add fill_between(::InterpolatedCls), misc

Author: marius311

src/chains.jl

@@ -167,7 +167,7 @@ Get the mean and standard deviation of a set of correlated `samples` from a
 chain where the error on the mean and standard deviation is estimated with
 bootstrap resampling using the calculated "effective sample size" of the chain.
 """
-function mean_std_and_errors(samples; N_bootstrap=10000)
+function mean_std_and_errors(samples; N_bootstrap=10000, N_in_paren=2)
 
     Neff = round(Int, length(samples) / @ondemand(PyCall.pyimport)(:emcee).autocorr.integrated_time(samples)[1])
 
@@ -177,7 +177,7 @@ function mean_std_and_errors(samples; N_bootstrap=10000)
     SEμ = std([mean(samples[rand(1:end, Neff)]) for i=1:N_bootstrap])
     SEσ = std([ std(samples[rand(1:end, Neff)]) for i=1:N_bootstrap])
 
-    "$(paren_errors(μ, SEμ)) ± $(paren_errors(σ, SEσ))"
+    "$(paren_errors(μ, SEμ; N_in_paren=N_in_paren)) ± $(paren_errors(σ, SEσ; N_in_paren=N_in_paren))"
 
 end
 

src/plotting.jl

@@ -1,7 +1,7 @@
 
 using .PyPlot
 using .PyPlot.PyCall
-import .PyPlot: loglog, plot, semilogx, semilogy, figure
+import .PyPlot: loglog, plot, semilogx, semilogy, figure, fill_between
 
 
 ### plotting Cℓs
@@ -17,6 +17,14 @@ for plot in (:plot, :loglog, :semilogx, :semilogy)
 	end
 end
 
+function fill_between(ic::InterpolatedCℓs{<:Measurement}, args...; kwargs...)
+	fill_between(
+		ic.ℓ, 
+		((@. Measurements.value(ic.Cℓ) - x * Measurements.uncertainty(ic.Cℓ)) for x in (-1,1))...,
+		args...; kwargs...
+	)
+end
+
 
 ### plotting FlatFields