def report_dds_fill(id_discdds, **params):
dds = get_conftools_discdds().instance(id_discdds)
r = Report('dds_fill-%s' % id_discdds)
ds = DiffeoSystemBounds(id_discdds, dds, **params)
ds.display(r)
return r
def make(id_discdds, outdir, **params):
discdds_library = get_conftools_discdds()
discdds = discdds_library.instance(id_discdds)
cases = make_logcases(id_discdds=id_discdds, discdds=discdds, **params)
for tc in cases:
tc.save(outdir)
def report_dds_fill(id_discdds, **params):
dds = get_conftools_discdds().instance(id_discdds)
r = Report('dds_fill-%s' % id_discdds)
ds = DiffeoSystemBounds(id_discdds, dds, **params)
ds.display(r)
return r
def make(id_discdds, outdir, **params):
discdds_library = get_conftools_discdds()
discdds = discdds_library.instance(id_discdds)
cases = make_logcases(id_discdds=id_discdds, discdds=discdds, **params)
for tc in cases:
tc.save(outdir)
def define_jobs_context(self, context):
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.discdds)
discdds = natsorted(discdds)
tolerance = self.options.tolerance
for c, id_discdds in iterate_context_names(context, discdds):
r = c.comp_config(report_dds_geometry, id_discdds, tolerance=tolerance)
c.add_report(r, 'dds_geometry', tolerance=tolerance, dds=id_discdds)
def FromImages(tcname, id_discdds, image1, image2, true_plan=None):
discdds = get_conftools_discdds().instance(id_discdds)
shape = discdds.get_shape()
images = get_conftools_uncertain_images()
y0 = images.instance(image1).resize(shape)
y1 = images.instance(image2).resize(shape)
tc = TestCase(id_tc=tcname, id_discdds=id_discdds,
y0=y0, y1=y1, true_plan=true_plan)
return tc
def define_jobs_context(self, context):
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.discdds)
discdds = natsorted(discdds)
for c, id_discdds in iterate_context_names(context, discdds):
options = self.options
params = dict(tolerance=options.tolerance,
collapse_threshold=options.collapse_threshold,
debug_it=options.debug_it,
max_it=options.num_iterations,
min_visibility=options.min_visibility)
r = c.comp_config(report_dds_fill, id_discdds, **params)
c.add_report(r, 'dds_fill', dds=id_discdds, **params)
def report_dds_geometry(id_discdds, tolerance):
dds = get_conftools_discdds().instance(id_discdds)
r = Report('dds_geometry-%s-%s' % (id_discdds, tolerance))
ds = DiffeoStructure(dds, tolerance=tolerance)
with r.subsection('display') as r:
ds.display(r)
with r.subsection('show_reduction_steps') as r:
ds.show_reduction_steps(r, max_nsteps=5)
with r.subsection('show_reduction') as r:
ds.show_reduction(r)
return r
def define_jobs_context(self, context):
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.discdds)
discdds = natsorted(discdds)
for c, id_discdds in iterate_context_names(context, discdds):
options = self.options
params = dict(tolerance=options.tolerance,
collapse_threshold=options.collapse_threshold,
debug_it=options.debug_it,
max_it=options.num_iterations,
min_visibility=options.min_visibility)
r = c.comp_config(report_dds_fill, id_discdds, **params)
c.add_report(r, 'dds_fill', dds=id_discdds, **params)
def report_dds_geometry(id_discdds, tolerance):
dds = get_conftools_discdds().instance(id_discdds)
r = Report('dds_geometry-%s-%s' % (id_discdds, tolerance))
ds = DiffeoStructure(dds, tolerance=tolerance)
with r.subsection('display') as r:
ds.display(r)
with r.subsection('show_reduction_steps') as r:
ds.show_reduction_steps(r, max_nsteps=5)
with r.subsection('show_reduction') as r:
ds.show_reduction(r)
return r
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.dds)
for c, id_discdds in iterate_context_names(context, discdds):
create_predstats_jobs(context=c, distances=distances,
id_discdds=id_discdds,
streams=streams, maxd=10)
def define_jobs_context(self, context):
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.discdds)
discdds = natsorted(discdds)
tolerance = self.options.tolerance
for c, id_discdds in iterate_context_names(context, discdds):
r = c.comp_config(report_dds_geometry,
id_discdds,
tolerance=tolerance)
c.add_report(r,
'dds_geometry',
tolerance=tolerance,
dds=id_discdds)
def display(self, report, discdds=None):
report.text('summary',
'Testcase: %s\nPlan: %s' % (self.id_tc, self.true_plan))
report.data('id_tc', self.id_tc)
report.data('id_discdds', self.id_discdds)
report.data('true_plan', self.true_plan)
def zoom(rgb):
""" Enlarge image so that pixels are distinguishable even though
the brows makes them smooth. """
return rgb_zoom(rgb, K=8)
f = report.figure(cols=4)
f.data_rgb('y0_rgb', zoom(self.y0.get_rgb()), caption='$y_0$ (rgb)')
f.data_rgb('y1_rgb', zoom(self.y1.get_rgb()), caption='$y_1$ (rgb)')
from diffeoplan.library.distances.distance_norm import DistanceNorm
d = DistanceNorm(2)
if discdds is None:
discdds = get_conftools_discdds().instance(self.id_discdds)
y1p = discdds.predict(self.y0, self.true_plan)
e_y0_y1_field = d.error_field(self.y1, self.y0)
e_y1p_y1_field = d.error_field(self.y1, y1p)
e_max = float(max(e_y0_y1_field.max(), e_y1p_y1_field.max()))
f.data_rgb('e_y0_y1',
zoom(scale(e_y0_y1_field, max_value=e_max)),
caption="Discrepancy between $y_0$ and $y_1$.")
f = report.figure('prediction_model',
cols=4,
caption="This is the prediction according to the "
"learned model."
"")
f.data_rgb('y1p_rgb',
zoom(y1p.get_rgb()),
caption="$p^\star \cdot y_0$")
f.data_rgb('y1p_rgb_u',
zoom(y1p.get_rgb_uncertain()),
caption="Uncertainty")
f.data_rgb('e_y1p_y1',
zoom(scale(e_y1p_y1_field, max_value=e_max)),
caption="Discrepancy between $y_1$ and $p^\star "
"\cdot y_0$.")
def FromImages(tcname, id_discdds, image1, image2, true_plan=None):
discdds = get_conftools_discdds().instance(id_discdds)
shape = discdds.get_shape()
images = get_conftools_uncertain_images()
y0 = images.instance(image1).resize(shape)
y1 = images.instance(image2).resize(shape)
tc = TestCase(id_tc=tcname,
id_discdds=id_discdds,
y0=y0,
y1=y1,
true_plan=true_plan)
return tc
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.dds)
for c, id_discdds in iterate_context_names(context, discdds):
create_predstats_jobs(context=c,
distances=distances,
id_discdds=id_discdds,
streams=streams,
maxd=10)
def display(self, report, discdds=None):
report.text('summary',
'Testcase: %s\nPlan: %s' % (self.id_tc, self.true_plan))
report.data('id_tc', self.id_tc)
report.data('id_discdds', self.id_discdds)
report.data('true_plan', self.true_plan)
def zoom(rgb):
""" Enlarge image so that pixels are distinguishable even though
the brows makes them smooth. """
return rgb_zoom(rgb, K=8)
f = report.figure(cols=4)
f.data_rgb('y0_rgb', zoom(self.y0.get_rgb()), caption='$y_0$ (rgb)')
f.data_rgb('y1_rgb', zoom(self.y1.get_rgb()), caption='$y_1$ (rgb)')
from diffeoplan.library.distances.distance_norm import DistanceNorm
d = DistanceNorm(2)
if discdds is None:
discdds = get_conftools_discdds().instance(self.id_discdds)
y1p = discdds.predict(self.y0, self.true_plan)
e_y0_y1_field = d.error_field(self.y1, self.y0)
e_y1p_y1_field = d.error_field(self.y1, y1p)
e_max = float(max(e_y0_y1_field.max(), e_y1p_y1_field.max()))
f.data_rgb('e_y0_y1', zoom(scale(e_y0_y1_field, max_value=e_max)),
caption="Discrepancy between $y_0$ and $y_1$.")
f = report.figure('prediction_model', cols=4,
caption="This is the prediction according to the "
"learned model.""")
f.data_rgb('y1p_rgb', zoom(y1p.get_rgb()),
caption="$p^\star \cdot y_0$")
f.data_rgb('y1p_rgb_u', zoom(y1p.get_rgb_uncertain()),
caption="Uncertainty")
f.data_rgb('e_y1p_y1', zoom(scale(e_y1p_y1_field, max_value=e_max)),
caption="Discrepancy between $y_1$ and $p^\star "
"\cdot y_0$.")
def ManualMotion(tcname, id_discdds, id_image, planstring):
discdds = get_conftools_discdds().instance(id_discdds)
shape = discdds.get_shape()
images = get_conftools_uncertain_images()
y0 = images.instance(id_image).resize(shape)
chars = "abcdefghilmnopqrst"
char2int = dict([(c, i) for i, c in enumerate(chars)])
plan = tuple(map(char2int.__getitem__, planstring))
# predict the result
y1 = discdds.predict(y0, plan)
tc = TestCase(id_tc=tcname, id_discdds=id_discdds,
y0=y0, y1=y1, true_plan=plan)
return tc
def ManualMotion(tcname, id_discdds, id_image, planstring):
discdds = get_conftools_discdds().instance(id_discdds)
shape = discdds.get_shape()
images = get_conftools_uncertain_images()
y0 = images.instance(id_image).resize(shape)
chars = "abcdefghilmnopqrst"
char2int = dict([(c, i) for i, c in enumerate(chars)])
plan = tuple(map(char2int.__getitem__, planstring))
# predict the result
y1 = discdds.predict(y0, plan)
tc = TestCase(id_tc=tcname,
id_discdds=id_discdds,
y0=y0,
y1=y1,
true_plan=plan)
return tc
def compute_predstats(id_discdds, id_stream, delta, id_distances):
dds = get_conftools_discdds().instance(id_discdds)
stream = get_conftools_streams().instance(id_stream)
distances_library = get_conftools_uncertain_image_distances()
distances = dict(map(lambda x: (x, distances_library.instance(x)), id_distances))
dtype = [(x, 'float32') for x in id_distances]
results = []
for logitem in iterate_testcases(stream.read_all(), delta):
assert_allclose(len(logitem.u), delta)
y0 = UncertainImage(logitem.y0)
y1 = UncertainImage(logitem.y1)
py0 = dds.predict(y0, dds.commands_to_indices(logitem.u))
ds = []
for name in id_distances:
d = distances[name].distance(y1, py0)
# d0 = distances[name].distance(y1, y0)
ds.append(d)
a = np.array(tuple(ds), dtype=dtype)
results.append(a)
return results
def compute_predstats(id_discdds, id_stream, delta, id_distances):
dds = get_conftools_discdds().instance(id_discdds)
stream = get_conftools_streams().instance(id_stream)
distances_library = get_conftools_uncertain_image_distances()
distances = dict(
map(lambda x: (x, distances_library.instance(x)), id_distances))
dtype = [(x, 'float32') for x in id_distances]
results = []
for logitem in iterate_testcases(stream.read_all(), delta):
assert_allclose(len(logitem.u), delta)
y0 = UncertainImage(logitem.y0)
y1 = UncertainImage(logitem.y1)
py0 = dds.predict(y0, dds.commands_to_indices(logitem.u))
ds = []
for name in id_distances:
d = distances[name].distance(y1, py0)
# d0 = distances[name].distance(y1, y0)
ds.append(d)
a = np.array(tuple(ds), dtype=dtype)
results.append(a)
return results
def from_yaml(discdds, servo):
_, i = get_conftools_discdds().instance_smarter(discdds)
return DiffeoPlanAgent(i, servo)
def from_yaml(discdds, servo):
_, i = get_conftools_discdds().instance_smarter(discdds)
return DiffeoPlanAgent(i, servo)
def instantiate_discdds(id_discdds):
warnings.warn('make sure it it called with comp_config')
return get_conftools_discdds().instance(id_discdds)
def instantiate_discdds(id_discdds):
warnings.warn('make sure it it called with comp_config')
return get_conftools_discdds().instance(id_discdds)
