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ankostis/wltp-upload_vehicles

Created September 16, 2014 00:30
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wltp-upload_vehicles
{
"metadata": {
"name": "",
"signature": "sha256:7d2e6d8d12fd7ead839ff65fc6f985a0822963ac03f6462cf56e4d511cd2abab"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Calculate multiple wltp-cycles from ``excel.xls`` | ``.csv`` files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instructions:\n",
"\n",
"1. For IPython 2+ only, because it uses *UI-widgets*!\n",
"2. Intially run all cells (``Menu|Cell|Run All``).\n",
"3. Then click the ``Upload`` button, 2 cells below,\n",
" and supply a a ``csv`` or an ``excel`` file with (at least) the filled-in columns below (for the excel, the table has to be in the 1st worksheet):\n",
"\n",
"| id | (*)test_mass | (*)v_max | (*)p_rated | (*)n_rated | (*)n_idle | (*)gear_ratios | (*)resistance_coeffs | unladen_mass | driver_mass | n_min | cycle_name |\n",
"|-----------|-------|---------|---------|--------|-------------|----------------------|----------------------|----|----|----|----|\n",
"| CAR_1 | 1700 | 247 | 180 | 5000 | 780 | 115,76,40,31,24,20.5 | 202.5,-0.833,0.03941 | | | | |\n",
"| CAR_2 | | | | | | | | | | | |\n",
"| ... | | | | | | | | | | | | |\n",
"\n",
"### Notes:\n",
"\n",
"1. Numbers-list (ie ``gear_ratios``) are parsed as *JSON* and can optionally be surrounded by ``[]``.\n",
"2. The ``id`` column is optional, and if not there, data-rows will be named ``0, 1, ...``.\n",
"3. Column-names are case sensitive.\n",
"4. Every time you edit a cell, you have to re-run the whole notebook for your changes to apply (due to widget-callbacks).\n",
"5. If your excel/csv is kind of different, modify the parsing code in ``read_uploaded_file_as_DataFrame()``, below. \n",
" It's easy! It is *pandas* :-) !!\n",
"6. The cells below are needed internally (ie for [implementing the uploading of files](http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/File%20Upload%20Widget.ipynb)). Ignore them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Uploading-widget's code\n",
"Use ``df_holder[0]`` to interactively inspect read-file in case of errors."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import base64\n",
"import io, os, sys\n",
"import json\n",
"import logging\n",
"\n",
"from IPython.html import widgets # Widget definitions.\n",
"from IPython.utils.traitlets import Unicode # Traitlet needed to add synced attributes to the widget.\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np, pandas as pd\n",
"\n",
"logger = logging.getLogger()\n",
"## To display driveability-problems\n",
"logger.setLevel(logging.DEBUG) ## Set to ``INFO`` for less pink-stuff!\n",
"log = logging.getLogger('notebook')\n",
"\n",
"## To display the complete cycle_run.\n",
"pd.set_option('max_rows', 20000)\n",
"\n",
"## For interactively checking uploaded-file's contents.\n",
"df_holder = [None]\n",
"\n",
"import wltp\n",
"from wltp import experiment\n",
"import wltp.cycles as cycles\n",
"import wltp.cycles.nedc as nedc\n",
"\n",
"## Should print wltp-library's version.\n",
"wltp.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"'0.0.9-alpha'"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class FileWidget(widgets.DOMWidget):\n",
" ## Modified widget's source to suport Excel from:\n",
" # http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/File%20Upload%20Widget.ipynb\n",
" \n",
" _view_name = Unicode('FilePickerView', sync=True)\n",
" value = Unicode(sync=True)\n",
" filename = Unicode(sync=True)\n",
" \n",
" def __init__(self, **kwargs):\n",
" widgets.DOMWidget.__init__(self, **kwargs) # Call the base.\n",
" self.errors = widgets.CallbackDispatcher(accepted_nargs=[0, 1])\n",
" self.on_msg(self._handle_custom_msg)\n",
"\n",
" def _handle_custom_msg(self, content):\n",
" if 'event' in content and content['event'] == 'error':\n",
" self.errors()\n",
" self.errors(self)\n",
" \n",
"def file_loading():\n",
" log.debug(\"Loading file(%s)...\", file_widget.filename)\n",
"\n",
"def file_uploaded():\n",
" df = read_uploaded_file_as_DataFrame()\n",
" log.info(\"Loaded file(%s) as DataFrame:\\n%s\", file_widget.filename, df)\n",
" process_file_df(file_widget.filename, df)\n",
"\n",
"def file_failed():\n",
" print(\"Failed loading file(%s)!\" % file_widget.filename)\n",
"\n",
"file_widget = FileWidget()\n",
"file_widget.on_trait_change(file_uploaded, 'value')\n",
"file_widget.on_trait_change(file_loading, 'filename')\n",
"file_widget.errors.register_callback(file_failed)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%javascript\n",
"\n",
"require([\"widgets/js/widget\"], function(WidgetManager){\n",
"\n",
" var FilePickerView = IPython.WidgetView.extend({\n",
" render: function(){\n",
" // Render the view.\n",
" this.setElement($('<input />')\n",
" .attr('type', 'file'));\n",
" },\n",
" \n",
" events: {\n",
" // List of events and their handlers.\n",
" 'change': 'handle_file_change',\n",
" },\n",
" \n",
" handle_file_change: function(evt) { \n",
" // Handle when the user has changed the file.\n",
" \n",
" // Retrieve the first (and only!) File from the FileList object\n",
" var file = evt.target.files[0];\n",
" if (file) {\n",
"\n",
" // Read the file's textual content and set value to those contents.\n",
" var that = this;\n",
" var file_reader = new FileReader();\n",
" file_reader.onload = function(e) {\n",
" that.model.set('value', e.target.result);\n",
" that.touch();\n",
" }\n",
" //file_reader.readAsText(file)\n",
" //file_reader.readAsArrayBuffer(file);\n",
" file_reader.readAsBinaryString(file);\n",
" } else {\n",
"\n",
" // The file couldn't be opened.\n",
" this.send({ 'event': 'error' });\n",
" }\n",
"\n",
" // Set the filename of the file.\n",
" this.model.set('filename', file.name);\n",
" this.touch();\n",
" },\n",
" });\n",
" \n",
" // Register the DatePickerView with the widget manager.\n",
" WidgetManager.register_widget_view('FilePickerView', FilePickerView);\n",
"});\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"\n",
"require([\"widgets/js/widget\"], function(WidgetManager){\n",
"\n",
" var FilePickerView = IPython.WidgetView.extend({\n",
" render: function(){\n",
" // Render the view.\n",
" this.setElement($('<input />')\n",
" .attr('type', 'file'));\n",
" },\n",
" \n",
" events: {\n",
" // List of events and their handlers.\n",
" 'change': 'handle_file_change',\n",
" },\n",
" \n",
" handle_file_change: function(evt) { \n",
" // Handle when the user has changed the file.\n",
" \n",
" // Retrieve the first (and only!) File from the FileList object\n",
" var file = evt.target.files[0];\n",
" if (file) {\n",
"\n",
" // Read the file's textual content and set value to those contents.\n",
" var that = this;\n",
" var file_reader = new FileReader();\n",
" file_reader.onload = function(e) {\n",
" that.model.set('value', e.target.result);\n",
" that.touch();\n",
" }\n",
" //file_reader.readAsText(file)\n",
" //file_reader.readAsArrayBuffer(file);\n",
" file_reader.readAsBinaryString(file);\n",
" } else {\n",
"\n",
" // The file couldn't be opened.\n",
" this.send({ 'event': 'error' });\n",
" }\n",
"\n",
" // Set the filename of the file.\n",
" this.model.set('filename', file.name);\n",
" this.touch();\n",
" },\n",
" });\n",
" \n",
" // Register the DatePickerView with the widget manager.\n",
" WidgetManager.register_widget_view('FilePickerView', FilePickerView);\n",
"});"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f9ad1f73d68>"
]
}
],
"prompt_number": 49
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Upload your Vehicles-file HERE:"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"\n",
"file_widget # Presents the \"Upload\" button, below."
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:notebook:Loading file(gf_wltp_input_cases.xlsx)...\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:notebook:Parsing file(gf_wltp_input_cases.xlsx) with(<function read_uploaded_file_as_DataFrame.<locals>.<lambda> at 0x7f9ad18c40d0>)...\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:notebook:Loaded file(gf_wltp_input_cases.xlsx) as DataFrame:\n",
" test_mass v_max p_rated n_rated n_idle \\\n",
"id \n",
"A 1700 247 180 5000 780 \n",
"B 1803 247 180 5000 780 \n",
"C 1983 247 180 5000 780 \n",
"D 1983 247 180 5000 780 \n",
"E 1804 247 180 5000 780 \n",
"F 1984 247 180 5000 780 \n",
"\n",
" gear_ratios resistance_coeffs \\\n",
"id \n",
"A [115.25,76.92,51.7,40.96,31.57,24.57,20.61,16.39] [202.5,-0.833,0.03941] \n",
"B [115.25,76.92,51.7,40.96,31.57,24.57,20.61,16.39] [198,-0.833,0.0393] \n",
"C [115.25,76.92,51.7,40.96,31.57,24.57,20.61,16.39] [278.5,-0.833,0.04281] \n",
"D [115.25,76.92,51.7,40.96,31.57,24.57,20.61,16.39] [258.7,-0.901,0.04359] \n",
"E [115.25,76.92,51.7,40.96,31.57,24.57,20.61,16.39] [221,-0.858,0.04059] \n",
"F [115.25,76.92,51.7,40.96,31.57,24.57,20.61,16.39] [324.8,-0.858,0.04376] \n",
"\n",
" cycle wltc_class \n",
"id \n",
"A NEDC class3b \n",
"B WLTC class3b \n",
"C WLTC class3b \n",
"D WLTC class3b \n",
"E WLTC class3b \n",
"F WLTC class3b \n",
"\n",
"[6 rows x 9 columns]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:notebook:mdl_in:\n",
"{'vehicle': {'n_rated': 5000, 'wltc_class': 'class3b', 'test_mass': 1700, 'resistance_coeffs': [202.5, -0.833, 0.03941], 'v_max': 247, 'p_rated': 180, 'gear_ratios': [115.25, 76.92, 51.7, 40.96, 31.57, 24.57, 20.61, 16.39], 'n_idle': 780}}\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:wltp.experiment:Found forced `cycle-run` table(0x0).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:wltp.experiment:Found forced wltc_class(class3b).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:wltp.experiment:35 g1: Revolutions too low!: [ 13 14 96 97 98 139 140 381 382 383 384 393 394 442 443\n",
" 513 514 527 528 534 535 536 566 602 603 983 984 985 1028 1450\n",
" 1451 1479 1480 1481 1794]\n"
]
},
{
"ename": "ValueError",
"evalue": "Length mismatch: Expected axis has 11 elements, new values have 1 elements",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/usr/local/lib/python3.4/dist-packages/IPython/html/widgets/widget.py\u001b[0m in \u001b[0;36m_handle_msg\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'backbone'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m'sync_data'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 271\u001b[0m \u001b[0msync_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sync_data'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 272\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_receive_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msync_data\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# handles all methods\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 273\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 274\u001b[0m \u001b[1;31m# Handle a custom msg from the front-end\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python3.4/dist-packages/IPython/html/widgets/widget.py\u001b[0m in \u001b[0;36m_handle_receive_state\u001b[1;34m(self, sync_data)\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_unpack_widgets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msync_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 285\u001b[1;33m \u001b[0msetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 287\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_handle_custom_msg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python3.4/dist-packages/IPython/utils/traitlets.py\u001b[0m in \u001b[0;36m__set__\u001b[1;34m(self, obj, value)\u001b[0m\n\u001b[0;32m 369\u001b[0m \u001b[1;31m# we explicitly compare silent to True just in case the equality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 370\u001b[0m \u001b[1;31m# comparison above returns something other than True/False\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 371\u001b[1;33m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_notify_trait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mold_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_validate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python3.4/dist-packages/IPython/utils/traitlets.py\u001b[0m in \u001b[0;36m_notify_trait\u001b[1;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[0;32m 512\u001b[0m \u001b[0moffset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 513\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnargs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0moffset\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 514\u001b[1;33m \u001b[0mc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 515\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mnargs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0moffset\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 516\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-48-38e6e211a9f5>\u001b[0m in \u001b[0;36mfile_uploaded\u001b[1;34m()\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mread_uploaded_file_as_DataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Loaded file(%s) as DataFrame:\\n%s\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_widget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mprocess_file_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_widget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfile_failed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-51-d97e17af9974>\u001b[0m in \u001b[0;36mprocess_file_df\u001b[1;34m(fname, df)\u001b[0m\n\u001b[0;32m 134\u001b[0m \u001b[1;31m## Append cycle-run as multi-indexed columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m \u001b[0mcycle_run\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mveh_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle_run\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'vehicle'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'cycle'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 137\u001b[0m \u001b[0mcycles_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcycles_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle_run\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python3/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m 1826\u001b[0m \u001b[0mexisting\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1827\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexisting\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1828\u001b[1;33m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1829\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1830\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python3/dist-packages/pandas/lib.cpython-34m-x86_64-linux-gnu.so\u001b[0m in \u001b[0;36mpandas.lib.AxisProperty.__set__ (pandas/lib.c:30018)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32m/usr/lib/python3/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m 394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 395\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 396\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 397\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python3/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[1;34m(self, axis, value, maybe_rename, check_axis)\u001b[0m\n\u001b[0;32m 2084\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2085\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaybe_rename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2086\u001b[1;33m \u001b[0mcur_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_axis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2087\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2088\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python3/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m_set_axis\u001b[1;34m(self, axis, value, check_axis)\u001b[0m\n\u001b[0;32m 2077\u001b[0m raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[0;32m 2078\u001b[0m 'new values have %d elements' % (len(cur_axis),\n\u001b[1;32m-> 2079\u001b[1;33m len(value)))\n\u001b[0m\u001b[0;32m 2080\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2081\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 11 elements, new values have 1 elements"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ARHVs9HX3jXFP+xmG7jRN9a/As8AgUG+aqhK42jD0j4DvA/9qmqqPQFv8n5wo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BK/k9PEv3rIjjYDg2SPmqi9+HLIYeeknQB8efhL4ogjY0IG85fM93oBp8UXamVJJXEdcSSTTCyx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\n7s4EUFLMdq0RvIhF3MVdlFFGEkk006zIZikQCv6/vTOPj6o6G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R7jAfji/34qYZSSwjXK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\nEARBEAThNEGUAIIgCIIgCIIgCIJwmvD/ZycabOJch6MAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f9ad1bb9ba8>"
]
}
],
"prompt_number": 50
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"WLTP code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def parse_numbers_list(numbers):\n",
" numbers = numbers.strip()\n",
" if not numbers.strip().startswith('['):\n",
" numbers = '[' + numbers + ']'\n",
" return json.loads(numbers)\n",
"\n",
"\n",
"def make_model(cycle=None, **kwds):\n",
" \"\"\"\n",
" ARG:\n",
" ----\n",
" Required: test_mass, v_max, p_rated, n_rated, n_idle,\n",
" Optional: n_min, \n",
" driver_mass, unladen_mass, ## If undefinedtest_mass - driver_mass(75 kg)\n",
" gear_ratios, resistance_coeffs, ## JSON lists\n",
" cycle_name ## \"NEDC\"?\n",
" \"\"\"\n",
" \n",
" kwds['gear_ratios'] = parse_numbers_list(kwds['gear_ratios'])\n",
" kwds['resistance_coeffs'] = parse_numbers_list(kwds['resistance_coeffs'])\n",
" mdl = {\"vehicle\": kwds}\n",
" \n",
" ## Overridde cycle with NEDC.\n",
" #\n",
" cycle_name = kwds.get('cycle')\n",
" if cycle_name and cycle_name.upper() == 'NEDC':\n",
" mdl['cycle_run'] = {'v_target': nedc.cycle_data()['cycle']}\n",
"\n",
" return mdl\n",
"\n",
"\n",
"def process_vehicle(mdl):\n",
" \n",
" wltp_exp = experiment.Experiment(mdl) ## Validate model\n",
" mdl_out = wltp_exp.run() ## Run experiment\n",
"\n",
" return mdl_out\n",
" \n",
" \n",
" \n",
"def plot_v_g_n(title, v_g_n, parts=None):\n",
" font_size = 12\n",
" tick_size = 9\n",
" title_size = 14\n",
"\n",
" ## Plot cycle-part limits.\n",
" #\n",
" if parts:\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",
"\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",
" \n",
"## Executed when file uploaded.\n",
"#\n",
"# Modify it if parsing fails.\n",
"#\n",
"def read_uploaded_file_as_DataFrame():\n",
" excel_reader = lambda value, *args: pd.read_excel(io.BytesIO(bytes(value, 'ISO-8859-1')), 0, *args)\n",
" csv_reader = lambda value, *args: pd.read_csv(io.StringIO(value), index_col=0, header=None, *args)\n",
" parsers = {\n",
" '.xls': excel_reader, \n",
" '.xlsx': excel_reader, \n",
" '.csv': csv_reader, \n",
" '.txt': csv_reader\n",
" }\n",
"\n",
" (_, ext) = os.path.splitext(file_widget.filename)\n",
" try:\n",
" parser = parsers[ext.lower()]\n",
" except KeyError:\n",
" raise ValueError(\"Unsupported file-extension(%s)!\"%ext)\n",
" log.info(\"Parsing file(%s) with(%s)...\", file_widget.filename, parser)\n",
" #log.debug(file_widget.value)\n",
" \n",
" df = parser(file_widget.value)\n",
" \n",
" if 'id' in df.columns:\n",
" df.set_index('id', inplace=True)\n",
" \n",
" df_holder[0] = df\n",
"\n",
" return df\n",
"\n",
"## Executed when file uploaded.\n",
"#\n",
"def process_file_df(fname, df):\n",
" cycles_df, vehs_df = pd.DataFrame(), pd.DataFrame() # To gather data for all vehicles.\n",
" for (veh_id, sr) in df.iterrows():\n",
" assert isinstance(sr, pd.Series)\n",
"\n",
" mdl_in = make_model(**sr)\n",
" log.info('mdl_in:\\n%s', mdl_in)\n",
" mdl_out = process_vehicle(mdl_in)\n",
" \n",
" cycle_run = mdl_out['cycle_run']\n",
"\n",
" plot_v_g_n(veh_id, cycle_run)\n",
"\n",
" ## Append cycle-run as multi-indexed columns\n",
" #\n",
" cycle_run.columns = pd.MultiIndex.from_arrays(([veh_id], cycle_run.columns), names=('vehicle', 'cycle'))\n",
" cycles_df = pd.concat((cycles_df, cycle_run), axis=1)\n",
" \n",
" \n",
"\n",
" ### One of : clutch, driveability, gears, gears_orig, p_available, p_required, rpm, rpm_norm, v_class, v_real, v_target\n",
" display(cycles_df, cycles_df.describe())\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax1 = plt.subplots()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x7f9ad1e08358>"
]
}
],
"prompt_number": 52
}
],
"metadata": {}
}
]
}
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