diff --git a/notebooks/leastSquaresFits.ipynb b/notebooks/leastSquaresFits.ipynb
deleted file mode 100644
index ab27e6f612d98b3d03318613e368e9cf67f768df..0000000000000000000000000000000000000000
--- a/notebooks/leastSquaresFits.ipynb
+++ /dev/null
@@ -1,442 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Least squares Fit\n",
-    "\n",
-    "A couple of examples to show how to use:  \n",
-    "- numpy.polyfit  \n",
-    "- scipy.optimize.curve_fit  \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import print_function\n",
-    "import numpy as np\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import curve_fit\n",
-    "from scipy.stats import norm, chi2, lognorm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit a polynomial\n",
-    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create some data distribuited as parabola with normally distributed errors.\n",
-    "def parabola(x, a, b, c):\n",
-    "    return a*x**2 + b*x + c\n",
-    "def error(x, sigma):\n",
-    "    return norm.rvs(0.0, sigma, x.size) \n",
-    "a = -0.1\n",
-    "b = 0\n",
-    "c = 1\n",
-    "sigma_y = 0.0015\n",
-    "\n",
-    "x = np.linspace(0, 1, 21)\n",
-    "y_true = parabola(x, a, b, c)\n",
-    "delta_y = error(x, sigma_y)\n",
-    "y = y_true + delta_y\n",
-    "y_error = sigma_y * np.ones(x.size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fit_polynomial(x, y, degree, weight):\n",
-    "    \"\"\"Fit polynomial of degree to data x, y with y weight = 1/sigma_y\n",
-    "    Return fit, covariance matrix, residuals and chi-squared and degrees of freedom.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    dof = x.shape[0] - degree\n",
-    "    fit, cov = np.polyfit(x, y, degree, w=weight, cov=True)\n",
-    "    residuals = np.sum((y - np.polyval(fit, x))**2 / y_error**2)\n",
-    "    chisq = residuals / (dof)\n",
-    "    return fit, cov, residuals, chisq, dof\n",
-    "    \n",
-    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
-    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
-    "\n",
-    "def evaluate_chisq(chisq, dof):\n",
-    "    return chi2.sf(chisq, dof)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fit with a straight line\n",
-      "a = -0.0955  +/- 0.0060\n",
-      "b = -0.0069  +/- 0.0035\n",
-      "Fit with a parabola\n",
-      "a = -0.0955  +/- 0.0039\n",
-      "b = -0.0069  +/- 0.0040\n",
-      "c = 1.0016  +/- 0.0009\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the fitted parameters and their uncertainties\n",
-    "fitPars = fit_1\n",
-    "errPars = np.sqrt(np.diag(cov_1))\n",
-    "print (\"Fit with a straight line\")\n",
-    "print ('a = {:.4f}'.format(fit[0]), ' +/- {:.4f}'.format(errPars[0]))\n",
-    "print ('b = {:.4f}'.format(fit[1]), ' +/- {:.4f}'.format(errPars[1]))\n",
-    "errPars = np.sqrt(np.diag(cov))\n",
-    "print (\"Fit with a parabola\")\n",
-    "print ('a = {:.4f}'.format(fit[0]), ' +/- {:.4f}'.format(errPars[0]))\n",
-    "print ('b = {:.4f}'.format(fit[1]), ' +/- {:.4f}'.format(errPars[1]))\n",
-    "print ('c = {:.4f}'.format(fit[2]), ' +/- {:.4f}'.format(errPars[2]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reduced chi^2:\n",
-      "parabola 0.8805087850199654\n",
-      "line 29.237037028605272\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Reduced chi^2:')\n",
-    "print('parabola', chisq)\n",
-    "print('line', chisq_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chi^2 distributions:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.999999999755556, 0.08319587293588597)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print('Chi^2 distributions:')\n",
-    "evaluate_chisq(chisq, dof), evaluate_chisq(chisq_1, dof_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
-    "ax[0].set_title('fits')\n",
-    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.')\n",
-    "ax[0].plot(x, y_true, 'k-')\n",
-    "ax[0].plot(x, np.polyval(fit, x), label='parabola', color='blue')\n",
-    "ax[0].plot(x, np.polyval(fit_1, x), label='line', color='green')\n",
-    "\n",
-    "ax[0].legend()\n",
-    "ax[1].plot(y_true - np.polyval(fit, x), '.', color='blue')\n",
-    "ax[1].plot(y_true - np.polyval(fit_1, x), '.', color='green')\n",
-    "ax[1].set_title('residuals')\n",
-    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
-    "ax[1].legend()\n",
-    "f.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Draw the plot form lecture notes \n",
-    "plt.figure(figsize=(7, 5))\n",
-    "chisq_arr = np.linspace(0, 100, 1001)\n",
-    "plt.ylabel(r'p-value for test $\\alpha$ for confidence interval') \n",
-    "plt.xlabel(r'$\\chi^2$')\n",
-    "for n in [1, 2, 3, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50]:\n",
-    "    plt.loglog(chisq_arr, chi2.sf(chisq_arr, n))\n",
-    "plt.loglog(chisq_arr, chi2.sf(chisq_arr, dof), 'k-', lw=2, label='dof={0}'.format(dof))\n",
-    "plt.ylim(1e-3, 1.1)\n",
-    "plt.xlim(1, 100)\n",
-    "\n",
-    "plt.legend()\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit of a gaussian\n",
-    "Next, we consider a Gaussian. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gaussian_parent(x, mu, sigma):\n",
-    "    return norm.pdf(x, mu, sigma)    \n",
-    "\n",
-    "def gaussian_sample(mu, sigma, sample_size):\n",
-    "    return norm.rvs(mu, sigma, sample_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create sample\n",
-    "## SAMPLE SIZE\n",
-    "sample_size = 200\n",
-    "##################\n",
-    "\n",
-    "# Prepare toy data\n",
-    "mu = 1540  # True values that we will try to estimate\n",
-    "sigma = 11 # using a least-squares fit\n",
-    "\n",
-    "x_arr = np.linspace(1500, 1600, 101)\n",
-    "bins = 12\n",
-    "sample = gaussian_sample(mu, sigma, sample_size)\n",
-    "hist = np.histogram(sample, bins=bins, range=(1500, 1580))\n",
-    "bin_width = np.diff(hist[1])[0]\n",
-    "normalization = bin_width * sample_size\n",
-    "x = hist[1][:-1]+bin_width/2\n",
-    "y_error_const = 0\n",
-    "y = hist[0]/normalization + gaussian_sample(0, y_error_const, bins)\n",
-    "y_errors = np.sqrt((np.sqrt(hist[0]) / normalization)**2 + y_error_const**2)\n",
-    "\n",
-    "# Plot our toy measurement results\n",
-    "plt.figure(figsize=(5, 4))\n",
-    "plt.xlabel(r'$E$ (meV)')\n",
-    "plt.ylabel('count rate')\n",
-    "plt.plot(x_arr, gaussian_parent(x_arr, mu, sigma), '-', color='black', label='parent')\n",
-    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Save data\n",
-    "data = np.vstack((x, y, y_errors))\n",
-    "np.savetxt('data', data)\n",
-    "np.savetxt('sample', sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
-    "data = np.loadtxt('data')\n",
-    "x = data[0, :]\n",
-    "y = data[1, :]\n",
-    "y_error = data[2, :]\n",
-    "# The sample used to generate\n",
-    "sample = np.loadtxt('sample')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function we want to fit to our data set\n",
-    "def model_function(x, *args):\n",
-    "    mu, sigma = args[0:2]\n",
-    "    return norm.pdf(x, mu, sigma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fit Results:\n",
-      "mu = 1541.0 +- 0.3\n",
-      "sigma = 9.9 +- 0.3\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Perform the fit minimizing least squares\n",
-    "starting_point = [1545, 9]\n",
-    "p_opt, p_cov = curve_fit(model_function, x, y, p0=starting_point, sigma=None, absolute_sigma=False, check_finite=True)\n",
-    "p_err = np.sqrt(np.diag(p_cov))\n",
-    "# pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N)\n",
-    "print('Fit Results:')\n",
-    "print('mu = {:1.1f} +- {:1.1f}'.format(p_opt[0], p_err[0]))\n",
-    "print('sigma = {:1.1f} +- {:1.1f}'.format(p_opt[1], p_err[1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_arr = np.linspace(1500, 1600, 101)\n",
-    "\n",
-    "plt.figure(figsize=(5, 4))\n",
-    "plt.xlabel(r'$E$ (meV)')\n",
-    "plt.ylabel('count rate')\n",
-    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
-    "plt.plot(x_arr, model_function(x_arr, *starting_point), '-', color='grey', label='starting point')\n",
-    "plt.plot(x_arr, model_function(x_arr, *p_opt), '-', color='black', label='fit')\n",
-    "plt.legend()\n",
-    "plt.tight_layout()"
-   ]
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "225.438px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}