def confidences(self):
if 'confidence' in self.raw:
return self.raw['confidence']
LoggingManager.instance().warning(
f"There are no 'confidences' for that sample {self.datasource.label}."
)
return None
def label_names(self):
'''Converts the category numbers in their corresponding names (e.g. 0 -> 'pedestrian') and returns the list of names for all boxes in the sample'''
label_source_name = categories.get_source(platform_utils.parse_datasource_name(self.datasource.label)[2])
try:
return [categories.CATEGORIES[label_source_name][str(category_number)]['name'] for category_number in self.raw['data']['classes']]
except:
LoggingManager.instance().warning(f"Can not find the CATEGORIES and NAMES of {label_source_name}.")
def raw(self):
if self._raw is None:
r = super(Echo, self).raw
r['das.sample'] = self
if r['data']['timestamps'][-1] == 0:
try:
cfg = self.datasource.sensor['cfg'].get_at_timestamp(
self.timestamp).raw
if self.datasource.sensor.specs is None:
self.datasource.sensor.specs = banks.extract_specs(
lambda n: cfg[n])
try:
if self.datasource.sensor.modules_angles is None:
self.datasource.sensor.modules_angles = banks.extract_intrinsics_modules_angles(
lambda n: cfg[n])
except:
LoggingManager.instance().warning(
"Sensor {} has no modules angles, or can not read modules angles from the sensor intrinsic calibratrion"
.format(self.datasource.sensor.name))
banks.add_timestamp_offsets(
r, self.datasource.sensor.name,
self.datasource.sensor.specs,
int(cfg['ID_ACCUMULATION_EXP']),
int(cfg['ID_OVERSAMPLING_EXP']),
int(cfg['ID_BASE_POINT_COUNT']))
except:
pass
self._raw = r
return self._raw
def _check_timestamps_consistency(self):
nts, nfiles = self._get_nb_timestamps_and_files_or_rows()
if nfiles != nts:
n = min(nts, nfiles)
LoggingManager.instance().warning(
'The number of timestamps and data files are '
'different for sensor %s (nfiles: %d != nts: %d). '
'Keeping the %d first timestamps and files' %
(self.path, nfiles, nts, n))
self.timestamps = self.timestamps[:n]
if (self.nb_data_per_pkl_file == 1):
self.files = self.files[:n]
else:
if n % self.nb_data_per_pkl_file == 0:
self.files = self.files[:int(n /
self.nb_data_per_pkl_file)]
else:
# on va conserver un fichier à la fin qui ne sera pas utilisé en entier
self.files = self.files[:n // self.nb_data_per_pkl_file +
1]
nts, nfiles = self._get_nb_timestamps_and_files_or_rows()
assert nfiles == nts
def attributes(self):
if 'attributes' in self.raw:
return self.raw['attributes']
LoggingManager.instance().warning(
f"There are no 'attributes' for that sample {self.datasource.label}."
)
return None
def _load_offline_datasources(self, name: str):
archives = glob.glob(os.path.join(self.platform.dataset, name + '_*'))
for archive in [os.path.basename(f) for f in archives]:
if archive.endswith('.zip'):
# remove the .zip and extracts the 'datasource' suffix:
ds_name = os.path.splitext(archive)[0].split('_')[
-1] #.split('-')[0]
try:
ds = ZipFileSource(
os.path.join(self.platform.dataset, archive))
self._sensors[name].add_datasource(
ds,
ds_name,
cache_size=self.platform.default_cache_size)
except:
LoggingManager.instance().warning(
f'Zip file for {name}_{ds_name} could not be loaded.')
continue
else:
ds_name = archive.split('_')[-1] #.split('-')[0]
try:
ds = DirSource(os.path.join(self.platform.dataset,
archive))
self._sensors[name].add_datasource(
ds,
ds_name,
cache_size=self.platform.default_cache_size)
except:
LoggingManager.instance().warning(
f'Data directory for {name}_{ds_name} could not be loaded.'
)
continue
def _add_imu_quality_data(self):
try:
metadata_entry = 'IMU_step_ratio'
ratio_ = vif.get_trajectory_step_ratio(
self.synchronized,
'flir_bfc_img',
'sbgekinox_bcc_navposvel',
traj_min_epsilon_precision=1e-3)
self._add_column(metadata_entry)
self.metadata_dirty[metadata_entry][self.frame_selection] = ratio_[
self.frame_selection]
except Exception as e:
LoggingManager.instance().warning(str(e))
try:
metadata_entry = 'IMU_standard_score'
ratio_ = vif.get_trajectory_standard_score(
self.synchronized,
'flir_bfc_img',
'sbgekinox_bcc_navposvel',
traj_seq_memory=200)
self._add_column(metadata_entry)
self.metadata_dirty[metadata_entry][self.frame_selection] = ratio_[
self.frame_selection]
except Exception as e:
LoggingManager.instance().warning(str(e))
self.window.isDirty = True
self._update()
def apply_temperature_correction(self, timestamp, distances):
"""Applies temperature-related distance corrections"""
if (self.temperature_slope is None
and self.temperature_reference is None):
self.temperature_compensation = MotorLidar.TemperatureCompensation.Deactivated
print(
'MotorLidar Log: Temperature compensation mode is deactivated')
return distances
offsets_ = 0
try:
temperature = self.datasources['temp'].get_at_timestamp(
timestamp).raw['data']
k = len(self.temperature_coeffs) - 1
offsets_ -= np.sum([
temperature**(k - i) * self.temperature_coeffs[i]
for i in range(k)
])
except Exception as e:
temperature = None
LoggingManager.instance().warning(
'Failed to apply the temperature for the distance correction. '
'Error: {}'.format(str(e)))
offsets_ -= self.temperature_coeffs[-1]
#print(f'Log: 3D points for {self.name} are corrected with temperature T={temperature}: global offset is {offsets_}')
return distances + offsets_
def load_specs_from_cfg(self):
try:
cfg = self['cfg'][0].raw
self.specs = banks.extract_specs(lambda n: cfg[n])
except:
LoggingManager.instance().warning(
"Unable to read the specs from the sensor {}".format(
self.name))
def does_path_exist(self, silent=True):
exists = self.src_sensor is not None and self.dst_sensor is not None
if not exists and not silent:
LoggingManager.instance().warning(
f"Undefined extrinsics path: {'undefined sensor' if self.src_sensor is None else self.src_sensor.name} -> \
{'undefined sensor' if self.dst_sensor is None else self.dst_sensor.name}"
)
return exists
def opengl_error_check(cf, prefix=""): # pass currentframe()
error = GL.glGetError()
if error != GL.GL_NO_ERROR:
frameinfo = getframeinfo(cf)
LoggingManager.instance().warning(
f"{prefix} @ line {frameinfo.lineno} : OpenGL error {error}")
return True
return False
def get_files(self):
files = []
timestamps = None
time_of_issues = None
nb_data_per_pkl_file = 1
for fullpath in glob.glob(f'{self.path}/*'):
name = fullpath.split('/')[-1]
if self.pattern is None:
match, self.pattern = try_all_patterns(name)
else:
match = re.match(self.pattern, name)
if match:
groups = match.groups()
if self.sort and not groups:
raise ValueError('no groups')
if self.sort and groups:
sample = (int(groups[0]), fullpath)
else:
sample = fullpath
files.append(sample)
# read config for multiple rows per .pkl file (high fps sensors)
elif (name == Constants.CONFIG_YML_PATTERN):
with open(fullpath) as f:
data_yaml = yaml.safe_load(f)
nb_data_per_pkl_file = data_yaml['nb_vectors_in_pkl']
# read timestamps
elif (name == Constants.TIMESTAMPS_CSV_PATTERN):
with open(fullpath) as f:
sensor_ts = pd.read_csv(f,
delimiter=" ",
dtype='u8',
header=None).values
timestamps = sensor_ts[:, 0]
# check if ts is always go up, for imu data (more than 1 data per pkl file) the test is not complet -> to improve
if len(timestamps) > 2 and (np.min(
np.diff(timestamps.astype(np.int64))) < 0):
LoggingManager.instance().warning(
'Timestamps are not strictly increasing for datasource file {}'
.format(self.path))
if sensor_ts.shape[1] > 1:
time_of_issues = sensor_ts[:, 1]
else:
time_of_issues = timestamps
if self.sort:
files.sort()
files = [s[1] for s in files]
return files, time_of_issues, timestamps, nb_data_per_pkl_file
def get_sampling_ordering_flat(v, h):
global _ordering_cache
if (v, h) not in _ordering_cache:
if v in [8, 16, 64, 128, 172, 256, 312]:
if platform.system() == "Windows":
ctypes.cdll.LoadLibrary(
os.path.join(os.path.dirname(__file__),
"leddar_utils_lcax.dll"))
lca_math = ctypes.cdll.leddar_utils_lcax
else:
ctypes.cdll.LoadLibrary(
os.path.join(os.path.dirname(__file__),
"libleddar_utils_lcax.so"))
lca_math = ctypes.CDLL("libleddar_utils_lcax.so")
lca_math.MATH_GenIndexMap(v, h)
fMATH_CopyMapTrc2Mem = lca_math.MATH_CopyMapTrc2Mem
fMATH_CopyMapTrc2Mem.restype = None
fMATH_CopyMapTrc2Mem.argtypes = [
ctypes.c_size_t,
np.ctypeslib.ndpointer(ctypes.c_uint16, flags="C_CONTIGUOUS"),
ctypes.c_bool
]
sampled_to_ordered = np.zeros((v * h), dtype=np.uint16, order='C')
fMATH_CopyMapTrc2Mem(v * h, sampled_to_ordered, False)
sampled_to_ordered_inv = np.zeros((v * h),
dtype=np.uint16,
order='C')
fMATH_CopyMapTrc2Mem(v * h, sampled_to_ordered_inv, True)
fMATH_CopyMapMem2Trc = lca_math.MATH_CopyMapMem2Trc
fMATH_CopyMapMem2Trc.restype = None
fMATH_CopyMapMem2Trc.argtypes = [
ctypes.c_size_t,
np.ctypeslib.ndpointer(ctypes.c_uint16, flags="C_CONTIGUOUS"),
ctypes.c_bool
]
ordered_to_sampled = np.zeros((v * h), dtype=np.uint16, order='C')
fMATH_CopyMapMem2Trc(v * h, ordered_to_sampled, False)
ordered_to_sampled_inv = np.zeros((v * h),
dtype=np.uint16,
order='C')
fMATH_CopyMapMem2Trc(v * h, ordered_to_sampled_inv, False)
_ordering_cache[(v, h)] = (ordered_to_sampled, sampled_to_ordered,
ordered_to_sampled_inv,
sampled_to_ordered_inv)
else:
LoggingManager.instance().warning("Unsupported v resolution")
return None, None
return _ordering_cache[(v, h)]
def load_static_noise_from_cfg(self):
try:
cfg = self['cfg'][0].raw
self.static_noise = banks.extract_intrinsics_static_noise(
lambda n: cfg[n])
except:
self.static_noise = 0
LoggingManager.instance().warning(
"Unable to read the static noise from the sensor {}".format(
self.name))
def _get_orientation_lut(self):
if self.orientation is not None:
orientation_f4 = self.orientation.astype('f4')
lut = Sample.__goc_lut()
if orientation_f4.tostring() in lut:
return lut[orientation_f4.tostring()]
LoggingManager.instance().warning(
'orientation {} could not be mapped to any image transform'.
format(self.orientation))
return None
def distortion_coeffs(self):
"""np.ndarray: Nx1 distortion coefficient, refer to opencv documentation
See also: https://docs.opencv.org/4.0.0/d9/d0c/group__calib3d.html#ga3207604e4b1a1758aa66acb6ed5aa65d
"""
try:
k = self.intrinsics['distortion']
except:
LoggingManager.instance().warning(
f'Distortion coeffients not found for {self.name}.')
k = np.zeros((5, 1))
return k
def create(cls, recordable, datasource, platform, synchronized, video_fps=None):
if video_fps is not None:
hz = video_fps
else:
ts = platform[datasource].timestamps
hz = 1e6/np.mean(ts[1:] - ts[:-1])
if hz < 1 or hz > 100:
hz_ = max(1, min(hz, 100)) #for the case units are wrong
LoggingManager.instance().warning(f"[For datasource {datasource}] Clipping video framerate to {hz_} fps (was {hz} fps)")
hz = hz_
return VideoRecorder(cls.__create_key, hz, recordable, datasource)
def load_time_base_delays_from_cfg(self):
try:
cfg = self['cfg'][0].raw
self.time_base_delays = banks.extract_intrinsics_timebase_delays(
lambda n: cfg[n])
except:
self.time_base_delays = 0 if not 'ftrr' in self else {
'high': 0,
'low': 0
}
LoggingManager.instance().warning(
"Unable to read the time base delays from the sensor {}".
format(self.name))
def add_virtual_datasources(self, virtual_datasources_config):
"""Add virtual datasources based upon the provided configuration dictionnary
Args:
-virtual_datasources_config (dict): Each key must correspond to a key available in
VIRTUAL_DATASOURCE_FACTORY (see api/datasources/virtual_datasources/__init__.py).
Under each key, a dictionnary must be provided with the parameters necessary
to create the corresponding virtual datasource. These parameters always contain at
least the 'reference_sensor' and 'dependencies' entries. Other entries depend upon
the specific virtual datasource type.
For exemple:
virtual_datasources_config = {
'echoes_from_traces': {
'reference_sensor': 'pixell_bfc',
'dependencies': ['pixell_bfc_ftrr'],
'nb_detections_max': 3,
}
}
Note: To create multiple virtual datasources of the same type, you can do something like:
virtual_datasources_config = {
'echoes_from_traces_bfc': {'reference_sensor': 'pixell_bfc', 'dependencies': ['pixell_bfc_ftrr']}
'echoes_from_traces_tfc': {'reference_sensor': 'pixell_tfc', 'dependencies': ['pixell_tfc_ftrr']}
}
"""
for virtual_ds_name in virtual_datasources_config:
args = virtual_datasources_config[virtual_ds_name]
# If multiple instances of the same VirtualDatasource class, their keys has to be different
# in the config file. So add a unique id such as: virtual_ds_name -> virtual_ds_name_id
if hasattr(virtual_datasources,
virtual_ds_name[:virtual_ds_name.rfind('_')]):
virtual_ds_name = virtual_ds_name[:virtual_ds_name.rfind('_')]
if hasattr(virtual_datasources, virtual_ds_name):
try:
virtual_datasource = virtual_datasources.VIRTUAL_DATASOURCE_FACTORY[
virtual_ds_name](**args)
self[virtual_datasource.reference_sensor].add_datasource(
virtual_datasource, virtual_datasource.ds_type)
except:
LoggingManager.instance().warning(
f"The virtual datasource {virtual_ds_name} could not be added."
)
else:
LoggingManager.instance().warning(
f"The virtual datasource {virtual_ds_name} does not exist."
)
def f_TIMESTAMPS_CSV_PATTERN(archive):
with archive.open(name) as stream:
# sensor_ts = np.loadtxt(stream, dtype='u8', delimiter=' ', ndmin=2)
sensor_ts = pd.read_csv(stream, delimiter=" ", dtype='u8', header=None).values
timestamps = sensor_ts[:,0]
# check if ts is always go up, for imu data (more than 1 data per pkl file) the test is not complet -> to improve
if len(timestamps)>2 and (np.min(np.diff(timestamps.astype(np.int64)))<0):
LoggingManager.instance().warning('Timestamps are not strictly increasing for datasource file {}'.format(self.path))
if sensor_ts.shape[1] > 1:
time_of_issues = sensor_ts[:,1]
else:
time_of_issues = timestamps
return time_of_issues, timestamps
def __init__(self, window, synchronized):
super(MetadataWindow, self).__init__(window, synchronized.platform)
self.synchronized = synchronized
self.backend = self.window.findChild(QObject, "figure")
self.ax = self.backend.getFigure().add_subplot(111)
if self.platform.metadata is not None:
if len(self.platform.metadata) != len(self.synchronized):
LoggingManager.instance().warning(
f'The current synchronized platform has a number of frames different than what is contained in the metadata file. '
)
self.metadata_dirty = pd.DataFrame(
index=range(len(self.synchronized)))
else:
self.metadata_dirty = copy.deepcopy(self.platform.metadata)
else:
self.metadata_dirty = pd.DataFrame(
index=range(len(self.synchronized)))
self.window.columnNames = list(self.metadata_dirty.columns)
self.window.showColumn = {c: False for c in self.window.columnNames}
#Dummy table to avoid a few warnings
self.table_model = DataFrameModel(self.metadata_dirty)
with open(
f'{os.path.dirname(os.path.abspath(__file__))}/../metadata.yml',
'r') as f:
self.standard_entries = yaml.safe_load(f)
self.window.entries.model = list(self.standard_entries.keys())
self.window.entries.currentIndex = 0
self.window.entryDescription.text = self.get_entry_description()
self.window.input.model = list(
self.standard_entries[self.get_entry()]['values'])
self.window.input.currentIndex = 0
self._select_all_frames()
self.last_cursor_value = int(self.window.playerCursor)
self.window.isDirty = False
self.window.addSyncQualityData.visible = CHECK_SYNC_AVAILABLE
self.window.addIMUQualityData.visible = 'sbgekinox_bcc_navposvel' in self.synchronized.platform.datasource_names(
)
self.window.addObjectQuantityData.visible = 'flir_bfc_box2d-detectron-cyl' in self.synchronized.platform.datasource_names(
)
def apply_temperature_correction(self, timestamp, indices, distances):
"""Applies temperature-related distance corrections"""
if (self.temperature_slope is not None
and self.temperature_reference is not None):
try:
temp = self.get_temperature_at(timestamp)
except Exception as e:
LoggingManager.instance().warning(
'Failed to apply temperature correction. '
'Error: {}'.format(str(e)))
return distances
temp_offset = self.temperature_slope * (temp -
self.temperature_reference)
return distances + temp_offset
return distances
def get_synchronized(self, dataset_index, progress_bar=False):
if self.cached_synchronized[dataset_index] is None:
try:
pf = Platform(self.datasets[dataset_index],
include=self.include,
ignore=self.ignore,
progress_bar=progress_bar,
default_cache_size=0)
if self.virtual_datasources_config is not None:
pf.add_virtual_datasources(self.virtual_datasources_config)
sync = pf.synchronized(self.sync_labels, self.interp_labels,
self.tolerance_us)
self.cached_synchronized[dataset_index] = sync
except Exception:
LoggingManager.instance().warning(
f"There is an issue with the dataset {self.datasets[dataset_index]}."
)
return self.cached_synchronized[dataset_index]
def load_intrinsics(self, intrinsics_config: str):
"""Looks for a pickle file containing intrinsics information for this sensor, e.g. 'eagle_tfc.pkl'
Args:
intrinsics_config: path to folder containing this sensor's intrinsics pickle file,
(absolute or relative to dataset path), e.g. '/nas/cam_intrinsics' or 'cam_intrinsics'
"""
paths = glob.glob(
os.path.join(self.pf.try_absolute_or_relative(intrinsics_config),
'{}*results.pkl'.format(self.name)))
if paths:
path = paths[0]
if len(paths) > 1:
LoggingManager.instance().warning(
'more than one intrinsics, using {}'.format(path))
with open(path, 'rb') as f:
self.intrinsics = pickle.load(f)
def _preload_lenghts(self):
self.lenghts = []
for dataset_index in tqdm.tqdm(range(len(self.datasets)),
'Grouping synchronized platforms'):
try:
if self.preload:
l = self._get_dataset_lenght(dataset_index)
else:
l = self._get_dataset_lenght_fast(dataset_index)
except Exception:
l = 0
LoggingManager.instance().warning(
f"The dataset {self.datasets[dataset_index]} could not be added to the SynchronizedGroup."
)
self.lenghts.append(l)
self.cumsum_lenghts = np.cumsum([0] + self.lenghts[:-1])
def camera_matrix(self):
"""np.ndarray: the 3x3 intrinsics matrix
See also: https://docs.opencv.org/4.0.0/d9/d0c/group__calib3d.html#ga3207604e4b1a1758aa66acb6ed5aa65d
"""
try:
matrix = self.intrinsics['matrix']
except:
LoggingManager.instance().warning(
f'Intrinsic matrix of {self.name} not found. Trying to make a generic one.'
)
h = self.yml['configurations']['img']['Width']
v = self.yml['configurations']['img']['Height']
h_fov = self.yml['configurations']['img']['h_fov']
matrix = np.identity(3)
matrix[0, 2] = h / 2
matrix[1, 2] = v / 2
matrix[0, 0] = matrix[1,
1] = h / (2 * np.tan(h_fov * np.pi / 360.0))
return matrix
def compute_iou(self, box, return_max=False, map2yaw=None):
"""Compute the iou score between all the elements of self and of box.
Return a matrix len(self), len(box) when row,col are indexed in the same order as self, box.
If return_max=True: return only a single number for each element of self (the max value).
Important note: By default the computation is performed in the sbg ref where only one angle (yaw) is not zero, unless
map2yaw is provided (a callable) which brings all the boxes in a referential where only one rotation (yaw).
"""
if map2yaw is not None:
box0 = map2yaw(self)
box1 = map2yaw(box)
else:
try: #must find either sbg or ENU.
referential = platform.referential_name('sbgekinox_bcc')
tf_TargetRef_from_Local = np.copy(
self.datasource.sensor.map_to(referential))
tf_TargetRef_from_Local[:3, :
3] = tf_TargetRef_from_Local[:3, :
3] @ self.orientation
except:
LoggingManager.instance().warning(
'IoU computation, falling back to ENU system transformation.'
)
tf_TargetRef_from_Local = np.eye(4)
tf_TargetRef_from_Local[:3, :3] = np.array(
[[0, 0, 1], [-1, 0, 0], [0, -1, 0]], dtype=np.float).T
box0 = self._mapto(tf=tf_TargetRef_from_Local)
box1 = box._mapto(tf=tf_TargetRef_from_Local)
Z0 = [box0['c'], box0['d'], 'z', box0['r'][:, 2]]
Z1 = [box1['c'], box1['d'], 'z', box1['r'][:, 2]]
matiou = IoU3d.matrixIoU(Z0=Z0, Z1=Z1)
if return_max:
return np.max(matiou, axis=1)
else:
return matiou
def __init__(self, pf: 'Platform', yml: dict):
"""Constructor
Args:
platform: the platform that holds this Sensors instance
yml: the YAML database
"""
self.platform = pf
self._sensors = {}
self._ordered_names = []
self._egomotion_provider = None
yml_sensor_items = [
s for s in yml.items() if s[0] not in
['ignore', 'virtual_datasources', 'synchronization']
]
yml_items_tqdm = tqdm.tqdm(
yml_sensor_items, 'Loading sensors'
) if self.platform.progress_bar else yml_sensor_items
for name, value in yml_items_tqdm:
sensor_type, _ = platform_utils.parse_sensor_name(name)
if sensor_type in SENSOR_FACTORY:
self._sensors[name] = SENSOR_FACTORY[sensor_type](
name, self.platform)
else:
self._sensors[name] = Sensor(name, self.platform)
self._ordered_names.append(name)
self._load_offline_datasources(name)
if 'orientation' in value:
m = np.array(value['orientation'], dtype='f4')
if m.shape != (3, 3):
LoggingManager.instance().warning(
'Ignoring orientation for sensor {}: {} is \
not a 3 x 3'.format(name, str(m)))
else:
self._sensors[name].orientation = m
if 'intrinsics' in value:
self._load_intrinsics(name, value['intrinsics'])
if 'extrinsics' in value:
self._load_extrinsics(name, value['extrinsics'])
try:
provider = self._sensors[name].create_egomotion_provider()
except:
LoggingManager.instance().warning(
f"The 'egomotion_provider' for sensor name {name} could not be created."
)
if provider is not None:
if self._egomotion_provider is not None:
LoggingManager.instance().warning(
f"Another 'egomotion_provider' found for sensor name {name}, ignoring it."
)
else:
self._egomotion_provider = provider
def amplitudes_to_color(amplitudes, power=1.0, mask=None, log_normalize=False):
cm = plt.get_cmap('viridis')
if mask is None:
mask = np.ones(amplitudes.shape, dtype=np.bool)
amplitudes = np.power(amplitudes, power)
masked_amps = amplitudes[mask]
if masked_amps.size == 0:
LoggingManager.instance().warning('Provided mask is empty')
dmin, dmax = amplitudes.min(), amplitudes.max()
else:
dmin, dmax = masked_amps.min(), masked_amps.max()
if log_normalize:
norm = matplotlib.colors.LogNorm(dmin, dmax)
else:
norm = matplotlib.colors.Normalize(dmin, dmax)
colors = norm(amplitudes)
colors = cm(colors)
colors = (colors[:, :3] * 255).astype('u1')
return colors
def get_corrected_projection_data(self,
timestamp: Union[int, float],
cache: dict,
type: str = 'directions'):
""" Returns temperature compensated projections directions or angles
Args:
timestamp: the timestamp at which the sample is needed (use to obtain the right temperature)
cache: the cache to search in (obtained by calling LCAx.cache(...))
type: the type of directions (e.g) 'directions' or 'quad_directions'.
**Must be present in the cache**
Returns:
the compensated directions if any, or the uncompensated direction otherwise
"""
default_directions = cache[type]
if self.mirror_temp_compensation is not None:
try:
temp = self.get_temperature_at(timestamp)
except Exception as e:
LoggingManager.instance().warning(
f"Failed to apply temperature correction. Error: {str(e)}")
return default_directions
temp = min(max(temp, self.mirror_temp_compensation['t_min']), 60)
temp_floor = np.floor(temp)
if (temp - temp_floor) >= 0.5:
temp = temp_floor + 0.5
else:
temp = temp_floor
return cache[f'{type}_temp_lut'][temp]
return default_directions
