lectures/L03_probability.ipynb

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   "source": [
    "# Probability & Statistics: II\n",
    "\n",
    "*Davide Gerosa (Milano-Bicocca)*\n",
    "\n",
    "##### Reading:\n",
    "\n",
    "- [Ivezic textbook](https://press.princeton.edu/books/hardcover/9780691198309/statistics-data-mining-and-machine-learning-in-astronomy) Chapter 3. \n",
    "\n",
    "\n",
    "This course is based on previous work by many people. See [here]((https://github.com/dgerosa/astrostatistics_bicocca_2024/blob/main/README.md) for credits.\n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import pylab as plt\n",
    "plt.rcParams['figure.figsize'] = [8, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo integration\n",
    "\n",
    "You might have seen this already, but Monte Carlo integration is an absolute crucial notion in modern statistics (modern because it relies on generating a large number of data points, which has been made possible by computers). Suppose you have very a complicated integral to solve and that you can write down the integrand as a product of $f(x)$ and $p(x)$ with $\\int p(x)=1$ (this is not restrictive at all! See below). My nasty integral is\n",
    "\n",
    "$$\\int f(x) p(x) dx$$\n",
    "\n",
    "\n",
    "Provided one can **evaluate** $f(x)$ and **sample** $p(x)$, then\n",
    "\n",
    "\n",
    "$$\\int f(x) p(x) dx \\approx \\frac{1}{N}\\sum_{i=1}^N f(x_i) $$\n",
    "\n",
    "where $x_i$ are samples drawn from $p$. "
   ]
  },
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   "metadata": {},
   "source": [
    "### An example.\n",
    "\n",
    "Ok try to integrate this crazy thing. Pen and paper? Good luck.\n",
    "    \n",
    "$$\\int_{0.3}^4 dx \\frac{\\exp(x) \\sqrt{4x+3}  \\log(x^6)}{\\tanh(x)}$$\n",
    "\n",
    "Pen and paper? Good luck. With a computer? Easy peasy!\n",
    "\n",
    "First, I divide and multiply by $4-0.3$. This is because (see below) 1/(4-0.3) is the uniform distribution $p(x)$ for x between 0.3 and 4.\n",
    "\n",
    "$$ (4-0.3) \\times \\int_{0.3}^{4} \\frac{dx}{(4-0.3)} \\frac{\\exp(x) \\sqrt{4x+3} * \\log(x^6)}{\\tanh(x)}$$\n",
    "\n",
    "Now I have $p(x)=\\frac{1}{4-0.3}$ and $f(x) = \\frac{\\exp(x) \\sqrt{4x+3} * \\log(x^6)}{\\tanh(x)}$. All I need to do is generate numbers from $p$, put them into $f$, and sum the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1390.409443434711"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "xmax= 4\n",
    "xmin=0.3\n",
    "\n",
    "xi=np.random.uniform(xmin,xmax, 100000)\n",
    "\n",
    "def fun(x):\n",
    "    return np.exp(x)*(4*x+3)**0.5 * np.log(x**6)/np.tanh(x)\n",
    "\n",
    "integral = (xmax-xmin) * np.mean(fun(xi))\n",
    "\n",
    "integral\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the result:\n",
    "https://www.wolframalpha.com/input/?i=integrate+from+0.3+to+4+exp%28x%29+sqrt%284x%2B3%29+*+ln%28x%5E6%29+%2Ftanh%28x%29\n",
    "\n",
    "Run the cell again. And again.  We'll understand the errors in a bit.\n",
    "\n",
    "#### Always remember Monte Carlo integration, it's extremely useful!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive statistics \n",
    "\n",
    "As we've said, our goal is to estimate $h(x)$ given some measured data, allowing us to reconstruct the data-based distribution $f(x)$. An arbitrary distribution can be characterized by **location** parameters (i.e., position), **scale** parameters (i.e., width), and **shape** parameters. These parameters are called ***descriptive statistics***.\n",
    "\n",
    "The distribution we're trying to characterize could be anything, e.g. (from my field) the distribution of masses of binary black-hole systems as discovered by gravitational-wave detectors. We really don't know the answer to this well, and the problem is made more complicated by things like detector selection effects (heavier systems are more likely to be observed), and blurring effects from measurement precision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get back to the distribution we've seen already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dgerosa/box/lib/python3.11/site-packages/astroML/linear_model/linear_regression_errors.py:10: UserWarning: LinearRegressionwithErrors requires PyMC3 to be installed\n",
      "  warnings.warn('LinearRegressionwithErrors requires PyMC3 to be installed')\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 841,
       "width": 875
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Based on Ivezic v2, Figure 6.8; edited by G. T. Richards, S. R. Taylor, and D. Gerosa\n",
    "\n",
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "from astropy.visualization import hist\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "if \"setup_text_plots\" not in globals():\n",
    "    from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=14, usetex=True)\n",
    "%config InlineBackend.figure_format='retina' # very useful command for high-res images\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Generate our data: a mix of several Cauchy distributions\n",
    "# In reality nature generates data for you\n",
    "\n",
    "\n",
    "random_state = np.random.RandomState(seed=0)\n",
    "N = 2000 # number of data points\n",
    "mu_gamma_f = [(5, 1.0, 0.1),\n",
    "              (7, 0.5, 0.5),\n",
    "              (9, 0.1, 0.1),\n",
    "              (12, 0.5, 0.2),\n",
    "              (14, 1.0, 0.1)]\n",
    "hx = lambda x: sum([f * stats.cauchy(mu, gamma).pdf(x)\n",
    "                          for (mu, gamma, f) in mu_gamma_f])\n",
    "x = np.concatenate([stats.cauchy(mu, gamma).rvs(int(f * N), random_state=random_state)\n",
    "                    for (mu, gamma, f) in mu_gamma_f])\n",
    "random_state.shuffle(x)\n",
    "x = x[x > -10]\n",
    "x = x[x < 30]\n",
    "\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# plot the results\n",
    "fig,ax = plt.subplots(figsize=(10, 10))\n",
    "xgrid = np.linspace(-10, 30, 1000)\n",
    "\n",
    "# True distribution: you typically don't have it! Only have the samples!\n",
    "if False:\n",
    "    ax.plot(xgrid, hx(xgrid), ':', color='black', zorder=3,\n",
    "            label=\"$h(x)$, Generating Distribution\")\n",
    "\n",
    "# A simple histogram\n",
    "# But try changing the number of bins!\n",
    "if True:\n",
    "    ax.hist(x,density=True,color='C0',bins=100,histtype='step',lw=2, label='Histogram, 100 bins')\n",
    "\n",
    "# Something more sophisticated: Kernel Density Estimation\n",
    "# But try changing the bandwith! \n",
    "if False:\n",
    "    kde = KernelDensity(bandwidth=0.1, kernel='gaussian')\n",
    "    kde.fit(x[:, None])\n",
    "    dens_kde = np.exp(kde.score_samples(xgrid[:, None]))\n",
    "    ax.plot(xgrid, dens_kde, '-', color='C1', zorder=3,\n",
    "            label=\"$f(x)$, non-parametric (KDE)\")\n",
    "\n",
    "# Use Gaussian Mixtures with a pre-defined number of clusters (13)\n",
    "if False:\n",
    "    gmm = GaussianMixture(n_components=13).fit(x.reshape(-1, 1))\n",
    "    logprob = gmm.score_samples(xgrid.reshape(-1, 1))\n",
    "    fx = lambda j : np.exp(gmm.score_samples(j.reshape(-1, 1)))\n",
    "    ax.plot(xgrid, fx(np.array(xgrid)), '-', color='C2',\n",
    "            label=\"$f(x)$, parametric (13 Gaussians)\")\n",
    "\n",
    "# Plot cosmetics\n",
    "ax.text(0.02, 0.95, \"%i points\" % N, ha='left', va='top',\n",
    "            transform=ax.transAxes)\n",
    "\n",
    "ax.set_ylabel('$p(x)$',fontsize=14)\n",
    "ax.legend(loc='upper right')\n",
    "\n",
    "ax.set_xlabel('$x$',fontsize=14)\n",
    "ax.set_xlim(0, 20)\n",
    "ax.set_ylim(-0.01, 0.4001)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We all know that the **mean** of a sample is \n",
    "\n",
    "$$\\bar{x} = \\frac{1}{N}\\sum_{i=1}^N x_i$$ \n",
    "\n",
    "This is actually known as the **sample arithmetic mean**, and derives from *Monte Carlo integration* to get the first moment of the distribution, i.e. \n",
    "\n",
    "$$\\mu = E(x) = \\langle x \\rangle = \\int_{-\\infty}^{\\infty} x h(x)\\,dx \\approx \\frac{1}{N}\\sum_{i=1}^N x_i $$\n",
    "\n",
    "where $\\{x_i\\}$ are random samples from the properly normalized $h(x)$, and $E(\\cdot)$ means the **expectation value**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the mean is straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.79491032639886\n"
     ]
    }
   ],
   "source": [
    "data =x.copy() #Why am I doing this?\n",
    "\n",
    "mean = np.mean(data)\n",
    "print(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it's most common to compute the mean, it may surprise you to learn that some distributions do not have formally calculable means (integration gives infinity). In these and other cases, the **median** is a more *robust* estimator of the (true) mean location of the distribution.  That's because it is less affected by **outliers**.\n",
    "\n",
    "To understand the previous statement, think about multiplying all numbers above the 50th percentile (i.e. the median) by 100, or even just replacing them with larger numbers. The mean would be strongly affected by these corrupted points, but **cumulative statistics based on the ordering of samples would remain unaffected by the outlier corruption**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.752731764708434"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = np.median(data)\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.752731764708434 7.752731764708434\n",
      "8.79491032639886 125.63017483107997\n"
     ]
    }
   ],
   "source": [
    "# Try to unpack what is happening here\n",
    "median = np.median(data)\n",
    "\n",
    "mask = data > 20\n",
    "data2 = data.copy()\n",
    "data2[mask] = 10000\n",
    "\n",
    "newmedian = np.median(data2)\n",
    "newmean = np.mean(data2)\n",
    "\n",
    "print(median, newmedian)\n",
    "print(mean, newmean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other descriptive statistics are related to higher order moments of the distribution. Beyond the \"average\" *location* value, we'd like to know something about **deviations** from the average (which is related to the *shape* of the distribution).  The simplest thing to compute is $$d_i = x_i - \\mu.$$  However, the average deviation is zero by definition of the mean.  The next simplest thing to do is to compute the **mean absolute deviation (MAD)**:\n",
    "\n",
    "$$\\frac{1}{N}\\sum|x_i-\\mu|,$$\n",
    "\n",
    "but the absolute values can hide the true scatter of the distribution [(example)](http://www.mathsisfun.com/data/standard-deviation.html). But this is used, see regularization schemes further on in the class. So the next simplest thing to do is to square the differences $$\\sigma^2 = \\frac{1}{N}\\sum(x_i-\\mu)^2,$$ which we call the **variance**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *variance* $V$ is the just expectation value of $(x-\\mu)^2$ (and related to the 2nd moment)\n",
    "\n",
    "$$\\sigma^2 = V = E((x-\\mu)^2)\\int_{-\\infty}^{\\infty}  (x-\\mu)^2 h(x) dx,$$\n",
    "\n",
    "where $\\sigma$ is the **standard deviation**. Again, the integral gets replaced by a sum for discrete distributions. While most familiar for Gaussian distributions, you can compute the variance even if your distribution is not Gaussian.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.026052739760294 3.876345281287555\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "var = np.var(data)\n",
    "std = np.std(data)\n",
    "print(var, std)\n",
    "print(np.isclose(std**2,var)) #Why am I not doing std**2==var?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also the **Median Absolute Deviation (also MAD)** given by\n",
    "\n",
    "$${\\rm median} (|x_i-{\\rm median}(\\{x_i\\})|)$$\n",
    "\n",
    "where $\\sigma = 1.4826\\,{\\rm MAD}$ for a Gaussian distribution (but note that we aren't using a Gaussian distribution above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6811130664731344 2.492418232353069\n"
     ]
    }
   ],
   "source": [
    "from astropy.stats import median_absolute_deviation\n",
    "MAD = median_absolute_deviation(data)\n",
    "print(MAD,MAD*1.4826)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$P\\%$ quantiles (or the $p^\\mathrm{th}$ percentile, $q_p$)** are computed as\n",
    "$$\\frac{p}{100} = H(q_p) = \\int_{-\\infty}^{q_p}h(x) dx$$\n",
    "\n",
    "The full integral from $-\\infty$ to $\\infty$ is 1 (100%).  So, here you are looking for the value of x that accounts for $p$ percent of the distribution.\n",
    "\n",
    "For example, the 25th, 50th, and 75th percentiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.674995612577169 7.752731764708434 11.637549888596837\n"
     ]
    }
   ],
   "source": [
    "# Execute this cell\n",
    "q25, q50, q75 = np.percentile(data, [25, 50, 75])\n",
    "print(q25, q50, q75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x16e3b1190>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 658,
       "width": 673
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data,bins=100,histtype='step');\n",
    "\n",
    "plt.axvline(np.percentile(data,50),c='red')\n",
    "plt.axvline(np.percentile(data,5),c='red',ls='dotted')\n",
    "plt.axvline(np.percentile(data,95),c='red',ls='dotted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The region between the 5th and the 95th percentile contains 90% of the samples. In Bayesian statistics, this is the 90% credible interval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **interquartile range** is the difference between the 25th and 75th percentiles, $q_{75} - q_{25}$.\n",
    "\n",
    "Just as with the median, the interquartile range is a more *robust* estimator of the scale of a distribution than the standard deviation.  So, one can create a standard-deviation-esque measurement (at least for a Gaussian) from the interquartile range as\n",
    "\n",
    "$$\\sigma_G = 0.7413\\times(q_{75} - q_{25})$$  \n",
    "\n",
    "The normalization makes it *unbiased* for a perfect Gaussian (more on that later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.678746989540611 3.876345281287555\n",
      "3.678746989540611 1998.538847396505\n"
     ]
    }
   ],
   "source": [
    "# Execute this cell. Think about and discuss the results.\n",
    "from astroML import stats as astroMLstats\n",
    "\n",
    "# original data\n",
    "print(astroMLstats.sigmaG(data), np.std(data))\n",
    "\n",
    "# corrupted by outliers\n",
    "print(astroMLstats.sigmaG(data2), np.std(data2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.89 µs ± 55 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
      "50.1 µs ± 21.2 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Execute this cell. Cumulative statistics take longer to compute, but are more robust.\n",
    "%timeit np.mean(data), np.std(data)\n",
    "%timeit np.median(data), astroMLstats.sigmaG(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **mode** is the most probable value, determined from the peak of the distribution, which is the value where the derivative is 0 (i.e. the turning point):\n",
    "\n",
    "$$ \\left(\\frac{dh(x)}{dx}\\right)_{x_m} = 0$$\n",
    "\n",
    "A good approximation for the mode is (the distribution needs to be somewhat close to Gaussian, for a proof: [Lupton 1993](https://press.princeton.edu/books/hardcover/9780691074290/statistics-in-theory-and-practice))\n",
    "\n",
    "$$x_m = 3q_{50} - 2\\mu$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.668374641327581 8.79491032639886 7.752731764708434\n",
      "ModeResult(mode=-9.890167554081579, count=1) This is non sense!\n"
     ]
    }
   ],
   "source": [
    "# Execute this cell\n",
    "mode = 3*q50 - 2*mean\n",
    "print(mode, mean, median)\n",
    "\n",
    "# Note: don't rely on scipy.stats.mode()!!\n",
    "# It gives the most common value of an array, \n",
    "# but we have a random sample of unique draws\n",
    "print(scipy.stats.mode(data,keepdims=False), \"This is non sense!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other useful ***shape*** measures include the \"higher order\" moments (the **skewness** and **kurtosis**):\n",
    "\n",
    "$$\\mathbf{Skewness}\\quad\\quad \\Sigma = \\int_{-\\infty}^{\\infty}  \\left(\\frac{x-\\mu}{\\sigma}\\right)^3 h(x) dx,$$\n",
    " \n",
    "$$\\mathbf{Kurtosis}\\quad\\quad K = \\int_{-\\infty}^{\\infty}  \\left(\\frac{x-\\mu}{\\sigma}\\right)^4 h(x) dx  - 3.$$\n",
    "\n",
    "The skewness measures the distribution's *asymmetry*. Distribution's with long tails to larger $x$ values have positive $\\Sigma$. \n",
    "\n",
    "The kurtosis measures how peaked or flat-topped a distribution is, with strongly peaked ones being positive and flat-topped ones being negative. $K$ is calibrated to a Gaussian distribution (hence the subtraction of $3$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![https://www.astroml.org/_images/fig_kurtosis_skew_1.png](https://www.astroml.org/_images/fig_kurtosis_skew_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.45629831452599 3.24867718616583\n"
     ]
    }
   ],
   "source": [
    "skew = scipy.stats.skew(data)\n",
    "kurt = scipy.stats.kurtosis(data)\n",
    "print(skew, kurt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location:  8.79491032639886 7.752731764708434 5.668374641327581\n",
      "Scale:  15.026052739760294 3.876345281287555 3.678746989540611\n",
      "Shape:  0.45629831452599 3.24867718616583\n",
      "Some percentiles:  6.674995612577169 7.752731764708434 11.637549888596837\n"
     ]
    }
   ],
   "source": [
    "# Summary descriptive statistics for our distribution\n",
    "print(\"Location: \", mean, median, mode)\n",
    "print(\"Scale: \", var, std, astroMLstats.sigmaG(data))\n",
    "print(\"Shape: \", skew, kurt)\n",
    "print(\"Some percentiles: \", q25, q50, q75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample versus Population statistics <a class=\"anchor\" id=\"two\"></a>\n",
    "\n",
    "Statistics estimated from the *data* are called **sample statistics** as compared to **population statistics** derived from knowing the functional form of the pdf.\n",
    "\n",
    "Specifically, $\\mu$ is the **population mean**, i.e., it is the expectation value of $x$ for $h(x)$.  But we don't *know* $h(x)$.  So the **sample mean**, $\\overline{x}$, is an ***estimator*** of $\\mu$, defined as\n",
    "\n",
    "$$\\overline{x} \\equiv \\frac{1}{N}\\sum_{i=1}^N x_i,$$\n",
    "\n",
    "which we determine from the data itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of the **population variance** $\\sigma^2$, we have the **sample variance**, $s^2$, where\n",
    "\n",
    "$$s^2 = \\frac{1}{N-1}\\sum_{i=1}^N(x_i-\\overline{x})^2$$\n",
    "\n",
    "The $N-1$ denominator (instead of $N$) accounts for the fact that we determine $\\overline{x}$ from the data itself instead of using a known $\\mu$. This is called [Bessel's correction](https://en.wikipedia.org/wiki/Bessel%27s_correction).\n",
    "\n",
    "Ideally one tries to work in a regime where $N$ is large enough that we can be lazy and ignore this. \n",
    "\n",
    "So the mean and variance of a distribution are $\\mu$ and $\\sigma^2$.  The *estimators* of the distribution are $\\overline{x}$ (or $\\hat{x}$) and $s^2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pill of modern astrophysics from my research.** We computed Bessel's correction in the context of Hierarchical Bayesian statistics in a recent paper. [Moore and Gerosa (2021), \"Population-informed priors in gravitational-wave astronomy\"](https://arxiv.org/abs/2108.02462). It's a fun paper to read if you're interested in the stats+gravity interplay and want to learn about probabilistic graphical models (which I wont'thave time to cover in this class)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Uncertainty of sample statistics\n",
    "\n",
    "We would also like to know the uncertainty of our estimates $\\overline{x}$ and $s$. \n",
    "\n",
    "Note that $s$ is the width estimate of the underlying distribution; it is **NOT** the uncertainty of $\\overline{x}$. This is a common misconception.\n",
    "\n",
    "Rather the uncertainty of $\\overline{x}$, $\\sigma_{\\overline{x}}$ is \n",
    "\n",
    "$$ \\sigma_{\\overline{x}} = \\frac{s}{\\sqrt{N}},$$\n",
    "\n",
    "which we call the **standard error of the mean**. The uncertainty of $s$ itself is\n",
    "\n",
    "$$\\sigma_s = \\frac{s}{\\sqrt{2(N-1)}} = \\frac{1}{\\sqrt{2}}\\sqrt{\\frac{N}{N-1}}\\sigma_{\\overline{x}}.$$\n",
    "\n",
    "Note that for large $N$, $\\sigma_{\\overline{x}} \\sim \\sqrt{2}\\sigma_s$ and for small $N$, $\\sigma_s$ is not much smaller than $s$.\n",
    "\n",
    "Another useful uncertainty estimate is for computing quantiles. The **standard error of a quantile** is\n",
    "\n",
    "$$ \\sigma_{q_p} = \\frac{1}{h_p}\\sqrt{\\frac{p(1-p)}{N}}$$\n",
    "\n",
    "where $p$ is between $0$ and $1$.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Try this at home***. \n",
    "- Generate some data\n",
    "- Compute $\\bar x$ and $s$\n",
    "- Do it many times\n",
    "- Estimate the errors\n",
    "- Compare with the expressions above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Univariate distributions  (and an intro to scipy.stats)\n",
    "\n",
    "If we are attempting to characterize our data in a way that is **parameterized**, then we need a functional form for a **distribution**.  There are many naturally occurring distributions.  The book goes through quite a few of them.  Here we'll just talk about a few basic ones to get us started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Uniform Distribution\n",
    "\n",
    "The uniform distribution is perhaps more commonly called a \"top-hat\" or a \"box\" distribution.  It is specified by a mean, $\\mu$, and a width, $W$, where\n",
    "\n",
    "$$p(x|\\mu,W) = \\frac{1}{W}$$\n",
    "\n",
    "over the range $|x-\\mu|\\le \\frac{W}{2}$ and $0$ otherwise.  That says that \"given $\\mu$ AND $W$, the probability of $x$ is $\\frac{1}{W}$\" (as long as we are within a certain range).\n",
    "\n",
    "Since we are used to thinking of a Gaussian as the *only* type of distribution the concept of $\\sigma$ (aside from the width) may seem strange.  But $\\sigma$ as mathematically defined above applies here and\n",
    "$$\\sigma = \\frac{W}{\\sqrt{12}}.$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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ggH788Uf99ttvWr16tR5++GEtWrRI/fr1c3eEpbMBsE2bNjp48KDmzJmjLVu2aOLEiUpJSVHTpk09trXrZ3dtw3OVVFdhYaHHshYtWmj8+PGaOHGie3veddddmj9/vrp06eI1UD3wwAOSpEGDBunHH3/Uzp079cYbb2j16tXuQOuq7Vzp6emSpA8++EDbt2/XP/7xD+Xm5urKK6/0+Pco/e/fjuvfeaNGjfTee+9Jkq6++mp9/vnn2rVrl5YsWaI+ffpo27Ztev7553Xttde6X8/1783l3J/b179NADCdmTchBAAEz+LFi42//OUvRrNmzYz4+HgjMjLSqFq1qtGhQwfjqaeeMvbs2eP1+1w3Li/6tWPHDo8bqZ/7NX36dI8bf5/79eyzz/pcJi837T506JCRkZFhtGjRwoiJiTESEhKMLl26GG+99ZZRWFjoMdZ1U++iX95uNO+6gbi3ceVdVpJXX33V588cExNj1KlTx7jiiiuMUaNGGVu2bPG6Dl+/b9fr79u3z5g0aZLxhz/8wahZs6YRFRVl1K5d2+jWrZvxzjvvGAUFBcXW+euvvxrdu3c3KleubCQkJBiXX365sXz5csMwDGPgwIE+t2Fp61qzZo3Hc+np6cb27duN2267zahdu7YRHR1tNGzY0HjqqaeMEydOeP25nU6nMWbMGKNx48ZGdHS0kZKSYjzxxBPGqVOnim2Pc3/G/Px8Y+jQoUatWrWM6Ohoo0mTJsb48eMNw/D973r69Oker71hwwZj4MCBRr169Yzo6GijevXqxh//+EdjwYIFxep03Xi+6M9rGL7/bQKA2RyGYeHLaQEAAAAAAoZDQAEAAAAgTBAAAQAAACBMEAABAAAAIEwQAAEAAAAgTBAAAQAAACBMEAABAAAAIEwQAAEAAAAgTIRtAFy6dKlatGghh8OhnTt3ml0OAAAAAARd2AXAvLw8jRgxQldccYW2bNni17qOHTumN998U3369FGdOnUUHR2tqlWrqkuXLho/frxOnToVoKoBAAAAwH9hFQAzMzPVpk0bzZkzR/PmzfNrXSdOnFCDBg10//33KykpSR9++KE2b96sTz75RImJiRoxYoQ6deqk/fv3B6h6AAAAAPCPwzAMw+wiKsrnn3+ur7/+WmPHjlWVKlXkcDgkSTt27FDDhg3LtK6cnBxVq1ZNN9xwg+bMmeOxzOl0qkuXLlq+fLluu+02zZw5M1A/AgAAAACUW1h1AK+99lpNnjxZVapUCdg677777mLPRUREaPDgwZKkjz/+WGfOnAnY6wEAAABAeUWZXUBFioyMDNi6kpKSdOTIESUmJnpdnpKSIkk6deqUTp48qYSEhIC9NgAAAACUR1gFwEByOByqWrWqz+XZ2dmSpAYNGhD+AAAAAFgCATBIvvrqK0nSgw8+WOrvOXXqlH799Vft27dP1atXV1RUyZunbt26ftUIAAAAwByuhpEvhYWFOnTokGrXrq3WrVsrNjY2IK8bVheBKcqfi8CUZP369Wrbtq1atWql//73v4qJiSnV961YsUKdOnUKWB0AAAAAQt/y5cvVsWPHgKwrrC4CUxHy8vL0l7/8RVWrVtWHH35Y6vAHAAAAAMHGIaABVFhYqJtvvlk7duzQwoUL1bRp0zJ9f82aNd3/P3fuXNWuXbvE8VY5BDQ7O9vduVy+fLll6kLZsB3tge1oD2xHe2A72gPb0R6suB3Pdwjovn379Kc//UmSZ07wFwEwQAoKCnT77bdr+fLlWrhwoS655JIyr+Pcc/7atGnjvpJoKKlbt25I1g1PbEd7YDvaA9vRHtiO9sB2tAerbMfz1ZCVleX+//NdG6QsCIABcOrUKfXv31+rV6/WDz/8oJYtW5pdEgAAAAAUwzmAfjp58qT++Mc/6tdff9XixYuLhb9rrrlGv/zyi0nVAQAAAMD/EADP4+uvv1bTpk01dOjQYsuOHj2qq6++Wrt27dLixYvVuHHjYmO++eYbHT58uCJKBQAAAIAShd0hoAcOHNCZM2eKPee6r0adOnU8lk2cOFGZmZmaNGmSnnvuOVWvXl2SdOzYMf3hD3/QihUr1LhxY/Xr169ifgAAAAAAKKewC4AdO3bUb7/95vHcuffeK3pbxAEDBmjx4sW6+uqr3eFPkjIzM7VixQpJ0vbt24NYMQAAAAAERtgFwJ07d5Zp/C233KJbbrml2PNt2rQpFhYBAAAAwMo4BxAAAAAAwoTDoI1lGVlZWUpNTZUk7d692xL3JwEAANaVl5eno0eP6sSJE8WucQDAWiIjIxUfH6/ExETFxcWdd3ywskHYHQIKAABgB7m5ufr999/NLgNAKRUWFio/P1+HDx/WBRdcoKSkJFPqIAACAACEmLy8vGLhLyqKj3WAlRUWFrr///fff1dMTIz7TgQViXcKAACAEHP06FH3/ycmJqpOnTqKjIw0sSIA53PmzBnt3bvXPX9zc3NNCYBcBAYAACDEnDhxwv3/hD8gNERGRnrcc/zceVyRCIAAAAAhxnXBl6ioKMIfEEIiIyPdh2ubdeEmAiAAAAAAhAkCIAAAAACECQIgAAAAAIQJAiAAAAAAhAkCIAAAAACECQIgAAAAAIQJAiAAAABgY7Nnz1b37t1VrVo1xcfH65JLLtHYsWNVUFAQ9Ndu1aqVHA6HvvzyS6/L9+/fr7i4ODkcDt1xxx0+15Oeni6Hw6GXXnopWKX6tHnzZr3++usaNGiQWrduraioKDkcDr3wwgsVXksgRJldAAAAAIDgeOSRRzRhwgRFRUWpR48eqlKlir777js98cQTmjt3rr799lvFxcUF7fWrVq0qSTp69KjX5ZMmTdKpU6ckSbm5uV7HrFq1SosWLVJ8fLyGDBkSlDpL8sYbb2jChAkV/rrBQgcQAAAAsKFPP/1UEyZMUJUqVbRs2TJ98803mjNnjrZu3arWrVtryZIleuaZZ4JagysAegt3eXl5mjx5smJiYnyOkaTx48dLku68805Vq1YtOIWWoFWrVnrsscc0c+ZMbdy4scROZSggAAIAAAA25DpcMiMjQ+3atXM/X6NGDU2ePFmSNHHiRJ/BKxBKCoDvvvuuDh48qLvuuktRUVHKyckpNmbPnj2aPXu2IiIiNHz48KDVWZLBgwfrb3/7m2677Ta1bNlSERGhHaFCu3oAAADABLt27ZLD4VD9+vV9jqlevbocDocOHz5cgZWdtWfPHq1YsUKSdNtttxVb3q1bN6Wmpio/P19fffVV0OrwdQioYRgaP368IiIiNGLECCUkJHgNia+//roKCgrUr18/NW7cOGh1hhPOAQQAALARp9OpQ4cOmV2GJVWvXj1g3RtXuOrQoYPX5du3b9fhw4fVqFEjJScnB+Q1y2LNmjWSpOTkZDVq1MjrmA4dOmj37t1as2aNbr311qDU4asDOHfuXG3ZskU33HCDmjRpoqSkpGIdwBMnTuitt96SJD322GNBqS8cEQABAABs5NChQ6pVq5bZZVjS/v37VbNmzYCsa+XKlZJ8B0DX8vbt25e4nkGDBumdd94p8+svXLhQ3bt397l8x44dklRihzI1NdVjbDD4CoDjxo2TJI0cOVKSlJiYqF27dskwDDkcDknS9OnTdeTIEV122WXq3Llzia8TrN+jHREAAQAAgDJydQB9BbxVq1aVuNylW7du5Xr9OnXqlLj82LFjkqT4+HifY6pUqSLJ9xU6A8HbIaArV67UokWL1LVrV3ewS0xMlNPp1LFjx9z/77ryZmm6f8H6PdoRARAAAAAoA8Mw3AHP3w7g4MGDNXjw4MAWaCHeOoCu7t+5wS4pKck9LjExUXPnztW2bdvUtGlTXX/99ed9Hbv/HgOJi8AAAAAAZbBt2zbl5OSoYcOGql69erHlhmFo9erVks4fAIMlISFB0tnz6Hw5fvy4pLPdt2ApGgB/++03ffTRR2revLmuu+469zhXDa5xrls/DB8+POSvumk1dAABAABspHr16tq/f7/ZZViSt7BWHufr7mVmZroD4vkuADNt2jQtWbKkzDVkZGSoZcuWPpc3bNhQkrR7926fY1zLXGODoeghoBMmTFBhYaEeffRRj2Dn6gDm5OS4b/xevXp1DRo0qFSvE6zfox0RAAEAAGwkIiIiYBc6gXeu8//atGnjdfmXX34pqXTdvyVLlpTr4iWDBg0qMbi0bdtW0tmLAu3YscPrlUBdQfbcewQG2rkdwNzcXE2bNk01a9bUwIEDPcad2wGcMmWKJGnIkCGqXLlyqV4nWL9HO6KfCgAAAJSBKzjFxcUVW5afn69p06ZJKl0AnDFjhgzDKPPX+a5cmZKSoo4dO0qS3n///WLLlyxZot27dysmJkZ9+vQ5b53ldW4AnDp1qo4dO6ahQ4cqNjbWY5wrAG7YsEEffvihYmJiNHTo0FK/TrB+j3ZEAAQAAABKyel0us/vmzVrlk6ePOledvjwYQ0cOFDr1q2TJNM7S0899ZQk6ZVXXnHXLJ3tCj7wwAOSpKFDh7oPv3SZMWOGHA5HQA4NdQXAwsJCjRs3TnFxce7XPperhvHjx6ugoEADBgxQ7dq1/X59FMchoAAAAEApbdy4USdOnFDdunW1ceNGNWjQQB06dNCxY8f0yy+/qE6dOqpRo4YOHjyoUaNGafPmzcrIyDCl1r59++qhhx7Sa6+9ps6dO6tnz56Kj4/XggULlJOTo65du+r5558v9n1Op1OSFB0d7XcNUVFRio+P14kTJ7R3714NGTJENWrUKDbO1QHcu3evHA6HHn30Ub9fO1BWr17tEVozMzMlSW+++aa++OIL9/OffPKJ6tatW+H1lRUBEAAAACgl1/l/6enpuueee/T4449r4cKFSkpKUv/+/fXSSy9p+vTpev755+V0OnXppZeaWu+ECRPUtWtXTZo0SUuXLlVBQYGaNGmijIwMDR8+XJUqVSr2Pa5bXNx9990BqaFq1ao6ceKEIiIifAa7c7uQvXv3VlpaWkBeOxCOHj2qZcuWFXs+KytLWVlZ7sf5+fkVWVa5OQzDMMwuAmdlZWUpNTVV0tmrMqWkpJhcEQAAsKKtW7eqsLBQUVFRatasmdnlhJWhQ4dq0qRJGjdunKW6VIHUrFkz5eXlaevWrV7Pc4R/Sjt/g5UN6AACAAAApeTqALousGI3O3fu1LZt2zR16lTCn00RAAEAAIBSKCgo0M8//6zIyEjTbvAebA0bNhQHCNobVwEFAAAASuHXX39Vfn6+0tLSSn1/OsBq6AACAAAApdCuXTu6Ywh5dAABAAAAIEwQAAEAAAAgTBAAAQAAACBMEAABAAAAIEwQAAEAAAAgTBAAAQAAACBMEAABAAAAIEwQAAEAAAAgTBAAAQAAACBMEAABAAAAIEwQAAEAAAAgTBAAAQAAACBMEAABAAAAIEwQAAEAAAAgTBAAAQAAABubPXu2unfvrmrVqik+Pl6XXHKJxo4dq4KCgqC/dqtWreRwOPTll196Xb5//37FxcXJ4XDojjvu8Lme9PR0ORwOvfTSS8Eq1auCggItWLBAI0eOVMeOHVW1alVFR0erTp06uu6663z+XFYWZXYBAAAAAILjkUce0YQJExQVFaUePXqoSpUq+u677/TEE09o7ty5+vbbbxUXFxe0169ataok6ejRo16XT5o0SadOnZIk5ebmeh2zatUqLVq0SPHx8RoyZEhQ6vTlhx9+UK9evSRJderUUbdu3RQfH68NGzZo7ty5mjt3ru69915NmTJFDoejQmsrLzqAAAAAgA19+umnmjBhgqpUqaJly5bpm2++0Zw5c7R161a1bt1aS5Ys0TPPPBPUGlwB0Fu4y8vL0+TJkxUTE+NzjCSNHz9eknTnnXeqWrVqwSnUh4iICN14441atGiRsrOz9cUXX2jWrFn69ddf9e9//1uRkZF666239K9//atC6/IHARAAAACwIdfhkhkZGWrXrp37+Ro1amjy5MmSpIkTJ/oMXoFQUgB89913dfDgQd11112KiopSTk5OsTF79uzR7NmzFRERoeHDhwetTl969Oihjz76SJdffnmxZX/+8581aNAgSWd/llBBAAQAAADKaNeuXXI4HKpfv77PMdWrV5fD4dDhw4crsLKz9uzZoxUrVkiSbrvttmLLu3XrptTUVOXn5+urr74KWh2+DgE1DEPjx49XRESERowYoYSEBK8h8fXXX1dBQYH69eunxo0bB63O8mrbtq0kaffu3SZXUnqcAwgAAGBTBw4cKPf3VqlSxee5YQcPHpRhGOVab+XKlRUfH+912eHDh3XmzJlyrTc2NlYJCQnl+t7ycIWrDh06eF2+fft2HT58WI0aNVJycnKF1eWyZs0aSVJycrIaNWrkdUyHDh20e/durVmzRrfeemtQ6vDVAZw7d662bNmiG264QU2aNFFSUlKxDuCJEyf01ltvSZIee+yxoNTnr61bt0qS6tata3IlpUcABAAAsKlatWqV+3snTpyoBx980OuyCy+8UAcPHizXep999lmNHj3a67LLL79cGzZsKNd6H3jgAU2aNKlc31seK1eulOQ7ALqWt2/fvsT1DBo0SO+8806ZX3/hwoXq3r27z+U7duyQpBI7lKmpqR5jg8FXABw3bpwkaeTIkZKkxMRE7dq1S4ZhuC+mMn36dB05ckSXXXaZOnfuXOLrBOv3WJK9e/dqxowZkqQbb7yxXOswAwEQAAAAKCNXB9BXwFu1alWJy126detWrtevU6dOicuPHTsmST67rdLZLq/k+wqdgeDtENCVK1dq0aJF6tq1qzvYJSYmyul06tixY+7/nzBhgqTSdf+C9Xv0pbCwUAMGDFBubq5at26t++67r1zrMQMBEAAAACgDwzDcAc/fDuDgwYM1ePDgwBZoId46gK7u37nBLikpyT0uMTFRc+fO1bZt29S0aVNdf/31532div493n///VqwYIGqV6+ujz76SJUqVaqw1/YXF4EBAAAAymDbtm3KyclRw4YNVb169WLLDcPQ6tWrJZ0/AAaL63zIEydO+Bxz/PhxSWe7b8FSNAD+9ttv+uijj9S8eXNdd9117nGuGlzjXLd+GD58uCIirBVZHn74Yf3zn/9UtWrVNH/+fDVv3tzsksqEDiAAAIBN7d+/v9zf6zo80JuNGzf6dREYXxYvXuzXRWAqyvm6e5mZme6AeL4LwEybNk1Lliwpcw0ZGRlq2bKlz+UNGzaUVPLVKV3LXGODoeghoBMmTFBhYaEeffRRj2Dn6gDm5OS4b/xevXp1920WzidYv8eiRowYoddee01Vq1bVt99+674KaCghAAIAANhUzZo1g7LeGjVqBGW9Zlwtszxc5/+1adPG6/Ivv/xSUum6f0uWLCnXxUsGDRpUYnBxBZNDhw5px44dXq8E6gqy594jMNDO7QDm5uZq2rRpqlmzpgYOHOgx7twO4JQpUyRJQ4YMKXGHwbmC9Xs81+OPP67x48crKSlJ3377rc/Df63OWv1UAAAAwOJcwcnbbTLy8/M1bdo0SaULgDNmzJBhGGX+Ot+VK1NSUtSxY0dJ0vvvv19s+ZIlS7R7927FxMSoT58+562zvM4NgFOnTtWxY8c0dOjQYh1bVwDcsGGDPvzwQ8XExGjo0KGlfp1g/R5dMjIy9Le//U1JSUmaP3+++3cbigiAAAAAQCk5nU73+X2zZs3SyZMn3csOHz6sgQMHat26dZJUpkMLg+Gpp56SJL3yyivumqWzXcEHHnhAkjR06FD34ZcuM2bMkMPhCMihoa4AWFhYqHHjxikuLs792udy1TB+/HgVFBRowIABql27tt+vHwhPP/20xowZo6pVq4Z8+JM4BBQAAAAotY0bN+rEiROqW7euNm7cqAYNGqhDhw46duyYfvnlF9WpU0c1atTQwYMHNWrUKG3evFkZGRmm1Nq3b1899NBDeu2119S5c2f17NlT8fHxWrBggXJyctS1a1c9//zzxb7P6XRKkqKjo/2uISoqSvHx8Tpx4oT27t2rIUOGeD2E2NUB3Lt3rxwOhx599FG/XzsQPv/8c7344ouSpKZNm/q812SNGjX097//vSJLKzcCIAAAAFBKrvP/0tPTdc899+jxxx/XwoULlZSUpP79++ull17S9OnT9fzzz8vpdOrSSy81td4JEyaoa9eumjRpkpYuXaqCggI1adJEGRkZGj58uNfbF7hucXH33XcHpIaqVavqxIkTioiI8Bnszu1C9u7dW2lpaQF5bX8dPnzY/f8rV650H/5bVIMGDUImADqM8l7CCQGXlZWl1NRUSWevypSSkmJyRQAAwIq2bt2qwsJCRUVFqVmzZmaXE1aGDh2qSZMmady4cZbpUgVas2bNlJeXp61bt3o9zxH+Ke38DVY2oAMIAAAAlJKrAxjq54H5snPnTm3btk1Tp04l/NkUARAAAAAohYKCAv3888+KjIw07QbvwdawYcNy3+MRoYGrgAIAAACl8Ouvvyo/P19paWmlvj8dYDV0AAEAAIBSaNeuHd0xhDw6gAAAAAAQJgiAAAAAABAmCIAAAAAAECYIgAAAAAAQJgiAAAAAABAmCIAAAAAAECYIgAAAAAAQJgiAAAAAABAmCIAAAAAAECbCNgAuXbpULVq0kMPh0M6dO80uBwAAAACCLuwCYF5enkaMGKErrrhCW7ZsCcg6N2/erAEDBqhu3bqKjY1VkyZNNHLkSOXm5gZk/QAAAAAQCGEVADMzM9WmTRvNmTNH8+bNC8g6v//+e7Vt21YrV67UjBkztHHjRj399NOaMmWK2rVrp+zs7IC8DgAAAAD4K8rsAirS+vXr1bNnT40dO1ZVqlTxe305OTnq37+/DMPQV199pcaNG0uSGjVqpJiYGN1+++36y1/+ovnz5/v9WgAAAADgr7DqAF577bWaPHlyQMKfJL3++us6ePCgrr/+enf4c7nlllt0wQUX6D//+Y9+/PHHgLweAAAAAPgjrAJgZGRkQNc3e/ZsSVLPnj2LLYuIiFCPHj0kSbNmzQro6wIAAAClNXv2bHXv3l3VqlVTfHy8LrnkEo0dO1YFBQVBf+1WrVrJ4XDoyy+/9Lp8//79iouLk8Ph0B133OFzPenp6XI4HHrppZeCVapPM2fO1F/+8hddcsklqlWrlqKjo5WUlKROnTrp5Zdf1vHjxyu8Jn+E1SGggXTixAmtX79ektSyZUuvY1zPr1ixosLqAoDCwkJt27ZNZ86cMbsUIOxFR0eradOmiogIq33usJBHHnlEEyZMUFRUlHr06KEqVarou+++0xNPPKG5c+fq22+/VVxcXNBev2rVqpKko0ePel0+adIknTp1SpJ8XkBx1apVWrRokeLj4zVkyJCg1FmSN954Q0uXLtWFF16odu3aKTk5Wfv27dN///tfrVixQm+//bZ++OEHXXDBBRVeW3kQAMtpx44dcjqdkqQ6dep4HVO3bl1JZy8+U1aluXhMSkpKmdcLwN5+/fVX9erVS/v27TO7FAD/X6NGjbRgwQI1atTI7FIQZj799FNNmDBBVapU0Q8//KB27dpJkg4ePKgePXpoyZIleuaZZ/T3v/89aDW4AqC3cJeXl6fJkycrJiZG+fn5PgPg+PHjJUl33nmnqlWrFrRafRk3bpyaNWum5ORkj+cPHTqkvn37asmSJRoxYoQ++OCDMq331KlTysrK8rk8WBeTZHdUOZ27F8PXXpPKlStL8r03oySdOnVSampqiV8AUNQrr7xC+AMsZseOHXrttdfMLgNhyHW4ZEZGhjv8SVKNGjU0efJkSdLEiRODeuuykgLgu+++q4MHD+quu+5SVFSUcnJyio3Zs2ePZs+erYiICA0fPjxodZbk0ksvLRb+JKl69eru3/G3335b5vUuXLiwxM/6nTp18rt2bwiAAGAju3fvNrsEAF4wN+1n165dcjgcql+/vs8x1atXl8Ph0OHDhyuwsrP27NnjPg3ptttuK7a8W7duSk1NVX5+vr766qug1eHrEFDDMDR+/HhFRERoxIgRSkhI8BoSX3/9dRUUFKhfv37FLrpoBVFRZw+ojImJMbmS0uMQ0HJKTEx0/39eXp7XMSdPnpQkJSUllXn9y5cvdx9CCgClZRiG2SUA8MKsuXngwAGPx9WqVXN/YD3f2KpVqyo6Otrr2IMHD3r8TElJSapUqZLXsYcOHXKfNiOd/Qzl68Py4cOHPc5fLmnskSNHVFhY6H6ckJCg2NhYr2ODwRWuOnTo4HX59u3bdfjwYTVq1Mhr9yjY1qxZI0lKTk72efhxhw4dtHv3bq1Zs0a33nprUOrw1QGcO3eutmzZohtuuEFNmjRRUlJSsQ7giRMn9NZbb0mSHnvssaDU549jx45p9OjRkqTrrruuzN9/5ZVXlrhzKDs7OyhdQDqA5dSoUSM5HA5J0t69e72OcR2326RJkzKvv27dukpJSSnxCwDOZ/LkyTIMgy+++Krgr+eee87s6S9JqlWrlsfX5s2bfY5t2LChx9i1a9f6HHvhhRd6jF26dKnPse3bt/cY+5///Mfn2Msvv9xj7Oeff+5z7FVXXeUx9t///rfPscGwcuVKSb4DoGt5+/btS1zPoEGD5HA4yvz1/fffl7jeHTt2SFKJHUrXKUWuscHgKwCOGzdOkjRy5EhJZ8P+0aNHZRj/27Ewffp0HTlyRJdddpk6d+5c4usE6/d4rm+//VaDBg3SX/7yF1199dWqV6+evv32W11zzTUaM2ZMqdfjEhsbW+Jn/WA1g+gAllN8fLwuuugirVu3Tps2bdLll19ebMymTZskSR07dqzo8gBAktw7qgAAgeXqAPoKeKtWrSpxuUu3bt3K9fq+LkLocuzYMUlnP7P64ro3tq8rdAaCt0NAV65cqUWLFqlr167uYJeYmCin06ljx465/3/ChAmSStf9C9bv8VwbNmzQO++84/HcbbfdpvHjx5friD+zEAD90L9/f61bt04LFizQPffc47HM6XTqu+++c48DgIpw7p5TANbB3LQXwzDcAc/fDuDgwYM1ePDgwBZoId46gK7u37nBzhWgcnNzlZiYqLlz52rbtm1q2rSprr/++vO+TkX8Hh955BE98sgjKigo0K5du/TZZ5/phRde0Ndff61PPvlEV1xxRVBfP1A4BPQ8vv76azVt2lRDhw4ttmzYsGGqXr26Pvvss2Kt83//+9/6/fff1aNHD6/dQQCoCHQAAXMw9+xt27ZtysnJUcOGDVW9evViyw3D0OrVqyWdPwAGS0JCgqSz59H54rqB+bnXtgi0ogHwt99+00cffaTmzZt7nDfnqsE1znXrh+HDh1vuPprR0dFq0qSJHn30Uc2bN09HjhzRgAEDfF4XxGrCrgN44MCBYjdHPnDggPuk4aJt4IkTJyozM1OTJk3Sc8895zHJq1Wrpg8//FDXXnutevfurddee03NmjXT999/r4ceekgNGzbUv/71r+D/UADw/9FlAKzJrLm5f/9+j8cl3UNt586dHo9dH9y92bhxo8fPVNLhb6tWrSp2ERhfFi9eXOwiML58++23xS4CU1HO193LzMx0B8TzXQBm2rRpWrJkSZlryMjIUMuWLX0ub9iwoaSSr0DrWuYaGwxFDwGdMGGCCgsL9eijj3oEO9e/oZycHPeN36tXr65BgwaV6nWC9Xs8n0svvVRpaWlav369Vq5cGRKNn7ALgB07dtRvv/3m8dy5V9cp+gY9YMAALV68WFdffbXXPTw9evTQmjVr9Ne//lV/+ctfdOTIEdWrV0/33nuvnnnmmRLfPAEg2OhCAOawytyrWbNmUMbWqFGj1GO9fX7ypSxXyzTjhuAurvP/2rRp43X5l19+Kal03b8lS5YUO6+sNAYNGlRicGnbtq2ks1dh3bFjh9crgbqC7Ln3CAy0czuAubm5mjZtmmrWrKmBAwd6jDu3AzhlyhRJ0pAhQ9z31T6fYP0eS8N1nmXRHS5WZa1+agXYuXNniVftKuqWW25Rbm6uPvzwQ5/rbNmypd5//33t3btX+fn52r59u8aNG0f4A1Dh6AAC1sTctBdXcIqLiyu2LD8/X9OmTZNUugA4Y8aMcl1ptnv37iWuNyUlxX0hwvfff7/Y8iVLlmj37t2KiYlRnz59zltneZ0bAKdOnapjx45p6NChxW7Z4QqAGzZs0IcffqiYmBivp2D5Eqzf4/kcPHhQP//8sySpefPmfq2rooRdAASAcGKVLgQQbph79uV0Ot3n982aNct932fp7H0MBw4cqHXr1kmS350lfz311FOSpFdeecVds3S2K/jAAw9IkoYOHVrsEN4ZM2bI4XAE5NBQVwAsLCzUuHHjFBcX537tc7lqGD9+vAoKCjRgwADVrl3b79f314YNGzRz5kydOnWq2LItW7aof//+ys/PV+fOndW6dWsTKiy7sDsEFADsjC4DYE3MTfvYuHGjTpw4obp162rjxo1q0KCBOnTooGPHjumXX35RnTp1VKNGDR08eFCjRo3S5s2blZGRYUqtffv21UMPPaTXXntNnTt3Vs+ePRUfH68FCxYoJydHXbt21fPPP1/s+1znbEZHR/tdQ1RUlOLj43XixAnt3btXQ4YM8XoIsasDuHfvXjkcDj366KN+v3Yg7N+/XwMGDNB9992ntm3bKiUlRadPn9auXbu0evVqOZ1OXXjhhZo1a5bZpZYaHUAAsDG6EIA5mHv25Tr/Lz09XZ999pkaNGighQsXauvWrerfv78WL16sESNGqHLlynI6nbr00ktNrXfChAmaNWuWunTpoqVLl+qrr75SSkqKXnnlFX333XdeD2N13eLi7rvvDkgNri5gRESEz2B3bheyd+/eSktLC8hr++uiiy7Siy++qMsvv1xZWVmaO3euvvjiC2VlZalnz5564403tGbNGtWvX9/sUkuNDiAA2AhdBsCamJv24Tr/r2PHjurRo4f78bkyMjJM6/p5c/PNN+vmm28u9fhvv/1W9erV08MPPxyQ18/KyjrvmOuvv96S86RmzZruQ2ntggAIADZGFwIwB3PPvlwdQNcFVuxm586d2rZtm6ZOneq1O4jQRwAEABux4t5TAMxNuygoKNDPP/+syMhI027wHmwNGzbk36vNcQ4gANgYXQjAHMw9e/r111+Vn5+vtLS0Ut+fDrAaOoAAYCPstQWsiblpD+3atWNbIuTRAQQAG6MLAZiDuQfAqgiAAGAj7JkGrIm5CcAqCIAAYGN0IQBzMPcAWBUBEABshC4DYE3MTQBWQQAEABujCwGYg7kHwKoIgABgI3QZAGtibgKwCgIgANgYXQjAHMGeexERZz/CnTlzhnAJhBDDMHTmzBlJ/5vHFY0ACAA2wgdBwJoCPTdjY2Pd683LywvougEET15envv9wDWPKxoBEABsjA4gYI5gz734+Hj3/+/fv1+nT58O6usB8N/p06e1f/9+9+Nz53FFijLlVQEAQUEHELCmQM/NhIQEHTx4UAUFBcrLy1NmZqYiIyPZ6QNY1LmHfkpSdHS0EhISTKmFAAgANsaHQcAcwZ57kZGRSklJ0c6dO93h8twPlwCsy+FwKCUlRZGRkaa8PgEQAGyEDiBgTcGYm7GxsWratKlycnJ07NgxFRYWBvw1AAROVFSUEhISVLVqVUVFmRfDCIAAYGN0AAFzVNTci4qKUo0aNVSjRo0KeT0AoY+LwACAjdABBKyJuQnAKgiAAGBjdAABczD3AFgVARAAbIQuA2BNzE0AVkEABAAbowsBmIO5B8CqCIAAAAAAECYIgABgI0UPM6MLAVgDh4ACsAoCIAAAQICx8wWAVREAAcBG6AAC1kQHEIBVEAABAAACjJ0vAKyKAAgANkKXAbAm5iYAqyAAAoCN0YUAzMHcA2BVBEAAsBG6DIA1MTcBWAUBEABsjC4EYA7mHgCrIgACgI3QZQCsibkJwCoIgABgY3QhAHMw9wBYFQEQAGyELgNgTcxNAFZBAAQAG6MLAZiDuQfAqgiAAGAjdBkAa2JuArAKAiAA2BhdCMAczD0AVkUABAAbocsAWBNzE4BVEAABwMboQgDmYO4BsCoCIADYCF0GwJqYmwCsggAIADZGFwIwB3MPgFURAAHARugyANbE3ARgFQRAALAxuhCAOZh7AKyKAAgANkKXAbAm5iYAqyAAAoCN0YUAzMHcA2BVBEAAsBG6DIA1MTcBWAUBEABsjC4EYA7mHgCrIgACgI3QZQCsibkJwCoIgABgY3QhAHMw9wBYFQEQAGyELgNgTcxNAFZBAAQAG6MLAZiDuQfAqgiAAGAjdBkAa2JuArAKAiAA2BhdCMAczD0AVkUABAAAAIAwQQAEABspepgZXQjAGjgEFIBVEAABAAACjJ0vAKyKAAgANkIHELAmOoAArIIACAAAEGDsfAFgVQRAALAROoCANdEBBGAVBEAAAIAAY+cLAKsiAAKAjdABBKyJDiAAqyAAAgAABBg7XwBYFQEQAGyEDiBgTXQAAVgFARAAACDA2PkCwKoIgABgI3QZAGtibgKwCgIgANgYXQjAHMw9AFZFAAQAG6HLAFgTcxOAVRAAAcDG6EIA5mDuAbAqAiAA2AhdBsCamJsArIIACAA2RhcCMAdzD4BVEQABwEboMgDWxNwEYBUEQACwMboQgDmYewCsigAIADZClwGwJuYmAKsgAAKAjdGFAMzB3ANgVQRAALARugyANTE3AVgFARAAbIwuBGAO5h4AqyIAAoCN0GUArIm5CcAqwjIAfv311+rVq5eSk5MVHx+vdu3aaeLEiXI6nWVe16lTpzRt2jRdfvnlSklJUaVKlVSrVi317t1bn3/+eRCqB4DSowsBmIO5B8Cqwi4AjhkzRr1791ZsbKzmz5+vNWvWqFevXho2bJj69u2rwsLCUq/r5MmT6tq1q+655x4lJCTogw8+0ObNm/Xee+/pyJEjuv766zVy5Mgg/jQA4IkuA2BNzE0AVhFldgEVafHixcrIyFBaWpo+/vhjRUdHSzobCo8cOaKpU6dqzJgxGjVqVKnW9+abb2r16tVq1KiRPv30U1WqVEmS1KhRI3Xo0EENGzbU3//+d916661q165d0H4uAPCFLgRgDuYeAKsKqw7g6NGjJUnDhg1zhz+XESNGSJLGjh2rvLy8Uq1v69atkqT27du7w59LcnKymjdvLklauHChP2UDQKnRZQCsibkJwCrCJgAeOHBA33//vSSpZ8+exZa3aNFCKSkpOnr0qObNm1eqdV5yySWSpI0bNxZ7Yz916pR27NghSYqLi/OjcgAoP7oQgDmYewCsKmwOAV25cqWcTqeio6PVpEkTr2NatmyprKwsrVixQjfccMN513n33Xdr3rx5+uyzz/TAAw/o//7v/1SnTh3t3LlTI0aM0OHDh1WpUiX16tWrzPVmZ2efd0xKSkqZ1wvA3ugyANbE3ARQVFZWVonLS5MHyiNsAmBmZqYkqWbNmoqI8N74rFu3rsfY84mKitKnn36qr7/+Wo8//rguuOACRUVFuS8kEx8frzfeeEPNmjUrc72dOnU67xj+mAA4H7oQgDmYewDOJzU11ZTXDZsAePToUUklH45ZuXJlSVJubm6p1ztp0iQ99thjat++vb766is1aNBA27dv1zfffKM77rijVEEOAAKFHUOANTE3AVhF2ATAYJg0aZKGDh2qRo0aacGCBYqJiZEkpaWl6corr1SLFi1000036W9/+1uxi86cz/Lly90dSQAoL7oQgDmYewDOZ/fu3SUuz87ODkozKWwCYGJioiSVeIXPkydPSpKSkpJKtc5XXnlF0tmrirrCn0t8fLyGDBmip59+WlFRUfr73/9epnrr1q3LOX4AAACATZn1WT9srgLquvDLgQMH5HQ6vY5xnWjp6yIx59q/f7/7xM0LL7zQ6xjX82+99ZbOnDlT5poBoKyKHmZGFwKwBg4BBWAVYRMA27dvr4iICBUUFPi8yMumTZskSR07dizTun19wHJdbObYsWPucxABAID9sfMFgFWFTQCsVauW0tPTJUkLFiwotnzz5s3KyspSQkKCrrnmmvOur0aNGqpWrZr7e71xPR8XF1fqw0oBwB90AAFrogMIwCrCJgBK0rPPPitJmjhxovtWDS7jxo2TJI0cOdJ9NVBJWr16tdLS0tS/f3+P74mIiNCAAQMknb0YTNH15efn64033pAk9e3b1+etJwAAgP2w8wWAVYVVKklPT9eLL76o9evXq1+/flq9erW2bNmijIwMTZ06VX369NGTTz7p8T1vv/22Nm7cqI8++kg///yzx7IXXnhBHTt21JYtW/THP/5RS5Ys0e7du/XDDz/o6quv1m+//abmzZtr/PjxFfljAghjdAABa6IDCMAqwioAStJTTz2lr776Snl5eerRo4fatm2rb775RhMmTNDnn3+uqCjPC6P2799fNWrU0BVXXKGLLrrIY1liYqKWLFmiCRMm6Pjx4/rTn/6kRo0a6frrr1d+fr5eeeUVrV69WnXq1KnIHxEAAJiMnS8ArCpsbgNxrt69e6t3796lGpuenq4DBw74XF6pUiU99NBDeuihhwJVHgCUGx1AwJroAAKwirDrAAIAAAQbO18AWBUBEABshA4gYE10AAFYBQEQAAAgwNj5AsCqCIAAYCN0AAFrogMIwCoIgAAAAAHGzhcAVkUABAAboQMIWBMdQABWQQAEAAAIMHa+ALAqAiAA2AhdBsCamJsArIIACAA2RhcCMAdzD4BVEQABwEboMgDWxNwEYBUEQACwMboQgDmYewCsigAIADZClwGwJuYmAKsgAAKAjdGFAMzB3ANgVQRAALARugyANTE3AVgFARAAbIwuBGAO5h4AqyIAAoCN0GUArIm5CcAqCIAAYGN0IQBzMPcAWBUBEABshC4DYE3MTQBWQQAEABujCwGYg7kHwKoIgABgI3QZAGtibgKwCgIgANgYXQjAHMw9AFZFAAQAAAgyOoAArIIACAA2RhcCMAdzD4BVEQABAAAAIEwQAAHAJrwdYkYXArAGDgEFYBUEQAAAgABj5wsAqyIAAoBN0AEErIsOIACrIAACAAAEGDtfAFgVARAAbIIOIGBddAABWAUBEAAAIMDY+QLAqgiAAGATdAAB66IDCMAqCIAAAAABxs4XAFZFAAQAm6ADCFgXHUAAVkEABAAACDB2vgCwKgIgANgEHUDAuugAArAKAiAAAECAsfMFgFURAAHAJugAAtZFBxCAVRAAAQAAAoydLwCsKqqiXmjfvn1asGCBVq9erX379unIkSOqVq2aateurfbt26tHjx6qXbt2RZUDALZDBxCwLjqAAKwiqAGwoKBAs2bN0qRJk7R8+XJJJX9AufTSS/Xggw/q5ptvVnR0dDBLAwAACBp2vgCwqqAFwH/961968sknlZ2dLcMwVLNmTXXp0kUXXXSRqlevrsTEROXm5urQoUNat26d/vvf/+qnn37SsmXLlJGRoZdfflkDBgwIVnkAYDt0AAHrogMIwCqCEgC7dOmi5cuXq0aNGnrooYc0aNAgXXLJJef9vrVr12r69On64IMPNHDgQE2ePFlLly4NRokAAABBw84XAFYVlIvAbN26VWPHjtWuXbv06quvlir8SVKbNm00YcIE7d69W6+88oq2bNkSjPIAwJboAALWRQcQgFUEpQO4fft2JSYmlvv7Y2JiNHLkSN13330BrAoAAKBisPMFgFUFpQM4efJk5ebm+r0ef0IkAIQbOgyAdTE/AVhFUALgU089pdTUVD3yyCPauXNnMF4CAFAKdCEAczD3AFhVUALg5ZdfruPHj+v1119Xs2bN9Oc//9l9GwgAQHDQYQCsi/kJwCqCEgB/+OEHrVy5UrfccosiIiI0e/ZsdenSRVdccYU+//zzYLwkAMALuhCAOZh7AKwqKAFQktq1a6eZM2dqx44devzxx1W1alUtWbJE/fr1U8uWLfXmm2/q1KlTwXp5AAg7dBgA62J+ArCKoAVAlwsuuECvvPKKdu/erddee02NGzfWli1b9MADD6h+/foaPXq0Dhw4EOwyACAs0YUAzMHcA2BVQQ+ALpUrV9bQoUO1ZcsWffLJJ+rWrZsOHjyo559/Xg0aNNB9992nTZs2VVQ5AGA7dBgA62J+ArCKCguALg6HQ9dff737PMFbb71VZ86c0bRp09SqVStdd911FV0SANgWXQjAHMw9AFYVlBvBl+T48ePKzc1Vbm6uTp06pdtvv11t2rTR+PHjtXfvXn355ZcVXRIA2AIdBsC6mJ8ArCIoAXDgwIHukJeTk+P+/6NHj8rpdHr9Ht4YASDw6EIA5mDuAbCqoATAf/3rX+43vsjISCUkJCgpKUmpqalKTExUUlKSx3+LPgcAAAAACLygBMCYmBjl5+crNjZWd911l0aMGKFGjRoF46UAAP+ftyMp6EIA5ig69zjSCYBVBOUiMLt27dIzzzyjKlWqaPLkyWrevLluvvlmrVixIhgvBwAAAAAohaAEwJo1a+q5557Trl279MYbb6hx48b66KOP1LlzZ6Wnp2vu3LnBeFkACGt0AAHrogMIwCqCehuI2NhY3Xfffdq8ebM+/fRTdevWTYsXL1bfvn2VlpamadOm6fTp08EsAQAAoMKx8wWAVVXYfQCvu+46/fDDD1q+fLluvvlmbdu2Tffdd5/q16+vF198UYcPH66oUgDAlugAAtZFBxCAVVT4jeA7dOigDz74QFu3btXDDz+svLw8PfPMM6pfv76GDRum7du3V3RJAAAAAcXOFwBWVeEB0KVBgwYaP368du/erb/97W9KTEzU5MmT1bJlS7NKAoCQRgcQsC46gACsIii3gZg3b56OHj3qcQP48/1/YWGhDMPQmTNnglESAABAhWHnCwCrCkoAvPbaa8t1/5v4+HhVq1YtGCUBgO3RAQSsiw4gAKsISgBMS0tTtWrVVLVq1TJ9RUZGBqMcAACACsXOFwBWFZQAuG7dumCsFgBQAjqAgHXRAQRgFaZdBAYAAMCu2PkCwKoIgABgE3QAAQDA+RAAAQAAAoydLwCsigAIADZBBxCwNs4DBGAFBEAAAIAAY+cLAKsiAAKATdABBKyNDiAAKyAAAgAABBg7XwBYFQEQAGyCDiBgbXQAAViBZQJg48aN1bJlS/3tb39Tfn6+2eUAAACUGztfAFiVZQLgzp07tWXLFmVkZKhZs2aaPn262SUBQEihAwhYGx1AAFZgmQA4ffp0/fOf/9SQIUMUExOjwYMHm10SAABAubDzBYBVRZldgMvAgQMlSXfeeack6bfffjOzHAAIOXQAAWujAwjACizTASyqQYMGZpcAAABQLux8AWBVFRIAc3JyKuJlSu3rr79Wr169lJycrPj4eLVr104TJ06U0+ks9zq/+OIL9e3bV3Xr1lVMTIzq1Kmjyy67TE888YSys7MDWD0AeEcHELA2OoAArKBCAmD16tXVrFkz/fnPf9bYsWO1YMECr6Fw7Nix+uGHH4Jay5gxY9S7d2/FxsZq/vz5WrNmjXr16qVhw4apb9++KiwsLNP6zpw5o4EDB2rAgAG67LLLNH/+fG3evFlvv/22jh8/rrFjx2r9+vVB+mkAAIAVsfMFgFVVyDmAVatWVWZmpjIzM/XRRx+5n2/YsKHat2+v9u3bq2XLlnrnnXf0zjvvBC0wLV68WBkZGUpLS9PHH3+s6OhoSWdD4ZEjRzR16lSNGTNGo0aNKvU6MzIyNHPmTC1dulSdOnVyP9+wYUO1aNFC3bp1U0xMTMB/FgAoiu4CYG3MUQBW4DAq6N1ox44dWrlypVatWqWVK1dq9erV7i6gay+ZYRiKj4/XsWPHglJDz5499d133+mNN97Q/fff77Fs8+bNatmypRITE7V3717FxcWdd32bN2/WRRddpJtvvlnvv/++3/VlZWUpNTVVkrR7926lpKT4vU4A4WP37t2qX7++x3O5ublKTEw0qSIgfG3btk3NmjXzeO7UqVPsFAZQasHKBhV2FdBGjRqpUaNG6t+/v/u5zMxMffHFF/rnP/+pdevWqWbNmnr00UeD8voHDhzQ999/L+lsECyqRYsWSklJUVZWlubNm6cbbrjhvOv85z//qTNnzqh3796BLhcAyozuAmBtzFEAVmDqbSCaNGmihx9+WEOGDNFf/vIXrV27VnfddVdQXmvlypVyOp2Kjo5WkyZNvI5p2bKlsrKytGLFilIFwPnz50uSmjZtqpkzZ2rq1KnauHGjIiIidNFFF2nQoEG6/fbby3UeQGkuHEOHEMD5cB4SYA7mHoDzycrKKnF5sC4kaYn7AFaqVEmTJ09Wo0aNNGrUKL311lsBf43MzExJUs2aNRUR4f3aN3Xr1vUYW5IzZ864z1XMyMjQ9u3b9fLLL6tdu3bKysrSc889pzvuuEPz5s3Te++9V+Y/BOeeT+gLexIBnIv3BMDamKMAzuU6vLOiWSIASlJycrIuvvhizZ07NyjrP3r0qCSVeG5f5cqVJZ09Z+Z8cnNzVVBQIElatGiRVq9erbZt20qS0tLS1K1bN7Vq1Urvv/++0tPTde+99/r7IwBAmdGFAMzB3ANgVZYJgJKUkJBQqvBlBSdPnnT//+WXX+4Ofy6VK1fWPffco6eeekqvv/56mQPg8uXL3R1JAAAAAPaye/fuEpdnZ2eX6qjAsqqQANiwYUN17NjRfcuHDh06qFq1ah5j8vLytHLlSrVq1SooNbiugpeXl+dzjCvUJSUlnXd953YS09LSvI5p3769JGn9+vU6efKku8NYGnXr1uUcPwBlwo3gAWvjEFAA5zLrs36FBMBdu3Zp165d+vjjj93Pue4B2KZNG8XFxWnWrFnKz8/X2LFjg1KD68IvBw4ckNPp9HoeoOtES18XiTlX1apVFRsbq1OnTik5OdnrmISEBEln3/BzcnLKFAABAEDoYucLAKuqkAD422+/acWKFVq5cqVWrFihVatWaceOHdqxY4fmzJnjHle3bl1NnTpVa9eudXcMY2NjA1JD+/btFRERoYKCAmVmZha7N48kbdq0SZLUsWPH864vMjJSF198sZYvX659+/Z5HeN63uFwqGrVquUvHgBKgQ4gYG10AAFYQYUEwNTUVKWmpnrcWmHbtm3uQOi6Mfzvv/+uDz74QP/+978lnQ1ZF110kdasWeN3DbVq1VJ6eroWLlyoBQsWFAuAmzdvVlZWlhISEnTNNdeUap19+/bV8uXL9fPPP3td7rpK6CWXXEL3DwCAMMLOFwBW5f1+CBWgadOmuuWWWzRu3Dj98MMPOnr0qNatW6fp06frgQceUMeOHRUREaFffvklYK/57LPPSpImTpyowsJCj2Xjxo2TJI0cOdIjrK1evVppaWnq379/se+57777VL16da1atUr//e9/PZbl5+drypQpkqQRI0YE7GcAAF/oAALWRgcQgBWYFgCLcjgcSktL08CBA/X666/rp59+0vHjx7Vy5cqAvUZ6erpefPFFrV+/Xv369dPq1au1ZcsWZWRkaOrUqerTp4+efPJJj+95++23tXHjRn300UfFOn3JycmaNWuWYmNjddNNN2n27NnatWuXli5dqmuvvVZZWVl66KGHNGDAgID9DAAAwPrY+QLAqiwTAL2JiooqdnsFfz311FP66quvlJeXpx49eqht27b65ptvNGHCBH3++eeKivI8KrZ///6qUaOGrrjiCl100UXF1tezZ0/9/PPP6tWrl4YPH66mTZvqT3/6k6Kjo/X5559rwoQJAa0fAHyhAwhYGx1AAFYQlHMAe/ToEfB1Dho0SH/5y18Csq7evXurd+/epRqbnp6uAwcOlDimefPmmjFjRgAqAwAAdsDOFwBWFZQA+P333wd8nd27dw/4OgHATugAAtZGBxCAFQQlAO7YsSPg6+Q2CgAAIFSw8wWAVQUlADZo0CAYqwUAlIAOIGBtdAABWIGlLwIDAAAQitj5AsCqKiQA5ubmVsTLAEBYowMIWBsdQABWUCEB8Morr9ShQ4cq4qUAAABMx84XAFZVIQFw7dq1uuKKK7R3797zji0oKKiAigDAfugAAtZGBxCAFVRIAHz88ce1ceNGXX755dq1a5fPcbNmzVLLli0roiQAAICgYecLAKuqkAD4yiuv6MUXX1RmZqYuv/xybd261WP5Tz/9pMsuu0y33Xabdu7cWRElAYDt0AEErI0OIAArqLCrgD755JOaNGmSsrKydMUVV+jXX3/Vzp079ec//1ldu3bVTz/9pPr16+udd96pqJIAAACCgp0vAKwqKPcB9GXIkCFKTEzUnXfeqcsvv1z5+fnKz89XcnKynnrqKQ0dOlSVKlWqyJIAwDboAALWRgcQgBVUaAB0Op06ceKEEhISdOTIETkcDt1yyy164403lJSUVJGlAAAABA07XwBYVYUdAvrJJ5+oVatWGjJkiI4cOaLLLrtMkvSf//xHO3bsqKgyAMC26AAC1kYHEIAVVEgAvOyyy3TTTTdp06ZNatu2rRYuXKglS5Zo2rRpOnLkiHr06KEff/yxIkoBAAAIOna+ALCqCgmAP/30k+rVq6d33nlHK1euVHp6uiTpzjvv1Pvvv68TJ07o6quv1vz58yuiHACwJTqAgLXRAQRgBRUSAJ9//nlt2bJFd9xxR7Fl/fv31yeffCKn06nrrrtOn3zySUWUBAAAEDTsfAFgVRUSAEeNGqXY2Fify/v06aN58+YpOjpat9xyS0WUBAC2Q3cBsDbmKAArqLCLwJxPenq6/vOf/yghIcHsUgAAAPxCBxCAVVkmAEpSp06d9P3335tdBgCEpKLdBT6AAtZCBxCAFQQlAD788MM6dOhQub63VatWkqQDBw7ooYceCmRZAAAAFYIdMACsKigBcNKkSWrUqJGefPJJbd26tUzfu3nzZo0cOVJNmjTRG2+8EYzyAMCW6AAC1kYHEIAVRAVjpStWrNCwYcM0ZswYjR07Vl26dFHPnj3VpUsXXXjhhapevbqqVKmi48eP69ChQ9qwYYP++9//av78+Vq+fLkMw1DXrl31+uuvB6M8AACAoGIHDACrCkoAbNu2rZYsWaKPPvpIr776qpYuXar//ve/JX6Pa6/YZZddpuHDh+vGG28MRmkAAAAAELaCEgBdbrrpJt10001au3atPv30U3333Xdas2aNTpw44R4THx+vdu3a6corr1Tfvn3Vpk2bYJYEALbFIaCAdXibfxwCCsAKghoAXdq0aaM2bdpo9OjRkqSTJ08qNzdXVatWVVxcXEWUAAAAAABhr0ICYFGVK1dW5cqVzXhpALAtOoCAtdEBBGAFFX4fwFOnTpX7FhEAAAChgB0wAKyqwgLg559/rnbt2qlKlSqqVauWKlWqpA4dOuiVV17RsWPHKqoMALAtOoCAtdEBBGAFFRIAP/vsM91www1au3atnE6nIiMjVVhYqNWrV2vUqFFq2rSpPv3004ooBQAAIOjYAQPAqiokAL7wwgtyOp266aabtHXrVuXn5+vkyZNasGCBBgwYoMOHD+umm27S5MmTK6IcALAlOoCAtdEBBGAFFRIA161bp8aNG+uDDz5QkyZN5HA4FBsbqyuvvFLvvPOOfvrpJ9WuXVsPP/yw1qxZUxElAQAABA07YABYVYUEwEqVKql9+/aKjIz0urx9+/b67LPP5HQ6NXbs2IooCQBshw4gYG10AAFYQYUEwGbNmmnv3r0ljunQoYM6d+6shQsXVkRJAAAAQcMOGABWVSEB8MYbb9TSpUu1bt26EsfVr19fR48erYiSAMB26AAC1kYHEIAVVEgA7N+/v9q0aaMbb7xRu3fv9jluw4YNatSoUUWUBAAAEDTsgAFgVRUSAFu0aKEtW7Zo69atat26tZ5//nnt2LHDvbygoECjRo3Sr7/+qgceeKAiSgIA26EDCFgbHUAAVhBVES9Su3Zt9zmAR48e1ejRozV69GglJycrMTFRv//+u06fPq2MjAw9+OCDFVESgCIMw9BHH32kFStWlPpDypVXXqk+ffp4XfaPf/xDe/bsKVctHTt21M033+x12dSpU7Vly5ZyrTctLU133nmn12UffPCBVq9eXa711q9fX8OGDfO67PPPP9fixYvLtd7q1asrIyPD67LvvvtO8+bN83iu6LnWBQUFGjlyZLHvjYmJ0QsvvOB1vcuXL9fs2bPLVa8kvfzyy4qKKv6nZf369ZoxY0a51ztq1ChVrVq12PO//fabJk6cWO71Pvzww0pJSSn2/MGDBzVmzJhyr3fw4MFq0aJFsedPnTqlZ555ptzrveWWW9S+fXuvyzIyMnTmzJlyrfdPf/qTrrjiCq/LXnjhBeXm5pZrveH8HnH69OlSjavI94jS4j3if3y9RwAhzagge/bsMT777DPjmWeeMXr37m3UrFnTcDgcHl8RERFG06ZNjVtvvdUYP368sXjxYuPEiRMVVaLpdu/ebUgyJBm7d+82uxyEmZdfftn976+0XxkZGT7X17Zt2zKvz/V11113+VzvVVddVe71/ulPf/K53gEDBpR7vZ07d/a53uHDh5d7vY0bN/a53pdeeqnc601ISPC53mnTppV7vZKMU6dOeV3vZ5995td6d+3a5XW9S5cu9Wu9q1at8rrebdu2+bXer7/+2ut6c3Nz/Vrvu+++63PbVapUqdzrHTdunM/1pqSklHu9vEd4fu3YsaPYenmPOCvU3iOAihCsbFAhHUBJuuCCC3Tdddfpuuuucz/322+/aeXKlVq5cqVWrFih1atXKzMzU5mZmZo1a5YkKTIystR70QCU34cffmh2CQAAAAiyCguA3jRo0EANGjTQjTfe6H5u27Zt7kC4YsUKrV271rwCgTBy4sQJs0sAAFszOAcQgAWYGgC9adq0qZo2bapbbrlFEm+WQEUpOte6d++uBg0alPg97dq187nsT3/6ky6++OJy1XLZZZf5XNarVy/VrVu3XOtt06aNz2WXX365IiMjy7XeJk2a+FzWsWNHDRw4sFzrrVmzps9lrVu3LrbeAwcO6KuvvnI/joqK0u23317se2NjY32ut1mzZuWuV5IiIrxfWyw1NdWv9VauXNnr8zVr1vRrvcnJyV6fT0hI8Gu9F1xwgdfno6Oj/Vpv48aNfS674447VFhYWK71pqWl+VzWv39/HT58uFzrDef3iMLCQs2cOfO84yryPaK0eI/4H1/vEUAocxgkLMvIyspSamqqJGn37t2cdIwK1axZM23bts39+OOPP1a/fv1MrAhl9dNPP6lLly7ux1WqVNGxY8dMrAgIXydPnlR8fLzHc5mZmSWGeAA4V7CygeU6gADMcc011yg7O1uGYcgwDJ8dDADA+XEbFnvYs2ePNmzY4P7bWLVqVV166aVmlwX4hQAIQJL0+uuvm10C/FT0gA4+gALWwkFXoeerr77Svffe637cpUsXLV261MSKAP9VyI3gAQAAwgk7YOyh6HYkxMMOCIAAYBN0AAFrIzyEHgIg7IgACAAAEGDsgLEHAiDsiAAIADZBBxCwNsJD6CEAwo4IgAAAAAHGDhh7IADCjrgKKABJ0pAhQ3Tw4EE5HA45HA5lZGSobdu2ZpeFMqADCFgb4SH0EABhRwRAAJKkL774QllZWe7HgwYNMq8YAAhx7ICxBwIg7IhDQAFIontkB2xDwNoID6GHAAg7IgACkER4AIBA4j3UHtiOsCMCIABJBEA7YhsCQGDRAYQdEAABSCIA2gEfTADr8PYeyhwNPRwCCjsiAAKQRAC0I7YhAPiHAAg7IgACkEQAtAM+mADWxhwNPQRA2BEBEIAkAqAdsQ0B8zD/7IEACDviPoAAJBEA7YAPJoC1MUdDT58+fbRp0yY5HA45HA7FxMSYXRLgNwIgAEkEQAAIJN5D7SExMVGJiYlmlwEEFIeAApBEALQDtiFgbXQAAVgBARCAJMIDAAQS76EArIoACEASAdAO2IaAtdEBBGAFBEAAkggPAAAA4YCLwACQJHXr1k3Hjh2TYRgyDIOT3kMQIR6wNjqAoSc3N1c7d+50/22MiYlRWlqa2WUBfiEAApAkffHFF2aXAAC24nA4CH0hbuHCherXr5/7cZMmTbRt2zYTKwL8xyGgAGATdAABayMMhh5uBA87IgACAAAEATthQh8BEHZEAAQAm6ADCFgb4SH0EABhRwRAAACAIGAnTOgjAMKOCIAAYBN0AAFrIzyEHgIg7IirgAKQJD3yyCMqKCiQw+GQw+HQyJEjVb9+fbPLAoCQxU6Y0Mc2hB0RAAFIkqZMmaL8/Hz340GDBhEAQwwdQMDa6B6FPrYh7IBDQAFIIjwAQKDxPhr6OAQUdkQABCCJAGgHbEPA2ggPoYcACDsiAAKQRHgAgEDjfTT0EQBhRwRAAJIIgHbANgSsjfAQegiAsCMCIABJhAcACDTeR0MfARB2RAAMsNWrVysqKkoOh0Pff/+92eUApUYADH1sQ8DaCA+hhwAIOwrLAPj111+rV69eSk5OVnx8vNq1a6eJEyfK6XT6td7CwkLdddddOnPmTIAqBSoO4QEAAov30dBHAIQdhV0AHDNmjHr37q3Y2FjNnz9fa9asUa9evTRs2DD17dtXhYWFfq17x44dqlq1auAKBkzCB5fQQ4gHrI3wEHrat2+vxYsXa8mSJfrxxx/15Zdfml0S4LewuhH84sWLlZGRobS0NH388ceKjo6WdDa4HTlyRFOnTtWYMWM0atSoMq978+bNev755/WPf/xDr7zyinJycgJcPRA83j6UEB4AwD+8j4a+qlWrqlu3bmaXAQRUWHUAR48eLUkaNmyYO/y5jBgxQpI0duxY5eXllWm9hmFo8ODB6tSpk+67776A1ApUJAKgPdABBKyNDiAAKwibAHjgwAH3RVl69uxZbHmLFi2UkpKio0ePat68eWVa96RJk7Ry5UpNmzaND1wISQRAAAg83kcBWFHYBMCVK1fK6XQqOjpaTZo08TqmZcuWkqQVK1aUer27du3Sk08+qf/7v/9T8+bNA1IrUNEIgPbENgQAAEWFzTmAmZmZkqSaNWsqIsJ77q1bt67H2NK4//771aRJE40cOdL/Is+RnZ193jEpKSkBfU2ELwKgPXB4GWAtXEESQEmysrJKXF6aPFAeYRMAjx49KkmKi4vzOaZy5cqSpNzc3FKt81//+pe+/fZbLVu2TFFRgf1VdurU6bxj+EOCQEpLS5NhGO6vSpUqmV0S/ESIBwD/nD59WgcPHnT/bXQ4HKpXr57ZZcEmUlNTTXndsAmAgbZ//34NHz5cjz76qNq3b292OYBfoqOjtX79erPLgJ/YKQRYCx3A0LdixQqPq4AmJiaWulEAWFXYBMDExERJKvEKnydPnpQkJSUlnXd9Dz30kKpWrarnnnsuMAUWsXz5cvchqQBQHnQAAcA/hHgE0+7du0tcnp2dXaqjAssqbAKg68IvBw4ckNPp9HoeoOs4W18XiTnXrFmzFB0drZo1axZb5gqSvXv3VmRkpCTpzTff1O23317qeuvWrcs5fgDKhA8mgLUxR0MPARDBZNZn/bAJgO3bt1dERIQKCgqUmZmpZs2aFRuzadMmSVLHjh3Pu76tW7f6XNa9e3ft2bNH06ZN06WXXipJql27djkrB4DyoQMImIs5GPrYhrCjsAmAtWrVUnp6uhYuXKgFCxYUC4CbN29WVlaWEhISdM0115x3fU2bNvW5zHVBmHr16pU4DgACiT3TgLUxR0Mf2xB2EDb3AZSkZ599VpI0ceJEFRYWeiwbN26cJGnkyJHuq4FK0urVq5WWlqb+/fsX+x4AsDL2XAPmYg6GPg4BhR2FTQdQktLT0/Xiiy9q1KhR6tevn5577jlVqVJFb7/9tqZOnao+ffroySef9Piet99+Wxs3btTGjRuVkZHh84qfx48f1/HjxyVJZ86ckSQdPnxYe/fuVWRkpNdzBQGryM/P18svv+z+Q+dwOPTwww+X6oJIsA4+mADWxhwNPQRA2FFYBUBJeuqpp9S2bVuNGzdOPXr0UEFBgZo3b64JEybowQcfdF+0xaV///6aNWuW0tLSdNFFF/lc79///vdiVwS98cYbJUkNGjTQzp07A/6zAIGSn59f7N/voEGDCIAhju4DYC7mYOgjAMKOwi4ASmevztm7d+9SjU1PT9eBAwfOO2706NEaPXq0n5UB5vD2B40PLqGHDyaAtTFHQw8BEHYUVucAAvCOAAgAgcf7aOgjAMKOCIAACIA2UXQ7sg0BayE8hB4CIOyIAAiAAAgAQcD7aOgjAMKOCIAACIA2QQcQsDbCQ+ghAMKOCIAACIAAEAS8j4Y+AiDsiAAIgABoE3QAAWsjPIQeAiDsKCxvAwHAEwEQAAKP99HQ16BBA82ZM0cOh8P9BYQ6AiAAAqBN0AEErI3uUehJSkrSDTfcYHYZQEBxCCgAAiAABAHvowCsiAAIgABoE3QAAWujAwjACgiAAAiAABAEvI8CsCICIAACoE3QAQSsjQ4gACvgIjAAFBERoerVq7s/nBiGoYgI9g8BgD/YCRP6DMNQYWGhDMNw/42sVKkS2xYhjQAIQHXr1tXBgwfNLgN+ogMIWBsdwNCzbds2NW/e3OO5/Px8VapUyaSKAP+xix8AACAI2AljTwR5hDoCIADYBB1AwNoIDqHH2/so2xGhjgAIAAAQBOyECX0EQNgRARAAbIIOIGBtBIfQQwCEHREAAQAAgoCdMKGPAAg74iqgAJSbm6v33ntPDofD/TV48GBFRfEWEUroAALWRnAIPQRA2BGf7hBw27dv16pVq9yPk5OT1bNnT69jd+3apWXLlrkfJyUl6aqrrvI69vfff9ePP/7ofhwfH68+ffp4Hbtv3z4tWrTI/Tg2NlZ/+tOfvI49dOiQvvvuO/fjqKgo9evXz+vYnJwczZ8/3/3Y4XDopptu8jr2+PHjmjdvnsdzN9xwgyIjI4uNzcvL0xdffOHx3PXXX+/1MtMFBQX69NNPPZ679tprVbly5WJjnU6n5syZ4/HcNddco4SEBI/nDh48qKFDh3o8l5CQoD59+qhatWrFfzhJn376qQoKCtyPr7zyStWoUcPr2C+++EJ5eXnux5dffrnq1Knjdey8efN0/Phx9+MuXbooJSXF69j58+crJyfH/bhTp05q0KCB17HfffedDh065H7crl07NWnSxOvYRYsWad++fe7Hl1xySbHLgLssXbpUe/bscT++6KKLlJaW5nXssmXLtGvXLvfjli1bqnXr1l7Hrlq1Stu3b3c/btq0qdq2bet17Nq1a7V161aPueTLunXrtHHjRvfj1NRUde7c2evYjRs3at26de7HdevWVbdu3byO3bp1q9auXet+XKtWLaWnp3sdy3vEWaH0HuEyZ84cOZ1O9+M//OEPvEfI93tE0fCwaNEipaSkmPYe4dKoUSN16NDB61jeI85yvUccOHCg2LKiAZD3iP8JtfeIsGXAMnbv3m1IMiQZu3fvNruccps6dar755BkdO7c2efY999/32Ns69atfY797LPPPMY2adLE59j58+d7jK1Tp47PsUuXLvUYm5CQ4HPszz//7DE2MjLS59itW7d6jJVknDp1yuvY33//vdjYQ4cOeR2bm5tbbKyvfy8FBQXFxm7atKlUtUoyVq1a5fPnq1atmsfYxYsX+xxbr149j7Fff/21z7HNmzf3GDtnzhyfY9u2besx9l//+pfPsV27dvUYO2XKFJ9jr7rqKo+x48aN8zm2X79+HmOff/55n2Nvv/12j7EZGRk+x957770eY4cNG+Zz7PDhw71uv7Zt2xYbO2rUKI8xt9xyi8/1vvTSSx5j//SnP/kcO2HCBI+xPXv29DmW94izQuk9wqVSpUq8Rxilf4+44IILiv1+rfAecffdd/scy3vEWUXfI879ys3N9RjLe8T/hNp7hNUFKxtwDiAADmcBgCDgMGx74m8mQh0BEAB/zGyKD58AEHj8zUSo4xxABFzVqlU9zm9o1KiRz7GJiYkeY32dbyGdPSft3LGpqak+x8bHx3uM9XVMuSTFxcV5jI2Pj/c5NiYmxmOst+PwXSpVqlTsPA9fH8ijoqKKjfW17oiIiGJjS7pYS9Gx3s4H8PbHLC0tTbGxsT7X26JFCx09etT92Nu5Ay7NmzdXUlKS+3FJv+OmTZt6/Dy+zjOQpMaNGys/P9/9+NzXKKphw4Y6cuSI+7GvcxIkqUGDBh6/t+rVq/scm5qaWup/a/Xq1fMYW6tWLZ9j69at6zHW17kOklS7dm2lpaUpJydHv//+u89xrtc8d70lnRdRo0aNUs+55ORkj7G+zrOSeI9wCaX3CJcLL7zQ45wd3iPOKuk94lz169c39T3C5YILLvA5lveIs1zvEYWFhdqyZYvHsqJ/M3mP+J9Qe48IVw6D3RiWkZWV5X4z2r17NyesosJs2rRJF154ocdzhYWFJf5hgvW8++67GjhwoPtx+/bttXLlShMrAsJbamqqsrKy3I/nzZuna665xsSKUFa///676tWr5/HcoUOHlJycbFJFCCfBygYcAgrAaweQwwdDD/vzAGtjjoYebgNhD99//73atm2rNm3a6OKLL/Z59ddwwSGg8FuHDh2Ul5eniIgIORwOzZw50+dlq2FNBEB7YhsC5mIOhj4CoD0cPXrU4zYk594mIhwRAOG3DRs2eNyb5dz/R2ggANoDH0oAa2OOhp7ExERNmTJFDofD/XexpPPPYE0REZ4HPYb7XCQAwm9FJ1HRSQbrC/c3QrsixAPmYg6GvsqVK+u+++4zuwz4qehcPPdm9eGIAAi/FZ1E/MELPUUDINswNBHkAWtjjgLmaNOmjd5991336UpVqlQxuyRTEQDht/fff19nzpyRYRhyOp0lXq4Z1kQAtCe2I2Au5iBgDfXq1dMdd9xhdhmWQQCE32688UazS4CfCID2QHcBsDbmKAAr4GQtAARAm2I7AuZiDgKwIgIggGL40BKa6C4A1sYcBWAFHAIKQG3atJHT6ZRhGO4vhD6CPGAu5mDoy83NVcOGDSXJ/fdx48aNuuCCC8wtDPADARCAJHnc4wihieAOWBtzNPQYhqGcnByP58L9FgIIfQRAAACAIGCnWujztg0J8qFn7dq1Gjp0qPuK9YmJifrmm2/MLss0BED4pbCwUJ07d5bD4XDfW2XWrFlq0KCB2aUBYYeL+QDWRnAIPQRAezh69Kh+/PFH9+Nq1aqZWI35CIDwi9Pp1KpVqzyey8/PN6kaAACsg50woY8AaA9Ft2O4b0OuAgq/eJtA/MEDzEEHELC2cP/QGYoIgPYQEeEZecL9PE46gPCLtwlUdJLB+g4dOqTvvvvOfSGY6OhoXXfddWaXBQAhjZ0woY8AaA9NmjTRlClT3KcsxcTEmF2SqQiA8Et0dLSmT5/uvjSy0+lUzZo1zS4LZbRlyxbdfPPN7sdVqlTRsWPHTKwI5UEHELA2gkPoIQDaQ506dXTfffeZXYZlEADhl6ioKA0aNMjsMuAnggMABB7vpaGPAAg74lg9AARAm2A7AtZGcAg9BEDYEQEQAMEBAIKA99LQRwCEHREAARAAbYLtCFgbwSH0EABhRwRAAAQHAAgC3ktDHwEQdsRFYAAQAG2C7QhYG8Eh9BAA7WH79u26//773Vesj4iI0Pz5880uyzQEQPglJydH119/vfu+Kg6HQx999JGqVatmdmkoA4IDAAQe76WhjwBoD8ePH/cIfOE+NwmA8Mvp06e1aNEij+cKCgpMqgblRQC0B7YjYG0Eh9ATERGh559/XtLZ91SHw8H9jkNQ0b+H4T4XCYDwi7cJxIfO0ENwAIDA47009DkcDj399NNmlwE/RUQUv+yJYRhhO0cJgPCLtwDobZLB2giA9sR2BABAqlOnjsaNG+c+XSnc/z4SAOGXhIQETZw40X1SrWEYio+PN7sslBEB0B7C/ZAWwGo47AywhurVq+vRRx81uwzLIADCL/Hx8XrwwQfNLgOAFwR5AABQFMfqAaADaBN0FwBroQMIwIoIgAAIgDbFdgQAAEVxCCgAde/eXfv27ZNhGDIMgwv5hCi6C4C1MUdDU6tWrXT69Gn338jPP/9caWlpZpcFlBsBEIAqVaqkWrVqmV0GAowOIGAu5qA9bNmyxeMex/n5+SZWA/iPAAgANkF3AbA25mho4lzO0HfgwAHde++9Hlet//DDDxUXF2d2aaYgAMIvv//+uwYPHuxxX5WPPvpIlSpVMrs0IOzRfQDMxRy0BwJg6MvLy9Onn37q8VxBQQEBECiP48ePa968eWaXAUB8KAGsjjkamgiAoc/btQ3CeTtypQf4xdvkYY8nYA3MRcBczEF7IACGPgKgJzqA8Iu3ycMVJEPPwYMHtW7dOvdhvHFxcerYsaPZZaGMwvmPGRAKmKOhiQAY+hISEvTXv/7V45SlmJgYs8syDQEQfqlZs6bGjh3rcVItATD0LF26VNdff737cePGjZWZmWliRQgEug+AuZiD9kAADH0JCQl65plnzC7DMgiA8Ev16tU1cuRIs8uAn7gRvD3woQSwNuZoaCIAwm5o1QAgANoU2xEwF3PQHgiAsBsCIAACoE3woQSwNuZoaCIAwm4IgAAIgDbFdgTMxRy0BwIg7IYACIAAaBN8KAGsjTkamgiAsBsuAgOAAGhTbEfAXMxBeyAAhr68vDzdc889MgzDfeX6SZMmqXr16maXZgoCIPySmZmpJ554wn1flZiYGL377rtml4UyIgDaAx9KAGtjjoYmAmDoKyws1MyZMz2e+9vf/kYABMrj8OHDmjNnjvtxXFwcAdAGCID2wHYEzMUctId7771XJ06ccN9AvF69emaXhDLyNhedTqcJlVgDARB+KTp5+GMXmtibaQ9sR8DamKOh6eWXXza7BPgpIqL4ZU/CeT4SAOGXopPH2wSD9XEIKAAEHu+lgDVER0crIyNDDofDfdpSYmKi2WWZhgAIv9SrV0/PPvus+4TaSpUqmV0SyoEAaA9sR8DawrnjAJgpOjqaTu45CIDwS2pqqkaPHm12GfATwQEAAo/3UgBWxPF6AAiANsF2BKyNDiAAKyAAAiA4AEAQ8F4KwIrCMgB+/fXX6tWrl5KTkxUfH6927dpp4sSJZb4cbH5+vmbOnKkbb7xRqampqlSpkhISEtS+fXuNHj1aOTk5wfkBgAC79tpr9euvv+qXX37Rzz//rA8//NDsklAOBHnA2ugAhqZ+/fqpa9euuuyyy9SlSxctW7bM7JIAv4RdABwzZox69+6t2NhYzZ8/X2vWrFGvXr00bNgw9e3bV4WFhaVeV+vWrTVgwAAdO3ZMb7/9tjZt2qSvv/5aF154oZ577jm1bt1aW7duDeJPAwRG1apV1apVK7Vu3VoXX3yxmjdvbnZJABDy2AljD8uXL9fSpUv13//+Vz/99BM7+BHywuoiMIsXL1ZGRobS0tL08ccfKzo6WtLZUHjkyBFNnTpVY8aM0ahRo0q1vv3796tTp0766quvFBV19lfZuHFjde3aVadOndKcOXM0aNAg/fjjj0H7mQDAhQ4gYG10AENT0fdStmNouv/++3XmzBn3lev/+te/KiUlxeyyTBFWHUDX1SqHDRvmDn8uI0aMkCSNHTtWeXl5pV7nwIED3eHvXPfdd58kaenSpdqzZ085K7a+9evXa+DAgbrzzjt111136dFHHzW7JAAALIGdMPZAALSHqVOnatq0afrnP/+p6dOn68iRI2aXZJqw6QAeOHBA33//vSSpZ8+exZa3aNFCKSkpysrK0rx583TDDTecd52//fab4uPjvS47d4/C4cOHVa9evfIVbnG///673n33XffjunXravz48SZWBMCFD58A4D8CoD0U3Y5lvfaHnYRNB3DlypVyOp2Kjo5WkyZNvI5p2bKlJGnFihWlWmdSUpLX7p8kZWdnS5JiY2PVtGnTclQcGopOHj5wAubhQwlgLQQHe2A72kNEhGfsCeftGDYdwMzMTElSzZo1i/0DcKlbt67HWH989dVXkqS77rpLcXFxZf5+V4AsiRWOWy46eXz9bmFtR44c0e+//y6HwyGHw6HKlSurQYMGZpcFP7FDBgD8RwC0h6FDh+rMmTOKiIiQw+FQjRo1zC5JWVlZJS4vTR4oj7AJgEePHpWkEsNY5cqVJUm5ubl+vda+ffv09ttvKyUlRc8//3y51tGpU6fzjrHCG1DDhg01cuRI9wm1SUlJZpeEcpgzZ47uuece9+NLL71UP/30k4kVoTys8J4A4H8IDvbAdrQHK56ilJqaasrrhk0ArChOp1N333238vPz9eWXXyo5OdnskoKqZcuWGjt2rNllwE9cPdKe2I4A4D8CIOwmbAJgYmKiJJV4hc+TJ09Kkl9drAcffFD/+c9/9Pnnn6tLly7lXs/y5cvdh6QCwUYAtAc+lADWxhwNTQRABMvu3btLXJ6dnV2qowLLKmwCoOvCLwcOHJDT6fR6rprrOFtfF4k5n+HDh+vdd9/VF198oT/84Q/lL1Znz0e0wjl+CA8EQHtiOwLmYg7aAwEQwWLWZ/2wuWJH+/btFRERoYKCAp8Xedm0aZMkqWPHjmVat9Pp1H333ae3335b33zzjd/hD6hoBEB74EMJYG3M0dBEAITdhE0ArFWrltLT0yVJCxYsKLZ88+bNysrKUkJCgq655ppSr/fMmTMaOHCgPvroI/3nP/9Rt27dPJYPHDhQ8+fP9694IMgIgPbEdgTMxRy0BwIg7CZsAqAkPfvss5KkiRMnqrCw0GPZuHHjJEkjR450Xw1UklavXq20tDT179+/2PcUFBToz3/+s7799lstXLjQa+fwhx9+0J49ewL9owBAMXwoAayNORqaCID2MGrUKD3yyCN6+OGHNWzYsIDc9i1Uhc05gJKUnp6uF198UaNGjVK/fv303HPPqUqVKnr77bc1depU9enTR08++aTH97z99tvauHGjNm7cqIyMDLVv317S2c5fv3799OWXX6pevXq66667vL5msO7fYRWrV6/W1KlT3fdUqVu3rkaNGmV2WSgjOoD2xHYEzMUctAcCoD1MmTJFhw8fdj+++eaby33dj1AXVgFQkp566im1bdtW48aNU48ePVRQUKDmzZtrwoQJevDBBxUZGekxvn///po1a5bS0tJ00UUXuZ8/duyYvvzyS0nSnj17wrbLl5mZqSlTprgfX3jhhQTAEEQAtAc+lADWxhwNTddee63atm0rh8Mhh8Nh2r3b4J+in22cTqdJlZgv7AKgJPXu3Vu9e/cu1dj09HQdOHCg2PNVq1bljVzFJ4+3q6vC+giA9sR2BMzFHLQH12lCCG1FP6OG8+f4sAyACByCgz2wHe0hnP+YAaGAOQqY5/bbb9exY8c8TlsKVwRA+KVp06Z64IEHZBiGnE6nLrjgArNLQjkQAO2J7QiYizkIWMerr75qdgmWQQCEXzp06KAOHTqYXQb8RAC0B7oLgLUxRwFYASdsASAA2hTbETAXcxCAFREAARAAbYLuAmBtzFEAVsAhoAB00003qU2bNjIMQ4ZhqFq1amaXhAAgyAPmYg7aw4gRI7R9+3Y5nU4ZhqFHH31U3bt3N7ssoNwIgACUmprKfY1sgO4CYG3M0dD03Xffae3ate7HN998s3nFAAFAAAQAm6L7AJiLOWgPRbcjQT40jR8/XocOHXJfuX7gwIG68MILzS7LFARA+GXZsmWaPXu2+54qDRs21JAhQ8wuCwhLfCgBrI05GpoIgPYwadIkbd++3f24a9euBECgPH755ReNGzfO/fiyyy4jAAIWQfcBMBdz0B4IgPZQdDs6nU6TKjEfVwGFX4pOHv7YAebhQwlgbczR0EQAtIeICM/YE84BkA4g/FL0TbDo5EJoOHHihE6cOCGHw6GIiAhVqlRJCQkJZpcFP7FDBjAXc9AeCID28Mc//lF79+51n7YUzhe/IwDCL82bN9fAgQPdl0Zu0aKF2SWhHF577TU99dRT7sd//OMfNXfuXBMrQnnwoQSwNuZoaCIA2sP48ePNLsEyCIDwS48ePdSjRw+zy4CfuBG8PbEdAXMxB+2BAAi74Xg9AARAm+BDCWBtzNHQRACE3RAAAQAAgoCdafZAAITdEAAB0AG0CbYjYG0Eh9BEAITdEAABEBwAIAh4L7UHAiDshovAACAA2gTbEbA2gkNoIgDaw9tvv63s7GwZhiGn06m+ffvq4osvNrssUxAA4Zcff/xR8+fPl8PhkMPhULNmzXTrrbeaXRbKiOAAAIHHe6k9EADtYdKkSVq9erX7ccOGDQmAQHksXbpUzz33nPtxnz59CIAhiABoT2xHAPBfhw4dVKlSJffO7pSUFLNLQjkU/ZvodDpNqsR8BED4pejk4QNnaCIA2gN7pQFroXNkD9xA3B6Yj/9DAIRfik6eiAiuKxSKCID2xHYEAOCs9PR0XXDBBYqIiJDD4VD9+vXNLsk0BED4pWXLlurfv7+cTqcMw1CHDh3MLgnlQAC0h3DemwlYER0HwDr+/ve/m12CZRAA4Ze+ffuqb9++ZpcBPxEA7YntCAAAiuJ4PQAEQJuguwBYG3MUgBUQAAEQAG2K7QiYizkIwIo4BBSAbr31VrVp00aGYcgwjLA+MTqU0V0ArI05GpomTJig9evXu/9G/vnPf1avXr3MLgsoNwIgALVu3VqtW7c2uwwEGN0HwFzMQXv48ssvNX/+fPfjtLQ0AiBCGgEQAGyC7gJgbczR0MTVXO3h448/1q5du9yd3CuvvFJt27Y1uyxTEADhl0WLFumnn36Sw+FQRESELrzwQvXp08fssgCI7gNgNuagPRAA7WHSpEn67rvv3I8nTJhAAATK45tvvtFLL73kfnzbbbcRAAGT8KEEsDbmaGgiANpD0e3odDpNqsR8XAUUfik6edjbCVgH8xEwF3PQHgiA9sB2/B86gPALtw8ArCOc/5gBoYA5GpoIDvbQvn17SXKfttSgQQOTKzIPARB+admypfr06SOn0ynDMLiSZIh69NFH9eqrr0o6+8Z41113adq0aSZXBX+xQwYwF3PQHgiA9vDKK6+YXYJlEADhl0GDBmnQoEFmlwE/nfvHzHV1LIQethtgbczR0EQAhN1wDiAADuW1KbYjYC7moD0QAGE3BEAABECb4EMJYG3M0dBEAITdEAABEABtiu0ImIs5aA8EQNgNARAAAdAm+FACWBtzNDQRAGE3XAQGAAHQptiOgLmYg/ZAALSH//znP9qxY4f7yvUdOnRQhw4dzC7LFARA+OWHH37QunXr3PdUueiii3T55ZebXRbKiABoD3woAayNORqaCID2MHnyZH3yySfux8899xwBECiPDz/8UJMnT3Y/fuCBBwiAIYgAaE9sR8BczEF7aNiwoS6++GI5HA45HA7VqVPH7JJQDhERnme+OZ1OkyoxHwEQfik6efhjF5oIgPbAXmnA2pijoWncuHFml4AAoJP7PwRA+IUAaA8EQHtiOwLmYg4C1tGiRQt16dLFfdpS/fr1zS7JNARA+KV58+ZKT093n1DbpEkTs0tCORAA7SGc92YCoYA5CpjnhRdeMLsEyyAAwi8jRozQiBEjzC4DfiIA2hPbETAXcxCAFXEfQAAEQJuguwBYG3MUgBUQAAEQAG2K7QiYizkIwIo4BBSAbr/9drVt21aGYcgwDF1yySVml4RyoLsAWBtzNDTNnj1ba9eudf+N7N69u66++mqzywLKjQAIQFdeeaWuvPJKs8tAgNF9AMzFHLSHzz77TDNnzvR4jgCIUEYABAAAAHzg/nH2sGzZMmVmZrqvXH/hhReqQ4cOZpdlCgIg/PLDDz9o+/btioiIUEREhFq1aqW2bduaXRYQlvhQAlgLwcEe2I728Oabb2r69Onux8OHDycAAuXx5ptv6oMPPnA/fvLJJwmAgEVw+BkA+I8AaA8REZ7Xvgzn7chVQOGXopOn6OQCUHHC+Y8ZYEUEB3tgO9pD0e3odDpNqsR8dADhl6KTh44DYB3MRwDwHwHQHlJTU3XxxRfL4XAoIiJCKSkpZpdkGgIg/NKkSRN17NjRfULtBRdcYHZJKIcHHnhAs2fPlsPhkMPh0LBhw/T000+bXRbKiA8lgLUxR0MTAdAe/u///k//93//Z3YZlkAAhF9eeuklvfTSS2aXAT8dPXpUBw8edD8+fvy4idUgUOgAAuZiDtoDARB2wwlbAIr9MeNDS2jiQwlgbczR0EQAhN0QAAEQAG2K7QiYizloDwRA2A0BEAAB0Cb4UAJYG3M0NBEAYTcEQAAEQJtiOwLmYg7aAwEQdsNFYAAQAG2CDyWAtTFHQxMB0B7Wr1+vbdu2ua9c36BBA7Vv397sskxBAIRfFi9erOzsbPc9VVq3bq3mzZubXRbKiABoT2xHwFzMQXsgANrDP//5T7366qvux3feeafefvttEysyDwEQfnn55Zc1b9489+MxY8bo8ccfN7EilAcB0B74UAJYG3M0NCUlJalOnTrue+UmJSWZXRLKISLC88y3cJ6PBED4pejkKTq5EBoIgPbEdgTMxRy0h1deeUWvvPKK2WXAT0Xno9PpNKkS8xEA4Zeik4c/dqGJAGgP4bw3EwgFzFHAPDVq1FDTpk3dpy3VqVPH7JJMQwCEXxo0aKALL7xQhmHIMAwlJyebXRLKgQBoT2xHwFzMQcA6nnjiCT3xxBNml2EJBED45a233jK7BAQAAdAe6C4A1sYcBWAFnLAFgABoU2xHwFzMQQBWRAAEQAC0CboLgLUxRwFYAYeAAtAtt9yiNm3auM/l7Natm9klIQAI8oC5mIP2sGjRIq1evdr9N7JVq1a66qqrzC4LKDcCIADdeuutZpeAAKC7AFgbczQ0ffLJJ/rHP/7hfnzXXXcRABHSCIAAYFN0HwBzMQftoeh2JMiHpu3btyszM1NOp1OGYahWrVpq166d2WWZggAIvyxdulRHjhxx31PloosuUmpqqtllAWGJDyWAtTFHQxMB0B7ee+89Pfvss+7H/fr108cff2xiReYhAMIvjz32mP773/+6H7/55pu69957TawIgAvdB8BczEF7IADaQ0SE57Uvw3k7chVQ+KXo5Ck6uQBUnHD+YwaEAuZoaCIA2kPR7eh0Ok2qxHx8Wodfik6enJwccwqB37KysuRwOORwOJSVlWV2OQiAY8eOmV0Cyon5aA8nT540uwQEwPHjxz0eEwBD05kzZ9z/X6dOHVWvXt3EasxFAIRf6tatq5SUFPfjypUrm1gNyuvee+9Vjx493I9nz55tYjUoLz6UANbGHA1NdADt4a677nL//4oVK/T222+bWI25CIDwy6effupxDuB1111nYjUor99++01bt251Pz5y5IiJ1SBQOP8IMBdz0B4IgLCbsAyAX3/9tXr16qXk5GTFx8erXbt2mjhxYrmPBc7Oztb999+v+vXrKyYmRvXr19f999+v33//PcCVA8FR9I8ZH1pCEx9KAGtjjoYmAiDsJuwC4JgxY9S7d2/FxsZq/vz5WrNmjXr16qVhw4apb9++KiwsLNP6NmzYoNatW+uzzz7Tq6++qk2bNunVV1/Vp59+qosvvlgbNmwI0k8CBA4B0J7YjoC5mIP2RABEqAur20AsXrxYGRkZSktL08cff6zo6GhJZ0PhkSNHNHXqVI0ZM0ajRo0q1foKCwt144036tChQ/rxxx912WWXSZIaNWqkOnXqqFu3brrpppv0yy+/KCoqrH7VCDEEQAAAvKMDCLsJqw7g6NGjJUnDhg1zhz+XESNGSJLGjh2rvLy8Uq3vgw8+0KZNm3TppZe6w59L165d1blzZ23cuFGzZs3yv3ggiAiA9sB2BKyF4GAPbEfYTdgEwAMHDuj777+XJPXs2bPY8hYtWiglJUVHjx7VvHnzSrVO15USva1Pkv7whz9IEgEQlkdwAADAOwKgPezbt8/9/8uWLdMvv/xiYjXmCpvjEleuXCmn06no6Gg1adLE65iWLVsqKytLK1as0A033HDedS5btsz9fb7WJ5291GxpnHv+YWpqaqm+xyUiIkJJSUlelx09etTj3idlVa1aNa/PHzt2rNg5k6Wtu6R68/Pz/bp3kq96T58+rRMnTpR7vVWrVvUajAoLC/2631pSUpIiIorvizlz5oyOHj1a7vUmJCR4PfTYMIzz3q/xpZde0quvvlru14Y5it6r6vjx49xDLkRlZ2d7/X+ElqJ/c9566y3NnDnTpGpQXkU/k3zyySdKTk72eC4nJ6fcwTAuLk6xsbFel/nzGS42NlZxcXFel3n7DFdaMTExPm/7deLECZ0+fbpc642OjlaVKlW8Ljt58qTy8/PLtd7IyEglJiZ6HOF30003STr7Ozp16lS51uv6LFu0ttjYWEVERPj1WbZq1arKy8vzWG95t5c3YRMAMzMzJUk1a9b0+mFbOntPu3PHluT48ePav3+/pLM3kyxpfXv37tXJkyfPe4+8AwcOnPd1fXE6nUG7dH8w1htq9UrBu8l9bm5uUNbrTyg9depUud8QYR3Tpk3TtGnTzC4DfurUqZPZJSBATp8+Xe4Px7COgoKCgH7WyMvLK/XpR2URrL/l+fn55Q5jJQn079XlzJkzPtfrz+/H12fZQPzOvX3mPHDggBo2bOj3uqUwOgTU1UnxtSdE+t9NzEvzgfzczoyvdZ4b+IL1IR8AAAAASitsOoChoHXr1lq+fLn27dun6tWrn/fKoa4OIwAAAIDQcr5D/AsLC3Xo0CHVrl1brVu3Dtjrhk0ATExMlKQSW+yuY3V9nZvmbX0lrfPcY39Ls87Y2Fh17NjxvOMAAAAAhLaUlBRTXjdsDgF1XfjlwIEDcjqdXse4Urivi8Scq0qVKqpVq5aks+f4lbS+OnXqnPf8PwAAAAAItrAJgO3bt1dERIQKCgp8XuRl06ZNklTqLpzrxHzX9/m7PgAAAAAIprAJgLVq1VJ6erokacGCBcWWb968WVlZWUpISNA111xTqnX279/f5/ok6T//+Y/HOAAAAAAwU9gEQEl69tlnJUkTJ04sdi+NcePGSZJGjhzpcbjm6tWrlZaWpv79+xf7nltvvVXNmzfXsmXLtHTpUo9lS5cu1U8//aQWLVro1ltvDcaPAwAAAABlElYBMD09XS+++KLWr1+vfv36afXq1dqyZYsyMjI0depU9enTR08++aTH97z99tvauHGjPvroI/38888ey6KjozVnzhwlJyfrxhtv1Mcff6ydO3fq448/1o033qjk5GR99NFH572aJwAAAABUBIdhGIbZRVS0efPmady4cVq5cqUKCgrUvHlz3XnnnXrwwQcVGRnpMfaHH37QTTfdpLS0NH3zzTeKjY0ttr49e/boueee07x587R//37VqlVLvXv31rPPPqt69epV1I8FAAAAACUKywAIAAAAAOEorA4BReCcOXNGY8aMUWxsrLp37252OSgntiMAIFwtXbpULVq0kMPh0M6dO80uB+XEdiw7AiDKbMOGDbrsssuUkZGh/Px8v9c3evRoORyOEr9ycnL8LxweAr0dXd5//31169ZNiYmJSkxMVNeuXTVz5syArR+e8vPz9eKLL+qiiy5SXFycatasqb59+2rZsmXlWt+MGTPOOx/Xrl0b2B8iTHz99dfq1auXkpOTFR8fr3bt2mnixIk+7017PtnZ2br//vtVv359xcTEqH79+rr//vv1+++/B7hynCuQ2/F8c+2RRx4J/A8A5eXlacSIEbriiiu0ZcuWgKxz8+bNGjBggOrWravY2Fg1adJEI0eOVG5ubkDWj+ICuR137tx53vn4j3/8IzCFWwABEGUybdo0tWvXTvXq1dMrr7wSsPUmJiaqRYsWPr+KnpsJ/wRrOz7wwAO6/fbb1bx5cy1dulRLly5V8+bNNWDAAD3wwAMBex2cdfz4cV1++eUaPXq07rrrLq1bt04ff/yx9u3bp65du+r9998v13rj4uJKnI/ezoVGycaMGaPevXsrNjZW8+fP15o1a9SrVy8NGzZMffv2LXaV6fPZsGGDWrdurc8++0yvvvqqNm3apFdffVWffvqpLr74Ym3YsCFIP0l4C/R2lKTGjRv7nGu1a9cOwk8R3jIzM9WmTRvNmTNH8+bNC8g6v//+e7Vt21YrV67UjBkztHHjRj399NOaMmWK2rVrp+zs7IC8Dv4nGNtRUol/+5KTkwP2OqYzgDIYPHiw8e9//9swDMOYPn26IclIT0/3a53PPvusMXDgQP+LQ6kFYzv+61//MiQZV111VbFlvXr1MiQZ7733nl+vAU933323Icl48cUXPZ4/cuSIUatWLSMmJsbIzMws0zqnT5/u978FeFq0aJEhyUhLSzNOnz7tseyee+4xJBkvvPBCqddXUFBgtGzZ0pBk/Pjjjx7LlixZYkgyLrzwQqOgoCAg9eOsQG9HwzAMScaOHTsCWCXO57PPPjOGDBliHDt2zDCMs9vAn+1w5MgRo0aNGkZsbGyx99uZM2cakow//OEP/paNIgK9HXfs2GGEUywKn58UAVFYWOj+fwJg6ArGdmzSpIkhyZg3b16xZV9//bUhyWjatKlfr4H/2blzpxEREWHExMQYOTk5xZZnZGQYkozBgweXab0EwMDr0aOHIcl44403ii3btGmTIclITEw0Tp48War1vfvuu4Yk49JLL/W6vHPnzuxwCYJAb0fDIACa4dy/f4bhf3D461//akgy/vznPxdbdubMGeOCCy4wJBlLliwp1/rhXaC3Y7gFQA4BRZlwKKY9BHo7rl69WpmZmYqKilJ6enqx5VdccYWio6O1bds2rV69OqCvHa7mzJkjp9Op9u3bKykpqdjyP/zhD+5xZ86cqejy8P8dOHBA33//vSSpZ8+exZa3aNFCKSkpOnr0aKkPY5o9e7bP9Un/2/azZs0qR8XwJhjbEeYI9N+/kuZjRESEevToIYn5GGh8HvUPARCWsG/fPj322GNq06aNatSoodTUVF177bX64IMPZHCnEstzXXCkfv36iouLK7Y8Li5ODRo0kCStWLGiQmuzK9fvvGXLll6Xu54/cuSItm3bVqZ1Hzt2TH/961/VoUMH1apVS/Xq1VPPnj01ZcoUnT592r/Cw8zKlSvldDoVHR2tJk2aeB3j2lalnRul3fbMtcAJxnZ0+eyzz3T11VerYcOGqlGjhi75f+3df0xV9R/H8Rc/BNQrIhqR2ZRAcajTgRqoWUZzWprlj1JbOV04s1ol5Q/csjJ0NjVbTa05XJozK1eEMXP56xpQTmnrWkllpkMxMXUqspvo5/uHu/cLca+CXu693vN8bPzh+Zzz2ftzPr7v4c0593P69tWsWbN05MiRm44bLaumpkY///yzJPIxVKxdu1b33Xef7rrrLiUkJGjAgAF67bXXVF1dHejQfIoCEEFh69atqqqq0tKlS2W32/XBBx+opqZGkydP1qhRo3y6SiV879ChQ5KkxMREr/vccccdDfbFzbneOa+/vbnnvLy8XGVlZXrjjTe0a9curVu3TnFxcXr22Wc1ZMgQnTlz5sYDtxjXub/tttsUHu75ktuc3Lhw4YJOnjwpyfvcu/o7ceKELl682OyY0Ziv57G+t99+W2PGjFFhYaG2bdumiRMn6sMPP1Tv3r25mxjkDh8+7F799Xr5yLXv1rBkyRI988wzKi4uVlFRkbKzs7Vo0SL16dNH+/fvD3R4PhMZ6ACAzMxMFRQUaOrUqe5taWlpGj58uAYOHKji4mLNnTtX77zzTgCjxLWcO3dOkjze/XNp06aNJLEkto9c75xHREQoOjpaTqezWec8LS1NS5cuVW5uboNt2dnZeuSRR1RUVKRp06bpiy++uLkBWISvc8PV37X6dPXn6rP+v3FjWuoz7uWXX1Zubq7uvPNO97b09HSlpqZq3Lhxevzxx3Xw4MEG7QgezclHrn3BLTY2VrNnz9b8+fMVGxvr3n7PPfcoISFBubm5GjNmjH777beQ+EylALSI2bNn66uvvmr2cWvWrNGQIUNaIKL/GzFihMftkZGRmj9/vsaPH69Vq1Zp4cKFstlsLRpLsAvmeUTTBfM8Dhw4UAMHDvTYtmDBAhUVFenLL7/U77//ru7du7doLECoW758ucftY8eOVZ8+feRwOLRy5Url5+f7OTLAWuLj47VkyRKPbTNnztTixYt17NgxbdiwQTk5OX6OzvcoAC3i+PHjqqioaPZxFy5caIFomi4jI0PS1Zdd79u3T/fff39A4wm0YJ1H11/Lamtrve7jehTN04IlVuOLebzeOb98+bL70WlfnfO+ffsqIiJCly9fVklJCQVgE/g6N+r/Zdpbn/Uf+yTffCMQn3Hp6elyOBwqKSnxSX/wvebkI7l464qJiVGvXr20e/dulZSUhEQByHcALeLjjz+Wufraj2b9eLs75y/1n6k/ffp0ACMJDsE6j65FEU6cOOF1H9eLcL0toGAlvpjH653z+tt9dc4jIyPVqVMnSeRjU7nOfXV1tfu7Qv/VnNyw2WxKSEiQ5H3uXf0lJiaGxKNKwcDX89gUrusfuRa8kpKSFBYWJun6+ci179YWavlIAYiA+ueff7RlyxavK33W/0CNi4vzU1RoLtcjg0ePHvX4V9Da2lr3inYDBgzwa2yhynXODx486LHdtT0uLk4pKSlN6rO2tlZbtmxRTU2Nx/a6ujqdOnXK3S+uLyMjQ+Hh4bp06ZLXRSBcc9XU3Gjq3JNrvtMS81hWVnbNJwFc1z9yLXi1bdtWvXr1kkQ+3up27Niho0ePem0PtXykAERAORwOjR492uty1z/++KOkq3ce+vfv78/Q0AwZGRm6++67VVdXp927dzdqt9vtunTpkpKTk5Wenh6ACEPPuHHjFB4erv3793tcXODbb7+VdPW7RE19X9Lff/+t0aNHe12u3OFwuN8pmJmZeYORW0tCQoL73Zjbt29v1F5RUaHKykq1a9euyXfqJ0yY4LU/6f9z79oPN68l5nHevHlavHix13bX9S8rK+sGIoa/XCsfr1y5oh07djTYD8Fp2rRpKigo8NjmdDr1yy+/SAqdfKQAhF/MnDlTKSkp2rZtm8f2jz76qNG2y5cva9GiRZKuJmb9Z+0RGNeaxwULFkiSx9VaXQsduPbBzevWrZumTJkip9OplStXNmg7e/asCgoKFBUVpby8vEbH5ufnKykpSevWrfPYt6d8lKSFCxdKkoYPH660tLSbHIF1uP7fv//++6qrq2vQtmzZMknSq6++2uBxzfLycqWlpWnChAmNjpk0aZJ69OihH374QaWlpQ3aSktL9f333ys1NVWTJk1qieFYlq/nUZK+/vpr9131+goLC/XTTz8pOjpaM2bM8OUwcAO2bt2qlJQUPf/8843aXnjhBXXs2FGFhYU6fPhwg7ZPPvlEx48f1wMPPKB7773XX+HCi2vNoyRt2rTJ42vHVq9ererqanXq1ElPPvlkS4fpHwZoBqfTaaqqqkxVVZVZsWKFkWSysrLc286ePdvomOrqaiPJSDKjRo1q0LZnzx4jyURGRpqXXnrJlJWVmSNHjpidO3ea4cOHG0lm6NCh5sKFC/4aoiX4eh5dpk+fbiSZqVOnmgMHDpgDBw6YqVOnGklm+vTpLT0syzl37pzJyMgwkZGRZunSpebQoUPmu+++M5mZmSY8PNysW7fO43Ft27Y1kkzv3r0bbD969KgJCwszkszTTz9t7Ha7+euvv0xZWZmZPHmykWTS0tJMVVWVP4YXUvLz8925s3//flNRUWHmzJljJJmHHnrIXLp0qcH+zz33nDvf9u3b16g/h8Nh4uPjTWJiotm8ebM5fPiw2bx5s0lMTDTx8fHG4XD4a2iW4st5zM7OdufUpk2bTEVFhamoqDArVqww7dq1MzExMebTTz/15/As4+TJk+7rnWt+9u7d6972Xw8//LB7v1OnTjVq3759u4mJiTGpqanmm2++MX/++acpKCgwNpvNdOvWzRw7dswfw7IcX85jcnKy+3ehoqIi88cff5gDBw6Y119/3bRq1crExcUZu93ur6G1OApANMvOnTvdyePpZ8qUKY2OuXLlihk7dqxp37692bRpU6P28vJy88orr5gBAwaY9u3bm4iICNOxY0czbNgws2bNGlNXV+eHkVlLS8yjy/r1601WVpax2WzGZrOZrKwss379+hYcjbXV1taaN9980/Ts2dNER0eb+Ph4M3r0aFNaWur1mBdffNHYbDazfPnyRm0VFRVmwYIFZvDgwSY+Pt5ERESYuLg4M2jQILNs2TJTU1PTksMJacXFxSY7O9u0b9/etGnTxvTr18+8++67Hj/jdu3aZTp16mSGDh1qamtrPfZXWVlpcnJyTJcuXUxUVJTp0qWLycnJMZWVlS09FEvz1TyeP3/eFBQUmMcee8x07drVREVFmZiYGNO9e3czY8YMc/DgQX8NyXK6du16zWvgf23cuNHExsaaCRMmeO3z119/NZMmTTK33367iYqKMklJSWbWrFnmzJkzLTgSa/PlPJ46dcq89957ZuTIkaZz586mVatWpk2bNqZ3794mNzc35D5Xw4zxsvoGAAAAACCk8B1AAAAAALAICkAAAAAAsAgKQAAAAACwCApAAAAAALAICkAAAAAAsAgKQAAAAACwCApAAAAAALAICkAAAAAAsAgKQAAAAACwCApAAAAAALAICkAAAAAAsAgKQAAAAACwCApAAAAAALAICkAAAAAAsAgKQAAAAACwCApAAAAAALAICkAAAAAAsAgKQAAAAACwCApAAAAAALAICkAAAAAAsAgKQAAAgsBbb72lsLAwZWZmemyfO3euwsLC1K9fP505c8bP0QEAQkWYMcYEOggAAKyutrZWPXr0UGVlpT7//HONGzfO3bZ48WLl5eUpNTVVdrtdCQkJAYwUAHAr4w4gAABBoHXr1srPz5ckzZ8/X3V1dZKkVatWKS8vT0lJSdq+fTvFHwDgpnAHEACAIGGMUf/+/VVeXq7Vq1fLZrPpqaeeUufOnbVnzx4lJSUFOkQAwC2OAhAAgCCya9cuDRs2TB06dND58+fVoUMH2e129ezZM9ChAQBCAAUgAABBZvDgwSotLVW7du1kt9vVr1+/QIcEAAgRfAcQAIAgsnbtWpWVlUmSnE6nYmNjAxwRACCUUAACABAkPvvsM+Xk5Cg+Pl5PPPGE/v33X82ZMyfQYQEAQgiPgAIAEASKi4v16KOPqnXr1tqxY4eSk5OVnJys06dPq6SkRIMGDQp0iACAEMAdQAAAAmz37t0aP368IiMjVVRUpIyMDMXFxSkvL0+SNGvWrABHCAAIFdwBBAAggPbu3asHH3xQTqdThYWFGjFihLvN6XQqNTVVR44c0caNGzVx4sQARgoACAXcAQQAIEAcDodGjhypixcvasOGDQ2KP0mKjo7WwoULJUnz5s2T0+kMRJgAgBDCHUAAAAAAsAjuAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEVQAAIAAACARVAAAgAAAIBFUAACAAAAgEX8D5xwOTcm6OqwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x375 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 448
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Example of a uniform distribution\n",
    "---------------------------------\n",
    "Figure 3.7.\n",
    "This shows an example of a uniform distribution with various parameters.\n",
    "We'll generate the distribution using::\n",
    "    dist = scipy.stats.uniform(...)\n",
    "Where ... should be filled in with the desired distribution parameters\n",
    "Once we have defined the distribution parameters in this way, these\n",
    "distribution objects have many useful methods; for example:\n",
    "* ``dist.pmf(x)`` computes the Probability Mass Function at values ``x``\n",
    "  in the case of discrete distributions\n",
    "* ``dist.pdf(x)`` computes the Probability Density Function at values ``x``\n",
    "  in the case of continuous distributions\n",
    "* ``dist.rvs(N)`` computes ``N`` random variables distributed according\n",
    "  to the given distribution\n",
    "Many further options exist; refer to the documentation of ``scipy.stats``\n",
    "for more details.\n",
    "\"\"\"\n",
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "import numpy as np\n",
    "from scipy.stats import uniform\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=False)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Define the distribution parameters to be plotted\n",
    "W_values = [1.0, 2.0, 3.0]\n",
    "linestyles = ['-', '--', ':']\n",
    "mu = 0\n",
    "x = np.linspace(-2, 2, 1000)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# plot the distributions\n",
    "fig, ax = plt.subplots(figsize=(5, 3.75))\n",
    "\n",
    "for W, ls in zip(W_values, linestyles):\n",
    "    left = mu - 0.5 * W\n",
    "    dist = uniform(left, W)\n",
    "\n",
    "    plt.plot(x, dist.pdf(x), ls=ls, c='black',\n",
    "             label=r'$\\mu=%i,\\ W=%i$' % (mu, W))\n",
    "\n",
    "plt.xlim(-1.7, 1.7)\n",
    "plt.ylim(0, 1.2)\n",
    "\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel(r'$p(x|\\mu, W)$')\n",
    "plt.title('Uniform Distribution')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can implement [uniform](http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.uniform.html#scipy.stats.uniform) in `scipy` as follows.  We'll use the methods listed at the bottom of the link to complete the cell: `dist.rvs(size=N)` which produces `N` random draws from the distribution and `dist.pdf(x)` which returns the value of the pdf at a given $x$. Lots of distributions can be accessed and used in a similar way.  \n",
    "\n",
    "Create a uniform distribution with parameters `loc=0`,  `scale=2`, and `N=10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.47149363 0.6082927  0.81125937 1.39991292 1.93563006 0.01012349\n",
      " 1.93941539 0.21465716 1.67758795 1.29713978]\n",
      "0.5\n"
     ]
    }
   ],
   "source": [
    "N = 10\n",
    "distU = scipy.stats.uniform(0,2)\n",
    "draws = distU.rvs(N) # ten random draws\n",
    "print(draws)\n",
    "\n",
    "p = distU.cdf(1) # pdf evaluated at x=1\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x16e43ae50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = distU.cdf(1) # pdf evaluated at x=1\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Distribution\n",
    "\n",
    "As many of you know, the Gaussian distribution pdf is given by\n",
    "\n",
    "$$p(x|\\mu,\\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} \\exp\\left(\\frac{-(x-\\mu)^2}{2\\sigma^2}\\right)$$\n",
    "\n",
    "It is also called the **normal distribution** and can be noted by $\\mathscr{N}(\\mu,\\sigma)$.\n",
    "\n",
    "\n",
    "We love using Gaussians in physics and astronomy because they can approximate many distributions and are also super easy to work with. **The convolution of two Gaussians results in a Gaussian.**  So $\\mathscr{N}(\\mu_1,\\sigma_1)$ convolved with $\\mathscr{N}(\\mu_2,\\sigma_2)$ is $\\mathscr{N}(\\mu_1+\\mu_2,\\sqrt{\\sigma_1^2+\\sigma_2^2})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 685,
       "width": 680
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Example of a Gaussian distribution\n",
    "----------------------------------\n",
    "Figure 3.8.\n",
    "This shows an example of a gaussian distribution with various parameters.\n",
    "We'll generate the distribution using::\n",
    "    dist = scipy.stats.norm(...)\n",
    "Where ... should be filled in with the desired distribution parameters\n",
    "Once we have defined the distribution parameters in this way, these\n",
    "distribution objects have many useful methods; for example:\n",
    "* ``dist.pmf(x)`` computes the Probability Mass Function at values ``x``\n",
    "  in the case of discrete distributions\n",
    "* ``dist.pdf(x)`` computes the Probability Density Function at values ``x``\n",
    "  in the case of continuous distributions\n",
    "* ``dist.rvs(N)`` computes ``N`` random variables distributed according\n",
    "  to the given distribution\n",
    "Many further options exist; refer to the documentation of ``scipy.stats``\n",
    "for more details.\n",
    "\"\"\"\n",
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "if \"setup_text_plots\" not in globals():\n",
    "    from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=False)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Define the distributions to be plotted\n",
    "sigma_values = [0.5, 1.0, 2.0]\n",
    "linestyles = ['-', '--', ':']\n",
    "mu = 0\n",
    "x = np.linspace(-10, 10, 1000)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# plot the distributions\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "for sigma, ls in zip(sigma_values, linestyles):\n",
    "    # create a gaussian / normal distribution\n",
    "    dist = norm(mu, sigma)\n",
    "\n",
    "    plt.plot(x, dist.pdf(x), ls=ls, c='black',\n",
    "             label=r'$\\mu=%i,\\ \\sigma=%.1f$' % (mu, sigma))\n",
    "\n",
    "plt.xlim(-5, 5)\n",
    "plt.ylim(0, 0.85)\n",
    "\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel(r'$p(x|\\mu,\\sigma)$')\n",
    "plt.title('Gaussian Distribution')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[114.23469611  97.2457498   96.08613021 109.61685283 108.09849237\n",
      " 113.25168812 114.58130231  78.84349072 101.79793731 111.71271199\n",
      " 107.14224414  78.58780611  86.91402817 103.07785267 118.59529777\n",
      " 111.69873529 111.85580695 100.30778363  94.89771119  75.0700713\n",
      " 115.01090496 118.20290656 111.72333778 126.74232669  79.64316027\n",
      "  98.35783318  59.67784917  99.82102342 111.36020114  97.23092903\n",
      " 117.43095019  83.27092044  88.52307232 103.94471951  68.31583699\n",
      "  97.68466211  97.67825098  94.24389267  64.86572279 108.48717409\n",
      "  92.14613684 111.8636257   99.22507861  94.6996936   91.10025503\n",
      " 120.18036903 105.25988366 117.48893171 110.88794047  93.95289433\n",
      " 105.46913926  90.30831589 109.02106466 106.07767205  80.76375343\n",
      " 109.23257479  86.12014532 111.83754451 115.28248216 101.03209187\n",
      " 106.28485512  90.31545181  84.74759318  83.84507535 101.23634956\n",
      " 105.0725596   97.98620407 108.58399333 119.17738009 101.8798268\n",
      "  93.59658552 115.88090439  93.07447662  88.0974974   95.45252701\n",
      "  98.60962601 123.11257123  91.97380628 103.22317489 101.62991967\n",
      " 119.62677686  94.45717843 107.96706485 101.88650661 102.76489271\n",
      " 102.50326154  94.21531827  99.96004388 111.51976889 105.28852994\n",
      "  98.66164367 103.61456521  95.78120031  97.72730328  98.6214921\n",
      "  74.27471708 108.32032331 100.71759217  96.33380755  98.63264267\n",
      " 112.53767978  86.00024467 125.08806467 104.96441827  91.93516078\n",
      " 112.32462026 129.89766951 100.98155864 110.5172422  104.50449348\n",
      " 106.64962973  90.72626659  89.14417994 101.84383836  89.34704163\n",
      " 128.82789901  93.64629782 100.8954546  115.40245341 100.67179543\n",
      " 126.37618918  96.84610132  93.03732123 122.9491171   79.83201504\n",
      " 113.32284569 112.74389503  93.91993261 126.20765546  82.92046509\n",
      "  86.51438135  69.92721353  91.37471351  89.25117009 110.88168954\n",
      "  90.09659015  98.51514281 114.90534772 107.28455837 116.92039299\n",
      "  99.86038651 134.06792254  67.3588725  122.42479366 106.45420857\n",
      " 105.38643244  83.56920914  83.7080622  105.10060557 124.60838937\n",
      "  95.69208095  63.08099395  78.46372769 111.87588174  68.34896326\n",
      "  94.04264872  85.85991055  87.61780616 151.00555862  99.3336401\n",
      "  95.75112963  91.64505097  99.90876256 117.98421525  99.02119199\n",
      "  85.10553058 111.26775069 102.24813974  99.48528888 100.79196054\n",
      " 140.98820268  87.6263453  127.63766064 104.84344548 103.73148446\n",
      "  91.18673362  95.51247194  91.10963019 125.22013337 126.34222118\n",
      "  81.40352565 117.36332703 103.89026103 122.22758795 133.12914263\n",
      "  96.35239045 117.62279359  77.69540718  85.4372348  109.54346319\n",
      " 137.28624866 115.25894219 105.96300947 115.04625118 113.00754794\n",
      " 102.13644382 108.47146906 112.95057786 109.82820215 106.96348071\n",
      "  90.46841132  98.8490125  116.24314973 105.94249487 107.84284648\n",
      "  98.96063862  88.09411209 118.10374871 111.7342131  110.33282789\n",
      " 116.82770307  84.9768404   75.08093066  92.05024809 112.33911102\n",
      " 116.23781799  94.07571893 104.27555127 103.94835499 110.02704717\n",
      " 109.1856238   89.14441293 102.32070802  95.3637537  115.53836689\n",
      "  77.81912542 145.08174108  99.94786521  84.55456784  73.40796425\n",
      " 107.45372941 101.34561194  52.38936941  99.36639132 130.51961231\n",
      "  71.38884512 122.57508546 111.44710753  90.02113618 105.12661814\n",
      " 110.70164832 123.97842665  97.53387515  92.79643169 124.87167571\n",
      " 105.84451182  89.79164057 100.1786354  113.93745177 103.22639511\n",
      " 102.17207612  76.12612089  88.56059661  67.99565527 108.87138204\n",
      " 115.00252762 117.27664995  77.10791498 106.47541288 113.46341369\n",
      "  85.90560158 119.68508537  99.40192656  86.43401564 109.74948457\n",
      "  84.00897549  85.79312769  86.65394934  77.5867675  113.63767739\n",
      "  99.4362224  128.20974276  83.13695982 101.65367691 111.16714338\n",
      "  90.96202817  90.15027064  93.07870623 110.07670832  91.72826751\n",
      " 100.27382319  89.17316962 107.62664837 114.89413675 104.82156812\n",
      " 100.4807102   94.00930515  94.7025121   96.11464604 114.16049963\n",
      "  89.01944062  93.25757544  94.58332827  77.2255178   89.26459059\n",
      " 102.01555539 111.76926032  87.88098458  98.45015581  95.88225555\n",
      " 107.42946929 119.16784546 113.00196405  81.99953874  92.14077676\n",
      "  95.14391227  97.56411019 102.33591293 108.55087867  89.18043385\n",
      "  89.53908928 111.46298304  95.1766603   96.59252968 118.65555481\n",
      " 103.54391091  68.4295701   81.68624157  90.73155667 100.94643916\n",
      "  85.37118812 102.84577012 100.16751772  87.06228172  97.1619907\n",
      " 107.68951075  98.01655427  95.26891392 107.13019394  96.62136556\n",
      "  80.5205354   93.0731487   64.76718322  85.5221697   81.56603163\n",
      " 114.1738846   99.43992263 111.9759502   74.72916722 110.16383556\n",
      "  79.94224147  95.60652405  78.4795614  126.02576281  95.81440033\n",
      " 123.74299248  94.30154271  65.85693839 111.05044436  95.77234724\n",
      "  89.42077425 111.39977665 100.14901161  81.00927906 131.50549055\n",
      " 110.3215495   91.49546154 105.46183249 106.63095545 112.4221628\n",
      " 100.55069125  78.02091349 107.16970945  95.99723518 127.34863356\n",
      " 111.60205201 100.25660298 117.62541897 121.87406876  99.71785157\n",
      "  66.24336824  99.62759798  91.20870583 109.54611824 106.45912782\n",
      "  90.27002176 116.55289201 105.40670838 102.88058029 112.27945013\n",
      " 102.75698341 107.13307179  88.43377806  72.89417608  95.10101468\n",
      " 103.73928197  87.77630694 102.96769355  94.45922299  89.56423145\n",
      " 120.06763095  72.17696178 103.32674009  84.22943807  86.78282278\n",
      " 103.25270896 100.14072489 101.77747108 110.26309284 105.98507183\n",
      "  94.86500673 135.41525179  84.61832688 102.01042862  86.78859851\n",
      " 120.80933345  91.71986747  96.19158298  98.69549653 113.19775407\n",
      " 117.82367067 100.69003656 113.2347317  115.57905951  83.53807121\n",
      " 114.4741854   74.65236356 133.44397953 109.23517709 102.25824802\n",
      "  86.66138461  93.75438091 107.29636041  90.64921117  82.4123187\n",
      " 103.31222038  80.42857765  94.51853528  89.11224356  85.67500794\n",
      "  85.41323295  74.57449949  81.42771059  97.52625755  94.48934377\n",
      " 106.76679412 106.69559225 103.60941771 118.37691382 105.65684914\n",
      " 121.63126575  63.36322905  98.883329    89.43773056 130.75421948\n",
      " 105.64900646  88.62343025  89.95261776  89.14962347  84.64422861\n",
      "  86.53344786 104.65739615  91.33676886  90.62286674 103.31253532\n",
      "  62.29754619  86.89326377 117.30077884  72.49992797  48.55562813\n",
      " 114.74773045  97.64396137  78.20624413 105.85671361 128.15205213\n",
      "  99.13745973 105.92876697  91.7495096   85.97216423  98.65934598\n",
      "  74.41708028  72.80115373  80.45287304 109.2641856  103.13161601\n",
      " 127.63461637  88.17257337 110.51774529 105.03043388 113.8241079\n",
      " 110.68567078  84.59931209 116.6340001  115.78705101  76.09056808\n",
      " 109.97824359  91.5088874   93.32741404 101.58650937 112.08353926\n",
      " 109.41750829 105.2541515   93.80585462  92.3044465  109.33781458\n",
      "  92.99517905 126.36282937  94.71856779 122.24153251 114.15373672\n",
      "  82.10324551 103.56254942 125.66423679  92.58084988  91.87617271\n",
      "  71.07697611  94.39363353 101.73437111  95.38770269 119.22339041\n",
      " 108.802867   125.20264325  85.89837411 117.40629402 113.49830747\n",
      "  88.76830137 116.17292037 103.19069756 112.40121312 107.3048943\n",
      " 120.27685272  64.50924661 113.32573576  75.89273054 103.75701991\n",
      "  85.1851224   92.56200417  90.5505975  119.47425836  97.84664857\n",
      " 105.59811226 120.87974824 108.64870569 119.36315615 122.20148245\n",
      " 104.70343961 114.78342683  91.20077729  98.43450068 119.39524158\n",
      "  94.928543    95.35218081  93.56261879  99.5774958  116.48006906\n",
      "  95.62248832 112.26685033 117.58008087 116.33876895 129.73386408\n",
      " 107.50148957 111.24294247  64.41857079 115.89046525 104.73959788\n",
      " 107.16980798 117.00839356 122.3201064  111.10961535 103.22354241\n",
      "  85.94638256  97.71503901 116.37898756  98.90526766 109.360888\n",
      " 104.56613013  95.4812206  108.57075597  83.76125045 106.56784249\n",
      " 109.65674562 102.58270928 109.36022506 104.95741634 112.54488888\n",
      "  92.5657843   97.15548027  98.68949647  90.21696762  85.98332267\n",
      " 106.10932633 110.2506413  105.34929199  81.69800647 112.10548766\n",
      " 100.13093084  84.71337445 106.64251807  88.26546626  89.67635741\n",
      "  98.78540752  75.57783753  95.26456411 110.54965024 110.7600699\n",
      "  96.50167167  82.2832615   69.52266076 114.35433953  95.23002342\n",
      "  78.92746475 100.39569672  69.49762623 112.65797839  93.17409833\n",
      " 111.15623343 106.35470898 100.08718021  81.74725329  89.76397924\n",
      "  97.20974824 105.86068607 121.68588781  81.26392689 115.37095342\n",
      "  98.54230555 134.93421776 108.84843753  87.88349245 108.94899108\n",
      "  86.2179523  104.9649644   94.35339782 115.04974136  82.66635682\n",
      "  94.59211757  86.52231877  99.26322651 114.64660375 138.03417659\n",
      "  91.49117862 116.75412142 110.40995415 100.02552139  94.77991268\n",
      " 104.97097095 122.23755041  75.56781218  84.30896576  97.20431935\n",
      "  93.3475894  116.31265793 109.28971349 115.81304932  96.44727701\n",
      " 109.86772497  95.58506362  98.64749559 106.46773154 111.92734978\n",
      "  95.03360594 109.7143656   82.23544144 113.42595327  95.71835117\n",
      " 104.2257348  113.70118997  96.31499357  90.31260422  91.26965273\n",
      " 104.16151774 111.4327195  108.14872957 120.32961172 103.50068305\n",
      " 100.7104664   89.84206857  85.60518019  95.52469642 108.24995657\n",
      " 120.98216143 114.61981662  94.09767501 114.49736127  99.60433791\n",
      "  63.60848085 101.46454367 128.18506219 101.17411001 124.61901202\n",
      "  85.44854579  86.41989767  71.11198129  74.81671235  96.02925045\n",
      "  95.76692597  54.92889018  94.34999086  82.84463142 114.54389428\n",
      " 106.53477847 105.66074106 102.46555674  79.78974605 120.13260405\n",
      "  85.63178025  93.03971554 121.39357224  96.19918228 101.00874275\n",
      " 101.24156857  95.20866221 120.9713265   95.6044999   98.93798255\n",
      " 115.85780015 128.88572689  89.33392561 104.82728852 118.39756576\n",
      "  82.84207446 129.80834374 109.85568301 115.59425298 100.85578176\n",
      "  70.19074811 121.67183267  82.18528561  77.3449525   96.30506714\n",
      " 105.18235714 103.72815638 116.73667452 121.53803815  90.24427984\n",
      "  95.3551433   77.26926178  86.95538653 111.89983601 110.43861809\n",
      " 106.93076242  90.79122664  95.96673345 109.1191152  135.46371502\n",
      "  95.97119772 117.91365324 149.4273862  102.03380147 112.49384972\n",
      "  90.62945301  86.57296009  77.32065559 118.88155274  81.83850064\n",
      " 104.37456687  96.87611664 122.88111837  87.20357311  80.29657425\n",
      "  93.94060533  91.77956278  73.49466089  76.65867719 100.14845886\n",
      "  82.37711006 108.70678821  87.99936822 122.04141214 107.8738897\n",
      "  93.50107516 100.01146629  91.89467511 108.08667286  82.06375412\n",
      " 106.92255943  83.62620866  98.85667414  85.18787175  95.76791694\n",
      " 100.18279732  96.65273931 113.70405883  77.6851746   89.6957084\n",
      " 121.10877878 128.63967166 121.50940459 111.49926835  85.04390665\n",
      " 108.45216469  99.87693587 101.28532075  98.45858209  80.5922042\n",
      " 103.8292392  120.96203602  82.75286192  90.88000495  89.4462693\n",
      " 119.91982355  99.68771118 108.08204788  81.73324662 111.03493861\n",
      " 101.39617426 104.01949807 118.01409893 113.85497486 109.80886463\n",
      "  57.44332237 100.06797801  94.16331169  96.89319325 105.97015583\n",
      "  78.19699218  80.95587858 151.39287324 100.04317428  99.63448542\n",
      "  77.76114115  89.55500246 125.15446756  80.37841765  99.63477925\n",
      " 113.0684435   99.86309146  75.8320604   88.55777025 104.91130024\n",
      "  95.05160901  91.54139628 115.42278373 105.16754691  92.52295815\n",
      " 122.39547383 100.82149102 104.51116763 121.39558941 106.60848196\n",
      "  84.70722505 100.6895014  101.02922035  83.33261145  97.40172158\n",
      " 129.56984548 115.37002577  83.06177933 146.80596849  99.87040085\n",
      "  96.78625646  98.23295674 101.48497082 109.44052965 111.72398998\n",
      "  87.56195251 104.24623787  91.48290292  85.81211353 109.25097357\n",
      " 110.66629365  90.9356698  109.69361593 104.61520392  97.98649866\n",
      "  81.61124752  98.30428103  98.10311739  86.02264613  58.98888298\n",
      " 126.54862838 119.40458136 110.87041144  95.92093455 110.19282009\n",
      "  97.76704674 112.10874443 117.95934056  89.26606275  95.9651262\n",
      "  89.6743527   94.56364681  83.77843931 102.06573096  96.82515313\n",
      " 105.86936216  81.3979314   85.49094019 119.65167409  89.14167801\n",
      " 132.96533061 100.17841502  82.38068924 108.3253397   90.67131271\n",
      "  96.07566716  99.05540724  89.65052451 102.45778872  96.12938939\n",
      "  83.17918194  93.67851548 127.66707135  95.4230488   94.88310173\n",
      "  87.04171799  96.44845469 106.59671854 103.2825368  118.74785119\n",
      " 108.2078306  106.21217717  68.9433185   99.01002742  92.38068241\n",
      "  87.86467936  79.34419195 108.82344102 104.1051943   98.2281323\n",
      "  69.26839659 101.69416211  95.8738476  102.77932395 101.23460814\n",
      " 114.58715622  95.50561207  84.1156472  120.02606713 103.59742749\n",
      " 121.92611707  73.39576238  83.62666826 112.91130819  90.19915217\n",
      " 111.86221928  91.49784868  94.66190856 105.18012889 108.64547391\n",
      "  88.42984314  92.37376218 108.27637039  79.93457972  81.4609819\n",
      "  80.65189732 130.1726579  103.37523215  92.40721899 110.70689639\n",
      " 113.72885221  73.7655231  111.20760946 113.9127471  112.31070248\n",
      " 105.4012722  104.54777705  81.37114123 109.22082645  84.20178463\n",
      " 124.30364109  97.04291532  87.65533995 112.60152905 105.25852487\n",
      " 104.45327064  98.4547283  115.4666523   86.23230096 130.35733553\n",
      "  97.8719996  114.70790093  69.38964818 108.97849132  76.50397918\n",
      "  94.35368886  95.84901716 112.64693076  89.6897216   67.56960105\n",
      " 107.17878488  69.6981731   76.1230637  106.19145799  98.94578345\n",
      " 105.60231204 111.82779031  67.57243725  97.58840857 103.30962403\n",
      "  95.87791523  81.07226734  80.4956855  110.63699549  72.32754269\n",
      " 102.46056243  99.97779463 105.83667873  82.81973211 112.00449974\n",
      "  95.36561902 112.40085702 135.90521891  80.62755964  88.02716173\n",
      "  78.23851785 129.02044027 109.62533423 118.92985036 113.66806783]\n",
      "3.2018043441388044e-05\n"
     ]
    }
   ],
   "source": [
    "distG = scipy.stats.norm(loc =100 , scale=15) # Normal distribution with mean = 100, stdev = 15\n",
    "draws = distG.rvs(1000) # 10 random draws\n",
    "p = distG.pdf(45) # pdf evaluated at x=0\n",
    "\n",
    "print(draws)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.,  20.,  56., 158., 250., 252., 179.,  64.,  12.,   5.]),\n",
       " array([ 48.55562813,  58.83935264,  69.12307715,  79.40680167,\n",
       "         89.69052618,  99.97425069, 110.2579752 , 120.54169971,\n",
       "        130.82542422, 141.10914873, 151.39287324]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 651,
       "width": 666
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(draws)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cumulative distribution function, cdf is the integral of pdf from $x'=-\\infty$ to $x'=x$:\n",
    "\n",
    "$$\\mathrm{cdf}(x|\\mu,\\sigma) = \\int_{-\\infty}^{x'} p(x'|\\mu,\\sigma) dx',$$\n",
    "\n",
    "where $\\mathrm{cdf}(\\infty) = 1$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 685,
       "width": 705
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Let's play with Gaussians! Or Normal distributions, N(mu,sigma)\n",
    "\n",
    "xgrid = np.linspace(-100,200,1000) # generate distribution for a uniform grid of x values\n",
    "gaussPDF = distG.pdf(xgrid)  # this is a function of xgrid\n",
    "\n",
    "# actual plotting\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "# Python3 f strings are awesome!\n",
    "plt.plot(xgrid, gaussPDF, ls='-', c='black', \n",
    "         label=f'$\\mu={mu},\\ \\sigma={sigma}$')\n",
    "plt.xlim(0, 200)\n",
    "plt.ylim(0, 0.03)\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel(r'$p(x|\\mu,\\sigma)$')\n",
    "plt.title('Gaussian Distribution')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16e731fd0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 685,
       "width": 691
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The same as above but now with the cdf method\n",
    "gaussCDF = distG.cdf(xgrid)\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "plt.plot(xgrid, gaussCDF, ls='-', c='black', \n",
    "         label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n",
    "plt.xlim(0, 200)\n",
    "plt.ylim(-0.01, 1.01)\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel(r'$CDF(x|\\mu,\\sigma)$')\n",
    "plt.title('Gaussian Distribution')\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian confidence levels\n",
    "\n",
    "The probability of a measurement drawn from a Gaussian distribution that is between $\\mu-a$ and $\\mu+b$ is\n",
    "\n",
    "$$\\int_{\\mu-a}^{\\mu+b} p(x|\\mu,\\sigma) dx.$$\n",
    "\n",
    "- For $a=b=1\\sigma$, we get the familar result of 68.3%.  \n",
    "- For $a=b=2\\sigma$ it is 95.4%.  \n",
    "- For $a=b=3\\sigma$ it is 99.7%. \n",
    "\n",
    "So we refer to the range $\\mu \\pm 1\\sigma$, $\\mu \\pm 2\\sigma$, and $\\mu \\pm 3\\sigma$ as the 68%, 95%, and 99.7% **confidence limits**, respectively. Note that if your distribution is not Gaussian, then these confidence intervals will be different!\n",
    "\n",
    "***We often still refer to uncertainty regions of distributions as $1\\sigma$ or $2\\sigma$ regions, which for non-Gaussian distributions usually means (for $1\\sigma$) the region enclosing the $16\\%$ and $84\\%$ quantiles.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6826894921370859\n"
     ]
    }
   ],
   "source": [
    "upper = distG.cdf(100+15)\n",
    "lower = distG.cdf(100-15)\n",
    "p = upper-lower\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the probability enclosed between $-2\\sigma$ and $+4\\sigma$? (*Verify first that you get the correct answer for the bullet points above!*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9772181968099877\n"
     ]
    }
   ],
   "source": [
    "upper = distG.cdf(100+4*15)\n",
    "lower = distG.cdf(100-2*15)\n",
    "p = upper-lower\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Log Normal\n",
    "\n",
    "Note that if $x$ is Gaussian distributed with $\\mathscr{N}(\\mu,\\sigma)$, then $y=\\exp(x)$ will have a **log-normal** distribution, where the mean of y is $\\exp(\\mu + \\sigma^2/2)$, the median is $\\exp(\\mu)$, and the mode is $\\exp(\\mu-\\sigma^2)$.  Try it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3678564938323101\n",
      "<scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x16e6d8590>\n"
     ]
    }
   ],
   "source": [
    "# Execute this cell\n",
    "x = scipy.stats.norm(0,1) # mean = 0, stdev = 1\n",
    "y = np.exp(x.rvs(100))\n",
    "\n",
    "print(y.mean())\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The catch here is that stats.norm(0,1) returns an *object* and not something that we can just do math on in the expected manner.  What *can* you do with it?  Try ```dir(x)``` to get a list of all the methods and properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " 'a',\n",
       " 'args',\n",
       " 'b',\n",
       " 'cdf',\n",
       " 'dist',\n",
       " 'entropy',\n",
       " 'expect',\n",
       " 'interval',\n",
       " 'isf',\n",
       " 'kwds',\n",
       " 'logcdf',\n",
       " 'logpdf',\n",
       " 'logsf',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'moment',\n",
       " 'pdf',\n",
       " 'ppf',\n",
       " 'random_state',\n",
       " 'rvs',\n",
       " 'sf',\n",
       " 'stats',\n",
       " 'std',\n",
       " 'support',\n",
       " 'var']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "? scipy.stats.norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6487212707001282 1.6652801222994493\n"
     ]
    }
   ],
   "source": [
    "distLN = scipy.stats.norm(0,1) # mean = 0, stdev = 1\n",
    "x = distLN.rvs(10000)\n",
    "y = np.exp(x)\n",
    "\n",
    "print(np.exp(0 + 0.5*1), y.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### $\\chi^2$ Distribution\n",
    "\n",
    "We'll run into the $\\chi^2$ distribution when we talk about Maximum Likelihood in the next lecture.\n",
    "\n",
    "If we have a Gaussian distribution with values ${x_i}$ and we scale and normalize them according to\n",
    "$$z_i = \\frac{x_i-\\mu}{\\sigma},$$\n",
    "then the sum of squares, $Q$ \n",
    "$$Q = \\sum_{i=1}^N z_i^2,$$\n",
    "will follow the $\\chi^2$ distribution.  The *number of degrees of freedom*, $k$ is given by the number of data points, $N$ (minus any constraints).  The pdf of $Q$ given $k$ defines $\\chi^2$ and is given by\n",
    "$$p(Q|k)\\equiv \\chi^2(Q|k) = \\frac{1}{2^{k/2}\\Gamma(k/2)}Q^{k/2-1}\\exp(-Q/2),$$\n",
    "where $Q>0$ and the $\\Gamma$ function would just be the usual factorial function if we were dealing with integers, but here we have half integers.\n",
    "\n",
    "This is ugly, but it is really just a formula like anything else.  Note that the shape of the distribution *only* depends on the sample size $N=k$ and not on $\\mu$ or $\\sigma$.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 679,
       "width": 687
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r\"\"\"\n",
    "Example of a chi-squared distribution\n",
    "---------------------------------------\n",
    "Figure 3.14.\n",
    "This shows an example of a :math:`\\chi^2` distribution with various parameters.\n",
    "We'll generate the distribution using::\n",
    "    dist = scipy.stats.chi2(...)\n",
    "Where ... should be filled in with the desired distribution parameters\n",
    "Once we have defined the distribution parameters in this way, these\n",
    "distribution objects have many useful methods; for example:\n",
    "* ``dist.pmf(x)`` computes the Probability Mass Function at values ``x``\n",
    "  in the case of discrete distributions\n",
    "* ``dist.pdf(x)`` computes the Probability Density Function at values ``x``\n",
    "  in the case of continuous distributions\n",
    "* ``dist.rvs(N)`` computes ``N`` random variables distributed according\n",
    "  to the given distribution\n",
    "Many further options exist; refer to the documentation of ``scipy.stats``\n",
    "for more details.\n",
    "\"\"\"\n",
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "import numpy as np\n",
    "from scipy.stats import chi2\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "if \"setup_text_plots\" not in globals():\n",
    "    from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=False)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Define the distribution parameters to be plotted\n",
    "k_values = [1, 2, 5, 7]\n",
    "linestyles = ['-', '--', ':', '-.']\n",
    "mu = 0\n",
    "x = np.linspace(-1, 20, 1000)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# plot the distributions\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "fig.subplots_adjust(bottom=0.12)\n",
    "\n",
    "for k, ls in zip(k_values, linestyles):\n",
    "    dist = chi2(k, mu)\n",
    "\n",
    "    plt.plot(x, dist.pdf(x), ls=ls, c='black',\n",
    "             label=r'$k=%i$' % k)\n",
    "\n",
    "plt.xlim(0, 10)\n",
    "plt.ylim(0, 0.5)\n",
    "\n",
    "plt.xlabel('$Q$')\n",
    "plt.ylabel(r'$p(Q|k)$')\n",
    "plt.title(r'$\\chi^2\\ \\mathrm{Distribution}$')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chi-squared per degree of freedom\n",
    "\n",
    "In practice we frequently divide $\\chi^2$ by the number of degrees of freedom, and work with:\n",
    "\n",
    "$$\\chi^2_\\mathrm{dof} = \\frac{1}{N-1} \\sum_{i=1}^N \\left(\\frac{x_i-\\overline{x}}{\\sigma}\\right)^2$$\n",
    "\n",
    "which (for large $k$) is distributed as\n",
    "\n",
    "$$ p(\\chi^2_\\mathrm{dof}) \\sim \\mathscr{N}\\left(1, \\sqrt{\\frac{2}{N-1}}\\right) $$\n",
    "\n",
    "(where $k = N-1$, and $N$ is the number of samples). Therefore, we expect $\\chi^2_\\mathrm{dof}$ to be 1, to within a few $\\sqrt{\\frac{2}{N-1}}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poisson distribution\n",
    "\n",
    "This is a distribution for a discrete variable, telling you the probability of $k$ events occuring within a certain time when the mean is $\\mu$. \n",
    "\n",
    "$$ p(k|\\mu) = \\frac{\\mu^k \\exp(-\\mu)}{k!} $$\n",
    "\n",
    "where the mean $\\mu$ completely characterizes the distribution. The mode is $(\\mu-1)$, the standard deviation is $\\sqrt{\\mu}$, the skewness is $1/\\sqrt{\\mu}$, and the kurtosis is $1/\\mu$.\n",
    "\n",
    "As $\\mu$ increases the Poisson distribution becomes more and more similar to a Gaussian with $\\mathcal{N}(\\mu,\\sqrt{\\mu})$. The Poisson distribution is sometimes called the ***law of small numbers*** or ***law of rare events***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 682,
       "width": 688
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "import numpy as np\n",
    "from scipy.stats import poisson\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "if \"setup_text_plots\" not in globals():\n",
    "    from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=True)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Define the distribution parameters to be plotted\n",
    "mu_values = [1, 5, 15]\n",
    "linestyles = ['-', '--', ':']\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# plot the distributions\n",
    "#   we generate it using scipy.stats.poisson().  Once the distribution\n",
    "#   object is created, we have many options: for example\n",
    "#   - dist.pmf(x) evaluates the probability mass function in the case of\n",
    "#     discrete distributions.\n",
    "#   - dist.pdf(x) evaluates the probability density function for\n",
    "#   evaluates\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "for mu, ls in zip(mu_values, linestyles):\n",
    "    # create a poisson distribution\n",
    "    # we could generate a random sample from this distribution using, e.g.\n",
    "    #   rand = dist.rvs(1000)\n",
    "    dist = poisson(mu)\n",
    "    x = np.arange(-1, 200)\n",
    "\n",
    "    plt.plot(x, dist.pmf(x), color='black',\n",
    "             linestyle=ls,\n",
    "             label=r'$\\mu=%i$' % mu)\n",
    "\n",
    "    plt.scatter(x, dist.pmf(x), edgecolor='black',facecolor='white')\n",
    "    \n",
    "plt.xlim(-0.5, 30)\n",
    "plt.ylim(0, 0.4)\n",
    "\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel(r'$p(x|\\mu)$')\n",
    "plt.title('Poisson Distribution')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Student's $t$ Distribution\n",
    "\n",
    "Another distribution that we'll see later is the Student's $t$ Distribution.\n",
    "\n",
    "If you have a sample of $N$ measurements, $\\{x_i\\}$, drawn from a Gaussian distribution, $\\mathscr{N}(\\mu,\\sigma)$, and you apply the transform\n",
    "\n",
    "$$t = \\frac{\\overline{x}-\\mu}{s/\\sqrt{N}},$$\n",
    "\n",
    "then $t$ will be distributed according to Student's $t$ with the following pdf (for $k$ degrees of freedom):\n",
    "\n",
    "$$p(x|k) = \\frac{\\Gamma(\\frac{k+1}{2})}{\\sqrt{\\pi k} \\Gamma(\\frac{k}{2})} \\left(1+\\frac{x^2}{k}\\right)^{-\\frac{k+1}{2}}$$\n",
    "\n",
    "As with a Gaussian, Student's $t$ is bell shaped, but has \"heavier\" tails.\n",
    "\n",
    "Note the similarity between $t$ and $z$ for a Gaussian (as defined in the $\\chi^2$ section above), which reflects the difference between data-derived estimates of the mean and standard deviation and their true values.\n",
    "\n",
    "In fact, although often approximated as a Gaussian distribution, the mean of a sample actually follows a Student's $t$ distribution (**Try this at home: check this property with scipy**). This matters when sample sizes are small, but mostly irrelevant for \"Big Data\" examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 685,
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   "source": [
    "\"\"\"\n",
    "Example of Student's t distribution\n",
    "-----------------------------------\n",
    "Figure 3.15.\n",
    "This shows an example of Student's t distribution with various parameters.\n",
    "We'll generate the distribution using::\n",
    "    dist = scipy.stats.student_t(...)\n",
    "Where ... should be filled in with the desired distribution parameters\n",
    "Once we have defined the distribution parameters in this way, these\n",
    "distribution objects have many useful methods; for example:\n",
    "* ``dist.pmf(x)`` computes the Probability Mass Function at values ``x``\n",
    "  in the case of discrete distributions\n",
    "* ``dist.pdf(x)`` computes the Probability Density Function at values ``x``\n",
    "  in the case of continuous distributions\n",
    "* ``dist.rvs(N)`` computes ``N`` random variables distributed according\n",
    "  to the given distribution\n",
    "Many further options exist; refer to the documentation of ``scipy.stats``\n",
    "for more details.\n",
    "\"\"\"\n",
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "import numpy as np\n",
    "from scipy.stats import t as student_t\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "if \"setup_text_plots\" not in globals():\n",
    "    from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=False)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Define the distribution parameters to be plotted\n",
    "mu = 0\n",
    "k_values = [1E10, 2, 1, 0.5]\n",
    "linestyles = ['-', '--', ':', '-.']\n",
    "x = np.linspace(-10, 10, 1000)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# plot the distributions\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "for k, ls in zip(k_values, linestyles):\n",
    "    dist = student_t(k, 0)\n",
    "\n",
    "    if k >= 1E10:\n",
    "        label = r'$\\mathrm{t}(k=\\infty)$'\n",
    "    else:\n",
    "        label = r'$\\mathrm{t}(k=%.1f)$' % k\n",
    "\n",
    "    plt.plot(x, dist.pdf(x), ls=ls, c='black', label=label)\n",
    "\n",
    "plt.xlim(-5, 5)\n",
    "plt.ylim(0.0, 0.45)\n",
    "\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel(r'$p(x|k)$')\n",
    "plt.title(\"Student's $t$ Distribution\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the point of all these distributions?\n",
    "\n",
    "* There are many other distributions that we haven't covered here (see the textbook).\n",
    "* The point is that we are going to make some measurement. \n",
    "* To understand the significance of our measurement, we want to know how likely it is that we would get that measurement in our experiment by random chance. \n",
    "* To determine that we need to know the shape of the distribution. Let's say that we find that $x=6$. If our data is $\\chi^2$ distributed with 2 degrees of freedom, then we would integrate the $k=2$ curve above from 6 to $\\infty$ to determine how likely it is that we would have gotten 6 or larger by chance.  If our distribution was instead $t$ distributed, we would get a *very* different answer.  \n",
    "\n",
    "Note that it is important that you decide *ahead of time* what the metric will be for deciding whether this result is significant or not.  More on this later, but see [this article](http://fivethirtyeight.com/features/science-isnt-broken/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**FUN FACT:** \"Student\" was the pen name of W. S. Gosset, who worked for the Guinness brewery in Dublin, Ireland. He was interested in the statistical analysis of small samples, e.g., the chemical properties of barley when the sample size might be as small as $3$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![https://thatsmaths.files.wordpress.com/2014/04/gosset-plaque.jpg](https://thatsmaths.files.wordpress.com/2014/04/gosset-plaque.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to get your hands dirty!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. A gaussian integral\n",
    "\n",
    "Using Monte Carlo integration, check that\n",
    "\n",
    "$$ \\int_0^\\infty x^3 \\exp\\left(-\\frac{x^2}{2\\sigma^2}\\right) \\,dx = 2\\sigma^4 .$$\n",
    "\n",
    "- Does the result converge with the number of samples? And how does the error go down?\n",
    "- Do it many times. For a given $N$, how are the result distributed? We'll talk about model fitting at lenght later on, but for now try to fit it by hand with a parametrized model. (If N is large enough you should get something that looks *very* accurate! And if $N$ is small?)\n",
    "- How does the distribution change if $N$ increases?\n",
    "\n",
    "(Hint: think about sample mean and sample variance)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[My solution](https://github.com/dgerosa/astrostatistics_bicocca_2024/blob/main/solutions/S03_cubegaussianintegral.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Kicking horses\n",
    "\n",
    "A famous early  application of low-number statistics was an analysis of Prussian cavalryman horse-kick deaths by [Bortkiewicz](https://www.wikiwand.com/en/Ladislaus_Bortkiewicz) in 1898.\n",
    "\n",
    "He studied the distribution of 122 men kicked to death by horses among 10 Prussian army corps within 20 years (so a total number of corpes of 200). He recorded the number of corps with a given number of deaths:\n",
    "\n",
    "| Number of deaths | Number of groups |\n",
    "| --- | --- |\n",
    "| 0 | 109 |\n",
    "| 1 | 65 |\n",
    "| 2 | 22 |\n",
    "| 3 | 3 |\n",
    "| 4 | 1 |\n",
    "\n",
    "- Plot the resulting probability distribution. Careful with the normalization\n",
    "- How does it look like? Again try to fit it by hand.\n",
    "\n",
    "(Hint: think about sample mean and sample variance)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[My solution](https://github.com/dgerosa/astrostatistics_bicocca_2024/blob/main/solutions/S04_horsekicks.ipynb)"
   ]
  },
  {
   "cell_type": "code",
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   "source": []
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   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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