def write_table_subfig(nrows, ncols, allcells={},
allcols=None, allrows=None, introws=None, intcols=None,
firstrow=None, firstcol=None, intcells={},
label_prefix=None):
""" Like write_table(), but use subfigures """
param_table = get_param_table(nrows, ncols, allcells, allcols, allrows,
introws, intcols, firstrow, firstcol, intcells)
# with latex_fragment(sys.stdout, graphics_path=get_resources_dir()) as frag:
# for r, param_row in enumerate(param_table):
# for c, param_cell in enumerate(param_row):
# internal = c > 0 and r > 0
# if internal:
# if label_prefix is not None:
# label = '%s-%s' % (r, c)
# label = label_prefix + '-' + label
# else:
# label = None
#
# with frag.subfigure(caption="", label=label) as subfigure:
# call_template(subfigure, param_cell)
# else:
# # with frag.minipage() as minip:
# call_template(frag, param_cell)
# frag.parbreak()
alignment = ['r'] + ['c'] * ncols
with latex_fragment(sys.stdout, graphics_path=get_resources_dir()) as frag:
with frag.tabular(alignment=alignment) as tabular:
for r, param_row in enumerate(param_table):
with tabular.row() as tex_row:
for c, param_cell in enumerate(param_row):
with tex_row.cell() as cell:
# print r, c, param_cell
logger.debug('%s %s : %s' % (r, c, param_cell))
# call_template(cell, param_cell)
internal = c > 0 and r > 0
if label_prefix is not None:
label = '%s-%s' % (r, c)
label = label_prefix + '-' + label
else:
label = None
if internal:
with cell.subfigure(caption="", label=label) as subfigure:
call_template(subfigure, param_cell)
else:
call_template(cell, param_cell)
def write_table(nrows, ncols, allcells={},
allcols=None, allrows=None, introws=None, intcols=None,
firstrow=None, firstcol=None, intcells={}):
"""
Use as follows:
Example 1: show correlation table
write_table(common=dict(id_set='id_set'),
rows=[dict(id_robot='robot1'),
dict(id_robot='robot2')],
cols=[dict(id_agent='bdse1'),
dict(id_agent='bdse2')],
header_row = dict(template='agent_name'),
header_col = dict(template='robot_name'),
cells=dict(template='predict_y_dot_corr'))
Example 2: show P and correlation for one agent
write_table(common=dict(id_set='id_set', id_agent='id_agent'),
rows=[dict(id_robot='robot1'),
dict(id_robot='robot2')],
cols=[dict(template='bds_P'),
dict(template='bds_T0'),
dict(template='predict_y_dot_corr')],
header_row = dict(template='empty'),
header_col = dict(template='robot_name'),
cells=dict())
"""
param_table = get_param_table(nrows, ncols, allcells, allcols, allrows,
introws, intcols, firstrow, firstcol, intcells)
alignment = ['r'] + ['c'] * ncols
with latex_fragment(sys.stdout, graphics_path=get_resources_dir()) as frag:
with frag.tabular(alignment=alignment) as tabular:
for r, param_row in enumerate(param_table):
with tabular.row() as tex_row:
for c, param_cell in enumerate(param_row):
with tex_row.cell() as cell:
# print r, c, param_cell
logger.debug('%s %s : %s' % (r, c, param_cell))
call_template(cell, param_cell)
def bvtemplate(name, **params):
""" Prints a string. Can be used from TeX code. """
with latex_fragment(sys.stdout, graphics_path=get_resources_dir()) as frag:
params['template'] = name
call_template(frag, params)
def bvvehicleimage(id_set, id_robot, width_em=3.5):
with latex_fragment(sys.stdout, graphics_path=get_resources_dir()) as frag:
create_robot_figure(frag, id_set, id_robot, width_em, write_world=False)
def bds_learn_reportA(id_set, agent, robot, width="3cm"):
""" This just prints on standard output """
report = load_report_phase(id_set=id_set, agent=agent, robot=robot, phase="learn")
prefix = "%s-%s-%s" % (id_set, robot, agent)
#
# bds_learn_reportA_frag(report, prefix=prefix, agent, robot, width=width,
# stream=sys.stdout,
# graphics_path=get_resources_dir())
stream = sys.stdout
graphics_path = get_resources_dir()
# def bds_learn_reportA_frag(report, prefix, agent, robot, width, stream=sys.stdout,
# graphics_path=None):
#
# if graphics_path is None:
# graphics_path = ()
#
assert isinstance(report, Node)
def save_data(node, gid):
fig.save_graphics_data(node.raw_data, node.mime, gid)
with latex_fragment(stream, graphics_path) as frag:
caption = (
"BDS learning and prediction statistics for the agent "
"\\bvid{%r} interacting with the robot \\bvid{%r}. \\bvseelegend" % (agent, robot)
)
label = "fig:%s-learn-rA" % prefix
with frag.figure(caption=caption, label=label, placement="p") as fig:
tsize = "3cm"
height = "3cm"
fig.hfill()
with fig.subfigure(caption="\\texttt{%s}" % robot, label="%s-%s" % (label, "vehicle")) as sub:
with sub.minipage("3cm", align="b") as mp:
mp.tex("\\vspace{0pt}\n")
mp.tex("\\centering")
create_robot_figure(mp, id_set, robot)
display_k_tensor(
fig,
report["estimator/tensors/U/value"].raw_data,
gid_pattern="%s-U%%d" % (prefix),
label_patterns="fig:%s" % prefix + "-U%d",
caption_patterns="$\TUe^{s}_{%d}$",
width=tsize,
xlabel="s",
ylabel="\TUe^{s}",
)
# fig.hfill() # added by display_k_tensor
fig.parbreak()
fig.hfill()
with fig.subfigure(caption="$\Tcove^{sv}$", label="%s-%s" % (label, "P")) as sub:
gid = "%s-P" % (prefix)
save_data(report["estimator/tensors/P/png"], gid)
tensor_figure(sub, gid=gid, xlabel="s", ylabel="v", width=width, label="P^{sv}")
display_3tensor(
fig,
V=report["estimator/tensors/T/value"].raw_data,
gid_pattern=prefix + "-T%d",
label_patterns="fig:%s" % prefix + "-T%d",
caption_patterns="\TTe^{s\,v\,%d}",
width=width,
label="\TTe^{s\,v\,%d}",
xlabel="s",
ylabel="v",
)
# fig.hfill() # added by display_3_tensor
fig.parbreak()
fig.hfill()
from .prediction import tab_predict_u_corr
with fig.subfigure(caption="\\labelpredu", label="%s-%s" % (label, "ucorr")) as sub:
# fig_predict_u_corr(sub, id_set, agent, robot, "1cm")
tab_predict_u_corr(sub, id_set, agent, robot)
display_k_tensor(
fig,
report["estimator/model/N/value"].raw_data,
gid_pattern=prefix + "-N%d",
label_patterns="fig:%s" % prefix + "-N%d",
caption_patterns="$\TNe^{s}_{%d}$",
width=tsize,
xlabel="s",
ylabel="\TNe^{s}",
)
fig.hfill()
fig.parbreak()
fig.hfill()
from .prediction import fig_predict_corr
with fig.subfigure(caption="\\labelpredydot", label="%s-%s" % (label, "corr")) as sub:
fig_predict_corr(sub, id_set, agent, robot, width=width, height=height)
display_3tensor(
fig,
V=report["estimator/model/M/value"].raw_data,
gid_pattern=prefix + "-M%d",
label_patterns="fig:%s" % prefix + "-M%d",
caption_patterns="\TMe^s_{v\,%d}",
width=width,
label="\TMe^s_{v\,%d}",
xlabel="s",
ylabel="v",
)
fig.hfill()
