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63 lines
2.7 KiB
Markdown
63 lines
2.7 KiB
Markdown
---
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title: My First Half Marathon!
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layout: post
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excerpt: |
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I ran my first half marathon! Let's look at the data.
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image: /assets/blog/running/time_vs_distance.svg
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social_image: /assets/blog/running/time_vs_distance.png
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thumbnail: /assets/blog/running/half_thumbnail.svg
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alt:
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image_class: invertable
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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
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---
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I recently ran my first half marathon! I'm also signed up for a full marathon in October. 😬
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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.
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<figure>
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<img src="/assets/blog/running/time_vs_distance_plus_hist.svg"/>
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<figcaption>
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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.
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</figcaption>
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</figure>
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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.
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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.
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Code:
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```python
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from matplotlib import pyplot as plt
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import numpy as np
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from datetime import datetime
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import pandas as pd
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runs = pd.read_csv("runs.csv", parse_dates = ["Date"]) # Get this from statshunter.com
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f, (ax2, ax) = plt.subplots(nrows=2, figsize = (5,5), sharex = True,
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gridspec_kw = dict(height_ratios = (1,2)))
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ax.set(ylabel = "Moving Time (mins)", xlabel = "Distance (km)")
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x = runs["Distance (m)"].values/1e3
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y = runs["Moving time"].values/60
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dists = np.linspace(1, 25, 2)
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for i in [5,6,7]:
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mins_per_km = i * dists
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ax.plot(dists, mins_per_km, color = "black", linestyle = "dotted", label = f"{i} min/km")
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ax.text(25.5, 25*i, f"{i} min/km", va = "center")
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ax.annotate("Half Marathon!", (x[0], y[0]-1), (20, 50), arrowprops = dict(arrowstyle = "->"))
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ax.scatter(x, y, s=20, alpha = 0.6*fade_out_by_date(runs["Date"]))
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for a in [ax, ax2]: a.spines[['right', 'top']].set_visible(False)
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ax2.hist(x, bins = 30, alpha = 0.5)
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ax2.set(yticks=[], ylabel = "Frequency Density")
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f.savefig("time_vs_distance_plus_hist.svg", transparent=True)
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``` |