tra-superscript/src/cli/config.py

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import math
import json
from multiprocessing import Pool
import os
from cerberus import Validator
from data import set_database_config, get_database_config
from interface import stderr, stdout, INF, ERR
config_path = "config.json"
sample_json = """
{
"persistent":{
"key":{
"database":"",
"tba":"",
"tra":{
"CLIENT_ID":"",
"CLIENT_SECRET":""
}
},
"config-preference":"local",
"synchronize-config":false
},
"variable":{
"max-threads":0.5,
"team":"",
"event-delay":false,
"loop-delay":0,
"reportable":true,
"teams":[
],
"modules":{
"match":{
"tests":{
"balls-blocked":[
"basic_stats",
"historical_analysis",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
],
"balls-collected":[
"basic_stats",
"historical_analysis",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
],
"balls-lower-teleop":[
"basic_stats",
"historical_analysis",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
],
"balls-lower-auto":[
"basic_stats",
"historical_analysis",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
],
"balls-started":[
"basic_stats",
"historical_analyss",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
],
"balls-upper-teleop":[
"basic_stats",
"historical_analysis",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
],
"balls-upper-auto":[
"basic_stats",
"historical_analysis",
"regression_linear",
"regression_logarithmic",
"regression_exponential",
"regression_polynomial",
"regression_sigmoidal"
]
}
},
"metric":{
"tests":{
"elo":{
"score":1500,
"N":400,
"K":24
},
"gl2":{
"score":1500,
"rd":250,
"vol":0.06
},
"ts":{
"mu":25,
"sigma":8.33
}
}
},
"pit":{
"tests":{
"wheel-mechanism":true,
"low-balls":true,
"high-balls":true,
"wheel-success":true,
"strategic-focus":true,
"climb-mechanism":true,
"attitude":true
}
}
}
}
}
"""
class ConfigurationError (Exception):
code = None
def __init__(self, str, code):
super().__init__(str)
self.code = code
def parse_config_persistent(send, config):
v = Validator(load_validation_schema(), allow_unknown = True)
isValidated = v.validate(config)
if not isValidated:
raise ConfigurationError(v.errors, 101)
apikey = config["persistent"]["key"]["database"]
tbakey = config["persistent"]["key"]["tba"]
preference = config["persistent"]["config-preference"]
sync = config["persistent"]["synchronize-config"]
return apikey, tbakey, preference, sync
def parse_config_variable(send, config):
sys_max_threads = os.cpu_count()
try:
cfg_max_threads = config["variable"]["max-threads"]
except:
raise ConfigurationError("variable/max-threads field is invalid or missing, refer to documentation for configuration options", 109)
if cfg_max_threads > -sys_max_threads and cfg_max_threads < 0 :
alloc_processes = sys_max_threads + cfg_max_threads
elif cfg_max_threads > 0 and cfg_max_threads < 1:
alloc_processes = math.floor(cfg_max_threads * sys_max_threads)
elif cfg_max_threads > 1 and cfg_max_threads <= sys_max_threads:
alloc_processes = cfg_max_threads
elif cfg_max_threads == 0:
alloc_processes = sys_max_threads
else:
raise ConfigurationError("variable/max-threads must be between -" + str(sys_max_threads) + " and " + str(sys_max_threads) + ", but got " + cfg_max_threads, 110)
try:
exec_threads = Pool(processes = alloc_processes)
except Exception as e:
send(stderr, INF, e)
raise ConfigurationError("unable to start threads", 200)
send(stdout, INF, "successfully initialized " + str(alloc_processes) + " threads")
try:
modules = config["variable"]["modules"]
except:
raise ConfigurationError("variable/modules field is invalid or missing", 102)
if modules == None:
raise ConfigurationError("variable/modules field is empty", 106)
send(stdout, INF, "found and loaded competition, match, metrics, pit from config")
return exec_threads, modules
def resolve_config_conflicts(send, client, config, preference, sync):
if sync:
if preference == "local" or preference == "client":
send(stdout, INF, "config-preference set to local/client, loading local config information")
remote_config = get_database_config(client)
if remote_config != config["variable"]:
set_database_config(client, config["variable"])
send(stdout, INF, "database config was different and was updated")
return config
elif preference == "remote" or preference == "database":
send(stdout, INF, "config-preference set to remote/database, loading remote config information")
remote_config= get_database_config(client)
if remote_config != config["variable"]:
config["variable"] = remote_config
if save_config(config_path, config):
raise ConfigurationError("local config was different but could not be updated", 121)
send(stdout, INF, "local config was different and was updated")
return config
else:
raise ConfigurationError("persistent/config-preference field must be \"local\"/\"client\" or \"remote\"/\"database\"", 120)
else:
if preference == "local" or preference == "client":
send(stdout, INF, "config-preference set to local/client, loading local config information")
return config
elif preference == "remote" or preference == "database":
send(stdout, INF, "config-preference set to remote/database, loading database config information")
config["variable"] = get_database_config(client)
return config
else:
raise ConfigurationError("persistent/config-preference field must be \"local\"/\"client\" or \"remote\"/\"database\"", 120)
def load_config(path, config_vector):
try:
f = open(path, "r")
config_vector.update(json.load(f))
f.close()
return 0
except:
f = open(path, "w")
f.write(sample_json)
f.close()
return 1
def load_validation_schema():
try:
with open("validation-schema.json", "r") as f:
return json.load(f)
except:
raise FileNotFoundError("Validation schema not found at validation-schema.json")
def save_config(path, config_vector):
f = open(path, "w+")
json.dump(config_vector, f, ensure_ascii=False, indent=4)
f.close()
return 0