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using GLVisualize, GeometryTypes, Colors
using Reactive, GLAbstraction

window = glscreen()
timesignal = loop(linspace(0f0, 1f0, 360))

large_sphere = HyperSphere(Point3f0(0), 1f0)
rotation_angle = const_lift(*, timesignal, 2f0*pi)
rotation = map(rotationmatrix_z, rotation_angle)

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# -*- coding: utf-8 -*-
"""
Tutorial example showing how to use the implicit solver Radau5DAE. 
It simulates a falling mass.
"""
import numpy as np
import pylab as plt
from assimulo.problem import Implicit_Problem #Imports the problem formulation from Assimulo
from assimulo.solvers import Radau5DAE #Imports the solver IDA from Assimulo

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# Custom type, that can store scalars of vectors in Julia

NumberOrVector = Union{Number, Vector}

# input data
type Project
    p_el_nom::NumberOrVector          # electrical generator power in kW
    rel_drum_diameter::NumberOrVector # ratio of the drum diameter and the tether diameter
    rpm_generator::NumberOrVector     # nominal speed of the electrical machine
    z_ref::NumberOrVector             # reference height for wind profile law

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from datetime import datetime
import time

def check_sleep(amount):
    start = datetime.now()
    time.sleep(amount)
    end = datetime.now()
    delta = end-start
    return delta.seconds + delta.microseconds/1000000.

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function check_sleep(amount)
    start = time()
    sleep(amount)
    final = time()
    delta = final - start
    return delta
end

sum = 0.0
abs(check_sleep(0.020)-0.020) # precompile check_sleep

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using Winston

const SIMUL_DUR  = 10.0      # simulation time in seconds
const PeriodTime = 20e-3     # period time for the simulation in seconds
const workload = 20000       # workload in vector additions/ multiplications per simulation step
const vec_size = 3

const cpu_usage = zeros(round(Int, SIMUL_DUR / PeriodTime)) # data array to log the cpu usage
const vec = zeros(vec_size, workload) # preallocate data array of 3D vectors

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# -*- coding: utf-8 -*-
"""
Demo code, showing the problem with using record arrays with Numba 0.18.
I replaced just three of the elements of vec3 with a record array with three elements. The execution time
increases from 22us to 45 us.

Model of a kite-power system in implicit form: residual = f(y, yd, sw)

This model implements a 3D mass-spring system with reel-out. It uses five tether segments (the number can be
configured in the file Settings.py). The kite is modelled as additional mass at the end of the tether.

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# -*- coding: utf-8 -*-
""" Accessing record arrays with numba 0.17 is 10 times slower than using two dimensional arrays. """
from numba import jit
import numpy as np
import time

V_WIND = 8.0

V_wind           = 0 # (westwind, downwind direction to the east)
V_wind_gnd       = 1

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# constants for accessing the array of scalars
ParamCD       =  0
ParamCL       =  1
Length        =  2 # length of one tether segment at zero force
C_spring      =  3 # spring constant of one tether segment
Damping       =  4 # damping constant of one tether segment
Depower       =  5
Steering      =  6
Alpha_depower =  7
Alpha_zero    =  8