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Change np.NaN to np.nan in FIMS-SPEAR notebook. (second try) #86

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Aug 13, 2024
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Change np.NaN to np.nan in FIMS-SPEAR notebook. (second try) #86

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76bb661
Change np.NaN to np.nan in FIMS-SPEAR notebook.
adrianlucy Aug 12, 2024
daa21e0
Fix PEP8 violation.
adrianlucy Aug 12, 2024
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18 changes: 9 additions & 9 deletions notebooks/MCCM/FIMS-SPEAR/hyperspectral_healpix_maps/hyperspectral_healpix_maps.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -266,7 +266,7 @@
"\n",
"# Load the data with astropy.\n",
"l_n064 = Table.read('mccm_fims-spear_fims-ap100-n064_sky-starless_long_v1.0_hp-map-hsi.fits.gz', \n",
" hdu=1).filled(np.NaN) # The \".filled(np.NaN)\" is important; see \"a note about NaNs\" below.\n",
" hdu=1).filled(np.nan) # The \".filled(np.nan)\" is important; see \"a note about NaNs\" below.\n",
"\n",
"# Integrate over wavelength and plot the data.\n",
"hp.mollview(np.sum(l_n064['INTEN_BSUB'], axis=1), coord='G', max=5e6, unit='photon cm-2 s-1 sr-1', title='') # Galactic coordinates"
Expand Down Expand Up @@ -367,21 +367,21 @@
"hdul.close()\n",
"\n",
"l_n064 = Table.read('mccm_fims-spear_fims-ap100-n064_sky-starless_long_v1.0_hp-map-hsi.fits.gz', \n",
" hdu=1).filled(np.NaN)\n",
" hdu=1).filled(np.nan)\n",
"\n",
"s_n064 = Table.read('mccm_fims-spear_fims-ap100-n064_sky-starless_short_v1.0_hp-map-hsi.fits.gz', \n",
" hdu=1).filled(np.NaN)\n",
" hdu=1).filled(np.nan)\n",
"\n",
"l_highres = Table.read(file_highres, \n",
" hdu=1).filled(np.NaN) # this one may take a minute"
"l_highres = Table.read(file_highres,\n",
" hdu=1).filled(np.nan) # this one may take a minute"
]
},
{
"cell_type": "markdown",
"id": "c6020dd5",
"metadata": {},
"source": [
"(A note about NaNs: You might be wondering why we needed `.filled(np.NaN)`. There are regions of the sky without coverage by FIMS-SPEAR, which are represented as NaN values in the FITS binary table. By default, `astropy.Table` masks these pixels with the `numpy` fill value for masking floats, 1e20. But the `healpy` fill value for masking bad data is -1.6375e+30. Plus, we're sometimes going to need to sum arrays over the wavelength axis, and we don't want to sum up either of these fill values. So, here we're bypassing the problem entirely by using NaNs instead of masking fill values.)"
"(A note about NaNs: You might be wondering why we needed `.filled(np.nan)`. There are regions of the sky without coverage by FIMS-SPEAR, which are represented as NaN values in the FITS binary table. By default, `astropy.Table` masks these pixels with the `numpy` fill value for masking floats, 1e20. But the `healpy` fill value for masking bad data is -1.6375e+30. Plus, we're sometimes going to need to sum arrays over the wavelength axis, and we don't want to sum up either of these fill values. So, here we're bypassing the problem entirely by using NaNs instead of masking fill values.)"
]
},
{
Expand Down Expand Up @@ -766,9 +766,9 @@
"source": [
"# Load the data\n",
"l_n064_spear = Table.read('mccm_fims-spear_spear-ap100-n064_sky-starless_long_v1.0_hp-map-hsi.fits.gz', \n",
" hdu=1).filled(np.NaN)\n",
" hdu=1).filled(np.nan)\n",
"s_n064_spear = Table.read('mccm_fims-spear_spear-ap100-n064_sky-starless_short_v1.0_hp-map-hsi.fits.gz', \n",
" hdu=1).filled(np.NaN)"
" hdu=1).filled(np.nan)"
]
},
{
Expand Down Expand Up @@ -1554,7 +1554,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
"version": "3.12.4"
}
},
"nbformat": 4,
Expand Down
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