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