def plot_survival_curves_and_histograms(sim_outcomes_mono, sim_outcomes_combo):
""" draws the survival curves and the histograms of time until HIV deaths
:param sim_outcomes_mono: outcomes of a cohort simulated under mono therapy
:param sim_outcomes_combo: outcomes of a cohort simulated under combination therapy
"""
# get survival curves of both treatments
survival_curves = [
sim_outcomes_mono.nLivingPatients, sim_outcomes_combo.nLivingPatients
]
# graph survival curve
Path.plot_sample_paths(sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step (year)',
y_label='Number of alive patients',
legends=['Mono Therapy', 'Combination Therapy'],
color_codes=['green', 'blue'])
# histograms of survival times
set_of_survival_times = [
sim_outcomes_mono.survivalTimes, sim_outcomes_combo.survivalTimes
]
# graph histograms
Hist.plot_histograms(data_sets=set_of_survival_times,
title='Histogram of patient survival time',
x_label='Survival time (year)',
y_label='Counts',
bin_width=1,
legends=['Mono Therapy', 'Combination Therapy'],
color_codes=['green', 'blue'],
transparency=0.6)
def extract_outcomes(self, simulated_patients):
""" extracts outcomes of a simulated cohort
:param simulated_patients: a list of simulated patients"""
# record patient outcomes
for patient in simulated_patients:
# survival time
if patient.stateMonitor.survivalTime is not None:
self.survivalTimes.append(patient.stateMonitor.survivalTime)
# time until AIDS
if patient.stateMonitor.timeToAIDS is not None:
self.timesToAIDS.append(patient.stateMonitor.timeToAIDS)
# discounted cost and discounted utility
self.costs.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedCost)
self.utilities.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedUtility)
# summary statistics
self.statSurvivalTime = Stat.SummaryStat(name='Survival time',
data=self.survivalTimes)
self.statTimeToAIDS = Stat.SummaryStat(name='Time until AIDS',
data=self.timesToAIDS)
self.statCost = Stat.SummaryStat(name='Discounted cost',
data=self.costs)
self.statUtility = Stat.SummaryStat(name='Discounted utility',
data=self.utilities)
# survival curve
self.nLivingPatients = Path.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=len(simulated_patients),
times_of_changes=self.survivalTimes,
increments=[-1] * len(self.survivalTimes))
def draw_survival_curves_and_histograms(calibrated_model_no_drug,
calibrated_model_with_drug):
""" draws the histograms of average survival time
:param calibrated_model_no_drug: calibrated model simulated when drug is not available
:param calibrated_model_with_drug: calibrated model simulated when drug is available
"""
# get survival curves of both treatments
survival_curves = [
calibrated_model_no_drug.multiCohorts.multiCohortOutcomes.
survivalCurves, calibrated_model_with_drug.multiCohorts.
multiCohortOutcomes.survivalCurves
]
# graph survival curve
Path.plot_sets_of_sample_paths(sets_of_sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step',
y_label='Number of alive patients',
legends=['No Drug', 'With Drug'],
color_codes=['blue', 'orange'],
transparency=0.25)
# histograms of average survival times
set_of_survival_times = [
calibrated_model_no_drug.multiCohorts.multiCohortOutcomes.
meanSurvivalTimes, calibrated_model_with_drug.multiCohorts.
multiCohortOutcomes.meanSurvivalTimes
]
# graph histograms
Hist.plot_histograms(data_sets=set_of_survival_times,
title='Histogram of average patient survival time',
x_label='Survival time',
y_label='Counts',
bin_width=0.5,
legends=['No Drug', 'With Drug'],
color_codes=['blue', 'orange'],
transparency=0.5,
x_range=[6, 20])
def plot_survival_curves_and_histograms(sim_outcomes_none, sim_outcomes_anticoag):
""" draws the survival curves and the histograms of time until HIV deaths
:param sim_outcomes_none: outcomes of a cohort simulated under no therapy
:param sim_outcomes_anticoag: outcomes of a cohort simulated under anticoagulation
"""
# get survival curves of both treatments
survival_curves = [
sim_outcomes_none.nLivingPatients,
sim_outcomes_anticoag.nLivingPatients
]
# graph survival curve
Path.plot_sample_paths(
sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step (year)',
y_label='Number of alive patients',
legends=['No Therapy', 'Anticoagulation'],
color_codes=['red', 'blue']
)
# histograms of survival times
set_of_strokes = [
sim_outcomes_none.nStrokes,
sim_outcomes_anticoag.nStrokes
]
# graph histograms
Hist.plot_histograms(
data_sets=set_of_strokes,
title='Histogram of number of strokes',
x_label='Number of Strokes',
y_label='Counts',
bin_width=1,
legends=['No Therapy', 'Anticoagulation'],
color_codes=['red', 'blue'],
transparency=0.5
)
def plot_survival_curves_and_histograms(multi_cohort_outcomes_mono,
multi_cohort_outcomes_combo):
""" plot the survival curves and the histograms of survival times
:param multi_cohort_outcomes_mono: outcomes of a multi-cohort simulated under mono therapy
:param multi_cohort_outcomes_combo: outcomes of a multi-cohort simulated under combination therapy
"""
# get survival curves of both treatments
sets_of_survival_curves = [
multi_cohort_outcomes_mono.survivalCurves,
multi_cohort_outcomes_combo.survivalCurves
]
# graph survival curve
Path.plot_sets_of_sample_paths(
sets_of_sample_paths=sets_of_survival_curves,
title='Survival Curves',
x_label='Simulation Time Step (year)',
y_label='Number of Patients Alive',
legends=['Mono Therapy', 'Combination Therapy'],
transparency=0.4,
color_codes=['green', 'blue'])
# histograms of survival times
set_of_survival_times = [
multi_cohort_outcomes_mono.meanSurvivalTimes,
multi_cohort_outcomes_combo.meanSurvivalTimes
]
# graph histograms
Hist.plot_histograms(data_sets=set_of_survival_times,
title='Histograms of Average Patient Survival Time',
x_label='Survival Time (year)',
y_label='Counts',
bin_width=0.25,
x_range=[5.25, 17.75],
legends=['Mono Therapy', 'Combination Therapy'],
color_codes=['green', 'blue'],
transparency=0.5)
def draw_survival_curves_and_histograms(cohort_no_drug, cohort_with_drug):
""" draws the survival curves and the histograms of survival time
:param cohort_no_drug: a cohort simulated when drug is not available
:param cohort_with_drug: a cohort simulated when drug is available
"""
# get survival curves of both treatments
survival_curves = [
cohort_no_drug.cohortOutcomes.nLivingPatients,
cohort_with_drug.cohortOutcomes.nLivingPatients
]
# graph survival curve
Path.plot_sample_paths(sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step',
y_label='Number of alive patients',
legends=['No Drug', 'With Drug'],
color_codes=['blue', 'orange'],
transparency=0.5)
# histograms of survival times
set_of_survival_times = [
cohort_no_drug.cohortOutcomes.survivalTimes,
cohort_with_drug.cohortOutcomes.survivalTimes
]
# graph histograms
Hist.plot_histograms(data_sets=set_of_survival_times,
title='Histogram of patient survival time',
x_label='Survival time',
y_label='Counts',
bin_width=2,
legends=['No Drug', 'With Drug'],
color_codes=['blue', 'orange'],
transparency=0.5)
def calculate_cohort_outcomes(self, initial_pop_size):
""" calculates the cohort outcomes
:param initial_pop_size: initial population size
"""
self.statSurvivalTime = Stat.SummaryStat(name='Survival Time',
data=self.survivalTimes)
self.statCost = Stat.SummaryStat(name='Discounted cost',
data=self.costs)
self.statUtility = Stat.SummaryStat(name='Discounted utility',
data=self.utilities)
self.statNumStrokes = Stat.SummaryStat(name='Total Number of Strokes',
data=self.nStrokes)
self.nLivingPatients = Path.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=initial_pop_size,
times_of_changes=self.survivalTimes,
increments=[-1] * len(self.survivalTimes))
def extract_outcomes(self, simulated_patients):
""" extracts outcomes of a simulated cohort
:param simulated_patients: a list of simulated patients"""
# record survival time and time until AIDS
for patient in simulated_patients:
if patient.stateMonitor.survivalTime is not None:
self.survivalTimes.append(patient.stateMonitor.survivalTime)
if patient.stateMonitor.timeToAIDS is not None:
self.timesToAIDS.append(patient.stateMonitor.timeToAIDS)
# calculate mean survival time
self.meanSurvivalTime = sum(self.survivalTimes) / len(
self.survivalTimes)
# calculate mean time to AIDS
self.meanTimeToAIDS = sum(self.timesToAIDS) / len(self.timesToAIDS)
# survival curve
self.nLivingPatients = Path.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=len(simulated_patients),
times_of_changes=self.survivalTimes,
increments=[-1] * len(self.survivalTimes))
size=Sets.PRIOR_N)
# create multiple cohorts
multiCohort = Cls.MultiCohort(
ids=range(Sets.PRIOR_N),
pop_sizes=[Sets.SIM_POP_SIZE] * Sets.PRIOR_N,
mortality_probs=mortality_samples # [p1, p2, ....]
)
# 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,
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)
import SimPy.Plots.SamplePaths as Path
# a sample path with initial size = 1
path1 = Path.IncidenceSamplePath('Path 1', delta_t=1, sim_rep=1)
# record the observations
path1.record_increment(1.2, 2)
path1.record_increment(2.3, 1)
path1.record_increment(5.1, 5)
path1.close(6)
# stats
print(path1.stat.get_mean())
# second sample path with initial size = 1
path2 = Path.IncidenceSamplePath('Path 2', delta_t=1, sim_rep=1)
# record the observations
path2.record_increment(0.5, 4)
path2.record_increment(1.8, 2)
path2.record_increment(5.5, 1)
path2.close(6)
# stats
print(path2.stat.get_mean())
# plot path 1 only
Path.plot_sample_path(
sample_path=path1,
title='Plotting a single sample path',
x_label='Time Period',
y_label='observed value',
legend='Path 1',
color_code='r',
connect='line')
from SimPy.Plots import SamplePaths as Path
# a sample path with initial size = 1
path1 = Path.PrevalenceSamplePath('Path 1', initial_size=1, sim_rep=1)
# record the observations
path1.record_increment(1.2, 2)
path1.record_increment(2.3, -1)
path1.record_increment(5.1, 0)
# second sample path with initial size = 1
path2 = Path.PrevalenceSamplePath('Path 2', initial_size=0, sim_rep=1)
# record the observations
path2.record_increment(0.5, 4)
path2.record_increment(1.8, -2)
path2.record_increment(5.5, 1)
# third sample path with initial size = 1
path3 = Path.PrevalencePathBatchUpdate('Path 3',
0,
times_of_changes=[1.5, 2, 5],
increments=[2, -1, 0],
sim_rep=1)
path4 = Path.PrevalencePathBatchUpdate('Path 4',
0,
times_of_changes=[0.5, 4, 4.5],
increments=[2.5, 1, 1],
sim_rep=1)
# stats
print(path1.stat.get_mean())
