Short tutorial

https://github.com/IAOCEA/demo-foscat-pangeo-eosc/blob/main/Demo_Synthesis.ipynb

FOSCAT_DEMO

The python scripts demo.py included in this package demonstrate how to use the foscat library to generate synthetic fields that have patterns with the same statistical properties as a specified image.

Install foscat library

Before installing, make sure you have python installed in your enviroment.
The last version of the foscat library can be installed using PyPi:

pip install foscat

Load the FOSCAT_DEMO package from github.

git clone https://github.com/jmdelouis/FOSCAT_DEMO.git

Recommended installing procedures for mac users

It is recomended to use python=3.9*.

micromamba create -n FOSCAT
micromamba install -n FOSCAT ‘python==3.9*’
micromamba activate FOSCAT
pip install foscat
git clone https://github.com/jmdelouis/FOSCAT_DEMO.git

Recommended installing procedures HPC users

It is recomended to install tensorflow in advance. For DATARMOR for using GPU ;

micromamba create -n FOSCAT
micromamba install -n FOSCAT ‘python==3.9*’
micromamba install -n FOSCAT ‘tensorflow==2.11.0’
micromamba activate FOSCAT
pip install foscat
git clone https://github.com/jmdelouis/FOSCAT_DEMO.git

Spherical data example

compute a synthetic image

python demo.py -n=32 -k -c -s=100

The demo.py script serves as a demonstration of the capabilities of the foscat library. It utilizes the Cross Wavelet Scattering Transform to generate a Healpix map that possesses the same characteristics as a specified input map.

• -n=32 computes map with nside=32.
• -k uses 5x5 kernel.
• -c uses Scattering Covariance.
• -l uses LBFGS minimizer.
• -s=100 computes 100 steps.

python demo.py -n=8 [-c|--cov][-s|--steps=3000][-S=1234|--seed=1234][-k|--k5x5][-d|--data][-o|--out][-r|--orient] [-p|--path][-a|--adam]

• The "-n" option specifies the nside of the input map. The maximum nside value is 256 with the default map.
• The "--cov" option (optional) uses scat_cov instead of scat.
• The "--steps" option (optional) specifies the number of iterations. If not specified, the default value is 1000.
• The "--seed" option (optional) specifies the seed of the random generator.
• The "--path" option (optional) allows you to define the path where the output files will be written. The default path is "data".
• The "--k5x5" option (optional) uses a 5x5 kernel instead of a 3x3.
• The "--data" option (optional) specifies the input data file to be used. If not specified, the default file "LSS_map_nside128.npy" will be used.
• The "--out" option (optional) specifies the output file name. If not specified, the output file will be saved in "demo".
• The "--orient" option (optional) specifies the number of orientations. If not specified, the default value is 4.
• The "--adam" option (optional) makes the synthesis using the ADAM optimizer instead of the L_BFGS.

plot the result

The following script generates a series of plots that showcase different aspects of the synthesis process using the demo.py script.

python plotdemo.py -n=32 -c

2D field demo

compute a synthetic turbulent field

The python scripts demo2D.py included in this package demonstrate how to use the foscat library to generate a 2D synthetic fields that have patterns with the same statistical properties as a specified 2D image. In this particular case, the input field is a sea surface temperature extracted from a north atlantic ocean simulation.

python demo2d.py -n=32 -k -c

plot the result

The following script generates a series of plots that showcase different aspects of the synthesis process using the demo2D.py script.

python plotdemo2d.py -n=32 -c