Add tests for Histogram and CentralityClasses

src/sparkx/CentralityClasses.py

@@ -1,4 +1,5 @@
 import numpy as np
+import warnings
 
 
 class CentralityClasses:
@@ -58,8 +59,32 @@ def __init__(self,events_multiplicity,centrality_bins):
         if not isinstance(centrality_bins, (list,np.ndarray)):
             raise TypeError("'centrality_bins' is not list or numpy.ndarray")
 
+        # Check if centrality_bins is sorted
+        if not all(centrality_bins[i] <= centrality_bins[i+1] for i in range(len(centrality_bins)-1)):
+            warnings.warn("'centrality_bins' is not sorted. Sorting automatically.")
+            centrality_bins.sort()
+
+        # Check for uniqueness of values
+        # Remove duplicates from the list
+        unique_bins = []
+        seen = set()
+        multiple_same_entries = False
+        for item in centrality_bins:
+            if item not in seen:
+                unique_bins.append(item)
+                seen.add(item)
+            else:
+                multiple_same_entries = True
+
+        if multiple_same_entries:
+            warnings.warn("'centrality_bins' contains duplicate values. They are removed automatically.")
+
+        # Check for negative values and values greater than 100
+        if any(value < 0.0 or value > 100.0 for value in centrality_bins):
+            raise ValueError("'centrality_bins' contains values less than 0 or greater than 100.")
+
         self.events_multiplicity_ = events_multiplicity
-        self.centrality_bins_ = centrality_bins
+        self.centrality_bins_ = unique_bins
 
         self.dNchdetaMin_ = []
         self.dNchdetaMax_ = []

src/sparkx/Histogram.py

@@ -444,6 +444,8 @@ def scale_histogram(self,value):
             raise ValueError("The scaling factor of the histogram cannot be negative")
         elif isinstance(value, (list, np.ndarray)) and sum(1 for number in value if number < 0) > 0:
             raise ValueError("The scaling factor of the histogram cannot be negative")
+        elif isinstance(value, (list, np.ndarray)) and len(value) != self.number_of_bins_:
+            raise ValueError("The length of list/array not compatible with number_of_bins_ of the histogram")
 
         if self.histograms_.ndim == 1:
             if isinstance(value, (int, float, np.number)):
@@ -479,7 +481,7 @@ def set_error(self,own_error):
         value: list, numpy.ndarray
             Values for the uncertainties of the individual bins.
         """
-        if len(own_error) != self.number_of_bins_ and\
+        if len(own_error) != self.number_of_bins_ or\
               not isinstance(own_error, (list,np.ndarray)):
             error_message = "The input error has a different length than the"\
                  + " number of histogram bins or it is not a list/numpy.ndarray"
@@ -498,7 +500,7 @@ def set_systematic_error(self,own_error):
         value: list, numpy.ndarray
             Values for the systematic uncertainties of the individual bins.
         """
-        if len(own_error) != self.number_of_bins_ and\
+        if len(own_error) != self.number_of_bins_ or\
               not isinstance(own_error, (list,np.ndarray)):
             error_message = "The input error has a different length than the"\
                  + " number of histogram bins or it is not a list/numpy.ndarray"

tests/test_CentralityClasses.py

@@ -0,0 +1,79 @@
+import pytest
+import numpy as np
+from sparkx.CentralityClasses import CentralityClasses
+
+
+@pytest.fixture
+def centrality_obj():
+    bins = [0,10,20,30,40,50,60,70,80,90,100]
+    numbers_sequence = range(1,101)
+    multiplicities = [num for num in numbers_sequence for _ in range(100)]
+    return CentralityClasses(events_multiplicity=multiplicities,
+                              centrality_bins=bins)
+
+def test_init_with_invalid_input():
+    with pytest.raises(TypeError):
+        CentralityClasses(events_multiplicity=10, centrality_bins=[0, 25, 50, 75, 100])
+    with pytest.raises(TypeError):
+        CentralityClasses(events_multiplicity=[0,10,20,30,40,50,60,70,80,90,100], 
+                          centrality_bins=0)
+
+    numbers_sequence = range(1,101)
+    multiplicities = [num for num in numbers_sequence for _ in range(100)]
+    with pytest.raises(ValueError):
+        CentralityClasses(events_multiplicity=multiplicities,centrality_bins=[0,10,20,30,40,50,60,70,80,90,100,110])
+    with pytest.raises(ValueError):
+        CentralityClasses(events_multiplicity=multiplicities,centrality_bins=[-10,0,10,20,30,40,50,60,70,80,90,100])
+    with pytest.warns(UserWarning, match=r"'centrality_bins' contains duplicate values. They are removed automatically."):
+        a = CentralityClasses(events_multiplicity=multiplicities,centrality_bins=[0,10,20,30,40,40,50,60,70,80,90,100])
+        assert a.centrality_bins_ == [0,10,20,30,40,50,60,70,80,90,100]
+
+def test_create_centrality_classes(centrality_obj): 
+    # Assuming there are 10 bins, so there should be 10 minimum values
+    assert len(centrality_obj.dNchdetaMin_) == 10
+    assert len(centrality_obj.dNchdetaMax_) == 10
+    assert len(centrality_obj.dNchdetaAvg_) == 10
+    assert len(centrality_obj.dNchdetaAvgErr_) == 10
+
+def test_get_centrality_class(centrality_obj):
+    assert centrality_obj.get_centrality_class(99) == 0
+    assert centrality_obj.get_centrality_class(105) == 0
+    assert centrality_obj.get_centrality_class(0) == 9
+
+    assert centrality_obj.get_centrality_class(1) == 9
+    assert centrality_obj.get_centrality_class(2) == 9
+    assert centrality_obj.get_centrality_class(3) == 9
+    assert centrality_obj.get_centrality_class(4) == 9
+    assert centrality_obj.get_centrality_class(5) == 9
+    assert centrality_obj.get_centrality_class(6) == 9
+    assert centrality_obj.get_centrality_class(7) == 9
+    assert centrality_obj.get_centrality_class(8) == 9
+    assert centrality_obj.get_centrality_class(9) == 9
+    assert centrality_obj.get_centrality_class(10) == 9
+    assert centrality_obj.get_centrality_class(11) == 8
+    assert centrality_obj.get_centrality_class(19) == 8
+    assert centrality_obj.get_centrality_class(20) == 8
+    assert centrality_obj.get_centrality_class(21) == 7
+    assert centrality_obj.get_centrality_class(29) == 7
+
+def test_output_centrality_classes(centrality_obj, tmp_path):
+    output_file = tmp_path / "centrality_output.txt"
+    centrality_obj.output_centrality_classes(str(output_file))
+    assert output_file.is_file()
+
+    # Check content of the output file
+    with open(output_file, 'r') as f:
+        lines = f.readlines()
+        assert lines[0].startswith("# CentralityMin CentralityMax")
+        assert len(lines) == 10
+
+def test_create_centrality_classes_error():
+    with pytest.raises(ValueError):
+        CentralityClasses(events_multiplicity=[1, 2, 3], centrality_bins=[0, 25, 50, 75, 100])
+
+    with pytest.raises(ValueError):
+        CentralityClasses(events_multiplicity=[10, 15, -20, 25], centrality_bins=[0, 25, 50, 75, 100])
+
+def test_output_centrality_classes_with_invalid_fname(centrality_obj):
+    with pytest.raises(TypeError):
+        centrality_obj.output_centrality_classes(123)

tests/test_Histogram.py

@@ -0,0 +1,130 @@
+import numpy as np
+import pytest
+import csv
+from sparkx.Histogram import Histogram
+
+def test_histogram_creation_with_tuple():
+    # Test histogram creation with a tuple
+    hist = Histogram((0, 10, 10))
+    assert hist.number_of_bins_ == 10
+    assert np.allclose(hist.bin_edges_, np.linspace(0, 10, num=11))
+
+def test_histogram_creation_with_list():
+    # Test histogram creation with a list
+    hist = Histogram([0, 2, 4, 6, 8, 10])
+    assert hist.number_of_bins_ == 5
+    assert np.allclose(hist.bin_edges_, np.array([0, 2, 4, 6, 8, 10]))
+
+def test_histogram_creation_with_invalid_input():
+    # Test histogram creation with invalid input
+    with pytest.raises(TypeError):
+        # Passing incorrect input type
+        hist = Histogram("invalid_input")
+
+    with pytest.raises(ValueError):
+        # Passing tuple with invalid values
+        hist = Histogram((10, 0, 10))  # hist_min > hist_max
+
+    with pytest.raises(ValueError):
+        # Passing tuple with non-integer number of bins
+        hist = Histogram((0, 10, 1.4))
+
+    with pytest.raises(ValueError):
+        hist = Histogram((0, 10, -1)) # num_bins <= 0
+
+def test_set_error_with_invalid_input():
+    # Test setting error with invalid input
+    hist = Histogram((0, 10, 10))
+    with pytest.raises(ValueError):
+        # Passing incorrect length of error list
+        hist.set_error([1, 2, 3])
+
+def test_set_systematic_error_with_invalid_input():
+    # Test setting systematic error with invalid input
+    hist = Histogram((0, 10, 10))
+    with pytest.raises(ValueError):
+        # Passing incorrect length of systematic error list
+        hist.set_systematic_error([1, 2, 3])
+
+def test_add_value_single_number():
+    # Test adding a single number to the histogram
+    hist = Histogram((0, 10, 10))
+    hist.add_value(4.5)
+    assert np.allclose(hist.histogram(), np.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0]))
+
+def test_add_value_list():
+    # Test adding a list of numbers to the histogram
+    hist = Histogram((0, 10, 10))
+    hist.add_value([1, 3, 5, 7, 9])
+    assert np.allclose(hist.histogram(), np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1]))
+
+def test_average():
+    # Test averaging histograms
+    hist = Histogram((0, 10, 10))
+    hist.add_value([1, 3, 5, 7, 9])
+    hist.add_histogram()
+    hist.add_value([2, 4, 6, 8, 9])
+    hist.average()
+    assert np.allclose(hist.histogram(), np.array([0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1.0]))
+
+def test_write_to_file(tmp_path):
+    # Test writing histograms to a file
+    hist = Histogram((0, 4, 4))
+    hist.add_value([1, 2, 3])
+    hist.statistical_error()
+    hist_labels = [{'bin_center': 'Bin Center', 'bin_low': 'Bin Low', 'bin_high': 'Bin High',
+                    'distribution': 'Distribution', 'stat_err+': 'Stat Error+', 'stat_err-': 'Stat Error-',
+                    'sys_err+': 'Sys Error+', 'sys_err-': 'Sys Error-'}]
+    filename = tmp_path / "test_histograms.csv"
+    hist.write_to_file(filename, hist_labels)
+
+    # Check if the file exists
+    assert filename.is_file()
+
+    # Read the file and verify its content
+    with open(filename, 'r') as f:
+        reader = csv.reader(f)
+        headers = next(reader)
+        expected_headers = ['Bin Center', 'Bin Low', 'Bin High', 'Distribution', 
+                            'Stat Error+', 'Stat Error-', 'Sys Error+', 'Sys Error-']
+        assert headers == expected_headers
+
+        # Check the content of the file
+        rows = [row for row in reader]
+        rows = rows[:-1] # neglect the last empty line
+        expected_rows = [
+            ['0.5', '0.0', '1.0', '0.0', '0.0', '0.0', '0.0', '0.0'],
+            ['1.5', '1.0', '2.0', '1.0', '1.0', '1.0', '0.0', '0.0'],
+            ['2.5', '2.0', '3.0', '1.0', '1.0', '1.0', '0.0', '0.0'],
+            ['3.5', '3.0', '4.0', '1.0', '1.0', '1.0', '0.0', '0.0']
+        ]
+        assert rows == expected_rows
+
+def test_average_weighted():
+    # Test weighted averaging histograms
+    hist = Histogram((0, 10, 10))
+    hist.add_value([1, 3, 5, 7, 9])
+    hist.add_histogram()
+    hist.add_value([2, 4, 6, 8, 9])
+    weights = np.array([0.5, 0.5])
+    hist.average_weighted(weights)
+    assert np.allclose(hist.histogram(), np.array([0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1.0]))
+
+def test_scale_histogram_single_factor():
+    # Test scaling histogram with a single factor
+    hist = Histogram((0, 10, 10))
+    hist.add_value([1, 3, 5, 7, 9])
+    hist.scale_histogram(2)
+    assert np.allclose(hist.histogram(), np.array([0, 2, 0, 2, 0, 2, 0, 2, 0, 2]))
+
+def test_scale_histogram_multiple_factors():
+    # Test scaling histogram with multiple factors
+    hist = Histogram((0, 10, 10))
+    hist.add_value([1, 3, 5, 7, 9])
+    hist.add_histogram()
+    hist.add_value([2, 4, 6, 8, 9])
+    with pytest.raises(ValueError):
+        # Passing incorrect length of scaling list
+        hist.scale_histogram([2, 0.5])
+    hist.scale_histogram([2,2,2,2,2,2,2,2,2,2])
+    assert np.allclose(hist.histogram(), np.array([[0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 2, 0, 2, 0, 2, 0, 2, 2]]))