File support-pytest-8.3.4.patch of Package python-sherpa
From 0fafdd4522408e15561ae79a502b096317bb0d9e Mon Sep 17 00:00:00 2001
From: Douglas Burke <dburke.gw@gmail.com>
Date: Fri, 6 Dec 2024 12:06:50 -0500
Subject: [PATCH 1/2] TEST: handle pytest 8.3.4 change in bool tests with
approx
Fix #2202
In pytest 8.3.3 and earlier
>>> import numpy as np; import pytest
>>> pytest.__version__
'8.3.3'
>>> np.ones(2, dtype=bool) == pytest.approx([True, True])
True
However, pytest 8.3.4 now causes this to fail
>>> import numpy as np; import pytest
>>> pytest.__version__
'8.3.4'
>>> np.ones(2, dtype=bool) == pytest.approx([True, True])
False
This is because of "pytest.approx considers boolean numeric types"
https://github.com/pytest-dev/pytest/issues/9353
The solution is to make the "expected" value be a ndarray, and so
>>> np.ones(2, dtype=bool) == pytest.approx(np.asarray([True, True]))
True
holds with both pytest 8.3.3 and 8.3.4.
So this commit basically goes through and updates the tests so that
we use a ndarray for boolean arrays.
An alternative would be to change from
assert got == pytest.approx(expected)
to something like
assert np.all(got == expected)
However, the error message when the array lengths are different or
an element is different are a **lot less** useful, and the change would
be even-more invasive than this change.
---
sherpa/astro/io/tests/test_io_pha.py | 2 +-
sherpa/astro/tests/test_astro_data.py | 43 +++---
sherpa/astro/tests/test_astro_data2.py | 131 +++++++++++-------
sherpa/astro/tests/test_astro_instrument.py | 3 +-
sherpa/astro/tests/test_astro_plot.py | 6 +-
sherpa/astro/ui/tests/test_astro_ui.py | 3 +-
sherpa/astro/ui/tests/test_astro_ui_unit.py | 9 +-
sherpa/astro/ui/tests/test_filtering.py | 8 +-
.../utils/tests/test_astro_utils_unit.py | 8 +-
sherpa/tests/test_data.py | 6 +-
sherpa/ui/tests/test_session.py | 3 +-
sherpa/ui/tests/test_ui_unit.py | 10 +-
sherpa/utils/tests/test_utils.py | 10 +-
13 files changed, 146 insertions(+), 96 deletions(-)
diff --git a/sherpa/astro/io/tests/test_io_pha.py b/sherpa/astro/io/tests/test_io_pha.py
index 84dcd4a98c..ceab1609a4 100644
--- a/sherpa/astro/io/tests/test_io_pha.py
+++ b/sherpa/astro/io/tests/test_io_pha.py
@@ -1504,7 +1504,7 @@ def test_write_pha_with_bad_quality(tmp_path):
counts = chans * 2
group = [1, -1, -1, 1, -1, 1, 1, 1, -1]
quality = [0, 5, 0, 0, 0, 0, 0, 2, 2]
- qfilt = [True, False] + [True] * 5 + [False] * 2
+ qfilt = np.asarray([True, False] + [True] * 5 + [False] * 2)
pha0 = DataPHA("qual", chans, counts, grouping=group,
quality=quality)
diff --git a/sherpa/astro/tests/test_astro_data.py b/sherpa/astro/tests/test_astro_data.py
index b4ab684ce5..9ca381763c 100644
--- a/sherpa/astro/tests/test_astro_data.py
+++ b/sherpa/astro/tests/test_astro_data.py
@@ -2367,7 +2367,7 @@ def test_pha_check_limit(ignore, lo, hi, evals):
pha.units = 'energy'
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 10)
+ assert pha.get_mask() == pytest.approx(np.ones(10, dtype=bool))
func = pha.ignore if ignore else pha.notice
func(lo, hi)
@@ -2380,7 +2380,7 @@ def test_pha_check_limit(ignore, lo, hi, evals):
vin = True
c1, c2, c3 = evals
- expected = [vout] * c1 + [vin] * c2 + [vout] * c3
+ expected = np.asarray([vout] * c1 + [vin] * c2 + [vout] * c3)
assert pha.mask == pytest.approx(pha.get_mask())
assert pha.mask == pytest.approx(expected)
@@ -2449,7 +2449,7 @@ def test_pha_check_limit_channel(ignore, lo, hi, evals):
pha.units = 'channel'
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 10)
+ assert pha.get_mask() == pytest.approx(np.ones(10, dtype=bool))
func = pha.ignore if ignore else pha.notice
func(lo, hi)
@@ -2462,7 +2462,7 @@ def test_pha_check_limit_channel(ignore, lo, hi, evals):
vin = True
c1, c2, c3 = evals
- expected = [vout] * c1 + [vin] * c2 + [vout] * c3
+ expected = np.asarray([vout] * c1 + [vin] * c2 + [vout] * c3)
assert pha.mask == pytest.approx(pha.get_mask())
assert pha.mask == pytest.approx(expected)
@@ -2672,7 +2672,9 @@ def test_is_mask_reset_pha(caplog):
# Pick a value somewhere within the independent axis
assert data.mask is True
data.ignore(None, 2)
- assert data.mask == pytest.approx([False, False, True])
+
+ mask = np.asarray([False, False, True])
+ assert data.mask == pytest.approx(mask)
# Change the independent axis, but to something of the same
# length.
@@ -2683,7 +2685,7 @@ def test_is_mask_reset_pha(caplog):
assert len(caplog.records) == 0
# The mask has *not* been cleared
- assert data.mask == pytest.approx([False, False, True])
+ assert data.mask == pytest.approx(mask)
def test_is_mask_reset_pha_channel(caplog):
@@ -2703,7 +2705,8 @@ def test_is_mask_reset_pha_channel(caplog):
assert len(caplog.records) == 0
# The mask has not been cleared
- assert data.mask == pytest.approx([False, False, True])
+ mask = np.asarray([False, False, True])
+ assert data.mask == pytest.approx(mask)
@requires_region
@@ -3376,9 +3379,11 @@ def test_pha_notice_bkg_id_none():
pha.notice(lo=2, bkg_id=None) # the default
- assert pha.mask == pytest.approx([False, True])
- assert b1.mask == pytest.approx([False, True])
- assert bup.mask == pytest.approx([False, True])
+ bfilt = np.asarray([False, True])
+
+ assert pha.mask == pytest.approx(bfilt)
+ assert b1.mask == pytest.approx(bfilt)
+ assert bup.mask == pytest.approx(bfilt)
@pytest.mark.parametrize("bkg_id", [1, "up"])
@@ -3394,13 +3399,15 @@ def test_pha_notice_bkg_id_scalar(bkg_id):
pha.notice(lo=2, bkg_id=bkg_id)
+ bfilt = np.asarray([False, True])
+
assert pha.mask is True
if bkg_id == 1:
- assert b1.mask == pytest.approx([False, True])
+ assert b1.mask == pytest.approx(bfilt)
assert bup.mask is True
else:
assert b1.mask is True
- assert bup.mask == pytest.approx([False, True])
+ assert bup.mask == pytest.approx(bfilt)
def test_pha_notice_bkg_id_array_all():
@@ -3415,9 +3422,11 @@ def test_pha_notice_bkg_id_array_all():
pha.notice(lo=2, bkg_id=["up", 1])
+ bfilt = np.asarray([False, True])
+
assert pha.mask is True
- assert b1.mask == pytest.approx([False, True])
- assert bup.mask == pytest.approx([False, True])
+ assert b1.mask == pytest.approx(bfilt)
+ assert bup.mask == pytest.approx(bfilt)
@pytest.mark.parametrize("bkg_id", [1, "up"])
@@ -3433,13 +3442,15 @@ def test_pha_notice_bkg_id_array_subset(bkg_id):
pha.notice(lo=2, bkg_id=[bkg_id])
+ bfilt = np.asarray([False, True])
+
assert pha.mask is True
if bkg_id == 1:
- assert b1.mask == pytest.approx([False, True])
+ assert b1.mask == pytest.approx(bfilt)
assert bup.mask is True
else:
assert b1.mask is True
- assert bup.mask == pytest.approx([False, True])
+ assert bup.mask == pytest.approx(bfilt)
def get_img_spatial_mask():
diff --git a/sherpa/astro/tests/test_astro_data2.py b/sherpa/astro/tests/test_astro_data2.py
index b5eaf3b598..d6bdb19e49 100644
--- a/sherpa/astro/tests/test_astro_data2.py
+++ b/sherpa/astro/tests/test_astro_data2.py
@@ -97,7 +97,7 @@ def test_get_mask_is_none():
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 3)
+ assert pha.get_mask() == pytest.approx(np.asarray([True] * 3))
def test_get_mask_is_none_when_all_filtered():
@@ -497,7 +497,7 @@ def test_288_a():
# with bools the use of approx is okay (it can tell the
# difference between 0 and 1, aka False and True).
#
- assert pha.mask == pytest.approx([True, False, True])
+ assert pha.mask == pytest.approx(np.asarray([True, False, True]))
def test_288_a_energy():
@@ -525,7 +525,7 @@ def test_288_a_energy():
# with bools the use of approx is okay (it can tell the
# difference between 0 and 1, aka False and True).
#
- assert pha.mask == pytest.approx([True, False, True])
+ assert pha.mask == pytest.approx(np.asarray([True, False, True]))
def test_288_b():
@@ -565,7 +565,7 @@ def test_288_b_energy():
assert pha.mask is True
pha.ignore(3.1, 4)
- assert pha.mask == pytest.approx([True, False, True])
+ assert pha.mask == pytest.approx(np.asarray([True, False, True]))
@requires_group
@@ -621,8 +621,8 @@ def test_416_a():
# grouped)
# - pha.get_mask() always returns the ungrouped mask
#
- mask_ungrouped = [False] * 3 + [True] * 3 + [False] * 4
- mask_grouped = [False] * 3 + [True] * 2 + [False] * 4
+ mask_ungrouped = np.asarray([False] * 3 + [True] * 3 + [False] * 4)
+ mask_grouped = np.asarray([False] * 3 + [True] * 2 + [False] * 4)
assert pha.mask == pytest.approx(mask_ungrouped)
assert pha.get_mask() == pytest.approx(mask_ungrouped)
@@ -685,7 +685,7 @@ def test_416_c():
pha.notice(4.5, 6.5)
# this should be ~pha.mask
- tabstops = [True] * 3 + [False] * 3 + [True] * 4
+ tabstops = np.asarray([True] * 3 + [False] * 3 + [True] * 4)
assert ~pha.mask == pytest.approx(tabstops)
assert pha.grouping is None
@@ -1688,10 +1688,13 @@ def test_pha_quality_all_bad_basic_checks():
"""
+ all4 = np.ones(4, dtype=bool)
+ none4 = np.zeros(4, dtype=bool)
+
pha = DataPHA("q", [1, 2, 3, 4], [9, 0, 1, 64])
fvals = [12, 2, 7, 8]
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 4)
+ assert pha.get_mask() == pytest.approx(all4)
assert pha.get_filter() == "1:4"
assert pha.get_x() == pytest.approx([1, 2, 3, 4])
assert pha.apply_filter(fvals) == pytest.approx(fvals)
@@ -1699,15 +1702,15 @@ def test_pha_quality_all_bad_basic_checks():
pha.quality = [2, 2, 2, 5]
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 4)
+ assert pha.get_mask() == pytest.approx(all4)
assert pha.get_filter() == "1:4"
assert pha.get_x() == pytest.approx([1, 2, 3, 4])
assert pha.apply_filter(fvals) == pytest.approx(fvals)
assert pha.apply_grouping(fvals) == pytest.approx(fvals)
pha.ignore_bad()
- assert pha.mask == pytest.approx([False] * 4)
- assert pha.get_mask() == pytest.approx([False] * 4)
+ assert pha.mask == pytest.approx(none4)
+ assert pha.get_mask() == pytest.approx(none4)
assert pha.get_filter() == ""
assert pha.get_x() == pytest.approx([1, 2, 3, 4])
assert pha.apply_filter(fvals) == pytest.approx([])
@@ -1742,7 +1745,7 @@ def test_pha_quality_change_mask(make_quality_pha):
pha.ignore_bad()
assert pha.mask is True
pha.mask = [1, 1, 0]
- assert pha.mask == pytest.approx([True, True, False])
+ assert pha.mask == pytest.approx(np.asarray([True, True, False]))
def test_pha_quality_change_mask_ungrouped(make_quality_pha):
@@ -1753,7 +1756,8 @@ def test_pha_quality_change_mask_ungrouped(make_quality_pha):
pha.ungroup()
assert pha.mask is True
pha.mask = [1, 1, 0, 1, 1, 0, 0, 1, 1]
- assert pha.mask == pytest.approx([True, True, False, True, True, False, False, True, True])
+ mask = np.asarray([True, True, False, True, True, False, False, True, True])
+ assert pha.mask == pytest.approx(mask)
def test_pha_quality_change_mask_fullsize(make_quality_pha):
@@ -1972,23 +1976,26 @@ def test_pha_quality_ignore_bad_clear_filter(make_quality_pha):
pha = make_quality_pha
+ mask0 = np.ones(9, dtype=bool)
assert pha.get_filter() == "1:9"
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 9)
+ assert pha.get_mask() == pytest.approx(mask0)
assert pha.quality_filter is None
# channels 2,3 and 7-9 are "bad"
pha.ignore(hi=3)
+ mask = np.asarray([False] + [True] * 3)
+ mask_full = np.asarray([False] * 4 + [True] * 5)
assert pha.get_filter() == "5:9"
- assert pha.mask == pytest.approx([False] + [True] * 3)
- assert pha.get_mask() == pytest.approx([False] * 4 + [True] * 5)
+ assert pha.mask == pytest.approx(mask)
+ assert pha.get_mask() == pytest.approx(mask_full)
assert pha.quality_filter is None
# This resets the previous filters
pha.ignore_bad()
- qflags = [True] * 1 + [False] * 2 + [True] * 3 + [False] * 3
+ qflags = np.asarray([True] * 1 + [False] * 2 + [True] * 3 + [False] * 3)
assert pha.get_filter() == "1:9"
assert pha.mask is True
assert pha.get_mask() == pytest.approx(qflags)
@@ -1996,16 +2003,19 @@ def test_pha_quality_ignore_bad_clear_filter(make_quality_pha):
pha.ignore(hi=3)
+ mask2 = np.asarray([False] + [True] * 2)
+ mask2_full = np.asarray([False] * 2 + [True] * 2)
+
assert pha.get_filter() == "5:6"
- assert pha.mask == pytest.approx([False] + [True] * 2)
- assert pha.get_mask() == pytest.approx([False] * 2 + [True] * 2)
+ assert pha.mask == pytest.approx(mask2)
+ assert pha.get_mask() == pytest.approx(mask2_full)
assert pha.quality_filter == pytest.approx(qflags)
pha.ignore(lo=2, hi=4)
assert pha.get_filter() == "5:6"
- assert pha.mask == pytest.approx([False] + [True] * 2)
- assert pha.get_mask() == pytest.approx([False] * 2 + [True] * 2)
+ assert pha.mask == pytest.approx(mask2)
+ assert pha.get_mask() == pytest.approx(mask2_full)
assert pha.quality_filter == pytest.approx(qflags)
# This removes the quality filter!
@@ -2013,7 +2023,7 @@ def test_pha_quality_ignore_bad_clear_filter(make_quality_pha):
assert pha.get_filter() == "1:9"
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 9)
+ assert pha.get_mask() == pytest.approx(mask0)
assert pha.quality_filter is None
@@ -2443,7 +2453,8 @@ def test_pha_change_quality_values(caplog):
pha.ignore_bad()
assert len(caplog.records) == 0
- assert pha.quality_filter == pytest.approx([True] * 5 + [False] * 2)
+ qfilt = np.asarray([True] * 5 + [False] * 2)
+ assert pha.quality_filter == pytest.approx(qfilt)
assert pha.get_dep(filter=True) == pytest.approx([6])
assert pha.get_filter() == '1:7'
@@ -2456,7 +2467,7 @@ def test_pha_change_quality_values(caplog):
assert pha.quality == pytest.approx([0, 0, 0, 2, 2, 0, 0])
# Should quality filter be reset?
- assert pha.quality_filter == pytest.approx([True] * 5 + [False] * 2)
+ assert pha.quality_filter == pytest.approx(qfilt)
assert pha.get_dep(filter=True) == pytest.approx([4, 2])
assert pha.get_filter() == '1:7'
@@ -2499,14 +2510,14 @@ def test_pha_group_ignore_bad_then_filter(caplog):
assert len(caplog.records) == 0
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 7)
+ assert pha.get_mask() == pytest.approx(np.ones(7, dtype=bool))
assert pha.get_filter() == '1:7'
assert pha.quality_filter is None
pha.ignore_bad()
assert len(caplog.records) == 0
- qual_mask = [True] * 2 + [False] + [True] * 3 + [False]
+ qual_mask = np.asarray([True] * 2 + [False] + [True] * 3 + [False])
assert pha.mask is True
assert pha.get_mask() == pytest.approx(qual_mask)
assert pha.get_filter() == '1:7'
@@ -2518,8 +2529,10 @@ def test_pha_group_ignore_bad_then_filter(caplog):
pha.ignore(4, 5)
assert len(caplog.records) == 0
- assert pha.mask == pytest.approx([True, False, True])
- assert pha.get_mask() == pytest.approx([True] * 2 + [False] * 2 + [True])
+ mask = np.asarray([True, False, True])
+ mask_full = np.asarray([True] * 2 + [False] * 2 + [True])
+ assert pha.mask == pytest.approx(mask)
+ assert pha.get_mask() == pytest.approx(mask_full)
assert pha.get_filter() == '1:2,6'
assert pha.quality_filter == pytest.approx(qual_mask)
assert pha.quality == pytest.approx([0, 0, 2, 0, 0, 0, 2])
@@ -2536,7 +2549,7 @@ def test_pha_group_ignore_bad_then_group(caplog):
pha.ignore_bad()
assert len(caplog.records) == 0
- qual_mask = [True] * 2 + [False] + [True] * 3 + [False]
+ qual_mask = np.asarray([True] * 2 + [False] + [True] * 3 + [False])
assert pha.mask is True
assert pha.get_mask() == pytest.approx(qual_mask)
assert pha.quality_filter == pytest.approx(qual_mask)
@@ -2562,7 +2575,7 @@ def test_pha_group_ignore_bad_then_group(caplog):
pha.ignore_bad()
assert len(caplog.records) == 0
- qual_mask = [True] + [False] + [True] * 5
+ qual_mask = np.asarray([True] + [False] + [True] * 5)
assert pha.mask is True
assert pha.get_mask() == pytest.approx(qual_mask)
assert pha.get_filter() == '1:7'
@@ -2584,7 +2597,7 @@ def test_pha_filter_ignore_bad_filter(caplog):
pha.ignore(lo=4, hi=4)
assert len(caplog.records) == 0
- data_mask = [True] * 3 + [False] + [True] * 3
+ data_mask = np.asarray([True] * 3 + [False] + [True] * 3)
assert pha.mask == pytest.approx(data_mask)
assert pha.get_mask() == pytest.approx(data_mask)
assert pha.get_filter() == '1:3,5:7'
@@ -2596,7 +2609,8 @@ def test_pha_filter_ignore_bad_filter(caplog):
pha.group_counts(5)
assert len(caplog.records) == 0
- assert pha.mask == pytest.approx([True] * 2 + [False] + [True] * 3)
+ data_mask2 = np.asarray([True] * 2 + [False] + [True] * 3)
+ assert pha.mask == pytest.approx(data_mask2)
assert pha.get_mask() == pytest.approx(data_mask)
assert pha.get_filter() == '1:3,5:7'
assert pha.quality_filter is None
@@ -2612,7 +2626,7 @@ def test_pha_filter_ignore_bad_filter(caplog):
assert r.levelname == "WARNING"
assert r.getMessage() == "filtering grouped data with quality flags, previous filters deleted"
- new_mask = [True] * 2 + [False] + [True] * 4
+ new_mask = np.asarray([True] * 2 + [False] + [True] * 4)
assert pha.mask is True
assert pha.get_mask() == pytest.approx(new_mask)
assert pha.get_filter() == '1:7'
@@ -2624,8 +2638,10 @@ def test_pha_filter_ignore_bad_filter(caplog):
pha.ignore(lo=2, hi=2)
assert len(caplog.records) == 1
- assert pha.mask == pytest.approx([False] + [True] * 4)
- assert pha.get_mask() == pytest.approx([False] * 2 + [True] * 4)
+ mask3 = np.asarray([False] + [True] * 4)
+ mask3_full = np.asarray([False] * 2 + [True] * 4)
+ assert pha.mask == pytest.approx(mask3)
+ assert pha.get_mask() == pytest.approx(mask3_full)
assert pha.get_filter() == '4:7'
assert pha.quality_filter == pytest.approx(new_mask)
assert pha.quality == pytest.approx([0, 0, 2, 0, 0, 0, 0])
@@ -2732,7 +2748,7 @@ def test_pha_ignore_bad_group_quality(caplog):
assert pha.get_filter(format="%.1f") == "3.0:7.0"
assert pha.get_noticed_channels() == pytest.approx(np.arange(3, 7))
- omask = [False] * 2 + [True] * 4 + [False] * 4
+ omask = np.asarray([False] * 2 + [True] * 4 + [False] * 4)
assert pha.mask == pytest.approx(omask)
assert pha.get_mask() == pytest.approx(omask)
@@ -2749,7 +2765,8 @@ def test_pha_ignore_bad_group_quality(caplog):
assert pha.get_dep(filter=False) == pytest.approx(y)
assert pha.get_dep(filter=True) == pytest.approx([3, 1])
- assert pha.mask == pytest.approx([False] * 2 + [True] * 2 + [False] * 4)
+ mask = np.asarray([False] * 2 + [True] * 2 + [False] * 4)
+ assert pha.mask == pytest.approx(mask)
assert pha.get_mask() == pytest.approx(omask)
grouping = [0, 0, 1, -1, -1, 1, 0, 0, 0, 0]
@@ -2788,7 +2805,7 @@ def test_pha_ignore_bad_group_quality(caplog):
# However, get_mask reflects the quality filter, so is all True
# except for the 6th element.
#
- single_bad = [True] * 5 + [False] + [True] * 4
+ single_bad = np.asarray([True] * 5 + [False] + [True] * 4)
assert pha.get_mask() == pytest.approx(single_bad)
# What about the quality fields?
@@ -2862,7 +2879,7 @@ def test_pha_ignore_bad_quality(groupit, caplog):
assert pha.get_dep(filter=False) == pytest.approx(y)
assert pha.get_dep(filter=True) == pytest.approx(y[2:6])
- mask = [False] * 2 + [True] * 4 + [False] * 4
+ mask = np.asarray([False] * 2 + [True] * 4 + [False] * 4)
assert pha.mask == pytest.approx(mask)
assert pha.get_mask() == pytest.approx(mask)
@@ -2900,7 +2917,7 @@ def test_pha_ignore_bad_quality(groupit, caplog):
# The mask changed (the channel=6 value is now filtered out).
#
- mask2 = [False] * 2 + [True] * 3 + [False] * 5
+ mask2 = np.asarray([False] * 2 + [True] * 3 + [False] * 5)
assert pha.mask == pytest.approx(mask2)
assert pha.get_mask() == pytest.approx(mask2)
@@ -3098,7 +3115,7 @@ def test_grouped_pha_mask(make_grouped_pha):
def test_grouped_pha_get_mask(make_grouped_pha):
"""What is the default get_mask value?"""
pha = make_grouped_pha
- assert pha.get_mask() == pytest.approx([True] * 4 + [False])
+ assert pha.get_mask() == pytest.approx(np.asarray([True] * 4 + [False]))
def test_quality_pha_mask(make_quality_pha):
@@ -3115,11 +3132,13 @@ def test_quality_pha_mask(make_quality_pha):
def test_quality_pha_get_mask(make_quality_pha):
"""What is the default get_mask value?"""
pha = make_quality_pha
- assert pha.get_mask() == pytest.approx([True] * 9)
+ m1 = np.ones(9, dtype=bool)
+ assert pha.get_mask() == pytest.approx(m1)
pha.ignore_bad()
# This is a regression test
- assert pha.get_mask() == pytest.approx([True] + [False] * 2 + [True] * 3 + [False] * 3)
+ m2 = np.asarray([True] + [False] * 2 + [True] * 3 + [False] * 3)
+ assert pha.get_mask() == pytest.approx(m2)
@pytest.mark.parametrize("field,expected",
@@ -3576,8 +3595,10 @@ def test_pha_quality_filtered_apply_filter_invalid_size(vals, make_grouped_pha):
pha.ignore(hi=1)
# safety check to make sure we've excluded points
- assert pha.mask == pytest.approx([False, True])
- assert pha.get_mask() == pytest.approx([False, False, False, True])
+ m1 = np.asarray([False, True])
+ m2 = np.asarray([False, False, False, True])
+ assert pha.mask == pytest.approx(m1)
+ assert pha.get_mask() == pytest.approx(m2)
with pytest.raises(DataErr,
match="^size mismatch between filtered data and array: 1 vs [28]$"):
@@ -4193,7 +4214,7 @@ def test_rmf_simple_filter_check(startchan, na, nb, nc):
assert rmf.apply_rmf(mvals) == pytest.approx(mvals)
selected = rmf.notice([startchan, startchan + 1, startchan + 2])
- expected = [False] * na + [True] * nb + [False] * nc
+ expected = np.asarray([False] * na + [True] * nb + [False] * nc)
assert selected == pytest.approx(expected)
# Drop everything but the selected values.
@@ -4295,7 +4316,7 @@ def test_rmf_offset_check_square(offset, caplog):
nchans = [offset + 2]
selected = rmf.notice(nchans)
- assert selected == pytest.approx([False, True, True, True])
+ assert selected == pytest.approx(np.asarray([False, True, True, True]))
expected2 = [0.0 * 0.2 + 0.6 * 0.4,
0.4 * 0.4 + 0.5 * 0.2,
@@ -4387,11 +4408,13 @@ def test_rmf_offset_check_rectangular(offset):
# - drop the last channel
nchans2 = offset + np.arange(0, 9)
+ mask = np.asarray([True] * 19 + [False])
+
selected2 = rmf.notice(nchans2)
- assert selected2 == pytest.approx([True] * 19 + [False])
+ assert selected2 == pytest.approx(mask)
selected2 = rmf.notice(offset + np.arange(0, 9))
- assert selected2 == pytest.approx([True] * 19 + [False])
+ assert selected2 == pytest.approx(mask)
expected2 = mvals[selected2] @ full_matrix[selected2, :]
got2 = rmf.apply_rmf(mvals[selected2])
@@ -4414,8 +4437,11 @@ def test_rmf_offset_check_rectangular(offset):
#
nchans3 = offset + np.asarray([4, 5, 6])
+ mask3 = np.asarray([True, False] * 2 + [True] * 10 +
+ [False] * 4 + [True, False])
+
selected3 = rmf.notice(nchans3)
- assert selected3 == pytest.approx([True, False] * 2 + [True] * 10 + [False] * 4 + [True, False])
+ assert selected3 == pytest.approx(mask3)
# It is not clear what the RMF application does here.
#
@@ -5970,7 +5996,7 @@ def test_group_xxx_tabtops_not_ndarray(asarray):
assert pha.get_y() == pytest.approx([2, 3, 4.5, 6])
assert pha.mask is True
- assert pha.get_mask() == pytest.approx([True] * 5)
+ assert pha.get_mask() == pytest.approx(np.ones(5, dtype=bool))
@requires_group
@@ -6002,7 +6028,8 @@ def test_group_xxx_tabstops_already_grouped():
assert pha.get_y(filter=True) == pytest.approx([12, 5.5, 4])
tstops = ~pha.mask
- assert tstops == pytest.approx([False, True, False, False, True])
+ mask = np.asarray([False, True, False, False, True])
+ assert tstops == pytest.approx(mask)
# Apply the mask as the tabStops (after inversion) where
# len(tstops) < nchannel but does match the number of groups.
diff --git a/sherpa/astro/tests/test_astro_instrument.py b/sherpa/astro/tests/test_astro_instrument.py
index b466e3fe5a..584fe371c6 100644
--- a/sherpa/astro/tests/test_astro_instrument.py
+++ b/sherpa/astro/tests/test_astro_instrument.py
@@ -712,7 +712,8 @@ def test_rmfmodelpha_delta_no_ebounds(analysis, caplog):
assert len(caplog.records) == 0
if analysis == "energy":
- assert pha.mask == pytest.approx([False, True, True, True, False])
+ expected = np.asarray([False, True, True, True, False])
+ assert pha.mask == pytest.approx(expected)
else:
assert not pha.mask.any()
diff --git a/sherpa/astro/tests/test_astro_plot.py b/sherpa/astro/tests/test_astro_plot.py
index 3f978728dc..a7df4cb1ee 100644
--- a/sherpa/astro/tests/test_astro_plot.py
+++ b/sherpa/astro/tests/test_astro_plot.py
@@ -227,8 +227,8 @@ def test_sourceplot_filtered(caplog, make_basic_datapha):
# The filtering should probably be this, but let's test the
# current behavior:
#
- # expected = [False] * 2 + [True] * 6 + [False] * 2
- expected = [False] * 3 + [True] * 5 + [False] * 2
+ # expected = np.asarray([False] * 2 + [True] * 6 + [False] * 2)
+ expected = np.asarray([False] * 3 + [True] * 5 + [False] * 2)
assert sp.mask == pytest.approx(expected)
assert len(caplog.records) == 0
check_sourceplot_energy(sp)
@@ -365,7 +365,7 @@ def test_sourceplot_wavelength_filtered(caplog, make_basic_datapha):
# Given the filtering for energy didn't quite match, DJB is
# slightly surprised this works.
#
- expected = [False] * 2 + [True] * 6 + [False] * 2
+ expected = np.asarray([False] * 2 + [True] * 6 + [False] * 2)
assert sp.mask == pytest.approx(expected)
assert len(caplog.records) == 0
check_sourceplot_wavelength(sp)
diff --git a/sherpa/astro/ui/tests/test_astro_ui.py b/sherpa/astro/ui/tests/test_astro_ui.py
index 26acfb96b0..f6872a3497 100644
--- a/sherpa/astro/ui/tests/test_astro_ui.py
+++ b/sherpa/astro/ui/tests/test_astro_ui.py
@@ -1962,7 +1962,8 @@ def check_pha_offset(specresp, matrix, energ_lo, energ_hi,
ui.notice(0.5, 0.8)
data = ui.get_data()
- assert data.mask == pytest.approx([False] * 3 + [True] * 3 + [False] * 3)
+ expected = np.asarray([False] * 3 + [True] * 3 + [False] * 3)
+ assert data.mask == pytest.approx(expected)
assert ui.get_filter(format="%.2f", delim="-") == "0.45-0.85"
diff --git a/sherpa/astro/ui/tests/test_astro_ui_unit.py b/sherpa/astro/ui/tests/test_astro_ui_unit.py
index 5a321fa733..a5c23dca45 100644
--- a/sherpa/astro/ui/tests/test_astro_ui_unit.py
+++ b/sherpa/astro/ui/tests/test_astro_ui_unit.py
@@ -2957,7 +2957,7 @@ def test_pha_set_filter_unmasked(simple_pha):
expected = [True, True, False, True, False]
ui.set_filter(expected)
- assert data.mask == pytest.approx(expected)
+ assert data.mask == pytest.approx(np.asarray(expected))
def test_pha_set_filter_unmasked_wrong(simple_pha):
@@ -2973,11 +2973,14 @@ def test_pha_set_filter_masked(simple_pha):
data = ui.get_data()
+ mask = np.asarray([True, False, False, False, True])
+ mask2 = np.asarray([True, False, True, False, True])
+
ui.ignore(4, 8)
- assert data.mask == pytest.approx([True, False, False, False, True])
+ assert data.mask == pytest.approx(mask)
ui.set_filter(np.asarray([True, False, True, False, False]))
- assert data.mask == pytest.approx([True, False, True, False, True])
+ assert data.mask == pytest.approx(mask2)
def test_pha_set_filter_masked_wrong(simple_pha):
diff --git a/sherpa/astro/ui/tests/test_filtering.py b/sherpa/astro/ui/tests/test_filtering.py
index 6e7c331456..eeb41cad3f 100644
--- a/sherpa/astro/ui/tests/test_filtering.py
+++ b/sherpa/astro/ui/tests/test_filtering.py
@@ -709,7 +709,7 @@ def test_ignore_bad_simple_comparison(caplog):
d = s.get_data(idval)
assert d.mask is True
- assert d.get_mask() == pytest.approx([True] * 5)
+ assert d.get_mask() == pytest.approx(np.ones(5, dtype=bool))
assert len(caplog.records) == 2
s.ignore_bad(1)
@@ -733,7 +733,7 @@ def test_ignore_bad_simple_comparison(caplog):
assert s.get_filter(1) == "1,3:5"
assert s.get_filter(2) == "1:5"
- mask = [True] + [False] + [True] * 3
+ mask = np.asarray([True] + [False] + [True] * 3)
d1 = s.get_data(1)
assert d1.mask == pytest.approx(mask)
assert d1.get_mask() == pytest.approx(mask)
@@ -762,12 +762,12 @@ def test_ignore_bad_simple_comparison(caplog):
assert s.get_filter(1) == "1,3"
assert s.get_filter(2) == "1:3"
- mask = [True] + [False] + [True] + [False] * 2
+ mask = np.asarray([True] + [False] + [True] + [False] * 2)
d1 = s.get_data(1)
assert d1.mask == pytest.approx(mask)
assert d1.get_mask() == pytest.approx(mask)
- mask = [True] * 2 + [False] * 2
+ mask = np.asarray([True] * 2 + [False] * 2)
d2 = s.get_data(2)
assert d2.mask == pytest.approx(mask)
assert d2.get_mask() == pytest.approx(mask)
diff --git a/sherpa/astro/utils/tests/test_astro_utils_unit.py b/sherpa/astro/utils/tests/test_astro_utils_unit.py
index 27551d5d22..de8b5e4bf5 100644
--- a/sherpa/astro/utils/tests/test_astro_utils_unit.py
+++ b/sherpa/astro/utils/tests/test_astro_utils_unit.py
@@ -194,7 +194,7 @@ def test_filter_resp_basics(offset, selected, ng, fch, nch, mat, msk):
assert f_chan2 == pytest.approx(np.asarray(fch) + delta)
assert n_chan2 == pytest.approx(nch)
assert matrix2 == pytest.approx(mat)
- assert mask2 == pytest.approx(msk)
+ assert mask2 == pytest.approx(np.asarray(msk))
@pytest.mark.parametrize("lo, hi, expected",
@@ -414,7 +414,8 @@ def test_qual_setting():
"""Regression test."""
pha = make_data("qual")
- assert pha.quality_filter == pytest.approx([True, True, False, True])
+ expected = np.asarray([True, True, False, True])
+ assert pha.quality_filter == pytest.approx(expected)
@pytest.mark.parametrize("data_class", ["1d", "1dint", "pha", "grp", "qual"])
@@ -869,4 +870,5 @@ def test_expand_grouped_mask_ingalid_group():
def test_expand_grouped_mask(mask, group, expected):
"""Check how test_expand_grouped_mask works."""
- assert expand_grouped_mask(mask, group) == pytest.approx(expected)
+ evals = np.asarray(expected)
+ assert expand_grouped_mask(mask, group) == pytest.approx(evals)
diff --git a/sherpa/tests/test_data.py b/sherpa/tests/test_data.py
index f7a41ebf79..e6744aeb8d 100644
--- a/sherpa/tests/test_data.py
+++ b/sherpa/tests/test_data.py
@@ -1321,7 +1321,7 @@ def test_data_filter_invalid_size_scalar():
x = numpy.asarray([1, 2, 5])
d = Data1D('x', x, x)
d.ignore(None, 2)
- assert d.mask == pytest.approx([False, False, True])
+ assert d.mask == pytest.approx(numpy.asarray([False, False, True]))
with pytest.raises(DataErr,
match="Array must be a sequence or None"):
@@ -1567,7 +1567,7 @@ def test_data1dint_check_limit(ignore, lo, hi, evals):
c1, c2, c3 = evals
expected = [vout] * c1 + [vin] * c2 + [vout] * c3
- assert d.mask == pytest.approx(expected)
+ assert d.mask == pytest.approx(numpy.asarray(expected))
def test_filter_apply_none():
@@ -1993,7 +1993,7 @@ def test_mask_sent_array_non_bool():
expected = [True, False, True, False, True, True, False, True, False, True]
data.mask = mask
- assert data.mask == pytest.approx(expected)
+ assert data.mask == pytest.approx(numpy.asarray(expected))
@pytest.mark.parametrize("data", ALL_DATA_CLASSES, indirect=True)
diff --git a/sherpa/ui/tests/test_session.py b/sherpa/ui/tests/test_session.py
index 4b352c203b..5723f4a655 100644
--- a/sherpa/ui/tests/test_session.py
+++ b/sherpa/ui/tests/test_session.py
@@ -1408,7 +1408,8 @@ def test_load_filter_simple(idval, tmp_path):
else:
s.load_filter(idval, str(infile), ncols=1, ignore=True)
- assert s.get_data().mask == pytest.approx([True, True, False])
+ expected = np.asarray([True, True, False])
+ assert s.get_data().mask == pytest.approx(expected)
@pytest.mark.parametrize("idval", [None, 1])
diff --git a/sherpa/ui/tests/test_ui_unit.py b/sherpa/ui/tests/test_ui_unit.py
index 99cc4e21b5..14f15a78e0 100644
--- a/sherpa/ui/tests/test_ui_unit.py
+++ b/sherpa/ui/tests/test_ui_unit.py
@@ -1209,7 +1209,7 @@ def test_set_filter_unmasked(ignore, clean_ui):
ui.load_arrays(1, x, y)
data = ui.get_data()
- assert data.mask
+ assert data.mask is True
if ignore:
expected = [False, True, False]
@@ -1217,7 +1217,7 @@ def test_set_filter_unmasked(ignore, clean_ui):
expected = [True, False, True]
ui.set_filter(np.asarray([True, False, True]), ignore=ignore)
- assert data.mask == pytest.approx(expected)
+ assert data.mask == pytest.approx(np.asarray(expected))
def test_set_filter_unmasked_wrong(clean_ui):
@@ -1245,7 +1245,9 @@ def test_set_filter_masked(ignore, clean_ui):
ui.ignore(lo=15, hi=45)
data = ui.get_data()
- assert data.mask == pytest.approx([True, False, False, False, True])
+
+ orig = np.asarray([True, False, False, False, True])
+ assert data.mask == pytest.approx(orig)
# Unlike test_set_filter_masked the two expected values are not
# logical inverses, since we need to consider the existing mask.
@@ -1256,7 +1258,7 @@ def test_set_filter_masked(ignore, clean_ui):
expected = [True, False, True, False, True]
ui.set_filter(np.asarray([True, False, True, False, False]), ignore=ignore)
- assert data.mask == pytest.approx(expected)
+ assert data.mask == pytest.approx(np.asarray(expected))
def test_set_filter_masked_wrong(clean_ui):
diff --git a/sherpa/utils/tests/test_utils.py b/sherpa/utils/tests/test_utils.py
index a11338d8b8..60e56f82ba 100644
--- a/sherpa/utils/tests/test_utils.py
+++ b/sherpa/utils/tests/test_utils.py
@@ -1,5 +1,5 @@
#
-# Copyright (C) 2010, 2016, 2018, 2019, 2020, 2021, 2022, 2023
+# Copyright (C) 2010, 2016, 2018 - 2024
# Smithsonian Astrophysical Observatory
#
#
@@ -374,9 +374,11 @@ def test_filter_bins_one(lo, hi, res):
lo>hi.
"""
+ expected = numpy.asarray(res)
+
dvals = numpy.asarray([1, 2, 3, 4, 5])
flags = utils.filter_bins([lo], [hi], [dvals])
- assert flags == pytest.approx(res)
+ assert flags == pytest.approx(expected)
# We can also check an identity: that
# a <= x <= b
@@ -385,7 +387,7 @@ def test_filter_bins_one(lo, hi, res):
# x <= b
#
flags = utils.filter_bins([lo, None], [None, hi], [dvals, dvals])
- assert flags == pytest.approx(res)
+ assert flags == pytest.approx(expected)
@@ -418,7 +420,7 @@ def test_filter_bins_one_int(lo, hi, res):
hivals = lovals + 1
flags = utils.filter_bins([None, lo], [hi, None], [lovals, hivals],
integrated=True)
- assert flags == pytest.approx(res)
+ assert flags == pytest.approx(numpy.asarray(res))
From af47af723c4fb054f037555e9c5fd219871c9695 Mon Sep 17 00:00:00 2001
From: Douglas Burke <dburke.gw@gmail.com>
Date: Fri, 6 Dec 2024 12:58:17 -0500
Subject: [PATCH 2/2] CI: use macos-13 for the macOS Intel run
This also
- bumps up the python version from 3.10 to 3.11
- adds bokeh to the mac test (as it's supposed to be a
"gull build")
- adds "Intel" to the name of the test, in preparation for
adding an ARM build, such as
https://github.com/sherpa/sherpa/pull/2198/
---
.github/workflows/ci-conda-workflow.yml | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/.github/workflows/ci-conda-workflow.yml b/.github/workflows/ci-conda-workflow.yml
index 6bab3c77ae..507c06efb2 100644
--- a/.github/workflows/ci-conda-workflow.yml
+++ b/.github/workflows/ci-conda-workflow.yml
@@ -30,13 +30,14 @@ jobs:
strategy:
matrix:
include:
- - name: MacOS Full Build
- os: macos-12
- python-version: "3.10"
+ - name: MacOS Intel Full Build
+ os: macos-13
+ python-version: "3.11"
install-type: develop
fits: astropy
test-data: submodule
matplotlib-version: 3
+ bokeh-version: 3
xspec-version: 12.14.0i
- name: Linux Minimum Setup (Python 3.10)
