def test_timing_data():
response = list()
tl = 12 # length of timestamp: len('00:00:00:000')
with open('fastf1/testing/reference_data/'
'2020_05_FP2/timing_data.raw', 'rb') as fobj:
for line in fobj.readlines():
dec = line.decode('utf-8-sig')
response.append([dec[:tl], api.parse(dec[tl:])])
# parse data; api path is unused here so it does not need to be valid
lap_data, stream_data = api.timing_data('api/path', response=response)
# ########## verify lap data
assert (isinstance(lap_data, pd.DataFrame)
and isinstance(stream_data, pd.DataFrame))
assert len(lap_data['Driver'].unique()) == 20 # 20 drivers
assert len(lap_data.columns) == 17
assert (lap_data.dtypes == [
'timedelta64[ns]', 'object', 'timedelta64[ns]', 'int64', 'int64',
'timedelta64[ns]', 'timedelta64[ns]', 'timedelta64[ns]',
'timedelta64[ns]', 'timedelta64[ns]', 'timedelta64[ns]',
'timedelta64[ns]', 'timedelta64[ns]', 'float64', 'float64', 'float64',
'float64'
]).all()
# these columns should not contain any NA values
assert not lap_data.isna().loc[:, ('Time', 'Driver', 'NumberOfLaps',
'NumberOfPitStops', 'Sector2Time',
'Sector3Time', 'Sector2SessionTime',
'Sector3SessionTime')].any().any()
# double .any(): 1st within column, 2nd column results combined
# these columns need to contain same number of NA values (outlap)
count1 = lap_data.isna().loc[:, 'Sector1Time'].sum()
count2 = lap_data.isna().loc[:, 'Sector1SessionTime'].sum()
assert count1 == count2
assert count1 > 50
# laps with no sector1time need to have a pit out time
mask = lap_data.isna().loc[:, 'Sector1Time']
assert not lap_data.loc[mask, 'PitOutTime'].isna().any()
# sum of sector times needs to be equal to lap time
mask = (~lap_data.isna().loc[:, ('Sector1Time', 'LapTime')]).all(axis=1)
sums = lap_data.loc[mask, ('Sector1Time', 'Sector2Time',
'Sector3Time')].sum(axis=1)
assert np.allclose(sums.to_numpy().astype(float),
lap_data.loc[mask, 'LapTime'].to_numpy().astype(float))
# ########## verify stream data
# columns are Time, Driver, Position, GapToLeader, IntervalToPositionAhead
assert len(stream_data.columns) == 5
assert (stream_data.dtypes == [
'timedelta64[ns]', 'object', 'int64', 'float64', 'float64'
]).all()
assert not stream_data.loc[:, ('Time', 'Driver', 'Position')]\
.isna().any().any()
def test_weather_data():
response = list()
tl = 12 # length of timestamp: len('00:00:00:000')
with open('fastf1/testing/reference_data/'
'2020_05_FP2/weather_data.raw', 'rb') as fobj:
for line in fobj.readlines():
dec = line.decode('utf-8-sig')
response.append([dec[:tl], api.parse(dec[tl:])])
# parse data; api path is unused here so it does not need to be valid
data = api.weather_data('api/path', response=response)
# ########## verify lap data
assert isinstance(data, dict)
assert len(data.keys()) == 8
dtypes = [datetime.timedelta, float, float, float, bool, float, int, float]
for col, dtype in zip(data.values(), dtypes):
assert isinstance(col[0], dtype)
assert len(col) == 100
def test_timing_app_data():
response = list()
tl = 12 # length of timestamp: len('00:00:00:000')
with open(
'fastf1/testing/reference_data/'
'2020_05_FP2/timing_app_data.raw', 'rb') as fobj:
for line in fobj.readlines():
dec = line.decode('utf-8-sig')
response.append([dec[:tl], api.parse(dec[tl:])])
# parse data; api path is unused here so it does not need to be valid
data = api.timing_app_data('api/path', response=response)
# ########## verify lap data
assert isinstance(data, pd.DataFrame)
assert len(data.columns) == 13
assert (data.dtypes == [
'float64', 'object', 'timedelta64[ns]', 'int64', 'float64', 'object',
'object', 'object', 'timedelta64[ns]', 'float64', 'object', 'float64',
'object'
]).all()