Fixed reproduction so it can be consistent between runs of the script…

…, but there is consistent variation between each of the individual runs of the model when those are run (and not the same result from every run)

evaluation/posts/2024_05_24/index.qmd

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+---
+title: "Day 3"
+author: "Amy Heather"
+date: "2024-05-23"
+categories: [reproduce]
+---
+
+::: {.callout-note}
+
+## Time elapsed
+
+TBC today (total: TBC%)
+
+:::
+
+## Work log
+
+## 11.29-12.38 Reproduction
+
+After some [untimed general revision of how random number generation works in Python](https://amyheather.github.io/programming_notes/python/seeds.html), I returned to trying to modify the code so it can reproduce results between runs.
+
+A simple fix of adding a seed to some of the Uniform() functions that were still using random_seed=None meant that the simulation would now run exactly the same each time.
+
+Run 1:
+
+<img src="figure_1a.png" width="500" /> 
+
+Run 2:
+
+<img src="figure_1b.png" width="500" />
+
+
+Then we needed a seperate seed with each run (else every run will produce identical results) This required changes to:
+    * `single_run()` within `sim_replicate.py` so it can accept random number set as an argument, and then when it was called in `multiple_replications()`, to use the rep number as the starting seed
+    * Scenario, changing it from a frozen dataclass to a class with a function that accepts a random number set, and then prompts generation of distributions from that
+
+These changes were based on [this model](https://pythonhealthdatascience.github.io/stars-simpy-example-docs/content/02_model_code/04_model.html).
+
+As can see, this has fixed it, as we have reproducible results between runs, and varying results within runs.
+
+Run 1:
+
+<img src="figure_2a.png" width="500" /> 
+
+Run 2:
+
+<img src="figure_2b.png" width="500" />