HTML: updating to not execute notebooks

_sources/forced_photometry/multiband_photometry.md.txt

@@ -8,7 +8,7 @@ jupytext:
 kernelspec:
   display_name: Python [conda env:tractor]
   language: python
-  name: python3
+  name: conda-env-tractor-py
 ---
 
 # Automated Multiband Forced Photometry on Large Datasets
@@ -17,7 +17,7 @@ kernelspec:
 ## Learning Goals:
 By the end of this tutorial, you will be able to:
 - get catalogs and images from NASA archives in the cloud where possible
-- measure fluxes at any location by running forced photometry using "The Tractor"
+- measure fluxes at any location by running forced photometry using "The Tractor" 
 - employ parallel processing to make this as fast as possible
 - cross match large catalogs
 - plot results
@@ -117,8 +117,8 @@ except ImportError:
 ```
 
 ## 1. Retrieve Initial Catalog from IRSA
-- Automatically set up a catalog with ra, dec, photometric redshifts, fiducial band fluxes, & probability that it is a star
-- Catalog we are using is COSMOS2015 (Laigle et al. 2016)
+- Automatically set up a catalog with ra, dec, photometric redshifts, fiducial band fluxes, & probability that it is a star  
+- Catalog we are using is COSMOS2015 (Laigle et al. 2016)  
 - Data exploration
 
 ```{code-cell} ipython3
@@ -128,7 +128,7 @@ except ImportError:
 coords = SkyCoord('150.01d 2.2d', frame='icrs')  #COSMOS center acording to Simbad
 
 #how large is the search radius, in arcmin
-radius = 48.0 * u.arcmin
+radius = 48.0 * u.arcmin  
 
 #specify only select columns to limit the size of the catalog
 cols = [
@@ -157,7 +157,7 @@ print("Number of objects: ", len(cosmos_table))
 - if desired could filter the initial catalog to only include desired sources
 
 ```{code-cell} ipython3
-#an example of how to filter the catalog to
+#an example of how to filter the catalog to 
 #select those rows with either chandra fluxes or Galex NUV fluxes
 
 #example_table = cosmos_table[(cosmos_table['flux_chandra_05_10']> 0) | (cosmos_table['flux_galex_fuv'] > 0)]
@@ -168,13 +168,13 @@ print("Number of objects: ", len(cosmos_table))
 
 +++
 
-#### Use the fornax cloud access API to obtain the IRAC data from the IRSA S3 bucket.
+#### Use the fornax cloud access API to obtain the IRAC data from the IRSA S3 bucket. 
 
 Details here may change as the prototype code is being added to the appropriate libraries, as well as the data holding to the appropriate NGAP storage as opposed to IRSA resources.
 
 ```{code-cell} ipython3
 # Temporary solution, remove when the fornax API is added to the image
-# This relies on the assumption that https://github.com/fornax-navo/fornax-cloud-access-API is being cloned to this environment.
+# This relies on the assumption that https://github.com/fornax-navo/fornax-cloud-access-API is being cloned to this environment. 
 # If it's not, then run a ``git clone https://github.com/fornax-navo/fornax-cloud-access-API --depth=1`` from a terminal at the highest directory root.
 # You may need to update the fork if you forked it in the past
 
@@ -204,7 +204,7 @@ spitzer = cosmos_results[cosmos_results['dataset'] == 'IRAC']
 ```
 
 ```{code-cell} ipython3
-# Temporarily add the cloud_access metadata to the Atlas response.
+# Temporarily add the cloud_access metadata to the Atlas response. 
 # This dataset has limited acces, thus 'region' should be used instead of 'open'.
 # S3 access should be available from the daskhub and those who has their IRSA token set up.
 
@@ -216,12 +216,12 @@ spitzer['cloud_access'] = [(f'{{"aws": {{ "bucket_name": "irsa-mast-tike-spitzer
 ```
 
 ```{code-cell} ipython3
-# Adding function to download multiple files using the fornax API.
+# Adding function to download multiple files using the fornax API. 
 # Requires https://github.com/fornax-navo/fornax-cloud-access-API/pull/4
 def fornax_download(data_table, data_subdirectory, access_url_column='access_url',
                     fname_filter=None, verbose=False):
     working_dir = os.getcwd()
-
+    
     data_directory = f"data/{data_subdirectory}"
     os.chdir(data_directory)
     for row in data_table:
@@ -232,12 +232,12 @@ def fornax_download(data_table, data_subdirectory, access_url_column='access_url
         # on-prem download returns temp file path, s3 download downloads file
         if temp_file:
             os.rename(temp_file, os.path.basename(row['fname']))
-
+        
     os.chdir(working_dir)
 ```
 
 ```{code-cell} ipython3
-fornax_download(spitzer, access_url_column='sia_url', fname_filter='go2_sci',
+fornax_download(spitzer, access_url_column='sia_url', fname_filter='go2_sci', 
                 data_subdirectory='IRAC', verbose=False)
 ```
 
@@ -445,13 +445,13 @@ sci_bkg_pairs = [
 ```{code-cell} ipython3
 def calc_instrflux(band, ra, dec, stype, ks_flux_aper2, img_pair, df):
     """
-    Calculate single-band instrumental fluxes and uncertainties at the given ra, dec
+    Calculate single-band instrumental fluxes and uncertainties at the given ra, dec 
     using tractor.
 
     Parameters:
     -----------
     band : `Band`
-        Collection of parameters for a single band.
+        Collection of parameters for a single band. 
         A `Band` is a named tuple with the following attributes:
             idx : int
                 Identifier for the band/channel.
@@ -494,7 +494,7 @@ def calc_instrflux(band, ra, dec, stype, ks_flux_aper2, img_pair, df):
     subimage, bkgsubimage, x1, y1, subimage_wcs = cutout.extract_pair(
         ra, dec, img_pair=img_pair, cutout_width=band.cutout_width, mosaic_pix_scale=band.mosaic_pix_scale
     )
-
+    
     # find nearby sources that are possible contaminants
     objsrc, nconfsrcs = find_nconfsources(
         ra, dec, stype, ks_flux_aper2, x1, y1, band.cutout_width, subimage_wcs, df
@@ -550,7 +550,7 @@ t1 = time.time()
 ```
 
 ### 3d. Calculate Forced Photometry - Parallelization
-- Parallelization: we can either interate over the rows of the dataframe and run the four bands in parallel; or we could zip together the row index, band, ra, dec,
+- Parallelization: we can either interate over the rows of the dataframe and run the four bands in parallel; or we could zip together the row index, band, ra, dec, 
 
 ```{code-cell} ipython3
 paramlist = []
@@ -625,7 +625,7 @@ df.rename(columns=cols, inplace = True)
 ```
 
 ```{code-cell} ipython3
-#When doing a large run of a large area, save the dataframe with the forced photometry
+#When doing a large run of a large area, save the dataframe with the forced photometry 
 # so we don't have to do the forced photometry every time
 
 df.to_pickle(f'output/COSMOS_{radius.value}arcmin.pkl')
@@ -637,7 +637,7 @@ df.to_pickle(f'output/COSMOS_{radius.value}arcmin.pkl')
 #df = pd.read_pickle('output/COSMOS_48.0arcmin.pkl')
 ```
 
-### 3f. Plot to Confirm our Photometry Results
+### 3f. Plot to Confirm our Photometry Results 
 - compare to published COSMOS 2015 catalog
 
 ```{code-cell} ipython3
@@ -650,8 +650,8 @@ fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
 fluxmax = 200
 ymax = 100
 xmax = 100
-#ch1
-#first shrink the dataframe to only those rows where I have tractor photometry
+#ch1 
+#first shrink the dataframe to only those rows where I have tractor photometry 
 df_tractor = df[(df.splash_1_flux> 0) & (df.splash_1_flux < fluxmax)] #200
 #sns.regplot(data = df_tractor, x = "splash_1_flux", y = "ch1flux", ax = ax1, robust = True)
 sns.scatterplot(data = df_tractor, x = "splash_1_flux", y = "ch1flux", ax = ax1)
@@ -669,8 +669,8 @@ ax1.set_ylim([0, ymax])
 ax1.set_xlim([0, xmax])
 
 
-#ch2
-#first shrink the dataframe to only those rows where I have tractor photometry
+#ch2 
+#first shrink the dataframe to only those rows where I have tractor photometry 
 df_tractor = df[(df.splash_2_flux> 0) & (df.splash_2_flux < fluxmax)]
 #sns.regplot(data = df_tractor, x = "splash_2_flux", y = "ch2flux", ax = ax2, robust = True)
 sns.scatterplot(data = df_tractor, x = "splash_2_flux", y = "ch2flux", ax = ax2)
@@ -688,7 +688,7 @@ ax2.set_ylim([0, ymax])
 ax2.set_xlim([0, xmax])
 
 
-#ch3
+#ch3 
 #first shrink the dataframe to only those rows where I have tractor photometry
 df_tractor = df[(df.splash_3_flux> 0) & (df.splash_3_flux < fluxmax)]
 
@@ -708,8 +708,8 @@ ax3.set_ylim([0, ymax])
 ax3.set_xlim([0, xmax])
 
 
-#ch4
-#first shrink the dataframe to only those rows where I have tractor photometry
+#ch4 
+#first shrink the dataframe to only those rows where I have tractor photometry 
 df_tractor = df[(df.splash_4_flux> 0) & (df.splash_4_flux < fluxmax)]
 
 #sns.regplot(data = df_tractor, x = "splash_4_flux", y = "ch4flux", ax = ax4, robust = True)
@@ -753,7 +753,7 @@ We are using `nway` as the tool to do the cross match (Salvato et al. 2017).
 heasarc = Heasarc()
 table = heasarc.query_mission_list()
 mask = (table['Mission'] =="CHANDRA")
-chandratable = table[mask]
+chandratable = table[mask]  
 
 #find out which tables exist on Heasarc
 #chandratable.pprint(max_lines = 200, max_width = 130)
@@ -773,7 +773,7 @@ ccosmoscat = heasarc.query_region(coords, mission=mission, radius='1 degree', re
 #astropy doesn't recognize capitalized units
 #so there might be some warnings here on writing out the file, but we can safely ignore those
 
-#need to make the chandra catalog into a fits table
+#need to make the chandra catalog into a fits table 
 #and needs to include area of the survey.
 nway_write_header('data/Chandra/COSMOS_chandra.fits', 'CHANDRA', float(ccosmoscat_rad**2) )
 
@@ -791,7 +791,7 @@ nway_write_header('data/multiband_phot.fits', 'OPT', float((2*rad_in_arcmin/60)*
 ```{code-cell} ipython3
 # call nway
 !/home/jovyan/.local/bin/nway.py 'data/Chandra/COSMOS_chandra.fits' :ERROR_RADIUS 'data/multiband_phot.fits' 0.1 --out=data/Chandra/chandra_multiband.fits --radius 15 --prior-completeness 0.9
-
+    
 #!/opt/conda/bin/nway.py 'data/Chandra/COSMOS_chandra.fits' :ERROR_RADIUS 'data/multiband_phot.fits' 0.1 --out=data/Chandra/chandra_multiband.fits --radius 15 --prior-completeness 0.9
 ```
 
@@ -813,7 +813,7 @@ merged = pd.merge(df, matched, 'outer',left_on='ID', right_on = 'OPT_ID')
 #remove all the rows which start with "OPT" because they are duplications of the original catalog
 merged = merged.loc[:, ~merged.columns.str.startswith('OPT')]
 
-#somehow the matching is giving negative fluxes in the band where there is no detection
+#somehow the matching is giving negative fluxes in the band where there is no detection 
 #if there is a detection in the other band
 #clean that up to make those negative fluxes = 0
 
@@ -850,8 +850,8 @@ print('number of Galex detections =',np.sum(merged.galex_detect > 0))
 #overplot galex sources
 #overplot xray sources
 
-#first select on 24 micron
-merged_24 = merged[(merged.flux_24 >= 0) ]
+#first select on 24 micron 
+merged_24 = merged[(merged.flux_24 >= 0) ] 
 
 #negative Galex fluxes are causing problems, so set those to zero
 merged_24.loc[merged_24.fuvflux < 0, 'fuvflux'] = 0
@@ -932,7 +932,7 @@ ax.set(xlabel = 'NUV - [3.6]', ylabel = 'FUV - NUV')
 #plt.legend([],[], frameon=False)
 
 #fig.savefig("output/color_color.png")
-#mpld3.display(fig)
+#mpld3.display(fig)  
 ```
 
 We extend the works of Bouquin et al. 2015 and Moutard et al. 2020 by showing a GALEX - Spitzer color color diagram over plotted with Chandra detections.  Blue galaxies in these colors are generated by O and B stars and so must currently be forming stars. We find a tight blue cloud in this color space identifying those star forming galaxies.  Galaxies off of the blue cloud have had their star formation quenched, quite possibly by the existence of an AGN through removal of the gas reservoir required for star formation.  Chandra detected galaxies host AGN, and while those are more limited in number, can be shown here to be a hosted by all kinds of galaxies, including quiescent galaxies which would be in the upper right of this plot.  This likely implies that AGN are indeed involved in quenching star formation.  Additionally, we show the Chandra hardness ratio (HR) color coded according to the vertical color bar on the right side of the plot.  Those AGN with higher hardness ratios have their soft x-ray bands heavily obscured and appear to reside preferentially toward the quiescent galaxies.