xoelop

// seen on http://makble.com/how-to-copy-text-from-chrome-console-to-file-using-javascript
console.save = function (data, filename) {
    // code to save console output as a json file

    if (!data) {
        console.error('Console.save: No data')
        return;
    }

    if (!filename) filename = 'console.json'

xoelop

var className = 'your-class-name'; // Replace your-class-name with the class name of the elements you want to click
var elements = Array.from(document.getElementsByClassName(className));
elements.map(x => x.click())

xoelop

import concurrent

def parallel_map(function, iterable, max_workers: int = 50):
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as pool:
        result = pool.map(function, iterable)
    return list(result)

xoelop

import pandas as pd
import numpy as np

# dataviz imports
import plotly as py
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

xoelop

import pandas as pd

data = pd.Series([1, 4, 4, 6, 2, 4, 7, 4, 7])

# data can be a series or a dataframe

# this calculates the inverse quantile for each value of data
inverse_quantiles = data.apply(lambda x: ((data <= x).sum()) / len(data)) 

# read more: https://stackoverflow.com/a/58186830/5031446

xoelop

{%- extends 'full.tpl' -%}


{%- block header -%}
{{ super() }}
<meta name='robots' content='noindex,nofollow' />
{%- endblock header -%}


{% block any_cell %}

xoelop

# plotly imports

import plotly.plotly as py

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, plot, iplot
import cufflinks as cf
init_notebook_mode(connected=True)
cf.go_offline()

xoelop

import pandas as pd
import numpy as np

def ratio(ret1: pd.Series,
          ret2: pd.Series = 0,
          ratio: str = 'sharpe',
          log: bool = True) -> float:
    """log: if True, convert ret1 and ret2 to log returns"""
    if log:
        ret1 = np.log1p(ret1)

xoelop

from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed


def parallel_process(array,
                     function,
                     type_pool: str = 'multithreading',
                     use_kwargs=False,
                     n_jobs=16,
                     front_num=3,

xoelop

df_gridsearch['size'] = (df_gridsearch.mean_test_score / 
                         df_gridsearch.mean_test_score.max()) ** 100 * 20 + 1