{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tokamak GOLEM - electron energy confinement $\\tau_E$ calculation" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Strategic flowchart\n", "
\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python Intro" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# NOTE: Please use python 3 version of language/kernel\n", "\n", "# First we import the necessary modules (libraries)\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sys\n", "from scipy.integrate import cumtrapz\n", "from scipy import constants\n", "from urllib.request import urlopen #python 3.0 \n", "from urllib.request import urlretrieve #python 3.0 \n", "import urllib\n", "import os\n", "\n", "\n", "# The following line activates the inline plotting backend in the notebook.\n", "%matplotlib inline\n", "\n", "# Next we define some fundamental constants and parameters\n", "RogowskiCalibration = 5.3e6 # Calibration for Rogowski coil\n", "UloopCalibration = 5.5 # Calibration for U loop measurement\n", "V_p = 0.057 #m^3 Plasma volume\n", "k_B = constants.k # Boltzmann constant\n", "eV = constants.e # 1 eV equivalent to J\n", "baseURL = \"http://golem.fjfi.cvut.cz/utils/data/\"\n", "#baseURL = \"file:///locgolem/database/operation/shots/\" #local solution\n", "VacuumShotNo = 22475\n", "PlasmaShotNo = 22471\n", "try:\n", " os.stat('shots/')\n", "except:\n", " os.mkdir('shots/') \n", "\n", "#The following function will download the text file of the given data in the given shot and read it into an array and return it.\n", "ds = np.DataSource()\n", "\n", "def open_data(shot_no, data_id):\n", " shotDIR = 'shots/' + str(shot_no)\n", " dataADR = str(shot_no) + '/' + str(data_id) + '.npz'\n", " dataURL = baseURL + dataADR\n", " try:\n", " os.stat(shotDIR)\n", " except:\n", " os.mkdir(shotDIR) \n", " try:\n", " data = np.load('shots/' + dataADR)\n", " except: \n", " urlretrieve (dataURL,'shots/' + dataADR)\n", " data = np.load('shots/' + dataADR)\n", " return data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chamber resistance $R_{ch}$ calculation from vacuum shot\n", "### Plot raw data from NIstandard DAS" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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hQnl1eeo9nFH2CoDdQQ9TQzz3TTYArRrX8zcQE3PZE4ZTr46VOEzZvOqwsL2q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\nqkZV8W4qL7T11P6CIurWsY6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# First let's get the data from the DAS in the columns specified by the given identifiers.\n", "IDuloop = open_data(VacuumShotNo,'uloop')\n", "IDirog = open_data(VacuumShotNo,'irog')\n", "time = np.linspace(IDuloop['t_start'],IDuloop['t_end'], len(IDuloop['data']))\n", "\n", "# Let's show raw data:\n", "f,ax = plt.subplots(2,sharex=True)\n", "f.suptitle('Vacuum shot: #'+str(VacuumShotNo)+ '- raw data from NIstandard DAS')\n", "\n", "f.subplots_adjust(hspace=0.05)\n", "ax[0].set_ylim(0,2)\n", "ax[0].set_ylabel('channel #1 [V]')\n", "ax[0].plot(time*1e3, IDuloop['data'],label='ID:uloop')\n", "ax[1].set_ylim(-.1,0.3)\n", "ax[1].set_ylabel('channel #3 [V]')\n", "ax[1].plot(time*1e3, IDirog['data'],label='ID:irog')\n", "ax[1].set_xlabel('Time [ms]')\n", "[a.legend(loc='best') for a in ax]\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get real physical quantities from raw data\n", "Physical quantities can be obtained by multiplying the raw data by appropriate calibration constants. In the case of the current, the derivative is measured, so the signal has to be numerically integrated." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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RD/PmDV0Z2DYqoDGWJiH9bSAi4cCrwEAgBVgkItNUdbW7kQXe+t3H6NKkZsD2\nd8xqKCbENK1bJTu55NWhD3DLe7/XZsIE1j01lIhwOxm2sEL926A7sFFVNwOIyEfACMCnCUVViXts\nBqmng3dcoctfn+/Kfi2hmFDk3aGvqrz/6zYe+WJljnUylRzXvgzv0IB/jexkfS9nEerfBg2BZK/5\nFOBcX+/kf0u2B3UycVOlCuFuh2BMiYgI1/dowvU9mgBw+EQaHR//7oz1pi/fyfTlO7Pn+7Sow3s3\ndrcE4yXUE0qBRGQMMAagcePGxdpGj2Z2A6n8NK1T2e0QjPGp6hUjsmsvAKt3HGHYy2fexmnOhn3E\njvu92axB9Uh+fvCCMt1EJqF83wEROQ8Yr6qDnflxAKr6bF7rd+3aVRcvXpzXopAVkzAdgB/uP5+m\ndau4HI0xpd/GPce48J8/FbjeeU1rM/mWc0tFDUZEElW1a0HrhXoNZRHQQkRige3ANcAf3Q3JGFOa\nNa9XJUcNJr8Es2Dz/hw1mJeuiWdEfMOAxOiWkE4oqpouIncBM/CcNjxJVVe5HJYrKpUP6bfSmJCV\nO8H8sHY3N75zZkvIPR8t5Z6PlmbPr35icKn7uw35V6OqXwNnnv9XxtSvbsPIGxMM+reOypFgXpu9\nkb9/u+6M9do+OiN7umndysz88/khf4FlyCcUY4wJZnf0a84d/TwjJKsqA1/4mY17juVYZ/Pe4zmu\ni1n75BAiI0LvDEpLKMYYEyAiwsz7zs+eTz6QSp+//3jGeq0f+TZ7+pWRnRjevkFI1F4soRhjjEsa\n1aqU3Tx2Oj2Tjo9/x4m0nNe8/d+Hv/F/H/4GwEUdGvBKEF9caQnFGGOCQPlyYax5ckj2/Lvzk3hs\nWs5zjL5avpOvnIsrI8KF1U8MCarrXiyhGGNMEBrVM4ZRPWMA2HX4JD2ezXm/l7QMzTE0zJonhlCx\nvLv9LpZQjDEmyNWv/vv9Xg6nptHxiTOHhmnzqKffpV+rukwa1c2VPhdLKMYYE0KqV/p9aJhT6Rm0\n+uu3OZbPXrc3+4yxL+7sRXyjGgGLzRKKMcaEqArlwrOTy6odhxn+8twcyy99dV72dCBORQ6e3hxj\njDHFFndOdZImDCdpwnD+d0fPM5Z7n4rsL5ZQjDGmlOncuCZJE4az4emhhDt9KeOGtvb7fq3Jyxhj\nSqmI8DA2PTMsYPuzGooxxhifsIQS4soH0UVNxpiyzZq8QtyCcf05dCLN7TCMMcYSSqirXaUCtatU\ncDsMY4zbsWoZAAAgAElEQVSxJi9jjDG+YQnFGGOMT4iquh1DwIjIXmBrMZ9eB9jnw3B8KVhjs7iK\nxuIqGouraEoSVxNVrVvQSmUqoZSEiCxW1a5ux5GXYI3N4ioai6toLK6iCURc1uRljDHGJyyhGGOM\n8QlLKIX3htsBnEWwxmZxFY3FVTQWV9H4PS7rQzHGGOMTZerCxjp16mhMTIzbYRhjTEhJTEzcV5iz\nvMpUQomJiWHx4sVuh2GMMSFFRAp1uYX1oRhjjPGJMlVDMaVDZqZy+EQaB1NPczItE0WJCA8jslw4\n1SqWo3rFCETE7TCNKXMsoZiglpaRyfKUQ8zbuJ8l2w6yae8xdh0+SVpG/ieTRIQLdapUcB7laViz\nIrF1qtCiXhXaNKhG3ao2mKYx/mAJxQQdVWXhlgO8Mz+Jn9fv5fjpDESgVVRVOkbX4KIOlahbpQK1\nKpcnMiIMENIzMzlxOoPDJ9LYd+w0+46dYt+xU+w5eool2w5x2GuI/3pVK9Ahujodo2vQJaYmnRvX\nJDIi3L0XHEBpaWmkpKRw8uRJt0MxQSgyMpLo6GgiIiKK9XxLKCZoqCrTV+xk4k+bWbH9MLUql+fS\nTg3p3bwOPZrWpmbl8sXe7v7jp1m/+yirdxxh1Y4jLE85xMw1ewCoUC6MrjE16dmsDr2a16F9w+rZ\n9+EubVJSUqhatSoxMTHWLGhyUFX2799PSkoKsbGxxdqGJRQTFLbsO85D/1vBgs37aVq3Ms9c1p4/\ndG7ok5qDyO9NYD2b1ckuP5yaxqKkA8zbtI/5G/fzjxnr+MeMdVSvGEGfFnXo37oeF7SqV+xEFoxO\nnjxpycTkSUSoXbs2e/fuLfY2LKEY132yKJlHpq6kfLkwnrmsPdd0a0RYAGoI1StFcGHbKC5sGwXA\n3qOnmL9pH3M27OOn9Xv5avlOwsOEbjE1GRJXn+EdzikV/S+WTEx+SvrZCHhCEZFJwEXAHlVt55TV\nAj4GYoAk4CpVPSieV/cSMAxIBUar6hLnOaOAvzqbfUpV3w3k6zAlp6pM+HYtE3/aTO/mdfjnVR2p\nVy3StXjqVq3AiPiGjIhvSGamsmL7Yb5fvZsZq3Yx/svVPDl9Db2a1+HS+HMYFFefKhXs95gx3tz4\ni3gH+BfwnldZAjBLVSeISIIz/xdgKNDCeZwLvA6c6ySgx4CugAKJIjJNVQ8G7FWYEvv7jHVM/Gkz\n1/VozPiL4ygXHjyXRYWFCR0b1aBjoxo8MLgVG3Yf5Yul25m6dAf3fbKMyIgVDGpbnyu6RNOreZ1S\n2+diTFEE/C9YVX8GDuQqHgFk1TDeBS71Kn9PPX4BaohIA2Aw8L2qHnCSyPfAEP9Hb3zljZ838frs\nTVx7bmOeHNEuqJJJXlpEVWXs4NbMefACptx2Hpd3juan9Xu5YdJCev/tB56bsY7th064HWbI2LVr\nF9dccw3NmjWjS5cuDBs2jPXr19OuXTu/7C8pKclv23bDoUOHeO211/JdPnHiRG699dYARuQRLH/F\nUaq605neBUQ50w2BZK/1Upyy/MrPICJjRGSxiCwuSWeT8Z0vl+3gma/XMrxDA54Y0S6k2vRFhK4x\ntXj6svb8+tAAXv1jZ1rXr8prszfS528/cPO7i/lp/V4yM23Q1fyoKpdddhn9+vVj06ZNJCYm8uyz\nz7J79263Q8uTqpKZmemXbRV32wUllBUrVtChQ4dixVgSwZJQsqln+GOf/TWq6huq2lVVu9atW+DY\nZsbPNu89RsJny+napCb/vKpjSDcVRUaEM7xDA/7zp+78/OAF3N6vGb9tO8ioSQu58J8/8e78JI6d\nSnc7zKDz448/EhERwW233ZZd1rFjRxo1akRGRga33HILcXFxDBo0iBMnfq/1XXrppXTp0oW4uDje\neMMzEntSUhKtW7dm9OjRtGzZkmuvvZaZM2fSq1cvWrRowcKFC7Ofn56ezrXXXkubNm244oorSE1N\nBeD999+ne/fuxMfHc+utt5KRkUFSUhKtWrXihhtuoF27diQne/9+hffee48OHTrQsWNHrr/++uxY\nvGtBzz33HOPHjz9jW3PmzMlz2/nF0aZNmzOOSUJCAps2bSI+Pp6xY8eecYyXL19O+/btS/pWFVmw\n9CruFpEGqrrTadLa45RvBxp5rRftlG0H+uUqnx2AOE0JZGQqD3y6jHLhYbzyx05UKFd6LiaMrlmJ\nsYNbc/eAFny7cheT5iXx2LRVPDdjHSPPbczonjGcU6Oi22Hm8PiXq1i944hPt9n2nGo8dnHcWddZ\nuXIlXbp0yXPZhg0b+PDDD3nzzTe56qqr+Oyzz7juuusAmDRpErVq1eLEiRN069aNyy+/HICNGzfy\n6aefMmnSJLp168bkyZOZO3cu06ZN45lnnuGLL74AYN26dbz99tv06tWLG2+8kddee43hw4fz8ccf\nM2/ePCIiIrjjjjv44IMP6Nu3Lxs2bODdd9+lR48eOWJctWoVTz31FPPnz6dOnTocOJC7BT/v15W1\nraSkpDO2vWbNmrPGkfuYTJgwgZUrV7J06dJ8j3FZrqFMA0Y506OAqV7lN4hHD+Cw0zQ2AxgkIjVF\npCYwyCkzQez9X7ayZNshHr8kjgbVg+vL1VcqlAtnRHxDpt7Zi//d0ZO+rery1pzN9Pn7j9zz0W+s\n2enbL/DSJjY2lvj4eAC6dOlCUlJS9rKXX36Zjh070qNHD5KTk9mwYUP2c9q3b09YWBhxcXEMGDAA\nEaF9+/Y5nt+oUSN69eoFwHXXXcfcuXOZNWsWiYmJdOvWjfj4eGbNmsXmzZsBaNKkyRnJBOCHH37g\nyiuvpE4dzzVNtWrVKvB15d5W7vmzxXG2Y5KX5ORkKleuTI0aNfJcfvPNNxcYb3G5cdrwh3hqF3VE\nJAXP2VoTgE9E5CZgK3CVs/rXeE4Z3ojntOE/AajqARF5EljkrPeEqhb8M8G4Zv+xUzz/3Tp6N6/D\niPhz3A4nIDo3rknnP9Yk+UAq78xP4qOF25i6dAcD20Zxd/8WtI+u7mp8BdUk/CUuLo4pU6bkuaxC\nhd+v8wkPD89u8po9ezYzZ85kwYIFVKpUiX79+mUPH+P9nLCwsOz5sLAw0tN/b3LM3VcnIqgqo0aN\n4tlnn82xLCkpicqVKxfpdZUrVy5Hf4j38Da5t5V7/mxx5HdM8uPdf5KRkcHYsWMREZo0acLNN9/M\nxo0befjhh1m9ejWff/55kV5jQdw4y2ukqjZQ1QhVjVbVt1V1v6oOUNUWqnphVnJwzu66U1WbqWp7\nVV3stZ1Jqtrcefwn0K/DFM0rP2zk+OkMxl/SNqQ64X2hUa1KPHJRW+YnDODeC1vw6+b9XPyvuYz+\nz0KWJR9yO7yA69+/P6dOncruBwFPm3/ufgpvhw8fpmbNmlSqVIm1a9fyyy+/FHm/27ZtY8GCBQBM\nnjyZ3r17M2DAAKZMmcKePZ5W9gMHDrB169lv/dG/f38+/fRT9u/fn/0cgKioKPbs2cP+/fs5deoU\nX331VaFjK2ocVatW5ejRo3ku8+4/ef311xkxYgTPP/88d999N0uWLOHyyy/n6aefLnLCLIxgafIy\npdjOwyeY/Os2rugcTfN6Vd0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Uloop from IDuloop needs only multiplication\n", "U_l = IDuloop['data']*UloopCalibration\n", "# Chamber current I_ch needs offset correction, integration and calibration\n", "i_offset = time.searchsorted(.004)\n", "offset = np.mean(IDirog['data'][:i_offset]) # Up to 4 ms no signal, good zero specification\n", "I_ch = cumtrapz(IDirog['data']-offset, time,initial=0) # offset correction and integration\n", "I_ch *= RogowskiCalibration # calibration\n", "\n", "# And let's plot it\n", "f,ax = plt.subplots(2,sharex=True)\n", "f.subplots_adjust(hspace=0.05)\n", "f.suptitle('Vacuum shot: #'+str(VacuumShotNo)+ '- real data')\n", "\n", "ax[0].set_ylabel('$U_l$ [V]')\n", "ax[0].plot(time*1e3,U_l,label='Loop voltage $U_l$')\n", "ax[1].set_ylabel('$I_{ch}$ [A]')\n", "ax[1].plot(time*1e3,I_ch,label='Chamber current $I_{ch}$')\n", "ax[1].set_xlabel('Time [ms]')\n", "[a.legend(loc='best') for a in ax]\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final chamber resistance specification\n", "The chanber resistnace should be calculated in the time span where the physical quantities are reasonably stationary." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chamber resistance = (9.38 +/- 0.02) mOhm\n" ] } ], "source": [ "t_start = time.searchsorted(0.014) \n", "t_end = time.searchsorted(0.015) \n", "# R_ch calculation via Ohm's law:\n", "R_ch = (U_l[t_start:t_end]/I_ch[t_start:t_end]) # in Ohms\n", "# final value\n", "R_ch, R_err = np.mean(R_ch), np.std(R_ch)\n", "\n", "print(\"Chamber resistance = (%.2f +/- %.2f) mOhm\"%(R_ch*1e3, R_err*1e3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Energy confinement time $\\tau_{E}$ calculation from plasma shot\n", "### Plot raw data from NIstandard DAS and interferometer" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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3CvyjLTLydHkyfLw9xJvoxS1RUNXpwM4gqwwG3lTHXKCR21DOhKmifd68dP0p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\nt0qqZ0jOQzhXI88r2DaFwG3AFGAF8J6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eLQmoOKZJQCWcqtoQmWkBBvfKZunmPdx8yqiYLj8lpeEdxXurtCSg4pcmAZUw\njDE8MnsNa4v3kp0e4IXvH8b20iq/w+Lh91bRMzut2V5JlfKbtgmobu+KJ+cy/e2lrC7ey93/XM4n\na0romZ1OfmYawwpzYxbHXWeN4+yDmvY8umTTHi5+bE7M4lCqLTQJqG5v1opi/vzB2gZ9BvXISot5\nHBdNGcK9508E4O1pR/HqtYfHPAal2kqrg1TC2F1RU/c4PRBdT59dxdvVtFLxTEsCKmF4k8CVR+7n\nYyRKdR+aBFS35h3GMZwEjhpRwEH79vIrJKW6FU0CqlvzjhewY281AI9ccnCX3AncHuExDLqqszql\nOkqTgOrWyj03Ys349woyUlPi6oD723PGAxDS7qRVnNIkoLq1vY26ZBjUKyvq4R9j4bRx/Zn2jZFU\n1oQI6eDzKg5pElDdwqZdFXy6pqTJ67aHznqj9smPVUhRC5dMvFVXSsULX5KAiARE5AsRecuP5avu\n56yHP+KCRz9tMkpX487ZBvXOimVYUQknAe1ITsUjv0oC1wPLfFq26oa27rHdPzQ+kDbunG3f3jkx\niylaWa6RukKTgIpDMU8CIjII+CbwWKyXrbq/8BVAYd7O2c6YOIDzJg+KdUitCo9lXF6jvYmq+ONH\nSeA+4GZAB15VbTbj3ytYumkPYO8L+PzrnQCkCPy/M8aSFoi/Zi6tDlLxLKZ7jIh8C9hmjJnfynzX\niMg8EZlXXFwco+hUPAsPEv/mwk2c9sAH7K2qZcId7/D4h2sB+OKXJ/nSX1A06hqGNQmoOBTr06Yj\ngdNFZB3wAjBVRJ5pPJMx5lFjzGRjzOTCwsIYh6jiUXajm7/u/+/KBs9zMuLn3oDG6qqDNAmoOBTT\nJGCM+bkxZpAxZihwATDTGPPdWMaguqfMRjeAPTp7TYPnqXFYDRSW5WL/8wdrWplTqdiL3z1HKY/K\nbnyNfbg6aO7aHQ06uVMqHvjWlbQx5j3gPb+Wr7oPY0zEqpTLjxjK8aP6xjiitvF2Y7FpV0Xctl2o\n5KTjCai4V1UbIhgynD95EGmBFJ6d8zVgO4o7ecw+PkfXuixPEthWWsWB/X0MRqlGtDpIxb0yd0PY\n2IE9uOXU+kHje+ek+xVSm6R72iu27qn0MRKlmtIkoOJe+K7g7PRU8jPrq1J6ZXePahURYfpZYwFY\nu32vz9Eo1ZAmARXX9lTWsGpbGQC5jS4D7ZHVPUoCABdP2ReA/3tvNbO/0ntfVPzQNgEV146f8R4l\nrquInIwr6Qh8AAAbuElEQVSGm2t3bWD9YGUxx+yv97+o+KAlARW3KmuCdQkAmh7001O75+abla7n\nXip+6Nao4lIwZJp0FtfTVf+889NjWF9S7kdYHfKdyYN5cd6GJt1hK+Wn7nkqpRLazr3VDP/FP/jj\nrFUNXu/hGoL375fHiaP7+RFah9x19jiy0gLs0RvGVBzRkoCKOxt22rP859z9AGdPGsjO8upu2wYQ\nFkgR+uSmU1qpXUqr+KFJQMWdneUNz5R/NHUEwwtzfYqmc+VlprGnUksCKn5odZCKOzv2VjV43ju7\n+1wK2pr8zFT2aElAxRFNAipu1AZDbNhRzo69Dc+U87t5NZBXflaatgmouKJJQMWN17/YyNH3zOLT\nNSUNXg8PKJMI8jJTtU1AxRVNAipu/HfZNgA+XVPSoL+dRJKvbQIqziTmnqa6pbxMe51CaWUtQwuy\nfY6ma+RnpVFWVUsopPcKqPigSUDFjaDnwNjLNQZnpcXvsJHtkZ+ZijFQVq1VQio+6CWiKm4Ul9Vf\nFdQ7J52FvzoJSbDTlHAvqHsqahr0iKqUX6JKAiLSO4rZQsaYXR2MRyWhDTvKOeOhjxp0E9EnN73u\nDuFEEr7SaVd5DYN6+RyMUkRfEtjk/lq6TCMADOlwRCqpPPzeKl6ZX9Skn6BEujfAa3DvLADeWrSZ\nsQN7+ByNUtG3CSwzxgwzxuwX6Q8oafVTlGrknn+tYE1x/UAr/XtkAlAdTMyG0/Cdz396f7XPkShl\nRZsEDu+keZSqU95M4+h5kwczsGcW5x48yIeIul6mp6FbrxBS8SCqJGCMaXVg1GjmUcrrg5XbGzzv\nk5POd6cM4aNbpzKib2L0FdScn7txkitrgz5HolQUSUBEfiAiT4vIBSLylohcG4vAVOLztgP075HJ\n/F+eSN/8TB8jio2sdFsaKK/WJKD8F01JYCpwGXCJMeZbwISuDUkli12e3kJTJHG6hmhNuEqoQpOA\nigPRJIESY4dC+pN7XtXSzEpFa1dFfUkgkfoHak2GGxazqjbkcyRKRZcE7gcwxvzdPX+t68JRyWS3\npySQmkRJIDXF7nZBbRhWcaDVJGCMWQ4gIgXu+ftdHZRKDt7qoGQqCaQG7HetDWlJQPmvLTflP9Fl\nUaiktHl3BWnugJidkTw9mIRLPbUJei+E6l7akgSS51RNxcTXO8rp564GyklPrI7iWhIu9dRqdZCK\nA21JAp2yxYpIpojMFZGFIrJERO7ojM9V3UtZVS07y2s4bVx/0lNT+PHUEX6HFDNpAW0TUPGjLWXw\nzioJVAFTjTFlIpIGfCgi/zTGfNpJn6+6gQ07ygEYN7AHX915qs/RxFZdSSCobQLKf21JAj/vjAW6\ny03L3NM096enREnEGMN3H5sDwJDeiTl4TEvCbQI1WhJQcSDqJGCMWSwik4HbgH3de8VOMuPbslAR\nCQDzgRHAQ8aYOY2mXwNcAzBkiHZMmmj2VNRS4u4WHpyMSaCuOkhLAsp/bb0k41ngZ8CXQLu3YGNM\nEJgoIj2B10VkrDFmsWf6o8CjAJMnT9bTpQSzrbS+m6leCThmQGv06iAVT9qaBIqNMW921sKNMbtE\nZBZwCrC4tflV9xcKGbaV1t90LknUXURY/X0CmgSU/9qaBH4tIo8B/8XTfYQxJuq7iEWkEKhxCSAL\nOBH4bRvjUN3Q4o27+e7jczh7ku0meuaNx/ockT9S9RJRFUfamgSuAEZhG3PD1UGGtnUl0R/4i2sX\nSAFeMsa81cY4VDf06ZoSdpXX8MRHawGSosfQ5oS7jdCrg1Q8aGsSOMQYc0BHFmiMWQRM6shnqO6p\nsqZhr5m5SXSXsFf4EtEbXlpIr5x0jj+gr88RqWTWlpvFAD4WkdFdEolKeMWl2gEt1N8sBvDSZxt8\njESptpcEDgMWiMhabJtAuy4RVckp3CB87sGDOH3CAJ+j8Y+3s7yQ0XYB5a+2JoFTuiQKlfBKK2v4\n5+ItDO2Tze/OS+5xicKd5gFoDlB+a1MSMMas76pAVGKbuXwbAOtKyn2OxH/ekkAyjaim4lNUbQIi\n8nlnzKOS02MfrOH6FxYA8NL3D/c5Gv952wSSaRwFFZ+iLQkcKCKLWpguQI9OiEcloHv+vaLu8aH7\n9fYxkvjQYBQ1zQHKZ9EmgVFRzKOjZqtmVetYug1odZCKJ1ElAW0LUO21pris7rEe7yxvVxl6dZDy\nW1vvE1CqTb7eUd8QrMe7pqpqtJSk/KVJQHWpDTsr/A4hrlXVai2q8pcmAdWlVm0tJSPVbmanjt3H\n52jij5YElN861HmLiNwP5BtjrhCRk4wx73RSXCpBfLZuJ2MH9uD+CyZSmJfhdzhxp1JLAspnHS0J\nhIC17vHUDn6WSjC7yqtZunkPx+1fyKBe2WSkBvwOKe407lRPqVjraBIoB3q4AeN1HEjVwJJNewCY\nMLinz5HEr0qtDlI+62gS+DWwGngIeK7j4ahEsrBoFwDjB+l9hJFoSUD5rU1tAiIyFbgY2IUdDnIR\n8LgxRvsIVk0s2rCbfftk0zM73e9Q4s7c277Bb/+5gv8s2+p3KCrJtbVh+AngJ9iRxcYDZwJjgBGd\nHJdKABt2ljOsIMfvMOJS37xMCvMyqNCSgPJZW5PAemPMG+7xy50djEosJWXVjO6f73cYcSsvM5Xq\n2hCVNUEy07TRXPmjrW0Cs0XkpyLaAYBqmTGGkr1VFOhloRHlZ6UBUFpZ63MkKpm1NQmMBq4FNovI\n2yIyXUTO64K4VDdmjOHCP39KTdDQJ0fbAyLJz7QF8d0VNT5HopJZWweVOQdARLKwCWEcMAWtGlIe\nm3dX8umaHQAU5GpJIJL8TFsS2FOpSUD5p113DBtjKoD57k+pOpt3V3DE3TPrnutdwpGFB5epDWrP\neso/2neQ6lTT315W9/i8gwdx0JBePkYT38LjCgRDmgSUfzrUd5BSjW3dU1n3eEaSDyjfmnAS0DEF\nlJ+0JKA6zWMfrOGzdTv9DqPb0JKAigeaBFSn2Lankjs9VUHPfW+Kj9F0D5oEVDzQJKA6bE9lDYfe\n9d+659+ZPJgjhhf4GFH3EBBNAsp/mgRUh42/veEwEumpullFI1wSqNUkoHykDcOqQxr3gvmzkw/g\nu4ft61M03Ys2DKt4EPMkICKDgaeBfoABHjXG3B/rOFTn2FleDdi7X7/41Ul1BzbVOi0JqHjgR7m9\nFrjRGDMaOAz4kYiM9iGOpLJ2+15uf3MJtcHOHcSkpMwmgXvOnaAJoI3qSgKaBJSPYp4EjDGbjTGf\nu8elwDJgYKzjSDaXPD6Hpz5ex4qtpe3+jAUbdrGmuKzBa+GSQJ9c7SOorVK1JKDigK8teCIyFJgE\nzGn0+jUiMk9E5hUXF/sRWsIp2lkBwEb3v612V9Rw5kMfMfX37zd4fcdemwR6Zad1LMAklKIlARUH\nfEsCIpILvAr8xBizxzvNGPOoMWayMWZyYWGhPwEmkOra+iqgDe1IAsWlVUy4o/4KoAf+u7Lussa9\nVbZhODdDk0BbaUlAxQNfkoAbmP5V4FljzGt+xJBMVm6rrwLaXtb2kUA37Cxv8Pzed79iztoSAMqr\nbV/4ORk6KEpbpYTvE9Crg5S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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get data\n", "IDuloop = open_data(PlasmaShotNo,'uloop')\n", "IDirog = open_data(PlasmaShotNo,'irog')\n", "#time = np.linspace(IDuloop['t_start'],IDuloop['t_end'], len(IDuloop['data']))\n", "\n", "IDelectron_density = open_data(PlasmaShotNo,'electron_density')\n", "#interpolate the density on the same time axis as the rest of the quantities\n", "n_e = IDelectron_density['data']\n", "time_ne = np.linspace(IDelectron_density['t_start'],IDelectron_density['t_end'], len(n_e))\n", "n_e = np.interp(time, time_ne, n_e)\n", "\n", "# Let's show raw data from NIstandard:\n", "f,ax = plt.subplots(2,sharex=True)\n", "f.subplots_adjust(hspace=0.1)\n", "f.suptitle('Plasma shot: #'+str(PlasmaShotNo)+ '- raw data from NIstandard DAS')\n", "\n", "ax[0].set_ylim(0,2)\n", "ax[0].set_ylabel('channel #1 [V]')\n", "ax[0].plot(time*1e3, IDuloop['data'],label='ID:uloop')\n", "ax[1].set_ylim(0,0.3)\n", "ax[1].set_ylabel('channel #3 [V]')\n", "ax[1].plot(time*1e3, IDirog['data'],label='ID:irog')\n", "ax[1].set_xlabel('Time [ms]')\n", "[a.legend(loc='best') for a in ax]\n", "plt.show()\n", "\n", "# Let's show raw data from interferometer diagnostics :\n", "f,ax = plt.subplots()\n", "ax.set_title('Plasma shot: #'+str(PlasmaShotNo)+ '- raw data from TektronixDPO DAS')\n", "\n", "ax.set_xlabel('Time [ms]')\n", "ax.set_ylabel('$n_e\\ \\mathrm{[m^{-3}]}$')\n", "ax.plot(time*1e3, n_e,label='ID:electron_density')\n", "ax.legend() \n", "ax.set_ylim(0,None)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get real physical data " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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OHQvA7NmzK/oLalqekJDA0KFDWbRoEXfccQfDhw/nnnvuqfFY4eHh5OTk0KdPH3r16gXA\nqFGjaj1euaCgICZMmEBERASXXHIJTz31FEuWLCEsLIyhQ4cyfvx4AMaMGcOvfvUrRowYQY8ePYiM\njKRr164MHz6cp556iqlTp2K1WnFzc2PRokX079+/4hhTp07lq6++YtiwYXh4eDB+/HiWLFlSsX7z\n5s1MmTLljD7rWqmqUx5AL2CU/bkfsB8YDvwdmGdfPg/4W337iomJUcPoiBJO52r/h1fpknUHVVV1\nX0q29n94la7cktjix46Pj2/xY7SGI0eOaHh4uLPDqJCTk6Oqqnl5eRoTE6OxsbHNst9f//rXWlpa\nWuO6mr5LYIs28PfaaU1PqnpCVePsz3OAPUAfYAaw3F5sOXCFcyI0DOdLyysGYJ995thB3Xzx9XB1\nSj+F0TzmzJlDdHQ0o0aN4uqrr2625qL//Oc/uLi4NMu+qmoTndkiEgqMBH4CeqjqCfuqFGxNUzVt\nMweYA9CvX7+WD9IwnMDPw/Zf9IJh3QFwsQiRfbqyPckkioYKDQ1l165dzg6jwooVK5wdQqM1ukYh\nIoENePg3Yn++wIfAA6paaaC2vXpUY6+dqi5T1dGqOrpbt26NfRuG0S6U2TspXCy/jNKJ6uvPnhPZ\nFJZUvyDLMFpCU2oUyfZHXZOcuwD1nuaLiBu2JPG2qn5kX3xSRHqp6gkR6QWkNiFGw+gQyqy/zPVU\nLrqvPyVlSvyJbEb1C3BWaEYn0pQ+ij2qOlBVB9T2ANLq24nYBjK/Yt/fcw6rPgNusz+/Dfi0CTEa\nRodgtdr+daxRRPe1Vdi3Hmv55ifVzjsMt6Noju+wKYni7GYqMwG4BZgsItvsj0uBhcBFInIAuND+\n2jA6pV+ann5Z1rOrJ/0CvfnxcL3nY2fE09OTtLQ0kyzaMbXfM9vT88ymfGlK09M/ROQdVa1+Dbyd\nqtY7a5l9+9qar1poMLBhtC81NT0BTDgriFU7TlBaZsXVpWUGL4aEhJCUlMSpU6daZP9G6/D09Kx0\nHUpTNCVR7AeesfcfvA+8o6pbzygKwzBqZK2hMxvgnEHBvPNzIjuPZzGyhfop3NzcGDBgQIvs22hf\nGn0qoqovqOrZwPnY+iJeFZG9IvInERnS7BEaRidWXqNwqVKjOHdwMC4WYc2ek84Iy+hkmlxnVdWj\nqvo3VR0JzMR2YdyeZovMMIyKOZ0sVWoU/t7ujBsQyFe7TaIwWl6TE4WIuIrI5SLyNvBfYB9wVbNF\nZhgGhaW2ayWqNj0BXBzek4OpuRxMzW3tsIxOpikX3F0kIq8CScCdwGpgkKreoKpmKKthNKM7Xt8C\nVO/MBpgabpu04KvdKa0ak9H5NKVG8QjwAxCmqr9S1RWq2rJ39jaMTi63qLTasl5dvYjq68/XJlEY\nLawpndmTVfU/aqb+NoxWs/lIeo3LLw7vwfakLJIzC1o5IqMzaUrTU1xzlDEMo37d/TwAuDyqd43r\np4Xb7p/8728PtlpMRufTlKanMBHZUcdjJxDc3IEaRmd00fAedPVyY2hPvxrXD+xmu3nRip+OtWZY\nRifTlAvuhjWgjJnW0jCagVUVd9e6z+cenx7GU6v3sCUhndGhga0UmdGZNKWP4mgDHkktEaxhdDZl\nVq12sV1VV47sA8BDH2xvjZCMTshpd7gzDKN+Vq35GgpHQb4eeLpZSEjLJy23qJUiMzoTkygMow2z\nWpV6KhQAvPXrcQA8+/X+Fo7I6IzO+FaoIvIakAvEAZuB3WrmJW5TQuetrnj++f0TiQzp6sRojMYo\nU623RgEwOjQQF4vwzs/HePTSYfh5urVCdEZn0ZThsV6Or1X1duAP2GaVvRBY2jyhGWdqX0pOpSQB\ncOXijU6KxmgKq1afELA2L9wQDcA/vznQkiEZnVBTmp6SRWSfiHwgIn8UkRmAp6puVNXnVXVOcwdp\nNN5Tq+K5+PnvK17HPn4h/t5u+Hq6mhvRtCMNbXoCuGxEb8YPDGTFz0dJTM9v2cCMTqUpo54CgGnA\nW/ZFNwO7ReQdETFtGm3A9Us38Z8NRwBYfNMoEhZOJ8jXg99fNITM/BIOnzYzrrQXZdaGNT2Ve+66\naFxEeOyTXeaEwGg2TerMVtUjqvqpqv5FVa8F+gC7geebNTqj0ULnreYn+3QP6x6axKWRvSrWTQmz\nTSI35R/fETpvNX/7cq9TYjTq9+qGI4TOW02pVWucELA2vf29eOjioXy//xSfbU9uwQiNzqRZRj2p\nzVPA+ObYn9E08z/bXfF86x8vIjTYp9L63v5eBPm4V7x+ad0hQuetJnTealbvONFqcTbVqh3JvPvz\nMULnream//zIp9uO8+m245SUWflqd0rF0NAyq/L59uRK02+v25dKQXEZqdmFbE6oPm9S+dl3SZm1\n4h4QLaGotAxVpbTMWusZf2FJGU+uigdgzZ6T7E3JadQxbj07lKiQrvzu3W2czK73rsSGUS9pbPVU\nRB4EdgA7VDXVYbkHsFdVW/3eiaNHj9YtW7a09mHblNij6Vz90iYA1v/hAvoGetdaVlW54/XNfLuv\n+r2Q7z5/EPMuacjF93VTVaSWM+GC4jIOn84lvPcvLZXFpVaOnM4jq6CEf/3vAPtSckjNcd41Aa/N\nGkNaXnGli9jOHRzM+gOnOXtgEDeO68dv3tnKo5cOo6DYyt2TBpKRV8KNL//I2YOCOHdwNyYN7caw\nP35Zbd99/L043shJ/BIWTm9U+d3JWUx/0XZb+0NPX9qo5iujcxCRWFUd3aCyTUgUfwMi7Q9XYCdw\nCBgDrFPVhxoX7pnraIli3b5UZr22mR8fmULPrp71li8pszL4sf8C8NG95zCqEfdQVlXufCO22i01\nG/vjUlxqZUdSJpEhXXl45Q4+2WZr9njwoiF09XLjyOk8zhkUxJw3Yxu8T+MXjU0UAI98tIN3fk5s\n8vZGx9aiiaLKgfyxJYyhwFFV/abJOzsDHS1R3PnGFr6JP8mSm2OYFtGz3vKLvj3IM1/t44ro3jx/\nw8gmHVNVGfToFzi2uni5uXB1TB++2JlCSakVDzcXzh0czMh+/qw/cJreXT25Z9JZrPj5GC+ubfkh\nmb4erowI6coV0X1IzSkkLa+Ym8f3Z8o/vqtUbtVvJjKwmw8vrD2AiwiL1x2qWF5SZuVAai5/WLmj\nxeNtTk35oVdVBjzyBQB+Hq7smD+11lqe0fm0WqJoKzpaorj7zVi+3J3C4ptGVeqMrsnB1FwufXE9\nF4Z1Z/FNMWd87OOZBUxY+L8z3k9jrXnwvIqLxDxdXfD1dKWgpAxfjzO+JrTBqjaXWa1a6V7V5eut\nVuW5b/Zz/+SzSEzPJ7uwhP5BPvx6+Rb+/Ktwovv6o6rc81YcN4ztS4C3O6t3nmBGdG++23+KmWP6\nsTclh7EDAnl5/WFuGd8fd1cLabnFnM4tIjTYBx93F1Rh4KO2H/qm1gjKrLYTgHKHn7602v23jc7J\nJIp27r6341i98wT/mjmy1vsQQOUzxs2PXUg3+70LzlRRaRlPrdrDmz8ePaP99Orqyf9dOISVsUkc\nTc/jmwfP540fErh/8uA6+zCMX5RfMHkmTUdVk8XGeZPp4+9VxxZGZ9CYRNF6p2tGg5Wf8VnrSeLl\nwx+7+Xk0W5IA8HB14S9XRPCXKyLqLKeqlFmVMlU8XF0qlp/OLUKVipiuG9O3Yt39kwcDmCTRQFv/\neFG9fwf1cbEIRxZcWnFSMWHh//hVVG9enNm0Zkqj8zGTArZBLvbf0LI6hmmqKr97dxsAP8yb3Bph\nVSMiuLpYKiUJgGDf5k1cnVmAjztBvmf+WYoICQuns+RmW/PkZ9uTCZ23+oxrjUbnYBJFG1Reo6gr\nUazbbxvaOiY0ADcX8zUaDTMtoie7/nxxxes/frKL0HmrGff0GkrLrE6MzGjLTNNTG1Q+CVxdTQ63\nv7YZgDft00sbRkP5eriSsHB6pYELJ7OLOMs+xDrIx53PfjPR9GMYFdpkohCRacALgAvwH1Vd6OSQ\nWlX5lA0FxTXfUTbBPlfToG4+eLq51FjGMOrTx9+LhIXTycovYczTaygutdUo0vKKKxKIn4crN5/d\nn2BfDy6J6Elvkzw6pTY36klEXLBNWX4RkITtHhczVTW+tm062qgnx6nBaxrt8oeV23l/SxIf3nM2\nMf3NPZKN5pOZX0z0kw27HGpID1/6B/nQxdONcQMDuXJkH9MM2o6091FPY4GDqnoYQETeBWYAtSaK\njiyroARXi+BiEURA1dZMEOTjbpKE0ez8vd0rnZxYrcr2pEye/mIPmxMyKpXdfzKX/Sdt82l9GJdU\n50WM919wFjH9AxgzIBAfdxcz6q2daYuJog+Q6PA6CWiRhvioP39NVkFJxWvHv13HP2PHP+rKyx3L\n17xxQ8rXdtzyGA3DWSwWYWS/AD64+5xq6wqKy9ibks1DH2zn0Km6p67/97cHK7329XAlt6i0WrkL\nw7oztKcfAd7ujOznDwgjQrqamoqTtcVE0SAiMgeYA9CvX78m7WPOeQMpsrfLYm+Cc2yIc2yVU4c1\nlZfXX76Wp5VmD3XctvxeEo7cXIQHLhwC2O5cd/uE0GplDKM1ebm7MLJfAGt/P6nG9arKtsRMHvlo\nJ1OH9+DVjQkVyWH8wKBq84sBrNmTypo9qdWW1+WOCQP4/sAp7jx3AH0DvIkJDag2ZNs4M22xj+Js\nYL6qXmx//QiAqi6obZvO1kdhGB1RUWkZKVmFpGQVkphRQGmZlXkf7Tzj/VoEXC0Wiu3Df389cQBf\n7U5hWnhPbp84gF5dPDvltCbtegoPEXHF1pk9BTiOrTP7RlXdXds2JlEYRudjtSpJGQVkF5bw0neH\nKCtTvtydwqxzQjmeWcA38dVrLA11/pBu9AnwIsDbjf6BPni5u3DBsO4dqn+lXXdmq2qpiNwPfIVt\neOyrdSWJjmxUP39nh2AYbZbFIvQLst13ZdGNo+otb7Uqu5OzyS8u5U+f7ebwqTyKy6wE+riTnldc\nqex3+6vfq6Uu4b27sDs5m19PHMCIkK5Mi+jZoZq/2lyiAFDVL4Av6i3YwcX0b/h9JQzDqJvFIkSG\n2G6W9eUD59VZVlVJziokMT2fQ6dyeezjXcw5byCfb0/mRFb1uwbuTs4G4JUa+her6uPvxcXhPbkm\nJoSwXn7toobSJhOFYdMe/oAMoyMSEfr4e9HH34vxA4O4aVx/AB69NKxa2dyiUrYdy+TmV35q0L6P\nZxbw6sYjvLqx9qTi6WbB292VgcE+PH1VJKFBPri7Om/kl0kUbdDY0EB+Tkhn6vAezg7FMIx6+Hq4\nMnFwcL39idmFJfxr7QFyi0or7jxYm8ISK4UlxaTnFTP1n9/XWm72xAE8fMmwFh8+3OY6s5uio3Vm\nqyo5RaV0sd/IxzCMziMjr5i9KTks/HIv2xMzmTq8B1/X0THf1JtRtevObMNW7TVJwjA6pwAfd84e\nFMSn902os5yqciKrsFWG9prLHQ3DMNohEWm1SRpbPVGISIKI7BSRbSJSrb1IbF4UkYMiskNE6h/3\nZhiGYbQYZzU9XaCqp2tZdwkw2P4YB7xEPXM9xcbGnhaRpt6qKxioLRZnMnE1jomrcUxcjdMR4+rf\n0IJtsY9iBvCG2nrZfxQRfxHppaonattAVbs19WAisqWhHTqtycTVOCauxjFxNU5nj8sZfRQKfC0i\nsfaJ/aqqafbYPq0SmWEYhlFNqw+PFZE+qnpcRLoD3wC/UdXvHdavAhaq6gb767XAw6q6pcp+KmaP\n9fHxiRk2bFirvQfDMIz2LjY29nRDW2NavelJVY/b/00VkY+x3ajI8YqS40Bfh9ch9mVV97MMWAYd\n7zoKwzCMltaYft1WTRQi4gNYVDXH/nwq8GSVYp8B99vvbDcOyKqrf8Iwmiq/uJS03GJyCkvJKSwh\nt6iU3KJSsgtLyS0sJbeohDIreLm52KdUcMHX05VAHw+CfNzp3sWDbr4eZqoVo8Nr7RpFD+Bj+38s\nV2CFqn4pIncDqOoSbJMBXgocBPKB21s5RqMDUVVO5xazLyWHvSnZ7D+Zw76TuRxLyyMjv6TObV0s\ngotIxX0MauLl5kK/QG/6B3nTN9CboT39GNXPn4HBvp3yHgdGx2Sm8DA6lBNZBfx8JJ1tiZnsPZHD\nvpM5laZoBhbwAAAgAElEQVSQDvZ1Z0gPP0KDfejj70U3Pw+6eLri6+GGn6crvp6u+Hm44ufphqeb\nBRGhzKoUlpSRX1xGTmEJaXnFpOUWczK7kKNp+RxLzychLY+kjHwKS2xJpauXG6P6+TM6NJAJZwUz\nok9XkziMNqVd37ioKUyi6LysVmVrYgZfx59k3d5T7DuZA9jO9If29GNYTz+G9LD/29OPYF+PFo3l\n8Olc4o5mEncsg9ijGRxIzQWgm58Hl0T05OpRIYwI6WqaqwynM4nC6NBKyqz8fCSdL3el8NXuFFJz\ninC1CGNCA7lgWDfOGRRMWK8uuLSBM/j0vGK+25/KN/EnWbMnleJSK4O7+3J1TAhXjuxDjy6ezg7R\n6KRMojA6pN3JWbz14zFWbU8mp6gUTzcLk4Z0Z1pETyaHdW/zEylmFZSwescJPoxLIvZoBhaBiYO7\ncU1MCFOH98DTrePcEc1o+0yiMDqMwpIyVu04wVs/HmVbYiYerhamj+jF1OE9OX9IN7zc2+eP6+FT\nuXwUd5yP4pJIziokyMedm8b355bx/enm13LNY4ZRziQKo907lVPEy+sP897mRLIKShjYzYebx/Xn\n6lEhdPVu2zWHxrBalR8OpfHaxiOs3ZuKh6uFW8b35zeTB3eo92m0PeZ+FEa7lZFXzNLvD7P8hwSK\ny6xMC+/JTeP7cfbAoA7ZAWyxCBMHBzNxcDCHT+Wy6NtDvLLxCCvjkvi/C4dw47h+LX73MsOoj6lR\nGG1CQXEZS78/xH/WHyGvuJQZUb353YVDGBDs4+zQWl18cjZ/WRXPpsNpRIV05aWbY1rtvgNG59Gs\nTU8iEtiA/VhVNbMhB2wJJlG0X6rK1/EnefLzeI5nFjAtvCcPTh3CkB5+zg7NqVSV1TtPMO/DnQT4\nuPHdQxeY6zCMZtXcTU/J9kddf6UuQL+GHNAwyiWczmP+57tZt+8UQ3v48d6c8YwbGOTssNoEEeGy\nEb0pLVMeeG8b25MyGdkvwNlhGZ1UQxLFHlUdWVcBEdnaTPEYnUCZVVny3SFeWHMAd1cLj08P47Zz\nQk1bfA0uGNodF4vw7b5TJlEYTtOQRHF2M5UxDI6m5fHg+9uJPZrBJRE9mf+rcHPRWR26ersxpIcf\n2xKd1rJrGPUnClUtrGm5iEwEZqrqfbWVMYxyqsq7mxP5y6p4XCzC89dHMyO6d4ccydTcRvTpylfx\nKaiq+bwMp2jU8FgRGQncCFwLHAE+aomgjI4lp7CEeR/tZPWOE0w4K4hnrokyo3gaYUTfrry3JZGk\njAL6Bno7OxyjE6o3UYjIEGCm/XEaeA/baKkLWjg2owPYnZzFfW/HkZhRwMPThnHXeQPN6J1Gigrx\nByDuWIZJFIZTNKT3cC8wGbhMVSeq6r+AspYNy+gIPtl6nKsW/0BBSRnvzhnPPZMGmSTRBMN6+uHt\n7kLs0Qxnh2J0Ug1peroKuAH4VkS+BN6l7qGyRidXWmblb1/u5eX1Rxg7IJBFN44y8xedAVcXC6P6\nBbA5wSQKwznqrVGo6ieqegMwDPgWeADoLiIvicjUlg7QaF8y84u5/fXNvLz+CLee3Z+3Z48zSaIZ\nxPQPYF9KNjmFdd+VzzBaQoMHrqtqnqquUNXLgRBgK/Bwi0VmtDv7T+bwq39v5MfDaSy8KpInZ0SY\nayOayejQAKwKW4+ZYbJG62vw/2IRiSl/rqoZqroM+GeLRGW0O+sPnOKqxT+QX2zrj7hhrLlQvzmN\n7BeARWBzQrqzQzE6ocYMj31ZRG5V1V0AIjITWzPUqhaJzGg3Pt6axNwPdnBWd19enTWmzQx9LSkp\nISkpicLCjnGZz2tX9gEK2LNnj7NDMdoRT09PQkJCcHNr+rT1jUkU1wArReRG4FzgVqDBfRQi0hd4\nA+gBKLBMVV+oUmYS8Cm2azQAPlLVJxsRo9HKXt1whCdXxXP2wCCW3hrTpu4yl5SUhJ+fH6GhoR3i\nQrWArEJO5RQyuFcXXE2TntEAqkpaWhpJSUkMGDCgyftpcKJQ1cMicgPwCXAMmKqqBY04Vinwe1WN\nExE/IFZEvlHV+Crl1qvqZY3Yr+EEqsrzaw7wwtoDXBzegxdnjsTDtW3dba6wsLDDJAkAP09XUnMg\nr6iUrt7uzg7HaAdEhKCgIE6dOnVG+2nIBXc7sdUAygVimy32JxFBVUc05ECqegI4YX+eIyJ7gD5A\n1URhtHGqypOr4nltYwLXxoSw4KrINnuG21GSBICXuwsuIuSYRGE0QnP8H2hIjaLZz+5FJBQYCfxU\nw+qzRWQ7tqnNH1LV3bXsYw4wB6BfP9Nx2lrKrMpjH+/k3c2J3DFhAH+8LKxD/Ri3ZRYRfD1dySks\nNfM+Ga2qIZMCHm3OA4qIL/Ah8ICqZldZHQf0V9VcEbkUWzPX4FriWgYsA9uNi5ozRqNmpWVWfv/B\ndj7dlsxvJp/FgxcNMT9WrczP042sghIKSsrwdjd3MjZaR73tBSIS1xxl7OXcsCWJt1W12oSCqpqt\nqrn2518AbiIS3JB9Gy2ruNTK/Su28um2ZOZePJTfTx1qkkQDpaSkcMMNNzBo0CBiYmK49NJL2b9/\nPxEREY3eVxdPV0SErILaL7xLSEho0r6bi6+vr9OO3VZkZmayePHiWtcvXbqUu+66qxUjOjMNaVgO\nE5EddTx2AvX+mIvtV+UVbDdCeq6WMj3t5RCRsfb40hr+doyWUFhSxpw3t/Dl7hSeuGw4911wlrND\najdUlSuvvJJJkyZx6NAhYmNjWbBgASdPnmzS/lxdLPh6uJKVX0JL3O9eVbFarRWv161bx6xZs5r9\nOG1V1fdf9XVD1Zcodu7cyYgRDerebRMakiiGAZfX8bgMOKcB+5kA3AJMFpFt9selInK3iNxtL3MN\nsMveR/EicIO2xP8Go8Hyi0u54/XNfLf/FAuuiuSOiU0fYtcZffvtt7i5uXH33XdXLIuKiqJv376U\nlZVx5513Eh4eztSpUyko+GUQ4RVXXEFMTAzh4eEsW7YMsNUUhg0bxqMP3MPFE0Zxw403smbNGiZM\nmMDgwYP5+eefK7YvLS3lpptuIiwsjGuuuYb8/HwA3nrrLcaOHUt0dDR33XUXZWVlJCQkMHToUG69\n9VYiIiJITEys932Vx1LTMRzV9D7y8vKYPn06UVFRRERE8N5771Xsb9asWQwZMoSbbrqp1vdW0z6r\neuONNxgxYgRRUVHccsstFTE71rSeffZZ5s+fX+39r1+/vtrnUdvnFhYWVuN3OG/ePA4dOkR0dDRz\n586tFt+OHTuIjIys93NuM1S13T9iYmLUaH65hSV67ZIfdMC8VfpRXKKzw2m0+Pj4iufzP9ul1y35\noVkf8z/bVW8ML7zwgj7wwAPVlh85ckRdXFx069atqqp67bXX6ptvvlmxPi0tTVVV8/PzNTw8XE+f\nPl2xTdzWbbrtWLpGRkXr7bffrlarVT/55BOdMWNGxb4B3bBhg6qq3n777frMM89ofHy8XnbZZVpc\nXKyqqvfcc48uX75cjxw5oiKimzZtqhbnt99+q7fddluN8dd0DFVVHx+fOt/HypUrdfbs2RVlMjMz\nK97bjh07tKysTEeNGlXje6t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Mzycxo4DE9HyS7Ikk/kQ238SfrHYBY3c/j4rEUV4bCQmw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\n413CzUlr7kMreHNTJQDLF3yWkC3wF3eZac5HdJS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Uloop from IDuloop needs only multiplication\n", "U_l = IDuloop['data']*UloopCalibration\n", "# Chamber current I_ch needs offset correction, integration and calibration\n", "offset = np.mean(IDirog['data'][:i_offset]) # Up to 4 ms no signal, good zero specification\n", "I_chANDp = cumtrapz(IDirog['data']-offset, time, initial=0) # offset correction and integration\n", "I_chANDp *= RogowskiCalibration # calibration\n", "I_p=I_chANDp-U_l/R_ch # getting real plasma current from Ohm's law\n", "\n", "# And let's plot it\n", "f,ax = plt.subplots(3,sharex=True )\n", "f.subplots_adjust(hspace=0.05)\n", "f.suptitle(r'Plasma shot: #'+str(PlasmaShotNo)+ '- real data')\n", "\n", "ax[0].set_ylabel('$U_l$ [V]')\n", "ax[0].plot(time*1e3, U_l,label='Loop voltage $U_l$')\n", "ax[1].set_ylabel('$I_{ch+p}$ [kA]')\n", "ax[1].plot(time*1e3,I_chANDp/1e3,label='Chamber+plasma current $I_{ch+p}$')\n", "ax[2].set_ylabel('$I_{p}$ [kA]')\n", "ax[2].plot(time*1e3,I_p/1e3,label='Plasma current $I_{p}$')\n", "ax[2].set_xlabel('Time [ms]')\n", "[a.legend(loc='best') for a in ax]\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data correlation .." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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M0setl6S4uAhm3BxuM9bfHksm9aFHqwD+M74z7RrVZdatnUoVM27gsYFtbJoo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\n9p3IYtW9oxjRy5mC0gq2RaUyJ8izUWPwv9h7ikq9ZNfSiVTo9bjZWdS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#select only a signal with the plasma \n", "i_start,i_end = time.searchsorted((IDuloop['plasma_start']+5e-4,IDuloop['plasma_end']-5e-4))\n", "\n", "n_e = n_e[i_start:i_end]\n", "I_p = I_p[i_start:i_end]\n", "U_l = U_l[i_start:i_end]\n", "time = time[i_start:i_end]\n", "\n", "\n", "# And let's plot it\n", "f,ax = plt.subplots(3,sharex=True)\n", "f.subplots_adjust(hspace=0.05)\n", "f.suptitle('Plasma shot: #'+str(PlasmaShotNo)+ '- real data')\n", "\n", "ax[0].set_ylabel('$U_l$ [V]')\n", "ax[0].plot(time*1e3, U_l,label='Loop voltage $U_l$')\n", "ax[1].set_ylabel('$I_{p}$ [kA]')\n", "ax[1].plot(time*1e3,I_p/1e3,label='Plasma current $I_{p}$')\n", "ax[2].set_ylabel('$n_{e}$ [$m^{-3}$]')\n", "ax[2].plot(time*1e3,n_e,label='Electron density $n_{e}$')\n", "ax[-1].set_xlabel('Time [ms]')\n", "[a.legend(loc='best') for a in ax]\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Final calculations" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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XiQ3r/EOns/ll7UFunxg8ZYq34Pzw911h3SsQmTnata/pXBdwC76SZN/JLA9vLCvSsz2/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Energy confinement time = (43 +/- 6) us\n" ] } ], "source": [ "R_p = np.maximum(0,U_l/I_p) # plasma resistivity adopted to avoid power(xx,-2./3) problems @ T_e calculation\n", "P_OH = U_l*I_p # Plasma heating power\n", "T_e = 0.9*R_p**(-2./3) # Electron temperature (the GOLEM specific case)\n", "W_p = V_p*n_e*eV*T_e/3 # Energy content in the plasma\n", "# Let's make final calculations in the stationary phase to avoid complex derivation calculations of plasma energy balance\n", "imin,imax = time.searchsorted((0.016, 0.020))\n", "P_OH = P_OH[imin:imax]\n", "W_p = W_p[imin:imax]\n", "time = time[imin:imax]\n", "tau = W_p/P_OH \n", "#Plot\n", "f,ax = plt.subplots(3, sharex=True)\n", "f.suptitle('Plasma shot: #'+str(PlasmaShotNo)+ ' energy balance')\n", "\n", "f.subplots_adjust(hspace=0.001)\n", "ax[0].set_ylabel('$P_{OH}$ [kW]')\n", "ax[0].plot(time*1e3, P_OH/1000,label='Ohming heating power $P_{OH}$')\n", "ax[1].set_ylabel('$W_p$ [J]')\n", "ax[1].plot(time*1e3,W_p,label='Plasma energy volume $W_p$')\n", "ax[2].set_ylabel(r'$\\tau_E$ [us]')\n", "ax[2].plot(time*1e3,tau*1e6,label=r'Energy confinement time $\\tau_E$')\n", "ax[2].set_ylim(0,80)\n", "ax[2].grid('on')\n", "[a.legend(loc='best') for a in ax]\n", "ax[-1].set_xlabel('Time [ms]')\n", "plt.show()\n", "\n", "# Final calculation\n", "tau, tau_err = np.mean(tau), np.std(tau)\n", "print(\"Energy confinement time = (%.0f +/- %.0f) us\"%(tau*1e6,tau_err*1e6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resources:\n", "\n", "* [Notebook (.ipynb)](script.ipynb)\n", "* [python (.py)](script.py)\n", "* [(.pdf) version](script.pdf)\n", "* [(.html) version](http://golem.fjfi.cvut.cz/wikiraw/Education/ExperimentMenu/ElectronEnergyConfinementTime/Code/Jupyter/script.html)\n", "* [flowchart](drawing.jpg)\n", "* [all in zip](script.zip)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 1 }