def test_plan_perfs_from_dic(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
csvdic = f_util.csv_value_dict_from_iter(csvlines)
plan, perfs, _, _ = f_util.plan_perfs_from_dic(csvdic, 'tss', '60m_critical_power', True)
self.assertTrue(len(plan) == len(perfs))
plan, perfs, _, _ = f_util.plan_perfs_from_dic(csvdic, 'tss', '60m_critical_power', True, 0.3)
self.assertTrue(len(plan) == len(perfs))
def test_accumulate_loads_per_day(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
csvdic = f_util.csv_value_dict_from_iter(csvlines)
dates = csvdic['date']
loads = list(map(float, csvdic['skiba_bike_score']))
acc_loads = f_util.accumulate_loads_per_day(dates, loads)
self.assertTrue(len(acc_loads) == 7)
self.assertTrue(acc_loads[1][1] == 200)
def test_span_best_over_weeks(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
csvdic = f_util.csv_value_dict_from_iter(csvlines)
dates, loads, perfs = f_util.span_best_over_weeks(csvdic,
'skiba_bike_score',
'60m_critical_power')
self.assertTrue(perfs[0] == 170)
self.assertTrue(perfs[0] == perfs[1])
self.assertTrue(perfs[1] == perfs[2])
self.assertTrue(perfs[3] == 180)
def test_collapse_perfs_per_day(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
csvdic = f_util.csv_value_dict_from_iter(csvlines)
dates, loads, perfs = f_util.span_best_over_weeks(csvdic,
'skiba_bike_score',
'60m_critical_power')
col_perfs = f_util.collapse_perfs_per_day(dates, perfs)
self.assertTrue(len(col_perfs) == 7)
expected = [(datetime.date(2014, 2, 15), 170),
(datetime.date(2014, 2, 16), 170),
(datetime.date(2014, 2, 17), 180),
(datetime.date(2014, 3, 1), 190),
(datetime.date(2014, 3, 29), 200),
(datetime.date(2015, 3, 1), 150),
(datetime.date(2015, 3, 29), 90)]
self.assertTrue(col_perfs == expected)
def test_span_best_over_time_frame(self):
def key_func(x):
return x[0].year, x[0].month # group by key (year, month)
csvlines = FittingUtilTestCase.csv_content.splitlines()
csvdic = f_util.csv_value_dict_from_iter(csvlines)
dates = f_util.parse_date_field(csvdic['date'])
loads = list(map(float, csvdic['skiba_bike_score']))
perfs = list(map(float, csvdic['60m_critical_power']))
trainings = zip(dates, loads, perfs)
dates, loads, perfs = f_util.span_best_over_time_frame(trainings,
key_func,
min_group_size=5)
self.assertTrue(dates == [])
self.assertTrue(loads == [])
self.assertTrue(perfs == [])
def test_csv_value_dict_from_iter(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
csvdic = f_util.csv_value_dict_from_iter(csvlines)
self.assertTrue(len(csvdic['date']) == 8)
self.assertTrue(len(csvdic['60m_critical_power']) == 8)
self.assertTrue(len(csvdic['skiba_bike_score']) == 8)
