A little nicer

README.md

@@ -2,6 +2,7 @@
 
 A datastructure and tokenization library for counting short DNA sequences for use in Bioinformatics. DNA Hash stores k-mer sequence counts by their up2bit encoding - a two-way hash that works with variable-length sequences. As such, DNA Hash uses considerably less memory than a lookup table that stores sequences in plaintext. In addition, DNA Hash's novel autoscaling Bloom filter eliminates the need to explicitly store counts for sequences that have only been seen once.
 
+- **Variable** sequence lengths
 - **Ultra-low** memory footprint
 - **Embarrassingly** parallelizable
 - **Open-source** and free to use commercially
@@ -17,13 +18,6 @@ Install DNA Hash using a Python package manager, example pip:
 pip install dnahash
 ```
 
-## Parameters
-| # | Name | Default | Type | Description |
-|---|---|---|---|---|
-| 1 | max_false_positive_rate | 0.01 | float | The upper bound on the false positivity rate. |
-| 2 | num_hashes | 4 | int | The number of hash functions used, i.e. the number of slices per layer. |
-| 3 | layer_size | 32000000 | int | The size of each layer of the Bloom filter in bits. |
-
 ## Example Usage
 
 ```python
@@ -57,6 +51,18 @@ plt.ylabel('Frequency')
 plt.show()
 ```
 
+```
+TAACAA: 70
+TTAAAA: 68
+ACAACA: 65
+...
+CATTAA: 49
+
+Total sequences: 29876
+# of unique sequences: 2013
+# of singletons: 100
+```
+
 ## References
 - [1] https://github.com/JohnLonginotto/ACGTrie/blob/master/docs/UP2BIT.md.
 - [2] P. Melsted et al. (2011). Efficient counting of k-mers in DNA sequences using a bloom filter.

src/dna_hash/dna_hash.py

@@ -55,9 +55,9 @@ def _decode(cls, hash: int) -> str:
         sequence = ''
 
         for i in range(0, int(math.log(hash, 2)), 2):
-            base = (hash >> i) & 3
+            encoding = (hash >> i) & 3
 
-            sequence += cls.BASE_DECODE_MAP[base]
+            sequence += cls.BASE_DECODE_MAP[encoding]
 
         return sequence