This is a cheat sheet for how to perform various actions to ZSH, which can be tricky to find on the web as the syntax is not intuitive and it is generally not very well-documented.
| Description | Syntax |
|---|---|
| Get the length of a string | ${#VARNAME} |
| Get a single character | ${VARNAME[index]} |
| GDB commands by function - simple guide | |
| --------------------------------------- | |
| More important commands have a (*) by them. | |
| Startup | |
| % gdb -help print startup help, show switches | |
| *% gdb object normal debug | |
| *% gdb object core core debug (must specify core file) | |
| %% gdb object pid attach to running process | |
| % gdb use file command to load object |
| def draw_graph3(networkx_graph,notebook=True,output_filename='graph.html',show_buttons=True,only_physics_buttons=False): | |
| """ | |
| This function accepts a networkx graph object, | |
| converts it to a pyvis network object preserving its node and edge attributes, | |
| and both returns and saves a dynamic network visualization. | |
| Valid node attributes include: | |
| "size", "value", "title", "x", "y", "label", "color". | |
| (For more info: https://pyvis.readthedocs.io/en/latest/documentation.html#pyvis.network.Network.add_node) |
| """ | |
| A minimal implementation of Monte Carlo tree search (MCTS) in Python 3 | |
| Luke Harold Miles, July 2019, Public Domain Dedication | |
| See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search | |
| https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1 | |
| """ | |
| from abc import ABC, abstractmethod | |
| from collections import defaultdict | |
| import math |
| # Use `%%capture` to hush 'noisy' stdout and stderr streams, but still combine with getting `%%time` after | |
| %%capture out_stream | |
| %%time | |
| ---rest of a cell that does something with LOTS of output-- | |
| #In cell after, put following to get time of completion from that: | |
| #time it took to run cell above | |
| for x in out_stream.stdout.split("\n")[-3:]: | |
| print(x) | |
| var tf = require("@tensorflow/tfjs"); | |
| //code modified from: | |
| //https://medium.com/tensorflow/a-gentle-introduction-to-tensorflow-js-dba2e5257702 | |
| //define our inputs (combinations of 2 bits, represented as 0s and 1s) | |
| //https://js.tensorflow.org/api/0.12.0/#tensor | |
| var xs = tf.tensor([[0, 0], [0, 1], [1, 0], [1, 1]]); | |
| //define our outputs (xor operation, a simple non-linear problem) |
| #=======================================================================# | |
| # extract_data.py # | |
| #=======================================================================# | |
| # usage: extract_data.py [-h] [-i INPUT_DIR] [-o OUTPUT_DIR] | |
| # | |
| # This program extracts provision numbers from a set of documents. | |
| # | |
| # optional arguments: | |
| # -h, --help show this help message and exit | |
| # -i INPUT_DIR, --input_dir INPUT_DIR |
