geraldyeo

//1.27323954  = 4/pi
//0.405284735 =-4/(pi^2)

/*********************************************************
 * low precision sine/cosine
 *********************************************************/

//always wrap input angle to -PI..PI
if (x < -3.14159265)
    x += 6.28318531;

monkstone

/**
 * Copyright (c) 2011 Martin Prout
 * 
 * This library is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Lesser General Public
 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 * 
 * http://creativecommons.org/licenses/LGPL/2.1/
 * 

rjeschke

package streaming;

import java.util.concurrent.Semaphore;

import javax.sound.sampled.AudioFormat;
import javax.sound.sampled.AudioSystem;
import javax.sound.sampled.DataLine;
import javax.sound.sampled.Line;
import javax.sound.sampled.LineUnavailableException;
import javax.sound.sampled.Mixer;

KdotJPG

/**
 * K.jpg's OpenSimplex 2, smooth variant ("SuperSimplex")
 *
 * More language ports, as well as legacy 2014 OpenSimplex, can be found here:
 * https://github.com/KdotJPG/OpenSimplex2
 */

public class OpenSimplex2S {

    private static final long PRIME_X = 0x5205402B9270C86FL;

p2or

# for https://blender.stackexchange.com/q/143599/3710

import bpy

mat_name = "Material_Name"

# check whether the material already exists
if bpy.data.materials.get(mat_name):
    mat = bpy.data.materials[mat_name]
else:

mateuszwojt

// Principled PBR Path tracer. Except where otherwise noted:

// Copyright © 2019 Markus Moenig Distributed under The MIT License.
// Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF 

innat

import tensorflow as tf

# credit: https://stackoverflow.com/a/66524901/9215780
class CustomTrainStep(tf.keras.Model):
    def __init__(self, n_gradients, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.n_gradients = tf.constant(n_gradients, dtype=tf.int32)
        self.n_acum_step = tf.Variable(0, dtype=tf.int32, trainable=False)
        self.gradient_accumulation = [tf.Variable(tf.zeros_like(v, dtype=tf.float32), 
                                                  trainable=False) for v in self.trainable_variables]

trygvebw

import torch
import numpy as np
import k_diffusion as K

from PIL import Image
from torch import autocast
from einops import rearrange, repeat

def pil_img_to_torch(pil_img, half=False):
    image = np.array(pil_img).astype(np.float32) / 255.0