def test_jsondecoder_scalars_only():
# like test_jsondecoder_basics, but with only scalar quantities
decoder = JSONDecoder(os.path.join('jsondecoder', 'fredrik.json'), ['cfrac', 'we'])
run_info = {'run_dir': 'tests'}
data = decoder.parse_sim_output(run_info)
assert(data['cfrac'] == 0.24000000131541285)
assert(data['we'] == -0.4910355508327484)
def test_json_nested():
decoder = JSONDecoder(os.path.join('jsondecoder', 'nested.json'),
[['root1', 'node1', 'leaf1'], ['root1', 'leaf2'], 'leaf3'])
run_info = {'run_dir': 'tests'}
data = decoder.parse_sim_output(run_info)
assert(data['root1.node1.leaf1'] == 0.33)
assert(data['root1.leaf2'] == 0.32)
assert(data['leaf3'] == [0.2, 0.3])
def test_jsondecoder_basic():
decoder = JSONDecoder(os.path.join('jsondecoder', 'fredrik.json'), ['cfrac', 'we', 'v'])
run_info = {'run_dir': 'tests'}
data = decoder.parse_sim_output(run_info)
assert(data['cfrac'] == 0.24000000131541285)
assert(data['we'] == -0.4910355508327484)
assert(len(data['v']) == 126)
assert(data['v'][:3] == [0.014841768890619278, 0.014779693447053432, 0.014733896590769291])
assert(data['v'][-3:] == [0.0010381652973592281, 0.0010054642334580421, 0.0009733123588375747])
def test_missing_column():
# Check if a RuntimeError is raised if a wrong column is specified
decoder = JSONDecoder(os.path.join('jsondecoder', 'nested.json'),
[['root1', 'node1', 'abcd'], ['root1', 'leaf2'], 'leaf3'])
run_info = {'run_dir': 'tests'}
with pytest.raises(RuntimeError) as excinfo:
data = decoder.parse_sim_output(run_info)
# Check if the missing column is reported in the exception message
assert("['root1', 'node1', 'abcd']" in str(excinfo.value))
def test_jsondecoder_basic():
decoder = JSONDecoder(os.path.join('jsondecoder', 'fredrik.json'),
['cfrac', 'we', 'v'])
run_info = {'run_dir': 'tests'}
data = decoder.parse_sim_output(run_info)
assert ((data['cfrac'] == np.array([0.24000000131541285])).all().all())
assert ((data['we'] == np.array([-0.4910355508327484])).all().all())
assert ((data['v'] == np.array([
0.014841768890619278, 0.014779693447053432, 0.014733896590769291,
0.014711864292621613, 0.01470966450870037, 0.014723854139447212,
0.01474687922745943, 0.014768443070352077, 0.01478823646903038,
0.01481111440807581, 0.014824368990957737, 0.014829156920313835,
0.014819246716797352, 0.014791729860007763, 0.014738374389708042,
0.014660114422440529, 0.014429694972932339, 0.01425190269947052,
0.014102380722761154, 0.013794410973787308, 0.013637131080031395,
0.013455528765916824, 0.013191652484238148, 0.013099015690386295,
0.012983563356101513, 0.012864833697676659, 0.01277084369212389,
0.012721200473606586, 0.012596715241670609, 0.012423157691955566,
0.012156027369201183, 0.011878175660967827, 0.01177501305937767,
0.011609729379415512, 0.011352979578077793, 0.011070813983678818,
0.010821142233908176, 0.010543721728026867, 0.010277168825268745,
0.010026565752923489, 0.009804881177842617, 0.009599489159882069,
0.009395209141075611, 0.00917865987867117, 0.008997873403131962,
0.008819280192255974, 0.008683303371071815, 0.008515624329447746,
0.00831963773816824, 0.008140306919813156, 0.007954764179885387,
0.007770225405693054, 0.00758766196668148, 0.007404009811580181,
0.00722180400043726, 0.007042537909001112, 0.006860550958663225,
0.006680922582745552, 0.006502178963273764, 0.006320721469819546,
0.00613985862582922, 0.00595864886417985, 0.0057795993052423,
0.005598034244030714, 0.005417921114712954, 0.005241408012807369,
0.005052104126662016, 0.004876876249909401, 0.004697349388152361,
0.004517038818448782, 0.004333806689828634, 0.00415557436645031,
0.003969619516283274, 0.003797980025410652, 0.003611199092119932,
0.0034324300941079855, 0.0032506941352039576, 0.0030704534146934748,
0.002888839691877365, 0.002707288833335042, 0.0025427646469324827,
0.002415195805951953, 0.002359578385949135, 0.0023344780784100294,
0.0023027241695672274, 0.002270693425089121, 0.0022391551174223423,
0.0022060663904994726, 0.00217291503213346, 0.0021398148965090513,
0.00210800813511014, 0.002074991352856159, 0.002041975734755397,
0.0020094725769013166, 0.0019798376597464085, 0.0019466927042230964,
0.0019146542763337493, 0.0018817033851519227, 0.0018483188468962908,
0.0018153132405132055, 0.0017822484951466322, 0.0017517984379082918,
0.0017170008504763246, 0.00168553926050663, 0.0016540634678676724,
0.0016217215452343225, 0.0015883957967162132, 0.0015583134954795241,
0.0015226758550852537, 0.001490586786530912, 0.0014588346239179373,
0.0014263952616602182, 0.001393234240822494, 0.0013616280630230904,
0.0013303045416250825, 0.0012980689061805606, 0.0012646319810301065,
0.0012320373207330704, 0.0011981468414887786, 0.0011661312310025096,
0.0011351823341101408, 0.0011039526434615254, 0.00107281981036067,
0.0010381652973592281, 0.0010054642334580421, 0.0009733123588375747
])).all().all())