Merge branch 's3_dev' into 'main'

ASPIS

=====

(ASi sPace weather InfraStructure)

The Italian Space Agency has produced a Space Weather (SWE) roadmap **([RD-2])** for a long-term strategy to

support the future scientific research of SWE, and the development of a related national scientific data centre

.. toctree::

   :caption: ASPIS documentation

   aspis/webapp

   ASPIS.py library<http://www.example.com/lib/ios/0.1.0/api/>

   webapp

   aspis_py

.. autosummary::

   .. toctree:: generated

   ASi sPace weather InfraStructure.

ASPIS.py library

----------------

Intro

^^^^^

ASPIS.py is a library of code that allows access to the data contained in ASPIS database and other

various astrophysics community databases, to perform client-side specific analyses of this data and to

visualize it. The data available are of two types: data in the internal database produced by the CAESAR

community and information on external data in other databases. Along with access to the data, the

ASPIS.py library contains code packages that can be imported as library objects, with which to perform

analysis and/or visualization of the data.

The criterion of ASPIS.py is to act as a collector for the data and analysis software of the astrophysics

community and to foster cooperation within the community. In fact, one can use the data and analysis

methods developed by the community and, likewise, share one’s own data and methods. Indeed, we

have defined a standard template in ASPIS.py for generic data analysis code, and provide a how-to for

developers in the astrophysics community to convert their own code into the standard template, making it

easily usable for the entire ASPIS.py community in a standard way. In this way ASPIS.py’s primary goal

is to promote collaboration and cooperation within the astrophysics community. It facilitates the sharing

of data and analysis methods among researchers, enabling them to benefit from each other’s work. In

summary, ASPIS.py is a tool designed to streamline the access, analysis, and sharing of astrophysical

data and analysis methods within the astrophysics community. It aims to enhance collaboration and

standardization, making it easier for researchers to work with and build upon each other’s work in the

field of astrophysics.

Functions

^^^^^^^^^^^^^^^^^^

The functions of the ASPIS.py module are conceived to implement the following key components of the

ASPIS service:

1. Access for Advanced Users: ASPIS.py is designed to cater to advanced users in the astrophysics

community. It offers a set of functions that can be executed programmatically through a Python

interpreter. This capability allows users to perform more complex computations and analysis tasks,

surpassing the limitations of a web application.

2. Basic Visualization Tools: While ASPIS.py provides basic visualization tools/functions, it also en-

courages users to leverage other freely available modules like Sunpy and Astropy for more ad-

vanced visualization needs. This approach ensures compatibility and flexibility.

3. Ready-to-Use Python Snippets: ASPIS.py intends to provide ready-to-use Python code snippets.

These snippets are designed to assist entry-level users, making it easier for them to get started

with the module. Advanced users can further customize these code snippets to suit their specific

research requirements.

4. Integration with User Programs: The expectation is that advanced users of ASPIS.py will seam-

lessly integrate the functions provided by the module into their own analysis and visualization pro-

grams. This promotes flexibility and allows users to tailor their solutions to their research needs.

5. Structured Information in the Database: ASPIS.py relies on structured information within the database.

This information likely includes details about various astrophysical entities such as phenomena,

data descriptions, and chains, which are essential for conducting meaningful analyses.

6. Data Outside the Database: ASPIS.py is designed to work with data that exists outside the database

but corresponds to the descriptions available within the database. This flexibility allows researchers

to combine data from various sources to enhance their analysis.

In summary, ASPIS.py is a versatile tool that caters to both entry-level and advanced users in the

astrophysics community. It provides functions for querying, visualizing, analyzing, and modeling astro-

physical data. The inclusion of code snippets and use case examples simplifies the usage for a broad

range of researchers, and advanced users can extend and customize the functionality to meet their spe-

cific research objectives.

Installation

^^^^^^^^^^^^

To install ASPIS.py, a user typically needs to follow these general steps. Please note that the specific

installation process may vary depending on the availability and distribution method of ASPIS.py. The user

has to ensure that Python is installed on the system before proceeding. The user can install ASPIS.py

using pip, which is the Python package manager:

``sudo -H pip3 install aspis-0.1.7-py3-none-any.whl``

The command is used to install a Python package from a Wheel file (.whl) using pip3 with adminis-

trative privileges (via sudo). The specific package being installed is "aspis-0.1.7-py3-none-any.whl".

Here’s a breakdown of the command:

* sudo: It is used to run the pip3 command with superuser privileges, which may be necessary for system-wide installations.

* -H: This flag is used to set the home directory to the target user’s home directory. It can help avoid potential issues with environment variables when using sudo.

* pip3: This is the Python package manager for Python 3.

* install: It’s the pip command to install packages.

* aspis-0.1.7-py3-none-any.whl: This is Wheel file that you want to install.

If everything is set up correctly, running this command install the ASPIS package version 0.1.7 from

the provided Wheel file.

Use case example

^^^^^^^^^^^^^^^^

Here we show an example of the way a user can use a specific product of the ASPIS.py library.

from aspis import products as aps

from datetime import datetime

product input:

``V0 = 1846 #* u.km / u.s``

``sigma_V0 = 362 #* u.km / u.s``

``Time_UTC = datetime ( 2017 , 9 , 6 , 14 , 1 , 0)``

call the class:

``obj = aps . pdbm ()``

compute the CME:

``obj . fit (V0 , sigma_V0 , Time_UTC )``

compute the CME on target = Earth:

``obj . predict ()``

plot:

``plt = obj . plot ( savefig = True )``

``plt = obj . plot ( type = ’ Travel time ’, savefig = True )``

``plt = obj . plot ( type = ’ ICME velocity ’, savefig = True )``

This example code performs the following steps:

#.  Import necessary modules and classes from the "aspis" library.

#.  Define input parameters, namely the initial velocity (V0), its uncertainty (sigma_V0), and a times-tamp (Time_UTC).

#.  Create an instance of the "pdbm" class from the "aspis.products" module.

#.  Use this instance to compute the CME (Coronal Mass Ejection) model parameters based on the provided input.

#.  Predict the behavior of the CME when it reaches its target, which is the Earth in this case.

#.  Generate and save plots related to the CME modeling and prediction including "Travel time" and "ICME velocity."

Additionally, it’s essential to check the specific classes and methods used in the script to understand

their usage and any additional requirements for proper execution.

Code snippets and standardization

.................................

The idea of ASPIS.py is that code developed by different members of the astrophysics community in contiguous 

fields can be made available to each other and shared in exactly the same way as is usually done

with data. In the area of data, standardization of formats and thus the use of databases in which to store

information in a consistent and shared manner has been adopted for years. As for software, the situation

is more complex because codes have very heterogeneous characteristics among themselves and code

groupings still have strong limitations. Inspired by some recent experiences aimed at standardizing the

use of codes and comparing the performance of different methods on the same data, ASPIS.py aims to

provide a structural scheme with the ability to make the use of code developed by different members of

the astrophysics community for even different purposes available to the entire community.

In particular, ASPIS provides four main functions:

``class < class_name >:``

``def __init__ ( self ) -> None :``

``# initialize parameters``

``def process ( self ):``

``# process data``

``def run ( self ):``

``# run method``

``def plot ( self ):``

``# plot results``

These functions have been thought with the following purposes:

* the "init" function is used to instantiate the object that will perform the data analysis by initializing the necessary parameters

* the "process" function is for analyzing the data according to a predetermined pattern

* the "run" function is for using the predetermined model

* the "plot" function is for making visible the parameters, data used and outputs of the model

All four functions are parameterizable so as to ensure that complex codes that depend on parameter

structures can be framed in the ASPIS system and can therefore perform tasks according to different

patterns.

The proposed structure is based on the following consideration: advanced methods for data analysis

in the field of astrophysics (and beyond) fall into two main classes: methods supported by a physical

model and those that lack it. The former are estimation problems - direct or inverse - for which the

"process" function should be implemented as the procedure for solving the inverse problem, meaning that

the optimization method that solves the inverse problem should be implemented in this part. In the "run"

section, the code that, conversely, solves the forward problem should be inserted. Regarding methods of

data analysis without a model, namely estimation or statistical learning methods, the "process" and "run"

functions correspond, respectively, to the training or learning process of the introduced (non-physical)

model and to the prediction procedure associated with the trained model. This concept resembles the

“fit” and “predict” methods of the “scikit-learn” library used in the significantly more homogeneous case of

matrix problem

CAESAR

======

=====

.. _intro:

Intro

-------------

The Italian Space Agency (ASI) funds the CAESAR_ (Comprehensive spAce wEather Studies for the ASPIS prototype Realization) project.

The project, officially started on December 21st and lasting 25 months, responds to a call created by the agreement between ASI and the National Institute of Astrophysics (INAF) aimed at carrying out "Study activities for the scientific community of Space Weather for population of the ASPIS scientific data center ".

.. _description:

What is CAESAR?

---------------

.. _description

Description

----------------

**CAESAR** (Comprehensive Space Weather Studies for the ASPIS Prototype Realization) is a project supported by the Italian Space Agency and the National Institute of Astrophysics through the ASI-INAF n.2020-35-HH.0 agreement for the development of the ASPIS prototype of scientific data centre for Space Weather. 

.. image:: assets/images/CAESAR_logo_fullsize_color.png

  :height: 220

  :alt: Alternative text

Check out the :doc:`caesar` section for further information, including the details on :ref:`ASPIS` the project.

Check out the :doc:`CAESAR` section for further information, including the details on :ref:`ASPIS` the project.

To read about the latest news of the project please, visit the website `CAESAR project <https://caesar.iaps.inaf.it>`_

.. _project:

.. _project

The project

-----------

----------------

The aim of the project is the creation, within the ASI Space Science Data Center, of a scientific center for the collection, processing and distribution of Space Weather data to the Italian scientific community, called ASPIS_ (ASI Space Weather Infrastructure).

CAESAR_ will design and build a prototype of ASPIS_ populated with high scientific level data and products.

.. _CAESAR: https://caesar.iaps.inaf.it/

.. _ASPIS: https://www.asi.it

   :glob:

   aspis

   webapp

   aspis_py

   caesar

   products/main

   <hr>

.. toctree::

   :maxdepth: 4

   genindex

   search

.. note::