def main(argv=None):
usage = """Data Model ERD Generator
Generates an entity relationship diagram for the data model specified. If
passing a custom URL, the data model returned must be in the JSON format
defined by the chop-dbhi/data-models package. The generated diagram is
saved to the named output file.
Usage: erd.py [options] <model> <version> <outfile>
Options:
-h --help Show this screen.
-u URL --url=URL Retrieve model JSON from this URL instead of the
default or environment-variable-passed URL.
""" # noqa
from docopt import docopt
# Ignore command name if called from command line.
argv = argv or sys.argv[1:]
args = docopt(usage, argv=argv, version=__version__)
url = args['--url'] or get_url(args['<model>'], args['<version>'])
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
render_er(metadata, args['<outfile>'])
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('i2b2_pedsnet', 'v2')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('pcornet', 'v3')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('omop', 'v4')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('pedsnet', 'v2')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
def main(argv=None):
usage = """Data Model DDL Generator
Generates data definition language for the data model specified in the
given DBMS dialect. If passing a custom URL, the data model returned must
be in the JSON format defined by the chop-dbhi/data-models package. See
http://docs.sqlalchemy.org/en/rel_1_0/dialects/index.html for available
dialects. The generated DDL is written to stdout.
Usage: ddl.py [options] <model> <version> <dialect>
Options:
-h --help Show this screen.
-t --xtables Exclude tables from the generated DDL.
-c --xconstraints Exclude constraints from the generated DDL.
-i --xindexes Exclude indexes from the generated DDL.
-d --drop Generate DDL to drop, instead of create, objects.
-x --delete-data Generate DML to delete data.
-u URL --url=URL Retrieve model JSON from this URL instead of the
default or environment-variable-passed URL.
-r --return Return DDL as python string object instead of
printing it to stdout.
""" # noqa
from docopt import docopt
# Ignore command name if called from command line.
argv = argv or sys.argv[1:]
args = docopt(usage, argv=argv, version=__version__)
url = args['--url'] or get_url(args['<model>'], args['<version>'])
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
engine = create_engine(args['<dialect>'] + '://')
output = []
if args['--delete-data']:
tables = reversed(metadata.sorted_tables)
output.extend(delete_data(tables, engine))
output = ''.join(output)
if args['--return']:
return output
else:
sys.stdout.write(output)
return
if not args['--xtables']:
if not args['--drop']:
tables = metadata.sorted_tables
output.extend(table_ddl(tables, engine, False))
else:
tables = reversed(metadata.sorted_tables)
output.extend(table_ddl(tables, engine, True))
if not args['--xconstraints']:
if not args['--drop']:
tables = metadata.sorted_tables
output.append('\n')
output.extend(constraint_ddl(tables, engine, False))
else:
tables = reversed(metadata.sorted_tables)
output.insert(0, '\n')
output[0:0] = constraint_ddl(tables, engine, True)
if not args['--xindexes']:
if not args['--drop']:
tables = metadata.sorted_tables
output.append('\n')
output.extend(index_ddl(tables, engine, False))
else:
tables = reversed(metadata.sorted_tables)
output.insert(0, '\n')
output[0:0] = index_ddl(tables, engine, True)
output = ''.join(output)
if args['--return']:
return output
else:
sys.stdout.write(output)
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('omop', 'v5')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('pedsnet', 'v1')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('i2b2', 'v1.7')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
import requests
from sqlalchemy import MetaData
from sqlalchemy.ext.declarative import declarative_base
from dmsa.settings import get_url
from dmsa.makers import make_model
url = get_url('pcornet', 'v2')
model_json = requests.get(url).json()
metadata = MetaData()
make_model(model_json, metadata)
Base = declarative_base(metadata=metadata)
for table in metadata.tables.values():
cls_name = ''.join(i.capitalize() for i in table.name.split('_'))
globals()[cls_name] = table
