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icweaver · Mar 15, 2022 · 60465e9 · 60465e9
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diff --git a/html/08_retrievals.html b/html/08_retrievals.html
diff --git a/notebooks/01_raw_data.jl b/notebooks/01_raw_data.jl
@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.18.1
+# v0.18.2
 
 using Markdown
 using InteractiveUtils

diff --git a/notebooks/02_reduced_data_IMACS.jl b/notebooks/02_reduced_data_IMACS.jl
@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.18.1
+# v0.18.2
 
 using Markdown
 using InteractiveUtils

diff --git a/notebooks/07_transmission_spectra.jl b/notebooks/07_transmission_spectra.jl
@@ -133,6 +133,7 @@ begin
 	 	cubes[transit]["δ_WLC"] = maxmeasure(p[1], p_u[1], p_d[1])^2 * 1e6
 	end
 
+	delete!(cubes["Transit 1 (IMACS)"]["tspec"], 3)
 	cubes = sort(cubes)
 end
 
@@ -164,9 +165,6 @@ df_common_0 = innerjoin(
 Conversely, we also store which points in the spectrum are not common between all nights. `Transit 2 (LDSS3C)` encompasses the spectra from all other nights, so we `antijoin` relative to this dataset:
 """
 
-# ╔═╡ 51357cde-d3da-4377-8ae0-10e81a9c5c8d
-occursin("_sp", DATA_DIR)
-
 # ╔═╡ 461097e9-a687-4ef2-a5b4-8bf4d9e1c98f
 dfs_unique = (
 	"Transit 1 (IMACS)" => antijoin(
@@ -263,7 +261,7 @@ end
 
 # ╔═╡ b32273bc-1bb5-406a-acfe-57fd643ded51
 df_tspecs = sort(vcat(df_common, df_extra), :Wcen)
-#df_tspecs = df_common
+# df_tspecs = df_common
 
 # ╔═╡ 64f608b9-76df-402e-801c-006dc3096f94
 latextabular(df_tspecs, latex=false) |> PlutoUI.Text
@@ -357,6 +355,9 @@ avg_prec_IMACS = getproperty.(df_IMACS[!, :Combined], :err) |> median
 # ╔═╡ b6fa6c00-14cf-47af-9593-c70514373db5
 avg_prec_LDSS3 = getproperty.(df_LDSS3[!, :Combined], :err) |> median
 
+# ╔═╡ 92029dbc-2633-414f-a8ee-0ec61eda0313
+df_common
+
 # ╔═╡ 3af0d3b0-c698-4845-a74e-c7186b03a721
 let
     #f = "$(DATA_DIR)/tspec_w50_IMACS.csv"
@@ -395,15 +396,11 @@ wbins_LDSS3
 Finally, we save the final combined transmission spectrum to file for our retrieval analysis, along with planet/star parameters computed from the WLC fits:
 """
 
-# ╔═╡ 5718672b-1bc6-4676-8703-5fc06b83f0f9
-let
-    #f = "$(DATA_DIR)/tspec_w50_all.csv"
-	#CSV.write(f, create_df(df_tspecs; instrument="IMACS+LDSS3C"))
-	#@info "Saved to $(f)"
-	#f = "$(DATA_DIR)/tspec_w50.csv"
-	#CSV.write(f, create_df(df_common; instrument="IMACS+LDSS3C"))
-	#@info "Saved to $(f)"
-end
+# ╔═╡ 4faac7de-8c38-4f2c-be85-569e8fc83d28
+df_tspecs
+
+# ╔═╡ 3846da6a-af41-47a0-9318-76757a1dba15
+df_common
 
 # ╔═╡ 9141dba4-4c11-404d-b18a-b22f3466caba
 Rₛ = 0.873u"Rsun"
@@ -430,6 +427,16 @@ function create_df(df; instrument="add_instrument")
 	end
 end
 
+# ╔═╡ 5718672b-1bc6-4676-8703-5fc06b83f0f9
+let
+    f = "$(DATA_DIR)/tspec_w50_3.csv"
+	CSV.write(f, create_df(df_tspecs; instrument="IMACS+LDSS3C"))
+	@info "Saved to $(f)"
+	#f = "$(DATA_DIR)/tspec_w50.csv"
+	#CSV.write(f, create_df(df_common; instrument="IMACS+LDSS3C"))
+	#@info "Saved to $(f)"
+end
+
 # ╔═╡ f8a86915-f7d8-4462-980e-7b8124b13a3f
 @mdx """
 ## Notebook setup 🔧
@@ -749,7 +756,6 @@ gₚ = G * Mₚ / Rₚ^2 |> u"cm/s^2"
 # ╟─11066667-9da2-4b36-b784-c3515c04a659
 # ╠═cb1b277b-aa92-44de-91ce-88122bc34bb9
 # ╟─acde40fd-8ed4-4175-9a52-13ed91dc5495
-# ╠═51357cde-d3da-4377-8ae0-10e81a9c5c8d
 # ╠═461097e9-a687-4ef2-a5b4-8bf4d9e1c98f
 # ╠═4b9cfc02-5e18-422d-b18e-6301a659561a
 # ╠═45acc116-e585-4ddf-943d-128db7736921
@@ -771,13 +777,16 @@ gₚ = G * Mₚ / Rₚ^2 |> u"cm/s^2"
 # ╠═940ad41b-910c-40a8-8752-e68e13ff4a1f
 # ╠═f37d9e45-575c-40d9-8f26-31bd6cc6d145
 # ╠═b6fa6c00-14cf-47af-9593-c70514373db5
+# ╠═92029dbc-2633-414f-a8ee-0ec61eda0313
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 # ╟─146a2be7-1c08-4d7c-802f-41f65aeae0d5
+# ╠═4faac7de-8c38-4f2c-be85-569e8fc83d28
+# ╠═3846da6a-af41-47a0-9318-76757a1dba15
 # ╠═5718672b-1bc6-4676-8703-5fc06b83f0f9
 # ╠═9141dba4-4c11-404d-b18a-b22f3466caba
 # ╠═54c341d9-2065-48cf-89bd-11acf72bdf9d

diff --git a/notebooks/08_retrievals.jl b/notebooks/08_retrievals.jl
@@ -30,9 +30,6 @@ begin
 	using PythonCall
 end
 
-# ╔═╡ 54d69f6d-cac0-4909-84b0-01f42cb7a4b0
-1400 + 250
-
 # ╔═╡ a21aad0b-5998-420e-b437-d7ad262d0fe4
 begin
 	const DATA_DIR = "data/retrievals"
@@ -91,7 +88,7 @@ species = [
 	"Na_TiO",
 	"K_TiO",
 	"Na_K_TiO",
-	"CO",
+	# "CO",
 	"H2O",
 	# "NH3", # too low
 	# "HCN", # too low
@@ -304,16 +301,22 @@ Dict(k => median(v) ± std(v) for (k, v) ∈ dists)
 cube |> keys
 
 # ╔═╡ 41d8cd60-aacb-46c4-9344-29a8228f43cb
-cube["K"] |> keys
+models = cube["K"] |> keys
+
+# ╔═╡ d5197c68-0045-4ca7-b5c6-f9cd7852d896
+models
+
+# ╔═╡ 9c6a178c-ac7a-4ee5-ada2-05690158fcdb
+models
 
 # ╔═╡ 6ed6b481-4b84-485a-af2f-d2ecb943cd33
-samples = pyconvert(Dict{String, Vector}, cube["K"]["clear"]["retr"]["samples"])
+samples = pyconvert(Dict{String, Vector}, cube["Na"]["clear"]["retr"]["samples"])
 
-# ╔═╡ d3ec52e6-205e-4639-bd50-c9ef45e44f8e
-hist(samples["T"])
+# ╔═╡ 8ac2b5d2-eb4b-4a4f-99f8-52830e5ef9e8
+keys(samples)
 
-# ╔═╡ b52a381b-438f-4224-b5f3-7e11db5cc045
-samples["Fhet"] |> median
+# ╔═╡ d3ec52e6-205e-4639-bd50-c9ef45e44f8e
+hist(samples["T"], bins=20)
 
 # ╔═╡ e43f1834-73dd-4859-b847-f4c552561897
 begin
@@ -405,14 +408,26 @@ let
 		xlabel = "Wavelength (Å)",
 		ylabel = "Transit depth (ppm)",
 		#limits = (0.3, 1.1, 17_500, 21_000),
-		#limits = (4_600, 9_800, 15_500, 22_500),
-		limits = (4_600, 9_800, 15_500, 22_500)
+		limits = (4_000, 9_800, 17_500, 21_500),
+		#limits = (4_600, 9_800, 17_000, 21_000)
+		#limits = (4_000, 13_000, 15_500, 22_500),
+		xticks = LinearTicks(7),
 	)
 
-	plot_retrieval!(ax, cube, "Na_K_TiO", "haze+spot"; color=COLORS[6])
-	plot_retrieval!(ax, cube, "Na", "cloud"; color=COLORS[3])
-	plot_retrieval!(ax, cube, "TiO", "haze"; color=COLORS[5])
-	plot_retrieval!(ax, cube, "CO", "clear"; color=COLORS[1], linewidth=6)
+	# for (i, sp) ∈ enumerate(species)
+	# 	plot_retrieval!(ax, cube, sp, "clear"; color=COLORS[mod1(i, 6)], linewidth=1)
+	# end
+	# for (i, model) ∈ enumerate(models)
+	# 	plot_retrieval!(ax, cube, "TiO", model; color=COLORS[i], linewidth=1)
+	# end
+	plot_retrieval!(ax, cube, "Na_K_TiO", "clear"; color=COLORS[1])
+	plot_retrieval!(ax, cube, "Na_K_TiO", "spot"; color=COLORS[2])
+	plot_retrieval!(ax, cube, "Na_K_TiO", "cloud"; color=COLORS[3])
+	plot_retrieval!(ax, cube, "Na_K_TiO", "haze"; color=COLORS[5])
+
+	# for sp ∈ species, model ∈ models
+	# 	plot_retrieval!(ax, cube, sp, model)
+	# end
 
 	fpath_suff = basename(base_dir)
 	if occursin("offs", fpath_suff)
@@ -457,10 +472,9 @@ body.disable_ui main {
 """
 
 # ╔═╡ Cell order:
-# ╠═54d69f6d-cac0-4909-84b0-01f42cb7a4b0
 # ╟─0132b4ab-0447-4546-b412-ec598b20d21d
 # ╠═a21aad0b-5998-420e-b437-d7ad262d0fe4
-# ╟─60dc161c-2aa2-4264-884d-6da3ead0e57b
+# ╠═60dc161c-2aa2-4264-884d-6da3ead0e57b
 # ╟─d7ce97c1-82f2-46f1-a5ac-73e38e032fc8
 # ╠═589afac8-0ea5-4962-b52b-7f035e91cf44
 # ╟─2c12ec4d-1184-4755-8bd8-0d7cd59fa205
@@ -480,17 +494,19 @@ body.disable_ui main {
 # ╠═930ec094-7b11-48b8-818e-15c63ed6f8a5
 # ╠═54b5c81a-835a-461c-9dfd-2d938fac3bc4
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+# ╠═d5197c68-0045-4ca7-b5c6-f9cd7852d896
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+# ╠═9c6a178c-ac7a-4ee5-ada2-05690158fcdb
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+# ╠═8ac2b5d2-eb4b-4a4f-99f8-52830e5ef9e8
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-# ╠═b52a381b-438f-4224-b5f3-7e11db5cc045
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