training/workshop_2019/harmonisation_interactive_lecture.ipynb

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    "# Interactive Lecture: Comparison of Fitting Algorithms\n",
    "\n",
    "In this exercise we will compare the effectiveness of two different fitting algorithms, classic *Least Squares* and a more rigorous *Orthogonal Distance Regression* approach.\n",
    "\n",
    "| It is structured as follows:                                                             |\n",
    "| ---------------------------------------------------------------------------------------- |\n",
    "|1. Define Example Measurement                                                             |\n",
    "|2. Define Test *True* Dataset                                                             |\n",
    "|3. Simulate *Measured* Dataset                                                            |\n",
    "|4. Fit the data with two approaches - *Least Squares* and *Othogonal Distance Regression* |\n",
    "|5. How do the fitting methods compare?                                                    |\n",
    "| *Extension Exercises* |\n",
    "\n",
    "NB: To download the interactive version of this notebook follow this [link](https://www.dropbox.com/s/editacaxbabrbex/harmonisation_interactive_lecture.ipynb?dl=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**``First import required python modules``**\n",
    "\n",
    "To run a Python cell in the iPython notebook select it and press [shift] + [Enter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from scipy.optimize import curve_fit    # scipy least squares implementation\n",
    "from scipy.odr import *                 # scipy othogonal-distance-regression implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Define Example Measurement\n",
    "\n",
    "Take a simple straight-line measurement function as an example,\n",
    "\n",
    "$y = a~x + b$\n",
    "\n",
    "where,\n",
    "* y is the measurand\n",
    "* x is it the observed quantity\n",
    "* a, b are the calibration parameters\n",
    "\n",
    "In this example set,\n",
    "\n",
    "* a = 1.0\n",
    "* b = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set true calibration parameters\n",
    "a_true = 1.0\n",
    "b_true = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``i. Define measurement function in Python``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def measurement_function(x, a, b):\n",
    "    \"\"\"\n",
    "    Return measurand for straight line measurement function\n",
    "    \n",
    "    :parameters:\n",
    "    x (numpy.ndarray) - measurement observed quantity\n",
    "    a (float) - calibration parameter\n",
    "    b (float) - calibration parameter\n",
    "    \n",
    "    :return:\n",
    "    y - measurement measurand\n",
    "    \"\"\"\n",
    "    \n",
    "    y = a*x + b\n",
    "\n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``ii. Test our measurement function``\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:\n",
      "x = 2.0\n",
      "\n",
      "Output:\n",
      "y = 2.0\n"
     ]
    }
   ],
   "source": [
    "# Choose test measurement observed quantity\n",
    "x_test = 2.0\n",
    "\n",
    "# Compute y from test observed quantity\n",
    "y_test = measurement_function(x_test, a_true, b_true)\n",
    "\n",
    "print(\"Input:\")\n",
    "print(\"x =\", x_test)\n",
    "\n",
    "print(\"\\nOutput:\")\n",
    "print(\"y =\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Define Test *True* Dataset\n",
    "\n",
    "Define set of true values for $x$ between $0 \\leq x \\leq 1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``i. Define dataset``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Number of measurements\n",
    "n_obs = 100\n",
    "\n",
    "# x values randomly drawn between 0 and 1 (rounded to 2 decimal places)\n",
    "x_true = np.round(np.random.rand(n_obs), decimals=2)\n",
    "\n",
    "# Evaluate y from x values with measurement function\n",
    "y_true = measurement_function(x_true, a_true, b_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``ii. Plot dataset``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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\">"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x783ccc0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot true data\n",
    "plt.figure()\n",
    "plt.plot(x_true, y_true, \"bo\")\n",
    "plt.ylabel(\"$y$\")\n",
    "plt.xlabel(\"$x$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Simulate *Measured* Dataset\n",
    "\n",
    "In reality each has an unknown error measurement,\n",
    "\n",
    "$y_{data} = y_{true} + \\epsilon_{y}$\n",
    "\n",
    "$x_{data} = x_{true} + \\epsilon_{x}$\n",
    "\n",
    "In our example lets assume each error is independent for each measurement. So for each mesaurement:\n",
    "\n",
    "* $\\epsilon_x \\in Norm[\\mu=0, \\sigma=u_x]$ -- i.e. $\\epsilon_{x}$ is a draw from the uncertainty distribution of $x$\n",
    "* $\\epsilon_y \\in Norm[\\mu=0, \\sigma=u_y]$ -- i.e. $\\epsilon_{y}$ is a draw from the uncertainty distribution of $y$\n",
    "\n",
    "\n",
    "Set uncertainty for each measurement to be:\n",
    "\n",
    "* $u(x) = 0.05$\n",
    "* $u(y) = 0.05$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_x: [ 0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05]\n",
      "u_y: [ 0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05]\n"
     ]
    }
   ],
   "source": [
    "# Define uncertainties\n",
    "u_x = np.full(n_obs, 0.05)\n",
    "u_y = np.full(n_obs, 0.05)\n",
    "\n",
    "# Print uncertainty for first 10 observations\n",
    "print(\"u_x:\", u_x[:10])\n",
    "print(\"u_y:\", u_x[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``i. Write a function to add errors to our data``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_errors(data, uncertainty):\n",
    "    \"\"\"\n",
    "    Return array with independent random gaussian error each element\n",
    "    \n",
    "    :parameters:\n",
    "    data (numpy.ndarray) - input array\n",
    "    uncertainty (float) - random error gaussian standard deviation\n",
    "    \n",
    "    :return:\n",
    "    data_error (numpy.ndarray) - input array + independent random gaussian errors\n",
    "    \"\"\"\n",
    "    \n",
    "    # Determine errors for each true value\n",
    "    errors = np.random.normal(loc=0, scale=uncertainty, size=data.shape[0])\n",
    "    \n",
    "    # Add error to true data to\n",
    "    data_error = data + errors\n",
    "    \n",
    "    return data_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``ii. Simulate set of measured data by adding errors to true values``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add errors to true data to simulate set of \"measured data\"\n",
    "x_data = add_errors(x_true, u_x)\n",
    "y_data = add_errors(y_true, u_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``iii. Plot measured dataset``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "output_type": "display_data"
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7c66fd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results\n",
    "plt.figure()\n",
    "\n",
    "# a. True data line\n",
    "plt.plot(x_true, y_true, 'blue', label=\"True\")\n",
    "\n",
    "# b. Plot measured data points (with errorbars)\n",
    "plt.plot(x_data, y_data, \"ro\", label=\"Data\")\n",
    "\n",
    "plt.ylabel(\"$y$\")\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Fitting the Data\n",
    "\n",
    "In this section we will attempt to fit the measured data we produced in *Section 3* with two difference approaches, standard *Least Squares* (LSQ) or a more rigorous *Orthogonal Distance Regression* (ODR) approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**a. Least Squares (LSQ)**\n",
    "\n",
    "*Least Squares* optimisation fits based on weighting of uncertainties in $y$ dimension only, i.e,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7c73b70>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot LSQ uncertainty picture\n",
    "plt.figure()\n",
    "\n",
    "# a. True data line\n",
    "plt.plot(x_true, y_true, 'blue', label=\"True\")\n",
    "\n",
    "# b. Plot measured data points (with y errorbars)\n",
    "plt.errorbar(x=x_data, y=y_data, yerr=u_y, fmt=\"ro\", ecolor='black', label=\"Data\")\n",
    "\n",
    "plt.ylabel(\"$y$\")\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``i. Define function to perform Least Squares fitting.``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use scipy LSQ implementation curve_fit\n",
    "\n",
    "def fit_lsq(measurement_function, x_data, y_data, u_y, p_initial):\n",
    "    \"\"\"\n",
    "    Return optimised parameters for a given measurement function and input data\n",
    "    \n",
    "    :parameters:\n",
    "    measurement_function (func) - measurement function\n",
    "    x_data (numpy.ndarray) - input x data\n",
    "    y_data (numpy.ndarray) - input y data\n",
    "    u_y (float) - y data uncertainty\n",
    "    p_initial (list) - intial parameter estimates\n",
    "    \n",
    "    :return:\n",
    "    p_est (list) - optimised parameter estimates\n",
    "    p_cov (numpy.ndarray) - parameter covariance matrix\n",
    "    \"\"\"\n",
    "\n",
    "    p_est, p_cov = curve_fit(measurement_function, x_data, y_data, p_initial, u_y)\n",
    "\n",
    "    return p_est, p_cov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``ii. Run optimisation``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSQ Parameter Estimate:\n",
      "a = 0.943573620886\n",
      "b = 0.0238050137032\n",
      "\n",
      "Covariance matrix:\n",
      "[[ 0.00060263 -0.00031173]\n",
      " [-0.00031173  0.00020664]]\n"
     ]
    }
   ],
   "source": [
    "# Run optimisation\n",
    "p_lsq, cov_lsq = fit_lsq(measurement_function, x_data, y_data, u_y, p_initial=[0.0, 0.0])\n",
    "\n",
    "a_lsq = p_lsq[0]\n",
    "b_lsq = p_lsq[1]\n",
    "\n",
    "# Print result\n",
    "print(\"LSQ Parameter Estimate:\")\n",
    "print(\"a =\", a_lsq)\n",
    "print(\"b =\", b_lsq)\n",
    "print(\"\\nCovariance matrix:\")\n",
    "print(cov_lsq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``iii. Plot results``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x81455f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results\n",
    "plt.figure()\n",
    "\n",
    "# a. True calibration line\n",
    "plt.plot(x_true, y_true, 'blue', label=\"True\")\n",
    "\n",
    "# b. Plot measured data points\n",
    "plt.errorbar(x=x_data, y=y_data, yerr=u_y, fmt=\"ro\", ecolor=\"black\", label=\"Data\")\n",
    "\n",
    "#c. Plot estimate\n",
    "y_lsq = measurement_function(x_true, a_lsq, b_lsq)\n",
    "plt.plot(x_true, y_lsq, 'orange', label=\"LSQ Fit\")\n",
    "\n",
    "plt.ylabel(\"$y$\")\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**b. Orthogonal Distance Regression (ODR)**\n",
    "\n",
    "*Orthogonal Distance Regression* optimisation methods weight based on uncertainties in *both* the $x$ and $y$ dimension, i.e,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x816b7b8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot ODR uncertainty picture\n",
    "plt.figure()\n",
    "\n",
    "# a. True data line\n",
    "plt.plot(x_true, y_true, 'blue', label=\"True\")\n",
    "\n",
    "# b. Plot measured data points (with errorbars)\n",
    "plt.errorbar(x=x_data, xerr=u_x, y=y_data, yerr=u_y, fmt=\"ro\", ecolor='black', label=\"Data\")\n",
    "\n",
    "plt.ylabel(\"$y$\")\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``i. Define function to perform Orthogonal Distance Regression fitting``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use scipy ODRPACK implementation \n",
    "\n",
    "def fit_odr(measurement_function, x_data, y_data, u_x, u_y, p_initial):\n",
    "    \"\"\"\n",
    "    Return optimised parameters for a given measurement function and input data\n",
    "    \n",
    "    :parameters:\n",
    "    measurement_function (func) - measurement function\n",
    "    x_data (numpy.ndarray) - input x data\n",
    "    y_data (numpy.ndarray) - input y data\n",
    "    u_y (float) - x data uncertainty\n",
    "    u_y (float) - y data uncertainty\n",
    "    p_initial (list) - intial parameter estimates\n",
    "    \n",
    "    :return:\n",
    "    p_est (list) - optimised parameter estimates\n",
    "    p_cov (numpy.ndarray) - parameter covariance matrix\n",
    "    \"\"\"\n",
    "\n",
    "    odr = ODR(RealData(x=x_data, y=y_data, sx=u_x, sy=u_y), Model(measurement_function), beta0=p_initial)\n",
    "    result = odr.run()\n",
    "\n",
    "    p_est = result.beta\n",
    "    p_cov = result.cov_beta\n",
    "\n",
    "    return p_est, p_cov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``ii. Run optimisation``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ODR Parameter Estimate:\n",
      "a = 0.97354019637\n",
      "b = 0.00830370430544\n",
      "\n",
      "Covariance matrix:\n",
      "[[ 0.00065633 -0.00033951]\n",
      " [-0.00033951  0.00022432]]\n"
     ]
    }
   ],
   "source": [
    "# scipy ODRPACK needs a slightly different form of input to the measurement function\n",
    "def measurement_function_odr(p, x):\n",
    "    return p[0] * x + p[1]\n",
    "\n",
    "# Run optimisation\n",
    "p_odr, cov_odr = fit_odr(measurement_function_odr, x_data, y_data, u_x, u_y, p_initial=[0.0, 0.0])\n",
    "a_odr = p_odr[0]\n",
    "b_odr = p_odr[1]\n",
    "\n",
    "# Print result\n",
    "print(\"ODR Parameter Estimate:\")\n",
    "print(\"a =\", a_odr)\n",
    "print(\"b =\", b_odr)\n",
    "print(\"\\nCovariance matrix:\")\n",
    "print(cov_odr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``iii. Plot results``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x82282e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results\n",
    "plt.figure()\n",
    "\n",
    "# a. True calibration line\n",
    "plt.plot(x_true, y_true, 'blue', label=\"True\")\n",
    "\n",
    "# b. Plot measured data points\n",
    "plt.errorbar(x=x_data, y=y_data, xerr=u_x, yerr=u_y, fmt=\"ro\", ecolor=\"black\", label=\"Data\")\n",
    "\n",
    "#c. Plot ODR estimate\n",
    "y_odr = measurement_function(x_true, a_odr, b_odr)\n",
    "plt.plot(x_true, y_odr, 'orange', label=\"ODR Fit\")\n",
    "\n",
    "plt.ylabel(\"$y$\")\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. How well does each perform\n",
    "\n",
    "Perform Monte Carlo analysis of fitting approaches:\n",
    "1. Generate ``n_trials`` instances of measured data, each with different random errors\n",
    "2. Fit each iteration of with both *Least Squares* and *Othogonal Distance Regression* approaches\n",
    "3. Compare results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``i. Perform Monte Carlo analysis``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define number of trials to perform\n",
    "n_trials = 1000\n",
    "\n",
    "# Initialise arrays to store parameter results \n",
    "a_lsqs = np.zeros(n_trials)\n",
    "b_lsqs = np.zeros(n_trials)\n",
    "\n",
    "a_odrs = np.zeros(n_trials)\n",
    "b_odrs = np.zeros(n_trials)\n",
    "\n",
    "y_lsqs = np.zeros((n_trials, n_obs))\n",
    "y_odrs = np.zeros((n_trials, n_obs))\n",
    "\n",
    "for i in range(n_trials):\n",
    "    \n",
    "    # Add errors to true data to generate set of \"measured data\"\n",
    "    x_data_i = add_errors(x_true, u_x)\n",
    "    y_data_i = add_errors(y_true, u_y)\n",
    "    \n",
    "    # Least squares fitting    \n",
    "    p_lsq_i, cov_lsq_i = fit_lsq(measurement_function, x_data_i, y_data_i, u_y, p_initial=[0.0, 0.0])\n",
    "    a_lsqs[i] = p_lsq_i[0]\n",
    "    b_lsqs[i] = p_lsq_i[1]\n",
    "    y_lsqs[i, :] = measurement_function(x_true, a_lsqs[i], b_lsqs[i])\n",
    "    \n",
    "    # ODR fitting\n",
    "    p_odr_i, cov_odr_i = fit_odr(measurement_function_odr, x_data_i, y_data_i, u_x, u_y, p_initial=[0.0, 0.0])\n",
    "    a_odrs[i] = p_odr_i[0]\n",
    "    b_odrs[i] = p_odr_i[1]\n",
    "    y_odrs[i, :] = measurement_function(x_true, a_odrs[i], b_odrs[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``ii. Plot Outputs``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results for a parameter\n",
    "plt.figure()\n",
    "\n",
    "# a_lsq estimates\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.hist(a_lsqs-a_true, bins=20)\n",
    "plt.title(\"a_lsq - a_true\")\n",
    "plt.xlim([-0.1, 0.1])\n",
    "\n",
    "# a_odr estimates\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.hist(a_odrs-a_true, bins=20)\n",
    "plt.title(\"a_odr - a_true\")\n",
    "plt.xlim([-0.1, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results for b parameter\n",
    "plt.figure()\n",
    "\n",
    "# b_lsq estimates\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.hist(b_lsqs-b_true, bins=20)\n",
    "plt.title(\"b_lsq - b_true\")\n",
    "plt.xlim([-0.1, 0.1])\n",
    "\n",
    "# b_odr estimates\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.hist(b_odrs-b_true, bins=20)\n",
    "plt.title(\"b_odr - b_true\")\n",
    "plt.xlim([-0.1, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.05, 0.05)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot results of impact on a given y\n",
    "plt.figure()\n",
    "\n",
    "# y_lsqs[0] estimates\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.hist(y_lsqs[:,0]-y_true[0], bins=20)\n",
    "plt.title(\"y_lsq[0] - y_true[0]\")\n",
    "plt.xlim([-0.05, 0.05])\n",
    "\n",
    "# y_odrs[0] estimates\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.hist(y_odrs[:,0]-y_true[0], bins=20)\n",
    "plt.title(\"y_odr[0] - y_true[0]\")\n",
    "plt.xlim([-0.05, 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "\n",
    "## EXTENSION EXERCISES\n",
    "\n",
    "The cell below contains the code we used in interactive lecture above. Try editing it to investigate the effects of the following:\n",
    "\n",
    "1. If the ``x_data`` fitted has no errors.\n",
    "2. If the ``x_data`` fitted has no errors or uncertainty (use a small number [i.e. 0.0001] - *Orthogonal Distance Regression Methods* cannot accept 0 uncertainty).\n",
    "3. If the actual uncertainty used to generate errors in the simulated measurements, ``x_data`` and/or ``y_data``, is different to the uncertainty given to the fitting algorithms. Try overestimating and underestimating.\n",
    "4. If uncertainty values vary between points - i.e. not just 0.05 for every point (could try a function like [np.linspace](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) or [np.random.rand](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rand.html) instead of [np.full](https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define uncertainties\n",
    "u_x = np.full(n_obs, 0.05)\n",
    "u_y = np.full(n_obs, 0.05)\n",
    "\n",
    "# Define number of trials to perform\n",
    "n_trials = 1000\n",
    "\n",
    "# Initialise arrays to store parameter results \n",
    "a_lsqs = np.zeros(n_trials)\n",
    "b_lsqs = np.zeros(n_trials)\n",
    "\n",
    "a_odrs = np.zeros(n_trials)\n",
    "b_odrs = np.zeros(n_trials)\n",
    "\n",
    "y_lsqs = np.zeros((n_trials, n_obs))\n",
    "y_odrs = np.zeros((n_trials, n_obs))\n",
    "\n",
    "for i in range(n_trials):\n",
    "    \n",
    "    # Add errors to true data to generate set of \"measured data\"\n",
    "    x_data_i = add_errors(x_true, u_x)\n",
    "    y_data_i = add_errors(y_true, u_y)\n",
    "    \n",
    "    # Least squares fitting    \n",
    "    p_lsq_i, cov_lsq_i = fit_lsq(measurement_function, x_data_i, y_data_i, u_y, p_initial=[0.0, 0.0])\n",
    "    a_lsqs[i] = p_lsq_i[0]\n",
    "    b_lsqs[i] = p_lsq_i[1]\n",
    "    y_lsqs[i, :] = measurement_function(x_true, a_lsqs[i], b_lsqs[i])\n",
    "    \n",
    "    # ODR fitting\n",
    "    p_odr_i, cov_odr_i = fit_odr(measurement_function_odr, x_data_i, y_data_i, u_x, u_y, p_initial=[0.0, 0.0])\n",
    "    a_odrs[i] = p_odr_i[0]\n",
    "    b_odrs[i] = p_odr_i[1]\n",
    "    y_odrs[i, :] = measurement_function(x_true, a_odrs[i], b_odrs[i])\n",
    "    \n",
    "\n",
    "# Plot results altogether\n",
    "plt.figure(figsize=(8, 12))\n",
    "\n",
    "# a_lsq estimates\n",
    "plt.subplot(3, 2, 1)\n",
    "plt.hist(a_lsqs-a_true, bins=20)\n",
    "plt.title(\"a_lsq - a_true\")\n",
    "plt.xlim([-0.1, 0.1])\n",
    "\n",
    "# a_odr estimates\n",
    "plt.subplot(3, 2, 2)\n",
    "plt.hist(a_odrs-a_true, bins=20)\n",
    "plt.title(\"a_odr - a_true\")\n",
    "plt.xlim([-0.1, 0.1])\n",
    "\n",
    "# b_lsq estimates\n",
    "plt.subplot(3, 2, 3)\n",
    "plt.hist(b_lsqs-b_true, bins=20)\n",
    "plt.title(\"b_lsq - b_true\")\n",
    "plt.xlim([-0.1, 0.1])\n",
    "\n",
    "# b_odr estimates\n",
    "plt.subplot(3, 2, 4)\n",
    "plt.hist(b_odrs-b_true, bins=20)\n",
    "plt.title(\"b_odr - b_true\")\n",
    "plt.xlim([-0.1, 0.1])\n",
    "\n",
    "# y_lsqs[0] estimates\n",
    "plt.subplot(3, 2, 5)\n",
    "plt.hist(y_lsqs[:,0]-y_true[0], bins=20)\n",
    "plt.title(\"y_lsq[0] - y_true[0]\")\n",
    "plt.xlim([-0.05, 0.05])\n",
    "\n",
    "# y_odrs[0] estimates\n",
    "plt.subplot(3, 2, 6)\n",
    "plt.hist(y_odrs[:,0]-y_true[0], bins=20)\n",
    "plt.title(\"y_odr[0] - y_true[0]\")\n",
    "plt.xlim([-0.05, 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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