def get_good_study(data_list, sig = True):
"""Return a list of studies that pass the criteria check"""
study_list_data = sorted(list(set(data_list['study'])))
good_study_list = []
for study in study_list_data:
data_study = data_list[data_list['study'] == study]
if tl.inclusion_criteria(data_study, sig = sig):
good_study_list.append(study)
return good_study_list
import matplotlib.pyplot as plt
import TL_functions as tl
import numpy as np
from scipy import stats
import random
# Figure 1 - visual representation using three studies
study_list = ['1_1', '10_1', '52_11']
fig = plt.figure(figsize = (10.5, 7))
iplot = 1
for feas_type in ['partition', 'composition']:
for study in study_list:
ax = plt.subplot(2, 3, iplot)
if iplot == 1 or iplot == 4: legend = True
else: legend = False
tl.plot_emp_vs_sim(study, feas_type = feas_type, ax = ax, legend = legend)
iplot += 1
plt.subplots_adjust(wspace = 0.29, hspace = 0.29)
plt.savefig('Fig1.pdf', dpi = 600)
# Figure 2 - compare the full distribution of empirical TLs and those from the feasible sets
study_info = tl.get_study_info('study_taxon_type.txt')
tl_pars_par = tl.get_tl_par_file('out_files/TL_form_partition.txt')
var_par = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_partition_1000_full.txt')
var_comp = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_composition_1000_full.txt')
par_quad = tl.get_val_ind_sample_file('out_files/TL_quad_p_partition.txt')
comp_quad = tl.get_val_ind_sample_file('out_files/TL_quad_p_composition.txt')
b_obs, b_par, b_comp, b_type = [], [], [], []
p_obs, p_par, p_comp = [], [], []
"""Print the summary for Results"""
from __future__ import division
import TL_functions as tl
import numpy as np
from scipy import stats
study_info = tl.get_study_info('study_taxon_type.txt')
tl_pars_par = tl.get_tl_par_file('TL_form_partition.txt')
tl_pars_comp = tl.get_tl_par_file('TL_form_composition.txt')
var_par = tl.get_var_sample_file('taylor_QN_var_predicted_partition_1000_full.txt')
var_comp = tl.get_var_sample_file('taylor_QN_var_predicted_composition_1000_full.txt')
study_spatial = [study for study in np.unique(var_par['study']) if study_info['type'] [study_info['study'] == study]== 'spatial']
study_temporal = [study for study in np.unique(var_par['study']) if study_info['type'][study_info['study'] == study] == 'temporal']
# 1. Curvature
par_quad = tl.get_val_ind_sample_file('TL_quad_p_partition.txt')
comp_quad = tl.get_val_ind_sample_file('TL_quad_p_composition.txt')
sig_spatial, sig_temporal, sig_par, sig_comp, tot_sig = 0, 0, 0, 0, 0
for study in study_spatial:
row_study_par = list(par_quad[par_quad['study'] == study][0])
row_study_comp = list(comp_quad[comp_quad['study'] == study][0])
if row_study_par[1] < 0.05: sig_spatial += 1
sig_par += len([x for x in row_study_par[2:] if x < 0.05])
sig_comp += len([x for x in row_study_comp[2:] if x < 0.05])
tot_sig += len(row_study_comp[2:])
for study in study_temporal:
row_study_par = list(par_quad[par_quad['study'] == study][0])
row_study_comp = list(comp_quad[comp_quad['study'] == study][0])
from __future__ import division
import TL_functions as tl
import multiprocessing
def get_good_study(data_list, sig = True):
"""Return a list of studies that pass the criteria check"""
study_list_data = sorted(list(set(data_list['study'])))
good_study_list = []
for study in study_list_data:
data_study = data_list[data_list['study'] == study]
if tl.inclusion_criteria(data_study, sig = sig):
good_study_list.append(study)
return good_study_list
data_lit = tl.get_QN_mean_var_data('data_literature.txt')
data_glenda = tl.get_QN_mean_var_data('data_Glenda.txt')
good_list_lit = get_good_study(data_lit, sig = False)
good_list_glenda = get_good_study(data_glenda)
def map_lit_partition(study):
tl.TL_analysis(data_lit, study)
def map_lit_composition(study):
tl.TL_analysis(data_lit, study, analysis = 'composition')
def map_glenda_partition(study):
tl.TL_analysis(data_glenda, study)
def map_glenda_composition(study):
tl.TL_analysis(data_glenda, study, analysis = 'composition')
