def print_trace(self,
filename,
directory='Trace',
delete_existing_files=True):
""" print the trace messages into a text file with the specified filename.
It creates a sub directory where the python script is located.
:param filename: filename of the text file where trace message should be exported to
:param directory: directory (relative to the current root) where the trace files should be located
:param delete_existing_files: set to True to delete the existing trace files in the directory
"""
if not self._on:
return
# create the directory if does not exist
if not os.path.exists(directory):
os.makedirs(directory)
# delete existing files
if delete_existing_files:
io.delete_files(extension='.txt',
path=os.getcwd() + '/' + directory)
# create a new file
filename = os.path.join(directory, filename)
# open the file with a write access
file = open(filename, 'w')
# write the trace messages
for message in self._messages:
file.write('%s\n' % message)
# close the file
file.close()
def compare_trajectories(csv_file_1, csv_file_2, legends, figure_location):
# clean the directory
IO.delete_files('.png', figure_location)
df1 = TrajsDataFrame(csv_file_1)
df2 = TrajsDataFrame(csv_file_2)
# for all trajectories
i = 0
for key, trajs_one_outcome in df1.allTrajs.items():
fig, ax = plt.subplots(figsize=(5, 5)) # figure size
ax.set(title=key) # title and labels
# first (base)
for traj in trajs_one_outcome.trajs:
ax.plot(traj.times, traj.obss, 'g', alpha=1, zorder=2)
# second (intervention)
for traj in df2.allTrajs[key].trajs:
ax.plot(traj.times, traj.obss, 'b', alpha=1, zorder=1)
plt.legend(legends) #[::-1]
file_name = figure_location + '/' + str(i) + ' ' + key
output_figure(plt, file_name)
i += 1
def clear_txt_files(path='..'):
""" removes every .txt files inside the directory
:param path: path (relative to the current root) where the .txt files are located
(the folder should already exist)
"""
io.delete_files('.txt', path)
def plot_histograms(self, par_names=None, csv_file_name_prior=None, posterior_fig_loc='figures_national'):
""" creates histograms of parameters specified by ids
:param par_names: (list) of parameter names to plot
:param csv_file_name_prior: (string) filename where parameter prior ranges are located
:param posterior_fig_loc: (string) location where posterior figures_national should be located
"""
raise ValueError('Needs to be debugged.')
# clean the directory
IO.delete_files('.png', posterior_fig_loc)
# read prior distributions
dict_of_priors = None
if csv_file_name_prior is not None:
dict_of_priors = self.get_dict_of_priors(prior_info_csv_file=csv_file_name_prior)
if par_names is None:
par_names = self.get_all_parameter_names()
# for all parameters, read sampled parameter values and create the histogram
for par_name in par_names:
# get values for this parameter
par_values = self.dictOfParamValues[par_name]
# get info of this parameter
title, multiplier, x_range = self.get_title_multiplier_x_range_decimal_format(
par_name=par_name, dict_of_priors=dict_of_priors)
# adjust parameter values
par_values = [v*multiplier for v in par_values]
# find the filename the histogram should be saved as
file_name = posterior_fig_loc + '/Par-' + par_name + ' ' + F.proper_file_name(par_name)
# plot histogram
Fig.plot_histogram(
data=par_values,
title=title.replace('!', '\n'),
x_range=x_range,
figure_size=HISTOGRAM_FIG_SIZE,
file_name=file_name
)
from apace import VisOptimClasses as Vis
import SimPy.InOutFunctions as IO
# delete existing figures_national
IO.delete_files('.png', '../tests/VisualizeOptimization/optimization_figures/')
Vis.plot_all_opt_itrs(
csv_directory='../tests/VisualizeOptimization/optimization_csvfiles/',
save_plots_directory='../tests/VisualizeOptimization/optimization_figures/',
#f_range=[0, 5*10e7],
#x_ranges=[[0, 0.3], [0, 0.2]],
#y_axis_labels=[r'$f(\tau, \theta)$', r'$\tau$', r'$\theta$']
)
from apace import VisOptimClasses as Vis
import SimPy.InOutFunctions as IO
# delete existing figures_national
IO.delete_files('.png', 'figures/optimization_figs/')
Vis.plot_all_opt_itrs(
csv_directory='csv_files/optimization_csvs/',
n_vars=2,
save_plots_directory='figures/optimization_figs/',
show_titles=False,
f_range=[-7500, -5000],
#var_ranges=[[0, 5], [-1, 0], [0, 0.5], [0, 1000], [-1, 0], [0, 1]],
y_axis_labels=[
r'$f$', r'$\tau_1$', r'$\tau_2$', r'$R: \rho$', r'$\%I: \tau_0$',
r'$\%I: \tau_1$', r'$\%I: rho$'
], # , r'$\rho_1$'
window=50)
def plot_histograms(self,
ids=None,
csv_file_name_prior=None,
posterior_fig_loc='figures_national'):
""" creates histograms of parameters specified by ids
:param ids: (list) list of parameter ids
:param csv_file_name_prior: (string) filename where parameter prior ranges are located
:param posterior_fig_loc: (string) location where posterior figures_national should be located
"""
# clean the directory
IO.delete_files('.png', posterior_fig_loc)
# read prior distributions
if csv_file_name_prior is not None:
priors = IO.read_csv_rows(file_name=csv_file_name_prior,
if_ignore_first_row=True,
delimiter=',',
if_convert_float=True)
# for all parameters, read sampled parameter values and create the histogram
par_id = 0
for key, par_values in self.dictOfParams.items():
# skip these columns
if key in ['Simulation Replication', 'Random Seed']:
continue
# check if the histogram should be created for this parameter
if_show = False
if ids is None:
if_show = True
elif par_id in ids:
if_show = True
# create the histogram
if if_show:
# find prior range
x_range = None
if priors is not None:
try:
x_range = [
float(priors[par_id][Column.LB.value]),
float(priors[par_id][Column.UB.value])
]
except:
print(
'Could not convert string to float to find the prior distribution of parameter:',
par_id)
else:
x_range = None
# find the filename the histogram should be saved as
file_name = posterior_fig_loc + '\Par-' + str(
par_id) + ' ' + F.proper_file_name(key)
# find title
if priors[par_id][Column.TITLE.value] in ('', None):
title = priors[par_id][Column.NAME.value]
else:
title = priors[par_id][Column.TITLE.value]
# find multiplier
if priors[par_id][Column.MULTIPLIER.value] in ('', None):
multiplier = 1
else:
multiplier = float(priors[par_id][Column.MULTIPLIER.value])
x_range = [x * multiplier for x in x_range]
par_values = [v * multiplier for v in par_values]
# plot histogram
Fig.plot_histogram(data=par_values,
title=title.replace('!', '\n'),
x_range=x_range,
figure_size=HISTOGRAM_FIG_SIZE,
file_name=file_name)
# move to the next parameter
par_id += 1
