env_vars = {'in_topic': os.getenv('IN_TOPIC'),
'in_group': os.getenv('IN_GROUP'),
'in_schema_file': os.getenv('IN_SCHEMA_FILE'),
'out_topic': os.getenv('OUT_TOPIC'),
'out_schema_file': os.getenv('OUT_SCHEMA_FILE')}
env_vars = {'in_topic': {'DB_raw_data': './schema/data.avsc'},
'in_group': 'monitoring',
'in_schema_file': './schema/data.avsc',
'out_topic': 'AB_monitoring',
'out_schema_file': './schema/monitoring.avsc',
'config_path': 'config.yaml'}
"""
new_pc = KafkaPC(**env_vars)
for msg in new_pc.consumer:
"""
"name": "Data",
"fields": [
{"name": "phase", "type": ["string"]},
{"name": "id_x", "type": ["int"]},
{"name": "x", "type": ["float"]},
{"name": "y", "type": ["float"]}
]
"""
new_data = new_pc.decode_avro_msg(msg)
"""
import os
import requests
from classes.KafkaPC import KafkaPC
env_vars = {'config_path': os.getenv('config_path'),
'config_section': os.getenv('config_section')}
new_pc = KafkaPC(**env_vars)
API_URL = new_pc.config['API_URL']
ENDPOINT = "/production_parameter/x"
URL = API_URL + ENDPOINT
for msg in new_pc.consumer:
"""
"name": "New X",
"fields": [
{"name": "new_x", "type": ["float"]}
]
"""
new_message = new_pc.decode_avro_msg(msg)
# defining a params dict for the parameters to be sent to the API
params = {"value": new_message['new_x']}
# sending get request and saving the response as response object
print(f"Send x={round(new_message['new_x'], 3)} to the CPPS Controller")
r = requests.put(url=URL, params=params)
"y": {
"new_x": msgdata["new_x"],
"algorithm": splitData[0]
},
}
new_c.send_msg(new_data_point)
print("message sent")
env_vars = {
"config_path": os.getenv("config_path"),
"config_section": os.getenv("config_section"),
}
new_c = KafkaPC(**env_vars)
api_dict = new_c.config["API_OUT"]
plot_dict = new_c.config["PLOT_TOPIC"]
API_URL = new_c.config["API_URL"]
ENDPOINT = new_c.config["API_ENDPOINT"]
for msg in new_c.consumer:
# tests if msg.topic is in api_dict and calls function from dict
try:
if api_dict.get(msg.topic) is not None:
eval(api_dict[msg.topic])(msg)
except Exception as e:
print(
f"Processing Topic: {msg.topic} with Function: {api_dict[msg.topic]}\n Error: {e}"
return MODEL_PARAMETERS
env_vars = {
'config_path': os.getenv('config_path'),
'config_section': os.getenv('config_section')
}
"""
env_vars = {'in_topic': {'DB_raw_data': './schema/data.avsc'},
'in_group': 'kriging',
'in_schema_file': './schema/data.avsc',
'out_topic': 'AB_model_data',
'out_schema_file': './schema/model.avsc'}
"""
new_pc = KafkaPC(**env_vars)
MODEL_ALGORITHM = new_pc.config['MODEL_ALGORITHM']
API_URL = new_pc.config['API_URL']
MODEL_PARAMETERS = get_model_parameters(API_URL)
new_window = DataWindow()
MIN_DATA_POINTS = 5
for msg in new_pc.consumer:
"""
"name": "Data",
"fields": [
{"name": "phase", "type": ["string"]},
{"name": "id_x", "type": ["int"]},
import requests
import json
import time
import os
from classes.KafkaPC import KafkaPC
from classes.caai_util import ObjectiveFunction
env_vars = {
'config_path': os.getenv('config_path'),
'config_section': os.getenv('config_section')
}
new_pc = KafkaPC(**env_vars)
new_objective = ObjectiveFunction()
new_objective.load_data(data_path=new_pc.config['data_path'],
x_columns=new_pc.config['x_columns'],
y_columns=new_pc.config['y_columns'])
new_objective.fit_model()
N_INITIAL_DESIGN = new_pc.config['N_INITIAL_DESIGN']
MAX_PRODUCTION_CYCLES = new_pc.config['MAX_PRODUCTION_CYCLES']
phase = 'init'
current_data_point = 0
time.sleep(5)
while current_data_point < MAX_PRODUCTION_CYCLES:
if current_data_point == N_INITIAL_DESIGN: