def plot(table_names=None):
"""
Plot relationships between columns and tables using Graphviz.
Parameters
----------
table_names : iterable of str, optional
Names of UrbanSim registered tables to plot.
Defaults to all registered tables.
Returns
-------
graph : pygraphviz.AGraph
PyGraphviz graph object.
"""
if not table_names:
# Default to all registered tables.
table_names = simulation.list_tables()
graph = AGraph(directed=True)
graph.graph_attr['fontname'] = 'Sans'
graph.graph_attr['fontsize'] = 28
graph.node_attr['shape'] = 'box'
graph.node_attr['fontname'] = 'Sans'
graph.node_attr['fontcolor'] = 'blue'
graph.edge_attr['weight'] = 2
# Add each registered table as a subgraph with columns as nodes.
for table_name in table_names:
subgraph = graph.add_subgraph(name='cluster_' + table_name,
label=table_name,
fontcolor='red')
table = simulation.get_table(table_name)
for column_name in table.columns:
full_name = table_name + '.' + column_name
subgraph.add_node(full_name, label=column_name)
# Iterate over computed columns to build nodes.
for key, wrapped_col in simulation._COLUMNS.items():
table_name = key[0]
column_name = key[1]
# Combine inputs from argument names and argument default values.
args = list(wrapped_col._argspec.args)
if wrapped_col._argspec.defaults:
default_args = list(wrapped_col._argspec.defaults)
else:
default_args = []
inputs = args[:len(args) - len(default_args)] + default_args
# Create edge from each input column to the computed column.
for input_name in inputs:
full_name = table_name + '.' + column_name
graph.add_edge(input_name, full_name)
graph.layout(prog='dot')
return graph
def plot(table_names=None):
"""
Plot relationships between columns and tables using Graphviz.
Parameters
----------
table_names : iterable of str, optional
Names of UrbanSim registered tables to plot.
Defaults to all registered tables.
Returns
-------
graph : pygraphviz.AGraph
PyGraphviz graph object.
"""
if not table_names:
# Default to all registered tables.
table_names = simulation.list_tables()
graph = AGraph(directed=True)
graph.graph_attr['fontname'] = 'Sans'
graph.graph_attr['fontsize'] = 28
graph.node_attr['shape'] = 'box'
graph.node_attr['fontname'] = 'Sans'
graph.node_attr['fontcolor'] = 'blue'
graph.edge_attr['weight'] = 2
# Add each registered table as a subgraph with columns as nodes.
for table_name in table_names:
subgraph = graph.add_subgraph(name='cluster_' + table_name,
label=table_name, fontcolor='red')
table = simulation.get_table(table_name)
for column_name in table.columns:
full_name = table_name + '.' + column_name
subgraph.add_node(full_name, label=column_name)
# Iterate over computed columns to build nodes.
for key, wrapped_col in simulation._COLUMNS.items():
table_name = key[0]
column_name = key[1]
# Combine inputs from argument names and argument default values.
args = list(wrapped_col._argspec.args)
if wrapped_col._argspec.defaults:
default_args = list(wrapped_col._argspec.defaults)
else:
default_args = []
inputs = args[:len(args) - len(default_args)] + default_args
# Create edge from each input column to the computed column.
for input_name in inputs:
full_name = table_name + '.' + column_name
graph.add_edge(input_name, full_name)
graph.layout(prog='dot')
return graph
def run_developer(forms, agents, buildings, supply_fname, parcel_size,
ave_unit_size, total_units, feasibility, year=None,
target_vacancy=.1, form_to_btype_callback=None,
add_more_columns_callback=None, max_parcel_size=2000000,
residential=True, bldg_sqft_per_job=400.0,
min_unit_size=400, remove_developed_buildings=True,
unplace_agents=['households', 'jobs']):
"""
Run the developer model to pick and build buildings
Parameters
----------
forms : string or list of strings
Passed directly dev.pick
agents : DataFrame Wrapper
Used to compute the current demand for units/floorspace in the area
buildings : DataFrame Wrapper
Used to compute the current supply of units/floorspace in the area
supply_fname : string
Identifies the column in buildings which indicates the supply of
units/floorspace
parcel_size : Series
Passed directly to dev.pick
ave_unit_size : Series
Passed directly to dev.pick - average residential unit size
total_units : Series
Passed directly to dev.pick - total current residential_units /
job_spaces
feasibility : DataFrame Wrapper
The output from feasibility above (the table called 'feasibility')
year : int
The year of the simulation - will be assigned to 'year_built' on the
new buildings
target_vacancy : float
The target vacancy rate - used to determine how much to build
form_to_btype_callback : function
Will be used to convert the 'forms' in the pro forma to
'building_type_id' in the larger model
add_more_columns_callback : function
Takes a dataframe and returns a dataframe - is used to make custom
modifications to the new buildings that get added
max_parcel_size : float
Passed directly to dev.pick - max parcel size to consider
min_unit_size : float
Passed directly to dev.pick - min unit size that is valid
residential : boolean
Passed directly to dev.pick - switches between adding/computing
residential_units and job_spaces
bldg_sqft_per_job : float
Passed directly to dev.pick - specified the multiplier between
floor spaces and job spaces for this form (does not vary by parcel
as ave_unit_size does)
remove_redeveloped_buildings : optional, boolean (default True)
Remove all buildings on the parcels which are being developed on
unplace_agents : optional : list of strings (default ['households', 'jobs'])
For all tables in the list, will look for field building_id and set
it to -1 for buildings which are removed - only executed if
remove_developed_buildings is true
Returns
-------
Writes the result back to the buildings table and returns the new
buildings with available debugging information on each new building
"""
dev = developer.Developer(feasibility.to_frame())
target_units = dev.\
compute_units_to_build(len(agents),
buildings[supply_fname].sum(),
target_vacancy)
print "{:,} feasible buildings before running developer".format(
len(dev.feasibility))
new_buildings = dev.pick(forms,
target_units,
parcel_size,
ave_unit_size,
total_units,
max_parcel_size=max_parcel_size,
min_unit_size=min_unit_size,
drop_after_build=True,
residential=residential,
bldg_sqft_per_job=bldg_sqft_per_job)
sim.add_table("feasibility", dev.feasibility)
if new_buildings is None:
return
if len(new_buildings) == 0:
return new_buildings
if year is not None:
new_buildings["year_built"] = year
if not isinstance(forms, list):
# form gets set only if forms is a list
new_buildings["form"] = forms
if form_to_btype_callback is not None:
new_buildings["building_type_id"] = new_buildings.\
apply(form_to_btype_callback, axis=1)
new_buildings["stories"] = new_buildings.stories.apply(np.ceil)
ret_buildings = new_buildings
if add_more_columns_callback is not None:
new_buildings = add_more_columns_callback(new_buildings)
print "Adding {:,} buildings with {:,} {}".\
format(len(new_buildings),
int(new_buildings[supply_fname].sum()),
supply_fname)
print "{:,} feasible buildings after running developer".format(
len(dev.feasibility))
old_buildings = buildings.to_frame(buildings.local_columns)
new_buildings = new_buildings[buildings.local_columns]
if remove_developed_buildings:
redev_buildings = old_buildings.parcel_id.isin(new_buildings.parcel_id)
l = len(old_buildings)
drop_buildings = old_buildings[redev_buildings]
old_buildings = old_buildings[np.logical_not(redev_buildings)]
l2 = len(old_buildings)
if l2-l > 0:
print "Dropped {} buildings because they were redeveloped".\
format(l2-l)
for tbl in unplace_agents:
agents = sim.get_table(tbl)
cols = agents.local_columns
if "building_id" not in cols:
# if it's a unit-level model, need to add building_id
# explicitly
cols += ["building_id"]
agents = agents.to_frame(cols)
displaced_agents = agents.building_id.isin(drop_buildings.index)
print "Unplaced {} before: {}".format(tbl, len(agents.query(
"building_id == -1")))
agents.building_id[displaced_agents] = -1
print "Unplaced {} after: {}".format(tbl, len(agents.query(
"building_id == -1")))
sim.add_table(tbl, agents)
all_buildings = dev.merge(old_buildings, new_buildings)
sim.add_table("buildings", all_buildings)
if "residential_units" in sim.list_tables() and residential:
# need to add units to the units table as well
old_units = sim.get_table("residential_units")
old_units = old_units.to_frame(old_units.local_columns)
new_units = pd.DataFrame({
"unit_residential_price": 0,
"num_units": 1,
"deed_restricted": 0,
"unit_num": np.concatenate([np.arange(i) for i in \
new_buildings.residential_units.values]),
"building_id": np.repeat(new_buildings.index.values,
new_buildings.residential_units.\
astype('int32').values)
}).sort(columns=["building_id", "unit_num"]).reset_index(drop=True)
print "Adding {:,} units to the residential_units table".\
format(len(new_units))
all_units = dev.merge(old_units, new_units)
all_units.index.name = "unit_id"
sim.add_table("residential_units", all_units)
return ret_buildings
# pondered returning ret_buildings, new_units but users can get_table
# the units if they want them - better to avoid breaking the api
return ret_buildings
