def test_pydh_invalid_small_single(fixture_small_invalid):
n_atoms, n_bins, coords, histo_ref = fixture_small_invalid
with pytest.raises(ValueError):
pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=1,
check_input=True)
def test_pydh_invalid_threads_small_double(fixture_small_invalid):
n_atoms, n_bins, coords, histo_ref = fixture_small_invalid
for nt in n_threads:
with pytest.raises(ValueError):
pydh.histograms(coords,
r_max,
n_bins,
precision="double",
n_threads=nt,
check_input=True)
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_pydh_large_single(fixture_large):
n_atoms, n_bins, coords, histo_ref = fixture_large
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=1)
util.compare(histo_ref, histo_pydh)
def test_n_threads_xlarge_single(fixture_xlarge):
n_atoms, n_bins, coords, histo_ref = fixture_xlarge
for nt in n_threads:
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=nt)
util.compare(histo_ref, histo_pydh)
def test_pydh_medium_manybins_single(fixture_medium_manybins):
n_atoms, n_bins, coords, histo_ref = fixture_medium_manybins
for check_input in [True, False]:
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=1,
check_input=check_input)
util.compare(histo_ref, histo_pydh)
def test_n_threads_medium_double(fixture_medium):
n_atoms, n_bins, coords, histo_ref = fixture_medium
for check_input in [True, False]:
for nt in n_threads:
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="double",
n_threads=nt,
check_input=check_input)
util.compare(histo_ref, histo_pydh)
def test_pydh_small_single(fixture_small):
"""Test if pydh gives the same answer as dist()."""
n_atoms, n_bins, coords, histo_ref = fixture_small
for check_input in [True, False]:
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=1,
check_input=check_input)
util.compare(histo_ref, histo_pydh)
def test_pydh_small_double_blocked(fixture_small):
n_atoms, n_bins, coords, histo_ref = fixture_small
for check_input in [True, False]:
for nt in n_threads:
histo_blocked = pydh.histograms(coords,
r_max,
n_bins,
precision="double",
n_threads=nt,
blocksize=200,
check_input=check_input)
util.compare(histo_ref, histo_blocked)
def test_pydh_small_triclinic_single(fixture_small_triclinic):
"""Test if the triclinic implementation gives the same answer in single precision."""
n_atoms, n_bins, coords, box, histo_ref = fixture_small_triclinic
for check_input in [True, False]:
histo = pydh.histograms(coords,
r_max,
n_bins,
box=box,
precision="single",
n_threads=1,
check_input=check_input)
util.compare(histo_ref, histo)
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 test_pydh_medium_scaled_single(fixture_medium):
n_atoms, n_bins, coords, histo_ref = fixture_medium
n_el = len(n_atoms)
scale_factors = np.ones(n_el * (n_el + 1) // 2)
scale_factors *= 0.5
for check_input in [True, False]:
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=1,
scale_factors=scale_factors,
check_input=check_input)
assert (histo_ref.sum() == 2.0 * histo_pydh.sum())
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 test_pydh_small_orthorhombic_triclinic(fixture_small_triclinic):
"""Test if the triclinic and orthorhombic implementations give the same answer for an orthorhombic box."""
n_atoms, n_bins, coords, box, histo_ref = fixture_small_triclinic
box_ort = get_orthorhombic_box()
box_tri = get_orthorhombic_triclinic_box()
for precision in ['single', 'double']:
for check_input in [True, False]:
histo_ort = pydh.histograms(coords,
r_max,
n_bins,
box=box_ort,
force_triclinic=False,
precision=precision,
n_threads=1,
check_input=check_input)
histo_tri = pydh.histograms(coords,
r_max,
n_bins,
box=box_tri,
force_triclinic=True,
precision=precision,
n_threads=1,
check_input=check_input)
util.compare(histo_ort, histo_tri)
def test_pydh_medium_masked_single(fixture_medium):
n_atoms, n_bins, coords, histo_ref = fixture_medium
n_el = len(n_atoms)
mask_array = np.ones(n_el * (n_el + 1) // 2)
mask_array[::2] = 0
for check_input in [True, False]:
histo_pydh = pydh.histograms(coords,
r_max,
n_bins,
precision="single",
n_threads=1,
mask_array=mask_array,
check_input=check_input)
col_sum = histo_pydh.sum(axis=0)
assert (np.sum(col_sum[1::2]) == 0)
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
r_max,
run_values['bins'],
run_values['precision'],
gpu_id=gpu_id,
do_histo2_only=run_values['histo2'],
thread_block_x=run_values['thread_block_x'],
check_input=run_values['check_input'],
box=box,
algorithm=run_values['gpu_algorithm'],
verbose=True)
else:
xxxx = pydh.histograms(coords,
r_max,
run_values['bins'],
run_values['precision'],
check_input=run_values['check_input'],
do_histo2_only=run_values['histo2'],
n_threads=run_values['threads'],
blocksize=run_values['blocksize'],
box=box,
verbose=True)
t1 = time.time()
dt = (t1 - t0)
bapps = old_div(bap, dt)
run_values['bap'] = bap
run_values['time'] = dt
run_values['bapps'] = bapps
if not run_values['silent']:
vals = [x + ":" + str(run_values[x]) for x in output_keys]
