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@venik
venik / build_tf.sh
Last active February 22, 2024 06:12
Bash script for local building TensorFlow on Mac/Linux with all CPU optimizations (default pip package has only SSE)
#!/usr/bin/env bash
# Author: Sasha Nikiforov
# source of inspiration
# https://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions
# Detect platform
if [ "$(uname)" == "Darwin" ]; then
# MacOS
@shanealynn
shanealynn / python batch geocoding.py
Last active March 14, 2026 01:45
Geocode as many addresses as you'd like with a powerful Python and Google Geocoding API combination
"""
Python script for batch geocoding of addresses using the Google Geocoding API.
This script allows for massive lists of addresses to be geocoded for free by pausing when the
geocoder hits the free rate limit set by Google (2500 per day). If you have an API key for paid
geocoding from Google, set it in the API key section.
Addresses for geocoding can be specified in a list of strings "addresses". In this script, addresses
come from a csv file with a column "Address". Adjust the code to your own requirements as needed.
After every 500 successul geocode operations, a temporary file with results is recorded in case of
script failure / loss of connection later.
Addresses and data are held in memory, so this script may need to be adjusted to process files line
@kastnerkyle
kastnerkyle / painless_q.py
Last active August 18, 2023 09:32
Painless Q-Learning Tutorial implementation in Python http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Author: Kyle Kastner
# License: BSD 3-Clause
# Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/
# Visualization based on code from Gael Varoquaux gael.varoquaux@normalesup.org
# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
@fawce
fawce / fetcher2dot0.md
Last active April 21, 2017 23:03
Draft documentation for Quantopian fetcher 2.0

Fetcher 2.0

Fetcher - Load any CSV file

Quantopian provides a 11-year history of US equity market data in minute and daily bars. The US market data provides a backbone for financial analysis, but some of the most promising areas of research are finding signals in non-market data. Fetcher provides your algorithm with access to external time series data. Any time series that can be retrieved as a csv file via http or https can be incorporated into a Quantopian algorithm.

Fetcher lets you load csv files over http. To use it, invoke fetch_csv(url) in your initialize method. fetch_csv will retrieve the text of the csv file using the (wonderful) requests module. The csv is parsed into a pandas dataframe using pandas.io.parsers.read_csv. You may then specify your own methods to modify the entire dataframe prior to the start of the simulation. During simulation, the rows of the csv/dataframe are streamed to your algorithm's handle_data method as additional properties of the data parameter.

Best of all, your F