Metadata-Version: 2.1
Name: discontinuitypy
Version: 0.1.1
Summary: A python package for identifying and analyzing discontinuities for time series data.
Author-Email: Beforerr <zzj956959688@gmail.com>
Project-URL: Homepage, https://beforerr.github.io/discontinuitypy
Project-URL: Repository, https://github.com/Beforerr/discontinuitypy
Requires-Python: >=3.10
Requires-Dist: space-analysis.py[io]
Requires-Dist: beforerr
Requires-Dist: plasmapy
Requires-Dist: xarray[accel]
Requires-Dist: xarray_einstats
Requires-Dist: polars
Requires-Dist: modin
Requires-Dist: pydantic
Requires-Dist: fastcore
Requires-Dist: pdpipe
Requires-Dist: loguru
Description-Content-Type: text/markdown

# DiscontinuityPy


# User Guide

This package is designed to identify and analyze discontinuities in time
series data.

1.  Finding the discontinuities, see [this
    notebook](./01_ids_detection.ipynb)
    - Corresponding to limited feature extraction / anomaly detection
2.  Calculating the properties of the discontinuities, see [this
    notebook](./02_ids_properties.ipynb)
    - One can use higher time resolution data

For how to use this project as a python library, please see [this
page](./00_ids_finder.ipynb).

## Installation

``` shell
pip install ids-finder
```

## Getting started

Import the package

``` python
from discontinuitypy.utils.basic import *
from discontinuitypy.core import *
```

# Related projects and publications

- [Solar wind discontinuities spatial evolution in the outer
  heliosphere](https://beforerr.github.io/ids_spatial_evolution_juno/)
- [Evolution of solar wind discontinuities in the inner heliosphere: PSP
  and Earth conjunctions and
  alignments](https://beforerr.github.io/psp_conjunction/)

# TODOs

Science part

- Analysis
  - [ ] Contribution of discontinuities to the power spectrum
  - [ ] Check Datagap
  - [ ] Distribution of \|B\| over radius
  - [ ] Add error bar
  - [ ] More accurate way to obtain the properties of the
    discontinuities
- Identifaction
  - [ ] Ensemble forest?
  - [ ] Smoothing is important?
  - [ ] Check change point algorithm
- Features
  - [ ] Thickness in N direction
  - [ ] Use high resolution data for feature extraction
- Compare with other methods of identifying IDs
  - [ ] Verify with other methods of identifying IDs

Code part

- Optimization
  - [ ] `JAX` library for `numpy` optimization
  - [ ] shorten import time
- Refactor
  - [x] `process_candidates` to exclude `sat_state` logics
  - [x] renaming feature layer `candidates`
- [x] Kedro
  - [x] Modular pipelines

### bugs

- [ ] STEREO `B` less than zero (after downsampling?)
