2019-11-20 20:24:16 +01:00
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{
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"cells": [
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{
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"cell_type": "code",
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2019-11-24 18:55:56 +01:00
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"execution_count": 2,
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2019-11-20 20:24:16 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from importlib import reload\n",
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"import utility\n",
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"\n",
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"reload(utility)\n",
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"from utility import *\n",
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"\n",
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"from scipy.optimize import curve_fit\n",
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"from SecondaryValue import SecondaryValue\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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2019-11-24 18:55:56 +01:00
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"execution_count": 3,
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2019-11-20 20:24:16 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"ccurves = load_and_analyze([\n",
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" ('30', 30),\n",
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" ('65', 65)],\n",
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" 1,\n",
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" area=26,\n",
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" formatter='../messungen/191114_OM_VB/4_{}.dat'.format,\n",
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" columns=['desc', 'curve', 'area', 'j_c', 'u_cc'])\n"
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]
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},
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{
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"cell_type": "code",
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2019-11-24 18:55:56 +01:00
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"execution_count": 5,
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2019-11-20 20:24:16 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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2019-11-24 18:55:56 +01:00
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"30 0.031243\n65 0.032597\nName: j_c, dtype: float64"
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2019-11-20 20:24:16 +01:00
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]
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},
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2019-11-24 18:55:56 +01:00
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"execution_count": 5,
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2019-11-20 20:24:16 +01:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ccurves['j_c']"
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]
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},
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2019-11-24 18:55:56 +01:00
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": [
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],
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": [
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"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
|
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|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
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|
|
|
]
|
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|
|
},
|
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|
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|
"metadata": {},
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|
|
"output_type": "display_data"
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|
}
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|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"for _, curve in ccurves.iterrows():\n",
|
|
|
|
|
" plot_ccurve(curve['curve'],\n",
|
|
|
|
|
" save=f'D/{curve[\"desc\"]}.pdf')"
|
|
|
|
|
]
|
|
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|
|
},
|
2019-11-20 20:24:16 +01:00
|
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|
{
|
|
|
|
|
"cell_type": "code",
|
2019-11-24 17:16:32 +01:00
|
|
|
"execution_count": 12,
|
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": [
|
|
|
|
|
"ucs = np.array([[30, 35, 40, 45, 50, 55, 60, 65],\n",
|
|
|
|
|
" [575, 570, 563, 555, 547, 536, 525, 512]])\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-11-24 17:16:32 +01:00
|
|
|
"execution_count": 31,
|
|
|
|
|
"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"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-11-24 18:55:56 +01:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 37,
|
|
|
|
|
"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)"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-11-24 17:16:32 +01:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-11-24 17:18:20 +01:00
|
|
|
"execution_count": 34,
|
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": {
|
2019-11-24 18:55:56 +01:00
|
|
|
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|
2019-11-20 20:24:16 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
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",
|
2019-11-20 20:24:16 +01:00
|
|
|
"plt.xlabel(r'Temparatur [$^\\circ$C]')\n",
|
2019-11-24 17:16:32 +01:00
|
|
|
"plt.ylabel(r'$V_{OC}$ [mV]')\n",
|
|
|
|
|
"plt.grid()\n",
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
"save_fig(plt.gcf(), 'D/ucc.pdf')"
|
2019-11-20 20:24:16 +01:00
|
|
|
]
|
|
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|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 26,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
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|
"slide_type": "-"
|
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|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"angles = pd.read_excel('../messungen/winkel.xlsx')"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 29,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
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|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc6f4b6ba00>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 29,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2019-11-24 17:16:32 +01:00
|
|
|
"image/png": "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
|
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",
|
|
|
|
|
"execution_count": 63,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"[<matplotlib.lines.Line2D at 0x7fc6eff42220>]"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 63,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2019-11-24 17:16:32 +01:00
|
|
|
"image/png": "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
|
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",
|
|
|
|
|
"execution_count": 107,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2019-11-24 17:16:32 +01:00
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEJCAYAAAB7UTvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3hU1dbA4d9OB9KooSSQgPQOkY4GAaWKUgRBUFERES/WK352vQqWK8WOYIGrIIqCIioQEjpIkV4DJCS00AIEUmf298eZjCGUTMK0ZNb7PHmSmbPPnLVJmDXn7LPXVlprhBBCCAAvVwcghBDCfUhSEEIIYSVJQQghhJUkBSGEEFaSFIQQQlj5uDqAG1WpUiUdGRlZrH0vXrxIuXLl7BuQm5M+ewbps2e4kT5v2rTplNa6csHnS3xSiIyMZOPGjcXaNz4+npiYGPsG5Oakz55B+uwZbqTPSqmkqz0vl4+EEEJYSVIQQghhJUlBCCGEVYkfU7ianJwcUlJSyMzMvG67kJAQdu/e7aSo3IOz+hwQEEB4eDi+vr4OP5YQwn5KZVJISUkhKCiIyMhIlFLXbHfhwgWCgoKcGJnrOaPPWmtOnz5NSkoKUVFRDj2WEMK+SuXlo8zMTCpWrHjdhCAcRylFxYoVCz1TE0K4n1KZFABJCC4m//5ClEyl8vKRrT5ZkcinKw9fc/u4rnV5qns9J0YkhBCu5dFJYcwtkTzfuykAgz9fC8D3j7a32+v//PPP9O/fn927d9OgQQO7va69PPzwwzz99NM0atTI5n3i4+N5//33WbhwoQMjE0JcVdwEWD7R+jAGIB64dTx0ecEuhyi1l4/cwezZs+nUqRNz5syxy+uZTCa7vE6e6dOnFykhCCFcrMsL8No5qNUJanUiPmaB8dhOCQEkKThMeno6q1evZsaMGdakkDclfeDAgTRo0IBhw4aRt/JdbGwsLVu2pGnTpowcOZKsrCzAKOPxxhtv0KlTJ3744Qc2bNhAs2bNaN++Pc899xxNmjQBIDExkc6dO9OqVStatWrFmjVrrnrMhx56yHrMmJgYNm7ciMlk4oEHHqBJkyY0bdqUSZMmAZCQkEC3bt1o3rw5rVq14sCBA9a+Xa0PQoiSr9RfPnr9153sOnr+qttMJhPe3t4A7DpmtMm7jHQ9jaoH82rfxtdtM3/+fHr06EG9evWoUKECmzdvBuDvv/9m586dVK9enY4dO7J69Wqio6N54IEHiI2NpV69eowYMYJPP/2UJ598EjDu+V+1ahUATZo0Ydq0aXTo0IHx48dbj1elShWWLFlCQEAA+/fv595777XWhMp/zHbt2rF69Wo6depk3XfLli0cOXKEHTt2AJCWlgbAsGHDGD9+PHfffTeZmZmYzWaSk5Ov2of8ryeEsJMCl4usQiIgtJZDDilnCg4ye/ZshgwZAsCQIUOYPXs2AG3atCE8PBwvLy9atGhBYmIie/fuJSoqinr1jEHt+++/nxUrVlhfa/DgwYDxZn3hwgU6dOgAwNChQ61tcnJyeOSRR2jatCmDBg1i165d1m35j9msWTMSExMvi7V27docPHiQJ554gj/++IPg4GAuXLjAkSNHuPvuuwEjMZUtW/aafRBCOECBy0W8ds74clBCAA84U7jeJ/r8E7nsOdB8+vRpli1bxo4dO1BKYTKZUErRq1cv/P39re28vb3Jzc0t9PJLXmnc67WbNGkSYWFhbN26FbPZTEBAgHVb/mN6eXmRm5t72b7ly5dn69at/Pnnn3z88cfMnTuXyZMnX/NYV+uDEKJ0kDMFB/jxxx8ZMWIESUlJJCYmkpycTFRUlPUSUEENGjQgMTGRhIQEAGbNmsWtt956Rbvy5csTFBTEunXrAC4bwD537hzVqlXDy8uLWbNmFWlQ+tSpU5jNZgYMGMCbb77J5s2bCQ4OJjw8nPnz5wOQlZXFpUuXbH5NIUQ+cRPgtZArv+ImuDqyK0hScIDZs2dbL7vkGTBgAN99991V2wcEBPDVV18xaNAgmjZtipeXF6NHj75q2xkzZjBq1Cjat2+P1pqQkBAAxowZwzfffEO7du3Yt29fkRbeOHLkCDExMbRo0YIHHniACROMP9RZs2YxdepUmjVrRocOHTh+/LjNrymEyOdal4HseNeQvShn3TmilPoS6AOkaq2bXGW7AqYAvYBLwANa682FvW50dLQuuMjO7t27adiwYaExOerykSOlp6cTGBgIwMSJEzl27BhTpkyxeX9n1nuy9ffgaLL4imcoEX3+qrfx/cHfirzfpFM3M+X0zddsUtTJtkqpTVrr6ILPO3NM4WvgI2DmNbb3BOpavtoCn1q+O8zVZjRHjv/nl+WOM5p/++03JkyYQG5uLrVq1eLrr792dUhCCCd4qtIGnnruNQB2vm3c7Xeyw3/sngidlhS01iuUUpHXadIPmKmNU5d1SqlQpVQ1rfUxR8WUf0ZzSTF48GDr3UhCCM90JjeAH3PaE5Nptvtru9PdRzWA5HyPUyzPXZEUlFKjgFEAYWFhxMfHX7Y9JCSECxcuFHpAk8lkU7vSxJl9zszMvOJ34wrp6eluEYczSZ/dUwvLHKAtRYzTul9cHFWPx9LG9DcHdSgZl9ravc/ulBSuVlbzqgMeWutpwDQwxhQKnj7t3r3bpuvmsp6CYwUEBNCyZUunHOt6SsS1ZjuTPrupQ6EARY/zUCiYc4k58y3s/YF15oZsV3XpUyHQ7n12p7uPUoCIfI/DgaMuikUIIVwv70ag3Ew4tgV2zGN95BiG5rxIjN8ehxzSnc4UfgHGKqXmYAwwn3PkeILVtaaR27HqoBBCXJfZBMe2QupuOLkbUvfAyT3Q7jFo/7jRRmtyRyxk3OxMmnrvIcr7JCcdEIrTkoJSajZGpddKSqkU4FXAF0Br/RmwCON21ASMW1IfdEpgXV4wvop7q9g1pKSk8Pjjj7Nr1y7MZjN9+vThvffew8/Pzy6vfzUbN25k5syZTJ06tUj7xcTE8P777xMdfcXdaUIIe9Iaci7B1u8hdRdUvAlaDQdTDkzvCtoM3n5QqR5EtDW2A3j7M9l/NJM/N8YWjhNJ7/MvwB8X4Q/jPcted0s68+6jewvZroHHnRSOQ2mt6d+/P4899hgLFizAZDIxatQoXnzxRd577z1ru9zcXHx87PcriI6Oljd2IdxVWrKRCDLPws+jwMsXWgw1koJvAAz9AcrXgvJR4F3gfUEpxlXaRFy5OzifkUNshffwUhAf9VzJvSXVkyxbtoyAgAAefNA42fH29mbSpElERUURFRVFXFwcmZmZXLx4kaVLlzJ27FiWL19OVFQUZrOZkSNHMnDgQN544w1+/fVXMjIy6NChA59//jlKKWJiYmjbti1xcXGkpaUxY8YMOnfufNkCOMuXL2fcuHGAsTTmihUrCAoKYvLkycydOxcvLy969uzJxInGpbMffviBMWPGXPZ6Qgg7WvwSZJ2D0EgY+j1UrAPevv9sr9vtsuaTluxjSux+y6Mxlu/GmULtU2MYV3EDLaPsH6ZnJIW8S0P5Nb4LGg6B7Evw7SA4vu3yti2GQsthcPE0zB1x+b6FXGLauXMnrVu3vuy54OBgatasSW5uLmvXrmXbtm1UqFCBH3/8kcTERLZv305qaioNGzZk5MiRAIwdO5ZXXnkFgOHDh7Nw4UL69u0LGGcZf/31F4sWLeL1119n6dKllx3v/fff5+OPP6Zjx46kp6cTEBDA77//zsKFC1m/fj1ly5blzJkz1vaFvZ4QohjOHDTOCEIjoMcESEsxzgqqFL4S41Pd61kvB+18uxMTLt3FNp+mrH2hK+W+6wdAvLH2ml25091HpYbW+qoL1+c93717dypUqADAqlWrGDRoEF5eXlStWpUuXbpY28fFxdG2bVuaNm3KsmXL2Llzp3Vb//79AWjduvVVS1d37NiRp59+mqlTp5KWloaPjw9Lly7lvvvus5bAzovBltcTQhSB2QRrPoJPOsAflnVPgqsbCaEYUs3BrMmtz71talLO37Gf5T3jTOFan+wvXAC/ssb2aw00l6tY5MH
|
2019-11-20 20:24:16 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig, ax1 = plt.subplots()\n",
|
|
|
|
|
"rel = SecondaryValue('i/m')\n",
|
|
|
|
|
"org = rel(m=(angles['Org'].max(), 0.01), i=(angles['Org'], .01))\n",
|
|
|
|
|
"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",
|
|
|
|
|
"ax1.errorbar(np.sin(angles['Winkel']/180*np.pi), anorg[0], yerr=anorg[1], label='Anorganisch', capsize=4)\n",
|
|
|
|
|
"ax1.errorbar(np.sin(angles['Winkel']/180*np.pi), org[0], yerr=org[1], label='Organisch', linestyle='--', capsize=3)\n",
|
|
|
|
|
"ax1.legend()\n",
|
|
|
|
|
"ax1.grid()\n",
|
|
|
|
|
"save_fig(fig, 'E/relativ.pdf')"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 70,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
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"data": {
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"text/plain": [
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2019-11-24 17:16:32 +01:00
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"(array([1. , 0.98611111, 0.97569444, 0.90972222, 0.82638889,\n",
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" 0.64236111, 0.61805556, 0.51388889, 0.36111111, 0.20486111]),\n",
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" array([0.04910464, 0.04876483, 0.04851155, 0.04694047, 0.04504422,\n",
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" 0.04126876, 0.04081881, 0.03903868, 0.03691678, 0.03544335]))"
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2019-11-20 20:24:16 +01:00
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]
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},
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"execution_count": 70,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"relangles"
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]
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2019-11-24 17:16:32 +01:00
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},
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{
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},
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"outputs": [
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2019-11-24 18:55:56 +01:00
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2019-11-24 17:16:32 +01:00
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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|
|
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|
"source": [
|
|
|
|
|
"ts = ucs[0]\n",
|
|
|
|
|
"plt.plot(ts, U(T=(273.15+ts,)))"
|
|
|
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]
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|
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"slide_type": "-"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"300668347958.8219"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"I0"
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]
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2019-11-20 20:24:16 +01:00
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}
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],
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"metadata": {
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"kernelspec": {
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"argv": [
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"/usr/bin/python3",
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"-m",
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"-f",
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"{connection_file}"
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],
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"display_name": "Python 3",
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"env": null,
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"interrupt_mode": "signal",
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"language": "python",
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"metadata": null,
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"name": "python3"
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},
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"name": "d.ipynb"
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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