combo: a list with dat_name and site
model - takes one of four values 'ssnt_0', 'ssnt_1', 'asne', or 'agsne'
in_dir - directory of raw data
out_dir - directory used both in input (obs_pred.csv file) and output
Niter - number of bootstrap samples
Output:
Writes to disk, with one file for R^2 and one for KS statistic.
"""
dat_name, site = name_site_combo
dat = wk.import_raw_data(in_dir + dat_name + '.csv')
dat_site = dat[dat['site'] == site]
dat_clean = clean_data_agsne(dat_site)
G, S, N, E = get_GSNE(dat_clean)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
isd_agsne = mete_distributions.psi_agsne([G, S, N, E], [lambda1, beta, lambda3, agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3])
isd_asne = mete_distributions.psi_epsilon_approx(S, N, E)
dbh_scaled = np.array(dat_clean['dbh'] / min(dat_clean['dbh']))
isd_ssnt_0 = ssnt_isd_bounded(1, N / (sum(dbh_scaled ** 1) - N))
isd_ssnt_1 = ssnt_isd_bounded(2/3, N / (sum(dbh_scaled ** (2/3)) - N))
dist_for_model = {'ssnt_0': isd_ssnt_0, 'ssnt_1': isd_ssnt_1, 'asne': isd_asne, 'agsne': isd_agsne}
dist = dist_for_model[model]
pred_obs = wk.import_obs_pred_data(out_dir + dat_name + '_obs_pred_isd_' + model + '.csv')
pred = pred_obs[pred_obs['site'] == site]['pred']
obs = pred_obs[pred_obs['site'] == site]['obs']
out_list_rsquare = [dat_name, site, str(mtools.obs_pred_rsquare(np.log10(obs), np.log10(pred)))]
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_rsquare.txt', ",".join(str(x) for x in out_list_rsquare), new_line = False)
emp_cdf = mtools.get_emp_cdf(obs)
out_list_ks = [dat_name, site, str(max(abs(emp_cdf - np.array([dist.cdf(x) for x in obs]))))]
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt', ",".join(str(x) for x in out_list_ks), new_line = False)
num_pools = 8 # Assuming that 8 pools are to be created
for i in xrange(Niter):
obs_boot = []
cdf_boot = []
while len(obs_boot) < N:
pool = multiprocessing.Pool(num_pools)
out_sample = pool.map(wk.generate_isd_sample, [dist for j in xrange(num_pools)])
for combo in out_sample:
cdf_sublist, sample_sublist = combo
obs_boot.extend(sample_sublist)
cdf_boot.extend(cdf_sublist)
pool.close()
pool.join()
if model in ['asne', 'agsne']: obs_boot = np.sort(obs_boot[:N]) ** 0.5 # Convert to diameter
else: obs_boot = np.sort(obs_boot[:N])
sample_rsquare = mtools.obs_pred_rsquare(np.log10(obs_boot), np.log10(pred))
sample_ks = max(abs(emp_cdf - np.sort(cdf_boot[:N])))
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_rsquare.txt', "".join([',', str(sample_rsquare)]), new_line = False)
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt', "".join([',', str(sample_ks)]), new_line = False)
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_rsquare.txt', '\t')
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt', '\t')
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt', ",".join(str(x) for x in out_list_ks))
def bootstrap_SDR(name_site_combo,
model,
in_dir='./data/',
out_dir='./out_files/',
Niter=200):
"""A general function of bootstrapping for ISD applying to all four models.
Inputs:
name_site_combo: a list with dat_name and site
model - takes one of four values 'ssnt_0', 'ssnt_1', 'asne', or 'agsne'
in_dir - directory of raw data
out_dir - directory used both in input (obs_pred.csv file) and output
Niter - number of bootstrap samples
Output:
Writes to one file on disk for R^2.
"""
dat_name, site = name_site_combo
dat = wk.import_raw_data(in_dir + dat_name + '.csv')
dat_site = dat[dat['site'] == site]
dat_clean = clean_data_agsne(dat_site)
G, S, N, E = get_GSNE(dat_clean)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
par_list = []
for sp in np.unique(dat_clean['sp']):
dat_sp = dat_clean[dat_clean['sp'] == sp]
n = len(dat_sp)
genus_sp = dat_sp['genus'][0]
m = len(np.unique(dat_clean[dat_clean['genus'] == genus_sp]['sp']))
par_list.append([m, n])
pred_obs = wk.import_obs_pred_data(out_dir + dat_name + '_obs_pred_sdr_' +
model + '.csv')
pred = pred_obs[pred_obs['site'] == site]['pred']
obs = pred_obs[pred_obs['site'] == site]['obs']
out_list_rsquare = [
dat_name, site,
str(mtools.obs_pred_rsquare(np.log10(obs), np.log10(pred)))
]
iisd_agsne = mete_distributions.theta_agsne([G, S, N, E], [
lambda1, beta, lambda3,
agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3
])
iisd_asne = mete_distributions.theta_epsilon(S, N, E)
dbh_scaled = np.array(dat_clean['dbh'] / min(dat_clean['dbh']))
iisd_ssnt_0 = ssnt_isd_bounded(1, N / (sum(dbh_scaled**1) - N))
iisd_ssnt_1 = ssnt_isd_bounded(2 / 3, N / (sum(dbh_scaled**(2 / 3)) - N))
dist_for_model = {
'ssnt_0': iisd_ssnt_0,
'ssnt_1': iisd_ssnt_1,
'asne': iisd_asne,
'agsne': iisd_agsne
}
dist = dist_for_model[model]
for i in range(Niter):
if model in ['ssnt_0', 'ssnt_1']:
obs_boot = np.array([
np.mean((dist.rvs(par[1]))**2) for par in par_list
]) # Here par[1] is n for each species
elif model == 'asne':
obs_boot = np.array([
np.mean(np.array(dist.rvs(par[1], par[1]))) for par in par_list
])
else:
obs_boot = np.array([
np.mean(np.array(dist.rvs(par[1], par[1], par[0])))
for par in par_list
])
out_list_rsquare.append(
str(mtools.obs_pred_rsquare(np.log10(obs_boot), np.log10(pred))))
wk.write_to_file(out_dir + 'SDR_bootstrap_' + model + '_rsquare.txt',
",".join(str(x) for x in out_list_rsquare))
def bootstrap_ISD(name_site_combo,
model,
in_dir='./data/',
out_dir='./out_files/',
Niter=200):
"""A general function of bootstrapping for ISD applying to all four models.
Inputs:
name_site_combo: a list with dat_name and site
model - takes one of four values 'ssnt_0', 'ssnt_1', 'asne', or 'agsne'
in_dir - directory of raw data
out_dir - directory used both in input (obs_pred.csv file) and output
Niter - number of bootstrap samples
Output:
Writes to disk, with one file for R^2 and one for KS statistic.
"""
dat_name, site = name_site_combo
dat = wk.import_raw_data(in_dir + dat_name + '.csv')
dat_site = dat[dat['site'] == site]
dat_clean = clean_data_agsne(dat_site)
G, S, N, E = get_GSNE(dat_clean)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
isd_agsne = mete_distributions.psi_agsne([G, S, N, E], [
lambda1, beta, lambda3,
agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3
])
isd_asne = mete_distributions.psi_epsilon_approx(S, N, E)
dbh_scaled = np.array(dat_clean['dbh'] / min(dat_clean['dbh']))
isd_ssnt_0 = ssnt_isd_bounded(1, N / (sum(dbh_scaled**1) - N))
isd_ssnt_1 = ssnt_isd_bounded(2 / 3, N / (sum(dbh_scaled**(2 / 3)) - N))
dist_for_model = {
'ssnt_0': isd_ssnt_0,
'ssnt_1': isd_ssnt_1,
'asne': isd_asne,
'agsne': isd_agsne
}
dist = dist_for_model[model]
pred_obs = wk.import_obs_pred_data(out_dir + dat_name + '_obs_pred_isd_' +
model + '.csv')
pred = pred_obs[pred_obs['site'] == site]['pred']
obs = pred_obs[pred_obs['site'] == site]['obs']
out_list_rsquare = [
dat_name, site,
str(mtools.obs_pred_rsquare(np.log10(obs), np.log10(pred)))
]
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_rsquare.txt',
",".join(str(x) for x in out_list_rsquare),
new_line=False)
emp_cdf = mtools.get_emp_cdf(obs)
out_list_ks = [
dat_name, site,
str(max(abs(emp_cdf - np.array([dist.cdf(x) for x in obs]))))
]
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt',
",".join(str(x) for x in out_list_ks),
new_line=False)
num_pools = 8 # Assuming that 8 pools are to be created
for i in xrange(Niter):
obs_boot = []
cdf_boot = []
while len(obs_boot) < N:
pool = multiprocessing.Pool(num_pools)
out_sample = pool.map(wk.generate_isd_sample,
[dist for j in xrange(num_pools)])
for combo in out_sample:
cdf_sublist, sample_sublist = combo
obs_boot.extend(sample_sublist)
cdf_boot.extend(cdf_sublist)
pool.close()
pool.join()
if model in ['asne', 'agsne']:
obs_boot = np.sort(obs_boot[:N])**0.5 # Convert to diameter
else:
obs_boot = np.sort(obs_boot[:N])
sample_rsquare = mtools.obs_pred_rsquare(np.log10(obs_boot),
np.log10(pred))
sample_ks = max(abs(emp_cdf - np.sort(cdf_boot[:N])))
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_rsquare.txt',
"".join([',', str(sample_rsquare)]),
new_line=False)
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt',
"".join([',', str(sample_ks)]),
new_line=False)
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_rsquare.txt', '\t')
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt', '\t')
wk.write_to_file(out_dir + 'ISD_bootstrap_' + model + '_ks.txt',
",".join(str(x) for x in out_list_ks))
def bootstrap_SAD(name_site_combo,
model,
in_dir='./data/',
out_dir='./out_files/',
Niter=200):
"""A general function of bootstrapping for SAD applying to all four models.
Inputs:
name_site_combo: a list with dat_name and site
model - takes one of four values 'ssnt_0', 'ssnt_1', 'asne', or 'agsne'
in_dir - directory of raw data
out_dir - directory used both in input (obs_pred.csv file) and output
Niter - number of bootstrap samples
Output:
Writes to disk, with one file for R^2 and one for KS statistic.
"""
dat_name, site = name_site_combo
dat = wk.import_raw_data(in_dir + dat_name + '.csv')
dat_site = dat[dat['site'] == site]
dat_clean = clean_data_agsne(dat_site)
G, S, N, E = get_GSNE(dat_clean)
beta_ssnt = mete.get_beta(S, N, version='untruncated')
beta_asne = mete.get_beta(S, N)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
sad_agsne = mete_distributions.sad_agsne([G, S, N, E], [
lambda1, beta, lambda3,
agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3
])
dist_for_model = {
'ssnt_0': stats.logser(np.exp(-beta_ssnt)),
'ssnt_1': stats.logser(np.exp(-beta_ssnt)),
'asne': md.trunc_logser(np.exp(-beta_asne), N),
'agsne': sad_agsne
}
dist = dist_for_model[model]
pred_obs = wk.import_obs_pred_data(out_dir + dat_name + '_obs_pred_rad_' +
model + '.csv')
pred = pred_obs[pred_obs['site'] == site]['pred'][::-1]
obs = pred_obs[pred_obs['site'] == site]['obs'][::-1]
out_list_rsquare = [
dat_name, site,
str(mtools.obs_pred_rsquare(np.log10(obs), np.log10(pred)))
]
emp_cdf = mtools.get_emp_cdf(obs)
out_list_ks = [
dat_name, site,
str(max(abs(emp_cdf - np.array([dist.cdf(x) for x in obs]))))
]
for i in range(Niter):
obs_boot = np.array(sorted(dist.rvs(S)))
cdf_boot = np.array([dist.cdf(x) for x in obs_boot])
emp_cdf_boot = mtools.get_emp_cdf(obs_boot)
out_list_rsquare.append(
str(mtools.obs_pred_rsquare(np.log10(obs_boot), np.log10(pred))))
out_list_ks.append(str(max(abs(emp_cdf_boot - np.array(cdf_boot)))))
wk.write_to_file(out_dir + 'SAD_bootstrap_' + model + '_rsquare.txt',
",".join(str(x) for x in out_list_rsquare))
wk.write_to_file(out_dir + 'SAD_bootstrap_' + model + '_ks.txt',
",".join(str(x) for x in out_list_ks))
