charles-l

import bpy
import numpy as np
from mathutils import noise

img = bpy.data.images['some-img']
w, h = img.size

# 4 channel image (RGBA), f64
a = np.array(img.pixels).reshape((h, w, 4))

charles-l

import pandas as pd

@pd.api.extensions.register_dataframe_accessor("rel")
class RelationshipAccessor:
    '''
    Add a relationship accessor to dataframe objects allowing Rails-like
    access to related dataframes. e.g.

    >>> authors = pd.DataFrame({'name': ['C. S. Lewis', 'Lewis Carroll']})
    >>> books = pd.DataFrame({'title': ["Alice's Adventures in Wonderland",

charles-l

'''
Expects probes to be defined in the target process, e.g.:

    import stapsdt
    provider = stapsdt.Provider('game')
    frame_begin_probe = provider.add_probe('frame_begin')
    frame_end_probe = provider.add_probe('frame_end')
    provider.load()
    
    ...

charles-l

const std = @import("std");

pub const BlockIterator = struct {
    buffer: []const u8,
    block_size: usize,
    index: usize,

    pub fn next(self: *BlockIterator) ?[]const u8 {
        if (self.index == self.buffer.len) {
            return null;

charles-l

# decodes hexahue images
import cv2
import numpy as np

codewords = {'mrgybc': 'a',
             'rmgybc': 'b',
             'rgmybc': 'c',
             'rgymbc': 'd',
             'rgybmc': 'e',
             'rgybcm': 'f',

charles-l

#!/sbin/openrc-run

description="daemon required by the guix package manager"
command=/root/.config/guix/current/bin/guix-daemon
command_args="--build-users-group=guixbuild"
command_background="yes"
pidfile="/run/${RC_SVCNAME}.pid"

charles-l

from typing import Callable, Any
from dataclasses import dataclass

class _CpsA: # (x, k) -> ()
    @staticmethod
    def lift(f) -> '_CpsA':
        return _CpsA(lambda x, k: k(f(x)))

    def __init__(self, cps: Callable[[Any, Callable], None]):
        self.cps = cps

charles-l

FILES=$(wildcard *.cpp)
NJOBS=$(shell echo $$(nproc) + 1 | bc)

all:
	g++ $(FILES)

generate_empty:
	echo "int main() {}" > main.cpp
	for i in `seq 500`; do touch f$$i.cpp; done

charles-l

NB. Port of https://scipy-cookbook.readthedocs.io/items/KalmanFiltering.html

require 'stats/distribs/normal'   
n =: 50 			NB. number of steps to compute
x =: _0.37227		NB. real value

z =: (x , 0.1) rnorm n	NB. readings (normal distribution)
Q =: 1e_5			NB. process variance
R =: 0.1^2			NB. estimate of variance

charles-l

def kalman_predict(x_hat, u, P, wheel_slip_std):
    # non-linear state transition
    def f(p, u):
        x, y, θ = p
        dΦ_l, dΦ_r = u
        return v(
            x + (((WHEEL_R * dΦ_l) / 2) +
                 ((WHEEL_R * dΦ_r) / 2)) * DT * cos(θ),
            y + (((WHEEL_R * dΦ_l) / 2) +
                 ((WHEEL_R * dΦ_r) / 2)) * DT * sin(θ),