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+# Video Prediction by GAN
+
+This project aims to adopt the GAN-based architectures,  which original proposed by [[Project Page]](https://alexlee-gk.github.io/video_prediction/) [[Paper]](https://arxiv.org/abs/1804.01523), to predict temperature based on ERA5 data
+ 
+## Getting Started ###
+### Prerequisites
+- Linux or macOS
+- Python 3
+- CPU or NVIDIA GPU + CUDA CuDNN
+
+### Installation 
+This project need to work with [Workflow_parallel_frame_prediction project](https://gitlab.version.fz-juelich.de/gong1/workflow_parallel_frame_prediction)
+- Clone this repo:
+```bash
+git clone master https://gitlab.version.fz-juelich.de/gong1/video_prediction_savp.git
+git clone master https://gitlab.version.fz-juelich.de/gong1/workflow_parallel_frame_prediction.git
+```
+
+### Set-up env on JUWELS
+
+- Set up env and install packages
+
+```bash
+cd video_prediction_savp
+source env_setup/create_env.sh <dir_name> <env_name>
+```
+
+## Workflow by steps
+
+### Data Extraction
+
+```python
+python3 ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py  --source_dir <input_dir1> --destination_dir <output_dir1>
+```
+
+e.g. 
+```python
+python3 ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py  --source_dir /p/fastdata/slmet/slmet111/met_data/ecmwf/era5/nc/2017/ --destination_dir /p/scratch/deepacf/bing/extractedData
+```
+
+### Data Preprocessing
+```python
+python3 ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir <output_dir1> --destination_dir <output_dir2> 
+
+python3 video_prediction/datasets/era5_dataset_v2.py  --source_dir   <output_dir2> --destination_dir <.data/exp_name>
+```
+
+Example
+```python
+python3 ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir /p/scratch/deepacf/bing/extractedData --destination_dir /p/scratch/deepacf/bing/preprocessedData
+
+python3 video_prediction/datasets/era5_dataset_v2.py /p/scratch/deepacf/bing/preprocessedData  ./data/era5_64_64_3_3t_norm
+ ```
+ 
+### Trarining
+
+```python
+python3 scripts/train_v2.py --input_dir <./data/exp_name> --dataset era5  --model <savp> --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir <./logs/{exp_name}/{mode}/>
+```
+
+Example
+```python
+python3 scripts/train_v2.py --input_dir ./data/era5_size_64_64_3_3t_norm --dataset era5  --model savp --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir logs/era5_64_64_3_3t_norm/end_to_end
+```
+### Postprocessing
+
+Generating prediction frames, model evaluation, and visulization
+You can trained your own model from the training step , or you can copy the Bing's trained model
+
+```python
+python3 scripts/generate_transfer_learning_finetune.py --input_dir <./data/exp_name>  --dataset_hparams sequence_length=20 --checkpoint <./logs/{exp_name}/{mode}/{model}> --mode test --results_dir <./results/{exp_name}/{mode}>  --batch_size <batch_size> --dataset era5
+```
+
+- example: use end_to_end training model from bing for exp_name:era5_size_64_64_3_3t_norm
+```python
+python3 scripts/generate_transfer_learning_finetune.py --input_dir data/era5_size_64_64_3_3t_norm --dataset_hparams sequence_length=20 --checkpoint /p/project/deepacf/deeprain/bing/video_prediction_savp/logs/era5_size_64_64_3_3t_norm/end_to_end/ours_savp --mode test --results_dir results_test_samples/era5_size_64_64_3_3t_norm/end_to_end  --batch_size 4 --dataset era5
+```
+
+![Groud Truth](/results_test_samples/era5_size_64_64_3_norm_dup/ours_savp/Sample_Batch_id_0_Sample_1.mp4)
+# End-to-End run the entire workflow
+
+```bash
+./bash/workflow_era5.sh <model>  <train_mode>  <exp_name>
+```
+
+example:
+```bash
+./bash/workflow_era5.sh savp end_to_end  era5_size_64_64_3_3t_norm
+```
+
+
+
+### Recomendation for output folder structure and name convention
+The details can be found [name_convention](docs/structure_name_convention.md)
+
+```
+├── ExtractedData
+│   ├── [Year]
+│   │   ├── [Month]
+│   │   │   ├── **/*.netCDF
+├── PreprocessedData
+│   ├── [Data_name_convention]
+│   │   ├── hickle
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   │   ├── test
+│   │   ├── tfrecords
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   │   ├── test
+├── Models
+│   ├── [Data_name_convention]
+│   │   ├── [model_name]
+│   │   ├── [model_name]
+├── Results
+│   ├── [Data_name_convention]
+│   │   ├── [training_mode]
+│   │   │   ├── [source_data_name_convention]
+│   │   │   │   ├── [model_name]
+
+```
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