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| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Outputs some information on CUDA-enabled devices on your computer, | |
| including current memory usage. | |
| It's a port of https://gist.github.com/f0k/0d6431e3faa60bffc788f8b4daa029b1 | |
| from C to Python with ctypes, so it can run without compiling anything. Note | |
| that this is a direct translation with no attempt to make the code Pythonic. |
Largely based on the Tensorflow 1.6 gist, and Tensorflow 1.7 gist for xcode and Tensorflow 1.7 gist for eGPU, this should hopefully simplify things a bit.
| # checkDuplicates.py | |
| # Python 2.7.6 | |
| """ | |
| Given a folder, walk through all files within the folder and subfolders | |
| and get list of all files that are duplicates | |
| The md5 checcksum for each file will determine the duplicates | |
| """ | |
| import os |
| #!/usr/bin/python | |
| """Convert CSV table to MindMap format | |
| Usage: python csv_to_mm.py sometable.csv > mymap.mm | |
| CSV format is rows representing tree leaves, e.g.: | |
| A1, | |
| A1,B1 | |
| A1,B1,C1 | |
| A1,B1,C2 |
| from scipy.stats.distributions import binom | |
| data = [ | |
| {'name': 'ISTJ', 'count': 12, 'expected': 0.1160}, | |
| {'name': 'ISFJ', 'count': 4, 'expected': 0.1380}, | |
| {'name': 'INFJ', 'count': 14, 'expected': 0.0150}, | |
| {'name': 'INTJ', 'count': 206, 'expected': 0.0210}, | |
| {'name': 'ISTP', 'count': 16, 'expected': 0.0540}, | |
| {'name': 'ISFP', 'count': 1, 'expected': 0.0880}, | |
| {'name': 'INFP', 'count': 56, 'expected': 0.0430}, |