{ "cells": [ { "cell_type": "markdown", "metadata": { "golem": {} }, "source": [ "# Measuring electron temperature with a swept Langmuir probe\n", "\n", "Written by Katerina Hromasova with input from Martina Lauerova, Georgiy Sarancha, Jan Stockel, Vojtech Svoboda, Michael Komm and others.\n", "\n", "\n", "## Theory of swept probe measurements\n", "\n", "The following figure shows the ideal Langmuir probe $I$-$V$ characteristic.\n", "\n", "\n", "\n", "The ion branch of the curve (left half of the plot) can be described by a three-parameter exponential function.\n", "\n", "$I(V) = I_{sat} \\left( 1 - \\exp \\left( -\\frac{V - V_f}{T_e} \\right)\\right)$\n", "\n", "The three parameters are the ion saturated current $I_{sat}$, the probe floating potential $V_f$ and the electron temperature $T_e$ \\[eV\\]. The shape of the characteristic changes depending on these parameters, and by fitting an experimental $I$-$V$ characteristic with an exponential function, one may retrieve their values.\n", "\n", "To collect the whole $I$-$V$ characteristic in experiment, the biasing voltage $V$ on the probe is swept (i.e. varied periodically). The exact voltage shape is irrelevant, though we most often encounter the sawtooth (zig-zag) shape and the sine shape. The biasing voltage $V$ is then plotted against the current $I$ flowing from the probe to the ground and the curve is fitted with the exponential.\n", "\n", "This notebook performs $I$-$V$ characteristic fitting throughout the current discharge. It documents the process step by step and concludes with drawing the temporal evolution of the ion saturated current $I_{sat}$, the probe floating potential $V_f$ and the electron temperature $T_e$.\n", "\n", "**Note: All the time variables are given in seconds.**\n", "\n", "## Import the basic libraries\n", "\n", "First we import basic libraries: Numpy and Matplotlib. We will import more libraries throughout the notebook as needed." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "#Pick the discharge to analyse\n", "shot_no = 35551 #35428 #test discharge for which the notebook will definitely work\n", "shot = shot_no" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Access the diagnostics data\n", "\n", "The Langmuir probe we shall be working with is placed on the PetiProbe.\n", "\n", "\n", "\n", "(The Langmuir probe is the small metal pin on the right.)\n", "\n", "The data directory of the PetiProbe is `http://golem.fjfi.cvut.cz/shots/{shot}/Diagnostics/PetiProbe/`. Here, we write the function `get_data` to download the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from urllib.error import HTTPError # recognise the error stemming from missing data\n", "import pandas as pd # for reading csv files\n", "\n", "#Define an exception which will be raised if the data is missing and stop the notebook execution\n", "class StopExecution(Exception):\n", " def _render_traceback_(self):\n", " pass\n", "\n", "def get_data(shot, identifier):\n", " URL = \"http://golem.fjfi.cvut.cz/shots/{shot}/Diagnostics/PetiProbe/{identifier}.csv\"\n", " url = URL.format(shot=shot, identifier=identifier)\n", " try:\n", " df = pd.read_csv(url, names=['time', identifier], index_col='time')\n", " except HTTPError:\n", " print('File not found at %s. Aborting notebook execution.' % url)\n", " raise StopExecution\n", " t = np.array(df.index)\n", " data = np.transpose(np.array(df))[0]\n", " return t, data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The biasing voltage $V$ is collected under the name `U_bias`. The voltage proportional to the probe current is called `U_current`. The probe current can be calculated as $I = V/R$, where $R=46.7 \\, \\Omega$ is the measuring resistor resistance.\n", "\n", "In the following, we load this data for the current shot, calculate the probe current $I$ and plot the time evolution of $I$ and $V$. Notice that at the discharge beginning, the current isn't flat zero. This is the effect of the parasitic current, which we will discuss shortly." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probe current: 0 infinities removed\n", "Probe voltage: 0 infinities removed\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Load the probe data\n", "t, U_bias = get_data(shot, 'U_bias')\n", "t, U_current = get_data(shot, 'U_current')\n", "I = U_current/46.7 #probe current\n", "V = U_bias # probe voltage\n", "\n", "#Prepare the figure\n", "fig = plt.figure('Time evolution of the raw probe data', figsize=(12,5))\n", "fig.tight_layout()\n", "\n", "#Plot the probe current\n", "ax1 = plt.subplot(121)\n", "ax1.set_title('Raw probe current')\n", "plt.plot(t*1000, I*1000)\n", "plt.xlabel('$t$ [ms]')\n", "plt.ylabel('$I$ [mA]')\n", "plt.grid(True)\n", "plt.axhline(c='k')\n", "\n", "#Plot the probe voltage\n", "ax2 = plt.subplot(122)\n", "ax2.set_title('Raw probe voltage')\n", "plt.plot(t*1000, V)\n", "plt.xlabel('$t$ [ms]')\n", "plt.ylabel('$V$ [V]')\n", "plt.grid(True)\n", "plt.axhline(c='k')\n", "\n", "# Cut all the signals so that they start at t=0 (for later calculation purposes)\n", "I = I[t>=0]\n", "V = V[t>=0]\n", "t = t[t>=0]\n", "\n", "# Remove infinities and NaNs from the signals\n", "def remove_bad_numbers(data):\n", " n_bad = np.sum(np.isnan(data) | np.isinf(data))\n", " if np.isnan(data[0]) or np.isinf(data[0]):\n", " data[0] = 0\n", " for i in range(1, data.size):\n", " if np.isnan(data[i]) or np.isinf(data[i]):\n", " data[i] = data[i-1]\n", " return data, n_bad\n", "\n", "I, n_bad = remove_bad_numbers(I)\n", "print('Probe current: %s infinities removed' % n_bad)\n", "V, n_bad = remove_bad_numbers(V)\n", "print('Probe voltage: %s infinities removed' % n_bad)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Remove the parasitic current\n", "\n", "The parasitic current appears due to the capacity of the data collection system. At high sweeping frequencies, the wires behave like capacitors and cause current oscillations proportional to the time derivative of the biasing voltage. This parasitic current adds up with the probe current, distorting it.\n", "\n", "$I_{total}(V) = I_{probe}(V) + c \\cdot \\frac{dV}{dt}$\n", "\n", "Since the biasing voltage is largely independent of the plasma parameters, $V(t)$ is periodically constant throughout the discharge and so is the parasitic current. We use this in the parasitic signal reconstruction and removal.\n", "\n", "First, we sample the parasitic current at the beginning of the discharge, where $I_{probe}=0$ and $I_{total}=c \\cdot \\frac{dV}{dt}$. This is the time period between the opening of the $B_t$ capacitor banks and the opening of the current drive capacitor banks." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://golem.fjfi.cvut.cz/shots/0/Production/Parameters/Tcd\n", "http://golem.fjfi.cvut.cz/shots/0/Production/Parameters/TBt\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from urllib.request import urlopen\n", "\n", "#Define a function for downloading single values from the GOLEM database\n", "def get_parameter(url, shot, silent=False):\n", " URL = 'http://golem.fjfi.cvut.cz/shots/%i/%s' % (shot, url)\n", " if not silent:\n", " print(URL)\n", " f = urlopen(URL)\n", " try:\n", " return np.loadtxt(f)\n", " except ValueError: # the data couldn't be converted to a row of numbers\n", " return np.array([np.nan])\n", "\n", "#Load the time of the thyristor opening\n", "Tcd = get_parameter('Production/Parameters/Tcd', shot) * 1e-6 #s\n", "TBt = get_parameter('Production/Parameters/TBt', shot) * 1e-6 #s\n", "T0 = Tcd - TBt #time before which the parasitic signal is sampled\n", "\n", "#Plot the parasitic signal\n", "plt.figure('Parasitic signal sample', figsize=(8,6))\n", "t_sample = t[t" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/svoboda/anaconda3/lib/python3.7/site-packages/IPython/core/events.py:88: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", " func(*args, **kwargs)\n", "/home/svoboda/anaconda3/lib/python3.7/site-packages/IPython/core/pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", " fig.canvas.print_figure(bytes_io, **kw)\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Clone the parasitic current sample\n", "n = int(np.floor(T0/T)) #number of whole periods in the parasitic signal\n", "par_sample = I[t < T*n] #parasitic signal sample, exactly n periods long\n", "n_final = int(np.ceil( t[-1] / (T*n) )) #number of sample repetitions required to cover the whole probe signal\n", "par = np.hstack(n_final * [par_sample]) #extended parasitic signal covering the probe signal and a bit\n", "par = par[:t.size] #extended parasitic signal covering exactly the whole probe signal\n", "\n", "#Plot the total current and the parasitic current\n", "plt.figure('Cloned parasitic signal', figsize=(8,6))\n", "plt.title('Cloned parasitic signal')\n", "plt.plot(t*1000, I*1000, label='Total current')\n", "plt.plot(t*1000, par*1000, label='Parasitic current')\n", "plt.legend()\n", "plt.xlabel('$t$ [ms]')\n", "plt.ylabel('$I$ [mA]')\n", "plt.grid(True)\n", "plt.axhline(c='k')\n", "\n", "#Plot the probe current alone\n", "plt.figure('Probe current after parasitic signal removal', figsize=(8,6))\n", "plt.title('Probe current after parasitic signal removal')\n", "I = I - par\n", "plt.plot(t*1000, I*1000, label='Probe current')\n", "plt.xlabel('$t$ [ms]')\n", "plt.ylabel('$I$ [mA]')\n", "plt.grid(True)\n", "plt.legend()\n", "plt.axhline(c='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cut the probe signal into individual $I-V$ characteristics\n", "\n", "The probe current $I$ and voltage $V$ are now ready to be plotted into the $I-V$ characteristic. However, we can't mix $I-V$ characteristics from different parts of the discharge - the plasma paramaters are different and so are the $I-V$ characteristics. We need to treat them separately, and that means breaking up the signal into individual periods of the sweeping voltage.\n", "\n", "In the following, we create a list of voltage peaks `maxima` and valleys `minima`. Specifically, we detect the first peak position in $V$ and \"predict\" the following peaks based on the sweeping period." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.signal import find_peaks\n", "from scipy.interpolate import UnivariateSpline\n", "\n", "#Prepare figure\n", "mask = (t < T)\n", "fig, axs = plt.subplots(1, 2, num='Probe voltage maxima detection', figsize=(11,5))\n", "fig.tight_layout()\n", "fig.subplots_adjust(wspace=0.3)\n", "\n", "#Select the first period of the voltage signal and plot it\n", "axs[0].set_title('First voltage period')\n", "axs[0].plot(1000*t[mask], V[mask])\n", "axs[0].set_xlabel('$t$ [ms]')\n", "axs[0].set_ylabel('$V$ [V]')\n", "axs[0].grid(True)\n", "\n", "#Smooth the voltage and plot it\n", "spline = UnivariateSpline(t[mask], V[mask])\n", "V_smooth = spline(t[mask])\n", "axs[0].plot(1000*t[mask], V_smooth)\n", "\n", "#Find the index of its peak and plot it\n", "peaks, _ = find_peaks(V_smooth)\n", "first_peak_index = peaks[0]\n", "axs[0].plot(1000*t[first_peak_index], V_smooth[first_peak_index], 'ro')\n", "\n", "#Find the number of samples that makes up one period of the probe voltage\n", "dt = t[1] - t[0]\n", "N_period = int(T/dt)\n", "\n", "#Create a list of maxima and minima indexes on the probe voltage\n", "maxima = np.array([i*N_period for i in range(100000)]) + first_peak_index\n", "maxima = maxima[maxima < t.size]\n", "minima = maxima[:-1] + int(N_period/2)\n", "extremes = np.sort(np.hstack((minima, maxima)))\n", "\n", "#Plot the voltage maxima and minima for check\n", "axs[1].set(title='Probe voltage', xlabel='$t$ [ms]', ylabel='$V$ [V]')\n", "axs[1].plot(t*1000, V)\n", "axs[1].plot(1000*t[mask], V_smooth)\n", "axs[1].plot(1000*t[maxima], V[maxima], 'ro')\n", "axs[1].plot(1000*t[minima], V[minima], 'bo')\n", "axs[1].grid(True)\n", "axs[1].axhline(c='k')\n", "#plt.xlim(0,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot a sample $I-V$ characteristic\n", "\n", "As an $I-V$ characteristic example, we take the first sweeping voltage period starting after $t = 7$ ms. We plot the $I$-$V$ characteristics separately for the voltage ramp up and ramp down to show any potential hysteresis." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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MFRzI3jegWMv06aGmP3Vq+DdfR7vq6tDS0MGab1utoztaCjqzVFSEvC9eHPIwZ074HPEv1rBhXTt+FzsZ9tdaWSFUNrn11/Kht9ToAcxsMvALb6/R73b3EdFrA96Iv0/ZrxaoBRgzZswpt99+e7fk56qrjue++8bGz0K85nnddQ8yc+bBnPuO+/nPGb1mDa3AyIQWhrXMZhmX8kfGcyE3U8v3AWhiNtXcTwsDoy2d8VV7OP/z2zj33K0Zz7Fx43B+/OMjefDBUW37JNeU2/MMcLjtYszIdzl40Dj+4JNcsfef22rN8XytZRZvMYKDVDLQWjh0eCt79g/hwAHj4MHKlGOnSv8+VbKfFgal7NO+XT21fJAnOI3f40mTEDnTeZRHOSVpj/gnyt7O0u69UaMYvGNHxpwWsn/itonrd594Ii8uWsRbJ5yQY6/s9u7dy7BhwwAYvnEjRzc08L7HHiu4PSU1b5C5TFLFt8n2WTN9ewo5Xmpa3P6qKjZ//vNsPffcPEdKllg+xbRx43DWrx/B9Om7OeGEt4p+vu7QU2XTW/XX8jnzzDN7dY1+d8r6N/IdozuH17mHfk+JlZX58ztx1Vhf7z58eN6aUNPoT/ni0T/xxdVPdOjWaOLt1KZZX/Hp/KGthg0H/Ai2eh1XF14jmz49Yw21qSn0vcu8W7b+DJnu9bf4ZJ5P6tQXWjvSt83YFyBXvufNS+4cGC+c+fND5rN/gPzL5Mndcs864/cn3tdg9OiQxwkTQk172jT3sWPDMmlS+HypHQ7LfTnssJD3eCtCnjIsdq0s3nBRURG6QAwa1HsGPfTXGmuh+mv50Fs644W8lk/TfaK6Ovdjj21vjez0l2nSpOw/ht05nrquLv/QuExLVVVBvbnnz8+0e6agnr7+JP6QNU/pTfzh9XxuzZ3vioqO9ULvTAe+Asb+xa8p8rXmd9uPUfxzxIcwVFaGi53Jk8N3Lf76sMM6H6SrqsKxJkzIvs3QoR0/7vTpWYN+MX+s6+vT+3mC+8iRBV2DlFx/DWSF6q/l09sD/XXAZdHry4Bl+Y5RjECfqktfpqlT039lijFpSjTpTsE/vNXVHT78vHnh48ybFyqflZXxw2XuazBn4H25f/ibmrJeREwb9JzXDf22Lx7yQ1887L+8qXpJp+6Pp32IXJPemLXXonOcIzXeJi4nnZTeCT7T9ydTp/n43AfTpuW5jimkh3x9fThQR74Pqcerrw9/6Lq69GF+8Q9w7LE5j1vPF5P6bziE1oqEMu7OH+vE72nqvA3Z/uTlPHlTfw1kheqv5dNrAj3wY2ArcADYAlwIjAR+Sxhedx9Qle84ZR/o3dt/dKdOLf546Pr6zEOzEpdubLusq3OfMOFtnzUr9Mk67DD3I45IuJaJ//LGo2KG2vicOZ2PRZ0WD1Tz5qU3/2cQn9Fv+vTscwJkryS/lxRM6usTL5JyXwsVkLXCPmd1dXvVtrKyvQNiAQdPLKrFizNcb2W4yGxitlezOunCbwKbfSqP+zzubG/lqa72P3z3u538cMl568SkhUkBP/X6O+kWWYlGH/bXQFao/lo+vSbQd9fSKwJ9KcSnsx0yJPSq7paokVlB5ZPnl3Ls2MJ/lLNM0Jd2unh8S60ld+RHu6mpawGkvZUjvO5Kl4GxY8PnmTcvebBAwX/STkSrpib3AQOyB8exY8PXrLrafXH1E14/9p+9uvIBb5/XIdvcDAfbRqc82MHpnLMH9kLmtsi9TJkSvi9VVZkv6PJdEHS3Xvnb04P6a/ko0BdBf/0yFao7yqe+vmM/yEOHtvdZaws0UW0z1+34wYOT31dVJQfLxKbfrgf4nlsyBaBEiRc+hV4gNDVlvvPU0YubfMM1jYOh82iWD9C5xwykDN+0Fq+fd4/XzX+lU10MUpd4VpuaQgNZIX+DztBvT279tXxyBfqyG17XHbprwpxc+uukDIXqrvJZsgSuvx5aW7uep55w2GFhLp+jjoKtW+G557Jt6RQ+OVHX1NXBvHnJ8ww9+ii89FLm7c1g7lw47jhYvx6mTw9z7GzcCD/6UQhrXRf//PGDZR6kN4d7+Ze6/TSOmMfu3fDzn8Nrr4X5eQo/T+p7p5oHuJbLw9BSM5qP/SxnbLqZA16ZIS+FGzs2zDG0c2dy+rHHwllndfgBkxnptye3/lo+uSbMUaDvpP76ZSpUd5ZP/Amw8R/6N94IP/bl9tUdOBDuvz/5h7y5OUyq9+ijIYCOGBEm8tu2LXugNwsT6h11VJhUb8YM+PKX2yep64xck+aVQmUltLQkpmQK+B6leNurwkb2J2phMi8xnQ2cw73sZBQ1NCbPuBhpZjbLWci2ARN5yGaz9cCoAs7XMfGLqK5MHqjfntz6a/nkCvR6Hr2UvVis/Udx6dLwbzz4r1wJ69aVJl9m8Nd/HWrxkLm2FovBXXel7/sf//Eod9xxMg880B6Ap08PM/FmOs6JJ7Y/2h5CjfbZZwuf/bdYQb66Ovy7fXuYGRdy5ys+izGEv19NDTz+ONxwg/HUU5AcqEON39sCfKYPkTmwH8UrTOOptimZCxFjbdj2YAj6Z9DIgbaJnpyqwXsZfiiMGPQ2bzACO2QIr78O77xT0OHDUTx8Z++9F1av7mCwb26G5cuZ8uqrYbrhrjYNSL+hQC+9Ujz4X345NDTADTcU1qR70knwpS+FptXEpuABA2DyZHjhhTCde64p6Csq4FOf6lqt7IQT3uL++9svWPJNPZ94sZMo+u1n27bQbDxjRggiK1d2Ll+FMoObboLa2szr4/lauxYeeyykDR4cgnz8cyT+W1sb38f4xZ3vsWX7YNJvb2QK9q1MZnP2wD5lCoyrDlcigwfDhg0FXfXEWMv91LCchQDhuPvWwr6EAhgzhobK+SziupQ85rdvXyifnN+feCFCeKbAN74BLS2Mh/DFra6Ga69VwJf8st28782LOuOVXqnKJ3HCmsQOdB19qmt9fdg33rFv3ryuD9lPVOzyyfUY4MQlPvIgcaa4fPt0dEhjZ3qgV499xNN75bc/G2Iym5KH5GVaMg3FSB1C2Q29K+v5ok/lcR/LKz554Cs+fcqetu9bXV32v4NZyncysXdkR4acFGMOjl6sv/42o8543a+/3gcqlMont54on3j/gGeeaW9Wf++90HqQ7QF2if0hGhth//72B9alPoSvmBobG3n2+8bN/zOcQ/a/GfLOIUnPhshoyhQ4/HC48MLszQ2pEptFnngCNm3qWuZTmjvif4ef/Sy9McEMHrzpcWIrvgxr1nT+nNOnw/e+p9o9/fe3R/foRfqhbP0D8u1TLrGi9rYzqL2NcG/mW98K92Wy3fzvSjN26oduaIDFizvfscEdFi2C55+HpUvb/g4NDSE58ZaEu7Ns0XPcReYg38xsGqlhJDvYyShGsoNHOJnXGAPALqp4iSPZt34wnAqH2GYGDh/Ku4cdwV//dXufFunfFOhFpLzV1rbXzuNNDiNHhqEM0P3NDLW16b0fIVxovPxyCOSDBuUaOxksWxbGJ9bUQE0NtbUxnr/5dyxbdyaJwX4lczmBx/kK30pqrWjgi3yZ/+AAFUBl0j5ZOfAm8KZHpzd+/euOFoD0NQr0ItJ79FSTQyHnSexJuXJlCOypVq0KC8DQoSx95x3Wspo1nBFtYEAFT3ICi2gAoJbv01z1CS7adSOtxMf1O1BBx+ZfcFatchpGXk7tYbeHMZs9ef9FyoYCvYhIZyReDMRicMwxuZv8o3F413I5p/FAQhCPB27nb/kuN075Jpu2DiN5jihL+TdR+pDExNdLdl0Ku3Zy70vn8Mya4zj+pmepq1tJbOm8Dn5g6a0qSp0BEZE+obYWHnwwjOHMIcZabuQijFZShwvuZzDrnxvG3r25jpA+299hlW8Tn/WPlEuE3VSxiAZW8mme4gRW8mlOW/ZJmsf9Weg4IH2eAr2ISHeJxcJ9+fnzc25WO/1hbpr/eyosHpwhV5P82LFhnofqajjxxN1MnRpeL14MTU0VvHVwGE1NFVxd/SvqWZxwEZHYYmBtSyuVfG7bNVyz6EWalxR50gUpOTXdi4h0t9tuC5H45pvDGMXdu8PYxsMPh0sugdpaaoETL4bLLss+ss4MLr00ufd8Y+OGjMPHYjGI3X8ONI/g+U/dyrIdXyA92Lff43+O47iCq6lY1sKNxxQ+GlF6HwV6EZFiSBwtkEUsFp6P0NAAV1/d3qkfujBiMBZj6fYYLNjCshXjs2wUv5cfaveLFrXy/Iq1HDN7DDf8/BjM4CtfUfDvK9R0LyJSYrW1sHlzuMV/9dXQ1JT+gKSOWnrbRJqaKpg3z5g61aiutoRbBYm990ONf9maGIuWvZ+nnnKefDKM+R83Trfx+wLV6EVEykR3jx5MnTSpubmCL30J1q9P7RuQ+ZHB27aFgH/nnWg8fi+mGr2ISD8Ri4V5hurrXmASL9Beu0+8l58+PHDVqvBcnU9/OkwfUKiGhtCJsKoKzjijY/tK9+kVgd7MPm5mz5jZJjO7rNT5ERHpzWqXHsPm+t/SZKcznWiGQRJr+Z7yPjzRceVKOPXUwoL2kiWhNeCll+CNN0KHw1NPhQULuv/zSG5lH+jNrBL4D+AcYBrwV2Y2rbS5EhHp5WpriT14PY/O+1fqx36NqRVPM42NzOe/mMpGhhJ/VnN6DT8etIcPhzFjQlBP1NCQeaJAgBUrFOx7WtkHemAWsMndX3D3/cDtwNwS50lEpPeLbuLXbv1/PNkylY1zvsptfI4nOZG3eR9zuDfn7nv2wOuvh6A+dGgI+PFnAuWyYoWa8XtSb+iMNwF4JeH9FuAjuXZ45plniv6Ywt27dzNixIiinqM3U/nkpvLJTeWTXdHL5tBD4e23ozef4LhDp/Di/vEcOJB7t3ffzV6Lz+S88+C44zqdy6z03UnXGwJ9QcysFqgFGDhwILt37y7q+VpaWop+jt5M5ZObyic3lU92xS6bAePGceimTW197w8fupshxw5l585BbNt2CAcPdqwhePDgFoYPP8D27Yckpb/11kF2784512+n6LuTgbuX9QLEgF8nvL8cuDzXPqeccooX2+rVq4t+jt5M5ZObyic3lU92PVI2dXXuYe6esMyZ07aqvt596lT3sWPdhw5N3ix1GTjQvakp8yHB/aST2td3l/763QEe9iwxsTfco/9fYIqZHW1mg4DzgbtLnCcRkb6rsTH5/apV8JGPwDXXUHtiM08+CVu3hhb++vowfC7VSSclT/qTqTV9wwY47TTdry+2sg/07n4Q+DLwa+Ap4A5331jaXImI9GHjM0ydu24dXHFF6G4/cmQYIP/pT1N7YjM7d4aAP2kSHHEE1NWFZ/skTv5TUwMVGSJOa2vH7u1Lx5V9oAdw91+6+3Hufoy7X1Xq/IiI9Gl1dZmjctyuXWGAfHxg/YIFbdP4vvZa8kN44mIxuPHGzIdbu7Zbci1Z9IpALyIiPSgWg9//Ho49trDtCxwcX1sbav6ptm3T2PpiUqAXEZF0sRg89xzMn1/Y9gUOjs8W7Fes0AN0ikWBXkREsrvtthCZp03L3OsuUYE322trw2N4U33ta53In+SlQC8iIrnV1sLGjbBzZ3iG7tVXh+A/blzydnffXXAX+muvBbPktG3bQud+6V4K9CIiUrhYDC6/PAT/O+9M7rTX2grLlxd8mEWL0tPXrdP9+u6mQC8iIp0Ti4WB8Im2bSt494ULM3fuX7ECpk/X+PruokAvIiKdl3rffteugneNd+4fPjx93YYNYeTe0Uerk15XKdCLiEj3+f3vO1QVj8XgV7/KPmx/8+bQxK9g33kK9CIi0nljxya/b21Nn0I3j3jNPrVvX6Krr+541iRQoBcRkc5buBAqK5PTRo7s8GFisdC3L/VQcS+9pHv2naVALyIinReLwbnnJqctWdKptvZYDB54AObNy7y+wA79kkKBXkREuia1+X737nBj/cgjw8D4DgT9WAzuuisM10897JNPdkNe+yEFehER6ZqFC9NnvwHYsiUMjF+0KNTyOyAWS6/Zd7Cfn0QU6EVEpGtiMbj00tzbXHddh6P0jBnJ7zswH48kUKAXEZGuW7o0PN42G3e47DK45pqCA/7Ond2Ut35OgV5ERLrH0qVhDkvGxXkAACAASURBVPxMzfgAa9bAFVeEmXCGDw8Pykm8f9/QAGef3ZZWUwMDBiQfItPkOpKbAr2IiHSf2lp48EFYvBgmT86+3Z498NRT4f798OEwcWJ4vWpV+HfBAmIx+OQnk3f7xjd0n76jBuTfREREpANisbA0N4e58Ftbc2+/Z09YEq1YARMmMHbs0qTklpYwH08s1r1Z7stUoxcRkeKIxeDGG7M35eezbBkLZzyeNj3uxo1dz1p/UhaB3sz+wsw2mlmrmc1MWXe5mW0ys2fM7OxS5VFERDoh3pR/0kkwdCiMHg3DhhW8e2zFl5k5MzltxQo133dEWQR64Angz4A1iYlmNg04HzgB+DjwPTPLMkGiiIiUpVgM1q+Ht9+G118PzfRNTVBdnT/or1nDhTXPpyUvW1akvPZBZRHo3f0pd38mw6q5wO3uvs/dXwQ2AbN6NnciItLtYjG4//72oL94cVjmz0/btPbZf0jr1/dMpoghGZm7lzoPbcysEfgHd384ev9dYK273xa9vxm4191/kmHfWqAWYMyYMafcfvvtRc3r3r17GdaB5qf+RuWTm8onN5VPdv2hbD7yV3/FIdu2YYAD744dy9wPPsJ997XPiVtR4Xz72+s54YS3kvbtD+WTyZlnnvkHd5+ZaV2P9bo3s/uAsRlWXenuP+vq8d29AWgAmDlzptfU1HT1kDk1NjZS7HP0Ziqf3FQ+ual8susXZfP1r4chdoABQ7dto6LqYPQuaG013nrrZFKLol+UTwf1WNO9u5/l7h/MsOQK8q8CRya8nxiliYhIX1VbC5MmJSWd9/Z/pW22e3dPZah3K4t79DncDZxvZoPN7GhgCrCuxHkSEZFiSwn0tYf/T9pDbjR5TmHKItCb2afNbAsQA+4xs18DuPtG4A7gSeBXwMXu3lK6nIqISI+YNi35/WOPUXfO41QmjLtqadFDbgpRFoHe3e9y94nuPtjdx7j72QnrrnL3Y9z9eHe/t5T5FBGRHrJwIUkz5bS2Env0e7z//cmbrV3b/rqhAS699ENJ0+dLmQR6ERGRJLEYfOhDyWlr16bdl9+8Ofy7ZEnov/fww4fHp8qXiAK9iIiUp0GDkt+vX8/U8W8mJe3eDR/5SOIEOqFn/ooVqGYfUaAXEZHydOGFaUnX7vhi2tT567J00b7zzpSE5ma45pp+14NPgV5ERMpThmF2sVd/wtwJ/5tjp/ZJ4KZPT0huaICPfhSuuAJOP71fBXsFehERKV9XXJGWVLfl/2KWewCWGYwYEb1ZsCDcwI/PBNvSAnPndnNGy5cCvYiIlK/aWpgyJSkpxlpu8otIrL1DeDhenDuMHEnopbdiRfpxt2+Hs/vHA1EV6EVEpLzdemtaUi3/SVP15VRXw8SJUFcXRuQlevRR4JZbsh931apwIdDHKdCLiEh5i8VCJE9NXrOU+69t5pVXYOnSDPs9uRF27Mh97Ouug4su6tP37BXoRUSk/C1dmtYxD0iqxs+YkbjCmfHAt/Mf1x1uuglOPbXPDr5XoBcRkd4hQ8c8Nm1qC9A7d7YnG63s9KrkbefNg6YmSHiM7RKuZjRbGc4uxqy4niWT/7sYOS8pBXoREekdamth/vz09BUr4IwzGLnx/ijBcSoYSUKzfUVFaP6PxeCsswA4m3tYxmXsYAx7GMHrjGHZS59h6KADferWvQK9iIj0HrfdBnPmpKevWcPOFb/CaAEMo4WdjGpfP3NmCPIAdXUs4RpWcU600hIWePfAAJYtCzPu9QUK9CIi0rv8+tdJze9xI9mBU0mo0Ve21eibmc0147/T1t+u4fEYy4h37kucZs9JDPjr1vWNTvkK9CIi0vt86UtpSTsZlVajb2Y2p9vvuWLlLE4/PUyQF3atoD3IO6lj8uMyDcHvbRToRUSk91m6NO1+fVqNfnQFCyf8lhYPD7FvaYG//dvwbzJnMi8CLbQH/RD4t27t/SPv8gZ6M6sqYBmR7zgiIiLd6rbbQi/6efNg7Fh2DpqA0Uqo0TuPnncVm14dmrTL/v3ph1m8uIIXq79AE6czgjei1FDbb22FxsaifoqiG1DANn+MFsuxTSVwVLfkSEREpFCxGNx1FwAjG8AXhZq4U1FQTbyyMhqKf8l7xFjHUpawiAba79eHR+H2ZoU03T/l7u9396OzLcDOvEcREREpokcfTX6/YUPu7c3ge9+LOuPX1ABQy/eZzAtJ2/X2Gn0hgT7WTdtkZWbXmdnTZvaYmd2VeCvAzC43s01m9oyZ9Y8nEIiISIdt21b4tmZhQrza2ijhrbfa1k0n+Qph/PhuyFwJ5Q307v5epnQzO83M/iPXNh3wG+CD7v4h4Fng8ugc04DzgROAjwPfM7PKLp5LRET6oLFjs6+bPj3MoHv44VBdDQ8+mBDkIekq4TieSdr3uOO6OaM9rEO97s1sRlT73gz8K/B0d2TC3Ve5+8Ho7VpgYvR6LnC7u+9z9xeBTcCs7jiniIj0LQsXgln6MLnKytBEv3kz7NoF99/fPndOm4SrhPUkTZrP+vVFyGwPKqTX/XFm9jUzexr4DvAyYO5+prt/pwh5ugC4N3o9AXglYd2WKE1ERCRJLAZ/+ZevJKWddBI88ECGwJ5q4UIYOBCA8ypWJq0677zuzGXPK6TX/dPAA8An3X0TgJn9XUdPZGb3AZkaVq50959F21wJHAQ6PEWBmdUCtQBjxoyhsci9J/bu3Vv0c/RmKp/cVD65qXyyU9nkNn/+XsaPf481a0ZTXb2dc8/dyr59+TvUDd+4kemtrRhwAk9QWdFKS2sFlZXOgQPraWx8K/cBylghgf7PCPfJV5vZr4DbyT3ULiN3PyvXejP7PPBJ4GPuHm97eRU4MmGziVFapuM3AA0AM2fO9JqoB2WxNDY2Uuxz9GYqn9xUPrmpfLJT2eTW2NjIN75xfPSuCjg+1+bt/vu/22bSub7172mJZs5raTGeeOJkLr64GLntGYV0xlvp7ucDHwBWA5cAR5jZjWaW4ckCHWdmHwfqgE+5+zsJq+4GzjezwWZ2NDAFWNcd5xQREWkTdcZrZjY/59wSZ6Z7FdwZz93fdvcfufu5hJr1I0B3Tff/XeAw4Ddmtt7MborOuRG4A3gS+BVwsbunTV4oIiLSJVFnvEZq8IQH21RURBPq9GKFNN0DYGYzgSuBSdF+RranAHSQux+bY91VwFXdcR4REZGMZoSe9jU0MoCD7KcSMCqjAd0LFsDdd8P73w833lhA574yUnCgJ3SQuxR4HGgtTnZERERKIJpWL8ZaLuAH3MQiwDh4ED7zGdiyJWy2YQN89KNhHH5vCfYdGUe/3d3vdvcX3f2l+FK0nImIiPSUhAlzhrObeNO9e3uQj3OHZct6MG9d1JEa/dfM7PvAb4F98UR3/2m350pERKREUifMyeSZZ/JuUjY6Eui/QOh5P5D2pnsHFOhFRKTPGM3rebcZPLgHMtJNOhLoP+zuBQ5IFBER6UUSpsDdzhF5N9+3L+8mZaMj9+iboofMiIiI9C1honwAzuPOKDH7wLLje1G1tyOBfjawPnpc7GNm9riZPVasjImIiPSYWAyGDQPCM+nntD1yJQT7WSmPUzvnnB7MWxd1pOn+40XLhYiISKlVVcGePQD8mk+whKv5KefxZ/OH8tZhE1mXMC9rNBqvV+jIzHgvZVqKmTkREZEec8UVSW+XcgXPcTxLD+vdc7YV8pjaR7pjGxERkbJWWwtTpqSnP/kkM2bQNkve4MG9a1rcQprup+a5F2/A+7opPyIiIqVz661w6qlJSc0PHOSSh1pwr2TgQPj2t3vPrHhQWKD/QAHb6EEzIiLS+8ViUF8PixeHKfCARq9m3772CWR27ixd9jojb6DXfXgREelXamvh3nth5UoARrKD1uhOd2srjBxZysx1XEeG14mIiPQPdXVtN+V32hHRzPdhqH1vq9Er0IuIiKR6/HFoCXelR/rrbVPnuPe+Gn3epnszuxF4jPB42sfc/a2i50pERKSU7ryz7eVORlFBK61UUlHRN2v0jwLTgKuAF83sJTP7uZldbWbnFzd7IiIiJXDeeW0va2hkQKVjBgMGQE1N6bLVGYV0xmtIfG9mE4ETgQ8BnwBuL07WRERESuTEE0NUP3gQKgdgFRXQ2jYdfpc0N0NjY7hg6Ilheh2ZAhcAd98CbIG2iYBFRET6lsbGtuF1y1vms78lzHp/8GBY1dkA3dwMp58ebv9XVsIDDxQ/2JdFZzwz+9foQTnrzWyVmY2P0s3Mvm1mm6L1J5c6ryIi0g/U1MCAATQT4wd8Ho/63VdWdq3p/ktfauvjR0sLXHZZl3OaV1kEeuA6d/+Qu08HfgH8c5R+DjAlWmqBG0uUPxER6W/caaSGgwwADDO44IKu1ebXr09Oe+GFLucyr7II9Ck9+Q+l/SHAc4HlHqwFRpjZuB7PoIiI9C+NjXDwIDWsppKDGK0MHNi1Oe6XL09PO+OMzh+vUB2+R18sZnYVsBB4EzgzSp4AvJKw2ZYobWuG/WsJtX7GjBlDY2NjMbPL3r17i36O3kzlk5vKJzeVT3Yqm9y6q3zG7djBca2tOLRNluPeyiOPrGffvs6NMn/iiROAUYlHZMiQF2lsfLnL+c2lxwK9md0HjM2w6kp3/5m7XwlcaWaXA18GvtaR40ejAxoAZs6c6TVFHv/Q2NhIsc/Rm6l8clP55KbyyU5lk1u3lU9zM1RUcH9rDQcYgFNBSwu89dbJnb5Hv3dv8nsz44IL3k8s9v4uZzeXHmu6d/ez3P2DGZafpWy6AogPYHwVODJh3cQoTUREpHhqamDwYEbaLlqpBDzvPPcLFoT1Cxakr8t0f37SpJ4ZXlcW9+jNLPEBwHOBp6PXdwMLo973s4E33T2t2V5ERKRbxWJwww08Ov6TWNSAn2tWvAULYMUK2LUr/Jsa7DPdnz/qqG7PdUblco/+WjM7nvAUwJeAxVH6L4E/BTYB7wBfKE32RESkX2lupvlvf8QP9v8qGlrnDBhgWZvt77or9/tMqqq6msnClEWgd/fzsqQ7cHEPZ0dERPq7xkYaD5zaPrSOVi64wApuah+QEl1nzOj2HBasLJruRUREysrIkdT4agaxnwoOUlnhWYN1czO8805y2sSJye8ffTR9v127uier+SjQi4iIpNq5k1jFOm7gK1TSSqsbl1wSgnqqTLPbffKTye+3bUvf5uXijqpro0AvIiKSqqYGBg5kJ6NpoYJWr2DfvjCPTqKGBlizJn33ESPyn8I9/zbdQYFeREQkE3dGsiPn8LqEx9a3SZ0Pv7kZ7rknfbtJk7ozs9kp0IuIiKRqbISWFh4lfmM+zGaXeK+9uTnzvfdTTkkeH798ORw4kL7dtGndltucyqLXvYiISFmpqYFBg9j2bvKErvF77WefDatWZd71wgsLO0VP9cRXjV5ERCRVNGEOo0enrfrIR7IH+cpKOPHE5LRMAT3X5DvdTYFeREQkVXMzXHIJY3c8kZS8axesW5d9t5aW9FnwMjXvDx7ctefad4QCvYiISKrly+G995jhf4gSQhf51PnqC5FpaN0NN/TMPPegQC8iIpKsuRluuQXceZSTo8TQGe+tDE+oHTUq+X1qU/3YlOe2mvVcsz0o0IuIiCSLetwXYvp0+PM/D8EbMt97Tw38Awb0XLM9KNCLiIgkq6kJ0diMhQNvj55el84MLroIfvCD9slvMgXx1Hv0n/hEzzXbgwK9iIhIuihyx2wtc6vfyLjJTTeF2nviGPkLLmgP4g0NYRjek08m75falF9sCvQiIiKJ4k337tDSQt209GntZs2C2towU15ra3t6vJm+oQEWLQrD8NasCbV/Mxg0CBYu7JmPEadALyIikiih6Z4BA4gtnEJTE0yZEobFzZkDDz0UNt25s/3+fGInuxtuSD6ke7h//53v9GyzPWhmPBERkXTxm+7xJvwYPPts+mYjRyZvOnJk6LT/9NPp27a0ZB5TX2yq0YuIiCSKT04fNd2nPbIuwc6doaYO7T3uGxt77sl0hVCgFxERiWtuhptvzt2NPkFKKz81NelPuIurrOz5+/OgQC8iItIu9VFz55yT96Z64j16yD4Zzle/2vP356HMAr2ZfdXM3MxGRe/NzL5tZpvM7DEzOznfMURERLpNnrFwjY3trfwHDoT3u3dn3jbTrHo9oWwCvZkdCcwBXk5IPgeYEi21wI0lyJqIiPQXw4cnv8/zLNnE4XWtrSHIf/ObmbdNHU/fU8om0APfBOogaQqiucByD9YCI8xsXElyJyIifVtzM/z7vyen/eM/hkHxWaR2xlu/PrnlP9F773VTPjuoLIbXmdlc4FV332DxmxzBBOCVhPdborStGY5RS6j1M2bMGBpz9JLsDnv37i36OXozlU9uKp/cVD7ZqWxy60r5HLViBUcfPIiRUOPcvh0WLeLZZ55h67nnpu2zY8c4zKZgBgMHOlVVW4CjorXxeBaOdtppz9LYmBa+is/de2QB7gOeyLDMBR4C3hdttxkYFb3+BXBawjF+C8zMd65TTjnFi2316tVFP0dvpvLJTeWTm8onO5VNbl0qn/p693C7PX2pqkrbvKnJfcgQ94oK94EDw+6LF2feffr0zmerEMDDniUm9ljTvbuf5e4fTF2AF4CjgQ1mthmYCDxiZmOBV4EjEw4zMUoTERHpXrlms9m1C5YsSUpqbIR9+8K9+ZaW0Iy/dm3m3WfP7r5sdlTJ79G7++PufoS7T3b3yYTm+ZPdfRtwN7Aw6n0/G3jT3UvQ7iEiIv3eD3+Y9DZTR7z169N3q6gozfj5tvOX7tQF+SWhxr8J+E/gS6XNjoiI9FkLF4ZZbbJ5992kt6nz3GcK8gDHHFOa8fNxZRfoo5r9jui1u/vF7n6Mu5/o7g+XOn8iItJHxWLwwAMwb154PN2UKcnr9+xJ6oGfOs/96NGZD5ttXH1PKbtALyIiUjKxGNx1V3g83a23pq//1rfaXqYOrdu+PfMhp04tQj47QIFeREQkk1gsfWa8t99ue1lTEx5bW1kZ/s1Uo6+shGuvLW4281GgFxERySa1u3zKTHmf+xz8zd/Ab3+b3ml/4sRwJ6CU9+ehTCbMERERKUvnnAMrVya/J0yi97GPwf79MGgQPPJI+hS3//RPpQ/yoBq9iIhIdqnV9Oh9Y2MI8i0toTP+unXJm02aBLW1PZPFfBToRUREstm2LeP7mppw/z151vZ2eZ6F06MU6EVERLJJ7YyX8N6sfXhdqrq6IuapgxToRUREslm4EAYODK8HDmyb4i7+HPpMJk0qj3vzcQr0IiIiuZiFpbUVHn8cSJ7+NtUVV/Rg3gqgQC8iIpJNYyMcPBja6Fta4OKLobk56/Nvpkwpn054cQr0IiIi2cR73cW1tobgn5HzscMfCWPvyogCvYiISDaxGPzd34U5bs3CFHg1NSxc2D79beBU0MLCdRfDaaeVVbBXoBcREcmmuRm+853wurISbrgBYjFiMbjxxnhlv5VKDnIjFxFjbaj1f+Yzpcx1EgV6ERGRbBobYd++ELxbW8OTbCK1tWGK26tHXM8DVFPL99v327KlbGr1mgJXREQkm8Tu9a2t4X2CGM3Edi/JvG9jY1mMs1ONXkREJJssU+C2WbYs+741Nd2enc5QoBcREemsVauyr0t8GE4JKdCLiIhkM2NG+6T2gwa1zYwHwOTJ8M472fdtaCh69gpRFoHezP7FzF41s/XR8qcJ6y43s01m9oyZnV3KfIqISD/S3AyXXBImy6msDL3v4/fcFyyAl17KvX+ui4AeVE6d8b7p7tcnJpjZNOB84ARgPHCfmR3n7i2lyKCIiPQj8WfRtraGGn1Cj3t+9KP8+w8ojxBbFjX6HOYCt7v7Pnd/EdgEzCpxnkREpD+oqWmfFaeior1z3ZIl2R9bl+iQQ4qVsw4pj8uN4MtmthB4GPiqu78BTADWJmyzJUpLY2a1QC3AmDFjaMw6RWH32Lt3b9HP0ZupfHJT+eSm8slOZZNbd5bPuJ//nOPij6g7cIBnf/ITtu7bx+xbbmEwYEA83L9XVYUBg3ftakt/d9Ag1pXD38rde2QB7gOeyLDMBcYAlYQWhquAW6J9vgssSDjGzcCf5zvXKaec4sW2evXqop+jN1P55KbyyU3lk53KJrduLZ85c9xD3b19mTLF3Sw5bdIk96Ym94qK5PR587ovL3kAD3uWmNhjNXp3P6uQ7czsP4FfRG9fBY5MWD0xShMRESmu3bvT0557Lj1t0aJwPz/1ubXHHVeUbHVUWdyjN7NxCW8/TajpA9wNnG9mg83saGAKsK6n8yciIv3Qxo35tzEL9+5TZswDcjzlrmeVyz36ZWY2nXBbYzOwCMDdN5rZHcCTwEHgYlePexER6QmHHQZvv517m0mTwpC7TDPkjR9fnHx1UFkEenf/bI51VxHu24uIiPScr389NMvnMn16+PfZZ9PX1dV1f546oSwCvYiISNmprQ3/3nwzrMtw19isPZgPGpS8bsqUsnigDZTJPXoREZGyVFsLDz0EU6emr/vwh9uDeWrHvTKZLAcU6EVERPI78sj0tBdeCP82N8PmzcnrRo8uepYKpUAvIiKSz/r16WnDhoV/ly9PXzdtWnHz0wEK9CIiIvkMGZKedvnl2befMaN4eekgBXoREZF8rrgi+f38+e2d9TIF9cQH4JRY+fQWEBERKVfxoH7nnXDeee3vAR59NH37TBPolIgCvYiISCFqa5MDfNy2belpZVSjV9O9iIhIV+zalfw+8ZG2ZUCBXkREpCtSZ8U78siymSwHFOhFREQ6r6Ehvel+0qTS5CULBXoREZHOuvPO9LQyGkMPCvQiIiKdl2kGvOHDez4fOSjQi4iIdNZzz6Wnlclz6OMU6EVERDrrkEPS08rkOfRxCvQiIiKd9dhj6Wll8hz6OAV6ERGRznrzzfS0MhpaBwr0IiIinZf6sJuhQ0uTjxwU6EVERDrrm9/M/b4MlE2gN7O/NbOnzWyjmS1LSL/czDaZ2TNmdnYp8ygiIpKkthbq62HOnPBvprnwS6wsHmpjZmcCc4GT3H2fmR0RpU8DzgdOAMYD95nZce7eUrrcioiIJMj2sJsyUS41+ouAa919H4C7vx6lzwVud/d97v4isAmYVaI8ioiI9Drm7qXOA2a2HvgZ8HHgPeAf3P1/zey7wFp3vy3a7mbgXnf/SYZj1AK1AGPGjDnl9ttvL2qe9+7dy7Bhw4p6jt5M5ZObyic3lU92Kpvc+mv5nHnmmX9w95mZ1vVY072Z3QeMzbDqyigfVcBs4MPAHWb2/o4c390bgAaAmTNnek2RHxHY2NhIsc/Rm6l8clP55KbyyU5lk5vKJ12PBXp3PyvbOjO7CPiph+aFdWbWCowCXgWOTNh0YpQmIiIiBSiXe/QrgTMBzOw4YBCwA7gbON/MBpvZ0cAUYF3JcikiItLLlEWve+AW4BYzewLYD3wuqt1vNLM7gCeBg8DF6nEvIiJSuLLojNfdzGw78FKRTzOK0Oogmal8clP55KbyyU5lk1t/LZ9J7p7hmbl9NND3BDN7OFsPR1H55KPyyU3lk53KJjeVT7pyuUcvIiIiRaBALyIi0ocp0HdeQ6kzUOZUPrmpfHJT+WSnsslN5ZNC9+hFRET6MNXoRURE+jAFehERkT5MgT4PM/sLM9toZq1mNjNl3eVmtsnMnjGzsxPSPx6lbTKzy3o+16VhZtPNbK2ZrTezh81sVpRuZvbtqDweM7OTS53XUjGzvzWzp6Pv1LKE9Izfpf7IzL5qZm5mo6L3+v4AZnZd9N15zMzuMrMRCev0/aH//vbm5e5acizAVOB4oBGYmZA+DdgADAaOBp4HKqPleeD9hKl8NwDTSv05eqisVgHnRK//FGhMeH0vYIQHFz1U6ryWqHzOBO4DBkfvj8j1XSp1fktURkcCvyZMeDVK35+kspkDDIheLwWW6vuTVD799rc336IafR7u/pS7P5Nh1Vzgdnff5+4vApuAWdGyyd1fcPf9wO3Rtv2BA8Oj1+8D/hi9ngss92AtMMLMxpUigyV2EXCtu+8DcPfXo/Rs36X+6JtAHeG7FKfvD+Duq9z9YPR2LeEhX6DvT1x//u3NSYG+8yYAryS83xKlZUvvDy4BrjOzV4Drgcuj9P5cJomOA043s4fM7H4z+3CUrvIBzGwu8Kq7b0hZpfJJdwGhlQNUPnEqhyzK5aE2JWVm9wFjM6y60t1/1tP5KWe5ygr4GPB37n6nmX0GuBnI+njivihP+QwAqgjNzx8G7jCz9/dg9kouT/lcQWie7rcK+S0ysysJD/la0ZN5k95LgR5w984Eo1cJ9xPjJkZp5Ejv9XKVlZktB74Svf0f4PvR61xl1afkKZ+LgJ96uKG4zsxaCQ/g6PflY2YnEu4vbzAzCGXwSNShs9+XT5yZfR74JPCx6HsE/ah88lA5ZKGm+867GzjfzAab2dHAFGAd8L/AFDM72swGAedH2/YHfwTOiF7/H+C56PXdwMKo9/Rs4E1331qKDJbYSkKHPMzsOEKHoR1k/y71G+7+uLsf4e6T3X0yodn1ZHffhr4/QOhRTui/8Cl3fydhVb///kT6829vTqrR52Fmnwa+A4wG7jGz9e5+trtvNLM7gCcJzWgXu3tLtM+XCT2HK4Fb3H1jibLf0/4G+JaZDQDeA2qj9F8Sek5vAt4BvlCa7JXcLcAtZvYEsB/4XFQry/pdEkDfn7jvEnrW/yZq9Vjr7otz/Rb1J+5+sB//9uakKXBFRET6MDXdi4iI9GEK9CIiIn2YAr2IiEgfpkAvIiLShynQi4iI9GEK9CIiIn2YAr2IZGVmx5jZ4ylpg83sRTM7ISFtspm9Gz2ieHXqo1LN7BIzu9HMhkTb7I8/hlZEikuBXkRyeRGYaGaJvxW1wJoMk5E87+7TgR8TZiVLdD7wY3d/N9rmj4hIj1CgF5Gs3L0VeBmYTkt9vAAAASNJREFUDGBmQ4CvAl/LsdtPgE9E05BiZpOB8cADRcyqiGShQC8i+TwFfCB6fTHwc3ffnG1jd99FmGv9nCjpfOAO1zScIiWhQC8i+TwFHG9mw4AvA/9WwD6JzffnR+9FpAQU6EUkn3iN/ivACnd/rYB9fgZ8zMxOBoa6+x+KmUERyU5PrxORfJ4CLgfOAk4pZAd332tmqwlP7FNtXqSEVKMXkXyeBU4EGtx9dwf2+zFwEgr0IiWlGr2I5OTu++jEb4W7rwSs+3MkIh2hGr2IdIcW4H1mtj7XRvEJc4CBQGuP5EyknzONeBEREem7VKMXERHpwxToRURE+jAFehERkT5MgV5ERKQPU6AXERHpwxToRURE+jAFehERkT5MgV5ERKQP+/9WUtf7WN7z5wAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Define where the sample extrema should start\n", "t_ext = t[extremes]\n", "sample_ext = extremes[t_ext>7e-3]\n", "\n", "#Pick samples for the first I-V characteristic\n", "sample_t1 = t[sample_ext[0] : sample_ext[1]]\n", "sample_I1 = I[sample_ext[0] : sample_ext[1]]\n", "sample_V1 = V[sample_ext[0] : sample_ext[1]]\n", "\n", "#Pick samples for the second I-V characteristic\n", "sample_t2 = t[sample_ext[1] : sample_ext[2]]\n", "sample_I2 = I[sample_ext[1] : sample_ext[2]]\n", "sample_V2 = V[sample_ext[1] : sample_ext[2]]\n", "\n", "#Plot the time evolution of the probe voltage\n", "plt.figure('One period of probe voltage', figsize=(8,4))\n", "plt.title('One period of probe voltage')\n", "plt.plot(sample_t1*1000, sample_V1, 'r.')\n", "plt.plot(sample_t2*1000, sample_V2, 'b.')\n", "plt.xlabel('$t$ [ms]')\n", "plt.ylabel('$V$ [V]')\n", "plt.grid(True)\n", "plt.axhline(c='k')\n", "\n", "#Plot the time evolution of the probe current\n", "plt.figure('One period of probe current', figsize=(8,4))\n", "plt.title('One period of probe current')\n", "plt.plot(sample_t1*1000, sample_I1*1000, 'r.')\n", "plt.plot(sample_t2*1000, sample_I2*1000, 'b.')\n", "plt.xlabel('$t$ [ms]')\n", "plt.ylabel('$I$ [mA]')\n", "plt.grid(True)\n", "plt.axhline(c='k')\n", "\n", "#Plot the two I-V characteristics\n", "plt.figure('Sample of the I-V characteristic', figsize=(8,4))\n", "plt.title('Sample of the I-V characteristic')\n", "plt.plot(sample_V1, sample_I1*1000, 'r.')\n", "plt.plot(sample_V2, sample_I2*1000, 'b.')\n", "plt.xlabel('$V$ [V]')\n", "plt.ylabel('$I$ [mA]')\n", "plt.grid(True)\n", "plt.axhline(c='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply the bin average to the $I-V$ characteristic\n", "\n", "$I$-$V$ characteristics often contain a lot of fluctuations. This can mean that the exponential fit will not converge. In the past, when fitting techniques were slow, this was alleviated by applying the *bin average* to the data.\n", "\n", "Bin averaging is breaking the data into individual \"bin\" and averaging them within that bin. Typically, the x axis (here the biasing voltage $V$) is split into even parts and all the samples within a given part (bin) are averaged. Each average is given an errorbar, calculated as the standard deviation of the averaged data. The errorbars can then be used as weights during the characteristic fitting.\n", "\n", "Today's fitting techniques are, however, much more powerful than they used to be. Bin averaging no longer provides faster result but, on the contrary, distorts the results. This is becuase its errorbars, pretty as they are, are not very representative of the actual uncertainties in the signal. It is much better to fit the $I-V$ characteristic as we collect it, sample by sample.\n", "\n", "We will demonstrate the difference between fitting the full and the bin-averaged $I-V$ characteristic in this notebook. Thereafter, we will use bin averaging to get a good first estimate of the plasma parameters. This can improve the fit quality of the real data.\n", "\n", "In the following, we calculate the bin average of the two $I$-$V$ characteristics shown in the figure above." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$I$ [mA]')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Define a function which performs bin averaging\n", "def bin_average(x, y, N_bins=100):\n", " dx = (np.nanmax(x) - np.nanmin(x)) / N_bins\n", " bins = np.linspace(np.nanmin(x) + dx/2, np.nanmax(x) - dx/2, N_bins)\n", " y_binned = []\n", " yerr = []\n", " for bin_centre in bins:\n", " y_samples = y[(bin_centre-dx/2 < x) & (x <= bin_centre+dx/2)]\n", " y_binned.append(np.mean(y_samples))\n", " yerr.append(np.std(y_samples))\n", " return bins, np.array(y_binned), np.array(yerr)\n", "\n", "#Select data from both the above I-V characteristics and apply bin averaging to them\n", "mask = slice(sample_ext[0], sample_ext[2])\n", "V_binned, I_binned, Ierr = bin_average(V[mask], I[mask], N_bins=40)\n", "\n", "#Plot the bin-averaged I-V characteristic\n", "plt.figure('Bin-averaged I-V characteristic', figsize=(8,6))\n", "plt.title('Bin-averaged I-V characteristic')\n", "plt.errorbar(V_binned, I_binned, yerr=Ierr, color='r', marker='o', ls='None', capsize=3, elinewidth=0.5)\n", "plt.grid(True)\n", "plt.axhline(c='k')\n", "plt.xlabel('$V$ [V]')\n", "plt.ylabel('$I$ [mA]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit the bin-averaged $I-V$ characteristic\n", "\n", "Next, we fit this binned $I$-$V$ characteristic by the exponential function and print the resulting plasma parameters.\n", "\n", "Notice that only a part of the curve is used as fit input, in particular the data points whose probe current value is above $-2 I_{sat}$. This improves the fit stability by disregarding the more volatile datapoints near the electron branch of the $I$-$V$ characteristic." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Isat = 10.7 mA\n", "V_fl = -12.5 V\n", "Te = 17.3 eV\n" ] }, { "data": { "image/png": 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rgSAIeu+99/jvf//LBRdcUOX2xhVXXMEvfvELDj/8cFasWMFxxx3Hxx9/zB133MHs2bM57LDDKCgooGPHjvV6XxNBAYGISDMViUTo2bNnhWUpKSl899139d7na6+9ximnnEJycjIAP/rRj6qkSUtLo2PHjlx44YWMHj2a0aNHl68744wzyqvz//3vf/P8889zxx13AMETFCtWrGDBggVcccUVAOy///7lF3yAOXPm8MADD1BSUsKqVatYunRp+fozzzwTgPXr17N+/XpGjBgBwLnnnss///nP8mN+8MEH5TUZGzZs4LPPPuO///0v48aNo3379uy8884cddRRMc9/wIABnH322YwdO5axY8eW7+Oyyy5j2bJlmBnFxcXl6Y888ki6dOlCly5dSEtL4+STTwagf//+fPDBB+Xpxo0bB8CIESPIz8+vEEQBvPzyyyxdurR8Pj8/n4KCAg477DB++ctfcvbZZ3Pqqac22O2geOiWgYhIM5WRkUFBQUGFZQUFBWRkxBysLmE6dOjA22+/zemnn87f//53jj/++PJ10SMCujt/+ctfeO+993jvvfdYsWIF++67b7X7XbZsGXfccQevvPIKH3zwASeddFKFR+PiGW3Q3cnOzi4/5rJlyzj22GPjPrd//OMfZGVlsWjRIoYMGUJJSQlTp05l+PDhLFmyhBdeeKFCnnbcccfy1+3atSufb9euHSUlJeXrKrfqrzxfWlrKW2+9VZ7vr7/+mpSUFK699loefPBBioqKOOyww/jkk0/iPpdEU0CwndZMmECeGQcfeSRuRqEZeWasmTChUY6fl51NYY8ewbF79CAvO7tRjisiDW/IkCGsX7+e/Px8SktLy395DhkypN77HDFiBM8++yxFRUVs3LiRF154oUqagoICNmzYwIknnsjvf/973n///Zj7Ou6448jOzsbdAVi8eDEAhx12GHPmzAFg6dKlfPjhh0Dwq7hz586kpaXx3Xfflf/qr6xr16507dqVBQsWAPD4449XOOa9995b/iv+008/ZdOmTYwYMYKnnnqKbdu2sWrVKubNm1dlv6WlpaxcuZIjjzyS2267jQ0bNpSf68477wwE7Qbq46mnngJgwYIFpKWlkZaWVmH9scceS3bU9/N7770HwBdffEH//v255pprGDJkSJMGBLplsJ0sM5PUpCSSwg9nMpCUlER+IzwjnJedTeqkSd8fOxIhadIk8oD0iRMb/Pgi0rB69+7NmDFjWLhwIV988QU777wzRxxxxHZVKx944IGceeaZHHDAAfTo0SNmcLFx40bGjBnD5s2bcXfuvPPOmPuaOnUqV155JQMGDKC0tJQ99tiDv//971x22WWMHz+e/fbbj3322Yd+/fqRlpbG3nvvzaBBg9hnn33YddddOeyww6rN58MPP8wFF1yAmVWoAbjoootYvnw5Bx54IO5ORkYGzz77LKeccgqvvvoq++23H7vtthvDhg2rss9t27ZxzjnnsGHDBtydyy+/nK5duzJ58mTOPfdcZsyYwUknnVSPUg26Ch40aBDFxcU89NBDVdbPnDmTrKwsBgwYQElJCSNGjOC+++7jrrvuYt68ebRr145+/fpxwgkn1Ov4CeHubXY66KCDfHttyshwhyrTpoyMKmnXzJzpmzIyvDRcv2bmzO069saOHWMee2PHjtu134Y0b968ps5Ci6Byql1LLaOlS5fWa7tPPvmkXtvl5+fXa7vtUVJS4kVFRe7u/vnnn3ufPn18y5YtjZ6PutiecjriiCN84cKFCcxNYsT6rAHveDXXRNUQbKdOkUhcy1cMH06vBQsoG2YiORIh6fLLWTFnDru99lq9jt25mm4pq1suIi1L5Z4Ky143954KCwsLOfLIIykuLsbdueeee9hhhx2aOltSCwUE26koI4PkGEFBUUYGyVHz3XNyqDzmVFK4vKGP3VDysrPpNH06nSIRijIyKJo6Vbcq6mHNhAl0j3rOGmAksCYri+4tpIczaRjRPRW2JF26dCnvd6AtmD9/flNnISHUqHA7FU2dSnGl4SWLk5Iomjq1wrJ4axLg+4aKhWY1NlRck5lJcaVti8Pl9bVmwgQwqzJVPvaK4cNJvfxykiMRjKDGI/Xyy1kxfHi9j93axFuW3WfNKr/hsyYrC9yZP2+eggERaVQKCLZT+sSJ5M+Ywfq0NBwozMggf8aMKr+Ui6p5TCjW8rKGiskQXGyB1KQkrNKFfrfXXiN/5kwKMzK+P/bMmVVuQcQbYEBwccqrtM+8mTOrXJwaosajtYl1ocddF3oRaZaa1S0DMzseuBtoDzzo7rdWWr8j8BhwEJAHnOnuy80sHXgGGAI84u6N88wf31f3Ps3plNABizh2+QI2/j2ZtIsupF274Idhx+5HcnTkaXbEy7fdgvF6xhHwUpCmLO2gqdPLnxwok1RcTIcbpvPuoRNp147yyY6cSLujg2Xt2wfL8pdRIc2WH2Sya4wnIdbskcnmzd9v164drJ0V35MLdanxqEtZ2uzZdAI6AUXh5Ko6FxFpcM0mIDCz9sBs4BggF1hoZs+7+9KoZBcC69x9LzM7C7gNOBPYDEwF9g+nRtN91iyYNYvxySUUFUUV57/DqdxTjONwbmE6uxFhBRlMYSpPLJ0IlfrU2Ebsi2qX9REGD657HpcxnSSqBhhbrppOp6smVkmbHiPtpiums+/0ibRvHwQQb7bLYNfSqvn8un0Gpx4MHToE6Tp0CKYRudlc9OV0RmyNsGqHDB7vN5X39ptYvr5DBxi4NJPz2iXRsfT7wKVd+ySe/SqTr2dAUlLsaYcdqv4tm3bcsfr5ljgcfaz2BqD2Bq3WjTfCr39ddfm0acE6kQQyd689VSMws2HAje5+XDh/HYC7/zYqzdwwzZtm1gH4FsgIH6XAzM4DBsdbQzB48GBPVMOXP/3pfwwePLTqM4AOpaXBVN38tm0V1w0c24OuG6pebNelZrDgT6vL05ftJ3qqvG7bNrj4Z0asa18pcNstXp5u2zaYdpPFvI9UCmRd6uXp9n1hElmRu+nEtvI0RXTgd12v4q1DfktJSZCupASGr8jmVysmkezfBxqFJHHNTjN4PmUiJSVBuoWRHuzmVc97ORnsQeyBSrZHUlIQIFSeOnas+jd66tSp6t+yKTn5+7/JydDpb9nsfv90UtZGKOyeQeH1U8m4omrDy8oNNN8/80yG1dLJ1JoJE9p0EDB//nxGjhzZ1Nmos48//rjG3vyqdeON9QoCNm7cSJcuXep+vAQZOXIkd9xxB4Pr82umAVUeUjm6nK6++mpefPFFTjzxRG6//fZ67X/cuHF89NFHnH/++axbt658WOm77rqLSy65pLzr6FtuuYUpU6aUb3fooYfyxhtvbOfZBWJ91szsXXeP+WY0mxoCYBdgZdR8LjC0ujTuXmJmG4B0YE2j5LAGvXsXsc8+idlX3vSpFEdV20PQULH0N1MJu9Guk8LrYz+NsDkjg+uuq5T23urT3ntv9JIZ5GX3wSs9ZTAtxlMGBZ0mVwgGAJIp5reFk8nO+z69W+yakd2JsGEDFBcH03fXXE+Xx/5MMUkUk8RWdqCYJCKnnEnyhF+ydSvl05YtwTZbtsDax58m6UdnVFhXNm2Y9wbtPv6czXRkCzuW/13Tow/bevVh82YoKgrSFhXB5s3BPmoyjmz+j0l0DmtcOq+JwJWT+Okk+EfaRFJSoHNnOHljNtO+/j5gSo5EGDj7fh77rC/fHR2kS02FLl2Cv2VTcWEKKZuDoKQy1SS0Io8/Dr/6FXz1FTzyCNx8M5x9dlPnqlV74IEHWLt2bdxDMFceCvrbb79l4cKFfP7551XS3nXXXZxzzjnVBgSJCgbqozkFBI3CzC4BLgHo2bNnwh4XKSgoSNi+ur36Kr2LiyveSy8uJvfVV/mwf/8672/HM8/koPvuY4eofre3dujA+2eeyZZKea5LWvr3h7B70nIxyuCIGvpLiC6zgWlpdN2woUq6DWlpvLcoar/nj6IwdQP7/vGPpG3YwIa0ND4+91xSTjsQmF/+S76ybul/Zt3gavqAPwNgtyDd3XezLhyYBZaHU1XbtsHWre3YurU9W7a0K5+2bm3P5s3tOHfqNDoXVgyEOlPM7zrcSP7wU9m8OUh3+We/rhIwdfJiRsy9iT3mVg2wxpHNLUxnTyKsePghrrfr+XvqpaSklNC58zZSUkpISZlGyom/okuXYrp/8Bodjh1Cly4lwXTvu6SmlpCaWkznziWkZ9/NAc8+W+U4748dG1UOddPt7sTvs7JE/s81prS0tLiHuO0wZw4dJ07EioqCBV99hV98MZs3b6bkxz+Oax/btm2rcLxNmzYxfvx4vvnmG7Zt28bkyZM57bTTuPXWW/nnP//J5s2bGTp0KHfffTdmxoknnsiAAQN488032bRpE/fffz933nknH330Eaeeeio33HADX331FaeeeioDBw7k/fffZ9999+X+++8nOTmZbdu2sWnTJjZu3Mgrr7zCLbfcwtatW9ljjz245557SElJYdq0abz44ot06NCBo446iptvvrnCOdxyyy2kpKRw+eWXAzB06FDmzJlD9+7dY57L4sWLmTJlCps2bWKnnXbivvvu4wc/+AGLFy8mKysLgKOOOorS0tLysikrpzPPPJOCggIGDRrEL3/5SwYPHkxWVhZ5eXl0796de+65h1133ZVLL72Ujh078v7773PIIYfw29+WV2YzatQovv76awYMGMDtt9/OY489xvHHH8+qVav45ptvOOKII0hPT2fw4MEUFRUxYMAA9tlnH/7whz/Qq1cvVq1axWuvvcZvf/tb0tPTWbp0KQMHDuTBBx/EzJg7dy5Tpkyhc+fODB06lOXLl/P0009Xee83V/qOrVV1PRY19gQMA+ZGzV8HXFcpzVxgWPi6A0HNgEWtPw+YFe8xE9FTYZnm3GtaJCvL14BvgqCXRPA14JGsrJjpE92jYry9Oa6ZOdO3JiVVSLM1KanK8eNNF6sc4lFbukhWVszzqbxdaYw0Hr4H8abbuNH9m2/cc3Lc33nH/Y0JM31zh4rnXtQuyWcfNtPPOsv9hBPchw1z328/9513du/UKeauy6d27dzT09379nU/9FD34/p84Bdc4D55svvtt7s/8oj7iy+6L1zo/tVX7mHnc3USb7nXVXP+n6tJnXoq3H332G/c7rvHvYvKPfA988wzftFFF5XPr1+/3t3d8/Lyypedc845/vzzz7t70Avf5MmT3d39rrvu8l69evk333zjmzdv9l122cXXrFnjy5Ytc8AXLFjg7u7nn3++33777eXbL1y40CORiA8fPtwLCgrc3f3WW2/1X//6175mzRrv27evl5aWurv7unXrqpzDtGnTyvfn7t6vXz9ftmxZzHPZunWrDxs2zFevXu3u7k8++aSff/757u7ev39//89//uPu7ldddZX369cvZjl17ty5/PXo0aP9kUcecXf3P/zhDz5mzBh3dx8/fryfdNJJXlJSUiW/y5Ytq7Dv8ePH+9NPP+3u7rvvvrtHIpGYx4qenzdvnqempvrKlSt927Ztfsghh/hrr73mRUVF3rt3b//yyy/d3f2ss87yk046qUoe3Ft2T4ULgb3NbA/ga+As4CeV0jwPjAfeBE4HXg1PUGpQ1vCxTHI4xVK5qjk5EiH58stZk5NT76rmNZmZ9IpEKjymWNZfwm5Ryzwnh/wYNSNe6VHGTtNjP4XRafp0aISOkaLLM/o+fvdK6eLtOKqmdCkpkJICvXoFywpPmM6OJRXPvWNpMed9Op3LFlQ89/J2CUURNqVn8NXPprLhpImsXUv5lJcX/F2zJnidu7EbH/4LIpHgVkssaaynJ9/Rk+/4Ad+S1r8He44bSa9eQT533hl22QW6dWuZDTeblRUr6rY8Dv3792fSpElcc801jB49muFh3yHz5s3jd7/7HYWFhaxdu5Z+/fqVD/VbNkRy//796devH73CD+See+7JypUr6dq1a4WxCc455xxmzpzJVVddVX7ct956i6VLl5an2bp1K8OGDatxqOX6nMuSJUtYsmQJxxxzDBD88u/Vq1eNQyrX5M033+Svf/1r+TaTJ08uXxc9FHRDOPjgg8vHrRg4cCDLl8isSVQAACAASURBVC8nJSWFPffckz322AMI2io88MADCTleswkIPGgTMIGgFqA98JC7f2RmNxFENM8DfwD+aGafA2sJggYAzGw5kArsYGZjgWO94hMKEofKwUP58u3Y526vvRazV8PdKl284w1c6vrIY9mx0yMRCufMqbZHxXjTxato6lSSYrQFKZo6tWJAECPd1g4dqqSr6RwrL6888FVKXoS+t08i/wc1D3y1ZsKtdJ81C3fYuDEIDCIRWL36++m777ry1YtfsH6XEXzwLaz6CvKnVN3XOe2CWxu7lEb49sE5/OOwqRSdMpFdd4XddgumnXZS0FCj3XYL2g7EWl5Pffv2ZdGiRbz44otcf/31HH300UyePJnLLruMd955h1133ZUbb7wx5hDA0cP/ls2XDQFc2/C/7s4xxxzDE088USVPb7/9Nq+88grPPPMMs2bN4tVXX62wvkOHDpSWlpbPl+Ut1rmccsop9OvXjzfffLPCPtavXx93GcUrnuGat0d0Wbdv377CcMsNoVl1TOTuL7p7X3f/obvfHC67IQwGcPfN7n6Gu+/l7ge7+5dR2/Zx953cPcXdeysYaF7SJ04kefVq/jNvHsmrV2/fhbYOnTyVXRgr9Kg4aVKVYaLjTRedvrBHD9Jnz6522OmyTqsqdBwVo9OqWOnevfTSmGUU77nXWIsSB7Og4eIPfwiHHAI/+hFcdBFMmQJ33w0PHvcw8+fDJ5/Ahg2waRN88QUsWABPPQVPnxI0qNy1NEI74AdbIpz16iTemJjN2LFw4IHQvTuk7LCFfvYRJ9qLXGb3cLtdzTN2Oq/8+DYa4Pu75bn55uBxlWjJycHyevrmm29ITk7mnHPO4eqrr2bRokXlF9ju3btTUFDAM888U+f9rlixovwi/Oc//5nDDz+8wvpDDjmE119/vbyh3aZNm/j000/jGmq5T58+LFq0CIBFixaxbNmyas8lMzOTSCRSnpfi4mI++uijGodUrsmhhx7Kk08+Wb7N8O3sjbVLly4V2nQkJSWVD+Ucj8zMTL788kuWL18OfD/sciI0q4BAJB7xdhcN8V8Y63IBrUvwUBYI5WVl1RgIVU635bTTtuvc61OLUluAU1O65GTYc0847DD48Y/hxAXTy/uTKNOZYh5In87ChfCXv8Dvfw+XTNiRc/d/lQfan8cssjiDR/krRzDq6Wvo1g3S02HoUPjJT4JH7//0J/jf/2DdupjZa33OPhseeAB23z2Y3333YH47njL48MMPOfjggxk4cCC//vWvuf766+natSsXX3wx+++/P8cdd1zMIZFrk5mZyezZs9l3331Zt24dP//5zyusz8jI4JFHHmHcuHEMGDCAYcOG8cknn7Bx40ZGjx7NgAEDOPzww2MOtXzaaaeV38aYNWsWffv2rfZcdthhB5555hmuueYaDjjgAAYOHFjecv/hhx8mKyuLgQMHEu/d5uzsbB5++GEGDBjAH//4R+6+++46l020Sy65hOOPP54jjzyyfH7AgAGcHed72qlTJ+655x6OP/54DjroILp06UJaWtp25alcdY0L2sLUVhoVNieJKqd4Gz4momFfZXUZ8rpMXRs01lRO8Zx7XYflTnRjznjLM9Y+t3RI8pcvmOm/+537z37mPmqUe58+7mYVd9et2xYfPtz94ovdZ8xw/9e/3FescA/bpjVb9R3+2KdNq9dmjTH8ceVGdC1RUwwTXV8bN250d/fS0lL/+c9/7nfeeWfMdHVtVNjkF+WmnBQQNL5ElFO8rfzd478w1uUCWpfgoUKe4z03j6+catpnXS7eDVFGDbHPzZvdly51f+4599/9zv3EE7/xww9379694uZdurgPHep+0UXud93l/sor7lGNuuulLp+52tQpIJg2LeZx6xIcKCCIT0sKCO68804/4IADfN999/Wf/OQnvmnTppjpFBAoIGjWGrucGuLXb1PXEMS7z6asRYm3POPdZ22PzkYi7v/5j/u997pPmOB+5JFVA4VddnE/8UT3665zf/pp9y+/rFttQqIex613DUE9taQLXVNqjeWkgEABQbPWFOUU7xd5XdLFc7Grz6/K2gKC7dlndRri17x7Ym9txL610KHGi3JpqfuqVe7//nfQp8I557j37+/eocP3uzmfW/1r0nwb+Fp28M9J9dWXVS2v+vZ9EYsCguapNZaTAgIFBM1aU5ZTojomck9s502JrI6udv81aMoOoeLdZ31qZapTVBR0tPTsj2d6YbuKxy4gyX/WZaafempwW+K//3UvLEzs8ZcuXVreCU9jaI0XuobQ2sqptLRUAUFdJgUEja+1BAT1SVsXiSynRAY49QmEEnFroz7tNmpT3UV+1Y4Zvuee3y9KSnLfFufx4wnuvvzyS49EIo0WFLS2C11DaU3lVFpa6pFIpLw3w2g1BQTNpmMiEUmc6B4nuwOEr6sb3Ch94kSYOLG858XqerKMN128x4+3Z8x4e32si+oewey5JcIXXwSdML35JrzxBqy6M4NdSqqmX5+aga2Hrl2D+e6zZpGXmVmlE67uUY+b9u7dm9zcXCLVHD/RNm/eTMdYg3tIBa2tnDp27Fjey2HcqosU2sKkGoLG19jlFG91fH2r7VtCDUFdNEQtSiLUtQ1BYzyaWUCSj2Omt2vnfvDB7lOmBGNNJKqtQaLouyk+baWcqKGGQB0TSavWfdasmJW9lX8lx5sOgl+1mIFZ8Os2fL1mwoTGOq02p2yci0IIenMENpaUVBnnAmDF8OGkXn55xY6jLr+cFZV6mFuTmUnl/uHKxtioLFZvkgV3zuDS/0zk+uuhQwe47TboNSu+Dq6iP0PRkz5D0qSqixTawqQagsancopPY5ZTQ9eiNJTqyqiuv/zjaRMRz7lv2FB9W4dt4K+95h5jYLwGLz/9z8WnrZQTqiEQkeo0RC1KU6pLt81lXUabe41dS8dz7qmp1Y81sZIMhg8PRoO86CL4xz/g2zvj6y5apLEoIBCRVqUug18l/NjVjDXR/rapPPkkHH00zJkDj4/OpksdBtMSaQwKCESkValL24BEq26Ey96TJ3LmmfDEE8Fw0vekTaczVdsatLt+OnUY+E4koRQQiEirsttrr5E/c2bFi/LMmez22msNfuw1EyaQXqlBY/rll1doLLjjjpC2IfZtjbT8CLvsAhMmBKM6ujd4lkXKKSAQkVYn3rYBiRZvO4vqbl9sSM1g5Ej4wx/gkEOgXz+YduizfGM71/pEwpoJE8gzo9AMD//mmdFtO4frlbZDAYGISCOrrq1B6W+mMmcOfPst/N//QbducNObY7mK61i1Q1Djsal7BnkzZ1YJMiwzk9SkJJIhqJ0AUpOSKKxr5zTSZikgEBFpZNW1NSiryUhLC55GeP11WHp9Ng+1m0SvrcFtiM5rInS+chJfTq/YALHT9Nh9IOz7xz/GzENedvCUg5vpKQcBFBCIiDSJstsaeVlZNd7W2P3+6XQsrXih71haTPsbpnPWWbBgQXBnorrHLdM2bKiyLN7Om6RtUUAgItKMVXeh35UI//oXDB8OBx0E67pU0y4hLa3Ksu45OSRVWpYULpe2SwGBiEgjq0v319U1QNyckcHXX8P998PmzTBh41QKqdou4eNzz62ybV06b5K2QwGBiEgjq0uvj9U1QCyaOpXOneGSS2DJEvjR6Pb8jotZTgalwGo683XxDiTn5lbdZxN23iTNlwICEZFmrLYGiADt2sFZL1zGjT6bdYtW85MznR9YAft2LOBGZrFiRcV91hRkSNulgEBEpBmLp7OjaIMGwZNPwiefwE9+Ai+80Iu99oKsLCirLIg1emR+cXHM0SOl7VBAICLSjNV3UKm+fYMOjh5//H9ccAE88ADstRdccQWUXD+LdHeS3YPOm9xJb4YDVUnjUkAgItKK9ey5hfvug88+g3POgdmzg8DghhsgP7/mbavr/bC62glp2RQQiIi0AX36wIMPBrcSRo+G6dPhhz+EmTNh69bY21TX+6E1wkBR0vgUEIiItCF77RW0MVi4EAYMCG4h7L8/vPBC1cGUquv9sNP06Y2YY2ksCghERNqgwYPh5ZfhxRehfXv40Y/guOOCRxjLqL+CtkUBgYhIG2UGJ5wAH3wQ3Dp45x0YOBB+8YugfYH6K2hbFBCIiLRxSUkwcWLQ8PDii+Huu2GffWDBieqvoC1RQCAiIgCkp8O998L//gc77wzHPTqRm4svZiPt1F9BG6CAQEREKhgyJAgK7rkHfp86m56dtnHnHc4OxbH7K4gemyF60uOJLYsCAhERqaJ9e/j5z2HpUjjmGLjqKhg2LGhvUFl050lrsrLi7jxJmhcFBCIiUq1ddoFnn4U5c2DFiuDphN/+FkpKmjpnkmgKCEREpEZmcMYZ8NFHMHYsTJkCw4fDp582dc4kkZpVQGBmx5tZjpl9bmbXxli/o5k9Fa7/n5n1iVp3Xbg8x8yOa8x8i4i0Bd27w1NPwZ//DDk5wSOK990X3CHIy86msEcP0mfPprBHD/Kys5s6u1JHzSYgMLP2wGzgBGA/YJyZ7Vcp2YXAOnffC/g9cFu47X7AWUA/4HjgnnB/IiKSQGYwblzQgdGIEUE7g9sGZJM6aVKFERlTJ01SUNDCNJuAADgY+Nzdv3T3rcCTwJhKacYAj4avnwGONjMLlz/p7lvcfRnwebg/ERFpADvvHPRyOGMGjFuiLo5bgw5NnYEouwAro+ZzgaHVpXH3EjPbAKSHy9+qtO0utR0wJyeHkSNHbkeWv7d+/Xq6du2akH21Ziqn+Kicaqcyik9jlNOVxO7KuGMkUuU7tvizz0j65psqaYt33pmkvfduiOzFRZ+n5hUQNAozuwS4BCApKYn169cnZL/btm1L2L5aM5VTfFROtVMZxacxyumb9u3pvW1bzOVVjp2REUxAh9xcSnr3/n5dE76f+jwB7t4sJmAYMDdq/jrgukpp5gLDwtcdgDUEo3JWSBudrqbpoIMO8kSZN29ewvbVmqmc4qNyqp3KKD6NUU5rZs70rUlJYe8DwbSJJF80aWaN20WysmpeF7W/sqmmbbZHW/k8Ae94NdfE5tSGYCGwt5ntYWY7EDQSfL5SmueB8eHr04FXwxN8HjgrfAphD2Bv4O1GyreISJuWPnEi+TNmUJiRgQMbumZwVdoMhs2ayB//WL99qrOjxtdsAgJ3LwEmEPy6/xiY4+4fmdlNZvajMNkfgHQz+xz4JXBtuO1HwBxgKfAvIMvdq9ZfiYhIg0ifOJHk1avJy8oibd1qbvp8IoceCj/9adDLoToyav6aVRsCd38ReLHSshuiXm8Gzqhm25uBmxs0gyIiEpfu3WHuXPjlL4MnEZYsgSeegG7dmjpnUp1mU0MgIiItU/TgRt1nzy5/veEXE8jOhgcegFdfDcZC+OKLps6tVEcBgYiIbJfo+/3RU9n9/osvhldegUgEDjkE3nqrlh1Kk1BAICIiDW74cHjzTUhLgyOPhL/8palzJJUpIBARkUbRt28QFPyyZzYHnd6DnTTuQbPSrBoViohI69buyWxu/GYSSQRdHSdHIiRNmkQewZMK0nRUQyAiIo2m0/TY4x50vEnjHjQ1BQQiItJoOkVij3vQaU2ELVsaOTNSgQICERFpNEXhOAaVrSCDsWOhqKiRMyTlFBCIiEijKZo6leKkpArLipOS+PCsqcydCyefDIWFTZS5Nk4BgYiINJrK4x4UZmSQP2MGJz8xkUcegXnz4MQToaCgqXPa9iggEBGRRhU97kHy6tXlTxf89Kfwpz/BggVw3HHw9e3ZFPboQboeT2wUeuxQRESajXHjYIcd4K+nZ9PtzUkkux5PbCyqIRARkWbltNMgu8v08mCgTFJxMZ2m6/HEhqKAQEREmp1uG6t5PLGaxxZl+ykgEBGRZqe6xxOrWy7bTwGBiIg0O7EeTyxql0TR9VObKEetnwICERFpdio/nri6UwYXls7g7jVqUNhQFBCIiEizFP14Ysam1SRfOJHp02HWrIrp1kyYAGZVpjUTJjRNxlsoBQQiItJooi/e3WfPjvvibQb33Qdjx8Lll8NTT32/rvusWeAO7qzJyip/3b1y5CA1UkAgIiKNJvriHT3Fc/Hu0AH+/Gc47DAYPx5ef70RMtyGKCAQEZEWo1MnePZZ2G03GDMGPv+8qXPUeiggEBGRFiU9Hf7xj6By4aSTYO3aps5R66CAQEREWpy99w5qCpYvh1NPha1bmzpHLZ8CAhERaZGGD4eHHoL//AcuuyyoMZD60+BGIiLSYp19Nnz8Mdx8MwwaBGc2dYZaMNUQiIhIi3bTTTB6NFx5Jbz+9V5NnZ0WSwGBiIi0aO3awZ/+BD/8IVz4rwv56qumzlHLpIBARERavJLHsnl7dQ9Wb06l/Q97sGpGdlNnqcVRGwIREWnR8rKzSZ00iaTiYgB6b4tQNHkSeTsE3R9LfFRDICIiLVqn6dPLg4HyZaXFMGV6E+WoZVJAICIizU5dxjzoFInE3Ee3gggffNDQOW09FBCIiEizU5cxD4oyMmLuI9cyOP10yM9v6Ny2DgoIRESkRSuaOpXipKQKy4qTklg1cSpffgkXXaROi+KhgEBERFq09IkTyZ8xg8KMDBwozMggf8YMht49kVtugaefhgcfbOpcNn8KCEREpMVLnziR5NWrycvKInn16vKnC666Co45Bq64Aj75pIkz2cwpIBARkVarXTt49FHo3Bl+8hPYsqWpc9R8NYuAwMx2MrOXzOyz8G+3atKND9N8Zmbjo5bfbGYrzayg8XItIiItQa9ewSBIixfDr37V1LlpvppFQABcC7zi7nsDr4TzFZjZTsA0YChwMDAtKnB4IVwmIiJSxcknQ1YWzJgB//53U+emeWouAcEY4NHw9aPA2BhpjgNecve17r4OeAk4HsDd33L3VY2SUxERaZFuvx369YPzzoO1a5s6N81PcwkIekZd0L8FesZIswuwMmo+N1wmIiJSq06d4I9/hEgE1KNxVY02loGZvQz8IMaqCnd03N3NrMGeGDWzS4BLAHr27Mn8+fMTst+CgoKE7as1UznFR+VUO5VRfNpaOXX7+muW1HK+55yzO488sgeZmUsYMWIN0PbKKSZ3b/IJyAF6ha97ATkx0owD7o+avx8YVylNQV2Oe9BBB3mizJs3L2H7as1UTvFROdVOZRSftlZOkaysWtNs3ep+0EHuGRnuq1cHy9pKOQHveDXXxOZyy+B5oOypgfHAczHSzAWONbNuYWPCY8NlIiIicUtKCh5F3LABLr1UvRiWaS4Bwa3AMWb2GTAqnMfMBpvZgwDuvhaYDiwMp5vCZZjZ78wsF0g2s1wzu7EJzkFERFqIfv3gppvgr3+FJ55o6tw0D80iIHD3PHc/2t33dvdRZRd6d3/H3S+KSveQu+8VTg9HLZ/s7r3dvV3498YmOA0REWkCdRkZMdpVV8HQoUEvhhs2JNWYti1oFgGBiIhIfdVlZMRo7dsHYxysXw+zZ/+wkXLbfCkgEBGRNmv//eHaa+Gll37Q5jssUkAgIiJt2q9+BbvuWsill8KmTU2dm6ajgEBERNq0jh1h0qQcli2DG29s6tw0HQUEIiLS5h1wwAYuuQTuvBMWLWrq3DQNBQQiItImRD+NED2VPY1w222QkQGXXQalpU2c2SaggEBERNqE6KcR1mRlVXkaoWvXYACk//0PHn64lp21QgoIREREQuecA4cfHjx50NZGRFRAICIiEjKD2bNh3Tq4/vqmzk3jUkAgIiISZcCAYHjk++6Dd99t6tw0HgUEIiIildx4I/To0bYaGCogEBERqSQtDe64AxYvbju1BAoIRESkzcjLzqawRw/SZ8+msEcP8rKzq0179tmQkwNDhjRiBptQh6bOgIiISGPIy84mddIkkoqLAUiOREiaNIk8gP79q6Q3gz32aNw8NiXVEIiISJvQafr08mCgTFJxMZ2mT2+iHDUvCghERKRN6BSJ1Gl5W6OAQERE2oSijIy4ltfWxXFrpYBARETahKKpUylOSqqwrDgpiaKpUyssq62L49ZKAYGIiLQJ6RMnkj9jBoUZGThQmJFB/owZpE+c2NRZaxYUEIiISJuRPnEiyatXk5eVRfLq1QoGoiggEBEREQUEIiIildWlA6PWQh0TiYiIRKmpA6PKtxhycnL49NNPAcjPzyc1NRWAvn37kpmZ2aj53l4KCERERKLU2IFRpYAgMzOz/MKfk5PT4oKAaLplICIiEqWtdmCkgEBERNqE6A6Hus+eXW2HQ/F2YNTaKCAQEZE2IbrDoeipcodD8XZg1NooIBAREYnSVjswqrVRoZntFMd+St19fQLyIyIi0uTSJ06EiRNZM2EC3WfNIrmpM9QI4nnK4JtwshrStAd2S0iOREREpNHFExB87O6DakpgZosTlB8RERFpAvG0IRiWoDQiIiLSTNUaELj75ljLzexwM5tdUxoREZGWJt7HE1ubOvVUaGaDgJ8AZwDLgL82RKZERESaSvdZs6DSo4gA3ZsgL40pnqcM+gLjwmkN8BRg7n5kA+dNREREGkk8NQSfAK8Bo939cwAz+0WD5kpEREQaVTyNCk8FVgHzzOz/zOxoan4Esc7MbCcze8nMPgv/dqsm3fgwzWdmNj5clmxm/zCzT8zsIzO7NZF5ExERaQviaVT4rLufBewDzAOuBHqY2b1mdmyC8nEt8Iq77w28Es5XEHaQNA0YChwMTIsKHO5w932AQcBhZnZCgvIlIiLSJsTddbG7b3L3P7v7yUBvYBFwTYLyMQZ4NHz9KDA2RprjgJfcfa27rwNeAo5390J3nxfmcWuYr94JypeIiEibEPdTBmY2GPgVsHu4nQGeoHz0dPdV4etvgZ4x0uwCrIyazw2XReexK3AycHeC8iUiItIm1OWxw8eBq4EPgdK6HsjMXgZ+EGPVr6Jn3N3NrM6Bhpl1AJ4AZrr7lzWkuwS4BKBnz57Mnz+/roeKqaCgIGH7as1UTvFROdVOZRQflVN8ElFOK1asYNWqVbUnbKbqEhBE3P35+h7I3UdVt87MvjOzXu6+ysx6AatjJPsaGBk13xuYHzX/APCZu99VSz4eCNMyePBgHzlyZE3J4zZ//nwSta/WTOUUH5VT7VRG8VE5xScR5ZSTk0NmZmZiMtQE6jL88TQze9DMxpnZqWVTgvLxPDA+fD0eeC5GmrnAsWbWLWxMeGy4DDP7DZBG0OBRRERE6qguNQTnEzxpkMT3twycxPRWeCswx8wuBL4Cfgzl7RYudfeL3H2tmU0HFobb3BQu601w2+ETYJGZAcxy9wcTkC8REZE2oS4BwRB3b5C6EHfPA46Osfwd4KKo+YeAhyqlySXB/SKIiIi0NXW5ZfCGme3XYDkRERGRJlOXGoJDgPfMbBmwhfCxQ3cf0CA5ExERkUZTl4Dg+AbLhYiIiDSpuAMCd/+qITMiIiIiTafWNgRmtigRaURERKT5iqeGYF8z+6CG9UbQB4CIiIi0UPEEBPvEkWbb9mZEREREmk6tAYHaDoiIiLR+demHQERERFopBQQiIiJS+y0DM7sX+IBg2OMP3D2/wXMlIiIijSqeRoWLgf7AWcD+ZlZAxQDhyQbMn4iIiDSCeBoVPhA9H44u2B8YAJwEKCAQERFp4erSdTFQPrpgLvDPxGdHREREmoIaFYqIiIgCAhEREVFAICIiIiggEBERERQQiIiICAoIREREBAUEIiIiggICERERQQGBiIiIoIBAREREqEfXxSIiIvK93NxcFi5cyIcffkj//v0ZMmQIvXv3bups1ZlqCEREROopNzeX5557jsLCQtLT0yksLOS5554jNze3qbNWZwoIRERE6mnhwoV07dqV1NRU2rVrR2pqKl27dmXhwoVNnbU6U0AgIiJST5FIhJSUlArLUlJSiEQiTZSj+lNAICIiUk8ZGRkUFBRUWFZQUEBGRkYT5aj+FBCIiIjU05AhQ1i/fj35+fmUlpaSn5/P+vXrGTJkSFNnrc4UEIiIiNRT7969GTNmDMnJyeTl5ZGcnMyYMWNa5FMGeuxQRERkO/Tu3ZvevXuz3377kZmZ2dTZqTfVEIiIiIgCAhEREVFAICIiIiggEBEREZpJQGBmO5nZS2b2Wfi3WzXpxodpPjOz8VHL/2Vm75vZR2Z2n5m1b7zci4iItHzNIiAArgVecfe9gVfC+QrMbCdgGjAUOBiYFhU4/NjdDwD2BzKAMxol1yIiIq1EcwkIxgCPhq8fBcbGSHMc8JK7r3X3dcBLwPEA7p4fpukA7AB4w2ZXRESkdWkuAUFPd18Vvv4W6BkjzS7Ayqj53HAZAGY2F1gNbASeaaB8ioiItEqN1jGRmb0M/CDGql9Fz7i7m1mdf+G7+3Fm1hF4HDiKoAYhVj4uAS4B6NmzJ/Pnz6/roWIqKChI2L5aM5VTfFROtVMZxUflFJ9ElNOKFStYtWpV7QmbqUYLCNx9VHXrzOw7M+vl7qvMrBfBL/3KvgZGRs33BuZXOsZmM3uO4BZEzIDA3R8AHgAYPHiwjxw5MlayOps/fz6J2ldrpnKKj8qpdiqj+Kic4pOIcsrJyVFPhQnwPFD21MB44LkYaeYCx5pZt7Ax4bHAXDNLCYMIzKwDcBLwSSPkWUREpNVoLgHBrcAxZvYZMCqcx8wGm9mDAO6+FpgOLAynm8JlnYHnzewD4D2C2oX7Gv8UREREWq5mMbiRu+cBR8dY/g5wUdT8Q8BDldJ8B7S8cSZFRESakeZSQyAiIiJNSAGBiIiIKCAQERERBQQiIiKCAgIRERFBAYGIiIiggEBERERQQCAiIiIoIBARERGaSU+FIiIiEsjJyeHTTz8FID8/n9TUVAD69u3boIMnKSAQERFpYHW5yGdmZpYva8wRFBUQiIiINLCmusjXhdoQiIiIiAICERERUUAgIiIiKCAQERERFBCIiIgICghEREQEBQQiIiKCoP2exgAADHhJREFUAgIRERFBAYGIiIiggEBERERQQCAiIiIoIBAREREUEIiIiAgKCERERAQFBCIiIoICAhEREUEBgYiIiKCAQERERFBAICIiIkCHps6AiIhIS5WTk8Onn34KQH5+fvnrvn37kpmZ2ZRZqzMFBCIiIvWUmZnZ4i781dEtAxEREVFAICIiIgoIREREhGYSEJjZTmb2kpl9Fv7tVk268WGaz8xsfIz1z5vZkobPsYiISOvSLAIC4FrgFXffG3glnK/AzHYCpgFDgYOBadGBg5mdChQ0TnZFRERal+YSEIwBHg1fPwqMjZHmOOAld1/r7uuAl4DjAcwsBfgl8JtGyKuIiEir01weO+zp7qvC198CPWOk2QVYGTWfGy4DmA7MAAobLIciIiKNJDc3l4ULF/Lhhx/Sv39/hgwZQu/evRv0mObuDXqA8gOZvQz8IMaqXwGPunvXqLTr3L1COwIzuwro6O6/CeenAkXAy8BN7v4jM+sD/N3d968hH5cAlwD07NnzoCeffHK7zqtMQUEBKSkpCdlXa6Zyio/KqXYqo/ionOLTmOW0YsUKdtttt2rXr169mjfeeIPOnTuzadOm8r+HHnooPXr02K5jH3nkke+6++BY6xqthsDdR1W3zsy+M7Ne7r7KzHoBq2Mk+xoYGTXfG5gPDAMGm9lygvPpYWbz3X0kMbj7A8ADAIMHD/aRI2Mmq7P58+eTqH21Ziqn+Kicaqcyio/KKT6NWU45OTk1dmb0t7/9jSFDhpCamkpubi69e/cmPz+fpKSkBs1jc2lD8DxQ9tTAeOC5GGnmAseaWbewMeGxwFx3v9fdd3b3PsDhwKfVBQMiIiLNXSQSqVJbkZKSQiQSadDjNpeA4FbgGDP7DBgVzmNmg83sQQB3X0vQVmBhON0ULhMRkf9v715D7DrrPY5/fzUaDBpjmzT2OHJSpRmNiKVmenxzaD2pbb3g6AslIFoVLYqKiiDWciic4wtvIEeFA1VzsKCVep2KSm3FUd9UUyWt1jpparVNGm+pcRyjLTV/X+yVZCfMZU/Onr12Zn8/sMlaz1p7zzN/Htb88qy119KqsWnTJubmTv7S3NzcHJs2bVrRnzsUFxVW1SFgxzztdwBv7lrfBexa5HN+DSx4/YAkScNuYmKCqanORPnRo0eZnZ3l8OHDXHLJJSv6c4dlhkCSJAFjY2NMTk6ybt06Dh06xLp165icnFzxbxkMxQyBJEk6YWxsjLGxMbZt2zawpyk6QyBJkgwEkiTJQCBJkjAQSJIkDASSJAm/ZSBJ0kC08cCi5XCGQJKkFbZ//36mpqY4cuQI55xzDkeOHGFqaor9+/e33bXjDASSJK2w3bt3s2HDBtavX89ZZ53F+vXr2bBhA7t37267a8cZCCRJWmFtPbBoOQwEkiStsLYeWLQcBgJJklbYxMQEhw8fZnZ29qQHFk1MTLTdteMMBJIkrbC2Hli0HH7tUJKkAWjjgUXL4QyBJEkyEEiSJAOBJEnCQCBJkjAQSJIkDASSJAkDgSRJwkAgSZIwEEiSJAwEkiQJA4EkScJAIEmSMBBIkiQMBJIkCQOBJEkC1rTdAUmSdMLMzAx79+4FYHZ29vjy1q1bGR8fX7GfayCQJGmIjI+Pr+gf/oV4ykCSJBkIJEmSgUCSJGEgkCRJDEkgSHJ2kluT3Nv8+9QF9ruq2efeJFd1tU8nmUmyp3mdO7jeS5J05huKQAC8H/huVV0AfLdZP0mSs4HrgH8DLgauOyU4vLaqLmxevx9EpyVJWi2GJRBMAp9rlj8HvHKefa4Abq2qh6vqT8CtwJUD6p8kSavasASCzVV1sFn+LbB5nn2eDjzYtb6/aTvm/5rTBf+ZJCvUT0mSVqWB3ZgoyW3A0+bZdG33SlVVklrmx7+2qg4keTLwFeB1wA0L9ONq4GqAzZs3Mz09vcwfNb+5ubm+fdZqZp16Y52WZo16Y516M8g6PfDAAxw8eHDpHQdsYIGgqi5baFuS3yU5r6oOJjkPmO8agAPApV3rY8B089kHmn//kuQLdK4xmDcQVNX1wPUA27dvr0svvXS+3ZZtenqafn3WamademOdlmaNemOdejPIOs3MzLRyJ8KlDMspg5uBY98auAqYmmefW4DLkzy1uZjwcuCWJGuSbARI8njg5cDPB9BnSZJWjWEJBB8CXpzkXuCyZp0k25N8BqCqHgb+G9jdvP6raVtLJxjcBeyhM5Pw6cH/CpIknbmG4uFGVXUI2DFP+x3Am7vWdwG7Ttnnr8ALVrqPkiStZsMyQyBJklpkIJAkSQYCSZJkIJAkSRgIJEkSBgJJkoSBQJIkYSCQJEkYCCRJEgYCSZLEkNy6WJKk1WxmZoa9e/cCMDs7e3x569atQ/PkQwOBJEkrbHx8fGj+8C/EUwaSJMlAIEmSDASSJAkDgSRJwkAgSZIwEEiSJAwEkiQJA4EkScJAIEmSMBBIkiQMBJIkCQOBJEnCQCBJkoBUVdt9aE2SPwC/6dPHbQT+2KfPWs2sU2+s09KsUW+sU29GpU7/WlWb5tsw0oGgn5LcUVXb2+7HsLNOvbFOS7NGvbFOvbFOnjKQJEkYCCRJEgaCfrq+7Q6cIaxTb6zT0qxRb6xTb0a+Tl5DIEmSnCGQJEkGgtOS5NVJ7k5yNMn2U7Zdk2RfkpkkV3S1X9m07Uvy/sH3ul1JLkxye5I9Se5IcnHTniSfaOpyV5KL2u5rm5K8M8kvm/H1ka72ecfVKEvy3iSVZGOz7ljqkuSjzVi6K8nXkmzo2uZ4aoz6sfkkVeVrmS/gOcA4MA1s72rfBtwJrAXOB+4DHte87gOeCTyh2Wdb27/HgGv2HeAlzfJLgemu5W8DAV4I/KjtvrZYoxcBtwFrm/VzFxtXbfe35Vo9A7iFzn1ENjqW5q3R5cCaZvnDwIebZcfTiRqN/LG5++UMwWmoqnuqamaeTZPAF6vqkaq6H9gHXNy89lXVr6rqUeCLzb6jpID1zfJTgIea5Unghuq4HdiQ5Lw2OjgE3gZ8qKoeAaiq3zftC42rUfZx4H10xtUxjqUuVfWdqnqsWb0dGGuWHU8neGzuYiDor6cDD3at72/aFmofJe8GPprkQeBjwDVNu7U5YSvw70l+lOT7SSaadmvUJckkcKCq7jxlk3Va2JvozJ6AdepmLbqsabsDwyrJbcDT5tl0bVVNDbo/Z4LFagbsAN5TVV9J8hrgs8Blg+zfMFiiRmuAs+lMd08ANyV55gC7NzSWqNMH6EyHj7xejlNJrgUeAz4/yL7pzGMgWEBVnc4fqwN0zm0eM9a0sUj7qrFYzZLcALyrWf0S8JlmebGarTpL1OhtwFerc3Lzx0mO0rm/+kjVCBauU5Ln0TnvfWcS6NTip81FqtbpFEneALwc2NGMKxjBOi3CWnTxlEF/3QzsTLI2yfnABcCPgd3ABUnOT/IEYGez7yh5CLikWf4P4N5m+Wbg9c0V4i8E/lxVB9vo4BD4Op0LC0mylc5FTn9k4XE1cqrqZ1V1blVtqaotdKZ4L6qq3+JYOkmSK+lcZ/GKqjrStcnxdILH5i7OEJyGJK8CPglsAr6ZZE9VXVFVdye5CfgFnSm6t1fVP5r3vIPOVdGPA3ZV1d0tdb8tbwH+J8ka4O/A1U37t+hcHb4POAK8sZ3uDYVdwK4kPwceBa5q/le34LjSSRxLJ/sUnW8S3NrMptxeVW9d7Dg1aqrqMY/NJ3inQkmS5CkDSZJkIJAkSRgIJEkSBgJJkoSBQJIkYSCQJEkYCCRJEgYCSX2Q5FlJfnZK29ok9yd5blfbliR/S7InyfeSXHHKe96d5H+TPLHZ59EkGwf1e0ijzEAgqR/uB8aSdB9TrgZ+MM+d3+6rqguBG+ncKrbbTuDGqvpbs89DSBoIA4Gk/7eqOgo8AGwBSPJE4L3AdYu87cvAy5p7yJNkC/AvwA9XsKuSFmAgkNQv9wDPbpbfDnyjqn690M5V9TCdh+q8pGnaCdxU3k9daoWBQFK/3AOMJ3kS8A7ggz28p/u0wc5mXVILDASS+uXYDMG7gM9X1e96eM8UsCPJRcC6qvrJSnZQ0sJ8/LGkfrkHuAa4DHhBL2+oqrkk36Pz6GdnB6QWOUMgqV/2As8Drq+qw8t4343A8zEQSK1yhkBSX1TVI5zGMaWqvg6k/z2StBzOEEgapH8AT0myZ7Gdjt2YCHg8cHQgPZNGXPyGjyRJcoZAkiQZCCRJkoFAkiRhIJAkSRgIJEkSBgJJkoSBQJIkYSCQJEnAPwEfhpPnB32EKAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import scipy.optimize as optimization # for fitting\n", "\n", "#Define the 3-parameter fit function; its arguments are a=Isat, b=Vf and c=Te\n", "def fit3par(x,a,b,c):\n", " return a*(1-np.exp((x-b)/c))\n", "\n", "#Trim the I-V characteristic\n", "Isat_estimate = np.mean(I_binned[:20])\n", "V_for_fit = V_binned[I_binned > -2*Isat_estimate]\n", "I_for_fit = I_binned[I_binned > -2*Isat_estimate]\n", "Ierr_for_fit = Ierr[I_binned > -2*Isat_estimate]\n", "\n", "#Fit the I-V characteristic\n", "p0 = [0.005, -5, 15] #initial guess of plasma parameters\n", "popt, pcov = optimization.curve_fit(fit3par, V_for_fit, I_for_fit, p0, sigma=Ierr_for_fit)\n", "\n", "#Keep a copy of the results for later\n", "popt_bin = popt\n", "pcov_bin = pcov\n", "\n", "#Plot the fitted I-V characteristic\n", "plt.figure('3-parameter fit of the binned I-V characteristic', figsize=(8,6))\n", "plt.title('3-parameter fit of the binned I-V characteristic')\n", "plt.errorbar(V_binned, I_binned, yerr=Ierr, color='k', marker='o', ls='None', capsize=3,\n", " elinewidth=0.5, alpha=0.3, label='disregarded samples')\n", "plt.errorbar(V_for_fit, I_for_fit, yerr=Ierr_for_fit, color='r', marker='o', ls='None',\n", " capsize=3, elinewidth=0.5, label='samples used for fitting')\n", "x = np.linspace(V_for_fit.min(), V_for_fit.max(), 100)\n", "plt.plot(x, fit3par(x,*popt), color = 'b')\n", "plt.grid(True)\n", "plt.legend()\n", "plt.axhline(c='k')\n", "plt.xlabel('$V$ [V]')\n", "plt.ylabel('$I$ [mA]')\n", "\n", "#Print the fit results\n", "print('Isat = %.1f mA' % (popt[0]*1000))\n", "print('V_fl = %.1f V' % popt[1])\n", "print('Te = %.1f eV' % popt[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit the full $I-V$ characteristic\n", "\n", "We use the fit of the bin-averaged data as initial guesses for the fit of the full data." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Isat = 10.7 mA (bin-averaged value: 10.7 mA)\n", "V_fl = -13.3 V (bin-averaged value: -12.5 V)\n", "Te = 17.0 eV (bin-averaged value: 17.3 eV)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Name the data to fit\n", "V_sample = V[mask]\n", "I_sample = I[mask]\n", "\n", "#Trim the I-V characteristic\n", "Isat_estimate = popt[0] #Isat of the bin-averaged I-V characteristic\n", "V_for_fit = V_sample[I_sample > -2*Isat_estimate]\n", "I_for_fit = I_sample[I_sample > -2*Isat_estimate]\n", "\n", "#Fit the I-V characteristic\n", "p0 = popt_bin #initial guess of plasma parameters\n", "popt, pcov = optimization.curve_fit(fit3par, V_for_fit, I_for_fit, p0)\n", "\n", "#Keep a copy of the results for later\n", "popt_orig = popt\n", "pcov_orig = pcov\n", "\n", "#Plot the fitted I-V characteristic\n", "plt.figure('3-parameter fit of the full I-V characteristic', figsize=(8,6))\n", "plt.title('3-parameter fit of the full I-V characteristic')\n", "plt.errorbar(V_sample, I_sample, color='k', marker='o', ls='None', alpha=0.3,\n", " label='disregarded samples', zorder=0)\n", "plt.errorbar(V_for_fit, I_for_fit, color='r', marker='o', ls='None',\n", " label='samples used for fitting', zorder=0)\n", "x = np.linspace(V_for_fit.min(), V_for_fit.max(), 100)\n", "plt.plot(x, fit3par(x,*popt), color = 'b', zorder=5, label='fit')\n", "plt.grid(True)\n", "plt.legend()\n", "plt.axhline(c='k')\n", "plt.xlabel('$V$ [V]')\n", "plt.ylabel('$I$ [mA]')\n", "\n", "#Print the fit results\n", "print('Isat = %.1f mA (bin-averaged value: %.1f mA)' % (popt[0]*1000, p0[0]*1000))\n", "print('V_fl = %.1f V (bin-averaged value: %.1f V)' % (popt[1], p0[1]))\n", "print('Te = %.1f eV (bin-averaged value: %.1f eV)' % (popt[2], p0[2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Investigate the fit result errorbars with the covariance matrix\n", "\n", "The Python fitting function `scipy.optimization.curve_fit` returns, beside the fit result values `popt`, also the so-called covariance matrix `pcov`. This is a 2D matrix whose diagonal contains the squares of the \"fit error\". They serve as an estimate of the fit results errorbars." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Isat = 10.7 +- 0.1 mA (bin-averaged value: 10.7 mA)\n", "V_fl = -13.3 +- 0.1 V (bin-averaged value: -12.5 V)\n", "Te = 17.0 +- 0.2 eV (bin-averaged value: 17.3 V)\n" ] } ], "source": [ "#Extract the diagonal components of the matrix and apply square root to them\n", "errs = np.sqrt(pcov.diagonal())\n", "\n", "#Print the fit results including the covariance matrix errorbars\n", "print('Isat = %.1f +- %.1f mA (bin-averaged value: %.1f mA)' % (popt[0]*1000, errs[0]*1000, popt_bin[0]*1000))\n", "print('V_fl = %.1f +- %.1f V (bin-averaged value: %.1f V)' % (popt[1], errs[1], popt_bin[1]))\n", "print('Te = %.1f +- %.1f eV (bin-averaged value: %.1f V)' % (popt[2], errs[2], popt_bin[2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the values obtained by fitting the bin-averaged $I-V$ characteristics may not fall within these errorbars.\n", "\n", "These errorbars, however, may not very representative of the uncertainty of the fit results. To get a real sense for the $I_{sat}$, $V_f$ and $T_e$ uncertainty due to the data fluctuation, we employ so-called *bootstrapping*.\n", "\n", "## Investigate the fit result errorbars with bootstraping\n", "\n", "Bootstraping ([Wikipedia article](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))) is a simple and flexible tool for calculating errorbars. The general idea is such:\n", "\n", "1. Calculate your quantity (here $I_{sat}$, $V_f$ and $T_e$) from your dataset (here $I-V$ characteristic).\n", "2. Create a large number of synthetic datasets, based on the original one.\n", "3. Calculate your quantity for each of the synthetic datasets.\n", "4. Look how your quantity varies between these datasets.\n", "\n", "In other words, the quantity uncertainty ($I_{sat}$, $V_f$ and $T_e$ errobars) are gauged based on how much the \"synthetic\" $I_{sat}$, $V_f$ and $T_e$ vary across the synthetic datasets. Bootstrapping has a lot of advantages, some of which will be showed later in the notebook. Among them is that it can be applied to *any* dataset and *any* quantity you calculate from it. It makes no assumptions on the distribution function of the data (which, in other methods, in frequently assumed to be Gaussian) and you can adjust its precision easily by changing the number of synthetic datasets. Its major drawback it that it takes a lot of time, particularly if calculating your quantity is complicated (or, God forbid, cannot be automatised). But in the case of our $I-V$ characteristics, the time needed is not that long. (In the current tests, the entire notebook takes about 30 seconds to execute on a personal computer.) Plus, the synthetic dataset are by nature independent, so the calculation can be easily parallelised.\n", "\n", "The following function creates a number of synthetic $I-V$ characteristics (by default 100), fits them and returns the resulting 100 samples of synthetic $I_{sat}$, $V_f$ and $T_e$." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'arch'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0march\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbootstrap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_bootstrap_fit_replicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mI\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mV\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.005\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m '''\n\u001b[1;32m 5\u001b[0m \u001b[0mReturn\u001b[0m \u001b[0mthree\u001b[0m \u001b[0marrays\u001b[0m \u001b[0mof\u001b[0m \u001b[0mbootstrap\u001b[0m \u001b[0mreplicates\u001b[0m \u001b[0mof\u001b[0m \u001b[0mI_sat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mV_f\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mT_e\u001b[0m \u001b[0mof\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mI\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mV\u001b[0m \u001b[0mcharacteristic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'arch'" ] } ], "source": [ "from arch import bootstrap\n", "\n", "def create_bootstrap_fit_replicates(I, V, p0=[0.005, -15, 15], bss=100, b=7):\n", " '''\n", " Return three arrays of bootstrap replicates of I_sat, V_f and T_e of given I-V characteristic.\n", " \n", " p0 ... initial parameter estimates\n", " bss ... number of bootstrap replicates to use for errorspan calculation\n", " b ... average size of bootstrap block (if 0, normal sample-by-sample bootstrap is used)\n", " '''\n", "\n", " #Fit the original I-V characteristic\n", " popt, pcov = optimization.curve_fit(fit3par, V, I, p0)\n", " p0 = popt #initial guess for all the replicates\n", "\n", " #Prepare variables and the bootstrap iterator\n", " indexes = np.arange(V.size)\n", " if b:\n", " bs = bootstrap.StationaryBootstrap(b, indexes)\n", " #something that will provide a number of bootstrapped indexes rather quickly\n", " else:\n", " bs = bootstrap.IIDBootstrap(indexes)\n", " \n", " #Perform bootstrapping\n", " synthetic_results = []\n", " for i_bs, ((bs_indexes,),_) in enumerate(bs.bootstrap(bss)):\n", " #print('bootstrap', i_bs+1)\n", " I_bs = I[bs_indexes]\n", " V_bs = V[bs_indexes]\n", " try:\n", " popt, pcov = optimization.curve_fit(fit3par, V_bs, I_bs, p0)\n", " except RuntimeError: #the fitting goes on for too long; is probably failing\n", " popt = np.array([np.nan]*3)\n", " synthetic_results.append(popt)\n", " \n", " return np.transpose(np.array(synthetic_results))\n", "\n", "# Create the bootstrap replicates\n", "synth_Isat, synth_Vf, synth_Te = create_bootstrap_fit_replicates(\n", " I_for_fit, V_for_fit, p0=popt, bss=1000, b=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we visualise the different fits by plotting them onto the $I-V$ characteristic." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Prepare the figure\n", "fig = plt.figure('Bootstrap 3-parameter fits of the real I-V characteristic', figsize=(8,6))\n", "fig.tight_layout()\n", "plt.title('Bootstrap 3-parameter fit of the real I-V characteristic')\n", "\n", "# Plot the probe data\n", "plt.errorbar(V_for_fit, I_for_fit, color='r', marker='o', ls='None',\n", " label='samples used for fitting', zorder=0)\n", "\n", "# Plot the synthetic fits\n", "x = np.linspace(V_for_fit.min(), V_for_fit.max(), 100)\n", "for i in range(synth_Isat.size):\n", " p = [synth_Isat[i], synth_Vf[i], synth_Te[i]]\n", " plt.plot(x, fit3par(x, *p), color='b', zorder=5, alpha=0.05)\n", " \n", "# Plot the original fit and label the plot\n", "plt.plot(x, fit3par(x, *popt_orig), color='y', zorder=10, label='original fit')\n", "plt.grid(True)\n", "plt.legend()\n", "plt.axhline(c='k')\n", "plt.xlabel('$V$ [V]')\n", "plt.ylabel('$I$ [mA]')\n", "plt.ylim(I_for_fit.min()-0.001, I_for_fit.max()+0.001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the original fit (yellow) remains in the middle of all the synthetic data fits (blue). This is a general feature of bootstrapping.\n", "\n", "Next, we investigate the fit result variability directly by plotting histograms of the synthetic values $I_{sat}$, $V_f$ and $T_e$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set the number of bins\n", "N = int(np.sqrt(synth_Isat.size))\n", "\n", "#Prepare the figure\n", "fig, axs = plt.subplots(1, 3, num='Synthetic data histograms', figsize=(12,5), sharey=True)\n", "fig.tight_layout()\n", "fig.subplots_adjust(wspace=0)\n", "\n", "# Draw Isat histogram\n", "axs[0].set_title('Synthetic $I_{sat}$')\n", "axs[0].hist(synth_Isat*1000, bins=N)\n", "axs[0].axvline(np.nanmean(synth_Isat)*1000, c='k')\n", "axs[0].set_xlabel('$I_{sat}$ [mA]')\n", "axs[0].grid(True)\n", "axs[0].set_ylabel('sample count')\n", "\n", "# Draw Vf histogram\n", "axs[1].set_title('Synthetic $V_f$')\n", "axs[1].hist(synth_Vf, bins=N)\n", "axs[1].axvline(np.nanmean(synth_Vf), c='k')\n", "axs[1].set_xlabel('$V_f$ [V]')\n", "axs[1].grid(True)\n", "\n", "# Draw Te histogram\n", "axs[2].set_title('Synthetic $T_e$')\n", "axs[2].hist(synth_Te, bins=N)\n", "axs[2].axvline(np.nanmean(synth_Te), c='k')\n", "axs[2].set_xlabel('$T_e$ [eV]')\n", "axs[2].grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the probability distributions of the synthetic fit results are Gaussian and their means are equal to the original fit values. This is another general feature of bootstrapping.\n", "\n", "To capture the variability within these probability distributions, one can simply use the standard deviation. The distributions are Gaussian, so their variability is symmetric and a symmetric standard deviation captures it well. However, sometimes it is more informative to use an alternative - the 95% *confidence interval*.\n", "\n", "A confidence interval ([Wikipedia page](https://en.wikipedia.org/wiki/Confidence_interval)) is a measure of variability which says \"I am 95 % certain that the real value is somewhere within this interval\". In other words, \"ignoring the top 2.5 % and the bottom 2.5 % of values, this is the minimum and maximum value I expect from the variable\". Ignoring the top and bottom 2.5 % removes the outliers (the really far-fetched variable samples) yet still captures most of the variable variability. We will test confidence intervals shortly.\n", "\n", "## Compare various ways to calculate $I_{sat}$, $V_f$ and $T_e$ and their errobars\n", "\n", "In the following, we compare several ways of determining the $I_{sat}$, $V_f$ and $T_e$ values and errorbars." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Results of bin averaging\n", "errs = np.sqrt(pcov_bin.diagonal())\n", "print('Data bin-averaged, covariance matrix errorbars:')\n", "print('Isat = %.1f +- %.1f mA' % (popt_bin[0]*1000, errs[0]*1000))\n", "print('V_fl = %.1f +- %.1f V' % (popt_bin[1], errs[1]))\n", "print('Te = %.1f +- %.1f eV' % (popt_bin[2], errs[2]))\n", "\n", "# Results of the whole data fit\n", "errs = np.sqrt(pcov_orig.diagonal())\n", "print('\\nFull data, covariance matrix errorbars:')\n", "print('Isat = %.1f +- %.1f mA' % (popt_orig[0]*1000, errs[0]*1000))\n", "print('V_fl = %.1f +- %.1f V' % (popt_orig[1], errs[1]))\n", "print('Te = %.1f +- %.1f eV' % (popt_orig[2], errs[2]))\n", "\n", "# Results of bootstrap with standard deviation errorbars\n", "print('\\nFull data, bootstrap fits with standard deviation errorbars:')\n", "print('Isat = %.1f +- %.1f mA' % (synth_Isat.mean()*1000, synth_Isat.std()*1000))\n", "print('V_fl = %.1f +- %.1f V' % (synth_Vf.mean(), synth_Vf.std()))\n", "print('Te = %.1f +- %.1f eV' % (synth_Te.mean(), synth_Te.std()))\n", "\n", "# Results of bootstrap with 95% confidence intervals\n", "print('\\nFull data, bootstrap fits with 95% confidence intervals:')\n", "print('Isat = %.1f mA (%.1f to %.1f mA)' % (synth_Isat.mean()*1000,\n", " np.percentile(synth_Isat, 2.5)*1000, np.percentile(synth_Isat, 97.5)*1000))\n", "print('V_fl = %.1f V (%.1f to %.1f V)' % (synth_Vf.mean(),\n", " np.percentile(synth_Vf, 2.5), np.percentile(synth_Vf, 97.5)))\n", "print('Te = %.1f eV (%.1f to %.1f eV)' % (synth_Te.mean(),\n", " np.percentile(synth_Te, 2.5), np.percentile(synth_Te, 97.5)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which method you pick depends, generally, on the kind of data you have. Can you afford to make a lot of synthetic bootstrap datasets? What kind of plots do you want to make with the data? How much do you need to trust the results? In this notebook, we will go with the third option and take the standard deviation of the bootstrapped $I_{sat}$, $V_f$ and $T_e$ as the errorbars. This is, in part, because I'm a bit too lazy to deal with asymmetric Y axis errors at the moment. (Plus the symmetric standard deviation is easier to read.)\n", "\n", "## Check correlations between the fit results\n", "\n", "There is one more thing to consider when judging the fit results uncertainty. Since $I_{sat}$, $V_f$ and $T_e$ come from a single fit, there can be trade-offs between them. For instance, a decrease in $I_{sat}$ may be partially compensated by a decrease in $T_e$. This correlation increases the overall uncertainty. Sadly, at the moment I don't know how to calculate exactly how much. So let's just plot the scatterplots and see if $I_{sat}$, $V_f$ and $T_e$ are correlated within a single $I-V$ characteristic and its synthetic replicates. The red star denotes the results of the original fit of the full data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Prepare the figure\n", "fig, axs = plt.subplots(1, 3, num='Synthetic data scatterplots', figsize=(12,5), constrained_layout=True)\n", "#fig.tight_layout()\n", "#fig.subplots_adjust(wspace=0.3)\n", "\n", "# Draw Isat x Te scatterplot\n", "axs[0].set_title('Synthetic $I_{sat}$ vs $T_e$')\n", "c0 = axs[0].scatter(synth_Isat*1000, synth_Te, marker='v', c=synth_Vf)\n", "axs[0].plot([popt_orig[0]*1000], [popt_orig[2]], 'r*')\n", "axs[0].set_xlabel('$I_{sat}$ [mA]')\n", "axs[0].set_ylabel('$T_e$ [eV]')\n", "axs[0].grid(True)\n", "cbar = fig.colorbar(c0, ax=axs[0], orientation='horizontal')\n", "cbar.set_label('$V_f$ [V]')\n", "\n", "# Draw Vf x Te histogram\n", "axs[1].set_title('Synthetic $V_f$ vs $T_e$')\n", "c1 = axs[1].scatter(synth_Vf, synth_Te, marker='v', c=synth_Isat*1000)\n", "axs[1].plot([popt_orig[1]], [popt_orig[2]], 'r*')\n", "axs[1].set_xlabel('$V_f$ [V]')\n", "axs[1].set_ylabel('$T_e$ [eV]')\n", "axs[1].grid(True)\n", "cbar = fig.colorbar(c1, ax=axs[1], orientation='horizontal')\n", "cbar.set_label('$I_{sat}$ [mA]')\n", "\n", "# Draw Isat x Vf histogram\n", "axs[2].set_title('Synthetic $I_{sat}$ vs $V_f$')\n", "c2 = axs[2].scatter(synth_Isat*1000, synth_Vf, marker='v', c=synth_Te)\n", "axs[2].plot([popt_orig[0]*1000], [popt_orig[1]], 'r*')\n", "axs[2].set_xlabel('$I_{sat}$ [mA]')\n", "axs[2].set_ylabel('$V_f$ [V]')\n", "axs[2].grid(True)\n", "cbar = fig.colorbar(c2, ax=axs[2], orientation='horizontal')\n", "cbar.set_label('$T_e$ [eV]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the fit results *are*, indeed, correlated. In particular, an increase in $T_e$ can be compensated by an increase in $I_{sat}$ or a decrease in $V_f$. The latter two, $I_{sat}$ and $V_f$, are not strongly correlated. This means that the variation in $T_e$ is, in part, not caused by the probe data fluctuation but by the variation of $I_{sat}$ and $V_f$. This finding requires more in-depth analysis to properly acknowledge in the errobars used in this notebook, so we leave it here as a point of interest and future research.\n", "\n", "\n", "\n", "## Fit all $I-V$ characteristics throughout the discharge\n", "\n", "Finally, we perform the fitting procedure described above throughout the discharge. The process is fully automatic. Occasionally the fit doesn't converge; this will be treated later." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define fit constants\n", "start = get_parameter('Diagnostics/BasicDiagnostics/Results/t_plasma_start', shot) * 1e-3 #s\n", "end = get_parameter('Diagnostics/BasicDiagnostics/Results/t_plasma_end', shot) * 1e-3 #s\n", "n = 2 #number of I-V characteristics to bin together\n", "\n", "# Perform the fitting\n", "extremes_for_calculation = extremes[(start < t_ext) & (t_ext < end)]\n", "fit_results = []\n", "for i in range(extremes_for_calculation.size-n):\n", " #Select full data sample\n", " k1 = extremes_for_calculation[i] #index of the sample beginning\n", " k2 = extremes_for_calculation[i+n] #index of the sample end\n", " V_sample = V[k1 : k2]\n", " I_sample = I[k1 : k2]\n", " \n", " #Perform bin averaging and fit the I-V characteristic as first guess\n", " Isat_estimate = np.mean(I_sample[I_sample > 0])\n", " mask = (I_binned > -2*Isat_estimate)\n", " V_binned, I_binned, Ierr = bin_average(V_sample, I_sample, N_bins=40)\n", " popt, pcov = optimization.curve_fit(fit3par, V_binned[mask], I_binned[mask], p0, sigma=Ierr[mask])\n", " \n", " #Fit the full data\n", " Isat_estimate = popt[0]\n", " mask = (I_sample > -2*Isat_estimate)\n", " popt, pcov = optimization.curve_fit(fit3par, V_sample[mask], I_sample[mask], popt)\n", " \n", " #Create synthetic fit results using bootstrapping and process them for errorbars\n", " synth_Isat, synth_Vf, synth_Te = create_bootstrap_fit_replicates(\n", " I_sample[mask], V_sample[mask], p0=popt, bss=100, b=5)\n", " err = [synth_Isat.std(), synth_Vf.std(), synth_Te.std()]\n", " fit_results.append(list(popt)+err)\n", "\n", "#Compile the fit results\n", "fit_results = np.transpose(np.array(fit_results))\n", "Isat, V_f, Te, Isat_err, Vf_err, Te_err = fit_results\n", "t_fit = np.convolve(t[extremes_for_calculation][:-1], np.ones(n), 'valid') / n + T/4\n", " # moving average of t_ext by n samples, except for the last, closing extreme\n", " # middle of the window where the used I-V characteristics were collected" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To improve the data quality, we remove the results where the fit evidently failed." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Create a mask of results to be kept\n", "mask = np.array([True]*Isat.size)\n", "\n", "#Throw out fit results with larger than 50 % errorbar in Isat or Te\n", "# (not V_f, since that can be expected to pass through 0)\n", "mask = mask & (Isat_err < 0.5*abs(Isat)) & (Te_err < 0.5*abs(Te))\n", "\n", "#Throw out fit results with unreasonable/unphysical values\n", "mask = mask & (Isat > 0) & (Isat < 0.03)\n", "mask = mask & (V_f > -100) & (V_f < 30)\n", "mask = mask & (Te > 0) & (Te < 100)\n", "\n", "#Remove fits which don't meet all the criteria\n", "Isat[~mask] = np.nan\n", "V_f[~mask] = np.nan\n", "Te[~mask] = np.nan\n", "print('%i %% of the fits did not converge.' % (np.sum(~mask)/Isat.size * 100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We save the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's plot the time evolution of the fit results - edge plasma parameters $I_{sat}$, $V_f$ and $T_e$ - during the whole discharge." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Prepare figure\n", "fig, axs = plt.subplots(3, 1, num='Time evolution of fit results', figsize=(8,8), sharex=True)\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)\n", "\n", "#Plot the ion saturated current\n", "axs[0].errorbar(1000*t_fit, Isat*1000, color='g', marker='o', yerr=Isat_err*1000,\n", " label='Ion saturation current', ls='None', capsize=3, elinewidth=0.5)\n", "axs[0].grid(True)\n", "axs[0].axhline(c='k')\n", "axs[0].set(ylabel='$I_{sat}$ [mA]', title='Swept Langmuir probe results, discharge #%i' % shot)\n", "axs[0].legend(loc=1)\n", "\n", "#Plot the floating potential\n", "axs[1].errorbar(1000*t_fit, V_f, color='b', marker='o', yerr=Vf_err,\n", " label='Floating potential', ls='None', capsize=3, elinewidth=0.5)\n", "axs[1].grid(True)\n", "axs[1].axhline(c='k')\n", "axs[1].set(ylabel='$V_f$ [V]')\n", "axs[1].legend(loc=1)\n", "\n", "#Plot the electron temperature\n", "axs[2].errorbar(1000*t_fit, Te, color='r', marker='o', yerr=Te_err,\n", " label='Electron temperature', ls='None', capsize=3, elinewidth=0.5)\n", "axs[2].grid(True)\n", "axs[2].axhline(c='k')\n", "axs[2].set(xlabel='$t$ [ms]', ylabel='$T_e$ [eV]')\n", "axs[2].legend(loc=1)\n", "\n", "# Save the figure\n", "plt.savefig('icon-fig')" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }