CoCalc -- populate_cast128_database

Path: Boolean-Cayley-graphs/sage-code / populate_cast128_database_psycopg2.py
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r"""
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Populate the cast128 database.
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"""
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#*****************************************************************************
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#       Copyright (C) 2017 Paul Leopardi [email protected]
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#
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#  Distributed under the terms of the GNU General Public License (GPL)
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#  as published by the Free Software Foundation; either version 2 of
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#  the License, or (at your option) any later version.
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#                  http://www.gnu.org/licenses/
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#*****************************************************************************
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import datetime
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import json
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import psycopg2
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from boolean_cayley_graphs.bent_function import BentFunction
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from boolean_cayley_graphs.bent_function_cayley_graph_classification import BentFunctionCayleyGraphClassification
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from boolean_cayley_graphs.classification_database_psycopg2 import *
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with open("BCG-DB.json") as auth_file:
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    auth = json.load(auth_file)
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conn = create_classification_tables(
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    "cast128",
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    user=auth["user"],
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    password=auth["passwd"],
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    host=auth["host"])
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for i in range(1,9):
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    stri = "%01d" % i
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    for j in range(32):
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        strj = "%02d" % j
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        sobj_name = "cast128_" + stri + "_" + str(j) + ".sobj"
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        name = "cast128_" + stri + "_" + strj
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        cgc = BentFunctionCayleyGraphClassification.load_mangled(
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            sobj_name,
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            directory="/data/sobj")
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        print(datetime.datetime.now(), stri, strj)
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        insert_classification(conn, cgc, name)
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print(datetime.datetime.now())
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curs = conn.cursor()
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print(datetime.datetime.now(), "before")
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curs.execute("SELECT COUNT(*) FROM cayley_graph")
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print(datetime.datetime.now(), "after")
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for row in curs:
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    for x in row:
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        print(x)
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print(datetime.datetime.now(), "before")
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curs.execute("SELECT COUNT(*) FROM graph")
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print(datetime.datetime.now(), "after")
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for row in curs:
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    for x in row:
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        print(x)
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cgc1 = select_classification_where_name(
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    conn,
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    "cast128_1_09")
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cgc1.report()
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cgc2 = BentFunctionCayleyGraphClassification.load_mangled(
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    "cast128_1_9",
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    directory="/data/sobj")
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bentf = BentFunction(cgc.algebraic_normal_form)
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cgc3 = select_classification_where_bent_function(
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    conn,
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    bentf)
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cgc3.report()
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conn.close()
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