Update readme to reflect wells with random population implemented

readme.md

@@ -131,10 +131,13 @@ Options when making a Sample Plate file:
     * Standard deviation size 
   * Exponential
     * Lambda value
-      * *(Based on the slope of the graph in Figure 4C of the pairSEQ paper, the distribution of the original experiment was exponential with a lambda of approximately 0.6. (Howie, et al. 2015))*
+      * *(Based on the slope of the graph in Figure 4C of the pairSEQ paper, the distribution of the original experiment was approximately exponential with a lambda ~0.6. (Howie, et al. 2015))*
 * Total number of wells on the plate
-* Number of sections on plate
+* Well populations random or fixed
-* Number of T cells per well
+  * If random, minimum and maximum population sizes
+  * If fixed
+    * Number of sections on plate
+    * Number of T cells per well
       * per section, if more than one section
 * Dropout rate
 
@@ -260,7 +263,7 @@ slightly less time than the simulation itself. Real elapsed time from start to f
   * ~~Apache Commons CSV library writes entries a row at a time~~ 
     * _Got this working, but at the cost of a profoundly strange bug in graph occupancy filtering. Have reverted the repo until I can figure out what caused that. Given how easily Thingiverse transposes CSV matrices in R, might not even be worth fixing._
 * Re-implement command line arguments, to enable scripting and statistical simulation studies
-* Implement sample plates with random numbers of T cells per well.
+* ~~Implement sample plates with random numbers of T cells per well.~~ DONE
   * Possible BiGpairSEQ advantage over pairSEQ: BiGpairSEQ is resilient to variations in well population sizes on a sample plate; pairSEQ is not.
     * preliminary data suggests that BiGpairSEQ behaves roughly as though the whole plate had whatever the *average* well concentration is, but that's still speculative.
 * Enable GraphML output in addition to serialized object binaries, for data portability