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)

MarcoGorelli

import functools

import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import numpyro.distributions as dist
import pandas as pd
import seaborn as sns
from matplotlib import patches