uplim/management/commands/load_survey.py
2025-03-25 18:18:56 +03:00

111 lines
3.2 KiB
Python

import numpy as np
from astropy.io import fits
from django.core.management.base import BaseCommand
from django.db import transaction
from axc_ul.models import Pixel
from itertools import islice
BATCH_SIZE = 1000000
def batch(iterable, size):
"""
Generator that yields successive chunks of size 'size' from 'iterable'.
"""
iterable = iter(iterable)
while True:
chunk = list(islice(iterable, size))
if not chunk:
break
yield chunk
class Command(BaseCommand):
help = "Process FITS files and store the data in the database"
def add_arguments(self, parser):
parser.add_argument(
'--counts',
type=str,
required=True,
help='Path of the counts file'
)
parser.add_argument(
'--exposure',
type=str,
required=True,
help='Path of the exposure file'
)
# parser.add_argument(
# '--survey_number',
# type=int,
# required=True,
# help='Integer ID of the survey being read'
# )
def handle(self, *args, **options):
counts_file = options['counts']
exposure_file = options['exposure']
# survey_number = options['survey_number']
self.stdout.write(f"Counts file: {counts_file}")
self.stdout.write(f"Exposure file: {exposure_file}")
with fits.open(counts_file) as hdul:
column_name = "T"
counts_map = hdul[1].data[column_name]
counts_data = counts_map.ravel()
with fits.open(exposure_file) as hdul:
column_name = "T"
exposure_map = hdul[1].data[column_name]
exposure_data = exposure_map.ravel()
self.stdout.write(f"Counts Data Shape: {counts_data.shape}")
self.stdout.write(f"Exposure Data Shape: {exposure_data.shape}")
assert counts_data.shape == exposure_data.shape, "Counts and exposure maps must have the same shape"
#rate_data = np.divide(counts_data, exposure_data)
# with transaction.atomic():
# survey,created = Survey.objects.get_or_create(number=survey_number)
# if created:
# self.stdout.write(f"Created a new survey instance with number: {survey.number}")
# else:
# self.stdout.write(f"Using existing survey instance with the number: {survey.number}")
# Create a generator that yields Pixel objects one by one.
pixel_generator = (
Pixel(
hpid=i,
counts=int(count),
exposure=float(exposure),
#rate=float(rate),
#survey=survey
)
for i, (count, exposure) in enumerate(zip(counts_data, exposure_data))
)
total_inserted = 0
# Process the generator in batches.
for pixel_batch in batch(pixel_generator, BATCH_SIZE):
with transaction.atomic():
Pixel.objects.bulk_create(pixel_batch)
total_inserted += len(pixel_batch)
self.stdout.write(f"Inserted {total_inserted} pixels")
self.stdout.write(f"Inserted a total of {total_inserted} pixels.")