Fixes comments

src/components/editor/distribution.js

@@ -1,11 +1,14 @@
-// This module defines an abstract BinnedDistribution class, which
-// should be implemented for each distribution. You need to decide
-// how to bin the distribution (use _adabin unless there's a nicer
-// way for your distr) and how to choose the distribution's support.
 const _math = require("mathjs");
 const math = _math.create(_math.all);
 const jStat = require("jstat");
 
+/**
+ * This module defines an abstract BinnedDistribution class, which
+ * should be implemented for each distribution. You need to decide
+ * how to bin the distribution (use _adabin unless there's a nicer
+ * way for your distr) and how to choose the distribution's support.
+ */
+
 math.import({
   normal: jStat.normal,
   beta: jStat.beta,
@@ -14,6 +17,9 @@ math.import({
 });
 
 class BaseDistributionBinned {
+  /**
+   * @param args
+   */
   constructor(args) {
     this._set_props();
     this.max_bin_size = 0.5;
@@ -30,11 +36,18 @@ class BaseDistributionBinned {
     [this.pdf_vals, this.divider_pts] = this.bin();
   }
 
+  /**
+   * this is hacky but class properties aren't always supported
+   * @private
+   */
   _set_props() {
-    // this is hacky but class properties aren't always supported
     throw new Error("NotImplementedError");
   }
 
+  /**
+   * @returns {(number[]|[*])[]}
+   * @private
+   */
   _adabin() {
     let point = this.start_point;
     let vals = [this.pdf_func(point)];
@@ -78,6 +91,10 @@ class BaseDistributionBinned {
     throw new Error("NotImplementedError");
   }
 
+  /**
+   * @param args
+   * @returns {(any|(function(*=): *))[]}
+   */
   get_params_and_pdf_func(args) {
     let args_str = args.toString() + ")";
     let substr = this.name + ".pdf(x, " + args_str;
@@ -95,11 +112,17 @@ class BaseDistributionBinned {
 }
 
 class NormalDistributionBinned extends BaseDistributionBinned {
+  /**
+   * @private
+   */
   _set_props() {
     this.name = "normal";
     this.param_names = ["mean", "std"];
   }
 
+  /**
+   * @returns {(number|*)[]}
+   */
   get_bounds() {
     return [
       this.params.mean - 4 * this.params.std,
@@ -107,22 +130,34 @@ class NormalDistributionBinned extends BaseDistributionBinned {
     ];
   }
 
+  /**
+   * @returns {[[*], [*]]}
+   */
   bin() {
     return this._adabin(this.params.std);
   }
 }
 
 class UniformDistributionBinned extends BaseDistributionBinned {
+  /**
+   * @private
+   */
   _set_props() {
     this.name = "uniform";
     this.param_names = ["start_point", "end_point"];
     this.num_bins = 200;
   }
 
+  /**
+   * @returns {*[]}
+   */
   get_bounds() {
     return [this.params.start_point, this.params.end_point];
   }
 
+  /**
+   * @returns {(*[])[]}
+   */
   bin() {
     let divider_pts = evenly_spaced_grid(
       this.params.start_point,
@@ -138,6 +173,9 @@ class UniformDistributionBinned extends BaseDistributionBinned {
 }
 
 class LogNormalDistributionBinned extends BaseDistributionBinned {
+  /**
+   * @private
+   */
   _set_props() {
     this.name = "lognormal";
     this.param_names = ["normal_mean", "normal_std"];
@@ -145,6 +183,12 @@ class LogNormalDistributionBinned extends BaseDistributionBinned {
     this.n_largest_bound_sample = 10;
   }
 
+  /**
+   * @param samples
+   * @param n
+   * @returns {any}
+   * @private
+   */
   _nth_largest(samples, n) {
     var largest_buffer = Array(n).fill(-Infinity);
     for (const sample of samples) {
@@ -159,6 +203,9 @@ class LogNormalDistributionBinned extends BaseDistributionBinned {
     return largest_buffer[n - 1];
   }
 
+  /**
+   * @returns {(*|any)[]}
+   */
   get_bounds() {
     let samples = Array(this.n_bounds_samples)
       .fill(0)
@@ -169,11 +216,20 @@ class LogNormalDistributionBinned extends BaseDistributionBinned {
     ];
   }
 
+  /**
+   * @returns {[[*], [*]]}
+   */
   bin() {
     return this._adabin();
   }
 }
 
+/**
+ * @param start
+ * @param stop
+ * @param numel
+ * @returns {*[]}
+ */
 function evenly_spaced_grid(start, stop, numel) {
   return Array(numel)
     .fill(0)

src/components/editor/main.js

@@ -1,16 +1,19 @@
-// The main algorithmic work is done by functions in this module.
+const _math = require("mathjs");
-// It also contains the main function, taking the user's string
+const bst = require("binary-search-tree");
-// and returning pdf values and x's.
 
 const distrs = require("./distribution.js").distrs;
 const parse = require("./parse.js");
-const _math = require("mathjs");
 const math = _math.create(_math.all);
-const bst = require("binary-search-tree");
 
 const NUM_MC_SAMPLES = 300;
 const OUTPUT_GRID_NUMEL = 300;
 
+/**
+ * The main algorithmic work is done by functions in this module.
+ * It also contains the main function, taking the user's string
+ * and returning pdf values and x's.
+ */
+
 /**
  * @param start
  * @param stop
@@ -24,22 +27,23 @@ function evenly_spaced_grid(start, stop, numel) {
 }
 
 /**
+ * Takes an array of strings like "normal(0, 1)" and
+ * returns the corresponding distribution objects
  * @param substrings
  * @returns {*}
  */
 function get_distributions(substrings) {
-  // Takes an array of strings like "normal(0, 1)" and
-  // returns the corresponding distribution objects
   let names_and_args = substrings.map(parse.get_distr_name_and_args);
   let pdfs = names_and_args.map(x => new distrs[x[0]](x[1]));
   return pdfs;
 }
 
-// update the binary search tree with bin points of
-// deterministic_pdf transformed by tansform func
-// (transfrom func can be a stocahstic func with parameters
-// sampled from mc_distrs)
 /**
+ * update the binary search tree with bin points of
+ * deterministic_pdf transformed by tansform func
+ * (transfrom func can be a stocahstic func with parameters
+ * sampled from mc_distrs)
+ *
  * @param transform_func
  * @param deterministic_pdf
  * @param mc_distrs
@@ -116,10 +120,11 @@ function update_transformed_divider_points_bst(
   return [start_pt, end_pt];
 }
 
-// Take the binary search tree with transformed bin points,
-// and an array of pdf values associated with the bins,
-// and return a pdf over an evenly spaced grid
 /**
+ * Take the binary search tree with transformed bin points,
+ * and an array of pdf values associated with the bins,
+ * and return a pdf over an evenly spaced grid
+ *
  * @param pdf_vals
  * @param bst_pts_and_idxs
  * @param output_grid
@@ -177,13 +182,14 @@ function get_final_pdf(pdf_vals, bst_pts_and_idxs, output_grid) {
   return final_pdf_vals;
 }
 
-// Entrypoint. Pass user input strings to this function,
-// get the corresponding pdf values and input points back.
-// If the pdf requires monte carlo (it contains a between-distr function)
-// we first determing which distr to have deterministic
-// and which to sample from. This is decided based on which
-// choice gives the least variance.
 /**
+ * Entrypoint. Pass user input strings to this function,
+ * get the corresponding pdf values and input points back.
+ * If the pdf requires monte carlo (it contains a between-distr function)
+ * we first determing which distr to have deterministic
+ * and which to sample from. This is decided based on which
+ * choice gives the least variance.
+ *
  * @param user_input_string
  * @returns {([]|*[])[]}
  */
@@ -261,10 +267,11 @@ function pluck_from_array(array, idx) {
   return [array[idx], array.slice(0, idx).concat(array.slice(idx + 1))];
 }
 
-// If distr_string requires MC, try all possible
-// choices for the deterministic distribution,
-// and pick the one with the least variance.
 /**
+ * If distr_string requires MC, try all possible
+ * choices for the deterministic distribution,
+ * and pick the one with the least variance.
+ *
  * @param distr_string
  * @returns {(*|*[])[]|*[]}
  */

src/components/editor/parse.js

@@ -1,7 +1,8 @@
-// Functions for parsing/processing user input strings are here
 const _math = require("mathjs");
 const math = _math.create(_math.all);
 
+// Functions for parsing/processing user input strings are here
+
 const DISTR_REGEXS = [
   /beta\(/g,
   /(log)?normal\(/g,
@@ -10,6 +11,11 @@ const DISTR_REGEXS = [
   /uniform\(/g
 ];
 
+/**
+ *
+ * @param user_input_string
+ * @returns {{mm_args_string: string, outer_string: string}}
+ */
 function parse_initial_string(user_input_string) {
   let outer_output_string = "";
   let mm_args_string = "";
@@ -42,6 +48,10 @@ function parse_initial_string(user_input_string) {
   };
 }
 
+/**
+ * @param mm_args_string
+ * @returns {{distrs: [], weights: string}}
+ */
 function separate_mm_args(mm_args_string) {
   if (mm_args_string.endsWith(",")) {
     mm_args_string = mm_args_string.slice(0, -1);
@@ -68,6 +78,10 @@ function separate_mm_args(mm_args_string) {
   };
 }
 
+/**
+ * @param distr_string
+ * @returns {[]}
+ */
 function get_distr_substrings(distr_string) {
   let substrings = [];
   for (let regex of DISTR_REGEXS) {
@@ -92,6 +106,10 @@ function get_distr_substrings(distr_string) {
   return substrings;
 }
 
+/**
+ * @param substr
+ * @returns {(string|*)[]}
+ */
 function get_distr_name_and_args(substr) {
   let distr_name = "";
   let args_str = "";