def get_data():
if len(request.args["from"]) < 10 or len(request.args["to"]) < 10:
return "date must have following format: 'yyyy-mm-dd'"
statistics.update("data_calls")
database = Database()
result = database.get_data(request.args["from"], request.args["to"])
compressed = False
if "compressed" in request.args.keys():
compressed = True
fetched_data = {}
row_count = 0
last_date = 0
for row in result:
row["weight"] = row["weight"] - config.tare[0]
for i in range(1, len(row.keys())):
if str(i) in row.keys() and row[str(i)] is not None:
if i in config.tare.keys():
row[str(i)] = row[str(i)] - config.tare[i]
else:
row[str(i)] = row[str(i)] - config.tare[0]
fetched_data[row_count] = row
row_count += 1
if compressed == True:
compressed_data = []
# All values for one day (for later calculating average value)
temp = []
weight = []
humidity = []
last_date = 0
for row in fetched_data:
if last_date == 0:
last_date = fetched_data[row]["measured"].date()
if last_date == fetched_data[row]["measured"].date():
temp.append(fetched_data[row]["temperature"])
weight.append(fetched_data[row]["weight"])
humidity.append(fetched_data[row]["humidity"])
else:
# Get an average int of all ints in array
temp_avg = sum(temp) / len(temp)
weight_avg = sum(weight) / len(weight)
humidity_avg = sum(humidity) / len(humidity)
# Clear all arrays for next day
temp.clear()
weight.clear()
humidity.clear()
# Append average value for day to compressed_data array
compressed_data.append({
"temperature": round(temp_avg, 2),
"weight": round(weight_avg, 2),
"humidity": round(humidity_avg, 2),
"measured": last_date
})
last_date = fetched_data[row]["measured"].date()
return jsonify(compressed_data)
return jsonify(fetched_data)
def getData():
statistics.update("data_calls")
return get_data.get_data(request.args)
def mainPage():
statistics.update("website")
return render_template("index.html")
def insertData():
statistics.update("insert_calls")
return insert_data.insert_data(request.args)
def dashboard():
statistics.update("website")
return render_template("index.html", pages=config.embedded_pages)
def define_production_statistics(parameters):
"""
Statistik-Arrays for performance evaluation
"""
statistics = dict()
stat_episode = dict()
statistics.update({
'stat_machines_working':
np.array([0.0] * parameters['NUM_MACHINES'])
})
statistics.update(
{'stat_machines_broken': np.array([0.0] * parameters['NUM_MACHINES'])})
statistics.update(
{'stat_machines_idle': np.array([0.0] * parameters['NUM_MACHINES'])})
statistics.update({
'stat_machines_changeover':
np.array([0.0] * parameters['NUM_MACHINES'])
})
statistics.update({
'stat_machines_processed_orders':
np.array([0.0] * parameters['NUM_MACHINES'])
})
statistics.update({
'stat_machines': [
statistics['stat_machines_working'],
statistics['stat_machines_broken'],
statistics['stat_machines_idle'],
statistics['stat_machines_changeover']
]
})
statistics.update({
'stat_transp_working':
np.array([0.0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({
'stat_transp_walking':
np.array([0.0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({
'stat_transp_handling':
np.array([0.0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({
'stat_transp_idle':
np.array([0.0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({
'stat_transp':
[statistics['stat_transp_walking'], statistics['stat_transp_idle']]
})
statistics.update({
'stat_transp_selected_idle':
np.array([0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({
'stat_transp_forced_idle':
np.array([0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({
'stat_transp_threshold_waiting_reached':
np.array([0] * parameters['NUM_TRANSP_AGENTS'])
})
statistics.update({'stat_order_sop': defaultdict(int)})
statistics.update({'stat_order_eop': defaultdict(int)})
statistics.update({'stat_order_waiting': defaultdict(int)})
statistics.update({'stat_order_processing': defaultdict(int)})
statistics.update({'stat_order_handling': defaultdict(int)})
statistics.update({'stat_order_leadtime': defaultdict(int)})
statistics.update({
'stat_order': [
statistics['stat_order_sop'], statistics['stat_order_eop'],
statistics['stat_order_waiting'],
statistics['stat_order_processing'],
statistics['stat_order_handling'],
statistics['stat_order_leadtime']
]
})
statistics.update(
{'stat_inv_buffer_in': np.array([0.0] * parameters['NUM_RESOURCES'])})
statistics.update(
{'stat_inv_buffer_out': np.array([0.0] * parameters['NUM_RESOURCES'])})
statistics.update({
'stat_inv_buffer_in_mean': [
np.array([0.0] * parameters['NUM_RESOURCES']),
np.array([0.0] * parameters['NUM_RESOURCES'])
]
})
statistics.update({
'stat_inv_buffer_out_mean': [
np.array([0.0] * parameters['NUM_RESOURCES']),
np.array([0.0] * parameters['NUM_RESOURCES'])
]
})
statistics.update({
'stat_inv':
[statistics['stat_inv_buffer_in'], statistics['stat_inv_buffer_out']]
})
statistics.update({'stat_inv_episode': [[0.0, 0]]})
statistics.update(
{'stat_agent_reward': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]})
statistics.update({
'agent_reward_log':
open(parameters['PATH_TIME'] + "_agent_reward_log.txt", "w")
})
statistics.update({
'episode_log':
open(parameters['PATH_TIME'] + "_episode_log.txt", "w")
})
statistics.update({
'episode_statistics': [
'stat_machines_working', 'stat_machines_changeover',
'stat_machines_broken', 'stat_machines_idle',
'stat_machines_processed_orders', 'stat_transp_working',
'stat_transp_walking', 'stat_transp_handling', 'stat_transp_idle'
]
})
statistics.update({'sim_start_time': ""})
statistics.update({'sim_end_time': ""})
statistics.update({'stat_prefilled_orders': ""})
# Episode KPI Logger
statistics.update({
'episode_log_header': [
'episode_counter', 'sim_step', 'sim_time', 'dt', 'dt_real_time',
'valid_actions', 'total_reward', 'machines_working',
'machines_changeover', 'machines_broken', 'machines_idle',
'processed_orders', 'transp_working', 'transp_walking',
'transp_handling', 'transp_idle', 'machines_total',
'selected_idle', 'forced_idle', 'threshold_waiting',
'finished_orders', 'order_waiting_time', 'alpha', 'inventory'
]
})
string = ""
for x in statistics['episode_log_header']:
string = string + x + ","
string = string[:-1]
statistics['episode_log'].write("%s\n" % (string))
# Reward Agent Logger
string = "episode,sim_step,sim_time,action,reward,action_valid,state"
statistics['agent_reward_log'].write("%s\n" % (string))
statistics['agent_reward_log'].close()
# Temp statistics
for stat in statistics['episode_statistics']:
stat_episode.update({stat: np.array([0.0] * len(statistics[stat]))})
statistics.update({'orders_done': deque()})
return statistics, stat_episode