semvec/pypi-packages huggingface dataset:
sdss 1.1.1
A python package for retrieving and analysing data from SDSS (Sloan Digital Sky Survey)
License: MIT
Requires Python: >=3.8

**Author:** [Behrouz Safari](https://behrouzz.github.io/)<br/>
**License:** [MIT](https://opensource.org/licenses/MIT)<br/>

# sdss
*A python package for retrieving and analysing data from SDSS (Sloan Digital Sky Survey)*


## Installation

Install the latest version of *sdss* from [PyPI](https://pypi.org/project/sdss/):

    pip install sdss

Requirements are *numpy*, *requests*, *Pillow*, *matplotlib*, *pandas* and *astropy*.
Versions before 1.0.0 are not dependent on *astropy*.

## Quick start

Let's create a Region:

```python
from sdss import Region

ra = 179.689293428354
dec = -0.454379056007667

reg = Region(ra, dec, fov=0.033)
```

To see the image:

```python
reg.show()
```

![alt text](https://raw.githubusercontent.com/behrouzz/astronomy/main/images/Region-show.png)

To see the image in three *gri* filter bands (green, red, infrared) separately:

```python
reg.show3b()
```

![alt text](https://raw.githubusercontent.com/behrouzz/astronomy/main/images/Region-show3b.png)

To find nearest objects:

```python
df_obj = reg.nearest_objects()
```

To find nearest objects with spectrum:

```python
df_sp = reg.nearest_spects()
```

## Photometry example

Let's download a frame, in fits and jpg, retrieve all of its objects.:

```python
from sdss.photometry import frame_filename, obj_frame_url, \
     download_file, unzip, get_df, df_radec2pixel

objid = 1237646587710014999

zip_file = 'data/' + frame_filename(objid) + '.fits.bz2'
fits_file = zip_file[:-4]
jpg_file = fits_file.replace('-r-', '-irg-').replace('fits', 'jpg')

zip_url = obj_frame_url(objid, 'r')
download_file(zip_url, 'data/')
unzip(zip_file)

jpg_url = obj_frame_url(objid, 'irg', jpg=True)
download_file(jpg_url, 'data/')

df = get_df(objid)
df = df_radec2pixel(df=df, fits_file=fits_file)

df.to_csv('data/COMP.csv', index=False)
```

Now we can plot our target image:

```python
import pandas as pd
import matplotlib.pyplot as plt
from sdss.photometry import frame_filename, obj_from_jpg

objid = 1237646587710014999

jpg_file = 'data/' + frame_filename(objid).replace('-r-', '-irg-') + '.jpg'

df = pd.read_csv('data/COMP.csv')

img = obj_from_jpg(jpg_file=jpg_file, df=df, objid=objid)

fig, ax = plt.subplots()
ax.imshow(img)
plt.show()
```

Let's find the best apparture:

```python
from sdss.photometry import flux

data = img[:,:,0]
half = data.shape[0]//2
center = (half, half)

ls_r_star = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
ls_background = []
ls_real_flux = []
for r_star in ls_r_star:
    background, real_flux = flux(data, center, r_star)
    ls_background.append(background)
    ls_real_flux.append(real_flux)

fig, ax = plt.subplots()
ax.scatter(ls_r_star, ls_real_flux, c='b')
ax.set_xlabel('R star')
ax.set_ylabel('Sky subtracted flux')
plt.grid()
plt.show()
```

See more examples at [astrodatascience.net](https://astrodatascience.net/)