MJacobs1985

import networkx as nx
G = nx.Graph()
G.add_node(1)
nx.draw(G, with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500)

G.add_nodes_from([2, 3])
nx.draw(G, with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500)

H = nx.path_graph(10)
G.add_nodes_from(H)

MJacobs1985

G = nx.Graph()
G.add_edge(1, 7)
e = (2, 3)
G.add_edge(*e)
nx.draw(G, with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500, alpha=0.8, edge_color='orange')

G.add_edges_from([(1, 2), (1, 3)])
nx.draw(G, with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500, alpha=0.8, edge_color='orange')

H = nx.path_graph(10)

MJacobs1985

K_5 = nx.complete_graph(5)
nx.draw(K_5, with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500, alpha=0.8, edge_color='orange')

K_3_5 = nx.complete_bipartite_graph(3, 5)
nx.draw(K_3_5, with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500, alpha=0.8, edge_color='orange')

barbell = nx.barbell_graph(10, 10)
nx.draw(barbell , with_labels=True, font_weight='bold', font_color='white', node_color='black', node_size=500, alpha=0.8, edge_color='orange')

lollipop = nx.lollipop_graph(10, 20)

MJacobs1985

# Visualization
import networkx as nx
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline

# Network topology
g = nx.random_geometric_graph(1000, 0.1, seed=896803) # Random Clustered graph 1000 nodes - distance =0.05, no self loops

MJacobs1985

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as ep
from ndlib.viz.mpl.DiffusionTrend import DiffusionTrend

# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)                    # 1000 nodes with 10% probability of edge creation

# Model selection
model  = ep.SEIRModel(g)

MJacobs1985

import networkx as nx
import matplotlib.pyplot as plt
import dynetx as dn
import ndlib.models.ModelConfig as mc
import ndlib.models.dynamic as dm
from past.builtins import xrange
import ndlib.models.epidemics as ep
from ndlib.utils import multi_runs
from ndlib.viz.mpl.DiffusionTrend import DiffusionTrend
from ndlib.viz.mpl.TrendComparison import DiffusionTrendComparison

MJacobs1985

%let ErrorVariance=1000;
%let BlockVariance=3000;
%let block=18;
%let nsim=1;
data factor;
length Treatment $30;
call streaminit(123);
    do isim = 1 to ≁
        do block=1 to █
        rndBlock=rand("Normal",0,sqrt(&BlockVariance)); /* create block specific deviates */

MJacobs1985

data Rsquared;
do i=1 to 1000 by 1;
x=rand("Normal");
y=1-2*x + 1*x*x + rand("Normal");
output;
end;
proc sgplot data=Rsquared;
scatter x=x y=y;
run;
proc reg data=Rsquared plots=fitplot;

MJacobs1985

%let ErrorVariance=2720;
%let BlockVariance=4851;
%let block=20;
%let nsim=1;
DATA RCBD;
 call streaminit(123);
 do isim = 1 to ≁
    do block=1 to █
    rndBlock=rand("Normal",0,sqrt(&BlockVariance)); /* create block specific deviates */
        do trt=1 to 2;

MJacobs1985

data normal;
call streaminit(123);
 length simulation 4 i 3; 
 do simulation = 1 to 1000;
  do i = 1 to 100;
   group='A';
    x = rand('normal');
 output;
 group='B';
    x = rand('normal');