Update README.md

Author: js51

README.md

@@ -11,55 +11,34 @@ Python package which implements split- and rank-based tools for inferring phylog
 The latest version of SplitP can be installed via the command
 `pip install splitp`
 
-### Examples
+### Example
+
+The following computes 100 split scores from a 10000bp sequence generated from a balanced 10-taxon tree under the Jukes Cantor model.
 
-Import `splitp` and the associated helper functions
-```python
-import splitp as sp
-from splitp import tree_helper_functions as hf
-```
-Define trees and work with splits
-```python
-splits = list(hf.all_splits(4))     # [01|23, 02|13, 03|12]
-tree = sp.NXTree('((0,1),(2,3));')	
-true_splits = tree.true_splits()    # 01|23
-```
-Let site patterns evolve under any submodel of the general markov model.
-```python
-JC_subs_matrix = tree.build_JC_matrix(branch_length:=0.05) # Or any other numpy Markov matrix
-tree.reassign_all_transition_matrices(JC_subs_matrix)
-pattern_probs = tree.get_pattern_probabilities()
-```
-```css
->             0         1
-      0    AAAA  0.185844
-      1    AAAC  0.003262
-      ..    ...       ...
-      254  TTTG  0.003262
-      255  TTTT  0.185844
-```
-Simulate sequence alignments from pattern distributions
-```python
-pattern_frequencies = tree.draw_from_multinomial(pattern_probs, 100)
-```
-```css
->         0    1
-    0  AAAA  0.22
-    1  AAAC  0.01
-    ..  ...   ...
-    2  CCGC  0.03
-    3  TTTT  0.14
-```
-Reconstruct trees using split based methods including flattenings:
-```python
-F1 = tree.flattening('01|23', pattern_frequencies)
-F2 = tree.flattening('02|13', pattern_frequencies)
-print(tree.split_score(F1) < tree.split_score(F2))    # True
-```
-Or subflattenings:
 ```python
-SF = tree.signed_sum_subflattening('01|23', pattern_probs)
-print(tree.split_score(SF))   # 0.0
+# Imports
+import splitp
+
+# Set parameters
+sequence_length = 10_000
+num_taxa = 10
+branch_length = 0.05
+number_of_splits = 100
+
+# Define model
+model = splitp.model.GTR.JukesCantor(1/2)
+
+# Generate tree, splits and alignment
+tree = splitp.trees.balanced_newick_tree(num_taxa, branch_length)
+splits = splitp.all_splits(tree)
+alignment = splitp.generate_alignment(tree, model, sequence_length)
+
+# Compute scores
+for s in range(number_of_splits):
+    split = next(splits)
+    flattening = splitp.flattening(split, alignment, splitp.FlatFormat.reduced)
+    score = splitp.split_score(flattening)
+    print(split, score)
 ```
 For more functionality please see the documentation at [splitp.joshuastevenson.me](http://splitp.joshuastevenson.me/splitp.html).