nils-werner

from __future__ import division
import numpy

n = 1000000
# 2D data spread uniformly across a square of A=1
data = numpy.random.uniform(-0.5, 0.5, size=(n, 2))

# Count points that are within distance 0.5 from center
inside = len(
    numpy.argwhere(

nils-werner

<?xml version="1.0" encoding="utf-8"?>
<xsl:stylesheet version="1.0"
	xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
  
<!--
	ISO-8859-1 based URL-encoding demo
	Written by Mike J. Brown, mike@skew.org.
	Updated 2002-05-20.

	No license; use freely, but credit me if reproducing in print.

nils-werner

from scipy.constants import physical_constants as pc
import numpy
import scipy.stats
import scipy.linalg


def a():
    def pnorm(v, p):
        return sum([x**p for x in v])**(1/p)

nils-werner

def sliding_window(data, size, stepsize=1, padded=False, axis=-1, copy=True):
    """
    Calculate a sliding window over a signal

    Parameters
    ----------
    data : numpy array
        The array to be slided over.
    size : int
        The sliding window size

nils-werner

BEGIN:VCALENDAR
VERSION:2.0
PRODID:ics.py - http://git.io/lLljaA
BEGIN:VEVENT
DTEND:20211020T081500Z
DTSTART:20211020T070000Z
SUMMARY:P4 (repeat): Array Processing\, Room Acoustics\, Spatial Audio\, and Audio Events\; Demonstrations
UID:8e2429fd-d719-4ade-bb37-593bbcadf6c6@8e24.org
END:VEVENT
BEGIN:VEVENT

nils-werner

#!/usr/bin/env python

from __future__ import print_function

import sys
import getpass
import hashlib
import argparse

try:

nils-werner

$ bash test.sh
Running virtualenv with interpreter /usr/local/bin/python3
Using base prefix '/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6'
New python executable in /Users/me/sotest/virtenv3/bin/python3.6
Also creating executable in /Users/me/sotest/virtenv3/bin/python
Installing setuptools, pip, wheel...done.
Running virtualenv with interpreter /usr/local/bin/python2
New python executable in /Users/me/sotest/virtenv2/bin/python2.7
Also creating executable in /Users/me/sotest/virtenv2/bin/python
Installing setuptools, pip, wheel...done.

nils-werner

'''Use this if you have a pre trained net and dont want to wait for a few hours'''

from __future__ import print_function
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys

nils-werner

import numpy
import matplotlib.pyplot as plt


def tupper(x,y):
    return 0.5 < ((y // 17) // (2**(17 * x + y % 17))) % 2

N = 4858450636189713423582095962494202044581400587983244549483093085061934704708809928450644769865524364849997247024915119110411605739177407856919754326571855442057210445735883681829823754139634338225199452191651284348332905131193199953502413758765239264874613394906870130562295813219481113685339535565290850023875092856892694555974281546386510730049106723058933586052544096664351265349363643957125565695936815184334857605266940161251266951421550539554519153785457525756590740540157929001765967965480064427829131488548259914721248506352686630476300

x, y = numpy.meshgrid(numpy.arange(107), numpy.arange(N, N + 17)[::-1])

nils-werner

\def\speaker{%
	(11mm,2mm) -- +(0mm,-4mm)
	(13mm,4mm) -- +(0mm,-8mm)
	(15mm,6mm) -- +(0mm,-12mm)
	(0mm,0mm) -- +(0mm,-3mm) -- +(4mm,-3mm) -- +(9mm,-7mm) -- +(9mm,7mm) -- +(4mm,3mm) -- +(0mm,3mm) -- +(0mm,0mm)
}