def plot_histogram(self, parameter_name, title, x_label=None, y_label=None, x_range=None, folder=''):
""" creates a histogram of one parameter """
Fig.plot_histogram(
data=self.dictOfParamValues[parameter_name],
title=title, x_label=x_label, y_label=y_label,
x_range=x_range, figure_size=HISTOGRAM_FIG_SIZE, file_name=folder+'/'+title
)
def plot_ratio_hist(self, numerator_par_name, denominator_par_names,
title, x_label=None, x_range=None, output_fig_loc='figures_national'):
ratio_obss = self.__calculate_ratio_obss(numerator_par_name, denominator_par_names)
file_name = output_fig_loc + '/Ratio-' + title
# create the histogram of ratio
Fig.plot_histogram(
data=ratio_obss,
title=title,
x_label=x_label,
x_range=x_range,
figure_size=HISTOGRAM_FIG_SIZE,
file_name=file_name)
def plot_histogram(self,
parameter_name,
title,
x_lable=None,
y_lable=None,
x_range=None):
""" creates a histogram of one parameter """
Fig.plot_histogram(self.dictOfParams[parameter_name],
title,
x_lable,
y_lable,
x_range=x_range,
figure_size=HISTOGRAM_FIG_SIZE,
output_type='jpg',
file_name='figures_national\Par-' + title)
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
)
import SimPy.InOutFunctions as IO
import SimPy.Plots.Histogram as Hist
import SimPy.Plots.ProbDist as Plot
import SimPy.RandomVariateGenerators as RVGs
import SimPy.Statistics as Stat
# read weekly number of drinks
cols = IO.read_csv_cols(file_name='dataNumOfDrinks.csv',
n_cols=1,
if_ignore_first_row=True,
if_convert_float=True)
# make a histogram
Hist.plot_histogram(data=cols[0], title='Weekly Number of Drinks', bin_width=1)
# mean and standard deviation
stat = Stat.SummaryStat(name='Weekly number of drinks', data=cols[0])
print('Mean = ', stat.get_mean())
print('StDev = ', stat.get_stdev())
# fit a Poisson distribution
fit_results = RVGs.Poisson.fit_ml(data=cols[0])
print('Fitting a Poisson distribution:', fit_results)
# plot the fitted Poisson distribution
Plot.plot_poisson_fit(data=cols[0],
fit_results=fit_results,
x_label='Weekly number of drinks',
x_range=(0, 40),
bin_width=1)
# simulate all cohorts
multiCohort.simulate(Sets.TIME_STEPS)
# plot the sample paths
Path.plot_sample_paths(
sample_paths=multiCohort.multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time-Step (Year)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of average survival time
Hist.plot_histogram(
data=multiCohort.multiCohortOutcomes.meanSurvivalTimes,
title='Histogram of Mean Survival Time',
x_label='Mean Survival Time (Year)',
y_label='Count',
x_range=[2.5, 21.5],
bin_width=0.5)
# create the histogram of the mortality probabilities
Hist.plot_histogram(
data=mortality_samples,
title='Histogram of Mortality Probabilities',
x_label='Mortality Probability',
y_label='Counts',
x_range=[Sets.PRIOR_L, Sets.PRIOR_U])
# print projected mean survival time (years)
print('Projected mean survival time (years)',
multiCohort.multiCohortOutcomes.statMeanSurvivalTime.get_mean())
import SimPy.Plots.Histogram as Hist
import SimPy.Plots.SamplePaths as Path
# selected therapy
therapy = P.Therapies.COMBO
# create a cohort
myCohort = Cls.Cohort(id=1,
pop_size=D.POP_SIZE,
parameters=P.Parameters(therapy=therapy))
# simulate the cohort over the specified time steps
myCohort.simulate(sim_length=D.SIM_LENGTH)
# plot the sample path (survival curve)
Path.plot_sample_path(sample_path=myCohort.cohortOutcomes.nLivingPatients,
title='Survival Curve',
x_label='Time-Step (Year)',
y_label='Number Survived')
# plot the histogram of survival times
Hist.plot_histogram(data=myCohort.cohortOutcomes.survivalTimes,
title='Histogram of Patient Survival Time',
x_label='Survival Time (Year)',
y_label='Count',
bin_width=1)
# print the outcomes of this simulated cohort
Support.print_outcomes(sim_outcomes=myCohort.cohortOutcomes,
therapy_name=therapy)
pop_sizes=[COHORT_POP_SIZE] *
N_COHORTS, # [COHORT_POP_SIZE, COHORT_POP_SIZE, ..., COHORT_POP_SIZE]
mortality_probs=[MORTALITY_PROB] * N_COHORTS # [p, p, ....]
)
# simulate all cohorts
multiCohort.simulate(TIME_STEPS)
# plot the sample paths
Path.plot_sample_paths(
sample_paths=multiCohort.multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time-Step (Year)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of average survival time
Hist.plot_histogram(data=multiCohort.multiCohortOutcomes.meanSurvivalTimes,
title='Histogram of Mean Survival Time',
x_label='Mean Survival Time (Year)',
y_label='Count')
# print projected mean survival time (years)
print('Projected mean survival time (years)',
multiCohort.multiCohortOutcomes.statMeanSurvivalTime.get_mean())
# print projection interval
print(
'95% projection (prediction, percentile, or uncertainty) interval of average survival time (years)',
multiCohort.multiCohortOutcomes.statMeanSurvivalTime.get_PI(alpha=ALPHA))
cohort_size=Sets.SIM_POP_SIZE,
time_steps=Sets.TIME_STEPS)
# plot the sample paths
Path.plot_sample_paths(sample_paths=calibrated_model.multiCohorts.
multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time-Step (Year)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of mean survival time
Hist.plot_histogram(
data=calibrated_model.multiCohorts.multiCohortOutcomes.meanSurvivalTimes,
title='Histogram of Mean Survival Time',
x_label='Mean Survival Time (Year)',
y_label='Count',
bin_width=0.25,
x_range=[2.5, 21.5])
# create the histogram of the resampled mortality probabilities
Hist.plot_histogram(data=calibrated_model.resampledMortalityProb,
title='Histogram of Resampled Mortality Probabilities',
x_label='Mortality Probability',
y_label='Counts',
x_range=[Sets.PRIOR_L, Sets.PRIOR_U])
# Estimate of mortality probability and the posterior interval
print(
'Estimate of mortality probability ({:.{prec}%} credible interval):'.
format(1 - Sets.ALPHA, prec=0),
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