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    "def my_random_sampler( func, x0, my_args=(), my_options=None ):\n    \"Simple optimiser: samples randomly and returns the minimum - used here as an example\"\n\n    my_options      = my_options or {}\n    points          = my_options.get('maxfev', 30)              # by default perform 30 function evaluations\n    num_parameters  = len(x0)                                   # get number of parameters needed for objective function\n\n    flist = []\n    xlist = []\n    for i in range(points):\n        x = [random.random() for i in range(num_parameters)]    # initialise parameters randomly from uniform distribution\n        fnew = func(x, my_args)\n        flist.append(fnew)\n        xlist.append(x)\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n\n    minimizer_output = { 'fun' : fmin, 'nfev' : points, 'nit' : points, 'x' : best_x }\n\n    return minimizer_output\n",
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