def test_count_out_of_range():
bins = [np.linspace(0., 5., 6)]
label = ['Random distribution']
scale = ['linear']
title = 'Test_histogram'
data = np.array(
[-1., 2.2, 3.2, 4.5, 6.3, 7.1, 4.9, 3.1, 0.2, 2.1, 2.2, 1.1])
test_data = [1, 1, 3, 2, 2]
test_out_range = [[1], [2]]
test_histo = Histogram(title, bins, label, scale)
out_range = test_histo.count_out_of_range(np.array(data, ndmin=2))
assert np.allclose(out_range, test_out_range)
def test_join_histo_managers(histogram_list):
args, list_of_histograms = histogram_list
histogram_manager = HistoManager(list_of_histograms)
joined_histogram_manager = histf.join_histo_managers(
histogram_manager, histogram_manager)
assert len(list_of_histograms) == len(joined_histogram_manager.histos)
for histoname, histogram in joined_histogram_manager.histos.items():
histo1 = histogram_manager[histoname]
true_histogram = Histogram(histoname, histo1.bins, histo1.labels,
histo1.scale)
true_histogram.data = 2 * histo1.data
true_histogram.errors = np.sqrt(2) * histo1.errors
true_histogram.out_range = 2 * histo1.out_range
assert_histogram_equality(histogram, true_histogram)
def test_histogram_initialization_with_values(bins):
label = ['Random distribution']
title = 'Test_histogram'
scale = ['linear']
data = []
out_range = []
for ibin in bins:
lower_limit = ibin[0] * 0.95
upper_limit = ibin[-1] * 1.05
data.append(np.random.uniform(lower_limit, upper_limit, 500))
out_range.append(
np.array([
np.count_nonzero(data[-1] < ibin[0]),
np.count_nonzero(data[-1] > ibin[-1])
]))
data = np.array(data)
out_range = np.array(out_range).T
binned_data = np.histogramdd(data.T, bins)[0]
test_histo = Histogram(title, bins, label, scale, data)
assert np.all(a == b for a, b in zip(test_histo.bins, bins))
assert np.allclose(test_histo.data, binned_data)
assert np.allclose(test_histo.out_range, out_range)
assert np.allclose(test_histo.errors, np.sqrt(binned_data))
assert test_histo.labels == label
assert test_histo.title == title
assert test_histo.scale == scale
def create_histomanager_from_dicts(histobins_dict,
histolabels_dict,
histoscales_dict,
init_fill_dict=None):
"""Creates and returns a HistoManager from a dict of bins
and a given of labels with identical keys.
Parameters
----------
histobins_dict : dict
Dictionary with keys equal to histogram names
and values equal to the binning.
histolabels_dict : dict
Dictionary with keys equal to histogram names
and values equal to the axis labels.
histoscales_dict : dict
Dictionary with keys equal to histogram names
and values equal to the y axis scale.
init_fill_dict : dict, optional
Dictionary with keys equal to histogram names
and values equal to an initial filling.
"""
histo_manager = HistoManager()
if init_fill_dict is None:
init_fill_dict = dict()
for title, bins in histobins_dict.items():
histo_manager.new_histogram(Histogram(title, bins,
histolabels_dict [title],
histoscales_dict [title],
init_fill_dict.get(title, None)))
return histo_manager
def test_join_histo_managers_with_different_histograms(histogram_list1,
histogram_list2):
_, list_of_histograms1 = histogram_list1
_, list_of_histograms2 = histogram_list2
histogram_manager1 = HistoManager(list_of_histograms1)
histogram_manager2 = HistoManager(list_of_histograms2)
joined_histogram_manager = histf.join_histo_managers(
histogram_manager1, histogram_manager2)
unique_histograms = set(histogram_manager1.histos) | set(
histogram_manager2.histos)
common_histograms = set(histogram_manager1.histos) & set(
histogram_manager2.histos)
remove_names = []
for name in unique_histograms:
if name in common_histograms:
if not np.all(a == b
for a, b in zip(histogram_manager1[name].bins,
histogram_manager2[name].bins)):
remove_names.append(name)
list_of_names = unique_histograms - set(remove_names)
for histoname, histogram in joined_histogram_manager.histos.items():
assert histoname in list_of_names
if (histoname in histogram_manager1.histos) and (
histoname in histogram_manager2.histos):
histo1 = histogram_manager1[histoname]
histo2 = histogram_manager2[histoname]
true_histogram = Histogram(histoname, histo1.bins, histo1.labels,
histo1.scale)
true_histogram.data = histo1.data + histo2.data
true_histogram.errors = np.sqrt(histo1.errors**2 +
histo2.errors**2)
true_histogram.out_range = histo1.out_range + histo2.out_range
assert_histogram_equality(histogram, true_histogram)
elif histoname in histogram_manager1.histos:
histo1 = histogram_manager1[histoname]
assert_histogram_equality(histogram, histo1)
elif histoname in histogram_manager2.histos:
histo2 = histogram_manager2[histoname]
assert_histogram_equality(histogram, histo2)
def test_bin_data():
bins = [np.linspace(0., 5., 6)]
label = ['Random distribution']
scale = ['linear']
title = 'Test_histogram'
data = np.array(
[-1., 2.2, 3.2, 4.5, 6.3, 7.1, 4.9, 3.1, 0.2, 2.1, 2.2, 1.1])
test_data = [1, 1, 3, 2, 2]
test_out_range = [[1], [2]]
test_histo = Histogram(title, bins, label, scale)
binned_data, out_range = test_histo.bin_data(data,
data_weights=np.ones(
len(data)))
assert np.allclose(binned_data, test_data)
assert np.allclose(out_range, test_out_range)
def get_histograms_from_file(file_input, group_name='HIST'):
histo_manager = HistoManager()
def name_selection(x):
selection = (('bins' not in x) and ('labels' not in x)
and ('errors' not in x) and ('outRange' not in x)
and ('scales' not in x))
return selection
with tb.open_file(file_input, "r") as h5in:
histogram_list = []
group = getattr(h5in.root, group_name)
for histoname in filter(name_selection, group._v_children):
entries = np.array(getattr(group, histoname)[:])
bins = getattr(group, histoname + '_bins')[:]
out_range = getattr(group, histoname + '_outRange')[:]
errors = np.array(getattr(group, histoname + '_errors')[:])
labels = getattr(group, histoname + '_labels')[:]
labels = [str(lab)[2:-1].replace('\\\\', '\\') for lab in labels]
try:
scale = getattr(group, histoname + '_scales')[:]
scale = [str(scl)[2:-1].replace('\\\\', '\\') for scl in scale]
except tb.NoSuchNodeError:
scale = ["linear"]
histogram = Histogram(histoname, bins, labels, scale)
histogram.data = entries
histogram.out_range = out_range
histogram.errors = errors
histogram.scale = scale
histogram_list.append(histogram)
return HistoManager(histogram_list)
def empty_histograms(draw, dimension=0):
if dimension <= 0:
dimension = draw(sampled_from((1, 2)))
bins = draw(bins_arrays(dimension=dimension))
labels = draw(
lists(text(characters, min_size=5),
min_size=dimension,
max_size=dimension))
scales = draw(
lists(text(characters, min_size=5),
min_size=dimension,
max_size=dimension))
args = draw(titles()), bins, labels, scales
return args, Histogram(*args)
def test_histogram_initialization(bins):
label = ['Random distribution']
title = 'Test_histogram'
scale = ['linear']
test_histo = Histogram(title, bins, label, scale)
assert np.all(a == b for a, b in zip(test_histo.bins, bins))
assert np.allclose(test_histo.data,
np.zeros(shape=tuple(len(x) - 1 for x in bins)))
assert np.allclose(test_histo.errors,
np.zeros(shape=tuple(len(x) - 1 for x in bins)))
assert np.allclose(test_histo.out_range, np.zeros(shape=(2, len(bins))))
assert test_histo.labels == label
assert test_histo.title == title
assert test_histo.scale == scale
def test_create_histomanager_from_dicts(bins):
histobins_dict = {}
histolabels_dict = {}
histoscales_dict = {}
histograms_dict = {}
for i, bins_element in enumerate(bins):
title = f"Histo_{i}"
labels = [f"Xlabel_{i}", f"Ylabel_{i}"]
scales = ["linear"]
histobins_dict[title] = bins_element
histolabels_dict[title] = labels
histoscales_dict[title] = scales
histograms_dict[title] = Histogram(title, bins_element, labels, scales)
histo_manager = histf.create_histomanager_from_dicts(
histobins_dict, histolabels_dict, histoscales_dict)
assert len(histograms_dict) == len(histo_manager.histos)
for histoname, histogram in histo_manager.histos.items():
assert_histogram_equality(histogram, histograms_dict[histoname])
def filled_histograms(draw, dimension=0, fixed_bins=None):
if fixed_bins is not None:
dimension = len(fixed_bins)
if dimension <= 0:
dimension = draw(sampled_from((1, 2)))
if fixed_bins is not None:
bins = [
np.linspace(bin_range[0], bin_range[1], bin_range[2] + 1)
for bin_range in fixed_bins
]
else:
bins = draw(bins_arrays(dimension=dimension))
labels = draw(
lists(text(characters, min_size=5),
min_size=dimension,
max_size=dimension))
scales = draw(
lists(text(characters, min_size=5),
min_size=dimension,
max_size=dimension))
shape = draw(integers(50, 100)),
data = []
for i in range(dimension):
lower_limit = bins[i][0] - draw(
floats(0.5, 1e8, allow_nan=False, allow_infinity=False))
upper_limit = bins[i][-1] + draw(
floats(0.5, 1e8, allow_nan=False, allow_infinity=False))
data.append(
draw(
arrays(
float, shape,
floats(lower_limit,
upper_limit,
allow_nan=False,
allow_infinity=False))))
data = np.array(data)
args = draw(titles()), bins, labels, scales, data
return args, Histogram(*args)