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@ankostis
Created December 18, 2017 16:57
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SRC_with_wltp-ToyotaAURIS14D
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cloned from `WLTC-SRC` on 15/12/2017"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"import numpy as np, pandas as pd\n",
"from pandalone import xleash\n",
"from io import StringIO \n",
"import logging\n",
"logging.basicConfig(level=logging.INFO)\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# wltc_path = !pwd\n",
"# wltc_path = 'PyWLTC'\n",
"# if (wltc_path not in sys.path):\n",
"# sys.path.append(wltc_path)\n",
"import wltp"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Display the complete cycle.\n",
"#pd.set_option('max_rows', 2000)\n",
"\n",
"\n",
"## INTRODUCA SOLO I VARIABILI\n",
"#---------------------------------------?---------\n",
"outfname = 'SRC_with_wltp-ToyotaAURIS14D.csv'\n",
"def goodVehicle():\n",
" goodVehicle = {\n",
" \"vehicle\": {\n",
" \"test_mass\": 1360, # Introduca la massa - test di WLTP qui [kg]\n",
" 'unladen_mass': 1220,\n",
" \"v_max\": 160, # Introduca la velocita massima qui [km/h]\n",
" \"p_rated\": 67.7, # Introduca la Potenza massima qui [kW]\n",
" \"n_rated\": 4000, # Introduca i RPM a potenza massima qui\n",
" \"n_idle\": 770, # Introduca i RPM idle qui \n",
" #\"n_min\": None, # Can be overriden by manufacturer.\n",
" \"gear_ratios\": [127.31, 64.683, 40.907, 30.422, 22.322, 19.493],\n",
" \"resistance_coeffs\":[140,\n",
" 0.45,\n",
" 0.0295],\n",
" }\n",
" }\n",
" return goodVehicle\n",
"\n",
"## Read and interpolate FullLoadCurve\n",
"#\n",
"def read_FLC_from_csv_txt(full_load_curve_txt, column_names=['Y']):\n",
" with StringIO(full_load_curve_txt) as fd: \n",
" df = pd.read_table(fd, header=None, names=column_names)\n",
" df['X'] = df.index.values\n",
" df = df.reset_index(drop=True)\n",
" df = df.apply(pd.to_numeric)\n",
" return df\n",
"\n",
"def interpolate_FLC(df, n_idle, n_rated, p_rated):\n",
" df.Y = df.Y / p_rated\n",
" df.X = (df.X - n_idle) / (n_rated - n_idle)\n",
"\n",
" Xout = np.arange(0.05, 1.3, 0.01)\n",
"\n",
" Yout = np.interp(Xout, df.X, df.Y)\n",
"\n",
" fld_curve = pd.DataFrame.from_items([\n",
" ('n_norm', Xout),\n",
" ('p_norm', Yout),\n",
" ])\n",
" \n",
" return fld_curve"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = goodVehicle()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc364d846d8>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc364dd10f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Q6aO9wONmNkS+vgCAu3++zr87Axh2970AZnYfsBHYU3bNFcAWd99f+JyvNRC7NGC6U9NU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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc364ca9470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"full_load_curve_txt = r\"\"\"\n",
"1000\t112.64\t11.8\n",
"1500\t220.3\t34.6\n",
"2000\t219.73\t46\n",
"2500\t214.59\t56.2\n",
"3000\t204.11\t64.1\n",
"3500\t183.3\t67.2\n",
"4000\t161.75\t67.7\n",
"4500\t130.23\t61.3\n",
"4900\t87.79\t45\n",
"\"\"\"\n",
"veh = model['vehicle']\n",
"flc_orig = read_FLC_from_csv_txt(full_load_curve_txt, column_names=['ignore', 'Y'])\n",
"flc_orig.rename_axis({'X': '$N[rpm]$', 'Y': '$P[kw]$'}, axis=1).plot.scatter('$N[rpm]$', '$P[kw]$')\n",
"\n",
"flc = interpolate_FLC(flc_orig,\n",
" veh['n_idle'], veh['n_rated'],\n",
" veh['p_rated'])\n",
"flc.plot.scatter('n_norm', 'p_norm')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pandalone.xleash.io._xlrd:Opening book '/opt/ipython/public/SRC.xlsx'...\n"
]
},
{
"data": {
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mYlw/AJH+Dlsl7y41bqfPVUBOagI5QkQBDGgDrmYWZ9M4ltXb+Ut6kVMjsJCM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\nYZgxhoUAwzDMGMNCgGEYZoz5/04nM+IwHSFDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc3649f79b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cycle = pd.DataFrame(xleash.lasso('SRC.xlsx#Sheet1!B5:..(RD)'), columns=list('tv'))\n",
"cycle.v.plot();"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model['vehicle']['full_load_curve'] = flc\n",
"model['cycle_run'] = {'v_target': cycle['v']}"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:wltp.experiment:Found forced `cycle-run` table(2012x1).\n",
"INFO:wltp.experiment:Found forced velocity(v_target).\n",
"INFO:wltp.experiment:12 Insufficient power!: [ 822 823 1090 1091 1092 1093 1094 1095 1096 1097 1753 1754]\n"
]
}
],
"source": [
"from wltp.experiment import Experiment\n",
"\n",
"wltp_exp = Experiment(model)\n",
"model = wltp_exp.run()\n",
"results = model['params']\n",
"cycle_table = pd.DataFrame(model['cycle_run']); cycle_table.index.name='TIME'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## One of : clutch, driveability, gears, gears_orig, p_available, p_required, rpm, rpm_norm, v_class, v_real, v_target\n",
"cycle_table.loc[:, ['v_target', 'rpm', 'gears']].to_csv(outfname)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_cycle(title, v_g_n, parts):\n",
" font_size = 12\n",
" tick_size = 9\n",
" title_size = 14\n",
"\n",
" t = np.arange(0, v_g_n.shape[0])\n",
"\n",
" #plt.figure(figsize=(16,3.5))\n",
" fig, ax1 = plt.subplots()\n",
" fig.set_size_inches((16,4.5))\n",
" ax1.set_xlabel('Time (s): ', size=font_size)\n",
" ax1.xaxis.set_label_coords(-0.06, -0.030)\n",
" for tl in ax1.get_xticklabels():\n",
" tl.set_size(tick_size)\n",
"\n",
" ax3 = ax1.twinx()\n",
" ax3.set_ylabel(r'Engine speed ($rpm$)', color='y', size=font_size)\n",
" for tl in ax3.get_yticklabels():\n",
" tl.set_color('y')\n",
" tl.set_size(tick_size)\n",
" l_n = ax3.plot(t, v_g_n.iloc[:, 2], color='y')[0]\n",
"\n",
" ax1.set_ylabel(r'Velocity ($km/h$)', color='b', size=font_size)\n",
" for tl in ax1.get_yticklabels():\n",
" tl.set_color('b')\n",
" tl.set_size(tick_size)\n",
" l_v = ax1.plot(t, v_g_n.iloc[:, 0], 'b-')[0]\n",
"\n",
" ax2 = ax1.twinx()\n",
" ax2.set_ylabel(r'Gears', color='m', size=font_size)\n",
" for tl in ax2.get_yticklabels():\n",
" tl.set_color('m')\n",
" tl.set_size(tick_size)\n",
" l_g = ax2.plot(t, v_g_n.iloc[:, 1], 'mo')[0]\n",
"\n",
" #plt.legend([l_vel, l_acc], ['Velocity', 'Acceleration'])\n",
"\n",
" plt.title(title, size=title_size, weight='bold')\n",
"\n",
" return ## SRC has no parts\n",
" ## Plot part-Limits\n",
" #\n",
" part_limits = [e+0.5 for (s, e) in parts[:-1]]\n",
" for limit in part_limits:\n",
" l = plt.axvline(limit, color='r', linewidth=2)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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66tt/cQY5pmtTZQBBM320B03jKFI/BRBERETqUC6ypTTXdrJ3b/IZCFB/Cn6aDQ7CwAB0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc397389828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wltc_data = wltp.model._get_wltc_data()\n",
"columns_visible = ['v_target', 'gears', 'rpm'] \n",
"\n",
"wltc_params = model['params']\n",
"#wltc_parts = wltc_data['classes'][model['vehicle']['wltc_class']]['parts']\n",
"plot_cycle('WLTC', cycle_table[columns_visible], None)#wltc_parts)\n",
"\n",
"#log.info('Class: %s\\nDownscale: %s', wltc_params.get('wltc_class'), wltc_params.get('f_downscale'))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
},
"widgets": {
"state": {
"C9029118D1D64A3CA01E49FC7D110EA2": {
"views": []
}
},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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