def main():
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--json', help='filename')
parser.add_argument('--dotoutput', help='filename')
parser.add_argument('--pngoutput', help='filename', default='output.png')
url_prefix = os.getenv('URL_PREFIX')
parser.add_argument('--url', help='filename', default=url_prefix)
args = parser.parse_args()
tree = Tree()
data = read_json_data(args.json)
if 'nodes' in data:
for x in data['nodes']:
name = x['name']
key = ''
parent = None
if 'key' in x:
key = get_key(name, x['key'])
else:
key = get_key(name)
if 'parent' in x:
parent = get_key(x['parent'])
#print (name, key, parent, x['parent'])
tree.create_node(name, key, parent)
print(map_keys)
tree.show()
x = recursive(tree.to_dict())
if 'edges' in data:
for d in data['edges']:
src = get_key(d['src'])
dst = get_key(d['dst'])
color = 'black'
if 'color' in d:
color = d['color']
x.edge(src, dst, color=color)
#print (x.source)
if args.dotoutput:
#print (json.dumps(data))
with open(args.dotoutput, 'w') as f:
f.write(x.source)
if args.url:
url = '%s' % (args.url)
r = requests.post(url, json={"datatype": "dotty", "data": x.source})
js = json.loads(r.content)
id = js['uuid']
url = '%s/%s/png' % (args.url, id)
print(url)
r = requests.get(url, stream=True)
print(r)
if r.status_code == 200:
with open(args.pngoutput, 'wb') as out_file:
shutil.copyfileobj(r.raw, out_file)
def buildTree(dictOfDatas):
tree = Tree()
used_datas = []
while dictOfDatas:
for key, value in dictOfDatas.items():
if value in used_datas:
tree.create_node(key, key, parent=value)
used_datas.append(key)
dictOfDatas.pop(key)
break
elif value is None:
tree.create_node(key, key)
used_datas.append(key)
dictOfDatas.pop(key)
break
tree.show()
return tree.to_dict()
def spaces(self, output="dictionary"):
spaces = self.get_all_spaces()
page_labels = self.get_page_labels()
spaces_tree = Tree()
#Add PDF name as top node
spaces_tree.create_node(self.file_name, self.file_name)
#Add pages nodes
for key, value in page_labels.items():
spaces_tree.create_node(value, value, parent=self.file_name)
spaces_tree = self.spacestree(spaces[key], spaces_tree, value)
if output == "tree":
return (spaces_tree)
elif output == "hierarchy":
#Getting a master space hierarchy by column
#Max depth starts calculating outside the doc name and sheet name
max_depth = max([len(i)
for i in spaces_tree.paths_to_leaves()]) - 2
space_depth = []
for level in range(max_depth):
space_depth.append("Space " + str(level + 1))
#Create the dictionary to hold the values
space_dict = {}
for idx, level in enumerate(space_depth):
space_dict[level] = []
#Loop through all space items
for item in spaces_tree.paths_to_leaves():
for level, space in enumerate(item[2:]):
res = list(space_dict.keys())[level]
if str(space)[7:-6] not in space_dict[res]:
space_dict[res].append(str(space)[7:-6])
return (space_dict)
else:
# Return the Python dictionary
spaces_dict = {'spaces': []}
spaces_dict['spaces'].append(spaces_tree.to_dict())
return (spaces_dict)
def do_md_section(sysid, levelid, connection_str, output_format, output_file,
compact, doc_handler, dotree):
"""
Given the MD ids, dump the content in the output file and produce the product tree diagrams
:param sysid: MagicDraw subsystem id
:param levelid: MagicDraw level id (package id containing the tree to extract)
:param connection_str: MagicDraw encoded connection string
:param output_format: OutputFormat
:param output_file: File to dump
:param compact: True if the portrait diagrams have to be in compact form
:param doc_handler: the document id
:param sub_name: name of the subsystem
:return: none
"""
global template_path
global productTree
global tree_dict
productTree = Tree()
products = []
tree_dict = {}
# get the information from MagicDraw
mdr = build_md_tree(sysid, levelid, connection_str)
print("\n Product tree depth:", productTree.depth())
nodes = productTree.expand_tree(key=lambda x: x.data.index, sorting=True)
for n in nodes:
products.append(productTree[n].data)
tree_dict[productTree[n].data.id] = productTree[n].data
print(f" Found {{np}} products (including container folders).".format(
np=len(tree_dict)))
envs = Environment(loader=ChoiceLoader([
FileSystemLoader(Config.TEMPLATE_DIRECTORY),
PackageLoader('ptree', 'templates')
]),
lstrip_blocks=True,
trim_blocks=True,
autoescape=None)
# dump a csv file
if dotree:
do_csv(products, output_file)
# create the diagrams tex files
do_trees_diagrams(productTree, output_file, products[0].shortname,
compact)
# define the template
template_path = f"section.{Config.TEMPLATE_LANGUAGE}.jinja2"
else:
# no diagrams are created
# use a simplified template
template_path = f"notree_section.{Config.TEMPLATE_LANGUAGE}.jinja2"
# sort tree dictionary based
mdp = productTree.to_dict(with_data=False)
# get ordered dictionary
template = envs.get_template(template_path)
new_mdpt = dict()
for k0 in mdp:
new_mdpt[k0] = order_tree_level(mdp[k0])
# dump the tex section
metadata = dict()
metadata["template"] = template.filename
text = template.render(metadata=metadata,
mdrev=mdr,
filename=output_file,
doc_handler=doc_handler,
mdt_dict=tree_dict,
mdp=new_mdpt,
mdps=products)
tex_file_name = output_file + ".tex"
file = open(tex_file_name, "w")
print(_as_output_format(text, output_format), file=file)
file.close()
return tree_dict
add_node(tree, None, kraken_data)
previous = tree.get_node(kraken_data['ncbi_taxonomy_id'])
elif kraken_data['depth'] > tree.depth(previous):
add_node(tree, previous, kraken_data)
previous = tree.get_node(kraken_data['ncbi_taxonomy_id'])
elif kraken_data['depth'] == tree.depth(previous):
add_node(tree, tree.parent(previous.identifier), kraken_data)
previous = tree.get_node(kraken_data['ncbi_taxonomy_id'])
elif kraken_data['depth'] < tree.depth(previous):
previous_search = previous
while(tree.depth(previous_search) > kraken_data['depth']):
previous_search = tree.parent(previous_search.identifier)
add_node(tree, tree.parent(previous_search.identifier), kraken_data)
previous = tree.get_node(kraken_data['ncbi_taxonomy_id'])
tree_dict = tree.to_dict(with_data=True)
def transform(tree_dict):
for key in tree_dict.keys():
for data_key in tree_dict[key]['data'].keys():
tree_dict[data_key] = tree_dict[key]['data'][data_key]
tree_dict[key].pop('data', None)
tree_dict.pop('depth', None)
if 'children' in tree_dict[key]:
for child in tree_dict[key]['children']:
transform(child)
tree_dict['children'] = tree_dict[key]['children']
tree_dict[key].pop('children', None)
tree_dict.pop(key, None)
tree_dict.pop('root', None)