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Merged
merged 11 commits into from
Sep 10, 2021
Merged

De-noise CI output #1294

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f056b61
clean up warnings
kandersolar Aug 20, 2021
0267672
Merge branch 'master' of https://github.com/pvlib/pvlib-python into w…
kandersolar Aug 25, 2021
147d01c
no timezones in numpy datetimes
kandersolar Aug 26, 2021
46dbb99
suppress the mess
kandersolar Aug 26, 2021
a1014b4
suppress another warning from netcdf4
kandersolar Aug 26, 2021
8899da6
fix typo
kandersolar Aug 26, 2021
441e71f
correct missed PVSystem deprecation for temperature_model_parameters
kandersolar Aug 26, 2021
cd289f9
Merge remote-tracking branch 'upstream/master' into warnings_cleanup
kandersolar Sep 7, 2021
ed05612
escape parens in regex
kandersolar Sep 7, 2021
3713005
numpy -> pandas in test_tracking.py
kandersolar Sep 8, 2021
69ba3da
remove unnecessary suppression for tables
kandersolar Sep 8, 2021
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2 changes: 1 addition & 1 deletion pvlib/clearsky.py
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Original file line number Diff line number Diff line change
Expand Up @@ -558,7 +558,7 @@ def _calc_taud(w, aod700, p):
# set up nan-tolerant masks
aod700_lt_0p05 = np.full_like(aod700, False, dtype='bool')
np.less(aod700, 0.05, where=~np.isnan(aod700), out=aod700_lt_0p05)
aod700_mask = np.array([aod700_lt_0p05, ~aod700_lt_0p05], dtype=np.int)
aod700_mask = np.array([aod700_lt_0p05, ~aod700_lt_0p05], dtype=int)

# create tuples of coefficients for
# aod700 < 0.05, aod700 >= 0.05
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2 changes: 1 addition & 1 deletion pvlib/tests/iotools/test_ecmwf_macc.py
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Expand Up @@ -57,7 +57,7 @@ def test_read_ecmwf_macc(expected_test_data):
expected_times = [
1351738800, 1351749600, 1351760400, 1351771200, 1351782000, 1351792800,
1351803600, 1351814400]
assert np.allclose(data.index.astype(int) // 1000000000, expected_times)
assert np.allclose(data.index.view(np.int64) // 1000000000, expected_times)
expected_aod = np.array([
0.39531226, 0.22371339, 0.18373083, 0.15010143, 0.130809, 0.11172834,
0.09741255, 0.0921606])
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2 changes: 1 addition & 1 deletion pvlib/tests/iotools/test_tmy.py
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Expand Up @@ -67,7 +67,7 @@ def test_gh865_read_tmy3_feb_leapyear_hr24():
assert all(data.index[:-1].year == 1990)
assert data.index[-1].year == 1991
# let's do a quick sanity check, are the indices monotonically increasing?
assert all(np.diff(data.index.astype(int)) == 3600000000000)
assert all(np.diff(data.index.view(np.int64)) == 3600000000000)
# according to the TMY3 manual, each record corresponds to the previous
# hour so check that the 1st hour is 1AM and the last hour is midnite
assert data.index[0].hour == 1
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2 changes: 1 addition & 1 deletion pvlib/tests/test_modelchain.py
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Expand Up @@ -717,7 +717,7 @@ def test_run_model_with_weather_noct_sam_temp(sapm_dc_snl_ac_system, location,
weather, mocker):
weather['wind_speed'] = 5
weather['temp_air'] = 10
sapm_dc_snl_ac_system.temperature_model_parameters = {
sapm_dc_snl_ac_system.arrays[0].temperature_model_parameters = {
'noct': 45, 'module_efficiency': 0.2
}
mc = ModelChain(sapm_dc_snl_ac_system, location)
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54 changes: 24 additions & 30 deletions pvlib/tests/test_solarposition.py
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Expand Up @@ -204,21 +204,18 @@ def test_sun_rise_set_transit_ephem(expected_rise_set_ephem, golden):
expected = pd.DataFrame(index=times,
columns=['sunrise', 'sunset'],
dtype='datetime64[ns]')
expected['sunrise'] = pd.Series(index=times, data=[
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunrise'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'sunrise'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'sunrise'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'sunrise']])
expected['sunset'] = pd.Series(index=times, data=[
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunset'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunset'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunset'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'sunset']])
expected['transit'] = pd.Series(index=times, data=[
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'transit'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'transit'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'transit'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'transit']])
idx_sunrise = pd.to_datetime(['2015-01-02', '2015-01-03', '2015-01-03',
'2015-01-03']).tz_localize('MST')
expected['sunrise'] = \
expected_rise_set_ephem.loc[idx_sunrise, 'sunrise'].tolist()
idx_sunset = pd.to_datetime(['2015-01-02', '2015-01-02', '2015-01-02',
'2015-01-03']).tz_localize('MST')
expected['sunset'] = \
expected_rise_set_ephem.loc[idx_sunset, 'sunset'].tolist()
idx_transit = pd.to_datetime(['2015-01-02', '2015-01-02', '2015-01-03',
'2015-01-03']).tz_localize('MST')
expected['transit'] = \
expected_rise_set_ephem.loc[idx_transit, 'transit'].tolist()

result = solarposition.sun_rise_set_transit_ephem(times,
golden.latitude,
Expand All @@ -243,21 +240,18 @@ def test_sun_rise_set_transit_ephem(expected_rise_set_ephem, golden):
expected = pd.DataFrame(index=times,
columns=['sunrise', 'sunset'],
dtype='datetime64[ns]')
expected['sunrise'] = pd.Series(index=times, data=[
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 1), 'sunrise'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunrise'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunrise'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'sunrise']])
expected['sunset'] = pd.Series(index=times, data=[
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 1), 'sunset'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 1), 'sunset'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunset'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'sunset']])
expected['transit'] = pd.Series(index=times, data=[
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 1), 'transit'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 1), 'transit'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 2), 'transit'],
expected_rise_set_ephem.loc[datetime.datetime(2015, 1, 3), 'transit']])
idx_sunrise = pd.to_datetime(['2015-01-01', '2015-01-02', '2015-01-02',
'2015-01-03']).tz_localize('MST')
expected['sunrise'] = \
expected_rise_set_ephem.loc[idx_sunrise, 'sunrise'].tolist()
idx_sunset = pd.to_datetime(['2015-01-01', '2015-01-01', '2015-01-02',
'2015-01-02']).tz_localize('MST')
expected['sunset'] = \
expected_rise_set_ephem.loc[idx_sunset, 'sunset'].tolist()
idx_transit = pd.to_datetime(['2015-01-01', '2015-01-01', '2015-01-02',
'2015-01-03']).tz_localize('MST')
expected['transit'] = \
expected_rise_set_ephem.loc[idx_transit, 'transit'].tolist()

result = solarposition.sun_rise_set_transit_ephem(
times,
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28 changes: 14 additions & 14 deletions pvlib/tests/test_spa.py
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Expand Up @@ -28,7 +28,7 @@

times = (pd.date_range('2003-10-17 12:30:30', periods=1, freq='D')
.tz_localize('MST'))
unixtimes = np.array(times.tz_convert('UTC').astype(np.int64)*1.0/10**9)
unixtimes = np.array(times.tz_convert('UTC').view(np.int64)*1.0/10**9)

lat = 39.742476
lon = -105.1786
Expand Down Expand Up @@ -266,15 +266,15 @@ def test_transit_sunrise_sunset(self):
times = pd.DatetimeIndex([dt.datetime(1996, 7, 5, 0),
dt.datetime(2004, 12, 4, 0)]
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
sunrise = pd.DatetimeIndex([dt.datetime(1996, 7, 5, 7, 8, 15),
dt.datetime(2004, 12, 4, 4, 38, 57)]
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
sunset = pd.DatetimeIndex([dt.datetime(1996, 7, 5, 17, 1, 4),
dt.datetime(2004, 12, 4, 19, 2, 2)]
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
times = np.array(times)
sunrise = np.array(sunrise)
sunset = np.array(sunset)
Expand All @@ -284,13 +284,13 @@ def test_transit_sunrise_sunset(self):

times = pd.DatetimeIndex([dt.datetime(1994, 1, 2), ]
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
sunset = pd.DatetimeIndex([dt.datetime(1994, 1, 2, 16, 59, 55), ]
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
sunrise = pd.DatetimeIndex([dt.datetime(1994, 1, 2, 7, 8, 12), ]
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
times = np.array(times)
sunrise = np.array(sunrise)
sunset = np.array(sunset)
Expand All @@ -305,19 +305,19 @@ def test_transit_sunrise_sunset(self):
dt.datetime(2015, 8, 2),
dt.datetime(2015, 12, 2)],
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
sunrise = pd.DatetimeIndex([dt.datetime(2015, 1, 2, 7, 19),
dt.datetime(2015, 4, 2, 5, 43),
dt.datetime(2015, 8, 2, 5, 1),
dt.datetime(2015, 12, 2, 7, 1)],
).tz_localize(
'MST').astype(np.int64)*1.0/10**9
'MST').view(np.int64)*1.0/10**9
sunset = pd.DatetimeIndex([dt.datetime(2015, 1, 2, 16, 49),
dt.datetime(2015, 4, 2, 18, 24),
dt.datetime(2015, 8, 2, 19, 10),
dt.datetime(2015, 12, 2, 16, 38)],
).tz_localize(
'MST').astype(np.int64)*1.0/10**9
'MST').view(np.int64)*1.0/10**9
times = np.array(times)
sunrise = np.array(sunrise)
sunset = np.array(sunset)
Expand All @@ -331,18 +331,18 @@ def test_transit_sunrise_sunset(self):
dt.datetime(2015, 8, 2),
dt.datetime(2015, 12, 2)],
).tz_localize(
'UTC').astype(np.int64)*1.0/10**9
'UTC').view(np.int64)*1.0/10**9
sunrise = pd.DatetimeIndex([dt.datetime(2015, 1, 2, 7, 36),
dt.datetime(2015, 4, 2, 5, 58),
dt.datetime(2015, 8, 2, 5, 13),
dt.datetime(2015, 12, 2, 7, 17)],
).tz_localize('Asia/Shanghai').astype(
).tz_localize('Asia/Shanghai').view(
np.int64)*1.0/10**9
sunset = pd.DatetimeIndex([dt.datetime(2015, 1, 2, 17, 0),
dt.datetime(2015, 4, 2, 18, 39),
dt.datetime(2015, 8, 2, 19, 28),
dt.datetime(2015, 12, 2, 16, 50)],
).tz_localize('Asia/Shanghai').astype(
).tz_localize('Asia/Shanghai').view(
np.int64)*1.0/10**9
times = np.array(times)
sunrise = np.array(sunrise)
Expand All @@ -355,7 +355,7 @@ def test_transit_sunrise_sunset(self):
def test_earthsun_distance(self):
times = (pd.date_range('2003-10-17 12:30:30', periods=1, freq='D')
.tz_localize('MST'))
unixtimes = times.tz_convert('UTC').astype(np.int64)*1.0/10**9
unixtimes = times.tz_convert('UTC').view(np.int64)*1.0/10**9
unixtimes = np.array(unixtimes)
result = self.spa.earthsun_distance(unixtimes, 64.0, 1)
assert_almost_equal(R, result, 6)
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21 changes: 11 additions & 10 deletions pvlib/tests/test_tracking.py
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Expand Up @@ -462,17 +462,18 @@ def test_slope_aware_backtracking():
"""
Test validation data set from https://www.nrel.gov/docs/fy20osti/76626.pdf
"""
# timestamps here are UTC instead of local standard time like in report
expected_data = np.array(
[('2019-01-01T08:00-0500', 2.404287, 122.79177, -84.440, -10.899),
('2019-01-01T09:00-0500', 11.263058, 133.288729, -72.604, -25.747),
('2019-01-01T10:00-0500', 18.733558, 145.285552, -59.861, -59.861),
('2019-01-01T11:00-0500', 24.109076, 158.939435, -45.578, -45.578),
('2019-01-01T12:00-0500', 26.810735, 173.931802, -28.764, -28.764),
('2019-01-01T13:00-0500', 26.482495, 189.371536, -8.475, -8.475),
('2019-01-01T14:00-0500', 23.170447, 204.13681, 15.120, 15.120),
('2019-01-01T15:00-0500', 17.296785, 217.446538, 39.562, 39.562),
('2019-01-01T16:00-0500', 9.461862, 229.102218, 61.587, 32.339),
('2019-01-01T17:00-0500', 0.524817, 239.330401, 79.530, 5.490)],
[('2019-01-01T13:00', 2.404287, 122.79177, -84.440, -10.899),
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@cwhanse cwhanse Sep 8, 2021

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I'm a bit surprised by this change. I'm sure it works but for style and clarity I would think including the timezone is better.

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@wholmgren wholmgren Sep 8, 2021

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Can we use DataFrame, assert_series_equal, and assert_frame_equal instead of array and assert_allclose? I think that would be more aligned with typical use cases and would solve the timezone problem.

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@kandersolar kandersolar Sep 8, 2021

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Some context here regarding numpy datetimes and timezones: https://numpy.org/doc/stable/release/1.11.0-notes.html#datetime64-changes

('2019-01-01T14:00', 11.263058, 133.288729, -72.604, -25.747),
('2019-01-01T15:00', 18.733558, 145.285552, -59.861, -59.861),
('2019-01-01T16:00', 24.109076, 158.939435, -45.578, -45.578),
('2019-01-01T17:00', 26.810735, 173.931802, -28.764, -28.764),
('2019-01-01T18:00', 26.482495, 189.371536, -8.475, -8.475),
('2019-01-01T19:00', 23.170447, 204.13681, 15.120, 15.120),
('2019-01-01T20:00', 17.296785, 217.446538, 39.562, 39.562),
('2019-01-01T21:00', 9.461862, 229.102218, 61.587, 32.339),
('2019-01-01T22:00', 0.524817, 239.330401, 79.530, 5.490)],
dtype=[
('Time', '<M8[h]'), ('ApparentElevation', '<f8'),
('SolarAzimuth', '<f8'), ('TrueTracking', '<f8'),
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21 changes: 20 additions & 1 deletion setup.cfg
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Expand Up @@ -21,4 +21,23 @@ exclude = pvlib/_version.py docs dist

[tool:pytest]
junit_family=xunit2
testpaths = pvlib/tests
testpaths = pvlib/tests
filterwarnings =
# warning messages to suppress from pytest output. useful in cases
# where a dependency hasn't addressed a deprecation yet, and there's
# nothing we can do to fix it ourselves.
# syntax is: action:message:category:module:lineno
# `message` is a regex matching start of warning message
# https://docs.python.org/3/library/warnings.html#the-warnings-filter

ignore:Using or importing the ABCs:DeprecationWarning:.*patsy:

# deprecation warnings from numpy 1.20
ignore:`np.object` is a deprecated alias:DeprecationWarning:.*tables:
ignore:`np.typeDict` is a deprecated alias:DeprecationWarning:.*tables:
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@wholmgren wholmgren Sep 8, 2021

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do we still need these?

ignore:`np.long` is a deprecated alias:DeprecationWarning:.*numba:
ignore:`np.int` is a deprecated alias:DeprecationWarning:.*(numba|scipy):
ignore:`np.bool` is a deprecated alias:DeprecationWarning:.*numba:
# warnings from netcdf4, but reported as coming from pvlib
ignore:`np.bool` is a deprecated alias:DeprecationWarning:.*(ecmwf_macc|forecast):
ignore:tostring\(\) is deprecated:DeprecationWarning:.*ecmwf_macc: