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reducing memory usage on OU TDE notebook #326

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25 changes: 13 additions & 12 deletions tutorials/simulated-data/OpenUniverse2024/openuniverse2024_TDE_light_curve.md
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ jupytext:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.19.1
jupytext_version: 1.19.3
kernelspec:
name: python3
display_name: python3
Expand Down Expand Up @@ -64,7 +64,7 @@ import numpy as np
import s3fs
from matplotlib import pyplot as plt
import pandas as pd
from photutils.aperture import SkyCircularAperture, aperture_photometry
from photutils.aperture import SkyCircularAperture, SkyCircularAnnulus, aperture_photometry, ApertureStats
from scipy.ndimage import rotate
from astropy import units as u
from astropy.coordinates import SkyCoord
Expand Down Expand Up @@ -177,6 +177,7 @@ def show_gallery(files, max_images=9):
vmin, vmax = np.nanpercentile(data, [5, 99])
# Show image in grayscale with good contrast
axes[i].imshow(data, origin="lower", cmap="gray", vmin=vmin, vmax=vmax)
del data
# Title: just the base filename
axes[i].set_title(f.split("/")[-1], fontsize=8)
# Remove tick marks and labels
Expand All @@ -193,7 +194,7 @@ def show_gallery(files, max_images=9):

```{code-cell} ipython3
# Number of example images to display. Increase to see more of the available images.
n_gallery_images = 6
n_gallery_images = 4
show_gallery(files, max_images=n_gallery_images)
```

Expand Down Expand Up @@ -423,10 +424,6 @@ The first function, `run_aperture_photometry()`, performs simple circular apertu
The two plotting functions then compile these measurements into time-ordered plots showing how the observed flux evolves across multiple visits, providing a first look at temporal variability that could signal transient activity or host-galaxy changes.

```{code-cell} ipython3
---
jupyter:
source_hidden: true
---
def run_aperture_photometry(host_galaxy, bandname, image_column="image_filenames", aperture_radius=1.0):
"""
Perform circular aperture photometry on a list of FITS images.
Expand Down Expand Up @@ -480,15 +477,18 @@ def run_aperture_photometry(host_galaxy, bandname, image_column="image_filenames
# Check output (optional)
#print(phot_table)

# Background estimate (median of finite pixels)
background = np.nanmedian(data)
# Background estimate from a local annulus around the source
annulus = SkyCircularAnnulus(sky_position, r_in=3 * aperture_radius * u.arcsec,
r_out=6 * aperture_radius * u.arcsec)
annulus_stats = ApertureStats(data, annulus, wcs=wcs)
background = annulus_stats.median

# Subtract background from aperture sum
aperture_area = pixel_aperture.area
flux = phot_table['aperture_sum'][0] - background * aperture_area

# Approximate uncertainty from background rms
flux_err = np.nanstd(data) * np.sqrt(aperture_area)
# Approximate uncertainty from background rms in annulus
flux_err = annulus_stats.std * np.sqrt(aperture_area)

# Observation mid-time from MJD-OBS
mjd_obs = header.get('MJD-OBS', None)
Expand Down Expand Up @@ -719,6 +719,7 @@ def make_cutout(fname, ra, dec, size=100):
except Exception as e:
print(f"Error creating cutout for {fname}: {e}")
return None
del img

# Step 2: determine rotation angle to place North at the top.
# Invert the CD matrix to find the North direction in pixel space.
Expand Down Expand Up @@ -830,7 +831,7 @@ def cutout_gallery(image_filenames, mjd_list, ra, dec, aperture_radius_pix_list,
# make cutout gallery of the host galaxy

# Number of cutout images to display. Increase to show more epochs.
n_cutout_images = 6
n_cutout_images = 4

single_gal = host_galaxy.iloc[0]

Expand Down
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