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# curvefit with non linear least squares (curve_fit function)
import numpy as np
from scipy.optimize import curve_fit

def func(x, a, b, c):
    return a*np.sin(x[0])+b*np.cos(x[1])+c

limits = [0, 2*np.pi, 0, 2*np.pi]  # [x1_min, x1_max, x2_min, x2_max]
side_x = np.linspace(limits[0], limits[1], 100)
side_y = np.linspace(limits[2], limits[3], 100)

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import JSON
###################
### Write data ####
###################
# dictionary to write
dict1 = Dict("param1" => 1, "param2" => 2,
            "dict" => Dict("d1"=>1.,"d2"=>1.,"d3"=>1.))
            
# pass data as a json string (how it shall be displayed in a file)
stringdata = JSON.json(dict1)

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#include <ros/ros.h>
#include <nav_msgs/OccupancyGrid.h>
#include <geometry_msgs/PoseStamped.h>
#include <tf/tf.h>
#include <tf/transform_listener.h>
#include <iterator>
#include <vector>

// some definitions of functions
#define MAP_INDEX(map, i, j) ((i) + (j) * map.size_x)

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Installation of IPOPT on LINUX

NOTE: this page is for a from-source install (useful if you want to contribute to CasADi). If you just want to evaluate CasADi quickly, use a binary install.

• To get started quickly, start off by grabbing as many dependencies as possible from repositories.

• Ubuntu: sudo apt-get install gcc g++ gfortran git cmake liblapack-dev pkg-config --install-recommends
• Fedora: sudo yum install gcc gcc-c++ gcc-gfortran git cmake lapack-devel

• To compile the Python interface, you also need SWIG and a decent Python installation:

• Ubuntu: sudo apt-get install swig ipython python-dev python-numpy python-scipy python-matplotlib --install-recommends
• Fedora: sudo yum install swig ipython python-devel numpy scipy python-matplotlib

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import matplotlib.pyplot as plt
from qiskit import *
from qiskit.tools.monitor import job_monitor
from qiskit.tools.visualization import plot_bloch_multivector, plot_histogram
from math import pi


qr = QuantumRegister(3, name="q")
crz, crx = ClassicalRegister(1, name="crz"), ClassicalRegister(1, name="crx")
c0 = ClassicalRegister(1, name="c0")

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import qutip
import matplotlib.pyplot as plt
import numpy as np

b = qutip.Bloch()
# vec = [0,1,0]
th = np.linspace(0, np.pi, 20)
xz = np.sin(th)
yz = np.zeros(20)
zz = np.cos(th)

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using SparseArrays
using LinearAlgebra
using BandedMatrices
using SpecialMatrices
using ToeplitzMatrices
using Random
using BlockArrays
using FFTW
BLAS.set_num_threads(1)

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import numpy as np
import cvxpy as cp
α = 0.3
A = np.array([[1., α], [α, 1.5]])
# maximum eigenvalue
t = cp.Variable()
prob = cp.Problem(cp.Minimize(t), [np.eye(2)*t-A>>0])
prob.solve()
# minimum eigenvalue
t = cp.Variable()

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Sanity check for convex optimization set

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Scan devices

hcitool scan

From: https://askubuntu.com/a/450751/269279 We can nicely control our Bluetooth device with the command line tool [hciconfig][1].

To enable visibility: