Metadata-Version: 2.1
Name: pygrb-lc
Version: 0.1.0
Summary: Python package for GRB analysis
Home-page: https://github.com/Jorezzz/pygrb_lc
Author: Mozgunov Georgiy
Author-email: georgiy99@bk.ru
Project-URL: Bug Tracker, https://github.com/Jorezzz/pygrb_lc/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# pygrb_lc
Package to load and handle GRB (Gamma-ray bursts) light curves, their furie transformations and various other functions.

To install run the following command

```bash
pip install --upgrade pygrb_lc
```
or
```bash
pip3 install --upgrade pygrb_lc
```

## Light curves

Main object in this package is LightCurve object and its children. Start by creating one
```python
from pygrb_lc.light_curves import LightCurve # base class for ligth curve

lc = LightCurve()
print(lc)
```
It is created but empty, semplest way to provide data is by ```data``` argument, that requires ```numpy.ndarray``` with 2 (time, signal) or 4 (time, time_err, signal, signal_err) columns
```python
import numpy as np

data = np.loadtxt('test.txt')
lc = LightCurve(data = data)
print(lc)
```

Base class can rebin data, subtract polynomial function, filter peaks, set intervals and load and save data to ```LIGHT_CURVE_SAVE``` folder in pickle format.

There are specific classes for INTERAL/SPI-ACS and Fermi/GBM instruments. They are able to load actual data from web, you need to specify it in loading_method parameter
```python
from pygrb_lc.light_curves import SPI_ACS_LightCurve, GBM_LightCurve

lc1 = SPI_ACS_LightCurve('2020-01-01 00:00:00', 500, loading_method = 'web')
lc2 = GBM_LightCurve('2020-01-01 00:00:00', ['na'], duration = 500, loading_method = 'web')
```
## Furie transformations

Furie transformation is performed by FurieLightCurve class. It requires LightCurve object as an argument
```python
from pygrb_lc.furie import FurieLightCurve

lc = LightCurve(data = np.loadtxt('test.txt'))
flc = FurieLightCurve(lc, interval_t90 = (0, 10))

flc.plot()
```

All classes have plot method, you can provide matplotlib.pyplot.Axes object as an argument to plot on existing plot
```python
fig,(ax1,ax2) = plt.subplots(2,1)
lc.plot(ax = ax1)
flc.plot(ax = ax2)
```


# Roadmap
Add support of main GRB catalogues and their connection with light curves. 
Create Catalogue class that can be compared and intersected to other. 
Add support of spectra (based on current ```photon_data``` in GBM_LightCurve class)
Add typical functions for approximation: Band function, power law, etc. and their interaction with ```LightCurve``` and ```FurieLightCurve``` classes.
