Metadata-Version: 1.1
Name: wltp
Version: 0.0.9-alpha.1
Summary: *wltp* gear-shifts calculator

Home-page: https://github.com/ankostis/wltp
Author: Kostis Anagnostopoulos @ European Commission (JRC)
Author-email: ankostis@gmail.com
License: European Union Public Licence 1.1 or later (EUPL 1.1+)
Description: ===============================
        *wltp* gear-shifts calculator
        ===============================
        
        |dev-status| |build-status| |cover-status| |docs-status| |pypi-status| |downloads-count| |github-issues|
        
        :Version:       x.x.x
        :Home:          https://github.com/ankostis/wltp
        :Documentation: https://wltp.readthedocs.org/
        :PyPI:          https://pypi.python.org/pypi/wltp
        :Copyright:     2013-2014 European Commission (`JRC-IET <http://iet.jrc.ec.europa.eu/>`_)
        :License:       `EUPL 1.1+ <https://joinup.ec.europa.eu/software/page/eupl>`_
        
        Calculates the *gear-shifts* of Light-duty vehicles running the **WLTP**
        driving-cycles, according to **UNECE**'s GTR (Global Technical Regulation) draft.
        
        ..  docs/wltc_class3b.png
            :align: center
        
            **Figure 1:** *WLTP cycle for class-3b Vehicles*
        
        
        .. Attention:: This project is still in *alpha* stage.  Its results are not
            considered "correct", and official approval procedures should not rely on them.
            Some of the known deficiencies are described in doc(``CHANGES``).
            On each build the calculations of this tool are automatically comparared with a pre-determined set
            of vehicles from Heinz-db, and the results are imprinted in the mod(``~wltp.test.wltp_db_tests``) test-case
            (currently, mean rpm differ from Heinz-db < 0.5% and gears diff < 5% for a 1800-step class-3 cycle).
        
        
        
        .. _begin-intro:
        
        Introduction
        ============
        
        The calculator accepts as input the vehicle's technical data, along with parameters for modifying the execution
        of the **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 the processing and augmentation of the **input-model**
        to produce the **output-model**.  The process is depicted in the following diagram::
        
                       .---------------------.                        .----------------------------.
                      ;     input-model     ;                        ;      output-model          ;
                     ;---------------------;                        ;----------------------------;
                    ; +--vehicle          ;     ___________        ; +---...                    ;
                   ;  +--params          ;     |           |      ;  +--cycle_run:             ;
                  ;       +--wltc_data  ;  ==> | Processor | ==> ;      t   v_class  gear     ;
                 ;                     ;       |___________|    ;      ------------------    ;
                ;                     ;                        ;       00       0.0     1   ;
               ;                     ;                        ;        01       1.3     1  ;
              ;                     ;                        ;         02       5.5     1 ;
             ;                     ;                        ;          ...               ;
            '---------------------'                        '----------------------------.
        
        
        
        .. _wltp_install:
        
        Install
        -------
        Requires Python-2.7+ or Python-3.3+ (preferred).
        
        .. Tip:: To install *python*, you can try the free (as in beer) distribution
            `Anaconda <http://docs.continuum.io/anaconda/pkg-docs.html>`_ for *Windows* and *OS X*, or
            the totally free `WinPython <http://winpython.sourceforge.net/>`_ distribution, but for *Windows* only:
        
            * For *Anaconda* you may need to install project's dependencies manually (see file(``setup.py``))
              using command(``conda``).
            * The most recent version of *WinPython* (python-3.4) is in the process of
              `changing maintainer <http://sourceforge.net/projects/stonebig.u/files/>`_,
              yet it still remains a higly active project, that can even compile native libraries using
              installation of *Visual Studio*, if any
              (required when upgrading ``numpy/scipy``,``pandas`` or ``matplotlib`` with command(``pip``)).
        
        
        You can install (or upgrade) the project directly from the `PyPI <https://pypi.python.org/pypi>`_ repository
        with command(``pip``).
        Notice that option(``--pre``) is required, since all realeased packages so far were *pre*-release (``-alpha``) versions:
        
        .. code-block:: console
        
            $ pip install wltp --pre -U                 ## Use `pip3` if both python-2 & 3 installed.
            $ wltp.py --version                         ## Check which version installed.
            wltp.py 0.0.9-alpha
        
        
        .. Tip::
            The commands above beginning with ``$`` work on an *unix* like operating system with a *POSIX* shell
            (*Linux*, *OS X*). If you're using *Windows*, you'll have to run their "windows counterparts".
            The same is true for the rest of this documentation.
        
            Although the commands are simple and easy to translate , it would be worthwile to install
            `cygwin <https://www.cygwin.com/>`_ to get the same environment on *Windows* machines.
            If you choose to do that, make sure that in the *cygwin*'s installation wizard the following packages
            are also included::
        
                * git, git-completion
                * make
                * openssh, curl, wget
        
        
        Alternatively you can build the latest version of the project from the sources,
        (assuming you have a working installation of `git <http://git-scm.com/>`_)
        and install it in `development mode <http://pythonhosted.org/setuptools/setuptools.html#development-mode>`_
        with the following series of commands:
        
        .. code-block:: console
        
            $ git clone "https://github.com/ankostis/wltp.git" wltp.git
            $ cd wltp.git
            $ python setup.py develop                   ## Use `python3` if you have installed both python-2 & 3.
        
        
        
        That way you get the complete source-tree of the project, ready for development
        (see doc(``contribute``) section, below)::
        
            +--wltp/            ## (package) The python-code of the calculator
            |   +--cycles/      ## (package) The python-code for the WLTC data
            |   +--test/        ## (package) Test-cases and the wltp_db
            |   +--model        ## (module) Describes the data for the calculation
            |   +--experiment   ## (module) The calculator
            +--docs/            ## Documentation folder
            +--devtools/        ## Scripts for preprocessing WLTC data and the wltp_db
            +--wltp.py          ## (script) The cmd-line entry-point script for the calculator
            +--setup.py         ## (script) The entry point for `setuptools`, installing, testing, etc
            +--requirements.txt ## The installation dependencies.
            +--README.rst
            +--CHANGES.rst
            +--LICENSE.txt
        
        
        The previous command installed also any *dependencies* inside the project-folder.  If you wish to install them
        on your system (or virtualenv), enter:
        
        .. code-block:: console
        
            pip install -r requirements.txt
        
        
        
        
        
        Python usage
        ------------
        Here is a quick-start python REPL (Read-Eval-Print Loop)-example to setup and run
        an *experiment*.  First run command(``python``) and try to import the project to check its version:
        
        code-block::
        
            >>> import wltp
        
            >>> wltp.__version__            ## Check version once more.
            '0.0.9-alpha.1'
        
            >>> wltp.__file__               ## To check where it was installed.         # doctest: +SKIP
            /usr/local/lib/site-package/wltp-...
        
        
        .. Tip::
            You can copy the the python commands starting with ``>>>`` and ``...`` and copy paste them directly
            into the python interpreter; it will remove these prefixes.
        
        If everything works, create the **pandas-model** that will hold the input-data (strings and numbers)
        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:
        
        code-block::
        
            >>> from wltp import model
            >>> from wltp.experiment import Experiment
            >>> from collections import OrderedDict as odic         ## It is handy to preserve keys-order.
        
            >>> mdl = odic(
            ...   vehicle = odic(
            ...     unladen_mass = 1430,
            ...     test_mass    = 1500,
            ...     v_max        = 195,
            ...     p_rated      = 100,
            ...     n_rated      = 5450,
            ...     n_idle       = 950,
            ...     n_min        = None,                            ## Manufacturers my overridde it
            ...     gear_ratios         = [120.5, 75, 50, 43, 37, 32],
            ...     resistance_coeffs   = [100, 0.5, 0.04],
            ...   )
            ... )
        
        
        For information on the accepted model-data, check its **JSON-schema**:
        
        code-block::
        
            >>> model.json_dumps(model.model_schema(), indent=2)                                # doctest: +SKIP
            {
              "properties": {
                "params": {
                  "properties": {
                    "f_n_min_gear2": {
                      "description": "Gear-2 is invalid when N :< f_n_min_gear2 * n_idle.",
                      "type": [
                        "number",
                        "null"
                      ],
                      "default": 0.9
                    },
                    "v_stopped_threshold": {
                      "description": "Velocity (Km/h) under which (<=) to idle gear-shift (Annex 2-3.3, p71).",
                      "type": [
            ...
        
        
        You then have to feed this model-tree to the class(``~wltp.experiment.Experiment``)
        constructor. Internally the class(``~wltp.pandel.Pandel``) resolves URIs, fills-in default values and
        validates the data based on the project's pre-defined JSON-schema:
        
        code-block::
        
            >>> 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:
        
        code-block::
        
            >>> mdl = processor.model               ## Returns the validated model with filled-in defaults.
            >>> sorted(mdl)                         ## The "defaulted" model now includes the `params` branch.
            ['params', 'vehicle']
            >>> 'full_load_curve' in mdl['vehicle'] ## A default wot was also provided in the `vehicle`.
            True
        
        
        Now you can run the experiment:
        
        code-block::
        
            >>> mdl = processor.run()               ## Runs experiment and augments the model with results.
            >>> sorted(mdl)                         ## Print the top-branches of the "augmented" model.
            ['cycle_run', 'params', 'vehicle']
        
        
        To access the time-based cycle-results it is better to use a class(``pandas.DataFrame``):
        
        code-block::
        
            >>> import pandas as pd
            >>> df = pd.DataFrame(mdl['cycle_run']); df.index.name = 't'
            >>> df.shape                            ## ROWS(time-steps) X COLUMNS.
            (1801, 11)
            >>> df.columns
            Index(['v_class', 'v_target', 'clutch', 'gears_orig', 'gears', 'v_real', 'p_available', 'p_required', 'rpm', 'rpm_norm', 'driveability'], dtype='object')
            >>> 'Mean engine_speed: %s' % df.rpm.mean()
            'Mean engine_speed: 1917.0407829'
            >>> df.describe()
                       v_class     v_target     clutch   gears_orig        gears  \
            count  1801.000000  1801.000000       1801  1801.000000  1801.000000
            mean     46.506718    46.506718  0.0660744     3.794003     3.683509
            std      36.119280    36.119280  0.2484811     2.278959     2.278108
            ...
            <BLANKLINE>
                        v_real  p_available   p_required          rpm     rpm_norm
            count  1801.000000  1801.000000  1801.000000  1801.000000  1801.000000
            mean     50.356222    28.846639     4.991915  1917.040783     0.214898
            std      32.336908    15.833262    12.139823   878.139758     0.195142
            ...
        
            >>> 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:
        
        .. code-block:: pycon
        
            >>> df.to_csv('cycle_run.csv')                                                      # doctest: +SKIP
        
        
        For more examples, download the sources and check the test-cases
        found under the file(``/wltp/test/``) folder.
        
        
        
        Cmd-line usage
        --------------
        .. Note:: Not implemented in yet.
        
        The examples presented so far required to execute multiple commands interactively inside
        the Python interpreter (REPL).
        The comand-line usage below still requires the Python environment to be installed, but 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.
        
        The entry-point script is called program(``wltp.py``), 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:
        
        .. code-block:: console
        
            $ wltp.py --help          ## to get generic help for cmd-line syntax
            $ wltp.py -M /vehicle     ## to get help for specific model-paths
        
        
        and 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:
        
        .. code-block:: console
        
            $ wltp.py -v \
                -I vehicle.csv file_frmt=SERIES model_path=/params header@=None \
                -m /vehicle/n_idle:=850 \
                -O cycle.csv model_path=/cycle_run
        
        
        
        
        
        IPython notebook usage
        ----------------------
        The list of *IPython notebooks* for wltp is maintained at the `wiki <https://github.com/ankostis/wltp/wiki>`_
        of the project.
        
        Requirements
        ^^^^^^^^^^^^
        In order to run them interactively, ensure that the following requirements are satisfied:
        
        a. A `ipython-notebook server <http://ipython.org/notebook.html>`_ >= v2.x.x is installed, up and running.
        b. The *wltp* is installed on your *python-3* of your system (see `wltp_install`_ above).
        
        Instructions
        ^^^^^^^^^^^^
        * Visit each *notebook* from the wiki-list that you wish to run and **download** it as file(``ipynb``) file
          from the menu (``File|Download as...|IPython Notebook(.ipynb)``).
        * Locate the downloaded file with your *file-browser* and **drag n' drop** it on the landing page
          of your notebook's server (the one with the folder-list).
        
        
        Enjoy!
        
        
        .. _begin-contribute:
        
        Getting Involved
        ================
        This project is hosted in **github**.
        To provide feedback about bugs and errors or questions and requests for enhancements,
        use `github's Issue-tracker <https://github.com/ankostis/wltp/issues>`_.
        
        
        
        Sources & Dependencies
        ----------------------
        To get involved with development, first you need to download the latest sources:
        
        .. code-block:: console
        
            $ git clone https://github.com/ankostis/wltp.git wltp.git
            $ cd wltp.git
        
        
        .. Admonition:: Virtualenv & Liclipse IDE
            :class: note
        
            You may choose to work in a `virtual-environment <http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_,
            to install dependency libraries isolated from system's ones, and/or without *admin-rights*
            (recommended for *Linux*/*Mac OS*).
        
            .. Attention::
                If you decide to reuse stystem-installed packages using  option(``--system-site-packages``)
                with ``virtualenv <= 1.11.6``
                (to avoid, for instance, having to reinstall *numpy* and *pandas* that require native-libraries)
                you may be bitten by `bug #461 <https://github.com/pypa/virtualenv/issues/461>`_ which
                prevents you from upgrading any of the pre-installed packages with command(``pip``).
        
            Within the sources it is included a file(``.project``) file for the comprehensive
            `LiClipse <https://brainwy.github.io/liclipse/>`_, an **eclipse** IDE pre-configured with the
            excellent **PyDev** environment.  If you also choose to use it, you may have to reorder 
            the 1st python interpreter known to eclipse under 
            ``&Windows --> &Preferences --> PyDev --> Interpreters --> Python Interpreter``, 
            since that is is the *default* interpreter, which is specified in the file(``.project``).
            You may change this choice of interpreter by ``Right-clicking`` on the Project and navigating
            to ``Properties --> PyDev - Interpreter/Grammar --> Interpreter``,
            but you have to remember not commit this change in file(``.project``).
        
        
        Then you can install all project's dependencies in *`development mode* using the file(``setup.py``) script:
        
        .. code-block:: console
        
            $ python setup.py --help                           ## Get help for this script.
            Common commands: (see '--help-commands' for more)
        
              setup.py build      will build the package underneath 'build/'
              setup.py install    will install the package
        
            Global options:
              --verbose (-v)      run verbosely (default)
              --quiet (-q)        run quietly (turns verbosity off)
              --dry-run (-n)      don't actually do anything
            ...
        
            $ python setup.py develop                           ## Also installs dependencies into project's folder.
            $ python setup.py build                             ## Check that the project indeed builds ok.
        
        
        You should now run the test-cases (see `Tests & Metrics`_, below) to check
        that the sources are in good shape:
        
        .. code-block:: console
        
           $ python setup.py test
        
        
        .. Note:: The above commands installed the dependencies inside the project folder and
            for the *virtual-environment*.  That is why all build and testing actions have to go through
            ``python setup.py {some_cmd}``.
        
            If you are dealing with installation problems and/or you want to permantly install dependant packages,
            you have to *deactivate* the virtual-environment and start installing them into your *base*
            python environment:
        
            .. code-block:: console
        
               $ deactivate
               $ python setup.py develop
        
            or even try the more *permanent* installation-mode:
        
            .. code-block:: console
        
               $ python setup.py install                # May require admin-rights
        
        
        
        Development procedure
        ---------------------
        For submitting code, use ``UTF-8`` everywhere, unix-eol(``LF``) and set ``git --config core.autocrlf = input``.
        
        The typical development procedure is like this:
        
        1. Modify the sources in small, isolated and well-defined changes, i.e.
           adding a single feature, or fixing a specific bug.
        2. Add test-cases "proving" your code.
        3. Rerun all test-cases to ensure that you didn't break anything,
           and check their *coverage* remain above 80%:
        
            .. code-block:: console
        
                $ python setup.py nosetests --with-coverage --cover-package wltp.model,wltp.experiment --cover-min-percentage=80
        
        
            .. Tip:: You can enter just: ``python setup.py test_all`` instead of the above cmd-line
                since it has been *aliased* in the file(``setup.cfg``) file.
                Check this file for more example commands to use during development.
        
        
        4. 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``):
        
            .. code-block:: console
        
                $ python setup.py build_sphinx                  # Builds html docs
                $ python setup.py build_sphinx -b doctest       # Checks if python-code embeded in comments runs ok.
        
        
        5. If there are no problems, commit your changes with a descriptive message.
        
        6. Repeat this cycle for other bugs/enhancements.
        7. 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 |build-status| on the integration-server's site (TravisCI).
        
            .. Hint:: Skim through the small IPython developer's documentantion on the matter:
                `The perfect pull request <https://github.com/ipython/ipython/wiki/Dev:-The-perfect-pull-request>`_
        
        
        
        Tests & Metrics
        ---------------
        In order to maintain the algorithm stable, a lot of effort has been put
        to setup a series of test-case and metrics to check the sanity of the results
        and to compare them with the Heinz-db tool or other datasets.
        These tests can be found in the file(``wltp/test/``) folders.
        Code for generating diagrams for the metrics below are located
        in the file(``docs/pyplot/``) folder.
        
        ..  pyplots/avg_p__pmr.py
           :include-source:
        
        
        
        
        Specs & Algorithm
        -----------------
        This program was implemented from scratch based on
        this download(``GTR specification <23.10.2013 ECE-TRANS-WP29-GRPE-2013-13 0930.docx>``)
        (included in the file(``docs/``) folder).  The latest version of this GTR, along
        with other related documents can be 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
        * Probably a more comprehensible but older spec is this one:
          https://www2.unece.org/wiki/display/trans/DHC+draft+technical+report
        
        The WLTC-profiles for the various classes in the file(``devtools/data/cycles/``) folder 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.
        
        Then use the file(``devtools/buildwltcclass.py``) to contruct the respective python-vars into the
        mod(``wltp/model.py``) sources.
        
        
        Data-files generated from Steven Heinz's ms-access ``vehicle info`` db-table can be processed
        with the  file(``devtools/preprocheinz.py``) script.
        
        
        Cycles
        ^^^^^^
        
        ..  docs/wltc_class1.png
            :align: center
        ..  docs/wltc_class2.png
            :align: center
        ..  docs/wltc_class3a.png
            :align: center
        ..  docs/wltc_class3b.png
            :align: center
        
        
        .. _dev-team:
        
        Development team
        ----------------
        
        * Author:
            * Kostis Anagnostopoulos
        * Contributing Authors:
            * Heinz Steven (test-data, validation and review)
            * Georgios Fontaras (simulation, physics & engineering support)
            * Alessandro Marotta (policy support)
        
        
        
        .. _begin-glossary:
        
        Glossary
        ========
        rubric::
        
            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
        
            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
                PMR (Power to Mass Ratio). Classes 1,2, 3a & 3b are split in 2, 4, 4 and 4 *parts* respectively.
        
            Unladen mass
                *UM* or *Curb weight*, the weight of the vehicle in running order minus
                the mass of the driver.
        
            Test mass
                *TM*, 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".
        
            pandas-model
                The *container* of data that the gear-shift calculator consumes and produces.
                It is implemented by class(``wltp.pandel.Pandel``) as a mergeable stack of **JSON-schema** abiding trees of
                strings and numbers, formed with sequences, dictionaries, mod(``pandas``)-instances and URI-references.
        
            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.
                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.
        
        
        
        .. _begin-replacements:
        
        .. |CO2| replace:: CO\ sub(``2``)
        
        .. |build-status| image:: https://travis-ci.org/ankostis/wltp.svg
            :alt: Integration-build status
            :scale: 100%
            :target: https://travis-ci.org/ankostis/wltp/builds
        
        .. |cover-status| image:: https://coveralls.io/repos/ankostis/wltp/badge.png?branch=master
                :target: https://coveralls.io/r/ankostis/wltp?branch=master
        
        .. |docs-status| image:: https://readthedocs.org/projects/wltp/badge/
            :alt: Documentation status
            :scale: 100%
            :target: https://readthedocs.org/builds/wltp/
        
        .. |pypi-status| image::  https://pypip.in/v/wltp/badge.png
            :target: https://pypi.python.org/pypi/wltp/
            :alt: Latest Version in PyPI
        
        .. |python-ver| image:: https://pypip.in/py_versions/wltp/badge.svg
            :target: https://pypi.python.org/pypi/wltp/
            :alt: Supported Python versions
        
        .. |dev-status| image:: https://pypip.in/status/wltp/badge.svg
            :target: https://pypi.python.org/pypi/wltp/
            :alt: Development Status
        
        .. |downloads-count| image:: https://pypip.in/download/wltp/badge.svg?period=week
            :target: https://pypi.python.org/pypi/wltp/
            :alt: Downloads
        
        .. |github-issues| image:: http://img.shields.io/github/issues/ankostis/wltp.svg
            :target: https://github.com/ankostis/wltp/issues
            :alt: Issues count
        
Keywords: automotive,vehicle,vehicles,car,cars,fuel,consumption,gears,gearshifs,simulation,simulator,driving,engine,wltc,UNECE,standard
Platform: UNKNOWN
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Development Status :: 3 - Alpha
Classifier: Natural Language :: English
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Manufacturing
Classifier: License :: OSI Approved :: European Union Public Licence 1.1 (EUPL 1.1)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
