"""This is a collection of various functions."""
import datetime
import warnings
from functools import reduce
from typing import (
Optional,
Union
)
import numpy as np
import pandas as pd
import fastf1
from fastf1.logger import get_logger
_logger = get_logger(__name__)
[docs]
def delta_time(
reference_lap: "fastf1.core.Lap",
compare_lap: "fastf1.core.Lap"
) -> tuple[pd.Series, "fastf1.core.Telemetry", "fastf1.core.Telemetry"]:
"""Calculates the delta time of a given lap, along the 'Distance' axis
of the reference lap.
.. deprecated:: 3.0.0
.. warning:: This function should no longer be considered as a stable part
of the API. Due to the reasons given below, this function will be
modified or removed at a future point.
.. 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(misc_mpl_mods=False, color_scheme='fastf1')
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 ensures the same applied interpolation and resampling
ax.plot(ref_tel['Distance'], ref_tel['Speed'],
color=plotting.get_team_color(ham['Team'], session))
ax.plot(compare_tel['Distance'], compare_tel['Speed'],
color=plotting.get_team_color(lec['Team'], session))
twin = ax.twinx()
twin.plot(ref_tel['Distance'], delta_time, '--', color='white')
twin.set_ylabel("<-- Lec ahead | Ham ahead -->")
plt.show()
Args:
reference_lap: The lap taken as reference
compare_lap: The lap to compare
Returns:
A tuple containing
- pd.Series of type `float64` with the delta in seconds.
- :class:`~fastf1.core.Telemetry` for the reference lap
- :class:`~fastf1.core.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!
"""
warnings.warn("`utils.delta_time` is considered deprecated and will"
"be modified or removed in a future release because it has"
"a tendency to give inaccurate results.",
FutureWarning)
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())
multiplier = ref.Distance.iat[-1]/comp.Distance.iat[-1]
ldistance = mini_pro(comp['Distance'].to_numpy())*multiplier
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: dict, *keys: str, default_none: bool = False):
"""Recursive dict get. Can take an arbitrary number of keys and returns an
empty dict if any key does not exist.
https://stackoverflow.com/a/28225747"""
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: Union[str, datetime.timedelta]) \
-> Optional[datetime.timedelta]:
"""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: timestamp
"""
# this is faster than using pd.timedelta on a string
if isinstance(x, str) and len(x):
try:
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 datetime.timedelta(
hours=int(hours), minutes=int(minutes),
seconds=int(seconds), microseconds=int(msus)
)
except Exception as exc:
_logger.debug(f"Failed to parse timedelta string '{x}'",
exc_info=exc)
return None
elif isinstance(x, datetime.timedelta):
return x
else:
return None
[docs]
def to_datetime(x: Union[str, datetime.datetime]) \
-> Optional[datetime.datetime]:
"""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: timestamp
"""
if isinstance(x, str) and x:
try:
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.datetime(
int(year), int(month), int(day), int(hours),
int(minutes), int(seconds), int(msus)
)
except Exception as exc:
_logger.debug(f"Failed to parse datetime string '{x}'",
exc_info=exc)
return None
elif isinstance(x, datetime.datetime):
return x
else:
return None