def backtest(session_id):
obj = Littlefield('group6', 'coronado91', session_id)
th_dict = {'queue_th': 100, 'util_th': 0.99}
_orders = []
_average_order = []
_average_sum = 0
ords = obj.completed_jobs.count()
ord = obj.orders.job_arrivals()
for ind in range(len(ords[0][2])):
calculate_station1_R(obj, ind, th_dict)
calculate_station2_R(obj, ind, th_dict)
calculate_station3_R(obj, ind, th_dict)
print("Order at time ", ind, " is ", ord[ind])
_orders.append(ord[ind][1])
print("#####################################################")
for ind, val in enumerate(_orders):
print(ind, " ,", val)
if ind <= len(ords[0][2]) - 5:
_average_order.append(sum(_orders[ind:ind + 5]) / 5)
print("order 5 day average:")
print(_average_order)
# export to a csv file
import csv
with open('average_demand.csv', 'w+') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
csv_writer.writerow(_average_order)
def backtest_throughput_rate(session_id):
obj = Littlefield('group6', 'coronado91', session_id)
th_dict = {'queue_th': 100, 'util_th': 0.90}
ord = obj.orders.job_arrivals()
rev = obj.completed_jobs.revenues()
jobs = obj.completed_jobs.count()
ave_demand = 0
for ind in range(len(ord) - 5, len(ord)):
calculate_station1_R(obj, ind, th_dict)
calculate_station2_R(obj, ind, th_dict)
calculate_station3_R(obj, ind, th_dict)
print("Order at time ", ind, " is ", ord[ind])
#print(jobs)
print("Contract 1 completed jobs for ", ind, " is ", jobs[0][2][ind])
print("Contract 2 completed jobs for ", ind, " is ", jobs[1][2][ind])
print("Contract 3 completed jobs for ", ind, " is ", jobs[2][2][ind])
print("Contract 1 Rev at time ", ind, " is ", rev[0][2][ind])
print("Contract 2 Rev at time ", ind, " is ", rev[1][2][ind])
print("Contract 3 Rev at time ", ind, " is ", rev[2][2][ind])
ave_demand = ave_demand + ord[ind][1]
print("#####################################################")
print("Average Demand = ", ave_demand / 5)
from raven import Client
raven = Client(os.getenv("LITTLEFIELD_RAVEN_DSN"))
try:
from littlefield import Littlefield
def avg(data, size):
return sum(data[((size*-1)-1):-1])/size
config = False
if len(sys.argv) > 1:
if sys.argv[1] == "config":
config = True
if config:
print("""graph_title Station Utilitization
graph_info Shows the utilitization of each station
graph_category littlefield
graph_scale no
graph_args --upper-limit 100 -l 0
graph_vlabel %""")
for station in range(1, 4):
if config:
print("station%s.label Station %s" % (station, station))
print("station%s.warning 10:80" % station)
else:
littlefield = Littlefield(os.getenv("LITTLEFIELD_USER"), os.getenv("LITTLEFIELD_PW"))
util = littlefield.get_data("S%sUTIL" % station)['average']
print("station%s.value %s" % (station, util[-1]*100))
except:
raven.captureException()
#!/usr/bin/env python
from littlefield import Littlefield
from configparser import ConfigParser
from bokeh.plotting import figure, output_server, show, gridplot
config = ConfigParser()
config.read(['littlefield.ini'])
littlefield = Littlefield(config['littlefield']['user'],
config['littlefield']['password'])
output_server("littlefield")
datatypes = [
"JOBIN", # Number of jobs accepted per day
"JOBQ", # Number of jobs waiting
"INV", # Inventory levels
"JOBOUT", # Number of completed jobs
"JOBT", # Lead times
"JOBREV", # Revenue
"S1Q", # Number of kits queued for station 1
"S1UTIL", # Station 1 utilitization by day
"S2Q", # number of kits queued for station 2
"S2UTIL", # Station 2 utilitization by day
"S3Q", # number of kits queued for station 3
"S3UTIL" # Station 3 utilitization by day
]
plots = []
for datatype in datatypes:
print(datatype)
data = littlefield.get_data(datatype)
