Update README.md

README.md

@@ -3,21 +3,28 @@
 
 **Method**: Convolutional Variational AutoEncoder.
 
-**Input**: "3D daily images", daily screenshots of Europe for three climate variables (maximum temperature, precipitation, wind).
+**Input**: "Daily images", daily screenshots of Europe for maximum temperature (CMCC-ESM2 model, 1° spatial resolution).
 
 **Output**: Error between original and reconstructed image: postprocessed for analysis in the "scenario_season_comparison.ipynb" file.
 
 **Idea**: The more unusual an image (anomaly), the higher error.
 
-**Use**:
-- Run "python train.py" once in the right working directory to train the network. Caution: It will overwrite the model saved in outputs unless you change the path name '../outputs/cvae_model_3d.pth' in the script.
+**Folder Architecture**:
+<img width="283" alt="image" src="https://github.com/cerfacs-globc/xtclim/assets/44207112/97813269-1632-4ae1-b30d-c0e17940734c">
 
-- Run "python anomaly.py" once in the right working directory to evaluate the model on the three available datasets - train, test, and projection.
 
-- The "data" folder could not be uploaded because of its weight. It contains the NetCDF files tasmax_day_CMCC-ESM2_historical_r1i1p1f1_gn_19750101-19991231.nc for both historical (1975-1999) and projection (2065-2089) time periods.
+**How to Run**:
 
-- The "input" folder is incomplete for the same reason. Only preprocessed test data could be uploaded. The missing files can be obtained by running "preprocessing/preprocess_functions_1d_temp_members.ipynb" then "preprocessing/preprocess_3d_seasons.ipynb" to split the files into seasons.
+_In the following part, "network" refers to the deep learning model, the Convolutional Variational Auto-Encoder (CVAE)._
 
-The 3D version on the "3d_cvae" branch has the same structure but for three climate variables simultaneously.
+- In the preprocessing folder, first run "preprocess_functions_2d_ssp.py" to load NetCDF files from the data folder. It will normalize and adjust the data for the network. Then run "preprocess_2d_seasons.py" to split the data into seasonal files. Preprocessed data is stored in the "input" folder.
 
-Estimation of the carbon footprint of the training with the codecarbon package (see first and last lines of "src/train.py").
+- Run "python train.py" to train the network. Caution: It will overwrite the weights of the network already saved in outputs (unless you change the path name '../outputs/cvae_model_3d.pth' in the script).
+
+- Run "python anomaly.py" to evaluate the network on the available datasets - train, test, and projection.
+
+- The "data" folder is too heavy to be stored on the repository. It should contain tasmax NetCDF files for both historical (1950-1999) and projection (2015-2100) time periods (e.g. tasmax_day_CMCC-ESM2_historical_r1i1p1f1_gn_19500101-19991231.nc).
+
+**More Details**:
+- "model.py" contains the details of the architecture of the network, and "engine.py" defines the train and evaluate functions.
+- The carbon footprint of the training is estimated with the codecarbon package (see first and last lines of "src/train.py").