def minimal_driver():
DUMP_DATA = bool(int(os.environ.get("DUMP_DATA", "0")))
n_atoms = [32768]
n_bins = 16384
coords = util.generate_random_coordinate_set(n_atoms)
box = None
# box = get_triclinic_box()
# box = get_orthorhombic_box()
force_triclinic = False
# box = get_orthorhombic_triclinic_box()
# force_triclinic = True
histo = pydh.histograms(coords,
r_max,
n_bins,
box=box,
precision="double",
force_triclinic=force_triclinic,
n_threads=util.get_n_cpu_cores(),
verbose=True)
if DUMP_DATA:
file_name = sys._getframe().f_code.co_name + ".dat"
util.dump_histograms(file_name, histo, r_max, n_bins)
def test_generate_random_coordinate_set():
n_atoms = [1024]
coords = util.generate_random_coordinate_set(n_atoms=n_atoms)
for coord_set in coords:
# print coord_set
assert (np.min(coord_set) >= 0.0)
assert (np.max(coord_set) <= 1.0)
def test_generate_random_coordinate_set_max():
n_atoms = [1024]
coord_max = (2., 3., 4.)
coords = util.generate_random_coordinate_set(n_atoms=n_atoms,
coord_max=coord_max)
for coord_set in coords:
# print coord_set
assert (np.min(coord_set) >= 0.0)
assert (np.all(np.max(coord_set, axis=0) <= coord_max))
def test_generate_random_coordinate_set_min():
n_atoms = [1024]
coord_min = (-1., -2., -3.)
coords = util.generate_random_coordinate_set(n_atoms=n_atoms,
coord_min=coord_min)
for coord_set in coords:
# print coord_set
assert (np.all(np.min(coord_set, axis=0) >= coord_min))
assert (np.max(coord_set) <= 1.0)
def fixture_medium_manybins():
global testcase_medium_manybins
if testcase_medium_manybins is None:
n_atoms = [3000, 5000, 3500]
n_bins = 68000
coords = util.generate_random_coordinate_set(n_atoms)
histo_ref = dist.histograms(coords, r_max, n_bins)
testcase_medium_manybins = (n_atoms, n_bins, coords, histo_ref)
return testcase_medium_manybins
def fixture_small_invalid():
global testcase_small_invalid
if testcase_small_invalid is None:
n_atoms = [2000, 1000]
n_bins = 1000
coords = util.generate_random_coordinate_set(n_atoms,
blowup_factor=3.14)
histo_ref = None
testcase_small_invalid = (n_atoms, n_bins, coords, histo_ref)
return testcase_small_invalid
def fixture_small():
"""Create a reference case using the very first dist implementation."""
global testcase_small
if testcase_small is None:
n_atoms = [2847, 3918]
n_bins = 1000
coords = util.generate_random_coordinate_set(n_atoms)
histo = dist.histograms(coords, r_max, n_bins)
# if DUMP_DATA:
# file_name = sys._getframe().f_code.co_name + ".dat"
# util.dump_histograms(file_name, histo, r_max, n_bins)
testcase_small = (n_atoms, n_bins, coords, histo)
return testcase_small
def fixture_large():
global testcase_large
if testcase_large is None:
n_atoms = [105000, 110000, 133000]
n_bins = 16000
coords = util.generate_random_coordinate_set(n_atoms)
# --- note that we use pydh to generate the test dataset
histo_ref = pydh.histograms(coords,
r_max,
n_bins,
precision="double",
n_threads=n_threads[-1])
testcase_large = (n_atoms, n_bins, coords, histo_ref)
return testcase_large
def fixture_xlarge():
global testcase_xlarge
if testcase_xlarge is None:
n_atoms = [250000, 275000, 225000]
n_bins = 18000
coords = util.generate_random_coordinate_set(n_atoms)
# --- note that we use pydh to generate the test dataset, max number of threads and no blocking
histo_ref = pydh.histograms(coords,
r_max,
n_bins,
precision="double",
n_threads=n_threads[-1],
blocksize=-1)
testcase_xlarge = (n_atoms, n_bins, coords, histo_ref)
return testcase_xlarge
def fixture_small_triclinic():
"""Create a triclinic reference case in double precision using pydh."""
global testcase_small_triclinic
if testcase_small_triclinic is None:
n_atoms = [2847, 3918]
n_bins = 1000
coords = util.generate_random_coordinate_set(n_atoms)
box = get_triclinic_box()
histo = pydh.histograms(coords,
r_max,
n_bins,
box=box,
precision="double",
n_threads=1)
testcase_small_triclinic = (n_atoms, n_bins, coords, box, histo)
return testcase_small_triclinic
def get_frame(self, i):
"""Create coordinate data sets for a single frame."""
# Cadishi and Capriqorn use base.Container() instances to handle data
frm = base.Container()
# Generate list of (Nx3) NumPy coordinate arrays.
# NOTE: This is the location where you would want to feed your own data in!
# We use random data in this simple example.
coord_set = util.generate_random_coordinate_set(self.n_objects)
for idx, coords in enumerate(coord_set):
# Each species carries a label.
# NOTE: You need to take care about labels fitting to your data!
label = "species_" + str(idx)
# add coordinates to frame at the following standardized location
location = base.loc_coordinates + '/' + label
frm.put_data(location, coords.astype(np.float64))
# finally, each frame is uniquely numbered
frm.i = i
return frm
bap += old_div(float(n_atoms[i] * n_atoms[j]), 1.e9)
else:
if not run_values['histo2']:
bap += old_div(
float(old_div((n_atoms[j] * (n_atoms[j] - 1)), 2)),
1.e9)
return bap
# ------------------------------------------------------------------------------
if run_values['numa']:
numa_topology = util.get_numa_domains()
util.set_numa_domain(0, numa_topology)
coords = util.generate_random_coordinate_set(run_values['size'])
n_bins = run_values['bins']
if run_values['box'].startswith('t'):
# triclinic dummy box
box = np.asarray([0.30, 0.32, 0.34, 60., 60., 90.])
elif run_values['box'].startswith('o'):
# orthorhombic dummy box
box = np.zeros((3, 3))
box[0][0] = 0.66
box[1][1] = 0.68
box[2][2] = 0.70
else:
# no box
box = []