Merge pull request #84 from bsipocz/MAINT_pip_installs

MAINT: fix pip install lines

Author: bsipocz

tutorials/cloud_access/cloud-access-intro.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.2
+    jupytext_version: 1.16.7
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -86,7 +86,7 @@ This cell will install them if needed:
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install astropy hpgeom pandas pyarrow pyvo>=1.5 s3fs matplotlib astroquery
+# !pip install astropy hpgeom pandas pyarrow 'pyvo>=1.5' s3fs matplotlib astroquery
 ```
 
 ## 4. Browse buckets
@@ -166,7 +166,6 @@ To request that additional datasets be made available in cloud storage, please c
 # from astroquery.ipac.irsa import Irsa
 
 # Irsa.list_collections()
-
 ```
 
 Use PyVO to execute a search for Spitzer SEIP Super Mosaics and find cloud access information:
@@ -339,6 +338,7 @@ results_df["W1-W2"] = results_df["w1mpro"] - results_df["w2mpro"]
 results_df["W2-W3"] = results_df["w2mpro"] - results_df["w3mpro"]
 results_df.plot.hexbin("W2-W3", "W1-W2", norm=colors.LogNorm(vmin=1, vmax=500))
 ```
+
 ***
 
 ## About this notebook

tutorials/cloud_access/euclid-cloud-access.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.3
+    jupytext_version: 1.16.7
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -48,7 +48,7 @@ If you have questions, please contact the [IRSA helpdesk](https://irsa.ipac.calt
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install s3fs astropy astroquery>=0.4.10 matploltib
+# !pip install s3fs astropy 'astroquery>=0.4.10' matplotlib
 ```
 
 ```{code-cell} ipython3

tutorials/euclid_access/1_Euclid_intro_MER_images.md

@@ -58,7 +58,7 @@ Each MER image is approximately 1.47 GB. Downloading can take some time.
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install numpy astropy>=5.3 matplotlib pyvo sep>=1.4 fsspec pandas
+# !pip install numpy 'astropy>=5.3' matplotlib pyvo 'sep>=1.4' fsspec pandas
 ```
 
 ```{code-cell} ipython3

tutorials/euclid_access/4_Euclid_intro_PHZ_catalog.md

@@ -51,7 +51,7 @@ If you have questions about this notebook, please contact the [IRSA helpdesk](ht
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install requests matplotlib pandas astropy>5.2 pyvo fsspec firefly_client
+# !pip install requests matplotlib pandas 'astropy>=5.3' pyvo fsspec firefly_client
 ```
 
 ```{code-cell} ipython3

tutorials/euclid_access/Euclid_ERO.md

@@ -53,7 +53,7 @@ First, we import all necessary packages.
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install tqdm numpy matplotlib astropy photutils>=2.0 astroquery>=0.4.10 sep>=1.4 firefly_client>=3.2
+# !pip install tqdm numpy matplotlib astropy 'photutils>=2.0' 'astroquery>=0.4.10' 'sep>=1.4' 'firefly_client>=3.2'
 ```
 
 ```{code-cell} ipython3
@@ -159,7 +159,6 @@ Irsa.list_collections(servicetype='SIA')
 
 Here we use the collection *euclid_ero*, containing the Euclid ERO images. We first create a `SkyCoord` object and then query the SIA.
 
-
 ```{code-cell} ipython3
 image_tab = Irsa.query_sia(pos=(coord, search_radius), collection='euclid_ero')
 print("Number of images available: {}".format(len(image_tab)))
@@ -319,7 +318,6 @@ mask = np.isnan(img)
 
 Next, we compute the background statistics what will be used by `sep` to extract the sources above a certain threshold.
 
-
 ```{code-cell} ipython3
 mean, median, std = sigma_clipped_stats(img, sigma=3.0)
 print(np.array((mean, median, std)))
@@ -359,7 +357,6 @@ psfphot = PSFPhotometry(psf_model,
                         finder = DAOStarFinder(fwhm=0.1, threshold=3.*std, exclude_border=True), # not needed because we are using fixed initial positions.
                         aperture_radius = 4,
                         progress_bar = True)
-
 ```
 
 After initiating the `PSFPhotometry` object, we can run the PSF photometry measurement. This can take a while (typically between 1 and 2 minutes).
@@ -448,7 +445,6 @@ mask2 = np.isnan(img2)
 
 ... and we also get some statistics on the sky background.
 
-
 ```{code-cell} ipython3
 mean2, median2, std2 = sigma_clipped_stats(img2, sigma=3.0)
 print(np.array((mean2, median2, std2)))
@@ -521,7 +517,6 @@ Gaia.ROW_LIMIT = -1
 
 Next, we request the Gaia catalog around the position of the globular cluster. We use the same size as the cutout size.
 
-
 ```{code-cell} ipython3
 gaia_objects = Gaia.query_object_async(coordinate=coord, radius = cutout_size/2)
 print("Number of Gaia stars found: {}".format(len(gaia_objects)))

tutorials/parquet-catalog-demos/wise-allwise-catalog-demo.md

@@ -4,9 +4,9 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.2
+    jupytext_version: 1.16.7
 kernelspec:
-  display_name: parquet
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -43,7 +43,7 @@ Each method accesses the parquet files a bit differently and is useful for diffe
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install pandas>=1.5.2 pyarrow>=10.0.1 matplotlib hpgeom astropy
+# !pip install 'pandas>=1.5.2' 'pyarrow>=10.0.1' matplotlib hpgeom astropy
 ```
 
 ```{code-cell} ipython3