add moments interactions (#19)

* Add moments to requirements.txt

* Add SFS under selection using moments applet.

book/_toc.yml

@@ -16,6 +16,9 @@ parts:
     - file: markdown/kingman_coalsecent/mutation_frequencies
     - file: markdown/kingman_coalsecent/expectations
     - file: markdown/kingman_coalsecent/distributions
+  - caption: Diffusion models
+    chapters:
+    - file: markdown/diffusion/sfs_selection
   - caption: Linked selection
     chapters:
     - file: markdown/classic_selection_drift/sweep_from_new_mutation

book/markdown/diffusion/sfs_selection.md

@@ -0,0 +1,39 @@
+---
+jupytext:
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# Mutation frequencies under selection (interactive Python)
+
+This notebook requires Binder for the interactive features to function.
+See {ref}`here <interactive_python_notebooks>` for more information.
+
+This applet uses [moments](https://moments.readthedocs.io) {cite:p}`Jouganous2017-tg` to calculate the site-frequency spectrum in a sample of size 20 as a function of $\gamma = 2N_es$, the scaled strength of selection.
+
+```{code-cell} python
+import holoviews as hv
+import moments
+
+hv.extension('bokeh')
+
+def fs_negative_selection(gamma):
+    fs_neg = moments.Spectrum(
+        moments.LinearSystem_1D.steady_state_1D(20, gamma=gamma)
+    )[1:-1]
+    b = hv.Histogram(([int(i)+1 for i in range(20)], fs_neg))
+    b = b.redim(y=hv.Dimension('y',range=(0, 1.1*fs_neg.max())))
+    b = b.opts(xlabel="Sample frequency", ylabel="Density")
+    return b
+
+kdims = [hv.Dimension('gamma',range=(-10, 10), step=1, default=0)]
+fsplot = hv.DynamicMap(fs_negative_selection,
+                       kdims=kdims).opts(framewise=True, width=500)
+fsplot
+```

book/references.bib

@@ -106,7 +106,10 @@ @ARTICLE{Jouganous2017-tg
   keywords  = "demographic inference; diffusion approximation; joint allele
                frequency spectrum; moments
                equations;polygenic\_inference\_R01;fwdpy11\_manual;popgen-course-material;Stehany",
-  language  = "en"
+  language  = "en",
+  issn      = "0016-6731, 1943-2631",
+  pmid      = "28495960",
+  pmc       = "PMC5500150"
 }
 
 @ARTICLE{Kreitman1983-xr,

requirements.txt

@@ -5,3 +5,4 @@ matplotlib
 numpy
 msprime==1.0.1
 pandas
+git+https://bitbucket.com/simongravel/moments