MarcoGorelli

deactivate
cp bytecode-parsing-tests.sh ../../temporary-polars-files/
cp just-make-it-anew.sh ../../temporary-polars-files/
cp pre-commit.sh ../../temporary-polars-files/
cp repl.sh ../../temporary-polars-files/
cp ../.vscode/settings.json ../../temporary-polars-files/
git clean -fxfd ../.
cargo clean
rustup self uninstall
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y

MarcoGorelli

ipython -i -c 'import pandas as pd; \
    from pandas.core.arrays import *; \
    import polars as pl; \
    from datetime import datetime, timezone, timedelta; \
    from pandas._libs.tslibs.parsing import guess_datetime_format; \
    from dateutil.parser import parse as du_parse; \
    import datetime as dt; \
    import numpy as np; \
    from pandas import *; \
    import pandas; \

MarcoGorelli

// ==UserScript==
// @name         myshipit
// @author       marcogorelli
// @match        https://github.com/*
// adapted from https://github.com/chriskuehl/shipit/blob/master/shipit.user.js,
// but I removed a handful of gifs (like the pikachu one) which I didn't like
// ==/UserScript==

(function () {
    var urls = [

MarcoGorelli

# usage:
# python -m cProfile  -o out.prof perf.py
# snakeviz out.prof --server

import pandas as pd

dates = pd.date_range("1900", "2000").tz_localize("+01:00").strftime("%Y-%d-%m %H:%M:%S%z").tolist()
dates.append("2020-01-01 00:00:00+02:00")

def main():

MarcoGorelli

import sys
import subprocess
import re
import shlex
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('command')
parser.add_argument('action', choices=['pull', 'push'])
args = parser.parse_args()

MarcoGorelli

import bornly as bns
import numpy as np
import pandas as pd
from pmdarima import auto_arima
from statsmodels.tsa.statespace.sarimax import SARIMAX

flights = bns.load_dataset("flights")
flights["t"] = np.arange(len(flights))
PERIOD = 12
n_steps = 12

MarcoGorelli

import numpy as np
import pandas as pd
from lightgbm import LGBMRegressor, log_evaluation
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold

data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)

MarcoGorelli

fig, axes = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
axes = axes.flatten()

data = pd.DataFrame(samples, columns=["x_0", "x_1"])
sns.kdeplot(data=data, x="x_0", y="x_1", ax=axes[0])

data = pd.DataFrame(pushforward_samples, columns=["x_tilde_0", "x_tilde_1"])
sns.kdeplot(data=data, x="x_tilde_0", y="x_tilde_1", ax=axes[1])

xyA = [2.5, -0.6]

MarcoGorelli

def inv_g(x_tilde):
    """Inverse of `g`."""
    return jnp.asarray([jax.scipy.special.logit(x_tilde[0]), jnp.log(x_tilde[1])])
  
  
x_tilde = jnp.column_stack(
    [jnp.linspace(0.001, 0.999, 1000), jnp.linspace(0.001, 3, 1000)]
)
pre_x_tilde = jax.vmap(inv_g)(x_tilde)

MarcoGorelli

samples = distribution.sample(rng_key, sample_shape=(1000,))
pushforward_samples = jax.vmap(g)(samples)