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Merge branch 'main' into dependabot/pip/requirements/scikit-learn-1.5.0
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@JecaTosovic
JecaTosovic authored Jun 23, 2024
2 parents ef648db + 08a241f commit 1002baf
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84 changes: 84 additions & 0 deletions tests/conftest.py
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import tempfile

import numpy as np
import pytest

from ConservedWaterSearch.water_clustering import WaterClustering


@pytest.fixture()
def orientations_normalized():
return np.asarray([[1, 0, 0], [-0.25038 * 2, 0.96814764 * 2, 0]])


@pytest.fixture()
def orientations_not_normalized():
return np.asarray([[1, 0, 0], [-0.25038, 0.96814764, 0]])


@pytest.fixture(
params=[
"tests/data/conserved_sample_FCW.dat",
"tests/data/conserved_sample_FCW2.dat",
"tests/data/dispersed_sample_HCW.dat",
"tests/data/dispersed_sample_HCW2.dat",
"tests/data/circ_sample_HCW.dat",
"tests/data/circ_sample_HCW2.dat",
"tests/data/circ_sample_HCW3.dat",
"tests/data/sample_WCW.dat",
"tests/data/sample_circular_only_WCW.dat",
"tests/data/dispersed_sample_WCW.dat",
"tests/data/2_by_2_WCW.dat",
"tests/data/dispersed_sample_not_conserved.dat",
"tests/data/not_conserved.dat",
]
)
def water_data(request):
return np.loadtxt(request.param), request.param.split("/")[-1].split(".")[0]


@pytest.fixture(autouse=True)
def _pymol_skip():
pytest.importorskip("pymol")


@pytest.fixture(params=[{"onlyO": False}, {"onlyO": True}])
def water_clustering_setup(request):
wc = WaterClustering(10)
# Common setup for both cases
wc._waterO.append(np.asarray([0.0, 0.0, 0.0]))
wc._waterO.append(np.asarray([0.0, 2.0, 0.0]))

if request.param["onlyO"]:
# Only oxygen scenario
wc._water_type.append("O_clust")
wc._water_type.append("O_clust")
else:
# Full water type clustering
wc._waterH1.append(np.asarray([1.0, 0.0, 0.0]))
wc._waterH1.append(np.asarray([0.0, 0.8, 0.5]))
wc._waterH2.append(np.asarray([2.5, 2.0, 2.8]))
wc._waterH2.append(np.asarray([0.0, 3.0, 0.0]))
wc._water_type.append("FCW")
wc._water_type.append("HCW")

return wc, request.param["onlyO"]


@pytest.fixture()
def water_clustering_setup_for_deletion():
with tempfile.NamedTemporaryFile(
mode="w+", delete=True
) as dat, tempfile.NamedTemporaryFile(mode="w+", delete=True) as res:
wc = WaterClustering(10, output_file=res.name, restart_data_file=dat.name)
Odata = np.asarray([[0.1, 0.1, 0.1], [1.5, 1.6, 1.7], [1.9, 5.8, 5.6]])
H1 = np.asarray([[0.8, 0.5, 0.8], [1.4, 0.6, 3.7], [3.9, 5.1, 5.9]])
H2 = np.asarray([[0.4, 0.7, 0.1], [1.7, 3.6, 2.7], [1.8, 3.8, 5.1]])
yield wc, dat.name, Odata, H1, H2


@pytest.fixture()
def water_clustering_data():
Opos = np.loadtxt("tests/data/testdataO.dat")
Hpos = np.loadtxt("tests/data/testdataH.dat")
return Opos, Hpos
147 changes: 147 additions & 0 deletions tests/data/CWS_input_3T73.dat
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42 changes: 42 additions & 0 deletions tests/data/Clustering_results_HDBSCAN_MSRC.dat
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10
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51 changes: 51 additions & 0 deletions tests/data/Clustering_results_HDBSCAN_QMSRC.dat
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10
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32 changes: 32 additions & 0 deletions tests/data/Clustering_results_HDBSCAN_QMSRC_onlyO.dat
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10
HDBSCAN
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O_clust 41.068180084228516 30.142730712890625 41.10182189941406
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O_clust 46.2599983215332 36.27909469604492 47.799095153808594
O_clust 45.247276306152344 33.96091079711914 49.6036376953125
37 changes: 37 additions & 0 deletions tests/data/Clustering_results_HDBSCAN_SC.dat
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10
HDBSCAN
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57 changes: 57 additions & 0 deletions tests/data/Clustering_results_OPTICS_MSRC.dat
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OPTICS
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WCW 47.64555740356445 33.075557708740234 50.49333572387695 46.68040084838867 33.10683822631836 50.23353576660156 47.751216888427734 33.867313385009766 51.094966888427734
WCW 45.247276306152344 33.96091079711914 49.6036376953125 45.019012451171875 33.98157501220703 48.63025665283203 46.20960998535156 34.230098724365234 49.565521240234375
WCW 45.247276306152344 33.96091079711914 49.6036376953125 45.019012451171875 33.98157501220703 48.63025665283203 45.35662841796875 34.91427230834961 49.88496398925781
WCW 45.247276306152344 33.96091079711914 49.6036376953125 44.58808135986328 33.21979522705078 49.73096466064453 45.22610855102539 34.317527770996094 48.66962814331055
WCW 45.247276306152344 33.96091079711914 49.6036376953125 44.58808135986328 33.21979522705078 49.73096466064453 45.11109161376953 34.746253967285156 50.20753479003906
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