From 62c99e26ebba6ad2019eccf88d7739050aed3433 Mon Sep 17 00:00:00 2001
From: Mo8it <mo8it@protonmail.com>
Date: Wed, 30 Mar 2022 03:03:24 +0200
Subject: [PATCH] Done day 3

---
 Day_3/Day_3.jl | 99 +++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 98 insertions(+), 1 deletion(-)

diff --git a/Day_3/Day_3.jl b/Day_3/Day_3.jl
index d44ff1d..f9ca3ca 100644
--- a/Day_3/Day_3.jl
+++ b/Day_3/Day_3.jl
@@ -388,8 +388,95 @@ savefig("resources/test_figure_export.pdf")
 # ╔═╡ f44cf458-4ec1-4e2b-b839-f3981683a151
 md"""
 # Data fitting
+Using data fitting, you can determine a model that does describe your measurements.
+
+It is best explained using an example!
 """
 
+# ╔═╡ 25362ad6-5b49-4900-8026-c2d54197ba94
+md"""
+## Example
+"""
+
+# ╔═╡ d6c9be8a-1025-449c-86c8-7d92ad240965
+# Defining a model.
+# Here, we did choose a linear model `f(x) = m * x + c`
+@. linear_model(x, p) = p[1] * x + p[2]
+
+# ╔═╡ 9befeddf-24fa-4af8-9b1d-df1a9865a9ff
+# Initial guess of the parameters in `p`
+# ⚠️ THE TYPE OF THIS VECTOR HAS TO BE FLOAT FOR NOW (because of a bug).
+p0 = [1.0, 1.0]
+
+# ╔═╡ e4c81d90-8d06-4c38-9886-252aa7285558
+# You have to strip units and remove errors
+fit = curve_fit(
+	linear_model, # First argument is the model
+	ustrip.(Measurements.value.(df_I_B_with_err.I)), # Measured x values
+	ustrip.(Measurements.value.(df_I_B_with_err.B)), # Measured y values
+	p0
+)
+
+# ╔═╡ ee858e78-6b69-485f-95d5-d735956e26d1
+# Our fit parameters
+param = fit.param
+
+# ╔═╡ 51a17b24-b71b-4670-9bc5-de7576d6fc75
+# The error of the fit parameters
+sigma = stderror(fit)
+
+# ╔═╡ 5957fce3-dcbd-4fc5-83b7-6730b6412dbd
+begin
+	custom_scatter(
+		df_I_B_with_err, "I", "B";
+		label="B(I)",
+		markershape=:diamond,
+		markersize=3,
+	)
+
+	x = Measurements.value.(ustrip.(LinRange(
+		minimum(df_I_B_with_err.I),
+		maximum(df_I_B_with_err.I),
+		250
+	)))
+	
+	plot!(
+		x, linear_model(x, param),
+		label="Linear fit",
+		linewidth=3, # Change line width
+	)
+end
+
+# ╔═╡ 385c3771-757c-49a7-8b8c-916e76524082
+md"""
+## Automation
+Again, you can automate this process!
+"""
+
+# ╔═╡ ef6274cf-f1fb-4f86-becf-eeb181ed92fc
+function automated_fit(model, df, x_column_name, y_column_name, p0=[1.0, 1.0])
+	fit = curve_fit(
+		model,
+		ustrip.(Measurements.value.(df[!, x_column_name])),
+		ustrip.(Measurements.value.(df[!, y_column_name])),
+		p0
+	)
+	
+	param = fit.param
+	sigma = stderror(fit)
+	
+	return (param, sigma)
+end
+
+# ╔═╡ 409a9f5a-138a-491f-9789-520926be9dbd
+# Test automation function
+param2, sigma2 = automated_fit(
+	linear_model,
+	df_I_B_with_err,
+	"I",
+	"B",
+)
+
 # ╔═╡ 00000000-0000-0000-0000-000000000001
 PLUTO_PROJECT_TOML_CONTENTS = """
 [deps]
@@ -1747,8 +1834,18 @@ version = "0.9.1+5"
 # ╠═6ab56f2d-759d-44ac-8427-55f85942615e
 # ╟─1d524a9b-84e9-4ffd-b104-331055c78845
 # ╠═1e14de84-7d87-4df4-8c88-2307cb2262ba
-# ╠═f44cf458-4ec1-4e2b-b839-f3981683a151
+# ╟─f44cf458-4ec1-4e2b-b839-f3981683a151
 # ╠═c985a351-cbc7-4385-b5f7-b709eee47092
+# ╟─25362ad6-5b49-4900-8026-c2d54197ba94
+# ╠═d6c9be8a-1025-449c-86c8-7d92ad240965
+# ╠═9befeddf-24fa-4af8-9b1d-df1a9865a9ff
+# ╠═e4c81d90-8d06-4c38-9886-252aa7285558
+# ╠═ee858e78-6b69-485f-95d5-d735956e26d1
+# ╠═51a17b24-b71b-4670-9bc5-de7576d6fc75
+# ╠═5957fce3-dcbd-4fc5-83b7-6730b6412dbd
+# ╟─385c3771-757c-49a7-8b8c-916e76524082
+# ╠═ef6274cf-f1fb-4f86-becf-eeb181ed92fc
+# ╠═409a9f5a-138a-491f-9789-520926be9dbd
 # ╟─32f4633c-af89-11ec-0059-5392abfd3bc3
 # ╟─00000000-0000-0000-0000-000000000001
 # ╟─00000000-0000-0000-0000-000000000002