def test_CI_sham():
import numpy as np
from synergy.combination import CombinationIndex
from synergy.datasets import sham
d1, d2, E = sham()
model = CombinationIndex()
synergy = model.fit(d1, d2, E)
assert np.abs(np.nanmean(np.log(synergy))) < 0.1
def test_HSA_sham():
import numpy as np
from synergy.combination import HSA
from synergy.datasets import sham
d1, d2, E = sham()
model = HSA()
synergy = model.fit(d1, d2, E)
assert np.nanmean(synergy)>0.08
def test_bliss_sham():
import numpy as np
from synergy.combination import Bliss
from synergy.datasets import sham
d1, d2, E = sham()
model = Bliss()
synergy = model.fit(d1, d2, E)
assert np.nanmean(synergy) < 0.1
def test_loewe_delta_variant():
import numpy as np
from synergy.combination import Loewe
from synergy.datasets import sham
d1, d2, E = sham()
model = Loewe(variant="delta")
synergy = model.fit(d1, d2, E)
assert np.nanmax(np.abs(synergy)) < 0.1
def test_loewe_sham():
import numpy as np
from synergy.combination import Loewe
from synergy.datasets import sham
d1, d2, E = sham()
model = Loewe()
synergy = model.fit(d1, d2, E)
assert np.abs(np.nanmean(np.log(synergy))) < 0.1
def test_plotly_linearscale():
import numpy as np
from synergy.datasets import sham
from synergy.utils.plots import plot_surface_plotly
d1, d2, E = sham()
d1 = np.hstack([d1, d1])
d2 = np.hstack([d2, d2])
E = np.hstack([E, E * 2])
plot_surface_plotly(d1, d2, E, fname="x.html", logscale=False)
assert 1 == 1
def test_plotly_replicates():
import numpy as np
from synergy.datasets import sham
from synergy.utils.plots import plot_surface_plotly
d1, d2, E = sham()
d1 = np.hstack([d1, d1])
d2 = np.hstack([d2, d2])
E = np.hstack([E, E * 2])
plot_surface_plotly(d1, d2, E, fname="x.html")
assert 1 == 1
def test_heatmap_linearscale():
import numpy as np
from synergy.datasets import sham
from synergy.utils.plots import plot_heatmap
d1, d2, E = sham()
d1 = np.hstack([d1, d1])
d2 = np.hstack([d2, d2])
E = np.hstack([E, E * 2])
plot_heatmap(d1, d2, E, fname="x.pdf", logscale=False)
assert 1 == 1
def test_heatmap_replicates():
import numpy as np
from synergy.datasets import sham
from synergy.utils.plots import plot_heatmap
d1, d2, E = sham()
d1 = np.hstack([d1, d1])
d2 = np.hstack([d2, d2])
E = np.hstack([E, E * 2])
plot_heatmap(d1, d2, E, fname="x.pdf")
assert 1 == 1