def plot_survival_curves_and_histograms(sim_outcomes_NO, sim_outcomes_ASP):
# get survival curves of both treatments
survival_curves = [
sim_outcomes_NO.nLivingPatients, sim_outcomes_ASP.nLivingPatients
]
# graph survival curve
PathCls.graph_sample_paths(sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step (year)',
y_label='Number of alive patients',
legends=['No Therapy', 'Aspirin Therapy'])
# histograms of survival times
set_of_survival_times = [
sim_outcomes_NO.survivalTimes, sim_outcomes_ASP.survivalTimes
]
# graph histograms
Figs.graph_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=['No Therapy', 'Aspirin Therapy'],
transparency=0.6)
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
PathCls.graph_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'])
# histograms of survival times
set_of_survival_times = [
sim_outcomes_mono.survivalTimes, sim_outcomes_combo.survivalTimes
]
# graph histograms
Figs.graph_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'],
transparency=0.6)
def graph_sample_path(sample_path,
title=None, x_label=None, y_label=None,
figure_size=None, output_type='show',
legend=None, color_code=None):
"""
plot a sample path
:param sample_path: a sample path
:param title: (string) title of the figure
:param x_label: (string) x-axis label
:param y_label: (string) y-axis label
:param figure_size: (tuple) figure size
:param output_type: select from 'show', 'pdf' or 'png'
:param legend: string for the legend
:param color_code: (string) 'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black
"""
if not isinstance(sample_path, _PrevalenceSamplePath):
raise ValueError(
'sample_path should be an instance of PrevalencePathRealTimeUpdate or PrevalencePathBatchUpdate.')
fig, ax = plt.subplots(figsize=figure_size)
ax.set_title(title) # title
ax.set_xlabel(x_label) # x-axis label
ax.set_ylabel(y_label) # y-axis label
# add a sample path to this ax
add_sample_path_to_ax(sample_path=sample_path,
ax=ax,
color_code=color_code,
legend=legend)
ax.set_ylim(bottom=0) # the minimum has to be set after plotting the values
# output figure
Fig.output_figure(fig, output_type, title)
def graph_sample_path(sample_path,
title,
x_label,
y_label,
output_type='show',
legend=None,
color_code=None):
"""
produces a sample path
:param sample_path: a sample path
:param title: (string) title of the figure
:param x_label: (string) x-axis label
:param y_label: (string) y-axis label
:param output_type: select from 'show', 'pdf' or 'png'
:param legend: string for the legend
:param color_code: (string) 'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black
"""
fig = plt.figure(title)
plt.title(title) # title
plt.xlabel(x_label) # x-axis label
plt.ylabel(y_label) # y-axis label
# x and y values
x_values = sample_path.get_times()
y_values = sample_path.get_values()
# color
color_marker_text = '-'
if not (color_code is None):
color_marker_text = color_code + color_marker_text
# plot
plt.plot(x_values, y_values, color_marker_text)
# add legend if provided
if not (legend is None):
plt.legend([legend])
# set the minimum of y-axis to zero
plt.ylim(bottom=0) # the minimum has to be set after plotting the values
# output figure
Fig.output_figure(plt, output_type, title)
def graph_sample_paths(sample_paths,
title=None, x_label=None, y_label=None,
figure_size=None, output_type='show',
legends=None, transparency=1, common_color_code=None):
""" graphs multiple sample paths
:param sample_paths: a list of sample paths
:param title: (string) title of the figure
:param x_label: (string) x-axis label
:param y_label: (string) y-axis label
:param figure_size: (tuple) figure size
:param output_type: select from 'show', 'pdf' or 'png'
:param legends: list of strings for legend
:param transparency: float (0.0 transparent through 1.0 opaque)
:param common_color_code: (string) color code if all sample paths should have the same color
'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black
"""
if len(sample_paths) == 1:
raise ValueError('Only one sample path is provided. Use graph_sample_path instead.')
fig, ax = plt.subplots(figsize=figure_size)
ax.set_title(title) # title
ax.set_xlabel(x_label) # x-axis label
ax.set_ylabel(y_label) # y-axis label
# add all sample paths
add_sample_paths_to_ax(sample_paths=sample_paths,
ax=ax,
common_color_code=common_color_code,
transparency=transparency)
# add legend if provided
if legends is not None:
if common_color_code is None:
ax.legend(legends)
else:
ax.legend([legends])
# set the minimum of y-axis to zero
ax.set_ylim(bottom=0) # the minimum has to be set after plotting the values
# output figure
Fig.output_figure(fig, output_type, title)
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
PathCls.graph_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
Figs.graph_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=['No Therapy', 'Aspirin Therapy'],
color_codes=['green', 'blue'],
transparency=0.5
)
def graph_sample_paths(sample_paths,
title,
x_label,
y_label,
output_type='show',
legends=None,
transparency=1,
common_color_code=None,
if_same_color=False):
""" graphs multiple sample paths
:param sample_paths: a list of sample paths
:param title: (string) title of the figure
:param x_label: (string) x-axis label
:param y_label: (string) y-axis label
:param output_type: select from 'show', 'pdf' or 'png'
:param legends: list of strings for legend
:param transparency: float (0.0 transparent through 1.0 opaque)
:param common_color_code: (string) color code if all sample paths should have the same color
'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black
:param if_same_color: logical, default False, if set True, paint the sample paths the same color
"""
if len(sample_paths) == 1:
raise ValueError(
'Only one sample path is provided. Use graph_sample_path instead.')
if if_same_color and common_color_code is None:
raise ValueError(
"Provide a color code (e.g. 'k') for common_color_code if all sample paths should have the same color ."
)
fig = plt.figure(title)
plt.title(title) # title
plt.xlabel(x_label) # x-axis label
plt.ylabel(y_label) # y-axis label
# color
color_marker_text = '-'
if not (common_color_code is None):
color_marker_text = common_color_code + color_marker_text
# x and y values
if if_same_color:
for path in sample_paths:
x_values = path.get_times()
y_values = path.get_values()
# plot
plt.plot(x_values, y_values, common_color_code, alpha=transparency)
else:
for path in sample_paths:
x_values = path.get_times()
y_values = path.get_values()
# plot
plt.plot(x_values, y_values, color_marker_text, alpha=transparency)
# add legend if provided
if not (legends is None):
if common_color_code is None:
plt.legend(legends)
else:
plt.legend([legends])
# set the minimum of y-axis to zero
plt.ylim(bottom=0) # the minimum has to be set after plotting the values
# output figure
Fig.output_figure(plt, output_type, title)
import CalibrationClasses as Cls
import CalibrationSettings as CalibSets
import SimPy.FigureSupport as Fig
# create a calibration object
calibration = Cls.Calibration()
# sample the posterior of the mortality probability
calibration.sample_posterior(n_samples=CalibSets.POST_N)
# create the histogram of the resampled mortality probabilities
Fig.graph_histogram(data=calibration.mortalityResamples,
title='Histogram of Resampled Mortality Probabilities',
x_label='Mortality Probability',
y_label='Counts',
x_range=[CalibSets.POST_L, CalibSets.POST_U])
# Estimate of mortality probability and the posterior interval
print(
'Estimate of mortality probability ({:.{prec}%} credible interval):'.
format(1 - CalibSets.ALPHA, prec=0),
calibration.get_mortality_estimate_credible_interval(
alpha=CalibSets.ALPHA))
# effective sample size
# txtEff = 'Effective sample size: {:.1f}'.format(calibration.get_effective_sample_size())
# print(txtEff)
import SimPy.FigureSupport as Fig import numpy as np import matplotlib.pyplot as plt data = [1, 2, 3, 4, 5] print(Fig.get_moving_average(data=data, window=3)) data = np.random.randn(100) fig = plt.figure(figsize=(4, 2)) ax = fig.add_subplot(111) ax.plot(range(100), data, alpha=0.5) ax.plot(range(100), Fig.get_moving_average(data, window=10), alpha=1) plt.show()
def print_histograms(sim_outcomes, diagnostic_name):
# plot the sample path (survival curve)
PathCls.graph_sample_path(sample_path=sim_outcomes.nLivingPatients,
title=f'Survival Curve {diagnostic_name}',
x_label='Time-Step (Week)',
y_label='Number Survived')
# plot the histogram of survival times
Figs.graph_histogram(
data=sim_outcomes.survivalTimes,
title=f'Histogram of Patient Survival Time {diagnostic_name}',
x_label='Survival Time (Week)',
y_label='Count',
bin_width=1)
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoHOSP_TBD,
# title=f'Histogram of INFECTED to HOSP_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoHOSP_TBM,
# title=f'Histogram of INFECTED to HOSP_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoDX_TBD,
# title=f'Histogram of INFECTED to DX_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoCLEARED,
# title=f'Histogram of INFECTED to CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesCLEAREDtoDEAD,
# title=f'Histogram of CLEARED to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBDtoDEAD,
# title=f'Histogram of HOSP_TBD to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBDtoDX_TBD,
# title=f'Histogram of HOSP_TBD to DX_TBD {diagnostic_name}',
# x_label='Survival Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBMtoDEAD,
# title=f'Histogram of HOSP_TBM to DEAD {diagnostic_name}',
# x_label='Survival Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBMtoDX_TBM,
# title=f'Histogram of HOSP_TBM to DX_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBDtoDEAD,
# title=f'Histogram of DX_TBD to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBDtoCLEARED,
# title=f'Histogram of DX_TBD to CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBMtoDEAD,
# title=f'Histogram of DX_TBM to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBMtoCLEARED,
# title=f'Histogram of DX_TBM to CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTED,
# title=f'Histogram of INFECTED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBM,
# title=f'Histogram of HOSP_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBD,
# title=f'Histogram of HOSP_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBD,
# title=f'Histogram of DX_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBM,
# title=f'Histogram of DX_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesCLEARED,
# title=f'Histogram of CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
# Figs.graph_histogram(
# data=sim_outcomes.costs,
# title=f'Histogram of Cost {diagnostic_name}',
# x_label='Cost (Dollars)',
# y_label='Count',
# bin_width=1)
Figs.graph_histogram(
data=sim_outcomes.costsPresenting,
title=f'Histogram of Cost (Patients Presenting) {diagnostic_name}',
x_label='Cost (Dollars)',
y_label='Count',
bin_width=1)
parameters=P.ParametersFixed(therapy=therapy_none))
# simulate the cohort over the specified time steps
myCohort_0.simulate(sim_length=D.SIM_LENGTH)
# plot the sample path (survival curve)
Path.graph_sample_path(
sample_path=myCohort_0.cohortOutcomes.nLivingPatients,
title='Survival Curvel for No Therapy',
x_label='Time-Step (Year)',
y_label='Number Survived')
# plot the histogram of survival times
Fig.graph_histogram(
data=myCohort_0.cohortOutcomes.survivalTimes,
title='Histogram of Patient Survival Times for No Therapy',
x_label='Survival Time (Year)',
y_label='Count',
bin_width=1)
# print the outcomes of this simulated cohort
Support.print_outcomes(sim_outcomes=myCohort_0.cohortOutcomes,therapy_name=therapy_none)
therapy_asp = P.Therapies.ASP
# create a cohort
myCohort_1 = Cls.Cohort(id=1,
pop_size=D.POP_SIZE,
parameters=P.ParametersFixed(therapy=therapy_asp))
# simulate the cohort over the specified time steps
pop_size=D.POP_SIZE,
parameters=P.ParametersFixed(diagnostic=P.Diagnostic.SOC))
multiCohort.simulate(sim_length=D.SIM_LENGTH)
# plot the sample paths
Path.graph_sample_paths(
sample_paths=multiCohort.multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time-Step (Week)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of average survival time
Fig.graph_histogram(data=multiCohort.multiCohortOutcomes.meanSurvivalTimes,
title='Histogram of Mean Survival Time',
x_label='Mean Survival Time (Week)',
y_label='Count')
# print the outcomes of this simulated cohort
Support.print_outcomes(multi_cohort_outcomes=multiCohort.multiCohortOutcomes,
diagnostic=P.Diagnostic.SOC)
# create multiple cohort
multiCohort_NSB = Cls.MultiCohort(
ids=range(D.N_COHORTS, 2 * D.N_COHORTS),
pop_size=D.POP_SIZE,
parameters=P.ParametersFixed(diagnostic=P.Diagnostic.NSB))
multiCohort_NSB.simulate(sim_length=D.SIM_LENGTH)
# plot the sample paths
pop_size=D.POP_SIZE,
therapy=therapy_no)
multiCohort_no.simulate(sim_length=D.SIM_LENGTH)
# plot the sample paths
Path.graph_sample_paths(
sample_paths=multiCohort_no.multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time-Step (Year)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of average survival time
Fig.graph_histogram(data=multiCohort_no.multiCohortOutcomes.meanSurvivalTimes,
title='Histogram of Mean Survival Time',
x_label='Mean Survival Time (Year)',
y_label='Count')
# print the outcomes of this simulated cohort
Support.print_outcomes(
multi_cohort_outcomes=multiCohort_no.multiCohortOutcomes,
therapy_name=therapy_no)
# ASPIRIN THERAPY
N_COHORTS = 20 # number of cohorts
therapy_asp = P.Therapies.ASP # selected therapy
# create multiple cohort
multiCohort_asp = Cls.MultiCohort(ids=range(N_COHORTS),
pop_size=D.POP_SIZE,
therapy=therapy_asp)
import numpy as np
import SimPy.FigureSupport as cls
obs = np.random.normal(4, 3, 1000)
cls.graph_histogram(
data=obs,
title='Histogram',
x_label='Values',
y_label='Counts',
color='g',
bin_width=1,
x_range=[-5, 20],
y_range=[0, 140],
legend='Number of patients')
obs_sets = [
np.random.normal(4, 3, 1000),
np.random.normal(8, 3, 1000)
]
cls.graph_histograms(
data_sets=obs_sets,
title='Two histograms',
x_label='Values',
y_label='Counts',
legends=['H 1', 'H 2'],
bin_width=1,
x_range=[-10, 20],
#y_range=[0, 100],
color_codes=['blue', 'green'],
import numpy as np
import SimPy.FigureSupport as cls
obs = np.random.normal(4, 3, 1000)
cls.graph_histogram(
data=obs,
title='Histogram',
x_label='Values',
y_label='Counts',
x_range=[-5, 20],
y_range=[0, 140],
legend='Number of patients')
obs_sets = [
np.random.normal(4, 3, 1000),
np.random.normal(8, 3, 1000)
]
cls.graph_histograms(
data_sets=obs_sets,
title='Two histograms',
x_label='Values',
y_label='Counts',
legend=['H 1', 'H 2'],
bin_width=0.5,
x_range=[-10, 20],
y_range=[0, 100],
transparency=0.6
)