Advanced Usage (Script)
After the installation, the next sections describe on how to get started. The sections configuration and logging describe the general methods we use, this is helpful to understand how you can change model parameters and similar.
Advanced (Script) Installation
This is a quick guide on how to install AvaFrame and the required dependencies on your machine. AvaFrame is developed on Linux machines (Ubuntu/Manjaro/Arch) with recent Python versions > 3.8. These instructions assume you are familiar with working in a terminal. This guide is currently described for Linux only, but expert users should be able to adapt it to Windows.
Install git and python, we suggest to work with miniconda/anaconda. For installation see miniconda or anaconda. Some operating systems might require the python headers (e.g python-dev on ubuntu) or other supporting libraries/packages (e.g. Visual Studio on Windows).
Create a new conda environment for AvaFrame, activate it and install pip, numpy and cython in this environment:
conda create --name avaframe_env
conda activate avaframe_env
conda install pip numpy cython
Clone the AvaFrame repository (in a directory of your choice: [YOURDIR]) and change into it:
git clone https://github.com/avaframe/AvaFrame.git
Compile the cython com1DFA part. You might also have to install a c-compiler (gcc or similar) through your systems package manager:
python setup.py build_ext --inplace
You will have to do this compilation every time something changes in the cython code. We also suggest to do this everytime updates from the repositories are pulled.
Install avaframe and its requirements:
pip install -e .
This installs avaframe in editable mode, so every time you import avaframe the current (local) version will be used.
Test it by starting
python and do an
import avaframe. If no error comes
up, you are good to go.
Head over to First run for the next steps.
To update go to your avaframe repository [YOURDIR]/Avaframe, pull the latest changes via:
and repeat the compilation step from above:
python setup.py build_ext --inplace
If there are updates on the requirements inside
setup.py, it might be necessary to run:
pip install -e .
again to get the additional requirements installed.
Follow these steps to run your first simulation:
change into your
AvaFramedirectory (replace [YOURDIR] with your path from the installation steps):
a similar output should show up:
logUtils - INFO - Started logging at: 03.11.2020 22:42:04 logUtils - INFO - Also logging to: data/avaParabola/runCom1DFA.log runCom1DFA - INFO - MAIN SCRIPT runCom1DFA - INFO - Current avalanche: data/avaParabola ...
This will perform a dense flow avalanche simulation using the
The results are saved to
data/avaParabola/Outputs/com1DFA. For a first
look at the results, got to the folder
reports, there you can find
a markdown report of the simulations performed including some plots of the results.
To display markdown files in a nice way use a markdown viewer of your choice. Some other options are:
Use the Atom editor with a markdown plugin
If you have pandoc installed use this to convert it to pdf/html
Some browsers have markdown extensions you can install easily
The following example should make it easier for you to find your way in AvaFrame and setup your own AvaFrame workflow after you did the full setup. There is also a directory with examples for different workflows, see more here: Example runscripts.
Make sure you change to your
AvaFrame directory by:
[YOURDIR] with the directory from your installation step.
To create the folder where the input data lies and where the
output results will be saved, specify the full path to the folder
local_avaframeCfg.ini (which is a copy of
avaframeCfg.ini that you need to create). So:
cp avaframeCfg.ini local_avaframeCfg.ini
local_avaframeCfg.ini with your favorite text editor and adjust the
This will create a new directory with the input required by AvaFrame structured as described in Initialize Project.
Check the input data required by the different modules you want to use and fill
Inputs/ inside the
[avalancheDir] folder from the initialize step accordingly.
For example the
com1DFA module needs input as described in
Input. You can also have a look at the default setting for
the module you want to use (for example
com1DFACfg.ini for module com1DFA).
If you want to use different settings, create a
local_ copy of the
file and modify the desired parameters.
More information about the configuration can be found here: Configuration
Building your run script
Create your own workflow by taking the
runOperational.py script as
We suggest you copy it and adjust it to your liking. There are annotations in the code that should help you to understand the structure.
A lot more examples can be found in the
runScripts directory (see also Example runscripts).
In order to set the configurations required by all the modules within Avaframe, the python module configparser is used.
This is done in two steps. The first step fetches the main settings:
from avaframe.in3Utils import cfgUtils
# Load avalanche directory from general configuration file
cfgMain = cfgUtils.getGeneralConfig()
avalancheDir = cfgMain['MAIN']['avalancheDir']
In the second step the specific settings to a given module are imported:
from avaframe.tmp1Ex import tmp1Ex
# Load all input Parameters from config file
# get the configuration of an already imported module
# Write config to log file
cfg = cfgUtils.getModuleConfig(tmp1Ex)
in3Utils.cfgUtils.getModuleConfig() function reads the settings from a configuration file (
in our example) and writes these settings to the log file. The default settings can be found in the
configuration file provided within each module.
It is possible to modify these settings, there are two options:
provide the path to your own configuration file when calling
cfgUtils.getModuleConfig(moduleName, path to config file)
create a copy of the module configuration file called
local_followed by the name of the original configuration file and set the desired values of the individual parameters.
see Override configuration for additional options to modify configuration
So the order is as follows:
if there is a path provided, configuration is read from this file.
if there is no path provided, the
local_...configuration file is read if it exists.
if there is no
getModuleConfigfunction reads the settings from the default configuration file with the default settings.
In the configuration file itself, there are multiple options to vary a parameter:
replace the default parameter value with desired value
provide a number of parameter values separated by
provide a number of parameter values using
relTh=1.:3.:3)) - a single value can be added by appending
If tools of one module, let’s call this module A for now, are called from another module B, there is the option to include an collectionName_A_override section in the
configuration file of module B. In this case, the default configuration of module A is read and the parameters in the A_override section
in the module B configuration file are used to update the configuration settings of module A. This has the advantage of gathering all
the configuration parameters used for one task in one configuration file.
An example of this usage can be found in
In order to generate simulation logs and to control what is prompted to the terminal, we use the python module logging.
Let’s have a look at the simple example in
tmp1Ex.tmp1Ex() on how
this is used within AvaFrame.
In your main script call:
from avaframe.in3Utils import logUtils
# log file name; leave empty to use default runLog.log
logName = 'runTmp1Ex'
# specify the working directory
avalancheDir = './'
# Start logging
log = logUtils.initiateLogger(avalancheDir, logName)
This will configure the logging (it sets the console output as well as the log file). In your modules/subscripts add:
log = logging.getLogger(__name__)
So you can use:
log.debug('Should be here')
log.info('DEM : %s',variable)
To get output that looks like this in your console:
tmp1Ex:DEBUG - Should be here
tmp1Ex:INFO - DEM : /path/to/DEM
and something similar in the
.log file which is saved in
./runTmp1Ex.log in this example.
The logging configuration is set in
You can modify this
logging.conf file to modify the levels or format of the messages to display
(python doc will help you).
runScripts we provide ready-to-use scripts for different applications of the modules provided within
Derive input data
Create a new project
Generate idealized/generic topography data