#!/usr/bin/env python
# coding: utf-8

# <a href="https://colab.research.google.com/github/klajosw/python/blob/master/kl_py_matplotlib_02.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

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#     <img src="https://raw.githubusercontent.com/klajosw/python/master/kl_mie_python_logo_250.jpg" 
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# # Python alapok Matplotlib használata 2.
# 
# <https://klajosw.blogspot.com/>
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# ---

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## csomagok importálása
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import networkx as nx
import random as rd

## A mágikus parancs  bekapcsolja az inline plots rajzolás lehetőséget. A grafika alaphelyzetbe állítása és betöltése.
## Az inline direktiva a grafikai megjelenitést engedélyezi külső program nélkül is
get_ipython().run_line_magic('matplotlib', 'inline')
a = """
  %matplotlib          ## megjelenítéshez használni fog egy csomagot  (pl. Qt5Agg)
  %matplotlib notebook ## interaktív rajzokhoz, amelyek be vannak ágyazva a notebookba
  %matplotlib inline   ## statikus képeket eredményez, amely be van ágyazva a notebookba

----------------

The pyplot interface provides 4 commands that are useful for interactive control.

  isinteractive() ## returns the interactive setting True|False
  ion()           ## turns interactive mode on
  ioff()          ## turns interactive mode off
  draw()          ## forces a figure redraw

"""


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## hálózat elemzés
g = nx.erdos_renyi_graph(30, 0.05, directed=True, seed=123)
nx.draw(g, pos=nx.kamada_kawai_layout(g))


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## elemzés
nx.pagerank(g)


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for i in g:
    print(f"Node {i} has neighbors {g[i]}")


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n = 4 # dimension 1
m = 4 # dimension 2
N = n * m # number of nodes
k = 4 # number of neighbors of each node

def initialize():
    global g
    g = nx.Graph()
    for i in range(n): # row
        for j in range(m): # column
            node = j + i*n
            if node+1 < n:
                g.add_edge(node, (node + 1))
            if (node - 1) >= i*n:
                g.add_edge(node, (node - 1))
            if (node + n) < N:
                g.add_edge(node, (node + n))
            if (node - n) >= 0:
                g.add_edge(node, (node - n))
    g.pos = nx.spring_layout(g)
    g.count = 0

def observe():
    global g
    nx.draw(g, pos = g.pos)

def update():
    global g
    g.count += 1
    if g.count % 20 == 0: # rewiring once in every 20 steps
        nds = list(g.nodes)
        i = rd.choice(nds)
        if g.degree[i] > 0:
            g.remove_edge(i, rd.choice(list(g.neighbors(i))))
            nds.remove(i)
            for j in g.neighbors(i):
                nds.remove(j)
            g.add_edge(i, rd.choice(nds))

    # simulation of node movement
    g.pos = nx.spring_layout(g, pos = g.pos, iterations = 5)
    
initialize()
observe()


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# run simulations
initialize()
for tstep in range(400):
    update()
observe()


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import matplotlib
# matplotlib.use('TkAgg')
from pylab import *
import networkx as nx

m0 = 5 # number of nodes in initial condition
m = 2 # number of edges per new node

def initialize():
    global g
    g = nx.complete_graph(m0)
    g.pos = nx.spring_layout(g)
    g.count = 0

def observe():
    global g
    cla()
    nx.draw(g, pos = g.pos)

def pref_select(nds):
    global g
    r = uniform(0, sum(g.degree(i) for i in nds))
    x=0
    for i in nds:
        x += g.degree[i]
        if r <= x:
            return i

def update():
    global g
    g.count += 1
    if g.count % 20 == 0: # network growth once in every 20 steps
        nds = list(g.nodes)
        newcomer = max(nds) + 1
        for i in range(m):
            j = pref_select(nds)
            g.add_edge(newcomer, j)
            nds.remove(j)
        g.pos[newcomer] = (0, 0)

    # simulation of node movement
    g.pos = nx.spring_layout(g, pos = g.pos, iterations = 5)
    
initialize()
plt.figure(0)
observe()
for _ in range(100):
    update()
plt.figure(1)
observe()


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initialize()
plt.figure(0)
observe()
for _ in range(100):
    update()
plt.figure(1)
observe()


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initialize()
plt.figure(0)
observe()
for _ in range(100):
    update()
plt.figure(1)
observe()


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m = 5
initialize()
plt.figure(0)
observe()
for _ in range(100):
    update()
plt.figure(1)
observe()


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