def read_tbt(file_path):
"""
Reads TbTData object from provided file_path
Args:
file_path: path to a file containing TbtData
Returns:
TbtData
"""
if _is_ascii_file(file_path):
matrices, date = _read_ascii(file_path)
return handler.TbtData(matrices, date, [0], matrices[0]["X"].shape[1])
sdds_file = sdds.read(file_path)
nbunches = sdds_file.values[N_BUNCHES]
bunch_ids = sdds_file.values[BUNCH_ID if BUNCH_ID in
sdds_file.values else HOR_BUNCH_ID]
if len(bunch_ids) > nbunches:
bunch_ids = bunch_ids[:nbunches]
nturns = sdds_file.values[N_TURNS]
date = pytz.utc.localize(
datetime.utcfromtimestamp(sdds_file.values[ACQ_STAMP] / 1e9))
bpm_names = sdds_file.values[BPM_NAMES]
nbpms = len(bpm_names)
data = {
k: sdds_file.values[POSITIONS[k]].reshape((nbpms, nbunches, nturns))
for k in PLANES
}
matrices = [{
k: pd.DataFrame(index=bpm_names, data=data[k][:, idx, :], dtype=float)
for k in data
} for idx in range(nbunches)]
return handler.TbtData(matrices, date, bunch_ids, nturns)
def read_tbt(file_path):
"""
Reads TbTData object from provided file_path
Args:
file_path: path to a file containing TbtData
Returns:
TbtData
"""
hdf_file = h5py.File(file_path, 'r')
bunch_ids = [1]
date = datetime.now()
for vers in VERSIONS[::-1]:
try:
bpm_names = FUNCTIONS[vers]['get_bpm_names'](hdf_file)
nturns = FUNCTIONS[vers]['get_nturns'](hdf_file, vers)
matrices = [{k: pd.DataFrame(index=bpm_names,
data=FUNCTIONS[vers]['get_tbtdata'](hdf_file, k, vers),
dtype=float) for k in PLANES}]
return handler.TbtData(matrices, date, bunch_ids, nturns)
except TypeError:
pass
def read_tbt(file_path):
"""
Reads TbtData object from PTC trackone output.
"""
LOGGER.debug(f"Reading path: {file_path}")
with open(file_path, "r") as tfs_data:
lines = tfs_data.readlines()
# header
date, header_length = _read_header(lines)
lines = lines[header_length:]
# parameters
bpms, particles, column_indices, n_turns, n_particles = _read_from_first_turn(lines)
# data (read into dict first for speed, then convert to DF)
matrices = [{p: {bpm: np.zeros(n_turns) for bpm in bpms} for p in PLANES} for _ in range(n_particles)]
matrices = _read_data(lines, matrices, column_indices)
for bunch in range(n_particles):
for plane in PLANES:
matrices[bunch][plane] = pd.DataFrame(matrices[bunch][plane]).transpose()
LOGGER.debug(f"Read Tbt data from : {file_path}")
return handler.TbtData(matrices, date, particles, n_turns)
def test_compare_average_Tbtdata():
npart = 10
data = {
plane: np.concatenate([[
_create_data(np.linspace(1, 10, 10, endpoint=False, dtype=int), 2,
(lambda x: np.random.randn(len(x))))
] for _ in range(npart)],
axis=0)
for plane in PLANES
}
origin = handler.TbtData(matrices=[{
'X':
pd.DataFrame(index=['IBPMA1C', 'IBPME2R'],
data=data['X'][i],
dtype=float),
'Y':
pd.DataFrame(index=['IBPMA1C', 'IBPME2R'],
data=data['Y'][i],
dtype=float)
} for i in range(npart)],
date=datetime.now(),
bunch_ids=range(npart),
nturns=10)
new = handler.TbtData(matrices=[{
'X':
pd.DataFrame(index=['IBPMA1C', 'IBPME2R'],
data=np.mean(data['X'], axis=0),
dtype=float),
'Y':
pd.DataFrame(index=['IBPMA1C', 'IBPME2R'],
data=np.mean(data['Y'], axis=0),
dtype=float)
}],
date=datetime.now(),
bunch_ids=[1],
nturns=10)
_compare_tbt(handler.generate_average_tbtdata(origin), new, False)
def _original_trackone(track=False):
names = np.array(["C1.BPM1"])
matrix = [
dict(
X=pd.DataFrame(index=names,
data=[[0.001, -0.0003606, -0.00165823,
-0.00266631]]),
Y=pd.DataFrame(index=names,
data=[[0.001, 0.00070558, -0.00020681,
-0.00093807]])),
dict(X=pd.DataFrame(
index=names,
data=[[0.0011, -0.00039666, -0.00182406, -0.00293294]]),
Y=pd.DataFrame(
index=names,
data=[[0.0011, 0.00077614, -0.00022749, -0.00103188]]))
]
origin = handler.TbtData(matrix, None, [0, 1] if track else [1, 2], 4)
return origin
def test_tbt_read_hdf5_v2(_hdf5_file_v2):
origin = handler.TbtData(matrices=[{
'X':
pd.DataFrame(index=['IBPMA1C', 'IBPME2R'],
data=_create_data(
np.linspace(-np.pi, np.pi, 2000, endpoint=False), 2,
np.sin),
dtype=float),
'Y':
pd.DataFrame(index=['IBPMA1C', 'IBPME2R'],
data=_create_data(
np.linspace(-np.pi, np.pi, 2000, endpoint=False), 2,
np.cos),
dtype=float)
}],
date=datetime.now(),
bunch_ids=[1],
nturns=2000)
new = reader_iota.read_tbt(_hdf5_file_v2)
_compare_tbt(origin, new, False)