mirror of
https://github.com/titanscouting/tra-superscript.git
synced 2024-09-20 00:47:56 +00:00
Merge pull request #19 from titanscouting/modularize
Reflect modularization changes into v1
Former-commit-id: b0a0632b99
This commit is contained in:
Arthur Lu
GitHub
commit
14ed3cc507
@ -2,4 +2,4 @@ set pathtospec="../src/cli/superscript.spec"
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set pathtodist="../dist/"
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set pathtowork="temp/"
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pyinstaller --onefile --clean --distpath %pathtodist% --workpath %pathtowork% %pathtospec%
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pyinstaller --clean --distpath %pathtodist% --workpath %pathtowork% %pathtospec%
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@ -2,4 +2,4 @@ pathtospec="../src/cli/superscript.spec"
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pathtodist="../dist/"
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pathtowork="temp/"
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pyinstaller --onefile --clean --distpath ${pathtodist} --workpath ${pathtowork} ${pathtospec}
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pyinstaller --clean --distpath ${pathtodist} --workpath ${pathtowork} ${pathtospec}
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@ -1,5 +1,4 @@
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import requests
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import pandas as pd
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def pull_new_tba_matches(apikey, competition, cutoff):
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api_key= apikey
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309
src/cli/module.py
Normal file
309
src/cli/module.py
Normal file
@ -0,0 +1,309 @@
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import abc
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import data as d
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import signal
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import numpy as np
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import tra_analysis as an
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class Module(metaclass = abc.ABCMeta):
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@classmethod
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def __subclasshook__(cls, subclass):
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return (hasattr(subclass, 'validate_config') and
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callable(subclass.validate_config) and
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hasattr(subclass, 'load_data') and
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callable(subclass.load_data) and
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hasattr(subclass, 'process_data') and
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callable(subclass.process_data) and
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hasattr(subclass, 'push_results') and
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callable(subclass.push_results)
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)
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@abc.abstractmethod
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def validate_config(self):
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raise NotImplementedError
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@abc.abstractmethod
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def load_data(self):
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raise NotImplementedError
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@abc.abstractmethod
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def process_data(self, exec_threads):
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raise NotImplementedError
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@abc.abstractmethod
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def push_results(self):
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raise NotImplementedError
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class Match (Module):
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config = None
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apikey = None
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tbakey = None
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timestamp = None
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competition = None
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data = None
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results = None
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def __init__(self, config, apikey, tbakey, timestamp, competition):
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self.config = config
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self.apikey = apikey
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self.tbakey = tbakey
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self.timestamp = timestamp
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self.competition = competition
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def validate_config(self):
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return True, ""
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def load_data(self):
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self.data = d.load_match(self.apikey, self.competition)
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def simplestats(data_test):
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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data = np.array(data_test[3])
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data = data[np.isfinite(data)]
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ranges = list(range(len(data)))
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test = data_test[2]
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if test == "basic_stats":
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return an.basic_stats(data)
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if test == "historical_analysis":
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return an.histo_analysis([ranges, data])
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if test == "regression_linear":
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return an.regression(ranges, data, ['lin'])
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if test == "regression_logarithmic":
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return an.regression(ranges, data, ['log'])
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if test == "regression_exponential":
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return an.regression(ranges, data, ['exp'])
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if test == "regression_polynomial":
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return an.regression(ranges, data, ['ply'])
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if test == "regression_sigmoidal":
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return an.regression(ranges, data, ['sig'])
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def process_data(self, exec_threads):
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tests = self.config["tests"]
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data = self.data
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input_vector = []
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for team in data:
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for variable in data[team]:
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if variable in tests:
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for test in tests[variable]:
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input_vector.append((team, variable, test, data[team][variable]))
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self.data = input_vector
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self.results = list(exec_threads.map(self.simplestats, self.data))
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def push_results(self):
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short_mapping = {"regression_linear": "lin", "regression_logarithmic": "log", "regression_exponential": "exp", "regression_polynomial": "ply", "regression_sigmoidal": "sig"}
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class AutoVivification(dict):
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def __getitem__(self, item):
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try:
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return dict.__getitem__(self, item)
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except KeyError:
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value = self[item] = type(self)()
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return value
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result_filtered = self.results
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input_vector = self.data
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return_vector = AutoVivification()
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i = 0
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for result in result_filtered:
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filtered = input_vector[i][2]
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try:
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short = short_mapping[filtered]
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return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result[short]
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except KeyError: # not in mapping
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return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result
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i += 1
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self.results = return_vector
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d.push_match(self.apikey, self.competition, self.results)
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class Metric (Module):
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config = None
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apikey = None
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tbakey = None
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timestamp = None
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competition = None
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data = None
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results = None
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def __init__(self, config, apikey, tbakey, timestamp, competition):
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self.config = config
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self.apikey = apikey
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self.tbakey = tbakey
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self.timestamp = timestamp
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self.competition = competition
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def validate_config(self):
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return True, ""
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def load_data(self):
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self.data = d.pull_new_tba_matches(self.tbakey, self.competition, self.timestamp)
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def process_data(self, exec_threads):
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elo_N = self.config["tests"]["elo"]["N"]
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elo_K = self.config["tests"]["elo"]["K"]
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matches = self.data
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red = {}
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blu = {}
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for match in matches:
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red = d.load_metric(self.apikey, self.competition, match, "red", self.config["tests"])
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blu = d.load_metric(self.apikey, self.competition, match, "blue", self.config["tests"])
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elo_red_total = 0
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elo_blu_total = 0
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gl2_red_score_total = 0
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gl2_blu_score_total = 0
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gl2_red_rd_total = 0
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gl2_blu_rd_total = 0
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gl2_red_vol_total = 0
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gl2_blu_vol_total = 0
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for team in red:
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elo_red_total += red[team]["elo"]["score"]
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gl2_red_score_total += red[team]["gl2"]["score"]
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gl2_red_rd_total += red[team]["gl2"]["rd"]
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gl2_red_vol_total += red[team]["gl2"]["vol"]
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for team in blu:
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elo_blu_total += blu[team]["elo"]["score"]
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gl2_blu_score_total += blu[team]["gl2"]["score"]
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gl2_blu_rd_total += blu[team]["gl2"]["rd"]
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gl2_blu_vol_total += blu[team]["gl2"]["vol"]
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red_elo = {"score": elo_red_total / len(red)}
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blu_elo = {"score": elo_blu_total / len(blu)}
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red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
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blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
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if match["winner"] == "red":
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observations = {"red": 1, "blu": 0}
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elif match["winner"] == "blue":
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observations = {"red": 0, "blu": 1}
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else:
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observations = {"red": 0.5, "blu": 0.5}
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red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
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blu_elo_delta = an.Metric().elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
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new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metric().glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
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new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metric().glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
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red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
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blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
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for team in red:
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red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
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red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
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red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
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red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
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for team in blu:
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blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
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blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
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blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
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blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
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temp_vector = {}
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temp_vector.update(red)
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temp_vector.update(blu)
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d.push_metric(self.client, self.competition, temp_vector)
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def push_results(self):
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pass
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class Pit (Module):
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config = None
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apikey = None
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tbakey = None
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timestamp = None
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competition = None
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data = None
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results = None
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def __init__(self, config, apikey, tbakey, timestamp, competition):
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self.config = config
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self.apikey = apikey
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self.tbakey = tbakey
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self.timestamp = timestamp
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self.competition = competition
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def validate_config(self):
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return True, ""
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def load_data(self):
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self.data = d.load_pit(self.apikey, self.competition)
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def process_data(self, exec_threads):
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return_vector = {}
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for team in self.data:
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for variable in self.data[team]:
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if variable in self.config:
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if not variable in return_vector:
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return_vector[variable] = []
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return_vector[variable].append(self.data[team][variable])
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self.results = return_vector
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def push_results(self):
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d.push_pit(self.apikey, self.competition, self.results)
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class Rating (Module):
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pass
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class Heatmap (Module):
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pass
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class Sentiment (Module):
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pass
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@ -1,188 +0,0 @@
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import numpy as np
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from tra_analysis import Analysis as an
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from data import pull_new_tba_matches, push_metric, load_metric
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import signal
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def simplestats(data_test):
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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data = np.array(data_test[3])
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data = data[np.isfinite(data)]
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ranges = list(range(len(data)))
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test = data_test[2]
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if test == "basic_stats":
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return an.basic_stats(data)
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if test == "historical_analysis":
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return an.histo_analysis([ranges, data])
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if test == "regression_linear":
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return an.regression(ranges, data, ['lin'])
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if test == "regression_logarithmic":
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return an.regression(ranges, data, ['log'])
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if test == "regression_exponential":
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return an.regression(ranges, data, ['exp'])
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if test == "regression_polynomial":
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return an.regression(ranges, data, ['ply'])
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if test == "regression_sigmoidal":
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return an.regression(ranges, data, ['sig'])
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def matchloop(client, competition, data, tests, exec_threads):
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short_mapping = {"regression_linear": "lin", "regression_logarithmic": "log", "regression_exponential": "exp", "regression_polynomial": "ply", "regression_sigmoidal": "sig"}
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class AutoVivification(dict):
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def __getitem__(self, item):
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try:
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return dict.__getitem__(self, item)
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except KeyError:
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value = self[item] = type(self)()
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return value
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input_vector = []
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return_vector = AutoVivification()
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for team in data:
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for variable in data[team]:
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if variable in tests:
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for test in tests[variable]:
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input_vector.append((team, variable, test, data[team][variable]))
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result_filtered = exec_threads.map(simplestats, input_vector)
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i = 0
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result_filtered = list(result_filtered)
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for result in result_filtered:
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filtered = input_vector[i][2]
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try:
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short = short_mapping[filtered]
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return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result[short]
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except KeyError: # not in mapping
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return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result
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i += 1
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return return_vector
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def metricloop(client, competition, data, metrics): # listener based metrics update
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elo_N = metrics["elo"]["N"]
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elo_K = metrics["elo"]["K"]
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matches = data
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#matches = pull_new_tba_matches(tbakey, competition, timestamp)
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red = {}
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blu = {}
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for match in matches:
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red = load_metric(client, competition, match, "red", metrics)
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blu = load_metric(client, competition, match, "blue", metrics)
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elo_red_total = 0
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elo_blu_total = 0
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gl2_red_score_total = 0
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gl2_blu_score_total = 0
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gl2_red_rd_total = 0
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gl2_blu_rd_total = 0
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gl2_red_vol_total = 0
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gl2_blu_vol_total = 0
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for team in red:
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elo_red_total += red[team]["elo"]["score"]
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gl2_red_score_total += red[team]["gl2"]["score"]
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gl2_red_rd_total += red[team]["gl2"]["rd"]
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gl2_red_vol_total += red[team]["gl2"]["vol"]
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for team in blu:
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elo_blu_total += blu[team]["elo"]["score"]
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gl2_blu_score_total += blu[team]["gl2"]["score"]
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gl2_blu_rd_total += blu[team]["gl2"]["rd"]
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gl2_blu_vol_total += blu[team]["gl2"]["vol"]
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red_elo = {"score": elo_red_total / len(red)}
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blu_elo = {"score": elo_blu_total / len(blu)}
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red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
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blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
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if match["winner"] == "red":
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observations = {"red": 1, "blu": 0}
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elif match["winner"] == "blue":
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observations = {"red": 0, "blu": 1}
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else:
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observations = {"red": 0.5, "blu": 0.5}
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||||
|
||||
red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
|
||||
blu_elo_delta = an.Metric().elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
|
||||
|
||||
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metric().glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
|
||||
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metric().glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
|
||||
|
||||
red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
|
||||
blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
|
||||
|
||||
for team in red:
|
||||
|
||||
red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
|
||||
|
||||
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
|
||||
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
|
||||
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
|
||||
|
||||
for team in blu:
|
||||
|
||||
blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
|
||||
|
||||
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
|
||||
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
|
||||
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
|
||||
|
||||
temp_vector = {}
|
||||
temp_vector.update(red)
|
||||
temp_vector.update(blu)
|
||||
|
||||
push_metric(client, competition, temp_vector)
|
||||
|
||||
def pitloop(client, competition, pit, tests):
|
||||
|
||||
return_vector = {}
|
||||
for team in pit:
|
||||
for variable in pit[team]:
|
||||
if variable in tests:
|
||||
if not variable in return_vector:
|
||||
return_vector[variable] = []
|
||||
return_vector[variable].append(pit[team][variable])
|
||||
|
||||
return return_vector
|
||||
@ -163,62 +163,78 @@ import warnings
|
||||
import zmq
|
||||
|
||||
from interface import splash, log, ERR, INF, stdout, stderr
|
||||
from data import get_previous_time, pull_new_tba_matches, set_current_time, load_match, push_match, load_pit, push_pit, get_database_config, set_database_config, check_new_database_matches
|
||||
from processing import matchloop, metricloop, pitloop
|
||||
from data import get_previous_time, set_current_time, get_database_config, set_database_config, check_new_database_matches
|
||||
from module import Match, Metric, Pit
|
||||
|
||||
config_path = "config.json"
|
||||
sample_json = """{
|
||||
"persistent":{
|
||||
"key":{
|
||||
"database":"mongodb+srv://analysis:MU2gPeEjEurRt2n@2022-scouting-4vfuu.mongodb.net/<dbname>?retryWrites=true&w=majority",
|
||||
"tba":"UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5"
|
||||
"database":"",
|
||||
"tba":""
|
||||
},
|
||||
"config-preference":"local",
|
||||
"synchronize-config":false
|
||||
},
|
||||
"variable":{
|
||||
|
||||
"max-threads":0.5,
|
||||
|
||||
"competition":"",
|
||||
"team":"",
|
||||
"competition": "2020ilch",
|
||||
"statistics":{
|
||||
|
||||
"event-delay":false,
|
||||
"loop-delay":0,
|
||||
"reportable":true,
|
||||
|
||||
"teams":[],
|
||||
|
||||
"modules":{
|
||||
|
||||
"match":{
|
||||
"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"]
|
||||
"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":{
|
||||
"elo":{
|
||||
"score":1500,
|
||||
"N":400,
|
||||
"K":24
|
||||
},
|
||||
"gl2":{
|
||||
"score":1500,
|
||||
"rd":250,
|
||||
"vol":0.06
|
||||
},
|
||||
"ts":{
|
||||
"mu":25,
|
||||
"sigma":8.33
|
||||
"tests":{
|
||||
"elo":{
|
||||
"score":1500,
|
||||
"N":400,
|
||||
"K":24
|
||||
},
|
||||
"gl2":{
|
||||
"score":1500,
|
||||
"rd":250,
|
||||
"vol":0.06
|
||||
},
|
||||
"ts":{
|
||||
"mu":25,
|
||||
"sigma":8.33
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"pit":{
|
||||
"wheel-mechanism":true,
|
||||
"low-balls":true,
|
||||
"high-balls":true,
|
||||
"wheel-success":true,
|
||||
"strategic-focus":true,
|
||||
"climb-mechanism":true,
|
||||
"attitude":true
|
||||
"tests":{
|
||||
"wheel-mechanism":true,
|
||||
"low-balls":true,
|
||||
"high-balls":true,
|
||||
"wheel-success":true,
|
||||
"strategic-focus":true,
|
||||
"climb-mechanism":true,
|
||||
"attitude":true
|
||||
}
|
||||
}
|
||||
},
|
||||
"event-delay":false,
|
||||
"loop-delay":60
|
||||
}
|
||||
}
|
||||
}"""
|
||||
|
||||
@ -238,6 +254,8 @@ def main(send, verbose = False, profile = False, debug = False):
|
||||
if verbose:
|
||||
splash(__version__)
|
||||
|
||||
modules = {"match": Match, "metric": Metric, "pit": Pit}
|
||||
|
||||
while True:
|
||||
|
||||
try:
|
||||
@ -273,40 +291,27 @@ def main(send, verbose = False, profile = False, debug = False):
|
||||
exit_code = 1
|
||||
close_all()
|
||||
break
|
||||
flag, exec_threads, competition, match_tests, metrics_tests, pit_tests = parse_config_variable(send, config)
|
||||
flag, exec_threads, competition, config_modules = parse_config_variable(send, config)
|
||||
if flag:
|
||||
exit_code = 1
|
||||
close_all()
|
||||
break
|
||||
|
||||
start = time.time()
|
||||
send(stdout, INF, "loading match, metric, pit data (this may take a few seconds)")
|
||||
match_data = load_match(client, competition)
|
||||
metrics_data = pull_new_tba_matches(tbakey, competition, loop_start)
|
||||
pit_data = load_pit(client, competition)
|
||||
send(stdout, INF, "finished loading match, metric, pit data in "+ str(time.time() - start) + " seconds")
|
||||
|
||||
start = time.time()
|
||||
send(stdout, INF, "performing analysis on match, metrics, pit data")
|
||||
match_results = matchloop(client, competition, match_data, match_tests, exec_threads)
|
||||
metrics_results = metricloop(client, competition, metrics_data, metrics_tests)
|
||||
pit_results = pitloop(client, competition, pit_data, pit_tests)
|
||||
send(stdout, INF, "finished analysis in " + str(time.time() - start) + " seconds")
|
||||
|
||||
start = time.time()
|
||||
send(stdout, INF, "uploading match, metrics, pit results to database")
|
||||
push_match(client, competition, match_results)
|
||||
push_pit(client, competition, pit_results)
|
||||
send(stdout, INF, "finished uploading results in " + str(time.time() - start) + " seconds")
|
||||
|
||||
if debug:
|
||||
f = open("matchloop.log", "w+")
|
||||
json.dump(match_results, f, ensure_ascii=False, indent=4)
|
||||
f.close()
|
||||
|
||||
f = open("pitloop.log", "w+")
|
||||
json.dump(pit_results, f, ensure_ascii=False, indent=4)
|
||||
f.close()
|
||||
for m in config_modules:
|
||||
if m in modules:
|
||||
start = time.time()
|
||||
current_module = modules[m](config_modules[m], client, tbakey, loop_start, competition)
|
||||
valid = current_module.validate_config()
|
||||
if not valid:
|
||||
continue
|
||||
current_module.load_data()
|
||||
current_module.process_data(exec_threads)
|
||||
current_module.push_results()
|
||||
send(stdout, INF, m + " module finished in " + str(time.time() - start) + " seconds")
|
||||
if debug:
|
||||
f = open(m + ".log", "w+")
|
||||
json.dump({"data": current_module.data, "results":current_module.results}, f, ensure_ascii=False, indent=4)
|
||||
f.close()
|
||||
|
||||
set_current_time(client, loop_start)
|
||||
close_all()
|
||||
@ -423,37 +428,21 @@ def parse_config_variable(send, config):
|
||||
send(stderr, ERR, "could not find competition field in config", code = 101)
|
||||
exit_flag = True
|
||||
try:
|
||||
match_tests = config["variable"]["statistics"]["match"]
|
||||
modules = config["variable"]["modules"]
|
||||
except:
|
||||
send(stderr, ERR, "could not find match field in config", code = 102)
|
||||
exit_flag = True
|
||||
try:
|
||||
metrics_tests = config["variable"]["statistics"]["metric"]
|
||||
except:
|
||||
send(stderr, ERR, "could not find metrics field in config", code = 103)
|
||||
exit_flag = True
|
||||
try:
|
||||
pit_tests = config["variable"]["statistics"]["pit"]
|
||||
except:
|
||||
send(stderr, ERR, "could not find pit field in config", code = 104)
|
||||
send(stderr, ERR, "could not find modules field in config", code = 102)
|
||||
exit_flag = True
|
||||
|
||||
if competition == None or competition == "":
|
||||
send(stderr, ERR, "competition field in config must not be empty", code = 105)
|
||||
exit_flag = True
|
||||
if match_tests == None:
|
||||
send(stderr, ERR, "matchfield in config must not be empty", code = 106)
|
||||
exit_flag = True
|
||||
if metrics_tests == None:
|
||||
send(stderr, ERR, "metrics field in config must not be empty", code = 107)
|
||||
exit_flag = True
|
||||
if pit_tests == None:
|
||||
send(stderr, ERR, "pit field in config must not be empty", code = 108)
|
||||
if modules == None:
|
||||
send(stderr, ERR, "modules in config must not be empty", code = 106)
|
||||
exit_flag = True
|
||||
|
||||
send(stdout, INF, "found and loaded competition, match, metrics, pit from config")
|
||||
|
||||
return exit_flag, exec_threads, competition, match_tests, metrics_tests, pit_tests
|
||||
return exit_flag, exec_threads, competition, modules
|
||||
|
||||
def resolve_config_conflicts(send, client, config, preference, sync):
|
||||
|
||||
|
||||
@ -13,7 +13,10 @@ a = Analysis(['superscript.py'],
|
||||
],
|
||||
hookspath=[],
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
excludes=[
|
||||
"matplotlib",
|
||||
"pandas"
|
||||
],
|
||||
win_no_prefer_redirects=False,
|
||||
win_private_assemblies=False,
|
||||
cipher=block_cipher,
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
requests
|
||||
pymongo
|
||||
pandas
|
||||
tra-analysis
|
||||
|
||||
dnspython
|
||||
@ -11,7 +10,6 @@ scipy
|
||||
scikit-learn
|
||||
six
|
||||
pyparsing
|
||||
pandas
|
||||
|
||||
kivy==2.0.0rc2
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user