def add_user_network_random(self,
df_interactions,
skip_hospitalised=True,
skip_quarantine=True,
name="user_network"):
"""[summary]
adds a bespoke user random network from a dataframe of people and number of interactions
the network is regenerates each day, but the number of interactions per person is statitc
hospitalsed and quarantined people can be skipped
Arguments:
df_interactions {[dataframe]} -- [list of indviduals and interactions, with 2 columns ID and N]
skip_hospitalised {[boolean]} -- [skip interaction if either person is in hospital]
skip_quarantine{[boolean]} -- [skip interaction if either person is in quarantined]
name{[char]} -- [the name of the network]
"""
n_indiv = len(df_interactions.index)
n_total = self._params_obj.get_param("n_total")
if not 'ID' in df_interactions.columns:
raise ParameterException("df_interactions must have column ID")
if not 'N' in df_interactions.columns:
raise ParameterException("df must have column N")
if not skip_hospitalised in [True, False]:
raise ParameterException("skip_hospitalised must be True or False")
if not skip_quarantine in [True, False]:
raise ParameterException("skip_quarantine must be True or False")
ID = df_interactions["ID"].to_list()
N = df_interactions["N"].to_list()
if (max(ID) >= n_total) or (min(ID) < 0):
raise ParameterException(
"all values of ID must be between 0 and n_total-1")
if (min(N) < 1):
raise ParameterException("all values of N must be greater than 0")
ID_c = covid19.longArray(n_indiv)
N_c = covid19.intArray(n_indiv)
for idx in range(n_indiv):
ID_c[idx] = ID[idx]
N_c[idx] = N[idx]
id = covid19.add_user_network_random(self.c_model, skip_hospitalised,
skip_quarantine, n_indiv, ID_c,
N_c, name)
return Network(self, id)
def get_network_info(self, max_ids= 1000):
if max_ids > 1e6 :
raise ModelException( "Maximum number of allowed network is 1e6" )
ids_c = covid19.intArray( max_ids )
n_ids = covid19.get_network_ids( self.c_model, ids_c, max_ids )
if n_ids == -1 :
return self.get_network_info( max_ids = max_ids * 10 )
ids = [None] * n_ids
names = [None] * n_ids
n_edges = [None] * n_ids
n_vertices = [None] * n_ids
type = [None] * n_ids
skip_hospitalised = [None] * n_ids
skip_quarantined = [None] * n_ids
daily_fraction = [None] * n_ids
for idx in range( n_ids ) :
network = Network( self, ids_c[idx] )
ids[idx] = ids_c[idx]
names[idx] = network.name()
n_edges[idx] = network.n_edges()
n_vertices[idx] = network.n_vertices()
type[idx] = network.type()
skip_hospitalised[idx] = network.skip_hospitalised()
skip_quarantined[idx] = network.skip_quarantined()
daily_fraction[idx] = network.daily_fraction()
return pd.DataFrame( {
'id' : ids,
'name' : names,
'n_edges' : n_edges,
'n_vertices' : n_vertices,
'type' : type,
'skip_hospitalised' : skip_hospitalised,
'skip_quarantined' : skip_quarantined,
'daily_fraction' : daily_fraction
} )
def get_network_by_id(self, network_id ):
return Network( self, network_id )
def add_user_network(
self,
df_network,
interaction_type = covid19.OCCUPATION,
skip_hospitalised = True,
skip_quarantine = True,
construction = covid19.NETWORK_CONSTRUCTION_BESPOKE,
daily_fraction = 1.0,
name = "user_network" ):
"""[summary]
adds as bespoke user network from a dataframe of edges
the network is static with the exception of skipping
hospitalised and quarantined people
Arguments:
df_network {[dataframe]} -- [list of edges, with 2 columns ID_1 and ID_2]
interaction {[int]} -- [type of interaction (e.g. household/occupation/random)]
skip_hospitalised {[boolean]} -- [skip interaction if either person is in hospital]
skip_quarantine{[boolean]} -- [skip interaction if either person is in quarantined]
construction{[int]} -- [the method used for network construction]
daily_fraction{[double]} -- [the fraction of edges on the network present each day (i.e. down-sampling the network)]
name{[char]} -- [the name of the network]
"""
n_edges = len( df_network.index )
n_total = self._params_obj.get_param("n_total")
if not 'ID_1' in df_network.columns:
raise ParameterException( "df_network must have column ID_1" )
if not 'ID_2' in df_network.columns:
raise ParameterException( "df_network must have column ID_2" )
if not interaction_type in [0,1,2]:
raise ParameterException( "interaction_type must be 0 (household), 1 (occupation) or 2 (random)" )
if (daily_fraction > 1) or( daily_fraction < 0):
raise ParameterException( "daily fraction must be in the range 0 to 1" )
if not skip_hospitalised in [ True, False ]:
raise ParameterException( "skip_hospitalised must be True or False" )
if not skip_quarantine in [ True, False ]:
raise ParameterException( "skip_quarantine must be True or False" )
ID_1 = df_network[ "ID_1" ].to_list()
ID_2 = df_network[ "ID_2" ].to_list()
if (max( ID_1 ) >= n_total) or (min( ID_1 ) < 0):
raise ParameterException( "all values of ID_1 must be between 0 and n_total-1" )
if (max( ID_2 ) >= n_total) or (min( ID_2 ) < 0):
raise ParameterException( "all values of ID_2 must be between 0 and n_total-1" )
ID_1_c = covid19.longArray(n_edges)
ID_2_c = covid19.longArray(n_edges)
for idx in range(n_edges):
ID_1_c[idx] = ID_1[idx]
ID_2_c[idx] = ID_2[idx]
id = covid19.add_user_network(self.c_model,interaction_type,skip_hospitalised,skip_quarantine,construction,daily_fraction, n_edges,ID_1_c, ID_2_c, name)
return Network( self, id )