import get_data
import pandas as pd
import random as rdm
static_networks = get_data.list_of_static_networks()
for time_series in ['Poisson', 'Circadian', 'Bursty']:
t_0 = 0
delta_t = 10**5
for data in static_networks:
df = pd.read_csv('../Data/Static_networks/' + data + '_edgelist.csv')
ID_list = list(set(pd.concat([df['ID1'], df['ID2']])))
# how long? N
N = len(ID_list)
total_number_of_contacts = sum(df['Weight'])
S = []
for ID in ID_list:
ID_df = df[(df['ID1'] == ID) | (df['ID2'] == ID)]
S.append(int(sum(ID_df['Weight'])))
contacts = {}
for node in ID_list:
contacts[node] = []
new_df = pd.DataFrame(columns=['ID1', 'ID2', 'start_time', 'end_time'])
import get_data
import pandas as pd
import pickle
import numpy as np
mean_weight_values={}
weight_heterogeneity_values={}
data_list=get_data.list_of_static_networks()+get_data.list_of_temporal_networks(bats=True)
for data in data_list:
df,t_0,delta_t,species,interaction,phi_zero=get_data.dataframe(data)
############################################################
if data=='bats':
ID_list=list(set(df['ID1']))
else:
ID_list=list(set(pd.concat([df['ID1'],df['ID2']])))
N=len(ID_list)
####################################################################
K=[len(set(pd.concat([df[df['ID1']==ID]['ID2'],df[df['ID2']==ID]['ID1']]))) for ID in ID_list]
S=[len(pd.concat([df[df['ID1']==ID]['ID2'],df[df['ID2']==ID]['ID1']])) for ID in ID_list]
W=[]
edge_list=[]
for i,row in df.iterrows():
edge=tuple(sorted([row['ID1'],row['ID2']]))
if edge not in edge_list:
edge_list.append(edge)
#
import matplotlib.pyplot as plt
from scipy.special import hyp2f1
import pandas as pd
import pickle as pk
import get_data
data_list = get_data.list_of_temporal_networks(
bats=True) + get_data.list_of_static_networks() #+get_data.twitter()
names = {
'R0_prediction': 'Prediction',
'phi': 'Social fluidity',
'population': 'Population size',
'degree': 'Mean degree',
'excess_degree': 'Excess degree',
'mean_strength': 'Mean strength',
'mean_weight': 'Mean edge weight',
'weight_heterogeneity': 'Edge weight heterogeneity',
'modularity': 'Modularity',
'clustering': 'Mean clustering'
}
#network_stats=['beta','Delta_I','population','mean_strength','degree','excess_degree','mean_weight','weight_heterogeneity','phi','R0_prediction','modularity','clustering']
network_stats = [
'phi', 'epsilon', 'population', 'degree', 'excess_degree', 'mean_strength',
'mean_weight', 'weight_heterogeneity', 'modularity', 'clustering'
]
R_star_range = [2, 3, 4]
time_series_range = ['Poisson', 'Circadian', 'Bursty']
}
names = {
'R0_prediction': 'Prediction',
'phi': 'Social fluidity',
'population': 'Population size',
'degree': 'Mean degree',
'excess_degree': 'Excess degree',
'mean_strength': 'Mean strength',
'mean_weight': 'Mean edge weight',
'weight_heterogeneity': 'Edge weight heterogeneity',
'modularity': 'Modularity',
'clustering': 'Mean clustering'
}
data_list = get_data.list_of_temporal_networks(
bats=False) + get_data.list_of_static_networks()
dic = pk.load(open('pickles/phi.p', 'rb'))
phi = [dic[d] for d in data_list]
dic = pk.load(open('pickles/mean_strength.p', 'rb'))
strength = [dic[d] for d in data_list]
dic = pk.load(open('pickles/population.p', 'rb'))
population = [dic[d] for d in data_list]
dic = pk.load(open('pickles/mean_weight.p', 'rb'))
weight = [dic[d] for d in data_list]
dic = pk.load(open('pickles/weight_heterogeneity.p', 'rb'))
weight_heterogeneity = [dic[d] for d in data_list]
'Shark': '#6eb791',
'Bee': '#f29bed'
}
position = {
'Aggression': 0.5,
'Food sharing': 1.5,
'Antennal contact': 2.5,
# 'Space sharing':3.5,
'Face-to-face': 3.5,
'Association': 4.5,
'Grooming': 5.5
}
data_list = get_data.list_of_temporal_networks(
bats=True) + get_data.list_of_static_networks()
X = []
Y = []
fig = plt.figure(figsize=(12.1, 7.7))
ax = fig.add_subplot(111)
#ax=plt.subplot2grid((10,20), (0, 0), colspan=10,rowspan=10)
#algorithm to spread the data out
x_value = {}
y_value = {}
int_type = {}
spec_type = {}
for data in data_list:
