add running blog post

_posts/2023-08-13-half-marathon.md

@@ -0,0 +1,58 @@
+---
+title: My First Half Marathon!
+layout: post
+image: /assets/blog/running/time_vs_distance.svg
+social_image: /assets/blog/running/time_vs_distance.png
+alt: A scatter graph of run time vs run distance for all my runs on strava. It shows that I mainly run between 5 and 6 min per kilometer, regardless of distance 
+---
+
+I recently ran my first half marathon! I'm also signed up for a full marathon in October. 😬
+
+Anyway, to celebrate the occasion and because I have now have a reason to think about how fast I might run a particular distance. I had a look at my historical run data. There's a great website called [statshunter](https://www.statshunters.com/) that you can authorise to Strava and from which you can download a little csv of all your runs. The first logical thing I could think to do is to see how fast I tend to run different distances.
+
+<figure>
+<img src="/assets/blog/running/time_vs_distance_plus_hist.svg"/>
+<figcaption>
+So it's pretty much a straight line. This is telling me what I kinda already knew: I'm comfortable running between 5 and 7 mins/km and that's basically how fast I always run regardless of distance. 
+</figcaption>
+</figure>
+
+A friend lent me a huge running book which I'm going to dig through more but I suspect one of the conclusions will be a bit obvious: I could run those shorter distances a lot faster.
+
+That same friend also lent me a heart rate watch which I've been playing with. So the next thing I want to learn about is what type of heart rates you should target when you train for a particular event. 
+
+
+Code:
+
+```python
+from matplotlib import pyplot as plt
+import numpy as np
+from datetime import datetime
+import pandas as pd
+
+runs = pd.read_csv("runs.csv", parse_dates = ["Date"]) # Get this from statshunter.com
+
+f, (ax2, ax) = plt.subplots(nrows=2, figsize = (5,5), sharex = True,
+                           gridspec_kw = dict(height_ratios = (1,2)))
+
+ax.set(ylabel = "Moving Time (mins)", xlabel = "Distance (km)")
+
+x = runs["Distance (m)"].values/1e3
+y = runs["Moving time"].values/60
+
+dists = np.linspace(1, 25, 2)
+for i in [5,6,7]:
+    mins_per_km = i * dists
+    ax.plot(dists, mins_per_km, color = "black", linestyle = "dotted", label = f"{i} min/km")
+    ax.text(25.5, 25*i, f"{i} min/km", va = "center")
+
+ax.annotate("Half Marathon!", (x[0], y[0]-1), (20, 50), arrowprops = dict(arrowstyle = "->"))
+
+ax.scatter(x, y, s=20, alpha = 0.6*fade_out_by_date(runs["Date"]))
+for a in [ax, ax2]: a.spines[['right', 'top']].set_visible(False)
+
+ax2.hist(x, bins = 30, alpha = 0.5)
+ax2.set(yticks=[], ylabel = "Frequency Density")
+
+f.savefig("time_vs_distance_plus_hist.svg", transparent=True)
+```

run.sh

@@ -1,3 +1,3 @@
 #!/usr/bin/env bash
-sleep 3 && open --url http://0.0.0.0:4000 &
+sleep 5 && open --url http://0.0.0.0:4000 &
 bundle exec jekyll serve --draft --future