def u_nk():
bz = load_benzene().data
u_nk_coul = alchemlyb.concat(
[extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']])
u_nk_coul.attrs = extract_u_nk(load_benzene().data['Coulomb'][0],
T=300).attrs
return u_nk_coul
def test_equilibrium_detection(self, dhdl):
'''Test if extract_u_nk assign the attr correctly'''
dataset = load_benzene()
dhdl = extract_dHdl(dataset['data']['Coulomb'][0], 310)
new_dhdl = equilibrium_detection(dhdl)
assert new_dhdl.attrs['temperature'] == 310
assert new_dhdl.attrs['energy_unit'] == 'kT'
def test_slicing(self, dhdl):
'''Test if extract_u_nk assign the attr correctly'''
dataset = load_benzene()
u_nk = extract_u_nk(dataset['data']['Coulomb'][0], 310)
new_u_nk = slicing(u_nk)
assert new_u_nk.attrs['temperature'] == 310
assert new_u_nk.attrs['energy_unit'] == 'kT'
def test_nounit():
'''Test no unit error'''
dataset = load_benzene()
dhdl = extract_dHdl(dataset['data']['Coulomb'][0], 310)
dhdl.attrs.pop('energy_unit', None)
with pytest.raises(TypeError):
to_kT(dhdl)
def test_plot_convergence_dataframe():
bz = load_benzene().data
data_list = [extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']]
df = forward_backward_convergence(data_list, 'mbar')
ax = plot_convergence(df)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close(ax.figure)
def test_statistical_inefficiency(self, dhdl):
'''Test if extract_u_nk assign the attr correctly'''
dataset = load_benzene()
dhdl = extract_dHdl(dataset['data']['Coulomb'][0], 310)
new_dhdl = statistical_inefficiency(dhdl)
assert new_dhdl.attrs['temperature'] == 310
assert new_dhdl.attrs['energy_unit'] == 'kT'
def test_noT():
'''Test no temperature error'''
dataset = load_benzene()
dhdl = extract_dHdl(dataset['data']['Coulomb'][0], 310)
dhdl.attrs.pop('temperature', None)
with pytest.raises(TypeError):
to_kT(dhdl)
def estimaters():
bz = load_benzene().data
dHdl_coul = alchemlyb.concat(
[extract_dHdl(xvg, T=300) for xvg in bz['Coulomb']])
ti = TI().fit(dHdl_coul)
u_nk_coul = alchemlyb.concat(
[extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']])
mbar = MBAR().fit(u_nk_coul)
return ti, mbar
def test_dHdl():
"""Test that dHdl has the correct form when extracted from files.
"""
dataset = load_benzene()
for leg in dataset['data']:
for filename in dataset['data'][leg]:
dHdl = extract_dHdl(filename, T=300)
assert dHdl.index.names == ['time', 'fep-lambda']
assert dHdl.shape == (4001, 1)
def test_u_nk():
"""Test that u_nk has the correct form when extracted from files.
"""
dataset = load_benzene()
for leg in dataset['data']:
for filename in dataset['data'][leg]:
u_nk = extract_u_nk(filename, T=300)
assert u_nk.index.names == ['time', 'fep-lambda']
if leg == 'Coulomb':
assert u_nk.shape == (4001, 5)
elif leg == 'VDW':
assert u_nk.shape == (4001, 16)
def test_plot_dF_state():
'''Just test if the plot runs'''
bz = load_benzene().data
u_nk_coul = pd.concat([extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']])
dHdl_coul = pd.concat([extract_dHdl(xvg, T=300) for xvg in bz['Coulomb']])
u_nk_vdw = pd.concat([extract_u_nk(xvg, T=300) for xvg in bz['VDW']])
dHdl_vdw = pd.concat([extract_dHdl(xvg, T=300) for xvg in bz['VDW']])
ti_coul = TI().fit(dHdl_coul)
ti_vdw = TI().fit(dHdl_vdw)
bar_coul = BAR().fit(u_nk_coul)
bar_vdw = BAR().fit(u_nk_vdw)
mbar_coul = MBAR().fit(u_nk_coul)
mbar_vdw = MBAR().fit(u_nk_vdw)
dhdl_data = [
(ti_coul, ti_vdw),
(bar_coul, bar_vdw),
(mbar_coul, mbar_vdw),
]
fig = plot_dF_state(dhdl_data, orientation='portrait')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state(dhdl_data, orientation='landscape')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state(dhdl_data, labels=['MBAR', 'TI', 'BAR'])
assert isinstance(fig, matplotlib.figure.Figure)
with pytest.raises(ValueError):
fig = plot_dF_state(dhdl_data, labels=[
'MBAR',
'TI',
])
fig = plot_dF_state(dhdl_data, colors=['#C45AEC', '#33CC33', '#F87431'])
assert isinstance(fig, matplotlib.figure.Figure)
with pytest.raises(ValueError):
fig = plot_dF_state(dhdl_data, colors=['#C45AEC', '#33CC33'])
with pytest.raises(NameError):
fig = plot_dF_state(dhdl_data, orientation='xxx')
fig = plot_dF_state(ti_coul, orientation='landscape')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state(ti_coul, orientation='portrait')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state([ti_coul, bar_coul])
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state([(ti_coul, ti_vdw)])
assert isinstance(fig, matplotlib.figure.Figure)
def test_plot_mbar_omatrix():
'''Just test if the plot runs'''
bz = load_benzene().data
u_nk_coul = pd.concat([extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']])
mbar_coul = MBAR()
mbar_coul.fit(u_nk_coul)
assert isinstance(plot_mbar_overlap_matrix(mbar_coul.overlap_matrix),
matplotlib.axes.Axes)
assert isinstance(
plot_mbar_overlap_matrix(mbar_coul.overlap_matrix, [
1,
]), matplotlib.axes.Axes)
# Bump up coverage
overlap_maxtrix = mbar_coul.overlap_matrix
overlap_maxtrix[0, 0] = 0.0025
overlap_maxtrix[-1, -1] = 0.9975
assert isinstance(plot_mbar_overlap_matrix(overlap_maxtrix),
matplotlib.axes.Axes)
def test_plot_ti_dhdl():
'''Just test if the plot runs'''
bz = load_benzene().data
dHdl_coul = pd.concat([extract_dHdl(xvg, T=300) for xvg in bz['Coulomb']])
ti_coul = TI()
ti_coul.fit(dHdl_coul)
assert isinstance(plot_ti_dhdl(ti_coul), matplotlib.axes.Axes)
fig, ax = plt.subplots(figsize=(8, 6))
assert isinstance(plot_ti_dhdl(ti_coul, ax=ax), matplotlib.axes.Axes)
assert isinstance(plot_ti_dhdl(ti_coul, labels=['Coul']),
matplotlib.axes.Axes)
assert isinstance(plot_ti_dhdl(ti_coul, labels=['Coul'], colors=['r']),
matplotlib.axes.Axes)
dHdl_vdw = pd.concat([extract_dHdl(xvg, T=300) for xvg in bz['VDW']])
ti_vdw = TI().fit(dHdl_vdw)
assert isinstance(plot_ti_dhdl([ti_coul, ti_vdw]), matplotlib.axes.Axes)
ti_coul.dhdl = pd.DataFrame.from_dict({
'fep': range(100)
},
orient='index',
columns=np.arange(100) / 100).T
assert isinstance(plot_ti_dhdl(ti_coul), matplotlib.axes.Axes)
def test_plot_convergence():
bz = load_benzene().data
data_list = [extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']]
forward = []
forward_error = []
backward = []
backward_error = []
num_points = 10
for i in range(1, num_points + 1):
# Do the forward
slice = int(len(data_list[0]) / num_points * i)
u_nk_coul = alchemlyb.concat([data[:slice] for data in data_list])
estimate = MBAR().fit(u_nk_coul)
forward.append(estimate.delta_f_.iloc[0, -1])
forward_error.append(estimate.d_delta_f_.iloc[0, -1])
# Do the backward
u_nk_coul = alchemlyb.concat([data[-slice:] for data in data_list])
estimate = MBAR().fit(u_nk_coul)
backward.append(estimate.delta_f_.iloc[0, -1])
backward_error.append(estimate.d_delta_f_.iloc[0, -1])
ax = plot_convergence(forward, forward_error, backward, backward_error)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close(ax.figure)
def gmx_benzene():
dataset = load_benzene()
return [gmx.extract_dHdl(dhdl, T=300) for dhdl in dataset['data']['Coulomb']], \
[gmx.extract_u_nk(dhdl, T=300) for dhdl in dataset['data']['Coulomb']]
def dhdl():
bz = load_benzene().data
dHdl_coul = alchemlyb.concat(
[extract_dHdl(xvg, T=300) for xvg in bz['Coulomb']])
return dHdl_coul
def data():
dhdl = extract_dHdl(load_benzene()['data']['Coulomb'][0], 310)
with bz2.open(load_benzene()['data']['Coulomb'][0], "rt") as bz_file:
text = bz_file.read()
return text, len(dhdl)
def test_extract_dHdl_unit():
'''Test if extract_u_nk assign the attr correctly'''
dataset = load_benzene()
dhdl = extract_dHdl(dataset['data']['Coulomb'][0], 310)
assert dhdl.attrs['temperature'] == 310
assert dhdl.attrs['energy_unit'] == 'kT'
def dhdl():
dataset = load_benzene()
dhdl = extract_dHdl(dataset['data']['Coulomb'][0], 310)
return dhdl
