jkbjh

def pid_controller(y, yc, h=1, Ti=1, Td=1, Kp=1, u0=0, e0=0)
	"""Calculate System Input using a PID Controller

	Arguments:
	y  .. Measured Output of the System
	yc .. Desired Output of the System
	h  .. Sampling Time
	Kp .. Controller Gain Constant
	Ti .. Controller Integration Constant
	Td .. Controller Derivation Constant

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#!/bin/bash
TARGETLINK=https://julialang-s3.julialang.org/bin/linux/x64/1.6/julia-1.6.2-linux-x86_64.tar.gz
VERSION=1.6.2
CURDIR=$(pwd)
set -e
set -x

ARCHIVE=julia-$VERSION.tar.gz
wget $TARGETLINK -O $ARCHIVE
UNCOMPRESSED_SIZE=$(zcat $ARCHIVE | wc -c)

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#!/usr/bin/env python
import subprocess
import socket
from contextlib import closing
import threading
import time
import os
import signal
import argparse

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# cross-entropy optimization
import numpy as np
import tqdm


def ce_minimize(X_mean, X_std, eval_loss, iterations=100, sample_size=25, keep_samples=10):
    assert 0 < sample_size
    assert 0 < keep_samples and keep_samples < sample_size
    # Alg settings:

jkbjh

"""Dark magic for interactive python."""
import builtins
import time


def ipy_here(leak_variables=True):
    import IPython
    import inspect

    parent_frame = inspect.currentframe().f_back

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import importlib
import subprocess
import re
import os
import joblib
from joblib import parallel_backend, Parallel, delayed


found_imports = sorted(list(set([x.lstrip().rstrip() for x in subprocess.check_output("findimports -p -l 1  |grep -v ':' |sort -u", shell=True).decode("utf-8").rstrip().lstrip().split("\n")])))
installed_packages = subprocess.check_output('pip list | tail -n +3 | cut -d" " -f1', shell=True).decode("utf-8").rstrip().lstrip()

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;;; diction.el --- minor mode to interface the command diction

;;; Copyright Sven Utcke <Sven.Utcke@gmx.de>

;; Emacs Lisp Archive Entry
;; Filename:      diction.el
;; Version:       $Id: diction.el,v 1.8 2005/09/29 12:55:52 utcke Exp $
;; Keywords:      diction, style
;; Author:        Sven Utcke <Sven.Utcke@gmx.de>
;; Maintainer:    Sven Utcke <Sven.Utcke@gmx.de>

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# a selection of small numpy helper functions
import joblib


def apply_along_axes(func, data, axes):
    """
    apply function along axes and use joblib parallel to execute the function on the selections.
    might result in a large allocation for reconstructing the result.
    only makes sense if the sub-executions are sufficiently costly.
    """

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import collections


JointInfo = collections.namedtuple(
    "JointInfo",
    [
        "index",
        "name",
        "type",
        "q_index",

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#!/bin/bash
set -e
set -x
# you can directly run this with: bash <(curl  -Ls "https://gist.githubusercontent.com/jkbjh/c83e5bf6d708f590f77ccdd6b2ef0a40/raw/mujoco-210-user-install.sh?$(date +%s)")
# (the date query parameter is to to prevent caching when downloading through curl.)
export START_DIR=$(pwd)
export MJ_DIR=$HOME/.mujoco
mkdir -p $MJ_DIR
pushd $MJ_DIR
curl -LJ 'https://github.com/deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz' -o $MJ_DIR/mujoco210_linux.tgz