WillKoehrsen

from bs4 import BeautifulSoup
# Create a soup and find the table
soup = BeautifulSoup(open(f'stats.html', 'r'))
table = soup.find_all(attrs={'class': "sortableTable-row js-statsTableRow"})

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entry = table[0]

# Reading time
read_time = int(entry.find_all(attrs={'class': 'readingTime'})[0].get('title').split(' ')[0])

# Unlisted vs published
type = 'unlisted' if len(entry.find_all(text=' Unlisted')) > 0 else 'published'

# Publication
publication = entry.find_all(attrs={'class': 'sortableTable-text'})

WillKoehrsen

Default Cell

Adds and runs a default cell at the top of each new notebook. To change the default cell, edit the main.js file in the code:

Jupyter.notebook.insert_cell_above('code', 0).set_text(``)

Set the text to whatever you would like.

This extension also adds a button to the toolbar allowing you to insert and run the

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define([
    'base/js/namespace',
    'base/js/events'
    ], function(Jupyter, events) {

        // Adds a cell above current cell (will be top if no cells)
        var add_cell = function() {
        Jupyter.notebook.
        insert_cell_above('code').
        // Define default cell here

WillKoehrsen

Type: Jupyter Notebook Extension
Compatibility: 3.x, 4.x, 5.x, 6.x
Name: Default cell
Main: main.js
Link: README.md
Description: |
  Adds and runs a default cell at the top of each new notebook. 
  Also adds a button to insert and run the default cell above the current cell. 
  Helpful for commonly used imports.

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def report_stats(text, channel):
    """Report training stats"""
    r = slack.chat.post_message(channel=channel, text=text,
                                username='Training Report',
                                icon_emoji=':clipboard:')
    
class SlackUpdate(Callback):
    """Custom Keras callback that posts to Slack while training a neural network"""

    def __init__(self, channel):

WillKoehrsen

# Observations from multiple trips
c = np.array([[3, 2, 1],
              [2, 3, 1],
              [3, 2, 1],
              [2, 3, 1]])

with pm.Model() as model:
    # Parameters are a dirichlet distribution
    parameters = pm.Dirichlet('parameters', a=alphas, shape=3)
    # Observed data is a multinomial distribution

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# Sample from the posterior
with model:
    samples = pm.sample_ppc(trace, samples = 1000)

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with model:
    # Sample from the posterior
    trace = pm.sample(draws=1000, chains=2, tune=500, 
                      discard_tuned_samples=True)

WillKoehrsen

import pymc3 as pm
import numpy as np

alphas = np.array([1, 1, 1])
c = np.array([3, 2, 1])

# Create model
with pm.Model() as model:
    # Parameters of the Multinomial are from a Dirichlet
    parameters = pm.Dirichlet('parameters', a=alphas, shape=3)