Nathan nmichlo

## array_chunking.py
# BASED ON @nmichlo's ISSUE AT:
# https://github.com/pytorch/pytorch/issues/60531

# NOTE: modern features of torch and numpy make this easier to implement in python
#       as modern one liners.

##### TORCH EXAMPLE #####
# chunks = 3
# dim = 0
# tensor = torch.arange(7)

## xterm_control_sequences.py
# XTerm Control Sequences based on:
# - https://invisible-island.net/xterm/ctlseqs/ctlseqs.html

# ========================================================================= #
# XTerm Control Sequences from invisible-island.net as pythonic code.
# Basic control sequences are string variables.
#   - eg: ESC = '\033'
#         CSI = ESC + '['
# Control sequences that have args can be called to return a string.
#   - eg: sgr = CSI + Ps + 'm'

## darwinkit_coreml_inference.go
package main

import (
	"github.com/progrium/darwinkit/macos/coreml"
	"github.com/progrium/darwinkit/macos/foundation"
	"github.com/progrium/darwinkit/objc"
	"log"
	"unsafe"
)

## Monokai.itermcolors
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Ansi 0 Color</key>
	<dict>
		<key>Alpha Component</key>
		<real>1</real>
		<key>Blue Component</key>
		<real>0.066666670143604279</real>

## cloudflare-ddns-update.sh
#!/bin/bash

# A bash script to update a Cloudflare DNS A record with the external IP of the source machine
# Used to provide DDNS service for my home
# Needs the DNS record pre-creating on Cloudflare

## Based on https://gist.github.com/Tras2/cba88201b17d765ec065ccbedfb16d9a with updates to use
## per-zone configurable access tokens available in the API sections of your Cloudflare profile
##  - info@foo-games.com

## colab_vscode.py
# Adapted from https://amitness.com/vscode-on-colab
# Copy this script into a colab cell

# install deps if needed
!(command -v "code-server" 1>/dev/null) || (curl -fsSL https://code-server.dev/install.sh | sh && echo)
!(python -c "import pyngrok" 2>/dev/null) || (pip install -qqq pyngrok)

# run vscode in background if needed
!(ps -ef|awk '/code-server/&&!/awk/{exit 1}') && (nohup code-server --port 9000 --auth none &)

## python_container_bench.py
"""
Benchmark of different python calls:

TL;DR:
- don't use dict(), dataclasses or namedtuples
- use {...}, [...], (...)
- tuples are fastest
- args are faster than kwargs for dataclasses and namedtuples

RESULTS:

## python_orm_bench.py
"""
py3.8 - macbook pro 16-inch, 2019, 2.3 GHz 8-Core Intel Core i9, 16 GB 2667 MHz DDR4

In memory benchmarks, working with 100_000 items at a time

SqlAlchemy - insert bulk: 1560.605ms
SqlAlchemy - select all: 1350.774ms
SqlAlchemy - insert singles: 7821.888ms

sqlite3 - insert singles: 140.412ms

## export_google_drive_folder.py
# MIT LICENSE
# Nathan Michlo

"""
Script to bypass rate limits for a GoogleDrive file
by creating an export job for the parent folder.

1. User manually specifies the file id of this parent folder
2. Script starts an export job for this folder
3. Script polls and waits for completion of the export job

## available_pypi_synonyms.py
# check for available pypi synonyms of a given word
# $ available_pypi_synonyms.py <words...>

import argparse
import itertools
import re

import diskcache as dc
import requests
from bs4 import BeautifulSoup
	# BASED ON @nmichlo's ISSUE AT:
	# https://github.com/pytorch/pytorch/issues/60531

	# NOTE: modern features of torch and numpy make this easier to implement in python
	# as modern one liners.

	##### TORCH EXAMPLE #####
	# chunks = 3
	# dim = 0
	# tensor = torch.arange(7)
	# XTerm Control Sequences based on:
	# - https://invisible-island.net/xterm/ctlseqs/ctlseqs.html

	# ========================================================================= #
	# XTerm Control Sequences from invisible-island.net as pythonic code.
	# Basic control sequences are string variables.
	# - eg: ESC = '\033'
	# CSI = ESC + '['
	# Control sequences that have args can be called to return a string.
	# - eg: sgr = CSI + Ps + 'm'
	package main

	import (
	"github.com/progrium/darwinkit/macos/coreml"
	"github.com/progrium/darwinkit/macos/foundation"
	"github.com/progrium/darwinkit/objc"
	"log"
	"unsafe"
	)
	<?xml version="1.0" encoding="UTF-8"?>
	<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
	<plist version="1.0">
	<dict>
	<key>Ansi 0 Color</key>
	<dict>
	<key>Alpha Component</key>
	<real>1</real>
	<key>Blue Component</key>
	<real>0.066666670143604279</real>
	#!/bin/bash

	# A bash script to update a Cloudflare DNS A record with the external IP of the source machine
	# Used to provide DDNS service for my home
	# Needs the DNS record pre-creating on Cloudflare

	## Based on https://gist.github.com/Tras2/cba88201b17d765ec065ccbedfb16d9a with updates to use
	## per-zone configurable access tokens available in the API sections of your Cloudflare profile
	## - info@foo-games.com
	# Adapted from https://amitness.com/vscode-on-colab
	# Copy this script into a colab cell

	# install deps if needed
	!(command -v "code-server" 1>/dev/null) \|\| (curl -fsSL https://code-server.dev/install.sh \| sh && echo)
	!(python -c "import pyngrok" 2>/dev/null) \|\| (pip install -qqq pyngrok)

	# run vscode in background if needed
	!(ps -ef\|awk '/code-server/&&!/awk/{exit 1}') && (nohup code-server --port 9000 --auth none &)
	"""
	Benchmark of different python calls:

	TL;DR:
	- don't use dict(), dataclasses or namedtuples
	- use {...}, [...], (...)
	- tuples are fastest
	- args are faster than kwargs for dataclasses and namedtuples

	RESULTS:
	"""
	py3.8 - macbook pro 16-inch, 2019, 2.3 GHz 8-Core Intel Core i9, 16 GB 2667 MHz DDR4

	In memory benchmarks, working with 100_000 items at a time

	SqlAlchemy - insert bulk: 1560.605ms
	SqlAlchemy - select all: 1350.774ms
	SqlAlchemy - insert singles: 7821.888ms

	sqlite3 - insert singles: 140.412ms
	# MIT LICENSE
	# Nathan Michlo

	"""
	Script to bypass rate limits for a GoogleDrive file
	by creating an export job for the parent folder.

	1. User manually specifies the file id of this parent folder
	2. Script starts an export job for this folder
	3. Script polls and waits for completion of the export job
	# check for available pypi synonyms of a given word
	# $ available_pypi_synonyms.py <words...>

	import argparse
	import itertools
	import re

	import diskcache as dc
	import requests
	from bs4 import BeautifulSoup