folyam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "# from sage.graphs.graph_plot import GraphPlot\n",
    "# from shapely.geometry import LineString\n",
    "from copy import deepcopy\n",
    "#from networkx.drawing.nx_pydot import graphviz_layout\n",
    "from networkx.drawing.nx_agraph import graphviz_layout\n",
    "from ipywidgets import HBox,VBox\n",
    "from ipywidgets.widgets import IntSlider,Textarea,Label,interactive,Button, Dropdown\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from networkx.drawing.nx_agraph import graphviz_layout\n",
    "from IPython.display import HTML\n",
    "\n",
    "from matplotlib.patches import ConnectionStyle\n",
    "\n",
    "from shutil import rmtree\n",
    "import os\n",
    "from IPython.display import display, FileLink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "The raw code for this IPython notebook is by default hidden for easier reading.\n",
       "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "The raw code for this IPython notebook is by default hidden for easier reading.\n",
    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "algoritmus=[u'Az aktualis folyam. Keszitsuk el a segedgrafot!',\n",
    "           u'Van-e iranyitott ut a segedgrafban S-bol T-be?',\n",
    "           u'\\t Van javito ut! Javitsunk es Vissza az elejere',\n",
    "           u'\\t Nincs javito ut. Az S-bol elerheto pontok kiszamitasa!',\n",
    "           u'Vagas ertekenek meghatarozasa!']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all(index):\n",
    "    return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "graph_select=Dropdown(\n",
    "    options=[('ZH_2018/5',0)('Szabad',1)],\n",
    "    value=0,\n",
    "    description='Gráfválasztás:',\n",
    "    layout={'width': 'max-content'},\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "pelda_grafok={0:'S:A5,B2,C6,E9\\n'\n",
    "              1:''}\n",
    "def trig(b):\n",
    "    input_graph.value=pelda_grafok[graph_select.value]\n",
    "graph_select.observe(trig ,names='value')\n",
    "graph_select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d_inc={'S':{'A':{'cap':5},'B':{'cap':2},'C':{'cap':6},'E':{'cap':9}},\n",
    "#        'A':{'B':{'cap':5},'T':{'cap':4}},\n",
    "#        'B':{'T':{'cap':10},'E':{'cap':2}},\n",
    "#        'C':{'B':{'cap':3},'D':{'cap':3}},\n",
    "#        'D':{'T':{'cap':5}},\n",
    "#        'E':{'D':{'cap':6},'T':{'cap':2}}}\n",
    "# G=nx.DiGraph(d_inc)\n",
    "# print(G.nodes())\n",
    "# G.get_edge_data('S','A')\n",
    "# forras='S'\n",
    "# nyelo='T'\n",
    "# pos_G=nx.spring_layout(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def folyam_ertek(f):\n",
    "    return sum([f[(forras,u)] for u in G.neighbors(forras)])\n",
    "def vagas_ertek(X):\n",
    "    return sum([G.get_edge_data(*e)['cap'] for e in G.edges if (e[0] in X) and (e[1] not in X)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "732cc2d31b7b4a03a2db028b0a35d38e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Gráfválasztás:', layout=Layout(width='max-content'), options=(('Jegyzet 2.14', 0), ('Gy9…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_select=Dropdown(\n",
    "    options=[('Jegyzet 2.14', 0), ('Gy9/2',1),('Gy9/4',2),('Gy9/6',3),('ures',4)],\n",
    "    value=0,\n",
    "    description='Gráfválasztás:',\n",
    "    layout={'width': 'max-content'},\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "pelda_grafok={0:['S:A5,B2,C6,E9\\nA:B5,T4\\nB:T10,E2\\nC:B3,D3\\nD:T5\\nE:D6,T2',{'S':(0,0),'A':(1,6),'B':(3,5),'C':(2,1.5),'D':(4,1.5),'E':(6,0),'T':(5,6)}],\n",
    "              1:['S:A7,D2\\nA:B2,F5\\nB:C5,F2\\nC:T6\\nD:A4,E5\\nE:A3,B3,F3\\nF:C1,T3',{'S':(0,1),'A':(1,1),'B':(2,1),'C':(3,1),'D':(0,0),'E':(1,0),'F':(2,0), 'T':(3,0)}],\n",
    "              2:['S:a11,d13,e4\\na:b6,f7\\nb:c9\\nc:T14\\nd:c2,e7\\ne:b4,f5\\nf:T13',{'S':(0,0),'a':(1,1),'b':(2,1),'c':(3,1),'d':(1,-1),'e':(2,-1),'f':(3,-1),'T':(4,0)}],\n",
    "              3:['S:A15,B6,C3,F2\\nA:D2,G9\\nB:D3,C11\\nC:F7,E9\\nD:T5\\nE:T21,D3\\nF:E7,G4\\nG:E4',{'S':(0,4),'A':(4,4),'F':(0,0),'G':(4,0),'T':(2,2),'B':(2,3.5),'C':(0.5,2),'D':(3.5,2),'E':(2,0.5)}],\n",
    "              4:['',{}]\n",
    "             }\n",
    "input_graph_pos={}\n",
    "def trig(b):\n",
    "    global input_graph_pos\n",
    "    input_graph.value=pelda_grafok[graph_select.value][0]\n",
    "    input_graph_pos=pelda_grafok[graph_select.value][1]\n",
    "graph_select.observe(trig ,names='value')\n",
    "forras='S'\n",
    "nyelo='T'\n",
    "\n",
    "graph_select\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_graph = Label(\"Gráf szomszédsági lista+Sulyok.\\n Példasor:\\n\\tA:B1,C2,D5\")\n",
    "input_graph = Textarea(value=pelda_grafok[graph_select.value][0],layout={'height': '55mm'})\n",
    "input_graph_pos=pelda_grafok[graph_select.value][1]\n",
    "#input_graph = Textarea(value='S:A5,B2,C6,E9\\nA:B5,T4\\nB:T10,E2\\nC:B3,D3\\nD:T5\\nE:D6,T2',layout={'height': '55mm'})\n",
    "\n",
    "label_fonat = Label(\"Javito utak.\\n Példasor: S,A,B,T:1\")\n",
    "input_fonat = Textarea(layout={'height': '55mm'})\n",
    "\n",
    "input_display = HBox([VBox([label_graph,input_graph]),VBox([label_fonat,input_fonat])])\n",
    "\n",
    "def textarea_reader(input_text,mode=\"fonat\"):\n",
    "    if mode==\"fonat\":\n",
    "        if len(input_text)==0:\n",
    "            return {}\n",
    "        edgelist = []\n",
    "        split = input_text.split(\"\\n\")\n",
    "        for line in split:\n",
    "            if len(line.split(':'))<2:\n",
    "                print(\"Figyelj a bemenetre! Üres/hosszú sorok, kettospont?\")\n",
    "                return\n",
    "            else:\n",
    "                a,k = line.split(\":\")\n",
    "                #print(k)\n",
    "                try:\n",
    "                    k=int(k)\n",
    "                    L=a.split(',')\n",
    "                    edgelist+=[(L[i],L[i+1],k) for i in range(len(L)-1)]\n",
    "                except:\n",
    "                    print(\"Egesz szammal novelunk csak\")\n",
    "                    return\n",
    "        sf={(e[0],e[1]):0 for e in edgelist}\n",
    "        for e in edgelist:\n",
    "            sf[(e[0],e[1])]+=e[2]\n",
    "        return sf\n",
    "    if mode==\"graph\":\n",
    "        if len(input_text)<4:\n",
    "            print(\"Üres/rövid bemenet!\")\n",
    "            return\n",
    "        d_inc = {}\n",
    "        split = input_text.split(\"\\n\")\n",
    "        for elem in split:\n",
    "            if \":\" not in elem:\n",
    "                print(\"Túl rövid/üres sor, van kettőspont?\")\n",
    "                return\n",
    "            a,b_dict = elem.split(\":\")\n",
    "            if b_dict==\"\":\n",
    "                b_dict={}\n",
    "                d_inc[a]=b_dict\n",
    "            else:\n",
    "                try:\n",
    "                    #b_list = [int(e) for e in b_list.split(\",\")]\n",
    "                    b_dict = {e[0]:{'cap':int(e[1:])} for e in b_dict.split(\",\")}\n",
    "                    d_inc[a]=b_dict\n",
    "                except:\n",
    "                    print(\"Rossz formátum!\")\n",
    "                    return\n",
    "        return d_inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_globals():\n",
    "    \n",
    "    d_inc = textarea_reader(input_graph.value,mode=\"graph\")\n",
    "#    d_inc={'S':{'A':{'cap':5},'B':{'cap':2},'C':{'cap':6},'E':{'cap':9}},\n",
    "#       'A':{'B':{'cap':5},'T':{'cap':4}},\n",
    "#       'B':{'T':{'cap':10},'E':{'cap':2}},\n",
    "#       'C':{'B':{'cap':3},'D':{'cap':3}},\n",
    "#       'D':{'T':{'cap':5}},\n",
    "#       'E':{'D':{'cap':6},'T':{'cap':2}}}\n",
    "    G=nx.DiGraph(d_inc)\n",
    "    \n",
    "    f_akt={e:0 for e in G.edges()}\n",
    "    sf=textarea_reader(input_fonat.value,mode='fonat')\n",
    "    #print(sf)\n",
    "    for e in sf:\n",
    "        f_akt[e]+=sf[e]\n",
    "    return G, f_akt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5615115d3434f5a99ea3dd1ef591c74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Label(value='Gráf szomszédsági lista+Sulyok.\\n Példasor:\\n\\tA:B1,C2,D5'), Textar…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_G=input_graph_pos\n",
    "G=init_globals()[0]\n",
    "f_akt=init_globals()[1]\n",
    "\n",
    "fig=plt.figure()\n",
    "nx.draw_networkx_edges(G,pos=pos_G)\n",
    "nx.draw_networkx_labels(G,pos=pos_G)\n",
    "nx.draw_networkx_edge_labels(G,pos=pos_G,edge_labels={e:(f_akt[e],G.get_edge_data(*e)['cap']) for e in G.edges()},label_pos=0.6)\n",
    "nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,node_color=\"red\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#f_akt={e:0 for e in G.edges()}\n",
    "#print(f_akt)\n",
    "#pos_G={'S':(0,0),'A':(1,6),'B':(3,5),'C':(2,1.5),'D':(4,1.5),'E':(6,0),'T':(5,6)}\n",
    "pos_G=input_graph_pos\n",
    "G=init_globals()[0]\n",
    "f_akt=init_globals()[1]\n",
    "b_found_alternating_path=True\n",
    "szamlalo=0\n",
    "d_movie={}\n",
    "\n",
    "while(b_found_alternating_path):\n",
    "    d_seged={}\n",
    "    #Aktualis folyam abrazolasa\n",
    "    d_seged[\"graph\"]=(G,pos_G)\n",
    "    d_seged['folyam_ertek']=folyam_ertek(f_akt)\n",
    "    d_seged['akt_folyam']=deepcopy(f_akt)\n",
    "    d_seged[\"code_index\"]=0\n",
    "    d_movie[szamlalo]=d_seged\n",
    "    szamlalo+=1\n",
    "\n",
    "    #seged graf epitese\n",
    "    d_seged=deepcopy(d_seged)\n",
    "    \n",
    "    d_seged_elek=[]\n",
    "    H=nx.DiGraph()\n",
    "    for e in G.edges:\n",
    "        if G.get_edge_data(*e)['cap']>f_akt[e]:\n",
    "            d_seged_elek.append((*e,{'gap':G.get_edge_data(*e)['cap']-f_akt[e],'color':'blue','elore':1,'original':e}))\n",
    "            \n",
    "        if f_akt[e]>0:\n",
    "            d_seged_elek.append((e[1],e[0],{'gap': f_akt[e], 'color':'red','elore':-1,'original':e}))\n",
    "    H.add_edges_from(d_seged_elek)\n",
    "    \n",
    "    d_seged['segedgraph']=(H,pos_G)\n",
    "    d_seged['code_index']=1\n",
    "    d_movie[szamlalo]=d_seged\n",
    "    szamlalo+=1\n",
    "\n",
    "#     \n",
    "    #BFS az forras csucsbol\n",
    "    d_seged=deepcopy(d_seged)\n",
    "    Q=[forras]\n",
    "    volt={u:False for u in H.nodes()}\n",
    "    honnan={u:'*' for u in H.nodes()}\n",
    "    volt[forras]=True\n",
    "    while(len(Q)>0):\n",
    "        akt=Q.pop(0)\n",
    "        \n",
    "        L_new=[v for v in H.neighbors(akt) if volt[v]==False]\n",
    "        Q+=L_new\n",
    "        for u in L_new:\n",
    "            volt[u]=True\n",
    "            honnan[u]=akt\n",
    "    \n",
    "    b_found_alternating_path=(nyelo in [u for u in volt if volt[u]])\n",
    "    \n",
    "    if(b_found_alternating_path):\n",
    "        #Javito ut meghatarozasa es a javitas merteke\n",
    "        L=[]\n",
    "        akt=nyelo\n",
    "        while(akt!='*'):\n",
    "            L.append(akt)\n",
    "            akt=honnan[akt]\n",
    "        L.reverse()\n",
    "        L_edges=[(L[i],L[i+1]) for i in range(len(L)-1)]\n",
    "        \n",
    "        javitas_merteke=min([H.get_edge_data(*e)['gap'] for e in L_edges])\n",
    "        \n",
    "\n",
    "        d_seged['javitout']=L_edges\n",
    "        d_seged['javitasi_mertek']=javitas_merteke\n",
    "        d_seged['code_index']=2\n",
    "        d_movie[szamlalo]=d_seged\n",
    "        szamlalo+=1\n",
    "        #Javitas\n",
    "        for e in L_edges:            \n",
    "            f_akt[H.get_edge_data(*e)['original']]+=H.get_edge_data(*e)['elore']*javitas_merteke\n",
    "    else:\n",
    "        X=[u for u in H.nodes() if volt[u]]\n",
    "        \n",
    "       \n",
    "        d_seged['vagas']=X\n",
    "        d_seged['code_index']=3\n",
    "        d_movie[szamlalo]=d_seged\n",
    "        szamlalo+=1\n",
    "        \n",
    "        \n",
    "        d_seged=deepcopy(d_seged)\n",
    "        d_seged={}\n",
    "        d_seged[\"folyam_ertek\"] = folyam_ertek(f_akt)\n",
    "        d_seged['graph']=(G,pos_G)\n",
    "        d_seged['vagas']=X\n",
    "        d_seged['code_index']=4\n",
    "        d_seged[\"akt_folyam\"] = deepcopy(f_akt)\n",
    "        d_movie[szamlalo]=d_seged\n",
    "        szamlalo+=1\n",
    "        d_seged=deepcopy(d_seged)\n",
    "        d_seged['cut_value']=vagas_ertek(X)\n",
    "        d_seged['leaving_edges']=[e for e in G.edges if (e[0] in X) and (e[1] not in X)]\n",
    "        d_movie[szamlalo]=d_seged\n",
    "        szamlalo+=1\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all(index):\n",
    "    fig,ax = plt.subplots(2,2,figsize=(21,14))\n",
    "    plt.subplots_adjust(wspace=0.05)\n",
    "    \n",
    "    G,pos_G = d_movie[index][\"graph\"]\n",
    "    code_index = d_movie[index][\"code_index\"]    \n",
    "    \n",
    "\n",
    "\n",
    "    ax[0,0].set_axis_off()\n",
    "    ax[1,0].set_axis_off()\n",
    "    ax[0,1].set_axis_off()\n",
    "    ax[1,1].set_axis_off()\n",
    "    \n",
    "    # graf\n",
    "    nx.draw_networkx_edges(G,pos=pos_G,ax=ax[0,0])\n",
    "    nx.draw_networkx_labels(G,pos=pos_G,ax=ax[0,0])\n",
    "    nx.draw_networkx_edge_labels(G,pos=pos_G,\n",
    "                                 edge_labels={e:(d_movie[index][\"akt_folyam\"][e],\n",
    "                                                 G.get_edge_data(*e)['cap']) for e in G.edges()},\n",
    "                                 label_pos=0.7,\n",
    "                                 font_size=14,\n",
    "                                 ax=ax[0,0])\n",
    "    nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,node_color=\"red\",ax=ax[0,0])\n",
    "    \n",
    "    \n",
    "    # jobb felso, algoritmus\n",
    "    \n",
    "    for i,line in enumerate(algoritmus):\n",
    "        tab_offset=0.2\n",
    "        dy=0.1\n",
    "        starting_height=1\n",
    "        if i!=code_index:\n",
    "            if line[0]==\"\\t\":\n",
    "                ax[0,1].annotate(line[1:],(0+tab_offset,starting_height-dy*i),fontsize=17)\n",
    "            else:\n",
    "                ax[0,1].annotate(line,(0,starting_height-dy*i),fontsize=17)\n",
    "        else:\n",
    "            if line[0]==\"\\t\":\n",
    "                ax[0,1].annotate(line[1:],(0+tab_offset,starting_height-dy*i),fontsize=17,weight=\"bold\")\n",
    "            else:\n",
    "                ax[0,1].annotate(line,(0,starting_height-dy*i),fontsize=17,weight=\"bold\")\n",
    "    ax[0,1].set_ylim(0,1.2)\n",
    "    \n",
    "    if \"folyam_ertek\" in d_movie[index]:\n",
    "        ax[0,1].annotate(\"Aktuális folyamérték: \"+str(d_movie[index][\"folyam_ertek\"]), \n",
    "            (0,starting_height-7*dy),fontsize=17)\n",
    "    \n",
    "    # segedgraph\n",
    "        # bal also\n",
    "    if \"segedgraph\" in d_movie[index]:\n",
    "        H,pos_H = d_movie[index][\"segedgraph\"]\n",
    "\n",
    "        \n",
    "        nx.draw_networkx_edges(H, pos=pos_H, \n",
    "                           connectionstyle=\"arc3,rad=0.1\",\n",
    "                           edge_color=[H.get_edge_data(*e)['color'] for e in H.edges()],\n",
    "                           edge_labels={e:H.get_edge_data(*e)['gap'] for e in H.edges()},\n",
    "                           label_pos=0.7,\n",
    "                           font_size=14,\n",
    "                           ax=ax[1,0])\n",
    "        nx.draw_networkx_labels(H,pos=pos_H,ax=ax[1,0])\n",
    "        nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,node_color=\"red\",ax=ax[1,0])\n",
    "    #     nx.draw_networkx_edge_labels(H,pos=pos_G,\n",
    "    #                                   edge_labels={e:H.get_edge_data(*e)['gap'] for e in H.edges()},\n",
    "    #                                   connectionstyle=\"arc3,rad=0.1\",\n",
    "    #                                   label_pos=0.7)\n",
    "\n",
    "    if \"javitasi_mertek\" in d_movie[index]:\n",
    "        ax[1,1].annotate(\"Javitás mértéke: \"+str(d_movie[index][\"javitasi_mertek\"]) + \".\",\n",
    "            (0,starting_height-dy),fontsize=17)\n",
    "        ax[1,1].annotate(\"Javitó út: \"+ \",\".join([str(t) for t in d_movie[index][\"javitout\"]]), \n",
    "            (0,starting_height-2*dy),fontsize=17)\n",
    "        ax[1,1].set_ylim(0,1.2)\n",
    "\n",
    "\n",
    "    #     print(javitas_merteke,':',L)\n",
    "    \n",
    "    # 2-es segedgraf\n",
    "    \n",
    "    # javitout\n",
    "    # javitasi_mertek\n",
    "    # code_index\n",
    "        L = list(set([n for t in d_movie[index][\"javitout\"] for n in t]))\n",
    "        L_edges = d_movie[index][\"javitout\"]\n",
    "    \n",
    "        nx.draw_networkx_edges(G,pos=pos_G,ax=ax[0,0])\n",
    "        nx.draw_networkx_labels(G,pos=pos_G,ax=ax[0,0])\n",
    "        nx.draw_networkx_edge_labels(\n",
    "            G,\n",
    "            pos=pos_G,\n",
    "            edge_labels={e:(d_movie[index][\"akt_folyam\"][e],G.get_edge_data(*e)['cap']) for e in G.edges()},\n",
    "            label_pos=0.7,font_size=14,ax=ax[0,0])\n",
    "        nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,node_color=\"red\",ax=ax[0,0])\n",
    "    #         fig=plt.figure()\n",
    "        nx.draw_networkx_edges(H, pos=pos_G, \n",
    "                           connectionstyle=\"arc3,rad=0.1\",\n",
    "                           edge_color=[H.get_edge_data(*e)['color'] for e in H.edges()],\n",
    "                           edge_labels={e:H.get_edge_data(*e)['gap'] for e in H.edges()},\n",
    "                           label_pos=0.7,ax=ax[1,0])\n",
    "        nx.draw_networkx_labels(H,pos=pos_G,ax=ax[1,0])\n",
    "        nx.draw_networkx_nodes(G,pos=pos_G,\n",
    "                               nodelist=L,\n",
    "                               node_size=400,node_color=\"lightgreen\",ax=ax[1,0])\n",
    "        nx.draw_networkx_nodes(G,pos=pos_G,\n",
    "                               nodelist=[u for u in G.nodes() if u not in L],\n",
    "                               node_size=400,node_color=\"red\",ax=ax[1,0])\n",
    "        nx.draw_networkx_edges(H, pos=pos_G, \n",
    "                           connectionstyle=\"arc3,rad=0.1\",\n",
    "                           edgelist=L_edges,\n",
    "                           edge_color=[H.get_edge_data(*e)['color'] for e in L_edges],\n",
    "                           edge_labels={e:H.get_edge_data(*e)['gap'] for e in L_edges},\n",
    "                           width=5,\n",
    "                           label_pos=0.7,ax=ax[1,0])\n",
    "        nx.draw_networkx_edge_labels(H,pos=pos_G, edgelist=L_edges,\n",
    "                                  edge_labels={e:H.get_edge_data(*e)['gap'] for e in L_edges},\n",
    "                                  connectionstyle=\"arc3,rad=0.1\",\n",
    "                                  font_size=14,\n",
    "                                  label_pos=0.7,ax=ax[1,0])\n",
    "\n",
    "    \n",
    "    if \"vagas\" in d_movie[index]:\n",
    "        # 3-as tipusu segedgraf, utolso fazis\n",
    "\n",
    "        # vagas\n",
    "        # code_index\n",
    "\n",
    "        nx.draw_networkx_edges(G,pos=pos_G,ax=ax[0,0])\n",
    "        nx.draw_networkx_labels(G,pos=pos_G,ax=ax[0,0])\n",
    "        nx.draw_networkx_edge_labels(G,pos=pos_G,\n",
    "                                     edge_labels={e:(d_movie[index][\"akt_folyam\"][e],\n",
    "                                                     G.get_edge_data(*e)['cap']) for e in G.edges()},\n",
    "                                     label_pos=0.7,font_size=14,ax=ax[0,0])\n",
    "        \n",
    "        if \"segedgraph\" not in d_movie[index]:\n",
    "            X = d_movie[index][\"vagas\"]\n",
    "            nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,nodelist=X,node_color=\"lightblue\",ax=ax[0,0])\n",
    "            nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,nodelist=[u for u in G.nodes() if u not in X],node_color=\"red\",ax=ax[0,0])\n",
    "        else:\n",
    "            nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,node_color=\"red\",ax=ax[0,0])\n",
    "\n",
    "            nx.draw_networkx_edges(H, pos=pos_G, \n",
    "                               connectionstyle=\"arc3,rad=0.1\",\n",
    "                               edge_color=[H.get_edge_data(*e)['color'] for e in H.edges()],\n",
    "                               edge_labels={e:H.get_edge_data(*e)['gap'] for e in H.edges()},\n",
    "                               label_pos=0.7,ax=ax[1,0])\n",
    "\n",
    "            nx.draw_networkx_labels(H,pos=pos_G,ax=ax[1,0])\n",
    "            X = d_movie[index][\"vagas\"]\n",
    "            nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,nodelist=X,node_color=\"lightblue\",ax=ax[1,0])\n",
    "            nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,nodelist=[u for u in G.nodes() if u not in X],node_color=\"red\",ax=ax[1,0])\n",
    "\n",
    "#         print('Talalt vagas: ',X)\n",
    "#         print('Talalt vagas erteke:',vagas_ertek(X))\n",
    "    \n",
    "    # cut_value\n",
    "    if \"leaving_edges\" in d_movie[index]:\n",
    "        nx.draw_networkx_edges(G,pos=pos_G,edgelist=d_movie[index][\"leaving_edges\"],width=5,ax=ax[0,0])\n",
    "        X = d_movie[index][\"vagas\"]\n",
    "        nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,nodelist=X,node_color=\"lightblue\",ax=ax[0,0])\n",
    "        nx.draw_networkx_nodes(G,pos=pos_G,node_size=400,nodelist=[u for u in G.nodes() if u not in X],node_color=\"red\",ax=ax[0,0])\n",
    "        \n",
    "        ax[0,1].annotate(\"Vágás értéke: \"+str(d_movie[index][\"cut_value\"]), \n",
    "            (0,starting_height-8*dy),fontsize=17)\n",
    "    \n",
    "    # leaving_edges - megvastagitani az grafban az utolso abranremmi\n",
    "    \n",
    "    # minden informacio egy abran, az eredeti graf egy masik rajzolasa\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "677c7c6a078f4c9a87f525197347b036",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='index', max=22), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive(plot_all,index=IntSlider(0,0,len(d_movie.keys())-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_movie_pdf(handler):\n",
    "    if 'figs' in os.listdir('.'):\n",
    "        rmtree('figs')\n",
    "    os.mkdir('figs')\n",
    "    for kepsorszam in d_movie:\n",
    "        fig=plot_all(kepsorszam)\n",
    "        fig.savefig('figs/f_'+str(kepsorszam).zfill(3)+'.pdf',density=300)\n",
    "        plt.close()\n",
    "\n",
    "    os.system('qpdf --empty --pages figs/f*.pdf -- f_movie.pdf')\n",
    "\n",
    "    local_file = FileLink('./f_movie.pdf', result_html_prefix=\"Összefűzött képek egy PDF-ben: \")\n",
    "    display(local_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44dc6c3f792e4d4b8ac4651fe8c4ed98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Fűzd össze!', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = Button(description = \"Fűzd össze!\")\n",
    "b.on_click(create_movie_pdf)\n",
    "b"
   ]
  }
 ],
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