Merge pull request #48 from popgensims/docfix

Minor doc fixes

dmel_example.py

@@ -24,7 +24,7 @@
     samples=samples,
     # recombination rate placeholder for quick runtime
     recombination_rate=1e-09,
-    mutation_rate=chrom.mean_mutation_rate,
+    mutation_rate=chrom.default_mutation_rate,
     **model.asdict())
 print("simulated:", ts.num_trees, ts.num_sites)
 
@@ -42,7 +42,7 @@
     samples=samples,
     # recombination rate placeholder for quick runtime
     recombination_rate=1e-09,
-    mutation_rate=chrom.mean_mutation_rate,
+    mutation_rate=chrom.default_mutation_rate,
     **model.asdict())
 print("simulated:", ts.num_trees, ts.num_sites)
 
@@ -61,7 +61,7 @@
     samples=samples,
     # actual recombination map; slow
     recombination_map=chrom.recombination_map(),
-    mutation_rate=chrom.mean_mutation_rate,
+    mutation_rate=chrom.default_mutation_rate,
     **model.asdict())
 print("simulated:", ts.num_trees, ts.num_sites)
 

docs/api.rst

@@ -25,10 +25,14 @@ might have:
     chrom = homo_sapiens.genome.chromosomes["chr22"]
     ts = msprime.simulate(
         sample_size=10,
-        recombination_rate=chrom.mean_recombination_rate,
-        mutation_rate=chrom.mean_mutation_rate,
+        recombination_rate=chrom.default_recombination_rate,
+        mutation_rate=chrom.default_mutation_rate,
         length=chrom.length)
 
+(This should be a very quick simulation, and the result will have very few
+variants, because although it performs a coalescent simulation of
+a 51,304,566bp chromosome, it does this with the effective population size of
+`Ne=1`.)
 
 The chromosome definitions also aware of recombination maps, so we can run
 more complex simulations like:
@@ -38,7 +42,7 @@ more complex simulations like:
     chrom = homo_sapiens.genome.chromosomes["chr22"]
     ts = msprime.simulate(
         sample_size=10,
-        mutation_rate=chrom.mean_mutation_rate,
+        mutation_rate=chrom.default_mutation_rate,
         recombination_map=chrom.recombination_map())
 
 Recombination maps will be downloaded on demand and cached in a
@@ -51,7 +55,7 @@ API will use the default. We can also ask for specific maps, if we want:
     chrom = homo_sapiens.genome.chromosomes["chr22"]
     ts = msprime.simulate(
         sample_size=10,
-        mutation_rate=chrom.mean_mutation_rate,
+        mutation_rate=chrom.default_mutation_rate,
         recombination_map=chrom.recombination_map("HapmapII_GRCh37"))
 
 
@@ -69,9 +73,11 @@ Demographic models can also be used. For example
     ts = msprime.simulate(
         samples=samples,
         recombination_map=chrom.recombination_map(),
-        mutation_rate=chrom.mean_mutation_rate,
+        mutation_rate=chrom.default_mutation_rate,
         **model.asdict())
 
+(This simulation now has a realistic effective population size,
+so will produce thousands of variant sties, but still runs very fast.)
 
 *****************
 General utilities

docs/development.rst

@@ -16,7 +16,7 @@ Overview
 The ``stdpopsim`` library requires Python 3.4 or later. For ``pip`` users,
 install the packages required for development using::
 
-    $ python3 -m pip install requirements/development.txt
+    $ python3 -m pip install -r requirements/development.txt
 
 We do require ``msprime``, so please see the the `installation notes
 <https://msprime.readthedocs.io/en/stable/installation.html>`_ if you

ecoli_example.py

@@ -17,7 +17,7 @@
 
 ts = msprime.simulate(
     samples=samples,
-    mutation_rate=chrom.mean_mutation_rate,
+    mutation_rate=chrom.default_mutation_rate,
     **model.asdict())
 print("simulated:", ts.num_trees, ts.num_sites)
-print("mean mut rate", chrom.mean_mutation_rate)
+print("mean mut rate", chrom.default_mutation_rate)

example.py

@@ -26,6 +26,6 @@
 ts = msprime.simulate(
     samples=samples,
     recombination_map=chrom.recombination_map(),
-    mutation_rate=chrom.mean_mutation_rate,
+    mutation_rate=chrom.default_mutation_rate,
     **model.asdict())
 print("simulated:", ts.num_trees, ts.num_sites)

stdpopsim/homo_sapiens.py

@@ -19,6 +19,9 @@
 
 class HapmapII_GRCh37(genetic_maps.GeneticMap):
     """
+    Usage: `hapmap = homo_sapiens.HapmapII_GRCh37()` (note the
+    parentheses).
+
     The Phase II HapMap Genetic map (lifted over to GRCh37) used in
     1000 Genomes. Please see the `README
     <ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/working/20110106_recombination_hotspots/README_hapmapII_GRCh37_map>`_