Adding new plots of N/O relations to the pipeline

colibre/auto_plotter/metallicity.yml

@@ -526,7 +526,7 @@ stellar_mass_gas_nitrogen_over_oxygen_lom_50_kpc:
       value: 1e12
       units: solar_mass
   metadata:
-    title: "Stellar mass - Gas Diffuse Nitrogen over Oxygen relation (log-of-mean, 50 kpc aperture)"
+    title: "Stellar mass - Gas Diffuse Nitrogen over Oxygen relation (log-of-mean, 50 kpc aperture, cold, dense gas only)"
     caption: Shown for galaxies with cold, dense gas. No minimum metallicity is imposed. All haloes are plotted, including subhaloes. This uses diffused element mass fractions, meaning that it does not include metals that are present in dust. This figure shows the log-of-mean, which means it first calculates the mass weighted average of N/O for each galaxy and then calculates the log.
     section: Gas Metallicity
     show_on_webpage: true
@@ -632,7 +632,7 @@ gas_metallicity_gas_nitrogen_over_oxygen_lom_50_kpc:
       value: 10
       units: "dimensionless"
   metadata:
-    title: "Diffuse Gas metallicity - Diffuse Gas Nitrogen over Oxygen relation (log-of-mean, 50 kpc aperture)"
+    title: "Diffuse Gas metallicity - Diffuse Gas Nitrogen over Oxygen relation (log-of-mean, 50 kpc aperture, Only dense gas)"
     caption: Shown for galaxies with cold, dense gas. Metallicity is represented by 12 + $\log_{10}$ O/H (where $\log_{10}$ O/H is averaged between gas particles with a [O/H]=-3 floor for diffuse O) of the cold, dense gas ($T < 10^{4.5}\;{\rm K}$,  $n_{\rm H} > 0.1 \; {\rm cm^{-3}}$). No minimum metallicity is imposed. All haloes are plotted, including subhaloes. This uses depleted gas metallicity, i.e. it does not include metals that are present in dust.
     section: Gas Metallicity
     show_on_webpage: true
@@ -641,6 +641,7 @@ gas_metallicity_gas_nitrogen_over_oxygen_lom_50_kpc:
     - filename: GalaxyStellarMassGasMetallicity/Nicholls_2017b.hdf5
     - filename: GalaxyStellarMassGasMetallicity/Berg_2020.hdf5
 
+
 gas_metallicity_gas_nitrogen_over_oxygen_lofloor_50_kpc:
   type: "scatter"
   legend_loc: "upper left"
@@ -746,7 +747,7 @@ gas_metallicity_gas_carbon_over_oxygen_lom_50_kpc:
       value: 10
       units: "dimensionless"
   metadata:
-    title: "Diffuse Gas metallicity - Diffuse Gas Carbon over Oxygen relation (log-of-mean, 50 kpc aperture)"
+    title: "Diffuse Gas metallicity - Diffuse Gas Carbon over Oxygen relation (log-of-mean, 50 kpc aperture, only cold dense gas)"
     caption: Only shown for galaxies with cold, dense gas. Metallicity is represented by 12 + $\log_{10}$ O/H (where $\log_{10}$ O/H is averaged between gas particles for diffuse O) of the cold, dense gas ($T < 10^{4.5}\;{\rm K}$,  $n_{\rm H} > 0.1 \; {\rm cm^{-3}}$). No minimum metallicity is imposed. All haloes are plotted, including subhaloes. This uses depleted gas metallicity, i.e. it does not include metals that are present in dust.
     section: Gas Metallicity
     show_on_webpage: true

colibre/config.yml

@@ -353,7 +353,7 @@ scripts:
       yvar: C_Fe
       dataset: GALAH
   - filename: scripts/stellar_abundances.py
-    caption: '[Fe/H] vs [C/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [C/H]Sun = 8.43. The median [C/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.25 bin size and a minimum star count of 10.'
+    caption: '[Fe/H] vs [C/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [C/H]Sun = 8.43. The median [C/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_FeH_CFe_APOGEE.png
     section: Stellar Metal Abundances
     title: '[Fe/H] vs [C/Fe]'
@@ -362,14 +362,32 @@ scripts:
       yvar: C_Fe
       dataset: APOGEE
   - filename: scripts/stellar_abundances.py
-    caption: '[Fe/H] vs [N/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [N/H]Sun = 7.83. The median [N/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.25 bin size and a minimum star count of 10.'
+    caption: '[Fe/H] vs [N/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [N/H]Sun = 7.83. The median [N/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_FeH_NFe_APOGEE.png
     section: Stellar Metal Abundances
     title: '[Fe/H] vs [N/Fe]'
     additional_arguments:
       xvar: Fe_H
       yvar: N_Fe
       dataset: APOGEE
+  - filename: scripts/stellar_abundances.py
+    caption: '[O/H] vs [N/O] using Asplund et al. (2009) values for [O/H]Sun = 8.69 and [N/H]Sun = 7.83. The median [N/O] vs median [O/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
+    output_file: stellar_abundances_OH_NO_APOGEE.png
+    section: Stellar Metal Abundances
+    title: '[O/H] vs [N/O]'
+    additional_arguments:
+      xvar: O_H
+      yvar: N_O
+      dataset: APOGEE
+  - filename: scripts/stellar_abundances.py
+    caption: '[Fe/H] vs [N/O] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5, [N/H]Sun = 7.83 and [O/H]Sun = 8.69. The median [N/O] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
+    output_file: stellar_abundances_FeH_NO_APOGEE.png
+    section: Stellar Metal Abundances
+    title: '[Fe/H] vs [N/O]'
+    additional_arguments:
+      xvar: Fe_H
+      yvar: N_O
+      dataset: APOGEE
   - filename: scripts/stellar_abundances.py
     caption: '[Fe/H] vs [O/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [O/H]Sun = 8.69. The median [O/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the GALAH survey (Buder et al. 2021). Contours use a log scale with 0.04 bin size and a minimum star count of 10. All recommended flags are applied to GALAH data to select stars (SN, FE/H and X/Fe quality flags).'
     output_file: stellar_abundances_FeH_OFe_GALAH.png
@@ -380,7 +398,7 @@ scripts:
       yvar: O_Fe
       dataset: GALAH
   - filename: scripts/stellar_abundances.py
-    caption: '[Fe/H] vs [O/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [O/H]Sun = 8.69. The median [O/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.25 bin size and a minimum star count of 10.'
+    caption: '[Fe/H] vs [O/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [O/H]Sun = 8.69. The median [O/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_FeH_OFe_APOGEE.png
     section: Stellar Metal Abundances
     title: '[Fe/H] vs [O/Fe]'
@@ -414,7 +432,7 @@ scripts:
       yvar: Mg_Fe
       dataset: GALAH
   - filename: scripts/stellar_abundances.py
-    caption: '[Fe/H] vs [Mg/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [Mg/H]Sun = 7.6. The median [Mg/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.25 bin size and a minimum star count of 10.'
+    caption: '[Fe/H] vs [Mg/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [Mg/H]Sun = 7.6. The median [Mg/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_FeH_MgFe_APOGEE.png
     section: Stellar Metal Abundances
     title: '[Fe/H] vs [Mg/Fe]'
@@ -466,7 +484,7 @@ scripts:
       yvar: Eu_Fe
       dataset: GALAH
   - filename: scripts/stellar_abundances.py
-    caption: '[O/H] vs [O/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [O/H]Sun = 8.69. The median [O/Fe] vs median [O/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.25 bin size and a minimum star count of 10.'
+    caption: '[O/H] vs [O/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [O/H]Sun = 8.69. The median [O/Fe] vs median [O/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_OH_OFe_APOGEE.png
     section: Stellar Metal Abundances
     title: '[O/H] vs [O/Fe]'
@@ -475,7 +493,7 @@ scripts:
       yvar: O_Fe
       dataset: APOGEE
   - filename: scripts/stellar_abundances.py
-    caption: '[O/H] vs [Mg/Fe] using Asplund et al. (2009) values for [O/H]Sun = 8.69 and [Mg/H]Sun = 7.6. The median [Mg/Fe] vs median [O/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.25 bin size and a minimum star count of 10.'
+    caption: '[O/H] vs [Mg/Fe] using Asplund et al. (2009) values for [O/H]Sun = 8.69 and [Mg/H]Sun = 7.6. The median [Mg/Fe] vs median [O/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_OH_MgFe_APOGEE.png
     section: Stellar Metal Abundances
     title: '[O/H] vs [Mg/Fe]'

colibre/registration.py

@@ -851,8 +851,7 @@ def register_nitrogen_to_oxygen(self, catalogue, aperture_sizes):
 
         # Fetch N over O times gas mass computed in apertures. The
         # mass ratio between N and O has already been accounted for.
-        # Note that here we are calling the diffuse quantities, for
-        # total quantities (diffuse + dust) call "lin_N_over_O_total_times_ .."
+        # Note that here we are calling the diffuse quantities
         log_N_over_O_times_gas_mass = catalogue.get_quantity(
             f"lin_element_ratios_times_masses.lin_N_over_O_times_gas_mass_{aperture_size}_kpc"
         )
@@ -875,7 +874,7 @@ def register_nitrogen_to_oxygen(self, catalogue, aperture_sizes):
         )
 
         log_N_over_O.name = (
-            f"Diffuse gas $\\log_{{10}}({{\\rm N/O}})$ ({aperture_size} kpc)"
+            f"Diffuse Gas $\\log_{{10}}({{\\rm N/O}})$ ({aperture_size} kpc)"
         )
 
         # Register the field
@@ -902,7 +901,7 @@ def register_nitrogen_to_oxygen(self, catalogue, aperture_sizes):
                 f"cold_dense_gas_properties.cold_dense_gas_mass_{aperture_size}_kpc"
             )
 
-            # Compute gas-mass weighted O over H
+            # Compute gas-mass weighted N over O
             log_N_over_O = unyt.unyt_array(
                 np.zeros_like(gas_cold_dense_mass), "dimensionless"
             )
@@ -914,7 +913,7 @@ def register_nitrogen_to_oxygen(self, catalogue, aperture_sizes):
 
             # Convert to units used in observations
             N_abundance = unyt.unyt_array(log_N_over_O, "dimensionless")
-            N_abundance.name = f"Diffuse gas $\\log_{{10}}({{\\rm N/O}})$ ({floor_label}, {aperture_size} kpc)"
+            N_abundance.name = f"Diffuse Gas $\\log_{{10}}({{\\rm N/O}})$ ({floor_label}, {aperture_size} kpc)"
 
             # Register the field
             setattr(
@@ -955,7 +954,7 @@ def register_carbon_to_oxygen(self, catalogue, aperture_sizes):
         )
 
         log_C_over_O.name = (
-            f"Diffuse gas $\\log_{{10}}({{\\rm C/O}})$ ({aperture_size} kpc)"
+            f"Diffuse Gas $\\log_{{10}}({{\\rm C/O}})$ ({aperture_size} kpc)"
         )
 
         # Register the field
@@ -994,7 +993,7 @@ def register_carbon_to_oxygen(self, catalogue, aperture_sizes):
 
             # Convert to units used in observations
             C_abundance = unyt.unyt_array(log_C_over_O, "dimensionless")
-            C_abundance.name = f"Diffuse gas $\\log_{{10}}({{\\rm C/O}})$ ({floor_label}, {aperture_size} kpc)"
+            C_abundance.name = f"Diffuse Gas $\\log_{{10}}({{\\rm C/O}})$ ({floor_label}, {aperture_size} kpc)"
 
             # Register the field
             setattr(