def test_sq_form(form):
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))[:100]
Q, S = structure_factor(trj,
Q_range=(0.5, 200),
framewise_rdf=False,
form=form)
def test_structure_factor(weighting_factor):
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))[:100]
Q, S = structure_factor(trj,
Q_range=(0.5, 200),
framewise_rdf=False,
weighting_factor=weighting_factor)
def test_vhf_from_pvhf():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
unique_atoms = get_unique_atoms(trj)
tuples_combination = combinations_with_replacement(unique_atoms, 2)
# obtaining g_r_t from total func
chunk_length = 20
r, t, g_r_t = compute_van_hove(trj, chunk_length=chunk_length)
partial_dict = compute_van_hove(trj, chunk_length=chunk_length, partial=True)
# obtating dict of np.array of pvhf
partial_dict = {}
for pairs in tuples_combination:
pair1 = pairs[0]
pair2 = pairs[1]
# Set in alphabetical order
if pairs[0].name > pairs[1].name:
pair2 = pairs[0]
pair1 = pairs[1]
x = compute_partial_van_hove(
trj,
chunk_length=chunk_length,
selection1=f"name {pair1.name}",
selection2=f"name {pair2.name}",
)
partial_dict[pairs] = x[1]
# obtaining total_grt from partial
total_g_r_t = vhf_from_pvhf(trj, partial_dict)
assert np.allclose(g_r_t, total_g_r_t)
def test_run_van_hove():
trj = md.load(get_fn('spce.xtc'), top=get_fn('spce.gro'))
chunk_length = 2
n_chunks = 2
run_total_vhf(trj, chunk_length, n_chunks)
def test_structure_factor():
trj = md.load(get_fn('spce.xtc'), top=get_fn('spce.gro'))[:100]
Q, S = structure_factor(trj,
Q_range=(0.5, 200),
framewise_rdf=False,
method='fz')
def test_invalid_sq_weigting_factor():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))[:100]
with pytest.raises(ValueError):
Q, S = structure_factor(trj,
Q_range=(0.5, 200),
framewise_rdf=False,
weighting_factor="invalid")
def test_invalid_form_style():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))[:100]
with pytest.raises(ValueError):
structure_factor(trj,
Q_range=(0.5, 200),
framewise_rdf=False,
form="random")
def test_van_hove_equal():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 2
r_p, t_p, g_r_t_p = compute_van_hove(trj, chunk_length=chunk_length)
r_s, t_s, g_r_t_s = compute_van_hove(trj, chunk_length=chunk_length, parallel=False)
assert np.allclose(r_p, r_s)
assert np.allclose(t_p, t_s)
assert np.allclose(g_r_t_p, g_r_t_s)
def test_self_warning(parallel):
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 2
with pytest.warns(UserWarning, match=r"Total VHF"):
compute_van_hove(
trj,
chunk_length=chunk_length,
self_correlation=True,
parallel=parallel,
)
def test_self_partial_warning():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 2
with pytest.warns(UserWarning, match=r"Partial VHF"):
compute_partial_van_hove(
trj,
chunk_length=chunk_length,
selection1="name O",
selection2="name H",
self_correlation=True,
)
def test_pvhf_error_is_tuple():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
unique_atoms = get_unique_atoms(trj)
key = frozenset({unique_atoms[0], unique_atoms[1]})
partial_dict = {}
x = compute_partial_van_hove(
trj,
chunk_length=20,
selection1="name O",
selection2="name O",
)
partial_dict[key] = x[1]
with pytest.raises(ValueError, match="Dictionary key not valid. Must be a tuple"):
vhf_from_pvhf(trj, partial_dict)
def test_pvhf_error_is_atom_type():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
unique_atoms = get_unique_atoms(trj)
partial_dict = {}
x = compute_partial_van_hove(
trj,
chunk_length=20,
selection1="name O",
selection2="name O",
)
partial_dict[("H", "O")] = x[1]
with pytest.raises(
ValueError, match="Dictionary key not valid. Must be type"
):
vhf_from_pvhf(trj, partial_dict)
def test_pvhf_error_2_atoms_per_pair():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
unique_atoms = get_unique_atoms(trj)
partial_dict = {}
x = compute_partial_van_hove(
trj,
chunk_length=20,
selection1="name O",
selection2="name O",
)
partial_dict[(unique_atoms[0], unique_atoms[1], unique_atoms[1])] = x[1]
with pytest.raises(
ValueError, match="Dictionary key not valid. Must only have 2 atoms per pair"
):
vhf_from_pvhf(trj, partial_dict)
def test_run_total_vhf(step):
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 4
n_chunks = 5
r, t, g_r_t = run_total_vhf(trj,
step=step,
chunk_length=chunk_length,
n_chunks=n_chunks)
assert len(t) == chunk_length / step
assert len(r) == 200
assert np.shape(g_r_t) == (chunk_length / step, 200)
# Check normalization to ~1
assert 0.95 < np.mean(g_r_t[:, -10:]) < 1.05
def test_van_hove():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 2
r, t, g_r_t = compute_van_hove(trj, chunk_length=chunk_length)
assert len(t) == 2
assert len(r) == 200
assert np.shape(g_r_t) == (2, 200)
# Check normalization to ~1
assert 0.95 < np.mean(g_r_t[:, -10:]) < 1.05
fig, ax = plt.subplots()
for i in range(len(t)):
ax.plot(r, g_r_t[i], ".-", label=t[i])
ax.set_ylim((0, 3))
ax.legend()
fig.savefig("vhf.pdf")
def test_pvhf_error_atoms_in_trj():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
unique_atoms = get_unique_atoms(trj)
atom = md.core.topology.Atom(
name="Na", element=md.core.element.sodium, index=0, residue=1
)
partial_dict = {}
x = compute_partial_van_hove(
trj,
chunk_length=20,
selection1="name O",
selection2="name O",
)
partial_dict[(atom, unique_atoms[0])] = x[1]
with pytest.raises(
ValueError, match="Dictionary key not valid, `Atom`"
):
vhf_from_pvhf(trj, partial_dict)
def test_run_partial_vhf(step):
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 4
n_chunks = 5
combo = ["O", "O"]
r, t, g_r_t = run_partial_vhf(
trj,
step=step,
selection1=f"element {combo[0]}",
selection2=f"element {combo[1]}",
chunk_length=chunk_length,
n_chunks=n_chunks,
)
assert len(t) == chunk_length / step
assert len(r) == 200
assert np.shape(g_r_t) == (chunk_length / step, 200)
# Check normalization to ~1
assert 0.95 < np.mean(g_r_t[:, -10:]) < 1.05
def test_local_maxima():
data = np.loadtxt(get_fn('rdf.txt'))
r = data[:, 0]
g_r = data[:, 1]
r_maxes = list()
for i, r_guess in enumerate([0.3, 0.45, 0.65]):
r_max, g_r_max = find_local_maxima(r, g_r, r_guess=r_guess)
r_maxes.append(r_max)
r_mins = list()
for i, r_guess in enumerate([0.3, 0.5]):
r_min, g_r_min = find_local_minima(r, g_r, r_guess=r_guess)
r_mins.append(r_min)
print(r_mins)
assert np.allclose(r_maxes, [0.2775, 0.4375, 0.6675])
assert np.allclose(r_mins, [0.3325, 0.5575])
def test_local_maxima():
""" Find maxs and mins for O-O RDF of SPC/E water"""
data = np.loadtxt(get_fn('rdf.txt'))
r = data[:, 0]
g_r = data[:, 1]
r_maxes = list()
for i, r_guess in enumerate([0.3, 0.45, 0.65]):
r_max, g_r_max = find_local_maxima(r, g_r, r_guess=r_guess)
r_maxes.append(r_max)
r_mins = list()
for i, r_guess in enumerate([0.3, 0.5]):
r_min, g_r_min = find_local_minima(r, g_r, r_guess=r_guess)
r_mins.append(r_min)
print(r_mins)
assert np.allclose(r_maxes, [0.2725, 0.4475, 0.6775])
assert np.allclose(r_mins, [0.3325, 0.5625])
import numpy as np
import matplotlib.pyplot as plt
from scattering.utils.features import *
from scattering.utils.io import get_fn
# Load data
data = np.loadtxt(get_fn('rdf.txt'))
r = data[:, 0]
g_r = data[:, 1]
fig, ax = plt.subplots()
ax.plot(r, g_r, '-')
for i, r_guess in enumerate([0.3, 0.45, 0.65]):
r_max, g_r_max = find_local_maxima(r, g_r, r_guess=r_guess)
plt.plot(r_max, g_r_max, 'k.')
for i, r_guess in enumerate([0.3, 0.5]):
r_min, g_r_min = find_local_minima(r, g_r, r_guess=r_guess)
plt.plot(r_min, g_r_min, 'r.')
ax.set_xlabel(r'Radial coordinate, $r$, $nm$')
ax.set_ylabel(r'Radial distribution function, $g(r)$, $unitiless$')
plt.savefig('maxima-minima.pdf')
def test_serial_van_hove():
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 2
r, t, g_r_t = compute_van_hove(trj, chunk_length=chunk_length, parallel=False)
import numpy as np
import mdtraj as md
import matplotlib.pyplot as plt
from scattering.scattering import compute_van_hove
from scattering.utils.io import get_fn
from scattering.utils.utils import get_dt
from scattering.utils.features import find_local_maxima
trj = md.load(get_fn('10fs.xtc'),
top=get_fn('10fs.gro'),
stride=1,
)[:10000]
chunk_length = 200
selection1 = 'name O'
selection2 = 'name O'
r, t, g_r_t = compute_van_hove(trj=trj, chunk_length=chunk_length, water=True,
r_range=(0, 0.8), bin_width=0.001, periodic=True, opt=True)
dt = get_dt(trj)
# Save output to text files
np.savetxt('vhf.txt', g_r_t, header='# Van Hove Function, dt: {} fs, dr: {}'.format(
dt,
np.unique(np.round(np.diff(trj.time), 6))[0],
))
np.savetxt('r.txt', r, header='# Times, ps')
np.savetxt('t.txt', t, header='# Positions, nm')
