MAINT: cleanup imports and empty cells

bsipocz · bsipocz · commit 70d4b9627c86 · 2025-03-12T21:15:44.000-07:00
diff --git a/tutorials/euclid_access/1_Euclid_intro_MER_images.md b/tutorials/euclid_access/1_Euclid_intro_MER_images.md
@@ -54,45 +54,29 @@ Each MER image is approximately 1.47 GB. On Caltech wifi this takes between 1-5
 ## Imports
 
 ```{code-cell} ipython3
-# Uncomment the next lines to install dependencies if needed
+# Uncomment the next line to install dependencies if needed.
+# !pip install numpy astropy matplotlib pyvo sep>=1.4 fsspec pandas
+```
 
-# Installation for pip
-# !pip install sep
-# !pip install astropy
-# !pip install pyvo
-# !pip install requests
-# !pip install fsspec[http]
+```{code-cell} ipython3
+import re
 
+import numpy as np
+import pandas as pd
 
-# Installation for conda
-# !conda install -c conda-forge sep
-# !conda install -c conda-forge astropy
-# !conda install -c conda-forge pyvo
-# !conda install requests
-# !conda install -c conda-forge fsspec requests aiohttp
-```
+import matplotlib.pyplot as plt
+from matplotlib.patches import Ellipse
 
-```{code-cell} ipython3
-from astropy.io import fits
 from astropy.coordinates import SkyCoord
-from astropy import units
-from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStretch,  ZScaleInterval, SquaredStretch
+from astropy.io import fits
 from astropy.nddata import Cutout2D
-from astropy.wcs import WCS
 from astropy.utils.data import download_file
+from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStretch,  ZScaleInterval, SquaredStretch
+from astropy.wcs import WCS
+from astropy import units as u
 
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.patches import Ellipse
-import os
-from os import listdir
-from os.path import isfile, join
-import pandas as pd
-import glob
-import sep
-import re
 import pyvo as vo
-import fsspec
+import sep
 ```
 
 # Introduction to Euclid Q1 MER mosaics
@@ -103,15 +87,8 @@ import fsspec
 Below are the object name and coordinates and our search radius
 
 ```{code-cell} ipython3
-name = 'HD 168151'
-
-ra = 273.474451
-dec = 64.397273
-
-search_radius = 1.5 * units.arcmin
-pos = SkyCoord(ra=ra, dec=dec, unit='deg')
-
-coord = SkyCoord(ra, dec, unit=(units.deg,units.deg), frame='icrs')
+search_radius = 10 * u.arcsec
+coord = SkyCoord.from_name('HD 168151')
 ```
 
 Use IRSA to search for all Euclid data on this target. 
@@ -121,14 +98,13 @@ This query will return any image with pixels that overlap the search region.
 ```{code-cell} ipython3
 irsa_service= vo.dal.sia2.SIA2Service('https://irsadev.ipac.caltech.edu/SIA')
 
-im_table = irsa_service.search(pos=(pos.ra.deg, pos.dec.deg, 10*units.arcsec),
-                                collection='euclid_DpdMerBksMosaic')
+image_table = irsa_service.search(pos=(coord, search_radius), collection='euclid_DpdMerBksMosaic')
 ```
 
 Convert the table to pandas dataframe
 
 ```{code-cell} ipython3
-df_im_irsa=im_table.to_table().to_pandas()
+df_im_irsa=image_table.to_table().to_pandas()
 ```
 
 Change the settings so we can see all the columns in the dataframe and the full column width (to see the full long URL)
@@ -155,14 +131,6 @@ df_im_euclid.head()
 print('There are',len(df_im_euclid),'MER images of this object/MER tile.')
 ```
 
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
-
 ## 2. Retrieve a Euclid Q1 MER mosaic image in the VIS bandpass
 
 +++
@@ -282,7 +250,7 @@ filters
 ```{code-cell} ipython3
 ######################## User defined section ############################
 ## How large do you want the image cutout to be?
-im_cutout= 1.0 * units.arcmin
+im_cutout= 1.0 * u.arcmin
 
 ## What is the center of the cutout? 
 ## For now choosing a random location on the image 
@@ -294,7 +262,7 @@ dec =  64.525
 # ra = 273.474451
 # dec = 64.397273
 
-coords_cutout = SkyCoord(ra, dec, unit=(units.deg,units.deg), frame='icrs')
+coords_cutout = SkyCoord(ra, dec, unit=(u.deg, u.deg), frame='icrs')
 
 ##########################################################################
 
@@ -434,7 +402,6 @@ print("Found", len(sources_thr), "objects above flux threshold")
 We plot the VIS cutout with the sources detected overplotted with a red ellipse
 
 ```{code-cell} ipython3
-
 fig, ax = plt.subplots()
 m, s = np.mean(data_sub), np.std(data_sub)
 im = ax.imshow(data_sub, cmap='gray', origin='lower', norm=ImageNormalize(img2, interval=ZScaleInterval(), stretch=SquaredStretch()))
diff --git a/tutorials/euclid_access/2_Euclid_intro_MER_catalog.md b/tutorials/euclid_access/2_Euclid_intro_MER_catalog.md
@@ -46,35 +46,14 @@ Each entry in the MER catalog is a single source containing all its photometry f
 ## Imports
 
 ```{code-cell} ipython3
-# Uncomment the next lines to install dependencies if needed
-
-# Installation for pip
-# !pip install sep
-# !pip install astropy
-# !pip install pyvo
-
-
-# Installation for conda
-# !conda install -c conda-forge sep
-# !conda install -c conda-forge astropy
-# !conda install -c conda-forge pyvo
+# Uncomment the next line to install dependencies if needed
+# !pip install numpy matplotlib pyvo
 ```
 
 ```{code-cell} ipython3
-from astropy.io import fits
-from astropy.coordinates import SkyCoord
-from astropy import units
-from astropy.wcs import WCS
-from astropy.io.votable import parse
-
 import numpy as np
 import matplotlib.pyplot as plt
-import os
-from os import listdir
-from os.path import isfile, join
-import pandas as pd
-import glob
-import sep
+
 import pyvo as vo
 ```
 
diff --git a/tutorials/euclid_access/3_Euclid_intro_1D_spectra.md b/tutorials/euclid_access/3_Euclid_intro_1D_spectra.md
@@ -50,38 +50,21 @@ This notebook provides an introduction to the SIR 1D spectra released as part of
 ## Imports
 
 ```{code-cell} ipython3
-# Uncomment the next lines to install dependencies if needed
-
-# Installation for pip
-# !pip install astropy
-# !pip install pyvo
-
-
-# Installation for conda
-# !conda install -c conda-forge astropy
-# !conda install -c conda-forge pyvo
+# Uncomment the next line to install dependencies if needed
+# !pip install matplotlib pandas requests astropy pyvo
 ```
 
 ```{code-cell} ipython3
-from astropy.io import fits
-from astropy.coordinates import SkyCoord
-from astropy import units
-from astropy.wcs import WCS
-from astropy.table import Table
-from astropy.units import UnitsWarning
+from io import BytesIO
 
-import warnings
-import math
-import numpy as np
 import matplotlib.pyplot as plt
-import os
-from os import listdir
-from os.path import isfile, join
 import pandas as pd
-import glob
-import pyvo as vo
-from io import BytesIO
 import requests
+
+from astropy.io import fits
+from astropy.table import Table
+
+import pyvo as vo
 ```
 
 # Introduction to Euclid Q1 1D spectra
@@ -121,10 +104,6 @@ pd.set_option('display.max_colwidth', None)
 # pd.reset_option('display.max_colwidth')
 ```
 
-```{code-cell} ipython3
-
-```
-
 ## 2. Search for the spectrum of a specific galaxy in the 1D spectra table
 
 ```{code-cell} ipython3
diff --git a/tutorials/euclid_access/5_Euclid_intro_SPE_catalog.md b/tutorials/euclid_access/5_Euclid_intro_SPE_catalog.md
@@ -37,9 +37,6 @@ These notebooks focus on how to access, download, and process Euclid Q1 data fro
 
 Every one dimensional spectrum is processed through a template and line fitting pipeline, producing several different 'SPE' catalogs. This notebook provides an introduction to the SPE catalogs released as part of Euclid Q1. Other Euclid notebooks show how to use other data products released as part of Euclid Q1.
 
-```{code-cell} ipython3
-
-```
 
 ### NOTE to testers -- please log in to IPAC vpn to access the IRSAdev data. After the Q1 data release, we will not need to use the IPAC VPN or the irsadev site.
 
@@ -48,47 +45,27 @@ Every one dimensional spectrum is processed through a template and line fitting
 ## Imports
 
 ```{code-cell} ipython3
-# Uncomment the next lines to install dependencies if needed
-
-# Installation for pip
-# !pip install sep
-# !pip install astropy
-# !pip install pyvo
-
-
-# Installation for conda
-# !conda install -c conda-forge sep
-# !conda install -c conda-forge astropy
-# !conda install -c conda-forge pyvo
+# Uncomment the next line to install dependencies if needed
+# !pip install matplotlib pandas astropy pyvo
 ```
 
 ```{code-cell} ipython3
-from astropy.io import fits
-from astropy.coordinates import SkyCoord
-from astropy import units
-from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStretch
-from astropy.nddata import Cutout2D
-from astropy.wcs import WCS
-from astropy.table import Table
-from astropy.utils.data import download_file
+from io import BytesIO
+import re
 
-import numpy as np
 import matplotlib.pyplot as plt
-import os
-from os import listdir
-from os.path import isfile, join
 import pandas as pd
-import glob
-import sep
-import pyvo as vo
-import re
-from io import BytesIO
 import requests
-```
 
-# Introduction to Euclid Q1 SPE catalog
+from astropy.coordinates import SkyCoord
+from astropy.io import fits
+from astropy.table import Table
+from astropy import units as u
+from astropy.utils.data import download_file
+from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStretch
 
-+++
+import pyvo as vo
+```
 
 ## 1. Find the MER Tile ID that corresponds to a given RA and Dec
 
@@ -98,10 +75,10 @@ In this case, choose the coordinates from the first notebook to save time downlo
 ra = 273.474451
 dec = 64.397273
 
-search_radius = 1.5 * units.arcmin
+search_radius = 1.5 * u.arcmin
 pos = SkyCoord(ra=ra, dec=dec, unit='deg')
 
-coord = SkyCoord(ra, dec, unit=(units.deg,units.deg), frame='icrs')
+coord = SkyCoord(ra, dec, unit=(u.deg, u.deg), frame='icrs')
 ```
 
 ### Use IRSA to search for all Euclid data on this target
@@ -111,12 +88,11 @@ This searches specifically in the euclid_DpdMerBksMosaic "collection" which is t
 ```{code-cell} ipython3
 irsa_service= vo.dal.sia2.SIA2Service('https://irsadev.ipac.caltech.edu/SIA')
 
-im_table = irsa_service.search(pos=(pos.ra.deg, pos.dec.deg, 10*units.arcsec),
+im_table = irsa_service.search(pos=(pos.ra.deg, pos.dec.deg, 10*u.arcsec),
                                 collection='euclid_DpdMerBksMosaic')
 
 ## Convert the table to pandas dataframe
 df_im_irsa=im_table.to_table().to_pandas()
-
 ```
 
 ```{code-cell} ipython3
@@ -346,41 +322,15 @@ plt.ylabel('Flux (erg / (Angstrom s cm2))')
 plt.title(obj_id)
 ```
 
-```{code-cell} ipython3
-
-```
-
 ## Additional Resources
 
 If you have any issues accessing data from the archives, please contact the helpdesk directly: IRSA (irsasupport@ipac.caltech.edu) and ESA (https://support.cosmos.esa.int/euclid).
 
-```{code-cell} ipython3
-
-```
++++
 
 ## About this Notebook
 
 **Author(s)**: Tiffany Meshkat <br>
 **Keyword(s)**: Euclid, Q1, spe catalog <br>
 **First published**: March 19, 2025 <br>
 **Last updated**: March 19, 2025
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