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backup_3_scheduler_script.py
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from subprocess import call
import json
import datetime
#from datetime import datetime,timedelta
import time
import os
from matplotlib import pyplot as plt
#Input to the algo_bw.js. algo_bw.js format all the info and send to glucosym server. An algorithm is running in glucosym server that calculated next glucose and send the value back.
algo_input_list = {"index":0,"BGTarget":130,"sens":45,"deltat_v":20,"dia":4,"dt":5.0,"time":6000,"bioavail":6.0,"Vg":253.0,"IRss":1.3,"events":{"bolus":[{ "amt": 0.0, "start":250}],"basal":[{ "amt":0, "start":50,"length":30}],"carb":[{"amt":0.0,"start":0,"length":0},{"amt":0.0,"start":0,"length":0}]}}
#write the algo_input_list to a file named algo_input.json so that algo_bw.js can read the input from that file
with open("../glucosym/closed_loop_algorithm_samples/algo_input.json", "w") as write_algo_input_init:
json.dump(algo_input_list, write_algo_input_init, indent=4)
write_algo_input_init.close()
suggested_data_to_dump = {}
list_suggested_data_to_dump = []
iteration_num = 1000;
#record the time 5 minutes ago, we need this time to attach with the recent glucose value
#time_5_minutes_back = ((time.time())*1000)-3000
for _ in range(iteration_num+1):
with open("monitor/glucose.json") as f:
data = json.load(f)
f.close()
data_to_prepend = data[0].copy()
read_glucose_from_glucosym = open("../glucosym/closed_loop_algorithm_samples/glucose_output_algo_bw.txt", "r")
loaded_glucose = read_glucose_from_glucosym.read()
data_to_prepend["glucose"] = loaded_glucose
data_to_prepend["date"] = int(time.time())*1000
data.insert(0, data_to_prepend)
with open('monitor/glucose.json', 'w') as outfile:
json.dump(data, outfile, indent=4)
outfile.close()
current_timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%dT%H:%M:%S-07:00')
##For the very first time get the time of 5 minutes ago from now and set it to the first glucose data
#if _==0:
# call("date -Ins -s $(date -Ins -d '-5 minute')", shell=True)
# first_glucose_to_prepend = data[0].copy()
# first_glucose_to_prepend["date"]=int(time.time())*1000
# print(data[0])
# print(data[0]["date"])
# with open("monitor/glucose.json", "w") as dump_first_glucose:
# json.dump(data[0], dump_first_glucose, indent=4)
# dump_first_glucose.close()
# #print(data_to_prepend["date"])
call("date -Ins -s $(date -Ins -d '+5 minute')", shell=True)
call(["openaps", "report", "invoke", "monitor/iob.json"])
#run openaps to get suggested tempbasal
call(["openaps", "report", "invoke", "enact/suggested.json"])
# current_timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%dT%H:%M:%S-07:00')
#read the output in suggested.json and append it to list_suggested_data_to_dump list. Basically we are trying to get all the suggest ed data and dump make a list lf that and then dump it to all_suggested.json file
with open("enact/suggested.json") as read_suggested:
loaded_suggested_data = json.load(read_suggested)
list_suggested_data_to_dump.insert(0,loaded_suggested_data)
#list_suggested_data_to_dump.append(loaded_suggested_data)
read_suggested.close()
#Update pumphistory only if there is value of temp basal. For 0 temp basal, no need to update pumphistory
if 'duration' in loaded_suggested_data.keys():
with open("monitor/pumphistory.json") as read_pump_history:
loaded_pump_history = json.load(read_pump_history) # read whole pump_history.json
pump_history_0 = loaded_pump_history[0].copy() #load first element
pump_history_1 = loaded_pump_history[1].copy() #load second element, fist and second are both for one temp basal
pump_history_0['duration (min)'] = loaded_suggested_data['duration'] #update the values
pump_history_1['rate'] = loaded_suggested_data['rate']
pump_history_0['timestamp'] = current_timestamp
pump_history_1['timestamp'] = current_timestamp
loaded_pump_history.insert(0, pump_history_1) # insert second element back to whatever we loaded from pumphistory
loaded_pump_history.insert(0, pump_history_0) #insert first element back to whatever we loaded from pumphistory
read_pump_history.close();
with open("monitor/pumphistory.json", "w") as write_pump_history:
json.dump(loaded_pump_history, write_pump_history, indent=4)
# Update temp_basal.json with the current temp_basal rate and duration
# if 'rate' in loaded_suggested_data.keys():
with open("monitor/temp_basal.json") as read_temp_basal:
loaded_temp_basal = json.load(read_temp_basal)
loaded_temp_basal['duration']-=5
if loaded_temp_basal['duration']<=0:
loaded_temp_basal['duration'] = loaded_suggested_data['duration']
loaded_temp_basal['rate'] = loaded_suggested_data['rate']
read_temp_basal.close()
with open("monitor/temp_basal.json", "w") as write_temp_basal:
json.dump(loaded_temp_basal, write_temp_basal, indent=4)
#print(suggested_data_to_dump)
#write the list_suggested_data_to_dump into all_suggested.json file
with open("enact/all_suggested.json", "w") as dump_suggested:
json.dump(list_suggested_data_to_dump, dump_suggested, indent=4)
dump_suggested.close()
if 'rate' in loaded_suggested_data.keys():
#update the insulin parameter input of glucosym. This insulin parameters is received from openaps(suggested.json)
algo_input_list["events"]['basal'][0]['amt'] = loaded_suggested_data['rate']
algo_input_list["events"]['basal'][0]['length'] = loaded_suggested_data['duration']
# else:
#
# algo_input_list["events"]['basal'][0]['amt'] = 0
# algo_input_list["events"]['basal'][0]['length'] = 30
algo_input_list["events"]['basal'][0]['start'] = _*5
#os.chdir("../glucosym/closed_loop_algorithm_samples")
call(["node", "../glucosym/closed_loop_algorithm_samples/algo_bw.js"]);
algo_input_list['index']=algo_input_list['index']+1
#print(algo_input_list)
with open("../glucosym/closed_loop_algorithm_samples/algo_input.json", "w") as write_algo_input:
json.dump(algo_input_list, write_algo_input, indent=4)
#os.chdir("../../myopenaps")
# data_to_prepend = data[0].copy()
#current_time = data_to_prepend["display_time"]
#mytime = datetime.strptime(current_time,"%Y-%m-%dT%H:%M:%S-07:00")
#dt = timedelta(minutes = 5)
#mytime += dt
#make_time_str = str(mytime).split(' ')
#new_time_str = make_time_str[0]+"T"+make_time_str[1]+"-07:00"
#data_to_prepend["display_time"] = new_time_str
#data_to_prepend["dateString"] = new_time_str
#current_time = data_to_prepend["system_time"]
#mytime = datetime.strptime(current_time,"%Y-%m-%dT%H:%M:%S-07:00")
#dt = timedelta(minutes = 5)
#mytime += dt
#make_time_str = str(mytime).split(' ')
#new_time_str = make_time_str[0]+"T"+make_time_str[1]+"-07:00"
#data_to_prepend["system_time"] = new_time_str
# read_glucose_from_glucosym = open("../glucosym/closed_loop_algorithm_samples/glucose_output_algo_bw.txt", "r")
# loaded_glucose = read_glucose_from_glucosym.read()
#data_to_prepend["glucose"] = int(data_to_prepend["glucose"])-5
# data_to_prepend["glucose"] = loaded_glucose
# data_to_prepend["date"]+= 300000
# call("date -Ins -s $(date -Ins -d '+5 minute')", shell=True)
# data.insert(0, data_to_prepend)
# with open('monitor/glucose.json', 'w') as outfile:
# json.dump(data, outfile, indent=4)
# outfile.close()
#This part is for ploting glucose and insulin data over the time. This section starts after all the iteration is finished
if _ == iteration_num:
with open("enact/all_suggested.json") as read_all_suggested:
loaded_all_suggested = json.load(read_all_suggested)
#y_list = [{"a":1, "b":1},{"a":4, "b":2},{"a":9, "b":3},{"a":16, "b":4}]
#print(loaded_all_suggested)
glucose = []
insulin = []
time = []
time_index = 0
for _ in loaded_all_suggested:
if 'bg' in _.keys() and 'rate' in _.keys():
glucose.insert(0,_['bg'])
insulin.insert(0,_['rate'])
time.append(time_index)
time_index+=5
#print(glucose)
#print(time)
plt.plot(time, glucose)
plt.plot(time, insulin)
plt.ylabel("glucose and Insulin")
plt.xlabel("time")
plt.show()
#print("glucose",glucose)
#print("insulin",insulin)
#print("time",time)