Update selection exercises

Fixed parameters to match updated script parameters

selection/mutation-drift-exercise.md

@@ -1,11 +1,11 @@
 # Exercise: effects of population size and mutation rate on population dynamics
 
-We'll go into this further in the next section, but a very important parameter is *&theta;*, which is equal to 2<i>N&mu;</i>, where *N* is equal to the population size and *&mu;* is equal to mutations per generation. If there are 100 sites and a per-site per-gen mutation rate of 0.0001, then *&mu;* = 0.01. If there are 50 individuals in the population, then *&theta;* = 2<i>N&mu;</i> = 1.
+We'll go into this further in the next section, but a very important parameter is *&theta;*, which is equal to 2<i>N&mu;</i>, where *N* is equal to the population size and *&mu;* is equal to mutations per generation. If there are 100 sites and a per-site per-gen mutation rate of 0.000025, then *&mu;* = 0.0025. If there are 200 individuals in the population, then *&theta;* = 2<i>N&mu;</i> = 1.
 
-This exercise can be completed by running the supplied Python script [`mutation-drift.py`](mutation-drift.py). To run the script with population size of 50, per-site per-gen mutation rate of 0.0001, 100 sites and 500 generations, input:
+This exercise can be completed by running the supplied Python script [`mutation-drift.py`](mutation-drift.py). To run the script with population size of 200, per-site per-gen mutation rate of 0.000025, 100 sites and 1000 generations, input:
 
 ```
-python mutation-drift.py --pop_size 50 --mutation_rate 0.0001 --seq_length 100 --generations 500
+python mutation-drift.py --pop_size 200 --mutation_rate 0.000025
 ```
 
 Again, these parameters give *&theta;* = 1.
@@ -16,10 +16,10 @@ Again, these parameters give *&theta;* = 1.
 
 > (3) Adjust *N* and *&mu;* up and down, while keeping *&theta;* = 1, to explore high *N* / low *&mu;* and low  *N* / high *&mu;* scenarios. What happens to diversity, divergence and haplotype dynamics?
 
-You might try increasing `generations` to something greater than 500 to get a better feel for equilibrium divergence and diversity, say `--generations 2500`. In this case, it may be easier to not plot the haplotype trajectories. This can be done with `--summary`, like so:
+You might try increasing `generations` to something greater than 1000 to get a better feel for equilibrium divergence and diversity, say `--generations 2500`. In this case, it may be easier to not plot the haplotype trajectories. This can be done with `--summary`, like so:
 
 ```
-python mutation-drift.py --pop_size 50 --mutation_rate 0.0001 --seq_length 100 --generations 2500 --summary
+python mutation-drift.py --pop_size 200 --mutation_rate 0.000025 --generations 2500 --summary
 ```
 
 --------------------------------------------

selection/mutation-drift-selection-exercise.md

@@ -2,13 +2,13 @@
 
 Here, we'll keep *N* and *μ* fixed and keep *θ* = 1, but include a small fraction of selectively advantageous mutations.
 
-This exercise can be completed by running the supplied Python script [`mutation-drift-selection.py`](mutation-drift-selection.py). To run the script with default population size of 100, per-site per-gen mutation rate of 0.0001, 100 sites and 500 generations, but with a chance of 0.002 for a mutation to have a fitness effect of 1.1:
+This exercise can be completed by running the supplied Python script [`mutation-drift-selection.py`](mutation-drift-selection.py). To run the script with default population size of 200, per-site per-gen mutation rate of 0.000025, 100 sites and 1000 generations, but with a chance of 0.01 for a mutation to have a fitness effect of 1.5:
 
 ```
 python mutation-drift-selection.py
 ```
 
-Again, you might try increasing `generations` to something greater than 500 to get a better feel for equilibrium divergence and diversity, say `--generations 2500`. In this case, it may be easier to not plot the haplotype trajectories, using `--summary`, like so:
+Again, you might try increasing `generations` to something greater than 1000 to get a better feel for equilibrium divergence and diversity, say `--generations 2500`. In this case, it may be easier to not plot the haplotype trajectories, using `--summary`, like so:
 
 ```
 python mutation-drift-selection.py --generations 2500 --summary

selection/mutation-drift.py

@@ -215,7 +215,7 @@ def snp_trajectory_plot(xlabel="generation"):
 
 if __name__=="__main__":
 	parser = argparse.ArgumentParser(description = "run wright-fisher simulation with mutation and genetic drift")
-	parser.add_argument('--pop_size', type = int, default = 200.0, help = "population size")
+	parser.add_argument('--pop_size', type = int, default = 200, help = "population size")
 	parser.add_argument('--mutation_rate', type = float, default = 0.000025, help = "mutation rate")
 	parser.add_argument('--seq_length', type = int, default = 100, help = "sequence length")
 	parser.add_argument('--generations', type = int, default = 1000, help = "generations")