Merge pull request #1012 from rhayes777/feature/concise_info

feature/concise info

autofit/mapper/prior_model/representative.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from typing import List
 
 
 def find_groups(path_value_tuples, limit=1):
@@ -28,6 +29,44 @@ def find_groups(path_value_tuples, limit=1):
     return path_value_tuples
 
 
+def integers_representative_key(integers: List[int]) -> str:
+    """
+    Given a list of integers, return a string that represents them in a concise way.
+
+    e.g.
+    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] -> "1 - 10"
+    [1, 2, 3, 5, 6, 7, 8, 9, 10] -> "1 - 3, 5 - 10"
+    [1, 2, 3, 5, 6, 7, 8, 9, 10, 12] -> "1 - 3, 5 - 10, 12"
+
+    Parameters
+    ----------
+    integers
+        The list of integers to represent
+
+    Returns
+    -------
+    A string representing the integers in a concise way
+    """
+    integers = sorted(integers)
+    ranges = []
+    start = integers[0]
+    end = integers[0]
+
+    for integer in integers[1:]:
+        if integer == end + 1:
+            end = integer
+        else:
+            ranges.append((start, end))
+            start = integer
+            end = integer
+
+    ranges.append((start, end))
+
+    return ", ".join(
+        f"{start} - {end}" if start != end else str(start) for start, end in ranges
+    )
+
+
 def _find_groups(path_value_tuples, position):
     groups = defaultdict(list)
     paths = []
@@ -43,11 +82,11 @@ def _find_groups(path_value_tuples, position):
     for (before, after, value), names in groups.items():
         try:
             names = list(map(int, names))
+            representative_key = integers_representative_key(names)
         except ValueError:
-            pass
+            representative_key = (
+                f"{min(names)} - {max(names)}" if len(set(names)) > 1 else names[0]
+            )
 
-        representative_key = (
-            f"{min(names)} - {max(names)}" if len(set(names)) > 1 else names[0]
-        )
         paths.append(((*before, representative_key, *after), value))
     return paths

autofit/text/samples_text.py

@@ -36,16 +36,18 @@ def summary(
 
     sigma_formatter = frm.TextFormatter(indent=indent, line_length=line_length)
 
-    paths = []
+    prior_result_map = {}
 
-    for i, prior_path in enumerate(samples.model.unique_prior_paths):
-        value_result = frm.value_result_string_from(
+    for i, (_, prior) in enumerate(samples.model.unique_path_prior_tuples):
+        prior_result_map[prior] = frm.value_result_string_from(
             parameter_name=parameter_names[i],
             value=values[i],
             values_at_sigma=values_at_sigma[i],
         )
 
-        paths.append((prior_path, value_result))
+    paths = []
+    for path, prior in samples.model.path_priors_tuples:
+        paths.append((path, prior_result_map[prior]))
 
     for path, value in find_groups(paths):
         sigma_formatter.add(path, value)

autofit/text/text_util.py

@@ -58,11 +58,13 @@ def result_info_from(samples) -> str:
 
     paths = []
 
-    for prior_path, value in zip(
-        samples.model.unique_prior_paths,
+    model = samples.model
+    for (_, prior), value in zip(
+        model.unique_path_prior_tuples,
         max_log_likelihood_sample,
     ):
-        paths.append((prior_path, value))
+        for path in model.all_paths_for_prior(prior):
+            paths.append((path, value))
 
     for path, value in find_groups(paths):
         formatter.add(path, format_str().format(value))

test_autofit/mapper/info/test_representation.py

@@ -36,10 +36,10 @@ def test_exception():
         ((2, 1), 4),
     ]
     groups = find_groups(info_paths)
-    assert groups == [(("0 - 2", 0), 1), ((0, 1), 2), ((1, 1), 3), ((2, 1), 4)]
+    assert groups == [(("0 - 2", 0), 1), (("0", 1), 2), (("1", 1), 3), (("2", 1), 4)]
 
 
 def test_prefix():
     info_paths = [((0, i, 1), 2) for i in range(3)]
     groups = find_groups(info_paths)
-    assert groups == [((0, "0 - 2", 1), 2)]
+    assert groups == [(("0", "0 - 2", 1), 2)]

test_autofit/text/test_concise.py

@@ -3,6 +3,7 @@
 import autofit as af
 import pytest
 
+from autofit.mapper.prior_model.representative import integers_representative_key
 from autofit.text.text_util import result_info_from
 
 
@@ -73,7 +74,7 @@ def test_model_results(samples):
 
 Maximum Log Likelihood Model:
 
-19
+0 - 19
     centre                                                                      1.000
     normalization                                                               2.000
     sigma                                                                       3.000
@@ -85,7 +86,7 @@ def test_model_results(samples):
 
 Summary (1.0 sigma limits):
 
-19
+0 - 19
     centre                                                                      1.00 (1.00, 1.00)
     normalization                                                               2.00 (2.00, 2.00)
     sigma                                                                       3.00 (3.00, 3.00)
@@ -94,3 +95,21 @@ def test_model_results(samples):
 
 """
     )
+
+
+def test_non_consecutive(collection):
+    collection[10].centre = af.UniformPrior(0.0, 1.0)
+    print(collection.info)
+
+
+@pytest.mark.parametrize(
+    "integers, expected",
+    [
+        (list(range(1, 11)), "1 - 10"),
+        ([1], "1"),
+        ([1, 10], "1, 10"),
+        ([1, 2, 3, 8, 9, 10], "1 - 3, 8 - 10"),
+    ],
+)
+def test_integers_representative_key(integers, expected):
+    assert integers_representative_key(integers) == expected