def test_calc_spectrum_multiple_molecules_inputerror(
verbose=True, plot=True, warnings=True, *args, **kwargs
):
"""Test calculations with multiple molecules
Note: try to keep the same wavelength ranges for each of the multi-molecule
tests, so that databases are only downloaded once, and cached!"""
# Contradictory:
with pytest.raises(ValueError):
calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
molecule=["CO2"], # contradictory
mole_fraction=1,
isotope={"CO2": "1,2", "CO": "1,2,3"},
verbose=verbose,
)
# Partial:
with pytest.raises(ValueError):
calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
molecule=["CO2", "CO"], # contradictory
mole_fraction=1,
isotope={"CO2": "1,2"}, # unclear for CO
verbose=verbose,
)
return True
def test_calc_spectrum_multiple_molecules_otherinputs(verbose=True,
plot=True,
warnings=True,
*args,
**kwargs):
"""Test calculations with differnet kind of inputs for multiple molecules
Note: try to keep the same wavelength ranges for each of the multi-molecule
tests, so that databases are only downloaded once, and cached!"""
# Give molecule:
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
molecule=["CO2", "CO"],
mole_fraction=1,
isotope={
"CO2": "1,2",
"CO": "1,2,3"
},
verbose=verbose,
)
assert set(s.conditions["molecule"]) == set(["CO2", "CO"])
# Give isotope only
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
isotope={
"CO2": "1,2",
"CO": "1,2,3"
},
verbose=verbose,
)
assert set(s.conditions["molecule"]) == set(["CO2", "CO"])
# Give mole fractions only
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
mole_fraction={
"CO2": 0.2,
"CO": 0.8
},
isotope="1,2",
verbose=verbose,
)
assert set(s.conditions["molecule"]) == set(["CO2", "CO"])
return True
def test_calc_spectrum_multiple_molecules(verbose=True,
plot=True,
warnings=True,
*args,
**kwargs):
"""Test calculations with multiple molecules
Note: try to keep the same wavelength ranges for each of the multi-molecule
tests, so that databases are only downloaded once, and cached!"""
s_co = calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
mole_fraction=1,
isotope={"CO": "1,2,3"},
verbose=verbose,
)
s_co2 = calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
mole_fraction=1,
isotope={"CO2": "1,2"},
verbose=verbose,
)
s_both = calc_spectrum(
wavelength_min=4165,
wavelength_max=5000,
Tgas=1000,
path_length=0.1,
mole_fraction=1,
isotope={
"CO2": "1,2",
"CO": "1,2,3"
},
verbose=verbose,
)
if plot:
s_both.plot(wunit="nm")
# Check calculation went fine:
assert set(s_both.conditions["molecule"]) == set(["CO2", "CO"])
# Compare
from radis.los.slabs import MergeSlabs
assert s_both.compare_with(MergeSlabs(s_co, s_co2), plot=False)
return True
def check_wavelength_range(verbose=True, warnings=True, *args, **kwargs):
"""Check that input wavelength is correctly taken into account.
See https://github.com/radis/radis/issues/214
"""
if verbose:
printm("Testing calc_spectrum wavelength range")
wstep = 0.01
s = calc_spectrum(
wavelength_min=4348, # nm
wavelength_max=5000,
molecule="CO",
isotope="1,2,3",
pressure=1.01325, # bar
Tvib=1700, # K
Trot=1700, # K
databank="HITRAN-CO-TEST",
wstep=wstep,
)
w, I = s.get("radiance_noslit", wunit="nm", Iunit="mW/sr/cm2/nm")
assert np.isclose(w.min(), 4348, atol=wstep)
assert np.isclose(w.max(), 5000, atol=wstep)
return True
def test_calc_spectrum(verbose=True, plot=True, warnings=True, *args, **kwargs):
""" Basic example, used as a non-regression test
Notes
-----
How long it tooks to calculate this Spectrum?
Performance test on old NeQ package, with the [CDSD-HITEMP-JMIN] databank.
See the caveats in the E. Pannier "Limits of CO2 NonEquilibrium Models" paper.
(just used here as a performance monitoring)
- neq 0.9.20: 18.7s
- neq 0.9.20*: 15.4s (removed 2nd loop on 1st isotope because of groupby().apply())
- neq 0.9.20**: 11.7s (after replacing fill_Evib with map() )
- neq 0.9.21: 9.4s (improve Qrot / nrot fetching performance)
- neq 0.9.22: 8.4s
Starting from RADIS 1.0.1, the test is run on [HITRAN-2016]_, which
is not valid for these temperatures but can be more conveniently
downloaded automatically and thus executed everytime with [Travis]_
(we also expect the test to be much faster than above, but that's just
because the database is smaller!)
- radis 0.9.20 : 2.49 s on [HITRAN-2016]
4.05 s on [CDSD-HITEMP-JMIN]
"""
if verbose:
printm("Testing calc_spectrum match reference")
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
import matplotlib.pyplot as plt
plt.ion()
try:
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=4200,
# databank='CDSD-HITEMP-JMIN',
databank="fetch", # not appropriate for these temperatures, but convenient for automatic testing
Tgas=300,
Tvib=1700,
Trot=1550,
path_length=0.1,
mole_fraction=0.5,
molecule="CO2",
isotope="1,2",
wstep=0.01,
cutoff=1e-25,
use_cached=True,
medium="vacuum",
verbose=verbose,
warnings={
"MissingSelfBroadeningWarning": "ignore",
"NegativeEnergiesWarning": "ignore",
"HighTemperatureWarning": "ignore",
},
)
s.apply_slit((2, 2.5), "nm", shape="trapezoidal")
if plot:
s.plot(wunit="nm")
w, I = s.get("radiance", wunit="nm")
w_ref = w[::100]
# Compare against hardcoded results (neq 0.9.22, 28/06/18)
# I_ref = np.array([0.28694463, 0.29141711, 0.32461613, 0.32909566, 0.21939511, 0.18606445,
# 0.19740763, 0.16948599, 0.16780345, 0.15572173, 0.16770853, 0.14966064,
# 0.13041356, 0.11751016, 0.10818072, 0.11592531, 0.04666677, 0.00177108,
# 0.00069339])
# Harcoded results changed for RADIS with the change of
# database (HITEMP-2010 -> HITRAN-2016) and of Tvib model
# CDSD with (P,C,Jmin,N) in CDSD polyad -> RADIS built-in constants)
# I_ref = np.array([ 0.29148768, 0.29646856, 0.32999337, 0.32249701, 0.2078451 ,
# 0.18974631, 0.2019285 , 0.17346687, 0.17211401, 0.15939359,
# 0.17240575, 0.15395179, 0.13374185, 0.11997065, 0.10858693,
# 0.11114162, 0.04575873, 0.00163863, 0.00062654])
# Updated again in RADIS 0.9.20 (19/08/19) to account for the use of DLM (difference
# not significant)
# I_ref = np.array([ 0.29060991, 0.29756722, 0.32972058, 0.3206278 , 0.20696867,
# 0.19218358, 0.20155747, 0.17336405, 0.17218653, 0.1589136 ,
# 0.17110649, 0.15403513, 0.13376804, 0.11932659, 0.10882006,
# 0.11112725, 0.0458288 , 0.00247956, 0.00144128])
# Updated again in RADIS 0.9.20 (02/09/19) with switch to tabulated Q(Tref)
I_ref = np.array(
[
0.29048064,
0.29743104,
0.32955513,
0.32047172,
0.20688813,
0.19210952,
0.20148265,
0.17330909,
0.17213373,
0.15887159,
0.17106096,
0.15400039,
0.13374285,
0.11930822,
0.10880631,
0.11111394,
0.04582291,
0.00247955,
0.00144128,
]
)
if plot:
plt.plot(w_ref, I_ref, "or", label="ref")
plt.legend()
assert np.allclose(I[::100], I_ref, atol=1e-6)
return True
except DatabankNotFound as err:
assert IgnoreMissingDatabase(err, __file__, warnings)
def test_calc_spectrum_overpopulations(
verbose=True, plot=False, warnings=True, *args, **kwargs
):
""" Non-regression test:
Example using overpopulation of the 001 asymmetric stretch first level of CO2,
which is written (p,c,N) = (3,1,4) in [CDSD-4000]_ notation
Notes
-----
In old Neq package (before RADIS):
the test uses a CDSD-PCN notation for vibrational energy assignation, i.e,
Evib = minimal energy of a (p,c,N) polyad. See the discussion on the implications
in the E. Pannier "Limits of CO2 NonEquilibrium models" paper.
Better use the assignation scheme suggested in the paper.
But it's okay here as a non-regression test.
Starting from RADIS 1.0.1, the test is run on [HITRAN-2016]_, which
is not valid for these temperatures but can be more conveniently
downloaded automatically and thus executed everytime with [Travis]_
"""
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
import matplotlib.pyplot as plt
plt.ion()
try:
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=4200,
# databank='CDSD-HITEMP-PCN',
databank="fetch", # not appropriate for these temperatures, but convenient for automatic testings
Tgas=300,
Tvib=1700,
Trot=1550,
# overpopulation={'(3,1,4)': 3}, # 00'0'1 in spectroscopic notation
overpopulation={"(0,0,0,1)": 3}, # 00'0'1 in spectroscopic notation
path_length=0.1,
mole_fraction=0.5,
molecule="CO2",
isotope="1,2",
wstep=0.01,
cutoff=1e-25,
use_cached=True,
medium="vacuum",
verbose=verbose,
warnings={
"MissingSelfBroadeningWarning": "ignore",
"NegativeEnergiesWarning": "ignore",
"HighTemperatureWarning": "ignore",
},
)
s.apply_slit((2, 2.5), "nm", shape="trapezoidal")
if plot:
s.plot()
w, I = s.get("radiance", wunit="nm")
w_ref = w[::100]
# Compare against hardcoded results (neq 0.9.22, 28/06/18)
# I_ref = np.array([0.61826008, 0.65598262, 0.79760003, 0.7958013 , 0.5792486 ,
# 0.56727691, 0.60361258, 0.51549598, 0.51012651, 0.47133131,
# 0.50770568, 0.45093953, 0.39129824, 0.35125324, 0.32238316,
# 0.34542781, 0.13908073, 0.00506012, 0.00189535])
# Harcoded results changed for RADIS v1.0.1 with the change of
# database (HITEMP-2010 -> HITRAN-2016) and of Tvib model
# CDSD with (P,C,Jmin,N) in CDSD polyad -> RADIS built-in constants)
#
# I_ref = np.array([ 0.62299838, 0.66229013, 0.81037059, 0.79899315, 0.57215806,
# 0.57626389, 0.61424273, 0.52454807, 0.5200812 , 0.47920924,
# 0.51843533, 0.46058817, 0.3983277 , 0.35582979, 0.32095204,
# 0.32821575, 0.13525543, 0.00469489, 0.00174166])
# Updated again in RADIS 0.9.20 (16/08/19) to account for the use of DLM (difference
# not significant)
# I_ref = np.array([ 0.62134142, 0.66722021, 0.81016539, 0.79387937, 0.56974945,
# 0.58280035, 0.6120114 , 0.52319075, 0.5193041 , 0.47686282,
# 0.51374777, 0.46022548, 0.3979033 , 0.3534643 , 0.32129239,
# 0.32786479, 0.1351593 , 0.0068877 , 0.00387545])
# Updated again in RADIS 0.9.20 (02/09/19) with switch to tabulated Q(Tref)
I_ref = np.array(
[
0.62109562,
0.66695661,
0.80983176,
0.79356445,
0.56958189,
0.58264143,
0.61185167,
0.52307454,
0.51919288,
0.47677519,
0.51365307,
0.46015383,
0.39785172,
0.35342697,
0.32126465,
0.32783797,
0.13514737,
0.00688769,
0.00387544,
]
)
if plot:
plt.plot(w_ref, I_ref, "or", label="ref")
plt.legend()
s.plot_populations()
assert np.allclose(I[::100], I_ref, atol=1e-6)
if verbose:
printm("Test overpopulations: OK")
return True
except DatabankNotFound as err:
assert IgnoreMissingDatabase(err, __file__, warnings)
def test_calc_spectrum(verbose=True,
plot=True,
warnings=True,
*args,
**kwargs):
"""Basic example, used as a non-regression test.
Notes
-----
How long it tooks to calculate this Spectrum?
Performance test on old NeQ package, with the [CDSD-HITEMP-JMIN] databank.
See the caveats in the E. Pannier "Limits of CO2 NonEquilibrium Models" paper.
(just used here as a performance monitoring)
- neq 0.9.20: 18.7s
- neq 0.9.20*: 15.4s (removed 2nd loop on 1st isotope because of groupby().apply())
- neq 0.9.20**: 11.7s (after replacing fill_Evib with map() )
- neq 0.9.21: 9.4s (improve Qrot / nrot fetching performance)
- neq 0.9.22: 8.4s
Starting from RADIS 1.0.1, the test is run on [HITRAN-2016]_, which
is not valid for these temperatures but can be more conveniently
downloaded automatically and thus executed everytime with `Travis CI <https://travis-ci.com/radis/radis>`_
(we also expect the test to be much faster than above, but that's just
because the database is smaller!)
- radis 0.9.20 : 2.49 s on [HITRAN-2016]
4.05 s on [CDSD-HITEMP-JMIN]
"""
if verbose:
printm("Testing calc_spectrum match reference")
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
import matplotlib.pyplot as plt
plt.ion()
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=4200,
# databank='CDSD-HITEMP-JMIN',
databank=
"hitran", # not appropriate for these temperatures, but convenient for automatic testing
Tgas=300,
Tvib=1700,
Trot=1550,
path_length=0.1,
mole_fraction=0.5,
molecule="CO2",
isotope="1,2",
wstep=0.01,
cutoff=1e-25,
use_cached=True,
medium="vacuum",
verbose=verbose,
optimization="simple",
warnings={
"MissingSelfBroadeningWarning": "ignore",
"NegativeEnergiesWarning": "ignore",
"HighTemperatureWarning": "ignore",
},
)
s.apply_slit((2, 2.5), "nm", shape="trapezoidal")
if plot:
s.plot(wunit="nm")
w, I = s.get("radiance", wunit="nm")
# Compare against hardcoded results (neq 0.9.22, 28/06/18)
# I_ref = np.array([0.28694463, 0.29141711, 0.32461613, 0.32909566, 0.21939511, 0.18606445,
# 0.19740763, 0.16948599, 0.16780345, 0.15572173, 0.16770853, 0.14966064,
# 0.13041356, 0.11751016, 0.10818072, 0.11592531, 0.04666677, 0.00177108,
# 0.00069339])
# Harcoded results changed for RADIS with the change of
# database (HITEMP-2010 -> HITRAN-2016) and of Tvib model
# CDSD with (P,C,Jmin,N) in CDSD polyad -> RADIS built-in constants)
# I_ref = np.array([ 0.29148768, 0.29646856, 0.32999337, 0.32249701, 0.2078451 ,
# 0.18974631, 0.2019285 , 0.17346687, 0.17211401, 0.15939359,
# 0.17240575, 0.15395179, 0.13374185, 0.11997065, 0.10858693,
# 0.11114162, 0.04575873, 0.00163863, 0.00062654])
# Updated again in RADIS 0.9.20 (19/08/19) to account for the use of DLM (difference
# not significant)
# I_ref = np.array([ 0.29060991, 0.29756722, 0.32972058, 0.3206278 , 0.20696867,
# 0.19218358, 0.20155747, 0.17336405, 0.17218653, 0.1589136 ,
# 0.17110649, 0.15403513, 0.13376804, 0.11932659, 0.10882006,
# 0.11112725, 0.0458288 , 0.00247956, 0.00144128])
# Updated again in RADIS 0.9.20 (02/09/19) with switch to tabulated Q(Tref)
# I_ref = np.array([ 0.29048064, 0.29743104, 0.32955513, 0.32047172, 0.20688813,
# 0.19210952, 0.20148265, 0.17330909, 0.17213373, 0.15887159,
# 0.17106096, 0.15400039, 0.13374285, 0.11930822, 0.10880631,
# 0.11111394, 0.04582291, 0.00247955, 0.00144128])
# Updated again on (05/08/20) with implementation of optimized weights:
# I_ref = np.array([0.29043204, 0.29740738, 0.32954171, 0.32045394, 0.20680637,
# 0.19205883, 0.2014279 , 0.17322236, 0.17206767, 0.15879478,
# 0.17107564, 0.15400038, 0.13372559, 0.11929585, 0.10881116,
# 0.11111882, 0.04581152, 0.00247154, 0.00143631])
# Updated with RADIS 0.9.26+ (13/12/20) and switch to latest HAPI version
# therefore TIPS 2017 (instead of TIPS 2011) which changed CO2 partition functions
# Q(Tref=300) 291.9025 --> 291.0405
w_ref = np.array([
4197.60321744,
4195.84148905,
4194.08123884,
4192.32246493,
4190.56516548,
4188.80933862,
4187.05498252,
4185.30209532,
4183.55067518,
4181.80072025,
4180.05222871,
4178.30519870,
4176.55962842,
4174.81551601,
4173.07285967,
4171.33165756,
4169.59190786,
4167.85360877,
4166.11675846,
])
I_ref = np.array([
0.29057002,
0.29755271,
0.32971832,
0.32062057,
0.20689238,
0.19213799,
0.20150788,
0.17328113,
0.17212415,
0.15883971,
0.17112437,
0.15403756,
0.13375255,
0.1193155,
0.10882585,
0.11113302,
0.04581781,
0.00247155,
0.00143631,
])
if plot:
plt.plot(w_ref, I_ref, "or", label="ref")
plt.legend()
assert np.allclose(w[::100], w_ref, atol=1e-6)
assert np.allclose(I[::100], I_ref, atol=1e-6)
return True
def test_calc_spectrum(verbose=True,
plot=True,
warnings=True,
*args,
**kwargs):
''' Basic example, used as a non-regression test
Note: test case not physically valid as overpopulation is currently calculated
with a post processing method that is only valid in optically thin cases
Notes
-----
How long it tooks to calculate this Spectrum?
Performance test on old NeQ package, with the [CDSD-HITEMP-JMIN] databank.
See the caveats in the E. Pannier "Limits of CO2 NonEquilibrium Models" paper.
(just used here as a performance monitoring)
- 0.9.20: 18.7s
- 0.9.20*: 15.4s (removed 2nd loop on 1st isotope because of groupby().apply())
- 0.9.20**: 11.7s (after replacing fill_Evib with map() )
- 0.9.21: 9.4s (improve Qrot / nrot fetching performance)
- 0.9.22: 8.4s
Starting from RADIS 1.0.1, the test is run on [HITRAN-2016]_, which
is not valid for these temperatures but can be more conveniently
downloaded automatically and thus executed everytime with [Travis]_
(we also expect the test to be much faster than above, but that's just
because the database is smaller!)
'''
if verbose:
printm('Testing calc_spectrum match reference')
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
import matplotlib.pyplot as plt
plt.ion()
try:
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=4200,
# databank='CDSD-HITEMP-JMIN',
databank=
'fetch', # not appropriate for these temperatures, but convenient for automatic testing
Tgas=300,
Tvib=1700,
Trot=1550,
path_length=0.1,
mole_fraction=0.5,
molecule='CO2',
isotope='1,2',
wstep=0.01,
cutoff=1e-25,
use_cached=True,
medium='vacuum',
verbose=verbose,
warnings={
'MissingSelfBroadeningWarning': 'ignore',
'NegativeEnergiesWarning': 'ignore',
'HighTemperatureWarning': 'ignore'
})
s.apply_slit((2, 2.5), 'nm', shape='trapezoidal')
if plot:
s.plot(wunit='nm')
w, I = s.get('radiance', wunit='nm')
w_ref = w[::100]
# Compare against hardcoded results (neq 0.9.22, 28/06/18)
# I_ref = np.array([0.28694463, 0.29141711, 0.32461613, 0.32909566, 0.21939511, 0.18606445,
# 0.19740763, 0.16948599, 0.16780345, 0.15572173, 0.16770853, 0.14966064,
# 0.13041356, 0.11751016, 0.10818072, 0.11592531, 0.04666677, 0.00177108,
# 0.00069339])
# Harcoded results changed for RADIS v1.0.1 with the change of
# database (HITEMP-2010 -> HITRAN-2016) and of Tvib model
# CDSD with (P,C,Jmin,N) in CDSD polyad -> RADIS built-in constants)
I_ref = np.array([
0.29148768, 0.29646856, 0.32999337, 0.32249701, 0.2078451,
0.18974631, 0.2019285, 0.17346687, 0.17211401, 0.15939359,
0.17240575, 0.15395179, 0.13374185, 0.11997065, 0.10858693,
0.11114162, 0.04575873, 0.00163863, 0.00062654
])
if plot:
plt.plot(w_ref, I_ref, 'or', label='ref')
plt.legend()
assert np.allclose(I[::100], I_ref, atol=1e-6)
return True
except DatabankNotFound as err:
assert IgnoreMissingDatabase(err, __file__, warnings)
def test_calc_spectrum_overpopulations(verbose=True,
plot=False,
warnings=True,
*args,
**kwargs):
''' Non-regression test:
Example using overpopulation of the 001 asymmetric stretch first level of CO2,
which is written (p,c,N) = (3,1,4) in [CDSD-4000]_ notation
Notes
-----
In old Neq package (before RADIS):
the test uses a CDSD-PCN notation for vibrational energy assignation, i.e,
Evib = minimal energy of a (p,c,N) polyad. See the discussion on the implications
in the E. Pannier "Limits of CO2 NonEquilibrium models" paper.
Better use the assignation scheme suggested in the paper.
But it's okay here as a non-regression test.
Starting from RADIS 1.0.1, the test is run on [HITRAN-2016]_, which
is not valid for these temperatures but can be more conveniently
downloaded automatically and thus executed everytime with [Travis]_
'''
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
import matplotlib.pyplot as plt
plt.ion()
try:
s = calc_spectrum(
wavelength_min=4165,
wavelength_max=4200,
# databank='CDSD-HITEMP-PCN',
databank=
'fetch', # not appropriate for these temperatures, but convenient for automatic testings
Tgas=300,
Tvib=1700,
Trot=1550,
# overpopulation={'(3,1,4)': 3}, # 00'0'1 in spectroscopic notation
overpopulation={'(0,0,0,1)':
3}, # 00'0'1 in spectroscopic notation
path_length=0.1,
mole_fraction=0.5,
molecule='CO2',
isotope='1,2',
wstep=0.01,
cutoff=1e-25,
use_cached=True,
medium='vacuum',
verbose=verbose,
warnings={
'MissingSelfBroadeningWarning': 'ignore',
'NegativeEnergiesWarning': 'ignore',
'HighTemperatureWarning': 'ignore'
})
s.apply_slit((2, 2.5), 'nm', shape='trapezoidal')
if plot:
s.plot()
w, I = s.get('radiance', wunit='nm')
w_ref = w[::100]
# Compare against hardcoded results (neq 0.9.22, 28/06/18)
I_ref = np.array([
0.61826008, 0.65598262, 0.79760003, 0.7958013, 0.5792486,
0.56727691, 0.60361258, 0.51549598, 0.51012651, 0.47133131,
0.50770568, 0.45093953, 0.39129824, 0.35125324, 0.32238316,
0.34542781, 0.13908073, 0.00506012, 0.00189535
])
# Harcoded results changed for RADIS v1.0.1 with the change of
# database (HITEMP-2010 -> HITRAN-2016) and of Tvib model
# CDSD with (P,C,Jmin,N) in CDSD polyad -> RADIS built-in constants)
I_ref = np.array([
0.62299838, 0.66229013, 0.81037059, 0.79899315, 0.57215806,
0.57626389, 0.61424273, 0.52454807, 0.5200812, 0.47920924,
0.51843533, 0.46058817, 0.3983277, 0.35582979, 0.32095204,
0.32821575, 0.13525543, 0.00469489, 0.00174166
])
if plot:
plt.plot(w_ref, I_ref, 'or', label='ref')
plt.legend()
s.plot_populations()
assert np.allclose(I[::100], I_ref, atol=1e-6)
if verbose:
printm('Test overpopulations: OK')
return True
except DatabankNotFound as err:
assert IgnoreMissingDatabase(err, __file__, warnings)
