151 lines
4.5 KiB
Python
151 lines
4.5 KiB
Python
# axc_ul/management/commands/load_survey.py
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import numpy as np
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from astropy.io import fits
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from django.core.management.base import BaseCommand
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from django.db import transaction
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from axc_ul.models import Pixel, Survey
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from django.db.models import Max
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from itertools import islice
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from datetime import datetime
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# DEFINE BATCH SIZE AND BATCH
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# **************************************************************
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BATCH_SIZE = 1000000
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def batch(iterable, size):
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"""
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Generator that yields successive chunks of size 'size' from 'iterable'.
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"""
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iterable = iter(iterable)
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while True:
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chunk = list(islice(iterable, size))
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if not chunk:
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break
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yield chunk
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class Command(BaseCommand):
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help = "Process FITS files and store the data in the database"
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# COMMAND LINE ARGUMENTS
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# **************************************************************
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def add_arguments(self, parser):
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parser.add_argument(
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'--counts',
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type=str,
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required=True,
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help='Path of the counts file'
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)
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parser.add_argument(
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'--exposure',
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type=str,
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required=True,
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help='Path of the exposure file'
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)
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parser.add_argument(
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'--survey_number',
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type=int,
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required=True,
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help='Integer ID of the survey being read'
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)
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def handle(self, *args, **options):
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# GET FILENAMES FROM ARGUMENTS
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# **************************************************************
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counts_file = options['counts']
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exposure_file = options['exposure']
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survey_number = options['survey_number']
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self.stdout.write(f"\nCounts file:\t{counts_file}")
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self.stdout.write(f"Exposure file:\t{exposure_file}")
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# OPEN BOTH FILES, RAVEL EACH
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# **************************************************************
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with fits.open(counts_file) as hdul:
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column_name = "T"
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counts_map = hdul[1].data[column_name]
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counts_data = counts_map.ravel()
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with fits.open(exposure_file) as hdul:
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column_name = "T"
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exposure_map = hdul[1].data[column_name]
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exposure_data = exposure_map.ravel()
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# COMPARE DATA SHAPES, ENSURE THEY'RE THE SAME
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# **************************************************************
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self.stdout.write(f"\nCounts Data Shape:\t{counts_data.shape}")
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self.stdout.write(f"Exposure Data Shape:\t{exposure_data.shape}")
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total_pixels = counts_data.shape[0]
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self.stdout.write(f"\nTotal pixels to insert:\t{total_pixels}")
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assert counts_data.shape == exposure_data.shape, "Counts and exposure maps must have the same shape"
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# CREATE THE SURVEY IF IT DOES NOT EXIST
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# **************************************************************
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with transaction.atomic():
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survey,created = Survey.objects.get_or_create(number=survey_number)
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if created:
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self.stdout.write(f"Created a new survey instance with number: {survey.number}")
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else:
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self.stdout.write(f"Using existing survey instance with the number: {survey.number}")
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# FETCH THE LAST PROCESSED HPID AND CONTINUE FROM IT
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# **************************************************************
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last_hpid = (
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Pixel.objects
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.filter(survey=survey)
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.aggregate(max_hpid=Max('hpid'))['max_hpid']
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or -1
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)
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start_index = last_hpid + 1
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pixel_generator = (
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Pixel(
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hpid=i,
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counts=int(count),
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exposure=float(exposure),
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survey=survey
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)
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for i, (count, exposure) in enumerate(zip(counts_data, exposure_data))
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if i >= start_index
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)
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total_inserted = start_index
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# Process in batches
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for pixel_batch in batch(pixel_generator, BATCH_SIZE):
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with transaction.atomic():
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Pixel.objects.bulk_create(pixel_batch)
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total_inserted += len(pixel_batch)
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percentage = total_inserted / total_pixels * 100
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timestamp = datetime.now().strftime("%H:%M:%S")
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self.stdout.write(
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f"[{timestamp}] {percentage:.2f}% inserted"
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)
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self.stdout.write(f"Inserted a total of {total_inserted} pixels.")
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