def main():
args = parse_arguments()
# checking if the arguments are valid
assert is_ndarray_folder(args.path), "Invalid path."
# factorizing the data
dims = [1] + list(range(5, args.max_dim, 5)) + [args.max_dim]
table = []
if args.matrix:
ndarray = sparse.load_npz(join(args.path, 'matrix.npz')).todense()
else:
ndarray = sparse.load_npz(join(args.path, 'tensor.npz')).todense()
for d in tqdm(dims):
err_sum, max_err_sum, mean_abs_err_sum, mean_sq_err_sum = 0, 0, 0, 0
args.dimensions = d
for _ in range(args.iterations):
err, max_err, mean_abs_err, mean_sq_err = \
get_factorization_error(ndarray, args)
err_sum += err
max_err_sum += max_err
mean_abs_err_sum += mean_abs_err
mean_sq_err_sum += mean_sq_err
table.append([
d, err_sum / args.iterations, max_err_sum / args.iterations,
mean_abs_err_sum / args.iterations,
mean_sq_err_sum / args.iterations
])
print(
tabulate(table,
headers=[
'Dim', 'Err.', 'Max Err.', 'Mean Abs. Err.',
'Mean Sq. Err.'
],
tablefmt='orgtbl'))
def run_cmtf(path,
dimensions=20,
is_matrix=True,
name='current',
noise='orig',
iterations=1,
fixed='False'):
# checking if the arguments are valid
assert is_ndarray_folder(path), "Not a valid path."
assert '_' not in name, "Name cannot contain the '_' symbol."
# creating some useful paths to store factorization results
mat_path = join(
path, 'mat_' + str(dimensions) + '_' + str(iterations) + '_' + name)
ten_path = join(
path, 'ten_' + str(dimensions) + '_' + str(iterations) + '_' + name)
# loading the meta data
with open(join(path, 'meta_data.json'), 'r') as json_file:
meta_data = json.load(json_file)
# removing old factorization with same name (if exists)
delete_factorization_by_name(name, path)
# factorizing the data
start = time.time()
if is_matrix:
matrix = sparse.load_npz(join(path, 'matrix.npz')).todense()
matrices = prepare_ndarrays(matrix, iterations, fixed, noise)
create_folder_if_absent(mat_path)
factorize_matrices(matrices, iterations, dimensions, mat_path, path)
else:
tensor = sparse.load_npz(join(path, 'tensor.npz')).todense()
tensors = prepare_ndarrays(tensor, iterations, fixed, noise)
create_folder_if_absent(ten_path)
factorize_tensors(tensors, iterations, dimensions, ten_path, meta_data,
path)
end = time.time()
print('Factorization completed in %d seconds' % (end - start))
def test_load_wrong_format_exception(tmp_path):
x = np.array([1, 2, 3])
filename = tmp_path / "mat.npz"
np.savez(filename, x)
with pytest.raises(RuntimeError):
load_npz(filename)
def test_load_wrong_format_exception():
with tempfile.TemporaryDirectory() as tmp_path_str:
tmp_path = pathlib.Path(tmp_path_str)
x = np.array([1, 2, 3])
filename = tmp_path / "mat.npz"
np.savez(filename, x)
with pytest.raises(RuntimeError):
load_npz(filename)
def test_load_wrong_format_exception():
x = np.array([1, 2, 3])
dir_name = tempfile.mkdtemp()
filename = os.path.join(dir_name, 'mat.npz')
np.savez(filename, x)
with pytest.raises(RuntimeError):
load_npz(filename)
shutil.rmtree(dir_name)
def get_benchmark_test_theta(fuse=False, theta=None, batch_size=64):
task = 'mortality'
duration = 48
timestep = 1.0
df_label_all = pd.read_csv(
data_path + 'population/{}_{}h.csv'.format(task, duration)).rename(
columns={'{}_LABEL'.format(task): 'LABEL'})
df_label = pd.read_csv(data_path +
'population/pop.mortality_benchmark.csv').rename(
columns={'{}_LABEL'.format(task): 'LABEL'})
X = sparse.load_npz(
data_path +
'features,comparison/theta={},outcome={},T={},dt={}/X.npz'.format(
theta, task, duration, timestep)).todense()
s = sparse.load_npz(
data_path +
'features,comparison/theta={},outcome={},T={},dt={}/s.npz'.format(
theta, task, duration, timestep)).todense()
te_idx = [
df_label_all[df_label_all['ICUSTAY_ID'] == ID].index.values[0]
for ID in df_label[df_label['partition'] == 'test']['ID']
]
def _select_examples(rows):
return (
X[rows],
s[rows],
df_label_all.iloc[rows][['LABEL']].values,
)
Xy_te = _select_examples(te_idx)
print('ICU stay splits:', len(te_idx))
te = EHRDataset(*Xy_te, fuse=fuse)
num_workers = 1
te_loader = DataLoader(te,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
print(te_loader.dataset.y.sum())
print('')
print('Time series shape, Static shape, Label shape, Class balance:')
print('\t', 'te', te_loader.dataset.X.shape, te_loader.dataset.s.shape,
te_loader.dataset.y.shape, te_loader.dataset.y.mean())
if fuse:
print('Fused dimensions:', te_loader.dataset[0][0].shape)
return te_loader
def _so_sparse(nspins):
"""
Either load a presaved set of spin operators as numpy arrays, or
calculate them and save them if a presaved set wasn't found.
Parameters
----------
nspins : int
the number of spins in the spin system
Returns
-------
(Lz, Lproduct) : a tuple of:
Lz : 3d sparse.COO array of shape (n, 2^n, 2^n) representing
[Lz1, Lz2, ...Lzn]
Lproduct : 4d sparse.COO array of shape (n, n, 2^n, 2^n), representing
an n x n array (cartesian product) for all combinations of
Lxa*Lxb + Lya*Lyb + Lza*Lzb, where 1 <= a, b <= n.
Side Effect
-----------
Saves the results as .npz files to the bin directory if they were not
found there.
"""
# TODO: once nmrsim demonstrates installing via the PyPI *test* server,
# need to determine how the saved solutions will be handled. For example,
# part of the final build may be generating these files then testing.
# Also, need to consider different users with different system capabilities
# (e.g. at extreme, Raspberry Pi). Some way to let user select, or select
# for user?
filename_Lz = f'Lz{nspins}.npz'
filename_Lproduct = f'Lproduct{nspins}.npz'
bin_path = _bin_path()
path_Lz = bin_path.joinpath(filename_Lz)
path_Lproduct = bin_path.joinpath(filename_Lproduct)
# with path_context_Lz as p:
# path_Lz = p
# with path_context_Lproduct as p:
# path_Lproduct = p
try:
Lz = sparse.load_npz(path_Lz)
Lproduct = sparse.load_npz(path_Lproduct)
return Lz, Lproduct
except FileNotFoundError:
print('no SO file ', path_Lz, ' found.')
print(f'creating {filename_Lz} and {filename_Lproduct}')
Lz, Lproduct = _so_dense(nspins)
Lz_sparse = sparse.COO(Lz)
Lproduct_sparse = sparse.COO(Lproduct)
sparse.save_npz(path_Lz, Lz_sparse)
sparse.save_npz(path_Lproduct, Lproduct_sparse)
return Lz_sparse, Lproduct_sparse
def load_one(path=PATH + "/" + SAVE_FILE_NAME):
"""
Load the matrix from a single file.
"""
data = sparse.load_npz(path).astype(np.int16).todense()
print("Finished loading " + path)
return data
def __init__(self, base_path, dataset_name, from_day_incl, dense=True):
if not isinstance(base_path, pathlib.Path):
base_path = pathlib.Path(base_path)
self.from_day_incl = from_day_incl
self.dataset_name = dataset_name
self.imls_log_daily = sparse.load_npz(base_path / f"interactions_{dataset_name}_sparse.npz")
self.dense = dense
if self.dense:
self.imls_log_daily = self.imls_log_daily.todense()
self.num_days = self.imls_log_daily.shape[0]
self.num_entities = self.imls_log_daily.shape[1]
self.num_matrices = self.imls_log_daily.shape[-1]
self.alive_df = pd.read_csv(
base_path / f"alive_{dataset_name}.csv", parse_dates=["date_emerged"]
)
self.num_classes = len(self.alive_df.date_emerged.unique())
indices_df = pd.read_csv(base_path / f"indices_{dataset_name}.csv")
self.id_to_idx = dict(indices_df.values)
self.idx_to_id = dict(indices_df.values[:, ::-1])
self.bee_ages, self.valid_ages = self.parse_bee_ages(
self.alive_df, from_day_incl, self.num_days, self.num_entities
)
self.labels = self.parse_labels(self.alive_df)
def cache_tm(nspins):
"""
Parameters
----------
nspins
Returns
-------
"""
"""spin11 test indicates this leads to faster overall simsignals().
11 spin x 6: 29.6 vs. 35.1 s
8 spin x 60: 2.2 vs 3.0 s"""
filename = f'T{nspins}.npz'
bin_dir = os.path.join(os.path.dirname(__file__), 'bin')
path = os.path.join(bin_dir, filename)
try:
T = sparse.load_npz(path)
return T
except FileNotFoundError:
print(f'creating {filename}')
T = transition_matrix_dense(nspins)
T_sparse = sparse.COO(T)
sparse.save_npz(path, T_sparse)
return T_sparse
def load_each():
"""
Load each file that has the name format containing the timestamps.
"""
files = os.listdir(PATH)
timestamps = []
files.sort()
prog = re.compile(r"20\d{6}\.\d{6}.npz$")
for file in files:
if prog.match(file):
timestamps.append(file)
# to get in order of timestamps
data = None
for ts_idx in range(len(timestamps)):
if (ts_idx + 1) % 10 == 0:
print(str(ts_idx + 1) + "/" + str(len(timestamps)) + " " + timestamps[ts_idx])
# prints progress
try:
record = sparse.load_npz(PATH + "/" + timestamps[ts_idx])
record = record.todense().astype(np.int16)
#record = record.astype(np.int16)
# if FIELDS != ALL_FIELDS:
# record = record[:, :, FIELDS]
if ts_idx == 0:
data = np.zeros((len(timestamps), *record.shape), dtype=np.int16)
data[ts_idx] += record
except BadZipFile as e:
print(e, file)
print("Loaded each")
return data
def extract_relation_ind(rel_file, rel_index_file):
"""
Save index array for nonzero entries of the
sparse relation tensor at rel_file.
"""
relations = sparse.load_npz(rel_file)
relation_ind = sparse.argwhere(relations > 0)
np.save(rel_index_file, relation_ind)
def test_save_load_npz_file(tmp_path, compression, format):
x = sparse.random((2, 3, 4, 5), density=0.25, format=format)
y = x.todense()
filename = tmp_path / "mat.npz"
save_npz(filename, x, compressed=compression)
z = load_npz(filename)
assert_eq(x, z)
assert_eq(y, z.todense())
def so_sparse(nspins):
filename_Lz = f'sparse_Lz{nspins}.npz'
filename_Lproduct = f'sparse_Lproduct{nspins}.npz'
try:
Lz = sparse.load_npz(filename_Lz)
Lproduct = sparse.load_npz(filename_Lproduct)
return Lz, Lproduct
except FileNotFoundError:
print(f'creating {filename_Lz} and {filename_Lproduct}')
sigma_x = np.array([[0, 1 / 2], [1 / 2, 0]])
sigma_y = np.array([[0, -1j / 2], [1j / 2, 0]])
sigma_z = np.array([[1 / 2, 0], [0, -1 / 2]])
unit = np.array([[1, 0], [0, 1]])
L = np.empty((3, nspins, 2 ** nspins, 2 ** nspins), dtype=np.complex128) # consider other dtype?
for n in range(nspins):
Lx_current = 1
Ly_current = 1
Lz_current = 1
for k in range(nspins):
if k == n:
Lx_current = np.kron(Lx_current, sigma_x)
Ly_current = np.kron(Ly_current, sigma_y)
Lz_current = np.kron(Lz_current, sigma_z)
else:
Lx_current = np.kron(Lx_current, unit)
Ly_current = np.kron(Ly_current, unit)
Lz_current = np.kron(Lz_current, unit)
L[0][n] = Lx_current
L[1][n] = Ly_current
L[2][n] = Lz_current
L_T = L.transpose(1, 0, 2, 3)
Lproduct = np.tensordot(L_T, L, axes=((1, 3), (0, 2))).swapaxes(1, 2)
Lz_sparse = sparse.COO(L[2])
# for i in range(nspins):
# for j in range(nspins):
# Lproduct[i, j] = csr_matrix(Lproduct[i, j])
Lproduct_sparse = sparse.COO(Lproduct)
sparse.save_npz(filename_Lz, Lz_sparse)
sparse.save_npz(filename_Lproduct, Lproduct_sparse)
return Lz_sparse, Lproduct_sparse
def get_benchmark_splits_ordinal(fuse=False, batch_size=64):
task = 'mortality'
duration = 48.0
timestep = 1.0
df_label = pd.read_csv('/data4/tangsp/FIDDLE/mimic3_experiments/data/processed/population/pop.mortality_benchmark.csv').rename(columns={'{}_LABEL'.format(task): 'LABEL'})
X = sparse.load_npz('../data/features,ablations/ordinal,benchmark,outcome={},T={},dt={}/X.npz'.format(task, duration, timestep)).todense()
s = sparse.load_npz('../data/features,ablations/ordinal,benchmark,outcome={},T={},dt={}/s.npz'.format(task, duration, timestep)).todense()
tr_idx = df_label[df_label['partition'] == 'train'].index.values
va_idx = df_label[df_label['partition'] == 'val' ].index.values
te_idx = df_label[df_label['partition'] == 'test' ].index.values
def _select_examples(rows):
return (
X[rows],
s[rows],
df_label.iloc[rows][['LABEL']].values,
)
Xy_tr = _select_examples(tr_idx)
Xy_va = _select_examples(va_idx)
Xy_te = _select_examples(te_idx)
print('ICU stay splits:', len(tr_idx), len(va_idx), len(te_idx))
te = EHRDataset(*Xy_te, fuse=fuse)
va = EHRDataset(*Xy_va, fuse=fuse)
tr = EHRDataset(*Xy_tr, fuse=fuse)
num_workers = 1
tr_loader = DataLoader(tr, batch_size=batch_size, shuffle=True , num_workers=num_workers, pin_memory=True)
va_loader = DataLoader(va, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)
te_loader = DataLoader(te, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)
print(tr_loader.dataset.y.sum() + va_loader.dataset.y.sum() + te_loader.dataset.y.sum(), '/', X.shape[0])
print('')
print('Time series shape, Static shape, Label shape, Class balance:')
print('\t', 'tr', tr_loader.dataset.X.shape, tr_loader.dataset.s.shape, tr_loader.dataset.y.shape, tr_loader.dataset.y.mean())
print('\t', 'va', va_loader.dataset.X.shape, va_loader.dataset.s.shape, va_loader.dataset.y.shape, va_loader.dataset.y.mean())
print('\t', 'te', te_loader.dataset.X.shape, te_loader.dataset.s.shape, te_loader.dataset.y.shape, te_loader.dataset.y.mean())
if fuse:
print('Fused dimensions:', tr_loader.dataset[0][0].shape)
return tr_loader, va_loader, te_loader
def so_sparse(nspins):
"""Either load a presaved set of spin operators as numpy arrays, or
calculate them and save them if a presaved set wasn't found.
Parameters
----------
nspins: int
the number of spins in the spin system
Returns
-------
(Lz, Lproduct): a tuple of:
Lz: 3d sparse.COO array of shape (n, 2^n, 2^n) representing
[Lz1, Lz2, ...Lzn]
Lproduct: 4d sparse.COO array of shape (n, n, 2^n, 2^n), representing
an n x n array (cartesian product) for all combinations of
Lxa*Lxb + Lya*Lyb + Lza*Lzb, where 1 <= a, b <= n.
Side Effect
-----------
Saves the results as .npz files to the bin directory if they were not
found there.
"""
filename_Lz = f'Lz{nspins}.npz'
filename_Lproduct = f'Lproduct{nspins}.npz'
bin_dir = os.path.join(os.path.dirname(__file__), 'bin')
path_Lz = os.path.join(bin_dir, filename_Lz)
path_Lproduct = os.path.join(bin_dir, filename_Lproduct)
try:
Lz = sparse.load_npz(path_Lz)
Lproduct = sparse.load_npz(path_Lproduct)
return Lz, Lproduct
except FileNotFoundError:
print('no SO file ', filename_Lz, ' found in: ', bin_dir)
print(f'creating {filename_Lz} and {filename_Lproduct}')
Lz, Lproduct = so_dense(nspins)
Lz_sparse = sparse.COO(Lz)
Lproduct_sparse = sparse.COO(Lproduct)
sparse.save_npz(path_Lz, Lz_sparse)
sparse.save_npz(path_Lproduct, Lproduct_sparse)
return Lz_sparse, Lproduct_sparse
def test_save_load_npz_file(compression, format):
with tempfile.TemporaryDirectory() as tmp_path_str:
tmp_path = pathlib.Path(tmp_path_str)
x = sparse.random((2, 3, 4, 5), density=0.25, format=format)
y = x.todense()
filename = tmp_path / "mat.npz"
save_npz(filename, x, compressed=compression)
z = load_npz(filename)
assert_eq(x, z)
assert_eq(y, z.todense())
def readMatrix(filename):
try:
return sparse.load_npz(filename.replace(".txt", ".npz"))
except:
with open(filename, 'r') as outfile:
dims = outfile.readline().replace("# Array shape: (", "").replace(
")", "").replace("\n", "").split(", ")
for i in range(len(dims)):
dims[i] = int(dims[i])
new_data = np.loadtxt(filename).reshape(dims)
return new_data
def test_save_load_npz_file(compression):
x = sparse.random((2, 3, 4, 5), density=.25)
y = x.todense()
dir_name = tempfile.mkdtemp()
filename = os.path.join(dir_name, 'mat.npz')
save_npz(filename, x, compressed=compression)
z = load_npz(filename)
assert_eq(x, z)
assert_eq(y, z.todense())
shutil.rmtree(dir_name)
def matrix_load(self, path):
''' Loads a previously saved matrix from a .npz file (containing the matrix) and a .nfo file (containing the matrix tags). '''
# load matrix
matrix = sparse.load_npz(os.path.splitext(path)[0] + '.npz')
matrix = sparse.DOK(
matrix) # convert to dict-of-keys for faster indexing
# load matrix tags
with open(os.path.splitext(path)[0] + '.nfo', 'rb') as f:
tags = pickle.load(f)
return matrix, tags
def load_expectation(expectation_file_name, type_=None): # pragma: no cover
"""Returns np.ndarray related to the *expectation_file_name*.
Expectation file path is rooted at tests/expectations.
"""
thisdir = os.path.dirname(__file__)
expectation_file_path = os.path.abspath(
os.path.join(thisdir, "expectations",
f"{expectation_file_name}.{type_}"))
if type_ == "npy":
expectation_data = np.load(expectation_file_path)
elif type_ == "npz":
expectation_data = sparse.load_npz(expectation_file_path)
elif type_ == "png":
expectation_data = Image.open(expectation_file_path)
else:
raise Exception("Type format not recognized")
return expectation_data
def resolve_relations(db_file, rel_file, meta_file, id_file):
"""
"""
conn = open_db_connection(db_file)
c = conn.cursor()
# load or compute unique IDs
if os.path.isfile(meta_file):
meta = np.load(meta_file)
off = meta[0]
num_unique = meta[1]
unique_ids = np.load(id_file)
else:
off = 0
c.execute("SELECT DISTINCT event1_id FROM Relations;")
event_ids = set(c.fetchall())
for id2 in c.execute("SELECT event2_id FROM Relations;"):
if not id2 in event_ids:
event_ids.add(id2)
unique_ids = np.char.array(list(event_ids))
num_unique = len(event_ids)
np.save(id_file, unique_ids)
np.save(meta_file, np.array([off, num_unique]))
id_lookup = dict()
for i, id_entr in enumerate(unique_ids):
id_lookup[id_entr[0]] = i
# load or compute (compressed) relations
if os.path.isfile(rel_file):
relations = sparse.load_npz(rel_file)
else:
relations = sparse.DOK((num_unique, num_unique, RELATION_COUNT),
dtype=np.float32)
for row in c.execute("SELECT * FROM Relations;"):
id_out = row[1]
id_in = row[2]
relations[id_lookup[id_out], id_lookup[id_in], :] = row[3:]
relations = sparse.COO(relations)
sparse.save_npz(rel_file, relations)
conn.close()
def cache_tm(n):
"""spin11 test indicates this leads to faster overall simsignals().
11 spin x 6: 29.6 vs. 35.1 s
8 spin x 60: 2.2 vs 3.0 s"""
filename = f'transitions{n}.npz'
try:
T = sparse.load_npz(filename)
return T
except FileNotFoundError:
print(f'creating {filename}')
T = np.zeros((n, n))
for i in range(n - 1):
for j in range(i + 1, n):
if bin(i ^ j).count('1') == 1:
T[i, j] = 1
# T = T + T.T
T += T.T
T_sparse = sparse.COO(T)
sparse.save_npz(filename, T_sparse)
return T_sparse
def _tm_cache(nspins):
"""
Loads a saved sparse transition matrix if it exists, or creates and saves
one if it is not.
Parameters
----------
nspins : int
The number of spins in the spin system.
Returns
-------
T_sparse : sparse.COO
The sparse transition matrix.
Side Effects
------------
Saves a sparse array to the bin folder if the required array was not
found there.
"""
# Speed tests indicated that using sparse-array transition matrices
# provides a modest speed improvement on larger spin systems.
filename = f'T{nspins}.npz'
# init_path_context = resources.path(nmrsim.bin, '__init__.py')
# with init_path_context as p:
# init_path = p
# print('path to init: ', init_path)
# bin_path = init_path.parent
bin_path = _bin_path()
path = bin_path.joinpath(filename)
try:
T_sparse = sparse.load_npz(path)
return T_sparse
except FileNotFoundError:
print(f'creating {filename}')
T_sparse = _transition_matrix_dense(nspins)
T_sparse = sparse.COO(T_sparse)
print('_tm_cache will save on path: ', path)
sparse.save_npz(path, T_sparse)
return T_sparse
def __init__(self, path, neg_sample_ratio, min_freq, is_matrix=True):
ndarray_file = 'matrix.npz' if is_matrix else 'tensor.npz'
self.ndarray = sparse.load_npz(join(path, ndarray_file)).todense()
# Creating positive instances
nonzeros = (self.ndarray > min_freq).nonzero()
print('# of non-zero cells is {}'.format(len(nonzeros[0])))
print('Density is {}%'.format(len(nonzeros[0]) / self.ndarray.size))
pos_points = list(zip(*nonzeros))
# Creating negative instances (if there are enough negatives)
assert len(nonzeros[0]) * (neg_sample_ratio + 1) < self.ndarray.size
neg_points, seen_points = [], set(pos_points)
while len(neg_points) < len(nonzeros[0]) * neg_sample_ratio:
sample = tuple([np.random.choice(d) for d in self.ndarray.shape])
if sample not in seen_points:
seen_points.add(tuple(sample))
neg_points.append(tuple(sample))
# Combining all points and labels
self.points = [np.array(x) for x in (pos_points + neg_points)]
self.labels = [1] * len(pos_points) + [0] * len(neg_points)
# Shuffling the points
zipped = list(zip(self.points, self.labels))
random.shuffle(zipped)
self.points, self.labels = zip(*zipped)
np.savetxt('fig8_rayflare.txt', RAT['A_bulk'][0])
from angles import make_angle_vector
from config import results_path
from sparse import load_npz
_, _, angle_vector = make_angle_vector(options['n_theta_bins'], options['phi_symmetry'],
options['c_azimuth'])
wl_to_plot = 1100e-9
wl_index = np.argmin(np.abs(wavelengths-wl_to_plot))
sprs = load_npz(os.path.join(results_path, options['project_name'], SC[0].name + 'rearRT.npz'))
full = sprs[wl_index].todense()
summat = theta_summary(full, angle_vector, options['n_theta_bins'], 'rear')
summat_r = summat[options['n_theta_bins']:, :]
summat_r= summat_r.rename({r'$\theta_{in}$': 'a', r'$\theta_{out}$': 'b'})
summat_r= summat_r.assign_coords(a=np.sin(summat_r.coords['a']).data,
b=np.sin(summat_r.coords['b']).data)
summat_r= summat_r.rename({'a': r'$\sin(\theta_{in})$', 'b': r'$\sin(\theta_{out})$'})
def matrix_multiplication(bulk_mats,
bulk_thick,
options,
layer_widths=[],
n_layers=[],
layer_names=[],
calc_prof_list=[]):
n_bulks = len(bulk_mats)
n_interfaces = n_bulks + 1
theta_intv, phi_intv, angle_vector = make_angle_vector(
options['n_theta_bins'], options['phi_symmetry'], options['c_azimuth'])
n_a_in = int(len(angle_vector) / 2)
num_wl = len(options['wavelengths'])
#wls = np.linspace(600, 1100, num_wl)*1e-9
#pol = 's'
# bulk thickness in m
thetas = angle_vector[:n_a_in, 1]
v0 = make_v0(options['theta_in'], options['phi_in'], num_wl,
options['n_theta_bins'], options['c_azimuth'],
options['phi_symmetry'])
up2down, down2up = out_to_in_matrix(options['phi_symmetry'], angle_vector,
theta_intv, phi_intv)
D = []
for i1 in range(n_bulks):
D.append(
make_D(bulk_mats[i1].alpha(options['wavelengths']), bulk_thick[i1],
thetas))
#unique_thetas = np.unique(thetas)
# front incidence matrices
Rf = []
Tf = []
Af = []
Pf = []
If = []
side = 1
for i1 in range(n_interfaces):
mat_path = os.path.join(results_path, options['project_name'],
layer_names[i1] + 'frontRT.npz')
absmat_path = os.path.join(results_path, options['project_name'],
layer_names[i1] + 'frontA.npz')
fullmat = load_npz(mat_path)
absmat = load_npz(absmat_path)
#if len(fullmat.shape) == 3:
Rf.append(fullmat[:, :n_a_in, :])
Tf.append(fullmat[:, n_a_in:, :])
Af.append(absmat)
#else:
# print(fullmat.shape)
# Rf.append(fullmat[:n_a_in, :])
# Tf.append(fullmat[n_a_in:, :])
# Af.append(absmat)
if len(calc_prof_list[i1]) > 0:
#profile, intgr = make_profile_data(options, unique_thetas, n_a_in, side,
# layer_names[i1], n_layers[i1], layer_widths[i1])
profmat_path = os.path.join(results_path, options['project_name'],
layer_names[i1] + 'frontprofmat.nc')
prof_int = xr.load_dataset(profmat_path)
profile = prof_int['profile']
intgr = prof_int['intgr']
Pf.append(profile)
If.append(intgr)
else:
Pf.append([])
If.append([])
# rear incidence matrices
Rb = []
Tb = []
Ab = []
Pb = []
Ib = []
paramsb = []
side = -1
for i1 in range(n_interfaces - 1):
mat_path = os.path.join(results_path, options['project_name'],
layer_names[i1] + 'rearRT.npz')
absmat_path = os.path.join(results_path, options['project_name'],
layer_names[i1] + 'rearA.npz')
fullmat = load_npz(mat_path)
absmat = load_npz(absmat_path)
#if len(fullmat.shape) == 3:
Rb.append(fullmat[:, :n_a_in, :])
Tb.append(fullmat[:, n_a_in:, :])
Ab.append(absmat)
#else:
# Rb.append(fullmat[:n_a_in, :])
# Tb.append(fullmat[n_a_in:, :])
# Ab.append(absmat)
if len(calc_prof_list[i1]) > 0:
#profile, intgr = make_profile_data(options, unique_thetas, n_a_in, side,
# layer_names[i1], n_layers[i1], layer_widths[i1])
profmat_path = os.path.join(results_path, options['project_name'],
layer_names[i1] + 'rearprofmat.nc')
prof_int = xr.load_dataset(profmat_path)
profile = prof_int['profile']
intgr = prof_int['intgr']
Pb.append(profile)
Ib.append(intgr)
else:
Pb.append([])
Ib.append([])
len_calcs = np.array([len(x) for x in calc_prof_list])
print(len_calcs)
print(np.any(len_calcs > 0))
if np.any(len_calcs > 0):
print('a')
a = [[] for _ in range(n_interfaces)]
a_prof = [[] for _ in range(n_interfaces)]
vr = [[] for _ in range(n_bulks)]
vt = [[] for _ in range(n_bulks)]
A = [[] for _ in range(n_bulks)]
A_prof = [[] for _ in range(n_bulks)]
vf_1 = [[] for _ in range(n_interfaces)]
vb_1 = [[] for _ in range(n_interfaces)]
vf_2 = [[] for _ in range(n_interfaces)]
vb_2 = [[] for _ in range(n_interfaces)]
for i1 in range(n_bulks):
#z = xr.DataArray(np.arange(0, bulk_thick[i1], options['nm_spacing']*1e-9), dims='z')
# v0 is actually travelling down, but no reason to start in 'outgoing' ray format.
vf_1[i1] = dot_wl(Tf[i1], v0) # pass through front surface
vr[i1].append(dot_wl(Rf[i1], v0)) # reflected from front surface
a[i1].append(dot_wl(
Af[i1], v0)) # absorbed in front surface at first interaction
#print(v0)
#print(If[i1])
if len(If[i1] > 0):
v_xr = xr.DataArray(v0,
dims=['wl', 'global_index'],
coords={
'wl': If[i1].coords['wl'],
'global_index': np.arange(0, n_a_in)
})
int_power = xr.dot(v_xr, If[i1], dims='global_index')
scale = (np.sum(dot_wl(Af[i1], v0), 1) / int_power).fillna(0)
a_prof[i1].append(
(scale * xr.dot(v_xr, Pf[i1], dims='global_index')).data)
power = np.sum(vf_1[i1], axis=1)
# rep
i2 = 1
while np.any(power > options['I_thresh']):
print(i2)
#print(power)
vf_1[i1] = dot_wl_u2d(down2up,
vf_1[i1]) # outgoing to incoming
vb_1[i1] = dot_wl(D[i1],
vf_1[i1]) # pass through bulk, downwards
# vb_1 already an incoming ray
if len(If[i1 + 1]) > 0:
v_xr = xr.DataArray(vb_1[i1],
dims=['wl', 'global_index'],
coords={
'wl': If[i1 + 1].coords['wl'],
'global_index':
np.arange(0, n_a_in)
})
int_power = xr.dot(v_xr, If[i1 + 1], dims='global_index')
scale = (np.sum(dot_wl(Af[i1 + 1], vb_1[i1]), 1) /
int_power).fillna(0)
#('front profile')
a_prof[i1 + 1].append(
(scale *
xr.dot(v_xr, Pf[i1 + 1], dims='global_index')).data)
#remaining_power.append(np.sum(vb_1, axis=1))
A[i1].append(np.sum(vf_1[i1], 1) - np.sum(vb_1[i1], 1))
nz_thetas = vf_1[i1] != 0
vb_2[i1] = dot_wl(
Rf[i1 + 1],
vb_1[i1]) # reflect from back surface. incoming -> up
vf_2[i1] = dot_wl(D[i1],
vb_2[i1]) # pass through bulk, upwards
#print('rear profile')
if len(Ib[i1]) > 0:
v_xr = xr.DataArray(vf_2[i1],
dims=['wl', 'global_index'],
coords={
'wl': Ib[i1].coords['wl'],
'global_index':
np.arange(0, n_a_in)
})
int_power = xr.dot(v_xr, Ib[i1], dims='global_index')
scale = (np.sum(dot_wl(Ab[i1], vf_2[i1]), 1) /
int_power).fillna(0)
a_prof[i1].append(
(scale *
xr.dot(v_xr, Pb[i1], dims='global_index')).data)
#remaining_power.append(np.sum(vf_2, axis=1))
A[i1].append(np.sum(vb_2[i1], 1) - np.sum(vf_2[i1], 1))
vf_2[i1] = dot_wl_u2d(up2down,
vf_2[i1]) # prepare for rear incidence
vf_1[i1] = dot_wl(Rb[i1],
vf_2[i1]) # reflect from front surface
power = np.sum(vf_1[i1], axis=1)
# nz_thetas = vb_2[i1] != 0
vr[i1].append(
dot_wl(Tb[i1], vf_2[i1])
) # matrix travelling up in medium 0, i.e. reflected overall by being transmitted through front surface
vt[i1].append(
dot_wl(Tf[i1 + 1], vb_1[i1])
) # transmitted into medium below through back surface
a[i1 + 1].append(dot_wl(Af[i1 + 1],
vb_1[i1])) # absorbed in 2nd surface
a[i1].append(dot_wl(
Ab[i1],
vf_2[i1])) # absorbed in 1st surface (from the back)
i2 += 1
vr = [np.array(item) for item in vr]
vt = [np.array(item) for item in vt]
a = [np.array(item) for item in a]
A = [np.array(item) for item in A]
a_prof = [np.array(item) for item in a_prof]
results_per_pass = {'r': vr, 't': vt, 'a': a, 'A': A, 'a_prof': a_prof}
# for i2 in range(3):
# for i1 in range(n_interfaces):
# plt.figure()
# z = np.arange(0, np.sum(layer_widths[i1]), options['nm_spacing'])
# plt.plot(z, a_prof[i1][i2, 0, :].T)
# plt.title(str(i2) + 'interface ' + str(i1))
# plt.show()
sum_dims = ['bulk_index', 'wl']
sum_coords = {
'bulk_index': np.arange(0, n_bulks),
'wl': options['wavelengths']
}
R = xr.DataArray(np.array([np.sum(item, (0, 2)) for item in vr]),
dims=sum_dims,
coords=sum_coords,
name='R')
T = xr.DataArray(np.array([np.sum(item, (0, 2)) for item in vt]),
dims=sum_dims,
coords=sum_coords,
name='T')
A_bulk = xr.DataArray(np.array([np.sum(item, 0) for item in A]),
dims=sum_dims,
coords=sum_coords,
name='A_bulk')
A_interface = xr.DataArray(
np.array([np.sum(item, (0, 2)) for item in a]),
dims=['surf_index', 'wl'],
coords={
'surf_index': np.arange(0, n_interfaces),
'wl': options['wavelengths']
},
name='A_interface')
profile = []
for j1, item in enumerate(a_prof):
if len(item) > 0:
profile.append(
xr.DataArray(np.sum(item, 0),
dims=['wl', 'z'],
coords={'wl': options['wavelengths']},
name='A_profile' + str(j1))
) # not necessarily same number of z coords per layer stack
bulk_profile = np.array(A_prof)
RAT = xr.merge([R, A_bulk, A_interface, T])
# for i2 in range(num_wl):
# plt.figure()
# for i1 in range(n_interfaces):
# z = np.arange(0, np.sum(layer_widths[i1]), options['nm_spacing'])
# plt.plot(z, profile[i1][i2].T)
#
# plt.show()
#
# plt.figure()
# for i2 in range(5):
# i1= 0
# z = np.arange(0, np.sum(layer_widths[i1]), options['nm_spacing'])
# plt.plot(z, a_prof[i1][i2, 0, :])
#
# plt.figure()
# plt.plot(options['wavelengths'], R.T)
# plt.plot(options['wavelengths'], T.T)
# plt.plot(options['wavelengths'], A_interface.T)
# plt.plot(options['wavelengths'], A_bulk.T)
# plt.plot(options['wavelengths'], R[0] + T[0] + A_interface[0] + A_interface[1]+A_bulk[0])
# plt.legend(['R', 'T', 'front', 'back', 'bulk'])
# plt.show()
#return R, T, A_bulk, A_interface, profile
return RAT, results_per_pass, profile, bulk_profile
else:
print('b')
a = [[] for _ in range(n_interfaces)]
vr = [[] for _ in range(n_bulks)]
vt = [[] for _ in range(n_bulks)]
A = [[] for _ in range(n_bulks)]
vf_1 = [[] for _ in range(n_interfaces)]
vb_1 = [[] for _ in range(n_interfaces)]
vf_2 = [[] for _ in range(n_interfaces)]
vb_2 = [[] for _ in range(n_interfaces)]
for i1 in range(n_bulks):
vf_1[i1] = dot_wl(Tf[i1], v0) # pass through front surface
vr[i1].append(dot_wl(Rf[i1], v0)) # reflected from front surface
a[i1].append(dot_wl(
Af[i1], v0)) # absorbed in front surface at first interaction
power = np.sum(vf_1[i1], axis=1)
# rep
i2 = 1
while np.any(power > options['I_thresh']):
print(i2)
print('before d2u', np.sum(vf_1[i1]))
vf_1[i1] = dot_wl_u2d(down2up,
vf_1[i1]) # outgoing to incoming
print('after 2du', np.sum(vf_1[i1]))
#print('vf_1 after', vf_1[i1])
vb_1[i1] = dot_wl(D[i1],
vf_1[i1]) # pass through bulk, downwards
print('before back ref', np.sum(vb_1[i1]))
# remaining_power.append(np.sum(vb_1, axis=1))
A[i1].append(np.sum(vf_1[i1], 1) - np.sum(vb_1[i1], 1))
vb_2[i1] = dot_wl(Rf[i1 + 1],
vb_1[i1]) # reflect from back surface
print('after back ref', np.sum(vb_2[i1]))
vf_2[i1] = dot_wl(D[i1],
vb_2[i1]) # pass through bulk, upwards
#print('vb_2', vb_2[i1])
print('after u2d', np.sum(vf_2[i1]))
vf_2[i1] = dot_wl_u2d(up2down,
vf_2[i1]) # prepare for rear incidence
print('after u2d/before front ref', np.sum(vf_2[i1]))
vf_1[i1] = dot_wl(Rb[i1],
vf_2[i1]) # reflect from front surface
print('after front ref', np.sum(vf_1[i1]))
#print('Rf, Rb, and vf2', Rf[i1][20].todense(), Rb[i1][20].todense(), vf_2[i1][20])
#print('powersrem', np.sum(vb_2[i1], 1), np.sum(vf_2[i1], 1), np.sum(vf_1[i1], 1))
# remaining_power.append(np.sum(vf_2, axis=1))
A[i1].append(np.sum(vb_2[i1], 1) - np.sum(vf_2[i1], 1))
power = np.sum(vf_1[i1], axis=1)
#print('power', power)
vr[i1].append(
dot_wl(Tb[i1], vf_2[i1])
) # matrix travelling up in medium 0, i.e. reflected overall by being transmitted through front surface
print('lost in front ref', np.sum(vr[i1]))
#print('Tf, vb1', Tf[i1 + 1][20].todense(), vb_1[i1][20])
vt[i1].append(
dot_wl(Tf[i1 + 1], vb_1[i1])
) # transmitted into medium below through back surface
print('lost in back ref', np.sum(vt[i1]))
a[i1 + 1].append(dot_wl(Af[i1 + 1],
vb_1[i1])) # absorbed in 2nd surface
a[i1].append(dot_wl(
Ab[i1],
vf_2[i1])) # absorbed in 1st surface (from the back)
i2 += 1
vr = [np.array(item) for item in vr]
vt = [np.array(item) for item in vt]
a = [np.array(item) for item in a]
A = [np.array(item) for item in A]
results_per_pass = {'r': vr, 't': vt, 'a': a, 'A': A}
sum_dims = ['bulk_index', 'wl']
sum_coords = {
'bulk_index': np.arange(0, n_bulks),
'wl': options['wavelengths']
}
R = xr.DataArray(np.array([np.sum(item, (0, 2)) for item in vr]),
dims=sum_dims,
coords=sum_coords,
name='R')
if i2 > 1:
A_bulk = xr.DataArray(np.array([np.sum(item, 0) for item in A]),
dims=sum_dims,
coords=sum_coords,
name='A_bulk')
T = xr.DataArray(np.array([np.sum(item, (0, 2)) for item in vt]),
dims=sum_dims,
coords=sum_coords,
name='T')
RAT = xr.merge([R, A_bulk, T])
else:
RAT = xr.merge([R])
return RAT, results_per_pass
plt.plot(options['wavelengths'] * 1e9,
TMM_res['A_per_layer'][len(front_materials) + 1],
label='Ge')
plt.plot(options['wavelengths'] * 1e9,
TMM_res['A_per_layer'][1] + TMM_res['A_per_layer'][2],
label='ARC')
plt.plot(options['wavelengths'] * 1e9,
TMM_res['A_per_layer'][3],
label='InGaP')
plt.plot(options['wavelengths'] * 1e9, TMM_res['A_per_layer'][4], label='GaAs')
plt.plot(options['wavelengths'] * 1e9,
TMM_res['A_per_layer'][5],
label='SiGeSn')
plt.xlabel('Wavelength (nm)')
plt.ylabel('Reflection / Absorption')
#plt.legend()
plt.show()
from sparse import load_npz
RTmat = load_npz(
'/home/phoebe/Documents/rayflare/results/test_matrix2/GaInP_GaAs_SiGeSn_RTfrontRT.npz'
)
TMMmat = load_npz(
'/home/phoebe/Documents/rayflare/results/test_matrix2/GaInP_GaAs_SiGeSn_TMMfrontRT.npz'
)
RTmat_0 = RTmat[0].todense()
TMMmat_0 = TMMmat[0].todense()
return clf
def save_test_predictions(y_true, y_score, model_name, save_dir):
# import pathlib
# pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
fname = save_dir + '{}.test.npz'.format(model_name)
np.savez(
open(fname, 'wb'),
y_score = y_score,
y_true = y_true,
)
print('Test predictions saved to', fname)
import sparse
X = sparse.load_npz('output.clinical/X.npz').todense().squeeze()
s = sparse.load_npz('output.icd[0,1,2]/s.npz').todense()
X = np.concatenate((s, X), axis=1)
Xtr = X[df.partition=="train"]
ytr = df[df.partition=="train"]['label']
Xte = X[df.partition=="test"]
yte = df[df.partition=="test"]['label']
print(Xtr.shape, ytr.shape, Xte.shape, yte.shape)
train_model(Xtr, ytr, Xte, yte, 'LR', 'clinical+ICD[0,1,2]')
train_model(Xtr, ytr, Xte, yte, 'RF', 'clinical+ICD[0,1,2]')
'lookuptable_angles': 200,
#'prof_layers': [1,2],
'n_rays': 500000,
'random_angles': False,
'nx': 15,
'ny': 15,
'parallel': True,
'n_jobs': -1,
'phi_symmetry': np.pi / 2,
'only_incidence_angle': True
}
Si = material('Si_OPTOS')()
sprs = load_npz(
os.path.join(results_path, options['project_name'],
'planar_back' + str(options['n_rays']) + 'frontRT.npz'))
_, _, angle_vector = make_angle_vector(options['n_theta_bins'],
options['phi_symmetry'],
options['c_azimuth'])
R = sprs.todense()[:, 0:int(len(angle_vector) / 2), :]
a, unique_index = np.unique(angle_vector[:, 1], return_index=True)
unique_index = unique_index[unique_index < 1300]
only_one_theta = R[:, unique_index, unique_index]
plt.figure()
a = plt.imshow(only_one_theta, extent=[0, 1, 900, 1200], aspect='auto')
plt.colorbar(a)
