mirror of
https://github.com/vale981/fpraktikum
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calculations for c
This commit is contained in:
parent
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commit
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16 changed files with 698 additions and 11 deletions
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"cells": [
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"cell_type": "code",
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"execution_count": 55,
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"cell_type": "code",
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"execution_count": 56,
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"execution_count": 58,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"an_light (-0.02630074615384615, 0.5599925566487172, 0.30999911523151735, 0.156638522040846, 0.1282742291712253, 0.06024558540032538)\n",
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"fol_light (-3.2946186e-05, 7.129314361652983, 4.02000070175182, 0.002029756267025768, 17.568978096781162, 0.0008119025068103072)\n",
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"org_light (-0.0040643187499999995, 0.9183601229960922, 0.7400008818112345, 0.00016271274581558095, 0.5744693424183512, 0.025423866533684523)\n"
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"an_light (0.02630074615384615, 0.5599925566487172, 0.30999911523151735, 0.156638522040846, 0.40904859440059965, 0.06024558540032538)\n",
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"fol_light (3.2946186e-05, 7.129314361652983, 4.02000070175182, 0.002029756267025768, 0.3456614690624081, 0.0008119025068103072)\n",
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"org_light (0.0040643187499999995, 0.9183601229960922, 0.7400008818112345, 0.00016271274581558095, 0.6811469583998824, 0.025423866533684523)\n"
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]
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}
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],
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396
SZ/auswertung/b.ipynb
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SZ/auswertung/b.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 139,
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"metadata": {
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"autoscroll": 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"
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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": 114,
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"metadata": {
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"autoscroll": 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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"org_compliance = 0.0098\n",
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"a_an = 26 # cm^2\n",
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"a_org = 6.4e-2 # cm^2\n",
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"a_fol = 25 # cm^2\n",
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"i_ein = 100e-3 # watt/cm^2\n",
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"u_ref = 32.2e-3 # volt"
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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": 122,
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"metadata": {
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"autoscroll": 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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"intensity = SecondaryValue('u/u_ref*i0', defaults=dict(i0=i_ein, u_ref=u_ref))"
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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": 170,
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"metadata": {
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"autoscroll": 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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"intensities, d_intensites = intensity(u=([.011, 0.017, .021, .026, .032], 1e-3))"
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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": 188,
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"metadata": {
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"autoscroll": 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 = [parse_ccurve(f'../messungen/191114_OM_VB/2_{point}.dat') \\\n",
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" for point in ['a', 'b', 'c', 'd', 'e']]\n",
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"\n",
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"ccurve_specs = [(intsy, analyze_ccurve(curve, a_an, intsy)) \\\n",
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" for curve, intsy in zip(ccurves, intensities)]"
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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": 187,
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"metadata": {
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"autoscroll": false,
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"ein.tags": "worksheet-0",
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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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"%matplotlib qt5\n",
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"fig, ax = plot_ccurve(ccurves[0], label=intensities[0]*1000, area=a_an)\n",
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"\n",
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"for ccurve, intsy in zip(ccurves[1:], intensities[1:]):\n",
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" plot_ccurve_line(ax, ccurve, label=intsy*1000, area=a_an)\n",
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"\n",
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"ax.legend()\n",
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"fig.show()"
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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": 168,
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"metadata": {
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0.04347826, 0.05279503, 0.06521739, 0.08074534, 0.09937888])"
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]
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},
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"execution_count": 168,
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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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"intensities"
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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": 241,
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"metadata": {
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"autoscroll": 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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"plt.clf()\n",
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"plt.plot(*np.array([[intsy*1000, params['j_c']] \\\n",
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" for intsy, params in ccurve_specs]).T, marker='*')\n",
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"plt.xlabel('Intensitaet [$mW/cm^2$]')\n",
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"plt.ylabel('$j_{SC}$ [$A/cm^2$]')\n",
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"plt.savefig('./figs/B/j_sc.pdf', dpi=300)\n",
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"plt.grid()\n",
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"#plt.xscale('log')\n",
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"#plt.yscale('log')\n"
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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": 243,
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"metadata": {
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"autoscroll": 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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"plt.clf()\n",
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"plt.plot(*np.array([[(intsy*1000), params['u_cc']] \\\n",
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" for intsy, params in ccurve_specs]).T, marker='*')\n",
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"plt.xlabel('Intensitaet [$mW/cm^2$]')\n",
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"plt.ylabel('$U_{CC}$ [$V$]')\n",
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"plt.xscale('log')\n",
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"plt.grid(which='both')\n",
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"plt.savefig('./figs/B/u_cc.pdf', dpi=300)\n",
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"#plt.yscale('log')\n"
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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": 103,
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"metadata": {
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"autoscroll": 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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"0.0168614765323676"
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]
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},
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"execution_count": 103,
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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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"0.011 * (.032/.011)**(2/5)"
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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": 105,
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"metadata": {
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"autoscroll": 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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"[(0.034161490683229816,\n",
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" {'j_c': 0.0025912392307692305,\n",
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" 'u_cc': 0.5086008577882963,\n",
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" 'u_mlp': 0.3977111360504424,\n",
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" 'p_mlp': 0.022268705563519103,\n",
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" 'ff': 0.6498857684076272,\n",
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" 'eta': 0.025071759410675354}),\n",
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" (0.052795031055900624,\n",
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" {'j_c': 0.01341955,\n",
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" 'u_cc': 0.5600084576663334,\n",
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" 'u_mlp': 0.37999930322896436,\n",
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" 'p_mlp': 0.11432399757691399,\n",
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" 'ff': 0.585101909146971,\n",
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" 'eta': 0.08328580818951652}),\n",
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" (0.06521739130434784,\n",
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" {'j_c': 0.016769280769230767,\n",
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" 'u_cc': 0.5613136094311547,\n",
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" 'u_mlp': 0.36000019842026715,\n",
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" 'p_mlp': 0.13293315750161686,\n",
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" 'ff': 0.5431752390708393,\n",
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" 'eta': 0.07839647750095351}),\n",
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" (0.08074534161490683,\n",
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" {'j_c': 0.02099583076923077,\n",
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" 'u_cc': 0.5641834857878043,\n",
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" 'u_mlp': 0.3399999063957519,\n",
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" 'p_mlp': 0.1534276805047185,\n",
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" 'ff': 0.4981692745272282,\n",
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" 'eta': 0.07308241586171502}),\n",
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" (0.09937888198757765,\n",
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" {'j_c': 0.025274496153846155,\n",
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" 'u_cc': 0.5675239554252917,\n",
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" 'u_mlp': 0.32765580118957827,\n",
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" 'p_mlp': 0.17174272136274898,\n",
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" 'ff': 0.4605091752711381,\n",
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" 'eta': 0.06646773591202546})]"
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]
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},
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"execution_count": 105,
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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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"ccurve_specs"
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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": 161,
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"metadata": {
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"autoscroll": 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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"[<matplotlib.lines.Line2D at 0x7f0eb414d940>,\n",
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" <matplotlib.lines.Line2D at 0x7f0eb414dc50>]"
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]
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},
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"execution_count": 161,
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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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"plt.clf()\n",
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"points = np.arange(0,6)\n",
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"ints = .011 * (.032/.011)**(points/5)\n",
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"plt.plot(np.arange(1,6), intensities, ints)"
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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": 162,
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"metadata": {
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0.03416149, 0.05279503, 0.06521739, 0.08074534, 0.09937888])"
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]
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},
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"execution_count": 162,
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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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"intensities"
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]
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0.011 , 0.01361897, 0.01686148, 0.02087599, 0.02584631,\n",
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" 0.032 ])"
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]
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},
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"execution_count": 163,
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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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"ints"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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@ -1,7 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.figure import Figure
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import seaborn
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from matplotlib import rc
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from scipy import interpolate
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from scipy import optimize
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@ -37,7 +36,7 @@ def plot_ccurve(ccurve, log=False, area=None, compliance=.99, median=False,
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if median:
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compliance = np.median(ccurve[:, 0])
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plot_ccurve_line(ax, ccurve, compliance=compliance, **pyplot_args)
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plot_ccurve_line(ax, ccurve, area=area, compliance=compliance, **pyplot_args)
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if log:
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ax.set_yscale('log')
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@ -51,11 +50,11 @@ def plot_ccurve_line(ax, ccurve, area=None, marker='.', compliance=.99, **pyplot
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if area:
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c /= area
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ax.errorbar(v, c, linestyle='None', marker=marker, markersize=1.5, alpha=1,
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ax.errorbar(v, c, linestyle='None', marker=marker, markersize=2, alpha=1,
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**pyplot_args)
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ax.set_xlabel("Spannung V [V]")
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ax.set_ylabel("Stromstaerke I [A]" \
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if area else r"Stromdichte j [$\frac{A}{cm^2}$]")
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if not area else r"Stromdichte j [$\frac{A}{cm^2}$]")
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ax.grid(True, which='both')
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ax.set_xlim(v[0], v[-1])
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@ -84,7 +83,8 @@ def analyze_ccurve(ccurve, area, int_ein):
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bracket=(0, u_cc), bounds=(0, u_cc),
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method='bounded').x
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p_mlp = -interpolated(u_mlp)*u_mlp
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ff = -p_mlp / i_c * u_cc
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ff = -p_mlp / (i_c * u_cc)
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eta = p_mlp / (int_ein * area)
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return -j_c, u_cc, u_mlp, p_mlp, ff, eta
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return {'j_c': -j_c, 'u_cc': u_cc, 'u_mlp': u_mlp, 'p_mlp': p_mlp,
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'ff': ff, 'eta': eta}
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|
|
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Loading…
Add table
Reference in a new issue