# Time a task
TT.tick()
<some task>
seconds_used = TT.tock()

# Profile a for loop
for i in range(100):
    TT.profile("Repeated code")
    <some task>
    TT.profile("Subtask")
    <some subtask>
    TT.end_profile()
    TT.end_profile()
print(TT)  # Prints a table view of profiled code sections

# Alternative syntax using with statement
with TT.profile("The best task"):
    <some task>

# When using multiprocessing, it can be useful to simulate multiple hits of the same profile
with mp.Pool() as p, TT.profile("Processing 100 items on multiple threads", hits=100):
    p.map(100 items)
# Similar for very quick loops
a = 0
with TT.profile("Adding 1 to a", hits=100):
    for _ in range(100):
        a += 1

# Examples so far use a global TickTock instance, which is convenient,
# but it can also be desirable to use for multiple different timers, e.g.
tt1 = TickTock()
tt2 = TickTock()
t1_interval = 1  # Do task 1 every second
t2_interval = 2  # Do task 2 every other second
tt1.tick()
tt2.tick()
while True:
    if tt1.tock() > t1_interval:
        <task 1>
        tt1.tick()
    if tt2.tock() > t2_interval:
        <task 2>
        tt2.tick()
    time.sleep(0.01)

@dataclass
class ResultData(DataStorage):
    shots: int
    goalscorers: list
    dists: np.ndarray

rdata = ResultData(shots=1, goalscorers=["Max Fenger"], dists=np.ones(22)*10)
rdata.save("max")
# Now shots and goalscorers are saved in <pwd>/max/ResultData.json and dists in <pwd>/max/ResultData.pkl

# Then to load
rdata = ResultData.load("max")
print(rdata.goalscorers)  # ["Max Fenger"]

options = [
    # Mandatory argument with set abbreviation -p
    Argument("data-path", help="Path to where data is located", abbrv"-p"),
    # Optional argument with auto-generated abbreviation -l
    Option("learning-rate", default=1e-5, help="Learning rate to use for gradient descent steps"),
    # Boolean flag with auto-generated abbreviation -f
    Flag("fp16", help="Use mixed precision for training"),
]
parser = Parser(*options, multiple_jobs=True)  # Two jobs are specified in the config file, so multiple_jobs=True
location = parser.location  # Experiments are stored here. In this case path/to/put/results
job_descriptions = parser.parse_args()
# Run each experiment
for job in job_descriptions:
    # Get the job as a dictionary
    job_dict = job.todict()
    # Clear directory where job is located and put a documentation file there
    job.prepare_directory()
    # Get location of this job as job.location
    run_experiment(job)

# Configure logger for the script
log.configure("path/to/save/log.log")

# Start logging
for i in range(70):  # Nice
    log("Execution %i" % i)

# Sections
log.section("New section in the logfile")

# Adjust logging levels
log.warning("Will be logged")
with log.level(LogLevels.ERROR):  # Only log at ERROR level or above
    log.warning("Will not be logged")
with log.no_log:
    log.section("I will not be logged")

# Error handling
# The zero-division error and stacktrace is logged
with log.log_errors:
    0 / 0
# Entire chained stacktrace is logged
with log.log_errors:
    try:
        0 / 0
    except ZeroDivisionError as e:
        raise ValueError("Denominator must be non-zero") from e

# User input - acts like built-in input but logs both prompt and user input
inp = log.input("Continue [Y/n]? ")
# Parse yes/no user input
cont = log.parse_bool_input(inp, default=True)

# Log all logs from a function at the same time
# This is especially useful when using multiple threads so logging does not get mixed up
def fun():
    with log.collect:
        log("Hello there")
        log("General Kenobi!")
with mp.Pool() as p:
    p.map(fun, args)

# It is also possible to create multiple loggers by importing the Logger class, e.g.
log2 = Logger()
log2.configure("path/to/save/log2.log")

# Get one sided z value for exponential(lambda=2) distribution with a significance level of 1 %
zval = z(alpha=0.01, two_sided=False, distribution=scipy.stats.expon(loc=1/2))

# Get correlation, confidence interval, and p value for two vectors
a, b = np.random.randn(100), np.random.randn(100)
r, lower_r, upper_r, p = corr_ci(a, b, alpha=0.01)

from pelutils.ds.plots import Figure

# The following makes a plot and saves it to `plot.png`.
# The seaborn is style is used for demonstration, but if the `style` argument
# is not given, the default matplotlib style is used.
# The figure and font size are also given for demonstration, but their default
# values are increased compared to matplotlib's default, as these are generally
# too small for finished plots.
with Figure("plot.png", figsize=(20, 10), style="seaborn", fontsize=20):
    plt.scatter(x, y, label="Data")
    plt.grid()
    plt.title("Very nice plot")
# The figure is automatically saved to `plot.png` and closed, such that
# plt.plot can be used again from here.
# Figure changes `matplotlib.rcParams`, but these changes are also undone
# after the end of the `with statement`.

import matplotlib.pyplot as plt
import numpy as np
from pelutils.ds.plots import histogram, normal_binning

# Generate normally distributed data
x = np.random.randn(100)
# Plot distribution
plt.plot(*histogram(x, binning_fn=normal_binning))

from pelutils.ds.plots import double_moving_avg

# Generate noisy data
n = 100
x = np.linspace(-1, 1, n)
y = np.random.randn(n)

# Plot data along with smoothed curve
plt.plot(*double_moving_avg(x, y))
# If x is not given, it is assumed to go from 0 to n-1 in steps of 1
plt.plot(*double_moving_avg(y))