def get_signals(self):
bounds = [[], []]
resources = set()
log_signals = self.rawlog.get_signals()
timerefs = set()
for _, signal_name in self.inputs.items():
check_is_in('signal', signal_name, log_signals)
s = log_signals[signal_name]
b = s.get_time_bounds()
bounds[0].append(b[0])
bounds[1].append(b[1])
timerefs.add(s.get_time_reference())
resources.update(s.get_resources())
bounds = min(bounds[0]), max(bounds[1])
resources = list(resources)
timeref = '-'.join(sorted(timerefs))
signals = {}
for _, signal_name in self.outputs.items():
warnings.warn('to finish')
signal_type = '???' # XXX
s = ProcgraphFilterSignal(signal_type, timeref, resources, bounds)
signals[signal_name] = s
return signals
def read_log(rawlog, signals=None, start=None, stop=None):
log_signals = rawlog.get_signals()
if not signals:
signals = list(log_signals.keys())
else:
for x in signals:
check_is_in('signal', x, log_signals)
# sync to first signal
signal = log_signals[signals[0]]
bounds = signal.get_time_bounds()
if start is not None:
start = bounds[0] + start
else:
start = bounds[0]
if stop is not None:
stop = bounds[0] + stop
else:
stop = bounds[1]
print('start: %s' % start)
print('stop: %s' % stop)
for timestamp, (name, value) in rawlog.read(signals, start, stop): # @UnusedVariable
if not (start <= timestamp <= stop + 0.001):
msg = 'Messages not filtered (%.5f <= %.5f <= %.5f)' % (start, timestamp, stop)
msg += '\nname: %s' % name
raise Exception(msg)
print('reading %.5f (%6.4f) %s' % (timestamp, timestamp - start, name))
def __init__(self, match_method):
"""
:param max_displ: Maximum pointwise displacement for the diffeomorphism d_max
:param match_method: Either "continuous" or "binary" (to compute the
error function).
"""
self.shape = None
self.last_y0 = None
self.last_y1 = None
accepted = [MATCH_CONTINUOUS, MATCH_BINARY]
check_is_in('match method', match_method, accepted)
self.match_method = match_method
self.num_samples = 0
def __init__(self, match_method):
"""
:param max_displ: Maximum pointwise displacement for the diffeomorphism d_max
:param match_method: Either "continuous" or "binary" (to compute the
error function).
"""
self.shape = None
self.last_y0 = None
self.last_y1 = None
accepted = [MATCH_CONTINUOUS, MATCH_BINARY]
check_is_in('match method', match_method, accepted)
self.match_method = match_method
self.num_samples = 0
def get_resource_job(self, context, rtype: str, **params):
# print('RM %s %s get_resource %s %s' % (id(self), self._context, rtype, params))
key = dict(rtype=rtype, **params)
already_done = key in self.allresources
if already_done:
return self.allresources[key]
check_is_in('resource type', rtype, self.providers)
prefix = self._make_prefix(rtype, **params)
# print('adding job prefix %r' % prefix)
c = context.child(name=rtype, add_job_prefix=prefix, add_outdir=rtype)
c._job_prefix = prefix
# Add this point we should check if we already created the job
ok = []
errors = []
for provider in self.providers[rtype]:
try:
res_i = provider(c, **params)
ok.append((provider, res_i))
except ResourceManager.CannotProvide as e:
errors.append(e)
except Exception as e:
msg = 'Error while trying to get resource.\n'
msg += ' type: %r params: %s\n' % (rtype, params)
msg += 'While calling provider %r:\n' % provider
msg += indent(traceback.format_exc(), '> ')
raise Exception(msg)
if not ok:
msg = 'No provider could create this resource:\n'
msg += "\n".join('- %s' % str(e) for e in errors)
raise Exception(msg)
if len(ok) >= 2:
msg = 'The same resource could be created by two providers.'
msg += '\n%s %s' % (rtype, params)
for prov, _ in ok:
msg += '\n - %s' % prov
raise Exception(msg)
assert len(ok) == 1
res = ok[0][1]
self.set_resource(res, rtype, **params)
return res
def get_resource_job(self, context, rtype, **params):
# print('RM %s %s get_resource %s %s' % (id(self), self._context, rtype, params))
key = dict(rtype=rtype, **params)
already_done = key in self.allresources
if already_done:
return self.allresources[key]
check_is_in('resource type', rtype, self.providers)
prefix = self._make_prefix(rtype, **params)
# print('adding job prefix %r' % prefix)
c = context.child(name=rtype, add_job_prefix=prefix, add_outdir=rtype)
c._job_prefix = prefix
# Add this point we should check if we already created the job
ok = []
errors = []
for provider in self.providers[rtype]:
try:
res_i = provider(c, **params)
ok.append((provider, res_i))
except ResourceManager.CannotProvide as e:
errors.append(e)
except Exception as e:
msg = 'Error while trying to get resource.\n'
msg += ' type: %r params: %s\n' % (rtype, params)
msg += 'While calling provider %r:\n' % provider
msg += indent(traceback.format_exc(), '> ')
raise Exception(msg)
if not ok:
msg = 'No provider could create this resource:\n'
msg += "\n".join('- %s' % str(e) for e in errors)
raise Exception(msg)
if len(ok) >= 2:
msg = 'The same resource could be created by two providers.'
msg += '\n%s %s' % (rtype, params)
for prov, _ in ok:
msg += '\n - %s' % prov
raise Exception(msg)
assert len(ok) == 1
res = ok[0][1]
self.set_resource(res, rtype, **params)
return res
def __init__(self, shape, neighborarea, centers=None, topology='plane'):
'''
:param shape:
:param neighborarea:
:param centers: if not None, area around centers
Suppose
shape = (H, W)
nighborarea = (X, Y)
A = X * Y
N = H * W
Then
self.neighbor_indices_flat
is a K x N array.
'''
neighborarea = np.array(neighborarea)
if not np.all(neighborarea <= shape):
neighborarea = np.minimum(neighborarea, shape)
assert neighborarea[0] <= shape[0]
assert neighborarea[1] <= shape[1]
# for each sensel, create an area
cmg = cmap(np.array(neighborarea), max_shape=shape)
# if the area size is even, it is bumbed to the next integer
assert cmg.shape[0] >= neighborarea[0]
assert cmg.shape[1] >= neighborarea[1]
assert cmg.shape[0] <= neighborarea[0] + 1
assert cmg.shape[1] <= neighborarea[1] + 1
# but we cannot be bigger than the area anyway
assert cmg.shape[0] <= shape[0]
assert cmg.shape[1] <= shape[1]
self.area_shape = cmg.shape[0], cmg.shape[1]
self.shape = shape
self.H, self.W = shape
self.X = self.area_shape[0]
self.Y = self.area_shape[1]
self.N = self.H * self.W
self.A = self.X * self.Y
self.topology = topology
check_is_in(topology, self.topology, ['torus', 'plane'])
# this is the important state
self.neighbor_indices_flat = np.zeros((self.N, self.A), 'int32')
# logger.info('Creating Flattening..')
self.flattening = Flattening.by_rows(tuple(shape))
# logger.info('..done')
if centers is None:
self.centers = diffeo_identity(shape)
else:
if centers.dtype == 'float':
# continuous diffeo
centers = np.round(centers).astype('int')
self.centers = centers
for coord in coords_iterate(shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
center = self.centers[coord]
cm[:, :, 0] += center[0]
cm[:, :, 1] += center[1]
# torus topology here
if self.topology == 'torus':
cm[:, :, 0] = cm[:, :, 0] % shape[0]
cm[:, :, 1] = cm[:, :, 1] % shape[1]
elif self.topology == 'plane':
for i in range(2):
cmin = cm[:, :, i].min()
if cmin < 0:
cm[:, :, i] -= cmin
assert cm[:, :, i].min() >= 0
cmax = cm[:, :, i].max()
if cmax >= shape[i]:
delta = -(cmax - (shape[i] - 1))
cm[:, :, i] += delta
assert cm[:, :, i].max() < shape[i]
assert cm[:, :, i].min() >= 0
else:
assert False
for i in range(2):
cmin, cmax = cm[:, :, i].min(), cm[:, :, i].max()
assert cmin >= 0
assert cmax < shape[i]
indices = np.zeros(self.area_shape, 'int32')
for a, b in coords_iterate(self.area_shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
# XXX using numpy's flattening
indices = np.array(indices.flat)
# warnings.warn('remove bias due to ordering')
# indices = np.random.permutation(indices)
self.neighbor_indices_flat[k, :] = indices
def __init__(self, score, **kwargs):
DiffeoActionEstimatorSimple.__init__(self, **kwargs)
self.score = score
check_is_in('score function', score,
DiffeoActionEstimatorNewUnc.scores)
def __init__(self, shape, neighborarea, centers=None, topology="plane"):
"""
:param shape:
:param neighborarea:
:param centers: if not None, area around centers
Suppose
shape = (H, W)
nighborarea = (X, Y)
A = X * Y
N = H * W
Then
self.neighbor_indices_flat
is a K x N array.
"""
neighborarea = np.array(neighborarea)
if not np.all(neighborarea <= shape):
neighborarea = np.minimum(neighborarea, shape)
assert neighborarea[0] <= shape[0]
assert neighborarea[1] <= shape[1]
# for each sensel, create an area
cmg = cmap(np.array(neighborarea), max_shape=shape)
# if the area size is even, it is bumbed to the next integer
assert cmg.shape[0] >= neighborarea[0]
assert cmg.shape[1] >= neighborarea[1]
assert cmg.shape[0] <= neighborarea[0] + 1
assert cmg.shape[1] <= neighborarea[1] + 1
# but we cannot be bigger than the area anyway
assert cmg.shape[0] <= shape[0]
assert cmg.shape[1] <= shape[1]
self.area_shape = cmg.shape[0], cmg.shape[1]
self.shape = shape
self.H, self.W = shape
self.X = self.area_shape[0]
self.Y = self.area_shape[1]
self.N = self.H * self.W
self.A = self.X * self.Y
self.topology = topology
check_is_in(topology, self.topology, ["torus", "plane"])
# this is the important state
self.neighbor_indices_flat = np.zeros((self.N, self.A), "int32")
# logger.info('Creating Flattening..')
self.flattening = Flattening.by_rows(tuple(shape))
# logger.info('..done')
if centers is None:
self.centers = diffeo_identity(shape)
else:
if centers.dtype == "float":
# continuous diffeo
centers = np.round(centers).astype("int")
self.centers = centers
for coord in coords_iterate(shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
center = self.centers[coord]
cm[:, :, 0] += center[0]
cm[:, :, 1] += center[1]
# torus topology here
if self.topology == "torus":
cm[:, :, 0] = cm[:, :, 0] % shape[0]
cm[:, :, 1] = cm[:, :, 1] % shape[1]
elif self.topology == "plane":
for i in range(2):
cmin = cm[:, :, i].min()
if cmin < 0:
cm[:, :, i] -= cmin
assert cm[:, :, i].min() >= 0
cmax = cm[:, :, i].max()
if cmax >= shape[i]:
delta = -(cmax - (shape[i] - 1))
cm[:, :, i] += delta
assert cm[:, :, i].max() < shape[i]
assert cm[:, :, i].min() >= 0
else:
assert False
for i in range(2):
cmin, cmax = cm[:, :, i].min(), cm[:, :, i].max()
assert cmin >= 0
assert cmax < shape[i]
indices = np.zeros(self.area_shape, "int32")
for a, b in coords_iterate(self.area_shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
# XXX using numpy's flattening
indices = np.array(indices.flat)
# warnings.warn('remove bias due to ordering')
# indices = np.random.permutation(indices)
self.neighbor_indices_flat[k, :] = indices
def __init__(self, score, **kwargs):
DiffeoActionEstimatorSimple.__init__(self, **kwargs)
self.score = score
check_is_in("score function", score, DiffeoActionEstimatorNewUnc.scores)
