Source code for fastf1.utils

Utils module - :mod:`fastf1.utils`
from functools import reduce

import numpy as np
from datetime import datetime, timedelta

[docs]def delta_time(reference_lap, compare_lap): # TODO move somewhere else """Calculates the delta time of a given lap, along the 'Distance' axis of the reference lap. .. warning:: This is a nice gimmick but not actually very accurate which is an inherent problem from the way this is calculated currently (There may not be a better way though). In comparison with the sector times and the differences that can be calculated from these, there are notable differences! You should always verify the result against sector time differences or find a different way for verification. Here is an example that compares the quickest laps of Leclerc and Hamilton from Bahrain 2021 Qualifying: .. plot:: :include-source: import fastf1 as ff1 from fastf1 import plotting from fastf1 import utils from matplotlib import pyplot as plt plotting.setup_mpl() session = ff1.get_session(2021, 'Emilia Romagna', 'Q') session.load() lec = session.laps.pick_driver('LEC').pick_fastest() ham = session.laps.pick_driver('HAM').pick_fastest() delta_time, ref_tel, compare_tel = utils.delta_time(ham, lec) # ham is reference, lec is compared fig, ax = plt.subplots() # use telemetry returned by .delta_time for best accuracy, # this ensure the same applied interpolation and resampling ax.plot(ref_tel['Distance'], ref_tel['Speed'], color=plotting.team_color(ham['Team'])) ax.plot(compare_tel['Distance'], compare_tel['Speed'], color=plotting.team_color(lec['Team'])) twin = ax.twinx() twin.plot(ref_tel['Distance'], delta_time, '--', color='white') twin.set_ylabel("<-- Lec ahead | Ham ahead -->") Args: reference_lap (pd.Series): The lap taken as reference compare_lap (pd.Series): The lap to compare Returns: tuple: (delta, reference, comparison) - pd.Series of type `float64` with the delta in seconds. - :class:`Telemetry` for the reference lap - :class:`Telemetry` for the comparison lap Use the return telemetry for plotting to make sure you have telemetry data that was created with the same interpolation and resampling options! """ ref = reference_lap.get_car_data(interpolate_edges=True).add_distance() comp = compare_lap.get_car_data(interpolate_edges=True).add_distance() def mini_pro(stream): # Ensure that all samples are interpolated dstream_start = stream[1] - stream[0] dstream_end = stream[-1] - stream[-2] return np.concatenate([[stream[0] - dstream_start], stream, [stream[-1] + dstream_end]]) ltime = mini_pro(comp['Time'].dt.total_seconds().to_numpy()) ldistance = mini_pro(comp['Distance'].to_numpy()) lap_time = np.interp(ref['Distance'], ldistance, ltime) delta = lap_time - ref['Time'].dt.total_seconds() return delta, ref, comp
[docs]def recursive_dict_get(d, *keys, default_none=False): """Recursive dict get. Can take an arbitrary number of keys and returns an empty dict if any key does not exist.""" ret = reduce(lambda c, k: c.get(k, {}), keys, d) if default_none and ret == {}: return None else: return ret
[docs]def to_timedelta(x): """Fast timedelta object creation from a time string Permissible string formats: For example: `13:24:46.320215` with: - optional hours and minutes - optional microseconds and milliseconds with arbitrary precision (1 to 6 digits) Examples of valid formats: - `24.3564` (seconds + milli/microseconds) - `36:54` (minutes + seconds) - `8:45:46` (hours, minutes, seconds) Args: x (str or timedelta): Returns: datetime.timedelta """ # this is faster than using pd.timedelta on a string if isinstance(x, str) and len(x): hours, minutes = 0, 0 if len(hms := x.split(':')) == 3: hours, minutes, seconds = hms elif len(hms) == 2: minutes, seconds = hms else: seconds = hms[0] if '.' in seconds: seconds, msus = seconds.split('.') if len(msus) < 6: msus = msus + '0' * (6 - len(msus)) elif len(msus) > 6: msus = msus[0:6] else: msus = 0 return timedelta(hours=int(hours), minutes=int(minutes), seconds=int(seconds), microseconds=int(msus)) elif isinstance(x, timedelta): return x
[docs]def to_datetime(x): """Fast datetime object creation from a date string. Permissible string formats: For example '2020-12-13T13:27:15.320000Z' with: - optional milliseconds and microseconds with arbitrary precision (1 to 6 digits) - with optional trailing letter 'Z' Examples of valid formats: - `2020-12-13T13:27:15.320000` - `2020-12-13T13:27:15.32Z` - `2020-12-13T13:27:15` Args: x (str or datetime) Returns: datetime.datetime """ if isinstance(x, str): date, time = x.strip('Z').split('T') year, month, day = date.split('-') hours, minutes, seconds = time.split(':') if '.' in seconds: seconds, msus = seconds.split('.') if len(msus) < 6: msus = msus+'0'*(6-len(msus)) elif len(msus) > 6: msus = msus[0:6] else: msus = 0 return datetime(int(year), int(month), int(day), int(hours), int(minutes), int(seconds), int(msus)) elif isinstance(x, datetime): return x