| Name | sdss JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | https://github.com/behrouzz/sdss |
| Summary | A python package for retrieving and analysing data from SDSS (Sloan Digital Sky Survey) |
| upload_time | 2023-02-09 13:07:14 |
| maintainer | |
| docs_url | None |
| author | Behrouz Safari |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
**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()
```
To see the image in three *gri* filter bands (green, red, infrared) separately:
```python
reg.show3b()
```
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/)
Raw data
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