def run() -> None:
"""Main program loop"""
args = parse_args()
dm = DataModel(args.url, args.http_username, args.http_password, args.uid,
args.cacert)
dm.load()
if dm.status != 200:
print("{} Status code {}".format(str(datetime.datetime.now()),
dm.status))
return
try:
old_dm = pickle.load(open("cache.dat", "rb"))
if old_dm == dm:
return
except FileNotFoundError:
print("First run")
print("{} Graphing changes".format(str(datetime.datetime.now())))
pickle.dump(dm, open("cache.dat", "wb"))
dot = graphviz.Digraph(comment="Double edge facts")
# for obj in dm.objects:
# dot.node(obj, obj)
for fact in dm.facts:
name, s, d, di = fact
if name == "mentions":
continue
if not d:
continue
dot.node(s, s)
dot.node(d, d)
if di:
dot.edge(s, d, label=name, dir="both")
else:
dot.edge(s, d, label=name)
if args.dump_source:
with open(os.path.join(args.dump_source, "double.dot"), "w") as f:
f.write(dot.source)
dot.render("output/double", format="png", renderer="cairo")
dot = graphviz.Digraph(comment="Single edge facts")
for fact in dm.facts:
name, s, d, di = fact
if d:
continue
dot.node(name, label=name, shape="diamond")
dot.node(s, s)
dot.edge(s, name)
if args.dump_source:
with open(os.path.join(args.dump_source, "single.dot"), "w") as f:
f.write(dot.source)
dot.render("output/single", format="png", renderer="cairo")
dot = graphviz.Digraph(comment="All Double edge facts")
for obj in dm.objects:
dot.node(obj, obj)
for fact in dm.facts:
name, s, d, di = fact
if not d:
continue
dot.node(s, s)
dot.node(d, d)
if di:
dot.edge(s, d, label=name, dir="both")
else:
dot.edge(s, d, label=name)
if args.dump_source:
with open(os.path.join(args.dump_source, "complete.dot"), "w") as f:
f.write(dot.source)
dot.render("output/complete", format="png", renderer="cairo")
if args.parent_id:
try:
os.environ.pop("https_proxy")
except KeyError:
pass
try:
os.environ.pop("http_proxy")
except KeyError:
pass
verify_ssl = False if args.cacert else True
confluence = Confluence(
url=args.confluence_url,
username=args.confluence_user,
password=args.confluence_password,
verify_ssl=verify_ssl,
)
confluence.attach_file(
"output/double.cairo.png",
page_id=args.parent_id,
title="Double Edged Facts",
)
confluence.attach_file(
"output/single.cairo.png",
page_id=args.parent_id,
title="Single Edged Facts",
)
confluence.attach_file(
"output/complete.cairo.png",
page_id=args.parent_id,
title="Single Edged Facts",
)
if args.dump_source:
confluence.attach_file(
os.path.join(args.dump_source, "complete.dot"),
page_id=args.parent_id,
title="complete source",
)
confluence.attach_file(
os.path.join(args.dump_source, "double.dot"),
page_id=args.parent_id,
title="double source",
)
confluence.attach_file(
os.path.join(args.dump_source, "single.dot"),
page_id=args.parent_id,
title="single source",
)
def draw_net(config,
genome,
view=False,
filename=None,
node_names=None,
show_disabled=True,
prune_unused=False,
node_colors=None,
fmt='svg'):
""" Receives a genome and draws a neural network with arbitrary topology. """
# Attributes for network nodes.
if graphviz is None:
warnings.warn(
"This display is not available due to a missing optional dependency (graphviz)"
)
return
if node_names is None:
node_names = {}
assert type(node_names) is dict
if node_colors is None:
node_colors = {}
assert type(node_colors) is dict
node_attrs = {
'shape': 'circle',
'fontsize': '9',
'height': '0.2',
'width': '0.2'
}
dot = graphviz.Digraph(format=fmt, node_attr=node_attrs)
inputs = set()
for k in config.genome_config.input_keys:
inputs.add(k)
name = node_names.get(k, str(k))
input_attrs = {
'style': 'filled',
'shape': 'box',
'fillcolor': node_colors.get(k, 'lightgray')
}
dot.node(name, _attributes=input_attrs)
outputs = set()
for k in config.genome_config.output_keys:
outputs.add(k)
name = node_names.get(k, str(k))
node_attrs = {
'style': 'filled',
'fillcolor': node_colors.get(k, 'lightblue')
}
dot.node(name, _attributes=node_attrs)
if prune_unused:
connections = set()
for cg in genome.connections.values():
if cg.enabled or show_disabled:
connections.add((cg.in_node_id, cg.out_node_id))
used_nodes = copy.copy(outputs)
pending = copy.copy(outputs)
while pending:
new_pending = set()
for a, b in connections:
if b in pending and a not in used_nodes:
new_pending.add(a)
used_nodes.add(a)
pending = new_pending
else:
used_nodes = set(genome.nodes.keys())
for n in used_nodes:
if n in inputs or n in outputs:
continue
attrs = {'style': 'filled', 'fillcolor': node_colors.get(n, 'white')}
dot.node(str(n), _attributes=attrs)
for cg in genome.connections.values():
if cg.enabled or show_disabled:
#if cg.input not in used_nodes or cg.output not in used_nodes:
# continue
input, output = cg.key
a = node_names.get(input, str(input))
b = node_names.get(output, str(output))
style = 'solid' if cg.enabled else 'dotted'
color = 'green' if cg.weight > 0 else 'red'
width = str(0.1 + abs(cg.weight / 5.0))
dot.edge(a,
b,
_attributes={
'style': style,
'color': color,
'penwidth': width
})
dot.render(filename, view=view)
return dot
def tree_dot(outfile, tree):
tree_dot = gv.Digraph(format='svg', engine='dot')
traversal(tree, 'root', tree_dot)
f = open(outfile, 'w+')
f.write(tree_dot.source)
f.close()
#print(model.labels_)
print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % list(model.labels_).count(-1))
print(model2.cluster_centers_)
color_groups = []
for i in all_predictions:
if i == 0:
color_groups.append("green")
if i == 1:
color_groups.append("red")
if i == 2:
color_groups.append("blue")
d = gv.Digraph(directory=None,
edge_attr=dict(dir='none', labeldistance='1.5', minlen='2'))
red = Color("red")
file1 = open("concepts.txt", "a")
for m in matrix:
#print(m)
file1.write(str(m) + '\n')
file1.close()
matrixForGraph = []
for tup in matrix:
if len([x for x in matrixForGraph if x[1] == tup[1]]) == 0:
matrixForGraph.append(tup)
matrixForGraph = sorted(matrixForGraph, key=lambda x: x[2])
colors = list(red.range_to(Color("green"), len(matrixForGraph)))
color_counter = 0
def __init__(self, blocks):
self.blocks = blocks
self.dot = graphviz.Digraph()
self.dot.attr('graph', rankdir='LR')
def visualizeTree(self, root, fname):
"""
Visualizes tree from the root node
"""
g = gv.Digraph(fname, filename=fname)
g.format = 'png'
nodes, edges = self.getAllnodesAndEdges(root)
searched_nodes = self.getSearchedNum(nodes)
total_nodes = len(nodes)
# Default values
searched = False
node_shape = 'box'
pen_color = 'black'
node_style = ''
color = ''
if root['searched']: # If the root is searched then search algortihm worked
searched = True
for node in nodes:
if node['type'] == 'MAX':
node_shape = 'box'
elif node['type'] == 'MIN':
node_shape = 'circle'
else:
if node['parent_type'] == 'MIN':
node_shape = 'box'
else:
node_shape = 'circle'
if searched and not node['searched']:
pen_color = 'lightgrey'
node_style = 'filled'
color = 'lightgrey'
else:
pen_color = 'black'
node_style = ''
color = ''
g.attr('node',
shape=node_shape,
pencolor=pen_color,
style=node_style,
color=color)
node_label = "?"
node_xlabel = ""
try:
node_label = str(node['value'])
node_xlabel = node['name']
except KeyError:
node_label = "?"
node_xlabel = ""
if node['id'] == root['id']:
node_xlabel = f"Total number of nodes: {total_nodes}\nSearched nodes: {searched_nodes}"
g.node(node['id'], label=node_label, xlabel=node_xlabel)
for node1, node2 in edges:
g.edge(node1['id'], node2['id'])
# Styling
# penwidth='4'
g.edge_attr.update(arrowhead='none')
g.render(view=True, cleanup=True, format='png')
def to_graphviz(topology_class, node_attr=None, edge_attr=None, **kwargs):
"""Convert a Topology into a DiGraph"""
if not HAVE_GRAPHVIZ:
raise ImportError(
"The visualize command requires the `graphviz` Python"
" library and `graphviz` system library to be "
"installed."
)
attributes = {
"fontsize": "16",
"fontcolor": "white",
"bgcolor": "#333333",
"rankdir": "LR",
}
node_attributes = {
"fontname": "Helvetica",
"fontcolor": "white",
"color": "white",
"style": "filled",
"fillcolor": "#006699",
}
edge_attributes = {
"style": "solid",
"color": "white",
"arrowhead": "open",
"fontname": "Helvetica",
"fontsize": "12",
"fontcolor": "white",
}
attributes.update(kwargs)
if node_attr is not None:
node_attributes.update(node_attr)
if edge_attr is not None:
edge_attributes.update(edge_attr)
g = graphviz.Digraph(
graph_attr=attributes, node_attr=node_attributes, edge_attr=edge_attributes
)
all_specs = {}
all_specs.update(topology_class.thrift_bolts)
all_specs.update(topology_class.thrift_spouts)
sametail_nodes = set()
for spec in topology_class.specs:
if isinstance(spec, (JavaSpoutSpec, ShellSpoutSpec)):
shape = "box"
else:
shape = None
g.node(spec.name, label=spec.name, shape=shape)
for stream_id, grouping in list(iteritems(spec.inputs)):
parent = stream_id.componentId
outputs = all_specs[parent].common.streams[stream_id.streamId].output_fields
label = "Stream: {}\lFields: {}\lGrouping: {}\l".format(
stream_id.streamId, outputs, grouping
)
sametail = "{}-{}".format(parent, stream_id.streamId)
if sametail not in sametail_nodes:
g.node(sametail, shape="point", width="0")
g.edge(parent, sametail, label=label, dir="none")
sametail_nodes.add(sametail)
g.edge(sametail, spec.name, samehead=str(outputs))
return g
#!/usr/bin/env python3
"""https://graphviz.org/Gallery/directed/hello.html"""
import graphviz
g = graphviz.Digraph('G', filename='hello.gv')
g.edge('Hello', 'World')
g.view()
def _make_graph(bsname, version=None, data_dir=None):
'''
Create a DOT graph file of the files included in a basis set
'''
if not graphviz_avail:
raise RuntimeError("graphviz package is not installed")
data_dir = api.fix_data_dir(data_dir)
md = api._get_basis_metadata(bsname, data_dir)
if version is None:
version = md['latest_version']
else:
version = str(version)
if version not in md['versions']:
raise RuntimeError("Version {} of {} doesn't exist".format(version, bsname))
gr = graphviz.Digraph(comment='Basis Set Graph: ' + bsname)
# Read the table file
table_path = os.path.join(data_dir, md['versions'][version]['file_relpath'])
table_data = fileio.read_json_basis(table_path)
table_edges = {}
for el, entry in table_data['elements'].items():
if entry not in table_edges:
table_edges[entry] = []
table_edges[entry].append(el)
for k, v in table_edges.items():
gr.edge(bsname, k, label=compact_elements(v))
# Element file
for elfile in table_edges.keys():
element_path = os.path.join(data_dir, elfile)
element_data = fileio.read_json_basis(element_path)
element_edges = {}
for el, components in element_data['elements'].items():
components = components['components']
components_str = '\n'.join(components)
# skip if this element for the table basis doesn't come from this file
if el not in table_data['elements']:
continue
if table_data['elements'][el] != elfile:
continue
if components_str not in element_edges:
element_edges[components_str] = []
element_edges[components_str].append(el)
for k, v in element_edges.items():
if len(v):
gr.edge(elfile, k, label=compact_elements(v))
return gr
if __name__ == '__main__':
import sys
fname = 'hello.bin'
if len(sys.argv) >= 2:
fname = sys.argv[1]
disass, instr_eq = make_disassembler(fname)
bbs = {}
grp = {}
call_targets = []
call_triples = []
construct_cfg(bbs, grp, 0, disass, call_targets, call_triples)
if '--graphviz' in sys.argv:
g1 = apply_styles(gv.Digraph(format='svg', node_attr={"shape": "box"}),
styles)
for i in bbs:
if i in call_targets:
g1.node(hex(i),
label="\l".join(bbs[i]).replace("\x00", "") + "\l",
color='red')
else:
g1.node(hex(i),
label="\l".join(bbs[i]).replace("\x00", "") + "\l")
for i in grp:
for j in grp[i]:
g1.edge(hex(i), hex(j))
call_counts = {}
for (sourcebb, destbb, sourceinst) in call_triples:
g1.edge(hex(sourcebb),
def __init__(self):
self.graph = gv.Digraph()
def draw(self, max_depth: int = None):
"""Draws the tree using the `graphviz` library.
Parameters:
max_depth: Only the root will be drawn when set to `0`. Every node will be drawn when
set to `None`.
Example:
>>> from creme import datasets
>>> from creme import tree
>>> model = tree.DecisionTreeClassifier(
... patience=10,
... confidence=1e-5,
... criterion='gini',
... max_depth=10,
... tie_threshold=0.05,
... min_child_samples=0,
... )
>>> for x, y in datasets.Phishing():
... model = model.fit_one(x, y)
>>> dot = model.draw()
.. image:: /img/dtree_draw.svg
:align: center
"""
if max_depth is None:
max_depth = math.inf
dot = graphviz.Digraph(graph_attr={'splines': 'ortho'},
node_attr={
'shape': 'box',
'penwidth': '1.2',
'fontname': 'trebuchet',
'fontsize': '11',
'margin': '0.1,0.0'
},
edge_attr={
'penwidth': '0.6',
'center': 'true'
})
# Do a first pass to guess the number of classes
n_classes = len(
set(
itertools.chain(*[
list(node.target_dist) for node, _ in self.root.iter_dfs()
])))
# Pick a color palette which maps classes to colors
new_color = functools.partial(next, iter(_color_brew(n_classes)))
palette = collections.defaultdict(new_color)
for parent_no, child_no, _, child, child_depth in self.root.iter_edges(
):
if child_depth > max_depth:
continue
if isinstance(child, leaf.Branch):
text = f'{child.split} \n {child.target_dist} \n samples: {child.n_samples}'
elif isinstance(child, leaf.Leaf):
text = f'{child.target_dist} \n samples: {child.n_samples}'
# Pick a color, the hue depends on the class and the transparency on the distribution
mode = child.target_dist.mode
if mode is not None:
p_mode = child.target_dist.pmf(mode)
alpha = (p_mode - 1 / n_classes) / (1 - 1 / n_classes)
fillcolor = str(
transparency_hex(color=palette[mode], alpha=alpha))
else:
fillcolor = '#FFFFFF'
dot.node(f'{child_no}', text, fillcolor=fillcolor, style='filled')
if parent_no is not None:
dot.edge(f'{parent_no}', f'{child_no}')
return dot
def plot(self):
gtree = gv.Digraph(format='png')
return self.racine.to_graph(gtree)
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
import graphviz as graphviz
# basic graph
hello = graphviz.Digraph("Hello World")
hello.edge("Hello", "World")
# styled graph
styled = graphviz.Graph("G", filename="g_c_n.gv")
styled.attr(bgcolor="purple:pink", label="agraph", fontcolor="white")
with styled.subgraph(name="cluster1") as c:
c.attr(
fillcolor="blue:cyan",
label="acluster",
fontcolor="white",
style="filled",
gradientangle="270",
)
c.attr("node",
def main(argv):
with open(argv[1], encoding='utf-8') as f:
s = re.sub(r'\s+', ' ', f.read(), flags=re.M)
f=re.split(r'(?<=[.!?…]) ',s)
sentens=[]
for i,t in enumerate(f):
sentens.append(t)
print(str(i)," ",t)
morph = pymorphy2.MorphAnalyzer()
ZnakiP=[",","!","/n",".",":",";",'"',"'","\n","...","?","!","(",")","-"," "," "]
t = Mystem()
PARS=[]
for sent in sentens:
input_file=open("input.txt","w",encoding="utf-8")
input_file.write(sent)
input_file.close()
# Делаем синтаксический анализ текста, находим граматические основы
process = subprocess.Popen('tomitaparser.exe config.proto', stdout=subprocess.PIPE,shell=True)
process.communicate()
process.wait()
predicate=[]
Nouns=[]
DOP=[]
DOP.append({})
OPR=[]
with open("pretty.html",encoding='utf8') as fp:
soup = BeautifulSoup(fp,"html.parser")
par_f=soup.find_all('table')
for table in par_f:
th=table.find('th')
if(th.text=="Noun1"):
slovo=th.find_parent("table").find('a').text
Nouns.append(slovo)
if(th.text=="Verb1"):
slovo=th.find_parent("table").find('a').text
predicate.append(slovo)
if(th.text=="OPR1"):
sl=th.find_parent("table").find_all('a')
for slovo in sl:
OPR.append(slovo.text)
if(th.text=="DOP1"):
sl=th.find_parent("table").find_all('a')
for slovo in sl:
DOP[0][slovo.text.lower()]=slovo.next_element.next_element.next_element.next_element
TREE={}
TREE[Nouns[0]]={}
for v in predicate:
TREE[Nouns[0]][v]={}
if(OPR!=[]):
for temp in OPR:
for noun in TREE:
if(len(re.split(r"[,' ']",temp))==1):
TREE[Nouns[0]][temp]=t.analyze(temp)[0]['analysis'][0]['gr']
else:
m2=[]
for f in re.split(r"[,' ']",temp):
if(f!=''):
m2.append(f)
if(noun in m2):
mk=t.analyze(temp)
wsp=[]
for tr in mk:
if(not tr['text'] in ZnakiP):
if(not 'CONJ' in tr['analysis'][0]['gr']):
wsp.append(tr['text'])
for tl in wsp:
if(tl!=noun):
TREE[Nouns[0]][tl]=t.analyze(tl)[0]['analysis'][0]['gr']
for temp in TREE[Nouns[0]]:
if(temp in DOP[0].values()):
for sp in DOP[0]:
if(DOP[0][sp]==temp):
m2=[]
for f in re.split(r"[,' ']",sp):
if(f!=''):
m2.append(f)
for rg in m2:
TREE[Nouns[0]][temp][rg]={}
for _opr in OPR:
reg=re.split(r"[,' ']",temp)
if(noun in reg):
mk=t.analyze(_opr)
wsp=[]
for tr in mk:
if(not tr['text'] in ZnakiP):
if(not 'CONJ' in tr['analysis'][0]['gr']):
wsp.append(tr['text'])
for tl in wsp:
if(tl!=rg):
TREE[Nouns[0]][temp][rg][tl]=t.analyze(tl)[0]['analysis'][0]['gr']
for noun in TREE:
d1=[noun]
for verb in TREE[noun]:
if(morph.parse(verb)[0].tag.POS=='ADJF'):
d2=[noun,'быть']
d2.append(verb)
if(not d2 in PARS):
PARS.append(d2.copy())
d2.pop()
else:
d4=[verb,"может быть"]
d1.append(verb)
for temp in TREE[noun][verb]:
if(morph.parse(temp)[0].tag.POS=='NOUN'):
d1.append(morph.parse(temp)[0].normal_form)
if(not d1 in PARS):
PARS.append(d1.copy())
d1.pop()
d3=[temp,'быть']
for temp2 in TREE[noun][verb][temp]:
d3.append(temp2)
PARS.append(d3.copy())
d3.pop()
else:
d4.append(temp)
if(not d4 in PARS):
PARS.append(d4.copy())
d4.pop()
obj = PARS.copy()
g1=gv.Digraph(format='png')
for temp in obj:
a=morph.parse(temp[0])[0].tag.POS
if(a=='VERB' or a=='INFN'):
for t in obj:
if(t[1]==temp[0]):
g1.node(t[0],shape='rect',style='filled',fillcolor='#cccccc')
g1.node(temp[0])
g1.node(temp[2],shape='rect',style='filled',fillcolor='#cccccc')
g1.edge(t[0],temp[0])
g1.edge(temp[0],temp[2],label=temp[1])
g1.edge(temp[0],t[2])
else:
g1.node(temp[0],shape='rect',style='filled',fillcolor='#cccccc')
g1.node(temp[2],shape='rect',style='filled',fillcolor='#cccccc')
g1.edge(temp[0],temp[2],label=temp[1])
print(g1.source)
g1.render('img/'+argv[2])
def visualize_ast(context,
comment='MOA AST',
with_attrs=True,
vector_value=True):
if graphviz is None:
raise ImportError(
'The graphviz package is required to draw expressions')
dot = graphviz.Digraph(comment=comment)
counter = itertools.count()
default_node_attr = dict(color='black',
fillcolor='white',
fontcolor='black')
def _visualize_node_label(dot, context):
unique_id = str(next(counter))
node_label = _node_label(context)
for key, value in node_label.items():
node_label[key] = escape_dot_string(value)
labels = []
if ast.is_array(context):
shape = 'box'
else: # operation
shape = 'ellipse'
if len(node_label) > 1:
labels.append('<TR><TD>{}</TD></TR>'.format(node_label['name']))
if 'shape' in node_label:
labels.append('\n<TR><TD>{}</TD></TR>'.format(
node_label['shape']))
if 'value' in node_label:
labels.append('\n<TR><TD>{}</TD></TR>'.format(
node_label['value']))
node_description = f'''<
<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
{''.join(labels)}
</TABLE>>'''
else:
node_description = node_label['name']
dot.node(unique_id, label=node_description, shape=shape)
return unique_id
def _visualize_node(dot, context):
node_id = _visualize_node_label(dot, context)
# no need to traverse condition node since converted to python source
if context.ast.symbol == (ast.NodeSymbol.CONDITION, ):
child_context = ast.select_node(context, (1, ))
child_node_id = _visualize_node(dot, child_context)
dot.edge(node_id, child_node_id)
return node_id
for i in range(ast.num_node_children(context)):
child_context = ast.select_node(context, (i, ))
child_node_id = _visualize_node(dot, child_context)
dot.edge(node_id, child_node_id)
return node_id
_visualize_node(dot, context)
return dot
def __init__(self, format='png'):
self.graph = gv.Digraph(format=format)
def make_graph(
model,
current_tags,
existing_resources,
existing_dependencies,
resources_to_up,
resources_to_down
):
# TODO show renames
graph = graphviz.Digraph()
def res_color(res_name):
if res_name in resources_to_up:
return 'green'
if res_name in resources_to_down:
return 'red'
return 'black'
for res_name, res in sorted(model.resources.items()):
is_existing = res_name in existing_resources
is_dirty = is_existing and existing_resources[res_name]['dirty']
label_suffix = ''
if is_dirty:
label_suffix += '*'
graph.node(
'res-' + res_name,
label=res_name + label_suffix,
color=res_color(res_name),
style=['solid', 'bold'][is_existing],
group=['new', 'existing'][is_existing]
)
dependencies = {}
for imp_name, (dep_res_name, dep_export_name) in res.data.imports.items():
dependencies.setdefault(dep_res_name, []).append(dep_export_name)
for dep_res_name, imports in sorted(dependencies.items()):
graph.edge(
'res-' + dep_res_name,
'res-' + res_name,
label=",\n".join(sorted(imports))
)
for res_name, _ in sorted(existing_resources.items()):
if res_name in model.resources:
continue
graph.node(
'res-' + res_name,
label=res_name,
color=res_color(res_name),
style='dashed',
group='old'
)
for dep_res_name in sorted(existing_dependencies[res_name]):
graph.edge(
'res-' + dep_res_name,
'res-' + res_name
)
all_tags = set()
for res_name, tags in current_tags.items():
for tag in tags:
all_tags.add(tag)
graph.edge(
'tag-' + tag,
'res-' + res_name,
style='dashed',
arrowhead='none'
)
with graph.subgraph(name='cluster_tags', graph_attr=dict(style='invis')) as subgraph:
for tag in all_tags:
subgraph.node(
'tag-' + tag,
label=tag,
shape='rectangle',
fillcolor='yellow',
style='filled'
)
for seq, seq_style in (
(resources_to_down, dict(color='red')),
(resources_to_up, dict(color='green'))
):
for i, j in zip(seq[:-1], seq[1:]):
graph.edge(
'res-' + i,
'res-' + j,
constraint='false',
**seq_style
)
return graph
def workflow_to_digraph(name, workflow):
"""
Generate a `graphviz.Digraph` from a `xworkflows.Workflow`.
:param workflow: workflows to generate the digraph from
:type workflows: xworkflows.Workflow
:returns: a dot digraph
:rtype: graphviz.Digraph
"""
sources, targets, edges = set(), set(), set()
# dump nodes and edges
for transition in workflow.transitions._transitions.values():
for source in transition.source:
source_name = '%s.%s' % (name, source)
target_name = '%s.%s' % (name, transition.target)
sources.add((source_name, str(source)))
targets.add((target_name, str(transition.target)))
edges.add((source_name, target_name, (('label',
str(transition.name)), )))
# construct subgraph
subgraph = graphviz.Digraph(
name="cluster_%s" % (name, ),
graph_attr={
'label': "%s" % (name, ),
'labelloc': 'top',
'labeljust': 'left',
},
)
final_states = targets - sources
for name, label in final_states:
subgraph.node(name, label=label, shape='doublecircle')
for name, label in (sources | targets) - final_states:
label = label.replace('_', '\n') # XXX hack to avoid lenghty names
# HACK customize some states with specific colors
if label == 'on':
subgraph.node(name,
label=label,
shape='doublecircle',
fillcolor='palegreen',
style='filled')
elif label == 'error':
subgraph.node(name,
label=label,
shape='doublecircle',
fillcolor='red',
style='filled')
elif label == 'off':
subgraph.node(name,
label=label,
shape='doublecircle',
style='filled')
else:
# "normal" state
subgraph.node(name, label=label, shape='circle')
if workflow.initial_state: # Adding initial state notation
if label == workflow.initial_state:
subgraph.node('.', shape='point')
subgraph.edge('.', name)
for source_name, target_name, attrs in edges:
subgraph.edge(source_name, target_name, **dict(attrs))
return subgraph
def render_graph(graph_specification, render_distributions=False):
"""
Create a graphviz object given a graph specification.
:param bool render_distributions: Show distribution of each RV in plot.
"""
try:
import graphviz # noqa: F401
except ImportError as e:
raise ImportError(
"Looks like you want to use graphviz (https://graphviz.org/) "
"to render your model. "
"You need to install `graphviz` to be able to use this feature. "
"It can be installed with `pip install graphviz`."
) from e
plate_groups = graph_specification["plate_groups"]
plate_data = graph_specification["plate_data"]
node_data = graph_specification["node_data"]
edge_list = graph_specification["edge_list"]
graph = graphviz.Digraph()
# add plates
plate_graph_dict = {
plate: graphviz.Digraph(name=f"cluster_{plate}")
for plate in plate_groups
if plate is not None
}
for plate, plate_graph in plate_graph_dict.items():
plate_graph.attr(label=plate.split("__CLONE")[0], labeljust="r", labelloc="b")
plate_graph_dict[None] = graph
# add nodes
for plate, rv_list in plate_groups.items():
cur_graph = plate_graph_dict[plate]
for rv in rv_list:
color = "grey" if node_data[rv]["is_observed"] else "white"
cur_graph.node(
rv, label=rv, shape="ellipse", style="filled", fillcolor=color
)
# add leaf nodes first
while len(plate_data) >= 1:
for plate, data in plate_data.items():
parent_plate = data["parent"]
is_leaf = True
for plate2, data2 in plate_data.items():
if plate == data2["parent"]:
is_leaf = False
break
if is_leaf:
plate_graph_dict[parent_plate].subgraph(plate_graph_dict[plate])
plate_data.pop(plate)
break
# add edges
for source, target in edge_list:
graph.edge(source, target)
# render distributions if requested
if render_distributions:
dist_label = ""
for rv, data in node_data.items():
rv_dist = data["distribution"]
dist_label += rf"{rv} ~ {rv_dist}\l"
graph.node("distribution_description_node", label=dist_label, shape="plaintext")
# return whole graph
return graph
def build_datastructure_doc(is_nt):
pd.set_option('display.max_colwidth', int(1e9))
st = get_context(is_nt)
xT = 'nT' if is_nt else '1T'
# Too lazy to write proper graph sorter
# Make dictionary {total number of dependencies below -> list of plugins}
plugins_by_deps = get_plugins_deps(st)
# Make graph for each suffix ('' referring to TPC)
for suffix in tree_suffices:
title = titles[suffix].format(xT=xT)
out = page_header.format(
title=title, context='xenonnt_online' if is_nt else 'xenon1t_dali')
if not is_nt and suffix != '':
# No NV/MV/HE for 1T
continue
print(f'------------ {xT}{suffix} ------------')
os.makedirs(this_dir + f'/graphs{suffix}_{xT}', exist_ok=True)
for n_deps in list(
reversed(sorted(list(plugins_by_deps[suffix].keys())))):
for data_type in plugins_by_deps[suffix][n_deps]:
plugins = st._get_plugins((data_type, ), run_id='0')
# Create dependency graph
g = graphviz.Digraph(format='svg')
# g.attr('graph', autosize='false', size="25.7,8.3!")
for d, p in plugins.items():
if skip(p, d, suffix, data_type):
continue
g.node(d,
style='filled',
href='#' + d.replace('_', '-'),
fillcolor=kind_colors.get(p.data_kind_for(d),
'grey'))
for dep in p.depends_on:
g.edge(d, dep)
fn = this_dir + f'/graphs{suffix}_{xT}/' + data_type
g.render(fn)
with open(f'{fn}.svg', mode='r') as f:
svg = add_spaces(f.readlines()[5:])
config_df = st.show_config(data_type).sort_values(by='option')
# Filter out the config options of lower level datatypes
config_mask = []
for ap_to in config_df['applies_to'].values:
config_mask.append(any([data_type in a for a in ap_to]))
keep_cols = ['option', 'default', 'current', 'help']
config_df = config_df[config_mask][keep_cols]
# Shorten long default values
config_df['default'] = [
x[:10] + '...' +
x[-10:] if isinstance(x, str) and len(x) > 30 else x
for x in config_df['default'].values
]
p = plugins[data_type]
out += template.format(
p=p,
context='',
module=str(p.__module__).replace('.', '/'),
svg=svg,
data_type=data_type,
columns=add_spaces(
st.data_info(data_type).to_html(index=False)),
kind=p.data_kind_for(data_type),
docstring=p.__doc__
if p.__doc__ else '(no plugin description)',
config_options=add_spaces(config_df.to_html(index=False)))
with open(this_dir + f'/reference/datastructure{suffix}_{xT}.rst',
mode='w') as f:
f.write(out)
shutil.rmtree(this_dir + f'/graphs{suffix}_{xT}')
def _make_graphviz(self):
d = graphviz.Digraph()
d.attr(labelloc='t', label=str(self))
self._add_nodes(d)
return d
def draw_graph(outputs,
draw_hyperparameters=True,
draw_io_labels=True,
draw_module_hyperparameter_info=True,
out_folderpath=None,
graph_name='graph',
print_to_screen=True):
"""Draws a graph representation of the current state of the search space.
All edges are directed. An edge between two modules represents the output of
the first module going into the input of the second module. An edge between
a module and an hyperparameter represents the dependency of that module on
that hyperparameter.
This visualization functionality is useful to verify that the description of
the search space done through the domain-specific language encodes the
intended search space.
.. note::
All drawing options are set to `True` to give a sense of all the
information that can be displayed simultaneously. This can lead to
cluttered graphs and slow rendering. We recommend changing the defaults
according to the desired behavior.
For generating a representation for a fully specified model,
we recommend setting `draw_hyperparameters` to `False`, as if we are
only concerned with the resulting model, the sharing structure of
hyperparameters does not really matter. We recommend using
`draw_hyperparameters` set to `True` when the user wishes to visualize
the hyperparameter sharing pattern, e.g., to verify that it has been
implemented correctly.
Args:
outputs (dict[str, deep_architect.core.Output]): Dictionary of named
outputs from which we can reach all the modules in the search space
by backwards traversal.
draw_hyperparameters (bool, optional): Draw hyperparameter nodes in the
graph, representing the dependencies between hyperparameters and
modules.
draw_io_labels (bool, optional): If `True`,
draw edge labels connecting different modules
with the local names of the input and output that are being connected.
draw_module_hyperparameter_info (bool, optional): If `True`,
draw the hyperparameters of a module alongside the module.
graph_name (str, optional): Name of the file used to store the
rendered graph. Only needs to be provided if we desire to output
the graph to a file, rather than just show it.
out_folderpath (str, optional): Folder to which to store the PDF file with
the rendered graph. If no path is provided, no file is created.
print_to_screen (bool, optional): Shows the result of rendering the
graph directly to screen.
"""
assert print_to_screen or out_folderpath is not None
g = graphviz.Digraph()
edge_fs = '10'
h_fs = '10'
penwidth = '1'
def _draw_connected_input(ix_localname, ix):
ox = ix.get_connected_output()
if not draw_io_labels:
label = ''
else:
ox_localname = None
for ox_iter_localname, ox_iter in ox.get_module().outputs.items():
if ox_iter == ox:
ox_localname = ox_iter_localname
break
assert ox_localname is not None
label = ix_localname + ':' + ox_localname
g.edge(ox.get_module().get_name(),
ix.get_module().get_name(),
label=label,
fontsize=edge_fs)
def _draw_unconnected_input(ix_localname, ix):
g.node(ix.get_name(),
shape='invhouse',
penwidth=penwidth,
fillcolor='firebrick',
style='filled')
g.edge(ix.get_name(), ix.get_module().get_name())
def _draw_module_hyperparameter(m, h_localname, h):
if h.has_value_assigned():
label = h_localname + '=' + str(h.get_value())
else:
label = h_localname
g.edge(h.get_name(), m.get_name(), label=label, fontsize=edge_fs)
def _draw_dependent_hyperparameter_relations(h_dep):
for h_localname, h in h_dep._hyperps.items():
if h.has_value_assigned():
label = h_localname + '=' + str(h.get_value())
else:
label = h_localname
g.edge(h.get_name(),
h_dep.get_name(),
label=label,
fontsize=edge_fs)
def _draw_module_hyperparameter_info(m):
g.node(
m.get_name(),
xlabel="<" + '<br align="right"/>'.join([
'<FONT POINT-SIZE="%s">' % h_fs + h_localname +
('=' + str(h.get_value()) if h.has_value_assigned() else '') +
"</FONT>" for h_localname, h in m.hyperps.items()
]) + ">")
def _draw_output_terminal(ox_localname, ox):
g.node(ox.get_name(),
shape='house',
penwidth=penwidth,
fillcolor='deepskyblue',
style='filled')
g.edge(ox.get_module().get_name(), ox.get_name())
nodes = set()
def fn(m):
"""Adds the module information to the graph that is local to the module.
"""
nodes.add(m.get_name())
for ix_localname, ix in m.inputs.items():
if ix.is_connected():
_draw_connected_input(ix_localname, ix)
else:
_draw_unconnected_input(ix_localname, ix)
if draw_hyperparameters:
for h_localname, h in m.hyperps.items():
_draw_module_hyperparameter(m, h_localname, h)
if draw_module_hyperparameter_info:
_draw_module_hyperparameter_info(m)
return False
# generate most of the graph.
co.traverse_backward(outputs, fn)
# drawing the hyperparameter graph.
if draw_hyperparameters:
hs = co.get_all_hyperparameters(outputs)
for h in hs:
if isinstance(h, co.DependentHyperparameter):
_draw_dependent_hyperparameter_relations(h)
g.node(h.get_name(),
fontsize=h_fs,
fillcolor='darkseagreen',
style='filled')
# add the output terminals.
for m in co.extract_unique_modules(list(outputs.values())):
for ox_localname, ox in m.outputs.items():
_draw_output_terminal(ox_localname, ox)
# minor adjustments to attributes.
for s in nodes:
g.node(s,
shape='rectangle',
penwidth=penwidth,
fillcolor='goldenrod',
style='filled')
if print_to_screen or out_folderpath is not None:
g.render(graph_name,
out_folderpath,
view=print_to_screen,
cleanup=True)
def make_sitemap_graph(df,
layers=graph_depth,
limit=limit,
size=size,
output_format=output_format,
skip=skip):
''' Make a sitemap graph up to a specified layer depth.
sitemap_layers : DataFrame
The dataframe created by the peel_layers function
containing sitemap information.
layers : int
Maximum depth to plot.
limit : int
The maximum number node edge connections. Good to set this
low for visualizing deep into site maps.
output_format : string
The type of file you want to save in PDF, PNG, TIFF, JPG
skip : list
List of branches that you do not want to expand.
'''
# Check to make sure we are not trying to plot too many layers
if layers > len(df) - 1:
layers = len(df) - 1
print('There are only %d layers available to plot, setting layers=%d' %
(layers, layers))
# Initialize graph
f = graphviz.Digraph('sitemap',
filename='sitemap_graph_%d_layer' % layers,
format='%s' % output_format)
f.body.extend(['rankdir=LR', 'size="%s"' % size])
def add_branch(f, names, vals, limit, connect_to=''):
''' Adds a set of nodes and edges to nodes on the previous layer. '''
# Get the currently existing node names
node_names = [item.split('"')[1] for item in f.body if 'label' in item]
# Only add a new branch it it will connect to a previously created node
if connect_to:
if connect_to in node_names:
for name, val in list(zip(names, vals))[:limit]:
f.node(name='%s-%s' % (connect_to, name), label=name)
f.edge(connect_to,
'%s-%s' % (connect_to, name),
label='{:,}'.format(val))
f.attr('node', shape='rectangle') # Plot nodes as rectangles
# Add the first layer of nodes
for name, counts in df.groupby(['0'])['counts'].sum().reset_index()\
.sort_values(['counts'], ascending=False).values:
f.node(name=name, label='{} ({:,})'.format(name, counts))
if layers == 0:
return f
f.attr('node', shape='oval') # Plot nodes as ovals
f.graph_attr.update()
# Loop over each layer adding nodes and edges to prior nodes
for i in range(1, layers + 1):
cols = [str(i_) for i_ in range(i)]
nodes = df[cols].drop_duplicates().values
for j, k in enumerate(nodes):
# Compute the mask to select correct data
mask = True
for j_, ki in enumerate(k):
mask &= df[str(j_)] == ki
# Select the data then count branch size, sort, and truncate
data = df[mask].groupby([str(i)])['counts'].sum()\
.reset_index().sort_values(['counts'], ascending=False)
# Add to the graph unless specified that we do not want to expand k-1
if (not skip) or (k[-1] not in skip):
add_branch(f,
names=data[str(i)].values,
vals=data['counts'].values,
limit=limit,
connect_to='-'.join(['%s'] * i) % tuple(k))
print(('Built graph up to node %d / %d in layer %d' % (j, len(nodes), i))\
.ljust(50), end='\r')
return f
df=df.sort_values(by='R_num', ascending=True)
df=df.reset_index()
df=df.drop('index',axis=1)
df['level']=nan # using nan to keep all value float
for num,nam in enumerate(df.name):
df.loc[num,'level']=next((df.loc[i].R_num for i, val in enumerate(df.ORG) if (val == nam and i>num and (df.loc[i].R_num not in list(df.level)))), nan)
df.level=df.level.map('{:.0f}'.format) # int format
df.loc[df['level']=='nan','level'] = df[df['level']=='nan']['R_num'] # replace nan with R_num to distinct feeds
print('GRAPH')################## GRAPH #################
os.environ["PATH"] += os.pathsep + r'C:\Users\e650188\AppData\Local\graphviz238\bin'
dot = gv.Digraph(node_attr={'shape': 'box'},edge_attr={'dir':'forward'},graph_attr={'rankdir':'RL'})
for num in range(df.shape[0]):
dot.edge(df.iloc[num]['ORG']+' '+str(df.iloc[num]['R_num']),df.iloc[num]['name']+' '+str(df.iloc[num]['level']))
dot.render(r'C:\Users\e650188\Desktop\automation_files\test-output\{}_{}.gv'.format(mart_name,(str(datetime.datetime.now())[:-7]).replace(":","-")), view=True)
print('DM formula')################### DM formula #################
dfcol_param=['DM_Name','Parameter','Default_Value'] # if this list changes often, will need a dict
df_param=pd.DataFrame(columns=dfcol_param)
dfcol_element = ['DM_Name','Element','status','alias','key','Formula','displayName'] # if this list changes often, will need a dict
df_element=pd.DataFrame(columns=dfcol_element)
dfcol_join = ['DM_Name','alias','Source_Element','Target_Element','Main_Table(Target)']
df_join=pd.DataFrame(columns=dfcol_join)
class Arbre:
# @classmethod
# def sortie(cls, noeud, nom_fichier, format):
# cls._graphe = graphviz.Digraph()
# for k in Arbre._options_graphe:
# cls._graphe.attr(k, **Arbre._options_graphe[k])
# #
# cls._visunoeud(noeud)
# cls._graphe.render(nom_fichier, format=format)
# @classmethod
@classmethod
def options(cls, classe, dictionnaire):
cls._options_graphe[classe].update(dictionnaire)
etiquette = "valeur"
_graphe = graphviz.Digraph()
_options_graphe = {
"node": {
"shape": "record"
},
"edge": {
"headport": "n",
"tailclip": "false",
"arrowsize": ".5",
"arrowhead": "vee"
},
"graph": {
"engine": "dot",
"splines": "false"
}
}
def __init__(self, racine=None):
self.racine = racine
self._fonction_ordre = lambda x, y: x.valeur < y.valeur
def __str__(self):
return str(self.racine)
def __len__(self):
return len(self.infixe)
def est_vide(self):
return self.racine is None
@property
def fonction_ordre(self):
return inspect.getsource(self._fonction_ordre)
@fonction_ordre.setter
def fonction_ordre(self, fonction):
self._fonction_ordre = fonction
@property
def prefixe(self):
return self._parcours_prof("prefixe")
@property
def infixe(self):
return self._parcours_prof("infixe")
@property
def suffixe(self):
return self._parcours_prof("suffixe")
@property
def largeur(self):
return self._parcours_largeur()
def liste_aplatie(self, noeud=None):
return self._parcours_largeur_numerote(noeud)
def inserer(self, noeud, noeud_courant=None):
if self.racine is None:
self.racine = noeud
return noeud
# racine de l'arbre
if noeud_courant is None:
noeud_courant = self.racine
if self._fonction_ordre(noeud, noeud_courant):
if noeud_courant.gauche is None:
noeud_courant.gauche = noeud
noeud.parent = noeud_courant
return noeud
else:
return self.inserer(noeud, noeud_courant.gauche)
else:
if noeud_courant.droit is None:
noeud_courant.droit = noeud
noeud.parent = noeud_courant
return noeud
else:
return self.inserer(noeud, noeud_courant.droit)
def maximum(self, noeud):
# renvoie le maximum et le noeud
courant = noeud
while courant.droit is not None:
courant = courant.droit
return courant, courant.valeur
def supprimer(self, noeud, noeud_courant=None, orientation=None):
"""Se placer au niveau du parent pour pouvoir pointer vers None, en
effet supprimer un objet par lui-même semble impossible en
Python !
Donc on sait que l'élément cherché n'est pas celui actuel
mais peut-être un des deux descendants.
Néanmoins en ajoutant un attribut parent à chaque noeud, il
est aisé de remonter au parent.
orientation permet de savoir si on est à gauche ou à droite
du parent pour supprimer le bon lien
"""
if noeud_courant is None:
noeud_courant = self.racine
#
if noeud.valeur == noeud_courant.valeur:
# pas de descendant
if noeud_courant.gauche is None and noeud_courant.droit is None:
# on supprime le lien qui le lie à son parent
# le garbage collector prend la suite
if orientation == 'g':
# print(noeud_courant.parent.valeur)
noeud_courant.parent.gauche = None
else:
noeud_courant.parent.droit = None
# un seul à droite
elif noeud_courant.gauche is None:
if orientation == 'g':
noeud_courant.parent.gauche = noeud_courant.droit
else:
noeud_courant.parent.droit = noeud_courant.droit
noeud_courant.droit.parent = noeud_courant.parent
# un seul à gauche
elif noeud_courant.droit is None:
if orientation == 'g':
noeud_courant.parent.gauche = noeud_courant.gauche
else:
noeud_courant.parent.droit = noeud_courant.gauche
noeud_courant.gauche.parent = noeud_courant.parent
# deux ! on choisit le plus grand (le plus à
# droite) du sous-arbre gauche qu'on devra donc
# supprimer
else:
n, M = self.maximum(noeud_courant.gauche)
# destruction du lien de droite du parent (le noeud
# max vient de la droite !)
# n.parent.droit = None
self.supprimer(n, noeud_courant.gauche, "g")
noeud_courant.valeur = M
#
elif noeud.valeur < noeud_courant.valeur:
self.supprimer(noeud, noeud_courant.gauche, 'g')
else:
self.supprimer(noeud, noeud_courant.droit, 'd')
def rechercher(self, element_a_chercher, type_element="valeur"):
# Besoin d'un noeud pour la fonction de comparaison
if type_element == "valeur":
noeud_factice = Noeud(element_a_chercher)
elif type_element == "contenu":
noeud_factice = Noeud(-1, contenu=element_a_chercher)
#
courant = self.racine
#
# recherche simple
if type_element == "valeur":
while courant.valeur != element_a_chercher:
if self._fonction_ordre(noeud_factice, courant):
courant = courant.gauche
else:
courant = courant.droit
elif type_element == "contenu":
# parcours préfixe
liste = self.prefixe
courant = liste.pop(0)
while courant.contenu != element_a_chercher:
courant = liste.pop(0)
return courant
def chemin_vers(self, noeud):
courant = self.racine
liste_noeuds = [courant]
while courant != noeud:
if self._fonction_ordre(noeud, courant):
courant = courant.gauche
else:
courant = courant.droit
liste_noeuds.append(courant)
return liste_noeuds
def sortie(self, noeud, nom_fichier, format, style="compact"):
graphe = graphviz.Digraph()
for k in Arbre._options_graphe:
graphe.attr(k, **Arbre._options_graphe[k])
#
if style == "compact":
self._visunoeud(noeud, graphe)
elif style == "complet":
self._visunoeud2(noeud, graphe)
graphe.render(nom_fichier, format=format)
def _visunoeud(self, noeud, graphe):
identifiant = noeud.valeur
contenu = eval(f"noeud.{Arbre.etiquette}")
if noeud.gauche is not None:
identifiantg = noeud.gauche.valeur
else:
identifiantg = None
if noeud.droit is not None:
identifiantd = noeud.droit.valeur
else:
identifiantd = None
with graphe.subgraph(name=f"sub_{identifiant}") as gsub:
gsub.node(str(identifiant),
label="{" + str(contenu) + "|{<g>|<d>}}")
if identifiantg is None:
gsub.node(str(identifiant) + "invisg",
label="",
width=".1",
style="invis")
gsub.edge(str(identifiant) + ":g:c",
str(identifiant) + "invisg",
style="invis")
else:
gsub.edge(str(identifiant) + ":g:c", str(identifiantg))
if identifiantd is None:
gsub.node(str(identifiant) + "invisd",
label="",
width=".1",
style="invis")
gsub.edge(str(identifiant) + ":d:c",
str(identifiant) + "invisd",
style="invis")
else:
gsub.edge(str(identifiant) + ":d:c", str(identifiantd))
if noeud.gauche is not None:
self._visunoeud(noeud.gauche, gsub)
if noeud.droit is not None:
self._visunoeud(noeud.droit, gsub)
def _visunoeud2(self, noeud, graphe):
compteur = 1
liste = self.liste_aplatie(noeud)
liste += [None
] * (2**(int(math.log2(len(liste))) + 1) - 1 - len(liste))
for i in range(self.hauteur(noeud)):
# sub (ou autre) plutôt que cluster qui contraint à
# l'intérieur du rectangle
with graphe.subgraph(name=f"sub_{i}") as gsub:
gsub.attr(rankdir="LR")
# hack pourri parce que sinon les boeuds ne sont
# pas dans le bon ordre sur une même ligne
for indice in range(2**(i + 1) - 2, 2**i - 2, -1):
noeud = liste[indice]
compteur_du_parent = int((indice - 1) // 2)
identifiant = str(indice)
contenu = " " if not noeud \
else eval(f"noeud.{Arbre.etiquette}")
style = "invis" if not noeud else "solid"
# contenu, style))
gsub.node(identifiant,
label="{" + str(contenu) + "|{<g>|<d>}}",
style=style)
#
provenance = ":g:c" if compteur % 2 else ":d:c"
identifiant_parent = str(compteur_du_parent)
if compteur != 1:
graphe.edge(identifiant_parent + provenance,
identifiant,
style=style) # , weight=str(2**i))
compteur += 1
#
# ci-dessous première version
# mais les noeuds sont dans l'ordre contraire !!!
#
# for i in range(self.hauteur()):
# with graphe.subgraph(name=f"sub_{i}") as gsub:
# gsub.attr(rankdir="RL")
# while compteur <= 2 ** (i + 1) - 1:
# noeud = liste[compteur - 1]
# compteur_du_parent = int(compteur // 2)
# if noeud is not None:
# # valeur = noeud.valeur
# contenu = eval(f"noeud.{Arbre.etiquette}")
# gsub.node(str(compteur),
# label="{" + str(contenu) + "|{<g>|<d>}}")
# if compteur % 2 == 0:
# gsub.edge(str(compteur_du_parent) + ":d:c",
# str(compteur)) # , weight=str(2**i-1))
# elif compteur != 1:
# gsub.edge(str(compteur_du_parent) + ":g:c",
# str(compteur)) # , weight=str(2**i-1))
# else:
# gsub.node(str(compteur),
# label="{ |{<g>|<d>}}",
# style="invis")
# if compteur % 2 == 0:
# gsub.edge(str(compteur_du_parent) + ":d:c",
# str(compteur),
# style="invis") # , weight=str(2**i-1))
# elif compteur != 1:
# gsub.edge(str(compteur_du_parent) + ":g:c",
# str(compteur),
# style="invis") # , weight=str(2**i-1))
# compteur += 1
def hauteur(self, noeud=None):
if noeud is None:
noeud = self.racine
return self._hauteur_rec(noeud, 0)
def _hauteur_rec(self, noeud, h):
if noeud.gauche is None and noeud.droit is None:
return h + 1
g = h if noeud.gauche is None else self._hauteur_rec(
noeud.gauche, h + 1)
d = h if noeud.droit is None else self._hauteur_rec(noeud.droit, h + 1)
return max(g, d)
def _parcours_prof(self, type_parcours, noeud=None):
if noeud is None:
noeud = self.racine
return self._parcours_prof_rec(type_parcours, noeud, [])
def _parcours_prof_rec(self, type_parcours, noeud_courant=None, liste=[]):
#
if type_parcours == "prefixe":
liste.append(noeud_courant)
if noeud_courant.gauche is not None:
self._parcours_prof_rec(type_parcours, noeud_courant.gauche, liste)
if type_parcours == "infixe":
liste.append(noeud_courant)
if noeud_courant.droit is not None:
self._parcours_prof_rec(type_parcours, noeud_courant.droit, liste)
if type_parcours == "suffixe":
liste.append(noeud_courant)
return liste
def _parcours_largeur(self, noeud=None):
liste = []
f = File()
if noeud is None:
noeud = self.racine
f.enfiler(noeud)
while not f.est_vide:
n = f.defiler()
liste.append(n)
if n.gauche is not None:
f.enfiler(n.gauche)
if n.droit is not None:
f.enfiler(n.droit)
return liste
def _parcours_largeur_numerote(self, noeud=None):
liste = []
f = File()
if not noeud:
noeud = self.racine
#
f.enfiler((noeud, 1))
while not f.est_vide:
n, numero = f.defiler()
# on comble le vide depuis la dernière insertion
liste += [None] * (numero - len(liste) - 1)
liste.append(n)
if n.gauche is not None:
f.enfiler((n.gauche, 2 * numero))
if n.droit is not None:
f.enfiler((n.droit, 2 * numero + 1))
return liste
def rotation_droite(self, noeud):
changement_racine = noeud == self.racine
# processus à partir de :
# https://upload.wikimedia.org/wikipedia/commons/1/15/Tree_Rotations.gif
pivot = noeud.gauche
if pivot.droit is not None:
noeud.gauche = pivot.droit
pivot.droit.parent = noeud
else:
noeud.gauche = None
pivot.droit = noeud
# on agit aussi sur le parent de noeud
if noeud.parent is not None and noeud.parent.gauche == noeud:
noeud.parent.gauche = pivot
elif noeud.parent is not None and noeud.parent.droit == noeud:
noeud.parent.droit = pivot
#
pivot.parent = noeud.parent
noeud.parent = pivot
#
if changement_racine:
self.racine = pivot
def rotation_gauche(self, noeud):
changement_racine = noeud == self.racine
# processus à partir de :
# https://upload.wikimedia.org/wikipedia/commons/1/15/Tree_Rotations.gif
pivot = noeud.droit
if pivot.gauche is not None:
pivot.gauche.parent = noeud
noeud.droit = pivot.gauche
else:
noeud.droit = None
pivot.gauche = noeud
# on agit aussi sur le parent de noeud
if noeud.parent is not None and noeud.parent.gauche == noeud:
noeud.parent.gauche = pivot
elif noeud.parent is not None and noeud.parent.droit == noeud:
noeud.parent.droit = pivot
#
pivot.parent = noeud.parent
noeud.parent = pivot
#
if changement_racine:
self.racine = pivot
# Dependencies
import numpy as np
import io
from mydict import dictionary
from Tree_Node import Node, BinaryTree
import random
import pandas as pd
from pprint import pprint
import pydot
import os
# from PIL import Image
import graphviz
from graphviz import Source
Tree_plot = graphviz.Digraph('Tree', format='png')
G = pydot.Dot(graph_type="digraph")
# first, this function is used to make sure that the data after splits will be pure to continue with the next step in
# the decision treee algorithm
def CheckPurity(data):
label_column = data[:, -1]
unique_classific = np.unique(label_column)
if len(unique_classific) == 1:
return True
else:
return False
#this function is used to work on data classification where it classify data base on
# majority class
def type_graph(simplify=False):
"""
Render graph of current type system.
"""
graph = graphviz.Digraph(format='svg')
graph.node(
u'\u03b5',
shape='circle',
style='dotted',
)
edge_pairs = set()
def add_edge(*pair):
if pair in edge_pairs:
return
graph.edge(*pair)
edge_pairs.add(pair)
simplified_graph = defaultdict(set)
for t in REGISTERED_TYPES:
targeting_functions = lookup_rtype(t, convert=False)
pretty_t = prettify_converted_type(t)
for targeting_function in targeting_functions:
params = targeting_function.readable_param_list
if not params:
add_edge(u'\u03b5', pretty_t)
continue
for param in params:
simplified_graph[param].add(pretty_t)
if simplify:
# pretend these can go directly to the return type
for param in params:
add_edge(param, pretty_t)
continue
elif len(list(params)) > 1:
# show composition of constructed type from constituents
graph.node(
targeting_function.readable_params,
shape='rect',
style='dashed',
)
for param in params:
add_edge(param, targeting_function.readable_params)
add_edge(targeting_function.readable_params, pretty_t)
end_states = {
t
for t in map(prettify_converted_type, REGISTERED_TYPES)
if not simplified_graph[t] or simplified_graph[t] == {t}
}
for end_state in end_states:
graph.node(
end_state,
peripheries='2',
)
return graph
def trt_network_to_dot_graph(network):
dot = graphviz.Digraph(comment='Network')
# add nodes (layers)
for i in range(network.num_layers):
layer = network.get_layer(i)
dot.node(layer.name)
# add nodes (inputs)
for i in range(network.num_inputs):
dot.node(network.get_input(i).name)
# add nodes (outputs)
for i in range(network.num_outputs):
dot.node(network.get_output(i).name)
# add layer->layer edges
for a in range(network.num_layers):
layer_a = network.get_layer(a)
for b in range(network.num_layers):
layer_b = network.get_layer(b)
for i in range(layer_a.num_outputs):
output_i = layer_a.get_output(i)
for j in range(layer_b.num_inputs):
input_j = layer_b.get_input(j)
if output_i == input_j:
dot.edge(layer_a.name,
layer_b.name,
label=str(input_j.shape))
# add input->layer edges
for i in range(network.num_inputs):
input_i = network.get_input(i)
for b in range(network.num_layers):
layer_b = network.get_layer(b)
for j in range(layer_b.num_inputs):
input_j = layer_b.get_input(j)
if input_i == input_j:
dot.edge(input_i.name,
layer_b.name,
label=str(input_j.shape))
# add layer->output edges
for i in range(network.num_outputs):
input_i = network.get_output(i)
for b in range(network.num_layers):
layer_b = network.get_layer(b)
for j in range(layer_b.num_outputs):
input_j = layer_b.get_output(j)
if input_i == input_j:
dot.edge(layer_b.name,
input_i.name,
label=str(input_j.shape))
return dot
def draw(self):
import graphviz
G = graphviz.Digraph()
drawn_subclusters = set()
def draw_node(a):
if isinstance(a, Network):
for part in a:
draw_node(part)
else:
G.node(a)
for a in self:
draw_node(a)
def draw_link(a, b):
if isinstance(a, Network):
# Connect the last part of a with b
if a.directed:
draw_link(a[-1], b)
# Connect each part of a with b
else:
for part in a:
draw_link(part, b)
elif isinstance(b, Network):
# Connect the first part of b with a
if b.directed:
if str(b) not in drawn_subclusters:
sub = b.draw()
# If the graph has a name, then we treat is as a cluster
if b.name is not None:
sub.attr(label=b.name, labelloc=b.labelloc)
sub.name = f"cluster_{b.name}"
G.subgraph(sub)
drawn_subclusters.add(str(b))
draw_link(a, b[0])
# Connect each part of b with a
else:
for part in b:
draw_link(a, part)
else:
G.edge(a, b)
for a, b in self.edges:
draw_link(self[a], self[b])
return G
