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fpraktikum/SZ/auswertung/d.ipynb

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done with scripting
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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from importlib import reload\n",
"import utility\n",
"\n",
"reload(utility)\n",
"from utility import *\n",
"\n",
"from scipy.optimize import curve_fit\n",
"from SecondaryValue import SecondaryValue\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"ccurves = load_and_analyze([\n",
" ('30', 30),\n",
" ('65', 65)],\n",
" 1,\n",
" area=26,\n",
" formatter='../messungen/191114_OM_VB/4_{}.dat'.format,\n",
" columns=['desc', 'curve', 'area', 'j_c', 'u_cc'])\n"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
finish D
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"30 0.031243\n65 0.032597\nName: j_c, dtype: float64"
done with scripting
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]
},
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"execution_count": 3,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ccurves['j_c']"
]
},
finish D
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{
"cell_type": "code",
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"execution_count": 4,
finish D
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"image/png": [
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finish D
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],
"text/plain": [
pgf all the things
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"<Figure size 360x288 with 1 Axes>"
finish D
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": [
fazit, winkelabhängigkeit
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"iVBORw0KGgoAAAANSUhEUgAAAWAAAAEYCAYAAABiECzgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAbgUlEQVR4nO3d32+c2X3f8c+3ySoFJNdcqrSNuot4R46Zq2BNUleFEcpLNkiA3jSk0j/AS7m9WgOu5M0/YEgO4MuEdAHrVhS3F71wkZC2GTjtjSiu4KssAnHZKA5cIKZmvZRTc5f49mLOIx0N58cznHnmnJl5vwCC8/zifPVQ89XR9/x4zN0FABi+f5E6AACYVCRgAEiEBAwAiZCAASAREjAAJPKbqQOoytTUlH/xi19MHcYZz54908WLF1OHcUaucUl5xvaLX/xCp6en+sxnPpM6lJZyvGdSvnFJ1cb28OHDf3L3meb9Y5uAP/vZz2pvby91GGfs7u5qcXExdRhn5BqXlGdsd+/eVb1e19tvv506lJZyvGdSvnFJ1cZmZv+n1X5KEACQSLIEbGYrZrbd5fiSma112gcAoypZAnb3rXbHzGwlnLMTtpda7RtGnABQlVxLEFclHYTXB5Lm2uwDgJGVayfcVNP25Tb7XhJKE2uSNDMzo93d3UqC68fx8TFx9SjH2Or1uk5PT7OLq5DjPZPyjUtKE1uuCbguabrEvpe4+4akDUmanZ31HHtbc+0FzjUuKc/YDg8PVa/Xs4urkOM9k/KNS0oTW64J+IFetHhrkrbDdvM+ABhZKUdBLElaKDrXwr5t6XkHXS2cM+XuO632JQkcAAYkWQs4JNBXm/YtR6/vhJc7nfYBQM6Onp3oNy5Nf7bVsVxHQQDAWLi/90S/cWn637Y6RgIGgAqtLrym0+Ojf2h1jAQMABWavnhBp8dH/7fVMRIwACRCAgaAREjAADBgR89OtP7Xj3X07KTjeSRgABiw+3tP9O3/+be6v/ek43m5zoQDgJG1uvDaS9/bIQEDwIBNX7ygG79/pet5lCAAIBESMAAkQgIGgD6VHfXQjAQMAH0qO+qhGZ1wANCnsqMempGAAaBPZUc9NKMEAQCJkIAB4BzO2/EWIwEDwDmct+MtRg0YAM7hvB1vMRIwAJzDeTveYpQgACAREjAAJJIsAZvZipktmdlai2NzZvbYzB6Gr9th/1Mz2zazm8OPGMCkG8TIh1iSGrCZrUiSu++Y2ZqZLbn7TnTKtLtfCefOSaqH/atN5wHA0BQjHyT1Xf+V0nXCXZV0L7w+kDQn6XlibUqyNXffCq+nzKzm7gfDCRMAXhjEyIdYqgQ81bR9udVJZrbm7hvRrmlJR2a27u43Wp0vaU2SZmZmtLu7O6BwB+f4+Ji4epRjbPV6Xaenp9nFVcjxnkn5xiWVj21W0k8fnH/sbyxVAq6rkUy7WZb0PAEXydjM6ma2ErWM4+MbkjQ7O+uLi4sDC3hQdnd3RVy9yTG2w8ND1ev17OIq5HjPpHzjktLElqoT7oFetIJrkrabTzCzqabttVAPBoCxkCQBh5ZrzcyWJE0VNV8zixPxtKSjaHsznLMS/QwAqNSgRz7Eks2Ec/c74WXc+bYcvT6QdCParkvaD18kXwBDMeiRDzGmIgNAB4Me+RAjAQNAB4NY86EdpiIDQCIkYABoUmXHW4wEDABNBrHYehnUgAGgSZUdbzESMAA0qbLjLUYJAgASIQEDQCIkYACQ9NGJD2XkQ4waMABI+snPPtbm+9VMOW6HBAwAkr7y+Vd0pXal8pEPMRIwAEj61AUbWsu3QA0YABIhAQNAIiRgABNrWGs+tEMNGMDEihdbn03w/iRgABMrXvNhUE867gUJGMDEGtaaD+1QAwaAREjAAJAICRjAREk98iGWLAGb2YqZLZnZWpvjT81s28xulr0GALoZ1tMuykjSCWdmK5Lk7jtmtmZmS+6+03Taaryv5DUA0NGwnnZRhrn78N/U7Lake+6+b2ZLkubc/U7TOSuS9t39oIdr1iStSdLMzMz85ubmMP44PTk+PtalS5dSh3FGrnFJecb26NEjnZ6ean5+PnUoLeV4z6R845Kqje3atWsP3X2heX+qYWhTTduXW5wzLenIzNbd/UaZa9x9Q9KGJM3Ozvri4uIAQh2s3d1dEVdvcozt8PBQ9Xo9u7gKOd4zKd+4pDSxpUrAdTUSbFshmcrM6qE13PUaABglqTrhHuhFi7YmaTs+GGq8c71cAwDt5DTyIZYkAbv7lqRaqOVOFZ1pZlYk1c2wXXS8bbW7BgC6yWnkQyzZVOSoA20n2rccvtcl7YevrU7XAEA3OY18iLEWBICxl3rNh3aYCQcAiZCAASAREjCAsZTryIcYCRjAWMp15EOMTjgAYynXkQ8xEjCAsZTryIcYJQgASIQEDACJkIABjI1RGPkQa1sDNrMvS1qQ1GrBYAvfXdKeuz+qIDYA6Ekx8kFS9vVfqXMn3LS7f6/bDzCzP5ZEAgaQ3CiMfIi1LUG4+w/bHTOzr0XnvTvooADgPIqRD9MXL6QOpZTSNWAz+4KZfdPM9iTdrjAmAJgIHccBm9m/UuMZa/9JjcXQH0ta1dnHAwEAetRtIsZ/U+PpE19z90dm9qa7fzCEuACglKNnJ7q/90SrC6+NTOmh0LEE4e7Xw5M8r5jZW5JWpEY5ovrQAKC7UVjzoZ1SU5HjjrYw6mFN0h9UFRQAlDVqIx9iPa8F4e7vmtlBFcEAQK9GYc2HdtqWIMzsjXbH3P29MucBANrr1AK+YmYLJX7GkZiIAQA9a5uAq55gER45X5dUc/eNpmNTaoy+qEm66u63wv6nkvYkbUdPSAYwYUZ55EMsyWI8IfnK3XfC9lLTKdclLbj7Vji+FvavuvsyyReYbKM88iGWakH2q5LuhdcHkuYk7RQHm1rENUnb4fWUmdXcnU5AYIKN8siHWKoE3DyT7nKrk8ysJumoaClLmpZ0ZGbr7n6jxflragyR08zMjHZ3dwcX8YAcHx8TV49yjK1er+v09DS7uAo53jNpsHHNSvrpg8G1gFPcs65Tkd39l2X396CuRjLtZiVOtEXL2MzqZrZSlCiajm9I0uzsrC8uLvYRYjV2d3dFXL3JMbbDw0PV6/Xs4irkeM+kfOOS0sTWrQX8jpnda7H/TyS908f7PtCLVnBcYnguJNg74fWcGmsT77n7fh/vCwDZ6JaAl9VIkNa0/3X1kYDdfcvMbobOt6moM27b3ZfD/ttmVrzHLUmbkmpRB95Wyx8OYOyMy6iHZt0S8FvxpItCeFpGX6KRDHHn23L4viOp1dSW/fBF8gUmyKg96aKsjgm4VfLttB8AqjAuox6apRoFAQCljfJ6D53wVGQASKRUAjazt8zsz83sDTP7tJl9terAAGDclW0BP3b3/yzJ3P3DKgMCAKkx8mH9rx/r6NlJ6lAqUzYBz4dlJ18Nrd/5CmMCgLFZ76GTsp1wG2qM+52T9Ffu/p3qQgKA8R35ECubgN90928VG2b2bXfvZyYcAHQ0riMfYmUT8NfNbF+N6cN/KunT1YUEAJOh7EM5/72ZvSlpzd2vm9nrFccFAGOv22pom5K82FRjLYY/V2NhnKsVxwZgwozrmg/tdGsBr7v7D5t3htYwAAzUuK750E63tSB+KElm9k13/7Pm/QAwSJMw8iFWdhzwS48A4lH0AKpQjHyYhPKD1NsoiNtqLAVpkr4s6XcqiwoAJkDZBHw7LjtQAwYwKJPW8RYrW4KoxYvx6MXICADoyyRMOW6nbAv4sbt/z8y+7O4fmjU/oQgAzmfSOt5iLMYDIKlJ63iLsRgPACRSdiryh5LixXj+o7v/98qiAoAJUPaJGG+a2V+Z2V+a2V+q8bh6ADiXSVhsvYyyJYjXw4I8xSpotX7f2MxWJNUl1dx9o8zxbtcAGA2TNuW4nbKdcB+Y2ddCKWJNfXbChUQqd98J20vdjne7BsDoWF14Te/84e9O5MiHWNka8A+jJSh31H8L+Kqke+H1gRqdeztdjl/ucs1LfvWrX+nu3bt9hjl49Xpdh4eHqcM4I9e4pDx
finish D
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],
"text/plain": [
pgf all the things
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"<Figure size 360x288 with 1 Axes>"
finish D
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for _, curve in ccurves.iterrows():\n",
" plot_ccurve(curve['curve'],\n",
pgf all the things
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" save=f'D/{curve[\"desc\"]}.pgf')"
finish D
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]
},
done with scripting
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{
"cell_type": "code",
fazit, winkelabhängigkeit
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"execution_count": 5,
done with scripting
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"ucs = np.array([[30, 35, 40, 45, 50, 55, 60, 65],\n",
" [575, 570, 563, 555, 547, 536, 525, 512]])\n"
]
},
{
"cell_type": "code",
fazit, winkelabhängigkeit
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"execution_count": 6,
some D
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"I_300 = 9.56e-8 # A\n",
"e_g = 1.12 # eV\n",
"kb = 8.617333262145e-5 # eV/K\n",
"T = 273.15 + 32 # K\n",
"a = 1.42\n",
"I0 = I_300 * np.exp(e_g/(T*kb))\n",
"I_p = 0.682171 # A\n",
"U = SecondaryValue('ln(ip/i0*exp(e_g/(k*T)))*T*k*a', defaults=dict(k=kb, e_g=e_g, ip=I_p, a=a, i0=I0))\n"
]
},
finish D
2019-11-24 18:55:56 +01:00
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 7,
finish D
2019-11-24 18:55:56 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"300667958386.67993\n"
]
}
],
"source": [
"print(I0)"
]
},
some D
2019-11-24 17:16:32 +01:00
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 8,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
pgf all the things
2019-11-24 21:34:22 +01:00
"image/png": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"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
pgf all the things
2019-11-24 21:34:22 +01:00
],
done with scripting
2019-11-20 20:24:16 +01:00
"text/plain": [
pgf all the things
2019-11-24 21:34:22 +01:00
"<Figure size 360x288 with 1 Axes>"
done with scripting
2019-11-20 20:24:16 +01:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
some D
2019-11-24 17:16:32 +01:00
"plt.plot(*ucs, label='Messung', linestyle='None', marker='x')\n",
"plt.plot(ucs[0], U(T=(273.15+ucs[0],))*1000, label='Theorie')\n",
done with scripting
2019-11-20 20:24:16 +01:00
"plt.xlabel(r'Temparatur [$^\\circ$C]')\n",
some D
2019-11-24 17:16:32 +01:00
"plt.ylabel(r'$V_{OC}$ [mV]')\n",
"plt.grid()\n",
"plt.legend()\n",
pgf all the things
2019-11-24 21:34:22 +01:00
"save_fig(plt.gcf(), 'D/ucc.pgf')"
done with scripting
2019-11-20 20:24:16 +01:00
]
},
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 9,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"angles = pd.read_excel('../messungen/winkel.xlsx')"
]
},
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 10,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"<matplotlib.axes._subplots.AxesSubplot at 0x7efc91cf64f0>"
done with scripting
2019-11-20 20:24:16 +01:00
]
},
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 10,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
pgf all the things
2019-11-24 21:34:22 +01:00
"image/png": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"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
pgf all the things
2019-11-24 21:34:22 +01:00
],
done with scripting
2019-11-20 20:24:16 +01:00
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"angles.plot(x='Winkel')"
]
},
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 11,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"[<matplotlib.lines.Line2D at 0x7efc91aa9040>]"
done with scripting
2019-11-20 20:24:16 +01:00
]
},
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 11,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
pgf all the things
2019-11-24 21:34:22 +01:00
"image/png": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"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
pgf all the things
2019-11-24 21:34:22 +01:00
],
done with scripting
2019-11-20 20:24:16 +01:00
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax1 = plt.subplots()\n",
"\n",
"ax1.plot(np.sin(angles['Winkel']/180*np.pi), angles['Org']/angles['Org'].max(), label='Organisch', linestyle='--')\n",
"ax1.set_ylabel('$I_{SC}$ Organisch [mA]')\n",
"ax1.set_xlabel(r'$\\sin(\\theta)$')\n",
"ax1.set_ylim([0,1])\n",
"#ax1.set_yscale('log')\n",
"#ax1.set_xscale('log')\n",
"ax2 = ax1.twinx()\n",
"ax2.set_ylim([0,1])\n",
"ax2.set_ylabel('$I_{SC}$ Anorganisch [A]')\n",
"ax2.plot(np.sin(angles['Winkel']/180*np.pi), angles['Anorg']/angles['Anorg'].max(), label='Anorganisch',)\n"
]
},
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 18,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
pgf all the things
2019-11-24 21:34:22 +01:00
"image/png": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"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
pgf all the things
2019-11-24 21:34:22 +01:00
],
done with scripting
2019-11-20 20:24:16 +01:00
"text/plain": [
pgf all the things
2019-11-24 21:34:22 +01:00
"<Figure size 360x288 with 1 Axes>"
done with scripting
2019-11-20 20:24:16 +01:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax1 = plt.subplots()\n",
"rel = SecondaryValue('i/m')\n",
pgf all the things
2019-11-24 21:34:22 +01:00
"org = rel(m=(angles['Org'].max(), 0.5), i=(angles['Org'], .5))\n",
done with scripting
2019-11-20 20:24:16 +01:00
"anorg = rel(m=(angles['Anorg'].max(), 0.01), i=(angles['Anorg'], .01))\n",
"ax1.set_ylabel('$I_{SC}/\\max({I_{SC}})$')\n",
"ax1.set_xlabel(r'$\\sin(\\theta)$')\n",
pgf all the things
2019-11-24 21:34:22 +01:00
"ax1.errorbar(np.sin(angles['Winkel']/180*np.pi), anorg[0], label='Anorganisch', capsize=4, marker='X')\n",
"ax1.errorbar(np.sin(angles['Winkel']/180*np.pi), org[0], label='Organisch', linestyle='--', capsize=3, marker='*')\n",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"area = np.linspace(0,1,1000)\n",
"ax1.plot(area, area, label='Effektive Flaeche')\n",
done with scripting
2019-11-20 20:24:16 +01:00
"ax1.legend()\n",
"ax1.grid()\n",
pgf all the things
2019-11-24 21:34:22 +01:00
"save_fig(fig, 'E/relativ.pgf')"
done with scripting
2019-11-20 20:24:16 +01:00
]
},
{
"cell_type": "code",
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"execution_count": 13,
done with scripting
2019-11-20 20:24:16 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"ename": "NameError",
"evalue": "name 'relangles' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m\u001b[0m",
"\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-047cd24c6f05>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrelangles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'relangles' is not defined"
]
done with scripting
2019-11-20 20:24:16 +01:00
}
],
"source": [
"relangles"
]
some D
2019-11-24 17:16:32 +01:00
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"[<matplotlib.lines.Line2D at 0x7efc91d343a0>]"
some D
2019-11-24 17:16:32 +01:00
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
fazit, winkelabhängigkeit
2019-11-30 13:35:25 +01:00
"image/png": [
"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
],
some D
2019-11-24 17:16:32 +01:00
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ts = ucs[0]\n",
"plt.plot(ts, U(T=(273.15+ts,)))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"300668347958.8219"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"I0"
]
done with scripting
2019-11-20 20:24:16 +01:00
}
],
"metadata": {
"kernelspec": {
"argv": [
"/usr/bin/python3",
"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "Python 3",
"env": null,
"interrupt_mode": "signal",
"language": "python",
"metadata": null,
"name": "python3"
},
"name": "d.ipynb"
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink