co2mpas 1.3.1

A vehicle simulator predicting CO2 emissions for NEDC using WLTP time-series

Release:

1.3.1

Date:

2016-07-18 20:37:01

Home:

http://co2mpas.io/

Releases:

http://files.co2mpas.io/

Sources:

https://github.com/JRCSTU/co2mpas

pypi-repo:

https://pypi.python.org/pypi/co2mpas

Keywords:

CO2, fuel-consumption, WLTP, NEDC, vehicle, automotive, EU, JRC, IET, STU, back-translation, policy, monitoring, M1, N1, simulator, engineering, scientific

Developers:

see: AUTHORS.rst

Copyright:

2015-2016 European Commission (JRC-IET)

License:

EUPL 1.1+

CO₂MPAS is backward-looking longitudinal-dynamics CO₂ and fuel-consumption simulator for light-duty M1 & N1 vehicles (cars and vans), specially crafted to back-translate consumption figures from WLTP cycles into NEDC ones, according to the EU legislation.

It is an open-source project developed with Python-3.4+, using Anaconda & WinPython under Windows 7, Anaconda under MacOS, and Linux’s standard python environment. It runs as a console command consuming (mostly) excel-files with input-data.

History

The European Commission is supporting the introduction of the WLTP cycle for Light-duty vehicles developed at the United Nations (UN/ECE) level, in the shortest possible time-frame. Its introduction requires the adaptation of CO₂ certification and monitoring procedures set by European regulations. European Commission’s Joint Research Centre (JRC) has been assigned the development of this vehicle simulator to facilitate this adaptation.

For recent activity, check the changes.

Quickstart

Tip

About console-commands appearing in the examples of the documentation:

• Console-commands beginning with $ symbol are for the bash shell (UNIX). You can install it on Windows with cygwin: https://www.cygwin.com/ along with these useful utilities:

  * git, git-completion
  * make, zip, unzip, bzip2, 7z, dos2unix
  * openssh, curl, wget

• Console-commands beginning with > symbol are for Windows cmd.exe command-prompt. You can augment it with bash-like capabilities using Clink: http://mridgers.github.io/clink/

• You can adapt commands between the two shells with minor modifications (i.e. ls <--> dir, rm -r <--> rmdir /s/q).

• You may download and install the all-in-one archive which contains both shells configured in a console supporting decent copy-paste and resizing capabilities (see ref: all-in-one).

IF you are familiar with python, AND IF you already have a full-blown python-3 environment (i.e. Linux or the all-in-one archive), AND IF you have familiarity with v1 release, THEN you can immediately start working with the following bash commands; otherwise follow the detailed instructions under sections ref: install and ref: usage.

## Install co2mpas.
## NOTE: If behind proxy, specify additionally this option:
##    --proxy http://user:password@yourProxyUrl:yourProxyPort
##
$ pip install co2mpas

## Where to store input and output files.
## In *Windows* cmd-prompt use `md` command instead.
$ mkdir input output

## Create a template excel-file for inputs.
$ co2mpas template input/vehicle_1.xlsx

###################################################
## Edit generated `./input/vehicle_1.xlsx` file. ##
###################################################

## Run simulator.
$ co2mpas batch  input -O output

###################################################
## Inspect generated results inside `./output/`. ##
###################################################

Table of Contents

• History

• Quickstart

• Install

  • All-In-One Installation under Windows

  • Python Installation

    • Install WinPython

    • Install Anaconda

  • Install co2mpas package

    • Install extras

    • Upgrade CO₂MPAS (with internet connectivity)

    • Uninstall CO₂MPAS

    • Alternative installation methods

    • Install Multiple versions in parallel

  • Autocompletion

• Usage

  • Demo files

  • Output files

  • Entering new vehicles

  • Simulation plan

  • Using custom output xl-files as templates

  • Launch CO₂MPAS from Jupyter(aka IPython)

  • Synchronizing time-series

    • Examples

  • Debugging and investigating results

• Model

  • Execution Model

  • Excel input: data naming conventions

    • Description of the name-parts

  • Calibrated Physical Models

  • Model selection

Install

These are the installation options:

1. On Windows you may install the latest all-In-One archive and ensure it contains (or upgrade to) the latest co2mpas python package; alternatively, you may perform the following 2 steps.

   Tip

   This is the easiest procedure for all non-technical users.

2. Manually install everything needed: 1. Install (or Upgrade) Python (2 choices under Windows), then proceed to 2. install the co2mpas python package:

   1. Install (or Upgrade) executable.

   2. (optional) Install documents.

   3. (optional) Install sources.

All-In-One Installation under Windows

• Download all-in-one archive from http://files.co2mpas.io/ (it only runs on 64bit PCs).

  Tip

  Search in older releases if the latest does not contain an ALLINONE archive, ansd remember to upgrade CO₂MPAS afterwords.

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

  Note

  Prefer to extract it in a folder without any spaces in its path.

• 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.

• Use Windows start-menu to launch CO₂MPAS CONSOLE or the GUI dialog-boxes.

• Visit the guidelines for its usage: allinone (also contained within the archive).

Note

If you have downloaded an all-in-one from previous version of CO₂MPAS you may upgrade CO₂MPAS contained within. Follow the instructions in the “Upgrade” section, below.

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.

Note

Installing Python under Windows:

The program requires CPython-3, and depends on numpy, scipy, pandas, sklearn and matplotlib packages, which depend on C-native backends and need a C-compiler to install from sources.

In Windows it is strongly suggested NOT to install the standard CPython distribution that comes up first(!) when you google for “python windows”, unless you are an experienced python-developer, and you know how to hunt down pre-compiled dependencies from the PyPi repository and/or from the Unofficial Windows Binaries for Python Extension Packages.

Therefore we suggest that you download one of the following two scientific-python distributions:

1. WinPython python-3 (64 bit)

2. Anaconda python-3 (64 bit)

Install WinPython

The WinPython distribution is just a collection of the standard pre-compiled binaries for Windows containing all the scientific packages, and much more. It is not update-able, and has a quasi-regular release-cycle of 3 months.

1. Install the latest python-3.4+ 64 bit from https://winpython.github.io/. Prefer an installation-folder without any spaces leading to it.

2. Open the WinPython’s command-prompt console, by locating the folder where you just installed it and run (double-click) the following file:

   <winpython-folder>\"WinPython Command Prompt.exe"

3. In the console-window check that you have the correct version of WinPython installed, and expect a similar response:

   > python -V
   Python 3.4.3
   
   REM Check your python is indeed where you installed it.
   > where python
   ....

4. Use this console and follow ref: install-co2mpas-package instructions, below.

Install Anaconda

The Anaconda distribution is a non-standard Python environment that for Windows containing all the scientific packages we need, and much more. It is not update-able, and has a semi-regular release-cycle of 3 months.

1. Install Anaconda python-3.4+ 64 bit from http://continuum.io/downloads. Prefer an installation-folder without any spaces leading to it.

   Note

   When asked by the installation wizard, ensure that Anaconda gets to be registered as the default python-environment for the user’s account.

2. Open a Windows command-prompt console:

   "windows start button" --> `cmd.exe`

3. In the console-window check that you have the correct version of Anaconda-python installed, by typing:

   > python -V
   Python 3.4.3 :: Anaconda 2.3.0 (64-bit)
   
   REM Check your python is indeed where you installed it.
   > where python
   ....

4. Use this console and follow ref: install-co2mpas-package instructions, below.

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-1.3.1

   Warning

   Installation failures:

   The previous step require http-connectivity for pip command to Python’s “standard” repository (https://pypi.python.org/) and to co2mpas-site (http://files.co2mpas.io). In case you are behind a corporate proxy, you may try one of the methods described in section Alternative installation methods, below.

   If all methods to install CO₂MPAS fail, re-run pip command adding extra verbose flags -vv, copy-paste the console-output, and report it to JRC.

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: 1.3.1
   co2mpas_rel_date: 2016-07-18 20:37:01
   co2mpas_path: d:\co2mpas_ALLINONE-64bit-v1.3.1\Apps\WinPython\python-3.4.3\lib\site-packages\co2mpas
   python_path: D:\co2mpas_ALLINONE-64bit-v1.3.1\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-v1.3.1\WinPython...

   Note

   The above procedure installs the latest CO₂MPAS, which might be more up-to-date than the version described here!

   In that case you can either:

   1. Visit the documents for the newer version actually installed.

   2. “Pin” the exact version you wish to install with a pip command (see section below).

Install extras

Internally CO₂MPAS uses an algorithmic scheduler to execute model functions. In order to visualize the design-time models and run-time workflows you need to install the Graphviz visualization library from: http://www.graphviz.org/.

If you skip this step, the modelgraph sub-command and the --plot-workflow option would both fail to run (see ref: debug).

Upgrade CO₂MPAS (with internet connectivity)

1. Uninstall (see below) and re-install it.

Uninstall CO₂MPAS

To uninstall CO₂MPAS type the following command, and confirm it with y:

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

Re-run the command again, to make sure that no dangling installations are left over; disregard any errors this time.

Alternative installation methods

You may get multiple versions of CO₂MPAS, from various places, but all require the use of pip command from a console to install:

Warning

In all cases below, remember to uninstall CO₂MPAS if it’s already installed.

• Latest STABLE: use the default pip described command above.

• Latest PRE-RELEASE: append the --pre option in the pip command.

• Specific version: modify the pip command like that, with optionally appending --pre:

  pip install co2mpas==1.0.1 ... # Other options, like above.

• Specific branch from the GitHub-sources:

  pip install git+https://github.com/JRCSTU/co2mpas.git@dev

• Specific commit from the GitHub-sources:

  pip install git+https://github.com/JRCSTU/co2mpas.git@2927346f4c513a

• Speed-up download: append the --use-mirrors option in the pip command.

• (for all of the above) When you are behind an http-proxy: append an appropriately adapted option --proxy http://user:password@yourProxyUrl:yourProxyPort.

  Important

  To avert any security deliberations for this http-proxy “tunnel”, JRC cryptographically signs all final releases with one of those keys: - GPG key ID: 9CF277C40A8A1B08 form @ankostis - GPG key ID: 1831F9C2294A33CC for @vinci1it2000

  Your IT staff may validate their authenticity and detect man-in-the-middle attacks, however impossible.

• (for all of the above) Without internet connectivity or when the above proxy cmd fails:

  1. With with a “regular” browser and when connected to the Internet, pre-download locally all files present in the packages folder located in the desired CO₂MPAS version in the |co2mpas| site (e.g. http://files.co2mpas.io/CO2MPAS-1.3.1/packages/).

  2. Install co2mpas, referencing the above folder. Assuming that you downloaded the packages in the folder path/to/co2mpas_packages, use a console-command like this:

     pip install co2mpas  --no-index  -f path/to/co2mpas_packages

Install Multiple versions in parallel

In order to run and compare results from different CO₂MPAS versions, you may use virtualenv command.

The virtualenv command creates isolated python-environments (“children-venvs”) where in each one you can install a different versions of CO₂MPAS.

Note

The virtualenv command does NOT run under the “conda” python-environment. Use the conda command in similar manner to create child conda-environments instead.

1. Ensure virtualenv command installed in your “parent” python-environment, i.e the “WinPython” you use:

   > pip install virtualenv

   Note

   The pip command above has to run only once for each parent python-env. If virtualenv is already installed, pip will exit gracefully.

2. Ensure co2mpas uninstalled in your parent-env:

   > pip uninstall co2mpas

   Warning

   It is important for the “parent” python-env NOT to have CO₂MPAS installed! The reason is that you must set “children venvs” to inherit all packages installed on their “parent” (i.e. numpy and pandas), and you cannot update any inherited package from within a child-env.

3. Move to the folder where you want your “venvs” to reside and create the “venv” with this command:

   > virtualenv --system-site-packages co2mpas_v1.0.1.venv.venv

   The --system-site-packages option instructs the child-venv to inherit all “parent” packages (numpy, pandas).

   Select a venv’s name to signify the version it will contains, e.g. co2mpas_v1.0.1.venv. The .venv at the end is not required, it is just for tagging the venv folders.

4. “Activate” the new “venv” by running the following command (notice the dot(.) at the begining of the command):

   > .\co2mpas_v1.0.1.venv.venv\Scripts\activate.bat

   Or type this in bash:

   $ source co2mpas_v1.0.1.venv.venv\Scripts\activate.bat

   You must now see that your prompt has been prefixed with the venv’s name.

6. Install the co2mpas version you want inside the activated venv. See the ref: install-co2mpas-package section, above.

   Don’t forget to check that what you get when running co2mpas is what you installed.

7. To “deactivate” the active venv, type:

   > deactivate

   The prompt-prefix with the venv-name should now dissappear. And if you try to invoke co2mpas, it should fail.

Tip

• Repeat steps 2–>5 to create venvs for different versions of co2mpas.

• Use steps (6: Activate) and (9: Deactivate) to switch between different venvs.

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

Usage

Note

The following commands are for the bash console, specifically tailored for the all-in-one archive. In cmd.exe the commands are rougly similar, but remember to substitute the slashes (/) in paths with backslashes(\).

The allinone contains additionally batch-files (e.g. RUN_CO2MPAS.bat, NEW_TEMPLATE.bat, etc) that offer roughly the same capabillities described below. When you double-click them, the output from these commands gets to be written in the ALLINONE/CO2MPAS/co2mpas.log file.

First ensure that the latest version of CO₂MPAS is properly installed, and that its version match the version declared on this file.

The main entry for the simulator is the co2mpas console-command, which is not visible, but it is installed in your PATH. To get the syntax of the co2mpas console-command, open a console where you have installed CO₂MPAS (see ref: install above) and type:

Predict NEDC CO2 emissions from WLTP.

:Home:         http://co2mpas.io/
:Copyright:    2015-2016 European Commission (JRC-IET <https://ec.europa.eu/jrc/en/institutes/iet>
: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 batch       [-v | --logconf=<conf-file>] [--gui] [-f]
                      [--overwrite-cache] [--out-template=<xlsx-file>]
                      [--plot-workflow] [-O=<output-folder>]
                      [--only-summary] [--soft-validation]
                      [<input-path>]...
  co2mpas demo        [-v | --logconf=<conf-file>] [--gui] [-f]
                      [<output-folder>]
  co2mpas template    [-v | --logconf=<conf-file>] [--gui] [-f]
                      [<excel-file-path> ...]
  co2mpas ipynb       [-v | --logconf=<conf-file>] [--gui] [-f]
                      [<output-folder>]
  co2mpas modelgraph  [-v | --logconf=<conf-file>] [-O=<output-folder>]
                      (--list | [--graph-depth=<levels>] [<models> ...])
  co2mpas             [--verbose | -v]  (--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.
  --gui                       Launches GUI dialog-boxes to choose Input, Output
                              and Options. [default: False].
  --only-summary              Do not save vehicle outputs, just the summary.
  --overwrite-cache           Overwrite the cached file.
  --soft-validation           Validate only partially input-data (no schema).
  --out-template=<xlsx-file>  Clone the given excel-file and appends results into it.
                              By default, results are appended into an empty excel-file.
                              Use `--out-template=-` to use input-file as template.
  --plot-workflow             Open workflow-plot in browser, after run finished.
  -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).

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.
  --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:
    batch           Simulate vehicle for all <input-path> excel-files & folder.
                    If no <input-path> given, reads all excel-files from current-dir.
                    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.


EXAMPLES::

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

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

    # Launch GUI dialog-boxes on the sample-vehicles just created:
    co2mpas  batch  --gui  input

    # or specify them with output-charts and workflow plots:
    co2mpas  batch  input  -O output  --plot-workflow

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

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

    # View full version specs:
    co2mpas -vV

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.

Demo files

The simulator contains input-files for demo-vehicles that are a nice starting point to try out:

id	AT	cal WLTP-H	cal WLTP-L	S/S	BERS	trg NEDC-H	trg NEDC-L	pre WLTP	plan
0		X	X			X
1		X	X	X	X	X
2	X	X	X				X
3		X	X	X		X		X
4	X		X		X		X
5		X	X		X	X
6	X	X	X	X		X		X
7	X	X		X	X	X
8		X	X			X	X
9	X	X	X	X	X	X
simplan		X	X			X			X

To run them, do the following:

1. Choose a folder where you will store the input and output files:

   ## Skip this if ``tutorial`` folder already exists.
   $ mkdir tutorial
   $ cd tutorial
   
   ## Skip also this if folders exist.
   $ mkdir input output

Note

The input & output folders do not have to reside in the same parent, neither to have these names. It is only for demonstration purposes that we decided to group them both under a hypothetical some-folder.

2. Create the demo vehicles inside the input-folder with the demo sub-command:

   $ co2mpas demo input
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-0.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-1.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-10.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-2.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-3.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-4.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-5.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-6.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-7.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-8.xlsx'...
   INFO:co2mpas.__main__:Creating INPUT-DEMO file 't\co2mpas_demo-9.xlsx'...
   INFO:co2mpas.__main__:You may run DEMOS with:
       co2mpas batch input

3. Run the simulator on all demo-files (note, it might take considerable time):

   $ co2mpas batch input -O output
   Processing ['input'] --> 'output'...
   Processing: co2mpas_demo-0
   ...
   ...
   Done! [499.579 sec]

   Note

   The last demo-file, the “simulation-plan” will take longer to complete, and, for demonstration purposes, some some of the actual models will fail; check the summary file.

4. Inspect the results (explained in the next section):

   $ start output/*summary.xlsx       ## More summaries might exist in the folder from previous runs.
   $ start output                     ## View the folder with all files generated.

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 the 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.

Tip

Additionally, a sample output file is provide here: http://files.co2mpas.io/CO2MPAS-1.3.1/co2mpas-empty_output-2.2.xlsx

Entering new vehicles

You may modify the samples vehicles and run again the model. But to be sure that your vehicle does not contain by accident any of the sample-data, use the template sub-command to make an empty input excel-file:

1. Decide the input/output folders. Assuming we are still in the tutorial folder and we wish to re-use the input/output folders from the example above, we may clear all their contents with this:

   $ rm -r ./input/* ./output/*      ## Replace `rm` with `del` in *Windows* (`cmd.exe`)

2. Create an empty vehicle template-file (eg. vehicle_1.xlsx) inside the input-folder with the template sub-command:

   $ co2mpas template input/vehicle_1.xlsx  ## Note that here we specify the filename, not the folder!
   Creating TEMPLATE INPUT file 'input/vehicle_1.xlsx'...

3. Open the template excel-file to fill-in your vehicle data (and save it afterwards):

   $ start input/vehicle_1.xlsx      ## Opens the excel-file. Use `start` in *cmd.exe*.

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

   Tip

   You may also read the “annotated” input excel-file to get an understanding of each scalar paramet and series required, but DO NOT USE THIS “fatty” xl-file (~10Mb) when running the model.

   For an explanation of the naming of the fields, read below the ref: excel-model section

   You may repeat these last 2 steps if you want to add more vehicles in the batch-run.

4. Run the simulator. Specify the single excel-file as input:

   $ co2mpas batch ./input/vehicle_1.xlsx -O output
   Processing './input/vehicle_1.xlsx' --> 'output'...
   Processing: vehicle_1
   ...
   Done! [12.938986 sec]

5. Assuming you do receive any error, you may now inspect the results:

   $ start output/*summary.xlsx      ## More summaries might open from previous runs.
   $ start output                    ## View all files generated (see below).

6. In the case of errors, or if the results are not satisfactory, repeat the above procedure from step 3 to modify the vehicle and re-run the model. See also ref: debug, below.

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 must 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.

• defaults: this is a a list of file paths. The calibrated models of these files are used as default models of the base model. This behavior is needed to simulate, for example, a manual car (base) as A/T, because the A/T strategy and the torque converter are not in the base model.

Using custom output xl-files as templates

You may have defined customized xl-files for summarizing time-series and scalar parameters. To have CO₂MPAS fill those “output-template” files with its results, execute it with the --out-template option.

To create/modify one output-template yourself, do the following:

1. Open a typical CO₂MPAS output-file for some vehicle.

2. Add one or more sheets and specify/referring CO₂MPAS result-data using named-ranges.

   Warning

   Do not use simple/absolute excel references (e.g. “=B2”). Use excel functions (indirect, lookup, offset, etc.) and array-functions together with string references to the named ranges (e.g. “=indirect(“nedc_predictions_time_series!_fuel_consumptions”)”).

3. (Optional) Delete the old sheets and save your file.

4. Use that file together with the --out-template argument.

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:

   Warning

   This step requires too many libraries to provide as standalone files, so unless you have it already installed, you will need a proper http-connectivity to the standard python-repo.

   $ 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.

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.

As an aid tool, you may use the datasync command-line tool to “synchronize” your data-tables. This command reads one or more tables from excel-files and synchronizes their columns. The syntax of this utility command is given by typing datasync --help in the command line (listing below just the main fields):

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

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

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:
                         - 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.
                         [default: .].
  -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
                         [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'
  -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.

All input tables must share 2 common columns: <x-label> and <y-label>, as if those tables describe 2D cartesian data, with a common X-axis and multiple data-series on the Y-Axis.

Tip

The <x-label> usually refers to the “time” dimension.

The 1st table given (<ref-table>) is considered to contain the “reference” X/Y values; the data-columns to shift-and-resample are contained in one or more tables (<sync-table>) specified subsequently in the command line, that are possibly read from different excel work-books.

• Shifting is based on the cross-correlation of <y-label> columns;

• resampling is based on the values of <x-label> columns among the different tables.

All tables are read from excel-sheets using the xl-ref syntax, which is best explained with some examples.

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  velocity  folder/Book.xlsx

• Sync Sheet1 using Sheet3 as reference:

  datasync times  velocity  wbook.xlsx#Sheet3!  Sheet1!

• The same as above but with integeres used to index excel-sheets:

  datasync times  velocity  wbook.xlsx#2!  0

  Note

  Sheet-indices are zero based!

• A more complex xlr-ref example which reads the synce-table from sheet2 of wbook-2 starting at D5 cell, or more Down ‘n Right if that was empty, till the first empty cell Down n Right, and synchronizes that based on 1st sheet of wbook-1:

  datasync times  velocity wbook-1.xlsx  wbook-2.xlsx#0!D5(DR):..(DR)

• Typical usage for CO₂MPAS velocity time-series from Dyno and OBD:

  1. Create a template pre-populated with the theoretical velocity profile:

     datasync template --cycle wltp.class3b template.xlsx

  Note

  the ref sheet contains the theoretical velocity profile.

  2. Fill the dyno and obd sheet with the raw data. Synchronize the data:

     datasync -O ./output times velocities template.xlsx#ref dyno obd

  3. Copy paste the synchronized signal into the CO2MPAS template.

Debugging and investigating results

• Make sure that you have installed graphviz, and when running the simulation, append also the --plot-workflow option.

  $ co2mpas batch --plot-workflow bad-file.xlsx

  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

• 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.

Model

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:

Tip

The models in the diagram are nested; explore by clicking on them.

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──────────────────────────────────────────────┘

Note

• The dot(.) may be replaced by space.

• The usage and stage parts may end with an s, denoting plural, and are not case-insensitive, e.g. Inputs.

Description of the name-parts

1. scope:

   • base [default]: values provided by the user as input to CO₂MPAS.

   • plan: values selected (see previous section) to calibrate the models and to predict the CO₂ emission.

2. usage:

   • input [default]: values provided by the user as input to CO₂MPAS.

   • data: values selected (see previous section) to calibrate the models and to predict the CO₂ emission.

   • output: CO₂MPAS precondition, calibration, and prediction results.

   • target: reference-values (NOT USED IN CALIBRATION OR PREDICTION) to be compared with the CO₂MPAS results. This comparison is performed in the report sub-model by compare_outputs_vs_targets() function.

3. stage:

   • precondition [imposed when: wltp-p is specified as cycle]: data related to the precondition stage.

   • calibration [default]: data related to the calibration stage.

   • prediction [imposed when: nedc is specified as cycle]: data related to the prediction stage.

4. cycle:

   • nedc-h: data related to the NEDC High cycle.

   • nedc-l: data related to the NEDC Low cycle.

   • wltp-h: data related to the WLTP High cycle.

   • wltp-l: data related to the WLTP Low cycle.

   • wltp-precon: data related to the preconditioning WLTP cycle.

   • wltp-p: is a shortcut of wltp-precon.

   • nedc: is a shortcut to set values for both nedc-h and nedc-l cycles.

   • wltp: is a shortcut to set values for both wltp-h and wltp-l cycles.

   • all [default]: is a shortcut to set values for nedc, wltp, and wltp-p cycles.

5. param: any data node name (e.g. vehicle_mass) used in the physical model.

6. sheet_type: there are three sheet types, which are parsed according to their contained data:

   • pl [parsed range is #A1:__]: table of scalar and time-depended values used into the simulation plan as variation from the base model.

   • pa [parsed range is #B2:C_]: scalar or not time-depended values (e.g. r_dynamic, gear_box_ratios, full_load_speeds).

   • ts [parsed range is #A2:__]: time-depended values (e.g. times, velocities, gears). Columns without values are skipped. COLUMNS MUST HAVE THE SAME LENGTH!

   ..note:: If it is not defined, the default value follows these rules:

   When scope is plan, the sheet is parsed as pl. If scope is base and cycle is missing in the sheet-name, the sheet is parsed as pa, otherwise it is parsed as ts.

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.

Note

The co2_params model has a third possible calibration configuration (so called ALL) using data from both WLTP_H and WLTP_L combined (when both are present).

Model selection

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.

Note

The overall score attributed to a specific calibration of a model is the average score achieved when compared against each one of the input cycles (WLTP_H and WLTP_L).

For example, the score of electric_model calibrated based on WLTP_H when predicting WLTP_H is 20, and when predicting WLTP_L is 14. In this case the overall score of the the electric_model calibrated based on WLTP_H is 17. Assuming that the calibration of the same model over WLTP_L was 18 and 12 respectively, this would give an overall score of 15.

In this case the second calibration (WLTP_L) would be chosen for predicting the NEDC.

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: