Alyssa Bulatek abulatek

## madcuba_ascii_writer.py
from astropy.io import fits
from spectral_cube import OneDSpectrum
from glob import glob

def write_madcuba_ascii(fn):
    # Write the ASCII header based on the FITS spectrum provided
    hdul = fits.open(fn)
    # Get header parts
    spec_unit = f"UnitSpectral: {hdul[0].header['CUNIT1']}"
    int_unit = f"UnitInten: {hdul[0].header['BUNIT']}"

## aspect_ratios.py
# I hope this is a MWE

# Generate random signal mask, using seed
numpy.random.seed(24)
signal_mask = np.random.rand(37,512,512)

import matplotlib.pyplot as plt
from astropy.visualization import simple_norm

# Generate data that we're plotting (from a cube called `signal_mask`

## SpectralCubeReprojectMaskExample_v3.ipynb

      
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                abulatek
                / SpectralCubeReprojectMaskExample_v3.ipynb
            
            
              Last active
              July 2, 2021 15:59
            
              
                Compiled from tutorials by Adam Ginsburg (reprojection) and Eric Koch, Adam Ginsburg, Tom Robitaille, and Erik Rosolowsky (signal masking). Example of reprojecting one spectral cube onto another, and masking the signal of one cube with the signal of another. Now with better noise estimation, more annotations, continuum subtraction, and PV projec…
              
          
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## glue_GUI_attempt.py
from spectral_cube import SpectralCube, StokesSpectralCube # maybe don't need StokesSpectralCube
from glue.core import Data, DataCollection
from glue.app.qt.application import GlueApplication
from glue.viewers.image.qt import ImageViewer
from glue.core.coordinates import coordinates_from_wcs

image_dir = '/orange/adamginsburg/brick_alma_linesurvey/2019.1.00092.S/imaging_results/'
image_file = image_dir + 'BrickMaser_87_spw25.residual'
cube = SpectralCube.read(image_file, format = 'casa_image')
	from astropy.io import fits
	from spectral_cube import OneDSpectrum
	from glob import glob

	def write_madcuba_ascii(fn):
	# Write the ASCII header based on the FITS spectrum provided
	hdul = fits.open(fn)
	# Get header parts
	spec_unit = f"UnitSpectral: {hdul[0].header['CUNIT1']}"
	int_unit = f"UnitInten: {hdul[0].header['BUNIT']}"
	# I hope this is a MWE

	# Generate random signal mask, using seed
	numpy.random.seed(24)
	signal_mask = np.random.rand(37,512,512)

	import matplotlib.pyplot as plt
	from astropy.visualization import simple_norm

	# Generate data that we're plotting (from a cube called `signal_mask`
	from spectral_cube import SpectralCube, StokesSpectralCube # maybe don't need StokesSpectralCube
	from glue.core import Data, DataCollection
	from glue.app.qt.application import GlueApplication
	from glue.viewers.image.qt import ImageViewer
	from glue.core.coordinates import coordinates_from_wcs

	image_dir = '/orange/adamginsburg/brick_alma_linesurvey/2019.1.00092.S/imaging_results/'
	image_file = image_dir + 'BrickMaser_87_spw25.residual'
	cube = SpectralCube.read(image_file, format = 'casa_image')