python/oms-Python/life_vs_mortality.py at master · openmpp/python · GitHub

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#
# Python integration example using NewCaseBased model:
#   loop over MortalityHazard parameter
#   to analyze DurationOfLife output value

# Prerequisite:
#
# download openM++ release from https://github.com/openmpp/main/releases/latest
# unpack it into any directory
# start oms web-service:
#   Windows:
#     cd C:\my-openmpp-release
#     bin\ompp_ui.bat
#   Linux:
#     cd ~/my-openmpp-release
#     bin/oms
#
# Script below is using openM++ web-service "oms"
# to run the model, modify parameters and read output values.

# Important:
#   Script below starts 20 instances of the model simultaneously.
#   Obviously this can work only if model relatively simple.
#
# DO NOT USE this in production, please use modeling task instead.
#
# Also script below does not handle errors, please use try/except in production.

import time
import requests
import matplotlib.pyplot as plt

# analyze NewCaseBased model varying mortality hazard values
#
mortalityData = [0.014 + i * 0.005 for i in range(20)]

# Use openM++ oms web-service to run NewCaseBased model 20 times
# with different values of MortalityHazard parameter:
#
# NewCaseBased.exe -OpenM.ProgressPercent 100 -OpenM.SubValues 16 OpenM.Threads 4 -Parameter.MortalityHazard 0.014
# NewCaseBased.exe -OpenM.ProgressPercent 100 -OpenM.SubValues 16 OpenM.Threads 4 -Parameter.MortalityHazard 0.019
#   .... and 18 more mortality hazard values ....
#
# For each request to run the model web-service respond with JSON containing RunStamp
# We can use this RunStamp to find model run status and results.
#
runStampLst = []

for m in mortalityData:
    runModelRq = {
        'ModelName': 'NewCaseBased',
        'Opts': {
            'Parameter.MortalityHazard': str(m),
            'OpenM.ProgressPercent': '100',     # reduce amount of progress messages in the log file
            'OpenM.SubValues': '16',            # use 16 sub-values (sub-samples)
            'OpenM.Threads': '4'                # use 4 modeling threads
        }
    }
    #
    # submit request to web-service to run the model
    #
    rsp = requests.post('http://127.0.0.1:4040/api/run', json=runModelRq)
    rsp.raise_for_status()
    js = rsp.json()
    #
    runStamp = js['RunStamp']
    if runStamp is None or runStamp == '':
        raise Exception('Model fail to start, run stamp is empty')
    #
    runStampLst.append(runStamp)
    #
    print("MortalityHazard:", m, "model run stamp:", runStamp)

# wait until all model runs completed
#
n = len(runStampLst)
runDigestLst = ['' for i in range(n)]
done = [False for i in range(n)]

while n > 0:
    print("Waiting for", n, "model runs to be completed...")
    n = 0
    #
    for i in range(len(runStampLst)):
        if done[i]:
            continue    # run already completed
        #
        rsp = requests.get('http://127.0.0.1:4040/api/model/NewCaseBased/run/' + runStampLst[i] + '/status')
        rsp.raise_for_status()
        js = rsp.json()
        runDigestLst[i], status = js['RunDigest'], js['Status']
        #
        if runDigestLst[i] is None or runDigestLst[i] == '' or \
            status is None or status == '' or \
            status in 'i' 'p':  # i = run not started yet, p = run in progress
            #
            n += 1
            continue
        #
        if status == 's':       # success
            done[i] = True
            continue
        #
        raise Exception("Model run failed, run stamp:", runStampLst[i], "status:", status)
        #
    #
    if n > 0:
        time.sleep(1)

# all model runs completed successfully
print("All model runs completed, retrive output values...")

# for each run get output value
#   average duration of life: DurationOfLife.Expr3
#
lifeDurationData = []

for runDigest in runDigestLst:
    rsp = requests.get('http://127.0.0.1:4040/api/model/NewCaseBased/run/' + runDigest + '/table/DurationOfLife/expr')
    rsp.raise_for_status()
    js = rsp.json()
    lifeDurationData.append(js[3]['Value'])

# display the results
#
plt.plot(mortalityData, lifeDurationData, 'ro', ls='-')
plt.xlabel('Mortality Hazard')
plt.ylabel('Duration of Life')
plt.show()