Lennart Landsmeer llandsmeer

Shape(ptr)
array_polar(shape, n, center=(0, 0))

Iterates a shape about an optional center position
array_polar_z(shape, n, center=(0, 0))

Iterates a shape about an optional center position
array_x(shape, nx, dx)

Iterates a part in a 1D array
array_xy(shape, nx, ny, delta)

Iterates a part in a 2D array
array_xyz(shape, nx, ny, nz, delta)

	# Adapted from https://hodgkin-huxley-tutorial.readthedocs.io/en/latest/_static/Hodgkin%20Huxley.html

	%matplotlib inline

	import numpy as np
	import matplotlib.pyplot as plt
	from scipy.integrate import solve_ivp

	C_m = 1.0
	g_Na = 120.0

	<!doctype html>
	<meta charset="utf-8">

	<div id=live style='overflow-wrap: break-word; font-family: monospace'></div>

	<script>
	class Controller {
	constructor(ip) {
	this.ip = ip
	this.connect()

	import json
	import random
	import time
	import numpy as np
	import matplotlib.pyplot as plt
	from matplotlib import cm
	import numba

	@numba.jit(fastmath=True, cache=True, nopython=True)
	def simulate(transient=10, seconds=1, delta=0.015, record_every=10):

	import arbor

	class master_recipe(arbor.recipe):
	def __init__(self, *recipes):
	arbor.recipe.__init__(self)
	self.recipes = list(recipes)
	self.setup_master_recipe()
	### :::THIS IS THE ONLY PROBLEM:::
	### global_properties() can not be specified up to subrecipe level
	### which means catalogue mechanism names can not collide

	# Dirichlet bc eliminated
	import numpy as np
	import matplotlib.pyplot as plt
	import scipy.sparse as sp
	import scipy.sparse.linalg as spla

	N = 64
	sources = [
	(0.3, 0.3, 15),
	(0.7, 0.7, 25)

	#!/usr/bin/env python3

	import ast
	import sys
	import requests
	from bs4 import BeautifulSoup
	import dbm
	import unidecode
	import subprocess

	import numpy as np

	def to_float32(exponent, significand):
	assert exponent.dtype == np.uint8
	assert significand.dtype == np.uint8
	negative = ((exponent & 128) >> 7) == 1
	exponent = (exponent & 127).astype(float)-64
	value = 2.0*exponent (1.0 + significand.astype(float) / 256.0)
	value[negative] *= -1
	return value

	import numpy as np
	import tensorflow as tf
	import tflite_runtime.interpreter as tflite

	# Fib function

	@tf.function
	def fibonacci(n):
	a = 0
	b = 1

	'''
	Automatically saves all you matplotlib plot using pickle to a sqlite database
	such that you can open them later, interactively.

	Put this file somewhere on your PYTHONPATH and add this to your .bashrc

	export PYTHONPATH="/path/to/directory"
	export MPLBACKEND="module://autosave_mpl_background"
	'''