fschwar4

import numpy as np
from numpy.typing import NDArray

def sinc_interpolation(x: NDArray, s: NDArray, u: NDArray) -> NDArray:
            """Whittaker–Shannon or sinc or bandlimited interpolation.

            Args:
                x (NDArray): signal to be interpolated, can be 1D or 2D
                s (NDArray): time points of x (*s* for *samples*) 
                u (NDArray): time points of y (*u* for *upsampled*)

paddymccrudden

import numpy as np
from numpy.linalg import solve
import logging
logging.basicConfig(level = logging.DEBUG)
from scipy.stats import moment,norm

def fleishman(b, c, d):
    """calculate the variance, skew and kurtois of a Fleishman distribution
    F = -c + bZ + cZ^2 + dZ^3, where Z ~ N(0,1)
    """

kirlf

"""
Developed by Vladimir Fadeev
(https://github.com/kirlf)
Kazan, 2017 / 2020

Python 3.7

The result is uploaded in 
https://commons.wikimedia.org/wiki/File:AdaptiveBeamForming.png
"""

ihoromi4

def seed_everything(seed: int):
    import random, os
    import numpy as np
    import torch
    
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

mdouze

mdouze

guillefix

# coding: utf-8

# In[1]:


import math
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F

goldsborough

#include <cudnn.h>
#include <cassert>
#include <cstdlib>
#include <iostream>
#include <opencv2/opencv.hpp>

#define checkCUDNN(expression)                               \
  {                                                          \
    cudnnStatus_t status = (expression);                     \
    if (status != CUDNN_STATUS_SUCCESS) {                    \

ajbrock

# Resample.py
# Andrew Brock, 2017
# This code resamples a 3d grid using catmull-rom spline interpolation, and is GPU accelerated.

# Resample along the trailing dimension
# Assumes a more-than-1D array? Or just directly assumes a 3D array? we'll find out
# 

# TODO: Some things could be shared (such as the mgrid call, which can presumably be done once? hmm)
#       between resample1d calls.

rtqichen

## Weight norm is now added to pytorch as a pre-hook, so use that instead :)

import torch
import torch.nn as nn
from torch.nn import Parameter
from functools import wraps

class WeightNorm(nn.Module):
    append_g = '_g'
    append_v = '_v'