In [10]:
# some_file.py
import sys

sys.path.insert(0, './shared_code')



import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import sqlite3 as db
import json
import pandas as pd
import importlib
from IPython.core.display import display, HTML

import os
import RHgenerate_states
import RHcomponents
import RHdisplay
import RHdistance_partition
import RHutilities as util

%matplotlib inline

In [2]:
con = db.connect('data/rush_hour.db')
cur = con.cursor()

#states.to_sql('states',con,if_exists="append")

In [7]:
importlib.reload(RHgenerate_states)
#importlib.reload(RHcomponents)


Out[7]:
<module 'RHgenerate_states' from './shared_code\\RHgenerate_states.py'>

In [38]:
num_cars = 4
num_trucks = 2
combinatorial_class = 2**num_cars * 3**num_trucks
#RHgenerate_states.generate_states(num_cars,num_trucks,con)

In [5]:
len(states) , combinatorial_class


Out[5]:
(18763346, 144)

In [ ]:
# prototype components algorithm using sqlite source for nodes

In [ ]:
comb_class = 144
solvable_count = 0
unsolvable_count = 0
cross_components_max_depth = 0

component_columns = ['comb_class','repr_board_int_s1','repr_board_int_s2',\
                     'repr_red_col', 'is_solvable','num_nodes',\
                     'density','max_solution_distance']

comb_class_columns = ['comb_class','num_nodes','num_components','num_solvable_components',\
                      'num_unsolvable_components','max_solution_depth']

select_node_sql = "select * from node where comb_class = 144 and included_in_component = 0 limit 1"
update_nodes_sql = ""

In [67]:
# Prime data for while condition
df = pd.read_sql_query(select_sql,con)
df


Out[67]:
id comb_class board_int_s1 board_int_s2 red_col included_in_component
0 4 144 15437151837839400 2748779072896 1 0

In [32]:


In [ ]:
# create component
# seteup component data to store
# save component data
# upate node data to mark nodes as inluded in a component
# select next node not yet inlcuded in a component

total_nodes = 780 + 930 + 2400 + 1000 + 660
component_count = 5
while not df.empty:
    
    s1 = df.iloc[0]['board_int_s1']
    s2 = df.iloc[0]['board_int_s2']
    red_col = df.iloc[0]['red_col']
    board_int = util.combine_ints(s1,s2)
    board_int,red_col

    g = RHcomponents.components([(board_int,red_col)])[0]


    if not g.graph['solvable']:
        unsolvable_count +=1
        node_dict = g.node[0]
        max_soln_distance = None
    else:
        solvable_count +=1

        RHdistance_partition.distance_partition(g)

        max_soln_distance = max(g.graph['distance_partition'].keys())
        if max_soln_distance > cross_components_max_depth:
            cross_components_max_depth = max_soln_distance

        # select a node in outermost partition
        for node in g.graph['distance_partition'][max_soln_distance]:
            break
        node_dict = g.node[node]


    s1,s2 = util.split_int( node_dict['board_int'])    

    df_component = pd.DataFrame( [[\
                                     combinatorial_class
                                    ,s1
                                    ,s2
                                    ,node_dict['red_col']
                                    ,g.graph['solvable']
                                    ,len(g.nodes())
                                    ,nx.density(g)
                                    ,max_soln_distance                         
                                  ]],columns =  component_columns)

    df_component.to_sql('component',con,if_exists="append",index=False)        


    # Now update nodes
    cur = con.cursor()
    cur.executemany("update node set included_in_component = 1 where board_int_s1 = ? and board_int_s2 = ? and red_col = ?", \
                    [
        [  int(util.split_int(g.node[x]['board_int'])[0]),\
           int(util.split_int(g.node[x]['board_int'])[1]),\
           int(g.node[x]['red_col'])\
        ] for x in g.nodes() 
     ]
    )
    con.commit()


    df = pd.read_sql_query(select_sql,con)
    
    total_nodes = total_nodes + len(g.nodes())
    component_count += 1
    print ("%d | total nodes: %d"%(component_count,total_nodes) )


6 | total nodes: 6466
7 | total nodes: 7352
8 | total nodes: 7949
9 | total nodes: 8511
10 | total nodes: 8721
11 | total nodes: 8874
12 | total nodes: 9907
13 | total nodes: 10758
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11783 | total nodes: 7998097
11784 | total nodes: 7999069
11785 | total nodes: 7999856
11786 | total nodes: 7999892
11787 | total nodes: 8000084
11788 | total nodes: 8000326
11789 | total nodes: 8000522
11790 | total nodes: 8001902
11791 | total nodes: 8002136
11792 | total nodes: 8002703
11793 | total nodes: 8002868
11794 | total nodes: 8003434
11795 | total nodes: 8003647
11796 | total nodes: 8003930
11797 | total nodes: 8004337
11798 | total nodes: 8004579
11799 | total nodes: 8005267
11800 | total nodes: 8011752
11801 | total nodes: 8012050
11802 | total nodes: 8012897
11803 | total nodes: 8013712
11804 | total nodes: 8013805
11805 | total nodes: 8013885
11806 | total nodes: 8013925
11807 | total nodes: 8014174
11808 | total nodes: 8014317
11809 | total nodes: 8014440
11810 | total nodes: 8015112
11811 | total nodes: 8021517
11812 | total nodes: 8022342
11813 | total nodes: 8022522
11814 | total nodes: 8023194
11815 | total nodes: 8024006
11816 | total nodes: 8024266
11817 | total nodes: 8025394
11818 | total nodes: 8025430
11819 | total nodes: 8025590
11820 | total nodes: 8025770
11821 | total nodes: 8027156
11822 | total nodes: 8027192
11823 | total nodes: 8027336
11824 | total nodes: 8027528
11825 | total nodes: 8027724
11826 | total nodes: 8028072
11827 | total nodes: 8029120
11828 | total nodes: 8029247
11829 | total nodes: 8029436
11830 | total nodes: 8029628
11831 | total nodes: 8031104
11832 | total nodes: 8032550
11833 | total nodes: 8032748
11834 | total nodes: 8032863
11835 | total nodes: 8033700
11836 | total nodes: 8034512
11837 | total nodes: 8035299
11838 | total nodes: 8036369
11839 | total nodes: 8036492
11840 | total nodes: 8036579
11841 | total nodes: 8036645
11842 | total nodes: 8036697
11843 | total nodes: 8038161
11844 | total nodes: 8038202
11845 | total nodes: 8038910
11846 | total nodes: 8039043
11847 | total nodes: 8039084
11848 | total nodes: 8039186
11849 | total nodes: 8039309
11850 | total nodes: 8040701
11851 | total nodes: 8040890
11852 | total nodes: 8041151
11853 | total nodes: 8041630
11854 | total nodes: 8041742
11855 | total nodes: 8041793
11856 | total nodes: 8041849
11857 | total nodes: 8042253
11858 | total nodes: 8042654
11859 | total nodes: 8042793
11860 | total nodes: 8043049
11861 | total nodes: 8043101
11862 | total nodes: 8043375
11863 | total nodes: 8043789
11864 | total nodes: 8044049
11865 | total nodes: 8044105
11866 | total nodes: 8044207
11867 | total nodes: 8044289
11868 | total nodes: 8044382
11869 | total nodes: 8044624
11870 | total nodes: 8045596
11871 | total nodes: 8045862
11872 | total nodes: 8046188
11873 | total nodes: 8046481
11874 | total nodes: 8046561
11875 | total nodes: 8046663
11876 | total nodes: 8046731
11877 | total nodes: 8047571
11878 | total nodes: 8047954
11879 | total nodes: 8048791
11880 | total nodes: 8048947
11881 | total nodes: 8049268
11882 | total nodes: 8049487
11883 | total nodes: 8049926
11884 | total nodes: 8050703
11885 | total nodes: 8052986
11886 | total nodes: 8053811
11887 | total nodes: 8054087
11888 | total nodes: 8054711
11889 | total nodes: 8054763

In [70]:
len(g.nodes()), len(g.nodes())  + 780


Out[70]:
(930, 1710)

In [66]:
# now to update all 780 nodes with 
cur = con.cursor()
cur.executemany("update node set included_in_component = 1 where board_int_s1 = ? and board_int_s2 = ? and red_col = ?", \
                [
    [  int(util.split_int(g.node[x]['board_int'])[0]),\
       int(util.split_int(g.node[x]['board_int'])[1]),\
       int(g.node[x]['red_col'])\
    ] for x in g.nodes() 
 ]
)
con.commit()

In [ ]:
[
    [  int(util.split_int(g.node[x]['board_int'])[0]),\
       int(util.split_int(g.node[x]['board_int'])[1]),\
       int(g.node[x]['red_col'])\
    ] for x in g.nodes() 
 ]

In [52]:
node_dict,g.graph['distance_partition'][11],s1,s2


Out[52]:
({'board_int': 278140368219519116774982815580214,
  'inner_nbrs': {195, 197, 626, 675},
  'is_soln_state': False,
  'red_col': 1,
  'soln_distance': 11},
 {625},
 15439892043751464,
 133955638)

In [ ]:


In [ ]:


In [ ]:


In [46]:
df_component


Out[46]:
comb_class repr_board_int_s1 repr_board_int_s2 repr_red_col is_solvable num_nodes density max_solution_distance
0 144 15439892043751464 133955638 1 True 780 0.00891 11

In [35]:
g.graph


Out[35]:
{'solvable': True}

In [ ]:


In [22]:
#g = RHcomponents.components([(board_int,red_col)])[0]
if g.graph['solvable']:
    RHdistance_partition.distance_partition(g)

In [25]:
max(g.graph['distance_partition'].keys()) , nx.density(g)


Out[25]:
(11, 0.008910174121984134)

In [ ]:
component

In [29]:
df_empty = pd.read_sql_query("select * from component where comb_class = 3",con)
df_empty.empty


Out[29]:
True

In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:
# prep for processing generated list of components
SAVE_BATCH_SIZE = 2000  # Number of Graphs to save in batch; Equals number of rows in DataFrame
db_components = []

comb_class_max_depth = 0

solvable_count = 0
unsolvable_count = 0
save_counter = 0

component_columns = ['comb_class','repr_board_int_s1','repr_board_int_s2',\
                     'repr_red_col', 'is_solvable','num_nodes',\
                     'density','max_solution_distance']

comb_class_columns = ['comb_class','num_nodes','num_components','num_solvable_components',\
                      'num_unsolvable_components','max_solution_depth']

for g in  RHcomponents.gen_components(states):
    
    save_counter +=1
    if save_counter >= SAVE_BATCH_SIZE:
        df = pd.DataFrame([x for x in db_components if x is not None],columns = component_columns)
        df.to_sql('component',con,if_exists="append")
        
        db_components = [None]*SAVE_BATCH_SIZE
        save_counter = 1
        
    if not g.graph['solvable']:
        unsolvable_count +=1
        node_dict = g.node[0]
        max_soln_distance = None
        
    else:
        
        solvable_count += 1
        
        RHdistance_partition.distance_partition(g)
        
        max_soln_distance = max(g.graph['distance_partition'].keys())
        if max_soln_distance > comb_class_max_depth:
            comb_class_max_depth = max_soln_distance
        for node in g.graph['distance_partition'][max_soln_distance]:
            break
        node_dict = g.node[node]
        
    
    s1,s2 = RHutilities.split_int( node_dict['board_int'])    
    db_components.append( [\
                             combinatorial_class
                            ,s1
                            ,s2
                            ,node_dict['red_col']
                            ,g.graph['solvable']
                            ,len(g.nodes())
                            ,nx.density(g)
                            ,max_soln_distance                         
                          ]
                        )
        
        
if  db_components:
    df = pd.DataFrame([x for x in db_components if x is not None],columns = component_columns)
    df.to_sql('component',con,if_exists="append",index=False)

    
#comb_class_columns = ['comb_class','num_nodes','num_components','num_solvable_components',\
#                      'num_unsolvable_components','max_solution_depth']

comb_class_data = [[combinatorial_class,num_nodes,unsolvable_count + solvable_count,\
                   solvable_count,unsolvable_count,  \
                   comb_class_max_depth]]

df_class = pd.DataFrame(comb_class_data,columns = comb_class_columns)
df_class.to_sql('comb_class',con,if_exists="append",index=False)

In [17]:
df


Out[17]:
comb_class repr_board_int_s1 repr_board_int_s2 repr_red_col is_solvable num_nodes density max_solution_distance
0 4 221184 113246208 1 True 25 0.133333 4.0
1 4 224640 0 1 False 6 0.400000 NaN
2 4 223232 140737488355328 1 True 21 0.133333 5.0
3 4 906190848 0 1 True 25 0.133333 4.0
4 4 1269764 0 1 True 23 0.138340 5.0
5 4 221184 237494511599616 1 True 25 0.133333 4.0
6 4 221184 9007233614479360 1 True 23 0.138340 4.0
7 4 67330304 0 1 True 21 0.133333 5.0
8 4 221184 432 1 True 25 0.133333 4.0
9 4 1125904202031104 0 1 True 21 0.142857 4.0
10 4 224640 0 3 True 6 0.400000 2.0
11 4 8609824 0 1 True 21 0.142857 5.0
12 4 237494511820800 0 1 True 25 0.133333 4.0

In [18]:
df2 = pd.read_sql_query("select * from comb_class",con)
df2


Out[18]:
comb_class num_nodes num_components num_solvable_components num_unsolvable_components max_solution_depth
0 4 267 13 12 1 5
1 36 116650 1361 1024 337 19

In [69]:
if g.graph['solvable']:
    RHdistance_partition.distance_partition(g)
    max_distance = max(g.graph['distance_partition'].keys())
    for node in g.graph['distance_partition'][max_distance]:
        break
    repr_node_dict = g.node[node]
else:
    repr_node_dict = g.node[0]
    max_distance = None

s1,s2 = RHutilities.split_int(repr_node_dict['board_int'])
red_col = repr_node_dict['red_col']
            
    
36, g.graph['solvable'],s1,s2,red_col, len(g.nodes()), nx.density(g),max_distance


Out[69]:
(36, True, 67330304, 11259042018267128, 1, 204, 0.024244180430793006, 5)

In [11]:
len(comps),sum(len(x.nodes()) for x in comps)


Out[11]:
(1361, 116650)

In [12]:
len(comps[0].nodes())


Out[12]:
308

In [14]:
solvable_comps = [g for g in comps if g.graph['solvable']==True]
unsolvable_comps = [g for g in comps if g.graph['solvable'] == False]

In [15]:
df_solvable = pd.DataFrame([len(x.nodes()) for x in solvable_comps])
df_unsolvable = pd.DataFrame([len(x.nodes()) for x in unsolvable_comps])

Here -

  • Select representative for each component
  • save json to file for each component (class_2_2_comp_39823484994949)
  • draw graph with d3

  • What I really want is real time back and forth to pull out components on the fly rather than build them all and save mass files to harddrive.


In [18]:
#df.describe()
#%matplotlib inline
df_solvable.hist(bins=20)


Out[18]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000250BD956EB8>]], dtype=object)

In [19]:
df_unsolvable.hist(bins=20)


Out[19]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000250BC07E9B0>]], dtype=object)

In [82]:
comp = comps[0]
min_int = min(comp.node[node]['board_int'] for node in comp.nodes())
min_int


Out[82]:
632587360075802098027458330624

In [16]:
df_solvable.describe()


Out[16]:
0
count 1024.000000
mean 107.013672
std 100.871674
min 1.000000
25% 20.000000
50% 60.000000
75% 178.000000
max 400.000000

In [17]:
df_unsolvable.describe()


Out[17]:
0
count 337.000000
mean 20.973294
std 20.752708
min 1.000000
25% 6.000000
50% 15.000000
75% 27.000000
max 96.000000

In [83]:
#node = 0
comp_repr_node = [comp[node] for node in comp.nodes() if comp.node[node]['board_int'] == min_int]
comp_repr_node


Out[83]:
[AtlasView({233: {}, 270: {}, 231: {}, 284: {}})]

In [113]:
len([g for g in comps if len(g.nodes())==1])


Out[113]:
18

In [37]:
n = solvable_comps[40].node[0]

#HTML(RHdisplay.svg_from_state(solvable_comps[40].nodes(0)))

HTML(RHdisplay.svg_from_state(n['board_int'],n['red_col']))


Out[37]:
XLZW

In [39]:
df = pd.DataFrame( [  [len(g.nodes()),g.graph['solvable'] ] for g in comps])

In [55]:
g = solvable_comps[40]
g


Out[55]:
<networkx.classes.graph.Graph at 0x250b11594a8>

In [58]:
RHdistance_partition.distance_partition(g)


Out[58]:
{'board_int': 207885568651978971296788249575424,
 'inner_nbrs': {11, 49},
 'is_soln_state': False,
 'outer_nbrs': {9},
 'red_col': 2,
 'soln_distance': 4}

In [72]:
df_dist = pd.DataFrame( [g.node[n]['soln_distance'] for n in g.nodes() ], columns = ['distance'])

In [74]:
df_dist.groupby(['distance']).size()


Out[74]:
distance
0    41
1    41
2    67
3    71
4    41
5    11
6     1
dtype: int64

In [76]:
HTML(RHdisplay.svg_from_state( g.node[0]['board_int'] , g.node[0]['red_col']))


Out[76]:
XLZW

In [77]:
len(solvable_comps) , len(unsolvable_comps)


Out[77]:
(1024, 337)

In [79]:
len(solvable_comps) + len(unsolvable_comps)


Out[79]:
1361

In [85]:
importlib.reload(RHdistance_partition)
for g in solvable_comps:
    RHdistance_partition.distance_partition(g)

In [142]:
for g in solvable_comps:
    g.graph['max_distance'] = max(g.graph['distance_partition'].keys())
    for node in g.graph['distance_partition'][g.graph['max_distance']]:
        break
    node_dict = g.node[node]
    split_board_int = RHutilities.split_int( node_dict['board_int'])
    g.graph['repr_board_int_s1'] = split_board_int[0]
    g.graph['repr_board_int_s2'] = split_board_int[1]
    g.graph['repr_red_col'] = node_dict['red_col']

In [162]:
df_max_dist = pd.DataFrame( \
            [ [g.graph['max_distance'],\
               g.graph['repr_board_int_s1'],\
               g.graph['repr_board_int_s2'],\
               g.graph['repr_red_col']\
               ,len(g.nodes())\
               ,g.size()\
               ,nx.density(g)\
              ]\
               for g in solvable_comps], columns=['distance','int_s1','int_s2','repr_red_col','num_nodes','num_edges','density'])

In [163]:
df_max_dist.sort_values(['distance'],ascending=False)


Out[163]:
distance int_s1 int_s2 repr_red_col num_nodes num_edges density
168 19 2748789558696 179633383279104 3 56 86 0.055844
769 17 2748789558696 175922545691136 3 80 149 0.047152
642 17 343598694837 25701168185664 4 116 260 0.038981
739 16 2748789776424 25701168185664 1 102 228 0.044263
830 15 2748789558696 670828544 3 84 172 0.049340
870 15 343598694837 21990330597696 4 144 344 0.033411
387 15 2748789558696 175922531535414 3 104 214 0.039955
572 15 21990316469568 670828544 2 86 161 0.044049
408 14 2748789776424 21990330597696 1 126 299 0.037968
506 14 175922531756544 68586307588 1 112 231 0.037162
455 14 175922531756544 68652368128 1 86 161 0.044049
765 14 175922531756544 68593647648 1 64 117 0.058036
505 13 22333915357509 54 1 172 432 0.029376
402 13 24739106218344 54 1 154 388 0.032934
20 12 197912848198464 237494511599616 1 102 164 0.031838
323 12 3092388470829 3710851743744 1 174 476 0.031626
795 12 175922531756800 17996806323437568 1 66 92 0.042890
88 12 197912848198464 905969664 1 126 234 0.029714
1 12 21990316663104 34493825024 1 112 231 0.037162
135 12 3092388470829 54 1 222 634 0.025845
693 12 197912848198464 3456 1 154 320 0.027162
532 12 21990316665152 175853140967424 1 66 92 0.042890
759 12 3092388470829 14155776 1 195 544 0.028760
508 12 175922531756548 17979489015300096 1 77 121 0.041353
776 12 175922531756576 17981413160648704 1 63 89 0.045571
149 11 21990316663136 2199023256063 1 159 378 0.030093
344 11 175922531756544 8650272 1 130 270 0.032200
637 11 21990316663104 1161019854422016 1 63 89 0.045571
345 11 24739643091304 0 1 165 388 0.028677
330 11 2748789776424 133955638 1 180 492 0.030540
... ... ... ... ... ... ... ...
1012 0 21990316441974 343665803525 5 3 2 0.666667
436 0 21990316703606 17592253153280 5 3 2 0.666667
1016 0 21990330597750 21990316441920 5 1 0 0.000000
468 0 21990316605814 11276634271252480 5 12 17 0.257576
815 0 21990316444534 193514784686080 5 12 17 0.257576
1018 0 15221639058817398 21990316441920 5 2 1 1.000000
1019 0 1407380252286390 1407380252282880 5 1 0 0.000000
1020 0 21990316441974 21990324830560 5 2 1 1.000000
587 0 21990316441974 22024676311360 5 5 4 0.400000
1022 0 21990316441974 21990330597696 5 1 0 0.000000
892 0 21990316441974 21990317490500 5 2 1 1.000000
1004 0 21990316441974 21990316469568 5 2 1 1.000000
738 0 21990316441974 21990853314880 5 5 4 0.400000
944 0 24189348086134 21990316441920 5 1 0 0.000000
957 0 21990316441974 17592253414912 5 6 5 0.333333
930 0 25701168185718 21990316441920 5 1 0 0.000000
931 0 22048298500470 21990316441920 5 2 1 1.000000
992 0 18001204453835126 17592253153280 5 6 5 0.333333
993 0 21990316441974 17996806390546432 5 6 5 0.333333
994 0 21990316441974 17660838412288 5 3 2 0.666667
995 0 21990316441974 15480573963272192 5 3 2 0.666667
932 0 21990316441974 15221639058817344 5 2 1 1.000000
800 0 11259042018295798 34359869440 5 3 2 0.666667
933 0 21990316441974 1147894518251840 5 5 4 0.400000
821 0 22058901700982 17592253153280 5 3 2 0.666667
1000 0 1429370568724854 67109120 5 12 17 0.257576
1001 0 21990316441974 25701168185664 5 1 0 0.000000
1002 0 21990316441974 22048298500416 5 2 1 1.000000
1003 0 21990316441974 21990316441974 5 1 0 0.000000
997 0 21990316441974 20341042708520 5 3 2 0.666667

1024 rows × 7 columns


In [164]:
board_int = RHutilities.combine_ints(21990316605814,11276634271252480)
red_col = 5
HTML(RHdisplay.svg_from_state(board_int,red_col))


Out[164]:
XLZW

In [166]:
df_max_dist.groupby(['distance']).agg(['count'])


Out[166]:
int_s1 int_s2 repr_red_col num_nodes num_edges density
count count count count count count
distance
0 34 34 34 34 34 34
1 165 165 165 165 165 165
2 73 73 73 73 73 73
3 18 18 18 18 18 18
4 221 221 221 221 221 221
5 93 93 93 93 93 93
6 55 55 55 55 55 55
7 72 72 72 72 72 72
8 110 110 110 110 110 110
9 53 53 53 53 53 53
10 55 55 55 55 55 55
11 50 50 50 50 50 50
12 11 11 11 11 11 11
13 2 2 2 2 2 2
14 4 4 4 4 4 4
15 4 4 4 4 4 4
16 1 1 1 1 1 1
17 2 2 2 2 2 2
19 1 1 1 1 1 1

In [119]:
df_max_dist.sort_values(by=['distance'] , ascending = False)


Out[119]:
distance int_s1 int_s2 repr_red_col
168 19 175922531535792 240243301154856 4
769 17 2748789558696 175922644781568 3
642 17 343598722053 25701168185664 2
739 16 28008 1924695198793728 2
830 15 140738035933224 2749850714112 1
870 15 27968 22341162893317 2
387 15 671312424 178670650027008 1
572 15 83886454 162728391737344 5
408 14 221504 24746353754152 1
506 14 175922531756544 343464214528 1
455 14 17592253155894 175922665488384 5
765 14 178121563179574 1071644672 5
505 13 27648 22333915163973 2
402 13 2748789776424 21990316441974 1
20 12 197912848198464 29686813949952 1
323 12 343609401389 1902704871866368 1
795 12 67330304 211038184147456 1
88 12 2934 197970830032896 5
1 12 9007233614700544 22058901700928 1
135 12 2748789558696 343598694837 3
693 12 374 197912847977014 5
532 12 21990316441974 17979214674266112 5
759 12 1311157 3093292908584 4
508 12 175922531756544 17979489016348672 1
776 12 175922531756576 17981413160648704 1
149 11 27968 24189348089848 2
344 11 175922531535792 8650272 4
637 11 1125904201810294 18001204453834752 5
345 11 140738025226672 24739105997160 4
330 11 432 2817374841896 4
... ... ... ... ...
1012 0 21990316441974 343665803525 5
436 0 21990316703606 17592253153280 5
1016 0 21990330597750 21990316441920 5
468 0 21990316605814 11276634271252480 5
815 0 21990316444534 193514784686080 5
1018 0 15221639058817398 21990316441920 5
1019 0 1407380252286390 1407380252282880 5
1020 0 21990316441974 21990324830560 5
587 0 21990316441974 22024676311360 5
1022 0 21990316441974 21990330597696 5
892 0 21990316441974 21990317490500 5
1004 0 21990316441974 21990316469568 5
738 0 21990316441974 21990853314880 5
944 0 24189348086134 21990316441920 5
957 0 21990316441974 17592253414912 5
930 0 25701168185718 21990316441920 5
931 0 22048298500470 21990316441920 5
992 0 18001204453835126 17592253153280 5
993 0 21990316441974 17996806390546432 5
994 0 21990316441974 17660838412288 5
995 0 21990316441974 15480573963272192 5
932 0 21990316441974 15221639058817344 5
800 0 11259042018295798 34359869440 5
933 0 21990316441974 1147894518251840 5
821 0 22058901700982 17592253153280 5
1000 0 1429370568724854 67109120 5
1001 0 21990316441974 25701168185664 5
1002 0 21990316441974 22048298500416 5
1003 0 21990316441974 21990316441974 5
997 0 21990316441974 20341042708520 5

1024 rows × 4 columns


In [135]:
df_max_dist.loc[df_max_dist['distance'] == 19]
#HTML(RHdisplay.svg_from_state(n['board_int'],n['red_col']))


Out[135]:
distance int_s1 int_s2 repr_red_col
168 19 175922531535792 240243301154856 4

In [137]:
board_int = RHutilities.combine_ints(2748789558696,240243301154856)
red_col = 4
HTML(RHdisplay.svg_from_state(board_int,red_col))


Out[137]:
XLZW

In [139]:



Out[139]:
{0: {0}, 1: {1}}

In [ ]: