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[Python] Fix Numpy 2.0 compatibility issues
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6
.github/workflows/main.yml
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6
.github/workflows/main.yml
vendored
@ -450,8 +450,10 @@ jobs:
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find samples/python -type f -iname "*.py" \
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-exec sh -c 'for n; do echo "$n" | tee -a results.txt && python3 "$n" >> results.txt || exit 1; done' sh {} +
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env:
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# The ignore setting here is due to a new warning introduced in Matplotlib==3.6.0
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PYTHONWARNINGS: "error,ignore:warn_name_set_on_empty_Forward::pyparsing,ignore:datetime.datetime.utcfromtimestamp:DeprecationWarning:"
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# The pyparsing ignore setting is due to a new warning introduced in Matplotlib==3.6.0
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# @todo: Remove the trapz-related ignore when dropping support for NumPy 1.x
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# and replacing np.trapz with np.trapezoid
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PYTHONWARNINGS: "error,ignore:warn_name_set_on_empty_Forward::pyparsing,ignore:datetime.datetime.utcfromtimestamp:DeprecationWarning:,ignore:`trapz`:DeprecationWarning"
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MPLBACKEND: Agg
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- name: Save the results file for inspection
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uses: actions/upload-artifact@v4
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@ -305,10 +305,10 @@ def analyze_errors(grids, speeds, true_speed):
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and compare these with our estimated errors.
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This will show if our estimates are reasonable, or conservative, or too optimistic.
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"""
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true_speed_estimates = np.full_like(speeds, np.NaN)
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total_percent_error_estimates = np.full_like(speeds, np.NaN)
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actual_extrapolated_percent_errors = np.full_like(speeds, np.NaN)
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actual_raw_percent_errors = np.full_like(speeds, np.NaN)
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true_speed_estimates = np.full_like(speeds, np.nan)
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total_percent_error_estimates = np.full_like(speeds, np.nan)
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actual_extrapolated_percent_errors = np.full_like(speeds, np.nan)
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actual_raw_percent_errors = np.full_like(speeds, np.nan)
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for i in range(3, len(grids)):
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print(grids[: i + 1])
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true_speed_estimate, total_percent_error_estimate = extrapolate_uncertainty(
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@ -972,9 +972,9 @@ def compare_profiles(
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"pos. err"
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))
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header.append(f"{10*'-'} ----- {14*'-'} {14*'-'} {9*'-'} {9*'-'} {9*'-'}")
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ref_ptp = reference.ptp(axis=1)
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ref_ptp = np.ptp(reference, axis=1)
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ref_max = np.abs(reference).max(axis=1)
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sample_ptp = sample.ptp(axis=1)
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sample_ptp = np.ptp(sample, axis=1)
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sample_max = np.abs(sample).max(axis=1)
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scale = np.maximum(
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np.maximum(ref_ptp[1:], ref_max[1:]),
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@ -487,11 +487,11 @@ class TestFreeFlame(utilities.CanteraTest):
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self.sim.transport_model = 'unity-Lewis-number'
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self.sim.set_refine_criteria(ratio=3.0, slope=0.08, curve=0.12)
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self.sim.solve(loglevel=0, auto=True)
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dh_unity_lewis = self.sim.enthalpy_mass.ptp()
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dh_unity_lewis = np.ptp(self.sim.enthalpy_mass)
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self.sim.transport_model = 'mixture-averaged'
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self.sim.solve(loglevel=0)
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dh_mix = self.sim.enthalpy_mass.ptp()
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dh_mix = np.ptp(self.sim.enthalpy_mass)
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# deviation of enthalpy should be much lower for unity Le model (tends
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# towards zero as grid is refined)
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@ -1851,7 +1851,7 @@ class TestExtensible3(utilities.CanteraTest):
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class InterfaceReactionTests(ReactionTests):
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# test suite for surface reaction expressions
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_value = np.NAN # reference value
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_value = np.nan # reference value
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_coverage_deps = None
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@classmethod
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@ -2383,7 +2383,9 @@ class TestReactorSensitivities(utilities.CanteraTest):
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return dtdp
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# See https://github.com/Cantera/enhancements/issues/55
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# @todo: replace np.trapz with np.trapezoid when dropping support for NumPy 1.x
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@unittest.skip("Integration of sensitivity ODEs is unreliable")
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@pytest.mark.filterwarnings("ignore:`trapz` is deprecated")
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def test_ignition_delay_sensitivity(self):
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species = ('H2', 'H', 'O2', 'H2O2', 'H2O', 'OH', 'HO2')
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dtigdh_cvodes = self.calc_dtdh(species)
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@ -1208,6 +1208,8 @@ class TestPlasmaPhase(utilities.CanteraTest):
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(ct.avogadro * ct.electron_charge))
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self.assertNear(mean_electron_energy, self.phase.mean_electron_energy)
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# @todo: replace np.trapz with np.trapezoid when dropping support for NumPy 1.x
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@pytest.mark.filterwarnings("ignore:`trapz` is deprecated")
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def test_discretized_electron_energy_distribution(self):
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levels = np.array([0.0, 1.0, 10.0])
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dist = np.array([0.0, 0.9, 0.01])
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@ -181,10 +181,10 @@ def compareProfiles(reference, sample, rtol=1e-5, atol=1e-12, xtol=1e-5):
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bad = []
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template = '{0:9.4e} {1: 3d} {2:14.7e} {3:14.7e} {4:9.3e} {5:9.3e} {6:9.3e}'
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for i in range(1, nVars):
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scale = max(max(abs(reference[i])), reference[i].ptp(),
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max(abs(sample[i])), sample[i].ptp())
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scale = max(max(abs(reference[i])), np.ptp(reference[i]),
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max(abs(sample[i])), np.ptp(sample[i]))
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slope = np.zeros(nTimes)
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slope[1:] = np.diff(reference[i]) / np.diff(reference[0]) * reference[0].ptp()
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slope[1:] = np.diff(reference[i]) / np.diff(reference[0]) * np.ptp(reference[0])
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comp = np.interp(reference[0], sample[0], sample[i])
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for j in range(nTimes):
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