seekshreyas

Electron apps

• Marp: Markdown presentation ecosystem.

• muryoimpl/slippr: markdown presentation app by Electron

• joe-re/cafe-pitch Markdown-driven presentation tool built on Electron.

Slide deck frameworks supporting Markdown source

seekshreyas

from pylab import *
from scipy.stats import uniform, binom, expon, beta

true_gamma = 0.5

N = 600
T = 15
data = zeros((2, N), dtype=float)
event_times = data[0,:]
event_times[:] = uniform(0,15).rvs(N)

seekshreyas

from scipy.stats import t


def retroDesign(A, s, alpha=0.05, df=1000000, n_sims=10000):
 """

 :param A: the hypothesized true effect size
 :param s: standard error
 :param alpha: confidence level
 :param df: degree of freedom

seekshreyas

#!/usr/bin/env python

from numpy import asmatrix, asarray, ones, zeros, mean, sum, arange, prod, dot, loadtxt
from numpy.random import random, randint
import pickle

MISSING_VALUE = -1 # a constant I will use to denote missing integer values

def impute_hidden_node(E, I, theta, sample_hidden):

seekshreyas

seekshreyas

var open = window.XMLHttpRequest.prototype.open,
    send = window.XMLHttpRequest.prototype.send,
    onReadyStateChange;

function openReplacement(method, url, async, user, password) {
    var syncMode = async !== false ? 'async' : 'sync';
    console.warn(
        'Preparing ' +
        syncMode +
        ' HTTP request : ' +

seekshreyas

Screencapture and animated gifs

I say "animated gif" but in reality I think it's irresponsible to be serving "real" GIF files to people now. You should be serving gfy's, gifv's, webm, mp4s, whatever. They're a fraction of the filesize making it easier for you to deliver high fidelity, full color animation very quickly, especially on bad mobile connections. (But I suppose if you're just doing this for small audiences (like bug reporting), then LICEcap is a good solution).

Capturing (Easy)

1. Launch quicktime player
2. do Screen recording

seekshreyas

"""quick way to create a data frame to try things out""" 
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

df['A'] """ will bring out a col """ df.ix[0] """will bring out a row, #0 in this case""" 

"""Given a dataframe df to filter by a series s:""" 
df[df['col_name'].isin(s)]

"""to do the same filter on the index instead of arbitrary column"""
df.ix[s]

seekshreyas

#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

seekshreyas

import sys
import logging
import psycopg2
import pandas as pd
import pandas.io.sql as sqlio
import ConfigParser
import argparse
import statistics
from pandas import pivot_table, crosstab
from datetime import datetime