from earm.lopez_embedded import model from max_plus_consumption_production import run_tropical import numpy as np import os import helper_functions as hf directory = os.path.dirname(__file__) parameters_path = os.path.join(directory, "parameters_5000") all_parameters = hf.listdir_fullpath(parameters_path) parameters = hf.read_pars(all_parameters[0]) t = np.linspace(0, 20000, 100) run_tropical(model, t, parameters, diff_par=1, type_sign='consumption', sp_visualize=[6])
axHistx.hist(column(cparp_info, 1), bins=np.arange(min(solver.tspan), max(solver.tspan) + binwidthx, binwidthx),
weights=weightsx)
# axHistx.axis["bottom"].major_ticklabels.set_visible(False)
for tl in axHistx.get_xticklabels():
tl.set_visible(False)
axHistx.set_yticks([0, 0.5, 1])
# axHisty.axis["left"].major_ticklabels.set_visible(False)
for tl in axHisty.get_yticklabels():
tl.set_visible(False)
axHisty.set_xticks([0, 0.5, 1])
axApop.legend(loc=0)
fig.savefig('/home/oscar/Documents/tropical_project/all_parameters_earm.png', format='png', dpi=400)
return
all_parameters_path = hf.listdir_fullpath('/home/oscar/tropical_project_new/parameters_5000')
clusters_path = hf.listdir_fullpath('/home/oscar/tropical_project_new/parameters_clusters')
cluster_pars_path = {}
for sc in clusters_path:
ff = open(sc)
data_paths = csv.reader(ff)
params_path = [dd[0] for dd in data_paths]
cluster_pars_path[sc.split('clusters/')[1]] = params_path
display_observables(all_parameters_path)
def display_all_species(cluster_parameters):
"""Saves figures of all species for each cluster of parameters
print (tropical_data)
drivers_all = [set(dr.keys()) for dr in tropical_data]
drivers_over_pars = set.intersection(*drivers_all)
drivers_to_df = {}
for sp in drivers_over_pars:
tmp = [0] * len(drivers_all)
for idx, tro in enumerate(tropical_data):
tmp[idx] = tro[sp]
drivers_to_df[sp] = tmp
for sp in drivers_to_df.keys():
pandas.DataFrame(np.array(drivers_to_df[sp]),
index=rindex,
columns=cindex).to_csv(path + '/data_frame%d' % sp + '.csv')
return
from earm.lopez_embedded import model
t = np.linspace(0, 20000, 100)
pars = hf.listdir_fullpath('/home/oscar/home/oscar/Documents/tropical_project/parameters_5000')
compare_all_drivers_signatures(model, t, pars[:10], to_data_frame=True, dir_path='/home/oscar/Desktop')
# all_drivers = np.load('/home/oscar/Documents/tropical_projetct/drivers_all_parameters5000.npy')
# drivers_all = {idx: dr.keys() for idx, dr in enumerate(all_drivers)}
#
# for i in drivers_all:
# for j in drivers_all:
# if set(drivers_all[i]) == set(drivers_all[j]):
# if i != j:
# print (i, j)
from miscellaneous_analysis import parameter_distribution
from earm.lopez_embedded import model
import helper_functions as hf
# Script to get the comparison of parameter distribution between different parameter clusters in EARM
clus = hf.listdir_fullpath('/home/oscar/Documents/tropical_earm/clustered_parameters_bid')
new_path = '/home/oscar/Documents/tropical_earm/parameters_5000'
for par in model.parameters:
parameter_distribution(clus, par.name, new_path)
