test push readme, devcont, installer

.devcontainer/Dockerfile

@@ -0,0 +1,20 @@
+############################################# INSTRUCTIONS #############################################
+# build image: docker build -t pyraws:latest  --build-arg CACHEBUST=$(date +%s) -f dockerfile . 
+# run image: docker run -it --rm -p 8888:8888 pyraws:latest                
+#############################################     END     #############################################
+
+# Use the official PyTorch base image
+FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime
+
+# Set the working directory inside the container
+WORKDIR /workspace
+
+# Install Python dependencies for pyraws:
+RUN apt-get update && apt-get install -y git ffmpeg libsm6 libxext6
+# install pyraws
+
+RUN git clone -b main https://github.com/ESA-PhiLab/PyRawS.git
+
+RUN cd PyRawS && /bin/bash -c "source ./pyraws_install.sh" 
+RUN echo "source activate pyraws" > ~/.bashrc
+ENV PATH /opt/conda/envs/pyraws/bin:$PATH

.devcontainer/devontainer.json

@@ -0,0 +1,22 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/anaconda
+
+{
+    "name": "pyraws",
+    "dockerComposeFile": "docker-compose.yml",
+    "service": "pyraws",
+    "workspaceFolder": "/workspace",
+    "settings": {
+        "terminal.integrated.shell.linux": "/bin/bash"
+    },
+    "extensions": [
+        "ms-toolsai.jupyter",
+        "ms-toolsai.vscode-jupyter-cell-tags",
+        "ms-toolsai.jupyter-renderers",
+        "ms-toolsai.vscode-jupyter-powertoys",
+        "ms-toolsai.vscode-jupyter-slideshow",
+        "donjayamanne.python-extension-pack"
+    ],
+    // Use 'postCreateCommand' to run commands after the container is created.
+	//"postCreateCommand": "conda install -n simulator ipykernel --update-deps --force-reinstall"
+}

.devcontainer/docker-compose.yml

@@ -0,0 +1,20 @@
+version: "3.8"
+
+services:
+  pyraws:
+    image: pyraws:latest
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [gpu]
+    build:
+      context: .
+      dockerfile: Dockerfile
+    volumes:
+      - ../:/workspace/
+    platform: linux/amd64
+
+    command: sleep infinity

docs/GLOSSARY.md

@@ -0,0 +1,34 @@
+
+## Glossary
+* ### Coarse coregistration
+  Lightweight spatial coregistration method optimized for onboard-satellite applications. It simply shifts the various bands of a fixed factor that depends only on the bands, the satellite and detector number.
+
+* ### Sentinel-2 L0 data
+  Sentinel-2 data at `level-0` (`L0`) are data that are transmitted to Ground from Sentinel-2 satellites. The `L0` format is compressed to diminish downlink bandwidth requirements. For more information, refer to the [Sentinel-2 Products Specification Document](https://sentinel.esa.int/documents/247904/685211/sentinel-2-products-specification-document)
+
+* ### Sentinel-2 Raw data
+  In the frame of this project, the [Sentinel-2 Raw](#sentinel-2-raw-data) represents a particular product in the Sentinel-2 processing chain that matches a  decompressed version of [Sentinel-2 L0 data](sentinel-2-l0-data) with additional metadata that are produced on ground. Once decompressed, `Sentinel-2 Raw data` are the data available on Ground that better emulate the one produced by Sentinel-2 detectors with the exception of the effects due to compression and onboard equalization, which are not compensated at this stage. Therefore, `Sentinel-2 raw data` are those exploited in this project. For more information, refer to the [Sentinel-2 Products Specification Document](https://sentinel.esa.int/documents/247904/685211/sentinel-2-products-specification-document). <br>
+**N.B**: the nomenclature ```raw data``` and its location in the Sentinel-2 processing chain is specific for this project only.
+
+* ### Sentinel-2 Raw granule
+    A `granule` is the image acquired by a Sentinel-2 detector during a single acquisition lasting 3.6 s. Granules are defined at [L0](#sentinel-2-raw-data) level. However, since the processing perfomed on the ground between L0 and raw data does not alter the image content (with the exception of the decompression process) but just provide additional metadata, granules are defined also at [Sentinel-2 Raw](#sentinel-2-raw-data) level.
+    Given the pushbroom nature of the Sentinel-2 sensor, bands do not look at the same area at [Raw](#sentinel-2-raw-data) level. For more information, refer to the [Sentinel-2 Products Specification Document](https://sentinel.esa.int/documents/247904/685211/sentinel-2-products-specification-document)
+
+* ### Sentinel-2 Raw event
+   [Sentinel-2 Raw data](#sentinel-2-raw-data) are produced by decompressing [Sentinel-2 L0 data](sentinel-2-raw-data). To download L0 data,  it is necessary to specify one polygon that surrounds a particular area-of-interest. This leads to download all those [Sentinel-2 Raw granules](#sentinel-2-raw-granule) whose reference band intersects the specified polygon. Such collection is a `Raw-event`. Each `Raw-event` matches one of the `ID_event` entry of the database. <br>
+    For each `Raw-event`, we do not provide all the collection of  [Sentinel-2 Raw granules](#sentinel-2-Raw-granule), but only the set of [Raw data useful granules](#raw-data-useful-granule) and [Raw data complementary granules](#Raw-data-complementary-granule). For an intuitive example, please, check [Raw events and granules](#raw-events-and-raw-granules).
+
+* ### Sentinel-2 L1C data
+  The `Level 1-C` (`L1C`) is one format for `Sentinel-2` data. To convert [Sentinel-2 Raw data](#sentinel-2-raw-data) to `L1C` data, numerous processing steps are applied to correct defects, including bands coregistration, ortho-rectification, decompression, noise-suppression and other. For more information, refer to the [Sentinel-2 Products Specification Document](https://sentinel.esa.int/documents/247904/685211/sentinel-2-products-specification-document).
+
+* ### Sentinel-2 L1C event
+  Same concept for [Sentinel-2 Raw events](#sentinel-2-raw-event) but applied on [Sentinel-2 L1C data](#sentinel-2-l1c-data).
+
+* ### Sentinel-2 L1C tile
+  The `Sentinel-2 L1C tile` is the minimum `L1C` product that can be downloaded.
+
+* ### Raw complementary granule
+  Given a certain set of bands of interest `[Bx,By,...,Bz]`, `Raw complementarey granules` are the granules adjacents at [Raw-useful-granules](#raw-useful-granule) that that can be used to fill missing pixels of `[By,...,Bz]` bands due to their coregistration with respecto the band `Bx`. For an intuitive example, please, check [Raw events and granules](#raw-events-and-raw-granules).
+
+* ### Raw useful granule
+  Given a certain set of bands of interest `[Bx,By,...,Bz]`, where `Bx` is the first band in the set, an `Raw useful granule` is one of the collection of [Sentinel-2 Raw granules](#sentinel-2-raw-granule) that compose a [Sentinel-2 Raw event](#sentinel-2-raw-event) whose band `Bx` include (or intersects) a certain area of interest (e.g., an eruption or an area covered by a fire). For an intuitive example, please, check [Raw data events and granules](#raw-events-and-raw-granules).

pyraws_install.sh

@@ -9,10 +9,10 @@ then
     exit 1
 fi
 
-# Create a new Conda environment called openmmlab
+# Create a new Conda environment called pyraws
 conda create --name pyraws python=3.9 -y
 
-# Activate the AINavi environment
+# Activate the environment
 source $(conda info --base)/etc/profile.d/conda.sh
 if conda activate pyraws; then
     echo "pyraws environment activated"
@@ -21,62 +21,45 @@ else
     exit 1
 fi
 
-#!/bin/bash
-# Check the operating system and install pytorch accordingly
-if [[ "$OSTYPE" == "darwin"* ]]; then
-  # macOS
-  echo "Detected macOS"
-  conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 -c pytorch
-elif [[ "$OSTYPE" == "linux-gnu" ]]; then
-  # Linux
-  echo "Detected Linux"
-  if command -v nvcc >/dev/null 2>&1; then
-    # CUDA is available
-    if nvcc --version | grep "release 10\.2" >/dev/null 2>&1; then
-      # CUDA 10.2
-      echo "Detected CUDA 10.2"
-      conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=10.2 -c pytorch
-    elif nvcc --version | grep "release 11\.3" >/dev/null 2>&1; then
-      # CUDA 11.3
-      echo "Detected CUDA 11.3"
-      conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
-    else
-      # CUDA version not supported, installing CPU version
-      echo "CUDA version not supported, installing CPU version"
-      conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cpuonly -c pytorch
-    fi
-  else
-    # CUDA is not available, installing CPU version
-    echo "CUDA not detected, installing CPU version"
-    conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cpuonly -c pytorch
-  fi
-elif [[ "$OSTYPE" == "msys" ]]; then
-  # Windows
-  echo "Detected Windows"
-  if where nvcc >/dev/null 2>&1; then
-    # CUDA is available
-    if nvcc --version | grep "release 10\.2" >/dev/null 2>&1; then
-      # CUDA 10.2
-      echo "Detected CUDA 10.2"
-      conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=10.2 -c pytorch
-    elif nvcc --version | grep "release 11\.3" >/dev/null 2>&1; then
-      # CUDA 11.3
-      echo "Detected CUDA 11.3"
-      conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
-    else
-      # CUDA version not supported, installing CPU version
-      echo "CUDA version not supported, installing CPU version"
-      conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cpuonly -c pytorch
-    fi
-  else
-    # CUDA is not available, installing CPU version
-    echo "CUDA not detected, installing CPU version"
-    conda install -y pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cpuonly -c pytorch 
-  fi
-else
-  echo "Unsupported operating system"
-  exit 1
-fi
+
+# Define PyTorch version and components
+pytorch_version="1.11.0"
+torchvision_version="0.12.0"
+torchaudio_version="0.11.0"
+
+# Function to install PyTorch
+install_pytorch() {
+  conda install -y pytorch==$pytorch_version torchvision==$torchvision_version torchaudio==$torchaudio_version $1 -c pytorch
+}
+
+# Install via pip:
+# pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
+
+# Detect operating system
+case "$OSTYPE" in
+  "darwin"*)
+    # macOS
+    echo "Detected macOS"
+    install_pytorch "cpuonly"
+    ;;
+  "linux-gnu"|"linux-gnueabihf")
+    # Linux
+    echo "Detected Linux"
+    # Install PyTorch with CUDA 11.3 support
+    install_pytorch "cudatoolkit=11.3"
+    ;;
+  "msys"|"win32")
+    # Windows
+    echo "Detected Windows"
+    # Install PyTorch with CUDA 11.3 support
+    install_pytorch "cudatoolkit=11.3"
+    ;;
+  *)
+    echo "Unsupported operating system"
+    exit 1
+    ;;
+esac
+
 
 # get absolute path of current working directory
 # and setup the sys_cfg.py file