remvoe print statements and update readme

Author: alexsweeten

README.md

@@ -121,9 +121,9 @@ Minimum sequence identity cutoff threshold. Default is 86. While it is possible
 
 Each partition takes into account a fraction of its neighboring partitions k-mers. This is to avoid sub-optimal identity scores when partitons don't overlap identically. Default is 0.5, and the accepted range is between 0 and 1. Anything greater than 0.5 is not recommended.
 
-`-s / --sparsity <int>`
+`-m / --modimizer <int>`
 
-A higher sparsity value means less k-mers to compare, at the expense of lower accuracy. Modimizers are selected `0 mod s`, an inverse of the selected k-mer density. The default is `s = 2/Mbp` of sequence for fast performance without compromising accuracy. For example, on a human Y chromosome ~ 62Mbp, ModDotPlot will set `s = 124`. Interactive mode will automatically round up to the nearest power of 2, in order to make computations easier. 
+Modimizer sketch size. Must be higher than window size `w`. A lower sketch size means less k-mers to compare (and faster runtime), at the expense of lower accuracy. Recommended to be kept > 1000.
 
 
 `-r / --resolution <int>`
@@ -165,7 +165,6 @@ Save the matrices produced in interactive mode. By default, a folder called `int
 
 Load previously saved matrices. Used instead of `-f/--fasta`
 
-
 --- 
 
 ### Static Mode Commands
@@ -330,14 +329,16 @@ Progress: |███████████████████████
 
 Chr1:14M-18M k-mers retrieved! 
 
-Computing self identity matrix for Chr1:14M-18M... 
-
-        Sparsity value s: 10
+Computing self identity matrix for chr1:14M-18M... 
 
-        Sequence length n: 4000001
+        Sequence length n: 4000000
 
         Window size w: 4000
 
+        Modimizer sketch value: 1000
+
+        Plot Resolution r: 1000
+
 Progress: |████████████████████████████████████████| 100.0% Completed
 
 

src/moddotplot/estimate_identity.py

@@ -37,7 +37,6 @@ def createSelfMatrix(
     ambiguous,
     sketch_size,
 ):
-    print(f"{sketch_size}\n")
     no_neighbors = partitionOverlaps(sequence, window_size, 0, sequence_length, k)
     if delta > 0:
         neighbors = partitionOverlaps(sequence, window_size, delta, sequence_length, k)