marmaduke woodman maedoc

organically grown, free range, artificially intelligent

maedoc / np_tf_bench.py

Created November 26, 2020 09:49

Benchmarking NumPy and TensorFlow2 on simple network model

	import time
	import tqdm
	import numpy as np
	import tensorflow as tf
	import numba as nb
	#tf.config.experimental.set_visible_devices([], 'GPU')
	tf.zeros((1,), tf.float32)

maedoc / specfit.py

Created August 25, 2020 20:24

Spectral fitting TVB with autograd

	import sys
	import time
	from autograd import numpy as np, grad
	from autograd.misc.optimizers import adam, rmsprop, sgd
	from scipy.optimize import minimize
	import pylab as pl


	# simplification of the generic 2d oscillator
	def dfun(state, w, theta):

maedoc / vdpo-autograd.py

Created March 11, 2020 08:11

Simple autograd odeint model

	import time
	from autograd import numpy as np, grad
	from autograd.scipy.integrate import odeint
	from autograd.builtins import tuple
	from autograd.scipy.stats import norm
	from autograd.misc.optimizers import adam


	def vdpo(y, t0, a, k, tau=3.0):
	x, y = np.reshape(y, (2, -1))

maedoc / jr95.py

Created January 15, 2020 12:47

Reproduce results of Jansen, Rit 1995 paper with TVB

	import numpy as np
	import tvb.simulator.lab as tvb
	from tvb.basic.neotraits.api import Final, List

	# setup model based on paper's parameters
	model_pars = dict(
	A=3.25,
	B=22.0,
	v0=6.0,
	a=0.1, # TVB uses ms, not s

maedoc / sdde.py

Last active August 4, 2020 07:28

SDDE estimation with Stan

	import pystan

	try:
	model # avoid recompiling during IPython/notebook session
	except:
	model = pystan.StanModel('sdde.stan')

	data = dict(
	nt=500,
	d=50,

maedoc / simplevi.py

Last active October 20, 2021 06:33

Simple example of variational inference with autograd

	from autograd import grad, numpy as np
	from autograd.scipy.stats import norm
	from autograd.misc.optimizers import adam

	def simple_vi(n=2000):
	x = np.random.normal(loc=+1.5, scale=+0.3, size=10)
	log_p = lambda z: np.mean(norm.logpdf(x[:, None], z[0], np.exp(z[1])), axis=0)
	log_q = lambda z, l: norm.logpdf(z, l[:, None], 0.3)
	samp_q = lambda l: np.random.normal(l[:, None], 0.3, (2, n))
	# https://arxiv.org/pdf/1401.0118.pdf, eq 3

maedoc / trace_tvb.py

Created November 19, 2019 06:58

Trace a TVB simulation to produce pretty call graph

	from pycallgraph import PyCallGraph
	from pycallgraph.output import GraphvizOutput

	from pycallgraph import GlobbingFilter
	from pycallgraph import Config

	from tvb.simulator.models.wong_wang import ReducedWongWang
	from tvb.simulator.lab import *
	import numpy

maedoc / anon-manifold.md

Last active November 12, 2019 20:13

anon estimates manifolds

> be me
> dukenukem.jpg
> tired of playing ITCrowd.exe
> drool while looking at HMC.jpeg

> install numpyro
> assume homogeneous coupling

maedoc / ssh_magic.py

Created October 22, 2019 20:36

SSH cell magic for IPython/Jupyter

	import IPython.core.magic, subprocess
	@IPython.core.magic.register_cell_magic
	def ssh(line, cell):
	proc = subprocess.Popen(['ssh'] + line.split(),
	stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
	out, _ = proc.communicate(cell.encode('ascii'))
	print(out.decode('ascii'))

maedoc / lsmod.out

Last active September 27, 2019 13:13

R510 info on OpenSuse