profConradi

def energy(z, n, m, L):
    return np.cos(n* np.pi *np.real(z) / L) *np.cos(m *np.pi*np.imag(z) / L) - np.cos(m*np.pi*np.real(z)/ L) *np.cos(n* np.pi *np.imag(z)  / L)


class ChladniPlate:
    def __init__(self, n, m, L=1, n_particles=10000):
        self.L = L
        self.n_particles = n_particles
        self.n = n
        self.m = m

anmonteiro

Using dune-release to release OCaml packages

Originally written 2020-05-16

dune-release is a good improvement over the old opam-publish, but releasing software is still clearly not a solved problem, and I find it hard to remember the exact steps involved in releasing an opam package, especially if some time has passed since the last release. This note is an attempt at having a place

Leonidas-from-XIV

(* Some kind of function that needs to be [Some _] for the rest of the body *)
let bigger_than x v = match v > x with true -> Some (v - 1) | false -> None

(* Sample inputs *)
let xs = [ 0; 1; 2; 3; 4; 5; 6; 7; 8; 9 ]

(* Why `fold` here in the first place? Maybe your data structure doesn't have `filter_map` etc.
 * This is an example
 *)

Jademaster

import matplotlib.pyplot as plt
import numpy as np
import math 
from PIL import Image

#this code takes a matrix M, regards it as a weighted graph where the entry M_ij indicates the weight between vertex i and vertex j. The free category on this graph is computed using the geometric series formula F(M) = Sum_{n geq 0} M^n with operations valued in the min plus semiring. The function freemetricspace(M,res) computes this formula to the first res terms. In words, this computes the shortest paths which occur in <= n steps.

#this function computes naive matrix multiplication in the min plus semiring
def mult(m1,m2):
  assert (m1.shape[1] == m2.shape[0]), "shape mismatch"

rougier

@import url('https://fonts.googleapis.com/css2?family=Roboto+Condensed:wght@300;400&family=Source+Code+Pro:wght@300;400&display=swap');
 
.rendered_html h1,
.rendered_html h2,
.rendered_html h3,
.rendered_html h4 {
    color: #000099;
    font-weight: 400;
}

twostraws

// A URLSession extension that fetches data from a URL and decodes to some Decodable type.
// Usage: let user = try await URLSession.shared.decode(UserData.self, from: someURL)
// Note: this requires Swift 5.5.
extension URLSession {
    func decode<T: Decodable>(
        _ type: T.Type = T.self,
        from url: URL,
        keyDecodingStrategy: JSONDecoder.KeyDecodingStrategy = .useDefaultKeys,
        dataDecodingStrategy: JSONDecoder.DataDecodingStrategy = .deferredToData,
        dateDecodingStrategy: JSONDecoder.DateDecodingStrategy = .deferredToDate

hcarty

let max_ocaml_int = Int64.of_int max_int

let read_file input_channel (header : Tar_cstruct.Header.t) =
  let file_size = header.file_size in
  assert (file_size <= max_ocaml_int);
  let buf = Cstruct.create (Int64.to_int file_size) in
  Tar_cstruct.really_read input_channel buf;
  buf

let rec read_files input_channel accu =

dpiponi

import math

import numpy as np
import random
import matplotlib.pyplot as plt

medium_size = 1024
x = np.linspace(-1., 1., medium_size)
y = np.linspace(-1., 1., medium_size)
x, y = np.meshgrid(x, y, indexing='ij')

anuragsoni

module Trie = struct
  type t = { mutable count : int; children : t option Array.t }

  let[@inline] empty () = { count = 0; children = Array.make 128 None }

  let add buf pos len t =
    let rec loop idx t =
      if idx > pos + len - 1 then t.count <- t.count + 1
      else
        let c = Char.lowercase_ascii (Bytes.get buf idx) in

EduardoRFS

[@ocaml.ppx.context
  {
    tool_name: "ppxlib_driver",
    include_dirs: [],
    load_path: [
      "",
      "/home/eduardo/.esy/3_________________________________________________________________/i/ocaml-4.10.2000-76cdf1b9/lib/ocaml",
    ],
    open_modules: [],
    for_package: None,