#!/usr/bin/env python
# coding: utf-8

# This notebook is a brief guide of downloading KPF raw (level 0) data
# in a given period. It requires PI credentials to access proprietary
# data. For more detail about PyKOA, please see:
# http://koa.ipac.caltech.edu/UserGuide/PyKOA/notebooks/PyKOA_KPF_introduction.html

# First, we need to install the KOA python client PyKOA. This line can
# be removed if PyKOA is already installed.

# In[1]:


get_ipython().system('pip install pykoa')


# The next steop is to import pre-requisite packages

# In[2]:


import sys
import io
import os
from pykoa.koa import Koa 
from astropy.table import Table,Column


# Then we need to assign directory that stores the query results:

# In[3]:


try:
    os.mkdir('./output')
except:
    print(" Directory exists already", flush=True)


# After that, we use a cookie for KOA log in. We can also spell out
# the credentials, but for security purpose, it is better to enter the
# userid and password interactively

# In[4]:


Koa.login ('./tapcookie.txt')
#Koa.login ('./tapcookie.txt', userid='clee', password='salami')


# We can then proceed to query KPF data taken in a given period. Note
# the time period is specified in UT date. The output table will be
# saved in the designated directory and in IPAC table format.

# In[5]:


Koa.query_datetime ('kpf', \
    '2023-05-30 00:00:00/2023-05-30 23:59:59', \
    './output/kpf_daterange.tbl', overwrite=True, format='ipac', \
    cookiepath='./tapcookie.txt')

rec = Table.read ('./output/kpf_daterange.tbl',format='ipac')
print (rec)
                    


# Once we have the query result, we can download files to the
# designated directory, in this case to dnload_dir_kpf/. If the
# download function is called several times, it will check the
# designated directory and only download new files.

# In[7]:


Koa.download ('./output/kpf_daterange.tbl',  
    'ipac', \
    'dnload_dir_kpf' )