def check_relationships(branches):
ancestors = {b: set() for b in branches}
length = len(branches) * (len(branches) - 1)
for b1, b2 in ub.ProgIter(it.combinations(branches, 2), length=length):
ret = ub.cmd('git merge-base --is-ancestor {} {}'.format(b1, b2))['ret']
if ret == 0:
ancestors[b1].add(b2)
ret = ub.cmd('git merge-base --is-ancestor {} {}'.format(b2, b1))['ret']
if ret == 0:
ancestors[b2].add(b1)
print('<key> is an ancestor of <value>')
print(ub.repr2(ancestors))
descendants = {b: set() for b in branches}
for key, others in ancestors.items():
for o in others:
descendants[o].add(key)
print('<key> descends from <value>')
print(ub.repr2(descendants))
import plottool as pt
import networkx as nx
G = nx.DiGraph()
G.add_nodes_from(branches)
for key, others in ancestors.items():
for o in others:
# G.add_edge(key, o)
G.add_edge(o, key)
from networkx.algorithms.connectivity.edge_augmentation import collapse
flag = True
G2 = G
while flag:
flag = False
for u, v in list(G2.edges()):
if G2.has_edge(v, u):
G2 = collapse(G2, [[u, v]])
node_relabel = ub.ddict(list)
for old, new in G2.graph['mapping'].items():
node_relabel[new].append(old)
G2 = nx.relabel_nodes(G2, {k: '\n'.join(v) for k, v in node_relabel.items()})
flag = True
break
G3 = nx.transitive_reduction(G2)
pt.show_nx(G3, arrow_width=1.5, prog='dot', layoutkw=dict(prog='dot'))
pt.zoom_factory()
pt.pan_factory()
pt.plt.show()
def draw_bayesian_model(model,
evidence={},
soft_evidence={},
fnum=None,
pnum=None,
**kwargs):
from pgmpy.models import BayesianModel
if not isinstance(model, BayesianModel):
model = model.to_bayesian_model()
import plottool as pt
import networkx as nx
kwargs = kwargs.copy()
factor_list = kwargs.pop('factor_list', [])
ttype_colors, ttype_scalars = make_colorcodes(model)
textprops = {
'horizontalalignment': 'left',
'family': 'monospace',
'size': 8,
}
# build graph attrs
tup = get_node_viz_attrs(model, evidence, soft_evidence, factor_list,
ttype_colors, **kwargs)
node_color, pos_list, pos_dict, takws = tup
# draw graph
has_infered = evidence or 'factor_list' in kwargs
if False:
fig = pt.figure(fnum=fnum, pnum=pnum, doclf=True) # NOQA
ax = pt.gca()
drawkw = dict(pos=pos_dict,
ax=ax,
with_labels=True,
node_size=1100,
node_color=node_color)
nx.draw(model, **drawkw)
else:
# BE VERY CAREFUL
if 1:
graph = model.copy()
graph.__class__ = nx.DiGraph
graph.graph['groupattrs'] = ut.ddict(dict)
#graph = model.
if getattr(graph, 'ttype2_cpds', None) is not None:
# Add invis edges and ttype groups
for ttype in model.ttype2_cpds.keys():
ttype_cpds = model.ttype2_cpds[ttype]
# use defined ordering
ttype_nodes = ut.list_getattr(ttype_cpds, 'variable')
# ttype_nodes = sorted(ttype_nodes)
invis_edges = list(ut.itertwo(ttype_nodes))
graph.add_edges_from(invis_edges)
nx.set_edge_attributes(
graph, 'style',
{edge: 'invis'
for edge in invis_edges})
nx.set_node_attributes(
graph, 'groupid',
{node: ttype
for node in ttype_nodes})
graph.graph['groupattrs'][ttype]['rank'] = 'same'
graph.graph['groupattrs'][ttype]['cluster'] = False
else:
graph = model
pt.show_nx(graph,
layout_kw={'prog': 'dot'},
fnum=fnum,
pnum=pnum,
verbose=0)
pt.zoom_factory()
fig = pt.gcf()
ax = pt.gca()
pass
hacks = [
pt.draw_text_annotations(textprops=textprops, **takw) for takw in takws
if takw
]
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
if 'name' in model.ttype2_template:
num_names = len(model.ttype2_template['name'].basis)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
title = 'num_names=%r, num_annots=%r' % (
num_names,
num_annots,
)
else:
title = ''
map_assign = kwargs.get('map_assign', None)
def word_insert(text):
return '' if len(text) == 0 else text + ' '
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
title += '\n%sMAP: ' % (word_insert(kwargs.get('method', '')))
title += map_assign + ' @' + '%.2f%%' % (100 * map_prob, )
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
for hack in hacks:
hack()
if has_infered:
# Hack in colorbars
# if ut.list_type(basis) is int:
# pt.colorbar(scalars, colors, lbl='score', ticklabels=np.array(basis) + 1)
# else:
# pt.colorbar(scalars, colors, lbl='score', ticklabels=basis)
keys = ['name', 'score']
locs = ['left', 'right']
for key, loc in zip(keys, locs):
if key in ttype_colors:
basis = model.ttype2_template[key].basis
# scalars =
colors = ttype_colors[key]
scalars = ttype_scalars[key]
pt.colorbar(scalars,
colors,
lbl=key,
ticklabels=basis,
ticklocation=loc)
def intraoccurrence_connected():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --postcut
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --smaller
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = intraoccurrence_connected()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='PZ_Master1')
nid2_aid = {
#4880: [3690, 3696, 3703, 3706, 3712, 3721],
4880: [3690, 3696, 3703],
6537: [3739],
6653: [7671],
6610: [7566, 7408],
#6612: [7664, 7462, 7522],
#6624: [7465, 7360],
#6625: [7746, 7383, 7390, 7477, 7376, 7579],
6630: [7586, 7377, 7464, 7478],
#6677: [7500]
}
nid2_dbaids = {4880: [33, 6120, 7164], 6537: [7017, 7206], 6653: [7660]}
if ut.get_argflag('--small') or ut.get_argflag('--smaller'):
del nid2_aid[6630]
del nid2_aid[6537]
del nid2_dbaids[6537]
if ut.get_argflag('--smaller'):
nid2_dbaids[4880].remove(33)
nid2_aid[4880].remove(3690)
nid2_aid[6610].remove(7408)
#del nid2_aid[4880]
#del nid2_dbaids[4880]
aids = ut.flatten(nid2_aid.values())
temp_nids = [1] * len(aids)
postcut = ut.get_argflag('--postcut')
aids_list = ibs.group_annots_by_name(aids)[0]
ensure_edges = 'all' if True or not postcut else None
unlabeled_graph = viz_graph.make_netx_graph_from_aid_groups(
ibs,
aids_list,
#invis_edges=invis_edges,
ensure_edges=ensure_edges,
temp_nids=temp_nids)
viz_graph.color_by_nids(unlabeled_graph,
unique_nids=[1] *
len(list(unlabeled_graph.nodes())))
viz_graph.ensure_node_images(ibs, unlabeled_graph)
nx.set_node_attributes(unlabeled_graph, 'shape', 'rect')
#unlabeled_graph = unlabeled_graph.to_undirected()
# Find the "database exemplars for these annots"
if False:
gt_aids = ibs.get_annot_groundtruth(aids)
gt_aids = [ut.setdiff(s, aids) for s in gt_aids]
dbaids = ut.unique(ut.flatten(gt_aids))
dbaids = ibs.filter_annots_general(dbaids, minqual='good')
ibs.get_annot_quality_texts(dbaids)
else:
dbaids = ut.flatten(nid2_dbaids.values())
exemplars = nx.DiGraph()
#graph = exemplars # NOQA
exemplars.add_nodes_from(dbaids)
def add_clique(graph, nodes, edgeattrs={}, nodeattrs={}):
edge_list = ut.upper_diag_self_prodx(nodes)
graph.add_edges_from(edge_list, **edgeattrs)
return edge_list
for aids_, nid in zip(*ibs.group_annots_by_name(dbaids)):
add_clique(exemplars, aids_)
viz_graph.ensure_node_images(ibs, exemplars)
viz_graph.color_by_nids(exemplars, ibs=ibs)
nx.set_node_attributes(unlabeled_graph, 'framewidth', False)
nx.set_node_attributes(exemplars, 'framewidth', 4.0)
nx.set_node_attributes(unlabeled_graph, 'group', 'unlab')
nx.set_node_attributes(exemplars, 'group', 'exemp')
#big_graph = nx.compose_all([unlabeled_graph])
big_graph = nx.compose_all([exemplars, unlabeled_graph])
# add sparse connections from unlabeled to exemplars
import numpy as np
rng = np.random.RandomState(0)
if True or not postcut:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(list(exemplars.nodes())))
flags = np.logical_or(exnids == nid_, flags)
exmatches = ut.compress(list(exemplars.nodes()), flags)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)),
color=pt.ORANGE,
implicit=True)
else:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
exmatches = ut.compress(list(exemplars.nodes()), flags)
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(exmatches))
exmatches = ut.compress(exmatches, exnids == nid_)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)))
pass
nx.set_node_attributes(big_graph, 'shape', 'rect')
#if False and postcut:
# ut.nx_delete_node_attr(big_graph, 'nid')
# ut.nx_delete_edge_attr(big_graph, 'color')
# viz_graph.ensure_graph_nid_labels(big_graph, ibs=ibs)
# viz_graph.color_by_nids(big_graph, ibs=ibs)
# big_graph = big_graph.to_undirected()
layoutkw = {
'sep': 1 / 5,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
as_directed = False
#as_directed = True
#hacknode = True
hacknode = 0
graph = big_graph
ut.nx_ensure_agraph_color(graph)
if hacknode:
nx.set_edge_attributes(graph, 'taillabel',
{e: str(e[0])
for e in graph.edges()})
nx.set_edge_attributes(graph, 'headlabel',
{e: str(e[1])
for e in graph.edges()})
explicit_graph = pt.get_explicit_graph(graph)
_, layout_info = pt.nx_agraph_layout(explicit_graph,
orig_graph=graph,
inplace=True,
**layoutkw)
if ut.get_argflag('--smaller'):
graph.node[7660]['pos'] = np.array([550, 350])
graph.node[6120]['pos'] = np.array([200, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([200, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw)
elif ut.get_argflag('--small'):
graph.node[7660]['pos'] = np.array([750, 350])
graph.node[33]['pos'] = np.array([300, 600]) + np.array([350, -400])
graph.node[6120]['pos'] = np.array([500, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([410, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw)
if not postcut:
#pt.show_nx(graph.to_undirected(), layout='agraph', layoutkw=layoutkw,
# as_directed=False)
#pt.show_nx(graph, layout='agraph', layoutkw=layoutkw,
# as_directed=as_directed, hacknode=hacknode)
pt.show_nx(graph,
layout='custom',
layoutkw=layoutkw,
as_directed=as_directed,
hacknode=hacknode)
else:
#explicit_graph = pt.get_explicit_graph(graph)
#_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph,
# **layoutkw)
#layout_info['edge']['alpha'] = .8
#pt.apply_graph_layout_attrs(graph, layout_info)
#graph_layout_attrs = layout_info['graph']
##edge_layout_attrs = layout_info['edge']
##node_layout_attrs = layout_info['node']
#for key, vals in layout_info['node'].items():
# #print('[special] key = %r' % (key,))
# nx.set_node_attributes(graph, key, vals)
#for key, vals in layout_info['edge'].items():
# #print('[special] key = %r' % (key,))
# nx.set_edge_attributes(graph, key, vals)
#nx.set_edge_attributes(graph, 'alpha', .8)
#graph.graph['splines'] = graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'polyline' # graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'line'
cut_graph = graph.copy()
edge_list = list(cut_graph.edges())
edge_nids = np.array(ibs.unflat_map(ibs.get_annot_nids, edge_list))
cut_flags = edge_nids.T[0] != edge_nids.T[1]
cut_edges = ut.compress(edge_list, cut_flags)
cut_graph.remove_edges_from(cut_edges)
ut.nx_delete_node_attr(cut_graph, 'nid')
viz_graph.ensure_graph_nid_labels(cut_graph, ibs=ibs)
#ut.nx_get_default_node_attributes(exemplars, 'color', None)
ut.nx_delete_node_attr(cut_graph,
'color',
nodes=unlabeled_graph.nodes())
aid2_color = ut.nx_get_default_node_attributes(cut_graph, 'color',
None)
nid2_colors = ut.group_items(aid2_color.values(),
ibs.get_annot_nids(aid2_color.keys()))
nid2_colors = ut.map_dict_vals(ut.filter_Nones, nid2_colors)
nid2_colors = ut.map_dict_vals(ut.unique, nid2_colors)
#for val in nid2_colors.values():
# assert len(val) <= 1
# Get initial colors
nid2_color_ = {
nid: colors_[0]
for nid, colors_ in nid2_colors.items() if len(colors_) == 1
}
graph = cut_graph
viz_graph.color_by_nids(cut_graph, ibs=ibs, nid2_color_=nid2_color_)
nx.set_node_attributes(cut_graph, 'framewidth', 4)
pt.show_nx(cut_graph,
layout='custom',
layoutkw=layoutkw,
as_directed=as_directed,
hacknode=hacknode)
pt.zoom_factory()
def setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(
ut.hz_str(
'prob_annots = ',
ut.array2string2(prob_annots,
precision=2,
max_line_width=140,
suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2]
candset = {
point: thresh_points[np.abs(thresh_points - point) < quant]
for point in thresh_points
}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(
ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {
ax: others
for ax, others in enumerate(covering_sets)
}
soln_cover = ut.setcover_ilp(candidate_sets_dict,
max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2])
for ax, other_xs in enumerate(current_covers)
for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids],
allow_directed=True,
ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth',
{aid: 4.0
for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep': 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
def double_depcache_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=.5
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=5
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = double_depcache_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
ibs = ibeis.opendb('testdb1')
reduced = True
implicit = True
annot_graph = ibs.depc_annot.make_graph(reduced=reduced, implicit=implicit)
image_graph = ibs.depc_image.make_graph(reduced=reduced, implicit=implicit)
to_rename = ut.isect(image_graph.nodes(), annot_graph.nodes())
nx.relabel_nodes(annot_graph, {x: 'annot_' + x
for x in to_rename},
copy=False)
nx.relabel_nodes(image_graph, {x: 'image_' + x
for x in to_rename},
copy=False)
graph = nx.compose_all([image_graph, annot_graph])
#graph = nx.union_all([image_graph, annot_graph], rename=('image', 'annot'))
# userdecision = ut.nx_makenode(graph, 'user decision', shape='rect', color=pt.DARK_YELLOW, style='diagonals')
# userdecision = ut.nx_makenode(graph, 'user decision', shape='circle', color=pt.DARK_YELLOW)
userdecision = ut.nx_makenode(
graph,
'User decision',
shape='rect',
#width=100, height=100,
color=pt.YELLOW,
style='diagonals')
#longcat = True
longcat = False
#edge = ('feat', 'neighbor_index')
#data = graph.get_edge_data(*edge)[0]
#print('data = %r' % (data,))
#graph.remove_edge(*edge)
## hack
#graph.add_edge('featweight', 'neighbor_index', **data)
graph.add_edge('detections',
userdecision,
constraint=longcat,
color=pt.PINK)
graph.add_edge(userdecision,
'annotations',
constraint=longcat,
color=pt.PINK)
# graph.add_edge(userdecision, 'annotations', implicit=True, color=[0, 0, 0])
if not longcat:
pass
#graph.add_edge('images', 'annotations', style='invis')
#graph.add_edge('thumbnails', 'annotations', style='invis')
#graph.add_edge('thumbnails', userdecision, style='invis')
graph.remove_node('Has_Notch')
graph.remove_node('annotmask')
layoutkw = {
'ranksep': 5,
'nodesep': 5,
'dpi': 96,
# 'nodesep': 1,
}
ns = 1000
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', 'Ubuntu')
ut.nx_set_default_node_attributes(graph, 'style', 'filled')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
#for u, v, d in graph.edge(data=True):
for u, vkd in graph.edge.items():
for v, dk in vkd.items():
for k, d in dk.items():
localid = d.get('local_input_id')
if localid:
# d['headlabel'] = localid
if localid not in ['1']:
d['taillabel'] = localid
#d['label'] = localid
if d.get('taillabel') in {'1'}:
del d['taillabel']
node_alias = {
'chips': 'Chip',
'images': 'Image',
'feat': 'Feat',
'featweight': 'Feat Weights',
'thumbnails': 'Thumbnail',
'detections': 'Detections',
'annotations': 'Annotation',
'Notch_Tips': 'Notch Tips',
'probchip': 'Prob Chip',
'Cropped_Chips': 'Croped Chip',
'Trailing_Edge': 'Trailing\nEdge',
'Block_Curvature': 'Block\nCurvature',
# 'BC_DTW': 'block curvature /\n dynamic time warp',
'BC_DTW': 'DTW Distance',
'vsone': 'Hots vsone',
'feat_neighbs': 'Nearest\nNeighbors',
'neighbor_index': 'Neighbor\nIndex',
'vsmany': 'Hots vsmany',
'annot_labeler': 'Annot Labeler',
'labeler': 'Labeler',
'localizations': 'Localizations',
'classifier': 'Classifier',
'sver': 'Spatial\nVerification',
'Classifier': 'Existence',
'image_labeler': 'Image Labeler',
}
node_alias = {
'Classifier': 'existence',
'feat_neighbs': 'neighbors',
'sver': 'spatial_verification',
'Cropped_Chips': 'cropped_chip',
'BC_DTW': 'dtw_distance',
'Block_Curvature': 'curvature',
'Trailing_Edge': 'trailing_edge',
'Notch_Tips': 'notch_tips',
'thumbnails': 'thumbnail',
'images': 'image',
'annotations': 'annotation',
'chips': 'chip',
#userdecision: 'User de'
}
node_alias = ut.delete_dict_keys(
node_alias, ut.setdiff(node_alias.keys(), graph.nodes()))
nx.relabel_nodes(graph, node_alias, copy=False)
fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#pt.gca().set_aspect('equal')
#pt.figure()
pt.show_nx(graph, layoutkw=layoutkw, fontkw=fontkw)
pt.zoom_factory()
def graphcut_flow():
r"""
Returns:
?: name
CommandLine:
python -m ibeis.scripts.specialdraw graphcut_flow --show --save cutflow.png --diskshow --clipwhite
python -m ibeis.scripts.specialdraw graphcut_flow --save figures4/cutiden.png --diskshow --clipwhite --dpath ~/latex/crall-candidacy-2015/ --figsize=24,10 --arrow-width=2.0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> graphcut_flow()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
pt.ensure_pylab_qt4()
import networkx as nx
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [
ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)
]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
ns = 512
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
annot1 = ut.nx_makenode(graph,
'Unlabeled\nannotations\n(query)',
width=ns,
height=ns,
groupid='annot',
color=p_shade2)
annot2 = ut.nx_makenode(graph,
'Labeled\nannotations\n(database)',
width=ns,
height=ns,
groupid='annot',
color=s1_shade2)
occurprob = ut.nx_makenode(graph,
'Dense \nprobabilities',
color=lighten_hex(p_shade2, .1))
cacheprob = ut.nx_makenode(graph,
'Cached \nprobabilities',
color=lighten_hex(s1_shade2, .1))
sparseprob = ut.nx_makenode(graph,
'Sparse\nprobabilities',
color=lighten_hex(c_shade2, .1))
graph.add_edge(annot1, occurprob)
graph.add_edge(annot1, sparseprob)
graph.add_edge(annot2, sparseprob)
graph.add_edge(annot2, cacheprob)
matchgraph = ut.nx_makenode(graph,
'Graph of\npotential matches',
color=lighten_hex(s2_shade2, .1))
cutalgo = ut.nx_makenode(graph,
'Graph cut algorithm',
color=lighten_hex(s2_shade2, .2),
shape='ellipse')
cc_names = ut.nx_makenode(
graph,
'Identifications,\n splits, and merges are\nconnected compoments',
color=lighten_hex(s2_shade2, .3))
graph.add_edge(occurprob, matchgraph)
graph.add_edge(sparseprob, matchgraph)
graph.add_edge(cacheprob, matchgraph)
graph.add_edge(matchgraph, cutalgo)
graph.add_edge(cutalgo, cc_names)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
ut.nx_set_default_node_attributes(graph, 'regular', False)
layoutkw = {
'prog': 'dot',
'rankdir': 'LR',
'splines': 'line',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
}
fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=14)
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def general_identify_flow():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw general_identify_flow --show --save pairsim.png --dpi=100 --diskshow --clipwhite
python -m ibeis.scripts.specialdraw general_identify_flow --dpi=200 --diskshow --clipwhite --dpath ~/latex/cand/ --figsize=20,10 --save figures4/pairprob.png --arrow-width=2.0
Example:
>>> # SCRIPT
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> general_identify_flow()
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(name + str(n), **attrkw) for n in range(num)]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
ns = 512
ut.inject_func_as_method(graph, ut.nx_makenode)
annot1_color = p_shade2
annot2_color = s1_shade2
#annot1_color2 = pt.color_funcs.lighten_rgb(colors.hex2color(annot1_color), .01)
annot1 = graph.nx_makenode('Annotation X',
width=ns,
height=ns,
groupid='annot',
color=annot1_color)
annot2 = graph.nx_makenode('Annotation Y',
width=ns,
height=ns,
groupid='annot',
color=annot2_color)
featX = graph.nx_makenode('Features X',
size=(ns / 1.2, ns / 2),
groupid='feats',
color=lighten_hex(annot1_color, .1))
featY = graph.nx_makenode('Features Y',
size=(ns / 1.2, ns / 2),
groupid='feats',
color=lighten_hex(annot2_color, .1))
#'#4771B3')
global_pairvec = graph.nx_makenode(
'Global similarity\n(viewpoint, quality, ...)',
width=ns * ut.PHI * 1.2,
color=s2_shade2)
findnn = graph.nx_makenode('Find correspondences\n(nearest neighbors)',
shape='ellipse',
color=c_shade2)
local_pairvec = graph.nx_makenode(
'Local similarities\n(LNBNN, spatial error, ...)',
size=(ns * 2.2, ns),
color=lighten_hex(c_shade2, .1))
agglocal = graph.nx_makenode('Aggregate',
size=(ns / 1.1, ns / 2),
shape='ellipse',
color=lighten_hex(c_shade2, .2))
catvecs = graph.nx_makenode('Concatenate',
size=(ns / 1.1, ns / 2),
shape='ellipse',
color=lighten_hex(s2_shade2, .1))
pairvec = graph.nx_makenode('Vector of\npairwise similarities',
color=lighten_hex(s2_shade2, .2))
classifier = graph.nx_makenode('Classifier\n(SVM/RF/DNN)',
color=lighten_hex(s2_shade2, .3))
prob = graph.nx_makenode(
'Matching Probability\n(same individual given\nsimilar viewpoint)',
color=lighten_hex(s2_shade2, .4))
graph.add_edge(annot1, global_pairvec)
graph.add_edge(annot2, global_pairvec)
add_edge2(annot1, featX)
add_edge2(annot2, featY)
add_edge2(featX, findnn)
add_edge2(featY, findnn)
add_edge2(findnn, local_pairvec)
graph.add_edge(local_pairvec, agglocal, constraint=True)
graph.add_edge(agglocal, catvecs, constraint=False)
graph.add_edge(global_pairvec, catvecs)
graph.add_edge(catvecs, pairvec)
# graph.add_edge(annot1, classifier, style='invis')
# graph.add_edge(pairvec, classifier , constraint=False)
graph.add_edge(pairvec, classifier)
graph.add_edge(classifier, prob)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
#ut.nx_set_default_node_attributes(graph, 'fillcolor', nx.get_node_attributes(graph, 'color'))
#ut.nx_set_default_node_attributes(graph, 'style', 'rounded')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'xlabel',
nx.get_node_attributes(graph, 'label'))
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns)
ut.nx_set_default_node_attributes(graph, 'regular', False)
#font = 'MonoDyslexic'
#font = 'Mono_Dyslexic'
font = 'Ubuntu'
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', font)
#ut.nx_delete_node_attr(graph, 'width')
#ut.nx_delete_node_attr(graph, 'height')
#ut.nx_delete_node_attr(graph, 'fixedsize')
#ut.nx_delete_node_attr(graph, 'style')
#ut.nx_delete_node_attr(graph, 'regular')
#ut.nx_delete_node_attr(graph, 'shape')
#graph.node[annot1]['label'] = "<f0> left|<f1> mid\ dle|<f2> right"
#graph.node[annot2]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['shape'] = 'none'
#graph.node[annot1]['margin'] = '0'
layoutkw = {
'forcelabels': True,
'prog': 'dot',
'rankdir': 'LR',
# 'splines': 'curved',
'splines': 'line',
'samplepoints': 20,
'showboxes': 1,
# 'splines': 'polyline',
#'splines': 'spline',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
#'inputscale': 72,
# 'inputscale': 1,
# 'dpi': 72,
# 'concentrate': 'true', # merges edge lines
# 'splines': 'ortho',
# 'aspect': 1,
# 'ratio': 'compress',
# 'size': '5,4000',
# 'rank': 'max',
}
#fontkw = dict(fontfamilty='sans-serif', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=20)
fontkw = dict(fontname=font, fontweight='light', fontsize=12)
#prop = fm.FontProperties(fname='/usr/share/fonts/truetype/groovygh.ttf')
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def draw_bayesian_model(model, evidence={}, soft_evidence={}, fnum=None,
pnum=None, **kwargs):
from pgmpy.models import BayesianModel
if not isinstance(model, BayesianModel):
model = model.to_bayesian_model()
import plottool as pt
import networkx as nx
kwargs = kwargs.copy()
factor_list = kwargs.pop('factor_list', [])
ttype_colors, ttype_scalars = make_colorcodes(model)
textprops = {
'horizontalalignment': 'left', 'family': 'monospace', 'size': 8, }
# build graph attrs
tup = get_node_viz_attrs(
model, evidence, soft_evidence, factor_list, ttype_colors, **kwargs)
node_color, pos_list, pos_dict, takws = tup
# draw graph
has_infered = evidence or 'factor_list' in kwargs
if False:
fig = pt.figure(fnum=fnum, pnum=pnum, doclf=True) # NOQA
ax = pt.gca()
drawkw = dict(pos=pos_dict, ax=ax, with_labels=True, node_size=1100,
node_color=node_color)
nx.draw(model, **drawkw)
else:
# BE VERY CAREFUL
if 1:
graph = model.copy()
graph.__class__ = nx.DiGraph
graph.graph['groupattrs'] = ut.ddict(dict)
#graph = model.
if getattr(graph, 'ttype2_cpds', None) is not None:
# Add invis edges and ttype groups
for ttype in model.ttype2_cpds.keys():
ttype_cpds = model.ttype2_cpds[ttype]
# use defined ordering
ttype_nodes = ut.list_getattr(ttype_cpds, 'variable')
# ttype_nodes = sorted(ttype_nodes)
invis_edges = list(ut.itertwo(ttype_nodes))
graph.add_edges_from(invis_edges)
nx.set_edge_attributes(graph, 'style', {edge: 'invis' for edge in invis_edges})
nx.set_node_attributes(graph, 'groupid', {node: ttype for node in ttype_nodes})
graph.graph['groupattrs'][ttype]['rank'] = 'same'
graph.graph['groupattrs'][ttype]['cluster'] = False
else:
graph = model
pt.show_nx(graph, layout_kw={'prog': 'dot'}, fnum=fnum, pnum=pnum, verbose=0)
pt.zoom_factory()
fig = pt.gcf()
ax = pt.gca()
pass
hacks = [pt.draw_text_annotations(textprops=textprops, **takw)
for takw in takws if takw]
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
if 'name' in model.ttype2_template:
num_names = len(model.ttype2_template['name'].basis)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
title = 'num_names=%r, num_annots=%r' % (num_names, num_annots,)
else:
title = ''
map_assign = kwargs.get('map_assign', None)
def word_insert(text):
return '' if len(text) == 0 else text + ' '
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
title += '\n%sMAP: ' % (word_insert(kwargs.get('method', '')))
title += map_assign + ' @' + '%.2f%%' % (100 * map_prob,)
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
for hack in hacks:
hack()
if has_infered:
# Hack in colorbars
# if ut.list_type(basis) is int:
# pt.colorbar(scalars, colors, lbl='score', ticklabels=np.array(basis) + 1)
# else:
# pt.colorbar(scalars, colors, lbl='score', ticklabels=basis)
keys = ['name', 'score']
locs = ['left', 'right']
for key, loc in zip(keys, locs):
if key in ttype_colors:
basis = model.ttype2_template[key].basis
# scalars =
colors = ttype_colors[key]
scalars = ttype_scalars[key]
pt.colorbar(scalars, colors, lbl=key, ticklabels=basis,
ticklocation=loc)
def _model_data_flow_to_networkx(model_info):
layers = model_info['layer']
import networkx as nx
G = nx.DiGraph()
prev = None
# Stores last node with the data for this layer in it
prev_map = {}
SHOW_LOOPS = False
for layer in layers:
name = layer.get('name')
print('name = {!r}'.format(name))
G.add_node(name)
bottom = set(layer.get('bottom', []))
top = set(layer.get('top', []))
both = top.intersection(bottom)
if both:
if prev is None:
prev = both
for b in both:
prev_map[b] = name
for b in prev:
print(' * b = {!r}'.format(b))
G.add_edge(b, name, constraint=False)
for b in both:
print(' * b = {!r}'.format(b))
kw = {}
if not G.has_edge(b, name):
kw['color'] = 'red'
G.add_edge(b, name, constraint=True, **kw)
prev = [name]
else:
prev = None
# for b in (bottom - both):
for b in bottom:
print(' * b = {!r}'.format(b))
constraint = True
G.add_edge(prev_map.get(b, b), name, constraint=constraint)
if SHOW_LOOPS:
G.add_edge(b, name)
# for t in (bottom - top):
for t in top:
print(' * t = {!r}'.format(t))
constraint = True
G.add_edge(name, prev_map.get(t, t), constraint=constraint)
if SHOW_LOOPS:
G.add_edge(name, t)
G.remove_edges_from(list(G.selfloop_edges()))
import plottool as pt
pt.qtensure()
pt.show_nx(G, arrow_width=1)
pt.adjust_subplots(left=0, right=1, top=1, bottom=0)
pt.pan_factory()
pt.zoom_factory()
list(nx.topological_sort(G))
def show_graph(infr,
graph=None,
use_image=False,
update_attrs=True,
with_colorbar=False,
pnum=(1, 1, 1),
zoomable=True,
pickable=False,
**kwargs):
r"""
Args:
infr (?):
graph (None): (default = None)
use_image (bool): (default = False)
update_attrs (bool): (default = True)
with_colorbar (bool): (default = False)
pnum (tuple): plot number(default = (1, 1, 1))
zoomable (bool): (default = True)
pickable (bool): (de = False)
**kwargs: verbose, with_labels, fnum, layout, ax, pos, img_dict,
title, layoutkw, framewidth, modify_ax, as_directed,
hacknoedge, hacknode, node_labels, arrow_width, fontsize,
fontweight, fontname, fontfamilty, fontproperties
CommandLine:
python -m ibeis.algo.graph.mixin_viz GraphVisualization.show_graph --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.graph.mixin_viz import * # NOQA
>>> from ibeis.algo.graph import demo
>>> import plottool as pt
>>> infr = demo.demodata_infr(ccs=ut.estarmap(
>>> range, [(1, 6), (6, 10), (10, 13), (13, 15), (15, 16),
>>> (17, 20)]))
>>> pnum_ = pt.make_pnum_nextgen(nRows=1, nCols=3)
>>> infr.show_graph(show_cand=True, simple_labels=True, pickable=True, fnum=1, pnum=pnum_())
>>> infr.add_feedback((1, 5), INCMP)
>>> infr.add_feedback((14, 18), INCMP)
>>> infr.refresh_candidate_edges()
>>> infr.show_graph(show_cand=True, simple_labels=True, pickable=True, fnum=1, pnum=pnum_())
>>> infr.add_feedback((17, 18), NEGTV) # add inconsistency
>>> infr.apply_nondynamic_update()
>>> infr.show_graph(show_cand=True, simple_labels=True, pickable=True, fnum=1, pnum=pnum_())
>>> ut.show_if_requested()
"""
import plottool as pt
if graph is None:
graph = infr.graph
# kwargs['fontsize'] = kwargs.get('fontsize', 8)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# default_update_kw = ut.get_func_kwargs(infr.update_visual_attrs)
# update_kw = ut.update_existing(default_update_kw, kwargs)
# infr.update_visual_attrs(**update_kw)
if update_attrs:
infr.update_visual_attrs(graph=graph, **kwargs)
verbose = kwargs.pop('verbose', infr.verbose)
pt.show_nx(graph,
layout='custom',
as_directed=False,
modify_ax=False,
use_image=use_image,
pnum=pnum,
verbose=verbose,
**kwargs)
if zoomable:
pt.zoom_factory()
pt.pan_factory(pt.gca())
# if with_colorbar:
# # Draw a colorbar
# _normal_ticks = np.linspace(0, 1, num=11)
# _normal_scores = np.linspace(0, 1, num=500)
# _normal_colors = infr.get_colored_weights(_normal_scores)
# cb = pt.colorbar(_normal_scores, _normal_colors, lbl='weights',
# ticklabels=_normal_ticks)
# # point to threshold location
# thresh = None
# if thresh is not None:
# xy = (1, thresh)
# xytext = (2.5, .3 if thresh < .5 else .7)
# cb.ax.annotate('threshold', xy=xy, xytext=xytext,
# arrowprops=dict(
# alpha=.5, fc="0.6",
# connectionstyle="angle3,angleA=90,angleB=0"),)
# infr.graph
if graph.graph.get('dark_background', None):
pt.dark_background(force=True)
if pickable:
fig = pt.gcf()
fig.canvas.mpl_connect('pick_event', ut.partial(on_pick,
infr=infr))
def intraoccurrence_connected():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --postcut
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --smaller
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = intraoccurrence_connected()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='PZ_Master1')
nid2_aid = {
#4880: [3690, 3696, 3703, 3706, 3712, 3721],
4880: [3690, 3696, 3703],
6537: [3739],
6653: [7671],
6610: [7566, 7408],
#6612: [7664, 7462, 7522],
#6624: [7465, 7360],
#6625: [7746, 7383, 7390, 7477, 7376, 7579],
6630: [7586, 7377, 7464, 7478],
#6677: [7500]
}
nid2_dbaids = {
4880: [33, 6120, 7164],
6537: [7017, 7206],
6653: [7660]
}
if ut.get_argflag('--small') or ut.get_argflag('--smaller'):
del nid2_aid[6630]
del nid2_aid[6537]
del nid2_dbaids[6537]
if ut.get_argflag('--smaller'):
nid2_dbaids[4880].remove(33)
nid2_aid[4880].remove(3690)
nid2_aid[6610].remove(7408)
#del nid2_aid[4880]
#del nid2_dbaids[4880]
aids = ut.flatten(nid2_aid.values())
temp_nids = [1] * len(aids)
postcut = ut.get_argflag('--postcut')
aids_list = ibs.group_annots_by_name(aids)[0]
ensure_edges = 'all' if True or not postcut else None
unlabeled_graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, aids_list,
#invis_edges=invis_edges,
ensure_edges=ensure_edges, temp_nids=temp_nids)
viz_graph.color_by_nids(unlabeled_graph, unique_nids=[1] *
len(list(unlabeled_graph.nodes())))
viz_graph.ensure_node_images(ibs, unlabeled_graph)
nx.set_node_attributes(unlabeled_graph, 'shape', 'rect')
#unlabeled_graph = unlabeled_graph.to_undirected()
# Find the "database exemplars for these annots"
if False:
gt_aids = ibs.get_annot_groundtruth(aids)
gt_aids = [ut.setdiff(s, aids) for s in gt_aids]
dbaids = ut.unique(ut.flatten(gt_aids))
dbaids = ibs.filter_annots_general(dbaids, minqual='good')
ibs.get_annot_quality_texts(dbaids)
else:
dbaids = ut.flatten(nid2_dbaids.values())
exemplars = nx.DiGraph()
#graph = exemplars # NOQA
exemplars.add_nodes_from(dbaids)
def add_clique(graph, nodes, edgeattrs={}, nodeattrs={}):
edge_list = ut.upper_diag_self_prodx(nodes)
graph.add_edges_from(edge_list, **edgeattrs)
return edge_list
for aids_, nid in zip(*ibs.group_annots_by_name(dbaids)):
add_clique(exemplars, aids_)
viz_graph.ensure_node_images(ibs, exemplars)
viz_graph.color_by_nids(exemplars, ibs=ibs)
nx.set_node_attributes(unlabeled_graph, 'framewidth', False)
nx.set_node_attributes(exemplars, 'framewidth', 4.0)
nx.set_node_attributes(unlabeled_graph, 'group', 'unlab')
nx.set_node_attributes(exemplars, 'group', 'exemp')
#big_graph = nx.compose_all([unlabeled_graph])
big_graph = nx.compose_all([exemplars, unlabeled_graph])
# add sparse connections from unlabeled to exemplars
import numpy as np
rng = np.random.RandomState(0)
if True or not postcut:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(list(exemplars.nodes())))
flags = np.logical_or(exnids == nid_, flags)
exmatches = ut.compress(list(exemplars.nodes()), flags)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)),
color=pt.ORANGE, implicit=True)
else:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
exmatches = ut.compress(list(exemplars.nodes()), flags)
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(exmatches))
exmatches = ut.compress(exmatches, exnids == nid_)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)))
pass
nx.set_node_attributes(big_graph, 'shape', 'rect')
#if False and postcut:
# ut.nx_delete_node_attr(big_graph, 'nid')
# ut.nx_delete_edge_attr(big_graph, 'color')
# viz_graph.ensure_graph_nid_labels(big_graph, ibs=ibs)
# viz_graph.color_by_nids(big_graph, ibs=ibs)
# big_graph = big_graph.to_undirected()
layoutkw = {
'sep' : 1 / 5,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
as_directed = False
#as_directed = True
#hacknode = True
hacknode = 0
graph = big_graph
ut.nx_ensure_agraph_color(graph)
if hacknode:
nx.set_edge_attributes(graph, 'taillabel', {e: str(e[0]) for e in graph.edges()})
nx.set_edge_attributes(graph, 'headlabel', {e: str(e[1]) for e in graph.edges()})
explicit_graph = pt.get_explicit_graph(graph)
_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph,
inplace=True, **layoutkw)
if ut.get_argflag('--smaller'):
graph.node[7660]['pos'] = np.array([550, 350])
graph.node[6120]['pos'] = np.array([200, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([200, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph,
inplace=True, **layoutkw)
elif ut.get_argflag('--small'):
graph.node[7660]['pos'] = np.array([750, 350])
graph.node[33]['pos'] = np.array([300, 600]) + np.array([350, -400])
graph.node[6120]['pos'] = np.array([500, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([410, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph,
inplace=True, **layoutkw)
if not postcut:
#pt.show_nx(graph.to_undirected(), layout='agraph', layoutkw=layoutkw,
# as_directed=False)
#pt.show_nx(graph, layout='agraph', layoutkw=layoutkw,
# as_directed=as_directed, hacknode=hacknode)
pt.show_nx(graph, layout='custom', layoutkw=layoutkw,
as_directed=as_directed, hacknode=hacknode)
else:
#explicit_graph = pt.get_explicit_graph(graph)
#_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph,
# **layoutkw)
#layout_info['edge']['alpha'] = .8
#pt.apply_graph_layout_attrs(graph, layout_info)
#graph_layout_attrs = layout_info['graph']
##edge_layout_attrs = layout_info['edge']
##node_layout_attrs = layout_info['node']
#for key, vals in layout_info['node'].items():
# #print('[special] key = %r' % (key,))
# nx.set_node_attributes(graph, key, vals)
#for key, vals in layout_info['edge'].items():
# #print('[special] key = %r' % (key,))
# nx.set_edge_attributes(graph, key, vals)
#nx.set_edge_attributes(graph, 'alpha', .8)
#graph.graph['splines'] = graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'polyline' # graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'line'
cut_graph = graph.copy()
edge_list = list(cut_graph.edges())
edge_nids = np.array(ibs.unflat_map(ibs.get_annot_nids, edge_list))
cut_flags = edge_nids.T[0] != edge_nids.T[1]
cut_edges = ut.compress(edge_list, cut_flags)
cut_graph.remove_edges_from(cut_edges)
ut.nx_delete_node_attr(cut_graph, 'nid')
viz_graph.ensure_graph_nid_labels(cut_graph, ibs=ibs)
#ut.nx_get_default_node_attributes(exemplars, 'color', None)
ut.nx_delete_node_attr(cut_graph, 'color', nodes=unlabeled_graph.nodes())
aid2_color = ut.nx_get_default_node_attributes(cut_graph, 'color', None)
nid2_colors = ut.group_items(aid2_color.values(), ibs.get_annot_nids(aid2_color.keys()))
nid2_colors = ut.map_dict_vals(ut.filter_Nones, nid2_colors)
nid2_colors = ut.map_dict_vals(ut.unique, nid2_colors)
#for val in nid2_colors.values():
# assert len(val) <= 1
# Get initial colors
nid2_color_ = {nid: colors_[0] for nid, colors_ in nid2_colors.items()
if len(colors_) == 1}
graph = cut_graph
viz_graph.color_by_nids(cut_graph, ibs=ibs, nid2_color_=nid2_color_)
nx.set_node_attributes(cut_graph, 'framewidth', 4)
pt.show_nx(cut_graph, layout='custom', layoutkw=layoutkw,
as_directed=as_directed, hacknode=hacknode)
pt.zoom_factory()
def double_depcache_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=.5
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=5
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = double_depcache_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
ibs = ibeis.opendb('testdb1')
reduced = True
implicit = True
annot_graph = ibs.depc_annot.make_graph(reduced=reduced, implicit=implicit)
image_graph = ibs.depc_image.make_graph(reduced=reduced, implicit=implicit)
to_rename = ut.isect(image_graph.nodes(), annot_graph.nodes())
nx.relabel_nodes(annot_graph, {x: 'annot_' + x for x in to_rename}, copy=False)
nx.relabel_nodes(image_graph, {x: 'image_' + x for x in to_rename}, copy=False)
graph = nx.compose_all([image_graph, annot_graph])
#graph = nx.union_all([image_graph, annot_graph], rename=('image', 'annot'))
# userdecision = ut.nx_makenode(graph, 'user decision', shape='rect', color=pt.DARK_YELLOW, style='diagonals')
# userdecision = ut.nx_makenode(graph, 'user decision', shape='circle', color=pt.DARK_YELLOW)
userdecision = ut.nx_makenode(graph, 'User decision', shape='rect',
#width=100, height=100,
color=pt.YELLOW, style='diagonals')
#longcat = True
longcat = False
#edge = ('feat', 'neighbor_index')
#data = graph.get_edge_data(*edge)[0]
#print('data = %r' % (data,))
#graph.remove_edge(*edge)
## hack
#graph.add_edge('featweight', 'neighbor_index', **data)
graph.add_edge('detections', userdecision, constraint=longcat, color=pt.PINK)
graph.add_edge(userdecision, 'annotations', constraint=longcat, color=pt.PINK)
# graph.add_edge(userdecision, 'annotations', implicit=True, color=[0, 0, 0])
if not longcat:
pass
#graph.add_edge('images', 'annotations', style='invis')
#graph.add_edge('thumbnails', 'annotations', style='invis')
#graph.add_edge('thumbnails', userdecision, style='invis')
graph.remove_node('Has_Notch')
graph.remove_node('annotmask')
layoutkw = {
'ranksep': 5,
'nodesep': 5,
'dpi': 96,
# 'nodesep': 1,
}
ns = 1000
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', 'Ubuntu')
ut.nx_set_default_node_attributes(graph, 'style', 'filled')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
#for u, v, d in graph.edge(data=True):
for u, vkd in graph.edge.items():
for v, dk in vkd.items():
for k, d in dk.items():
localid = d.get('local_input_id')
if localid:
# d['headlabel'] = localid
if localid not in ['1']:
d['taillabel'] = localid
#d['label'] = localid
if d.get('taillabel') in {'1'}:
del d['taillabel']
node_alias = {
'chips': 'Chip',
'images': 'Image',
'feat': 'Feat',
'featweight': 'Feat Weights',
'thumbnails': 'Thumbnail',
'detections': 'Detections',
'annotations': 'Annotation',
'Notch_Tips': 'Notch Tips',
'probchip': 'Prob Chip',
'Cropped_Chips': 'Croped Chip',
'Trailing_Edge': 'Trailing\nEdge',
'Block_Curvature': 'Block\nCurvature',
# 'BC_DTW': 'block curvature /\n dynamic time warp',
'BC_DTW': 'DTW Distance',
'vsone': 'Hots vsone',
'feat_neighbs': 'Nearest\nNeighbors',
'neighbor_index': 'Neighbor\nIndex',
'vsmany': 'Hots vsmany',
'annot_labeler': 'Annot Labeler',
'labeler': 'Labeler',
'localizations': 'Localizations',
'classifier': 'Classifier',
'sver': 'Spatial\nVerification',
'Classifier': 'Existence',
'image_labeler': 'Image Labeler',
}
node_alias = {
'Classifier': 'existence',
'feat_neighbs': 'neighbors',
'sver': 'spatial_verification',
'Cropped_Chips': 'cropped_chip',
'BC_DTW': 'dtw_distance',
'Block_Curvature': 'curvature',
'Trailing_Edge': 'trailing_edge',
'Notch_Tips': 'notch_tips',
'thumbnails': 'thumbnail',
'images': 'image',
'annotations': 'annotation',
'chips': 'chip',
#userdecision: 'User de'
}
node_alias = ut.delete_dict_keys(node_alias, ut.setdiff(node_alias.keys(),
graph.nodes()))
nx.relabel_nodes(graph, node_alias, copy=False)
fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#pt.gca().set_aspect('equal')
#pt.figure()
pt.show_nx(graph, layoutkw=layoutkw, fontkw=fontkw)
pt.zoom_factory()
def setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(ut.hz_str('prob_annots = ', ut.array2string2(prob_annots, precision=2, max_line_width=140, suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2 ]
candset = {point: thresh_points[np.abs(thresh_points - point) < quant] for point in thresh_points}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {ax: others for ax, others in enumerate(covering_sets)}
soln_cover = ut.setcover_ilp(candidate_sets_dict, max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2]) for ax, other_xs in enumerate(current_covers) for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids], allow_directed=True, ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth', {aid: 4.0 for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep' : 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
def graphcut_flow():
r"""
Returns:
?: name
CommandLine:
python -m ibeis.scripts.specialdraw graphcut_flow --show --save cutflow.png --diskshow --clipwhite
python -m ibeis.scripts.specialdraw graphcut_flow --save figures4/cutiden.png --diskshow --clipwhite --dpath ~/latex/crall-candidacy-2015/ --figsize=24,10 --arrow-width=2.0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> graphcut_flow()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
pt.ensure_pylab_qt4()
import networkx as nx
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
ns = 512
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
annot1 = ut.nx_makenode(graph, 'Unlabeled\nannotations\n(query)', width=ns, height=ns,
groupid='annot', color=p_shade2)
annot2 = ut.nx_makenode(graph, 'Labeled\nannotations\n(database)', width=ns, height=ns,
groupid='annot', color=s1_shade2)
occurprob = ut.nx_makenode(graph, 'Dense \nprobabilities', color=lighten_hex(p_shade2, .1))
cacheprob = ut.nx_makenode(graph, 'Cached \nprobabilities', color=lighten_hex(s1_shade2, .1))
sparseprob = ut.nx_makenode(graph, 'Sparse\nprobabilities', color=lighten_hex(c_shade2, .1))
graph.add_edge(annot1, occurprob)
graph.add_edge(annot1, sparseprob)
graph.add_edge(annot2, sparseprob)
graph.add_edge(annot2, cacheprob)
matchgraph = ut.nx_makenode(graph, 'Graph of\npotential matches', color=lighten_hex(s2_shade2, .1))
cutalgo = ut.nx_makenode(graph, 'Graph cut algorithm', color=lighten_hex(s2_shade2, .2), shape='ellipse')
cc_names = ut.nx_makenode(graph, 'Identifications,\n splits, and merges are\nconnected compoments', color=lighten_hex(s2_shade2, .3))
graph.add_edge(occurprob, matchgraph)
graph.add_edge(sparseprob, matchgraph)
graph.add_edge(cacheprob, matchgraph)
graph.add_edge(matchgraph, cutalgo)
graph.add_edge(cutalgo, cc_names)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
ut.nx_set_default_node_attributes(graph, 'regular', False)
layoutkw = {
'prog': 'dot',
'rankdir': 'LR',
'splines': 'line',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
}
fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=14)
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def general_identify_flow():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw general_identify_flow --show --save pairsim.png --dpi=100 --diskshow --clipwhite
python -m ibeis.scripts.specialdraw general_identify_flow --dpi=200 --diskshow --clipwhite --dpath ~/latex/cand/ --figsize=20,10 --save figures4/pairprob.png --arrow-width=2.0
Example:
>>> # SCRIPT
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> general_identify_flow()
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(name + str(n), **attrkw) for n in range(num)]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
ns = 512
ut.inject_func_as_method(graph, ut.nx_makenode)
annot1_color = p_shade2
annot2_color = s1_shade2
#annot1_color2 = pt.color_funcs.lighten_rgb(colors.hex2color(annot1_color), .01)
annot1 = graph.nx_makenode('Annotation X', width=ns, height=ns, groupid='annot', color=annot1_color)
annot2 = graph.nx_makenode('Annotation Y', width=ns, height=ns, groupid='annot', color=annot2_color)
featX = graph.nx_makenode('Features X', size=(ns / 1.2, ns / 2), groupid='feats', color=lighten_hex(annot1_color, .1))
featY = graph.nx_makenode('Features Y', size=(ns / 1.2, ns / 2), groupid='feats', color=lighten_hex(annot2_color, .1))
#'#4771B3')
global_pairvec = graph.nx_makenode('Global similarity\n(viewpoint, quality, ...)', width=ns * ut.PHI * 1.2, color=s2_shade2)
findnn = graph.nx_makenode('Find correspondences\n(nearest neighbors)', shape='ellipse', color=c_shade2)
local_pairvec = graph.nx_makenode('Local similarities\n(LNBNN, spatial error, ...)',
size=(ns * 2.2, ns), color=lighten_hex(c_shade2, .1))
agglocal = graph.nx_makenode('Aggregate', size=(ns / 1.1, ns / 2), shape='ellipse', color=lighten_hex(c_shade2, .2))
catvecs = graph.nx_makenode('Concatenate', size=(ns / 1.1, ns / 2), shape='ellipse', color=lighten_hex(s2_shade2, .1))
pairvec = graph.nx_makenode('Vector of\npairwise similarities', color=lighten_hex(s2_shade2, .2))
classifier = graph.nx_makenode('Classifier\n(SVM/RF/DNN)', color=lighten_hex(s2_shade2, .3))
prob = graph.nx_makenode('Matching Probability\n(same individual given\nsimilar viewpoint)', color=lighten_hex(s2_shade2, .4))
graph.add_edge(annot1, global_pairvec)
graph.add_edge(annot2, global_pairvec)
add_edge2(annot1, featX)
add_edge2(annot2, featY)
add_edge2(featX, findnn)
add_edge2(featY, findnn)
add_edge2(findnn, local_pairvec)
graph.add_edge(local_pairvec, agglocal, constraint=True)
graph.add_edge(agglocal, catvecs, constraint=False)
graph.add_edge(global_pairvec, catvecs)
graph.add_edge(catvecs, pairvec)
# graph.add_edge(annot1, classifier, style='invis')
# graph.add_edge(pairvec, classifier , constraint=False)
graph.add_edge(pairvec, classifier)
graph.add_edge(classifier, prob)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
#ut.nx_set_default_node_attributes(graph, 'fillcolor', nx.get_node_attributes(graph, 'color'))
#ut.nx_set_default_node_attributes(graph, 'style', 'rounded')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'xlabel', nx.get_node_attributes(graph, 'label'))
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns)
ut.nx_set_default_node_attributes(graph, 'regular', False)
#font = 'MonoDyslexic'
#font = 'Mono_Dyslexic'
font = 'Ubuntu'
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', font)
#ut.nx_delete_node_attr(graph, 'width')
#ut.nx_delete_node_attr(graph, 'height')
#ut.nx_delete_node_attr(graph, 'fixedsize')
#ut.nx_delete_node_attr(graph, 'style')
#ut.nx_delete_node_attr(graph, 'regular')
#ut.nx_delete_node_attr(graph, 'shape')
#graph.node[annot1]['label'] = "<f0> left|<f1> mid\ dle|<f2> right"
#graph.node[annot2]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['shape'] = 'none'
#graph.node[annot1]['margin'] = '0'
layoutkw = {
'forcelabels': True,
'prog': 'dot',
'rankdir': 'LR',
# 'splines': 'curved',
'splines': 'line',
'samplepoints': 20,
'showboxes': 1,
# 'splines': 'polyline',
#'splines': 'spline',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
#'inputscale': 72,
# 'inputscale': 1,
# 'dpi': 72,
# 'concentrate': 'true', # merges edge lines
# 'splines': 'ortho',
# 'aspect': 1,
# 'ratio': 'compress',
# 'size': '5,4000',
# 'rank': 'max',
}
#fontkw = dict(fontfamilty='sans-serif', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=20)
fontkw = dict(fontname=font, fontweight='light', fontsize=12)
#prop = fm.FontProperties(fname='/usr/share/fonts/truetype/groovygh.ttf')
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()