def pick(self, idx):
limit = tools.threshold(self.no, self.judges())
logic = (self.data.loc[idx, 'Total'] +
self.data.loc[idx, 'Penalty']) >= limit
self.data.loc[idx, 'Penalty'] = self.data.loc[idx, 'Penalty'].where(
logic, limit - self.data.loc[idx, 'Total']).where(
self.data.loc[idx, 'Total'] < limit, 0)
return self.data
def ban(self, idx):
limit = tools.threshold(self.no, self.judges())
logic = (self.data.loc[idx, 'Total'] +
self.data.loc[idx, 'Penalty']) < limit
self.data.loc[idx, 'Penalty'] = self.data.loc[idx, 'Penalty'].where(
logic, (self.data.loc[idx, 'Total'] - (limit - 1)) * -1).where(
self.data.loc[idx, 'Total'] >= limit, 0)
return self.data
def sort(self):
data = self.data
threshold = tools.threshold(self.no, self.judges())
data = data.assign(Penaltized=data['Total'] +
data['Penalty']).sort_values(
['Penaltized', 'ID', 'Penalty'],
ascending=[False, True, False])
logic = data['Penaltized'] >= threshold
self.data = tools.pd.concat(
[data.loc[logic].sort_index(),
data.loc[~logic].sort_index()]).drop('Penaltized', axis=1)
from model.constants import *
from model import tools
import numpy as np
import math
import glob
if __name__ == "__main__":
MIN_NUMBER_SAMPLES = 30 # sample rate is 2samples/sec
for file_path in glob.iglob('../data/classes/**/*.dat', recursive=True):
if 'threshold' not in file_path and 'day' in file_path:
data = tools.load_data_scilab(file_path)
data = tools.convert(data)
threshold_data = tools.threshold(data)
if np.shape(threshold_data[:, 0])[0] > MIN_NUMBER_SAMPLES:
threshold_file_path = '{}_threshold.dat'.format(
file_path.rstrip('.dat'))
print('Applying thresholds to {} and storing in {}'.format(
file_path, threshold_file_path))
with open(threshold_file_path, 'wb') as outfile:
np.save(outfile, threshold_data)
def threshold(self):
no = self.cur_stage_no()
nJudges = len(self.judges)
return tools.threshold(no, nJudges)
def highlights(self):
no = self.no
nJudges = self.judges()
threshold = tools.threshold(no, nJudges)
return [tools.invalid_candidates(self.data, threshold)]
def validity(self, idx):
''' return valid, invalid counts '''
threshold = tools.threshold(self.no, self.judges())
invalid = self.data['Total'] + self.data['Penalty'] < threshold
return (~invalid).loc[idx].sum(), invalid.loc[idx].sum()
def dropped(self, is_overall=False):
threshold = tools.threshold(self.no, self.judges())
sliced = self.data.iloc[:self.manpl.get(
)] if not is_overall else self.data
logic = (sliced['Total'] + sliced['Penalty']) < threshold
return logic.sum()