nils-werner

# Maintainer: speps <speps at aur dot archlinux dot org>

pkgname=rtmidi
pkgver=2.1.0
pkgrel=1
pkgdesc="A set of C++ classes that provides a common API for realtime MIDI input/output."
arch=(i686 x86_64)
url="http://www.music.mcgill.ca/~gary/rtmidi/"
license=('MIT')
depends=('alsa-lib')

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

#!/usr/bin/python
import time
import BaseHTTPServer
from pprint import pprint

HOST_NAME = '0.0.0.0'
PORT_NUMBER = 8000


class MyHandler(BaseHTTPServer.BaseHTTPRequestHandler):

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{'connection': 'close',
 'content-length': '1598',
 'content-type': 'application/json',
 'host': 'mymac.employer.server.de:8000'}
 

{
    "before": "781f005120f77fbc69d1077d57b3b10ee792f232",
    "after": "0bba8ee1bc7d3f5849b530ef6a4bdef1e97f8ecb",
    "ref": "refs/heads/master",

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#!/usr/bin/env bash

set -e

PYV=`python -c "import sys;t='{v[0]}.{v[1]}'.format(v=list(sys.version_info[:2]));sys.stdout.write(t)";`

# Make sure we are on a recent version of PIP and have wheels
pip install --upgrade pip
pip install --upgrade setuptools
pip install --upgrade six

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@pytest.fixture(params=(True, False))
def odd(request):
    return request.param

@pytest.fixture(params=(1, 2))
def channels(request):
    return request.param

@pytest.fixture(params=(16000, 44100))
def samplerate(request):

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from __future__ import division

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from pysoundcard import Stream, continue_flag, complete_flag

number_of_blocks = 100
number_of_channels = 6

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'''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

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

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])