# PyXCSAO
Replicates functionality of IRAF XCSAO
To run:
### Import
from pyxcsao.crosscorrelate import PyXCSAO
### Initiates instance:
b=PyXCSAO(st_lambda=5000,end_lambda=10000)
---optional parameters: ncols=8192,low_bin=0,top_low=10,top_nrun=125,nrun=255,bell_window=0.05,minvel=-500,maxvel=500
### Adds Synthetic grid
First time running:
b.add_grid(grid_pickle='phoenix.p',grid_path='phoenix/*0.0/*4.5*.fits',grid_class='phoenix')
---options: phoenix, phoenixhires, coelho
From a precompiled pickle file:
b.add_grid(grid_pickle='phoenix.p')
### Adds data
b.add_spectrum('file.fits',data_class='boss')
---options: boss,lamost,segue,user
### Run XCSAO and get parameters
print(b.run_XCSAO())
### Optimized for large grids:
print(b.run_XCSAO_optimized())
### Plot CCF:
plt.plot(b.lag,b.best_ccf)
### Example Code
```python
import glob
import pandas as pd
from pyxcsao.crosscorrelate import PyXCSAO
from astropy.table import Table
import time
cat=Table.read('path.fits')
best=[]
b=PyXCSAO(st_lambda=5000,end_lambda=10000)
b.add_grid(grid_pickle='phoenix_full1.p')
batchsize=500
for j in range(0,len(cat),batchsize):
cat1=cat[j:j+batchsize]
print(j)
for i in range(len(cat1)):
path=cat1['path'][i]
try:
b.add_spectrum(path)
x=b.run_XCSAO_optimized()
best.append(x.copy())
except:
print(path)
df = pd.DataFrame(best)
df.to_csv('batch.csv')
```
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