CoCalc -- test_sqlite3_p6_p8_database

Path: Boolean-Cayley-graphs/sage-code / test_sqlite3_p6_p8_database_prototypes.ipynb

Views: ⁸⁸⁰⁰

Kernel: SageMath 9.5

In [1]:

import datetime
import os
import sqlite3

from boolean_cayley_graphs.bent_function import BentFunction
from boolean_cayley_graphs.classification_database_sqlite3 import *
from boolean_cayley_graphs.bent_function_cayley_graph_classification import BentFunctionCayleyGraphClassification

In [2]:

db_dirname=os.path.join("..","db")
sobj_dirname=os.path.join("..","sobj")

In [3]:

rm -f test_p6.db

In [4]:

conn = create_database("test_p6.db")
conn = create_classification_tables("test_p6.db")

In [5]:

for i in range(1,5):
    print(datetime.datetime.now(), i)
    name = "p6_"+str(i)
    cgc = BentFunctionCayleyGraphClassification.load_mangled(name + ".sobj", dir=sobj_dirname)
    insert_classification(conn, cgc, name)

2023-01-18 00:09:44.738566 1
2023-01-18 00:09:47.971304 2
2023-01-18 00:09:50.392083 3
2023-01-18 00:09:50.798809 4

In [6]:

cgc.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x3 + x1*x3*x4 + x1*x5 + x2*x3*x5 + x2*x3 + x2*x4 + x2*x5 + x3*x4 + x3*x5
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 64, 28, 12))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 3 extended Cayley classes in the extended translation class.

In [7]:

bentf = BentFunction(cgc.algebraic_normal_form)
c = select_classification_where_bent_function(conn, bentf)
c.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x3 + x1*x3*x4 + x1*x5 + x2*x3*x5 + x2*x3 + x2*x4 + x2*x5 + x3*x4 + x3*x5
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 64, 28, 12))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 3 extended Cayley classes in the extended translation class.

In [8]:

c = select_classification_where_name(conn, "p6_1")
c.report()

Algebraic normal form of Boolean function: x0*x1 + x2*x3 + x4*x5
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 64, 28, 12))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 2 extended Cayley classes in the extended translation class.

In [9]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM cayley_graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2023-01-18 00:09:52.012755 before
2023-01-18 00:09:52.014381 after
12

In [10]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT * FROM graph")
print(datetime.datetime.now(), "after")

2023-01-18 00:09:52.250714 before
2023-01-18 00:09:52.252170 after

In [11]:

print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2023-01-18 00:09:52.514735 before
2023-01-18 00:09:52.516550 after
11

In [12]:

conn.close()

In [13]:

rm -f test_p8.db

In [14]:

conn = create_database("test_p8.db")
conn = create_classification_tables("test_p8.db")

In [15]:

for i in range(1,11):
    name = "p8_"+str(i)
    cgc = BentFunctionCayleyGraphClassification.load_mangled(name + ".sobj", dir=sobj_dirname)
    print(datetime.datetime.now(), i)
    insert_classification(conn, cgc, name)
print(datetime.datetime.now())

2023-01-18 00:09:56.874949 1
2023-01-18 00:10:12.527393 2
2023-01-18 00:10:21.122624 3
2023-01-18 00:10:28.028806 4
2023-01-18 00:10:33.545277 5
2023-01-18 00:10:36.488421 6
2023-01-18 00:10:38.124097 7
2023-01-18 00:10:39.397405 8
2023-01-18 00:10:40.682930 9
2023-01-18 00:10:42.339054 10
2023-01-18 00:10:43.627746

In [16]:

c8_5 = select_classification_where_name(conn, "p8_5")
c8_5.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x6 + x1*x3*x4 + x1*x4 + x1*x5 + x2*x3*x5 + x2*x4 + x3*x7
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 256, 120, 56))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 9 extended Cayley classes in the extended translation class.

In [17]:

bentf = BentFunction(c8_5.algebraic_normal_form)

c = select_classification_where_bent_function(conn, bentf)
c.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x6 + x1*x3*x4 + x1*x4 + x1*x5 + x2*x3*x5 + x2*x4 + x3*x7
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 256, 120, 56))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 9 extended Cayley classes in the extended translation class.

In [18]:

c8_6 = select_classification_where_name(conn, "p8_6")
c8_6.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x2 + x0*x3 + x1*x3*x4 + x1*x6 + x2*x3*x5 + x2*x4 + x5*x7
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 256, 120, 56))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 9 extended Cayley classes in the extended translation class.

In [19]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(*) FROM cayley_graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2023-01-18 00:10:47.954266 before
2023-01-18 00:10:47.955176 after
66

In [20]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM cayley_graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2023-01-18 00:10:48.461690 before
2023-01-18 00:10:48.463025 after
66

In [21]:

print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2023-01-18 00:10:48.694081 before
2023-01-18 00:10:48.695756 after
55

In [22]:

conn.close()

In [23]:

conn = connect_to_database('test_p8.db')

In [24]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")

curs.execute("""
select name, cayley_graph_index, graph.rowid, count(*)
from matrices, (
    select name, bent_function, cayley_graph_index, canonical_label_hash
    from (
        select canonical_label_hash
        from cayley_graph
        group by canonical_label_hash
        having count (canonical_label_hash) > 1) as repeats
    natural join cayley_graph
    natural join bent_function )
as repeats_with_counts, graph
where matrices.bent_function = repeats_with_counts.bent_function
and matrices.bent_cayley_graph_index = repeats_with_counts.cayley_graph_index
and graph.canonical_label_hash = repeats_with_counts.canonical_label_hash
group by name, cayley_graph_index, graph.rowid
order by graph.rowid, name, cayley_graph_index
""")

print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x,end=" ")
    print("")

2023-01-18 00:10:49.621418 before
2023-01-18 00:10:51.116214 after
p8_1 0 1 34816 
p8_2 0 1 6144 
p8_1 1 2 30720 
p8_2 3 2 2048 
p8_5 0 17 4096 
p8_6 0 17 4096 
p8_5 1 18 6144 
p8_6 1 18 6144 
p8_5 2 19 6144 
p8_6 2 19 6144 
p8_5 3 20 2048 
p8_6 5 20 2048 
p8_5 4 21 2048 
p8_6 8 21 2048 
p8_5 5 22 6144 
p8_6 6 22 6144 
p8_5 6 23 6144 
p8_6 7 23 6144 
p8_5 7 24 16384 
p8_6 3 24 16384 
p8_5 8 25 16384 
p8_6 4 25 16384 

In [25]:

conn.close()

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