| versions: |    
(build-version: |release|, build-date: |today|)
  |
|---|---|
| documentation: | https://wltp.readthedocs.org/ |
| live-demo: |   |
| sources: | https://github.com/JRCSTU/wltp 
 
    |
| keywords: | UNECE, automotive, car, cars, driving, engine, emissions, fuel-consumption, gears, gearshifts, rpm, simulation, simulator, standard, vehicle, vehicles, WLTC, NEDC |
| Copyright: | 2013-2020 European Commission (JRC-IET) 
|
A python-3.6+ package to generate the gear-shifts of Light-duty vehicles running the :term:`WLTP` driving-cycles, according to :term:`UNECE`'s :term:`GTR`s.
Attention!
This wltp python project is still in alpha stage, in the sense that its results are not "correct" by the standard, and no WLTP dyno-tests should rely currently on them.
Some of the known limitations are described in these places:
- In the :doc:`CHANGES`.
- Compare results with AccDB in
Notebooks/CarsDB-compare.ipynbnotebook; launch your private demo-server () to view it.
Table of Contents
The calculator accepts as input the vehicle's technical data, along with parameters for modifying the execution of the :term:`WLTC` cycle, and it then spits-out the gear-shifts of the vehicle, the attained speed-profile, and any warnings. It does not calculate any CO2 emissions.
An "execution" or a "run" of an experiment is depicted in the following diagram:
.-----------------. .------------------.
: Input : : Output :
;-----------------; ;------------------;
; +--test_mass ; ____________ ; +--pmr ;
; +--n_idle ; | | ; +--wltc_class ;
; +--f0,f1,f2 ; ==> | Cycle | ==> ; +--... ;
; +--wot/ ; | Generator | ; +--cycle ;
; +-- ; |____________| ; | +-- ;
; +--n2vs ; ; +--gwots ;
; +-- ; ; +-- ;
'-----------------' '------------------'
The Input, Output and all its contents are instances of :term:`datamodel` (trees of strings, numbers & pandas objects)
- Launch the example jupyter notebooks in a private demo server (
- Otherwise, install it locally, preferably from the sources (instructions below).
pip install:abbr:`"extras" (e.g. pip install wltp[all])`:plot, excel, all, dev, notebook, test, doc
Python-3.6+ is required and Python-3.7 or Python-3.8 recommended. It requires numpy/scipy and pandas libraries with native backends.
Tip
On Windows, it is preferable to use the miniconda
distribution; although its conda command adds another layer of complexity on top of pip,
unlike standard Python, it has pre-built all native libraries required
(e.g. numpy/scipy and pandas).
If nevertheless you choose the standard Python, and some packages fail to build when pip-installing them, download these packages from Gohlke's "Unofficial Windows Binaries" and install them manually with:
pip install <package-file-v1.2.3.whl>
Download the sources,
either with git, by giving this command to the terminal:
git clone https://github.com/JRCSTU/wltp/ --depth=1
or download and extract the project-archive from the release page: https://github.com/JRCSTU/wltp/archive/v1.1.0.dev0.zip
From within the project directory, run one of these commands to install it:
for standard python, installing with
pipis enough (but might):pip install -e .[test]
for conda, prefer to install the conda-packages listed in :file:`Notebooks/conda/conda-reqs.txt`, before running the same pip command, like this:
conda install --override-channels -c ankostis -c conda-forge -c defaults --file Notebooks/conda/conda-reqs.txt pip install -e .[dev]
Check installation:
$ wltp --version ... $ wltp --help ... See: :ref:`wltp-usage`Recreate jupyter notebooks from the paired
*.py"py:percent" files (only these files are stored in git-repo), by executing the bash-script:Notebooks/recreate_ipynbs.sh
Run pyalgo on all AccDB cars to re-create the H5 file needed for
CarsDB-comparenotebook, etc:Notebooks/recreate_pyalgo_h5.sh
import pandas as pd
from wltp import datamodel
from wltp.experiment import Experiment
inp_mdl = datamodel.get_model_base()
inp_mdl.update({
"unladen_mass": None,
"test_mass": 1100, # in kg
"p_rated": 95.3, # in kW
"n_rated": 3000, # in RPM
"n_idle": 600,
"n2v_ratios": [122.88, 75.12, 50.06, 38.26, 33.63],
## For giving absolute P numbers,
# rename `p_norm` column to `p`.
#
"wot": pd.DataFrame(
[[600, 0.1],
[2500, 1],
[3500, 1],
[5000, 0.7]], columns=["n", "p_norm"]
),
'f0': 395.78,
'f1': 0,
'f2': 0.15,
})
datamodel.validate_model(inp_mdl, additional_properties=True)
exp = Experiment(inp_mdl, skip_model_validation=True)
# exp = Experiment(inp_mdl)
out_mdl = exp.run()
print(f"Available values: \n{list(out_mdl.keys())}")
print(f"Cycle: \n{out_mdl['cycle']}")See: :ref:`python-usage`
The files and folders of the project are listed below (see also :ref:`architecture:Architecture`):
+--bin/ # (shell-scripts) Utilities & preprocessing of WLTC data on GTR and the wltp_db
| +--bumpver.py # (script) Update project's version-string
+--wltp/ # (package) python-code of the calculator
| +--cycles/ # (package) code & data for the WLTC data
| +--experiment # top-level code running the algo
| +--datamodel # schemas & defaults for data of algo
| +--cycler # code for generating the cycle
| +--engine # formulae for engine power & revolutions and gear-box
| +--vehicle # formulae for cycle/vehicle dynamics
| +--vmax # formulae estimating `v_max` from wot
| +--downscale # formulae downscaling cycles based on pmr/test_mass ratio
| +--invariants # definitions & idempotent formulae for physics/engineering
| +--io # utilities for starting-up, parsing, naming and spitting data
| +--utils # software utils unrelated to physics or engineering
| +--cli # (OUTDATED) command-line entry-point for launching this wltp tool
| +--plots # (OUTDATED) code for plotting diagrams related to wltp cycles & results
| +--idgears # (OUTDATED) reconstructs the gears-profile by identifying the actual gears
+--tests/ # (package) Test-TestCases
+--vehdb # Utils for manipulating h5db with accdb & pyalgo cases.
+--docs/ # (folder) documentation
| +--pyplots/ # (DEPRECATED by notebooks) scripts plotting the metric diagrams embedded in the README
+--Notebooks/ # Jupyter notebooks for running & comparing results (see `Notebooks/README.md`)
+--AccDB_src/ # AccDB code & queries extracted and stored as text
+--setup.py # (script) The entry point for `setuptools`, installing, testing, etc
+--requirements/ # (txt-files) Various pip-dependencies for tools.
+--README.rst
+--CHANGES.rst
+--LICENSE.txt
First run :command:`python` or :command:`ipython` :abbr:`REPL (Read-Eval-Print Loop)` and try to import the project to check its version:
>>> import wltp
>>> wltp.__version__ ## Check version once more.
'1.1.0.dev0'
>>> wltp.__file__ ## To check where it was installed. # doctest: +SKIP
/usr/local/lib/site-package/wltp-...If everything works, create the :term:`datamodel` of the experiment. You can assemble the model-tree by the use of:
- sequences,
- dictionaries,
- :class:`pandas.DataFrame`,
- :class:`pandas.Series`, and
- URI-references to other model-trees.
For instance:
>>> from wltp import datamodel
>>> from wltp.experiment import Experiment
>>> mdl = {
... "unladen_mass": 1430,
... "test_mass": 1500,
... "v_max": 195,
... "p_rated": 100,
... "n_rated": 5450,
... "n_idle": 950,
... "n_min": None, ## Manufacturers my override it
... "n2v_ratios": [120.5, 75, 50, 43, 37, 32],
... "f0": 100,
... "f1": 0.5,
... "f2": 0.04,
... }
>>> mdl = datamodel.upd_default_load_curve(mdl) ## need some WOTFor information on the accepted model-data, check the :ref:`code:Schemas`:
>>> from wltp import utils
>>> utils.yaml_dumps(datamodel.model_schema(), indent=2) # doctest: +SKIP
$schema: http://json-schema.org/draft-07/schema#
$id: /wltc
title: WLTC data
type: object
additionalProperties: false
required:
- classes
properties:
classes:
...You then have to feed this model-tree to the :class:`~wltp.experiment.Experiment` constructor. Internally the :class:`pandalone.pandel.Pandel` resolves URIs, fills-in default values and validates the data based on the project's pre-defined :term:`JSON-schema`:
>>> processor = Experiment(mdl) ## Fills-in defaults and Validates model.Assuming validation passes without errors, you can now inspect the defaulted-model before running the experiment:
>>> mdl = processor.model ## Returns the validated model with filled-in defaults.
>>> sorted(mdl) ## The "defaulted" model now includes the `params` branch.
['driver_mass', 'f0', 'f1', 'f2', 'f_dsc_decimals', 'f_dsc_threshold', 'f_inertial',
'f_n_clutch_gear2', 'f_n_min', 'f_n_min_gear2', 'f_running_threshold', 'f_safety_margin',
'f_up_threshold', 'n2v_ratios', 'n_idle', 'n_min_drive1', 'n_min_drive2', 'n_min_drive2_stopdecel',
'n_min_drive2_up', 'n_min_drive_down', 'n_min_drive_down_start', 'n_min_drive_set',
'n_min_drive_up', 'n_min_drive_up_start', 'n_rated', 'p_rated', 't_cold_end', 'test_mass',
'unladen_mass', 'v_cap', 'v_max', 'v_stopped_threshold', 'wltc_data', 'wot']Now you can run the experiment:
>>> mdl = processor.run() ## Runs experiment and augments the model with results.
>>> sorted(mdl) ## Print the top-branches of the "augmented" model.
[`cycle`, 'driver_mass', 'f0', 'f1', 'f2', `f_dsc`, 'f_dsc_decimals', `f_dsc_raw`,
'f_dsc_threshold', 'f_inertial', 'f_n_clutch_gear2', 'f_n_min', 'f_n_min_gear2',
'f_running_threshold', 'f_safety_margin', 'f_up_threshold', `g_vmax`, `is_n_lim_vmax`,
'n2v_ratios', `n95_high`, `n95_low`, 'n_idle', `n_max`, `n_max1`, `n_max2`, `n_max3`,
'n_min_drive1', 'n_min_drive2', 'n_min_drive2_stopdecel', 'n_min_drive2_up', 'n_min_drive_down',
'n_min_drive_down_start', 'n_min_drive_set', 'n_min_drive_up', 'n_min_drive_up_start',
'n_rated', `n_vmax`, 'p_rated', `pmr`, 't_cold_end', 'test_mass', 'unladen_mass', 'v_cap',
'v_max', 'v_stopped_threshold', `wltc_class`, 'wltc_data', 'wot', `wots_vmax`]To access the time-based cycle-results it is better to use a :class:`pandas.DataFrame`:
>>> import pandas as pd, wltp.cycler as cycler, wltp.io as wio
>>> df = pd.DataFrame(mdl['cycle']); df.index.name = 't'
>>> df.shape ## ROWS(time-steps) X COLUMNS.
(1801, 107)
>>> wio.flatten_columns(df.columns)
['t', 'V_cycle', 'v_target', 'a', 'phase_1', 'phase_2', 'phase_3', 'phase_4', 'accel_raw',
'run', 'stop', 'accel', 'cruise', 'decel', 'initaccel', 'stopdecel', 'up', 'p_inert', 'n/g1',
'n/g2', 'n/g3', 'n/g4', 'n/g5', 'n/g6', 'n_norm/g1', 'n_norm/g2', 'n_norm/g3', 'n_norm/g4',
'n_norm/g5', 'n_norm/g6', 'p/g1', 'p/g2', 'p/g3', 'p/g4', 'p/g5', 'p/g6', 'p_avail/g1',
'p_avail/g2', 'p_avail/g3', 'p_avail/g4', 'p_avail/g5', 'p_avail/g6', 'p_avail_stable/g1',
'p_avail_stable/g2', 'p_avail_stable/g3', 'p_avail_stable/g4', 'p_avail_stable/g5',
'p_avail_stable/g6', 'p_norm/g1', 'p_norm/g2', 'p_norm/g3', 'p_norm/g4', 'p_norm/g5',
'p_norm/g6', 'p_resist', 'p_req', 'P_remain/g1', 'P_remain/g2', 'P_remain/g3',
'P_remain/g4', 'P_remain/g5', 'P_remain/g6', 'ok_p/g3', 'ok_p/g4', 'ok_p/g5', 'ok_p/g6',
'ok_gear0/g0', 'ok_max_n/g1', 'ok_max_n/g2', 'ok_max_n/g3', 'ok_max_n/g4', 'ok_max_n/g5',
'ok_max_n/g6', 'ok_min_n_g1/g1', 'ok_min_n_g1_initaccel/g1', 'ok_min_n_g2/g2',
'ok_min_n_g2_stopdecel/g2', 'ok_min_n_g3plus_dns/g3', 'ok_min_n_g3plus_dns/g4',
'ok_min_n_g3plus_dns/g5', 'ok_min_n_g3plus_dns/g6', 'ok_min_n_g3plus_ups/g3',
'ok_min_n_g3plus_ups/g4', 'ok_min_n_g3plus_ups/g5', 'ok_min_n_g3plus_ups/g6', 'ok_n/g1',
'ok_n/g2', 'ok_n/g3', 'ok_n/g4', 'ok_n/g5', 'ok_n/g6', 'ok_gear/g0', 'ok_gear/g1',
'ok_gear/g2', 'ok_gear/g3', 'ok_gear/g4', 'ok_gear/g5', 'ok_gear/g6', 'G_scala/g0', 'G_scala/g1',
'G_scala/g2', 'G_scala/g3', 'G_scala/g4', 'G_scala/g5', 'G_scala/g6', 'g_min', 'g_max0']
>>> 'Mean engine_speed: %s' % df.n.mean() # doctest: +SKIP
'Mean engine_speed: 1908.9266796224322'
>>> df.describe() # doctest: +SKIP
v_class v_target ... rpm_norm v_real
count 1801.000000 1801.000000 ... 1801.000000 1801.000000
mean 46.361410 46.361410 ... 0.209621 50.235126
std 36.107745 36.107745 ... 0.192395 32.317776
min 0.000000 0.000000 ... -0.205756 0.200000
25% 17.700000 17.700000 ... 0.083889 28.100000
50% 41.300000 41.300000 ... 0.167778 41.300000
75% 69.100000 69.100000 ... 0.285556 69.100000
max 131.300000 131.300000 ... 0.722578 131.300000
<BLANKLINE>
[8 rows x 10 columns]
>>> processor.driveability_report() # doctest: +SKIP
...
12: (a: X-->0)
13: g1: Revolutions too low!
14: g1: Revolutions too low!
...
30: (b2(2): 5-->4)
...
38: (c1: 4-->3)
39: (c1: 4-->3)
40: Rule e or g missed downshift(40: 4-->3) in acceleration?
...
42: Rule e or g missed downshift(42: 3-->2) in acceleration?
...You can export the cycle-run results in a CSV-file with the following pandas command:
>>> df.to_csv('cycle.csv') # doctest: +SKIPFor more examples, download the sources and check the test-cases found under the :file:`/tests/` folder.
Warning
Not implemented in yet.
The command-line usage below requires the Python environment to be installed, and provides for executing an experiment directly from the OS's shell (i.e. :program:`cmd` in windows or :program:`bash` in POSIX), and in a single command. To have precise control over the inputs and outputs (i.e. experiments in a "batch" and/or in a design of experiments) you have to run the experiments using the API python, as explained below.
The entry-point script is called :program:`wltp`, and it must have been placed in your :envvar:`PATH` during installation. This script can construct a model by reading input-data from multiple files and/or overriding specific single-value items. Conversely, it can output multiple parts of the resulting-model into files.
To get help for this script, use the following commands:
$ wltp --help ## to get generic help for cmd-line syntax
$ wltcmdp.py -M vehicle/full_load_curve ## to get help for specific model-pathsand then, assuming vehicle.csv is a CSV file with the vehicle parameters
for which you want to override the n_idle only, run the following:
$ wltp -v \
-I vehicle.csv file_frmt=SERIES model_path=params header@=None \
-m vehicle/n_idle:=850 \
-O cycle.csv model_path=cycleAttention!
OUTDATED!!! Excel-integration requires Python 3 and Windows or OS X!
In Windows and OS X you may utilize the excellent xlwings library to use Excel files for providing input and output to the experiment.
To create the necessary template-files in your current-directory you should enter:
$ wltp --excelYou could type instead :samp:`wltp --excel {file_path}` to specify a different destination path.
In windows/OS X you can type :samp:`wltp --excelrun` and the files will be created in your home-directory and the excel will open them in one-shot.
All the above commands creates two files:
- :file:`wltp_excel_runner.xlsm`
The python-enabled excel-file where input and output data are written, as seen in the screenshot below:
After opening it the first tie, enable the macros on the workbook, select the python-code at the left and click the :menuselection:`Run Selection as Python` button; one sheet per vehicle should be created.
The excel-file contains additionally appropriate VBA modules allowing you to invoke Python code present in selected cells with a click of a button, and python-functions declared in the python-script, below, using the
mypynamespace.To add more input-columns, you need to set as column Headers the json-pointers path of the desired model item (see :ref:`python-usage` below,).
- :file:`wltp_excel_runner.py`
Utility python functions used by the above xls-file for running a batch of experiments.
The particular functions included reads multiple vehicles from the input table with various vehicle characteristics and/or experiment parameters, and then it adds a new worksheet containing the cycle-run of each vehicle . Of course you can edit it to further fit your needs.
Note
You may reverse the procedure described above and run the python-script instead. The script will open the excel-file, run the experiments and add the new sheets, but in case any errors occur, this time you can debug them, if you had executed the script through LiClipse, or IPython!
Some general notes regarding the python-code from excel-cells:
- On each invocation, the predefined VBA module
pandalonexecutes a dynamically generated python-script file in the same folder where the excel-file resides, which, among others, imports the "sister" python-script file. You can read & modify the sister python-script to import libraries such as 'numpy' and 'pandas', or pre-define utility python functions. - The name of the sister python-script is automatically calculated from the name of the Excel-file,
and it must be valid as a python module-name. Therefore do not use non-alphanumeric characters such as
spaces(``
), dashes(-) and dots(.``) on the Excel-file. - On errors, a log-file is written in the same folder where the excel-file resides, for as long as the message-box is visible, and it is deleted automatically after you click 'ok'!
- Read http://docs.xlwings.org/quickstart.html
The Python code is highly modular, with testability in mind. so that specific parts can run in isolation. This facilitates studying tough issues, such as, double-precision reproducibility, boundary conditions, comparison of numeric outputs, and studying the code in sub-routines.
Tip
Run test-cases with pytest command.
Computations are vectorial, based on hierarchical dataframes, all of them stored in a single structure, the datamodel. In case the computation breaks, you can still retrieve all intermediate results till that point.
.. TODO::
Almost all of the names of the `datamodel` and `formulae` can be remapped,
For instance, it is possible to run the tool on data containing ``n_idling_speed``
instead of ``n_idle`` (which is the default), without renaming the input data.
.. glossary::
mdl
datamodel
The container of all the scalar Input & Output values, the WLTC constants factors,
and 3 matrices: `WOT`, `gwots`, and the `cycle run` time series.
It is composed by a stack of mergeable `JSON-schema` abiding trees of *string, numbers & pandas objects*,
formed with python *sequences & dictionaries, and URI-references*.
It is implemented in :mod:`~wltp.datamodel`, supported by :class:`pandalone.pandata.Pandel`.
WOT
Full Load Curve
An *input* array/dict/dataframe with the full load power curves for (at least) 2 columns for ``(n, p)``
or their normalized values ``(n_norm, p_norm)``.
See also https://en.wikipedia.org/wiki/Wide_open_throttle
gwots
grid WOTs
A dataframe produced from `WOT` for all gear-ratios, indexed by a grid of rounded velocities,
and with 2-level columns ``(item, gear)``.
It is generated by :func:`~wltp.engine.interpolate_wot_on_v_grid()`, and augmented
by :func:`~wltp.engine.attach_p_avail_in_gwots()` & :func:`~wltp.vehicle.calc_p_resist()` .
.. TODO::
Move `grid WOTs` code in own module :mod:`~wltp.gwots`.
cycle
cycle run
A dataframe with all the time-series, indexed by the time of the samples.
The velocities for each time-sample must exist in the `gwots`.
The columns are the same 2-level columns like *gwots*.
it is implemented in :mod:`~wltp.cycler`.
The computation code is roughly divided in these python modules:
.. glossary::
formulae
Physics and engineering code, implemented in modules:
- :mod:`~wltp.engine`
- :mod:`~wltp.vmax`
- :mod:`~wltp.downscale`
- :mod:`~wltp.vehicle`
- orchestration
The code producing the actual gear-shifting, implemented in modules:
- :mod:`~wltp.datamodel`
- :mod:`~wltp.cycler`
- :mod:`~wltp.gridwots` (TODO)
- :mod:`~wltp.scheduler` (TODO)
- :mod:`~wltp.experiment` (TO BE DROPPED, :mod:`~wltp.datamodel` will assume all functionality)
scheduler
graphtik
The internal software component :mod:`graphtik` which decides which
`formulae` to execute based on given inputs and requested outputs.
The blueprint for the underlying software ideas is given with this diagram:
Note that currently there is no scheduler component, which will allow to execute the tool with a varying list of available inputs & required data, and automatically compute only what is not already given.
This program imitates to some degree the MS Access DB (as of July 2019), following this 08.07.2019_HS rev2_23072019 GTR specification (:download:`docs/_static/WLTP-GS-TF-41 GTR 15 annex 1 and annex 2 08.07.2019_HS rev2_23072019.docx`, included in the :file:`docs/_static` folder).
Note
There is a distinctive difference between this implementation and the AccDB:
All computations are vectorial, meaning that all intermediate results are calculated & stored, for all time sample-points, and not just the side of the conditions that evaluate to true on each sample.
The latest official version of this GTR, along with other related documents maybe found at UNECE's site:
- http://www.unece.org/trans/main/wp29/wp29wgs/wp29grpe/grpedoc_2013.html
- https://www2.unece.org/wiki/pages/viewpage.action?pageId=2523179
The WLTC-profiles for the various classes were generated from the tables of the specs above using the :file:`devtools/csvcolumns8to2.py` script, but it still requires an intermediate manual step involving a spreadsheet to copy the table into ands save them as CSV.
The :term:`GTR`'s velocity traces have overlapping split-time values, i.e. belonging to 2 phases, and e.g. for class1 these are the sample-values @ times 589 & 1022:
| class1 | phase-1 | phase-2 | phase-3 | cycle |
|---|---|---|---|---|
| Boundaries | [0, 589] | [589, 1022] | [1022, 1611] | [0, 1611] |
| Duration | 589 | 433 | 589 | 1611 |
| # of samples | 590 | 434 | 590 | 1612 |
Some programs and most spreadsheets do not handle overlapping split-time values like that (i.e. keeping a separate column for each class-phase), and assign split-times either to the earlier or the later phase, distorting thus the duration & number of time samples some phases contain!
For instance, Access-DB tables assign split-times on the lower parts, distorting the start-times & durations for all phases except the 1st one (deviations from GTR in bold):
| class1 | phase-1 | phase-2 | phase-3 | cycle |
|---|---|---|---|---|
| Boundaries | [0, 589] | [590, 1022] | [1023, 1611] | [0, 1611] |
| Duration | 589 | 432 | 588 | 1611 |
| # of samples | 590 | 433 | 589 | 1612 |
Note
The algorithms contained in Access DB are carefully crafted to do the right thing.
The inverse distortion (assigning split-times on the higher parts) would preserve phase starting times (hint: downscaling algorithm depends on those absolute timings being precisely correct):
| class1 | phase-1 | phase-2 | phase-3 | cycle |
|---|---|---|---|---|
| Boundaries | [0, 588] | [589, 1021] | [1022, 1611] | [0, 1611] |
| Duration | 588 | 432 | 589 | 1611 |
| # of samples | 589 | 433 | 590 | 1612 |
On a related issue, GTR's formula for Acceleration (Annex 1 3.1) produces one less value than the number of velocity samples (like the majority of the distorted phases above). GTR prescribes to (optionally) append and extra A=0 sample at the end, to equalize Acceleration & Velocities lengths, but that is not totally ok (hint: mean Acceleration values do not add up like mean-Velocities do, see next point about averaging).
Since most calculated and measured quantities (like cycle Power) are tied to the acceleration, we could refrain from adding the extra 0, and leave all phases with -1 samples, without any overlapping split-times:
| class1 | phase-1 | phase-2 | phase-3 | cycle |
|---|---|---|---|---|
| Boundaries | [0, 588] | [589, 1021] | [1022, 1610] | [0, 1610] |
| Duration | 588 | 432 | 588 | 1610 |
| # of samples | 589 | 433 | 589 | 1611 |
Actually this is "semi-VA0" phasings, above, with the last part equally distorted by -1 @ 1610. But now the whole cycle has (disturbingly) -1 # of samples & duration:
We can resolve this, conceptually, by assuming that each Acceleration-dependent sample signifies a time-duration, so that although the # of samples are still -1, the phase & cycle durations (in sec) are as expected:
| class1 | phase-1 | phase-2 | phase-3 | cycle |
|---|---|---|---|---|
| Boundaries | [0, 589 ) | [589, 1022 ) | [1022, 1611 ) | [0, 1611 ) |
| Duration | 589 | 433 | 589 | 1611 |
| # of samples | 589 | 433 | 589 | 1611 |
Summarizing the last "VA0+" phasing scheme:
- each step signifies a "duration" of 1 sec,
- the duration of the final sample @ 1610 reaches just before 1611sec,
- # of samples for all phases are symmetrically -1 compared to Velocity phases,
- it is valid for Acceleration-dependent quantities only,
- it is valid for any sampling frequency (not just 1Hz),
- respects the Dijkstra counting (notice the parenthesis signifying open right intervals, in the above table), BUT ...
- Velocity-related quantities cannot utilize this phasing scheme, must stick to the original, with overlapping split-times.
Calculating mean values for Acceleration-related quantities produce correct results only with non-overlapping split-times.
It's easier to demonstrate the issues with a hypothetical 4-sec cycle, composed of 2 symmetrical ramp-up/ramp-down 2-sec phases (the "blue" line in the plot, below):
| t | V-phase1 | V-phase2 | V | Distance | VA-phase | A |
|---|---|---|---|---|---|---|
| [sec] | [kmh] | [m x 3.6] | [m/sec²] | |||
| 0 | X | 0 | 0 | 1 | 5 | |
| 1 | X | 5 | 2.5 | 1 | 5 | |
| 2 | X | X | 10 | 10 | 2 | -5 |
| 3 | X | 5 | 17.5 | 2 | -5 | |
| 4 | X | 0 | 20 | <blank> | <blank> |
- The final A value has been kept blank, so that mean values per-phase add up, and phases no longer overlap.
| mean(V) | mean(S) | mean(A) | |
|---|---|---|---|
| [kmh] | [m x 3.6] | [m/sec²] | |
| phase1: | 5 | 10 | 5 |
| phase2: | 5 | 10 | -5 |
- Applying the V-phasings and the extra 0 on mean(A) would have arrived
to counterintuitive values, that don't even sum up to 0:
- up-ramp: \left(\frac{5 + 5 + (-5)}{3} =\right) 1.66m/sec^2
- down-ramp: \left(\frac{(-5) + (-5) + 0}{3} =\right) -3.33m/sec^2
All phases in WLTC begin and finish with consecutive zeros(0), therefore the deliberations above do not manifest as problems; but at the same time, discovering off-by-one errors & shifts in time (wherever this really matters e.g. for syncing data), on arbitrary files containing calculated and/or measured traces is really hard: SUMs & CUMSUMs do not produce any difference at all.
The tables in the next section, along with accompanying CRC functions developed in Python, come as an aid to the problems above.
As reported by :func:`wltp.cycles.cycle_phases()`, and neglecting the advice to optionally add a final 0 when calculating the cycle Acceleration (Annex 1 2-3.1), the following 3 phasing are identified from velocity traces of 1Hz:
- V: phases for quantities dependent on Velocity samples, overlapping split-times.
- VA0: phases for Acceleration-dependent quantities, -1 length, NON overlapping split-times, starting on t=0.
- VA1: phases for Acceleration-dependent quantities, -1 length, NON overlapping split-times, starting on t=1. (e.g. Energy in Annex 7).
| class | phasing | phase-1 | phase-2 | phase-3 | phase-4 |
|---|---|---|---|---|---|
| class1 | V | [0, 589] | [589, 1022] | [1022, 1611] | |
| VA0 | [0, 588] | [589, 1021] | [1022, 1610] | ||
| VA1 | [1, 589] | [590, 1022] | [1023, 1611] | ||
| class2 | V | [0, 589] | [589, 1022] | [1022, 1477] | [1477, 1800] |
| VA0 | [0, 588] | [589, 1021] | [1022, 1476] | [1477, 1799] | |
| VA1 | [1, 589] | [590, 1022] | [1023, 1477] | [1478, 1800] | |
| class3a | V | [0, 589] | [589, 1022] | [1022, 1477] | [1477, 1800] |
| VA0 | [0, 588] | [589, 1021] | [1022, 1476] | [1477, 1799] | |
| VA1 | [1, 589] | [590, 1022] | [1023, 1477] | [1478, 1800] | |
| class3b | V | [0, 589] | [589, 1022] | [1022, 1477] | [1477, 1800] |
| VA0 | [0, 588] | [589, 1021] | [1022, 1476] | [1477, 1799] | |
| VA1 | [1, 589] | [590, 1022] | [1023, 1477] | [1478, 1800] |
- The :func:`~wltp.cycles.crc_velocity()` function has been specially crafted to consume series of floats with 2-digits precision (:data:`~wltp.invariants.v_decimals`) denoting Velocity-traces, and spitting out a hexadecimal string, the CRC, without neglecting any zeros(0) in the trace.
- The checksums for all :ref:`phasings` are reported by :func:`~wltp.cycles.cycle_checksums()`, and the the table below is constructed. The original checksums of the :term:`GTR` are also included in the final 2 columns.
- Based on this table of CRCs, the :func:`~wltp.cycles.identify_cycle_v_crc()` function tries to match and identify any given Velocity-trace:
| CRC32 | SUM | |||||||
|---|---|---|---|---|---|---|---|---|
| by_phase | cumulative | by_phase | cumulative | |||||
| phasing⇨ phase⇩ | V | VA0 | VA1 | V | VA0 | VA1 | V | V |
| class1 | ||||||||
| phase1 | 9840 | 4438 | 97DB | 9840 | 4438 | 97DB | 11988.4 | 11988.4 |
| phase2 | 8C34 | 8C8D | D9E8 | DCF2 | 090B | 4295 | 17162.8 | 29151.2 |
| phase3 | 9840 | 4438 | 97DB | 6D1D | 4691 | F523 | 11988.4 | 41139.6 |
| class2 | ||||||||
| phase1 | 8591 | CDD1 | 8A0A | 8591 | CDD1 | 8A0A | 11162.2 | 11162.2 |
| phase2 | 312D | 391A | 64F1 | A010 | 606E | 3E77 | 17054.3 | 28216.5 |
| phase3 | 81CD | E29E | 9560 | 28FB | 9261 | D162 | 24450.6 | 52667.1 |
| phase4 | 8994 | 0D25 | 2181 | 474B | 262A | F70F | 28869.8 | 81536.9 |
| class3a | ||||||||
| phase1 | 48E5 | 910C | 477E | 48E5 | 910C | 477E | 11140.3 | 11140.3 |
| phase2 | 1494 | D93B | 4148 | 403D | 2487 | DE5A | 16995.7 | 28136.0 |
| phase3 | 8B3B | 9887 | 9F96 | D770 | 3F67 | 2EE9 | 25646.0 | 53782.0 |
| phase4 | F962 | 1A0A | 5177 | 9BCE | 9853 | 2B8A | 29714.9 | 83496.9 |
| class3b | ||||||||
| phase1 | 48E5 | 910C | 477E | 48E5 | 910C | 477E | 11140.3 | 11140.3 |
| phase2 | AF1D | E501 | FAC1 | FBB4 | 18BD | 65D3 | 17121.2 | 28261.5 |
| phase3 | 15F6 | A779 | 015B | 43BC | B997 | BA25 | 25782.2 | 54043.7 |
| phase4 | F962 | 1A0A | 5177 | 639B | 0B7A | D3DF | 29714.9 | 83758.6 |
... where if a some cycle-phase is identified as:
- V phasing, it contains all samples;
- VA0 phasing, it lacks -1 sample from the end;
- VA1 phasing, it lacks -1 sample from the start.
For instance, let's identify the V-trace of class1's full cycle:
>>> from wltp.datamodel import get_class_v_cycle as wltc
>>> from wltp.cycles import identify_cycle_v as crc
>>> V = wltc("class1")>>> crc(V) # full cycle
('class1', None, 'V')
>>> crc(V[:-1]) # -1 (last) sample
('class1', None, 'VA0')The :func:`crc() <wltp.cycles.crc_velocity>` function returns a 3-tuple:
(i-class, i-phase, i-kind):
- When
i-phaseis None, the trace was a full-cycle.
Now let's identify the phases of "Access-DB":
>>> crc(V[:590]) # AccDB phase1 respects GTR
('class1', 'phase-1', 'V')
>>> crc(V[590:1023]) # AccDB phase2 has -1 (first) sample
('class1', 'phase-2', 'VA1')
>>> crc(V[:1023]) # cumulative AccDB phase2 respects GTR
('class1', 'PHASE-2', 'V')- When
i-phaseis CAPITALIZED, the trace was cumulative. - Phase2 is missing -1 sample from the start (
i-kind == VA1).
>>> crc(V[1023:]) # AccDB phase3
('class1', 'phase-1', 'VA1')- Phase3 was identified again as phase1, since they are identical.
Finally, clipping both samples from start & end, matches no CRC:
>>> crc(V[1:-1])
(None, None, None)To be note, all cases above would have had identical CUMSUM (GTR's) CRCs.
This project is hosted in github. To provide feedback about bugs and errors or questions and requests for enhancements, use github's Issue-tracker.
For submitting code, use UTF-8 everywhere, unix-eol(LF) and set git --config core.autocrlf = input.
The typical development procedure is like this:
Install and arm a pre-commit hook with black to auto-format you python-code.
Modify the sources in small, isolated and well-defined changes, i.e. adding a single feature, or fixing a specific bug.
Add test-cases "proving" your code.
Rerun all test-cases to ensure that you didn't break anything, and check their coverage remain above the limit set in :file:`setup.cfg`.
If you made a rather important modification, update also the :doc:`CHANGES` file and/or other documents (i.e. README.rst). To see the rendered results of the documents, issue the following commands and read the result html at :file:`build/sphinx/html/index.html`:
python setup.py build_sphinx # Builds html docs python setup.py build_sphinx -b doctest # Checks if python-code embedded in comments runs ok.
If there are no problems, commit your changes with a descriptive message.
Repeat this cycle for other bugs/enhancements.
When you are finished, push the changes upstream to github and make a merge_request. You can check whether your merge-request indeed passed the tests by checking its build-status
on the integration-server's site (TravisCI).
Hint
Skim through the small IPython developer's documentation on the matter: The perfect pull request
Generate the Sphinx documents in :file:`./wltp.git/docs/_build/html/` with this command:
python setup.py build_sphinx
Access the generated documents through a a web-server, for :term:`graphtik` graphs to work correctly, with this bash-command (remove the final
&on Windows):python -m http.server 8080 --directory ./wltp.git/docs/_build/html/ &
- Author:
- Kostis Anagnostopoulos
- Contributing Authors:
- Heinz Steven (test-data, validation and review)
- Georgios Fontaras (simulation, physics & engineering support)
- Alessandro Marotta (policy support)
- Jelica Pavlovic (policy support)
- Eckhard Schlichte (discussions & advice)
See also :ref:`architecture:Architecture`.
.. glossary::
WLTP
The `Worldwide harmonised Light duty vehicles Test Procedure <https://www2.unece.org/wiki/pages/viewpage.action?pageId=2523179>`_,
a `GRPE` informal working group
UNECE
The United Nations Economic Commission for Europe, which has assumed the steering role
on the `WLTP`.
GRPE
`UNECE` Working party on Pollution and Energy - Transport Programme
GTR
Any of the *Global Technical Regulation* documents of the `WLTP` .
GS Task-Force
The Gear-shift Task-force of the `GRPE`. It is the team of automotive experts drafting
the gear-shifting strategy for vehicles running the `WLTP` cycles.
WLTC
The family of pre-defined *driving-cycles* corresponding to vehicles with different
:abbr:`PMR (Power to Mass Ratio)`. Classes 1,2, 3a/b are split in 3, 4 and 4 *parts* respectively.
AccDB
MS Access DB
The original implementation of the algorithm in *MS Access* by Heinz Steven.
To facilitate searching and cross-referencing the existing routines,
all the code & queries of the database have been extracted and stored in as text
under the `Notebooks/AccDB_src/
<https://github.com/JRCSTU/wltp/tree/master/Notebooks/AccDB_src/>`_ folder
of this project.
MRO
Mass in running order
The mass of the vehicle, with its fuel tank(s) filled to at least 90 per cent
of its or their capacity/capacities, including the mass of the driver and the liquids,
fitted with the standard equipment in accordance with the manufacturer’s specifications and,
where they are fitted, the mass of the bodywork, the cabin,
the coupling and the spare wheel(s) as well as the tools when they are fitted.
UM
Kerb mass
Curb weight
Unladen mass
The `Mass in running order` minus the `Driver mass`.
Driver weight
Driver mass
75 kgr
TM
Test mass
The representative weight of the vehicle used as input for the calculations of the simulation,
derived by interpolating between high and low values for the |CO2|-family of the vehicle.
Downscaling
Reduction of the top-velocity of the original drive trace to be followed, to ensure that the vehicle
is not driven in an unduly high proportion of "full throttle".
JSON-schema
The `JSON schema <http://json-schema.org/>`_ is an `IETF draft <http://tools.ietf.org/html/draft-zyp-json-schema-03>`_
that provides a *contract* for what JSON-data is required for a given application and how to interact
with it. JSON Schema is intended to define validation, documentation, hyperlink navigation, and
interaction control of JSON data.
The schema of this project has its own section: :ref:`code:Schemas`
You can learn more about it from this `excellent guide <http://spacetelescope.github.io/understanding-json-schema/>`_,
and experiment with this `on-line validator <http://www.jsonschema.net/>`_.
JSON-pointer
JSON Pointer(:rfc:`6901`) defines a string syntax for identifying a specific value within
a JavaScript Object Notation (JSON) document. It aims to serve the same purpose as *XPath* from the XML world,
but it is much simpler.
sphinx
The text-oriented language, a superset of `Restructured Text <https://en.wikipedia.org/wiki/ReStructuredText>`_,
used to write the documentation for this project, with similar capabilities to *LaTeX*,
but for humans, e.g., the Linux kernel adopted this textual format on 2016.
http://sphinx-doc.org/
notebook
jupyter notebook
Jupyter
*Jupyter* is a web-based interactive computational environment for creating *Jupyter notebook* documents.
The "notebook" term can colloquially make reference to many different entities,
mainly the Jupyter web application, Jupyter Python web server, or Jupyter document format,
depending on context.
A *Jupyter Notebook* document is composed of an ordered list of input/output *cells*
which contain code in various languages, text (using Markdown), mathematics, plots and
rich media, usually ending with the ".ipynb" extension.
![@dependabot[bot]](https://avatars.githubusercontent.com/in/29110?s=64&v=4){kind=small}