co2mpas 1.6.0.dev1

Requirements

• These are the minimum IT requirements for the Computer to run CO2MPAS & DICE:

• 64-bit Intel or AMD processor (x86_64, aka x64, aka AMD64);

• Microsoft Windows 7, or later;

• 4 GB RAM (more recommended);

• 2.4 GB hard disk storage for extracting the software, more space for the input/output files;

• Execution-rights to the installation folder (but no Admin-rights).

• An e-mail account to send & receive DICE e-mails;

• Unhindered SMTP access to this email-server (no firewall on ports 25, 465, 587); or access through SOCKS Proxy (see diagram below);

• Unhindered HTTP/HTTPS web-access (no firewall on ports 80, 443); or access through HTTP Proxy;

• (optional) Excel, to view & edit simulation’s input and output files;

• (optional) GitHub account to submit and resolve issues.

All-In-One Installation under Windows

• Download all-in-one archive from https://github.com/JRCSTU/CO2MPAS-TA/releases/ (it only runs on 64bit PCs).

• Use the original “7z” extractor, since “plain-zip” application produces out-of-memory errors when expanding long directories.

• Run INSTALL.bat script contained in the root of the unzipped folder. It will install links for commons CO₂MPAS tasks under your Windows Start-Menu.

• You can start CO₂MPAS from Windows start-menu by pressing the [WinKey] and start typing ‘co2mpas’, or by selecting the CO₂MPAS menu item from All Programs.

  Alternatively, advanced users may continue to use the Console.

Upgrade CO₂MPAS

Uninstall and re-install it from the CO₂MPAS CONSOLE:

pip uninstall co2mpas
pip install co2mpas

File Contents

RUN_CO2MPAS.bat            ## Asks for Input & Output folders, and runs CO2MPAS for all Excel-files in Input.
CONSOLE.bat                ## Open a python+cygwin enabled `cmd.exe` console.

co2mpas-env.bat            ## Sets env-vars for python+cygwin and launches arguments as new command
                           ## !!!!! DO NOT MODIFY !!!!! used by Windows StartMenu shortcuts.
bash-console.bat           ## Open a python+cygwin enabled `bash` console.


CO2MPAS/                   ## User's HOME directory containing release-files and tutorial-folders.
CO2MPAS/.*                 ## Configuration-files auto-generated by various programs, starting with dot(.).

Apps/Cygwin/               ## Unix-folders for *Cygwin* environment (i.e. bash).
Apps/WinPython/            ## Python environment (co2mpas is pre-installed inside it).
Apps/Console2/             ## A versatile console-window supporting decent copy-paste.
Apps/graphviz/             ## Graph-plotting library (needed to generate model-plots).
Apps/gpg4win-2.3.3.exe     ## GPG cryptographic suite installer for Windows.
vc_redist.x64.exe          ## Microsoft Visual C++ Redistributable for Visual Studio 2015
                           #  (KB2977003 Windows update, prerequisite for running Python-3.5.x).
CO2MPAS_logo.ico           ## The logos used by the INSTALL.bat script.

README                     ## This file, with instructions on this pre-populated folder.

Generic Tips

• You may freely move & copy this folder around. But prefer NOT TO HAVE SPACES IN THE PATH LEADING TO IT.

• To view & edit textual files, such as .txt, .bat or config-files starting with dot(.), you may use the “ancient” Window notepad editor, but it will save you from a lot of trouble if you download and install notepad++ from: http://portableapps.com/apps/development/notepadpp_portable (no admin-rights needed).

  Even better if you combine it with the “gem” file-manager of the ’90s, TotalCommander, from http://www.ghisler.com/ (no admin-rights needed). From inside this file-manager, F3 key-shortcut views files.

• The Cygwin POSIX-environment and its accompanying bash-shell are a much better choice to give console-commands compare to cmd.exe prompt, supporting auto-completion for various commands (with [TAB] key) and enhanced history search (with [UP]/[DOWN] cursor-keys).

  There are MANY tutorials and crash-courses for bash:

  • a concise one: http://www.ks.uiuc.edu/Training/Tutorials/Reference/unixprimer.html

  • a more detailed guide (just ignore the Linux-specific part): http://linuxcommand.org/lc3_lts0020.php

  • a useful poster with all fundamental bash-commands (eg. ls, pwd, cd): http://www.improgrammer.net/linux-commands-cheat-sheet/

• The console automatically copies into clipboard anything that is selected with the mouse. In case of errors, copy and paste the offending commands and their error-messages to emails sent to JRC.

• When a new CO₂MPAS version comes out it is not necessary to download the full ALLINONE archive, but you choose instead to just upgrade co2mpas.

  Please follow the upgrade procedure in the main documentation.

CO₂MPAS GUI

>From “Rally” release, CO₂MPAS can be launched through a Graphical User Interface (GUI). Its core functionality is provided from within the GUI. Just ensure that the latest version of CO₂MPAS is properly installed, and that its version is the latest released, by checking the “About” menu, as shown in the animation, below:

Alternatively, open the CONSOLE and type the following command:

## Check co2mpas version.
$ co2mpas -V
co2mpas-1.6.0.dev1

CO₂MPAS command syntax

To get the syntax of the CO₂MPAS console-command, open a console where you have installed CO₂MPAS (see ref: install above) and type:

## co2mpas help.
$ co2mpas --help

Predict NEDC CO2 emissions from WLTP.

:Home:         http://co2mpas.io/
:Copyright:    2015-2016 European Commission, JRC <https://ec.europa.eu/jrc/>
:License:       EUPL 1.1+ <https://joinup.ec.europa.eu/software/page/eupl>

Use the `batch` sub-command to simulate a vehicle contained in an excel-file.


USAGE:
  co2mpas gui         [-v | -q | --logconf=<conf-file>]
  co2mpas ta          [-f] [-O=<output-folder>] [<input-path>]...
  co2mpas batch       [-v | -q | --logconf=<conf-file>] [-f]
                      [--overwrite-cache] [-O=<output-folder>]
                      [--modelconf=<yaml-file>]
                      [-D=<key=value>]... [<input-path>]...
  co2mpas demo        [-v | -q | --logconf=<conf-file>] [-f]
                      [<output-folder>]
  co2mpas template    [-v | -q | --logconf=<conf-file>] [-f]
                      [<excel-file-path> ...]
  co2mpas ipynb       [-v | -q | --logconf=<conf-file>] [-f] [<output-folder>]
  co2mpas modelgraph  [-v | -q | --logconf=<conf-file>] [-O=<output-folder>]
                      [--modelconf=<yaml-file>]
                      (--list | [--graph-depth=<levels>] [<models> ...])
  co2mpas modelconf   [-v | -q | --logconf=<conf-file>] [-f]
                      [--modelconf=<yaml-file>] [-O=<output-folder>]
  co2mpas gui         [-v | -q | --logconf=<conf-file>]
  co2mpas             [-v | -q | --logconf=<conf-file>] (--version | -V)
  co2mpas             --help

Syntax tip:
  The brackets `[ ]`, parens `( )`, pipes `|` and ellipsis `...` signify
  "optional", "required", "mutually exclusive", and "repeating elements";
  for more syntax-help see: http://docopt.org/


OPTIONS:
  <input-path>                Input xlsx-file or folder. Assumes current-dir if missing.
  -O=<output-folder>          Output folder or file [default: .].
  <excel-file-path>           Output file.
  --modelconf=<yaml-file>     Path to a model-configuration file, according to YAML:
                                https://docs.python.org/3.5/library/logging.config.html#logging-config-dictschema
  --overwrite-cache           Overwrite the cached input file.
  --override, -D=<key=value>  Input data overrides (e.g., `-D fuel_type=diesel`,
                              `-D prediction.nedc_h.vehicle_mass=1000`).
  -l, --list                  List available models.
  --graph-depth=<levels>      An integer to Limit the levels of sub-models plotted.
  -f, --force                 Overwrite output/template/demo excel-file(s).


Model flags (-D flag.xxx, example -D flag.engineering_mode=True):
 engineering_mode=<bool>     Use all data and not only the declaration data.
 soft_validation=<bool>      Relax some Input-data validations, to facilitate experimentation.
 use_selector=<bool>         Select internally the best model to predict both NEDC H/L cycles.
 only_summary=<bool>         Do not save vehicle outputs, just the summary.
 plot_workflow=<bool>        Open workflow-plot in browser, after run finished.
 output_template=<xlsx-file> Clone the given excel-file and appends results into
                             it. By default, results are appended into an empty
                             excel-file. Use `output_template=-` to use
                             input-file as template.

Miscellaneous:
  -h, --help                  Show this help message and exit.
  -V, --version               Print version of the program, with --verbose
                              list release-date and installation details.
  -v, --verbose               Print more verbosely messages - overridden by --logconf.
  -q, --quite                 Print less verbosely messages (warnings) - overridden by --logconf.
  --logconf=<conf-file>       Path to a logging-configuration file, according to:
                                https://docs.python.org/3/library/logging.config.html#configuration-file-format
                              If the file-extension is '.yaml' or '.yml', it reads a dict-schema from YAML:
                                https://docs.python.org/3.5/library/logging.config.html#logging-config-dictschema


SUB-COMMANDS:
    gui             Launches co2mpas GUI (DEPRECATED: Use `co2gui` command).
    ta              Simulate vehicle in type approval mode for all <input-path>
                    excel-files & folder. If no <input-path> given, reads all
                    excel-files from current-dir. It reads just the declaration
                    inputs, if it finds some extra input will raise a warning
                    and will not produce any result.
                    Read this for explanations of the param names:
                      http://co2mpas.io/explanation.html#excel-input-data-naming-conventions
    batch           Simulate vehicle in scientific mode for all <input-path>
                    excel-files & folder. If no <input-path> given, reads all
                    excel-files from current-dir. By default reads just the
                    declaration inputs and skip the extra inputs. Thus, it will
                    produce always a result. To read all inputs the flag
                    `engineering_mode` have to be set to True.
                    Read this for explanations of the param names:
                      http://co2mpas.io/explanation.html#excel-input-data-naming-conventions
    demo            Generate demo input-files for the `batch` cmd inside <output-folder>.
    template        Generate "empty" input-file for the `batch` cmd as <excel-file-path>.
    ipynb           Generate IPython notebooks inside <output-folder>; view them with cmd:
                      jupyter --notebook-dir=<output-folder>
    modelgraph      List or plot available models. If no model(s) specified, all assumed.
    modelconf       Save a copy of all model defaults in yaml format.


EXAMPLES::

    # Don't enter lines starting with `#`.

    # View full version specs:
    co2mpas -vV

    # Create an empty vehicle-file inside `input` folder:
    co2mpas  template  input/vehicle_1.xlsx

    # Create work folders and then fill `input` with sample-vehicles:
    md input output
    co2mpas  demo  input

    # View a specific submodel on your browser:
    co2mpas  modelgraph  co2mpas.model.physical.wheels.wheels

    # Run co2mpas with batch cmd plotting the workflow:
    co2mpas  batch  input  -O output  -D flag.plot_workflow=True

    # Run co2mpas with ta cmd:
    co2mpas  batch  input/co2mpas_demo-0.xlsx  -O output

    # or launch the co2mpas GUI:
    co2mpas  gui

    # View all model defaults in yaml format:
    co2maps modelconf -O output

Input template

The sub-commands batch (Run) and ta (Run TA) accept either a single input-excel-file or a folder with multiple input-files for each vehicle. You can download an empty input excel-file from the GUI:

Or you can create an empty vehicle template-file (e.g., vehicle_1.xlsx) inside the input-folder with the template sub-command:

$ co2mpas template input/vehicle_1.xlsx -f
Creating TEMPLATE INPUT file 'input/vehicle_1.xlsx'...

The generated file contains descriptions to help you populate it with vehicle data. For items where an array of values is required (e.g. gear-box ratios) you may reference different parts of the spreadsheet following the syntax of the “xlref” mini-language.

Demo files

The simulator contains demo-files that are a nice starting point to try out. You can download the demo vehicles from the GUI:

Or you can create the demo files inside the input-folder with the demo sub-command:

$ co2mpas demo input -f
2016-11-14 16:33:07,520: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-0.xlsx'...
2016-11-14 16:33:07,525: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-1.xlsx'...
2016-11-14 16:33:07,530: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-2.xlsx'...
2016-11-14 16:33:07,535: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-3.xlsx'...
2016-11-14 16:33:07,540: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-4.xlsx'...
2016-11-14 16:33:07,546: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-5.xlsx'...
2016-11-14 16:33:07,551: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-6.xlsx'...
2016-11-14 16:33:07,556: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-7.xlsx'...
2016-11-14 16:33:07,560: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-8.xlsx'...
2016-11-14 16:33:07,565: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_demo-9.xlsx'...
2016-11-14 16:33:07,570: INFO:co2mpas_main:Creating INPUT-DEMO file 'input\co2mpas_simplan.xlsx'...
2016-11-14 16:33:07,574: INFO:co2mpas_main:You may run DEMOS with:
    co2mpas batch input

Synchronizing time-series

The model might fail in case your time-series signals are time-shifted and/or with different sampling rates. Even if the run succeeds, the results will not be accurate enough, because the data are not synchronized with the theoretical cycle.

As an aid tool, you may use the datasync tool to “synchronize” and “resample” your data, which have been acquired from different sources.

To get the syntax of the datasync console-command, open a console where you have installed CO₂MPAS and type:

## datasync help.
$ datasync --help

Shift and resample excel-tables; see http://co2mpas.io/usage.html#Synchronizing-time-series.

Usage:
  datasync template [-f] [--cycle <cycle>] <excel-file-path>...
  datasync          [-v | -q | --logconf=<conf-file>] [--force | -f]
                    [--interp <method>] [--no-clone] [--prefix-cols]
                    [-O <output>] <x-label> <y-label> <ref-table>
                    [<sync-table> ...] [-i=<label=interp> ...]
  datasync          [-v | -q | --logconf=<conf-file>] (--version | -V)
  datasync          (--interp-methods | -l)
  datasync          --help

Options:
  <x-label>              Column-name of the common x-axis (e.g. 'times') to be
                         re-sampled if needed.
  <y-label>              Column-name of y-axis cross-correlated between all
                         <sync-table> and <ref-table>.
  <ref-table>            The reference table, in *xl-ref* notation (usually
                         given as `file#sheet!`); synced columns will be
                         appended into this table.
                         The captured table must contain <x_label> & <y_label>
                         as column labels.
                         If hash(`#`) symbol missing, assumed as file-path and
                         the table is read from its 1st sheet .
  <sync-table>           Sheets to be synced in relation to <ref-table>, also in
                         *xl-ref* notation.
                         All tables must contain <x_label> & <y_label> as column
                         labels.
                         Each xlref may omit file or sheet-name parts; in that
                         case, those from the previous xlref(s) are reused.
                         If hash(`#`) symbol missing, assumed as sheet-name.
                         If none given, all non-empty sheets of <ref-table> are
                         synced against the 1st one.
  -O=<output>            Output folder or file path to write the results
                         [default: .]:

                         - Non-existent path: taken as the new file-path; fails
                           if intermediate folders do not exist, unless --force.
                         - Existent file: file-path to overwrite if --force,
                           fails otherwise.
                         - Existent folder: writes a new file
                           `<ref-file>.sync<.ext>` in that folder; --force
                           required if that file exists.

  -f, --force            Overwrite excel-file(s) and create any missing
                         intermediate folders.
  --prefix-cols          Prefix all synced column names with their source
                         sheet-names. By default, only clashing column-names are
                         prefixed.
  --no-clone             Do not clone excel-sheets contained in <ref-table>
                         workbook into output.
  --interp=<method>      Interpolation method used in the resampling for all
                         signals [default: linear]: 'linear', 'nearest', 'zero',
                         'slinear', 'quadratic', 'cubic', 'barycentric',
                         'polynomial', 'spline' is passed to
                         scipy.interpolate.interp1d. Both 'polynomial' and
                         'spline' require that you also specify an order (int),
                         e.g. df.interpolate(--interp=polynomial4).
                         'krogh', 'piecewise_polynomial', 'pchip' and 'akima'
                         are all wrappers around the scipy interpolation methods
                         of similar names.
                         'integral' is respect the signal integral.
  -i=<label=interp>      Interpolation method used in the resampling for a
                         signal with a specific label
                         (e.g., `-i alternator_currents=integral`).
  -l, --interp-methods   List of all interpolation methods that can be used in
                         the resampling.
  --cycle=<cycle>        If set (e.g., --cycle=nedc.manual), the <ref-table> is
                         populated with the theoretical velocity profile.
                         Options: 'nedc.manual', 'nedc.automatic',
                         'wltp.class1', 'wltp.class2', 'wltp.class3a', and
                         'wltp.class3b'.

  <excel-file-path>      Output file.

Miscellaneous:
  -h, --help             Show this help message and exit.
  -V, --version          Print version of the program, with --verbose
                         list release-date and installation details.
  -v, --verbose          Print more verbosely messages - overridden by --logconf.
  -q, --quite            Print less verbosely messages (warnings) - overridden by --logconf.
  --logconf=<conf-file>  Path to a logging-configuration file, according to:
                         See https://docs.python.org/3/library/logging.config.html#configuration-file-format
                         Uses reads a dict-schema if file ends with '.yaml' or '.yml'.
                         See https://docs.python.org/3.5/library/logging.config.html#logging-config-dictschema

* For xl-refs see: https://pandalone.readthedocs.org/en/latest/reference.html#module-pandalone.xleash

SUB-COMMANDS:
    template             Generate "empty" input-file for the `datasync` cmd as
                         <excel-file-path>.


Examples::

    ## Read the full contents from all `wbook.xlsx` sheets as tables and
    ## sync their columns using the table from the 1st sheet as reference:
    datasync times velocities folder/Book.xlsx

    ## Sync `Sheet1` using `Sheet3` as reference:
    datasync times velocities wbook.xlsx#Sheet3!  Sheet1!

    ## The same as above but with integers used to index excel-sheets.
    ## NOTE that sheet-indices are zero based!
    datasync times velocities wbook.xlsx#2! 0

    ## Complex Xlr-ref example:
    ## Read the table in sheet2 of wbook-2 starting at D5 cell
    ## or more Down 'n Right if that was empty, till Down n Right,
    ## and sync this based on 1st sheet of wbook-1:
    datasync times velocities wbook-1.xlsx  wbook-2.xlsx#0!D5(DR):..(DR)

    ## Typical usage for CO2MPAS velocity time-series from Dyno and OBD
    ## (the ref sheet contains the theoretical velocity profile):
    datasync template --cycle wltp.class3b template.xlsx
    datasync -O ./output times velocities template.xlsx#ref! dyno obd -i alternator_currents=integral -i battery_currents=integral

$ datasync template sync/datasync.xlsx --cycle wltp.class3b -f
2016-11-14 17:14:00,919: INFO:__main__:Creating INPUT-TEMPLATE file 'sync/datasync.xlsx'...

datasync times velocities template.xlsx#ref! dyno obd -i alternator_currents=integral -i battery_currents=integral

Run batch

The default sub-command (batch) accepts either a single input-excel-file or a folder with multiple input-files for each vehicle, and generates a summary-excel-file aggregating the major result-values from these vehicles, and (optionally) multiple output-excel-files for each vehicle run.

To run all demo-files (note, it might take considerable time), you can use the GUI as follows:

Or you can run CO₂MPAS with the batch sub-command:

$ co2mpas batch input -O output
2016-11-15 17:00:31,286: INFO:co2mpas_main:Processing ['../input'] --> '../output'...
  0%|          | 0/11 [00:00<?, ?it/s]: Processing ../input\co2mpas_demo-0.xlsx
...
...
Done! [527.420557 sec]

Run Type-Approval (ta) command

The Type Approval command simulates the NEDC fuel consumption and CO2 emission of the given vehicle using just the required declaration inputs (marked as compulsory inputs in input file version >= 2.2.5) and produces an NEDC prediction. If CO₂MPAS finds some extra input it will raise a warning and it will not produce any result. The type approval command is the CO₂MPAS running mode that is fully aligned to the WLTP-NEDC correlation Regulation.

The sub-command ta accepts either a single input-excel-file or a folder with multiple input-files for each vehicle, and generates a summary-excel-file aggregating the major result-values from these vehicles, and multiple output-excel-files for each vehicle run.

To run the type approval command you can use the GUI as follows:

Or you can run CO₂MPAS with the ta sub-command:

$ co2mpas ta input -O output
2016-11-15 17:00:31,286: INFO:co2mpas_main:Processing ['../input'] --> '../output'...
  0%|          | 0/1 [00:00<?, ?it/s]: Processing ../input\co2mpas_demo-0.xlsx
...
...
Done! [51.6874 sec]

Output files

The output-files produced on each run are the following:

• One file per vehicle, named as <timestamp>-<inp-fname>.xls: This file contains all inputs and calculation results for each vehicle contained in the batch-run: scalar-parameters and time series for target, calibration and prediction phases, for all cycles. In addition, the file contains all the specific submodel-functions that generated the results, a comparison summary, and information on the python libraries installed on the system (for investigating reproducibility issues).

• A Summary-file named as <timestamp>-summary.xls: Major CO₂ emissions values, optimized CO₂ parameters values and success/fail flags of CO₂MPAS submodels for all vehicles in the batch-run.

Simulation plan

It is possible to launch CO₂MPAS once, and have it run the model multiple times, with variations on the input-data, all contained in a single (or more) input file(s).

The data for base model are contained in the regular sheets, and any variations are provided in additional sheets which names starting with the plan. prefix. These sheets must contain a table where each row is a single simulation, while the columns names are the parameters that the user want to vary. The columns of these tables can contain the following special names:

• id: Identifies the variation id.

• base: this is a file path of a CO₂MPAS excel input, this model will be used as new base vehicle.

• run_base: this is a boolean. If true the base model results are computed and stored, otherwise the data are just loaded.

You can use the GUI as follows:

Or you can run CO₂MPAS with the batch sub-command:

$ co2mpas batch input/co2mpas_simplan.xlsx -O output
2016-11-15 17:00:31,286: INFO:co2mpas_main:Processing ['../input/co2mpas_simplan.xlsx'] --> '../output'...
  0%|          | 0/4 [00:00<?, ?it/s]: Processing ../input\co2mpas_simplan.xlsx
...
...
Done! [180.4692 sec]

Launch CO₂MPAS from Jupyter(aka IPython)

You may enter the data for a single vehicle and run its simulation, plot its results and experiment in your browser using IPython.

The usage pattern is similar to “demos” but requires to have ipython installed:

1. Ensure ipython with notebook “extra” is installed:

   $ pip install ipython[notebook]
   Installing collected packages: ipython[notebook]
   ...
   Successfully installed ipython-x.x.x notebook-x.x.x

2. Then create the demo ipython-notebook(s) into some folder (i.e. assuming the same setup from above, tutorial/input):

   $ pwd                     ## Check our current folder (``cd`` alone for Windows).
   .../tutorial
   
   $ co2mpas ipynb ./input

3. Start-up the server and open a browser page to run the vehicle-simulation:

   $ ipython notebook ./input

4. A new window should open to your default browser (AVOID IEXPLORER) listing the simVehicle.ipynb notebook (and all the demo xls-files). Click on the *.ipynb file to “load” the notebook in a new tab.

   The results are of a simulation run already pre-generated for this notebook but you may run it yourself again, by clicking the menu:

   "menu" --> `Cell` --> `Run All`

   And watch it as it re-calculates cell by cell.

5. You may edit the python code on the cells by selecting them and clicking Enter (the frame should become green), and then re-run them, with Ctrl + Enter.

   Navigate your self around by taking the tutorial at:

   "menu" --> `Help` --> `User Interface Tour`

   And study the example code and diagrams.

6. When you have finished, return to the console and issue twice Ctrl + C to shutdown the ipython-server.

Debugging and investigating results

• Make sure that you have installed graphviz, and when running the simulation, append also the -D flag.plot_workflow=True option.

  $ co2mpas batch bad-file.xlsx -D flag.plot_workflow=True

  A browser tab will open at the end with the nodes processed.

• Use the modelgraph sub-command to plot the offending model (or just out of curiosity). For instance:

  $ co2mpas modelgraph co2mpas.model.physical.wheels.wheels

  code-block:: co2mpas

  code-block:: d

  alt:

  Flow-diagram Wheel-to-Engine speed ratio calculations.

  height:

  240

  width:

  320

  >>> import co2mpas
  >>> d = co2mpas.model.physical.wheels.wheels()
  

• Inspect the functions mentioned in the workflow and models and search them in CO2MPAS documentation ensuring you are visiting the documents for the actual version you are using.

Execution Model

The execution of CO₂MPAS model for a single vehicle is a stepwise procedure of 3 stages: precondition, calibration, and prediction. These are invoked repeatedly, and subsequently combined, for the various cycles, as shown in the “active” flow-diagram of the execution, below:

code-block:: co2mpas

code-block:: dsp

opt:

depth=-1

alt:

Flow-diagram of the execution of various Stages and Cycles sub-models.

width:

640

>>> import co2mpas
>>> dsp = co2mpas.model.model()

1. Precondition: identifies the initial state of the vehicle by running a preconditioning WLTP cycle, before running the WLTP-H and WLTP-L cycles. The inputs are defined by the input.precondition.wltp_p node, while the outputs are stored in output.precondition.wltp_p.

2. Calibration: the scope of the stage is to identify, calibrate and select (see next sections) the best physical models from the WLTP-H and WLTP-L inputs (input.calibration.wltp_x). If some of the inputs needed to calibrate the physical models are not provided (e.g. initial_state_of_charge), the model will select the missing ones from precondition-stage’s outputs (output.precondition.wltp_p). Note that all data provided in input.calibration.wltp_x overwrite those in output.precondition.wltp_p.

3. Prediction: executed for the NEDC and as well as for the WLTP-H and WLTP-L cycles. All predictions use the calibrated_models. The inputs to predict the cycles are defined by the user in input.prediction.xxx nodes. If some or all inputs for the prediction of WLTP-H and WLTP-L cycles are not provided, the model will select from `output.calibration.wltp_x nodes a minimum set required to predict CO₂ emissions.

Excel input: data naming conventions

This section describes the data naming convention used in the CO₂MPAS template (.xlsx file). In it, the names used as sheet-names, parameter-names and column-names are “sensitive”, in the sense that they construct a data-values tree which is then fed into into the simulation model as input. These names are split in “parts”, as explained below with examples:

• sheet-names parts:

              base.input.precondition.WLTP-H.ts
              └┬─┘ └─┬─┘ └────┬─────┘ └─┬──┘ └┬┘
  scope────────┘     │        │         │     │
  usage──────────────┘        │         │     │
  stage───────────────────────┘         │     │
  cycle─────────────────────────────────┘     │
  sheet_type──────────────────────────────────┘

  First 4 parts above are optional, but at least one of them must be present on a sheet-name; those parts are then used as defaults for all parameter-names contained in that sheet. type is optional and specify the type of sheet.

• parameter-names/columns-names parts:

                 plan.target.prediction.initial_state_of_charge.WLTP-H
                 └┬─┘ └─┬─┘ └────┬────┘ └──────────┬──────────┘ └──┬─┘
  scope(optional)─┘     │        │                 │               │
  usage(optional)───────┘        │                 │               │
  stage(optional)────────────────┘                 │               │
  parameter────────────────────────────────────────┘               │
  cycle(optional)──────────────────────────────────────────────────┘

  OR with the last 2 parts reversed:

                plan.target.prediction.WLTP-H.initial_state_of_charge
                                       └──┬─┘ └──────────┬──────────┘
  cycle(optional)─────────────────────────┘              │
  parameter──────────────────────────────────────────────┘

Calibrated Physical Models

There are potentially eight models calibrated from input scalar-values and time-series (see reference):

1. AT_model,

2. electric_model,

3. clutch_torque_converter_model,

4. co2_params,

5. engine_cold_start_speed_model,

6. engine_coolant_temperature_model,

7. engine_speed_model, and

8. start_stop_model.

Each model is calibrated separately over WLTP_H and WLTP_L. A model can contain one or several functions predicting different quantities. For example, the electric_model contains the following functions/data:

• alternator_current_model,

• alternator_status_model,

• electric_load,

• max_battery_charging_current,

• start_demand.

These functions/data are calibrated/estimated based on the provided input (in the particular case: alternator current, battery current, and initial SOC) over both cycles, assuming that data for both WLTP_H and WLTP_L are provided.

Model selection

For the type approval mode the selection is fixed. The criteria is to select the models calibrated from WLTP_H to predict WLTP_H and NEDC_H; and from WLTP_L to predict WLTP_L and NEDC_L.

While for the engineering mode the automatic selection can be enabled adding -D flag.use_selector=True to the batch command. Then to select which is the best calibration (from WLTP_H or WLTP_L or ALL) to be used in the prediction phase, the results of each stage are compared against the provided input data (used in the calibration). The calibrated models are THEN used to recalculate (predict) the inputs of the WLTP_H and WLTP_L cycles. A score (weighted average of all computed metrics) is attributed to each calibration of each model as a result of this comparison.

In addition to the above, a success flag is defined according to upper or lower limits of scores which have been defined empirically by the JRC. If a model fails these limits, priority is then given to a model that succeeds, even if it has achieved a worse score.

The following table describes the scores, targets, and metrics for each model:

Python Installation

If you already have a suitable python-3 installation with all scientific packages updated to their latest versions, you may skip this 1st stage.

Install CO₂MPAS package

1. Install CO₂MPAS executable internally into your python-environment with the following console-commands (there is no prob if the 1st uninstall command fails):

   > pip uninstall co2mpas
   > pip install co2mpas
   Collecting co2mpas
   Downloading http://pypi.co2mpas.io/packages/co2mpas-...
   ...
   Installing collected packages: co2mpas
   Successfully installed co2mpas-1.6.0.dev1

2. Check that when you run CO₂MPAS, the version executed is indeed the one installed above (check both version-identifiers and paths):

   > co2mpas -vV
   co2mpas_version: 1.6.0.dev1
   co2mpas_rel_date: 2017-05-04 18:25:09
   co2mpas_path: d:\co2mpas_ALLINONE-64bit-v1.4.1\Apps\WinPython\python-3.4.3\lib\site-packages\co2mpas
   python_path: D:\co2mpas_ALLINONE-64bit-v1.4.1\WinPython\python-3.4.3
   python_version: 3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:44:40) [MSC v.1600 XXX]
   PATH: D:\co2mpas_ALLINONE-64bit-v1.4.1\WinPython...

> pip uninstall co2mpas
Uninstalling co2mpas-<installed-version>
...
Proceed (y/n)?

Autocompletion

In order to press [Tab] and get completions, do the following in your environment (ALLINONE is pre-configured with them):

• For the Clink environment, on cmd.exe, add the following lua script inside clink’s profile folder: clink/profile/co2mpas_autocompletion.lua

  --[[ clink-autocompletion for CO2MPAS
  --]]
  local handle = io.popen('co2mpas-autocompletions')
  words_str = handle:read("*a")
  handle:close()
  
  function words_generator(prefix, first, last)
      local cmd = 'co2mpas'
      local prefix_len = #prefix
  
      --print('P:'..prefix..', F:'..first..', L:'..last..', l:'..rl_state.line_buffer)
      if prefix_len == 0 or rl_state.line_buffer:sub(1, cmd:len()) ~= cmd then
          return false
      end
  
      for w in string.gmatch(words_str, "%S+") do
          -- Add matching app-words.
          --
          if w:sub(1, prefix_len) == prefix then
              clink.add_match(w)
          end
  
          -- Add matching files & dirs.
          --
          full_path = true
          nf = clink.match_files(prefix..'*', full_path)
          if nf > 0 then
              clink.matches_are_files()
          end
      end
      return clink.match_count() > 0
  end
  
  sort_id = 100
  clink.register_match_generator(words_generator)

• For the bash shell just add this command in your ~/.bashrc (or type it every time you open a new console):

  complete -fdev -W "`co2mpas-autocompletions`" co2mpas