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*fuefit* fits engine-maps on physical parameters

Project description

*fuefit* fits engine-maps on physical parameters
|dev-status| |build-status| |docs-status| |pypi-status| |downloads-count| |github-issues|

:Release: x.x.x
:Copyright: 2014 European Commission (`JRC-IET <>`_)
:License: `EUPL 1.1+ <>`_

*Fuefit* is a python package that calculates fitted fuel-maps from measured engine data-points based on coefficients with physical meaning.

.. _before-intro:


The *Fuefit* calculator performs the following:

1) Accepts **fuel-consumption engine data points** as input
(RPM, Power and Fuel-Consumption or equivalent quantities such as CM, PME/Torque and PMF/FC).
2) Uses those points to **fit the coefficients** math(``a, b, c, a2, b2, loss0, loss2``) in the following formula:[#]_

.. (a + b*cm + c*cm**2)*pmf + (a2 + b2*cm)*pmf**2 + loss0 + loss2*cm**2
.. math::

\mathbf{pme} = (a + b\times{\mathbf{cm}} + c\times{\mathbf{cm^2}})\times{\mathbf{pmf}} +
(a2 + b2\times{\mathbf{cm}})\times{\mathbf{pmf^2}} + loss0 + loss2\times{\mathbf{cm^2}}

3) **Spits-out the input engine-points** according to the fitting.

An "execution" or a "run" of a calculation along with the most important pieces of data
are depicted in the following diagram::

.-----------------------------. .-------------------------------.
/ Input-Model / / Output-Model /
/-----------------------------/ /-------------------------------/
/ +--engine / / +--engine /
/ | +--... / / | +--fc_map_coeffs /
/ +--params / ____________ / +--measured_eng_points /
/ | +--... / | | / | n p fc pme ... /
/ +--measured_eng_points / ==> | Calculator | ==> / | ... ... ... ... ... /
/ n p fc / |____________| / +--fitted_eng_points /
/ -- ---- --- / / | n p fc /
/ 0 0.0 0 / / | ... ... ... /
/ 600 42.5 25 / / +--mesh_eng_points /
/ ... ... ... / / n p fc /
/ / / ... ... ... /
'-----------------------------' '-------------------------------'

The *Input & Output Model* are trees of strings and numbers, assembled with:

* sequences,
* dictionaries,
* class(``pandas.DataFrame``),
* class(``pandas.Series``), and
* URI-references to other model-trees (TODO).

Apart from various engine-characteristics under ``/engine`` the table-columns such as `capacity` and `p_rated`,
the table under ``/measured_eng_points`` must contain *at least* one column
from each of the following categories (column-headers are case-insensitive):

1. Engine-speed::

N (1/min)
N_norm (1/min) : normalized against N_idle + (N_rated - N_idle)
CM (m/sec) : Mean Piston speed

2. Work-capability::

P (kW)
P_norm (kW) : normalized against P_MAX
T (Nm)
PME (bar)

3. Fuel-consumption::

FC (g/h)
FC_norm (g/h) : normalized against P_MAX
PMF (bar)

On *Windows*/*OS X*, it is recommended to use one of the scientific Python distributions:

* `WinPython <>`_ (*Windows* only),
* `Anaconda <>`_ or `miniconda <>`_
* `Canopy <>`_,

as they already include *numpy/scipy*, *pandas* and *win32* native-libraries.

Assuming you have a working python-environment, open a *command-shell*,
(in *Windows* use program(``cmd.exe``) BUT ensure program(``python.exe``) is in its envvar(``PATH``)),
you can try the following commands:

.. code-block:: console

$ pip install fuefit --pre
$ fuefit --winmenus ## Adds StartMenu-items, Windows only.

.. code-block:: console

$ fuefit --version

$ fuefit --help

## Change-directory into the `fuefit/test/` folder in the *sources*.
$ fuefit -I FuelFit_real.csv header+=0 \
--irenames n_norm _ fc_norm \
-I engine.csv file_frmt=SERIES model_path=/engine header@=None \
--irenames \
-m /engine/fuel=petrol \
-O - model_path=/engine/fc_map_coeffs \
-m /params/plot_maps@=True

.. code-block:: console

$ fuefit --excelrun ## Windows & OS X only

.. code-block:: python

import pandas as pd
from fuefit import model, processor

input_model = mdl = datamodel.base_model()
input_model.update({...}) ## See "Python Usage" below.
input_model['engine_points'] = pd.read_csv('measured.csv') ## Can also read Excel, matlab, ...
mdl = datamodel.validate_model(mdl, additional_properties=False)

output_model =

print(datamodel.resolve_jsonpointer(output_model, '/engine/fc_map_coeffs'))

For more elaborate instructions, read the next sections.

.. Tip::
The commands beginning with ``$``, above, imply a *Unix* like operating system with a *POSIX* shell
(*Linux*, *OS X*). Although the commands are simple and easy to translate in its *Windows* counterparts,
it would be worthwile to install `Cygwin <>`_ to get the same environment on *Windows*.
If you choose to do that, include also the following packages in the *Cygwin*'s installation wizard::

* git, git-completion
* make, zip, unzip, bzip2
* openssh, curl, wget

But do not install/rely on cygwin's outdated python environment.

.. _before-install:

Fuefit-x.x.x runs on Python-3.3+, and it is distributed on `Wheels <>`_.

.. Note::
This project depends on the *numpy/scipy*, *pandas* and *win32* python-packages
that themselfs require the use of *C* and *Fortran* compilers to build from sources.
To avoid this hussle, you can choose instead a self-wrapped python distribution like
*Anaconda/minoconda*, *Winpython*, or *Canopy*.

.. Tip::
* You can try to install the `Anaconda <>`_
cross-platform distribution (*Windows*, *Linux* and *OS X*), or its lighter-weight alternative,
`miniconda <>`_.

On this environment you will need to install this project's dependencies manually
using a combination of program(``conda``) and program(``pip``) commands.
See file(``conda_requirements.txt``), and peek at the example script commands in file(``.travis.yaml``).

* Under *Windows* you can try the self-wrapped `WinPython <>`_ distribution,
a higly active project, that can even compile native libraries using an installations of *Visual Studio*,
if available (required for instance when upgrading ``numpy/scipy``, ``pandas`` or ``matplotlib`` with command(``pip``)).

Just remember to **Register your WinPython installation** after installation and
**add your installation into** envvar(``PATH``) (see doc(``faq``)):

* To register it, go to ``Start menu --> All Programs --> WinPython --> WinPython ControlPanel``, and then
``Options --> Register Distribution`` .
* For the path, add or modify the registry string-key ``[HKEY_CURRENT_USER\Environment] "PATH"``.

* Check for alternative installation instructions on the various python environments and platforms
at `the pandas site <>`_.

Before installing it, make sure that there are no older versions left over.
So run this command until you cannot find any project installed:

.. code-block:: console

$ pip uninstall fuefit ## Use `pip3` if both python-2 & 3 are in PATH.

You can install the project directly from the |pypi|_ the "standard" way,
by typing the command(``pip``) in the console:

.. code-block:: console

$ pip install fuefit

* If you want to install a *pre-release* version (the version-string is not plain numbers, but
ends with ``alpha``, ``beta.2`` or something else), use additionally option(``--pre``).

* If you want to upgrade an existing instalation along with all its dependencies,
add also option(``--upgrade``) (or option(``-U``) equivalently), but then the build might take some
considerable time to finish. Also there is the possibility the upgraded libraries might break
existing programs(!) so use it with caution, or from within a |virtualenv|_.

* To install an older version issue the console command:

.. code-block:: console

$ pip install fuefit=1.1.1 ## Use `--pre` if version-string has a build-suffix.

* To install it for different Python environments, repeat the procedure using
the appropriate program(``python.exe``) interpreter for each environment.

* .. Tip::
To debug installation problems, you can export a non-empty envvar(``DISTUTILS_DEBUG``)
and *distutils* will print detailed information about what it is doing and/or
print the whole command line when an external program (like a C compiler) fails.

After installation, it is important that you check which version is visible in your envvar(``PATH``):

.. code-block:: console

$ fuefit --version

Installing from sources
If you download the sources you have more options for installation.
There are various methods to get hold of them:

* Download and extract a `release-snapshot from github <>`_.
* Download and extract a ``sdist`` *source* distribution from |pypi|_.
* Clone the *git-repository* at *github*. Assuming you have a working installation of `git <>`_
you can fetch and install the latest version of the project with the following series of commands:

.. code-block:: console

$ git clone "" fuefit.git
$ cd fuefit.git
$ python install ## Use `python3` if both python-2 & 3 installed.

When working with sources, you need to have installed all libraries that the project depends on.
Particularly for the latest *WinPython* environments (*Windows* / *OS X*) you can install
the necessary dependencies with:

.. code-block:: console

$ pip install -r WinPython_requirements.txt -U .

The previous command installs a "snapshot" of the project as it is found in the sources.
If you wish to link the project's sources with your python environment, install the project
in `development mode <>`_:

.. code-block:: console

$ python develop

.. Note:: This last command installs any missing dependencies inside the project-folder.

.. _before-usage:

Excel usage
.. Attention:: 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 processor.

To create the necessary template-files in your current-directory you should enter:

.. code-block:: console

$ fuefit --excel

You could type instead ``fuefit --excel {file_path}`` to specify a different destination path.

In *windows*/*OS X* you can type ``fuefit --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:

The python-enabled excel-file where input and output data are written, as seen in the screenshot below:

.. docs/xlwings_screenshot.png
:scale: 50%
:alt: Screenshot of the `FuefitExcelRunner.xlsm` file.

After opening it the first tie, enable the macros on the workbook, select the python-code at the left and click
the ``Run Selection as Pyhon`` 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 `mypy` namespace.

To add more input-columns, you need to set as column *Headers* the *json-pointers* path of the desired
model item (see `Python usage`_ below,).

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 coefficients, 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:

.. code-block:: console

$ python

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:

* An elaborate syntax to reference excel *cells*, *rows*, *columns* or *tables* from python code, and
to read them as class(``pandas.DataFrame``) is utilized by the Excel .
Read its syntax at func(``~fuefit.excel.FuefitExcelRunner.resolve_excel_ref``).
* On each invocation, the predefined VBA module `pandalon` executes 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.
* If you rename the excel-file, rename also the python-file, or add this python ``import <old_py_file> as mypy```
* 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

Cmd-line usage
Example command::

fuefit -v\
-I fuefit/test/FuelFit.xlsx sheetname+=0 header@=None names:='["p","rpm","fc"]' \
-I fuefit/test/engine.csv file_frmt=SERIES model_path=/engine header@=None \
-m /engine/fuel=petrol \
-O ~t2.csv model_path=/fitted_eng_points index?=false \
-O ~t2.csv model_path=/mesh_eng_points index?=false \
-O ~t.csv model_path= -m /params/plot_maps@=True

Python usage
Example python REPL (Read-Eval-Print Loop) example-commands are given below
that setup and run an *experiment*.

First run command(``python``) or command(``ipython``) and try to import the project to check its version:


>>> import fuefit

>>> fuefit.__version__ ## Check version once more.

>>> fuefit.__file__ ## To check where it was installed. # doctest: +SKIP

.. Tip:
The use command(``ipython``) is preffered over command(``python``) since it offers various user-friendly
facilities, such as pressing kbd(``Tab``) for completions, or allowing you to suffix commands with `?` or `??`
to get help and read their source-code.

Additionally you can <b>copy any python commands starting with ``>>>`` and ``...``</b> and copy paste them directly
into the ipython interpreter; it will remove these prefixes.
But in command(``python``) you have to remove it youself.

If everything works, take the **base-model** and extend it your input-data (strings and numbers):

.. code-block:: pycon

>>> from fuefit import datamodel, processor

>>> input_model = datamodel.base_model()
>>> input_model.update({
... "engine": {
... "fuel": "diesel",
... "p_max": 95,
... "n_idle": 850,
... "n_rated": 6500,
... "stroke": 94.2,
... "capacity": 2000,
... "bore": None, ##You do not have to include these,
... "cylinders": None, ## they are just for displaying some more engine properties.
... }
... })

For information on the accepted model-data, check both its **JSON-schema** at func(``~fuefit.datamodel.model_schema``),
and the func(``~fuefit.datamodel.base_model``):

Next you have to *validate* it against its *JSON-schema*:

.. code-block:: pycon

>>> datamodel.validate_model(input_model, additional_properties=False)

If validation is successful, you may then feed this model-tree to the mod(``fuefit.processor``),
and get back the results:

.. code-block:: pycon

>>> output_model = # doctest: +SKIP

>>> print(output_model['fitted_eng_maps']) # doctest: +SKIP
>>> print(output_model['fitted_eng_points'].shape) # doctest: +SKIP

.. Hint::
You can always check the sample code at the Test-cases and in the cmdline tool mod(``fuefit.__main__``).

.. _before-contribute:

sad [TBD]

Development team

* Author:
* Kostis Anagnostopoulos
* Contributing Authors:
* Giorgos Fontaras for the testing, physics, policy and admin support.

.. _before-indices:


.. _before-footer:

.. [#] Bastiaan Zuurendonk, Maarten Steinbuch(2005):
"Advanced Fuel Consumption and Emission Modeling using Willans line scaling techniques for engines",
*Technische Universiteit Eindhoven*, 2005,
Department Mechanical Engineering, Dynamics and Control Technology Group,

.. |virtualenv| replace:: *virtualenv* (isolated Python environment)
.. _virtualenv:

.. |pypi| replace:: *PyPi* repo
.. _pypi:

.. |build-status| image::
:alt: Integration-build status
:scale: 100%

.. |docs-status| image::
:alt: Documentation status
:scale: 100%

.. |pypi-status| image::
:alt: Latest Version in PyPI

.. |python-ver| image::
:alt: Supported Python versions

.. |dev-status| image::
:alt: Development Status

.. |downloads-count| image::
:alt: Downloads

.. |github-issues| image::
:alt: Issues count


Mean piston speed (measure for the engines operating speed)

Mean effective pressure (the engines ability to produce mechanical work)

Available mean effective pressure (the maximum mean effective pressure which could be produced if n = 1)

The `JSON schema <>`_ is an `IETF draft <>`_
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 <>`_,
and experiment with this `on-line validator <>`_.

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.

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