def _test_write_dx(counts=100, ndim=3, nptype="float32", dxtype="float"):
h, edges = np.histogramdd(np.random.random((counts, ndim)), bins=10)
g = Grid(h, edges)
# hack the grid to be a different dtype
g.grid = g.grid.astype(nptype)
assert_equal(g.grid.sum(), counts)
with tempdir.in_tempdir():
outfile = "grid.dx"
g.export(outfile)
g2 = Grid(outfile)
# check that dxtype was written
dx = gridData.OpenDX.field(0)
dx.read(outfile)
data = dx.components['data']
out_dxtype = data.type
assert_almost_equal(g.grid, g2.grid,
err_msg="written grid does not match original")
assert_almost_equal(
g.delta, g2.delta,
decimal=6,
err_msg="deltas of written grid do not match original")
assert_equal(out_dxtype, dxtype)
def test_write_dx(tmpdir, nptype, dxtype, outfile, counts=100, ndim=3):
# conversion from numpy array to DX file
h, edges = np.histogramdd(np.random.random((counts, ndim)), bins=10)
g = Grid(h, edges)
# hack the grid to be a different dtype
g.grid = g.grid.astype(nptype)
assert_equal(g.grid.sum(), counts)
with tmpdir.as_cwd():
g.export(outfile)
g2 = Grid(outfile)
# check that dxtype was written
dx = gridData.OpenDX.field(0)
dx.read(outfile)
data = dx.components['data']
out_dxtype = data.type
assert_almost_equal(g.grid,
g2.grid,
err_msg="written grid does not match original")
assert_almost_equal(g.delta,
g2.delta,
decimal=6,
err_msg="deltas of written grid do not match original")
assert_equal(out_dxtype, dxtype)
def write_dx_input(self, param_charge, param_diel):
''' Generate the dielectric constant and charge grid for APBS
calculations.
``generate_charge`` and ``generate_diel`` are used to generate the
profile of the charge and dielectric constant on the one-dimensional
z-axis which is then broadcast to cover the whole 3D space. The
generated files are named as ``dielx.dx``, ``diely.dx``,
``dielz.dx`` and ``charge.dx``.
Parameters
----------
**kwargs : dict
``generate_charge`` uses the arguments from kwargs['charge'] and
``generate_diel`` uses the arguments from kwargs['diel'].
'''
# generate the one dimensional z axis
z_list = np.linspace(0 * pq.angstrom, self.dim[-1], self.grid[-1])
charge = self.generate_charge(z_list, param_charge)
diel = self.generate_diel(z_list, param_diel)
# expand to 3D
diel = np.ones((self.grid[0], self.grid[1], 1)) * diel.reshape(
(1, 1, self.grid[2]))
charge = np.ones((self.grid[0], self.grid[1], 1)) * charge.reshape(
(1, 1, self.grid[2]))
origin = self.dim / 2 * -1
origin = origin.rescale(pq.angstrom)
delta = self.delta.rescale(pq.angstrom)
# Write diel
# write x
origin_x = origin.copy()
origin_x[0] += self.delta[0] / 2
g = Grid(diel, origin=origin_x.magnitude, delta=delta.magnitude)
g.export('dielx.dx', typequote='')
# write y
origin_y = origin.copy()
origin_y[1] += self.delta[1] / 2
g = Grid(diel, origin=origin_y.magnitude, delta=delta.magnitude)
g.export('diely.dx', typequote='')
# write z
origin_z = origin.copy()
origin_z[2] += self.delta[2] / 2
g = Grid(diel, origin=origin_z.magnitude, delta=delta.magnitude)
g.export('dielz.dx', typequote='')
# Write charge
charge = charge.rescale(pq.e / pq.angstrom**3)
g = Grid(charge.magnitude,
origin=origin.magnitude,
delta=delta.magnitude)
g.export('charge.dx', typequote='')
def test_ccp4():
g = Grid(datafiles.CCP4)
POINTS = 192
assert_equal(g.grid.flat, np.arange(1, POINTS+1))
assert_equal(g.grid.size, POINTS)
assert_almost_equal(g.delta, [3./4, .5, 2./3])
assert_equal(g.origin, np.zeros(3))
def analyze_dx(_f):
"""analyzes dx _file
Args:
_f (string): filename with extension of the dx file that we want to analyze
Returns:
pandas: pandas dataframe columns=['coord_phi','phiz']
"""
g = Grid(_f)
nx, ny, nz = g.grid.shape
Lx = g.edges[0][0]
Ly = g.edges[1][0]
Lzl = g.edges[2][0]
Lzr = g.edges[2][-1]
g_all = np.zeros(nz)
count = 0
for i in range(nx):
for j in range(ny):
g_all += g.grid[i][j]
count += 1
r_array = np.linspace(Lzl, Lzr, nz)
yy = g_all / count
A = np.vstack([r_array, yy])
df = pd.DataFrame(A.T, columns=['coord_phi', 'phiz'])
return df
def test_read_dx():
g = Grid('gridData/tests/test.dx')
POINTS = 8
assert_array_equal(g.grid.flat, np.ones(POINTS))
assert_equal(g.grid.size, POINTS)
assert_array_equal(g.delta, np.ones(3))
assert_array_equal(g.origin, np.zeros(3))
def test_ccp4():
g = Grid('gridData/tests/test.ccp4')
POINTS = 192
assert_equal(g.grid.flat, np.arange(1, POINTS + 1))
assert_equal(g.grid.size, POINTS)
assert_almost_equal(g.delta, [3. / 4, .5, 2. / 3])
assert_equal(g.origin, np.zeros(3))
def analyze_dx(f):
"""analyzes dx file"""
g = Grid(f + ".dx")
nx, ny, nz = g.grid.shape
Lx = g.edges[0][0]
Ly = g.edges[1][0]
Lzl = g.edges[2][0]
Lzr = g.edges[2][-1]
# g_all = g.grid[nx//2][ny//2]
# g_vmd = g_all
# L = 100.
g_all = np.zeros(nz)
count = 0
for i in range(nx):
for j in range(ny):
g_all += g.grid[i][j]
count += 1
# vmd_unit = 25*ureg.mV
# vmd_unit = 1.
# units = nu.e/(25*nu.mV)
# print units
yy = g_all[2:-2]/count
r_array = np.linspace(Lzl, Lzr, len(yy))
print len(r_array), len(yy)
return r_array, yy
def __init__(self, fileName):
try:
from gridData import Grid
except ModuleNotFoundError:
raise ModuleNotFoundError("The dependency 'GridDataFormats' is required for this functionality!")
self.grid = Grid(fileName) # stores the grid data
def extractISOPdb(input, output, iso):
g = Grid(input)
# JD: this is clunky but i'm not sure what's a better way
data = np.array(OpenDX.array(3, g.grid).array.flat)
data = data.reshape((len(data) / 3, 3))
x, y, z = 0, 0, 0
counter = 1
lines = []
for point in data:
for pos in point:
if (iso < 0 and pos < iso) or (iso > 0 and pos > iso):
line = 'ATOM %5d C PTH 1 %8.3f%8.3f%8.3f%6.2f%6.2f\n' % (
counter, g.origin[0] + float(x) * g.delta[0, 0],
g.origin[1] + float(y) * g.delta[1, 1],
g.origin[2] + float(z) * g.delta[2, 2], 0.0, 0.0)
lines.append(line)
counter += 1
z += 1
if z >= g.grid.shape[2]:
z = 0
y += 1
if y >= g.grid.shape[1]:
y = 0
x += 1
with open(output, "w") as f:
f.writelines(lines)
return g
def data():
d = dict(
griddata=np.arange(1, 28).reshape(3, 3, 3),
origin=np.zeros(3),
delta=np.ones(3))
d['grid'] = Grid(d['griddata'], origin=d['origin'],
delta=d['delta'])
return d
def read_APBS(self):
''' Read the APBS output.
Returns
-------
charge : array
The three-dimensional array of the charge density.
diel : array
The three-dimensional array of the dielectric constant in the x
direction.
lipid : array
The three-dimensional array of the ESP.
'''
charge = Grid('charge_check.dx').grid
diel = Grid('dielx_check.dx').grid
lipid = Grid('lipid.dx').grid
return charge, diel, lipid
def mask_generator(maskfile, referencefile=None, distance=5):
if os.path.splitext(maskfile)[1] == ".pdb":
mask = GridUtil.gen_distance_grid(referencefile, maskfile)
else:
mask = Grid(maskfile)
mask.grid = mask.grid < distance
return mask
def test_write_dx(counts=100, ndim=3):
h, edges = np.histogramdd(np.random.random((counts, ndim)), bins=10)
g = Grid(h, edges)
assert_equal(g.grid.sum(), counts)
with tempdir.in_tempdir():
outfile = "grid.dx"
g.export(outfile)
g2 = Grid(outfile)
assert_array_almost_equal(g.grid,
g2.grid,
err_msg="written grid does not match original")
assert_array_almost_equal(
g.delta,
g2.delta,
err_msg="deltas of written grid do not match original")
def test_init_pickle_pathobjects(self, data, tmpdir):
g = data['grid']
fn = tmpdir.mkdir('grid').join('grid.pickle')
g.save(fn)
h = Grid(fn)
assert h == g
def test_centers(self, data):
# this only checks the edges. If you know an alternative
# algorithm that isn't an exact duplicate of the one in
# g.centers to test this please implement it.
g = Grid(data['griddata'], origin=np.ones(3), delta=data['delta'])
centers = np.array(list(g.centers()))
assert_array_equal(centers[0], g.origin)
assert_array_equal(centers[-1] - g.origin,
(np.array(g.grid.shape) - 1) * data['delta'])
def test_load_pickle(self, data, tmpdir):
g = data['grid']
fn = str(tmpdir.mkdir('grid').join('grid.pkl'))
g.save(fn)
h = Grid()
h.load(fn)
assert h == g
def test_read_dx(infile):
g = Grid(infile)
POINTS = 8
ref = np.ones(POINTS)
ref[4] = 1e-6
ref[5] = -1e+6
assert_equal(g.grid.flat, ref)
assert_equal(g.grid.size, POINTS)
assert_equal(g.delta, np.ones(3))
assert_equal(g.origin, np.array([20.1, 3., -10.]))
def read(self):
DX = Path(self.fname).glob("*.dx")
files = [p for p in DX if p.is_file()]
if not files:
raise Exception("No .dx files found")
for dx in files:
name = dx.stem.split('.')
if (name[0].endswith("_"+self.suffix[0])):
self.grids[self.suffix[0]+name[1]] = Grid(self.fname + "/" + dx.stem + ".dx")
self.delta = np.prod(self.grids[self.suffix[0]+name[1]].delta)
elif (name[0].endswith("_"+self.suffix[1])):
self.grids[self.suffix[1]+name[1]] = Grid(self.fname + "/" + dx.stem + ".dx")
elif (name[0].endswith("_"+self.suffix[2])):
self.grids[self.suffix[2]+name[1]] = Grid(self.fname + "/" + dx.stem + ".dx")
elif (name[0].endswith("_"+self.suffix[3])):
self.grids[self.suffix[3]+name[1]] = Grid(self.fname + "/" + dx.stem + ".dx")
if not self.grids:
raise Exception("No tagged .dx files found")
def test_write_dx_ValueError(tmpdir, nptype, outfile, counts=100, ndim=3):
h, edges = np.histogramdd(np.random.random((counts, ndim)), bins=10)
g = Grid(h, edges)
# hack the grid to be a different dtype
g.grid = g.grid.astype(nptype)
with pytest.raises(ValueError):
with tmpdir.as_cwd():
g.export(outfile)
def get_density(lipid_xyz, ul_max, x):
xbin, ybin, zbin = int(ul_max[0]*x), int(ul_max[1]*x), int(ul_max[2]*x)
system_h, system_e = coordinate_histogram(lipid_xyz, xbin, ybin, zbin)
histogram, edges = system_h, system_e
grid = Grid(histogram, edges=[e * 10 for e in edges])
return grid
def _conclude(self):
# concatenate lists into arrays
all_coords = np.concatenate(self.coord_list, axis=0)
# compute histogram
self.data, self.edges = np.histogramdd(
all_coords, [self.xedges, self.yedges, self.zedges])
# normalize to occupancy per frame
self.data = self.data / self.frame_count
# place into a grid object for writing to .dx
self.gridobj = Grid(self.data, edges=self.edges)
def test_anyarray(data):
ma = np.ma.MaskedArray(data['griddata'])
mg = Grid(ma, origin=data['origin'], delta=data['delta'])
assert isinstance(mg.grid, ma.__class__)
result = f_arithmetic(mg)
ref = f_arithmetic(data['grid'])
assert_almost_equal(result.grid, ref.grid)
def read_central_ESP(self):
''' Read the one-dimensional ESP along the Z-axis at the center of
the x-y plane.
Returns
-------
out_ESP : array
The one-dimensional ESP.
'''
lipid = Grid('lipid.dx').grid
out_ESP = lipid[int(np.rint(self.grid[0] / 2)),
int(np.rint(self.grid[1] / 2)), :]
return out_ESP
def density_data(self, dens, quantiles=np.arange(0, 1.05, 0.05)):
g = Grid(dens)
g_max = np.max(g.grid.flat)
g_min = np.min(g.grid.flat)
g_mean = np.median(g.grid.flat)
g_std = np.std(g.grid.flat)
filtered = g.grid[g.grid > 0.001]
g_q = np.quantile(filtered.flat, q=quantiles)
print(f'Mean: {g_mean}\nSt. dev: {g_std}\nMax: {g_max}\nMin: {g_min}')
return (quantiles, g_q, g.grid, g_mean, g_std, g_max, filtered)
def test_gOpenMol():
g = Grid(datafiles.gOpenMol)
POINTS = 192
assert g.grid.size == 165048
assert_almost_equal(g.delta, [1.0, 1.0, 1.0])
assert_equal(g.grid.shape, (46, 46, 78))
assert_almost_equal(g.origin, [0.5995016, 0.5995016, 0.5919984])
assert_almost_equal(
g.grid[::20, ::20, ::30],
np.array([[[1.02196848, 0., 0.88893718], [0.99051529, 0., 0.95906246],
[0.96112466, 0., 0.88996845]],
[[0.97247058, 0., 0.91574967],
[1.00237465, 1.34423399, 0.87810922],
[0.97917157, 0., 0.84717268]],
[[0.99103099, 0., 0.86521846], [0.96421844, 0., 0.],
[0.98432779, 0., 0.8817184]]]))
assert_almost_equal(g.grid.mean(), 0.5403224581733577)
def Grid(self, delta, **kwargs):
"""Package the PMF as a :class:`gridData.Grid` object.
*delta* should be the original spacing of the points in angstroem.
With a *resample_factor*, the data are interpolated on a new grid
with *resample_factor* times as many bins as in the original
histogram (See :meth:`gridData.Grid.resample_factor`).
*interpolation_order* sets the interpolation order for the
resampling procedure.
.. Warning:: Interpolating can lead to artifacts in the 3D PMF. If
in doubt, **do not resample**.
"""
from gridData import Grid
resample_factor = kwargs.pop('resample_factor', None)
kwargs.setdefault('interpolation_spline_order', 3)
g = Grid(*self.histogramdd(delta), **kwargs)
if not resample_factor:
return g
return g.resample_factor(resample_factor)
def grid_to_dx(self, save_dir, grid, bin_size, use_channel_names=False):
#origin = np.array([7.473, 4.334, 7.701]) - 5.0
if grid.shape[0] % 2 != 0:
raise ValueError('Number of channels must be even (%i)' %
grid.shape[0])
ch_per_mol = grid.shape[0] / 2
for shift, prefix in [(0, 'rec'), (ch_per_mol, 'lig')]:
for ch in range(ch_per_mol):
ch += shift
if use_channel_names:
names = self.prop_table.columns[1:]
names *= 2
channel_name = names[ch]
else:
channel_name = str(ch)
g = Grid(grid[ch],
origin=np.array([7.473, 4.334, 7.701]) - 5.0,
delta=bin_size)
g.export(Path(save_dir).joinpath(channel_name + '.dx'), 'DX')
import numpy as np
from gridData import Grid
import sys
import argparse
# one PMEpot unit (kT/e) of electrostatic potential is equivalent to 0.0258 Volts. (or 25.8 mV)
# Parse inputs
parser = argparse.ArgumentParser()
parser.add_argument("-f",dest="infile",action="store")
parser.add_argument("-e",dest="eField",type=float,action="store")
parser.add_argument("-o",dest="outname",action="store")
args = parser.parse_args()
# Open the .dx from PMEPot, and calculate grid-demensions
PMEPotIN = Grid(args.infile)
lenx, leny, lenz = PMEPotIN.grid.shape
# loop through the z-axis of the reaction potential and add the applied linear-potential
for z in range(lenz):
for x in range(lenx):
for y in range(leny):
PMEPotIN.grid[x][y][z] = PMEPotIN.grid[x][y][z] + (z*args.eField)
# writeout new potential grid
PMEPotIN.export(args.outname+'.dx')
def test_equality(self, data):
assert data['grid'] == data['grid']
assert data['grid'] != 'foo'
g = Grid(data['griddata'], origin=data['origin'] + 1, delta=data['delta'])
assert data['grid'] != g
