"""
===================
Querying PUNCH Data
===================

A notebook guide to querying and loading PUNCH data using SunPy.
"""

# %%
# This notebook provides a guide on how to query PUNCH data from the SDAC / VSO using Python tools, and how to load and display this data.
# Note that PUNCH provides some bespoke tools to assist Fido in querying the complex range of data products and versions of PUNCH.


# %%
# Load libraries

from sunpy.net import Fido
from sunpy.net import attrs as a

import punchbowl  # Note that this import is needed to register PUNCH fido tools
from punchbowl.data import punch_io, visualize

# %%
# With a range of dates and a PUNCH data product in mind, we can begin querying data.
# Here we'll search for level 3 clear low-noise mosaics from 1-2 November 2025.
# We're looking for CAM data, so a product code of "CA" and a instrument code of "M".
# We can construct a query using the Fido tool, specifying search attributes:

# %%
result = Fido.search(a.Time('2025/10/30 12:00:00', '2025/10/31 12:00:00'),
                     a.punch.ProductCode.ca, # (ca for clear low-noise), or pa for polarized low-noise, etc.
                     a.Instrument.m, # (m for mosaic), or a.Instrument.nfi_4, etc for earlier levels.
                     a.Level.three,
                     a.punch.DataVersion.newest, # or a.punch.DataVersion.zero_j, etc.
                     a.punch.FileType.fits) # or a.punch.FileType.jp2

result

# %%
# This results in a table of available data products that match the search criteria.
# Next, let's download the first file from this list of results:

# %%
try:
    files = Fido.fetch(result[0][0])
except IndexError:
    print("Oops no files were found!")
    files = None
# %%
# This returns a list of paths to files that have been downloaded. Note that the Fido.fetch tool can specify a particular download directory for larger data searches.
# With that file downloaded, we can plot the data:

# %%
if files:
    fig, ax = visualize.plot_punch(files[0])

# %%
# And that's it! From here the data is loaded into the PUNCH plotting tool and displayed.
# Of course this is just one path, you could always load the data using the punchbowl.data.punch_io.load_ndcube_from_fits() function or Astropy fits tools.
# As an example you could load this file using:

# %%
if files:
    datacube = punch_io.load_ndcube_from_fits(files[0])

datacube