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Commit aa3956a5 authored by Thibault Hallouin's avatar Thibault Hallouin
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include newly added prob. metric CONT_TBL in evalhyd-cpp

parent 4b977b7a
main dev v0.1.2.0
1 merge request!3release v0.1.2.0
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+218 -5
evalhyd-cpp @ 6f17c3e0
Compare 1d815c68...6f17c3e0
Subproject commit 1d815c6808c61511864b38cee7d8b03be1187cb8 Subproject commit 6f17c3e0f32f935c0a4f34dc1d3c6a9ee780497b
xtensor @ 44b56bba
Compare e534928c...44b56bba
Subproject commit e534928cc30eb3a4a05539747d98e1d6868c2d62 Subproject commit 44b56bbae2185ebf19e6f617ac5690344b9e35a4
environment.yml
+ 1
− 1
View file @ aa3956a5
...@@ -12,7 +12,7 @@ dependencies: ...@@ -12,7 +12,7 @@ dependencies:
- numpy - numpy
- pybind11 - pybind11
- xtl==0.7.5 - xtl==0.7.5
- xtensor==0.24.6 - xtensor==0.24.7
- xtensor-python==0.26.1 - xtensor-python==0.26.1
# - evalhyd-cpp==0.1.0 # - evalhyd-cpp==0.1.0
# Test dependencies # Test dependencies
......
tests/expected/evalp/CONT_TBL.csv 0 → 100644
+ 208
− 0
View file @ aa3956a5
0.44694534,0.55305466,0.,0.
0.33118971,0.66881029,0.,0.
0.2733119,0.7266881,0.,0.
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.37942122,0.04501608,0.06752412,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.37942122,0.04501608,0.06752412,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.37942122,0.04501608,0.06752412,0.50803859
0.28617363,0.02572347,0.04501608,0.64308682
0.20578778,0.01607717,0.06752412,0.71061093
nan,nan,nan,nan
tests/test_probabilist.py
+ 7
− 2
View file @ aa3956a5
...@@ -20,7 +20,7 @@ _all_metrics = ( ...@@ -20,7 +20,7 @@ _all_metrics = (
# quantile-based # quantile-based
'QS', 'CRPS_FROM_QS', 'QS', 'CRPS_FROM_QS',
# contingency table-based # contingency table-based
'POD', 'POFD', 'FAR', 'CSI', 'ROCSS', 'CONT_TBL', 'POD', 'POFD', 'FAR', 'CSI', 'ROCSS',
# ranks-based # ranks-based
'RANK_HIST', 'DS', 'AS', 'RANK_HIST', 'DS', 'AS',
# intervals # intervals
...@@ -68,8 +68,13 @@ class TestMetrics(unittest.TestCase): ...@@ -68,8 +68,13 @@ class TestMetrics(unittest.TestCase):
metric: ( metric: (
numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',') numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
[numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...] [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
) for metric in ('POD', 'POFD', 'FAR', 'CSI', 'ROCSS') ) for metric in ('CONT_TBL', 'POD', 'POFD', 'FAR', 'CSI', 'ROCSS')
} }
# /!\ stacked-up thresholds in CSV file for CONT_TBL
# because 7D metric so need to reshape array
expected_ct['CONT_TBL'] = (
expected_ct['CONT_TBL'].reshape(expected_ct['POD'].shape + (4,))
)
expected_rk = { expected_rk = {
metric: ( metric: (
......
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