def get_lik_sp_abd_dbh_four_models(raw_data_site,
dataset_name,
out_dir='./out_files/'):
"""Obtain the summed log likelihood of each species having abundance n and its individuals having
their specific dbh values for the three models METE, SSNT on D, and SSNT on D ** (2/3).
"""
site = raw_data_site['site'][0]
G, S, N, E = get_GSNE(raw_data_site)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
beta_ssnt = mete.get_beta(S, N, version='untruncated')
beta_asne = mete.get_beta(S, N)
d_list = raw_data_site['dbh'] / min(raw_data_site['dbh'])
lik_asne, lik_agsne, lik_ssnt_0, lik_ssnt_1 = 0, 0, 0, 0
for sp in np.unique(raw_data_site['sp']):
sp_dbh = d_list[raw_data_site['sp'] == sp]
lik_asne += lik_sp_abd_dbh_asne([G, S, N, E], np.exp(-beta_asne),
len(sp_dbh), sp_dbh)
lik_agsne += lik_sp_abd_dbh_agsne([G, S, N, E], [
lambda1, beta, lambda3,
agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3
], len(sp_dbh), sp_dbh)
lik_ssnt_0 += lik_sp_abd_dbh_ssnt([G, S, N, E],
np.exp(-beta_ssnt), 'ssnt_0',
len(sp_dbh), sp_dbh, d_list)
lik_ssnt_1 += lik_sp_abd_dbh_ssnt([G, S, N, E],
np.exp(-beta_ssnt), 'ssnt_1',
len(sp_dbh), sp_dbh, d_list)
out = open(out_dir + 'lik_sp_abd_dbh_four_models.txt', 'a')
print >> out, dataset_name, site, str(lik_asne), str(lik_agsne), str(
lik_ssnt_0), str(lik_ssnt_1)
out.close()
def get_obs_pred_sdr(raw_data_site,
dataset_name,
model,
out_dir='./out_files/'):
"""Write the observed and predicted SDR (in unit of D^2) to file for a given model.
Inputs:
raw_data_site - data in the same format as obtained by clean_data_genera(), with
four columns site, sp, dbh, and genus, and only for one site.
dataset_name - name of the dataset for raw_data_site.
model - can take one of four values 'ssnt_0' (constant growth of diameter D),
'ssnt_1' (constant growth of D^2/3), 'asne', or 'agsne'.
out_dir - directory for output file.
"""
scaled_d = raw_data_site['dbh'] / min(raw_data_site['dbh'])
scaled_d2 = scaled_d**2
G, S, N, E = get_GSNE(raw_data_site)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
theta_agsne = mete_distributions.theta_agsne([G, S, N, E], [
lambda1, beta, lambda3,
agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3
])
theta_asne = mete_distributions.theta_epsilon(S, N, E)
if model == 'ssnt_1': alpha = 2 / 3
else: alpha = 1
par = N / (sum(scaled_d**alpha) - N)
iisd_ssnt = ssnt_isd_bounded(alpha, par)
pred, obs = [], []
for sp in np.unique(raw_data_site['sp']):
n = len(raw_data_site[raw_data_site['sp'] ==
sp]) # Number of individuals within species
if model == 'agsne':
genus_sp = raw_data_site['genus'][raw_data_site['sp'] == sp][0]
m = len(
np.unique(
raw_data_site['sp'][raw_data_site['genus'] == genus_sp])
) # Number of specis within genus
pred.append(theta_agsne.expected(m, n))
elif model == 'asne':
pred.append(theta_asne.E(n))
elif model in ['ssnt_0', 'ssnt_1']:
pred.append(iisd_ssnt.expected_square())
obs.append(np.mean(scaled_d2[raw_data_site['sp'] == sp]))
results = np.zeros((S, ), dtype=('S15, f8, f8'))
results['f0'] = np.array([raw_data_site['site'][0]] * S)
results['f1'] = obs
results['f2'] = pred
f1_write = open(out_dir + dataset_name + '_obs_pred_sdr_' + model + '.csv',
'ab')
f1 = csv.writer(f1_write)
f1.writerows(results)
f1_write.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_obs_pred_sdr(raw_data_site, dataset_name, model, out_dir = './out_files/'):
"""Write the observed and predicted SDR (in unit of D^2) to file for a given model.
Inputs:
raw_data_site - data in the same format as obtained by clean_data_genera(), with
four columns site, sp, dbh, and genus, and only for one site.
dataset_name - name of the dataset for raw_data_site.
model - can take one of four values 'ssnt_0' (constant growth of diameter D),
'ssnt_1' (constant growth of D^2/3), 'asne', or 'agsne'.
out_dir - directory for output file.
"""
scaled_d = raw_data_site['dbh'] / min(raw_data_site['dbh'])
scaled_d2 = scaled_d **2
G, S, N, E = get_GSNE(raw_data_site)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
theta_agsne = mete_distributions.theta_agsne([G, S, N, E], [lambda1, beta, lambda3, agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3])
theta_asne = mete_distributions.theta_epsilon(S, N, E)
if model == 'ssnt_1': alpha = 2/3
else: alpha = 1
par = N / (sum(scaled_d ** alpha) - N)
iisd_ssnt = ssnt_isd_bounded(alpha, par)
pred, obs = [], []
for sp in np.unique(raw_data_site['sp']):
n = len(raw_data_site[raw_data_site['sp'] == sp]) # Number of individuals within species
if model == 'agsne':
genus_sp = raw_data_site['genus'][raw_data_site['sp'] == sp][0]
m = len(np.unique(raw_data_site['sp'][raw_data_site['genus'] == genus_sp])) # Number of specis within genus
pred.append(theta_agsne.expected(m, n))
elif model == 'asne': pred.append(theta_asne.E(n))
elif model in ['ssnt_0', 'ssnt_1']: pred.append(iisd_ssnt.expected_square())
obs.append(np.mean(scaled_d2[raw_data_site['sp'] == sp]))
results = np.zeros((S, ), dtype = ('S15, f8, f8'))
results['f0'] = np.array([raw_data_site['site'][0]] * S)
results['f1'] = obs
results['f2'] = pred
f1_write = open(out_dir + dataset_name + '_obs_pred_sdr_' + model + '.csv', 'ab')
f1 = csv.writer(f1_write)
f1.writerows(results)
f1_write.close()
def get_lik_sp_abd_dbh_four_models(raw_data_site, dataset_name, out_dir = './out_files/'):
"""Obtain the summed log likelihood of each species having abundance n and its individuals having
their specific dbh values for the three models METE, SSNT on D, and SSNT on D ** (2/3).
"""
site = raw_data_site['site'][0]
G, S, N, E = get_GSNE(raw_data_site)
lambda1, beta, lambda3 = agsne.get_agsne_lambdas(G, S, N, E)
beta_ssnt = mete.get_beta(S, N, version = 'untruncated')
beta_asne = mete.get_beta(S, N)
d_list = raw_data_site['dbh'] / min(raw_data_site['dbh'])
lik_asne, lik_agsne, lik_ssnt_0, lik_ssnt_1 = 0, 0, 0, 0
for sp in np.unique(raw_data_site['sp']):
sp_dbh = d_list[raw_data_site['sp'] == sp]
lik_asne += lik_sp_abd_dbh_asne([G, S, N, E], np.exp(-beta_asne), len(sp_dbh), sp_dbh)
lik_agsne += lik_sp_abd_dbh_agsne([G, S, N, E],
[lambda1, beta, lambda3, agsne.agsne_lambda3_z(lambda1, beta, S) / lambda3], len(sp_dbh), sp_dbh)
lik_ssnt_0 += lik_sp_abd_dbh_ssnt([G, S, N, E], np.exp(-beta_ssnt), 'ssnt_0', len(sp_dbh), sp_dbh, d_list)
lik_ssnt_1 += lik_sp_abd_dbh_ssnt([G, S, N, E], np.exp(-beta_ssnt), 'ssnt_1', len(sp_dbh), sp_dbh, d_list)
out = open(out_dir + 'lik_sp_abd_dbh_four_models.txt', 'a')
print>>out, dataset_name, site, str(lik_asne), str(lik_agsne), str(lik_ssnt_0), str(lik_ssnt_1)
out.close()
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))
