From 6ae8b034102d31c9159c9d1671b99a801aa57657 Mon Sep 17 00:00:00 2001 From: Yi-Mu Chen Date: Mon, 30 Oct 2023 16:27:25 +0100 Subject: [PATCH] Fixed pylint for test scripts --- tests/test_histread.py | 77 ++++++++++++++++++++++++++---------------- 1 file changed, 48 insertions(+), 29 deletions(-) diff --git a/tests/test_histread.py b/tests/test_histread.py index 2e6d1742..93607563 100644 --- a/tests/test_histread.py +++ b/tests/test_histread.py @@ -4,7 +4,7 @@ import numpy as np import hist import hist.intervals -from hepdata_lib.hist_utils import * +from hepdata_lib.hist_utils import read_hist, hist_as_variable, create_hist_base_table class TestHistUtils(TestCase): @@ -41,7 +41,10 @@ class TestHistUtils(TestCase): ) def test_default_read(self): - # Default read with no projection + """ + Ensure basic readout function generates arrays with compatible + dimensions with given the base-level histogram. + """ try: readout = read_hist(TestHistUtils.base_hist) except: @@ -51,34 +54,46 @@ def test_default_read(self): self.assertTrue(len(readout["dataset"]) == len(readout["flavor"])) self.assertTrue(len(readout["dataset"]) == len(readout["eta"])) self.assertTrue(len(readout["dataset"]) == len(readout["pt"])) + self.doCleanups() def test_projection_read(self): + """ + Ensure basic readout function generates arrays with compatible + dimensions with histogram slicing operations. + """ # Default read with simple projection try: - r1 = read_hist(TestHistUtils.base_hist[{"dataset": "data", "flavor": sum}]) - r2 = read_hist(TestHistUtils.base_hist[{"dataset": "QCD", "flavor": 0j}]) + read1 = read_hist( + TestHistUtils.base_hist[{"dataset": "data", "flavor": sum}] + ) + read2 = read_hist(TestHistUtils.base_hist[{"dataset": "QCD", "flavor": 0j}]) except: self.fail("Histogram reading raised an unexpected exception.") # Checking dimension compatibility - self.assertTrue(len(r1["eta"]) == len(r1["pt"])) - self.assertTrue(len(r1["eta"]) == len(r2["pt"])) - self.assertTrue(np.all(r1["eta"] == r2["eta"])) - self.assertTrue(np.all(r1["pt"] == r2["pt"])) + self.assertTrue(len(read1["eta"]) == len(read1["pt"])) + self.assertTrue(len(read1["eta"]) == len(read2["pt"])) + self.assertTrue(np.all(read1["eta"] == read2["eta"])) + self.assertTrue(np.all(read1["pt"] == read2["pt"])) # Clean up self.doCleanups() def test_uncertainty_generation(self): - h = TestHistUtils.base_hist[{"dataset": "data"}] + """ + Exhaustively testing automatic variable generation with all defined + uncertainty formats + """ + + d_h = TestHistUtils.base_hist[{"dataset": "data"}] q_h = TestHistUtils.base_hist[{"dataset": "QCD"}] t_h = TestHistUtils.base_hist[{"dataset": "ttbar"}] - h_arr = h.view(flow=True)["value"].flatten() - unc_arr = np.ones_like(h_arr) * np.random.random(size=h_arr.shape[0]) + d_arr = d_h.view(flow=True)["value"].flatten() + unc_arr = np.ones_like(d_arr) * np.random.random(size=d_arr.shape[0]) try: - r = hist_as_variable( + auto_var = hist_as_variable( "testing", - h, + d_h, flow=True, uncertainty={ "symmetric stat": "poisson_sym", @@ -94,41 +109,45 @@ def test_uncertainty_generation(self): except: self.fail("Unexpected exception of automatic uncertainty generation.") - def check_val(a, b): - return self.assertTrue(np.all(np.isclose(a, b) | np.isnan(a) | np.isnan(b))) + def check_val(arr1, arr2): + return self.assertTrue( + np.all(np.isclose(arr1, arr2) | np.isnan(arr1) | np.isnan(arr2)) + ) - h_arr = h.view(flow=True)["value"].flatten() - check_val(r.values, h_arr) + check_val(auto_var.values, d_arr) # Symmetric Poisson - check_val(r.uncertainties[0].values, np.sqrt(h_arr)) + check_val(auto_var.uncertainties[0].values, np.sqrt(d_arr)) # Asymmetric Poisson - l, u = hist.intervals.poisson_interval(h_arr) - l, u = l - h_arr, u - h_arr - check_val(r.uncertainties[1].values, list(zip(l, u))) + _l, _u = hist.intervals.poisson_interval(d_arr) + _l, _u = _l - d_arr, _u - d_arr + check_val(auto_var.uncertainties[1].values, list(zip(_l, _u))) # Flat uncertainties - check_val(r.uncertainties[2].values, 1.5) - check_val(r.uncertainties[3].values, (1.5, 2.2)) + check_val(auto_var.uncertainties[2].values, 1.5) + check_val(auto_var.uncertainties[3].values, (1.5, 2.2)) # Array defined uncertainties - check_val(r.uncertainties[4].values, unc_arr) - check_val(r.uncertainties[5].values, list(zip(-0.8 * unc_arr, unc_arr))) + check_val(auto_var.uncertainties[4].values, unc_arr) + check_val(auto_var.uncertainties[5].values, list(zip(-0.8 * unc_arr, unc_arr))) # Histogram defined uncertainties q_arr = q_h.view(flow=True)["value"].flatten() t_arr = t_h.view(flow=True)["value"].flatten() - check_val(r.uncertainties[6].values, q_arr) - check_val(r.uncertainties[7].values, list(zip(q_arr, t_arr))) + check_val(auto_var.uncertainties[6].values, q_arr) + check_val(auto_var.uncertainties[7].values, list(zip(q_arr, t_arr))) self.doCleanups() def test_table_generation(self): + """ + Base table generation with base histogram + """ _rename_ = { "dataset": "Data set", "flavor": "Jet flavor", - "eta": "Jet $\eta$", - "pt": "Jet $p_{T}$", + "eta": r"Jet $\eta$", + "pt": r"Jet $p_{T}$", } _units_ = {"pt": "GeV"} try: