hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1484ee49b2cef09281d37246bf94204586d1304b
| 13,367
|
py
|
Python
|
tests/test_prepare_dutchf3.py
|
yohanesnuwara/seismic-deeplearning
|
a0318b4a9f02b9c1f988ccd37971df525f5aa41f
|
[
"MIT"
] | 2
|
2020-10-19T08:00:01.000Z
|
2021-05-16T10:04:04.000Z
|
tests/test_prepare_dutchf3.py
|
regginalee/seismic-deeplearning
|
a0318b4a9f02b9c1f988ccd37971df525f5aa41f
|
[
"MIT"
] | 3
|
2020-02-21T23:49:10.000Z
|
2020-04-09T16:12:50.000Z
|
tests/test_prepare_dutchf3.py
|
regginalee/seismic-deeplearning
|
a0318b4a9f02b9c1f988ccd37971df525f5aa41f
|
[
"MIT"
] | 2
|
2020-09-26T09:27:43.000Z
|
2020-11-16T10:33:34.000Z
|
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Test the extract functions against a variety of SEGY files and trace_header scenarioes
"""
import pytest
import numpy as np
import pandas as pd
import tempfile
import scripts.prepare_dutchf3 as prep_dutchf3
import math
# Setup
OUTPUT = None
ILINE = 551
XLINE = 1008
DEPTH = 351
ALINE = np.zeros((ILINE, XLINE, DEPTH))
STRIDE = 100
PATCH = 50
PER_VAL = 0.2
LOG_CONFIG = None
def test_get_aline_range_step_one():
"""check if it includes the step in the range if step = 1
"""
SLICE_STEPS = 1
# Test
output_iline = prep_dutchf3._get_aline_range(ILINE, PER_VAL, SLICE_STEPS)
output_xline = prep_dutchf3._get_aline_range(XLINE, PER_VAL, SLICE_STEPS)
assert str(output_iline[0].step) == str(SLICE_STEPS)
assert str(output_xline[0].step) == str(SLICE_STEPS)
def test_get_aline_range_step_zero():
"""check if a ValueError exception is raised when slice_steps = 0
"""
with pytest.raises(ValueError, match=r'slice_steps cannot be zero or a negative number'):
SLICE_STEPS = 0
# Test
output_iline = prep_dutchf3._get_aline_range(ILINE, PER_VAL, SLICE_STEPS)
output_xline = prep_dutchf3._get_aline_range(XLINE, PER_VAL, SLICE_STEPS)
assert output_iline
assert output_xline
def test_get_aline_range_negative_step():
"""check if a ValueError exception is raised when slice_steps = -1
"""
with pytest.raises(ValueError, match='slice_steps cannot be zero or a negative number'):
SLICE_STEPS = -1
# Test
output_iline = prep_dutchf3._get_aline_range(ILINE, PER_VAL, SLICE_STEPS)
output_xline = prep_dutchf3._get_aline_range(XLINE, PER_VAL, SLICE_STEPS)
assert output_iline
assert output_xline
def test_get_aline_range_float_step():
"""check if a ValueError exception is raised when slice_steps = 1.1
"""
with pytest.raises(TypeError, match="'float' object cannot be interpreted as an integer"):
SLICE_STEPS = 1.
# Test
output_iline = prep_dutchf3._get_aline_range(ILINE, PER_VAL, SLICE_STEPS)
output_xline = prep_dutchf3._get_aline_range(XLINE, PER_VAL, SLICE_STEPS)
assert output_iline
assert output_xline
def test_get_aline_range_single_digit_step():
"""check if it includes the step in the range if 1 < step < 10
"""
SLICE_STEPS = 1
# Test
output_iline = prep_dutchf3._get_aline_range(ILINE, PER_VAL, SLICE_STEPS)
output_xline = prep_dutchf3._get_aline_range(XLINE, PER_VAL, SLICE_STEPS)
assert str(output_iline[0].step) == str(SLICE_STEPS)
assert str(output_xline[0].step) == str(SLICE_STEPS)
def test_get_aline_range_double_digit_step():
"""check if it includes the step in the range if step > 10
"""
SLICE_STEPS = 17
# Test
output_iline = prep_dutchf3._get_aline_range(ILINE, PER_VAL, SLICE_STEPS)
output_xline = prep_dutchf3._get_aline_range(XLINE, PER_VAL, SLICE_STEPS)
assert str(output_iline[0].step) == str(SLICE_STEPS)
assert str(output_xline[0].step) == str(SLICE_STEPS)
def test_prepare_dutchf3_patch_step_1():
"""check a complete run for the script in case further changes are needed
"""
# setting a value to SLICE_STEPS as needed to test the values
SLICE_STEPS = 1
# use a temp dir that will be discarded at the end of the execution
with tempfile.TemporaryDirectory() as tmpdirname:
# saving the file to be used by the script
label_file = tmpdirname + '/label_file.npy'
np.save(label_file, ALINE)
# stting the output directory to be used by the script
output = tmpdirname + '/split'
# calling the main function of the script without SLICE_STEPS, to check default value
prep_dutchf3.split_patch_train_val(data_dir=tmpdirname, output_dir=output, label_file=label_file,
slice_steps=SLICE_STEPS, stride=STRIDE, patch_size=PATCH, per_val=PER_VAL,log_config=LOG_CONFIG)
# reading the file and splitting the data
patch_train = pd.read_csv(output + '/patch_train.txt', header=None, names=['row', 'a', 'b'])
patch_train = pd.DataFrame(patch_train.row.str.split('_').tolist(), columns=['aline', 'x', 'y', 'z'])
# test patch_train and slice_steps=2
y = list(sorted(set(patch_train.y.astype(int))))
x = list(sorted(set(patch_train.x.astype(int))))
assert (int(y[1]) - int(y[0])) == SLICE_STEPS
assert (int(x[1]) - int(x[0])) == SLICE_STEPS
# reading the file and splitting the data
patch_val = pd.read_csv(output + '/patch_val.txt', header=None, names=['row', 'a', 'b'])
patch_val = pd.DataFrame(patch_val.row.str.split('_').tolist(), columns=['aline', 'x', 'y', 'z'])
# test patch_val and slice_steps=2
y = list(sorted(set(patch_val.y.astype(int))))
x = list(sorted(set(patch_val.x.astype(int))))
assert (int(y[1]) - int(y[0])) != SLICE_STEPS
assert (int(x[1]) - int(x[0])) != SLICE_STEPS
# test validation set is, at least, PER_VAL
PER_VAL_CHK = len(set(patch_train.y))/(len(set(patch_train.y))+len(set(patch_val.y))) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
PER_VAL_CHK = len(set(patch_train.x))/(len(set(patch_train.x))+len(set(patch_val.x))) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
def test_prepare_dutchf3_patch_step_2():
"""check a complete run for the script in case further changes are needed
"""
# setting a value to SLICE_STEPS as needed to test the values
SLICE_STEPS = 2
# use a temp dir that will be discarded at the end of the execution
with tempfile.TemporaryDirectory() as tmpdirname:
# saving the file to be used by the script
label_file = tmpdirname + '/label_file.npy'
np.save(label_file, ALINE)
# stting the output directory to be used by the script
output = tmpdirname + '/split'
# calling the main function of the script without SLICE_STEPS, to check default value
prep_dutchf3.split_patch_train_val(data_dir=tmpdirname, output_dir=output, label_file=label_file,
slice_steps=SLICE_STEPS, stride=STRIDE, patch_size=PATCH, per_val=PER_VAL,log_config=LOG_CONFIG)
# reading the file and splitting the data
patch_train = pd.read_csv(output + '/patch_train.txt', header=None, names=['row', 'a', 'b'])
patch_train = pd.DataFrame(patch_train.row.str.split('_').tolist(), columns=['aline', 'x', 'y', 'z'])
# test patch_train and slice_steps=2
y = list(sorted(set(patch_train.y.astype(int))))
x = list(sorted(set(patch_train.x.astype(int))))
assert (int(y[1]) - int(y[0])) == SLICE_STEPS
assert (int(x[1]) - int(x[0])) == SLICE_STEPS
# reading the file and splitting the data
patch_val = pd.read_csv(output + '/patch_val.txt', header=None, names=['row', 'a', 'b'])
patch_val = pd.DataFrame(patch_val.row.str.split('_').tolist(), columns=['aline', 'x', 'y', 'z'])
# test patch_val and slice_steps=2
y = list(sorted(set(patch_val.y.astype(int))))
x = list(sorted(set(patch_val.x.astype(int))))
assert (int(y[1]) - int(y[0])) != SLICE_STEPS
assert (int(x[1]) - int(x[0])) != SLICE_STEPS
# test validation set is, at least, PER_VAL
PER_VAL_CHK = len(set(patch_train.y))/(len(set(patch_train.y))+len(set(patch_val.y))) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
PER_VAL_CHK = len(set(patch_train.x))/(len(set(patch_train.x))+len(set(patch_val.x))) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
def test_prepare_dutchf3_patch_train_and_test_sets():
"""check a complete run for the script in case further changes are needed
"""
# setting a value to SLICE_STEPS as needed to test the values
SLICE_STEPS = 1
# use a temp dir that will be discarded at the end of the execution
with tempfile.TemporaryDirectory() as tmpdirname:
# saving the file to be used by the script
label_file = tmpdirname + '/label_file.npy'
np.save(label_file, ALINE)
# stting the output directory to be used by the script
output = tmpdirname + '/split'
# calling the main function of the script without SLICE_STEPS, to check default value
prep_dutchf3.split_patch_train_val(data_dir=tmpdirname, output_dir=output, label_file=label_file,
slice_steps=SLICE_STEPS, stride=STRIDE, patch_size=PATCH, per_val=PER_VAL,log_config=LOG_CONFIG)
# reading the file and splitting the data
patch_train = pd.read_csv(output + '/patch_train.txt', header=None, names=['row', 'a', 'b'])
patch_train = pd.DataFrame(patch_train.row.str.split('_').tolist(), columns=['aline', 'x', 'y', 'z'])
# reading the file and splitting the data
patch_val = pd.read_csv(output + '/patch_val.txt', header=None, names=['row', 'a', 'b'])
patch_val = pd.DataFrame(patch_val.row.str.split('_').tolist(), columns=['aline', 'x', 'y', 'z'])
y_train = set(patch_train.y)
x_train = set(patch_train.x)
y_val = set(patch_val.y)
x_val = set(patch_val.x)
# The sets must not contain values common to both
assert y_train & y_val == set()
assert x_train & x_val == set()
# test validation set is, at least, PER_VAL
PER_VAL_CHK = len(set(patch_train.y))/(len(set(patch_train.y))+len(set(patch_val.y))) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
PER_VAL_CHK = len(set(patch_train.x))/(len(set(patch_train.x))+len(set(patch_val.x))) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
def test_prepare_dutchf3_section_step_1():
"""check a complete run for the script in case further changes are needed
"""
# setting a value to SLICE_STEPS as needed to test the values
SLICE_STEPS = 1
# use a temp dir that will be discarded at the end of the execution
with tempfile.TemporaryDirectory() as tmpdirname:
# saving the file to be used by the script
label_file = tmpdirname + '/label_file.npy'
np.save(label_file, ALINE)
# stting the output directory to be used by the script
output = tmpdirname + '/split'
# calling the main function of the script without SLICE_STEPS, to check default value
prep_dutchf3.split_section_train_val(data_dir=tmpdirname, output_dir=output, label_file=label_file,slice_steps=SLICE_STEPS, per_val=PER_VAL, log_config=LOG_CONFIG)
# reading the file and splitting the data
section_train = pd.read_csv(output + '/section_train.txt', header=None, names=['row'])
section_train = pd.DataFrame(section_train.row.str.split('_').tolist(), columns=['aline', 'section'])
section_val = pd.read_csv(output + '/section_val.txt', header=None, names=['row'])
section_val = pd.DataFrame(section_val.row.str.split('_').tolist(), columns=['aline', 'section'])
# test
assert (float(section_train.section[1]) - float(section_train.section[0])) % float(SLICE_STEPS) == 0.0
assert (float(section_val.section[1]) - float(section_val.section[0])) % float(SLICE_STEPS) == 0.0
# test validation set is, at least, PER_VAL
PER_VAL_CHK = len(section_val)/(len(section_val)+len(section_train)) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
def test_prepare_dutchf3_section_step_2():
"""check a complete run for the script in case further changes are needed
"""
# setting a value to SLICE_STEPS as needed to test the values
SLICE_STEPS = 2
# use a temp dir that will be discarded at the end of the execution
with tempfile.TemporaryDirectory() as tmpdirname:
# saving the file to be used by the script
label_file = tmpdirname + '/label_file.npy'
np.save(label_file, ALINE)
# stting the output directory to be used by the script
output = tmpdirname + '/split'
# calling the main function of the script without SLICE_STEPS, to check default value
prep_dutchf3.split_section_train_val(data_dir=tmpdirname, output_dir=output, label_file=label_file,
slice_steps=SLICE_STEPS, per_val=PER_VAL, log_config=LOG_CONFIG)
# reading the file and splitting the data
section_train = pd.read_csv(output + '/section_train.txt', header=None, names=['row'])
section_train = pd.DataFrame(section_train.row.str.split('_').tolist(), columns=['aline', 'section'])
section_val = pd.read_csv(output + '/section_val.txt', header=None, names=['row'])
section_val = pd.DataFrame(section_val.row.str.split('_').tolist(), columns=['aline', 'section'])
# test
assert (float(section_train.section[1]) - float(section_train.section[0])) % float(SLICE_STEPS) == 0.0
assert (float(section_val.section[1]) - float(section_val.section[0])) % float(SLICE_STEPS) > 0.0
# test validation set is, at least, PER_VAL
PER_VAL_CHK = len(section_val)/(len(section_val)+len(section_train)) * 100
assert round(PER_VAL_CHK,0) >= int(PER_VAL * 100)
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0
| 7
|
14ac12121f88c5983c10772480712014a4108af9
| 21,085
|
py
|
Python
|
tests/test_packages/test_connections/test_ledger/test_contract_api.py
|
ejfitzgerald/agents-aea
|
6411fcba8af2cdf55a3005939ae8129df92e8c3e
|
[
"Apache-2.0"
] | null | null | null |
tests/test_packages/test_connections/test_ledger/test_contract_api.py
|
ejfitzgerald/agents-aea
|
6411fcba8af2cdf55a3005939ae8129df92e8c3e
|
[
"Apache-2.0"
] | null | null | null |
tests/test_packages/test_connections/test_ledger/test_contract_api.py
|
ejfitzgerald/agents-aea
|
6411fcba8af2cdf55a3005939ae8129df92e8c3e
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2018-2019 Fetch.AI Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ------------------------------------------------------------------------------
"""This module contains the tests of the ledger API connection for the contract APIs."""
import asyncio
import copy
import logging
import unittest.mock
from typing import cast
import pytest
from aea.helpers.transaction.base import RawMessage, RawTransaction, State
from aea.mail.base import Envelope
from aea.multiplexer import ConnectionStatus
from packages.fetchai.connections.ledger.contract_dispatcher import (
ContractApiRequestDispatcher,
)
from packages.fetchai.protocols.contract_api.dialogues import ContractApiDialogues
from packages.fetchai.protocols.contract_api.message import ContractApiMessage
from tests.conftest import ETHEREUM, ETHEREUM_ADDRESS_ONE
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_erc1155_get_deploy_transaction(erc1155_contract, ledger_apis_connection):
"""Test get state with contract erc1155."""
# TODO to fix
address = ETHEREUM_ADDRESS_ONE
contract_api_dialogues = ContractApiDialogues("agent_address")
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_DEPLOY_TRANSACTION,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
callable="get_deploy_transaction",
kwargs=ContractApiMessage.Kwargs({"deployer_address": address}),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
assert response is not None
assert type(response.message) == ContractApiMessage
response_message_orig = cast(ContractApiMessage, response.message)
response_message = copy.copy(response_message_orig)
response_message.is_incoming = True
response_message.counterparty = response_message_orig.sender
assert (
response_message.performative == ContractApiMessage.Performative.RAW_TRANSACTION
), "Error: {}".format(response_message.message)
response_dialogue = contract_api_dialogues.update(response_message)
assert response_dialogue == contract_api_dialogue
assert type(response_message.raw_transaction) == RawTransaction
assert response_message.raw_transaction.ledger_id == ETHEREUM
assert len(response.message.raw_transaction.body) == 6
assert len(response.message.raw_transaction.body["data"]) > 0
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_erc1155_get_raw_transaction(erc1155_contract, ledger_apis_connection):
"""Test get state with contract erc1155."""
address = ETHEREUM_ADDRESS_ONE
contract_address = "0x250A2aeb3eB84782e83365b4c42dbE3CDA9920e4"
contract_api_dialogues = ContractApiDialogues("agent_address")
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_RAW_TRANSACTION,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address=contract_address,
callable="get_create_batch_transaction",
kwargs=ContractApiMessage.Kwargs(
{"deployer_address": address, "token_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}
),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
assert response is not None
assert type(response.message) == ContractApiMessage
response_message_orig = cast(ContractApiMessage, response.message)
response_message = copy.copy(response_message_orig)
response_message.is_incoming = True
response_message.counterparty = response_message_orig.sender
assert (
response_message.performative == ContractApiMessage.Performative.RAW_TRANSACTION
), "Error: {}".format(response_message.message)
response_dialogue = contract_api_dialogues.update(response_message)
assert response_dialogue == contract_api_dialogue
assert type(response_message.raw_transaction) == RawTransaction
assert response_message.raw_transaction.ledger_id == ETHEREUM
assert len(response.message.raw_transaction.body) == 7
assert len(response.message.raw_transaction.body["data"]) > 0
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_erc1155_get_raw_message(erc1155_contract, ledger_apis_connection):
"""Test get state with contract erc1155."""
address = ETHEREUM_ADDRESS_ONE
contract_address = "0x250A2aeb3eB84782e83365b4c42dbE3CDA9920e4"
contract_api_dialogues = ContractApiDialogues("agent_address")
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_RAW_MESSAGE,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address=contract_address,
callable="get_hash_single",
kwargs=ContractApiMessage.Kwargs(
{
"from_address": address,
"to_address": address,
"token_id": 1,
"from_supply": 10,
"to_supply": 0,
"value": 0,
"trade_nonce": 1,
}
),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
assert response is not None
assert type(response.message) == ContractApiMessage
response_message_orig = cast(ContractApiMessage, response.message)
response_message = copy.copy(response_message_orig)
response_message.is_incoming = True
response_message.counterparty = response_message_orig.sender
assert (
response_message.performative == ContractApiMessage.Performative.RAW_MESSAGE
), "Error: {}".format(response_message.message)
response_dialogue = contract_api_dialogues.update(response_message)
assert response_dialogue == contract_api_dialogue
assert type(response_message.raw_message) == RawMessage
assert response_message.raw_message.ledger_id == ETHEREUM
assert type(response.message.raw_message.body) == bytes
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_erc1155_get_state(erc1155_contract, ledger_apis_connection):
"""Test get state with contract erc1155."""
address = ETHEREUM_ADDRESS_ONE
contract_address = "0x250A2aeb3eB84782e83365b4c42dbE3CDA9920e4"
contract_api_dialogues = ContractApiDialogues("agent_address")
token_id = 1
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_STATE,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address=contract_address,
callable="get_balance",
kwargs=ContractApiMessage.Kwargs(
{"agent_address": address, "token_id": token_id}
),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
assert response is not None
assert type(response.message) == ContractApiMessage
response_message_orig = cast(ContractApiMessage, response.message)
response_message = copy.copy(response_message_orig)
response_message.is_incoming = True
response_message.counterparty = response_message_orig.sender
assert (
response_message.performative == ContractApiMessage.Performative.STATE
), "Error: {}".format(response_message.message)
response_dialogue = contract_api_dialogues.update(response_message)
assert response_dialogue == contract_api_dialogue
assert type(response_message.state) == State
assert response_message.state.ledger_id == ETHEREUM
result = response_message.state.body.get("balance", None)
expected_result = {token_id: 0}
assert result is not None and result == expected_result
@pytest.mark.asyncio
async def test_run_async():
"""Test run async error handled."""
# for pydocstyle
def _raise():
raise Exception("Expected")
contract_api_dialogues = ContractApiDialogues("agent_address")
message = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_RAW_TRANSACTION,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address="test addr",
callable="get_create_batch_transaction",
kwargs=ContractApiMessage.Kwargs(
{
"deployer_address": "test_addr",
"token_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
}
),
)
message.counterparty = "test"
dialogue = contract_api_dialogues.update(message)
api = None
msg = await ContractApiRequestDispatcher(ConnectionStatus()).run_async(
_raise, api, message, dialogue
)
assert msg.performative == ContractApiMessage.Performative.ERROR
@pytest.mark.asyncio
async def test_get_handler():
"""Test failed to get handler."""
with pytest.raises(Exception, match="Performative not recognized."):
ContractApiRequestDispatcher(ConnectionStatus()).get_handler(
ContractApiMessage.Performative.ERROR
)
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_callable_wrong_number_of_arguments_api_and_contract_address(
erc1155_contract, ledger_apis_connection
):
"""
Test a contract callable with wrong number of arguments.
Test the case of either GET_STATE, GET_RAW_MESSAGE or GET_RAW_TRANSACTION.
"""
address = ETHEREUM_ADDRESS_ONE
contract_api_dialogues = ContractApiDialogues("agent_address")
token_id = 1
contract_address = "0x250A2aeb3eB84782e83365b4c42dbE3CDA9920e4"
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_STATE,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address=contract_address,
callable="get_balance",
kwargs=ContractApiMessage.Kwargs(
{"agent_address": address, "token_id": token_id}
),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
with unittest.mock.patch(
"inspect.getfullargspec", return_value=unittest.mock.MagicMock(args=[None])
):
with unittest.mock.patch.object(
ledger_apis_connection._logger, "error"
) as mock_logger:
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
mock_logger.assert_any_call(
"Expected two or more positional arguments, got 1"
)
assert (
response.message.performative == ContractApiMessage.Performative.ERROR
)
assert (
response.message.message
== "Expected two or more positional arguments, got 1"
)
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_callable_wrong_number_of_arguments_apis(
erc1155_contract, ledger_apis_connection
):
"""
Test a contract callable with wrong number of arguments.
Test the case of either GET_DEPLOY_TRANSACTION.
"""
address = ETHEREUM_ADDRESS_ONE
contract_api_dialogues = ContractApiDialogues("agent_address")
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_DEPLOY_TRANSACTION,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
callable="get_deploy_transaction",
kwargs=ContractApiMessage.Kwargs({}),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
with unittest.mock.patch(
"inspect.getfullargspec", return_value=unittest.mock.MagicMock(args=[])
):
with unittest.mock.patch.object(
ledger_apis_connection._contract_dispatcher, "_call_stub", return_value=None
):
with unittest.mock.patch.object(
ledger_apis_connection._contract_dispatcher.logger, "error"
) as mock_logger:
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
mock_logger.assert_any_call(
"Expected one or more positional arguments, got 0"
)
assert (
response.message.performative
== ContractApiMessage.Performative.ERROR
)
assert (
response.message.message
== "Expected one or more positional arguments, got 0"
)
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_callable_wrong_number_of_arguments_apis_method_call(
erc1155_contract, ledger_apis_connection, caplog
):
"""
Test a contract callable with wrong number of arguments.
Test the case of either GET_DEPLOY_TRANSACTION.
"""
address = ETHEREUM_ADDRESS_ONE
contract_api_dialogues = ContractApiDialogues("agent_address")
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_DEPLOY_TRANSACTION,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
callable="get_deploy_transaction",
kwargs=ContractApiMessage.Kwargs({}),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
with unittest.mock.patch.object(
ledger_apis_connection._contract_dispatcher, "_call_stub", return_value=None
):
with caplog.at_level(logging.DEBUG, "aea.packages.fetchai.connections.ledger"):
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
assert (
"An error occurred while processing the contract api request: 'get_deploy_transaction() missing 1 required positional argument: 'deployer_address''."
in caplog.text
)
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_callable_generic_error(erc1155_contract, ledger_apis_connection):
"""Test error messages when an exception is raised while processing the request."""
address = ETHEREUM_ADDRESS_ONE
contract_api_dialogues = ContractApiDialogues("agent_address")
token_id = 1
contract_address = "0x250A2aeb3eB84782e83365b4c42dbE3CDA9920e4"
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_STATE,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address=contract_address,
callable="get_balance",
kwargs=ContractApiMessage.Kwargs(
{"agent_address": address, "token_id": token_id}
),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
with unittest.mock.patch(
"inspect.getfullargspec", side_effect=Exception("Generic error")
):
with unittest.mock.patch.object(
ledger_apis_connection._logger, "error"
) as mock_logger:
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
response = await ledger_apis_connection.receive()
mock_logger.assert_any_call(
"An error occurred while processing the contract api request: 'Generic error'."
)
assert (
response.message.performative == ContractApiMessage.Performative.ERROR
)
assert response.message.message == "Generic error"
@pytest.mark.integration
@pytest.mark.ledger
@pytest.mark.asyncio
async def test_callable_cannot_find(erc1155_contract, ledger_apis_connection, caplog):
"""Test error messages when an exception is raised while processing the request."""
address = ETHEREUM_ADDRESS_ONE
contract_api_dialogues = ContractApiDialogues("agent_address")
token_id = 1
contract_address = "0x250A2aeb3eB84782e83365b4c42dbE3CDA9920e4"
request = ContractApiMessage(
performative=ContractApiMessage.Performative.GET_STATE,
dialogue_reference=contract_api_dialogues.new_self_initiated_dialogue_reference(),
ledger_id=ETHEREUM,
contract_id="fetchai/erc1155:0.8.0",
contract_address=contract_address,
callable="unknown_callable",
kwargs=ContractApiMessage.Kwargs(
{"agent_address": address, "token_id": token_id}
),
)
request.counterparty = str(ledger_apis_connection.connection_id)
contract_api_dialogue = contract_api_dialogues.update(request)
assert contract_api_dialogue is not None
envelope = Envelope(
to=str(ledger_apis_connection.connection_id),
sender=address,
protocol_id=request.protocol_id,
message=request,
)
with caplog.at_level(logging.DEBUG, "aea.packages.fetchai.connections.ledger"):
await ledger_apis_connection.send(envelope)
await asyncio.sleep(0.01)
assert f"Cannot find {request.callable} in contract" in caplog.text
| 39.933712
| 165
| 0.715295
| 2,280
| 21,085
| 6.342105
| 0.1
| 0.06639
| 0.06639
| 0.028631
| 0.861964
| 0.855118
| 0.836791
| 0.829115
| 0.820332
| 0.812448
| 0
| 0.0219
| 0.198719
| 21,085
| 527
| 166
| 40.009488
| 0.833975
| 0.041214
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| 0.738636
| 0
| 0.002273
| 0.091111
| 0.038848
| 0
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| 0.013001
| 0.001898
| 0.115909
| 1
| 0.002273
| false
| 0
| 0.029545
| 0
| 0.031818
| 0
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| 0
| 0
| null | 0
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| 1
| 1
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| 1
| 1
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| null | 0
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| 0
|
0
| 7
|
1afef8b2e3e3eb656ca7349cecd647b6df73f70f
| 1,140
|
py
|
Python
|
emulated_hue/light_definitions.py
|
amahlaka/hass_emulated_hue
|
491e88fa174a93d67a06b6ae081c00ea9da5dd1b
|
[
"Apache-2.0"
] | null | null | null |
emulated_hue/light_definitions.py
|
amahlaka/hass_emulated_hue
|
491e88fa174a93d67a06b6ae081c00ea9da5dd1b
|
[
"Apache-2.0"
] | null | null | null |
emulated_hue/light_definitions.py
|
amahlaka/hass_emulated_hue
|
491e88fa174a93d67a06b6ae081c00ea9da5dd1b
|
[
"Apache-2.0"
] | 1
|
2020-11-12T22:13:35.000Z
|
2020-11-12T22:13:35.000Z
|
"""Common model info for Light models."""
LST002 = {
"config": {
"archetype": "huelightstrip",
"function": "mixed",
"direction": "omnidirectional",
},
"capabilities": {
"certified": True,
"control": {
"mindimlevel": 40,
"maxlumen": 1600,
"colorgamuttype": "C",
"colorgamut": [[0.6915, 0.3083], [0.17, 0.7], [0.1532, 0.0475]],
"ct": {"min": 153, "max": 500},
},
"streaming": {"renderer": True, "proxy": True},
},
"swversion": "5.127.1.26581",
}
ESPRESSIF_ESP_WROVER_KIT = {
"config": {
"archetype": "huelightstrip",
"function": "mixed",
"direction": "omnidirectional",
},
"capabilities": {
"certified": True,
"control": {
"mindimlevel": 40,
"maxlumen": 1600,
"colorgamuttype": "C",
"colorgamut": [[0.6915, 0.3083], [0.17, 0.7], [0.1532, 0.0475]],
"ct": {"min": 153, "max": 500},
},
"streaming": {"renderer": True, "proxy": True},
},
"swversion": "5.127.1.26581",
}
| 25.909091
| 76
| 0.464035
| 101
| 1,140
| 5.207921
| 0.485149
| 0.057034
| 0.106464
| 0.136882
| 0.893536
| 0.893536
| 0.893536
| 0.893536
| 0.893536
| 0.893536
| 0
| 0.126632
| 0.32807
| 1,140
| 43
| 77
| 26.511628
| 0.560052
| 0.030702
| 0
| 0.736842
| 0
| 0
| 0.343949
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| 1
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2166eff1b1fc8036ca89a7e0775c2ed4c8dfe801
| 3,217
|
py
|
Python
|
HexMeshCylinders/headers.py
|
gui-miotto/HexMeshCylinders
|
5f86c85d7f13e6705ae10f70e04e80328de0c6cc
|
[
"MIT"
] | null | null | null |
HexMeshCylinders/headers.py
|
gui-miotto/HexMeshCylinders
|
5f86c85d7f13e6705ae10f70e04e80328de0c6cc
|
[
"MIT"
] | 1
|
2020-07-09T02:45:29.000Z
|
2020-07-09T07:54:51.000Z
|
HexMeshCylinders/headers.py
|
gui-miotto/HexMeshCylinders
|
5f86c85d7f13e6705ae10f70e04e80328de0c6cc
|
[
"MIT"
] | null | null | null |
point_header = r"""/*--------------------------------*- C++ -*----------------------------------*\
========= |
\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
\\ / O peration | Website: https://openfoam.org
\\ / A nd | Version: 7
\\/ M anipulation |
\*---------------------------------------------------------------------------*/
FoamFile
{
version 2.0;
format ascii;
class vectorField;
location "constant/polyMesh";
object points;
}
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
"""
faces_header = r"""/*--------------------------------*- C++ -*----------------------------------*\
========= |
\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
\\ / O peration | Website: https://openfoam.org
\\ / A nd | Version: 7
\\/ M anipulation |
\*---------------------------------------------------------------------------*/
FoamFile
{
version 2.0;
format ascii;
class faceList;
location "constant/polyMesh";
object faces;
}
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
"""
boundary_header = r"""/*--------------------------------*- C++ -*----------------------------------*\
========= |
\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
\\ / O peration | Website: https://openfoam.org
\\ / A nd | Version: 7
\\/ M anipulation |
\*---------------------------------------------------------------------------*/
FoamFile
{
version 2.0;
format ascii;
class polyBoundaryMesh;
location "constant/polyMesh";
object boundary;
}
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
"""
neighbour_header = r"""/*--------------------------------*- C++ -*----------------------------------*\
========= |
\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
\\ / O peration | Website: https://openfoam.org
\\ / A nd | Version: 7
\\/ M anipulation |
\*---------------------------------------------------------------------------*/
FoamFile
{
version 2.0;
format ascii;
class labelList;
location "constant/polyMesh";
object neighbour;
}
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
"""
owner_header = r"""/*--------------------------------*- C++ -*----------------------------------*\
========= |
\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
\\ / O peration | Website: https://openfoam.org
\\ / A nd | Version: 7
\\/ M anipulation |
\*---------------------------------------------------------------------------*/
FoamFile
{
version 2.0;
format ascii;
class labelList;
location "constant/polyMesh";
object owner;
}
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
"""
| 35.744444
| 102
| 0.290022
| 185
| 3,217
| 5.016216
| 0.227027
| 0.037716
| 0.043103
| 0.048491
| 0.795259
| 0.795259
| 0.795259
| 0.795259
| 0.795259
| 0.795259
| 0
| 0.006919
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| 103
| 36.146067
| 0.421125
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| 0.960833
| 0.237799
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
|
0
| 8
|
0d57704ddb69d22b0e912d26acf2692924241e2f
| 23,300
|
py
|
Python
|
mapmatrix_build.py
|
livkosterlitz/AWD
|
2402ee455282d09a14216c1f448c3e211762721e
|
[
"MIT"
] | 1
|
2019-08-19T20:39:48.000Z
|
2019-08-19T20:39:48.000Z
|
mapmatrix_build.py
|
livkosterlitz/AWD
|
2402ee455282d09a14216c1f448c3e211762721e
|
[
"MIT"
] | null | null | null |
mapmatrix_build.py
|
livkosterlitz/AWD
|
2402ee455282d09a14216c1f448c3e211762721e
|
[
"MIT"
] | 1
|
2019-08-20T15:03:23.000Z
|
2019-08-20T15:03:23.000Z
|
#python2 mapmatrix_build.py -t Output_files/window_mapping/mapmatrix.txt
import sys
import argparse
script_description = """
uses the matrix txt file to build the map
"""
parser = argparse.ArgumentParser(
description = script_description,
formatter_class = argparse.RawDescriptionHelpFormatter)
parser.add_argument("-t", "--txt_file", required = True)
args = parser.parse_args()
matrix_file = args.txt_file
nopairsleft = []
reference_dict = {}
scaff_pairs_used = []
allscaff_used = []
allscaff_duplicates = []
reciprocal_list = []
reciprocal_list_1 = []
reciprocal_list_2 = []
reciprocal_list_3 = []
reciprocal_list_4 = []
reciprocal_list_5 = []
allscaff_pairs = []
allscaff_used_round1 = []
allscaff_used_round2 = []
allscaff_used_round3 = []
allscaff_used_round4 = []
scaff_used_round1 = []
scaff_used_round2 = []
scaff_used_round3 = []
scaff_used_round4 = []
scaff_unused_round1 = []
scaff_unused_round2 = []
scaff_unused_round3 = []
scaff_unused_round4 = []
round1_emptyranks = []
round2_emptyranks = []
round3_emptyranks = []
round4_emptyranks = []
outfile1 = open('%s/reciprocalpairs.txt' % ('/'.join(matrix_file.split("/")[:-1])), 'w')
outfile2 = open('%s.round2.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'w')
outfile3 = open('%s.round3.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'w')
outfile4 = open('%s.round4.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'w')
outfile5 = open('%s.round5.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'w')
with open(matrix_file, "r") as openmatrix:
for line in openmatrix:
line = line.strip().split("\t")
ref = line[0]
ref_scaff = "_".join(ref.split("_")[:-1])
if ref_scaff not in reference_dict:
reference_dict[ref_scaff] = {}
ref_orient = ref.split("_")[-1]
reference_dict[ref_scaff][ref_orient] = line[1:7]
for ref_scaff in reference_dict:
for ref_orient in reference_dict[ref_scaff]:
rank_1_ref = reference_dict[ref_scaff][ref_orient][0]
rank_2_ref = reference_dict[ref_scaff][ref_orient][1]
ref_scaff_check = "%s_" % (ref_scaff)
ref_scaff_combine = "%s_%s" % (ref_scaff, ref_orient)
if ref_scaff_check in rank_1_ref:
rank_1_ref = rank_2_ref
for compare_scaff in reference_dict:
for compare_orient in reference_dict[compare_scaff]:
compare_combine = "%s_%s" % (compare_scaff, compare_orient)
if compare_combine in rank_1_ref:
rank_1_compare = reference_dict[compare_scaff][compare_orient][0]
rank_2_compare = reference_dict[compare_scaff][compare_orient][1]
if compare_scaff in rank_1_compare:
rank_1_compare = rank_2_compare
if ref_scaff_combine in rank_1_compare:
if [compare_combine, ref_scaff_combine] in reciprocal_list or [ref_scaff_combine, compare_combine] in reciprocal_list:
pass
else:
if "all" in ref_scaff_combine and "all" in compare_combine:
if [compare_combine, ref_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([ref_scaff_combine, compare_combine])
reciprocal_list.append([ref_scaff_combine, compare_combine])
reciprocal_list_1.append([ref_scaff_combine, compare_combine])
scaff_pairs_used.append([ref_scaff, compare_scaff])
if "all" not in ref_scaff_combine:
scaff_used_round1.append(ref_scaff_combine)
else:
allscaff_used_round1.append(ref_scaff_combine)
allscaff_used.append(ref_scaff_combine)
if "all" not in compare_combine:
scaff_used_round1.append(compare_combine)
else:
allscaff_used_round1.append(compare_combine)
allscaff_used.append(compare_combine)
else:
scaff_unused_round1.append(ref_scaff_combine)
for pairs in reciprocal_list_1:
outfile1.write('%s\n' % (pairs))
round2_dict = {}
with open(matrix_file, "r") as openmatrix:
for line in openmatrix:
line = line.strip().split("\t")
column_1 = line[0]
column_1_scaff = "_".join(column_1.split("_")[:-1])
column_1_scaff_check = "%s_" % (column_1_scaff)
column_1_orient = column_1.split("_")[-1]
if column_1 in scaff_used_round1:
pass
elif column_1 in allscaff_used_round1:
all_count = allscaff_used.count(column_1)
if all_count == 2:
pass
else:
if column_1_scaff not in round2_dict:
round2_dict[column_1_scaff] = {}
round2_dict[column_1_scaff][column_1_orient] = []
for info in line[1:6]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round1:
if column_1_scaff not in info_split:
if [column_1, info_split] in allscaff_pairs or [info_split, column_1] in allscaff_pairs:
pass
else:
round2_dict[column_1_scaff][column_1_orient].append(info)
allscaff_duplicates.append(column_1_scaff)
else:
if column_1_scaff not in round2_dict:
round2_dict[column_1_scaff] = {}
round2_dict[column_1_scaff][column_1_orient] = []
for info in line[1:7]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round1:
if column_1_scaff not in info_split:
if "all" in info_split:
all_count = allscaff_used.count(info_split)
if all_count == 2:
pass
else:
round2_dict[column_1_scaff][column_1_orient].append(info)
else:
round2_dict[column_1_scaff][column_1_orient].append(info)
for key in round2_dict:
for key_2 in round2_dict[key]:
outfile2.write("%s_%s\t%s\n" % (key, key_2, "\t".join(round2_dict[key][key_2])))
outfile2.close()
for round2_scaff in round2_dict:
for round2_orient in round2_dict[round2_scaff]:
round2_scaff_combine = "%s_%s" % (round2_scaff, round2_orient)
if round2_dict[round2_scaff][round2_orient] != []:
rank_1_round2 = round2_dict[round2_scaff][round2_orient][0]
else:
round1_emptyranks.append(round2_scaff)
nopairsleft.append(round2_scaff_combine)
break
for round2_compare_scaff in round2_dict:
for round2_compare_orient in round2_dict[round2_compare_scaff]:
round2_compare_combine = "%s_%s" % (round2_compare_scaff, round2_compare_orient)
if round2_dict[round2_compare_scaff][round2_compare_orient] != []:
rank_1_round2_compare = round2_dict[round2_compare_scaff][round2_compare_orient][0]
else:
pass
if round2_compare_combine in rank_1_round2:
if round2_scaff_combine in rank_1_round2_compare:
if [round2_compare_combine, round2_scaff_combine] in reciprocal_list or [round2_scaff_combine, round2_compare_combine] in reciprocal_list:
pass
elif [round2_scaff, round2_compare_scaff] in scaff_pairs_used or [round2_compare_scaff, round2_scaff] in scaff_pairs_used:
if len(round2_dict[round2_scaff][round2_orient]) <= 1:
pass
else:
rank_1_round2 = round2_dict[round2_scaff][round2_orient][1]
round2_scaff_combine = "%s_%s" % (round2_scaff, round2_orient)
for round2_compare_scaff in round2_dict:
for round2_compare_orient in round2_dict[round2_compare_scaff]:
round2_compare_combine = "%s_%s" % (round2_compare_scaff, round2_compare_orient)
if round2_dict[round2_compare_scaff][round2_compare_orient] != []:
rank_1_round2_compare = round2_dict[round2_compare_scaff][round2_compare_orient][0]
else:
pass
if round2_compare_combine in rank_1_round2:
if round2_scaff_combine in rank_1_round2_compare:
if [round2_compare_combine, round2_scaff_combine] in reciprocal_list or [round2_scaff_combine, round2_compare_combine] in reciprocal_list:
pass
else:
if "all" in round2_scaff_combine and "all" in round2_compare_combine:
if [round2_compare_combine, round2_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([round2_scaff_combine, round2_compare_combine])
reciprocal_list.append([round2_scaff_combine, round2_compare_combine])
reciprocal_list_2.append([round2_scaff_combine, round2_compare_combine])
scaff_pairs_used.append([round2_scaff, round2_compare_scaff])
if "all" not in round2_scaff_combine:
scaff_used_round2.append(round2_scaff_combine)
else:
allscaff_used_round2.append(round2_scaff_combine)
allscaff_used.append(round2_scaff_combine)
if "all" not in round2_compare_combine:
scaff_used_round2.append(round2_compare_combine)
else:
allscaff_used_round2.append(round2_compare_combine)
allscaff_used.append(round2_compare_combine)
else:
if "all" in round2_scaff_combine and "all" in round2_compare_combine:
if [round2_compare_combine, round2_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([round2_scaff_combine, round2_compare_combine])
reciprocal_list.append([round2_scaff_combine, round2_compare_combine])
reciprocal_list_2.append([round2_scaff_combine, round2_compare_combine])
scaff_pairs_used.append([round2_scaff, round2_compare_scaff])
if "all" not in round2_scaff_combine:
scaff_used_round2.append(round2_scaff_combine)
else:
allscaff_used_round2.append(round2_scaff_combine)
allscaff_used.append(round2_scaff_combine)
if "all" not in round2_compare_combine:
scaff_used_round2.append(round2_compare_combine)
else:
allscaff_used_round2.append(round2_compare_combine)
allscaff_used.append(round2_compare_combine)
else:
scaff_unused_round2.append(round2_scaff_combine)
round3_dict = {}
outfile2_read = open('%s.round2.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'r')
for pairs in reciprocal_list_2:
outfile1.write('%s\n' % (pairs))
for line in outfile2_read:
line = line.strip().split("\t")
column_1 = line[0]
column_1_scaff = "_".join(column_1.split("_")[:-1])
column_1_scaff_check = "%s_" % (column_1_scaff)
column_1_orient = column_1.split("_")[-1]
if column_1 in scaff_used_round2 or column_1 in nopairsleft:
pass
elif column_1 in allscaff_used:
all_count = allscaff_used.count(column_1)
if all_count == 2:
pass
else:
if column_1_scaff not in round3_dict:
round3_dict[column_1_scaff] = {}
round3_dict[column_1_scaff][column_1_orient] = []
for info in line[1:7]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round2 and info_split not in nopairsleft:
if column_1_scaff not in info_split:
if [column_1, info_split] in allscaff_pairs or [info_split, column_1] in allscaff_pairs:
pass
else:
round3_dict[column_1_scaff][column_1_orient].append(info)
allscaff_duplicates.append(column_1_scaff)
else:
if column_1_scaff not in round3_dict:
round3_dict[column_1_scaff] = {}
round3_dict[column_1_scaff][column_1_orient] = []
for info in line[1:7]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round2 and info_split not in nopairsleft:
if column_1_scaff not in info_split:
if "all" in info_split:
all_count = allscaff_used.count(info_split)
if all_count == 2:
pass
else:
round3_dict[column_1_scaff][column_1_orient].append(info)
else:
round3_dict[column_1_scaff][column_1_orient].append(info)
for key in round3_dict:
for key_2 in round3_dict[key]:
outfile3.write("%s_%s\t%s\n" % (key, key_2, "\t".join(round3_dict[key][key_2])))
outfile3.close()
for round3_scaff in round3_dict:
for round3_orient in round3_dict[round3_scaff]:
round3_scaff_combine = "%s_%s" % (round3_scaff, round3_orient)
if round3_dict[round3_scaff][round3_orient] != []:
rank_1_round3 = round3_dict[round3_scaff][round3_orient][0]
else:
round2_emptyranks.append(round3_scaff)
nopairsleft.append(round3_scaff_combine)
break
for round3_compare_scaff in round3_dict:
for round3_compare_orient in round3_dict[round3_compare_scaff]:
round3_compare_combine = "%s_%s" % (round3_compare_scaff, round3_compare_orient)
if round3_dict[round3_compare_scaff][round3_compare_orient] != []:
rank_1_round3_compare = round3_dict[round3_compare_scaff][round3_compare_orient][0]
else:
break
if round3_compare_combine in rank_1_round3:
if round3_scaff_combine in rank_1_round3_compare:
if [round3_compare_combine, round3_scaff_combine] in reciprocal_list or [round3_scaff_combine, round3_compare_combine] in reciprocal_list:
pass
elif [round3_scaff, round3_compare_scaff] in scaff_pairs_used or [round3_compare_scaff, round3_scaff] in scaff_pairs_used:
rank_1_round3 = round3_dict[round3_scaff][round3_orient][1]
round3_scaff_combine = "%s_%s" % (round3_scaff, round3_orient)
for round3_compare_scaff in round3_dict:
for round3_compare_orient in round3_dict[round3_compare_scaff]:
round3_compare_combine = "%s_%s" % (round3_compare_scaff, round3_compare_orient)
if round3_dict[round3_compare_scaff][round3_compare_orient] != []:
rank_1_round3_compare = round3_dict[round3_compare_scaff][round3_compare_orient][0]
else:
pass
if round3_compare_combine in rank_1_round3:
if round3_scaff_combine in rank_1_round3_compare:
if [round3_compare_combine, round3_scaff_combine] in reciprocal_list or [round3_scaff_combine, round3_compare_combine] in reciprocal_list:
pass
else:
if "all" in round3_scaff_combine and "all" in round3_compare_combine:
if [round3_compare_combine, round3_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([round3_scaff_combine, round3_compare_combine])
reciprocal_list.append([round3_scaff_combine, round3_compare_combine])
reciprocal_list_3.append([round3_scaff_combine, round3_compare_combine])
scaff_pairs_used.append([round3_scaff, round3_compare_scaff])
if "all" not in round3_scaff_combine:
scaff_used_round3.append(round3_scaff_combine)
else:
allscaff_used_round3.append(round3_scaff_combine)
allscaff_used.append(round3_scaff_combine)
if "all" not in round3_compare_combine:
scaff_used_round3.append(round3_compare_combine)
else:
allscaff_used_round3.append(round3_compare_combine)
allscaff_used.append(round3_compare_combine)
else:
if "all" in round3_scaff_combine and "all" in round3_compare_combine:
if [round3_compare_combine, round3_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([round3_scaff_combine, round3_compare_combine])
reciprocal_list.append([round3_scaff_combine, round3_compare_combine])
reciprocal_list_3.append([round3_scaff_combine, round3_compare_combine])
scaff_pairs_used.append([round3_scaff, round3_compare_scaff])
if "all" not in round3_scaff_combine:
scaff_used_round3.append(round3_scaff_combine)
else:
allscaff_used_round3.append(round3_scaff_combine)
allscaff_used.append(round3_scaff_combine)
if "all" not in round3_compare_combine:
scaff_used_round3.append(round3_compare_combine)
else:
allscaff_used_round3.append(round3_compare_combine)
allscaff_used.append(round3_compare_combine)
else:
scaff_unused_round3.append(round3_scaff_combine)
for pairs in reciprocal_list_3:
outfile1.write('%s\n' % (pairs))
round4_dict = {}
outfile3_read = open('%s.round3.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'r')
for line in outfile3_read:
line = line.strip().split("\t")
column_1 = line[0]
column_1_scaff = "_".join(column_1.split("_")[:-1])
column_1_scaff_check = "%s_" % (column_1_scaff)
column_1_orient = column_1.split("_")[-1]
if column_1 in scaff_used_round3 or column_1 in nopairsleft:
pass
elif column_1 in allscaff_used:
all_count = allscaff_used.count(column_1)
if all_count == 2:
pass
else:
if column_1_scaff not in round4_dict:
round4_dict[column_1_scaff] = {}
round4_dict[column_1_scaff][column_1_orient] = []
for info in line[1:7]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round3 and info_split not in nopairsleft:
if column_1_scaff not in info_split:
if [column_1, info_split] in allscaff_pairs or [info_split, column_1] in allscaff_pairs:
pass
else:
round4_dict[column_1_scaff][column_1_orient].append(info)
allscaff_duplicates.append(column_1_scaff)
else:
if column_1_scaff not in round4_dict:
round4_dict[column_1_scaff] = {}
round4_dict[column_1_scaff][column_1_orient] = []
for info in line[1:7]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round3 and info_split not in nopairsleft:
if column_1_scaff not in info_split:
if "all" in info_split:
all_count = allscaff_used.count(info_split)
if all_count == 2:
pass
else:
round4_dict[column_1_scaff][column_1_orient].append(info)
else:
round4_dict[column_1_scaff][column_1_orient].append(info)
for key in round4_dict:
for key_2 in round4_dict[key]:
outfile4.write("%s_%s\t%s\n" % (key, key_2, "\t".join(round4_dict[key][key_2])))
outfile4.close()
for round4_scaff in round4_dict:
for round4_orient in round4_dict[round4_scaff]:
round4_scaff_combine = "%s_%s" % (round4_scaff, round4_orient)
if round4_dict[round4_scaff][round4_orient] != []:
rank_1_round4 = round4_dict[round4_scaff][round4_orient][0]
else:
round3_emptyranks.append(round4_scaff)
nopairsleft.append(round4_scaff_combine)
break
for round4_compare_scaff in round4_dict:
for round4_compare_orient in round4_dict[round4_compare_scaff]:
round4_compare_combine = "%s_%s" % (round4_compare_scaff, round4_compare_orient)
if round4_dict[round4_compare_scaff][round4_compare_orient] != []:
rank_1_round4_compare = round4_dict[round4_compare_scaff][round4_compare_orient][0]
else:
break
if round4_compare_combine in rank_1_round4:
if round4_scaff_combine in rank_1_round4_compare:
if [round4_compare_combine, round4_scaff_combine] in reciprocal_list:
pass
elif [round4_scaff, round4_compare_scaff] in scaff_pairs_used or [round4_compare_scaff, round4_scaff] in scaff_pairs_used:
if len(round4_dict[round4_scaff][round4_orient]) == 1:
pass
else:
rank_1_round4 = round4_dict[round4_scaff][round4_orient][1]
round4_scaff_combine = "%s_%s" % (round4_scaff, round4_orient)
for round4_compare_scaff in round4_dict:
for round4_compare_orient in round4_dict[round4_compare_scaff]:
round4_compare_combine = "%s_%s" % (round4_compare_scaff, round4_compare_orient)
if round4_dict[round4_compare_scaff][round4_compare_orient] != []:
rank_1_round4_compare = round4_dict[round4_compare_scaff][round4_compare_orient][0]
else:
pass
if round4_compare_combine in rank_1_round4:
if round4_scaff_combine in rank_1_round4_compare:
if [round4_compare_combine, round4_scaff_combine] in reciprocal_list:
pass
else:
if "all" in round4_scaff_combine and "all" in round4_compare_combine:
if [round4_compare_combine, round4_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([round4_scaff_combine, round4_compare_combine])
reciprocal_list.append([round4_scaff_combine, round4_compare_combine])
reciprocal_list_4.append([round4_scaff_combine, round4_compare_combine])
scaff_pairs_used.append([round4_scaff, round4_compare_scaff])
if "all" not in round4_scaff_combine:
scaff_used_round4.append(round4_scaff_combine)
else:
allscaff_used_round4.append(round4_scaff_combine)
allscaff_used.append(round4_scaff_combine)
if "all" not in round4_compare_combine:
scaff_used_round4.append(round4_compare_combine)
else:
allscaff_used_round4.append(round4_compare_combine)
allscaff_used.append(round4_compare_combine)
else:
if "all" in round4_scaff_combine and "all" in round4_compare_combine:
if [round4_compare_combine, round4_scaff_combine] not in reciprocal_list:
allscaff_pairs.append([round4_scaff_combine, round4_compare_combine])
reciprocal_list.append([round4_scaff_combine, round4_compare_combine])
reciprocal_list_4.append([round4_scaff_combine, round4_compare_combine])
scaff_pairs_used.append([round4_scaff, round4_compare_scaff])
if "all" not in round4_scaff_combine:
scaff_used_round4.append(round4_scaff_combine)
else:
allscaff_used_round4.append(round4_scaff_combine)
allscaff_used.append(round4_scaff_combine)
if "all" not in round4_compare_combine:
scaff_used_round4.append(round4_compare_combine)
else:
allscaff_used_round4.append(round4_compare_combine)
allscaff_used.append(round4_compare_combine)
else:
scaff_unused_round4.append(round4_scaff_combine)
for pairs in reciprocal_list_4:
outfile1.write('%s\n' % (pairs))
round5_dict = {}
outfile4_read = open('%s.round4.txt' % ('.'.join(matrix_file.split(".")[:-1])), 'r')
for line in outfile4_read:
line = line.strip().split("\t")
column_1 = line[0]
column_1_scaff = "_".join(column_1.split("_")[:-1])
column_1_scaff_check = "%s_" % (column_1_scaff)
column_1_orient = column_1.split("_")[-1]
if column_1 in scaff_used_round4 or column_1 in nopairsleft:
pass
elif column_1 in allscaff_used:
all_count = allscaff_used.count(column_1)
if all_count == 2:
pass
else:
if column_1_scaff not in round5_dict:
round5_dict[column_1_scaff] = {}
round5_dict[column_1_scaff][column_1_orient] = []
for info in line[1:6]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round4 and info_split not in nopairsleft:
if column_1_scaff not in info_split:
if [column_1, info_split] in allscaff_pairs or [info_split, column_1] in allscaff_pairs:
pass
else:
round5_dict[column_1_scaff][column_1_orient].append(info)
allscaff_duplicates.append(column_1_scaff)
else:
if column_1_scaff not in round5_dict:
round5_dict[column_1_scaff] = {}
round5_dict[column_1_scaff][column_1_orient] = []
for info in line[1:6]:
info_split = info.split(",")[0][2:-1]
if info_split not in scaff_used_round4 and info_split not in nopairsleft:
if column_1_scaff not in info_split:
if "all" in info_split:
all_count = allscaff_used.count(info_split)
if all_count == 2:
pass
else:
round5_dict[column_1_scaff][column_1_orient].append(info)
else:
round5_dict[column_1_scaff][column_1_orient].append(info)
for key in round5_dict:
for key_2 in round5_dict[key]:
outfile5.write("%s_%s\t%s\n" % (key, key_2, "\t".join(round5_dict[key][key_2])))
for key in nopairsleft:
outfile5.write("%s\tnopairslist\n" % (key))
outfile1.close()
outfile2.close()
outfile3.close()
outfile4.close()
outfile5.close()
| 41.756272
| 150
| 0.717554
| 3,279
| 23,300
| 4.681305
| 0.031107
| 0.054267
| 0.046906
| 0.029186
| 0.869316
| 0.823453
| 0.785603
| 0.74886
| 0.742606
| 0.695049
| 0
| 0.0408
| 0.186867
| 23,300
| 557
| 151
| 41.831239
| 0.769397
| 0.003047
| 0
| 0.711198
| 0
| 0
| 0.021235
| 0.000948
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.056974
| 0.003929
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
b4a76e0d18da0fea1115637b0e9701f30f122c60
| 144
|
py
|
Python
|
iaas/iaas.py
|
manoelhc/elektro
|
33c8a58e7028ebec132f66219816e7d64d7ca566
|
[
"MIT"
] | null | null | null |
iaas/iaas.py
|
manoelhc/elektro
|
33c8a58e7028ebec132f66219816e7d64d7ca566
|
[
"MIT"
] | null | null | null |
iaas/iaas.py
|
manoelhc/elektro
|
33c8a58e7028ebec132f66219816e7d64d7ca566
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
class iaas:
def __init__(self):
pass
def list(self):
pass
def apply(self, es):
pass
| 14.4
| 24
| 0.534722
| 19
| 144
| 3.842105
| 0.684211
| 0.219178
| 0.30137
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010753
| 0.354167
| 144
| 9
| 25
| 16
| 0.774194
| 0.145833
| 0
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0.428571
| 0
| 0
| 0.571429
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
372dc3d5c4ab083bd2573ce0a73c6802244721bf
| 162
|
py
|
Python
|
rex/exploit/cgc/type2/__init__.py
|
tiedaoxiaotubie/rex
|
049bbce3ab2717cbb4d2f0fc10fe8c0433b39c1d
|
[
"BSD-2-Clause"
] | 471
|
2016-08-20T19:25:33.000Z
|
2019-12-06T04:46:55.000Z
|
rex/exploit/cgc/type2/__init__.py
|
tiedaoxiaotubie/rex
|
049bbce3ab2717cbb4d2f0fc10fe8c0433b39c1d
|
[
"BSD-2-Clause"
] | 42
|
2016-08-21T04:26:43.000Z
|
2019-12-18T02:02:58.000Z
|
rex/exploit/cgc/type2/__init__.py
|
tiedaoxiaotubie/rex
|
049bbce3ab2717cbb4d2f0fc10fe8c0433b39c1d
|
[
"BSD-2-Clause"
] | 101
|
2016-08-21T02:21:26.000Z
|
2019-09-12T07:22:24.000Z
|
from rex.exploit.cgc.type2.cgc_type2_rop_exploit import CGCType2RopExploit
from rex.exploit.cgc.type2.cgc_type2_shellcode_exploit import CGCType2ShellcodeExploit
| 54
| 86
| 0.901235
| 22
| 162
| 6.363636
| 0.454545
| 0.228571
| 0.2
| 0.242857
| 0.428571
| 0.428571
| 0.428571
| 0
| 0
| 0
| 0
| 0.038961
| 0.049383
| 162
| 2
| 87
| 81
| 0.87013
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
2eafe5d8e83a8b1e8fa6a10d2c54ea642c45caf9
| 315
|
py
|
Python
|
test_readlisp.py
|
olemb/readlisp
|
4b82a8101c023f7a7da127f2d5dd3280428794d1
|
[
"MIT"
] | 3
|
2018-06-17T18:36:48.000Z
|
2020-03-12T01:41:21.000Z
|
test_readlisp.py
|
olemb/readlisp
|
4b82a8101c023f7a7da127f2d5dd3280428794d1
|
[
"MIT"
] | 2
|
2018-06-17T18:36:23.000Z
|
2020-11-25T16:24:35.000Z
|
test_readlisp.py
|
olemb/readlisp
|
4b82a8101c023f7a7da127f2d5dd3280428794d1
|
[
"MIT"
] | 2
|
2018-04-07T13:29:29.000Z
|
2021-11-18T14:48:49.000Z
|
import pytest
from readlisp import readlisp, LispSymbol
assert readlisp('(a |b|)') == [LispSymbol('a'), LispSymbol('b')]
assert readlisp('(1 2 3 "four")') == [1, 2, 3, "four"]
# TODO: How do we handle these cases? (And what does this mean?)
# assert readlisp("(a '|b|)") == ?
# assert readlisp("(a :|b|)") == ?
| 26.25
| 64
| 0.609524
| 45
| 315
| 4.266667
| 0.533333
| 0.291667
| 0.234375
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022814
| 0.165079
| 315
| 11
| 65
| 28.636364
| 0.707224
| 0.406349
| 0
| 0
| 0
| 0
| 0.148352
| 0
| 0
| 0
| 0
| 0.090909
| 0.5
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
2ebf63a6b9f17f1e88bb4f191ee9c6fe8b71125f
| 4,466
|
py
|
Python
|
tests/decorators/test_auth_required.py
|
briancappello/flask-security-bundle
|
d9b97a4408c2001a0d29f5c55a4540a4917abd24
|
[
"MIT"
] | null | null | null |
tests/decorators/test_auth_required.py
|
briancappello/flask-security-bundle
|
d9b97a4408c2001a0d29f5c55a4540a4917abd24
|
[
"MIT"
] | null | null | null |
tests/decorators/test_auth_required.py
|
briancappello/flask-security-bundle
|
d9b97a4408c2001a0d29f5c55a4540a4917abd24
|
[
"MIT"
] | null | null | null |
import pytest
from flask_security_bundle.decorators import (
auth_required,
# roles_accepted, # tested by tests for auth_required
# roles_required, # tested by tests for auth_required
)
from werkzeug.exceptions import Forbidden, Unauthorized
class MethodCalled(Exception):
pass
@pytest.mark.usefixtures('user')
class TestAuthRequired:
def test_decorated_with_parenthesis(self):
@auth_required()
def method():
raise MethodCalled
with pytest.raises(Unauthorized):
method()
def test_decorated_without_parenthesis(self):
@auth_required
def method():
raise MethodCalled
with pytest.raises(Unauthorized):
method()
def test_anonymous_user_unauthorized(self):
@auth_required
def method():
raise MethodCalled
with pytest.raises(Unauthorized):
method()
def test_authed_user_allowed(self, client):
client.login_user()
@auth_required
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
def test_with_role(self, client):
client.login_user()
@auth_required(role='ROLE_USER')
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
def test_without_role(self, client):
client.login_user()
@auth_required(role='ROLE_FAIL')
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
def test_with_all_roles(self, client):
client.login_user()
@auth_required(roles=['ROLE_USER', 'ROLE_USER1'])
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
def test_without_all_roles(self, client):
client.login_user()
@auth_required(roles=['ROLE_USER', 'ROLE_FAIL'])
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
def test_with_one_of_roles(self, client):
client.login_user()
@auth_required(one_of=['ROLE_USER', 'ROLE_FAIL'])
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
def test_without_one_of_roles(self, client):
client.login_user()
@auth_required(one_of=['ROLE_FAIL', 'ROLE_ALSO_FAIL'])
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
def test_with_role_and_one_of_roles(self, client):
client.login_user()
@auth_required(role='ROLE_USER', one_of=['ROLE_FAIL', 'ROLE_USER1'])
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
@auth_required(roles=['ROLE_USER'], one_of=['ROLE_FAIL', 'ROLE_USER1'])
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
def test_without_role_and_one_of_roles(self, client):
client.login_user()
@auth_required(role='ROLE_FAIL', one_of=['ROLE_USER'])
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
@auth_required(roles=['ROLE_FAIL'], one_of=['ROLE_USER'])
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
@auth_required(role='ROLE_USER', one_of=['ROLE_FAIL'])
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
@auth_required(roles=['ROLE_USER'], one_of=['ROLE_FAIL'])
def method():
raise MethodCalled
with pytest.raises(Forbidden):
method()
def test_only_one_of_role_or_roles_allowed(self, client):
client.login_user()
with pytest.raises(RuntimeError) as e:
@auth_required(role='ROLE_USER', roles=['ROLE_USER1'])
def method():
raise MethodCalled
assert 'specify only one of `role` or `roles` kwargs' in str(e)
def test_works_with_token_auth(self, client, user):
client.login_as(user)
@auth_required(role='ROLE_USER')
def method():
raise MethodCalled
with pytest.raises(MethodCalled):
method()
| 25.231638
| 79
| 0.601209
| 478
| 4,466
| 5.355649
| 0.129707
| 0.098438
| 0.098438
| 0.182813
| 0.842969
| 0.828906
| 0.7625
| 0.752734
| 0.752734
| 0.746484
| 0
| 0.001279
| 0.299821
| 4,466
| 176
| 80
| 25.375
| 0.817397
| 0.023063
| 0
| 0.685484
| 0
| 0
| 0.068839
| 0
| 0
| 0
| 0
| 0
| 0.008065
| 1
| 0.258065
| false
| 0.008065
| 0.024194
| 0
| 0.298387
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
25747199704ca2f22c3cb999d578ede1db4380df
| 3,691
|
py
|
Python
|
bot_essentials/scrapper/Profile.py
|
France-Alliance/Bot-Discord
|
33c031469e4841cad45366438f5cfb889c63d60e
|
[
"BSD-3-Clause"
] | 1
|
2021-03-17T19:01:12.000Z
|
2021-03-17T19:01:12.000Z
|
bot_essentials/scrapper/Profile.py
|
O-Schell/Bot-Discord
|
b85bb3801650d4e40a95849b5ced466ab9219e3d
|
[
"BSD-3-Clause"
] | 2
|
2021-01-05T10:44:11.000Z
|
2021-04-23T15:16:00.000Z
|
bot_essentials/scrapper/Profile.py
|
O-Schell/Bot-Discord
|
b85bb3801650d4e40a95849b5ced466ab9219e3d
|
[
"BSD-3-Clause"
] | null | null | null |
def Profile(driver, result):
result["Name"] = driver.find_element_by_xpath(
'//*[@id="alliance_profile"]/div[1]/div[2]/ul/li[1]/b').text
result["ID"] = int(driver.find_element_by_xpath(
f'//*[@id="alliance_profile"]/div[2]/div[1]/a').get_attribute('href').split("/")[-1])
result["Classement"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile"]/div[1]/div[3]/ul/li[4]').text.replace(" ", ""))
Profile = result["Profile"]
Profile["General"]["Created"] = driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_infos"]/tbody/tr[1]/td[1]/div/span[2]/span').text
Profile["General"]["nbCompanies"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_infos"]/tbody/tr[2]/td[1]/div/span[2]/span').text)
Profile["General"]["Solde"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_infos"]/tbody/tr[2]/td[2]/div/span[2]/span').text.replace(" ", "").replace("$", ""))
Profile["General"]["BeneficeHebdo"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_infos"]/tbody/tr[3]/td[1]/div/span[2]/span').text.replace(" ", "").replace("$", ""))
Profile["General"]["TaxeHebdo"] = float(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_infos"]/tbody/tr[1]/td[2]/div/span[2]/span').text.split(" ")[0].replace("%", "").replace(",", "."))
Profile["Hub"]["HubsDispo"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_hubs"]/tbody/tr[1]/td[1]/div/span[2]/span').text)
Profile["Hub"]["KmPartage"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_hubs"]/tbody/tr[2]/td[1]/div/span[2]').text.replace(" ", "").replace("km", "").replace(",", ""))
Profile["Hub"]["TaxeLigne"] = float(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_hubs"]/tbody/tr[1]/td[2]/div/span[2]').text.split(" ")[0].replace("%", "").replace(",", "."))
Profile["Hub"]["TaxeCompagnies"] = float(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_hubs"]/tbody/tr[2]/td[2]/div/span[2]/span').text.split(" ")[0].replace("%", "").replace(",", "."))
Profile["AG"]["nbAvionProposer"] = driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_sells"]/tbody/tr[1]/td[1]/div/span[2]/span').text
Profile["AG"]["ReducMax"] = float(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_sells"]/tbody/tr[2]/td[1]/div/span[2]/span').text.replace(" %", "").replace(",", "."))
Profile["AG"]["Reduc30j"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_sells"]/tbody/tr[3]/td[1]/div/span[2]/span').text.replace(" ", "").replace("$", ""))
Profile["AG"]["nbAvionAchter"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_sells"]/tbody/tr[1]/td[2]/div/span[2]').text)
Profile["AG"]["AideAchatMax"] = float(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_sells"]/tbody/tr[2]/td[2]/div/span[2]/span').text.replace(" %", "").replace(",", "."))
Profile["AG"]["AideAchat30j"] = driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_sells"]/tbody/tr[3]/td[2]/div/span[2]/span').text
Profile["R&D"] = int(driver.find_element_by_xpath(
'//*[@id="alliance_profile_statistiques_general_research"]/tbody/tr/td[1]/div/div/div').get_attribute('title').replace("%", ""))
return result
| 87.880952
| 155
| 0.65592
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| 4.674134
| 0.12831
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| 0.157298
| 0.808715
| 0.808715
| 0.7939
| 0.772113
| 0.768192
| 0.739434
| 0
| 0.018563
| 0.095096
| 3,691
| 41
| 156
| 90.02439
| 0.668563
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| 0.365854
| 0.494717
| 0.418586
| 0
| 0
| 0
| 0
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| 1
| 0.02439
| false
| 0
| 0
| 0
| 0.04878
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
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| 0
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|
0
| 8
|
25c28d1a2be57180186da6a538168f26cec5453f
| 31,896
|
py
|
Python
|
sket/utils/utils.py
|
ExaNLP/sket
|
5ebe516f4454da246301532c353af57c1660ec69
|
[
"MIT"
] | 4
|
2021-07-30T16:06:35.000Z
|
2022-01-07T09:44:11.000Z
|
sket/utils/utils.py
|
ExaNLP/sket
|
5ebe516f4454da246301532c353af57c1660ec69
|
[
"MIT"
] | null | null | null |
sket/utils/utils.py
|
ExaNLP/sket
|
5ebe516f4454da246301532c353af57c1660ec69
|
[
"MIT"
] | 1
|
2021-09-07T04:58:31.000Z
|
2021-09-07T04:58:31.000Z
|
import os
import json
def assign_gpu(tknz_out, gpu):
"""
Assign tokenizer tensors to GPU(s)
Params:
tknz_out (dict): dict containing tokenizer tensors within CPU
gpu (int): gpu device
Returns: the dict containing tokenizer tensors within GPU(s)
"""
if type(gpu) == int:
device = 'cuda:' + str(gpu)
else:
device = 'cpu'
tokens_tensor = tknz_out['input_ids'].to(device)
token_type_ids = tknz_out['token_type_ids'].to(device)
attention_mask = tknz_out['attention_mask'].to(device)
# assign GPU(s) tokenizer tensors to output dict
output = {
'input_ids': tokens_tensor,
'token_type_ids': token_type_ids,
'attention_mask': attention_mask
}
return output
def en_sanitize_record(record, use_case): # @smarchesin TODO: define sanitize use-case functions that read replacements from file
"""
Sanitize record to avoid translation errors
Params:
record (str): target record
Returns: the sanitized record
"""
if record:
if use_case == 'colon':
record = record.replace('octopus', 'polyp')
record = record.replace('hairy', 'villous')
record = record.replace('villous adenoma-tubule', 'tubulo-villous adenoma')
record = record.replace('villous adenomas-tubule', 'tubulo-villous adenoma')
record = record.replace('villous adenomas tubule', 'tubulo-villous adenoma')
record = record.replace('tubule adenoma-villous', 'tubulo-villous adenoma')
record = record.replace('tubular adenoma-villous', 'tubulo-villous adenoma')
record = record.replace('villous adenoma tubule-', 'tubulo-villous adenoma ')
record = record.replace('villous adenoma tubule', 'tubulo-villous adenoma')
record = record.replace('tubulovilloso adenoma', 'tubulo-villous adenoma')
record = record.replace('blind', 'caecum')
record = record.replace('cecal', 'caecum')
record = record.replace('rectal', 'rectum')
record = record.replace('sigma', 'sigmoid')
record = record.replace('hyperplasia', 'hyperplastic') # MarianMT translates 'iperplastico' as 'hyperplasia' instead of 'hyperplastic'
record = record.replace('proximal colon', 'right colon')
if use_case == 'cervix':
record = record.replace('octopus', 'polyp')
record = record.replace('his cassock', 'lamina propria')
record = record.replace('tunica propria', 'lamina propria')
record = record.replace('l-sil', 'lsil')
record = record.replace('h-sil', 'hsil')
record = record.replace('cin ii / iii', 'cin23')
record = record.replace('cin iii', 'cin3')
record = record.replace('cin ii', 'cin2')
record = record.replace('cin i', 'cin1')
record = record.replace('cin-iii', 'cin3')
record = record.replace('cin-ii', 'cin2')
record = record.replace('cin-i', 'cin1')
record = record.replace('cin1-2', 'cin1 cin2')
record = record.replace('cin2-3', 'cin2 cin3')
record = record.replace('cin-1', 'cin1')
record = record.replace('cin-2', 'cin2')
record = record.replace('cin-3', 'cin3')
record = record.replace('cin 2 / 3', 'cin23')
record = record.replace('cin 2/3', 'cin23')
record = record.replace('cin 1-2', 'cin1 cin2')
record = record.replace('cin 2-3', 'cin2 cin3')
record = record.replace('cin 1', 'cin1')
record = record.replace('cin 2', 'cin2')
record = record.replace('cin 3', 'cin3')
record = record.replace('ii-iii cin', 'cin2 cin3')
record = record.replace('i-ii cin', 'cin1 cin2')
record = record.replace('iii cin', 'cin3')
record = record.replace('ii cin', 'cin2')
record = record.replace('i cin', 'cin1')
record = record.replace('port biopsy', 'portio biopsy')
return record
def nl_sanitize_record(record, use_case):
"""
Sanitize record to avoid translation errors
Params:
record (str): target record
Returns: the sanitized record
"""
if record:
if use_case == 'cervix':
record = record.replace('cin ii - iii', 'cin2 cin3')
record = record.replace('cin ii-iii', 'cin2 cin3')
record = record.replace('cin ii en iii', 'cin2 cin3')
record = record.replace('cin i - iii', 'cin1 cin3')
record = record.replace('cin i-iii', 'cin1 cin3')
record = record.replace('cin i en iii', 'cin1 cin3')
record = record.replace('cin i - ii', 'cin1 cin2')
record = record.replace('cin i-ii', 'cin1 cin2')
record = record.replace('cin i en ii', 'cin1 cin2')
record = record.replace('cin ii / iii', 'cin23')
record = record.replace('cin iii', 'cin3')
record = record.replace('cin ii', 'cin2')
record = record.replace('cin i', 'cin1')
record = record.replace('cin-iii', 'cin3')
record = record.replace('cin-ii', 'cin2')
record = record.replace('cin-i', 'cin1')
record = record.replace('ii-iii cin', 'cin2 cin3')
record = record.replace('i-ii cin', 'cin1 cin2')
record = record.replace('iii cin', 'cin3')
record = record.replace('ii cin', 'cin2')
record = record.replace('i cin', 'cin1')
record = record.replace('kin ii - iii', 'kin2 kin3')
record = record.replace('kin ii-iii', 'kin2 kin3')
record = record.replace('kin ii en iii', 'kin2 kin3')
record = record.replace('kin i - iii', 'kin1 kin3')
record = record.replace('kin i-iii', 'kin1 kin3')
record = record.replace('kin i en iii', 'kin1 kin3')
record = record.replace('kin i - ii', 'kin1 kin2')
record = record.replace('kin i-ii', 'kin1 kin2')
record = record.replace('kin i en ii', 'kin1 kin2')
record = record.replace('kin ii / iii', 'kin2 kin3')
record = record.replace('kin iii', 'kin3')
record = record.replace('kin ii', 'kin2')
record = record.replace('kin i', 'kin1')
record = record.replace('kin-iii', 'kin3')
record = record.replace('kin-ii', 'kin2')
record = record.replace('kin-i', 'kin1')
record = record.replace('ii-iii kin', 'kin2 kin3')
record = record.replace('i-ii kin', 'kin1 kin2')
record = record.replace('iii kin', 'kin3')
record = record.replace('ii kin', 'kin2')
record = record.replace('i kin', 'kin1')
return record
def sanitize_code(code):
"""
Sanitize code removing unnecessary characters
Params:
code (str): target code
Returns: the sanitized code
"""
if code:
code = code.replace('-', '')
code = code.ljust(7, '0')
return code
def sanitize_codes(codes):
"""
Sanitize codes by splitting and removing unnecessarsy characters
Params:
codes (list): target codes
Returns: the sanitized codes
"""
codes = codes.split(';')
codes = [sanitize_code(code) for code in codes]
return codes
def read_rules(rules):
"""
Read rules stored within file
Params:
rules (str): path to rules file
Returns: a dict of trigger: [candidates] representing rules for each use-case
"""
with open(rules, 'r') as file:
lines = file.readlines()
rules = {'colon': {}, 'cervix': {}, 'celiac': {}, 'lung': {}}
for line in lines:
trigger, candidates, position, mode, use_cases = line.strip().split('\t')
use_cases = use_cases.split(',')
for use_case in use_cases:
rules[use_case][trigger] = (candidates.split(','), position, mode)
return rules
def read_dysplasia_mappings(mappings):
"""
Read dysplasia mappings stored within file
Params:
mappings (str): path to dysplasia mappings file
Returns: a dict of {trigger: grade} representing mappings for each use-case
"""
with open(mappings, 'r') as file:
lines = file.readlines()
mappings = {'colon': {}, 'cervix': {}, 'celiac': {}, 'lung': {}}
for line in lines:
trigger, grade, use_cases = line.strip().split('\t')
use_cases = use_cases.split(',')
for use_case in use_cases:
mappings[use_case][trigger] = grade.split(',')
return mappings
def read_cin_mappings(mappings):
"""
Read cin mappings stored within file
Params:
mappings (str): path to cin mappings file
Returns: a dict of {trigger: grade} representing mappings for cervical intraephitelial neoplasia
"""
with open(mappings, 'r') as file:
lines = file.readlines()
mappings = {}
for line in lines:
trigger, grade = line.strip().split('\t')
mappings[trigger] = grade
return mappings
def read_hierarchies(hrels):
"""
Read hierarchy relations stored within file
Params:
hrels (str): hierarchy relations file path
Returns: the list of hierarchical relations
"""
with open(hrels, 'r') as f:
rels = f.readlines()
return [rel.strip() for rel in rels]
def read_report_fields(rfields):
"""
Read considered report fields stored within file
Params:
rfields (str): report fields file path
Returns: the list of report fields
"""
with open(rfields, 'r') as f:
fields = f.read().splitlines()
return [field.strip() for field in fields if field]
def store_concepts(concepts, out_path, indent=4, sort_keys=False):
"""
Store report concepts
Params:
concepts (dict): report concepts
out_path (str): output file-path w/o extension
indent (int): indentation level
sort_keys (bool): sort keys
Returns: True
"""
os.makedirs(os.path.dirname(out_path), exist_ok=True)
with open(out_path, 'w') as out:
json.dump(concepts, out, indent=indent, sort_keys=sort_keys)
return True
def load_concepts(concept_fpath):
"""
Load stored concepts
Params:
concept_fpath (str): file-path to stored concepts
Returns: the dict containing the report (stored) concepts
"""
with open(concept_fpath, 'r') as f:
concepts = json.load(f)
return concepts
def store_labels(labels, out_path, indent=4, sort_keys=False):
"""
Store report labels
Params:
labels (dict): report labels
out_path (str): output file-path w/o extension
indent (int): indentation level
sort_keys (bool): sort keys
Returns: True
"""
os.makedirs(os.path.dirname(out_path), exist_ok=True)
with open(out_path, 'w') as out:
json.dump(labels, out, indent=indent, sort_keys=sort_keys)
return True
def load_labels(label_fpath):
"""
Load stored labels
Params:
label_fpath (str): file-path to stored labels
Returns: the dict containing the report (stored) labels
"""
with open(label_fpath, 'r') as f:
labels = json.load(f)
return labels
# AOEC RELATED FUNCTIONS
def aoec_colon_concepts2labels(report_concepts):
"""
Convert the concepts extracted from colon reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each colon report the extracted concepts
Returns: a dict containing for each colon report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {'cancer': 0, 'hgd': 0, 'lgd': 0, 'hyperplastic': 0, 'ni': 0}
# textify diagnosis section
diagnosis = ' '.join([concept[1].lower() for concept in rconcepts['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
if 'colon adenocarcinoma' in diagnosis: # update cancer
report_labels[rid]['cancer'] = 1
if 'dysplasia' in diagnosis: # diagnosis contains dysplasia
if 'mild' in diagnosis: # update lgd
report_labels[rid]['lgd'] = 1
if 'moderate' in diagnosis: # update lgd
report_labels[rid]['lgd'] = 1
if 'severe' in diagnosis: # update hgd
report_labels[rid]['hgd'] = 1
if 'hyperplastic polyp' in diagnosis: # update hyperplastic
report_labels[rid]['hyperplastic'] = 1
if sum(report_labels[rid].values()) == 0: # update ni
report_labels[rid]['ni'] = 1
return report_labels
def aoec_colon_labels2binary(report_labels):
"""
Convert the pre-defined labels extracted from colon reports to binary labels used for classification
Params:
report_labels (dict(list)): the dict containing for each colon report the pre-defined labels
Returns: a dict containing for each colon report the set of binary labels where 0 = absence and 1 = presence
"""
binary_labels = dict()
# loop over reports
for rid, rlabels in report_labels.items():
# assign binary labels to current report
binary_labels[rid] = {'cancer_or_dysplasia': 0, 'other': 0}
# update binary labels w/ 1 in case of label presence
if rlabels['cancer'] == 1 or rlabels['lgd'] == 1 or rlabels['hgd'] == 1: # update cancer_or_dysplasia label
binary_labels[rid]['cancer_or_dysplasia'] = 1
else: # update other label
binary_labels[rid]['other'] = 1
return binary_labels
def aoec_cervix_concepts2labels(report_concepts):
"""
Convert the concepts extracted from cervix reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each cervix report the extracted concepts
Returns: a dict containing for each cervix report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {
'cancer_scc_inv': 0, 'cancer_scc_insitu': 0, 'cancer_adeno_inv': 0, 'cancer_adeno_insitu': 0,
'lgd': 0, 'hgd': 0,
'hpv': 0, 'koilocytes': 0,
'glands_norm': 0, 'squamous_norm': 0
}
# make diagnosis section a set
diagnosis = set([concept[1].lower() for concept in rconcepts['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
for d in diagnosis:
if 'cervical squamous cell carcinoma' == d:
report_labels[rid]['cancer_scc_inv'] = 1
if 'squamous carcinoma in situ' == d or 'squamous intraepithelial neoplasia' == d:
report_labels[rid]['cancer_scc_insitu'] = 1
if 'cervical adenocarcinoma' in d:
if 'cervical adenocarcinoma in situ' == d:
report_labels[rid]['cancer_adeno_insitu'] = 1
else:
report_labels[rid]['cancer_adeno_inv'] = 1
if 'low grade cervical squamous intraepithelial neoplasia' == d:
report_labels[rid]['lgd'] = 1
if 'squamous carcinoma in situ' == d or \
'squamous intraepithelial neoplasia' == d or \
'cervical squamous intraepithelial neoplasia 2' == d or \
'cervical intraepithelial neoplasia grade 2/3' == d:
report_labels[rid]['hgd'] = 1
if 'human papilloma virus infection' == d:
report_labels[rid]['hpv'] = 1
if 'koilocytotic squamous cell' == d:
report_labels[rid]['koilocytes'] = 1
# update when no label has been set to 1
if sum(report_labels[rid].values()) == 0:
report_labels[rid]['glands_norm'] = 1
report_labels[rid]['squamous_norm'] = 1
return report_labels
def aoec_cervix_labels2aggregates(report_labels):
"""
Convert the pre-defined labels extracted from cervix reports to coarse- and fine-grained aggregated labels
Params:
report_labels (dict(list)): the dict containing for each cervix report the pre-defined labels
Returns: two dicts containing for each cervix report the set of aggregated labels where 0 = absence and 1 = presence
"""
coarse_labels = dict()
fine_labels = dict()
# loop over reports
for rid, rlabels in report_labels.items():
# assign aggregated labels to current report
coarse_labels[rid] = {'cancer': 0, 'dysplasia': 0, 'normal': 0}
fine_labels[rid] = {'cancer_adeno': 0, 'cancer_scc': 0, 'dysplasia': 0, 'glands_norm': 0, 'squamous_norm': 0}
# update aggregated labels w/ 1 in case of label presence
if rlabels['cancer_adeno_inv'] == 1 or rlabels['cancer_adeno_insitu'] == 1:
coarse_labels[rid]['cancer'] = 1
fine_labels[rid]['cancer_adeno'] = 1
if rlabels['cancer_scc_inv'] == 1 or rlabels['cancer_scc_insitu'] == 1:
coarse_labels[rid]['cancer'] = 1
fine_labels[rid]['cancer_scc'] = 1
if rlabels['lgd'] == 1 or rlabels['hgd'] == 1:
coarse_labels[rid]['dysplasia'] = 1
fine_labels[rid]['dysplasia'] = 1
if rlabels['glands_norm'] == 1:
coarse_labels[rid]['normal'] = 1
fine_labels[rid]['glands_norm'] = 1
if rlabels['squamous_norm'] == 1:
coarse_labels[rid]['normal'] = 1
fine_labels[rid]['squamous_norm'] = 1
return coarse_labels, fine_labels
def aoec_lung_concepts2labels(report_concepts):
"""
Convert the concepts extracted from lung reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each lung report the extracted concepts
Returns: a dict containing for each lung report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {
'cancer_scc': 0, 'cancer_nscc_adeno': 0, 'cancer_nscc_squamous': 0, 'cancer_nscc_large': 0, 'no_cancer': 0}
# make diagnosis section a set
diagnosis = set([concept[1].lower() for concept in rconcepts['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
for d in diagnosis:
if 'small cell lung carcinoma' == d:
report_labels[rid]['cancer_scc'] = 1
if 'lung adenocarcinoma' == d or 'clear cell adenocarcinoma' == d or 'metastatic neoplasm' == d:
report_labels[rid]['cancer_nscc_adeno'] = 1
if 'non-small cell squamous lung carcinoma' == d:
report_labels[rid]['cancer_nscc_squamous'] = 1
if 'lung large cell carcinoma' == d:
report_labels[rid]['cancer_nscc_large'] = 1
# update when no label has been set to 1
if sum(report_labels[rid].values()) == 0:
report_labels[rid]['no_cancer'] = 1
return report_labels
# RADBOUD RELATED FUNCTIONS
def radboud_colon_concepts2labels(report_concepts):
"""
Convert the concepts extracted from reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each report the extracted concepts
Returns: a dict containing for each report the set of pre-defined labels where 0 = abscence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
report_labels[rid] = dict()
# assign pre-defined set of labels to current report
report_labels[rid]['labels'] = {'cancer': 0, 'hgd': 0, 'lgd': 0, 'hyperplastic': 0, 'ni': 0}
# textify diagnosis section
diagnosis = ' '.join([concept[1].lower() for concept in rconcepts['concepts']['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
if 'colon adenocarcinoma' in diagnosis: # update cancer
report_labels[rid]['labels']['cancer'] = 1
if 'dysplasia' in diagnosis: # diagnosis contains dysplasia
if 'mild' in diagnosis: # update lgd
report_labels[rid]['labels']['lgd'] = 1
if 'moderate' in diagnosis: # update lgd
report_labels[rid]['labels']['lgd'] = 1
if 'severe' in diagnosis: # update hgd
report_labels[rid]['labels']['hgd'] = 1
if 'hyperplastic polyp' in diagnosis: # update hyperplastic
report_labels[rid]['labels']['hyperplastic'] = 1
if sum(report_labels[rid]['labels'].values()) == 0: # update ni
report_labels[rid]['labels']['ni'] = 1
if 'slide_ids' in rconcepts:
report_labels[rid]['slide_ids'] = rconcepts['slide_ids']
return report_labels
def radboud_colon_labels2binary(report_labels):
"""
Convert the pre-defined labels extracted from reports to binary labels used for classification
Params:
report_labels (dict(list)): the dict containing for each report the pre-defined labels
Returns: a dict containing for each report the set of binary labels where 0 = abscence and 1 = presence
"""
binary_labels = dict()
# loop over reports
for rid, rlabels in report_labels.items():
binary_labels[rid] = dict()
# assign binary labels to current report
binary_labels[rid]['labels'] = {'cancer_or_dysplasia': 0, 'other': 0}
# update binary labels w/ 1 in case of label presence
if rlabels['labels']['cancer'] == 1 or rlabels['labels']['lgd'] == 1 or rlabels['labels']['hgd'] == 1: # update cancer_or_adenoma label
binary_labels[rid]['labels']['cancer_or_dysplasia'] = 1
else: # update other label
binary_labels[rid]['labels']['other'] = 1
if 'slide_ids' in rlabels:
binary_labels[rid]['slide_ids'] = rlabels['slide_ids']
return binary_labels
def radboud_cervix_concepts2labels(report_concepts):
"""
Convert the concepts extracted from cervix reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each cervix report the extracted concepts
Returns: a dict containing for each cervix report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {
'cancer_scc_inv': 0, 'cancer_scc_insitu': 0, 'cancer_adeno_inv': 0, 'cancer_adeno_insitu': 0,
'lgd': 0, 'hgd': 0,
'hpv': 0, 'koilocytes': 0,
'glands_norm': 0, 'squamous_norm': 0
}
# make diagnosis section a set
diagnosis = set([concept[1].lower() for concept in rconcepts['concepts']['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
for d in diagnosis:
if 'cervical squamous cell carcinoma' == d:
report_labels[rid]['cancer_scc_inv'] = 1
if 'squamous carcinoma in situ' == d or 'squamous intraepithelial neoplasia' == d:
report_labels[rid]['cancer_scc_insitu'] = 1
if 'cervical adenocarcinoma' in d:
if 'cervical adenocarcinoma in situ' == d:
report_labels[rid]['cancer_adeno_insitu'] = 1
else:
report_labels[rid]['cancer_adeno_inv'] = 1
if 'low grade cervical squamous intraepithelial neoplasia' == d:
report_labels[rid]['lgd'] = 1
if 'squamous carcinoma in situ' == d or \
'squamous intraepithelial neoplasia' == d or \
'cervical squamous intraepithelial neoplasia 2' == d or \
'cervical intraepithelial neoplasia grade 2/3' == d:
report_labels[rid]['hgd'] = 1
if 'human papilloma virus infection' == d:
report_labels[rid]['hpv'] = 1
if 'koilocytotic squamous cell' == d:
report_labels[rid]['koilocytes'] = 1
# update when no label has been set to 1
if sum(report_labels[rid].values()) == 0:
report_labels[rid]['glands_norm'] = 1
report_labels[rid]['squamous_norm'] = 1
if 'slide_ids' in rconcepts:
report_labels[rid]['slide_ids'] = rconcepts['slide_ids']
return report_labels
def radboud_cervix_labels2aggregates(report_labels):
"""
Convert the pre-defined labels extracted from cervix reports to coarse- and fine-grained aggregated labels
Params:
report_labels (dict(list)): the dict containing for each cervix report the pre-defined labels
Returns: two dicts containing for each cervix report the set of aggregated labels where 0 = absence and 1 = presence
"""
coarse_labels = dict()
fine_labels = dict()
# loop over reports
for rid, rlabels in report_labels.items():
# assign aggregated labels to current report
coarse_labels[rid] = {'cancer': 0, 'dysplasia': 0, 'normal': 0}
fine_labels[rid] = {'cancer_adeno': 0, 'cancer_scc': 0, 'dysplasia': 0, 'glands_norm': 0, 'squamous_norm': 0}
# update aggregated labels w/ 1 in case of label presence
if rlabels['cancer_adeno_inv'] == 1 or rlabels['cancer_adeno_insitu'] == 1:
coarse_labels[rid]['cancer'] = 1
fine_labels[rid]['cancer_adeno'] = 1
if rlabels['cancer_scc_inv'] == 1 or rlabels['cancer_scc_insitu'] == 1:
coarse_labels[rid]['cancer'] = 1
fine_labels[rid]['cancer_scc'] = 1
if rlabels['lgd'] == 1 or rlabels['hgd'] == 1:
coarse_labels[rid]['dysplasia'] = 1
fine_labels[rid]['dysplasia'] = 1
if rlabels['glands_norm'] == 1:
coarse_labels[rid]['normal'] = 1
fine_labels[rid]['glands_norm'] = 1
if rlabels['squamous_norm'] == 1:
coarse_labels[rid]['normal'] = 1
fine_labels[rid]['squamous_norm'] = 1
if 'slide_ids' in rlabels:
coarse_labels[rid]['slide_ids'] = rlabels['slide_ids']
fine_labels[rid]['slide_ids'] = rlabels['slide_ids']
return coarse_labels, fine_labels
# GENERAL-PURPOSE FUNCTIONS
def colon_concepts2labels(report_concepts):
"""
Convert the concepts extracted from colon reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each colon report the extracted concepts
Returns: a dict containing for each colon report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {'cancer': 0, 'hgd': 0, 'lgd': 0, 'hyperplastic': 0, 'ni': 0}
# textify diagnosis section
diagnosis = ' '.join([concept[1].lower() for concept in rconcepts['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
if 'colon adenocarcinoma' in diagnosis: # update cancer
report_labels[rid]['cancer'] = 1
if 'dysplasia' in diagnosis: # diagnosis contains dysplasia
if 'mild' in diagnosis: # update lgd
report_labels[rid]['lgd'] = 1
if 'moderate' in diagnosis: # update lgd
report_labels[rid]['lgd'] = 1
if 'severe' in diagnosis: # update hgd
report_labels[rid]['hgd'] = 1
if 'hyperplastic polyp' in diagnosis: # update hyperplastic
report_labels[rid]['hyperplastic'] = 1
if sum(report_labels[rid].values()) == 0: # update ni
report_labels[rid]['ni'] = 1
return report_labels
def colon_labels2binary(report_labels):
"""
Convert the pre-defined labels extracted from colon reports to binary labels used for classification
Params:
report_labels (dict(list)): the dict containing for each colon report the pre-defined labels
Returns: a dict containing for each colon report the set of binary labels where 0 = absence and 1 = presence
"""
binary_labels = dict()
# loop over reports
for rid, rlabels in report_labels.items():
# assign binary labels to current report
binary_labels[rid] = {'cancer_or_dysplasia': 0, 'other': 0}
# update binary labels w/ 1 in case of label presence
if rlabels['cancer'] == 1 or rlabels['lgd'] == 1 or rlabels['hgd'] == 1: # update cancer_or_dysplasia label
binary_labels[rid]['cancer_or_dysplasia'] = 1
else: # update other label
binary_labels[rid]['other'] = 1
return binary_labels
def cervix_concepts2labels(report_concepts):
"""
Convert the concepts extracted from cervix reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each cervix report the extracted concepts
Returns: a dict containing for each cervix report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {
'cancer_scc_inv': 0, 'cancer_scc_insitu': 0, 'cancer_adeno_inv': 0, 'cancer_adeno_insitu': 0,
'lgd': 0, 'hgd': 0,
'hpv': 0, 'koilocytes': 0,
'glands_norm': 0, 'squamous_norm': 0
}
# make diagnosis section a set
diagnosis = set([concept[1].lower() for concept in rconcepts['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
for d in diagnosis:
if 'cervical squamous cell carcinoma' == d:
report_labels[rid]['cancer_scc_inv'] = 1
if 'squamous carcinoma in situ' == d or 'squamous intraepithelial neoplasia' == d:
report_labels[rid]['cancer_scc_insitu'] = 1
if 'cervical adenocarcinoma' in d:
if 'cervical adenocarcinoma in situ' == d:
report_labels[rid]['cancer_adeno_insitu'] = 1
else:
report_labels[rid]['cancer_adeno_inv'] = 1
if 'low grade cervical squamous intraepithelial neoplasia' == d:
report_labels[rid]['lgd'] = 1
if 'squamous carcinoma in situ' == d or \
'squamous intraepithelial neoplasia' == d or \
'cervical squamous intraepithelial neoplasia 2' == d or \
'cervical intraepithelial neoplasia grade 2/3' == d:
report_labels[rid]['hgd'] = 1
if 'human papilloma virus infection' == d:
report_labels[rid]['hpv'] = 1
if 'koilocytotic squamous cell' == d:
report_labels[rid]['koilocytes'] = 1
# update when no label has been set to 1
if sum(report_labels[rid].values()) == 0:
report_labels[rid]['glands_norm'] = 1
report_labels[rid]['squamous_norm'] = 1
return report_labels
def cervix_labels2aggregates(report_labels):
"""
Convert the pre-defined labels extracted from cervix reports to coarse- and fine-grained aggregated labels
Params:
report_labels (dict(list)): the dict containing for each cervix report the pre-defined labels
Returns: two dicts containing for each cervix report the set of aggregated labels where 0 = absence and 1 = presence
"""
coarse_labels = dict()
fine_labels = dict()
# loop over reports
for rid, rlabels in report_labels.items():
# assign aggregated labels to current report
coarse_labels[rid] = {'cancer': 0, 'dysplasia': 0, 'normal': 0}
fine_labels[rid] = {'cancer_adeno': 0, 'cancer_scc': 0, 'dysplasia': 0, 'glands_norm': 0, 'squamous_norm': 0}
# update aggregated labels w/ 1 in case of label presence
if rlabels['cancer_adeno_inv'] == 1 or rlabels['cancer_adeno_insitu'] == 1:
coarse_labels[rid]['cancer'] = 1
fine_labels[rid]['cancer_adeno'] = 1
if rlabels['cancer_scc_inv'] == 1 or rlabels['cancer_scc_insitu'] == 1:
coarse_labels[rid]['cancer'] = 1
fine_labels[rid]['cancer_scc'] = 1
if rlabels['lgd'] == 1 or rlabels['hgd'] == 1:
coarse_labels[rid]['dysplasia'] = 1
fine_labels[rid]['dysplasia'] = 1
if rlabels['glands_norm'] == 1:
coarse_labels[rid]['normal'] = 1
fine_labels[rid]['glands_norm'] = 1
if rlabels['squamous_norm'] == 1:
coarse_labels[rid]['normal'] = 1
fine_labels[rid]['squamous_norm'] = 1
return coarse_labels, fine_labels
def lung_concepts2labels(report_concepts):
"""
Convert the concepts extracted from lung reports to the set of pre-defined labels used for classification
Params:
report_concepts (dict(list)): the dict containing for each lung report the extracted concepts
Returns: a dict containing for each lung report the set of pre-defined labels where 0 = absence and 1 = presence
"""
report_labels = dict()
# loop over reports
for rid, rconcepts in report_concepts.items():
# assign pre-defined set of labels to current report
report_labels[rid] = {
'cancer_scc': 0, 'cancer_nscc_adeno': 0, 'cancer_nscc_squamous': 0, 'cancer_nscc_large': 0, 'no_cancer': 0}
# make diagnosis section a set
diagnosis = set([concept[1].lower() for concept in rconcepts['Diagnosis']])
# update pre-defined labels w/ 1 in case of label presence
for d in diagnosis:
if 'small cell lung carcinoma' == d:
report_labels[rid]['cancer_scc'] = 1
if 'lung adenocarcinoma' == d or 'clear cell adenocarcinoma' == d or 'metastatic neoplasm' == d:
report_labels[rid]['cancer_nscc_adeno'] = 1
if 'non-small cell squamous lung carcinoma' == d:
report_labels[rid]['cancer_nscc_squamous'] = 1
if 'lung large cell carcinoma' == d:
report_labels[rid]['cancer_nscc_large'] = 1
# update when no label has been set to 1
if sum(report_labels[rid].values()) == 0:
report_labels[rid]['no_cancer'] = 1
return report_labels
| 37.305263
| 139
| 0.681183
| 4,400
| 31,896
| 4.827727
| 0.063864
| 0.053385
| 0.078712
| 0.034178
| 0.873788
| 0.858912
| 0.842341
| 0.829583
| 0.81626
| 0.797383
| 0
| 0.01633
| 0.195542
| 31,896
| 854
| 140
| 37.348946
| 0.811528
| 0.307813
| 0
| 0.709205
| 0
| 0
| 0.272693
| 0
| 0
| 0
| 0
| 0.001171
| 0
| 1
| 0.058577
| false
| 0
| 0.004184
| 0
| 0.121339
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d30a0513c12f57e07256cfba61b802e65b57aa7f
| 4,670
|
py
|
Python
|
code/best_models.py
|
sealuzh/branch-prediction
|
4cb3d3fbd765a88961f5cb41f80ebfb6fb44b0ea
|
[
"MIT"
] | null | null | null |
code/best_models.py
|
sealuzh/branch-prediction
|
4cb3d3fbd765a88961f5cb41f80ebfb6fb44b0ea
|
[
"MIT"
] | null | null | null |
code/best_models.py
|
sealuzh/branch-prediction
|
4cb3d3fbd765a88961f5cb41f80ebfb6fb44b0ea
|
[
"MIT"
] | 1
|
2020-09-02T14:40:39.000Z
|
2020-09-02T14:40:39.000Z
|
from sklearn.ensemble import RandomForestRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.linear_model import HuberRegressor
import numpy as np
def get_best_models(budget, tool):
"""
Statically returns the best model we obtained with our tests; needed to compute some additional analysis
:param budget: the budget for the model version
:param tool: the tool for the model version
:return a random forest regressor with the right configuration
"""
if tool == 'evosuite':
if budget == '180':
return RandomForestRegressor(
bootstrap=True, criterion='mse', max_depth=25,
max_features=71, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=16, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
elif budget == '300':
return RandomForestRegressor(
bootstrap=True, criterion='mse', max_depth=30,
max_features=79, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=20, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
elif budget == '600':
return RandomForestRegressor(
bootstrap=True, criterion='mse', max_depth=45,
max_features=63, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
else:
return RandomForestRegressor(
bootstrap=True, criterion='mse', max_depth=45,
max_features=71, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=16, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
else:
if budget == '180':
return RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=35,
max_features=71, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=16, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
elif budget == '300':
return RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=30,
max_features=55, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=6, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
elif budget == '600':
return RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=35,
max_features=79, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=3,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=4, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
else:
return RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=40,
max_features=55, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=3,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=14, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
| 60.649351
| 108
| 0.574946
| 540
| 4,670
| 4.668519
| 0.17963
| 0.044427
| 0.11424
| 0.126934
| 0.836969
| 0.836969
| 0.836969
| 0.836969
| 0.836969
| 0.836969
| 0
| 0.041804
| 0.349465
| 4,670
| 76
| 109
| 61.447368
| 0.788018
| 0.05546
| 0
| 0.761194
| 0
| 0
| 0.01141
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.014925
| false
| 0
| 0.059701
| 0
| 0.19403
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d37e5c4b9b8eb352266e4b4cf96c1f7ed071302a
| 275
|
py
|
Python
|
tests/test_requests.py
|
mannuan/dspider
|
bf1bbad375b3b61f800cb25d1c839659a66f3e12
|
[
"Apache-2.0"
] | 15
|
2018-05-12T17:15:59.000Z
|
2020-09-06T04:32:47.000Z
|
tests/test_requests.py
|
mannuan/dspider
|
bf1bbad375b3b61f800cb25d1c839659a66f3e12
|
[
"Apache-2.0"
] | null | null | null |
tests/test_requests.py
|
mannuan/dspider
|
bf1bbad375b3b61f800cb25d1c839659a66f3e12
|
[
"Apache-2.0"
] | 2
|
2018-06-29T00:44:52.000Z
|
2020-07-07T01:58:03.000Z
|
import requests
import os,re
print(re.findall(r'[\d]{1,3}.[\d]{1,3}.[\d]{1,3}.[\d]{1,3}',os.popen("tsocks wget -q -O - http://pv.sohu.com/cityjson | awk '{print $5}'").read())[0])
# print(os.popen("tsocks wget -q -O - http://pv.sohu.com/cityjson | awk '{print $5}'").read())
| 55
| 150
| 0.592727
| 53
| 275
| 3.075472
| 0.45283
| 0.04908
| 0.07362
| 0.07362
| 0.723926
| 0.723926
| 0.723926
| 0.723926
| 0.723926
| 0.650307
| 0
| 0.044
| 0.090909
| 275
| 5
| 151
| 55
| 0.608
| 0.334545
| 0
| 0
| 0
| 0.666667
| 0.576923
| 0.214286
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0.333333
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 10
|
d3af3479e5c483ca3806b4c5f5c195e39dcd6ca0
| 9,177
|
py
|
Python
|
sql/tests/accuracy/test_simple_accuracy.py
|
AprilXiaoyanLiu/whitenoise-system
|
0e94d2cc8114b97a61d5d2e45278428f91f1e687
|
[
"MIT"
] | 56
|
2021-02-21T19:45:47.000Z
|
2022-03-20T16:45:56.000Z
|
sql/tests/accuracy/test_simple_accuracy.py
|
AprilXiaoyanLiu/whitenoise-system
|
0e94d2cc8114b97a61d5d2e45278428f91f1e687
|
[
"MIT"
] | 87
|
2021-02-20T20:43:49.000Z
|
2022-03-31T16:24:46.000Z
|
sql/tests/accuracy/test_simple_accuracy.py
|
AprilXiaoyanLiu/whitenoise-system
|
0e94d2cc8114b97a61d5d2e45278428f91f1e687
|
[
"MIT"
] | 17
|
2021-02-18T18:47:09.000Z
|
2022-03-01T06:44:17.000Z
|
import numpy as np
from snsql.sql._mechanisms import *
from snsql.sql.privacy import Privacy, Stat
# grid of (alpha, epsilon, delta, max_contrib) to test
grid = [
(0.01, 0.5, 0.0, 3),
(0.01, 0.1, 1/3333, 1),
(0.05, 1.0, 0.0, 2),
(0.25, 1.3, 1/1000, 3)
]
class TestSimpleAccuracy:
def test_geom_count(self, test_databases):
query = 'SELECT COUNT(educ) FROM PUMS.PUMS'
sensitivity = 1
for alpha, epsilon, delta, max_contrib in grid:
privacy = Privacy(epsilon=epsilon, delta=delta)
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'count', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.geometric)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_geom_small_sum(self, test_databases):
query = 'SELECT SUM(age) FROM PUMS.PUMS'
sensitivity = 100
for alpha, epsilon, delta, max_contrib in grid:
privacy = Privacy(epsilon=epsilon, delta=delta)
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'sum', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.geometric)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_geom_large_sum(self, test_databases):
# reverts to laplace because it's large
query = 'SELECT SUM(income) FROM PUMS.PUMS'
sensitivity = 500_000
for alpha, epsilon, delta, max_contrib in grid:
if delta == 0.0:
delta = 1/100_000
privacy = Privacy(epsilon=epsilon, delta=delta)
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'sum', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.laplace)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_geom_key_count(self, test_databases):
# reverts to laplace because we need a threshold
query = 'SELECT COUNT(DISTINCT pid) FROM PUMS.PUMS'
sensitivity = 1
for alpha, epsilon, delta, max_contrib in grid:
if delta == 0.0: # not permitted when thresholding
delta = 1/100_000
privacy = Privacy(epsilon=epsilon, delta=delta)
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'threshold', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.laplace)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_geom_key_count_gauss(self, test_databases):
# reverts to gaussian because we need a threshold
query = 'SELECT COUNT(DISTINCT pid) FROM PUMS.PUMS'
sensitivity = 1
for alpha, epsilon, delta, max_contrib in grid:
if delta == 0.0: # not permitted when thresholding
delta = 1/100_000
privacy = Privacy(epsilon=epsilon, delta=delta)
privacy.mechanisms.map[Stat.threshold] = Mechanism.gaussian
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'threshold', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.gaussian)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_gauss_count(self, test_databases):
query = 'SELECT COUNT(educ) FROM PUMS.PUMS'
sensitivity = 1
for alpha, epsilon, delta, max_contrib in grid:
if delta == 0.0:
delta = 1/100_000
privacy = Privacy(epsilon=epsilon, delta=delta)
privacy.mechanisms.map[Stat.count] = Mechanism.gaussian
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'count', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.gaussian)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_gauss_large_sum(self, test_databases):
query = 'SELECT SUM(income) FROM PUMS.PUMS'
sensitivity = 500_000
for alpha, epsilon, delta, max_contrib in grid:
if delta == 0.0:
delta = 1/100_000
privacy = Privacy(epsilon=epsilon, delta=delta)
privacy.mechanisms.map[Stat.sum_large_int] = Mechanism.gaussian
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'sum', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.gaussian)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
def test_lap_count(self, test_databases):
query = 'SELECT COUNT(educ) FROM PUMS.PUMS'
sensitivity = 1
for alpha, epsilon, delta, max_contrib in grid:
if delta == 0.0:
delta = 1/100_000
privacy = Privacy(epsilon=epsilon, delta=delta)
privacy.mechanisms.map[Stat.count] = Mechanism.laplace
reader = test_databases.get_private_reader(database='PUMS_pid', engine="pandas", privacy=privacy, overrides={'max_contrib': max_contrib})
if reader:
mech_class = privacy.mechanisms.get_mechanism(sensitivity, 'count', 'int')
mech = mech_class(epsilon, delta=delta, sensitivity=sensitivity, max_contrib=max_contrib)
assert(mech.mechanism == Mechanism.laplace)
acc = reader.get_simple_accuracy(query, alpha)
assert(np.isclose(acc[0], mech.accuracy(alpha)))
class TestSimpleMatch:
"""
Compare accuracies obtained from the reader without executing query
with accuracies provided inline with query result.
"""
def test_simple_pid(self, test_databases):
max_ids = 2
alpha = 0.05
privacy = Privacy(alphas=[alpha], epsilon=1.5, delta=1/100_000)
privacy.mechanisms.map[Stat.threshold] = Mechanism.gaussian
query = 'SELECT COUNT(DISTINCT pid), COUNT(*), COUNT(educ), SUM(age) FROM PUMS.PUMS'
reader = test_databases.get_private_reader(
database='PUMS_pid',
engine="pandas",
privacy=privacy,
overrides={'max_ids': max_ids})
if reader:
simple_a = reader.get_simple_accuracy(query, alpha=alpha)
res = reader.execute(query, accuracy=True)
simple_b = res[1][1][0]
assert (all([a == b for a, b in zip(simple_a, simple_b)]))
def test_simple_row_privacy(self, test_databases):
alpha = 0.07
privacy = Privacy(alphas=[alpha], epsilon=0.5, delta=1/1000)
query = 'SELECT COUNT(*), COUNT(educ), SUM(age) FROM PUMS.PUMS'
reader = test_databases.get_private_reader(
database='PUMS',
engine="pandas",
privacy=privacy
)
if reader:
simple_a = reader.get_simple_accuracy(query, alpha=alpha)
res = reader.execute(query, accuracy=True)
simple_b = res[1][1][0]
assert (all([a == b for a, b in zip(simple_a, simple_b)]))
| 54.952096
| 149
| 0.632342
| 1,098
| 9,177
| 5.117486
| 0.099271
| 0.072967
| 0.048407
| 0.05695
| 0.909948
| 0.877202
| 0.877202
| 0.8441
| 0.8441
| 0.8441
| 0
| 0.021996
| 0.26185
| 9,177
| 166
| 150
| 55.283133
| 0.807499
| 0.040209
| 0
| 0.745098
| 0
| 0
| 0.079822
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 1
| 0.065359
| false
| 0
| 0.019608
| 0
| 0.098039
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6cb01c674f9d8f335acf24a378be5803bcc3ad61
| 3,803
|
py
|
Python
|
Semester 6/SPCC/Exp 05/threeadresscode.py
|
atharva8300/Engineering-Practical-Experiments
|
3f7fe4abbbe69a3bbb8aa19892dd7209e70c69ac
|
[
"Unlicense"
] | 7
|
2020-04-20T19:32:23.000Z
|
2021-08-03T16:50:15.000Z
|
Semester 6/SPCC/Exp 05/threeadresscode.py
|
atharva8300/Engineering-Practical-Experiments
|
3f7fe4abbbe69a3bbb8aa19892dd7209e70c69ac
|
[
"Unlicense"
] | null | null | null |
Semester 6/SPCC/Exp 05/threeadresscode.py
|
atharva8300/Engineering-Practical-Experiments
|
3f7fe4abbbe69a3bbb8aa19892dd7209e70c69ac
|
[
"Unlicense"
] | 5
|
2019-04-20T06:35:25.000Z
|
2021-12-12T12:25:08.000Z
|
import StackClass
def T_A_C(exp):
stack = []
x = 1
# an instance of the StackClass module
obj = StackClass.Conversion(len(exp))
# an instance that converts a given infix notation to post fix
# ab-*ab-*+
postfix = obj.infixToPostfix(exp)
for i in postfix:
if i in "abcdefghijklmnopqrstuvwxyz" or i in "0123456789":
stack.append(i)
elif i == '-':
op1 = stack.pop()
print("t(", x, ")", "=", i, op1)
stack.append("t(%s)" % x)
x = x+1
if stack != []:
op2 = stack.pop()
op1 = stack.pop()
print("t(", x, ")", "=", op1, "+", op2)
stack.append("t(%s)" % x)
x = x+1
elif i == '=':
op2 = stack.pop()
op1 = stack.pop()
print(op1, i, op2)
else:
op1 = stack.pop()
if stack != []:
op2 = stack.pop()
print("t(", x, ")", "=", op2, i, op1)
stack.append("t(%s)" % x)
x = x+1
def Quadruple(exp):
stack = []
op = []
x = 1
# an instance of the StackClass module
obj = StackClass.Conversion(len(exp))
# an instance that converts a given infix notation to post fix
postfix = obj.infixToPostfix(exp)
print("{0:^4s} | {1:^4s} | {2:^4s}|{3:4s}".format(
'op', 'arg1', 'arg2', 'result'))
for i in postfix:
if i in "abcdefghijklmnopqrstuvwxyz" or i in "0123456789":
stack.append(i)
elif i == '-':
op1 = stack.pop()
stack.append("t(%s)" % x)
print("{0:^4s} | {1:^4s} | {2:^4s}|{3:4s}".format(
i, op1, "(-)", " t(%s)" % x))
x = x+1
if stack != []:
op2 = stack.pop()
op1 = stack.pop()
print("{0:^4s} | {1:^4s} | {2:^4s}|{3:4s}".format(
"+", op1, op2, " t(%s)" % x))
stack.append("t(%s)" % x)
x = x+1
elif i == '=':
op2 = stack.pop()
op1 = stack.pop()
print("{0:^4s} | {1:^4s} | {2:^4s}|{3:4s}".format(i, op2, "(-)", op1))
else:
op1 = stack.pop()
op2 = stack.pop()
print("{0:^4s} | {1:^4s} | {2:^4s}|{3:4s}".format(
i, op2, op1, " t(%s)" % x))
stack.append("t(%s)" % x)
x = x+1
def Triple(exp):
stack = []
op = []
x = 0
# an instance of the StackClass module
obj = StackClass.Conversion(len(exp))
# an instance that converts a given infix notation to postfix
postfix = obj.infixToPostfix(exp)
print("{0:^4s} | {1:^4s} | {2:^4s}".format('op', 'arg1', 'arg2'))
for i in postfix:
if i in "abcdefghijklmnopqrstuvwxyz" or i in "0123456789":
stack.append(i)
elif i == '-':
op1 = stack.pop()
stack.append("(%s)" % x)
print("{0:^4s} | {1:^4s} | {2:^4s}".format(i, op1, "(-)"))
x = x+1
if stack != []:
op2 = stack.pop()
op1 = stack.pop()
print("{0:^4s} | {1:^4s} | {2:^4s}".format("+", op1, op2))
stack.append("(%s)" % x)
x = x+1
elif i == '=':
op2 = stack.pop()
op1 = stack.pop()
print("{0:^4s} | {1:^4s} | {2:^4s}".format(i, op1, op2))
else:
op1 = stack.pop()
if stack != []:
op2 = stack.pop()
print("{0:^4s} | {1:^4s} | {2:^4s}".format(i, op2, op1))
stack.append("(%s)" % x)
x = x+1
exp = input("Enter a valid infix expression \n")
T_A_C(exp)
Quadruple(exp)
Triple(exp)
| 31.172131
| 82
| 0.414673
| 467
| 3,803
| 3.368308
| 0.12848
| 0.106802
| 0.083916
| 0.057216
| 0.862683
| 0.844247
| 0.838525
| 0.82136
| 0.817546
| 0.811189
| 0
| 0.068785
| 0.396003
| 3,803
| 121
| 83
| 31.429752
| 0.616021
| 0.079411
| 0
| 0.77451
| 0
| 0.04902
| 0.16118
| 0.02233
| 0
| 0
| 0
| 0
| 0
| 1
| 0.029412
| false
| 0
| 0.009804
| 0
| 0.039216
| 0.137255
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9f49742ae2eac3c037020e7a4f4cedbc2dac1e26
| 228
|
py
|
Python
|
src/kernel/testdata/repo/oopl/python/metax/test_test/.FakeFcntlTestMeta.py
|
metaesque/meta
|
c3e6413ca6cc6ff5456158b128070b36baf2d36a
|
[
"AML",
"TCL",
"Ruby"
] | null | null | null |
src/kernel/testdata/repo/oopl/python/metax/test_test/.FakeFcntlTestMeta.py
|
metaesque/meta
|
c3e6413ca6cc6ff5456158b128070b36baf2d36a
|
[
"AML",
"TCL",
"Ruby"
] | 1
|
2018-10-30T03:14:34.000Z
|
2018-10-30T03:19:35.000Z
|
src/kernel/testdata/repo/oopl/python/metax/test_test/.FakeFcntlTestMeta.py
|
metaesque/meta
|
c3e6413ca6cc6ff5456158b128070b36baf2d36a
|
[
"AML",
"TCL",
"Ruby"
] | null | null | null |
import metax.test # target=//metax/test:test
########## End Imports ##########
class FakeFcntlTestMeta(metax.test.TestCaseMeta):
"""Auto-generated meta class for auto-generated test class metax.test_test.FakeFcntlTest"""
| 32.571429
| 93
| 0.70614
| 27
| 228
| 5.925926
| 0.518519
| 0.225
| 0.1625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109649
| 228
| 6
| 94
| 38
| 0.788177
| 0.54386
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
9f79e85a380747d2a07da77510678a294dd2a414
| 40,617
|
py
|
Python
|
files_sdk/models/remote_server.py
|
Files-com/files-sdk-python
|
84cedc9be099cd9e4db6249ef7a9d60595487090
|
[
"MIT"
] | 14
|
2020-08-05T15:48:06.000Z
|
2021-08-18T13:13:39.000Z
|
files_sdk/models/remote_server.py
|
Files-com/files-sdk-python
|
84cedc9be099cd9e4db6249ef7a9d60595487090
|
[
"MIT"
] | 4
|
2020-10-30T14:49:25.000Z
|
2021-09-29T17:11:53.000Z
|
files_sdk/models/remote_server.py
|
Files-com/files-sdk-python
|
84cedc9be099cd9e4db6249ef7a9d60595487090
|
[
"MIT"
] | null | null | null |
import builtins
import datetime
from files_sdk.api import Api
from files_sdk.list_obj import ListObj
from files_sdk.exceptions import InvalidParameterError, MissingParameterError, NotImplementedError
class RemoteServer:
default_attributes = {
'id': None, # int64 - Remote server ID
'authentication_method': None, # string - Type of authentication method
'hostname': None, # string - Hostname or IP address
'remote_home_path': None, # string - Initial home folder on remote server
'name': None, # string - Internal name for your reference
'port': None, # int64 - Port for remote server. Not needed for S3.
'max_connections': None, # int64 - Max number of parallel connections. Ignored for S3 connections (we will parallelize these as much as possible).
's3_bucket': None, # string - S3 bucket name
's3_region': None, # string - S3 region
'server_certificate': None, # string - Remote server certificate
'server_host_key': None, # string - Remote server SSH Host Key. If provided, we will require that the server host key matches the provided key. Uses OpenSSH format similar to what would go into ~/.ssh/known_hosts
'server_type': None, # string - Remote server type.
'ssl': None, # string - Should we require SSL?
'username': None, # string - Remote server username. Not needed for S3 buckets.
'google_cloud_storage_bucket': None, # string - Google Cloud Storage bucket name
'google_cloud_storage_project_id': None, # string - Google Cloud Project ID
'backblaze_b2_s3_endpoint': None, # string - Backblaze B2 Cloud Storage S3 Endpoint
'backblaze_b2_bucket': None, # string - Backblaze B2 Cloud Storage Bucket name
'wasabi_bucket': None, # string - Wasabi Bucket name
'wasabi_region': None, # string - Wasabi region
'rackspace_username': None, # string - Rackspace username used to login to the Rackspace Cloud Control Panel.
'rackspace_region': None, # string - Three letter airport code for Rackspace region. See https://support.rackspace.com/how-to/about-regions/
'rackspace_container': None, # string - The name of the container (top level directory) where files will sync.
'auth_setup_link': None, # string - Returns link to login with an Oauth provider
'auth_status': None, # string - Either `in_setup` or `complete`
'auth_account_name': None, # string - Describes the authorized account
'one_drive_account_type': None, # string - Either personal or business_other account types
'azure_blob_storage_account': None, # string - Azure Blob Storage Account name
'azure_blob_storage_container': None, # string - Azure Blob Storage Container name
's3_compatible_bucket': None, # string - S3-compatible Bucket name
's3_compatible_endpoint': None, # string - S3-compatible endpoint
'enable_dedicated_ips': None, # boolean - `true` if remote server only accepts connections from dedicated IPs
'aws_access_key': None, # string - AWS Access Key.
'aws_secret_key': None, # string - AWS secret key.
'password': None, # string - Password if needed.
'private_key': None, # string - Private key if needed.
'ssl_certificate': None, # string - SSL client certificate.
'google_cloud_storage_credentials_json': None, # string - A JSON file that contains the private key. To generate see https://cloud.google.com/storage/docs/json_api/v1/how-tos/authorizing#APIKey
'wasabi_access_key': None, # string - Wasabi access key.
'wasabi_secret_key': None, # string - Wasabi secret key.
'backblaze_b2_key_id': None, # string - Backblaze B2 Cloud Storage keyID.
'backblaze_b2_application_key': None, # string - Backblaze B2 Cloud Storage applicationKey.
'rackspace_api_key': None, # string - Rackspace API key from the Rackspace Cloud Control Panel.
'reset_authentication': None, # boolean - Reset authenticated account
'azure_blob_storage_access_key': None, # string - Azure Blob Storage secret key.
's3_compatible_access_key': None, # string - S3-compatible access key
's3_compatible_secret_key': None, # string - S3-compatible secret key
}
def __init__(self, attributes=None, options=None):
if not isinstance(attributes, dict):
attributes = {}
if not isinstance(options, dict):
options = {}
self.set_attributes(attributes)
self.options = options
def set_attributes(self, attributes):
for (attribute, default_value) in RemoteServer.default_attributes.items():
setattr(self, attribute, attributes.get(attribute, default_value))
def get_attributes(self):
return {k: getattr(self, k, None) for k in RemoteServer.default_attributes if getattr(self, k, None) is not None}
# Parameters:
# aws_access_key - string - AWS Access Key.
# aws_secret_key - string - AWS secret key.
# password - string - Password if needed.
# private_key - string - Private key if needed.
# ssl_certificate - string - SSL client certificate.
# google_cloud_storage_credentials_json - string - A JSON file that contains the private key. To generate see https://cloud.google.com/storage/docs/json_api/v1/how-tos/authorizing#APIKey
# wasabi_access_key - string - Wasabi access key.
# wasabi_secret_key - string - Wasabi secret key.
# backblaze_b2_key_id - string - Backblaze B2 Cloud Storage keyID.
# backblaze_b2_application_key - string - Backblaze B2 Cloud Storage applicationKey.
# rackspace_api_key - string - Rackspace API key from the Rackspace Cloud Control Panel.
# reset_authentication - boolean - Reset authenticated account
# azure_blob_storage_access_key - string - Azure Blob Storage secret key.
# hostname - string - Hostname or IP address
# name - string - Internal name for your reference
# max_connections - int64 - Max number of parallel connections. Ignored for S3 connections (we will parallelize these as much as possible).
# port - int64 - Port for remote server. Not needed for S3.
# s3_bucket - string - S3 bucket name
# s3_region - string - S3 region
# server_certificate - string - Remote server certificate
# server_host_key - string - Remote server SSH Host Key. If provided, we will require that the server host key matches the provided key. Uses OpenSSH format similar to what would go into ~/.ssh/known_hosts
# server_type - string - Remote server type.
# ssl - string - Should we require SSL?
# username - string - Remote server username. Not needed for S3 buckets.
# google_cloud_storage_bucket - string - Google Cloud Storage bucket name
# google_cloud_storage_project_id - string - Google Cloud Project ID
# backblaze_b2_bucket - string - Backblaze B2 Cloud Storage Bucket name
# backblaze_b2_s3_endpoint - string - Backblaze B2 Cloud Storage S3 Endpoint
# wasabi_bucket - string - Wasabi Bucket name
# wasabi_region - string - Wasabi region
# rackspace_username - string - Rackspace username used to login to the Rackspace Cloud Control Panel.
# rackspace_region - string - Three letter airport code for Rackspace region. See https://support.rackspace.com/how-to/about-regions/
# rackspace_container - string - The name of the container (top level directory) where files will sync.
# one_drive_account_type - string - Either personal or business_other account types
# azure_blob_storage_account - string - Azure Blob Storage Account name
# azure_blob_storage_container - string - Azure Blob Storage Container name
# s3_compatible_bucket - string - S3-compatible Bucket name
# s3_compatible_endpoint - string - S3-compatible endpoint
# enable_dedicated_ips - boolean - `true` if remote server only accepts connections from dedicated IPs
# s3_compatible_access_key - string - S3-compatible access key
# s3_compatible_secret_key - string - S3-compatible secret key
def update(self, params = None):
if not isinstance(params, dict):
params = {}
if hasattr(self, "id") and self.id:
params['id'] = self.id
else:
raise MissingParameterError("Current object doesn't have a id")
if "id" not in params:
raise MissingParameterError("Parameter missing: id")
if "id" in params and not isinstance(params["id"], int):
raise InvalidParameterError("Bad parameter: id must be an int")
if "aws_access_key" in params and not isinstance(params["aws_access_key"], str):
raise InvalidParameterError("Bad parameter: aws_access_key must be an str")
if "aws_secret_key" in params and not isinstance(params["aws_secret_key"], str):
raise InvalidParameterError("Bad parameter: aws_secret_key must be an str")
if "password" in params and not isinstance(params["password"], str):
raise InvalidParameterError("Bad parameter: password must be an str")
if "private_key" in params and not isinstance(params["private_key"], str):
raise InvalidParameterError("Bad parameter: private_key must be an str")
if "ssl_certificate" in params and not isinstance(params["ssl_certificate"], str):
raise InvalidParameterError("Bad parameter: ssl_certificate must be an str")
if "google_cloud_storage_credentials_json" in params and not isinstance(params["google_cloud_storage_credentials_json"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_credentials_json must be an str")
if "wasabi_access_key" in params and not isinstance(params["wasabi_access_key"], str):
raise InvalidParameterError("Bad parameter: wasabi_access_key must be an str")
if "wasabi_secret_key" in params and not isinstance(params["wasabi_secret_key"], str):
raise InvalidParameterError("Bad parameter: wasabi_secret_key must be an str")
if "backblaze_b2_key_id" in params and not isinstance(params["backblaze_b2_key_id"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_key_id must be an str")
if "backblaze_b2_application_key" in params and not isinstance(params["backblaze_b2_application_key"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_application_key must be an str")
if "rackspace_api_key" in params and not isinstance(params["rackspace_api_key"], str):
raise InvalidParameterError("Bad parameter: rackspace_api_key must be an str")
if "azure_blob_storage_access_key" in params and not isinstance(params["azure_blob_storage_access_key"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_access_key must be an str")
if "hostname" in params and not isinstance(params["hostname"], str):
raise InvalidParameterError("Bad parameter: hostname must be an str")
if "name" in params and not isinstance(params["name"], str):
raise InvalidParameterError("Bad parameter: name must be an str")
if "max_connections" in params and not isinstance(params["max_connections"], int):
raise InvalidParameterError("Bad parameter: max_connections must be an int")
if "port" in params and not isinstance(params["port"], int):
raise InvalidParameterError("Bad parameter: port must be an int")
if "s3_bucket" in params and not isinstance(params["s3_bucket"], str):
raise InvalidParameterError("Bad parameter: s3_bucket must be an str")
if "s3_region" in params and not isinstance(params["s3_region"], str):
raise InvalidParameterError("Bad parameter: s3_region must be an str")
if "server_certificate" in params and not isinstance(params["server_certificate"], str):
raise InvalidParameterError("Bad parameter: server_certificate must be an str")
if "server_host_key" in params and not isinstance(params["server_host_key"], str):
raise InvalidParameterError("Bad parameter: server_host_key must be an str")
if "server_type" in params and not isinstance(params["server_type"], str):
raise InvalidParameterError("Bad parameter: server_type must be an str")
if "ssl" in params and not isinstance(params["ssl"], str):
raise InvalidParameterError("Bad parameter: ssl must be an str")
if "username" in params and not isinstance(params["username"], str):
raise InvalidParameterError("Bad parameter: username must be an str")
if "google_cloud_storage_bucket" in params and not isinstance(params["google_cloud_storage_bucket"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_bucket must be an str")
if "google_cloud_storage_project_id" in params and not isinstance(params["google_cloud_storage_project_id"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_project_id must be an str")
if "backblaze_b2_bucket" in params and not isinstance(params["backblaze_b2_bucket"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_bucket must be an str")
if "backblaze_b2_s3_endpoint" in params and not isinstance(params["backblaze_b2_s3_endpoint"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_s3_endpoint must be an str")
if "wasabi_bucket" in params and not isinstance(params["wasabi_bucket"], str):
raise InvalidParameterError("Bad parameter: wasabi_bucket must be an str")
if "wasabi_region" in params and not isinstance(params["wasabi_region"], str):
raise InvalidParameterError("Bad parameter: wasabi_region must be an str")
if "rackspace_username" in params and not isinstance(params["rackspace_username"], str):
raise InvalidParameterError("Bad parameter: rackspace_username must be an str")
if "rackspace_region" in params and not isinstance(params["rackspace_region"], str):
raise InvalidParameterError("Bad parameter: rackspace_region must be an str")
if "rackspace_container" in params and not isinstance(params["rackspace_container"], str):
raise InvalidParameterError("Bad parameter: rackspace_container must be an str")
if "one_drive_account_type" in params and not isinstance(params["one_drive_account_type"], str):
raise InvalidParameterError("Bad parameter: one_drive_account_type must be an str")
if "azure_blob_storage_account" in params and not isinstance(params["azure_blob_storage_account"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_account must be an str")
if "azure_blob_storage_container" in params and not isinstance(params["azure_blob_storage_container"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_container must be an str")
if "s3_compatible_bucket" in params and not isinstance(params["s3_compatible_bucket"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_bucket must be an str")
if "s3_compatible_endpoint" in params and not isinstance(params["s3_compatible_endpoint"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_endpoint must be an str")
if "s3_compatible_access_key" in params and not isinstance(params["s3_compatible_access_key"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_access_key must be an str")
if "s3_compatible_secret_key" in params and not isinstance(params["s3_compatible_secret_key"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_secret_key must be an str")
response, _options = Api.send_request("PATCH", "/remote_servers/{id}".format(id=params['id']), params, self.options)
return response.data
def delete(self, params = None):
if not isinstance(params, dict):
params = {}
if hasattr(self, "id") and self.id:
params['id'] = self.id
else:
raise MissingParameterError("Current object doesn't have a id")
if "id" not in params:
raise MissingParameterError("Parameter missing: id")
if "id" in params and not isinstance(params["id"], int):
raise InvalidParameterError("Bad parameter: id must be an int")
response, _options = Api.send_request("DELETE", "/remote_servers/{id}".format(id=params['id']), params, self.options)
return response.data
def destroy(self, params = None):
self.delete(params)
def save(self):
if hasattr(self, "id") and self.id:
self.update(self.get_attributes())
else:
new_obj = create(self.get_attributes(), self.options)
self.set_attributes(new_obj.get_attributes())
# Parameters:
# cursor - string - Used for pagination. Send a cursor value to resume an existing list from the point at which you left off. Get a cursor from an existing list via the X-Files-Cursor-Next header.
# per_page - int64 - Number of records to show per page. (Max: 10,000, 1,000 or less is recommended).
def list(params = None, options = None):
if not isinstance(params, dict):
params = {}
if not isinstance(options, dict):
options = {}
if "cursor" in params and not isinstance(params["cursor"], str):
raise InvalidParameterError("Bad parameter: cursor must be an str")
if "per_page" in params and not isinstance(params["per_page"], int):
raise InvalidParameterError("Bad parameter: per_page must be an int")
return ListObj(RemoteServer,"GET", "/remote_servers", params, options)
def all(params = None, options = None):
list(params, options)
# Parameters:
# id (required) - int64 - Remote Server ID.
def find(id, params = None, options = None):
if not isinstance(params, dict):
params = {}
if not isinstance(options, dict):
options = {}
params["id"] = id
if "id" in params and not isinstance(params["id"], int):
raise InvalidParameterError("Bad parameter: id must be an int")
if "id" not in params:
raise MissingParameterError("Parameter missing: id")
response, options = Api.send_request("GET", "/remote_servers/{id}".format(id=params['id']), params, options)
return RemoteServer(response.data, options)
def get(id, params = None, options = None):
find(id, params, options)
# Parameters:
# aws_access_key - string - AWS Access Key.
# aws_secret_key - string - AWS secret key.
# password - string - Password if needed.
# private_key - string - Private key if needed.
# ssl_certificate - string - SSL client certificate.
# google_cloud_storage_credentials_json - string - A JSON file that contains the private key. To generate see https://cloud.google.com/storage/docs/json_api/v1/how-tos/authorizing#APIKey
# wasabi_access_key - string - Wasabi access key.
# wasabi_secret_key - string - Wasabi secret key.
# backblaze_b2_key_id - string - Backblaze B2 Cloud Storage keyID.
# backblaze_b2_application_key - string - Backblaze B2 Cloud Storage applicationKey.
# rackspace_api_key - string - Rackspace API key from the Rackspace Cloud Control Panel.
# reset_authentication - boolean - Reset authenticated account
# azure_blob_storage_access_key - string - Azure Blob Storage secret key.
# hostname - string - Hostname or IP address
# name - string - Internal name for your reference
# max_connections - int64 - Max number of parallel connections. Ignored for S3 connections (we will parallelize these as much as possible).
# port - int64 - Port for remote server. Not needed for S3.
# s3_bucket - string - S3 bucket name
# s3_region - string - S3 region
# server_certificate - string - Remote server certificate
# server_host_key - string - Remote server SSH Host Key. If provided, we will require that the server host key matches the provided key. Uses OpenSSH format similar to what would go into ~/.ssh/known_hosts
# server_type - string - Remote server type.
# ssl - string - Should we require SSL?
# username - string - Remote server username. Not needed for S3 buckets.
# google_cloud_storage_bucket - string - Google Cloud Storage bucket name
# google_cloud_storage_project_id - string - Google Cloud Project ID
# backblaze_b2_bucket - string - Backblaze B2 Cloud Storage Bucket name
# backblaze_b2_s3_endpoint - string - Backblaze B2 Cloud Storage S3 Endpoint
# wasabi_bucket - string - Wasabi Bucket name
# wasabi_region - string - Wasabi region
# rackspace_username - string - Rackspace username used to login to the Rackspace Cloud Control Panel.
# rackspace_region - string - Three letter airport code for Rackspace region. See https://support.rackspace.com/how-to/about-regions/
# rackspace_container - string - The name of the container (top level directory) where files will sync.
# one_drive_account_type - string - Either personal or business_other account types
# azure_blob_storage_account - string - Azure Blob Storage Account name
# azure_blob_storage_container - string - Azure Blob Storage Container name
# s3_compatible_bucket - string - S3-compatible Bucket name
# s3_compatible_endpoint - string - S3-compatible endpoint
# enable_dedicated_ips - boolean - `true` if remote server only accepts connections from dedicated IPs
# s3_compatible_access_key - string - S3-compatible access key
# s3_compatible_secret_key - string - S3-compatible secret key
def create(params = None, options = None):
if not isinstance(params, dict):
params = {}
if not isinstance(options, dict):
options = {}
if "aws_access_key" in params and not isinstance(params["aws_access_key"], str):
raise InvalidParameterError("Bad parameter: aws_access_key must be an str")
if "aws_secret_key" in params and not isinstance(params["aws_secret_key"], str):
raise InvalidParameterError("Bad parameter: aws_secret_key must be an str")
if "password" in params and not isinstance(params["password"], str):
raise InvalidParameterError("Bad parameter: password must be an str")
if "private_key" in params and not isinstance(params["private_key"], str):
raise InvalidParameterError("Bad parameter: private_key must be an str")
if "ssl_certificate" in params and not isinstance(params["ssl_certificate"], str):
raise InvalidParameterError("Bad parameter: ssl_certificate must be an str")
if "google_cloud_storage_credentials_json" in params and not isinstance(params["google_cloud_storage_credentials_json"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_credentials_json must be an str")
if "wasabi_access_key" in params and not isinstance(params["wasabi_access_key"], str):
raise InvalidParameterError("Bad parameter: wasabi_access_key must be an str")
if "wasabi_secret_key" in params and not isinstance(params["wasabi_secret_key"], str):
raise InvalidParameterError("Bad parameter: wasabi_secret_key must be an str")
if "backblaze_b2_key_id" in params and not isinstance(params["backblaze_b2_key_id"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_key_id must be an str")
if "backblaze_b2_application_key" in params and not isinstance(params["backblaze_b2_application_key"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_application_key must be an str")
if "rackspace_api_key" in params and not isinstance(params["rackspace_api_key"], str):
raise InvalidParameterError("Bad parameter: rackspace_api_key must be an str")
if "azure_blob_storage_access_key" in params and not isinstance(params["azure_blob_storage_access_key"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_access_key must be an str")
if "hostname" in params and not isinstance(params["hostname"], str):
raise InvalidParameterError("Bad parameter: hostname must be an str")
if "name" in params and not isinstance(params["name"], str):
raise InvalidParameterError("Bad parameter: name must be an str")
if "max_connections" in params and not isinstance(params["max_connections"], int):
raise InvalidParameterError("Bad parameter: max_connections must be an int")
if "port" in params and not isinstance(params["port"], int):
raise InvalidParameterError("Bad parameter: port must be an int")
if "s3_bucket" in params and not isinstance(params["s3_bucket"], str):
raise InvalidParameterError("Bad parameter: s3_bucket must be an str")
if "s3_region" in params and not isinstance(params["s3_region"], str):
raise InvalidParameterError("Bad parameter: s3_region must be an str")
if "server_certificate" in params and not isinstance(params["server_certificate"], str):
raise InvalidParameterError("Bad parameter: server_certificate must be an str")
if "server_host_key" in params and not isinstance(params["server_host_key"], str):
raise InvalidParameterError("Bad parameter: server_host_key must be an str")
if "server_type" in params and not isinstance(params["server_type"], str):
raise InvalidParameterError("Bad parameter: server_type must be an str")
if "ssl" in params and not isinstance(params["ssl"], str):
raise InvalidParameterError("Bad parameter: ssl must be an str")
if "username" in params and not isinstance(params["username"], str):
raise InvalidParameterError("Bad parameter: username must be an str")
if "google_cloud_storage_bucket" in params and not isinstance(params["google_cloud_storage_bucket"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_bucket must be an str")
if "google_cloud_storage_project_id" in params and not isinstance(params["google_cloud_storage_project_id"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_project_id must be an str")
if "backblaze_b2_bucket" in params and not isinstance(params["backblaze_b2_bucket"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_bucket must be an str")
if "backblaze_b2_s3_endpoint" in params and not isinstance(params["backblaze_b2_s3_endpoint"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_s3_endpoint must be an str")
if "wasabi_bucket" in params and not isinstance(params["wasabi_bucket"], str):
raise InvalidParameterError("Bad parameter: wasabi_bucket must be an str")
if "wasabi_region" in params and not isinstance(params["wasabi_region"], str):
raise InvalidParameterError("Bad parameter: wasabi_region must be an str")
if "rackspace_username" in params and not isinstance(params["rackspace_username"], str):
raise InvalidParameterError("Bad parameter: rackspace_username must be an str")
if "rackspace_region" in params and not isinstance(params["rackspace_region"], str):
raise InvalidParameterError("Bad parameter: rackspace_region must be an str")
if "rackspace_container" in params and not isinstance(params["rackspace_container"], str):
raise InvalidParameterError("Bad parameter: rackspace_container must be an str")
if "one_drive_account_type" in params and not isinstance(params["one_drive_account_type"], str):
raise InvalidParameterError("Bad parameter: one_drive_account_type must be an str")
if "azure_blob_storage_account" in params and not isinstance(params["azure_blob_storage_account"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_account must be an str")
if "azure_blob_storage_container" in params and not isinstance(params["azure_blob_storage_container"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_container must be an str")
if "s3_compatible_bucket" in params and not isinstance(params["s3_compatible_bucket"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_bucket must be an str")
if "s3_compatible_endpoint" in params and not isinstance(params["s3_compatible_endpoint"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_endpoint must be an str")
if "s3_compatible_access_key" in params and not isinstance(params["s3_compatible_access_key"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_access_key must be an str")
if "s3_compatible_secret_key" in params and not isinstance(params["s3_compatible_secret_key"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_secret_key must be an str")
response, options = Api.send_request("POST", "/remote_servers", params, options)
return RemoteServer(response.data, options)
# Parameters:
# aws_access_key - string - AWS Access Key.
# aws_secret_key - string - AWS secret key.
# password - string - Password if needed.
# private_key - string - Private key if needed.
# ssl_certificate - string - SSL client certificate.
# google_cloud_storage_credentials_json - string - A JSON file that contains the private key. To generate see https://cloud.google.com/storage/docs/json_api/v1/how-tos/authorizing#APIKey
# wasabi_access_key - string - Wasabi access key.
# wasabi_secret_key - string - Wasabi secret key.
# backblaze_b2_key_id - string - Backblaze B2 Cloud Storage keyID.
# backblaze_b2_application_key - string - Backblaze B2 Cloud Storage applicationKey.
# rackspace_api_key - string - Rackspace API key from the Rackspace Cloud Control Panel.
# reset_authentication - boolean - Reset authenticated account
# azure_blob_storage_access_key - string - Azure Blob Storage secret key.
# hostname - string - Hostname or IP address
# name - string - Internal name for your reference
# max_connections - int64 - Max number of parallel connections. Ignored for S3 connections (we will parallelize these as much as possible).
# port - int64 - Port for remote server. Not needed for S3.
# s3_bucket - string - S3 bucket name
# s3_region - string - S3 region
# server_certificate - string - Remote server certificate
# server_host_key - string - Remote server SSH Host Key. If provided, we will require that the server host key matches the provided key. Uses OpenSSH format similar to what would go into ~/.ssh/known_hosts
# server_type - string - Remote server type.
# ssl - string - Should we require SSL?
# username - string - Remote server username. Not needed for S3 buckets.
# google_cloud_storage_bucket - string - Google Cloud Storage bucket name
# google_cloud_storage_project_id - string - Google Cloud Project ID
# backblaze_b2_bucket - string - Backblaze B2 Cloud Storage Bucket name
# backblaze_b2_s3_endpoint - string - Backblaze B2 Cloud Storage S3 Endpoint
# wasabi_bucket - string - Wasabi Bucket name
# wasabi_region - string - Wasabi region
# rackspace_username - string - Rackspace username used to login to the Rackspace Cloud Control Panel.
# rackspace_region - string - Three letter airport code for Rackspace region. See https://support.rackspace.com/how-to/about-regions/
# rackspace_container - string - The name of the container (top level directory) where files will sync.
# one_drive_account_type - string - Either personal or business_other account types
# azure_blob_storage_account - string - Azure Blob Storage Account name
# azure_blob_storage_container - string - Azure Blob Storage Container name
# s3_compatible_bucket - string - S3-compatible Bucket name
# s3_compatible_endpoint - string - S3-compatible endpoint
# enable_dedicated_ips - boolean - `true` if remote server only accepts connections from dedicated IPs
# s3_compatible_access_key - string - S3-compatible access key
# s3_compatible_secret_key - string - S3-compatible secret key
def update(id, params = None, options = None):
if not isinstance(params, dict):
params = {}
if not isinstance(options, dict):
options = {}
params["id"] = id
if "id" in params and not isinstance(params["id"], int):
raise InvalidParameterError("Bad parameter: id must be an int")
if "aws_access_key" in params and not isinstance(params["aws_access_key"], str):
raise InvalidParameterError("Bad parameter: aws_access_key must be an str")
if "aws_secret_key" in params and not isinstance(params["aws_secret_key"], str):
raise InvalidParameterError("Bad parameter: aws_secret_key must be an str")
if "password" in params and not isinstance(params["password"], str):
raise InvalidParameterError("Bad parameter: password must be an str")
if "private_key" in params and not isinstance(params["private_key"], str):
raise InvalidParameterError("Bad parameter: private_key must be an str")
if "ssl_certificate" in params and not isinstance(params["ssl_certificate"], str):
raise InvalidParameterError("Bad parameter: ssl_certificate must be an str")
if "google_cloud_storage_credentials_json" in params and not isinstance(params["google_cloud_storage_credentials_json"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_credentials_json must be an str")
if "wasabi_access_key" in params and not isinstance(params["wasabi_access_key"], str):
raise InvalidParameterError("Bad parameter: wasabi_access_key must be an str")
if "wasabi_secret_key" in params and not isinstance(params["wasabi_secret_key"], str):
raise InvalidParameterError("Bad parameter: wasabi_secret_key must be an str")
if "backblaze_b2_key_id" in params and not isinstance(params["backblaze_b2_key_id"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_key_id must be an str")
if "backblaze_b2_application_key" in params and not isinstance(params["backblaze_b2_application_key"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_application_key must be an str")
if "rackspace_api_key" in params and not isinstance(params["rackspace_api_key"], str):
raise InvalidParameterError("Bad parameter: rackspace_api_key must be an str")
if "azure_blob_storage_access_key" in params and not isinstance(params["azure_blob_storage_access_key"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_access_key must be an str")
if "hostname" in params and not isinstance(params["hostname"], str):
raise InvalidParameterError("Bad parameter: hostname must be an str")
if "name" in params and not isinstance(params["name"], str):
raise InvalidParameterError("Bad parameter: name must be an str")
if "max_connections" in params and not isinstance(params["max_connections"], int):
raise InvalidParameterError("Bad parameter: max_connections must be an int")
if "port" in params and not isinstance(params["port"], int):
raise InvalidParameterError("Bad parameter: port must be an int")
if "s3_bucket" in params and not isinstance(params["s3_bucket"], str):
raise InvalidParameterError("Bad parameter: s3_bucket must be an str")
if "s3_region" in params and not isinstance(params["s3_region"], str):
raise InvalidParameterError("Bad parameter: s3_region must be an str")
if "server_certificate" in params and not isinstance(params["server_certificate"], str):
raise InvalidParameterError("Bad parameter: server_certificate must be an str")
if "server_host_key" in params and not isinstance(params["server_host_key"], str):
raise InvalidParameterError("Bad parameter: server_host_key must be an str")
if "server_type" in params and not isinstance(params["server_type"], str):
raise InvalidParameterError("Bad parameter: server_type must be an str")
if "ssl" in params and not isinstance(params["ssl"], str):
raise InvalidParameterError("Bad parameter: ssl must be an str")
if "username" in params and not isinstance(params["username"], str):
raise InvalidParameterError("Bad parameter: username must be an str")
if "google_cloud_storage_bucket" in params and not isinstance(params["google_cloud_storage_bucket"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_bucket must be an str")
if "google_cloud_storage_project_id" in params and not isinstance(params["google_cloud_storage_project_id"], str):
raise InvalidParameterError("Bad parameter: google_cloud_storage_project_id must be an str")
if "backblaze_b2_bucket" in params and not isinstance(params["backblaze_b2_bucket"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_bucket must be an str")
if "backblaze_b2_s3_endpoint" in params and not isinstance(params["backblaze_b2_s3_endpoint"], str):
raise InvalidParameterError("Bad parameter: backblaze_b2_s3_endpoint must be an str")
if "wasabi_bucket" in params and not isinstance(params["wasabi_bucket"], str):
raise InvalidParameterError("Bad parameter: wasabi_bucket must be an str")
if "wasabi_region" in params and not isinstance(params["wasabi_region"], str):
raise InvalidParameterError("Bad parameter: wasabi_region must be an str")
if "rackspace_username" in params and not isinstance(params["rackspace_username"], str):
raise InvalidParameterError("Bad parameter: rackspace_username must be an str")
if "rackspace_region" in params and not isinstance(params["rackspace_region"], str):
raise InvalidParameterError("Bad parameter: rackspace_region must be an str")
if "rackspace_container" in params and not isinstance(params["rackspace_container"], str):
raise InvalidParameterError("Bad parameter: rackspace_container must be an str")
if "one_drive_account_type" in params and not isinstance(params["one_drive_account_type"], str):
raise InvalidParameterError("Bad parameter: one_drive_account_type must be an str")
if "azure_blob_storage_account" in params and not isinstance(params["azure_blob_storage_account"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_account must be an str")
if "azure_blob_storage_container" in params and not isinstance(params["azure_blob_storage_container"], str):
raise InvalidParameterError("Bad parameter: azure_blob_storage_container must be an str")
if "s3_compatible_bucket" in params and not isinstance(params["s3_compatible_bucket"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_bucket must be an str")
if "s3_compatible_endpoint" in params and not isinstance(params["s3_compatible_endpoint"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_endpoint must be an str")
if "s3_compatible_access_key" in params and not isinstance(params["s3_compatible_access_key"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_access_key must be an str")
if "s3_compatible_secret_key" in params and not isinstance(params["s3_compatible_secret_key"], str):
raise InvalidParameterError("Bad parameter: s3_compatible_secret_key must be an str")
if "id" not in params:
raise MissingParameterError("Parameter missing: id")
response, options = Api.send_request("PATCH", "/remote_servers/{id}".format(id=params['id']), params, options)
return RemoteServer(response.data, options)
def delete(id, params = None, options = None):
if not isinstance(params, dict):
params = {}
if not isinstance(options, dict):
options = {}
params["id"] = id
if "id" in params and not isinstance(params["id"], int):
raise InvalidParameterError("Bad parameter: id must be an int")
if "id" not in params:
raise MissingParameterError("Parameter missing: id")
response, _options = Api.send_request("DELETE", "/remote_servers/{id}".format(id=params['id']), params, options)
return response.data
def destroy(id, params = None, options = None):
delete(id, params, options)
def new(*args, **kwargs):
return RemoteServer(*args, **kwargs)
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0
| 7
|
9f79f23602d95759a679fe16b7f90e36d7921681
| 12,854
|
py
|
Python
|
tests/logic/test_components.py
|
NicolasCARPi/sampledb
|
d6fd0f4d28d05010d7e0c022fbf2576e25435077
|
[
"MIT"
] | null | null | null |
tests/logic/test_components.py
|
NicolasCARPi/sampledb
|
d6fd0f4d28d05010d7e0c022fbf2576e25435077
|
[
"MIT"
] | null | null | null |
tests/logic/test_components.py
|
NicolasCARPi/sampledb
|
d6fd0f4d28d05010d7e0c022fbf2576e25435077
|
[
"MIT"
] | null | null | null |
# coding: utf-8
'''
'''
from uuid import uuid4
import pytest
from sampledb import models, logic
from sampledb.logic.components import add_component, update_component, validate_address
from sampledb.logic.errors import InvalidComponentAddressError, ComponentAlreadyExistsError, InvalidComponentUUIDError, ComponentDoesNotExistError, InvalidComponentNameError, InsecureComponentAddressError
def test_create_component():
assert len(models.components.Component.query.all()) == 0
component_id = add_component('28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', address='https://example.com', description='').id
assert len(models.components.Component.query.all()) == 1
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com'
assert component.uuid == '28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71'
assert component.name == 'Example Component'
assert component.description == ''
def test_create_duplicate_component():
add_component(uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', address='https://example.com', description='')
assert len(models.components.Component.query.all()) == 1
with pytest.raises(ComponentAlreadyExistsError):
add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component 2', description='')
with pytest.raises(ComponentAlreadyExistsError):
add_component(address='https://example.com', uuid='cf7118a7-6976-5b1a-9a39-7adc72f591a4', name='Example Component', description='')
assert len(models.components.Component.query.all()) == 1
def test_create_component_with_empty_address():
assert len(models.components.Component.query.all()) == 0
with pytest.raises(InvalidComponentAddressError):
add_component(uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', address='', description='')
assert len(models.components.Component.query.all()) == 0
def test_create_component_with_empty_uuid():
assert len(models.components.Component.query.all()) == 0
with pytest.raises(InvalidComponentUUIDError):
add_component(uuid='', name='Example Component', address='https://example.com', description='')
assert len(models.components.Component.query.all()) == 0
def test_create_component_with_long_name():
assert len(models.components.Component.query.all()) == 0
with pytest.raises(InvalidComponentNameError):
add_component(name='A' * 101, uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', description='')
assert len(models.components.Component.query.all()) == 0
component_id = add_component(name='A' * 100, uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', description='').id
assert len(models.components.Component.query.all()) == 1
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.name == 'A' * 100
assert component.uuid == '28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71'
assert component.address is None
assert component.description == ''
def test_create_component_with_invalid_uuid():
assert len(models.components.Component.query.all()) == 0
with pytest.raises(InvalidComponentUUIDError):
add_component(uuid='28b8d3ca-fb5f-59d9-8090-bfdd6d07a71', name='Example Component', description='', address=None)
assert len(models.components.Component.query.all()) == 0
def test_get_component():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
component = logic.components.get_component(component_id)
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com'
assert component.uuid == '28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71'
assert component.name == 'Example Component'
assert component.description == ''
def test_get_component_by_uuid():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
component = logic.components.get_component_by_uuid('28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71')
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com'
assert component.uuid == '28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71'
assert component.name == 'Example Component'
assert component.description == ''
def test_get_component_that_does_not_exist():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
with pytest.raises(ComponentDoesNotExistError):
logic.components.get_component(component_id + 1)
def test_get_component_by_uuid_that_does_not_exist():
add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='')
assert len(models.components.Component.query.all()) == 1
with pytest.raises(ComponentDoesNotExistError):
logic.components.get_component_by_uuid('28b8d3ca-fb5f-59d9-8090-bfdbd6d07a72')
def test_get_components():
components = logic.components.get_components()
assert len(components) == 0
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component 1', description='').id
components = logic.components.get_components()
assert len(components) == 1
component = components[0]
assert component.id == component_id
assert component.address == 'https://example.com'
assert component.uuid == '28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71'
assert component.name == 'Example Component 1'
assert component.description == ''
add_component(address='https://example.com/sampledb', uuid='cf7118a7-6976-5b1a-9a39-7adc72f591a4', name='Example Component 2', description='')
components = logic.components.get_components()
assert len(components) == 2
assert {components[0].name, components[1].name} == {'Example Component 1', 'Example Component 2'}
def test_update_component():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
update_component(component_id=component_id, address='https://example.com/sampledb', name='Test Component', description='Test Description')
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com/sampledb'
assert component.uuid == '28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71'
assert component.name == 'Test Component'
assert component.description == 'Test Description'
def test_update_component_that_does_not_exist():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
with pytest.raises(ComponentDoesNotExistError):
update_component(component_id=component_id + 1, address='https://example.com', name='Test Component', description='Test Description')
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.name == 'Example Component'
assert component.description == ''
def test_update_component_with_existing_name():
add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='')
component_id = add_component(address='https://example.com/sampledb', uuid='cf7118a7-6976-5b1a-9a39-7adc72f591a4', name='Example Component 2', description='').id
assert len(models.components.Component.query.all()) == 2
with pytest.raises(ComponentAlreadyExistsError):
update_component(component_id=component_id, address='https://example.com/sampledb', name='Example Component', description='')
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com/sampledb'
assert component.uuid == 'cf7118a7-6976-5b1a-9a39-7adc72f591a4'
assert component.name == 'Example Component 2'
assert component.description == ''
def test_update_component_with_empty_address():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
with pytest.raises(InvalidComponentAddressError):
update_component(component_id=component_id, address='', name='Test Component', description='Test Description')
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com'
assert component.name == 'Example Component'
assert component.description == ''
def test_update_component_with_long_name():
component_id = add_component(address='https://example.com', uuid='28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71', name='Example Component', description='').id
assert len(models.components.Component.query.all()) == 1
with pytest.raises(InvalidComponentNameError):
update_component(component_id=component_id, name='A' * 101, description='')
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.address == 'https://example.com'
assert component.name == 'Example Component'
assert component.description == ''
update_component(component_id=component_id, name='A' * 100, description='')
component = models.components.Component.query.get(component_id)
assert component is not None
assert component.id == component_id
assert component.name == 'A' * 100
assert component.address is None
assert component.description == ''
def test_get_name():
uuid = uuid4()
component = add_component(address='https://example.com', uuid=str(uuid), name='Example Component', description='')
assert component.get_name() == component.name
assert component.get_name() == 'Example Component'
uuid = uuid4()
component = add_component(address='https://example.com/sampledb', uuid=str(uuid), name=None, description='')
assert component.get_name() == 'example.com/sampledb'
uuid = uuid4()
component = add_component(address=None, uuid=str(uuid), name=None, description='')
assert component.get_name() == 'Database #' + str(component.id)
def test_add_component_no_https():
uuid = uuid4()
address = 'example.com'
component = add_component(address=address, uuid=str(uuid), name='Component', description='')
assert component.address == 'https://' + address
def test_validate_address():
assert validate_address('example.com') == 'https://example.com'
assert validate_address('www.example.com') == 'https://www.example.com'
assert validate_address('https://www.example.com') == 'https://www.example.com'
assert validate_address('https://example.com:1234') == 'https://example.com:1234'
assert validate_address('https://example.com') == 'https://example.com'
assert validate_address('https://example.com', allow_http=True) == 'https://example.com'
assert validate_address('https://example.com', max_length=23) == 'https://example.com'
assert validate_address('https://www.{}.com'.format('a' * 61)) == 'https://www.{}.com'.format('a' * 61)
with pytest.raises(InvalidComponentAddressError):
validate_address('example')
with pytest.raises(InvalidComponentAddressError):
validate_address('')
with pytest.raises(InvalidComponentAddressError):
validate_address('com')
with pytest.raises(InvalidComponentAddressError):
validate_address('ftp://example.com')
with pytest.raises(InvalidComponentAddressError):
validate_address('https://www.example.com', max_length=10)
with pytest.raises(InsecureComponentAddressError):
validate_address('http://example.com')
| 45.420495
| 204
| 0.736658
| 1,503
| 12,854
| 6.174983
| 0.056554
| 0.105053
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| 0.080595
| 0.854757
| 0.834285
| 0.785583
| 0.752397
| 0.708868
| 0.677298
| 0
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| 12,854
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| 0.497487
| 1
| 0.095477
| false
| 0
| 0.025126
| 0
| 0.120603
| 0
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| 0
| null | 0
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0
| 7
|
9faa0a4efb52652a69742f614713f4377e83c6e3
| 16,966
|
py
|
Python
|
OkDatabase.py
|
vanavski/vk_inst_ok_mutual_friends
|
6e8e1ff52222656af6186148977d6e0972ac789e
|
[
"Apache-2.0"
] | null | null | null |
OkDatabase.py
|
vanavski/vk_inst_ok_mutual_friends
|
6e8e1ff52222656af6186148977d6e0972ac789e
|
[
"Apache-2.0"
] | null | null | null |
OkDatabase.py
|
vanavski/vk_inst_ok_mutual_friends
|
6e8e1ff52222656af6186148977d6e0972ac789e
|
[
"Apache-2.0"
] | null | null | null |
import psycopg2
class OkDatabase(object):
# create tables
def create_table_ok(self):
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
create_table_query = '''CREATE TABLE ok_tables
(us_id REAL PRIMARY KEY NOT NULL,
ok_id TEXT NOT NULL,
table_name TEXT NOT NULL); '''
cursor.execute(create_table_query)
connection.commit()
print("Table created successfully in PostgreSQL ")
except (Exception, psycopg2.DatabaseError) as error:
print("Error while creating PostgreSQL table", error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
# methods to add ok friends of user by link
def add_user_to_ok_db(self, us_id, ok_us_id):
name = None
is_contains = None
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
name = '{}_{}_{}'.format('ok', us_id, ok_us_id)
if (self.check_id_ok_tables(us_id, ok_us_id) == 0):
postgres_insert_query = """ INSERT INTO ok_tables (us_id, ok_id, table_name) VALUES (%s, %s, %s)"""
record_to_insert = (us_id, ok_us_id, name)
cursor.execute(postgres_insert_query, record_to_insert)
connection.commit()
count = cursor.rowcount
print(count, "Record inserted successfully into ok_tables table")
is_contains = False
else:
is_contains = True
except (Exception, psycopg2.Error) as error:
if (connection):
is_contains = True
print("Failed to insert record into ok_tables table", error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
return name, is_contains
#
def create_table_friends_ok(self, name):
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
create_table_query = '''CREATE TABLE {}
(ID SERIAL PRIMARY KEY NOT NULL,
friend_id TEXT NOT NULL); '''.format(name)
cursor.execute(create_table_query)
connection.commit()
print("Table created successfully in PostgreSQL ")
except (Exception, psycopg2.Error) as error:
if (connection):
print("Failed to insert record into {} table".format(name), error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
#
def check_id_ok_tables(self, user_id, ok_id):
count = 0
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
postgres_insert_query = """SELECT ok_id FROM ok_tables WHERE ok_id = '{}' and us_id = '{}'""".format(
ok_id, user_id)
cursor.execute(postgres_insert_query)
connection.commit()
count = cursor.rowcount
print(count)
except (Exception, psycopg2.Error) as error:
if (connection):
print("Failed command into ok_tables table", error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
return count
#
def add_ok_user_to_db(self, us_id, ok_us_id, friends):
name, is_contains = self.add_user_to_ok_db(us_id, ok_us_id)
if (is_contains == False):
self.create_table_friends_ok(name)
self.add_friends_table_ok(name, friends, is_contains)
#
def add_friends_table_ok(self, name, friends, is_contains):
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
if (is_contains == False):
# print
postgres_insert_query = """ INSERT INTO {} (friend_id) VALUES """.format(name)
for i in range(len(friends) - 1):
postgres_insert_query = postgres_insert_query + '({}), '.format(friends[i])
postgres_insert_query = postgres_insert_query + '({});'.format(friends[i])
else:
postgres_insert_query = """ DROP TABLE {};
CREATE TABLE {} (ID SERIAL PRIMARY KEY NOT NULL,
friend_id TEXT NOT NULL);
INSERT INTO {} (friend_id) VALUES """.format(name, name, name)
for i in range(len(friends) - 1):
postgres_insert_query = postgres_insert_query + '({}), '.format(friends[i])
postgres_insert_query = postgres_insert_query + '({});'.format(friends[i])
cursor.execute(postgres_insert_query)
connection.commit()
count = cursor.rowcount
print(count, "Record inserted successfully into {} table".format(name))
except (Exception, psycopg2.Error) as error:
if (connection):
print("Failed to insert record into {} table".format(name), error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
# methods to get list of friends ok on website
def get_friends_us_id_by_table_name(self, name):
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
postgres_insert_query = """SELECT us_id FROM ok_tables JOIN {} ON ok_tables.ok_id = {}.friend_id""".format(
name, name)
cursor.execute(postgres_insert_query)
connection.commit()
records = cursor.fetchall()
print("Successfully in PostgreSQL ")
pr_fr_l = []
for item in records:
pr_fr_l.append(item[0])
records = pr_fr_l
except (Exception, psycopg2.DatabaseError) as error:
print("Error while creating PostgreSQL table", error)
records = None
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
return records
def get_table_name_by_user_id(self, us_id):
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
postgres_insert_query = """SELECT table_name FROM ok_tables WHERE ok_tables.us_id = '{}'""".format(
us_id)
cursor.execute(postgres_insert_query)
connection.commit()
records = cursor.fetchall()
# print(records)
print("{} Successfully in PostgreSQL ".format(postgres_insert_query))
except (Exception, psycopg2.DatabaseError) as error:
print("Error while creating PostgreSQL table", error)
return None
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
if (len(records) == 0):
return None
return records[0][0]
def create_table_confidance_on_site(self, us_id):
table_name = 'friends_on_site_{}'.format(us_id)
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
create_table_query = '''CREATE TABLE {}
(FRIEND_ID SERIAL PRIMARY KEY NOT NULL,
confidance REAL NOT NULL); '''.format(table_name)
cursor.execute(create_table_query)
connection.commit()
print("Table {} created successfully in PostgreSQL ".format(table_name))
except (Exception, psycopg2.Error) as error:
if (connection):
print("Failed to create {} table".format(table_name), error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
def check_user_in_confidence_list(self, us_id, fr_id):
count = 0
is_contains_table = None
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
postgres_insert_query = """SELECT FRIEND_ID FROM friends_on_site_{} WHERE FRIEND_ID = {}""".format(
us_id, fr_id)
cursor.execute(postgres_insert_query)
connection.commit()
count = cursor.rowcount
print(count)
is_contains_table = True
except (Exception, psycopg2.Error) as error:
if (connection):
print("Failed command into friends_on_site_{} table".format(us_id), error)
is_contains_table = False
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
return count, is_contains_table
def check_users_in_confidence_list(self, friends_ids, us_id):
proposed_friends_list = []
for friend in friends_ids:
is_contains = False
while (is_contains == False):
count, is_contains = self.check_user_in_confidence_list(us_id, friend)
if (is_contains == False):
self.create_table_confidance_on_site(us_id)
if (count == 0):
proposed_friends_list.append(friend)
return proposed_friends_list
def push_notifications(self, us_id, friends):
push_list = []
for friend in friends:
new_friend = self.check_users_in_confidence_list([us_id], friend)
if (len(new_friend) == 1):
push_list.append(friend)
return push_list, us_id
def get_view_proposed_fr_list(self, friend_list):
# method for web programmer. This method get list of friends id, which need to show
return 0
def update_mark_score_for_friend(self, us_id, friend_id, score):
is_contains_table = None
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
postgres_insert_query = """ UPDATE friends_on_site_{} SET confidance = {} WHERE friend_id = {}""".format(
us_id, score, friend_id)
cursor.execute(postgres_insert_query)
connection.commit()
count = cursor.rowcount
print(count, "Record updated successfully into friends_on_site_{} table".format(us_id))
except (Exception, psycopg2.Error) as error:
if (connection):
print("Failed command into {}_friends_on_site table".format(us_id), error)
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
return count
def insert_mark_score_for_friend(self, us_id, friend_id, score):
is_contains = None
try:
connection = psycopg2.connect(dbname="d1un08c4ikop2s",
user="zbvsjseotlonov",
password="92a9bc55b03b692658e5776cbce5cc8769e72d90a9806bbe824f2cec215f42ce",
host="ec2-54-217-213-79.eu-west-1.compute.amazonaws.com",
port="5432")
cursor = connection.cursor()
postgres_insert_query = """ INSERT INTO friends_on_site_{} (friend_id, confidance) VALUES ({}, {})""".format(
us_id, friend_id, score)
cursor.execute(postgres_insert_query)
connection.commit()
# colnames = [desc[0] for desc in cursor.description]
records = cursor.fetchall()
# print(records)
print("Successfully in PostgreSQL ")
is_contains = False
except (Exception, psycopg2.DatabaseError) as error:
print("Error request: {}. ".format(postgres_insert_query), error)
is_contains = True
finally:
# closing database connection.
if (connection):
cursor.close()
connection.close()
print("PostgreSQL connection is closed")
return is_contains
| 41.380488
| 121
| 0.530944
| 1,547
| 16,966
| 5.625727
| 0.092437
| 0.017006
| 0.058945
| 0.03539
| 0.801218
| 0.764334
| 0.744571
| 0.714466
| 0.70447
| 0.68689
| 0
| 0.066565
| 0.383709
| 16,966
| 410
| 122
| 41.380488
| 0.76578
| 0.036367
| 0
| 0.706452
| 0
| 0.035484
| 0.241394
| 0.076136
| 0
| 0
| 0
| 0
| 0
| 1
| 0.048387
| false
| 0.035484
| 0.003226
| 0.003226
| 0.093548
| 0.106452
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
4ca4cdb80fc2040d5aef0cc44dcd8ef66f3aa721
| 6,366
|
py
|
Python
|
test/unit/skuba_update_test.py
|
MaximilianMeister/skuba-update
|
7de3095c246c4b85d0e249fe5e7f9304c7ac2676
|
[
"Apache-2.0"
] | null | null | null |
test/unit/skuba_update_test.py
|
MaximilianMeister/skuba-update
|
7de3095c246c4b85d0e249fe5e7f9304c7ac2676
|
[
"Apache-2.0"
] | null | null | null |
test/unit/skuba_update_test.py
|
MaximilianMeister/skuba-update
|
7de3095c246c4b85d0e249fe5e7f9304c7ac2676
|
[
"Apache-2.0"
] | null | null | null |
from mock import patch, call, Mock, ANY
from skuba_update.skuba_update import (
main,
run_command,
run_zypper_command,
REBOOT_NEEDED_PATH,
ZYPPER_EXIT_INF_UPDATE_NEEDED,
ZYPPER_EXIT_INF_REBOOT_NEEDED
)
@patch('subprocess.Popen')
def test_run_command(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (b'stdout', b'')
mock_process.returncode = 0
mock_subprocess.return_value = mock_process
result = run_command(['/bin/dummycmd', 'arg1'])
assert result.output == "stdout"
assert result.returncode == 0
assert result.error == '(no output on stderr)'
mock_process.returncode = 1
result = run_command(['/bin/dummycmd', 'arg1'])
assert result.output == "stdout"
assert result.returncode == 1
mock_process.communicate.return_value = (b'', b'stderr')
result = run_command(['/bin/dummycmd', 'arg1'])
assert result.output == ""
assert result.returncode == 1
@patch('subprocess.Popen')
def test_main_wrong_version(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (b'zypper 1.13.0', b'stderr')
mock_process.returncode = 0
mock_subprocess.return_value = mock_process
exception = False
try:
main()
except Exception as e:
exception = True
assert 'higher is required' in str(e)
assert exception
@patch('subprocess.Popen')
def test_main_bad_format_version(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (b'zypper', b'stderr')
mock_process.returncode = 0
mock_subprocess.return_value = mock_process
exception = False
try:
main()
except Exception as e:
exception = True
assert 'Could not parse' in str(e)
assert exception
@patch('subprocess.Popen')
def test_main_no_root(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (b'zypper 1.14.15', b'stderr')
mock_process.returncode = 0
mock_subprocess.return_value = mock_process
exception = False
try:
main()
except Exception as e:
exception = True
assert 'root privileges' in str(e)
assert exception
@patch('os.environ.get', new={}.get, spec_set=True)
@patch('os.geteuid')
@patch('subprocess.Popen')
def test_main(mock_subprocess, mock_geteuid):
return_values = [
(b'some_service1\nsome_service2', b''), (b'zypper 1.14.15', b'')
]
def mock_communicate():
if len(return_values) > 1:
return return_values.pop()
else:
return return_values[0]
mock_geteuid.return_value = 0
mock_process = Mock()
mock_process.communicate.side_effect = mock_communicate
mock_process.returncode = 0
mock_subprocess.return_value = mock_process
main()
assert mock_subprocess.call_args_list == [
call(['zypper', '--version'], stdout=ANY, stderr=ANY, env=ANY),
call(['zypper', 'ref', '-s'], env=ANY),
call([
'zypper', '--non-interactive',
'--non-interactive-include-reboot-patches', 'patch'
], env=ANY),
call([
'zypper', '--non-interactive-include-reboot-patches', 'patch-check'
], env=ANY),
call(
['zypper', 'ps', '-sss'],
stdout=-1, stderr=-1, env=ANY
),
call(
['systemctl', 'restart', 'some_service1'],
stdout=-1, stderr=-1, env=ANY
),
call(
['systemctl', 'restart', 'some_service2'],
stdout=-1, stderr=-1, env=ANY
)
]
@patch('os.environ.get', new={}.get, spec_set=True)
@patch('os.geteuid')
@patch('subprocess.Popen')
def test_main_zypper_returns_100(mock_subprocess, mock_geteuid):
return_values = [(b'', b''), (b'zypper 1.14.15', b'')]
def mock_communicate():
if len(return_values) > 1:
return return_values.pop()
else:
return return_values[0]
mock_geteuid.return_value = 0
mock_process = Mock()
mock_process.communicate.side_effect = mock_communicate
mock_process.returncode = 100
mock_subprocess.return_value = mock_process
main()
assert mock_subprocess.call_args_list == [
call(['zypper', '--version'], stdout=-1, stderr=-1, env=ANY),
call(['zypper', 'ref', '-s'], env=ANY),
call([
'zypper', '--non-interactive',
'--non-interactive-include-reboot-patches', 'patch'
], env=ANY),
call([
'zypper', '--non-interactive-include-reboot-patches', 'patch-check'
], env=ANY),
call([
'zypper', '--non-interactive',
'--non-interactive-include-reboot-patches', 'patch'
], env=ANY),
call(
['zypper', 'ps', '-sss'],
stdout=-1, stderr=-1, env=ANY
)
]
@patch('subprocess.Popen')
def test_run_zypper_command(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (b'stdout', b'stderr')
mock_process.returncode = 0
mock_subprocess.return_value = mock_process
assert run_zypper_command(['zypper', 'patch']) == 0
mock_process.returncode = ZYPPER_EXIT_INF_UPDATE_NEEDED
mock_subprocess.return_value = mock_process
assert run_zypper_command(['zypper', 'patch']) == 100
@patch('subprocess.Popen')
def test_run_zypper_command_failure(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (None, None)
mock_process.returncode = 1
mock_subprocess.return_value = mock_process
exception = False
try:
run_zypper_command(['zypper', 'patch']) == 'stdout'
except Exception as e:
exception = True
assert '"zypper patch" failed' in str(e)
assert exception
@patch('subprocess.Popen')
def test_run_zypper_command_creates_file_on_102(mock_subprocess):
mock_process = Mock()
mock_process.communicate.return_value = (None, None)
mock_process.returncode = ZYPPER_EXIT_INF_REBOOT_NEEDED
mock_subprocess.return_value = mock_process
exception = False
try:
run_zypper_command(['zypper', 'patch']) == 'stdout'
except PermissionError as e:
exception = True
msg = 'Permission denied: \'{0}\''.format(REBOOT_NEEDED_PATH)
assert msg in str(e)
assert exception
| 31.053659
| 79
| 0.64169
| 763
| 6,366
| 5.123198
| 0.141547
| 0.112561
| 0.059094
| 0.063955
| 0.882323
| 0.86109
| 0.818368
| 0.780762
| 0.756459
| 0.743924
| 0
| 0.013651
| 0.229029
| 6,366
| 204
| 80
| 31.205882
| 0.782804
| 0
| 0
| 0.715084
| 0
| 0
| 0.157713
| 0.035815
| 0
| 0
| 0
| 0
| 0.117318
| 1
| 0.061453
| false
| 0
| 0.011173
| 0
| 0.094972
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
981c167025f065af4bfcc692ad6be8d5b6196731
| 1,201
|
py
|
Python
|
binary_search_tree/tests/m_bst_from_preorder_test.py
|
dhrubach/python-code-recipes
|
14356c6adb1946417482eaaf6f42dde4b8351d2f
|
[
"MIT"
] | null | null | null |
binary_search_tree/tests/m_bst_from_preorder_test.py
|
dhrubach/python-code-recipes
|
14356c6adb1946417482eaaf6f42dde4b8351d2f
|
[
"MIT"
] | null | null | null |
binary_search_tree/tests/m_bst_from_preorder_test.py
|
dhrubach/python-code-recipes
|
14356c6adb1946417482eaaf6f42dde4b8351d2f
|
[
"MIT"
] | null | null | null |
from binary_search_tree.m_bst_from_preorder import BstFromPreOrder
class TestBstFromPreOrder:
def test_single_node(self):
bst = BstFromPreOrder()
preorder = [8]
ans = bst.create(preorder)
assert ans.val == 8
def test_lc_data_1(self):
bst = BstFromPreOrder()
preorder = [8, 5, 1, 3, 4, 7, 10, 12]
ans = bst.create(preorder)
assert ans.left.val == 5
assert ans.right.right.val == 12
def test_lc_data_2(self):
bst = BstFromPreOrder()
preorder = [18, 9, 6, 3, 15, 12, 27, 24, 21, 30]
ans = bst.create(preorder)
assert ans.left.right.left.val == 12
assert ans.right.right.val == 30
def test_inorder_lc_data_1(self):
bst = BstFromPreOrder()
preorder = [8, 5, 1, 3, 4, 7, 10, 12]
ans = bst.create_inorder(preorder)
assert ans.left.val == 5
assert ans.right.right.val == 12
def test_inorder_lc_data_2(self):
bst = BstFromPreOrder()
preorder = [18, 9, 6, 3, 15, 12, 27, 24, 21, 30]
ans = bst.create_inorder(preorder)
assert ans.left.right.left.val == 12
assert ans.right.right.val == 30
| 27.930233
| 66
| 0.588676
| 169
| 1,201
| 4.047337
| 0.248521
| 0.118421
| 0.160819
| 0.219298
| 0.840643
| 0.76462
| 0.722222
| 0.722222
| 0.701754
| 0.701754
| 0
| 0.087161
| 0.293089
| 1,201
| 42
| 67
| 28.595238
| 0.718492
| 0
| 0
| 0.709677
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.290323
| 1
| 0.16129
| false
| 0
| 0.032258
| 0
| 0.225806
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
e236267dc58f44a780aa74dec3f52aebab657357
| 97
|
py
|
Python
|
pyparcels/__init__.py
|
kgalliher/arcgis-pythonapi
|
6e1b726e56bd537b39499d8dfec31e8009b663fd
|
[
"MIT"
] | null | null | null |
pyparcels/__init__.py
|
kgalliher/arcgis-pythonapi
|
6e1b726e56bd537b39499d8dfec31e8009b663fd
|
[
"MIT"
] | null | null | null |
pyparcels/__init__.py
|
kgalliher/arcgis-pythonapi
|
6e1b726e56bd537b39499d8dfec31e8009b663fd
|
[
"MIT"
] | null | null | null |
from pyparcels import features
from pyparcels import versioning
from pyparcels import parcels
| 24.25
| 33
| 0.845361
| 12
| 97
| 6.833333
| 0.5
| 0.47561
| 0.695122
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.154639
| 97
| 3
| 34
| 32.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
e2a15a9cda421ec287f60b6dba59325be372cc7a
| 213
|
py
|
Python
|
server/languages/python/module/__init__.py
|
adamrehn/language-toolbox
|
f86c39784b2a6952719afdd7c3769a6a6b5f2630
|
[
"MIT"
] | 2
|
2018-12-18T07:53:06.000Z
|
2020-02-28T11:13:21.000Z
|
server/languages/python/module/__init__.py
|
adamrehn/language-toolbox
|
f86c39784b2a6952719afdd7c3769a6a6b5f2630
|
[
"MIT"
] | null | null | null |
server/languages/python/module/__init__.py
|
adamrehn/language-toolbox
|
f86c39784b2a6952719afdd7c3769a6a6b5f2630
|
[
"MIT"
] | null | null | null |
from . import common_pb2 as common_messages
from . import language_pb2 as language_messages
from . import language_pb2_grpc as service
from .LanguageModuleImp import LanguageModuleImp
from .Utility import Utility
| 35.5
| 48
| 0.85446
| 29
| 213
| 6.068966
| 0.37931
| 0.170455
| 0.204545
| 0.295455
| 0.329545
| 0
| 0
| 0
| 0
| 0
| 0
| 0.016043
| 0.122066
| 213
| 5
| 49
| 42.6
| 0.925134
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
2c89e8375d290afb9fa72c4af52608a1f7f3fbd2
| 3,313
|
py
|
Python
|
retinopathy/rounder.py
|
RamsteinWR/Diabetic-Retinopathy-Blindness-Detection
|
24390aeefd197600255a961189872dd4dfc77092
|
[
"MIT"
] | 68
|
2019-09-08T20:04:23.000Z
|
2021-05-05T10:05:14.000Z
|
retinopathy/rounder.py
|
RamsteinWR/Diabetic-Retinopathy-Blindness-Detection
|
24390aeefd197600255a961189872dd4dfc77092
|
[
"MIT"
] | 1
|
2019-09-24T06:40:33.000Z
|
2019-10-04T09:13:35.000Z
|
retinopathy/rounder.py
|
RamsteinWR/Diabetic-Retinopathy-Blindness-Detection
|
24390aeefd197600255a961189872dd4dfc77092
|
[
"MIT"
] | 25
|
2019-09-09T04:42:51.000Z
|
2022-03-28T15:01:30.000Z
|
from functools import partial
import numpy as np
import scipy as sp
from sklearn import metrics
class OptimizedRounder(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
X_p = np.copy(X)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
ll = metrics.cohen_kappa_score(y, X_p, weights='quadratic')
return -ll
def fit(self, X, y):
loss_partial = partial(self._kappa_loss, X=X, y=y)
initial_coef = [0.5, 1.5, 2.5, 3.5]
self.coef_ = sp.optimize.minimize(loss_partial, initial_coef, method='nelder-mead')
return self.coefficients()
def predict(self, X, coef):
X_p = np.copy(X)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
return X_p
def coefficients(self):
return self.coef_['x']
class OptimizedRounderV2(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
weights = coef[4:]
X_p = np.average(X, axis=1, weights=weights)
return metrics.mean_squared_error(y, X_p)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
ll = metrics.cohen_kappa_score(y, X_p, weights='quadratic')
return -ll
def fit(self, X, y):
loss_partial = partial(self._kappa_loss, X=X, y=y)
initial_coef = [0.5, 1.5, 2.5, 3.5]
initial_weights = [1.] * X.shape[1]
self.coef_ = sp.optimize.minimize(loss_partial, initial_coef + initial_weights,
method='Nelder-Mead',
options={'disp': True})
return self.coefficients()
def predict(self, X, coef=None):
if coef is None:
coef = self.coefficients()
weights = coef[4:]
X_p = np.average(X, axis=1, weights=weights)
return np.around(X_p)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
return X_p
def coefficients(self):
return self.coef_['x']
| 30.118182
| 91
| 0.477815
| 472
| 3,313
| 3.201271
| 0.141949
| 0.045003
| 0.039709
| 0.026473
| 0.818001
| 0.818001
| 0.818001
| 0.818001
| 0.763733
| 0.700199
| 0
| 0.036757
| 0.400543
| 3,313
| 109
| 92
| 30.394495
| 0.724068
| 0
| 0
| 0.813187
| 0
| 0
| 0.013885
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.10989
| false
| 0
| 0.043956
| 0.021978
| 0.285714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2cacc155b184d0ef2c719981c58c4f3a9675b8b9
| 7,572
|
py
|
Python
|
mmur/models/dirichlet_multinomial.py
|
RUrlus/ModelMetricUncertaintyResearch
|
37daa1421a3a45a6adaea3788e2d00493477ff96
|
[
"Apache-2.0"
] | null | null | null |
mmur/models/dirichlet_multinomial.py
|
RUrlus/ModelMetricUncertaintyResearch
|
37daa1421a3a45a6adaea3788e2d00493477ff96
|
[
"Apache-2.0"
] | null | null | null |
mmur/models/dirichlet_multinomial.py
|
RUrlus/ModelMetricUncertaintyResearch
|
37daa1421a3a45a6adaea3788e2d00493477ff96
|
[
"Apache-2.0"
] | null | null | null |
from mmur.models.base import ConfusionMatrixBase
from mmur.stan import _dm_code
from mmur.stan import _dm_multi_code
class DirichletMultinomialConfusionMatrix(ConfusionMatrixBase):
"""Model a confusion matrix using a Dirichlet-multinomial."""
def __init__(self, random_state=None):
"""Initialise the class."""
self.code = _dm_code
self.prior_vars = ['phi', 'theta']
self.post_vars = ['y_hat', 'theta_hat']
self.predictive_var = 'y_hat'
# exclude transformed variables
self._set_random_state(random_state)
def fit_predict(
self,
X,
y=None,
v=2.0,
alpha=1.0,
n_samples=10000,
sample_factor=1.5,
total_count=None,
n_cores=None,
n_warmup=500,
threshold=0.5,
sampling_kwargs=None,
):
"""Fit Dirichlet-Multinomial model and sample from the posterior.
Parameters
----------
X : np.ndarray[np.int64, float64]
the confusion matrix, should have shape (N, 4) where N is
the number confusion matrices if ``y``=None, else the probabilities
for the test set used to compute the confusion matrix
y : np.ndarray[bool, np.int64], default=None
the labels for the probabilities, must be set if X are probabilities
rather than the confusion matrix
v : float, default=2
prior strength, should be value between 1 and 2.
alpha : float, default=1
concentration parameter hyper-prior dirichlet
n_samples : int, default=10000
Number of samples to draw, used both during fit as well as draw
from posterior.
sample_factor : float, int, default=1.5
a factor to increase the number of samples drawn from the posterior
which increases the probability that the number non-divergent
samples is as least as big as n_samples.
total_count : int, default=None
the number of entries a single sampled confusion matrix should have,
if None the sum of the first row of X is used.
n_cores : int, default=None
the number of cores to use during fit and sampling. Default's
4 cores if present other the number of cores available. Setting it
to -1 will use all cores available - 1.
n_warmup : int, default=500
number of warmup itertations used during fitting of Stan model
threshold : float, default=0.5
the classification threshold used to compute the confusion matrix
is ``y`` is not None
sampling_kwargs : dict, default=None
keyword arguments passed to sample function of Stan
"""
if y is not None:
X = self._compute_confusion_matrix(
X, y, threshold, ensure_1d=True
)
else:
X = self._check_X(X).flatten()
if isinstance(v, int):
v = float(v)
elif not isinstance(v, float):
raise TypeError('``v`` must be a float.')
if isinstance(alpha, int):
alpha = float(alpha)
elif not isinstance(alpha, float):
raise TypeError('``alpha`` must be a float.')
if total_count is None:
total_count = int(X.sum())
elif isinstance(total_count, (int, float)):
total_count = int(total_count)
else:
raise TypeError('``total_count`` must be `None` or `int`')
data = {
'alpha': alpha,
'v': v,
'total_count': total_count,
'y': X.flatten(),
}
return self._fit_predict(
data, n_samples, sample_factor, n_cores, n_warmup, sampling_kwargs
)
class DirichletMultinomialMultiConfusionMatrix(ConfusionMatrixBase):
"""Model a confusion matrix using a Dirichlet-multinomial."""
def __init__(self, random_state=None):
"""Initialise the class."""
self.code = _dm_multi_code
self.prior_vars = ['phi', 'theta']
self.post_vars = ['y_hat', 'theta_hat']
self.predictive_var = 'y_hat'
# exclude transformed variables
self._set_random_state(random_state)
def fit_predict(
self,
X,
y=None,
v=2.0,
alpha=1.0,
n_samples=10000,
sample_factor=1.5,
total_count=None,
n_cores=None,
n_warmup=500,
threshold=0.5,
sampling_kwargs=None,
):
"""Fit Dirichlet-Multinomial model over multiple observations and sample from the posterior.
Parameters
----------
X : np.ndarray[np.int64, float64]
the confusion matrix, should have shape (N, 4) where N is
the number confusion matrices if ``y``=None, else the probabilities
for the test set used to compute the confusion matrix
y : np.ndarray[bool, np.int64], default=None
the labels for the probabilities, must be set if X are probabilities
rather than the confusion matrix
v : float, default=2
prior strength, should be value between 1 and 2.
alpha : float, default=1
concentration parameter hyper-prior dirichlet
n_samples : int, default=10000
Number of samples to draw, used both during fit as well as draw
from posterior.
sample_factor : float, int, default=1.5
a factor to increase the number of samples drawn from the posterior
which increases the probability that the number non-divergent
samples is as least as big as n_samples.
total_count : int, default=None
the number of entries a single sampled confusion matrix should have,
if None the sum of the first row of X is used.
n_cores : int, default=None
the number of cores to use during fit and sampling. Default's
4 cores if present other the number of cores available. Setting it
to -1 will use all cores available - 1.
n_warmup : int, default=500
number of warmup itertations used during fitting of Stan model
threshold : float, default=0.5
the classification threshold used to compute the confusion matrix
is ``y`` is not None
sampling_kwargs : dict, default=None
keyword arguments passed to sample function of Stan
"""
if y is not None:
X = self._compute_confusion_matrix(
X, y, threshold, ensure_1d=False
)
else:
X = self._check_X(X)
if isinstance(v, int):
v = float(v)
elif not isinstance(v, float):
raise TypeError('``v`` must be a float.')
if isinstance(alpha, int):
alpha = float(alpha)
elif not isinstance(alpha, float):
raise TypeError('``alpha`` must be a float.')
if total_count is None:
total_count = int(X.sum())
elif isinstance(total_count, (int, float)):
total_count = int(total_count)
else:
raise TypeError('``total_count`` must be `None` or `int`')
data = {
'v': v,
'alpha': alpha,
'N': int(X.shape[0]),
'total_count': total_count,
'y': X,
}
return self._fit_predict(
data, n_samples, sample_factor, n_cores, n_warmup, sampling_kwargs
)
| 37.300493
| 100
| 0.591918
| 960
| 7,572
| 4.554167
| 0.164583
| 0.045746
| 0.032937
| 0.022873
| 0.94785
| 0.938701
| 0.921317
| 0.921317
| 0.921317
| 0.921317
| 0
| 0.01717
| 0.330824
| 7,572
| 202
| 101
| 37.485149
| 0.845668
| 0.464474
| 0
| 0.807692
| 0
| 0
| 0.076612
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.038462
| false
| 0
| 0.028846
| 0
| 0.105769
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e2f105953c6e22c5d8408a24d7e09306fc8089e9
| 3,294
|
py
|
Python
|
functions/get_sidebar.py
|
myarist/Interactive-Machine-Learning-Dashboard
|
8e982b09799eb82d2d2d6ddd8644ab02c70c7286
|
[
"MIT"
] | 1
|
2021-09-13T09:15:59.000Z
|
2021-09-13T09:15:59.000Z
|
functions/get_sidebar.py
|
myarist/Interactive-Machine-Learning-Dashboard
|
8e982b09799eb82d2d2d6ddd8644ab02c70c7286
|
[
"MIT"
] | 3
|
2021-08-12T06:34:45.000Z
|
2021-08-12T19:31:17.000Z
|
functions/get_sidebar.py
|
myarist/Interactive-Machine-Learning-Dashboard
|
8e982b09799eb82d2d2d6ddd8644ab02c70c7286
|
[
"MIT"
] | 3
|
2021-09-26T15:36:14.000Z
|
2022-02-24T05:11:44.000Z
|
from functions.get_best import *
from functions.get_data import *
from functions.get_result import *
import streamlit as st
def get_sidebar(dataset_name):
X, y = get_data(dataset_name)
st.sidebar.subheader('Classifier')
best_button = st.sidebar.button('Find the Best Classifier Automatically ')
if best_button:
classifier_name = get_best_classifier(X, y)
if classifier_name == 'None':
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset'}</h1>",
unsafe_allow_html=True)
else:
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset' + ' using ' + classifier_name}</h1>",
unsafe_allow_html=True)
get_best_result(classifier_name, X, y)
else:
classifier_name = st.sidebar.selectbox(
'Or Choose Classifier Manually',
('None', 'k-NN', 'SVC', 'Perceptron', 'Decision Tree', 'Random Forest'),
index=0)
if classifier_name == 'None':
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset'}</h1>",
unsafe_allow_html=True)
get_plot_data(X, y, st, (5, 3))
elif classifier_name != 'None':
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset' + ' using ' + classifier_name}</h1>",
unsafe_allow_html=True)
get_result(classifier_name, X, y)
def get_sidebar_xy(dataset_name, X, y):
st.sidebar.subheader('Classifier')
best_button = st.sidebar.button('Find the Best Classifier Automatically ')
if best_button:
classifier_name = get_best_classifier(X, y)
if classifier_name == 'None':
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset'}</h1>",
unsafe_allow_html=True)
else:
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset' + ' using ' + classifier_name}</h1>",
unsafe_allow_html=True)
get_best_result(classifier_name, X, y)
else:
classifier_name = st.sidebar.selectbox(
'Or Choose Classifier Manually',
('None', 'k-NN', 'SVC', 'Perceptron', 'Decision Tree', 'Random Forest'),
index=0)
if classifier_name == 'None':
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset'}</h1>",
unsafe_allow_html=True)
get_plot_data(X, y, st, (5, 3))
elif classifier_name != 'None':
st.markdown(f"<h1 style='text-align: center;'>\
{dataset_name + ' Dataset' + ' using ' + classifier_name}</h1>",
unsafe_allow_html=True)
get_result(classifier_name, X, y)
| 38.752941
| 94
| 0.502429
| 336
| 3,294
| 4.717262
| 0.166667
| 0.158991
| 0.055521
| 0.065615
| 0.892114
| 0.892114
| 0.892114
| 0.892114
| 0.892114
| 0.892114
| 0
| 0.010768
| 0.379781
| 3,294
| 85
| 95
| 38.752941
| 0.765051
| 0
| 0
| 0.888889
| 0
| 0
| 0.16132
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.031746
| false
| 0
| 0.063492
| 0
| 0.095238
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
39178b319310a326289d8bbd22f6e8462523014c
| 17,168
|
py
|
Python
|
project/hog/tests/04b.py
|
FyisFe/UCB-CS61A-20Fall
|
40312a939e17c3de6735df7fc2eb2d66286f87ad
|
[
"MIT"
] | 3
|
2022-01-21T15:52:32.000Z
|
2022-03-06T11:02:34.000Z
|
project/hog/tests/04b.py
|
FyisFe/UCB-CS61A-20Fall
|
40312a939e17c3de6735df7fc2eb2d66286f87ad
|
[
"MIT"
] | null | null | null |
project/hog/tests/04b.py
|
FyisFe/UCB-CS61A-20Fall
|
40312a939e17c3de6735df7fc2eb2d66286f87ad
|
[
"MIT"
] | 1
|
2022-02-13T08:28:27.000Z
|
2022-02-13T08:28:27.000Z
|
test = {
'name': 'Question 4b',
'points': 1,
'suites': [
{
'cases': [
{
'code': r"""
>>> pig_pass(5, 4)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(13, 10)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(7, 10)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(28, 30)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(23, 23)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(22, 23)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(3, 92)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(47, 64)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(28, 29)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(15, 72)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(24, 3)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(46, 55)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(27, 29)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(73, 99)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(27, 28)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(96, 98)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(92, 54)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(11, 12)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(1, 3)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(2, 3)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(48, 74)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(75, 77)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(12, 5)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(18, 20)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(56, 57)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(19, 69)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(9, 11)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(76, 78)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(24, 25)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(97, 98)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(5, 7)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(3, 64)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(62, 90)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(7, 9)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(28, 30)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(92, 94)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(76, 78)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(9, 11)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(87, 16)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(98, 99)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(7, 88)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(43, 44)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(55, 56)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(43, 45)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(15, 16)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(46, 48)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(83, 84)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(91, 7)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(35, 12)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(51, 92)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(64, 49)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(35, 45)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(19, 21)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(20, 21)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(64, 66)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(51, 81)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(45, 40)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(96, 11)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(57, 96)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(97, 99)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(52, 54)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(12, 13)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(51, 52)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(65, 67)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(12, 13)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(94, 96)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(24, 26)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(38, 39)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(79, 81)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(24, 81)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(11, 87)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(38, 54)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(63, 40)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(60, 51)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(82, 83)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(88, 89)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(75, 73)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(86, 24)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(95, 97)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(19, 46)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(18, 46)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(91, 9)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(19, 81)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(56, 58)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(45, 47)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(22, 24)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(43, 44)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(50, 51)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(64, 65)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(74, 75)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(23, 39)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(89, 56)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(32, 13)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(96, 44)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(77, 59)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(32, 79)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(9, 10)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(29, 31)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(46, 82)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(42, 67)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(93, 95)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(61, 62)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(41, 19)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(90, 38)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(35, 51)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(42, 52)
False
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> pig_pass(93, 95)
True
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
>>> from hog import *
""",
'teardown': '',
'type': 'doctest'
}
]
}
| 19.665521
| 30
| 0.274581
| 1,195
| 17,168
| 3.85523
| 0.090377
| 0.116128
| 0.185804
| 0.278706
| 0.940742
| 0.937703
| 0.937703
| 0.937703
| 0.937703
| 0.665726
| 0
| 0.054172
| 0.560228
| 17,168
| 872
| 31
| 19.688073
| 0.556026
| 0
| 0
| 0.62844
| 0
| 0
| 0.462314
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.122706
| 0.001147
| 0
| 0.001147
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 10
|
394cdb0335a9f8368c2637fd89fbf19c6b08e4f2
| 13,814
|
py
|
Python
|
test/testSlackGenerator.py
|
stolostron/canary-reporting
|
118ef9eae242ca63ec420a3041675d3c4d2c3c0d
|
[
"Apache-2.0"
] | 1
|
2022-01-18T18:58:16.000Z
|
2022-01-18T18:58:16.000Z
|
test/testSlackGenerator.py
|
stolostron/canary-reporting
|
118ef9eae242ca63ec420a3041675d3c4d2c3c0d
|
[
"Apache-2.0"
] | 18
|
2022-01-05T22:10:36.000Z
|
2022-03-22T17:58:07.000Z
|
test/testSlackGenerator.py
|
stolostron/canary-reporting
|
118ef9eae242ca63ec420a3041675d3c4d2c3c0d
|
[
"Apache-2.0"
] | null | null | null |
import unittest, os, sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
from generators import SlackGenerator
class TestSlackGenerator(unittest.TestCase):
results_folder = f"{os.path.dirname(os.path.abspath(__file__))}/test_results_dir"
ignorelist = [
{
"name": "Search: Viewer is NOT able to edit configmaps",
"squad": "search",
"owner": "@anonymous-user-that-I-wont-name"
},
{
"name": "Search: Load page",
"squad": "search",
"owner": "@anonymous-user-that-I-wont-name"
}
]
def test_slack_report_full(self):
_sl_generator = SlackGenerator.SlackGenerator(
[TestSlackGenerator.results_folder],
snapshot="TEST_SNAPSHOT",
branch="TEST_BRANCH",
verification_level="BVT",
stage="TEST_STAGE",
hub_version="TEST_HUB_VERSION",
hub_platform="TEST_HUB_PLATFORM",
import_cluster_details=[
{
"clustername": "cluster1",
"version": "4.7.0",
"platform": "aws"
},
{
"clustername": "cluster2",
"version": "4.7.1",
"platform": "gcp"
}
],
job_url="TEST_JOB_URL",
build_id="TEST_BUILD_ID",
sd_url="TEST_SD_URL",
issue_url="TEST_ISSUE_URL",
md_url="TEST_MD_URL"
)
_sl_report = _sl_generator.generate_slack_report()
self.assertEqual(_sl_report, """*:red_circle:TEST_SNAPSHOT Failed BVT on branch Test_stage*
*Job URL:* TEST_JOB_URL
*Results Markdown:* TEST_MD_URL
*Snapshot Diff:* TEST_SD_URL
*Hub Cluster Platform:* TEST_HUB_PLATFORM *Hub Cluster Version:* TEST_HUB_VERSION
*Import Cluster(s):*
• *Import Cluster Platform:* aws *Import Cluster Version:* 4.7.0
• *Import Cluster Platform:* gcp *Import Cluster Version:* 4.7.1
*Issue generation information:*
TEST_ISSUE_URL
*Quality Gates (100% ; 100%):*
:red_jenkins_circle:*92.31% Executed ; 75.0% Passing*
*Results:*
*:white_check_mark: 9 Tests Passed*
*:failed: 3 Tests Failed*
*:warning: 0 Failures Ignored*
*:blue_question: 1 Test Case Skipped*
*Failing Tests*
:failed: Provider connections -> Provider connections should be able to be created
```CypressError: Timed out retrying: `cy.click()` failed because this element is `disabled`:
`<button tabindex="0" class="bx--btn bx--btn--primary" disabled="" type="button">Go to p...</button>`
Fix this problem, or use `{force: true}` to disable error checking.
https://on.cypress.io/element-cannot-be-interacted-with
at cypressErr (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146621:16)
at cypressErrByPath (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146630:10)
at Object.throwErrByPath (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146593:11)
at Object.ensureNotDisabled (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:137570:24)
at runAllChecks (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:127486:14)
at retryActionability (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:127542:16)
at tryCatcher (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:9065:23)
at Function.Promise.attempt.Promise.try (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:6339:29)
at tryFn (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140680:21)
at whenStable (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140715:12)
at https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140259:16
at tryCatcher (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:9065:23)
at Promise._settlePromiseFromHandler (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7000:31)
at Promise._settlePromise (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7057:18)
at Promise._settlePromise0 (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7102:10)
at Promise._settlePromises (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7182:18)```
:failed: adminSearch.test -> Search: Search for secret
``` at Page.enterTextInSearchbar (/tests/page-objects/SearchPage.js:73:8)
at Object.Search: Search for secret (/tests/e2e/adminSearch.test.js:34:16)```
:failed: viewerSearch.test -> Search: Viewer is NOT able to edit configmaps
``` at Page.enterTextInSearchbar (/tests/page-objects/SearchPage.js:73:8)
at Object.Search: Viewer is NOT able to edit configmaps (/tests/e2e/viewerSearch.test.js:41:16)```
""")
def test_slack_report_ignorelist(self):
_sl_generator = SlackGenerator.SlackGenerator(
[TestSlackGenerator.results_folder],
snapshot="TEST_SNAPSHOT",
branch="TEST_BRANCH",
verification_level="BVT",
stage="TEST_STAGE",
hub_version="TEST_HUB_VERSION",
hub_platform="TEST_HUB_PLATFORM",
import_cluster_details=[
{
"clustername": "cluster1",
"version": "4.7.0",
"platform": "aws"
},
{
"clustername": "cluster2",
"version": "4.7.1",
"platform": "gcp"
}
],
job_url="TEST_JOB_URL",
build_id="TEST_BUILD_ID",
sd_url="TEST_SD_URL",
issue_url="TEST_ISSUE_URL",
md_url="TEST_MD_URL",
ignorelist=TestSlackGenerator.ignorelist
)
_sl_report = _sl_generator.generate_slack_report()
self.assertEqual(_sl_report, """*:red_circle:TEST_SNAPSHOT Failed BVT on branch Test_stage*
*Job URL:* TEST_JOB_URL
*Results Markdown:* TEST_MD_URL
*Snapshot Diff:* TEST_SD_URL
*Hub Cluster Platform:* TEST_HUB_PLATFORM *Hub Cluster Version:* TEST_HUB_VERSION
*Import Cluster(s):*
• *Import Cluster Platform:* aws *Import Cluster Version:* 4.7.0
• *Import Cluster Platform:* gcp *Import Cluster Version:* 4.7.1
*Issue generation information:*
TEST_ISSUE_URL
*Quality Gates (100% ; 100%):*
:red_jenkins_circle:*92.31% Executed ; 75.0% Passing*
*Results:*
*:white_check_mark: 9 Tests Passed*
*:failed: 2 Tests Failed*
*:warning: 1 Failure Ignored*
*:blue_question: 1 Test Case Skipped*
*Failing Tests*
:failed: Provider connections -> Provider connections should be able to be created
```CypressError: Timed out retrying: `cy.click()` failed because this element is `disabled`:
`<button tabindex="0" class="bx--btn bx--btn--primary" disabled="" type="button">Go to p...</button>`
Fix this problem, or use `{force: true}` to disable error checking.
https://on.cypress.io/element-cannot-be-interacted-with
at cypressErr (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146621:16)
at cypressErrByPath (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146630:10)
at Object.throwErrByPath (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146593:11)
at Object.ensureNotDisabled (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:137570:24)
at runAllChecks (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:127486:14)
at retryActionability (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:127542:16)
at tryCatcher (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:9065:23)
at Function.Promise.attempt.Promise.try (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:6339:29)
at tryFn (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140680:21)
at whenStable (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140715:12)
at https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140259:16
at tryCatcher (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:9065:23)
at Promise._settlePromiseFromHandler (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7000:31)
at Promise._settlePromise (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7057:18)
at Promise._settlePromise0 (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7102:10)
at Promise._settlePromises (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7182:18)```
:failed: adminSearch.test -> Search: Search for secret
``` at Page.enterTextInSearchbar (/tests/page-objects/SearchPage.js:73:8)
at Object.Search: Search for secret (/tests/e2e/adminSearch.test.js:34:16)```
""")
def test_slack_report_min(self):
_sl_generator = SlackGenerator.SlackGenerator([TestSlackGenerator.results_folder])
_sl_report = _sl_generator.generate_slack_report()
self.assertEqual(_sl_report, """*:red_circle: Failed Verification Test*
*Quality Gates (100% ; 100%):*
:red_jenkins_circle:*92.31% Executed ; 75.0% Passing*
*Results:*
*:white_check_mark: 9 Tests Passed*
*:failed: 3 Tests Failed*
*:warning: 0 Failures Ignored*
*:blue_question: 1 Test Case Skipped*
*Failing Tests*
:failed: Provider connections -> Provider connections should be able to be created
```CypressError: Timed out retrying: `cy.click()` failed because this element is `disabled`:
`<button tabindex="0" class="bx--btn bx--btn--primary" disabled="" type="button">Go to p...</button>`
Fix this problem, or use `{force: true}` to disable error checking.
https://on.cypress.io/element-cannot-be-interacted-with
at cypressErr (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146621:16)
at cypressErrByPath (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146630:10)
at Object.throwErrByPath (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:146593:11)
at Object.ensureNotDisabled (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:137570:24)
at runAllChecks (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:127486:14)
at retryActionability (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:127542:16)
at tryCatcher (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:9065:23)
at Function.Promise.attempt.Promise.try (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:6339:29)
at tryFn (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140680:21)
at whenStable (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140715:12)
at https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:140259:16
at tryCatcher (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:9065:23)
at Promise._settlePromiseFromHandler (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7000:31)
at Promise._settlePromise (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7057:18)
at Promise._settlePromise0 (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7102:10)
at Promise._settlePromises (https://multicloud-console.apps.ds4-aws-444.aws.red-chesterfield.com/__cypress/runner/cypress_runner.js:7182:18)```
:failed: adminSearch.test -> Search: Search for secret
``` at Page.enterTextInSearchbar (/tests/page-objects/SearchPage.js:73:8)
at Object.Search: Search for secret (/tests/e2e/adminSearch.test.js:34:16)```
:failed: viewerSearch.test -> Search: Viewer is NOT able to edit configmaps
``` at Page.enterTextInSearchbar (/tests/page-objects/SearchPage.js:73:8)
at Object.Search: Viewer is NOT able to edit configmaps (/tests/e2e/viewerSearch.test.js:41:16)```
""")
if __name__ == '__main__':
unittest.main()
| 58.042017
| 157
| 0.722528
| 1,862
| 13,814
| 5.192266
| 0.117078
| 0.129086
| 0.109226
| 0.129086
| 0.960695
| 0.956971
| 0.956971
| 0.956971
| 0.94549
| 0.936802
| 0
| 0.055467
| 0.138627
| 13,814
| 237
| 158
| 58.28692
| 0.756702
| 0
| 0
| 0.829268
| 0
| 0.273171
| 0.826553
| 0.096713
| 0
| 0
| 0
| 0
| 0.014634
| 1
| 0.014634
| false
| 0.029268
| 0.04878
| 0
| 0.078049
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
1a5279c769c4e481d5e3d5e78057c73d091bb5c3
| 3,910
|
py
|
Python
|
insights/parsers/tests/test_gluster_vol.py
|
ritwik12/insights-core
|
9ece3f37358987ed6d245585e065237aedcce4cc
|
[
"Apache-2.0"
] | null | null | null |
insights/parsers/tests/test_gluster_vol.py
|
ritwik12/insights-core
|
9ece3f37358987ed6d245585e065237aedcce4cc
|
[
"Apache-2.0"
] | null | null | null |
insights/parsers/tests/test_gluster_vol.py
|
ritwik12/insights-core
|
9ece3f37358987ed6d245585e065237aedcce4cc
|
[
"Apache-2.0"
] | null | null | null |
import pytest
from insights.parsers import ParseException
from insights.tests import context_wrap
from insights.parsers.gluster_vol import GlusterVolInfo
TRACKING_VALID = """
Volume Name: test_vol
cluster.choose-local: off
network.remote-dio: enable
performance.low-prio-threads: 32
performance.io-cache: off
performance.read-ahead: off
performance.quick-read: off
nfs.disable: on
performance.client-io-threads: off
"""
TRACKING_INVALID = """ """
MULTIPLE_VOLUMES = """
Volume Name: test_vol
Type: Replicate
Volume ID: 2c32ed8d-5a07-4a76-a73a-123859556974
Status: Started
Snapshot Count: 0
Number of Bricks: 1 x 3 = 3
Transport-type: tcp
Bricks:
Brick1: 172.17.18.42:/home/brick
Brick2: 172.17.18.43:/home/brick
Brick3: 172.17.18.44:/home/brick
Options Reconfigured:
cluster.choose-local: off
user.cifs: off
features.shard: on
cluster.shd-wait-qlength: 10000
cluster.shd-max-threads: 8
cluster.locking-scheme: granular
cluster.data-self-heal-algorithm: full
cluster.server-quorum-type: server
cluster.quorum-type: auto
cluster.eager-lock: enable
network.remote-dio: enable
performance.low-prio-threads: 32
performance.io-cache: off
performance.read-ahead: off
performance.quick-read: off
transport.address-family: inet
nfs.disable: on
performance.client-io-threads: off
Volume Name: test_vol_2
Type: Replicate
Volume ID: dd821df9-ee2e-429c-98a0-81b1b794433d
Status: Started
Snapshot Count: 0
Number of Bricks: 1 x 3 = 3
Transport-type: tcp
Bricks:
Brick1: 172.17.18.42:/home/brick2
Brick2: 172.17.18.43:/home/brick2
Brick3: 172.17.18.44:/home/brick2
Options Reconfigured:
cluster.choose-local: off
user.cifs: off
features.shard: on
cluster.shd-wait-qlength: 10000
cluster.shd-max-threads: 8
cluster.locking-scheme: granular
cluster.data-self-heal-algorithm: full
cluster.server-quorum-type: server
cluster.quorum-type: auto
cluster.eager-lock: enable
network.remote-dio: enable
performance.low-prio-threads: 32
performance.io-cache: off
performance.read-ahead: off
performance.quick-read: off
transport.address-family: inet
nfs.disable: on
performance.client-io-threads: off
""".strip()
def test_invalid():
with pytest.raises(ParseException) as e:
GlusterVolInfo(context_wrap(TRACKING_INVALID))
assert "Unable to parse gluster volume options: []" in str(e)
def test_gluster_volume_options():
parser_result = GlusterVolInfo(context_wrap(TRACKING_VALID))
assert parser_result is not None
data = parser_result.data["test_vol"]
assert data['network.remote-dio'] == 'enable'
assert data['cluster.choose-local'] == 'off'
assert data['performance.client-io-threads'] == 'off'
assert data['performance.quick-read'] == 'off'
assert data['performance.low-prio-threads'] == '32'
assert data['performance.io-cache'] == 'off'
assert data['performance.read-ahead'] == 'off'
assert data['nfs.disable'] == 'on'
def test_gluster_multiple_volume_options():
parser_result = GlusterVolInfo(context_wrap(MULTIPLE_VOLUMES))
assert parser_result is not None
data = parser_result.data["test_vol"]
assert data['network.remote-dio'] == 'enable'
assert data['cluster.choose-local'] == 'off'
assert data['performance.client-io-threads'] == 'off'
assert data['performance.quick-read'] == 'off'
assert data['performance.low-prio-threads'] == '32'
assert data['performance.io-cache'] == 'off'
assert data['performance.read-ahead'] == 'off'
assert data['nfs.disable'] == 'on'
data = parser_result.data["test_vol_2"]
assert data['network.remote-dio'] == 'enable'
assert data['cluster.choose-local'] == 'off'
assert data['performance.client-io-threads'] == 'off'
assert data['performance.quick-read'] == 'off'
assert data['performance.low-prio-threads'] == '32'
assert data['performance.io-cache'] == 'off'
assert data['performance.read-ahead'] == 'off'
assert data['nfs.disable'] == 'on'
| 29.847328
| 66
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| 3,910
| 5.220803
| 0.228102
| 0.083887
| 0.068158
| 0.100664
| 0.793429
| 0.793429
| 0.757427
| 0.722475
| 0.708144
| 0.708144
| 0
| 0.041084
| 0.122251
| 3,910
| 130
| 67
| 30.076923
| 0.792541
| 0
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| 0.765217
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| 0.640409
| 0.326598
| 0
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| 0.234783
| 1
| 0.026087
| false
| 0
| 0.034783
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| 0.06087
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| 0
| null | 0
| 0
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| 1
| 1
| 1
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| null | 0
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| 0
| 0
|
0
| 7
|
1a89e646aa5e685da0a51f656044e4b3e403bad3
| 107
|
py
|
Python
|
modules/unit_tests/eden/__init__.py
|
andygimma/eden
|
716d5e11ec0030493b582fa67d6f1c35de0af50d
|
[
"MIT"
] | 1
|
2019-08-20T16:32:33.000Z
|
2019-08-20T16:32:33.000Z
|
modules/unit_tests/eden/__init__.py
|
andygimma/eden
|
716d5e11ec0030493b582fa67d6f1c35de0af50d
|
[
"MIT"
] | null | null | null |
modules/unit_tests/eden/__init__.py
|
andygimma/eden
|
716d5e11ec0030493b582fa67d6f1c35de0af50d
|
[
"MIT"
] | null | null | null |
from unit_tests.eden.layouts import *
from unit_tests.eden.pr import *
from unit_tests.eden.stats import *
| 26.75
| 37
| 0.803738
| 18
| 107
| 4.611111
| 0.444444
| 0.289157
| 0.46988
| 0.614458
| 0.554217
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11215
| 107
| 3
| 38
| 35.666667
| 0.873684
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| 1
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| true
| 0
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 8
|
1a96de73732d2c814e6c7f804ddae75217e0e28e
| 156
|
py
|
Python
|
epythet/docs_gen.py
|
sylvainbonnot/epythet
|
4b0b06f93e187c914c1782696a5bdf0387a230ab
|
[
"Apache-2.0"
] | 1
|
2020-10-08T00:24:25.000Z
|
2020-10-08T00:24:25.000Z
|
epythet/docs_gen.py
|
sylvainbonnot/epythet
|
4b0b06f93e187c914c1782696a5bdf0387a230ab
|
[
"Apache-2.0"
] | 9
|
2021-09-03T17:11:47.000Z
|
2021-11-03T19:19:44.000Z
|
epythet/docs_gen.py
|
sylvainbonnot/epythet
|
4b0b06f93e187c914c1782696a5bdf0387a230ab
|
[
"Apache-2.0"
] | 1
|
2021-09-08T20:04:32.000Z
|
2021-09-08T20:04:32.000Z
|
"""
Documentation generation
"""
from epythet.setup_docsrc import make_docsrc
from epythet.autogen import make_autodocs
from epythet.call_make import make
| 19.5
| 44
| 0.833333
| 21
| 156
| 6
| 0.52381
| 0.261905
| 0
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| 0
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| 0
| 0.108974
| 156
| 7
| 45
| 22.285714
| 0.906475
| 0.153846
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| null | 0
| 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
1ab43f58ccadba996e1c062e290ef94bd8b2b344
| 96,655
|
py
|
Python
|
tests/tests/test_threading.py
|
diybitcoinhardware/emb
|
470187ce18136ed6d2afcf6ee4ee4ceff2408c36
|
[
"MIT"
] | 24
|
2020-01-23T21:01:58.000Z
|
2022-01-31T14:59:40.000Z
|
tests/tests/test_threading.py
|
diybitcoinhardware/emb
|
470187ce18136ed6d2afcf6ee4ee4ceff2408c36
|
[
"MIT"
] | 16
|
2020-11-04T10:35:17.000Z
|
2021-10-02T07:35:04.000Z
|
tests/tests/test_threading.py
|
diybitcoinhardware/emb
|
470187ce18136ed6d2afcf6ee4ee4ceff2408c36
|
[
"MIT"
] | 15
|
2020-01-23T19:00:23.000Z
|
2022-02-21T20:36:17.000Z
|
from unittest import TestCase
from embit.ec import PrivateKey
from embit.liquid.pset import PSET
from embit.hashes import tagged_hash
from embit.liquid import slip77
import threading
mbkey = PrivateKey.from_string("L2U2zGBgimb2vNee3bTw2y936PDJZXq3p7nMXEWuPP5MmpE1nCfv")
B64PSET = "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"
class TaprootTest(TestCase):
# run blind-unblind in a thread or multiple threads
def unblind_thread(self, pset, i, unblinded=None, blinded=None, reblinded=None):
# check we can unblind
pset.unblind(mbkey)
for inp in pset.inputs:
self.assertIsNotNone(inp.value)
self.assertIsNotNone(inp.asset)
if unblinded:
self.assertEqual(str(pset), unblinded)
# check we can blind
seed = tagged_hash("liquid/blinding_seed", mbkey.secret)
pset.blind(seed)
for out in pset.outputs:
if out.blinding_pubkey is None:
continue
self.assertIsNotNone(out.value_commitment)
self.assertIsNotNone(out.asset_commitment)
self.assertIsNotNone(out.value_proof)
self.assertIsNotNone(out.asset_proof)
self.assertIsNotNone(out.range_proof)
self.assertIsNotNone(out.surjection_proof)
if blinded:
self.assertEqual(str(pset), blinded)
# check we can add extra message into the rangeproof
txseed = pset.txseed(seed)
nonce = tagged_hash("liquid/range_proof", txseed + (0).to_bytes(4, "little"))
out = pset.outputs[i]
msg = b"I can put any message here"
out.reblind(nonce, extra_message=msg)
if reblinded:
self.assertEqual(str(pset), reblinded)
# check we can unblind this output and extract the message
vout = out.blinded_vout
bkey = slip77.blinding_key(mbkey, vout.script_pubkey)
res = vout.unblind(bkey.secret, message_length=200)
value, asset, vbf, abf, extramsg, min_value, max_value = res
self.assertEqual(extramsg.rstrip(b"\x00"), msg)
def test_threading(self):
# reverse order for pset inputs and outputs
pset = PSET.from_string(B64PSET)
pset.inputs = pset.inputs[::-1]
pset.outputs = pset.outputs[::-1]
pset2 = PSET.from_string(str(pset))
pset1 = PSET.from_string(B64PSET)
self.assertTrue(str(pset1) != str(pset2))
tarr = [
threading.Thread(target=self.unblind_thread, args=(PSET.from_string(str(pset2)),-1))
for i in range(5)
]
tarr.append(threading.Thread(target=self.unblind_thread, args=(pset1,0,UNBLINDED,BLINDED,REBLINDED)))
tarr += [
threading.Thread(target=self.unblind_thread, args=(PSET.from_string(str(pset2)),-1))
for i in range(5)
]
for t in tarr:
t.start()
for t in tarr:
t.join()
self.assertEqual(str(pset1), REBLINDED)
| 1,150.654762
| 32,870
| 0.957747
| 3,243
| 96,655
| 28.535307
| 0.344434
| 0.002334
| 0.001426
| 0.004711
| 0.969008
| 0.965809
| 0.965809
| 0.963108
| 0.963108
| 0.963108
| 0
| 0.149889
| 0.008111
| 96,655
| 83
| 32,871
| 1,164.518072
| 0.815366
| 0.002473
| 0
| 0.092308
| 0
| 0.061538
| 0.972917
| 0.97215
| 0
| 1
| 0
| 0
| 0.215385
| 1
| 0.030769
| false
| 0
| 0.092308
| 0
| 0.138462
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 12
|
2047300b74c557f25594f0ec5250060f2a667deb
| 24,254
|
py
|
Python
|
newstareg/newstareg/doctype/guarantee_transaction/guarantee_transaction.py
|
erpcloudsystems/newstarg
|
6e34c285af19e4964b68877337e166aa99699423
|
[
"MIT"
] | null | null | null |
newstareg/newstareg/doctype/guarantee_transaction/guarantee_transaction.py
|
erpcloudsystems/newstarg
|
6e34c285af19e4964b68877337e166aa99699423
|
[
"MIT"
] | null | null | null |
newstareg/newstareg/doctype/guarantee_transaction/guarantee_transaction.py
|
erpcloudsystems/newstarg
|
6e34c285af19e4964b68877337e166aa99699423
|
[
"MIT"
] | null | null | null |
# Copyright (c) 2021, erpcloud.systems and contributors
# For license information, please see license.txt
# import frappe
from frappe.model.document import Document
import frappe, json
from frappe.model.document import Document
from frappe import _
from frappe.desk.search import sanitize_searchfield
from frappe.utils import (flt, getdate, get_url, now,
nowtime, get_time, today, get_datetime, add_days)
from frappe.utils import add_to_date, now, nowdate
class GuaranteeTransaction(Document):
def on_submit(self):
self.make_journal_entry()
def make_journal_entry(self):
# Account From - Account To - Without Fees
if not self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and not self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.acc_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# Account From - Account To - With Fees
if not self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and not self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.acc_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Party - Account To - Without Fees
if self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and not self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.acc_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"party_type": self.party_type_from,
"party": self.party_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Party - Account To - With Fees
if self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and not self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.acc_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"party_type": self.party_type_from,
"party": self.party_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Bank - Account To - Without Fees
if not self.paid_from_party and self.paid_from_bank and not self.paid_to_party and not self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.acc_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Bank - Account To - With Fees
if not self.paid_from_party and self.paid_from_bank and not self.paid_to_party and not self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.acc_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# Account From - To Party - Without Fees
if not self.paid_from_party and not self.paid_from_bank and self.paid_to_party and not self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.account_paid_to,
"party_type": self.party_type_to,
"party": self.party_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# Account From - To Party - With Fees
if not self.paid_from_party and not self.paid_from_bank and self.paid_to_party and not self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.account_paid_to,
"party_type": self.party_type_to,
"party": self.party_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Party - To Party - Without Fees
if self.paid_from_party and not self.paid_from_bank and self.paid_to_party and not self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.account_paid_to,
"party_type": self.party_type_to,
"party": self.party_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"party_type": self.party_type_from,
"party": self.party_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Party - To Party - With Fees
if self.paid_from_party and not self.paid_from_bank and self.paid_to_party and not self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.account_paid_to,
"party_type": self.party_type_to,
"party": self.party_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"party_type": self.party_type_from,
"party": self.party_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Bank - To Party - Without Fees
if not self.paid_from_party and self.paid_from_bank and self.paid_to_party and not self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.account_paid_to,
"party_type": self.party_type_to,
"party": self.party_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Bank - To Party - With Fees
if not self.paid_from_party and self.paid_from_bank and self.paid_to_party and not self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.account_paid_to,
"party_type": self.party_type_to,
"party": self.party_to,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# Account From - To Bank - Without Fees
if not self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.bank_account_to_acc,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# Account From - To Bank - With Fees
if not self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.bank_account_to_acc,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.account_from,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Party - To Bank - Without Fees
if self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.bank_account_to_acc,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"party_type": self.party_type_from,
"party": self.party_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Party - To Bank - With Fees
if self.paid_from_party and not self.paid_from_bank and not self.paid_to_party and self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.bank_account_to_acc,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"party_type": self.party_type_from,
"party": self.party_from,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.party_account_from,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Bank - To Bank - Without Fees
if not self.paid_from_party and self.paid_from_bank and not self.paid_to_party and self.paid_to_bank and not self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.bank_account_to_acc,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
# From Bank - To Bank - With Fees
if not self.paid_from_party and self.paid_from_bank and not self.paid_to_party and self.paid_to_bank and self.fees:
accounts = [
{
"doctype": "Journal Entry Account",
"account": self.bank_account_to_acc,
"debit": self.paid_amount,
"credit": 0,
"debit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.fees_account,
"debit": self.fees_amount,
"credit": 0,
"debit_in_account_currency": self.fees_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.paid_amount,
"credit_in_account_currency": self.paid_amount,
"user_remark": self.name
},
{
"doctype": "Journal Entry Account",
"account": self.bank_account_from_acc,
"debit": 0,
"credit": self.fees_amount,
"credit_in_account_currency": self.fees_amount,
"user_remark": self.name
}
]
doc = frappe.get_doc({
"doctype": "Journal Entry",
"voucher_type": "Journal Entry",
"gur": self.name,
"company": self.company,
"posting_date": self.posting_date,
"accounts": accounts,
"cheque_no": self.name,
"cheque_date": self.posting_date,
"user_remark": self.notes,
"remark": _('Guarantee Transaction {0}').format(self.name)
})
doc.insert()
doc.submit()
pass
| 28.942721
| 129
| 0.641131
| 3,060
| 24,254
| 4.823529
| 0.026797
| 0.078049
| 0.092683
| 0.095122
| 0.973171
| 0.973171
| 0.968157
| 0.968157
| 0.968157
| 0.968157
| 0
| 0.004018
| 0.220087
| 24,254
| 837
| 130
| 28.9773
| 0.776274
| 0.031582
| 0
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| 0
| 0.303375
| 0.058681
| 0
| 0
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| 0
| 0
| 1
| 0.002581
| false
| 0.00129
| 0.009032
| 0
| 0.012903
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
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| 0
| 0
| 0
|
0
| 7
|
204a797e8d66b60c59c0e2417960c33dcb9ccb19
| 42,607
|
py
|
Python
|
data/projects/flutils/tests/unit/packages/test_bump_version.py
|
se2p/artifact-pynguin-ssbse2020
|
32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6
|
[
"CC-BY-4.0"
] | 3
|
2020-08-20T10:27:13.000Z
|
2021-11-02T20:28:16.000Z
|
data/projects/flutils/tests/unit/packages/test_bump_version.py
|
se2p/artifact-pynguin-ssbse2020
|
32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6
|
[
"CC-BY-4.0"
] | null | null | null |
data/projects/flutils/tests/unit/packages/test_bump_version.py
|
se2p/artifact-pynguin-ssbse2020
|
32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6
|
[
"CC-BY-4.0"
] | null | null | null |
import unittest
from unittest.mock import patch
# noinspection PyProtectedMember
from flutils.packages import (
_BUMP_VERSION_MAJOR,
_BUMP_VERSION_MINOR,
_BUMP_VERSION_MINOR_ALPHA,
_BUMP_VERSION_MINOR_BETA,
_BUMP_VERSION_PATCH,
_BUMP_VERSION_PATCH_ALPHA,
_BUMP_VERSION_PATCH_BETA,
_VersionInfo,
_VersionPart,
bump_version,
)
class TestBumpVersion(unittest.TestCase):
def test_bump_version__00(self) -> None:
version = '1.2.3'
position = 1
pre_release = ''
bump_type = _BUMP_VERSION_MAJOR
exp = '2.0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='3',
num=3,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__01(self) -> None:
version = '1.2.3'
position = 2
pre_release = ''
bump_type = _BUMP_VERSION_MINOR
exp = '1.3'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='3',
num=3,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__02(self) -> None:
version = '1.2a19'
position = 2
pre_release = ''
bump_type = _BUMP_VERSION_MINOR
exp = '1.2'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2a19',
num=2,
pre_txt='a',
pre_num=19,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__03(self) -> None:
version = '1.2.3'
position = 2
pre_release = 'a'
bump_type = _BUMP_VERSION_MINOR_ALPHA
exp = '1.3a0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='3',
num=3,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__04(self) -> None:
version = '1.2a0'
position = 2
pre_release = 'a'
bump_type = _BUMP_VERSION_MINOR_ALPHA
exp = '1.2a1'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2a0',
num=2,
pre_txt='a',
pre_num=0,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__05(self) -> None:
version = '1.2b0'
position = 2
pre_release = 'a'
bump_type = _BUMP_VERSION_MINOR_ALPHA
exp = '1.3a0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2b0',
num=2,
pre_txt='b',
pre_num=0,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__06(self) -> None:
version = '1.2b0'
position = 2
pre_release = 'b'
bump_type = _BUMP_VERSION_MINOR_BETA
exp = '1.2b1'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2b0',
num=2,
pre_txt='b',
pre_num=0,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__07(self) -> None:
version = '1.2a0'
position = 2
pre_release = 'b'
bump_type = _BUMP_VERSION_MINOR_BETA
exp = '1.2b0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2a0',
num=2,
pre_txt='a',
pre_num=0,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__08(self) -> None:
version = '1.2'
position = 2
pre_release = 'b'
bump_type = _BUMP_VERSION_MINOR_BETA
exp = '1.3b0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__09(self) -> None:
version = '1.2.3'
position = 3
pre_release = ''
bump_type = _BUMP_VERSION_PATCH
exp = '1.2.4'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='3',
num=3,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__10(self) -> None:
version = '1.140.2b20'
position = 3
pre_release = None
bump_type = _BUMP_VERSION_PATCH
exp = '1.140.2'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='140',
num=140,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='2b20',
num=2,
pre_txt='b',
pre_num=20,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__11(self) -> None:
version = '1.140'
position = 3
pre_release = None
bump_type = _BUMP_VERSION_PATCH
exp = '1.140.1'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='140',
num=140,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='',
num=0,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__12(self) -> None:
version = '1.2.3'
position = 3
pre_release = 'a'
bump_type = _BUMP_VERSION_PATCH_ALPHA
exp = '1.2.4a0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='3',
num=3,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__13(self) -> None:
version = '1.2.1b10'
position = 3
pre_release = 'a'
bump_type = _BUMP_VERSION_PATCH_ALPHA
exp = '1.2.2a0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='1b10',
num=1,
pre_txt='b',
pre_num=10,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__14(self) -> None:
version = '1.2.1a137'
position = 3
pre_release = 'a'
bump_type = _BUMP_VERSION_PATCH_ALPHA
exp = '1.2.1a138'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='1a137',
num=1,
pre_txt='a',
pre_num=137,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__15(self) -> None:
version = '1.2.1a137'
position = 3
pre_release = 'beta'
bump_type = _BUMP_VERSION_PATCH_BETA
exp = '1.2.1b0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='1a137',
num=1,
pre_txt='a',
pre_num=137,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__16(self) -> None:
version = '1.2.1b137'
position = 3
pre_release = 'beta'
bump_type = _BUMP_VERSION_PATCH_BETA
exp = '1.2.1b138'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='1',
num=1,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='2',
num=2,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='1b137',
num=1,
pre_txt='b',
pre_num=137,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
def test_bump_version__17(self) -> None:
version = '7.6.14'
position = 3
pre_release = 'beta'
bump_type = _BUMP_VERSION_PATCH_BETA
exp = '7.6.15b0'
ver_info = _VersionInfo(
version=version,
major=_VersionPart(
pos=0,
txt='7',
num=7,
pre_txt='',
pre_num=-1,
name='major'
),
minor=_VersionPart(
pos=1,
txt='6',
num=6,
pre_txt='',
pre_num=-1,
name='minor'
),
patch=_VersionPart(
pos=2,
txt='14',
num=14,
pre_txt='',
pre_num=-1,
name='patch'
),
pre_pos=-1
)
patcher = patch(
'flutils.packages._build_version_info',
return_value=ver_info
)
build_version_info = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_position',
return_value=position
)
build_version_bump_position = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'flutils.packages._build_version_bump_type',
return_value=bump_type
)
build_version_bump_type = patcher.start()
self.addCleanup(patcher.stop)
ret = bump_version(version, position=position, pre_release=pre_release)
self.assertEqual(
ret,
exp,
msg=(
'\n\n'
'bump_version({version!r}, '
'position={position!r}, '
'pre_release={pre_release!r})\n'
'expected: {exp!r}\n'
' got: {ret!r}\n'
).format(
version=version,
position=position,
pre_release=pre_release,
exp=exp,
ret=ret
)
)
build_version_info.assert_called_once_with(version)
build_version_bump_position.assert_called_once_with(position)
build_version_bump_type.assert_called_once_with(position, pre_release)
| 29.547157
| 79
| 0.476049
| 4,001
| 42,607
| 4.740065
| 0.025244
| 0.102505
| 0.091115
| 0.076878
| 0.974057
| 0.969365
| 0.967835
| 0.961719
| 0.961719
| 0.957712
| 0
| 0.018804
| 0.430845
| 42,607
| 1,441
| 80
| 29.567661
| 0.763258
| 0.000704
| 0
| 0.867112
| 0
| 0
| 0.116759
| 0.084134
| 0
| 0
| 0
| 0
| 0.053452
| 1
| 0.013363
| false
| 0
| 0.002227
| 0
| 0.016333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
204f1c8f6671ef95d84b643e8a5c6aa1887b7f4d
| 1,879
|
py
|
Python
|
snake_project/extra/tools/asker.py
|
gitter-badger/snake1976-win
|
5b8b66a57db5f847f463f2661ca51351cef3a215
|
[
"MIT"
] | null | null | null |
snake_project/extra/tools/asker.py
|
gitter-badger/snake1976-win
|
5b8b66a57db5f847f463f2661ca51351cef3a215
|
[
"MIT"
] | null | null | null |
snake_project/extra/tools/asker.py
|
gitter-badger/snake1976-win
|
5b8b66a57db5f847f463f2661ca51351cef3a215
|
[
"MIT"
] | null | null | null |
def complex_ask(menu_instance, mode: int) -> dict:
'''Asks player about the game settings and then returns dictionary
with them'''
player_choice = {}
if mode == 1:
# Classic mode
menu_instance.set_gamemode(1)
menu_instance.ask_player_about_field_size()
menu_instance.ask_player_about_snake_length()
menu_instance.ask_player_about_snake_and_walls()
menu_instance.ask_plyaer_about_speed()
player_choice['width'] = menu_instance.field_size_set_width()
player_choice['height'] = menu_instance.field_size_set_height()
player_choice['length'] = menu_instance.set_default_length()
player_choice['walls'] = menu_instance.set_snake_and_walls_setting()
player_choice['speed'] = menu_instance.set_game_speed()
return player_choice
elif mode == 2:
# Survival mode
menu_instance.set_gamemode(2)
menu_instance.ask_player_about_field_size()
menu_instance.ask_player_about_snake_length()
menu_instance.ask_player_about_snake_and_walls()
menu_instance.ask_plyaer_about_speed()
player_choice['width'] = menu_instance.field_size_set_width()
player_choice['height'] = menu_instance.field_size_set_height()
player_choice['length'] = menu_instance.set_default_length()
player_choice['walls'] = menu_instance.set_snake_and_walls_setting()
player_choice['speed'] = menu_instance.set_game_speed()
return player_choice
else:
# Battle mode
menu_instance.set_gamemode(3)
menu_instance.ask_player_about_snake_length()
menu_instance.ask_player_about_snake_and_walls()
menu_instance.ask_plyaer_about_speed()
menu_instance.ask_player_about_game_time()
player_choice['length'] = menu_instance.set_default_length()
player_choice['walls'] = menu_instance.set_snake_and_walls_setting()
player_choice['speed'] = menu_instance.set_game_speed()
player_choice['game_time'] = menu_instance.set_game_time()
return player_choice
| 36.843137
| 70
| 0.798829
| 266
| 1,879
| 5.120301
| 0.169173
| 0.264317
| 0.143172
| 0.138767
| 0.83627
| 0.757709
| 0.757709
| 0.757709
| 0.757709
| 0.757709
| 0
| 0.002969
| 0.103779
| 1,879
| 50
| 71
| 37.58
| 0.805819
| 0.060671
| 0
| 0.72973
| 0
| 0
| 0.045014
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.027027
| false
| 0
| 0
| 0
| 0.108108
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
648a35c820a1beaea1a20addc8a027d52f3fb0d1
| 11,730
|
py
|
Python
|
examples/scatter_velocity.py
|
MoLochmann/larda
|
9fe87d27ca9bac1ec1fadcaf716b129c9c89ffff
|
[
"MIT"
] | 4
|
2019-09-16T17:32:33.000Z
|
2022-03-02T10:23:16.000Z
|
examples/scatter_velocity.py
|
MoLochmann/larda
|
9fe87d27ca9bac1ec1fadcaf716b129c9c89ffff
|
[
"MIT"
] | 12
|
2020-01-13T11:11:46.000Z
|
2022-02-07T07:58:31.000Z
|
examples/scatter_velocity.py
|
MoLochmann/larda
|
9fe87d27ca9bac1ec1fadcaf716b129c9c89ffff
|
[
"MIT"
] | 4
|
2019-09-16T17:32:39.000Z
|
2020-12-17T09:26:17.000Z
|
#!/usr/bin/python3
import sys
# just needed to find pyLARDA from this location
sys.path.append('../')
sys.path.append('.')
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pyLARDA
import pyLARDA.helpers as h
import datetime
import numpy as np
import scipy.ndimage as spn
from scipy import stats
#Load LARDA
larda = pyLARDA.LARDA().connect('lacros_dacapo')
c_info = [larda.camp.LOCATION, larda.camp.VALID_DATES]
print(larda.days_with_data())
#print("array_avail()", larda.array_avail(2015, 6))
#print("single month with new interface ", larda.instr_status(2015, 6))
#begin_dt=datetime.datetime(2018,12,6,0,1)
begin_dt=datetime.datetime(2018,12,6,1,40)
end_dt=datetime.datetime(2018,12,6,4,0,0)
plot_range = [300, 10000]
case_prefix = 'plots/scatter_case_studies/comparison_cloud_'
#### load the velocity data
MIRA_VELg=larda.read("MIRA","VELg",[begin_dt,end_dt],[0,'max'])
MIRA_VELg['var_lims'] = [-6,6]
fig, ax = pyLARDA.Transformations.plot2d(MIRA_VELg, range_interval=plot_range)
fig.savefig(case_prefix+'mira_vel.png', dpi=250)
LIMRAD94_VEL=larda.read("LIMRAD94","VEL",[begin_dt,end_dt],[0,'max'])
fig, ax = pyLARDA.Transformations.plot2d(LIMRAD94_VEL, range_interval=plot_range)
fig.savefig(case_prefix+'limrad_vel.png', dpi=250)
LIMRAD94_VEL_interp = pyLARDA.Transformations.interpolate2d(LIMRAD94_VEL, new_time=MIRA_VELg['ts'], new_range=MIRA_VELg['rg'])
fig, ax = pyLARDA.Transformations.plot2d(LIMRAD94_VEL_interp, range_interval=plot_range)
fig.savefig(case_prefix+'limrad_vel_interp.png', dpi=250)
# vel scatterplot
combined_mask = np.logical_or(MIRA_VELg['mask'], LIMRAD94_VEL_interp['mask'])
Mira_VELg_scatter = MIRA_VELg['var'][~combined_mask].ravel()
Limrad_VEL_scatter = LIMRAD94_VEL_interp['var'][~combined_mask].ravel()
s, i, r, p, std_err = stats.linregress(Mira_VELg_scatter, Limrad_VEL_scatter)
H, xedges, yedges = np.histogram2d(Mira_VELg_scatter, Limrad_VEL_scatter,
bins=120, range=[[-4, 4], [-4, 4]])
X, Y = np.meshgrid(xedges,yedges)
fig, ax = plt.subplots(1, figsize=(5.7, 5.7))
ax.pcolormesh(X, Y, np.transpose(H),
norm=matplotlib.colors.LogNorm(),
)
ax.text(0.01, 0.93, 'slope {:5.3f}\nintercept {:5.3f}\nR^2 {:5.3f}'.format(s,i,r**2),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
#ax.scatter(Mira_Z_scatter, Limrad_Z_scatter, s=1)
ax.plot([-10,10],[-10,10], color='salmon')
ax.plot([-10,10],[-8,12], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-9,11], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-11,9], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-12,8], color='salmon', linewidth=0.7, linestyle='--')
ax.set_xlim([-4,4])
ax.set_ylim([-4,4])
ax.set_xlabel("MIRA VELg")
ax.set_ylabel("LIMRAD94 VEL")
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.tick_params(axis='both', which='both', right=True, top=True)
fig.savefig(case_prefix+'scatter_vel_mira_limrad.png', dpi=250)
# other day for the mrr
begin_dt=datetime.datetime(2018,12,9,2,0)
end_dt=datetime.datetime(2018,12,9,5,0,0)
case_prefix = 'plots/scatter_case_studies/comparison_rain_'
MIRA_VELg=larda.read("MIRA","VELg",[begin_dt,end_dt],[0,4000])
MIRA_VELg['var_lims'] = [-6,6]
fig, ax = pyLARDA.Transformations.plot2d(MIRA_VELg, range_interval=plot_range)
fig.savefig(case_prefix+'mira_vel.png', dpi=250)
LIMRAD94_VEL=larda.read("LIMRAD94","VEL",[begin_dt,end_dt],[0,4000])
fig, ax = pyLARDA.Transformations.plot2d(LIMRAD94_VEL, range_interval=plot_range)
fig.savefig(case_prefix+'limrad_vel.png', dpi=250)
LIMRAD94_VEL_interp = pyLARDA.Transformations.interpolate2d(LIMRAD94_VEL, new_time=MIRA_VELg['ts'], new_range=MIRA_VELg['rg'])
fig, ax = pyLARDA.Transformations.plot2d(LIMRAD94_VEL_interp, range_interval=plot_range)
fig.savefig(case_prefix+'limrad_vel_interp.png', dpi=250)
# vel scatterplot
combined_mask = np.logical_or(MIRA_VELg['mask'], LIMRAD94_VEL_interp['mask'])
Mira_VELg_scatter = MIRA_VELg['var'][~combined_mask].ravel()
Limrad_VEL_scatter = LIMRAD94_VEL_interp['var'][~combined_mask].ravel()
s, i, r, p, std_err = stats.linregress(Mira_VELg_scatter, Limrad_VEL_scatter)
H, xedges, yedges = np.histogram2d(Mira_VELg_scatter, Limrad_VEL_scatter,
bins=120, range=[[-4, 4], [-4, 4]])
X, Y = np.meshgrid(xedges,yedges)
fig, ax = plt.subplots(1, figsize=(5.7, 5.7))
ax.pcolormesh(X, Y, np.transpose(H),
norm=matplotlib.colors.LogNorm(),
)
ax.text(0.01, 0.93, 'slope {:5.3f}\nintercept {:5.3f}\nR^2 {:5.3f}'.format(s,i,r**2),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
#ax.scatter(Mira_Z_scatter, Limrad_Z_scatter, s=1)
ax.plot([-10,10],[-10,10], color='salmon')
ax.plot([-10,10],[-8,12], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-9,11], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-11,9], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-12,8], color='salmon', linewidth=0.7, linestyle='--')
ax.set_xlim([-4,4])
ax.set_ylim([-4,4])
ax.set_xlabel("MIRA VELg")
ax.set_ylabel("LIMRAD94 VEL")
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.tick_params(axis='both', which='both', right=True, top=True)
fig.savefig(case_prefix+'scatter_vel_mira_limrad.png', dpi=250)
MRR_VEL=larda.read("MRRPRO","VEL",[begin_dt,end_dt],[0,4000])
fig, ax = pyLARDA.Transformations.plot2d(MRR_VEL, range_interval=plot_range)
fig.savefig(case_prefix+'mrr_vel.png', dpi=250)
MRR_VEL_interp = pyLARDA.Transformations.interpolate2d(MRR_VEL, new_time=MIRA_VELg['ts'], new_range=MIRA_VELg['rg'])
fig, ax = pyLARDA.Transformations.plot2d(MRR_VEL_interp, range_interval=plot_range)
fig.savefig(case_prefix+'mrr_vel_interp.png', dpi=250)
# scatterplot
combined_mask = np.logical_or(MIRA_VELg['mask'], MRR_VEL_interp['mask'])
Mira_VELg_scatter = MIRA_VELg['var'][~combined_mask].ravel()
MRR_VEL_scatter = MRR_VEL_interp['var'][~combined_mask].ravel()
s, i, r, p, std_err = stats.linregress(Mira_VELg_scatter, MRR_VEL_scatter)
H, xedges, yedges = np.histogram2d(Mira_VELg_scatter, MRR_VEL_scatter,
bins=120, range=[[-4, 4], [-4, 4]])
X, Y = np.meshgrid(xedges,yedges)
fig, ax = plt.subplots(1, figsize=(5.7, 5.7))
ax.pcolormesh(X, Y, np.transpose(H),
norm=matplotlib.colors.LogNorm(),
)
ax.text(0.01, 0.93, 'slope {:5.3f}\nintercept {:5.3f}\nR^2 {:5.3f}'.format(s,i,r**2),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
#ax.scatter(Mira_Z_scatter, Limrad_Z_scatter, s=1)
ax.plot([-10,10],[-10,10], color='salmon')
ax.plot([-10,10],[-8,12], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-9,11], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-11,9], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-12,8], color='salmon', linewidth=0.7, linestyle='--')
ax.set_xlim([-4,4])
ax.set_ylim([-4,4])
ax.set_xlabel("MIRA VELg")
ax.set_ylabel("MRRPRO VEL")
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.tick_params(axis='both', which='both', right=True, top=True)
fig.savefig(case_prefix+'scatter_vel_mira_mrr.png', dpi=250)
MRR_VEL_interp = pyLARDA.Transformations.interpolate2d(MRR_VEL, new_time=LIMRAD94_VEL['ts'], new_range=LIMRAD94_VEL['rg'])
fig, ax = pyLARDA.Transformations.plot2d(MRR_VEL_interp, range_interval=plot_range)
fig.savefig(case_prefix+'mrr_vel_interp_to_limrad.png', dpi=250)
combined_mask = np.logical_or(LIMRAD94_VEL['mask'], MRR_VEL_interp['mask'])
LIMRAD94_VEL_scatter = LIMRAD94_VEL['var'][~combined_mask].ravel()
MRR_VEL_scatter = MRR_VEL_interp['var'][~combined_mask].ravel()
s, i, r, p, std_err = stats.linregress(LIMRAD94_VEL_scatter, MRR_VEL_scatter)
H, xedges, yedges = np.histogram2d(LIMRAD94_VEL_scatter, MRR_VEL_scatter,
bins=120, range=[[-4, 4], [-4, 4]])
X, Y = np.meshgrid(xedges,yedges)
fig, ax = plt.subplots(1, figsize=(5.7, 5.7))
ax.pcolormesh(X, Y, np.transpose(H),
norm=matplotlib.colors.LogNorm(),
)
ax.text(0.01, 0.93, 'slope {:5.3f}\nintercept {:5.3f}\nR^2 {:5.3f}'.format(s,i,r**2),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
#ax.scatter(Mira_Z_scatter, Limrad_Z_scatter, s=1)
ax.plot([-10,10],[-10,10], color='salmon')
ax.plot([-10,10],[-8,12], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-9,11], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-11,9], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-12,8], color='salmon', linewidth=0.7, linestyle='--')
ax.set_xlim([-4,4])
ax.set_ylim([-4,4])
ax.set_xlabel("LIMRAD VEL")
ax.set_ylabel("MRRPRO VEL")
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.tick_params(axis='both', which='both', right=True, top=True)
fig.savefig(case_prefix+'scatter_vel_limrad_mrr.png', dpi=250)
begin_dt=datetime.datetime(2018,12,14,6,0)
end_dt=datetime.datetime(2018,12,14,9,0)
plot_range = [300, 10000]
case_prefix = 'plots/scatter_case_studies/comparison_c14_'
#### load the velocity data
MIRA_VELg=larda.read("MIRA","VELg",[begin_dt,end_dt],[0,'max'])
MIRA_VELg['var_lims'] = [-6,6]
fig, ax = pyLARDA.Transformations.plot2d(MIRA_VELg, range_interval=plot_range)
fig.savefig(case_prefix+'mira_vel.png', dpi=250)
LIMRAD94_VEL=larda.read("LIMRAD94","VEL",[begin_dt,end_dt],[0,'max'])
fig, ax = pyLARDA.Transformations.plot2d(LIMRAD94_VEL, range_interval=plot_range)
fig.savefig(case_prefix+'limrad_vel.png', dpi=250)
LIMRAD94_VEL_interp = pyLARDA.Transformations.interpolate2d(LIMRAD94_VEL, new_time=MIRA_VELg['ts'], new_range=MIRA_VELg['rg'])
fig, ax = pyLARDA.Transformations.plot2d(LIMRAD94_VEL_interp, range_interval=plot_range)
fig.savefig(case_prefix+'limrad_vel_interp.png', dpi=250)
# vel scatterplot
combined_mask = np.logical_or(MIRA_VELg['mask'], LIMRAD94_VEL_interp['mask'])
Mira_VELg_scatter = MIRA_VELg['var'][~combined_mask].ravel()
Limrad_VEL_scatter = LIMRAD94_VEL_interp['var'][~combined_mask].ravel()
s, i, r, p, std_err = stats.linregress(Mira_VELg_scatter, Limrad_VEL_scatter)
H, xedges, yedges = np.histogram2d(Mira_VELg_scatter, Limrad_VEL_scatter,
bins=120, range=[[-4, 4], [-4, 4]])
X, Y = np.meshgrid(xedges,yedges)
fig, ax = plt.subplots(1, figsize=(5.7, 5.7))
ax.pcolormesh(X, Y, np.transpose(H),
norm=matplotlib.colors.LogNorm(),
)
ax.text(0.01, 0.93, 'slope {:5.3f}\nintercept {:5.3f}\nR^2 {:5.3f}'.format(s,i,r**2),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
#ax.scatter(Mira_Z_scatter, Limrad_Z_scatter, s=1)
ax.plot([-10,10],[-10,10], color='salmon')
ax.plot([-10,10],[-8,12], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-9,11], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-11,9], color='salmon', linewidth=0.7, linestyle='--')
ax.plot([-10,10],[-12,8], color='salmon', linewidth=0.7, linestyle='--')
ax.set_xlim([-4,4])
ax.set_ylim([-4,4])
ax.set_xlabel("MIRA VELg")
ax.set_ylabel("LIMRAD94 VEL")
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.tick_params(axis='both', which='both', right=True, top=True)
fig.savefig(case_prefix+'scatter_vel_mira_limrad.png', dpi=250)
| 44.431818
| 126
| 0.718073
| 1,863
| 11,730
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| 0.097155
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| 0.031029
| 0.922304
| 0.914733
| 0.900087
| 0.87998
| 0.87998
| 0.867817
| 0
| 0.059788
| 0.090281
| 11,730
| 263
| 127
| 44.60076
| 0.695249
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| 0.03163
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| false
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| 0.045
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| 0.005
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| null | 0
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| 1
| 1
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0
| 7
|
64a0662fad3f8047831bcf934fd27858eee0fe96
| 513,212
|
py
|
Python
|
tests/examples/minlplib/fdesign50.py
|
ouyang-w-19/decogo
|
52546480e49776251d4d27856e18a46f40c824a1
|
[
"MIT"
] | 2
|
2021-07-03T13:19:10.000Z
|
2022-02-06T10:48:13.000Z
|
tests/examples/minlplib/fdesign50.py
|
ouyang-w-19/decogo
|
52546480e49776251d4d27856e18a46f40c824a1
|
[
"MIT"
] | 1
|
2021-07-04T14:52:14.000Z
|
2021-07-15T10:17:11.000Z
|
tests/examples/minlplib/fdesign50.py
|
ouyang-w-19/decogo
|
52546480e49776251d4d27856e18a46f40c824a1
|
[
"MIT"
] | null | null | null |
# NLP written by GAMS Convert at 04/21/18 13:52:11
#
# Equation counts
# Total E G L N X C B
# 307 2 1 304 0 0 0 0
#
# Variable counts
# x b i s1s s2s sc si
# Total cont binary integer sos1 sos2 scont sint
# 55 55 0 0 0 0 0 0
# FX 1 1 0 0 0 0 0 0
#
# Nonzero counts
# Total const NL DLL
# 15391 15388 3 0
from pyomo.environ import *
model = m = ConcreteModel()
m.x1 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x2 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x3 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x4 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x5 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x6 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x7 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x8 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x9 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x10 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x11 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x12 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x13 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x14 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x15 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x16 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x17 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x18 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x19 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x20 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x21 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x22 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x23 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x24 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x25 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x26 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x27 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x28 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x29 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x30 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x31 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x32 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x33 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x34 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x35 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x36 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x37 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x38 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x39 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x40 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x41 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x42 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x43 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x44 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x45 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x46 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x47 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x48 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x49 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x50 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x51 = Var(within=Reals,bounds=(1,None),initialize=1)
m.x52 = Var(within=Reals,bounds=(None,None),initialize=0)
m.x53 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x54 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x55 = Var(within=Reals,bounds=(2,2),initialize=2)
m.obj = Objective(expr=m.x51, sense=minimize)
m.c1 = Constraint(expr= 2*m.x1 + 2*m.x2 + 2*m.x3 + 2*m.x4 + 2*m.x5 + 2*m.x6 + 2*m.x7 + 2*m.x8 + 2*m.x9 + 2*m.x10
+ 2*m.x11 + 2*m.x12 + 2*m.x13 + 2*m.x14 + 2*m.x15 + 2*m.x16 + 2*m.x17 + 2*m.x18 + 2*m.x19
+ 2*m.x20 + 2*m.x21 + 2*m.x22 + 2*m.x23 + 2*m.x24 + 2*m.x25 + 2*m.x26 + 2*m.x27 + 2*m.x28
+ 2*m.x29 + 2*m.x30 + 2*m.x31 + 2*m.x32 + 2*m.x33 + 2*m.x34 + 2*m.x35 + 2*m.x36 + 2*m.x37
+ 2*m.x38 + 2*m.x39 + 2*m.x40 + 2*m.x41 + 2*m.x42 + 2*m.x43 + 2*m.x44 + 2*m.x45 + 2*m.x46
+ 2*m.x47 + 2*m.x48 + 2*m.x49 + 2*m.x50 - m.x51 <= 0)
m.c2 = Constraint(expr= 1.29889609666037*m.x1 + 1.32524009643148*m.x2 + 1.35118041523132*m.x3 + 1.37670915138751*m.x4
+ 1.4018185285997*m.x5 + 1.42650089830836*m.x6 + 1.45074874202458*m.x7 + 1.47455467362025*m.x8
+ 1.497911441578*m.x9 + 1.52081193120006*m.x10 + 1.54324916677544*m.x11 + 1.56521631370483*m.x12
+ 1.58670668058247*m.x13 + 1.60771372123443*m.x14 + 1.62823103671264*m.x15
+ 1.64825237724403*m.x16 + 1.66777164413434*m.x17 + 1.68678289162577*m.x18
+ 1.70528032870818*m.x19 + 1.72325832088305*m.x20 + 1.7407113918798*m.x21
+ 1.75763422532393*m.x22 + 1.77402166635644*m.x23 + 1.78986872320405*m.x24
+ 1.80517056869972*m.x25 + 1.81992254175309*m.x26 + 1.83412014877025*m.x27
+ 1.84775906502257*m.x28 + 1.86083513596405*m.x29 + 1.8733443784968*m.x30
+ 1.88528298218436*m.x31 + 1.8966473104124*m.x32 + 1.90743390149645*m.x33
+ 1.91763946973639*m.x34 + 1.92726090641725*m.x35 + 1.93629528075622*m.x36
+ 1.94473984079535*m.x37 + 1.95259201423987*m.x38 + 1.95984940924166*m.x39
+ 1.96650981512791*m.x40 + 1.97257120307446*m.x41 + 1.97803172672383*m.x42
+ 1.98288972274762*m.x43 + 1.98714371135317*m.x44 + 1.99079239673436*m.x45
+ 1.99383466746626*m.x46 + 1.99626959684373*m.x47 + 1.99809644316372*m.x48
+ 1.99931464995111*m.x49 + 1.99992384612834*m.x50 - m.x51 <= 0)
m.c3 = Constraint(expr= - 0.312868930080462*m.x1 - 0.243738686810295*m.x2 - 0.174311485495316*m.x3
- 0.104671912485888*m.x4 - 0.034904812874567*m.x5 + 0.0349048128745672*m.x6
+ 0.104671912485888*m.x7 + 0.174311485495316*m.x8 + 0.243738686810295*m.x9
+ 0.312868930080462*m.x10 + 0.38161799075309*m.x11 + 0.44990210868773*m.x12
+ 0.517638090205041*m.x13 + 0.584743409445474*m.x14 + 0.651136308914314*m.x15
+ 0.716735899090601*m.x16 + 0.781462256978547*m.x17 + 0.845236523481399*m.x18
+ 0.907980999479094*m.x19 + 0.969619240492674*m.x20 + 1.03007614982011*m.x21
+ 1.08927807003005*m.x22 + 1.14715287270209*m.x23 + 1.2036300463041*m.x24
+ 1.25864078209968*m.x25 + 1.31211805798101*m.x26 + 1.363996720125*m.x27 + 1.4142135623731*m.x28
+ 1.46270740323834*m.x29 + 1.50941916044554*m.x30 + 1.55429192291394*m.x31
+ 1.59727102009459*m.x32 + 1.63830408857798*m.x33 + 1.67734113589085*m.x34
+ 1.71433460140422*m.x35 + 1.74923941427879*m.x36 + 1.78201304837674*m.x37
+ 1.8126155740733*m.x38 + 1.84100970690488*m.x39 + 1.8671608529944*m.x40
+ 1.89103715119863*m.x41 + 1.91260951192607*m.x42 + 1.93185165257814*m.x43
+ 1.94874012957047*m.x44 + 1.96325436689533*m.x45 + 1.97537668119028*m.x46
+ 1.98509230328264*m.x47 + 1.99238939618349*m.x48 + 1.99725906950915*m.x49
+ 1.99969539031278*m.x50 - m.x51 <= 0)
m.c4 = Constraint(expr= - 1.70528032870818*m.x1 - 1.64825237724403*m.x2 - 1.58670668058247*m.x3 - 1.52081193120006*m.x4
- 1.45074874202458*m.x5 - 1.37670915138751*m.x6 - 1.29889609666037*m.x7 - 1.21752285801744*m.x8
- 1.13281247384967*m.x9 - 1.0449971294319*m.x10 - 0.954317520519217*m.x11
- 0.861022193616591*m.x12 - 0.76536686473018*m.x13 - 0.667613718467542*m.x14
- 0.568030689407845*m.x15 - 0.466890727711811*m.x16 - 0.364471050984295*m.x17
- 0.261052384440103*m.x18 - 0.15691819145569*m.x19 - 0.0523538966157465*m.x20
+ 0.0523538966157463*m.x21 + 0.15691819145569*m.x22 + 0.261052384440103*m.x23
+ 0.364471050984295*m.x24 + 0.466890727711811*m.x25 + 0.568030689407845*m.x26
+ 0.667613718467542*m.x27 + 0.76536686473018*m.x28 + 0.86102219361659*m.x29
+ 0.954317520519217*m.x30 + 1.0449971294319*m.x31 + 1.13281247384967*m.x32
+ 1.21752285801744*m.x33 + 1.29889609666037*m.x34 + 1.37670915138751*m.x35
+ 1.45074874202458*m.x36 + 1.52081193120006*m.x37 + 1.58670668058247*m.x38
+ 1.64825237724403*m.x39 + 1.70528032870818*m.x40 + 1.75763422532393*m.x41
+ 1.80517056869972*m.x42 + 1.84775906502257*m.x43 + 1.88528298218436*m.x44
+ 1.91763946973639*m.x45 + 1.94473984079535*m.x46 + 1.96650981512791*m.x47
+ 1.98288972274762*m.x48 + 1.99383466746626*m.x49 + 1.99931464995111*m.x50 - m.x51 <= 0)
m.c5 = Constraint(expr= - 1.90211303259031*m.x1 - 1.94059145255199*m.x2 - 1.96961550602442*m.x3 - 1.98904379073655*m.x4
- 1.99878165403819*m.x5 - 1.99878165403819*m.x6 - 1.98904379073655*m.x7 - 1.96961550602442*m.x8
- 1.94059145255199*m.x9 - 1.90211303259031*m.x10 - 1.85436770913357*m.x11
- 1.79758809259833*m.x12 - 1.73205080756888*m.x13 - 1.65807514511008*m.x14
- 1.57602150721344*m.x15 - 1.48628965095479*m.x16 - 1.38931674091799*m.x17
- 1.28557521937308*m.x18 - 1.17557050458495*m.x19 - 1.05983852846641*m.x20
- 0.938943125571782*m.x21 - 0.813473286151601*m.x22 - 0.684040286651337*m.x23
- 0.551274711633998*m.x24 - 0.415823381635519*m.x25 - 0.278346201920131*m.x26
- 0.139512947488251*m.x27 + 1.22464679914735E-16*m.x28 + 0.13951294748825*m.x29
+ 0.278346201920131*m.x30 + 0.415823381635519*m.x31 + 0.551274711633998*m.x32
+ 0.684040286651338*m.x33 + 0.8134732861516*m.x34 + 0.938943125571782*m.x35
+ 1.05983852846641*m.x36 + 1.17557050458495*m.x37 + 1.28557521937308*m.x38
+ 1.38931674091799*m.x39 + 1.48628965095479*m.x40 + 1.57602150721344*m.x41
+ 1.65807514511008*m.x42 + 1.73205080756888*m.x43 + 1.79758809259833*m.x44
+ 1.85436770913357*m.x45 + 1.90211303259031*m.x46 + 1.94059145255199*m.x47
+ 1.96961550602442*m.x48 + 1.98904379073655*m.x49 + 1.99878165403819*m.x50 - m.x51 <= 0)
m.c6 = Constraint(expr= - 0.765366864730181*m.x1 - 0.923497226470069*m.x2 - 1.07459921669365*m.x3
- 1.21752285801744*m.x4 - 1.35118041523132*m.x5 - 1.47455467362025*m.x6 - 1.58670668058247*m.x7
- 1.68678289162577*m.x8 - 1.77402166635644*m.x9 - 1.84775906502257*m.x10
- 1.90743390149645*m.x11 - 1.95259201423987*m.x12 - 1.98288972274762*m.x13
- 1.99809644316372*m.x14 - 1.99809644316372*m.x15 - 1.98288972274762*m.x16
- 1.95259201423987*m.x17 - 1.90743390149645*m.x18 - 1.84775906502257*m.x19
- 1.77402166635644*m.x20 - 1.68678289162577*m.x21 - 1.58670668058247*m.x22
- 1.47455467362025*m.x23 - 1.35118041523132*m.x24 - 1.21752285801744*m.x25
- 1.07459921669365*m.x26 - 0.923497226470067*m.x27 - 0.765366864730179*m.x28
- 0.601411599008546*m.x29 - 0.432879227876206*m.x30 - 0.261052384440103*m.x31
- 0.0872387747306718*m.x32 + 0.087238774730672*m.x33 + 0.261052384440103*m.x34
+ 0.432879227876206*m.x35 + 0.601411599008547*m.x36 + 0.76536686473018*m.x37
+ 0.923497226470068*m.x38 + 1.07459921669365*m.x39 + 1.21752285801744*m.x40
+ 1.35118041523132*m.x41 + 1.47455467362025*m.x42 + 1.58670668058247*m.x43
+ 1.68678289162577*m.x44 + 1.77402166635644*m.x45 + 1.84775906502257*m.x46
+ 1.90743390149645*m.x47 + 1.95259201423987*m.x48 + 1.98288972274762*m.x49
+ 1.99809644316372*m.x50 - m.x51 <= 0)
m.c7 = Constraint(expr= 0.907980999479095*m.x1 + 0.716735899090601*m.x2 + 0.517638090205042*m.x3
+ 0.312868930080461*m.x4 + 0.104671912485888*m.x5 - 0.104671912485887*m.x6
- 0.312868930080462*m.x7 - 0.517638090205041*m.x8 - 0.7167358990906*m.x9
- 0.907980999479094*m.x10 - 1.08927807003005*m.x11 - 1.25864078209967*m.x12
- 1.41421356237309*m.x13 - 1.55429192291394*m.x14 - 1.67734113589085*m.x15
- 1.78201304837674*m.x16 - 1.8671608529944*m.x17 - 1.93185165257814*m.x18
- 1.97537668119028*m.x19 - 1.99725906950915*m.x20 - 1.99725906950915*m.x21
- 1.97537668119028*m.x22 - 1.93185165257814*m.x23 - 1.8671608529944*m.x24
- 1.78201304837674*m.x25 - 1.67734113589085*m.x26 - 1.55429192291394*m.x27
- 1.41421356237309*m.x28 - 1.25864078209968*m.x29 - 1.08927807003005*m.x30
- 0.907980999479093*m.x31 - 0.716735899090601*m.x32 - 0.517638090205042*m.x33
- 0.312868930080462*m.x34 - 0.104671912485888*m.x35 + 0.104671912485887*m.x36
+ 0.312868930080461*m.x37 + 0.517638090205041*m.x38 + 0.7167358990906*m.x39
+ 0.907980999479094*m.x40 + 1.08927807003005*m.x41 + 1.25864078209968*m.x42
+ 1.41421356237309*m.x43 + 1.55429192291394*m.x44 + 1.67734113589085*m.x45
+ 1.78201304837674*m.x46 + 1.8671608529944*m.x47 + 1.93185165257814*m.x48
+ 1.97537668119028*m.x49 + 1.99725906950915*m.x50 - m.x51 <= 0)
m.c8 = Constraint(expr= 1.94473984079535*m.x1 + 1.8733443784968*m.x2 + 1.77402166635644*m.x3 + 1.64825237724403*m.x4
+ 1.497911441578*m.x5 + 1.32524009643148*m.x6 + 1.13281247384966*m.x7 + 0.923497226470068*m.x8
+ 0.700414762518934*m.x9 + 0.466890727711811*m.x10 + 0.226406427535813*m.x11
- 0.0174530709967472*m.x12 - 0.261052384440103*m.x13 - 0.500760008108882*m.x14
- 0.733002453448594*m.x15 - 0.954317520519217*m.x16 - 1.16140591142188*m.x17
- 1.35118041523132*m.x18 - 1.52081193120006*m.x19 - 1.66777164413434*m.x20
- 1.78986872320405*m.x21 - 1.88528298218436*m.x22 - 1.95259201423987*m.x23
- 1.99079239673436*m.x24 - 1.99931464995111*m.x25 - 1.97803172672383*m.x26
- 1.92726090641725*m.x27 - 1.84775906502257*m.x28 - 1.7407113918798*m.x29
- 1.60771372123443*m.x30 - 1.45074874202458*m.x31 - 1.27215644055553*m.x32
- 1.07459921669365*m.x33 - 0.861022193616591*m.x34 - 0.634609312810184*m.x35
- 0.398735868834395*m.x36 - 0.15691819145569*m.x37 + 0.087238774730672*m.x38
+ 0.330095211721355*m.x39 + 0.568030689407845*m.x40 + 0.797498137850493*m.x41
+ 1.01507672592141*m.x42 + 1.21752285801744*m.x43 + 1.4018185285997*m.x44
+ 1.56521631370483*m.x45 + 1.70528032870818*m.x46 + 1.81992254175309*m.x47
+ 1.90743390149645*m.x48 + 1.96650981512791*m.x49 + 1.99626959684373*m.x50 - m.x51 <= 0)
m.c9 = Constraint(expr= 1.6180339887499*m.x1 + 1.76589518571785*m.x2 + 1.87938524157182*m.x3 + 1.95629520146761*m.x4
+ 1.99512810051965*m.x5 + 1.99512810051965*m.x6 + 1.95629520146761*m.x7 + 1.87938524157182*m.x8
+ 1.76589518571785*m.x9 + 1.61803398874989*m.x10 + 1.4386796006773*m.x11
+ 1.23132295065132*m.x12 + m.x13 + 0.749213186831824*m.x14 + 0.483843791199335*m.x15
+ 0.209056926535306*m.x16 - 0.0697989934050015*m.x17 - 0.347296355333861*m.x18
- 0.618033988749895*m.x19 - 0.876742293578155*m.x20 - 1.11838580694149*m.x21
- 1.33826121271772*m.x22 - 1.53208888623796*m.x23 - 1.69609619231285*m.x24
- 1.8270909152852*m.x25 - 1.92252339187664*m.x26 - 1.98053613748314*m.x27 - 2*m.x28
- 1.98053613748314*m.x29 - 1.92252339187664*m.x30 - 1.8270909152852*m.x31
- 1.69609619231285*m.x32 - 1.53208888623796*m.x33 - 1.33826121271772*m.x34
- 1.11838580694149*m.x35 - 0.876742293578155*m.x36 - 0.618033988749895*m.x37
- 0.347296355333861*m.x38 - 0.0697989934050019*m.x39 + 0.209056926535307*m.x40
+ 0.483843791199335*m.x41 + 0.749213186831824*m.x42 + m.x43 + 1.23132295065132*m.x44
+ 1.4386796006773*m.x45 + 1.61803398874989*m.x46 + 1.76589518571785*m.x47
+ 1.87938524157182*m.x48 + 1.95629520146761*m.x49 + 1.99512810051965*m.x50 - m.x51 <= 0)
m.c10 = Constraint(expr= 0.156918191455691*m.x1 + 0.466890727711811*m.x2 + 0.765366864730181*m.x3
+ 1.0449971294319*m.x4 + 1.29889609666037*m.x5 + 1.52081193120006*m.x6 + 1.70528032870819*m.x7
+ 1.84775906502257*m.x8 + 1.94473984079535*m.x9 + 1.99383466746626*m.x10
+ 1.99383466746626*m.x11 + 1.94473984079535*m.x12 + 1.84775906502257*m.x13
+ 1.70528032870818*m.x14 + 1.52081193120006*m.x15 + 1.29889609666037*m.x16
+ 1.0449971294319*m.x17 + 0.76536686473018*m.x18 + 0.46689072771181*m.x19
+ 0.15691819145569*m.x20 - 0.156918191455691*m.x21 - 0.46689072771181*m.x22
- 0.765366864730181*m.x23 - 1.0449971294319*m.x24 - 1.29889609666037*m.x25
- 1.52081193120006*m.x26 - 1.70528032870818*m.x27 - 1.84775906502257*m.x28
- 1.94473984079535*m.x29 - 1.99383466746626*m.x30 - 1.99383466746626*m.x31
- 1.94473984079535*m.x32 - 1.84775906502257*m.x33 - 1.70528032870818*m.x34
- 1.52081193120006*m.x35 - 1.29889609666037*m.x36 - 1.0449971294319*m.x37
- 0.765366864730179*m.x38 - 0.466890727711811*m.x39 - 0.15691819145569*m.x40
+ 0.15691819145569*m.x41 + 0.466890727711811*m.x42 + 0.76536686473018*m.x43
+ 1.0449971294319*m.x44 + 1.29889609666037*m.x45 + 1.52081193120006*m.x46
+ 1.70528032870818*m.x47 + 1.84775906502257*m.x48 + 1.94473984079535*m.x49
+ 1.99383466746626*m.x50 - m.x51 <= 0)
m.c11 = Constraint(expr= - 1.41421356237309*m.x1 - 1.14715287270209*m.x2 - 0.845236523481397*m.x3
- 0.51763809020504*m.x4 - 0.174311485495316*m.x5 + 0.174311485495317*m.x6
+ 0.517638090205041*m.x7 + 0.8452365234814*m.x8 + 1.14715287270209*m.x9 + 1.4142135623731*m.x10
+ 1.63830408857798*m.x11 + 1.8126155740733*m.x12 + 1.93185165257814*m.x13
+ 1.99238939618349*m.x14 + 1.99238939618349*m.x15 + 1.93185165257814*m.x16
+ 1.8126155740733*m.x17 + 1.63830408857798*m.x18 + 1.41421356237309*m.x19
+ 1.14715287270209*m.x20 + 0.845236523481398*m.x21 + 0.517638090205041*m.x22
+ 0.174311485495316*m.x23 - 0.174311485495316*m.x24 - 0.517638090205041*m.x25
- 0.8452365234814*m.x26 - 1.14715287270209*m.x27 - 1.4142135623731*m.x28
- 1.63830408857798*m.x29 - 1.8126155740733*m.x30 - 1.93185165257814*m.x31
- 1.99238939618349*m.x32 - 1.99238939618349*m.x33 - 1.93185165257814*m.x34
- 1.8126155740733*m.x35 - 1.63830408857798*m.x36 - 1.41421356237309*m.x37
- 1.14715287270209*m.x38 - 0.845236523481399*m.x39 - 0.517638090205041*m.x40
- 0.174311485495316*m.x41 + 0.174311485495317*m.x42 + 0.517638090205041*m.x43
+ 0.845236523481399*m.x44 + 1.14715287270209*m.x45 + 1.4142135623731*m.x46
+ 1.63830408857798*m.x47 + 1.8126155740733*m.x48 + 1.93185165257814*m.x49
+ 1.99238939618349*m.x50 - m.x51 <= 0)
m.c12 = Constraint(expr= - 1.99383466746626*m.x1 - 1.98714371135317*m.x2 - 1.90743390149645*m.x3 - 1.75763422532393*m.x4
- 1.54324916677544*m.x5 - 1.27215644055553*m.x6 - 0.954317520519215*m.x7
- 0.601411599008547*m.x8 - 0.226406427535813*m.x9 + 0.156918191455691*m.x10
+ 0.534476752156514*m.x11 + 0.892395626219619*m.x12 + 1.21752285801744*m.x13
+ 1.49791144157801*m.x14 + 1.72325832088305*m.x15 + 1.88528298218436*m.x16
+ 1.97803172672383*m.x17 + 1.99809644316372*m.x18 + 1.94473984079535*m.x19
+ 1.81992254175309*m.x20 + 1.62823103671264*m.x21 + 1.37670915138751*m.x22
+ 1.07459921669365*m.x23 + 0.733002453448594*m.x24 + 0.364471050984295*m.x25
- 0.0174530709967489*m.x26 - 0.398735868834395*m.x27 - 0.765366864730181*m.x28
- 1.10387397062412*m.x29 - 1.4018185285997*m.x30 - 1.64825237724403*m.x31
- 1.83412014877025*m.x32 - 1.95259201423987*m.x33 - 1.99931464995111*m.x34
- 1.97257120307446*m.x35 - 1.8733443784968*m.x36 - 1.70528032870818*m.x37
- 1.47455467362025*m.x38 - 1.18964557350268*m.x39 - 0.86102219361659*m.x40
- 0.500760008108883*m.x41 - 0.122097079069714*m.x42 + 0.261052384440103*m.x43
+ 0.634609312810185*m.x44 + 0.984847120206934*m.x45 + 1.29889609666037*m.x46
+ 1.56521631370483*m.x47 + 1.77402166635644*m.x48 + 1.91763946973639*m.x49
+ 1.99079239673436*m.x50 - m.x51 <= 0)
m.c13 = Constraint(expr= - 1.17557050458494*m.x1 - 1.48628965095479*m.x2 - 1.73205080756888*m.x3 - 1.90211303259031*m.x4
- 1.98904379073655*m.x5 - 1.98904379073655*m.x6 - 1.90211303259031*m.x7 - 1.73205080756888*m.x8
- 1.48628965095479*m.x9 - 1.17557050458495*m.x10 - 0.813473286151601*m.x11
- 0.41582338163552*m.x12 - 1.16403343982657E-15*m.x13 + 0.415823381635518*m.x14
+ 0.8134732861516*m.x15 + 1.17557050458495*m.x16 + 1.48628965095479*m.x17
+ 1.73205080756888*m.x18 + 1.90211303259031*m.x19 + 1.98904379073655*m.x20
+ 1.98904379073655*m.x21 + 1.90211303259031*m.x22 + 1.73205080756888*m.x23
+ 1.48628965095479*m.x24 + 1.17557050458495*m.x25 + 0.813473286151601*m.x26
+ 0.415823381635519*m.x27 - 3.67394039744206E-16*m.x28 - 0.415823381635518*m.x29
- 0.8134732861516*m.x30 - 1.17557050458495*m.x31 - 1.48628965095479*m.x32
- 1.73205080756888*m.x33 - 1.90211303259031*m.x34 - 1.98904379073655*m.x35
- 1.98904379073655*m.x36 - 1.90211303259031*m.x37 - 1.73205080756888*m.x38
- 1.48628965095479*m.x39 - 1.17557050458495*m.x40 - 0.813473286151601*m.x41
- 0.415823381635519*m.x42 - 3.21624529935327E-16*m.x43 + 0.415823381635518*m.x44
+ 0.8134732861516*m.x45 + 1.17557050458495*m.x46 + 1.48628965095479*m.x47
+ 1.73205080756888*m.x48 + 1.90211303259031*m.x49 + 1.98904379073655*m.x50 - m.x51 <= 0)
m.c14 = Constraint(expr= 0.466890727711813*m.x1 + 0.0174530709967495*m.x2 - 0.432879227876204*m.x3
- 0.861022193616589*m.x4 - 1.24502927327524*m.x5 - 1.56521631370483*m.x6
- 1.80517056869972*m.x7 - 1.95259201423987*m.x8 - 1.99992384612834*m.x9
- 1.94473984079535*m.x10 - 1.78986872320405*m.x11 - 1.54324916677544*m.x12
- 1.21752285801744*m.x13 - 0.829386485312478*m.x14 - 0.398735868834394*m.x15
+ 0.0523538966157454*m.x16 + 0.500760008108882*m.x17 + 0.923497226470067*m.x18
+ 1.29889609666037*m.x19 + 1.60771372123443*m.x20 + 1.83412014877025*m.x21
+ 1.96650981512791*m.x22 + 1.99809644316372*m.x23 + 1.92726090641725*m.x24
+ 1.75763422532393*m.x25 + 1.497911441578*m.x26 + 1.16140591142188*m.x27
+ 0.76536686473018*m.x28 + 0.330095211721356*m.x29 - 0.122097079069714*m.x30
- 0.568030689407845*m.x31 - 0.984847120206934*m.x32 - 1.35118041523132*m.x33
- 1.64825237724403*m.x34 - 1.86083513596405*m.x35 - 1.97803172672383*m.x36
- 1.99383466746626*m.x37 - 1.90743390149645*m.x38 - 1.72325832088305*m.x39
- 1.45074874202458*m.x40 - 1.10387397062412*m.x41 - 0.700414762518935*m.x42
- 0.261052384440103*m.x43 + 0.191691505040448*m.x44 + 0.634609312810184*m.x45
+ 1.0449971294319*m.x46 + 1.4018185285997*m.x47 + 1.68678289162577*m.x48
+ 1.88528298218436*m.x49 + 1.98714371135317*m.x50 - m.x51 <= 0)
m.c15 = Constraint(expr= 1.78201304837674*m.x1 + 1.50941916044554*m.x2 + 1.14715287270209*m.x3 + 0.7167358990906*m.x4
+ 0.243738686810297*m.x5 - 0.243738686810295*m.x6 - 0.716735899090602*m.x7
- 1.14715287270209*m.x8 - 1.50941916044554*m.x9 - 1.78201304837674*m.x10
- 1.94874012957047*m.x11 - 1.99969539031278*m.x12 - 1.93185165257814*m.x13
- 1.74923941427879*m.x14 - 1.46270740323834*m.x15 - 1.08927807003005*m.x16
- 0.651136308914314*m.x17 - 0.174311485495317*m.x18 + 0.312868930080462*m.x19
+ 0.781462256978548*m.x20 + 1.2036300463041*m.x21 + 1.55429192291394*m.x22
+ 1.8126155740733*m.x23 + 1.96325436689533*m.x24 + 1.99725906950915*m.x25
+ 1.91260951192607*m.x26 + 1.71433460140422*m.x27 + 1.41421356237309*m.x28
+ 1.03007614982011*m.x29 + 0.584743409445473*m.x30 + 0.104671912485888*m.x31
- 0.381617990753089*m.x32 - 0.845236523481398*m.x33 - 1.25864078209967*m.x34
- 1.59727102009459*m.x35 - 1.84100970690488*m.x36 - 1.97537668119028*m.x37
- 1.99238939618349*m.x38 - 1.89103715119863*m.x39 - 1.67734113589085*m.x40
- 1.363996720125*m.x41 - 0.969619240492674*m.x42 - 0.517638090205042*m.x43
- 0.034904812874567*m.x44 + 0.44990210868773*m.x45 + 0.907980999479094*m.x46
+ 1.31211805798101*m.x47 + 1.63830408857798*m.x48 + 1.8671608529944*m.x49
+ 1.98509230328264*m.x50 - m.x51 <= 0)
m.c16 = Constraint(expr= 1.84775906502257*m.x1 + 1.98288972274762*m.x2 + 1.98288972274762*m.x3 + 1.84775906502257*m.x4
+ 1.58670668058247*m.x5 + 1.21752285801744*m.x6 + 0.765366864730176*m.x7 + 0.2610523844401*m.x8
- 0.261052384440105*m.x9 - 0.765366864730181*m.x10 - 1.21752285801744*m.x11
- 1.58670668058247*m.x12 - 1.84775906502257*m.x13 - 1.98288972274762*m.x14
- 1.98288972274762*m.x15 - 1.84775906502257*m.x16 - 1.58670668058247*m.x17
- 1.21752285801744*m.x18 - 0.765366864730176*m.x19 - 0.261052384440102*m.x20
+ 0.261052384440105*m.x21 + 0.765366864730181*m.x22 + 1.21752285801744*m.x23
+ 1.58670668058247*m.x24 + 1.84775906502257*m.x25 + 1.98288972274762*m.x26
+ 1.98288972274762*m.x27 + 1.84775906502257*m.x28 + 1.58670668058247*m.x29
+ 1.21752285801744*m.x30 + 0.765366864730178*m.x31 + 0.261052384440103*m.x32
- 0.261052384440105*m.x33 - 0.765366864730181*m.x34 - 1.21752285801744*m.x35
- 1.58670668058247*m.x36 - 1.84775906502257*m.x37 - 1.98288972274762*m.x38
- 1.98288972274762*m.x39 - 1.84775906502257*m.x40 - 1.58670668058247*m.x41
- 1.21752285801744*m.x42 - 0.765366864730179*m.x43 - 0.261052384440103*m.x44
+ 0.261052384440103*m.x45 + 0.76536686473018*m.x46 + 1.21752285801744*m.x47
+ 1.58670668058247*m.x48 + 1.84775906502257*m.x49 + 1.98288972274762*m.x50 - m.x51 <= 0)
m.c17 = Constraint(expr= 0.618033988749896*m.x1 + 1.1183858069415*m.x2 + 1.53208888623796*m.x3 + 1.8270909152852*m.x4
+ 1.98053613748314*m.x5 + 1.98053613748314*m.x6 + 1.8270909152852*m.x7 + 1.53208888623796*m.x8
+ 1.11838580694149*m.x9 + 0.618033988749894*m.x10 + 0.0697989934050003*m.x11
- 0.483843791199334*m.x12 - m.x13 - 1.4386796006773*m.x14 - 1.76589518571785*m.x15
- 1.95629520146761*m.x16 - 1.99512810051965*m.x17 - 1.87938524157182*m.x18
- 1.61803398874989*m.x19 - 1.23132295065132*m.x20 - 0.749213186831825*m.x21
- 0.209056926535307*m.x22 + 0.347296355333861*m.x23 + 0.876742293578156*m.x24
+ 1.33826121271772*m.x25 + 1.69609619231285*m.x26 + 1.92252339187664*m.x27 + 2*m.x28
+ 1.92252339187664*m.x29 + 1.69609619231285*m.x30 + 1.33826121271772*m.x31
+ 0.876742293578155*m.x32 + 0.34729635533386*m.x33 - 0.209056926535307*m.x34
- 0.749213186831825*m.x35 - 1.23132295065132*m.x36 - 1.61803398874989*m.x37
- 1.87938524157182*m.x38 - 1.99512810051965*m.x39 - 1.95629520146761*m.x40
- 1.76589518571785*m.x41 - 1.4386796006773*m.x42 - m.x43 - 0.483843791199336*m.x44
+ 0.0697989934050022*m.x45 + 0.618033988749895*m.x46 + 1.11838580694149*m.x47
+ 1.53208888623796*m.x48 + 1.8270909152852*m.x49 + 1.98053613748314*m.x50 - m.x51 <= 0)
m.c18 = Constraint(expr= - 1.0449971294319*m.x1 - 0.500760008108886*m.x2 + 0.0872387747306712*m.x3
+ 0.66761371846754*m.x4 + 1.18964557350268*m.x5 + 1.60771372123443*m.x6 + 1.88528298218436*m.x7
+ 1.99809644316372*m.x8 + 1.93629528075622*m.x9 + 1.70528032870818*m.x10
+ 1.32524009643148*m.x11 + 0.829386485312481*m.x12 + 0.261052384440104*m.x13
- 0.330095211721353*m.x14 - 0.892395626219618*m.x15 - 1.37670915138751*m.x16
- 1.7407113918798*m.x17 - 1.95259201423987*m.x18 - 1.99383466746626*m.x19
- 1.86083513596405*m.x20 - 1.56521631370483*m.x21 - 1.13281247384967*m.x22
- 0.601411599008547*m.x23 - 0.0174530709967497*m.x24 + 0.568030689407844*m.x25
+ 1.10387397062412*m.x26 + 1.54324916677544*m.x27 + 1.84775906502257*m.x28
+ 1.99079239673436*m.x29 + 1.95984940924166*m.x30 + 1.75763422532393*m.x31
+ 1.4018185285997*m.x32 + 0.923497226470068*m.x33 + 0.364471050984295*m.x34
- 0.226406427535812*m.x35 - 0.797498137850492*m.x36 - 1.29889609666037*m.x37
- 1.68678289162577*m.x38 - 1.92726090641725*m.x39 - 1.99931464995111*m.x40
- 1.8966473104124*m.x41 - 1.62823103671264*m.x42 - 1.21752285801744*m.x43
- 0.700414762518935*m.x44 - 0.122097079069714*m.x45 + 0.466890727711811*m.x46
+ 1.01507672592141*m.x47 + 1.47455467362025*m.x48 + 1.80517056869972*m.x49
+ 1.97803172672383*m.x50 - m.x51 <= 0)
m.c19 = Constraint(expr= - 1.97537668119027*m.x1 - 1.78201304837674*m.x2 - 1.41421356237309*m.x3
- 0.907980999479094*m.x4 - 0.312868930080459*m.x5 + 0.312868930080461*m.x6
+ 0.907980999479096*m.x7 + 1.41421356237309*m.x8 + 1.78201304837674*m.x9
+ 1.97537668119028*m.x10 + 1.97537668119028*m.x11 + 1.78201304837674*m.x12
+ 1.41421356237309*m.x13 + 0.907980999479094*m.x14 + 0.312868930080459*m.x15
- 0.312868930080461*m.x16 - 0.907980999479096*m.x17 - 1.41421356237309*m.x18
- 1.78201304837674*m.x19 - 1.97537668119028*m.x20 - 1.97537668119028*m.x21
- 1.78201304837674*m.x22 - 1.41421356237309*m.x23 - 0.907980999479095*m.x24
- 0.312868930080459*m.x25 + 0.312868930080462*m.x26 + 0.907980999479094*m.x27
+ 1.4142135623731*m.x28 + 1.78201304837674*m.x29 + 1.97537668119028*m.x30
+ 1.97537668119028*m.x31 + 1.78201304837674*m.x32 + 1.41421356237309*m.x33
+ 0.907980999479093*m.x34 + 0.312868930080461*m.x35 - 0.312868930080462*m.x36
- 0.907980999479094*m.x37 - 1.4142135623731*m.x38 - 1.78201304837674*m.x39
- 1.97537668119028*m.x40 - 1.97537668119028*m.x41 - 1.78201304837674*m.x42
- 1.41421356237309*m.x43 - 0.907980999479093*m.x44 - 0.312868930080462*m.x45
+ 0.312868930080462*m.x46 + 0.907980999479094*m.x47 + 1.4142135623731*m.x48
+ 1.78201304837674*m.x49 + 1.97537668119028*m.x50 - m.x51 <= 0)
m.c20 = Constraint(expr= - 1.52081193120006*m.x1 - 1.86083513596405*m.x2 - 1.99809644316372*m.x3 - 1.91763946973639*m.x4
- 1.62823103671264*m.x5 - 1.16140591142188*m.x6 - 0.568030689407847*m.x7
+ 0.0872387747306712*m.x8 + 0.733002453448595*m.x9 + 1.29889609666037*m.x10
+ 1.72325832088305*m.x11 + 1.95984940924166*m.x12 + 1.98288972274762*m.x13
+ 1.78986872320405*m.x14 + 1.4018185285997*m.x15 + 0.861022193616591*m.x16
+ 0.226406427535813*m.x17 - 0.432879227876204*m.x18 - 1.0449971294319*m.x19
- 1.54324916677544*m.x20 - 1.87334437849679*m.x21 - 1.99931464995111*m.x22
- 1.90743390149645*m.x23 - 1.60771372123443*m.x24 - 1.13281247384966*m.x25
- 0.534476752156515*m.x26 + 0.122097079069714*m.x27 + 0.765366864730179*m.x28
+ 1.32524009643147*m.x29 + 1.7407113918798*m.x30 + 1.96650981512791*m.x31
+ 1.97803172672383*m.x32 + 1.77402166635644*m.x33 + 1.37670915138751*m.x34
+ 0.829386485312478*m.x35 + 0.191691505040448*m.x36 - 0.46689072771181*m.x37
- 1.07459921669365*m.x38 - 1.56521631370483*m.x39 - 1.88528298218436*m.x40
- 1.99992384612834*m.x41 - 1.8966473104124*m.x42 - 1.58670668058247*m.x43
- 1.10387397062412*m.x44 - 0.500760008108883*m.x45 + 0.15691819145569*m.x46
+ 0.797498137850492*m.x47 + 1.35118041523132*m.x48 + 1.75763422532393*m.x49
+ 1.97257120307446*m.x50 - m.x51 <= 0)
m.c21 = Constraint(expr= - 4.89982515786259E-15*m.x1 - 0.684040286651341*m.x2 - 1.28557521937308*m.x3
- 1.73205080756888*m.x4 - 1.96961550602442*m.x5 - 1.96961550602442*m.x6 - 1.73205080756888*m.x7
- 1.28557521937308*m.x8 - 0.684040286651336*m.x9 + 1.10218211923262E-15*m.x10
+ 0.684040286651338*m.x11 + 1.28557521937308*m.x12 + 1.73205080756888*m.x13
+ 1.96961550602442*m.x14 + 1.96961550602442*m.x15 + 1.73205080756888*m.x16
+ 1.28557521937308*m.x17 + 0.684040286651336*m.x18 - 8.57252759403147E-16*m.x19
- 0.684040286651338*m.x20 - 1.28557521937308*m.x21 - 1.73205080756888*m.x22
- 1.96961550602442*m.x23 - 1.96961550602442*m.x24 - 1.73205080756888*m.x25
- 1.28557521937308*m.x26 - 0.684040286651336*m.x27 + 6.12323399573677E-16*m.x28
+ 0.684040286651339*m.x29 + 1.28557521937308*m.x30 + 1.73205080756888*m.x31
+ 1.96961550602442*m.x32 + 1.96961550602442*m.x33 + 1.73205080756888*m.x34
+ 1.28557521937308*m.x35 + 0.684040286651336*m.x36 - 3.67394039744206E-16*m.x37
- 0.684040286651339*m.x38 - 1.28557521937308*m.x39 - 1.73205080756888*m.x40
- 1.96961550602442*m.x41 - 1.96961550602442*m.x42 - 1.73205080756888*m.x43
- 1.28557521937308*m.x44 - 0.684040286651337*m.x45 + 1.22464679914735E-16*m.x46
+ 0.684040286651338*m.x47 + 1.28557521937308*m.x48 + 1.73205080756888*m.x49
+ 1.96961550602442*m.x50 - m.x51 <= 0)
m.c22 = Constraint(expr= 1.52081193120006*m.x1 + 0.954317520519217*m.x2 + 0.261052384440107*m.x3
- 0.466890727711811*m.x4 - 1.13281247384966*m.x5 - 1.64825237724403*m.x6
- 1.94473984079535*m.x7 - 1.98288972274762*m.x8 - 1.75763422532393*m.x9
- 1.29889609666037*m.x10 - 0.667613718467541*m.x11 + 0.0523538966157477*m.x12
+ 0.765366864730178*m.x13 + 1.37670915138751*m.x14 + 1.80517056869972*m.x15
+ 1.99383466746626*m.x16 + 1.91763946973639*m.x17 + 1.58670668058247*m.x18
+ 1.0449971294319*m.x19 + 0.364471050984296*m.x20 - 0.364471050984294*m.x21
- 1.0449971294319*m.x22 - 1.58670668058247*m.x23 - 1.91763946973639*m.x24
- 1.99383466746626*m.x25 - 1.80517056869972*m.x26 - 1.37670915138751*m.x27
- 0.76536686473018*m.x28 - 0.052353896615746*m.x29 + 0.667613718467541*m.x30
+ 1.29889609666037*m.x31 + 1.75763422532393*m.x32 + 1.98288972274762*m.x33
+ 1.94473984079535*m.x34 + 1.64825237724403*m.x35 + 1.13281247384966*m.x36
+ 0.466890727711811*m.x37 - 0.261052384440103*m.x38 - 0.954317520519217*m.x39
- 1.52081193120006*m.x40 - 1.88528298218436*m.x41 - 1.99931464995111*m.x42
- 1.84775906502257*m.x43 - 1.45074874202458*m.x44 - 0.861022193616591*m.x45
- 0.15691819145569*m.x46 + 0.568030689407845*m.x47 + 1.21752285801744*m.x48
+ 1.70528032870818*m.x49 + 1.96650981512791*m.x50 - m.x51 <= 0)
m.c23 = Constraint(expr= 1.97537668119028*m.x1 + 1.94874012957047*m.x2 + 1.63830408857798*m.x3 + 1.08927807003005*m.x4
+ 0.381617990753089*m.x5 - 0.381617990753092*m.x6 - 1.08927807003006*m.x7
- 1.63830408857798*m.x8 - 1.94874012957047*m.x9 - 1.97537668119027*m.x10
- 1.71433460140422*m.x11 - 1.20363004630409*m.x12 - 0.51763809020504*m.x13
+ 0.243738686810299*m.x14 + 0.969619240492676*m.x15 + 1.55429192291394*m.x16
+ 1.91260951192607*m.x17 + 1.99238939618349*m.x18 + 1.78201304837673*m.x19
+ 1.31211805798101*m.x20 + 0.65113630891431*m.x21 - 0.104671912485889*m.x22
- 0.845236523481399*m.x23 - 1.46270740323834*m.x24 - 1.8671608529944*m.x25
- 1.99969539031278*m.x26 - 1.84100970690488*m.x27 - 1.41421356237309*m.x28
- 0.781462256978547*m.x29 - 0.034904812874566*m.x30 + 0.716735899090602*m.x31
+ 1.363996720125*m.x32 + 1.8126155740733*m.x33 + 1.99725906950915*m.x34
+ 1.89103715119863*m.x35 + 1.50941916044554*m.x36 + 0.907980999479093*m.x37
+ 0.174311485495316*m.x38 - 0.584743409445474*m.x39 - 1.25864078209968*m.x40
- 1.74923941427879*m.x41 - 1.98509230328264*m.x42 - 1.93185165257814*m.x43
- 1.59727102009459*m.x44 - 1.03007614982011*m.x45 - 0.312868930080462*m.x46
+ 0.44990210868773*m.x47 + 1.14715287270209*m.x48 + 1.67734113589085*m.x49
+ 1.96325436689533*m.x50 - m.x51 <= 0)
m.c24 = Constraint(expr= 1.0449971294319*m.x1 + 1.62823103671264*m.x2 + 1.95259201423987*m.x3 + 1.96650981512791*m.x4
+ 1.66777164413433*m.x5 + 1.10387397062411*m.x6 + 0.364471050984292*m.x7
- 0.432879227876208*m.x8 - 1.16140591142188*m.x9 - 1.70528032870819*m.x10
- 1.97803172672383*m.x11 - 1.93629528075622*m.x12 - 1.58670668058247*m.x13
- 0.984847120206933*m.x14 - 0.226406427535812*m.x15 + 0.568030689407846*m.x16
+ 1.27215644055553*m.x17 + 1.77402166635644*m.x18 + 1.99383466746626*m.x19
+ 1.8966473104124*m.x20 + 1.497911441578*m.x21 + 0.861022193616591*m.x22
+ 0.0872387747306728*m.x23 - 0.700414762518934*m.x24 - 1.37670915138751*m.x25
- 1.83412014877025*m.x26 - 1.99992384612834*m.x27 - 1.84775906502257*m.x28
- 1.4018185285997*m.x29 - 0.733002453448593*m.x30 + 0.0523538966157472*m.x31
+ 0.829386485312479*m.x32 + 1.47455467362025*m.x33 + 1.88528298218436*m.x34
+ 1.99626959684373*m.x35 + 1.78986872320405*m.x36 + 1.29889609666037*m.x37
+ 0.601411599008547*m.x38 - 0.191691505040449*m.x39 - 0.954317520519217*m.x40
- 1.56521631370483*m.x41 - 1.92726090641725*m.x42 - 1.98288972274762*m.x43
- 1.72325832088305*m.x44 - 1.18964557350268*m.x45 - 0.466890727711811*m.x46
+ 0.330095211721355*m.x47 + 1.07459921669365*m.x48 + 1.64825237724403*m.x49
+ 1.95984940924166*m.x50 - m.x51 <= 0)
m.c25 = Constraint(expr= - 0.6180339887499*m.x1 + 0.209056926535306*m.x2 + 0.999999999999997*m.x3 + 1.6180339887499*m.x4
+ 1.95629520146761*m.x5 + 1.95629520146761*m.x6 + 1.61803398874989*m.x7 + m.x8
+ 0.20905692653531*m.x9 - 0.618033988749896*m.x10 - 1.33826121271772*m.x11
- 1.8270909152852*m.x12 - 2*m.x13 - 1.8270909152852*m.x14 - 1.33826121271772*m.x15
- 0.618033988749897*m.x16 + 0.209056926535306*m.x17 + m.x18 + 1.61803398874989*m.x19
+ 1.95629520146761*m.x20 + 1.95629520146761*m.x21 + 1.6180339887499*m.x22 + m.x23
+ 0.209056926535307*m.x24 - 0.618033988749892*m.x25 - 1.33826121271772*m.x26
- 1.8270909152852*m.x27 - 2*m.x28 - 1.8270909152852*m.x29 - 1.33826121271772*m.x30
- 0.618033988749894*m.x31 + 0.209056926535307*m.x32 + m.x33 + 1.61803398874989*m.x34
+ 1.95629520146761*m.x35 + 1.95629520146761*m.x36 + 1.6180339887499*m.x37 + m.x38
+ 0.209056926535308*m.x39 - 0.618033988749895*m.x40 - 1.33826121271772*m.x41
- 1.8270909152852*m.x42 - 2*m.x43 - 1.8270909152852*m.x44 - 1.33826121271772*m.x45
- 0.618033988749895*m.x46 + 0.209056926535307*m.x47 + m.x48 + 1.61803398874989*m.x49
+ 1.95629520146761*m.x50 - m.x51 <= 0)
m.c26 = Constraint(expr= - 1.84775906502257*m.x1 - 1.35118041523132*m.x2 - 0.601411599008542*m.x3
+ 0.261052384440103*m.x4 + 1.07459921669365*m.x5 + 1.68678289162577*m.x6
+ 1.98288972274762*m.x7 + 1.90743390149645*m.x8 + 1.47455467362025*m.x9
+ 0.765366864730179*m.x10 - 0.087238774730675*m.x11 - 0.923497226470066*m.x12
- 1.58670668058247*m.x13 - 1.95259201423987*m.x14 - 1.95259201423987*m.x15
- 1.58670668058247*m.x16 - 0.923497226470067*m.x17 - 0.0872387747306726*m.x18
+ 0.765366864730181*m.x19 + 1.47455467362025*m.x20 + 1.90743390149645*m.x21
+ 1.98288972274762*m.x22 + 1.68678289162577*m.x23 + 1.07459921669365*m.x24
+ 0.261052384440104*m.x25 - 0.601411599008548*m.x26 - 1.35118041523132*m.x27
- 1.84775906502257*m.x28 - 1.99809644316372*m.x29 - 1.77402166635644*m.x30
- 1.21752285801744*m.x31 - 0.432879227876206*m.x32 + 0.432879227876206*m.x33
+ 1.21752285801744*m.x34 + 1.77402166635644*m.x35 + 1.99809644316372*m.x36
+ 1.84775906502257*m.x37 + 1.35118041523132*m.x38 + 0.601411599008545*m.x39
- 0.261052384440103*m.x40 - 1.07459921669365*m.x41 - 1.68678289162577*m.x42
- 1.98288972274762*m.x43 - 1.90743390149645*m.x44 - 1.47455467362025*m.x45
- 0.765366864730179*m.x46 + 0.087238774730672*m.x47 + 0.923497226470068*m.x48
+ 1.58670668058247*m.x49 + 1.95259201423987*m.x50 - m.x51 <= 0)
m.c27 = Constraint(expr= - 1.78201304837673*m.x1 - 1.99969539031278*m.x2 - 1.8126155740733*m.x3 - 1.25864078209968*m.x4
- 0.449902108687732*m.x5 + 0.449902108687728*m.x6 + 1.25864078209967*m.x7
+ 1.8126155740733*m.x8 + 1.99969539031278*m.x9 + 1.78201304837674*m.x10 + 1.2036300463041*m.x11
+ 0.381617990753089*m.x12 - 0.517638090205042*m.x13 - 1.31211805798102*m.x14
- 1.84100970690488*m.x15 - 1.99725906950915*m.x16 - 1.74923941427879*m.x17
- 1.14715287270209*m.x18 - 0.312868930080462*m.x19 + 0.584743409445473*m.x20
+ 1.363996720125*m.x21 + 1.8671608529944*m.x22 + 1.99238939618349*m.x23
+ 1.71433460140423*m.x24 + 1.08927807003006*m.x25 + 0.243738686810297*m.x26
- 0.651136308914312*m.x27 - 1.41421356237309*m.x28 - 1.89103715119863*m.x29
- 1.98509230328264*m.x30 - 1.67734113589085*m.x31 - 1.03007614982011*m.x32
- 0.174311485495317*m.x33 + 0.7167358990906*m.x34 + 1.46270740323834*m.x35
+ 1.91260951192607*m.x36 + 1.97537668119028*m.x37 + 1.63830408857798*m.x38
+ 0.969619240492675*m.x39 + 0.104671912485888*m.x40 - 0.781462256978548*m.x41
- 1.50941916044554*m.x42 - 1.93185165257814*m.x43 - 1.96325436689533*m.x44
- 1.59727102009459*m.x45 - 0.907980999479093*m.x46 - 0.034904812874567*m.x47
+ 0.845236523481399*m.x48 + 1.55429192291394*m.x49 + 1.94874012957047*m.x50 - m.x51 <= 0)
m.c28 = Constraint(expr= - 0.466890727711812*m.x1 - 1.29889609666037*m.x2 - 1.84775906502258*m.x3
- 1.99383466746626*m.x4 - 1.70528032870818*m.x5 - 1.0449971294319*m.x6 - 0.156918191455686*m.x7
+ 0.765366864730182*m.x8 + 1.52081193120006*m.x9 + 1.94473984079535*m.x10
+ 1.94473984079535*m.x11 + 1.52081193120006*m.x12 + 0.765366864730179*m.x13
- 0.156918191455689*m.x14 - 1.0449971294319*m.x15 - 1.70528032870819*m.x16
- 1.99383466746626*m.x17 - 1.84775906502257*m.x18 - 1.29889609666037*m.x19
- 0.466890727711809*m.x20 + 0.466890727711811*m.x21 + 1.29889609666037*m.x22
+ 1.84775906502257*m.x23 + 1.99383466746626*m.x24 + 1.70528032870818*m.x25
+ 1.0449971294319*m.x26 + 0.15691819145569*m.x27 - 0.765366864730181*m.x28
- 1.52081193120006*m.x29 - 1.94473984079535*m.x30 - 1.94473984079535*m.x31
- 1.52081193120006*m.x32 - 0.76536686473018*m.x33 + 0.156918191455691*m.x34
+ 1.0449971294319*m.x35 + 1.70528032870819*m.x36 + 1.99383466746626*m.x37
+ 1.84775906502257*m.x38 + 1.29889609666037*m.x39 + 0.46689072771181*m.x40
- 0.46689072771181*m.x41 - 1.29889609666037*m.x42 - 1.84775906502257*m.x43
- 1.99383466746626*m.x44 - 1.70528032870818*m.x45 - 1.0449971294319*m.x46
- 0.15691819145569*m.x47 + 0.76536686473018*m.x48 + 1.52081193120006*m.x49
+ 1.94473984079535*m.x50 - m.x51 <= 0)
m.c29 = Constraint(expr= 1.17557050458495*m.x1 + 0.278346201920129*m.x2 - 0.684040286651335*m.x3
- 1.48628965095479*m.x4 - 1.94059145255199*m.x5 - 1.94059145255199*m.x6 - 1.48628965095479*m.x7
- 0.684040286651339*m.x8 + 0.278346201920133*m.x9 + 1.17557050458494*m.x10
+ 1.79758809259833*m.x11 + 1.99878165403819*m.x12 + 1.73205080756888*m.x13
+ 1.05983852846641*m.x14 + 0.139512947488251*m.x15 - 0.813473286151603*m.x16
- 1.57602150721344*m.x17 - 1.96961550602442*m.x18 - 1.90211303259031*m.x19
- 1.38931674091799*m.x20 - 0.551274711633998*m.x21 + 0.415823381635519*m.x22
+ 1.28557521937308*m.x23 + 1.85436770913357*m.x24 + 1.98904379073655*m.x25
+ 1.65807514511008*m.x26 + 0.93894312557178*m.x27 - 8.57252759403147E-16*m.x28
- 0.938943125571782*m.x29 - 1.65807514511008*m.x30 - 1.98904379073655*m.x31
- 1.85436770913358*m.x32 - 1.28557521937308*m.x33 - 0.41582338163552*m.x34
+ 0.551274711633998*m.x35 + 1.38931674091799*m.x36 + 1.90211303259031*m.x37
+ 1.96961550602442*m.x38 + 1.57602150721344*m.x39 + 0.813473286151601*m.x40
- 0.139512947488251*m.x41 - 1.05983852846641*m.x42 - 1.73205080756888*m.x43
- 1.99878165403819*m.x44 - 1.79758809259833*m.x45 - 1.17557050458495*m.x46
- 0.278346201920131*m.x47 + 0.684040286651338*m.x48 + 1.48628965095479*m.x49
+ 1.94059145255199*m.x50 - m.x51 <= 0)
m.c30 = Constraint(expr= 1.99383466746626*m.x1 + 1.66777164413434*m.x2 + 0.92349722647007*m.x3
- 0.0523538966157449*m.x4 - 1.01507672592141*m.x5 - 1.72325832088305*m.x6
- 1.99931464995111*m.x7 - 1.77402166635645*m.x8 - 1.10387397062412*m.x9
- 0.156918191455693*m.x10 + 0.829386485312476*m.x11 + 1.60771372123443*m.x12
+ 1.98288972274762*m.x13 + 1.86083513596405*m.x14 + 1.27215644055553*m.x15
+ 0.364471050984299*m.x16 - 0.634609312810181*m.x17 - 1.47455467362025*m.x18
- 1.94473984079535*m.x19 - 1.92726090641725*m.x20 - 1.42650089830837*m.x21
- 0.568030689407847*m.x22 + 0.432879227876205*m.x23 + 1.32524009643147*m.x24
+ 1.88528298218436*m.x25 + 1.97257120307446*m.x26 + 1.56521631370483*m.x27
+ 0.76536686473018*m.x28 - 0.226406427535811*m.x29 - 1.16140591142188*m.x30
- 1.80517056869972*m.x31 - 1.99626959684373*m.x32 - 1.68678289162577*m.x33
- 0.954317520519218*m.x34 + 0.0174530709967474*m.x35 + 0.984847120206933*m.x36
+ 1.70528032870818*m.x37 + 1.99809644316372*m.x38 + 1.78986872320405*m.x39
+ 1.13281247384967*m.x40 + 0.191691505040448*m.x41 - 0.797498137850492*m.x42
- 1.58670668058247*m.x43 - 1.97803172672383*m.x44 - 1.8733443784968*m.x45
- 1.29889609666037*m.x46 - 0.398735868834395*m.x47 + 0.601411599008546*m.x48
+ 1.45074874202458*m.x49 + 1.93629528075622*m.x50 - m.x51 <= 0)
m.c31 = Constraint(expr= 1.4142135623731*m.x1 + 1.93185165257814*m.x2 + 1.93185165257814*m.x3 + 1.41421356237309*m.x4
+ 0.517638090205039*m.x5 - 0.51763809020505*m.x6 - 1.4142135623731*m.x7 - 1.93185165257814*m.x8
- 1.93185165257814*m.x9 - 1.41421356237309*m.x10 - 0.517638090205039*m.x11
+ 0.517638090205042*m.x12 + 1.4142135623731*m.x13 + 1.93185165257814*m.x14
+ 1.93185165257814*m.x15 + 1.41421356237309*m.x16 + 0.51763809020504*m.x17
- 0.517638090205042*m.x18 - 1.4142135623731*m.x19 - 1.93185165257814*m.x20
- 1.93185165257814*m.x21 - 1.41421356237309*m.x22 - 0.51763809020504*m.x23
+ 0.517638090205045*m.x24 + 1.4142135623731*m.x25 + 1.93185165257814*m.x26
+ 1.93185165257814*m.x27 + 1.41421356237309*m.x28 + 0.51763809020504*m.x29
- 0.517638090205045*m.x30 - 1.4142135623731*m.x31 - 1.93185165257814*m.x32
- 1.93185165257814*m.x33 - 1.41421356237309*m.x34 - 0.51763809020504*m.x35
+ 0.517638090205043*m.x36 + 1.4142135623731*m.x37 + 1.93185165257814*m.x38
+ 1.93185165257814*m.x39 + 1.41421356237309*m.x40 + 0.517638090205041*m.x41
- 0.517638090205043*m.x42 - 1.4142135623731*m.x43 - 1.93185165257814*m.x44
- 1.93185165257814*m.x45 - 1.41421356237309*m.x46 - 0.517638090205041*m.x47
+ 0.517638090205042*m.x48 + 1.4142135623731*m.x49 + 1.93185165257814*m.x50 - m.x51 <= 0)
m.c32 = Constraint(expr= - 0.156918191455685*m.x1 + 0.892395626219619*m.x2 + 1.68678289162577*m.x3
+ 1.99931464995111*m.x4 + 1.7407113918798*m.x5 + 0.984847120206933*m.x6
- 0.052353896615752*m.x7 - 1.07459921669365*m.x8 - 1.78986872320405*m.x9
- 1.99383466746626*m.x10 - 1.62823103671264*m.x11 - 0.79749813785049*m.x12
+ 0.261052384440103*m.x13 + 1.24502927327524*m.x14 + 1.8733443784968*m.x15
+ 1.96650981512791*m.x16 + 1.497911441578*m.x17 + 0.601411599008543*m.x18
- 0.466890727711811*m.x19 - 1.4018185285997*m.x20 - 1.93629528075622*m.x21
- 1.91763946973639*m.x22 - 1.35118041523132*m.x23 - 0.398735868834393*m.x24
+ 0.667613718467543*m.x25 + 1.54324916677544*m.x26 + 1.97803172672383*m.x27
+ 1.84775906502257*m.x28 + 1.18964557350268*m.x29 + 0.191691505040446*m.x30
- 0.861022193616592*m.x31 - 1.66777164413434*m.x32 - 1.99809644316372*m.x33
- 1.75763422532393*m.x34 - 1.01507672592141*m.x35 + 0.0174530709967492*m.x36
+ 1.0449971294319*m.x37 + 1.77402166635644*m.x38 + 1.99626959684373*m.x39
+ 1.64825237724403*m.x40 + 0.829386485312478*m.x41 - 0.226406427535814*m.x42
- 1.21752285801744*m.x43 - 1.86083513596405*m.x44 - 1.97257120307446*m.x45
- 1.52081193120006*m.x46 - 0.634609312810184*m.x47 + 0.432879227876206*m.x48
+ 1.37670915138751*m.x49 + 1.92726090641725*m.x50 - m.x51 <= 0)
m.c33 = Constraint(expr= - 1.61803398874989*m.x1 - 0.749213186831821*m.x2 + 0.347296355333859*m.x3
+ 1.33826121271772*m.x4 + 1.92252339187664*m.x5 + 1.92252339187664*m.x6 + 1.33826121271771*m.x7
+ 0.347296355333862*m.x8 - 0.749213186831824*m.x9 - 1.6180339887499*m.x10
- 1.99512810051965*m.x11 - 1.76589518571785*m.x12 - m.x13 + 0.0697989934050027*m.x14
+ 1.1183858069415*m.x15 + 1.8270909152852*m.x16 + 1.98053613748314*m.x17
+ 1.53208888623796*m.x18 + 0.618033988749894*m.x19 - 0.483843791199338*m.x20
- 1.4386796006773*m.x21 - 1.95629520146761*m.x22 - 1.87938524157182*m.x23
- 1.23132295065132*m.x24 - 0.209056926535307*m.x25 + 0.876742293578156*m.x26
+ 1.69609619231285*m.x27 + 2*m.x28 + 1.69609619231285*m.x29 + 0.876742293578154*m.x30
- 0.209056926535309*m.x31 - 1.23132295065132*m.x32 - 1.87938524157182*m.x33
- 1.95629520146761*m.x34 - 1.4386796006773*m.x35 - 0.483843791199336*m.x36
+ 0.618033988749895*m.x37 + 1.53208888623796*m.x38 + 1.98053613748314*m.x39
+ 1.8270909152852*m.x40 + 1.11838580694149*m.x41 + 0.0697989934050026*m.x42 - m.x43
- 1.76589518571785*m.x44 - 1.99512810051965*m.x45 - 1.61803398874989*m.x46
- 0.749213186831824*m.x47 + 0.347296355333861*m.x48 + 1.33826121271772*m.x49
+ 1.92252339187664*m.x50 - m.x51 <= 0)
m.c34 = Constraint(expr= - 1.94473984079535*m.x1 - 1.88528298218436*m.x2 - 1.21752285801744*m.x3
- 0.156918191455692*m.x4 + 0.954317520519214*m.x5 + 1.75763422532393*m.x6
+ 1.99383466746626*m.x7 + 1.58670668058247*m.x8 + 0.66761371846754*m.x9
- 0.466890727711812*m.x10 - 1.45074874202458*m.x11 - 1.96650981512791*m.x12
- 1.84775906502257*m.x13 - 1.13281247384967*m.x14 - 0.0523538966157486*m.x15
+ 1.0449971294319*m.x16 + 1.80517056869972*m.x17 + 1.98288972274762*m.x18
+ 1.52081193120006*m.x19 + 0.568030689407844*m.x20 - 0.568030689407846*m.x21
- 1.52081193120006*m.x22 - 1.98288972274762*m.x23 - 1.80517056869972*m.x24
- 1.0449971294319*m.x25 + 0.0523538966157441*m.x26 + 1.13281247384967*m.x27
+ 1.84775906502257*m.x28 + 1.96650981512791*m.x29 + 1.45074874202458*m.x30
+ 0.466890727711813*m.x31 - 0.667613718467543*m.x32 - 1.58670668058247*m.x33
- 1.99383466746626*m.x34 - 1.75763422532393*m.x35 - 0.954317520519218*m.x36
+ 0.15691819145569*m.x37 + 1.21752285801744*m.x38 + 1.88528298218436*m.x39
+ 1.94473984079535*m.x40 + 1.37670915138751*m.x41 + 0.364471050984295*m.x42
- 0.765366864730179*m.x43 - 1.64825237724403*m.x44 - 1.99931464995111*m.x45
- 1.70528032870818*m.x46 - 0.861022193616591*m.x47 + 0.261052384440103*m.x48
+ 1.29889609666037*m.x49 + 1.91763946973639*m.x50 - m.x51 <= 0)
m.c35 = Constraint(expr= - 0.907980999479091*m.x1 - 1.74923941427879*m.x2 - 1.99238939618349*m.x3
- 1.55429192291394*m.x4 - 0.584743409445474*m.x5 + 0.584743409445471*m.x6
+ 1.55429192291394*m.x7 + 1.99238939618349*m.x8 + 1.74923941427879*m.x9
+ 0.907980999479093*m.x10 - 0.243738686810292*m.x11 - 1.31211805798101*m.x12
- 1.93185165257814*m.x13 - 1.89103715119864*m.x14 - 1.2036300463041*m.x15
- 0.10467191248589*m.x16 + 1.0300761498201*m.x17 + 1.8126155740733*m.x18
+ 1.97537668119028*m.x19 + 1.46270740323834*m.x20 + 0.449902108687732*m.x21
- 0.716735899090596*m.x22 - 1.63830408857798*m.x23 - 1.99969539031278*m.x24
- 1.67734113589085*m.x25 - 0.78146225697855*m.x26 + 0.381617990753088*m.x27
+ 1.41421356237309*m.x28 + 1.96325436689533*m.x29 + 1.84100970690488*m.x30
+ 1.08927807003006*m.x31 - 0.0349048128745657*m.x32 - 1.14715287270209*m.x33
- 1.8671608529944*m.x34 - 1.94874012957047*m.x35 - 1.363996720125*m.x36
- 0.312868930080463*m.x37 + 0.845236523481399*m.x38 + 1.71433460140422*m.x39
+ 1.99725906950915*m.x40 + 1.59727102009459*m.x41 + 0.651136308914314*m.x42
- 0.517638090205041*m.x43 - 1.50941916044554*m.x44 - 1.98509230328264*m.x45
- 1.78201304837674*m.x46 - 0.969619240492674*m.x47 + 0.174311485495316*m.x48
+ 1.25864078209967*m.x49 + 1.91260951192607*m.x50 - m.x51 <= 0)
m.c36 = Constraint(expr= 0.765366864730178*m.x1 - 0.432879227876209*m.x2 - 1.47455467362025*m.x3
- 1.98288972274762*m.x4 - 1.77402166635644*m.x5 - 0.923497226470063*m.x6
+ 0.26105238444011*m.x7 + 1.35118041523132*m.x8 + 1.95259201423987*m.x9
+ 1.84775906502257*m.x10 + 1.07459921669365*m.x11 - 0.0872387747306755*m.x12
- 1.21752285801745*m.x13 - 1.90743390149645*m.x14 - 1.90743390149645*m.x15
- 1.21752285801744*m.x16 - 0.0872387747306721*m.x17 + 1.07459921669365*m.x18
+ 1.84775906502258*m.x19 + 1.95259201423987*m.x20 + 1.35118041523132*m.x21
+ 0.2610523844401*m.x22 - 0.923497226470073*m.x23 - 1.77402166635644*m.x24
- 1.98288972274762*m.x25 - 1.47455467362025*m.x26 - 0.432879227876202*m.x27
+ 0.765366864730181*m.x28 + 1.68678289162577*m.x29 + 1.99809644316372*m.x30
+ 1.58670668058247*m.x31 + 0.601411599008543*m.x32 - 0.601411599008548*m.x33
- 1.58670668058247*m.x34 - 1.99809644316372*m.x35 - 1.68678289162577*m.x36
- 0.76536686473018*m.x37 + 0.432879227876208*m.x38 + 1.47455467362025*m.x39
+ 1.98288972274762*m.x40 + 1.77402166635644*m.x41 + 0.923497226470066*m.x42
- 0.261052384440103*m.x43 - 1.35118041523132*m.x44 - 1.95259201423987*m.x45
- 1.84775906502257*m.x46 - 1.07459921669365*m.x47 + 0.087238774730672*m.x48
+ 1.21752285801744*m.x49 + 1.90743390149645*m.x50 - m.x51 <= 0)
m.c37 = Constraint(expr= 1.90211303259031*m.x1 + 1.17557050458495*m.x2 - 5.87954259718047E-15*m.x3
- 1.17557050458495*m.x4 - 1.90211303259031*m.x5 - 1.90211303259031*m.x6 - 1.17557050458494*m.x7
- 1.47081412025E-15*m.x8 + 1.17557050458495*m.x9 + 1.90211303259031*m.x10
+ 1.90211303259031*m.x11 + 1.17557050458495*m.x12 - 5.38968387752153E-15*m.x13
- 1.17557050458494*m.x14 - 1.90211303259031*m.x15 - 1.90211303259031*m.x16
- 1.17557050458494*m.x17 - 1.96067283990894E-15*m.x18 + 1.17557050458495*m.x19
+ 1.90211303259031*m.x20 + 1.90211303259031*m.x21 + 1.17557050458495*m.x22
- 4.89982515786259E-15*m.x23 - 1.17557050458494*m.x24 - 1.90211303259031*m.x25
- 1.90211303259031*m.x26 - 1.17557050458495*m.x27 + 1.10218211923262E-15*m.x28
+ 1.17557050458495*m.x29 + 1.90211303259031*m.x30 + 1.90211303259031*m.x31
+ 1.17557050458495*m.x32 - 8.57252759403147E-16*m.x33 - 1.17557050458495*m.x34
- 1.90211303259031*m.x35 - 1.90211303259031*m.x36 - 1.17557050458495*m.x37
+ 6.12323399573677E-16*m.x38 + 1.17557050458495*m.x39 + 1.90211303259031*m.x40
+ 1.90211303259031*m.x41 + 1.17557050458495*m.x42 - 3.67394039744206E-16*m.x43
- 1.17557050458495*m.x44 - 1.90211303259031*m.x45 - 1.90211303259031*m.x46
- 1.17557050458495*m.x47 + 1.22464679914735E-16*m.x48 + 1.17557050458495*m.x49
+ 1.90211303259031*m.x50 - m.x51 <= 0)
m.c38 = Constraint(expr= 1.70528032870819*m.x1 + 1.99079239673436*m.x2 + 1.47455467362025*m.x3
+ 0.364471050984298*m.x4 - 0.892395626219619*m.x5 - 1.78986872320405*m.x6
- 1.96650981512791*m.x7 - 1.35118041523132*m.x8 - 0.191691505040448*m.x9
+ 1.0449971294319*m.x10 + 1.86083513596405*m.x11 + 1.92726090641725*m.x12
+ 1.21752285801744*m.x13 + 0.0174530709967449*m.x14 - 1.18964557350268*m.x15
- 1.91763946973639*m.x16 - 1.87334437849679*m.x17 - 1.07459921669365*m.x18
+ 0.156918191455689*m.x19 + 1.32524009643147*m.x20 + 1.95984940924166*m.x21
+ 1.80517056869972*m.x22 + 0.92349722647007*m.x23 - 0.330095211721357*m.x24
- 1.45074874202458*m.x25 - 1.98714371135318*m.x26 - 1.72325832088305*m.x27
- 0.765366864730179*m.x28 + 0.500760008108884*m.x29 + 1.56521631370483*m.x30
+ 1.99931464995111*m.x31 + 1.62823103671264*m.x32 + 0.601411599008546*m.x33
- 0.667613718467543*m.x34 - 1.66777164413434*m.x35 - 1.99626959684373*m.x36
- 1.52081193120006*m.x37 - 0.432879227876206*m.x38 + 0.829386485312479*m.x39
+ 1.75763422532393*m.x40 + 1.97803172672383*m.x41 + 1.4018185285997*m.x42
+ 0.261052384440103*m.x43 - 0.984847120206934*m.x44 - 1.83412014877025*m.x45
- 1.94473984079535*m.x46 - 1.27215644055553*m.x47 - 0.0872387747306718*m.x48
+ 1.13281247384967*m.x49 + 1.8966473104124*m.x50 - m.x51 <= 0)
m.c39 = Constraint(expr= 0.312868930080463*m.x1 + 1.46270740323834*m.x2 + 1.99238939618349*m.x3
+ 1.67734113589085*m.x4 + 0.651136308914316*m.x5 - 0.651136308914314*m.x6
- 1.67734113589085*m.x7 - 1.99238939618349*m.x8 - 1.46270740323834*m.x9
- 0.312868930080465*m.x10 + 0.969619240492674*m.x11 + 1.84100970690488*m.x12
+ 1.93185165257814*m.x13 + 1.2036300463041*m.x14 - 0.0349048128745631*m.x15
- 1.25864078209967*m.x16 - 1.94874012957047*m.x17 - 1.8126155740733*m.x18
- 0.907980999479094*m.x19 + 0.381617990753085*m.x20 + 1.50941916044554*m.x21
+ 1.99725906950915*m.x22 + 1.63830408857799*m.x23 + 0.584743409445474*m.x24
- 0.716735899090602*m.x25 - 1.71433460140422*m.x26 - 1.98509230328264*m.x27
- 1.4142135623731*m.x28 - 0.243738686810296*m.x29 + 1.03007614982011*m.x30
+ 1.8671608529944*m.x31 + 1.91260951192607*m.x32 + 1.14715287270209*m.x33
- 0.104671912485886*m.x34 - 1.31211805798101*m.x35 - 1.96325436689533*m.x36
- 1.78201304837674*m.x37 - 0.845236523481401*m.x38 + 0.449902108687729*m.x39
+ 1.55429192291394*m.x40 + 1.99969539031278*m.x41 + 1.59727102009459*m.x42
+ 0.517638090205042*m.x43 - 0.781462256978548*m.x44 - 1.74923941427879*m.x45
- 1.97537668119028*m.x46 - 1.363996720125*m.x47 - 0.174311485495316*m.x48
+ 1.08927807003005*m.x49 + 1.89103715119863*m.x50 - m.x51 <= 0)
m.c40 = Constraint(expr= - 1.29889609666036*m.x1 - 0.0523538966157405*m.x2 + 1.21752285801744*m.x3
+ 1.94473984079535*m.x4 + 1.80517056869972*m.x5 + 0.861022193616583*m.x6
- 0.466890727711819*m.x7 - 1.58670668058247*m.x8 - 1.99931464995111*m.x9
- 1.52081193120006*m.x10 - 0.364471050984291*m.x11 + 0.95431752051922*m.x12
+ 1.84775906502258*m.x13 + 1.91763946973638*m.x14 + 1.13281247384966*m.x15
- 0.156918191455696*m.x16 - 1.37670915138751*m.x17 - 1.98288972274762*m.x18
- 1.70528032870818*m.x19 - 0.667613718467541*m.x20 + 0.667613718467544*m.x21
+ 1.70528032870819*m.x22 + 1.98288972274762*m.x23 + 1.37670915138751*m.x24
+ 0.156918191455686*m.x25 - 1.13281247384967*m.x26 - 1.91763946973639*m.x27
- 1.84775906502257*m.x28 - 0.954317520519214*m.x29 + 0.364471050984298*m.x30
+ 1.52081193120006*m.x31 + 1.99931464995111*m.x32 + 1.58670668058247*m.x33
+ 0.466890727711809*m.x34 - 0.861022193616592*m.x35 - 1.80517056869972*m.x36
- 1.94473984079535*m.x37 - 1.21752285801744*m.x38 + 0.0523538966157472*m.x39
+ 1.29889609666037*m.x40 + 1.96650981512791*m.x41 + 1.75763422532393*m.x42
+ 0.765366864730178*m.x43 - 0.568030689407845*m.x44 - 1.64825237724403*m.x45
- 1.99383466746626*m.x46 - 1.45074874202457*m.x47 - 0.261052384440103*m.x48
+ 1.0449971294319*m.x49 + 1.88528298218436*m.x50 - m.x51 <= 0)
m.c41 = Constraint(expr= - 2*m.x1 - 1.53208888623795*m.x2 - 0.347296355333854*m.x3 + m.x4 + 1.87938524157182*m.x5
+ 1.87938524157182*m.x6 + m.x7 - 0.347296355333866*m.x8 - 1.53208888623796*m.x9 - 2*m.x10
- 1.53208888623796*m.x11 - 0.347296355333855*m.x12 + m.x13 + 1.87938524157182*m.x14
+ 1.87938524157182*m.x15 + m.x16 - 0.347296355333866*m.x17 - 1.53208888623796*m.x18 - 2*m.x19
- 1.53208888623796*m.x20 - 0.347296355333855*m.x21 + m.x22 + 1.87938524157182*m.x23
+ 1.87938524157182*m.x24 + m.x25 - 0.347296355333865*m.x26 - 1.53208888623796*m.x27 - 2*m.x28
- 1.53208888623795*m.x29 - 0.347296355333859*m.x30 + m.x31 + 1.87938524157182*m.x32
+ 1.87938524157182*m.x33 + 0.999999999999998*m.x34 - 0.347296355333861*m.x35
- 1.53208888623796*m.x36 - 2*m.x37 - 1.53208888623795*m.x38 - 0.34729635533386*m.x39 + m.x40
+ 1.87938524157182*m.x41 + 1.87938524157182*m.x42 + m.x43 - 0.347296355333861*m.x44
- 1.53208888623796*m.x45 - 2*m.x46 - 1.53208888623796*m.x47 - 0.347296355333861*m.x48 + m.x49
+ 1.87938524157182*m.x50 - m.x51 <= 0)
m.c42 = Constraint(expr= - 1.29889609666037*m.x1 - 1.97803172672383*m.x2 - 1.68678289162577*m.x3
- 0.568030689407843*m.x4 + 0.829386485312484*m.x5 + 1.81992254175309*m.x6
+ 1.91763946973639*m.x7 + 1.07459921669365*m.x8 - 0.295618822259219*m.x9
- 1.52081193120006*m.x10 - 1.99992384612834*m.x11 - 1.497911441578*m.x12
- 0.261052384440099*m.x13 + 1.10387397062412*m.x14 + 1.92726090641725*m.x15
+ 1.80517056869972*m.x16 + 0.79749813785049*m.x17 - 0.601411599008546*m.x18
- 1.70528032870819*m.x19 - 1.97257120307446*m.x20 - 1.27215644055553*m.x21
+ 0.0523538966157446*m.x22 + 1.35118041523132*m.x23 + 1.98714371135317*m.x24
+ 1.64825237724403*m.x25 + 0.500760008108885*m.x26 - 0.892395626219618*m.x27
- 1.84775906502258*m.x28 - 1.8966473104124*m.x29 - 1.01507672592141*m.x30
+ 0.364471050984295*m.x31 + 1.56521631370483*m.x32 + 1.99809644316372*m.x33
+ 1.45074874202458*m.x34 + 0.191691505040446*m.x35 - 1.16140591142188*m.x36
- 1.94473984079535*m.x37 - 1.77402166635644*m.x38 - 0.733002453448593*m.x39
+ 0.667613718467543*m.x40 + 1.7407113918798*m.x41 + 1.95984940924166*m.x42
+ 1.21752285801744*m.x43 - 0.122097079069714*m.x44 - 1.4018185285997*m.x45
- 1.99383466746626*m.x46 - 1.60771372123443*m.x47 - 0.432879227876206*m.x48
+ 0.954317520519217*m.x49 + 1.8733443784968*m.x50 - m.x51 <= 0)
m.c43 = Constraint(expr= 0.312868930080457*m.x1 - 1.08927807003005*m.x2 - 1.93185165257813*m.x3
- 1.78201304837674*m.x4 - 0.716735899090605*m.x5 + 0.716735899090603*m.x6
+ 1.78201304837673*m.x7 + 1.93185165257814*m.x8 + 1.08927807003005*m.x9
- 0.312868930080462*m.x10 - 1.55429192291394*m.x11 - 1.99725906950915*m.x12
- 1.4142135623731*m.x13 - 0.10467191248589*m.x14 + 1.25864078209967*m.x15
+ 1.97537668119028*m.x16 + 1.67734113589085*m.x17 + 0.517638090205039*m.x18
- 0.90798099947909*m.x19 - 1.8671608529944*m.x20 - 1.8671608529944*m.x21
- 0.907980999479094*m.x22 + 0.517638090205042*m.x23 + 1.67734113589085*m.x24
+ 1.97537668119028*m.x25 + 1.25864078209968*m.x26 - 0.104671912485886*m.x27
- 1.41421356237309*m.x28 - 1.99725906950915*m.x29 - 1.55429192291394*m.x30
- 0.312868930080462*m.x31 + 1.08927807003005*m.x32 + 1.93185165257814*m.x33
+ 1.78201304837674*m.x34 + 0.7167358990906*m.x35 - 0.716735899090602*m.x36
- 1.78201304837674*m.x37 - 1.93185165257814*m.x38 - 1.08927807003005*m.x39
+ 0.312868930080462*m.x40 + 1.55429192291394*m.x41 + 1.99725906950915*m.x42
+ 1.41421356237309*m.x43 + 0.104671912485888*m.x44 - 1.25864078209967*m.x45
- 1.97537668119028*m.x46 - 1.67734113589085*m.x47 - 0.517638090205042*m.x48
+ 0.907980999479094*m.x49 + 1.8671608529944*m.x50 - m.x51 <= 0)
m.c44 = Constraint(expr= 1.70528032870818*m.x1 + 0.534476752156513*m.x2 - 0.923497226470068*m.x3
- 1.88528298218436*m.x4 - 1.83412014877025*m.x5 - 0.797498137850495*m.x6
+ 0.667613718467538*m.x7 + 1.77402166635644*m.x8 + 1.92726090641725*m.x9
+ 1.0449971294319*m.x10 - 0.398735868834393*m.x11 - 1.62823103671264*m.x12
- 1.98288972274762*m.x13 - 1.27215644055553*m.x14 + 0.122097079069708*m.x15
+ 1.45074874202458*m.x16 + 1.99992384612834*m.x17 + 1.47455467362025*m.x18
+ 0.156918191455692*m.x19 - 1.24502927327524*m.x20 - 1.97803172672383*m.x21
- 1.64825237724403*m.x22 - 0.432879227876205*m.x23 + 1.01507672592141*m.x24
+ 1.91763946973639*m.x25 + 1.78986872320405*m.x26 + 0.700414762518939*m.x27
- 0.765366864730175*m.x28 - 1.81992254175309*m.x29 - 1.8966473104124*m.x30
- 0.954317520519218*m.x31 + 0.500760008108881*m.x32 + 1.68678289162577*m.x33
+ 1.96650981512791*m.x34 + 1.18964557350268*m.x35 - 0.226406427535811*m.x36
- 1.52081193120006*m.x37 - 1.99809644316372*m.x38 - 1.4018185285997*m.x39
- 0.0523538966157478*m.x40 + 1.32524009643147*m.x41 + 1.99079239673436*m.x42
+ 1.58670668058247*m.x43 + 0.330095211721356*m.x44 - 1.10387397062412*m.x45
- 1.94473984079535*m.x46 - 1.7407113918798*m.x47 - 0.601411599008546*m.x48
+ 0.86102219361659*m.x49 + 1.86083513596405*m.x50 - m.x51 <= 0)
m.c45 = Constraint(expr= 1.90211303259031*m.x1 + 1.79758809259833*m.x2 + 0.684040286651331*m.x3
- 0.813473286151611*m.x4 - 1.85436770913358*m.x5 - 1.85436770913357*m.x6
- 0.813473286151593*m.x7 + 0.684040286651336*m.x8 + 1.79758809259833*m.x9
+ 1.90211303259031*m.x10 + 0.938943125571779*m.x11 - 0.551274711634005*m.x12
- 1.73205080756888*m.x13 - 1.94059145255199*m.x14 - 1.05983852846641*m.x15
+ 0.41582338163552*m.x16 + 1.65807514511009*m.x17 + 1.96961550602442*m.x18
+ 1.17557050458494*m.x19 - 0.278346201920133*m.x20 - 1.57602150721345*m.x21
- 1.98904379073655*m.x22 - 1.28557521937308*m.x23 + 0.139512947488254*m.x24
+ 1.48628965095479*m.x25 + 1.99878165403819*m.x26 + 1.38931674091799*m.x27
- 4.89982515786259E-15*m.x28 - 1.389316740918*m.x29 - 1.99878165403819*m.x30
- 1.48628965095479*m.x31 - 0.139512947488248*m.x32 + 1.28557521937308*m.x33
+ 1.98904379073655*m.x34 + 1.57602150721344*m.x35 + 0.27834620192013*m.x36
- 1.17557050458495*m.x37 - 1.96961550602442*m.x38 - 1.65807514511008*m.x39
- 0.415823381635517*m.x40 + 1.05983852846641*m.x41 + 1.94059145255199*m.x42
+ 1.73205080756888*m.x43 + 0.551274711633997*m.x44 - 0.938943125571782*m.x45
- 1.90211303259031*m.x46 - 1.79758809259833*m.x47 - 0.684040286651337*m.x48
+ 0.813473286151601*m.x49 + 1.85436770913357*m.x50 - m.x51 <= 0)
m.c46 = Constraint(expr= 0.765366864730177*m.x1 + 1.84775906502258*m.x2 + 1.84775906502257*m.x3
+ 0.765366864730171*m.x4 - 0.765366864730183*m.x5 - 1.84775906502257*m.x6
- 1.84775906502257*m.x7 - 0.765366864730178*m.x8 + 0.765366864730176*m.x9
+ 1.84775906502258*m.x10 + 1.84775906502257*m.x11 + 0.765366864730178*m.x12
- 0.765366864730182*m.x13 - 1.84775906502258*m.x14 - 1.84775906502257*m.x15
- 0.765366864730178*m.x16 + 0.765366864730182*m.x17 + 1.84775906502258*m.x18
+ 1.84775906502257*m.x19 + 0.765366864730179*m.x20 - 0.765366864730182*m.x21
- 1.84775906502258*m.x22 - 1.84775906502257*m.x23 - 0.765366864730179*m.x24
+ 0.765366864730182*m.x25 + 1.84775906502258*m.x26 + 1.84775906502257*m.x27
+ 0.765366864730179*m.x28 - 0.765366864730182*m.x29 - 1.84775906502258*m.x30
- 1.84775906502257*m.x31 - 0.765366864730179*m.x32 + 0.765366864730181*m.x33
+ 1.84775906502257*m.x34 + 1.84775906502257*m.x35 + 0.76536686473018*m.x36
- 0.765366864730181*m.x37 - 1.84775906502257*m.x38 - 1.84775906502257*m.x39
- 0.76536686473018*m.x40 + 0.765366864730181*m.x41 + 1.84775906502257*m.x42
+ 1.84775906502257*m.x43 + 0.76536686473018*m.x44 - 0.765366864730181*m.x45
- 1.84775906502257*m.x46 - 1.84775906502257*m.x47 - 0.765366864730179*m.x48
+ 0.76536686473018*m.x49 + 1.84775906502257*m.x50 - m.x51 <= 0)
m.c47 = Constraint(expr= - 0.907980999479099*m.x1 + 0.651136308914307*m.x2 + 1.8126155740733*m.x3 + 1.8671608529944*m.x4
+ 0.781462256978541*m.x5 - 0.781462256978553*m.x6 - 1.86716085299441*m.x7
- 1.8126155740733*m.x8 - 0.651136308914309*m.x9 + 0.907980999479097*m.x10
+ 1.91260951192607*m.x11 + 1.74923941427879*m.x12 + 0.517638090205039*m.x13
- 1.03007614982011*m.x14 - 1.94874012957047*m.x15 - 1.67734113589085*m.x16
- 0.381617990753089*m.x17 + 1.14715287270209*m.x18 + 1.97537668119028*m.x19
+ 1.59727102009459*m.x20 + 0.243738686810296*m.x21 - 1.25864078209967*m.x22
- 1.99238939618349*m.x23 - 1.50941916044555*m.x24 - 0.10467191248589*m.x25
+ 1.36399672012499*m.x26 + 1.99969539031278*m.x27 + 1.41421356237309*m.x28
- 0.0349048128745698*m.x29 - 1.46270740323834*m.x30 - 1.99725906950915*m.x31
- 1.31211805798101*m.x32 + 0.174311485495317*m.x33 + 1.55429192291394*m.x34
+ 1.98509230328264*m.x35 + 1.2036300463041*m.x36 - 0.312868930080461*m.x37
- 1.63830408857798*m.x38 - 1.96325436689533*m.x39 - 1.08927807003005*m.x40
+ 0.449902108687731*m.x41 + 1.71433460140422*m.x42 + 1.93185165257814*m.x43
+ 0.969619240492675*m.x44 - 0.584743409445474*m.x45 - 1.78201304837674*m.x46
- 1.89103715119863*m.x47 - 0.845236523481399*m.x48 + 0.7167358990906*m.x49
+ 1.84100970690488*m.x50 - m.x51 <= 0)
m.c48 = Constraint(expr= - 1.94473984079536*m.x1 - 0.984847120206938*m.x2 + 0.601411599008547*m.x3
+ 1.80517056869972*m.x4 + 1.86083513596405*m.x5 + 0.733002453448595*m.x6
- 0.861022193616594*m.x7 - 1.90743390149645*m.x8 - 1.7407113918798*m.x9
- 0.466890727711807*m.x10 + 1.10387397062411*m.x11 + 1.97257120307446*m.x12
+ 1.58670668058247*m.x13 + 0.191691505040448*m.x14 - 1.32524009643147*m.x15
- 1.99931464995111*m.x16 - 1.4018185285997*m.x17 + 0.0872387747306687*m.x18
+ 1.52081193120006*m.x19 + 1.98714371135318*m.x20 + 1.18964557350269*m.x21
- 0.364471050984295*m.x22 - 1.68678289162577*m.x23 - 1.93629528075622*m.x24
- 0.954317520519217*m.x25 + 0.634609312810181*m.x26 + 1.81992254175309*m.x27
+ 1.84775906502257*m.x28 + 0.700414762518939*m.x29 - 0.892395626219618*m.x30
- 1.91763946973639*m.x31 - 1.72325832088305*m.x32 - 0.432879227876206*m.x33
+ 1.13281247384967*m.x34 + 1.97803172672383*m.x35 + 1.56521631370483*m.x36
+ 0.15691819145569*m.x37 - 1.35118041523132*m.x38 - 1.99992384612834*m.x39
- 1.37670915138751*m.x40 + 0.122097079069714*m.x41 + 1.54324916677544*m.x42
+ 1.98288972274762*m.x43 + 1.16140591142188*m.x44 - 0.398735868834395*m.x45
- 1.70528032870818*m.x46 - 1.92726090641725*m.x47 - 0.923497226470068*m.x48
+ 0.667613718467542*m.x49 + 1.83412014877025*m.x50 - m.x51 <= 0)
m.c49 = Constraint(expr= - 1.61803398874989*m.x1 - 1.95629520146761*m.x2 - 1.00000000000001*m.x3
+ 0.618033988749898*m.x4 + 1.8270909152852*m.x5 + 1.8270909152852*m.x6 + 0.618033988749892*m.x7
- 0.999999999999998*m.x8 - 1.95629520146761*m.x9 - 1.61803398874989*m.x10
- 0.209056926535309*m.x11 + 1.33826121271771*m.x12 + 2*m.x13 + 1.33826121271772*m.x14
- 0.209056926535307*m.x15 - 1.61803398874989*m.x16 - 1.95629520146761*m.x17 - m.x18
+ 0.61803398874989*m.x19 + 1.8270909152852*m.x20 + 1.8270909152852*m.x21
+ 0.6180339887499*m.x22 - 0.999999999999997*m.x23 - 1.95629520146761*m.x24
- 1.6180339887499*m.x25 - 0.20905692653531*m.x26 + 1.33826121271772*m.x27 + 2*m.x28
+ 1.33826121271772*m.x29 - 0.209056926535306*m.x30 - 1.6180339887499*m.x31
- 1.95629520146761*m.x32 - m.x33 + 0.618033988749892*m.x34 + 1.8270909152852*m.x35
+ 1.8270909152852*m.x36 + 0.618033988749897*m.x37 - m.x38 - 1.95629520146761*m.x39
- 1.61803398874989*m.x40 - 0.209056926535307*m.x41 + 1.33826121271772*m.x42 + 2*m.x43
+ 1.33826121271772*m.x44 - 0.209056926535307*m.x45 - 1.61803398874989*m.x46
- 1.95629520146761*m.x47 - m.x48 + 0.618033988749895*m.x49 + 1.8270909152852*m.x50 - m.x51
<= 0)
m.c50 = Constraint(expr= - 0.156918191455691*m.x1 - 1.60771372123444*m.x2 - 1.95259201423987*m.x3
- 0.954317520519216*m.x4 + 0.700414762518936*m.x5 + 1.8733443784968*m.x6
+ 1.75763422532393*m.x7 + 0.432879227876204*m.x8 - 1.18964557350268*m.x9
- 1.99383466746626*m.x10 - 1.42650089830836*m.x11 + 0.122097079069716*m.x12
+ 1.58670668058247*m.x13 + 1.95984940924166*m.x14 + 0.984847120206932*m.x15
- 0.667613718467544*m.x16 - 1.86083513596405*m.x17 - 1.77402166635644*m.x18
- 0.466890727711808*m.x19 + 1.16140591142188*m.x20 + 1.99079239673436*m.x21
+ 1.45074874202457*m.x22 - 0.0872387747306755*m.x23 - 1.56521631370483*m.x24
- 1.96650981512791*m.x25 - 1.0150767259214*m.x26 + 0.634609312810188*m.x27
+ 1.84775906502258*m.x28 + 1.78986872320405*m.x29 + 0.500760008108879*m.x30
- 1.13281247384967*m.x31 - 1.98714371135318*m.x32 - 1.47455467362025*m.x33
+ 0.0523538966157477*m.x34 + 1.54324916677544*m.x35 + 1.97257120307446*m.x36
+ 1.0449971294319*m.x37 - 0.601411599008548*m.x38 - 1.83412014877025*m.x39
- 1.80517056869972*m.x40 - 0.534476752156511*m.x41 + 1.10387397062412*m.x42
+ 1.98288972274762*m.x43 + 1.497911441578*m.x44 - 0.0174530709967489*m.x45
- 1.52081193120006*m.x46 - 1.97803172672383*m.x47 - 1.07459921669365*m.x48
+ 0.568030689407846*m.x49 + 1.81992254175309*m.x50 - m.x51 <= 0)
m.c51 = Constraint(expr= 1.4142135623731*m.x1 - 0.174311485495319*m.x2 - 1.63830408857799*m.x3 - 1.93185165257814*m.x4
- 0.845236523481395*m.x5 + 0.845236523481394*m.x6 + 1.93185165257814*m.x7
+ 1.63830408857798*m.x8 + 0.17431148549532*m.x9 - 1.4142135623731*m.x10
- 1.99238939618349*m.x11 - 1.14715287270209*m.x12 + 0.517638090205043*m.x13
+ 1.8126155740733*m.x14 + 1.8126155740733*m.x15 + 0.517638090205039*m.x16
- 1.14715287270209*m.x17 - 1.99238939618349*m.x18 - 1.41421356237309*m.x19
+ 0.174311485495318*m.x20 + 1.63830408857798*m.x21 + 1.93185165257814*m.x22
+ 0.845236523481396*m.x23 - 0.8452365234814*m.x24 - 1.93185165257814*m.x25
- 1.63830408857798*m.x26 - 0.174311485495315*m.x27 + 1.41421356237309*m.x28
+ 1.99238939618349*m.x29 + 1.14715287270209*m.x30 - 0.517638090205042*m.x31
- 1.8126155740733*m.x32 - 1.8126155740733*m.x33 - 0.51763809020504*m.x34
+ 1.14715287270209*m.x35 + 1.99238939618349*m.x36 + 1.4142135623731*m.x37
- 0.174311485495317*m.x38 - 1.63830408857798*m.x39 - 1.93185165257814*m.x40
- 0.845236523481397*m.x41 + 0.845236523481399*m.x42 + 1.93185165257814*m.x43
+ 1.63830408857798*m.x44 + 0.174311485495316*m.x45 - 1.4142135623731*m.x46
- 1.99238939618349*m.x47 - 1.14715287270209*m.x48 + 0.517638090205041*m.x49
+ 1.8126155740733*m.x50 - m.x51 <= 0)
m.c52 = Constraint(expr= 1.99383466746626*m.x1 + 1.3767091513875*m.x2 - 0.261052384440104*m.x3 - 1.70528032870818*m.x4
- 1.88528298218436*m.x5 - 0.667613718467539*m.x6 + 1.0449971294319*m.x7 + 1.98288972274762*m.x8
+ 1.45074874202457*m.x9 - 0.15691819145569*m.x10 - 1.64825237724403*m.x11
- 1.91763946973638*m.x12 - 0.765366864730178*m.x13 + 0.954317520519215*m.x14
+ 1.96650981512791*m.x15 + 1.52081193120006*m.x16 - 0.0523538966157454*m.x17
- 1.58670668058247*m.x18 - 1.94473984079535*m.x19 - 0.86102219361659*m.x20
+ 0.861022193616587*m.x21 + 1.94473984079535*m.x22 + 1.58670668058247*m.x23
+ 0.0523538966157483*m.x24 - 1.52081193120006*m.x25 - 1.96650981512791*m.x26
- 0.954317520519217*m.x27 + 0.765366864730182*m.x28 + 1.91763946973639*m.x29
+ 1.64825237724403*m.x30 + 0.156918191455693*m.x31 - 1.45074874202458*m.x32
- 1.98288972274762*m.x33 - 1.0449971294319*m.x34 + 0.667613718467543*m.x35
+ 1.88528298218436*m.x36 + 1.70528032870818*m.x37 + 0.261052384440104*m.x38
- 1.37670915138751*m.x39 - 1.99383466746626*m.x40 - 1.13281247384966*m.x41
+ 0.568030689407846*m.x42 + 1.84775906502257*m.x43 + 1.75763422532393*m.x44
+ 0.364471050984295*m.x45 - 1.29889609666037*m.x46 - 1.99931464995111*m.x47
- 1.21752285801744*m.x48 + 0.466890727711811*m.x49 + 1.80517056869972*m.x50 - m.x51 <= 0)
m.c53 = Constraint(expr= 1.17557050458494*m.x1 + 1.99878165403819*m.x2 + 1.28557521937308*m.x3
- 0.415823381635514*m.x4 - 1.79758809259833*m.x5 - 1.79758809259834*m.x6
- 0.415823381635521*m.x7 + 1.28557521937308*m.x8 + 1.99878165403819*m.x9
+ 1.17557050458495*m.x10 - 0.551274711633998*m.x11 - 1.85436770913358*m.x12
- 1.73205080756888*m.x13 - 0.278346201920129*m.x14 + 1.389316740918*m.x15
+ 1.98904379073655*m.x16 + 1.05983852846641*m.x17 - 0.684040286651336*m.x18
- 1.90211303259031*m.x19 - 1.65807514511008*m.x20 - 0.13951294748825*m.x21
+ 1.48628965095479*m.x22 + 1.96961550602442*m.x23 + 0.938943125571786*m.x24
- 0.813473286151597*m.x25 - 1.94059145255199*m.x26 - 1.57602150721345*m.x27
- 1.96067283990894E-15*m.x28 + 1.57602150721344*m.x29 + 1.94059145255199*m.x30
+ 0.8134732861516*m.x31 - 0.938943125571782*m.x32 - 1.96961550602442*m.x33
- 1.48628965095479*m.x34 + 0.13951294748825*m.x35 + 1.65807514511008*m.x36
+ 1.90211303259031*m.x37 + 0.684040286651339*m.x38 - 1.05983852846641*m.x39
- 1.98904379073655*m.x40 - 1.38931674091799*m.x41 + 0.27834620192013*m.x42
+ 1.73205080756888*m.x43 + 1.85436770913358*m.x44 + 0.551274711633999*m.x45
- 1.17557050458495*m.x46 - 1.99878165403819*m.x47 - 1.28557521937308*m.x48
+ 0.415823381635518*m.x49 + 1.79758809259833*m.x50 - m.x51 <= 0)
m.c54 = Constraint(expr= - 0.466890727711807*m.x1 + 1.27215644055554*m.x2 + 1.99809644316372*m.x3
+ 1.13281247384966*m.x4 - 0.634609312810183*m.x5 - 1.8966473104124*m.x6 - 1.64825237724403*m.x7
- 0.0872387747306706*m.x8 + 1.54324916677544*m.x9 + 1.94473984079535*m.x10
+ 0.797498137850489*m.x11 - 0.984847120206943*m.x12 - 1.98288972274762*m.x13
- 1.4018185285997*m.x14 + 0.29561882225922*m.x15 + 1.75763422532393*m.x16
+ 1.81992254175309*m.x17 + 0.432879227876205*m.x18 - 1.29889609666037*m.x19
- 1.99626959684373*m.x20 - 1.10387397062411*m.x21 + 0.667613718467544*m.x22
+ 1.90743390149645*m.x23 + 1.62823103671263*m.x24 + 0.0523538966157412*m.x25
- 1.56521631370483*m.x26 - 1.93629528075622*m.x27 - 0.765366864730179*m.x28
+ 1.01507672592141*m.x29 + 1.98714371135318*m.x30 + 1.37670915138751*m.x31
- 0.330095211721357*m.x32 - 1.77402166635644*m.x33 - 1.80517056869972*m.x34
- 0.398735868834393*m.x35 + 1.32524009643148*m.x36 + 1.99383466746626*m.x37
+ 1.07459921669365*m.x38 - 0.700414762518937*m.x39 - 1.91763946973639*m.x40
- 1.60771372123443*m.x41 - 0.0174530709967462*m.x42 + 1.58670668058247*m.x43
+ 1.92726090641725*m.x44 + 0.733002453448594*m.x45 - 1.0449971294319*m.x46
- 1.99079239673436*m.x47 - 1.35118041523132*m.x48 + 0.364471050984295*m.x49
+ 1.78986872320405*m.x50 - m.x51 <= 0)
m.c55 = Constraint(expr= - 1.78201304837673*m.x1 - 0.312868930080463*m.x2 + 1.4142135623731*m.x3 + 1.97537668119027*m.x4
+ 0.907980999479092*m.x5 - 0.907980999479092*m.x6 - 1.97537668119028*m.x7
- 1.41421356237309*m.x8 + 0.312868930080463*m.x9 + 1.78201304837673*m.x10
+ 1.78201304837673*m.x11 + 0.312868930080457*m.x12 - 1.4142135623731*m.x13
- 1.97537668119028*m.x14 - 0.907980999479087*m.x15 + 0.907980999479097*m.x16
+ 1.97537668119028*m.x17 + 1.41421356237309*m.x18 - 0.312868930080462*m.x19
- 1.78201304837674*m.x20 - 1.78201304837674*m.x21 - 0.312868930080458*m.x22
+ 1.4142135623731*m.x23 + 1.97537668119027*m.x24 + 0.907980999479093*m.x25
- 0.907980999479097*m.x26 - 1.97537668119028*m.x27 - 1.41421356237309*m.x28
+ 0.312868930080462*m.x29 + 1.78201304837674*m.x30 + 1.78201304837674*m.x31
+ 0.312868930080459*m.x32 - 1.41421356237309*m.x33 - 1.97537668119027*m.x34
- 0.907980999479094*m.x35 + 0.907980999479096*m.x36 + 1.97537668119028*m.x37
+ 1.41421356237309*m.x38 - 0.312868930080461*m.x39 - 1.78201304837674*m.x40
- 1.78201304837674*m.x41 - 0.312868930080459*m.x42 + 1.4142135623731*m.x43
+ 1.97537668119028*m.x44 + 0.907980999479093*m.x45 - 0.907980999479094*m.x46
- 1.97537668119028*m.x47 - 1.41421356237309*m.x48 + 0.312868930080462*m.x49
+ 1.78201304837674*m.x50 - m.x51 <= 0)
m.c56 = Constraint(expr= - 1.84775906502257*m.x1 - 1.68678289162577*m.x2 - 0.0872387747306701*m.x3
+ 1.58670668058247*m.x4 + 1.90743390149645*m.x5 + 0.601411599008541*m.x6
- 1.21752285801745*m.x7 - 1.99809644316372*m.x8 - 1.07459921669365*m.x9
+ 0.765366864730177*m.x10 + 1.95259201423987*m.x11 + 1.47455467362025*m.x12
- 0.261052384440104*m.x13 - 1.77402166635644*m.x14 - 1.77402166635644*m.x15
- 0.261052384440099*m.x16 + 1.47455467362025*m.x17 + 1.95259201423987*m.x18
+ 0.765366864730178*m.x19 - 1.07459921669365*m.x20 - 1.99809644316372*m.x21
- 1.21752285801744*m.x22 + 0.601411599008546*m.x23 + 1.90743390149645*m.x24
+ 1.58670668058247*m.x25 - 0.0872387747306755*m.x26 - 1.68678289162577*m.x27
- 1.84775906502257*m.x28 - 0.432879227876205*m.x29 + 1.35118041523132*m.x30
+ 1.98288972274762*m.x31 + 0.92349722647007*m.x32 - 0.923497226470066*m.x33
- 1.98288972274762*m.x34 - 1.35118041523132*m.x35 + 0.432879227876205*m.x36
+ 1.84775906502257*m.x37 + 1.68678289162577*m.x38 + 0.0872387747306728*m.x39
- 1.58670668058247*m.x40 - 1.90743390149645*m.x41 - 0.601411599008547*m.x42
+ 1.21752285801744*m.x43 + 1.99809644316372*m.x44 + 1.07459921669365*m.x45
- 0.765366864730179*m.x46 - 1.95259201423987*m.x47 - 1.47455467362025*m.x48
+ 0.261052384440103*m.x49 + 1.77402166635644*m.x50 - m.x51 <= 0)
m.c57 = Constraint(expr= - 0.618033988749892*m.x1 - 1.92252339187664*m.x2 - 1.53208888623796*m.x3
+ 0.209056926535308*m.x4 + 1.76589518571785*m.x5 + 1.76589518571785*m.x6
+ 0.209056926535301*m.x7 - 1.53208888623795*m.x8 - 1.92252339187664*m.x9
- 0.618033988749899*m.x10 + 1.23132295065132*m.x11 + 1.99512810051965*m.x12 + m.x13
- 0.876742293578148*m.x14 - 1.98053613748314*m.x15 - 1.33826121271771*m.x16
+ 0.483843791199333*m.x17 + 1.87938524157182*m.x18 + 1.61803398874989*m.x19
- 0.0697989934050035*m.x20 - 1.69609619231285*m.x21 - 1.8270909152852*m.x22
- 0.347296355333862*m.x23 + 1.4386796006773*m.x24 + 1.95629520146761*m.x25
+ 0.749213186831827*m.x26 - 1.1183858069415*m.x27 - 2*m.x28 - 1.11838580694149*m.x29
+ 0.749213186831824*m.x30 + 1.95629520146761*m.x31 + 1.4386796006773*m.x32
- 0.347296355333858*m.x33 - 1.8270909152852*m.x34 - 1.69609619231285*m.x35
- 0.0697989934050036*m.x36 + 1.6180339887499*m.x37 + 1.87938524157182*m.x38
+ 0.483843791199336*m.x39 - 1.33826121271772*m.x40 - 1.98053613748314*m.x41
- 0.876742293578155*m.x42 + m.x43 + 1.99512810051965*m.x44 + 1.23132295065132*m.x45
- 0.618033988749895*m.x46 - 1.92252339187664*m.x47 - 1.53208888623796*m.x48
+ 0.209056926535307*m.x49 + 1.76589518571785*m.x50 - m.x51 <= 0)
m.c58 = Constraint(expr= 1.0449971294319*m.x1 - 0.861022193616582*m.x2 - 1.98288972274762*m.x3 - 1.29889609666037*m.x4
+ 0.568030689407842*m.x5 + 1.91763946973638*m.x6 + 1.52081193120007*m.x7
- 0.261052384440104*m.x8 - 1.80517056869972*m.x9 - 1.70528032870819*m.x10
- 0.0523538966157542*m.x11 + 1.64825237724403*m.x12 + 1.84775906502257*m.x13
+ 0.364471050984298*m.x14 - 1.45074874202457*m.x15 - 1.94473984079536*m.x16
- 0.66761371846754*m.x17 + 1.21752285801744*m.x18 + 1.99383466746626*m.x19
+ 0.954317520519217*m.x20 - 0.954317520519214*m.x21 - 1.99383466746626*m.x22
- 1.21752285801744*m.x23 + 0.667613718467537*m.x24 + 1.94473984079535*m.x25
+ 1.45074874202458*m.x26 - 0.364471050984295*m.x27 - 1.84775906502257*m.x28
- 1.64825237724403*m.x29 + 0.0523538966157446*m.x30 + 1.70528032870818*m.x31
+ 1.80517056869972*m.x32 + 0.261052384440107*m.x33 - 1.52081193120006*m.x34
- 1.91763946973639*m.x35 - 0.568030689407847*m.x36 + 1.29889609666037*m.x37
+ 1.98288972274762*m.x38 + 0.861022193616591*m.x39 - 1.0449971294319*m.x40
- 1.99931464995111*m.x41 - 1.13281247384966*m.x42 + 0.765366864730179*m.x43
+ 1.96650981512791*m.x44 + 1.37670915138751*m.x45 - 0.46689072771181*m.x46
- 1.88528298218436*m.x47 - 1.58670668058247*m.x48 + 0.15691819145569*m.x49
+ 1.75763422532393*m.x50 - m.x51 <= 0)
m.c59 = Constraint(expr= 1.97537668119028*m.x1 + 0.781462256978554*m.x2 - 1.14715287270209*m.x3
- 1.99725906950915*m.x4 - 0.969619240492675*m.x5 + 0.969619240492675*m.x6
+ 1.99725906950915*m.x7 + 1.1471528727021*m.x8 - 0.781462256978541*m.x9
- 1.97537668119027*m.x10 - 1.31211805798102*m.x11 + 0.584743409445472*m.x12
+ 1.93185165257814*m.x13 + 1.46270740323834*m.x14 - 0.381617990753079*m.x15
- 1.8671608529944*m.x16 - 1.59727102009459*m.x17 + 0.174311485495312*m.x18
+ 1.78201304837673*m.x19 + 1.71433460140423*m.x20 + 0.0349048128745732*m.x21
- 1.67734113589085*m.x22 - 1.8126155740733*m.x23 - 0.243738686810295*m.x24
+ 1.55429192291394*m.x25 + 1.89103715119863*m.x26 + 0.449902108687732*m.x27
- 1.4142135623731*m.x28 - 1.94874012957047*m.x29 - 0.651136308914317*m.x30
+ 1.25864078209967*m.x31 + 1.98509230328264*m.x32 + 0.845236523481403*m.x33
- 1.08927807003005*m.x34 - 1.99969539031278*m.x35 - 1.03007614982011*m.x36
+ 0.907980999479093*m.x37 + 1.99238939618349*m.x38 + 1.2036300463041*m.x39
- 0.716735899090599*m.x40 - 1.96325436689533*m.x41 - 1.363996720125*m.x42
+ 0.517638090205041*m.x43 + 1.91260951192607*m.x44 + 1.50941916044554*m.x45
- 0.31286893008046*m.x46 - 1.84100970690488*m.x47 - 1.63830408857798*m.x48
+ 0.104671912485887*m.x49 + 1.74923941427879*m.x50 - m.x51 <= 0)
m.c60 = Constraint(expr= 1.52081193120005*m.x1 + 1.8966473104124*m.x2 + 0.432879227876217*m.x3 - 1.45074874202457*m.x4
- 1.92726090641725*m.x5 - 0.534476752156519*m.x6 + 1.3767091513875*m.x7 + 1.95259201423987*m.x8
+ 0.634609312810197*m.x9 - 1.29889609666036*m.x10 - 1.97257120307446*m.x11
- 0.733002453448601*m.x12 + 1.21752285801744*m.x13 + 1.98714371135318*m.x14
+ 0.829386485312479*m.x15 - 1.13281247384966*m.x16 - 1.99626959684373*m.x17
- 0.923497226470076*m.x18 + 1.0449971294319*m.x19 + 1.99992384612834*m.x20
+ 1.01507672592141*m.x21 - 0.954317520519214*m.x22 - 1.99809644316372*m.x23
- 1.10387397062412*m.x24 + 0.861022193616587*m.x25 + 1.99079239673436*m.x26
+ 1.18964557350269*m.x27 - 0.765366864730175*m.x28 - 1.97803172672383*m.x29
- 1.27215644055553*m.x30 + 0.667613718467537*m.x31 + 1.95984940924166*m.x32
+ 1.35118041523132*m.x33 - 0.568030689407839*m.x34 - 1.93629528075621*m.x35
- 1.42650089830837*m.x36 + 0.466890727711808*m.x37 + 1.90743390149645*m.x38
+ 1.497911441578*m.x39 - 0.364471050984294*m.x40 - 1.87334437849679*m.x41
- 1.56521631370483*m.x42 + 0.261052384440102*m.x43 + 1.83412014877025*m.x44
+ 1.62823103671264*m.x45 - 0.156918191455689*m.x46 - 1.78986872320405*m.x47
- 1.68678289162577*m.x48 + 0.0523538966157458*m.x49 + 1.7407113918798*m.x50 - m.x51 <= 0)
m.c61 = Constraint(expr= 1.46994754735878E-14*m.x1 + 1.73205080756888*m.x2 + 1.73205080756887*m.x3
- 7.3491187561573E-15*m.x4 - 1.73205080756888*m.x5 - 1.73205080756887*m.x6
+ 1.42096167539288E-14*m.x7 + 1.73205080756888*m.x8 + 1.73205080756887*m.x9
- 6.85926003649835E-15*m.x10 - 1.73205080756888*m.x11 - 1.73205080756888*m.x12
+ 1.37197580342699E-14*m.x13 + 1.73205080756888*m.x14 + 1.73205080756887*m.x15
- 6.36940131683941E-15*m.x16 - 1.73205080756888*m.x17 - 1.73205080756888*m.x18
+ 1.32298993146109E-14*m.x19 + 1.73205080756888*m.x20 + 1.73205080756887*m.x21
- 5.87954259718047E-15*m.x22 - 1.73205080756888*m.x23 - 1.73205080756887*m.x24
+ 5.634613237351E-15*m.x25 + 1.73205080756888*m.x26 + 1.73205080756887*m.x27
- 5.38968387752153E-15*m.x28 - 1.73205080756888*m.x29 - 1.73205080756887*m.x30
+ 5.14475451769206E-15*m.x31 + 1.73205080756888*m.x32 + 1.73205080756887*m.x33
- 4.89982515786259E-15*m.x34 - 1.73205080756888*m.x35 - 1.73205080756888*m.x36
+ 4.65489579803312E-15*m.x37 + 1.73205080756888*m.x38 + 1.73205080756888*m.x39
- 8.57252759403147E-16*m.x40 - 1.73205080756888*m.x41 - 1.73205080756888*m.x42
+ 2.38868023897393E-15*m.x43 + 1.73205080756888*m.x44 + 1.73205080756888*m.x45
- 3.67394039744206E-16*m.x46 - 1.73205080756888*m.x47 - 1.73205080756888*m.x48
+ 1.22464679914735E-16*m.x49 + 1.73205080756888*m.x50 - m.x51 <= 0)
m.c62 = Constraint(expr= - 1.52081193120005*m.x1 + 0.398735868834409*m.x2 + 1.90743390149645*m.x3
+ 1.45074874202457*m.x4 - 0.50076000810889*m.x5 - 1.93629528075622*m.x6 - 1.3767091513875*m.x7
+ 0.601411599008547*m.x8 + 1.95984940924166*m.x9 + 1.29889609666036*m.x10
- 0.700414762518943*m.x11 - 1.97803172672384*m.x12 - 1.21752285801743*m.x13
+ 0.797498137850494*m.x14 + 1.99079239673436*m.x15 + 1.13281247384966*m.x16
- 0.892395626219626*m.x17 - 1.99809644316372*m.x18 - 1.0449971294319*m.x19
+ 0.984847120206936*m.x20 + 1.99992384612834*m.x21 + 0.95431752051921*m.x22
- 1.07459921669365*m.x23 - 1.99626959684373*m.x24 - 0.861022193616583*m.x25
+ 1.16140591142188*m.x26 + 1.98714371135317*m.x27 + 0.765366864730179*m.x28
- 1.24502927327524*m.x29 - 1.97257120307446*m.x30 - 0.667613718467541*m.x31
+ 1.32524009643148*m.x32 + 1.95259201423987*m.x33 + 0.568030689407844*m.x34
- 1.4018185285997*m.x35 - 1.92726090641725*m.x36 - 0.466890727711809*m.x37
+ 1.47455467362025*m.x38 + 1.8966473104124*m.x39 + 0.364471050984293*m.x40
- 1.54324916677544*m.x41 - 1.86083513596405*m.x42 - 0.261052384440102*m.x43
+ 1.60771372123444*m.x44 + 1.81992254175309*m.x45 + 0.156918191455689*m.x46
- 1.66777164413434*m.x47 - 1.77402166635644*m.x48 - 0.052353896615746*m.x49
+ 1.72325832088305*m.x50 - m.x51 <= 0)
m.c63 = Constraint(expr= - 1.97537668119028*m.x1 - 1.20363004630409*m.x2 + 0.845236523481408*m.x3
+ 1.99725906950915*m.x4 + 1.0300761498201*m.x5 - 1.03007614982011*m.x6 - 1.99725906950915*m.x7
- 0.845236523481395*m.x8 + 1.20363004630411*m.x9 + 1.97537668119027*m.x10
+ 0.651136308914315*m.x11 - 1.363996720125*m.x12 - 1.93185165257814*m.x13
- 0.449902108687724*m.x14 + 1.50941916044554*m.x15 + 1.8671608529944*m.x16
+ 0.243738686810295*m.x17 - 1.63830408857799*m.x18 - 1.78201304837674*m.x19
- 0.0349048128745587*m.x20 + 1.74923941427879*m.x21 + 1.67734113589085*m.x22
- 0.174311485495318*m.x23 - 1.84100970690488*m.x24 - 1.55429192291394*m.x25
+ 0.381617990753093*m.x26 + 1.91260951192607*m.x27 + 1.41421356237309*m.x28
- 0.584743409445477*m.x29 - 1.96325436689533*m.x30 - 1.25864078209967*m.x31
+ 0.781462256978552*m.x32 + 1.99238939618349*m.x33 + 1.08927807003005*m.x34
- 0.969619240492673*m.x35 - 1.99969539031278*m.x36 - 0.907980999479091*m.x37
+ 1.14715287270209*m.x38 + 1.98509230328264*m.x39 + 0.7167358990906*m.x40
- 1.31211805798101*m.x41 - 1.94874012957047*m.x42 - 0.51763809020504*m.x43
+ 1.46270740323834*m.x44 + 1.89103715119863*m.x45 + 0.312868930080461*m.x46
- 1.59727102009459*m.x47 - 1.8126155740733*m.x48 - 0.104671912485887*m.x49
+ 1.71433460140422*m.x50 - m.x51 <= 0)
m.c64 = Constraint(expr= - 1.0449971294319*m.x1 - 1.99383466746626*m.x2 - 0.765366864730177*m.x3 + 1.29889609666037*m.x4
+ 1.94473984079535*m.x5 + 0.466890727711813*m.x6 - 1.52081193120007*m.x7
- 1.84775906502257*m.x8 - 0.156918191455684*m.x9 + 1.70528032870819*m.x10
+ 1.70528032870818*m.x11 - 0.156918191455691*m.x12 - 1.84775906502257*m.x13
- 1.52081193120006*m.x14 + 0.46689072771182*m.x15 + 1.94473984079535*m.x16
+ 1.29889609666036*m.x17 - 0.765366864730183*m.x18 - 1.99383466746626*m.x19
- 1.0449971294319*m.x20 + 1.0449971294319*m.x21 + 1.99383466746626*m.x22
+ 0.765366864730178*m.x23 - 1.29889609666037*m.x24 - 1.94473984079535*m.x25
- 0.466890727711808*m.x26 + 1.52081193120006*m.x27 + 1.84775906502257*m.x28
+ 0.156918191455685*m.x29 - 1.70528032870819*m.x30 - 1.70528032870818*m.x31
+ 0.156918191455689*m.x32 + 1.84775906502258*m.x33 + 1.52081193120006*m.x34
- 0.466890727711811*m.x35 - 1.94473984079535*m.x36 - 1.29889609666037*m.x37
+ 0.765366864730181*m.x38 + 1.99383466746626*m.x39 + 1.0449971294319*m.x40
- 1.0449971294319*m.x41 - 1.99383466746626*m.x42 - 0.76536686473018*m.x43
+ 1.29889609666037*m.x44 + 1.94473984079535*m.x45 + 0.46689072771181*m.x46
- 1.52081193120006*m.x47 - 1.84775906502257*m.x48 - 0.15691819145569*m.x49
+ 1.70528032870818*m.x50 - m.x51 <= 0)
m.c65 = Constraint(expr= 0.618033988749891*m.x1 - 1.43867960067731*m.x2 - 1.87938524157182*m.x3
- 0.209056926535308*m.x4 + 1.69609619231285*m.x5 + 1.69609619231285*m.x6
- 0.209056926535315*m.x7 - 1.87938524157182*m.x8 - 1.4386796006773*m.x9
+ 0.618033988749898*m.x10 + 1.98053613748314*m.x11 + 1.1183858069415*m.x12 - m.x13
- 1.99512810051965*m.x14 - 0.74921318683182*m.x15 + 1.33826121271772*m.x16
+ 1.92252339187664*m.x17 + 0.347296355333861*m.x18 - 1.6180339887499*m.x19
- 1.76589518571785*m.x20 + 0.0697989934049966*m.x21 + 1.8270909152852*m.x22
+ 1.53208888623796*m.x23 - 0.483843791199339*m.x24 - 1.95629520146761*m.x25
- 1.23132295065131*m.x26 + 0.876742293578154*m.x27 + 2*m.x28 + 0.876742293578157*m.x29
- 1.23132295065132*m.x30 - 1.95629520146761*m.x31 - 0.483843791199335*m.x32
+ 1.53208888623796*m.x33 + 1.8270909152852*m.x34 + 0.0697989934049998*m.x35
- 1.76589518571785*m.x36 - 1.61803398874989*m.x37 + 0.347296355333861*m.x38
+ 1.92252339187664*m.x39 + 1.33826121271772*m.x40 - 0.749213186831823*m.x41
- 1.99512810051965*m.x42 - 0.999999999999998*m.x43 + 1.11838580694149*m.x44
+ 1.98053613748314*m.x45 + 0.618033988749894*m.x46 - 1.4386796006773*m.x47
- 1.87938524157182*m.x48 - 0.209056926535307*m.x49 + 1.69609619231285*m.x50 - m.x51 <= 0)
m.c66 = Constraint(expr= 1.84775906502257*m.x1 + 0.0872387747306765*m.x2 - 1.77402166635644*m.x3
- 1.58670668058247*m.x4 + 0.432879227876211*m.x5 + 1.95259201423987*m.x6
+ 1.21752285801745*m.x7 - 0.923497226470069*m.x8 - 1.99809644316372*m.x9
- 0.765366864730177*m.x10 + 1.35118041523132*m.x11 + 1.90743390149645*m.x12
+ 0.261052384440105*m.x13 - 1.68678289162577*m.x14 - 1.68678289162577*m.x15
+ 0.261052384440097*m.x16 + 1.90743390149645*m.x17 + 1.35118041523132*m.x18
- 0.765366864730183*m.x19 - 1.99809644316372*m.x20 - 0.923497226470063*m.x21
+ 1.21752285801744*m.x22 + 1.95259201423987*m.x23 + 0.432879227876205*m.x24
- 1.58670668058247*m.x25 - 1.77402166635644*m.x26 + 0.0872387747306687*m.x27
+ 1.84775906502257*m.x28 + 1.47455467362025*m.x29 - 0.601411599008546*m.x30
- 1.98288972274762*m.x31 - 1.07459921669365*m.x32 + 1.07459921669365*m.x33
+ 1.98288972274762*m.x34 + 0.601411599008549*m.x35 - 1.47455467362025*m.x36
- 1.84775906502257*m.x37 - 0.0872387747306726*m.x38 + 1.77402166635644*m.x39
+ 1.58670668058247*m.x40 - 0.432879227876204*m.x41 - 1.95259201423987*m.x42
- 1.21752285801744*m.x43 + 0.923497226470067*m.x44 + 1.99809644316372*m.x45
+ 0.76536686473018*m.x46 - 1.35118041523132*m.x47 - 1.90743390149645*m.x48
- 0.261052384440103*m.x49 + 1.68678289162577*m.x50 - m.x51 <= 0)
m.c67 = Constraint(expr= 1.78201304837674*m.x1 + 1.55429192291394*m.x2 - 0.517638090205038*m.x3
- 1.97537668119027*m.x4 - 1.08927807003006*m.x5 + 1.08927807003005*m.x6 + 1.97537668119028*m.x7
+ 0.517638090205037*m.x8 - 1.55429192291394*m.x9 - 1.78201304837673*m.x10
+ 0.104671912485888*m.x11 + 1.8671608529944*m.x12 + 1.4142135623731*m.x13
- 0.716735899090598*m.x14 - 1.99725906950915*m.x15 - 0.907980999479099*m.x16
+ 1.25864078209967*m.x17 + 1.93185165257814*m.x18 + 0.312868930080457*m.x19
- 1.67734113589085*m.x20 - 1.67734113589085*m.x21 + 0.312868930080463*m.x22
+ 1.93185165257814*m.x23 + 1.25864078209968*m.x24 - 0.907980999479091*m.x25
- 1.99725906950915*m.x26 - 0.716735899090599*m.x27 + 1.4142135623731*m.x28
+ 1.8671608529944*m.x29 + 0.10467191248589*m.x30 - 1.78201304837673*m.x31
- 1.55429192291394*m.x32 + 0.517638090205042*m.x33 + 1.97537668119028*m.x34
+ 1.08927807003006*m.x35 - 1.08927807003005*m.x36 - 1.97537668119028*m.x37
- 0.517638090205043*m.x38 + 1.55429192291394*m.x39 + 1.78201304837674*m.x40
- 0.104671912485886*m.x41 - 1.8671608529944*m.x42 - 1.4142135623731*m.x43
+ 0.7167358990906*m.x44 + 1.99725906950915*m.x45 + 0.907980999479093*m.x46
- 1.25864078209967*m.x47 - 1.93185165257814*m.x48 - 0.312868930080462*m.x49
+ 1.67734113589085*m.x50 - m.x51 <= 0)
m.c68 = Constraint(expr= 0.466890727711807*m.x1 + 1.97257120307446*m.x2 + 1.07459921669366*m.x3
- 1.13281247384966*m.x4 - 1.95984940924166*m.x5 - 0.398735868834394*m.x6
+ 1.64825237724403*m.x7 + 1.68678289162577*m.x8 - 0.330095211721353*m.x9
- 1.94473984079535*m.x10 - 1.18964557350269*m.x11 + 1.0150767259214*m.x12
+ 1.98288972274762*m.x13 + 0.534476752156512*m.x14 - 1.56521631370482*m.x15
- 1.75763422532393*m.x16 + 0.191691505040446*m.x17 + 1.90743390149645*m.x18
+ 1.29889609666037*m.x19 - 0.892395626219613*m.x20 - 1.99626959684373*m.x21
- 0.66761371846754*m.x22 + 1.47455467362024*m.x23 + 1.81992254175309*m.x24
- 0.0523538966157454*m.x25 - 1.86083513596405*m.x26 - 1.4018185285997*m.x27
+ 0.765366864730176*m.x28 + 1.99992384612834*m.x29 + 0.797498137850496*m.x30
- 1.37670915138751*m.x31 - 1.8733443784968*m.x32 - 0.0872387747306721*m.x33
+ 1.80517056869972*m.x34 + 1.497911441578*m.x35 - 0.634609312810181*m.x36
- 1.99383466746626*m.x37 - 0.923497226470071*m.x38 + 1.27215644055553*m.x39
+ 1.91763946973639*m.x40 + 0.226406427535813*m.x41 - 1.7407113918798*m.x42
- 1.58670668058247*m.x43 + 0.500760008108882*m.x44 + 1.97803172672383*m.x45
+ 1.0449971294319*m.x46 - 1.16140591142188*m.x47 - 1.95259201423987*m.x48
- 0.364471050984295*m.x49 + 1.66777164413434*m.x50 - m.x51 <= 0)
m.c69 = Constraint(expr= - 1.17557050458495*m.x1 + 1.0598385284664*m.x2 + 1.96961550602442*m.x3 + 0.415823381635527*m.x4
- 1.65807514511008*m.x5 - 1.65807514511009*m.x6 + 0.415823381635508*m.x7
+ 1.96961550602442*m.x8 + 1.05983852846642*m.x9 - 1.17557050458495*m.x10
- 1.94059145255199*m.x11 - 0.278346201920142*m.x12 + 1.73205080756888*m.x13
+ 1.57602150721345*m.x14 - 0.551274711633999*m.x15 - 1.98904379073655*m.x16
- 0.938943125571791*m.x17 + 1.28557521937308*m.x18 + 1.90211303259031*m.x19
+ 0.13951294748825*m.x20 - 1.79758809259833*m.x21 - 1.4862896509548*m.x22
+ 0.684040286651336*m.x23 + 1.99878165403819*m.x24 + 0.813473286151606*m.x25
- 1.38931674091799*m.x26 - 1.85436770913358*m.x27 - 1.47081412025E-15*m.x28
+ 1.85436770913357*m.x29 + 1.389316740918*m.x30 - 0.813473286151597*m.x31
- 1.99878165403819*m.x32 - 0.684040286651339*m.x33 + 1.48628965095479*m.x34
+ 1.79758809259834*m.x35 - 0.139512947488247*m.x36 - 1.90211303259031*m.x37
- 1.28557521937308*m.x38 + 0.938943125571779*m.x39 + 1.98904379073655*m.x40
+ 0.551274711633998*m.x41 - 1.57602150721344*m.x42 - 1.73205080756888*m.x43
+ 0.27834620192013*m.x44 + 1.94059145255199*m.x45 + 1.17557050458495*m.x46
- 1.05983852846641*m.x47 - 1.96961550602442*m.x48 - 0.415823381635519*m.x49
+ 1.65807514511008*m.x50 - m.x51 <= 0)
m.c70 = Constraint(expr= - 1.99383466746626*m.x1 - 0.568030689407841*m.x2 + 1.58670668058248*m.x3
+ 1.70528032870818*m.x4 - 0.364471050984312*m.x5 - 1.96650981512791*m.x6
- 1.04499712943188*m.x7 + 1.21752285801745*m.x8 + 1.91763946973638*m.x9
+ 0.156918191455676*m.x10 - 1.80517056869973*m.x11 - 1.45074874202457*m.x12
+ 0.76536686473019*m.x13 + 1.99931464995111*m.x14 + 0.667613718467532*m.x15
- 1.52081193120007*m.x16 - 1.75763422532393*m.x17 + 0.261052384440111*m.x18
+ 1.94473984079535*m.x19 + 1.13281247384966*m.x20 - 1.13281247384967*m.x21
- 1.94473984079535*m.x22 - 0.261052384440099*m.x23 + 1.75763422532393*m.x24
+ 1.52081193120006*m.x25 - 0.667613718467544*m.x26 - 1.99931464995111*m.x27
- 0.765366864730172*m.x28 + 1.45074874202458*m.x29 + 1.80517056869972*m.x30
- 0.156918191455696*m.x31 - 1.91763946973639*m.x32 - 1.21752285801744*m.x33
+ 1.0449971294319*m.x34 + 1.96650981512791*m.x35 + 0.364471050984292*m.x36
- 1.70528032870819*m.x37 - 1.58670668058247*m.x38 + 0.568030689407849*m.x39
+ 1.99383466746626*m.x40 + 0.861022193616588*m.x41 - 1.37670915138751*m.x42
- 1.84775906502257*m.x43 + 0.0523538966157472*m.x44 + 1.88528298218436*m.x45
+ 1.29889609666037*m.x46 - 0.954317520519217*m.x47 - 1.98288972274762*m.x48
- 0.466890727711811*m.x49 + 1.64825237724403*m.x50 - m.x51 <= 0)
m.c71 = Constraint(expr= - 1.4142135623731*m.x1 - 1.8126155740733*m.x2 + 0.174311485495328*m.x3 + 1.93185165257814*m.x4
+ 1.14715287270209*m.x5 - 1.1471528727021*m.x6 - 1.93185165257813*m.x7 - 0.174311485495312*m.x8
+ 1.8126155740733*m.x9 + 1.41421356237309*m.x10 - 0.845236523481401*m.x11
- 1.99238939618349*m.x12 - 0.517638090205031*m.x13 + 1.63830408857798*m.x14
+ 1.63830408857798*m.x15 - 0.517638090205051*m.x16 - 1.99238939618349*m.x17
- 0.845236523481395*m.x18 + 1.4142135623731*m.x19 + 1.81261557407329*m.x20
- 0.174311485495319*m.x21 - 1.93185165257814*m.x22 - 1.14715287270208*m.x23
+ 1.14715287270209*m.x24 + 1.93185165257814*m.x25 + 0.174311485495314*m.x26
- 1.8126155740733*m.x27 - 1.41421356237309*m.x28 + 0.8452365234814*m.x29
+ 1.99238939618349*m.x30 + 0.517638090205039*m.x31 - 1.63830408857799*m.x32
- 1.63830408857798*m.x33 + 0.517638090205042*m.x34 + 1.99238939618349*m.x35
+ 0.845236523481397*m.x36 - 1.41421356237309*m.x37 - 1.8126155740733*m.x38
+ 0.174311485495317*m.x39 + 1.93185165257814*m.x40 + 1.14715287270209*m.x41
- 1.14715287270209*m.x42 - 1.93185165257814*m.x43 - 0.174311485495316*m.x44
+ 1.8126155740733*m.x45 + 1.41421356237309*m.x46 - 0.8452365234814*m.x47
- 1.99238939618349*m.x48 - 0.517638090205041*m.x49 + 1.63830408857798*m.x50 - m.x51 <= 0)
m.c72 = Constraint(expr= 0.15691819145569*m.x1 - 1.83412014877025*m.x2 - 1.35118041523132*m.x3
+ 0.954317520519229*m.x4 + 1.97257120307446*m.x5 + 0.330095211721352*m.x6
- 1.75763422532394*m.x7 - 1.47455467362024*m.x8 + 0.797498137850495*m.x9
+ 1.99383466746626*m.x10 + 0.500760008108876*m.x11 - 1.66777164413434*m.x12
- 1.58670668058246*m.x13 + 0.63460931281019*m.x14 + 1.99992384612834*m.x15
+ 0.667613718467532*m.x16 - 1.56521631370483*m.x17 - 1.68678289162577*m.x18
+ 0.46689072771182*m.x19 + 1.99079239673436*m.x20 + 0.829386485312479*m.x21
- 1.45074874202458*m.x22 - 1.77402166635644*m.x23 + 0.29561882225922*m.x24
+ 1.96650981512791*m.x25 + 0.984847120206932*m.x26 - 1.32524009643148*m.x27
- 1.84775906502257*m.x28 + 0.122097079069715*m.x29 + 1.92726090641725*m.x30
+ 1.13281247384966*m.x31 - 1.18964557350268*m.x32 - 1.90743390149645*m.x33
- 0.0523538966157415*m.x34 + 1.8733443784968*m.x35 + 1.27215644055553*m.x36
- 1.0449971294319*m.x37 - 1.95259201423987*m.x38 - 0.226406427535812*m.x39
+ 1.80517056869972*m.x40 + 1.4018185285997*m.x41 - 0.892395626219618*m.x42
- 1.98288972274762*m.x43 - 0.398735868834394*m.x44 + 1.72325832088305*m.x45
+ 1.52081193120006*m.x46 - 0.733002453448594*m.x47 - 1.99809644316372*m.x48
- 0.568030689407845*m.x49 + 1.62823103671264*m.x50 - m.x51 <= 0)
m.c73 = Constraint(expr= 1.61803398874989*m.x1 - 0.618033988749893*m.x2 - 2*m.x3 - 0.618033988749897*m.x4
+ 1.6180339887499*m.x5 + 1.6180339887499*m.x6 - 0.618033988749906*m.x7 - 2*m.x8
- 0.618033988749884*m.x9 + 1.61803398874989*m.x10 + 1.61803398874989*m.x11
- 0.618033988749892*m.x12 - 2*m.x13 - 0.618033988749898*m.x14 + 1.6180339887499*m.x15
+ 1.6180339887499*m.x16 - 0.618033988749905*m.x17 - 2*m.x18 - 0.618033988749885*m.x19
+ 1.61803398874989*m.x20 + 1.61803398874989*m.x21 - 0.618033988749891*m.x22 - 2*m.x23
- 0.618033988749899*m.x24 + 1.6180339887499*m.x25 + 1.61803398874989*m.x26
- 0.618033988749897*m.x27 - 2*m.x28 - 0.618033988749893*m.x29 + 1.6180339887499*m.x30
+ 1.61803398874989*m.x31 - 0.618033988749897*m.x32 - 2*m.x33 - 0.618033988749893*m.x34
+ 1.6180339887499*m.x35 + 1.61803398874989*m.x36 - 0.618033988749896*m.x37 - 2*m.x38
- 0.618033988749894*m.x39 + 1.6180339887499*m.x40 + 1.61803398874989*m.x41
- 0.618033988749896*m.x42 - 2*m.x43 - 0.618033988749894*m.x44 + 1.6180339887499*m.x45
+ 1.61803398874989*m.x46 - 0.618033988749895*m.x47 - 2*m.x48 - 0.618033988749895*m.x49
+ 1.61803398874989*m.x50 - m.x51 <= 0)
m.c74 = Constraint(expr= 1.94473984079536*m.x1 + 1.01507672592141*m.x2 - 1.35118041523132*m.x3 - 1.80517056869972*m.x4
+ 0.295618822259222*m.x5 + 1.97803172672383*m.x6 + 0.861022193616594*m.x7
- 1.47455467362025*m.x8 - 1.72325832088305*m.x9 + 0.466890727711807*m.x10
+ 1.99626959684373*m.x11 + 0.70041476251893*m.x12 - 1.58670668058247*m.x13
- 1.62823103671264*m.x14 + 0.63460931281019*m.x15 + 1.99931464995111*m.x16
+ 0.534476752156512*m.x17 - 1.68678289162577*m.x18 - 1.52081193120006*m.x19
+ 0.797498137850494*m.x20 + 1.98714371135317*m.x21 + 0.364471050984298*m.x22
- 1.77402166635644*m.x23 - 1.4018185285997*m.x24 + 0.954317520519215*m.x25
+ 1.95984940924166*m.x26 + 0.191691505040448*m.x27 - 1.84775906502257*m.x28
- 1.27215644055553*m.x29 + 1.10387397062412*m.x30 + 1.91763946973639*m.x31
+ 0.0174530709967449*m.x32 - 1.90743390149645*m.x33 - 1.13281247384967*m.x34
+ 1.24502927327524*m.x35 + 1.86083513596405*m.x36 - 0.156918191455689*m.x37
- 1.95259201423987*m.x38 - 0.984847120206933*m.x39 + 1.37670915138751*m.x40
+ 1.78986872320405*m.x41 - 0.330095211721357*m.x42 - 1.98288972274762*m.x43
- 0.829386485312478*m.x44 + 1.497911441578*m.x45 + 1.70528032870818*m.x46
- 0.500760008108884*m.x47 - 1.99809644316372*m.x48 - 0.667613718467542*m.x49
+ 1.60771372123443*m.x50 - m.x51 <= 0)
m.c75 = Constraint(expr= 0.9079809994791*m.x1 + 1.96325436689533*m.x2 + 0.174311485495318*m.x3 - 1.8671608529944*m.x4
- 1.20363004630409*m.x5 + 1.2036300463041*m.x6 + 1.86716085299441*m.x7 - 0.17431148549532*m.x8
- 1.96325436689533*m.x9 - 0.907980999479098*m.x10 + 1.46270740323834*m.x11
+ 1.71433460140422*m.x12 - 0.517638090205038*m.x13 - 1.99969539031278*m.x14
- 0.584743409445472*m.x15 + 1.67734113589085*m.x16 + 1.50941916044554*m.x17
- 0.845236523481401*m.x18 - 1.97537668119028*m.x19 - 0.243738686810302*m.x20
+ 1.84100970690488*m.x21 + 1.25864078209968*m.x22 - 1.14715287270209*m.x23
- 1.89103715119863*m.x24 + 0.104671912485887*m.x25 + 1.94874012957047*m.x26
+ 0.969619240492676*m.x27 - 1.4142135623731*m.x28 - 1.74923941427879*m.x29
+ 0.449902108687729*m.x30 + 1.99725906950915*m.x31 + 0.651136308914316*m.x32
- 1.63830408857798*m.x33 - 1.55429192291394*m.x34 + 0.781462256978546*m.x35
+ 1.98509230328264*m.x36 + 0.312868930080459*m.x37 - 1.8126155740733*m.x38
- 1.31211805798101*m.x39 + 1.08927807003005*m.x40 + 1.91260951192607*m.x41
- 0.0349048128745657*m.x42 - 1.93185165257814*m.x43 - 1.03007614982011*m.x44
+ 1.363996720125*m.x45 + 1.78201304837674*m.x46 - 0.381617990753089*m.x47
- 1.99238939618349*m.x48 - 0.716735899090601*m.x49 + 1.59727102009459*m.x50 - m.x51 <= 0)
m.c76 = Constraint(expr= - 0.765366864730189*m.x1 + 1.58670668058247*m.x2 + 1.58670668058248*m.x3
- 0.765366864730172*m.x4 - 1.98288972274762*m.x5 - 0.261052384440111*m.x6
+ 1.84775906502257*m.x7 + 1.21752285801745*m.x8 - 1.21752285801744*m.x9
- 1.84775906502258*m.x10 + 0.261052384440098*m.x11 + 1.98288972274762*m.x12
+ 0.765366864730183*m.x13 - 1.58670668058247*m.x14 - 1.58670668058247*m.x15
+ 0.765366864730177*m.x16 + 1.98288972274762*m.x17 + 0.261052384440105*m.x18
- 1.84775906502257*m.x19 - 1.21752285801744*m.x20 + 1.21752285801744*m.x21
+ 1.84775906502257*m.x22 - 0.261052384440104*m.x23 - 1.98288972274762*m.x24
- 0.765366864730178*m.x25 + 1.58670668058247*m.x26 + 1.58670668058247*m.x27
- 0.765366864730176*m.x28 - 1.98288972274762*m.x29 - 0.261052384440106*m.x30
+ 1.84775906502257*m.x31 + 1.21752285801744*m.x32 - 1.21752285801744*m.x33
- 1.84775906502257*m.x34 + 0.261052384440103*m.x35 + 1.98288972274762*m.x36
+ 0.765366864730179*m.x37 - 1.58670668058247*m.x38 - 1.58670668058247*m.x39
+ 0.765366864730178*m.x40 + 1.98288972274762*m.x41 + 0.261052384440104*m.x42
- 1.84775906502257*m.x43 - 1.21752285801744*m.x44 + 1.21752285801744*m.x45
+ 1.84775906502257*m.x46 - 0.261052384440103*m.x47 - 1.98288972274762*m.x48
- 0.765366864730179*m.x49 + 1.58670668058247*m.x50 - m.x51 <= 0)
m.c77 = Constraint(expr= - 1.90211303259031*m.x1 + 0.139512947488258*m.x2 + 1.96961550602442*m.x3
+ 0.81347328615161*m.x4 - 1.57602150721344*m.x5 - 1.57602150721344*m.x6
+ 0.813473286151593*m.x7 + 1.96961550602442*m.x8 + 0.139512947488248*m.x9
- 1.90211303259031*m.x10 - 1.05983852846641*m.x11 + 1.38931674091799*m.x12
+ 1.73205080756888*m.x13 - 0.551274711633999*m.x14 - 1.99878165403819*m.x15
- 0.415823381635521*m.x16 + 1.79758809259834*m.x17 + 1.28557521937308*m.x18
- 1.17557050458495*m.x19 - 1.85436770913358*m.x20 + 0.278346201920127*m.x21
+ 1.98904379073655*m.x22 + 0.684040286651344*m.x23 - 1.65807514511008*m.x24
- 1.48628965095479*m.x25 + 0.938943125571777*m.x26 + 1.94059145255199*m.x27
+ 1.22588476042053E-15*m.x28 - 1.94059145255199*m.x29 - 0.938943125571786*m.x30
+ 1.48628965095479*m.x31 + 1.65807514511008*m.x32 - 0.684040286651335*m.x33
- 1.98904379073655*m.x34 - 0.27834620192013*m.x35 + 1.85436770913357*m.x36
+ 1.17557050458495*m.x37 - 1.28557521937308*m.x38 - 1.79758809259833*m.x39
+ 0.415823381635519*m.x40 + 1.99878165403819*m.x41 + 0.551274711633998*m.x42
- 1.73205080756888*m.x43 - 1.38931674091799*m.x44 + 1.05983852846641*m.x45
+ 1.90211303259031*m.x46 - 0.139512947488249*m.x47 - 1.96961550602442*m.x48
- 0.813473286151601*m.x49 + 1.57602150721344*m.x50 - m.x51 <= 0)
m.c78 = Constraint(expr= - 1.70528032870818*m.x1 - 1.40181852859971*m.x2 + 1.07459921669364*m.x3 + 1.88528298218436*m.x4
- 0.226406427535811*m.x5 - 1.98714371135317*m.x6 - 0.667613718467551*m.x7
+ 1.68678289162577*m.x8 + 1.42650089830837*m.x9 - 1.04499712943189*m.x10
- 1.8966473104124*m.x11 + 0.191691505040447*m.x12 + 1.98288972274762*m.x13
+ 0.700414762518943*m.x14 - 1.66777164413434*m.x15 - 1.45074874202458*m.x16
+ 1.0150767259214*m.x17 + 1.90743390149646*m.x18 - 0.156918191455691*m.x19
- 1.97803172672383*m.x20 - 0.733002453448601*m.x21 + 1.64825237724403*m.x22
+ 1.47455467362025*m.x23 - 0.98484712020693*m.x24 - 1.91763946973639*m.x25
+ 0.122097079069716*m.x26 + 1.97257120307446*m.x27 + 0.765366864730185*m.x28
- 1.62823103671264*m.x29 - 1.49791144157801*m.x30 + 0.954317520519214*m.x31
+ 1.92726090641725*m.x32 - 0.0872387747306684*m.x33 - 1.96650981512791*m.x34
- 0.797498137850496*m.x35 + 1.60771372123443*m.x36 + 1.52081193120006*m.x37
- 0.923497226470066*m.x38 - 1.93629528075622*m.x39 + 0.0523538966157441*m.x40
+ 1.95984940924166*m.x41 + 0.829386485312481*m.x42 - 1.58670668058247*m.x43
- 1.54324916677544*m.x44 + 0.892395626219618*m.x45 + 1.94473984079535*m.x46
- 0.0174530709967472*m.x47 - 1.95259201423987*m.x48 - 0.861022193616591*m.x49
+ 1.56521631370483*m.x50 - m.x51 <= 0)
m.c79 = Constraint(expr= - 0.312868930080486*m.x1 - 1.99725906950915*m.x2 - 0.517638090205043*m.x3
+ 1.78201304837674*m.x4 + 1.25864078209966*m.x5 - 1.25864078209969*m.x6 - 1.78201304837673*m.x7
+ 0.517638090205045*m.x8 + 1.99725906950915*m.x9 + 0.312868930080456*m.x10
- 1.86716085299441*m.x11 - 1.08927807003005*m.x12 + 1.4142135623731*m.x13
+ 1.67734113589084*m.x14 - 0.716735899090611*m.x15 - 1.97537668119027*m.x16
- 0.104671912485874*m.x17 + 1.93185165257814*m.x18 + 0.907980999479092*m.x19
- 1.55429192291394*m.x20 - 1.55429192291394*m.x21 + 0.907980999479098*m.x22
+ 1.93185165257814*m.x23 - 0.104671912485895*m.x24 - 1.97537668119028*m.x25
- 0.716735899090592*m.x26 + 1.67734113589085*m.x27 + 1.41421356237309*m.x28
- 1.08927807003006*m.x29 - 1.8671608529944*m.x30 + 0.312868930080469*m.x31
+ 1.99725906950915*m.x32 + 0.517638090205039*m.x33 - 1.78201304837674*m.x34
- 1.25864078209967*m.x35 + 1.25864078209968*m.x36 + 1.78201304837673*m.x37
- 0.517638090205042*m.x38 - 1.99725906950915*m.x39 - 0.312868930080459*m.x40
+ 1.8671608529944*m.x41 + 1.08927807003005*m.x42 - 1.4142135623731*m.x43
- 1.67734113589085*m.x44 + 0.716735899090602*m.x45 + 1.97537668119028*m.x46
+ 0.104671912485886*m.x47 - 1.93185165257814*m.x48 - 0.907980999479093*m.x49
+ 1.55429192291394*m.x50 - m.x51 <= 0)
m.c80 = Constraint(expr= 1.29889609666036*m.x1 - 1.24502927327524*m.x2 - 1.77402166635643*m.x3
+ 0.568030689407857*m.x4 + 1.99079239673436*m.x5 + 0.191691505040438*m.x6
- 1.91763946973639*m.x7 - 0.923497226470061*m.x8 + 1.56521631370484*m.x9
+ 1.52081193120006*m.x10 - 0.984847120206944*m.x11 - 1.8966473104124*m.x12
+ 0.261052384440112*m.x13 + 1.99626959684373*m.x14 + 0.500760008108876*m.x15
- 1.80517056869973*m.x16 - 1.18964557350268*m.x17 + 1.35118041523133*m.x18
+ 1.70528032870818*m.x19 - 0.700414762518943*m.x20 - 1.97257120307446*m.x21
- 0.05235389661574*m.x22 + 1.95259201423987*m.x23 + 0.797498137850489*m.x24
- 1.64825237724404*m.x25 - 1.42650089830836*m.x26 + 1.10387397062412*m.x27
+ 1.84775906502257*m.x28 - 0.3987358688344*m.x29 - 1.99992384612834*m.x30
- 0.364471050984291*m.x31 + 1.86083513596405*m.x32 + 1.07459921669365*m.x33
- 1.45074874202458*m.x34 - 1.62823103671263*m.x35 + 0.829386485312483*m.x36
+ 1.94473984079535*m.x37 - 0.087238774730675*m.x38 - 1.97803172672383*m.x39
- 0.667613718467541*m.x40 + 1.72325832088305*m.x41 + 1.32524009643147*m.x42
- 1.21752285801744*m.x43 - 1.78986872320405*m.x44 + 0.534476752156514*m.x45
+ 1.99383466746626*m.x46 + 0.226406427535813*m.x47 - 1.90743390149645*m.x48
- 0.954317520519217*m.x49 + 1.54324916677544*m.x50 - m.x51 <= 0)
m.c81 = Constraint(expr= 2*m.x1 + 0.347296355333845*m.x2 - 1.87938524157182*m.x3 - 0.999999999999992*m.x4
+ 1.53208888623796*m.x5 + 1.53208888623795*m.x6 - m.x7 - 1.87938524157181*m.x8
+ 0.347296355333868*m.x9 + 2*m.x10 + 0.34729635533386*m.x11 - 1.87938524157182*m.x12
- 0.999999999999992*m.x13 + 1.53208888623796*m.x14 + 1.53208888623795*m.x15 - m.x16
- 1.87938524157181*m.x17 + 0.347296355333867*m.x18 + 2*m.x19 + 0.347296355333861*m.x20
- 1.87938524157182*m.x21 - 0.999999999999993*m.x22 + 1.53208888623796*m.x23
+ 1.53208888623796*m.x24 - 0.999999999999998*m.x25 - 1.87938524157181*m.x26
+ 0.347296355333866*m.x27 + 2*m.x28 + 0.347296355333855*m.x29 - 1.87938524157182*m.x30 - m.x31
+ 1.53208888623796*m.x32 + 1.53208888623796*m.x33 - m.x34 - 1.87938524157182*m.x35
+ 0.347296355333865*m.x36 + 2*m.x37 + 0.347296355333855*m.x38 - 1.87938524157182*m.x39
- 0.999999999999998*m.x40 + 1.53208888623796*m.x41 + 1.53208888623795*m.x42 - m.x43
- 1.87938524157182*m.x44 + 0.347296355333861*m.x45 + 2*m.x46 + 0.34729635533386*m.x47
- 1.87938524157182*m.x48 - m.x49 + 1.53208888623796*m.x50 - m.x51 <= 0)
m.c82 = Constraint(expr= 1.29889609666037*m.x1 + 1.70528032870818*m.x2 - 0.765366864730185*m.x3
- 1.94473984079535*m.x4 + 0.156918191455699*m.x5 + 1.99383466746626*m.x6
+ 0.466890727711812*m.x7 - 1.84775906502257*m.x8 - 1.0449971294319*m.x9
+ 1.52081193120006*m.x10 + 1.52081193120006*m.x11 - 1.0449971294319*m.x12
- 1.84775906502257*m.x13 + 0.466890727711821*m.x14 + 1.99383466746626*m.x15
+ 0.156918191455691*m.x16 - 1.94473984079535*m.x17 - 0.765366864730177*m.x18
+ 1.70528032870819*m.x19 + 1.29889609666036*m.x20 - 1.29889609666037*m.x21
- 1.70528032870818*m.x22 + 0.765366864730177*m.x23 + 1.94473984079535*m.x24
- 0.15691819145569*m.x25 - 1.99383466746626*m.x26 - 0.466890727711807*m.x27
+ 1.84775906502258*m.x28 + 1.0449971294319*m.x29 - 1.52081193120006*m.x30
- 1.52081193120006*m.x31 + 1.0449971294319*m.x32 + 1.84775906502257*m.x33
- 0.466890727711812*m.x34 - 1.99383466746626*m.x35 - 0.156918191455686*m.x36
+ 1.94473984079535*m.x37 + 0.765366864730179*m.x38 - 1.70528032870819*m.x39
- 1.29889609666037*m.x40 + 1.29889609666037*m.x41 + 1.70528032870818*m.x42
- 0.765366864730181*m.x43 - 1.94473984079535*m.x44 + 0.156918191455691*m.x45
+ 1.99383466746626*m.x46 + 0.46689072771181*m.x47 - 1.84775906502257*m.x48
- 1.0449971294319*m.x49 + 1.52081193120006*m.x50 - m.x51 <= 0)
m.c83 = Constraint(expr= - 0.312868930080448*m.x1 + 1.91260951192607*m.x2 + 0.8452365234814*m.x3 - 1.67734113589085*m.x4
- 1.31211805798101*m.x5 + 1.31211805798102*m.x6 + 1.67734113589085*m.x7
- 0.845236523481402*m.x8 - 1.91260951192607*m.x9 + 0.312868930080464*m.x10
+ 1.99969539031278*m.x11 + 0.243738686810293*m.x12 - 1.93185165257814*m.x13
- 0.781462256978547*m.x14 + 1.71433460140423*m.x15 + 1.25864078209968*m.x16
- 1.363996720125*m.x17 - 1.63830408857798*m.x18 + 0.907980999479098*m.x19
+ 1.89103715119863*m.x20 - 0.381617990753094*m.x21 - 1.99725906950915*m.x22
- 0.174311485495313*m.x23 + 1.94874012957047*m.x24 + 0.716735899090598*m.x25
- 1.74923941427879*m.x26 - 1.2036300463041*m.x27 + 1.4142135623731*m.x28
+ 1.59727102009459*m.x29 - 0.969619240492674*m.x30 - 1.8671608529944*m.x31
+ 0.449902108687729*m.x32 + 1.99238939618349*m.x33 + 0.10467191248589*m.x34
- 1.96325436689533*m.x35 - 0.65113630891431*m.x36 + 1.78201304837674*m.x37
+ 1.14715287270209*m.x38 - 1.46270740323834*m.x39 - 1.55429192291394*m.x40
+ 1.03007614982011*m.x41 + 1.84100970690488*m.x42 - 0.517638090205042*m.x43
- 1.98509230328264*m.x44 - 0.034904812874566*m.x45 + 1.97537668119028*m.x46
+ 0.584743409445473*m.x47 - 1.8126155740733*m.x48 - 1.08927807003005*m.x49
+ 1.50941916044554*m.x50 - m.x51 <= 0)
m.c84 = Constraint(expr= - 1.70528032870819*m.x1 + 0.829386485312486*m.x2 + 1.90743390149646*m.x3
- 0.364471050984299*m.x4 - 1.99626959684373*m.x5 - 0.122097079069715*m.x6
+ 1.96650981512791*m.x7 + 0.601411599008539*m.x8 - 1.81992254175309*m.x9
- 1.0449971294319*m.x10 + 1.56521631370483*m.x11 + 1.42650089830837*m.x12
- 1.21752285801744*m.x13 - 1.72325832088305*m.x14 + 0.797498137850495*m.x15
+ 1.91763946973639*m.x16 - 0.330095211721353*m.x17 - 1.99809644316372*m.x18
- 0.156918191455684*m.x19 + 1.95984940924166*m.x20 + 0.634609312810183*m.x21
- 1.80517056869972*m.x22 - 1.07459921669365*m.x23 + 1.54324916677544*m.x24
+ 1.45074874202457*m.x25 - 1.18964557350268*m.x26 - 1.7407113918798*m.x27
+ 0.765366864730176*m.x28 + 1.92726090641725*m.x29 - 0.295618822259219*m.x30
- 1.99931464995111*m.x31 - 0.191691505040448*m.x32 + 1.95259201423987*m.x33
+ 0.66761371846754*m.x34 - 1.78986872320405*m.x35 - 1.10387397062411*m.x36
+ 1.52081193120006*m.x37 + 1.47455467362025*m.x38 - 1.16140591142188*m.x39
- 1.75763422532393*m.x40 + 0.733002453448595*m.x41 + 1.93629528075622*m.x42
- 0.261052384440102*m.x43 - 1.99992384612834*m.x44 - 0.226406427535813*m.x45
+ 1.94473984079535*m.x46 + 0.700414762518934*m.x47 - 1.77402166635644*m.x48
- 1.13281247384967*m.x49 + 1.497911441578*m.x50 - m.x51 <= 0)
m.c85 = Constraint(expr= - 1.90211303259031*m.x1 - 0.813473286151603*m.x2 + 1.73205080756887*m.x3
+ 1.17557050458494*m.x4 - 1.48628965095478*m.x5 - 1.48628965095478*m.x6 + 1.17557050458494*m.x7
+ 1.73205080756888*m.x8 - 0.813473286151599*m.x9 - 1.90211303259031*m.x10
+ 0.415823381635522*m.x11 + 1.98904379073655*m.x12 + 6.37187723938577E-15*m.x13
- 1.98904379073655*m.x14 - 0.415823381635521*m.x15 + 1.90211303259031*m.x16
+ 0.813473286151598*m.x17 - 1.73205080756888*m.x18 - 1.17557050458495*m.x19
+ 1.48628965095479*m.x20 + 1.48628965095479*m.x21 - 1.17557050458495*m.x22
- 1.73205080756888*m.x23 + 0.813473286151604*m.x24 + 1.90211303259031*m.x25
- 0.415823381635513*m.x26 - 1.98904379073655*m.x27 - 9.80955400591059E-16*m.x28
+ 1.98904379073655*m.x29 + 0.415823381635522*m.x30 - 1.90211303259031*m.x31
- 0.8134732861516*m.x32 + 1.73205080756888*m.x33 + 1.17557050458495*m.x34
- 1.48628965095479*m.x35 - 1.48628965095479*m.x36 + 1.17557050458494*m.x37
+ 1.73205080756888*m.x38 - 0.813473286151603*m.x39 - 1.90211303259031*m.x40
+ 0.415823381635519*m.x41 + 1.98904379073655*m.x42 - 8.57252759403147E-16*m.x43
- 1.98904379073655*m.x44 - 0.41582338163552*m.x45 + 1.90211303259031*m.x46
+ 0.813473286151601*m.x47 - 1.73205080756888*m.x48 - 1.17557050458495*m.x49
+ 1.48628965095479*m.x50 - m.x51 <= 0)
m.c86 = Constraint(expr= - 0.765366864730173*m.x1 - 1.90743390149646*m.x2 + 0.432879227876212*m.x3
+ 1.98288972274762*m.x4 - 0.0872387747306647*m.x5 - 1.99809644316371*m.x6
- 0.261052384440096*m.x7 + 1.95259201423987*m.x8 + 0.601411599008553*m.x9
- 1.84775906502257*m.x10 - 0.923497226470074*m.x11 + 1.68678289162577*m.x12
+ 1.21752285801745*m.x13 - 1.47455467362025*m.x14 - 1.47455467362025*m.x15
+ 1.21752285801744*m.x16 + 1.68678289162577*m.x17 - 0.923497226470069*m.x18
- 1.84775906502258*m.x19 + 0.601411599008547*m.x20 + 1.95259201423987*m.x21
- 0.261052384440104*m.x22 - 1.99809644316372*m.x23 - 0.0872387747306706*m.x24
+ 1.98288972274762*m.x25 + 0.432879227876204*m.x26 - 1.90743390149645*m.x27
- 0.765366864730178*m.x28 + 1.77402166635644*m.x29 + 1.07459921669365*m.x30
- 1.58670668058247*m.x31 - 1.35118041523132*m.x32 + 1.35118041523132*m.x33
+ 1.58670668058247*m.x34 - 1.07459921669364*m.x35 - 1.77402166635644*m.x36
+ 0.765366864730175*m.x37 + 1.90743390149645*m.x38 - 0.432879227876201*m.x39
- 1.98288972274762*m.x40 + 0.0872387747306712*m.x41 + 1.99809644316372*m.x42
+ 0.261052384440104*m.x43 - 1.95259201423987*m.x44 - 0.601411599008547*m.x45
+ 1.84775906502257*m.x46 + 0.923497226470068*m.x47 - 1.68678289162577*m.x48
- 1.21752285801744*m.x49 + 1.47455467362025*m.x50 - m.x51 <= 0)
m.c87 = Constraint(expr= 0.90798099947909*m.x1 - 1.71433460140423*m.x2 - 1.14715287270209*m.x3 + 1.55429192291394*m.x4
+ 1.363996720125*m.x5 - 1.36399672012499*m.x6 - 1.55429192291395*m.x7 + 1.14715287270208*m.x8
+ 1.71433460140422*m.x9 - 0.907980999479093*m.x10 - 1.84100970690488*m.x11
+ 0.651136308914309*m.x12 + 1.93185165257814*m.x13 - 0.381617990753081*m.x14
- 1.98509230328265*m.x15 + 0.104671912485889*m.x16 + 1.99969539031278*m.x17
+ 0.174311485495319*m.x18 - 1.97537668119027*m.x19 - 0.449902108687737*m.x20
+ 1.91260951192607*m.x21 + 0.716735899090598*m.x22 - 1.8126155740733*m.x23
- 0.969619240492675*m.x24 + 1.67734113589085*m.x25 + 1.2036300463041*m.x26
- 1.50941916044554*m.x27 - 1.4142135623731*m.x28 + 1.31211805798102*m.x29
+ 1.59727102009459*m.x30 - 1.08927807003005*m.x31 - 1.74923941427879*m.x32
+ 0.8452365234814*m.x33 + 1.8671608529944*m.x34 - 0.58474340944547*m.x35
- 1.94874012957047*m.x36 + 0.312868930080462*m.x37 + 1.99238939618349*m.x38
- 0.0349048128745626*m.x39 - 1.99725906950915*m.x40 - 0.243738686810296*m.x41
+ 1.96325436689533*m.x42 + 0.517638090205044*m.x43 - 1.89103715119863*m.x44
- 0.781462256978547*m.x45 + 1.78201304837674*m.x46 + 1.03007614982011*m.x47
- 1.63830408857798*m.x48 - 1.25864078209968*m.x49 + 1.46270740323834*m.x50 - m.x51 <= 0)
m.c88 = Constraint(expr= 1.94473984079535*m.x1 - 0.364471050984313*m.x2 - 1.98288972274762*m.x3
+ 0.156918191455714*m.x4 + 1.99931464995111*m.x5 + 0.0523538966157449*m.x6
- 1.99383466746626*m.x7 - 0.261052384440096*m.x8 + 1.96650981512791*m.x9
+ 0.466890727711798*m.x10 - 1.91763946973639*m.x11 - 0.667613718467538*m.x12
+ 1.84775906502258*m.x13 + 0.861022193616581*m.x14 - 1.75763422532394*m.x15
- 1.04499712943188*m.x16 + 1.64825237724403*m.x17 + 1.21752285801744*m.x18
- 1.52081193120007*m.x19 - 1.3767091513875*m.x20 + 1.37670915138751*m.x21
+ 1.52081193120006*m.x22 - 1.21752285801745*m.x23 - 1.64825237724403*m.x24
+ 1.04499712943191*m.x25 + 1.75763422532393*m.x26 - 0.861022193616594*m.x27
- 1.84775906502257*m.x28 + 0.667613718467551*m.x29 + 1.91763946973638*m.x30
- 0.466890727711819*m.x31 - 1.96650981512791*m.x32 + 0.26105238444011*m.x33
+ 1.99383466746626*m.x34 - 0.052353896615752*m.x35 - 1.99931464995111*m.x36
- 0.156918191455686*m.x37 + 1.98288972274762*m.x38 + 0.364471050984292*m.x39
- 1.94473984079535*m.x40 - 0.568030689407844*m.x41 + 1.88528298218436*m.x42
+ 0.765366864730176*m.x43 - 1.80517056869972*m.x44 - 0.954317520519215*m.x45
+ 1.70528032870819*m.x46 + 1.13281247384966*m.x47 - 1.58670668058247*m.x48
- 1.29889609666037*m.x49 + 1.45074874202458*m.x50 - m.x51 <= 0)
m.c89 = Constraint(expr= 1.61803398874989*m.x1 + 1.23132295065131*m.x2 - 1.53208888623797*m.x3 - 1.3382612127177*m.x4
+ 1.4386796006773*m.x5 + 1.4386796006773*m.x6 - 1.33826121271773*m.x7 - 1.53208888623796*m.x8
+ 1.23132295065132*m.x9 + 1.61803398874989*m.x10 - 1.1183858069415*m.x11
- 1.69609619231284*m.x12 + 1.00000000000001*m.x13 + 1.76589518571785*m.x14
- 0.876742293578162*m.x15 - 1.8270909152852*m.x16 + 0.749213186831833*m.x17
+ 1.87938524157182*m.x18 - 0.618033988749905*m.x19 - 1.92252339187664*m.x20
+ 0.483843791199347*m.x21 + 1.95629520146761*m.x22 - 0.34729635533386*m.x23
- 1.98053613748314*m.x24 + 0.209056926535308*m.x25 + 1.99512810051965*m.x26
- 0.069798993405004*m.x27 - 2*m.x28 - 0.0697989934049986*m.x29 + 1.99512810051965*m.x30
+ 0.209056926535302*m.x31 - 1.98053613748314*m.x32 - 0.347296355333855*m.x33
+ 1.95629520146761*m.x34 + 0.483843791199335*m.x35 - 1.92252339187664*m.x36
- 0.618033988749893*m.x37 + 1.87938524157182*m.x38 + 0.749213186831821*m.x39
- 1.8270909152852*m.x40 - 0.876742293578154*m.x41 + 1.76589518571785*m.x42
+ 0.999999999999998*m.x43 - 1.69609619231285*m.x44 - 1.11838580694149*m.x45
+ 1.6180339887499*m.x46 + 1.23132295065132*m.x47 - 1.53208888623796*m.x48
- 1.33826121271772*m.x49 + 1.4386796006773*m.x50 - m.x51 <= 0)
m.c90 = Constraint(expr= 0.1569181914557*m.x1 + 1.99626959684373*m.x2 - 0.0872387747306794*m.x3
- 1.99931464995111*m.x4 + 0.0174530709967523*m.x5 + 1.99992384612834*m.x6
+ 0.0523538966157449*m.x7 - 1.99809644316372*m.x8 - 0.122097079069715*m.x9
+ 1.99383466746626*m.x10 + 0.191691505040438*m.x11 - 1.98714371135318*m.x12
- 0.261052384440097*m.x13 + 1.97803172672383*m.x14 + 0.330095211721352*m.x15
- 1.96650981512791*m.x16 - 0.398735868834394*m.x17 + 1.95259201423987*m.x18
+ 0.466890727711799*m.x19 - 1.93629528075622*m.x20 - 0.534476752156505*m.x21
+ 1.91763946973639*m.x22 + 0.601411599008541*m.x23 - 1.8966473104124*m.x24
- 0.667613718467539*m.x25 + 1.8733443784968*m.x26 + 0.733002453448595*m.x27
- 1.84775906502257*m.x28 - 0.797498137850495*m.x29 + 1.81992254175309*m.x30
+ 0.861022193616589*m.x31 - 1.78986872320405*m.x32 - 0.923497226470063*m.x33
+ 1.75763422532393*m.x34 + 0.984847120206933*m.x35 - 1.72325832088305*m.x36
- 1.0449971294319*m.x37 + 1.68678289162577*m.x38 + 1.10387397062411*m.x39
- 1.64825237724403*m.x40 - 1.16140591142188*m.x41 + 1.60771372123444*m.x42
+ 1.21752285801744*m.x43 - 1.56521631370483*m.x44 - 1.27215644055553*m.x45
+ 1.52081193120006*m.x46 + 1.32524009643148*m.x47 - 1.47455467362025*m.x48
- 1.37670915138751*m.x49 + 1.42650089830836*m.x50 - m.x51 <= 0)
m.c91 = Constraint(expr= - 1.4142135623731*m.x1 + 1.4142135623731*m.x2 + 1.41421356237309*m.x3 - 1.41421356237311*m.x4
- 1.41421356237309*m.x5 + 1.41421356237309*m.x6 + 1.41421356237308*m.x7 - 1.4142135623731*m.x8
- 1.4142135623731*m.x9 + 1.4142135623731*m.x10 + 1.4142135623731*m.x11 - 1.4142135623731*m.x12
- 1.41421356237309*m.x13 + 1.4142135623731*m.x14 + 1.4142135623731*m.x15
- 1.4142135623731*m.x16 - 1.41421356237309*m.x17 + 1.4142135623731*m.x18
+ 1.4142135623731*m.x19 - 1.4142135623731*m.x20 - 1.41421356237309*m.x21
+ 1.4142135623731*m.x22 + 1.4142135623731*m.x23 - 1.4142135623731*m.x24
- 1.41421356237309*m.x25 + 1.4142135623731*m.x26 + 1.4142135623731*m.x27
- 1.4142135623731*m.x28 - 1.41421356237309*m.x29 + 1.4142135623731*m.x30
+ 1.41421356237309*m.x31 - 1.4142135623731*m.x32 - 1.41421356237309*m.x33
+ 1.4142135623731*m.x34 + 1.41421356237309*m.x35 - 1.4142135623731*m.x36
- 1.41421356237309*m.x37 + 1.41421356237309*m.x38 + 1.41421356237309*m.x39
- 1.41421356237309*m.x40 - 1.41421356237309*m.x41 + 1.41421356237309*m.x42
+ 1.41421356237309*m.x43 - 1.41421356237309*m.x44 - 1.41421356237309*m.x45
+ 1.4142135623731*m.x46 + 1.41421356237309*m.x47 - 1.4142135623731*m.x48
- 1.41421356237309*m.x49 + 1.4142135623731*m.x50 - m.x51 <= 0)
m.c92 = Constraint(expr= - m.x51 + m.x53 - m.x54 == 0)
m.c93 = Constraint(expr= m.x51 + m.x52 - m.x54 == 0)
m.c94 = Constraint(expr=m.x53**2 - (m.x55**2 + m.x52**2) >= 0)
m.c95 = Constraint(expr= - 2*m.x1 - 2*m.x2 - 2*m.x3 - 2*m.x4 - 2*m.x5 - 2*m.x6 - 2*m.x7 - 2*m.x8 - 2*m.x9 - 2*m.x10
- 2*m.x11 - 2*m.x12 - 2*m.x13 - 2*m.x14 - 2*m.x15 - 2*m.x16 - 2*m.x17 - 2*m.x18 - 2*m.x19
- 2*m.x20 - 2*m.x21 - 2*m.x22 - 2*m.x23 - 2*m.x24 - 2*m.x25 - 2*m.x26 - 2*m.x27 - 2*m.x28
- 2*m.x29 - 2*m.x30 - 2*m.x31 - 2*m.x32 - 2*m.x33 - 2*m.x34 - 2*m.x35 - 2*m.x36 - 2*m.x37
- 2*m.x38 - 2*m.x39 - 2*m.x40 - 2*m.x41 - 2*m.x42 - 2*m.x43 - 2*m.x44 - 2*m.x45 - 2*m.x46
- 2*m.x47 - 2*m.x48 - 2*m.x49 - 2*m.x50 + m.x54 <= 0)
m.c96 = Constraint(expr= - 1.29889609666037*m.x1 - 1.32524009643148*m.x2 - 1.35118041523132*m.x3 - 1.37670915138751*m.x4
- 1.4018185285997*m.x5 - 1.42650089830836*m.x6 - 1.45074874202458*m.x7 - 1.47455467362025*m.x8
- 1.497911441578*m.x9 - 1.52081193120006*m.x10 - 1.54324916677544*m.x11
- 1.56521631370483*m.x12 - 1.58670668058247*m.x13 - 1.60771372123443*m.x14
- 1.62823103671264*m.x15 - 1.64825237724403*m.x16 - 1.66777164413434*m.x17
- 1.68678289162577*m.x18 - 1.70528032870818*m.x19 - 1.72325832088305*m.x20
- 1.7407113918798*m.x21 - 1.75763422532393*m.x22 - 1.77402166635644*m.x23
- 1.78986872320405*m.x24 - 1.80517056869972*m.x25 - 1.81992254175309*m.x26
- 1.83412014877025*m.x27 - 1.84775906502257*m.x28 - 1.86083513596405*m.x29
- 1.8733443784968*m.x30 - 1.88528298218436*m.x31 - 1.8966473104124*m.x32
- 1.90743390149645*m.x33 - 1.91763946973639*m.x34 - 1.92726090641725*m.x35
- 1.93629528075622*m.x36 - 1.94473984079535*m.x37 - 1.95259201423987*m.x38
- 1.95984940924166*m.x39 - 1.96650981512791*m.x40 - 1.97257120307446*m.x41
- 1.97803172672383*m.x42 - 1.98288972274762*m.x43 - 1.98714371135317*m.x44
- 1.99079239673436*m.x45 - 1.99383466746626*m.x46 - 1.99626959684373*m.x47
- 1.99809644316372*m.x48 - 1.99931464995111*m.x49 - 1.99992384612834*m.x50 + m.x54 <= 0)
m.c97 = Constraint(expr= 0.312868930080462*m.x1 + 0.243738686810295*m.x2 + 0.174311485495316*m.x3
+ 0.104671912485888*m.x4 + 0.034904812874567*m.x5 - 0.0349048128745672*m.x6
- 0.104671912485888*m.x7 - 0.174311485495316*m.x8 - 0.243738686810295*m.x9
- 0.312868930080462*m.x10 - 0.38161799075309*m.x11 - 0.44990210868773*m.x12
- 0.517638090205041*m.x13 - 0.584743409445474*m.x14 - 0.651136308914314*m.x15
- 0.716735899090601*m.x16 - 0.781462256978547*m.x17 - 0.845236523481399*m.x18
- 0.907980999479094*m.x19 - 0.969619240492674*m.x20 - 1.03007614982011*m.x21
- 1.08927807003005*m.x22 - 1.14715287270209*m.x23 - 1.2036300463041*m.x24
- 1.25864078209968*m.x25 - 1.31211805798101*m.x26 - 1.363996720125*m.x27
- 1.4142135623731*m.x28 - 1.46270740323834*m.x29 - 1.50941916044554*m.x30
- 1.55429192291394*m.x31 - 1.59727102009459*m.x32 - 1.63830408857798*m.x33
- 1.67734113589085*m.x34 - 1.71433460140422*m.x35 - 1.74923941427879*m.x36
- 1.78201304837674*m.x37 - 1.8126155740733*m.x38 - 1.84100970690488*m.x39
- 1.8671608529944*m.x40 - 1.89103715119863*m.x41 - 1.91260951192607*m.x42
- 1.93185165257814*m.x43 - 1.94874012957047*m.x44 - 1.96325436689533*m.x45
- 1.97537668119028*m.x46 - 1.98509230328264*m.x47 - 1.99238939618349*m.x48
- 1.99725906950915*m.x49 - 1.99969539031278*m.x50 + m.x54 <= 0)
m.c98 = Constraint(expr= 1.70528032870818*m.x1 + 1.64825237724403*m.x2 + 1.58670668058247*m.x3 + 1.52081193120006*m.x4
+ 1.45074874202458*m.x5 + 1.37670915138751*m.x6 + 1.29889609666037*m.x7 + 1.21752285801744*m.x8
+ 1.13281247384967*m.x9 + 1.0449971294319*m.x10 + 0.954317520519217*m.x11
+ 0.861022193616591*m.x12 + 0.76536686473018*m.x13 + 0.667613718467542*m.x14
+ 0.568030689407845*m.x15 + 0.466890727711811*m.x16 + 0.364471050984295*m.x17
+ 0.261052384440103*m.x18 + 0.15691819145569*m.x19 + 0.0523538966157465*m.x20
- 0.0523538966157463*m.x21 - 0.15691819145569*m.x22 - 0.261052384440103*m.x23
- 0.364471050984295*m.x24 - 0.466890727711811*m.x25 - 0.568030689407845*m.x26
- 0.667613718467542*m.x27 - 0.76536686473018*m.x28 - 0.86102219361659*m.x29
- 0.954317520519217*m.x30 - 1.0449971294319*m.x31 - 1.13281247384967*m.x32
- 1.21752285801744*m.x33 - 1.29889609666037*m.x34 - 1.37670915138751*m.x35
- 1.45074874202458*m.x36 - 1.52081193120006*m.x37 - 1.58670668058247*m.x38
- 1.64825237724403*m.x39 - 1.70528032870818*m.x40 - 1.75763422532393*m.x41
- 1.80517056869972*m.x42 - 1.84775906502257*m.x43 - 1.88528298218436*m.x44
- 1.91763946973639*m.x45 - 1.94473984079535*m.x46 - 1.96650981512791*m.x47
- 1.98288972274762*m.x48 - 1.99383466746626*m.x49 - 1.99931464995111*m.x50 + m.x54 <= 0)
m.c99 = Constraint(expr= 1.90211303259031*m.x1 + 1.94059145255199*m.x2 + 1.96961550602442*m.x3 + 1.98904379073655*m.x4
+ 1.99878165403819*m.x5 + 1.99878165403819*m.x6 + 1.98904379073655*m.x7 + 1.96961550602442*m.x8
+ 1.94059145255199*m.x9 + 1.90211303259031*m.x10 + 1.85436770913357*m.x11
+ 1.79758809259833*m.x12 + 1.73205080756888*m.x13 + 1.65807514511008*m.x14
+ 1.57602150721344*m.x15 + 1.48628965095479*m.x16 + 1.38931674091799*m.x17
+ 1.28557521937308*m.x18 + 1.17557050458495*m.x19 + 1.05983852846641*m.x20
+ 0.938943125571782*m.x21 + 0.813473286151601*m.x22 + 0.684040286651337*m.x23
+ 0.551274711633998*m.x24 + 0.415823381635519*m.x25 + 0.278346201920131*m.x26
+ 0.139512947488251*m.x27 - 1.22464679914735E-16*m.x28 - 0.13951294748825*m.x29
- 0.278346201920131*m.x30 - 0.415823381635519*m.x31 - 0.551274711633998*m.x32
- 0.684040286651338*m.x33 - 0.8134732861516*m.x34 - 0.938943125571782*m.x35
- 1.05983852846641*m.x36 - 1.17557050458495*m.x37 - 1.28557521937308*m.x38
- 1.38931674091799*m.x39 - 1.48628965095479*m.x40 - 1.57602150721344*m.x41
- 1.65807514511008*m.x42 - 1.73205080756888*m.x43 - 1.79758809259833*m.x44
- 1.85436770913357*m.x45 - 1.90211303259031*m.x46 - 1.94059145255199*m.x47
- 1.96961550602442*m.x48 - 1.98904379073655*m.x49 - 1.99878165403819*m.x50 + m.x54 <= 0)
m.c100 = Constraint(expr= 0.765366864730181*m.x1 + 0.923497226470069*m.x2 + 1.07459921669365*m.x3
+ 1.21752285801744*m.x4 + 1.35118041523132*m.x5 + 1.47455467362025*m.x6
+ 1.58670668058247*m.x7 + 1.68678289162577*m.x8 + 1.77402166635644*m.x9
+ 1.84775906502257*m.x10 + 1.90743390149645*m.x11 + 1.95259201423987*m.x12
+ 1.98288972274762*m.x13 + 1.99809644316372*m.x14 + 1.99809644316372*m.x15
+ 1.98288972274762*m.x16 + 1.95259201423987*m.x17 + 1.90743390149645*m.x18
+ 1.84775906502257*m.x19 + 1.77402166635644*m.x20 + 1.68678289162577*m.x21
+ 1.58670668058247*m.x22 + 1.47455467362025*m.x23 + 1.35118041523132*m.x24
+ 1.21752285801744*m.x25 + 1.07459921669365*m.x26 + 0.923497226470067*m.x27
+ 0.765366864730179*m.x28 + 0.601411599008546*m.x29 + 0.432879227876206*m.x30
+ 0.261052384440103*m.x31 + 0.0872387747306718*m.x32 - 0.087238774730672*m.x33
- 0.261052384440103*m.x34 - 0.432879227876206*m.x35 - 0.601411599008547*m.x36
- 0.76536686473018*m.x37 - 0.923497226470068*m.x38 - 1.07459921669365*m.x39
- 1.21752285801744*m.x40 - 1.35118041523132*m.x41 - 1.47455467362025*m.x42
- 1.58670668058247*m.x43 - 1.68678289162577*m.x44 - 1.77402166635644*m.x45
- 1.84775906502257*m.x46 - 1.90743390149645*m.x47 - 1.95259201423987*m.x48
- 1.98288972274762*m.x49 - 1.99809644316372*m.x50 + m.x54 <= 0)
m.c101 = Constraint(expr= - 0.907980999479095*m.x1 - 0.716735899090601*m.x2 - 0.517638090205042*m.x3
- 0.312868930080461*m.x4 - 0.104671912485888*m.x5 + 0.104671912485887*m.x6
+ 0.312868930080462*m.x7 + 0.517638090205041*m.x8 + 0.7167358990906*m.x9
+ 0.907980999479094*m.x10 + 1.08927807003005*m.x11 + 1.25864078209967*m.x12
+ 1.41421356237309*m.x13 + 1.55429192291394*m.x14 + 1.67734113589085*m.x15
+ 1.78201304837674*m.x16 + 1.8671608529944*m.x17 + 1.93185165257814*m.x18
+ 1.97537668119028*m.x19 + 1.99725906950915*m.x20 + 1.99725906950915*m.x21
+ 1.97537668119028*m.x22 + 1.93185165257814*m.x23 + 1.8671608529944*m.x24
+ 1.78201304837674*m.x25 + 1.67734113589085*m.x26 + 1.55429192291394*m.x27
+ 1.41421356237309*m.x28 + 1.25864078209968*m.x29 + 1.08927807003005*m.x30
+ 0.907980999479093*m.x31 + 0.716735899090601*m.x32 + 0.517638090205042*m.x33
+ 0.312868930080462*m.x34 + 0.104671912485888*m.x35 - 0.104671912485887*m.x36
- 0.312868930080461*m.x37 - 0.517638090205041*m.x38 - 0.7167358990906*m.x39
- 0.907980999479094*m.x40 - 1.08927807003005*m.x41 - 1.25864078209968*m.x42
- 1.41421356237309*m.x43 - 1.55429192291394*m.x44 - 1.67734113589085*m.x45
- 1.78201304837674*m.x46 - 1.8671608529944*m.x47 - 1.93185165257814*m.x48
- 1.97537668119028*m.x49 - 1.99725906950915*m.x50 + m.x54 <= 0)
m.c102 = Constraint(expr= - 1.94473984079535*m.x1 - 1.8733443784968*m.x2 - 1.77402166635644*m.x3 - 1.64825237724403*m.x4
- 1.497911441578*m.x5 - 1.32524009643148*m.x6 - 1.13281247384966*m.x7 - 0.923497226470068*m.x8
- 0.700414762518934*m.x9 - 0.466890727711811*m.x10 - 0.226406427535813*m.x11
+ 0.0174530709967472*m.x12 + 0.261052384440103*m.x13 + 0.500760008108882*m.x14
+ 0.733002453448594*m.x15 + 0.954317520519217*m.x16 + 1.16140591142188*m.x17
+ 1.35118041523132*m.x18 + 1.52081193120006*m.x19 + 1.66777164413434*m.x20
+ 1.78986872320405*m.x21 + 1.88528298218436*m.x22 + 1.95259201423987*m.x23
+ 1.99079239673436*m.x24 + 1.99931464995111*m.x25 + 1.97803172672383*m.x26
+ 1.92726090641725*m.x27 + 1.84775906502257*m.x28 + 1.7407113918798*m.x29
+ 1.60771372123443*m.x30 + 1.45074874202458*m.x31 + 1.27215644055553*m.x32
+ 1.07459921669365*m.x33 + 0.861022193616591*m.x34 + 0.634609312810184*m.x35
+ 0.398735868834395*m.x36 + 0.15691819145569*m.x37 - 0.087238774730672*m.x38
- 0.330095211721355*m.x39 - 0.568030689407845*m.x40 - 0.797498137850493*m.x41
- 1.01507672592141*m.x42 - 1.21752285801744*m.x43 - 1.4018185285997*m.x44
- 1.56521631370483*m.x45 - 1.70528032870818*m.x46 - 1.81992254175309*m.x47
- 1.90743390149645*m.x48 - 1.96650981512791*m.x49 - 1.99626959684373*m.x50 + m.x54 <= 0)
m.c103 = Constraint(expr= - 1.6180339887499*m.x1 - 1.76589518571785*m.x2 - 1.87938524157182*m.x3 - 1.95629520146761*m.x4
- 1.99512810051965*m.x5 - 1.99512810051965*m.x6 - 1.95629520146761*m.x7
- 1.87938524157182*m.x8 - 1.76589518571785*m.x9 - 1.61803398874989*m.x10
- 1.4386796006773*m.x11 - 1.23132295065132*m.x12 - m.x13 - 0.749213186831824*m.x14
- 0.483843791199335*m.x15 - 0.209056926535306*m.x16 + 0.0697989934050015*m.x17
+ 0.347296355333861*m.x18 + 0.618033988749895*m.x19 + 0.876742293578155*m.x20
+ 1.11838580694149*m.x21 + 1.33826121271772*m.x22 + 1.53208888623796*m.x23
+ 1.69609619231285*m.x24 + 1.8270909152852*m.x25 + 1.92252339187664*m.x26
+ 1.98053613748314*m.x27 + 2*m.x28 + 1.98053613748314*m.x29 + 1.92252339187664*m.x30
+ 1.8270909152852*m.x31 + 1.69609619231285*m.x32 + 1.53208888623796*m.x33
+ 1.33826121271772*m.x34 + 1.11838580694149*m.x35 + 0.876742293578155*m.x36
+ 0.618033988749895*m.x37 + 0.347296355333861*m.x38 + 0.0697989934050019*m.x39
- 0.209056926535307*m.x40 - 0.483843791199335*m.x41 - 0.749213186831824*m.x42 - m.x43
- 1.23132295065132*m.x44 - 1.4386796006773*m.x45 - 1.61803398874989*m.x46
- 1.76589518571785*m.x47 - 1.87938524157182*m.x48 - 1.95629520146761*m.x49
- 1.99512810051965*m.x50 + m.x54 <= 0)
m.c104 = Constraint(expr= - 0.156918191455691*m.x1 - 0.466890727711811*m.x2 - 0.765366864730181*m.x3
- 1.0449971294319*m.x4 - 1.29889609666037*m.x5 - 1.52081193120006*m.x6 - 1.70528032870819*m.x7
- 1.84775906502257*m.x8 - 1.94473984079535*m.x9 - 1.99383466746626*m.x10
- 1.99383466746626*m.x11 - 1.94473984079535*m.x12 - 1.84775906502257*m.x13
- 1.70528032870818*m.x14 - 1.52081193120006*m.x15 - 1.29889609666037*m.x16
- 1.0449971294319*m.x17 - 0.76536686473018*m.x18 - 0.46689072771181*m.x19
- 0.15691819145569*m.x20 + 0.156918191455691*m.x21 + 0.46689072771181*m.x22
+ 0.765366864730181*m.x23 + 1.0449971294319*m.x24 + 1.29889609666037*m.x25
+ 1.52081193120006*m.x26 + 1.70528032870818*m.x27 + 1.84775906502257*m.x28
+ 1.94473984079535*m.x29 + 1.99383466746626*m.x30 + 1.99383466746626*m.x31
+ 1.94473984079535*m.x32 + 1.84775906502257*m.x33 + 1.70528032870818*m.x34
+ 1.52081193120006*m.x35 + 1.29889609666037*m.x36 + 1.0449971294319*m.x37
+ 0.765366864730179*m.x38 + 0.466890727711811*m.x39 + 0.15691819145569*m.x40
- 0.15691819145569*m.x41 - 0.466890727711811*m.x42 - 0.76536686473018*m.x43
- 1.0449971294319*m.x44 - 1.29889609666037*m.x45 - 1.52081193120006*m.x46
- 1.70528032870818*m.x47 - 1.84775906502257*m.x48 - 1.94473984079535*m.x49
- 1.99383466746626*m.x50 + m.x54 <= 0)
m.c105 = Constraint(expr= 1.41421356237309*m.x1 + 1.14715287270209*m.x2 + 0.845236523481397*m.x3
+ 0.51763809020504*m.x4 + 0.174311485495316*m.x5 - 0.174311485495317*m.x6
- 0.517638090205041*m.x7 - 0.8452365234814*m.x8 - 1.14715287270209*m.x9
- 1.4142135623731*m.x10 - 1.63830408857798*m.x11 - 1.8126155740733*m.x12
- 1.93185165257814*m.x13 - 1.99238939618349*m.x14 - 1.99238939618349*m.x15
- 1.93185165257814*m.x16 - 1.8126155740733*m.x17 - 1.63830408857798*m.x18
- 1.41421356237309*m.x19 - 1.14715287270209*m.x20 - 0.845236523481398*m.x21
- 0.517638090205041*m.x22 - 0.174311485495316*m.x23 + 0.174311485495316*m.x24
+ 0.517638090205041*m.x25 + 0.8452365234814*m.x26 + 1.14715287270209*m.x27
+ 1.4142135623731*m.x28 + 1.63830408857798*m.x29 + 1.8126155740733*m.x30
+ 1.93185165257814*m.x31 + 1.99238939618349*m.x32 + 1.99238939618349*m.x33
+ 1.93185165257814*m.x34 + 1.8126155740733*m.x35 + 1.63830408857798*m.x36
+ 1.41421356237309*m.x37 + 1.14715287270209*m.x38 + 0.845236523481399*m.x39
+ 0.517638090205041*m.x40 + 0.174311485495316*m.x41 - 0.174311485495317*m.x42
- 0.517638090205041*m.x43 - 0.845236523481399*m.x44 - 1.14715287270209*m.x45
- 1.4142135623731*m.x46 - 1.63830408857798*m.x47 - 1.8126155740733*m.x48
- 1.93185165257814*m.x49 - 1.99238939618349*m.x50 + m.x54 <= 0)
m.c106 = Constraint(expr= 1.99383466746626*m.x1 + 1.98714371135317*m.x2 + 1.90743390149645*m.x3
+ 1.75763422532393*m.x4 + 1.54324916677544*m.x5 + 1.27215644055553*m.x6
+ 0.954317520519215*m.x7 + 0.601411599008547*m.x8 + 0.226406427535813*m.x9
- 0.156918191455691*m.x10 - 0.534476752156514*m.x11 - 0.892395626219619*m.x12
- 1.21752285801744*m.x13 - 1.49791144157801*m.x14 - 1.72325832088305*m.x15
- 1.88528298218436*m.x16 - 1.97803172672383*m.x17 - 1.99809644316372*m.x18
- 1.94473984079535*m.x19 - 1.81992254175309*m.x20 - 1.62823103671264*m.x21
- 1.37670915138751*m.x22 - 1.07459921669365*m.x23 - 0.733002453448594*m.x24
- 0.364471050984295*m.x25 + 0.0174530709967489*m.x26 + 0.398735868834395*m.x27
+ 0.765366864730181*m.x28 + 1.10387397062412*m.x29 + 1.4018185285997*m.x30
+ 1.64825237724403*m.x31 + 1.83412014877025*m.x32 + 1.95259201423987*m.x33
+ 1.99931464995111*m.x34 + 1.97257120307446*m.x35 + 1.8733443784968*m.x36
+ 1.70528032870818*m.x37 + 1.47455467362025*m.x38 + 1.18964557350268*m.x39
+ 0.86102219361659*m.x40 + 0.500760008108883*m.x41 + 0.122097079069714*m.x42
- 0.261052384440103*m.x43 - 0.634609312810185*m.x44 - 0.984847120206934*m.x45
- 1.29889609666037*m.x46 - 1.56521631370483*m.x47 - 1.77402166635644*m.x48
- 1.91763946973639*m.x49 - 1.99079239673436*m.x50 + m.x54 <= 0)
m.c107 = Constraint(expr= 1.17557050458494*m.x1 + 1.48628965095479*m.x2 + 1.73205080756888*m.x3
+ 1.90211303259031*m.x4 + 1.98904379073655*m.x5 + 1.98904379073655*m.x6
+ 1.90211303259031*m.x7 + 1.73205080756888*m.x8 + 1.48628965095479*m.x9
+ 1.17557050458495*m.x10 + 0.813473286151601*m.x11 + 0.41582338163552*m.x12
+ 1.16403343982657E-15*m.x13 - 0.415823381635518*m.x14 - 0.8134732861516*m.x15
- 1.17557050458495*m.x16 - 1.48628965095479*m.x17 - 1.73205080756888*m.x18
- 1.90211303259031*m.x19 - 1.98904379073655*m.x20 - 1.98904379073655*m.x21
- 1.90211303259031*m.x22 - 1.73205080756888*m.x23 - 1.48628965095479*m.x24
- 1.17557050458495*m.x25 - 0.813473286151601*m.x26 - 0.415823381635519*m.x27
+ 3.67394039744206E-16*m.x28 + 0.415823381635518*m.x29 + 0.8134732861516*m.x30
+ 1.17557050458495*m.x31 + 1.48628965095479*m.x32 + 1.73205080756888*m.x33
+ 1.90211303259031*m.x34 + 1.98904379073655*m.x35 + 1.98904379073655*m.x36
+ 1.90211303259031*m.x37 + 1.73205080756888*m.x38 + 1.48628965095479*m.x39
+ 1.17557050458495*m.x40 + 0.813473286151601*m.x41 + 0.415823381635519*m.x42
+ 3.21624529935327E-16*m.x43 - 0.415823381635518*m.x44 - 0.8134732861516*m.x45
- 1.17557050458495*m.x46 - 1.48628965095479*m.x47 - 1.73205080756888*m.x48
- 1.90211303259031*m.x49 - 1.98904379073655*m.x50 + m.x54 <= 0)
m.c108 = Constraint(expr= - 0.466890727711813*m.x1 - 0.0174530709967495*m.x2 + 0.432879227876204*m.x3
+ 0.861022193616589*m.x4 + 1.24502927327524*m.x5 + 1.56521631370483*m.x6
+ 1.80517056869972*m.x7 + 1.95259201423987*m.x8 + 1.99992384612834*m.x9
+ 1.94473984079535*m.x10 + 1.78986872320405*m.x11 + 1.54324916677544*m.x12
+ 1.21752285801744*m.x13 + 0.829386485312478*m.x14 + 0.398735868834394*m.x15
- 0.0523538966157454*m.x16 - 0.500760008108882*m.x17 - 0.923497226470067*m.x18
- 1.29889609666037*m.x19 - 1.60771372123443*m.x20 - 1.83412014877025*m.x21
- 1.96650981512791*m.x22 - 1.99809644316372*m.x23 - 1.92726090641725*m.x24
- 1.75763422532393*m.x25 - 1.497911441578*m.x26 - 1.16140591142188*m.x27
- 0.76536686473018*m.x28 - 0.330095211721356*m.x29 + 0.122097079069714*m.x30
+ 0.568030689407845*m.x31 + 0.984847120206934*m.x32 + 1.35118041523132*m.x33
+ 1.64825237724403*m.x34 + 1.86083513596405*m.x35 + 1.97803172672383*m.x36
+ 1.99383466746626*m.x37 + 1.90743390149645*m.x38 + 1.72325832088305*m.x39
+ 1.45074874202458*m.x40 + 1.10387397062412*m.x41 + 0.700414762518935*m.x42
+ 0.261052384440103*m.x43 - 0.191691505040448*m.x44 - 0.634609312810184*m.x45
- 1.0449971294319*m.x46 - 1.4018185285997*m.x47 - 1.68678289162577*m.x48
- 1.88528298218436*m.x49 - 1.98714371135317*m.x50 + m.x54 <= 0)
m.c109 = Constraint(expr= - 1.78201304837674*m.x1 - 1.50941916044554*m.x2 - 1.14715287270209*m.x3 - 0.7167358990906*m.x4
- 0.243738686810297*m.x5 + 0.243738686810295*m.x6 + 0.716735899090602*m.x7
+ 1.14715287270209*m.x8 + 1.50941916044554*m.x9 + 1.78201304837674*m.x10
+ 1.94874012957047*m.x11 + 1.99969539031278*m.x12 + 1.93185165257814*m.x13
+ 1.74923941427879*m.x14 + 1.46270740323834*m.x15 + 1.08927807003005*m.x16
+ 0.651136308914314*m.x17 + 0.174311485495317*m.x18 - 0.312868930080462*m.x19
- 0.781462256978548*m.x20 - 1.2036300463041*m.x21 - 1.55429192291394*m.x22
- 1.8126155740733*m.x23 - 1.96325436689533*m.x24 - 1.99725906950915*m.x25
- 1.91260951192607*m.x26 - 1.71433460140422*m.x27 - 1.41421356237309*m.x28
- 1.03007614982011*m.x29 - 0.584743409445473*m.x30 - 0.104671912485888*m.x31
+ 0.381617990753089*m.x32 + 0.845236523481398*m.x33 + 1.25864078209967*m.x34
+ 1.59727102009459*m.x35 + 1.84100970690488*m.x36 + 1.97537668119028*m.x37
+ 1.99238939618349*m.x38 + 1.89103715119863*m.x39 + 1.67734113589085*m.x40
+ 1.363996720125*m.x41 + 0.969619240492674*m.x42 + 0.517638090205042*m.x43
+ 0.034904812874567*m.x44 - 0.44990210868773*m.x45 - 0.907980999479094*m.x46
- 1.31211805798101*m.x47 - 1.63830408857798*m.x48 - 1.8671608529944*m.x49
- 1.98509230328264*m.x50 + m.x54 <= 0)
m.c110 = Constraint(expr= - 1.84775906502257*m.x1 - 1.98288972274762*m.x2 - 1.98288972274762*m.x3
- 1.84775906502257*m.x4 - 1.58670668058247*m.x5 - 1.21752285801744*m.x6
- 0.765366864730176*m.x7 - 0.2610523844401*m.x8 + 0.261052384440105*m.x9
+ 0.765366864730181*m.x10 + 1.21752285801744*m.x11 + 1.58670668058247*m.x12
+ 1.84775906502257*m.x13 + 1.98288972274762*m.x14 + 1.98288972274762*m.x15
+ 1.84775906502257*m.x16 + 1.58670668058247*m.x17 + 1.21752285801744*m.x18
+ 0.765366864730176*m.x19 + 0.261052384440102*m.x20 - 0.261052384440105*m.x21
- 0.765366864730181*m.x22 - 1.21752285801744*m.x23 - 1.58670668058247*m.x24
- 1.84775906502257*m.x25 - 1.98288972274762*m.x26 - 1.98288972274762*m.x27
- 1.84775906502257*m.x28 - 1.58670668058247*m.x29 - 1.21752285801744*m.x30
- 0.765366864730178*m.x31 - 0.261052384440103*m.x32 + 0.261052384440105*m.x33
+ 0.765366864730181*m.x34 + 1.21752285801744*m.x35 + 1.58670668058247*m.x36
+ 1.84775906502257*m.x37 + 1.98288972274762*m.x38 + 1.98288972274762*m.x39
+ 1.84775906502257*m.x40 + 1.58670668058247*m.x41 + 1.21752285801744*m.x42
+ 0.765366864730179*m.x43 + 0.261052384440103*m.x44 - 0.261052384440103*m.x45
- 0.76536686473018*m.x46 - 1.21752285801744*m.x47 - 1.58670668058247*m.x48
- 1.84775906502257*m.x49 - 1.98288972274762*m.x50 + m.x54 <= 0)
m.c111 = Constraint(expr= - 0.618033988749896*m.x1 - 1.1183858069415*m.x2 - 1.53208888623796*m.x3 - 1.8270909152852*m.x4
- 1.98053613748314*m.x5 - 1.98053613748314*m.x6 - 1.8270909152852*m.x7 - 1.53208888623796*m.x8
- 1.11838580694149*m.x9 - 0.618033988749894*m.x10 - 0.0697989934050003*m.x11
+ 0.483843791199334*m.x12 + m.x13 + 1.4386796006773*m.x14 + 1.76589518571785*m.x15
+ 1.95629520146761*m.x16 + 1.99512810051965*m.x17 + 1.87938524157182*m.x18
+ 1.61803398874989*m.x19 + 1.23132295065132*m.x20 + 0.749213186831825*m.x21
+ 0.209056926535307*m.x22 - 0.347296355333861*m.x23 - 0.876742293578156*m.x24
- 1.33826121271772*m.x25 - 1.69609619231285*m.x26 - 1.92252339187664*m.x27 - 2*m.x28
- 1.92252339187664*m.x29 - 1.69609619231285*m.x30 - 1.33826121271772*m.x31
- 0.876742293578155*m.x32 - 0.34729635533386*m.x33 + 0.209056926535307*m.x34
+ 0.749213186831825*m.x35 + 1.23132295065132*m.x36 + 1.61803398874989*m.x37
+ 1.87938524157182*m.x38 + 1.99512810051965*m.x39 + 1.95629520146761*m.x40
+ 1.76589518571785*m.x41 + 1.4386796006773*m.x42 + m.x43 + 0.483843791199336*m.x44
- 0.0697989934050022*m.x45 - 0.618033988749895*m.x46 - 1.11838580694149*m.x47
- 1.53208888623796*m.x48 - 1.8270909152852*m.x49 - 1.98053613748314*m.x50 + m.x54 <= 0)
m.c112 = Constraint(expr= 1.0449971294319*m.x1 + 0.500760008108886*m.x2 - 0.0872387747306712*m.x3
- 0.66761371846754*m.x4 - 1.18964557350268*m.x5 - 1.60771372123443*m.x6
- 1.88528298218436*m.x7 - 1.99809644316372*m.x8 - 1.93629528075622*m.x9
- 1.70528032870818*m.x10 - 1.32524009643148*m.x11 - 0.829386485312481*m.x12
- 0.261052384440104*m.x13 + 0.330095211721353*m.x14 + 0.892395626219618*m.x15
+ 1.37670915138751*m.x16 + 1.7407113918798*m.x17 + 1.95259201423987*m.x18
+ 1.99383466746626*m.x19 + 1.86083513596405*m.x20 + 1.56521631370483*m.x21
+ 1.13281247384967*m.x22 + 0.601411599008547*m.x23 + 0.0174530709967497*m.x24
- 0.568030689407844*m.x25 - 1.10387397062412*m.x26 - 1.54324916677544*m.x27
- 1.84775906502257*m.x28 - 1.99079239673436*m.x29 - 1.95984940924166*m.x30
- 1.75763422532393*m.x31 - 1.4018185285997*m.x32 - 0.923497226470068*m.x33
- 0.364471050984295*m.x34 + 0.226406427535812*m.x35 + 0.797498137850492*m.x36
+ 1.29889609666037*m.x37 + 1.68678289162577*m.x38 + 1.92726090641725*m.x39
+ 1.99931464995111*m.x40 + 1.8966473104124*m.x41 + 1.62823103671264*m.x42
+ 1.21752285801744*m.x43 + 0.700414762518935*m.x44 + 0.122097079069714*m.x45
- 0.466890727711811*m.x46 - 1.01507672592141*m.x47 - 1.47455467362025*m.x48
- 1.80517056869972*m.x49 - 1.97803172672383*m.x50 + m.x54 <= 0)
m.c113 = Constraint(expr= 1.97537668119027*m.x1 + 1.78201304837674*m.x2 + 1.41421356237309*m.x3
+ 0.907980999479094*m.x4 + 0.312868930080459*m.x5 - 0.312868930080461*m.x6
- 0.907980999479096*m.x7 - 1.41421356237309*m.x8 - 1.78201304837674*m.x9
- 1.97537668119028*m.x10 - 1.97537668119028*m.x11 - 1.78201304837674*m.x12
- 1.41421356237309*m.x13 - 0.907980999479094*m.x14 - 0.312868930080459*m.x15
+ 0.312868930080461*m.x16 + 0.907980999479096*m.x17 + 1.41421356237309*m.x18
+ 1.78201304837674*m.x19 + 1.97537668119028*m.x20 + 1.97537668119028*m.x21
+ 1.78201304837674*m.x22 + 1.41421356237309*m.x23 + 0.907980999479095*m.x24
+ 0.312868930080459*m.x25 - 0.312868930080462*m.x26 - 0.907980999479094*m.x27
- 1.4142135623731*m.x28 - 1.78201304837674*m.x29 - 1.97537668119028*m.x30
- 1.97537668119028*m.x31 - 1.78201304837674*m.x32 - 1.41421356237309*m.x33
- 0.907980999479093*m.x34 - 0.312868930080461*m.x35 + 0.312868930080462*m.x36
+ 0.907980999479094*m.x37 + 1.4142135623731*m.x38 + 1.78201304837674*m.x39
+ 1.97537668119028*m.x40 + 1.97537668119028*m.x41 + 1.78201304837674*m.x42
+ 1.41421356237309*m.x43 + 0.907980999479093*m.x44 + 0.312868930080462*m.x45
- 0.312868930080462*m.x46 - 0.907980999479094*m.x47 - 1.4142135623731*m.x48
- 1.78201304837674*m.x49 - 1.97537668119028*m.x50 + m.x54 <= 0)
m.c114 = Constraint(expr= 1.52081193120006*m.x1 + 1.86083513596405*m.x2 + 1.99809644316372*m.x3
+ 1.91763946973639*m.x4 + 1.62823103671264*m.x5 + 1.16140591142188*m.x6
+ 0.568030689407847*m.x7 - 0.0872387747306712*m.x8 - 0.733002453448595*m.x9
- 1.29889609666037*m.x10 - 1.72325832088305*m.x11 - 1.95984940924166*m.x12
- 1.98288972274762*m.x13 - 1.78986872320405*m.x14 - 1.4018185285997*m.x15
- 0.861022193616591*m.x16 - 0.226406427535813*m.x17 + 0.432879227876204*m.x18
+ 1.0449971294319*m.x19 + 1.54324916677544*m.x20 + 1.87334437849679*m.x21
+ 1.99931464995111*m.x22 + 1.90743390149645*m.x23 + 1.60771372123443*m.x24
+ 1.13281247384966*m.x25 + 0.534476752156515*m.x26 - 0.122097079069714*m.x27
- 0.765366864730179*m.x28 - 1.32524009643147*m.x29 - 1.7407113918798*m.x30
- 1.96650981512791*m.x31 - 1.97803172672383*m.x32 - 1.77402166635644*m.x33
- 1.37670915138751*m.x34 - 0.829386485312478*m.x35 - 0.191691505040448*m.x36
+ 0.46689072771181*m.x37 + 1.07459921669365*m.x38 + 1.56521631370483*m.x39
+ 1.88528298218436*m.x40 + 1.99992384612834*m.x41 + 1.8966473104124*m.x42
+ 1.58670668058247*m.x43 + 1.10387397062412*m.x44 + 0.500760008108883*m.x45
- 0.15691819145569*m.x46 - 0.797498137850492*m.x47 - 1.35118041523132*m.x48
- 1.75763422532393*m.x49 - 1.97257120307446*m.x50 + m.x54 <= 0)
m.c115 = Constraint(expr= 4.89982515786259E-15*m.x1 + 0.684040286651341*m.x2 + 1.28557521937308*m.x3
+ 1.73205080756888*m.x4 + 1.96961550602442*m.x5 + 1.96961550602442*m.x6
+ 1.73205080756888*m.x7 + 1.28557521937308*m.x8 + 0.684040286651336*m.x9
- 1.10218211923262E-15*m.x10 - 0.684040286651338*m.x11 - 1.28557521937308*m.x12
- 1.73205080756888*m.x13 - 1.96961550602442*m.x14 - 1.96961550602442*m.x15
- 1.73205080756888*m.x16 - 1.28557521937308*m.x17 - 0.684040286651336*m.x18
+ 8.57252759403147E-16*m.x19 + 0.684040286651338*m.x20 + 1.28557521937308*m.x21
+ 1.73205080756888*m.x22 + 1.96961550602442*m.x23 + 1.96961550602442*m.x24
+ 1.73205080756888*m.x25 + 1.28557521937308*m.x26 + 0.684040286651336*m.x27
- 6.12323399573677E-16*m.x28 - 0.684040286651339*m.x29 - 1.28557521937308*m.x30
- 1.73205080756888*m.x31 - 1.96961550602442*m.x32 - 1.96961550602442*m.x33
- 1.73205080756888*m.x34 - 1.28557521937308*m.x35 - 0.684040286651336*m.x36
+ 3.67394039744206E-16*m.x37 + 0.684040286651339*m.x38 + 1.28557521937308*m.x39
+ 1.73205080756888*m.x40 + 1.96961550602442*m.x41 + 1.96961550602442*m.x42
+ 1.73205080756888*m.x43 + 1.28557521937308*m.x44 + 0.684040286651337*m.x45
- 1.22464679914735E-16*m.x46 - 0.684040286651338*m.x47 - 1.28557521937308*m.x48
- 1.73205080756888*m.x49 - 1.96961550602442*m.x50 + m.x54 <= 0)
m.c116 = Constraint(expr= - 1.52081193120006*m.x1 - 0.954317520519217*m.x2 - 0.261052384440107*m.x3
+ 0.466890727711811*m.x4 + 1.13281247384966*m.x5 + 1.64825237724403*m.x6
+ 1.94473984079535*m.x7 + 1.98288972274762*m.x8 + 1.75763422532393*m.x9
+ 1.29889609666037*m.x10 + 0.667613718467541*m.x11 - 0.0523538966157477*m.x12
- 0.765366864730178*m.x13 - 1.37670915138751*m.x14 - 1.80517056869972*m.x15
- 1.99383466746626*m.x16 - 1.91763946973639*m.x17 - 1.58670668058247*m.x18
- 1.0449971294319*m.x19 - 0.364471050984296*m.x20 + 0.364471050984294*m.x21
+ 1.0449971294319*m.x22 + 1.58670668058247*m.x23 + 1.91763946973639*m.x24
+ 1.99383466746626*m.x25 + 1.80517056869972*m.x26 + 1.37670915138751*m.x27
+ 0.76536686473018*m.x28 + 0.052353896615746*m.x29 - 0.667613718467541*m.x30
- 1.29889609666037*m.x31 - 1.75763422532393*m.x32 - 1.98288972274762*m.x33
- 1.94473984079535*m.x34 - 1.64825237724403*m.x35 - 1.13281247384966*m.x36
- 0.466890727711811*m.x37 + 0.261052384440103*m.x38 + 0.954317520519217*m.x39
+ 1.52081193120006*m.x40 + 1.88528298218436*m.x41 + 1.99931464995111*m.x42
+ 1.84775906502257*m.x43 + 1.45074874202458*m.x44 + 0.861022193616591*m.x45
+ 0.15691819145569*m.x46 - 0.568030689407845*m.x47 - 1.21752285801744*m.x48
- 1.70528032870818*m.x49 - 1.96650981512791*m.x50 + m.x54 <= 0)
m.c117 = Constraint(expr= - 1.97537668119028*m.x1 - 1.94874012957047*m.x2 - 1.63830408857798*m.x3
- 1.08927807003005*m.x4 - 0.381617990753089*m.x5 + 0.381617990753092*m.x6
+ 1.08927807003006*m.x7 + 1.63830408857798*m.x8 + 1.94874012957047*m.x9
+ 1.97537668119027*m.x10 + 1.71433460140422*m.x11 + 1.20363004630409*m.x12
+ 0.51763809020504*m.x13 - 0.243738686810299*m.x14 - 0.969619240492676*m.x15
- 1.55429192291394*m.x16 - 1.91260951192607*m.x17 - 1.99238939618349*m.x18
- 1.78201304837673*m.x19 - 1.31211805798101*m.x20 - 0.65113630891431*m.x21
+ 0.104671912485889*m.x22 + 0.845236523481399*m.x23 + 1.46270740323834*m.x24
+ 1.8671608529944*m.x25 + 1.99969539031278*m.x26 + 1.84100970690488*m.x27
+ 1.41421356237309*m.x28 + 0.781462256978547*m.x29 + 0.034904812874566*m.x30
- 0.716735899090602*m.x31 - 1.363996720125*m.x32 - 1.8126155740733*m.x33
- 1.99725906950915*m.x34 - 1.89103715119863*m.x35 - 1.50941916044554*m.x36
- 0.907980999479093*m.x37 - 0.174311485495316*m.x38 + 0.584743409445474*m.x39
+ 1.25864078209968*m.x40 + 1.74923941427879*m.x41 + 1.98509230328264*m.x42
+ 1.93185165257814*m.x43 + 1.59727102009459*m.x44 + 1.03007614982011*m.x45
+ 0.312868930080462*m.x46 - 0.44990210868773*m.x47 - 1.14715287270209*m.x48
- 1.67734113589085*m.x49 - 1.96325436689533*m.x50 + m.x54 <= 0)
m.c118 = Constraint(expr= - 1.0449971294319*m.x1 - 1.62823103671264*m.x2 - 1.95259201423987*m.x3 - 1.96650981512791*m.x4
- 1.66777164413433*m.x5 - 1.10387397062411*m.x6 - 0.364471050984292*m.x7
+ 0.432879227876208*m.x8 + 1.16140591142188*m.x9 + 1.70528032870819*m.x10
+ 1.97803172672383*m.x11 + 1.93629528075622*m.x12 + 1.58670668058247*m.x13
+ 0.984847120206933*m.x14 + 0.226406427535812*m.x15 - 0.568030689407846*m.x16
- 1.27215644055553*m.x17 - 1.77402166635644*m.x18 - 1.99383466746626*m.x19
- 1.8966473104124*m.x20 - 1.497911441578*m.x21 - 0.861022193616591*m.x22
- 0.0872387747306728*m.x23 + 0.700414762518934*m.x24 + 1.37670915138751*m.x25
+ 1.83412014877025*m.x26 + 1.99992384612834*m.x27 + 1.84775906502257*m.x28
+ 1.4018185285997*m.x29 + 0.733002453448593*m.x30 - 0.0523538966157472*m.x31
- 0.829386485312479*m.x32 - 1.47455467362025*m.x33 - 1.88528298218436*m.x34
- 1.99626959684373*m.x35 - 1.78986872320405*m.x36 - 1.29889609666037*m.x37
- 0.601411599008547*m.x38 + 0.191691505040449*m.x39 + 0.954317520519217*m.x40
+ 1.56521631370483*m.x41 + 1.92726090641725*m.x42 + 1.98288972274762*m.x43
+ 1.72325832088305*m.x44 + 1.18964557350268*m.x45 + 0.466890727711811*m.x46
- 0.330095211721355*m.x47 - 1.07459921669365*m.x48 - 1.64825237724403*m.x49
- 1.95984940924166*m.x50 + m.x54 <= 0)
m.c119 = Constraint(expr= 0.6180339887499*m.x1 - 0.209056926535306*m.x2 - 0.999999999999997*m.x3
- 1.6180339887499*m.x4 - 1.95629520146761*m.x5 - 1.95629520146761*m.x6 - 1.61803398874989*m.x7
- m.x8 - 0.20905692653531*m.x9 + 0.618033988749896*m.x10 + 1.33826121271772*m.x11
+ 1.8270909152852*m.x12 + 2*m.x13 + 1.8270909152852*m.x14 + 1.33826121271772*m.x15
+ 0.618033988749897*m.x16 - 0.209056926535306*m.x17 - m.x18 - 1.61803398874989*m.x19
- 1.95629520146761*m.x20 - 1.95629520146761*m.x21 - 1.6180339887499*m.x22 - m.x23
- 0.209056926535307*m.x24 + 0.618033988749892*m.x25 + 1.33826121271772*m.x26
+ 1.8270909152852*m.x27 + 2*m.x28 + 1.8270909152852*m.x29 + 1.33826121271772*m.x30
+ 0.618033988749894*m.x31 - 0.209056926535307*m.x32 - m.x33 - 1.61803398874989*m.x34
- 1.95629520146761*m.x35 - 1.95629520146761*m.x36 - 1.6180339887499*m.x37 - m.x38
- 0.209056926535308*m.x39 + 0.618033988749895*m.x40 + 1.33826121271772*m.x41
+ 1.8270909152852*m.x42 + 2*m.x43 + 1.8270909152852*m.x44 + 1.33826121271772*m.x45
+ 0.618033988749895*m.x46 - 0.209056926535307*m.x47 - m.x48 - 1.61803398874989*m.x49
- 1.95629520146761*m.x50 + m.x54 <= 0)
m.c120 = Constraint(expr= 1.84775906502257*m.x1 + 1.35118041523132*m.x2 + 0.601411599008542*m.x3
- 0.261052384440103*m.x4 - 1.07459921669365*m.x5 - 1.68678289162577*m.x6
- 1.98288972274762*m.x7 - 1.90743390149645*m.x8 - 1.47455467362025*m.x9
- 0.765366864730179*m.x10 + 0.087238774730675*m.x11 + 0.923497226470066*m.x12
+ 1.58670668058247*m.x13 + 1.95259201423987*m.x14 + 1.95259201423987*m.x15
+ 1.58670668058247*m.x16 + 0.923497226470067*m.x17 + 0.0872387747306726*m.x18
- 0.765366864730181*m.x19 - 1.47455467362025*m.x20 - 1.90743390149645*m.x21
- 1.98288972274762*m.x22 - 1.68678289162577*m.x23 - 1.07459921669365*m.x24
- 0.261052384440104*m.x25 + 0.601411599008548*m.x26 + 1.35118041523132*m.x27
+ 1.84775906502257*m.x28 + 1.99809644316372*m.x29 + 1.77402166635644*m.x30
+ 1.21752285801744*m.x31 + 0.432879227876206*m.x32 - 0.432879227876206*m.x33
- 1.21752285801744*m.x34 - 1.77402166635644*m.x35 - 1.99809644316372*m.x36
- 1.84775906502257*m.x37 - 1.35118041523132*m.x38 - 0.601411599008545*m.x39
+ 0.261052384440103*m.x40 + 1.07459921669365*m.x41 + 1.68678289162577*m.x42
+ 1.98288972274762*m.x43 + 1.90743390149645*m.x44 + 1.47455467362025*m.x45
+ 0.765366864730179*m.x46 - 0.087238774730672*m.x47 - 0.923497226470068*m.x48
- 1.58670668058247*m.x49 - 1.95259201423987*m.x50 + m.x54 <= 0)
m.c121 = Constraint(expr= 1.78201304837673*m.x1 + 1.99969539031278*m.x2 + 1.8126155740733*m.x3 + 1.25864078209968*m.x4
+ 0.449902108687732*m.x5 - 0.449902108687728*m.x6 - 1.25864078209967*m.x7
- 1.8126155740733*m.x8 - 1.99969539031278*m.x9 - 1.78201304837674*m.x10
- 1.2036300463041*m.x11 - 0.381617990753089*m.x12 + 0.517638090205042*m.x13
+ 1.31211805798102*m.x14 + 1.84100970690488*m.x15 + 1.99725906950915*m.x16
+ 1.74923941427879*m.x17 + 1.14715287270209*m.x18 + 0.312868930080462*m.x19
- 0.584743409445473*m.x20 - 1.363996720125*m.x21 - 1.8671608529944*m.x22
- 1.99238939618349*m.x23 - 1.71433460140423*m.x24 - 1.08927807003006*m.x25
- 0.243738686810297*m.x26 + 0.651136308914312*m.x27 + 1.41421356237309*m.x28
+ 1.89103715119863*m.x29 + 1.98509230328264*m.x30 + 1.67734113589085*m.x31
+ 1.03007614982011*m.x32 + 0.174311485495317*m.x33 - 0.7167358990906*m.x34
- 1.46270740323834*m.x35 - 1.91260951192607*m.x36 - 1.97537668119028*m.x37
- 1.63830408857798*m.x38 - 0.969619240492675*m.x39 - 0.104671912485888*m.x40
+ 0.781462256978548*m.x41 + 1.50941916044554*m.x42 + 1.93185165257814*m.x43
+ 1.96325436689533*m.x44 + 1.59727102009459*m.x45 + 0.907980999479093*m.x46
+ 0.034904812874567*m.x47 - 0.845236523481399*m.x48 - 1.55429192291394*m.x49
- 1.94874012957047*m.x50 + m.x54 <= 0)
m.c122 = Constraint(expr= 0.466890727711812*m.x1 + 1.29889609666037*m.x2 + 1.84775906502258*m.x3
+ 1.99383466746626*m.x4 + 1.70528032870818*m.x5 + 1.0449971294319*m.x6
+ 0.156918191455686*m.x7 - 0.765366864730182*m.x8 - 1.52081193120006*m.x9
- 1.94473984079535*m.x10 - 1.94473984079535*m.x11 - 1.52081193120006*m.x12
- 0.765366864730179*m.x13 + 0.156918191455689*m.x14 + 1.0449971294319*m.x15
+ 1.70528032870819*m.x16 + 1.99383466746626*m.x17 + 1.84775906502257*m.x18
+ 1.29889609666037*m.x19 + 0.466890727711809*m.x20 - 0.466890727711811*m.x21
- 1.29889609666037*m.x22 - 1.84775906502257*m.x23 - 1.99383466746626*m.x24
- 1.70528032870818*m.x25 - 1.0449971294319*m.x26 - 0.15691819145569*m.x27
+ 0.765366864730181*m.x28 + 1.52081193120006*m.x29 + 1.94473984079535*m.x30
+ 1.94473984079535*m.x31 + 1.52081193120006*m.x32 + 0.76536686473018*m.x33
- 0.156918191455691*m.x34 - 1.0449971294319*m.x35 - 1.70528032870819*m.x36
- 1.99383466746626*m.x37 - 1.84775906502257*m.x38 - 1.29889609666037*m.x39
- 0.46689072771181*m.x40 + 0.46689072771181*m.x41 + 1.29889609666037*m.x42
+ 1.84775906502257*m.x43 + 1.99383466746626*m.x44 + 1.70528032870818*m.x45
+ 1.0449971294319*m.x46 + 0.15691819145569*m.x47 - 0.76536686473018*m.x48
- 1.52081193120006*m.x49 - 1.94473984079535*m.x50 + m.x54 <= 0)
m.c123 = Constraint(expr= - 1.17557050458495*m.x1 - 0.278346201920129*m.x2 + 0.684040286651335*m.x3
+ 1.48628965095479*m.x4 + 1.94059145255199*m.x5 + 1.94059145255199*m.x6
+ 1.48628965095479*m.x7 + 0.684040286651339*m.x8 - 0.278346201920133*m.x9
- 1.17557050458494*m.x10 - 1.79758809259833*m.x11 - 1.99878165403819*m.x12
- 1.73205080756888*m.x13 - 1.05983852846641*m.x14 - 0.139512947488251*m.x15
+ 0.813473286151603*m.x16 + 1.57602150721344*m.x17 + 1.96961550602442*m.x18
+ 1.90211303259031*m.x19 + 1.38931674091799*m.x20 + 0.551274711633998*m.x21
- 0.415823381635519*m.x22 - 1.28557521937308*m.x23 - 1.85436770913357*m.x24
- 1.98904379073655*m.x25 - 1.65807514511008*m.x26 - 0.93894312557178*m.x27
+ 8.57252759403147E-16*m.x28 + 0.938943125571782*m.x29 + 1.65807514511008*m.x30
+ 1.98904379073655*m.x31 + 1.85436770913358*m.x32 + 1.28557521937308*m.x33
+ 0.41582338163552*m.x34 - 0.551274711633998*m.x35 - 1.38931674091799*m.x36
- 1.90211303259031*m.x37 - 1.96961550602442*m.x38 - 1.57602150721344*m.x39
- 0.813473286151601*m.x40 + 0.139512947488251*m.x41 + 1.05983852846641*m.x42
+ 1.73205080756888*m.x43 + 1.99878165403819*m.x44 + 1.79758809259833*m.x45
+ 1.17557050458495*m.x46 + 0.278346201920131*m.x47 - 0.684040286651338*m.x48
- 1.48628965095479*m.x49 - 1.94059145255199*m.x50 + m.x54 <= 0)
m.c124 = Constraint(expr= - 1.99383466746626*m.x1 - 1.66777164413434*m.x2 - 0.92349722647007*m.x3
+ 0.0523538966157449*m.x4 + 1.01507672592141*m.x5 + 1.72325832088305*m.x6
+ 1.99931464995111*m.x7 + 1.77402166635645*m.x8 + 1.10387397062412*m.x9
+ 0.156918191455693*m.x10 - 0.829386485312476*m.x11 - 1.60771372123443*m.x12
- 1.98288972274762*m.x13 - 1.86083513596405*m.x14 - 1.27215644055553*m.x15
- 0.364471050984299*m.x16 + 0.634609312810181*m.x17 + 1.47455467362025*m.x18
+ 1.94473984079535*m.x19 + 1.92726090641725*m.x20 + 1.42650089830837*m.x21
+ 0.568030689407847*m.x22 - 0.432879227876205*m.x23 - 1.32524009643147*m.x24
- 1.88528298218436*m.x25 - 1.97257120307446*m.x26 - 1.56521631370483*m.x27
- 0.76536686473018*m.x28 + 0.226406427535811*m.x29 + 1.16140591142188*m.x30
+ 1.80517056869972*m.x31 + 1.99626959684373*m.x32 + 1.68678289162577*m.x33
+ 0.954317520519218*m.x34 - 0.0174530709967474*m.x35 - 0.984847120206933*m.x36
- 1.70528032870818*m.x37 - 1.99809644316372*m.x38 - 1.78986872320405*m.x39
- 1.13281247384967*m.x40 - 0.191691505040448*m.x41 + 0.797498137850492*m.x42
+ 1.58670668058247*m.x43 + 1.97803172672383*m.x44 + 1.8733443784968*m.x45
+ 1.29889609666037*m.x46 + 0.398735868834395*m.x47 - 0.601411599008546*m.x48
- 1.45074874202458*m.x49 - 1.93629528075622*m.x50 + m.x54 <= 0)
m.c125 = Constraint(expr= - 1.4142135623731*m.x1 - 1.93185165257814*m.x2 - 1.93185165257814*m.x3 - 1.41421356237309*m.x4
- 0.517638090205039*m.x5 + 0.51763809020505*m.x6 + 1.4142135623731*m.x7
+ 1.93185165257814*m.x8 + 1.93185165257814*m.x9 + 1.41421356237309*m.x10
+ 0.517638090205039*m.x11 - 0.517638090205042*m.x12 - 1.4142135623731*m.x13
- 1.93185165257814*m.x14 - 1.93185165257814*m.x15 - 1.41421356237309*m.x16
- 0.51763809020504*m.x17 + 0.517638090205042*m.x18 + 1.4142135623731*m.x19
+ 1.93185165257814*m.x20 + 1.93185165257814*m.x21 + 1.41421356237309*m.x22
+ 0.51763809020504*m.x23 - 0.517638090205045*m.x24 - 1.4142135623731*m.x25
- 1.93185165257814*m.x26 - 1.93185165257814*m.x27 - 1.41421356237309*m.x28
- 0.51763809020504*m.x29 + 0.517638090205045*m.x30 + 1.4142135623731*m.x31
+ 1.93185165257814*m.x32 + 1.93185165257814*m.x33 + 1.41421356237309*m.x34
+ 0.51763809020504*m.x35 - 0.517638090205043*m.x36 - 1.4142135623731*m.x37
- 1.93185165257814*m.x38 - 1.93185165257814*m.x39 - 1.41421356237309*m.x40
- 0.517638090205041*m.x41 + 0.517638090205043*m.x42 + 1.4142135623731*m.x43
+ 1.93185165257814*m.x44 + 1.93185165257814*m.x45 + 1.41421356237309*m.x46
+ 0.517638090205041*m.x47 - 0.517638090205042*m.x48 - 1.4142135623731*m.x49
- 1.93185165257814*m.x50 + m.x54 <= 0)
m.c126 = Constraint(expr= 0.156918191455685*m.x1 - 0.892395626219619*m.x2 - 1.68678289162577*m.x3
- 1.99931464995111*m.x4 - 1.7407113918798*m.x5 - 0.984847120206933*m.x6
+ 0.052353896615752*m.x7 + 1.07459921669365*m.x8 + 1.78986872320405*m.x9
+ 1.99383466746626*m.x10 + 1.62823103671264*m.x11 + 0.79749813785049*m.x12
- 0.261052384440103*m.x13 - 1.24502927327524*m.x14 - 1.8733443784968*m.x15
- 1.96650981512791*m.x16 - 1.497911441578*m.x17 - 0.601411599008543*m.x18
+ 0.466890727711811*m.x19 + 1.4018185285997*m.x20 + 1.93629528075622*m.x21
+ 1.91763946973639*m.x22 + 1.35118041523132*m.x23 + 0.398735868834393*m.x24
- 0.667613718467543*m.x25 - 1.54324916677544*m.x26 - 1.97803172672383*m.x27
- 1.84775906502257*m.x28 - 1.18964557350268*m.x29 - 0.191691505040446*m.x30
+ 0.861022193616592*m.x31 + 1.66777164413434*m.x32 + 1.99809644316372*m.x33
+ 1.75763422532393*m.x34 + 1.01507672592141*m.x35 - 0.0174530709967492*m.x36
- 1.0449971294319*m.x37 - 1.77402166635644*m.x38 - 1.99626959684373*m.x39
- 1.64825237724403*m.x40 - 0.829386485312478*m.x41 + 0.226406427535814*m.x42
+ 1.21752285801744*m.x43 + 1.86083513596405*m.x44 + 1.97257120307446*m.x45
+ 1.52081193120006*m.x46 + 0.634609312810184*m.x47 - 0.432879227876206*m.x48
- 1.37670915138751*m.x49 - 1.92726090641725*m.x50 + m.x54 <= 0)
m.c127 = Constraint(expr= 1.61803398874989*m.x1 + 0.749213186831821*m.x2 - 0.347296355333859*m.x3
- 1.33826121271772*m.x4 - 1.92252339187664*m.x5 - 1.92252339187664*m.x6
- 1.33826121271771*m.x7 - 0.347296355333862*m.x8 + 0.749213186831824*m.x9
+ 1.6180339887499*m.x10 + 1.99512810051965*m.x11 + 1.76589518571785*m.x12 + m.x13
- 0.0697989934050027*m.x14 - 1.1183858069415*m.x15 - 1.8270909152852*m.x16
- 1.98053613748314*m.x17 - 1.53208888623796*m.x18 - 0.618033988749894*m.x19
+ 0.483843791199338*m.x20 + 1.4386796006773*m.x21 + 1.95629520146761*m.x22
+ 1.87938524157182*m.x23 + 1.23132295065132*m.x24 + 0.209056926535307*m.x25
- 0.876742293578156*m.x26 - 1.69609619231285*m.x27 - 2*m.x28 - 1.69609619231285*m.x29
- 0.876742293578154*m.x30 + 0.209056926535309*m.x31 + 1.23132295065132*m.x32
+ 1.87938524157182*m.x33 + 1.95629520146761*m.x34 + 1.4386796006773*m.x35
+ 0.483843791199336*m.x36 - 0.618033988749895*m.x37 - 1.53208888623796*m.x38
- 1.98053613748314*m.x39 - 1.8270909152852*m.x40 - 1.11838580694149*m.x41
- 0.0697989934050026*m.x42 + m.x43 + 1.76589518571785*m.x44 + 1.99512810051965*m.x45
+ 1.61803398874989*m.x46 + 0.749213186831824*m.x47 - 0.347296355333861*m.x48
- 1.33826121271772*m.x49 - 1.92252339187664*m.x50 + m.x54 <= 0)
m.c128 = Constraint(expr= 1.94473984079535*m.x1 + 1.88528298218436*m.x2 + 1.21752285801744*m.x3
+ 0.156918191455692*m.x4 - 0.954317520519214*m.x5 - 1.75763422532393*m.x6
- 1.99383466746626*m.x7 - 1.58670668058247*m.x8 - 0.66761371846754*m.x9
+ 0.466890727711812*m.x10 + 1.45074874202458*m.x11 + 1.96650981512791*m.x12
+ 1.84775906502257*m.x13 + 1.13281247384967*m.x14 + 0.0523538966157486*m.x15
- 1.0449971294319*m.x16 - 1.80517056869972*m.x17 - 1.98288972274762*m.x18
- 1.52081193120006*m.x19 - 0.568030689407844*m.x20 + 0.568030689407846*m.x21
+ 1.52081193120006*m.x22 + 1.98288972274762*m.x23 + 1.80517056869972*m.x24
+ 1.0449971294319*m.x25 - 0.0523538966157441*m.x26 - 1.13281247384967*m.x27
- 1.84775906502257*m.x28 - 1.96650981512791*m.x29 - 1.45074874202458*m.x30
- 0.466890727711813*m.x31 + 0.667613718467543*m.x32 + 1.58670668058247*m.x33
+ 1.99383466746626*m.x34 + 1.75763422532393*m.x35 + 0.954317520519218*m.x36
- 0.15691819145569*m.x37 - 1.21752285801744*m.x38 - 1.88528298218436*m.x39
- 1.94473984079535*m.x40 - 1.37670915138751*m.x41 - 0.364471050984295*m.x42
+ 0.765366864730179*m.x43 + 1.64825237724403*m.x44 + 1.99931464995111*m.x45
+ 1.70528032870818*m.x46 + 0.861022193616591*m.x47 - 0.261052384440103*m.x48
- 1.29889609666037*m.x49 - 1.91763946973639*m.x50 + m.x54 <= 0)
m.c129 = Constraint(expr= 0.907980999479091*m.x1 + 1.74923941427879*m.x2 + 1.99238939618349*m.x3
+ 1.55429192291394*m.x4 + 0.584743409445474*m.x5 - 0.584743409445471*m.x6
- 1.55429192291394*m.x7 - 1.99238939618349*m.x8 - 1.74923941427879*m.x9
- 0.907980999479093*m.x10 + 0.243738686810292*m.x11 + 1.31211805798101*m.x12
+ 1.93185165257814*m.x13 + 1.89103715119864*m.x14 + 1.2036300463041*m.x15
+ 0.10467191248589*m.x16 - 1.0300761498201*m.x17 - 1.8126155740733*m.x18
- 1.97537668119028*m.x19 - 1.46270740323834*m.x20 - 0.449902108687732*m.x21
+ 0.716735899090596*m.x22 + 1.63830408857798*m.x23 + 1.99969539031278*m.x24
+ 1.67734113589085*m.x25 + 0.78146225697855*m.x26 - 0.381617990753088*m.x27
- 1.41421356237309*m.x28 - 1.96325436689533*m.x29 - 1.84100970690488*m.x30
- 1.08927807003006*m.x31 + 0.0349048128745657*m.x32 + 1.14715287270209*m.x33
+ 1.8671608529944*m.x34 + 1.94874012957047*m.x35 + 1.363996720125*m.x36
+ 0.312868930080463*m.x37 - 0.845236523481399*m.x38 - 1.71433460140422*m.x39
- 1.99725906950915*m.x40 - 1.59727102009459*m.x41 - 0.651136308914314*m.x42
+ 0.517638090205041*m.x43 + 1.50941916044554*m.x44 + 1.98509230328264*m.x45
+ 1.78201304837674*m.x46 + 0.969619240492674*m.x47 - 0.174311485495316*m.x48
- 1.25864078209967*m.x49 - 1.91260951192607*m.x50 + m.x54 <= 0)
m.c130 = Constraint(expr= - 0.765366864730178*m.x1 + 0.432879227876209*m.x2 + 1.47455467362025*m.x3
+ 1.98288972274762*m.x4 + 1.77402166635644*m.x5 + 0.923497226470063*m.x6
- 0.26105238444011*m.x7 - 1.35118041523132*m.x8 - 1.95259201423987*m.x9
- 1.84775906502257*m.x10 - 1.07459921669365*m.x11 + 0.0872387747306755*m.x12
+ 1.21752285801745*m.x13 + 1.90743390149645*m.x14 + 1.90743390149645*m.x15
+ 1.21752285801744*m.x16 + 0.0872387747306721*m.x17 - 1.07459921669365*m.x18
- 1.84775906502258*m.x19 - 1.95259201423987*m.x20 - 1.35118041523132*m.x21
- 0.2610523844401*m.x22 + 0.923497226470073*m.x23 + 1.77402166635644*m.x24
+ 1.98288972274762*m.x25 + 1.47455467362025*m.x26 + 0.432879227876202*m.x27
- 0.765366864730181*m.x28 - 1.68678289162577*m.x29 - 1.99809644316372*m.x30
- 1.58670668058247*m.x31 - 0.601411599008543*m.x32 + 0.601411599008548*m.x33
+ 1.58670668058247*m.x34 + 1.99809644316372*m.x35 + 1.68678289162577*m.x36
+ 0.76536686473018*m.x37 - 0.432879227876208*m.x38 - 1.47455467362025*m.x39
- 1.98288972274762*m.x40 - 1.77402166635644*m.x41 - 0.923497226470066*m.x42
+ 0.261052384440103*m.x43 + 1.35118041523132*m.x44 + 1.95259201423987*m.x45
+ 1.84775906502257*m.x46 + 1.07459921669365*m.x47 - 0.087238774730672*m.x48
- 1.21752285801744*m.x49 - 1.90743390149645*m.x50 + m.x54 <= 0)
m.c131 = Constraint(expr= - 1.90211303259031*m.x1 - 1.17557050458495*m.x2 + 5.87954259718047E-15*m.x3
+ 1.17557050458495*m.x4 + 1.90211303259031*m.x5 + 1.90211303259031*m.x6
+ 1.17557050458494*m.x7 + 1.47081412025E-15*m.x8 - 1.17557050458495*m.x9
- 1.90211303259031*m.x10 - 1.90211303259031*m.x11 - 1.17557050458495*m.x12
+ 5.38968387752153E-15*m.x13 + 1.17557050458494*m.x14 + 1.90211303259031*m.x15
+ 1.90211303259031*m.x16 + 1.17557050458494*m.x17 + 1.96067283990894E-15*m.x18
- 1.17557050458495*m.x19 - 1.90211303259031*m.x20 - 1.90211303259031*m.x21
- 1.17557050458495*m.x22 + 4.89982515786259E-15*m.x23 + 1.17557050458494*m.x24
+ 1.90211303259031*m.x25 + 1.90211303259031*m.x26 + 1.17557050458495*m.x27
- 1.10218211923262E-15*m.x28 - 1.17557050458495*m.x29 - 1.90211303259031*m.x30
- 1.90211303259031*m.x31 - 1.17557050458495*m.x32 + 8.57252759403147E-16*m.x33
+ 1.17557050458495*m.x34 + 1.90211303259031*m.x35 + 1.90211303259031*m.x36
+ 1.17557050458495*m.x37 - 6.12323399573677E-16*m.x38 - 1.17557050458495*m.x39
- 1.90211303259031*m.x40 - 1.90211303259031*m.x41 - 1.17557050458495*m.x42
+ 3.67394039744206E-16*m.x43 + 1.17557050458495*m.x44 + 1.90211303259031*m.x45
+ 1.90211303259031*m.x46 + 1.17557050458495*m.x47 - 1.22464679914735E-16*m.x48
- 1.17557050458495*m.x49 - 1.90211303259031*m.x50 + m.x54 <= 0)
m.c132 = Constraint(expr= - 1.70528032870819*m.x1 - 1.99079239673436*m.x2 - 1.47455467362025*m.x3
- 0.364471050984298*m.x4 + 0.892395626219619*m.x5 + 1.78986872320405*m.x6
+ 1.96650981512791*m.x7 + 1.35118041523132*m.x8 + 0.191691505040448*m.x9
- 1.0449971294319*m.x10 - 1.86083513596405*m.x11 - 1.92726090641725*m.x12
- 1.21752285801744*m.x13 - 0.0174530709967449*m.x14 + 1.18964557350268*m.x15
+ 1.91763946973639*m.x16 + 1.87334437849679*m.x17 + 1.07459921669365*m.x18
- 0.156918191455689*m.x19 - 1.32524009643147*m.x20 - 1.95984940924166*m.x21
- 1.80517056869972*m.x22 - 0.92349722647007*m.x23 + 0.330095211721357*m.x24
+ 1.45074874202458*m.x25 + 1.98714371135318*m.x26 + 1.72325832088305*m.x27
+ 0.765366864730179*m.x28 - 0.500760008108884*m.x29 - 1.56521631370483*m.x30
- 1.99931464995111*m.x31 - 1.62823103671264*m.x32 - 0.601411599008546*m.x33
+ 0.667613718467543*m.x34 + 1.66777164413434*m.x35 + 1.99626959684373*m.x36
+ 1.52081193120006*m.x37 + 0.432879227876206*m.x38 - 0.829386485312479*m.x39
- 1.75763422532393*m.x40 - 1.97803172672383*m.x41 - 1.4018185285997*m.x42
- 0.261052384440103*m.x43 + 0.984847120206934*m.x44 + 1.83412014877025*m.x45
+ 1.94473984079535*m.x46 + 1.27215644055553*m.x47 + 0.0872387747306718*m.x48
- 1.13281247384967*m.x49 - 1.8966473104124*m.x50 + m.x54 <= 0)
m.c133 = Constraint(expr= - 0.312868930080463*m.x1 - 1.46270740323834*m.x2 - 1.99238939618349*m.x3
- 1.67734113589085*m.x4 - 0.651136308914316*m.x5 + 0.651136308914314*m.x6
+ 1.67734113589085*m.x7 + 1.99238939618349*m.x8 + 1.46270740323834*m.x9
+ 0.312868930080465*m.x10 - 0.969619240492674*m.x11 - 1.84100970690488*m.x12
- 1.93185165257814*m.x13 - 1.2036300463041*m.x14 + 0.0349048128745631*m.x15
+ 1.25864078209967*m.x16 + 1.94874012957047*m.x17 + 1.8126155740733*m.x18
+ 0.907980999479094*m.x19 - 0.381617990753085*m.x20 - 1.50941916044554*m.x21
- 1.99725906950915*m.x22 - 1.63830408857799*m.x23 - 0.584743409445474*m.x24
+ 0.716735899090602*m.x25 + 1.71433460140422*m.x26 + 1.98509230328264*m.x27
+ 1.4142135623731*m.x28 + 0.243738686810296*m.x29 - 1.03007614982011*m.x30
- 1.8671608529944*m.x31 - 1.91260951192607*m.x32 - 1.14715287270209*m.x33
+ 0.104671912485886*m.x34 + 1.31211805798101*m.x35 + 1.96325436689533*m.x36
+ 1.78201304837674*m.x37 + 0.845236523481401*m.x38 - 0.449902108687729*m.x39
- 1.55429192291394*m.x40 - 1.99969539031278*m.x41 - 1.59727102009459*m.x42
- 0.517638090205042*m.x43 + 0.781462256978548*m.x44 + 1.74923941427879*m.x45
+ 1.97537668119028*m.x46 + 1.363996720125*m.x47 + 0.174311485495316*m.x48
- 1.08927807003005*m.x49 - 1.89103715119863*m.x50 + m.x54 <= 0)
m.c134 = Constraint(expr= 1.29889609666036*m.x1 + 0.0523538966157405*m.x2 - 1.21752285801744*m.x3
- 1.94473984079535*m.x4 - 1.80517056869972*m.x5 - 0.861022193616583*m.x6
+ 0.466890727711819*m.x7 + 1.58670668058247*m.x8 + 1.99931464995111*m.x9
+ 1.52081193120006*m.x10 + 0.364471050984291*m.x11 - 0.95431752051922*m.x12
- 1.84775906502258*m.x13 - 1.91763946973638*m.x14 - 1.13281247384966*m.x15
+ 0.156918191455696*m.x16 + 1.37670915138751*m.x17 + 1.98288972274762*m.x18
+ 1.70528032870818*m.x19 + 0.667613718467541*m.x20 - 0.667613718467544*m.x21
- 1.70528032870819*m.x22 - 1.98288972274762*m.x23 - 1.37670915138751*m.x24
- 0.156918191455686*m.x25 + 1.13281247384967*m.x26 + 1.91763946973639*m.x27
+ 1.84775906502257*m.x28 + 0.954317520519214*m.x29 - 0.364471050984298*m.x30
- 1.52081193120006*m.x31 - 1.99931464995111*m.x32 - 1.58670668058247*m.x33
- 0.466890727711809*m.x34 + 0.861022193616592*m.x35 + 1.80517056869972*m.x36
+ 1.94473984079535*m.x37 + 1.21752285801744*m.x38 - 0.0523538966157472*m.x39
- 1.29889609666037*m.x40 - 1.96650981512791*m.x41 - 1.75763422532393*m.x42
- 0.765366864730178*m.x43 + 0.568030689407845*m.x44 + 1.64825237724403*m.x45
+ 1.99383466746626*m.x46 + 1.45074874202457*m.x47 + 0.261052384440103*m.x48
- 1.0449971294319*m.x49 - 1.88528298218436*m.x50 + m.x54 <= 0)
m.c135 = Constraint(expr= 2*m.x1 + 1.53208888623795*m.x2 + 0.347296355333854*m.x3 - m.x4 - 1.87938524157182*m.x5
- 1.87938524157182*m.x6 - m.x7 + 0.347296355333866*m.x8 + 1.53208888623796*m.x9 + 2*m.x10
+ 1.53208888623796*m.x11 + 0.347296355333855*m.x12 - m.x13 - 1.87938524157182*m.x14
- 1.87938524157182*m.x15 - m.x16 + 0.347296355333866*m.x17 + 1.53208888623796*m.x18 + 2*m.x19
+ 1.53208888623796*m.x20 + 0.347296355333855*m.x21 - m.x22 - 1.87938524157182*m.x23
- 1.87938524157182*m.x24 - m.x25 + 0.347296355333865*m.x26 + 1.53208888623796*m.x27 + 2*m.x28
+ 1.53208888623795*m.x29 + 0.347296355333859*m.x30 - m.x31 - 1.87938524157182*m.x32
- 1.87938524157182*m.x33 - 0.999999999999998*m.x34 + 0.347296355333861*m.x35
+ 1.53208888623796*m.x36 + 2*m.x37 + 1.53208888623795*m.x38 + 0.34729635533386*m.x39 - m.x40
- 1.87938524157182*m.x41 - 1.87938524157182*m.x42 - m.x43 + 0.347296355333861*m.x44
+ 1.53208888623796*m.x45 + 2*m.x46 + 1.53208888623796*m.x47 + 0.347296355333861*m.x48 - m.x49
- 1.87938524157182*m.x50 + m.x54 <= 0)
m.c136 = Constraint(expr= 1.29889609666037*m.x1 + 1.97803172672383*m.x2 + 1.68678289162577*m.x3
+ 0.568030689407843*m.x4 - 0.829386485312484*m.x5 - 1.81992254175309*m.x6
- 1.91763946973639*m.x7 - 1.07459921669365*m.x8 + 0.295618822259219*m.x9
+ 1.52081193120006*m.x10 + 1.99992384612834*m.x11 + 1.497911441578*m.x12
+ 0.261052384440099*m.x13 - 1.10387397062412*m.x14 - 1.92726090641725*m.x15
- 1.80517056869972*m.x16 - 0.79749813785049*m.x17 + 0.601411599008546*m.x18
+ 1.70528032870819*m.x19 + 1.97257120307446*m.x20 + 1.27215644055553*m.x21
- 0.0523538966157446*m.x22 - 1.35118041523132*m.x23 - 1.98714371135317*m.x24
- 1.64825237724403*m.x25 - 0.500760008108885*m.x26 + 0.892395626219618*m.x27
+ 1.84775906502258*m.x28 + 1.8966473104124*m.x29 + 1.01507672592141*m.x30
- 0.364471050984295*m.x31 - 1.56521631370483*m.x32 - 1.99809644316372*m.x33
- 1.45074874202458*m.x34 - 0.191691505040446*m.x35 + 1.16140591142188*m.x36
+ 1.94473984079535*m.x37 + 1.77402166635644*m.x38 + 0.733002453448593*m.x39
- 0.667613718467543*m.x40 - 1.7407113918798*m.x41 - 1.95984940924166*m.x42
- 1.21752285801744*m.x43 + 0.122097079069714*m.x44 + 1.4018185285997*m.x45
+ 1.99383466746626*m.x46 + 1.60771372123443*m.x47 + 0.432879227876206*m.x48
- 0.954317520519217*m.x49 - 1.8733443784968*m.x50 + m.x54 <= 0)
m.c137 = Constraint(expr= - 0.312868930080457*m.x1 + 1.08927807003005*m.x2 + 1.93185165257813*m.x3
+ 1.78201304837674*m.x4 + 0.716735899090605*m.x5 - 0.716735899090603*m.x6
- 1.78201304837673*m.x7 - 1.93185165257814*m.x8 - 1.08927807003005*m.x9
+ 0.312868930080462*m.x10 + 1.55429192291394*m.x11 + 1.99725906950915*m.x12
+ 1.4142135623731*m.x13 + 0.10467191248589*m.x14 - 1.25864078209967*m.x15
- 1.97537668119028*m.x16 - 1.67734113589085*m.x17 - 0.517638090205039*m.x18
+ 0.90798099947909*m.x19 + 1.8671608529944*m.x20 + 1.8671608529944*m.x21
+ 0.907980999479094*m.x22 - 0.517638090205042*m.x23 - 1.67734113589085*m.x24
- 1.97537668119028*m.x25 - 1.25864078209968*m.x26 + 0.104671912485886*m.x27
+ 1.41421356237309*m.x28 + 1.99725906950915*m.x29 + 1.55429192291394*m.x30
+ 0.312868930080462*m.x31 - 1.08927807003005*m.x32 - 1.93185165257814*m.x33
- 1.78201304837674*m.x34 - 0.7167358990906*m.x35 + 0.716735899090602*m.x36
+ 1.78201304837674*m.x37 + 1.93185165257814*m.x38 + 1.08927807003005*m.x39
- 0.312868930080462*m.x40 - 1.55429192291394*m.x41 - 1.99725906950915*m.x42
- 1.41421356237309*m.x43 - 0.104671912485888*m.x44 + 1.25864078209967*m.x45
+ 1.97537668119028*m.x46 + 1.67734113589085*m.x47 + 0.517638090205042*m.x48
- 0.907980999479094*m.x49 - 1.8671608529944*m.x50 + m.x54 <= 0)
m.c138 = Constraint(expr= - 1.70528032870818*m.x1 - 0.534476752156513*m.x2 + 0.923497226470068*m.x3
+ 1.88528298218436*m.x4 + 1.83412014877025*m.x5 + 0.797498137850495*m.x6
- 0.667613718467538*m.x7 - 1.77402166635644*m.x8 - 1.92726090641725*m.x9
- 1.0449971294319*m.x10 + 0.398735868834393*m.x11 + 1.62823103671264*m.x12
+ 1.98288972274762*m.x13 + 1.27215644055553*m.x14 - 0.122097079069708*m.x15
- 1.45074874202458*m.x16 - 1.99992384612834*m.x17 - 1.47455467362025*m.x18
- 0.156918191455692*m.x19 + 1.24502927327524*m.x20 + 1.97803172672383*m.x21
+ 1.64825237724403*m.x22 + 0.432879227876205*m.x23 - 1.01507672592141*m.x24
- 1.91763946973639*m.x25 - 1.78986872320405*m.x26 - 0.700414762518939*m.x27
+ 0.765366864730175*m.x28 + 1.81992254175309*m.x29 + 1.8966473104124*m.x30
+ 0.954317520519218*m.x31 - 0.500760008108881*m.x32 - 1.68678289162577*m.x33
- 1.96650981512791*m.x34 - 1.18964557350268*m.x35 + 0.226406427535811*m.x36
+ 1.52081193120006*m.x37 + 1.99809644316372*m.x38 + 1.4018185285997*m.x39
+ 0.0523538966157478*m.x40 - 1.32524009643147*m.x41 - 1.99079239673436*m.x42
- 1.58670668058247*m.x43 - 0.330095211721356*m.x44 + 1.10387397062412*m.x45
+ 1.94473984079535*m.x46 + 1.7407113918798*m.x47 + 0.601411599008546*m.x48
- 0.86102219361659*m.x49 - 1.86083513596405*m.x50 + m.x54 <= 0)
m.c139 = Constraint(expr= - 1.90211303259031*m.x1 - 1.79758809259833*m.x2 - 0.684040286651331*m.x3
+ 0.813473286151611*m.x4 + 1.85436770913358*m.x5 + 1.85436770913357*m.x6
+ 0.813473286151593*m.x7 - 0.684040286651336*m.x8 - 1.79758809259833*m.x9
- 1.90211303259031*m.x10 - 0.938943125571779*m.x11 + 0.551274711634005*m.x12
+ 1.73205080756888*m.x13 + 1.94059145255199*m.x14 + 1.05983852846641*m.x15
- 0.41582338163552*m.x16 - 1.65807514511009*m.x17 - 1.96961550602442*m.x18
- 1.17557050458494*m.x19 + 0.278346201920133*m.x20 + 1.57602150721345*m.x21
+ 1.98904379073655*m.x22 + 1.28557521937308*m.x23 - 0.139512947488254*m.x24
- 1.48628965095479*m.x25 - 1.99878165403819*m.x26 - 1.38931674091799*m.x27
+ 4.89982515786259E-15*m.x28 + 1.389316740918*m.x29 + 1.99878165403819*m.x30
+ 1.48628965095479*m.x31 + 0.139512947488248*m.x32 - 1.28557521937308*m.x33
- 1.98904379073655*m.x34 - 1.57602150721344*m.x35 - 0.27834620192013*m.x36
+ 1.17557050458495*m.x37 + 1.96961550602442*m.x38 + 1.65807514511008*m.x39
+ 0.415823381635517*m.x40 - 1.05983852846641*m.x41 - 1.94059145255199*m.x42
- 1.73205080756888*m.x43 - 0.551274711633997*m.x44 + 0.938943125571782*m.x45
+ 1.90211303259031*m.x46 + 1.79758809259833*m.x47 + 0.684040286651337*m.x48
- 0.813473286151601*m.x49 - 1.85436770913357*m.x50 + m.x54 <= 0)
m.c140 = Constraint(expr= - 0.765366864730177*m.x1 - 1.84775906502258*m.x2 - 1.84775906502257*m.x3
- 0.765366864730171*m.x4 + 0.765366864730183*m.x5 + 1.84775906502257*m.x6
+ 1.84775906502257*m.x7 + 0.765366864730178*m.x8 - 0.765366864730176*m.x9
- 1.84775906502258*m.x10 - 1.84775906502257*m.x11 - 0.765366864730178*m.x12
+ 0.765366864730182*m.x13 + 1.84775906502258*m.x14 + 1.84775906502257*m.x15
+ 0.765366864730178*m.x16 - 0.765366864730182*m.x17 - 1.84775906502258*m.x18
- 1.84775906502257*m.x19 - 0.765366864730179*m.x20 + 0.765366864730182*m.x21
+ 1.84775906502258*m.x22 + 1.84775906502257*m.x23 + 0.765366864730179*m.x24
- 0.765366864730182*m.x25 - 1.84775906502258*m.x26 - 1.84775906502257*m.x27
- 0.765366864730179*m.x28 + 0.765366864730182*m.x29 + 1.84775906502258*m.x30
+ 1.84775906502257*m.x31 + 0.765366864730179*m.x32 - 0.765366864730181*m.x33
- 1.84775906502257*m.x34 - 1.84775906502257*m.x35 - 0.76536686473018*m.x36
+ 0.765366864730181*m.x37 + 1.84775906502257*m.x38 + 1.84775906502257*m.x39
+ 0.76536686473018*m.x40 - 0.765366864730181*m.x41 - 1.84775906502257*m.x42
- 1.84775906502257*m.x43 - 0.76536686473018*m.x44 + 0.765366864730181*m.x45
+ 1.84775906502257*m.x46 + 1.84775906502257*m.x47 + 0.765366864730179*m.x48
- 0.76536686473018*m.x49 - 1.84775906502257*m.x50 + m.x54 <= 0)
m.c141 = Constraint(expr= 0.907980999479099*m.x1 - 0.651136308914307*m.x2 - 1.8126155740733*m.x3
- 1.8671608529944*m.x4 - 0.781462256978541*m.x5 + 0.781462256978553*m.x6
+ 1.86716085299441*m.x7 + 1.8126155740733*m.x8 + 0.651136308914309*m.x9
- 0.907980999479097*m.x10 - 1.91260951192607*m.x11 - 1.74923941427879*m.x12
- 0.517638090205039*m.x13 + 1.03007614982011*m.x14 + 1.94874012957047*m.x15
+ 1.67734113589085*m.x16 + 0.381617990753089*m.x17 - 1.14715287270209*m.x18
- 1.97537668119028*m.x19 - 1.59727102009459*m.x20 - 0.243738686810296*m.x21
+ 1.25864078209967*m.x22 + 1.99238939618349*m.x23 + 1.50941916044555*m.x24
+ 0.10467191248589*m.x25 - 1.36399672012499*m.x26 - 1.99969539031278*m.x27
- 1.41421356237309*m.x28 + 0.0349048128745698*m.x29 + 1.46270740323834*m.x30
+ 1.99725906950915*m.x31 + 1.31211805798101*m.x32 - 0.174311485495317*m.x33
- 1.55429192291394*m.x34 - 1.98509230328264*m.x35 - 1.2036300463041*m.x36
+ 0.312868930080461*m.x37 + 1.63830408857798*m.x38 + 1.96325436689533*m.x39
+ 1.08927807003005*m.x40 - 0.449902108687731*m.x41 - 1.71433460140422*m.x42
- 1.93185165257814*m.x43 - 0.969619240492675*m.x44 + 0.584743409445474*m.x45
+ 1.78201304837674*m.x46 + 1.89103715119863*m.x47 + 0.845236523481399*m.x48
- 0.7167358990906*m.x49 - 1.84100970690488*m.x50 + m.x54 <= 0)
m.c142 = Constraint(expr= 1.94473984079536*m.x1 + 0.984847120206938*m.x2 - 0.601411599008547*m.x3
- 1.80517056869972*m.x4 - 1.86083513596405*m.x5 - 0.733002453448595*m.x6
+ 0.861022193616594*m.x7 + 1.90743390149645*m.x8 + 1.7407113918798*m.x9
+ 0.466890727711807*m.x10 - 1.10387397062411*m.x11 - 1.97257120307446*m.x12
- 1.58670668058247*m.x13 - 0.191691505040448*m.x14 + 1.32524009643147*m.x15
+ 1.99931464995111*m.x16 + 1.4018185285997*m.x17 - 0.0872387747306687*m.x18
- 1.52081193120006*m.x19 - 1.98714371135318*m.x20 - 1.18964557350269*m.x21
+ 0.364471050984295*m.x22 + 1.68678289162577*m.x23 + 1.93629528075622*m.x24
+ 0.954317520519217*m.x25 - 0.634609312810181*m.x26 - 1.81992254175309*m.x27
- 1.84775906502257*m.x28 - 0.700414762518939*m.x29 + 0.892395626219618*m.x30
+ 1.91763946973639*m.x31 + 1.72325832088305*m.x32 + 0.432879227876206*m.x33
- 1.13281247384967*m.x34 - 1.97803172672383*m.x35 - 1.56521631370483*m.x36
- 0.15691819145569*m.x37 + 1.35118041523132*m.x38 + 1.99992384612834*m.x39
+ 1.37670915138751*m.x40 - 0.122097079069714*m.x41 - 1.54324916677544*m.x42
- 1.98288972274762*m.x43 - 1.16140591142188*m.x44 + 0.398735868834395*m.x45
+ 1.70528032870818*m.x46 + 1.92726090641725*m.x47 + 0.923497226470068*m.x48
- 0.667613718467542*m.x49 - 1.83412014877025*m.x50 + m.x54 <= 0)
m.c143 = Constraint(expr= 1.61803398874989*m.x1 + 1.95629520146761*m.x2 + 1.00000000000001*m.x3
- 0.618033988749898*m.x4 - 1.8270909152852*m.x5 - 1.8270909152852*m.x6
- 0.618033988749892*m.x7 + 0.999999999999998*m.x8 + 1.95629520146761*m.x9
+ 1.61803398874989*m.x10 + 0.209056926535309*m.x11 - 1.33826121271771*m.x12 - 2*m.x13
- 1.33826121271772*m.x14 + 0.209056926535307*m.x15 + 1.61803398874989*m.x16
+ 1.95629520146761*m.x17 + m.x18 - 0.61803398874989*m.x19 - 1.8270909152852*m.x20
- 1.8270909152852*m.x21 - 0.6180339887499*m.x22 + 0.999999999999997*m.x23
+ 1.95629520146761*m.x24 + 1.6180339887499*m.x25 + 0.20905692653531*m.x26
- 1.33826121271772*m.x27 - 2*m.x28 - 1.33826121271772*m.x29 + 0.209056926535306*m.x30
+ 1.6180339887499*m.x31 + 1.95629520146761*m.x32 + m.x33 - 0.618033988749892*m.x34
- 1.8270909152852*m.x35 - 1.8270909152852*m.x36 - 0.618033988749897*m.x37 + m.x38
+ 1.95629520146761*m.x39 + 1.61803398874989*m.x40 + 0.209056926535307*m.x41
- 1.33826121271772*m.x42 - 2*m.x43 - 1.33826121271772*m.x44 + 0.209056926535307*m.x45
+ 1.61803398874989*m.x46 + 1.95629520146761*m.x47 + m.x48 - 0.618033988749895*m.x49
- 1.8270909152852*m.x50 + m.x54 <= 0)
m.c144 = Constraint(expr= 0.156918191455691*m.x1 + 1.60771372123444*m.x2 + 1.95259201423987*m.x3
+ 0.954317520519216*m.x4 - 0.700414762518936*m.x5 - 1.8733443784968*m.x6
- 1.75763422532393*m.x7 - 0.432879227876204*m.x8 + 1.18964557350268*m.x9
+ 1.99383466746626*m.x10 + 1.42650089830836*m.x11 - 0.122097079069716*m.x12
- 1.58670668058247*m.x13 - 1.95984940924166*m.x14 - 0.984847120206932*m.x15
+ 0.667613718467544*m.x16 + 1.86083513596405*m.x17 + 1.77402166635644*m.x18
+ 0.466890727711808*m.x19 - 1.16140591142188*m.x20 - 1.99079239673436*m.x21
- 1.45074874202457*m.x22 + 0.0872387747306755*m.x23 + 1.56521631370483*m.x24
+ 1.96650981512791*m.x25 + 1.0150767259214*m.x26 - 0.634609312810188*m.x27
- 1.84775906502258*m.x28 - 1.78986872320405*m.x29 - 0.500760008108879*m.x30
+ 1.13281247384967*m.x31 + 1.98714371135318*m.x32 + 1.47455467362025*m.x33
- 0.0523538966157477*m.x34 - 1.54324916677544*m.x35 - 1.97257120307446*m.x36
- 1.0449971294319*m.x37 + 0.601411599008548*m.x38 + 1.83412014877025*m.x39
+ 1.80517056869972*m.x40 + 0.534476752156511*m.x41 - 1.10387397062412*m.x42
- 1.98288972274762*m.x43 - 1.497911441578*m.x44 + 0.0174530709967489*m.x45
+ 1.52081193120006*m.x46 + 1.97803172672383*m.x47 + 1.07459921669365*m.x48
- 0.568030689407846*m.x49 - 1.81992254175309*m.x50 + m.x54 <= 0)
m.c145 = Constraint(expr= - 1.4142135623731*m.x1 + 0.174311485495319*m.x2 + 1.63830408857799*m.x3
+ 1.93185165257814*m.x4 + 0.845236523481395*m.x5 - 0.845236523481394*m.x6
- 1.93185165257814*m.x7 - 1.63830408857798*m.x8 - 0.17431148549532*m.x9
+ 1.4142135623731*m.x10 + 1.99238939618349*m.x11 + 1.14715287270209*m.x12
- 0.517638090205043*m.x13 - 1.8126155740733*m.x14 - 1.8126155740733*m.x15
- 0.517638090205039*m.x16 + 1.14715287270209*m.x17 + 1.99238939618349*m.x18
+ 1.41421356237309*m.x19 - 0.174311485495318*m.x20 - 1.63830408857798*m.x21
- 1.93185165257814*m.x22 - 0.845236523481396*m.x23 + 0.8452365234814*m.x24
+ 1.93185165257814*m.x25 + 1.63830408857798*m.x26 + 0.174311485495315*m.x27
- 1.41421356237309*m.x28 - 1.99238939618349*m.x29 - 1.14715287270209*m.x30
+ 0.517638090205042*m.x31 + 1.8126155740733*m.x32 + 1.8126155740733*m.x33
+ 0.51763809020504*m.x34 - 1.14715287270209*m.x35 - 1.99238939618349*m.x36
- 1.4142135623731*m.x37 + 0.174311485495317*m.x38 + 1.63830408857798*m.x39
+ 1.93185165257814*m.x40 + 0.845236523481397*m.x41 - 0.845236523481399*m.x42
- 1.93185165257814*m.x43 - 1.63830408857798*m.x44 - 0.174311485495316*m.x45
+ 1.4142135623731*m.x46 + 1.99238939618349*m.x47 + 1.14715287270209*m.x48
- 0.517638090205041*m.x49 - 1.8126155740733*m.x50 + m.x54 <= 0)
m.c146 = Constraint(expr= - 1.99383466746626*m.x1 - 1.3767091513875*m.x2 + 0.261052384440104*m.x3
+ 1.70528032870818*m.x4 + 1.88528298218436*m.x5 + 0.667613718467539*m.x6
- 1.0449971294319*m.x7 - 1.98288972274762*m.x8 - 1.45074874202457*m.x9
+ 0.15691819145569*m.x10 + 1.64825237724403*m.x11 + 1.91763946973638*m.x12
+ 0.765366864730178*m.x13 - 0.954317520519215*m.x14 - 1.96650981512791*m.x15
- 1.52081193120006*m.x16 + 0.0523538966157454*m.x17 + 1.58670668058247*m.x18
+ 1.94473984079535*m.x19 + 0.86102219361659*m.x20 - 0.861022193616587*m.x21
- 1.94473984079535*m.x22 - 1.58670668058247*m.x23 - 0.0523538966157483*m.x24
+ 1.52081193120006*m.x25 + 1.96650981512791*m.x26 + 0.954317520519217*m.x27
- 0.765366864730182*m.x28 - 1.91763946973639*m.x29 - 1.64825237724403*m.x30
- 0.156918191455693*m.x31 + 1.45074874202458*m.x32 + 1.98288972274762*m.x33
+ 1.0449971294319*m.x34 - 0.667613718467543*m.x35 - 1.88528298218436*m.x36
- 1.70528032870818*m.x37 - 0.261052384440104*m.x38 + 1.37670915138751*m.x39
+ 1.99383466746626*m.x40 + 1.13281247384966*m.x41 - 0.568030689407846*m.x42
- 1.84775906502257*m.x43 - 1.75763422532393*m.x44 - 0.364471050984295*m.x45
+ 1.29889609666037*m.x46 + 1.99931464995111*m.x47 + 1.21752285801744*m.x48
- 0.466890727711811*m.x49 - 1.80517056869972*m.x50 + m.x54 <= 0)
m.c147 = Constraint(expr= - 1.17557050458494*m.x1 - 1.99878165403819*m.x2 - 1.28557521937308*m.x3
+ 0.415823381635514*m.x4 + 1.79758809259833*m.x5 + 1.79758809259834*m.x6
+ 0.415823381635521*m.x7 - 1.28557521937308*m.x8 - 1.99878165403819*m.x9
- 1.17557050458495*m.x10 + 0.551274711633998*m.x11 + 1.85436770913358*m.x12
+ 1.73205080756888*m.x13 + 0.278346201920129*m.x14 - 1.389316740918*m.x15
- 1.98904379073655*m.x16 - 1.05983852846641*m.x17 + 0.684040286651336*m.x18
+ 1.90211303259031*m.x19 + 1.65807514511008*m.x20 + 0.13951294748825*m.x21
- 1.48628965095479*m.x22 - 1.96961550602442*m.x23 - 0.938943125571786*m.x24
+ 0.813473286151597*m.x25 + 1.94059145255199*m.x26 + 1.57602150721345*m.x27
+ 1.96067283990894E-15*m.x28 - 1.57602150721344*m.x29 - 1.94059145255199*m.x30
- 0.8134732861516*m.x31 + 0.938943125571782*m.x32 + 1.96961550602442*m.x33
+ 1.48628965095479*m.x34 - 0.13951294748825*m.x35 - 1.65807514511008*m.x36
- 1.90211303259031*m.x37 - 0.684040286651339*m.x38 + 1.05983852846641*m.x39
+ 1.98904379073655*m.x40 + 1.38931674091799*m.x41 - 0.27834620192013*m.x42
- 1.73205080756888*m.x43 - 1.85436770913358*m.x44 - 0.551274711633999*m.x45
+ 1.17557050458495*m.x46 + 1.99878165403819*m.x47 + 1.28557521937308*m.x48
- 0.415823381635518*m.x49 - 1.79758809259833*m.x50 + m.x54 <= 0)
m.c148 = Constraint(expr= 0.466890727711807*m.x1 - 1.27215644055554*m.x2 - 1.99809644316372*m.x3
- 1.13281247384966*m.x4 + 0.634609312810183*m.x5 + 1.8966473104124*m.x6
+ 1.64825237724403*m.x7 + 0.0872387747306706*m.x8 - 1.54324916677544*m.x9
- 1.94473984079535*m.x10 - 0.797498137850489*m.x11 + 0.984847120206943*m.x12
+ 1.98288972274762*m.x13 + 1.4018185285997*m.x14 - 0.29561882225922*m.x15
- 1.75763422532393*m.x16 - 1.81992254175309*m.x17 - 0.432879227876205*m.x18
+ 1.29889609666037*m.x19 + 1.99626959684373*m.x20 + 1.10387397062411*m.x21
- 0.667613718467544*m.x22 - 1.90743390149645*m.x23 - 1.62823103671263*m.x24
- 0.0523538966157412*m.x25 + 1.56521631370483*m.x26 + 1.93629528075622*m.x27
+ 0.765366864730179*m.x28 - 1.01507672592141*m.x29 - 1.98714371135318*m.x30
- 1.37670915138751*m.x31 + 0.330095211721357*m.x32 + 1.77402166635644*m.x33
+ 1.80517056869972*m.x34 + 0.398735868834393*m.x35 - 1.32524009643148*m.x36
- 1.99383466746626*m.x37 - 1.07459921669365*m.x38 + 0.700414762518937*m.x39
+ 1.91763946973639*m.x40 + 1.60771372123443*m.x41 + 0.0174530709967462*m.x42
- 1.58670668058247*m.x43 - 1.92726090641725*m.x44 - 0.733002453448594*m.x45
+ 1.0449971294319*m.x46 + 1.99079239673436*m.x47 + 1.35118041523132*m.x48
- 0.364471050984295*m.x49 - 1.78986872320405*m.x50 + m.x54 <= 0)
m.c149 = Constraint(expr= 1.78201304837673*m.x1 + 0.312868930080463*m.x2 - 1.4142135623731*m.x3
- 1.97537668119027*m.x4 - 0.907980999479092*m.x5 + 0.907980999479092*m.x6
+ 1.97537668119028*m.x7 + 1.41421356237309*m.x8 - 0.312868930080463*m.x9
- 1.78201304837673*m.x10 - 1.78201304837673*m.x11 - 0.312868930080457*m.x12
+ 1.4142135623731*m.x13 + 1.97537668119028*m.x14 + 0.907980999479087*m.x15
- 0.907980999479097*m.x16 - 1.97537668119028*m.x17 - 1.41421356237309*m.x18
+ 0.312868930080462*m.x19 + 1.78201304837674*m.x20 + 1.78201304837674*m.x21
+ 0.312868930080458*m.x22 - 1.4142135623731*m.x23 - 1.97537668119027*m.x24
- 0.907980999479093*m.x25 + 0.907980999479097*m.x26 + 1.97537668119028*m.x27
+ 1.41421356237309*m.x28 - 0.312868930080462*m.x29 - 1.78201304837674*m.x30
- 1.78201304837674*m.x31 - 0.312868930080459*m.x32 + 1.41421356237309*m.x33
+ 1.97537668119027*m.x34 + 0.907980999479094*m.x35 - 0.907980999479096*m.x36
- 1.97537668119028*m.x37 - 1.41421356237309*m.x38 + 0.312868930080461*m.x39
+ 1.78201304837674*m.x40 + 1.78201304837674*m.x41 + 0.312868930080459*m.x42
- 1.4142135623731*m.x43 - 1.97537668119028*m.x44 - 0.907980999479093*m.x45
+ 0.907980999479094*m.x46 + 1.97537668119028*m.x47 + 1.41421356237309*m.x48
- 0.312868930080462*m.x49 - 1.78201304837674*m.x50 + m.x54 <= 0)
m.c150 = Constraint(expr= 1.84775906502257*m.x1 + 1.68678289162577*m.x2 + 0.0872387747306701*m.x3
- 1.58670668058247*m.x4 - 1.90743390149645*m.x5 - 0.601411599008541*m.x6
+ 1.21752285801745*m.x7 + 1.99809644316372*m.x8 + 1.07459921669365*m.x9
- 0.765366864730177*m.x10 - 1.95259201423987*m.x11 - 1.47455467362025*m.x12
+ 0.261052384440104*m.x13 + 1.77402166635644*m.x14 + 1.77402166635644*m.x15
+ 0.261052384440099*m.x16 - 1.47455467362025*m.x17 - 1.95259201423987*m.x18
- 0.765366864730178*m.x19 + 1.07459921669365*m.x20 + 1.99809644316372*m.x21
+ 1.21752285801744*m.x22 - 0.601411599008546*m.x23 - 1.90743390149645*m.x24
- 1.58670668058247*m.x25 + 0.0872387747306755*m.x26 + 1.68678289162577*m.x27
+ 1.84775906502257*m.x28 + 0.432879227876205*m.x29 - 1.35118041523132*m.x30
- 1.98288972274762*m.x31 - 0.92349722647007*m.x32 + 0.923497226470066*m.x33
+ 1.98288972274762*m.x34 + 1.35118041523132*m.x35 - 0.432879227876205*m.x36
- 1.84775906502257*m.x37 - 1.68678289162577*m.x38 - 0.0872387747306728*m.x39
+ 1.58670668058247*m.x40 + 1.90743390149645*m.x41 + 0.601411599008547*m.x42
- 1.21752285801744*m.x43 - 1.99809644316372*m.x44 - 1.07459921669365*m.x45
+ 0.765366864730179*m.x46 + 1.95259201423987*m.x47 + 1.47455467362025*m.x48
- 0.261052384440103*m.x49 - 1.77402166635644*m.x50 + m.x54 <= 0)
m.c151 = Constraint(expr= 0.618033988749892*m.x1 + 1.92252339187664*m.x2 + 1.53208888623796*m.x3
- 0.209056926535308*m.x4 - 1.76589518571785*m.x5 - 1.76589518571785*m.x6
- 0.209056926535301*m.x7 + 1.53208888623795*m.x8 + 1.92252339187664*m.x9
+ 0.618033988749899*m.x10 - 1.23132295065132*m.x11 - 1.99512810051965*m.x12 - m.x13
+ 0.876742293578148*m.x14 + 1.98053613748314*m.x15 + 1.33826121271771*m.x16
- 0.483843791199333*m.x17 - 1.87938524157182*m.x18 - 1.61803398874989*m.x19
+ 0.0697989934050035*m.x20 + 1.69609619231285*m.x21 + 1.8270909152852*m.x22
+ 0.347296355333862*m.x23 - 1.4386796006773*m.x24 - 1.95629520146761*m.x25
- 0.749213186831827*m.x26 + 1.1183858069415*m.x27 + 2*m.x28 + 1.11838580694149*m.x29
- 0.749213186831824*m.x30 - 1.95629520146761*m.x31 - 1.4386796006773*m.x32
+ 0.347296355333858*m.x33 + 1.8270909152852*m.x34 + 1.69609619231285*m.x35
+ 0.0697989934050036*m.x36 - 1.6180339887499*m.x37 - 1.87938524157182*m.x38
- 0.483843791199336*m.x39 + 1.33826121271772*m.x40 + 1.98053613748314*m.x41
+ 0.876742293578155*m.x42 - m.x43 - 1.99512810051965*m.x44 - 1.23132295065132*m.x45
+ 0.618033988749895*m.x46 + 1.92252339187664*m.x47 + 1.53208888623796*m.x48
- 0.209056926535307*m.x49 - 1.76589518571785*m.x50 + m.x54 <= 0)
m.c152 = Constraint(expr= - 1.0449971294319*m.x1 + 0.861022193616582*m.x2 + 1.98288972274762*m.x3
+ 1.29889609666037*m.x4 - 0.568030689407842*m.x5 - 1.91763946973638*m.x6
- 1.52081193120007*m.x7 + 0.261052384440104*m.x8 + 1.80517056869972*m.x9
+ 1.70528032870819*m.x10 + 0.0523538966157542*m.x11 - 1.64825237724403*m.x12
- 1.84775906502257*m.x13 - 0.364471050984298*m.x14 + 1.45074874202457*m.x15
+ 1.94473984079536*m.x16 + 0.66761371846754*m.x17 - 1.21752285801744*m.x18
- 1.99383466746626*m.x19 - 0.954317520519217*m.x20 + 0.954317520519214*m.x21
+ 1.99383466746626*m.x22 + 1.21752285801744*m.x23 - 0.667613718467537*m.x24
- 1.94473984079535*m.x25 - 1.45074874202458*m.x26 + 0.364471050984295*m.x27
+ 1.84775906502257*m.x28 + 1.64825237724403*m.x29 - 0.0523538966157446*m.x30
- 1.70528032870818*m.x31 - 1.80517056869972*m.x32 - 0.261052384440107*m.x33
+ 1.52081193120006*m.x34 + 1.91763946973639*m.x35 + 0.568030689407847*m.x36
- 1.29889609666037*m.x37 - 1.98288972274762*m.x38 - 0.861022193616591*m.x39
+ 1.0449971294319*m.x40 + 1.99931464995111*m.x41 + 1.13281247384966*m.x42
- 0.765366864730179*m.x43 - 1.96650981512791*m.x44 - 1.37670915138751*m.x45
+ 0.46689072771181*m.x46 + 1.88528298218436*m.x47 + 1.58670668058247*m.x48
- 0.15691819145569*m.x49 - 1.75763422532393*m.x50 + m.x54 <= 0)
m.c153 = Constraint(expr= - 1.97537668119028*m.x1 - 0.781462256978554*m.x2 + 1.14715287270209*m.x3
+ 1.99725906950915*m.x4 + 0.969619240492675*m.x5 - 0.969619240492675*m.x6
- 1.99725906950915*m.x7 - 1.1471528727021*m.x8 + 0.781462256978541*m.x9
+ 1.97537668119027*m.x10 + 1.31211805798102*m.x11 - 0.584743409445472*m.x12
- 1.93185165257814*m.x13 - 1.46270740323834*m.x14 + 0.381617990753079*m.x15
+ 1.8671608529944*m.x16 + 1.59727102009459*m.x17 - 0.174311485495312*m.x18
- 1.78201304837673*m.x19 - 1.71433460140423*m.x20 - 0.0349048128745732*m.x21
+ 1.67734113589085*m.x22 + 1.8126155740733*m.x23 + 0.243738686810295*m.x24
- 1.55429192291394*m.x25 - 1.89103715119863*m.x26 - 0.449902108687732*m.x27
+ 1.4142135623731*m.x28 + 1.94874012957047*m.x29 + 0.651136308914317*m.x30
- 1.25864078209967*m.x31 - 1.98509230328264*m.x32 - 0.845236523481403*m.x33
+ 1.08927807003005*m.x34 + 1.99969539031278*m.x35 + 1.03007614982011*m.x36
- 0.907980999479093*m.x37 - 1.99238939618349*m.x38 - 1.2036300463041*m.x39
+ 0.716735899090599*m.x40 + 1.96325436689533*m.x41 + 1.363996720125*m.x42
- 0.517638090205041*m.x43 - 1.91260951192607*m.x44 - 1.50941916044554*m.x45
+ 0.31286893008046*m.x46 + 1.84100970690488*m.x47 + 1.63830408857798*m.x48
- 0.104671912485887*m.x49 - 1.74923941427879*m.x50 + m.x54 <= 0)
m.c154 = Constraint(expr= - 1.52081193120005*m.x1 - 1.8966473104124*m.x2 - 0.432879227876217*m.x3
+ 1.45074874202457*m.x4 + 1.92726090641725*m.x5 + 0.534476752156519*m.x6
- 1.3767091513875*m.x7 - 1.95259201423987*m.x8 - 0.634609312810197*m.x9
+ 1.29889609666036*m.x10 + 1.97257120307446*m.x11 + 0.733002453448601*m.x12
- 1.21752285801744*m.x13 - 1.98714371135318*m.x14 - 0.829386485312479*m.x15
+ 1.13281247384966*m.x16 + 1.99626959684373*m.x17 + 0.923497226470076*m.x18
- 1.0449971294319*m.x19 - 1.99992384612834*m.x20 - 1.01507672592141*m.x21
+ 0.954317520519214*m.x22 + 1.99809644316372*m.x23 + 1.10387397062412*m.x24
- 0.861022193616587*m.x25 - 1.99079239673436*m.x26 - 1.18964557350269*m.x27
+ 0.765366864730175*m.x28 + 1.97803172672383*m.x29 + 1.27215644055553*m.x30
- 0.667613718467537*m.x31 - 1.95984940924166*m.x32 - 1.35118041523132*m.x33
+ 0.568030689407839*m.x34 + 1.93629528075621*m.x35 + 1.42650089830837*m.x36
- 0.466890727711808*m.x37 - 1.90743390149645*m.x38 - 1.497911441578*m.x39
+ 0.364471050984294*m.x40 + 1.87334437849679*m.x41 + 1.56521631370483*m.x42
- 0.261052384440102*m.x43 - 1.83412014877025*m.x44 - 1.62823103671264*m.x45
+ 0.156918191455689*m.x46 + 1.78986872320405*m.x47 + 1.68678289162577*m.x48
- 0.0523538966157458*m.x49 - 1.7407113918798*m.x50 + m.x54 <= 0)
m.c155 = Constraint(expr= - 1.46994754735878E-14*m.x1 - 1.73205080756888*m.x2 - 1.73205080756887*m.x3
+ 7.3491187561573E-15*m.x4 + 1.73205080756888*m.x5 + 1.73205080756887*m.x6
- 1.42096167539288E-14*m.x7 - 1.73205080756888*m.x8 - 1.73205080756887*m.x9
+ 6.85926003649835E-15*m.x10 + 1.73205080756888*m.x11 + 1.73205080756888*m.x12
- 1.37197580342699E-14*m.x13 - 1.73205080756888*m.x14 - 1.73205080756887*m.x15
+ 6.36940131683941E-15*m.x16 + 1.73205080756888*m.x17 + 1.73205080756888*m.x18
- 1.32298993146109E-14*m.x19 - 1.73205080756888*m.x20 - 1.73205080756887*m.x21
+ 5.87954259718047E-15*m.x22 + 1.73205080756888*m.x23 + 1.73205080756887*m.x24
- 5.634613237351E-15*m.x25 - 1.73205080756888*m.x26 - 1.73205080756887*m.x27
+ 5.38968387752153E-15*m.x28 + 1.73205080756888*m.x29 + 1.73205080756887*m.x30
- 5.14475451769206E-15*m.x31 - 1.73205080756888*m.x32 - 1.73205080756887*m.x33
+ 4.89982515786259E-15*m.x34 + 1.73205080756888*m.x35 + 1.73205080756888*m.x36
- 4.65489579803312E-15*m.x37 - 1.73205080756888*m.x38 - 1.73205080756888*m.x39
+ 8.57252759403147E-16*m.x40 + 1.73205080756888*m.x41 + 1.73205080756888*m.x42
- 2.38868023897393E-15*m.x43 - 1.73205080756888*m.x44 - 1.73205080756888*m.x45
+ 3.67394039744206E-16*m.x46 + 1.73205080756888*m.x47 + 1.73205080756888*m.x48
- 1.22464679914735E-16*m.x49 - 1.73205080756888*m.x50 + m.x54 <= 0)
m.c156 = Constraint(expr= 1.52081193120005*m.x1 - 0.398735868834409*m.x2 - 1.90743390149645*m.x3
- 1.45074874202457*m.x4 + 0.50076000810889*m.x5 + 1.93629528075622*m.x6 + 1.3767091513875*m.x7
- 0.601411599008547*m.x8 - 1.95984940924166*m.x9 - 1.29889609666036*m.x10
+ 0.700414762518943*m.x11 + 1.97803172672384*m.x12 + 1.21752285801743*m.x13
- 0.797498137850494*m.x14 - 1.99079239673436*m.x15 - 1.13281247384966*m.x16
+ 0.892395626219626*m.x17 + 1.99809644316372*m.x18 + 1.0449971294319*m.x19
- 0.984847120206936*m.x20 - 1.99992384612834*m.x21 - 0.95431752051921*m.x22
+ 1.07459921669365*m.x23 + 1.99626959684373*m.x24 + 0.861022193616583*m.x25
- 1.16140591142188*m.x26 - 1.98714371135317*m.x27 - 0.765366864730179*m.x28
+ 1.24502927327524*m.x29 + 1.97257120307446*m.x30 + 0.667613718467541*m.x31
- 1.32524009643148*m.x32 - 1.95259201423987*m.x33 - 0.568030689407844*m.x34
+ 1.4018185285997*m.x35 + 1.92726090641725*m.x36 + 0.466890727711809*m.x37
- 1.47455467362025*m.x38 - 1.8966473104124*m.x39 - 0.364471050984293*m.x40
+ 1.54324916677544*m.x41 + 1.86083513596405*m.x42 + 0.261052384440102*m.x43
- 1.60771372123444*m.x44 - 1.81992254175309*m.x45 - 0.156918191455689*m.x46
+ 1.66777164413434*m.x47 + 1.77402166635644*m.x48 + 0.052353896615746*m.x49
- 1.72325832088305*m.x50 + m.x54 <= 0)
m.c157 = Constraint(expr= 1.97537668119028*m.x1 + 1.20363004630409*m.x2 - 0.845236523481408*m.x3
- 1.99725906950915*m.x4 - 1.0300761498201*m.x5 + 1.03007614982011*m.x6 + 1.99725906950915*m.x7
+ 0.845236523481395*m.x8 - 1.20363004630411*m.x9 - 1.97537668119027*m.x10
- 0.651136308914315*m.x11 + 1.363996720125*m.x12 + 1.93185165257814*m.x13
+ 0.449902108687724*m.x14 - 1.50941916044554*m.x15 - 1.8671608529944*m.x16
- 0.243738686810295*m.x17 + 1.63830408857799*m.x18 + 1.78201304837674*m.x19
+ 0.0349048128745587*m.x20 - 1.74923941427879*m.x21 - 1.67734113589085*m.x22
+ 0.174311485495318*m.x23 + 1.84100970690488*m.x24 + 1.55429192291394*m.x25
- 0.381617990753093*m.x26 - 1.91260951192607*m.x27 - 1.41421356237309*m.x28
+ 0.584743409445477*m.x29 + 1.96325436689533*m.x30 + 1.25864078209967*m.x31
- 0.781462256978552*m.x32 - 1.99238939618349*m.x33 - 1.08927807003005*m.x34
+ 0.969619240492673*m.x35 + 1.99969539031278*m.x36 + 0.907980999479091*m.x37
- 1.14715287270209*m.x38 - 1.98509230328264*m.x39 - 0.7167358990906*m.x40
+ 1.31211805798101*m.x41 + 1.94874012957047*m.x42 + 0.51763809020504*m.x43
- 1.46270740323834*m.x44 - 1.89103715119863*m.x45 - 0.312868930080461*m.x46
+ 1.59727102009459*m.x47 + 1.8126155740733*m.x48 + 0.104671912485887*m.x49
- 1.71433460140422*m.x50 + m.x54 <= 0)
m.c158 = Constraint(expr= 1.0449971294319*m.x1 + 1.99383466746626*m.x2 + 0.765366864730177*m.x3
- 1.29889609666037*m.x4 - 1.94473984079535*m.x5 - 0.466890727711813*m.x6
+ 1.52081193120007*m.x7 + 1.84775906502257*m.x8 + 0.156918191455684*m.x9
- 1.70528032870819*m.x10 - 1.70528032870818*m.x11 + 0.156918191455691*m.x12
+ 1.84775906502257*m.x13 + 1.52081193120006*m.x14 - 0.46689072771182*m.x15
- 1.94473984079535*m.x16 - 1.29889609666036*m.x17 + 0.765366864730183*m.x18
+ 1.99383466746626*m.x19 + 1.0449971294319*m.x20 - 1.0449971294319*m.x21
- 1.99383466746626*m.x22 - 0.765366864730178*m.x23 + 1.29889609666037*m.x24
+ 1.94473984079535*m.x25 + 0.466890727711808*m.x26 - 1.52081193120006*m.x27
- 1.84775906502257*m.x28 - 0.156918191455685*m.x29 + 1.70528032870819*m.x30
+ 1.70528032870818*m.x31 - 0.156918191455689*m.x32 - 1.84775906502258*m.x33
- 1.52081193120006*m.x34 + 0.466890727711811*m.x35 + 1.94473984079535*m.x36
+ 1.29889609666037*m.x37 - 0.765366864730181*m.x38 - 1.99383466746626*m.x39
- 1.0449971294319*m.x40 + 1.0449971294319*m.x41 + 1.99383466746626*m.x42
+ 0.76536686473018*m.x43 - 1.29889609666037*m.x44 - 1.94473984079535*m.x45
- 0.46689072771181*m.x46 + 1.52081193120006*m.x47 + 1.84775906502257*m.x48
+ 0.15691819145569*m.x49 - 1.70528032870818*m.x50 + m.x54 <= 0)
m.c159 = Constraint(expr= - 0.618033988749891*m.x1 + 1.43867960067731*m.x2 + 1.87938524157182*m.x3
+ 0.209056926535308*m.x4 - 1.69609619231285*m.x5 - 1.69609619231285*m.x6
+ 0.209056926535315*m.x7 + 1.87938524157182*m.x8 + 1.4386796006773*m.x9
- 0.618033988749898*m.x10 - 1.98053613748314*m.x11 - 1.1183858069415*m.x12 + m.x13
+ 1.99512810051965*m.x14 + 0.74921318683182*m.x15 - 1.33826121271772*m.x16
- 1.92252339187664*m.x17 - 0.347296355333861*m.x18 + 1.6180339887499*m.x19
+ 1.76589518571785*m.x20 - 0.0697989934049966*m.x21 - 1.8270909152852*m.x22
- 1.53208888623796*m.x23 + 0.483843791199339*m.x24 + 1.95629520146761*m.x25
+ 1.23132295065131*m.x26 - 0.876742293578154*m.x27 - 2*m.x28 - 0.876742293578157*m.x29
+ 1.23132295065132*m.x30 + 1.95629520146761*m.x31 + 0.483843791199335*m.x32
- 1.53208888623796*m.x33 - 1.8270909152852*m.x34 - 0.0697989934049998*m.x35
+ 1.76589518571785*m.x36 + 1.61803398874989*m.x37 - 0.347296355333861*m.x38
- 1.92252339187664*m.x39 - 1.33826121271772*m.x40 + 0.749213186831823*m.x41
+ 1.99512810051965*m.x42 + 0.999999999999998*m.x43 - 1.11838580694149*m.x44
- 1.98053613748314*m.x45 - 0.618033988749894*m.x46 + 1.4386796006773*m.x47
+ 1.87938524157182*m.x48 + 0.209056926535307*m.x49 - 1.69609619231285*m.x50 + m.x54 <= 0)
m.c160 = Constraint(expr= - 1.84775906502257*m.x1 - 0.0872387747306765*m.x2 + 1.77402166635644*m.x3
+ 1.58670668058247*m.x4 - 0.432879227876211*m.x5 - 1.95259201423987*m.x6
- 1.21752285801745*m.x7 + 0.923497226470069*m.x8 + 1.99809644316372*m.x9
+ 0.765366864730177*m.x10 - 1.35118041523132*m.x11 - 1.90743390149645*m.x12
- 0.261052384440105*m.x13 + 1.68678289162577*m.x14 + 1.68678289162577*m.x15
- 0.261052384440097*m.x16 - 1.90743390149645*m.x17 - 1.35118041523132*m.x18
+ 0.765366864730183*m.x19 + 1.99809644316372*m.x20 + 0.923497226470063*m.x21
- 1.21752285801744*m.x22 - 1.95259201423987*m.x23 - 0.432879227876205*m.x24
+ 1.58670668058247*m.x25 + 1.77402166635644*m.x26 - 0.0872387747306687*m.x27
- 1.84775906502257*m.x28 - 1.47455467362025*m.x29 + 0.601411599008546*m.x30
+ 1.98288972274762*m.x31 + 1.07459921669365*m.x32 - 1.07459921669365*m.x33
- 1.98288972274762*m.x34 - 0.601411599008549*m.x35 + 1.47455467362025*m.x36
+ 1.84775906502257*m.x37 + 0.0872387747306726*m.x38 - 1.77402166635644*m.x39
- 1.58670668058247*m.x40 + 0.432879227876204*m.x41 + 1.95259201423987*m.x42
+ 1.21752285801744*m.x43 - 0.923497226470067*m.x44 - 1.99809644316372*m.x45
- 0.76536686473018*m.x46 + 1.35118041523132*m.x47 + 1.90743390149645*m.x48
+ 0.261052384440103*m.x49 - 1.68678289162577*m.x50 + m.x54 <= 0)
m.c161 = Constraint(expr= - 1.78201304837674*m.x1 - 1.55429192291394*m.x2 + 0.517638090205038*m.x3
+ 1.97537668119027*m.x4 + 1.08927807003006*m.x5 - 1.08927807003005*m.x6
- 1.97537668119028*m.x7 - 0.517638090205037*m.x8 + 1.55429192291394*m.x9
+ 1.78201304837673*m.x10 - 0.104671912485888*m.x11 - 1.8671608529944*m.x12
- 1.4142135623731*m.x13 + 0.716735899090598*m.x14 + 1.99725906950915*m.x15
+ 0.907980999479099*m.x16 - 1.25864078209967*m.x17 - 1.93185165257814*m.x18
- 0.312868930080457*m.x19 + 1.67734113589085*m.x20 + 1.67734113589085*m.x21
- 0.312868930080463*m.x22 - 1.93185165257814*m.x23 - 1.25864078209968*m.x24
+ 0.907980999479091*m.x25 + 1.99725906950915*m.x26 + 0.716735899090599*m.x27
- 1.4142135623731*m.x28 - 1.8671608529944*m.x29 - 0.10467191248589*m.x30
+ 1.78201304837673*m.x31 + 1.55429192291394*m.x32 - 0.517638090205042*m.x33
- 1.97537668119028*m.x34 - 1.08927807003006*m.x35 + 1.08927807003005*m.x36
+ 1.97537668119028*m.x37 + 0.517638090205043*m.x38 - 1.55429192291394*m.x39
- 1.78201304837674*m.x40 + 0.104671912485886*m.x41 + 1.8671608529944*m.x42
+ 1.4142135623731*m.x43 - 0.7167358990906*m.x44 - 1.99725906950915*m.x45
- 0.907980999479093*m.x46 + 1.25864078209967*m.x47 + 1.93185165257814*m.x48
+ 0.312868930080462*m.x49 - 1.67734113589085*m.x50 + m.x54 <= 0)
m.c162 = Constraint(expr= - 0.466890727711807*m.x1 - 1.97257120307446*m.x2 - 1.07459921669366*m.x3
+ 1.13281247384966*m.x4 + 1.95984940924166*m.x5 + 0.398735868834394*m.x6
- 1.64825237724403*m.x7 - 1.68678289162577*m.x8 + 0.330095211721353*m.x9
+ 1.94473984079535*m.x10 + 1.18964557350269*m.x11 - 1.0150767259214*m.x12
- 1.98288972274762*m.x13 - 0.534476752156512*m.x14 + 1.56521631370482*m.x15
+ 1.75763422532393*m.x16 - 0.191691505040446*m.x17 - 1.90743390149645*m.x18
- 1.29889609666037*m.x19 + 0.892395626219613*m.x20 + 1.99626959684373*m.x21
+ 0.66761371846754*m.x22 - 1.47455467362024*m.x23 - 1.81992254175309*m.x24
+ 0.0523538966157454*m.x25 + 1.86083513596405*m.x26 + 1.4018185285997*m.x27
- 0.765366864730176*m.x28 - 1.99992384612834*m.x29 - 0.797498137850496*m.x30
+ 1.37670915138751*m.x31 + 1.8733443784968*m.x32 + 0.0872387747306721*m.x33
- 1.80517056869972*m.x34 - 1.497911441578*m.x35 + 0.634609312810181*m.x36
+ 1.99383466746626*m.x37 + 0.923497226470071*m.x38 - 1.27215644055553*m.x39
- 1.91763946973639*m.x40 - 0.226406427535813*m.x41 + 1.7407113918798*m.x42
+ 1.58670668058247*m.x43 - 0.500760008108882*m.x44 - 1.97803172672383*m.x45
- 1.0449971294319*m.x46 + 1.16140591142188*m.x47 + 1.95259201423987*m.x48
+ 0.364471050984295*m.x49 - 1.66777164413434*m.x50 + m.x54 <= 0)
m.c163 = Constraint(expr= 1.17557050458495*m.x1 - 1.0598385284664*m.x2 - 1.96961550602442*m.x3
- 0.415823381635527*m.x4 + 1.65807514511008*m.x5 + 1.65807514511009*m.x6
- 0.415823381635508*m.x7 - 1.96961550602442*m.x8 - 1.05983852846642*m.x9
+ 1.17557050458495*m.x10 + 1.94059145255199*m.x11 + 0.278346201920142*m.x12
- 1.73205080756888*m.x13 - 1.57602150721345*m.x14 + 0.551274711633999*m.x15
+ 1.98904379073655*m.x16 + 0.938943125571791*m.x17 - 1.28557521937308*m.x18
- 1.90211303259031*m.x19 - 0.13951294748825*m.x20 + 1.79758809259833*m.x21
+ 1.4862896509548*m.x22 - 0.684040286651336*m.x23 - 1.99878165403819*m.x24
- 0.813473286151606*m.x25 + 1.38931674091799*m.x26 + 1.85436770913358*m.x27
+ 1.47081412025E-15*m.x28 - 1.85436770913357*m.x29 - 1.389316740918*m.x30
+ 0.813473286151597*m.x31 + 1.99878165403819*m.x32 + 0.684040286651339*m.x33
- 1.48628965095479*m.x34 - 1.79758809259834*m.x35 + 0.139512947488247*m.x36
+ 1.90211303259031*m.x37 + 1.28557521937308*m.x38 - 0.938943125571779*m.x39
- 1.98904379073655*m.x40 - 0.551274711633998*m.x41 + 1.57602150721344*m.x42
+ 1.73205080756888*m.x43 - 0.27834620192013*m.x44 - 1.94059145255199*m.x45
- 1.17557050458495*m.x46 + 1.05983852846641*m.x47 + 1.96961550602442*m.x48
+ 0.415823381635519*m.x49 - 1.65807514511008*m.x50 + m.x54 <= 0)
m.c164 = Constraint(expr= 1.99383466746626*m.x1 + 0.568030689407841*m.x2 - 1.58670668058248*m.x3
- 1.70528032870818*m.x4 + 0.364471050984312*m.x5 + 1.96650981512791*m.x6
+ 1.04499712943188*m.x7 - 1.21752285801745*m.x8 - 1.91763946973638*m.x9
- 0.156918191455676*m.x10 + 1.80517056869973*m.x11 + 1.45074874202457*m.x12
- 0.76536686473019*m.x13 - 1.99931464995111*m.x14 - 0.667613718467532*m.x15
+ 1.52081193120007*m.x16 + 1.75763422532393*m.x17 - 0.261052384440111*m.x18
- 1.94473984079535*m.x19 - 1.13281247384966*m.x20 + 1.13281247384967*m.x21
+ 1.94473984079535*m.x22 + 0.261052384440099*m.x23 - 1.75763422532393*m.x24
- 1.52081193120006*m.x25 + 0.667613718467544*m.x26 + 1.99931464995111*m.x27
+ 0.765366864730172*m.x28 - 1.45074874202458*m.x29 - 1.80517056869972*m.x30
+ 0.156918191455696*m.x31 + 1.91763946973639*m.x32 + 1.21752285801744*m.x33
- 1.0449971294319*m.x34 - 1.96650981512791*m.x35 - 0.364471050984292*m.x36
+ 1.70528032870819*m.x37 + 1.58670668058247*m.x38 - 0.568030689407849*m.x39
- 1.99383466746626*m.x40 - 0.861022193616588*m.x41 + 1.37670915138751*m.x42
+ 1.84775906502257*m.x43 - 0.0523538966157472*m.x44 - 1.88528298218436*m.x45
- 1.29889609666037*m.x46 + 0.954317520519217*m.x47 + 1.98288972274762*m.x48
+ 0.466890727711811*m.x49 - 1.64825237724403*m.x50 + m.x54 <= 0)
m.c165 = Constraint(expr= 1.4142135623731*m.x1 + 1.8126155740733*m.x2 - 0.174311485495328*m.x3 - 1.93185165257814*m.x4
- 1.14715287270209*m.x5 + 1.1471528727021*m.x6 + 1.93185165257813*m.x7
+ 0.174311485495312*m.x8 - 1.8126155740733*m.x9 - 1.41421356237309*m.x10
+ 0.845236523481401*m.x11 + 1.99238939618349*m.x12 + 0.517638090205031*m.x13
- 1.63830408857798*m.x14 - 1.63830408857798*m.x15 + 0.517638090205051*m.x16
+ 1.99238939618349*m.x17 + 0.845236523481395*m.x18 - 1.4142135623731*m.x19
- 1.81261557407329*m.x20 + 0.174311485495319*m.x21 + 1.93185165257814*m.x22
+ 1.14715287270208*m.x23 - 1.14715287270209*m.x24 - 1.93185165257814*m.x25
- 0.174311485495314*m.x26 + 1.8126155740733*m.x27 + 1.41421356237309*m.x28
- 0.8452365234814*m.x29 - 1.99238939618349*m.x30 - 0.517638090205039*m.x31
+ 1.63830408857799*m.x32 + 1.63830408857798*m.x33 - 0.517638090205042*m.x34
- 1.99238939618349*m.x35 - 0.845236523481397*m.x36 + 1.41421356237309*m.x37
+ 1.8126155740733*m.x38 - 0.174311485495317*m.x39 - 1.93185165257814*m.x40
- 1.14715287270209*m.x41 + 1.14715287270209*m.x42 + 1.93185165257814*m.x43
+ 0.174311485495316*m.x44 - 1.8126155740733*m.x45 - 1.41421356237309*m.x46
+ 0.8452365234814*m.x47 + 1.99238939618349*m.x48 + 0.517638090205041*m.x49
- 1.63830408857798*m.x50 + m.x54 <= 0)
m.c166 = Constraint(expr= - 0.15691819145569*m.x1 + 1.83412014877025*m.x2 + 1.35118041523132*m.x3
- 0.954317520519229*m.x4 - 1.97257120307446*m.x5 - 0.330095211721352*m.x6
+ 1.75763422532394*m.x7 + 1.47455467362024*m.x8 - 0.797498137850495*m.x9
- 1.99383466746626*m.x10 - 0.500760008108876*m.x11 + 1.66777164413434*m.x12
+ 1.58670668058246*m.x13 - 0.63460931281019*m.x14 - 1.99992384612834*m.x15
- 0.667613718467532*m.x16 + 1.56521631370483*m.x17 + 1.68678289162577*m.x18
- 0.46689072771182*m.x19 - 1.99079239673436*m.x20 - 0.829386485312479*m.x21
+ 1.45074874202458*m.x22 + 1.77402166635644*m.x23 - 0.29561882225922*m.x24
- 1.96650981512791*m.x25 - 0.984847120206932*m.x26 + 1.32524009643148*m.x27
+ 1.84775906502257*m.x28 - 0.122097079069715*m.x29 - 1.92726090641725*m.x30
- 1.13281247384966*m.x31 + 1.18964557350268*m.x32 + 1.90743390149645*m.x33
+ 0.0523538966157415*m.x34 - 1.8733443784968*m.x35 - 1.27215644055553*m.x36
+ 1.0449971294319*m.x37 + 1.95259201423987*m.x38 + 0.226406427535812*m.x39
- 1.80517056869972*m.x40 - 1.4018185285997*m.x41 + 0.892395626219618*m.x42
+ 1.98288972274762*m.x43 + 0.398735868834394*m.x44 - 1.72325832088305*m.x45
- 1.52081193120006*m.x46 + 0.733002453448594*m.x47 + 1.99809644316372*m.x48
+ 0.568030689407845*m.x49 - 1.62823103671264*m.x50 + m.x54 <= 0)
m.c167 = Constraint(expr= - 1.61803398874989*m.x1 + 0.618033988749893*m.x2 + 2*m.x3 + 0.618033988749897*m.x4
- 1.6180339887499*m.x5 - 1.6180339887499*m.x6 + 0.618033988749906*m.x7 + 2*m.x8
+ 0.618033988749884*m.x9 - 1.61803398874989*m.x10 - 1.61803398874989*m.x11
+ 0.618033988749892*m.x12 + 2*m.x13 + 0.618033988749898*m.x14 - 1.6180339887499*m.x15
- 1.6180339887499*m.x16 + 0.618033988749905*m.x17 + 2*m.x18 + 0.618033988749885*m.x19
- 1.61803398874989*m.x20 - 1.61803398874989*m.x21 + 0.618033988749891*m.x22 + 2*m.x23
+ 0.618033988749899*m.x24 - 1.6180339887499*m.x25 - 1.61803398874989*m.x26
+ 0.618033988749897*m.x27 + 2*m.x28 + 0.618033988749893*m.x29 - 1.6180339887499*m.x30
- 1.61803398874989*m.x31 + 0.618033988749897*m.x32 + 2*m.x33 + 0.618033988749893*m.x34
- 1.6180339887499*m.x35 - 1.61803398874989*m.x36 + 0.618033988749896*m.x37 + 2*m.x38
+ 0.618033988749894*m.x39 - 1.6180339887499*m.x40 - 1.61803398874989*m.x41
+ 0.618033988749896*m.x42 + 2*m.x43 + 0.618033988749894*m.x44 - 1.6180339887499*m.x45
- 1.61803398874989*m.x46 + 0.618033988749895*m.x47 + 2*m.x48 + 0.618033988749895*m.x49
- 1.61803398874989*m.x50 + m.x54 <= 0)
m.c168 = Constraint(expr= - 1.94473984079536*m.x1 - 1.01507672592141*m.x2 + 1.35118041523132*m.x3
+ 1.80517056869972*m.x4 - 0.295618822259222*m.x5 - 1.97803172672383*m.x6
- 0.861022193616594*m.x7 + 1.47455467362025*m.x8 + 1.72325832088305*m.x9
- 0.466890727711807*m.x10 - 1.99626959684373*m.x11 - 0.70041476251893*m.x12
+ 1.58670668058247*m.x13 + 1.62823103671264*m.x14 - 0.63460931281019*m.x15
- 1.99931464995111*m.x16 - 0.534476752156512*m.x17 + 1.68678289162577*m.x18
+ 1.52081193120006*m.x19 - 0.797498137850494*m.x20 - 1.98714371135317*m.x21
- 0.364471050984298*m.x22 + 1.77402166635644*m.x23 + 1.4018185285997*m.x24
- 0.954317520519215*m.x25 - 1.95984940924166*m.x26 - 0.191691505040448*m.x27
+ 1.84775906502257*m.x28 + 1.27215644055553*m.x29 - 1.10387397062412*m.x30
- 1.91763946973639*m.x31 - 0.0174530709967449*m.x32 + 1.90743390149645*m.x33
+ 1.13281247384967*m.x34 - 1.24502927327524*m.x35 - 1.86083513596405*m.x36
+ 0.156918191455689*m.x37 + 1.95259201423987*m.x38 + 0.984847120206933*m.x39
- 1.37670915138751*m.x40 - 1.78986872320405*m.x41 + 0.330095211721357*m.x42
+ 1.98288972274762*m.x43 + 0.829386485312478*m.x44 - 1.497911441578*m.x45
- 1.70528032870818*m.x46 + 0.500760008108884*m.x47 + 1.99809644316372*m.x48
+ 0.667613718467542*m.x49 - 1.60771372123443*m.x50 + m.x54 <= 0)
m.c169 = Constraint(expr= - 0.9079809994791*m.x1 - 1.96325436689533*m.x2 - 0.174311485495318*m.x3 + 1.8671608529944*m.x4
+ 1.20363004630409*m.x5 - 1.2036300463041*m.x6 - 1.86716085299441*m.x7 + 0.17431148549532*m.x8
+ 1.96325436689533*m.x9 + 0.907980999479098*m.x10 - 1.46270740323834*m.x11
- 1.71433460140422*m.x12 + 0.517638090205038*m.x13 + 1.99969539031278*m.x14
+ 0.584743409445472*m.x15 - 1.67734113589085*m.x16 - 1.50941916044554*m.x17
+ 0.845236523481401*m.x18 + 1.97537668119028*m.x19 + 0.243738686810302*m.x20
- 1.84100970690488*m.x21 - 1.25864078209968*m.x22 + 1.14715287270209*m.x23
+ 1.89103715119863*m.x24 - 0.104671912485887*m.x25 - 1.94874012957047*m.x26
- 0.969619240492676*m.x27 + 1.4142135623731*m.x28 + 1.74923941427879*m.x29
- 0.449902108687729*m.x30 - 1.99725906950915*m.x31 - 0.651136308914316*m.x32
+ 1.63830408857798*m.x33 + 1.55429192291394*m.x34 - 0.781462256978546*m.x35
- 1.98509230328264*m.x36 - 0.312868930080459*m.x37 + 1.8126155740733*m.x38
+ 1.31211805798101*m.x39 - 1.08927807003005*m.x40 - 1.91260951192607*m.x41
+ 0.0349048128745657*m.x42 + 1.93185165257814*m.x43 + 1.03007614982011*m.x44
- 1.363996720125*m.x45 - 1.78201304837674*m.x46 + 0.381617990753089*m.x47
+ 1.99238939618349*m.x48 + 0.716735899090601*m.x49 - 1.59727102009459*m.x50 + m.x54 <= 0)
m.c170 = Constraint(expr= 0.765366864730189*m.x1 - 1.58670668058247*m.x2 - 1.58670668058248*m.x3
+ 0.765366864730172*m.x4 + 1.98288972274762*m.x5 + 0.261052384440111*m.x6
- 1.84775906502257*m.x7 - 1.21752285801745*m.x8 + 1.21752285801744*m.x9
+ 1.84775906502258*m.x10 - 0.261052384440098*m.x11 - 1.98288972274762*m.x12
- 0.765366864730183*m.x13 + 1.58670668058247*m.x14 + 1.58670668058247*m.x15
- 0.765366864730177*m.x16 - 1.98288972274762*m.x17 - 0.261052384440105*m.x18
+ 1.84775906502257*m.x19 + 1.21752285801744*m.x20 - 1.21752285801744*m.x21
- 1.84775906502257*m.x22 + 0.261052384440104*m.x23 + 1.98288972274762*m.x24
+ 0.765366864730178*m.x25 - 1.58670668058247*m.x26 - 1.58670668058247*m.x27
+ 0.765366864730176*m.x28 + 1.98288972274762*m.x29 + 0.261052384440106*m.x30
- 1.84775906502257*m.x31 - 1.21752285801744*m.x32 + 1.21752285801744*m.x33
+ 1.84775906502257*m.x34 - 0.261052384440103*m.x35 - 1.98288972274762*m.x36
- 0.765366864730179*m.x37 + 1.58670668058247*m.x38 + 1.58670668058247*m.x39
- 0.765366864730178*m.x40 - 1.98288972274762*m.x41 - 0.261052384440104*m.x42
+ 1.84775906502257*m.x43 + 1.21752285801744*m.x44 - 1.21752285801744*m.x45
- 1.84775906502257*m.x46 + 0.261052384440103*m.x47 + 1.98288972274762*m.x48
+ 0.765366864730179*m.x49 - 1.58670668058247*m.x50 + m.x54 <= 0)
m.c171 = Constraint(expr= 1.90211303259031*m.x1 - 0.139512947488258*m.x2 - 1.96961550602442*m.x3
- 0.81347328615161*m.x4 + 1.57602150721344*m.x5 + 1.57602150721344*m.x6
- 0.813473286151593*m.x7 - 1.96961550602442*m.x8 - 0.139512947488248*m.x9
+ 1.90211303259031*m.x10 + 1.05983852846641*m.x11 - 1.38931674091799*m.x12
- 1.73205080756888*m.x13 + 0.551274711633999*m.x14 + 1.99878165403819*m.x15
+ 0.415823381635521*m.x16 - 1.79758809259834*m.x17 - 1.28557521937308*m.x18
+ 1.17557050458495*m.x19 + 1.85436770913358*m.x20 - 0.278346201920127*m.x21
- 1.98904379073655*m.x22 - 0.684040286651344*m.x23 + 1.65807514511008*m.x24
+ 1.48628965095479*m.x25 - 0.938943125571777*m.x26 - 1.94059145255199*m.x27
- 1.22588476042053E-15*m.x28 + 1.94059145255199*m.x29 + 0.938943125571786*m.x30
- 1.48628965095479*m.x31 - 1.65807514511008*m.x32 + 0.684040286651335*m.x33
+ 1.98904379073655*m.x34 + 0.27834620192013*m.x35 - 1.85436770913357*m.x36
- 1.17557050458495*m.x37 + 1.28557521937308*m.x38 + 1.79758809259833*m.x39
- 0.415823381635519*m.x40 - 1.99878165403819*m.x41 - 0.551274711633998*m.x42
+ 1.73205080756888*m.x43 + 1.38931674091799*m.x44 - 1.05983852846641*m.x45
- 1.90211303259031*m.x46 + 0.139512947488249*m.x47 + 1.96961550602442*m.x48
+ 0.813473286151601*m.x49 - 1.57602150721344*m.x50 + m.x54 <= 0)
m.c172 = Constraint(expr= 1.70528032870818*m.x1 + 1.40181852859971*m.x2 - 1.07459921669364*m.x3
- 1.88528298218436*m.x4 + 0.226406427535811*m.x5 + 1.98714371135317*m.x6
+ 0.667613718467551*m.x7 - 1.68678289162577*m.x8 - 1.42650089830837*m.x9
+ 1.04499712943189*m.x10 + 1.8966473104124*m.x11 - 0.191691505040447*m.x12
- 1.98288972274762*m.x13 - 0.700414762518943*m.x14 + 1.66777164413434*m.x15
+ 1.45074874202458*m.x16 - 1.0150767259214*m.x17 - 1.90743390149646*m.x18
+ 0.156918191455691*m.x19 + 1.97803172672383*m.x20 + 0.733002453448601*m.x21
- 1.64825237724403*m.x22 - 1.47455467362025*m.x23 + 0.98484712020693*m.x24
+ 1.91763946973639*m.x25 - 0.122097079069716*m.x26 - 1.97257120307446*m.x27
- 0.765366864730185*m.x28 + 1.62823103671264*m.x29 + 1.49791144157801*m.x30
- 0.954317520519214*m.x31 - 1.92726090641725*m.x32 + 0.0872387747306684*m.x33
+ 1.96650981512791*m.x34 + 0.797498137850496*m.x35 - 1.60771372123443*m.x36
- 1.52081193120006*m.x37 + 0.923497226470066*m.x38 + 1.93629528075622*m.x39
- 0.0523538966157441*m.x40 - 1.95984940924166*m.x41 - 0.829386485312481*m.x42
+ 1.58670668058247*m.x43 + 1.54324916677544*m.x44 - 0.892395626219618*m.x45
- 1.94473984079535*m.x46 + 0.0174530709967472*m.x47 + 1.95259201423987*m.x48
+ 0.861022193616591*m.x49 - 1.56521631370483*m.x50 + m.x54 <= 0)
m.c173 = Constraint(expr= 0.312868930080486*m.x1 + 1.99725906950915*m.x2 + 0.517638090205043*m.x3
- 1.78201304837674*m.x4 - 1.25864078209966*m.x5 + 1.25864078209969*m.x6
+ 1.78201304837673*m.x7 - 0.517638090205045*m.x8 - 1.99725906950915*m.x9
- 0.312868930080456*m.x10 + 1.86716085299441*m.x11 + 1.08927807003005*m.x12
- 1.4142135623731*m.x13 - 1.67734113589084*m.x14 + 0.716735899090611*m.x15
+ 1.97537668119027*m.x16 + 0.104671912485874*m.x17 - 1.93185165257814*m.x18
- 0.907980999479092*m.x19 + 1.55429192291394*m.x20 + 1.55429192291394*m.x21
- 0.907980999479098*m.x22 - 1.93185165257814*m.x23 + 0.104671912485895*m.x24
+ 1.97537668119028*m.x25 + 0.716735899090592*m.x26 - 1.67734113589085*m.x27
- 1.41421356237309*m.x28 + 1.08927807003006*m.x29 + 1.8671608529944*m.x30
- 0.312868930080469*m.x31 - 1.99725906950915*m.x32 - 0.517638090205039*m.x33
+ 1.78201304837674*m.x34 + 1.25864078209967*m.x35 - 1.25864078209968*m.x36
- 1.78201304837673*m.x37 + 0.517638090205042*m.x38 + 1.99725906950915*m.x39
+ 0.312868930080459*m.x40 - 1.8671608529944*m.x41 - 1.08927807003005*m.x42
+ 1.4142135623731*m.x43 + 1.67734113589085*m.x44 - 0.716735899090602*m.x45
- 1.97537668119028*m.x46 - 0.104671912485886*m.x47 + 1.93185165257814*m.x48
+ 0.907980999479093*m.x49 - 1.55429192291394*m.x50 + m.x54 <= 0)
m.c174 = Constraint(expr= - 1.29889609666036*m.x1 + 1.24502927327524*m.x2 + 1.77402166635643*m.x3
- 0.568030689407857*m.x4 - 1.99079239673436*m.x5 - 0.191691505040438*m.x6
+ 1.91763946973639*m.x7 + 0.923497226470061*m.x8 - 1.56521631370484*m.x9
- 1.52081193120006*m.x10 + 0.984847120206944*m.x11 + 1.8966473104124*m.x12
- 0.261052384440112*m.x13 - 1.99626959684373*m.x14 - 0.500760008108876*m.x15
+ 1.80517056869973*m.x16 + 1.18964557350268*m.x17 - 1.35118041523133*m.x18
- 1.70528032870818*m.x19 + 0.700414762518943*m.x20 + 1.97257120307446*m.x21
+ 0.05235389661574*m.x22 - 1.95259201423987*m.x23 - 0.797498137850489*m.x24
+ 1.64825237724404*m.x25 + 1.42650089830836*m.x26 - 1.10387397062412*m.x27
- 1.84775906502257*m.x28 + 0.3987358688344*m.x29 + 1.99992384612834*m.x30
+ 0.364471050984291*m.x31 - 1.86083513596405*m.x32 - 1.07459921669365*m.x33
+ 1.45074874202458*m.x34 + 1.62823103671263*m.x35 - 0.829386485312483*m.x36
- 1.94473984079535*m.x37 + 0.087238774730675*m.x38 + 1.97803172672383*m.x39
+ 0.667613718467541*m.x40 - 1.72325832088305*m.x41 - 1.32524009643147*m.x42
+ 1.21752285801744*m.x43 + 1.78986872320405*m.x44 - 0.534476752156514*m.x45
- 1.99383466746626*m.x46 - 0.226406427535813*m.x47 + 1.90743390149645*m.x48
+ 0.954317520519217*m.x49 - 1.54324916677544*m.x50 + m.x54 <= 0)
m.c175 = Constraint(expr= - 2*m.x1 - 0.347296355333845*m.x2 + 1.87938524157182*m.x3 + 0.999999999999992*m.x4
- 1.53208888623796*m.x5 - 1.53208888623795*m.x6 + m.x7 + 1.87938524157181*m.x8
- 0.347296355333868*m.x9 - 2*m.x10 - 0.34729635533386*m.x11 + 1.87938524157182*m.x12
+ 0.999999999999992*m.x13 - 1.53208888623796*m.x14 - 1.53208888623795*m.x15 + m.x16
+ 1.87938524157181*m.x17 - 0.347296355333867*m.x18 - 2*m.x19 - 0.347296355333861*m.x20
+ 1.87938524157182*m.x21 + 0.999999999999993*m.x22 - 1.53208888623796*m.x23
- 1.53208888623796*m.x24 + 0.999999999999998*m.x25 + 1.87938524157181*m.x26
- 0.347296355333866*m.x27 - 2*m.x28 - 0.347296355333855*m.x29 + 1.87938524157182*m.x30 + m.x31
- 1.53208888623796*m.x32 - 1.53208888623796*m.x33 + m.x34 + 1.87938524157182*m.x35
- 0.347296355333865*m.x36 - 2*m.x37 - 0.347296355333855*m.x38 + 1.87938524157182*m.x39
+ 0.999999999999998*m.x40 - 1.53208888623796*m.x41 - 1.53208888623795*m.x42 + m.x43
+ 1.87938524157182*m.x44 - 0.347296355333861*m.x45 - 2*m.x46 - 0.34729635533386*m.x47
+ 1.87938524157182*m.x48 + m.x49 - 1.53208888623796*m.x50 + m.x54 <= 0)
m.c176 = Constraint(expr= - 1.29889609666037*m.x1 - 1.70528032870818*m.x2 + 0.765366864730185*m.x3
+ 1.94473984079535*m.x4 - 0.156918191455699*m.x5 - 1.99383466746626*m.x6
- 0.466890727711812*m.x7 + 1.84775906502257*m.x8 + 1.0449971294319*m.x9
- 1.52081193120006*m.x10 - 1.52081193120006*m.x11 + 1.0449971294319*m.x12
+ 1.84775906502257*m.x13 - 0.466890727711821*m.x14 - 1.99383466746626*m.x15
- 0.156918191455691*m.x16 + 1.94473984079535*m.x17 + 0.765366864730177*m.x18
- 1.70528032870819*m.x19 - 1.29889609666036*m.x20 + 1.29889609666037*m.x21
+ 1.70528032870818*m.x22 - 0.765366864730177*m.x23 - 1.94473984079535*m.x24
+ 0.15691819145569*m.x25 + 1.99383466746626*m.x26 + 0.466890727711807*m.x27
- 1.84775906502258*m.x28 - 1.0449971294319*m.x29 + 1.52081193120006*m.x30
+ 1.52081193120006*m.x31 - 1.0449971294319*m.x32 - 1.84775906502257*m.x33
+ 0.466890727711812*m.x34 + 1.99383466746626*m.x35 + 0.156918191455686*m.x36
- 1.94473984079535*m.x37 - 0.765366864730179*m.x38 + 1.70528032870819*m.x39
+ 1.29889609666037*m.x40 - 1.29889609666037*m.x41 - 1.70528032870818*m.x42
+ 0.765366864730181*m.x43 + 1.94473984079535*m.x44 - 0.156918191455691*m.x45
- 1.99383466746626*m.x46 - 0.46689072771181*m.x47 + 1.84775906502257*m.x48
+ 1.0449971294319*m.x49 - 1.52081193120006*m.x50 + m.x54 <= 0)
m.c177 = Constraint(expr= 0.312868930080448*m.x1 - 1.91260951192607*m.x2 - 0.8452365234814*m.x3
+ 1.67734113589085*m.x4 + 1.31211805798101*m.x5 - 1.31211805798102*m.x6
- 1.67734113589085*m.x7 + 0.845236523481402*m.x8 + 1.91260951192607*m.x9
- 0.312868930080464*m.x10 - 1.99969539031278*m.x11 - 0.243738686810293*m.x12
+ 1.93185165257814*m.x13 + 0.781462256978547*m.x14 - 1.71433460140423*m.x15
- 1.25864078209968*m.x16 + 1.363996720125*m.x17 + 1.63830408857798*m.x18
- 0.907980999479098*m.x19 - 1.89103715119863*m.x20 + 0.381617990753094*m.x21
+ 1.99725906950915*m.x22 + 0.174311485495313*m.x23 - 1.94874012957047*m.x24
- 0.716735899090598*m.x25 + 1.74923941427879*m.x26 + 1.2036300463041*m.x27
- 1.4142135623731*m.x28 - 1.59727102009459*m.x29 + 0.969619240492674*m.x30
+ 1.8671608529944*m.x31 - 0.449902108687729*m.x32 - 1.99238939618349*m.x33
- 0.10467191248589*m.x34 + 1.96325436689533*m.x35 + 0.65113630891431*m.x36
- 1.78201304837674*m.x37 - 1.14715287270209*m.x38 + 1.46270740323834*m.x39
+ 1.55429192291394*m.x40 - 1.03007614982011*m.x41 - 1.84100970690488*m.x42
+ 0.517638090205042*m.x43 + 1.98509230328264*m.x44 + 0.034904812874566*m.x45
- 1.97537668119028*m.x46 - 0.584743409445473*m.x47 + 1.8126155740733*m.x48
+ 1.08927807003005*m.x49 - 1.50941916044554*m.x50 + m.x54 <= 0)
m.c178 = Constraint(expr= 1.70528032870819*m.x1 - 0.829386485312486*m.x2 - 1.90743390149646*m.x3
+ 0.364471050984299*m.x4 + 1.99626959684373*m.x5 + 0.122097079069715*m.x6
- 1.96650981512791*m.x7 - 0.601411599008539*m.x8 + 1.81992254175309*m.x9
+ 1.0449971294319*m.x10 - 1.56521631370483*m.x11 - 1.42650089830837*m.x12
+ 1.21752285801744*m.x13 + 1.72325832088305*m.x14 - 0.797498137850495*m.x15
- 1.91763946973639*m.x16 + 0.330095211721353*m.x17 + 1.99809644316372*m.x18
+ 0.156918191455684*m.x19 - 1.95984940924166*m.x20 - 0.634609312810183*m.x21
+ 1.80517056869972*m.x22 + 1.07459921669365*m.x23 - 1.54324916677544*m.x24
- 1.45074874202457*m.x25 + 1.18964557350268*m.x26 + 1.7407113918798*m.x27
- 0.765366864730176*m.x28 - 1.92726090641725*m.x29 + 0.295618822259219*m.x30
+ 1.99931464995111*m.x31 + 0.191691505040448*m.x32 - 1.95259201423987*m.x33
- 0.66761371846754*m.x34 + 1.78986872320405*m.x35 + 1.10387397062411*m.x36
- 1.52081193120006*m.x37 - 1.47455467362025*m.x38 + 1.16140591142188*m.x39
+ 1.75763422532393*m.x40 - 0.733002453448595*m.x41 - 1.93629528075622*m.x42
+ 0.261052384440102*m.x43 + 1.99992384612834*m.x44 + 0.226406427535813*m.x45
- 1.94473984079535*m.x46 - 0.700414762518934*m.x47 + 1.77402166635644*m.x48
+ 1.13281247384967*m.x49 - 1.497911441578*m.x50 + m.x54 <= 0)
m.c179 = Constraint(expr= 1.90211303259031*m.x1 + 0.813473286151603*m.x2 - 1.73205080756887*m.x3
- 1.17557050458494*m.x4 + 1.48628965095478*m.x5 + 1.48628965095478*m.x6
- 1.17557050458494*m.x7 - 1.73205080756888*m.x8 + 0.813473286151599*m.x9
+ 1.90211303259031*m.x10 - 0.415823381635522*m.x11 - 1.98904379073655*m.x12
- 6.37187723938577E-15*m.x13 + 1.98904379073655*m.x14 + 0.415823381635521*m.x15
- 1.90211303259031*m.x16 - 0.813473286151598*m.x17 + 1.73205080756888*m.x18
+ 1.17557050458495*m.x19 - 1.48628965095479*m.x20 - 1.48628965095479*m.x21
+ 1.17557050458495*m.x22 + 1.73205080756888*m.x23 - 0.813473286151604*m.x24
- 1.90211303259031*m.x25 + 0.415823381635513*m.x26 + 1.98904379073655*m.x27
+ 9.80955400591059E-16*m.x28 - 1.98904379073655*m.x29 - 0.415823381635522*m.x30
+ 1.90211303259031*m.x31 + 0.8134732861516*m.x32 - 1.73205080756888*m.x33
- 1.17557050458495*m.x34 + 1.48628965095479*m.x35 + 1.48628965095479*m.x36
- 1.17557050458494*m.x37 - 1.73205080756888*m.x38 + 0.813473286151603*m.x39
+ 1.90211303259031*m.x40 - 0.415823381635519*m.x41 - 1.98904379073655*m.x42
+ 8.57252759403147E-16*m.x43 + 1.98904379073655*m.x44 + 0.41582338163552*m.x45
- 1.90211303259031*m.x46 - 0.813473286151601*m.x47 + 1.73205080756888*m.x48
+ 1.17557050458495*m.x49 - 1.48628965095479*m.x50 + m.x54 <= 0)
m.c180 = Constraint(expr= 0.765366864730173*m.x1 + 1.90743390149646*m.x2 - 0.432879227876212*m.x3
- 1.98288972274762*m.x4 + 0.0872387747306647*m.x5 + 1.99809644316371*m.x6
+ 0.261052384440096*m.x7 - 1.95259201423987*m.x8 - 0.601411599008553*m.x9
+ 1.84775906502257*m.x10 + 0.923497226470074*m.x11 - 1.68678289162577*m.x12
- 1.21752285801745*m.x13 + 1.47455467362025*m.x14 + 1.47455467362025*m.x15
- 1.21752285801744*m.x16 - 1.68678289162577*m.x17 + 0.923497226470069*m.x18
+ 1.84775906502258*m.x19 - 0.601411599008547*m.x20 - 1.95259201423987*m.x21
+ 0.261052384440104*m.x22 + 1.99809644316372*m.x23 + 0.0872387747306706*m.x24
- 1.98288972274762*m.x25 - 0.432879227876204*m.x26 + 1.90743390149645*m.x27
+ 0.765366864730178*m.x28 - 1.77402166635644*m.x29 - 1.07459921669365*m.x30
+ 1.58670668058247*m.x31 + 1.35118041523132*m.x32 - 1.35118041523132*m.x33
- 1.58670668058247*m.x34 + 1.07459921669364*m.x35 + 1.77402166635644*m.x36
- 0.765366864730175*m.x37 - 1.90743390149645*m.x38 + 0.432879227876201*m.x39
+ 1.98288972274762*m.x40 - 0.0872387747306712*m.x41 - 1.99809644316372*m.x42
- 0.261052384440104*m.x43 + 1.95259201423987*m.x44 + 0.601411599008547*m.x45
- 1.84775906502257*m.x46 - 0.923497226470068*m.x47 + 1.68678289162577*m.x48
+ 1.21752285801744*m.x49 - 1.47455467362025*m.x50 + m.x54 <= 0)
m.c181 = Constraint(expr= - 0.90798099947909*m.x1 + 1.71433460140423*m.x2 + 1.14715287270209*m.x3
- 1.55429192291394*m.x4 - 1.363996720125*m.x5 + 1.36399672012499*m.x6 + 1.55429192291395*m.x7
- 1.14715287270208*m.x8 - 1.71433460140422*m.x9 + 0.907980999479093*m.x10
+ 1.84100970690488*m.x11 - 0.651136308914309*m.x12 - 1.93185165257814*m.x13
+ 0.381617990753081*m.x14 + 1.98509230328265*m.x15 - 0.104671912485889*m.x16
- 1.99969539031278*m.x17 - 0.174311485495319*m.x18 + 1.97537668119027*m.x19
+ 0.449902108687737*m.x20 - 1.91260951192607*m.x21 - 0.716735899090598*m.x22
+ 1.8126155740733*m.x23 + 0.969619240492675*m.x24 - 1.67734113589085*m.x25
- 1.2036300463041*m.x26 + 1.50941916044554*m.x27 + 1.4142135623731*m.x28
- 1.31211805798102*m.x29 - 1.59727102009459*m.x30 + 1.08927807003005*m.x31
+ 1.74923941427879*m.x32 - 0.8452365234814*m.x33 - 1.8671608529944*m.x34
+ 0.58474340944547*m.x35 + 1.94874012957047*m.x36 - 0.312868930080462*m.x37
- 1.99238939618349*m.x38 + 0.0349048128745626*m.x39 + 1.99725906950915*m.x40
+ 0.243738686810296*m.x41 - 1.96325436689533*m.x42 - 0.517638090205044*m.x43
+ 1.89103715119863*m.x44 + 0.781462256978547*m.x45 - 1.78201304837674*m.x46
- 1.03007614982011*m.x47 + 1.63830408857798*m.x48 + 1.25864078209968*m.x49
- 1.46270740323834*m.x50 + m.x54 <= 0)
m.c182 = Constraint(expr= - 1.94473984079535*m.x1 + 0.364471050984313*m.x2 + 1.98288972274762*m.x3
- 0.156918191455714*m.x4 - 1.99931464995111*m.x5 - 0.0523538966157449*m.x6
+ 1.99383466746626*m.x7 + 0.261052384440096*m.x8 - 1.96650981512791*m.x9
- 0.466890727711798*m.x10 + 1.91763946973639*m.x11 + 0.667613718467538*m.x12
- 1.84775906502258*m.x13 - 0.861022193616581*m.x14 + 1.75763422532394*m.x15
+ 1.04499712943188*m.x16 - 1.64825237724403*m.x17 - 1.21752285801744*m.x18
+ 1.52081193120007*m.x19 + 1.3767091513875*m.x20 - 1.37670915138751*m.x21
- 1.52081193120006*m.x22 + 1.21752285801745*m.x23 + 1.64825237724403*m.x24
- 1.04499712943191*m.x25 - 1.75763422532393*m.x26 + 0.861022193616594*m.x27
+ 1.84775906502257*m.x28 - 0.667613718467551*m.x29 - 1.91763946973638*m.x30
+ 0.466890727711819*m.x31 + 1.96650981512791*m.x32 - 0.26105238444011*m.x33
- 1.99383466746626*m.x34 + 0.052353896615752*m.x35 + 1.99931464995111*m.x36
+ 0.156918191455686*m.x37 - 1.98288972274762*m.x38 - 0.364471050984292*m.x39
+ 1.94473984079535*m.x40 + 0.568030689407844*m.x41 - 1.88528298218436*m.x42
- 0.765366864730176*m.x43 + 1.80517056869972*m.x44 + 0.954317520519215*m.x45
- 1.70528032870819*m.x46 - 1.13281247384966*m.x47 + 1.58670668058247*m.x48
+ 1.29889609666037*m.x49 - 1.45074874202458*m.x50 + m.x54 <= 0)
m.c183 = Constraint(expr= - 1.61803398874989*m.x1 - 1.23132295065131*m.x2 + 1.53208888623797*m.x3 + 1.3382612127177*m.x4
- 1.4386796006773*m.x5 - 1.4386796006773*m.x6 + 1.33826121271773*m.x7 + 1.53208888623796*m.x8
- 1.23132295065132*m.x9 - 1.61803398874989*m.x10 + 1.1183858069415*m.x11
+ 1.69609619231284*m.x12 - 1.00000000000001*m.x13 - 1.76589518571785*m.x14
+ 0.876742293578162*m.x15 + 1.8270909152852*m.x16 - 0.749213186831833*m.x17
- 1.87938524157182*m.x18 + 0.618033988749905*m.x19 + 1.92252339187664*m.x20
- 0.483843791199347*m.x21 - 1.95629520146761*m.x22 + 0.34729635533386*m.x23
+ 1.98053613748314*m.x24 - 0.209056926535308*m.x25 - 1.99512810051965*m.x26
+ 0.069798993405004*m.x27 + 2*m.x28 + 0.0697989934049986*m.x29 - 1.99512810051965*m.x30
- 0.209056926535302*m.x31 + 1.98053613748314*m.x32 + 0.347296355333855*m.x33
- 1.95629520146761*m.x34 - 0.483843791199335*m.x35 + 1.92252339187664*m.x36
+ 0.618033988749893*m.x37 - 1.87938524157182*m.x38 - 0.749213186831821*m.x39
+ 1.8270909152852*m.x40 + 0.876742293578154*m.x41 - 1.76589518571785*m.x42
- 0.999999999999998*m.x43 + 1.69609619231285*m.x44 + 1.11838580694149*m.x45
- 1.6180339887499*m.x46 - 1.23132295065132*m.x47 + 1.53208888623796*m.x48
+ 1.33826121271772*m.x49 - 1.4386796006773*m.x50 + m.x54 <= 0)
m.c184 = Constraint(expr= - 0.1569181914557*m.x1 - 1.99626959684373*m.x2 + 0.0872387747306794*m.x3
+ 1.99931464995111*m.x4 - 0.0174530709967523*m.x5 - 1.99992384612834*m.x6
- 0.0523538966157449*m.x7 + 1.99809644316372*m.x8 + 0.122097079069715*m.x9
- 1.99383466746626*m.x10 - 0.191691505040438*m.x11 + 1.98714371135318*m.x12
+ 0.261052384440097*m.x13 - 1.97803172672383*m.x14 - 0.330095211721352*m.x15
+ 1.96650981512791*m.x16 + 0.398735868834394*m.x17 - 1.95259201423987*m.x18
- 0.466890727711799*m.x19 + 1.93629528075622*m.x20 + 0.534476752156505*m.x21
- 1.91763946973639*m.x22 - 0.601411599008541*m.x23 + 1.8966473104124*m.x24
+ 0.667613718467539*m.x25 - 1.8733443784968*m.x26 - 0.733002453448595*m.x27
+ 1.84775906502257*m.x28 + 0.797498137850495*m.x29 - 1.81992254175309*m.x30
- 0.861022193616589*m.x31 + 1.78986872320405*m.x32 + 0.923497226470063*m.x33
- 1.75763422532393*m.x34 - 0.984847120206933*m.x35 + 1.72325832088305*m.x36
+ 1.0449971294319*m.x37 - 1.68678289162577*m.x38 - 1.10387397062411*m.x39
+ 1.64825237724403*m.x40 + 1.16140591142188*m.x41 - 1.60771372123444*m.x42
- 1.21752285801744*m.x43 + 1.56521631370483*m.x44 + 1.27215644055553*m.x45
- 1.52081193120006*m.x46 - 1.32524009643148*m.x47 + 1.47455467362025*m.x48
+ 1.37670915138751*m.x49 - 1.42650089830836*m.x50 + m.x54 <= 0)
m.c185 = Constraint(expr= 1.4142135623731*m.x1 - 1.4142135623731*m.x2 - 1.41421356237309*m.x3 + 1.41421356237311*m.x4
+ 1.41421356237309*m.x5 - 1.41421356237309*m.x6 - 1.41421356237308*m.x7 + 1.4142135623731*m.x8
+ 1.4142135623731*m.x9 - 1.4142135623731*m.x10 - 1.4142135623731*m.x11 + 1.4142135623731*m.x12
+ 1.41421356237309*m.x13 - 1.4142135623731*m.x14 - 1.4142135623731*m.x15
+ 1.4142135623731*m.x16 + 1.41421356237309*m.x17 - 1.4142135623731*m.x18
- 1.4142135623731*m.x19 + 1.4142135623731*m.x20 + 1.41421356237309*m.x21
- 1.4142135623731*m.x22 - 1.4142135623731*m.x23 + 1.4142135623731*m.x24
+ 1.41421356237309*m.x25 - 1.4142135623731*m.x26 - 1.4142135623731*m.x27
+ 1.4142135623731*m.x28 + 1.41421356237309*m.x29 - 1.4142135623731*m.x30
- 1.41421356237309*m.x31 + 1.4142135623731*m.x32 + 1.41421356237309*m.x33
- 1.4142135623731*m.x34 - 1.41421356237309*m.x35 + 1.4142135623731*m.x36
+ 1.41421356237309*m.x37 - 1.41421356237309*m.x38 - 1.41421356237309*m.x39
+ 1.41421356237309*m.x40 + 1.41421356237309*m.x41 - 1.41421356237309*m.x42
- 1.41421356237309*m.x43 + 1.41421356237309*m.x44 + 1.41421356237309*m.x45
- 1.4142135623731*m.x46 - 1.41421356237309*m.x47 + 1.4142135623731*m.x48
+ 1.41421356237309*m.x49 - 1.4142135623731*m.x50 + m.x54 <= 0)
m.c186 = Constraint(expr= 2*m.x1 - 1.00000000000002*m.x2 - 0.999999999999983*m.x3 + 2*m.x4 - 1.00000000000001*m.x5
- 0.999999999999971*m.x6 + 2*m.x7 - 1.00000000000002*m.x8 - 0.999999999999984*m.x9 + 2*m.x10
- 1.00000000000001*m.x11 - 0.999999999999997*m.x12 + 2*m.x13 - 1.00000000000002*m.x14
- 0.999999999999985*m.x15 + 2*m.x16 - 1.00000000000001*m.x17 - 0.999999999999997*m.x18
+ 2*m.x19 - 1.00000000000001*m.x20 - 0.999999999999986*m.x21 + 2*m.x22
- 1.00000000000001*m.x23 - 0.999999999999986*m.x24 + 2*m.x25 - 1.00000000000001*m.x26
- 0.999999999999986*m.x27 + 2*m.x28 - 1.00000000000001*m.x29 - 0.999999999999987*m.x30
+ 2*m.x31 - m.x32 - 0.999999999999987*m.x33 + 2*m.x34 - m.x35 - 0.999999999999994*m.x36
+ 2*m.x37 - m.x38 - 0.999999999999994*m.x39 + 2*m.x40 - m.x41 - 0.999999999999995*m.x42
+ 2*m.x43 - m.x44 - 0.999999999999998*m.x45 + 2*m.x46 - m.x47 - m.x48 + 2*m.x49 - m.x50
<= 0.01)
m.c187 = Constraint(expr= 1.29889609666036*m.x1 + 0.634609312810172*m.x2 - 1.95259201423987*m.x3
+ 1.37670915138752*m.x4 + 0.534476752156529*m.x5 - 1.92726090641724*m.x6
+ 1.45074874202456*m.x7 + 0.432879227876193*m.x8 - 1.8966473104124*m.x9
+ 1.52081193120007*m.x10 + 0.330095211721371*m.x11 - 1.86083513596404*m.x12
+ 1.58670668058246*m.x13 + 0.226406427535801*m.x14 - 1.81992254175309*m.x15
+ 1.64825237724404*m.x16 + 0.122097079069729*m.x17 - 1.77402166635644*m.x18
+ 1.70528032870818*m.x19 + 0.0174530709967347*m.x20 - 1.72325832088305*m.x21
+ 1.75763422532393*m.x22 - 0.0872387747306711*m.x23 - 1.66777164413434*m.x24
+ 1.80517056869972*m.x25 - 0.191691505040447*m.x26 - 1.60771372123444*m.x27
+ 1.84775906502257*m.x28 - 0.295618822259221*m.x29 - 1.54324916677544*m.x30
+ 1.88528298218436*m.x31 - 0.398735868834394*m.x32 - 1.47455467362025*m.x33
+ 1.91763946973639*m.x34 - 0.500760008108882*m.x35 - 1.4018185285997*m.x36
+ 1.94473984079535*m.x37 - 0.601411599008546*m.x38 - 1.32524009643148*m.x39
+ 1.96650981512791*m.x40 - 0.700414762518935*m.x41 - 1.24502927327524*m.x42
+ 1.98288972274762*m.x43 - 0.797498137850492*m.x44 - 1.16140591142188*m.x45
+ 1.99383466746626*m.x46 - 0.892395626219618*m.x47 - 1.07459921669365*m.x48
+ 1.99931464995111*m.x49 - 0.984847120206934*m.x50 <= 0.01)
m.c188 = Constraint(expr= - 0.312868930080438*m.x1 + 1.84100970690487*m.x2 - 1.63830408857799*m.x3
- 0.104671912485877*m.x4 + 1.74923941427878*m.x5 - 1.7492394142788*m.x6
+ 0.104671912485913*m.x7 + 1.63830408857798*m.x8 - 1.84100970690488*m.x9
+ 0.312868930080474*m.x10 + 1.50941916044553*m.x11 - 1.91260951192608*m.x12
+ 0.517638090205068*m.x13 + 1.36399672012499*m.x14 - 1.96325436689533*m.x15
+ 0.716735899090613*m.x16 + 1.20363004630408*m.x17 - 1.99238939618349*m.x18
+ 0.907980999479093*m.x19 + 1.0300761498201*m.x20 - 1.99969539031278*m.x21
+ 1.08927807003007*m.x22 + 0.845236523481394*m.x23 - 1.98509230328264*m.x24
+ 1.25864078209969*m.x25 + 0.651136308914307*m.x26 - 1.94874012957047*m.x27
+ 1.4142135623731*m.x28 + 0.449902108687723*m.x29 - 1.89103715119863*m.x30
+ 1.55429192291394*m.x31 + 0.243738686810287*m.x32 - 1.81261557407329*m.x33
+ 1.67734113589085*m.x34 + 0.0349048128745587*m.x35 - 1.71433460140422*m.x36
+ 1.78201304837674*m.x37 - 0.174311485495318*m.x38 - 1.59727102009458*m.x39
+ 1.86716085299441*m.x40 - 0.381617990753092*m.x41 - 1.46270740323834*m.x42
+ 1.93185165257814*m.x43 - 0.584743409445477*m.x44 - 1.31211805798101*m.x45
+ 1.97537668119028*m.x46 - 0.781462256978548*m.x47 - 1.14715287270209*m.x48
+ 1.99725906950915*m.x49 - 0.969619240492674*m.x50 <= 0.01)
m.c189 = Constraint(expr= - 1.70528032870818*m.x1 + 1.80517056869972*m.x2 - 0.261052384440102*m.x3
- 1.52081193120007*m.x4 + 1.91763946973638*m.x5 - 0.568030689407845*m.x6
- 1.29889609666038*m.x7 + 1.98288972274762*m.x8 - 0.861022193616591*m.x9
- 1.04499712943191*m.x10 + 1.99931464995111*m.x11 - 1.13281247384967*m.x12
- 0.765366864730194*m.x13 + 1.96650981512791*m.x14 - 1.37670915138751*m.x15
- 0.466890727711825*m.x16 + 1.88528298218436*m.x17 - 1.58670668058247*m.x18
- 0.156918191455703*m.x19 + 1.75763422532393*m.x20 - 1.75763422532393*m.x21
+ 0.156918191455692*m.x22 + 1.58670668058247*m.x23 - 1.88528298218435*m.x24
+ 0.466890727711814*m.x25 + 1.37670915138751*m.x26 - 1.96650981512791*m.x27
+ 0.765366864730171*m.x28 + 1.13281247384967*m.x29 - 1.99931464995111*m.x30
+ 1.04499712943189*m.x31 + 0.861022193616589*m.x32 - 1.98288972274762*m.x33
+ 1.29889609666036*m.x34 + 0.568030689407843*m.x35 - 1.91763946973639*m.x36
+ 1.52081193120006*m.x37 + 0.261052384440106*m.x38 - 1.80517056869972*m.x39
+ 1.70528032870818*m.x40 - 0.0523538966157446*m.x41 - 1.64825237724403*m.x42
+ 1.84775906502257*m.x43 - 0.364471050984295*m.x44 - 1.45074874202458*m.x45
+ 1.94473984079535*m.x46 - 0.667613718467541*m.x47 - 1.21752285801744*m.x48
+ 1.99383466746626*m.x49 - 0.954317520519217*m.x50 <= 0.01)
m.c190 = Constraint(expr= - 1.90211303259031*m.x1 + 0.551274711634004*m.x2 + 1.28557521937306*m.x3
- 1.98904379073655*m.x4 + 0.9389431255718*m.x5 + 0.938943125571775*m.x6
- 1.98904379073654*m.x7 + 1.28557521937309*m.x8 + 0.551274711633975*m.x9
- 1.9021130325903*m.x10 + 1.57602150721344*m.x11 + 0.139512947488239*m.x12
- 1.73205080756888*m.x13 + 1.79758809259834*m.x14 - 0.27834620192013*m.x15
- 1.48628965095478*m.x16 + 1.94059145255199*m.x17 - 0.684040286651352*m.x18
- 1.17557050458494*m.x19 + 1.99878165403819*m.x20 - 1.05983852846641*m.x21
- 0.813473286151597*m.x22 + 1.96961550602442*m.x23 - 1.389316740918*m.x24
- 0.415823381635513*m.x25 + 1.85436770913357*m.x26 - 1.65807514511009*m.x27
+ 7.3491187561573E-15*m.x28 + 1.65807514511008*m.x29 - 1.85436770913358*m.x30
+ 0.415823381635528*m.x31 + 1.38931674091799*m.x32 - 1.96961550602442*m.x33
+ 0.813473286151611*m.x34 + 1.05983852846641*m.x35 - 1.99878165403819*m.x36
+ 1.17557050458495*m.x37 + 0.684040286651331*m.x38 - 1.94059145255199*m.x39
+ 1.48628965095479*m.x40 + 0.27834620192013*m.x41 - 1.79758809259833*m.x42
+ 1.73205080756888*m.x43 - 0.139512947488254*m.x44 - 1.57602150721344*m.x45
+ 1.90211303259031*m.x46 - 0.551274711634*m.x47 - 1.28557521937308*m.x48
+ 1.98904379073655*m.x49 - 0.938943125571782*m.x50 <= 0.01)
m.c191 = Constraint(expr= - 0.765366864730182*m.x1 - 1.07459921669365*m.x2 + 1.99809644316372*m.x3
- 1.21752285801742*m.x4 - 0.60141159900855*m.x5 + 1.90743390149646*m.x6
- 1.58670668058246*m.x7 - 0.0872387747306736*m.x8 + 1.68678289162578*m.x9
- 1.84775906502257*m.x10 + 0.432879227876207*m.x11 + 1.35118041523133*m.x12
- 1.98288972274762*m.x13 + 0.923497226470071*m.x14 + 0.923497226470073*m.x15
- 1.98288972274762*m.x16 + 1.35118041523132*m.x17 + 0.432879227876209*m.x18
- 1.84775906502258*m.x19 + 1.68678289162576*m.x20 - 0.0872387747306716*m.x21
- 1.58670668058248*m.x22 + 1.90743390149645*m.x23 - 0.601411599008535*m.x24
- 1.21752285801745*m.x25 + 1.99809644316372*m.x26 - 1.07459921669364*m.x27
- 0.765366864730183*m.x28 + 1.95259201423987*m.x29 - 1.47455467362024*m.x30
- 0.261052384440105*m.x31 + 1.77402166635645*m.x32 - 1.77402166635644*m.x33
+ 0.261052384440104*m.x34 + 1.47455467362025*m.x35 - 1.95259201423987*m.x36
+ 0.765366864730176*m.x37 + 1.07459921669365*m.x38 - 1.99809644316372*m.x39
+ 1.21752285801744*m.x40 + 0.601411599008549*m.x41 - 1.90743390149646*m.x42
+ 1.58670668058247*m.x43 + 0.0872387747306726*m.x44 - 1.68678289162577*m.x45
+ 1.84775906502257*m.x46 - 0.432879227876204*m.x47 - 1.35118041523132*m.x48
+ 1.98288972274762*m.x49 - 0.923497226470068*m.x50 <= 0.01)
m.c192 = Constraint(expr= 0.907980999479081*m.x1 - 1.97537668119027*m.x2 + 1.4142135623731*m.x3
+ 0.312868930080459*m.x4 - 1.78201304837674*m.x5 + 1.78201304837673*m.x6
- 0.312868930080481*m.x7 - 1.41421356237308*m.x8 + 1.97537668119028*m.x9
- 0.907980999479101*m.x10 - 0.907980999479089*m.x11 + 1.97537668119028*m.x12
- 1.41421356237309*m.x13 - 0.312868930080468*m.x14 + 1.78201304837673*m.x15
- 1.78201304837674*m.x16 + 0.312868930080473*m.x17 + 1.41421356237309*m.x18
- 1.97537668119028*m.x19 + 0.907980999479093*m.x20 + 0.907980999479097*m.x21
- 1.97537668119027*m.x22 + 1.4142135623731*m.x23 + 0.312868930080463*m.x24
- 1.78201304837673*m.x25 + 1.78201304837674*m.x26 - 0.312868930080464*m.x27
- 1.4142135623731*m.x28 + 1.97537668119028*m.x29 - 0.907980999479098*m.x30
- 0.907980999479092*m.x31 + 1.97537668119028*m.x32 - 1.4142135623731*m.x33
- 0.312868930080457*m.x34 + 1.78201304837674*m.x35 - 1.78201304837674*m.x36
+ 0.312868930080462*m.x37 + 1.41421356237309*m.x38 - 1.97537668119028*m.x39
+ 0.907980999479097*m.x40 + 0.907980999479094*m.x41 - 1.97537668119027*m.x42
+ 1.41421356237309*m.x43 + 0.312868930080459*m.x44 - 1.78201304837674*m.x45
+ 1.78201304837674*m.x46 - 0.312868930080462*m.x47 - 1.41421356237309*m.x48
+ 1.97537668119028*m.x49 - 0.907980999479094*m.x50 <= 0.01)
m.c193 = Constraint(expr= 1.94473984079535*m.x1 - 1.54324916677544*m.x2 - 0.087238774730701*m.x3
+ 1.64825237724405*m.x4 - 1.89664731041239*m.x5 + 0.63460931281016*m.x6
+ 1.13281247384969*m.x7 - 1.99809644316372*m.x8 + 1.27215644055551*m.x9
+ 0.466890727711831*m.x10 - 1.83412014877026*m.x11 + 1.74071139187979*m.x12
- 0.261052384440086*m.x13 - 1.42650089830837*m.x14 + 1.97803172672383*m.x15
- 0.954317520519205*m.x16 - 0.829386485312489*m.x17 + 1.95259201423987*m.x18
- 1.52081193120006*m.x19 - 0.122097079069722*m.x20 + 1.66777164413434*m.x21
- 1.88528298218435*m.x22 + 0.601411599008542*m.x23 + 1.16140591142188*m.x24
- 1.99931464995111*m.x25 + 1.24502927327524*m.x26 + 0.500760008108897*m.x27
- 1.84775906502258*m.x28 + 1.72325832088305*m.x29 - 0.226406427535803*m.x30
- 1.45074874202458*m.x31 + 1.97257120307446*m.x32 - 0.923497226470062*m.x33
- 0.861022193616595*m.x34 + 1.95984940924166*m.x35 - 1.497911441578*m.x36
- 0.156918191455692*m.x37 + 1.68678289162577*m.x38 - 1.87334437849679*m.x39
+ 0.56803068940784*m.x40 + 1.18964557350269*m.x41 - 1.99992384612834*m.x42
+ 1.21752285801744*m.x43 + 0.534476752156514*m.x44 - 1.86083513596405*m.x45
+ 1.70528032870818*m.x46 - 0.191691505040447*m.x47 - 1.47455467362025*m.x48
+ 1.96650981512791*m.x49 - 0.892395626219617*m.x50 <= 0.01)
m.c194 = Constraint(expr= 1.6180339887499*m.x1 - 0.0697989934050167*m.x2 - 1.53208888623796*m.x3
+ 1.95629520146761*m.x4 - 0.876742293578159*m.x5 - 0.876742293578145*m.x6
+ 1.95629520146761*m.x7 - 1.53208888623795*m.x8 - 0.069798993405001*m.x9
+ 1.61803398874989*m.x10 - 1.92252339187664*m.x11 + 0.749213186831816*m.x12 + m.x13
- 1.98053613748314*m.x14 + 1.43867960067731*m.x15 + 0.209056926535292*m.x16
- 1.69609619231286*m.x17 + 1.87938524157182*m.x18 - 0.6180339887499*m.x19
- 1.11838580694148*m.x20 + 1.99512810051965*m.x21 - 1.33826121271771*m.x22
- 0.347296355333859*m.x23 + 1.76589518571785*m.x24 - 1.8270909152852*m.x25
+ 0.483843791199341*m.x26 + 1.23132295065132*m.x27 - 2*m.x28 + 1.23132295065131*m.x29
+ 0.483843791199334*m.x30 - 1.8270909152852*m.x31 + 1.76589518571785*m.x32
- 0.347296355333867*m.x33 - 1.33826121271772*m.x34 + 1.99512810051965*m.x35
- 1.11838580694149*m.x36 - 0.618033988749893*m.x37 + 1.87938524157182*m.x38
- 1.69609619231285*m.x39 + 0.209056926535306*m.x40 + 1.4386796006773*m.x41
- 1.98053613748314*m.x42 + m.x43 + 0.749213186831825*m.x44 - 1.92252339187664*m.x45
+ 1.6180339887499*m.x46 - 0.0697989934050018*m.x47 - 1.53208888623796*m.x48
+ 1.95629520146761*m.x49 - 0.876742293578155*m.x50 <= 0.01)
m.c195 = Constraint(expr= 0.15691819145571*m.x1 + 1.45074874202455*m.x2 - 1.98288972274762*m.x3
+ 1.04499712943192*m.x4 + 0.667613718467535*m.x5 - 1.88528298218435*m.x6
+ 1.7052803287082*m.x7 - 0.261052384440116*m.x8 - 1.37670915138749*m.x9
+ 1.99383466746626*m.x10 - 1.13281247384968*m.x11 - 0.568030689407818*m.x12
+ 1.84775906502257*m.x13 - 1.75763422532394*m.x14 + 0.3644710509843*m.x15
+ 1.29889609666036*m.x16 - 1.99931464995112*m.x17 + 1.21752285801745*m.x18
+ 0.466890727711791*m.x19 - 1.80517056869972*m.x20 + 1.80517056869973*m.x21
- 0.466890727711836*m.x22 - 1.21752285801743*m.x23 + 1.99931464995111*m.x24
- 1.29889609666038*m.x25 - 0.364471050984282*m.x26 + 1.75763422532393*m.x27
- 1.84775906502258*m.x28 + 0.568030689407849*m.x29 + 1.13281247384965*m.x30
- 1.99383466746626*m.x31 + 1.37670915138751*m.x32 + 0.261052384440098*m.x33
- 1.70528032870818*m.x34 + 1.88528298218436*m.x35 - 0.667613718467551*m.x36
- 1.04499712943189*m.x37 + 1.98288972274762*m.x38 - 1.45074874202458*m.x39
- 0.156918191455685*m.x40 + 1.64825237724403*m.x41 - 1.91763946973639*m.x42
+ 0.765366864730182*m.x43 + 0.954317520519214*m.x44 - 1.96650981512791*m.x45
+ 1.52081193120006*m.x46 + 0.0523538966157442*m.x47 - 1.58670668058247*m.x48
+ 1.94473984079535*m.x49 - 0.86102219361659*m.x50 <= 0.01)
m.c196 = Constraint(expr= - 1.41421356237309*m.x1 + 1.99238939618349*m.x2 - 1.1471528727021*m.x3
- 0.517638090205047*m.x4 + 1.8126155740733*m.x5 - 1.8126155740733*m.x6
+ 0.517638090205028*m.x7 + 1.14715287270209*m.x8 - 1.99238939618349*m.x9
+ 1.4142135623731*m.x10 + 0.174311485495323*m.x11 - 1.63830408857798*m.x12
+ 1.93185165257814*m.x13 - 0.84523652348141*m.x14 - 0.845236523481399*m.x15
+ 1.93185165257814*m.x16 - 1.63830408857799*m.x17 + 0.174311485495308*m.x18
+ 1.41421356237309*m.x19 - 1.99238939618349*m.x20 + 1.1471528727021*m.x21
+ 0.517638090205043*m.x22 - 1.8126155740733*m.x23 + 1.8126155740733*m.x24
- 0.517638090205045*m.x25 - 1.14715287270209*m.x26 + 1.99238939618349*m.x27
- 1.41421356237309*m.x28 - 0.174311485495319*m.x29 + 1.63830408857798*m.x30
- 1.93185165257814*m.x31 + 0.845236523481401*m.x32 + 0.845236523481395*m.x33
- 1.93185165257814*m.x34 + 1.63830408857798*m.x35 - 0.174311485495312*m.x36
- 1.4142135623731*m.x37 + 1.99238939618349*m.x38 - 1.14715287270209*m.x39
- 0.517638090205039*m.x40 + 1.8126155740733*m.x41 - 1.8126155740733*m.x42
+ 0.517638090205042*m.x43 + 1.14715287270209*m.x44 - 1.99238939618349*m.x45
+ 1.41421356237309*m.x46 + 0.174311485495316*m.x47 - 1.63830408857798*m.x48
+ 1.93185165257814*m.x49 - 0.845236523481399*m.x50 <= 0.01)
m.c197 = Constraint(expr= - 1.99383466746626*m.x1 + 1.18964557350269*m.x2 + 0.432879227876178*m.x3
- 1.75763422532392*m.x4 + 1.8733443784968*m.x5 - 0.700414762518941*m.x6
- 0.954317520519193*m.x7 + 1.95259201423986*m.x8 - 1.60771372123444*m.x9
+ 0.156918191455694*m.x10 + 1.40181852859968*m.x11 - 1.99626959684373*m.x12
+ 1.21752285801745*m.x13 + 0.398735868834391*m.x14 - 1.74071139187979*m.x15
+ 1.88528298218436*m.x16 - 0.733002453448603*m.x17 - 0.923497226470066*m.x18
+ 1.94473984079535*m.x19 - 1.62823103671265*m.x20 + 0.191691505040455*m.x21
+ 1.37670915138751*m.x22 - 1.99809644316372*m.x23 + 1.24502927327525*m.x24
+ 0.364471050984289*m.x25 - 1.72325832088304*m.x26 + 1.8966473104124*m.x27
- 0.765366864730191*m.x28 - 0.892395626219614*m.x29 + 1.93629528075621*m.x30
- 1.64825237724403*m.x31 + 0.226406427535824*m.x32 + 1.35118041523132*m.x33
- 1.99931464995111*m.x34 + 1.27215644055553*m.x35 + 0.330095211721346*m.x36
- 1.70528032870818*m.x37 + 1.90743390149646*m.x38 - 0.797498137850493*m.x39
- 0.861022193616583*m.x40 + 1.92726090641725*m.x41 - 1.66777164413434*m.x42
+ 0.261052384440102*m.x43 + 1.32524009643147*m.x44 - 1.99992384612834*m.x45
+ 1.29889609666037*m.x46 + 0.29561882225922*m.x47 - 1.68678289162577*m.x48
+ 1.91763946973639*m.x49 - 0.829386485312478*m.x50 <= 0.01)
m.c198 = Constraint(expr= - 1.17557050458494*m.x1 - 0.415823381635523*m.x2 + 1.73205080756888*m.x3
- 1.9021130325903*m.x4 + 0.81347328615159*m.x5 + 0.813473286151614*m.x6
- 1.90211303259031*m.x7 + 1.73205080756888*m.x8 - 0.415823381635525*m.x9
- 1.17557050458494*m.x10 + 1.98904379073655*m.x11 - 1.48628965095479*m.x12
- 3.92258364109106E-15*m.x13 + 1.48628965095479*m.x14 - 1.98904379073655*m.x15
+ 1.17557050458494*m.x16 + 0.415823381635532*m.x17 - 1.73205080756889*m.x18
+ 1.90211303259031*m.x19 - 0.813473286151607*m.x20 - 0.813473286151597*m.x21
+ 1.90211303259031*m.x22 - 1.73205080756888*m.x23 + 0.415823381635515*m.x24
+ 1.17557050458495*m.x25 - 1.98904379073655*m.x26 + 1.48628965095479*m.x27
- 4.88620758385765E-16*m.x28 - 1.48628965095479*m.x29 + 1.98904379073655*m.x30
- 1.17557050458494*m.x31 - 0.415823381635514*m.x32 + 1.73205080756888*m.x33
- 1.90211303259031*m.x34 + 0.813473286151598*m.x35 + 0.813473286151606*m.x36
- 1.90211303259031*m.x37 + 1.73205080756888*m.x38 - 0.41582338163552*m.x39
- 1.17557050458495*m.x40 + 1.98904379073655*m.x41 - 1.48628965095479*m.x42
- 2.20560219973841E-15*m.x43 + 1.48628965095479*m.x44 - 1.98904379073655*m.x45
+ 1.17557050458495*m.x46 + 0.41582338163552*m.x47 - 1.73205080756888*m.x48
+ 1.90211303259031*m.x49 - 0.8134732861516*m.x50 <= 0.01)
m.c199 = Constraint(expr= 0.466890727711801*m.x1 - 1.74071139187979*m.x2 + 1.90743390149646*m.x3
- 0.861022193616606*m.x4 - 0.733002453448577*m.x5 + 1.86083513596404*m.x6
- 1.80517056869973*m.x7 + 0.601411599008544*m.x8 + 0.984847120206934*m.x9
- 1.94473984079535*m.x10 + 1.66777164413434*m.x11 - 0.330095211721363*m.x12
- 1.21752285801743*m.x13 + 1.99079239673436*m.x14 - 1.49791144157801*m.x15
+ 0.0523538966157635*m.x16 + 1.42650089830835*m.x17 - 1.99809644316372*m.x18
+ 1.29889609666036*m.x19 + 0.226406427535815*m.x20 - 1.60771372123443*m.x21
+ 1.96650981512791*m.x22 - 1.07459921669365*m.x23 - 0.500760008108875*m.x24
+ 1.75763422532393*m.x25 - 1.8966473104124*m.x26 + 0.829386485312479*m.x27
+ 0.765366864730177*m.x28 - 1.87334437849679*m.x29 + 1.78986872320405*m.x30
- 0.568030689407855*m.x31 - 1.01507672592141*m.x32 + 1.95259201423987*m.x33
- 1.64825237724403*m.x34 + 0.295618822259227*m.x35 + 1.24502927327523*m.x36
- 1.99383466746626*m.x37 + 1.47455467362025*m.x38 - 0.0174530709967489*m.x39
- 1.45074874202457*m.x40 + 1.99626959684373*m.x41 - 1.27215644055553*m.x42
- 0.2610523844401*m.x43 + 1.62823103671264*m.x44 - 1.95984940924166*m.x45
+ 1.0449971294319*m.x46 + 0.534476752156511*m.x47 - 1.77402166635644*m.x48
+ 1.88528298218436*m.x49 - 0.797498137850493*m.x50 <= 0.01)
m.c200 = Constraint(expr= 1.78201304837674*m.x1 - 1.89103715119863*m.x2 + 0.84523652348138*m.x3
+ 0.716735899090613*m.x4 - 1.84100970690488*m.x5 + 1.84100970690488*m.x6
- 0.716735899090582*m.x7 - 0.84523652348141*m.x8 + 1.89103715119864*m.x9
- 1.78201304837674*m.x10 + 0.584743409445455*m.x11 + 0.969619240492684*m.x12
- 1.93185165257814*m.x13 + 1.71433460140423*m.x14 - 0.449902108687712*m.x15
- 1.08927807003006*m.x16 + 1.96325436689533*m.x17 - 1.63830408857799*m.x18
+ 0.312868930080445*m.x19 + 1.2036300463041*m.x20 - 1.98509230328264*m.x21
+ 1.55429192291394*m.x22 - 0.1743114854953*m.x23 - 1.31211805798102*m.x24
+ 1.99725906950915*m.x25 - 1.46270740323833*m.x26 + 0.0349048128745656*m.x27
+ 1.4142135623731*m.x28 - 1.99969539031278*m.x29 + 1.36399672012499*m.x30
+ 0.104671912485888*m.x31 - 1.50941916044555*m.x32 + 1.99238939618349*m.x33
- 1.25864078209967*m.x34 - 0.243738686810295*m.x35 + 1.59727102009459*m.x36
- 1.97537668119028*m.x37 + 1.14715287270209*m.x38 + 0.381617990753089*m.x39
- 1.67734113589085*m.x40 + 1.94874012957047*m.x41 - 1.0300761498201*m.x42
- 0.51763809020504*m.x43 + 1.74923941427879*m.x44 - 1.91260951192607*m.x45
+ 0.907980999479093*m.x46 + 0.651136308914314*m.x47 - 1.8126155740733*m.x48
+ 1.8671608529944*m.x49 - 0.781462256978547*m.x50 <= 0.01)
m.c201 = Constraint(expr= 1.84775906502258*m.x1 - 0.765366864730195*m.x2 - 0.765366864730179*m.x3
+ 1.84775906502257*m.x4 - 1.84775906502257*m.x5 + 0.765366864730188*m.x6
+ 0.76536686473016*m.x7 - 1.84775906502257*m.x8 + 1.84775906502258*m.x9
- 0.765366864730181*m.x10 - 0.765366864730167*m.x11 + 1.84775906502257*m.x12
- 1.84775906502258*m.x13 + 0.765366864730173*m.x14 + 0.765366864730174*m.x15
- 1.84775906502257*m.x16 + 1.84775906502257*m.x17 - 0.765366864730192*m.x18
- 0.765366864730182*m.x19 + 1.84775906502257*m.x20 - 1.84775906502257*m.x21
+ 0.765366864730185*m.x22 + 0.765366864730163*m.x23 - 1.84775906502257*m.x24
+ 1.84775906502257*m.x25 - 0.765366864730178*m.x26 - 0.76536686473017*m.x27
+ 1.84775906502257*m.x28 - 1.84775906502258*m.x29 + 0.765366864730184*m.x30
+ 0.765366864730177*m.x31 - 1.84775906502257*m.x32 + 1.84775906502257*m.x33
- 0.765366864730177*m.x34 - 0.765366864730171*m.x35 + 1.84775906502257*m.x36
- 1.84775906502258*m.x37 + 0.765366864730182*m.x38 + 0.765366864730178*m.x39
- 1.84775906502257*m.x40 + 1.84775906502258*m.x41 - 0.765366864730182*m.x42
- 0.765366864730179*m.x43 + 1.84775906502257*m.x44 - 1.84775906502257*m.x45
+ 0.765366864730181*m.x46 + 0.76536686473018*m.x47 - 1.84775906502257*m.x48
+ 1.84775906502257*m.x49 - 0.76536686473018*m.x50 <= 0.01)
m.c202 = Constraint(expr= 0.618033988749883*m.x1 + 0.876742293578176*m.x2 - 1.87938524157182*m.x3
+ 1.82709091528519*m.x4 - 0.749213186831824*m.x5 - 0.749213186831835*m.x6
+ 1.82709091528521*m.x7 - 1.87938524157181*m.x8 + 0.87674229357814*m.x9
+ 0.618033988749894*m.x10 - 1.76589518571786*m.x11 + 1.92252339187663*m.x12
- 0.999999999999996*m.x13 - 0.483843791199351*m.x14 + 1.69609619231285*m.x15
- 1.95629520146761*m.x16 + 1.11838580694148*m.x17 + 0.347296355333865*m.x18
- 1.6180339887499*m.x19 + 1.98053613748314*m.x20 - 1.23132295065131*m.x21
- 0.209056926535328*m.x22 + 1.53208888623796*m.x23 - 1.99512810051965*m.x24
+ 1.33826121271771*m.x25 + 0.0697989934050108*m.x26 - 1.43867960067731*m.x27 + 2*m.x28
- 1.4386796006773*m.x29 + 0.0697989934049907*m.x30 + 1.33826121271772*m.x31
- 1.99512810051965*m.x32 + 1.53208888623795*m.x33 - 0.209056926535308*m.x34
- 1.23132295065132*m.x35 + 1.98053613748314*m.x36 - 1.61803398874989*m.x37
+ 0.347296355333859*m.x38 + 1.1183858069415*m.x39 - 1.95629520146761*m.x40
+ 1.69609619231285*m.x41 - 0.483843791199332*m.x42 - m.x43 + 1.92252339187664*m.x44
- 1.76589518571785*m.x45 + 0.618033988749892*m.x46 + 0.876742293578155*m.x47
- 1.87938524157182*m.x48 + 1.8270909152852*m.x49 - 0.749213186831824*m.x50 <= 0.01)
m.c203 = Constraint(expr= - 1.0449971294319*m.x1 + 1.92726090641725*m.x2 - 1.77402166635645*m.x3 + 0.66761371846755*m.x4
+ 0.79749813785049*m.x5 - 1.83412014877025*m.x6 + 1.88528298218435*m.x7
- 0.923497226470079*m.x8 - 0.534476752156508*m.x9 + 1.70528032870818*m.x10
- 1.95984940924166*m.x11 + 1.16140591142189*m.x12 + 0.261052384440094*m.x13
- 1.54324916677544*m.x14 + 1.99626959684373*m.x15 - 1.3767091513875*m.x16
+ 0.0174530709967601*m.x17 + 1.35118041523132*m.x18 - 1.99383466746626*m.x19
+ 1.56521631370482*m.x20 - 0.295618822259237*m.x21 - 1.13281247384966*m.x22
+ 1.95259201423987*m.x23 - 1.72325832088305*m.x24 + 0.56803068940785*m.x25
+ 0.892395626219619*m.x26 - 1.87334437849679*m.x27 + 1.84775906502257*m.x28
- 0.829386485312485*m.x29 - 0.634609312810183*m.x30 + 1.75763422532393*m.x31
- 1.93629528075622*m.x32 + 1.07459921669364*m.x33 + 0.36447105098429*m.x34
- 1.60771372123444*m.x35 + 1.98714371135318*m.x36 - 1.29889609666037*m.x37
- 0.0872387747306713*m.x38 + 1.42650089830836*m.x39 - 1.99931464995111*m.x40
+ 1.49791144157801*m.x41 - 0.191691505040452*m.x42 - 1.21752285801744*m.x43
+ 1.97257120307446*m.x44 - 1.66777164413434*m.x45 + 0.466890727711811*m.x46
+ 0.984847120206934*m.x47 - 1.90743390149645*m.x48 + 1.80517056869972*m.x49
- 0.733002453448595*m.x50 <= 0.01)
m.c204 = Constraint(expr= - 1.97537668119027*m.x1 + 1.67734113589085*m.x2 - 0.517638090205077*m.x3
- 0.907980999479062*m.x4 + 1.86716085299439*m.x5 - 1.86716085299442*m.x6
+ 0.907980999479121*m.x7 + 0.517638090205013*m.x8 - 1.67734113589083*m.x9
+ 1.97537668119028*m.x10 - 1.25864078209969*m.x11 - 0.104671912485863*m.x12
+ 1.41421356237308*m.x13 - 1.99725906950915*m.x14 + 1.55429192291395*m.x15
- 0.31286893008048*m.x16 - 1.08927807003004*m.x17 + 1.93185165257813*m.x18
- 1.78201304837674*m.x19 + 0.716735899090613*m.x20 + 0.716735899090589*m.x21
- 1.78201304837673*m.x22 + 1.93185165257814*m.x23 - 1.08927807003006*m.x24
- 0.312868930080455*m.x25 + 1.55429192291394*m.x26 - 1.99725906950915*m.x27
+ 1.41421356237311*m.x28 - 0.104671912485903*m.x29 - 1.25864078209966*m.x30
+ 1.97537668119027*m.x31 - 1.67734113589085*m.x32 + 0.517638090205051*m.x33
+ 0.907980999479086*m.x34 - 1.8671608529944*m.x35 + 1.86716085299441*m.x36
- 0.907980999479097*m.x37 - 0.517638090205039*m.x38 + 1.67734113589084*m.x39
- 1.97537668119028*m.x40 + 1.25864078209968*m.x41 + 0.104671912485883*m.x42
- 1.41421356237309*m.x43 + 1.99725906950915*m.x44 - 1.55429192291394*m.x45
+ 0.312868930080464*m.x46 + 1.08927807003005*m.x47 - 1.93185165257814*m.x48
+ 1.78201304837674*m.x49 - 0.716735899090601*m.x50 <= 0.01)
m.c205 = Constraint(expr= - 1.52081193120006*m.x1 + 0.295618822259219*m.x2 + 1.07459921669365*m.x3
- 1.91763946973638*m.x4 + 1.81992254175309*m.x5 - 0.829386485312489*m.x6
- 0.568030689407858*m.x7 + 1.68678289162578*m.x8 - 1.97803172672383*m.x9
+ 1.29889609666037*m.x10 + 0.0174530709967464*m.x11 - 1.32524009643147*m.x12
+ 1.98288972274762*m.x13 - 1.66777164413434*m.x14 + 0.534476752156501*m.x15
+ 0.861022193616599*m.x16 - 1.83412014877025*m.x17 + 1.90743390149645*m.x18
- 1.0449971294319*m.x19 - 0.33009521172135*m.x20 + 1.54324916677543*m.x21
- 1.99931464995111*m.x22 + 1.47455467362024*m.x23 - 0.226406427535804*m.x24
- 1.13281247384967*m.x25 + 1.93629528075622*m.x26 - 1.78986872320405*m.x27
+ 0.765366864730184*m.x28 + 0.63460931281019*m.x29 - 1.72325832088305*m.x30
+ 1.96650981512791*m.x31 - 1.24502927327524*m.x32 - 0.0872387747306775*m.x33
+ 1.37670915138751*m.x34 - 1.99079239673436*m.x35 + 1.62823103671264*m.x36
- 0.466890727711806*m.x37 - 0.923497226470069*m.x38 + 1.86083513596405*m.x39
- 1.88528298218436*m.x40 + 0.984847120206936*m.x41 + 0.398735868834396*m.x42
- 1.58670668058247*m.x43 + 1.99626959684373*m.x44 - 1.42650089830836*m.x45
+ 0.156918191455688*m.x46 + 1.18964557350268*m.x47 - 1.95259201423987*m.x48
+ 1.75763422532393*m.x49 - 0.700414762518935*m.x50 <= 0.01)
m.c206 = Constraint(expr= - 5.87706667463412E-15*m.x1 - 1.28557521937307*m.x2 + 1.96961550602441*m.x3
- 1.73205080756889*m.x4 + 0.684040286651348*m.x5 + 0.684040286651319*m.x6
- 1.73205080756886*m.x7 + 1.96961550602442*m.x8 - 1.28557521937309*m.x9
+ 2.54788432286308E-14*m.x10 + 1.28557521937307*m.x11 - 1.96961550602441*m.x12
+ 1.73205080756889*m.x13 - 0.68404028665134*m.x14 - 0.684040286651327*m.x15
+ 1.73205080756887*m.x16 - 1.96961550602442*m.x17 + 1.28557521937308*m.x18
- 1.66589103522235E-14*m.x19 - 1.28557521937306*m.x20 + 1.96961550602442*m.x21
- 1.73205080756888*m.x22 + 0.684040286651358*m.x23 + 0.684040286651335*m.x24
- 1.73205080756887*m.x25 + 1.96961550602442*m.x26 - 1.28557521937309*m.x27
+ 7.83897747581624E-15*m.x28 + 1.28557521937308*m.x29 - 1.96961550602441*m.x30
+ 1.73205080756888*m.x31 - 0.68404028665135*m.x32 - 0.68404028665133*m.x33
+ 1.73205080756888*m.x34 - 1.96961550602442*m.x35 + 1.28557521937308*m.x36
+ 9.80955400591059E-16*m.x37 - 1.28557521937307*m.x38 + 1.96961550602442*m.x39
- 1.73205080756888*m.x40 + 0.684040286651342*m.x41 + 0.684040286651332*m.x42
- 1.73205080756888*m.x43 + 1.96961550602442*m.x44 - 1.28557521937308*m.x45
+ 8.57252759403147E-16*m.x46 + 1.28557521937308*m.x47 - 1.96961550602442*m.x48
+ 1.73205080756888*m.x49 - 0.684040286651338*m.x50 <= 0.01)
m.c207 = Constraint(expr= 1.52081193120007*m.x1 - 1.99931464995111*m.x2 + 1.58670668058246*m.x3
- 0.466890727711812*m.x4 - 0.861022193616603*m.x5 + 1.80517056869972*m.x6
- 1.94473984079535*m.x7 + 1.21752285801744*m.x8 + 0.0523538966157635*m.x9
- 1.29889609666037*m.x10 + 1.96650981512791*m.x11 - 1.75763422532393*m.x12
+ 0.765366864730187*m.x13 + 0.568030689407852*m.x14 - 1.64825237724403*m.x15
+ 1.99383466746626*m.x16 - 1.45074874202458*m.x17 + 0.261052384440093*m.x18
+ 1.04499712943189*m.x19 - 1.88528298218436*m.x20 + 1.88528298218436*m.x21
- 1.04499712943189*m.x22 - 0.261052384440103*m.x23 + 1.45074874202459*m.x24
- 1.99383466746626*m.x25 + 1.64825237724403*m.x26 - 0.568030689407843*m.x27
- 0.765366864730183*m.x28 + 1.75763422532393*m.x29 - 1.96650981512791*m.x30
+ 1.29889609666036*m.x31 - 0.0523538966157395*m.x32 - 1.21752285801745*m.x33
+ 1.94473984079536*m.x34 - 1.80517056869972*m.x35 + 0.861022193616594*m.x36
+ 0.466890727711807*m.x37 - 1.58670668058247*m.x38 + 1.99931464995111*m.x39
- 1.52081193120006*m.x40 + 0.364471050984295*m.x41 + 0.954317520519217*m.x42
- 1.84775906502257*m.x43 + 1.91763946973639*m.x44 - 1.13281247384967*m.x45
- 0.15691819145569*m.x46 + 1.37670915138751*m.x47 - 1.98288972274762*m.x48
+ 1.70528032870818*m.x49 - 0.667613718467542*m.x50 <= 0.01)
m.c208 = Constraint(expr= 1.97537668119028*m.x1 - 1.36399672012501*m.x2 + 0.174311485495325*m.x3
+ 1.08927807003003*m.x4 - 1.89103715119863*m.x5 + 1.89103715119864*m.x6
- 1.08927807003008*m.x7 - 0.174311485495301*m.x8 + 1.36399672012499*m.x9
- 1.97537668119027*m.x10 + 1.7492394142788*m.x11 - 0.781462256978551*m.x12
- 0.51763809020502*m.x13 + 1.59727102009458*m.x14 - 1.99969539031278*m.x15
+ 1.55429192291395*m.x16 - 0.44990210868774*m.x17 - 0.845236523481399*m.x18
+ 1.78201304837673*m.x19 - 1.96325436689533*m.x20 + 1.31211805798101*m.x21
- 0.104671912485904*m.x22 - 1.14715287270209*m.x23 + 1.91260951192607*m.x24
- 1.8671608529944*m.x25 + 1.03007614982011*m.x26 + 0.243738686810286*m.x27
- 1.41421356237309*m.x28 + 1.98509230328264*m.x29 - 1.71433460140423*m.x30
+ 0.716735899090611*m.x31 + 0.584743409445472*m.x32 - 1.63830408857798*m.x33
+ 1.99725906950915*m.x34 - 1.50941916044554*m.x35 + 0.381617990753093*m.x36
+ 0.907980999479087*m.x37 - 1.8126155740733*m.x38 + 1.94874012957047*m.x39
- 1.25864078209968*m.x40 + 0.0349048128745702*m.x41 + 1.2036300463041*m.x42
- 1.93185165257814*m.x43 + 1.84100970690488*m.x44 - 0.969619240492676*m.x45
- 0.312868930080459*m.x46 + 1.46270740323834*m.x47 - 1.99238939618349*m.x48
+ 1.67734113589085*m.x49 - 0.651136308914314*m.x50 <= 0.01)
m.c209 = Constraint(expr= 1.04499712943188*m.x1 + 0.191691505040469*m.x2 - 1.35118041523132*m.x3
+ 1.96650981512791*m.x4 - 1.78986872320405*m.x5 + 0.892395626219606*m.x6
+ 0.364471050984313*m.x7 - 1.47455467362024*m.x8 + 1.99079239673436*m.x9
- 1.70528032870818*m.x10 + 0.733002453448585*m.x11 + 0.534476752156529*m.x12
- 1.58670668058248*m.x13 + 1.99992384612834*m.x14 - 1.60771372123443*m.x15
+ 0.568030689407838*m.x16 + 0.700414762518947*m.x17 - 1.68678289162578*m.x18
+ 1.99383466746626*m.x19 - 1.497911441578*m.x20 + 0.398735868834389*m.x21
+ 0.861022193616599*m.x22 - 1.77402166635645*m.x23 + 1.97257120307446*m.x24
- 1.3767091513875*m.x25 + 0.226406427535811*m.x26 + 1.01507672592141*m.x27
- 1.84775906502257*m.x28 + 1.93629528075621*m.x29 - 1.24502927327523*m.x30
+ 0.0523538966157468*m.x31 + 1.16140591142188*m.x32 - 1.90743390149646*m.x33
+ 1.88528298218436*m.x34 - 1.10387397062411*m.x35 - 0.12209707906971*m.x36
+ 1.29889609666037*m.x37 - 1.95259201423987*m.x38 + 1.81992254175308*m.x39
- 0.954317520519214*m.x40 - 0.295618822259222*m.x41 + 1.42650089830836*m.x42
- 1.98288972274762*m.x43 + 1.7407113918798*m.x44 - 0.797498137850492*m.x45
- 0.466890727711813*m.x46 + 1.54324916677544*m.x47 - 1.99809644316372*m.x48
+ 1.64825237724403*m.x49 - 0.634609312810184*m.x50 <= 0.01)
m.c210 = Constraint(expr= - 0.618033988749872*m.x1 + 1.6180339887499*m.x2 - 2*m.x3 + 1.61803398874989*m.x4
- 0.618033988749917*m.x5 - 0.6180339887499*m.x6 + 1.61803398874988*m.x7 - 2*m.x8
+ 1.61803398874991*m.x9 - 0.618033988749889*m.x10 - 0.618033988749874*m.x11
+ 1.6180339887499*m.x12 - 2*m.x13 + 1.61803398874989*m.x14 - 0.618033988749915*m.x15
- 0.618033988749902*m.x16 + 1.61803398874988*m.x17 - 2*m.x18 + 1.61803398874991*m.x19
- 0.618033988749887*m.x20 - 0.618033988749876*m.x21 + 1.6180339887499*m.x22 - 2*m.x23
+ 1.61803398874989*m.x24 - 0.618033988749913*m.x25 - 0.61803398874989*m.x26
+ 1.61803398874989*m.x27 - 2*m.x28 + 1.6180339887499*m.x29 - 0.618033988749899*m.x30
- 0.618033988749891*m.x31 + 1.61803398874989*m.x32 - 2*m.x33 + 1.6180339887499*m.x34
- 0.618033988749898*m.x35 - 0.618033988749892*m.x36 + 1.61803398874989*m.x37 - 2*m.x38
+ 1.6180339887499*m.x39 - 0.618033988749897*m.x40 - 0.618033988749893*m.x41
+ 1.61803398874989*m.x42 - 2*m.x43 + 1.6180339887499*m.x44 - 0.618033988749896*m.x45
- 0.618033988749894*m.x46 + 1.61803398874989*m.x47 - 2*m.x48 + 1.61803398874989*m.x49
- 0.618033988749895*m.x50 <= 0.01)
m.c211 = Constraint(expr= - 1.84775906502258*m.x1 + 1.95259201423986*m.x2 - 1.35118041523131*m.x3
+ 0.261052384440096*m.x4 + 0.92349722647007*m.x5 - 1.77402166635646*m.x6
+ 1.98288972274762*m.x7 - 1.47455467362024*m.x8 + 0.432879227876194*m.x9
+ 0.765366864730186*m.x10 - 1.68678289162577*m.x11 + 1.99809644316372*m.x12
- 1.58670668058246*m.x13 + 0.60141159900853*m.x14 + 0.601411599008558*m.x15
- 1.58670668058247*m.x16 + 1.99809644316372*m.x17 - 1.68678289162577*m.x18
+ 0.76536686473016*m.x19 + 0.432879227876222*m.x20 - 1.47455467362026*m.x21
+ 1.98288972274762*m.x22 - 1.77402166635644*m.x23 + 0.92349722647007*m.x24
+ 0.261052384440124*m.x25 - 1.35118041523132*m.x26 + 1.95259201423987*m.x27
- 1.84775906502257*m.x28 + 1.07459921669365*m.x29 + 0.0872387747306838*m.x30
- 1.21752285801745*m.x31 + 1.90743390149645*m.x32 - 1.90743390149645*m.x33
+ 1.21752285801744*m.x34 - 0.0872387747306696*m.x35 - 1.07459921669365*m.x36
+ 1.84775906502258*m.x37 - 1.95259201423987*m.x38 + 1.35118041523132*m.x39
- 0.261052384440103*m.x40 - 0.92349722647007*m.x41 + 1.77402166635645*m.x42
- 1.98288972274762*m.x43 + 1.47455467362025*m.x44 - 0.432879227876205*m.x45
- 0.76536686473018*m.x46 + 1.68678289162577*m.x47 - 1.99809644316372*m.x48
+ 1.58670668058247*m.x49 - 0.601411599008546*m.x50 <= 0.01)
m.c212 = Constraint(expr= - 1.78201304837674*m.x1 + 0.969619240492674*m.x2 + 0.174311485495307*m.x3
- 1.25864078209968*m.x4 + 1.91260951192607*m.x5 - 1.91260951192607*m.x6
+ 1.25864078209967*m.x7 - 0.174311485495317*m.x8 - 0.969619240492665*m.x9
+ 1.78201304837674*m.x10 - 1.98509230328264*m.x11 + 1.50941916044555*m.x12
- 0.517638090205034*m.x13 - 0.651136308914311*m.x14 + 1.59727102009458*m.x15
- 1.99725906950915*m.x16 + 1.71433460140423*m.x17 - 0.84523652348141*m.x18
- 0.312868930080468*m.x19 + 1.36399672012499*m.x20 - 1.94874012957047*m.x21
+ 1.8671608529944*m.x22 - 1.1471528727021*m.x23 + 0.0349048128745808*m.x24
+ 1.08927807003006*m.x25 - 1.84100970690488*m.x26 + 1.96325436689533*m.x27
- 1.41421356237309*m.x28 + 0.381617990753095*m.x29 + 0.781462256978547*m.x30
- 1.67734113589085*m.x31 + 1.99969539031278*m.x32 - 1.63830408857798*m.x33
+ 0.716735899090598*m.x34 + 0.449902108687724*m.x35 - 1.46270740323834*m.x36
+ 1.97537668119028*m.x37 - 1.8126155740733*m.x38 + 1.03007614982011*m.x39
+ 0.10467191248589*m.x40 - 1.2036300463041*m.x41 + 1.89103715119863*m.x42
- 1.93185165257814*m.x43 + 1.31211805798102*m.x44 - 0.243738686810295*m.x45
- 0.907980999479094*m.x46 + 1.74923941427879*m.x47 - 1.99238939618349*m.x48
+ 1.55429192291394*m.x49 - 0.584743409445474*m.x50 <= 0.01)
m.c213 = Constraint(expr= - 0.46689072771184*m.x1 - 0.667613718467526*m.x2 + 1.58670668058245*m.x3
- 1.99383466746625*m.x4 + 1.75763422532395*m.x5 - 0.954317520519233*m.x6
- 0.156918191455657*m.x7 + 1.21752285801743*m.x8 - 1.88528298218435*m.x9
+ 1.94473984079536*m.x10 - 1.37670915138751*m.x11 + 0.364471050984315*m.x12
+ 0.765366864730173*m.x13 - 1.64825237724402*m.x14 + 1.99931464995111*m.x15
- 1.7052803287082*m.x16 + 0.861022193616598*m.x17 + 0.261052384440081*m.x18
- 1.29889609666036*m.x19 + 1.91763946973638*m.x20 - 1.91763946973639*m.x21
+ 1.29889609666039*m.x22 - 0.261052384440114*m.x23 - 0.861022193616568*m.x24
+ 1.70528032870818*m.x25 - 1.99931464995111*m.x26 + 1.64825237724404*m.x27
- 0.765366864730191*m.x28 - 0.364471050984282*m.x29 + 1.3767091513875*m.x30
- 1.94473984079535*m.x31 + 1.88528298218436*m.x32 - 1.21752285801745*m.x33
+ 0.156918191455705*m.x34 + 0.954317520519216*m.x35 - 1.75763422532393*m.x36
+ 1.99383466746626*m.x37 - 1.58670668058247*m.x38 + 0.667613718467544*m.x39
+ 0.466890727711808*m.x40 - 1.45074874202457*m.x41 + 1.96650981512791*m.x42
- 1.84775906502258*m.x43 + 1.13281247384967*m.x44 - 0.0523538966157477*m.x45
- 1.0449971294319*m.x46 + 1.80517056869972*m.x47 - 1.98288972274762*m.x48
+ 1.52081193120006*m.x49 - 0.568030689407846*m.x50 <= 0.01)
m.c214 = Constraint(expr= 1.17557050458494*m.x1 - 1.85436770913357*m.x2 + 1.96961550602442*m.x3
- 1.48628965095478*m.x4 + 0.551274711634005*m.x5 + 0.551274711634001*m.x6
- 1.4862896509548*m.x7 + 1.96961550602441*m.x8 - 1.85436770913357*m.x9
+ 1.17557050458494*m.x10 - 0.139512947488261*m.x11 - 0.938943125571781*m.x12
+ 1.73205080756888*m.x13 - 1.99878165403819*m.x14 + 1.65807514511009*m.x15
- 0.813473286151595*m.x16 - 0.278346201920118*m.x17 + 1.28557521937308*m.x18
- 1.90211303259031*m.x19 + 1.94059145255199*m.x20 - 1.389316740918*m.x21
+ 0.415823381635516*m.x22 + 0.684040286651348*m.x23 - 1.57602150721344*m.x24
+ 1.98904379073655*m.x25 - 1.79758809259834*m.x26 + 1.05983852846641*m.x27
- 9.78479478044706E-16*m.x28 - 1.0598385284664*m.x29 + 1.79758809259834*m.x30
- 1.98904379073655*m.x31 + 1.57602150721344*m.x32 - 0.684040286651337*m.x33
- 0.415823381635514*m.x34 + 1.389316740918*m.x35 - 1.94059145255199*m.x36
+ 1.90211303259031*m.x37 - 1.28557521937308*m.x38 + 0.278346201920134*m.x39
+ 0.8134732861516*m.x40 - 1.65807514511008*m.x41 + 1.99878165403819*m.x42
- 1.73205080756888*m.x43 + 0.938943125571782*m.x44 + 0.139512947488251*m.x45
- 1.17557050458495*m.x46 + 1.85436770913358*m.x47 - 1.96961550602442*m.x48
+ 1.48628965095479*m.x49 - 0.551274711633998*m.x50 <= 0.01)
m.c215 = Constraint(expr= 1.99383466746625*m.x1 - 1.78986872320406*m.x2 + 1.07459921669366*m.x3
- 0.0523538966157596*m.x4 - 0.984847120206926*m.x5 + 1.7407113918798*m.x6
- 1.99931464995112*m.x7 + 1.68678289162579*m.x8 - 0.892395626219637*m.x9
- 0.156918191455672*m.x10 + 1.16140591142187*m.x11 - 1.83412014877024*m.x12
+ 1.98288972274762*m.x13 - 1.56521631370483*m.x14 + 0.70041476251896*m.x15
+ 0.364471050984273*m.x16 - 1.32524009643146*m.x17 + 1.90743390149645*m.x18
- 1.94473984079536*m.x19 + 1.42650089830837*m.x20 - 0.500760008108886*m.x21
- 0.568030689407846*m.x22 + 1.47455467362023*m.x23 - 1.95984940924166*m.x24
+ 1.88528298218436*m.x25 - 1.27215644055554*m.x26 + 0.295618822259229*m.x27
+ 0.765366864730176*m.x28 - 1.60771372123443*m.x29 + 1.99079239673436*m.x30
- 1.80517056869972*m.x31 + 1.10387397062412*m.x32 - 0.0872387747306843*m.x33
- 0.954317520519209*m.x34 + 1.72325832088305*m.x35 - 1.99992384612834*m.x36
+ 1.70528032870819*m.x37 - 0.923497226470074*m.x38 - 0.122097079069711*m.x39
+ 1.13281247384966*m.x40 - 1.81992254175309*m.x41 + 1.98714371135318*m.x42
- 1.58670668058247*m.x43 + 0.733002453448599*m.x44 + 0.330095211721355*m.x45
- 1.29889609666036*m.x46 + 1.8966473104124*m.x47 - 1.95259201423987*m.x48
+ 1.45074874202458*m.x49 - 0.534476752156514*m.x50 <= 0.01)
m.c216 = Constraint(expr= 1.41421356237308*m.x1 - 0.51763809020505*m.x2 - 0.517638090205059*m.x3
+ 1.41421356237311*m.x4 - 1.93185165257814*m.x5 + 1.93185165257813*m.x6
- 1.41421356237308*m.x7 + 0.517638090205029*m.x8 + 0.517638090205053*m.x9
- 1.4142135623731*m.x10 + 1.93185165257814*m.x11 - 1.93185165257813*m.x12
+ 1.41421356237309*m.x13 - 0.517638090205034*m.x14 - 0.517638090205048*m.x15
+ 1.4142135623731*m.x16 - 1.93185165257814*m.x17 + 1.93185165257814*m.x18
- 1.41421356237309*m.x19 + 0.51763809020504*m.x20 + 0.517638090205042*m.x21
- 1.41421356237309*m.x22 + 1.93185165257814*m.x23 - 1.93185165257814*m.x24
+ 1.4142135623731*m.x25 - 0.517638090205046*m.x26 - 0.51763809020505*m.x27
+ 1.4142135623731*m.x28 - 1.93185165257814*m.x29 + 1.93185165257814*m.x30
- 1.41421356237309*m.x31 + 0.517638090205038*m.x32 + 0.517638090205044*m.x33
- 1.4142135623731*m.x34 + 1.93185165257814*m.x35 - 1.93185165257814*m.x36
+ 1.4142135623731*m.x37 - 0.517638090205043*m.x38 - 0.517638090205046*m.x39
+ 1.4142135623731*m.x40 - 1.93185165257814*m.x41 + 1.93185165257814*m.x42
- 1.41421356237309*m.x43 + 0.517638090205042*m.x44 + 0.517638090205043*m.x45
- 1.4142135623731*m.x46 + 1.93185165257814*m.x47 - 1.93185165257814*m.x48
+ 1.4142135623731*m.x49 - 0.517638090205041*m.x50 <= 0.01)
m.c217 = Constraint(expr= - 0.156918191455698*m.x1 + 1.10387397062409*m.x2 - 1.77402166635644*m.x3
+ 1.99931464995111*m.x4 - 1.72325832088306*m.x5 + 1.01507672592143*m.x6
- 0.0523538966157733*m.x7 - 0.923497226470064*m.x8 + 1.66777164413433*m.x9
- 1.99383466746625*m.x10 + 1.8199225417531*m.x11 - 1.18964557350268*m.x12
+ 0.261052384440109*m.x13 + 0.733002453448584*m.x14 - 1.54324916677543*m.x15
+ 1.96650981512791*m.x16 - 1.8966473104124*m.x17 + 1.35118041523133*m.x18
- 0.466890727711823*m.x19 - 0.534476752156496*m.x20 + 1.4018185285997*m.x21
- 1.91763946973639*m.x22 + 1.95259201423987*m.x23 - 1.49791144157801*m.x24
+ 0.66761371846756*m.x25 + 0.330095211721358*m.x26 - 1.24502927327524*m.x27
+ 1.84775906502257*m.x28 - 1.98714371135318*m.x29 + 1.62823103671264*m.x30
- 0.861022193616602*m.x31 - 0.122097079069709*m.x32 + 1.07459921669364*m.x33
- 1.75763422532393*m.x34 + 1.99992384612834*m.x35 - 1.7407113918798*m.x36
+ 1.0449971294319*m.x37 - 0.0872387747306691*m.x38 - 0.892395626219615*m.x39
+ 1.64825237724403*m.x40 - 1.99079239673436*m.x41 + 1.83412014877025*m.x42
- 1.21752285801745*m.x43 + 0.295618822259225*m.x44 + 0.700414762518932*m.x45
- 1.52081193120006*m.x46 + 1.95984940924166*m.x47 - 1.90743390149645*m.x48
+ 1.37670915138751*m.x49 - 0.500760008108883*m.x50 <= 0.01)
m.c218 = Constraint(expr= - 1.61803398874989*m.x1 + 1.98053613748314*m.x2 - 1.87938524157182*m.x3 + 1.3382612127177*m.x4
- 0.483843791199325*m.x5 - 0.483843791199335*m.x6 + 1.33826121271773*m.x7
- 1.87938524157182*m.x8 + 1.98053613748314*m.x9 - 1.61803398874989*m.x10
+ 0.876742293578153*m.x11 + 0.0697989934050216*m.x12 - 1.00000000000001*m.x13
+ 1.69609619231285*m.x14 - 1.99512810051965*m.x15 + 1.8270909152852*m.x16
- 1.23132295065132*m.x17 + 0.347296355333849*m.x18 + 0.618033988749895*m.x19
- 1.43867960067731*m.x20 + 1.92252339187664*m.x21 - 1.95629520146761*m.x22
+ 1.53208888623795*m.x23 - 0.749213186831821*m.x24 - 0.209056926535299*m.x25
+ 1.1183858069415*m.x26 - 1.76589518571785*m.x27 + 2*m.x28 - 1.76589518571785*m.x29
+ 1.11838580694149*m.x30 - 0.209056926535309*m.x31 - 0.749213186831826*m.x32
+ 1.53208888623796*m.x33 - 1.95629520146761*m.x34 + 1.92252339187664*m.x35
- 1.4386796006773*m.x36 + 0.618033988749891*m.x37 + 0.347296355333868*m.x38
- 1.23132295065132*m.x39 + 1.8270909152852*m.x40 - 1.99512810051965*m.x41
+ 1.69609619231285*m.x42 - 0.999999999999997*m.x43 + 0.0697989934050025*m.x44
+ 0.876742293578157*m.x45 - 1.61803398874989*m.x46 + 1.98053613748314*m.x47
- 1.87938524157182*m.x48 + 1.33826121271772*m.x49 - 0.483843791199335*m.x50 <= 0.01)
m.c219 = Constraint(expr= - 1.94473984079536*m.x1 + 1.52081193120008*m.x2 - 0.765366864730196*m.x3
- 0.156918191455685*m.x4 + 1.04499712943188*m.x5 - 1.70528032870818*m.x6
+ 1.99383466746626*m.x7 - 1.84775906502258*m.x8 + 1.29889609666037*m.x9
- 0.466890727711825*m.x10 - 0.466890727711809*m.x11 + 1.29889609666035*m.x12
- 1.84775906502257*m.x13 + 1.99383466746626*m.x14 - 1.70528032870819*m.x15
+ 1.04499712943189*m.x16 - 0.156918191455701*m.x17 - 0.765366864730181*m.x18
+ 1.52081193120005*m.x19 - 1.94473984079535*m.x20 + 1.94473984079536*m.x21
- 1.52081193120007*m.x22 + 0.765366864730173*m.x23 + 0.156918191455682*m.x24
- 1.0449971294319*m.x25 + 1.70528032870818*m.x26 - 1.99383466746626*m.x27
+ 1.84775906502257*m.x28 - 1.29889609666037*m.x29 + 0.466890727711814*m.x30
+ 0.466890727711806*m.x31 - 1.29889609666036*m.x32 + 1.84775906502257*m.x33
- 1.99383466746626*m.x34 + 1.70528032870818*m.x35 - 1.0449971294319*m.x36
+ 0.15691819145569*m.x37 + 0.765366864730178*m.x38 - 1.52081193120006*m.x39
+ 1.94473984079535*m.x40 - 1.94473984079535*m.x41 + 1.52081193120006*m.x42
- 0.765366864730182*m.x43 - 0.156918191455686*m.x44 + 1.0449971294319*m.x45
- 1.70528032870818*m.x46 + 1.99383466746626*m.x47 - 1.84775906502257*m.x48
+ 1.29889609666037*m.x49 - 0.466890727711811*m.x50 <= 0.01)
m.c220 = Constraint(expr= - 0.907980999479066*m.x1 + 0.0349048128745573*m.x2 + 0.845236523481415*m.x3
- 1.55429192291396*m.x4 + 1.94874012957047*m.x5 - 1.94874012957047*m.x6
+ 1.55429192291393*m.x7 - 0.8452365234814*m.x8 - 0.0349048128745739*m.x9
+ 0.907980999479107*m.x10 - 1.5972710200946*m.x11 + 1.96325436689533*m.x12
- 1.93185165257813*m.x13 + 1.50941916044553*m.x14 - 0.781462256978551*m.x15
- 0.104671912485892*m.x16 + 0.969619240492684*m.x17 - 1.63830408857799*m.x18
+ 1.97537668119028*m.x19 - 1.91260951192607*m.x20 + 1.46270740323833*m.x21
- 0.71673589909058*m.x22 - 0.174311485495317*m.x23 + 1.03007614982012*m.x24
- 1.67734113589086*m.x25 + 1.98509230328264*m.x26 - 1.89103715119863*m.x27
+ 1.41421356237309*m.x28 - 0.651136308914309*m.x29 - 0.243738686810307*m.x30
+ 1.08927807003006*m.x31 - 1.71433460140422*m.x32 + 1.99238939618349*m.x33
- 1.8671608529944*m.x34 + 1.36399672012499*m.x35 - 0.584743409445472*m.x36
- 0.312868930080471*m.x37 + 1.14715287270209*m.x38 - 1.74923941427879*m.x39
+ 1.99725906950915*m.x40 - 1.84100970690488*m.x41 + 1.31211805798101*m.x42
- 0.517638090205042*m.x43 - 0.381617990753089*m.x44 + 1.2036300463041*m.x45
- 1.78201304837674*m.x46 + 1.99969539031278*m.x47 - 1.8126155740733*m.x48
+ 1.25864078209967*m.x49 - 0.44990210868773*m.x50 <= 0.01)
m.c221 = Constraint(expr= 0.765366864730197*m.x1 - 1.47455467362024*m.x2 + 1.90743390149644*m.x3
- 1.98288972274762*m.x4 + 1.68678289162577*m.x5 - 1.07459921669364*m.x6
+ 0.261052384440096*m.x7 + 0.601411599008529*m.x8 - 1.35118041523131*m.x9
+ 1.84775906502257*m.x10 - 1.99809644316372*m.x11 + 1.77402166635645*m.x12
- 1.21752285801744*m.x13 + 0.432879227876208*m.x14 + 0.432879227876207*m.x15
- 1.21752285801744*m.x16 + 1.77402166635645*m.x17 - 1.99809644316372*m.x18
+ 1.84775906502258*m.x19 - 1.35118041523133*m.x20 + 0.601411599008557*m.x21
+ 0.261052384440095*m.x22 - 1.07459921669364*m.x23 + 1.68678289162577*m.x24
- 1.98288972274762*m.x25 + 1.90743390149645*m.x26 - 1.47455467362025*m.x27
+ 0.765366864730185*m.x28 + 0.0872387747306691*m.x29 - 0.923497226470068*m.x30
+ 1.58670668058247*m.x31 - 1.95259201423986*m.x32 + 1.95259201423987*m.x33
- 1.58670668058247*m.x34 + 0.923497226470068*m.x35 - 0.0872387747306696*m.x36
- 0.765366864730184*m.x37 + 1.47455467362024*m.x38 - 1.90743390149645*m.x39
+ 1.98288972274762*m.x40 - 1.68678289162577*m.x41 + 1.07459921669365*m.x42
- 0.261052384440103*m.x43 - 0.601411599008543*m.x44 + 1.35118041523132*m.x45
- 1.84775906502257*m.x46 + 1.99809644316372*m.x47 - 1.77402166635644*m.x48
+ 1.21752285801744*m.x49 - 0.432879227876206*m.x50 <= 0.01)
m.c222 = Constraint(expr= 1.90211303259029*m.x1 - 1.98904379073655*m.x2 + 1.73205080756888*m.x3
- 1.17557050458497*m.x4 + 0.415823381635548*m.x5 + 0.415823381635487*m.x6
- 1.17557050458492*m.x7 + 1.73205080756887*m.x8 - 1.98904379073655*m.x9
+ 1.90211303259031*m.x10 - 1.4862896509548*m.x11 + 0.813473286151615*m.x12
- 1.86183452308593E-14*m.x13 - 0.813473286151581*m.x14 + 1.48628965095477*m.x15
- 1.9021130325903*m.x16 + 1.98904379073655*m.x17 - 1.73205080756889*m.x18
+ 1.17557050458495*m.x19 - 0.415823381635524*m.x20 - 0.415823381635511*m.x21
+ 1.17557050458494*m.x22 - 1.73205080756887*m.x23 + 1.98904379073655*m.x24
- 1.90211303259031*m.x25 + 1.4862896509548*m.x26 - 0.813473286151606*m.x27
+ 8.32883619547518E-15*m.x28 + 0.813473286151591*m.x29 - 1.48628965095478*m.x30
+ 1.9021130325903*m.x31 - 1.98904379073655*m.x32 + 1.73205080756888*m.x33
- 1.17557050458495*m.x34 + 0.415823381635528*m.x35 + 0.415823381635508*m.x36
- 1.17557050458494*m.x37 + 1.73205080756888*m.x38 - 1.98904379073655*m.x39
+ 1.90211303259031*m.x40 - 1.48628965095479*m.x41 + 0.813473286151603*m.x42
- 5.14475451769206E-15*m.x43 - 0.8134732861516*m.x44 + 1.48628965095479*m.x45
- 1.90211303259031*m.x46 + 1.98904379073655*m.x47 - 1.73205080756888*m.x48
+ 1.17557050458495*m.x49 - 0.415823381635519*m.x50 <= 0.01)
m.c223 = Constraint(expr= 1.70528032870819*m.x1 - 1.16140591142187*m.x2 + 0.432879227876224*m.x3
+ 0.364471050984298*m.x4 - 1.10387397062411*m.x5 + 1.66777164413433*m.x6
- 1.96650981512791*m.x7 + 1.95259201423987*m.x8 - 1.62823103671264*m.x9
+ 1.04499712943191*m.x10 - 0.295618822259211*m.x11 - 0.500760008108886*m.x12
+ 1.21752285801744*m.x13 - 1.74071139187979*m.x14 + 1.98714371135318*m.x15
- 1.91763946973639*m.x16 + 1.54324916677544*m.x17 - 0.923497226470078*m.x18
+ 0.15691819145568*m.x19 + 0.634609312810187*m.x20 - 1.32524009643147*m.x21
+ 1.80517056869972*m.x22 - 1.99809644316372*m.x23 + 1.87334437849679*m.x24
- 1.45074874202458*m.x25 + 0.797498137850503*m.x26 - 0.0174530709967376*m.x27
- 0.765366864730182*m.x28 + 1.42650089830836*m.x29 - 1.86083513596405*m.x30
+ 1.99931464995111*m.x31 - 1.81992254175309*m.x32 + 1.35118041523132*m.x33
- 0.667613718467539*m.x34 - 0.12209707906971*m.x35 + 0.892395626219621*m.x36
- 1.52081193120006*m.x37 + 1.90743390149645*m.x38 - 1.99079239673436*m.x39
+ 1.75763422532393*m.x40 - 1.24502927327524*m.x41 + 0.53447675215651*m.x42
+ 0.2610523844401*m.x43 - 1.01507672592141*m.x44 + 1.60771372123443*m.x45
- 1.94473984079535*m.x46 + 1.97257120307446*m.x47 - 1.68678289162577*m.x48
+ 1.13281247384967*m.x49 - 0.398735868834394*m.x50 <= 0.01)
m.c224 = Constraint(expr= 0.312868930080478*m.x1 + 0.44990210868773*m.x2 - 1.14715287270206*m.x3
+ 1.67734113589084*m.x4 - 1.96325436689533*m.x5 + 1.96325436689533*m.x6
- 1.67734113589086*m.x7 + 1.1471528727021*m.x8 - 0.449902108687756*m.x9
- 0.312868930080451*m.x10 + 1.03007614982009*m.x11 - 1.59727102009458*m.x12
+ 1.93185165257813*m.x13 - 1.98509230328264*m.x14 + 1.7492394142788*m.x15
- 1.25864078209969*m.x16 + 0.584743409445482*m.x17 + 0.174311485495295*m.x18
- 0.907980999479089*m.x19 + 1.50941916044553*m.x20 - 1.89103715119863*m.x21
+ 1.99725906950915*m.x22 - 1.81261557407331*m.x23 + 1.363996720125*m.x24
- 0.71673589909062*m.x25 - 0.0349048128745631*m.x26 + 0.781462256978533*m.x27
- 1.41421356237309*m.x28 + 1.84100970690488*m.x29 - 1.99969539031278*m.x30
+ 1.86716085299441*m.x31 - 1.46270740323834*m.x32 + 0.845236523481401*m.x33
- 0.104671912485888*m.x34 - 0.651136308914301*m.x35 + 1.31211805798101*m.x36
- 1.78201304837673*m.x37 + 1.99238939618349*m.x38 - 1.91260951192607*m.x39
+ 1.55429192291394*m.x40 - 0.96961924049268*m.x41 + 0.243738686810299*m.x42
+ 0.517638090205039*m.x43 - 1.2036300463041*m.x44 + 1.71433460140422*m.x45
- 1.97537668119028*m.x46 + 1.94874012957047*m.x47 - 1.63830408857798*m.x48
+ 1.08927807003005*m.x49 - 0.38161799075309*m.x50 <= 0.01)
m.c225 = Constraint(expr= - 1.29889609666039*m.x1 + 1.75763422532392*m.x2 - 1.98288972274762*m.x3
+ 1.94473984079535*m.x4 - 1.64825237724404*m.x5 + 1.13281247384966*m.x6
- 0.466890727711798*m.x7 - 0.261052384440099*m.x8 + 0.954317520519223*m.x9
- 1.52081193120006*m.x10 + 1.88528298218436*m.x11 - 1.99931464995111*m.x12
+ 1.84775906502257*m.x13 - 1.45074874202457*m.x14 + 0.861022193616599*m.x15
- 0.156918191455688*m.x16 - 0.568030689407858*m.x17 + 1.21752285801744*m.x18
- 1.70528032870819*m.x19 + 1.96650981512791*m.x20 - 1.96650981512791*m.x21
+ 1.70528032870818*m.x22 - 1.21752285801744*m.x23 + 0.568030689407837*m.x24
+ 0.156918191455682*m.x25 - 0.861022193616593*m.x26 + 1.45074874202459*m.x27
- 1.84775906502257*m.x28 + 1.99931464995111*m.x29 - 1.88528298218435*m.x30
+ 1.52081193120006*m.x31 - 0.954317520519216*m.x32 + 0.261052384440105*m.x33
+ 0.466890727711807*m.x34 - 1.13281247384967*m.x35 + 1.64825237724403*m.x36
- 1.94473984079535*m.x37 + 1.98288972274762*m.x38 - 1.75763422532393*m.x39
+ 1.29889609666037*m.x40 - 0.667613718467544*m.x41 - 0.0523538966157483*m.x42
+ 0.765366864730179*m.x43 - 1.37670915138751*m.x44 + 1.80517056869972*m.x45
- 1.99383466746626*m.x46 + 1.91763946973639*m.x47 - 1.58670668058247*m.x48
+ 1.0449971294319*m.x49 - 0.364471050984295*m.x50 <= 0.01)
m.c226 = Constraint(expr= - 2*m.x1 + 1.87938524157183*m.x2 - 1.53208888623797*m.x3 + 1.00000000000002*m.x4
- 0.347296355333874*m.x5 - 0.347296355333854*m.x6 + m.x7 - 1.53208888623794*m.x8
+ 1.87938524157181*m.x9 - 2*m.x10 + 1.87938524157182*m.x11 - 1.53208888623797*m.x12
+ 1.00000000000002*m.x13 - 0.347296355333872*m.x14 - 0.347296355333856*m.x15 + m.x16
- 1.53208888623794*m.x17 + 1.87938524157181*m.x18 - 2*m.x19 + 1.87938524157182*m.x20
- 1.53208888623797*m.x21 + 1.00000000000001*m.x22 - 0.34729635533387*m.x23
- 0.347296355333858*m.x24 + m.x25 - 1.53208888623794*m.x26 + 1.87938524157181*m.x27 - 2*m.x28
+ 1.87938524157182*m.x29 - 1.53208888623796*m.x30 + m.x31 - 0.347296355333868*m.x32
- 0.34729635533386*m.x33 + 0.999999999999993*m.x34 - 1.53208888623795*m.x35
+ 1.87938524157181*m.x36 - 2*m.x37 + 1.87938524157182*m.x38 - 1.53208888623796*m.x39 + m.x40
- 0.347296355333866*m.x41 - 0.347296355333855*m.x42 + m.x43 - 1.53208888623796*m.x44
+ 1.87938524157182*m.x45 - 2*m.x46 + 1.87938524157182*m.x47 - 1.53208888623796*m.x48 + m.x49
- 0.347296355333861*m.x50 <= 0.01)
m.c227 = Constraint(expr= - 1.29889609666036*m.x1 + 0.733002453448587*m.x2 - 0.0872387747306628*m.x3
- 0.568030689407856*m.x4 + 1.16140591142189*m.x5 - 1.62823103671265*m.x6
+ 1.91763946973639*m.x7 - 1.99809644316371*m.x8 + 1.86083513596404*m.x9
- 1.52081193120005*m.x10 + 1.01507672592141*m.x11 - 0.398735868834399*m.x12
- 0.2610523844401*m.x13 + 0.892395626219616*m.x14 - 1.42650089830836*m.x15
+ 1.80517056869972*m.x16 - 1.98714371135318*m.x17 + 1.95259201423987*m.x18
- 1.70528032870818*m.x19 + 1.27215644055552*m.x20 - 0.700414762518926*m.x21
+ 0.0523538966157351*m.x22 + 0.601411599008558*m.x23 - 1.18964557350269*m.x24
+ 1.64825237724404*m.x25 - 1.92726090641725*m.x26 + 1.99626959684373*m.x27
- 1.84775906502257*m.x28 + 1.497911441578*m.x29 - 0.984847120206926*m.x30
+ 0.364471050984298*m.x31 + 0.29561882225922*m.x32 - 0.923497226470068*m.x33
+ 1.45074874202458*m.x34 - 1.81992254175309*m.x35 + 1.99079239673436*m.x36
- 1.94473984079535*m.x37 + 1.68678289162577*m.x38 - 1.24502927327524*m.x39
+ 0.667613718467538*m.x40 - 0.0174530709967489*m.x41 - 0.634609312810185*m.x42
+ 1.21752285801744*m.x43 - 1.66777164413434*m.x44 + 1.93629528075622*m.x45
- 1.99383466746626*m.x46 + 1.83412014877025*m.x47 - 1.47455467362025*m.x48
+ 0.954317520519216*m.x49 - 0.330095211721355*m.x50 <= 0.01)
m.c228 = Constraint(expr= 0.312868930080456*m.x1 - 0.907980999479086*m.x2 + 1.41421356237309*m.x3
- 1.78201304837673*m.x4 + 1.97537668119027*m.x5 - 1.97537668119028*m.x6
+ 1.78201304837673*m.x7 - 1.4142135623731*m.x8 + 0.907980999479097*m.x9
- 0.312868930080469*m.x10 - 0.312868930080451*m.x11 + 0.907980999479081*m.x12
- 1.41421356237308*m.x13 + 1.78201304837673*m.x14 - 1.97537668119028*m.x15
+ 1.97537668119028*m.x16 - 1.78201304837674*m.x17 + 1.4142135623731*m.x18
- 0.907980999479101*m.x19 + 0.312868930080474*m.x20 + 0.312868930080447*m.x21
- 0.907980999479077*m.x22 + 1.4142135623731*m.x23 - 1.78201304837674*m.x24
+ 1.97537668119028*m.x25 - 1.97537668119028*m.x26 + 1.78201304837674*m.x27
- 1.4142135623731*m.x28 + 0.907980999479093*m.x29 - 0.312868930080464*m.x30
- 0.312868930080456*m.x31 + 0.907980999479085*m.x32 - 1.4142135623731*m.x33
+ 1.78201304837673*m.x34 - 1.97537668119027*m.x35 + 1.97537668119028*m.x36
- 1.78201304837673*m.x37 + 1.4142135623731*m.x38 - 0.907980999479097*m.x39
+ 0.312868930080462*m.x40 + 0.312868930080458*m.x41 - 0.907980999479093*m.x42
+ 1.41421356237309*m.x43 - 1.78201304837674*m.x44 + 1.97537668119027*m.x45
- 1.97537668119028*m.x46 + 1.78201304837674*m.x47 - 1.4142135623731*m.x48
+ 0.907980999479094*m.x49 - 0.312868930080462*m.x50 <= 0.01)
m.c229 = Constraint(expr= 1.70528032870821*m.x1 - 1.93629528075622*m.x2 + 1.99809644316371*m.x3
- 1.88528298218435*m.x4 + 1.60771372123443*m.x5 - 1.18964557350268*m.x6
+ 0.667613718467524*m.x7 - 0.0872387747306618*m.x8 - 0.500760008108885*m.x9
+ 1.04499712943192*m.x10 - 1.49791144157801*m.x11 + 1.81992254175309*m.x12
- 1.98288972274762*m.x13 + 1.97257120307446*m.x14 - 1.78986872320405*m.x15
+ 1.45074874202457*m.x16 - 0.984847120206916*m.x17 + 0.432879227876193*m.x18
+ 0.156918191455694*m.x19 - 0.733002453448591*m.x20 + 1.24502927327525*m.x21
- 1.64825237724404*m.x22 + 1.90743390149645*m.x23 - 1.99992384612834*m.x24
+ 1.91763946973638*m.x25 - 1.66777164413433*m.x26 + 1.27215644055553*m.x27
- 0.765366864730165*m.x28 + 0.191691505040441*m.x29 + 0.398735868834393*m.x30
- 0.954317520519221*m.x31 + 1.42650089830837*m.x32 - 1.77402166635644*m.x33
+ 1.96650981512791*m.x34 - 1.98714371135317*m.x35 + 1.83412014877025*m.x36
- 1.52081193120006*m.x37 + 1.07459921669364*m.x38 - 0.534476752156504*m.x39
- 0.0523538966157478*m.x40 + 0.634609312810185*m.x41 - 1.16140591142188*m.x42
+ 1.58670668058247*m.x43 - 1.8733443784968*m.x44 + 1.99626959684373*m.x45
- 1.94473984079535*m.x46 + 1.72325832088305*m.x47 - 1.35118041523132*m.x48
+ 0.86102219361659*m.x49 - 0.295618822259221*m.x50 <= 0.01)
m.c230 = Constraint(expr= 1.90211303259032*m.x1 - 1.65807514511008*m.x2 + 1.28557521937308*m.x3
- 0.813473286151611*m.x4 + 0.278346201920155*m.x5 + 0.278346201920121*m.x6
- 0.813473286151606*m.x7 + 1.28557521937307*m.x8 - 1.65807514511009*m.x9
+ 1.90211303259031*m.x10 - 1.99878165403819*m.x11 + 1.94059145255199*m.x12
- 1.73205080756889*m.x13 + 1.389316740918*m.x14 - 0.938943125571794*m.x15
+ 0.415823381635518*m.x16 + 0.139512947488238*m.x17 - 0.68404028665134*m.x18
+ 1.17557050458494*m.x19 - 1.57602150721345*m.x20 + 1.85436770913357*m.x21
- 1.98904379073655*m.x22 + 1.96961550602442*m.x23 - 1.79758809259833*m.x24
+ 1.48628965095479*m.x25 - 1.0598385284664*m.x26 + 0.551274711634001*m.x27
- 1.56791929129057E-14*m.x28 - 0.551274711633998*m.x29 + 1.05983852846641*m.x30
- 1.48628965095479*m.x31 + 1.79758809259834*m.x32 - 1.96961550602442*m.x33
+ 1.98904379073655*m.x34 - 1.85436770913358*m.x35 + 1.57602150721345*m.x36
- 1.17557050458495*m.x37 + 0.684040286651343*m.x38 - 0.139512947488255*m.x39
- 0.415823381635515*m.x40 + 0.938943125571779*m.x41 - 1.38931674091799*m.x42
+ 1.73205080756888*m.x43 - 1.94059145255199*m.x44 + 1.99878165403819*m.x45
- 1.90211303259031*m.x46 + 1.65807514511008*m.x47 - 1.28557521937308*m.x48
+ 0.8134732861516*m.x49 - 0.278346201920131*m.x50 <= 0.01)
m.c231 = Constraint(expr= 0.765366864730217*m.x1 - 0.261052384440105*m.x2 - 0.261052384440083*m.x3
+ 0.765366864730144*m.x4 - 1.21752285801744*m.x5 + 1.58670668058246*m.x6
- 1.84775906502256*m.x7 + 1.98288972274762*m.x8 - 1.98288972274762*m.x9
+ 1.84775906502259*m.x10 - 1.58670668058248*m.x11 + 1.21752285801745*m.x12
- 0.765366864730208*m.x13 + 0.261052384440123*m.x14 + 0.261052384440093*m.x15
- 0.765366864730153*m.x16 + 1.21752285801743*m.x17 - 1.58670668058247*m.x18
+ 1.84775906502256*m.x19 - 1.98288972274762*m.x20 + 1.98288972274762*m.x21
- 1.84775906502258*m.x22 + 1.58670668058248*m.x23 - 1.21752285801744*m.x24
+ 0.765366864730199*m.x25 - 0.261052384440114*m.x26 - 0.261052384440103*m.x27
+ 0.765366864730163*m.x28 - 1.21752285801743*m.x29 + 1.58670668058246*m.x30
- 1.84775906502257*m.x31 + 1.98288972274762*m.x32 - 1.98288972274762*m.x33
+ 1.84775906502258*m.x34 - 1.58670668058247*m.x35 + 1.21752285801745*m.x36
- 0.76536686473019*m.x37 + 0.261052384440104*m.x38 + 0.261052384440099*m.x39
- 0.765366864730172*m.x40 + 1.21752285801744*m.x41 - 1.58670668058247*m.x42
+ 1.84775906502257*m.x43 - 1.98288972274762*m.x44 + 1.98288972274762*m.x45
- 1.84775906502257*m.x46 + 1.58670668058247*m.x47 - 1.21752285801744*m.x48
+ 0.765366864730181*m.x49 - 0.261052384440103*m.x50 <= 0.01)
m.c232 = Constraint(expr= - 0.907980999479098*m.x1 + 1.312118057981*m.x2 - 1.63830408857799*m.x3 + 1.8671608529944*m.x4
- 1.98509230328265*m.x5 + 1.98509230328264*m.x6 - 1.8671608529944*m.x7 + 1.63830408857799*m.x8
- 1.31211805798102*m.x9 + 0.907980999479097*m.x10 - 0.449902108687729*m.x11
- 0.0349048128745739*m.x12 + 0.517638090205053*m.x13 - 0.969619240492665*m.x14
+ 1.36399672012499*m.x15 - 1.67734113589085*m.x16 + 1.89103715119864*m.x17
- 1.99238939618349*m.x18 + 1.97537668119028*m.x19 - 1.84100970690488*m.x20
+ 1.59727102009459*m.x21 - 1.25864078209967*m.x22 + 0.845236523481391*m.x23
- 0.381617990753103*m.x24 - 0.104671912485879*m.x25 + 0.58474340944547*m.x26
- 1.03007614982011*m.x27 + 1.4142135623731*m.x28 - 1.71433460140422*m.x29
+ 1.91260951192607*m.x30 - 1.99725906950915*m.x31 + 1.96325436689533*m.x32
- 1.8126155740733*m.x33 + 1.55429192291394*m.x34 - 1.20363004630409*m.x35
+ 0.781462256978554*m.x36 - 0.312868930080463*m.x37 - 0.17431148549532*m.x38
+ 0.651136308914309*m.x39 - 1.08927807003005*m.x40 + 1.46270740323834*m.x41
- 1.74923941427879*m.x42 + 1.93185165257814*m.x43 - 1.99969539031278*m.x44
+ 1.94874012957047*m.x45 - 1.78201304837674*m.x46 + 1.50941916044554*m.x47
- 1.14715287270209*m.x48 + 0.716735899090601*m.x49 - 0.243738686810295*m.x50 <= 0.01)
m.c233 = Constraint(expr= - 1.94473984079535*m.x1 + 1.99992384612834*m.x2 - 1.95259201423987*m.x3
+ 1.80517056869974*m.x4 - 1.56521631370483*m.x5 + 1.24502927327527*m.x6
- 0.861022193616594*m.x7 + 0.432879227876237*m.x8 + 0.0174530709967445*m.x9
- 0.46689072771178*m.x10 + 0.892395626219615*m.x11 - 1.2721564405555*m.x12
+ 1.58670668058247*m.x13 - 1.81992254175307*m.x14 + 1.95984940924166*m.x15
- 1.99931464995112*m.x16 + 1.93629528075622*m.x17 - 1.77402166635646*m.x18
+ 1.52081193120006*m.x19 - 1.1896455735027*m.x20 + 0.797498137850491*m.x21
- 0.364471050984321*m.x22 - 0.0872387747306745*m.x23 + 0.534476752156489*m.x24
- 0.95431752051922*m.x25 + 1.32524009643146*m.x26 - 1.62823103671264*m.x27
+ 1.84775906502256*m.x28 - 1.97257120307446*m.x29 + 1.99626959684373*m.x30
- 1.91763946973639*m.x31 + 1.7407113918798*m.x32 - 1.47455467362025*m.x33
+ 1.13281247384967*m.x34 - 0.733002453448601*m.x35 + 0.295618822259227*m.x36
+ 0.156918191455684*m.x37 - 0.601411599008541*m.x38 + 1.0150767259214*m.x39
- 1.3767091513875*m.x40 + 1.66777164413433*m.x41 - 1.87334437849679*m.x42
+ 1.98288972274762*m.x43 - 1.99079239673436*m.x44 + 1.8966473104124*m.x45
- 1.70528032870819*m.x46 + 1.42650089830836*m.x47 - 1.07459921669365*m.x48
+ 0.667613718467542*m.x49 - 0.226406427535814*m.x50 <= 0.01)
m.c234 = Constraint(expr= - 1.61803398874991*m.x1 + 1.33826121271771*m.x2 - 0.999999999999974*m.x3
+ 0.618033988749898*m.x4 - 0.209056926535287*m.x5 - 0.209056926535294*m.x6
+ 0.618033988749906*m.x7 - 1.00000000000001*m.x8 + 1.33826121271772*m.x9
- 1.61803398874989*m.x10 + 1.8270909152852*m.x11 - 1.95629520146761*m.x12 + 2*m.x13
- 1.95629520146761*m.x14 + 1.8270909152852*m.x15 - 1.6180339887499*m.x16
+ 1.33826121271772*m.x17 - 1.00000000000001*m.x18 + 0.618033988749881*m.x19
- 0.209056926535297*m.x20 - 0.209056926535312*m.x21 + 0.618033988749895*m.x22
- 0.999999999999997*m.x23 + 1.33826121271771*m.x24 - 1.6180339887499*m.x25
+ 1.82709091528521*m.x26 - 1.95629520146761*m.x27 + 2*m.x28 - 1.95629520146761*m.x29
+ 1.8270909152852*m.x30 - 1.61803398874989*m.x31 + 1.33826121271772*m.x32
- 0.999999999999993*m.x33 + 0.618033988749892*m.x34 - 0.209056926535308*m.x35
- 0.209056926535301*m.x36 + 0.618033988749899*m.x37 - m.x38 + 1.33826121271771*m.x39
- 1.61803398874989*m.x40 + 1.8270909152852*m.x41 - 1.95629520146761*m.x42 + 2*m.x43
- 1.95629520146761*m.x44 + 1.8270909152852*m.x45 - 1.6180339887499*m.x46
+ 1.33826121271772*m.x47 - m.x48 + 0.618033988749895*m.x49 - 0.209056926535307*m.x50 <= 0.01)
m.c235 = Constraint(expr= - 0.15691819145572*m.x1 - 0.226406427535802*m.x2 + 0.601411599008554*m.x3
- 0.95431752051919*m.x4 + 1.27215644055552*m.x5 - 1.54324916677545*m.x6
+ 1.75763422532392*m.x7 - 1.90743390149645*m.x8 + 1.98714371135317*m.x9
- 1.99383466746626*m.x10 + 1.92726090641725*m.x11 - 1.78986872320406*m.x12
+ 1.58670668058247*m.x13 - 1.32524009643148*m.x14 + 1.01507672592143*m.x15
- 0.667613718467543*m.x16 + 0.295618822259232*m.x17 + 0.087238774730652*m.x18
- 0.46689072771181*m.x19 + 0.829386485312469*m.x20 - 1.16140591142186*m.x21
+ 1.45074874202457*m.x22 - 1.68678289162577*m.x23 + 1.86083513596404*m.x24
- 1.96650981512791*m.x25 + 1.99992384612834*m.x26 - 1.95984940924166*m.x27
+ 1.84775906502257*m.x28 - 1.66777164413434*m.x29 + 1.42650089830837*m.x30
- 1.13281247384967*m.x31 + 0.797498137850502*m.x32 - 0.432879227876211*m.x33
+ 0.0523538966157468*m.x34 + 0.330095211721345*m.x35 - 0.70041476251893*m.x36
+ 1.0449971294319*m.x37 - 1.35118041523131*m.x38 + 1.60771372123443*m.x39
- 1.80517056869972*m.x40 + 1.93629528075621*m.x41 - 1.99626959684373*m.x42
+ 1.98288972274762*m.x43 - 1.8966473104124*m.x44 + 1.7407113918798*m.x45
- 1.52081193120006*m.x46 + 1.24502927327524*m.x47 - 0.923497226470069*m.x48
+ 0.568030689407845*m.x49 - 0.191691505040448*m.x50 <= 0.01)
m.c236 = Constraint(expr= 1.41421356237311*m.x1 - 1.638304088578*m.x2 + 1.81261557407329*m.x3 - 1.93185165257814*m.x4
+ 1.99238939618349*m.x5 - 1.99238939618349*m.x6 + 1.93185165257814*m.x7 - 1.8126155740733*m.x8
+ 1.63830408857798*m.x9 - 1.4142135623731*m.x10 + 1.14715287270208*m.x11
- 0.8452365234814*m.x12 + 0.517638090205029*m.x13 - 0.174311485495317*m.x14
- 0.174311485495329*m.x15 + 0.51763809020504*m.x16 - 0.84523652348141*m.x17
+ 1.14715287270209*m.x18 - 1.4142135623731*m.x19 + 1.63830408857798*m.x20
- 1.8126155740733*m.x21 + 1.93185165257814*m.x22 - 1.99238939618349*m.x23
+ 1.99238939618349*m.x24 - 1.93185165257813*m.x25 + 1.8126155740733*m.x26
- 1.63830408857798*m.x27 + 1.4142135623731*m.x28 - 1.14715287270208*m.x29
+ 0.845236523481402*m.x30 - 0.517638090205032*m.x31 + 0.17431148549532*m.x32
+ 0.174311485495326*m.x33 - 0.517638090205037*m.x34 + 0.845236523481408*m.x35
- 1.14715287270209*m.x36 + 1.4142135623731*m.x37 - 1.63830408857798*m.x38
+ 1.8126155740733*m.x39 - 1.93185165257814*m.x40 + 1.99238939618349*m.x41
- 1.99238939618349*m.x42 + 1.93185165257814*m.x43 - 1.8126155740733*m.x44
+ 1.63830408857798*m.x45 - 1.41421356237309*m.x46 + 1.14715287270209*m.x47
- 0.845236523481399*m.x48 + 0.517638090205041*m.x49 - 0.174311485495316*m.x50 <= 0.01)
m.c237 = Constraint(expr= 1.99383466746626*m.x1 - 1.94473984079535*m.x2 + 1.84775906502259*m.x3 - 1.7052803287082*m.x4
+ 1.52081193120007*m.x5 - 1.29889609666037*m.x6 + 1.0449971294319*m.x7
- 0.765366864730196*m.x8 + 0.466890727711819*m.x9 - 0.15691819145569*m.x10
- 0.156918191455671*m.x11 + 0.466890727711801*m.x12 - 0.765366864730179*m.x13
+ 1.04499712943188*m.x14 - 1.29889609666036*m.x15 + 1.52081193120006*m.x16
- 1.70528032870817*m.x17 + 1.84775906502257*m.x18 - 1.94473984079535*m.x19
+ 1.99383466746626*m.x20 - 1.99383466746626*m.x21 + 1.94473984079535*m.x22
- 1.84775906502257*m.x23 + 1.70528032870819*m.x24 - 1.52081193120007*m.x25
+ 1.29889609666036*m.x26 - 1.04499712943191*m.x27 + 0.765366864730185*m.x28
- 0.466890727711808*m.x29 + 0.156918191455692*m.x30 + 0.156918191455683*m.x31
- 0.466890727711813*m.x32 + 0.765366864730177*m.x33 - 1.04499712943189*m.x34
+ 1.29889609666037*m.x35 - 1.52081193120006*m.x36 + 1.70528032870818*m.x37
- 1.84775906502257*m.x38 + 1.94473984079535*m.x39 - 1.99383466746626*m.x40
+ 1.99383466746626*m.x41 - 1.94473984079535*m.x42 + 1.84775906502258*m.x43
- 1.70528032870819*m.x44 + 1.52081193120006*m.x45 - 1.29889609666037*m.x46
+ 1.0449971294319*m.x47 - 0.765366864730181*m.x48 + 0.46689072771181*m.x49
- 0.15691819145569*m.x50 <= 0.01)
m.c238 = Constraint(expr= 1.17557050458495*m.x1 - 0.938943125571785*m.x2 + 0.684040286651337*m.x3
- 0.415823381635514*m.x4 + 0.139512947488242*m.x5 + 0.139512947488263*m.x6
- 0.415823381635535*m.x7 + 0.684040286651357*m.x8 - 0.938943125571779*m.x9
+ 1.17557050458495*m.x10 - 1.389316740918*m.x11 + 1.57602150721345*m.x12
- 1.73205080756888*m.x13 + 1.85436770913358*m.x14 - 1.940591452552*m.x15
+ 1.98904379073655*m.x16 - 1.99878165403819*m.x17 + 1.96961550602441*m.x18
- 1.9021130325903*m.x19 + 1.79758809259833*m.x20 - 1.65807514511007*m.x21
+ 1.48628965095479*m.x22 - 1.28557521937308*m.x23 + 1.05983852846641*m.x24
- 0.813473286151594*m.x25 + 0.551274711633988*m.x26 - 0.278346201920116*m.x27
- 1.96030145152699E-14*m.x28 + 0.278346201920126*m.x29 - 0.551274711633998*m.x30
+ 0.813473286151604*m.x31 - 1.05983852846642*m.x32 + 1.28557521937308*m.x33
- 1.48628965095479*m.x34 + 1.65807514511009*m.x35 - 1.79758809259834*m.x36
+ 1.90211303259031*m.x37 - 1.96961550602442*m.x38 + 1.99878165403819*m.x39
- 1.98904379073655*m.x40 + 1.94059145255199*m.x41 - 1.85436770913357*m.x42
+ 1.73205080756888*m.x43 - 1.57602150721344*m.x44 + 1.389316740918*m.x45
- 1.17557050458494*m.x46 + 0.938943125571782*m.x47 - 0.684040286651337*m.x48
+ 0.415823381635518*m.x49 - 0.13951294748825*m.x50 <= 0.01)
m.c239 = Constraint(expr= - 0.466890727711792*m.x1 + 0.700414762518916*m.x2 - 0.923497226470049*m.x3
+ 1.13281247384965*m.x4 - 1.32524009643146*m.x5 + 1.49791144157799*m.x6
- 1.64825237724402*m.x7 + 1.77402166635643*m.x8 - 1.8733443784968*m.x9
+ 1.94473984079535*m.x10 - 1.98714371135318*m.x11 + 1.99992384612834*m.x12
- 1.98288972274762*m.x13 + 1.93629528075622*m.x14 - 1.86083513596405*m.x15
+ 1.75763422532393*m.x16 - 1.62823103671264*m.x17 + 1.47455467362025*m.x18
- 1.29889609666037*m.x19 + 1.10387397062412*m.x20 - 0.892395626219623*m.x21
+ 0.667613718467548*m.x22 - 0.432879227876213*m.x23 + 0.191691505040456*m.x24
+ 0.052353896615737*m.x25 - 0.295618822259211*m.x26 + 0.534476752156503*m.x27
- 0.765366864730169*m.x28 + 0.984847120206924*m.x29 - 1.18964557350268*m.x30
+ 1.37670915138751*m.x31 - 1.54324916677544*m.x32 + 1.68678289162577*m.x33
- 1.80517056869972*m.x34 + 1.8966473104124*m.x35 - 1.95984940924166*m.x36
+ 1.99383466746626*m.x37 - 1.99809644316372*m.x38 + 1.97257120307446*m.x39
- 1.91763946973639*m.x40 + 1.83412014877025*m.x41 - 1.72325832088305*m.x42
+ 1.58670668058247*m.x43 - 1.42650089830836*m.x44 + 1.24502927327524*m.x45
- 1.0449971294319*m.x46 + 0.829386485312479*m.x47 - 0.601411599008546*m.x48
+ 0.364471050984296*m.x49 - 0.122097079069714*m.x50 <= 0.01)
m.c240 = Constraint(expr= - 1.78201304837672*m.x1 + 1.86716085299439*m.x2 - 1.93185165257813*m.x3
+ 1.97537668119027*m.x4 - 1.99725906950915*m.x5 + 1.99725906950915*m.x6
- 1.97537668119028*m.x7 + 1.93185165257814*m.x8 - 1.86716085299441*m.x9
+ 1.78201304837675*m.x10 - 1.67734113589086*m.x11 + 1.55429192291395*m.x12
- 1.41421356237311*m.x13 + 1.25864078209969*m.x14 - 1.08927807003008*m.x15
+ 0.907980999479121*m.x16 - 0.716735899090609*m.x17 + 0.517638090205055*m.x18
- 0.312868930080481*m.x19 + 0.104671912485913*m.x20 + 0.104671912485885*m.x21
- 0.312868930080453*m.x22 + 0.517638090205028*m.x23 - 0.716735899090582*m.x24
+ 0.907980999479071*m.x25 - 1.08927807003005*m.x26 + 1.25864078209967*m.x27
- 1.41421356237308*m.x28 + 1.55429192291393*m.x29 - 1.67734113589084*m.x30
+ 1.78201304837673*m.x31 - 1.8671608529944*m.x32 + 1.93185165257813*m.x33
- 1.97537668119027*m.x34 + 1.99725906950915*m.x35 - 1.99725906950915*m.x36
+ 1.97537668119028*m.x37 - 1.93185165257814*m.x38 + 1.86716085299441*m.x39
- 1.78201304837674*m.x40 + 1.67734113589085*m.x41 - 1.55429192291394*m.x42
+ 1.4142135623731*m.x43 - 1.25864078209968*m.x44 + 1.08927807003006*m.x45
- 0.907980999479096*m.x46 + 0.716735899090602*m.x47 - 0.517638090205043*m.x48
+ 0.312868930080462*m.x49 - 0.104671912485888*m.x50 <= 0.01)
m.c241 = Constraint(expr= - 1.84775906502257*m.x1 + 1.77402166635644*m.x2 - 1.68678289162578*m.x3
+ 1.58670668058248*m.x4 - 1.47455467362027*m.x5 + 1.35118041523131*m.x6
- 1.21752285801744*m.x7 + 1.07459921669365*m.x8 - 0.92349722647008*m.x9
+ 0.765366864730176*m.x10 - 0.601411599008553*m.x11 + 0.432879227876195*m.x12
- 0.261052384440103*m.x13 + 0.0872387747306824*m.x14 + 0.0872387747306794*m.x15
- 0.2610523844401*m.x16 + 0.432879227876192*m.x17 - 0.60141159900855*m.x18
+ 0.765366864730173*m.x19 - 0.923497226470078*m.x20 + 1.07459921669365*m.x21
- 1.21752285801743*m.x22 + 1.35118041523133*m.x23 - 1.47455467362025*m.x24
+ 1.58670668058246*m.x25 - 1.68678289162577*m.x26 + 1.77402166635644*m.x27
- 1.84775906502258*m.x28 + 1.90743390149645*m.x29 - 1.95259201423987*m.x30
+ 1.98288972274762*m.x31 - 1.99809644316372*m.x32 + 1.99809644316372*m.x33
- 1.98288972274762*m.x34 + 1.95259201423987*m.x35 - 1.90743390149645*m.x36
+ 1.84775906502257*m.x37 - 1.77402166635644*m.x38 + 1.68678289162577*m.x39
- 1.58670668058247*m.x40 + 1.47455467362025*m.x41 - 1.35118041523132*m.x42
+ 1.21752285801744*m.x43 - 1.07459921669365*m.x44 + 0.923497226470066*m.x45
- 0.765366864730181*m.x46 + 0.601411599008545*m.x47 - 0.432879227876206*m.x48
+ 0.261052384440103*m.x49 - 0.087238774730672*m.x50 <= 0.01)
m.c242 = Constraint(expr= - 0.618033988749893*m.x1 + 0.483843791199348*m.x2 - 0.347296355333889*m.x3
+ 0.209056926535351*m.x4 - 0.0697989934050049*m.x5 - 0.0697989934049834*m.x6
+ 0.209056926535273*m.x7 - 0.347296355333868*m.x8 + 0.483843791199327*m.x9
- 0.618033988749872*m.x10 + 0.749213186831814*m.x11 - 0.876742293578131*m.x12
+ 0.999999999999988*m.x13 - 1.11838580694147*m.x14 + 1.2313229506513*m.x15
- 1.33826121271771*m.x16 + 1.43867960067729*m.x17 - 1.53208888623795*m.x18
+ 1.61803398874988*m.x19 - 1.69609619231285*m.x20 + 1.76589518571784*m.x21
- 1.8270909152852*m.x22 + 1.87938524157182*m.x23 - 1.92252339187663*m.x24
+ 1.95629520146761*m.x25 - 1.98053613748314*m.x26 + 1.99512810051965*m.x27 - 2*m.x28
+ 1.99512810051965*m.x29 - 1.98053613748314*m.x30 + 1.95629520146761*m.x31
- 1.92252339187664*m.x32 + 1.87938524157182*m.x33 - 1.82709091528521*m.x34
+ 1.76589518571785*m.x35 - 1.69609619231285*m.x36 + 1.6180339887499*m.x37
- 1.53208888623796*m.x38 + 1.43867960067731*m.x39 - 1.33826121271772*m.x40
+ 1.23132295065132*m.x41 - 1.1183858069415*m.x42 + m.x43 - 0.87674229357816*m.x44
+ 0.749213186831824*m.x45 - 0.618033988749896*m.x46 + 0.483843791199338*m.x47
- 0.347296355333861*m.x48 + 0.209056926535308*m.x49 - 0.0697989934050022*m.x50 <= 0.01)
m.c243 = Constraint(expr= 1.04499712943189*m.x1 - 1.13281247384969*m.x2 + 1.21752285801745*m.x3
- 1.29889609666036*m.x4 + 1.37670915138752*m.x5 - 1.45074874202458*m.x6
+ 1.52081193120005*m.x7 - 1.58670668058248*m.x8 + 1.64825237724403*m.x9
- 1.70528032870819*m.x10 + 1.75763422532394*m.x11 - 1.80517056869972*m.x12
+ 1.84775906502257*m.x13 - 1.88528298218436*m.x14 + 1.91763946973638*m.x15
- 1.94473984079535*m.x16 + 1.96650981512791*m.x17 - 1.98288972274762*m.x18
+ 1.99383466746626*m.x19 - 1.99931464995111*m.x20 + 1.99931464995111*m.x21
- 1.99383466746625*m.x22 + 1.98288972274762*m.x23 - 1.96650981512791*m.x24
+ 1.94473984079535*m.x25 - 1.91763946973639*m.x26 + 1.88528298218436*m.x27
- 1.84775906502257*m.x28 + 1.80517056869972*m.x29 - 1.75763422532393*m.x30
+ 1.70528032870818*m.x31 - 1.64825237724403*m.x32 + 1.58670668058247*m.x33
- 1.52081193120006*m.x34 + 1.45074874202457*m.x35 - 1.37670915138751*m.x36
+ 1.29889609666036*m.x37 - 1.21752285801744*m.x38 + 1.13281247384967*m.x39
- 1.0449971294319*m.x40 + 0.954317520519214*m.x41 - 0.861022193616587*m.x42
+ 0.765366864730182*m.x43 - 0.667613718467544*m.x44 + 0.568030689407846*m.x45
- 0.466890727711811*m.x46 + 0.364471050984294*m.x47 - 0.261052384440103*m.x48
+ 0.156918191455689*m.x49 - 0.0523538966157463*m.x50 <= 0.01)
m.c244 = Constraint(expr= 1.97537668119027*m.x1 - 1.98509230328265*m.x2 + 1.99238939618349*m.x3
- 1.99725906950915*m.x4 + 1.99969539031278*m.x5 - 1.99969539031278*m.x6
+ 1.99725906950915*m.x7 - 1.99238939618349*m.x8 + 1.98509230328264*m.x9
- 1.97537668119028*m.x10 + 1.96325436689533*m.x11 - 1.94874012957047*m.x12
+ 1.93185165257814*m.x13 - 1.91260951192607*m.x14 + 1.89103715119864*m.x15
- 1.86716085299441*m.x16 + 1.84100970690488*m.x17 - 1.8126155740733*m.x18
+ 1.78201304837675*m.x19 - 1.7492394142788*m.x20 + 1.71433460140423*m.x21
- 1.67734113589086*m.x22 + 1.63830408857799*m.x23 - 1.59727102009459*m.x24
+ 1.55429192291395*m.x25 - 1.50941916044555*m.x26 + 1.46270740323834*m.x27
- 1.41421356237309*m.x28 + 1.36399672012499*m.x29 - 1.31211805798102*m.x30
+ 1.25864078209968*m.x31 - 1.20363004630411*m.x32 + 1.1471528727021*m.x33
- 1.08927807003006*m.x34 + 1.03007614982011*m.x35 - 0.969619240492675*m.x36
+ 0.907980999479092*m.x37 - 0.845236523481407*m.x38 + 0.781462256978553*m.x39
- 0.716735899090603*m.x40 + 0.651136308914314*m.x41 - 0.584743409445478*m.x42
+ 0.517638090205043*m.x43 - 0.449902108687735*m.x44 + 0.381617990753092*m.x45
- 0.312868930080465*m.x46 + 0.243738686810295*m.x47 - 0.174311485495317*m.x48
+ 0.104671912485889*m.x49 - 0.0349048128745672*m.x50 <= 0.01)
m.c245 = Constraint(expr= 1.52081193120005*m.x1 - 1.49791144157801*m.x2 + 1.47455467362023*m.x3
- 1.45074874202458*m.x4 + 1.42650089830835*m.x5 - 1.40181852859971*m.x6 + 1.3767091513875*m.x7
- 1.35118041523133*m.x8 + 1.32524009643147*m.x9 - 1.29889609666036*m.x10
+ 1.27215644055552*m.x11 - 1.24502927327524*m.x12 + 1.21752285801744*m.x13
- 1.18964557350268*m.x14 + 1.16140591142188*m.x15 - 1.13281247384967*m.x16
+ 1.10387397062412*m.x17 - 1.07459921669365*m.x18 + 1.04499712943188*m.x19
- 1.01507672592139*m.x20 + 0.984847120206922*m.x21 - 0.954317520519206*m.x22
+ 0.923497226470059*m.x23 - 0.89239562621961*m.x24 + 0.861022193616584*m.x25
- 0.829386485312474*m.x26 + 0.79749813785049*m.x27 - 0.765366864730179*m.x28
+ 0.733002453448596*m.x29 - 0.700414762518938*m.x30 + 0.667613718467533*m.x31
- 0.634609312810177*m.x32 + 0.601411599008541*m.x33 - 0.568030689407842*m.x34
+ 0.534476752156512*m.x35 - 0.500760008108883*m.x36 + 0.466890727711813*m.x37
- 0.43287922787621*m.x38 + 0.398735868834387*m.x39 - 0.364471050984289*m.x40
+ 0.330095211721351*m.x41 - 0.295618822259219*m.x42 + 0.261052384440103*m.x43
- 0.226406427535815*m.x44 + 0.191691505040444*m.x45 - 0.156918191455688*m.x46
+ 0.122097079069714*m.x47 - 0.0872387747306707*m.x48 + 0.052353896615747*m.x49
- 0.0174530709967478*m.x50 <= 0.01)
m.c246 = Constraint(expr= - 1.27449924400447E-14*m.x1 - 2.25372149881308E-14*m.x2 + 9.76003555498352E-16*m.x3
- 3.62582109836739E-14*m.x4 + 1.46969995510414E-14*m.x5 + 6.86421188159106E-15*m.x6
+ 2.84179955465845E-14*m.x7 - 6.856784113952E-15*m.x8 - 1.47044273186805E-14*m.x9
- 2.05777801094951E-14*m.x10 - 9.83431323137413E-16*m.x11 - 5.87706667463412E-15*m.x12
+ 1.27375646724056E-14*m.x13 - 1.95980626701772E-14*m.x14 - 1.9631487624553E-15*m.x15
- 4.89734923531624E-15*m.x16 + 1.17578472330878E-14*m.x17 - 1.86183452308593E-14*m.x18
- 2.94286620177318E-15*m.x19 - 3.91763179599835E-15*m.x20 + 1.07781297937699E-14*m.x21
- 1.76386277915414E-14*m.x22 - 3.92258364109106E-15*m.x23 - 2.93791435668047E-15*m.x24
+ 9.798412354452E-15*m.x25 - 1.66589103522235E-14*m.x26 - 4.90230108040894E-15*m.x27
- 1.95819691736259E-15*m.x28 + 8.81869491513412E-15*m.x29 - 1.56791929129057E-14*m.x30
+ 8.32883619547518E-15*m.x31 - 9.78479478044706E-16*m.x32 + 7.83897747581624E-15*m.x33
- 4.88620758385765E-16*m.x34 + 7.3491187561573E-15*m.x35 + 1.23796127317672E-18*m.x36
+ 6.85926003649835E-15*m.x37 + 4.91096680932118E-16*m.x38 + 6.36940131683941E-15*m.x39
+ 9.80955400591059E-16*m.x40 + 5.87954259718047E-15*m.x41 + 1.47081412025E-15*m.x42
+ 5.38968387752153E-15*m.x43 + 1.96067283990894E-15*m.x44 + 4.89982515786259E-15*m.x45
- 1.10218211923262E-15*m.x46 + 8.57252759403147E-16*m.x47 - 6.12323399573677E-16*m.x48
+ 3.67394039744206E-16*m.x49 - 1.22464679914735E-16*m.x50 <= 0.01)
m.c247 = Constraint(expr= - 2*m.x1 + 1.00000000000002*m.x2 + 0.999999999999983*m.x3 - 2*m.x4 + 1.00000000000001*m.x5
+ 0.999999999999971*m.x6 - 2*m.x7 + 1.00000000000002*m.x8 + 0.999999999999984*m.x9 - 2*m.x10
+ 1.00000000000001*m.x11 + 0.999999999999997*m.x12 - 2*m.x13 + 1.00000000000002*m.x14
+ 0.999999999999985*m.x15 - 2*m.x16 + 1.00000000000001*m.x17 + 0.999999999999997*m.x18
- 2*m.x19 + 1.00000000000001*m.x20 + 0.999999999999986*m.x21 - 2*m.x22
+ 1.00000000000001*m.x23 + 0.999999999999986*m.x24 - 2*m.x25 + 1.00000000000001*m.x26
+ 0.999999999999986*m.x27 - 2*m.x28 + 1.00000000000001*m.x29 + 0.999999999999987*m.x30
- 2*m.x31 + m.x32 + 0.999999999999987*m.x33 - 2*m.x34 + m.x35 + 0.999999999999994*m.x36
- 2*m.x37 + m.x38 + 0.999999999999994*m.x39 - 2*m.x40 + m.x41 + 0.999999999999995*m.x42
- 2*m.x43 + m.x44 + 0.999999999999998*m.x45 - 2*m.x46 + m.x47 + m.x48 - 2*m.x49 + m.x50
<= 0.01)
m.c248 = Constraint(expr= - 1.29889609666036*m.x1 - 0.634609312810172*m.x2 + 1.95259201423987*m.x3
- 1.37670915138752*m.x4 - 0.534476752156529*m.x5 + 1.92726090641724*m.x6
- 1.45074874202456*m.x7 - 0.432879227876193*m.x8 + 1.8966473104124*m.x9
- 1.52081193120007*m.x10 - 0.330095211721371*m.x11 + 1.86083513596404*m.x12
- 1.58670668058246*m.x13 - 0.226406427535801*m.x14 + 1.81992254175309*m.x15
- 1.64825237724404*m.x16 - 0.122097079069729*m.x17 + 1.77402166635644*m.x18
- 1.70528032870818*m.x19 - 0.0174530709967347*m.x20 + 1.72325832088305*m.x21
- 1.75763422532393*m.x22 + 0.0872387747306711*m.x23 + 1.66777164413434*m.x24
- 1.80517056869972*m.x25 + 0.191691505040447*m.x26 + 1.60771372123444*m.x27
- 1.84775906502257*m.x28 + 0.295618822259221*m.x29 + 1.54324916677544*m.x30
- 1.88528298218436*m.x31 + 0.398735868834394*m.x32 + 1.47455467362025*m.x33
- 1.91763946973639*m.x34 + 0.500760008108882*m.x35 + 1.4018185285997*m.x36
- 1.94473984079535*m.x37 + 0.601411599008546*m.x38 + 1.32524009643148*m.x39
- 1.96650981512791*m.x40 + 0.700414762518935*m.x41 + 1.24502927327524*m.x42
- 1.98288972274762*m.x43 + 0.797498137850492*m.x44 + 1.16140591142188*m.x45
- 1.99383466746626*m.x46 + 0.892395626219618*m.x47 + 1.07459921669365*m.x48
- 1.99931464995111*m.x49 + 0.984847120206934*m.x50 <= 0.01)
m.c249 = Constraint(expr= 0.312868930080438*m.x1 - 1.84100970690487*m.x2 + 1.63830408857799*m.x3
+ 0.104671912485877*m.x4 - 1.74923941427878*m.x5 + 1.7492394142788*m.x6
- 0.104671912485913*m.x7 - 1.63830408857798*m.x8 + 1.84100970690488*m.x9
- 0.312868930080474*m.x10 - 1.50941916044553*m.x11 + 1.91260951192608*m.x12
- 0.517638090205068*m.x13 - 1.36399672012499*m.x14 + 1.96325436689533*m.x15
- 0.716735899090613*m.x16 - 1.20363004630408*m.x17 + 1.99238939618349*m.x18
- 0.907980999479093*m.x19 - 1.0300761498201*m.x20 + 1.99969539031278*m.x21
- 1.08927807003007*m.x22 - 0.845236523481394*m.x23 + 1.98509230328264*m.x24
- 1.25864078209969*m.x25 - 0.651136308914307*m.x26 + 1.94874012957047*m.x27
- 1.4142135623731*m.x28 - 0.449902108687723*m.x29 + 1.89103715119863*m.x30
- 1.55429192291394*m.x31 - 0.243738686810287*m.x32 + 1.81261557407329*m.x33
- 1.67734113589085*m.x34 - 0.0349048128745587*m.x35 + 1.71433460140422*m.x36
- 1.78201304837674*m.x37 + 0.174311485495318*m.x38 + 1.59727102009458*m.x39
- 1.86716085299441*m.x40 + 0.381617990753092*m.x41 + 1.46270740323834*m.x42
- 1.93185165257814*m.x43 + 0.584743409445477*m.x44 + 1.31211805798101*m.x45
- 1.97537668119028*m.x46 + 0.781462256978548*m.x47 + 1.14715287270209*m.x48
- 1.99725906950915*m.x49 + 0.969619240492674*m.x50 <= 0.01)
m.c250 = Constraint(expr= 1.70528032870818*m.x1 - 1.80517056869972*m.x2 + 0.261052384440102*m.x3
+ 1.52081193120007*m.x4 - 1.91763946973638*m.x5 + 0.568030689407845*m.x6
+ 1.29889609666038*m.x7 - 1.98288972274762*m.x8 + 0.861022193616591*m.x9
+ 1.04499712943191*m.x10 - 1.99931464995111*m.x11 + 1.13281247384967*m.x12
+ 0.765366864730194*m.x13 - 1.96650981512791*m.x14 + 1.37670915138751*m.x15
+ 0.466890727711825*m.x16 - 1.88528298218436*m.x17 + 1.58670668058247*m.x18
+ 0.156918191455703*m.x19 - 1.75763422532393*m.x20 + 1.75763422532393*m.x21
- 0.156918191455692*m.x22 - 1.58670668058247*m.x23 + 1.88528298218435*m.x24
- 0.466890727711814*m.x25 - 1.37670915138751*m.x26 + 1.96650981512791*m.x27
- 0.765366864730171*m.x28 - 1.13281247384967*m.x29 + 1.99931464995111*m.x30
- 1.04499712943189*m.x31 - 0.861022193616589*m.x32 + 1.98288972274762*m.x33
- 1.29889609666036*m.x34 - 0.568030689407843*m.x35 + 1.91763946973639*m.x36
- 1.52081193120006*m.x37 - 0.261052384440106*m.x38 + 1.80517056869972*m.x39
- 1.70528032870818*m.x40 + 0.0523538966157446*m.x41 + 1.64825237724403*m.x42
- 1.84775906502257*m.x43 + 0.364471050984295*m.x44 + 1.45074874202458*m.x45
- 1.94473984079535*m.x46 + 0.667613718467541*m.x47 + 1.21752285801744*m.x48
- 1.99383466746626*m.x49 + 0.954317520519217*m.x50 <= 0.01)
m.c251 = Constraint(expr= 1.90211303259031*m.x1 - 0.551274711634004*m.x2 - 1.28557521937306*m.x3
+ 1.98904379073655*m.x4 - 0.9389431255718*m.x5 - 0.938943125571775*m.x6
+ 1.98904379073654*m.x7 - 1.28557521937309*m.x8 - 0.551274711633975*m.x9
+ 1.9021130325903*m.x10 - 1.57602150721344*m.x11 - 0.139512947488239*m.x12
+ 1.73205080756888*m.x13 - 1.79758809259834*m.x14 + 0.27834620192013*m.x15
+ 1.48628965095478*m.x16 - 1.94059145255199*m.x17 + 0.684040286651352*m.x18
+ 1.17557050458494*m.x19 - 1.99878165403819*m.x20 + 1.05983852846641*m.x21
+ 0.813473286151597*m.x22 - 1.96961550602442*m.x23 + 1.389316740918*m.x24
+ 0.415823381635513*m.x25 - 1.85436770913357*m.x26 + 1.65807514511009*m.x27
- 7.3491187561573E-15*m.x28 - 1.65807514511008*m.x29 + 1.85436770913358*m.x30
- 0.415823381635528*m.x31 - 1.38931674091799*m.x32 + 1.96961550602442*m.x33
- 0.813473286151611*m.x34 - 1.05983852846641*m.x35 + 1.99878165403819*m.x36
- 1.17557050458495*m.x37 - 0.684040286651331*m.x38 + 1.94059145255199*m.x39
- 1.48628965095479*m.x40 - 0.27834620192013*m.x41 + 1.79758809259833*m.x42
- 1.73205080756888*m.x43 + 0.139512947488254*m.x44 + 1.57602150721344*m.x45
- 1.90211303259031*m.x46 + 0.551274711634*m.x47 + 1.28557521937308*m.x48
- 1.98904379073655*m.x49 + 0.938943125571782*m.x50 <= 0.01)
m.c252 = Constraint(expr= 0.765366864730182*m.x1 + 1.07459921669365*m.x2 - 1.99809644316372*m.x3
+ 1.21752285801742*m.x4 + 0.60141159900855*m.x5 - 1.90743390149646*m.x6
+ 1.58670668058246*m.x7 + 0.0872387747306736*m.x8 - 1.68678289162578*m.x9
+ 1.84775906502257*m.x10 - 0.432879227876207*m.x11 - 1.35118041523133*m.x12
+ 1.98288972274762*m.x13 - 0.923497226470071*m.x14 - 0.923497226470073*m.x15
+ 1.98288972274762*m.x16 - 1.35118041523132*m.x17 - 0.432879227876209*m.x18
+ 1.84775906502258*m.x19 - 1.68678289162576*m.x20 + 0.0872387747306716*m.x21
+ 1.58670668058248*m.x22 - 1.90743390149645*m.x23 + 0.601411599008535*m.x24
+ 1.21752285801745*m.x25 - 1.99809644316372*m.x26 + 1.07459921669364*m.x27
+ 0.765366864730183*m.x28 - 1.95259201423987*m.x29 + 1.47455467362024*m.x30
+ 0.261052384440105*m.x31 - 1.77402166635645*m.x32 + 1.77402166635644*m.x33
- 0.261052384440104*m.x34 - 1.47455467362025*m.x35 + 1.95259201423987*m.x36
- 0.765366864730176*m.x37 - 1.07459921669365*m.x38 + 1.99809644316372*m.x39
- 1.21752285801744*m.x40 - 0.601411599008549*m.x41 + 1.90743390149646*m.x42
- 1.58670668058247*m.x43 - 0.0872387747306726*m.x44 + 1.68678289162577*m.x45
- 1.84775906502257*m.x46 + 0.432879227876204*m.x47 + 1.35118041523132*m.x48
- 1.98288972274762*m.x49 + 0.923497226470068*m.x50 <= 0.01)
m.c253 = Constraint(expr= - 0.907980999479081*m.x1 + 1.97537668119027*m.x2 - 1.4142135623731*m.x3
- 0.312868930080459*m.x4 + 1.78201304837674*m.x5 - 1.78201304837673*m.x6
+ 0.312868930080481*m.x7 + 1.41421356237308*m.x8 - 1.97537668119028*m.x9
+ 0.907980999479101*m.x10 + 0.907980999479089*m.x11 - 1.97537668119028*m.x12
+ 1.41421356237309*m.x13 + 0.312868930080468*m.x14 - 1.78201304837673*m.x15
+ 1.78201304837674*m.x16 - 0.312868930080473*m.x17 - 1.41421356237309*m.x18
+ 1.97537668119028*m.x19 - 0.907980999479093*m.x20 - 0.907980999479097*m.x21
+ 1.97537668119027*m.x22 - 1.4142135623731*m.x23 - 0.312868930080463*m.x24
+ 1.78201304837673*m.x25 - 1.78201304837674*m.x26 + 0.312868930080464*m.x27
+ 1.4142135623731*m.x28 - 1.97537668119028*m.x29 + 0.907980999479098*m.x30
+ 0.907980999479092*m.x31 - 1.97537668119028*m.x32 + 1.4142135623731*m.x33
+ 0.312868930080457*m.x34 - 1.78201304837674*m.x35 + 1.78201304837674*m.x36
- 0.312868930080462*m.x37 - 1.41421356237309*m.x38 + 1.97537668119028*m.x39
- 0.907980999479097*m.x40 - 0.907980999479094*m.x41 + 1.97537668119027*m.x42
- 1.41421356237309*m.x43 - 0.312868930080459*m.x44 + 1.78201304837674*m.x45
- 1.78201304837674*m.x46 + 0.312868930080462*m.x47 + 1.41421356237309*m.x48
- 1.97537668119028*m.x49 + 0.907980999479094*m.x50 <= 0.01)
m.c254 = Constraint(expr= - 1.94473984079535*m.x1 + 1.54324916677544*m.x2 + 0.087238774730701*m.x3
- 1.64825237724405*m.x4 + 1.89664731041239*m.x5 - 0.63460931281016*m.x6
- 1.13281247384969*m.x7 + 1.99809644316372*m.x8 - 1.27215644055551*m.x9
- 0.466890727711831*m.x10 + 1.83412014877026*m.x11 - 1.74071139187979*m.x12
+ 0.261052384440086*m.x13 + 1.42650089830837*m.x14 - 1.97803172672383*m.x15
+ 0.954317520519205*m.x16 + 0.829386485312489*m.x17 - 1.95259201423987*m.x18
+ 1.52081193120006*m.x19 + 0.122097079069722*m.x20 - 1.66777164413434*m.x21
+ 1.88528298218435*m.x22 - 0.601411599008542*m.x23 - 1.16140591142188*m.x24
+ 1.99931464995111*m.x25 - 1.24502927327524*m.x26 - 0.500760008108897*m.x27
+ 1.84775906502258*m.x28 - 1.72325832088305*m.x29 + 0.226406427535803*m.x30
+ 1.45074874202458*m.x31 - 1.97257120307446*m.x32 + 0.923497226470062*m.x33
+ 0.861022193616595*m.x34 - 1.95984940924166*m.x35 + 1.497911441578*m.x36
+ 0.156918191455692*m.x37 - 1.68678289162577*m.x38 + 1.87334437849679*m.x39
- 0.56803068940784*m.x40 - 1.18964557350269*m.x41 + 1.99992384612834*m.x42
- 1.21752285801744*m.x43 - 0.534476752156514*m.x44 + 1.86083513596405*m.x45
- 1.70528032870818*m.x46 + 0.191691505040447*m.x47 + 1.47455467362025*m.x48
- 1.96650981512791*m.x49 + 0.892395626219617*m.x50 <= 0.01)
m.c255 = Constraint(expr= - 1.6180339887499*m.x1 + 0.0697989934050167*m.x2 + 1.53208888623796*m.x3
- 1.95629520146761*m.x4 + 0.876742293578159*m.x5 + 0.876742293578145*m.x6
- 1.95629520146761*m.x7 + 1.53208888623795*m.x8 + 0.069798993405001*m.x9
- 1.61803398874989*m.x10 + 1.92252339187664*m.x11 - 0.749213186831816*m.x12 - m.x13
+ 1.98053613748314*m.x14 - 1.43867960067731*m.x15 - 0.209056926535292*m.x16
+ 1.69609619231286*m.x17 - 1.87938524157182*m.x18 + 0.6180339887499*m.x19
+ 1.11838580694148*m.x20 - 1.99512810051965*m.x21 + 1.33826121271771*m.x22
+ 0.347296355333859*m.x23 - 1.76589518571785*m.x24 + 1.8270909152852*m.x25
- 0.483843791199341*m.x26 - 1.23132295065132*m.x27 + 2*m.x28 - 1.23132295065131*m.x29
- 0.483843791199334*m.x30 + 1.8270909152852*m.x31 - 1.76589518571785*m.x32
+ 0.347296355333867*m.x33 + 1.33826121271772*m.x34 - 1.99512810051965*m.x35
+ 1.11838580694149*m.x36 + 0.618033988749893*m.x37 - 1.87938524157182*m.x38
+ 1.69609619231285*m.x39 - 0.209056926535306*m.x40 - 1.4386796006773*m.x41
+ 1.98053613748314*m.x42 - m.x43 - 0.749213186831825*m.x44 + 1.92252339187664*m.x45
- 1.6180339887499*m.x46 + 0.0697989934050018*m.x47 + 1.53208888623796*m.x48
- 1.95629520146761*m.x49 + 0.876742293578155*m.x50 <= 0.01)
m.c256 = Constraint(expr= - 0.15691819145571*m.x1 - 1.45074874202455*m.x2 + 1.98288972274762*m.x3
- 1.04499712943192*m.x4 - 0.667613718467535*m.x5 + 1.88528298218435*m.x6
- 1.7052803287082*m.x7 + 0.261052384440116*m.x8 + 1.37670915138749*m.x9
- 1.99383466746626*m.x10 + 1.13281247384968*m.x11 + 0.568030689407818*m.x12
- 1.84775906502257*m.x13 + 1.75763422532394*m.x14 - 0.3644710509843*m.x15
- 1.29889609666036*m.x16 + 1.99931464995112*m.x17 - 1.21752285801745*m.x18
- 0.466890727711791*m.x19 + 1.80517056869972*m.x20 - 1.80517056869973*m.x21
+ 0.466890727711836*m.x22 + 1.21752285801743*m.x23 - 1.99931464995111*m.x24
+ 1.29889609666038*m.x25 + 0.364471050984282*m.x26 - 1.75763422532393*m.x27
+ 1.84775906502258*m.x28 - 0.568030689407849*m.x29 - 1.13281247384965*m.x30
+ 1.99383466746626*m.x31 - 1.37670915138751*m.x32 - 0.261052384440098*m.x33
+ 1.70528032870818*m.x34 - 1.88528298218436*m.x35 + 0.667613718467551*m.x36
+ 1.04499712943189*m.x37 - 1.98288972274762*m.x38 + 1.45074874202458*m.x39
+ 0.156918191455685*m.x40 - 1.64825237724403*m.x41 + 1.91763946973639*m.x42
- 0.765366864730182*m.x43 - 0.954317520519214*m.x44 + 1.96650981512791*m.x45
- 1.52081193120006*m.x46 - 0.0523538966157442*m.x47 + 1.58670668058247*m.x48
- 1.94473984079535*m.x49 + 0.86102219361659*m.x50 <= 0.01)
m.c257 = Constraint(expr= 1.41421356237309*m.x1 - 1.99238939618349*m.x2 + 1.1471528727021*m.x3
+ 0.517638090205047*m.x4 - 1.8126155740733*m.x5 + 1.8126155740733*m.x6
- 0.517638090205028*m.x7 - 1.14715287270209*m.x8 + 1.99238939618349*m.x9
- 1.4142135623731*m.x10 - 0.174311485495323*m.x11 + 1.63830408857798*m.x12
- 1.93185165257814*m.x13 + 0.84523652348141*m.x14 + 0.845236523481399*m.x15
- 1.93185165257814*m.x16 + 1.63830408857799*m.x17 - 0.174311485495308*m.x18
- 1.41421356237309*m.x19 + 1.99238939618349*m.x20 - 1.1471528727021*m.x21
- 0.517638090205043*m.x22 + 1.8126155740733*m.x23 - 1.8126155740733*m.x24
+ 0.517638090205045*m.x25 + 1.14715287270209*m.x26 - 1.99238939618349*m.x27
+ 1.41421356237309*m.x28 + 0.174311485495319*m.x29 - 1.63830408857798*m.x30
+ 1.93185165257814*m.x31 - 0.845236523481401*m.x32 - 0.845236523481395*m.x33
+ 1.93185165257814*m.x34 - 1.63830408857798*m.x35 + 0.174311485495312*m.x36
+ 1.4142135623731*m.x37 - 1.99238939618349*m.x38 + 1.14715287270209*m.x39
+ 0.517638090205039*m.x40 - 1.8126155740733*m.x41 + 1.8126155740733*m.x42
- 0.517638090205042*m.x43 - 1.14715287270209*m.x44 + 1.99238939618349*m.x45
- 1.41421356237309*m.x46 - 0.174311485495316*m.x47 + 1.63830408857798*m.x48
- 1.93185165257814*m.x49 + 0.845236523481399*m.x50 <= 0.01)
m.c258 = Constraint(expr= 1.99383466746626*m.x1 - 1.18964557350269*m.x2 - 0.432879227876178*m.x3
+ 1.75763422532392*m.x4 - 1.8733443784968*m.x5 + 0.700414762518941*m.x6
+ 0.954317520519193*m.x7 - 1.95259201423986*m.x8 + 1.60771372123444*m.x9
- 0.156918191455694*m.x10 - 1.40181852859968*m.x11 + 1.99626959684373*m.x12
- 1.21752285801745*m.x13 - 0.398735868834391*m.x14 + 1.74071139187979*m.x15
- 1.88528298218436*m.x16 + 0.733002453448603*m.x17 + 0.923497226470066*m.x18
- 1.94473984079535*m.x19 + 1.62823103671265*m.x20 - 0.191691505040455*m.x21
- 1.37670915138751*m.x22 + 1.99809644316372*m.x23 - 1.24502927327525*m.x24
- 0.364471050984289*m.x25 + 1.72325832088304*m.x26 - 1.8966473104124*m.x27
+ 0.765366864730191*m.x28 + 0.892395626219614*m.x29 - 1.93629528075621*m.x30
+ 1.64825237724403*m.x31 - 0.226406427535824*m.x32 - 1.35118041523132*m.x33
+ 1.99931464995111*m.x34 - 1.27215644055553*m.x35 - 0.330095211721346*m.x36
+ 1.70528032870818*m.x37 - 1.90743390149646*m.x38 + 0.797498137850493*m.x39
+ 0.861022193616583*m.x40 - 1.92726090641725*m.x41 + 1.66777164413434*m.x42
- 0.261052384440102*m.x43 - 1.32524009643147*m.x44 + 1.99992384612834*m.x45
- 1.29889609666037*m.x46 - 0.29561882225922*m.x47 + 1.68678289162577*m.x48
- 1.91763946973639*m.x49 + 0.829386485312478*m.x50 <= 0.01)
m.c259 = Constraint(expr= 1.17557050458494*m.x1 + 0.415823381635523*m.x2 - 1.73205080756888*m.x3
+ 1.9021130325903*m.x4 - 0.81347328615159*m.x5 - 0.813473286151614*m.x6
+ 1.90211303259031*m.x7 - 1.73205080756888*m.x8 + 0.415823381635525*m.x9
+ 1.17557050458494*m.x10 - 1.98904379073655*m.x11 + 1.48628965095479*m.x12
+ 3.92258364109106E-15*m.x13 - 1.48628965095479*m.x14 + 1.98904379073655*m.x15
- 1.17557050458494*m.x16 - 0.415823381635532*m.x17 + 1.73205080756889*m.x18
- 1.90211303259031*m.x19 + 0.813473286151607*m.x20 + 0.813473286151597*m.x21
- 1.90211303259031*m.x22 + 1.73205080756888*m.x23 - 0.415823381635515*m.x24
- 1.17557050458495*m.x25 + 1.98904379073655*m.x26 - 1.48628965095479*m.x27
+ 4.88620758385765E-16*m.x28 + 1.48628965095479*m.x29 - 1.98904379073655*m.x30
+ 1.17557050458494*m.x31 + 0.415823381635514*m.x32 - 1.73205080756888*m.x33
+ 1.90211303259031*m.x34 - 0.813473286151598*m.x35 - 0.813473286151606*m.x36
+ 1.90211303259031*m.x37 - 1.73205080756888*m.x38 + 0.41582338163552*m.x39
+ 1.17557050458495*m.x40 - 1.98904379073655*m.x41 + 1.48628965095479*m.x42
+ 2.20560219973841E-15*m.x43 - 1.48628965095479*m.x44 + 1.98904379073655*m.x45
- 1.17557050458495*m.x46 - 0.41582338163552*m.x47 + 1.73205080756888*m.x48
- 1.90211303259031*m.x49 + 0.8134732861516*m.x50 <= 0.01)
m.c260 = Constraint(expr= - 0.466890727711801*m.x1 + 1.74071139187979*m.x2 - 1.90743390149646*m.x3
+ 0.861022193616606*m.x4 + 0.733002453448577*m.x5 - 1.86083513596404*m.x6
+ 1.80517056869973*m.x7 - 0.601411599008544*m.x8 - 0.984847120206934*m.x9
+ 1.94473984079535*m.x10 - 1.66777164413434*m.x11 + 0.330095211721363*m.x12
+ 1.21752285801743*m.x13 - 1.99079239673436*m.x14 + 1.49791144157801*m.x15
- 0.0523538966157635*m.x16 - 1.42650089830835*m.x17 + 1.99809644316372*m.x18
- 1.29889609666036*m.x19 - 0.226406427535815*m.x20 + 1.60771372123443*m.x21
- 1.96650981512791*m.x22 + 1.07459921669365*m.x23 + 0.500760008108875*m.x24
- 1.75763422532393*m.x25 + 1.8966473104124*m.x26 - 0.829386485312479*m.x27
- 0.765366864730177*m.x28 + 1.87334437849679*m.x29 - 1.78986872320405*m.x30
+ 0.568030689407855*m.x31 + 1.01507672592141*m.x32 - 1.95259201423987*m.x33
+ 1.64825237724403*m.x34 - 0.295618822259227*m.x35 - 1.24502927327523*m.x36
+ 1.99383466746626*m.x37 - 1.47455467362025*m.x38 + 0.0174530709967489*m.x39
+ 1.45074874202457*m.x40 - 1.99626959684373*m.x41 + 1.27215644055553*m.x42
+ 0.2610523844401*m.x43 - 1.62823103671264*m.x44 + 1.95984940924166*m.x45
- 1.0449971294319*m.x46 - 0.534476752156511*m.x47 + 1.77402166635644*m.x48
- 1.88528298218436*m.x49 + 0.797498137850493*m.x50 <= 0.01)
m.c261 = Constraint(expr= - 1.78201304837674*m.x1 + 1.89103715119863*m.x2 - 0.84523652348138*m.x3
- 0.716735899090613*m.x4 + 1.84100970690488*m.x5 - 1.84100970690488*m.x6
+ 0.716735899090582*m.x7 + 0.84523652348141*m.x8 - 1.89103715119864*m.x9
+ 1.78201304837674*m.x10 - 0.584743409445455*m.x11 - 0.969619240492684*m.x12
+ 1.93185165257814*m.x13 - 1.71433460140423*m.x14 + 0.449902108687712*m.x15
+ 1.08927807003006*m.x16 - 1.96325436689533*m.x17 + 1.63830408857799*m.x18
- 0.312868930080445*m.x19 - 1.2036300463041*m.x20 + 1.98509230328264*m.x21
- 1.55429192291394*m.x22 + 0.1743114854953*m.x23 + 1.31211805798102*m.x24
- 1.99725906950915*m.x25 + 1.46270740323833*m.x26 - 0.0349048128745656*m.x27
- 1.4142135623731*m.x28 + 1.99969539031278*m.x29 - 1.36399672012499*m.x30
- 0.104671912485888*m.x31 + 1.50941916044555*m.x32 - 1.99238939618349*m.x33
+ 1.25864078209967*m.x34 + 0.243738686810295*m.x35 - 1.59727102009459*m.x36
+ 1.97537668119028*m.x37 - 1.14715287270209*m.x38 - 0.381617990753089*m.x39
+ 1.67734113589085*m.x40 - 1.94874012957047*m.x41 + 1.0300761498201*m.x42
+ 0.51763809020504*m.x43 - 1.74923941427879*m.x44 + 1.91260951192607*m.x45
- 0.907980999479093*m.x46 - 0.651136308914314*m.x47 + 1.8126155740733*m.x48
- 1.8671608529944*m.x49 + 0.781462256978547*m.x50 <= 0.01)
m.c262 = Constraint(expr= - 1.84775906502258*m.x1 + 0.765366864730195*m.x2 + 0.765366864730179*m.x3
- 1.84775906502257*m.x4 + 1.84775906502257*m.x5 - 0.765366864730188*m.x6
- 0.76536686473016*m.x7 + 1.84775906502257*m.x8 - 1.84775906502258*m.x9
+ 0.765366864730181*m.x10 + 0.765366864730167*m.x11 - 1.84775906502257*m.x12
+ 1.84775906502258*m.x13 - 0.765366864730173*m.x14 - 0.765366864730174*m.x15
+ 1.84775906502257*m.x16 - 1.84775906502257*m.x17 + 0.765366864730192*m.x18
+ 0.765366864730182*m.x19 - 1.84775906502257*m.x20 + 1.84775906502257*m.x21
- 0.765366864730185*m.x22 - 0.765366864730163*m.x23 + 1.84775906502257*m.x24
- 1.84775906502257*m.x25 + 0.765366864730178*m.x26 + 0.76536686473017*m.x27
- 1.84775906502257*m.x28 + 1.84775906502258*m.x29 - 0.765366864730184*m.x30
- 0.765366864730177*m.x31 + 1.84775906502257*m.x32 - 1.84775906502257*m.x33
+ 0.765366864730177*m.x34 + 0.765366864730171*m.x35 - 1.84775906502257*m.x36
+ 1.84775906502258*m.x37 - 0.765366864730182*m.x38 - 0.765366864730178*m.x39
+ 1.84775906502257*m.x40 - 1.84775906502258*m.x41 + 0.765366864730182*m.x42
+ 0.765366864730179*m.x43 - 1.84775906502257*m.x44 + 1.84775906502257*m.x45
- 0.765366864730181*m.x46 - 0.76536686473018*m.x47 + 1.84775906502257*m.x48
- 1.84775906502257*m.x49 + 0.76536686473018*m.x50 <= 0.01)
m.c263 = Constraint(expr= - 0.618033988749883*m.x1 - 0.876742293578176*m.x2 + 1.87938524157182*m.x3
- 1.82709091528519*m.x4 + 0.749213186831824*m.x5 + 0.749213186831835*m.x6
- 1.82709091528521*m.x7 + 1.87938524157181*m.x8 - 0.87674229357814*m.x9
- 0.618033988749894*m.x10 + 1.76589518571786*m.x11 - 1.92252339187663*m.x12
+ 0.999999999999996*m.x13 + 0.483843791199351*m.x14 - 1.69609619231285*m.x15
+ 1.95629520146761*m.x16 - 1.11838580694148*m.x17 - 0.347296355333865*m.x18
+ 1.6180339887499*m.x19 - 1.98053613748314*m.x20 + 1.23132295065131*m.x21
+ 0.209056926535328*m.x22 - 1.53208888623796*m.x23 + 1.99512810051965*m.x24
- 1.33826121271771*m.x25 - 0.0697989934050108*m.x26 + 1.43867960067731*m.x27 - 2*m.x28
+ 1.4386796006773*m.x29 - 0.0697989934049907*m.x30 - 1.33826121271772*m.x31
+ 1.99512810051965*m.x32 - 1.53208888623795*m.x33 + 0.209056926535308*m.x34
+ 1.23132295065132*m.x35 - 1.98053613748314*m.x36 + 1.61803398874989*m.x37
- 0.347296355333859*m.x38 - 1.1183858069415*m.x39 + 1.95629520146761*m.x40
- 1.69609619231285*m.x41 + 0.483843791199332*m.x42 + m.x43 - 1.92252339187664*m.x44
+ 1.76589518571785*m.x45 - 0.618033988749892*m.x46 - 0.876742293578155*m.x47
+ 1.87938524157182*m.x48 - 1.8270909152852*m.x49 + 0.749213186831824*m.x50 <= 0.01)
m.c264 = Constraint(expr= 1.0449971294319*m.x1 - 1.92726090641725*m.x2 + 1.77402166635645*m.x3 - 0.66761371846755*m.x4
- 0.79749813785049*m.x5 + 1.83412014877025*m.x6 - 1.88528298218435*m.x7
+ 0.923497226470079*m.x8 + 0.534476752156508*m.x9 - 1.70528032870818*m.x10
+ 1.95984940924166*m.x11 - 1.16140591142189*m.x12 - 0.261052384440094*m.x13
+ 1.54324916677544*m.x14 - 1.99626959684373*m.x15 + 1.3767091513875*m.x16
- 0.0174530709967601*m.x17 - 1.35118041523132*m.x18 + 1.99383466746626*m.x19
- 1.56521631370482*m.x20 + 0.295618822259237*m.x21 + 1.13281247384966*m.x22
- 1.95259201423987*m.x23 + 1.72325832088305*m.x24 - 0.56803068940785*m.x25
- 0.892395626219619*m.x26 + 1.87334437849679*m.x27 - 1.84775906502257*m.x28
+ 0.829386485312485*m.x29 + 0.634609312810183*m.x30 - 1.75763422532393*m.x31
+ 1.93629528075622*m.x32 - 1.07459921669364*m.x33 - 0.36447105098429*m.x34
+ 1.60771372123444*m.x35 - 1.98714371135318*m.x36 + 1.29889609666037*m.x37
+ 0.0872387747306713*m.x38 - 1.42650089830836*m.x39 + 1.99931464995111*m.x40
- 1.49791144157801*m.x41 + 0.191691505040452*m.x42 + 1.21752285801744*m.x43
- 1.97257120307446*m.x44 + 1.66777164413434*m.x45 - 0.466890727711811*m.x46
- 0.984847120206934*m.x47 + 1.90743390149645*m.x48 - 1.80517056869972*m.x49
+ 0.733002453448595*m.x50 <= 0.01)
m.c265 = Constraint(expr= 1.97537668119027*m.x1 - 1.67734113589085*m.x2 + 0.517638090205077*m.x3
+ 0.907980999479062*m.x4 - 1.86716085299439*m.x5 + 1.86716085299442*m.x6
- 0.907980999479121*m.x7 - 0.517638090205013*m.x8 + 1.67734113589083*m.x9
- 1.97537668119028*m.x10 + 1.25864078209969*m.x11 + 0.104671912485863*m.x12
- 1.41421356237308*m.x13 + 1.99725906950915*m.x14 - 1.55429192291395*m.x15
+ 0.31286893008048*m.x16 + 1.08927807003004*m.x17 - 1.93185165257813*m.x18
+ 1.78201304837674*m.x19 - 0.716735899090613*m.x20 - 0.716735899090589*m.x21
+ 1.78201304837673*m.x22 - 1.93185165257814*m.x23 + 1.08927807003006*m.x24
+ 0.312868930080455*m.x25 - 1.55429192291394*m.x26 + 1.99725906950915*m.x27
- 1.41421356237311*m.x28 + 0.104671912485903*m.x29 + 1.25864078209966*m.x30
- 1.97537668119027*m.x31 + 1.67734113589085*m.x32 - 0.517638090205051*m.x33
- 0.907980999479086*m.x34 + 1.8671608529944*m.x35 - 1.86716085299441*m.x36
+ 0.907980999479097*m.x37 + 0.517638090205039*m.x38 - 1.67734113589084*m.x39
+ 1.97537668119028*m.x40 - 1.25864078209968*m.x41 - 0.104671912485883*m.x42
+ 1.41421356237309*m.x43 - 1.99725906950915*m.x44 + 1.55429192291394*m.x45
- 0.312868930080464*m.x46 - 1.08927807003005*m.x47 + 1.93185165257814*m.x48
- 1.78201304837674*m.x49 + 0.716735899090601*m.x50 <= 0.01)
m.c266 = Constraint(expr= 1.52081193120006*m.x1 - 0.295618822259219*m.x2 - 1.07459921669365*m.x3
+ 1.91763946973638*m.x4 - 1.81992254175309*m.x5 + 0.829386485312489*m.x6
+ 0.568030689407858*m.x7 - 1.68678289162578*m.x8 + 1.97803172672383*m.x9
- 1.29889609666037*m.x10 - 0.0174530709967464*m.x11 + 1.32524009643147*m.x12
- 1.98288972274762*m.x13 + 1.66777164413434*m.x14 - 0.534476752156501*m.x15
- 0.861022193616599*m.x16 + 1.83412014877025*m.x17 - 1.90743390149645*m.x18
+ 1.0449971294319*m.x19 + 0.33009521172135*m.x20 - 1.54324916677543*m.x21
+ 1.99931464995111*m.x22 - 1.47455467362024*m.x23 + 0.226406427535804*m.x24
+ 1.13281247384967*m.x25 - 1.93629528075622*m.x26 + 1.78986872320405*m.x27
- 0.765366864730184*m.x28 - 0.63460931281019*m.x29 + 1.72325832088305*m.x30
- 1.96650981512791*m.x31 + 1.24502927327524*m.x32 + 0.0872387747306775*m.x33
- 1.37670915138751*m.x34 + 1.99079239673436*m.x35 - 1.62823103671264*m.x36
+ 0.466890727711806*m.x37 + 0.923497226470069*m.x38 - 1.86083513596405*m.x39
+ 1.88528298218436*m.x40 - 0.984847120206936*m.x41 - 0.398735868834396*m.x42
+ 1.58670668058247*m.x43 - 1.99626959684373*m.x44 + 1.42650089830836*m.x45
- 0.156918191455688*m.x46 - 1.18964557350268*m.x47 + 1.95259201423987*m.x48
- 1.75763422532393*m.x49 + 0.700414762518935*m.x50 <= 0.01)
m.c267 = Constraint(expr= 5.87706667463412E-15*m.x1 + 1.28557521937307*m.x2 - 1.96961550602441*m.x3
+ 1.73205080756889*m.x4 - 0.684040286651348*m.x5 - 0.684040286651319*m.x6
+ 1.73205080756886*m.x7 - 1.96961550602442*m.x8 + 1.28557521937309*m.x9
- 2.54788432286308E-14*m.x10 - 1.28557521937307*m.x11 + 1.96961550602441*m.x12
- 1.73205080756889*m.x13 + 0.68404028665134*m.x14 + 0.684040286651327*m.x15
- 1.73205080756887*m.x16 + 1.96961550602442*m.x17 - 1.28557521937308*m.x18
+ 1.66589103522235E-14*m.x19 + 1.28557521937306*m.x20 - 1.96961550602442*m.x21
+ 1.73205080756888*m.x22 - 0.684040286651358*m.x23 - 0.684040286651335*m.x24
+ 1.73205080756887*m.x25 - 1.96961550602442*m.x26 + 1.28557521937309*m.x27
- 7.83897747581624E-15*m.x28 - 1.28557521937308*m.x29 + 1.96961550602441*m.x30
- 1.73205080756888*m.x31 + 0.68404028665135*m.x32 + 0.68404028665133*m.x33
- 1.73205080756888*m.x34 + 1.96961550602442*m.x35 - 1.28557521937308*m.x36
- 9.80955400591059E-16*m.x37 + 1.28557521937307*m.x38 - 1.96961550602442*m.x39
+ 1.73205080756888*m.x40 - 0.684040286651342*m.x41 - 0.684040286651332*m.x42
+ 1.73205080756888*m.x43 - 1.96961550602442*m.x44 + 1.28557521937308*m.x45
- 8.57252759403147E-16*m.x46 - 1.28557521937308*m.x47 + 1.96961550602442*m.x48
- 1.73205080756888*m.x49 + 0.684040286651338*m.x50 <= 0.01)
m.c268 = Constraint(expr= - 1.52081193120007*m.x1 + 1.99931464995111*m.x2 - 1.58670668058246*m.x3
+ 0.466890727711812*m.x4 + 0.861022193616603*m.x5 - 1.80517056869972*m.x6
+ 1.94473984079535*m.x7 - 1.21752285801744*m.x8 - 0.0523538966157635*m.x9
+ 1.29889609666037*m.x10 - 1.96650981512791*m.x11 + 1.75763422532393*m.x12
- 0.765366864730187*m.x13 - 0.568030689407852*m.x14 + 1.64825237724403*m.x15
- 1.99383466746626*m.x16 + 1.45074874202458*m.x17 - 0.261052384440093*m.x18
- 1.04499712943189*m.x19 + 1.88528298218436*m.x20 - 1.88528298218436*m.x21
+ 1.04499712943189*m.x22 + 0.261052384440103*m.x23 - 1.45074874202459*m.x24
+ 1.99383466746626*m.x25 - 1.64825237724403*m.x26 + 0.568030689407843*m.x27
+ 0.765366864730183*m.x28 - 1.75763422532393*m.x29 + 1.96650981512791*m.x30
- 1.29889609666036*m.x31 + 0.0523538966157395*m.x32 + 1.21752285801745*m.x33
- 1.94473984079536*m.x34 + 1.80517056869972*m.x35 - 0.861022193616594*m.x36
- 0.466890727711807*m.x37 + 1.58670668058247*m.x38 - 1.99931464995111*m.x39
+ 1.52081193120006*m.x40 - 0.364471050984295*m.x41 - 0.954317520519217*m.x42
+ 1.84775906502257*m.x43 - 1.91763946973639*m.x44 + 1.13281247384967*m.x45
+ 0.15691819145569*m.x46 - 1.37670915138751*m.x47 + 1.98288972274762*m.x48
- 1.70528032870818*m.x49 + 0.667613718467542*m.x50 <= 0.01)
m.c269 = Constraint(expr= - 1.97537668119028*m.x1 + 1.36399672012501*m.x2 - 0.174311485495325*m.x3
- 1.08927807003003*m.x4 + 1.89103715119863*m.x5 - 1.89103715119864*m.x6
+ 1.08927807003008*m.x7 + 0.174311485495301*m.x8 - 1.36399672012499*m.x9
+ 1.97537668119027*m.x10 - 1.7492394142788*m.x11 + 0.781462256978551*m.x12
+ 0.51763809020502*m.x13 - 1.59727102009458*m.x14 + 1.99969539031278*m.x15
- 1.55429192291395*m.x16 + 0.44990210868774*m.x17 + 0.845236523481399*m.x18
- 1.78201304837673*m.x19 + 1.96325436689533*m.x20 - 1.31211805798101*m.x21
+ 0.104671912485904*m.x22 + 1.14715287270209*m.x23 - 1.91260951192607*m.x24
+ 1.8671608529944*m.x25 - 1.03007614982011*m.x26 - 0.243738686810286*m.x27
+ 1.41421356237309*m.x28 - 1.98509230328264*m.x29 + 1.71433460140423*m.x30
- 0.716735899090611*m.x31 - 0.584743409445472*m.x32 + 1.63830408857798*m.x33
- 1.99725906950915*m.x34 + 1.50941916044554*m.x35 - 0.381617990753093*m.x36
- 0.907980999479087*m.x37 + 1.8126155740733*m.x38 - 1.94874012957047*m.x39
+ 1.25864078209968*m.x40 - 0.0349048128745702*m.x41 - 1.2036300463041*m.x42
+ 1.93185165257814*m.x43 - 1.84100970690488*m.x44 + 0.969619240492676*m.x45
+ 0.312868930080459*m.x46 - 1.46270740323834*m.x47 + 1.99238939618349*m.x48
- 1.67734113589085*m.x49 + 0.651136308914314*m.x50 <= 0.01)
m.c270 = Constraint(expr= - 1.04499712943188*m.x1 - 0.191691505040469*m.x2 + 1.35118041523132*m.x3
- 1.96650981512791*m.x4 + 1.78986872320405*m.x5 - 0.892395626219606*m.x6
- 0.364471050984313*m.x7 + 1.47455467362024*m.x8 - 1.99079239673436*m.x9
+ 1.70528032870818*m.x10 - 0.733002453448585*m.x11 - 0.534476752156529*m.x12
+ 1.58670668058248*m.x13 - 1.99992384612834*m.x14 + 1.60771372123443*m.x15
- 0.568030689407838*m.x16 - 0.700414762518947*m.x17 + 1.68678289162578*m.x18
- 1.99383466746626*m.x19 + 1.497911441578*m.x20 - 0.398735868834389*m.x21
- 0.861022193616599*m.x22 + 1.77402166635645*m.x23 - 1.97257120307446*m.x24
+ 1.3767091513875*m.x25 - 0.226406427535811*m.x26 - 1.01507672592141*m.x27
+ 1.84775906502257*m.x28 - 1.93629528075621*m.x29 + 1.24502927327523*m.x30
- 0.0523538966157468*m.x31 - 1.16140591142188*m.x32 + 1.90743390149646*m.x33
- 1.88528298218436*m.x34 + 1.10387397062411*m.x35 + 0.12209707906971*m.x36
- 1.29889609666037*m.x37 + 1.95259201423987*m.x38 - 1.81992254175308*m.x39
+ 0.954317520519214*m.x40 + 0.295618822259222*m.x41 - 1.42650089830836*m.x42
+ 1.98288972274762*m.x43 - 1.7407113918798*m.x44 + 0.797498137850492*m.x45
+ 0.466890727711813*m.x46 - 1.54324916677544*m.x47 + 1.99809644316372*m.x48
- 1.64825237724403*m.x49 + 0.634609312810184*m.x50 <= 0.01)
m.c271 = Constraint(expr= 0.618033988749872*m.x1 - 1.6180339887499*m.x2 + 2*m.x3 - 1.61803398874989*m.x4
+ 0.618033988749917*m.x5 + 0.6180339887499*m.x6 - 1.61803398874988*m.x7 + 2*m.x8
- 1.61803398874991*m.x9 + 0.618033988749889*m.x10 + 0.618033988749874*m.x11
- 1.6180339887499*m.x12 + 2*m.x13 - 1.61803398874989*m.x14 + 0.618033988749915*m.x15
+ 0.618033988749902*m.x16 - 1.61803398874988*m.x17 + 2*m.x18 - 1.61803398874991*m.x19
+ 0.618033988749887*m.x20 + 0.618033988749876*m.x21 - 1.6180339887499*m.x22 + 2*m.x23
- 1.61803398874989*m.x24 + 0.618033988749913*m.x25 + 0.61803398874989*m.x26
- 1.61803398874989*m.x27 + 2*m.x28 - 1.6180339887499*m.x29 + 0.618033988749899*m.x30
+ 0.618033988749891*m.x31 - 1.61803398874989*m.x32 + 2*m.x33 - 1.6180339887499*m.x34
+ 0.618033988749898*m.x35 + 0.618033988749892*m.x36 - 1.61803398874989*m.x37 + 2*m.x38
- 1.6180339887499*m.x39 + 0.618033988749897*m.x40 + 0.618033988749893*m.x41
- 1.61803398874989*m.x42 + 2*m.x43 - 1.6180339887499*m.x44 + 0.618033988749896*m.x45
+ 0.618033988749894*m.x46 - 1.61803398874989*m.x47 + 2*m.x48 - 1.61803398874989*m.x49
+ 0.618033988749895*m.x50 <= 0.01)
m.c272 = Constraint(expr= 1.84775906502258*m.x1 - 1.95259201423986*m.x2 + 1.35118041523131*m.x3
- 0.261052384440096*m.x4 - 0.92349722647007*m.x5 + 1.77402166635646*m.x6
- 1.98288972274762*m.x7 + 1.47455467362024*m.x8 - 0.432879227876194*m.x9
- 0.765366864730186*m.x10 + 1.68678289162577*m.x11 - 1.99809644316372*m.x12
+ 1.58670668058246*m.x13 - 0.60141159900853*m.x14 - 0.601411599008558*m.x15
+ 1.58670668058247*m.x16 - 1.99809644316372*m.x17 + 1.68678289162577*m.x18
- 0.76536686473016*m.x19 - 0.432879227876222*m.x20 + 1.47455467362026*m.x21
- 1.98288972274762*m.x22 + 1.77402166635644*m.x23 - 0.92349722647007*m.x24
- 0.261052384440124*m.x25 + 1.35118041523132*m.x26 - 1.95259201423987*m.x27
+ 1.84775906502257*m.x28 - 1.07459921669365*m.x29 - 0.0872387747306838*m.x30
+ 1.21752285801745*m.x31 - 1.90743390149645*m.x32 + 1.90743390149645*m.x33
- 1.21752285801744*m.x34 + 0.0872387747306696*m.x35 + 1.07459921669365*m.x36
- 1.84775906502258*m.x37 + 1.95259201423987*m.x38 - 1.35118041523132*m.x39
+ 0.261052384440103*m.x40 + 0.92349722647007*m.x41 - 1.77402166635645*m.x42
+ 1.98288972274762*m.x43 - 1.47455467362025*m.x44 + 0.432879227876205*m.x45
+ 0.76536686473018*m.x46 - 1.68678289162577*m.x47 + 1.99809644316372*m.x48
- 1.58670668058247*m.x49 + 0.601411599008546*m.x50 <= 0.01)
m.c273 = Constraint(expr= 1.78201304837674*m.x1 - 0.969619240492674*m.x2 - 0.174311485495307*m.x3
+ 1.25864078209968*m.x4 - 1.91260951192607*m.x5 + 1.91260951192607*m.x6
- 1.25864078209967*m.x7 + 0.174311485495317*m.x8 + 0.969619240492665*m.x9
- 1.78201304837674*m.x10 + 1.98509230328264*m.x11 - 1.50941916044555*m.x12
+ 0.517638090205034*m.x13 + 0.651136308914311*m.x14 - 1.59727102009458*m.x15
+ 1.99725906950915*m.x16 - 1.71433460140423*m.x17 + 0.84523652348141*m.x18
+ 0.312868930080468*m.x19 - 1.36399672012499*m.x20 + 1.94874012957047*m.x21
- 1.8671608529944*m.x22 + 1.1471528727021*m.x23 - 0.0349048128745808*m.x24
- 1.08927807003006*m.x25 + 1.84100970690488*m.x26 - 1.96325436689533*m.x27
+ 1.41421356237309*m.x28 - 0.381617990753095*m.x29 - 0.781462256978547*m.x30
+ 1.67734113589085*m.x31 - 1.99969539031278*m.x32 + 1.63830408857798*m.x33
- 0.716735899090598*m.x34 - 0.449902108687724*m.x35 + 1.46270740323834*m.x36
- 1.97537668119028*m.x37 + 1.8126155740733*m.x38 - 1.03007614982011*m.x39
- 0.10467191248589*m.x40 + 1.2036300463041*m.x41 - 1.89103715119863*m.x42
+ 1.93185165257814*m.x43 - 1.31211805798102*m.x44 + 0.243738686810295*m.x45
+ 0.907980999479094*m.x46 - 1.74923941427879*m.x47 + 1.99238939618349*m.x48
- 1.55429192291394*m.x49 + 0.584743409445474*m.x50 <= 0.01)
m.c274 = Constraint(expr= 0.46689072771184*m.x1 + 0.667613718467526*m.x2 - 1.58670668058245*m.x3
+ 1.99383466746625*m.x4 - 1.75763422532395*m.x5 + 0.954317520519233*m.x6
+ 0.156918191455657*m.x7 - 1.21752285801743*m.x8 + 1.88528298218435*m.x9
- 1.94473984079536*m.x10 + 1.37670915138751*m.x11 - 0.364471050984315*m.x12
- 0.765366864730173*m.x13 + 1.64825237724402*m.x14 - 1.99931464995111*m.x15
+ 1.7052803287082*m.x16 - 0.861022193616598*m.x17 - 0.261052384440081*m.x18
+ 1.29889609666036*m.x19 - 1.91763946973638*m.x20 + 1.91763946973639*m.x21
- 1.29889609666039*m.x22 + 0.261052384440114*m.x23 + 0.861022193616568*m.x24
- 1.70528032870818*m.x25 + 1.99931464995111*m.x26 - 1.64825237724404*m.x27
+ 0.765366864730191*m.x28 + 0.364471050984282*m.x29 - 1.3767091513875*m.x30
+ 1.94473984079535*m.x31 - 1.88528298218436*m.x32 + 1.21752285801745*m.x33
- 0.156918191455705*m.x34 - 0.954317520519216*m.x35 + 1.75763422532393*m.x36
- 1.99383466746626*m.x37 + 1.58670668058247*m.x38 - 0.667613718467544*m.x39
- 0.466890727711808*m.x40 + 1.45074874202457*m.x41 - 1.96650981512791*m.x42
+ 1.84775906502258*m.x43 - 1.13281247384967*m.x44 + 0.0523538966157477*m.x45
+ 1.0449971294319*m.x46 - 1.80517056869972*m.x47 + 1.98288972274762*m.x48
- 1.52081193120006*m.x49 + 0.568030689407846*m.x50 <= 0.01)
m.c275 = Constraint(expr= - 1.17557050458494*m.x1 + 1.85436770913357*m.x2 - 1.96961550602442*m.x3
+ 1.48628965095478*m.x4 - 0.551274711634005*m.x5 - 0.551274711634001*m.x6
+ 1.4862896509548*m.x7 - 1.96961550602441*m.x8 + 1.85436770913357*m.x9
- 1.17557050458494*m.x10 + 0.139512947488261*m.x11 + 0.938943125571781*m.x12
- 1.73205080756888*m.x13 + 1.99878165403819*m.x14 - 1.65807514511009*m.x15
+ 0.813473286151595*m.x16 + 0.278346201920118*m.x17 - 1.28557521937308*m.x18
+ 1.90211303259031*m.x19 - 1.94059145255199*m.x20 + 1.389316740918*m.x21
- 0.415823381635516*m.x22 - 0.684040286651348*m.x23 + 1.57602150721344*m.x24
- 1.98904379073655*m.x25 + 1.79758809259834*m.x26 - 1.05983852846641*m.x27
+ 9.78479478044706E-16*m.x28 + 1.0598385284664*m.x29 - 1.79758809259834*m.x30
+ 1.98904379073655*m.x31 - 1.57602150721344*m.x32 + 0.684040286651337*m.x33
+ 0.415823381635514*m.x34 - 1.389316740918*m.x35 + 1.94059145255199*m.x36
- 1.90211303259031*m.x37 + 1.28557521937308*m.x38 - 0.278346201920134*m.x39
- 0.8134732861516*m.x40 + 1.65807514511008*m.x41 - 1.99878165403819*m.x42
+ 1.73205080756888*m.x43 - 0.938943125571782*m.x44 - 0.139512947488251*m.x45
+ 1.17557050458495*m.x46 - 1.85436770913358*m.x47 + 1.96961550602442*m.x48
- 1.48628965095479*m.x49 + 0.551274711633998*m.x50 <= 0.01)
m.c276 = Constraint(expr= - 1.99383466746625*m.x1 + 1.78986872320406*m.x2 - 1.07459921669366*m.x3
+ 0.0523538966157596*m.x4 + 0.984847120206926*m.x5 - 1.7407113918798*m.x6
+ 1.99931464995112*m.x7 - 1.68678289162579*m.x8 + 0.892395626219637*m.x9
+ 0.156918191455672*m.x10 - 1.16140591142187*m.x11 + 1.83412014877024*m.x12
- 1.98288972274762*m.x13 + 1.56521631370483*m.x14 - 0.70041476251896*m.x15
- 0.364471050984273*m.x16 + 1.32524009643146*m.x17 - 1.90743390149645*m.x18
+ 1.94473984079536*m.x19 - 1.42650089830837*m.x20 + 0.500760008108886*m.x21
+ 0.568030689407846*m.x22 - 1.47455467362023*m.x23 + 1.95984940924166*m.x24
- 1.88528298218436*m.x25 + 1.27215644055554*m.x26 - 0.295618822259229*m.x27
- 0.765366864730176*m.x28 + 1.60771372123443*m.x29 - 1.99079239673436*m.x30
+ 1.80517056869972*m.x31 - 1.10387397062412*m.x32 + 0.0872387747306843*m.x33
+ 0.954317520519209*m.x34 - 1.72325832088305*m.x35 + 1.99992384612834*m.x36
- 1.70528032870819*m.x37 + 0.923497226470074*m.x38 + 0.122097079069711*m.x39
- 1.13281247384966*m.x40 + 1.81992254175309*m.x41 - 1.98714371135318*m.x42
+ 1.58670668058247*m.x43 - 0.733002453448599*m.x44 - 0.330095211721355*m.x45
+ 1.29889609666036*m.x46 - 1.8966473104124*m.x47 + 1.95259201423987*m.x48
- 1.45074874202458*m.x49 + 0.534476752156514*m.x50 <= 0.01)
m.c277 = Constraint(expr= - 1.41421356237308*m.x1 + 0.51763809020505*m.x2 + 0.517638090205059*m.x3
- 1.41421356237311*m.x4 + 1.93185165257814*m.x5 - 1.93185165257813*m.x6
+ 1.41421356237308*m.x7 - 0.517638090205029*m.x8 - 0.517638090205053*m.x9
+ 1.4142135623731*m.x10 - 1.93185165257814*m.x11 + 1.93185165257813*m.x12
- 1.41421356237309*m.x13 + 0.517638090205034*m.x14 + 0.517638090205048*m.x15
- 1.4142135623731*m.x16 + 1.93185165257814*m.x17 - 1.93185165257814*m.x18
+ 1.41421356237309*m.x19 - 0.51763809020504*m.x20 - 0.517638090205042*m.x21
+ 1.41421356237309*m.x22 - 1.93185165257814*m.x23 + 1.93185165257814*m.x24
- 1.4142135623731*m.x25 + 0.517638090205046*m.x26 + 0.51763809020505*m.x27
- 1.4142135623731*m.x28 + 1.93185165257814*m.x29 - 1.93185165257814*m.x30
+ 1.41421356237309*m.x31 - 0.517638090205038*m.x32 - 0.517638090205044*m.x33
+ 1.4142135623731*m.x34 - 1.93185165257814*m.x35 + 1.93185165257814*m.x36
- 1.4142135623731*m.x37 + 0.517638090205043*m.x38 + 0.517638090205046*m.x39
- 1.4142135623731*m.x40 + 1.93185165257814*m.x41 - 1.93185165257814*m.x42
+ 1.41421356237309*m.x43 - 0.517638090205042*m.x44 - 0.517638090205043*m.x45
+ 1.4142135623731*m.x46 - 1.93185165257814*m.x47 + 1.93185165257814*m.x48
- 1.4142135623731*m.x49 + 0.517638090205041*m.x50 <= 0.01)
m.c278 = Constraint(expr= 0.156918191455698*m.x1 - 1.10387397062409*m.x2 + 1.77402166635644*m.x3
- 1.99931464995111*m.x4 + 1.72325832088306*m.x5 - 1.01507672592143*m.x6
+ 0.0523538966157733*m.x7 + 0.923497226470064*m.x8 - 1.66777164413433*m.x9
+ 1.99383466746625*m.x10 - 1.8199225417531*m.x11 + 1.18964557350268*m.x12
- 0.261052384440109*m.x13 - 0.733002453448584*m.x14 + 1.54324916677543*m.x15
- 1.96650981512791*m.x16 + 1.8966473104124*m.x17 - 1.35118041523133*m.x18
+ 0.466890727711823*m.x19 + 0.534476752156496*m.x20 - 1.4018185285997*m.x21
+ 1.91763946973639*m.x22 - 1.95259201423987*m.x23 + 1.49791144157801*m.x24
- 0.66761371846756*m.x25 - 0.330095211721358*m.x26 + 1.24502927327524*m.x27
- 1.84775906502257*m.x28 + 1.98714371135318*m.x29 - 1.62823103671264*m.x30
+ 0.861022193616602*m.x31 + 0.122097079069709*m.x32 - 1.07459921669364*m.x33
+ 1.75763422532393*m.x34 - 1.99992384612834*m.x35 + 1.7407113918798*m.x36
- 1.0449971294319*m.x37 + 0.0872387747306691*m.x38 + 0.892395626219615*m.x39
- 1.64825237724403*m.x40 + 1.99079239673436*m.x41 - 1.83412014877025*m.x42
+ 1.21752285801745*m.x43 - 0.295618822259225*m.x44 - 0.700414762518932*m.x45
+ 1.52081193120006*m.x46 - 1.95984940924166*m.x47 + 1.90743390149645*m.x48
- 1.37670915138751*m.x49 + 0.500760008108883*m.x50 <= 0.01)
m.c279 = Constraint(expr= 1.61803398874989*m.x1 - 1.98053613748314*m.x2 + 1.87938524157182*m.x3 - 1.3382612127177*m.x4
+ 0.483843791199325*m.x5 + 0.483843791199335*m.x6 - 1.33826121271773*m.x7
+ 1.87938524157182*m.x8 - 1.98053613748314*m.x9 + 1.61803398874989*m.x10
- 0.876742293578153*m.x11 - 0.0697989934050216*m.x12 + 1.00000000000001*m.x13
- 1.69609619231285*m.x14 + 1.99512810051965*m.x15 - 1.8270909152852*m.x16
+ 1.23132295065132*m.x17 - 0.347296355333849*m.x18 - 0.618033988749895*m.x19
+ 1.43867960067731*m.x20 - 1.92252339187664*m.x21 + 1.95629520146761*m.x22
- 1.53208888623795*m.x23 + 0.749213186831821*m.x24 + 0.209056926535299*m.x25
- 1.1183858069415*m.x26 + 1.76589518571785*m.x27 - 2*m.x28 + 1.76589518571785*m.x29
- 1.11838580694149*m.x30 + 0.209056926535309*m.x31 + 0.749213186831826*m.x32
- 1.53208888623796*m.x33 + 1.95629520146761*m.x34 - 1.92252339187664*m.x35
+ 1.4386796006773*m.x36 - 0.618033988749891*m.x37 - 0.347296355333868*m.x38
+ 1.23132295065132*m.x39 - 1.8270909152852*m.x40 + 1.99512810051965*m.x41
- 1.69609619231285*m.x42 + 0.999999999999997*m.x43 - 0.0697989934050025*m.x44
- 0.876742293578157*m.x45 + 1.61803398874989*m.x46 - 1.98053613748314*m.x47
+ 1.87938524157182*m.x48 - 1.33826121271772*m.x49 + 0.483843791199335*m.x50 <= 0.01)
m.c280 = Constraint(expr= 1.94473984079536*m.x1 - 1.52081193120008*m.x2 + 0.765366864730196*m.x3
+ 0.156918191455685*m.x4 - 1.04499712943188*m.x5 + 1.70528032870818*m.x6
- 1.99383466746626*m.x7 + 1.84775906502258*m.x8 - 1.29889609666037*m.x9
+ 0.466890727711825*m.x10 + 0.466890727711809*m.x11 - 1.29889609666035*m.x12
+ 1.84775906502257*m.x13 - 1.99383466746626*m.x14 + 1.70528032870819*m.x15
- 1.04499712943189*m.x16 + 0.156918191455701*m.x17 + 0.765366864730181*m.x18
- 1.52081193120005*m.x19 + 1.94473984079535*m.x20 - 1.94473984079536*m.x21
+ 1.52081193120007*m.x22 - 0.765366864730173*m.x23 - 0.156918191455682*m.x24
+ 1.0449971294319*m.x25 - 1.70528032870818*m.x26 + 1.99383466746626*m.x27
- 1.84775906502257*m.x28 + 1.29889609666037*m.x29 - 0.466890727711814*m.x30
- 0.466890727711806*m.x31 + 1.29889609666036*m.x32 - 1.84775906502257*m.x33
+ 1.99383466746626*m.x34 - 1.70528032870818*m.x35 + 1.0449971294319*m.x36
- 0.15691819145569*m.x37 - 0.765366864730178*m.x38 + 1.52081193120006*m.x39
- 1.94473984079535*m.x40 + 1.94473984079535*m.x41 - 1.52081193120006*m.x42
+ 0.765366864730182*m.x43 + 0.156918191455686*m.x44 - 1.0449971294319*m.x45
+ 1.70528032870818*m.x46 - 1.99383466746626*m.x47 + 1.84775906502257*m.x48
- 1.29889609666037*m.x49 + 0.466890727711811*m.x50 <= 0.01)
m.c281 = Constraint(expr= 0.907980999479066*m.x1 - 0.0349048128745573*m.x2 - 0.845236523481415*m.x3
+ 1.55429192291396*m.x4 - 1.94874012957047*m.x5 + 1.94874012957047*m.x6
- 1.55429192291393*m.x7 + 0.8452365234814*m.x8 + 0.0349048128745739*m.x9
- 0.907980999479107*m.x10 + 1.5972710200946*m.x11 - 1.96325436689533*m.x12
+ 1.93185165257813*m.x13 - 1.50941916044553*m.x14 + 0.781462256978551*m.x15
+ 0.104671912485892*m.x16 - 0.969619240492684*m.x17 + 1.63830408857799*m.x18
- 1.97537668119028*m.x19 + 1.91260951192607*m.x20 - 1.46270740323833*m.x21
+ 0.71673589909058*m.x22 + 0.174311485495317*m.x23 - 1.03007614982012*m.x24
+ 1.67734113589086*m.x25 - 1.98509230328264*m.x26 + 1.89103715119863*m.x27
- 1.41421356237309*m.x28 + 0.651136308914309*m.x29 + 0.243738686810307*m.x30
- 1.08927807003006*m.x31 + 1.71433460140422*m.x32 - 1.99238939618349*m.x33
+ 1.8671608529944*m.x34 - 1.36399672012499*m.x35 + 0.584743409445472*m.x36
+ 0.312868930080471*m.x37 - 1.14715287270209*m.x38 + 1.74923941427879*m.x39
- 1.99725906950915*m.x40 + 1.84100970690488*m.x41 - 1.31211805798101*m.x42
+ 0.517638090205042*m.x43 + 0.381617990753089*m.x44 - 1.2036300463041*m.x45
+ 1.78201304837674*m.x46 - 1.99969539031278*m.x47 + 1.8126155740733*m.x48
- 1.25864078209967*m.x49 + 0.44990210868773*m.x50 <= 0.01)
m.c282 = Constraint(expr= - 0.765366864730197*m.x1 + 1.47455467362024*m.x2 - 1.90743390149644*m.x3
+ 1.98288972274762*m.x4 - 1.68678289162577*m.x5 + 1.07459921669364*m.x6
- 0.261052384440096*m.x7 - 0.601411599008529*m.x8 + 1.35118041523131*m.x9
- 1.84775906502257*m.x10 + 1.99809644316372*m.x11 - 1.77402166635645*m.x12
+ 1.21752285801744*m.x13 - 0.432879227876208*m.x14 - 0.432879227876207*m.x15
+ 1.21752285801744*m.x16 - 1.77402166635645*m.x17 + 1.99809644316372*m.x18
- 1.84775906502258*m.x19 + 1.35118041523133*m.x20 - 0.601411599008557*m.x21
- 0.261052384440095*m.x22 + 1.07459921669364*m.x23 - 1.68678289162577*m.x24
+ 1.98288972274762*m.x25 - 1.90743390149645*m.x26 + 1.47455467362025*m.x27
- 0.765366864730185*m.x28 - 0.0872387747306691*m.x29 + 0.923497226470068*m.x30
- 1.58670668058247*m.x31 + 1.95259201423986*m.x32 - 1.95259201423987*m.x33
+ 1.58670668058247*m.x34 - 0.923497226470068*m.x35 + 0.0872387747306696*m.x36
+ 0.765366864730184*m.x37 - 1.47455467362024*m.x38 + 1.90743390149645*m.x39
- 1.98288972274762*m.x40 + 1.68678289162577*m.x41 - 1.07459921669365*m.x42
+ 0.261052384440103*m.x43 + 0.601411599008543*m.x44 - 1.35118041523132*m.x45
+ 1.84775906502257*m.x46 - 1.99809644316372*m.x47 + 1.77402166635644*m.x48
- 1.21752285801744*m.x49 + 0.432879227876206*m.x50 <= 0.01)
m.c283 = Constraint(expr= - 1.90211303259029*m.x1 + 1.98904379073655*m.x2 - 1.73205080756888*m.x3
+ 1.17557050458497*m.x4 - 0.415823381635548*m.x5 - 0.415823381635487*m.x6
+ 1.17557050458492*m.x7 - 1.73205080756887*m.x8 + 1.98904379073655*m.x9
- 1.90211303259031*m.x10 + 1.4862896509548*m.x11 - 0.813473286151615*m.x12
+ 1.86183452308593E-14*m.x13 + 0.813473286151581*m.x14 - 1.48628965095477*m.x15
+ 1.9021130325903*m.x16 - 1.98904379073655*m.x17 + 1.73205080756889*m.x18
- 1.17557050458495*m.x19 + 0.415823381635524*m.x20 + 0.415823381635511*m.x21
- 1.17557050458494*m.x22 + 1.73205080756887*m.x23 - 1.98904379073655*m.x24
+ 1.90211303259031*m.x25 - 1.4862896509548*m.x26 + 0.813473286151606*m.x27
- 8.32883619547518E-15*m.x28 - 0.813473286151591*m.x29 + 1.48628965095478*m.x30
- 1.9021130325903*m.x31 + 1.98904379073655*m.x32 - 1.73205080756888*m.x33
+ 1.17557050458495*m.x34 - 0.415823381635528*m.x35 - 0.415823381635508*m.x36
+ 1.17557050458494*m.x37 - 1.73205080756888*m.x38 + 1.98904379073655*m.x39
- 1.90211303259031*m.x40 + 1.48628965095479*m.x41 - 0.813473286151603*m.x42
+ 5.14475451769206E-15*m.x43 + 0.8134732861516*m.x44 - 1.48628965095479*m.x45
+ 1.90211303259031*m.x46 - 1.98904379073655*m.x47 + 1.73205080756888*m.x48
- 1.17557050458495*m.x49 + 0.415823381635519*m.x50 <= 0.01)
m.c284 = Constraint(expr= - 1.70528032870819*m.x1 + 1.16140591142187*m.x2 - 0.432879227876224*m.x3
- 0.364471050984298*m.x4 + 1.10387397062411*m.x5 - 1.66777164413433*m.x6
+ 1.96650981512791*m.x7 - 1.95259201423987*m.x8 + 1.62823103671264*m.x9
- 1.04499712943191*m.x10 + 0.295618822259211*m.x11 + 0.500760008108886*m.x12
- 1.21752285801744*m.x13 + 1.74071139187979*m.x14 - 1.98714371135318*m.x15
+ 1.91763946973639*m.x16 - 1.54324916677544*m.x17 + 0.923497226470078*m.x18
- 0.15691819145568*m.x19 - 0.634609312810187*m.x20 + 1.32524009643147*m.x21
- 1.80517056869972*m.x22 + 1.99809644316372*m.x23 - 1.87334437849679*m.x24
+ 1.45074874202458*m.x25 - 0.797498137850503*m.x26 + 0.0174530709967376*m.x27
+ 0.765366864730182*m.x28 - 1.42650089830836*m.x29 + 1.86083513596405*m.x30
- 1.99931464995111*m.x31 + 1.81992254175309*m.x32 - 1.35118041523132*m.x33
+ 0.667613718467539*m.x34 + 0.12209707906971*m.x35 - 0.892395626219621*m.x36
+ 1.52081193120006*m.x37 - 1.90743390149645*m.x38 + 1.99079239673436*m.x39
- 1.75763422532393*m.x40 + 1.24502927327524*m.x41 - 0.53447675215651*m.x42
- 0.2610523844401*m.x43 + 1.01507672592141*m.x44 - 1.60771372123443*m.x45
+ 1.94473984079535*m.x46 - 1.97257120307446*m.x47 + 1.68678289162577*m.x48
- 1.13281247384967*m.x49 + 0.398735868834394*m.x50 <= 0.01)
m.c285 = Constraint(expr= - 0.312868930080478*m.x1 - 0.44990210868773*m.x2 + 1.14715287270206*m.x3
- 1.67734113589084*m.x4 + 1.96325436689533*m.x5 - 1.96325436689533*m.x6
+ 1.67734113589086*m.x7 - 1.1471528727021*m.x8 + 0.449902108687756*m.x9
+ 0.312868930080451*m.x10 - 1.03007614982009*m.x11 + 1.59727102009458*m.x12
- 1.93185165257813*m.x13 + 1.98509230328264*m.x14 - 1.7492394142788*m.x15
+ 1.25864078209969*m.x16 - 0.584743409445482*m.x17 - 0.174311485495295*m.x18
+ 0.907980999479089*m.x19 - 1.50941916044553*m.x20 + 1.89103715119863*m.x21
- 1.99725906950915*m.x22 + 1.81261557407331*m.x23 - 1.363996720125*m.x24
+ 0.71673589909062*m.x25 + 0.0349048128745631*m.x26 - 0.781462256978533*m.x27
+ 1.41421356237309*m.x28 - 1.84100970690488*m.x29 + 1.99969539031278*m.x30
- 1.86716085299441*m.x31 + 1.46270740323834*m.x32 - 0.845236523481401*m.x33
+ 0.104671912485888*m.x34 + 0.651136308914301*m.x35 - 1.31211805798101*m.x36
+ 1.78201304837673*m.x37 - 1.99238939618349*m.x38 + 1.91260951192607*m.x39
- 1.55429192291394*m.x40 + 0.96961924049268*m.x41 - 0.243738686810299*m.x42
- 0.517638090205039*m.x43 + 1.2036300463041*m.x44 - 1.71433460140422*m.x45
+ 1.97537668119028*m.x46 - 1.94874012957047*m.x47 + 1.63830408857798*m.x48
- 1.08927807003005*m.x49 + 0.38161799075309*m.x50 <= 0.01)
m.c286 = Constraint(expr= 1.29889609666039*m.x1 - 1.75763422532392*m.x2 + 1.98288972274762*m.x3
- 1.94473984079535*m.x4 + 1.64825237724404*m.x5 - 1.13281247384966*m.x6
+ 0.466890727711798*m.x7 + 0.261052384440099*m.x8 - 0.954317520519223*m.x9
+ 1.52081193120006*m.x10 - 1.88528298218436*m.x11 + 1.99931464995111*m.x12
- 1.84775906502257*m.x13 + 1.45074874202457*m.x14 - 0.861022193616599*m.x15
+ 0.156918191455688*m.x16 + 0.568030689407858*m.x17 - 1.21752285801744*m.x18
+ 1.70528032870819*m.x19 - 1.96650981512791*m.x20 + 1.96650981512791*m.x21
- 1.70528032870818*m.x22 + 1.21752285801744*m.x23 - 0.568030689407837*m.x24
- 0.156918191455682*m.x25 + 0.861022193616593*m.x26 - 1.45074874202459*m.x27
+ 1.84775906502257*m.x28 - 1.99931464995111*m.x29 + 1.88528298218435*m.x30
- 1.52081193120006*m.x31 + 0.954317520519216*m.x32 - 0.261052384440105*m.x33
- 0.466890727711807*m.x34 + 1.13281247384967*m.x35 - 1.64825237724403*m.x36
+ 1.94473984079535*m.x37 - 1.98288972274762*m.x38 + 1.75763422532393*m.x39
- 1.29889609666037*m.x40 + 0.667613718467544*m.x41 + 0.0523538966157483*m.x42
- 0.765366864730179*m.x43 + 1.37670915138751*m.x44 - 1.80517056869972*m.x45
+ 1.99383466746626*m.x46 - 1.91763946973639*m.x47 + 1.58670668058247*m.x48
- 1.0449971294319*m.x49 + 0.364471050984295*m.x50 <= 0.01)
m.c287 = Constraint(expr= 2*m.x1 - 1.87938524157183*m.x2 + 1.53208888623797*m.x3 - 1.00000000000002*m.x4
+ 0.347296355333874*m.x5 + 0.347296355333854*m.x6 - m.x7 + 1.53208888623794*m.x8
- 1.87938524157181*m.x9 + 2*m.x10 - 1.87938524157182*m.x11 + 1.53208888623797*m.x12
- 1.00000000000002*m.x13 + 0.347296355333872*m.x14 + 0.347296355333856*m.x15 - m.x16
+ 1.53208888623794*m.x17 - 1.87938524157181*m.x18 + 2*m.x19 - 1.87938524157182*m.x20
+ 1.53208888623797*m.x21 - 1.00000000000001*m.x22 + 0.34729635533387*m.x23
+ 0.347296355333858*m.x24 - m.x25 + 1.53208888623794*m.x26 - 1.87938524157181*m.x27 + 2*m.x28
- 1.87938524157182*m.x29 + 1.53208888623796*m.x30 - m.x31 + 0.347296355333868*m.x32
+ 0.34729635533386*m.x33 - 0.999999999999993*m.x34 + 1.53208888623795*m.x35
- 1.87938524157181*m.x36 + 2*m.x37 - 1.87938524157182*m.x38 + 1.53208888623796*m.x39 - m.x40
+ 0.347296355333866*m.x41 + 0.347296355333855*m.x42 - m.x43 + 1.53208888623796*m.x44
- 1.87938524157182*m.x45 + 2*m.x46 - 1.87938524157182*m.x47 + 1.53208888623796*m.x48 - m.x49
+ 0.347296355333861*m.x50 <= 0.01)
m.c288 = Constraint(expr= 1.29889609666036*m.x1 - 0.733002453448587*m.x2 + 0.0872387747306628*m.x3
+ 0.568030689407856*m.x4 - 1.16140591142189*m.x5 + 1.62823103671265*m.x6
- 1.91763946973639*m.x7 + 1.99809644316371*m.x8 - 1.86083513596404*m.x9
+ 1.52081193120005*m.x10 - 1.01507672592141*m.x11 + 0.398735868834399*m.x12
+ 0.2610523844401*m.x13 - 0.892395626219616*m.x14 + 1.42650089830836*m.x15
- 1.80517056869972*m.x16 + 1.98714371135318*m.x17 - 1.95259201423987*m.x18
+ 1.70528032870818*m.x19 - 1.27215644055552*m.x20 + 0.700414762518926*m.x21
- 0.0523538966157351*m.x22 - 0.601411599008558*m.x23 + 1.18964557350269*m.x24
- 1.64825237724404*m.x25 + 1.92726090641725*m.x26 - 1.99626959684373*m.x27
+ 1.84775906502257*m.x28 - 1.497911441578*m.x29 + 0.984847120206926*m.x30
- 0.364471050984298*m.x31 - 0.29561882225922*m.x32 + 0.923497226470068*m.x33
- 1.45074874202458*m.x34 + 1.81992254175309*m.x35 - 1.99079239673436*m.x36
+ 1.94473984079535*m.x37 - 1.68678289162577*m.x38 + 1.24502927327524*m.x39
- 0.667613718467538*m.x40 + 0.0174530709967489*m.x41 + 0.634609312810185*m.x42
- 1.21752285801744*m.x43 + 1.66777164413434*m.x44 - 1.93629528075622*m.x45
+ 1.99383466746626*m.x46 - 1.83412014877025*m.x47 + 1.47455467362025*m.x48
- 0.954317520519216*m.x49 + 0.330095211721355*m.x50 <= 0.01)
m.c289 = Constraint(expr= - 0.312868930080456*m.x1 + 0.907980999479086*m.x2 - 1.41421356237309*m.x3
+ 1.78201304837673*m.x4 - 1.97537668119027*m.x5 + 1.97537668119028*m.x6
- 1.78201304837673*m.x7 + 1.4142135623731*m.x8 - 0.907980999479097*m.x9
+ 0.312868930080469*m.x10 + 0.312868930080451*m.x11 - 0.907980999479081*m.x12
+ 1.41421356237308*m.x13 - 1.78201304837673*m.x14 + 1.97537668119028*m.x15
- 1.97537668119028*m.x16 + 1.78201304837674*m.x17 - 1.4142135623731*m.x18
+ 0.907980999479101*m.x19 - 0.312868930080474*m.x20 - 0.312868930080447*m.x21
+ 0.907980999479077*m.x22 - 1.4142135623731*m.x23 + 1.78201304837674*m.x24
- 1.97537668119028*m.x25 + 1.97537668119028*m.x26 - 1.78201304837674*m.x27
+ 1.4142135623731*m.x28 - 0.907980999479093*m.x29 + 0.312868930080464*m.x30
+ 0.312868930080456*m.x31 - 0.907980999479085*m.x32 + 1.4142135623731*m.x33
- 1.78201304837673*m.x34 + 1.97537668119027*m.x35 - 1.97537668119028*m.x36
+ 1.78201304837673*m.x37 - 1.4142135623731*m.x38 + 0.907980999479097*m.x39
- 0.312868930080462*m.x40 - 0.312868930080458*m.x41 + 0.907980999479093*m.x42
- 1.41421356237309*m.x43 + 1.78201304837674*m.x44 - 1.97537668119027*m.x45
+ 1.97537668119028*m.x46 - 1.78201304837674*m.x47 + 1.4142135623731*m.x48
- 0.907980999479094*m.x49 + 0.312868930080462*m.x50 <= 0.01)
m.c290 = Constraint(expr= - 1.70528032870821*m.x1 + 1.93629528075622*m.x2 - 1.99809644316371*m.x3
+ 1.88528298218435*m.x4 - 1.60771372123443*m.x5 + 1.18964557350268*m.x6
- 0.667613718467524*m.x7 + 0.0872387747306618*m.x8 + 0.500760008108885*m.x9
- 1.04499712943192*m.x10 + 1.49791144157801*m.x11 - 1.81992254175309*m.x12
+ 1.98288972274762*m.x13 - 1.97257120307446*m.x14 + 1.78986872320405*m.x15
- 1.45074874202457*m.x16 + 0.984847120206916*m.x17 - 0.432879227876193*m.x18
- 0.156918191455694*m.x19 + 0.733002453448591*m.x20 - 1.24502927327525*m.x21
+ 1.64825237724404*m.x22 - 1.90743390149645*m.x23 + 1.99992384612834*m.x24
- 1.91763946973638*m.x25 + 1.66777164413433*m.x26 - 1.27215644055553*m.x27
+ 0.765366864730165*m.x28 - 0.191691505040441*m.x29 - 0.398735868834393*m.x30
+ 0.954317520519221*m.x31 - 1.42650089830837*m.x32 + 1.77402166635644*m.x33
- 1.96650981512791*m.x34 + 1.98714371135317*m.x35 - 1.83412014877025*m.x36
+ 1.52081193120006*m.x37 - 1.07459921669364*m.x38 + 0.534476752156504*m.x39
+ 0.0523538966157478*m.x40 - 0.634609312810185*m.x41 + 1.16140591142188*m.x42
- 1.58670668058247*m.x43 + 1.8733443784968*m.x44 - 1.99626959684373*m.x45
+ 1.94473984079535*m.x46 - 1.72325832088305*m.x47 + 1.35118041523132*m.x48
- 0.86102219361659*m.x49 + 0.295618822259221*m.x50 <= 0.01)
m.c291 = Constraint(expr= - 1.90211303259032*m.x1 + 1.65807514511008*m.x2 - 1.28557521937308*m.x3
+ 0.813473286151611*m.x4 - 0.278346201920155*m.x5 - 0.278346201920121*m.x6
+ 0.813473286151606*m.x7 - 1.28557521937307*m.x8 + 1.65807514511009*m.x9
- 1.90211303259031*m.x10 + 1.99878165403819*m.x11 - 1.94059145255199*m.x12
+ 1.73205080756889*m.x13 - 1.389316740918*m.x14 + 0.938943125571794*m.x15
- 0.415823381635518*m.x16 - 0.139512947488238*m.x17 + 0.68404028665134*m.x18
- 1.17557050458494*m.x19 + 1.57602150721345*m.x20 - 1.85436770913357*m.x21
+ 1.98904379073655*m.x22 - 1.96961550602442*m.x23 + 1.79758809259833*m.x24
- 1.48628965095479*m.x25 + 1.0598385284664*m.x26 - 0.551274711634001*m.x27
+ 1.56791929129057E-14*m.x28 + 0.551274711633998*m.x29 - 1.05983852846641*m.x30
+ 1.48628965095479*m.x31 - 1.79758809259834*m.x32 + 1.96961550602442*m.x33
- 1.98904379073655*m.x34 + 1.85436770913358*m.x35 - 1.57602150721345*m.x36
+ 1.17557050458495*m.x37 - 0.684040286651343*m.x38 + 0.139512947488255*m.x39
+ 0.415823381635515*m.x40 - 0.938943125571779*m.x41 + 1.38931674091799*m.x42
- 1.73205080756888*m.x43 + 1.94059145255199*m.x44 - 1.99878165403819*m.x45
+ 1.90211303259031*m.x46 - 1.65807514511008*m.x47 + 1.28557521937308*m.x48
- 0.8134732861516*m.x49 + 0.278346201920131*m.x50 <= 0.01)
m.c292 = Constraint(expr= - 0.765366864730217*m.x1 + 0.261052384440105*m.x2 + 0.261052384440083*m.x3
- 0.765366864730144*m.x4 + 1.21752285801744*m.x5 - 1.58670668058246*m.x6
+ 1.84775906502256*m.x7 - 1.98288972274762*m.x8 + 1.98288972274762*m.x9
- 1.84775906502259*m.x10 + 1.58670668058248*m.x11 - 1.21752285801745*m.x12
+ 0.765366864730208*m.x13 - 0.261052384440123*m.x14 - 0.261052384440093*m.x15
+ 0.765366864730153*m.x16 - 1.21752285801743*m.x17 + 1.58670668058247*m.x18
- 1.84775906502256*m.x19 + 1.98288972274762*m.x20 - 1.98288972274762*m.x21
+ 1.84775906502258*m.x22 - 1.58670668058248*m.x23 + 1.21752285801744*m.x24
- 0.765366864730199*m.x25 + 0.261052384440114*m.x26 + 0.261052384440103*m.x27
- 0.765366864730163*m.x28 + 1.21752285801743*m.x29 - 1.58670668058246*m.x30
+ 1.84775906502257*m.x31 - 1.98288972274762*m.x32 + 1.98288972274762*m.x33
- 1.84775906502258*m.x34 + 1.58670668058247*m.x35 - 1.21752285801745*m.x36
+ 0.76536686473019*m.x37 - 0.261052384440104*m.x38 - 0.261052384440099*m.x39
+ 0.765366864730172*m.x40 - 1.21752285801744*m.x41 + 1.58670668058247*m.x42
- 1.84775906502257*m.x43 + 1.98288972274762*m.x44 - 1.98288972274762*m.x45
+ 1.84775906502257*m.x46 - 1.58670668058247*m.x47 + 1.21752285801744*m.x48
- 0.765366864730181*m.x49 + 0.261052384440103*m.x50 <= 0.01)
m.c293 = Constraint(expr= 0.907980999479098*m.x1 - 1.312118057981*m.x2 + 1.63830408857799*m.x3 - 1.8671608529944*m.x4
+ 1.98509230328265*m.x5 - 1.98509230328264*m.x6 + 1.8671608529944*m.x7 - 1.63830408857799*m.x8
+ 1.31211805798102*m.x9 - 0.907980999479097*m.x10 + 0.449902108687729*m.x11
+ 0.0349048128745739*m.x12 - 0.517638090205053*m.x13 + 0.969619240492665*m.x14
- 1.36399672012499*m.x15 + 1.67734113589085*m.x16 - 1.89103715119864*m.x17
+ 1.99238939618349*m.x18 - 1.97537668119028*m.x19 + 1.84100970690488*m.x20
- 1.59727102009459*m.x21 + 1.25864078209967*m.x22 - 0.845236523481391*m.x23
+ 0.381617990753103*m.x24 + 0.104671912485879*m.x25 - 0.58474340944547*m.x26
+ 1.03007614982011*m.x27 - 1.4142135623731*m.x28 + 1.71433460140422*m.x29
- 1.91260951192607*m.x30 + 1.99725906950915*m.x31 - 1.96325436689533*m.x32
+ 1.8126155740733*m.x33 - 1.55429192291394*m.x34 + 1.20363004630409*m.x35
- 0.781462256978554*m.x36 + 0.312868930080463*m.x37 + 0.17431148549532*m.x38
- 0.651136308914309*m.x39 + 1.08927807003005*m.x40 - 1.46270740323834*m.x41
+ 1.74923941427879*m.x42 - 1.93185165257814*m.x43 + 1.99969539031278*m.x44
- 1.94874012957047*m.x45 + 1.78201304837674*m.x46 - 1.50941916044554*m.x47
+ 1.14715287270209*m.x48 - 0.716735899090601*m.x49 + 0.243738686810295*m.x50 <= 0.01)
m.c294 = Constraint(expr= 1.94473984079535*m.x1 - 1.99992384612834*m.x2 + 1.95259201423987*m.x3
- 1.80517056869974*m.x4 + 1.56521631370483*m.x5 - 1.24502927327527*m.x6
+ 0.861022193616594*m.x7 - 0.432879227876237*m.x8 - 0.0174530709967445*m.x9
+ 0.46689072771178*m.x10 - 0.892395626219615*m.x11 + 1.2721564405555*m.x12
- 1.58670668058247*m.x13 + 1.81992254175307*m.x14 - 1.95984940924166*m.x15
+ 1.99931464995112*m.x16 - 1.93629528075622*m.x17 + 1.77402166635646*m.x18
- 1.52081193120006*m.x19 + 1.1896455735027*m.x20 - 0.797498137850491*m.x21
+ 0.364471050984321*m.x22 + 0.0872387747306745*m.x23 - 0.534476752156489*m.x24
+ 0.95431752051922*m.x25 - 1.32524009643146*m.x26 + 1.62823103671264*m.x27
- 1.84775906502256*m.x28 + 1.97257120307446*m.x29 - 1.99626959684373*m.x30
+ 1.91763946973639*m.x31 - 1.7407113918798*m.x32 + 1.47455467362025*m.x33
- 1.13281247384967*m.x34 + 0.733002453448601*m.x35 - 0.295618822259227*m.x36
- 0.156918191455684*m.x37 + 0.601411599008541*m.x38 - 1.0150767259214*m.x39
+ 1.3767091513875*m.x40 - 1.66777164413433*m.x41 + 1.87334437849679*m.x42
- 1.98288972274762*m.x43 + 1.99079239673436*m.x44 - 1.8966473104124*m.x45
+ 1.70528032870819*m.x46 - 1.42650089830836*m.x47 + 1.07459921669365*m.x48
- 0.667613718467542*m.x49 + 0.226406427535814*m.x50 <= 0.01)
m.c295 = Constraint(expr= 1.61803398874991*m.x1 - 1.33826121271771*m.x2 + 0.999999999999974*m.x3
- 0.618033988749898*m.x4 + 0.209056926535287*m.x5 + 0.209056926535294*m.x6
- 0.618033988749906*m.x7 + 1.00000000000001*m.x8 - 1.33826121271772*m.x9
+ 1.61803398874989*m.x10 - 1.8270909152852*m.x11 + 1.95629520146761*m.x12 - 2*m.x13
+ 1.95629520146761*m.x14 - 1.8270909152852*m.x15 + 1.6180339887499*m.x16
- 1.33826121271772*m.x17 + 1.00000000000001*m.x18 - 0.618033988749881*m.x19
+ 0.209056926535297*m.x20 + 0.209056926535312*m.x21 - 0.618033988749895*m.x22
+ 0.999999999999997*m.x23 - 1.33826121271771*m.x24 + 1.6180339887499*m.x25
- 1.82709091528521*m.x26 + 1.95629520146761*m.x27 - 2*m.x28 + 1.95629520146761*m.x29
- 1.8270909152852*m.x30 + 1.61803398874989*m.x31 - 1.33826121271772*m.x32
+ 0.999999999999993*m.x33 - 0.618033988749892*m.x34 + 0.209056926535308*m.x35
+ 0.209056926535301*m.x36 - 0.618033988749899*m.x37 + m.x38 - 1.33826121271771*m.x39
+ 1.61803398874989*m.x40 - 1.8270909152852*m.x41 + 1.95629520146761*m.x42 - 2*m.x43
+ 1.95629520146761*m.x44 - 1.8270909152852*m.x45 + 1.6180339887499*m.x46
- 1.33826121271772*m.x47 + m.x48 - 0.618033988749895*m.x49 + 0.209056926535307*m.x50 <= 0.01)
m.c296 = Constraint(expr= 0.15691819145572*m.x1 + 0.226406427535802*m.x2 - 0.601411599008554*m.x3
+ 0.95431752051919*m.x4 - 1.27215644055552*m.x5 + 1.54324916677545*m.x6
- 1.75763422532392*m.x7 + 1.90743390149645*m.x8 - 1.98714371135317*m.x9
+ 1.99383466746626*m.x10 - 1.92726090641725*m.x11 + 1.78986872320406*m.x12
- 1.58670668058247*m.x13 + 1.32524009643148*m.x14 - 1.01507672592143*m.x15
+ 0.667613718467543*m.x16 - 0.295618822259232*m.x17 - 0.087238774730652*m.x18
+ 0.46689072771181*m.x19 - 0.829386485312469*m.x20 + 1.16140591142186*m.x21
- 1.45074874202457*m.x22 + 1.68678289162577*m.x23 - 1.86083513596404*m.x24
+ 1.96650981512791*m.x25 - 1.99992384612834*m.x26 + 1.95984940924166*m.x27
- 1.84775906502257*m.x28 + 1.66777164413434*m.x29 - 1.42650089830837*m.x30
+ 1.13281247384967*m.x31 - 0.797498137850502*m.x32 + 0.432879227876211*m.x33
- 0.0523538966157468*m.x34 - 0.330095211721345*m.x35 + 0.70041476251893*m.x36
- 1.0449971294319*m.x37 + 1.35118041523131*m.x38 - 1.60771372123443*m.x39
+ 1.80517056869972*m.x40 - 1.93629528075621*m.x41 + 1.99626959684373*m.x42
- 1.98288972274762*m.x43 + 1.8966473104124*m.x44 - 1.7407113918798*m.x45
+ 1.52081193120006*m.x46 - 1.24502927327524*m.x47 + 0.923497226470069*m.x48
- 0.568030689407845*m.x49 + 0.191691505040448*m.x50 <= 0.01)
m.c297 = Constraint(expr= - 1.41421356237311*m.x1 + 1.638304088578*m.x2 - 1.81261557407329*m.x3 + 1.93185165257814*m.x4
- 1.99238939618349*m.x5 + 1.99238939618349*m.x6 - 1.93185165257814*m.x7 + 1.8126155740733*m.x8
- 1.63830408857798*m.x9 + 1.4142135623731*m.x10 - 1.14715287270208*m.x11
+ 0.8452365234814*m.x12 - 0.517638090205029*m.x13 + 0.174311485495317*m.x14
+ 0.174311485495329*m.x15 - 0.51763809020504*m.x16 + 0.84523652348141*m.x17
- 1.14715287270209*m.x18 + 1.4142135623731*m.x19 - 1.63830408857798*m.x20
+ 1.8126155740733*m.x21 - 1.93185165257814*m.x22 + 1.99238939618349*m.x23
- 1.99238939618349*m.x24 + 1.93185165257813*m.x25 - 1.8126155740733*m.x26
+ 1.63830408857798*m.x27 - 1.4142135623731*m.x28 + 1.14715287270208*m.x29
- 0.845236523481402*m.x30 + 0.517638090205032*m.x31 - 0.17431148549532*m.x32
- 0.174311485495326*m.x33 + 0.517638090205037*m.x34 - 0.845236523481408*m.x35
+ 1.14715287270209*m.x36 - 1.4142135623731*m.x37 + 1.63830408857798*m.x38
- 1.8126155740733*m.x39 + 1.93185165257814*m.x40 - 1.99238939618349*m.x41
+ 1.99238939618349*m.x42 - 1.93185165257814*m.x43 + 1.8126155740733*m.x44
- 1.63830408857798*m.x45 + 1.41421356237309*m.x46 - 1.14715287270209*m.x47
+ 0.845236523481399*m.x48 - 0.517638090205041*m.x49 + 0.174311485495316*m.x50 <= 0.01)
m.c298 = Constraint(expr= - 1.99383466746626*m.x1 + 1.94473984079535*m.x2 - 1.84775906502259*m.x3 + 1.7052803287082*m.x4
- 1.52081193120007*m.x5 + 1.29889609666037*m.x6 - 1.0449971294319*m.x7
+ 0.765366864730196*m.x8 - 0.466890727711819*m.x9 + 0.15691819145569*m.x10
+ 0.156918191455671*m.x11 - 0.466890727711801*m.x12 + 0.765366864730179*m.x13
- 1.04499712943188*m.x14 + 1.29889609666036*m.x15 - 1.52081193120006*m.x16
+ 1.70528032870817*m.x17 - 1.84775906502257*m.x18 + 1.94473984079535*m.x19
- 1.99383466746626*m.x20 + 1.99383466746626*m.x21 - 1.94473984079535*m.x22
+ 1.84775906502257*m.x23 - 1.70528032870819*m.x24 + 1.52081193120007*m.x25
- 1.29889609666036*m.x26 + 1.04499712943191*m.x27 - 0.765366864730185*m.x28
+ 0.466890727711808*m.x29 - 0.156918191455692*m.x30 - 0.156918191455683*m.x31
+ 0.466890727711813*m.x32 - 0.765366864730177*m.x33 + 1.04499712943189*m.x34
- 1.29889609666037*m.x35 + 1.52081193120006*m.x36 - 1.70528032870818*m.x37
+ 1.84775906502257*m.x38 - 1.94473984079535*m.x39 + 1.99383466746626*m.x40
- 1.99383466746626*m.x41 + 1.94473984079535*m.x42 - 1.84775906502258*m.x43
+ 1.70528032870819*m.x44 - 1.52081193120006*m.x45 + 1.29889609666037*m.x46
- 1.0449971294319*m.x47 + 0.765366864730181*m.x48 - 0.46689072771181*m.x49
+ 0.15691819145569*m.x50 <= 0.01)
m.c299 = Constraint(expr= - 1.17557050458495*m.x1 + 0.938943125571785*m.x2 - 0.684040286651337*m.x3
+ 0.415823381635514*m.x4 - 0.139512947488242*m.x5 - 0.139512947488263*m.x6
+ 0.415823381635535*m.x7 - 0.684040286651357*m.x8 + 0.938943125571779*m.x9
- 1.17557050458495*m.x10 + 1.389316740918*m.x11 - 1.57602150721345*m.x12
+ 1.73205080756888*m.x13 - 1.85436770913358*m.x14 + 1.940591452552*m.x15
- 1.98904379073655*m.x16 + 1.99878165403819*m.x17 - 1.96961550602441*m.x18
+ 1.9021130325903*m.x19 - 1.79758809259833*m.x20 + 1.65807514511007*m.x21
- 1.48628965095479*m.x22 + 1.28557521937308*m.x23 - 1.05983852846641*m.x24
+ 0.813473286151594*m.x25 - 0.551274711633988*m.x26 + 0.278346201920116*m.x27
+ 1.96030145152699E-14*m.x28 - 0.278346201920126*m.x29 + 0.551274711633998*m.x30
- 0.813473286151604*m.x31 + 1.05983852846642*m.x32 - 1.28557521937308*m.x33
+ 1.48628965095479*m.x34 - 1.65807514511009*m.x35 + 1.79758809259834*m.x36
- 1.90211303259031*m.x37 + 1.96961550602442*m.x38 - 1.99878165403819*m.x39
+ 1.98904379073655*m.x40 - 1.94059145255199*m.x41 + 1.85436770913357*m.x42
- 1.73205080756888*m.x43 + 1.57602150721344*m.x44 - 1.389316740918*m.x45
+ 1.17557050458494*m.x46 - 0.938943125571782*m.x47 + 0.684040286651337*m.x48
- 0.415823381635518*m.x49 + 0.13951294748825*m.x50 <= 0.01)
m.c300 = Constraint(expr= 0.466890727711792*m.x1 - 0.700414762518916*m.x2 + 0.923497226470049*m.x3
- 1.13281247384965*m.x4 + 1.32524009643146*m.x5 - 1.49791144157799*m.x6
+ 1.64825237724402*m.x7 - 1.77402166635643*m.x8 + 1.8733443784968*m.x9
- 1.94473984079535*m.x10 + 1.98714371135318*m.x11 - 1.99992384612834*m.x12
+ 1.98288972274762*m.x13 - 1.93629528075622*m.x14 + 1.86083513596405*m.x15
- 1.75763422532393*m.x16 + 1.62823103671264*m.x17 - 1.47455467362025*m.x18
+ 1.29889609666037*m.x19 - 1.10387397062412*m.x20 + 0.892395626219623*m.x21
- 0.667613718467548*m.x22 + 0.432879227876213*m.x23 - 0.191691505040456*m.x24
- 0.052353896615737*m.x25 + 0.295618822259211*m.x26 - 0.534476752156503*m.x27
+ 0.765366864730169*m.x28 - 0.984847120206924*m.x29 + 1.18964557350268*m.x30
- 1.37670915138751*m.x31 + 1.54324916677544*m.x32 - 1.68678289162577*m.x33
+ 1.80517056869972*m.x34 - 1.8966473104124*m.x35 + 1.95984940924166*m.x36
- 1.99383466746626*m.x37 + 1.99809644316372*m.x38 - 1.97257120307446*m.x39
+ 1.91763946973639*m.x40 - 1.83412014877025*m.x41 + 1.72325832088305*m.x42
- 1.58670668058247*m.x43 + 1.42650089830836*m.x44 - 1.24502927327524*m.x45
+ 1.0449971294319*m.x46 - 0.829386485312479*m.x47 + 0.601411599008546*m.x48
- 0.364471050984296*m.x49 + 0.122097079069714*m.x50 <= 0.01)
m.c301 = Constraint(expr= 1.78201304837672*m.x1 - 1.86716085299439*m.x2 + 1.93185165257813*m.x3
- 1.97537668119027*m.x4 + 1.99725906950915*m.x5 - 1.99725906950915*m.x6
+ 1.97537668119028*m.x7 - 1.93185165257814*m.x8 + 1.86716085299441*m.x9
- 1.78201304837675*m.x10 + 1.67734113589086*m.x11 - 1.55429192291395*m.x12
+ 1.41421356237311*m.x13 - 1.25864078209969*m.x14 + 1.08927807003008*m.x15
- 0.907980999479121*m.x16 + 0.716735899090609*m.x17 - 0.517638090205055*m.x18
+ 0.312868930080481*m.x19 - 0.104671912485913*m.x20 - 0.104671912485885*m.x21
+ 0.312868930080453*m.x22 - 0.517638090205028*m.x23 + 0.716735899090582*m.x24
- 0.907980999479071*m.x25 + 1.08927807003005*m.x26 - 1.25864078209967*m.x27
+ 1.41421356237308*m.x28 - 1.55429192291393*m.x29 + 1.67734113589084*m.x30
- 1.78201304837673*m.x31 + 1.8671608529944*m.x32 - 1.93185165257813*m.x33
+ 1.97537668119027*m.x34 - 1.99725906950915*m.x35 + 1.99725906950915*m.x36
- 1.97537668119028*m.x37 + 1.93185165257814*m.x38 - 1.86716085299441*m.x39
+ 1.78201304837674*m.x40 - 1.67734113589085*m.x41 + 1.55429192291394*m.x42
- 1.4142135623731*m.x43 + 1.25864078209968*m.x44 - 1.08927807003006*m.x45
+ 0.907980999479096*m.x46 - 0.716735899090602*m.x47 + 0.517638090205043*m.x48
- 0.312868930080462*m.x49 + 0.104671912485888*m.x50 <= 0.01)
m.c302 = Constraint(expr= 1.84775906502257*m.x1 - 1.77402166635644*m.x2 + 1.68678289162578*m.x3
- 1.58670668058248*m.x4 + 1.47455467362027*m.x5 - 1.35118041523131*m.x6
+ 1.21752285801744*m.x7 - 1.07459921669365*m.x8 + 0.92349722647008*m.x9
- 0.765366864730176*m.x10 + 0.601411599008553*m.x11 - 0.432879227876195*m.x12
+ 0.261052384440103*m.x13 - 0.0872387747306824*m.x14 - 0.0872387747306794*m.x15
+ 0.2610523844401*m.x16 - 0.432879227876192*m.x17 + 0.60141159900855*m.x18
- 0.765366864730173*m.x19 + 0.923497226470078*m.x20 - 1.07459921669365*m.x21
+ 1.21752285801743*m.x22 - 1.35118041523133*m.x23 + 1.47455467362025*m.x24
- 1.58670668058246*m.x25 + 1.68678289162577*m.x26 - 1.77402166635644*m.x27
+ 1.84775906502258*m.x28 - 1.90743390149645*m.x29 + 1.95259201423987*m.x30
- 1.98288972274762*m.x31 + 1.99809644316372*m.x32 - 1.99809644316372*m.x33
+ 1.98288972274762*m.x34 - 1.95259201423987*m.x35 + 1.90743390149645*m.x36
- 1.84775906502257*m.x37 + 1.77402166635644*m.x38 - 1.68678289162577*m.x39
+ 1.58670668058247*m.x40 - 1.47455467362025*m.x41 + 1.35118041523132*m.x42
- 1.21752285801744*m.x43 + 1.07459921669365*m.x44 - 0.923497226470066*m.x45
+ 0.765366864730181*m.x46 - 0.601411599008545*m.x47 + 0.432879227876206*m.x48
- 0.261052384440103*m.x49 + 0.087238774730672*m.x50 <= 0.01)
m.c303 = Constraint(expr= 0.618033988749893*m.x1 - 0.483843791199348*m.x2 + 0.347296355333889*m.x3
- 0.209056926535351*m.x4 + 0.0697989934050049*m.x5 + 0.0697989934049834*m.x6
- 0.209056926535273*m.x7 + 0.347296355333868*m.x8 - 0.483843791199327*m.x9
+ 0.618033988749872*m.x10 - 0.749213186831814*m.x11 + 0.876742293578131*m.x12
- 0.999999999999988*m.x13 + 1.11838580694147*m.x14 - 1.2313229506513*m.x15
+ 1.33826121271771*m.x16 - 1.43867960067729*m.x17 + 1.53208888623795*m.x18
- 1.61803398874988*m.x19 + 1.69609619231285*m.x20 - 1.76589518571784*m.x21
+ 1.8270909152852*m.x22 - 1.87938524157182*m.x23 + 1.92252339187663*m.x24
- 1.95629520146761*m.x25 + 1.98053613748314*m.x26 - 1.99512810051965*m.x27 + 2*m.x28
- 1.99512810051965*m.x29 + 1.98053613748314*m.x30 - 1.95629520146761*m.x31
+ 1.92252339187664*m.x32 - 1.87938524157182*m.x33 + 1.82709091528521*m.x34
- 1.76589518571785*m.x35 + 1.69609619231285*m.x36 - 1.6180339887499*m.x37
+ 1.53208888623796*m.x38 - 1.43867960067731*m.x39 + 1.33826121271772*m.x40
- 1.23132295065132*m.x41 + 1.1183858069415*m.x42 - m.x43 + 0.87674229357816*m.x44
- 0.749213186831824*m.x45 + 0.618033988749896*m.x46 - 0.483843791199338*m.x47
+ 0.347296355333861*m.x48 - 0.209056926535308*m.x49 + 0.0697989934050022*m.x50 <= 0.01)
m.c304 = Constraint(expr= - 1.04499712943189*m.x1 + 1.13281247384969*m.x2 - 1.21752285801745*m.x3
+ 1.29889609666036*m.x4 - 1.37670915138752*m.x5 + 1.45074874202458*m.x6
- 1.52081193120005*m.x7 + 1.58670668058248*m.x8 - 1.64825237724403*m.x9
+ 1.70528032870819*m.x10 - 1.75763422532394*m.x11 + 1.80517056869972*m.x12
- 1.84775906502257*m.x13 + 1.88528298218436*m.x14 - 1.91763946973638*m.x15
+ 1.94473984079535*m.x16 - 1.96650981512791*m.x17 + 1.98288972274762*m.x18
- 1.99383466746626*m.x19 + 1.99931464995111*m.x20 - 1.99931464995111*m.x21
+ 1.99383466746625*m.x22 - 1.98288972274762*m.x23 + 1.96650981512791*m.x24
- 1.94473984079535*m.x25 + 1.91763946973639*m.x26 - 1.88528298218436*m.x27
+ 1.84775906502257*m.x28 - 1.80517056869972*m.x29 + 1.75763422532393*m.x30
- 1.70528032870818*m.x31 + 1.64825237724403*m.x32 - 1.58670668058247*m.x33
+ 1.52081193120006*m.x34 - 1.45074874202457*m.x35 + 1.37670915138751*m.x36
- 1.29889609666036*m.x37 + 1.21752285801744*m.x38 - 1.13281247384967*m.x39
+ 1.0449971294319*m.x40 - 0.954317520519214*m.x41 + 0.861022193616587*m.x42
- 0.765366864730182*m.x43 + 0.667613718467544*m.x44 - 0.568030689407846*m.x45
+ 0.466890727711811*m.x46 - 0.364471050984294*m.x47 + 0.261052384440103*m.x48
- 0.156918191455689*m.x49 + 0.0523538966157463*m.x50 <= 0.01)
m.c305 = Constraint(expr= - 1.97537668119027*m.x1 + 1.98509230328265*m.x2 - 1.99238939618349*m.x3
+ 1.99725906950915*m.x4 - 1.99969539031278*m.x5 + 1.99969539031278*m.x6
- 1.99725906950915*m.x7 + 1.99238939618349*m.x8 - 1.98509230328264*m.x9
+ 1.97537668119028*m.x10 - 1.96325436689533*m.x11 + 1.94874012957047*m.x12
- 1.93185165257814*m.x13 + 1.91260951192607*m.x14 - 1.89103715119864*m.x15
+ 1.86716085299441*m.x16 - 1.84100970690488*m.x17 + 1.8126155740733*m.x18
- 1.78201304837675*m.x19 + 1.7492394142788*m.x20 - 1.71433460140423*m.x21
+ 1.67734113589086*m.x22 - 1.63830408857799*m.x23 + 1.59727102009459*m.x24
- 1.55429192291395*m.x25 + 1.50941916044555*m.x26 - 1.46270740323834*m.x27
+ 1.41421356237309*m.x28 - 1.36399672012499*m.x29 + 1.31211805798102*m.x30
- 1.25864078209968*m.x31 + 1.20363004630411*m.x32 - 1.1471528727021*m.x33
+ 1.08927807003006*m.x34 - 1.03007614982011*m.x35 + 0.969619240492675*m.x36
- 0.907980999479092*m.x37 + 0.845236523481407*m.x38 - 0.781462256978553*m.x39
+ 0.716735899090603*m.x40 - 0.651136308914314*m.x41 + 0.584743409445478*m.x42
- 0.517638090205043*m.x43 + 0.449902108687735*m.x44 - 0.381617990753092*m.x45
+ 0.312868930080465*m.x46 - 0.243738686810295*m.x47 + 0.174311485495317*m.x48
- 0.104671912485889*m.x49 + 0.0349048128745672*m.x50 <= 0.01)
m.c306 = Constraint(expr= - 1.52081193120005*m.x1 + 1.49791144157801*m.x2 - 1.47455467362023*m.x3
+ 1.45074874202458*m.x4 - 1.42650089830835*m.x5 + 1.40181852859971*m.x6 - 1.3767091513875*m.x7
+ 1.35118041523133*m.x8 - 1.32524009643147*m.x9 + 1.29889609666036*m.x10
- 1.27215644055552*m.x11 + 1.24502927327524*m.x12 - 1.21752285801744*m.x13
+ 1.18964557350268*m.x14 - 1.16140591142188*m.x15 + 1.13281247384967*m.x16
- 1.10387397062412*m.x17 + 1.07459921669365*m.x18 - 1.04499712943188*m.x19
+ 1.01507672592139*m.x20 - 0.984847120206922*m.x21 + 0.954317520519206*m.x22
- 0.923497226470059*m.x23 + 0.89239562621961*m.x24 - 0.861022193616584*m.x25
+ 0.829386485312474*m.x26 - 0.79749813785049*m.x27 + 0.765366864730179*m.x28
- 0.733002453448596*m.x29 + 0.700414762518938*m.x30 - 0.667613718467533*m.x31
+ 0.634609312810177*m.x32 - 0.601411599008541*m.x33 + 0.568030689407842*m.x34
- 0.534476752156512*m.x35 + 0.500760008108883*m.x36 - 0.466890727711813*m.x37
+ 0.43287922787621*m.x38 - 0.398735868834387*m.x39 + 0.364471050984289*m.x40
- 0.330095211721351*m.x41 + 0.295618822259219*m.x42 - 0.261052384440103*m.x43
+ 0.226406427535815*m.x44 - 0.191691505040444*m.x45 + 0.156918191455688*m.x46
- 0.122097079069714*m.x47 + 0.0872387747306707*m.x48 - 0.052353896615747*m.x49
+ 0.0174530709967478*m.x50 <= 0.01)
m.c307 = Constraint(expr= 1.27449924400447E-14*m.x1 + 2.25372149881308E-14*m.x2 - 9.76003555498352E-16*m.x3
+ 3.62582109836739E-14*m.x4 - 1.46969995510414E-14*m.x5 - 6.86421188159106E-15*m.x6
- 2.84179955465845E-14*m.x7 + 6.856784113952E-15*m.x8 + 1.47044273186805E-14*m.x9
+ 2.05777801094951E-14*m.x10 + 9.83431323137413E-16*m.x11 + 5.87706667463412E-15*m.x12
- 1.27375646724056E-14*m.x13 + 1.95980626701772E-14*m.x14 + 1.9631487624553E-15*m.x15
+ 4.89734923531624E-15*m.x16 - 1.17578472330878E-14*m.x17 + 1.86183452308593E-14*m.x18
+ 2.94286620177318E-15*m.x19 + 3.91763179599835E-15*m.x20 - 1.07781297937699E-14*m.x21
+ 1.76386277915414E-14*m.x22 + 3.92258364109106E-15*m.x23 + 2.93791435668047E-15*m.x24
- 9.798412354452E-15*m.x25 + 1.66589103522235E-14*m.x26 + 4.90230108040894E-15*m.x27
+ 1.95819691736259E-15*m.x28 - 8.81869491513412E-15*m.x29 + 1.56791929129057E-14*m.x30
- 8.32883619547518E-15*m.x31 + 9.78479478044706E-16*m.x32 - 7.83897747581624E-15*m.x33
+ 4.88620758385765E-16*m.x34 - 7.3491187561573E-15*m.x35 - 1.23796127317672E-18*m.x36
- 6.85926003649835E-15*m.x37 - 4.91096680932118E-16*m.x38 - 6.36940131683941E-15*m.x39
- 9.80955400591059E-16*m.x40 - 5.87954259718047E-15*m.x41 - 1.47081412025E-15*m.x42
- 5.38968387752153E-15*m.x43 - 1.96067283990894E-15*m.x44 - 4.89982515786259E-15*m.x45
+ 1.10218211923262E-15*m.x46 - 8.57252759403147E-16*m.x47 + 6.12323399573677E-16*m.x48
- 3.67394039744206E-16*m.x49 + 1.22464679914735E-16*m.x50 <= 0.01)
| 94.60129
| 120
| 0.574149
| 63,231
| 513,212
| 4.660056
| 0.028625
| 0.014586
| 0.01477
| 0.002898
| 0.994363
| 0.994231
| 0.991102
| 0.991102
| 0.991102
| 0.983927
| 0
| 0.715237
| 0.301634
| 513,212
| 5,424
| 121
| 94.618732
| 0.106894
| 0.001216
| 0
| 0.000392
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.000196
| 0
| 0.000196
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
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| 0
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| 0
| 0
|
0
| 8
|
64a13e4d7c3202ab7b17688e22f4ad17c5e726bf
| 8,088
|
py
|
Python
|
tests/unit/test_groupby.py
|
rjzamora/NVTabular
|
068ce230b34b0eeae3fd15379291a0e79632b731
|
[
"Apache-2.0"
] | null | null | null |
tests/unit/test_groupby.py
|
rjzamora/NVTabular
|
068ce230b34b0eeae3fd15379291a0e79632b731
|
[
"Apache-2.0"
] | null | null | null |
tests/unit/test_groupby.py
|
rjzamora/NVTabular
|
068ce230b34b0eeae3fd15379291a0e79632b731
|
[
"Apache-2.0"
] | null | null | null |
#
# Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import glob
import cudf
import pytest
import nvtabular.groupby as groupby
from tests.conftest import allcols_csv, mycols_csv
@pytest.mark.parametrize("batch", [0, 100, 1000])
@pytest.mark.parametrize("dskey", ["csv", "csv-no-header"])
@pytest.mark.parametrize("gpu_mem_trans_use", [0.00000001, 0.5])
def test_groupby_fit_merge_fim(datasets, batch, dskey, gpu_mem_trans_use):
paths = glob.glob(str(datasets[dskey]) + "/*.csv")
names = allcols_csv if dskey == "csv-no-header" else None
df_expect = cudf.read_csv(paths[0], header=False, names=names)[mycols_csv]
df_expect["id"] = df_expect["id"].astype("int64")
grouby_stats = groupby.GroupByMomentsCal(
col="name-string",
col_count="x",
cont_col=["x", "y"],
stats=["count", "sum"],
gpu_mem_trans_use=gpu_mem_trans_use,
)
grouby_stats.fit(df_expect[["name-string", "x", "y"]])
grouby_stats.fit_finalize()
new_fea = grouby_stats.merge(df_expect[["name-string", "x", "y"]])
new_fea_host = new_fea.to_pandas()
new_fea_sum_host = new_fea_host[["name-string_x_sum", "name-string_y_sum"]]
new_fea_count_host = new_fea_host[["name-string_count"]]
df_expect_pd = df_expect.to_pandas()
groups_pd = df_expect_pd[["name-string", "x", "y"]].groupby(["name-string"])
sums_pd = groups_pd.sum()
sums_pd["name-string"] = sums_pd.index
sums_pd = sums_pd.reset_index(drop=True)
new_fea_pd = df_expect_pd.merge(sums_pd, on=["name-string"], how="left")
new_fea_pd = new_fea_pd[["x_y", "y_y"]]
error = new_fea_sum_host.subtract(new_fea_pd, axis=1)
e_x_y = error["x_y"].abs()
e_x_y = e_x_y[e_x_y > 1e-10]
assert e_x_y.shape[0] == 0
e_y_y = error["y_y"].abs()
e_y_y = e_y_y[e_y_y > 1e-10]
assert e_y_y.shape[0] == 0
count_pd = groups_pd.count()
count_pd["name-string"] = count_pd.index
count_pd = count_pd.reset_index(drop=True)
new_fea_pd = df_expect_pd.merge(count_pd, on=["name-string"], how="left")
new_fea_pd = new_fea_pd[["x_y"]]
error = new_fea_count_host.subtract(new_fea_pd, axis=1)
e_x_y = error["x_y"].abs()
e_x_y = e_x_y[e_x_y > 0.0]
assert e_x_y.shape[0] == 0
@pytest.mark.parametrize("batch", [0, 100, 1000])
@pytest.mark.parametrize("dskey", ["csv", "csv-no-header"])
@pytest.mark.parametrize("gpu_mem_trans_use", [0.00000001, 0.5])
def test_groupby_fit_merge_countonly(datasets, batch, dskey, gpu_mem_trans_use):
paths = glob.glob(str(datasets[dskey]) + "/*.csv")
names = allcols_csv if dskey == "csv-no-header" else None
df_expect = cudf.read_csv(paths[0], header=False, names=names)[mycols_csv]
df_expect["id"] = df_expect["id"].astype("int64")
grouby_stats = groupby.GroupByMomentsCal(
col="name-string",
col_count="x",
cont_col=None,
stats=["count"],
gpu_mem_trans_use=gpu_mem_trans_use,
)
grouby_stats.fit(df_expect[["name-string", "x", "y"]])
grouby_stats.fit_finalize()
new_fea = grouby_stats.merge(df_expect[["name-string", "x", "y"]])
new_fea_host = new_fea.to_pandas()
new_fea_count_host = new_fea_host[["name-string_count"]]
df_expect_pd = df_expect.to_pandas()
groups_pd = df_expect_pd[["name-string", "x", "y"]].groupby(["name-string"])
count_pd = groups_pd.count()
count_pd["name-string"] = count_pd.index
count_pd = count_pd.reset_index(drop=True)
new_fea_pd = df_expect_pd.merge(count_pd, on=["name-string"], how="left")
new_fea_pd = new_fea_pd[["x_y"]]
error = new_fea_count_host.subtract(new_fea_pd, axis=1)
e_x_y = error["x_y"].abs()
e_x_y = e_x_y[e_x_y > 0.0]
assert e_x_y.shape[0] == 0
@pytest.mark.parametrize("batch", [0, 100, 1000])
@pytest.mark.parametrize("dskey", ["csv", "csv-no-header"])
@pytest.mark.parametrize("gpu_mem_trans_use", [0.00000001, 0.5])
def test_groupby_fit_merge_mean(datasets, batch, dskey, gpu_mem_trans_use):
paths = glob.glob(str(datasets[dskey]) + "/*.csv")
names = allcols_csv if dskey == "csv-no-header" else None
df_expect = cudf.read_csv(paths[0], header=False, names=names)[mycols_csv]
df_expect["id"] = df_expect["id"].astype("int64")
grouby_stats = groupby.GroupByMomentsCal(
col="name-string",
col_count="x",
cont_col=["x", "y"],
stats=["mean"],
gpu_mem_trans_use=gpu_mem_trans_use,
)
grouby_stats.fit(df_expect[["name-string", "x", "y"]])
grouby_stats.fit_finalize()
new_fea = grouby_stats.merge(df_expect[["name-string", "x", "y"]])
new_fea_host = new_fea.to_pandas()
new_fea_mean_host = new_fea_host[["name-string_x_mean", "name-string_y_mean"]]
df_expect_pd = df_expect.to_pandas()
groups_pd = df_expect_pd[["name-string", "x", "y"]].groupby(["name-string"])
mean_pd = groups_pd.mean()
mean_pd["name-string"] = mean_pd.index
mean_pd = mean_pd.reset_index(drop=True)
new_fea_pd = df_expect_pd.merge(mean_pd, on=["name-string"], how="left")
new_fea_pd = new_fea_pd[["x_y", "y_y"]]
error = new_fea_mean_host.subtract(new_fea_pd, axis=1)
e_x_y = error["x_y"].abs()
e_x_y = e_x_y[e_x_y > 1e-10]
assert e_x_y.shape[0] == 0
e_y_y = error["y_y"].abs()
e_y_y = e_y_y[e_y_y > 1e-10]
assert e_y_y.shape[0] == 0
@pytest.mark.parametrize("batch", [0, 100, 1000])
@pytest.mark.parametrize("dskey", ["csv", "csv-no-header"])
@pytest.mark.parametrize("gpu_mem_trans_use", [0.00000001, 0.5])
def test_groupby_fit_merge_stdvar(datasets, batch, dskey, gpu_mem_trans_use):
paths = glob.glob(str(datasets[dskey]) + "/*.csv")
names = allcols_csv if dskey == "csv-no-header" else None
df_expect = cudf.read_csv(paths[0], header=False, names=names)[mycols_csv]
df_expect["id"] = df_expect["id"].astype("int64")
grouby_stats = groupby.GroupByMomentsCal(
col="name-string",
col_count="x",
cont_col=["x", "y"],
stats=["std", "var"],
gpu_mem_trans_use=gpu_mem_trans_use,
)
grouby_stats.fit(df_expect[["name-string", "x", "y"]])
grouby_stats.fit_finalize()
new_fea = grouby_stats.merge(df_expect[["name-string", "x", "y"]])
new_fea_host = new_fea.to_pandas()
new_fea_std_host = new_fea_host[["name-string_x_std", "name-string_y_std"]]
new_fea_var_host = new_fea_host[["name-string_x_var", "name-string_y_var"]]
df_expect_pd = df_expect.to_pandas()
groups_pd = df_expect_pd[["name-string", "x", "y"]].groupby(["name-string"])
# STD test
std_pd = groups_pd.std(ddof=1)
std_pd["name-string"] = std_pd.index
std_pd = std_pd.reset_index(drop=True)
new_fea_pd = df_expect_pd.merge(std_pd, on=["name-string"], how="left")
new_fea_pd = new_fea_pd[["x_y", "y_y"]]
error = new_fea_std_host.subtract(new_fea_pd, axis=1)
e_x_y = error["x_y"].abs()
e_x_y = e_x_y[e_x_y > 1e-10]
assert e_x_y.shape[0] == 0
e_y_y = error["y_y"].abs()
e_y_y = e_y_y[e_y_y > 1e-10]
assert e_y_y.shape[0] == 0
# VAR Test
var_pd = groups_pd.var(ddof=1)
var_pd["name-string"] = var_pd.index
var_pd = var_pd.reset_index(drop=True)
new_fea_pd = df_expect_pd.merge(var_pd, on=["name-string"], how="left")
new_fea_pd = new_fea_pd[["x_y", "y_y"]]
error = new_fea_var_host.subtract(new_fea_pd, axis=1)
e_x_y = error["x_y"].abs()
e_x_y = e_x_y[e_x_y > 1e-10]
assert e_x_y.shape[0] == 0
e_y_y = error["y_y"].abs()
e_y_y = e_y_y[e_y_y > 1e-10]
assert e_y_y.shape[0] == 0
| 35.473684
| 82
| 0.666914
| 1,379
| 8,088
| 3.572154
| 0.108049
| 0.023143
| 0.01827
| 0.045473
| 0.806537
| 0.806537
| 0.806537
| 0.786236
| 0.786236
| 0.786236
| 0
| 0.022596
| 0.168274
| 8,088
| 227
| 83
| 35.629956
| 0.709677
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| 0.126685
| 0
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| 0
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| 0.062112
| 1
| 0.024845
| false
| 0
| 0.031056
| 0
| 0.055901
| 0
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| 0
| null | 0
| 0
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| 1
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0
| 7
|
b3bca1c594ae52a8216d361f5b400791f87209ce
| 10,507
|
py
|
Python
|
tests/libs/measurements/test_logicanalyzer.py
|
izm51/obniz-python-sdk
|
40a738b5fe2c0a415cdc09f46d28c143982bfb07
|
[
"MIT"
] | 11
|
2019-03-22T12:02:11.000Z
|
2021-01-21T04:57:18.000Z
|
tests/libs/measurements/test_logicanalyzer.py
|
izm51/obniz-python-sdk
|
40a738b5fe2c0a415cdc09f46d28c143982bfb07
|
[
"MIT"
] | 5
|
2019-03-02T08:28:25.000Z
|
2021-02-02T22:06:37.000Z
|
tests/libs/measurements/test_logicanalyzer.py
|
izm51/obniz-python-sdk
|
40a738b5fe2c0a415cdc09f46d28c143982bfb07
|
[
"MIT"
] | 3
|
2019-07-20T06:55:09.000Z
|
2019-12-04T05:05:00.000Z
|
from ...utils import assert_finished, assert_send, receive_json
class TestLogicAnalyzer:
def test_start(self, obniz):
obniz.logicAnalyzer.start({"io": 1, "interval": 0.1, "duration": 100})
assert_send(
obniz, [{"logic_analyzer": {"interval": 0.1, "io": [1], "duration": 100}}]
)
assert_finished(obniz)
def test_start_with_trigger(self, obniz):
obniz.logicAnalyzer.start(
{
"io": 1,
"interval": 0.1,
"duration": 100,
"triggerValue": False,
"triggerValueSamples": 3,
}
)
assert_send(
obniz,
[
{
"logic_analyzer": {
"interval": 0.1,
"io": [1],
"duration": 100,
"trigger": {"samples": 3, "value": False},
}
}
],
)
assert_finished(obniz)
def test_start_with_trigger2(self, obniz):
obniz.logicAnalyzer.start(
{
"io": 1,
"interval": 0.1,
"duration": 100,
"triggerValue": 1,
"triggerValueSamples": 3,
}
)
assert_send(
obniz,
[
{
"logic_analyzer": {
"interval": 0.1,
"io": [1],
"duration": 100,
"trigger": {"samples": 3, "value": True},
}
}
],
)
assert_finished(obniz)
def test_onmeasured(self, mocker, obniz):
stub = mocker.stub()
obniz.logicAnalyzer.start(
{
"io": 1,
"interval": 0.1,
"duration": 100,
"triggerValue": False,
"triggerValueSamples": 3,
}
)
assert_send(
obniz,
[
{
"logic_analyzer": {
"interval": 0.1,
"io": [1],
"duration": 100,
"trigger": {"samples": 3, "value": False},
}
}
],
)
obniz.logicAnalyzer.onmeasured = stub
data = [
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]
receive_json(obniz, [{"logic_analyzer": {"data": data}}])
assert stub.call_count == 1
assert stub.call_args[0][0] == data
assert_finished(obniz)
def test_onmeasured_need_pin_no(self):
pass
def test_finished(self, obniz):
obniz.logicAnalyzer.start({"io": 1, "interval": 0.1, "duration": 100})
assert_send(
obniz, [{"logic_analyzer": {"interval": 0.1, "io": [1], "duration": 100}}]
)
assert_finished(obniz)
obniz.logicAnalyzer.end()
assert_send(obniz, [{"logic_analyzer": None}])
| 17.252874
| 86
| 0.165223
| 733
| 10,507
| 2.317872
| 0.06412
| 0.474397
| 0.626839
| 0.725132
| 0.819305
| 0.802825
| 0.760447
| 0.73043
| 0.73043
| 0.73043
| 0
| 0.216992
| 0.751309
| 10,507
| 608
| 87
| 17.28125
| 0.433219
| 0
| 0
| 0.922428
| 0
| 0
| 0.041115
| 0
| 0
| 0
| 0
| 0
| 0.023609
| 1
| 0.010118
| false
| 0.001686
| 0.001686
| 0
| 0.013491
| 0
| 0
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| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
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| 1
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| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 13
|
b3cef08306b8db1008206b03863259a553242560
| 13,909
|
py
|
Python
|
tests/test_sha3.py
|
djboni/emb-crypto
|
300e806387d6a74761ece1ee4b1bf8e086062421
|
[
"Apache-2.0"
] | null | null | null |
tests/test_sha3.py
|
djboni/emb-crypto
|
300e806387d6a74761ece1ee4b1bf8e086062421
|
[
"Apache-2.0"
] | null | null | null |
tests/test_sha3.py
|
djboni/emb-crypto
|
300e806387d6a74761ece1ee4b1bf8e086062421
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python3
# Copyright 2021 Djones A. Boni
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from unit_test_c_with_python.load_c import load
import unittest
import os
import random
module_name = 'sha3_'
source_files = [
'../source/sha3.c',
'../source/keccak.c',
]
include_paths = [
'../include',
]
compiler_options = [
'-std=c90',
'-pedantic',
'-DKECCAK_WORD=8',
]
module, ffi = load(
source_files, include_paths, compiler_options,
module_name=module_name)
import hashlib
class TestSHA3_512(unittest.TestCase):
def testSHA3_512Empty(self):
HASH_BITS = 512
length = 0
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_512_t[1]')
hash_ = phash_[0]
module.SHA3_512Init(phash_)
module.SHA3_512Update(phash_, data, length)
module.SHA3_512Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_512(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_512Zero(self):
HASH_BITS = 512
length = 1
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_512_t[1]')
hash_ = phash_[0]
module.SHA3_512Init(phash_)
module.SHA3_512Update(phash_, data, length)
module.SHA3_512Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_512(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_512Zeros(self):
HASH_BITS = 512
length = random.randint(0, 1024)
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_512_t[1]')
hash_ = phash_[0]
module.SHA3_512Init(phash_)
module.SHA3_512Update(phash_, data, length)
module.SHA3_512Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_512(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_512Random(self):
HASH_BITS = 512
length = random.randint(0, 1024)
data = os.urandom(length)
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_512_t[1]')
hash_ = phash_[0]
module.SHA3_512Init(phash_)
module.SHA3_512Update(phash_, data, length)
module.SHA3_512Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_512(data).digest()
self.assertEqual(hash_module, hash_reference)
class TestSHA3_384(unittest.TestCase):
def testSHA3_384Empty(self):
HASH_BITS = 384
length = 0
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_384_t[1]')
hash_ = phash_[0]
module.SHA3_384Init(phash_)
module.SHA3_384Update(phash_, data, length)
module.SHA3_384Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_384(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_384Zero(self):
HASH_BITS = 384
length = 1
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_384_t[1]')
hash_ = phash_[0]
module.SHA3_384Init(phash_)
module.SHA3_384Update(phash_, data, length)
module.SHA3_384Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_384(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_384Zeros(self):
HASH_BITS = 384
length = random.randint(0, 1024)
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_384_t[1]')
hash_ = phash_[0]
module.SHA3_384Init(phash_)
module.SHA3_384Update(phash_, data, length)
module.SHA3_384Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_384(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_384Random(self):
HASH_BITS = 384
length = random.randint(0, 1024)
data = os.urandom(length)
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_384_t[1]')
hash_ = phash_[0]
module.SHA3_384Init(phash_)
module.SHA3_384Update(phash_, data, length)
module.SHA3_384Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_384(data).digest()
self.assertEqual(hash_module, hash_reference)
class TestSHA3_256(unittest.TestCase):
def testSHA3_256Empty(self):
HASH_BITS = 256
length = 0
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_256_t[1]')
hash_ = phash_[0]
module.SHA3_256Init(phash_)
module.SHA3_256Update(phash_, data, length)
module.SHA3_256Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_256(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_256Zero(self):
HASH_BITS = 256
length = 1
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_256_t[1]')
hash_ = phash_[0]
module.SHA3_256Init(phash_)
module.SHA3_256Update(phash_, data, length)
module.SHA3_256Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_256(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_256Zeros(self):
HASH_BITS = 256
length = random.randint(0, 1024)
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_256_t[1]')
hash_ = phash_[0]
module.SHA3_256Init(phash_)
module.SHA3_256Update(phash_, data, length)
module.SHA3_256Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_256(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_256Random(self):
HASH_BITS = 256
length = random.randint(0, 1024)
data = os.urandom(length)
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_256_t[1]')
hash_ = phash_[0]
module.SHA3_256Init(phash_)
module.SHA3_256Update(phash_, data, length)
module.SHA3_256Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_256(data).digest()
self.assertEqual(hash_module, hash_reference)
class TestSHA3_224(unittest.TestCase):
def testSHA3_224Empty(self):
HASH_BITS = 224
length = 0
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_224_t[1]')
hash_ = phash_[0]
module.SHA3_224Init(phash_)
module.SHA3_224Update(phash_, data, length)
module.SHA3_224Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_224(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_256Zero(self):
HASH_BITS = 224
length = 1
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_224_t[1]')
hash_ = phash_[0]
module.SHA3_224Init(phash_)
module.SHA3_224Update(phash_, data, length)
module.SHA3_224Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_224(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_256Zeros(self):
HASH_BITS = 224
length = random.randint(0, 1024)
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_224_t[1]')
hash_ = phash_[0]
module.SHA3_224Init(phash_)
module.SHA3_224Update(phash_, data, length)
module.SHA3_224Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_224(data).digest()
self.assertEqual(hash_module, hash_reference)
def testSHA3_256Random(self):
HASH_BITS = 224
length = random.randint(0, 1024)
data = os.urandom(length)
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct sha3_224_t[1]')
hash_ = phash_[0]
module.SHA3_224Init(phash_)
module.SHA3_224Update(phash_, data, length)
module.SHA3_224Finish(phash_)
hash_module = ffi.buffer(hash_.hash.a, hash_length)
hash_module = hash_module[:]
hash_reference = hashlib.sha3_224(data).digest()
self.assertEqual(hash_module, hash_reference)
class TestSHAKE_256(unittest.TestCase):
def testSHAKE_256Empty(self):
HASH_BITS = 256
length = 0
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_256_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE256Init(phash_)
module.SHAKE256Absorb(phash_, data, length)
module.SHAKE256Finish(phash_)
module.SHAKE256Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_256(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
def testSHAKE_256Zero(self):
HASH_BITS = 256
length = 1
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_256_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE256Init(phash_)
module.SHAKE256Absorb(phash_, data, length)
module.SHAKE256Finish(phash_)
module.SHAKE256Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_256(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
def testSHAKE_256Zeros(self):
HASH_BITS = 256
length = random.randint(0, 1024)
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_256_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE256Init(phash_)
module.SHAKE256Absorb(phash_, data, length)
module.SHAKE256Finish(phash_)
module.SHAKE256Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_256(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
def testSHAKE_256Random(self):
HASH_BITS = 256
length = random.randint(0, 1024)
data = os.urandom(length)
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_256_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE256Init(phash_)
module.SHAKE256Absorb(phash_, data, length)
module.SHAKE256Finish(phash_)
module.SHAKE256Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_256(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
class TestSHAKE_128(unittest.TestCase):
def testSHAKE_128Empty(self):
HASH_BITS = 128
length = 0
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_128_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE128Init(phash_)
module.SHAKE128Absorb(phash_, data, length)
module.SHAKE128Finish(phash_)
module.SHAKE128Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_128(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
def testSHAKE_128Zero(self):
HASH_BITS = 128
length = 1
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_128_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE128Init(phash_)
module.SHAKE128Absorb(phash_, data, length)
module.SHAKE128Finish(phash_)
module.SHAKE128Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_128(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
def testSHAKE_128Zeros(self):
HASH_BITS = 128
length = random.randint(0, 1024)
data = b'\x00' * length
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_128_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE128Init(phash_)
module.SHAKE128Absorb(phash_, data, length)
module.SHAKE128Finish(phash_)
module.SHAKE128Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_128(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
def testSHAKE_128Random(self):
HASH_BITS = 128
length = random.randint(0, 1024)
data = os.urandom(length)
hash_length = HASH_BITS // 8
phash_ = ffi.new('struct shake_128_t[1]')
hash_ = phash_[0]
hash_module = b'\x00' * hash_length
module.SHAKE128Init(phash_)
module.SHAKE128Absorb(phash_, data, length)
module.SHAKE128Finish(phash_)
module.SHAKE128Squeeze(phash_, hash_module, hash_length)
hash_reference = hashlib.shake_128(data).digest(hash_length)
self.assertEqual(hash_module, hash_reference)
if __name__ == '__main__':
unittest.main()
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|
0
| 7
|
b3e4b67f56c6daf9286adb98cc9817ab8b2685b0
| 40
|
py
|
Python
|
quantlib/editing/fx/__init__.py
|
mdatres/quantlab
|
09fb24ede78f49768f829afe0fac2ac291b8fd4f
|
[
"Apache-2.0"
] | null | null | null |
quantlib/editing/fx/__init__.py
|
mdatres/quantlab
|
09fb24ede78f49768f829afe0fac2ac291b8fd4f
|
[
"Apache-2.0"
] | null | null | null |
quantlib/editing/fx/__init__.py
|
mdatres/quantlab
|
09fb24ede78f49768f829afe0fac2ac291b8fd4f
|
[
"Apache-2.0"
] | 1
|
2022-01-02T10:10:46.000Z
|
2022-01-02T10:10:46.000Z
|
from . import passes
from . import util
| 13.333333
| 20
| 0.75
| 6
| 40
| 5
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| 0.666667
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0
| 8
|
b603f83e0e56985a1f245dc30a8f3731a06d562c
| 27,258
|
py
|
Python
|
ConvNet.py
|
srk1995/2D3DRegistration
|
8d65d06c93532574d831055fce2d3f9bbe315f09
|
[
"MIT"
] | 9
|
2020-09-16T16:29:28.000Z
|
2021-07-28T11:23:09.000Z
|
ConvNet.py
|
srk1995/2D3DRegistration
|
8d65d06c93532574d831055fce2d3f9bbe315f09
|
[
"MIT"
] | null | null | null |
ConvNet.py
|
srk1995/2D3DRegistration
|
8d65d06c93532574d831055fce2d3f9bbe315f09
|
[
"MIT"
] | 1
|
2020-09-24T07:37:27.000Z
|
2020-09-24T07:37:27.000Z
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from pointnet_util import PointNetSetAbstractionMsg, PointNetSetAbstraction
from unet.unet_parts import *
def double_conv(in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
nn.ReLU(inplace=True)
)
def double_conv3(in_channels, out_channels):
return nn.Sequential(
nn.Conv3d(in_channels, out_channels, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv3d(out_channels, out_channels, 3, padding=1),
nn.ReLU(inplace=True)
)
class UNet(nn.Module):
def __init__(self, input, hidden, n_class):
super().__init__()
self.dconv_down1 = double_conv3(input, hidden)
self.dconv_down2 = double_conv3(hidden, hidden*2)
self.dconv_down3 = double_conv3(hidden*2, hidden*4)
self.dconv_down4 = double_conv3(hidden*4, hidden*8)
self.maxpool = nn.MaxPool3d(2)
self.upsample = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=True)
self.dconv_up3 = double_conv3(hidden*4 + hidden*8, hidden*4)
self.dconv_up2 = double_conv3(hidden*2 + hidden*4, hidden*2)
self.dconv_up1 = double_conv3(hidden*2 + hidden, hidden)
self.conv_last = nn.Conv3d(hidden, 1, 1)
self.fc_ct = nn.Linear(32*32*32, 128)
self.dconv_down1_x = double_conv(input, hidden)
self.dconv_down2_x = double_conv(hidden, hidden*2)
self.dconv_down3_x = double_conv(hidden*2, hidden*4)
self.dconv_down4_x = double_conv(hidden*4, hidden*8)
self.maxpool_x = nn.MaxPool2d(2)
self.upsample_x = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.dconv_up3_x = double_conv(hidden*4 + hidden*8, hidden*4)
self.dconv_up2_x = double_conv(hidden*2 + hidden*4, hidden*2)
self.dconv_up1_x = double_conv(hidden*2 + hidden, hidden)
self.conv_last_x = nn.Conv2d(hidden, 1, 1)
self.fc_x = nn.Linear(32 * 32, 128)
self.fc = nn.Linear(256, 6)
def forward(self, x, xray):
x = F.interpolate(x, (64, 64, 64))
conv1 = self.dconv_down1(x)
x = self.maxpool(conv1)
conv2 = self.dconv_down2(x)
x = self.maxpool(conv2)
conv3 = self.dconv_down3(x)
x = self.maxpool(conv3)
x = self.dconv_down4(x)
x = self.upsample(x)
x = torch.cat([x, conv3], dim=1)
x = self.dconv_up3(x)
x = self.upsample(x)
x = torch.cat([x, conv2], dim=1)
x = self.dconv_up2(x)
x = self.upsample(x)
x = torch.cat([x, conv1], dim=1)
x = self.dconv_up1(x)
out = F.relu(self.conv_last(x))
out = self.maxpool(out)
out = self.fc_ct(out.view((1, -1)))
# X-ray
xray = F.interpolate(xray, (64, 64))
conv1 = self.dconv_down1_x(xray)
x = self.maxpool_x(conv1)
conv2 = self.dconv_down2_x(x)
x = self.maxpool_x(conv2)
conv3 = self.dconv_down3_x(x)
x = self.maxpool_x(conv3)
x = self.dconv_down4_x(x)
x = self.upsample_x(x)
x = torch.cat([x, conv3], dim=1)
x = self.dconv_up3_x(x)
x = self.upsample_x(x)
x = torch.cat([x, conv2], dim=1)
x = self.dconv_up2_x(x)
x = self.upsample_x(x)
x = torch.cat([x, conv1], dim=1)
x = self.dconv_up1_x(x)
out_x = F.relu(self.conv_last_x(x))
out_x = self.maxpool_x(out_x)
out_x = self.fc_x(out_x.view((1, -1)))
out = torch.cat((out, out_x), dim=-1)
out = self.fc(out)
return out
class Net_split(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Net_split, self).__init__()
# layers for CT
self.map1 = nn.Conv3d(input_size, hidden_size, 3, padding=1, bias=False)
self.bn1 = nn.BatchNorm3d(hidden_size)
self.map2 = nn.Conv3d(hidden_size, hidden_size, 3, padding=1, bias=False)
self.bn2 = nn.BatchNorm3d(hidden_size)
self.map3 = nn.Conv3d(hidden_size, hidden_size*2, 3, padding=1, bias=False)
self.bn3 = nn.BatchNorm3d(hidden_size*2)
self.map4 = nn.Conv3d(hidden_size*2, hidden_size*2, 3, padding=1, bias=False)
self.bn4 = nn.BatchNorm3d(hidden_size*2)
self.map5 = nn.Conv3d(hidden_size * 2, hidden_size * 4, 3, padding=1, bias=False)
self.bn5 = nn.BatchNorm3d(hidden_size*4)
self.map6 = nn.Conv3d(hidden_size * 4, hidden_size * 4, 3, padding=1, bias=False)
self.bn6 = nn.BatchNorm3d(hidden_size*4)
self.map7 = nn.Conv3d(hidden_size * 4, hidden_size * 8, 3, padding=1, bias=False)
self.bn7 = nn.BatchNorm3d(hidden_size*8)
self.map8 = nn.Conv3d(hidden_size * 8, hidden_size * 8, 3, padding=1, bias=False)
self.bn8 = nn.BatchNorm3d(hidden_size*8)
self.fc_ct = nn.Linear(hidden_size*8*8*8*8, output_size*2)
# layers for X-ray
self.conv1 = nn.Conv2d(input_size, hidden_size, 3, padding=1)
self.bn1_x = nn.BatchNorm2d(hidden_size)
self.conv2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn2_x = nn.BatchNorm2d(hidden_size)
self.conv3 = nn.Conv2d(hidden_size, hidden_size*2, 3, padding=1)
self.bn3_x = nn.BatchNorm2d(hidden_size*2)
self.conv4 = nn.Conv2d(hidden_size*2, hidden_size * 2, 3, padding=1)
self.bn4_x = nn.BatchNorm2d(hidden_size*2)
self.conv5 = nn.Conv2d(hidden_size * 2, hidden_size * 4, 3, padding=1)
self.bn5_x = nn.BatchNorm2d(hidden_size*4)
self.conv6 = nn.Conv2d(hidden_size * 4, hidden_size*4, 3, padding=1)
self.bn6_x = nn.BatchNorm2d(hidden_size*4)
self.conv7 = nn.Conv2d(hidden_size * 4, hidden_size*8, 3, padding=1)
self.bn7_x = nn.BatchNorm2d(hidden_size*8)
self.conv8 = nn.Conv2d(hidden_size * 8, hidden_size*8, 3, padding=1)
self.bn8_x = nn.BatchNorm2d(hidden_size*8)
self.fc_x = nn.Linear(hidden_size * 8 * 8 * 8, output_size * 2)
self.fc1 = nn.Linear(output_size * 4, output_size * 3)
self.fc2 = nn.Linear(output_size * 3, output_size * 2)
self.fc3 = nn.Linear(output_size * 2, output_size)
self.fc_out = nn.Linear(output_size, output_size)
def forward(self, x, xray):
# CT
x = F.interpolate(x, (128, 128, 128))
out = self.map1(x)
out = F.relu(self.bn1(out))
out = nn.AdaptiveMaxPool3d((128, 128, 128))(out)
out = self.map2(out)
out = F.relu(self.bn2(out))
out = nn.AdaptiveMaxPool3d((64, 64, 64))(out)
out = self.map3(out)
out = F.relu(self.bn3(out))
out = nn.AdaptiveMaxPool3d((64, 64, 64))(out)
out = self.map4(out)
out = F.relu(self.bn4(out))
out = nn.AdaptiveMaxPool3d((32, 32, 32))(out)
out = self.map5(out)
out = F.relu(self.bn5(out))
out = nn.AdaptiveMaxPool3d((16, 16, 16))(out)
out = self.map6(out)
out = F.relu(self.bn6(out))
out = nn.AdaptiveMaxPool3d((8, 8, 8))(out)
out = self.map7(out)
out = F.relu(self.bn7(out))
out = nn.AdaptiveMaxPool3d((8, 8, 8))(out)
out = self.map8(out)
out = F.relu(self.bn8(out))
out = nn.AdaptiveMaxPool3d((8, 8, 8))(out)
out = out.view((1, -1))
out_ct = F.relu(self.fc_ct(out))
# X-ray
xray = F.interpolate(xray, (xray.shape[2] // 2, xray.shape[3] // 2))
out = self.conv1(xray)
out = F.relu(self.bn1_x(out))
out = nn.AdaptiveMaxPool2d((120, 155))(out)
out = self.conv2(out)
out = F.relu(self.bn2_x(out))
out = nn.AdaptiveMaxPool2d((64, 64))(out)
out = self.conv3(out)
out = F.relu(self.bn3_x(out))
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = self.conv4(out)
out = F.relu(self.bn4_x(out))
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = self.conv5(out)
out = F.relu(self.bn5_x(out))
out = nn.AdaptiveMaxPool2d((16, 16))(out)
out = self.conv6(out)
out = F.relu(self.bn6_x(out))
out = nn.AdaptiveMaxPool2d((8, 8))(out)
out = self.conv7(out)
out = F.relu(self.bn7_x(out))
out = nn.AdaptiveMaxPool2d((8, 8))(out)
out = self.conv8(out)
out = F.relu(self.bn8_x(out))
out = nn.AdaptiveMaxPool2d((8, 8))(out)
out = out.view((1, -1))
out_xray = F.relu(self.fc_x(out))
out = torch.cat((out_ct, out_xray), dim=-1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
out = F.relu(self.fc3(out))
out = self.fc_out(out)
return out
class layer6Net(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(layer6Net, self).__init__()
# layers for CT
self.map1 = nn.Conv3d(input_size, hidden_size, 5, padding=0, bias=False)
self.bn1 = nn.BatchNorm3d(hidden_size)
self.map2 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn2 = nn.BatchNorm3d(hidden_size)
self.map3 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn3 = nn.BatchNorm3d(hidden_size)
self.fc_ct = nn.Linear(hidden_size*16*16*16, output_size*2)
# layers for X-ray
self.conv1 = nn.Conv2d(input_size, hidden_size, 3, padding=1)
self.bn1_x = nn.BatchNorm2d(hidden_size)
self.conv2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn2_x = nn.BatchNorm2d(hidden_size)
self.conv3 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn3_x = nn.BatchNorm2d(hidden_size*2)
self.fc_x = nn.Linear(hidden_size * 16 * 16, output_size * 2)
self.fc1 = nn.Linear(output_size * 4, output_size * 3)
self.fc2 = nn.Linear(output_size * 3, output_size * 2)
self.fc3 = nn.Linear(output_size * 2, output_size)
self.fc_out = nn.Linear(output_size, output_size)
def forward(self, x, xray):
# CT
x = F.interpolate(x, (64, 64, 64))
out = self.map1(x)
# out = F.relu(self.bn1(out))
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((64, 64, 64))(out)
out = self.map2(out)
# out = F.relu(self.bn2(out))
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((32, 32, 32))(out)
out = self.map3(out)
# out = F.relu(self.bn3(out))
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((16, 16, 16))(out)
out = out.view((1, -1))
out_ct = F.relu(self.fc_ct(out))
# X-ray
xray = F.interpolate(xray, (xray.shape[2]//2, xray.shape[3]//2))
out = self.conv1(xray)
# out = F.relu(self.bn1_x(out))
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((64, 64))(out)
out = self.conv2(out)
# out = F.relu(self.bn2_x(out))
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = self.conv3(out)
# out = F.relu(self.bn3_x(out))
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((16, 16))(out)
out = out.view((1, -1))
out_xray = F.relu(self.fc_x(out))
out = torch.cat((out_ct, out_xray), dim=-1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
out = F.relu(self.fc3(out))
out = self.fc_out(out)
return out
class HomographyNet(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(HomographyNet, self).__init__()
# layers for CT
self.map1 = nn.Conv3d(input_size, hidden_size, 5, padding=0, bias=False)
self.bn1 = nn.BatchNorm3d(hidden_size)
self.map2 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn2 = nn.BatchNorm3d(hidden_size)
self.map3 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn3 = nn.BatchNorm3d(hidden_size)
self.map4 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn4 = nn.BatchNorm3d(hidden_size)
self.map5 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn5 = nn.BatchNorm3d(hidden_size)
self.map6 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn6 = nn.BatchNorm3d(hidden_size)
self.fc_ct = nn.Linear(hidden_size * 16 * 16 * 16, 512)
# layers for X-ray
self.conv1 = nn.Conv2d(input_size, hidden_size, 3, padding=1)
self.bn1_x = nn.BatchNorm2d(hidden_size)
self.conv2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn2_x = nn.BatchNorm2d(hidden_size)
self.conv3 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn3_x = nn.BatchNorm2d(hidden_size)
self.conv4 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn4_x = nn.BatchNorm2d(hidden_size)
self.conv5 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn5_x = nn.BatchNorm2d(hidden_size)
self.conv6 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn6_x = nn.BatchNorm2d(hidden_size)
self.fc_x = nn.Linear(hidden_size * 16 * 16, 512)
self.fc_out = nn.Linear(1024, output_size)
def forward(self, x, xray):
# CT
x = F.interpolate(x, (64, 64, 64))
out = self.map1(x)
out = self.bn1(out)
out = F.relu(out)
out = self.map2(out)
out = self.bn2(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((64, 64, 64))(out)
out = self.map3(out)
out = self.bn3(out)
out = F.relu(out)
out = self.map4(out)
out = self.bn4(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((32, 32, 32))(out)
out = self.map5(out)
out = self.bn5(out)
out = F.relu(out)
out = self.map6(out)
out = self.bn6(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((16, 16, 16))(out)
out = out.view((1, -1))
out_ct = F.relu(self.fc_ct(out))
# X-ray
xray = F.interpolate(xray, (xray.shape[2] // 2, xray.shape[3] // 2))
out = self.conv1(xray)
out = self.bn1_x(out)
out = F.relu(out)
out = self.conv2(out)
out = self.bn2_x(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((64, 64))(out)
out = self.conv3(out)
out = self.bn3_x(out)
out = F.relu(out)
out = self.conv4(out)
out = self.bn4_x(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = self.conv5(out)
out = self.bn5_x(out)
out = F.relu(out)
out = self.conv6(out)
out = self.bn6_x(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((16, 16))(out)
out = out.view((1, -1))
out_xray = F.relu(self.fc_x(out))
out = torch.cat((out_ct, out_xray), dim=-1)
out = self.fc_out(out)
return out
class HomographyNet_bn(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(HomographyNet_bn, self).__init__()
# layers for CT
self.map1 = nn.Conv3d(input_size, hidden_size, 5, padding=0, bias=False)
self.bn1 = nn.BatchNorm3d(hidden_size)
self.map2 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn2 = nn.BatchNorm3d(hidden_size)
self.map3 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn3 = nn.BatchNorm3d(hidden_size)
self.map4 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn4 = nn.BatchNorm3d(hidden_size)
self.map5 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn5 = nn.BatchNorm3d(hidden_size)
self.map6 = nn.Conv3d(hidden_size, hidden_size, 5, padding=0, bias=False)
self.bn6 = nn.BatchNorm3d(hidden_size)
self.fc_ct = nn.Linear(hidden_size*16*16*16, output_size*2)
# layers for X-ray
self.conv1 = nn.Conv2d(input_size, hidden_size, 3, padding=1)
self.bn1_x = nn.BatchNorm2d(hidden_size)
self.conv2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn2_x = nn.BatchNorm2d(hidden_size)
self.conv3 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn3_x = nn.BatchNorm2d(hidden_size)
self.conv4 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn4_x = nn.BatchNorm2d(hidden_size)
self.conv5 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn5_x = nn.BatchNorm2d(hidden_size)
self.conv6 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
self.bn6_x = nn.BatchNorm2d(hidden_size)
self.fc_x = nn.Linear(hidden_size * 16 * 16, output_size * 2)
self.fc1 = nn.Linear(output_size * 4, output_size * 2)
self.fc2 = nn.Linear(output_size * 2, output_size)
self.fc_out = nn.Linear(output_size, output_size)
def forward(self, x, xray):
# CT
x = F.interpolate(x, (64, 64, 64))
out = self.map1(x)
out = self.bn1(out)
out = F.relu(out)
out = self.map2(out)
out = self.bn2(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((64, 64, 64))(out)
out = self.map3(out)
out = self.bn3(out)
out = F.relu(out)
out = self.map4(out)
out = self.bn4(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((32, 32, 32))(out)
out = self.map5(out)
out = self.bn5(out)
out = F.relu(out)
out = self.map6(out)
out = self.bn6(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool3d((16, 16, 16))(out)
out = out.view((1, -1))
out_ct = F.relu(self.fc_ct(out))
# X-ray
xray = F.interpolate(xray, (xray.shape[2]//2, xray.shape[3]//2))
out = self.conv1(xray)
out = self.bn1_x(out)
out = F.relu(out)
out = self.conv2(out)
out = self.bn2_x(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((64, 64))(out)
out = self.conv3(out)
out = self.bn3_x(out)
out = F.relu(out)
out = self.conv4(out)
out = self.bn4_x(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = self.conv5(out)
out = self.bn5_x(out)
out = F.relu(out)
out = self.conv6(out)
out = self.bn6_x(out)
out = F.relu(out)
out = nn.AdaptiveMaxPool2d((16, 16))(out)
out = out.view((1, -1))
out_xray = F.relu(self.fc_x(out))
out = torch.cat((out_ct, out_xray), dim=-1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
out = self.fc_out(out)
return out
class layer8Net(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(layer8Net, self).__init__()
# layers for CT
self.map1 = nn.Conv3d(input_size, hidden_size*2, 5, padding=2, bias=False)
self.bn1 = nn.BatchNorm3d(hidden_size*2)
self.map2 = nn.Conv3d(hidden_size*2, hidden_size*2, 5, padding=2, bias=False)
self.bn2 = nn.BatchNorm3d(hidden_size*2)
self.map3 = nn.Conv3d(hidden_size*2, hidden_size*4, 5, padding=2, bias=False)
self.bn3 = nn.BatchNorm3d(hidden_size*4)
self.map4 = nn.Conv3d(hidden_size*4, hidden_size*4, 5, padding=2, bias=False)
self.bn4 = nn.BatchNorm3d(hidden_size*4)
self.fc_ct = nn.Linear(hidden_size*4*32*32*32, output_size*2)
# layers for X-ray
self.conv1 = nn.Conv2d(input_size, hidden_size*2, 3, padding=1)
self.bn1_x = nn.BatchNorm2d(hidden_size*2)
self.conv2 = nn.Conv2d(hidden_size*2, hidden_size*2, 3, padding=1)
self.bn2_x = nn.BatchNorm2d(hidden_size*2)
self.conv3 = nn.Conv2d(hidden_size*2, hidden_size*4, 3, padding=1)
self.bn3_x = nn.BatchNorm2d(hidden_size*4)
self.conv4 = nn.Conv2d(hidden_size*4, hidden_size*4, 3, padding=1)
self.bn4_x = nn.BatchNorm2d(hidden_size*4)
self.fc_x = nn.Linear(hidden_size * 4 * 32 * 32, output_size * 2)
self.fc1 = nn.Linear(output_size * 4, output_size * 3)
self.fc2 = nn.Linear(output_size * 3, output_size * 2)
self.fc3 = nn.Linear(output_size * 2, output_size)
self.fc_out = nn.Linear(output_size, output_size)
def forward(self, x, xray):
# CT
x = F.interpolate(x, (128, 128, 128))
out = self.map1(x)
out = F.relu(self.bn1(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool3d((128, 128, 128))(out)
out = self.map2(out)
out = F.relu(self.bn2(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool3d((64, 64, 64))(out)
out = self.map3(out)
out = F.relu(self.bn3(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool3d((32, 32, 32))(out)
out = self.map4(out)
out = F.relu(self.bn4(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool3d((32, 32, 32))(out)
out = out.view((1, -1))
out_ct = F.relu(self.fc_ct(out))
# X-ray
xray = F.interpolate(xray, (xray.shape[2]//2, xray.shape[3]//2))
out = self.conv1(xray)
out = F.relu(self.bn1_x(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool2d((128, 128))(out)
out = self.conv2(out)
out = F.relu(self.bn2_x(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool2d((64, 64))(out)
out = self.conv3(out)
out = F.relu(self.bn3_x(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = self.conv4(out)
out = F.relu(self.bn4_x(out))
# out = F.relu(out)
out = nn.AdaptiveMaxPool2d((32, 32))(out)
out = out.view((1, -1))
out_xray = F.relu(self.fc_x(out))
out = torch.cat((out_ct, out_xray), dim=-1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
out = F.relu(self.fc3(out))
out = self.fc_out(out)
return out
## PointNet
class PointReg(nn.Module):
def __init__(self, out_dim, normal_channel=False):
super(PointReg, self).__init__()
in_channel = 3 if normal_channel else 0
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [16, 32, 128], in_channel, [[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2 = PointNetSetAbstractionMsg(128, [0.2, 0.4, 0.8], [32, 64, 128], 320, [[64, 64, 128], [128, 128, 256], [128, 128, 256]])
self.sa3 = PointNetSetAbstraction(None, None, None, 640 + 3, [256, 256, 512], True)
self.sa1_x = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [16, 32, 128], -1, [[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2_x = PointNetSetAbstractionMsg(128, [0.2, 0.4, 0.8], [32, 64, 128], 319, [[64, 64, 128], [128, 128, 256], [128, 128, 256]])
self.sa3_x = PointNetSetAbstraction(None, None, None, 640 + 2, [256, 256, 512], True)
self.fc1 = nn.Linear(1024, 512)
self.bn1 = nn.BatchNorm1d(512)
self.drop1 = nn.Dropout(0.4)
self.fc2 = nn.Linear(1024, 512)
self.bn2 = nn.BatchNorm1d(512)
self.drop2 = nn.Dropout(0.5)
self.fc3 = nn.Linear(512, 256)
self.bn3 = nn.BatchNorm1d(256)
self.drop3 = nn.Dropout(0.5)
self.fc4 = nn.Linear(256, out_dim)
def forward(self, xyz, xy):
B, _, _ = xyz.shape
if self.normal_channel:
norm = xyz[:, 3:, :]
xyz = xyz[:, :3, :]
xy = xy[:, :2, :]
else:
norm = None
l1_xyz, l1_points = self.sa1(xyz, norm)
l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)
x = l3_points.view(B, 512)
l1_xy, l1_points = self.sa1_x(xy, norm)
l2_xy, l2_points = self.sa2_x(l1_xy, l1_points)
l3_xy, l3_points = self.sa3_x(l2_xy, l2_points)
x_2 = l3_points.view(B, 512)
x_t = torch.cat((x, x_2), dim=1)
pi_x = self.drop1(F.relu(self.fc1(x_t)))
x = x + pi_x
x_2 = x_2 + pi_x
x = torch.cat((x, x_2), dim=1)
x = self.drop2(F.relu(self.fc2(x)))
x = self.drop3(F.relu(self.fc3(x)))
x = self.fc4(x)
x = x.reshape((xyz.size()[0], -1, 1))
x = torch.softmax(x, dim=1)
return x
class PointReg_dcp(nn.Module):
def __init__(self, out_dim, normal_channel=False):
super(PointReg_dcp, self).__init__()
in_channel = 3 if normal_channel else 0
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [16, 32, 128], in_channel, [[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2 = PointNetSetAbstractionMsg(128, [0.2, 0.4, 0.8], [32, 64, 128], 320, [[64, 64, 128], [128, 128, 256], [128, 128, 256]])
self.sa3 = PointNetSetAbstraction(None, None, None, 640 + 3, [256, 256, 512], True)
self.sa1_x = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [16, 32, 128], -1, [[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2_x = PointNetSetAbstractionMsg(128, [0.2, 0.4, 0.8], [32, 64, 128], 319, [[64, 64, 128], [128, 128, 256], [128, 128, 256]])
self.sa3_x = PointNetSetAbstraction(None, None, None, 640 + 2, [256, 256, 512], True)
self.fc1 = nn.Linear(1024, 512)
self.bn1 = nn.BatchNorm1d(512)
self.drop1 = nn.Dropout(0.4)
self.fc2 = nn.Linear(1024, 512)
self.bn2 = nn.BatchNorm1d(512)
self.drop2 = nn.Dropout(0.5)
self.fc3 = nn.Linear(512, 256)
self.bn3 = nn.BatchNorm1d(256)
self.drop3 = nn.Dropout(0.5)
self.fc4 = nn.Linear(256, out_dim)
def forward(self, xyz, xy):
B, _, _ = xyz.shape
if self.normal_channel:
norm = xyz[:, 3:, :]
xyz = xyz[:, :3, :]
xy = xy[:, :2, :]
else:
norm = None
l1_xyz, l1_points = self.sa1(xyz, norm)
l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)
x = l3_points.view(B, 512)
l1_xy, l1_points = self.sa1_x(xy, norm)
l2_xy, l2_points = self.sa2_x(l1_xy, l1_points)
l3_xy, l3_points = self.sa3_x(l2_xy, l2_points)
x_2 = l3_points.view(B, 512)
x_t = torch.cat((x, x_2), dim=1)
pi_x = self.drop1(F.relu(self.fc1(x_t)))
x = x + pi_x
x_2 = x_2 + pi_x
x = torch.cat((x, x_2), dim=1)
x = self.drop2(F.relu(self.fc2(x)))
x = self.drop3(F.relu(self.fc3(x)))
x = self.fc4(x)
x = x.reshape((xyz.size()[0], -1, 6))
x = torch.softmax(x, dim=1)
return x
if __name__ == "__main__":
# The number of block is 5.
net = Net(3, 16, 6)
| 34.946154
| 139
| 0.579646
| 4,151
| 27,258
| 3.659841
| 0.03999
| 0.083333
| 0.042127
| 0.049961
| 0.947209
| 0.93569
| 0.891061
| 0.850843
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0
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373c35b1dc521fe204f01c78aba0466b4b07b84d
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py
|
Python
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test/conftest.py
|
rporres/qontract-schemas
|
eed3c11702e11dbdb9cae161830e55190ede283f
|
[
"Apache-2.0"
] | null | null | null |
test/conftest.py
|
rporres/qontract-schemas
|
eed3c11702e11dbdb9cae161830e55190ede283f
|
[
"Apache-2.0"
] | 34
|
2021-11-10T17:42:19.000Z
|
2022-03-31T17:38:47.000Z
|
test/conftest.py
|
rporres/qontract-schemas
|
eed3c11702e11dbdb9cae161830e55190ede283f
|
[
"Apache-2.0"
] | 19
|
2021-11-01T13:34:26.000Z
|
2022-02-10T21:05:01.000Z
|
import pytest
from utils import load_schemas
@pytest.fixture(scope="session")
def schemas():
return load_schemas()
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0
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37ccbb315c6de069a706c39ee63d7fcec22dfea7
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py
|
Python
|
MatplotLib/MatplotLib.py
|
jrderek/Data-science-master-resources
|
95adab02dccbf5fbe6333389324a1f8d032d3165
|
[
"MIT"
] | 14
|
2020-09-17T17:04:04.000Z
|
2021-08-19T05:08:49.000Z
|
MatplotLib/MatplotLib.py
|
jrderek/Data-science-master-resources
|
95adab02dccbf5fbe6333389324a1f8d032d3165
|
[
"MIT"
] | 85
|
2020-10-01T16:53:21.000Z
|
2021-07-08T17:44:17.000Z
|
MatplotLib/MatplotLib.py
|
jrderek/Data-science-master-resources
|
95adab02dccbf5fbe6333389324a1f8d032d3165
|
[
"MIT"
] | 5
|
2020-09-18T08:53:01.000Z
|
2021-08-19T05:12:52.000Z
|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional Method"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(0, 5, 11)\n",
"y = x**2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.25, 1. , 2.25, 4. , 6.25, 9. , 12.25, 16. ,\n",
" 20.25, 25. ])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Title and Labels"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('X values')\n",
"plt.ylabel('Y values')\n",
"plt.title('Graph of $y = x^2$')\n",
"\n",
"plt.plot(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"x1 = np.linspace(-np.pi, np.pi, 256)\n",
"x2 = np.linspace(-np.pi, np.pi, 256)\n",
"\n",
"y1 = np.cos(x1)\n",
"y2 = np.sin(x2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2bc46623848>,\n",
" <matplotlib.lines.Line2D at 0x2bc4662c808>]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x1, y1, x2, y2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Figure Size and dpi"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1)\n",
"plt.plot(x2, y2)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Styling Figure"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, 'r-.')\n",
"plt.plot(x2, y2, 'b--')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, color = 'purple')\n",
"plt.plot(x2, y2, color = 'green')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, color = 'purple', lw = 2.5)\n",
"plt.plot(x2, y2, color = 'green', lw = 2.5)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, color = 'purple', lw = 2.5, ls = '--')\n",
"plt.plot(x2, y2, color = 'green', lw = 2.5, ls = '-.')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## xticks and yticks"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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48PjKx6n1UC3FyeC3E7+x79I+3uv8ntzxbyDpOeksP7ac4U2Hy/+XEupO11DKlFchhBIXdlzgx14/2spk3d516b+8P44u8m1JNZOjib7f90XXdY7/fBxztplNb24iKCTItie4Cr+d+I2Bvwwkz5KHRbcwvct0KS8GUda5LCOajbjp8ey8bFwcXRQkEsVNTnkLIYrdpX2X+L779+RmWLdSrNu7LgN+GSBl0kD+r1TW71efio0q8sSaJ5SWSYDz18/btlK8lH4JHdn+z8g2RW+i9pzaHI4/rDqKKAZyylsIUaziD8fzdcevyUrOAiAoNIjHVz0uZdKgzLlmslOzca9gjOsTlxxcwo4LO1jQawEOJgfVccQtbL+wnZDvQsjKy6JimYpsHbaVuj51VccSBSBzKIUQhpESk8Ki4EXcuHoDgOodqjN43WDZl9vO5Gbmkn45He+acke1+Hdp2WmEfBfCnot7APD39Gfb8G3U8KqhNpi4ZzKHUghhGJ7+ntR+uDYAfsF+DFo9SMqkncm6nsX33b5naYelpMSkFOl7nb52mjWn1xTpe4ii4eHiwbrB62hSqQkAcalxdPm2C/Hp8YqTiaIihVIIUWxMjiZ6L+pN9zndGbxuMC4ecrG+vdkwbgPnt54n7WIa34V8V2RzQ+NS4+j6bVf6/NSHH478UCTvIYqWt5s3G4dspJ6PdQzVueRz9PyhJ+k5Mmu2JJJCKYQoVpqm0Wp0K9y83VRHEfeg64yuVKhr3b0o6UwS33f/nuy07EJ9j+tZ1+nxfQ9iU2Mx62Y+3vmx7WYcYV8qlqlI2JAwAjwDANh/eT8Dfh5ArjlXcTJR2KRQCiGKjG7RWTd2HfFH5DRXSVHGtwxDwobgGeAJwJWDV1jx+AoseZZCef0ccw79f+7PkatHAKjpXZN1g9fhaJKbtuyVv6c/6wavw8vVC4D1Z9bz3OrnUH0PhyhcUiiFEEVmwysb2DtnL0vbLyVmS4zqOKKQlAsox5MbnsTVyxWAqLVRrB2ztsAFQdd1nvvjOcLPhQNQwa0C6wevp1LZSgXOLNRqWLEhqx5fhYuD9TKXrw99zZRNUxSnEoVJCqUQokjsnbuXPZ9b7/DMSc8h94ac4ipJfOv78thvj2Fysv4Y2f/VfnZ+vLNAr/nulnf55q9vAHB1dOWPQX9Qu0LtAmcVxtC+enuW9V2GhnWe6Xvb3mPh/oWKU4nCIoVSCFHozqw/w/qX1tvWvRb0onYPKQYlTY2ONeizpI9tHT4x3LZd491aenAp7255FwANjWWPLqN1QOtCySmMo3+D/nzW7TPbetTaUWyK3qQwkSgsUiiFEIXq6rGr/PLYL+gW6+nPtq+1pdmIZopTiaLS+MnGdJrWybY+tPTQXZ/63nZ+GyNXj7StZz00i34N+hVaRmEsY4PH8nLwywDkWfLot7wfUdeiFKcSBSWDzYUQheZGwg0WtVpkm09Y79F6DPxloPIt+0TR0nWdVc+swpJjodeiXne161FMSgwtF7YkMSMRgDGtxjC7++yiiioMwmwx0/un3qyNWgtAnQp12P30brzdZFi+UclOOUKIYpGXnce3Xb4ldkcsAFXur8KwrcNwLuOsOJkoDpY8C5qDhqbl/5eH9Jx02ixuY7ujO6RmCGsHr5U7ukuJ1OxU2ixuw7EE62USXQK7sG7wOpwcZLMDI5KdcoQQRU7XddaMWmMrkx5VPXh81eNSJksRk6PppjKZnZZ9y9PfFt3Ck78+aSuTdSrU4T/9/yNlshTxdPHkj0F/4OPuA0BsaiwJGQmKU4l7JYVSCFEofOr6oJk0HN0ceXzV43j6eaqOJBRKOJHAgvsXsGPGjn99fsqmKaw8tRKAci7lWPX4KjndWQoFegfy22O/0bN2T3Y/vZuqHlVVRxL3SE55CyEKzdmNZ8lOzaZB/waqowiFMhIzmF1rNtnXs0GDQasGUefhOrbnVxxfQf+f+wNg0kysG7yO0KBQVXGFEPkgp7yFEMUmKDRIyqTA3cedNq+2sS50+HXwrySdSQLgZOJJhq0cZvvYT0I/kTIpbpJjziE5M1l1DHEXpFAKIe5JXnYesbtiVccQBtX+jfa2Xy6yU7NZ3m85uRm5eLt606JqCwCebPwkLwW/pDKmMKBLaZfo9E0n+i7vK3u42xEplEKIe7JuzDqWtlvKrlm7ZE9ecRNN0+i9pDc+9aw3XMQfjmf186upWKYiYUPC+DjkY+Y/PP+u7goXJZ+u6/T+sTc7Y3eyOWYzr4e/rjqSyCcplEKIu3bom0McWHgA3aIT+UYkKdEpqiMJA3LxcGHgrwNxKmMdA3P4u8Ps+2ofjiZHxrcZj7uTu+KEwmg0TWN299k4mZwI8AzgsUaPqY4k8kkKpRDirlw9epU1L6yxrR+e/zDeNeXuXPHvfOv7UuuLWmS4ZQCw/qX1XNx7UXEqYWRtAtrwy8Bf2P/cftvlEcL4pFAKIfItJz2Hnwf8TF6m9bqmZs80o8nQJopTCSOLT4/npcSX+HrC11yqcglLroXl/ZeTmZSpOpowsN51e+Nbxld1DHEXpFAKIfJF13VWj1xN4knrFnmVmlSi++zuilMJoxuzbgxX0q9w1XSVvX33ojlotBzVElcvV9XRhB0xW8ysOrVKdQxxG7IlgRAiXw4sPMCRH6y7mjh7ODPg5wE4uckWaeL2Pg79mItpF4lOjmbFkytwfNyRau2qqY4l7Eh8ejyDfx1MRHQEyx5dxuDGg1VHEv9CCqUQ4o6uHLrCurHrbOvei3tToXYFhYmEvahWrhqbn9pMdEo0NSvUhCDViYS9iYiOICI6AoCRq0fSvGpz6vnUU5xK/C855S2EuC3dovP7sN8xZ5sBaDWmFQ0HNFScStgTJwcn6lSo86/Pxe6MJSslq5gTCXvyxH1PMLzpcABu5N5gwM8DyMyVa3CNRgqlEOK2NJPGI18/gk89H6rcX4WQmSGqIwkDs+gWZu6YSWp26u0/zmxh6/tbWdphKaueWSWzTMVtze0xl0YVGwFw9OpRJoZNVJxI/C8plEKIO6rctDLP7nuWx35/DEcXuVJG3Nrnuz9nYvhEmi9ozsHLB2/5cTfib7Dr413oZp0TK06wf/7+Ykwp7I27kzv/6f8fXB2tN3PN/XMuf5z6Q3Eq8f+TQimEyBfnMs6UCyinOoYwsL+u/MVrEa8BcCbpDAkZCbf8WI+qHvRZ2se23vDKBhJPJRZ5RmG/Gvg24NOHPrWth68czqW0SwoTif+fFEohxE0seRb2frEXc65ZdRRhJzJzM3ni1yfIMecA8MoDrxAaFHrbz6n3SD1ajLIOrs7LzOPXwb/K15y4rZHNR/JIvUcAuJZ5jaG/DcWiWxSnEiCFUgjxL7ZM28K60da9upPOJqmOI+zAqxtf5XjCcQCaVm7K9C7T8/V5oTNDqVDXOjHg8v7LbHl3S5FlFPZP0zQW9VpEVY+qgPUO8I93fqw4lQAplEKI/xG3O45t720D4NL+S2QkZChOJIxu9enVzNs3DwBXR1d+6PsDLo4u+fpcJ3cn+n7fF5Oj9cfR9g+2c2H7hSLLKuxfBfcKLHt0GRoaAG9EvsH+S3INrmpSKIUQNjk3cvht6G/oFusdtw++/SD+D/grTiWM7Er6FUasHGFbzwqdRX3f+nf1GlWbV6Xj1I6AdUzVb0N+Izs1uxBTipKmU2AnXmtnvV43z5LHkN+GyCghxaRQCiFswieFkxRlPcXtF+xH+9fbK04kjEzXdZ5Z9Yzt5ptedXrxfIvn7+m12k5sa9tBJyUmhfXj1hdaTlEyvdPxHe6vcj8AJxJP8Gbkm4oTlW5SKIUQAJzdeJY/v/gTAEc3Rx799lHbaUgh/s3SQ0tZE7UGgEplKrG492I0Tbun1zI5mHj0u0dx9nDGt6EvwWODCzOqKIGcHZz59pFvcXGwXl7x6e5P2RIj1+CqIj8thBBkJmeycvhK2zpkZggV6sjWiuLWzqec5+X1L9vWC3otwLeMb4Fe06uGF0PDh/Lcvueo3KRyQSOKUqBhxYa83/l9AHR0hq0cRlp2muJUpZMUSiEE60avI+2S9ZtwzZCatBzVUnEiYWQW3cKIVSNIy7F+zQxrOozedXsXymv7tfLD0VWG54v8e/mBl+lQvQMAMSkxvLLhFcWJSicplEKUcqf+OMWRH44A4OrlSp8lfe75tKUoHb7880sioyMBCPAM4LOHPiuy9zLnmrm0T4ZXi1tzMDnwdZ+vKetcFj8PPwY2HKg6UqkkhVKIUi4oJIg2E9qABj2+6IGnv6fqSMLAoq5FMTH8732Ul/RZQjnXotlBKf5IPIsfWMzXHb8mOTq5SN5DlAyB3oGsenwVR144QkhQiOo4pZKm67rSAP7+/npcXJzSDEIISDiegE99Hzk6KW7JbDHT4esO7IzdCcCoFqP4oucXRfZ+a15cw755+wCo0akGQ8OHopnk61MIFTRNu6jr+i3nyMkRSiEEAL4NfKVMitv6dPentjIZ5B3EjJAZRfp+XT/sSrlq1qOfMZti2L9AhleL/NN1nTNJZ1THKDWkUApRCqVdSuPa6WuqYwg706tOL4L9gtHQ+PoR6zVrRcnFw4Xei/++2SdsQhgpMSlF+p6iZLiYepEeP/Sg1cJWXEm/ojpOqSCFUohSRtd1Vo9czVdNvmLHRzuw5FlURxJ2oq5PXbaP2E740HDaVWtXLO9Zs2tN7n/OOrw6Jz2HVc+sQvWlWsL43tn8DuvPrCc5K5nRa0erjlMqSKEUopQ58sMRTq8+TV5WHrs/3U12mmxxJ/LP0eRI58DOxfqeoTND8Qyw3iwWHRHNgYUHivX9hf2Z3mU6Pu4+VPWoytAmQ1XHKRWkUApRity4eoP1L/29pV3PL3vi5u2mMJEwuivpV8jOU/tLh4unC70X/X3qe+P4jaScl1Pf4tZ8y/iyetBqjo06VmgzUsXtSaEUohRZN2YdmdcyAWg4sCH1HqmnOJEwMotuYeDPA2k2v5ntZhxVgkKDaPZMM8B66vuPZ/6QU9/itoL9g/Fy9VIdo9SQQilEKXHitxMcW34MALcKbnSf011xImF0C/YvYNuFbZxIPMFTvz9FniVPaZ7Qj0Px9PdEM2lUblZZrv8Vd0XXdblBpwjJ/lZClAKZyZmsHbXWtu72eTfKVCyjMJGwB20C2tCiagv2XdrH/Ifn42hS+yPDtZwrjy57FEcXR/wfuOU4PCFuEpcaxwtrXuBw/GGOvnAUDxcP1ZFKHBlsLkQpsHL4Sg59fQiAOg/X4fFVj8vMSZEveZY8Is5F8FCth1RHEeKePfLTI6w8tRKA0S1HM6fHHMWJ7I8MNheilDuz/oytTLp4utDzq55SJkW+OZocDV8m5dS3uJNPQj/BzdF6A+LcP+ey/cJ2xYlKHimUQpRwTmWc8K7pDUDIxyF4+sle3eLWUrNTyTHnqI6RL5Y8Czs/3skXDb4gO1XGX4lbCyofxPud37etn171NFl5WQoTlTxSKIUo4aq3r87zh5+n+9zu3P/M/arjCIMbvXY0zRc0Z0/cHtVR7mjjhI2ETQgjKSqJiMkRquMIgxsbPJZgv2AATl87zdQtUxUnKlnkGkohhBAArItaR48fegBQwa0C518+Txln4968lRKTwryG88jNyAUNRuwYQUDrANWxhIEdu3qMZvObkWvJxdHkyKGRh2hYsaHqWHZBrqEUohTSLbrM6BN35UbODV5Y84JtPTNkpqHLJIBXDS86TetkXeiw+rnVmHPMakMJQ2tYsSGvtXsNsN5w9vya57Hocg1uYZBCKUQJtPeLvXwX8h3Xoq6pjiLsxLSt0zh//TwAXQK7MKzpMLWB8il4bDBVmlcB4OrRq+yYuUNxImF0k9tPplb5WgBsv7CdpQeXKk5UMkihFKKESb2YSuQbkURHRPNVk69Iu5ymOpIwuKNXj/LJrk8AcHFw4cueX9rNJACTo4leC3uhOVjzbp22lWun5RcpcWuujq582fNL23pC2ASu3riqMFHJIIVSiBJm/UvryUmz3qXb+MnGeFSRAb7i1iy6hedXP2/bBef1dq9Tu0JtxanuTpVmVXhg3AMAmLPNrB65Wi75ELfVtWZXBt83GIDkrGRe3fiq4kT2TwqlECXIqT9OcWLFCS+WVHUAACAASURBVADKVCxD1w+7Kk4kjG7pwaXsiLWeJq5dvjaT2k1SnOjedHynI141rPs2x2yOsc1eFeJWPgn9xLbX93eHvyPinEwKKAgplEKUEDnpOawbvc62fujTh3Ar76YwkTC6hBsJTAyfaFvP6zkPV0dXhYnunXMZZ3p+2dO23jt7rxylFLdVqWwlZnSdYVu/sOYFmU1ZAFIohSghNr+zmesXrgNQM6QmjQY1UpxIGN2EsAkkZSYB8MR9T9C1pn0f0a7VrRb3Db6PNhPaMHzbcLu5DlSo88z9z9AmoA0AUUlRfLj9Q8WJ7JfMoRSiBLhy6AoLWixAN+s4uDgw6ugoytcqrzqWMLDNMZvp9I115E45l3KcGn2KSmUrKU5VcLquS5EUd+Xo1aM0m9+MPEsezg7OHH7+MHV96qqOZTgyh1KIEk636Kx+fjW62frLYYe3OkiZFLeVY875x8zJD7t+WCLKJCBlUty1RhUbMb71eMD6b2NSuH1eR6yaFEoh7Fx0ZDQX91wEwKeeD20ntFWcSBjdxzs/5mTiSQCC/YJ5rvlzihMVnZSYFKLWRqmOIQxuyoNTCPQKZFCjQXz18Feq49glOeUtRAlwNuws68aso+e8ngR2DlQdRxjYueRzNJzXkKy8LEyaif3P7adp5aaqYxU6XdfZMWMHW6ZuwcHZgdEnR1O2clnVsYSBJWcm4+3mrTqGYckpbyFKgaCQIEYdHSVlUtzRS+tfst3J+lLwSyWyTIL11Pe1qGvkZeaRfT2bsAlhqiMJg5MyWTBSKIUoIUyO8s9Z3N76M+tZfXo1AFU9qvJux3cVJypaITNCbKOzDi87TMzmGLWBhF1JzkzmfMp51THshvwEEsIO6Rad06tPy5w9cVc6VO/AlA5TcHFw4ZPQT/BwKdm7KLn7uNPlgy629ZpRazDnmBUmEvbAoltYenApdefWZejvQ+X7bD5JoRTCDh1ccpAfe/3IN52+IeFEguo4wk64O7nzbqd3iRoTxWMNH1Mdp1jc/8z9+LXyAyDxRCK7Pt2lOJEwulxzLu9ve5+EjAS2nt/KD0d+UB3JLkihFMLOZFzLIPy1cADObznPjfgbihMJexNQLqDUjNfRTBo9v+yJZrL+fbdO3cr12OuKUwkjc3F0YU73OQAMaDCADtU7KE5kH6RQCmFnIt+IJPNaJgCNBjWiRscaagMJw7uSfkV1BKWq3F+FFqNaAJCbkUvYq3KDjri97rW7c+C5AywfsJyAcgGq49gFKZRC2JGLf15k/4L9ADiXdSb041DFiYTRbTizgRqf1WDKpilk5GaojqNMp6mdcPdxB+DY8mNEb4pWnEgYXbMqzVRHsCtSKIWwExazhbWj1sJ/rw/v+G5HPKqW7JsqRMFk52UzZt0Yss3ZTNs6jVWnVqmOpIybt5vtBp1y1cvZdpYSIr/Sc9LlBp3bkEIphJ04tPQQl/ZdAsC3oS+txrRSnEgYnUW30Ld+X5xMTrQNaFtqbsS5lWYjmtFjXg9ePPEiNbvWVB1H2Ald1/nhyA/UnlObH4/+qDqOYclOOULYgayULObUmUNGgvWU5VObnpJrJ0W+nUo8hY5OPZ96qqMIYXd2XNhBu6XtAOv81lOjT1HWufTtuiQ75QhRAmyZusVWJhsMaCBlUtyVuj51pUwKcY/aVmtL77q9AbiUdokPtn2gOJExSaEUwuB0Xbfd1e3o6kjIzBDFiYTRWXSL6gh2IelMEj/2/tF2KYkQt/JJ6Cc4Ozhb/7zrE84ln1OcyHikUAphcJqm8cg3jzB823C6z+2OV3Uv1ZGEgZktZtovbc+0LdPIzM1UHcewLmy/wLyG8zj9x2nWjV2HbpGbLcSt1Spfi3EPjAMg25zNhLAJihMZjxRKIexEtXbVuP/p+1XHEAa3+OBidsbuZMrmKQxaMUh1HMPya+WHd01vAOJ2xXF42WHFiYTRvdH+DSqXrQzAryd+JTI6UnEiY5FCKYQQJURKVgpvRr5pW49vPV5hGmNzcHag2+xutnX4pHCyU7MVJhJG5+HiwQdd/r5+8uX1L5NnyVOYyFikUAphUPsX7ufQ14fkVJzIt2lbppGQYd3b/bGGj9G+envFiYwtKCSIeo9Yb1ZKv5LOlmlbFCcSRje0yVBaVLXuunTk6hEW7l+oOJFxSKEUwoDS49PZOH4jK4evZHGbxZhzzKojCYM7lXiK2XtnA+Dq6MpHIR8pTmQfQmeF4ujqCMCez/dwLeqa4kTCyEyaidndZtvWb256k6TMJIWJjEMKpRAGFDE5gpy0HAAqNamEg7OD4kTC6MZvHG87/TaxzUSqlaumOJF98A70pvWrrQGw5FoImyD7fIvbax3QmicbPwlAUmYS72x+R20gg5BCKYTBXNp3iUNLDwHgUs6Fzu91VpxIGN36M+tZE7UGAH9Pfya2nag4kX1pN6kdZatYB1WfWnmKcxEyEkbc3oddPsTdybo3/Lw/53Hs6jHFidSTQimEgei6zvqX1v+9X/c7HSnjW0ZtKGFoueZcxm0YZ1vP6DqDMs7yNXM3nMs62/b5BtgwbgMWs8zyFLfm5+nH5HaTATDrZsZtGFfq9/mWQimEgRz98SixO2MB8KnnQ8sXWypOJIxu3p/zOJl4EoA2AW0Y1EhGBd2LJkOaUKV5FSrUrUDXD7uimTTVkYTBvdL6FWp41QAg7FwYf5z+Q20gxWQvbyEMIudGDnPrziXtYhoAg9cPptZDtRSnEkaWmJFI7Tm1SclKAWDvM3tp6Se/hNyr1LhUylQqg4OTXLMs8mfF8RX0/7k/AEHeQRwbdQwXRxfFqYqG7OUthJ3Y/sF2W5ms83AdKZPijqZsmmIrk8OaDpMyWUCe/p5SJsVd6Vu/Lx1rdATgbPJZPtv9mdpACkmhFMIAUmJS2PnxTgBMTiZCZ4UqTiSM7nD8Yebvnw9AWeeyTO88XXGikkfXdXJu5KiOIQxM0zQ+7/Y5Js2Ej7sPVTyqqI6kjBRKIQzA0c2Rxk82Bg2CXwqmQu0KqiMJA9N1nZfXv4xFt9448kb7N0r1D7KiEH8knu9CvuO3Ib+pjiIMrnGlxizvv5yoMVEMbTJUdRxl5BpKIQzkyqEreAV64VrOVXUUYWC/nfiNvsv7AlDTuybHRh3D1VG+ZgqLOdfM7JqzSY1LBWBo5FACOwUqTiWEWnINpRB2pHLTylImxW1l52XzatirtvXHIR9LmSxkDk4OdJza0baWMUJC3JkUSiEUUn2GQNif2NRYnB2cAehUoxOP1HtEcaKSqelTTancrDIA8X/Fc3DJQcWJhL24lHaJEStHsPLkStVRipUUSiEUycvKY1GrRfw5708seXL0Q+RPrfK1OPz8Yb7o8QWzHpqFpsm8xKKgmTS6fdbNto58I5Ks61kKEwl7cCrxFLXn1GbpoaVMCJtAjrn03NQlhVIIRXZ/vptL+y6x9sW1rHlxjeo4wo44OTgxquUomlZuqjpKiVa9Q3Ua9G8AQEZCBtumb1OcSBhdnQp1aF6lOQBXb1wtVVsySqEUQoEbV2+w7X3rDyfNpNFqdCvFiYQQ/6brR11xcLHOptzz2R6SziYpTiSMTNM0Zj00i9EtR3Nm7BmaVWmmOlKxkUIphAKb3t5ETpr1VEizZ5pR6b5KihMJo5u2ZRp/XflLdYxSxzvQm9avtAbAnGMmfFK44kTC6FpUbcGcHnPwcfdRHaVYSaEUophdPXaVAwsOAOBc1plOUzspTiSMbnPMZqZsnkKz+c14Pfx11XFKnXavt6NMpTIAnFhxgoTjCYoTCWE8UiiFKGZhr4ahW6x3d7eb3I6ylcoqTiSMbvae2QDo6NSpUEdxmtLHxcOFTlM7UblZZYZGDMW3ga/qSMKOJNxIYMH+BapjFDkZbC5EMTqz/gzfd/8egHLVyvHiyRdxcnNSnEoYXXZeNnP2zmFN1BrCh4TjYJL9poubxWxB0zQ0k9xVL/Jv4f6FvBr2KqnZqWwZtoUO1TuojnTPZLC5EAZhybOw8dWNtnWXD7tImRT54uLowqttXiVyaKSUSUVMDiYpk+KumTQTqdnWHZde2fCKbbvUkkgKpRDF5OCSgyQcs1575RfsR6PHGylOJOyNzJw0Dl3XSTojd3yL2xvWdBiNKzUGYP/l/Xx/+HvFiYqOFEohikn9fvVpNbYVJicTD816SMqBuK349HhOJp5UHUP8i9hdsSxpu4SFrRaSmZSpOo4wMAeTA5+EfmJbvx7xOhm5GQoTFR0plEIUE/cK7nT/vDsvn3+ZgDYBquMIg3sz8k0azWvE6LWjScqUI2FGsn/+fuJ2xZGVnMXW97aqjiMMrmvNrvSs3ROAi2kXmbVrluJERUMKpRDFzKOKh+oIwuAOxx9m8cHFmHUzyw4vw2wxq44k/j+d3+uMo6sjAHvn7pVh5+KOZobMxEGzXv/84fYPuZx2WXGiwieFUogilpuRqzqCsCO6rjN+43h0rBM43mj/Br5lZEyNkXj6e9J6vHXYuSXXQsTrEYoTCaOr71uf51s8D8CN3BtM2TRFcaLCJ4VSiCJ0+eBlZvnPYsdHO8jNlGIp7mzdmXWEn7PuxlLDqwZjgscoTiT+TdtJbSlT0Trs/PjPx4ndFas4kTC6dzq+g6eLJwBLDi0pcft8S6EUoojouk74xHCykrMInxTOoaWHVEcSBpdnyWNC2ATbekbXGbg6uipMJG7FxcOFju92tK03jt+I6rnOwth83H14vZ11pyuLbmFS+CTFiQqXFEohisjZjWc5F34OAK9AL5o93UxxImF0Xx/6muMJxwF4wP8BBjQYoDiRuJ37n7kfn/rW/ZrjdsVxYsUJxYmE0b0U/BL+ntbZ4Gui1rApepPiRIVHCqUQRcBithA+Mdy27jK9C44ujgoTCaO7kfPP66pmhsyU0VIGZ3I0ETIzxLYOnxSOOUduoBK35ubkxnud3rOtJ4RNKDHDzqVQClEEDi87TPzheACqtqhKw4ENFScSRvfp7k+5nG698/OReo/Qrlo7xYlEftTuUZvAzoEAJJ9L5mzYWcWJhNE92fhJmlRqAliHnf909CfFiQqHFEohClluZi6b3vz7NEbIzBDZsk3c1tUbV5mxYwYADpoDH3T5QHEikV+aphHycQjVH6zOM3ueoU7POqojCYNzMDnwUchHtvXkiMlk52UrTFQ4pFAKUcj2fL6H1Djr3q11Hq5DjY411AYShjd1y1TSc9IBePb+Z6nnU09xInE3qjSrwrDNw/Br5ac6irAToUGhhAaFAnD++nnm7p2rOFHBSaEUohBlJGaw/YPtAGgmjS4fdlGcSBjd6Wunmb9/PgBlnMrwdse3FScSQhSHj7p+hIb17NV7294jJStFcaKCkUIpRCHa+fFOslOtpy6ajmhKxYYVFScSRjc5YjJ5ljwAJrSZQOWylRUnEgVlMVuIjoxWHUMYXJPKTRjaZCi1ytdiUa9FlHMppzpSgWiq52b5+/vrcXFxSjMIUViyrmex46Md7J+/nxcOv4BHVdlmUdzarthdtFnSBoBKZSpxZuwZyjqXVZxKFETM5hjWv7Se+MPxjNg5goDWAaojCQNLyUrB3ckdZwdn1VHuSNO0i7qu+9/qeTlCKUQhci3nSpf3uzDuwjgpk+K2dF3/xxDzdzu+K2WyBEg4kWCb8BA2IUyGnYvb8nL1sosymR9SKIUoAk7uTqojCINbeWolO2J3AFC3Ql2evv9pxYlEYWj+bHN86lmHncfuiOXk7ycVJxL2JiM3Q3WEeyKFUogC0nWdG1dvqI4h7EyAZwBtA9oC1i0WHU0y+L4kMDma6Dqjq20dPikcc64MOxd3duzqMR7+4WEe/uFhuzyyLYVSiAI6/cdpPqvxGZFvRZKdZv+zxETxaF61OduGbyNyaCS96/ZWHUcUojq96lC9Q3UAkqKSOLDwgOJEwugsuoV+y/tZt2OM2cTaqLWqI901KZRCFIAlz0L4pHDyMvPY9t42oiPkzk6Rf5qm0Smwk2yxWMJomvaPLRk3v7PZNv1BiH9j0ky819m6JWOAZ4BdbscohVKIAji45CCJJxMBCGgTQN0+dRUnEkIYgV8rPxo+Zt1yNSMhgx0f7VCcSBhdv/r9WNJ7CadGn6JX3V6q49w1KZRC3KOc9Bw2v73Ztg6ZGSJHmsRtXU67TLsl7Qg7G6Y6iigGXaZ3weRk/TG7a9YuUi+mKk4kjEzTNIY3G46bk5vqKPdECqUQ92jXrF2kX7Ful1e/b30C2si8OXF772x+hx2xOwhdFsoXe79QHUcUMe+a3rR8sSUA5mwz58LPKU4kRNGRQinEPUiPT7edwjI5mujygWyxKG4v15zLwSsHAfB08WRgw4GKE4ni0OHNDjQY0IDn/3qepk81VR1H2JEzSWcY8tsQ4tPjVUfJF5lTIcQ92PLuFnJv5ALQfGRzKtSpoDiRMDonByd2P7Obn47+RHpOOr5lfFVHEsXAvYI7A5YPUB1D2Jlfjv/CEyueINeSS1mnsnz58JeqI92RbL0oxF1KPJXIvIbz0M06zmWdGXt2LGUqllEdSwghRAlx9cZVgmYHkZ6TjoPmwNFRR6nnU09pJtl6UYhCtnPmTnSz9RextpPaSpkUQuSbOdfM4WWH0S32N7haFJ+KZSoyqe0kAMy6mdfCX1Oc6M6kUApxl7rP7k6XD7rgU9+H1q+0Vh1HGFzEuQh2xu5UHUMYQHRkNPMazuO3Ib9x5IcjquMIg3ul9StU9agKWLdq3XZ+m+JEtyeFUoi75OTuRLvX2vHCkRdkz25xWznmHJ5b/Rxtl7Sl3/J+drtHrygcuq6TFJUEQOQbkeRl5SlOJIzM3cmdqR2n2tavhr1q6C0ZpVAKcY9MDvLPR9zeV/u+4lyydVRMSlYKbo72OV9OFI6aXWpSq3stAK5fuM6eOXsUJxJGN6zpMBpVbATA3ot7+eX4L4oT3Zr8RBQiH8w5ZpKjk1XHEHbketZ1pm75++jCR10/ksH3gq4zuqKZrF8H297fRsY1OWotbs3B5MCHXT60rSdHTibHnKMw0a1JoRQiH/Yv2M/cunNZP269/AAQ+TJjxwyuZV4D4In7nqB51eaKEwkjqHRfJZoMawJA9vVstr1v7OvihHo9avegY42OgHU25YL9C9QGugUplELcQXZqNlve3YIl18Kez/aQfE6OVIrbi0uN49PdnwLg7ODMe53eU5xIGEmnqZ1wdLOOgd47d698TxG3pWkaH3X9yLaeumUqqdnG28azUAulpmm1NU3bqWnaaU3T9mqa1qAwX18IFbbP2E5GovWoZKPHG+HX0k9xImF0UzZNISsvC4DRLUcT6B2oOJEwEk8/T9uECEuuhYjJEYoTCaNr6deSxxo+BkBCRgIzd8xUnOhmhX2Ecj6wQNf1OsBHwOJCfn0hilVqXCq7Z+0GwORkovP0zooTCaM7En+Erw99DYCXqxdvdHhDbSBhSG0ntsXd1x2AY/85xsW9FxUnEkb3fuf3cTJZJ4vM2j2Ly2mXFSf6p0IrlJqmVQTuB5b996EVQKCmaTUK6z2EKG6b3t5kG+3RanQrvAO9FScSRjcpfBI61tEek9tNprxbecWJhBG5eLrQ8Z2OgPWX1Uv7L6kNJAwvqHwQL7R4AYCM3Aze2fyO2kD/ozCPUAYAl3RdzwPQrcOSLgDVCvE9CuxEwgl+OvqT6hjCDsQfieevr/8CwKWcC+3faK84kTC6iHMRrDuzDoBq5aoxJniM4kTCyO5/9n6CXwrmxRMv0vKFlqrjCDvwZoc38XD2ACAtJw2LblGc6G+Ohfx6/ztx86YZGZqmvQK88n/rcuXKFXKEf6frOi+tf4l5f87DxdGFDtU72CbQC/FvwieF27ZHaz+5Pe4V3BUnEkZm0S1MDJ9oW7/X6T1cHV0VJhJG5+DkQLfPuqmOIeyIbxlf5j88n6DyQbTya6U6zj8U5hHKWMBf0zRHAM06cC0A61FKG13XZ+m67v9//5UtW7YQI9yapmloaJh1syEPFQtjORdxjjPrzgDgGeBJqzHG+ocrjOenoz9x4PIBAJpWbsrgxoMVJxJClESD7htkuDIJhVgodV2/ChwEnvzvQ/2AGF3XYwrrPQrqrQffwtPFE4DFBxdz7OoxxYmEUe3/ar/tz53f74yTm2yxKG4tOy+byRGTbeuZITMxaTKVTdyd3Mxcds3aRXZatuooQty1wv6ONxIYqWnaaeA14OlCfv0C8XH34fV2rwPW01OvRbymOJEwqr7f96X7nO7UDKlJ48GNVccRBvfFn19w/vp5AB4KeoiuNbsqTiTsTczmGObWmcvG8RvZ+fFO1XGEuGua6o3G/f399bi4uGJ7v8zcTOrMrUNcqvU9Nz21yTaBXggh7lZyZjJBs4NIzkpGQ+PgyIM0qdxEdSxhZ5LOJPFF/S+w5FlwcndizJkxeFTxUB1LCBtN0y7quu5/q+dL3TkZNyc3pnWaZltPCJtgqLukhBD25YPtH5CcZd3pZGiToVImxT0pX6s8LV5oAUBuRi6b396sNpAQd6nUFUqAIY2H0LiS9TTmvkv7WH5sueJEwggykzO5euyq6hjCzjzX/DkGNBiAq6MrUztNVR1H2LEHpzyIi6cLAAcXH5TvR8KulMpC6WBy+Me+mJMjJpOdJxdBl3bbpm/jq8ZfserZVaTHp6uOI+xErfK1WD5gOWfGnKFaOUON3RV2xt3HnXavtwNAt+hEvCZbMgr7USoLJUBoUKjtwvnolGjm/TlPcSKhUkpMCntn70W36BxZdgRzjll1JGFn/Dxlj3dRcMEvBePpb51Gcnr1aWI2x6gNJEQ+ldpCqWkaH3X9CO2/s9enbZ1Gcmay4lRClcg3I20lMvjlYMoFFM/AfWGfdF2XsxqiSDi5OdHpvU62ddiEMNsGC0IYWaktlADNqjTjycbWsZnJWcl8uP1DxYmECpcPXObI90cAcKvgRrvX2ilOJIxuw9kN1J5Tm+/++k5u6hOFrvGTjanUpBIAl/Zd4uh/jipOJMSdlepCCTCt0zRcHKwXQX++53POp5xXnEgUJ13XCZsQZls/OOVBXMvJdnni1swWMxPCJhCbGsvQ34eyNmqt6kiihDE5mAj5KAQARzdHMhIzFCcS4s5KfaGs7lWdscFjAcg2Z/PWprcUJxLF6cz6M0RHRgPgHeRNi+dbKE4kjC4lK8V2800rv1b0rN1TcSJREgWFBhH6SShjosYQPCZYdRwh7qjUDTb/NylZKQTNDiIpMwkNjf3P7adZlWZKM4miZzFbmN90PlePWkdz9F/en4YDGipOJexFZHQkHs4etPRrqTqKEEIUORlsng9erl682f5NAHR0JoVPUpxIFIe/vvnLVib9gv1o0L+B4kTCnnQO7CxlUggh/ksK5X+NajmKQK9AHqz+IO93fl91HFEMjv983PbnkJkhaJqmMI0QQtxadmo2kW9Fkhwt00iEMTmqDmAULo4u7Hx6J5XKVJJiUUoMWj2Iw98dJm5PHNXbV1cdRxjcjO0zqF2hNo/We1S+R4hiFbsrlp/6/ERGQgYp51Lo+31f1ZGEuIlcQymEEHcQdS2KBvMakGfJIzQolPWD10upFMUm63oWs4Nmk3ktE4Bn/3yWqi2qKk4lShu5hrIAzBYzOeYc1TGEEIpNjpxMniUPgNb+raVMimLlWs6VDm91sK3DJoSh+mCQEP9LCuW/0HWd9WfW02x+M2btmqU6jihESWeTuPjnRdUxhB3ZFbuLX47/AkClMpV4tc2rihOJ0qjlCy3xDvIGIGZzDFFroxQnEuKfpFD+i5iUGHr+0JMjV4/wwfYPSLiRoDqSKCRhE8JY1GoRKwat4MbVG6rjCIPTdZ0JYRNs63c7vktZ57IKE4nSysHZgS4fdLGtwyeGY8mTXZqEcUih/BeB3oGMaDoCgLoV6pKYkag4kSgMF3Zc4ORvJwHrb/hO7k6KEwmj+/3k7+yI3QFAPZ96PH3/04oTidKsQf8G+AX7AZBwPIGDSw8qTiTE36RQ3sLUTlP5qd9P7HlmD/V966uOIwrof7dY7Di1I85lndUFEoaXa87ltYjXbOsZXWfgaJLBGEIdTdMI/TjUtt48ZTM5N+Q6f2EMUihvoYpHFR5r9JhcfF9CnPj1BHG7rNMEfOr70Gy47IQkbm/hgYWcvnYagA7VO9CrTi/FiYSAau2qUe+RegCkX0ln1ye7FCcSwkoKpSjxzLlmIl6LsK1DPgrB5Chf+uLW0rLTeGfzO7b1zJCZ8sulMIwuH3ZBc9Bw8XTB1ctVdRwhABlsnm9bz29l+bHlzOk+R36w2Jn98/eTdCYJgOoPVqd2z9qKEwmj+2jHRyRkWG/GG9hwIK38WilOJMTffOr60O/HfgR2CsTdx111HCEAKZT5Mm79OD7b8xkAoUGh9K7bW3EikV/ZqdlseXeLbS1bLIo7uZR2iU92fQKAk8mJ6Z2nK04kxM0aDmioOoIQ/yDn/fLhwRoP2v48KXySbcCxML7tM7aTkZgBQKPHG+HX0k9xImF0b296m8w8644ko1qOIqh8kOJEQghhfFIo86FP3T60DWgLwMnEkyw5uERxIpEfuq5zcbd1iLnJyUTn6Z0VJxJGd+zqMZYcsv77LudSjrc6vKU4kRB3lpGYwbqX1nF+23nVUUQpJoUyHzRNY2bITNt6yqYppOekK0wk8kPTNIaED2HAzwPoMr0L3oHeqiMJg5sUPgmLbh0W/Xq716ngXkFxIiFuL/5IPLODZrN39l7ZklEoJYUyn1oHtKZ/g/4AxN+I55OdnyhOJPJD0zQa9G9Am1fbqI4iDC78XDhrotYAEOAZwNjgsYoTCXFnvg188QzwBODinosc//m44kSitJJCeRemd55uG2w8c+dMLqddVpxICFFY3tv6nu3P73d+HzcnN4VphMgfk4OJkI9CbOuI1yMw55gVJhKllRTKu1C7Qm2eb/48ADdybzBl0xTFicS/Ob/tfl0ovQAAIABJREFUPNGR0apjCDvzy8BfGN1yNA/4P8DgxoNVxxEi32p1r0Vg50AAks8l8+eXfypOJEojTfX1Fv7+/npcXJzSDHcj4UYCtebUIjU7FZNm4uDIgzSu1Fh1LPFfljwLXzb+ksQTidTpVYe+y/ri4umiOpawI3mWPNliUdidywcus6D5AgDcyrsx9uxYGXouCpWmaRd1Xfe/1fNyhPIu+Zbx5c32bwJg0S2M3zheLoI2kAOLDpB4IhGAjIQMnD1kv25xd6RMCntU5f4q3Df4PgAykzLZ/uF2xYlEaSOF8h6MCR5DoJf19EL4uXDWnVmnOJEAyLqexaYpm2zr0FmhMsRc3FbCjQQSMxJVxxCiUHR+vzMOLg4A7P5sN9cvXFecSJQmUijvgaujKzO6zrCtx28cT645V2EiAbD9g+1kJFiHmDd8rCEBrQMUJxJGNzF8IrVm12Lmjplk52WrjiNEgXhV9yJ4bDAA5mwzkW9GKk4kShMplPeof4P+tAmwjqI5mXiShQcWKk5UuqXEpLD7s90AOLg40PXDrooTCaM7cPkA3xz6huvZ15m+fTo3cm+ojiREgbV7vR2u3q64+7rj3/qWl7sJUeikUN4jTdP4JPTvWZRvb36b61lyekGViNcjMGdbR2UEvxSMVw0vxYmE0VX1qMqIZiPQ0Hirw1uUdyuvOpIQBebm7cagPwYx9sxYWr7QUnUcUYrIXd4F9MSKJ/jx6I8ATGwzkRkhM+7wGaKwxe2OY3HrxQC4+7gz5swYXMvJ3Y0ifw7HH/5/7d13dFTV2sfx704lhYTQAiGELiC9FwHpvQQRAUGKoHIRVOyFV/QqViwI1itSVIoEpCi9CdJb6DXUEFpICARCymS/f0wcAxIpmWTPTJ7PWlkrp8w5vyGHzJOz99mbioUq4u0howEIIURW5CnvHPZ+q/fxdrd+EH2z7RsuJ182nChv0Vqz5PkltuXmbzeXYlLclerB1aWYFEKIbJKCMptKFSjFyIYj6VWlF5FPRRLgHWA6Up6SnJCMl791aKDClQtT58k6hhMJR/fXXN1C5AWXT19m/pD5RE6JNB1FuDhp8raDdJ2Om5La3BStNUcWH8HTx5PSzUubjiMc2KXrl2g0sRHD6w3nyTpP4unuaTqSEDkm4VQCEypOIC0pjfwh+Rl+aDhefjI2r7g30uSdC6SYNEspRYUOFaSYFLc1Zs0YDsQeYPii4by24jXTcYTIUYElAynbuiwAV2KusP7j9YYTCVcmlVAOuJpyld3ndpuOIYTI5Gj8Ub7Y/AVgHUv2mQbPGE4kRM5r83Eb3DysH/XrPlpHwikZjUTkDCko7UhrzdSdU6k4oSKdp3cmKTXJdCSX9eeHf3Jw/kGZ9lLcsVeXv0qKJQWAkQ1HEhYYZjiREDmvcMXC1B9RH4C0pDSWv7LccCLhqqSgtCOlFNP3TOf0ldOcTDjJ5xs/Nx3JJV3Yf4GVb6xkRrcZzAyfKUWluK01J9Ywa98sAIr6FeXVJq8aTiRE7mn2f83wKeQDwJ7pezi1/pThRMIVSUFpZ2PbjMVNudHlvi50r9zddByXo7VmyXNL0BZrEVm8bnGZr1v8K0u6hWcXP2tbHtNyjIzGIPIUnyAfWrzTwra8+LnF6HT5Q1zYlxSUdlalaBX2DdvH/D7zqVS4kuk4Lufw74eJWhoFQGBYII1faGw4kXB0P+z4gciz1iFTahWrxaCagwwnEiL31XmiDkWrFgUgZksMu37eZTiRcDVSUOaAioUrmo7gkiwplhsGMW/zcRs8fWXYF5G1hOsJvLHyDdvyuPbjcHdzN5hICDPcPNxo91k72/KKV1eQcjXFYCLhaqSgzAXX065LPz872DR+E3GH4wAIaxrG/T3vN5xIOLp31rzDhWsXAHikyiM0LdXUcCIhzCnbuiwVu1Ykf4n8tP6oNZ4+8ge5sB8pKHOQ1pqIfRFUmlDJNt+3uDeJ5xJZ89811gUF7ce1l76T4l8duniILzb9PUzQR60/MpxICPM6f9uZ4QeHU71vdZSb/A4V9iMFZQ7aEL2BnrN6ciLhBC8ve5mrKVdNR3JaK0etJPlyMgC1h9SmeK3ihhMJR/fC0hdITU8F4KXGL1GqQCnDiYQwz7+Yv8yWI3KEFJQ5qHHJxnSq0AmA01dO88GfHxhO5JzObD/Djok7APAO8Kbluy0NJxKObsmRJfx26DcASuQvwSsPvGI4kRCOS/pSCnuQgjKHfdruUzzdrP1UPl7/MccvHTcbyAn5FPKh8kOVAXhw9IP4FfUznEg4slRLKiOXjLQtf9j6Q/y85JoR4mbxx+KZ1XMWU5pPkWGERLZJQZnD7it0H882sI6Bl2xJ5qVlLxlO5HwKlCrAIxGPMHDNQOoPr286jnBwX2/9mv2x+wFoFNqIR6s9ajiREI5pdu/Z7IvYR8zWGHZO3Wk6jnByUlDmglHNRlHUzzr+V8S+CFYfX202kJMq1bQU7l4y5IvIWuy1WEavHm1bHtd+nDy8JUQWWn3Qyvb9itdWkJIoTd/i3klBmQsC8wXyXsv3bMvPLn4WS7rFYCLnIE0w4m4dv3TcNgvOgBoDqFeinuFEQjiuMi3KUCncOgFH4tlE1r6/1nAi4cykoMwlA2sOpHbx2gDsOreL/23/n+FEji3hVAJfVv6S3dN2yxie4o7VDanLgacP8E6Ld3iv1Xu3f4EQeVybsW1sLT8bPtlAXFSc4UTCWUlBmUvc3dwZ136cbXnUylHEJ8UbTOTYlr+ynIuHLjKn7xw2fr7RdBzhRHw8fRjVbBQh+UNMRxHC4RUsV5CGIxsCYEm2sGTkktu8Qohbk4IyFzUJa0Kfqn0AuJh0kbf/eNtwIsd08s+T7Jm+BwDfwr7UHFjTcCIhhHBdzUY1I39IfgAOLTjE4YWHDScSzkgKylz2YesP8fHwAWDC5gnsu7DPcCLHkm5JZ/Fzi23LLd5tgU+Qj8FEwtElpSYxZP4QjsQdMR1FCKfk5e9Fm4/b2JYXP7uYtOQ0g4mEM5KCMpeVDCzJq01eBcCiLYxcMlL6CGay/fvtnNl2BoDg6sHUHlLbcCLh6D5c9yETd0ykyldV+HHnj6bjCOGUqvapSljTMADijsSxf/Z+w4mEs5GC0oAXG79IWKD1P+7SqKXMPTDXcCLHcO3iNVa+vtK23GF8B9zc5RIVWUu1pPLz7p8BSNfp1A2paziREM5JKUWH8R0oWL4gveb2omqfqqYjCSejTN8dCw0N1dHR0UYzmDB732wenvUwAGGBYex/ej++nr6GU5m14KkFbP9uOwDV+lbjoZ8eMpxIOIPElETGrBmDm3JjTKsxpuMI4dTSLenyh7y4JaXUaa11aJbbpaA0Q2tNu5/asezoMgDeaPoG77Z813Aqc2K2xvC/+v8DDV75vRh+cDj5i+c3HUsIIYQQ3L6glD9DDFFKMb7DeDzdPAnKF0TpAqVNRzJqxWsrIONvm+ZvNZdiUgghDNNaE7U0ivS0dNNRhBOQgtKgioUrMvPhmRwcfpAhtYeYjmNU+NRwqverTtGqRak/QubrFv9u9r7ZHL903HQMIVxW3JE4pnWcxk/tfmLL11tMxxFOQJq8hUNJSUzBy9/LdAzhwE5cOkHlLyuj0bzZ7E1ea/qa6UhCuJzoTdFMbDgRAO9Ab0YcGoFfUT/DqYRJ0uTthEwX+SZJMSlu54WlL5CUlsT1tOucu3rOdBwhXFJog1BqDrJOKpGckMzyV5YbTiQcnRSUDuTC1QsMnjeY0atHm46SK+Ki4kiKTzIdQziRZVHLmL1/NgBF/YryVvO3zAYSwoW1er8V3oHeAEROjuTE2hOGEwlHJgWlg7iWeo1qX1fjh8gf+HDdhxy+6NpTX+l0zZy+c5hw3wR2/LADnZ5378qKO5NiSWHEohG25Y9af0SBfAUMJhLCtfkH+9PqvVa25YXDFmJJtRhMJByZFJQOwtfTl0E1BwGQzyMf+2Nde5aCyMmRnN50mmux11g/dr08RShua+z6sRy8eBCAxiUb81iNxwwnEsL11XmqDiF1QwA4v+c8Gz/faDiRcFRSUDqQUc1G8Uz9Zzg4/CBdK3Y1HSfHXIu9xrKXl9mWO07oiLuXu8FEwtEdjT/KO2veAcBNufFlxy9xU/LrS4ic5ubuRqevO4GyLv/x1h8knEwwG0o4JPmN7ED8vPwY12EcxfyLmY6So5a9vIyki9a+k1V7V6VMyzKGEwlHprXm6YVPcz3tOgDPNniWmsVqGk4lRN4RUjeEesPqAZB6LZXFzy42nEg4IikoRa46seYEkZMiAetQFO0+a2c4kXB0s/fPZvER6wdYaEAobzd/23AiIfKelu+2xC/YOmyQclekXU8znEg4Gg/TAUTW4pLieG35azxV9ylqF69tOk62WVIs/Db0N9tyq/da4V/M32Ai4eguJ1/m2cXP2pa/aP8F+b1lFiUhclu+Avno8l0X3DzdqNChguk4wgFJQemg9pzfQ8spLblw7QLbz25n4+CNuLs5dz/D9WPXE7s/FoAS9UtQ56k6hhMJR/d/K/+PmCsxAHS+rzPhlcINJxIi76rYtaLpCMKBSZO3g7qv0H0U8SsCwNaYrXy99WvDibInLiqONe+sAUC5KTp/2xk3d7n8RNa2xWxjwpYJAPh4+DC+w3iUUoZTCSGEuBX5RHdQXu5efNv5W9vy6ytet92pcUbxUfF4+noC0ODZBhSr6doPHonssaRbGPr7UNK1dTip0Q+OpnSB0mZDCSFstNbsmbGH2Y/OztOzu4m/SUHpwJqENWFIrSEAXEm5ckNfMmdTrm05nj7wNA2ea0Dzt5ubjiMc3Ddbv2FrzFYAqhSpwvONnjecSAiR2YInFjC7z2z2TN/Dzqk7TccRDkAKSgf3YZsPKeJrbfqO2BfBwsMLDSe6d35F/Gj/WXu883ubjiIcWMyVGF5f+bpt+ZvO3+Dp7mkwkRDiZpUfqmz7funzS7l6/qrBNMIRSEHp4Ar6FOSTtp/Ylof9PoyrKfIfV7iu+Qfnczn5MgCDaw2mSVgTw4mEEDer0LECVftUBSApLonFz8nYlHmdFJROoF/1frQs0xKAEwkn+O8f/zWc6M5Eb4pm7sC5XL0gBbC4c0PrDmXVgFU0CWvCh60/NB1HCJGF9p+3x6egDwB7pu/h8MLDhhMJk5TpzrShoaE6OjraaAZncDD2INW/qU6KJQV35c62J7dRo1gN07GyZEmx8G3tb7mw9wL5gvIxcPVAgqsHm44lhBDCjiInRzJv0DwAAsMCGbZ3GF7+XoZTiZyglDqttQ7NarvcoXQSFQtX5PUm1n5lFm1h8PzBpKU77kwFa99fy4W9FwAIKhNEkfuLGE4khBDC3moMqEGZVtbpcxNOJrBy1ErDiYQpUlA6kVebvEqVIlUA2HZmG5+s/+Q2rzDj/J7zrB2zFgA3Dze6/tAVNw+51ETW1pxYw7dbv7UNEySEcA5KWccV9vCxzpOy6YtNRG+SVse8SD7lnYi3hzcTu07ETVl/bKNXj+bQxUOGU90o3ZLO/MHzSU+1FgYPvPIAxWrImJMia0mpSQyeP5ihvw+lxZQWXLx20XQkIcRdKFiuIM3fam5d0LD8leVG8wgzpKB0Mg1CG/Bcg+cASLYkM3j+YIe6q7Ppi02c3nwagMKVCtNsVDPDiYSj++3QbxyJOwJAWnoaQT5BhhMJIe5Wo+cbUaxWMar2rsrDMx82HUcYIA/lOKFrqdeo9nU1jsYfBeDLjl8yrN4ww6kg/mg8X1X9irSkNFAwaO0gwh4IMx1LOIHlR5czYtEI5jwyh8pFKt/+BUIIh5N8JVnGGXZh8lCOC/L19OX7Lt/bll9Z/gonLp0wmMg6DdeCJxZYi0mg/vD6UkyKO9a6bGv2DtsrxaQQTkyKybxNCkon1aJMC56s/SQAjUIboZQymufIoiMcW3kMsA4d0eq9VkbzCOfzV99gIYRruHr+KocXydiUeYX8BndiH7X5iB+7/8iSfksICzR7N7B8h/J0/7E7PoV86PxdZxmHTPyrg7EH+X7795juciOEyBn7IvbxVZWv+KXHL8QdiTMdR+QC6UMp7Er60IjbsaRbaDKpCRujN9KidAt+fuhniucvbjqWEMKOFj2ziM3jNwNQ8oGSDPxjIG7ucg/LmUkfyjwmMSXR6F0fKSbF7YxdP5aN0RsBOHX5FAHeAYYTCSHsrdX7rQgqZx2x4dS6U2z6YpPhRCKnSUHpQpZFLaPShEr8vPvnXDnf5ejLHJh7IFfOJVzD7nO7eXP1m4C1z+SU8Cn4efkZTiWEsDcvPy+6TeoGGd37V76+kouHZIxZVyYFpYuIPBtJ25/acvrKaYYvHM6phFM5ej6tNfMen8fM7jP5tf+vXE+4nqPnE84vxZLCgLkDSLGkAPBioxdpXLKx4VRCiJxSqmkpGjzTAIC062nMHTiXdIvjjJss7EsKShdRs1hN+lbrC0C9EvVy/InZrd9s5egy6ziYx1YeQ6fLwxXi341ZM4YdZ3cAUKVIFd5u8bbhREKInNbqvVYULF8QgOgN0Wz8bKPhRCKnSEHpQsZ3GM93nb9jSb8llAgokWPniTsSx7IXl9mWu07sik+QT46dTzi/bTHbGLN2DAAebh5MCZ9CPo98hlMJIXKap68n3SZnavoetZLYA7FmQ4kcIQWlCwnyCeKJOk/k6N3JdEs6cwfOJfVaKgB1htahfLvyOXY+4fyup12n/9z+WLQFgFFNR1EnpI7hVEKI3BL2QBgNRzYEwJJsYe4Aafp2RVJQurjLyZdJS0+z2/E2fLqBU+us/TODygbR9uO2dju2cE1vrnqTfRf2AVC7eG1eb/q64URCiNzW8t2WFLqvEPmC8tHg2QYoN7OTcQj7k4LSha08tpKqX1Xl43Uf2+V45/ecZ9WoVdYFBeFTwmUAc/Gv1p1cx9j1YwHwcvdiavhUPN09DacSQuQ2Tx9Pekb0ZNjeYVR7tJrx2d2E/UlB6aJOJpyk3U/tOHX5FKNXj2bn2Z3ZOp4lxcKv/X/FkmJttmz0QiPCmshc3SJriSmJDJg7AI31ga13W7xLlaJVDKcSQpgSXC2Y/MXzm44hcogUlC4qLDCMlxq/BEBqeiqP/foYyWnJ93y8dR+t4+yOswAUqVKElu+0tEtO4bqeW/wcUfFRADQu2ZjnGz1vOJEQwtFEb5KZ8lyFFJQu7K3mb1EjuAYAu8/v5o2Vb9zzsWoPqU25duVw83AjfEo4Hvk87BVTuKDZ+2YzccdEAPy9/JkSPgV3N3fDqYQQjuJ6wnXm9JvDxIYT2T9nv+k4wg6koHRhXu5eTO0+FS93az/HTzZ8wtKopfd0LP9i/vRd2JfH1z9OSJ0Qe8YULuZUwimeWPCEbXl8h/GULygjAQgh/nbot0Ps/nk3AAueWMDl05cNJxLZJQWli6seXJ0PW39oW+7/a3/OXz1/T8dSbooS9XJufEvh/LTWDJw3kPjr8QA8UuURBtQYYDiVEMLRVHu0GpV7VAYgKS6JuQPmygQZTk4Kyjzg2QbP0qF8BwDOXT3HoHmD0Pr2/3GPrjjKtYvXcjqecCFKKV554BWK+RejZEBJvun0jTzNKYT4B6UUXb7rQv4S1od0jq04xobPNhhOJbJDCso8QCnF5PDJBPsFA7Dw8ELGbx7/r6+5ePgiM7rN4Jsa33Bs1bHciClcRNtybdk1dBfzes8jyCfIdBwhhIPyKehD96nd/55F5/WVnI08azaUuGdSUOYRRf2KMiV8im35pWUvZTmUUFpyGrP7zCb1aipXTl9hz/Q9uRVTuIgifkWoVbyW6RhCCAdXpmUZGr/YGLAOTzf70dmkXE0xnErcCyko85B25dvxfEPr0C0plhR6z+5NYkriP/Zb9vIyzmw7A0ChioVo91m7XM0pnIvWmvkH599RNwohhLhZi3daUKxWMQBi98eyaPgiw4nEvZCCMo95r9V71CpmvXN0IPYA//n9PzcUAvvn7GfzF5sBcPd25+EZD+PlJ7PhiKx9s/Ubus3oRtcZXbl47aLpOEIIJ+Ph7UGP6T3w9LPOohU5OZLd03YbTiXulhSUeYy3hzczH55Jfi9rR+ifdv1kGy8w/mg88x6fZ9u3/eftKVazmJGcwjmcSzzH80utd71/O/Qb60+tN5xICOGMClcsTOdvOwNQoVMFyrUrZziRuFvKdDNVaGiojo6WkfJz2y97f6FXRC8AvN29Wdd/Hdt7bCdmawwAVXpVocf0HvKErritxUcW89ivj/Fo1UcZ12Gc6ThCCCd2ZPERyrUth3KTzx5Ho5Q6rbUOzXK7FJR519O/P81XW78CIMQSQv+P+pMvOR8FyxfkyW1P4h3gbTihcBYxV2Io5FMIbw+5ZoQQwhXdrqCUJu887NN2n1K7eG0A8h3Ph8XdYu03+cvDUkyKuxKSP0SKSSGE3V2JucKh3w6ZjiHugBSUeZi3hze/PPwLY1qO4ZeWvxCYHki7z9pRvFZx09GEA1t3ch2fbvhUnuoWQuSoE2tP8F2d7/jl4V+I2RZjOo64DWnyFjbxR+MpUKaA9JsUWTpz5Qy1v6vN2cSz9Ly/J5PDJ+Pr6Ws6lhDCBS0cvpAtX24BIDAskCe3PYlvYfl9Y4o0eYs7FlQ2CKUU5xLPEXNF/hoUN0q1pNIrohdnE60zWcRei8XLXYaUEkLkjHaftiO0obV+STiZwOw+s0m3pBtOJbIiBWUeteHTDeyZ+c8ZcDZGb6T2d7V5aOZDJKclG0gmHNXLy15m7cm1AIQGhDLj4Rl4uHkYTiWEcFXuXu70jOiJX1E/AI4uP8rKUSsNpxJZkYIyD4paGsXSF5cyu/dslr601NYXLi09jQFzBxBzJYZNpzcxevVow0mFo/h51898vulzADzdPInoGUFRv6KGUwkhXF1AiQB6zuqJcrd2xVr3wTr2z9lvOJW4FSko85iLhy8S0TsCMrrOegd42/pMerh5ML3HdPJ55KNZqWaMbDjSYFLhKDZFb2Lw/MG25XHtx9EgtIHBREKIvKRUs1K0/aStbfnX/r9ybtc5g4nErUhBmYdcv3Sd6V2mcz3+OgD3dbmPZm80u2Gf2sVrs3rAapY/tpxg/2ATMYUDOX35NN1ndifZYu3+8ETtJxhad6jhVEKIvKbBMw2o9mg1AFKvpjK9y3Sunr9qOJXITArKPCI9LZ2IXhFcPGida7lIlSI89NNDt5yNoEFoAzzdPXM7onAwSalJhM8M50ziGQCalWrGhI4TZBQAIUSuU0rR5fsuhNQLAawP6RyYd8BwKpGZFJR5xJLnlxC1NAoA38K+9FnQ544HL4+9FsvgeYO5dP1STkYUDkRrzePzH2drzFYAShcoTUTPCHmqWwhhjKePJ73n9iaobBDhU8Op80Qd05FEJvKIZh6w9dutbB6/GQA3TzcemfMIQWWC7ui1B2IP0GlaJ47GHyX6SjS/P/q7PNmbB7y39j1m7JkBgL+XP/N7z6eIXxHDqYQQeV3+kPwM2zcMD2/5HHI0cofSxUUti2LR8EW25c7fdKZU01J3/Hovdy8SricAsDRqKSMXy4M6ru7nXT8zatUoABSKn7r/RLXgaoZTCSGE1a2KyaT4JANJRGZSULo430K++BTyAaDh8w2p9Xitu3p92aCyzOk1B083a5/KCVsm8OmGT+2eUziGVcdWMWjeINvy+63ep1ulbgYTCSHEv9v5407GlR7HqQ2nTEfJ02TqxTzg0vFLbPpiE20+boOb+739DTE5cvINhcb0HtPpXbW3vSIKB7D3/F4e+OEBEpKtd6SH1hnKV52+kodwhBAO6+CCg8zoau2e41PIh8HrB1PovkKGU7kmmXpRUKB0Adp92u6ei0mAgTUH8taDb9mWB8wdwKpjq+yQTjiC+KR4Ok7raCsmO1XoxPiO46WYFEI4tPLtylOmVRkAki4m8XOHn2U4IUOkoHQxaclpbPpiU47Md/rmg28ypNYQAFIsKYTPDGf3ud12P4/IfQXyFeDJ2k8CUKd4HZlWUQjhFNy93Hlk9iMUrWaduSv+aDzTOk8j5WqK4WR5jzR5u5D0tHQiekewf/Z+KoVX4qFpD+HpY9/xJNPS0wifEc7vh38HICR/CBsGbyAsMMyu5xFmzNo7i6almlLMv5jpKEIIcccuR1/m+4bfc+X0FQDKty9P73m9cfdyN5zMdUiTdx6h0zULnljA/tnWOU6jlkZx8dBFu5/Hw82DmQ/PpH6J+gDEXImh9dTWnE08a/dzidzXs0pPKSaFEE4nIDSAvov62sZXPrL4CHP6zcmR1jpxa1JQugCtNYtHLiZyciSQMdbk7EcoViNnCgM/Lz9+6/MbFQpWAOBw3GHa/tiWuKS4HDmfsD9LuoUh84ew+Mhi01GEEMIugqsF02dBHzzyWbvr7Ju1jwVPLkCnm22JzSukoHQBq0evZvMX1oHLlZuix/QelG9fPkfPWcSvCMv7L7c1de8+v5v2P7XncvLlHD2vyD6tNcMXDmfijol0nd6VWXtnmY4khBB2UapZKXr92gs3T2t5s3fmXi4etn9rnfgnKSid3Pqx61nzzhrbctcfunJ/j/tz5dxhgWEsf2w5wX7BAGyJ2ULnaZ25lnotV84v7k1aehqxSbEApOt08nnkM5xICCHsp3z78vSY1gPfwr70X96fwhULm46UJ8hDOU5sw6cbWPrCUtty+y/a02BEg1zPsef8Hh6c/KCtybtN2TbM6z0PH0+fXM8i7owl3cJ/fv8PD5Z6kL7V+5qOI4QQdpd8OdnWp1JknzyU46J2/rjzhmKyxbstjBSTAFWLVmVJvyXk98oPwMbojRyOO2wki7gz7m7ufNflOykmhRAu6+ZiUmvNod8PYfpGmquSgtJJVexakZB6IQA0/29zmr3RzGieuiF1WdR3EcX9i7Oo7yKqB1c3mkf8TWvN26vfZv+F/aajCCGEEVprloxcwvTO01n5xkopKnNZr4VeAAAUIklEQVSANHk7seuXrrP/1/3UGnR383PnpKTUJGnqdiDpOp3nlzzPuE3jCPYLZtWAVVQuUtl0LCGEyFUn151kUpNJtuXGLzem9QetZTawuyBN3i5Ca01SfNIN6/IVyOdQxSTwj2JSa824jeNkSCEDUi2pDJo3iHGbxgFw/up5tp3ZZjiVEELkvrAHwuj4VUfb8vqP1rNoxCIZp9KOpKB0ApZUC/MGzWNS00kkxSXd/gUOQmvNK8tf4bklz9HkhyacTDhpOlKekZSaxEO/PMTUnVMBcFNuTOw6kX7V+xlOJoQQZtT7Tz06f9vZtrzlyy3M7jObtOQ0g6lchxSUDi71Wiozu89k55SdXNh7gRnhM5xmkNbzV8/bCpoDsQfYcWaH4UR5w6Xrl2j7U1t+O/QbAF7uXszqOYtBtQYZTiaEEGbVebIO4VPDcfOwlj/7Zu1jWsdpJF9ONpzM+UlB6cCS4pL4sc2PHP7d+sS0u5c7DZ5pgHJzjj4fwf7BrB+8ngoFKzCh4wS6VepmOpLLi7kSw4OTH+TPk38C4O/lz6K+i3io8kOGkwkhhGOo8VgNes/vjaevJwDHVh5jcvPJJJ5LNJzMuclDOQ7q4uGLzOg6g9gD1gGovfJ70Xteb8q0KGM42d27lnoNX09f0zFc3raYbXSd0ZWYKzEAFPEtwqK+i6gTUsdwMiGEcDzRG6OZ1mmarStZ1d5V6TG9h+FUjkseynFCR1cc5fsG39uKSb9gPwb+MdApi0nglsXk2PVjGb1qNOlaOkTbQ8S+CJpOamorJksXKM2fj/8pxaQQQmQhtGEog/4cREDJAAJKBtDus3amIzk1D9MBxI22fLWFRc8sQlusd46L3F+EPgv6EFQ2yHAy+1l4eCEvL3sZjWb3+d1M7T4Vfy9/07Gcktaa99a+x6hVo2zrHij5AHN6zaGoX1GDyYQQwvEVqVyEwesHkxSfhH8x+RzKDrlD6UCilkWx8OmFtmKyQqcKDN4w2KWKSYBj8cdsY3/9euBXGk9szLH4Y4ZTOZ8ryVd4dM6jNxSTj1V/jBX9V0gxKYQQdyggNIDgasE3rEuKT2L5a8tJTUo1lMr5SEHpQMq2LkuNATUAaPxSY3rP6+2S85A+Xf9pfn/0dwK9AwHYfX43df9X1/ZUsri93ees/2Yz9sywrXuv5XtMCZ+Ct4frXTNCCJFb0tPSiXgkgnUfrGNiw4nEHow1HckpyEM5DiYtOY2opVFU7FLRdJQcdzD2IF1ndOXQxUO2dSMbjuSD1h/g5e5lMJljW3F0BV2mdyEpzdqRPL9XfqaET6F75e6GkwkhhPM7s/0MPzT5gbQk6/iUnn6edP62M9X75u0pheWhHAeVlpzGomcWsS9i3w3rPbw98kQxCVCxcEU2DdlEt4p/Dyf02cbPaDyxMUfijhhM5tjqhtSleP7iANQsVpPtT22XYlIIIeykeO3iPLHlCQpXLgxA6tVUfu33K/OHzCf1mjSBZ0XuUBoQsy2GeQPncX7PebwDvHlqx1Mu10/ybmit+XLLl7yw9AVSLCmA9a7b5+0/Z1DNQTLX6i1sP7OdH3b8wNi2Y8nnkc90HCGEcDkpV1NYNHwRkZMjbesK3VeIbpO6UbJxSYPJzLjdHUopKHNRWnIaf/z3D9Z9uM724I27tzsP/fwQ9/e433A68yLPRtIrotcNTeBtyrbhuy7fUbpAaXPBDLqacpVRK0fxdP2nKV+wvOk4QgiR50ROiWThsIV/351U0HBkQ1q+09I2OHpeIAWlgzi9+TTzHp/Hhb0XbOuCawQTPiWcYjWKGUzmWBJTEhmxaASTIyfb1vl5+vFB6w8YVm8Ybirv9NI4Gn+Utj+2JSo+iiZhTVg9YDXubu6mYwkhRJ4TeyCWuQPmcnrzadu6fkv6Ua5tOYOpcpf0oTQs8Vwi85+Yz/cNv7cVk26ebjR/uzlPbHlCismb+Hv5M6nbJH5/9HdCA6zX7dXUq4xYNIIHfniAzac3G06Ye0rkL2EroLfFbGPXuV2GEwkhRN5UuFJhHl/3OK0/ao27tzs1+tfIU8XknZA7lDno5J8nmdbpxknni9cuTrdJ3QiuHvwvrxQAl5Mv88qyV/hm2ze2dYHegUQ/H+2SA6Gn6/R/3IFdeHghY9aOYVK3SdxX6D5DyYQQQvwl9kAsfkX98CnoY1un0zXbv99Ojf418MjnmnPGyB1Kg4rVLIann7V/hXeAN23GtmHwxsFSTN6hAO8Avu78NasGrKJS4UoAvNH0DZcrJlMsKXy79VsqjK/Avgs3PvXfsUJH/hz0pxSTQgjhIApXKnxDMQnWfpa/PfUbX1b+ksgpkaSn5b1pheUOpZ2kp6VzevPpfzz5teunXZxYc4KW77bEr6ifoXTOL9WSytSdU+lXvd8NA3efuHSC77Z9x3MNn6OIXxGDCe9eiiWFyZGTGbN2DCcTTgLQqkwrlj22TJ5sF0IIJ2FJtTCuzDiunL5iWxdUNogmrzehxmM1cPdyjb7v8lBODku+kszuabtZ//F6Lh2/xIhDI/L0EEC5bfjC4Xy55Ut8PX2J6BlBhwodTEe6rYvXLjIpchITNk/gRMKJG7Z1qtCJaT2mEeAdYCidEEKIu3V+z3mWPL+Eo8uO3rA+sFQgjV5oRI3+NcgX6NxDvElBmUPO7DjDtm+3sfvn3aQkptjW1xpSi67/62owWd5xNeUqIZ+GcDn5Mn6efpx47gSFfAuZjpWlzac389WWr5ixZwbJluQbtnWs0JG3HnyLeiXqGUonhBAiu06sPcGad9b8o7D09PWkSu8qtPhvCwJKOOcNg9sVlK7ZczSHXDx0kf2/7md/xH5itsb8Y3uZVmWo8VgNA8nyJj8vP/b8Zw9j148lMF/gP4rJV5a9Qmp6Kt0rdadRyUZ4uOXu5a61Zs/5PczeP5uIfRHsvbD3H/t0KN+B0Q+OpkFog1zNJoQQwv5KNS3FY0sf49SGU6x5Zw1HFllnfUu9lsrun3fT5qM2hhPmHLvcoVRKPQ6MBCoDz2mtJ9zpa53lDuXy15az7oN1/1jv6edJtb7VqPtUXYrXLm4gmbiVy8mXKf5Jca6lXgOgkE8hOt3XiS73daFpWFOC/XPmwahL1y+x7uQ61pxYw9yDc28YpP0vBfIVYFDNQQytO1QethFCCBd2bvc5tn27jV0/7qJi14p0//HGaXKXvrgUNw83yrYuS4n6JfAO8M7iSOblSpO3UqoGkAK8Bmx2toJSp2uuxFwhLiqO+Kh4zmw/Q6MXGhFU5u++kAfmHWBm+EzbcrGaxajzVB2qPVrNoS+AvGpZ1DI6TetEavqt510tFViK+iXq06BEA6oFV6N0gdKEBYbd8TSG6TqdFEvKDftvjN5I44mN0dz6/1Tjko0ZXGswvav2xtfT9+7flBBCCKeUcjWF5MvJ5C+e37YuNSmVj4t8TOrVv2fgCa4WTGjjUELqhFCwfEGCygYREBqAcjP/oGauNHlrrXdmnMzhn5Nf8sIS4g7FkZKYQkpiCtcTrpNwMgFLsuWG/ULqhtxQUJZrW47SLUpToWMFKoVXomD5grmcXNyNNuXacPbFsyw8vJD5B+ez+MhirqT8/QTeiYQTnEg4wax9s254XXH/4pQIKIGvpy8+Hj4MrTuU8Erhtu17zu+h24xuxFyJ4dUHXmV089G2bdWDq+Pu5k5aehoAbsqNZqWa0aNyD7pX6k6JgBI5/K6FEEI4Ii8/L7z8vG5Yd2bbGdKS0v5eoeHcrnOc23WObWyzrXb3cmfwxsEUr/V3K+jpzacpUd+xPlPyXB/K46uOc3bH2dvud2r9KWoOrGlb9vTxZMDKATkZTdhZQZ+C9Kvej37V+5GclswfJ/5g+dHlbDq9ia0xW23N4ZmdSTzDmcQztuXO93W+YbuHmwdH462drQ/F3dic7evpS7/q/QjwCqBpqaY8WOpBpxvKSAghRO4IaxLGC2dfIGppFNEbojm1/hTndp5Dp9/YymVJsfzjQZ6EUwnOWVAqpdZi7R95K7W01qfu9IRKqeeB5/9aDgwMvNOX2oWX/99/ISg3haefJ4ElAwkqF2T9KhtE4UqFHe4HJbLH28ObtuXa0rZcWwDS0tPYe34vW2K2EBUXxfGE4xy/ZP06l3jO1mzt43Hj4LUF8hWgsG9hCvsWJsQ/5B/nmdRtUs6/GSGEEC7Br4gf1ftWp3rf6gCkJKZwevNpLuy/QPzReOKj4kk8k4hvEcfvJmXXYYOUUpOBrY7chzLxXCLKTeHl74VHPg8ZQFr8g9aaFEsK19Ou4+3hfcf9KoUQQojckHI15R9N6DlNhg26iX+wa03bJ+xPKYW3h/cNM/IIIYQQjiK3i8k7YZe5vJVS/ZRS0UBP4B2lVLRSqpY9ji2EEEIIIRybvZ7y/gn4yR7HEkIIIYQQzsUudyiFEEIIIUTeJQWlEEIIIYTIFikohRBCCCFEtkhBKYQQQgghskUKSiGEEEIIkS1SUAohhBBCiGyRglIIIYQQQmSLFJRCCCGEECJbpKAUQgghhBDZIgWlEEIIIYTIFikohRBCCCFEtiittdkASiUDF3LxlP5AYi6eTzg/uWbE3ZJrRtwtuWbE3crta6aI1to7q43GC8rcppSK1lqHms4hnIdcM+JuyTUj7pZcM+JuOdo1I03eQgghhBAiW6SgFEIIIYQQ2ZIXC8pPTQcQTkeuGXG35JoRd0uuGXG3HOqayXN9KIUQQgghhH3lxTuUQgghhBDCjqSgFEIIIYQQ2eISBaVSaoxSardSKjLjq1cW+/krpZYopWKVUrFZ7KOUUisyb1dKVVNKrVFKHcg4z3dKqSzHYhKOTyn1eMbPMk0pNfxf9iudsU9kpq9yGdta37Q+Rim1PdNrIzLWaaWUf268L2FfSqkKSqn1SqlDSqnNSqn7s9hvlFIqKuPrHXtsE85HKfWFUup4xv/5qlnsM1ApdSnT741Vmba9etPvlMtKqU8ztpVWSq1WSiUopbbm1nsSOU8ptVQptSvjZ75WKVXzFvvUy/hddE0pFZHFcYoopc5l3q6U6qWU2qGU2pPxmTcix96I1trpv4ACmb4PAS4DQbfYzxtoBdQEYrM41ghgYubtQAWgesb37sBM4HXT71u+snXN1AAqA1OB4f+yX+msrpVb7Psb8EKm5dZAUUAD/qbfs3zd03WyEhiY8f3DwIZb7NMM2Av4ZfyO2Qq0y842+XLOr4yfaShwHKiaxT4DgYg7OJYXEAvUyVguCDQBOgFbTb9X+bLrdZO5hgkHtt9in1CgPvBUVtcPMAuYlHk78ABQLOP7QOAI8EBOvA+XuEOptb6UaTE/1g/wf7w3rXWy1noFcOnmbWC9GwH0Bj646XWHtda7Mr63AFuAsvZJL0zQWu/UWu8H0u1xPKVUCNAS+DHTOZZrrc/b4/gi9ymligK1gZ8yVs0GyiilSt+0ay9gstb6qtY6GfgB6JPNbcIJaa3XaK2j7XS4cCBaa70t49hxWus/gat2Or5wEDfVMIHc4nNJax2ttd4MJN/qGEqpvsA54I+bXrdOa3024/sE4ABQxk7Rb+ASBSWAUuoZpdRBYDvwpNb64l2+3g34H/A0kPov+/kBQ4AF2YgrnEuAUmqLUmq7UupNpZT7LfYZACySAtKllARitNZpANr6J/5JIOym/cKAE5mWj2fa5163Cdf2YEbz5jql1MNZ7DMYa2uZyAOUUlOVUqeAd7F+ntzNa0OA54FXb7Pf/UAjrC0vducUBWVGn4LYLL5KAmitv9BaVwQaA6OUUoXu8jQvAmu01pH/ksMTa3P3Uq31vHt9PyLn3ck1c4fOAKFa63pYm7CbAi/cYr9ByC9/V3TzuGrqDva7eZ973SZc029AKa11Taw3Jz5TSjXMvEPG76gmwM8G8gkDtNb9tdYlgVHAx3f58v8BL2uts5zXWykVCswDhmqtY+49adY8cuKg9qa1bnoX++5USp0GmmNtorpTzYDqSqn+WP9dgpRSx4FaWuv4jGLyF6wFxrN3cVxhwN1cM7c5TjJwPuP7OKXUD8CjwEd/7aOUagb4AkvscU7hME4BoUopD611mlJKYb1refKm/U5i7Wv7l1KZ9rnXbcJFaa1jM32/Xym1EGs/t42ZdhsEzNdax+V2PmGW1nqKUuobpVShu2hpbQRMtP6Kwh/wUUot0Vq3A9sdzOXAu1rrWTkSHCe5Q3k7SqnKmb4vB9QC9t3NMbTWnbXWYVrr0lj/MozXWpfOKCY9gBlAHNbmdBkNPo9QShXN+GMCZX2y/yFgx027PY61L5wlt/OJnJPRfWEH0C9jVQ/guNb6+E27zgIGKKX8Mq6Rx7H+vsjONuGilFIlMn0fjLXv9Y5M6xTWB3ekxSMPUEoFZBR8fy13By5irTfuiNa6YEa9Uhpra+uiTMVkcWAF8KHWeopdw9/EJQpK4AOl1F6lVCTWJunhGQ9coJT6r1Jq6F87KuuwLhuw3oGMVkr9eOtD3qAX1kKiLrAjo+/Ll/Z/GyK3KKX6KaWigZ7AOxnXQq2MbZmvmSZYf+Y7sfbPPQuMyXSc/FgLjR9ucY75GecAOKiUWp1jb0jklKeAp5RSh7D2TxoMoJRaqJSqC6C1Xo219WI3sB9rl5jF2dkmnJNS6suM//OhwHKl1JGM9bbrBXg60+fVMuAzrXXmPm0tsXZ/WHHTsb0zjj0La2tatFLq/Zx+TyLHBQJzM4b02Yn1OY7OWmud+bpRSpXL+Pl/CnTM+PkPu4Pj/xdr3+xn1d/DUQ3KiTciUy8KIYQQQohscZU7lEIIIYQQwhApKIUQQgghRLZIQSmEEEIIIbJFCkohhBBCCJEtUlAKIYQQQohskYJSCCGEEEJkixSUQgghhBAiW6SgFEIIIYQQ2SIFpRBCCCGEyJb/B8rwaMkGb4HRAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, color = 'purple', lw = 2.5, ls = '--')\n",
"plt.plot(x2, y2, color = 'green', lw = 2.5, ls = '-.')\n",
"\n",
"plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n",
"plt.yticks([-1, 0, 1])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, color = 'purple', lw = 2.5, ls = '--')\n",
"plt.plot(x2, y2, color = 'green', lw = 2.5, ls = '-.')\n",
"\n",
"plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r'$-\\pi$', r'$-\\pi/2$', r'$0$',r'$\\pi/2$',r'$\\pi$'])\n",
"plt.yticks([-1, 0, 1])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Legend"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.plot(x1, y1, color = 'purple', lw = 2.5, ls = '--', label = '$y = cosx$')\n",
"plt.plot(x2, y2, color = 'green', lw = 2.5, ls = '-.', label = '$y = sinx$')\n",
"\n",
"plt.xlabel('X')\n",
"plt.ylabel('Y')\n",
"\n",
"plt.title('Graph')\n",
"\n",
"plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r'$-\\pi$', r'$-\\pi/2$', r'$0$',r'$\\pi/2$',r'$\\pi$'])\n",
"plt.yticks([-1, 0, 1])\n",
"\n",
"plt.legend(loc = 0)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"plt.legend??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Marker"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10,6), dpi = 80)\n",
"\n",
"plt.plot([1,2,3,4,5], [1,4,9,16, 25], color = 'red', lw = 2.5, marker = '^', mec = 'blue', mew = 5)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"plt.plot??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Subplot"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([1,2,3,4,5])\n",
"\n",
"y1 = x**2\n",
"y2 = np.exp(x)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2bc46794748>]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x480 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6), dpi = 80)\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(x, y1, 'r')\n",
"\n",
"plt.subplot(1,2, 2)\n",
"plt.plot(x, y2, 'b-.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Object Oriented Methods"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([1,2,3,4,5,6,7,8,9,10])\n",
"y = x**2"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10,6))\n",
"\n",
"axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
"\n",
"axes.plot(x, y)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Axes in Figure"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Small Plot')"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10,6))\n",
"\n",
"axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
"axes2 = fig.add_axes([0.15, 0.55, 0.4, 0.3])\n",
"\n",
"axes1.plot(x,y, 'r--')\n",
"axes1.set_title('Large Plot')\n",
"\n",
"axes2.plot(y,x, 'g-.')\n",
"axes2.set_title('Small Plot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Subplots"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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y82JcAzxWVcdqeO6Qx3jzkEprgpmQxpkJaTLT+HXk6c6LsdznkJFWKzMhjTMT0gKmcZ6w050XYynnV9nJcBc155577ocuueSSKZQlTcf+/fu/X1Ubl7CKmdBbmpmQTpogD2+YRhN2uvNizDE8cdvo/CcXeoCq2s3wuk8MBoOanZ2dQlnSdCT5zyWuYib0lmYmpJMmyMMbpvF15Azw293Vwz8M/LCqvgc8ClydZEN3oOXV3Tzprc5MSOPMhLSARfeEJbmf4SeV87prMt0B/CRAVX2R4TWarmV4npaXgd/plh1Lciewr3uoXVU1zTMZS02YCWmcmZAms2gTVlXXL7K8GJ61eKFle4A9k5UmrU5mQhpnJqTJtLp2pCRJ0tuaTZgkSVIDNmGSJEkN2IRJkiQ1YBMmSZLUgE2YJElSAzZhkiRJDdiESZIkNWATJkmS1IBNmCRJUgM2YZIkSQ3YhEmSJDVgEyZJktSATZgkSVIDNmGSJEkN9GrCkmxLcijJ4SS3LbD8riRPd7dvJ/nByLLXRpbNTLN4qRUzIY0zE9LSrV9sQJJ1wN3AVcAcsC/JTFUdPDGmqv5wZPxngCtGHuJHVfWB6ZUstWUmpHFmQppMnz1hVwKHq+pIVb0KPABsf5Px1wP3T6M4aZUyE9I4MyFNoE8Tdj7w/Mj0XDfvFEkuBLYAj4/MPifJbJK9ST4xcaXS6mEmpHFmQprAol9HAllgXp1m7A7g4ap6bWTe5qo6muRi4PEkz1bVc2MbSHYCOwE2b97coySpKTMhjTMT0gT67AmbAzaNTF8AHD3N2B3M28VcVUe7v0eAJxk/DuDEmN1VNaiqwcaNG3uUJDVlJqRxZkKaQJ8mbB+wNcmWJGcxDNApv15J8j5gA/D1kXkbkpzd3T8P+AhwcP660hpjJqRxZkKawKJfR1bV8SS3Ao8C64A9VXUgyS5gtqpOBO164IGqGt0FfSlwT5LXGTZ8nx/9tYy0FpkJaZyZkCaT8Sy0NxgManZ2tnUZ0huS7K+qQavtmwmtNmZCOulM8uAZ8yVJkhqwCZMkSWrAJkySJKkBmzBJkqQGbMIkSZIasAmTJElqwCZMkiSpAZswSZKkBmzCJEmSGrAJkyRJasAmTJIkqQGbMEmSpAZswiRJkhqwCZMkSWrAJkySJKmBXk1Ykm1JDiU5nOS2BZbflOTFJE93t98dWXZjkv/objdOs3ipFTMhjTMT0tKtX2xAknXA3cBVwBywL8lMVR2cN/TBqrp13rrvAu4ABkAB+7t1X5pK9VIDZkIaZyakyfTZE3YlcLiqjlTVq8ADwPaej38N8FhVHesC9RiwbbJSpVXDTEjjzIQ0gT5N2PnA8yPTc928+X4jyTNJHk6yaYnrSmuJmZDGmQlpAn2asCwwr+ZN/xNwUVX9AvBl4L4lrEuSnUlmk8y++OKLPUqSmjIT0jgzIU2gTxM2B2wamb4AODo6oKr+p6pe6Sb/BvhQ33W79XdX1aCqBhs3buxbu9SKmZDGmQlpAn2asH3A1iRbkpwF7ABmRgckec/I5HXAv3f3HwWuTrIhyQbg6m6etJaZCWmcmZAmsOivI6vqeJJbGYZiHbCnqg4k2QXMVtUM8AdJrgOOA8eAm7p1jyW5k2FAAXZV1bFl+D+kFWMmpHFmQppMqk756r2pwWBQs7OzrcuQ3pBkf1UNWm3fTGi1MRPSSWeSB8+YL0mS1IBNmCRJUgM2YZIkSQ3YhEmSJDVgEyZJktSATZgkSVIDNmGSJEkN2IRJkiQ1YBMmSZLUgE2YJElSAzZhkiRJDdiESZIkNWATJkmS1IBNmCRJUgM2YZIkSQ30asKSbEtyKMnhJLctsPyzSQ4meSbJV5JcOLLstSRPd7eZaRYvtWImpHFmQlq69YsNSLIOuBu4CpgD9iWZqaqDI8OeAgZV9XKSW4AvAL/VLftRVX1gynVLzZgJaZyZkCbTZ0/YlcDhqjpSVa8CDwDbRwdU1RNV9XI3uRe4YLplSquKmZDGmQlpAn2asPOB50em57p5p3Mz8MjI9DlJZpPsTfKJCWqUVhszIY0zE9IEFv06EsgC82rBgckNwAD46MjszVV1NMnFwONJnq2q5+attxPYCbB58+ZehUsNmQlpnJmQJtBnT9gcsGlk+gLg6PxBST4G3A5cV1WvnJhfVUe7v0eAJ4Er5q9bVburalBVg40bNy7pH5AaMBPSODMhTaBPE7YP2JpkS5KzgB3A2K9XklwB3MMwWC+MzN+Q5Ozu/nnAR4DRAzWltchMSOPMhDSBRb+OrKrjSW4FHgXWAXuq6kCSXcBsVc0Afwb8NPClJAD/VVXXAZcC9yR5nWHD9/l5v5aR1hwzIY0zE9JkUrXg1/bNDAaDmp2dbV2G9IYk+6tq0Gr7ZkKrjZmQTjqTPHjGfEmSpAZswiRJkhqwCZMkSWrAJkySJKkBmzBJkqQGbMIkSZIasAmTJElqwCZMkiSpAZswSZKkBmzCJEmSGrAJkyRJasAmTJIkqQGbMEmSpAZswiRJkhqwCZMkSWqgVxOWZFuSQ0kOJ7ltgeVnJ3mwW/6NJBeNLPujbv6hJNdMr3SpHTMhjTMT0tIt2oQlWQfcDXwcuAy4Psll84bdDLxUVT8P3AX8abfuZcAO4P3ANuCvuseT1iwzIY0zE9Jk+uwJuxI4XFVHqupV4AFg+7wx24H7uvsPA7+WJN38B6rqlar6DnC4ezxpLTMT0jgzIU2gTxN2PvD8yPRcN2/BMVV1HPgh8O6e60prjZmQxpkJaQLre4zJAvOq55g+65JkJ7Czm3wlybd61LWczgO+bw3W0HnfvGkzYQ1v9xrMxOp4HaxhddQwPw+99WnC5oBNI9MXAEdPM2YuyXrgncCxnutSVbuB3QBJZqtq0PcfWA7WYA3za5g3y0xYw9u+hnmzzIQ1vG1rWCAPvfX5OnIfsDXJliRnMTyAcmbemBngxu7+J4HHq6q6+Tu6X8VsAbYC35y0WGmVMBPSODMhTWDRPWFVdTzJrcCjwDpgT1UdSLILmK2qGeBvgb9PcpjhJ5sd3boHkjwEHASOA5+uqteW6X+RVoSZkMaZCWlCVbWqbsBOa7AGa1g927cGa1htNbTevjVYw7S2n+4BJEmStIK8bJEkSVIDzZqwM7nExQrW8NkkB5M8k+QrSS5c6RpGxn0ySSWZ+i9A+tSQ5De75+JAkn9Yye0n2ZzkiSRPda/FtdPcfreNPUleON3P3jP0l12NzyT54DLUYCZ61DAyzkyYCTMxPm5ZMtE6D31qWLOZaPT96TrgOeBi4Czg34DL5o35feCL3f0dwIMNavhV4Ke6+7e0qKEb9w7gq8BeYNDgedgKPAVs6KZ/boW3vxu4pbt/GfDdZXhP/jLwQeBbp1l+LfAIw3MafRj4RoPXwUycHGcmzISZGB+3LJlonYcl1LAmM9FqT9iZXOJixWqoqieq6uVuci/D89dMU5/nAeBO4AvA/055+31r+D3g7qp6CaCqXljh7RfwM939d7LAOYTOVFV9leEvtk5nO/B3NbQX+Nkk75liCWaiZw0dM2EmTtRgJoaWKxOt89C3hjWZiVZN2Jlc4mIlaxh1M8MOd5oWrSHJFcCmqvrnKW+7dw3Ae4H3Jvlakr1Jtq3w9j8H3JBkDvgX4DNT3H5fy31pFTPRswYzAZiJU8aYiWXLROs89K3hc6zBTPQ5Y/5yOJNLXKxkDcOByQ3AAPjoFLe/aA1JfgK4C7hpytvtXUNnPcPdzb/C8FPevya5vKp+sELbvx64t6r+PMkvMTzX0OVV9foUtt/Xang/roYahgPNhJlYHe/H1VDDcOBbNxOt89C3hjWZiVZ7wpZyiQsyfomLlayBJB8Dbgeuq6pXprj9PjW8A7gceDLJdxl+xzwz5YMu+74W/1hVP66q7wCHGAZupbZ/M/AQQFV9HTiH4bXCVlKv98syP76ZMBMnmIl5Y8zEsmWidR761rA2MzHtA9f63Bh2zUeALZw8yO7988Z8mvEDLh9qUMMVDA8G3NrqeZg3/kmmfxByn+dhG3Bfd/88hrtb372C238EuKm7f2n3ps4yvB4XcfoDLn+d8QMuv9ngdTATp443E2bCTIyPn2omWudhCTWsyUxM/Q2zhH/kWuDb3Zv39m7eLoafJGDYxX4JOMzwOmIXN6jhy8B/A093t5mVrmHe2KmGawnPQ4C/YHhZkWeBHSu8/cuAr3XBexq4ehmeg/uB7wE/Zvhp5mbgU8CnRp6Du7san230OpiJU8eaCTNhJsbHTj0TrfPQs4Y1mYlFz5ifZA/wf4AXquryBZYH+L/dE/Qyw070/3XLbgT+uBv6J1V13/z1pbXGTEjjzIQ0mT7HhN3LcFfj6Xyc4Xe/W4GdwF8DJHkXcAfwiwx/XnpHkg1nUqy0StyLmZBG3YuZkJZs0SasJj8vxjXAY1V1rIbnDnmMNw+ptCaYCWmcmZAmM41fR57uvBjLfQ4ZabUyE9I4MyEtYBrnCTvdeTGWcn6VnQx3UXPuued+6JJLLplCWdJ07N+///tVtXEJq5gJvaWZCemkCfLwhmk0Yac7L8YcwxO3jc5/cqEHqKrdDK/7xGAwqNnZ2SmUJU1Hkv9c4ipmQm9pZkI6aYI8vGEaX0fOAL/dXT38w8APq+p7wKPA1Uk2dAdaXt3Nk97qzIQ0zkxIC1h0T1iS+xl+UjmvuybTHcBPAlTVFxleo+lahudpeRn4nW7ZsSR3Avu6h9pVVdM8k7HUhJmQxpkJaTKLNmFVdf0iy4vhWYsXWrYH2DNZadLqZCakcWZCmkyra0dKkiS9rdmESZIkNWATJkmS1IBNmCRJUgM2YZIkSQ3YhEmSJDVgEyZJktSATZgkSVIDNmGSJEkN2IRJkiQ1YBMmSZLUgE2YJElSAzZhkiRJDdiESZIkNWATJkmS1ECvJizJtiSHkhxOctsCy+9K8nR3+3aSH4wse21k2cw0i5daMRPSODMhLd36xQYkWQfcDVwFzAH7ksxU1cETY6rqD0fGfwa4YuQhflRVH5heyVJbZkIaZyakyfTZE3YlcLiqjlTVq8ADwPY3GX89cP80ipNWKTMhjTMT0gT6NGHnA8+PTM91806R5EJgC/D4yOxzkswm2ZvkExNXKq0eZkIaZyakCSz6dSSQBebVacbuAB6uqtdG5m2uqqNJLgYeT/JsVT03toFkJ7ATYPPmzT1KkpoyE9I4MyFNoM+esDlg08j0BcDR04zdwbxdzFV1tPt7BHiS8eMATozZXVWDqhps3LixR0lSU2ZCGmcmpAn0acL2AVuTbElyFsMAnfLrlSTvAzYAXx+ZtyHJ2d3984CPAAfnryutMWZCGmcmpAks+nVkVR1PcivwKLAO2FNVB5LsAmar6kTQrgceqKrRXdCXAvckeZ1hw/f50V/LSGuRmZDGmQlpMhnPQnuDwaBmZ2dblyG9Icn+qhq02r6Z0GpjJqSTziQPnjFfkiSpAZswSZKkBmzCJEmSGrAJkyRJasAmTJIkqQGbMEmSpAZswiRJkhqwCZMkSWrAJkySJKkBmzBJkqQGbMIkSZIasAmTJElqwCZMkiSpAZswSZKkBmzCJEmSGujVhCXZluRQksNJbltg+U1JXkzydHf73ZFlNyb5j+524zSLl1oxE9I4MyEt3frFBiRZB9wNXAXMAfuSzFTVwXlDH6yqW+et+y7gDmAAFLC/W/elqVQvNWAmpHFmQppMnz1hVwKHq+pIVb0KPABs7/n41wCPVdWxLlCPAdsmK1VaNcyENM5MSBPo04SdDzw/Mj3XzZvvN5I8k+ThJJuWuK60lpgJaZyZkCbQpwnLAvNq3vQ/ARdV1S8AXwbuW8K6JNmZZDbJ7IsvvtijJKkpMyGNMxPSBPo0YXPAppHpC4CjowOq6n+q6pVu8m+AD/Vdt1t/d1UNqmqwcePGvrVLrZgJaZyZkCbQpwnbB2xNsiXJWcAOYGZ0QJL3jExeB/x7d/9R4OokG5JsAK7u5klrmZmQxpkJaQKL/jqyqo4nuZVhKNYBe6rqQJJdwGxVzQB/kOQ64DhwDLipW/dYkjsZBhRgV1UdW4b/Q1oxZkIaZyakyaTqlK/emxoMBjU7O9u6DOkNSfZX1aDV9s2EVhszIZ10JnnwjHS8X5kAAAfISURBVPmSJEkN2IRJkiQ1YBMmSZLUgE2YJElSAzZhkiRJDdiESZIkNWATJkmS1IBNmCRJUgM2YZIkSQ3YhEmSJDVgEyZJktSATZgkSVIDNmGSJEkN2IRJkiQ1YBMmSZLUQK8mLMm2JIeSHE5y2wLLP5vkYJJnknwlyYUjy15L8nR3m5lm8VIrZkIaZyakpVu/2IAk64C7gauAOWBfkpmqOjgy7ClgUFUvJ7kF+ALwW92yH1XVB6Zct9SMmZDGmQlpMn32hF0JHK6qI1X1KvAAsH10QFU9UVUvd5N7gQumW6a0qpgJaZyZkCbQpwk7H3h+ZHqum3c6NwOPjEyfk2Q2yd4kn5igRmm1MRPSODMhTWDRryOBLDCvFhyY3AAMgI+OzN5cVUeTXAw8nuTZqnpu3no7gZ0Amzdv7lW41JCZkMaZCWkCffaEzQGbRqYvAI7OH5TkY8DtwHVV9cqJ+VV1tPt7BHgSuGL+ulW1u6oGVTXYuHHjkv4BqQEzIY0zE9IE+jRh+4CtSbYkOQvYAYz9eiXJFcA9DIP1wsj8DUnO7u6fB3wEGD1QU1qLzIQ0zkxIE1j068iqOp7kVuBRYB2wp6oOJNkFzFbVDPBnwE8DX0oC8F9VdR1wKXBPktcZNnyfn/drGWnNMRPSODMhTSZVC35t38xgMKjZ2dnWZUhvSLK/qgattm8mtNqYCemkM8mDZ8yXJElqwCZMkiSpAZswSZKkBmzCJEmSGrAJkyRJasAmTJIkqQGbMEmSpAZswiRJkhqwCZMkSWrAJkySJKkBmzBJkqQGbMIkSZIasAmTJElqwCZMkiSpAZswSZKkBno1YUm2JTmU5HCS2xZYfnaSB7vl30hy0ciyP+rmH0pyzfRKl9oxE9I4MyEt3aJNWJJ1wN3Ax4HLgOuTXDZv2M3AS1X188BdwJ92614G7ADeD2wD/qp7PGnNMhPSODMhTabPnrArgcNVdaSqXgUeALbPG7MduK+7/zDwa0nSzX+gql6pqu8Ah7vHk9YyMyGNMxPSBPo0YecDz49Mz3XzFhxTVceBHwLv7rmutNaYCWmcmZAmsL7HmCwwr3qO6bMuSXYCO7vJV5J8q0ddy+k84PvWYA2d982bNhPW8HavwUysjtfBGlZHDfPz0FufJmwO2DQyfQFw9DRj5pKsB94JHOu5LlW1G9gNkGS2qgZ9/4HlYA3WML+GebPMhDW87WuYN8tMWMPbtoYF8tBbn68j9wFbk2xJchbDAyhn5o2ZAW7s7n8SeLyqqpu/o/tVzBZgK/DNSYuVVgkzIY0zE9IEFt0TVlXHk9wKPAqsA/ZU1YEku4DZqpoB/hb4+ySHGX6y2dGteyDJQ8BB4Djw6ap6bZn+F2lFmAlpnJmQJlRVq+oG7LQGa7CG1bN9a7CG1VZD6+1bgzVMa/vpHkCSJEkryMsWSZIkNdCsCTuTS1ysYA2fTXIwyTNJvpLkwpWuYWTcJ5NUkqn/AqRPDUl+s3suDiT5h5XcfpLNSZ5I8lT3Wlw7ze1329iT5IXT/ew9Q3/Z1fhMkg8uQw1mokcNI+PMhJkwE+PjliUTrfPQp4Y1m4lG35+uA54DLgbOAv4NuGzemN8Hvtjd3wE82KCGXwV+qrt/S4saunHvAL4K7AUGDZ6HrcBTwIZu+udWePu7gVu6+5cB312G9+QvAx8EvnWa5dcCjzA8p9GHgW80eB3MxMlxZsJMmInxccuSidZ5WEINazITrfaEncklLlashqp6oqpe7ib3Mjx/zTT1eR4A7gS+APzvlLfft4bfA+6uqpcAquqFFd5+AT/T3X8nC5xD6ExV1VcZ/mLrdLYDf1dDe4GfTfKeKZZgJnrW0DETZuJEDWZiaLky0ToPfWtYk5lo1YSdySUuVrKGUTcz7HCnadEaklwBbKqqf57ytnvXALwXeG+SryXZm2TbCm//c8ANSeaAfwE+M8Xt97Xcl1YxEz1rMBOAmThljJlYtky0zkPfGj7HGsxEnzPmL4czucTFStYwHJjcAAyAj05x+4vWkOQngLuAm6a83d41dNYz3N38Kww/5f1rksur6gcrtP3rgXur6s+T/BLDcw1dXlWvT2H7fa2G9+NqqGE40EyYidXxflwNNQwHvnUz0ToPfWtYk5lotSdsKZe4IOOXuFjJGkjyMeB24LqqemWK2+9TwzuAy4Enk3yX4XfMM1M+6LLva/GPVfXjqvoOcIhh4FZq+zcDDwFU1deBcxheK2wl9Xq/LPPjmwkzcYKZmDfGTCxbJlrnoW8NazMT0z5wrc+NYdd8BNjCyYPs3j9vzKcZP+DyoQY1XMHwYMCtrZ6HeeOfZPoHIfd5HrYB93X3z2O4u/XdK7j9R4CbuvuXdm/qLMPrcRGnP+Dy1xk/4PKbDV4HM3HqeDNhJszE+PipZqJ1HpZQw5rMxNTfMEv4R64Fvt29eW/v5u1i+EkChl3sl4DDDK8jdnGDGr4M/DfwdHebWeka5o2dariW8DwE+AuGlxV5Ftixwtu/DPhaF7yngauX4Tm4H/ge8GOGn2ZuBj4FfGrkObi7q/HZRq+DmTh1rJkwE2ZifOzUM9E6Dz1rWJOZ8Iz5kiRJDXjGfEmSpAZswiRJkhqwCZMkSWrAJkySJKkBmzBJkqQGbMIkSZIasAmTJElqwCZMkiSpgf8P4mnRiSZy9/gAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x432 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10, 6))\n",
"\n",
"axes = fig.subplots(3, 3)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(axes)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10, 6))\n",
"\n",
"axes = fig.subplots(2, 3)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10, 6))\n",
"\n",
"axes = fig.subplots(2, 3)\n",
"\n",
"axes[0][0].plot(x,y,'r--')\n",
"axes[0][0].set_title('0,0')\n",
"\n",
"axes[0][1].plot(x,y,'b-.')\n",
"axes[0][1].set_title('0,1')\n",
"\n",
"axes[0][2].plot(x,y,'go')\n",
"axes[0][2].set_title('0,2')\n",
"\n",
"axes[1,0].plot(y,x,'r--')\n",
"axes[1,0].set_title('1,0')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2D Plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line Plot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"t = np.arange(0, 2,0.01)\n",
"s = 1 + np.sin(2*np.pi*t)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.plot(t, s)\n",
"ax.set(xlabel = 'Time', ylabel = 'Voltages', title = 'Time vs Voltage')\n",
"\n",
"ax.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"x = np.random.randint(100, size = 50)\n",
"\n",
"y = np.random.randint(100, size = 50)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x25d1aed3ec8>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAU4klEQVR4nO3df4xlZXnA8e9TQF2wZkAWAgPbXROCkpJ2ycSi2xgLJgga2RBNNVY3hmb/sS1agy72D+NfrNGoNTGYDahrY0SLZCFCSgiLsZpIO+tSQYFCscIOK6yxi8ZuCtinf9wzZZi9c2fmnvvjnPd8P8lm5p45c+975r732fc873PeE5mJJKlcvzftBkiSxstAL0mFM9BLUuEM9JJUOAO9JBXuxGk3AOD000/PzZs3T7sZktQqBw4c+GVmblxtv0YE+s2bNzM/Pz/tZkhSq0TEz9eyn6kbSSrcqoE+Ir4cEc9ExINLtp0WEXdHxKPV11Or7RERX4iIxyLixxFx0TgbL0la3VpG9F8F3rps2y7gnsw8D7inegxwOXBe9W8ncMNomilJGtaqgT4zvwf8atnmK4G91fd7ge1Ltn8te34IzETEWaNqrCRp/YbN0Z+ZmYcBqq9nVNtngSeX7Heo2naciNgZEfMRMX/kyJEhmyFJWs2oq26iz7a+q6Zl5h5gD8Dc3Jwrq4l9Bxf49F2P8NTRY5w9s4FrLzuf7Vv7jhMkrcOwgf7piDgrMw9XqZlnqu2HgHOX7HcO8FSdBqob9h1c4LpbH+DY878DYOHoMa679QEAg71U07Cpm9uBHdX3O4Dblmx/f1V9czHw7GKKRxrk03c98v9BftGx53/Hp+96ZEotksqx6og+Ir4BvBk4PSIOAZ8AdgPfioirgSeAd1W73wlcATwG/DfwgTG0WQV66uixdW2XtHarBvrMfM8KP7q0z74JfLBuo9Q9Z89sYKFPUE9g2+795uulGrwyVo1w7WXns+GkE/r+bDFfv+/gwoRbJZXBQK9G2L51luuvupDZmQ19f26+XhqegV6NsX3rLD/YdUnfGl0wXy8NqxGrV06LddvNtFK+/uwVRvuSBuvsiH6xbnvh6DES88BN0i9fv+GkE7j2svOn1CKp3To7oh9Ut920UX3XzjwWj61Lx6zumeTnurOBvi112129YnT71tmij0/dNunPdWdTNyvle5uWB/aKUak8k/5cdzbQtyUP3JYzD0lrN+nPdWcD/dK67QBmZzZw/VUXNi5d0JYzD0lrN+nPdWdz9NCOPPC1l53/klweNPPMQ9LxVppwnfTnutOBvg2sQJHaaS0TrpP6XEdvHbLpmpuby/n5+Wk3Q5JGZtvu/X0v/Jud2cAPdl0ykteIiAOZObfafp3N0UvSODWpkMJAL0lj0KRCCgO9JI1Bk0q4nYyVNBEu5TG9YzbQSxo7l/KYLlM3ksbOpTymy0AvaeyaVIHSRQZ6SWPXpAqULjLQSxq7JlWgdJGTsZLGrkkVKF1koJc0EU2pQOkiUzeSVDgDvSQVztRNh3XtSkWpqwz0HdXVKxWlLjJ101FeqSh1h4G+o7xSUeoOA31HeaWi1B0G+o7ySkWpO2pNxkbEh4G/BBJ4APgAcBZwM3Aa8CPgfZn5XM12HqerFSOjOm6vVGymrvZrjdfQNwePiFng+8AFmXksIr4F3AlcAdyamTdHxJeAf8vMGwY913pvDr68YgR6o9Hrr7qw6A9FV4+7K3x/tV6Tujn4icCGiDgROBk4DFwC3FL9fC+wveZrHKerFSNtOu59BxfYtns/W3bdwbbd+9l3cGHaTWq8Nr2/apehA31mLgCfAZ6gF+CfBQ4ARzPzhWq3Q0DfoUhE7IyI+YiYP3LkyLpeu6sVI2057sWR6cLRYyQv1ugb7Adry/ur9hk60EfEqcCVwBbgbOAU4PI+u/bNDWXmnsycy8y5jRs3ruu1u1ox0pbjdmQ6nLa8v2qfOqmbtwA/y8wjmfk8cCvwRmCmSuUAnAM8VbONx+lqxUhbjtuR6XDa8v6qfeoE+ieAiyPi5IgI4FLgp8C9wDurfXYAt9Vr4vG2b53l+qsuZHZmAwHMzmzoxIRVW47bkelw2vL+qn2GrroBiIhPAn8OvAAcpFdqOcuL5ZUHgb/IzP8Z9DzrrbpRs1k9Ik3GWqtuatXRZ+YngE8s2/w48Po6zzsM64+bowk1+vYH6UVFrF7pSozNM827CdkfpJcqYgkEqzy01Hr6g/X+6oIiRvRWeWiptfYHR/7qiiJG9NOq8nA02Exr7Q+eCaorigj006g/9urP5lprf/BMUF1RRKCfRv2xo8HmWmt/sN5fXVFEjh4mX+XhaLDZ1tIfrr3s/L71/l6JqtIUMaKfBkeD7eeVqOqKYkb0k+ZosAzTrPeXJsVAP6QmXP0pSWthoK/B0aCkNjBHL0mFM9BLUuFM3WhorhAptYOBXkNxnRipPQz0GsqgK4O3b511tC81iIFeQxl0ZbCjfalZnIzVUAZdGew6QFKzGOg1lEErRLoOkNQsBnoNZdA6Ma4DJDWLOXoNbaUrg10HSGoWA71GznWApGYx0GssXAdIag5z9JJUOAO9JBXOQC9JhTNHL0k1NX3JDwO9JNXQhiU/TN1IUg1tWPLDQC9JNbRhyQ8DvSTV0IYlPwz0klTDoAX+mqJWoI+ImYi4JSIejoiHIuINEXFaRNwdEY9WX08dVWMlqWkGLfDXFJGZw/9yxF7gnzPzxoh4GXAy8HHgV5m5OyJ2Aadm5scGPc/c3FzOz88P3Q5J6qKIOJCZc6vtN/SIPiJeBbwJuAkgM5/LzKPAlcDeare9wPZhX0OSVF+d1M1rgCPAVyLiYETcGBGnAGdm5mGA6usZ/X45InZGxHxEzB85cqRGMyRJg9QJ9CcCFwE3ZOZW4LfArrX+cmbuycy5zJzbuHFjjWZIkgapE+gPAYcy877q8S30Av/TEXEWQPX1mXpNlCTVMXSgz8xfAE9GxGIN0aXAT4HbgR3Vth3AbbVaKEmqpe5aN38NfL2quHkc+AC9/zy+FRFXA08A76r5GpKkGmoF+sy8H+hX2nNpneeVJI2OV8ZKUuEM9JJUOAO9JBXOQC9JhTPQS1LhvJWgitT0e3hKk2SgV3HacA9PaZIM9CrOoHt4lh7oPZNRPwZ6FacN9/AcB89ktBInY1WcNtzDcxwGncmo2wz0Kk4b7uE5Dl09k9HqDPQqThvu4TkOXT2T0erM0atI27fOFh/Yl7v2svNfkqOHbpzJaHXFBHqrDdR1i/3dz4GWKyLQW20g9XTxTEarKyJHb7WBJK2siEBvtYEkrayI1M3ZMxtY6BPUrTboFudppP6KGNF3tW5aL1qcp1k4eozkxXmafQcXpt00aeqKCPRdrZvWi5ynkVZWROoGrDboOudppJUVE+jVbSXO0zjnoFEpInUjlTZP45yDRslAryKUNk/jnINGydSNilHSPI1zDholA73UQNOac3BeoEymbqQGmsacg/MC5XJEr8ZwNPmiaaxE2eV77ZbOQK9GcAXS4016zsF5gXIZ6CuOJqfL0eT0DZoX8PPRbuboMTfZBI4mp2+leYE/e+1GPx8tVzvQR8QJEXEwIr5TPd4SEfdFxKMR8c2IeFn9Zo6XNcvT5/1Op2+laxHuffiIn4+WG0Xq5hrgIeBV1eNPAZ/LzJsj4kvA1cANI3idsXE0OX3e77QZ+s0LfPib9/fd189He9Qa0UfEOcDbgBurxwFcAtxS7bIX2F7nNSbB0eT0TevK1n0HF9i2ez9bdt3Btt37TUf04eej/eqO6D8PfBT4/erxq4GjmflC9fgQ0PeTGhE7gZ0AmzZtqtmMehxNNsOkq0ys9FkbPx/tN/SIPiLeDjyTmQeWbu6za/b7/czck5lzmTm3cePGYZsxEqWtk6K1cW5mbfx8tF+dEf024B0RcQXwCno5+s8DMxFxYjWqPwd4qn4zx6+kdVK0Ns7NrJ2fj3YbekSfmddl5jmZuRl4N7A/M98L3Au8s9ptB3Bb7VZKY2DuWV0xjjr6jwF/GxGP0cvZ3zSG15BqK20Ne2klI7kyNjO/C3y3+v5x4PWjeF5pnKaxnow0DS6BoE4z96wucAkESSqcI3qp4sJdKpWBXsKLp1Q2UzcSXjylshnoJbx4SmUz0Et48ZTKZqCX8OIplc3J2BawGmT8unzxlP2rfAb6hrMaZHK6ePGU/asbTN00nNUgk9PFm5DYv7rBEX3DWQ0yGV0d2dq/usERfcNZDTIZXR3ZrtSPEjpzVtMFBvqGsxpkMro6su3XvxYtntUY7NvPQN9w3sZtMrp65rS0f/XThbOaLjBH3wJdrAaZtC7fAHuxf23ZdUffGzyXflbTBQb6Gqw/Lse06uib1IfOntnAQp+gXvpZTRcY6IfU1SqNkk36zKlpfajLZzWlM0c/pK5WaWh0mtaHnA8qlyP6IXW1SkOj08Q+5HxQmRzRD6mrVRoaHfuQJsVAPyTr21WXfUiTUkzqZtLVC11e7VCjYR/SpERmv8rZyZqbm8v5+fmhf3959QL0RkZOJEkqWUQcyMy51fYrYkQ/qHrBQK9hNKm+XaqriEDfxOoFtVfT6tuluoqYjLV6QaPUtPp2qa4iAr3VCxolzxBVmiJSN1YvaJRc86XdnF85XhGBHryiT6Pjmi/t5fxKf0WkbqRRcs2X9nJ+pb9iRvTSKHmG2E7Or/Q39Ig+Is6NiHsj4qGI+ElEXFNtPy0i7o6IR6uvp46uuZKW23dwgW2797Nl1x2dv8+rFXj91UndvAB8JDNfB1wMfDAiLgB2Afdk5nnAPdVjSWOwmJNeOHqMxPu8WoHX39CBPjMPZ+aPqu9/AzwEzAJXAnur3fYC2+s2UlJ/5qRfyvmV/kaSo4+IzcBW4D7gzMw8DL3/DCLijBV+ZyewE2DTpk2jaIbUOeakj+f8yvFqB/qIeCXwbeBDmfnriFjT72XmHmAP9BY1q9sOqYus+R9O12rta5VXRsRJ9IL81zPz1mrz0xFxVvXzs4Bn6jVR0krMSa9fF+c16lTdBHAT8FBmfnbJj24HdlTf7wBuG755kgYxJ71+XZzXqJO62Qa8D3ggIu6vtn0c2A18KyKuBp4A3lWviZIGMSe9Pl2c1xg60Gfm94GVEvKXDvu8kjROw85rtDmv7xIIkjplmHmNtuf1XQJBKkiTR51Nadswq922/S52BnqpEE1eubFpbVvvvEbb8/qmbqRCNLmapMltW4u2r6FjoJcK0eRRZ5PbthZtv17BQC8Vosmjzia3bS3afr2COXqpEE2+M1aT27ZWbb5ewUC/iqZUCkirafK9k5vcti6IzOmvJzY3N5fz8/PTbsZxllcKQG8U0qZTNknliogDmTm32n7m6Adoe6WAJIGBfqC2VwpIEhjoB2p7pYAkgYF+oLbXzkoSWHUzkJUCkkpgoF9Fm2tnJQlM3UhS8Qz0klQ4A70kFc4cfR8ueyCpJAb6ZZp2gwRJqstAv0zbbxkmTYJnve1ioF/GZQ+kwTzrbR8nY5dx2QNpMBf7ax8D/TIueyAN5llv+5i6WcZlDwYzN6uzZzaw0Ceoe9bbXAb6Plz2oD9zs4IybgvYNaZutGbmZgXtv1F2Fzmi15qZm9Uiz3rbxUDfMtPMkZubldrJ1E2LLObIF44eI3kxR77v4MJEXt+KJKmdHNG3yLSv2rUiSeqv6dVoBvoWaUKO3Nys9FJtqEYbS+omIt4aEY9ExGMRsWscr9FFXrUrNU8bqtFGHugj4gTgi8DlwAXAeyLiglG/TheZI5eapwln2qsZx4j+9cBjmfl4Zj4H3AxcOYbX6Rzrl6XmacOZ9jhy9LPAk0seHwL+ZPlOEbET2AmwadOmMTSjTObIpWZpw5XC4xjRR59tedyGzD2ZOZeZcxs3bhxDMyRp/Npwpj2OEf0h4Nwlj88BnhrD60hSIzT9THscI/p/Bc6LiC0R8TLg3cDtY3gdSdIajHxEn5kvRMRfAXcBJwBfzsyfjPp1JElrM5YLpjLzTuDOcTy3JGl9XOtGkgpnoJekwkXmcZWPk29ExBHg531+dDrwywk3p0m6fvzg3wD8G4B/g5WO/w8yc9X69EYE+pVExHxmzk27HdPS9eMH/wbg3wD8G9Q9flM3klQ4A70kFa7pgX7PtBswZV0/fvBvAP4NwL9BreNvdI5eklRf00f0kqSaDPSSVLhGBvou3oowIs6NiHsj4qGI+ElEXFNtPy0i7o6IR6uvp067reMUESdExMGI+E71eEtE3Fcd/zerhfKKFREzEXFLRDxc9YU3dLAPfLj6DDwYEd+IiFeU3g8i4ssR8UxEPLhkW9/3PXq+UMXHH0fERas9f+MCfYdvRfgC8JHMfB1wMfDB6rh3Afdk5nnAPdXjkl0DPLTk8aeAz1XH/1/A1VNp1eT8PfBPmfla4I/o/S060wciYhb4G2AuM/+Q3sKI76b8fvBV4K3Ltq30vl8OnFf92wncsNqTNy7Q09FbEWbm4cz8UfX9b+h9wGfpHfveare9wPbptHD8IuIc4G3AjdXjAC4Bbql2Kf34XwW8CbgJIDOfy8yjdKgPVE4ENkTEicDJwGEK7weZ+T3gV8s2r/S+Xwl8LXt+CMxExFmDnr+Jgb7frQibu6L/GETEZmArcB9wZmYeht5/BsAZ02vZ2H0e+Cjwv9XjVwNHM/OF6nHpfeE1wBHgK1X66saIOIUO9YHMXAA+AzxBL8A/CxygW/1g0Urv+7pjZBMD/ZpuRViqiHgl8G3gQ5n562m3Z1Ii4u3AM5l5YOnmPruW3BdOBC4CbsjMrcBvKThN00+Vh74S2AKcDZxCL1WxXMn9YDXr/lw0MdB39laEEXESvSD/9cy8tdr89OJpWfX1mWm1b8y2Ae+IiP+kl667hN4If6Y6hYfy+8Ih4FBm3lc9voVe4O9KHwB4C/CzzDySmc8DtwJvpFv9YNFK7/u6Y2QTA30nb0VY5aNvAh7KzM8u+dHtwI7q+x3AbZNu2yRk5nWZeU5mbqb3nu/PzPcC9wLvrHYr9vgBMvMXwJMRcX616VLgp3SkD1SeAC6OiJOrz8Ti36Az/WCJld7324H3V9U3FwPPLqZ4VpSZjfsHXAH8O/AfwN9Nuz0TOuY/pXf69WPg/urfFfTy1PcAj1ZfT5t2Wyfwt3gz8J3q+9cA/wI8Bvwj8PJpt2/Mx/7HwHzVD/YBp3atDwCfBB4GHgT+AXh56f0A+Aa9OYnn6Y3Yr17pfaeXuvliFR8foFehNPD5XQJBkgrXxNSNJGmEDPSSVDgDvSQVzkAvSYUz0EtS4Qz0klQ4A70kFe7/AGUGo/kyYtAvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y, marker = 'o')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3D Plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line Plot"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from mpl_toolkits import mplot3d"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize= (10,6))\n",
"\n",
"ax = plt.axes(projection = '3d')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<mpl_toolkits.mplot3d.art3d.Line3D at 0x25d1e3bd1c8>]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize= (10,6))\n",
"\n",
"ax = plt.axes(projection = '3d')\n",
"\n",
"zline = np.linspace(0, 15, 1000)\n",
"xline = np.sin(zline)\n",
"yline = np.cos(zline)\n",
"\n",
"ax.plot3D(xline, yline, zline, 'red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3D Scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x25d1eb25d48>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10, 6))\n",
"\n",
"ax = plt.axes(projection = '3d')\n",
"\n",
"x = np.random.randint(100, size = 200)\n",
"y = np.random.randint(100, size = 200)\n",
"z = np.random.randint(100, size = 200)\n",
"\n",
"ax.scatter3D(x, y, z, c = 'red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3D Surface Plot"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def f(x, y):\n",
" return np.sin(np.sqrt(x**2+y**2))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"r = np.linspace(0, 6, 20)\n",
"theta = np.linspace(-0.9*np.pi, 0.8*np.pi, 40)\n",
"\n",
"r,theta = np.meshgrid(r, theta)\n",
"\n",
"X = r*np.sin(theta)\n",
"Y = r*np.cos(theta)\n",
"Z = f(X,Y)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10, 6))\n",
"\n",
"ax = plt.axes(projection = '3d')\n",
"\n",
"ax.plot_surface(X, Y, Z, cmap = 'winter')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ax.view_init(60, 0)\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Saving Figure"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"fig.savefig('test.pdf', dpi = 200)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 851.823423
| 90,840
| 0.956531
| 29,564
| 945,524
| 30.584427
| 0.852354
| 0.000531
| 0.000544
| 0.000952
| 0.064348
| 0.063239
| 0.062328
| 0.061965
| 0.061386
| 0.060458
| 0
| 0.154992
| 0.00697
| 945,524
| 1,109
| 90,841
| 852.591524
| 0.808013
| 0
| 0
| 0.518485
| 0
| 0.040577
| 0.989942
| 0.978812
| 0
| 1
| 0.000082
| 0
| 0
| 1
| 0
| true
| 0.000902
| 0.002705
| 0
| 0.002705
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
80a26ae86bcfd1808b56a19b270fa2a903687472
| 111
|
py
|
Python
|
icpc/2020-8-15/F-gen.py
|
Riteme/test
|
b511d6616a25f4ae8c3861e2029789b8ee4dcb8d
|
[
"BSD-Source-Code"
] | 3
|
2018-08-30T09:43:20.000Z
|
2019-12-03T04:53:43.000Z
|
icpc/2020-8-15/F-gen.py
|
Riteme/test
|
b511d6616a25f4ae8c3861e2029789b8ee4dcb8d
|
[
"BSD-Source-Code"
] | null | null | null |
icpc/2020-8-15/F-gen.py
|
Riteme/test
|
b511d6616a25f4ae8c3861e2029789b8ee4dcb8d
|
[
"BSD-Source-Code"
] | null | null | null |
print(1)
n=25000
print(n*2)
for i in range(n):
print(-999999, 14)
for i in range(n):
print(999999, 14)
| 13.875
| 22
| 0.621622
| 23
| 111
| 3
| 0.478261
| 0.115942
| 0.173913
| 0.318841
| 0.724638
| 0.724638
| 0.724638
| 0.724638
| 0
| 0
| 0
| 0.261364
| 0.207207
| 111
| 7
| 23
| 15.857143
| 0.522727
| 0
| 0
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.571429
| 1
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 8
|
80f37830b0cac510042310d4477ebfa8286a8b91
| 25,757
|
py
|
Python
|
python/hjung/io.py
|
jht0664/Utility_python_gromacs
|
4457b62e2f0252bcb38021d5deda0cfb932e3ed9
|
[
"MIT"
] | 1
|
2022-01-02T11:27:59.000Z
|
2022-01-02T11:27:59.000Z
|
python/hjung/io.py
|
jht0664/Utility_python_gromacs
|
4457b62e2f0252bcb38021d5deda0cfb932e3ed9
|
[
"MIT"
] | null | null | null |
python/hjung/io.py
|
jht0664/Utility_python_gromacs
|
4457b62e2f0252bcb38021d5deda0cfb932e3ed9
|
[
"MIT"
] | null | null | null |
# Read 2 3D-coordinates from .trr trajectory files
# input: tpr_filename, the filename of .tpr file in Gromacs
# trr_filename, the filename of .trr file in Gromacs
# select_atoms_filename#, the filename including a command-line
# for keeping trajectory of the selected atoms
# output: coordinates#, xyz position of atoms, (#frame x #atoms x 3)
# unit_cells, box dimension (#frame x (x,y,z))
# Example: coordinates1, coordinates2, unit_cells = read_coord_trr_3d_select2('topol.tpr','traj.trr','b.select','peo.select')
def read_trr_3d_select2(tpr_filename, trr_filename, select_atoms_filename1, select_atoms_filename2, mode):
print("io.read_trr_3d_select2:")
# import
import MDAnalysis
import numpy as np
# check the arg, mode
outmode = np.zeros(3,dtype=bool) # use true and false in python as 1 and 0
if 'pos' in mode:
outmode[0] = True
if 'vel' in mode:
outmode[1] = True
if 'forc' in mode:
outmode[2] = True
if not np.any(outmode): # if all is false
raise ValueError(" wrong arg mode {}".format(mode))
ndata = sum(bool(x) for x in outmode)
print(" output data #sets = {} by your mode setting {} ".format(ndata,mode))
# read a line of select command-line for MDAnalysis
select_command = []
for select_atoms_filename in [select_atoms_filename1, select_atoms_filename2]:
if select_atoms_filename is not None:
try:
open_file = open(select_atoms_filename, 'r')
except IOError:
raise IOError(" problem with opening ",select_atoms_filename)
select_command_temp = open_file.readline().strip()
open_file.close()
print(" select written in {}: {}".format(select_atoms_filename,select_command_temp))
select_command.append(select_command_temp)
else:
raise ValueError(" wrong select atom files {}".format(select_atoms_filename))
# Read trajectory using MDAnalysis
u = MDAnalysis.Universe(tpr_filename,trr_filename)
n_frames = len(u.trajectory)
# obtain a set of atom index
n_atoms = []
atoms = []
for iselect in select_command:
list_atoms = u.select_atoms(iselect).indices
print(" You selected {} atoms for selection {}".format(len(list_atoms),iselect))
if len(list_atoms) == 0:
raise ValueError(" No atom is selected. {} may be wrong in grammer.".format(iselect))
atoms.append(list_atoms)
n_atoms.append(len(list_atoms))
print(" selected total #atoms: {}".format(n_atoms))
# initailize variables
if ndata > 1:
data1 = np.zeros((ndata, n_frames, n_atoms[0], 3))
data2 = np.zeros((ndata, n_frames, n_atoms[1], 3))
else:
data1 = np.zeros((n_frames, n_atoms[0], 3))
data2 = np.zeros((n_frames, n_atoms[1], 3))
unit_cells = np.zeros((n_frames, 6))
# read trajectory
print(" starting reading trajectory...")
i_frame = 0
mod_frame = process_init()
for ts in u.trajectory:
try:
if ndata == 1:
if outmode[0]:
tmp = np.array(ts._pos)
if outmode[1]:
tmp = np.array(ts._velocities)
if outmode[2]:
tmp = np.array(ts._forces)
data1[i_frame, :, :] = tmp[atoms[0]]
data2[i_frame, :, :] = tmp[atoms[1]]
else:
dataset = 0
if outmode[0]:
tmp = np.array(ts._pos)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if outmode[1]:
tmp = np.array(ts._velocities)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if outmode[2]:
tmp = np.array(ts._forces)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if dataset != ndata:
raise ValueError(" weird number of data set reading trajectory. {} {}".format(dataset,ndata))
unit_cells[i_frame, :] = ts._unitcell
except IndexError:
raise ValueError(" There are more coordinates to be read than indicated in the header.")
i_frame += 1
mod_frame = process_print(i_frame,n_frames,mod_frame)
# check consistency; final i_frame should be the same as # frames
if i_frame != n_frames:
print(" actual nframes {} in trajectory != the length claimed in header of trajectory {}".format(i_frame, n_frames))
print(" saving trajectory is problem (due to limit of disk quota). Size of your data will be {} by force".format(i_frame))
print(" # frames will be extracted from trajectory = {} (excludes t=0)".format(i_frame-1))
# box info
if all(unit_cells[0,:] == unit_cells[1,:]):
print(" The system may be in NVT ensemble")
else:
# for gromacs (tpr, trr files)
# unit_cells = [length_x, length_y, length_z, angles, ...]
if 'trr' in trr_filename and 'tpr' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][1] == unit_cells[1][1]:
print(" may be in NPAT ensemble")
else:
print(" may be in NPT ensemble")
# for openmm (pdb, dcd files)
# unit_cells = [length_x, alpha angle, length_y, beta angle, theta angle, length_z]
if 'dcd' in trr_filename and 'pdb' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][2] == unit_cells[1][2]:
print(" may be in NPAT ensemble")
else:
print(" may be in NPT ensemble")
if ndata == 1:
#return data1[0:i_frame-1], data2[0:i_frame-1], unit_cells[0:i_frame-1]
return data1[1:i_frame], data2[1:i_frame], unit_cells[1:i_frame]
else:
return data1[:,1:i_frame,:,:], data2[:,1:i_frame,:,:], unit_cells[1:i_frame]
# Read 3D-coordinates from .trr trajectory files
# input: tpr_filename, the filename of .tpr file in Gromacs
# trr_filename, the filename of .trr file in Gromacs
# select_atoms_filename, the filename including a command-line
# for keeping trajectory of the selected atoms
# output: coordinates, xyz position of atoms, (#frame x #atoms x 3)
# unit_cells, box dimension (#frame x (x,y,z))
# Example: coordinates, unit_cells = read_coord_trr_3d('topol.tpr','traj.trr','b.select')
def read_trr_3d_select1(tpr_filename, trr_filename, select_atoms_filename=None, mode='pos'):
print("read_trr_3d_select1:")
# import
import MDAnalysis
import numpy as np
# check the arg, mode
outmode = np.zeros(3,dtype=bool) # use true and false in python as 1 and 0
if 'pos' in mode:
outmode[0] = True
if 'vel' in mode:
outmode[1] = True
if 'forc' in mode:
outmode[2] = True
if not np.any(outmode): # if all is false
raise ValueError(" wrong arg mode {}".format(mode))
ndata = sum(bool(x) for x in outmode)
print(" output data #sets = {} by your mode setting {} ".format(ndata,mode))
# read a line of select command-line for MDAnalysis
select_command = []
if select_atoms_filename is not None:
try:
open_file = open(select_atoms_filename, 'r')
except IOError:
raise IOError(" problem with opening ",select_atoms_filename)
select_command_temp = open_file.readline().strip()
open_file.close()
print(" select written in {}: {}".format(select_atoms_filename,select_command_temp))
select_command.append(select_command_temp)
#else:
# raise ValueError(" wrong select atom files {}".format(select_atoms_filename))
# Read trajectory using MDAnalysis
u = MDAnalysis.Universe(tpr_filename,trr_filename)
n_frames = len(u.trajectory)
# obtain a set of atom index
n_atoms = []
atoms = []
if select_atoms_filename is not None:
for iselect in select_command:
list_atoms = u.select_atoms(iselect).indices
if len(list_atoms) == 0:
raise ValueError(" No atom is selected. {} may be wrong in grammer.".format(iselect))
else:
list_atoms = u.select_atoms("all").indices
atoms.append(list_atoms)
n_atoms.append(len(list_atoms))
print(" selected total #atoms: {}".format(n_atoms))
# initailize variables
if ndata > 1:
data1 = np.zeros((ndata, n_frames, n_atoms[0], 3))
else:
data1 = np.zeros((n_frames, n_atoms[0], 3))
unit_cells = np.zeros((n_frames, 6))
# read trajectory
print(" starting reading trajectory...")
i_frame = 0
mod_frame = process_init()
for ts in u.trajectory:
try:
if ndata == 1:
if outmode[0]:
tmp = np.array(ts._pos)
if outmode[1]:
tmp = np.array(ts._velocities)
if outmode[2]:
tmp = np.array(ts._forces)
data1[i_frame, :, :] = tmp[atoms[0]]
else:
dataset = 0
if outmode[0]:
tmp = np.array(ts._pos)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
dataset = dataset + 1
if outmode[1]:
tmp = np.array(ts._velocities)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
dataset = dataset + 1
if outmode[2]:
tmp = np.array(ts._forces)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
dataset = dataset + 1
if dataset != ndata:
raise ValueError(" weird number of data set reading trajectory. {} {}".format(dataset,ndata))
unit_cells[i_frame, :] = ts._unitcell
except IndexError:
raise ValueError(" There are more coordinates to be read than indicated in the header.")
i_frame += 1
mod_frame = process_print(i_frame,n_frames,mod_frame)
# check consistency; final i_frame should be the same as # frames
if i_frame != n_frames:
print(" actual nframes {} in trajectory != the length claimed in header of trajectory {}".format(i_frame, n_frames))
print(" saving trajectory is problem (due to limit of disk quota). Size of your data will be {} by force".format(i_frame))
print("# frames will be extracted from trajectory = {} (excludes t=0)".format(i_frame-1))
# box info
if all(unit_cells[0,:] == unit_cells[1,:]):
print("The system may be in NVT ensemble")
else:
# for gromacs (tpr, trr files)
# unit_cells = [length_x, length_y, length_z, angles, ...]
if 'trr' in trr_filename and 'tpr' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][1] == unit_cells[1][1]:
print("may be in NPAT ensemble")
else:
print("may be in NPT ensemble")
# for openmm (pdb, dcd files)
# unit_cells = [length_x, alpha angle, length_y, beta angle, theta angle, length_z]
if 'dcd' in trr_filename and 'pdb' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][2] == unit_cells[1][2]:
print("may be in NPAT ensemble")
else:
print("may be in NPT ensemble")
if ndata == 1:
return data1[1:i_frame], unit_cells[1:i_frame] #return data1[0:i_frame-1], data2[0:i_frame-1], unit_cells[0:i_frame-1]
else:
return data1[:,1:i_frame,:,:], unit_cells[1:i_frame] # return data1[0]:pos, data1[1]:force if your mode is "pos force"
# Read 2 3D-coordinates from .trr trajectory files
# input: tpr_filename, the filename of .tpr file in Gromacs
# trr_filename, the filename of .trr file in Gromacs
# select_atoms_filename#, the filename including a command-line
# for keeping trajectory of the selected atoms
# output: coordinates#, xyz position of atoms, (#frame x #atoms x 3)
# unit_cells, box dimension (#frame x (x,y,z))
# Example: coordinates1, coordinates2, unit_cells = read_coord_trr_3d_select2('topol.tpr','traj.trr','b.select','peo.select')
def read_trr_3d_select2(tpr_filename, trr_filename, select_atoms_filename1, select_atoms_filename2, mode):
print("io.read_trr_3d_select2:")
# import
import MDAnalysis
import numpy as np
# check the arg, mode
outmode = np.zeros(3,dtype=bool) # use true and false in python as 1 and 0
if 'pos' in mode:
outmode[0] = True
if 'vel' in mode:
outmode[1] = True
if 'forc' in mode:
outmode[2] = True
if not np.any(outmode): # if all is false
raise ValueError(" wrong arg mode {}".format(mode))
ndata = sum(bool(x) for x in outmode)
print(" output data #sets = {} by your mode setting {} ".format(ndata,mode))
# read a line of select command-line for MDAnalysis
select_command = []
for select_atoms_filename in [select_atoms_filename1, select_atoms_filename2]:
if select_atoms_filename is not None:
try:
open_file = open(select_atoms_filename, 'r')
except IOError:
raise IOError(" problem with opening ",select_atoms_filename)
select_command_temp = open_file.readline().strip()
open_file.close()
print(" select written in {}: {}".format(select_atoms_filename,select_command_temp))
select_command.append(select_command_temp)
else:
raise ValueError(" wrong select atom files {}".format(select_atoms_filename))
# Read trajectory using MDAnalysis
u = MDAnalysis.Universe(tpr_filename,trr_filename)
n_frames = len(u.trajectory)
# obtain a set of atom index
n_atoms = []
atoms = []
for iselect in select_command:
list_atoms = u.select_atoms(iselect).indices
print(" You selected {} atoms for selection {}".format(len(list_atoms),iselect))
if len(list_atoms) == 0:
raise ValueError(" No atom is selected. {} may be wrong in grammer.".format(iselect))
atoms.append(list_atoms)
n_atoms.append(len(list_atoms))
print(" selected total #atoms: {}".format(n_atoms))
# initailize variables
if ndata > 1:
data1 = np.zeros((ndata, n_frames, n_atoms[0], 3))
data2 = np.zeros((ndata, n_frames, n_atoms[1], 3))
else:
data1 = np.zeros((n_frames, n_atoms[0], 3))
data2 = np.zeros((n_frames, n_atoms[1], 3))
unit_cells = np.zeros((n_frames, 6))
# read trajectory
print(" starting reading trajectory...")
i_frame = 0
mod_frame = process_init()
for ts in u.trajectory:
try:
if ndata == 1:
if outmode[0]:
tmp = np.array(ts._pos)
if outmode[1]:
tmp = np.array(ts._velocities)
if outmode[2]:
tmp = np.array(ts._forces)
data1[i_frame, :, :] = tmp[atoms[0]]
data2[i_frame, :, :] = tmp[atoms[1]]
else:
dataset = 0
if outmode[0]:
tmp = np.array(ts._pos)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if outmode[1]:
tmp = np.array(ts._velocities)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if outmode[2]:
tmp = np.array(ts._forces)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if dataset != ndata:
raise ValueError(" weird number of data set reading trajectory. {} {}".format(dataset,ndata))
unit_cells[i_frame, :] = ts._unitcell
except IndexError:
raise ValueError(" There are more coordinates to be read than indicated in the header.")
i_frame += 1
mod_frame = process_print(i_frame,n_frames,mod_frame)
# check consistency; final i_frame should be the same as # frames
if i_frame != n_frames:
print(" actual nframes {} in trajectory != the length claimed in header of trajectory {}".format(i_frame, n_frames))
print(" saving trajectory is problem (due to limit of disk quota). Size of your data will be {} by force".format(i_frame))
print(" # frames will be extracted from trajectory = {} (excludes t=0)".format(i_frame-1))
# box info
if all(unit_cells[0,:] == unit_cells[1,:]):
print(" The system may be in NVT ensemble")
else:
# for gromacs (tpr, trr files)
# unit_cells = [length_x, length_y, length_z, angles, ...]
if 'trr' in trr_filename and 'tpr' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][1] == unit_cells[1][1]:
print(" may be in NPAT ensemble")
else:
print(" may be in NPT ensemble")
# for openmm (pdb, dcd files)
# unit_cells = [length_x, alpha angle, length_y, beta angle, theta angle, length_z]
if 'dcd' in trr_filename and 'pdb' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][2] == unit_cells[1][2]:
print(" may be in NPAT ensemble")
else:
print(" may be in NPT ensemble")
if ndata == 1:
#return data1[0:i_frame-1], data2[0:i_frame-1], unit_cells[0:i_frame-1]
return data1[1:i_frame], data2[1:i_frame], unit_cells[1:i_frame]
else:
return data1[:,1:i_frame,:,:], data2[:,1:i_frame,:,:], unit_cells[1:i_frame]
# Read some 3D-coordinates from .trr trajectory files
# input: tpr_filename, the filename of .tpr file in Gromacs
# trr_filename, the filename of .trr file in Gromacs
# select_atoms_filename#, the filename including a command-line
# for keeping trajectory of the selected atoms
# output: coordinates#, xyz position of atoms, (#frame x #atoms x 3)
# unit_cells, box dimension (#frame x (x,y,z))
# Example: coordinates, unit_cells = read_coord_trr_3d_select2('topol.tpr','traj.trr','b.select','peo.select')
def read_trr_3d_selects(tpr_filename, trr_filename, select_filename, mode):
print("io.read_trr_3d_selects: ######## INCOMPLETE coding #### ")
# import
import MDAnalysis
import numpy as np
# check the arg, mode
outmode = np.zeros(3,dtype=bool) # use true and false in python as 1 and 0
if 'pos' in mode:
outmode[0] = True
if 'vel' in mode:
outmode[1] = True
if 'forc' in mode:
outmode[2] = True
if not np.any(outmode): # if all is false
raise ValueError(" wrong arg mode {}".format(mode))
ndata = sum(bool(x) for x in outmode)
print(" output data #sets = {} by your mode setting {} ".format(ndata,mode))
# read a line of select command-line for MDAnalysis
select_command = []
if select_filename is not None:
try:
open_file = open(select_filename, 'r')
except IOError:
raise IOError(" problem with opening ",select_atoms_filename)
for line in oepn_file:
iline = line.strip()
# line is blank or comment line
if not iline or iline.startswith('#') or iline.startswith('@'):
continue
print(" select written in {}: {}".format(select_filename,iline))
select_command.append(iline)
n_select = len(select_command)
open_file.close()
else:
raise ValueError(" wrong select atom files {}".format(select_atoms_filename))
# Read trajectory using MDAnalysis
u = MDAnalysis.Universe(tpr_filename,trr_filename)
n_frames = len(u.trajectory)
# obtain a set of atom index
n_atoms = []
atoms = []
for iselect in select_command:
list_atoms = u.select_atoms(iselect).indices
if len(list_atoms) == 0:
raise ValueError(" No atom is selected. {} may be wrong in grammer.".format(iselect))
atoms.append(list_atoms)
n_atoms.append(len(list_atoms))
print(" selected total #atoms: {}".format(n_atoms))
######################################## followings are not complete
# initailize variables with zeros
if ndata > 1:
data1 = np.zeros((ndata, n_frames, n_atoms[0], 3))
data2 = np.zeros((ndata, n_frames, n_atoms[1], 3))
else:
data1 = np.zeros((n_frames, n_atoms[0], 3))
data2 = np.zeros((n_frames, n_atoms[1], 3))
unit_cells = np.zeros((n_frames, 6))
# read trajectory
print(" starting reading trajectory...")
i_frame = 0
mod_frame = process_init()
for ts in u.trajectory:
try:
if ndata == 1:
if outmode[0]:
tmp = np.array(ts._pos)
if outmode[1]:
tmp = np.array(ts._velocities)
if outmode[2]:
tmp = np.array(ts._forces)
data1[i_frame, :, :] = tmp[atoms[0]]
data2[i_frame, :, :] = tmp[atoms[1]]
else:
dataset = 0
if outmode[0]:
tmp = np.array(ts._pos)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if outmode[1]:
tmp = np.array(ts._velocities)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if outmode[2]:
tmp = np.array(ts._forces)
data1[dataset, i_frame, :, :] = tmp[atoms[0]]
data2[dataset, i_frame, :, :] = tmp[atoms[1]]
dataset = dataset + 1
if dataset != ndata:
raise ValueError(" weird number of data set reading trajectory. {} {}".format(dataset,ndata))
unit_cells[i_frame, :] = ts._unitcell
except IndexError:
raise ValueError(" There are more coordinates to be read than indicated in the header.")
i_frame += 1
mod_frame = process_print(i_frame,n_frames,mod_frame)
# check consistency; final i_frame should be the same as # frames
if i_frame != n_frames:
print(" actual nframes {} in trajectory != the length claimed in header of trajectory {}".format(i_frame, n_frames))
print(" saving trajectory is problem (due to limit of disk quota). Size of your data will be {} by force".format(i_frame))
print(" # frames will be extracted from trajectory = {} (excludes t=0)".format(i_frame-1))
# box info
if all(unit_cells[0,:] == unit_cells[1,:]):
print(" The system may be in NVT ensemble")
else:
# for gromacs (tpr, trr files)
# unit_cells = [length_x, length_y, length_z, angles, ...]
if 'trr' in trr_filename and 'tpr' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][1] == unit_cells[1][1]:
print(" may be in NPAT ensemble")
else:
print(" may be in NPT ensemble")
# for openmm (pdb, dcd files)
# unit_cells = [length_x, alpha angle, length_y, beta angle, theta angle, length_z]
if 'dcd' in trr_filename and 'pdb' in tpr_filename:
if unit_cells[0][0] == unit_cells[1][0] and unit_cells[0][2] == unit_cells[1][2]:
print(" may be in NPAT ensemble")
else:
print(" may be in NPT ensemble")
if ndata == 1:
#return data1[0:i_frame-1], data2[0:i_frame-1], unit_cells[0:i_frame-1]
return data1[1:i_frame], data2[1:i_frame], unit_cells[1:i_frame]
else:
return data1[:,1:i_frame,:,:], data2[:,1:i_frame,:,:], unit_cells[1:i_frame]
# rename the existing filename if the filename already exists
# input: filename is a filename what you want to rename if the filename already exists
# Example: rename_existing_file("../../fit.plot")
# ref: http://code.activestate.com/recipes/578116-move-files-with-rename-if-required/
def rename_existing_file(filename):
import sys, os
print('backup_existing_file:')
if not os.path.exists(filename):
print(" %s does not exist." %filename)
return
# get info
dirname = os.path.dirname(filename)
basename = os.path.basename(filename) # basename is a fiilname excluding path. e.g. ../../fit.plot -> fit.plot
head, tail = os.path.splitext(basename) # head = fit, tail = .plot
#dst_file = os.path.join(dst_dir, basename) # dst_dir is target folder path
# find better filename (= filename2)
filename2 = filename
count = 0
while os.path.exists(filename2):
filename2 = os.path.join(dirname, '{0}.{1}{2}'.format(head, count, tail))
count += 1
print(" rename {0} to {1}".format(filename,filename2))
os.rename(filename, filename2)
# initialize processing bar
# output: process tiem and wall time
# Example: mod_frame = process_init()
def process_init():
print("io.process_init: ")
return 10 # initial mode number = 10
# print process bar
# input: itime, current time
# ftime, final time
# mod_time, frequency of printing.
# output: mod_time, new or old printing frequency
# Example: mod_frame = process_print(itime+1, ftime, mod_frame)
def process_print(itime, ftime, mod_time):
if itime%mod_time == 0:
print("... {0} th frame reading ({1:.0%}) ...".format(itime,itime/ftime))
if (itime/mod_time)%10 == 0:
mod_time = mod_time*10
return mod_time
# read mass values
# input: filename
# output: mw, molecular weight
# divider, number of selected atoms per molecule or polymer chain length
# Example: mw, divider = read_mass2(filename)
def read_mass2(filename):
print("io.read_mass2:")
import numpy as np
# read mass file for two selection
print(" reading the mass file, {}".format(filename))
try:
massinfo = open(filename, 'r')
except IOError:
print(" Problem with opening ",filename)
exit()
divider = []
mw = []
for line in massinfo:
line = line.strip()
line_m = line.rsplit()
divider.append(float(line_m[0]))
mw.append(float(line_m[1]))
massinfo.close()
# type
divider = np.array(divider,dtype=np.float)
mw = np.array(mw,dtype=np.float)
if len(divider) != 2 or len(mw) != 2:
ValueError("Wrong format in {} file".format(filename))
print(" dividers[select1,select2] = {}".format(divider))
print(" mw[select1,select2] = {}".format(mw))
return mw, divider
# read a file simply
# input:
# inputfile input file name
# begin beginning i-frame
# end end i-frame
# output:
# data 1d or 2d data array
# Example: data = read_simple(filename,-1,-1)
def read_simple(inputfile,begin,end):
print("io.read_simple:")
import numpy as np
## load rdf files
if '.npy' in inputfile:
data = np.load(inputfile)
ftype='npy'
elif '.txt': # assume txt input file
## you may use basic open and append functions
#try:
# xvg = open(inputfile, 'r')
#except IOError:
# print(" Problem with opening {}".format(inputfile))
# exit()
#data = []
#for line in xvg:
# line = line.strip()
# if not line or line.startswith('#') or line.startswith('@'): # line is blank or comment line
# continue
# data.append(float(line.split(' ')[0])) # to get numbers in the first column
#xvg.close()
#data = np.array(data)
## or use np library
data = np.loadtxt(inputfile)
ftype='txt'
else:
data = np.loadtxt(inputfile,comments='#',unpack=True)
ftype='loadtxt'
# refine size of datas
n_frames = data.shape[0]
if begin == -1:
begin = int(n_frames/2)
if end == -1:
end = n_frames
elif end <= begin:
raise ValueError(" you may set wrong begin and end to read")
data = data[begin:end]
n_frames = data.shape[0]
# refine
if len(data.shape) > 1:
n_species = data.shape[1] # number of independent molecules to average # 2d data
else:
n_species = 1 # 1d data
print(" total {} frames and {} species in {} file {}".format(
n_frames,n_species,str(ftype),inputfile))
return data
# read text by line
def read_text_line(inputfile,nlines):
print("io.read_text_line:")
data_text = []
try:
open_file = open(inputfile, 'r')
except IOError:
raise IOError(" problem with opening ",inputfile)
for iline in range(nlines):
tmp = open_file.readline().strip()
data_text.append(str(tmp))
open_file.close()
print(" select written in {} for selection command: \n{}".format(inputfile,data_text))
return data_text
| 38.616192
| 125
| 0.68296
| 4,002
| 25,757
| 4.251624
| 0.07946
| 0.035263
| 0.01481
| 0.023039
| 0.812518
| 0.802351
| 0.793006
| 0.784954
| 0.782369
| 0.780135
| 0
| 0.021013
| 0.17964
| 25,757
| 667
| 126
| 38.616192
| 0.78424
| 0.243973
| 0
| 0.81336
| 0
| 0.007859
| 0.191209
| 0.005975
| 0
| 0
| 0
| 0
| 0
| 1
| 0.019646
| false
| 0
| 0.021611
| 0
| 0.068762
| 0.143418
| 0
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0
| 7
|
038f0085643066c48e85ef02126b488f82494409
| 17,755
|
py
|
Python
|
squareconnect/models/catalog_info_response_limits.py
|
xethorn/connect-python-sdk
|
a0543b2f7ea498865c6d916de0b10370f89ebc77
|
[
"Apache-2.0"
] | 53
|
2016-08-06T17:12:16.000Z
|
2020-08-02T19:43:58.000Z
|
squareconnect/models/catalog_info_response_limits.py
|
xethorn/connect-python-sdk
|
a0543b2f7ea498865c6d916de0b10370f89ebc77
|
[
"Apache-2.0"
] | 32
|
2016-08-19T16:32:30.000Z
|
2020-01-14T18:01:37.000Z
|
squareconnect/models/catalog_info_response_limits.py
|
xethorn/connect-python-sdk
|
a0543b2f7ea498865c6d916de0b10370f89ebc77
|
[
"Apache-2.0"
] | 45
|
2016-09-05T11:58:09.000Z
|
2020-11-15T16:26:41.000Z
|
# coding: utf-8
"""
Copyright 2017 Square, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from pprint import pformat
from six import iteritems
import re
class CatalogInfoResponseLimits(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, batch_upsert_max_objects_per_batch=None, batch_upsert_max_total_objects=None, batch_retrieve_max_object_ids=None, search_max_page_limit=None, batch_delete_max_object_ids=None, update_item_taxes_max_item_ids=None, update_item_taxes_max_taxes_to_enable=None, update_item_taxes_max_taxes_to_disable=None, update_item_modifier_lists_max_item_ids=None, update_item_modifier_lists_max_modifier_lists_to_enable=None, update_item_modifier_lists_max_modifier_lists_to_disable=None):
"""
CatalogInfoResponseLimits - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'batch_upsert_max_objects_per_batch': 'int',
'batch_upsert_max_total_objects': 'int',
'batch_retrieve_max_object_ids': 'int',
'search_max_page_limit': 'int',
'batch_delete_max_object_ids': 'int',
'update_item_taxes_max_item_ids': 'int',
'update_item_taxes_max_taxes_to_enable': 'int',
'update_item_taxes_max_taxes_to_disable': 'int',
'update_item_modifier_lists_max_item_ids': 'int',
'update_item_modifier_lists_max_modifier_lists_to_enable': 'int',
'update_item_modifier_lists_max_modifier_lists_to_disable': 'int'
}
self.attribute_map = {
'batch_upsert_max_objects_per_batch': 'batch_upsert_max_objects_per_batch',
'batch_upsert_max_total_objects': 'batch_upsert_max_total_objects',
'batch_retrieve_max_object_ids': 'batch_retrieve_max_object_ids',
'search_max_page_limit': 'search_max_page_limit',
'batch_delete_max_object_ids': 'batch_delete_max_object_ids',
'update_item_taxes_max_item_ids': 'update_item_taxes_max_item_ids',
'update_item_taxes_max_taxes_to_enable': 'update_item_taxes_max_taxes_to_enable',
'update_item_taxes_max_taxes_to_disable': 'update_item_taxes_max_taxes_to_disable',
'update_item_modifier_lists_max_item_ids': 'update_item_modifier_lists_max_item_ids',
'update_item_modifier_lists_max_modifier_lists_to_enable': 'update_item_modifier_lists_max_modifier_lists_to_enable',
'update_item_modifier_lists_max_modifier_lists_to_disable': 'update_item_modifier_lists_max_modifier_lists_to_disable'
}
self._batch_upsert_max_objects_per_batch = batch_upsert_max_objects_per_batch
self._batch_upsert_max_total_objects = batch_upsert_max_total_objects
self._batch_retrieve_max_object_ids = batch_retrieve_max_object_ids
self._search_max_page_limit = search_max_page_limit
self._batch_delete_max_object_ids = batch_delete_max_object_ids
self._update_item_taxes_max_item_ids = update_item_taxes_max_item_ids
self._update_item_taxes_max_taxes_to_enable = update_item_taxes_max_taxes_to_enable
self._update_item_taxes_max_taxes_to_disable = update_item_taxes_max_taxes_to_disable
self._update_item_modifier_lists_max_item_ids = update_item_modifier_lists_max_item_ids
self._update_item_modifier_lists_max_modifier_lists_to_enable = update_item_modifier_lists_max_modifier_lists_to_enable
self._update_item_modifier_lists_max_modifier_lists_to_disable = update_item_modifier_lists_max_modifier_lists_to_disable
@property
def batch_upsert_max_objects_per_batch(self):
"""
Gets the batch_upsert_max_objects_per_batch of this CatalogInfoResponseLimits.
The maximum number of objects that may appear within a single batch in a `/v2/catalog/batch-upsert` request.
:return: The batch_upsert_max_objects_per_batch of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._batch_upsert_max_objects_per_batch
@batch_upsert_max_objects_per_batch.setter
def batch_upsert_max_objects_per_batch(self, batch_upsert_max_objects_per_batch):
"""
Sets the batch_upsert_max_objects_per_batch of this CatalogInfoResponseLimits.
The maximum number of objects that may appear within a single batch in a `/v2/catalog/batch-upsert` request.
:param batch_upsert_max_objects_per_batch: The batch_upsert_max_objects_per_batch of this CatalogInfoResponseLimits.
:type: int
"""
self._batch_upsert_max_objects_per_batch = batch_upsert_max_objects_per_batch
@property
def batch_upsert_max_total_objects(self):
"""
Gets the batch_upsert_max_total_objects of this CatalogInfoResponseLimits.
The maximum number of objects that may appear across all batches in a `/v2/catalog/batch-upsert` request.
:return: The batch_upsert_max_total_objects of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._batch_upsert_max_total_objects
@batch_upsert_max_total_objects.setter
def batch_upsert_max_total_objects(self, batch_upsert_max_total_objects):
"""
Sets the batch_upsert_max_total_objects of this CatalogInfoResponseLimits.
The maximum number of objects that may appear across all batches in a `/v2/catalog/batch-upsert` request.
:param batch_upsert_max_total_objects: The batch_upsert_max_total_objects of this CatalogInfoResponseLimits.
:type: int
"""
self._batch_upsert_max_total_objects = batch_upsert_max_total_objects
@property
def batch_retrieve_max_object_ids(self):
"""
Gets the batch_retrieve_max_object_ids of this CatalogInfoResponseLimits.
The maximum number of object IDs that may appear in a `/v2/catalog/batch-retrieve` request.
:return: The batch_retrieve_max_object_ids of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._batch_retrieve_max_object_ids
@batch_retrieve_max_object_ids.setter
def batch_retrieve_max_object_ids(self, batch_retrieve_max_object_ids):
"""
Sets the batch_retrieve_max_object_ids of this CatalogInfoResponseLimits.
The maximum number of object IDs that may appear in a `/v2/catalog/batch-retrieve` request.
:param batch_retrieve_max_object_ids: The batch_retrieve_max_object_ids of this CatalogInfoResponseLimits.
:type: int
"""
self._batch_retrieve_max_object_ids = batch_retrieve_max_object_ids
@property
def search_max_page_limit(self):
"""
Gets the search_max_page_limit of this CatalogInfoResponseLimits.
The maximum number of results that may be returned in a page of a `/v2/catalog/search` response.
:return: The search_max_page_limit of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._search_max_page_limit
@search_max_page_limit.setter
def search_max_page_limit(self, search_max_page_limit):
"""
Sets the search_max_page_limit of this CatalogInfoResponseLimits.
The maximum number of results that may be returned in a page of a `/v2/catalog/search` response.
:param search_max_page_limit: The search_max_page_limit of this CatalogInfoResponseLimits.
:type: int
"""
self._search_max_page_limit = search_max_page_limit
@property
def batch_delete_max_object_ids(self):
"""
Gets the batch_delete_max_object_ids of this CatalogInfoResponseLimits.
The maximum number of object IDs that may be included in a single `/v2/catalog/batch-delete` request.
:return: The batch_delete_max_object_ids of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._batch_delete_max_object_ids
@batch_delete_max_object_ids.setter
def batch_delete_max_object_ids(self, batch_delete_max_object_ids):
"""
Sets the batch_delete_max_object_ids of this CatalogInfoResponseLimits.
The maximum number of object IDs that may be included in a single `/v2/catalog/batch-delete` request.
:param batch_delete_max_object_ids: The batch_delete_max_object_ids of this CatalogInfoResponseLimits.
:type: int
"""
self._batch_delete_max_object_ids = batch_delete_max_object_ids
@property
def update_item_taxes_max_item_ids(self):
"""
Gets the update_item_taxes_max_item_ids of this CatalogInfoResponseLimits.
The maximum number of item IDs that may be included in a single `/v2/catalog/update-item-taxes` request.
:return: The update_item_taxes_max_item_ids of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._update_item_taxes_max_item_ids
@update_item_taxes_max_item_ids.setter
def update_item_taxes_max_item_ids(self, update_item_taxes_max_item_ids):
"""
Sets the update_item_taxes_max_item_ids of this CatalogInfoResponseLimits.
The maximum number of item IDs that may be included in a single `/v2/catalog/update-item-taxes` request.
:param update_item_taxes_max_item_ids: The update_item_taxes_max_item_ids of this CatalogInfoResponseLimits.
:type: int
"""
self._update_item_taxes_max_item_ids = update_item_taxes_max_item_ids
@property
def update_item_taxes_max_taxes_to_enable(self):
"""
Gets the update_item_taxes_max_taxes_to_enable of this CatalogInfoResponseLimits.
The maximum number of tax IDs to be enabled that may be included in a single `/v2/catalog/update-item-taxes` request.
:return: The update_item_taxes_max_taxes_to_enable of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._update_item_taxes_max_taxes_to_enable
@update_item_taxes_max_taxes_to_enable.setter
def update_item_taxes_max_taxes_to_enable(self, update_item_taxes_max_taxes_to_enable):
"""
Sets the update_item_taxes_max_taxes_to_enable of this CatalogInfoResponseLimits.
The maximum number of tax IDs to be enabled that may be included in a single `/v2/catalog/update-item-taxes` request.
:param update_item_taxes_max_taxes_to_enable: The update_item_taxes_max_taxes_to_enable of this CatalogInfoResponseLimits.
:type: int
"""
self._update_item_taxes_max_taxes_to_enable = update_item_taxes_max_taxes_to_enable
@property
def update_item_taxes_max_taxes_to_disable(self):
"""
Gets the update_item_taxes_max_taxes_to_disable of this CatalogInfoResponseLimits.
The maximum number of tax IDs to be disabled that may be included in a single `/v2/catalog/update-item-taxes` request.
:return: The update_item_taxes_max_taxes_to_disable of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._update_item_taxes_max_taxes_to_disable
@update_item_taxes_max_taxes_to_disable.setter
def update_item_taxes_max_taxes_to_disable(self, update_item_taxes_max_taxes_to_disable):
"""
Sets the update_item_taxes_max_taxes_to_disable of this CatalogInfoResponseLimits.
The maximum number of tax IDs to be disabled that may be included in a single `/v2/catalog/update-item-taxes` request.
:param update_item_taxes_max_taxes_to_disable: The update_item_taxes_max_taxes_to_disable of this CatalogInfoResponseLimits.
:type: int
"""
self._update_item_taxes_max_taxes_to_disable = update_item_taxes_max_taxes_to_disable
@property
def update_item_modifier_lists_max_item_ids(self):
"""
Gets the update_item_modifier_lists_max_item_ids of this CatalogInfoResponseLimits.
The maximum number of item IDs that may be included in a single `/v2/catalog/update-item-modifier-lists` request.
:return: The update_item_modifier_lists_max_item_ids of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._update_item_modifier_lists_max_item_ids
@update_item_modifier_lists_max_item_ids.setter
def update_item_modifier_lists_max_item_ids(self, update_item_modifier_lists_max_item_ids):
"""
Sets the update_item_modifier_lists_max_item_ids of this CatalogInfoResponseLimits.
The maximum number of item IDs that may be included in a single `/v2/catalog/update-item-modifier-lists` request.
:param update_item_modifier_lists_max_item_ids: The update_item_modifier_lists_max_item_ids of this CatalogInfoResponseLimits.
:type: int
"""
self._update_item_modifier_lists_max_item_ids = update_item_modifier_lists_max_item_ids
@property
def update_item_modifier_lists_max_modifier_lists_to_enable(self):
"""
Gets the update_item_modifier_lists_max_modifier_lists_to_enable of this CatalogInfoResponseLimits.
The maximum number of modifier list IDs to be enabled that may be included in a single `/v2/catalog/update-item-modifier-lists` request.
:return: The update_item_modifier_lists_max_modifier_lists_to_enable of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._update_item_modifier_lists_max_modifier_lists_to_enable
@update_item_modifier_lists_max_modifier_lists_to_enable.setter
def update_item_modifier_lists_max_modifier_lists_to_enable(self, update_item_modifier_lists_max_modifier_lists_to_enable):
"""
Sets the update_item_modifier_lists_max_modifier_lists_to_enable of this CatalogInfoResponseLimits.
The maximum number of modifier list IDs to be enabled that may be included in a single `/v2/catalog/update-item-modifier-lists` request.
:param update_item_modifier_lists_max_modifier_lists_to_enable: The update_item_modifier_lists_max_modifier_lists_to_enable of this CatalogInfoResponseLimits.
:type: int
"""
self._update_item_modifier_lists_max_modifier_lists_to_enable = update_item_modifier_lists_max_modifier_lists_to_enable
@property
def update_item_modifier_lists_max_modifier_lists_to_disable(self):
"""
Gets the update_item_modifier_lists_max_modifier_lists_to_disable of this CatalogInfoResponseLimits.
The maximum number of modifier list IDs to be disabled that may be included in a single `/v2/catalog/update-item-modifier-lists` request.
:return: The update_item_modifier_lists_max_modifier_lists_to_disable of this CatalogInfoResponseLimits.
:rtype: int
"""
return self._update_item_modifier_lists_max_modifier_lists_to_disable
@update_item_modifier_lists_max_modifier_lists_to_disable.setter
def update_item_modifier_lists_max_modifier_lists_to_disable(self, update_item_modifier_lists_max_modifier_lists_to_disable):
"""
Sets the update_item_modifier_lists_max_modifier_lists_to_disable of this CatalogInfoResponseLimits.
The maximum number of modifier list IDs to be disabled that may be included in a single `/v2/catalog/update-item-modifier-lists` request.
:param update_item_modifier_lists_max_modifier_lists_to_disable: The update_item_modifier_lists_max_modifier_lists_to_disable of this CatalogInfoResponseLimits.
:type: int
"""
self._update_item_modifier_lists_max_modifier_lists_to_disable = update_item_modifier_lists_max_modifier_lists_to_disable
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
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0
| 7
|
03ac286d8d0d93e49fee48891850dfd76042f0ac
| 13,615
|
py
|
Python
|
api/tracker/migrations/0001_initial.py
|
NimbusInformatics/bdcat-data-tracker
|
968449105d4400122a22b143cd84eb8860f06411
|
[
"Apache-2.0"
] | 1
|
2021-11-24T16:17:44.000Z
|
2021-11-24T16:17:44.000Z
|
api/tracker/migrations/0001_initial.py
|
NimbusInformatics/bdcat-data-tracker
|
968449105d4400122a22b143cd84eb8860f06411
|
[
"Apache-2.0"
] | 7
|
2021-11-29T16:59:57.000Z
|
2022-01-27T15:02:01.000Z
|
api/tracker/migrations/0001_initial.py
|
NimbusInformatics/bdcat-data-tracker
|
968449105d4400122a22b143cd84eb8860f06411
|
[
"Apache-2.0"
] | null | null | null |
# Generated by Django 4.0.2 on 2022-02-24 16:58
from django.conf import settings
import django.core.validators
from django.db import migrations, models
import django.db.models.deletion
import simple_history.models
class Migration(migrations.Migration):
initial = True
dependencies = [
('auth', '0012_alter_user_first_name_max_length'),
]
operations = [
migrations.CreateModel(
name='User',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('password', models.CharField(max_length=128, verbose_name='password')),
('email', models.EmailField(max_length=254, unique=True, verbose_name='Email')),
('name', models.CharField(max_length=100, verbose_name='Name')),
('is_staff', models.BooleanField(default=False)),
('is_active', models.BooleanField(default=True)),
('is_superuser', models.BooleanField(default=False)),
('last_login', models.DateTimeField(blank=True, null=True)),
('date_joined', models.DateTimeField(auto_now_add=True)),
('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')),
('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Ticket',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(default='', help_text='Name of primary contact', max_length=100, verbose_name='Name')),
('email', models.EmailField(default='', max_length=254, verbose_name='Email')),
('organization', models.CharField(default='', help_text='Name of requesting organization', max_length=250, verbose_name='Organization')),
('study_name', models.CharField(default='', help_text='Name of Study or Dataset', max_length=250, verbose_name='Study Name')),
('study_id', models.CharField(default='', help_text='Please refer to Data Custodian Instructions for more information', max_length=100, validators=[django.core.validators.RegexValidator('^[a-z0-9][a-z0-9.]{0,59}[a-z0-9]$', 'Study ID format invalid')], verbose_name='Study ID')),
('consent_code', models.CharField(default='', help_text='Please refer to Data Custodian Instructions for more information', max_length=100, validators=[django.core.validators.RegexValidator('^[a-z0-9][a-z0-9.]{0,59}[a-z0-9]$', 'Consent Code format invalid')], verbose_name='Consent Code')),
('data_size', models.CharField(default='', help_text='Please provide an estimated size of your data set(s) (ex. 100 GB)', max_length=100, validators=[django.core.validators.RegexValidator('^[0-9]{1,5}(.[0-9]{1,5})?\\s?(MB|GB|TB|PB)$', 'Data Size format invalid. Please add a unit of measurement (MB, GB, TB, PB)')], verbose_name='Data Size')),
('dataset_description', models.CharField(blank=True, default='', help_text='Describe the dataset you are uploading', max_length=2500, verbose_name='Dataset Description')),
('google_email', models.EmailField(blank=True, default='', help_text="If you're uploading to Google, please provide your google email for access", max_length=254, verbose_name='Google Email')),
('aws_iam', models.CharField(blank=True, default='', help_text="If you're uploading to Amazon, please provide your AWS IAM (ex: arn:aws:iam::123456789012:user/username)", max_length=100, validators=[django.core.validators.RegexValidator('^arn:aws:iam::[0-9]{12}:user/[a-zA-Z0-9-_]{1,64}$', 'AWS IAM format invalid. Please use the following format: arn:aws:iam::123456789012:user/username')], verbose_name='AWS IAM')),
('is_test_data', models.BooleanField(blank=True, default=False, help_text='Check this box if this is test data', verbose_name='Is Test Data')),
('ticket_review_comment', models.CharField(blank=True, default='', help_text='Please provide a comment for approval or rejection', max_length=1000, verbose_name='Ticket Review Comment')),
('created_dt', models.DateTimeField(auto_now_add=True, verbose_name='Created Date')),
('ticket_approved_dt', models.DateTimeField(blank=True, null=True, verbose_name='Intake Form Approved Date')),
('ticket_rejected_dt', models.DateTimeField(blank=True, null=True, verbose_name='Intake Form Rejected Date')),
('bucket_created_dt', models.DateTimeField(blank=True, null=True, verbose_name='Bucket Created Date')),
('data_uploaded_started_dt', models.DateTimeField(blank=True, null=True, verbose_name='Data Uploaded Started Date')),
('data_uploaded_completed_dt', models.DateTimeField(blank=True, null=True, verbose_name='Data Uploaded Completed Date')),
('data_accepted_dt', models.DateTimeField(blank=True, null=True, verbose_name='Gen3 Accepted Date')),
('ticket_approved_by', models.EmailField(default='', max_length=254, verbose_name='Ticket approved by')),
('ticket_rejected_by', models.EmailField(default='', max_length=254, verbose_name='Ticket rejected by')),
('bucket_created_by', models.EmailField(default='', max_length=254, verbose_name='Bucket created by')),
('data_uploaded_started_by', models.EmailField(default='', max_length=254, verbose_name='Data upload started by')),
('data_uploaded_completed_by', models.EmailField(default='', max_length=254, verbose_name='Data upload completed by')),
('data_accepted_by', models.EmailField(default='', max_length=254, verbose_name='Data accepted by')),
('created_by', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='HistoricalUser',
fields=[
('id', models.BigIntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')),
('password', models.CharField(max_length=128, verbose_name='password')),
('email', models.EmailField(db_index=True, max_length=254, verbose_name='Email')),
('name', models.CharField(max_length=100, verbose_name='Name')),
('is_staff', models.BooleanField(default=False)),
('is_active', models.BooleanField(default=True)),
('is_superuser', models.BooleanField(default=False)),
('last_login', models.DateTimeField(blank=True, null=True)),
('date_joined', models.DateTimeField(blank=True, editable=False)),
('history_id', models.AutoField(primary_key=True, serialize=False)),
('history_date', models.DateTimeField()),
('history_change_reason', models.CharField(max_length=100, null=True)),
('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)),
('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name': 'historical user',
'ordering': ('-history_date', '-history_id'),
'get_latest_by': 'history_date',
},
bases=(simple_history.models.HistoricalChanges, models.Model),
),
migrations.CreateModel(
name='HistoricalTicket',
fields=[
('id', models.BigIntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')),
('name', models.CharField(default='', help_text='Name of primary contact', max_length=100, verbose_name='Name')),
('email', models.EmailField(default='', max_length=254, verbose_name='Email')),
('organization', models.CharField(default='', help_text='Name of requesting organization', max_length=250, verbose_name='Organization')),
('study_name', models.CharField(default='', help_text='Name of Study or Dataset', max_length=250, verbose_name='Study Name')),
('study_id', models.CharField(default='', help_text='Please refer to Data Custodian Instructions for more information', max_length=100, validators=[django.core.validators.RegexValidator('^[a-z0-9][a-z0-9.]{0,59}[a-z0-9]$', 'Study ID format invalid')], verbose_name='Study ID')),
('consent_code', models.CharField(default='', help_text='Please refer to Data Custodian Instructions for more information', max_length=100, validators=[django.core.validators.RegexValidator('^[a-z0-9][a-z0-9.]{0,59}[a-z0-9]$', 'Consent Code format invalid')], verbose_name='Consent Code')),
('data_size', models.CharField(default='', help_text='Please provide an estimated size of your data set(s) (ex. 100 GB)', max_length=100, validators=[django.core.validators.RegexValidator('^[0-9]{1,5}(.[0-9]{1,5})?\\s?(MB|GB|TB|PB)$', 'Data Size format invalid. Please add a unit of measurement (MB, GB, TB, PB)')], verbose_name='Data Size')),
('dataset_description', models.CharField(blank=True, default='', help_text='Describe the dataset you are uploading', max_length=2500, verbose_name='Dataset Description')),
('google_email', models.EmailField(blank=True, default='', help_text="If you're uploading to Google, please provide your google email for access", max_length=254, verbose_name='Google Email')),
('aws_iam', models.CharField(blank=True, default='', help_text="If you're uploading to Amazon, please provide your AWS IAM (ex: arn:aws:iam::123456789012:user/username)", max_length=100, validators=[django.core.validators.RegexValidator('^arn:aws:iam::[0-9]{12}:user/[a-zA-Z0-9-_]{1,64}$', 'AWS IAM format invalid. Please use the following format: arn:aws:iam::123456789012:user/username')], verbose_name='AWS IAM')),
('is_test_data', models.BooleanField(blank=True, default=False, help_text='Check this box if this is test data', verbose_name='Is Test Data')),
('ticket_review_comment', models.CharField(blank=True, default='', help_text='Please provide a comment for approval or rejection', max_length=1000, verbose_name='Ticket Review Comment')),
('created_dt', models.DateTimeField(blank=True, editable=False, verbose_name='Created Date')),
('ticket_approved_dt', models.DateTimeField(blank=True, null=True, verbose_name='Intake Form Approved Date')),
('ticket_rejected_dt', models.DateTimeField(blank=True, null=True, verbose_name='Intake Form Rejected Date')),
('bucket_created_dt', models.DateTimeField(blank=True, null=True, verbose_name='Bucket Created Date')),
('data_uploaded_started_dt', models.DateTimeField(blank=True, null=True, verbose_name='Data Uploaded Started Date')),
('data_uploaded_completed_dt', models.DateTimeField(blank=True, null=True, verbose_name='Data Uploaded Completed Date')),
('data_accepted_dt', models.DateTimeField(blank=True, null=True, verbose_name='Gen3 Accepted Date')),
('ticket_approved_by', models.EmailField(default='', max_length=254, verbose_name='Ticket approved by')),
('ticket_rejected_by', models.EmailField(default='', max_length=254, verbose_name='Ticket rejected by')),
('bucket_created_by', models.EmailField(default='', max_length=254, verbose_name='Bucket created by')),
('data_uploaded_started_by', models.EmailField(default='', max_length=254, verbose_name='Data upload started by')),
('data_uploaded_completed_by', models.EmailField(default='', max_length=254, verbose_name='Data upload completed by')),
('data_accepted_by', models.EmailField(default='', max_length=254, verbose_name='Data accepted by')),
('history_id', models.AutoField(primary_key=True, serialize=False)),
('history_date', models.DateTimeField()),
('history_change_reason', models.CharField(max_length=100, null=True)),
('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)),
('created_by', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to=settings.AUTH_USER_MODEL)),
('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name': 'historical ticket',
'ordering': ('-history_date', '-history_id'),
'get_latest_by': 'history_date',
},
bases=(simple_history.models.HistoricalChanges, models.Model),
),
]
| 97.94964
| 433
| 0.660668
| 1,649
| 13,615
| 5.272893
| 0.121892
| 0.080966
| 0.034503
| 0.037148
| 0.912823
| 0.910178
| 0.888327
| 0.884416
| 0.880046
| 0.880046
| 0
| 0.025084
| 0.188909
| 13,615
| 138
| 434
| 98.65942
| 0.762293
| 0.003305
| 0
| 0.755725
| 1
| 0.091603
| 0.319207
| 0.058594
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.015267
| 0.038168
| 0
| 0.068702
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
03f68f8d144cdd93f5dcee9e784f93b7be7897c2
| 78
|
py
|
Python
|
src/python/test_module/another_file.py
|
davidjstevenson/cpp-embedded-python
|
798e99bde8bb969fda18335f36e286e2dfcbf9e2
|
[
"MIT"
] | null | null | null |
src/python/test_module/another_file.py
|
davidjstevenson/cpp-embedded-python
|
798e99bde8bb969fda18335f36e286e2dfcbf9e2
|
[
"MIT"
] | null | null | null |
src/python/test_module/another_file.py
|
davidjstevenson/cpp-embedded-python
|
798e99bde8bb969fda18335f36e286e2dfcbf9e2
|
[
"MIT"
] | null | null | null |
def another_file():
print("test_module/another_file.py:another_file()")
| 26
| 55
| 0.730769
| 11
| 78
| 4.818182
| 0.636364
| 0.622642
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 78
| 3
| 55
| 26
| 0.768116
| 0
| 0
| 0
| 0
| 0
| 0.552632
| 0.552632
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
03f6bb140af99a50e36a8e2526f8fd23c8c8dd0e
| 4,894
|
py
|
Python
|
tests/test_api.py
|
prometheusresearch/rios.conversion
|
388ff233020f2ab834b30a2b7b7e9f8c3b3b4dd3
|
[
"Apache-2.0"
] | null | null | null |
tests/test_api.py
|
prometheusresearch/rios.conversion
|
388ff233020f2ab834b30a2b7b7e9f8c3b3b4dd3
|
[
"Apache-2.0"
] | null | null | null |
tests/test_api.py
|
prometheusresearch/rios.conversion
|
388ff233020f2ab834b30a2b7b7e9f8c3b3b4dd3
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import print_function
import traceback
import sys
from rios.conversion.exception import Error, ConversionFailureError
from rios.conversion.base import SUCCESS_MESSAGE
from rios.conversion import (
redcap_to_rios,
qualtrics_to_rios,
rios_to_redcap,
rios_to_qualtrics,
)
from utils import (
show_tst,
redcap_to_rios_tsts,
qualtrics_to_rios_tsts,
rios_tsts,
no_error_tst_to_rios,
no_error_tst_from_rios,
)
def to_rios_api_tst(api_func, tests):
for test in tests:
show_tst(api_func, test)
try:
package = api_func(**test)
except Exception as exc:
ex_type, ex, tb = sys.exc_info()
if isinstance(exc, Error) \
or isinstance(exc, ConversionFailureError):
print("Successful error handling (exception)")
print("= EXCEPTION:")
traceback.print_tb(tb)
print(str(exc))
else:
print("= EXCEPTION:")
traceback.print_tb(tb)
print(repr(exc))
raise exc
else:
if 'suppress' in test and test['suppress']:
# Error output is suppressed
if 'failure' in package:
# We have an error situation
if not package['failure']:
raise ValueError('Error output is empty')
elif ('instrument' in package
or 'form' in package
or 'calculationset' in package):
raise ValueError(
'Error output should not contain references'
' to instrument, form, or calculationset'
' configuration definitions')
else:
print("Successful error handling (logged)")
else:
# We do NOT have an error situation
no_error_tst_to_rios(package)
else:
# Error output is NOT suppressed
if 'failure' in package:
raise ValueError(
'Errors should only be logged if error suppression'
' is set'
)
else:
# We do NOT have an error situation
no_error_tst_to_rios(package)
def from_rios_api_tst(api_func, tests):
for test in tests:
show_tst(api_func, test)
try:
package = api_func(**test)
except Exception as exc:
ex_type, ex, tb = sys.exc_info()
if isinstance(exc, Error) \
or isinstance(exc, ConversionFailureError):
print("Successful error handling (exception)")
print("= EXCEPTION:")
traceback.print_tb(tb)
print(str(exc))
else:
print("= EXCEPTION:")
traceback.print_tb(tb)
print(repr(exc))
raise exc
else:
if 'suppress' in test and test['suppress']:
# Error output is suppressed
if 'failure' in package:
# We have an error situation
if not package['failure']:
raise ValueError('Error output is empty')
elif ('instrument' in package
or 'form' in package
or 'calculationset' in package):
raise ValueError(
'Error output should not contain references'
' to instrument, form, or calculationset'
' configuration definitions')
else:
print("Successful error handling (logged)")
else:
# We do NOT have an error situation
no_error_tst_from_rios(package)
else:
# Error output is NOT suppressed
if 'failure' in package:
raise ValueError(
'Errors should only be logged if error suppression'
' is set'
)
else:
# We do NOT have an error situation
no_error_tst_from_rios(package)
print("\n====== API TESTS ======")
def test_redcap_to_rios_api():
to_rios_api_tst(redcap_to_rios, redcap_to_rios_tsts)
def test_qualtrics_to_rios_api():
to_rios_api_tst(qualtrics_to_rios, qualtrics_to_rios_tsts)
def test_rios_to_redcap_api():
from_rios_api_tst(rios_to_redcap, rios_tsts)
def test_rios_to_qualtrics_api():
from_rios_api_tst(rios_to_qualtrics, rios_tsts)
| 36.251852
| 75
| 0.509808
| 494
| 4,894
| 4.823887
| 0.157895
| 0.040285
| 0.025178
| 0.050357
| 0.836341
| 0.798993
| 0.781368
| 0.74444
| 0.74444
| 0.74444
| 0
| 0
| 0.42501
| 4,894
| 134
| 76
| 36.522388
| 0.846837
| 0.062321
| 0
| 0.714286
| 0
| 0
| 0.155745
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.053571
| false
| 0
| 0.0625
| 0
| 0.116071
| 0.160714
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
206a5f15e60dfa374c955d9d35d211a51e050a43
| 112
|
py
|
Python
|
notint/__init__.py
|
donno2048/notint
|
de3c0db1713a297912df9785912ff59a757ea64d
|
[
"MIT"
] | null | null | null |
notint/__init__.py
|
donno2048/notint
|
de3c0db1713a297912df9785912ff59a757ea64d
|
[
"MIT"
] | null | null | null |
notint/__init__.py
|
donno2048/notint
|
de3c0db1713a297912df9785912ff59a757ea64d
|
[
"MIT"
] | null | null | null |
class NotInt(int): __eq__, __ne__ = lambda _, __: bool(int(_).__ne__(__)), lambda _, __: bool(int(_).__eq__(__))
| 112
| 112
| 0.678571
| 13
| 112
| 3.692308
| 0.538462
| 0.208333
| 0.5
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 112
| 1
| 112
| 112
| 0.48
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
20955618f1014cbd38124d6f67219b4ff0c57297
| 151
|
py
|
Python
|
daoliproxy/agent/__init__.py
|
foruy/openflow-multiopenstack
|
74140b041ac25ed83898ff3998e8dcbed35572bb
|
[
"Apache-2.0"
] | 1
|
2019-09-11T11:56:19.000Z
|
2019-09-11T11:56:19.000Z
|
daoliproxy/agent/__init__.py
|
foruy/openflow-multiopenstack
|
74140b041ac25ed83898ff3998e8dcbed35572bb
|
[
"Apache-2.0"
] | null | null | null |
daoliproxy/agent/__init__.py
|
foruy/openflow-multiopenstack
|
74140b041ac25ed83898ff3998e8dcbed35572bb
|
[
"Apache-2.0"
] | null | null | null |
from daoliproxy.agent.proxy import *
from daoliproxy.agent.nova import *
from daoliproxy.agent.glance import *
from daoliproxy.agent.keystone import *
| 30.2
| 39
| 0.81457
| 20
| 151
| 6.15
| 0.4
| 0.455285
| 0.617886
| 0.609756
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10596
| 151
| 4
| 40
| 37.75
| 0.911111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
20abcb8d66c2a76dc39cf2c39ff4f74e7c7be2c0
| 103
|
py
|
Python
|
saleor/dashboard/graphql/page/types.py
|
djlowes/russell-westbark
|
7c3857e8a35b3bd0fea575ef0031360a1c5c0254
|
[
"BSD-3-Clause"
] | 6
|
2017-04-22T11:47:54.000Z
|
2021-03-27T17:15:40.000Z
|
saleor/dashboard/graphql/page/types.py
|
djlowes/russell-westbark
|
7c3857e8a35b3bd0fea575ef0031360a1c5c0254
|
[
"BSD-3-Clause"
] | 30
|
2017-11-02T20:46:02.000Z
|
2018-08-06T18:13:18.000Z
|
saleor/dashboard/graphql/page/types.py
|
djlowes/russell-westbark
|
7c3857e8a35b3bd0fea575ef0031360a1c5c0254
|
[
"BSD-3-Clause"
] | 2
|
2018-01-25T06:09:07.000Z
|
2018-01-25T20:55:34.000Z
|
from ....page import models
def resolve_all_pages():
return models.Page.objects.all().distinct()
| 17.166667
| 47
| 0.718447
| 14
| 103
| 5.142857
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135922
| 103
| 5
| 48
| 20.6
| 0.808989
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
20c6ee6961182b1d528f0abbad4f0021fa34f444
| 144
|
py
|
Python
|
src/tequila/apps/adapt/__init__.py
|
kottmanj/tequila
|
07156342d82893249747994c2ebcc528eddc9934
|
[
"MIT"
] | 214
|
2020-04-29T13:50:58.000Z
|
2021-10-17T09:36:42.000Z
|
src/tequila/apps/adapt/__init__.py
|
kottmanj/tequila
|
07156342d82893249747994c2ebcc528eddc9934
|
[
"MIT"
] | 28
|
2020-06-02T01:30:25.000Z
|
2021-09-23T14:36:37.000Z
|
src/tequila/apps/adapt/__init__.py
|
kottmanj/tequila
|
07156342d82893249747994c2ebcc528eddc9934
|
[
"MIT"
] | 62
|
2020-04-29T16:07:44.000Z
|
2021-10-18T18:44:36.000Z
|
from .adapt import Adapt, AdaptPoolBase, ObjectiveFactoryBase, ObjectiveFactorySequentialExcitedState, MolecularPool, PseudoSingletMolecularPool
| 144
| 144
| 0.902778
| 9
| 144
| 14.444444
| 0.888889
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.055556
| 144
| 1
| 144
| 144
| 0.955882
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| 0
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| true
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
20ea47d7f5fcffe4126c2c627fca80981ecf7fe2
| 3,831
|
py
|
Python
|
proplem11.py
|
raedeid/python-puzzle
|
89829d50ba4ace533ebe5b532ba43a880e01b89f
|
[
"MIT"
] | null | null | null |
proplem11.py
|
raedeid/python-puzzle
|
89829d50ba4ace533ebe5b532ba43a880e01b89f
|
[
"MIT"
] | null | null | null |
proplem11.py
|
raedeid/python-puzzle
|
89829d50ba4ace533ebe5b532ba43a880e01b89f
|
[
"MIT"
] | 2
|
2019-03-23T18:51:55.000Z
|
2019-09-27T14:16:13.000Z
|
#In the 20×20 grid below, four numbers along a diagonal line have been marked in red.
#08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
#49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
#81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
#52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
#22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
#24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
#32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
#67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
#24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
#21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
#78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
#16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
#86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
#19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
#04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
#88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
#04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
#20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
#20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
#01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
#The product of these numbers is 26 × 63 × 78 × 14 = 1788696.
#What is the greatest product of four adjacent numbers in the same direction (up, down, left, right, or diagonally) in the 20×20 grid
l=[]
l.append('08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08')
l.append('49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00')
l.append('81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65')
l.append('52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91')
l.append('22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80')
l.append('24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50')
l.append('32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70')
l.append('67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21')
l.append('24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72')
l.append('21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95')
l.append('78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92')
l.append('16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57')
l.append('86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58')
l.append('19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40')
l.append('04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66')
l.append('88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69')
l.append('04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36')
l.append('20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16')
l.append('20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54')
l.append('01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48')
g=[i.split() for i in l]#separate the number
g=[[int(r) for r in i]for i in g]#make as inteager
max_number=0
for b in range(20):
for c in range(16):
production=g[b][c]*g[b][c+1]*g[b][c+2]*g[b][c+3]#for number from right to left
if production>max_number:
max_number=production
production=g[c][b]*g[c+1][b]*g[c+2][b]*g[c+3][b]#for number from up to down
if production>max_number:
max_number=production
for b in range(16):
for c in range(16):
production= g[b][c]*g[b+1][c+1]*g[b+2][c+2]*g[b+3][c+3]#for number in diagonal
if production > max_number:
max_number = production
for b in range(3,20):
for c in range(16):
production= g[b][c]*g[b-1][c+1]*g[b-2][c+2]*g[b-3][c+3]
if production > max_number:
max_number = production
print(max_number)
| 53.208333
| 133
| 0.641608
| 1,073
| 3,831
| 2.286114
| 0.145387
| 0.057073
| 0.007338
| 0.034244
| 0.784346
| 0.784346
| 0.772931
| 0.740318
| 0.740318
| 0.740318
| 0
| 0.602037
| 0.282433
| 3,831
| 71
| 134
| 53.957746
| 0.288469
| 0.409815
| 0
| 0.255814
| 0
| 0
| 0.528437
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.023256
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
4554deb6f8380596fa12e09f3e5b6e545495139c
| 74
|
py
|
Python
|
gumbi/utils/__init__.py
|
JohnGoertz/Gumbi
|
7a7df9bf97bf10cdf5dc8af36026dba578e161c9
|
[
"Apache-2.0"
] | 34
|
2021-11-29T11:40:52.000Z
|
2022-03-10T09:08:59.000Z
|
gumbi/utils/__init__.py
|
JohnGoertz/Gumbi
|
7a7df9bf97bf10cdf5dc8af36026dba578e161c9
|
[
"Apache-2.0"
] | 13
|
2021-12-30T17:07:34.000Z
|
2022-02-18T18:46:37.000Z
|
gumbi/utils/__init__.py
|
JohnGoertz/Gumbi
|
7a7df9bf97bf10cdf5dc8af36026dba578e161c9
|
[
"Apache-2.0"
] | null | null | null |
from .generic_utils import *
from .gp_utils import *
from .misc import *
| 24.666667
| 29
| 0.743243
| 11
| 74
| 4.818182
| 0.545455
| 0.415094
| 0.566038
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175676
| 74
| 3
| 30
| 24.666667
| 0.868852
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
4564aecc7feb128db6eabb89f8a3e1d1c212a3f2
| 6,908
|
py
|
Python
|
resources/emails.py
|
axonepro/sdk-ooti
|
146ba758f571352d02daa56349e8b3affd8ca5a9
|
[
"Unlicense"
] | 1
|
2021-03-13T16:04:54.000Z
|
2021-03-13T16:04:54.000Z
|
resources/emails.py
|
axonepro/sdk-ooti
|
146ba758f571352d02daa56349e8b3affd8ca5a9
|
[
"Unlicense"
] | 7
|
2021-07-21T12:42:39.000Z
|
2022-01-06T10:34:04.000Z
|
resources/emails.py
|
axonepro/sdk-ooti
|
146ba758f571352d02daa56349e8b3affd8ca5a9
|
[
"Unlicense"
] | 2
|
2021-06-22T08:10:48.000Z
|
2021-09-01T09:16:41.000Z
|
import requests
import json
from .helper import Helper
class Emails(Helper):
def __init__(self, base_url, org_pk, teams_pk, access_token, _csrf_token, headers, pagination):
super().__init__(base_url, org_pk, teams_pk, access_token, _csrf_token, headers, pagination)
def get_emails_list(self, page=1):
""" Get the emails list """
route = 'v1/emails/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page)
response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None)
return self.process_response(response, True)
def get_email_details(self, pk):
""" Get the email details
Keyword arguments:
pk -- the pk of the email
"""
route = 'v1/emails/{0}/'.format(pk)
response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None)
return self.process_response(response)
def create_email(self, data):
""" Create an email
Keyword arguments:
data -- data create, fields:
{
"team": 0,
"name": "string", (name of the template)
"type": "", ('invoice', 'followup' or 'contractor_notification')
"email_to": "string",
"email_from": "string",
"name_from": "string",
"email_cc": "string",
"email_bcc": "string",
"email_subject": "string",
"email_body": "string",
"smtp_setting": 0,
"projects": [],
"invoices": []
}
Note that there is no required fields to create the email template.
"""
route = 'v1/emails/list/{0}/'.format(self.org_pk)
response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data))
return self.process_response(response)
def update_email(self, pk, data):
""" Update an email
Keyword arguments:
pk -- the pk of the email template
data -- data create, fields:
{
"team": 0,
"name": "string", (name of the template)
"type": "", ('invoice', 'followup' or 'contractor_notification')
"email_to": "string",
"email_from": "string",
"name_from": "string",
"email_cc": "string",
"email_bcc": "string",
"email_subject": "string",
"email_body": "string",
"smtp_setting": 0,
"projects": [],
"invoices": []
}
Note that there is no required fields to create the email template.
"""
route = 'v1/emails/{0}/'.format(pk)
response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data))
return self.process_response(response)
def delete_email(self, pk):
"""Delete an email
Keyword arguments:
pk -- pk of the email
"""
route = 'v1/emails/{0}'.format(pk)
response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None)
return self.process_response(response)
def send_test_email(self, pk):
""" Send test email to the email of the account
Keyword arguments:
pk -- the pk of the email template
"""
route = 'v1/emails/{0}/send-test/'.format(pk)
response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None)
return self.process_response(response)
def apply_email(self, pk):
""" Apply the template to related projects and unsent invoices
Keyword arguments:
pk -- pk of the email template
"""
route = 'v1/emails/{0}/apply/'.format(pk)
response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None)
return self.process_response(response)
def get_emails_smtp_list(self, page=1):
""" Get the emails smtp list """
route = 'v1/emails/smtp/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page)
response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None)
return self.process_response(response, True)
def get_email_smtp_details(self, pk):
""" Get the email smtp details
Keyword arguments:
pk -- the pk of the email smtp
"""
route = 'v1/emails/smtp/{0}/'.format(pk)
response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None)
return self.process_response(response)
def create_email_smtp(self, data):
""" Create an email smtp
Keyword Arguments:
data -- data create: fields:
{
"from_name": "string",
"from_email": "string",
"username": "string",
"password": "string",
"protocol": "TLS" or "SSL",
"host": "string",
"port": 0
}
"""
route = 'v1/emails/smtp/list/{0}/'.format(self.org_pk)
response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data))
return self.process_response(response)
def update_email_smtp(self, pk, data):
""" Update an email smtp
Keyword arguments:
pk -- the pk of the email smtp
data -- data create: fields:
{
"from_name": "string",
"from_email": "string",
"username": "string",
"password": "string",
"protocol": "TLS" or "SSL",
"host": "string",
"port": 0
}
"""
route = 'v1/emails/smtp/{0}/'.format(pk)
response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data))
return self.process_response(response)
def delete_email_smtp(self, pk):
""" Delete an email smtp
Keyword arguments:
pk -- pk of the email smtp
"""
route = 'v1/emails/smtp/{0}'.format(pk)
response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None)
return self.process_response(response)
def send_test_email_smtp(self, pk):
""" Verify the status of the smtp
Keyword arguments:
pk -- the pk of the email template
"""
route = 'v1/emails/smtp/{0}/send-test/'.format(pk)
response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None)
return self.process_response(response)
| 33.862745
| 118
| 0.564852
| 794
| 6,908
| 4.7733
| 0.117128
| 0.075462
| 0.040633
| 0.089182
| 0.925594
| 0.900792
| 0.860158
| 0.832718
| 0.832718
| 0.78628
| 0
| 0.007945
| 0.307614
| 6,908
| 204
| 119
| 33.862745
| 0.784445
| 0.348147
| 0
| 0.517241
| 0
| 0
| 0.095315
| 0.044444
| 0
| 0
| 0
| 0
| 0
| 1
| 0.241379
| false
| 0
| 0.051724
| 0
| 0.534483
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 8
|
457b4b11e629f8eade519fcf3bbf7c54bcc94bb0
| 1,794
|
py
|
Python
|
pruebas automatizadas/pruebas modulo jugar/p1.py
|
HighDeFing/ATI-2019-1
|
c9b5cd89408dc620f87acda5c81c09e178089595
|
[
"Apache-2.0"
] | null | null | null |
pruebas automatizadas/pruebas modulo jugar/p1.py
|
HighDeFing/ATI-2019-1
|
c9b5cd89408dc620f87acda5c81c09e178089595
|
[
"Apache-2.0"
] | 81
|
2019-07-04T18:55:19.000Z
|
2019-09-27T01:57:11.000Z
|
pruebas automatizadas/pruebas modulo jugar/p1.py
|
HighDeFing/ATI-2019-1
|
c9b5cd89408dc620f87acda5c81c09e178089595
|
[
"Apache-2.0"
] | 2
|
2019-09-21T00:15:04.000Z
|
2020-02-25T23:56:33.000Z
|
from selenium import webdriver
driver=webdriver.Chrome()
driver.get("http://127.0.0.1:5000/home.html")
driver.find_element_by_xpath("//*[contains(text(), 'Go trivia')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Go trivia')]").click()
#prueba todas las respuestas incorrectas
driver.find_element_by_id("choiceB").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Cerrar')]").click()
driver.find_element_by_id("choiceA").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Cerrar')]").click()
driver.find_element_by_id("choiceA").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Cerrar')]").click()
driver.find_element_by_id("choiceC").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Cerrar')]").click()
driver.find_element_by_id("choiceA").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Cerrar')]").click()
driver.find_element_by_id("choiceD").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'Cerrar')]").click()
driver.find_element_by_id("choiceC").click()
driver.find_element_by_xpath("//*[contains(text(), 'Siguiente')]").click()
driver.find_element_by_xpath("//*[contains(text(), 'iniciar otro juego')]").click()
# driver.find_element_by_class_name("btn btn-info")
# driver.find_element_by_xpath("//*[contains(text(), 'User Profile')]").click()
| 44.85
| 83
| 0.723523
| 238
| 1,794
| 5.134454
| 0.184874
| 0.204583
| 0.347791
| 0.388707
| 0.853519
| 0.816694
| 0.816694
| 0.787234
| 0.787234
| 0.787234
| 0
| 0.005811
| 0.040691
| 1,794
| 39
| 84
| 46
| 0.704242
| 0.094203
| 0
| 0.769231
| 0
| 0
| 0.379162
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| false
| 0
| 0.038462
| 0
| 0.038462
| 0
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| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
b319e27fbca378fe593a6e58ab83f43390875353
| 12,843
|
py
|
Python
|
biskivy/utils/winpreferences/themes.py
|
manahter/biskivy
|
8fe87c94b42d9563a8d8939a517401c8221542aa
|
[
"MIT"
] | null | null | null |
biskivy/utils/winpreferences/themes.py
|
manahter/biskivy
|
8fe87c94b42d9563a8d8939a517401c8221542aa
|
[
"MIT"
] | null | null | null |
biskivy/utils/winpreferences/themes.py
|
manahter/biskivy
|
8fe87c94b42d9563a8d8939a517401c8221542aa
|
[
"MIT"
] | null | null | null |
from kivy.lang import Builder
from biskivy.uix.panel import BisPanel
Builder.load_string("""
<BisWinPrefThemes>:
orientation: "vertical"
BisPanelItem:
prop_title: "User Interface"
prop_padding: [0,]
### LABEL
BisMenuItem:
prop_text: "Label"
prop_orientation: "horizontal"
prop_padding: [10,]
prop_spacing: 10
BisBoxLayout:
prop_orientation: "vertical"
spacing: 5
BisBoxLayout:
spacing: 5
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Text"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Item"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.label.renk_text
on_get_rgba:
bkv.prefs.label.renk_text = self.get_rgba
BisColor:
rgba: bkv.prefs.label.renk_t_selected
on_get_rgba:
bkv.prefs.label.renk_t_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.label.renk_t_item
on_get_rgba:
bkv.prefs.label.renk_t_item = self.get_rgba
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Inner"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Outline"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.label.renk_inner
on_get_rgba:
bkv.prefs.label.renk_inner = self.get_rgba
BisColor:
rgba: bkv.prefs.label.renk_selected
on_get_rgba:
bkv.prefs.label.renk_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.label.renk_outline
on_get_rgba:
bkv.prefs.label.renk_outline = self.get_rgba
### BUTTON
BisMenuItem:
prop_text: "Button"
prop_orientation: "horizontal"
prop_padding: [10,]
prop_spacing: 10
BisBoxLayout:
prop_orientation: "vertical"
spacing: 5
BisBoxLayout:
spacing: 5
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Text"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Item"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.button.renk_text
on_get_rgba:
bkv.prefs.button.renk_text = self.get_rgba
BisColor:
rgba: bkv.prefs.button.renk_t_selected
on_get_rgba:
bkv.prefs.button.renk_t_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.button.renk_t_item
on_get_rgba:
bkv.prefs.button.renk_t_item = self.get_rgba
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Inner"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Outline"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.button.renk_inner
on_get_rgba:
bkv.prefs.button.renk_inner = self.get_rgba
BisColor:
rgba: bkv.prefs.button.renk_selected
on_get_rgba:
bkv.prefs.button.renk_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.button.renk_outline
on_get_rgba:
bkv.prefs.button.renk_outline = self.get_rgba
BisBoxLayout:
#spacing: 5
BisLabelAligned:
prop_text: "Roundness"
BisSliderInput:
size_hint_x: 3
prop_min: 0
prop_max: 1
prop_step: 0.001
prop_value: bkv.prefs.button.scale_round
on_prop_value:
bkv.prefs.button.scale_round = round(self.prop_value, 3)
### INPUT
BisMenuItem:
prop_text: "Input"
prop_orientation: "horizontal"
prop_padding: [10,]
prop_spacing: 10
BisBoxLayout:
prop_orientation: "vertical"
spacing: 5
BisBoxLayout:
spacing: 5
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Text"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Item"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.input.renk_text
on_get_rgba:
bkv.prefs.input.renk_text = self.get_rgba
BisColor:
rgba: bkv.prefs.input.renk_t_selected
on_get_rgba:
bkv.prefs.input.renk_t_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.input.renk_t_item
on_get_rgba:
bkv.prefs.input.renk_t_item = self.get_rgba
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Inner"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Outline"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.input.renk_inner
on_get_rgba:
bkv.prefs.input.renk_inner = self.get_rgba
BisColor:
rgba: bkv.prefs.input.renk_selected
on_get_rgba:
bkv.prefs.input.renk_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.input.renk_outline
on_get_rgba:
bkv.prefs.input.renk_outline = self.get_rgba
BisBoxLayout:
#spacing: 5
BisLabelAligned:
prop_text: "Roundness"
BisSliderInput:
size_hint_x: 3
prop_min: 0
prop_max: 1
prop_step: 0.001
prop_value: bkv.prefs.input.scale_round
on_prop_value:
bkv.prefs.input.scale_round = round(self.prop_value, 3)
### TEXT INPUT
BisMenuItem:
prop_text: "Text Input"
prop_orientation: "horizontal"
prop_padding: [10,]
prop_spacing: 10
BisBoxLayout:
prop_orientation: "vertical"
spacing: 5
BisBoxLayout:
spacing: 5
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Text"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Item"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.textinput.renk_text
on_get_rgba:
bkv.prefs.textinput.renk_text = self.get_rgba
BisColor:
rgba: bkv.prefs.textinput.renk_t_selected
on_get_rgba:
bkv.prefs.textinput.renk_t_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.textinput.renk_t_item
on_get_rgba:
bkv.prefs.textinput.renk_t_item = self.get_rgba
BisBoxLayout:
prop_orientation: "vertical"
BisLabelAligned:
prop_text: "Inner"
BisLabelAligned:
prop_text: "Selected"
BisLabelAligned:
prop_text: "Outline"
BisCombinLayout:
prop_orientation: "vertical"
BisColor:
rgba: bkv.prefs.textinput.renk_inner
on_get_rgba:
bkv.prefs.textinput.renk_inner = self.get_rgba
BisColor:
rgba: bkv.prefs.textinput.renk_selected
on_get_rgba:
bkv.prefs.textinput.renk_selected = self.get_rgba
BisColor:
rgba: bkv.prefs.textinput.renk_outline
on_get_rgba:
bkv.prefs.textinput.renk_outline = self.get_rgba
BisBoxLayout:
#spacing: 5
BisLabelAligned:
prop_text: "Roundness"
BisSliderInput:
size_hint_x: 3
prop_min: 0
prop_max: 1
prop_step: 0.001
prop_value: bkv.prefs.textinput.scale_round
on_prop_value:
bkv.prefs.textinput.scale_round = round(self.prop_value, 3)
### COLOR
BisMenuItem:
prop_text: "Color"
prop_orientation: "horizontal"
prop_padding: [10,]
prop_spacing: 10
BisBoxLayout:
#spacing: 5
BisLabelAligned:
prop_text: "Roundness"
BisSliderInput:
size_hint_x: 3
prop_min: 0
prop_max: 1
prop_step: 0.001
prop_value: bkv.prefs.color.scale_round
on_prop_value:
bkv.prefs.color.scale_round = round(self.prop_value, 3)
""")
class BisWinPrefThemes(BisPanel):
pass
| 42.52649
| 87
| 0.396792
| 913
| 12,843
| 5.306681
| 0.070099
| 0.092466
| 0.118885
| 0.099071
| 0.932714
| 0.930444
| 0.928999
| 0.799174
| 0.725077
| 0.673684
| 0
| 0.011293
| 0.551818
| 12,843
| 302
| 88
| 42.52649
| 0.830438
| 0
| 0
| 0.729825
| 0
| 0
| 0.988789
| 0.132513
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.003509
| 0.007018
| 0
| 0.010526
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
b32a3fbd4b6816d912a5aa5f6a2b3240ace133ba
| 1,641
|
py
|
Python
|
Rota_System/UI/Duration/command_durations.py
|
ergoregion/Rota-Program
|
44dab4cb11add184619d88aa0fcab61532d128e6
|
[
"MIT"
] | null | null | null |
Rota_System/UI/Duration/command_durations.py
|
ergoregion/Rota-Program
|
44dab4cb11add184619d88aa0fcab61532d128e6
|
[
"MIT"
] | null | null | null |
Rota_System/UI/Duration/command_durations.py
|
ergoregion/Rota-Program
|
44dab4cb11add184619d88aa0fcab61532d128e6
|
[
"MIT"
] | null | null | null |
__author__ = 'Neil Butcher'
from PyQt4 import QtGui
class CommandAddDuration(QtGui.QUndoCommand):
def __init__(self, model, row, parent):
super(CommandAddDuration, self).__init__('Added a new duration')
self.model = model
self.row = row
self.parent = parent
self.duration = model.newDuration()
def redo(self):
self.model.beginInsertRows(self.parent, self.row, self.row)
self.model.durations.insert(self.row, self.duration)
self.duration.nameChanged.connect(self.model._durationNameChanged)
self.model.endInsertRows()
def undo(self):
self.model.beginRemoveRows(self.parent, self.row, self.row)
self.model.durations.pop(self.row)
self.duration.nameChanged.disconnect(self.model._durationNameChanged)
self.model.endRemoveRows()
class CommandRemoveDuration(QtGui.QUndoCommand):
def __init__(self, model, row, parent):
super(CommandRemoveDuration, self).__init__('Removed a duration')
self.model = model
self.row = row
self.parent = parent
self.duration = model.durations[row]
def undo(self):
self.model.beginInsertRows(self.parent, self.row, self.row)
self.model.durations.insert(self.row, self.duration)
self.duration.nameChanged.connect(self.model._durationNameChanged)
self.model.endInsertRows()
def redo(self):
self.model.beginRemoveRows(self.parent, self.row, self.row)
self.model.durations.pop(self.row)
self.duration.nameChanged.disconnect(self.model._durationNameChanged)
self.model.endRemoveRows()
| 38.162791
| 77
| 0.690433
| 185
| 1,641
| 5.994595
| 0.2
| 0.162308
| 0.119026
| 0.061317
| 0.83679
| 0.816952
| 0.816952
| 0.816952
| 0.816952
| 0.732191
| 0
| 0.000761
| 0.199269
| 1,641
| 43
| 78
| 38.162791
| 0.843227
| 0
| 0
| 0.777778
| 0
| 0
| 0.030451
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.027778
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b359e3b1c54f10bfe6246855aab5a31bddd4c87a
| 6,921
|
py
|
Python
|
tests/test_newscatcherapi_client.py
|
NewscatcherAPI/newscatcherapi-sdk-python
|
7ca643805c117ce3f33311419a4308f759b53311
|
[
"MIT"
] | 3
|
2022-01-21T07:18:24.000Z
|
2022-03-18T07:25:23.000Z
|
tests/test_newscatcherapi_client.py
|
NewscatcherAPI/newscatcherapi-sdk-python
|
7ca643805c117ce3f33311419a4308f759b53311
|
[
"MIT"
] | null | null | null |
tests/test_newscatcherapi_client.py
|
NewscatcherAPI/newscatcherapi-sdk-python
|
7ca643805c117ce3f33311419a4308f759b53311
|
[
"MIT"
] | null | null | null |
import os
import unittest
from newscatcherapi.newscatcherapi_client import NewsCatcherApiClient
class NewsCatcherApiTest(unittest.TestCase):
def setUp(self):
key = os.environ.get("newscatcher_api_secret")
self.api = NewsCatcherApiClient(key)
def test_api_latest_headlines(self):
# Raise TypeError if lang is not of type str
lang = 1
with self.assertRaises(TypeError):
self.api.get_latest_headlines(lang=lang)
# Raise ValueError if lang is not in list
lang = 'aer'
with self.assertRaises(ValueError):
self.api.get_latest_headlines(lang=lang)
# Raise TypeError if not_lang is not of type str
not_lang = 1
with self.assertRaises(TypeError):
self.api.get_latest_headlines(not_lang=not_lang)
# Raise ValueError if lang is not in list
not_lang = 'aer'
with self.assertRaises(ValueError):
self.api.get_latest_headlines(not_lang=not_lang)
# Raise TypeError if sources param is not of type str
sources = 0
with self.assertRaises(TypeError):
self.api.get_latest_headlines(sources=sources)
# Raise TypeError if country param is not of type str
countries = 0
with self.assertRaises(TypeError):
self.api.get_latest_headlines(countries=countries)
# Raises TypeError if topic param is not of type str
topic = 0
with self.assertRaises(TypeError):
self.api.get_latest_headlines(topic=topic)
# Raises ValueError if category param is invalid
topic = "dogcoin"
with self.assertRaises(ValueError):
self.api.get_latest_headlines(topic=topic)
# Raises TypeError if page_size param is not an int
page_size = "1"
with self.assertRaises(TypeError):
self.api.get_latest_headlines(page_size=page_size)
# Raises ValueError if page_size param is less than zero(0) or greater than 100
page_size = -1
with self.assertRaises(ValueError):
self.api.get_latest_headlines(page_size=page_size)
page_size = 1000
with self.assertRaises(ValueError):
self.api.get_latest_headlines(page_size=page_size)
# Raises a TypeError is page param is not an int
page = "1"
with self.assertRaises(TypeError):
self.api.get_latest_headlines(page=page)
# Raises a ValueError if page param is less than zero(0)
page = -1
with self.assertRaises(ValueError):
self.api.get_latest_headlines(page=page)
def test_api_get_search(self):
# Raise TypeError if q param is None
q = 0
with self.assertRaises(TypeError):
self.api.get_search(q=q)
# Raise TypeError if lang is not of type str
lang = 1
with self.assertRaises(TypeError):
self.api.get_search(lang=lang)
# Raise ValueError if lang is not in list
lang = 'aer'
with self.assertRaises(ValueError):
self.api.get_search(lang=lang)
# Raise TypeError if not_lang is not of type str
not_lang = 1
with self.assertRaises(TypeError):
self.api.get_search(not_lang=not_lang)
# Raise ValueError if lang is not in list
not_lang = 'aer'
with self.assertRaises(ValueError):
self.api.get_search(not_lang=not_lang)
# Raise TypeError if sources param is not of type str
sources = 0
with self.assertRaises(TypeError):
self.api.get_search(sources=sources)
# Raise TypeError if country param is not of type str
countries = 0
with self.assertRaises(TypeError):
self.api.get_search(countries=countries)
# Raise TypeError if not_countries param is not of type str
not_countries = 0
with self.assertRaises(TypeError):
self.api.get_search(not_countries=not_countries)
# Raises TypeError if topic param is not of type str
topic = 0
with self.assertRaises(TypeError):
self.api.get_search(topic=topic)
# Raises ValueError if category param is invalid
topic = "dogcoin"
with self.assertRaises(ValueError):
self.api.get_search(topic=topic)
# Raises TypeError if page_size param is not an int
page_size = "1"
with self.assertRaises(TypeError):
self.api.get_search(page_size=page_size)
# Raises ValueError if page_size param is less than zero(0) or greater than 100
page_size = -1
with self.assertRaises(ValueError):
self.api.get_search(page_size=page_size)
page_size = 1000
with self.assertRaises(ValueError):
self.api.get_search(page_size=page_size)
# Raises a TypeError is page param is not an int
page = "1"
with self.assertRaises(TypeError):
self.api.get_search(page=page)
# Raises a ValueError if page param is less than zero(0)
page = -1
with self.assertRaises(ValueError):
self.api.get_search(page=page)
# Raise TypeError is sort_by param is not of type str
sort_by = 1
with self.assertRaises(TypeError):
self.api.get_search(sort_by=sort_by)
# Raise ValueError if soft_by param is invalid
sort_by = "sort"
with self.assertRaises(ValueError):
self.api.get_search(sort_by=sort_by)
# Raise ValueError if soft_by param is invalid
published_date_precision = "score"
with self.assertRaises(ValueError):
self.api.get_search(published_date_precision=published_date_precision)
# Raise ValueError if soft_by param is invalid
search_in = "published_date"
with self.assertRaises(ValueError):
self.api.get_search(search_in=search_in)
# Raises a TypeError is from_rank param is not an int
from_rank = "1"
with self.assertRaises(TypeError):
self.api.get_search(from_rank=from_rank)
# Raises a TypeError is from_rank param is not an int
to_rank = "1"
with self.assertRaises(TypeError):
self.api.get_search(to_rank=to_rank)
def test_api_get_sources(self):
# Raise TypeError if not_countries param is not of type str
not_countries = 0
with self.assertRaises(TypeError):
self.api.get_search(not_countries=not_countries)
# Raises TypeError if topic param is not of type str
topic = 0
with self.assertRaises(TypeError):
self.api.get_search(topic=topic)
# Raises ValueError if category param is invalid
topic = "dogcoin"
with self.assertRaises(ValueError):
self.api.get_search(topic=topic)
| 34.778894
| 87
| 0.64326
| 900
| 6,921
| 4.797778
| 0.08
| 0.054192
| 0.171376
| 0.08893
| 0.8805
| 0.8805
| 0.875174
| 0.874247
| 0.837425
| 0.814961
| 0
| 0.008724
| 0.28782
| 6,921
| 198
| 88
| 34.954545
| 0.867316
| 0.251842
| 0
| 0.785124
| 0
| 0
| 0.016336
| 0.004278
| 0
| 0
| 0
| 0
| 0.305785
| 1
| 0.033058
| false
| 0
| 0.024793
| 0
| 0.066116
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b369590befcc793c19da17659dc180c5164a28f6
| 14,389
|
py
|
Python
|
sdk/keyvault/azure-keyvault-certificates/tests/certs.py
|
vincenttran-msft/azure-sdk-for-python
|
348b56f9f03eeb3f7b502eed51daf494ffff874d
|
[
"MIT"
] | 1
|
2022-03-09T08:59:13.000Z
|
2022-03-09T08:59:13.000Z
|
sdk/keyvault/azure-keyvault-certificates/tests/certs.py
|
vincenttran-msft/azure-sdk-for-python
|
348b56f9f03eeb3f7b502eed51daf494ffff874d
|
[
"MIT"
] | null | null | null |
sdk/keyvault/azure-keyvault-certificates/tests/certs.py
|
vincenttran-msft/azure-sdk-for-python
|
348b56f9f03eeb3f7b502eed51daf494ffff874d
|
[
"MIT"
] | null | null | null |
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""Keys for use in test cases"""
CERT_CONTENT_PASSWORD_ENCODED = b'0\x82\t\xb1\x02\x01\x030\x82\tw\x06\t*\x86H\x86\xf7\r\x01\x07\x01\xa0\x82\th\x04\x82\td0\x82\t`0\x82\x04\x17\x06\t*\x86H\x86\xf7\r\x01\x07\x06\xa0\x82\x04\x080\x82\x04\x04\x02\x01\x000\x82\x03\xfd\x06\t*\x86H\x86\xf7\r\x01\x07\x010\x1c\x06\n*\x86H\x86\xf7\r\x01\x0c\x01\x060\x0e\x04\x08X\xb2\x8c\xa9\xed\x830\xd7\x02\x02\x08\x00\x80\x82\x03\xd0\xd7\xf2Au\xa4\x18\xcbL\x8c&\xbd\x07\x01\xb6C\xadse\xb6\xc6C \xa85\xee}\xc9<6uU\x9b\x03xK\xb8\xc6\x8e\xc6\xbb\x12\xfb\x96\x03\x89\x15a\xcb\xff5\xe5i\xecKnqG\x88\xee\xd9c}\xea?\x19\x17\xbd\x035\x87\xf2O_\x12-\x1aJ\xc5\xadf\xa4\xf15\x18\xa4Cb\x86\xb1\xf5,\xb8\xb3\'nX\x9c\x18\x19\xce\xbf\xfa\xb0g!\x1a y\xb1;|\xa9@\xe5\x90\x92\xb1\xe0vJD\x06\xf7|\xac\x9a\x11k\x86Jl\xe1K\xdaa\xb0\xb2GY^\xb9>\xdb9`\x1b^~\xd9\xb5\xebx\xe8\x9c\xf8X\xab\xa7\xe4\xdc\x8a!{V\xa6C\xbc\xb2\x0b2\xdbK\xaeBJ\x92\x8den8\xa6\xba\x9c$dg\x98\x98\xfeF\xf7\x02E\x91\xa8\xae\xec\x9c\x1b}\xf7\x806\xd6B\x86\xf1\xf6T\xeb\xbd\x03\xc3[7\xb6,m.Q6X<\x18\x9d\xbd\xdfP\xb2s\x9a\xd4\xd7bL\x9aDV\xd6\xa0RO\x1e\xb3\xcc\xbb\xd2\xbde\x04\xd8\xfde\xd3pT\xa3\xda\xa8\x10R]9I\x03\x81\x88\x9a;n}dU?\x97M:\xc2?\x93\x84,\xed/\xbd\xa2\x14W\xda&3\x0b\x9a\xf6\x85\x1e\x81h\x12\xd0i\x00\x0e\x1b\xdc\xee\r\x8f\x90\x9c9\xba\n\x16\xdbs\x05\xac\xb3\xd4\xec\x9bv0\xb5\xe7\x04]\xdd\x86\xee\xb1\x91\xfe\xcd0`\x9a\xd3}\xf4:}\xa8\x03.+\xbbx\xec"\xa2\xa4\xaf\x1c3\xdb\xab\x91\xac<\xf8g-\x86\xdeU\x1bp\xf6\x8f\xed>\xc8a\xdb\xb8\xe0O\xf5\xdf\xd7M\xc4vo\xa0k\x91\x16\xa5\x88I\xb8\xf9\x9eF9\xa5\xc4\x14\xae\x01\x99:\x93\x9e\xf7(&9\'\x94U\x8e\xdd \x9cE)n\xd7\xd4\xed\xcd\x97\x82\xe9\xa1\xa2\x96\xe3i\xd33\xbcn\xa1T8;\xf1\xa0j\x91\xea\xbe\x0c\x8a\x8e$\xb3/\xdc\xebE\x90\x17\x12\x9a\xca@\x04\x0c\t\xba\xdebk\xb3x\x9a\x17:\xe8*`Op\x1e\x91\'x\xf5\xa9+\xb1\xa9\x03\xed0\x00\x9d\xe0\xb2\xa1Dh\xb4\xba<\x05\x175\xaaW\xe5\xa7\xfdT\xfa\xd9zpA`U\x08\xc4\xf5\'\xe2\xe2s\xfa\x9d\x8f\x18\xc5\x80\xf7A\xf89{\xf8y9P\xdaZ\xaa\x12\xa0\xd8g\x93.\xc2\xd20\xb7\xab\xc7\xd4S\xbb\x87a\xaa\x88\xc6\xd57\xf7$M*d\x14\xa6\x94n\xc8\xaa\x11"\x9f\xcc>\xf5\xd9\x8c\x8d*k\n)M={g7I\xa8\x02\to3">\xe4\x82\xca\xda\r\xc2\xae\xbe\xa3\xaa\x8es\x12^\\h\xf8\xc8\rq\xa62Hp34<K\xf7\x85\xb2\xee\xc3\x88\x1a\x88\x19\x991\x03\x11WO\xbegYC\x94\xca\xb5\xa9\xf7c\x0b\xecS\x1ef\xb7\x15=\xb7[\x90")J\xec\xf2\x1b\x7fTkB)\xaa\xe9E\xe9BF\xadsZ\xf5\xb7\xe3`\xc3-\x99\x9e&s\xc1\xa9.U\xce\x03\x91j\xb4y\xa0\xc7\x8d\x1d\x92\xe5\xadVf\xd0\x1c\xf1B\x073\xf1\x105\xc5Yi\x07\xa3\x064{@\x99\x8fc\x97\x93\x82a>\xec\x86\xd01\xb9\xfc\xf2s]\x12R\xa2\xa6ijKw:\xc0\xee{/L\xd1J\xf6\xeb\x04,T\xb9R8\x077U}\x11.\xc6o\x8az\xfdih\x04\xb8\xe2\xb72\x81\x1c\xecDj\xfd\x80J\x1e{\xf2:V\x03[\x1a0\x95\xd3\xc1\xfcR!y+O\xd5l\xdfw\xf8KZy\xf3P\x05\xa54|R\xd9Q\xfdg\xc4\xc8-\x812J\xa9M\xa4\xbb\xf7zG\xcd\x81\xff\x82\xd6\x089\xef\\\x00U\t\x0f\xb5Z`y\x8du3\xc1\x80\x05Uq\xea\xa5\x96\xc6tN\xea+\xae\xb7\x19\x1b\xd3\xd0"\r\xd8\\%\x8d\x93\x13\xb5\x83W\t\x03\xbaN[\x1eT\xb5\xfc\n\xb2\xdd\xc4h\xa8&\x89|\xe4\xd5\xab\x8fv\x12\x0f\xff\xe0\x1bp\xec\xd1\x18\x87\xfbG\xd7r\xe3z\xee)\xf6D-\xd6)\xf6\x94\t\xc5\x13\xba\xbe\xf5M\xab\xb6\x7f~\xd2Y\x9fE\xaf3\x87B\xa2U\xa0\xff\xdd)1\xb5P\x9f0\x82\x05A\x06\t*\x86H\x86\xf7\r\x01\x07\x01\xa0\x82\x052\x04\x82\x05.0\x82\x05*0\x82\x05&\x06\x0b*\x86H\x86\xf7\r\x01\x0c\n\x01\x02\xa0\x82\x04\xee0\x82\x04\xea0\x1c\x06\n*\x86H\x86\xf7\r\x01\x0c\x01\x030\x0e\x04\x08\x00\xc9N\x15u\x14D\xf1\x02\x02\x08\x00\x04\x82\x04\xc8V.\xb2P\x8f\xab+k\xd2m\xc0\xbc\x1aS$\xc9\xb7Rk\xfd\xe1x:\xcf\xe9a\x97\xaa\x10\xfc)_W\xb4\xc1iF\xf5\x82\xf35\xdb>\xfe\x85&\xee\xc9\x97\xc5\'\t\xbf\xac\xae\\\xdb\xc0GV\x96K\xe7;\xde\x86xQ\x00\xb8\xed\xeb\xbc\x88\x11XL\x9az\x9eJ\xffO!\x1e\xc5\xd6\xbc\xeb\x18\x11\x97\x95\xed\xe2\xd8\xc0\xb3\r\x00\xf7\xf9\x04C\xfem\xc7P_\xd6\x0f\xd2\xcb\xc7\xd44\xb9\xc5\x1fw>"\xd2\xad;!\xb4@\x0bP\xcf\xff\x19&\x03k\xaa\x8b\xfc\xd6X\x87^s}mv\xa6\xf2G\xbb@\xda\x152]zU\x1b\x96q\xc7!\xee\xc2Q\x05dG\x07\xa6\xc1\xe7\rN4\xe6(x\xe7\xe5\xa1p\x0f\x91\xec#:o\x0c\xcb\xb1\x17\xf5M\x9e\xcb\x8f\x1d\xc5\xc3\xce<\x1bD:\x93\x83\xaf:drZ\xd0\x80?\xfb($#\xef\xb8\xe5\xe7"\xf2W8\x93d\x0e\xdb\xf9\x17*\x10\x17\xecrk\xd8\x1fo\xa6\xbe\xb7\xf9\x1b\xc0qa3^-\xea\xbc\x82g\x9d\xb9\x7f\xce\x8f\x12,\xdc~(\xd3\x0e\xf7D\xd8MD\xa8VenpU\x08|\t\xf5\xe2nr4\xb1^j`\xb2V\xdd\xae\x83Gf\x93\r\x03\xe6\xc3f*\xa0r\x07\x0e\x1c\xf2\xbe1\xa1;(J"D\xa9\t\xed\x0e\x13\xddu\x9f\xac\xba\x83h\x0bE\x9a\x12\r\'\x0e\xc7\x99+\x9e\x96,\x8e\x10\xcfG\xbdQ\xda\xf0"\x80\xd4\x83<\xc5\xd1\x7f\x9e\xbb\x06\x11\x8a\xf0\xfce-=\xa1ueq\x01\xa1\xe1N\xd7!\xfe\xe8\x99\x13\xd9,\x86o7\xa2\xd8\xa0\x02J\x1a\xaef\x91j&\xf1\xfa\x81+qd\xfehh\xe4\xf4P\x816\x10\xac\xfd\xfbj^M\xa8D\xe5r\xf7/\xb5\xe6\xab\xf5*{;\x11(\xe0A\xb1\x1f\xaeM z\xc3\xc7V\x9b\x07\x9ajZ\x010\xbdbF\xc3I,1md\xf2o\x8b\xbc\xd62\xad1\x8dLh/{\xa50\x9f#?o\xee\xd6k\xa6\xd2\\\xa5;n\x88\x16\xa8;W\xbe8\x81+u\x14\xf1\xd1\xebl\xf0\x1bl\xd5\xd9\xbea\xbb_\xe16xWxOJ2\xc7p\xc1\xd7\xcbRG*^\xb3A\x06\xa9\x03\x8c]vK\x8a\xed\xecp\x9a\x02<u|O\xce\xfe\x83*\x0b\xc6\x04;t\xd8k\xb9\x8b\xe2f\xa3\xeeT}%\xad\'\xee\x05G\xa4\xa1\x88\xcb\xa2\x14z\xf6\x9d>\x1c\\\xbc\xcd\x7ft\xc6\xb1;\xf8]\xe3\x10\x83@-U\xf0\x16*\xc6\xcb\xfa@~\x1do\x13\xa3\xdd\x84\xee\xc4>\xe6ED\x88\xeen A\xb1\xb6\x8c9Ta5\x8ct&\x90\xa3\r\x91\x08\xf0D\xec\x95\xa4\xed\xe7\xef_\xf4p\x92t\x17\x8bn\x93pk\x15\x84\x06t\xff\x94\xdf\xdb\xe2\x8f\xd2\x93\x81g\x96>\xe2\xc4\x1cm H]4*M\x1e>\xa91&N\x8b\x12\x8f\x83\x93\x0c\xe3\xbam/\x9bVy\xach\xd0\t\xf4\xe5Z\xf5\xb3@]#\xa5\xfb\xa5\x8f\x123Q\xdf>6[\xf7\xdd\xfa\xc5$\xd1\x8d\x06S\x86\x1c6 H|\xc3\xe4\x91\t,Vj\x94\xdd\x8d2\xca\xe6\xb3\xad\x99$\xff}\xa4\xa9*\xce5\xfcU\xf4\x96\xe4\x80\xb1\xb0K\x83\xb6,\x10\xc2\xb9\xbcG\xc8\x1a\x1a\x92\xa2\xef\x8f!b{\xf8\xd8\xa7\xb1\xee\xe1\x1e\x97\x11\xf0\xf4\x9a\na\xa3\xa8aXx{\x83.x\xc8\x0f\xd1\x88\xaa\x19g\xb22&\xfdx)\xb1s\xb3G\xfc<F\xd2\xc7\xbeQ\xa5\xf5D\xb5\x00\xfeE\x9c\x88o.\xe8\x9a\x9c\x11\x9cr\xef\xc3N \x8fI\x16\xcbS\x11\xf3\x84\x14 \xbe\xf2\xa0\x92H:\x02\xfa)\xc8\x1c\x8b\x839e\xa5]p\xd0+\xd0\'f\x8c\xd2\x9f8\x1e\xc3"\xf0\xd2\xc4)Y\x8c\xbfx\xcc\xe6\xca\xc6\xc9\xc4\xc5\xa9\xb1\xb3\x9fw\x1c\xb9\n&A\x1d\x86\xc9)>f\xaa/\xf4\xed\xdc\x86\x8b\xab\x1b\x89\xb0\\\xfb\x88\x8d\x8a=\x1c\x03\xf4\xdff|OR}\xcfr\xbc\x98\x8b\xe7t7\xb1\x9a\xbd\x8fz*\xd5\xb5\x18k[\x9bn\xfd>\xe0X\xf7K\x088\x16cs\xed0\x8d\xee\xcc-o`Q\x9f\x04\xcb\x87\xd0\x917p`Y\xa4O \xecf\t\xea,\x97Q\xb3\xa9k\xfe\xb0\xdf\xc6l\xa7\x89\xf0\xdc\xa5\x9aMM\xcd\xa5^&\r\r\x90\xad\x93\xf6\x19l\xb5W\xe6u\x19\xbe^N\x15\xdfJF\xfcP\xfa\xefnuO[\xe4,!\xcf\xe82r\xb2\x81\x06\xb1\xf8\x92\xd8\xe3>\xff\x1d\xe8\x9c\xcb\xeb\x07\xa4\xfe\x85\xf5&\xb6\xfcJrt;w\x80\xcc~a\xc2\xf7\x08\xffa\x99\x14,\xb6\x08k\xeb\x1d\xf4\xf6\xeb\xd5M\xa6\'\xa9j\xc9k\x0fP\xce\xd7,3\x1f\xcc\xddW:\x02\x03\xbb7\x18\xc54Q\xe50\xe2c(=\x85\x89,0\xd7\xaaJ+\x02\xb3/\xce\x0e\xd3\n\xc2N\xf7\x9bo\xb4\x19\xdb\xc8\x19\xb3\xd0}^\xdc\x80`D\x073}Z\x1d\x81-klf\xadt\xed\x97\xab3M\x97\x99\x11f\xab\xf3\x17\xd16\x8e\xb2eW\x851%0#\x06\t*\x86H\x86\xf7\r\x01\t\x151\x16\x04\x14\xecw|\xc23\rW\xad`\xbf0\x18\xe0\xb7\xc2\x1b\x15\xb0\x8b\xf2010!0\t\x06\x05+\x0e\x03\x02\x1a\x05\x00\x04\x14.\xa0\x17\x15I\xbb\xf1\x94;\x08$na\xc4\xb7;\xda\xaa\xdbQ\x04\x08\x9a\xba\x98\xec*6\xda`\x02\x02\x08\x00'
CERT_CONTENT_NOT_PASSWORD_ENCODED = b'0\x82\t\xb1\x02\x01\x030\x82\tw\x06\t*\x86H\x86\xf7\r\x01\x07\x01\xa0\x82\th\x04\x82\td0\x82\t`0\x82\x04\x17\x06\t*\x86H\x86\xf7\r\x01\x07\x06\xa0\x82\x04\x080\x82\x04\x04\x02\x01\x000\x82\x03\xfd\x06\t*\x86H\x86\xf7\r\x01\x07\x010\x1c\x06\n*\x86H\x86\xf7\r\x01\x0c\x01\x060\x0e\x04\x08\x13\xba]\x97\x87\xd3\xaal\x02\x02\x08\x00\x80\x82\x03\xd0\xc0\xe5q\x11PP}\x18\x0f\x1a)\xba\xa5r\xb8Gx8\xb7\xda\xc2\xe7\x04\x03\x11\xafv?~H\xcb7\xe0,\xf5\xb5\\\x99\x81\xf1nS\x830\x94\xb9:p\r]\xa2\x0f"\xc3\xdf\xdf\xf3\xf5e\x03\xac\x12\xd01\xe1\xb2R\xcb\xdc\x9c\xd2\xc5\x9a"\xe8\x94\x10\xd0\x86 \x8df\x88\xbf\x87>\xc1=\x08p\x18\\\xd9D\x88\xdeS\xea\xa2\xa0\x0b\x1b^jr\xcf\x1f\x00JC"\xcb\x14\xa6P\xdc\x98\x0e\xe0-[\xc1\xbf\rn\xb4\x08\xa9\x89\xbf2\xf7r{\xd6\xd2\x08y\xd0\xda\x7fJ@\xb9[\x82\xe8\xadOD\x8d\xc7YY\xd4z\xd0\x9a\x8a\xae\x00[h\x90\xbb\xb7\xf4\xaf\x12U\r\xc2\xaencb\x9cH\xaf\xc9\x18\xb8\xca]\x90\xd6\r\x8e\xc0ij\xc1\x91\x00\x9c\x85\xb4\xda\xe1>\x178\x05\x93\x9dN\x03~{\xbe\xfa(*\'\x1b\xd1\xf7C\xcf\xe8lF\xec\xe5X\xc8l\xd9\xc8\x852\x0e\xf5\x8eP\x94 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| 1,598.777778
| 7,147
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| 0.101507
| 0.101507
| 0.0975
| 0.092921
| 0.092921
| 0
| 0.244073
| 0.003405
| 14,389
| 8
| 7,148
| 1,798.625
| 0.48689
| 0.011815
| 0
| 0
| 0
| 8
| 0.600774
| 0.599859
| 0
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| 0
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| 0
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| false
| 1
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| 0
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| null | 0
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| 0
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| 1
| 1
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| null | 0
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|
0
| 9
|
2fbdb71a89cd6b4bfe54744d534ad2c1849784a9
| 4,859
|
py
|
Python
|
test_main.py
|
dwsilk/lds-feed-action
|
0c4f74eb72a9b832627bc73ec2ef90c74aecce59
|
[
"MIT"
] | 1
|
2020-02-26T20:45:37.000Z
|
2020-02-26T20:45:37.000Z
|
test_main.py
|
dwsilk/lds-feed-action
|
0c4f74eb72a9b832627bc73ec2ef90c74aecce59
|
[
"MIT"
] | 55
|
2020-02-24T17:16:42.000Z
|
2020-09-28T09:50:37.000Z
|
test_main.py
|
dwsilk/lds-feed-action
|
0c4f74eb72a9b832627bc73ec2ef90c74aecce59
|
[
"MIT"
] | null | null | null |
"""Tests"""
import os
import pytest
from main import main
def test_update_found_using_days(capsys):
"""Test that an update is found using days."""
os.environ["INPUT_LAYERID"] = "52054"
os.environ["INPUT_TIMEFRAME"] = "10000"
os.environ["INPUT_UNITS"] = "days"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::True
::set-output name=datasetTitle::Landonline: Street Address (Deprecated)
::set-output name=revisionNumber::146
::set-output name=publishedTime::Jul 2nd 2017 at 01:47
::set-output name=totalFeatures::1,993,687
::set-output name=adds::43,445
::set-output name=modifies::84,108
::set-output name=deletes::1
"""
)
def test_update_found_using_hours(capsys):
"""Test that an update is found using hours."""
os.environ["INPUT_LAYERID"] = "52054"
os.environ["INPUT_TIMEFRAME"] = "1000000"
os.environ["INPUT_UNITS"] = "hours"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::True
::set-output name=datasetTitle::Landonline: Street Address (Deprecated)
::set-output name=revisionNumber::146
::set-output name=publishedTime::Jul 2nd 2017 at 01:47
::set-output name=totalFeatures::1,993,687
::set-output name=adds::43,445
::set-output name=modifies::84,108
::set-output name=deletes::1
"""
)
def test_no_update_found_using_minutes(capsys):
"""Test that no update is found using minutes."""
os.environ["INPUT_LAYERID"] = "52054"
os.environ["INPUT_TIMEFRAME"] = "5"
os.environ["INPUT_UNITS"] = "minutes"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::False
::set-output name=datasetTitle::None
::set-output name=revisionNumber::None
::set-output name=publishedTime::None
::set-output name=totalFeatures::None
::set-output name=adds::None
::set-output name=modifies::None
::set-output name=deletes::None
"""
)
def test_raises_value_error_wrong_units():
"""Test that ValueError is raised when incorrect units provided."""
with pytest.raises(ValueError):
os.environ["INPUT_LAYERID"] = "52054"
os.environ["INPUT_TIMEFRAME"] = "1"
os.environ["INPUT_UNITS"] = "minute"
main()
def test_update_found_raster(capsys):
"""Test that no update is found using minutes."""
os.environ["INPUT_LAYERID"] = "104485"
os.environ["INPUT_TIMEFRAME"] = "10000"
os.environ["INPUT_UNITS"] = "days"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::True
::set-output name=datasetTitle::Chatham Islands 0.5m Satellite Imagery (2014-2019)
::set-output name=revisionNumber::4
::set-output name=publishedTime::Feb 20th 2020 at 11:32
::set-output name=totalFeatures::None
::set-output name=adds::None
::set-output name=modifies::None
::set-output name=deletes::None
"""
)
def test_no_update_found_raster(capsys):
"""Test that no update is found using minutes."""
os.environ["INPUT_LAYERID"] = "104485"
os.environ["INPUT_TIMEFRAME"] = "5"
os.environ["INPUT_UNITS"] = "minutes"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::False
::set-output name=datasetTitle::None
::set-output name=revisionNumber::None
::set-output name=publishedTime::None
::set-output name=totalFeatures::None
::set-output name=adds::None
::set-output name=modifies::None
::set-output name=deletes::None
"""
)
def test_update_found_table(capsys):
"""Test that no update is found using minutes."""
os.environ["INPUT_LAYERID"] = "51741"
os.environ["INPUT_TIMEFRAME"] = "10000"
os.environ["INPUT_UNITS"] = "days"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::True
::set-output name=datasetTitle::Landonline: Road Name Association (Deprecated)
::set-output name=revisionNumber::148
::set-output name=publishedTime::Jul 2nd 2017 at 01:24
::set-output name=totalFeatures::258,503
::set-output name=adds::26,865
::set-output name=modifies::0
::set-output name=deletes::26,823
"""
)
def test_no_update_found_table(capsys):
"""Test that no update is found using minutes."""
os.environ["INPUT_LAYERID"] = "51741"
os.environ["INPUT_TIMEFRAME"] = "5"
os.environ["INPUT_UNITS"] = "minutes"
main()
captured = capsys.readouterr()
assert (
captured.out
== """::set-output name=updateFound::False
::set-output name=datasetTitle::None
::set-output name=revisionNumber::None
::set-output name=publishedTime::None
::set-output name=totalFeatures::None
::set-output name=adds::None
::set-output name=modifies::None
::set-output name=deletes::None
"""
)
| 29.628049
| 82
| 0.683062
| 631
| 4,859
| 5.171157
| 0.161648
| 0.154459
| 0.223108
| 0.109409
| 0.85688
| 0.821637
| 0.821637
| 0.821637
| 0.800797
| 0.749004
| 0
| 0.043563
| 0.159086
| 4,859
| 163
| 83
| 29.809816
| 0.755017
| 0.076147
| 0
| 0.755556
| 0
| 0
| 0.593743
| 0.202116
| 0
| 0
| 0
| 0
| 0.051852
| 1
| 0.059259
| false
| 0
| 0.022222
| 0
| 0.081481
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
2fcfa27fd982cf430d17d02f3a603d6ee576e27e
| 520
|
py
|
Python
|
tests/test_graphtranslation.py
|
breecummins/dsgrn_utilities
|
65eb691c6f038060c803ea0a9f058f3d52fa0e4c
|
[
"MIT"
] | null | null | null |
tests/test_graphtranslation.py
|
breecummins/dsgrn_utilities
|
65eb691c6f038060c803ea0a9f058f3d52fa0e4c
|
[
"MIT"
] | null | null | null |
tests/test_graphtranslation.py
|
breecummins/dsgrn_utilities
|
65eb691c6f038060c803ea0a9f058f3d52fa0e4c
|
[
"MIT"
] | null | null | null |
import dsgrn_utilities.graphtranslation as gt
def test():
netspec = "A : (A + B) : E\nB : (B)(~A) : E\nC : (A) : E"
assert(netspec == gt.createEssentialNetworkSpecFromGraph(gt.getGraphFromNetworkSpec(netspec)))
assert(netspec == gt.nxgraph2netspec(gt.netspec2nxgraph(netspec)))
netspec = "Z : (~x) : E\nx : (y)(~Z) : E\ny : (y) : E"
assert(netspec == gt.createEssentialNetworkSpecFromGraph(gt.getGraphFromNetworkSpec(netspec)))
assert(netspec == gt.nxgraph2netspec(gt.netspec2nxgraph(netspec)))
| 52
| 98
| 0.690385
| 59
| 520
| 6.067797
| 0.40678
| 0.145251
| 0.167598
| 0.089385
| 0.765363
| 0.765363
| 0.765363
| 0.765363
| 0.765363
| 0.765363
| 0
| 0.008989
| 0.144231
| 520
| 9
| 99
| 57.777778
| 0.795506
| 0
| 0
| 0.5
| 0
| 0.25
| 0.167308
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.125
| false
| 0
| 0.125
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
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| null | 0
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| 0
|
0
| 8
|
643cd919ced648d12169149bb045a708870494c9
| 79,768
|
py
|
Python
|
parser/team25/code/astDML.py
|
webdev188/tytus
|
847071edb17b218f51bb969d335a8ec093d13f94
|
[
"MIT"
] | 35
|
2020-12-07T03:11:43.000Z
|
2021-04-15T17:38:16.000Z
|
parser/team25/code/astDML.py
|
webdev188/tytus
|
847071edb17b218f51bb969d335a8ec093d13f94
|
[
"MIT"
] | 47
|
2020-12-09T01:29:09.000Z
|
2021-01-13T05:37:50.000Z
|
parser/team25/code/astDML.py
|
webdev188/tytus
|
847071edb17b218f51bb969d335a8ec093d13f94
|
[
"MIT"
] | 556
|
2020-12-07T03:13:31.000Z
|
2021-06-17T17:41:10.000Z
|
import typeChecker.typeEnum as typeEnum
from astExpresion import ExpresionID
from astDDL import Instruccion
import storageManager.jsonMode as DBMS
from useDB.instanciaDB import DB_ACTUAL
import typeChecker.typeReference as TypeCheck
import entorno as env
from astExpresion import TIPO_DE_DATO
from astFunciones import FuncionTime,FuncionCadena
from useDB import instanciaDB
from reporteErrores.errorReport import ErrorReport
import sqlErrors as sqlErrors
def parseToNumber(input:str):
try:
result=int(input)
# print("Es un entero")
return result
except ValueError:
try:
result=float(input)
# print("Es un float")
return result
except ValueError:
# print("No es un número")
return None
# Returns True if the column is already a key in this dictionary, otherwise, returns False
def checkColumnsDictionary(dictionary:dict,key:str) -> bool:
try:
a=dictionary[key]
return True
except:
return False
def getColumnIndex(columnList:list,name:str):
try:
return columnList.index(name)
except:
return None
# ------------------------ DML ----------------------------
# Insert into table
class InsertTable(Instruccion):
def __init__(self, tabla, valores,listaColumnas=None,linea=0):
self.tabla = tabla
self.valores = valores
self.listaColumnas=listaColumnas
self.linea=linea
def dibujar(self):
identificador = str(hash(self))
nodo = "\n" + identificador + "[ label = \"INSERT TABLE\" ];"
nodo += "\nINTO" + identificador + "[ label = \"INTO\" ];"
nodo += "\n" + identificador + " -> INTO" + identificador + ";"
nodo += "\nNAME" + identificador + "[ label = \"" + self.tabla + "\" ];"
nodo += "\nINTO" + identificador + " -> NAME" + identificador + ";"
nodo += "\nVALUES" + identificador + "[ label = \"VALUES\" ];"
nodo += "\n" + identificador + " -> VALUES" + identificador + ";"
for valor in self.valores:
nodo += "\nVALUES" + identificador + " -> " + str(hash(valor)) + ";"
nodo += valor.dibujar()
return nodo
def ejecutar(self,ts):
tabla1=self.tabla
valores1=self.valores
listaCol=self.listaColumnas
dbName=DB_ACTUAL.getName()
# dbName="test"
# Check if a db is in use
if dbName==None:
# Add this error to the errors Tree
return ErrorReport('Semántico',"ERROR: database does not exist",self.linea)
else:
# Check if the table exists into the db
tables=TypeCheck.tableExist(dbName,tabla1)
if tables == False:
sqlError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_table
return ErrorReport('Semántico',"ERROR "+sqlError.value+": "+str(sqlError.name)+" \'"+tabla1+"\'",self.linea)
else:
flag=False
counter=0
result = []
columnsNameList=list(TypeCheck.getColumns(dbName,tabla1).keys())
if listaCol==None:
# INSERT CHECKING ALL COLUMNS ON THEIR RESPECTIVE ORDER
if len(columnsNameList)==len(valores1):
for val in valores1:
if isinstance(val,FuncionTime) or isinstance(val,FuncionCadena):
val=val.ejecutar(ts)
if val.tipo==TIPO_DE_DATO.CADENA:
if TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="varchar".casefold() or TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="character varying".casefold():
if len(val.val)<=TypeCheck.getLenght(dbName,tabla1,columnsNameList[counter]):
if len(val.val)==0:
if TypeCheck.getNull(dbName,tabla1,columnsNameList[counter]):
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter])!=None:
result.append(TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter]))
else:
result.append(None)
else:
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter])!=None:
result.append(TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter]))
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.null_value_not_allowed
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
print("ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name))
flag=True
break
else:
result.append(val.val)
else:
return ErrorReport('Semántico',"ERROR: la longitud de la cadena es mayor a lo permitido por el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="char".casefold() or TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="character".casefold():
if len(val.val.encode('utf-8'))<=TypeCheck.getLenght(dbName,tabla1,columnsNameList[counter]):
if len(val.val)==0:
if TypeCheck.getNull(dbName,tabla1,columnsNameList[counter]):
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter])!=None:
result.append(TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter]))
else:
result.append(None)
else:
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter])!=None:
result.append(TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter]))
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.null_value_not_allowed
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
else:
return ErrorReport('Semántico',"ERROR: el tamaño de la cadena es mayor a lo permitido por el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="text".casefold():
if len(val.val)==0:
if TypeCheck.getNull(dbName,tabla1,columnsNameList[counter]):
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter])!=None:
result.append(TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter]))
else:
result.append(None)
else:
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter])!=None:
result.append(TypeCheck.detDefault(dbName,tabla1,columnsNameList[counter]))
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.null_value_not_allowed
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="date".casefold() or\
TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="time".casefold() or\
TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="interval".casefold() :
if val.isFecha:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[counter])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[counter]))
if val.val in enumVals:
result.append(val.val)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[counter]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
elif val.tipo==TIPO_DE_DATO.ENTERO:
if TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="smallint".casefold():
if val.val>=-32768 and val.val<=32767:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
else:
return ErrorReport('Semántico',"ERROR: el tamaño del número no coincide con el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="integer".casefold():
if val.val>=-2147483648 and val.val<=2147483647:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
else:
return ErrorReport('Semántico',"ERROR: el tamaño del número no coincide con el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="bigint".casefold():
if val.val>=-9223372036854775808 and val.val<=9223372036854775807:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
else:
return ErrorReport('Semántico',"ERROR: el tamaño del número no coincide con el tipo de dato",self.linea)
flag=True
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[counter])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[counter]))
if val.val in enumVals:
result.append(val.val)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[counter]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
elif val.tipo==TIPO_DE_DATO.DECIMAL:
if TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="decimal".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="numeric".casefold():
if not TypeCheck.getLenght(dbName,tabla1,columnsNameList[counter])==None:
precision=TypeCheck.getPrecision(dbName,tabla1,columnsNameList[counter])
scale_=TypeCheck.getScale(dbName,tabla1,columnsNameList[counter])
flagPrecision=False
flagScale=False
if str(val.val).index('.')==len(str(val.val))-scale_-1 or str(val.val).index('.')==len(str(val.val))-scale_:
flagPrecision=True
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.numeric_value_out_of_range
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+str(val.val)+"' no coincide con con la escala del campo",self.linea)
if len(str(val.val).replace('.',''))<=precision:
flagScale=True
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.numeric_value_out_of_range
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+str(val.val)+"' no coincide con con la precisión del campo",self.linea)
if flagPrecision and flagScale:
result.append(val.val)
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="real".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="double_precision".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="money".casefold():
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
result.append(val.val)
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[counter])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[counter]))
if val.val in enumVals:
result.append(val.val)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[counter]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
elif val.tipo==TIPO_DE_DATO.BOOLEANO:
if TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="boolean".casefold():
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[counter]).lower()
dictCheck={columnsNameList[counter]:val.val}
if eval(check,dictCheck):
result.append(val.val)
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
result.append(val.val)
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[counter])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[counter]))
if val.val in enumVals:
result.append(val.val)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[counter]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
counter+=1
if not flag:
print("Llamando a la funcion insert("+dbName+","+tabla1+","+str(result)+").")
print(DBMS.insert(dbName,tabla1,result))
else:
print("ERROR AL INSERTAR EN LA BD")
else:
return ErrorReport('Semántico',"ERROR: La cantidad de parámetros indicados no coincide con los campos de la tabla",self.linea)
# INSERT VALIDATING THE COLUMNS LIST
else:
if len(listaCol)==len(valores1):
columnsDict={}
columnsIndex=0
counter=0
for val in valores1:
if isinstance(val,FuncionTime):
val=val.ejecutar(ts)
colIndex=getColumnIndex(columnsNameList,listaCol[counter])
if not colIndex==None:
if not checkColumnsDictionary(columnsDict,listaCol[counter]):
if val.tipo==TIPO_DE_DATO.CADENA:
if TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="varchar".casefold() or TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="character varying".casefold():
if len(val.val)<=TypeCheck.getLenght(dbName,tabla1,columnsNameList[colIndex]):
if len(val.val)==0:
if TypeCheck.getNull(dbName,tabla1,columnsNameList[colIndex]):
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])!=None:
columnsDict[listaCol[counter]]=TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])
else:
columnsDict[listaCol[counter]]=None
else:
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])!=None:
columnsDict[listaCol[counter]]=TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.null_value_not_allowed
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
else:
return ErrorReport('Semántico',"ERROR: la longitud de la cadena es mayor a lo permitido por el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="char".casefold() or TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="character".casefold():
if len(val.val.encode('utf-8'))<=TypeCheck.getLenght(dbName,tabla1,columnsNameList[colIndex]):
if len(val.val)==0:
if TypeCheck.getNull(dbName,tabla1,columnsNameList[colIndex]):
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])!=None:
columnsDict[listaCol[counter]]=TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])
else:
columnsDict[listaCol[counter]]=None
else:
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])!=None:
columnsDict[listaCol[counter]]=TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.null_value_not_allowed
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
else:
return ErrorReport('Semántico',"ERROR: el tamaño de la cadena es mayor a lo permitido por el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[counter]).casefold()=="text".casefold():
if len(val.val)==0:
if TypeCheck.getNull(dbName,tabla1,columnsNameList[colIndex]):
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])!=None:
columnsDict[listaCol[counter]]=TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])
else:
columnsDict[listaCol[counter]]=None
else:
if TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])!=None:
columnsDict[listaCol[counter]]=TypeCheck.detDefault(dbName,tabla1,columnsNameList[colIndex])
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.null_value_not_allowed
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
else:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
elif TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="date".casefold() or\
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="time".casefold() or\
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="interval".casefold() :
if val.isFecha:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]))
if val.val in enumVals:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_column
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+" 1 "+listaCol[counter],self.linea)
elif val.tipo==TIPO_DE_DATO.ENTERO:
if TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="smallint".casefold():
if val.val>=-32768 and val.val<=32767:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
print("ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name))
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
else:
return ErrorReport('Semántico',"ERROR: el tamaño del número no coincide con el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="integer".casefold():
if val.val>=-2147483648 and val.val<=2147483647:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
print("ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name))
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
else:
return ErrorReport('Semántico',"ERROR: el tamaño del número no coincide con el tipo de dato",self.linea)
flag=True
elif TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="bigint".casefold():
if val.val>=-9223372036854775808 and val.val<=9223372036854775807:
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
else:
return ErrorReport('Semántico',"ERROR: el tamaño del número no coincide con el tipo de dato",self.linea)
flag=True
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]))
if val.val in enumVals:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
elif val.tipo==TIPO_DE_DATO.DECIMAL:
if TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="decimal".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="numeric".casefold():
if not TypeCheck.getLenght(dbName,tabla1,columnsNameList[colIndex])==None:
precision=TypeCheck.getPrecision(dbName,tabla1,columnsNameList[colIndex])
scale_=TypeCheck.getScale(dbName,tabla1,columnsNameList[colIndex])
flagPrecision=False
flagScale=False
if str(val.val).index('.')==len(str(val.val))-scale_-1 or str(val.val).index('.')==len(str(val.val))-scale_:
flagPrecision=True
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.numeric_value_out_of_range
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+str(val.val)+"' no coincide con con la escala del campo",self.linea)
if len(str(val.val).replace('.',''))<=precision:
flagScale=True
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.numeric_value_out_of_range
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+str(val.val)+"' no coincide con con la precisión del campo",self.linea)
if flagPrecision and flagScale:
columnsDict[listaCol[counter]]=val.val
elif TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="real".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="double_precision".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="money".casefold():
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]))
if val.val in enumVals:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+ " "+columnsNameList[colIndex] + " "+str(val.val),self.linea)
# FALTA COMPROBAR FECHAS Y TYPES
elif val.tipo==TIPO_DE_DATO.DATE:
if TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="date".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="time".casefold() or \
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="interval".casefold():
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
else:
columnsDict[listaCol[counter]]=val.val
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]))
if val.val in enumVals:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]),self.linea)
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+ " "+columnsNameList[colIndex] + " "+str(val.val),self.linea)
elif val.tipo==TIPO_DE_DATO.BOOLEANO and \
TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]).casefold()=="boolean".casefold():
if not TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex])==None:
check=TypeCheck.getCheck(dbName,tabla1,columnsNameList[colIndex]).lower()
dictCheck={columnsNameList[colIndex]:val.val}
if eval(check,dictCheck):
columnsDict[listaCol[counter]]=val.val
else:
sqlTypeError=sqlErrors.sql_error_integrity_constraint_violation.check_violation
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name),self.linea)
flag=True
break
elif typeEnum.enumExist(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex])):
enumVals=typeEnum.getEnum(TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]))
if val.val in enumVals:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_data_exception.invalid_parameter_value
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+": el valor indicado '"+val.val+"' no coincide con el tipo "+TypeCheck.getType(dbName,tabla1,columnsNameList[colIndex]),self.linea)
else:
columnsDict[listaCol[counter]]=val.val
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name) + " "+columnsNameList[colIndex] + " "+str(val.val),self.linea)
else:
flag=True
return ErrorReport('Semántico',"ERROR: se está intentando ingresar una misma columna dentro de una sentencia INSERT",self.linea)
break
counter+=1
else:
flag=True
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_column
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+" 2 "+listaCol[counter],self.linea)
if not flag:
# THIS SECOND FLAG WILL CHECK IF A REMAINING COLUMN CAN BE NULL, IF NOT, RETURNS TRUE AND IS AN ERROR
secondFlag=False
for column in columnsNameList:
# print(column)
if not checkColumnsDictionary(columnsDict,column):
# print("La columna "+column+" no esta especificada en el diccionario, buscando si puede tener un valor nulo")
# THIS MEANS A COLUMN EXISTS IN THE TABLE, BUT IT WAS NOT DECLARED ON THE INSERT SENTENCE
# print("La anulabilidad de "+column+" es "+ str(TypeCheck.getNull(dbName,tabla1,column)))
# print("El valor default de "+column+ " es "+str(TypeCheck.getDefault(dbName,tabla1,column)))
if not TypeCheck.getNull(dbName,tabla1,column):
# IF GETNULL RETURNS TRUE, THE DEFAULT VALUE HAS TO BE INSERTED, SO LET'S FIND IT
if TypeCheck.getDefault(dbName,tabla1,column)!=None:
# IF THE DEFAULT VALUE IS DISTINCT FROM NULL (IN THIS CASE IS NONE), APPEND IT TO THE INSERT LIST
result.append(TypeCheck.getDefault(dbName,tabla1,column))
else:
# THERE WAS NOT A DEFAULT VALUE FOR THIS COLUMN, SO THIS IS AN ERROR, SO WE HAVE TO BREAK THE CYCLE
secondFlag=True
break
else:
if TypeCheck.getDefault(dbName,tabla1,column)!=None:
result.append(TypeCheck.getDefault(dbName,tabla1,column))
else:
# THERE WAS NOT A DEFAULT VALUE FOR THIS COLUMN, SO THIS IS AN ERROR, SO WE HAVE TO BREAK THE CYCLE
result.append(None)
else:
# IF THE COLUMN EXISTS IN THE DICTIONARY, THE JUST APPEND IT TO THE INSERT LIST
# THE ORDER IS DEFINED BY COLUMNSNAMELIST
result.append(columnsDict[column])
# CHECKING IF NO ERRORS WERE PRODUCED
if not secondFlag:
# FINALLY, WE MUST ENSURE ALL THE COLUMNS FROM THE DICTIONARY ARE COLUMNS FROM THE TABLE
# WE ARE GONNA USE A THIRD FLAG :/
thirdFlag=False
for itemDict in list(columnsDict.keys()):
# print(itemDict)
if not itemDict in columnsNameList:
thirdFlag=True
break
if not thirdFlag:
print("Llamando a la funcion insert("+dbName+","+tabla1+","+str(result)+").")
print(DBMS.insert(dbName,tabla1,result))
else:
sqlTypeError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_column
return ErrorReport('Semántico',"ERROR "+sqlTypeError.value+": "+str(sqlTypeError.name)+" 3 "+listaCol[counter],self.linea)
else:
return ErrorReport('Semántico',"ERROR: Una o varias columnas no han sido especificadas y no pueden ser nulas",self.linea)
else:
print("ERROR AL INSERTAR EN LA BD")
else:
return ErrorReport('Semántico',"ERROR: La cantidad de parámetros no coincide con la cantidad de datos a ingresar",self.linea)
# Delete from a table
class DeleteTabla(Instruccion):
def __init__(self, tabla, condiciones = None,linea=0):
self.tabla = tabla
self.condiciones = condiciones
self.linea=linea
def dibujar(self):
identificador = str(hash(self))
nodo = "\n" + identificador + "[ label = \"DELETE TABLE\" ];"
nodo += "\nFROM" + identificador + "[ label = \"FROM\" ];"
nodo += "\n" + identificador + " -> FROM" + identificador + ";"
nodo += "\nNAME" + identificador + "[ label = \"" + self.tabla + "\" ];"
nodo += "\nFROM" + identificador + " -> NAME" + identificador + ";"
if self.condiciones:
nodo += "\nWHERE" + identificador + "[ label = \"WHERE\" ];"
nodo += "\n" + identificador + " -> WHERE" + identificador + ";"
for condicion in self.condiciones:
nodo += "\nWHERE" + identificador + " -> " + str(hash(condicion)) + ";"
nodo += condicion.dibujar()
return nodo
def transalateExpresion(self,input_:str)->str:
input_=input_.replace(" = "," == ")
input_=input_.replace(" ^ "," ** ")
return input_
def ejecutar(self,ts):
# print(self.tabla)
# print(self.condiciones.getExpresionToString())
# print(self.linea)
dbName=DB_ACTUAL.getName()
# dbName="test"
# Check if a db is in use
if dbName==None:
# Add this error to the errors Tree
return ErrorReport('Semántico',"ERROR: database \'"+str(dbName)+"\' does not exist",self.linea)
else:
# Check if the table exists into the db
tables=TypeCheck.tableExist(dbName,self.tabla)
if not tables:
sqlError=sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_table
return ErrorReport('Semántico',"ERROR "+sqlError.value+": "+str(sqlError.name)+" \'"+self.tabla+"\'",self.linea)
else:
# dataList returns a list of lists
dataList=DBMS.extractTable(dbName,self.tabla)
# columnsList returns a list with the name of the columns
if not self.condiciones==None:
print(self.transalateExpresion(self.condiciones.getExpresionToString()))
try:
columnsList=list(TypeCheck.getColumns(dbName,self.tabla))
evalDict={}
deleted=False
counter=0
for data in dataList:
index=len(data)
for i in range(index):
evalDict[columnsList[i]]=data[i]
if eval(self.transalateExpresion(self.condiciones.getExpresionToString()),evalDict):
pkFound=False
pkLists=[]
for item in columnsList:
if TypeCheck.getPK(dbName,self.tabla,item):
pkIndex=columnsList.index(item)
pkLists.append(data[pkIndex])
pkFound=True
if pkFound:
print(DBMS.delete(dbName,self.tabla,pkLists))
deleted=True
counter+=1
else:
print(DBMS.delete(dbName,self.tabla,dataList.index(data)))
deleted=True
counter+=1
if deleted:
print("Sentencia DELETE ejecutada correctamente, "+str(counter)+" filas afectadas.")
else:
print("Ninguna fila fue eliminada.")
except Exception as e:
sqlError=sqlErrors.sql_error_internal_error.internal_error
print(e)
return ErrorReport('Semántico',"ERROR "+sqlError.value+": "+str(sqlError.name),self.linea)
else:
try:
columnsList=list(TypeCheck.getColumns(dbName,self.tabla))
evalDict={}
deleted=False
counter=0
for data in dataList:
index=len(data)
for i in range(index):
evalDict[columnsList[i]]=data[i]
pkFound=False
pkLists=[]
for item in columnsList:
if TypeCheck.getPK(dbName,self.tabla,item):
pkIndex=columnsList.index(item)
pkLists.append(data[pkIndex])
pkFound=True
if pkFound:
print(DBMS.delete(dbName,self.tabla,pkLists))
deleted=True
counter+=1
else:
print(DBMS.delete(dbName,self.tabla,dataList.index(data)))
deleted=True
counter+=1
if deleted:
print("Sentencia DELETE ejecutada correctamente, "+str(counter)+" filas afectadas.")
else:
print("Ninguna fila fue eliminada.")
except:
sqlError=sqlErrors.sql_error_internal_error.internal_error
return ErrorReport('Semántico',"ERROR "+sqlError.value+": "+str(sqlError.name),self.linea)
class UpdateTable(Instruccion):
def __init__(self, tabla, asignaciones, condiciones = None):
self.tabla = tabla
self.asignaciones = asignaciones
self.condiciones = condiciones
def dibujar(self):
identificador = str(hash(self))
nodo = "\n" + identificador + "[ label = \"DELETE TABLE\" ];"
nodo += "\nNAME" + identificador + "[ label = \"" + self.tabla + "\" ];"
nodo += "\n" + identificador + " -> NAME" + identificador + ";"
nodo += "\nSET" + identificador + "[ label = \"SET\" ];"
nodo += "\n" + identificador + " -> SET" + identificador + ";"
for asignacion in self.asignaciones:
nodo += "\nSET" + identificador + " -> " + str(hash(asignacion)) + ";"
nodo += "\n" + str(hash(asignacion)) + "[ label = \" " + asignacion + " = " + str(self.asignaciones[asignacion].val) + "\" ];"
if self.condiciones:
nodo += "\nWHERE" + identificador + "[ label = \"WHERE\" ];"
nodo += "\n" + identificador + " -> WHERE" + identificador + ";"
for condicion in self.condiciones:
nodo += "\nWHERE" + identificador + " -> " + str(hash(condicion)) + ";"
nodo += condicion.dibujar()
return nodo
def ejecutar(self, ts):
try:
if DB_ACTUAL.getName() == None:
return ErrorReport('Semantico', 'Not defined database to used', 0)
elif not TypeCheck.databaseExist(DB_ACTUAL.getName()):
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_invalid_schema_name.invalid_schema_name), 0)
elif not TypeCheck.tableExist(DB_ACTUAL.getName(), self.tabla):
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_table), 0)
columnas = TypeCheck.getColumns(DB_ACTUAL.getName(),self.tabla)
columnasIndex = self.__auxDict(columnas)
#Evaluamos las nuevas expresiones xd
posiblesValores = dict()
for asign in self.asignaciones:
temp = self.asignaciones[asign].ejecutar(None)
if isinstance(temp, ErrorReport):
return temp
posiblesValores[asign] = temp
#Procesamos cada valor para determinar si podemos ingresar
for valor in posiblesValores:
temp = self.__processUpdateField(columnas[valor], posiblesValores[valor])
if isinstance(temp, ErrorReport):
return temp
posiblesValores[valor] = temp
#Ahora evaluamos los checks en base a cada registroCambiado
for cheq in posiblesValores:
if columnas[cheq]['Check']:
try:
posible = eval(columnas[cheq]['Check'], posiblesValores)
if not posible:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.check_violation), 0)
except:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.integrity_constraint_violation), 0)
#Traemos todos los valores de las columnas
registros = DBMS.extractTable(DB_ACTUAL.getName(),self.tabla)
#Validacion WHERE
if self.condiciones:
tsLocal = env.Entorno()
tsLocal = env.toEnviroment(columnas,tsLocal)
auxAceptadas = list()
for reg in range(len(registros)):
#Insertamos valores en la tabla de simbolos
for field in columnasIndex:
tsLocal.modificarSimbolo(field, registros[reg][columnasIndex[field]])
valido = self.condiciones.ejecutar(tsLocal)
if valido.tipo != TIPO_DE_DATO.BOOLEANO:
raise Exception()
if valido.val:
auxAceptadas.append( reg )
pkWhere = TypeCheck.getIndexPK(DB_ACTUAL.getName(), self.tabla)
#Con PK
if len(pkWhere) != 0:
for regCamb in auxAceptadas:
pkReg = list()
for pk in pkWhere:
pkReg.append(registros[regCamb][pk])
DBMS.update(DB_ACTUAL.getName(),self.tabla,posiblesValores,pkReg)
#Sin PK
else:
for regCamb in auxAceptadas:
DBMS.update(DB_ACTUAL.getName(),self.tabla,posiblesValores,[regCamb])
#SIN WHERE
else:
pkNormal = TypeCheck.getIndexPK(DB_ACTUAL.getName(), self.tabla)
#Con PK
if len(pkNormal) != 0:
for regCamb in registros:
pkReg = list()
for pk in pkNormal:
pkReg.append(regCamb[pk])
DBMS.update(DB_ACTUAL.getName(),self.tabla,posiblesValores,pkReg)
#Sin PK
else:
for regCamb in range(len(registros)):
DBMS.update(DB_ACTUAL.getName(),self.tabla,posiblesValores,[regCamb])
return 'Deleted successful'
except:
return ErrorReport('Semantico', 'Error Fatal', 0)
def __auxDict(self,columns: dict) -> dict:
dicc = dict()
aux = 0
for item in columns:
dicc[item] = aux
aux += 1
return dicc
def __processUpdateField(self, pureColumn: dict, newValue: any) -> any:
#Comprobamos esto antes de seguir
if pureColumn['PK']:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.integrity_constraint_violation), 0)
if pureColumn['FK']:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.foreign_key_violation), 0)
if pureColumn['Unique']:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.unique_violation), 0)
#Comprobación de tipos
pureValue = newValue.val
if pureValue != None:
if pureColumn['Type'] in ("SMALLINT", "BIGINT", "INTEGER") and newValue.tipo == TIPO_DE_DATO.ENTERO:
if pureColumn['Type'] == "SMALLINT" and ( pureValue <= 32767 and pureValue >= -32768 ):
pass
elif pureColumn['Type'] == "BIGINT" and ( pureValue <= 9223372036854775807 and pureValue >= -9223372036854775808 ):
pass
elif pureColumn['Type'] == "INTEGER" and ( pureValue <= 2147483647 and pureValue >= -2147483648 ):
pass
else:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch), 0)
elif pureColumn['Type'] in ("DECIMAL", "NUMERIC", "REAL", "DOUBLE_PRECISION", "MONEY") and \
(newValue.tipo == TIPO_DE_DATO.ENTERO or newValue.tipo == TIPO_DE_DATO.DECIMAL):
#Forma de calcular la forma xd
auxDecimal = (len(str(pureValue)),0)
if isinstance(pureValue, float):
auxDecimal = str(pureValue).split('.')
auxDecimal = (len(str(pureValue)),len(auxDecimal[1]))
if pureColumn['Type'] in ("DECIMAL","NUMERIC") and \
(auxDecimal[0] <= pureColumn['Lenght']['Precision'] and auxDecimal[1] <= pureColumn['Lenght']['Scale']) :
pass
elif pureColumn['Type'] == "REAL" and auxDecimal[1] < 6:
pass
elif pureColumn['Type'] == "DOUBLE_PRECISION" and auxDecimal[1] < 15:
pass
elif pureColumn['Type'] == "MONEY" and auxDecimal[1] < 2:
pass
else:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch), 0)
elif pureColumn['Type'] in ("CHAR", "TEXT", "VARCHAR", "DATE") and newValue.tipo == TIPO_DE_DATO.CADENA:
if pureColumn['Type'] in ("CHAR","VARCHAR") and len(pureValue) < pureColumn['Lenght']:
pass
elif pureColumn['Type'] == "DATE" and newValue.isFecha:
pass
elif pureColumn['Type'] == "TEXT":
pass
else:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch), 0)
elif pureColumn['Type'] == "BOOLEAN" and newValue.tipo == TIPO_DE_DATO.BOOLEANO:
pass
elif typeEnum.enumExist(pureColumn['Type']):
if not pureValue in typeEnum.getEnum(pureColumn['Type']):
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch), 0)
else:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.datatype_mismatch), 0)
#Comprobaciones especiales, excluyendo checks
if not pureColumn['Null'] and pureValue == None:
return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.not_null_violation), 0)
return pureValue
| 75.111111
| 266
| 0.455032
| 5,835
| 79,768
| 6.133162
| 0.073522
| 0.051974
| 0.107134
| 0.072373
| 0.820018
| 0.803141
| 0.796434
| 0.780647
| 0.76743
| 0.750161
| 0
| 0.009885
| 0.464785
| 79,768
| 1,062
| 267
| 75.111111
| 0.828356
| 0.029172
| 0
| 0.760383
| 0
| 0
| 0.056599
| 0.000491
| 0
| 0
| 0
| 0.000942
| 0
| 1
| 0.015974
| false
| 0.011715
| 0.01278
| 0
| 0.138445
| 0.020234
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ff27dd9ff8a033afd495a6f1e5bc3bd916ee493a
| 46,778
|
py
|
Python
|
poropy/nucleardata/nucleardata.py
|
robertsj/poropy
|
481a604339c9fa797817b2b0a55448329685c1d8
|
[
"MIT"
] | 1
|
2018-02-11T11:24:14.000Z
|
2018-02-11T11:24:14.000Z
|
poropy/nucleardata/nucleardata.py
|
robertsj/poropy
|
481a604339c9fa797817b2b0a55448329685c1d8
|
[
"MIT"
] | 1
|
2018-06-12T16:15:31.000Z
|
2018-06-12T16:15:31.000Z
|
poropy/nucleardata/nucleardata.py
|
robertsj/poropy
|
481a604339c9fa797817b2b0a55448329685c1d8
|
[
"MIT"
] | 7
|
2015-03-27T16:58:07.000Z
|
2022-01-01T17:44:02.000Z
|
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 1.3.40
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
# This file is compatible with both classic and new-style classes.
from sys import version_info
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import dirname
import imp
fp = None
try:
fp, pathname, description = imp.find_module('_nucleardata', [dirname(__file__)])
except ImportError:
import _nucleardata
return _nucleardata
if fp is not None:
try:
_mod = imp.load_module('_nucleardata', fp, pathname, description)
finally:
fp.close()
return _mod
_nucleardata = swig_import_helper()
del swig_import_helper
else:
import _nucleardata
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
if (name == "thisown"): return self.this.own(value)
if (name == "this"):
if type(value).__name__ == 'SwigPyObject':
self.__dict__[name] = value
return
method = class_type.__swig_setmethods__.get(name,None)
if method: return method(self,value)
if (not static) or hasattr(self,name):
self.__dict__[name] = value
else:
raise AttributeError("You cannot add attributes to %s" % self)
def _swig_setattr(self,class_type,name,value):
return _swig_setattr_nondynamic(self,class_type,name,value,0)
def _swig_getattr(self,class_type,name):
if (name == "thisown"): return self.this.own()
method = class_type.__swig_getmethods__.get(name,None)
if method: return method(self)
raise AttributeError(name)
def _swig_repr(self):
try: strthis = "proxy of " + self.this.__repr__()
except: strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
try:
_object = object
_newclass = 1
except AttributeError:
class _object : pass
_newclass = 0
class NUCLEAR_DATA(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, NUCLEAR_DATA, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, NUCLEAR_DATA, name)
__repr__ = _swig_repr
__swig_setmethods__["SM2_NO_XE"] = _nucleardata.NUCLEAR_DATA_SM2_NO_XE_set
__swig_getmethods__["SM2_NO_XE"] = _nucleardata.NUCLEAR_DATA_SM2_NO_XE_get
if _newclass:SM2_NO_XE = _swig_property(_nucleardata.NUCLEAR_DATA_SM2_NO_XE_get, _nucleardata.NUCLEAR_DATA_SM2_NO_XE_set)
__swig_setmethods__["M2_NO_XE"] = _nucleardata.NUCLEAR_DATA_M2_NO_XE_set
__swig_getmethods__["M2_NO_XE"] = _nucleardata.NUCLEAR_DATA_M2_NO_XE_get
if _newclass:M2_NO_XE = _swig_property(_nucleardata.NUCLEAR_DATA_M2_NO_XE_get, _nucleardata.NUCLEAR_DATA_M2_NO_XE_set)
__swig_setmethods__["M2_XE"] = _nucleardata.NUCLEAR_DATA_M2_XE_set
__swig_getmethods__["M2_XE"] = _nucleardata.NUCLEAR_DATA_M2_XE_get
if _newclass:M2_XE = _swig_property(_nucleardata.NUCLEAR_DATA_M2_XE_get, _nucleardata.NUCLEAR_DATA_M2_XE_set)
__swig_setmethods__["NUFISS1"] = _nucleardata.NUCLEAR_DATA_NUFISS1_set
__swig_getmethods__["NUFISS1"] = _nucleardata.NUCLEAR_DATA_NUFISS1_get
if _newclass:NUFISS1 = _swig_property(_nucleardata.NUCLEAR_DATA_NUFISS1_get, _nucleardata.NUCLEAR_DATA_NUFISS1_set)
__swig_setmethods__["K2"] = _nucleardata.NUCLEAR_DATA_K2_set
__swig_getmethods__["K2"] = _nucleardata.NUCLEAR_DATA_K2_get
if _newclass:K2 = _swig_property(_nucleardata.NUCLEAR_DATA_K2_get, _nucleardata.NUCLEAR_DATA_K2_set)
__swig_setmethods__["K1"] = _nucleardata.NUCLEAR_DATA_K1_set
__swig_getmethods__["K1"] = _nucleardata.NUCLEAR_DATA_K1_get
if _newclass:K1 = _swig_property(_nucleardata.NUCLEAR_DATA_K1_get, _nucleardata.NUCLEAR_DATA_K1_set)
__swig_setmethods__["NUFISS2"] = _nucleardata.NUCLEAR_DATA_NUFISS2_set
__swig_getmethods__["NUFISS2"] = _nucleardata.NUCLEAR_DATA_NUFISS2_get
if _newclass:NUFISS2 = _swig_property(_nucleardata.NUCLEAR_DATA_NUFISS2_get, _nucleardata.NUCLEAR_DATA_NUFISS2_set)
__swig_setmethods__["BOR1_NO_XE"] = _nucleardata.NUCLEAR_DATA_BOR1_NO_XE_set
__swig_getmethods__["BOR1_NO_XE"] = _nucleardata.NUCLEAR_DATA_BOR1_NO_XE_get
if _newclass:BOR1_NO_XE = _swig_property(_nucleardata.NUCLEAR_DATA_BOR1_NO_XE_get, _nucleardata.NUCLEAR_DATA_BOR1_NO_XE_set)
__swig_setmethods__["BOR2_XE"] = _nucleardata.NUCLEAR_DATA_BOR2_XE_set
__swig_getmethods__["BOR2_XE"] = _nucleardata.NUCLEAR_DATA_BOR2_XE_get
if _newclass:BOR2_XE = _swig_property(_nucleardata.NUCLEAR_DATA_BOR2_XE_get, _nucleardata.NUCLEAR_DATA_BOR2_XE_set)
__swig_setmethods__["REMOV1"] = _nucleardata.NUCLEAR_DATA_REMOV1_set
__swig_getmethods__["REMOV1"] = _nucleardata.NUCLEAR_DATA_REMOV1_get
if _newclass:REMOV1 = _swig_property(_nucleardata.NUCLEAR_DATA_REMOV1_get, _nucleardata.NUCLEAR_DATA_REMOV1_set)
__swig_setmethods__["DIFF2"] = _nucleardata.NUCLEAR_DATA_DIFF2_set
__swig_getmethods__["DIFF2"] = _nucleardata.NUCLEAR_DATA_DIFF2_get
if _newclass:DIFF2 = _swig_property(_nucleardata.NUCLEAR_DATA_DIFF2_get, _nucleardata.NUCLEAR_DATA_DIFF2_set)
__swig_setmethods__["DIFF1"] = _nucleardata.NUCLEAR_DATA_DIFF1_set
__swig_getmethods__["DIFF1"] = _nucleardata.NUCLEAR_DATA_DIFF1_get
if _newclass:DIFF1 = _swig_property(_nucleardata.NUCLEAR_DATA_DIFF1_get, _nucleardata.NUCLEAR_DATA_DIFF1_set)
__swig_setmethods__["BOR1_XE"] = _nucleardata.NUCLEAR_DATA_BOR1_XE_set
__swig_getmethods__["BOR1_XE"] = _nucleardata.NUCLEAR_DATA_BOR1_XE_get
if _newclass:BOR1_XE = _swig_property(_nucleardata.NUCLEAR_DATA_BOR1_XE_get, _nucleardata.NUCLEAR_DATA_BOR1_XE_set)
__swig_setmethods__["XE_YIELD"] = _nucleardata.NUCLEAR_DATA_XE_YIELD_set
__swig_getmethods__["XE_YIELD"] = _nucleardata.NUCLEAR_DATA_XE_YIELD_get
if _newclass:XE_YIELD = _swig_property(_nucleardata.NUCLEAR_DATA_XE_YIELD_get, _nucleardata.NUCLEAR_DATA_XE_YIELD_set)
__swig_setmethods__["NU"] = _nucleardata.NUCLEAR_DATA_NU_set
__swig_getmethods__["NU"] = _nucleardata.NUCLEAR_DATA_NU_get
if _newclass:NU = _swig_property(_nucleardata.NUCLEAR_DATA_NU_get, _nucleardata.NUCLEAR_DATA_NU_set)
__swig_setmethods__["BOR2_NO_XE"] = _nucleardata.NUCLEAR_DATA_BOR2_NO_XE_set
__swig_getmethods__["BOR2_NO_XE"] = _nucleardata.NUCLEAR_DATA_BOR2_NO_XE_get
if _newclass:BOR2_NO_XE = _swig_property(_nucleardata.NUCLEAR_DATA_BOR2_NO_XE_get, _nucleardata.NUCLEAR_DATA_BOR2_NO_XE_set)
__swig_setmethods__["KAPPA"] = _nucleardata.NUCLEAR_DATA_KAPPA_set
__swig_getmethods__["KAPPA"] = _nucleardata.NUCLEAR_DATA_KAPPA_get
if _newclass:KAPPA = _swig_property(_nucleardata.NUCLEAR_DATA_KAPPA_get, _nucleardata.NUCLEAR_DATA_KAPPA_set)
__swig_setmethods__["ABS1"] = _nucleardata.NUCLEAR_DATA_ABS1_set
__swig_getmethods__["ABS1"] = _nucleardata.NUCLEAR_DATA_ABS1_get
if _newclass:ABS1 = _swig_property(_nucleardata.NUCLEAR_DATA_ABS1_get, _nucleardata.NUCLEAR_DATA_ABS1_set)
__swig_setmethods__["ABS2"] = _nucleardata.NUCLEAR_DATA_ABS2_set
__swig_getmethods__["ABS2"] = _nucleardata.NUCLEAR_DATA_ABS2_get
if _newclass:ABS2 = _swig_property(_nucleardata.NUCLEAR_DATA_ABS2_get, _nucleardata.NUCLEAR_DATA_ABS2_set)
__swig_setmethods__["XE2_MAC"] = _nucleardata.NUCLEAR_DATA_XE2_MAC_set
__swig_getmethods__["XE2_MAC"] = _nucleardata.NUCLEAR_DATA_XE2_MAC_get
if _newclass:XE2_MAC = _swig_property(_nucleardata.NUCLEAR_DATA_XE2_MAC_get, _nucleardata.NUCLEAR_DATA_XE2_MAC_set)
__swig_setmethods__["K_INF_NO_XE"] = _nucleardata.NUCLEAR_DATA_K_INF_NO_XE_set
__swig_getmethods__["K_INF_NO_XE"] = _nucleardata.NUCLEAR_DATA_K_INF_NO_XE_get
if _newclass:K_INF_NO_XE = _swig_property(_nucleardata.NUCLEAR_DATA_K_INF_NO_XE_get, _nucleardata.NUCLEAR_DATA_K_INF_NO_XE_set)
__swig_setmethods__["XE2_MIC"] = _nucleardata.NUCLEAR_DATA_XE2_MIC_set
__swig_getmethods__["XE2_MIC"] = _nucleardata.NUCLEAR_DATA_XE2_MIC_get
if _newclass:XE2_MIC = _swig_property(_nucleardata.NUCLEAR_DATA_XE2_MIC_get, _nucleardata.NUCLEAR_DATA_XE2_MIC_set)
__swig_setmethods__["SM2_XE"] = _nucleardata.NUCLEAR_DATA_SM2_XE_set
__swig_getmethods__["SM2_XE"] = _nucleardata.NUCLEAR_DATA_SM2_XE_get
if _newclass:SM2_XE = _swig_property(_nucleardata.NUCLEAR_DATA_SM2_XE_get, _nucleardata.NUCLEAR_DATA_SM2_XE_set)
__swig_setmethods__["K_INF_XE"] = _nucleardata.NUCLEAR_DATA_K_INF_XE_set
__swig_getmethods__["K_INF_XE"] = _nucleardata.NUCLEAR_DATA_K_INF_XE_get
if _newclass:K_INF_XE = _swig_property(_nucleardata.NUCLEAR_DATA_K_INF_XE_get, _nucleardata.NUCLEAR_DATA_K_INF_XE_set)
def __init__(self):
this = _nucleardata.new_NUCLEAR_DATA()
try: self.this.append(this)
except: self.this = this
__swig_destroy__ = _nucleardata.delete_NUCLEAR_DATA
__del__ = lambda self : None;
NUCLEAR_DATA_swigregister = _nucleardata.NUCLEAR_DATA_swigregister
NUCLEAR_DATA_swigregister(NUCLEAR_DATA)
def get_2g_parms(*args):
return _nucleardata.get_2g_parms(*args)
get_2g_parms = _nucleardata.get_2g_parms
def get_SM2_NO_XE_0(*args):
return _nucleardata.get_SM2_NO_XE_0(*args)
get_SM2_NO_XE_0 = _nucleardata.get_SM2_NO_XE_0
def get_SM2_NO_XE_1(*args):
return _nucleardata.get_SM2_NO_XE_1(*args)
get_SM2_NO_XE_1 = _nucleardata.get_SM2_NO_XE_1
def get_M2_NO_XE_0(*args):
return _nucleardata.get_M2_NO_XE_0(*args)
get_M2_NO_XE_0 = _nucleardata.get_M2_NO_XE_0
def get_M2_NO_XE_1(*args):
return _nucleardata.get_M2_NO_XE_1(*args)
get_M2_NO_XE_1 = _nucleardata.get_M2_NO_XE_1
def get_M2_XE_0(*args):
return _nucleardata.get_M2_XE_0(*args)
get_M2_XE_0 = _nucleardata.get_M2_XE_0
def get_M2_XE_1(*args):
return _nucleardata.get_M2_XE_1(*args)
get_M2_XE_1 = _nucleardata.get_M2_XE_1
def get_NUFISS1_0(*args):
return _nucleardata.get_NUFISS1_0(*args)
get_NUFISS1_0 = _nucleardata.get_NUFISS1_0
def get_NUFISS1_1(*args):
return _nucleardata.get_NUFISS1_1(*args)
get_NUFISS1_1 = _nucleardata.get_NUFISS1_1
def get_K2_0(*args):
return _nucleardata.get_K2_0(*args)
get_K2_0 = _nucleardata.get_K2_0
def get_K2_1(*args):
return _nucleardata.get_K2_1(*args)
get_K2_1 = _nucleardata.get_K2_1
def get_K1_0(*args):
return _nucleardata.get_K1_0(*args)
get_K1_0 = _nucleardata.get_K1_0
def get_K1_1(*args):
return _nucleardata.get_K1_1(*args)
get_K1_1 = _nucleardata.get_K1_1
def get_NUFISS2_0(*args):
return _nucleardata.get_NUFISS2_0(*args)
get_NUFISS2_0 = _nucleardata.get_NUFISS2_0
def get_NUFISS2_1(*args):
return _nucleardata.get_NUFISS2_1(*args)
get_NUFISS2_1 = _nucleardata.get_NUFISS2_1
def get_BOR1_NO_XE_0(*args):
return _nucleardata.get_BOR1_NO_XE_0(*args)
get_BOR1_NO_XE_0 = _nucleardata.get_BOR1_NO_XE_0
def get_BOR1_NO_XE_1(*args):
return _nucleardata.get_BOR1_NO_XE_1(*args)
get_BOR1_NO_XE_1 = _nucleardata.get_BOR1_NO_XE_1
def get_BOR2_XE_0(*args):
return _nucleardata.get_BOR2_XE_0(*args)
get_BOR2_XE_0 = _nucleardata.get_BOR2_XE_0
def get_BOR2_XE_1(*args):
return _nucleardata.get_BOR2_XE_1(*args)
get_BOR2_XE_1 = _nucleardata.get_BOR2_XE_1
def get_REMOV1_0(*args):
return _nucleardata.get_REMOV1_0(*args)
get_REMOV1_0 = _nucleardata.get_REMOV1_0
def get_REMOV1_1(*args):
return _nucleardata.get_REMOV1_1(*args)
get_REMOV1_1 = _nucleardata.get_REMOV1_1
def get_DIFF2_0(*args):
return _nucleardata.get_DIFF2_0(*args)
get_DIFF2_0 = _nucleardata.get_DIFF2_0
def get_DIFF2_1(*args):
return _nucleardata.get_DIFF2_1(*args)
get_DIFF2_1 = _nucleardata.get_DIFF2_1
def get_DIFF1_0(*args):
return _nucleardata.get_DIFF1_0(*args)
get_DIFF1_0 = _nucleardata.get_DIFF1_0
def get_DIFF1_1(*args):
return _nucleardata.get_DIFF1_1(*args)
get_DIFF1_1 = _nucleardata.get_DIFF1_1
def get_BOR1_XE_0(*args):
return _nucleardata.get_BOR1_XE_0(*args)
get_BOR1_XE_0 = _nucleardata.get_BOR1_XE_0
def get_BOR1_XE_1(*args):
return _nucleardata.get_BOR1_XE_1(*args)
get_BOR1_XE_1 = _nucleardata.get_BOR1_XE_1
def get_XE_YIELD_0(*args):
return _nucleardata.get_XE_YIELD_0(*args)
get_XE_YIELD_0 = _nucleardata.get_XE_YIELD_0
def get_XE_YIELD_1(*args):
return _nucleardata.get_XE_YIELD_1(*args)
get_XE_YIELD_1 = _nucleardata.get_XE_YIELD_1
def get_NU_0(*args):
return _nucleardata.get_NU_0(*args)
get_NU_0 = _nucleardata.get_NU_0
def get_NU_1(*args):
return _nucleardata.get_NU_1(*args)
get_NU_1 = _nucleardata.get_NU_1
def get_BOR2_NO_XE_0(*args):
return _nucleardata.get_BOR2_NO_XE_0(*args)
get_BOR2_NO_XE_0 = _nucleardata.get_BOR2_NO_XE_0
def get_BOR2_NO_XE_1(*args):
return _nucleardata.get_BOR2_NO_XE_1(*args)
get_BOR2_NO_XE_1 = _nucleardata.get_BOR2_NO_XE_1
def get_KAPPA_0(*args):
return _nucleardata.get_KAPPA_0(*args)
get_KAPPA_0 = _nucleardata.get_KAPPA_0
def get_KAPPA_1(*args):
return _nucleardata.get_KAPPA_1(*args)
get_KAPPA_1 = _nucleardata.get_KAPPA_1
def get_ABS1_0(*args):
return _nucleardata.get_ABS1_0(*args)
get_ABS1_0 = _nucleardata.get_ABS1_0
def get_ABS1_1(*args):
return _nucleardata.get_ABS1_1(*args)
get_ABS1_1 = _nucleardata.get_ABS1_1
def get_ABS2_0(*args):
return _nucleardata.get_ABS2_0(*args)
get_ABS2_0 = _nucleardata.get_ABS2_0
def get_ABS2_1(*args):
return _nucleardata.get_ABS2_1(*args)
get_ABS2_1 = _nucleardata.get_ABS2_1
def get_XE2_MAC_0(*args):
return _nucleardata.get_XE2_MAC_0(*args)
get_XE2_MAC_0 = _nucleardata.get_XE2_MAC_0
def get_XE2_MAC_1(*args):
return _nucleardata.get_XE2_MAC_1(*args)
get_XE2_MAC_1 = _nucleardata.get_XE2_MAC_1
def get_K_INF_NO_XE_0(*args):
return _nucleardata.get_K_INF_NO_XE_0(*args)
get_K_INF_NO_XE_0 = _nucleardata.get_K_INF_NO_XE_0
def get_K_INF_NO_XE_1(*args):
return _nucleardata.get_K_INF_NO_XE_1(*args)
get_K_INF_NO_XE_1 = _nucleardata.get_K_INF_NO_XE_1
def get_XE2_MIC_0(*args):
return _nucleardata.get_XE2_MIC_0(*args)
get_XE2_MIC_0 = _nucleardata.get_XE2_MIC_0
def get_XE2_MIC_1(*args):
return _nucleardata.get_XE2_MIC_1(*args)
get_XE2_MIC_1 = _nucleardata.get_XE2_MIC_1
def get_SM2_XE_0(*args):
return _nucleardata.get_SM2_XE_0(*args)
get_SM2_XE_0 = _nucleardata.get_SM2_XE_0
def get_SM2_XE_1(*args):
return _nucleardata.get_SM2_XE_1(*args)
get_SM2_XE_1 = _nucleardata.get_SM2_XE_1
def get_K_INF_XE_0(*args):
return _nucleardata.get_K_INF_XE_0(*args)
get_K_INF_XE_0 = _nucleardata.get_K_INF_XE_0
def get_K_INF_XE_1(*args):
return _nucleardata.get_K_INF_XE_1(*args)
get_K_INF_XE_1 = _nucleardata.get_K_INF_XE_1
def get_SM2_NO_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_SM2_NO_XE_IFBA_DIFF_0(*args)
get_SM2_NO_XE_IFBA_DIFF_0 = _nucleardata.get_SM2_NO_XE_IFBA_DIFF_0
def get_SM2_NO_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_SM2_NO_XE_IFBA_DIFF_1(*args)
get_SM2_NO_XE_IFBA_DIFF_1 = _nucleardata.get_SM2_NO_XE_IFBA_DIFF_1
def get_SM2_NO_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_SM2_NO_XE_IFBA_DIFF_2(*args)
get_SM2_NO_XE_IFBA_DIFF_2 = _nucleardata.get_SM2_NO_XE_IFBA_DIFF_2
def get_M2_NO_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_M2_NO_XE_IFBA_DIFF_0(*args)
get_M2_NO_XE_IFBA_DIFF_0 = _nucleardata.get_M2_NO_XE_IFBA_DIFF_0
def get_M2_NO_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_M2_NO_XE_IFBA_DIFF_1(*args)
get_M2_NO_XE_IFBA_DIFF_1 = _nucleardata.get_M2_NO_XE_IFBA_DIFF_1
def get_M2_NO_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_M2_NO_XE_IFBA_DIFF_2(*args)
get_M2_NO_XE_IFBA_DIFF_2 = _nucleardata.get_M2_NO_XE_IFBA_DIFF_2
def get_M2_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_M2_XE_IFBA_DIFF_0(*args)
get_M2_XE_IFBA_DIFF_0 = _nucleardata.get_M2_XE_IFBA_DIFF_0
def get_M2_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_M2_XE_IFBA_DIFF_1(*args)
get_M2_XE_IFBA_DIFF_1 = _nucleardata.get_M2_XE_IFBA_DIFF_1
def get_M2_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_M2_XE_IFBA_DIFF_2(*args)
get_M2_XE_IFBA_DIFF_2 = _nucleardata.get_M2_XE_IFBA_DIFF_2
def get_NUFISS1_IFBA_DIFF_0(*args):
return _nucleardata.get_NUFISS1_IFBA_DIFF_0(*args)
get_NUFISS1_IFBA_DIFF_0 = _nucleardata.get_NUFISS1_IFBA_DIFF_0
def get_NUFISS1_IFBA_DIFF_1(*args):
return _nucleardata.get_NUFISS1_IFBA_DIFF_1(*args)
get_NUFISS1_IFBA_DIFF_1 = _nucleardata.get_NUFISS1_IFBA_DIFF_1
def get_NUFISS1_IFBA_DIFF_2(*args):
return _nucleardata.get_NUFISS1_IFBA_DIFF_2(*args)
get_NUFISS1_IFBA_DIFF_2 = _nucleardata.get_NUFISS1_IFBA_DIFF_2
def get_K2_IFBA_DIFF_0(*args):
return _nucleardata.get_K2_IFBA_DIFF_0(*args)
get_K2_IFBA_DIFF_0 = _nucleardata.get_K2_IFBA_DIFF_0
def get_K2_IFBA_DIFF_1(*args):
return _nucleardata.get_K2_IFBA_DIFF_1(*args)
get_K2_IFBA_DIFF_1 = _nucleardata.get_K2_IFBA_DIFF_1
def get_K2_IFBA_DIFF_2(*args):
return _nucleardata.get_K2_IFBA_DIFF_2(*args)
get_K2_IFBA_DIFF_2 = _nucleardata.get_K2_IFBA_DIFF_2
def get_K1_IFBA_DIFF_0(*args):
return _nucleardata.get_K1_IFBA_DIFF_0(*args)
get_K1_IFBA_DIFF_0 = _nucleardata.get_K1_IFBA_DIFF_0
def get_K1_IFBA_DIFF_1(*args):
return _nucleardata.get_K1_IFBA_DIFF_1(*args)
get_K1_IFBA_DIFF_1 = _nucleardata.get_K1_IFBA_DIFF_1
def get_K1_IFBA_DIFF_2(*args):
return _nucleardata.get_K1_IFBA_DIFF_2(*args)
get_K1_IFBA_DIFF_2 = _nucleardata.get_K1_IFBA_DIFF_2
def get_NUFISS2_IFBA_DIFF_0(*args):
return _nucleardata.get_NUFISS2_IFBA_DIFF_0(*args)
get_NUFISS2_IFBA_DIFF_0 = _nucleardata.get_NUFISS2_IFBA_DIFF_0
def get_NUFISS2_IFBA_DIFF_1(*args):
return _nucleardata.get_NUFISS2_IFBA_DIFF_1(*args)
get_NUFISS2_IFBA_DIFF_1 = _nucleardata.get_NUFISS2_IFBA_DIFF_1
def get_NUFISS2_IFBA_DIFF_2(*args):
return _nucleardata.get_NUFISS2_IFBA_DIFF_2(*args)
get_NUFISS2_IFBA_DIFF_2 = _nucleardata.get_NUFISS2_IFBA_DIFF_2
def get_BOR1_NO_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_BOR1_NO_XE_IFBA_DIFF_0(*args)
get_BOR1_NO_XE_IFBA_DIFF_0 = _nucleardata.get_BOR1_NO_XE_IFBA_DIFF_0
def get_BOR1_NO_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_BOR1_NO_XE_IFBA_DIFF_1(*args)
get_BOR1_NO_XE_IFBA_DIFF_1 = _nucleardata.get_BOR1_NO_XE_IFBA_DIFF_1
def get_BOR1_NO_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_BOR1_NO_XE_IFBA_DIFF_2(*args)
get_BOR1_NO_XE_IFBA_DIFF_2 = _nucleardata.get_BOR1_NO_XE_IFBA_DIFF_2
def get_BOR2_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_BOR2_XE_IFBA_DIFF_0(*args)
get_BOR2_XE_IFBA_DIFF_0 = _nucleardata.get_BOR2_XE_IFBA_DIFF_0
def get_BOR2_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_BOR2_XE_IFBA_DIFF_1(*args)
get_BOR2_XE_IFBA_DIFF_1 = _nucleardata.get_BOR2_XE_IFBA_DIFF_1
def get_BOR2_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_BOR2_XE_IFBA_DIFF_2(*args)
get_BOR2_XE_IFBA_DIFF_2 = _nucleardata.get_BOR2_XE_IFBA_DIFF_2
def get_REMOV1_IFBA_DIFF_0(*args):
return _nucleardata.get_REMOV1_IFBA_DIFF_0(*args)
get_REMOV1_IFBA_DIFF_0 = _nucleardata.get_REMOV1_IFBA_DIFF_0
def get_REMOV1_IFBA_DIFF_1(*args):
return _nucleardata.get_REMOV1_IFBA_DIFF_1(*args)
get_REMOV1_IFBA_DIFF_1 = _nucleardata.get_REMOV1_IFBA_DIFF_1
def get_REMOV1_IFBA_DIFF_2(*args):
return _nucleardata.get_REMOV1_IFBA_DIFF_2(*args)
get_REMOV1_IFBA_DIFF_2 = _nucleardata.get_REMOV1_IFBA_DIFF_2
def get_DIFF2_IFBA_DIFF_0(*args):
return _nucleardata.get_DIFF2_IFBA_DIFF_0(*args)
get_DIFF2_IFBA_DIFF_0 = _nucleardata.get_DIFF2_IFBA_DIFF_0
def get_DIFF2_IFBA_DIFF_1(*args):
return _nucleardata.get_DIFF2_IFBA_DIFF_1(*args)
get_DIFF2_IFBA_DIFF_1 = _nucleardata.get_DIFF2_IFBA_DIFF_1
def get_DIFF2_IFBA_DIFF_2(*args):
return _nucleardata.get_DIFF2_IFBA_DIFF_2(*args)
get_DIFF2_IFBA_DIFF_2 = _nucleardata.get_DIFF2_IFBA_DIFF_2
def get_DIFF1_IFBA_DIFF_0(*args):
return _nucleardata.get_DIFF1_IFBA_DIFF_0(*args)
get_DIFF1_IFBA_DIFF_0 = _nucleardata.get_DIFF1_IFBA_DIFF_0
def get_DIFF1_IFBA_DIFF_1(*args):
return _nucleardata.get_DIFF1_IFBA_DIFF_1(*args)
get_DIFF1_IFBA_DIFF_1 = _nucleardata.get_DIFF1_IFBA_DIFF_1
def get_DIFF1_IFBA_DIFF_2(*args):
return _nucleardata.get_DIFF1_IFBA_DIFF_2(*args)
get_DIFF1_IFBA_DIFF_2 = _nucleardata.get_DIFF1_IFBA_DIFF_2
def get_BOR1_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_BOR1_XE_IFBA_DIFF_0(*args)
get_BOR1_XE_IFBA_DIFF_0 = _nucleardata.get_BOR1_XE_IFBA_DIFF_0
def get_BOR1_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_BOR1_XE_IFBA_DIFF_1(*args)
get_BOR1_XE_IFBA_DIFF_1 = _nucleardata.get_BOR1_XE_IFBA_DIFF_1
def get_BOR1_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_BOR1_XE_IFBA_DIFF_2(*args)
get_BOR1_XE_IFBA_DIFF_2 = _nucleardata.get_BOR1_XE_IFBA_DIFF_2
def get_XE_YIELD_IFBA_DIFF_0(*args):
return _nucleardata.get_XE_YIELD_IFBA_DIFF_0(*args)
get_XE_YIELD_IFBA_DIFF_0 = _nucleardata.get_XE_YIELD_IFBA_DIFF_0
def get_XE_YIELD_IFBA_DIFF_1(*args):
return _nucleardata.get_XE_YIELD_IFBA_DIFF_1(*args)
get_XE_YIELD_IFBA_DIFF_1 = _nucleardata.get_XE_YIELD_IFBA_DIFF_1
def get_XE_YIELD_IFBA_DIFF_2(*args):
return _nucleardata.get_XE_YIELD_IFBA_DIFF_2(*args)
get_XE_YIELD_IFBA_DIFF_2 = _nucleardata.get_XE_YIELD_IFBA_DIFF_2
def get_NU_IFBA_DIFF_0(*args):
return _nucleardata.get_NU_IFBA_DIFF_0(*args)
get_NU_IFBA_DIFF_0 = _nucleardata.get_NU_IFBA_DIFF_0
def get_NU_IFBA_DIFF_1(*args):
return _nucleardata.get_NU_IFBA_DIFF_1(*args)
get_NU_IFBA_DIFF_1 = _nucleardata.get_NU_IFBA_DIFF_1
def get_NU_IFBA_DIFF_2(*args):
return _nucleardata.get_NU_IFBA_DIFF_2(*args)
get_NU_IFBA_DIFF_2 = _nucleardata.get_NU_IFBA_DIFF_2
def get_BOR2_NO_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_BOR2_NO_XE_IFBA_DIFF_0(*args)
get_BOR2_NO_XE_IFBA_DIFF_0 = _nucleardata.get_BOR2_NO_XE_IFBA_DIFF_0
def get_BOR2_NO_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_BOR2_NO_XE_IFBA_DIFF_1(*args)
get_BOR2_NO_XE_IFBA_DIFF_1 = _nucleardata.get_BOR2_NO_XE_IFBA_DIFF_1
def get_BOR2_NO_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_BOR2_NO_XE_IFBA_DIFF_2(*args)
get_BOR2_NO_XE_IFBA_DIFF_2 = _nucleardata.get_BOR2_NO_XE_IFBA_DIFF_2
def get_KAPPA_IFBA_DIFF_0(*args):
return _nucleardata.get_KAPPA_IFBA_DIFF_0(*args)
get_KAPPA_IFBA_DIFF_0 = _nucleardata.get_KAPPA_IFBA_DIFF_0
def get_KAPPA_IFBA_DIFF_1(*args):
return _nucleardata.get_KAPPA_IFBA_DIFF_1(*args)
get_KAPPA_IFBA_DIFF_1 = _nucleardata.get_KAPPA_IFBA_DIFF_1
def get_KAPPA_IFBA_DIFF_2(*args):
return _nucleardata.get_KAPPA_IFBA_DIFF_2(*args)
get_KAPPA_IFBA_DIFF_2 = _nucleardata.get_KAPPA_IFBA_DIFF_2
def get_ABS1_IFBA_DIFF_0(*args):
return _nucleardata.get_ABS1_IFBA_DIFF_0(*args)
get_ABS1_IFBA_DIFF_0 = _nucleardata.get_ABS1_IFBA_DIFF_0
def get_ABS1_IFBA_DIFF_1(*args):
return _nucleardata.get_ABS1_IFBA_DIFF_1(*args)
get_ABS1_IFBA_DIFF_1 = _nucleardata.get_ABS1_IFBA_DIFF_1
def get_ABS1_IFBA_DIFF_2(*args):
return _nucleardata.get_ABS1_IFBA_DIFF_2(*args)
get_ABS1_IFBA_DIFF_2 = _nucleardata.get_ABS1_IFBA_DIFF_2
def get_ABS2_IFBA_DIFF_0(*args):
return _nucleardata.get_ABS2_IFBA_DIFF_0(*args)
get_ABS2_IFBA_DIFF_0 = _nucleardata.get_ABS2_IFBA_DIFF_0
def get_ABS2_IFBA_DIFF_1(*args):
return _nucleardata.get_ABS2_IFBA_DIFF_1(*args)
get_ABS2_IFBA_DIFF_1 = _nucleardata.get_ABS2_IFBA_DIFF_1
def get_ABS2_IFBA_DIFF_2(*args):
return _nucleardata.get_ABS2_IFBA_DIFF_2(*args)
get_ABS2_IFBA_DIFF_2 = _nucleardata.get_ABS2_IFBA_DIFF_2
def get_XE2_MAC_IFBA_DIFF_0(*args):
return _nucleardata.get_XE2_MAC_IFBA_DIFF_0(*args)
get_XE2_MAC_IFBA_DIFF_0 = _nucleardata.get_XE2_MAC_IFBA_DIFF_0
def get_XE2_MAC_IFBA_DIFF_1(*args):
return _nucleardata.get_XE2_MAC_IFBA_DIFF_1(*args)
get_XE2_MAC_IFBA_DIFF_1 = _nucleardata.get_XE2_MAC_IFBA_DIFF_1
def get_XE2_MAC_IFBA_DIFF_2(*args):
return _nucleardata.get_XE2_MAC_IFBA_DIFF_2(*args)
get_XE2_MAC_IFBA_DIFF_2 = _nucleardata.get_XE2_MAC_IFBA_DIFF_2
def get_K_INF_NO_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_K_INF_NO_XE_IFBA_DIFF_0(*args)
get_K_INF_NO_XE_IFBA_DIFF_0 = _nucleardata.get_K_INF_NO_XE_IFBA_DIFF_0
def get_K_INF_NO_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_K_INF_NO_XE_IFBA_DIFF_1(*args)
get_K_INF_NO_XE_IFBA_DIFF_1 = _nucleardata.get_K_INF_NO_XE_IFBA_DIFF_1
def get_K_INF_NO_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_K_INF_NO_XE_IFBA_DIFF_2(*args)
get_K_INF_NO_XE_IFBA_DIFF_2 = _nucleardata.get_K_INF_NO_XE_IFBA_DIFF_2
def get_XE2_MIC_IFBA_DIFF_0(*args):
return _nucleardata.get_XE2_MIC_IFBA_DIFF_0(*args)
get_XE2_MIC_IFBA_DIFF_0 = _nucleardata.get_XE2_MIC_IFBA_DIFF_0
def get_XE2_MIC_IFBA_DIFF_1(*args):
return _nucleardata.get_XE2_MIC_IFBA_DIFF_1(*args)
get_XE2_MIC_IFBA_DIFF_1 = _nucleardata.get_XE2_MIC_IFBA_DIFF_1
def get_XE2_MIC_IFBA_DIFF_2(*args):
return _nucleardata.get_XE2_MIC_IFBA_DIFF_2(*args)
get_XE2_MIC_IFBA_DIFF_2 = _nucleardata.get_XE2_MIC_IFBA_DIFF_2
def get_SM2_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_SM2_XE_IFBA_DIFF_0(*args)
get_SM2_XE_IFBA_DIFF_0 = _nucleardata.get_SM2_XE_IFBA_DIFF_0
def get_SM2_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_SM2_XE_IFBA_DIFF_1(*args)
get_SM2_XE_IFBA_DIFF_1 = _nucleardata.get_SM2_XE_IFBA_DIFF_1
def get_SM2_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_SM2_XE_IFBA_DIFF_2(*args)
get_SM2_XE_IFBA_DIFF_2 = _nucleardata.get_SM2_XE_IFBA_DIFF_2
def get_K_INF_XE_IFBA_DIFF_0(*args):
return _nucleardata.get_K_INF_XE_IFBA_DIFF_0(*args)
get_K_INF_XE_IFBA_DIFF_0 = _nucleardata.get_K_INF_XE_IFBA_DIFF_0
def get_K_INF_XE_IFBA_DIFF_1(*args):
return _nucleardata.get_K_INF_XE_IFBA_DIFF_1(*args)
get_K_INF_XE_IFBA_DIFF_1 = _nucleardata.get_K_INF_XE_IFBA_DIFF_1
def get_K_INF_XE_IFBA_DIFF_2(*args):
return _nucleardata.get_K_INF_XE_IFBA_DIFF_2(*args)
get_K_INF_XE_IFBA_DIFF_2 = _nucleardata.get_K_INF_XE_IFBA_DIFF_2
def get_SM2_NO_XE_WABA_DIFF_0(*args):
return _nucleardata.get_SM2_NO_XE_WABA_DIFF_0(*args)
get_SM2_NO_XE_WABA_DIFF_0 = _nucleardata.get_SM2_NO_XE_WABA_DIFF_0
def get_SM2_NO_XE_WABA_DIFF_1(*args):
return _nucleardata.get_SM2_NO_XE_WABA_DIFF_1(*args)
get_SM2_NO_XE_WABA_DIFF_1 = _nucleardata.get_SM2_NO_XE_WABA_DIFF_1
def get_SM2_NO_XE_WABA_DIFF_2(*args):
return _nucleardata.get_SM2_NO_XE_WABA_DIFF_2(*args)
get_SM2_NO_XE_WABA_DIFF_2 = _nucleardata.get_SM2_NO_XE_WABA_DIFF_2
def get_M2_NO_XE_WABA_DIFF_0(*args):
return _nucleardata.get_M2_NO_XE_WABA_DIFF_0(*args)
get_M2_NO_XE_WABA_DIFF_0 = _nucleardata.get_M2_NO_XE_WABA_DIFF_0
def get_M2_NO_XE_WABA_DIFF_1(*args):
return _nucleardata.get_M2_NO_XE_WABA_DIFF_1(*args)
get_M2_NO_XE_WABA_DIFF_1 = _nucleardata.get_M2_NO_XE_WABA_DIFF_1
def get_M2_NO_XE_WABA_DIFF_2(*args):
return _nucleardata.get_M2_NO_XE_WABA_DIFF_2(*args)
get_M2_NO_XE_WABA_DIFF_2 = _nucleardata.get_M2_NO_XE_WABA_DIFF_2
def get_M2_XE_WABA_DIFF_0(*args):
return _nucleardata.get_M2_XE_WABA_DIFF_0(*args)
get_M2_XE_WABA_DIFF_0 = _nucleardata.get_M2_XE_WABA_DIFF_0
def get_M2_XE_WABA_DIFF_1(*args):
return _nucleardata.get_M2_XE_WABA_DIFF_1(*args)
get_M2_XE_WABA_DIFF_1 = _nucleardata.get_M2_XE_WABA_DIFF_1
def get_M2_XE_WABA_DIFF_2(*args):
return _nucleardata.get_M2_XE_WABA_DIFF_2(*args)
get_M2_XE_WABA_DIFF_2 = _nucleardata.get_M2_XE_WABA_DIFF_2
def get_NUFISS1_WABA_DIFF_0(*args):
return _nucleardata.get_NUFISS1_WABA_DIFF_0(*args)
get_NUFISS1_WABA_DIFF_0 = _nucleardata.get_NUFISS1_WABA_DIFF_0
def get_NUFISS1_WABA_DIFF_1(*args):
return _nucleardata.get_NUFISS1_WABA_DIFF_1(*args)
get_NUFISS1_WABA_DIFF_1 = _nucleardata.get_NUFISS1_WABA_DIFF_1
def get_NUFISS1_WABA_DIFF_2(*args):
return _nucleardata.get_NUFISS1_WABA_DIFF_2(*args)
get_NUFISS1_WABA_DIFF_2 = _nucleardata.get_NUFISS1_WABA_DIFF_2
def get_K2_WABA_DIFF_0(*args):
return _nucleardata.get_K2_WABA_DIFF_0(*args)
get_K2_WABA_DIFF_0 = _nucleardata.get_K2_WABA_DIFF_0
def get_K2_WABA_DIFF_1(*args):
return _nucleardata.get_K2_WABA_DIFF_1(*args)
get_K2_WABA_DIFF_1 = _nucleardata.get_K2_WABA_DIFF_1
def get_K2_WABA_DIFF_2(*args):
return _nucleardata.get_K2_WABA_DIFF_2(*args)
get_K2_WABA_DIFF_2 = _nucleardata.get_K2_WABA_DIFF_2
def get_K1_WABA_DIFF_0(*args):
return _nucleardata.get_K1_WABA_DIFF_0(*args)
get_K1_WABA_DIFF_0 = _nucleardata.get_K1_WABA_DIFF_0
def get_K1_WABA_DIFF_1(*args):
return _nucleardata.get_K1_WABA_DIFF_1(*args)
get_K1_WABA_DIFF_1 = _nucleardata.get_K1_WABA_DIFF_1
def get_K1_WABA_DIFF_2(*args):
return _nucleardata.get_K1_WABA_DIFF_2(*args)
get_K1_WABA_DIFF_2 = _nucleardata.get_K1_WABA_DIFF_2
def get_NUFISS2_WABA_DIFF_0(*args):
return _nucleardata.get_NUFISS2_WABA_DIFF_0(*args)
get_NUFISS2_WABA_DIFF_0 = _nucleardata.get_NUFISS2_WABA_DIFF_0
def get_NUFISS2_WABA_DIFF_1(*args):
return _nucleardata.get_NUFISS2_WABA_DIFF_1(*args)
get_NUFISS2_WABA_DIFF_1 = _nucleardata.get_NUFISS2_WABA_DIFF_1
def get_NUFISS2_WABA_DIFF_2(*args):
return _nucleardata.get_NUFISS2_WABA_DIFF_2(*args)
get_NUFISS2_WABA_DIFF_2 = _nucleardata.get_NUFISS2_WABA_DIFF_2
def get_BOR1_NO_XE_WABA_DIFF_0(*args):
return _nucleardata.get_BOR1_NO_XE_WABA_DIFF_0(*args)
get_BOR1_NO_XE_WABA_DIFF_0 = _nucleardata.get_BOR1_NO_XE_WABA_DIFF_0
def get_BOR1_NO_XE_WABA_DIFF_1(*args):
return _nucleardata.get_BOR1_NO_XE_WABA_DIFF_1(*args)
get_BOR1_NO_XE_WABA_DIFF_1 = _nucleardata.get_BOR1_NO_XE_WABA_DIFF_1
def get_BOR1_NO_XE_WABA_DIFF_2(*args):
return _nucleardata.get_BOR1_NO_XE_WABA_DIFF_2(*args)
get_BOR1_NO_XE_WABA_DIFF_2 = _nucleardata.get_BOR1_NO_XE_WABA_DIFF_2
def get_BOR2_XE_WABA_DIFF_0(*args):
return _nucleardata.get_BOR2_XE_WABA_DIFF_0(*args)
get_BOR2_XE_WABA_DIFF_0 = _nucleardata.get_BOR2_XE_WABA_DIFF_0
def get_BOR2_XE_WABA_DIFF_1(*args):
return _nucleardata.get_BOR2_XE_WABA_DIFF_1(*args)
get_BOR2_XE_WABA_DIFF_1 = _nucleardata.get_BOR2_XE_WABA_DIFF_1
def get_BOR2_XE_WABA_DIFF_2(*args):
return _nucleardata.get_BOR2_XE_WABA_DIFF_2(*args)
get_BOR2_XE_WABA_DIFF_2 = _nucleardata.get_BOR2_XE_WABA_DIFF_2
def get_REMOV1_WABA_DIFF_0(*args):
return _nucleardata.get_REMOV1_WABA_DIFF_0(*args)
get_REMOV1_WABA_DIFF_0 = _nucleardata.get_REMOV1_WABA_DIFF_0
def get_REMOV1_WABA_DIFF_1(*args):
return _nucleardata.get_REMOV1_WABA_DIFF_1(*args)
get_REMOV1_WABA_DIFF_1 = _nucleardata.get_REMOV1_WABA_DIFF_1
def get_REMOV1_WABA_DIFF_2(*args):
return _nucleardata.get_REMOV1_WABA_DIFF_2(*args)
get_REMOV1_WABA_DIFF_2 = _nucleardata.get_REMOV1_WABA_DIFF_2
def get_DIFF2_WABA_DIFF_0(*args):
return _nucleardata.get_DIFF2_WABA_DIFF_0(*args)
get_DIFF2_WABA_DIFF_0 = _nucleardata.get_DIFF2_WABA_DIFF_0
def get_DIFF2_WABA_DIFF_1(*args):
return _nucleardata.get_DIFF2_WABA_DIFF_1(*args)
get_DIFF2_WABA_DIFF_1 = _nucleardata.get_DIFF2_WABA_DIFF_1
def get_DIFF2_WABA_DIFF_2(*args):
return _nucleardata.get_DIFF2_WABA_DIFF_2(*args)
get_DIFF2_WABA_DIFF_2 = _nucleardata.get_DIFF2_WABA_DIFF_2
def get_DIFF1_WABA_DIFF_0(*args):
return _nucleardata.get_DIFF1_WABA_DIFF_0(*args)
get_DIFF1_WABA_DIFF_0 = _nucleardata.get_DIFF1_WABA_DIFF_0
def get_DIFF1_WABA_DIFF_1(*args):
return _nucleardata.get_DIFF1_WABA_DIFF_1(*args)
get_DIFF1_WABA_DIFF_1 = _nucleardata.get_DIFF1_WABA_DIFF_1
def get_DIFF1_WABA_DIFF_2(*args):
return _nucleardata.get_DIFF1_WABA_DIFF_2(*args)
get_DIFF1_WABA_DIFF_2 = _nucleardata.get_DIFF1_WABA_DIFF_2
def get_BOR1_XE_WABA_DIFF_0(*args):
return _nucleardata.get_BOR1_XE_WABA_DIFF_0(*args)
get_BOR1_XE_WABA_DIFF_0 = _nucleardata.get_BOR1_XE_WABA_DIFF_0
def get_BOR1_XE_WABA_DIFF_1(*args):
return _nucleardata.get_BOR1_XE_WABA_DIFF_1(*args)
get_BOR1_XE_WABA_DIFF_1 = _nucleardata.get_BOR1_XE_WABA_DIFF_1
def get_BOR1_XE_WABA_DIFF_2(*args):
return _nucleardata.get_BOR1_XE_WABA_DIFF_2(*args)
get_BOR1_XE_WABA_DIFF_2 = _nucleardata.get_BOR1_XE_WABA_DIFF_2
def get_XE_YIELD_WABA_DIFF_0(*args):
return _nucleardata.get_XE_YIELD_WABA_DIFF_0(*args)
get_XE_YIELD_WABA_DIFF_0 = _nucleardata.get_XE_YIELD_WABA_DIFF_0
def get_XE_YIELD_WABA_DIFF_1(*args):
return _nucleardata.get_XE_YIELD_WABA_DIFF_1(*args)
get_XE_YIELD_WABA_DIFF_1 = _nucleardata.get_XE_YIELD_WABA_DIFF_1
def get_XE_YIELD_WABA_DIFF_2(*args):
return _nucleardata.get_XE_YIELD_WABA_DIFF_2(*args)
get_XE_YIELD_WABA_DIFF_2 = _nucleardata.get_XE_YIELD_WABA_DIFF_2
def get_NU_WABA_DIFF_0(*args):
return _nucleardata.get_NU_WABA_DIFF_0(*args)
get_NU_WABA_DIFF_0 = _nucleardata.get_NU_WABA_DIFF_0
def get_NU_WABA_DIFF_1(*args):
return _nucleardata.get_NU_WABA_DIFF_1(*args)
get_NU_WABA_DIFF_1 = _nucleardata.get_NU_WABA_DIFF_1
def get_NU_WABA_DIFF_2(*args):
return _nucleardata.get_NU_WABA_DIFF_2(*args)
get_NU_WABA_DIFF_2 = _nucleardata.get_NU_WABA_DIFF_2
def get_BOR2_NO_XE_WABA_DIFF_0(*args):
return _nucleardata.get_BOR2_NO_XE_WABA_DIFF_0(*args)
get_BOR2_NO_XE_WABA_DIFF_0 = _nucleardata.get_BOR2_NO_XE_WABA_DIFF_0
def get_BOR2_NO_XE_WABA_DIFF_1(*args):
return _nucleardata.get_BOR2_NO_XE_WABA_DIFF_1(*args)
get_BOR2_NO_XE_WABA_DIFF_1 = _nucleardata.get_BOR2_NO_XE_WABA_DIFF_1
def get_BOR2_NO_XE_WABA_DIFF_2(*args):
return _nucleardata.get_BOR2_NO_XE_WABA_DIFF_2(*args)
get_BOR2_NO_XE_WABA_DIFF_2 = _nucleardata.get_BOR2_NO_XE_WABA_DIFF_2
def get_KAPPA_WABA_DIFF_0(*args):
return _nucleardata.get_KAPPA_WABA_DIFF_0(*args)
get_KAPPA_WABA_DIFF_0 = _nucleardata.get_KAPPA_WABA_DIFF_0
def get_KAPPA_WABA_DIFF_1(*args):
return _nucleardata.get_KAPPA_WABA_DIFF_1(*args)
get_KAPPA_WABA_DIFF_1 = _nucleardata.get_KAPPA_WABA_DIFF_1
def get_KAPPA_WABA_DIFF_2(*args):
return _nucleardata.get_KAPPA_WABA_DIFF_2(*args)
get_KAPPA_WABA_DIFF_2 = _nucleardata.get_KAPPA_WABA_DIFF_2
def get_ABS1_WABA_DIFF_0(*args):
return _nucleardata.get_ABS1_WABA_DIFF_0(*args)
get_ABS1_WABA_DIFF_0 = _nucleardata.get_ABS1_WABA_DIFF_0
def get_ABS1_WABA_DIFF_1(*args):
return _nucleardata.get_ABS1_WABA_DIFF_1(*args)
get_ABS1_WABA_DIFF_1 = _nucleardata.get_ABS1_WABA_DIFF_1
def get_ABS1_WABA_DIFF_2(*args):
return _nucleardata.get_ABS1_WABA_DIFF_2(*args)
get_ABS1_WABA_DIFF_2 = _nucleardata.get_ABS1_WABA_DIFF_2
def get_ABS2_WABA_DIFF_0(*args):
return _nucleardata.get_ABS2_WABA_DIFF_0(*args)
get_ABS2_WABA_DIFF_0 = _nucleardata.get_ABS2_WABA_DIFF_0
def get_ABS2_WABA_DIFF_1(*args):
return _nucleardata.get_ABS2_WABA_DIFF_1(*args)
get_ABS2_WABA_DIFF_1 = _nucleardata.get_ABS2_WABA_DIFF_1
def get_ABS2_WABA_DIFF_2(*args):
return _nucleardata.get_ABS2_WABA_DIFF_2(*args)
get_ABS2_WABA_DIFF_2 = _nucleardata.get_ABS2_WABA_DIFF_2
def get_XE2_MAC_WABA_DIFF_0(*args):
return _nucleardata.get_XE2_MAC_WABA_DIFF_0(*args)
get_XE2_MAC_WABA_DIFF_0 = _nucleardata.get_XE2_MAC_WABA_DIFF_0
def get_XE2_MAC_WABA_DIFF_1(*args):
return _nucleardata.get_XE2_MAC_WABA_DIFF_1(*args)
get_XE2_MAC_WABA_DIFF_1 = _nucleardata.get_XE2_MAC_WABA_DIFF_1
def get_XE2_MAC_WABA_DIFF_2(*args):
return _nucleardata.get_XE2_MAC_WABA_DIFF_2(*args)
get_XE2_MAC_WABA_DIFF_2 = _nucleardata.get_XE2_MAC_WABA_DIFF_2
def get_K_INF_NO_XE_WABA_DIFF_0(*args):
return _nucleardata.get_K_INF_NO_XE_WABA_DIFF_0(*args)
get_K_INF_NO_XE_WABA_DIFF_0 = _nucleardata.get_K_INF_NO_XE_WABA_DIFF_0
def get_K_INF_NO_XE_WABA_DIFF_1(*args):
return _nucleardata.get_K_INF_NO_XE_WABA_DIFF_1(*args)
get_K_INF_NO_XE_WABA_DIFF_1 = _nucleardata.get_K_INF_NO_XE_WABA_DIFF_1
def get_K_INF_NO_XE_WABA_DIFF_2(*args):
return _nucleardata.get_K_INF_NO_XE_WABA_DIFF_2(*args)
get_K_INF_NO_XE_WABA_DIFF_2 = _nucleardata.get_K_INF_NO_XE_WABA_DIFF_2
def get_XE2_MIC_WABA_DIFF_0(*args):
return _nucleardata.get_XE2_MIC_WABA_DIFF_0(*args)
get_XE2_MIC_WABA_DIFF_0 = _nucleardata.get_XE2_MIC_WABA_DIFF_0
def get_XE2_MIC_WABA_DIFF_1(*args):
return _nucleardata.get_XE2_MIC_WABA_DIFF_1(*args)
get_XE2_MIC_WABA_DIFF_1 = _nucleardata.get_XE2_MIC_WABA_DIFF_1
def get_XE2_MIC_WABA_DIFF_2(*args):
return _nucleardata.get_XE2_MIC_WABA_DIFF_2(*args)
get_XE2_MIC_WABA_DIFF_2 = _nucleardata.get_XE2_MIC_WABA_DIFF_2
def get_SM2_XE_WABA_DIFF_0(*args):
return _nucleardata.get_SM2_XE_WABA_DIFF_0(*args)
get_SM2_XE_WABA_DIFF_0 = _nucleardata.get_SM2_XE_WABA_DIFF_0
def get_SM2_XE_WABA_DIFF_1(*args):
return _nucleardata.get_SM2_XE_WABA_DIFF_1(*args)
get_SM2_XE_WABA_DIFF_1 = _nucleardata.get_SM2_XE_WABA_DIFF_1
def get_SM2_XE_WABA_DIFF_2(*args):
return _nucleardata.get_SM2_XE_WABA_DIFF_2(*args)
get_SM2_XE_WABA_DIFF_2 = _nucleardata.get_SM2_XE_WABA_DIFF_2
def get_K_INF_XE_WABA_DIFF_0(*args):
return _nucleardata.get_K_INF_XE_WABA_DIFF_0(*args)
get_K_INF_XE_WABA_DIFF_0 = _nucleardata.get_K_INF_XE_WABA_DIFF_0
def get_K_INF_XE_WABA_DIFF_1(*args):
return _nucleardata.get_K_INF_XE_WABA_DIFF_1(*args)
get_K_INF_XE_WABA_DIFF_1 = _nucleardata.get_K_INF_XE_WABA_DIFF_1
def get_K_INF_XE_WABA_DIFF_2(*args):
return _nucleardata.get_K_INF_XE_WABA_DIFF_2(*args)
get_K_INF_XE_WABA_DIFF_2 = _nucleardata.get_K_INF_XE_WABA_DIFF_2
def get_SM2_NO_XE_GAD_DIFF_0(*args):
return _nucleardata.get_SM2_NO_XE_GAD_DIFF_0(*args)
get_SM2_NO_XE_GAD_DIFF_0 = _nucleardata.get_SM2_NO_XE_GAD_DIFF_0
def get_SM2_NO_XE_GAD_DIFF_1(*args):
return _nucleardata.get_SM2_NO_XE_GAD_DIFF_1(*args)
get_SM2_NO_XE_GAD_DIFF_1 = _nucleardata.get_SM2_NO_XE_GAD_DIFF_1
def get_SM2_NO_XE_GAD_DIFF_2(*args):
return _nucleardata.get_SM2_NO_XE_GAD_DIFF_2(*args)
get_SM2_NO_XE_GAD_DIFF_2 = _nucleardata.get_SM2_NO_XE_GAD_DIFF_2
def get_M2_NO_XE_GAD_DIFF_0(*args):
return _nucleardata.get_M2_NO_XE_GAD_DIFF_0(*args)
get_M2_NO_XE_GAD_DIFF_0 = _nucleardata.get_M2_NO_XE_GAD_DIFF_0
def get_M2_NO_XE_GAD_DIFF_1(*args):
return _nucleardata.get_M2_NO_XE_GAD_DIFF_1(*args)
get_M2_NO_XE_GAD_DIFF_1 = _nucleardata.get_M2_NO_XE_GAD_DIFF_1
def get_M2_NO_XE_GAD_DIFF_2(*args):
return _nucleardata.get_M2_NO_XE_GAD_DIFF_2(*args)
get_M2_NO_XE_GAD_DIFF_2 = _nucleardata.get_M2_NO_XE_GAD_DIFF_2
def get_M2_XE_GAD_DIFF_0(*args):
return _nucleardata.get_M2_XE_GAD_DIFF_0(*args)
get_M2_XE_GAD_DIFF_0 = _nucleardata.get_M2_XE_GAD_DIFF_0
def get_M2_XE_GAD_DIFF_1(*args):
return _nucleardata.get_M2_XE_GAD_DIFF_1(*args)
get_M2_XE_GAD_DIFF_1 = _nucleardata.get_M2_XE_GAD_DIFF_1
def get_M2_XE_GAD_DIFF_2(*args):
return _nucleardata.get_M2_XE_GAD_DIFF_2(*args)
get_M2_XE_GAD_DIFF_2 = _nucleardata.get_M2_XE_GAD_DIFF_2
def get_NUFISS1_GAD_DIFF_0(*args):
return _nucleardata.get_NUFISS1_GAD_DIFF_0(*args)
get_NUFISS1_GAD_DIFF_0 = _nucleardata.get_NUFISS1_GAD_DIFF_0
def get_NUFISS1_GAD_DIFF_1(*args):
return _nucleardata.get_NUFISS1_GAD_DIFF_1(*args)
get_NUFISS1_GAD_DIFF_1 = _nucleardata.get_NUFISS1_GAD_DIFF_1
def get_NUFISS1_GAD_DIFF_2(*args):
return _nucleardata.get_NUFISS1_GAD_DIFF_2(*args)
get_NUFISS1_GAD_DIFF_2 = _nucleardata.get_NUFISS1_GAD_DIFF_2
def get_K2_GAD_DIFF_0(*args):
return _nucleardata.get_K2_GAD_DIFF_0(*args)
get_K2_GAD_DIFF_0 = _nucleardata.get_K2_GAD_DIFF_0
def get_K2_GAD_DIFF_1(*args):
return _nucleardata.get_K2_GAD_DIFF_1(*args)
get_K2_GAD_DIFF_1 = _nucleardata.get_K2_GAD_DIFF_1
def get_K2_GAD_DIFF_2(*args):
return _nucleardata.get_K2_GAD_DIFF_2(*args)
get_K2_GAD_DIFF_2 = _nucleardata.get_K2_GAD_DIFF_2
def get_K1_GAD_DIFF_0(*args):
return _nucleardata.get_K1_GAD_DIFF_0(*args)
get_K1_GAD_DIFF_0 = _nucleardata.get_K1_GAD_DIFF_0
def get_K1_GAD_DIFF_1(*args):
return _nucleardata.get_K1_GAD_DIFF_1(*args)
get_K1_GAD_DIFF_1 = _nucleardata.get_K1_GAD_DIFF_1
def get_K1_GAD_DIFF_2(*args):
return _nucleardata.get_K1_GAD_DIFF_2(*args)
get_K1_GAD_DIFF_2 = _nucleardata.get_K1_GAD_DIFF_2
def get_NUFISS2_GAD_DIFF_0(*args):
return _nucleardata.get_NUFISS2_GAD_DIFF_0(*args)
get_NUFISS2_GAD_DIFF_0 = _nucleardata.get_NUFISS2_GAD_DIFF_0
def get_NUFISS2_GAD_DIFF_1(*args):
return _nucleardata.get_NUFISS2_GAD_DIFF_1(*args)
get_NUFISS2_GAD_DIFF_1 = _nucleardata.get_NUFISS2_GAD_DIFF_1
def get_NUFISS2_GAD_DIFF_2(*args):
return _nucleardata.get_NUFISS2_GAD_DIFF_2(*args)
get_NUFISS2_GAD_DIFF_2 = _nucleardata.get_NUFISS2_GAD_DIFF_2
def get_BOR1_NO_XE_GAD_DIFF_0(*args):
return _nucleardata.get_BOR1_NO_XE_GAD_DIFF_0(*args)
get_BOR1_NO_XE_GAD_DIFF_0 = _nucleardata.get_BOR1_NO_XE_GAD_DIFF_0
def get_BOR1_NO_XE_GAD_DIFF_1(*args):
return _nucleardata.get_BOR1_NO_XE_GAD_DIFF_1(*args)
get_BOR1_NO_XE_GAD_DIFF_1 = _nucleardata.get_BOR1_NO_XE_GAD_DIFF_1
def get_BOR1_NO_XE_GAD_DIFF_2(*args):
return _nucleardata.get_BOR1_NO_XE_GAD_DIFF_2(*args)
get_BOR1_NO_XE_GAD_DIFF_2 = _nucleardata.get_BOR1_NO_XE_GAD_DIFF_2
def get_BOR2_XE_GAD_DIFF_0(*args):
return _nucleardata.get_BOR2_XE_GAD_DIFF_0(*args)
get_BOR2_XE_GAD_DIFF_0 = _nucleardata.get_BOR2_XE_GAD_DIFF_0
def get_BOR2_XE_GAD_DIFF_1(*args):
return _nucleardata.get_BOR2_XE_GAD_DIFF_1(*args)
get_BOR2_XE_GAD_DIFF_1 = _nucleardata.get_BOR2_XE_GAD_DIFF_1
def get_BOR2_XE_GAD_DIFF_2(*args):
return _nucleardata.get_BOR2_XE_GAD_DIFF_2(*args)
get_BOR2_XE_GAD_DIFF_2 = _nucleardata.get_BOR2_XE_GAD_DIFF_2
def get_REMOV1_GAD_DIFF_0(*args):
return _nucleardata.get_REMOV1_GAD_DIFF_0(*args)
get_REMOV1_GAD_DIFF_0 = _nucleardata.get_REMOV1_GAD_DIFF_0
def get_REMOV1_GAD_DIFF_1(*args):
return _nucleardata.get_REMOV1_GAD_DIFF_1(*args)
get_REMOV1_GAD_DIFF_1 = _nucleardata.get_REMOV1_GAD_DIFF_1
def get_REMOV1_GAD_DIFF_2(*args):
return _nucleardata.get_REMOV1_GAD_DIFF_2(*args)
get_REMOV1_GAD_DIFF_2 = _nucleardata.get_REMOV1_GAD_DIFF_2
def get_DIFF2_GAD_DIFF_0(*args):
return _nucleardata.get_DIFF2_GAD_DIFF_0(*args)
get_DIFF2_GAD_DIFF_0 = _nucleardata.get_DIFF2_GAD_DIFF_0
def get_DIFF2_GAD_DIFF_1(*args):
return _nucleardata.get_DIFF2_GAD_DIFF_1(*args)
get_DIFF2_GAD_DIFF_1 = _nucleardata.get_DIFF2_GAD_DIFF_1
def get_DIFF2_GAD_DIFF_2(*args):
return _nucleardata.get_DIFF2_GAD_DIFF_2(*args)
get_DIFF2_GAD_DIFF_2 = _nucleardata.get_DIFF2_GAD_DIFF_2
def get_DIFF1_GAD_DIFF_0(*args):
return _nucleardata.get_DIFF1_GAD_DIFF_0(*args)
get_DIFF1_GAD_DIFF_0 = _nucleardata.get_DIFF1_GAD_DIFF_0
def get_DIFF1_GAD_DIFF_1(*args):
return _nucleardata.get_DIFF1_GAD_DIFF_1(*args)
get_DIFF1_GAD_DIFF_1 = _nucleardata.get_DIFF1_GAD_DIFF_1
def get_DIFF1_GAD_DIFF_2(*args):
return _nucleardata.get_DIFF1_GAD_DIFF_2(*args)
get_DIFF1_GAD_DIFF_2 = _nucleardata.get_DIFF1_GAD_DIFF_2
def get_BOR1_XE_GAD_DIFF_0(*args):
return _nucleardata.get_BOR1_XE_GAD_DIFF_0(*args)
get_BOR1_XE_GAD_DIFF_0 = _nucleardata.get_BOR1_XE_GAD_DIFF_0
def get_BOR1_XE_GAD_DIFF_1(*args):
return _nucleardata.get_BOR1_XE_GAD_DIFF_1(*args)
get_BOR1_XE_GAD_DIFF_1 = _nucleardata.get_BOR1_XE_GAD_DIFF_1
def get_BOR1_XE_GAD_DIFF_2(*args):
return _nucleardata.get_BOR1_XE_GAD_DIFF_2(*args)
get_BOR1_XE_GAD_DIFF_2 = _nucleardata.get_BOR1_XE_GAD_DIFF_2
def get_XE_YIELD_GAD_DIFF_0(*args):
return _nucleardata.get_XE_YIELD_GAD_DIFF_0(*args)
get_XE_YIELD_GAD_DIFF_0 = _nucleardata.get_XE_YIELD_GAD_DIFF_0
def get_XE_YIELD_GAD_DIFF_1(*args):
return _nucleardata.get_XE_YIELD_GAD_DIFF_1(*args)
get_XE_YIELD_GAD_DIFF_1 = _nucleardata.get_XE_YIELD_GAD_DIFF_1
def get_XE_YIELD_GAD_DIFF_2(*args):
return _nucleardata.get_XE_YIELD_GAD_DIFF_2(*args)
get_XE_YIELD_GAD_DIFF_2 = _nucleardata.get_XE_YIELD_GAD_DIFF_2
def get_NU_GAD_DIFF_0(*args):
return _nucleardata.get_NU_GAD_DIFF_0(*args)
get_NU_GAD_DIFF_0 = _nucleardata.get_NU_GAD_DIFF_0
def get_NU_GAD_DIFF_1(*args):
return _nucleardata.get_NU_GAD_DIFF_1(*args)
get_NU_GAD_DIFF_1 = _nucleardata.get_NU_GAD_DIFF_1
def get_NU_GAD_DIFF_2(*args):
return _nucleardata.get_NU_GAD_DIFF_2(*args)
get_NU_GAD_DIFF_2 = _nucleardata.get_NU_GAD_DIFF_2
def get_BOR2_NO_XE_GAD_DIFF_0(*args):
return _nucleardata.get_BOR2_NO_XE_GAD_DIFF_0(*args)
get_BOR2_NO_XE_GAD_DIFF_0 = _nucleardata.get_BOR2_NO_XE_GAD_DIFF_0
def get_BOR2_NO_XE_GAD_DIFF_1(*args):
return _nucleardata.get_BOR2_NO_XE_GAD_DIFF_1(*args)
get_BOR2_NO_XE_GAD_DIFF_1 = _nucleardata.get_BOR2_NO_XE_GAD_DIFF_1
def get_BOR2_NO_XE_GAD_DIFF_2(*args):
return _nucleardata.get_BOR2_NO_XE_GAD_DIFF_2(*args)
get_BOR2_NO_XE_GAD_DIFF_2 = _nucleardata.get_BOR2_NO_XE_GAD_DIFF_2
def get_KAPPA_GAD_DIFF_0(*args):
return _nucleardata.get_KAPPA_GAD_DIFF_0(*args)
get_KAPPA_GAD_DIFF_0 = _nucleardata.get_KAPPA_GAD_DIFF_0
def get_KAPPA_GAD_DIFF_1(*args):
return _nucleardata.get_KAPPA_GAD_DIFF_1(*args)
get_KAPPA_GAD_DIFF_1 = _nucleardata.get_KAPPA_GAD_DIFF_1
def get_KAPPA_GAD_DIFF_2(*args):
return _nucleardata.get_KAPPA_GAD_DIFF_2(*args)
get_KAPPA_GAD_DIFF_2 = _nucleardata.get_KAPPA_GAD_DIFF_2
def get_ABS1_GAD_DIFF_0(*args):
return _nucleardata.get_ABS1_GAD_DIFF_0(*args)
get_ABS1_GAD_DIFF_0 = _nucleardata.get_ABS1_GAD_DIFF_0
def get_ABS1_GAD_DIFF_1(*args):
return _nucleardata.get_ABS1_GAD_DIFF_1(*args)
get_ABS1_GAD_DIFF_1 = _nucleardata.get_ABS1_GAD_DIFF_1
def get_ABS1_GAD_DIFF_2(*args):
return _nucleardata.get_ABS1_GAD_DIFF_2(*args)
get_ABS1_GAD_DIFF_2 = _nucleardata.get_ABS1_GAD_DIFF_2
def get_ABS2_GAD_DIFF_0(*args):
return _nucleardata.get_ABS2_GAD_DIFF_0(*args)
get_ABS2_GAD_DIFF_0 = _nucleardata.get_ABS2_GAD_DIFF_0
def get_ABS2_GAD_DIFF_1(*args):
return _nucleardata.get_ABS2_GAD_DIFF_1(*args)
get_ABS2_GAD_DIFF_1 = _nucleardata.get_ABS2_GAD_DIFF_1
def get_ABS2_GAD_DIFF_2(*args):
return _nucleardata.get_ABS2_GAD_DIFF_2(*args)
get_ABS2_GAD_DIFF_2 = _nucleardata.get_ABS2_GAD_DIFF_2
def get_XE2_MAC_GAD_DIFF_0(*args):
return _nucleardata.get_XE2_MAC_GAD_DIFF_0(*args)
get_XE2_MAC_GAD_DIFF_0 = _nucleardata.get_XE2_MAC_GAD_DIFF_0
def get_XE2_MAC_GAD_DIFF_1(*args):
return _nucleardata.get_XE2_MAC_GAD_DIFF_1(*args)
get_XE2_MAC_GAD_DIFF_1 = _nucleardata.get_XE2_MAC_GAD_DIFF_1
def get_XE2_MAC_GAD_DIFF_2(*args):
return _nucleardata.get_XE2_MAC_GAD_DIFF_2(*args)
get_XE2_MAC_GAD_DIFF_2 = _nucleardata.get_XE2_MAC_GAD_DIFF_2
def get_K_INF_NO_XE_GAD_DIFF_0(*args):
return _nucleardata.get_K_INF_NO_XE_GAD_DIFF_0(*args)
get_K_INF_NO_XE_GAD_DIFF_0 = _nucleardata.get_K_INF_NO_XE_GAD_DIFF_0
def get_K_INF_NO_XE_GAD_DIFF_1(*args):
return _nucleardata.get_K_INF_NO_XE_GAD_DIFF_1(*args)
get_K_INF_NO_XE_GAD_DIFF_1 = _nucleardata.get_K_INF_NO_XE_GAD_DIFF_1
def get_K_INF_NO_XE_GAD_DIFF_2(*args):
return _nucleardata.get_K_INF_NO_XE_GAD_DIFF_2(*args)
get_K_INF_NO_XE_GAD_DIFF_2 = _nucleardata.get_K_INF_NO_XE_GAD_DIFF_2
def get_XE2_MIC_GAD_DIFF_0(*args):
return _nucleardata.get_XE2_MIC_GAD_DIFF_0(*args)
get_XE2_MIC_GAD_DIFF_0 = _nucleardata.get_XE2_MIC_GAD_DIFF_0
def get_XE2_MIC_GAD_DIFF_1(*args):
return _nucleardata.get_XE2_MIC_GAD_DIFF_1(*args)
get_XE2_MIC_GAD_DIFF_1 = _nucleardata.get_XE2_MIC_GAD_DIFF_1
def get_XE2_MIC_GAD_DIFF_2(*args):
return _nucleardata.get_XE2_MIC_GAD_DIFF_2(*args)
get_XE2_MIC_GAD_DIFF_2 = _nucleardata.get_XE2_MIC_GAD_DIFF_2
def get_SM2_XE_GAD_DIFF_0(*args):
return _nucleardata.get_SM2_XE_GAD_DIFF_0(*args)
get_SM2_XE_GAD_DIFF_0 = _nucleardata.get_SM2_XE_GAD_DIFF_0
def get_SM2_XE_GAD_DIFF_1(*args):
return _nucleardata.get_SM2_XE_GAD_DIFF_1(*args)
get_SM2_XE_GAD_DIFF_1 = _nucleardata.get_SM2_XE_GAD_DIFF_1
def get_SM2_XE_GAD_DIFF_2(*args):
return _nucleardata.get_SM2_XE_GAD_DIFF_2(*args)
get_SM2_XE_GAD_DIFF_2 = _nucleardata.get_SM2_XE_GAD_DIFF_2
def get_K_INF_XE_GAD_DIFF_0(*args):
return _nucleardata.get_K_INF_XE_GAD_DIFF_0(*args)
get_K_INF_XE_GAD_DIFF_0 = _nucleardata.get_K_INF_XE_GAD_DIFF_0
def get_K_INF_XE_GAD_DIFF_1(*args):
return _nucleardata.get_K_INF_XE_GAD_DIFF_1(*args)
get_K_INF_XE_GAD_DIFF_1 = _nucleardata.get_K_INF_XE_GAD_DIFF_1
def get_K_INF_XE_GAD_DIFF_2(*args):
return _nucleardata.get_K_INF_XE_GAD_DIFF_2(*args)
get_K_INF_XE_GAD_DIFF_2 = _nucleardata.get_K_INF_XE_GAD_DIFF_2
| 38.405583
| 131
| 0.845718
| 8,618
| 46,778
| 3.86621
| 0.016941
| 0.222696
| 0.167022
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| 0.909541
| 0.798013
| 0.600858
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| 0.151805
| 0.026321
| 0
| 0.047556
| 0.082068
| 46,778
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| 132
| 38.437141
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| 1
| 0
| 0.008822
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| 1
| 0.28953
| false
| 0.002137
| 0.009615
| 0.284188
| 0.597222
| 0
| 0
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| null | 1
| 0
| 1
| 1
| 1
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|
0
| 7
|
ff4212cc340c136668856fcafdf45ddfa814ac63
| 29,332
|
py
|
Python
|
sdk/python/pulumi_azure/kusto/database_principal.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 109
|
2018-06-18T00:19:44.000Z
|
2022-02-20T05:32:57.000Z
|
sdk/python/pulumi_azure/kusto/database_principal.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 663
|
2018-06-18T21:08:46.000Z
|
2022-03-31T20:10:11.000Z
|
sdk/python/pulumi_azure/kusto/database_principal.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 41
|
2018-07-19T22:37:38.000Z
|
2022-03-14T10:56:26.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['DatabasePrincipalArgs', 'DatabasePrincipal']
@pulumi.input_type
class DatabasePrincipalArgs:
def __init__(__self__, *,
client_id: pulumi.Input[str],
cluster_name: pulumi.Input[str],
database_name: pulumi.Input[str],
object_id: pulumi.Input[str],
resource_group_name: pulumi.Input[str],
role: pulumi.Input[str],
type: pulumi.Input[str]):
"""
The set of arguments for constructing a DatabasePrincipal resource.
:param pulumi.Input[str] client_id: The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
:param pulumi.Input[str] cluster_name: Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] database_name: Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] object_id: An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
:param pulumi.Input[str] role: Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
:param pulumi.Input[str] type: Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
pulumi.set(__self__, "client_id", client_id)
pulumi.set(__self__, "cluster_name", cluster_name)
pulumi.set(__self__, "database_name", database_name)
pulumi.set(__self__, "object_id", object_id)
pulumi.set(__self__, "resource_group_name", resource_group_name)
pulumi.set(__self__, "role", role)
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="clientId")
def client_id(self) -> pulumi.Input[str]:
"""
The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "client_id")
@client_id.setter
def client_id(self, value: pulumi.Input[str]):
pulumi.set(self, "client_id", value)
@property
@pulumi.getter(name="clusterName")
def cluster_name(self) -> pulumi.Input[str]:
"""
Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "cluster_name")
@cluster_name.setter
def cluster_name(self, value: pulumi.Input[str]):
pulumi.set(self, "cluster_name", value)
@property
@pulumi.getter(name="databaseName")
def database_name(self) -> pulumi.Input[str]:
"""
Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "database_name")
@database_name.setter
def database_name(self, value: pulumi.Input[str]):
pulumi.set(self, "database_name", value)
@property
@pulumi.getter(name="objectId")
def object_id(self) -> pulumi.Input[str]:
"""
An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "object_id")
@object_id.setter
def object_id(self, value: pulumi.Input[str]):
pulumi.set(self, "object_id", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter
def role(self) -> pulumi.Input[str]:
"""
Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "role")
@role.setter
def role(self, value: pulumi.Input[str]):
pulumi.set(self, "role", value)
@property
@pulumi.getter
def type(self) -> pulumi.Input[str]:
"""
Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: pulumi.Input[str]):
pulumi.set(self, "type", value)
@pulumi.input_type
class _DatabasePrincipalState:
def __init__(__self__, *,
app_id: Optional[pulumi.Input[str]] = None,
client_id: Optional[pulumi.Input[str]] = None,
cluster_name: Optional[pulumi.Input[str]] = None,
database_name: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
fully_qualified_name: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
object_id: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
role: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering DatabasePrincipal resources.
:param pulumi.Input[str] app_id: The app id, if not empty, of the principal.
:param pulumi.Input[str] client_id: The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
:param pulumi.Input[str] cluster_name: Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] database_name: Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] email: The email, if not empty, of the principal.
:param pulumi.Input[str] fully_qualified_name: The fully qualified name of the principal.
:param pulumi.Input[str] name: The name of the Kusto Database Principal.
:param pulumi.Input[str] object_id: An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
:param pulumi.Input[str] role: Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
:param pulumi.Input[str] type: Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
if app_id is not None:
pulumi.set(__self__, "app_id", app_id)
if client_id is not None:
pulumi.set(__self__, "client_id", client_id)
if cluster_name is not None:
pulumi.set(__self__, "cluster_name", cluster_name)
if database_name is not None:
pulumi.set(__self__, "database_name", database_name)
if email is not None:
pulumi.set(__self__, "email", email)
if fully_qualified_name is not None:
pulumi.set(__self__, "fully_qualified_name", fully_qualified_name)
if name is not None:
pulumi.set(__self__, "name", name)
if object_id is not None:
pulumi.set(__self__, "object_id", object_id)
if resource_group_name is not None:
pulumi.set(__self__, "resource_group_name", resource_group_name)
if role is not None:
pulumi.set(__self__, "role", role)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="appId")
def app_id(self) -> Optional[pulumi.Input[str]]:
"""
The app id, if not empty, of the principal.
"""
return pulumi.get(self, "app_id")
@app_id.setter
def app_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "app_id", value)
@property
@pulumi.getter(name="clientId")
def client_id(self) -> Optional[pulumi.Input[str]]:
"""
The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "client_id")
@client_id.setter
def client_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "client_id", value)
@property
@pulumi.getter(name="clusterName")
def cluster_name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "cluster_name")
@cluster_name.setter
def cluster_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cluster_name", value)
@property
@pulumi.getter(name="databaseName")
def database_name(self) -> Optional[pulumi.Input[str]]:
"""
Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "database_name")
@database_name.setter
def database_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "database_name", value)
@property
@pulumi.getter
def email(self) -> Optional[pulumi.Input[str]]:
"""
The email, if not empty, of the principal.
"""
return pulumi.get(self, "email")
@email.setter
def email(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "email", value)
@property
@pulumi.getter(name="fullyQualifiedName")
def fully_qualified_name(self) -> Optional[pulumi.Input[str]]:
"""
The fully qualified name of the principal.
"""
return pulumi.get(self, "fully_qualified_name")
@fully_qualified_name.setter
def fully_qualified_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "fully_qualified_name", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the Kusto Database Principal.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="objectId")
def object_id(self) -> Optional[pulumi.Input[str]]:
"""
An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "object_id")
@object_id.setter
def object_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "object_id", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter
def role(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "role")
@role.setter
def role(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "role", value)
@property
@pulumi.getter
def type(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "type", value)
class DatabasePrincipal(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
client_id: Optional[pulumi.Input[str]] = None,
cluster_name: Optional[pulumi.Input[str]] = None,
database_name: Optional[pulumi.Input[str]] = None,
object_id: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
role: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Manages a Kusto (also known as Azure Data Explorer) Database Principal
> **NOTE:** This resource is being **deprecated** due to API updates and should no longer be used. Please use kusto.DatabasePrincipalAssignment instead.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
current = azure.core.get_client_config()
rg = azure.core.ResourceGroup("rg", location="West Europe")
cluster = azure.kusto.Cluster("cluster",
location=rg.location,
resource_group_name=rg.name,
sku=azure.kusto.ClusterSkuArgs(
name="Standard_D13_v2",
capacity=2,
))
database = azure.kusto.Database("database",
resource_group_name=rg.name,
location=rg.location,
cluster_name=cluster.name,
hot_cache_period="P7D",
soft_delete_period="P31D")
principal = azure.kusto.DatabasePrincipal("principal",
resource_group_name=rg.name,
cluster_name=cluster.name,
database_name=azurerm_kusto_database["test"]["name"],
role="Viewer",
type="User",
client_id=current.tenant_id,
object_id=current.client_id)
```
## Import
Kusto Database Principals can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:kusto/databasePrincipal:DatabasePrincipal example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Kusto/Clusters/cluster1/Databases/database1/Role/role1/FQN/some-guid
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] client_id: The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
:param pulumi.Input[str] cluster_name: Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] database_name: Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] object_id: An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
:param pulumi.Input[str] role: Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
:param pulumi.Input[str] type: Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: DatabasePrincipalArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Manages a Kusto (also known as Azure Data Explorer) Database Principal
> **NOTE:** This resource is being **deprecated** due to API updates and should no longer be used. Please use kusto.DatabasePrincipalAssignment instead.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
current = azure.core.get_client_config()
rg = azure.core.ResourceGroup("rg", location="West Europe")
cluster = azure.kusto.Cluster("cluster",
location=rg.location,
resource_group_name=rg.name,
sku=azure.kusto.ClusterSkuArgs(
name="Standard_D13_v2",
capacity=2,
))
database = azure.kusto.Database("database",
resource_group_name=rg.name,
location=rg.location,
cluster_name=cluster.name,
hot_cache_period="P7D",
soft_delete_period="P31D")
principal = azure.kusto.DatabasePrincipal("principal",
resource_group_name=rg.name,
cluster_name=cluster.name,
database_name=azurerm_kusto_database["test"]["name"],
role="Viewer",
type="User",
client_id=current.tenant_id,
object_id=current.client_id)
```
## Import
Kusto Database Principals can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:kusto/databasePrincipal:DatabasePrincipal example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Kusto/Clusters/cluster1/Databases/database1/Role/role1/FQN/some-guid
```
:param str resource_name: The name of the resource.
:param DatabasePrincipalArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(DatabasePrincipalArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
client_id: Optional[pulumi.Input[str]] = None,
cluster_name: Optional[pulumi.Input[str]] = None,
database_name: Optional[pulumi.Input[str]] = None,
object_id: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
role: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = DatabasePrincipalArgs.__new__(DatabasePrincipalArgs)
if client_id is None and not opts.urn:
raise TypeError("Missing required property 'client_id'")
__props__.__dict__["client_id"] = client_id
if cluster_name is None and not opts.urn:
raise TypeError("Missing required property 'cluster_name'")
__props__.__dict__["cluster_name"] = cluster_name
if database_name is None and not opts.urn:
raise TypeError("Missing required property 'database_name'")
__props__.__dict__["database_name"] = database_name
if object_id is None and not opts.urn:
raise TypeError("Missing required property 'object_id'")
__props__.__dict__["object_id"] = object_id
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
if role is None and not opts.urn:
raise TypeError("Missing required property 'role'")
__props__.__dict__["role"] = role
if type is None and not opts.urn:
raise TypeError("Missing required property 'type'")
__props__.__dict__["type"] = type
__props__.__dict__["app_id"] = None
__props__.__dict__["email"] = None
__props__.__dict__["fully_qualified_name"] = None
__props__.__dict__["name"] = None
super(DatabasePrincipal, __self__).__init__(
'azure:kusto/databasePrincipal:DatabasePrincipal',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
app_id: Optional[pulumi.Input[str]] = None,
client_id: Optional[pulumi.Input[str]] = None,
cluster_name: Optional[pulumi.Input[str]] = None,
database_name: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
fully_qualified_name: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
object_id: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
role: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None) -> 'DatabasePrincipal':
"""
Get an existing DatabasePrincipal resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] app_id: The app id, if not empty, of the principal.
:param pulumi.Input[str] client_id: The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
:param pulumi.Input[str] cluster_name: Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] database_name: Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
:param pulumi.Input[str] email: The email, if not empty, of the principal.
:param pulumi.Input[str] fully_qualified_name: The fully qualified name of the principal.
:param pulumi.Input[str] name: The name of the Kusto Database Principal.
:param pulumi.Input[str] object_id: An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
:param pulumi.Input[str] role: Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
:param pulumi.Input[str] type: Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _DatabasePrincipalState.__new__(_DatabasePrincipalState)
__props__.__dict__["app_id"] = app_id
__props__.__dict__["client_id"] = client_id
__props__.__dict__["cluster_name"] = cluster_name
__props__.__dict__["database_name"] = database_name
__props__.__dict__["email"] = email
__props__.__dict__["fully_qualified_name"] = fully_qualified_name
__props__.__dict__["name"] = name
__props__.__dict__["object_id"] = object_id
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["role"] = role
__props__.__dict__["type"] = type
return DatabasePrincipal(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="appId")
def app_id(self) -> pulumi.Output[str]:
"""
The app id, if not empty, of the principal.
"""
return pulumi.get(self, "app_id")
@property
@pulumi.getter(name="clientId")
def client_id(self) -> pulumi.Output[str]:
"""
The Client ID that owns the specified `object_id`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "client_id")
@property
@pulumi.getter(name="clusterName")
def cluster_name(self) -> pulumi.Output[str]:
"""
Specifies the name of the Kusto Cluster this database principal will be added to. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "cluster_name")
@property
@pulumi.getter(name="databaseName")
def database_name(self) -> pulumi.Output[str]:
"""
Specified the name of the Kusto Database this principal will be added to. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "database_name")
@property
@pulumi.getter
def email(self) -> pulumi.Output[str]:
"""
The email, if not empty, of the principal.
"""
return pulumi.get(self, "email")
@property
@pulumi.getter(name="fullyQualifiedName")
def fully_qualified_name(self) -> pulumi.Output[str]:
"""
The fully qualified name of the principal.
"""
return pulumi.get(self, "fully_qualified_name")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
The name of the Kusto Database Principal.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="objectId")
def object_id(self) -> pulumi.Output[str]:
"""
An Object ID of a User, Group, or App. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "object_id")
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Output[str]:
"""
Specifies the Resource Group where the Kusto Database Principal should exist. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@property
@pulumi.getter
def role(self) -> pulumi.Output[str]:
"""
Specifies the permissions the Principal will have. Valid values include `Admin`, `Ingestor`, `Monitor`, `UnrestrictedViewers`, `User`, `Viewer`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "role")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Specifies the type of object the principal is. Valid values include `App`, `Group`, `User`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "type")
| 46.484945
| 243
| 0.650314
| 3,637
| 29,332
| 5.054715
| 0.064339
| 0.071203
| 0.089099
| 0.069408
| 0.888327
| 0.866079
| 0.841873
| 0.801349
| 0.785574
| 0.760117
| 0
| 0.003957
| 0.250443
| 29,332
| 630
| 244
| 46.55873
| 0.832211
| 0.417189
| 0
| 0.58209
| 1
| 0
| 0.102499
| 0.005777
| 0
| 0
| 0
| 0
| 0
| 1
| 0.161194
| false
| 0.002985
| 0.014925
| 0
| 0.274627
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ff4c8d9be9d193424a64e55cc13a9ca37ee972ab
| 46
|
py
|
Python
|
03/slicing.py
|
mradrianhh/Introduction-to-scientific-programming
|
29d7312626d06a0778e130b2ddf9f21180581c4d
|
[
"MIT"
] | null | null | null |
03/slicing.py
|
mradrianhh/Introduction-to-scientific-programming
|
29d7312626d06a0778e130b2ddf9f21180581c4d
|
[
"MIT"
] | null | null | null |
03/slicing.py
|
mradrianhh/Introduction-to-scientific-programming
|
29d7312626d06a0778e130b2ddf9f21180581c4d
|
[
"MIT"
] | null | null | null |
n = [0,1,2,3,4,5]
a = n[2:4]
print(n)
print(a)
| 11.5
| 17
| 0.5
| 15
| 46
| 1.533333
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205128
| 0.152174
| 46
| 4
| 18
| 11.5
| 0.384615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
ff8502af3c6ba72b81c3ecd0ad8326b416625d34
| 23,878
|
py
|
Python
|
gate/config/dataset/avec2014.py
|
atranitell/TensorGate
|
855ae0c69a706c179c26ba4a75a8067a514285fe
|
[
"Apache-2.0"
] | null | null | null |
gate/config/dataset/avec2014.py
|
atranitell/TensorGate
|
855ae0c69a706c179c26ba4a75a8067a514285fe
|
[
"Apache-2.0"
] | null | null | null |
gate/config/dataset/avec2014.py
|
atranitell/TensorGate
|
855ae0c69a706c179c26ba4a75a8067a514285fe
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017 The KaiJIN Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""AVEC2014 DATASET
- avec2014: normal single image
- avec2014.flow: normal single optical flow image
- avec2014.bicnn: .normal/.rgb/.opt/.shared
"""
import gate.config.config_params as params
from gate.config.config_base import Configbase
class AVEC2014(Configbase):
def __init__(self, args):
"""AVEC2014 dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014'
self.target = 'avec2014.img.cnn'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=120000)
"""network model"""
self.net = [params.NET()]
self.net[0].resnet_v2(
depth='50',
num_classes=1,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=0.001)
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014/trn_img.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014/tst_img.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
""" data.heatmap """
self.heatmap = params.Phase('heatmap')
self.heatmap.data = params.DATA(
batchsize=1,
entry_path='../_datasets/AVEC2014/pp_tst_img_paper.txt',
shuffle=False)
self.heatmap.data.set_queue_loader(
loader='load_image',
reader_thread=1,
min_queue_num=1)
self.set_default_data_attr(self.heatmap.data)
def set_default_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=224,
output_width=224,
preprocessing_method='avec2014',
gray=False)
data.add(image)
data.set_entry_attr((str, int), (True, False))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
class AVEC2014_DLDL(Configbase):
def __init__(self, args):
"""AVEC2014 dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014.dldl'
self.target = 'avec2014.img.cnn.dldl'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=120000)
"""network model"""
self.net = [params.NET()]
self.net[0].resnet_v2(
depth='50',
num_classes=64,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=0.001)
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014/pp_trn_0_img.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014/pp_tst_img.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
def set_default_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=224,
output_width=224,
preprocessing_method='avec2014',
gray=False)
data.add(image)
data.set_entry_attr((str, int), (True, False))
data.set_label(num_classes=64, span=64, one_hot=False, scale=False)
class AVEC2014_EX1(Configbase):
def __init__(self, args):
"""AVEC2014 dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014.ex1'
self.target = 'avec2014.img.cnn.ex1'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=250000)
"""network model"""
self.net = [params.NET()]
self.net[0].resnet_v2(
depth='50',
num_classes=64,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
"""learning rate"""
self.lr = [params.LR(), params.LR()]
self.lr[0].set_fixed(learning_rate=0.0001)
self.lr[1].set_fixed(learning_rate=0.00001)
"""optimizer"""
self.opt = [params.OPT(), params.OPT()]
self.opt[0].set_adam()
self.opt[1].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014/pp_trn_0_img_with_c.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_image',
reader_thread=1,
min_queue_num=32)
self.set_train_data_attr(self.train.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014/pp_tst_img.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
def set_train_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=224,
output_width=224,
preprocessing_method='avec2014',
gray=False)
data.add(image)
data.set_entry_attr((str, int, int), (True, False, False))
data.set_label(num_classes=64, span=64, one_hot=False, scale=False)
def set_default_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=224,
output_width=224,
preprocessing_method='avec2014',
gray=False)
data.add(image)
data.set_entry_attr((str, int), (True, False))
data.set_label(num_classes=64, span=64, one_hot=False, scale=False)
class AVEC2014_68(Configbase):
def __init__(self, args):
"""AVEC2014 dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014.68'
self.target = 'avec2014.68.img.cnn'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=120000)
"""network model"""
self.net = [params.NET()]
self.net[0].resnet_v2_critical(
depth='50',
num_classes=1,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=0.001)
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014/pp_trn_0_img_aligned_int.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014/pp_tst_img_aligned_int.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
""" data.heatmap """
self.heatmap = params.Phase('heatmap')
self.heatmap.data = params.DATA(
batchsize=1,
entry_path='../_datasets/AVEC2014/pp_tst_img_paper.txt',
shuffle=False)
self.heatmap.data.set_queue_loader(
loader='load_image',
reader_thread=1,
min_queue_num=1)
self.set_default_data_attr(self.heatmap.data)
def set_default_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=256,
output_width=256,
preprocessing_method='avec2014.68',
gray=False)
data.add(image)
data.set_entry_attr(
tuple([str, ]+[float, float, float]*68+[float, ]),
tuple([True, ]+[False]*205))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
class AVEC2014_FLOW(Configbase):
def __init__(self, args):
"""AVEC2014_FLOW dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014.flow'
self.target = 'avec2014.img.cnn'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=120000)
"""network model"""
self.net = [params.NET()]
self.net[0].resnet_v2(
depth='50',
num_classes=1,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=0.001)
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014/pp_trn_0_flow.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014/pp_tst_flow.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
def set_default_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=224,
output_width=224,
preprocessing_method='avec2014',
gray=False)
data.add(image)
data.set_entry_attr((str, int), (True, False))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
class AVEC2014_BICNN(Configbase):
def __init__(self, args):
"""AVEC2014_BICNN dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014'
self.target = 'avec2014.img.bicnn.shared'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=200000)
"""network model"""
# self.net = [params.NET()]
# self.net[0].resnet_v2_bishared(
# depth='50',
# num_classes=1,
# weight_decay=0.0005,
# batch_norm_decay=0.997,
# batch_norm_epsilon=1e-5,
# batch_norm_scale=True,
# use_batch_norm=True,
# activation_fn='relu',
# global_pool=True)
self.net = [params.NET(), params.NET()]
self.net[0].resnet_v2(
depth='50',
num_classes=1,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
self.net[1].resnet_v2(
depth='50',
num_classes=1,
weight_decay=0.0005,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=True,
activation_fn='relu',
global_pool=True)
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=0.001)
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014/pp_trn_0_img_flow.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014/pp_tst_img_flow.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_image',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
def set_default_data_attr(self, data):
image = params.Image()
image.set_fixed_length_image(
channels=3,
frames=1,
raw_height=256,
raw_width=256,
output_height=224,
output_width=224,
preprocessing_method='avec2014',
gray=False)
data.add([image, image])
data.set_entry_attr((str, str, int), (True, True, False))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
class AVEC2014_AUDIO_CNN(Configbase):
def __init__(self, args):
"""AVEC2014_AUDIO dataset for Depressive Detection"""
Configbase.__init__(self, args)
r = self._read_config_file
self.name = r('avec2014', 'name')
self.target = r('avec2014.audio.cnn', 'target')
self.output_dir = r(None, 'output_dir')
self.ckpt_file = r(None, 'ckpt_file')
self.task = r('train', 'task')
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=150000)
"""network model"""
self.net = [params.NET()]
self.net[0].sensnet(
num_classes=1,
weight_decay=0.0001,
unit_type=r('multi_addition', 'net.unit_type'),
unit_num=r([1, 1, 1, 1], 'net.unit_num'),
batch_norm_decay=0.999,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
use_batch_norm=r(False, 'net.use_batch_norm'),
use_pre_batch_norm=r(False, 'net.use_pre_batch_norm'),
dropout_keep=0.5,
activation_fn=r('leaky_relu', 'net.activation_fn'),
version=r('sensnet_v2_fpn', 'net.version'))
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=r(0.00003, 'train.lr'))
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=r(32, 'train.batchsize'),
entry_path=r('../_datasets/AVEC2014_Audio/pp_trn_raw.txt',
'train.entry_path'),
shuffle=True)
self.train.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""val"""
self.val = params.Phase('val')
self.val.data = params.DATA(
batchsize=r(50, 'val.batchsize'),
entry_path=r('../_datasets/AVEC2014_Audio/pp_val_16.txt',
'val.entry_path'),
shuffle=False)
self.val.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.val.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=r(50, 'test.batchsize'),
entry_path=r('../_datasets/AVEC2014_Audio/pp_tst_16.txt',
'test.entry_path'),
shuffle=False)
self.test.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
"""extract_feature"""
self.extract_feature = params.Phase('extract_feature')
self.extract_feature.data = params.DATA(
batchsize=r(50, 'extract_feature.batchsize'),
entry_path=r('../_datasets/AVEC2014_Audio/pp_tst_16.txt',
'extract_feature.entry_path'),
shuffle=False)
self.extract_feature.data.set_queue_loader(
loader='load_audio',
reader_thread=1, # keep order
min_queue_num=128)
self.set_default_data_attr(self.extract_feature.data)
def set_default_data_attr(self, data):
r = self._read_config_file
audio = params.Audio()
audio.set_fixed_length_audio(
frame_num=r(16, 'audio.frame_num'),
frame_length=r(200, 'audio.frame_length'),
frame_invl=r(200, 'audio.frame_invl'))
data.add(audio)
data.set_entry_attr((str, int, int), (True, False, False))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
class AVEC2014_AUDIO_FCN(Configbase):
def __init__(self, args):
"""AVEC2014_AUDIO dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014'
self.target = 'avec2014.audio.fcn'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=120000)
"""learning rate"""
self.lr = [params.LR()]
self.lr[0].set_fixed(learning_rate=0.00003)
"""optimizer"""
self.opt = [params.OPT()]
self.opt[0].set_adam()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014_Audio/pp_trn_raw.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""val"""
self.val = params.Phase('val')
self.val.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014_Audio/pp_val_16.txt',
shuffle=False)
self.val.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.val.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014_Audio/pp_tst_16.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
def set_default_data_attr(self, data):
audio = params.Audio()
audio.set_fixed_length_audio(
frame_num=16, frame_length=200, frame_invl=200)
data.add(audio)
data.set_entry_attr((str, int, int), (True, False, False))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
class AVEC2014_AUDIO_VAE(Configbase):
def __init__(self, args):
"""AVEC2014_AUDIO dataset for Depressive Detection"""
Configbase.__init__(self, args)
self.name = 'avec2014'
self.target = 'avec2014.audio.vae'
self.output_dir = None
self.task = 'train'
"""iteration controller"""
self.log = params.LOG(
print_invl=20,
save_summary_invl=20,
save_model_invl=1000,
test_invl=5000,
val_invl=5000,
max_iter=1500000)
"""learning rate"""
self.lr = [params.LR(), params.LR()]
self.lr[0].set_fixed(learning_rate=0.00001)
self.lr[1].set_fixed(learning_rate=0.00003)
"""optimizer"""
self.opt = [params.OPT(), params.OPT()]
self.opt[0].set_rmsprop()
self.opt[1].set_rmsprop()
"""train"""
self.train = params.Phase('train')
self.train.data = params.DATA(
batchsize=32,
entry_path='../_datasets/AVEC2014_Audio/pp_trn_raw.txt',
shuffle=True)
self.train.data.set_queue_loader(
loader='load_pair_audio',
reader_thread=8,
min_queue_num=32)
self.set_default_data_attr(self.train.data)
"""val"""
self.val = params.Phase('val')
self.val.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014_Audio/pp_val_32.txt',
shuffle=False)
self.val.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.val.data)
"""test"""
self.test = params.Phase('test')
self.test.data = params.DATA(
batchsize=50,
entry_path='../_datasets/AVEC2014_Audio/pp_tst_32.txt',
shuffle=False)
self.test.data.set_queue_loader(
loader='load_audio',
reader_thread=8,
min_queue_num=128)
self.set_default_data_attr(self.test.data)
def set_default_data_attr(self, data):
audio = params.Audio()
audio.set_fixed_length_audio(
frame_num=32, frame_length=200, frame_invl=200)
data.add(audio)
data.set_entry_attr((str, int, int), (True, False, False))
data.set_label(num_classes=1, span=63, one_hot=False, scale=True)
| 28.94303
| 80
| 0.627314
| 3,177
| 23,878
| 4.446333
| 0.074599
| 0.021804
| 0.028883
| 0.040776
| 0.894096
| 0.877956
| 0.86401
| 0.862523
| 0.85148
| 0.843126
| 0
| 0.056822
| 0.235698
| 23,878
| 824
| 81
| 28.978155
| 0.717205
| 0.062526
| 0
| 0.843949
| 0
| 0
| 0.098558
| 0.051269
| 0
| 0
| 0
| 0
| 0
| 1
| 0.030255
| false
| 0
| 0.003185
| 0
| 0.047771
| 0.014331
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ff98e8bbc10b2e3ee4c8768a350c940d4baf39fc
| 72
|
py
|
Python
|
test_app.py
|
SturmUDrang/chime
|
0fedb8d9e9106ac02c18259c3025b533fa8b6f0d
|
[
"MIT"
] | 156
|
2020-03-15T01:34:35.000Z
|
2022-01-28T09:52:23.000Z
|
test_app.py
|
SturmUDrang/chime
|
0fedb8d9e9106ac02c18259c3025b533fa8b6f0d
|
[
"MIT"
] | 57
|
2020-03-14T12:07:45.000Z
|
2022-03-12T00:26:53.000Z
|
test_app.py
|
SturmUDrang/chime
|
0fedb8d9e9106ac02c18259c3025b533fa8b6f0d
|
[
"MIT"
] | 45
|
2020-03-14T21:11:42.000Z
|
2020-10-16T18:43:54.000Z
|
import chime.app
def test_sample():
assert chime.app.S == 4119405
| 12
| 33
| 0.694444
| 11
| 72
| 4.454545
| 0.818182
| 0.326531
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12069
| 0.194444
| 72
| 5
| 34
| 14.4
| 0.724138
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
4418dbbecd356485f1921ed5fc7b10240a7ab335
| 48,683
|
py
|
Python
|
3pxnet-training/network.py
|
SRavit1/3pxnet
|
1f81a2bdcbb97c42163e914b01dba4e6c73ade60
|
[
"MIT"
] | null | null | null |
3pxnet-training/network.py
|
SRavit1/3pxnet
|
1f81a2bdcbb97c42163e914b01dba4e6c73ade60
|
[
"MIT"
] | null | null | null |
3pxnet-training/network.py
|
SRavit1/3pxnet
|
1f81a2bdcbb97c42163e914b01dba4e6c73ade60
|
[
"MIT"
] | null | null | null |
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import utils
import utils_own
import binarized_modules
import binarized_modules_multi
import uuid
def getModelFilename(model, modelName):
extension = modelName + "_model"
if model.full:
extension += "_full"
else:
if model.binary:
extension += "_binary"
else:
extension += "_ternary"
extension += "_" + str(model.conv_thres)
extension += "_bw" + str(model.bitwidth)
extension += "_input_bw" + str(model.input_bitwidth)
extension += "_" + model.id
return extension
class pnet(nn.Module):
def __init__(self, full=False, binary=True, conv_thres=0.7, align=True, bitwidth=1, input_bitwidth=1, binarize_input=True):
super(pnet, self).__init__()
self.align = align
self.pruned = False
self.full = full
self.binary = binary
self.bitwidth = bitwidth
self.input_bitwidth = input_bitwidth
self.binarize_input = binarize_input
self.conv_thres = conv_thres
self.id = str(uuid.uuid4())[:8]
self.name = "pnet"
self.filename = getModelFilename(self, self.name)
if full:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv4 = nn.Conv2d(32, 2, kernel_size=1, stride=1, padding=(0, 0), bias=False)
self.conv5 = nn.Conv2d(32, 4, kernel_size=1, stride=1, padding=(0, 0), bias=False)
elif binary:
if self.binarize_input:
self.conv1 = binarized_modules_multi.BinarizeConv2d(input_bitwidth, bitwidth, 3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
else:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
#self.conv4 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 2, kernel_size=1, stride=1, padding=(0, 0), bias=False)
self.conv4 = nn.Conv2d(32, 2, kernel_size=1, stride=1, padding=(0, 0), bias=False)
#self.conv5 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 4, kernel_size=1, stride=1, padding=(0, 0), bias=False)
self.conv5 = nn.Conv2d(32, 4, kernel_size=1, stride=1, padding=(0, 0), bias=False)
else:
if self.binarize_input:
self.conv1 = binarized_modules_multi.TernarizeConv2d(conv_thres, input_bitwidth, bitwidth, 3, 32, kernel_size=3, stride=1, padding=(0, 0), align=False, bias=False) #cannot be aligned since # input channels < 32
else:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), align=self.align, bias=False)
self.conv3 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), align=self.align, bias=False)
#self.conv4 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 2, kernel_size=1, stride=1, padding=(0, 0), align=self.align, bias=False)
self.conv4 = nn.Conv2d(32, 2, kernel_size=1, stride=1, padding=(0, 0), bias=False)
#self.conv5 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 4, kernel_size=1, stride=1, padding=(0, 0), align=self.align, bias=False)
self.conv5 = nn.Conv2d(32, 4, kernel_size=1, stride=1, padding=(0, 0), bias=False)
self.softmax1 = torch.nn.Softmax(dim = 1)
self.act = F.relu if self.full else nn.Hardtanh()
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(32)
self.bn3 = nn.BatchNorm2d(32)
self.bn4 = nn.BatchNorm2d(2)
self.bn5 = nn.BatchNorm2d(4)
def forward(self, x):
pad_width = 0 if x.shape[-1]%2 == 0 else 1
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=(pad_width, pad_width))
x = self.act(self.bn1(self.conv1(x)))
x = self.pool1(x)
x = self.act(self.bn2(self.conv2(x)))
x = self.act(self.bn3(self.conv3(x)))
out1 = self.softmax1(self.bn4(self.conv4(x)))
out2 = self.bn5(self.conv5(x))
return out1, out2
class rnet(nn.Module):
def __init__(self, full=False, binary=True, first_sparsity=0.8, rest_sparsity=0.9, conv_thres=0.7, align=True, bitwidth=1, input_bitwidth=1, binarize_input=True):
super(rnet, self).__init__()
self.align = align
self.pruned = False
self.full = full
self.binary = binary
self.bitwidth = bitwidth
self.input_bitwidth = input_bitwidth
self.binarize_input = binarize_input
self.conv_thres = conv_thres
self.first_sparsity = first_sparsity
self.rest_sparsity = rest_sparsity
self.id = str(uuid.uuid4())[:8]
self.name = "rnet"
self.filename = getModelFilename(self, self.name)
if full:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.fc1 = nn.Linear(64, 128, bias=False)
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
elif binary:
if self.binarize_input:
self.conv1 = binarized_modules_multi.BinarizeConv2d(input_bitwidth, bitwidth, 3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
else:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 64, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.fc1 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 64, 128, bias=False)
#self.fc2 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 128, 2, bias=False)
#self.fc3 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 128, 4, bias=False)
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
else:
if self.binarize_input:
self.conv1 = binarized_modules_multi.TernarizeConv2d(conv_thres, input_bitwidth, bitwidth, 3, 32, kernel_size=3, stride=1, padding=(0, 0), align=False, bias=False)
else:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), align=self.align, bias=False)
self.conv3 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 64, kernel_size=3, stride=1, padding=(0, 0), align=self.align, bias=False)
self.fc1 = binarized_modules_multi.TernarizeLinear(first_sparsity, bitwidth, bitwidth, 64, 128, align=self.align, bias=False)
#self.fc2 = binarized_modules_multi.TernarizeLinear(rest_sparsity, bitwidth, bitwidth, 128, 2, align=self.align, bias=False)
#self.fc3 = binarized_modules_multi.TernarizeLinear(rest_sparsity, bitwidth, bitwidth, 128, 4, align=self.align, bias=False)
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
self.softmax1 = torch.nn.Softmax(dim = 1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=(0, 0)) #TODO: Find the padding for "SAME"
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=(0, 0))
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, padding=(0, 0))
self.act = F.relu if self.full else nn.Hardtanh()
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(32)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm1d(128)
self.bn5 = nn.BatchNorm1d(2)
self.bn6 = nn.BatchNorm1d(4)
def forward(self, x):
x = self.act(self.bn1(self.conv1(x)))
x = self.pool1(x)
x = self.act(self.bn2(self.conv2(x)))
x = self.pool2(x)
x = self.act(self.bn3(self.conv3(x)))
x = self.pool3(x)
x = torch.reshape(x, (-1, 64))
x = self.act(self.bn4(self.fc1(x)))
out1 = self.softmax1(self.bn5(self.fc2(x)))
out2 = self.bn6(self.fc3(x))
return out1, out2
class onet(nn.Module):
def __init__(self, full=False, binary=True, first_sparsity=0.8, rest_sparsity=0.9, conv_thres=0.7, align=True, bitwidth=1, input_bitwidth=1, binarize_input=True):
super(onet, self).__init__()
self.align = align
self.pruned = False
self.full = full
self.binary = binary
self.bitwidth = bitwidth
self.input_bitwidth = input_bitwidth
self.binarize_input = binarize_input
self.conv_thres = conv_thres
self.first_sparsity = first_sparsity
self.rest_sparsity = rest_sparsity
self.id = str(uuid.uuid4())[:8]
self.name = "onet"
self.filename = getModelFilename(self, self.name)
if full:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv4 = nn.Conv2d(32, 64, kernel_size=2, stride=1, padding=(0, 0), bias=False)
self.fc1 = nn.Linear(64, 128, bias=False)
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
self.fc4 = nn.Linear(128, 10, bias=False)
elif binary:
if self.binarize_input:
self.conv1 = binarized_modules_multi.BinarizeConv2d(input_bitwidth, bitwidth, 3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
else:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv4 = binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, 32, 64, kernel_size=2, stride=1, padding=(0, 0), bias=False)
self.fc1 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 64, 128, bias=False)
self.fc2 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 128, 2, bias=False)
self.fc3 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 128, 4, bias=False)
self.fc4 = binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 128, 10, bias=False)
"""
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
self.fc4 = nn.Linear(128, 10, bias=False)
"""
"""
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=(0, 0), bias=False)
self.conv4 = nn.Conv2d(32, 64, kernel_size=2, stride=1, padding=(0, 0), bias=False)
self.fc1 = nn.Linear(64, 128, bias=False)
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
self.fc4 = nn.Linear(128, 10, bias=False)
"""
else:
if self.binarize_input:
self.conv1 = binarized_modules_multi.TernarizeConv2d(conv_thres, input_bitwidth, bitwidth, 3, 32, kernel_size=3, stride=1, padding=(0, 0), align=False, bias=False)
else:
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=(0, 0), align=False, bias=False)
self.conv2 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), align=self.align, bias=False)
self.conv3 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 32, kernel_size=3, stride=1, padding=(0, 0), align=self.align, bias=False)
self.conv4 = binarized_modules_multi.TernarizeConv2d(conv_thres, bitwidth, bitwidth, 32, 64, kernel_size=2, stride=1, padding=(0, 0), align=self.align, bias=False)
self.fc1 = binarized_modules_multi.TernarizeLinear(first_sparsity, bitwidth, bitwidth, 64, 128, align=self.align, bias=False)
#self.fc2 = binarized_modules_multi.TernarizeLinear(rest_sparsity, bitwidth, bitwidth, 128, 2, align=self.align, bias=False)
#self.fc3 = binarized_modules_multi.TernarizeLinear(rest_sparsity, bitwidth, bitwidth, 128, 4, align=self.align, bias=False)
#self.fc4 = binarized_modules_multi.TernarizeLinear(rest_sparsity, bitwidth, bitwidth, 128, 10, align=self.align, bias=False)
self.fc2 = nn.Linear(128, 2, bias=False)
self.fc3 = nn.Linear(128, 4, bias=False)
self.fc4 = nn.Linear(128, 10, bias=False)
self.softmax1 = torch.nn.Softmax(dim = 1)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=3, padding=(0, 0))
self.pool2 = nn.MaxPool2d(kernel_size=3, stride=3, padding=(0, 0))
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, padding=(0, 0))
self.act = F.relu if self.full else nn.Hardtanh()
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(32)
self.bn3 = nn.BatchNorm2d(32)
self.bn4 = nn.BatchNorm2d(64)
self.bn5 = nn.BatchNorm1d(128)
self.bn6 = nn.BatchNorm1d(2)
self.bn7 = nn.BatchNorm1d(4)
self.bn8 = nn.BatchNorm1d(10)
def forward(self, x):
x = torch.tensor(torch.full((1, 3, 48, 48), 3.))
conv_layer = self.conv1
bn_layer = self.bn1
pool_layer = self.pool1
print([param.shape for param in conv_layer.parameters()])
x_ = x
x_ = torch.transpose(x_, 1, 3)
x_ = torch.transpose(x_, 1, 2)
print("input (nwhc)", torch.flatten(x_)[:10])
# converting nchw to nhwc (nchw -> nwhc -> nhwc)
conv_parameters = conv_layer.weight
conv_parameters = torch.transpose(conv_parameters, 1, 3)
conv_parameters = torch.transpose(conv_parameters, 1, 2)
parameters = self.fc2.weight
#make parameters nchw for conv1
print("parameter values (nhwc):", torch.flatten(parameters)[:27])
with torch.no_grad():
print("parameter count of nonnegative", str(int(torch.count_nonzero(torch.greater_equal(parameters, 0)))) + '/' + str(len(torch.flatten(parameters))))
#x = bn_layer(conv_layer(x))
x = self.pool1(self.bn1(self.conv1(x)))
x = self.pool2(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
x = self.bn4(self.conv4(x))
x = torch.reshape(x, (-1, 64))
x = binarized_modules_multi.Binarize(x-0.01, quant_mode='det', bitwidth=1) # -0.01 to count 0 as negative
#expected_out = torch.nn.functional.linear(x, parameters).flatten()
#x.fill_(1)
x = self.bn5(self.fc1(x))
x = binarized_modules_multi.Binarize(x-0.01, quant_mode='det', bitwidth=1)
print("input values (nhwc)", [elem.item() for elem in list(torch.flatten(x)[:64])])
x1 = self.fc2(x)#self.bn6(self.fc2(x))
x2 = self.bn7(self.fc3(x))
x3 = self.bn8(self.fc4(x))
x = x1
#print("output values before binarization (nhwc)", [elem.item() for elem in list(torch.flatten(x)[:64])])
#x = self.fc1(x)
"""
x = conv_layer(x)
x = bn_layer(x)
if pool_layer:
x = pool_layer(x)
x = binarized_modules_multi.Binarize(x, quant_mode='det', bitwidth=1)
"""
x_ = x
"""
# converting nchw to nhwc
x_ = torch.transpose(x_, 1, 3)
x_ = torch.transpose(x_, 1, 2)
"""
print("output values (nhwc)", [elem.item() for elem in list(torch.flatten(x_)[:128])])
with torch.no_grad():
print("output count of nonnegative", str(int(torch.count_nonzero(torch.greater_equal(x_, 0)))) + '/' + str(len(torch.flatten(x_))))
# converting nchw to nhwc
"""
x = self.act(self.bn1(self.conv1(x)))
x = self.pool1(x)
x = self.act(self.bn2(self.conv2(x)))
x = self.pool2(x)
x = self.act(self.bn3(self.conv3(x)))
x = self.act(self.bn4(self.conv4(x)))
x = torch.reshape(x, (-1, 64))
x = self.act(self.bn5(self.fc1(x)))
out1 = self.softmax1(self.bn6(self.fc2(x)))
out2 = self.bn7(self.fc3(x))
out3 = self.bn8(self.fc4(x))
return out1, out2, out3
"""
"""
class VGGM(nn.Module):
def __init__(self, n_classes=1251, full=False, binary=True, first_sparsity=0.8, rest_sparsity=0.9, conv_thres=0.7, align=True, bitwidth=1, input_bitwidth=1, binarize_input_True):
super(VGGM, self).__init__()
self.n_classes=n_classes
if full:
self.features=nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(in_channels=1, out_channels=96, kernel_size=(7,7), stride=(2,2), padding=1)),
('bn1', nn.BatchNorm2d(96, momentum=0.5)),
('relu1', nn.ReLU()),
('mpool1', nn.MaxPool2d(kernel_size=(3,3), stride=(2,2))),
('conv2', nn.Conv2d(in_channels=96, out_channels=256, kernel_size=(5,5), stride=(2,2), padding=1)),
('bn2', nn.BatchNorm2d(256, momentum=0.5)),
('relu2', nn.ReLU()),
('mpool2', nn.MaxPool2d(kernel_size=(3,3), stride=(2,2))),
('conv3', nn.Conv2d(in_channels=256, out_channels=384, kernel_size=(3,3), stride=(1,1), padding=1)),
('bn3', nn.BatchNorm2d(384, momentum=0.5)),
('relu3', nn.ReLU()),
('conv4', nn.Conv2d(in_channels=384, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=1)),
('bn4', nn.BatchNorm2d(256, momentum=0.5)),
('relu4', nn.ReLU()),
('conv5', nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=1)),
('bn5', nn.BatchNorm2d(256, momentum=0.5)),
('relu5', nn.ReLU()),
('mpool5', nn.MaxPool2d(kernel_size=(5,3), stride=(3,2))),
('fc6', nn.Conv2d(in_channels=256, out_channels=4096, kernel_size=(9,1), stride=(1,1))),
('bn6', nn.BatchNorm2d(4096, momentum=0.5)),
('relu6', nn.ReLU()),
('apool6', nn.AdaptiveAvgPool2d((1,1))),
('flatten', nn.Flatten())]))
self.classifier=nn.Sequential(OrderedDict([
('fc7', nn.Linear(4096, 1024)),
#('drop1', nn.Dropout()),
('relu7', nn.ReLU()),
('fc8', nn.Linear(1024, n_classes))]))
elif binary:
self.features=nn.Sequential(OrderedDict([
('conv1', binarized_modules_multi.BinarizeConv2d(input_bitwidth, bitwidth, in_channels=1, out_channels=96, kernel_size=(7,7), stride=(2,2), padding=1)),
('bn1', nn.BatchNorm2d(96, momentum=0.5)),
('relu1', nn.functional.tanh),
('mpool1', nn.MaxPool2d(kernel_size=(3,3), stride=(2,2))),
('conv2', binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, in_channels=96, out_channels=256, kernel_size=(5,5), stride=(2,2), padding=1)),
('bn2', nn.BatchNorm2d(256, momentum=0.5)),
('relu2', nn.functional.tanh),
('mpool2', nn.MaxPool2d(kernel_size=(3,3), stride=(2,2))),
('conv3', binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, in_channels=256, out_channels=384, kernel_size=(3,3), stride=(1,1), padding=1)),
('bn3', nn.BatchNorm2d(384, momentum=0.5)),
('relu3', nn.functional.tanh),
('conv4', binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, in_channels=384, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=1)),
('bn4', nn.BatchNorm2d(256, momentum=0.5)),
('relu4', nn.functional.tanh),
('conv5', binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, in_channels=256, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=1)),
('bn5', nn.BatchNorm2d(256, momentum=0.5)),
('relu5', nn.functional.tanh),
('mpool5', nn.MaxPool2d(kernel_size=(5,3), stride=(3,2))),
('fc6', binarized_modules_multi.BinarizeConv2d(bitwidth, bitwidth, in_channels=256, out_channels=4096, kernel_size=(9,1), stride=(1,1))),
('bn6', nn.BatchNorm2d(4096, momentum=0.5)),
('relu6', nn.functional.tanh),
('apool6', nn.AdaptiveAvgPool2d((1,1))),
('flatten', nn.Flatten())]))
self.classifier=nn.Sequential(OrderedDict([
('fc7', binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 4096, 1024, bias=False)),
#('drop1', nn.Dropout()),
('relu7', nn.functional.tanh),
('fc8', binarized_modules_multi.BinarizeLinear(bitwidth, bitwidth, 1024, n_classes, bias=False))]))
else:
#TODO
pass
def forward(self, inp):
inp=self.features(inp)
#inp=inp.view(inp.size()[0],-1)
inp=self.classifier(inp)
return inp
"""
class FC_small(nn.Module):
def __init__(self, full=False, binary=True, first_sparsity=0.8, rest_sparsity=0.9, hid=512, ind=784, align=False):
super(FC_small, self).__init__()
self.align = align
self.pruned = False
self.hid = hid
self.ind = ind
full = True
self.full = full
self.binary = binary
if full:
self.fc1 = nn.Linear(ind, hid)
self.fc2 = nn.Linear(hid, 10)
elif binary:
self.fc1 = binarized_modules.BinarizeLinear(ind, hid)
self.fc2 = binarized_modules.BinarizeLinear(hid, 10)
else:
self.fc1 = binarized_modules.TernarizeLinear(first_sparsity, ind, hid, align=align)
self.fc2 = binarized_modules.TernarizeLinear(rest_sparsity, hid, 10, align=align)
self.htanh1 = nn.Hardtanh()
self.bn1 = nn.BatchNorm1d(hid)
self.bn2 = nn.BatchNorm1d(10, affine=True)
self.logsoftmax=nn.LogSoftmax(dim=1)
def forward(self, x):
if self.full:
x = x.view(-1, 784)
if x.size(1)==784:
x = x[:,:768]
x = F.relu(self.fc1(x))
x = self.bn1(x)
x = self.fc2(x)
else:
x = x.view(-1, 784)
if x.size(1)==784:
x = x[:,:768]
if self.binary:
x = self.fc1(x)
else:
x = self.fc1(x, self.pruned)
x = self.bn1(x)
self.htanh1(x)
if self.binary:
x = self.fc2(x)
else:
x = self.fc2(x, self.pruned)
x = self.bn2(x)
return x
class FC_large(nn.Module):
def __init__(self, full=False, binary=True, first_sparsity=0.8, rest_sparsity=0.9, hid=4096, ind=768, align=False):
super(FC_large, self).__init__()
self.align = align
self.pruned = False
self.hid = hid
self.ind = ind
self.full = full
self.binary = binary
if full:
self.fc1 = nn.Linear(ind, hid)
self.fc2 = nn.Linear(hid, hid)
self.fc3 = nn.Linear(hid, hid)
self.fc4 = nn.Linear(hid, 10)
elif binary:
self.fc1 = binarized_modules.BinarizeLinear(ind, hid)
self.fc2 = binarized_modules.BinarizeLinear(hid, hid)
self.fc3 = binarized_modules.BinarizeLinear(hid, hid)
self.fc4 = binarized_modules.BinarizeLinear(hid, 10)
else:
self.fc1 = binarized_modules.TernarizeLinear(first_sparsity, ind, hid, align=align)
self.fc2 = binarized_modules.TernarizeLinear(rest_sparsity, hid, hid, align=align)
self.fc3 = binarized_modules.TernarizeLinear(rest_sparsity, hid, hid, align=align)
self.fc4 = binarized_modules.TernarizeLinear(rest_sparsity, hid, 10, align=align)
self.htanh1 = nn.Hardtanh()
self.bn1 = nn.BatchNorm1d(hid)
self.htanh2 = nn.Hardtanh()
self.bn2 = nn.BatchNorm1d(hid)
self.htanh3 = nn.Hardtanh()
self.bn3 = nn.BatchNorm1d(hid)
self.bn4 = nn.BatchNorm1d(10, affine=True)
self.logsoftmax=nn.LogSoftmax(dim=1)
def forward(self, x):
if self.full:
x = x.view(-1, 784)
if x.size(1)==784:
x = x[:,:768]
x = F.relu(self.fc1(x))
x = self.bn1(x)
x = F.relu(self.fc2(x))
x = self.bn2(x)
x = F.relu(self.fc3(x))
x = self.bn3(x)
x = self.fc4(x)
else:
x = x.view(-1, 784)
if x.size(1)==784:
x = x[:,:768]
if self.binary:
x = self.fc1(x)
else:
x = self.fc1(x, self.pruned)
x = self.bn1(x)
self.htanh1(x)
if self.binary:
x = self.fc2(x)
else:
x = self.fc2(x, self.pruned)
x = self.bn2(x)
self.htanh2(x)
if self.binary:
x = self.fc3(x)
else:
x = self.fc3(x, self.pruned)
x = self.bn3(x)
self.htanh3(x)
if self.binary:
x = self.fc4(x)
else:
x = self.fc4(x, self.pruned)
x = self.bn4(x)
return self.logsoftmax(x)
class CNN_medium(nn.Module):
def __init__(self, full=False, binary=True, conv_thres=0.7, fc_thres=0.9, align=False, pad=0):
super(CNN_medium, self).__init__()
self.pruned = False
self.full = full
self.binary = binary
self.pad = pad
self.conv1 = binarized_modules.BinarizeConv2d(3, 128, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(128)
self.htanh1 = nn.Hardtanh(inplace=True)
if full:
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv6 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512*4*4, 10)
elif binary:
self.conv1 = binarized_modules.BinarizeConv2d(3, 128, kernel_size=3, stride=1, padding=0)
self.conv2 = binarized_modules.BinarizeConv2d(128, 128, kernel_size=3, stride=1, padding=0)
self.conv3 = binarized_modules.BinarizeConv2d(128, 256, kernel_size=3, stride=1, padding=0)
self.conv4 = binarized_modules.BinarizeConv2d(256, 256, kernel_size=3, stride=1, padding=0)
self.conv5 = binarized_modules.BinarizeConv2d(256, 512, kernel_size=3, stride=1, padding=0)
self.conv6 = binarized_modules.BinarizeConv2d(512, 512, kernel_size=3, stride=1, padding=0)
self.conv7 = binarized_modules.BinarizeConv2d(512, 10, kernel_size=4, padding=0)
self.fc1 = binarized_modules.BinarizeLinear(1024, 1024)
else:
self.conv1 = binarized_modules.BinarizeConv2d(3, 128, kernel_size=3, stride=1, padding=0)
self.conv2 = binarized_modules.TernarizeConv2d(conv_thres, 128, 128, kernel_size=3, padding=0, align=align)
self.conv3 = binarized_modules.TernarizeConv2d(conv_thres, 128, 256, kernel_size=3, padding=0, align=align)
self.conv4 = binarized_modules.TernarizeConv2d(conv_thres, 256, 256, kernel_size=3, padding=0, align=align)
self.conv5 = binarized_modules.TernarizeConv2d(conv_thres, 256, 512, kernel_size=3, padding=0, align=align)
self.conv6 = binarized_modules.TernarizeConv2d(conv_thres, 512, 512, kernel_size=3, padding=0, align=align)
self.conv7 = binarized_modules.TernarizeConv2d(0.49, 512, 10, kernel_size=4, padding=0, align=align)
self.fc1 = binarized_modules.BinarizeLinear(1024, 1024)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn2 = nn.BatchNorm2d(128)
self.htanh2 = nn.Hardtanh(inplace=True)
self.bn3 = nn.BatchNorm2d(256)
self.htanh3 = nn.Hardtanh(inplace=True)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn4 = nn.BatchNorm2d(256)
self.htanh4 = nn.Hardtanh(inplace=True)
self.bn5 = nn.BatchNorm2d(512)
self.htanh5 = nn.Hardtanh(inplace=True)
self.pool6 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn6 = nn.BatchNorm2d(512)
self.htanh6 = nn.Hardtanh(inplace=True)
self.bnfc1 = nn.BatchNorm1d(10, affine=True)
self.logsoftmax = nn.LogSoftmax(dim=1)
self.regime = {
0: {'optimizer': 'Adam', 'betas': (0.9, 0.999),'lr': 5e-3},
40: {'lr': 1e-3},
80: {'lr': 5e-4},
100: {'lr': 1e-4},
120: {'lr': 5e-5},
140: {'lr': 1e-5}
}
def forward(self, x):
if self.full:
x = F.relu(self.conv1(x))
x = self.bn1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = self.bn2(x)
x = F.relu(self.conv3(x))
x = self.bn3(x)
x = F.relu(self.conv4(x))
x = self.pool4(x)
x = self.bn4(x)
x = F.relu(self.conv5(x))
x = self.bn5(x)
x = F.relu(self.conv6(x))
x = self.pool6(x)
x = self.bn6(x)
x = x.view(-1, 512*4*4)
x = F.relu(self.fc1(x))
self.fc1_result = x.data.clone()
else:
x = F.pad(x, (1,1,1,1), value=self.pad)
x = self.conv1(x)
x = self.bn1(x)
self.htanh1(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv2(x)
else:
x = self.conv2(x, self.pruned)
x = self.pool2(x)
x = self.bn2(x)
self.htanh2(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv3(x)
else:
x = self.conv3(x, self.pruned)
x = self.bn3(x)
self.htanh3(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv4(x)
else:
x = self.conv4(x, self.pruned)
x = self.pool4(x)
x = self.bn4(x)
self.htanh4(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv5(x)
else:
x = self.conv5(x, self.pruned)
x = self.bn5(x)
self.htanh5(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv6(x)
else:
x = self.conv6(x, self.pruned)
x = self.pool6(x)
x = self.bn6(x)
self.htanh6(x)
if self.binary:
x = self.conv7(x)
else:
x = self.conv7(x, self.pruned)
x = x.view(-1, 10)
x = self.bnfc1(x)
return self.logsoftmax(x)
class CNN_large(nn.Module):
def __init__(self, full=False, binary=True, conv_thres=0.7, fc_thres=0.9, align=False, pad=0):
super(CNN_large, self).__init__()
self.pruned = False
self.full = full
self.binary = binary
self.pad = pad
self.conv1 = binarized_modules.BinarizeConv2d(3, 128, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(128)
self.htanh1 = nn.Hardtanh(inplace=True)
if full:
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv6 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512*4*4, 1024)
self.fc2 = nn.Linear(1024, 1024)
self.fc3 = nn.Linear(1024, 10)
elif binary:
self.conv1 = binarized_modules.BinarizeConv2d(3, 128, kernel_size=3, stride=1, padding=0)
self.conv2 = binarized_modules.BinarizeConv2d(128, 128, kernel_size=3, stride=1, padding=0)
self.conv3 = binarized_modules.BinarizeConv2d(128, 256, kernel_size=3, stride=1, padding=0)
self.conv4 = binarized_modules.BinarizeConv2d(256, 256, kernel_size=3, stride=1, padding=0)
self.conv5 = binarized_modules.BinarizeConv2d(256, 512, kernel_size=3, stride=1, padding=0)
self.conv6 = binarized_modules.BinarizeConv2d(512, 512, kernel_size=3, stride=1, padding=0)
self.conv7 = binarized_modules.BinarizeConv2d(512, 1024, kernel_size=4, padding=0)
self.fc1 = binarized_modules.BinarizeLinear(1024, 1024)
self.fc2 = binarized_modules.BinarizeLinear(1024, 10)
else:
self.conv1 = binarized_modules.BinarizeConv2d(3, 128, kernel_size=3, stride=1, padding=0)
self.conv2 = binarized_modules.TernarizeConv2d(conv_thres, 128, 128, kernel_size=3, padding=0, align=align)
self.conv3 = binarized_modules.TernarizeConv2d(conv_thres, 128, 256, kernel_size=3, padding=0, align=align)
self.conv4 = binarized_modules.TernarizeConv2d(conv_thres, 256, 256, kernel_size=3, padding=0, align=align)
self.conv5 = binarized_modules.TernarizeConv2d(conv_thres, 256, 512, kernel_size=3, padding=0, align=align)
self.conv6 = binarized_modules.TernarizeConv2d(conv_thres, 512, 512, kernel_size=3, padding=0, align=align)
self.conv7 = binarized_modules.TernarizeConv2d(fc_thres, 512, 1024, kernel_size=4, padding=0, align=align)
self.fc1 = binarized_modules.TernarizeLinear(fc_thres, 1024, 1024, align=align)
self.fc2 = binarized_modules.TernarizeLinear(0.49, 1024, 10)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn2 = nn.BatchNorm2d(128)
self.htanh2 = nn.Hardtanh(inplace=True)
self.bn3 = nn.BatchNorm2d(256)
self.htanh3 = nn.Hardtanh(inplace=True)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn4 = nn.BatchNorm2d(256)
self.htanh4 = nn.Hardtanh(inplace=True)
self.bn5 = nn.BatchNorm2d(512)
self.htanh5 = nn.Hardtanh(inplace=True)
self.pool6 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn6 = nn.BatchNorm2d(512)
self.htanh6 = nn.Hardtanh(inplace=True)
self.bnfc1 = nn.BatchNorm1d(1024)
self.htanhfc1 = nn.Hardtanh(inplace=True)
self.bnfc2 = nn.BatchNorm1d(1024)
self.htanhfc2 = nn.Hardtanh(inplace=True)
self.bnfc3 = nn.BatchNorm1d(10, affine=True)
self.logsoftmax = nn.LogSoftmax(dim=1)
self.regime = {
0: {'optimizer': 'Adam', 'betas': (0.9, 0.999),'lr': 5e-3},
40: {'lr': 1e-3},
80: {'lr': 5e-4},
100: {'lr': 1e-4},
120: {'lr': 5e-5},
140: {'lr': 1e-5}
}
def forward(self, x):
if self.full:
x = F.relu(self.conv1(x))
x = self.bn1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = self.bn2(x)
x = F.relu(self.conv3(x))
x = self.bn3(x)
x = F.relu(self.conv4(x))
x = self.pool4(x)
x = self.bn4(x)
x = F.relu(self.conv5(x))
x = self.bn5(x)
x = F.relu(self.conv6(x))
x = self.pool6(x)
x = self.bn6(x)
x = x.view(-1, 512*4*4)
x = F.relu(self.fc1(x))
x = self.bnfc1(x)
x = F.relu(self.fc2(x))
x = self.bnfc2(x)
x = F.relu(self.fc3(x))
self.fc3_result = x.data.clone()
else:
x = F.pad(x, (1,1,1,1), value=self.pad)
x = self.conv1(x)
x = self.bn1(x)
self.htanh1(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv2(x)
else:
x = self.conv2(x, self.pruned)
x = self.pool2(x)
x = self.bn2(x)
self.htanh2(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv3(x)
else:
x = self.conv3(x, self.pruned)
x = self.bn3(x)
self.htanh3(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv4(x)
else:
x = self.conv4(x, self.pruned)
x = self.pool4(x)
x = self.bn4(x)
self.htanh4(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv5(x)
else:
x = self.conv5(x, self.pruned)
x = self.bn5(x)
self.htanh5(x)
x = F.pad(x, (1,1,1,1), value=self.pad)
if self.binary:
x = self.conv6(x)
else:
x = self.conv6(x, self.pruned)
x = self.pool6(x)
x = self.bn6(x)
self.htanh6(x)
if self.binary:
x = self.conv7(x)
else:
x = self.conv7(x, self.pruned)
print(x.shape)
x = x.view(-1, 1024)
x = self.bnfc1(x)
self.htanhfc1(x)
if self.binary:
x = self.fc1(x)
else:
x = self.fc1(x, self.pruned)
x = self.bnfc2(x)
self.htanhfc2(x)
if self.binary:
x = self.fc2(x)
else:
x = self.fc2(x, self.pruned)
x = self.bnfc3(x)
return self.logsoftmax(x)
class CNN_tiny(nn.Module):
def __init__(self, full=False, binary=True, conv_thres=0.7, fc_thres=0.9, align=False):
super(CNN_tiny, self).__init__()
self.pruned = False
self.full = full
self.binary = binary
if full:
self.conv1 = nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=0)
self.conv2 = nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=0)
self.fc1 = nn.Linear(32*4*4, 10)
elif binary:
self.conv1 = binarized_modules.BinarizeConv2d(1, 32, kernel_size=5, stride=1, padding=0)
self.conv2 = binarized_modules.BinarizeConv2d(32, 32, kernel_size=5, stride=1, padding=0)
self.fc1 = binarized_modules.BinarizeConv2d(32, 10, kernel_size=4, padding=0)
else:
self.conv1 = binarized_modules.BinarizeConv2d(1, 32, kernel_size=5, stride=1, padding=0)
self.conv2 = binarized_modules.TernarizeConv2d(conv_thres, 32, 32, kernel_size=5, stride=1, padding=0, align=align)
self.fc1 = binarized_modules.TernarizeConv2d(0.49, 32, 10, kernel_size=4, padding=0, align=align)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn1 = nn.BatchNorm2d(32)
self.htanh1 = nn.Hardtanh(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn2 = nn.BatchNorm2d(32)
self.htanh2 = nn.Hardtanh(inplace=True)
self.bnfc1 = nn.BatchNorm1d(10, affine=True)
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, x):
if self.full:
x = F.relu(self.conv1(x))
x = self.pool1(x)
x = self.bn1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = self.bn2(x)
x = x.view(-1, 32*4*4)
x = F.relu(self.fc1(x))
else:
x = self.conv1(x)
x = self.pool1(x)
x = self.bn1(x)
self.htanh1(x)
if self.binary:
x = self.conv2(x)
else:
x = self.conv2(x, self.pruned)
x = self.pool2(x)
x = self.bn2(x)
self.htanh2(x)
if self.binary:
x = self.fc1(x)
else:
x = self.fc1(x, self.pruned)
x = x.view(-1, 10)
x = self.bnfc1(x)
return self.logsoftmax(x)
#Deep autoencoder model
#https://github.com/mlcommons/tiny/tree/master/v0.5
class DeepAutoEncoder(nn.Module):
def __init__(self, full=True, binary=True, sparsity=0.1, align=False):
super(DeepAutoEncoder, self).__init__()
self.full = full
self.binary = binary
self.align = align
self.pruned = False
ind = 640
hid = 128
mid = 8
self.act = F.relu if self.full else nn.Hardtanh()
if full:
self.fc1 = nn.Linear(ind, hid, bias=False)
self.fc2 = nn.Linear(hid, hid, bias=False)
self.fc3 = nn.Linear(hid, hid, bias=False)
self.fc4 = nn.Linear(hid, hid, bias=False)
self.fc5 = nn.Linear(hid, mid, bias=False)
self.fc6 = nn.Linear(mid, hid, bias=False)
self.fc7 = nn.Linear(hid, hid, bias=False)
self.fc8 = nn.Linear(hid, hid, bias=False)
self.fc9 = nn.Linear(hid, hid, bias=False)
self.fc10 = nn.Linear(hid, ind, bias=False)
elif binary:
self.fc1 = nn.Linear(ind, hid, bias=False)
self.fc2 = binarized_modules_multi.BinarizeLinear(1, 1, hid, hid, bias=False)
self.fc3 = binarized_modules_multi.BinarizeLinear(1, 1, hid, hid, bias=False)
self.fc4 = binarized_modules_multi.BinarizeLinear(1, 1, hid, hid, bias=False)
self.fc5 = binarized_modules_multi.BinarizeLinear(1, 1, hid, mid, bias=False)
self.fc6 = binarized_modules_multi.BinarizeLinear(1, 1, mid, hid, bias=False)
self.fc7 = binarized_modules_multi.BinarizeLinear(1, 1, hid, hid, bias=False)
self.fc8 = binarized_modules_multi.BinarizeLinear(1, 1, hid, hid, bias=False)
self.fc9 = binarized_modules_multi.BinarizeLinear(1, 1, hid, hid, bias=False)
self.fc10 = nn.Linear(hid, ind, bias=False)
else:
self.fc1 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, ind, hid, bias=False, align=self.align)
self.fc2 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, hid, bias=False, align=self.align)
self.fc3 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, hid, bias=False, align=self.align)
self.fc4 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, hid, bias=False, align=self.align)
self.fc5 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, mid, bias=False, align=self.align)
self.fc6 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, mid, hid, bias=False, align=self.align)
self.fc7 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, hid, bias=False, align=self.align)
self.fc8 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, hid, bias=False, align=self.align)
self.fc9 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, hid, bias=False, align=self.align)
self.fc10 = binarized_modules_multi.TernarizeLinear(sparsity, 1, 1, hid, ind, bias=False, align=self.align)
self.batchnorm1 = nn.BatchNorm1d(hid)
self.batchnorm2 = nn.BatchNorm1d(hid)
self.batchnorm3 = nn.BatchNorm1d(hid)
self.batchnorm4 = nn.BatchNorm1d(hid)
self.batchnorm5 = nn.BatchNorm1d(mid)
self.batchnorm6 = nn.BatchNorm1d(hid)
self.batchnorm7 = nn.BatchNorm1d(hid)
self.batchnorm8 = nn.BatchNorm1d(hid)
self.batchnorm9 = nn.BatchNorm1d(hid)
self.batchnorm10 = nn.BatchNorm1d(ind)
def forward(self, x):
x = self.fc1(x)
x = self.batchnorm1(x)
x = self.act(x)
x = self.fc2(x)
x = self.batchnorm2(x)
x = self.act(x)
x = self.fc3(x)
x = self.batchnorm3(x)
x = self.act(x)
x = self.fc4(x)
x = self.batchnorm4(x)
x = self.act(x)
x = self.fc5(x)
x = self.batchnorm5(x)
x = self.act(x)
x = self.fc6(x)
x = self.batchnorm6(x)
x = self.act(x)
x = self.fc7(x)
x = self.batchnorm7(x)
x = self.act(x)
x = self.fc8(x)
x = self.batchnorm8(x)
x = self.act(x)
x = self.fc9(x)
x = self.batchnorm9(x)
x = self.act(x)
x = self.fc10(x)
x = self.batchnorm10(x)
#x = self.act(x)
return x
| 45.160482
| 225
| 0.555677
| 6,358
| 48,683
| 4.159327
| 0.044511
| 0.039516
| 0.044734
| 0.039705
| 0.888296
| 0.864927
| 0.836756
| 0.799282
| 0.783173
| 0.771942
| 0
| 0.07698
| 0.310766
| 48,683
| 1,077
| 226
| 45.202414
| 0.711152
| 0.039377
| 0
| 0.722861
| 0
| 0
| 0.006426
| 0
| 0
| 0
| 0
| 0.001857
| 0
| 1
| 0.024266
| false
| 0
| 0.012771
| 0
| 0.060026
| 0.010217
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 1
| 0
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| null | 0
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| 0
|
0
| 7
|
442be051c05866586200c29f5896d6ea66ff1520
| 36,954
|
py
|
Python
|
sdk/python/pulumi_aws/appstream/stack.py
|
rapzo/pulumi-aws
|
390a098221315d98a54ba97d1559e750dc3053b7
|
[
"ECL-2.0",
"Apache-2.0"
] | 260
|
2018-06-18T14:57:00.000Z
|
2022-03-29T11:41:03.000Z
|
sdk/python/pulumi_aws/appstream/stack.py
|
rapzo/pulumi-aws
|
390a098221315d98a54ba97d1559e750dc3053b7
|
[
"ECL-2.0",
"Apache-2.0"
] | 1,154
|
2018-06-19T20:38:20.000Z
|
2022-03-31T19:48:16.000Z
|
sdk/python/pulumi_aws/appstream/stack.py
|
rapzo/pulumi-aws
|
390a098221315d98a54ba97d1559e750dc3053b7
|
[
"ECL-2.0",
"Apache-2.0"
] | 115
|
2018-06-28T03:20:27.000Z
|
2022-03-29T11:41:06.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['StackArgs', 'Stack']
@pulumi.input_type
class StackArgs:
def __init__(__self__, *,
access_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input['StackAccessEndpointArgs']]]] = None,
application_settings: Optional[pulumi.Input['StackApplicationSettingsArgs']] = None,
description: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
embed_host_domains: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
feedback_url: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
redirect_url: Optional[pulumi.Input[str]] = None,
storage_connectors: Optional[pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
user_settings: Optional[pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]]] = None):
"""
The set of arguments for constructing a Stack resource.
:param pulumi.Input['StackApplicationSettingsArgs'] application_settings: Settings for application settings persistence.
:param pulumi.Input[str] description: Description for the AppStream stack.
:param pulumi.Input[str] display_name: Stack name to display.
:param pulumi.Input[Sequence[pulumi.Input[str]]] embed_host_domains: Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
:param pulumi.Input[str] feedback_url: URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
:param pulumi.Input[str] name: Unique name for the AppStream stack.
:param pulumi.Input[str] redirect_url: URL that users are redirected to after their streaming session ends.
:param pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]] storage_connectors: Configuration block for the storage connectors to enable. See below.
:param pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]] user_settings: Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
if access_endpoints is not None:
pulumi.set(__self__, "access_endpoints", access_endpoints)
if application_settings is not None:
pulumi.set(__self__, "application_settings", application_settings)
if description is not None:
pulumi.set(__self__, "description", description)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if embed_host_domains is not None:
pulumi.set(__self__, "embed_host_domains", embed_host_domains)
if feedback_url is not None:
pulumi.set(__self__, "feedback_url", feedback_url)
if name is not None:
pulumi.set(__self__, "name", name)
if redirect_url is not None:
pulumi.set(__self__, "redirect_url", redirect_url)
if storage_connectors is not None:
pulumi.set(__self__, "storage_connectors", storage_connectors)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if tags_all is not None:
pulumi.set(__self__, "tags_all", tags_all)
if user_settings is not None:
pulumi.set(__self__, "user_settings", user_settings)
@property
@pulumi.getter(name="accessEndpoints")
def access_endpoints(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['StackAccessEndpointArgs']]]]:
return pulumi.get(self, "access_endpoints")
@access_endpoints.setter
def access_endpoints(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['StackAccessEndpointArgs']]]]):
pulumi.set(self, "access_endpoints", value)
@property
@pulumi.getter(name="applicationSettings")
def application_settings(self) -> Optional[pulumi.Input['StackApplicationSettingsArgs']]:
"""
Settings for application settings persistence.
"""
return pulumi.get(self, "application_settings")
@application_settings.setter
def application_settings(self, value: Optional[pulumi.Input['StackApplicationSettingsArgs']]):
pulumi.set(self, "application_settings", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
Description for the AppStream stack.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
Stack name to display.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="embedHostDomains")
def embed_host_domains(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
"""
return pulumi.get(self, "embed_host_domains")
@embed_host_domains.setter
def embed_host_domains(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "embed_host_domains", value)
@property
@pulumi.getter(name="feedbackUrl")
def feedback_url(self) -> Optional[pulumi.Input[str]]:
"""
URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
"""
return pulumi.get(self, "feedback_url")
@feedback_url.setter
def feedback_url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "feedback_url", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Unique name for the AppStream stack.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="redirectUrl")
def redirect_url(self) -> Optional[pulumi.Input[str]]:
"""
URL that users are redirected to after their streaming session ends.
"""
return pulumi.get(self, "redirect_url")
@redirect_url.setter
def redirect_url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "redirect_url", value)
@property
@pulumi.getter(name="storageConnectors")
def storage_connectors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]]]:
"""
Configuration block for the storage connectors to enable. See below.
"""
return pulumi.get(self, "storage_connectors")
@storage_connectors.setter
def storage_connectors(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]]]):
pulumi.set(self, "storage_connectors", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="tagsAll")
def tags_all(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "tags_all")
@tags_all.setter
def tags_all(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags_all", value)
@property
@pulumi.getter(name="userSettings")
def user_settings(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]]]:
"""
Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
return pulumi.get(self, "user_settings")
@user_settings.setter
def user_settings(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]]]):
pulumi.set(self, "user_settings", value)
@pulumi.input_type
class _StackState:
def __init__(__self__, *,
access_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input['StackAccessEndpointArgs']]]] = None,
application_settings: Optional[pulumi.Input['StackApplicationSettingsArgs']] = None,
arn: Optional[pulumi.Input[str]] = None,
created_time: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
embed_host_domains: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
feedback_url: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
redirect_url: Optional[pulumi.Input[str]] = None,
storage_connectors: Optional[pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
user_settings: Optional[pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]]] = None):
"""
Input properties used for looking up and filtering Stack resources.
:param pulumi.Input['StackApplicationSettingsArgs'] application_settings: Settings for application settings persistence.
:param pulumi.Input[str] arn: ARN of the appstream stack.
:param pulumi.Input[str] created_time: Date and time, in UTC and extended RFC 3339 format, when the stack was created.
:param pulumi.Input[str] description: Description for the AppStream stack.
:param pulumi.Input[str] display_name: Stack name to display.
:param pulumi.Input[Sequence[pulumi.Input[str]]] embed_host_domains: Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
:param pulumi.Input[str] feedback_url: URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
:param pulumi.Input[str] name: Unique name for the AppStream stack.
:param pulumi.Input[str] redirect_url: URL that users are redirected to after their streaming session ends.
:param pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]] storage_connectors: Configuration block for the storage connectors to enable. See below.
:param pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]] user_settings: Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
if access_endpoints is not None:
pulumi.set(__self__, "access_endpoints", access_endpoints)
if application_settings is not None:
pulumi.set(__self__, "application_settings", application_settings)
if arn is not None:
pulumi.set(__self__, "arn", arn)
if created_time is not None:
pulumi.set(__self__, "created_time", created_time)
if description is not None:
pulumi.set(__self__, "description", description)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if embed_host_domains is not None:
pulumi.set(__self__, "embed_host_domains", embed_host_domains)
if feedback_url is not None:
pulumi.set(__self__, "feedback_url", feedback_url)
if name is not None:
pulumi.set(__self__, "name", name)
if redirect_url is not None:
pulumi.set(__self__, "redirect_url", redirect_url)
if storage_connectors is not None:
pulumi.set(__self__, "storage_connectors", storage_connectors)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if tags_all is not None:
pulumi.set(__self__, "tags_all", tags_all)
if user_settings is not None:
pulumi.set(__self__, "user_settings", user_settings)
@property
@pulumi.getter(name="accessEndpoints")
def access_endpoints(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['StackAccessEndpointArgs']]]]:
return pulumi.get(self, "access_endpoints")
@access_endpoints.setter
def access_endpoints(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['StackAccessEndpointArgs']]]]):
pulumi.set(self, "access_endpoints", value)
@property
@pulumi.getter(name="applicationSettings")
def application_settings(self) -> Optional[pulumi.Input['StackApplicationSettingsArgs']]:
"""
Settings for application settings persistence.
"""
return pulumi.get(self, "application_settings")
@application_settings.setter
def application_settings(self, value: Optional[pulumi.Input['StackApplicationSettingsArgs']]):
pulumi.set(self, "application_settings", value)
@property
@pulumi.getter
def arn(self) -> Optional[pulumi.Input[str]]:
"""
ARN of the appstream stack.
"""
return pulumi.get(self, "arn")
@arn.setter
def arn(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "arn", value)
@property
@pulumi.getter(name="createdTime")
def created_time(self) -> Optional[pulumi.Input[str]]:
"""
Date and time, in UTC and extended RFC 3339 format, when the stack was created.
"""
return pulumi.get(self, "created_time")
@created_time.setter
def created_time(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "created_time", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
Description for the AppStream stack.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
Stack name to display.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="embedHostDomains")
def embed_host_domains(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
"""
return pulumi.get(self, "embed_host_domains")
@embed_host_domains.setter
def embed_host_domains(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "embed_host_domains", value)
@property
@pulumi.getter(name="feedbackUrl")
def feedback_url(self) -> Optional[pulumi.Input[str]]:
"""
URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
"""
return pulumi.get(self, "feedback_url")
@feedback_url.setter
def feedback_url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "feedback_url", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Unique name for the AppStream stack.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="redirectUrl")
def redirect_url(self) -> Optional[pulumi.Input[str]]:
"""
URL that users are redirected to after their streaming session ends.
"""
return pulumi.get(self, "redirect_url")
@redirect_url.setter
def redirect_url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "redirect_url", value)
@property
@pulumi.getter(name="storageConnectors")
def storage_connectors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]]]:
"""
Configuration block for the storage connectors to enable. See below.
"""
return pulumi.get(self, "storage_connectors")
@storage_connectors.setter
def storage_connectors(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['StackStorageConnectorArgs']]]]):
pulumi.set(self, "storage_connectors", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="tagsAll")
def tags_all(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "tags_all")
@tags_all.setter
def tags_all(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags_all", value)
@property
@pulumi.getter(name="userSettings")
def user_settings(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]]]:
"""
Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
return pulumi.get(self, "user_settings")
@user_settings.setter
def user_settings(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['StackUserSettingArgs']]]]):
pulumi.set(self, "user_settings", value)
class Stack(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
access_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackAccessEndpointArgs']]]]] = None,
application_settings: Optional[pulumi.Input[pulumi.InputType['StackApplicationSettingsArgs']]] = None,
description: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
embed_host_domains: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
feedback_url: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
redirect_url: Optional[pulumi.Input[str]] = None,
storage_connectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackStorageConnectorArgs']]]]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
user_settings: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackUserSettingArgs']]]]] = None,
__props__=None):
"""
Provides an AppStream stack.
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.appstream.Stack("example",
application_settings=aws.appstream.StackApplicationSettingsArgs(
enabled=True,
settings_group="SettingsGroup",
),
description="stack description",
display_name="stack display name",
feedback_url="http://your-domain/feedback",
redirect_url="http://your-domain/redirect",
storage_connectors=[aws.appstream.StackStorageConnectorArgs(
connector_type="HOMEFOLDERS",
)],
tags={
"TagName": "TagValue",
},
user_settings=[
aws.appstream.StackUserSettingArgs(
action="CLIPBOARD_COPY_FROM_LOCAL_DEVICE",
permission="ENABLED",
),
aws.appstream.StackUserSettingArgs(
action="CLIPBOARD_COPY_TO_LOCAL_DEVICE",
permission="ENABLED",
),
aws.appstream.StackUserSettingArgs(
action="FILE_UPLOAD",
permission="ENABLED",
),
aws.appstream.StackUserSettingArgs(
action="FILE_DOWNLOAD",
permission="ENABLED",
),
])
```
## Import
`aws_appstream_stack` can be imported using the id, e.g.
```sh
$ pulumi import aws:appstream/stack:Stack example stackID
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['StackApplicationSettingsArgs']] application_settings: Settings for application settings persistence.
:param pulumi.Input[str] description: Description for the AppStream stack.
:param pulumi.Input[str] display_name: Stack name to display.
:param pulumi.Input[Sequence[pulumi.Input[str]]] embed_host_domains: Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
:param pulumi.Input[str] feedback_url: URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
:param pulumi.Input[str] name: Unique name for the AppStream stack.
:param pulumi.Input[str] redirect_url: URL that users are redirected to after their streaming session ends.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackStorageConnectorArgs']]]] storage_connectors: Configuration block for the storage connectors to enable. See below.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackUserSettingArgs']]]] user_settings: Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[StackArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Provides an AppStream stack.
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.appstream.Stack("example",
application_settings=aws.appstream.StackApplicationSettingsArgs(
enabled=True,
settings_group="SettingsGroup",
),
description="stack description",
display_name="stack display name",
feedback_url="http://your-domain/feedback",
redirect_url="http://your-domain/redirect",
storage_connectors=[aws.appstream.StackStorageConnectorArgs(
connector_type="HOMEFOLDERS",
)],
tags={
"TagName": "TagValue",
},
user_settings=[
aws.appstream.StackUserSettingArgs(
action="CLIPBOARD_COPY_FROM_LOCAL_DEVICE",
permission="ENABLED",
),
aws.appstream.StackUserSettingArgs(
action="CLIPBOARD_COPY_TO_LOCAL_DEVICE",
permission="ENABLED",
),
aws.appstream.StackUserSettingArgs(
action="FILE_UPLOAD",
permission="ENABLED",
),
aws.appstream.StackUserSettingArgs(
action="FILE_DOWNLOAD",
permission="ENABLED",
),
])
```
## Import
`aws_appstream_stack` can be imported using the id, e.g.
```sh
$ pulumi import aws:appstream/stack:Stack example stackID
```
:param str resource_name: The name of the resource.
:param StackArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(StackArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
access_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackAccessEndpointArgs']]]]] = None,
application_settings: Optional[pulumi.Input[pulumi.InputType['StackApplicationSettingsArgs']]] = None,
description: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
embed_host_domains: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
feedback_url: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
redirect_url: Optional[pulumi.Input[str]] = None,
storage_connectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackStorageConnectorArgs']]]]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
user_settings: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackUserSettingArgs']]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = StackArgs.__new__(StackArgs)
__props__.__dict__["access_endpoints"] = access_endpoints
__props__.__dict__["application_settings"] = application_settings
__props__.__dict__["description"] = description
__props__.__dict__["display_name"] = display_name
__props__.__dict__["embed_host_domains"] = embed_host_domains
__props__.__dict__["feedback_url"] = feedback_url
__props__.__dict__["name"] = name
__props__.__dict__["redirect_url"] = redirect_url
__props__.__dict__["storage_connectors"] = storage_connectors
__props__.__dict__["tags"] = tags
__props__.__dict__["tags_all"] = tags_all
__props__.__dict__["user_settings"] = user_settings
__props__.__dict__["arn"] = None
__props__.__dict__["created_time"] = None
super(Stack, __self__).__init__(
'aws:appstream/stack:Stack',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
access_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackAccessEndpointArgs']]]]] = None,
application_settings: Optional[pulumi.Input[pulumi.InputType['StackApplicationSettingsArgs']]] = None,
arn: Optional[pulumi.Input[str]] = None,
created_time: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
embed_host_domains: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
feedback_url: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
redirect_url: Optional[pulumi.Input[str]] = None,
storage_connectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackStorageConnectorArgs']]]]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
user_settings: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackUserSettingArgs']]]]] = None) -> 'Stack':
"""
Get an existing Stack resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['StackApplicationSettingsArgs']] application_settings: Settings for application settings persistence.
:param pulumi.Input[str] arn: ARN of the appstream stack.
:param pulumi.Input[str] created_time: Date and time, in UTC and extended RFC 3339 format, when the stack was created.
:param pulumi.Input[str] description: Description for the AppStream stack.
:param pulumi.Input[str] display_name: Stack name to display.
:param pulumi.Input[Sequence[pulumi.Input[str]]] embed_host_domains: Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
:param pulumi.Input[str] feedback_url: URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
:param pulumi.Input[str] name: Unique name for the AppStream stack.
:param pulumi.Input[str] redirect_url: URL that users are redirected to after their streaming session ends.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackStorageConnectorArgs']]]] storage_connectors: Configuration block for the storage connectors to enable. See below.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['StackUserSettingArgs']]]] user_settings: Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _StackState.__new__(_StackState)
__props__.__dict__["access_endpoints"] = access_endpoints
__props__.__dict__["application_settings"] = application_settings
__props__.__dict__["arn"] = arn
__props__.__dict__["created_time"] = created_time
__props__.__dict__["description"] = description
__props__.__dict__["display_name"] = display_name
__props__.__dict__["embed_host_domains"] = embed_host_domains
__props__.__dict__["feedback_url"] = feedback_url
__props__.__dict__["name"] = name
__props__.__dict__["redirect_url"] = redirect_url
__props__.__dict__["storage_connectors"] = storage_connectors
__props__.__dict__["tags"] = tags
__props__.__dict__["tags_all"] = tags_all
__props__.__dict__["user_settings"] = user_settings
return Stack(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="accessEndpoints")
def access_endpoints(self) -> pulumi.Output[Sequence['outputs.StackAccessEndpoint']]:
return pulumi.get(self, "access_endpoints")
@property
@pulumi.getter(name="applicationSettings")
def application_settings(self) -> pulumi.Output['outputs.StackApplicationSettings']:
"""
Settings for application settings persistence.
"""
return pulumi.get(self, "application_settings")
@property
@pulumi.getter
def arn(self) -> pulumi.Output[str]:
"""
ARN of the appstream stack.
"""
return pulumi.get(self, "arn")
@property
@pulumi.getter(name="createdTime")
def created_time(self) -> pulumi.Output[str]:
"""
Date and time, in UTC and extended RFC 3339 format, when the stack was created.
"""
return pulumi.get(self, "created_time")
@property
@pulumi.getter
def description(self) -> pulumi.Output[str]:
"""
Description for the AppStream stack.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter(name="displayName")
def display_name(self) -> pulumi.Output[str]:
"""
Stack name to display.
"""
return pulumi.get(self, "display_name")
@property
@pulumi.getter(name="embedHostDomains")
def embed_host_domains(self) -> pulumi.Output[Sequence[str]]:
"""
Domains where AppStream 2.0 streaming sessions can be embedded in an iframe. You must approve the domains that you want to host embedded AppStream 2.0 streaming sessions.
"""
return pulumi.get(self, "embed_host_domains")
@property
@pulumi.getter(name="feedbackUrl")
def feedback_url(self) -> pulumi.Output[str]:
"""
URL that users are redirected to after they click the Send Feedback link. If no URL is specified, no Send Feedback link is displayed. .
"""
return pulumi.get(self, "feedback_url")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Unique name for the AppStream stack.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="redirectUrl")
def redirect_url(self) -> pulumi.Output[str]:
"""
URL that users are redirected to after their streaming session ends.
"""
return pulumi.get(self, "redirect_url")
@property
@pulumi.getter(name="storageConnectors")
def storage_connectors(self) -> pulumi.Output[Sequence['outputs.StackStorageConnector']]:
"""
Configuration block for the storage connectors to enable. See below.
"""
return pulumi.get(self, "storage_connectors")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="tagsAll")
def tags_all(self) -> pulumi.Output[Mapping[str, str]]:
return pulumi.get(self, "tags_all")
@property
@pulumi.getter(name="userSettings")
def user_settings(self) -> pulumi.Output[Sequence['outputs.StackUserSetting']]:
"""
Configuration block for the actions that are enabled or disabled for users during their streaming sessions. By default, these actions are enabled. See below.
"""
return pulumi.get(self, "user_settings")
| 47.255754
| 266
| 0.657628
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| 0.002268
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|
0
| 8
|
923bef6f7fc1d2c51d5e5784fc0b965254952f55
| 13,475
|
py
|
Python
|
examples/projects/adaptive_controllers/adaptive_computed_torque.py
|
SherbyRobotics/PyRobotics
|
86eb1189258f6f41642a149c813dd2fd6853bcc1
|
[
"MIT"
] | 14
|
2019-05-03T15:22:38.000Z
|
2022-03-14T15:31:54.000Z
|
examples/projects/adaptive_controllers/adaptive_computed_torque.py
|
SherbyRobotics/PyRobotics
|
86eb1189258f6f41642a149c813dd2fd6853bcc1
|
[
"MIT"
] | 9
|
2019-08-01T14:22:13.000Z
|
2021-06-12T01:44:50.000Z
|
examples/projects/adaptive_controllers/adaptive_computed_torque.py
|
SherbyRobotics/PyRobotics
|
86eb1189258f6f41642a149c813dd2fd6853bcc1
|
[
"MIT"
] | 9
|
2019-05-21T12:38:36.000Z
|
2022-03-29T16:28:45.000Z
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 24 21:15:49 2020
@author: alex
"""
import numpy as np
from pyro.control import controller
##############################################################################
class SinglePendulumAdaptativeController( controller.DynamicController ):
"""
Adaptative Controller for fully actuated mechanical systems (single pendulum)
"""
############################
def __init__( self , model , traj = None ):
""" """
self.name = 'Adaptive controller'
# Params
self.A = np.zeros(2)
self.T=np.eye(2)
self.Kd = 1
self.lam = 1 # Sliding surface slope
self.nab = 0.1 # Min convergence rate
self.model=model
k = model.dof
m = model.m
p = model.p
l = 2
super().__init__(k, l, m, p)
# Init internal states
self.z0 = np.array([0.2,0.2])
self.internal_state_label = []
self.internal_state_units = []
for i in range(l):
self.internal_state_label.append('a' +str(i))
self.internal_state_units.append('')
############################
def adaptative_variables( self , ddq_d , dq_d , q_d , dq , q ):
"""
Given desired trajectory and actual state
"""
q_e = q - q_d
dq_e = dq - dq_d
s = dq_e + self.lam * q_e
dq_r = dq_d - self.lam * q_e
ddq_r = ddq_d - self.lam * dq_e
return [ s , dq_r , ddq_r ]
############################
def adaptative_torque( self , Y , s , q , t ):
"""
Given actual state, compute torque necessarly to guarantee convergence
"""
u_computed = np.dot( Y , self.A )
u_discontinuous = self.Kd*s
u_tot = u_computed - u_discontinuous
return u_tot
############################
def b(self, z, x, q_d, t):
[ q , dq ] = self.model.x2q( x )
ddq_d = np.zeros( self.model.dof )
dq_d = np.zeros( self.model.dof )
[ s , dq_r , ddq_r ] = self.adaptative_variables( ddq_d , dq_d ,
q_d , dq , q )
Y = np.zeros(2)
dz = np.zeros(2)
Y[0]=ddq_r
Y[1]=np.sin(q)
b = Y * s
dz=-1*np.dot( self.T , b )
return dz
############################
def c(self , z , x , q_d , t = 0):
"""
Given desired fixed goal state and actual state, compute torques
"""
[ q , dq ] = self.model.x2q( x )
ddq_d = np.zeros( self.model.dof )
dq_d = np.zeros( self.model.dof )
[ s , dq_r , ddq_r ] = self.adaptative_variables( ddq_d , dq_d ,
q_d , dq , q )
Y = np.zeros(2)
Y[0]=ddq_r
Y[1]=np.sin(q)
self.A = self.get_z_integral(z)
u = self.adaptative_torque( Y , s , q , t )
return u
############################
def get_z_integral(self, z):
""" get intergral error internal states """
return z[:self.l]
##############################################################################
class DoublePendulumAdaptativeController( controller.DynamicController ):
"""
"""
############################
def __init__( self , model , traj = None ):
""" """
self.name = 'Adaptive controller'
self.A = np.zeros(5)
self.guess = np.zeros(5)
self.T=np.eye(5)
self.Kd = np.eye(2)
self.lam = 1 # Sliding surface slope
self.nab = 0.1 # Min convergence rate
self.model=model
k = model.dof
m = model.m
p = model.p
l = 5
super().__init__(k, l, m, p)
# Init states
self.z0 = np.zeros(5)
self.internal_state_label = []
self.internal_state_units = []
for i in range(l):
self.internal_state_label.append('a' +str(i))
self.internal_state_units.append('')
##############################
def trig(self, q ):
"""
Compute cos and sin usefull in other computation
------------------------------------------------
"""
c1 = np.cos( q[0] )
s1 = np.sin( q[0] )
c2 = np.cos( q[1] )
s2 = np.sin( q[1] )
c12 = np.cos( q[0] + q[1] )
s12 = np.sin( q[0] + q[1] )
return [c1,s1,c2,s2,c12,s12]
############################
def adaptative_variables( self , ddq_d , dq_d , q_d , dq , q ):
"""
Given desired trajectory and actual state
"""
q_e = q - q_d
dq_e = dq - dq_d
s = dq_e + self.lam * q_e
dq_r = dq_d - self.lam * q_e
ddq_r = ddq_d - self.lam * dq_e
return [ s , dq_r , ddq_r ]
############################
def adaptative_torque( self , Y , s , q , t ):
"""
Given actual state, compute torque necessarly to guarantee convergence
"""
u_computed = np.dot( Y , self.A )
u_discontinuous = np.dot(self.Kd,s)
u_tot = u_computed - u_discontinuous
return u_tot
############################
def b(self, z, x, q_d, t):
[ q , dq ] = self.model.x2q( x )
ddq_d = np.zeros( self.model.dof )
dq_d = np.zeros( self.model.dof )
[ s , dq_r , ddq_r ] = self.adaptative_variables( ddq_d , dq_d ,
q_d , dq , q )
[c1,s1,c2,s2,c12,s12] = self.trig( q )
Y = np.zeros((2,5))
dz = np.zeros(5)
Y[0,0]=ddq_r[0]*c2
Y[0,1]=ddq_r[1]*c2
Y[0,2]=s2*dq[1]*dq_r[0]
Y[0,3]=s2*(dq[0]+dq[1])*dq_r[1]
Y[0,4]=s1+s12
Y[1,0]=ddq_r[0]*c2
Y[1,1]=ddq_r[1]
Y[1,2]=s2*dq[0]*dq_r[0]
Y[1,3]=0
Y[1,4]=s12
b = np.dot(Y.T,s)
dz=-1*np.dot( self.T , b )
return dz
############################
def c( self , z , x , q_d , t = 0 ):
"""
Given desired fixed goal state and actual state, compute torques
"""
[ q , dq ] = self.model.x2q( x )
ddq_d = np.zeros( self.model.dof )
dq_d = np.zeros( self.model.dof )
[ s , dq_r , ddq_r ] = self.adaptative_variables( ddq_d , dq_d ,
q_d , dq , q )
[c1,s1,c2,s2,c12,s12] = self.trig( q )
Y = np.zeros((2,5))
Y[0,0]=ddq_r[0]*c2
Y[0,1]=ddq_r[1]*c2
Y[0,2]=s2*dq[1]*dq_r[0]
Y[0,3]=s2*(dq[0]+dq[1])*dq_r[1]
Y[0,4]=s1+s12
Y[1,0]=ddq_r[0]*c2
Y[1,1]=ddq_r[1]
Y[1,2]=s2*dq[0]*dq_r[0]
Y[1,3]=0
Y[1,4]=s12
self.A= self.guess + self.get_z_integral(z)
u = self.adaptative_torque( Y , s , q , t )
return u
############################
def get_z_integral(self, z):
""" get intergral error internal states """
return z[:self.l]
##############################################################################
class AdaptativeController_WCRT( controller.DynamicController ):
"""
Adaptative Controller for fully actuated mechanical systems (single pendulum)
"""
############################
def __init__( self , model , traj = None ):
""" """
self.name = 'Adaptive controller'
# Params
self.A = np.zeros(8)
self.T=np.eye(8)
self.Kd = np.eye(3)
self.lam = 1 # Sliding surface slope
self.nab = 0.1 # Min convergence rate
self.model=model
k = model.dof
m = model.m
p = model.p
l = self.A.shape[0]
super().__init__(k, l, m, p)
##############################
def trig(self, q ):
"""
Compute cos and sin usefull in other computation
------------------------------------------------
"""
c1 = np.cos( q[0] )
s1 = np.sin( q[0] )
c2 = np.cos( q[1] )
s2 = np.sin( q[1] )
c3 = np.cos( q[2] )
s3 = np.sin( q[2] )
c23 = np.cos( q[2] + q[1] )
s23 = np.sin( q[2] + q[1] )
return [c1,s1,c2,s2,c3,s3,c23,s23]
############################
def adaptative_variables( self , ddq_d , dq_d , q_d , dq , q ):
"""
Given desired trajectory and actual state
"""
q_e = q - q_d
dq_e = dq - dq_d
s = dq_e + self.lam * q_e
dq_r = dq_d - self.lam * q_e
ddq_r = ddq_d - self.lam * dq_e
return [ s , dq_r , ddq_r ]
############################
def adaptative_torque( self , Y , s , q , t ):
"""
Given actual state, compute torque necessarly to guarantee convergence
"""
u_computed = np.dot( Y , self.A )
u_discontinuous = np.dot(self.Kd,s)
u_tot = u_computed - u_discontinuous
return u_tot
############################
def b(self, z, x, q_d, t):
[ q , dq ] = self.model.x2q( x )
ddq_d = np.zeros( self.model.dof )
dq_d = np.zeros( self.model.dof )
[ s , dq_r , ddq_r ] = self.adaptative_variables( ddq_d , dq_d ,
q_d , dq , q )
[c1,s1,c2,s2,c3,s3,c23,s23] = self.trig( q )
Y = np.zeros((3,8))
dz = np.zeros(8)
Y[0,0]=ddq_r[0]
Y[0,1]=ddq_r[1]*(s2+s2*c3)
Y[0,2]=ddq_r[2]*s23
Y[0,3]=(dq[2]*s2*s3+dq[1]*c2+dq[1]*c2*c3)*dq_r[1]
Y[0,4]=(dq[2]+dq[1])*c23*dq_r[2]
Y[0,5]=0
Y[0,6]=0
Y[0,7]=q[0]
Y[1,0]=ddq_r[0]*(s2+s2*c3)
Y[1,1]=ddq_r[1]*(1+c3+c3**2)
Y[1,2]=ddq_r[2]*(c3+c3**2)
Y[1,3]=(dq[2]*(s3+s3*c3))*dq_r[1]
Y[1,4]=(dq[2]*(s3+s3*c3)+dq[1]*(s3+s3*c3)+dq[0]*(c2*c3))*dq_r[2]
Y[1,5]=c2
Y[1,6]=c23
Y[1,7]=q[1]
Y[2,0]=ddq_r[0]*s23
Y[2,1]=ddq_r[1]*(c3+c3**2)
Y[2,2]=ddq_r[2]
Y[2,3]=(dq[1]*(s3+s3*c3)+dq[1]*c2*c3)*dq_r[1]
Y[2,4]=0
Y[2,5]=0
Y[2,6]=c23
Y[2,7]=q[2]
b = np.dot(Y.T,s)
dz=-1*np.dot( self.T , b )
return dz
############################
def c( self , z , x , q_d , t = 0 ):
"""
Given desired fixed goal state and actual state, compute torques
"""
[ q , dq ] = self.model.x2q( x )
ddq_d = np.zeros( self.model.dof )
dq_d = np.zeros( self.model.dof )
[ s , dq_r , ddq_r ] = self.adaptative_variables( ddq_d , dq_d ,
q_d , dq , q )
[c1,s1,c2,s2,c3,s3,c23,s23] = self.trig( q )
Y = np.zeros((3,8))
Y[0,0]=ddq_r[0]
Y[0,1]=ddq_r[1]*(s2+s2*c3)
Y[0,2]=ddq_r[2]*s23
Y[0,3]=(dq[2]*s2*s3+dq[1]*c2+dq[1]*c2*c3)*dq_r[1]
Y[0,4]=(dq[2]+dq[1])*c23*dq_r[2]
Y[0,5]=0
Y[0,6]=0
Y[0,7]=q[0]
Y[1,0]=ddq_r[0]*(s2+s2*c3)
Y[1,1]=ddq_r[1]*(1+c3+c3**2)
Y[1,2]=ddq_r[2]*(c3+c3**2)
Y[1,3]=(dq[2]*(s3+s3*c3))*dq_r[1]
Y[1,4]=(dq[2]*(s3+s3*c3)+dq[1]*(s3+s3*c3)+dq[0]*(c2*c3))*dq_r[2]
Y[1,5]=c2
Y[1,6]=c23
Y[1,7]=q[1]
Y[2,0]=ddq_r[0]*s23
Y[2,1]=ddq_r[1]*(c3+c3**2)
Y[2,2]=ddq_r[2]
Y[2,3]=(dq[1]*(s3+s3*c3)+dq[1]*c2*c3)*dq_r[1]
Y[2,4]=0
Y[2,5]=0
Y[2,6]=c23
Y[2,7]=q[2]
self.A =self.get_z_integral( z )
u = self.adaptative_torque( Y , s , q , t )
return u
############################
def get_z_integral(self, z):
""" get intergral error internal states """
return z[:self.l]
##############################################################################
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| 81
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2b86afd6ab3d0e8c682c33a04a53f6d122f84731
| 2,280
|
py
|
Python
|
test/operations/compares/test_less_than.py
|
xpenalosa/PyPc
|
fff3ae29b800d127d261492098aecbbf6719bd07
|
[
"MIT"
] | null | null | null |
test/operations/compares/test_less_than.py
|
xpenalosa/PyPc
|
fff3ae29b800d127d261492098aecbbf6719bd07
|
[
"MIT"
] | null | null | null |
test/operations/compares/test_less_than.py
|
xpenalosa/PyPc
|
fff3ae29b800d127d261492098aecbbf6719bd07
|
[
"MIT"
] | null | null | null |
from pypc.memory import MemoryTypes
from pypc.operations import Operations
from test.operations.operation_test import OperationTestBase
class LessThanTest(OperationTestBase):
def test_lt_basic_true(self):
memory_type = MemoryTypes.BASIC
initial_data = "\n".join([
"LT 1, 2, 0"])
expected_data = list(
[1, 1, 2, 0,
Operations.END.value])
result_mem = self.run_pc(memory_type, self.parse_input(initial_data))
self.assertEqual(expected_data, result_mem.data)
self.assertEqual(result_mem.address, 4)
def test_lt_basic_false(self):
memory_type = MemoryTypes.BASIC
initial_data = "\n".join([
"LT 2, 2, 0"])
expected_data = list(
[0, 2, 2, 0,
Operations.END.value])
result_mem = self.run_pc(memory_type, self.parse_input(initial_data))
self.assertEqual(expected_data, result_mem.data)
self.assertEqual(result_mem.address, 4)
def test_lt_mod_access_ll(self):
memory_type = MemoryTypes.MODIFIED_ACCESS
initial_data = "\n".join([
"LT 2L, 3L, 0"])
expected_data = list(
[1, 2, 3, 0,
Operations.END.value])
result_mem = self.run_pc(memory_type, self.parse_input(initial_data))
self.assertEqual(expected_data, result_mem.data)
self.assertEqual(result_mem.address, 4)
def test_lt_mod_access_lr(self):
memory_type = MemoryTypes.MODIFIED_ACCESS
initial_data = "\n".join([
"LT 2L, 1, 0"])
expected_data = list(
[0, 2, 1, 0,
Operations.END.value])
result_mem = self.run_pc(memory_type, self.parse_input(initial_data))
self.assertEqual(expected_data, result_mem.data)
self.assertEqual(result_mem.address, 4)
def test_lt_mod_access_rr(self):
memory_type = MemoryTypes.MODIFIED_ACCESS
initial_data = "\n".join([
"LT 1, 2, 0"])
expected_data = list(
[1, 1, 2, 0,
Operations.END.value])
result_mem = self.run_pc(memory_type, self.parse_input(initial_data))
self.assertEqual(expected_data, result_mem.data)
self.assertEqual(result_mem.address, 4)
| 34.029851
| 77
| 0.625877
| 291
| 2,280
| 4.639175
| 0.161512
| 0.1
| 0.140741
| 0.092593
| 0.856296
| 0.842222
| 0.814074
| 0.814074
| 0.814074
| 0.814074
| 0
| 0.023966
| 0.267982
| 2,280
| 66
| 78
| 34.545455
| 0.784901
| 0
| 0
| 0.722222
| 0
| 0
| 0.027632
| 0
| 0
| 0
| 0
| 0
| 0.185185
| 1
| 0.092593
| false
| 0
| 0.055556
| 0
| 0.166667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2bdfdeb12673a1f8d69908ad35fbf7d7949223da
| 3,004
|
py
|
Python
|
scripts/dict/amber_detail_food_r_rich.py
|
Shadybloom/amber-in-the-dark
|
688a6c6fe1a251860174d14a0ca76b5b1c5a5527
|
[
"WTFPL"
] | null | null | null |
scripts/dict/amber_detail_food_r_rich.py
|
Shadybloom/amber-in-the-dark
|
688a6c6fe1a251860174d14a0ca76b5b1c5a5527
|
[
"WTFPL"
] | 3
|
2018-06-25T21:10:44.000Z
|
2019-07-03T13:51:09.000Z
|
scripts/dict/amber_detail_food_r_rich.py
|
Shadybloom/amber-in-the-dark
|
688a6c6fe1a251860174d14a0ca76b5b1c5a5527
|
[
"WTFPL"
] | 2
|
2018-06-24T23:00:17.000Z
|
2019-06-29T10:42:16.000Z
|
#----
# Изысканный рацион:
metadict_detail['Изысканный рацион (пони/год)'] = {
# Объединение всех городских и сельских рационов.
'Изысканный весенний рацион (пони/день)':90,
'Изысканный летний рацион (пони/день)':90,
'Изысканный осенний рацион (пони/день)':90,
'Изысканный зимний рацион (пони/день)':90,
}
#----
# Изысканный рацион (времена года):
metadict_detail['Изысканный весенний рацион (пони/день)'] = {
# TODO:
# Это ресторанное питание и заказ готовой еды.
# Следовательно, нужно учесть доставку.
'Североморский весенний повседневный рацион (пони/день)':1 / 8,
'Североморский весенний праздничный рацион (пони/день)':1 / 8,
'Среднеземный весенний повседневный рацион (пони/день)':1 / 8,
'Среднеземный весенний праздничный рацион (пони/день)':1 / 8,
'Южноморский весенний повседневный рацион (пони/день)':1 / 8,
'Южноморский весенний праздничный рацион (пони/день)':1 / 8,
'Городской весенний повседневный рацион (пони/день)':1 / 8,
'Городской весенний праздничный рацион (пони/день)':1 / 8,
}
metadict_detail['Изысканный летний рацион (пони/день)'] = {
'Североморский летний повседневный рацион (пони/день)':1 / 8,
'Североморский летний праздничный рацион (пони/день)':1 / 8,
'Среднеземный летний повседневный рацион (пони/день)':1 / 8,
'Среднеземный летний праздничный рацион (пони/день)':1 / 8,
'Южноморский летний повседневный рацион (пони/день)':1 / 8,
'Южноморский летний праздничный рацион (пони/день)':1 / 8,
'Городской летний повседневный рацион (пони/день)':1 / 8,
'Городской летний праздничный рацион (пони/день)':1 / 8,
}
metadict_detail['Изысканный осенний рацион (пони/день)'] = {
'Североморский осенний повседневный рацион (пони/день)':1 / 8,
'Североморский осенний праздничный рацион (пони/день)':1 / 8,
'Среднеземный осенний повседневный рацион (пони/день)':1 / 8,
'Среднеземный осенний праздничный рацион (пони/день)':1 / 8,
'Южноморский осенний повседневный рацион (пони/день)':1 / 8,
'Южноморский осенний праздничный рацион (пони/день)':1 / 8,
'Городской осенний повседневный рацион (пони/день)':1 / 8,
'Городской осенний праздничный рацион (пони/день)':1 / 8,
}
metadict_detail['Изысканный зимний рацион (пони/день)'] = {
'Североморский зимний повседневный рацион (пони/день)':1 / 8,
'Североморский зимний праздничный рацион (пони/день)':1 / 8,
'Среднеземный зимний повседневный рацион (пони/день)':1 / 8,
'Среднеземный зимний праздничный рацион (пони/день)':1 / 8,
'Южноморский зимний повседневный рацион (пони/день)':1 / 8,
'Южноморский зимний праздничный рацион (пони/день)':1 / 8,
'Городской зимний повседневный рацион (пони/день)':1 / 8,
'Городской зимний праздничный рацион (пони/день)':1 / 8,
}
| 49.245902
| 71
| 0.654128
| 338
| 3,004
| 5.798817
| 0.115385
| 0.209184
| 0.285714
| 0.244898
| 0.872449
| 0.753061
| 0.753061
| 0.078061
| 0.078061
| 0
| 0
| 0.030981
| 0.226365
| 3,004
| 60
| 72
| 50.066667
| 0.812392
| 0.065912
| 0
| 0
| 0
| 0
| 0.692638
| 0
| 0
| 0
| 0
| 0.016667
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
a60a1ca6f3962bb9fa69b9160a0b37e2d9c0c905
| 10,179
|
py
|
Python
|
airmozilla/main/tests/views/test_related.py
|
mozilla/airmozilla
|
fa6acbbbacc1e22553457807bcea7ce7a9ef6fe3
|
[
"BSD-3-Clause"
] | 115
|
2015-01-06T18:45:39.000Z
|
2022-02-07T10:56:49.000Z
|
airmozilla/main/tests/views/test_related.py
|
april/airmozilla
|
ee357f5396cdcb50147c72ff1e81a610f9cb292c
|
[
"BSD-3-Clause"
] | 321
|
2015-01-02T15:19:25.000Z
|
2018-07-05T14:58:50.000Z
|
airmozilla/main/tests/views/test_related.py
|
april/airmozilla
|
ee357f5396cdcb50147c72ff1e81a610f9cb292c
|
[
"BSD-3-Clause"
] | 101
|
2015-01-13T17:59:15.000Z
|
2020-12-15T02:58:38.000Z
|
from nose.tools import eq_, ok_
from django.contrib.auth.models import User
from django.core.urlresolvers import reverse
from airmozilla.manage import related
from airmozilla.main.models import (
Event,
Tag,
UserProfile,
)
from airmozilla.base.tests.testbase import DjangoTestCase
class RelatedTestCase(DjangoTestCase):
fixtures = ['airmozilla/manage/tests/main_testdata.json']
main_image = 'airmozilla/manage/tests/firefox.png'
def setUp(self):
super(RelatedTestCase, self).setUp()
related.flush()
def test_related_public(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events similar but with different tags
other = Event.objects.create(
title='Mozilla Event',
description='bla bla',
status=event.status,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=event.privacy,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
event.tags.add(tag1)
tag2 = Tag.objects.create(name='PartyTag')
other.tags.add(tag2)
related.index(all=True)
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
ok_('Mozilla Event' in response.content)
def test_unrelated_privacy(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events with different privacies and logged in
other = Event.objects.create(
title='Happiness event',
description='bla bla',
status=event.status,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=Event.PRIVACY_COMPANY,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
event.tags.add(tag1)
other.tags.add(tag1)
related.index(all=True)
self._login()
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
ok_('Hapiness event' not in response.content)
# more tests for multiple types of users
def test_related_content_logged_users(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events similar with use logged-in
other = Event.objects.create(
title='Event test',
description='bla bla',
status=event.status,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=event.privacy,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
other.tags.add(tag1)
event.tags.add(tag1)
related.index(all=True)
self._login()
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Event test' in response.content)
User.objects.create_user(
'helen', 'helen@mozilla.com', 'secret'
)
assert self.client.login(username='helen', password='secret')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Event test' in response.content)
contributor = User.objects.create_user(
'nigel', 'nigel@live.com', 'secret'
)
UserProfile.objects.create(
user=contributor,
contributor=True
)
assert self.client.login(username='nigel', password='secret')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Event test' in response.content)
def test_unrelated_users(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events that are dissimilar in tags and title
other = Event.objects.create(
title='Mozilla Festival',
description='bla bla',
status=event.status,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=Event.PRIVACY_PUBLIC,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
event.tags.add(tag1)
tag2 = Tag.objects.create(name='PartyTag')
other.tags.add(tag2)
related.index(all=True)
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
ok_('Mozilla Festival' not in response.content)
User.objects.create_user(
'gloria', 'gloria@mozilla.com', 'starlight'
)
assert self.client.login(username='gloria', password='starlight')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Mozilla Festival' not in response.content)
contributor = User.objects.create_user(
'nigel', 'nigel@live.com', 'secret'
)
UserProfile.objects.create(
user=contributor,
contributor=True
)
assert self.client.login(username='nigel', password='secret')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Mozilla Festival' not in response.content)
def test_unrelated_privacy_company(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events with privacy company i.e staff
other = Event.objects.create(
title='Happiness event',
description='bla bla',
status=event.status,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=Event.PRIVACY_COMPANY,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
event.tags.add(tag1)
other.tags.add(tag1)
related.index(all=True)
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
ok_('Happiness event' not in response.content)
User.objects.create_user(
'gloria', 'gloria@mozilla.com', 'starlight'
)
assert self.client.login(username='gloria', password='starlight')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Happiness event' in response.content)
contributor = User.objects.create_user(
'nigel', 'nigel@live.com', 'secret'
)
UserProfile.objects.create(
user=contributor,
contributor=True
)
assert self.client.login(username='nigel', password='secret')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Happiness event' not in response.content)
def test_unrelated_privacy_contributor(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events with privacy contributors
other = Event.objects.create(
title='Happiness event',
description='bla bla',
status=event.status,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=Event.PRIVACY_CONTRIBUTORS,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
event.tags.add(tag1)
other.tags.add(tag1)
related.index(all=True)
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
ok_('Happiness event' not in response.content)
User.objects.create_user(
'gloria', 'gloria@mozilla.com', 'starlight'
)
assert self.client.login(username='gloria', password='starlight')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Happiness event' in response.content)
contributor = User.objects.create_user(
'nigel', 'nigel@live.com', 'secret'
)
UserProfile.objects.create(
user=contributor,
contributor=True
)
assert self.client.login(username='nigel', password='secret')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Happiness event' in response.content)
def test_related_event_remove(self):
event = Event.objects.get(title='Test event')
self._attach_file(event, self.main_image)
# events similar but with different tags
other = Event.objects.create(
title='Mozilla Event',
description='bla bla',
status=event.STATUS_REMOVED,
start_time=event.start_time,
archive_time=event.archive_time,
privacy=event.privacy,
placeholder_img=event.placeholder_img,
)
tag1 = Tag.objects.create(name='SomeTag')
event.tags.add(tag1)
other.tags.add(tag1)
related.index(all=True)
url = reverse('main:related_content', args=(event.slug,))
response = self.client.get(url)
ok_('Mozilla Event' not in response.content)
User.objects.create_user(
'gloria', 'gloria@mozilla.com', 'starlight'
)
assert self.client.login(username='gloria', password='starlight')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Mozilla Festival' not in response.content)
contributor = User.objects.create_user(
'nigel', 'nigel@live.com', 'secret'
)
UserProfile.objects.create(
user=contributor,
contributor=True
)
assert self.client.login(username='nigel', password='secret')
response = self.client.get(url)
eq_(response.status_code, 200)
ok_('Mozilla Festival' not in response.content)
| 35.466899
| 73
| 0.613322
| 1,136
| 10,179
| 5.364437
| 0.103873
| 0.066131
| 0.050213
| 0.058582
| 0.884148
| 0.875287
| 0.86577
| 0.847227
| 0.836396
| 0.836396
| 0
| 0.007609
| 0.276943
| 10,179
| 286
| 74
| 35.590909
| 0.82038
| 0.030651
| 0
| 0.77459
| 0
| 0
| 0.116555
| 0.007811
| 0
| 0
| 0
| 0
| 0.040984
| 1
| 0.032787
| false
| 0.040984
| 0.02459
| 0
| 0.069672
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a61b1fd9906bab51324aa78c1aead399a1cb07cd
| 2,044
|
py
|
Python
|
aerosandbox/numpy/test_numpy/test_determine_type.py
|
raihaan123/AeroSandbox
|
1e7c78f04b066415f671237a4833ba98901bb9ec
|
[
"MIT"
] | 322
|
2019-05-29T20:40:04.000Z
|
2022-03-29T12:46:45.000Z
|
aerosandbox/numpy/test_numpy/test_determine_type.py
|
raihaan123/AeroSandbox
|
1e7c78f04b066415f671237a4833ba98901bb9ec
|
[
"MIT"
] | 55
|
2019-07-14T09:52:59.000Z
|
2022-03-28T16:02:21.000Z
|
aerosandbox/numpy/test_numpy/test_determine_type.py
|
raihaan123/AeroSandbox
|
1e7c78f04b066415f671237a4833ba98901bb9ec
|
[
"MIT"
] | 68
|
2019-06-02T09:57:26.000Z
|
2022-03-28T15:03:47.000Z
|
from aerosandbox.numpy.determine_type import *
import pytest
import numpy as np
import casadi as cas
def test_int():
assert is_casadi_type(5, recursive=True) == False
assert is_casadi_type(5, recursive=False) == False
def test_float():
assert is_casadi_type(5., recursive=True) == False
assert is_casadi_type(5., recursive=False) == False
def test_numpy():
assert is_casadi_type(
np.array([1, 2, 3]),
recursive=True
) == False
assert is_casadi_type(
np.array([1, 2, 3]),
recursive=False
) == False
def test_casadi():
assert is_casadi_type(
cas.GenMX_ones(5),
recursive=False
) == True
assert is_casadi_type(
cas.GenMX_ones(5),
recursive=True
) == True
def test_numpy_list():
assert is_casadi_type(
[np.array(5), np.array(7)],
recursive=False
) == False
assert is_casadi_type(
[np.array(5), np.array(7)],
recursive=True
) == False
def test_casadi_list():
assert is_casadi_type(
[cas.GenMX_ones(5), cas.GenMX_ones(5)],
recursive=False
) == False
assert is_casadi_type(
[cas.GenMX_ones(5), cas.GenMX_ones(5)],
recursive=True
) == True
def test_mixed_list():
assert is_casadi_type(
[np.array(5), cas.GenMX_ones(5)],
recursive=False
) == False
assert is_casadi_type(
[np.array(5), cas.GenMX_ones(5)],
recursive=True
) == True
def test_multi_level_contaminated_list():
a = [[1 for _ in range(10)] for _ in range(10)]
assert is_casadi_type(a, recursive=False) == False
assert is_casadi_type(a, recursive=True) == False
a[5][5] = cas.MX(1)
assert is_casadi_type(a, recursive=False) == False
assert is_casadi_type(a, recursive=True) == True
a[5][5] = np.array(cas.DM(1), dtype="O")
assert is_casadi_type(a, recursive=False) == False
assert is_casadi_type(a, recursive=True) == False
if __name__ == '__main__':
pytest.main([__file__])
| 22.461538
| 55
| 0.624755
| 288
| 2,044
| 4.166667
| 0.159722
| 0.133333
| 0.233333
| 0.3
| 0.786667
| 0.769167
| 0.765
| 0.7575
| 0.750833
| 0.671667
| 0
| 0.022698
| 0.245597
| 2,044
| 90
| 56
| 22.711111
| 0.755512
| 0
| 0
| 0.671642
| 0
| 0
| 0.004403
| 0
| 0
| 0
| 0
| 0
| 0.298507
| 1
| 0.119403
| false
| 0
| 0.059701
| 0
| 0.179104
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a65fa42c8af234072e1ff92ca4e6fbee69ad14ce
| 136
|
py
|
Python
|
icevision/models/mmdet/models/yolox/backbones/__init__.py
|
matt-deboer/icevision
|
bf886e558cc0c5ba5c84559514b8330c64d72f8d
|
[
"Apache-2.0"
] | null | null | null |
icevision/models/mmdet/models/yolox/backbones/__init__.py
|
matt-deboer/icevision
|
bf886e558cc0c5ba5c84559514b8330c64d72f8d
|
[
"Apache-2.0"
] | null | null | null |
icevision/models/mmdet/models/yolox/backbones/__init__.py
|
matt-deboer/icevision
|
bf886e558cc0c5ba5c84559514b8330c64d72f8d
|
[
"Apache-2.0"
] | null | null | null |
from icevision.models.mmdet.models.yolox.backbones.resnet_fpn import *
from icevision.models.mmdet.models.yolox.backbones.swin import *
| 45.333333
| 70
| 0.838235
| 19
| 136
| 5.947368
| 0.526316
| 0.230089
| 0.336283
| 0.424779
| 0.778761
| 0.778761
| 0.778761
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 136
| 2
| 71
| 68
| 0.882813
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 10
|
a6dfb231421dbda62053dc109783f960ad279a5c
| 12,579
|
py
|
Python
|
tensorflow/deepflow.py
|
JSHBaxter/DeepFlow
|
aff9ebff59a96289918a7efabddab4c38872f7b9
|
[
"Unlicense"
] | null | null | null |
tensorflow/deepflow.py
|
JSHBaxter/DeepFlow
|
aff9ebff59a96289918a7efabddab4c38872f7b9
|
[
"Unlicense"
] | null | null | null |
tensorflow/deepflow.py
|
JSHBaxter/DeepFlow
|
aff9ebff59a96289918a7efabddab4c38872f7b9
|
[
"Unlicense"
] | null | null | null |
import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np
import os
flow_path = os.path.dirname(os.path.realpath(__file__))
module = tf.load_op_library(flow_path + '/flow.so')
@ops.RegisterGradient("BinaryMeanpass3d")
def _binary_meanpass3d_grad_cc(op, grad):
"""
The gradient for `binary_meanpass3d` using the operation implemented in C++.
:param op: `binary_meanpass3d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `binary_meanpass3d` op.
:return: gradients with respect to the input of `binary_meanpass3d`.
"""
return module.binary_meanpass3d_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], op.outputs[0])
@ops.RegisterGradient("BinaryMeanpass3dWithInit")
def _binary_meanpass3d_with_init_grad_cc(op, grad):
"""
The gradient for `binary_meanpass3d` using the operation implemented in C++.
:param op: `binary_meanpass3d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `binary_meanpass3d` op.
:return: gradients with respect to the input of `binary_meanpass3d`.
"""
return module.binary_meanpass3d_with_init_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], op.outputs[0])
@ops.RegisterGradient("BinaryMeanpass2d")
def _binary_meanpass2d_grad_cc(op, grad):
"""
The gradient for `binary_meanpass2d` using the operation implemented in C++.
:param op: `binary_meanpass2d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `binary_meanpass2d` op.
:return: gradients with respect to the input of `binary_meanpass2d`.
"""
return module.binary_meanpass2d_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.outputs[0])
@ops.RegisterGradient("BinaryMeanpass2dWithInit")
def _binary_meanpass2d_with_init_grad_cc(op, grad):
"""
The gradient for `binary_meanpass2d` using the operation implemented in C++.
:param op: `binary_meanpass2d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `binary_meanpass2d` op.
:return: gradients with respect to the input of `binary_meanpass2d`.
"""
return module.binary_meanpass2d_with_init_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.outputs[0])
@ops.RegisterGradient("BinaryMeanpass1d")
def _binary_meanpass1d_grad_cc(op, grad):
"""
The gradient for `binary_meanpass1d` using the operation implemented in C++.
:param op: `binary_meanpass1d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `binary_meanpass1d` op.
:return: gradients with respect to the input of `binary_meanpass1d`.
"""
return module.binary_meanpass1d_grad(grad, op.inputs[0], op.inputs[1], op.outputs[0])
@ops.RegisterGradient("BinaryMeanpass1dWithInit")
def _binary_meanpass1d_with_init_grad_cc(op, grad):
"""
The gradient for `binary_meanpass1d` using the operation implemented in C++.
:param op: `binary_meanpass1d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `binary_meanpass1d` op.
:return: gradients with respect to the input of `binary_meanpass1d`.
"""
return module.binary_meanpass1d_with_init_grad(grad, op.inputs[0], op.inputs[1], op.outputs[0])
@ops.RegisterGradient("PottsMeanpass3d")
def _potts_meanpass3d_grad_cc(op, grad):
"""
The gradient for `potts_meanpass3d` using the operation implemented in C++.
:param op: `potts_meanpass3d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `potts_meanpass3d` op.
:return: gradients with respect to the input of `potts_meanpass3d`.
"""
return module.potts_meanpass3d_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], op.outputs[0])
@ops.RegisterGradient("PottsMeanpass3dWithInit")
def _potts_meanpass3d_with_init_grad_cc(op, grad):
"""
The gradient for `potts_meanpass3d_with_init` using the operation implemented in C++.
:param op: `potts_meanpass3d_with_init` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `potts_meanpass3d_with_init` op.
:return: gradients with respect to the input of `potts_meanpass3d_with_init`.
"""
return module.potts_meanpass3d_with_init_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], op.outputs[0])
@ops.RegisterGradient("PottsMeanpass2d")
def _potts_meanpass2d_grad_cc(op, grad):
"""
The gradient for `potts_meanpass2d` using the operation implemented in C++.
:param op: `potts_meanpass2d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `potts_meanpass2d` op.
:return: gradients with respect to the input of `potts_meanpass2d`.
"""
return module.potts_meanpass2d_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.outputs[0])
@ops.RegisterGradient("PottsMeanpass2dWithInit")
def _potts_meanpass2d_with_init_grad_cc(op, grad):
"""
The gradient for `potts_meanpass2d` using the operation implemented in C++.
:param op: `potts_meanpass2d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `potts_meanpass2d` op.
:return: gradients with respect to the input of `potts_meanpass2d`.
"""
return module.potts_meanpass2d_with_init_grad(grad, op.inputs[0], op.inputs[1], op.inputs[2], op.outputs[0])
@ops.RegisterGradient("PottsMeanpass1d")
def _potts_meanpass1d_grad_cc(op, grad):
"""
The gradient for `potts_meanpass1d` using the operation implemented in C++.
:param op: `potts_meanpass1d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `potts_meanpass1d` op.
:return: gradients with respect to the input of `potts_meanpass1d`.
"""
return module.potts_meanpass1d_grad(grad, op.inputs[0], op.inputs[1], op.outputs[0])
@ops.RegisterGradient("PottsMeanpass1dWithInit")
def _potts_meanpass1d_with_init_grad_cc(op, grad):
"""
The gradient for `potts_meanpass1d` using the operation implemented in C++.
:param op: `potts_meanpass1d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `potts_meanpass1d` op.
:return: gradients with respect to the input of `potts_meanpass1d`.
"""
return module.potts_meanpass1d_with_init_grad(grad, op.inputs[0], op.inputs[1], op.outputs[0])
@ops.RegisterGradient("HmfMeanpass3d")
def _hmf_meanpass3d_grad_cc(op, grad):
"""
The gradient for `hmf_meanpass3d` using the operation implemented in C++.
:param op: `hmf_meanpass3d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `hmf_meanpass3d` op.
:return: gradients with respect to the input of `hmf_meanpass3d`.
"""
gradient = module.hmf_meanpass3d_grad(op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], grad, op.outputs[0], op.inputs[4], op.inputs[5])
return gradient
@ops.RegisterGradient("HmfMeanpass3dWithInit")
def _hmf_meanpass3d_with_init_grad_cc(op, grad):
"""
The gradient for `hmf_meanpass3d` using the operation implemented in C++.
:param op: `hmf_meanpass3d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `hmf_meanpass3d` op.
:return: gradients with respect to the input of `hmf_meanpass3d`.
"""
gradient = module.hmf_meanpass3d_with_init_grad(op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], grad, op.outputs[0], op.inputs[5], op.inputs[6])
return gradient
@ops.RegisterGradient("HmfMeanpass2d")
def _hmf_meanpass2d_grad_cc(op, grad):
"""
The gradient for `hmf_meanpass2d` using the operation implemented in C++.
:param op: `hmf_meanpass2d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `hmf_meanpass2d` op.
:return: gradients with respect to the input of `hmf_meanpass2d`.
"""
gradient = module.hmf_meanpass2d_grad(op.inputs[0], op.inputs[1], op.inputs[2], grad, op.outputs[0], op.inputs[3], op.inputs[4])
return gradient
@ops.RegisterGradient("HmfMeanpass2dWithInit")
def _hmf_meanpass2d_with_init_grad_cc(op, grad):
"""
The gradient for `hmf_meanpass2d` using the operation implemented in C++.
:param op: `hmf_meanpass2d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `hmf_meanpass2d` op.
:return: gradients with respect to the input of `hmf_meanpass2d`.
"""
gradient = module.hmf_meanpass2d_with_init_grad(op.inputs[0], op.inputs[1], op.inputs[2], grad, op.outputs[0], op.inputs[4], op.inputs[5])
return gradient
@ops.RegisterGradient("HmfMeanpass1d")
def _hmf_meanpass1d_grad_cc(op, grad):
"""
The gradient for `hmf_meanpass1d` using the operation implemented in C++.
:param op: `hmf_meanpass1d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `hmf_meanpass1d` op.
:return: gradients with respect to the input of `hmf_meanpass1d`.
"""
gradient = module.hmf_meanpass1d_grad(op.inputs[0], op.inputs[1], grad, op.outputs[0], op.inputs[2], op.inputs[3])
return gradient
@ops.RegisterGradient("HmfMeanpass1dWithInit")
def _hmf_meanpass1d_with_init_grad_cc(op, grad):
"""
The gradient for `hmf_meanpass1d` using the operation implemented in C++.
:param op: `hmf_meanpass1d` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
:param grad: gradient with respect to the output of the `hmf_meanpass1d` op.
:return: gradients with respect to the input of `hmf_meanpass1d`.
"""
gradient = module.hmf_meanpass1d_with_init_grad(op.inputs[0], op.inputs[1], grad, op.outputs[0], op.inputs[3], op.inputs[4])
return gradient
@ops.RegisterGradient("TaylorSeriesNCS")
def _taylor_series_ncs_grad_cc(op, grad):
"""
The gradient for `TaylorSeriesNCS` using the operation implemented in C++.
:param op: `TaylorSeriesNCS` `Operation` that we are differentiating, which we can use
to find the inputs of the original op.
:param grad: gradient with respect to the output of the `TaylorSeriesNCS` op.
:return: gradients with respect to the input of `TaylorSeriesNCS`.
"""
gradient = module.taylor_series_grad_ncs(op.inputs[0], op.inputs[1], grad)
return gradient
@ops.RegisterGradient("TaylorSeriesNSC")
def _taylor_series_nsc_grad_cc(op, grad):
"""
The gradient for `TaylorSeriesNSC` using the operation implemented in C++.
:param op: `TaylorSeriesNSC` `Operation` that we are differentiating, which we can use
to find the inputs of the original op.
:param grad: gradient with respect to the output of the `TaylorSeriesNSC` op.
:return: gradients with respect to the input of `TaylorSeriesNSC`.
"""
gradient = module.taylor_series_grad_nsc(op.inputs[0], op.inputs[1], grad)
return gradient
| 46.762082
| 156
| 0.727164
| 1,810
| 12,579
| 4.916575
| 0.050829
| 0.062928
| 0.058434
| 0.071918
| 0.892797
| 0.880773
| 0.879649
| 0.873806
| 0.855152
| 0.831442
| 0
| 0.020642
| 0.175849
| 12,579
| 268
| 157
| 46.936567
| 0.837754
| 0.582161
| 0
| 0.108108
| 0
| 0
| 0.080708
| 0.044022
| 0
| 0
| 0
| 0
| 0
| 1
| 0.27027
| false
| 0.72973
| 0.054054
| 0
| 0.594595
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 9
|
471ff82d938360aa7e1b179841b00b2a05c65f19
| 5,716
|
py
|
Python
|
tests/test_analyser/test_sniffer_wbsi.py
|
di-unipi-socc/micro-tosca
|
5d5c9361b34eeabaed8955ddc62282607672bd81
|
[
"MIT"
] | null | null | null |
tests/test_analyser/test_sniffer_wbsi.py
|
di-unipi-socc/micro-tosca
|
5d5c9361b34eeabaed8955ddc62282607672bd81
|
[
"MIT"
] | 3
|
2019-10-02T13:55:39.000Z
|
2021-06-01T22:55:20.000Z
|
tests/test_analyser/test_sniffer_wbsi.py
|
di-unipi-socc/microFreshener-core
|
5d5c9361b34eeabaed8955ddc62282607672bd81
|
[
"MIT"
] | null | null | null |
from unittest import TestCase
from microfreshener.core.importer import YMLImporter
from microfreshener.core.analyser.sniffer import WobblyServiceInteractionSmellSniffer
from microfreshener.core.analyser.smell import WobblyServiceInteractionSmell
from microfreshener.core.model.groups import Edge
class TestWobblyserviceInteractionSmell(TestCase):
@classmethod
def setUpClass(self):
file = 'data/tests/test_sniffer_wbsi.yml'
loader = YMLImporter()
self.micro_model = loader.Import(file)
self.wbsiSniffer = WobblyServiceInteractionSmellSniffer()
def test_wbsi(self):
source = self.micro_model["source"]
smell = self.wbsiSniffer.snif(source)
self.assertIsInstance(smell, WobblyServiceInteractionSmell)
self.assertFalse(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 1)
self.assertEqual(smell.getLinkCause()[0].target, self.micro_model["target"])
def test_wbsi_with_c(self):
source = self.micro_model["source_c"]
smell = self.wbsiSniffer.snif(source)
self.assertIsInstance(smell, WobblyServiceInteractionSmell)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_with_d(self):
source = self.micro_model["source_d"]
smell = self.wbsiSniffer.snif(source)
self.assertFalse(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 1)
self.assertEqual(len(smell.getNodeCause()), 0)
self.assertEqual(smell.getLinkCause()[0].target, self.micro_model["target"])
def test_wbsi_with_cd(self):
source = self.micro_model["source_cd"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_with_t(self):
source = self.micro_model["source_t"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_with_tc(self):
source = self.micro_model["source_tc"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_with_td(self):
source = self.micro_model["source_td"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_with_tcd(self):
source = self.micro_model["source_tcd"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter(self):
source = self.micro_model["source_mr"]
smell = self.wbsiSniffer.snif(source)
self.assertFalse(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 1)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_t(self):
source = self.micro_model["source_mr_t"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_c(self):
source = self.micro_model["source_mr_c"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_d(self):
source = self.micro_model["source_mr_d"]
smell = self.wbsiSniffer.snif(source)
self.assertFalse(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 1)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_tc(self):
source = self.micro_model["source_mr_tc"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_td(self):
source = self.micro_model["source_mr_td"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_cd(self):
source = self.micro_model["source_mr_cd"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_to_messagerouter_with_tcd(self):
source = self.micro_model["source_mr_tcd"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell.isEmpty())
self.assertEqual(len(smell.getLinkCause()), 0)
self.assertEqual(len(smell.getNodeCause()), 0)
def test_wbsi_from_message_router_to_service(self):
source = self.micro_model["mr"]
smell = self.wbsiSniffer.snif(source)
self.assertTrue(smell is None)
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0
| 8
|
5b6e82a0c8d33fe3bc11bb6d347af94af01d8d68
| 3,186
|
py
|
Python
|
question/migrations/0001_initial.py
|
belloshehu/multiple-choice-questions
|
abfb7ac8cc24bc3f9ee34e9505bc6c6944786ac0
|
[
"MIT"
] | null | null | null |
question/migrations/0001_initial.py
|
belloshehu/multiple-choice-questions
|
abfb7ac8cc24bc3f9ee34e9505bc6c6944786ac0
|
[
"MIT"
] | 2
|
2020-09-03T21:48:33.000Z
|
2020-09-22T08:51:14.000Z
|
question/migrations/0001_initial.py
|
belloshehu/multiple-choice-questions
|
abfb7ac8cc24bc3f9ee34e9505bc6c6944786ac0
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.1 on 2020-12-06 14:35
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('cbt', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='InstitutionQuestionPassage',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(blank=True, max_length=50, null=True)),
('body', models.TextField(blank=True, max_length=500, null=True)),
('no_of_questions', models.IntegerField(blank=True, default=4, null=True)),
('assessment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='cbt.institutionassessment')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='InstitutionQuestion',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('question_asked', models.TextField(unique=True)),
('score', models.IntegerField(default=5)),
('no_of_choices', models.IntegerField(default=4)),
('assessment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='cbt.institutionassessment')),
('passage', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='question.institutionquestionpassage')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='IndividualQuestionPassage',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(blank=True, max_length=50, null=True)),
('body', models.TextField(blank=True, max_length=500, null=True)),
('no_of_questions', models.IntegerField(blank=True, default=4, null=True)),
('assessment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='cbt.individualassessment')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='IndividualQuestion',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('question_asked', models.TextField(unique=True)),
('score', models.IntegerField(default=5)),
('no_of_choices', models.IntegerField(default=4)),
('assessment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='cbt.individualassessment')),
('passage', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='question.individualquestionpassage')),
],
options={
'abstract': False,
},
),
]
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| 134
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| 3,186
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0
| 8
|
5b8f9f11b78fcb15c5a09db47b17b07f8cbcda15
| 513,018
|
py
|
Python
|
src/intensio_obfuscator/obfuscation_examples/python/advanced/output/basicRAT-example/zOVbwWlnrcgdxwiaqBzrNMmeEGcKUKIUFNvwPSfYXmIciiGmPMHDYwgAcnOaoARWWAcoqWBJYDIMLlpnVAJVtfONkUMruKSGSTpZlvAhsOSwHkUWPPvhbFgcMNfNWMsV.py
|
bbhunter/Intensio-Obfuscator
|
f66a22b50c19793edac673cfd7dc319405205c39
|
[
"MIT"
] | 553
|
2019-06-08T17:47:41.000Z
|
2022-03-29T03:12:11.000Z
|
src/intensio_obfuscator/obfuscation_examples/python/advanced/output/basicRAT-example/zOVbwWlnrcgdxwiaqBzrNMmeEGcKUKIUFNvwPSfYXmIciiGmPMHDYwgAcnOaoARWWAcoqWBJYDIMLlpnVAJVtfONkUMruKSGSTpZlvAhsOSwHkUWPPvhbFgcMNfNWMsV.py
|
bbhunter/Intensio-Obfuscator
|
f66a22b50c19793edac673cfd7dc319405205c39
|
[
"MIT"
] | 69
|
2019-06-08T13:25:47.000Z
|
2022-02-15T08:34:07.000Z
|
src/intensio_obfuscator/obfuscation_examples/python/advanced/output/basicRAT-example/zOVbwWlnrcgdxwiaqBzrNMmeEGcKUKIUFNvwPSfYXmIciiGmPMHDYwgAcnOaoARWWAcoqWBJYDIMLlpnVAJVtfONkUMruKSGSTpZlvAhsOSwHkUWPPvhbFgcMNfNWMsV.py
|
bbhunter/Intensio-Obfuscator
|
f66a22b50c19793edac673cfd7dc319405205c39
|
[
"MIT"
] | 130
|
2019-06-08T18:44:13.000Z
|
2022-03-27T01:00:52.000Z
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exec(xXrZUZUNkuZHresTPrcnEWwyOrFRBspCNMJVOYEYJdTDOIECpJOmNWwwFRmPhfLwWTFRZjLrtXXHHkZVXJxtDILbSZRGfAwYJddoQRgdrrIfRXgigIuuXSYFCtBkBhAh)
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| 2
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0
| 15
|
754e8abd8cfa268cf1d3fa4213f8a39aec8c7490
| 47
|
py
|
Python
|
access.py
|
francisk/accessible
|
11d9a4d1aa8259b47b526caca4a9c390090d0d3a
|
[
"MIT"
] | null | null | null |
access.py
|
francisk/accessible
|
11d9a4d1aa8259b47b526caca4a9c390090d0d3a
|
[
"MIT"
] | null | null | null |
access.py
|
francisk/accessible
|
11d9a4d1aa8259b47b526caca4a9c390090d0d3a
|
[
"MIT"
] | null | null | null |
import numpy as np
def access():
return
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| 18
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|
0
| 8
|
f3858ab6c3c1de9b4dd9fb82e5ca280a50c0ed30
| 23,054
|
py
|
Python
|
tests/test_operations.py
|
fvalverd/AutoApi
|
3ceb320fe6a36d24032df121e335a8470fb929af
|
[
"MIT"
] | 6
|
2015-04-28T13:03:04.000Z
|
2021-08-24T19:15:53.000Z
|
tests/test_operations.py
|
fvalverd/AutoApi
|
3ceb320fe6a36d24032df121e335a8470fb929af
|
[
"MIT"
] | 6
|
2017-06-19T20:59:10.000Z
|
2020-05-22T16:22:28.000Z
|
tests/test_operations.py
|
fvalverd/AutoApi
|
3ceb320fe6a36d24032df121e335a8470fb929af
|
[
"MIT"
] | 2
|
2015-11-10T14:38:39.000Z
|
2017-05-18T05:46:03.000Z
|
# -*- coding: utf-8 -*-
import json
import unittest
from auto_api.messages import message
from . import BaseTest, BaseAuthTest, MoviesTest
class TestLogin(BaseAuthTest):
def test_unauthorized(self):
for verb in [self.app.get, self.app.post, self.app.put, self.app.patch, self.app.delete]:
response = verb('/%s/movies' % self.api)
self.assertEqual(response.status_code, 401)
response_json = json.loads(response.data or '{}')
self.assertDictEqual(
response_json,
{'message': u'You must be logged in "%s" api' % self.api}
)
def test_incomplete_login(self):
data = {'password': self.password}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 400)
response_json = json.loads(response.data or '{}')
self.assertDictContainsSubset(
{'message': u'Missing "api" parameter'},
response_json
)
def test_missing_parameters(self):
data = {'password': self.password, 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 400)
response_json = json.loads(response.data or '{}')
self.assertDictContainsSubset(
{'message': u'Missing parameters'},
response_json
)
def test_login_bad_pass(self):
data = {'email': self.user, 'password': 'bad_pass', 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 401)
response_json = json.loads(response.data or '{}')
self.assertDictContainsSubset(
{'message': u'Invalid email/password/api'},
response_json
)
def test_login(self):
data = {'email': self.user, 'password': self.password, 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
response = self.app.get('/%s/movies' % self.api, headers=headers)
self.assertEqual(response.status_code, 200)
class TestLogout(BaseAuthTest):
def test_logout(self):
data = {'email': self.user, 'password': self.password, 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
response = self.app.post('/logout', headers=headers, data={'api': self.api})
self.assertEqual(response.status_code, 204)
def test_missing_api(self):
data = {'email': self.user, 'password': self.password, 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
response = self.app.post('/logout', headers=headers)
self.assertEqual(response.status_code, 400)
response_json = json.loads(response.data or '{}')
self.assertDictEqual(
response_json,
{'message': u'Missing "api" parameter'}
)
def test_logout_but_unauthorized_in_other_api(self):
data = {'email': self.user, 'password': self.password, 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
response = self.app.post('/logout', headers=headers, data={'api': 'bad_api'})
self.assertEqual(response.status_code, 401)
response_json = json.loads(response.data or '{}')
self.assertDictEqual(
response_json,
{'message': u'You must be logged in "%s" api' % u'bad_api'}
)
class TestCreateUser(MoviesTest):
def tearDown(self):
self.remove_user(self.api, 'fvalverd')
super(BaseTest, self).tearDown()
def test_missing_parameters(self):
test_data = {'email': u'fvalverd', 'api': self.api, 'roles': ['read']}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 400)
response_json = json.loads(response.data or '{}')
self.assertDictContainsSubset(
{'message': u'Missing parameters'},
response_json
)
def test_already_existing_user(self):
test_data = {'email': self.user, 'password': self.password, 'api': self.api}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 409)
response_json = json.loads(response.data or '{}')
self.assertDictContainsSubset(
{'message': u'User already exists'},
response_json
)
def test_read_role(self):
test_data = {'email': u'fvalverd', 'password': u'pass', 'api': self.api, 'roles': ['read']}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=test_data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
# read
response = self.app.get('/%s/movies' % self.api, headers=headers)
self.assertEqual(response.status_code, 200)
# create
response = self.app.post('/%s/movies' % self.api, headers=headers, data='')
self.assertEqual(response.status_code, 403)
# update put
response = self.app.put('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
# update patch
response = self.app.patch('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
# delete
response = self.app.delete('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
def test_create_role(self):
test_data = {'email': u'fvalverd', 'password': u'pass', 'api': self.api, 'roles': ['create']}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=test_data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
# read
response = self.app.get('/%s/movies' % self.api, headers=headers)
self.assertEqual(response.status_code, 403)
# create
response = self.app.post(
'/%s/movies' % self.api,
headers=headers,
data=json.dumps({}),
content_type='application/json'
)
self.assertEqual(response.status_code, 201)
# update put
response = self.app.put('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
# update patch
response = self.app.patch('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
# delete
response = self.app.delete('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
def test_update_role(self):
test_data = {'email': u'fvalverd', 'password': u'pass', 'api': self.api, 'roles': ['update']}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=test_data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
# read
response = self.app.get('/%s/movies' % self.api, headers=headers)
self.assertEqual(response.status_code, 403)
# create
response = self.app.post('/%s/movies' % self.api, headers=headers, data='')
self.assertEqual(response.status_code, 403)
# update put
response = self.app.put(
'/%s/movies/%s' % (self.api, self.movies[0]['id']),
headers=headers,
content_type='application/json',
data=json.dumps({'field': 'value'})
)
self.assertEqual(response.status_code, 204)
# update patch
response = self.app.patch(
'/%s/movies/%s' % (self.api, self.movies[0]['id']),
headers=headers,
content_type='application/json',
data=json.dumps({'another_field': 'value'})
)
self.assertEqual(response.status_code, 204)
# delete
response = self.app.delete('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
def test_delete_role(self):
test_data = {'email': u'fvalverd', 'password': u'pass', 'api': self.api, 'roles': ['delete']}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=test_data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
# read
response = self.app.get('/%s/movies' % self.api, headers=headers)
self.assertEqual(response.status_code, 403)
# create
response = self.app.post('/%s/movies' % self.api, headers=headers, data='')
self.assertEqual(response.status_code, 403)
# update put
response = self.app.put('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
# update patch
response = self.app.patch('/%s/movies/%s' % (self.api, self.movies[0]['id']), headers=headers)
self.assertEqual(response.status_code, 403)
# delete
response = self.app.delete('/%s/movies/%s' % (self.api, self.movies[1]['id']), headers=headers)
self.assertEqual(response.status_code, 204)
def test_admin_role(self):
test_data = {'email': u'fvalverd', 'password': u'pass', 'api': self.api, 'roles': ['admin']}
admin_headers = self.get_admin_headers()
response = self.app.post(
'/user',
headers=admin_headers,
data=json.dumps(test_data),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=test_data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
# read
response = self.app.get('/%s/movies' % self.api, headers=headers)
self.assertEqual(response.status_code, 200)
# create
response = self.app.post(
'/%s/movies' % self.api,
headers=headers,
data=json.dumps({}),
content_type='application/json'
)
self.assertEqual(response.status_code, 201)
# update put
response = self.app.put(
'/%s/movies/%s' % (self.api, self.movies[0]['id']),
headers=headers,
content_type='application/json',
data=json.dumps({'field': 'value'})
)
self.assertEqual(response.status_code, 204)
# update patch
response = self.app.patch(
'/%s/movies/%s' % (self.api, self.movies[0]['id']),
headers=headers,
content_type='application/json',
data=json.dumps({'another_field': 'value'})
)
self.assertEqual(response.status_code, 204)
# delete
response = self.app.delete('/%s/movies/%s' % (self.api, self.movies[2]['id']), headers=headers)
self.assertEqual(response.status_code, 204)
class TestEditRoles(MoviesTest):
@classmethod
def setUpClass(cls):
super(TestEditRoles, cls).setUpClass()
cls.read_user = {'email': u'fvalverd', 'password': u'pass', 'api': cls.api, 'roles': ['read']}
cls.app.post(
'/user',
headers=cls.headers,
data=json.dumps(cls.read_user),
content_type='application/json'
)
response = cls.app.post('/login', data=cls.read_user)
cls.read_user_header = cls.response_to_headers(response)
@classmethod
def tearDownClass(cls):
cls.remove_user(cls.api, 'fvalverd')
cls.remove_user(cls.api, 'user_1')
cls.remove_user(cls.api, 'user_2')
super(TestEditRoles, cls).tearDownClass()
def test_missing_parameters(self):
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': self.read_user['email'], 'api': self.api}),
content_type='application/json'
)
self.assertEqual(response.status_code, 400)
def test_exist_user(self):
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': 'fake_user', 'api': self.api, 'roles': {'read': True}}),
content_type='application/json'
)
self.assertEqual(response.status_code, 403)
def test_edit_read_role(self):
for read, status_code in [(False, 403), (True, 200)]:
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': self.read_user['email'], 'api': self.api, 'roles': {'read': read}}),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.get('/%s/movies' % self.api, headers=self.read_user_header)
self.assertEqual(response.status_code, status_code)
def test_edit_create_role(self):
for create, status_code in [(True, 201), (False, 403)]:
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': self.read_user['email'], 'api': self.api, 'roles': {'create': create}}),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post(
'/%s/movies' % self.api,
headers=self.read_user_header,
data=json.dumps({}),
content_type='application/json'
)
self.assertEqual(response.status_code, status_code)
def test_edit_update_role(self):
for update, status_code in [(True, 204), (False, 403)]:
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': self.read_user['email'], 'api': self.api, 'roles': {'update': update}}),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.put(
'/%s/movies/%s' % (self.api, self.movies[0]['id']),
headers=self.read_user_header,
data=json.dumps({}),
content_type='application/json'
)
self.assertEqual(response.status_code, status_code)
def test_edit_delete_role(self):
for delete, movie_pos, status_code in [(True, 1, 204), (False, 2, 403)]:
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': self.read_user['email'], 'api': self.api, 'roles': {'delete': delete}}),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.delete(
'/%s/movies/%s' % (self.api, self.movies[movie_pos]['id']),
headers=self.read_user_header,
data=json.dumps({}),
content_type='application/json'
)
self.assertEqual(response.status_code, status_code)
def test_edit_admin_role(self):
for admin, email, status_code in [(True, 'user_1', 204), (False, 'user_2', 403)]:
response = self.app.post(
'/roles',
headers=self.headers,
data=json.dumps({'email': self.read_user['email'], 'api': self.api, 'roles': {'admin': admin}}),
content_type='application/json'
)
self.assertEqual(response.status_code, 204)
response = self.app.post(
'/user',
headers=self.read_user_header,
data=json.dumps({'email': email, 'password': u'pass', 'api': self.api}),
content_type='application/json'
)
self.assertEqual(response.status_code, status_code)
class TestChangePassword(BaseAuthTest):
def setUp(self):
super(TestChangePassword, self).setUp()
self._user = u'fvalverd'
self._pass = u'pass'
self.remove_user(self.api, self._user)
self.create_user(self.api, self._user, self._pass, ['read'])
def tearDown(self):
self.remove_user(self.api, self._user)
super(TestChangePassword, self).tearDown()
def test_user_change_password(self):
data = {'email': self._user, 'password': self._pass, 'api': self.api}
response = self.app.post('/login', data=data)
headers = self.response_to_headers(response)
data['password'] = u'new_pass'
response = self.app.post('/password', data=data, headers=headers)
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
def test_missing_parameters(self):
data = {'email': self._user, 'password': self._pass, 'api': self.api}
response = self.app.post('/login', data=data)
headers = self.response_to_headers(response)
del data['password']
response = self.app.post('/password', data=data, headers=headers)
self.assertEqual(response.status_code, 400)
response_json = json.loads(response.data or '{}')
self.assertDictEqual(
response_json,
{'message': u'Missing parameters'}
)
def test_admin_change_user_password(self):
data = {'email': self._user, 'password': u'new_pass', 'api': self.api}
response = self.app.post('/password', data=data, headers=self.get_admin_headers())
self.assertEqual(response.status_code, 204)
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
def test_only_its_own_password_can_be_change(self):
data = {'email': self._user, 'password': self._pass, 'api': self.api}
response = self.app.post('/login', data=data)
headers = self.response_to_headers(response)
data = {'email': self.user, 'password': self.password, 'api': self.api}
response = self.app.post('/password', data=data, headers=headers)
self.assertEqual(response.status_code, 409)
response_json = json.loads(response.data or '{}')
self.assertDictEqual(
response_json,
{'message': u'A user can only change their own password'}
)
class TestOtherOperations(BaseTest):
def test_welcome(self):
for verb in [self.app.get, self.app.post, self.app.delete, self.app.put, self.app.patch]:
response = verb('/')
self.assertEqual(response.status_code, 200)
self.assertDictEqual(
json.loads(response.data or '{}'),
json.loads(self.autoapi.welcome().data)
)
def test_invalid_operation(self):
response = self.app.post('/asdf')
response_json = json.loads(response.data or '{}')
self.assertEqual(response.status_code, 400)
self.assertDictEqual(
response_json,
{'message': u'This is not a valid operation'}
)
class TestOtherOperationsAuth(BaseAuthTest):
def test_welcome(self):
for verb in [self.app.get, self.app.post, self.app.delete, self.app.put, self.app.patch]:
response = verb('/')
self.assertEqual(response.status_code, 200)
self.assertDictEqual(
json.loads(response.data or '{}'),
json.loads(self.autoapi.welcome().data)
)
def test_invalid_operation(self):
response = self.app.post('/asdf')
response_json = json.loads(response.data or '{}')
self.assertEqual(response.status_code, 400)
self.assertDictEqual(
response_json,
{'message': u'This is not a valid operation'}
)
def test_invalid_operation_auth(self):
data = {'email': self.user, 'password': self.password, 'api': self.api}
response = self.app.post('/login', data=data)
self.assertEqual(response.status_code, 200)
headers = self.response_to_headers(response)
response = self.app.post('/asdf', headers=headers)
response_json = json.loads(response.data or '{}')
self.assertEqual(response.status_code, 400)
self.assertDictEqual(
response_json,
{'message': u'This is not a valid operation'}
)
class TestExternalDecorator(BaseTest):
def test_autoapi_decorator(self):
text = 'There is not best movie, only interpretations'
path = '/{}/best-movie'.format(self.api)
@self.autoapi.route(path, api=self.api, method='GET')
def best_movie():
return message(text)
response = self.app.get(path)
self.assertEqual(response.status_code, 200)
response_json = json.loads(response.data or '{}')
self.assertEqual({'message': text}, response_json)
if __name__ == '__main__':
unittest.main()
| 37.917763
| 114
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| 0.079665
| 0.008386
| 0.002096
| 0.106918
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| null | 0
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0
| 8
|
f38870eeb12da8579188b32d04e057e4c0280b75
| 21,850
|
py
|
Python
|
kinow_client/apis/configuration_api.py
|
kinow-io/kinow-python-sdk
|
4c1699a3c78048b84287bd049a669651a5b4e2d5
|
[
"Apache-2.0"
] | 1
|
2019-06-26T14:24:54.000Z
|
2019-06-26T14:24:54.000Z
|
kinow_client/apis/configuration_api.py
|
kinow-io/kinow-python-sdk
|
4c1699a3c78048b84287bd049a669651a5b4e2d5
|
[
"Apache-2.0"
] | null | null | null |
kinow_client/apis/configuration_api.py
|
kinow-io/kinow-python-sdk
|
4c1699a3c78048b84287bd049a669651a5b4e2d5
|
[
"Apache-2.0"
] | 1
|
2018-02-01T10:08:40.000Z
|
2018-02-01T10:08:40.000Z
|
# coding: utf-8
"""
Server API
Reference for Server API (REST/Json)
OpenAPI spec version: 1.4.58
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class ConfigurationApi(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
config = Configuration()
if api_client:
self.api_client = api_client
else:
if not config.api_client:
config.api_client = ApiClient()
self.api_client = config.api_client
def get_configuration(self, **kwargs):
"""
Get configuration by name. Available : - PLATFORM_NAME - ACTIVE - LANG_DEFAULT - CURRENCY_DEFAULT - COUNTRY_DEFAULT - TIMEZONE - COPYRIGHT - COOKIE_WARNING - RECAPTCHA_KEY - CUSTOMER_REGISTRATION - CATALOG_RESTRICTED - CATALOG_SUBSCRIPTION - PRODUCTS_ORDER_BY - PRODUCTS_ORDER_WAY - PRODUCTS_RAIL_NB - PRODUCTS_NEW_DAYS - FORCE_TAX_ID - CMS_CONDITIONS_ID - GEOLOCATION_WHITELIST - PASSWORD_MIN_LENGTH - PASSWORD_MIN_CAPITALIZE - PASSWORD_MIN_NUMERIC - PASSWORD_MIN_SPECIAL
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int page:
:param int per_page:
:return: ConfigurationList
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_configuration_with_http_info(**kwargs)
else:
(data) = self.get_configuration_with_http_info(**kwargs)
return data
def get_configuration_with_http_info(self, **kwargs):
"""
Get configuration by name. Available : - PLATFORM_NAME - ACTIVE - LANG_DEFAULT - CURRENCY_DEFAULT - COUNTRY_DEFAULT - TIMEZONE - COPYRIGHT - COOKIE_WARNING - RECAPTCHA_KEY - CUSTOMER_REGISTRATION - CATALOG_RESTRICTED - CATALOG_SUBSCRIPTION - PRODUCTS_ORDER_BY - PRODUCTS_ORDER_WAY - PRODUCTS_RAIL_NB - PRODUCTS_NEW_DAYS - FORCE_TAX_ID - CMS_CONDITIONS_ID - GEOLOCATION_WHITELIST - PASSWORD_MIN_LENGTH - PASSWORD_MIN_CAPITALIZE - PASSWORD_MIN_NUMERIC - PASSWORD_MIN_SPECIAL
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int page:
:param int per_page:
:return: ConfigurationList
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page', 'per_page']
all_params.append('callback')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_configuration" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
resource_path = '/configuration'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'page' in params:
query_params['page'] = params['page']
if 'per_page' in params:
query_params['per_page'] = params['per_page']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['ApiClientId', 'ApiClientSecret']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigurationList',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_configuration_analytics(self, **kwargs):
"""
Get analytics configuration
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_analytics(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int page:
:param int per_page:
:return: Analytics
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_configuration_analytics_with_http_info(**kwargs)
else:
(data) = self.get_configuration_analytics_with_http_info(**kwargs)
return data
def get_configuration_analytics_with_http_info(self, **kwargs):
"""
Get analytics configuration
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_analytics_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int page:
:param int per_page:
:return: Analytics
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page', 'per_page']
all_params.append('callback')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_configuration_analytics" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
resource_path = '/configuration/analytics'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'page' in params:
query_params['page'] = params['page']
if 'per_page' in params:
query_params['per_page'] = params['per_page']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['ApiClientId', 'ApiClientSecret']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Analytics',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_configuration_by_name(self, configuration_name, **kwargs):
"""
Get configuration by name. Available : - LANG_DEFAULT - CURRENCY_DEFAULT - COUNTRY_DEFAULT - TIMEZONE
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_by_name(configuration_name, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str configuration_name: (required)
:return: Configuration
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_configuration_by_name_with_http_info(configuration_name, **kwargs)
else:
(data) = self.get_configuration_by_name_with_http_info(configuration_name, **kwargs)
return data
def get_configuration_by_name_with_http_info(self, configuration_name, **kwargs):
"""
Get configuration by name. Available : - LANG_DEFAULT - CURRENCY_DEFAULT - COUNTRY_DEFAULT - TIMEZONE
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_by_name_with_http_info(configuration_name, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str configuration_name: (required)
:return: Configuration
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['configuration_name']
all_params.append('callback')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_configuration_by_name" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'configuration_name' is set
if ('configuration_name' not in params) or (params['configuration_name'] is None):
raise ValueError("Missing the required parameter `configuration_name` when calling `get_configuration_by_name`")
collection_formats = {}
resource_path = '/configuration/{configuration_name}'.replace('{format}', 'json')
path_params = {}
if 'configuration_name' in params:
path_params['configuration_name'] = params['configuration_name']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['ApiClientId', 'ApiClientSecret']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Configuration',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_configuration_logo(self, **kwargs):
"""
Get logo settings
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_logo(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:return: LogoSettings
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_configuration_logo_with_http_info(**kwargs)
else:
(data) = self.get_configuration_logo_with_http_info(**kwargs)
return data
def get_configuration_logo_with_http_info(self, **kwargs):
"""
Get logo settings
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_logo_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:return: LogoSettings
If the method is called asynchronously,
returns the request thread.
"""
all_params = []
all_params.append('callback')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_configuration_logo" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
resource_path = '/configuration/logo'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['ApiClientId', 'ApiClientSecret']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='LogoSettings',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_configuration_social(self, **kwargs):
"""
Get social networks settings
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_social(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:return: SocialSettings
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_configuration_social_with_http_info(**kwargs)
else:
(data) = self.get_configuration_social_with_http_info(**kwargs)
return data
def get_configuration_social_with_http_info(self, **kwargs):
"""
Get social networks settings
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_configuration_social_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:return: SocialSettings
If the method is called asynchronously,
returns the request thread.
"""
all_params = []
all_params.append('callback')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_configuration_social" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
resource_path = '/configuration/social'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['ApiClientId', 'ApiClientSecret']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SocialSettings',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 42.592593
| 681
| 0.560137
| 2,055
| 21,850
| 5.677859
| 0.094891
| 0.068564
| 0.023997
| 0.030854
| 0.92578
| 0.908896
| 0.901354
| 0.889356
| 0.886099
| 0.853188
| 0
| 0.000506
| 0.366453
| 21,850
| 512
| 682
| 42.675781
| 0.842375
| 0.353638
| 0
| 0.762846
| 1
| 0
| 0.146463
| 0.041813
| 0
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| 0.043478
| false
| 0
| 0.027668
| 0
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| 0
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| null | 0
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| 1
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| 0
| 0
|
0
| 8
|
f392d6533c2242677ab03331ec8f267b7350e14c
| 150
|
py
|
Python
|
EOSS/dialogue/dialogue_functions.py
|
seakers/daphne-brain
|
1d703d468cd503a21395f986dd72e67b6e556451
|
[
"MIT"
] | null | null | null |
EOSS/dialogue/dialogue_functions.py
|
seakers/daphne-brain
|
1d703d468cd503a21395f986dd72e67b6e556451
|
[
"MIT"
] | null | null | null |
EOSS/dialogue/dialogue_functions.py
|
seakers/daphne-brain
|
1d703d468cd503a21395f986dd72e67b6e556451
|
[
"MIT"
] | null | null | null |
import EOSS.analyst.dialogue_functions as analyst
import EOSS.engineer.dialogue_functions as engineer
import EOSS.critic.dialogue_functions as critic
| 37.5
| 51
| 0.88
| 21
| 150
| 6.142857
| 0.380952
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| 0.44186
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| 150
| 3
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|
0
| 7
|
45eb30b727474e06ad7de8f47bece42b357feb19
| 68,596
|
py
|
Python
|
benchmarks/SimResults/micro_pinned_train_combos/cmpA_astarlbmtontoh264ref/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/micro_pinned_train_combos/cmpA_astarlbmtontoh264ref/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/micro_pinned_train_combos/cmpA_astarlbmtontoh264ref/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0691322,
'Subthreshold Leakage with power gating': 0.0259246},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.148091,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.319006,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 1.04574,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.268999,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.465808,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.267154,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.00196,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.105566,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.79587,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.197563,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00975141,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.115955,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0721177,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.313518,
'Execution Unit/Register Files/Runtime Dynamic': 0.0818691,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.320766,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.728094,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 2.53631,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000148537,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000148537,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000128592,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
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'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
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'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00103598,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00146164,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00145214,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0693285,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.40989,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.139797,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
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'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 6.8457,
'Instruction Fetch Unit/Runtime Dynamic': 0.447511,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
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'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.179189,
'L2/Runtime Dynamic': 0.109643,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
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'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
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'Load Store Unit/LoadQ/Runtime Dynamic': 0.0510257,
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'Load Store Unit/Peak Dynamic': 3.05624,
'Load Store Unit/Runtime Dynamic': 1.32511,
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'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
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'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Peak Dynamic': 0.509887,
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'Renaming Unit/FP Front End RAT/Area': 0.168486,
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'Renaming Unit/Free List/Area': 0.0414755,
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'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
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'Renaming Unit/Peak Dynamic': 4.56169,
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'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
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'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
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'Execution Unit/Area': 7.68434,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Gate Leakage': 0.120359,
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'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
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'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
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'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.10902,
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'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.458906,
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'Execution Unit/Integer ALUs/Peak Dynamic': 0.153146,
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'Execution Unit/Peak Dynamic': 4.12249,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00561652,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0406144,
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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0406144,
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'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
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'Execution Unit/Runtime Dynamic': 1.35899,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.00049359,
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'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000596691,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00439835,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0104419,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0399312,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.53997,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.103132,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.135624,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.88175,
'Instruction Fetch Unit/Runtime Dynamic': 0.293528,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0179347,
'L2/Runtime Dynamic': 0.00412594,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.45962,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.592373,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0395509,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0395508,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.64639,
'Load Store Unit/Runtime Dynamic': 0.826975,
'Load Store Unit/StoreQ/Area': 0.322079,
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'Load Store Unit/StoreQ/Peak Dynamic': 0.0975257,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.195051,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Gate Leakage': 0.00808595,
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'Memory Management Unit/Peak Dynamic': 0.373492,
'Memory Management Unit/Runtime Dynamic': 0.0517893,
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'Peak Dynamic': 15.6315,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.0755594,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.61097,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
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'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.349956,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.176646,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.743568,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
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'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.200119,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.79873,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0591804,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00910049,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0937184,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0673037,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.152899,
'Execution Unit/Register Files/Runtime Dynamic': 0.0764042,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.214551,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.545431,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 2.02722,
'Execution Unit/Subthreshold Leakage': 1.79543,
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'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00131772,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00131772,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00118858,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000482458,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000966823,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00479084,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0111749,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0647007,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.11552,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.182521,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.219753,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 6.53377,
'Instruction Fetch Unit/Runtime Dynamic': 0.48294,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0164893,
'L2/Runtime Dynamic': 0.00395448,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 3.60185,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.14314,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0765046,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0765047,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 3.96312,
'Load Store Unit/Runtime Dynamic': 1.59694,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.188647,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.377295,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0669515,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0671128,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.255888,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0301776,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.527008,
'Memory Management Unit/Runtime Dynamic': 0.0972903,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 19.4286,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.155677,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.0116834,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.108384,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.275744,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 4.48409,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 4.15664e-05,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202721,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.000273244,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.315513,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.508911,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.256881,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.0813,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.360813,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.52957,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 5.16216e-05,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.013234,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0957123,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0978738,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0957639,
'Execution Unit/Register Files/Runtime Dynamic': 0.111108,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.201649,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.634113,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 2.43462,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00151715,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00151715,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00133974,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000528652,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00140596,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00578,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.013892,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0940885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.98484,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.25818,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.319567,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.49381,
'Instruction Fetch Unit/Runtime Dynamic': 0.691508,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0171255,
'L2/Runtime Dynamic': 0.00564793,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 4.71436,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.67758,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.112497,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.112497,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.2456,
'Load Store Unit/Runtime Dynamic': 2.34487,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.277399,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.554798,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0984497,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0986844,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.372115,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0423912,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.697344,
'Memory Management Unit/Runtime Dynamic': 0.141076,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 22.5729,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.000135479,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.0142367,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.166599,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.180971,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 5.7987,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 6.432711566366688,
'Runtime Dynamic': 6.432711566366688,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.294712,
'Runtime Dynamic': 0.197683,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 79.8763,
'Peak Power': 112.989,
'Runtime Dynamic': 18.4151,
'Subthreshold Leakage': 31.5774,
'Subthreshold Leakage with power gating': 13.9484,
'Total Cores/Area': 128.669,
'Total Cores/Gate Leakage': 1.4798,
'Total Cores/Peak Dynamic': 79.5816,
'Total Cores/Runtime Dynamic': 18.2175,
'Total Cores/Subthreshold Leakage': 24.7074,
'Total Cores/Subthreshold Leakage with power gating': 10.2429,
'Total L3s/Area': 61.9075,
'Total L3s/Gate Leakage': 0.0484137,
'Total L3s/Peak Dynamic': 0.294712,
'Total L3s/Runtime Dynamic': 0.197683,
'Total L3s/Subthreshold Leakage': 6.80085,
'Total L3s/Subthreshold Leakage with power gating': 3.32364,
'Total Leakage': 33.1122,
'Total NoCs/Area': 1.33155,
'Total NoCs/Gate Leakage': 0.00662954,
'Total NoCs/Peak Dynamic': 0.0,
'Total NoCs/Runtime Dynamic': 0.0,
'Total NoCs/Subthreshold Leakage': 0.0691322,
'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}
| 75.050328
| 124
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| 0
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| 0.224372
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| 914
| 125
| 75.050328
| 0.746734
| 0
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| 0.642232
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| 0.048107
| 0
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| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
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|
0
| 7
|
45edd7033b846987d942d379e515a1d60b2d5568
| 11,253
|
py
|
Python
|
tests/ecdsa/secp256k1_test.py
|
jaschadub/pycoin
|
1e8d0d9fe20ce0347b97847bb529cd1bd84c7442
|
[
"MIT"
] | 1,210
|
2015-01-02T13:36:28.000Z
|
2022-03-30T00:52:22.000Z
|
tests/ecdsa/secp256k1_test.py
|
impactog/pycoin
|
3db6f82afa3054d8d07caca4909e1aed3de2fceb
|
[
"MIT"
] | 280
|
2015-01-05T23:16:47.000Z
|
2022-02-22T22:02:17.000Z
|
tests/ecdsa/secp256k1_test.py
|
impactog/pycoin
|
3db6f82afa3054d8d07caca4909e1aed3de2fceb
|
[
"MIT"
] | 459
|
2015-01-10T00:15:57.000Z
|
2022-03-16T12:04:40.000Z
|
import unittest
from pycoin.ecdsa.secp256k1 import secp256k1_generator
from pycoin.key.Key import Key, InvalidPublicPairError
# From <https://crypto.stackexchange.com/questions/784/are-there-any-secp256k1-ecdsa-test-examples-available>
VECTORS = """
k = 1
x = 0x79BE667EF9DCBBAC55A06295CE870B07029BFCDB2DCE28D959F2815B16F81798
y = 0x483ADA7726A3C4655DA4FBFC0E1108A8FD17B448A68554199C47D08FFB10D4B8
k = 2
x = 0xC6047F9441ED7D6D3045406E95C07CD85C778E4B8CEF3CA7ABAC09B95C709EE5
y = 0x1AE168FEA63DC339A3C58419466CEAEEF7F632653266D0E1236431A950CFE52A
k = 3
x = 0xF9308A019258C31049344F85F89D5229B531C845836F99B08601F113BCE036F9
y = 0x388F7B0F632DE8140FE337E62A37F3566500A99934C2231B6CB9FD7584B8E672
k = 4
x = 0xE493DBF1C10D80F3581E4904930B1404CC6C13900EE0758474FA94ABE8C4CD13
y = 0x51ED993EA0D455B75642E2098EA51448D967AE33BFBDFE40CFE97BDC47739922
k = 5
x = 0x2F8BDE4D1A07209355B4A7250A5C5128E88B84BDDC619AB7CBA8D569B240EFE4
y = 0xD8AC222636E5E3D6D4DBA9DDA6C9C426F788271BAB0D6840DCA87D3AA6AC62D6
k = 6
x = 0xFFF97BD5755EEEA420453A14355235D382F6472F8568A18B2F057A1460297556
y = 0xAE12777AACFBB620F3BE96017F45C560DE80F0F6518FE4A03C870C36B075F297
k = 7
x = 0x5CBDF0646E5DB4EAA398F365F2EA7A0E3D419B7E0330E39CE92BDDEDCAC4F9BC
y = 0x6AEBCA40BA255960A3178D6D861A54DBA813D0B813FDE7B5A5082628087264DA
k = 8
x = 0x2F01E5E15CCA351DAFF3843FB70F3C2F0A1BDD05E5AF888A67784EF3E10A2A01
y = 0x5C4DA8A741539949293D082A132D13B4C2E213D6BA5B7617B5DA2CB76CBDE904
k = 9
x = 0xACD484E2F0C7F65309AD178A9F559ABDE09796974C57E714C35F110DFC27CCBE
y = 0xCC338921B0A7D9FD64380971763B61E9ADD888A4375F8E0F05CC262AC64F9C37
k = 10
x = 0xA0434D9E47F3C86235477C7B1AE6AE5D3442D49B1943C2B752A68E2A47E247C7
y = 0x893ABA425419BC27A3B6C7E693A24C696F794C2ED877A1593CBEE53B037368D7
k = 11
x = 0x774AE7F858A9411E5EF4246B70C65AAC5649980BE5C17891BBEC17895DA008CB
y = 0xD984A032EB6B5E190243DD56D7B7B365372DB1E2DFF9D6A8301D74C9C953C61B
k = 12
x = 0xD01115D548E7561B15C38F004D734633687CF4419620095BC5B0F47070AFE85A
y = 0xA9F34FFDC815E0D7A8B64537E17BD81579238C5DD9A86D526B051B13F4062327
k = 13
x = 0xF28773C2D975288BC7D1D205C3748651B075FBC6610E58CDDEEDDF8F19405AA8
y = 0x0AB0902E8D880A89758212EB65CDAF473A1A06DA521FA91F29B5CB52DB03ED81
k = 14
x = 0x499FDF9E895E719CFD64E67F07D38E3226AA7B63678949E6E49B241A60E823E4
y = 0xCAC2F6C4B54E855190F044E4A7B3D464464279C27A3F95BCC65F40D403A13F5B
k = 15
x = 0xD7924D4F7D43EA965A465AE3095FF41131E5946F3C85F79E44ADBCF8E27E080E
y = 0x581E2872A86C72A683842EC228CC6DEFEA40AF2BD896D3A5C504DC9FF6A26B58
k = 16
x = 0xE60FCE93B59E9EC53011AABC21C23E97B2A31369B87A5AE9C44EE89E2A6DEC0A
y = 0xF7E3507399E595929DB99F34F57937101296891E44D23F0BE1F32CCE69616821
k = 17
x = 0xDEFDEA4CDB677750A420FEE807EACF21EB9898AE79B9768766E4FAA04A2D4A34
y = 0x4211AB0694635168E997B0EAD2A93DAECED1F4A04A95C0F6CFB199F69E56EB77
k = 18
x = 0x5601570CB47F238D2B0286DB4A990FA0F3BA28D1A319F5E7CF55C2A2444DA7CC
y = 0xC136C1DC0CBEB930E9E298043589351D81D8E0BC736AE2A1F5192E5E8B061D58
k = 19
x = 0x2B4EA0A797A443D293EF5CFF444F4979F06ACFEBD7E86D277475656138385B6C
y = 0x85E89BC037945D93B343083B5A1C86131A01F60C50269763B570C854E5C09B7A
k = 20
x = 0x4CE119C96E2FA357200B559B2F7DD5A5F02D5290AFF74B03F3E471B273211C97
y = 0x12BA26DCB10EC1625DA61FA10A844C676162948271D96967450288EE9233DC3A
k = 112233445566778899
x = 0xA90CC3D3F3E146DAADFC74CA1372207CB4B725AE708CEF713A98EDD73D99EF29
y = 0x5A79D6B289610C68BC3B47F3D72F9788A26A06868B4D8E433E1E2AD76FB7DC76
k = 112233445566778899112233445566778899
x = 0xE5A2636BCFD412EBF36EC45B19BFB68A1BC5F8632E678132B885F7DF99C5E9B3
y = 0x736C1CE161AE27B405CAFD2A7520370153C2C861AC51D6C1D5985D9606B45F39
k = 28948022309329048855892746252171976963209391069768726095651290785379540373584
x = 0xA6B594B38FB3E77C6EDF78161FADE2041F4E09FD8497DB776E546C41567FEB3C
y = 0x71444009192228730CD8237A490FEBA2AFE3D27D7CC1136BC97E439D13330D55
k = 57896044618658097711785492504343953926418782139537452191302581570759080747168
x = 0x00000000000000000000003B78CE563F89A0ED9414F5AA28AD0D96D6795F9C63
y = 0x3F3979BF72AE8202983DC989AEC7F2FF2ED91BDD69CE02FC0700CA100E59DDF3
k = 86844066927987146567678238756515930889628173209306178286953872356138621120752
x = 0xE24CE4BEEE294AA6350FAA67512B99D388693AE4E7F53D19882A6EA169FC1CE1
y = 0x8B71E83545FC2B5872589F99D948C03108D36797C4DE363EBD3FF6A9E1A95B10
k = 115792089237316195423570985008687907852837564279074904382605163141518161494317
x = 0x4CE119C96E2FA357200B559B2F7DD5A5F02D5290AFF74B03F3E471B273211C97
y = 0xED45D9234EF13E9DA259E05EF57BB3989E9D6B7D8E269698BAFD77106DCC1FF5
k = 115792089237316195423570985008687907852837564279074904382605163141518161494318
x = 0x2B4EA0A797A443D293EF5CFF444F4979F06ACFEBD7E86D277475656138385B6C
y = 0x7A17643FC86BA26C4CBCF7C4A5E379ECE5FE09F3AFD9689C4A8F37AA1A3F60B5
k = 115792089237316195423570985008687907852837564279074904382605163141518161494319
x = 0x5601570CB47F238D2B0286DB4A990FA0F3BA28D1A319F5E7CF55C2A2444DA7CC
y = 0x3EC93E23F34146CF161D67FBCA76CAE27E271F438C951D5E0AE6D1A074F9DED7
k = 115792089237316195423570985008687907852837564279074904382605163141518161494320
x = 0xDEFDEA4CDB677750A420FEE807EACF21EB9898AE79B9768766E4FAA04A2D4A34
y = 0xBDEE54F96B9CAE9716684F152D56C251312E0B5FB56A3F09304E660861A910B8
k = 115792089237316195423570985008687907852837564279074904382605163141518161494321
x = 0xE60FCE93B59E9EC53011AABC21C23E97B2A31369B87A5AE9C44EE89E2A6DEC0A
y = 0x081CAF8C661A6A6D624660CB0A86C8EFED6976E1BB2DC0F41E0CD330969E940E
k = 115792089237316195423570985008687907852837564279074904382605163141518161494322
x = 0xD7924D4F7D43EA965A465AE3095FF41131E5946F3C85F79E44ADBCF8E27E080E
y = 0xA7E1D78D57938D597C7BD13DD733921015BF50D427692C5A3AFB235F095D90D7
k = 115792089237316195423570985008687907852837564279074904382605163141518161494323
x = 0x499FDF9E895E719CFD64E67F07D38E3226AA7B63678949E6E49B241A60E823E4
y = 0x353D093B4AB17AAE6F0FBB1B584C2B9BB9BD863D85C06A4339A0BF2AFC5EBCD4
k = 115792089237316195423570985008687907852837564279074904382605163141518161494324
x = 0xF28773C2D975288BC7D1D205C3748651B075FBC6610E58CDDEEDDF8F19405AA8
y = 0xF54F6FD17277F5768A7DED149A3250B8C5E5F925ADE056E0D64A34AC24FC0EAE
k = 115792089237316195423570985008687907852837564279074904382605163141518161494325
x = 0xD01115D548E7561B15C38F004D734633687CF4419620095BC5B0F47070AFE85A
y = 0x560CB00237EA1F285749BAC81E8427EA86DC73A2265792AD94FAE4EB0BF9D908
k = 115792089237316195423570985008687907852837564279074904382605163141518161494326
x = 0x774AE7F858A9411E5EF4246B70C65AAC5649980BE5C17891BBEC17895DA008CB
y = 0x267B5FCD1494A1E6FDBC22A928484C9AC8D24E1D20062957CFE28B3536AC3614
k = 115792089237316195423570985008687907852837564279074904382605163141518161494327
x = 0xA0434D9E47F3C86235477C7B1AE6AE5D3442D49B1943C2B752A68E2A47E247C7
y = 0x76C545BDABE643D85C4938196C5DB3969086B3D127885EA6C3411AC3FC8C9358
k = 115792089237316195423570985008687907852837564279074904382605163141518161494328
x = 0xACD484E2F0C7F65309AD178A9F559ABDE09796974C57E714C35F110DFC27CCBE
y = 0x33CC76DE4F5826029BC7F68E89C49E165227775BC8A071F0FA33D9D439B05FF8
k = 115792089237316195423570985008687907852837564279074904382605163141518161494329
x = 0x2F01E5E15CCA351DAFF3843FB70F3C2F0A1BDD05E5AF888A67784EF3E10A2A01
y = 0xA3B25758BEAC66B6D6C2F7D5ECD2EC4B3D1DEC2945A489E84A25D3479342132B
k = 115792089237316195423570985008687907852837564279074904382605163141518161494330
x = 0x5CBDF0646E5DB4EAA398F365F2EA7A0E3D419B7E0330E39CE92BDDEDCAC4F9BC
y = 0x951435BF45DAA69F5CE8729279E5AB2457EC2F47EC02184A5AF7D9D6F78D9755
k = 115792089237316195423570985008687907852837564279074904382605163141518161494331
x = 0xFFF97BD5755EEEA420453A14355235D382F6472F8568A18B2F057A1460297556
y = 0x51ED8885530449DF0C4169FE80BA3A9F217F0F09AE701B5FC378F3C84F8A0998
k = 115792089237316195423570985008687907852837564279074904382605163141518161494332
x = 0x2F8BDE4D1A07209355B4A7250A5C5128E88B84BDDC619AB7CBA8D569B240EFE4
y = 0x2753DDD9C91A1C292B24562259363BD90877D8E454F297BF235782C459539959
k = 115792089237316195423570985008687907852837564279074904382605163141518161494333
x = 0xE493DBF1C10D80F3581E4904930B1404CC6C13900EE0758474FA94ABE8C4CD13
y = 0xAE1266C15F2BAA48A9BD1DF6715AEBB7269851CC404201BF30168422B88C630D
k = 115792089237316195423570985008687907852837564279074904382605163141518161494334
x = 0xF9308A019258C31049344F85F89D5229B531C845836F99B08601F113BCE036F9
y = 0xC77084F09CD217EBF01CC819D5C80CA99AFF5666CB3DDCE4934602897B4715BD
k = 115792089237316195423570985008687907852837564279074904382605163141518161494335
x = 0xC6047F9441ED7D6D3045406E95C07CD85C778E4B8CEF3CA7ABAC09B95C709EE5
y = 0xE51E970159C23CC65C3A7BE6B99315110809CD9ACD992F1EDC9BCE55AF301705
k = 115792089237316195423570985008687907852837564279074904382605163141518161494336
x = 0x79BE667EF9DCBBAC55A06295CE870B07029BFCDB2DCE28D959F2815B16F81798
y = 0xB7C52588D95C3B9AA25B0403F1EEF75702E84BB7597AABE663B82F6F04EF2777
k = 0xaa5e28d6a97a2479a65527f7290311a3624d4cc0fa1578598ee3c2613bf99522
x = 0x34f9460f0e4f08393d192b3c5133a6ba099aa0ad9fd54ebccfacdfa239ff49c6
y = 0x0b71ea9bd730fd8923f6d25a7a91e7dd7728a960686cb5a901bb419e0f2ca232
k = 0x7e2b897b8cebc6361663ad410835639826d590f393d90a9538881735256dfae3
x = 0xd74bf844b0862475103d96a611cf2d898447e288d34b360bc885cb8ce7c00575
y = 0x131c670d414c4546b88ac3ff664611b1c38ceb1c21d76369d7a7a0969d61d97d
k = 0x6461e6df0fe7dfd05329f41bf771b86578143d4dd1f7866fb4ca7e97c5fa945d
x = 0xe8aecc370aedd953483719a116711963ce201ac3eb21d3f3257bb48668c6a72f
y = 0xc25caf2f0eba1ddb2f0f3f47866299ef907867b7d27e95b3873bf98397b24ee1
k = 0x376a3a2cdcd12581efff13ee4ad44c4044b8a0524c42422a7e1e181e4deeccec
x = 0x14890e61fcd4b0bd92e5b36c81372ca6fed471ef3aa60a3e415ee4fe987daba1
y = 0x297b858d9f752ab42d3bca67ee0eb6dcd1c2b7b0dbe23397e66adc272263f982
k = 0x1b22644a7be026548810c378d0b2994eefa6d2b9881803cb02ceff865287d1b9
x = 0xf73c65ead01c5126f28f442d087689bfa08e12763e0cec1d35b01751fd735ed3
y = 0xf449a8376906482a84ed01479bd18882b919c140d638307f0c0934ba12590bde
"""
class Secp256k1Test(unittest.TestCase):
def test_multiply(self):
for f1 in [15, 10000, 73**38]:
for f2 in [2, 3, 78192, 71**39]:
k1 = f1 * secp256k1_generator
k2 = (f1 * f2) * secp256k1_generator
self.assertEqual(f2 * k1, k2)
def test_bad_0_generator(self):
self.assertRaises(ValueError, lambda: secp256k1_generator.Point(0, 0))
def test_bad_0_key(self):
class MyKey(Key):
_generator = secp256k1_generator
self.assertRaises(InvalidPublicPairError, MyKey, public_pair=(0, 0))
def inject():
for triplets in VECTORS.strip().split("\n\n"):
k_line, x_line, y_line = triplets.split("\n")
if k_line.startswith("k = ") and x_line.startswith("x = ") and y_line.startswith("y = "):
if k_line[4:].startswith("0x"):
k = int(k_line[4:], 16)
else:
k = int(k_line[4:])
x = int(x_line[4:], 16)
y = int(y_line[4:], 16)
name_of_f = "test_vector_%d" % k
def make_test(k, x, y):
def the_test(self):
self.assertEqual(k * secp256k1_generator, (x, y))
return the_test
setattr(Secp256k1Test, name_of_f, make_test(k, x, y))
else:
print("WARNING: bad vector %s" % repr(triplets))
inject()
| 44.478261
| 109
| 0.881632
| 514
| 11,253
| 19.231518
| 0.404669
| 0.010926
| 0.001821
| 0.002226
| 0.004249
| 0
| 0
| 0
| 0
| 0
| 0
| 0.614704
| 0.086199
| 11,253
| 252
| 110
| 44.654762
| 0.346591
| 0.009509
| 0
| 0.224599
| 0
| 0
| 0.867014
| 0.785804
| 0
| 1
| 0.621859
| 0
| 0.02139
| 1
| 0.032086
| false
| 0
| 0.016043
| 0
| 0.064171
| 0.005348
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3460b305e33fa89a712f1881b7350679a02561cf
| 80
|
py
|
Python
|
pystsup/evolutionary/selection/__init__.py
|
rithinch/GeneticAlgorithms-StudentSupervisorAllocation
|
cbfec0dad469323e49a9affb0cfa550dda5e5a4f
|
[
"MIT"
] | 12
|
2019-08-30T18:42:05.000Z
|
2022-03-26T15:26:44.000Z
|
pystsup/evolutionary/selection/__init__.py
|
rithinch/GeneticAlgorithms-StudentSupervisorAllocation
|
cbfec0dad469323e49a9affb0cfa550dda5e5a4f
|
[
"MIT"
] | 6
|
2019-09-18T19:28:39.000Z
|
2022-02-04T19:09:07.000Z
|
pystsup/evolutionary/selection/__init__.py
|
rithinch/GeneticAlgorithms-StudentSupervisorAllocation
|
cbfec0dad469323e49a9affb0cfa550dda5e5a4f
|
[
"MIT"
] | 2
|
2020-12-21T11:32:29.000Z
|
2021-06-12T14:49:29.000Z
|
from .selection import tournamentSelection
from .selection import rouletteWheel
| 26.666667
| 42
| 0.875
| 8
| 80
| 8.75
| 0.625
| 0.371429
| 0.542857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 80
| 2
| 43
| 40
| 0.972222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
34665277770c47cacf08731f763c566f39d30b38
| 1,419
|
py
|
Python
|
cytology/models.py
|
rodrigoncalves/anato-hub
|
bb6c5a39bfbc247476a6ef4fe410c972b5e561c4
|
[
"MIT"
] | null | null | null |
cytology/models.py
|
rodrigoncalves/anato-hub
|
bb6c5a39bfbc247476a6ef4fe410c972b5e561c4
|
[
"MIT"
] | null | null | null |
cytology/models.py
|
rodrigoncalves/anato-hub
|
bb6c5a39bfbc247476a6ef4fe410c972b5e561c4
|
[
"MIT"
] | null | null | null |
# -*- conding: utf-8 -*-
from django.db import models
from exam.models import Exam, ReportStatus
class CytologyStatus(models.Model):
description = models.CharField(max_length=50)
class Cytology(models.Model):
clinical_information = models.CharField(
max_length=255, null=True, blank=True)
quantity = models.CharField(
max_length=255, null=True, blank=True)
microscopic = models.CharField(
max_length=255, null=True, blank=True)
conclusion = models.CharField(
max_length=255, null=True, blank=True)
note = models.CharField(
max_length=255, null=True, blank=True)
footer = models.CharField(
max_length=255, null=True, blank=True)
status = models.ForeignKey(CytologyStatus, default=1)
exam = models.ForeignKey(Exam)
class CytologyReport(models.Model):
clinical_information = models.CharField(
max_length=255, null=True, blank=True)
quantity = models.CharField(
max_length=255, null=True, blank=True)
microscopic = models.CharField(
max_length=255, null=True, blank=True)
conclusion = models.CharField(
max_length=255, null=True, blank=True)
note = models.CharField(
max_length=255, null=True, blank=True)
footer = models.CharField(
max_length=255, null=True, blank=True)
status = models.ForeignKey(ReportStatus)
cytology = models.ForeignKey(Cytology)
| 33
| 57
| 0.694151
| 172
| 1,419
| 5.639535
| 0.203488
| 0.201031
| 0.241237
| 0.321649
| 0.731959
| 0.731959
| 0.731959
| 0.731959
| 0.731959
| 0.731959
| 0
| 0.035088
| 0.196617
| 1,419
| 42
| 58
| 33.785714
| 0.815789
| 0.015504
| 0
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.058824
| 0
| 0.647059
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
347e899d01e13be89be459eaa46013119572a084
| 194
|
py
|
Python
|
npt/isis/mosaic.py
|
chbrandt/gpt-neanias
|
aa7c2e88972f9af280b7f02ee11170df6c967b55
|
[
"MIT"
] | 2
|
2020-09-28T08:22:54.000Z
|
2020-09-28T13:17:25.000Z
|
npt/isis/mosaic.py
|
chbrandt/gpt-neanias
|
aa7c2e88972f9af280b7f02ee11170df6c967b55
|
[
"MIT"
] | null | null | null |
npt/isis/mosaic.py
|
chbrandt/gpt-neanias
|
aa7c2e88972f9af280b7f02ee11170df6c967b55
|
[
"MIT"
] | null | null | null |
from npt import log
from . import sh
def mosaic(filename_mapcubs_list='mapcubs.list', filename_mosaic='mosaic.cub'):
isissh.automos(FROMLIST=filename_mapcubs_list, MOSAIC=filename_mosaic)
| 27.714286
| 79
| 0.804124
| 27
| 194
| 5.555556
| 0.518519
| 0.22
| 0.253333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097938
| 194
| 6
| 80
| 32.333333
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0.113402
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ca9f46b44f89e354033846a04c1c9a50ab244110
| 227
|
py
|
Python
|
spider/cmdline.py
|
tangchengdong/tang_project
|
b236f7c0029b6514747e51e1efdc8d9d710a0b94
|
[
"MIT"
] | 1
|
2018-04-04T02:47:01.000Z
|
2018-04-04T02:47:01.000Z
|
spider/cmdline.py
|
tangchengdong/tang_project
|
b236f7c0029b6514747e51e1efdc8d9d710a0b94
|
[
"MIT"
] | null | null | null |
spider/cmdline.py
|
tangchengdong/tang_project
|
b236f7c0029b6514747e51e1efdc8d9d710a0b94
|
[
"MIT"
] | null | null | null |
import scrapy.cmdline
if __name__ == '__main__':
#scrapy.cmdline.execute(argv=['scrapy','crawl','dmoz'])
# scrapy.cmdline.execute("scrapy crawl by --nolog".split())
scrapy.cmdline.execute("scrapy crawl by".split())
| 37.833333
| 63
| 0.69163
| 28
| 227
| 5.321429
| 0.464286
| 0.348993
| 0.402685
| 0.348993
| 0.442953
| 0.442953
| 0
| 0
| 0
| 0
| 0
| 0
| 0.118943
| 227
| 6
| 64
| 37.833333
| 0.745
| 0.493392
| 0
| 0
| 0
| 0
| 0.201754
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
cac20425c7d2c84dc9972658fa7752ebdaf353c9
| 9,793
|
py
|
Python
|
tests/to_jsonschema/test_pattern_properties.py
|
pglass/py-openapi-jsonschema-converter
|
ca84a49fc1e218ae4686d4ca6ee89781ac26c213
|
[
"MIT"
] | 12
|
2018-12-10T19:11:47.000Z
|
2021-08-02T13:55:04.000Z
|
tests/to_jsonschema/test_pattern_properties.py
|
pglass/py-openapi-jsonschema-converter
|
ca84a49fc1e218ae4686d4ca6ee89781ac26c213
|
[
"MIT"
] | 6
|
2018-07-06T19:36:47.000Z
|
2020-07-27T18:43:50.000Z
|
tests/to_jsonschema/test_pattern_properties.py
|
pglass/py-openapi-jsonschema-converter
|
ca84a49fc1e218ae4686d4ca6ee89781ac26c213
|
[
"MIT"
] | 3
|
2018-07-05T23:06:18.000Z
|
2020-07-11T03:32:07.000Z
|
from openapi_schema_to_json_schema import to_json_schema as convert
def test_handling_additional_properties_of_the_same_type_string():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'string'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": False,
"patternProperties": {
'^[a-z]*$': {
"type": 'string'
}
}
}
assert result == expected
def test_handling_additional_properties_of_the_same_type_number():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'number'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'number'
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": False,
"patternProperties": {
'^[a-z]*$': {
"type": 'number'
}
}
}
assert result == expected
def test_handling_additional_properties_with_one_of_patternProperty_types():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'number'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'number'
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": False,
"patternProperties": {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'number'
}
}
}
assert result == expected
def test_handling_additionalProperties_with_matching_objects():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'object',
"properties": {
"test": {
"type": 'string'
}
}
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'object',
"properties": {
"test": {
"type": 'string'
}
}
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": False,
"patternProperties": {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'object',
"properties": {
"test": {
"type": 'string'
}
}
}
}
}
assert result == expected
def test_handling_additionalProperties_with_non_matching_objects():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'object',
"properties": {
"test": {
"type": 'string'
}
}
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'object',
"properties": {
"test": {
"type": 'integer'
}
}
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": {
"type": 'object',
"properties": {
"test": {
"type": 'string'
}
}
},
"patternProperties": {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'object',
"properties": {
"test": {
"type": 'integer'
}
}
}
}
}
assert result == expected
def test_handling_additionalProperties_with_matching_array():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'array',
"items": {
"type": 'string'
}
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'array',
"items": {
"type": 'string'
}
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": False,
"patternProperties": {
'^[a-z]*$': {
"type": 'string'
},
'^[A-Z]*$': {
"type": 'array',
"items": {
"type": 'string'
}
}
}
}
assert result == expected
def test_handling_additionalProperties_with_composition_types():
schema = {
"type": 'object',
"additionalProperties": {
"oneOf": [
{
"type": 'string'
},
{
"type": 'integer'
}
]
},
'x-patternProperties': {
'^[a-z]*$': {
"oneOf": [
{
"type": 'string'
},
{
"type": 'integer'
}
]
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": False,
"patternProperties": {
'^[a-z]*$': {
"oneOf": [
{
"type": 'string'
},
{
"type": 'integer'
}
]
}
}
}
assert result == expected
def test_not_supporting_patternProperties():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'string'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
result = convert(schema, {"supportPatternProperties": False})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": {
"type": 'string'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
assert result == expected
def test_not_supporting_patternProperties_by_default():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'string'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
result = convert(schema)
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": {
"type": 'string'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
assert result == expected
def test_setting_custom_pattern_properties_handler():
schema = {
"type": 'object',
"additionalProperties": {
"type": 'string'
},
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
def handler(sch):
sch['patternProperties'] = False
return sch
options = {
"supportPatternProperties": True,
"patternPropertiesHandler": handler,
}
result = convert(schema, options)
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": {
"type": 'string'
},
"patternProperties": False
}
assert result == expected
def test_additionalProperties_not_modified_if_set_to_true():
schema = {
"type": 'object',
"additionalProperties": True,
'x-patternProperties': {
'^[a-z]*$': {
"type": 'string'
}
}
}
result = convert(schema, {"supportPatternProperties": True})
expected = {
"$schema": 'http://json-schema.org/draft-04/schema#',
"type": 'object',
"additionalProperties": True,
"patternProperties": {
'^[a-z]*$': {
"type": 'string'
}
}
}
assert result == expected
| 25.110256
| 76
| 0.396508
| 607
| 9,793
| 6.280066
| 0.102142
| 0.094439
| 0.042497
| 0.207765
| 0.903987
| 0.853358
| 0.853358
| 0.853358
| 0.796695
| 0.747901
| 0
| 0.004083
| 0.449811
| 9,793
| 389
| 77
| 25.174807
| 0.703415
| 0
| 0
| 0.61157
| 0
| 0
| 0.282753
| 0.026958
| 0
| 0
| 0
| 0
| 0.030303
| 1
| 0.033058
| false
| 0
| 0.002755
| 0
| 0.038567
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1b5caf2724fa5a3a0baa6368bbb5b8f374b979b1
| 152
|
py
|
Python
|
xdg.py
|
wingerse/audio_adversarial_examples
|
f747efea967e64351e9d5dc1b36fa1ca8d52b066
|
[
"BSD-2-Clause"
] | null | null | null |
xdg.py
|
wingerse/audio_adversarial_examples
|
f747efea967e64351e9d5dc1b36fa1ca8d52b066
|
[
"BSD-2-Clause"
] | null | null | null |
xdg.py
|
wingerse/audio_adversarial_examples
|
f747efea967e64351e9d5dc1b36fa1ca8d52b066
|
[
"BSD-2-Clause"
] | null | null | null |
# Even more hacks.
class BaseDirectory:
def save_data_path(*args, **kwargs):
return
def save_data_path(*args, **kwargs):
return
| 21.714286
| 40
| 0.638158
| 19
| 152
| 4.894737
| 0.631579
| 0.150538
| 0.236559
| 0.322581
| 0.666667
| 0.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0.25
| 152
| 7
| 41
| 21.714286
| 0.815789
| 0.105263
| 0
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| true
| 0
| 0
| 0.4
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 9
|
1b7582a056f9458324d580fd175fd8cc3a71e3cc
| 99
|
py
|
Python
|
data_reader/__init__.py
|
xyvivian/adlib
|
79a93baa8aa542080bbf55734168eb89317df83c
|
[
"MIT"
] | null | null | null |
data_reader/__init__.py
|
xyvivian/adlib
|
79a93baa8aa542080bbf55734168eb89317df83c
|
[
"MIT"
] | null | null | null |
data_reader/__init__.py
|
xyvivian/adlib
|
79a93baa8aa542080bbf55734168eb89317df83c
|
[
"MIT"
] | null | null | null |
import scipy
# import data_reader
# from data_reader import input
# from data_reader import output
| 19.8
| 32
| 0.818182
| 15
| 99
| 5.2
| 0.466667
| 0.384615
| 0.358974
| 0.512821
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151515
| 99
| 4
| 33
| 24.75
| 0.928571
| 0.79798
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
1b8d56bc9873e0695f6f7443b706954f7a454b71
| 79
|
py
|
Python
|
tests/test_import.py
|
mdmix4/pymdmix
|
52ab6a2cde241267348d1d502e821e5d9359d018
|
[
"MIT"
] | null | null | null |
tests/test_import.py
|
mdmix4/pymdmix
|
52ab6a2cde241267348d1d502e821e5d9359d018
|
[
"MIT"
] | 1
|
2021-02-23T18:20:09.000Z
|
2021-03-13T18:27:19.000Z
|
tests/test_import.py
|
mdmix4/pymdmix-plugin-template
|
166ba41cbb82a525ea1cae249e9ca3c5ed11d6ef
|
[
"MIT"
] | null | null | null |
def test_import():
import pymdmix_core
assert pymdmix_core is not None
| 19.75
| 35
| 0.746835
| 12
| 79
| 4.666667
| 0.75
| 0.392857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.21519
| 79
| 3
| 36
| 26.333333
| 0.903226
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.666667
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
94221183a4298f8464ed046bf43f5f552dcad09a
| 247
|
py
|
Python
|
scripts/item/consume_2435674.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 54
|
2019-04-16T23:24:48.000Z
|
2021-12-18T11:41:50.000Z
|
scripts/item/consume_2435674.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 3
|
2019-05-19T15:19:41.000Z
|
2020-04-27T16:29:16.000Z
|
scripts/item/consume_2435674.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 49
|
2020-11-25T23:29:16.000Z
|
2022-03-26T16:20:24.000Z
|
# Created by MechAviv
# Resistance Water Warrior Damage Skin | (2435674)
if sm.addDamageSkin(2435674):
sm.chat("'Resistance Water Warrior Damage Skin' Damage Skin has been added to your account's damage skin collection.")
sm.consumeItem()
| 49.4
| 123
| 0.757085
| 34
| 247
| 5.5
| 0.647059
| 0.213904
| 0.235294
| 0.299465
| 0.342246
| 0
| 0
| 0
| 0
| 0
| 0
| 0.067308
| 0.157895
| 247
| 5
| 124
| 49.4
| 0.831731
| 0.275304
| 0
| 0
| 0
| 0.333333
| 0.610169
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
8479b975b905f1634ea0dd07900185b4b42e1879
| 149,023
|
py
|
Python
|
scipy/sparse/sparsetools/bsr.py
|
tomspur/scipy
|
5309706537dbd96e0409f890a20fc6f5badfbac3
|
[
"BSD-3-Clause"
] | null | null | null |
scipy/sparse/sparsetools/bsr.py
|
tomspur/scipy
|
5309706537dbd96e0409f890a20fc6f5badfbac3
|
[
"BSD-3-Clause"
] | null | null | null |
scipy/sparse/sparsetools/bsr.py
|
tomspur/scipy
|
5309706537dbd96e0409f890a20fc6f5badfbac3
|
[
"BSD-3-Clause"
] | null | null | null |
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 2.0.10
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import dirname
import imp
fp = None
try:
fp, pathname, description = imp.find_module('_bsr', [dirname(__file__)])
except ImportError:
import _bsr
return _bsr
if fp is not None:
try:
_mod = imp.load_module('_bsr', fp, pathname, description)
finally:
fp.close()
return _mod
_bsr = swig_import_helper()
del swig_import_helper
else:
import _bsr
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
if (name == "thisown"): return self.this.own(value)
if (name == "this"):
if type(value).__name__ == 'SwigPyObject':
self.__dict__[name] = value
return
method = class_type.__swig_setmethods__.get(name,None)
if method: return method(self,value)
if (not static):
self.__dict__[name] = value
else:
raise AttributeError("You cannot add attributes to %s" % self)
def _swig_setattr(self,class_type,name,value):
return _swig_setattr_nondynamic(self,class_type,name,value,0)
def _swig_getattr(self,class_type,name):
if (name == "thisown"): return self.this.own()
method = class_type.__swig_getmethods__.get(name,None)
if method: return method(self)
raise AttributeError(name)
def _swig_repr(self):
try: strthis = "proxy of " + self.this.__repr__()
except: strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
try:
_object = object
_newclass = 1
except AttributeError:
class _object : pass
_newclass = 0
def bsr_diagonal(*args):
"""
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_bool_wrapper [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax,
signed char [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
unsigned char [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, short [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
unsigned short [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, int [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
unsigned int [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, long long [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
unsigned long long [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, float [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, double [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax,
long double [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_cfloat_wrapper [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_cdouble_wrapper [] Yx)
bsr_diagonal(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_clongdouble_wrapper [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_bool_wrapper [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax,
signed char [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
unsigned char [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, short [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
unsigned short [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, int [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
unsigned int [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, long long [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
unsigned long long [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, float [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, double [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax,
long double [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_cfloat_wrapper [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_cdouble_wrapper [] Yx)
bsr_diagonal(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_clongdouble_wrapper [] Yx)
"""
return _bsr.bsr_diagonal(*args)
def bsr_scale_rows(*args):
"""
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper [] Ax,
npy_bool_wrapper const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char [] Ax, signed char const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char [] Ax, unsigned char const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short [] Ax, short const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short [] Ax, unsigned short const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int [] Ax, int const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int [] Ax, unsigned int const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long [] Ax, long long const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long [] Ax,
unsigned long long const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float [] Ax, float const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double [] Ax, double const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double [] Ax, long double const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper [] Ax,
npy_cfloat_wrapper const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper [] Ax,
npy_cdouble_wrapper const [] Xx)
bsr_scale_rows(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper [] Ax,
npy_clongdouble_wrapper const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper [] Ax,
npy_bool_wrapper const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char [] Ax, signed char const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char [] Ax, unsigned char const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short [] Ax, short const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short [] Ax, unsigned short const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int [] Ax, int const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int [] Ax, unsigned int const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long [] Ax, long long const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long [] Ax,
unsigned long long const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float [] Ax, float const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double [] Ax, double const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double [] Ax, long double const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper [] Ax,
npy_cfloat_wrapper const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper [] Ax,
npy_cdouble_wrapper const [] Xx)
bsr_scale_rows(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper [] Ax,
npy_clongdouble_wrapper const [] Xx)
"""
return _bsr.bsr_scale_rows(*args)
def bsr_scale_columns(*args):
"""
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper [] Ax,
npy_bool_wrapper const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char [] Ax, signed char const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char [] Ax, unsigned char const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short [] Ax, short const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short [] Ax, unsigned short const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int [] Ax, int const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int [] Ax, unsigned int const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long [] Ax, long long const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long [] Ax,
unsigned long long const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float [] Ax, float const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double [] Ax, double const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double [] Ax, long double const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper [] Ax,
npy_cfloat_wrapper const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper [] Ax,
npy_cdouble_wrapper const [] Xx)
bsr_scale_columns(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper [] Ax,
npy_clongdouble_wrapper const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper [] Ax,
npy_bool_wrapper const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char [] Ax, signed char const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char [] Ax, unsigned char const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short [] Ax, short const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short [] Ax, unsigned short const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int [] Ax, int const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int [] Ax, unsigned int const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long [] Ax, long long const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long [] Ax,
unsigned long long const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float [] Ax, float const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double [] Ax, double const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double [] Ax, long double const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper [] Ax,
npy_cfloat_wrapper const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper [] Ax,
npy_cdouble_wrapper const [] Xx)
bsr_scale_columns(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper [] Ax,
npy_clongdouble_wrapper const [] Xx)
"""
return _bsr.bsr_scale_columns(*args)
def bsr_transpose(*args):
"""
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, npy_bool_wrapper [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, signed char [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, unsigned char [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 [] Bp,
npy_int32 [] Bj, short [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, unsigned short [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 [] Bp,
npy_int32 [] Bj, int [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, unsigned int [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 [] Bp,
npy_int32 [] Bj, long long [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, unsigned long long [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 [] Bp,
npy_int32 [] Bj, float [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 [] Bp,
npy_int32 [] Bj, double [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, long double [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, npy_cfloat_wrapper [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, npy_cdouble_wrapper [] Bx)
bsr_transpose(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 [] Bp, npy_int32 [] Bj, npy_clongdouble_wrapper [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, npy_bool_wrapper [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, signed char [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, unsigned char [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 [] Bp,
npy_int64 [] Bj, short [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, unsigned short [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 [] Bp,
npy_int64 [] Bj, int [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, unsigned int [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 [] Bp,
npy_int64 [] Bj, long long [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, unsigned long long [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 [] Bp,
npy_int64 [] Bj, float [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 [] Bp,
npy_int64 [] Bj, double [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, long double [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, npy_cfloat_wrapper [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, npy_cdouble_wrapper [] Bx)
bsr_transpose(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 [] Bp, npy_int64 [] Bj, npy_clongdouble_wrapper [] Bx)
"""
return _bsr.bsr_transpose(*args)
def bsr_matmat_pass2(*args):
"""
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, signed char const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, signed char [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned char [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, short [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned short [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, int const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, int [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned int [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, long long [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned long long [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, float const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, float [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, double const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, double [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, long double const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, long double [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_matmat_pass2(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const N, npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj,
npy_clongdouble_wrapper const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj, npy_clongdouble_wrapper [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, signed char const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, signed char [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned char [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, short [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned short [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, int const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, int [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned int [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, long long [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned long long [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, float const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, float [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, double const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, double [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, long double const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, long double [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_matmat_pass2(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const N, npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj,
npy_clongdouble_wrapper const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj, npy_clongdouble_wrapper [] Cx)
"""
return _bsr.bsr_matmat_pass2(*args)
def bsr_matvec(*args):
"""
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_bool_wrapper const [] Xx, npy_bool_wrapper [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax,
signed char const [] Xx, signed char [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
unsigned char const [] Xx, unsigned char [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, short const [] Xx,
short [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
unsigned short const [] Xx, unsigned short [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, int const [] Xx,
int [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
unsigned int const [] Xx, unsigned int [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, long long const [] Xx,
long long [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
unsigned long long const [] Xx, unsigned long long [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, float const [] Xx,
float [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, double const [] Xx,
double [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax,
long double const [] Xx, long double [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_cfloat_wrapper const [] Xx, npy_cfloat_wrapper [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_cdouble_wrapper const [] Xx, npy_cdouble_wrapper [] Yx)
bsr_matvec(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_clongdouble_wrapper const [] Xx, npy_clongdouble_wrapper [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_bool_wrapper const [] Xx, npy_bool_wrapper [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax,
signed char const [] Xx, signed char [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
unsigned char const [] Xx, unsigned char [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, short const [] Xx,
short [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
unsigned short const [] Xx, unsigned short [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, int const [] Xx,
int [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
unsigned int const [] Xx, unsigned int [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, long long const [] Xx,
long long [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
unsigned long long const [] Xx, unsigned long long [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, float const [] Xx,
float [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, double const [] Xx,
double [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax,
long double const [] Xx, long double [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_cfloat_wrapper const [] Xx, npy_cfloat_wrapper [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_cdouble_wrapper const [] Xx, npy_cdouble_wrapper [] Yx)
bsr_matvec(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_clongdouble_wrapper const [] Xx, npy_clongdouble_wrapper [] Yx)
"""
return _bsr.bsr_matvec(*args)
def bsr_matvecs(*args):
"""
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
npy_bool_wrapper const [] Ax, npy_bool_wrapper const [] Xx, npy_bool_wrapper [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
signed char const [] Ax, signed char const [] Xx, signed char [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
unsigned char const [] Ax, unsigned char const [] Xx, unsigned char [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
short const [] Ax, short const [] Xx, short [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
unsigned short const [] Ax, unsigned short const [] Xx, unsigned short [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
int const [] Ax, int const [] Xx, int [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
unsigned int const [] Ax, unsigned int const [] Xx, unsigned int [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
long long const [] Ax, long long const [] Xx, long long [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
unsigned long long const [] Ax, unsigned long long const [] Xx, unsigned long long [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
float const [] Ax, float const [] Xx, float [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
double const [] Ax, double const [] Xx, double [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
long double const [] Ax, long double const [] Xx, long double [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
npy_cfloat_wrapper const [] Ax, npy_cfloat_wrapper const [] Xx, npy_cfloat_wrapper [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
npy_cdouble_wrapper const [] Ax, npy_cdouble_wrapper const [] Xx, npy_cdouble_wrapper [] Yx)
bsr_matvecs(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const n_vecs, npy_int32 const R,
npy_int32 const C, npy_int32 const [] Ap, npy_int32 const [] Aj,
npy_clongdouble_wrapper const [] Ax, npy_clongdouble_wrapper const [] Xx, npy_clongdouble_wrapper [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
npy_bool_wrapper const [] Ax, npy_bool_wrapper const [] Xx, npy_bool_wrapper [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
signed char const [] Ax, signed char const [] Xx, signed char [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
unsigned char const [] Ax, unsigned char const [] Xx, unsigned char [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
short const [] Ax, short const [] Xx, short [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
unsigned short const [] Ax, unsigned short const [] Xx, unsigned short [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
int const [] Ax, int const [] Xx, int [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
unsigned int const [] Ax, unsigned int const [] Xx, unsigned int [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
long long const [] Ax, long long const [] Xx, long long [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
unsigned long long const [] Ax, unsigned long long const [] Xx, unsigned long long [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
float const [] Ax, float const [] Xx, float [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
double const [] Ax, double const [] Xx, double [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
long double const [] Ax, long double const [] Xx, long double [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
npy_cfloat_wrapper const [] Ax, npy_cfloat_wrapper const [] Xx, npy_cfloat_wrapper [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
npy_cdouble_wrapper const [] Ax, npy_cdouble_wrapper const [] Xx, npy_cdouble_wrapper [] Yx)
bsr_matvecs(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const n_vecs, npy_int64 const R,
npy_int64 const C, npy_int64 const [] Ap, npy_int64 const [] Aj,
npy_clongdouble_wrapper const [] Ax, npy_clongdouble_wrapper const [] Xx, npy_clongdouble_wrapper [] Yx)
"""
return _bsr.bsr_matvecs(*args)
def bsr_elmul_bsr(*args):
"""
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, signed char [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned char [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, short [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned short [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
int [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned int [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long long [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned long long [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, float [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, double [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long double [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_elmul_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_clongdouble_wrapper [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, signed char [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned char [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, short [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned short [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
int [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned int [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long long [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned long long [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, float [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, double [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long double [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_elmul_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_clongdouble_wrapper [] Cx)
"""
return _bsr.bsr_elmul_bsr(*args)
def bsr_eldiv_bsr(*args):
"""
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, signed char [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned char [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, short [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned short [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
int [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned int [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long long [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned long long [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, float [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, double [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long double [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_eldiv_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_clongdouble_wrapper [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, signed char [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned char [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, short [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned short [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
int [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned int [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long long [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned long long [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, float [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, double [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long double [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_eldiv_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_clongdouble_wrapper [] Cx)
"""
return _bsr.bsr_eldiv_bsr(*args)
def bsr_plus_bsr(*args):
"""
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, signed char [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned char [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, short [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned short [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
int [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned int [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long long [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned long long [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, float [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, double [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long double [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_plus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_clongdouble_wrapper [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, signed char [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned char [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, short [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned short [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
int [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned int [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long long [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned long long [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, float [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, double [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long double [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_plus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_clongdouble_wrapper [] Cx)
"""
return _bsr.bsr_plus_bsr(*args)
def bsr_minus_bsr(*args):
"""
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, signed char [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned char [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, short [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned short [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
int [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, unsigned int [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long long [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, unsigned long long [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, float [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, double [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, long double [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_minus_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_clongdouble_wrapper [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, signed char [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned char [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, short [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned short [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
int [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, unsigned int [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long long [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, unsigned long long [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, float [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, double [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, long double [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cfloat_wrapper [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_cdouble_wrapper [] Cx)
bsr_minus_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_clongdouble_wrapper [] Cx)
"""
return _bsr.bsr_minus_bsr(*args)
def bsr_sort_indices(*args):
"""
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, npy_bool_wrapper [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, signed char [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, unsigned char [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, short [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, unsigned short [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, int [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, unsigned int [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, long long [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, unsigned long long [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, float [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, double [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, long double [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, npy_cfloat_wrapper [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, npy_cdouble_wrapper [] Ax)
bsr_sort_indices(npy_int32 const n_brow, npy_int32 const n_bcol, npy_int32 const R, npy_int32 const C,
npy_int32 [] Ap, npy_int32 [] Aj, npy_clongdouble_wrapper [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, npy_bool_wrapper [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, signed char [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, unsigned char [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, short [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, unsigned short [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, int [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, unsigned int [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, long long [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, unsigned long long [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, float [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, double [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, long double [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, npy_cfloat_wrapper [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, npy_cdouble_wrapper [] Ax)
bsr_sort_indices(npy_int64 const n_brow, npy_int64 const n_bcol, npy_int64 const R, npy_int64 const C,
npy_int64 [] Ap, npy_int64 [] Aj, npy_clongdouble_wrapper [] Ax)
"""
return _bsr.bsr_sort_indices(*args)
def bsr_ne_bsr(*args):
"""
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ne_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
"""
return _bsr.bsr_ne_bsr(*args)
def bsr_lt_bsr(*args):
"""
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_lt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
"""
return _bsr.bsr_lt_bsr(*args)
def bsr_gt_bsr(*args):
"""
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_gt_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
"""
return _bsr.bsr_gt_bsr(*args)
def bsr_le_bsr(*args):
"""
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_le_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
"""
return _bsr.bsr_le_bsr(*args)
def bsr_ge_bsr(*args):
"""
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, signed char const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, signed char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned char const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned char const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, short const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, short const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned short const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned short const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, int const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, int const [] Bx, npy_int32 [] Cp, npy_int32 [] Cj,
npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned int const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned int const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long long const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long long const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, unsigned long long const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, unsigned long long const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, float const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, float const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, long double const [] Ax, npy_int32 const [] Bp,
npy_int32 const [] Bj, long double const [] Bx, npy_int32 [] Cp,
npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int32 const n_row, npy_int32 const n_col, npy_int32 const R, npy_int32 const C,
npy_int32 const [] Ap, npy_int32 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int32 const [] Bp, npy_int32 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int32 [] Cp, npy_int32 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_bool_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_bool_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, signed char const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, signed char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned char const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned char const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, short const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, short const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned short const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned short const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, int const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, int const [] Bx, npy_int64 [] Cp, npy_int64 [] Cj,
npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned int const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned int const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long long const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long long const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, unsigned long long const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, unsigned long long const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, float const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, float const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, long double const [] Ax, npy_int64 const [] Bp,
npy_int64 const [] Bj, long double const [] Bx, npy_int64 [] Cp,
npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cfloat_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cfloat_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_cdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_cdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
bsr_ge_bsr(npy_int64 const n_row, npy_int64 const n_col, npy_int64 const R, npy_int64 const C,
npy_int64 const [] Ap, npy_int64 const [] Aj, npy_clongdouble_wrapper const [] Ax,
npy_int64 const [] Bp, npy_int64 const [] Bj, npy_clongdouble_wrapper const [] Bx,
npy_int64 [] Cp, npy_int64 [] Cj, npy_bool_wrapper [] Cx)
"""
return _bsr.bsr_ge_bsr(*args)
# This file is compatible with both classic and new-style classes.
| 78.104298
| 112
| 0.676775
| 24,839
| 149,023
| 3.760981
| 0.006482
| 0.187542
| 0.254659
| 0.080926
| 0.983183
| 0.982284
| 0.982177
| 0.981171
| 0.978752
| 0.975562
| 0
| 0.076691
| 0.229569
| 149,023
| 1,907
| 113
| 78.145254
| 0.736979
| 0.932178
| 0
| 0.11236
| 1
| 0
| 0.030917
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.247191
| false
| 0.044944
| 0.101124
| 0.011236
| 0.606742
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 11
|
ca5d43f0b4820f127a7e33ebe004b77b0782793f
| 191
|
py
|
Python
|
accounts/views.py
|
jubayer-hossain/real-estate-app
|
b2999f5b0d6311aeaa87275f7c979a9573f2b4ed
|
[
"MIT"
] | null | null | null |
accounts/views.py
|
jubayer-hossain/real-estate-app
|
b2999f5b0d6311aeaa87275f7c979a9573f2b4ed
|
[
"MIT"
] | null | null | null |
accounts/views.py
|
jubayer-hossain/real-estate-app
|
b2999f5b0d6311aeaa87275f7c979a9573f2b4ed
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
def register(request):
return render(request, 'accounts/register.html')
def dashboard(request):
return render(request, 'accounts/dashboard.html')
| 27.285714
| 53
| 0.769634
| 23
| 191
| 6.391304
| 0.521739
| 0.176871
| 0.258503
| 0.353742
| 0.462585
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120419
| 191
| 7
| 53
| 27.285714
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0.234375
| 0.234375
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.2
| 0.4
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
ca7b84e958a365ff7419d334404bef634e40ac73
| 34,007
|
py
|
Python
|
com/vmware/nsx/directory/domains_client.py
|
adammillerio/vsphere-automation-sdk-python
|
c07e1be98615201139b26c28db3aa584c4254b66
|
[
"MIT"
] | null | null | null |
com/vmware/nsx/directory/domains_client.py
|
adammillerio/vsphere-automation-sdk-python
|
c07e1be98615201139b26c28db3aa584c4254b66
|
[
"MIT"
] | null | null | null |
com/vmware/nsx/directory/domains_client.py
|
adammillerio/vsphere-automation-sdk-python
|
c07e1be98615201139b26c28db3aa584c4254b66
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
#---------------------------------------------------------------------------
# Copyright 2020 VMware, Inc. All rights reserved.
# AUTO GENERATED FILE -- DO NOT MODIFY!
#
# vAPI stub file for package com.vmware.nsx.directory.domains.
#---------------------------------------------------------------------------
"""
"""
__author__ = 'VMware, Inc.'
__docformat__ = 'restructuredtext en'
import sys
from vmware.vapi.bindings import type
from vmware.vapi.bindings.converter import TypeConverter
from vmware.vapi.bindings.enum import Enum
from vmware.vapi.bindings.error import VapiError
from vmware.vapi.bindings.struct import VapiStruct
from vmware.vapi.bindings.stub import (
ApiInterfaceStub, StubFactoryBase, VapiInterface)
from vmware.vapi.bindings.common import raise_core_exception
from vmware.vapi.data.validator import (UnionValidator, HasFieldsOfValidator)
from vmware.vapi.exception import CoreException
from vmware.vapi.lib.constants import TaskType
from vmware.vapi.lib.rest import OperationRestMetadata
class Groups(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.directory.domains.groups'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _GroupsStub)
self._VAPI_OPERATION_IDS = {}
def list(self,
domain_id,
filter_value,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Search for directory groups within a domain based on the substring of a
distinguished name. (e.g. CN=User,DC=acme,DC=com) The search filter
pattern can optionally support multiple (up to 100 maximum) search
pattern separated by '|' (url encoded %7C). In this case, the search
results will be returned as the union of all matching criteria. (e.g.
CN=Ann,CN=Users,DC=acme,DC=com|CN=Bob,CN=Users,DC=acme,DC=com)
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type filter_value: :class:`str`
:param filter_value: Name search filter value (required)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.DirectoryGroupListResults`
:return: com.vmware.nsx.model.DirectoryGroupListResults
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'domain_id': domain_id,
'filter_value': filter_value,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class LdapServers(VapiInterface):
"""
"""
CREATE_0_ACTION_CONNECTIVITY = "CONNECTIVITY"
"""
Possible value for ``action`` of method :func:`LdapServers.create_0`.
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.directory.domains.ldap_servers'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _LdapServersStub)
self._VAPI_OPERATION_IDS = {}
def create(self,
domain_id,
directory_ldap_server,
):
"""
More than one LDAP server can be created and only one LDAP server is
used to synchronize directory objects. If more than one LDAP server is
configured, NSX will try all the servers until it is able to
successfully connect to one.
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type directory_ldap_server: :class:`com.vmware.nsx.model_client.DirectoryLdapServer`
:param directory_ldap_server: (required)
:rtype: :class:`com.vmware.nsx.model_client.DirectoryLdapServer`
:return: com.vmware.nsx.model.DirectoryLdapServer
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create',
{
'domain_id': domain_id,
'directory_ldap_server': directory_ldap_server,
})
def create_0(self,
domain_id,
server_id,
action,
):
"""
The API tests a LDAP server connection for an already configured
domain. If the connection is successful, the response will be HTTP
status 200. Otherwise the response will be HTTP status 500 and
corresponding error message will be returned.
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type server_id: :class:`str`
:param server_id: LDAP server identifier (required)
:type action: :class:`str`
:param action: LDAP server test requested (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create_0',
{
'domain_id': domain_id,
'server_id': server_id,
'action': action,
})
def delete(self,
domain_id,
server_id,
):
"""
Delete a LDAP server for directory domain
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type server_id: :class:`str`
:param server_id: LDAP server identifier (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('delete',
{
'domain_id': domain_id,
'server_id': server_id,
})
def get(self,
domain_id,
server_id,
):
"""
Get a specific LDAP server for a given directory domain
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type server_id: :class:`str`
:param server_id: LDAP server identifier (required)
:rtype: :class:`com.vmware.nsx.model_client.DirectoryLdapServer`
:return: com.vmware.nsx.model.DirectoryLdapServer
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'domain_id': domain_id,
'server_id': server_id,
})
def list(self,
domain_id,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
List all configured domain LDAP servers
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.DirectoryLdapServerListResults`
:return: com.vmware.nsx.model.DirectoryLdapServerListResults
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'domain_id': domain_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
def update(self,
domain_id,
server_id,
directory_ldap_server,
):
"""
Update a LDAP server for directory domain
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:type server_id: :class:`str`
:param server_id: LDAP server identifier (required)
:type directory_ldap_server: :class:`com.vmware.nsx.model_client.DirectoryLdapServer`
:param directory_ldap_server: (required)
:rtype: :class:`com.vmware.nsx.model_client.DirectoryLdapServer`
:return: com.vmware.nsx.model.DirectoryLdapServer
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('update',
{
'domain_id': domain_id,
'server_id': server_id,
'directory_ldap_server': directory_ldap_server,
})
class SyncStats(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.directory.domains.sync_stats'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _SyncStatsStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
domain_id,
):
"""
Get domain sync statistics for the given identifier
:type domain_id: :class:`str`
:param domain_id: Directory domain identifier (required)
:rtype: :class:`com.vmware.nsx.model_client.DirectoryDomainSyncStats`
:return: com.vmware.nsx.model.DirectoryDomainSyncStats
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'domain_id': domain_id,
})
class _GroupsStub(ApiInterfaceStub):
def __init__(self, config):
# properties for list operation
list_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'filter_value': type.StringType(),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
HasFieldsOfValidator()
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/directory/domains/{domain-id}/groups',
path_variables={
'domain_id': 'domain-id',
},
query_parameters={
'filter_value': 'filter_value',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryGroupListResults'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.directory.domains.groups',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _LdapServersStub(ApiInterfaceStub):
def __init__(self, config):
# properties for create operation
create_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'directory_ldap_server': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryLdapServer'),
})
create_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_input_value_validator_list = [
]
create_output_validator_list = [
]
create_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/directory/domains/{domain-id}/ldap-servers',
request_body_parameter='directory_ldap_server',
path_variables={
'domain_id': 'domain-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for create_0 operation
create_0_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'server_id': type.StringType(),
'action': type.StringType(),
})
create_0_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_0_input_value_validator_list = [
]
create_0_output_validator_list = [
]
create_0_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/directory/domains/{domain-id}/ldap-servers/{server-id}',
path_variables={
'domain_id': 'domain-id',
'server_id': 'server-id',
},
query_parameters={
'action': 'action',
},
content_type='application/json'
)
# properties for delete operation
delete_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'server_id': type.StringType(),
})
delete_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
delete_input_value_validator_list = [
]
delete_output_validator_list = [
]
delete_rest_metadata = OperationRestMetadata(
http_method='DELETE',
url_template='/api/v1/directory/domains/{domain-id}/ldap-servers/{server-id}',
path_variables={
'domain_id': 'domain-id',
'server_id': 'server-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'server_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/directory/domains/{domain-id}/ldap-servers/{server-id}',
path_variables={
'domain_id': 'domain-id',
'server_id': 'server-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/directory/domains/{domain-id}/ldap-servers',
path_variables={
'domain_id': 'domain-id',
},
query_parameters={
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
# properties for update operation
update_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
'server_id': type.StringType(),
'directory_ldap_server': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryLdapServer'),
})
update_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
update_input_value_validator_list = [
]
update_output_validator_list = [
]
update_rest_metadata = OperationRestMetadata(
http_method='PUT',
url_template='/api/v1/directory/domains/{domain-id}/ldap-servers/{server-id}',
request_body_parameter='directory_ldap_server',
path_variables={
'domain_id': 'domain-id',
'server_id': 'server-id',
},
query_parameters={
},
content_type='application/json'
)
operations = {
'create': {
'input_type': create_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryLdapServer'),
'errors': create_error_dict,
'input_value_validator_list': create_input_value_validator_list,
'output_validator_list': create_output_validator_list,
'task_type': TaskType.NONE,
},
'create_0': {
'input_type': create_0_input_type,
'output_type': type.VoidType(),
'errors': create_0_error_dict,
'input_value_validator_list': create_0_input_value_validator_list,
'output_validator_list': create_0_output_validator_list,
'task_type': TaskType.NONE,
},
'delete': {
'input_type': delete_input_type,
'output_type': type.VoidType(),
'errors': delete_error_dict,
'input_value_validator_list': delete_input_value_validator_list,
'output_validator_list': delete_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryLdapServer'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryLdapServerListResults'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
'update': {
'input_type': update_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryLdapServer'),
'errors': update_error_dict,
'input_value_validator_list': update_input_value_validator_list,
'output_validator_list': update_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'create': create_rest_metadata,
'create_0': create_0_rest_metadata,
'delete': delete_rest_metadata,
'get': get_rest_metadata,
'list': list_rest_metadata,
'update': update_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.directory.domains.ldap_servers',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _SyncStatsStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'domain_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/directory/domains/{domain-id}/sync-stats',
path_variables={
'domain_id': 'domain-id',
},
query_parameters={
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DirectoryDomainSyncStats'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.directory.domains.sync_stats',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class StubFactory(StubFactoryBase):
_attrs = {
'Groups': Groups,
'LdapServers': LdapServers,
'SyncStats': SyncStats,
'groups': 'com.vmware.nsx.directory.domains.groups_client.StubFactory',
}
| 43.046835
| 115
| 0.59029
| 3,385
| 34,007
| 5.703102
| 0.079173
| 0.06993
| 0.080808
| 0.099456
| 0.877389
| 0.850557
| 0.817353
| 0.785185
| 0.769179
| 0.75452
| 0
| 0.002014
| 0.299085
| 34,007
| 789
| 116
| 43.101394
| 0.807896
| 0.272826
| 0
| 0.648703
| 1
| 0.007984
| 0.30195
| 0.19965
| 0
| 0
| 0
| 0
| 0
| 1
| 0.027944
| false
| 0
| 0.023952
| 0
| 0.091816
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
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