Matthew Muller mwmuni

## example_python_concurrent_interpreters.py
# /// script
# requires-python = ">=3.14"
# ///
import concurrent.interpreters as ci
from time import sleep

def consume_queue(qin: ci.Queue, qout: ci.Queue, id: int = 0):
    while True:
        item = qin.get()
        if item is None:  # Use None as a sentinel to stop the consumer

## input.txt
abd
ccd
adadfsgsgs
sadagfa

## top-k-byte-counting.c
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define BINS 256
#define NUM_BYTES (sizeof(int) / sizeof(char))
#define K 3

void find_top_k(int *data, int n, int byte_pos, int k, int *top_k, int *current_k) {
    if (n == 0 || *current_k >= k || byte_pos >= NUM_BYTES) {

## restore_from_torchscript.py
from torch.nn import Sequential, Linear, ReLU, Tanh

# Model had an 'actor' attribute that was a Sequential type in this case

model = torch.jit.load(path)
model_modules = model.actor._modules._python_modules
def convert_module(module):
    if module.original_name == "Sequential":
        layers = []
        for sub_key in module._modules._python_modules:

## torch_gaussian.py
# Use torch to smooth the pixel art

import os
# Set environment QT_QPA_PLATFORM
os.environ['QT_QPA_PLATFORM'] = 'offscreen'
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2

## how-to-compile.md

      
        
          
            
              
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                mwmuni
                / how-to-compile.md
            
            
              Last active
              December 23, 2023 11:58
            
              
                Animated Sine wave with OpenGL in C++
              
          
        
      
        
  
      
    Compile the code with:
$ g++ sine_opengl.cpp -o sine-gl -lGL -lGLU -lglut
There will now be a sine-gl binary, which can be run:
$ ./sine-gl
	# /// script
	# requires-python = ">=3.14"
	# ///
	import concurrent.interpreters as ci
	from time import sleep

	def consume_queue(qin: ci.Queue, qout: ci.Queue, id: int = 0):
	while True:
	item = qin.get()
	if item is None: # Use None as a sentinel to stop the consumer
	#include <stdio.h>
	#include <stdlib.h>
	#include <string.h>

	#define BINS 256
	#define NUM_BYTES (sizeof(int) / sizeof(char))
	#define K 3

	void find_top_k(int data, int n, int byte_pos, int k, int top_k, int *current_k) {
	if (n == 0 \|\| *current_k >= k \|\| byte_pos >= NUM_BYTES) {
	from torch.nn import Sequential, Linear, ReLU, Tanh

	# Model had an 'actor' attribute that was a Sequential type in this case

	model = torch.jit.load(path)
	model_modules = model.actor._modules._python_modules
	def convert_module(module):
	if module.original_name == "Sequential":
	layers = []
	for sub_key in module._modules._python_modules:
	# Use torch to smooth the pixel art

	import os
	# Set environment QT_QPA_PLATFORM
	os.environ['QT_QPA_PLATFORM'] = 'offscreen'
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import numpy as np
	import cv2