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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
276fbd1be1b3fa7a07902c9991a92c375d8ec021 | 75,279 | py | Python | cardano-node-tests/cardano_node_tests/tests/test_transactions.py | MitchellTesla/Cardano-SCK | f394506eb0875622093805c009951f6905261778 | [
"Apache-2.0"
] | 6 | 2021-08-30T00:49:12.000Z | 2022-01-27T07:07:53.000Z | cardano-node-tests/cardano_node_tests/tests/test_transactions.py | c-spider/Cardano-SCK | 1accb0426289489e371eb67422ccb19ffaab5f3c | [
"Apache-2.0"
] | 17 | 2021-08-31T23:27:44.000Z | 2022-03-25T20:35:16.000Z | cardano-node-tests/cardano_node_tests/tests/test_transactions.py | c-spider/Cardano-SCK | 1accb0426289489e371eb67422ccb19ffaab5f3c | [
"Apache-2.0"
] | 3 | 2021-05-20T08:26:00.000Z | 2022-03-27T22:31:36.000Z | """Tests for general transactions.
* transfering funds (from 1 address to many, many to 1, many to many)
* not balanced transactions
* other negative tests like duplicated transaction, sending funds to wrong addresses,
wrong fee, wrong ttl
* transactions with metadata
* transactions with many UTxOs
"""
import functools
import itertools
import json
import logging
import random
import string
import time
from pathlib import Path
from typing import List
from typing import Tuple
import allure
import cbor2
import hypothesis
import hypothesis.strategies as st
import pytest
from _pytest.tmpdir import TempdirFactory
from cardano_clusterlib import clusterlib
from cardano_node_tests.utils import cluster_management
from cardano_node_tests.utils import clusterlib_utils
from cardano_node_tests.utils import dbsync_utils
from cardano_node_tests.utils import helpers
from cardano_node_tests.utils.versions import VERSIONS
LOGGER = logging.getLogger(__name__)
DATA_DIR = Path(__file__).parent / "data"
ADDR_ALPHABET = list(f"{string.ascii_lowercase}{string.digits}")
@pytest.fixture(scope="module")
def create_temp_dir(tmp_path_factory: TempdirFactory):
"""Create a temporary dir."""
p = Path(tmp_path_factory.getbasetemp()).joinpath(helpers.get_id_for_mktemp(__file__)).resolve()
p.mkdir(exist_ok=True, parents=True)
return p
@pytest.fixture
def temp_dir(create_temp_dir: Path):
"""Change to a temporary dir."""
with helpers.change_cwd(create_temp_dir):
yield create_temp_dir
# use the "temp_dir" fixture for all tests automatically
pytestmark = pytest.mark.usefixtures("temp_dir")
def _get_raw_tx_values(
cluster_obj: clusterlib.ClusterLib,
tx_name: str,
src_record: clusterlib.AddressRecord,
dst_record: clusterlib.AddressRecord,
temp_dir: Path,
) -> clusterlib.TxRawOutput:
"""Get values for building raw TX using `clusterlib.build_raw_tx_bare`."""
src_address = src_record.address
dst_address = dst_record.address
tx_files = clusterlib.TxFiles(signing_key_files=[src_record.skey_file])
ttl = cluster_obj.calculate_tx_ttl()
fee = cluster_obj.calculate_tx_fee(
src_address=src_address,
tx_name=tx_name,
dst_addresses=[dst_address],
tx_files=tx_files,
ttl=ttl,
)
src_addr_highest_utxo = cluster_obj.get_utxo_with_highest_amount(src_address)
# use only the UTxO with highest amount
txins = [src_addr_highest_utxo]
txouts = [
clusterlib.TxOut(address=dst_address, amount=src_addr_highest_utxo.amount - fee),
]
out_file = temp_dir / f"{helpers.get_timestamped_rand_str()}_tx.body"
return clusterlib.TxRawOutput(
txins=txins,
txouts=txouts,
tx_files=tx_files,
out_file=out_file,
fee=fee,
invalid_hereafter=ttl,
)
def _get_txins_txouts(
txins: List[clusterlib.UTXOData], txouts: List[clusterlib.TxOut]
) -> Tuple[List[str], List[str]]:
txins_combined = [f"{x.utxo_hash}#{x.utxo_ix}" for x in txins]
txouts_combined = [f"{x.address}+{x.amount}" for x in txouts]
return txins_combined, txouts_combined
@pytest.mark.testnets
class TestBasic:
"""Test basic transactions - transfering funds, transaction IDs."""
@pytest.fixture
def payment_addrs(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
) -> List[clusterlib.AddressRecord]:
"""Create 2 new payment addresses."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
addrs = clusterlib_utils.create_payment_addr_records(
f"addr_basic_ci{cluster_manager.cluster_instance_num}_0",
f"addr_basic_ci{cluster_manager.cluster_instance_num}_1",
cluster_obj=cluster,
)
fixture_cache.value = addrs
# fund source addresses
clusterlib_utils.fund_from_faucet(
*addrs,
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
)
return addrs
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
@pytest.mark.parametrize("amount", (1, 10, 200, 2000, 100_000))
def test_transfer_funds(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
amount: int,
):
"""Send funds to payment address.
* send funds from 1 source address to 1 destination address
* check expected balances for both source and destination addresses
"""
temp_template = f"{helpers.get_func_name()}_{amount}"
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
tx_raw_output = cluster.send_funds(
src_address=src_address,
destinations=destinations,
tx_name=temp_template,
tx_files=tx_files,
)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - tx_raw_output.fee - len(destinations) * amount
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address) == dst_init_balance + amount
), f"Incorrect balance for destination address `{dst_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_transfer_all_funds(
self, cluster: clusterlib.ClusterLib, payment_addrs: List[clusterlib.AddressRecord]
):
"""Send ALL funds from one payment address to another.
* send all available funds from 1 source address to 1 destination address
* check expected balance for destination addresses
* check that balance for source address is 0 Lovelace
"""
temp_template = helpers.get_func_name()
src_address = payment_addrs[1].address
dst_address = payment_addrs[0].address
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
# amount value -1 means all available funds
destinations = [clusterlib.TxOut(address=dst_address, amount=-1)]
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[1].skey_file])
tx_raw_output = cluster.send_funds(
src_address=src_address,
destinations=destinations,
tx_name=temp_template,
tx_files=tx_files,
)
assert (
cluster.get_address_balance(src_address) == 0
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address)
== dst_init_balance + src_init_balance - tx_raw_output.fee
), f"Incorrect balance for destination address `{dst_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_funds_to_valid_address(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
):
"""Send funds to a valid payment address.
The destination address is a valid address that was generated sometime
in the past. The test verifies it is possible to use a valid address
even though it was not generated while running a speciffic cardano
network.
* send funds from 1 source address to 1 destination address
* check expected balances for both source and destination addresses
"""
temp_template = helpers.get_func_name()
amount = 100
src_address = payment_addrs[0].address
dst_address = "addr_test1vpst87uzwafqkxumyf446zr2jsyn44cfpu9fe8yqanyuh6glj2hkl"
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
tx_raw_output = cluster.send_funds(
src_address=src_address,
destinations=destinations,
tx_name=temp_template,
tx_files=tx_files,
)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - tx_raw_output.fee - len(destinations) * amount
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address) == dst_init_balance + amount
), f"Incorrect balance for destination address `{dst_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_get_txid(
self, cluster: clusterlib.ClusterLib, payment_addrs: List[clusterlib.AddressRecord]
):
"""Get transaction ID (txid) from transaction body.
Transaction ID is a hash of transaction body and doesn't change for a signed TX.
* send funds from 1 source address to 1 destination address
* get txid from transaction body
* get txid from signed transaction
* check that txid from transaction body matches the txid from signed transaction
* check that txid has expected lenght
* check that the txid is listed in UTxO hashes for both source and destination addresses
"""
temp_template = helpers.get_func_name()
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
destinations = [clusterlib.TxOut(address=dst_address, amount=2000)]
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
tx_raw_output = cluster.send_funds(
src_address=src_address,
destinations=destinations,
tx_name=temp_template,
tx_files=tx_files,
)
txid_body = cluster.get_txid(tx_body_file=tx_raw_output.out_file)
txid_signed = cluster.get_txid(tx_file=tx_raw_output.out_file.with_suffix(".signed"))
assert txid_body == txid_signed
utxo_src = cluster.get_utxo(src_address)
utxo_dst = cluster.get_utxo(dst_address)
assert len(txid_body) == 64
assert txid_body in (u.utxo_hash for u in utxo_src)
assert txid_body in (u.utxo_hash for u in utxo_dst)
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_extra_signing_keys(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
):
"""Send a transaction with extra signing key.
Check that it is possible to use unneded signing key in addition to the necessary
signing keys for signing the transaction.
"""
temp_template = helpers.get_func_name()
amount = 100
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
# use extra signing key
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addrs[0].skey_file, payment_addrs[1].skey_file]
)
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
# it should be possible to submit a transaction with extra signing key
tx_raw_output = cluster.send_tx(
src_address=src_address, tx_name=temp_template, txouts=destinations, tx_files=tx_files
)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - tx_raw_output.fee - len(destinations) * amount
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address) == dst_init_balance + amount
), f"Incorrect balance for destination address `{dst_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_duplicate_signing_keys(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
):
"""Send a transaction with duplicate signing key.
Check that it is possible to specify the same signing key twice.
"""
temp_template = helpers.get_func_name()
amount = 100
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
# use extra signing key
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addrs[0].skey_file, payment_addrs[0].skey_file]
)
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
# it should be possible to submit a transaction with duplicate signing key
tx_raw_output = cluster.send_tx(
src_address=src_address, tx_name=temp_template, txouts=destinations, tx_files=tx_files
)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - tx_raw_output.fee - len(destinations) * amount
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address) == dst_init_balance + amount
), f"Incorrect balance for destination address `{dst_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_no_txout(
self,
cluster: clusterlib.ClusterLib,
cluster_manager: cluster_management.ClusterManager,
):
"""Send transaction with just fee, no UTxO is produced.
* submit a transaction where all funds available on source address is used for fee
* check that no UTxOs are created by the transaction
* check that there are no funds left on source address
"""
temp_template = helpers.get_func_name()
src_record = clusterlib_utils.create_payment_addr_records(
f"{temp_template}_0", cluster_obj=cluster
)[0]
clusterlib_utils.fund_from_faucet(
src_record,
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
amount=200_000,
)
tx_files = clusterlib.TxFiles(signing_key_files=[src_record.skey_file])
fee = cluster.get_address_balance(src_record.address)
tx_raw_output = cluster.send_tx(
src_address=src_record.address, tx_name=temp_template, tx_files=tx_files, fee=fee
)
assert not tx_raw_output.txouts, "Transaction has unexpected txouts"
assert (
cluster.get_address_balance(src_record.address) == 0
), f"Incorrect balance for source address `{src_record.address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
def test_missing_tx_out(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
temp_dir: Path,
):
"""Try to build a transaction with a missing `--tx-out` parameter.
Expect failure on node version < 1.24.2 commit 3d201869.
"""
temp_template = helpers.get_func_name()
tx_raw_output = _get_raw_tx_values(
cluster_obj=cluster,
tx_name=temp_template,
src_record=payment_addrs[0],
dst_record=payment_addrs[1],
temp_dir=temp_dir,
)
txins, __ = _get_txins_txouts(tx_raw_output.txins, tx_raw_output.txouts)
cli_args = [
"transaction",
"build-raw",
"--invalid-hereafter",
str(tx_raw_output.invalid_hereafter),
"--fee",
str(tx_raw_output.fee),
"--out-file",
str(tx_raw_output.out_file),
*cluster._prepend_flag("--tx-in", txins),
]
cluster.cli(cli_args)
@allure.link(helpers.get_vcs_link())
@pytest.mark.skipif(
VERSIONS.transaction_era == VERSIONS.SHELLEY,
reason="doesn't run with Shelley TX",
)
@pytest.mark.dbsync
def test_missing_ttl(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
temp_dir: Path,
):
"""Submit a transaction with a missing `--ttl` (`--invalid-hereafter`) parameter."""
temp_template = helpers.get_func_name()
src_address = payment_addrs[0].address
init_balance = cluster.get_address_balance(src_address)
tx_raw_template = _get_raw_tx_values(
cluster_obj=cluster,
tx_name=temp_template,
src_record=payment_addrs[0],
dst_record=payment_addrs[0],
temp_dir=temp_dir,
)
txins, txouts = _get_txins_txouts(tx_raw_template.txins, tx_raw_template.txouts)
tx_raw_output = tx_raw_template._replace(invalid_hereafter=None)
cluster.cli(
[
"transaction",
"build-raw",
"--fee",
str(tx_raw_output.fee),
"--out-file",
str(tx_raw_output.out_file),
*cluster._prepend_flag("--tx-in", txins),
*cluster._prepend_flag("--tx-out", txouts),
*cluster.tx_era_arg,
]
)
tx_signed_file = cluster.sign_tx(
tx_body_file=tx_raw_output.out_file,
tx_name=temp_template,
signing_key_files=[payment_addrs[0].skey_file],
)
cluster.submit_tx(tx_file=tx_signed_file, txins=tx_raw_output.txins)
assert (
cluster.get_address_balance(src_address) == init_balance - tx_raw_output.fee
), f"Incorrect balance for source address `{src_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@pytest.mark.testnets
class TestMultiInOut:
"""Test transactions with multiple txins and/or txouts."""
@pytest.fixture
def payment_addrs(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
) -> List[clusterlib.AddressRecord]:
"""Create 201 new payment addresses."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
addrs = clusterlib_utils.create_payment_addr_records(
*[
f"multi_in_out_addr_ci{cluster_manager.cluster_instance_num}_{i}"
for i in range(201)
],
cluster_obj=cluster,
)
fixture_cache.value = addrs
# fund source addresses
clusterlib_utils.fund_from_faucet(
addrs[0],
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
amount=100_000_000,
)
return addrs
def _from_to_transactions(
self,
cluster_obj: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
tx_name: str,
from_num: int,
to_num: int,
amount: int,
):
"""Test 1 tx from `from_num` payment addresses to `to_num` payment addresses."""
src_address = payment_addrs[0].address
# addr1..addr<from_num+1>
from_addr_recs = payment_addrs[1 : from_num + 1]
# addr<from_num+1>..addr<from_num+to_num+1>
dst_addresses = [
payment_addrs[i].address for i in range(from_num + 1, from_num + to_num + 1)
]
# fund "from" addresses
# Using `src_address` to fund the "from" addresses. In `send_tx`, all remaining change is
# returned to `src_address`, so it should always have enough funds. The "from" addresses has
# zero balance after each test.
fund_amount = int(amount * len(dst_addresses) / len(from_addr_recs))
fund_dst = [
clusterlib.TxOut(address=d.address, amount=fund_amount) for d in from_addr_recs[:-1]
]
# add more funds to the last "from" address so it can cover TX fee
last_from_addr_rec = from_addr_recs[-1]
fund_dst.append(
clusterlib.TxOut(address=last_from_addr_rec.address, amount=fund_amount + 5_000_000)
)
fund_tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
cluster_obj.send_funds(
src_address=src_address,
destinations=fund_dst,
tx_name=f"{tx_name}_add_funds",
tx_files=fund_tx_files,
)
# record initial balances
src_init_balance = cluster_obj.get_address_balance(src_address)
from_init_total_balance = functools.reduce(
lambda x, y: x + y,
(cluster_obj.get_address_balance(r.address) for r in from_addr_recs),
0,
)
dst_init_balances = {addr: cluster_obj.get_address_balance(addr) for addr in dst_addresses}
# create TX data
_txins = [cluster_obj.get_utxo(r.address) for r in from_addr_recs]
# flatten the list of lists that is _txins
txins = list(itertools.chain.from_iterable(_txins))
txouts = [clusterlib.TxOut(address=addr, amount=amount) for addr in dst_addresses]
tx_files = clusterlib.TxFiles(signing_key_files=[r.skey_file for r in from_addr_recs])
# send TX
tx_raw_output = cluster_obj.send_tx(
src_address=src_address, # change is returned to `src_address`
tx_name=tx_name,
txins=txins,
txouts=txouts,
tx_files=tx_files,
)
# check balances
from_final_balance = functools.reduce(
lambda x, y: x + y,
(cluster_obj.get_address_balance(r.address) for r in from_addr_recs),
0,
)
src_final_balance = cluster_obj.get_address_balance(src_address)
assert (
from_final_balance == 0
), f"The output addresses should have no balance, they have {from_final_balance}"
assert (
src_final_balance
== src_init_balance
+ from_init_total_balance
- tx_raw_output.fee
- amount * len(dst_addresses)
), f"Incorrect balance for source address `{src_address}`"
for addr in dst_addresses:
assert (
cluster_obj.get_address_balance(addr) == dst_init_balances[addr] + amount
), f"Incorrect balance for destination address `{addr}`"
dbsync_utils.check_tx(cluster_obj=cluster_obj, tx_raw_output=tx_raw_output)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_10_transactions(
self, cluster: clusterlib.ClusterLib, payment_addrs: List[clusterlib.AddressRecord]
):
"""Send 10 transactions to payment address.
* send funds from 1 source address to 1 destination address in 10 separate transactions
* check expected balances for both source and destination addresses
"""
temp_template = helpers.get_func_name()
no_of_transactions = 10
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
ttl = cluster.calculate_tx_ttl()
fee = cluster.calculate_tx_fee(
src_address=src_address,
tx_name=temp_template,
dst_addresses=[dst_address],
tx_files=tx_files,
ttl=ttl,
)
amount = int(fee / no_of_transactions + 1000)
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
for i in range(no_of_transactions):
tx_raw_output = cluster.send_funds(
src_address=src_address,
destinations=destinations,
tx_name=f"{temp_template}_{i}",
tx_files=tx_files,
fee=fee,
ttl=ttl,
)
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - fee * no_of_transactions - amount * no_of_transactions
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address)
== dst_init_balance + amount * no_of_transactions
), f"Incorrect balance for destination address `{dst_address}`"
@allure.link(helpers.get_vcs_link())
@pytest.mark.parametrize("amount", (1, 100, 11_000))
@pytest.mark.dbsync
def test_transaction_to_10_addrs_from_1_addr(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
amount: int,
):
"""Test 1 transaction from 1 payment address to 10 payment addresses.
* send funds from 1 source address to 10 destination addresses
* check expected balances for both source and destination addresses
"""
self._from_to_transactions(
cluster_obj=cluster,
payment_addrs=payment_addrs,
tx_name=f"{helpers.get_func_name()}_{amount}",
from_num=1,
to_num=10,
amount=amount,
)
@allure.link(helpers.get_vcs_link())
@pytest.mark.parametrize("amount", (1, 100, 11_000, 100_000))
@pytest.mark.dbsync
def test_transaction_to_1_addr_from_10_addrs(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
amount: int,
):
"""Test 1 transaction from 10 payment addresses to 1 payment address.
* send funds from 10 source addresses to 1 destination address
* check expected balances for both source and destination addresses
"""
self._from_to_transactions(
cluster_obj=cluster,
payment_addrs=payment_addrs,
tx_name=f"{helpers.get_func_name()}_{amount}",
from_num=10,
to_num=1,
amount=amount,
)
@allure.link(helpers.get_vcs_link())
@pytest.mark.parametrize("amount", (1, 100, 11_000))
@pytest.mark.dbsync
def test_transaction_to_10_addrs_from_10_addrs(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
amount: int,
):
"""Test 1 transaction from 10 payment addresses to 10 payment addresses.
* send funds from 10 source addresses to 10 destination addresses
* check expected balances for both source and destination addresses
"""
self._from_to_transactions(
cluster_obj=cluster,
payment_addrs=payment_addrs,
tx_name=f"{helpers.get_func_name()}_{amount}",
from_num=10,
to_num=10,
amount=amount,
)
@allure.link(helpers.get_vcs_link())
@pytest.mark.parametrize("amount", (1, 100, 1000))
@pytest.mark.dbsync
def test_transaction_to_100_addrs_from_50_addrs(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
amount: int,
):
"""Test 1 transaction from 50 payment addresses to 100 payment addresses.
* send funds from 50 source addresses to 100 destination addresses
* check expected balances for both source and destination addresses
"""
self._from_to_transactions(
cluster_obj=cluster,
payment_addrs=payment_addrs,
tx_name=f"{helpers.get_func_name()}_{amount}",
from_num=50,
to_num=100,
amount=amount,
)
class TestManyUTXOs:
"""Test transaction with many UTxOs and small amounts of Lovelace."""
@pytest.fixture
def payment_addrs(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
) -> List[clusterlib.AddressRecord]:
"""Create new payment addresses."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
addrs = clusterlib_utils.create_payment_addr_records(
*[f"tiny_tx_addr_ci{cluster_manager.cluster_instance_num}_{i}" for i in range(3)],
cluster_obj=cluster,
)
fixture_cache.value = addrs
# fund source addresses
clusterlib_utils.fund_from_faucet(
addrs[0],
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
amount=100_000_000_000,
)
return addrs
def _from_to_transactions(
self,
cluster_obj: clusterlib.ClusterLib,
payment_addr: clusterlib.AddressRecord,
out_addrs: List[clusterlib.AddressRecord],
tx_name: str,
amount: int,
):
"""Send `amount` of Lovelace to each address in `out_addrs`."""
src_address = payment_addr.address
dst_addresses = [rec.address for rec in out_addrs]
# create TX data
txouts = [clusterlib.TxOut(address=addr, amount=amount) for addr in dst_addresses]
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addr.skey_file])
# send TX
cluster_obj.send_tx(
src_address=src_address, # change is returned to `src_address`
tx_name=tx_name,
txouts=txouts,
tx_files=tx_files,
join_txouts=False,
)
@pytest.fixture
def many_utxos(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
) -> Tuple[clusterlib.AddressRecord, clusterlib.AddressRecord]:
"""Generate many UTxOs (100000+) with small amounts of Lovelace (1-1000000)."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
temp_template = helpers.get_func_name()
LOGGER.info("Generating lot of UTxO addresses, will take a while.")
start = time.time()
payment_addr = payment_addrs[0]
out_addrs1 = [payment_addrs[1] for __ in range(200)]
out_addrs2 = [payment_addrs[2] for __ in range(200)]
out_addrs = [*out_addrs1, *out_addrs2]
for i in range(25):
for amount in range(1, 21):
self._from_to_transactions(
cluster_obj=cluster,
payment_addr=payment_addr,
tx_name=f"{temp_template}_{amount}_{i}",
out_addrs=out_addrs,
amount=amount,
)
self._from_to_transactions(
cluster_obj=cluster,
payment_addr=payment_addr,
tx_name=f"{temp_template}_big",
out_addrs=out_addrs,
amount=1000_000,
)
end = time.time()
retval = payment_addrs[1], payment_addrs[2]
fixture_cache.value = retval
num_of_utxo = len(cluster.get_utxo(payment_addrs[1].address)) + len(
cluster.get_utxo(payment_addrs[2].address)
)
LOGGER.info(f"Generated {num_of_utxo} of UTxO addresses in {end - start} seconds.")
return retval
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
@pytest.mark.parametrize("amount", (1, 10, 200, 2000, 10_000, 100_000, 1000_000))
def test_mini_transactions(
self,
cluster: clusterlib.ClusterLib,
many_utxos: Tuple[clusterlib.AddressRecord, clusterlib.AddressRecord],
amount: int,
):
"""Test transaction with many UTxOs (300+) with tiny amounts of Lovelace (1-1000000).
* use source address with many UTxOs (100000+)
* use destination address with many UTxOs (100000+)
* sent transaction with many UTxOs (300+) with tiny amounts of Lovelace from source address
to destination address
* check expected balances for both source and destination addresses
"""
temp_template = f"{helpers.get_func_name()}_{amount}"
big_funds_idx = -190
src_address = many_utxos[0].address
dst_address = many_utxos[1].address
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
tx_files = clusterlib.TxFiles(signing_key_files=[many_utxos[0].skey_file])
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
# sort UTxOs by amount
utxos_sorted = sorted(cluster.get_utxo(src_address), key=lambda x: x.amount)
# select 350 UTxOs, so we are in a limit of command line arguments lenght and size of the TX
txins = random.sample(utxos_sorted[:big_funds_idx], k=350)
# add several UTxOs with "big funds" so we can pay fees
txins.extend(utxos_sorted[-30:])
ttl = cluster.calculate_tx_ttl()
fee = cluster.calculate_tx_fee(
src_address=src_address,
tx_name=temp_template,
txins=txins,
txouts=destinations,
tx_files=tx_files,
ttl=ttl,
)
# optimize list of txins so the total amount of funds in selected UTxOs is close
# to the amount of needed funds
needed_funds = amount + fee + 100_000 # add a buffer
total_funds = functools.reduce(lambda x, y: x + y.amount, txins, 0)
funds_optimized = total_funds
txins_optimized = txins[:]
while funds_optimized > needed_funds:
popped_txin = txins_optimized.pop()
funds_optimized -= popped_txin.amount
if funds_optimized < needed_funds:
txins_optimized.append(popped_txin)
break
# build, sign and submit the transaction
txins_filtered, txouts_balanced = cluster.get_tx_ins_outs(
src_address=src_address,
tx_files=tx_files,
txins=txins_optimized,
txouts=destinations,
fee=fee,
)
tx_raw_output = cluster.build_raw_tx_bare(
out_file=f"{temp_template}_tx.body",
txins=txins_filtered,
txouts=txouts_balanced,
tx_files=tx_files,
fee=fee,
ttl=ttl,
)
tx_signed_file = cluster.sign_tx(
tx_body_file=tx_raw_output.out_file,
tx_name=temp_template,
signing_key_files=tx_files.signing_key_files,
)
cluster.submit_tx(tx_file=tx_signed_file, txins=tx_raw_output.txins)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - tx_raw_output.fee - len(destinations) * amount
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address) == dst_init_balance + amount
), f"Incorrect balance for destination address `{dst_address}`"
dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
@pytest.mark.testnets
class TestNotBalanced:
"""Tests for not balanced transactions."""
@pytest.fixture
def payment_addrs(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
) -> List[clusterlib.AddressRecord]:
"""Create 2 new payment addresses."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
addrs = clusterlib_utils.create_payment_addr_records(
f"addr_not_balanced_ci{cluster_manager.cluster_instance_num}_0",
f"addr_not_balanced_ci{cluster_manager.cluster_instance_num}_1",
cluster_obj=cluster,
)
fixture_cache.value = addrs
# fund source addresses
clusterlib_utils.fund_from_faucet(
addrs[0],
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
)
return addrs
@allure.link(helpers.get_vcs_link())
def test_negative_change(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
temp_dir: Path,
):
"""Try to build a transaction with a negative change.
Check that it is not possible to built such transaction.
"""
temp_template = helpers.get_func_name()
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
ttl = cluster.calculate_tx_ttl()
fee = cluster.calculate_tx_fee(
src_address=src_address,
tx_name=temp_template,
dst_addresses=[dst_address],
tx_files=tx_files,
ttl=ttl,
)
src_addr_highest_utxo = cluster.get_utxo_with_highest_amount(src_address)
# use only the UTxO with highest amount
txins = [src_addr_highest_utxo]
# try to transfer +1 Lovelace more than available and use a negative change (-1)
txouts = [
clusterlib.TxOut(address=dst_address, amount=src_addr_highest_utxo.amount - fee + 1),
clusterlib.TxOut(address=src_address, amount=-1),
]
assert txins[0].amount - txouts[0].amount - fee == txouts[-1].amount
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.build_raw_tx_bare(
out_file=temp_dir / f"{helpers.get_timestamped_rand_str()}_tx.body",
txins=txins,
txouts=txouts,
tx_files=tx_files,
fee=fee,
ttl=ttl,
)
exc_val = str(excinfo.value)
assert (
"option --tx-out: Failed reading" in exc_val
or "TxOutAdaOnly" in exc_val
or "AdaAssetId,-1" in exc_val
)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(transfer_add=st.integers(), change_amount=st.integers(min_value=0))
@helpers.hypothesis_settings()
def test_wrong_balance(
self,
cluster: clusterlib.ClusterLib,
payment_addrs: List[clusterlib.AddressRecord],
temp_dir: Path,
transfer_add: int,
change_amount: int,
):
"""Build a transaction with unbalanced change (property-based test).
* build a not balanced transaction
* check that it is not possible to submit such transaction
"""
# we want to test only unbalanced transactions
hypothesis.assume((transfer_add + change_amount) != 0)
src_address = payment_addrs[0].address
dst_address = payment_addrs[1].address
src_addr_highest_utxo = cluster.get_utxo_with_highest_amount(src_address)
fee = 200_000
# add to `transferred_amount` the value from test's parameter to unbalance the transaction
transferred_amount = src_addr_highest_utxo.amount - fee + transfer_add
# make sure the change amount is valid
hypothesis.assume(0 <= transferred_amount <= src_addr_highest_utxo.amount)
tx_name = f"test_wrong_balance_{helpers.get_timestamped_rand_str()}"
out_file_tx = temp_dir / f"{tx_name}_tx.body"
tx_files = clusterlib.TxFiles(signing_key_files=[payment_addrs[0].skey_file])
ttl = cluster.calculate_tx_ttl()
# use only the UTxO with highest amount
txins = [src_addr_highest_utxo]
txouts = [
clusterlib.TxOut(address=dst_address, amount=transferred_amount),
# Add the value from test's parameter to unbalance the transaction. Since the correct
# change amount here is 0, the value from test's parameter can be used directly.
clusterlib.TxOut(address=src_address, amount=change_amount),
]
# it should be possible to build and sign an unbalanced transaction
try:
cluster.build_raw_tx_bare(
out_file=out_file_tx,
txins=txins,
txouts=txouts,
tx_files=tx_files,
fee=fee,
ttl=ttl,
)
except clusterlib.CLIError as exc:
if change_amount >= 2 ** 64:
exc_val = str(exc)
assert "out of bounds" in exc_val or "exceeds the max bound" in exc_val
return
raise
out_file_signed = cluster.sign_tx(
tx_body_file=out_file_tx,
signing_key_files=tx_files.signing_key_files,
tx_name=tx_name,
)
# it should NOT be possible to submit an unbalanced transaction
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.submit_tx_bare(out_file_signed)
assert "ValueNotConservedUTxO" in str(excinfo.value)
@pytest.mark.testnets
class TestNegative:
"""Transaction tests that are expected to fail."""
@pytest.fixture
def pool_users(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
) -> List[clusterlib.PoolUser]:
"""Create pool users."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
created_users = clusterlib_utils.create_pool_users(
cluster_obj=cluster,
name_template=f"test_negative_ci{cluster_manager.cluster_instance_num}",
no_of_addr=2,
)
fixture_cache.value = created_users
# fund source addresses
clusterlib_utils.fund_from_faucet(
*created_users,
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
amount=1_000_000,
)
return created_users
def _send_funds_to_invalid_address(
self, cluster_obj: clusterlib.ClusterLib, pool_users: List[clusterlib.PoolUser], addr: str
):
"""Send funds from payment address to invalid address."""
tx_files = clusterlib.TxFiles(signing_key_files=[pool_users[0].payment.skey_file])
destinations = [clusterlib.TxOut(address=addr, amount=1000)]
# it should NOT be possible to build a transaction using an invalid address
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster_obj.build_raw_tx(
src_address=pool_users[0].payment.address,
tx_name="to_invalid",
txouts=destinations,
tx_files=tx_files,
fee=0,
)
exc_val = str(excinfo.value)
assert "invalid address" in exc_val or "An error occurred" in exc_val # TODO: better match
def _send_funds_from_invalid_address(
self, cluster_obj: clusterlib.ClusterLib, pool_users: List[clusterlib.PoolUser], addr: str
):
"""Send funds from invalid payment address."""
tx_files = clusterlib.TxFiles(signing_key_files=[pool_users[0].payment.skey_file])
destinations = [clusterlib.TxOut(address=pool_users[1].payment.address, amount=1000)]
# it should NOT be possible to build a transaction using an invalid address
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster_obj.build_raw_tx(
src_address=addr,
tx_name="from_invalid",
txouts=destinations,
tx_files=tx_files,
fee=0,
)
assert "invalid address" in str(excinfo.value)
def _send_funds_with_invalid_utxo(
self,
cluster_obj: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
utxo: clusterlib.UTXOData,
temp_template: str,
) -> str:
"""Send funds with invalid UTxO."""
src_addr = pool_users[0].payment
tx_files = clusterlib.TxFiles(signing_key_files=[src_addr.skey_file])
destinations = [clusterlib.TxOut(address=pool_users[1].payment.address, amount=1000)]
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster_obj.send_tx(
src_address=src_addr.address,
tx_name=temp_template,
txins=[utxo],
txouts=destinations,
tx_files=tx_files,
)
return str(excinfo.value)
@allure.link(helpers.get_vcs_link())
def test_past_ttl(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to send a transaction with ttl in the past.
Expect failure.
"""
temp_template = helpers.get_func_name()
src_address = pool_users[0].payment.address
dst_address = pool_users[1].payment.address
tx_files = clusterlib.TxFiles(signing_key_files=[pool_users[0].payment.skey_file])
destinations = [clusterlib.TxOut(address=dst_address, amount=100)]
ttl = cluster.get_slot_no() - 1
fee = cluster.calculate_tx_fee(
src_address=src_address,
tx_name=temp_template,
txouts=destinations,
tx_files=tx_files,
ttl=ttl,
)
# it should be possible to build and sign a transaction with ttl in the past
tx_raw_output = cluster.build_raw_tx(
src_address=src_address,
tx_name=temp_template,
txouts=destinations,
tx_files=tx_files,
fee=fee,
ttl=ttl,
)
out_file_signed = cluster.sign_tx(
tx_body_file=tx_raw_output.out_file,
signing_key_files=tx_files.signing_key_files,
tx_name=temp_template,
)
# it should NOT be possible to submit a transaction with ttl in the past
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.submit_tx_bare(out_file_signed)
exc_val = str(excinfo.value)
assert "ExpiredUTxO" in exc_val or "ValidityIntervalUTxO" in exc_val
@allure.link(helpers.get_vcs_link())
def test_duplicated_tx(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to send an identical transaction twice.
Expect failure.
"""
temp_template = helpers.get_func_name()
amount = 100
src_address = pool_users[0].payment.address
dst_address = pool_users[1].payment.address
src_init_balance = cluster.get_address_balance(src_address)
dst_init_balance = cluster.get_address_balance(dst_address)
tx_files = clusterlib.TxFiles(signing_key_files=[pool_users[0].payment.skey_file])
destinations = [clusterlib.TxOut(address=dst_address, amount=amount)]
# build and sign a transaction
fee = cluster.calculate_tx_fee(
src_address=src_address,
tx_name=temp_template,
txouts=destinations,
tx_files=tx_files,
)
tx_raw_output = cluster.build_raw_tx(
src_address=src_address,
tx_name=temp_template,
txouts=destinations,
tx_files=tx_files,
fee=fee,
)
out_file_signed = cluster.sign_tx(
tx_body_file=tx_raw_output.out_file,
signing_key_files=tx_files.signing_key_files,
tx_name=temp_template,
)
# submit a transaction for the first time
cluster.submit_tx(tx_file=out_file_signed, txins=tx_raw_output.txins)
assert (
cluster.get_address_balance(src_address)
== src_init_balance - tx_raw_output.fee - len(destinations) * amount
), f"Incorrect balance for source address `{src_address}`"
assert (
cluster.get_address_balance(dst_address) == dst_init_balance + amount
), f"Incorrect balance for destination address `{dst_address}`"
# it should NOT be possible to submit a transaction twice
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.submit_tx_bare(out_file_signed)
assert "ValueNotConservedUTxO" in str(excinfo.value)
@allure.link(helpers.get_vcs_link())
def test_wrong_signing_key(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to send a transaction signed with wrong signing key.
Expect failure.
"""
temp_template = helpers.get_func_name()
# use wrong signing key
tx_files = clusterlib.TxFiles(signing_key_files=[pool_users[1].payment.skey_file])
destinations = [clusterlib.TxOut(address=pool_users[1].payment.address, amount=100)]
# it should NOT be possible to submit a transaction with wrong signing key
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.send_tx(
src_address=pool_users[0].payment.address,
tx_name=temp_template,
txouts=destinations,
tx_files=tx_files,
)
assert "MissingVKeyWitnessesUTXOW" in str(excinfo.value)
@allure.link(helpers.get_vcs_link())
def test_send_funds_to_reward_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to send funds from payment address to stake address.
Expect failure.
"""
addr = pool_users[0].stake.address
self._send_funds_to_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
def test_send_funds_to_utxo_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to send funds from payment address to UTxO address.
Expect failure.
"""
dst_addr = pool_users[1].payment.address
utxo_addr = cluster.get_utxo(dst_addr)[0].utxo_hash
self._send_funds_to_invalid_address(
cluster_obj=cluster, pool_users=pool_users, addr=utxo_addr
)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(addr=st.text(alphabet=ADDR_ALPHABET, min_size=98, max_size=98))
@helpers.hypothesis_settings()
def test_send_funds_to_invalid_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
addr: str,
):
"""Try to send funds from payment address to non-existent address (property-based test).
Expect failure.
"""
addr = f"addr_test1{addr}"
self._send_funds_to_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(addr=st.text(alphabet=ADDR_ALPHABET, min_size=50, max_size=250))
@helpers.hypothesis_settings()
def test_send_funds_to_invalid_length_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
addr: str,
):
"""Try to send funds from payment address to address with invalid length.
Expect failure. Property-based test.
"""
addr = f"addr_test1{addr}"
self._send_funds_to_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(
addr=st.text(alphabet=st.characters(blacklist_categories=["C"]), min_size=98, max_size=98)
)
@helpers.hypothesis_settings()
def test_send_funds_to_invalid_chars_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
addr: str,
):
"""Try to send funds from payment address to address with invalid characters.
Expect failure. Property-based test.
"""
addr = f"addr_test1{addr}"
self._send_funds_to_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(addr=st.text(alphabet=ADDR_ALPHABET, min_size=98, max_size=98))
@helpers.hypothesis_settings()
def test_send_funds_from_invalid_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
addr: str,
):
"""Try to send funds from non-existent address (property-based test).
Expect failure.
"""
addr = f"addr_test1{addr}"
self._send_funds_from_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(addr=st.text(alphabet=ADDR_ALPHABET, min_size=50, max_size=250))
@helpers.hypothesis_settings()
def test_send_funds_from_invalid_length_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
addr: str,
):
"""Try to send funds from address with invalid length (property-based test).
Expect failure.
"""
addr = f"addr_test1{addr}"
self._send_funds_from_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
@hypothesis.given(
addr=st.text(alphabet=st.characters(blacklist_categories=["C"]), min_size=98, max_size=98)
)
@helpers.hypothesis_settings()
def test_send_funds_from_invalid_chars_address(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
addr: str,
):
"""Try to send funds from address with invalid characters (property-based test).
Expect failure.
"""
addr = f"addr_test1{addr}"
self._send_funds_from_invalid_address(cluster_obj=cluster, pool_users=pool_users, addr=addr)
@allure.link(helpers.get_vcs_link())
def test_nonexistent_utxo_ix(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to use nonexistent UTxO TxIx as an input.
Expect failure.
"""
temp_template = helpers.get_func_name()
utxo = cluster.get_utxo(pool_users[0].payment.address)[0]
utxo_copy = utxo._replace(utxo_ix=5)
err = self._send_funds_with_invalid_utxo(
cluster_obj=cluster, pool_users=pool_users, utxo=utxo_copy, temp_template=temp_template
)
assert "BadInputsUTxO" in err
@allure.link(helpers.get_vcs_link())
def test_nonexistent_utxo_hash(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
):
"""Try to use nonexistent UTxO hash as an input.
Expect failure.
"""
temp_template = helpers.get_func_name()
utxo = cluster.get_utxo(pool_users[0].payment.address)[0]
new_hash = f"{utxo.utxo_hash[:-4]}fd42"
utxo_copy = utxo._replace(utxo_hash=new_hash)
err = self._send_funds_with_invalid_utxo(
cluster_obj=cluster, pool_users=pool_users, utxo=utxo_copy, temp_template=temp_template
)
assert "BadInputsUTxO" in err
@allure.link(helpers.get_vcs_link())
@hypothesis.given(utxo_hash=st.text(alphabet=ADDR_ALPHABET, min_size=10, max_size=550))
@helpers.hypothesis_settings()
def test_invalid_lenght_utxo_hash(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
utxo_hash: str,
):
"""Try to use invalid UTxO hash as an input (property-based test).
Expect failure.
"""
temp_template = "test_invalid_lenght_utxo_hash"
utxo = cluster.get_utxo(pool_users[0].payment.address)[0]
utxo_copy = utxo._replace(utxo_hash=utxo_hash)
err = self._send_funds_with_invalid_utxo(
cluster_obj=cluster, pool_users=pool_users, utxo=utxo_copy, temp_template=temp_template
)
assert "Incorrect transaction id format" in err or "Failed reading" in err
@allure.link(helpers.get_vcs_link())
def test_missing_fee(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
temp_dir: Path,
):
"""Try to build a transaction with a missing `--fee` parameter.
Expect failure.
"""
temp_template = helpers.get_func_name()
tx_raw_output = _get_raw_tx_values(
cluster_obj=cluster,
tx_name=temp_template,
src_record=pool_users[0].payment,
dst_record=pool_users[1].payment,
temp_dir=temp_dir,
)
txins, txouts = _get_txins_txouts(tx_raw_output.txins, tx_raw_output.txouts)
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.cli(
[
"transaction",
"build-raw",
"--invalid-hereafter",
str(tx_raw_output.invalid_hereafter),
"--out-file",
str(tx_raw_output.out_file),
*cluster._prepend_flag("--tx-in", txins),
*cluster._prepend_flag("--tx-out", txouts),
]
)
assert "fee must be specified" in str(excinfo.value)
@allure.link(helpers.get_vcs_link())
@pytest.mark.skipif(
VERSIONS.transaction_era != VERSIONS.SHELLEY,
reason="runs only with Shelley TX",
)
def test_missing_ttl(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
temp_dir: Path,
):
"""Try to build a Shelley era TX with a missing `--ttl` (`--invalid-hereafter`) parameter.
Expect failure.
"""
temp_template = helpers.get_func_name()
tx_raw_output = _get_raw_tx_values(
cluster_obj=cluster,
tx_name=temp_template,
src_record=pool_users[0].payment,
dst_record=pool_users[1].payment,
temp_dir=temp_dir,
)
txins, txouts = _get_txins_txouts(tx_raw_output.txins, tx_raw_output.txouts)
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.cli(
[
"transaction",
"build-raw",
"--fee",
str(tx_raw_output.fee),
"--out-file",
str(tx_raw_output.out_file),
*cluster._prepend_flag("--tx-in", txins),
*cluster._prepend_flag("--tx-out", txouts),
*cluster.tx_era_arg,
]
)
assert "TTL must be specified" in str(excinfo.value)
@allure.link(helpers.get_vcs_link())
def test_missing_tx_in(
self,
cluster: clusterlib.ClusterLib,
pool_users: List[clusterlib.PoolUser],
temp_dir: Path,
):
"""Try to build a transaction with a missing `--tx-in` parameter.
Expect failure.
"""
temp_template = helpers.get_func_name()
tx_raw_output = _get_raw_tx_values(
cluster_obj=cluster,
tx_name=temp_template,
src_record=pool_users[0].payment,
dst_record=pool_users[1].payment,
temp_dir=temp_dir,
)
__, txouts = _get_txins_txouts(tx_raw_output.txins, tx_raw_output.txouts)
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.cli(
[
"transaction",
"build-raw",
"--invalid-hereafter",
str(tx_raw_output.invalid_hereafter),
"--fee",
str(tx_raw_output.fee),
"--out-file",
str(tx_raw_output.out_file),
*cluster._prepend_flag("--tx-out", txouts),
]
)
assert "Missing: (--tx-in TX-IN)" in str(excinfo.value)
@pytest.mark.testnets
class TestMetadata:
"""Tests for transactions with metadata."""
JSON_METADATA_FILE = DATA_DIR / "tx_metadata.json"
JSON_METADATA_WRONG_FILE = DATA_DIR / "tx_metadata_wrong.json"
JSON_METADATA_INVALID_FILE = DATA_DIR / "tx_metadata_invalid.json"
JSON_METADATA_LONG_FILE = DATA_DIR / "tx_metadata_long.json"
CBOR_METADATA_FILE = DATA_DIR / "tx_metadata.cbor"
METADATA_DUPLICATES = "tx_metadata_duplicate_keys*.json"
@pytest.fixture
def payment_addr(
self,
cluster_manager: cluster_management.ClusterManager,
cluster: clusterlib.ClusterLib,
) -> clusterlib.AddressRecord:
"""Create new payment address."""
with cluster_manager.cache_fixture() as fixture_cache:
if fixture_cache.value:
return fixture_cache.value # type: ignore
addr = clusterlib_utils.create_payment_addr_records(
f"addr_test_metadata_ci{cluster_manager.cluster_instance_num}_0",
cluster_obj=cluster,
)[0]
fixture_cache.value = addr
# fund source addresses
clusterlib_utils.fund_from_faucet(
addr,
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
)
return addr
@allure.link(helpers.get_vcs_link())
def test_tx_wrong_json_metadata_format(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Try to build a transaction with wrong fromat of metadata JSON.
The metadata file is a valid JSON, but not in a format that is expected.
"""
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_json_files=[self.JSON_METADATA_WRONG_FILE],
)
# it should NOT be possible to build a transaction using wrongly formatted metadata JSON
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.build_raw_tx(
src_address=payment_addr.address,
tx_name="wrong_json_format",
tx_files=tx_files,
)
assert "The JSON metadata top level must be a map" in str(excinfo.value)
@allure.link(helpers.get_vcs_link())
def test_tx_invalid_json_metadata(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Try to build a transaction with invalid metadata JSON.
The metadata file is an invalid JSON.
"""
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_json_files=[self.JSON_METADATA_INVALID_FILE],
)
# it should NOT be possible to build a transaction using an invalid metadata JSON
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.build_raw_tx(
src_address=payment_addr.address,
tx_name="invalid_metadata",
tx_files=tx_files,
)
assert "Failed reading: satisfy" in str(excinfo.value)
@allure.link(helpers.get_vcs_link())
def test_tx_too_long_metadata_json(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Try to build a transaction with metadata JSON longer than 64 bytes."""
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_json_files=[self.JSON_METADATA_LONG_FILE],
)
# it should NOT be possible to build a transaction using too long metadata JSON
with pytest.raises(clusterlib.CLIError) as excinfo:
cluster.build_raw_tx(
src_address=payment_addr.address,
tx_name="too_long_metadata",
tx_files=tx_files,
)
assert "Text string metadata value must consist of at most 64 UTF8 bytes" in str(
excinfo.value
)
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_tx_metadata_json(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Send transaction with metadata JSON.
Check that the metadata in TX body matches the original metadata.
"""
temp_template = helpers.get_func_name()
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_json_files=[self.JSON_METADATA_FILE],
)
tx_raw_output = cluster.send_tx(
src_address=payment_addr.address, tx_name=temp_template, tx_files=tx_files
)
assert tx_raw_output.fee, "Transaction had no fee"
cbor_body_metadata = clusterlib_utils.load_tx_metadata(tx_body_file=tx_raw_output.out_file)
# dump it as JSON, so keys are converted to strings
json_body_metadata = json.loads(json.dumps(cbor_body_metadata))
with open(self.JSON_METADATA_FILE) as metadata_fp:
json_file_metadata = json.load(metadata_fp)
assert (
json_body_metadata == json_file_metadata
), "Metadata in TX body doesn't match the original metadata"
# check TX and metadata in db-sync if available
tx_db_record = dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
if tx_db_record:
db_metadata = tx_db_record._convert_metadata()
assert (
db_metadata == cbor_body_metadata
), "Metadata in db-sync doesn't match the original metadata"
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_tx_metadata_cbor(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Send transaction with metadata CBOR.
Check that the metadata in TX body matches the original metadata.
"""
temp_template = helpers.get_func_name()
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_cbor_files=[self.CBOR_METADATA_FILE],
)
tx_raw_output = cluster.send_tx(
src_address=payment_addr.address, tx_name=temp_template, tx_files=tx_files
)
assert tx_raw_output.fee, "Transaction had no fee"
cbor_body_metadata = clusterlib_utils.load_tx_metadata(tx_body_file=tx_raw_output.out_file)
with open(self.CBOR_METADATA_FILE, "rb") as metadata_fp:
cbor_file_metadata = cbor2.load(metadata_fp)
assert (
cbor_body_metadata == cbor_file_metadata
), "Metadata in TX body doesn't match original metadata"
# check TX and metadata in db-sync if available
tx_db_record = dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
if tx_db_record:
db_metadata = tx_db_record._convert_metadata()
assert (
db_metadata == cbor_file_metadata
), "Metadata in db-sync doesn't match the original metadata"
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_tx_metadata_both(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Send transaction with both metadata JSON and CBOR.
Check that the metadata in TX body matches the original metadata.
"""
temp_template = helpers.get_func_name()
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_json_files=[self.JSON_METADATA_FILE],
metadata_cbor_files=[self.CBOR_METADATA_FILE],
)
tx_raw_output = cluster.send_tx(
src_address=payment_addr.address, tx_name=temp_template, tx_files=tx_files
)
assert tx_raw_output.fee, "Transaction had no fee"
cbor_body_metadata = clusterlib_utils.load_tx_metadata(tx_body_file=tx_raw_output.out_file)
# dump it as JSON, so keys are converted to strings
json_body_metadata = json.loads(json.dumps(cbor_body_metadata))
with open(self.JSON_METADATA_FILE) as metadata_fp_json:
json_file_metadata = json.load(metadata_fp_json)
with open(self.CBOR_METADATA_FILE, "rb") as metadata_fp_cbor:
cbor_file_metadata = cbor2.load(metadata_fp_cbor)
cbor_file_metadata = json.loads(json.dumps(cbor_file_metadata))
assert json_body_metadata == {
**json_file_metadata,
**cbor_file_metadata,
}, "Metadata in TX body doesn't match original metadata"
# check TX and metadata in db-sync if available
tx_db_record = dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
if tx_db_record:
db_metadata = tx_db_record._convert_metadata()
assert (
db_metadata == cbor_body_metadata
), "Metadata in db-sync doesn't match the original metadata"
@allure.link(helpers.get_vcs_link())
def test_tx_duplicate_metadata_keys(
self, cluster: clusterlib.ClusterLib, payment_addr: clusterlib.AddressRecord
):
"""Send transaction with multiple metadata JSON files and with duplicate keys.
* check that the metadata in TX body matches the original metadata
* check that in case of duplicate keys the first occurrence is used
"""
temp_template = helpers.get_func_name()
metadata_json_files = list(DATA_DIR.glob(self.METADATA_DUPLICATES))
tx_files = clusterlib.TxFiles(
signing_key_files=[payment_addr.skey_file],
metadata_json_files=metadata_json_files,
)
tx_raw_output = cluster.send_tx(
src_address=payment_addr.address, tx_name=temp_template, tx_files=tx_files
)
assert tx_raw_output.fee, "Transaction had no fee"
cbor_body_metadata = clusterlib_utils.load_tx_metadata(tx_body_file=tx_raw_output.out_file)
# dump it as JSON, so keys are converted to strings
json_body_metadata = json.loads(json.dumps(cbor_body_metadata))
# merge the input JSON files and alter the result so it matches the expected metadata
with open(metadata_json_files[0]) as metadata_fp:
json_file_metadata1 = json.load(metadata_fp)
with open(metadata_json_files[1]) as metadata_fp:
json_file_metadata2 = json.load(metadata_fp)
json_file_metadata = {**json_file_metadata2, **json_file_metadata1}
json_file_metadata["5"] = "baz1"
assert (
json_body_metadata == json_file_metadata
), "Metadata in TX body doesn't match the original metadata"
# check TX and metadata in db-sync if available
tx_db_record = dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
if tx_db_record:
db_metadata = tx_db_record._convert_metadata()
assert (
db_metadata == cbor_body_metadata
), "Metadata in db-sync doesn't match the original metadata"
@allure.link(helpers.get_vcs_link())
@pytest.mark.dbsync
def test_tx_metadata_no_txout(
self,
cluster: clusterlib.ClusterLib,
cluster_manager: cluster_management.ClusterManager,
):
"""Send transaction with just metadata, no UTxO is produced.
* submit a transaction where all funds available on source address is used for fee
* check that no UTxOs are created by the transaction
* check that there are no funds left on source address
* check that the metadata in TX body matches the original metadata
"""
temp_template = helpers.get_func_name()
src_record = clusterlib_utils.create_payment_addr_records(
f"{temp_template}_0", cluster_obj=cluster
)[0]
clusterlib_utils.fund_from_faucet(
src_record,
cluster_obj=cluster,
faucet_data=cluster_manager.cache.addrs_data["user1"],
amount=500_000,
)
tx_files = clusterlib.TxFiles(
signing_key_files=[src_record.skey_file],
metadata_json_files=[self.JSON_METADATA_FILE],
)
fee = cluster.get_address_balance(src_record.address)
tx_raw_output = cluster.send_tx(
src_address=src_record.address, tx_name=temp_template, tx_files=tx_files, fee=fee
)
assert not tx_raw_output.txouts, "Transaction has unexpected txouts"
assert (
cluster.get_address_balance(src_record.address) == 0
), f"Incorrect balance for source address `{src_record.address}`"
cbor_body_metadata = clusterlib_utils.load_tx_metadata(tx_body_file=tx_raw_output.out_file)
# dump it as JSON, so keys are converted to strings
json_body_metadata = json.loads(json.dumps(cbor_body_metadata))
with open(self.JSON_METADATA_FILE) as metadata_fp:
json_file_metadata = json.load(metadata_fp)
assert (
json_body_metadata == json_file_metadata
), "Metadata in TX body doesn't match the original metadata"
# check TX and metadata in db-sync if available
tx_db_record = dbsync_utils.check_tx(cluster_obj=cluster, tx_raw_output=tx_raw_output)
if tx_db_record:
db_metadata = tx_db_record._convert_metadata()
assert (
db_metadata == cbor_body_metadata
), "Metadata in db-sync doesn't match the original metadata"
| 37.377855 | 100 | 0.644615 | 9,156 | 75,279 | 5.00284 | 0.055046 | 0.023578 | 0.024735 | 0.018338 | 0.809894 | 0.779483 | 0.756495 | 0.728922 | 0.701065 | 0.681788 | 0 | 0.010382 | 0.273224 | 75,279 | 2,013 | 101 | 37.396423 | 0.826854 | 0.142776 | 0 | 0.672378 | 0 | 0 | 0.070452 | 0.020109 | 0 | 0 | 0 | 0.000497 | 0.041124 | 1 | 0.039753 | false | 0 | 0.015079 | 0 | 0.07608 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
279d6c64fa260eb1980a0da83a5bd49724bc6482 | 94 | py | Python | controllers/interfaces.py | aboulay/goyave.py | ce46f582d87a9fc5a455e7f5e1b0103a4571f157 | [
"MIT"
] | null | null | null | controllers/interfaces.py | aboulay/goyave.py | ce46f582d87a9fc5a455e7f5e1b0103a4571f157 | [
"MIT"
] | null | null | null | controllers/interfaces.py | aboulay/goyave.py | ce46f582d87a9fc5a455e7f5e1b0103a4571f157 | [
"MIT"
] | null | null | null | class IController():
def reload(self):
pass
def get_data(self):
pass
| 13.428571 | 23 | 0.553191 | 11 | 94 | 4.636364 | 0.727273 | 0.313725 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.351064 | 94 | 6 | 24 | 15.666667 | 0.836066 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0.4 | 0 | 0 | 0.6 | 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 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
27ecbf91952d89d939b08d1a40f87fe52676668e | 81 | py | Python | utils/fmap_visualize/utils/__init__.py | qaz670756/LSNet | 6ecb6fa1b2e96ca46c2cae973274e5d8f117afe1 | [
"Apache-2.0"
] | 3 | 2022-01-28T02:17:14.000Z | 2022-02-25T00:51:21.000Z | utils/fmap_visualize/utils/__init__.py | qaz670756/LSNet | 6ecb6fa1b2e96ca46c2cae973274e5d8f117afe1 | [
"Apache-2.0"
] | 1 | 2022-02-24T12:23:19.000Z | 2022-02-25T02:31:48.000Z | utils/fmap_visualize/utils/__init__.py | qaz670756/LSNet | 6ecb6fa1b2e96ca46c2cae973274e5d8f117afe1 | [
"Apache-2.0"
] | 2 | 2022-01-23T01:48:33.000Z | 2022-02-16T09:05:49.000Z | from .image_misc import *
from .featuremap_vis import *
from .utils_misc import * | 27 | 29 | 0.790123 | 12 | 81 | 5.083333 | 0.583333 | 0.327869 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135802 | 81 | 3 | 30 | 27 | 0.871429 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7e07b217fd9a71042e0eaffb389e709f046ec48d | 27 | py | Python | tests/unit/game_pkchess/character/__init__.py | RaenonX/Jelly-Bot-API | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 5 | 2020-08-26T20:12:00.000Z | 2020-12-11T16:39:22.000Z | tests/unit/game_pkchess/character/__init__.py | RaenonX/Jelly-Bot | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 234 | 2019-12-14T03:45:19.000Z | 2020-08-26T18:55:19.000Z | tests/unit/game_pkchess/character/__init__.py | RaenonX/Jelly-Bot-API | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 2 | 2019-10-23T15:21:15.000Z | 2020-05-22T09:35:55.000Z | from .obj import * # noqa
| 13.5 | 26 | 0.62963 | 4 | 27 | 4.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.259259 | 27 | 1 | 27 | 27 | 0.85 | 0.148148 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e33aa62147db3b73cffd328109ea7887aa1c99f1 | 261,039 | py | Python | instances/passenger_demand/pas-20210422-1717-int16e/38.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int16e/38.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int16e/38.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 30308
passenger_arriving = (
(7, 8, 12, 7, 3, 0, 4, 4, 2, 0, 0, 2, 0, 10, 6, 1, 4, 4, 2, 4, 2, 5, 3, 0, 2, 0), # 0
(8, 10, 7, 12, 9, 3, 1, 1, 5, 2, 1, 0, 0, 7, 9, 8, 1, 14, 2, 1, 2, 1, 1, 0, 1, 0), # 1
(6, 10, 4, 12, 9, 1, 3, 4, 1, 1, 2, 0, 0, 12, 8, 5, 4, 10, 6, 1, 2, 5, 3, 3, 3, 0), # 2
(9, 5, 4, 6, 7, 4, 5, 5, 1, 6, 1, 4, 0, 14, 6, 5, 3, 6, 1, 5, 1, 2, 1, 3, 1, 0), # 3
(8, 8, 11, 13, 7, 1, 5, 5, 3, 1, 1, 0, 0, 7, 8, 7, 4, 6, 2, 4, 4, 4, 3, 1, 1, 0), # 4
(9, 7, 5, 11, 12, 5, 6, 4, 7, 3, 3, 1, 0, 8, 15, 7, 2, 6, 3, 4, 5, 6, 2, 2, 0, 0), # 5
(12, 11, 16, 12, 8, 3, 3, 2, 5, 3, 2, 1, 0, 8, 11, 5, 11, 11, 10, 4, 3, 4, 2, 1, 1, 0), # 6
(11, 9, 10, 6, 11, 7, 2, 4, 6, 2, 0, 2, 0, 10, 8, 17, 2, 6, 6, 4, 3, 3, 5, 1, 2, 0), # 7
(12, 14, 9, 13, 4, 1, 5, 12, 1, 2, 1, 2, 0, 9, 13, 6, 7, 16, 3, 5, 2, 6, 2, 1, 2, 0), # 8
(13, 8, 8, 10, 11, 2, 6, 4, 6, 6, 2, 1, 0, 11, 9, 8, 8, 9, 5, 10, 1, 2, 2, 1, 3, 0), # 9
(6, 14, 8, 16, 6, 3, 3, 2, 4, 3, 2, 2, 0, 10, 12, 10, 9, 7, 11, 8, 5, 4, 3, 4, 3, 0), # 10
(14, 8, 18, 15, 9, 4, 5, 2, 8, 1, 1, 0, 0, 19, 16, 11, 7, 12, 10, 3, 7, 8, 6, 4, 1, 0), # 11
(16, 13, 16, 11, 4, 3, 4, 5, 8, 0, 3, 1, 0, 14, 16, 6, 7, 12, 6, 7, 4, 9, 6, 3, 3, 0), # 12
(15, 15, 11, 14, 13, 5, 5, 2, 3, 2, 1, 1, 0, 13, 17, 9, 12, 14, 7, 6, 1, 3, 6, 3, 3, 0), # 13
(17, 15, 11, 18, 10, 4, 3, 3, 3, 1, 4, 0, 0, 11, 15, 6, 5, 16, 12, 3, 1, 8, 4, 4, 1, 0), # 14
(11, 18, 16, 10, 9, 3, 1, 3, 6, 4, 1, 1, 0, 14, 15, 10, 8, 7, 9, 12, 8, 9, 2, 2, 4, 0), # 15
(21, 12, 8, 10, 12, 5, 6, 5, 12, 2, 1, 1, 0, 9, 16, 12, 3, 18, 7, 5, 5, 5, 4, 2, 1, 0), # 16
(14, 16, 16, 20, 17, 10, 3, 5, 4, 1, 2, 1, 0, 15, 13, 14, 18, 12, 8, 6, 5, 6, 4, 3, 1, 0), # 17
(12, 11, 12, 16, 9, 5, 5, 7, 6, 5, 0, 1, 0, 14, 16, 8, 15, 20, 4, 5, 5, 4, 8, 1, 1, 0), # 18
(11, 24, 14, 12, 12, 3, 2, 7, 4, 4, 2, 1, 0, 16, 20, 8, 12, 11, 11, 3, 4, 3, 8, 2, 2, 0), # 19
(14, 16, 14, 21, 11, 6, 0, 4, 6, 2, 2, 0, 0, 11, 15, 12, 9, 14, 11, 9, 3, 5, 4, 1, 1, 0), # 20
(19, 11, 7, 18, 15, 6, 10, 8, 8, 0, 1, 2, 0, 17, 14, 15, 13, 16, 11, 2, 6, 3, 1, 3, 0, 0), # 21
(23, 13, 11, 15, 13, 6, 12, 11, 11, 1, 3, 2, 0, 16, 14, 12, 12, 18, 13, 2, 7, 9, 9, 4, 2, 0), # 22
(15, 21, 7, 13, 11, 9, 9, 8, 5, 0, 2, 4, 0, 12, 11, 11, 10, 15, 10, 6, 5, 7, 5, 7, 0, 0), # 23
(18, 17, 11, 11, 13, 7, 6, 6, 11, 1, 4, 1, 0, 16, 14, 12, 5, 14, 5, 5, 5, 9, 4, 3, 1, 0), # 24
(21, 23, 13, 15, 9, 4, 10, 1, 6, 5, 1, 0, 0, 15, 14, 6, 8, 13, 9, 7, 2, 9, 5, 4, 1, 0), # 25
(19, 16, 12, 19, 13, 4, 8, 6, 4, 0, 0, 2, 0, 15, 15, 15, 9, 10, 7, 6, 4, 3, 10, 4, 1, 0), # 26
(19, 15, 14, 13, 9, 3, 9, 5, 6, 6, 3, 0, 0, 25, 22, 8, 9, 16, 11, 5, 9, 3, 6, 4, 2, 0), # 27
(13, 15, 14, 18, 13, 9, 3, 8, 5, 2, 3, 1, 0, 15, 16, 13, 7, 15, 10, 6, 5, 9, 1, 2, 0, 0), # 28
(15, 12, 13, 11, 16, 2, 6, 8, 8, 4, 2, 2, 0, 14, 13, 12, 7, 14, 13, 8, 4, 5, 6, 1, 2, 0), # 29
(11, 17, 14, 20, 15, 7, 5, 3, 7, 4, 1, 1, 0, 20, 22, 15, 13, 11, 10, 10, 7, 3, 4, 3, 5, 0), # 30
(18, 12, 9, 18, 10, 5, 3, 5, 5, 3, 0, 2, 0, 18, 9, 12, 7, 15, 12, 4, 6, 13, 6, 4, 0, 0), # 31
(16, 13, 17, 14, 14, 5, 6, 5, 7, 5, 2, 0, 0, 16, 10, 13, 10, 18, 13, 3, 6, 4, 7, 5, 2, 0), # 32
(8, 14, 11, 14, 8, 4, 5, 2, 2, 0, 1, 0, 0, 16, 8, 14, 10, 13, 7, 5, 6, 4, 2, 1, 3, 0), # 33
(18, 18, 12, 16, 8, 5, 8, 5, 12, 4, 3, 1, 0, 21, 14, 8, 9, 17, 10, 4, 5, 5, 3, 3, 1, 0), # 34
(17, 10, 11, 9, 12, 7, 12, 7, 8, 7, 2, 2, 0, 19, 14, 7, 14, 14, 6, 3, 1, 5, 2, 3, 1, 0), # 35
(10, 17, 14, 19, 13, 4, 15, 6, 5, 3, 0, 0, 0, 12, 9, 9, 7, 13, 12, 7, 7, 2, 6, 4, 1, 0), # 36
(19, 13, 14, 17, 17, 4, 8, 7, 7, 4, 1, 0, 0, 12, 10, 13, 10, 12, 11, 7, 4, 7, 2, 3, 1, 0), # 37
(17, 12, 15, 24, 8, 2, 3, 6, 5, 0, 2, 1, 0, 17, 20, 9, 7, 16, 10, 7, 4, 2, 4, 2, 2, 0), # 38
(13, 19, 14, 12, 17, 4, 4, 6, 8, 1, 5, 1, 0, 21, 11, 6, 9, 9, 7, 8, 1, 5, 7, 1, 2, 0), # 39
(17, 21, 18, 13, 13, 7, 8, 3, 3, 7, 2, 0, 0, 18, 10, 11, 9, 16, 12, 4, 4, 4, 6, 4, 2, 0), # 40
(25, 24, 9, 17, 14, 4, 7, 2, 6, 4, 1, 1, 0, 15, 16, 13, 7, 12, 6, 6, 2, 7, 6, 2, 0, 0), # 41
(18, 19, 13, 17, 16, 7, 5, 3, 2, 5, 3, 1, 0, 19, 10, 6, 12, 16, 9, 1, 8, 9, 5, 1, 1, 0), # 42
(12, 9, 15, 15, 16, 4, 8, 7, 4, 5, 3, 2, 0, 21, 8, 15, 8, 11, 6, 8, 2, 3, 3, 0, 0, 0), # 43
(22, 14, 22, 13, 10, 3, 4, 5, 3, 7, 2, 2, 0, 20, 16, 9, 7, 13, 12, 6, 8, 7, 2, 4, 1, 0), # 44
(17, 10, 18, 15, 5, 4, 9, 6, 2, 1, 3, 1, 0, 16, 14, 11, 9, 18, 12, 12, 7, 5, 1, 5, 3, 0), # 45
(20, 11, 9, 13, 10, 5, 7, 10, 5, 1, 7, 2, 0, 21, 20, 12, 9, 14, 8, 6, 3, 7, 5, 3, 0, 0), # 46
(16, 19, 11, 10, 6, 8, 7, 6, 4, 2, 4, 1, 0, 17, 18, 16, 9, 16, 11, 7, 10, 6, 4, 2, 1, 0), # 47
(10, 18, 15, 14, 16, 6, 7, 6, 8, 3, 0, 2, 0, 11, 14, 10, 11, 17, 6, 7, 3, 7, 6, 2, 1, 0), # 48
(19, 17, 12, 12, 15, 9, 13, 6, 2, 5, 0, 3, 0, 14, 9, 7, 9, 10, 10, 8, 5, 5, 6, 5, 2, 0), # 49
(13, 15, 14, 11, 7, 4, 7, 6, 4, 3, 3, 0, 0, 14, 20, 11, 5, 18, 7, 12, 2, 8, 3, 3, 1, 0), # 50
(10, 20, 8, 13, 12, 7, 7, 8, 6, 3, 0, 3, 0, 22, 20, 13, 6, 11, 5, 3, 5, 9, 5, 1, 2, 0), # 51
(14, 15, 13, 15, 14, 5, 5, 8, 7, 4, 2, 2, 0, 16, 15, 7, 8, 16, 5, 5, 4, 7, 6, 3, 1, 0), # 52
(14, 14, 19, 20, 9, 11, 7, 8, 6, 4, 3, 3, 0, 19, 11, 14, 7, 12, 5, 3, 2, 3, 7, 1, 0, 0), # 53
(23, 20, 6, 17, 15, 2, 4, 3, 9, 3, 3, 1, 0, 14, 9, 15, 10, 12, 9, 8, 2, 5, 5, 4, 1, 0), # 54
(9, 15, 12, 21, 10, 4, 2, 6, 8, 3, 1, 2, 0, 13, 11, 14, 5, 15, 5, 6, 6, 10, 3, 4, 1, 0), # 55
(15, 13, 11, 7, 15, 6, 7, 2, 6, 1, 5, 1, 0, 11, 13, 9, 8, 7, 9, 6, 2, 10, 1, 3, 2, 0), # 56
(9, 11, 15, 13, 13, 9, 5, 5, 10, 2, 2, 2, 0, 11, 11, 14, 6, 12, 9, 1, 5, 6, 5, 4, 2, 0), # 57
(20, 16, 12, 10, 13, 4, 7, 3, 2, 3, 3, 2, 0, 12, 14, 13, 10, 9, 7, 7, 5, 8, 5, 0, 1, 0), # 58
(13, 10, 19, 14, 21, 4, 5, 5, 6, 6, 2, 1, 0, 15, 10, 7, 6, 17, 4, 7, 3, 3, 11, 2, 0, 0), # 59
(17, 13, 18, 14, 16, 5, 9, 4, 9, 2, 5, 1, 0, 20, 12, 7, 9, 11, 8, 5, 6, 3, 2, 2, 0, 0), # 60
(10, 11, 13, 12, 18, 4, 8, 8, 7, 5, 3, 2, 0, 22, 21, 6, 7, 11, 14, 4, 1, 4, 3, 2, 1, 0), # 61
(17, 11, 10, 18, 8, 14, 3, 4, 4, 1, 0, 1, 0, 14, 13, 16, 9, 12, 5, 7, 4, 13, 6, 1, 2, 0), # 62
(10, 16, 10, 16, 16, 7, 7, 6, 8, 1, 5, 2, 0, 9, 10, 16, 7, 14, 2, 5, 2, 9, 3, 6, 1, 0), # 63
(15, 14, 12, 13, 7, 6, 2, 3, 6, 2, 3, 2, 0, 18, 16, 17, 6, 5, 10, 4, 7, 1, 5, 3, 1, 0), # 64
(22, 10, 16, 18, 12, 12, 2, 5, 6, 3, 2, 1, 0, 15, 12, 7, 10, 11, 5, 5, 8, 8, 2, 2, 1, 0), # 65
(20, 16, 16, 16, 14, 6, 6, 2, 7, 1, 1, 3, 0, 9, 11, 5, 5, 15, 8, 9, 7, 7, 4, 2, 2, 0), # 66
(13, 7, 19, 17, 2, 6, 4, 3, 8, 1, 3, 2, 0, 22, 20, 11, 4, 10, 0, 5, 1, 4, 4, 1, 3, 0), # 67
(10, 18, 11, 20, 4, 6, 3, 3, 4, 2, 0, 1, 0, 14, 3, 13, 4, 16, 5, 4, 1, 9, 4, 3, 1, 0), # 68
(7, 20, 12, 18, 11, 5, 10, 4, 6, 2, 0, 2, 0, 18, 13, 13, 8, 20, 8, 8, 4, 6, 8, 1, 1, 0), # 69
(18, 13, 11, 16, 15, 5, 2, 9, 9, 0, 1, 1, 0, 21, 17, 5, 12, 14, 8, 5, 3, 3, 6, 0, 1, 0), # 70
(22, 19, 15, 12, 11, 5, 3, 6, 4, 2, 1, 2, 0, 19, 20, 12, 6, 12, 4, 3, 7, 8, 3, 5, 1, 0), # 71
(10, 11, 14, 8, 8, 4, 12, 1, 5, 5, 4, 3, 0, 12, 16, 13, 8, 13, 5, 10, 5, 14, 8, 4, 1, 0), # 72
(14, 14, 17, 15, 14, 9, 7, 8, 6, 5, 1, 0, 0, 12, 22, 11, 8, 18, 2, 5, 6, 4, 3, 5, 0, 0), # 73
(15, 14, 16, 14, 9, 5, 2, 3, 8, 4, 0, 0, 0, 13, 9, 9, 6, 19, 10, 8, 4, 6, 5, 1, 3, 0), # 74
(15, 13, 13, 14, 7, 2, 5, 3, 8, 3, 2, 4, 0, 20, 16, 14, 4, 11, 11, 1, 7, 10, 1, 3, 0, 0), # 75
(14, 6, 24, 15, 10, 5, 12, 5, 6, 1, 3, 1, 0, 16, 14, 10, 10, 8, 6, 5, 2, 5, 1, 4, 1, 0), # 76
(18, 22, 9, 16, 10, 8, 4, 8, 9, 2, 0, 4, 0, 16, 11, 11, 6, 12, 8, 7, 2, 8, 3, 0, 0, 0), # 77
(12, 16, 11, 10, 5, 4, 5, 3, 6, 1, 1, 0, 0, 14, 12, 11, 7, 7, 7, 9, 4, 4, 5, 1, 1, 0), # 78
(16, 10, 15, 15, 7, 7, 6, 5, 9, 2, 4, 1, 0, 18, 15, 6, 7, 10, 8, 4, 5, 7, 3, 3, 3, 0), # 79
(8, 12, 13, 20, 18, 3, 5, 7, 4, 3, 1, 1, 0, 20, 15, 7, 8, 16, 10, 4, 7, 12, 8, 1, 1, 0), # 80
(13, 22, 18, 8, 13, 5, 6, 6, 5, 2, 0, 0, 0, 16, 11, 10, 6, 14, 11, 5, 10, 6, 1, 4, 3, 0), # 81
(12, 15, 10, 10, 14, 4, 5, 2, 7, 3, 0, 0, 0, 11, 13, 13, 8, 11, 4, 3, 3, 5, 10, 3, 3, 0), # 82
(24, 16, 13, 12, 13, 2, 5, 10, 6, 3, 1, 0, 0, 17, 9, 12, 13, 6, 9, 10, 3, 2, 2, 6, 3, 0), # 83
(17, 13, 18, 15, 22, 5, 5, 7, 7, 2, 1, 2, 0, 18, 14, 9, 11, 16, 9, 9, 4, 10, 4, 2, 0, 0), # 84
(17, 8, 13, 13, 9, 2, 5, 2, 6, 2, 3, 1, 0, 16, 11, 11, 11, 15, 6, 2, 8, 6, 7, 1, 2, 0), # 85
(19, 13, 18, 16, 17, 8, 6, 4, 8, 1, 1, 1, 0, 13, 16, 16, 13, 14, 3, 5, 7, 3, 5, 3, 1, 0), # 86
(17, 10, 12, 19, 6, 8, 5, 1, 8, 3, 1, 2, 0, 9, 6, 17, 7, 16, 4, 7, 5, 5, 5, 1, 1, 0), # 87
(12, 7, 7, 14, 10, 7, 6, 3, 2, 5, 1, 2, 0, 13, 9, 14, 10, 13, 7, 9, 4, 4, 5, 1, 0, 0), # 88
(10, 13, 10, 17, 10, 2, 3, 6, 7, 2, 3, 0, 0, 16, 13, 19, 8, 12, 4, 6, 3, 10, 6, 3, 2, 0), # 89
(12, 7, 13, 18, 7, 4, 3, 1, 4, 8, 2, 1, 0, 21, 16, 10, 6, 18, 5, 6, 5, 2, 4, 2, 1, 0), # 90
(11, 10, 20, 11, 14, 6, 3, 5, 4, 4, 1, 0, 0, 14, 10, 5, 10, 7, 9, 0, 3, 6, 8, 2, 1, 0), # 91
(20, 12, 9, 16, 12, 5, 3, 5, 9, 2, 2, 1, 0, 27, 7, 9, 10, 8, 4, 2, 6, 5, 2, 1, 1, 0), # 92
(8, 16, 12, 13, 13, 5, 11, 4, 6, 4, 2, 2, 0, 20, 19, 7, 8, 14, 9, 5, 4, 6, 2, 2, 0, 0), # 93
(9, 13, 12, 15, 8, 3, 3, 1, 7, 1, 3, 1, 0, 17, 6, 8, 8, 10, 6, 10, 4, 6, 7, 3, 1, 0), # 94
(17, 14, 11, 13, 10, 3, 7, 5, 2, 3, 1, 4, 0, 25, 13, 11, 9, 12, 5, 6, 5, 3, 5, 3, 0, 0), # 95
(22, 13, 22, 15, 8, 6, 7, 4, 9, 1, 2, 1, 0, 10, 13, 16, 6, 11, 8, 6, 4, 10, 4, 5, 0, 0), # 96
(11, 11, 14, 19, 22, 4, 5, 6, 6, 4, 3, 2, 0, 15, 7, 11, 12, 9, 6, 2, 2, 8, 1, 6, 0, 0), # 97
(11, 12, 11, 10, 10, 9, 8, 6, 12, 2, 4, 1, 0, 10, 7, 14, 11, 13, 7, 3, 3, 4, 3, 7, 1, 0), # 98
(10, 12, 18, 10, 15, 5, 4, 4, 2, 2, 1, 3, 0, 13, 14, 7, 11, 14, 1, 5, 3, 5, 3, 4, 1, 0), # 99
(11, 7, 17, 16, 14, 4, 5, 5, 6, 2, 1, 0, 0, 21, 13, 7, 5, 10, 6, 6, 6, 7, 2, 3, 2, 0), # 100
(17, 14, 15, 16, 12, 6, 8, 4, 5, 2, 1, 1, 0, 12, 12, 10, 10, 7, 9, 3, 4, 5, 2, 2, 1, 0), # 101
(13, 12, 15, 4, 11, 7, 6, 1, 4, 5, 2, 0, 0, 18, 17, 12, 12, 15, 4, 8, 0, 11, 5, 3, 1, 0), # 102
(9, 11, 9, 13, 21, 7, 9, 7, 10, 2, 2, 2, 0, 16, 7, 13, 9, 5, 2, 4, 4, 5, 5, 4, 1, 0), # 103
(19, 11, 12, 14, 5, 12, 4, 4, 8, 3, 4, 0, 0, 14, 9, 12, 12, 13, 4, 5, 2, 4, 3, 3, 1, 0), # 104
(11, 12, 15, 20, 14, 5, 5, 4, 6, 2, 0, 0, 0, 15, 12, 6, 9, 14, 5, 6, 2, 7, 7, 2, 6, 0), # 105
(26, 12, 17, 14, 11, 7, 1, 3, 7, 3, 2, 0, 0, 18, 15, 16, 13, 17, 7, 8, 2, 7, 7, 1, 1, 0), # 106
(10, 15, 11, 9, 9, 8, 4, 1, 7, 3, 0, 3, 0, 14, 10, 9, 4, 10, 4, 5, 4, 6, 2, 2, 3, 0), # 107
(15, 10, 11, 8, 7, 6, 3, 7, 2, 4, 3, 0, 0, 18, 13, 7, 9, 10, 9, 10, 2, 5, 5, 4, 1, 0), # 108
(15, 12, 20, 13, 6, 3, 8, 4, 5, 0, 3, 1, 0, 17, 8, 13, 10, 15, 6, 5, 3, 4, 8, 3, 1, 0), # 109
(14, 19, 9, 11, 9, 6, 7, 5, 5, 5, 1, 1, 0, 15, 11, 9, 6, 13, 7, 5, 6, 5, 3, 1, 1, 0), # 110
(14, 8, 8, 8, 13, 5, 4, 5, 6, 2, 0, 1, 0, 11, 8, 10, 6, 14, 6, 4, 7, 7, 4, 2, 2, 0), # 111
(9, 12, 9, 13, 12, 5, 5, 2, 6, 5, 0, 1, 0, 7, 16, 9, 8, 9, 3, 2, 6, 7, 4, 1, 0, 0), # 112
(19, 15, 15, 20, 15, 4, 0, 7, 9, 1, 1, 0, 0, 17, 12, 13, 10, 8, 6, 6, 3, 5, 2, 2, 0, 0), # 113
(16, 14, 12, 11, 13, 8, 3, 6, 5, 3, 1, 0, 0, 7, 17, 7, 10, 11, 6, 6, 4, 4, 4, 3, 0, 0), # 114
(14, 11, 16, 18, 8, 4, 5, 4, 7, 2, 1, 0, 0, 15, 8, 7, 3, 12, 6, 4, 2, 6, 6, 3, 1, 0), # 115
(16, 13, 9, 9, 16, 6, 4, 4, 9, 2, 0, 1, 0, 17, 13, 8, 8, 17, 4, 3, 3, 4, 9, 3, 0, 0), # 116
(17, 18, 15, 12, 13, 6, 5, 5, 3, 5, 1, 1, 0, 14, 14, 11, 8, 10, 8, 7, 5, 6, 3, 2, 3, 0), # 117
(16, 15, 12, 7, 12, 3, 8, 6, 2, 7, 1, 3, 0, 22, 14, 11, 3, 14, 3, 2, 2, 7, 2, 3, 1, 0), # 118
(5, 14, 16, 11, 18, 3, 5, 3, 10, 2, 1, 2, 0, 16, 16, 10, 3, 12, 4, 7, 4, 5, 4, 3, 0, 0), # 119
(15, 10, 11, 12, 10, 2, 10, 3, 2, 6, 1, 3, 0, 15, 20, 12, 5, 11, 5, 5, 3, 7, 4, 1, 1, 0), # 120
(16, 10, 12, 15, 14, 4, 4, 4, 7, 3, 0, 0, 0, 14, 13, 4, 11, 12, 4, 5, 1, 9, 7, 6, 1, 0), # 121
(6, 9, 6, 11, 9, 4, 7, 4, 7, 2, 1, 1, 0, 10, 11, 11, 5, 15, 6, 4, 4, 4, 5, 3, 1, 0), # 122
(13, 11, 9, 12, 10, 8, 6, 3, 5, 1, 1, 1, 0, 15, 11, 8, 10, 11, 4, 5, 6, 12, 0, 1, 3, 0), # 123
(12, 9, 8, 10, 11, 4, 5, 5, 4, 2, 1, 3, 0, 18, 12, 5, 6, 15, 2, 4, 4, 6, 3, 1, 0, 0), # 124
(14, 15, 15, 20, 8, 5, 1, 3, 8, 4, 1, 2, 0, 11, 10, 12, 2, 10, 9, 6, 3, 6, 5, 5, 1, 0), # 125
(11, 12, 12, 15, 16, 6, 2, 1, 2, 0, 1, 2, 0, 17, 12, 9, 8, 7, 6, 5, 0, 4, 0, 5, 1, 0), # 126
(13, 8, 12, 10, 7, 2, 7, 2, 4, 2, 2, 0, 0, 17, 7, 10, 7, 16, 6, 5, 5, 5, 6, 0, 1, 0), # 127
(15, 16, 8, 6, 14, 4, 6, 4, 5, 3, 1, 1, 0, 10, 10, 9, 7, 9, 5, 3, 6, 5, 6, 4, 1, 0), # 128
(12, 10, 12, 9, 10, 5, 3, 4, 4, 2, 3, 1, 0, 9, 9, 4, 8, 19, 10, 2, 4, 4, 4, 2, 1, 0), # 129
(21, 11, 12, 14, 11, 3, 7, 2, 9, 4, 1, 1, 0, 10, 7, 10, 7, 8, 8, 3, 4, 6, 4, 4, 1, 0), # 130
(15, 13, 8, 19, 16, 3, 5, 3, 6, 2, 3, 0, 0, 13, 11, 12, 4, 13, 5, 5, 3, 2, 4, 3, 1, 0), # 131
(10, 12, 15, 14, 10, 6, 7, 1, 5, 0, 0, 0, 0, 12, 12, 11, 6, 6, 10, 7, 5, 7, 5, 0, 1, 0), # 132
(15, 11, 10, 19, 6, 3, 4, 4, 7, 2, 3, 1, 0, 15, 13, 6, 5, 10, 5, 4, 5, 8, 3, 1, 2, 0), # 133
(12, 15, 8, 13, 11, 8, 2, 5, 7, 2, 1, 1, 0, 17, 11, 7, 7, 11, 4, 5, 4, 3, 3, 1, 2, 0), # 134
(16, 5, 9, 11, 8, 6, 5, 5, 5, 1, 0, 0, 0, 15, 13, 4, 4, 14, 5, 3, 5, 2, 4, 2, 0, 0), # 135
(15, 10, 10, 11, 17, 5, 6, 3, 4, 1, 0, 0, 0, 11, 15, 4, 9, 11, 3, 5, 1, 6, 4, 3, 0, 0), # 136
(10, 9, 11, 15, 5, 7, 3, 4, 4, 1, 2, 0, 0, 12, 7, 12, 8, 15, 8, 0, 4, 0, 6, 2, 1, 0), # 137
(16, 10, 16, 10, 12, 3, 2, 5, 6, 3, 2, 0, 0, 14, 9, 7, 6, 11, 5, 3, 1, 2, 2, 2, 0, 0), # 138
(9, 7, 9, 17, 13, 4, 1, 7, 9, 4, 1, 1, 0, 15, 8, 4, 6, 12, 9, 6, 2, 3, 2, 1, 1, 0), # 139
(12, 12, 13, 9, 7, 6, 5, 4, 9, 2, 1, 0, 0, 13, 13, 14, 4, 11, 6, 5, 4, 7, 5, 4, 1, 0), # 140
(8, 12, 9, 10, 12, 3, 5, 4, 6, 1, 0, 0, 0, 15, 6, 10, 7, 8, 11, 0, 2, 6, 3, 2, 0, 0), # 141
(20, 11, 9, 11, 14, 2, 3, 4, 6, 3, 1, 1, 0, 9, 12, 7, 12, 16, 9, 7, 2, 3, 7, 1, 1, 0), # 142
(13, 10, 8, 12, 13, 4, 7, 4, 2, 2, 1, 4, 0, 10, 17, 11, 2, 9, 5, 5, 2, 6, 3, 0, 1, 0), # 143
(9, 14, 8, 7, 9, 4, 3, 4, 6, 1, 4, 0, 0, 9, 9, 5, 4, 7, 5, 3, 8, 2, 3, 1, 1, 0), # 144
(11, 14, 4, 13, 8, 4, 4, 1, 4, 5, 1, 0, 0, 12, 14, 11, 8, 10, 4, 3, 5, 5, 5, 3, 3, 0), # 145
(16, 3, 14, 14, 14, 3, 5, 2, 5, 3, 2, 1, 0, 13, 11, 6, 1, 6, 7, 0, 3, 4, 6, 2, 0, 0), # 146
(8, 7, 8, 9, 11, 7, 2, 4, 6, 3, 2, 1, 0, 22, 10, 12, 10, 11, 7, 2, 3, 3, 3, 3, 0, 0), # 147
(13, 9, 8, 9, 8, 8, 3, 3, 5, 2, 1, 1, 0, 13, 8, 6, 5, 7, 10, 4, 3, 6, 3, 3, 1, 0), # 148
(19, 6, 16, 8, 5, 4, 4, 2, 3, 1, 3, 0, 0, 20, 6, 11, 6, 8, 8, 4, 2, 5, 5, 1, 1, 0), # 149
(16, 11, 12, 15, 9, 6, 3, 2, 6, 2, 2, 1, 0, 8, 8, 10, 6, 9, 4, 2, 4, 5, 3, 1, 0, 0), # 150
(9, 13, 12, 11, 10, 2, 2, 6, 2, 4, 2, 1, 0, 17, 8, 6, 5, 11, 4, 4, 3, 5, 5, 1, 1, 0), # 151
(18, 8, 9, 7, 13, 6, 7, 1, 8, 0, 3, 3, 0, 11, 10, 10, 6, 13, 6, 3, 1, 7, 3, 4, 3, 0), # 152
(15, 6, 14, 15, 10, 7, 7, 4, 4, 2, 1, 1, 0, 16, 8, 6, 8, 7, 2, 4, 1, 4, 9, 3, 1, 0), # 153
(18, 6, 7, 12, 12, 3, 1, 4, 7, 2, 0, 0, 0, 14, 7, 8, 9, 7, 4, 2, 2, 7, 5, 1, 2, 0), # 154
(14, 8, 14, 11, 16, 6, 2, 4, 5, 4, 1, 0, 0, 14, 11, 9, 8, 7, 3, 2, 4, 7, 7, 1, 0, 0), # 155
(12, 11, 11, 10, 14, 6, 6, 4, 4, 2, 1, 0, 0, 14, 12, 6, 4, 9, 3, 5, 3, 7, 4, 1, 1, 0), # 156
(10, 9, 6, 11, 7, 8, 7, 3, 2, 5, 2, 1, 0, 11, 5, 4, 13, 11, 3, 3, 2, 3, 5, 3, 0, 0), # 157
(9, 8, 12, 9, 9, 9, 2, 8, 5, 1, 0, 0, 0, 15, 5, 6, 10, 13, 5, 8, 3, 4, 2, 1, 1, 0), # 158
(13, 11, 12, 10, 13, 5, 3, 3, 4, 1, 2, 0, 0, 13, 14, 6, 4, 13, 6, 1, 6, 4, 4, 4, 2, 0), # 159
(6, 9, 15, 5, 4, 4, 6, 4, 3, 3, 2, 1, 0, 8, 14, 4, 6, 10, 3, 0, 1, 4, 6, 0, 2, 0), # 160
(11, 7, 7, 6, 5, 3, 5, 3, 6, 3, 1, 2, 0, 11, 13, 6, 6, 5, 8, 5, 4, 5, 1, 2, 0, 0), # 161
(10, 7, 11, 15, 11, 7, 1, 2, 1, 2, 3, 1, 0, 11, 8, 7, 9, 5, 3, 3, 5, 5, 0, 3, 0, 0), # 162
(12, 4, 12, 13, 9, 4, 7, 0, 4, 2, 0, 3, 0, 17, 10, 2, 7, 12, 10, 3, 3, 3, 6, 2, 0, 0), # 163
(11, 3, 8, 7, 11, 3, 3, 6, 8, 0, 3, 1, 0, 9, 8, 6, 7, 13, 4, 2, 6, 5, 1, 2, 1, 0), # 164
(7, 11, 10, 13, 5, 6, 3, 1, 4, 2, 1, 0, 0, 12, 16, 10, 3, 8, 7, 3, 1, 4, 5, 4, 0, 0), # 165
(15, 10, 10, 8, 12, 6, 2, 5, 4, 0, 2, 1, 0, 8, 13, 7, 6, 9, 6, 1, 6, 4, 3, 0, 2, 0), # 166
(12, 10, 8, 10, 13, 1, 5, 3, 3, 1, 0, 0, 0, 19, 9, 6, 5, 6, 9, 2, 4, 4, 3, 2, 0, 0), # 167
(8, 8, 7, 11, 12, 4, 4, 0, 2, 3, 2, 0, 0, 8, 8, 9, 5, 7, 5, 5, 2, 6, 1, 0, 2, 0), # 168
(12, 9, 9, 13, 13, 1, 2, 6, 4, 1, 1, 0, 0, 9, 8, 9, 3, 7, 2, 4, 3, 4, 2, 0, 0, 0), # 169
(14, 5, 11, 6, 2, 4, 2, 6, 6, 0, 0, 0, 0, 7, 10, 6, 8, 8, 5, 2, 4, 1, 3, 3, 0, 0), # 170
(3, 6, 8, 11, 10, 7, 3, 2, 5, 1, 1, 0, 0, 11, 6, 3, 3, 5, 6, 0, 0, 2, 2, 1, 2, 0), # 171
(7, 3, 7, 3, 4, 3, 0, 4, 2, 0, 0, 1, 0, 7, 6, 3, 6, 8, 4, 4, 1, 4, 3, 2, 1, 0), # 172
(5, 4, 6, 6, 4, 8, 3, 3, 7, 1, 0, 2, 0, 12, 10, 8, 4, 13, 2, 3, 3, 5, 1, 0, 0, 0), # 173
(4, 6, 3, 5, 5, 3, 0, 3, 3, 1, 0, 1, 0, 7, 4, 2, 5, 9, 5, 0, 3, 3, 4, 2, 1, 0), # 174
(7, 2, 9, 6, 6, 6, 4, 1, 5, 0, 1, 0, 0, 5, 8, 6, 1, 6, 1, 2, 4, 4, 3, 2, 0, 0), # 175
(6, 5, 6, 8, 8, 0, 1, 3, 8, 1, 1, 1, 0, 6, 8, 5, 4, 3, 3, 1, 2, 3, 1, 1, 1, 0), # 176
(8, 1, 5, 11, 2, 1, 2, 3, 1, 0, 0, 0, 0, 10, 3, 1, 1, 4, 2, 0, 4, 4, 1, 0, 0, 0), # 177
(7, 1, 3, 8, 7, 0, 3, 2, 3, 1, 3, 2, 0, 5, 10, 6, 5, 2, 6, 0, 4, 0, 6, 2, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(8.033384925394829, 8.840461695509067, 8.33805316738001, 9.943468438181492, 8.887496972175379, 5.021847891259743, 6.6336569845982645, 7.445081876767077, 9.744158499468812, 6.332824024835792, 6.728424262216965, 7.836664125289878, 8.134208340125381), # 0
(8.566923443231959, 9.424097110631614, 8.888554546128244, 10.600230805242587, 9.475984539958779, 5.353573734468089, 7.07115030602191, 7.9352219566491335, 10.387592522132655, 6.75036910764344, 7.172953817529811, 8.353946657302968, 8.671666635903767), # 1
(9.09875681436757, 10.005416273425567, 9.436867656875862, 11.254380327463672, 10.062340757999591, 5.683976183219912, 7.506909612737127, 8.423400396647072, 11.028458891004078, 7.166262040032874, 7.615717038042101, 8.869172243284888, 9.206983725135505), # 2
(9.6268124690345, 10.582112803098315, 9.980817390911767, 11.903322252051318, 10.644258681603043, 6.011744996136181, 7.939205826636729, 8.907681851991212, 11.664216257473749, 7.578852317481889, 8.054957458923813, 9.380297095888738, 9.738036490006762), # 3
(10.149017837465571, 11.15188031885724, 10.518228639524859, 12.544461826212112, 11.219431366074389, 6.335569931837869, 8.366309869613534, 9.386130977911865, 12.292323272932332, 7.986489435468286, 8.48891861534492, 9.885277427767623, 10.262701812703709), # 4
(10.663300349893618, 11.712412439909741, 11.04692629400403, 13.17520429715263, 11.785551866718848, 6.654140748945943, 8.786492663560358, 9.856812429639348, 12.910238588770495, 8.387522889469862, 8.915844042475412, 10.382069451574637, 10.778856575412524), # 5
(11.167587436551466, 12.261402785463202, 11.564735245638186, 13.792954912079445, 12.34031323884167, 6.9661472060813825, 9.19802513037002, 10.317790862403982, 13.515420856378904, 8.780302174964413, 9.333977275485251, 10.868629379962893, 11.284377660319372), # 6
(11.65980652767195, 12.79654497472501, 12.069480385716217, 14.39511891819914, 12.881408537748086, 7.270279061865153, 9.599178191935335, 10.767130931436084, 14.105328727148231, 9.16317678742974, 9.74156184954443, 11.342913425585486, 11.777141949610431), # 7
(12.137885053487896, 13.31553262690256, 12.558986605527034, 14.979101562718284, 13.406530818743338, 7.565226074918224, 9.988222770149116, 11.20289729196596, 14.67742085246913, 9.53449622234364, 10.136841299822914, 11.802877801095525, 12.255026325471867), # 8
(12.599750444232136, 13.816059361203237, 13.031078796359527, 15.54230809284347, 13.913373137132655, 7.849678003861574, 10.363429786904192, 11.623154599223941, 15.229155883732279, 9.892609975183907, 10.518059161490685, 12.246478719146102, 12.71590767008986), # 9
(13.043330130137491, 14.295818796834425, 13.483581849502599, 16.08214375578126, 14.399628548221282, 8.122324607316171, 10.723070164093368, 12.025967508440338, 15.757992472328343, 10.235867541428343, 10.883458969717719, 12.671672392390324, 13.157662865650577), # 10
(13.466551541436809, 14.752504553003531, 13.914320656245145, 16.596013798738237, 14.862990107314454, 8.38185564390299, 11.065414823609466, 12.409400674845465, 16.26138926964799, 10.56261841655475, 11.231284259673998, 13.076415033481297, 13.57816879434018), # 11
(13.8673421083629, 15.183810248917917, 14.321120107876064, 17.08132346892098, 15.301150869717404, 8.626960872242991, 11.388734687345298, 12.771518753669634, 16.736804927081888, 10.871212096040916, 11.559778566529495, 13.45866285507211, 13.975302338344855), # 12
(14.243629261148602, 15.587429503784993, 14.701805095684259, 17.53547801353607, 15.711803890735363, 8.856330050957158, 11.69130067719369, 13.11038640014317, 17.181698096020693, 11.159998075364648, 11.86718542545419, 13.816372069815873, 14.346940379850777), # 13
(14.593340430026746, 15.961055936812143, 15.054200510958635, 17.95588267979007, 16.092642225673583, 9.068652938666455, 11.971383715047459, 13.424068269496395, 17.593527427855076, 11.427325850003735, 12.151748371618055, 14.147498890365696, 14.690959801044102), # 14
(14.914403045230168, 16.30238316720675, 15.376131244988068, 18.339942714889578, 16.441358929837293, 9.26261929399186, 12.227254722799401, 13.71062901695961, 17.96975157397571, 11.671544915435986, 12.411710940191071, 14.449999529374674, 15.00523748411101), # 15
(15.204744536991681, 16.609104814176213, 15.66542218906148, 18.685063366041145, 16.755647058531732, 9.436918875554335, 12.457184622342362, 13.968133297763139, 18.307829185773258, 11.891004767139194, 12.64531666634322, 14.721830199495905, 15.287650311237673), # 16
(15.46229233554412, 16.878914496927916, 15.919898234467764, 18.98864988045138, 17.033199667062142, 9.590241441974857, 12.659444335569138, 14.19464576713731, 18.605218914638375, 12.084054900591148, 12.850809085244478, 14.960947113382488, 15.536075164610265), # 17
(15.684973871120327, 17.10950583466924, 16.137384272495808, 19.248107505326846, 17.271709810733743, 9.721276751874406, 12.832304784372562, 14.388231080312417, 18.859379411961754, 12.249044811269659, 13.026431732064815, 15.165306483687544, 15.748388926414954), # 18
(15.870716573953118, 17.29857244660759, 16.315705194434525, 19.460841487874106, 17.468870544851786, 9.828714563873934, 12.974036890645431, 14.546953892518793, 19.067769329134048, 12.384323994652526, 13.170428141974206, 15.332864523064154, 15.922468478837914), # 19
(16.01744787427533, 17.44380795195034, 16.452685891572806, 19.624257075299766, 17.62237492472151, 9.91124463659443, 13.08291157628058, 14.668878858986748, 19.22784731754592, 12.488241946217535, 13.28104185014264, 15.461577444165426, 16.05619070406532), # 20
(16.123095202319785, 17.542905969904893, 16.54615125519955, 19.73575951481038, 17.729916005648143, 9.967556728656858, 13.157199763170816, 14.752070634946598, 19.337072028588036, 12.559148161442488, 13.356516391740096, 15.54940145964447, 16.147432484283325), # 21
(16.18558598831933, 17.59356011967863, 16.593926176603656, 19.79275405361254, 17.78918684293692, 9.996340598682188, 13.19517237320896, 14.794593875628664, 19.392902113651065, 12.595392135805188, 13.395095301936545, 15.594292782154383, 16.194070701678125), # 22
(16.208629381348224, 17.599557750342935, 16.599877091906723, 19.799889300411525, 17.804371289652156, 10.0, 13.199686403614942, 14.79919012345679, 19.399881975308645, 12.599667636031093, 13.399932859458785, 15.599836122542294, 16.2), # 23
(16.225619860854646, 17.59605925925926, 16.598903703703705, 19.799011111111113, 17.812972181783763, 10.0, 13.197206100217867, 14.7928, 19.398946666666667, 12.59704098765432, 13.39939932659933, 15.598538271604937, 16.2), # 24
(16.242251568338528, 17.589163237311386, 16.59698216735254, 19.797273662551444, 17.821383912951205, 10.0, 13.192318244170096, 14.78024691358025, 19.3970987654321, 12.591870141746686, 13.39834143908218, 15.595976223136716, 16.2), # 25
(16.258523230476854, 17.578975034293556, 16.594138820301787, 19.79469670781893, 17.82960618947377, 10.0, 13.185098749293955, 14.76176790123457, 19.39436197530864, 12.58424113397348, 13.396768774161368, 15.592185093735715, 16.2), # 26
(16.27443357394662, 17.5656, 16.5904, 19.7913, 17.837638717670742, 10.0, 13.175623529411766, 14.7376, 19.39076, 12.57424, 13.39469090909091, 15.587200000000003, 16.2), # 27
(16.2899813254248, 17.549143484224967, 16.585792043895747, 19.787103292181072, 17.845481203861443, 10.0, 13.163968498345842, 14.707980246913582, 19.386316543209876, 12.561952775491541, 13.39211742112483, 15.581056058527665, 16.2), # 28
(16.3051652115884, 17.52971083676269, 16.580341289437587, 19.78212633744856, 17.853133354365152, 10.0, 13.150209569918506, 14.673145679012345, 19.381055308641976, 12.547465496113398, 13.389057887517147, 15.57378838591678, 16.2), # 29
(16.319983959114396, 17.50740740740741, 16.574074074074073, 19.77638888888889, 17.860594875501178, 10.0, 13.13442265795207, 14.633333333333333, 19.375, 12.530864197530866, 13.385521885521886, 15.56543209876543, 16.2), # 30
(16.334436294679772, 17.482338545953365, 16.567016735253773, 19.76991069958848, 17.867865473588814, 10.0, 13.116683676268863, 14.588780246913581, 19.368174320987656, 12.512234915409238, 13.381518992393067, 15.556022313671699, 16.2), # 31
(16.34852094496153, 17.45460960219479, 16.55919561042524, 19.762711522633747, 17.874944854947355, 10.0, 13.097068538691198, 14.539723456790126, 19.360601975308644, 12.49166368541381, 13.377058785384712, 15.545594147233656, 16.2), # 32
(16.362236636636634, 17.424325925925924, 16.55063703703704, 19.75481111111111, 17.8818327258961, 10.0, 13.075653159041394, 14.486400000000001, 19.352306666666667, 12.469236543209878, 13.372150841750841, 15.534182716049381, 16.2), # 33
(16.375582096382097, 17.391592866941014, 16.541367352537723, 19.746229218106997, 17.888528792754347, 10.0, 13.052513451141776, 14.429046913580246, 19.343312098765438, 12.445039524462736, 13.36680473874548, 15.521823136716964, 16.2), # 34
(16.388556050874893, 17.356515775034293, 16.53141289437586, 19.736985596707818, 17.895032761841392, 10.0, 13.027725328814654, 14.367901234567903, 19.333641975308645, 12.419158664837678, 13.361030053622645, 15.508550525834478, 16.2), # 35
(16.40115722679201, 17.3192, 16.5208, 19.7271, 17.901344339476537, 10.0, 13.001364705882352, 14.303200000000002, 19.32332, 12.391680000000001, 13.354836363636364, 15.494400000000002, 16.2), # 36
(16.41338435081044, 17.27975089163237, 16.50955500685871, 19.71659218106996, 17.907463231979076, 10.0, 12.97350749616719, 14.23518024691358, 19.31236987654321, 12.362689565615, 13.348233246040657, 15.479406675811616, 16.2), # 37
(16.425236149607162, 17.238273799725654, 16.49770425240055, 19.70548189300412, 17.913389145668305, 10.0, 12.944229613491487, 14.164079012345681, 19.300815308641976, 12.332273397347967, 13.341230278089538, 15.4636056698674, 16.2), # 38
(16.436711349859177, 17.194874074074075, 16.485274074074077, 19.69378888888889, 17.919121786863524, 10.0, 12.913606971677561, 14.090133333333334, 19.288680000000003, 12.300517530864198, 13.333837037037037, 15.447032098765431, 16.2), # 39
(16.44780867824346, 17.149657064471878, 16.472290809327845, 19.6815329218107, 17.924660861884032, 10.0, 12.88171548454773, 14.013580246913584, 19.27598765432099, 12.267508001828991, 13.326063100137175, 15.429721079103798, 16.2), # 40
(16.458526861437004, 17.102728120713305, 16.458780795610426, 19.66873374485597, 17.930006077049125, 10.0, 12.848631065924312, 13.934656790123459, 19.262761975308642, 12.233330845907636, 13.317918044643973, 15.411707727480568, 16.2), # 41
(16.4688646261168, 17.054192592592596, 16.444770370370374, 19.655411111111114, 17.935157138678093, 10.0, 12.814429629629629, 13.8536, 19.24902666666667, 12.198072098765433, 13.30941144781145, 15.393027160493828, 16.2), # 42
(16.47882069895983, 17.00415582990398, 16.430285871056242, 19.641584773662554, 17.940113753090245, 10.0, 12.779187089486001, 13.770646913580249, 19.234805432098767, 12.161817796067673, 13.300552886893627, 15.373714494741657, 16.2), # 43
(16.488393806643085, 16.9527231824417, 16.4153536351166, 19.62727448559671, 17.944875626604873, 10.0, 12.742979359315743, 13.686034567901238, 19.220121975308643, 12.124653973479653, 13.291351939144532, 15.353804846822133, 16.2), # 44
(16.497582675843546, 16.900000000000002, 16.400000000000002, 19.6125, 17.949442465541274, 10.0, 12.705882352941178, 13.600000000000001, 19.205, 12.086666666666668, 13.281818181818181, 15.333333333333332, 16.2), # 45
(16.50638603323821, 16.846091632373113, 16.384251303155008, 19.59728106995885, 17.953813976218747, 10.0, 12.667971984184621, 13.512780246913582, 19.189463209876543, 12.04794191129401, 13.271961192168598, 15.312335070873344, 16.2), # 46
(16.514802605504055, 16.79110342935528, 16.36813388203018, 19.581637448559672, 17.957989864956588, 10.0, 12.629324166868395, 13.424612345679012, 19.173535308641977, 12.008565743026978, 13.261790547449806, 15.29084517604024, 16.2), # 47
(16.522831119318074, 16.735140740740743, 16.351674074074076, 19.565588888888893, 17.961969838074097, 10.0, 12.590014814814815, 13.335733333333335, 19.15724, 11.968624197530865, 13.251315824915824, 15.268898765432098, 16.2), # 48
(16.53047030135726, 16.67830891632373, 16.334898216735255, 19.549155144032923, 17.965753601890572, 10.0, 12.550119841846204, 13.246380246913581, 19.14060098765432, 11.928203310470966, 13.240546601820677, 15.246530955647007, 16.2), # 49
(16.537718878298588, 16.620713305898494, 16.31783264746228, 19.53235596707819, 17.969340862725304, 10.0, 12.50971516178488, 13.15679012345679, 19.12364197530864, 11.887389117512575, 13.22949245541838, 15.223776863283039, 16.2), # 50
(16.544575576819057, 16.56245925925926, 16.300503703703704, 19.515211111111114, 17.9727313268976, 10.0, 12.46887668845316, 13.0672, 19.10638666666667, 11.846267654320988, 13.218162962962964, 15.200671604938274, 16.2), # 51
(16.551039123595647, 16.503652126200276, 16.282937722908095, 19.497740329218107, 17.975924700726743, 10.0, 12.427680335673365, 12.977846913580246, 19.0888587654321, 11.8049249565615, 13.206567701708444, 15.177250297210794, 16.2), # 52
(16.55710824530535, 16.444397256515778, 16.26516104252401, 19.479963374485596, 17.978920690532046, 10.0, 12.386202017267813, 12.888967901234569, 19.071081975308644, 11.763447059899406, 13.194716248908842, 15.153548056698675, 16.2), # 53
(16.562781668625146, 16.384800000000002, 16.2472, 19.4619, 17.981719002632804, 10.0, 12.344517647058824, 12.800799999999999, 19.05308, 11.72192, 13.18261818181818, 15.1296, 16.2), # 54
(16.568058120232035, 16.324965706447188, 16.229080932784637, 19.443569958847736, 17.984319343348304, 10.0, 12.302703138868717, 12.71358024691358, 19.034876543209876, 11.68042981252858, 13.170283077690485, 15.10544124371285, 16.2), # 55
(16.572936326802996, 16.264999725651577, 16.210830178326475, 19.424993004115226, 17.986721418997856, 10.0, 12.26083440651981, 12.627545679012346, 19.016495308641975, 11.639062533150437, 13.157720513779774, 15.0811069044353, 16.2), # 56
(16.577415015015013, 16.205007407407408, 16.192474074074077, 19.40618888888889, 17.988924935900748, 10.0, 12.218987363834422, 12.542933333333336, 18.997960000000003, 11.597904197530866, 13.144940067340068, 15.056632098765432, 16.2), # 57
(16.581492911545087, 16.145094101508917, 16.174038957475997, 19.387177366255145, 17.99092960037628, 10.0, 12.177237924634875, 12.459980246913581, 18.979294320987655, 11.557040841335164, 13.131951315625393, 15.032051943301326, 16.2), # 58
(16.585168743070195, 16.085365157750342, 16.155551165980796, 19.367978189300413, 17.992735118743752, 10.0, 12.135662002743485, 12.378923456790124, 18.960521975308644, 11.516558500228626, 13.11876383588976, 15.007401554641062, 16.2), # 59
(16.588441236267325, 16.02592592592593, 16.137037037037036, 19.34861111111111, 17.99434119732246, 10.0, 12.094335511982571, 12.3, 18.94166666666667, 11.476543209876544, 13.105387205387206, 14.982716049382717, 16.2), # 60
(16.591309117813463, 15.966881755829906, 16.11852290809328, 19.329095884773665, 17.995747542431697, 10.0, 12.053334366174454, 12.223446913580247, 18.922752098765432, 11.437081005944217, 13.091831001371743, 14.958030544124373, 16.2), # 61
(16.593771114385607, 15.908337997256517, 16.100035116598082, 19.30945226337449, 17.996953860390775, 10.0, 12.01273447914145, 12.149501234567902, 18.903801975308642, 11.398257924096939, 13.078104801097394, 14.933380155464107, 16.2), # 62
(16.595825952660736, 15.8504, 16.0816, 19.289700000000003, 17.99795985751897, 10.0, 11.972611764705881, 12.078400000000002, 18.88484, 11.36016, 13.064218181818184, 14.9088, 16.2), # 63
(16.597472359315837, 15.793173113854596, 16.0632438957476, 19.26985884773663, 17.998765240135597, 10.0, 11.933042136690068, 12.010380246913583, 18.86588987654321, 11.322873269318702, 13.050180720788127, 14.884325194330135, 16.2), # 64
(16.5987090610279, 15.73676268861454, 16.04499314128944, 19.249948559670784, 17.999369714559947, 10.0, 11.894101508916325, 11.945679012345678, 18.846975308641976, 11.286483767718336, 13.036001995261257, 14.859990855052581, 16.2), # 65
(16.599534784473914, 15.681274074074077, 16.026874074074076, 19.22998888888889, 17.999772987111317, 10.0, 11.855865795206972, 11.884533333333335, 18.828120000000002, 11.251077530864197, 13.021691582491583, 14.835832098765435, 16.2), # 66
(16.59994825633087, 15.626812620027435, 16.00891303155007, 19.209999588477366, 17.99997476410901, 10.0, 11.81841090938433, 11.827180246913583, 18.809347654320987, 11.216740594421584, 13.007259059733137, 14.811884042066758, 16.2), # 67
(16.59966658316932, 15.573197822912517, 15.991049519890261, 19.189826784755773, 17.999804728475752, 9.99981441853376, 11.781624311727434, 11.77335016003658, 18.790540557841794, 11.183392706635466, 12.992457581664603, 14.788048035039589, 16.19980024005487), # 68
(16.597026731078905, 15.51879283154122, 15.97278148148148, 19.168453623188405, 17.99825708061002, 9.998347325102882, 11.744429090154583, 11.720158024691358, 18.770876543209877, 11.150090225127087, 12.975780542264753, 14.76355035737492, 16.198217592592595), # 69
(16.59181726009423, 15.463347935749368, 15.954029492455417, 19.14573939881911, 17.995198902606308, 9.995458009449779, 11.706656215298192, 11.667123914037496, 18.750244627343395, 11.116671239140375, 12.957038218441728, 14.738276418068494, 16.195091735253776), # 70
(16.584111457028687, 15.406896269746449, 15.93480013717421, 19.12171760601181, 17.990668926006617, 9.991193293705228, 11.668322655262381, 11.61426538637403, 18.728675537265662, 11.083136574948224, 12.936299793254179, 14.712244699540344, 16.190463820301783), # 71
(16.573982608695655, 15.349470967741935, 15.915099999999999, 19.096421739130435, 17.98470588235294, 9.985600000000002, 11.62944537815126, 11.5616, 18.706200000000003, 11.04948705882353, 12.913634449760767, 14.685473684210528, 16.184375), # 72
(16.561504001908514, 15.291105163945307, 15.894935665294923, 19.069885292538917, 17.977348503187283, 9.978724950464867, 11.590041352068948, 11.50914531321445, 18.682848742569732, 11.01572351703919, 12.889111371020142, 14.65798185449907, 16.1768664266118), # 73
(16.546748923480646, 15.231831992566043, 15.874313717421124, 19.04214176060118, 17.96863552005164, 9.970614967230606, 11.550127545119556, 11.456918884316416, 18.658652491998172, 10.9818467758681, 12.86279974009097, 14.629787692826028, 16.167979252400553), # 74
(16.52979066022544, 15.171684587813619, 15.85324074074074, 19.01322463768116, 17.95860566448802, 9.961316872427986, 11.509720925407201, 11.404938271604939, 18.63364197530864, 10.947857661583152, 12.834768740031897, 14.600909681611435, 16.157754629629633), # 75
(16.510702498956285, 15.11069608389752, 15.831723319615913, 18.98316741814278, 17.94729766803841, 9.950877488187778, 11.468838461035993, 11.353221033379059, 18.607847919524463, 10.913757000457247, 12.805087553901586, 14.571366303275333, 16.146233710562413), # 76
(16.48955772648655, 15.048899615027217, 15.809768038408777, 18.95200359634997, 17.934750262244815, 9.939343636640757, 11.427497120110047, 11.301784727937816, 18.581301051668955, 10.87954561876328, 12.7738253647587, 14.54117604023777, 16.13345764746228), # 77
(16.46642962962963, 14.98632831541219, 15.787381481481482, 18.919766666666668, 17.92100217864924, 9.926762139917695, 11.38571387073348, 11.250646913580248, 18.55403209876543, 10.845224342774147, 12.741051355661883, 14.510357374918781, 16.119467592592596), # 78
(16.441391495198904, 14.923015319261916, 15.76457023319616, 18.88649012345679, 17.906092148793675, 9.913179820149367, 11.343505681010402, 11.199825148605397, 18.52607178783722, 10.810793998762742, 12.706834709669796, 14.478928789738408, 16.104304698216733), # 79
(16.414516610007755, 14.858993760785877, 15.74134087791495, 18.852207461084273, 17.890058904220126, 9.898643499466544, 11.30088951904493, 11.149336991312301, 18.497450845907636, 10.776255413001962, 12.671244609841102, 14.446908767116696, 16.08801011659808), # 80
(16.385878260869568, 14.79429677419355, 15.7177, 18.816952173913048, 17.872941176470587, 9.8832, 11.257882352941177, 11.099200000000002, 18.4682, 10.741609411764706, 12.63435023923445, 14.414315789473685, 16.070625), # 81
(16.355549734597723, 14.728957493694413, 15.693654183813445, 18.780757756307032, 17.854777697087066, 9.866896143880508, 11.214501150803258, 11.049431732967536, 18.43834997713763, 10.706856821323866, 12.596220780908501, 14.381168339229419, 16.052190500685874), # 82
(16.323604318005607, 14.663009053497943, 15.669210013717422, 18.743657702630166, 17.835607197611555, 9.849778753238837, 11.170762880735285, 11.000049748513947, 18.40793150434385, 10.671998467952339, 12.55692541792191, 14.34748489880394, 16.03274777091907), # 83
(16.290115297906603, 14.59648458781362, 15.644374074074074, 18.70568550724638, 17.815468409586057, 9.831894650205761, 11.126684510841374, 10.95107160493827, 18.376975308641974, 10.637035177923023, 12.516533333333333, 14.313283950617285, 16.012337962962963), # 84
(16.255155961114095, 14.529417230850923, 15.61915294924554, 18.666874664519593, 17.794400064552573, 9.813290656912057, 11.08228300922564, 10.902514860539554, 18.345512117055325, 10.60196777750881, 12.47511371020143, 14.2785839770895, 15.991002229080934), # 85
(16.21879959444146, 14.46184011681933, 15.593553223593966, 18.627258668813745, 17.772440894053094, 9.794013595488494, 11.037575343992193, 10.854397073616827, 18.313572656607228, 10.566797092982599, 12.432735731584856, 14.24340346064063, 15.968781721536352), # 86
(16.18111948470209, 14.393786379928315, 15.567581481481481, 18.586871014492754, 17.749629629629634, 9.774110288065843, 10.99257848324515, 10.806735802469136, 18.28118765432099, 10.531523950617284, 12.389468580542264, 14.207760883690709, 15.945717592592594), # 87
(16.142188918709373, 14.325289154387361, 15.541244307270233, 18.54574519592056, 17.726005002824177, 9.753627556774882, 10.947309395088626, 10.75954860539552, 18.248387837219937, 10.496149176685762, 12.345381440132318, 14.171674728659784, 15.921850994513035), # 88
(16.102081183276677, 14.256381574405948, 15.51454828532236, 18.503914707461085, 17.701605745178732, 9.732612223746381, 10.901785047626733, 10.712853040695016, 18.21520393232739, 10.460673597460932, 12.30054349341367, 14.135163477967897, 15.897223079561043), # 89
(16.06086956521739, 14.187096774193549, 15.4875, 18.461413043478263, 17.676470588235297, 9.711111111111112, 10.856022408963586, 10.666666666666666, 18.18166666666667, 10.425098039215687, 12.255023923444977, 14.098245614035088, 15.871875000000001), # 90
(16.0186273513449, 14.117467887959643, 15.460106035665294, 18.41827369833602, 17.650638263535864, 9.689171040999847, 10.810038447203299, 10.621007041609511, 18.14780676726109, 10.389423328222922, 12.208891913284896, 14.060939619281399, 15.845847908093276), # 91
(15.975427828472597, 14.047528049913716, 15.432372976680384, 18.374530166398284, 17.624147502622446, 9.666838835543363, 10.763850130449988, 10.57589172382259, 18.113654961133975, 10.353650290755535, 12.162216645992086, 14.023263976126877, 15.819182956104251), # 92
(15.931344283413848, 13.977310394265235, 15.404307407407408, 18.33021594202899, 17.597037037037037, 9.644161316872427, 10.717474426807762, 10.53133827160494, 18.079241975308644, 10.31777975308642, 12.1150673046252, 13.985237166991553, 15.791921296296294), # 93
(15.886450002982048, 13.906848055223684, 15.375915912208507, 18.285364519592058, 17.569345598321632, 9.621185307117818, 10.670928304380737, 10.487364243255604, 18.044598536808415, 10.281812541488476, 12.067513072242896, 13.946877674295479, 15.764104080932785), # 94
(15.840818273990577, 13.836174166998541, 15.347205075445817, 18.240009393451423, 17.541111918018238, 9.597957628410304, 10.62422873127303, 10.443987197073618, 18.00975537265661, 10.245749482234594, 12.019623131903835, 13.908203980458689, 15.735772462277092), # 95
(15.79452238325282, 13.765321863799286, 15.318181481481483, 18.194184057971015, 17.512374727668846, 9.574525102880658, 10.577392675588754, 10.401224691358026, 17.974743209876543, 10.209591401597677, 11.971466666666668, 13.869234567901238, 15.706967592592594), # 96
(15.747635617582157, 13.694324279835394, 15.28885171467764, 18.14792200751476, 17.483172758815464, 9.550934552659655, 10.530437105432021, 10.359094284407867, 17.939592775491544, 10.173339125850616, 11.923112859590052, 13.829987919043152, 15.677730624142663), # 97
(15.700231263791975, 13.623214549316343, 15.259222359396432, 18.101256736446594, 17.453544743000084, 9.52723279987807, 10.48337898890695, 10.317613534522177, 17.904334796524918, 10.136993481266307, 11.87463089373265, 13.790482516304477, 15.648102709190674), # 98
(15.652382608695653, 13.552025806451613, 15.229300000000002, 18.054221739130437, 17.423529411764708, 9.503466666666666, 10.43623529411765, 10.276800000000001, 17.869, 10.100555294117648, 11.826089952153112, 13.750736842105264, 15.618125000000001), # 99
(15.60416293910658, 13.480791185450682, 15.19909122085048, 18.00685050993022, 17.393165496651335, 9.479682975156226, 10.389022989168232, 10.236671239140376, 17.833619112940102, 10.064025390677534, 11.777559217910095, 13.710769378865548, 15.58783864883402), # 100
(15.555645541838135, 13.409543820523034, 15.168602606310015, 17.959176543209878, 17.36249172920197, 9.455928547477518, 10.34175904216282, 10.19724481024234, 17.798222862368544, 10.027404597218862, 11.72910787406226, 13.670598609005365, 15.557284807956103), # 101
(15.506903703703706, 13.338316845878138, 15.13784074074074, 17.911233333333335, 17.331546840958605, 9.432250205761319, 10.294460421205521, 10.15853827160494, 17.762841975308643, 9.990693740014526, 11.680805103668263, 13.63024301494477, 15.526504629629631), # 102
(15.458010711516671, 13.267143395725476, 15.1068122085048, 17.86305437466452, 17.300369563463246, 9.408694772138395, 10.247144094400449, 10.120569181527207, 17.72750717878372, 9.953893645337423, 11.632720089786758, 13.589721079103796, 15.495539266117968), # 103
(15.409039852090416, 13.196056604274526, 15.075523593964334, 17.814673161567367, 17.268998628257886, 9.385309068739522, 10.199827029851722, 10.083355098308186, 17.692249199817102, 9.91700513946045, 11.584922015476401, 13.549051283902486, 15.464429869684501), # 104
(15.360064412238325, 13.125089605734766, 15.043981481481481, 17.766123188405796, 17.237472766884533, 9.362139917695474, 10.152526195663453, 10.046913580246915, 17.6570987654321, 9.880029048656501, 11.537480063795854, 13.508252111760886, 15.433217592592593), # 105
(15.311157678773782, 13.054275534315678, 15.012192455418381, 17.717437949543747, 17.205830710885177, 9.339234141137021, 10.105258559939752, 10.011262185642433, 17.622086602652033, 9.842966199198472, 11.490463417803769, 13.46734204509903, 15.401943587105624), # 106
(15.26239293851017, 12.983647524226738, 14.980163100137176, 17.66865093934514, 17.174111191801824, 9.31663856119494, 10.058041090784739, 9.976418472793783, 17.58724343850023, 9.805817417359263, 11.443941260558804, 13.426339566336967, 15.370649005486968), # 107
(15.21384347826087, 12.913238709677422, 14.947900000000002, 17.619795652173917, 17.14235294117647, 9.294400000000001, 10.010890756302521, 9.942400000000001, 17.5526, 9.768583529411766, 11.397982775119617, 13.38526315789474, 15.339375000000002), # 108
(15.16558258483927, 12.843082224877207, 14.915409739369, 17.570905582393987, 17.11059469055112, 9.272565279682976, 9.96382452459722, 9.90922432556013, 17.518187014174668, 9.731265361628877, 11.352657144544864, 13.34413130219238, 15.308162722908094), # 109
(15.117683545058746, 12.77321120403558, 14.882698902606315, 17.522014224369297, 17.078875171467768, 9.251181222374639, 9.916859363772943, 9.876909007773206, 17.484035208047555, 9.693863740283494, 11.308033551893201, 13.302962481649942, 15.277053326474624), # 110
(15.07021964573269, 12.703658781362009, 14.849774074074077, 17.47315507246377, 17.047233115468412, 9.230294650205762, 9.87001224193381, 9.845471604938272, 17.450175308641978, 9.656379491648512, 11.264181180223286, 13.261775178687461, 15.246087962962964), # 111
(15.02326417367448, 12.634458091065975, 14.816641838134434, 17.42436162104133, 17.015707254095055, 9.209952385307119, 9.823300127183934, 9.814929675354367, 17.41663804298125, 9.618813441996826, 11.221169212593775, 13.220587875724977, 15.215307784636488), # 112
(14.976806757924871, 12.565757790057525, 14.78338852520331, 17.375734211987265, 16.98428108827793, 9.190191630743222, 9.776841541850832, 9.78536411004897, 17.383540498013794, 9.581287578580367, 11.179078249844586, 13.179508698407085, 15.184710241349155), # 113
(14.930369436640104, 12.498235493640857, 14.75047308003459, 17.327663074043738, 16.952629367306123, 9.170967373647843, 9.731229133456928, 9.757138015208191, 17.351390457140898, 9.544504268660452, 11.137990939381115, 13.13905947538076, 15.154040662656056), # 114
(14.883815844806392, 12.431915517892875, 14.717915092331708, 17.280135208290847, 16.920652284621763, 9.152229619998023, 9.6864954403065, 9.730244246845935, 17.320199965870064, 9.508520524780923, 11.09784721828335, 13.099260132094162, 15.123210610656603), # 115
(14.837087797180216, 12.366701250066724, 14.685651503974197, 17.233065840426246, 16.888301642214046, 9.133934203659356, 9.64256770804463, 9.70460850063839, 17.28989014276453, 9.473269373519276, 11.05856949003437, 13.060037115979753, 15.092171615609425), # 116
(14.790127108518035, 12.302496077415555, 14.653619256841578, 17.18637019614759, 16.855529242072176, 9.116036958497425, 9.599373182316404, 9.680156472261736, 17.260382106387524, 9.438683841453006, 11.020080158117253, 13.021316874470001, 15.06087520777316), # 117
(14.742875593576338, 12.239203387192518, 14.621755292813388, 17.139963501152533, 16.82228688618535, 9.098493718377823, 9.556839108766905, 9.656813857392155, 17.231596975302296, 9.404696955159615, 10.98230162601508, 12.98302585499736, 15.02927291740644), # 118
(14.695275067111588, 12.176726566650768, 14.589996553769158, 17.09376098113873, 16.788526376542755, 9.081260317166132, 9.51489273304121, 9.634506351705832, 17.20345586807207, 9.371241741216595, 10.945156297210925, 12.945090504994296, 14.997316274767892), # 119
(14.647267343880259, 12.114969003043454, 14.55827998158842, 17.04767786180383, 16.754199515133596, 9.064292588727945, 9.473461300784406, 9.613159650878949, 17.175879903260093, 9.338251226201448, 10.908566575187866, 12.907437271893276, 14.964956810116156), # 120
(14.59879423863883, 12.053834083623727, 14.5265425181507, 17.001629368845496, 16.71925810394707, 9.047546366928849, 9.432472057641569, 9.592699450587691, 17.148790199429598, 9.305658436691674, 10.872454863428986, 12.869992603126756, 14.932146053709857), # 121
(14.549797566143766, 11.993225195644738, 14.494721105335538, 16.95553072796137, 16.683653944972374, 9.03097748563443, 9.391852249257788, 9.573051446508238, 17.122107875143822, 9.273396399264763, 10.836743565417363, 12.832682946127202, 14.898835535807633), # 122
(14.50021914115155, 11.933045726359639, 14.462752685022458, 16.90929716484911, 16.647338840198707, 9.01454177871028, 9.351529121278142, 9.554141334316773, 17.095754048966008, 9.24139814049822, 10.801355084636072, 12.795434748327075, 14.864976786668116), # 123
(14.450000778418648, 11.87319906302158, 14.430574199090993, 16.86284390520638, 16.61026459161526, 8.998195080021983, 9.311429919347711, 9.535894809689482, 17.069649839459384, 9.209596686969538, 10.766211824568192, 12.758174457158841, 14.830521336549939), # 124
(14.399084292701534, 11.813588592883713, 14.398122589420678, 16.816086174730817, 16.572383001211236, 8.98189322343513, 9.271481889111582, 9.518237568302546, 17.04371636518719, 9.177925065256215, 10.731236188696803, 12.720828520054958, 14.795420715711726), # 125
(14.347411498756685, 11.754117703199192, 14.365334797891038, 16.768939199120087, 16.53364587097583, 8.965592042815308, 9.231612276214832, 9.501095305832148, 17.017874744712667, 9.146316301935748, 10.696350580504982, 12.683323384447895, 14.759626454412127), # 126
(14.294924211340579, 11.69468978122116, 14.332147766381608, 16.72131820407184, 16.494005002898238, 8.949247372028104, 9.19174832630255, 9.484393717954474, 16.99204609659905, 9.114703423585638, 10.661477403475807, 12.645585497770107, 14.723090082909758), # 127
(14.241564245209673, 11.635208214202777, 14.29849843677192, 16.67313841528373, 16.453412198967666, 8.93281504493911, 9.151817285019812, 9.4680585003457, 16.966151539409577, 9.083019456783381, 10.626539061092359, 12.607541307454062, 14.68576313146326), # 128
(14.187273415120451, 11.575576389397186, 14.264323750941504, 16.624315058453412, 16.4118192611733, 8.916250895413912, 9.111746398011702, 9.452015348682016, 16.94011219170748, 9.051197428106473, 10.591457956837715, 12.569117260932218, 14.647597130331262), # 129
(14.131993535829388, 11.515697694057547, 14.229560650769887, 16.57476335927854, 16.36917799150434, 8.899510757318094, 9.0714629109233, 9.4361899586396, 16.913849172056, 9.019170364132412, 10.556156494194951, 12.530239805637045, 14.608543609772397), # 130
(14.07566642209295, 11.455475515437003, 14.19414607813661, 16.524398543456762, 16.32544019194999, 8.88255046451725, 9.030894069399695, 9.42050802589464, 16.887283599018378, 8.986871291438696, 10.52055707664715, 12.490835389000999, 14.568554100045299), # 131
(14.018233888667616, 11.39481324078871, 14.158016974921194, 16.47313583668574, 16.280557664499447, 8.865325850876964, 8.98996711908596, 9.404895246123317, 16.860336591157846, 8.954233236602823, 10.484582107677383, 12.450830458456547, 14.527580131408602), # 132
(13.959637750309861, 11.333614257365817, 14.121110283003175, 16.420890464663124, 16.2344822111419, 8.847792750262826, 8.948609305627183, 9.389277315001811, 16.832929267037642, 8.921189226202292, 10.448153990768738, 12.410151461436149, 14.485573234120938), # 133
(13.899819821776152, 11.271781952421478, 14.083362944262086, 16.367577653086567, 16.18716563386655, 8.829906996540425, 8.906747874668445, 9.37357992820631, 16.804982745221007, 8.887672286814597, 10.411195129404286, 12.368724845372267, 14.442484938440934), # 134
(13.838721917822966, 11.209219713208839, 14.044711900577454, 16.313112627653727, 16.138559734662593, 8.811624423575347, 8.86431007185483, 9.357728781412993, 16.77641814427117, 8.853615445017242, 10.373627927067108, 12.326477057697364, 14.398266774627231), # 135
(13.776285853206776, 11.145830926981056, 14.005094093828815, 16.25741061406225, 16.08861631551923, 8.792900865233184, 8.821223142831416, 9.341649570298044, 16.74715658275137, 8.818951727387716, 10.335374787240283, 12.283334545843907, 14.352870272938459), # 136
(13.712453442684055, 11.081518980991277, 13.964446465895698, 16.200386838009802, 16.037287178425654, 8.773692155379518, 8.77741433324329, 9.325267990537647, 16.717119179224852, 8.783614160503523, 10.296358113406889, 12.239223757244352, 14.306246963633242), # 137
(13.647166501011277, 11.016187262492654, 13.922705958657628, 16.141956525194022, 15.98452412537107, 8.753954127879942, 8.732810888735527, 9.308509737807984, 16.68622705225485, 8.747535770942156, 10.256500309050004, 12.194071139331164, 14.258348376970226), # 138
(13.58036684294491, 10.949739158738339, 13.879809513994145, 16.082034901312575, 15.930278958344665, 8.733642616600042, 8.687340054953216, 9.29130050778524, 16.654401320404595, 8.710649585281116, 10.215723777652705, 12.14780313953681, 14.20912604320803), # 139
(13.511996283241437, 10.88207805698148, 13.83569407378478, 16.020537192063113, 15.874503479335647, 8.712713455405407, 8.640929077541434, 9.273565996145594, 16.62156310223733, 8.672888630097898, 10.17395092269807, 12.100346205293746, 14.158531492605304), # 140
(13.44199663665733, 10.813107344475235, 13.790296579909057, 15.957378623143285, 15.817149490333206, 8.691122478161624, 8.593505202145272, 9.255231898565233, 16.587633516316288, 8.634185931970002, 10.131104147669182, 12.05162678403444, 14.106516255420662), # 141
(13.37030971794905, 10.742730408472745, 13.743553974246513, 15.892474420250753, 15.75816879332654, 8.668825518734284, 8.544995674409803, 9.236223910720339, 16.552533681204707, 8.594474517474925, 10.087105856049115, 12.001571323191351, 14.053031861912746), # 142
(13.29687734187308, 10.67085063622717, 13.695403198676681, 15.82573980908316, 15.697513190304846, 8.64577841098897, 8.49532773998011, 9.21646772828709, 16.516184715465837, 8.553687413190165, 10.04187845132095, 11.950106270196944, 13.998029842340188), # 143
(13.221641323185896, 10.597371414991658, 13.645781195079085, 15.757090015338171, 15.635134483257326, 8.621936988791274, 8.444428644501278, 9.195889046941678, 16.478507737662895, 8.511757645693216, 9.995344336967761, 11.897158072483679, 13.941461726961624), # 144
(13.144543476643964, 10.52219613201936, 13.594624905333262, 15.686440264713433, 15.570984474173173, 8.597257086006785, 8.39222563361839, 9.174413562360282, 16.439423866359128, 8.46861824156158, 9.947425916472632, 11.842653177484022, 13.88327904603568), # 145
(13.065525617003761, 10.445228174563427, 13.541871271318747, 15.613705782906601, 15.505014965041589, 8.57169453650109, 8.338645952976528, 9.151966970219084, 16.39885422011777, 8.424202227372753, 9.898045593318638, 11.786518032630433, 13.82343332982099), # 146
(12.98452955902176, 10.366370929877009, 13.487457234915055, 15.538801795615328, 15.437177757851764, 8.545205174139772, 8.28361684822077, 9.128474966194265, 16.356719917502065, 8.378442629704233, 9.847125770988859, 11.728679085355378, 13.761876108576189), # 147
(12.901497117454435, 10.285527785213262, 13.431319738001733, 15.461643528537275, 15.367424654592899, 8.517744832788429, 8.227065564996202, 9.103863245962012, 16.312942077075245, 8.331272475133515, 9.794588852966372, 11.669062783091313, 13.698558912559907), # 148
(12.81637010705826, 10.20260212782533, 13.37339572245831, 15.382146207370084, 15.295707457254194, 8.48926934631264, 8.168919348947906, 9.078057505198506, 16.26744181740054, 8.282624790238101, 9.740357242734255, 11.607595573270707, 13.63343327203078), # 149
(12.729090342589704, 10.117497344966367, 13.313622130164312, 15.30022505781142, 15.221977967824841, 8.459734548577998, 8.109105445720962, 9.05098343957993, 16.220140257041205, 8.232432601595482, 9.684353343775589, 11.544203903326022, 13.566450717247434), # 150
(12.63959963880524, 10.030116823889527, 13.251935902999268, 15.215795305558927, 15.146187988294043, 8.429096273450089, 8.047551100960453, 9.02256674478247, 16.170958514560464, 8.180628935783165, 9.626499559573448, 11.478814220689715, 13.49756277846851), # 151
(12.54783981046135, 9.940363951847957, 13.188273982842723, 15.128772176310271, 15.06828932065099, 8.397310354794502, 7.984183560311464, 8.992733116482306, 16.119817708521552, 8.12714681937864, 9.566718293610915, 11.411352972794255, 13.426720985952636), # 152
(12.453752672314497, 9.848142116094811, 13.12257331157419, 15.039070895763093, 14.988233766884889, 8.364332626476825, 7.918930069419071, 8.96140825035562, 16.06663895748772, 8.071919278959406, 9.504931949371066, 11.341746607072103, 13.353876869958444), # 153
(12.357280039121166, 9.75335470388324, 13.054770831073213, 14.946606689615056, 14.905973128984929, 8.330118922362647, 7.851717873928365, 8.928517842078596, 16.011343380022186, 8.014879341102965, 9.44106293033698, 11.26992157095572, 13.278981960744572), # 154
(12.258363725637818, 9.655905102466392, 12.984803483219322, 14.851294783563805, 14.821459208940315, 8.294625076317555, 7.782474219484418, 8.893987587327418, 15.953852094688205, 7.955960032386807, 9.375033639991733, 11.195804311877572, 13.201987788569642), # 155
(12.15694554662093, 9.555696699097421, 12.912608209892042, 14.753050403307, 14.734643808740238, 8.257806922207138, 7.71112635173232, 8.85774318177827, 15.894086220049003, 7.8950943793884365, 9.306766481818407, 11.119321277270117, 13.122845883692296), # 156
(12.05296731682698, 9.452632881029478, 12.838121952970909, 14.6517887745423, 14.645478730373895, 8.219620293896982, 7.637601516317151, 8.819710321107332, 15.831966874667822, 7.832215408685347, 9.236183859300079, 11.04039891456582, 13.041507776371162), # 157
(11.943489514248384, 9.344724993235614, 12.75774712624377, 14.54363133064199, 14.549889769393596, 8.177639162107376, 7.560170753484572, 8.777275123758995, 15.762659346558557, 7.76538546606583, 9.160953204062308, 10.956159302710944, 12.954377375064553), # 158
(11.811658827165445, 9.220904511359164, 12.65078050944478, 14.406363454061527, 14.424306095650605, 8.117903436811366, 7.469140421417146, 8.715541652423012, 15.658283617955432, 7.683649590557993, 9.06786709699039, 10.850180037892974, 12.840684235072311), # 159
(11.655795351846896, 9.080154765665142, 12.515073532729422, 14.237724016654177, 14.266272210154874, 8.038946073676295, 7.363589997414055, 8.632958703243755, 15.515880363565842, 7.58592904298063, 8.955615213775264, 10.720803118220555, 12.69827297422973), # 160
(11.477155287337537, 8.92339338892875, 12.352075155056495, 14.039316006010765, 14.077428998851381, 7.941723586512502, 7.244290313611002, 8.530560852975649, 15.337327627198428, 7.473053109073501, 8.825186647359532, 10.569227950252113, 12.528598471710556), # 161
(11.27699483268217, 8.751538013925183, 12.163234335384793, 13.812742409722123, 13.859417347685127, 7.827192489130329, 7.112012202143695, 8.409382678373124, 15.12450345266182, 7.3458510745763705, 8.677570490685794, 10.39665394054607, 12.333115606688533), # 162
(11.056570186925597, 8.565506273429639, 11.950000032673124, 13.559606215379095, 13.613878142601102, 7.696309295340116, 6.967526495147841, 8.2704587561906, 14.87928588376465, 7.205152225229, 8.513755836696653, 10.204280495660853, 12.113279258337407), # 163
(10.817137549112616, 8.366215800217313, 11.713821205880283, 13.281510410572508, 13.342452269544303, 7.550030518952207, 6.811604024759146, 8.114823663182511, 14.603552964315558, 7.05178584677115, 8.334731778334714, 9.993307022154886, 11.870544305830926), # 164
(10.559953118288028, 8.154584227063411, 11.45614681396507, 12.980057982893204, 13.046780614459719, 7.389312673776939, 6.6450156231133155, 7.943511976103274, 14.299182738123168, 6.8865812249425815, 8.141487408542579, 9.764932926586592, 11.606365628342832), # 165
(10.286273093496636, 7.931529186743127, 11.178425815886285, 12.656851919932002, 12.728504063292343, 7.215112273624654, 6.468532122346058, 7.757558271707324, 13.968053248996117, 6.71036764548306, 7.935011820262847, 9.520357615514403, 11.322198105046873), # 166
(9.997353673783238, 7.6979683120316595, 10.882107170602728, 12.31349520927975, 12.389263501987168, 7.028385832305694, 6.28292435459308, 7.557997126749083, 13.61204254074304, 6.523974394132343, 7.716294106438124, 9.260780495496734, 11.019496615116793), # 167
(9.694451058192634, 7.454819235704206, 10.568639837073198, 11.951590838527274, 12.030699816489188, 6.830089863630398, 6.088963151990087, 7.345863117982976, 13.233028657172568, 6.328230756630195, 7.48632336001101, 8.987400973092019, 10.69971603772634), # 168
(9.378821445769624, 7.202999590535967, 10.239472774256495, 11.572741795265413, 11.654453892743392, 6.621180881409112, 5.887419346672787, 7.122190822163432, 12.832889642093342, 6.123966018716379, 7.24608867392411, 8.701418454858675, 10.364311252049257), # 169
(9.051721035559014, 6.94342700930214, 9.896054941111416, 11.178551067084992, 11.262166616694774, 6.402615399452171, 5.679063770776885, 6.888014816044876, 12.413503539313982, 5.912009466130653, 6.996579141120026, 8.404032347355134, 10.014737137259289), # 170
(8.7144060266056, 6.677019124777921, 9.539835296596765, 10.770621641576858, 10.85547887428833, 6.175349931569918, 5.464667256438089, 6.644369676381733, 11.976748392643131, 5.693190384612782, 6.738783854541357, 8.096442057139818, 9.652448572530185), # 171
(8.368132617954185, 6.4046935697385114, 9.172262799671339, 10.350556506331834, 10.436031551469046, 5.940340991572694, 5.245000635792105, 6.392289979928433, 11.524502245889417, 5.468338059902528, 6.473691907130711, 7.779846990771154, 9.278900437035686), # 172
(8.014157008649567, 6.127367976959108, 8.79478640929394, 9.919958648940762, 10.005465534181923, 5.69854509327084, 5.02083474097464, 6.132810303439398, 11.058643142861477, 5.238281777739651, 6.202292391830685, 7.45544655480756, 8.89554760994954), # 173
(7.6537353977365505, 5.845959979214909, 8.408855084423363, 9.480431056994465, 9.565421708371947, 5.450918750474696, 4.792940404121401, 5.866965223669057, 10.581049127367942, 5.003850823863915, 5.9255744015838845, 7.124440155807469, 8.503844970445494), # 174
(7.288123984259929, 5.561387209281111, 8.015917784018413, 9.033576718083788, 9.11754095998411, 5.198418476994606, 4.562088457368093, 5.595789317371834, 10.09359824321745, 4.765874484015079, 5.644527029332911, 6.788027200329303, 8.105247397697292), # 175
(6.91857896726451, 5.274567299932917, 7.617423467037885, 8.58099861979956, 8.663464174963408, 4.942000786640907, 4.329049732850424, 5.3203171613021585, 9.598168534218628, 4.525182043932907, 5.360139368020368, 6.447407094931487, 7.701209770878679), # 176
(6.546356545795092, 4.986417883945522, 7.214821092440582, 8.124299749732613, 8.204832239254838, 4.682622193223941, 4.094595062704101, 5.0415833322144525, 9.096638044180112, 4.282602789357159, 5.073400510588858, 6.103779246172446, 7.2931869691634), # 177
(6.172712918896475, 4.697856594094126, 6.809559619185302, 7.665083095473786, 7.743286038803382, 4.421239210554052, 3.859495279064828, 4.760622406863145, 8.590884816910537, 4.0389660060276, 4.78529954998098, 5.758343060610604, 6.882633871725203), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(7, 8, 12, 7, 3, 0, 4, 4, 2, 0, 0, 2, 0, 10, 6, 1, 4, 4, 2, 4, 2, 5, 3, 0, 2, 0), # 0
(15, 18, 19, 19, 12, 3, 5, 5, 7, 2, 1, 2, 0, 17, 15, 9, 5, 18, 4, 5, 4, 6, 4, 0, 3, 0), # 1
(21, 28, 23, 31, 21, 4, 8, 9, 8, 3, 3, 2, 0, 29, 23, 14, 9, 28, 10, 6, 6, 11, 7, 3, 6, 0), # 2
(30, 33, 27, 37, 28, 8, 13, 14, 9, 9, 4, 6, 0, 43, 29, 19, 12, 34, 11, 11, 7, 13, 8, 6, 7, 0), # 3
(38, 41, 38, 50, 35, 9, 18, 19, 12, 10, 5, 6, 0, 50, 37, 26, 16, 40, 13, 15, 11, 17, 11, 7, 8, 0), # 4
(47, 48, 43, 61, 47, 14, 24, 23, 19, 13, 8, 7, 0, 58, 52, 33, 18, 46, 16, 19, 16, 23, 13, 9, 8, 0), # 5
(59, 59, 59, 73, 55, 17, 27, 25, 24, 16, 10, 8, 0, 66, 63, 38, 29, 57, 26, 23, 19, 27, 15, 10, 9, 0), # 6
(70, 68, 69, 79, 66, 24, 29, 29, 30, 18, 10, 10, 0, 76, 71, 55, 31, 63, 32, 27, 22, 30, 20, 11, 11, 0), # 7
(82, 82, 78, 92, 70, 25, 34, 41, 31, 20, 11, 12, 0, 85, 84, 61, 38, 79, 35, 32, 24, 36, 22, 12, 13, 0), # 8
(95, 90, 86, 102, 81, 27, 40, 45, 37, 26, 13, 13, 0, 96, 93, 69, 46, 88, 40, 42, 25, 38, 24, 13, 16, 0), # 9
(101, 104, 94, 118, 87, 30, 43, 47, 41, 29, 15, 15, 0, 106, 105, 79, 55, 95, 51, 50, 30, 42, 27, 17, 19, 0), # 10
(115, 112, 112, 133, 96, 34, 48, 49, 49, 30, 16, 15, 0, 125, 121, 90, 62, 107, 61, 53, 37, 50, 33, 21, 20, 0), # 11
(131, 125, 128, 144, 100, 37, 52, 54, 57, 30, 19, 16, 0, 139, 137, 96, 69, 119, 67, 60, 41, 59, 39, 24, 23, 0), # 12
(146, 140, 139, 158, 113, 42, 57, 56, 60, 32, 20, 17, 0, 152, 154, 105, 81, 133, 74, 66, 42, 62, 45, 27, 26, 0), # 13
(163, 155, 150, 176, 123, 46, 60, 59, 63, 33, 24, 17, 0, 163, 169, 111, 86, 149, 86, 69, 43, 70, 49, 31, 27, 0), # 14
(174, 173, 166, 186, 132, 49, 61, 62, 69, 37, 25, 18, 0, 177, 184, 121, 94, 156, 95, 81, 51, 79, 51, 33, 31, 0), # 15
(195, 185, 174, 196, 144, 54, 67, 67, 81, 39, 26, 19, 0, 186, 200, 133, 97, 174, 102, 86, 56, 84, 55, 35, 32, 0), # 16
(209, 201, 190, 216, 161, 64, 70, 72, 85, 40, 28, 20, 0, 201, 213, 147, 115, 186, 110, 92, 61, 90, 59, 38, 33, 0), # 17
(221, 212, 202, 232, 170, 69, 75, 79, 91, 45, 28, 21, 0, 215, 229, 155, 130, 206, 114, 97, 66, 94, 67, 39, 34, 0), # 18
(232, 236, 216, 244, 182, 72, 77, 86, 95, 49, 30, 22, 0, 231, 249, 163, 142, 217, 125, 100, 70, 97, 75, 41, 36, 0), # 19
(246, 252, 230, 265, 193, 78, 77, 90, 101, 51, 32, 22, 0, 242, 264, 175, 151, 231, 136, 109, 73, 102, 79, 42, 37, 0), # 20
(265, 263, 237, 283, 208, 84, 87, 98, 109, 51, 33, 24, 0, 259, 278, 190, 164, 247, 147, 111, 79, 105, 80, 45, 37, 0), # 21
(288, 276, 248, 298, 221, 90, 99, 109, 120, 52, 36, 26, 0, 275, 292, 202, 176, 265, 160, 113, 86, 114, 89, 49, 39, 0), # 22
(303, 297, 255, 311, 232, 99, 108, 117, 125, 52, 38, 30, 0, 287, 303, 213, 186, 280, 170, 119, 91, 121, 94, 56, 39, 0), # 23
(321, 314, 266, 322, 245, 106, 114, 123, 136, 53, 42, 31, 0, 303, 317, 225, 191, 294, 175, 124, 96, 130, 98, 59, 40, 0), # 24
(342, 337, 279, 337, 254, 110, 124, 124, 142, 58, 43, 31, 0, 318, 331, 231, 199, 307, 184, 131, 98, 139, 103, 63, 41, 0), # 25
(361, 353, 291, 356, 267, 114, 132, 130, 146, 58, 43, 33, 0, 333, 346, 246, 208, 317, 191, 137, 102, 142, 113, 67, 42, 0), # 26
(380, 368, 305, 369, 276, 117, 141, 135, 152, 64, 46, 33, 0, 358, 368, 254, 217, 333, 202, 142, 111, 145, 119, 71, 44, 0), # 27
(393, 383, 319, 387, 289, 126, 144, 143, 157, 66, 49, 34, 0, 373, 384, 267, 224, 348, 212, 148, 116, 154, 120, 73, 44, 0), # 28
(408, 395, 332, 398, 305, 128, 150, 151, 165, 70, 51, 36, 0, 387, 397, 279, 231, 362, 225, 156, 120, 159, 126, 74, 46, 0), # 29
(419, 412, 346, 418, 320, 135, 155, 154, 172, 74, 52, 37, 0, 407, 419, 294, 244, 373, 235, 166, 127, 162, 130, 77, 51, 0), # 30
(437, 424, 355, 436, 330, 140, 158, 159, 177, 77, 52, 39, 0, 425, 428, 306, 251, 388, 247, 170, 133, 175, 136, 81, 51, 0), # 31
(453, 437, 372, 450, 344, 145, 164, 164, 184, 82, 54, 39, 0, 441, 438, 319, 261, 406, 260, 173, 139, 179, 143, 86, 53, 0), # 32
(461, 451, 383, 464, 352, 149, 169, 166, 186, 82, 55, 39, 0, 457, 446, 333, 271, 419, 267, 178, 145, 183, 145, 87, 56, 0), # 33
(479, 469, 395, 480, 360, 154, 177, 171, 198, 86, 58, 40, 0, 478, 460, 341, 280, 436, 277, 182, 150, 188, 148, 90, 57, 0), # 34
(496, 479, 406, 489, 372, 161, 189, 178, 206, 93, 60, 42, 0, 497, 474, 348, 294, 450, 283, 185, 151, 193, 150, 93, 58, 0), # 35
(506, 496, 420, 508, 385, 165, 204, 184, 211, 96, 60, 42, 0, 509, 483, 357, 301, 463, 295, 192, 158, 195, 156, 97, 59, 0), # 36
(525, 509, 434, 525, 402, 169, 212, 191, 218, 100, 61, 42, 0, 521, 493, 370, 311, 475, 306, 199, 162, 202, 158, 100, 60, 0), # 37
(542, 521, 449, 549, 410, 171, 215, 197, 223, 100, 63, 43, 0, 538, 513, 379, 318, 491, 316, 206, 166, 204, 162, 102, 62, 0), # 38
(555, 540, 463, 561, 427, 175, 219, 203, 231, 101, 68, 44, 0, 559, 524, 385, 327, 500, 323, 214, 167, 209, 169, 103, 64, 0), # 39
(572, 561, 481, 574, 440, 182, 227, 206, 234, 108, 70, 44, 0, 577, 534, 396, 336, 516, 335, 218, 171, 213, 175, 107, 66, 0), # 40
(597, 585, 490, 591, 454, 186, 234, 208, 240, 112, 71, 45, 0, 592, 550, 409, 343, 528, 341, 224, 173, 220, 181, 109, 66, 0), # 41
(615, 604, 503, 608, 470, 193, 239, 211, 242, 117, 74, 46, 0, 611, 560, 415, 355, 544, 350, 225, 181, 229, 186, 110, 67, 0), # 42
(627, 613, 518, 623, 486, 197, 247, 218, 246, 122, 77, 48, 0, 632, 568, 430, 363, 555, 356, 233, 183, 232, 189, 110, 67, 0), # 43
(649, 627, 540, 636, 496, 200, 251, 223, 249, 129, 79, 50, 0, 652, 584, 439, 370, 568, 368, 239, 191, 239, 191, 114, 68, 0), # 44
(666, 637, 558, 651, 501, 204, 260, 229, 251, 130, 82, 51, 0, 668, 598, 450, 379, 586, 380, 251, 198, 244, 192, 119, 71, 0), # 45
(686, 648, 567, 664, 511, 209, 267, 239, 256, 131, 89, 53, 0, 689, 618, 462, 388, 600, 388, 257, 201, 251, 197, 122, 71, 0), # 46
(702, 667, 578, 674, 517, 217, 274, 245, 260, 133, 93, 54, 0, 706, 636, 478, 397, 616, 399, 264, 211, 257, 201, 124, 72, 0), # 47
(712, 685, 593, 688, 533, 223, 281, 251, 268, 136, 93, 56, 0, 717, 650, 488, 408, 633, 405, 271, 214, 264, 207, 126, 73, 0), # 48
(731, 702, 605, 700, 548, 232, 294, 257, 270, 141, 93, 59, 0, 731, 659, 495, 417, 643, 415, 279, 219, 269, 213, 131, 75, 0), # 49
(744, 717, 619, 711, 555, 236, 301, 263, 274, 144, 96, 59, 0, 745, 679, 506, 422, 661, 422, 291, 221, 277, 216, 134, 76, 0), # 50
(754, 737, 627, 724, 567, 243, 308, 271, 280, 147, 96, 62, 0, 767, 699, 519, 428, 672, 427, 294, 226, 286, 221, 135, 78, 0), # 51
(768, 752, 640, 739, 581, 248, 313, 279, 287, 151, 98, 64, 0, 783, 714, 526, 436, 688, 432, 299, 230, 293, 227, 138, 79, 0), # 52
(782, 766, 659, 759, 590, 259, 320, 287, 293, 155, 101, 67, 0, 802, 725, 540, 443, 700, 437, 302, 232, 296, 234, 139, 79, 0), # 53
(805, 786, 665, 776, 605, 261, 324, 290, 302, 158, 104, 68, 0, 816, 734, 555, 453, 712, 446, 310, 234, 301, 239, 143, 80, 0), # 54
(814, 801, 677, 797, 615, 265, 326, 296, 310, 161, 105, 70, 0, 829, 745, 569, 458, 727, 451, 316, 240, 311, 242, 147, 81, 0), # 55
(829, 814, 688, 804, 630, 271, 333, 298, 316, 162, 110, 71, 0, 840, 758, 578, 466, 734, 460, 322, 242, 321, 243, 150, 83, 0), # 56
(838, 825, 703, 817, 643, 280, 338, 303, 326, 164, 112, 73, 0, 851, 769, 592, 472, 746, 469, 323, 247, 327, 248, 154, 85, 0), # 57
(858, 841, 715, 827, 656, 284, 345, 306, 328, 167, 115, 75, 0, 863, 783, 605, 482, 755, 476, 330, 252, 335, 253, 154, 86, 0), # 58
(871, 851, 734, 841, 677, 288, 350, 311, 334, 173, 117, 76, 0, 878, 793, 612, 488, 772, 480, 337, 255, 338, 264, 156, 86, 0), # 59
(888, 864, 752, 855, 693, 293, 359, 315, 343, 175, 122, 77, 0, 898, 805, 619, 497, 783, 488, 342, 261, 341, 266, 158, 86, 0), # 60
(898, 875, 765, 867, 711, 297, 367, 323, 350, 180, 125, 79, 0, 920, 826, 625, 504, 794, 502, 346, 262, 345, 269, 160, 87, 0), # 61
(915, 886, 775, 885, 719, 311, 370, 327, 354, 181, 125, 80, 0, 934, 839, 641, 513, 806, 507, 353, 266, 358, 275, 161, 89, 0), # 62
(925, 902, 785, 901, 735, 318, 377, 333, 362, 182, 130, 82, 0, 943, 849, 657, 520, 820, 509, 358, 268, 367, 278, 167, 90, 0), # 63
(940, 916, 797, 914, 742, 324, 379, 336, 368, 184, 133, 84, 0, 961, 865, 674, 526, 825, 519, 362, 275, 368, 283, 170, 91, 0), # 64
(962, 926, 813, 932, 754, 336, 381, 341, 374, 187, 135, 85, 0, 976, 877, 681, 536, 836, 524, 367, 283, 376, 285, 172, 92, 0), # 65
(982, 942, 829, 948, 768, 342, 387, 343, 381, 188, 136, 88, 0, 985, 888, 686, 541, 851, 532, 376, 290, 383, 289, 174, 94, 0), # 66
(995, 949, 848, 965, 770, 348, 391, 346, 389, 189, 139, 90, 0, 1007, 908, 697, 545, 861, 532, 381, 291, 387, 293, 175, 97, 0), # 67
(1005, 967, 859, 985, 774, 354, 394, 349, 393, 191, 139, 91, 0, 1021, 911, 710, 549, 877, 537, 385, 292, 396, 297, 178, 98, 0), # 68
(1012, 987, 871, 1003, 785, 359, 404, 353, 399, 193, 139, 93, 0, 1039, 924, 723, 557, 897, 545, 393, 296, 402, 305, 179, 99, 0), # 69
(1030, 1000, 882, 1019, 800, 364, 406, 362, 408, 193, 140, 94, 0, 1060, 941, 728, 569, 911, 553, 398, 299, 405, 311, 179, 100, 0), # 70
(1052, 1019, 897, 1031, 811, 369, 409, 368, 412, 195, 141, 96, 0, 1079, 961, 740, 575, 923, 557, 401, 306, 413, 314, 184, 101, 0), # 71
(1062, 1030, 911, 1039, 819, 373, 421, 369, 417, 200, 145, 99, 0, 1091, 977, 753, 583, 936, 562, 411, 311, 427, 322, 188, 102, 0), # 72
(1076, 1044, 928, 1054, 833, 382, 428, 377, 423, 205, 146, 99, 0, 1103, 999, 764, 591, 954, 564, 416, 317, 431, 325, 193, 102, 0), # 73
(1091, 1058, 944, 1068, 842, 387, 430, 380, 431, 209, 146, 99, 0, 1116, 1008, 773, 597, 973, 574, 424, 321, 437, 330, 194, 105, 0), # 74
(1106, 1071, 957, 1082, 849, 389, 435, 383, 439, 212, 148, 103, 0, 1136, 1024, 787, 601, 984, 585, 425, 328, 447, 331, 197, 105, 0), # 75
(1120, 1077, 981, 1097, 859, 394, 447, 388, 445, 213, 151, 104, 0, 1152, 1038, 797, 611, 992, 591, 430, 330, 452, 332, 201, 106, 0), # 76
(1138, 1099, 990, 1113, 869, 402, 451, 396, 454, 215, 151, 108, 0, 1168, 1049, 808, 617, 1004, 599, 437, 332, 460, 335, 201, 106, 0), # 77
(1150, 1115, 1001, 1123, 874, 406, 456, 399, 460, 216, 152, 108, 0, 1182, 1061, 819, 624, 1011, 606, 446, 336, 464, 340, 202, 107, 0), # 78
(1166, 1125, 1016, 1138, 881, 413, 462, 404, 469, 218, 156, 109, 0, 1200, 1076, 825, 631, 1021, 614, 450, 341, 471, 343, 205, 110, 0), # 79
(1174, 1137, 1029, 1158, 899, 416, 467, 411, 473, 221, 157, 110, 0, 1220, 1091, 832, 639, 1037, 624, 454, 348, 483, 351, 206, 111, 0), # 80
(1187, 1159, 1047, 1166, 912, 421, 473, 417, 478, 223, 157, 110, 0, 1236, 1102, 842, 645, 1051, 635, 459, 358, 489, 352, 210, 114, 0), # 81
(1199, 1174, 1057, 1176, 926, 425, 478, 419, 485, 226, 157, 110, 0, 1247, 1115, 855, 653, 1062, 639, 462, 361, 494, 362, 213, 117, 0), # 82
(1223, 1190, 1070, 1188, 939, 427, 483, 429, 491, 229, 158, 110, 0, 1264, 1124, 867, 666, 1068, 648, 472, 364, 496, 364, 219, 120, 0), # 83
(1240, 1203, 1088, 1203, 961, 432, 488, 436, 498, 231, 159, 112, 0, 1282, 1138, 876, 677, 1084, 657, 481, 368, 506, 368, 221, 120, 0), # 84
(1257, 1211, 1101, 1216, 970, 434, 493, 438, 504, 233, 162, 113, 0, 1298, 1149, 887, 688, 1099, 663, 483, 376, 512, 375, 222, 122, 0), # 85
(1276, 1224, 1119, 1232, 987, 442, 499, 442, 512, 234, 163, 114, 0, 1311, 1165, 903, 701, 1113, 666, 488, 383, 515, 380, 225, 123, 0), # 86
(1293, 1234, 1131, 1251, 993, 450, 504, 443, 520, 237, 164, 116, 0, 1320, 1171, 920, 708, 1129, 670, 495, 388, 520, 385, 226, 124, 0), # 87
(1305, 1241, 1138, 1265, 1003, 457, 510, 446, 522, 242, 165, 118, 0, 1333, 1180, 934, 718, 1142, 677, 504, 392, 524, 390, 227, 124, 0), # 88
(1315, 1254, 1148, 1282, 1013, 459, 513, 452, 529, 244, 168, 118, 0, 1349, 1193, 953, 726, 1154, 681, 510, 395, 534, 396, 230, 126, 0), # 89
(1327, 1261, 1161, 1300, 1020, 463, 516, 453, 533, 252, 170, 119, 0, 1370, 1209, 963, 732, 1172, 686, 516, 400, 536, 400, 232, 127, 0), # 90
(1338, 1271, 1181, 1311, 1034, 469, 519, 458, 537, 256, 171, 119, 0, 1384, 1219, 968, 742, 1179, 695, 516, 403, 542, 408, 234, 128, 0), # 91
(1358, 1283, 1190, 1327, 1046, 474, 522, 463, 546, 258, 173, 120, 0, 1411, 1226, 977, 752, 1187, 699, 518, 409, 547, 410, 235, 129, 0), # 92
(1366, 1299, 1202, 1340, 1059, 479, 533, 467, 552, 262, 175, 122, 0, 1431, 1245, 984, 760, 1201, 708, 523, 413, 553, 412, 237, 129, 0), # 93
(1375, 1312, 1214, 1355, 1067, 482, 536, 468, 559, 263, 178, 123, 0, 1448, 1251, 992, 768, 1211, 714, 533, 417, 559, 419, 240, 130, 0), # 94
(1392, 1326, 1225, 1368, 1077, 485, 543, 473, 561, 266, 179, 127, 0, 1473, 1264, 1003, 777, 1223, 719, 539, 422, 562, 424, 243, 130, 0), # 95
(1414, 1339, 1247, 1383, 1085, 491, 550, 477, 570, 267, 181, 128, 0, 1483, 1277, 1019, 783, 1234, 727, 545, 426, 572, 428, 248, 130, 0), # 96
(1425, 1350, 1261, 1402, 1107, 495, 555, 483, 576, 271, 184, 130, 0, 1498, 1284, 1030, 795, 1243, 733, 547, 428, 580, 429, 254, 130, 0), # 97
(1436, 1362, 1272, 1412, 1117, 504, 563, 489, 588, 273, 188, 131, 0, 1508, 1291, 1044, 806, 1256, 740, 550, 431, 584, 432, 261, 131, 0), # 98
(1446, 1374, 1290, 1422, 1132, 509, 567, 493, 590, 275, 189, 134, 0, 1521, 1305, 1051, 817, 1270, 741, 555, 434, 589, 435, 265, 132, 0), # 99
(1457, 1381, 1307, 1438, 1146, 513, 572, 498, 596, 277, 190, 134, 0, 1542, 1318, 1058, 822, 1280, 747, 561, 440, 596, 437, 268, 134, 0), # 100
(1474, 1395, 1322, 1454, 1158, 519, 580, 502, 601, 279, 191, 135, 0, 1554, 1330, 1068, 832, 1287, 756, 564, 444, 601, 439, 270, 135, 0), # 101
(1487, 1407, 1337, 1458, 1169, 526, 586, 503, 605, 284, 193, 135, 0, 1572, 1347, 1080, 844, 1302, 760, 572, 444, 612, 444, 273, 136, 0), # 102
(1496, 1418, 1346, 1471, 1190, 533, 595, 510, 615, 286, 195, 137, 0, 1588, 1354, 1093, 853, 1307, 762, 576, 448, 617, 449, 277, 137, 0), # 103
(1515, 1429, 1358, 1485, 1195, 545, 599, 514, 623, 289, 199, 137, 0, 1602, 1363, 1105, 865, 1320, 766, 581, 450, 621, 452, 280, 138, 0), # 104
(1526, 1441, 1373, 1505, 1209, 550, 604, 518, 629, 291, 199, 137, 0, 1617, 1375, 1111, 874, 1334, 771, 587, 452, 628, 459, 282, 144, 0), # 105
(1552, 1453, 1390, 1519, 1220, 557, 605, 521, 636, 294, 201, 137, 0, 1635, 1390, 1127, 887, 1351, 778, 595, 454, 635, 466, 283, 145, 0), # 106
(1562, 1468, 1401, 1528, 1229, 565, 609, 522, 643, 297, 201, 140, 0, 1649, 1400, 1136, 891, 1361, 782, 600, 458, 641, 468, 285, 148, 0), # 107
(1577, 1478, 1412, 1536, 1236, 571, 612, 529, 645, 301, 204, 140, 0, 1667, 1413, 1143, 900, 1371, 791, 610, 460, 646, 473, 289, 149, 0), # 108
(1592, 1490, 1432, 1549, 1242, 574, 620, 533, 650, 301, 207, 141, 0, 1684, 1421, 1156, 910, 1386, 797, 615, 463, 650, 481, 292, 150, 0), # 109
(1606, 1509, 1441, 1560, 1251, 580, 627, 538, 655, 306, 208, 142, 0, 1699, 1432, 1165, 916, 1399, 804, 620, 469, 655, 484, 293, 151, 0), # 110
(1620, 1517, 1449, 1568, 1264, 585, 631, 543, 661, 308, 208, 143, 0, 1710, 1440, 1175, 922, 1413, 810, 624, 476, 662, 488, 295, 153, 0), # 111
(1629, 1529, 1458, 1581, 1276, 590, 636, 545, 667, 313, 208, 144, 0, 1717, 1456, 1184, 930, 1422, 813, 626, 482, 669, 492, 296, 153, 0), # 112
(1648, 1544, 1473, 1601, 1291, 594, 636, 552, 676, 314, 209, 144, 0, 1734, 1468, 1197, 940, 1430, 819, 632, 485, 674, 494, 298, 153, 0), # 113
(1664, 1558, 1485, 1612, 1304, 602, 639, 558, 681, 317, 210, 144, 0, 1741, 1485, 1204, 950, 1441, 825, 638, 489, 678, 498, 301, 153, 0), # 114
(1678, 1569, 1501, 1630, 1312, 606, 644, 562, 688, 319, 211, 144, 0, 1756, 1493, 1211, 953, 1453, 831, 642, 491, 684, 504, 304, 154, 0), # 115
(1694, 1582, 1510, 1639, 1328, 612, 648, 566, 697, 321, 211, 145, 0, 1773, 1506, 1219, 961, 1470, 835, 645, 494, 688, 513, 307, 154, 0), # 116
(1711, 1600, 1525, 1651, 1341, 618, 653, 571, 700, 326, 212, 146, 0, 1787, 1520, 1230, 969, 1480, 843, 652, 499, 694, 516, 309, 157, 0), # 117
(1727, 1615, 1537, 1658, 1353, 621, 661, 577, 702, 333, 213, 149, 0, 1809, 1534, 1241, 972, 1494, 846, 654, 501, 701, 518, 312, 158, 0), # 118
(1732, 1629, 1553, 1669, 1371, 624, 666, 580, 712, 335, 214, 151, 0, 1825, 1550, 1251, 975, 1506, 850, 661, 505, 706, 522, 315, 158, 0), # 119
(1747, 1639, 1564, 1681, 1381, 626, 676, 583, 714, 341, 215, 154, 0, 1840, 1570, 1263, 980, 1517, 855, 666, 508, 713, 526, 316, 159, 0), # 120
(1763, 1649, 1576, 1696, 1395, 630, 680, 587, 721, 344, 215, 154, 0, 1854, 1583, 1267, 991, 1529, 859, 671, 509, 722, 533, 322, 160, 0), # 121
(1769, 1658, 1582, 1707, 1404, 634, 687, 591, 728, 346, 216, 155, 0, 1864, 1594, 1278, 996, 1544, 865, 675, 513, 726, 538, 325, 161, 0), # 122
(1782, 1669, 1591, 1719, 1414, 642, 693, 594, 733, 347, 217, 156, 0, 1879, 1605, 1286, 1006, 1555, 869, 680, 519, 738, 538, 326, 164, 0), # 123
(1794, 1678, 1599, 1729, 1425, 646, 698, 599, 737, 349, 218, 159, 0, 1897, 1617, 1291, 1012, 1570, 871, 684, 523, 744, 541, 327, 164, 0), # 124
(1808, 1693, 1614, 1749, 1433, 651, 699, 602, 745, 353, 219, 161, 0, 1908, 1627, 1303, 1014, 1580, 880, 690, 526, 750, 546, 332, 165, 0), # 125
(1819, 1705, 1626, 1764, 1449, 657, 701, 603, 747, 353, 220, 163, 0, 1925, 1639, 1312, 1022, 1587, 886, 695, 526, 754, 546, 337, 166, 0), # 126
(1832, 1713, 1638, 1774, 1456, 659, 708, 605, 751, 355, 222, 163, 0, 1942, 1646, 1322, 1029, 1603, 892, 700, 531, 759, 552, 337, 167, 0), # 127
(1847, 1729, 1646, 1780, 1470, 663, 714, 609, 756, 358, 223, 164, 0, 1952, 1656, 1331, 1036, 1612, 897, 703, 537, 764, 558, 341, 168, 0), # 128
(1859, 1739, 1658, 1789, 1480, 668, 717, 613, 760, 360, 226, 165, 0, 1961, 1665, 1335, 1044, 1631, 907, 705, 541, 768, 562, 343, 169, 0), # 129
(1880, 1750, 1670, 1803, 1491, 671, 724, 615, 769, 364, 227, 166, 0, 1971, 1672, 1345, 1051, 1639, 915, 708, 545, 774, 566, 347, 170, 0), # 130
(1895, 1763, 1678, 1822, 1507, 674, 729, 618, 775, 366, 230, 166, 0, 1984, 1683, 1357, 1055, 1652, 920, 713, 548, 776, 570, 350, 171, 0), # 131
(1905, 1775, 1693, 1836, 1517, 680, 736, 619, 780, 366, 230, 166, 0, 1996, 1695, 1368, 1061, 1658, 930, 720, 553, 783, 575, 350, 172, 0), # 132
(1920, 1786, 1703, 1855, 1523, 683, 740, 623, 787, 368, 233, 167, 0, 2011, 1708, 1374, 1066, 1668, 935, 724, 558, 791, 578, 351, 174, 0), # 133
(1932, 1801, 1711, 1868, 1534, 691, 742, 628, 794, 370, 234, 168, 0, 2028, 1719, 1381, 1073, 1679, 939, 729, 562, 794, 581, 352, 176, 0), # 134
(1948, 1806, 1720, 1879, 1542, 697, 747, 633, 799, 371, 234, 168, 0, 2043, 1732, 1385, 1077, 1693, 944, 732, 567, 796, 585, 354, 176, 0), # 135
(1963, 1816, 1730, 1890, 1559, 702, 753, 636, 803, 372, 234, 168, 0, 2054, 1747, 1389, 1086, 1704, 947, 737, 568, 802, 589, 357, 176, 0), # 136
(1973, 1825, 1741, 1905, 1564, 709, 756, 640, 807, 373, 236, 168, 0, 2066, 1754, 1401, 1094, 1719, 955, 737, 572, 802, 595, 359, 177, 0), # 137
(1989, 1835, 1757, 1915, 1576, 712, 758, 645, 813, 376, 238, 168, 0, 2080, 1763, 1408, 1100, 1730, 960, 740, 573, 804, 597, 361, 177, 0), # 138
(1998, 1842, 1766, 1932, 1589, 716, 759, 652, 822, 380, 239, 169, 0, 2095, 1771, 1412, 1106, 1742, 969, 746, 575, 807, 599, 362, 178, 0), # 139
(2010, 1854, 1779, 1941, 1596, 722, 764, 656, 831, 382, 240, 169, 0, 2108, 1784, 1426, 1110, 1753, 975, 751, 579, 814, 604, 366, 179, 0), # 140
(2018, 1866, 1788, 1951, 1608, 725, 769, 660, 837, 383, 240, 169, 0, 2123, 1790, 1436, 1117, 1761, 986, 751, 581, 820, 607, 368, 179, 0), # 141
(2038, 1877, 1797, 1962, 1622, 727, 772, 664, 843, 386, 241, 170, 0, 2132, 1802, 1443, 1129, 1777, 995, 758, 583, 823, 614, 369, 180, 0), # 142
(2051, 1887, 1805, 1974, 1635, 731, 779, 668, 845, 388, 242, 174, 0, 2142, 1819, 1454, 1131, 1786, 1000, 763, 585, 829, 617, 369, 181, 0), # 143
(2060, 1901, 1813, 1981, 1644, 735, 782, 672, 851, 389, 246, 174, 0, 2151, 1828, 1459, 1135, 1793, 1005, 766, 593, 831, 620, 370, 182, 0), # 144
(2071, 1915, 1817, 1994, 1652, 739, 786, 673, 855, 394, 247, 174, 0, 2163, 1842, 1470, 1143, 1803, 1009, 769, 598, 836, 625, 373, 185, 0), # 145
(2087, 1918, 1831, 2008, 1666, 742, 791, 675, 860, 397, 249, 175, 0, 2176, 1853, 1476, 1144, 1809, 1016, 769, 601, 840, 631, 375, 185, 0), # 146
(2095, 1925, 1839, 2017, 1677, 749, 793, 679, 866, 400, 251, 176, 0, 2198, 1863, 1488, 1154, 1820, 1023, 771, 604, 843, 634, 378, 185, 0), # 147
(2108, 1934, 1847, 2026, 1685, 757, 796, 682, 871, 402, 252, 177, 0, 2211, 1871, 1494, 1159, 1827, 1033, 775, 607, 849, 637, 381, 186, 0), # 148
(2127, 1940, 1863, 2034, 1690, 761, 800, 684, 874, 403, 255, 177, 0, 2231, 1877, 1505, 1165, 1835, 1041, 779, 609, 854, 642, 382, 187, 0), # 149
(2143, 1951, 1875, 2049, 1699, 767, 803, 686, 880, 405, 257, 178, 0, 2239, 1885, 1515, 1171, 1844, 1045, 781, 613, 859, 645, 383, 187, 0), # 150
(2152, 1964, 1887, 2060, 1709, 769, 805, 692, 882, 409, 259, 179, 0, 2256, 1893, 1521, 1176, 1855, 1049, 785, 616, 864, 650, 384, 188, 0), # 151
(2170, 1972, 1896, 2067, 1722, 775, 812, 693, 890, 409, 262, 182, 0, 2267, 1903, 1531, 1182, 1868, 1055, 788, 617, 871, 653, 388, 191, 0), # 152
(2185, 1978, 1910, 2082, 1732, 782, 819, 697, 894, 411, 263, 183, 0, 2283, 1911, 1537, 1190, 1875, 1057, 792, 618, 875, 662, 391, 192, 0), # 153
(2203, 1984, 1917, 2094, 1744, 785, 820, 701, 901, 413, 263, 183, 0, 2297, 1918, 1545, 1199, 1882, 1061, 794, 620, 882, 667, 392, 194, 0), # 154
(2217, 1992, 1931, 2105, 1760, 791, 822, 705, 906, 417, 264, 183, 0, 2311, 1929, 1554, 1207, 1889, 1064, 796, 624, 889, 674, 393, 194, 0), # 155
(2229, 2003, 1942, 2115, 1774, 797, 828, 709, 910, 419, 265, 183, 0, 2325, 1941, 1560, 1211, 1898, 1067, 801, 627, 896, 678, 394, 195, 0), # 156
(2239, 2012, 1948, 2126, 1781, 805, 835, 712, 912, 424, 267, 184, 0, 2336, 1946, 1564, 1224, 1909, 1070, 804, 629, 899, 683, 397, 195, 0), # 157
(2248, 2020, 1960, 2135, 1790, 814, 837, 720, 917, 425, 267, 184, 0, 2351, 1951, 1570, 1234, 1922, 1075, 812, 632, 903, 685, 398, 196, 0), # 158
(2261, 2031, 1972, 2145, 1803, 819, 840, 723, 921, 426, 269, 184, 0, 2364, 1965, 1576, 1238, 1935, 1081, 813, 638, 907, 689, 402, 198, 0), # 159
(2267, 2040, 1987, 2150, 1807, 823, 846, 727, 924, 429, 271, 185, 0, 2372, 1979, 1580, 1244, 1945, 1084, 813, 639, 911, 695, 402, 200, 0), # 160
(2278, 2047, 1994, 2156, 1812, 826, 851, 730, 930, 432, 272, 187, 0, 2383, 1992, 1586, 1250, 1950, 1092, 818, 643, 916, 696, 404, 200, 0), # 161
(2288, 2054, 2005, 2171, 1823, 833, 852, 732, 931, 434, 275, 188, 0, 2394, 2000, 1593, 1259, 1955, 1095, 821, 648, 921, 696, 407, 200, 0), # 162
(2300, 2058, 2017, 2184, 1832, 837, 859, 732, 935, 436, 275, 191, 0, 2411, 2010, 1595, 1266, 1967, 1105, 824, 651, 924, 702, 409, 200, 0), # 163
(2311, 2061, 2025, 2191, 1843, 840, 862, 738, 943, 436, 278, 192, 0, 2420, 2018, 1601, 1273, 1980, 1109, 826, 657, 929, 703, 411, 201, 0), # 164
(2318, 2072, 2035, 2204, 1848, 846, 865, 739, 947, 438, 279, 192, 0, 2432, 2034, 1611, 1276, 1988, 1116, 829, 658, 933, 708, 415, 201, 0), # 165
(2333, 2082, 2045, 2212, 1860, 852, 867, 744, 951, 438, 281, 193, 0, 2440, 2047, 1618, 1282, 1997, 1122, 830, 664, 937, 711, 415, 203, 0), # 166
(2345, 2092, 2053, 2222, 1873, 853, 872, 747, 954, 439, 281, 193, 0, 2459, 2056, 1624, 1287, 2003, 1131, 832, 668, 941, 714, 417, 203, 0), # 167
(2353, 2100, 2060, 2233, 1885, 857, 876, 747, 956, 442, 283, 193, 0, 2467, 2064, 1633, 1292, 2010, 1136, 837, 670, 947, 715, 417, 205, 0), # 168
(2365, 2109, 2069, 2246, 1898, 858, 878, 753, 960, 443, 284, 193, 0, 2476, 2072, 1642, 1295, 2017, 1138, 841, 673, 951, 717, 417, 205, 0), # 169
(2379, 2114, 2080, 2252, 1900, 862, 880, 759, 966, 443, 284, 193, 0, 2483, 2082, 1648, 1303, 2025, 1143, 843, 677, 952, 720, 420, 205, 0), # 170
(2382, 2120, 2088, 2263, 1910, 869, 883, 761, 971, 444, 285, 193, 0, 2494, 2088, 1651, 1306, 2030, 1149, 843, 677, 954, 722, 421, 207, 0), # 171
(2389, 2123, 2095, 2266, 1914, 872, 883, 765, 973, 444, 285, 194, 0, 2501, 2094, 1654, 1312, 2038, 1153, 847, 678, 958, 725, 423, 208, 0), # 172
(2394, 2127, 2101, 2272, 1918, 880, 886, 768, 980, 445, 285, 196, 0, 2513, 2104, 1662, 1316, 2051, 1155, 850, 681, 963, 726, 423, 208, 0), # 173
(2398, 2133, 2104, 2277, 1923, 883, 886, 771, 983, 446, 285, 197, 0, 2520, 2108, 1664, 1321, 2060, 1160, 850, 684, 966, 730, 425, 209, 0), # 174
(2405, 2135, 2113, 2283, 1929, 889, 890, 772, 988, 446, 286, 197, 0, 2525, 2116, 1670, 1322, 2066, 1161, 852, 688, 970, 733, 427, 209, 0), # 175
(2411, 2140, 2119, 2291, 1937, 889, 891, 775, 996, 447, 287, 198, 0, 2531, 2124, 1675, 1326, 2069, 1164, 853, 690, 973, 734, 428, 210, 0), # 176
(2419, 2141, 2124, 2302, 1939, 890, 893, 778, 997, 447, 287, 198, 0, 2541, 2127, 1676, 1327, 2073, 1166, 853, 694, 977, 735, 428, 210, 0), # 177
(2426, 2142, 2127, 2310, 1946, 890, 896, 780, 1000, 448, 290, 200, 0, 2546, 2137, 1682, 1332, 2075, 1172, 853, 698, 977, 741, 430, 210, 0), # 178
(2426, 2142, 2127, 2310, 1946, 890, 896, 780, 1000, 448, 290, 200, 0, 2546, 2137, 1682, 1332, 2075, 1172, 853, 698, 977, 741, 430, 210, 0), # 179
)
passenger_arriving_rate = (
(8.033384925394829, 8.103756554216645, 6.9483776394833425, 7.45760132863612, 5.924997981450252, 2.9294112699015167, 3.3168284922991322, 3.102117448652949, 3.2480528331562706, 1.5832060062089484, 1.1214040437028276, 0.6530553437741565, 0.0, 8.134208340125381, 7.183608781515721, 5.607020218514138, 4.749618018626844, 6.496105666312541, 4.342964428114128, 3.3168284922991322, 2.0924366213582264, 2.962498990725126, 2.4858671095453735, 1.3896755278966686, 0.7367051412924223, 0.0), # 0
(8.566923443231959, 8.638755684745645, 7.407128788440204, 7.95017310393194, 6.317323026639185, 3.122918011773052, 3.535575153010955, 3.306342481937139, 3.462530840710885, 1.6875922769108604, 1.1954923029216353, 0.6961622214419141, 0.0, 8.671666635903767, 7.657784435861053, 5.9774615146081755, 5.06277683073258, 6.92506168142177, 4.628879474711995, 3.535575153010955, 2.230655722695037, 3.1586615133195926, 2.650057701310647, 1.4814257576880407, 0.7853414258859679, 0.0), # 1
(9.09875681436757, 9.171631583973436, 7.864056380729885, 8.440785245597754, 6.708227171999727, 3.3156527735449486, 3.7534548063685635, 3.5097501652696135, 3.676152963668026, 1.7915655100082188, 1.269286173007017, 0.7390976869404075, 0.0, 9.206983725135505, 8.13007455634448, 6.346430865035084, 5.374696530024655, 7.352305927336052, 4.913650231377459, 3.7534548063685635, 2.3683234096749635, 3.3541135859998636, 2.8135950818659183, 1.5728112761459772, 0.8337846894521307, 0.0), # 2
(9.6268124690345, 9.70027006950679, 8.317347825759807, 8.927491689038488, 7.096172454402028, 3.5068512477461056, 3.9696029133183646, 3.7115341049963386, 3.8880720858245827, 1.8947130793704727, 1.3424929098206355, 0.7816914246573948, 0.0, 9.738036490006762, 8.598605671231342, 6.712464549103178, 5.684139238111417, 7.7761441716491655, 5.196147746994874, 3.9696029133183646, 2.5048937483900753, 3.548086227201014, 2.97583056301283, 1.6634695651519613, 0.8818427335915264, 0.0), # 3
(10.149017837465571, 10.222556958952469, 8.765190532937382, 9.408346369659084, 7.479620910716259, 3.6957491269054237, 4.183154934806767, 3.910887907463277, 4.097441090977444, 1.996622358867072, 1.4148197692241535, 0.8237731189806353, 0.0, 10.262701812703709, 9.061504308786986, 7.074098846120767, 5.9898670766012145, 8.194882181954888, 5.475243070448588, 4.183154934806767, 2.6398208049324454, 3.7398104553581293, 3.136115456553029, 1.7530381065874767, 0.9293233599047701, 0.0), # 4
(10.663300349893618, 10.736378069917262, 9.205771911670025, 9.881403222864472, 7.8570345778125645, 3.8815821035518008, 4.393246331780179, 4.1070051790163955, 4.303412862923498, 2.096880722367466, 1.4859740070792353, 0.8651724542978865, 0.0, 10.778856575412524, 9.51689699727675, 7.429870035396177, 6.290642167102396, 8.606825725846996, 5.749807250622953, 4.393246331780179, 2.772558645394143, 3.9285172889062823, 3.2938010742881585, 1.841154382334005, 0.9760343699924785, 0.0), # 5
(11.167587436551466, 11.239619220007935, 9.637279371365155, 10.344716184059584, 8.226875492561113, 4.06358587021414, 4.59901256518501, 4.299079526001659, 4.5051402854596345, 2.195075543741104, 1.555662879247542, 0.9057191149969079, 0.0, 11.284377660319372, 9.962910264965986, 7.77831439623771, 6.5852266312233105, 9.010280570919269, 6.018711336402323, 4.59901256518501, 2.902561335867243, 4.113437746280557, 3.448238728019862, 1.9274558742730312, 1.021783565455267, 0.0), # 6
(11.65980652767195, 11.73016622683126, 10.05790032143018, 10.796339188649355, 8.587605691832056, 4.2409961194213395, 4.799589095967668, 4.486304554765035, 4.701776242382744, 2.2907941968574352, 1.6235936415907386, 0.9452427854654573, 0.0, 11.777141949610431, 10.397670640120028, 8.117968207953693, 6.872382590572304, 9.403552484765488, 6.280826376671049, 4.799589095967668, 3.029282942443814, 4.293802845916028, 3.598779729549786, 2.0115800642860364, 1.066378747893751, 0.0), # 7
(12.137885053487896, 12.205904907994013, 10.465822171272528, 11.234326172038713, 8.937687212495558, 4.413048543702297, 4.994111385074558, 4.667873871652484, 4.89247361748971, 2.3836240555859103, 1.6894735499704858, 0.9835731500912939, 0.0, 12.255026325471867, 10.81930465100423, 8.447367749852429, 7.150872166757729, 9.78494723497942, 6.535023420313477, 4.994111385074558, 3.152177531215927, 4.468843606247779, 3.744775390679572, 2.093164434254506, 1.1096277189085468, 0.0), # 8
(12.599750444232136, 12.664721081102966, 10.859232330299607, 11.656731069632603, 9.27558209142177, 4.578978835585919, 5.181714893452096, 4.842981083009976, 5.076385294577426, 2.4731524937959772, 1.7530098602484476, 1.0205398932621754, 0.0, 12.71590767008986, 11.225938825883926, 8.765049301242238, 7.41945748138793, 10.152770589154851, 6.780173516213966, 5.181714893452096, 3.270699168275656, 4.637791045710885, 3.8855770232108684, 2.1718464660599213, 1.1513382801002698, 0.0), # 9
(13.043330130137491, 13.104500563764889, 11.236318207918833, 12.061607816835945, 9.599752365480853, 4.7380226876011005, 5.361535082046684, 5.010819795183474, 5.252664157442781, 2.558966885357086, 1.8139098282862867, 1.0559726993658605, 0.0, 13.157662865650577, 11.615699693024464, 9.069549141431432, 7.676900656071258, 10.505328314885562, 7.015147713256865, 5.361535082046684, 3.3843019197150714, 4.799876182740427, 4.020535938945316, 2.247263641583767, 1.1913182330695355, 0.0), # 10
(13.466551541436809, 13.52312917358657, 11.595267213537621, 12.447010349053677, 9.908660071542968, 4.889415792276744, 5.532707411804733, 5.170583614518944, 5.420463089882663, 2.640654604138688, 1.8718807099456667, 1.0897012527901082, 0.0, 13.57816879434018, 11.986713780691188, 9.359403549728333, 7.921963812416063, 10.840926179765326, 7.238817060326522, 5.532707411804733, 3.4924398516262456, 4.954330035771484, 4.14900344968456, 2.3190534427075247, 1.229375379416961, 0.0), # 11
(13.8673421083629, 13.918492728174757, 11.934266756563387, 12.810992601690735, 10.200767246478268, 5.032393842141746, 5.694367343672649, 5.321466147362347, 5.578934975693962, 2.7178030240102293, 1.9266297610882495, 1.1215552379226759, 0.0, 13.975302338344855, 12.337107617149433, 9.633148805441246, 8.153409072030687, 11.157869951387925, 7.4500526063072865, 5.694367343672649, 3.5945670301012465, 5.100383623239134, 4.270330867230246, 2.3868533513126775, 1.26531752074316, 0.0), # 12
(14.243629261148602, 14.288477045136244, 12.251504246403549, 13.151608510152053, 10.474535927156907, 5.166192529725009, 5.845650338596845, 5.462661000059654, 5.727232698673564, 2.7899995188411624, 1.9778642375756985, 1.1513643391513229, 0.0, 14.346940379850777, 12.66500773066455, 9.889321187878492, 8.369998556523486, 11.454465397347128, 7.647725400083517, 5.845650338596845, 3.6901375212321494, 5.237267963578454, 4.383869503384019, 2.45030084928071, 1.2989524586487495, 0.0), # 13
(14.593340430026746, 14.630967942077797, 12.54516709246553, 13.466912009842552, 10.728428150449055, 5.2900475475554325, 5.9856918575237295, 5.593361778956831, 5.864509142618358, 2.856831462500934, 2.0252913952696763, 1.1789582408638082, 0.0, 14.690959801044102, 12.968540649501888, 10.12645697634838, 8.570494387502801, 11.729018285236716, 7.830706490539565, 5.9856918575237295, 3.778605391111023, 5.3642140752245275, 4.488970669947518, 2.509033418493106, 1.3300879947343454, 0.0), # 14
(14.914403045230168, 14.943851236606186, 12.813442704156724, 13.754957036167184, 10.960905953224861, 5.403194588161918, 6.1136273613997005, 5.7127620903998375, 5.989917191325237, 2.917886228858997, 2.0686184900318456, 1.2041666274478897, 0.0, 15.00523748411101, 13.245832901926784, 10.343092450159226, 8.753658686576989, 11.979834382650473, 7.997866926559773, 6.1136273613997005, 3.8594247058299413, 5.480452976612431, 4.584985678722395, 2.562688540831345, 1.3585319306005625, 0.0), # 15
(15.204744536991681, 15.225012746328195, 13.054518490884568, 14.013797524530858, 11.170431372354487, 5.504869344073363, 6.228592311171181, 5.820055540734641, 6.102609728591085, 2.972751191784799, 2.1075527777238703, 1.2268191832913256, 0.0, 15.287650311237673, 13.495011016204579, 10.53776388861935, 8.918253575354395, 12.20521945718217, 8.148077757028497, 6.228592311171181, 3.932049531480973, 5.585215686177244, 4.671265841510287, 2.6109036981769136, 1.384092067848018, 0.0), # 16
(15.46229233554412, 15.472338288850588, 13.266581862056471, 14.241487410338536, 11.355466444708094, 5.594307507818667, 6.329722167784569, 5.914435736307213, 6.201739638212791, 3.021013725147788, 2.141801514207413, 1.2467455927818742, 0.0, 15.536075164610265, 13.714201520600614, 10.709007571037066, 9.063041175443361, 12.403479276425582, 8.280210030830098, 6.329722167784569, 3.9959339341561906, 5.677733222354047, 4.747162470112846, 2.6533163724112945, 1.4065762080773265, 0.0), # 17
(15.684973871120327, 15.683713681780135, 13.447820227079841, 14.436080628995136, 11.514473207155827, 5.670744771926737, 6.416152392186281, 5.995096283463507, 6.286459803987251, 3.0622612028174157, 2.171071955344136, 1.2637755403072954, 0.0, 15.748388926414954, 13.901530943380248, 10.855359776720679, 9.186783608452245, 12.572919607974502, 8.39313479684891, 6.416152392186281, 4.050531979947669, 5.757236603577914, 4.812026876331712, 2.689564045415968, 1.4257921528891033, 0.0), # 18
(15.870716573953118, 15.857024742723624, 13.596420995362104, 14.59563111590558, 11.645913696567856, 5.733416828926462, 6.4870184453227155, 6.061230788549498, 6.355923109711349, 3.0960809986631324, 2.1950713569957014, 1.2777387102553464, 0.0, 15.922468478837914, 14.055125812808807, 10.975356784978505, 9.288242995989394, 12.711846219422698, 8.485723103969297, 6.4870184453227155, 4.095297734947473, 5.822956848283928, 4.865210371968527, 2.7192841990724212, 1.441547703883966, 0.0), # 19
(16.01744787427533, 15.990157289287811, 13.710571576310672, 14.718192806474825, 11.748249949814339, 5.781559371346751, 6.54145578814029, 6.112032857911145, 6.409282439181973, 3.1220604865543846, 2.213506975023774, 1.2884647870137858, 0.0, 16.05619070406532, 14.17311265715164, 11.067534875118868, 9.366181459663151, 12.818564878363945, 8.556846001075604, 6.54145578814029, 4.129685265247679, 5.874124974907169, 4.9060642688249425, 2.7421143152621346, 1.4536506626625285, 0.0), # 20
(16.123095202319785, 16.080997139079486, 13.78845937933296, 14.801819636107783, 11.819944003765428, 5.8144080917165, 6.578599881585408, 6.1466960978944165, 6.445690676196012, 3.139787040360623, 2.226086065290016, 1.2957834549703726, 0.0, 16.147432484283325, 14.253618004674097, 11.13043032645008, 9.419361121081867, 12.891381352392024, 8.605374537052183, 6.578599881585408, 4.153148636940357, 5.909972001882714, 4.933939878702596, 2.757691875866592, 1.461908830825408, 0.0), # 21
(16.18558598831933, 16.12743010970541, 13.82827181383638, 14.844565540209405, 11.85945789529128, 5.83119868256461, 6.59758618660448, 6.164414114845277, 6.464300704550355, 3.148848033951298, 2.232515883656091, 1.2995243985128655, 0.0, 16.194070701678125, 14.294768383641518, 11.162579418280455, 9.446544101853892, 12.92860140910071, 8.630179760783388, 6.59758618660448, 4.1651419161175784, 5.92972894764564, 4.948188513403136, 2.7656543627672763, 1.4661300099732195, 0.0), # 22
(16.208629381348224, 16.132927937814358, 13.83323090992227, 14.849916975308645, 11.869580859768103, 5.833333333333334, 6.599843201807471, 6.166329218106997, 6.466627325102881, 3.149916909007774, 2.233322143243131, 1.2999863435451913, 0.0, 16.2, 14.299849778997103, 11.166610716215654, 9.44975072702332, 12.933254650205763, 8.632860905349796, 6.599843201807471, 4.166666666666667, 5.9347904298840515, 4.949972325102882, 2.7666461819844543, 1.4666298125285782, 0.0), # 23
(16.225619860854646, 16.12972098765432, 13.832419753086421, 14.849258333333335, 11.875314787855842, 5.833333333333334, 6.598603050108934, 6.163666666666667, 6.466315555555555, 3.149260246913581, 2.2332332210998884, 1.2998781893004117, 0.0, 16.2, 14.298660082304526, 11.166166105499443, 9.44778074074074, 12.93263111111111, 8.629133333333334, 6.598603050108934, 4.166666666666667, 5.937657393927921, 4.949752777777779, 2.7664839506172845, 1.4663382716049385, 0.0), # 24
(16.242251568338528, 16.1233996342021, 13.830818472793784, 14.847955246913582, 11.880922608634137, 5.833333333333334, 6.596159122085048, 6.158436213991771, 6.465699588477367, 3.1479675354366723, 2.233056906513697, 1.2996646852613931, 0.0, 16.2, 14.296311537875322, 11.165284532568485, 9.443902606310015, 12.931399176954734, 8.62181069958848, 6.596159122085048, 4.166666666666667, 5.940461304317068, 4.949318415637862, 2.766163694558757, 1.4657636031092822, 0.0), # 25
(16.258523230476854, 16.114060448102425, 13.828449016918157, 14.846022530864197, 11.886404126315846, 5.833333333333334, 6.592549374646977, 6.150736625514405, 6.46478732510288, 3.146060283493371, 2.2327947956935614, 1.2993487578113097, 0.0, 16.2, 14.292836335924404, 11.163973978467807, 9.43818085048011, 12.92957465020576, 8.611031275720167, 6.592549374646977, 4.166666666666667, 5.943202063157923, 4.948674176954733, 2.7656898033836312, 1.46491458619113, 0.0), # 26
(16.27443357394662, 16.1018, 13.825333333333333, 14.843475, 11.891759145113827, 5.833333333333334, 6.587811764705883, 6.140666666666667, 6.463586666666666, 3.143560000000001, 2.232448484848485, 1.2989333333333337, 0.0, 16.2, 14.288266666666669, 11.162242424242425, 9.430679999999999, 12.927173333333332, 8.596933333333334, 6.587811764705883, 4.166666666666667, 5.945879572556914, 4.947825000000001, 2.765066666666667, 1.4638000000000002, 0.0), # 27
(16.2899813254248, 16.08671486053955, 13.821493369913123, 14.840327469135804, 11.896987469240962, 5.833333333333334, 6.581984249172921, 6.12832510288066, 6.462105514403292, 3.140488193872886, 2.232019570187472, 1.2984213382106389, 0.0, 16.2, 14.282634720317025, 11.160097850937358, 9.421464581618656, 12.924211028806583, 8.579655144032923, 6.581984249172921, 4.166666666666667, 5.948493734620481, 4.946775823045269, 2.764298673982625, 1.462428623685414, 0.0), # 28
(16.3051652115884, 16.0689016003658, 13.816951074531323, 14.83659475308642, 11.902088902910101, 5.833333333333334, 6.575104784959253, 6.113810699588477, 6.460351769547325, 3.1368663740283504, 2.2315096479195247, 1.2978156988263985, 0.0, 16.2, 14.27597268709038, 11.157548239597624, 9.41059912208505, 12.92070353909465, 8.559334979423868, 6.575104784959253, 4.166666666666667, 5.951044451455051, 4.945531584362141, 2.763390214906265, 1.460809236396891, 0.0), # 29
(16.319983959114396, 16.04845679012346, 13.811728395061728, 14.832291666666666, 11.907063250334119, 5.833333333333334, 6.567211328976035, 6.097222222222222, 6.458333333333333, 3.1327160493827173, 2.230920314253648, 1.297119341563786, 0.0, 16.2, 14.268312757201645, 11.15460157126824, 9.398148148148149, 12.916666666666666, 8.536111111111111, 6.567211328976035, 4.166666666666667, 5.953531625167059, 4.944097222222223, 2.7623456790123457, 1.458950617283951, 0.0), # 30
(16.334436294679772, 16.02547700045725, 13.805847279378145, 14.82743302469136, 11.911910315725876, 5.833333333333334, 6.558341838134432, 6.078658436213992, 6.456058106995885, 3.1280587288523103, 2.2302531653988447, 1.296335192805975, 0.0, 16.2, 14.259687120865724, 11.151265826994223, 9.384176186556928, 12.91211621399177, 8.510121810699589, 6.558341838134432, 4.166666666666667, 5.955955157862938, 4.942477674897121, 2.761169455875629, 1.4568615454961138, 0.0), # 31
(16.34852094496153, 16.00005880201189, 13.799329675354366, 14.82203364197531, 11.916629903298237, 5.833333333333334, 6.548534269345599, 6.058218106995886, 6.453533991769548, 3.1229159213534534, 2.229509797564119, 1.2954661789361381, 0.0, 16.2, 14.250127968297518, 11.147548987820594, 9.368747764060357, 12.907067983539095, 8.48150534979424, 6.548534269345599, 4.166666666666667, 5.958314951649118, 4.940677880658438, 2.759865935070873, 1.4545508001828993, 0.0), # 32
(16.362236636636634, 15.972298765432097, 13.792197530864199, 14.816108333333332, 11.921221817264065, 5.833333333333334, 6.537826579520697, 6.0360000000000005, 6.450768888888889, 3.1173091358024703, 2.228691806958474, 1.2945152263374486, 0.0, 16.2, 14.239667489711932, 11.143459034792368, 9.351927407407409, 12.901537777777778, 8.450400000000002, 6.537826579520697, 4.166666666666667, 5.960610908632033, 4.938702777777778, 2.75843950617284, 1.452027160493827, 0.0), # 33
(16.375582096382097, 15.942293461362596, 13.784472793781436, 14.809671913580248, 11.92568586183623, 5.833333333333334, 6.526256725570888, 6.012102880658436, 6.447770699588479, 3.111259881115685, 2.2278007897909133, 1.2934852613930805, 0.0, 16.2, 14.228337875323884, 11.139003948954567, 9.333779643347052, 12.895541399176958, 8.41694403292181, 6.526256725570888, 4.166666666666667, 5.962842930918115, 4.93655730452675, 2.7568945587562874, 1.449299405578418, 0.0), # 34
(16.388556050874893, 15.9101394604481, 13.776177411979882, 14.802739197530864, 11.930021841227594, 5.833333333333334, 6.513862664407327, 5.986625514403293, 6.4445473251028815, 3.1047896662094203, 2.226838342270441, 1.2923792104862066, 0.0, 16.2, 14.216171315348271, 11.134191711352205, 9.314368998628257, 12.889094650205763, 8.381275720164611, 6.513862664407327, 4.166666666666667, 5.965010920613797, 4.934246399176955, 2.755235482395977, 1.4463763145861912, 0.0), # 35
(16.40115722679201, 15.87593333333333, 13.767333333333335, 14.795325, 11.934229559651024, 5.833333333333334, 6.500682352941176, 5.959666666666668, 6.441106666666666, 3.097920000000001, 2.225806060606061, 1.2912000000000003, 0.0, 16.2, 14.203200000000002, 11.129030303030303, 9.29376, 12.882213333333333, 8.343533333333335, 6.500682352941176, 4.166666666666667, 5.967114779825512, 4.931775000000001, 2.753466666666667, 1.4432666666666667, 0.0), # 36
(16.41338435081044, 15.839771650663007, 13.757962505715593, 14.78744413580247, 11.938308821319383, 5.833333333333334, 6.486753748083595, 5.931325102880659, 6.437456625514404, 3.090672391403751, 2.2247055410067764, 1.2899505563176348, 0.0, 16.2, 14.18945611949398, 11.123527705033881, 9.27201717421125, 12.874913251028808, 8.303855144032923, 6.486753748083595, 4.166666666666667, 5.969154410659692, 4.929148045267491, 2.751592501143119, 1.4399792409693644, 0.0), # 37
(16.425236149607162, 15.801750983081849, 13.748086877000459, 14.77911141975309, 11.942259430445535, 5.833333333333334, 6.4721148067457435, 5.901699588477367, 6.433605102880659, 3.0830683493369926, 2.22353837968159, 1.2886338058222835, 0.0, 16.2, 14.174971864045116, 11.11769189840795, 9.249205048010975, 12.867210205761317, 8.262379423868314, 6.4721148067457435, 4.166666666666667, 5.971129715222768, 4.926370473251031, 2.7496173754000917, 1.4365228166438047, 0.0), # 38
(16.436711349859177, 15.761967901234568, 13.737728395061731, 14.770341666666667, 11.94608119124235, 5.833333333333334, 6.456803485838781, 5.8708888888888895, 6.42956, 3.0751293827160504, 2.2223061728395064, 1.2872526748971194, 0.0, 16.2, 14.159779423868311, 11.111530864197531, 9.225388148148149, 12.85912, 8.219244444444445, 6.456803485838781, 4.166666666666667, 5.973040595621175, 4.923447222222223, 2.7475456790123465, 1.4329061728395065, 0.0), # 39
(16.44780867824346, 15.720518975765888, 13.726909007773205, 14.761149691358025, 11.949773907922687, 5.833333333333334, 6.440857742273865, 5.838991769547327, 6.425329218106996, 3.0668770004572488, 2.2210105166895295, 1.2858100899253166, 0.0, 16.2, 14.143910989178481, 11.105052583447646, 9.200631001371743, 12.850658436213992, 8.174588477366258, 6.440857742273865, 4.166666666666667, 5.974886953961343, 4.920383230452676, 2.745381801554641, 1.42913808870599, 0.0), # 40
(16.458526861437004, 15.677500777320528, 13.71565066300869, 14.751550308641978, 11.953337384699417, 5.833333333333334, 6.424315532962156, 5.806106995884774, 6.420920658436214, 3.05833271147691, 2.2196530074406624, 1.2843089772900476, 0.0, 16.2, 14.12739875019052, 11.09826503720331, 9.174998134430727, 12.841841316872427, 8.128549794238685, 6.424315532962156, 4.166666666666667, 5.976668692349708, 4.9171834362139935, 2.743130132601738, 1.4252273433927756, 0.0), # 41
(16.4688646261168, 15.633009876543213, 13.70397530864198, 14.741558333333336, 11.956771425785394, 5.833333333333334, 6.4072148148148145, 5.772333333333334, 6.416342222222223, 3.049518024691359, 2.2182352413019086, 1.282752263374486, 0.0, 16.2, 14.110274897119341, 11.091176206509541, 9.148554074074074, 12.832684444444446, 8.081266666666668, 6.4072148148148145, 4.166666666666667, 5.978385712892697, 4.913852777777779, 2.740795061728396, 1.421182716049383, 0.0), # 42
(16.47882069895983, 15.587142844078647, 13.69190489254687, 14.731188580246915, 11.960075835393496, 5.833333333333334, 6.389593544743001, 5.737769547325104, 6.4116018106995885, 3.040454449016919, 2.2167588144822714, 1.281142874561805, 0.0, 16.2, 14.092571620179852, 11.083794072411356, 9.121363347050755, 12.823203621399177, 8.032877366255146, 6.389593544743001, 4.166666666666667, 5.980037917696748, 4.9103961934156395, 2.738380978509374, 1.4170129858253318, 0.0), # 43
(16.488393806643085, 15.539996250571559, 13.679461362597166, 14.720455864197532, 11.963250417736582, 5.833333333333334, 6.371489679657872, 5.702514403292183, 6.4067073251028805, 3.031163493369914, 2.2152253231907557, 1.279483737235178, 0.0, 16.2, 14.074321109586954, 11.076126615953777, 9.09349048010974, 12.813414650205761, 7.983520164609057, 6.371489679657872, 4.166666666666667, 5.981625208868291, 4.906818621399179, 2.7358922725194335, 1.4127269318701419, 0.0), # 44
(16.497582675843546, 15.491666666666667, 13.66666666666667, 14.709375000000001, 11.966294977027516, 5.833333333333334, 6.352941176470589, 5.666666666666668, 6.4016666666666655, 3.021666666666668, 2.213636363636364, 1.277777777777778, 0.0, 16.2, 14.055555555555554, 11.068181818181818, 9.065000000000001, 12.803333333333331, 7.9333333333333345, 6.352941176470589, 4.166666666666667, 5.983147488513758, 4.903125000000001, 2.733333333333334, 1.4083333333333337, 0.0), # 45
(16.50638603323821, 15.442250663008686, 13.653542752629173, 14.697960802469137, 11.969209317479164, 5.833333333333334, 6.333985992092311, 5.63032510288066, 6.396487736625514, 3.0119854778235036, 2.2119935320281, 1.2760279225727789, 0.0, 16.2, 14.036307148300564, 11.059967660140499, 9.035956433470508, 12.792975473251028, 7.882455144032924, 6.333985992092311, 4.166666666666667, 5.984604658739582, 4.899320267489713, 2.730708550525835, 1.4038409693644263, 0.0), # 46
(16.514802605504055, 15.391844810242342, 13.640111568358483, 14.686228086419753, 11.971993243304391, 5.833333333333334, 6.3146620834341975, 5.593588477366255, 6.391178436213992, 3.0021414357567453, 2.210298424574968, 1.2742370980033535, 0.0, 16.2, 14.016608078036885, 11.051492122874839, 9.006424307270233, 12.782356872427984, 7.831023868312758, 6.3146620834341975, 4.166666666666667, 5.985996621652196, 4.895409362139919, 2.728022313671697, 1.3992586191129404, 0.0), # 47
(16.522831119318074, 15.340545679012347, 13.626395061728397, 14.674191666666669, 11.974646558716064, 5.833333333333334, 6.295007407407407, 5.556555555555557, 6.385746666666667, 2.9921560493827166, 2.208552637485971, 1.272408230452675, 0.0, 16.2, 13.996490534979422, 11.042763187429854, 8.976468148148149, 12.771493333333334, 7.77917777777778, 6.295007407407407, 4.166666666666667, 5.987323279358032, 4.891397222222224, 2.7252790123456796, 1.3945950617283953, 0.0), # 48
(16.53047030135726, 15.288449839963418, 13.612415180612713, 14.661866358024692, 11.977169067927047, 5.833333333333334, 6.275059920923102, 5.519325102880659, 6.380200329218106, 2.982050827617742, 2.2067577669701133, 1.2705442463039174, 0.0, 16.2, 13.97598670934309, 11.033788834850565, 8.946152482853226, 12.760400658436213, 7.727055144032923, 6.275059920923102, 4.166666666666667, 5.9885845339635235, 4.887288786008232, 2.7224830361225427, 1.389859076360311, 0.0), # 49
(16.537718878298588, 15.235653863740286, 13.598193872885233, 14.649266975308642, 11.979560575150202, 5.833333333333334, 6.25485758089244, 5.481995884773663, 6.3745473251028795, 2.971847279378144, 2.204915409236397, 1.2686480719402533, 0.0, 16.2, 13.955128791342785, 11.024577046181985, 8.91554183813443, 12.749094650205759, 7.674794238683129, 6.25485758089244, 4.166666666666667, 5.989780287575101, 4.883088991769548, 2.7196387745770467, 1.385059442158208, 0.0), # 50
(16.544575576819057, 15.182254320987655, 13.583753086419755, 14.636408333333335, 11.981820884598399, 5.833333333333334, 6.23443834422658, 5.4446666666666665, 6.368795555555556, 2.9615669135802474, 2.2030271604938276, 1.2667226337448563, 0.0, 16.2, 13.933948971193416, 11.015135802469137, 8.88470074074074, 12.737591111111112, 7.622533333333334, 6.23443834422658, 4.166666666666667, 5.9909104422991994, 4.878802777777779, 2.716750617283951, 1.380204938271605, 0.0), # 51
(16.551039123595647, 15.128347782350252, 13.56911476909008, 14.623305246913581, 11.983949800484496, 5.833333333333334, 6.213840167836683, 5.407436213991769, 6.3629529218107, 2.9512312391403754, 2.2010946169514076, 1.2647708581008996, 0.0, 16.2, 13.912479439109894, 11.005473084757037, 8.853693717421125, 12.7259058436214, 7.570410699588477, 6.213840167836683, 4.166666666666667, 5.991974900242248, 4.874435082304528, 2.713822953818016, 1.3753043438500232, 0.0), # 52
(16.55710824530535, 15.074030818472796, 13.554300868770008, 14.609972530864198, 11.985947127021364, 5.833333333333334, 6.1931010086339064, 5.370403292181071, 6.357027325102881, 2.940861764974852, 2.1991193748181406, 1.2627956713915565, 0.0, 16.2, 13.890752385307119, 10.995596874090701, 8.822585294924554, 12.714054650205762, 7.518564609053499, 6.1931010086339064, 4.166666666666667, 5.992973563510682, 4.8699908436214, 2.710860173754002, 1.3703664380429816, 0.0), # 53
(16.562781668625146, 15.019400000000001, 13.539333333333333, 14.596425, 11.987812668421869, 5.833333333333334, 6.172258823529412, 5.333666666666667, 6.351026666666667, 2.9304800000000006, 2.19710303030303, 1.2608000000000001, 0.0, 16.2, 13.8688, 10.98551515151515, 8.791440000000001, 12.702053333333334, 7.467133333333333, 6.172258823529412, 4.166666666666667, 5.993906334210934, 4.865475000000001, 2.707866666666667, 1.3654000000000004, 0.0), # 54
(16.568058120232035, 14.964551897576587, 13.524234110653865, 14.582677469135803, 11.989546228898869, 5.833333333333334, 6.151351569434358, 5.2973251028806585, 6.344958847736625, 2.9201074531321454, 2.1950471796150812, 1.2587867703094042, 0.0, 16.2, 13.846654473403445, 10.975235898075404, 8.760322359396435, 12.68991769547325, 7.416255144032922, 6.151351569434358, 4.166666666666667, 5.994773114449434, 4.860892489711935, 2.704846822130773, 1.360413808870599, 0.0), # 55
(16.572936326802996, 14.909583081847279, 13.509025148605396, 14.56874475308642, 11.991147612665237, 5.833333333333334, 6.130417203259905, 5.261477366255145, 6.338831769547324, 2.9097656332876096, 2.1929534189632958, 1.2567589087029418, 0.0, 16.2, 13.824347995732358, 10.964767094816478, 8.729296899862828, 12.677663539094649, 7.366068312757203, 6.130417203259905, 4.166666666666667, 5.995573806332619, 4.856248251028807, 2.7018050297210796, 1.3554166438042983, 0.0), # 56
(16.577415015015013, 14.85459012345679, 13.493728395061732, 14.554641666666669, 11.99261662393383, 5.833333333333334, 6.109493681917211, 5.226222222222224, 6.332653333333334, 2.899476049382717, 2.1908233445566783, 1.254719341563786, 0.0, 16.2, 13.801912757201645, 10.95411672278339, 8.69842814814815, 12.665306666666668, 7.316711111111113, 6.109493681917211, 4.166666666666667, 5.996308311966915, 4.851547222222224, 2.6987456790123465, 1.3504172839506174, 0.0), # 57
(16.581492911545087, 14.79966959304984, 13.478365797896664, 14.540383024691359, 11.99395306691752, 5.833333333333334, 6.088618962317438, 5.191658436213992, 6.326431440329218, 2.8892602103337914, 2.1886585526042324, 1.2526709952751107, 0.0, 16.2, 13.779380948026215, 10.943292763021162, 8.667780631001373, 12.652862880658436, 7.2683218106995895, 6.088618962317438, 4.166666666666667, 5.99697653345876, 4.846794341563787, 2.695673159579333, 1.3454245084590766, 0.0), # 58
(16.585168743070195, 14.744918061271147, 13.462959304983997, 14.525983641975309, 11.995156745829167, 5.833333333333334, 6.067831001371743, 5.157884773662552, 6.320173991769548, 2.879139625057157, 2.1864606393149604, 1.2506167962200887, 0.0, 16.2, 13.756784758420972, 10.9323031965748, 8.63741887517147, 12.640347983539096, 7.221038683127573, 6.067831001371743, 4.166666666666667, 5.9975783729145835, 4.841994547325104, 2.6925918609968, 1.3404470964791952, 0.0), # 59
(16.588441236267325, 14.690432098765434, 13.44753086419753, 14.511458333333334, 11.996227464881638, 5.833333333333334, 6.0471677559912855, 5.125000000000001, 6.31388888888889, 2.8691358024691365, 2.184231200897868, 1.2485596707818931, 0.0, 16.2, 13.734156378600822, 10.921156004489339, 8.607407407407408, 12.62777777777778, 7.175000000000001, 6.0471677559912855, 4.166666666666667, 5.998113732440819, 4.837152777777779, 2.6895061728395064, 1.3354938271604941, 0.0), # 60
(16.591309117813463, 14.636308276177413, 13.432102423411067, 14.496821913580249, 11.997165028287798, 5.833333333333334, 6.026667183087227, 5.093102880658437, 6.3075840329218105, 2.8592702514860546, 2.1819718335619576, 1.246502545343698, 0.0, 16.2, 13.711527998780674, 10.909859167809786, 8.577810754458163, 12.615168065843621, 7.130344032921811, 6.026667183087227, 4.166666666666667, 5.998582514143899, 4.832273971193417, 2.6864204846822135, 1.3305734796524924, 0.0), # 61
(16.593771114385607, 14.582643164151806, 13.416695930498403, 14.482089197530867, 11.997969240260517, 5.833333333333334, 6.006367239570725, 5.062292181069959, 6.301267325102881, 2.849564481024235, 2.1796841335162327, 1.2444483462886757, 0.0, 16.2, 13.68893180917543, 10.898420667581162, 8.548693443072704, 12.602534650205762, 7.0872090534979435, 6.006367239570725, 4.166666666666667, 5.998984620130258, 4.827363065843623, 2.6833391860996807, 1.3256948331047098, 0.0), # 62
(16.595825952660736, 14.529533333333333, 13.401333333333335, 14.467275000000003, 11.998639905012647, 5.833333333333334, 5.986305882352941, 5.0326666666666675, 6.294946666666666, 2.8400400000000006, 2.1773696969696976, 1.2424000000000002, 0.0, 16.2, 13.6664, 10.886848484848487, 8.52012, 12.589893333333332, 7.045733333333335, 5.986305882352941, 4.166666666666667, 5.999319952506323, 4.822425000000002, 2.6802666666666672, 1.3208666666666669, 0.0), # 63
(16.597472359315837, 14.477075354366713, 13.386036579789668, 14.452394135802471, 11.999176826757065, 5.833333333333334, 5.966521068345034, 5.004325102880659, 6.288629958847737, 2.830718317329676, 2.1750301201313547, 1.2403604328608446, 0.0, 16.2, 13.64396476146929, 10.875150600656774, 8.492154951989026, 12.577259917695473, 7.006055144032923, 5.966521068345034, 4.166666666666667, 5.999588413378532, 4.817464711934158, 2.6772073159579337, 1.316097759487883, 0.0), # 64
(16.5987090610279, 14.425365797896662, 13.370827617741199, 14.437461419753088, 11.999579809706631, 5.833333333333334, 5.947050754458163, 4.977366255144033, 6.282325102880659, 2.8216209419295843, 2.1726669992102097, 1.238332571254382, 0.0, 16.2, 13.6216582837982, 10.863334996051048, 8.464862825788751, 12.564650205761318, 6.968312757201646, 5.947050754458163, 4.166666666666667, 5.999789904853316, 4.812487139917697, 2.67416552354824, 1.3113968907178786, 0.0), # 65
(16.599534784473914, 14.374501234567903, 13.35572839506173, 14.422491666666668, 11.99984865807421, 5.833333333333334, 5.927932897603486, 4.95188888888889, 6.27604, 2.81276938271605, 2.170281930415264, 1.2363193415637863, 0.0, 16.2, 13.599512757201648, 10.851409652076319, 8.438308148148149, 12.55208, 6.932644444444446, 5.927932897603486, 4.166666666666667, 5.999924329037105, 4.807497222222223, 2.6711456790123465, 1.3067728395061733, 0.0), # 66
(16.59994825633087, 14.324578235025148, 13.340760859625059, 14.407499691358025, 11.999983176072671, 5.833333333333334, 5.909205454692165, 4.927991769547327, 6.269782551440329, 2.8041851486053964, 2.1678765099555233, 1.23432367017223, 0.0, 16.2, 13.577560371894528, 10.839382549777614, 8.412555445816189, 12.539565102880658, 6.899188477366257, 5.909205454692165, 4.166666666666667, 5.999991588036336, 4.802499897119342, 2.6681521719250116, 1.3022343850022864, 0.0), # 67
(16.59966658316932, 14.275431337669806, 13.325874599908552, 14.39237008856683, 11.999869818983834, 5.833225077478026, 5.890812155863717, 4.905562566681908, 6.263513519280598, 2.795848176658867, 2.1654095969441007, 1.2323373362532992, 0.0, 16.19980024005487, 13.555710698786289, 10.827047984720503, 8.3875445299766, 12.527027038561195, 6.867787593354672, 5.890812155863717, 4.166589341055733, 5.999934909491917, 4.797456696188944, 2.6651749199817103, 1.29776648524271, 0.0), # 68
(16.597026731078905, 14.22556009557945, 13.310651234567901, 14.376340217391304, 11.998838053740013, 5.832369272976682, 5.872214545077291, 4.8833991769547325, 6.256958847736625, 2.7875225562817723, 2.162630090377459, 1.2302958631145768, 0.0, 16.198217592592595, 13.533254494260342, 10.813150451887294, 8.362567668845315, 12.51391769547325, 6.8367588477366255, 5.872214545077291, 4.165978052126201, 5.999419026870006, 4.792113405797102, 2.66213024691358, 1.2932327359617684, 0.0), # 69
(16.59181726009423, 14.174735607770254, 13.295024577046181, 14.359304549114333, 11.996799268404205, 5.8306838388457045, 5.853328107649096, 4.861301630848957, 6.2500815424477985, 2.7791678097850943, 2.159506369740288, 1.228189701505708, 0.0, 16.195091735253776, 13.510086716562785, 10.797531848701441, 8.337503429355282, 12.500163084895597, 6.80582228318854, 5.853328107649096, 4.164774170604074, 5.998399634202102, 4.786434849704778, 2.6590049154092363, 1.2886123279791142, 0.0), # 70
(16.584111457028687, 14.122988247267578, 13.279000114311843, 14.341288204508857, 11.993779284004411, 5.828196087994717, 5.8341613276311906, 4.8392772443225125, 6.242891845755221, 2.7707841437370564, 2.1560499655423633, 1.226020391628362, 0.0, 16.190463820301783, 13.486224307911982, 10.780249827711817, 8.312352431211167, 12.485783691510441, 6.774988142051518, 5.8341613276311906, 4.162997205710512, 5.9968896420022055, 4.780429401502953, 2.6558000228623686, 1.2839080224788708, 0.0), # 71
(16.573982608695655, 14.070348387096773, 13.262583333333334, 14.322316304347826, 11.989803921568626, 5.824933333333335, 5.81472268907563, 4.817333333333334, 6.2354, 2.762371764705883, 2.1522724082934617, 1.2237894736842108, 0.0, 16.184375, 13.461684210526316, 10.761362041467306, 8.287115294117648, 12.4708, 6.744266666666667, 5.81472268907563, 4.160666666666668, 5.994901960784313, 4.7741054347826095, 2.6525166666666666, 1.2791225806451614, 0.0), # 72
(16.561504001908514, 14.016846400283198, 13.245779721079103, 14.302413969404189, 11.984899002124855, 5.820922887771173, 5.795020676034474, 4.795477213839354, 6.227616247523244, 2.753930879259798, 2.1481852285033574, 1.2214984878749227, 0.0, 16.1768664266118, 13.436483366624147, 10.740926142516786, 8.261792637779392, 12.455232495046488, 6.713668099375096, 5.795020676034474, 4.157802062693695, 5.992449501062428, 4.76747132313473, 2.649155944215821, 1.274258763662109, 0.0), # 73
(16.546748923480646, 13.962512659852205, 13.228594764517604, 14.281606320450884, 11.979090346701094, 5.816192064217854, 5.775063772559778, 4.773716201798507, 6.219550830666057, 2.7454616939670253, 2.143799956681829, 1.219148974402169, 0.0, 16.167979252400553, 13.410638718423858, 10.718999783409142, 8.236385081901075, 12.439101661332113, 6.683202682517909, 5.775063772559778, 4.154422903012753, 5.989545173350547, 4.760535440150296, 2.645718952903521, 1.269319332713837, 0.0), # 74
(16.52979066022544, 13.90737753882915, 13.211033950617283, 14.259918478260868, 11.972403776325345, 5.810768175582992, 5.754860462703601, 4.752057613168724, 6.211213991769547, 2.7369644153957884, 2.13912812333865, 1.2167424734676198, 0.0, 16.157754629629633, 13.384167208143815, 10.695640616693249, 8.210893246187364, 12.422427983539094, 6.652880658436215, 5.754860462703601, 4.150548696844995, 5.986201888162673, 4.7533061594202906, 2.6422067901234567, 1.2643070489844683, 0.0), # 75
(16.510702498956285, 13.851471410239393, 13.193102766346595, 14.237375563607085, 11.964865112025606, 5.804678534776205, 5.734419230517997, 4.730508763907942, 6.2026159731748205, 2.728439250114312, 2.134181258983598, 1.2142805252729445, 0.0, 16.146233710562413, 13.357085778002387, 10.67090629491799, 8.185317750342936, 12.405231946349641, 6.622712269471118, 5.734419230517997, 4.146198953411575, 5.982432556012803, 4.745791854535696, 2.638620553269319, 1.259224673658127, 0.0), # 76
(16.48955772648655, 13.794824647108282, 13.174806698673981, 14.21400269726248, 11.956500174829877, 5.797950454707109, 5.7137485600550235, 4.70907696997409, 6.193767017222985, 2.7198864046908207, 2.1289708941264505, 1.2117646700198144, 0.0, 16.13345764746228, 13.329411370217956, 10.64485447063225, 8.15965921407246, 12.38753403444597, 6.592707757963726, 5.7137485600550235, 4.141393181933649, 5.9782500874149385, 4.738000899087494, 2.6349613397347964, 1.254074967918935, 0.0), # 77
(16.46642962962963, 13.737467622461173, 13.156151234567902, 14.189825, 11.94733478576616, 5.790611248285322, 5.69285693536674, 4.687769547325104, 6.184677366255142, 2.711306085693537, 2.123508559276981, 1.2091964479098987, 0.0, 16.119467592592596, 13.301160927008882, 10.617542796384903, 8.13391825708061, 12.369354732510285, 6.562877366255145, 5.69285693536674, 4.136150891632373, 5.97366739288308, 4.729941666666668, 2.6312302469135807, 1.248860692951016, 0.0), # 78
(16.441391495198904, 13.679430709323423, 13.1371418609968, 14.164867592592593, 11.93739476586245, 5.782688228420464, 5.671752840505201, 4.666593811918916, 6.1753572626124065, 2.702698499690686, 2.117805784944966, 1.2065773991448674, 0.0, 16.104304698216733, 13.27235139059354, 10.58902892472483, 8.108095499072057, 12.350714525224813, 6.533231336686482, 5.671752840505201, 4.130491591728903, 5.968697382931225, 4.721622530864199, 2.6274283721993603, 1.243584609938493, 0.0), # 79
(16.414516610007755, 13.620744280720386, 13.117784064929126, 14.139155595813204, 11.92670593614675, 5.774208708022151, 5.650444759522465, 4.645557079713459, 6.165816948635879, 2.694063853250491, 2.111874101640184, 1.2039090639263914, 0.0, 16.08801011659808, 13.242999703190304, 10.559370508200919, 8.082191559751472, 12.331633897271757, 6.503779911598843, 5.650444759522465, 4.1244347914443935, 5.963352968073375, 4.713051865271069, 2.6235568129858255, 1.23824948006549, 0.0), # 80
(16.385878260869568, 13.56143870967742, 13.098083333333335, 14.112714130434785, 11.915294117647058, 5.765200000000001, 5.628941176470589, 4.624666666666667, 6.156066666666666, 2.685402352941177, 2.1057250398724086, 1.2011929824561405, 0.0, 16.070625, 13.213122807017545, 10.528625199362043, 8.05620705882353, 12.312133333333332, 6.474533333333334, 5.628941176470589, 4.118, 5.957647058823529, 4.704238043478263, 2.619616666666667, 1.2328580645161293, 0.0), # 81
(16.355549734597723, 13.501544369219879, 13.078045153177872, 14.085568317230274, 11.903185131391377, 5.75568941726363, 5.607250575401629, 4.603929888736474, 6.146116659045877, 2.676714205330967, 2.099370130151417, 1.198430694935785, 0.0, 16.052190500685874, 13.182737644293633, 10.496850650757084, 8.030142615992899, 12.292233318091753, 6.445501844231063, 5.607250575401629, 4.111206726616879, 5.951592565695688, 4.695189439076759, 2.6156090306355746, 1.2274131244745345, 0.0), # 82
(16.323604318005607, 13.441091632373114, 13.057675011431185, 14.057743276972625, 11.890404798407703, 5.745704272722655, 5.585381440367643, 4.5833540618808115, 6.135977168114616, 2.667999616988085, 2.0928209029869853, 1.195623741566995, 0.0, 16.03274777091907, 13.151861157236944, 10.464104514934926, 8.003998850964255, 12.271954336229232, 6.416695686633136, 5.585381440367643, 4.104074480516182, 5.945202399203851, 4.6859144256575425, 2.6115350022862374, 1.2219174211248287, 0.0), # 83
(16.290115297906603, 13.380110872162485, 13.036978395061729, 14.029264130434784, 11.876978939724037, 5.735271879286694, 5.563342255420687, 4.562946502057613, 6.125658436213991, 2.659258794480756, 2.0860888888888893, 1.1927736625514405, 0.0, 16.012337962962963, 13.120510288065844, 10.430444444444445, 7.977776383442267, 12.251316872427982, 6.388125102880658, 5.563342255420687, 4.096622770919067, 5.938489469862018, 4.676421376811596, 2.607395679012346, 1.2163737156511352, 0.0), # 84
(16.255155961114095, 13.318632461613346, 13.015960791037951, 14.000155998389694, 11.862933376368382, 5.724419549865368, 5.54114150461282, 4.542714525224815, 6.115170705685108, 2.650491944377203, 2.0791856183669055, 1.1898819980907918, 0.0, 15.991002229080934, 13.088701978998708, 10.395928091834525, 7.951475833131607, 12.230341411370215, 6.35980033531474, 5.54114150461282, 4.088871107046691, 5.931466688184191, 4.666718666129899, 2.6031921582075905, 1.210784769237577, 0.0), # 85
(16.21879959444146, 13.256686773751051, 12.994627686328306, 13.970444001610309, 11.84829392936873, 5.713174597368289, 5.518787671996097, 4.522665447340345, 6.104524218869075, 2.64169927324565, 2.0721226219308098, 1.1869502883867193, 0.0, 15.968781721536352, 13.05645317225391, 10.360613109654047, 7.9250978197369495, 12.20904843773815, 6.331731626276483, 5.518787671996097, 4.080838998120206, 5.924146964684365, 4.656814667203437, 2.5989255372656612, 1.2051533430682777, 0.0), # 86
(16.18111948470209, 13.194304181600955, 12.972984567901234, 13.940153260869565, 11.833086419753089, 5.7015643347050755, 5.496289241622575, 4.5028065843621405, 6.093729218106997, 2.6328809876543215, 2.0649114300903775, 1.1839800736408925, 0.0, 15.945717592592594, 13.023780810049816, 10.324557150451888, 7.898642962962963, 12.187458436213994, 6.303929218106997, 5.496289241622575, 4.072545953360768, 5.9165432098765445, 4.646717753623189, 2.594596913580247, 1.1994821983273598, 0.0), # 87
(16.142188918709373, 13.131515058188414, 12.951036922725194, 13.90930889694042, 11.817336668549451, 5.689616074785349, 5.473654697544313, 4.483145252248133, 6.082795945739979, 2.624037294171441, 2.0575635733553868, 1.1809728940549822, 0.0, 15.921850994513035, 12.990701834604803, 10.287817866776932, 7.8721118825143215, 12.165591891479957, 6.276403353147386, 5.473654697544313, 4.064011481989534, 5.908668334274726, 4.636436298980141, 2.5902073845450393, 1.193774096198947, 0.0), # 88
(16.102081183276677, 13.068349776538785, 12.928790237768634, 13.877936030595814, 11.80107049678582, 5.677357130518723, 5.4508925238133665, 4.463688766956257, 6.07173464410913, 2.6151683993652335, 2.050090582235612, 1.1779302898306583, 0.0, 15.897223079561043, 12.957233188137238, 10.250452911178058, 7.845505198095699, 12.14346928821826, 6.24916427373876, 5.4508925238133665, 4.055255093227659, 5.90053524839291, 4.625978676865272, 2.585758047553727, 1.1880317978671624, 0.0), # 89
(16.06086956521739, 13.004838709677419, 12.906250000000002, 13.846059782608698, 11.784313725490197, 5.664814814814815, 5.428011204481793, 4.444444444444445, 6.060555555555556, 2.606274509803922, 2.04250398724083, 1.1748538011695908, 0.0, 15.871875000000001, 12.923391812865496, 10.212519936204147, 7.818823529411765, 12.121111111111112, 6.222222222222222, 5.428011204481793, 4.046296296296297, 5.892156862745098, 4.615353260869567, 2.5812500000000003, 1.1822580645161291, 0.0), # 90
(16.0186273513449, 12.941012230629672, 12.883421696387746, 13.813705273752014, 11.767092175690575, 5.652016440583244, 5.405019223601649, 4.4254196006706294, 6.049268922420364, 2.597355832055731, 2.0348153188808165, 1.17174496827345, 0.0, 15.845847908093276, 12.889194651007948, 10.174076594404081, 7.792067496167191, 12.098537844840727, 6.195587440938882, 5.405019223601649, 4.037154600416603, 5.883546087845287, 4.604568424584006, 2.5766843392775494, 1.1764556573299705, 0.0), # 91
(15.975427828472597, 12.876900712420905, 12.86031081390032, 13.780897624798712, 11.749431668414964, 5.638989320733629, 5.381925065224994, 4.406621551592746, 6.037884987044658, 2.5884125726888843, 2.027036107665348, 1.1686053313439067, 0.0, 15.819182956104251, 12.85465864478297, 10.135180538326738, 7.765237718066651, 12.075769974089315, 6.169270172229845, 5.381925065224994, 4.027849514809735, 5.874715834207482, 4.593632541599572, 2.5720621627800644, 1.1706273374928098, 0.0), # 92
(15.931344283413848, 12.812534528076466, 12.836922839506174, 13.747661956521743, 11.731358024691357, 5.625760768175583, 5.358737213403881, 4.388057613168725, 6.026413991769548, 2.5794449382716054, 2.0191778841042, 1.1654364305826295, 0.0, 15.791921296296294, 12.819800736408922, 10.095889420521, 7.738334814814815, 12.052827983539096, 6.143280658436215, 5.358737213403881, 4.018400548696845, 5.865679012345678, 4.582553985507248, 2.567384567901235, 1.1647758661887697, 0.0), # 93
(15.886450002982048, 12.74794405062171, 12.813263260173755, 13.714023389694043, 11.712897065547754, 5.612358095818728, 5.335464152190369, 4.369735101356501, 6.014866178936138, 2.5704531353721194, 2.01125217870715, 1.16223980619129, 0.0, 15.764104080932785, 12.784637868104188, 10.056260893535747, 7.711359406116356, 12.029732357872277, 6.117629141899102, 5.335464152190369, 4.008827211299091, 5.856448532773877, 4.571341129898015, 2.5626526520347515, 1.1589040046019738, 0.0), # 94
(15.840818273990577, 12.683159653081995, 12.789337562871514, 13.680007045088567, 11.694074612012159, 5.598808616572678, 5.312114365636515, 4.351661332114007, 6.003251790885536, 2.561437370558649, 2.0032705219839726, 1.1590169983715575, 0.0, 15.735772462277092, 12.749186982087132, 10.016352609919863, 7.684312111675945, 12.006503581771073, 6.09232586495961, 5.312114365636515, 3.999149011837627, 5.847037306006079, 4.560002348362857, 2.5578675125743033, 1.1530145139165453, 0.0), # 95
(15.79452238325282, 12.61821170848268, 12.765151234567902, 13.645638043478261, 11.674916485112563, 5.585139643347051, 5.288696337794377, 4.333843621399177, 5.991581069958848, 2.55239785039942, 1.9952444444444448, 1.1557695473251033, 0.0, 15.706967592592594, 12.713465020576134, 9.976222222222225, 7.657193551198258, 11.983162139917695, 6.067381069958849, 5.288696337794377, 3.9893854595336076, 5.8374582425562815, 4.5485460144927545, 2.553030246913581, 1.1471101553166074, 0.0), # 96
(15.747635617582157, 12.553130589849111, 12.740709762231369, 13.61094150563607, 11.655448505876976, 5.571378489051465, 5.265218552716011, 4.316289285169945, 5.979864258497181, 2.5433347814626543, 1.9871854765983423, 1.152498993253596, 0.0, 15.677730624142663, 12.677488925789556, 9.93592738299171, 7.630004344387961, 11.959728516994362, 6.042804999237923, 5.265218552716011, 3.9795560636081895, 5.827724252938488, 4.536980501878691, 2.5481419524462736, 1.141193689986283, 0.0), # 97
(15.700231263791975, 12.487946670206647, 12.71601863283036, 13.575942552334945, 11.635696495333388, 5.557552466595541, 5.241689494453475, 4.299005639384241, 5.968111598841639, 2.5342483703165772, 1.9791051489554419, 1.1492068763587067, 0.0, 15.648102709190674, 12.64127563994577, 9.89552574477721, 7.60274511094973, 11.936223197683278, 6.018607895137937, 5.241689494453475, 3.969680333282529, 5.817848247666694, 4.525314184111649, 2.5432037265660723, 1.1352678791096953, 0.0), # 98
(15.652382608695653, 12.422690322580646, 12.691083333333335, 13.540666304347827, 11.615686274509805, 5.543688888888889, 5.218117647058825, 4.282000000000001, 5.956333333333333, 2.5251388235294123, 1.9710149920255189, 1.1458947368421055, 0.0, 15.618125000000001, 12.604842105263158, 9.855074960127594, 7.575416470588236, 11.912666666666667, 5.9948000000000015, 5.218117647058825, 3.9597777777777776, 5.807843137254903, 4.51355543478261, 2.5382166666666675, 1.129335483870968, 0.0), # 99
(15.60416293910658, 12.357391919996457, 12.665909350708734, 13.505137882447666, 11.595443664434223, 5.529815068841132, 5.194511494584116, 4.265279682975157, 5.944539704313367, 2.516006347669384, 1.9629265363183495, 1.1425641149054624, 0.0, 15.58783864883402, 12.568205263960085, 9.814632681591746, 7.54801904300815, 11.889079408626735, 5.97139155616522, 5.194511494584116, 3.9498679063150943, 5.797721832217111, 4.501712627482556, 2.533181870141747, 1.1233992654542237, 0.0), # 100
(15.555645541838135, 12.292081835479447, 12.640502171925013, 13.469382407407409, 11.574994486134646, 5.515958319361886, 5.17087952108141, 4.248852004267642, 5.932740954122847, 2.506851149304716, 1.9548513123437101, 1.1392165507504473, 0.0, 15.557284807956103, 12.531382058254918, 9.77425656171855, 7.520553447914146, 11.865481908245695, 5.948392805974699, 5.17087952108141, 3.9399702281156324, 5.787497243067323, 4.48979413580247, 2.528100434385003, 1.1174619850435863, 0.0), # 101
(15.506903703703706, 12.22679044205496, 12.614867283950618, 13.433425000000002, 11.554364560639069, 5.5021459533607695, 5.1472302106027605, 4.2327242798353915, 5.920947325102881, 2.497673435003632, 1.9468008506113774, 1.135853584578731, 0.0, 15.526504629629631, 12.49438943036604, 9.734004253056886, 7.493020305010894, 11.841894650205761, 5.925813991769548, 5.1472302106027605, 3.93010425240055, 5.7771822803195345, 4.477808333333335, 2.522973456790124, 1.1115264038231782, 0.0), # 102
(15.458010711516671, 12.161548112748353, 12.589010173754001, 13.397290780998391, 11.533579708975497, 5.488405283747397, 5.123572047200224, 4.2169038256363365, 5.909169059594573, 2.4884734113343563, 1.9387866816311266, 1.132476756591983, 0.0, 15.495539266117968, 12.457244322511812, 9.693933408155633, 7.4654202340030675, 11.818338119189146, 5.903665355890872, 5.123572047200224, 3.920289488390998, 5.766789854487748, 4.465763593666131, 2.5178020347508006, 1.1055952829771232, 0.0), # 103
(15.409039852090416, 12.096385220584981, 12.562936328303612, 13.361004871175524, 11.512665752171923, 5.474763623431389, 5.099913514925861, 4.201397957628411, 5.897416399939034, 2.479251284865113, 1.9308203359127338, 1.129087606991874, 0.0, 15.464429869684501, 12.419963676910612, 9.654101679563668, 7.437753854595337, 11.794832799878067, 5.881957140679775, 5.099913514925861, 3.9105454453081343, 5.756332876085962, 4.4536682903918425, 2.5125872656607227, 1.099671383689544, 0.0), # 104
(15.360064412238325, 12.031332138590201, 12.536651234567902, 13.324592391304346, 11.491648511256354, 5.461248285322361, 5.076263097831727, 4.186213991769549, 5.885699588477366, 2.470007262164126, 1.922913343965976, 1.125687675980074, 0.0, 15.433217592592593, 12.382564435780811, 9.61456671982988, 7.410021786492376, 11.771399176954732, 5.860699588477368, 5.076263097831727, 3.9008916323731144, 5.745824255628177, 4.44153079710145, 2.5073302469135803, 1.093757467144564, 0.0), # 105
(15.311157678773782, 11.96641923978937, 12.510160379515318, 13.28807846215781, 11.470553807256785, 5.44788658232993, 5.052629279969876, 4.1713592440176805, 5.8740288675506775, 2.4607415497996183, 1.9150772363006283, 1.1222785037582528, 0.0, 15.401943587105624, 12.345063541340778, 9.575386181503141, 7.382224649398854, 11.748057735101355, 5.839902941624753, 5.052629279969876, 3.8913475588070923, 5.735276903628392, 4.429359487385938, 2.5020320759030636, 1.0878562945263066, 0.0), # 106
(15.26239293851017, 11.901676897207842, 12.483469250114315, 13.251488204508856, 11.449407461201215, 5.434705827363715, 5.0290205453923695, 4.156841030330743, 5.862414479500076, 2.451454354339816, 1.9073235434264675, 1.1188616305280807, 0.0, 15.370649005486968, 12.307477935808887, 9.536617717132337, 7.354363063019447, 11.724828959000153, 5.819577442463041, 5.0290205453923695, 3.8819327338312255, 5.724703730600607, 4.417162734836286, 2.496693850022863, 1.081970627018895, 0.0), # 107
(15.21384347826087, 11.83713548387097, 12.456583333333336, 13.214846739130437, 11.428235294117645, 5.421733333333335, 5.0054453781512604, 4.142666666666667, 5.850866666666667, 2.442145882352942, 1.8996637958532698, 1.1154385964912283, 0.0, 15.339375000000002, 12.26982456140351, 9.498318979266347, 7.326437647058825, 11.701733333333333, 5.799733333333334, 5.0054453781512604, 3.8726666666666674, 5.714117647058822, 4.40494891304348, 2.4913166666666675, 1.076103225806452, 0.0), # 108
(15.16558258483927, 11.772825372804107, 12.429508116140834, 13.17817918679549, 11.40706312703408, 5.408996413148403, 4.98191226229861, 4.128843468983388, 5.839395671391555, 2.4328163404072196, 1.8921095240908108, 1.112010941849365, 0.0, 15.308162722908094, 12.232120360343014, 9.460547620454054, 7.298449021221657, 11.67879134278311, 5.780380856576743, 4.98191226229861, 3.8635688665345733, 5.70353156351704, 4.392726395598498, 2.485901623228167, 1.0702568520731008, 0.0), # 109
(15.117683545058746, 11.708776937032614, 12.402249085505263, 13.141510668276972, 11.385916780978512, 5.396522379718539, 4.9584296818864715, 4.1153787532388355, 5.828011736015851, 2.423465935070874, 1.8846722586488671, 1.108580206804162, 0.0, 15.277053326474624, 12.194382274845779, 9.423361293244335, 7.27039780521262, 11.656023472031702, 5.76153025453437, 4.9584296818864715, 3.8546588426560997, 5.692958390489256, 4.380503556092325, 2.4804498171010527, 1.0644342670029652, 0.0), # 110
(15.07021964573269, 11.64502054958184, 12.374811728395064, 13.104866304347826, 11.36482207697894, 5.384338545953361, 4.935006120966905, 4.102279835390947, 5.816725102880659, 2.4140948729121283, 1.8773635300372145, 1.1051479315572885, 0.0, 15.246087962962964, 12.156627247130173, 9.386817650186073, 7.242284618736384, 11.633450205761317, 5.743191769547326, 4.935006120966905, 3.845956104252401, 5.68241103848947, 4.368288768115943, 2.474962345679013, 1.0586382317801675, 0.0), # 111
(15.02326417367448, 11.581586583477144, 12.347201531778696, 13.068271215781, 11.34380483606337, 5.372472224762486, 4.911650063591967, 4.089554031397653, 5.805546014327083, 2.404703360499207, 1.8701948687656293, 1.101715656310415, 0.0, 15.215307784636488, 12.118872219414563, 9.350974343828147, 7.214110081497619, 11.611092028654166, 5.725375643956714, 4.911650063591967, 3.837480160544633, 5.671902418031685, 4.356090405260334, 2.469440306355739, 1.0528715075888313, 0.0), # 112
(14.976806757924871, 11.51861130755273, 12.319490437669426, 13.031800658990448, 11.322854058851952, 5.3609451179335466, 4.888420770925416, 4.077235045853738, 5.794513499337931, 2.3953218946450923, 1.8631797083074313, 1.098292391533924, 0.0, 15.184710241349155, 12.081216306873161, 9.315898541537155, 7.185965683935276, 11.589026998675863, 5.708129064195233, 4.888420770925416, 3.829246512809676, 5.661427029425976, 4.343933552996817, 2.4638980875338854, 1.0471464825047938, 0.0), # 113
(14.930369436640104, 11.456715869170786, 12.292060900028826, 12.995747305532802, 11.301752911537415, 5.349730967961242, 4.865614566728464, 4.065474173003413, 5.783796819046966, 2.3861260671651134, 1.8563318232301862, 1.094921622948397, 0.0, 15.154040662656056, 12.044137852432362, 9.28165911615093, 7.1583782014953385, 11.567593638093932, 5.691663842204779, 4.865614566728464, 3.821236405686601, 5.6508764557687075, 4.331915768510935, 2.4584121800057654, 1.0415196244700715, 0.0), # 114
(14.883815844806392, 11.395922558068468, 12.264929243609757, 12.960101406218136, 11.280434856414509, 5.338800611665514, 4.84324772015325, 4.054268436185806, 5.773399988623354, 2.3771301311952313, 1.8496412030472253, 1.091605011007847, 0.0, 15.123210610656603, 12.007655121086316, 9.248206015236125, 7.131390393585693, 11.546799977246708, 5.675975810660129, 4.84324772015325, 3.8134290083325095, 5.640217428207254, 4.320033802072713, 2.452985848721952, 1.0359929598244064, 0.0), # 115
(14.837087797180216, 11.336142812561162, 12.238042919978499, 12.924799380319683, 11.25886776147603, 5.328128285467958, 4.821283854022315, 4.043586875265996, 5.763296714254843, 2.3683173433798195, 1.8430949150057288, 1.0883364263316462, 0.0, 15.092171615609425, 11.971700689648106, 9.215474575028642, 7.104952030139457, 11.526593428509686, 5.661021625372395, 4.821283854022315, 3.8058059181913984, 5.629433880738015, 4.308266460106562, 2.4476085839957, 1.0305584375055605, 0.0), # 116
(14.790127108518035, 11.277288070964257, 12.211349380701316, 12.88977764711069, 11.237019494714783, 5.317688225790165, 4.799686591158202, 4.033398530109057, 5.753460702129175, 2.359670960363252, 1.8366800263528757, 1.085109739539167, 0.0, 15.06087520777316, 11.936207134930834, 9.183400131764378, 7.079012881089755, 11.50692140425835, 5.6467579421526795, 4.799686591158202, 3.7983487327072605, 5.6185097473573915, 4.296592549036898, 2.4422698761402635, 1.0252080064512963, 0.0), # 117
(14.742875593576338, 11.21926977159314, 12.18479607734449, 12.854972625864399, 11.214857924123566, 5.3074546690537305, 4.7784195543834524, 4.023672440580065, 5.743865658434098, 2.351174238789904, 1.8303836043358468, 1.0819188212497801, 0.0, 15.02927291740644, 11.901107033747579, 9.151918021679233, 7.053522716369711, 11.487731316868196, 5.633141416812091, 4.7784195543834524, 3.791039049324093, 5.607428962061783, 4.284990875288134, 2.436959215468898, 1.0199336155993766, 0.0), # 118
(14.695275067111588, 11.161999352763203, 12.158330461474298, 12.820320735854047, 11.192350917695169, 5.297401851680244, 4.757446366520605, 4.014377646544097, 5.734485289357356, 2.3428104353041492, 1.824192716201821, 1.0787575420828581, 0.0, 14.997316274767892, 11.866332962911438, 9.120963581009105, 7.028431305912447, 11.468970578714712, 5.620128705161736, 4.757446366520605, 3.7838584654858884, 5.5961754588475845, 4.273440245284683, 2.43166609229486, 1.014727213887564, 0.0), # 119
(14.647267343880259, 11.105388252789831, 12.131899984657018, 12.785758396352872, 11.169466343422396, 5.287504010091301, 4.736730650392203, 4.005483187866229, 5.7252933010866975, 2.3345628065503625, 1.818094429197978, 1.0756197726577732, 0.0, 14.964956810116156, 11.831817499235502, 9.090472145989889, 7.003688419651086, 11.450586602173395, 5.60767646301272, 4.736730650392203, 3.7767885786366437, 5.584733171711198, 4.2619194654509585, 2.4263799969314035, 1.0095807502536214, 0.0), # 120
(14.59879423863883, 11.049347909988416, 12.105452098458917, 12.751222026634121, 11.146172069298046, 5.277735380708496, 4.716236028820784, 3.9969581044115383, 5.716263399809866, 2.326414609172919, 1.812075810571498, 1.0724993835938965, 0.0, 14.932146053709857, 11.797493219532859, 9.060379052857488, 6.979243827518756, 11.432526799619732, 5.595741346176154, 4.716236028820784, 3.769810986220354, 5.573086034649023, 4.250407342211375, 2.4210904196917835, 1.0044861736353108, 0.0), # 121
(14.549797566143766, 10.993789762674343, 12.078934254446281, 12.716648045971027, 11.122435963314915, 5.268070199953418, 4.695926124628894, 3.9887714360450994, 5.707369291714607, 2.3183490998161913, 1.8061239275695606, 1.0693902455106004, 0.0, 14.898835535807633, 11.763292700616601, 9.030619637847803, 6.955047299448573, 11.414738583429214, 5.584280010463139, 4.695926124628894, 3.762907285681013, 5.561217981657458, 4.238882681990344, 2.4157868508892566, 0.9994354329703949, 0.0), # 122
(14.50021914115155, 10.938625249163001, 12.052293904185383, 12.681972873636834, 11.098225893465804, 5.258482704247664, 4.675764560639071, 3.9808922226319887, 5.698584682988669, 2.3103495351245553, 1.8002258474393456, 1.0662862290272563, 0.0, 14.864976786668116, 11.729148519299818, 9.001129237196727, 6.931048605373665, 11.397169365977337, 5.573249111684785, 4.675764560639071, 3.7560590744626166, 5.549112946732902, 4.227324291212279, 2.4104587808370765, 0.9944204771966367, 0.0), # 123
(14.450000778418648, 10.883765807769782, 12.025478499242494, 12.647132928904785, 11.073509727743506, 5.248947130012824, 4.655714959673856, 3.9732895040372846, 5.689883279819794, 2.302399171742385, 1.794368637428032, 1.063181204763237, 0.0, 14.830521336549939, 11.694993252395603, 8.971843187140161, 6.907197515227153, 11.379766559639588, 5.562605305652198, 4.655714959673856, 3.74924795000916, 5.536754863871753, 4.215710976301596, 2.405095699848499, 0.9894332552517985, 0.0), # 124
(14.399084292701534, 10.82912287681007, 11.9984354911839, 12.612064631048113, 11.048255334140823, 5.239437713670492, 4.635740944555791, 3.965932320126061, 5.68123878839573, 2.294481266314054, 1.7885393647828007, 1.0600690433379134, 0.0, 14.795420715711726, 11.660759476717045, 8.942696823914003, 6.883443798942161, 11.36247757679146, 5.552305248176485, 4.635740944555791, 3.7424555097646373, 5.524127667070411, 4.204021543682705, 2.39968709823678, 0.9844657160736429, 0.0), # 125
(14.347411498756685, 10.774607894599258, 11.971112331575865, 12.576704399340066, 11.022430580650552, 5.229928691642264, 4.615806138107416, 3.958789710763395, 5.6726249149042225, 2.2865790754839375, 1.7827250967508306, 1.0569436153706582, 0.0, 14.759626454412127, 11.626379769077237, 8.913625483754151, 6.859737226451811, 11.345249829808445, 5.542305595068753, 4.615806138107416, 3.735663351173045, 5.511215290325276, 4.192234799780023, 2.394222466315173, 0.9795098085999328, 0.0), # 126
(14.294924211340579, 10.720132299452729, 11.943456471984673, 12.54098865305388, 10.996003335265492, 5.220394300349728, 4.595874163151275, 3.951830715814364, 5.664015365533016, 2.27867585589641, 1.7769129005793014, 1.0537987914808424, 0.0, 14.723090082909758, 11.591786706289264, 8.884564502896506, 6.836027567689229, 11.328030731066033, 5.53256300214011, 4.595874163151275, 3.728853071678377, 5.498001667632746, 4.1803295510179606, 2.388691294396935, 0.97455748176843, 0.0), # 127
(14.241564245209673, 10.665607529685879, 11.915415363976601, 12.504853811462798, 10.968941465978443, 5.210808776214481, 4.575908642509906, 3.9450243751440417, 5.655383846469858, 2.2707548641958457, 1.7710898435153934, 1.0506284422878387, 0.0, 14.68576313146326, 11.556912865166222, 8.855449217576966, 6.812264592587535, 11.310767692939717, 5.523034125201659, 4.575908642509906, 3.722006268724629, 5.484470732989221, 4.168284603820934, 2.3830830727953205, 0.9696006845168982, 0.0), # 128
(14.187273415120451, 10.610945023614088, 11.886936459117921, 12.468236293840059, 10.9412128407822, 5.201146355658116, 4.555873199005851, 3.938339728617507, 5.646704063902494, 2.2627993570266187, 1.765242992806286, 1.0474264384110183, 0.0, 14.647597130331262, 11.5216908225212, 8.82621496403143, 6.788398071079855, 11.293408127804987, 5.51367562006451, 4.555873199005851, 3.7151045397557967, 5.4706064203911, 4.156078764613354, 2.377387291823584, 0.9646313657830989, 0.0), # 129
(14.131993535829388, 10.556056219552751, 11.857967208974907, 12.431072519458905, 10.91278532766956, 5.191381275102222, 4.53573145546165, 3.9317458160998338, 5.637949724018666, 2.2547925910331035, 1.7593594156991588, 1.044186650469754, 0.0, 14.608543609772397, 11.48605315516729, 8.796797078495793, 6.764377773099309, 11.275899448037332, 5.504444142539767, 4.53573145546165, 3.7081294822158726, 5.45639266383478, 4.1436908398196355, 2.3715934417949813, 0.9596414745047956, 0.0), # 130
(14.07566642209295, 10.500852555817252, 11.828455065113841, 12.393298907592571, 10.883626794633326, 5.181487770968396, 4.515447034699847, 3.9252116774560997, 5.629094533006126, 2.2467178228596745, 1.7534261794411918, 1.0409029490834167, 0.0, 14.568554100045299, 11.449932439917582, 8.767130897205957, 6.740153468579022, 11.258189066012251, 5.49529634843854, 4.515447034699847, 3.701062693548854, 5.441813397316663, 4.131099635864191, 2.3656910130227686, 0.9546229596197504, 0.0), # 131
(14.018233888667616, 10.445245470722984, 11.798347479100995, 12.354851877514303, 10.853705109666297, 5.171440079678229, 4.49498355954298, 3.918706352551382, 5.620112197052615, 2.238558309150706, 1.7474303512795641, 1.0375692048713792, 0.0, 14.527580131408602, 11.413261253585167, 8.73715175639782, 6.715674927452117, 11.24022439410523, 5.486188893571935, 4.49498355954298, 3.693885771198735, 5.4268525548331485, 4.1182839591714355, 2.3596694958201994, 0.949567770065726, 0.0), # 132
(13.959637750309861, 10.38914640258533, 11.767591902502646, 12.315667848497343, 10.822988140761264, 5.161212437653315, 4.474304652813592, 3.9121988812507547, 5.61097642234588, 2.2302973065505736, 1.7413589984614566, 1.0341792884530125, 0.0, 14.485573234120938, 11.375972172983136, 8.706794992307282, 6.690891919651719, 11.22195284469176, 5.477078433751057, 4.474304652813592, 3.686580312609511, 5.411494070380632, 4.105222616165782, 2.3535183805005295, 0.9444678547804848, 0.0), # 133
(13.899819821776152, 10.332466789719687, 11.736135786885072, 12.275683239814924, 10.791443755911033, 5.150779081315248, 4.453373937334223, 3.9056583034192958, 5.601660915073669, 2.2219180717036497, 1.7351991882340478, 1.030727070447689, 0.0, 14.442484938440934, 11.337997774924577, 8.675995941170239, 6.6657542151109475, 11.203321830147338, 5.467921624787015, 4.453373937334223, 3.6791279152251772, 5.395721877955516, 4.091894413271643, 2.3472271573770147, 0.9393151627017899, 0.0), # 134
(13.838721917822966, 10.275118070441435, 11.703926583814546, 12.234834470740296, 10.759039823108395, 5.14011424708562, 4.432155035927415, 3.8990536589220803, 5.592139381423722, 2.213403861254311, 1.7289379878445184, 1.0272064214747805, 0.0, 14.398266774627231, 11.299270636222584, 8.64468993922259, 6.640211583762932, 11.184278762847445, 5.458675122490913, 4.432155035927415, 3.671510176489728, 5.379519911554198, 4.0782781569134325, 2.340785316762909, 0.9341016427674034, 0.0), # 135
(13.776285853206776, 10.217011683065968, 11.670911744857346, 12.193057960546685, 10.725744210346152, 5.129192171386024, 4.410611571415708, 3.892353987624185, 5.5823855275837895, 2.2047379318469296, 1.7225624645400475, 1.0236112121536591, 0.0, 14.352870272938459, 11.259723333690248, 8.612812322700236, 6.614213795540787, 11.164771055167579, 5.44929558267386, 4.410611571415708, 3.6637086938471604, 5.362872105173076, 4.064352653515563, 2.3341823489714693, 0.9288192439150881, 0.0), # 136
(13.712453442684055, 10.15805906590867, 11.63703872157975, 12.15029012850735, 10.691524785617101, 5.117987090638052, 4.388707166621645, 3.885528329390686, 5.572373059741617, 2.1959035401258813, 1.716059685567815, 1.0199353131036961, 0.0, 14.306246963633242, 11.219288444140656, 8.580298427839075, 6.587710620377642, 11.144746119483234, 5.439739661146961, 4.388707166621645, 3.6557050647414657, 5.345762392808551, 4.050096709502451, 2.3274077443159498, 0.9234599150826065, 0.0), # 137
(13.647166501011277, 10.098171657284933, 11.602254965548024, 12.106467393895517, 10.656349416914047, 5.106473241263299, 4.366405444367763, 3.8785457240866603, 5.56207568408495, 2.1868839427355393, 1.7094167181750008, 1.016172594944264, 0.0, 14.258348376970226, 11.1778985443869, 8.547083590875005, 6.560651828206616, 11.1241513681699, 5.4299640137213245, 4.366405444367763, 3.6474808866166426, 5.3281747084570235, 4.035489131298506, 2.320450993109605, 0.9180156052077213, 0.0), # 138
(13.58036684294491, 10.037260895510144, 11.566507928328454, 12.061526175984431, 10.620185972229777, 5.094624859683358, 4.343670027476608, 3.8713752115771833, 5.551467106801532, 2.1776623963202795, 1.7026206296087845, 1.0123169282947344, 0.0, 14.20912604320803, 11.135486211242075, 8.513103148043921, 6.532987188960837, 11.102934213603064, 5.419925296208056, 4.343670027476608, 3.6390177569166844, 5.3100929861148884, 4.020508725328145, 2.313301585665691, 0.912478263228195, 0.0), # 139
(13.511996283241437, 9.97523821889969, 11.529745061487317, 12.015402894047334, 10.583002319557098, 5.082416182319821, 4.320464538770717, 3.863985831727331, 5.54052103407911, 2.168222157524475, 1.6956584871163454, 1.008362183774479, 0.0, 14.158531492605304, 11.091984021519266, 8.478292435581725, 6.504666472573423, 11.08104206815822, 5.409580164418264, 4.320464538770717, 3.6302972730855863, 5.291501159778549, 4.005134298015779, 2.3059490122974635, 0.9068398380817901, 0.0), # 140
(13.44199663665733, 9.912015065768964, 11.491913816590882, 11.968033967357464, 10.544766326888803, 5.069821445594281, 4.296752601072636, 3.8563466244021805, 5.529211172105429, 2.158546482992501, 1.688517357944864, 1.00430223200287, 0.0, 14.106516255420662, 11.047324552031569, 8.442586789724318, 6.4756394489775015, 11.058422344210857, 5.398885274163053, 4.296752601072636, 3.6213010325673434, 5.272383163444402, 3.989344655785822, 2.2983827633181764, 0.9010922787062696, 0.0), # 141
(13.37030971794905, 9.84750287443335, 11.452961645205429, 11.919355815188066, 10.505445862217693, 5.056814885928333, 4.272497837204901, 3.848426629466808, 5.517511227068235, 2.1486186293687317, 1.6811843093415195, 1.0001309435992793, 0.0, 14.053031861912746, 11.001440379592072, 8.405921546707596, 6.445855888106194, 11.03502245413647, 5.3877972812535315, 4.272497837204901, 3.612010632805952, 5.252722931108846, 3.973118605062689, 2.2905923290410857, 0.8952275340393956, 0.0), # 142
(13.29687734187308, 9.781613083208239, 11.412835998897235, 11.86930485681237, 10.465008793536564, 5.043370739743566, 4.247663869990055, 3.840194886786288, 5.505394905155279, 2.1384218532975416, 1.6736464085534917, 0.9958421891830788, 0.0, 13.998029842340188, 10.954264081013864, 8.368232042767458, 6.415265559892624, 11.010789810310557, 5.376272841500803, 4.247663869990055, 3.6024076712454045, 5.232504396768282, 3.956434952270791, 2.282567199779447, 0.8892375530189309, 0.0), # 143
(13.221641323185896, 9.714257130409019, 11.37148432923257, 11.817817511503629, 10.423422988838217, 5.029463243461577, 4.222214322250639, 3.8316204362256996, 5.492835912554298, 2.1279394114233043, 1.6658907228279605, 0.99142983937364, 0.0, 13.941461726961624, 10.905728233110038, 8.329453614139801, 6.383818234269912, 10.985671825108597, 5.364268610715979, 4.222214322250639, 3.592473745329698, 5.2117114944191085, 3.9392725038345437, 2.2742968658465146, 0.8831142845826383, 0.0), # 144
(13.144543476643964, 9.64534645435108, 11.328854087777719, 11.764830198535075, 10.380656316115449, 5.015066633503958, 4.196112816809195, 3.8226723176501176, 5.479807955453042, 2.1171545603903956, 1.6579043194121055, 0.9868877647903354, 0.0, 13.88327904603568, 10.855765412693687, 8.289521597060528, 6.351463681171186, 10.959615910906084, 5.351741244710165, 4.196112816809195, 3.582190452502827, 5.190328158057724, 3.921610066178359, 2.265770817555544, 0.8768496776682801, 0.0), # 145
(13.065525617003761, 9.574792493349808, 11.284892726098956, 11.710279337179951, 10.33667664336106, 5.000155146292303, 4.169322976488264, 3.813319570924618, 5.4662847400392565, 2.1060505568431886, 1.6496742655531065, 0.9822098360525362, 0.0, 13.82343332982099, 10.804308196577896, 8.248371327765533, 6.318151670529565, 10.932569480078513, 5.338647399294466, 4.169322976488264, 3.5715393902087875, 5.16833832168053, 3.903426445726651, 2.2569785452197917, 0.870435681213619, 0.0), # 146
(12.98452955902176, 9.502506685720592, 11.239547695762546, 11.654101346711496, 10.291451838567841, 4.984703018248201, 4.141808424110385, 3.803531235914277, 5.4522399725006885, 2.094610657426059, 1.6411876284981433, 0.9773899237796149, 0.0, 13.761876108576189, 10.751289161575762, 8.205938142490716, 6.2838319722781755, 10.904479945001377, 5.324943730279988, 4.141808424110385, 3.5605021558915717, 5.145725919283921, 3.884700448903833, 2.2479095391525097, 0.8638642441564175, 0.0), # 147
(12.901497117454435, 9.428400469778822, 11.192766448334778, 11.596232646402957, 10.2449497697286, 4.968684485793251, 4.113532782498101, 3.7932763524841717, 5.437647359025082, 2.082818118783379, 1.6324314754943956, 0.9724218985909429, 0.0, 13.698558912559907, 10.69664088450037, 8.162157377471978, 6.248454356350136, 10.875294718050164, 5.310586893477841, 4.113532782498101, 3.5490603469951787, 5.1224748848643, 3.8654108821343196, 2.2385532896669558, 0.8571273154344385, 0.0), # 148
(12.81637010705826, 9.352385283839885, 11.144496435381926, 11.536609655527563, 10.197138304836129, 4.9520737853490395, 4.084459674473953, 3.7825239604993777, 5.42248060580018, 2.0706561975595257, 1.6233928737890426, 0.9672996311058923, 0.0, 13.63343327203078, 10.640295942164814, 8.116964368945213, 6.211968592678575, 10.84496121160036, 5.295533544699129, 4.084459674473953, 3.5371955609635997, 5.098569152418064, 3.845536551842522, 2.2288992870763855, 0.8502168439854443, 0.0), # 149
(12.729090342589704, 9.274372566219169, 11.09468510847026, 11.475168793358566, 10.147985311883227, 4.934845153337166, 4.054552722860481, 3.771243099824971, 5.406713419013735, 2.058108150398871, 1.614058890629265, 0.9620169919438353, 0.0, 13.566450717247434, 10.582186911382186, 8.070294453146325, 6.174324451196611, 10.81342683802747, 5.27974033975496, 4.054552722860481, 3.524889395240833, 5.0739926559416135, 3.825056264452856, 2.2189370216940523, 0.8431247787471974, 0.0), # 150
(12.63959963880524, 9.194273755232066, 11.043279919166057, 11.411846479169196, 10.097458658862696, 4.916972826179219, 4.023775550480226, 3.759402810326029, 5.390319504853488, 2.0451572339457917, 1.6044165932622414, 0.956567851724143, 0.0, 13.49756277846851, 10.522246368965572, 8.022082966311206, 6.135471701837374, 10.780639009706976, 5.263163934456441, 4.023775550480226, 3.5121234472708704, 5.048729329431348, 3.8039488263897328, 2.2086559838332116, 0.8358430686574607, 0.0), # 151
(12.54783981046135, 9.11200028919396, 10.990228319035603, 11.346579132232703, 10.045526213767326, 4.898431040296793, 3.992091780155732, 3.7469721318676275, 5.373272569507184, 2.0317867048446603, 1.5944530489351527, 0.950946081066188, 0.0, 13.426720985952636, 10.460406891728066, 7.9722652446757625, 6.09536011453398, 10.746545139014367, 5.245760984614678, 3.992091780155732, 3.4988793144977093, 5.022763106883663, 3.7821930440775686, 2.198045663807121, 0.8283636626539964, 0.0), # 152
(12.453752672314497, 9.027463606420243, 10.935477759645158, 11.27930317182232, 9.992155844589925, 4.8791940321114815, 3.9594650347095355, 3.7339201043148416, 5.355546319162572, 2.017979819739852, 1.5841553248951779, 0.945145550589342, 0.0, 13.353876869958444, 10.39660105648276, 7.920776624475889, 6.053939459219555, 10.711092638325145, 5.227488146040779, 3.9594650347095355, 3.485138594365344, 4.996077922294963, 3.759767723940774, 2.187095551929032, 0.8206785096745677, 0.0), # 153
(12.357280039121166, 8.940575145226303, 10.878975692561012, 11.209955017211293, 9.937315419323285, 4.859236038044878, 3.9258589369641825, 3.7202157675327485, 5.337114460007395, 2.0037198352757417, 1.5735104883894968, 0.9391601309129768, 0.0, 13.278981960744572, 10.330761440042743, 7.867552441947483, 6.011159505827224, 10.67422892001479, 5.208302074545848, 3.9258589369641825, 3.4708828843177697, 4.968657709661643, 3.736651672403765, 2.1757951385122025, 0.8127795586569367, 0.0), # 154
(12.258363725637818, 8.851246343927524, 10.820669569349436, 11.138471087672855, 9.880972805960209, 4.838531294518574, 3.891237109742209, 3.705828161386424, 5.317950698229401, 1.9889900080967022, 1.562505606665289, 0.9329836926564644, 0.0, 13.201987788569642, 10.262820619221108, 7.812528033326444, 5.966970024290106, 10.635901396458802, 5.188159425940994, 3.891237109742209, 3.456093781798981, 4.940486402980104, 3.712823695890952, 2.1641339138698874, 0.804658758538866, 0.0), # 155
(12.15694554662093, 8.759388640839303, 10.760506841576703, 11.06478780248025, 9.823095872493491, 4.817054037954164, 3.85556317586616, 3.690726325740946, 5.298028740016334, 1.9737735948471096, 1.5511277469697347, 0.9266101064391765, 0.0, 13.122845883692296, 10.19271117083094, 7.755638734848673, 5.921320784541328, 10.596057480032668, 5.167016856037325, 3.85556317586616, 3.440752884252974, 4.911547936246746, 3.688262600826751, 2.1521013683153405, 0.7963080582581185, 0.0), # 156
(12.05296731682698, 8.664913474277022, 10.698434960809092, 10.988841580906726, 9.76365248691593, 4.79477850477324, 3.8188007581585754, 3.6748793004613884, 5.27732229155594, 1.958053852171337, 1.5393639765500133, 0.9200332428804852, 0.0, 13.041507776371162, 10.120365671685335, 7.696819882750066, 5.87416155651401, 10.55464458311188, 5.1448310206459436, 3.8188007581585754, 3.4248417891237426, 4.881826243457965, 3.662947193635576, 2.1396869921618182, 0.7877194067524566, 0.0), # 157
(11.943489514248384, 8.56599791046598, 10.631455938536474, 10.907723497981493, 9.699926512929064, 4.7702895112293024, 3.780085376742286, 3.6571979682329148, 5.254219782186185, 1.9413463665164579, 1.5268255340103847, 0.9130132752259121, 0.0, 12.954377375064553, 10.043146027485031, 7.634127670051924, 5.824039099549372, 10.50843956437237, 5.120077155526081, 3.780085376742286, 3.407349650878073, 4.849963256464532, 3.6359078326604983, 2.126291187707295, 0.7787270827696345, 0.0), # 158
(11.811658827165445, 8.452495802079234, 10.542317091203984, 10.804772590546145, 9.61620406376707, 4.7354436714732975, 3.734570210708573, 3.6314756885095885, 5.21942787265181, 1.9209123976394986, 1.5113111828317318, 0.9041816698244146, 0.0, 12.840684235072311, 9.94599836806856, 7.556555914158659, 5.762737192918495, 10.43885574530362, 5.084065963913424, 3.734570210708573, 3.3824597653380692, 4.808102031883535, 3.6015908635153826, 2.108463418240797, 0.7684087092799304, 0.0), # 159
(11.655795351846896, 8.323475201859713, 10.429227943941186, 10.678293012490633, 9.51084814010325, 4.689385209644506, 3.6817949987070273, 3.5970661263515646, 5.171960121188613, 1.896482260745158, 1.4926025356292107, 0.893400259851713, 0.0, 12.69827297422973, 9.827402858368842, 7.463012678146054, 5.689446782235472, 10.343920242377227, 5.0358925768921905, 3.6817949987070273, 3.3495608640317895, 4.755424070051625, 3.559431004163545, 2.0858455887882372, 0.7566795638054286, 0.0), # 160
(11.477155287337537, 8.179777273184687, 10.293395962547079, 10.529487004508074, 9.38495266590092, 4.632672092132293, 3.622145156805501, 3.5544003554065204, 5.112442542399476, 1.8682632772683756, 1.4708644412265888, 0.8807689958543429, 0.0, 12.528598471710556, 9.68845895439777, 7.354322206132943, 5.6047898318051255, 10.224885084798952, 4.976160497569129, 3.622145156805501, 3.3090514943802094, 4.69247633295046, 3.509829001502692, 2.058679192509416, 0.7436161157440625, 0.0), # 161
(11.27699483268217, 8.022243179431417, 10.136028612820661, 10.359556807291593, 9.239611565123418, 4.565862285326026, 3.5560061010718473, 3.503909449322135, 5.041501150887273, 1.836462768644093, 1.4462617484476323, 0.8663878283788393, 0.0, 12.333115606688533, 9.530266112167231, 7.231308742238162, 5.509388305932278, 10.083002301774545, 4.9054732290509895, 3.5560061010718473, 3.261330203804304, 4.619805782561709, 3.4531856024305316, 2.0272057225641325, 0.7292948344937653, 0.0), # 162
(11.056570186925597, 7.851714083977169, 9.958333360560937, 10.169704661534322, 9.075918761734068, 4.489513755615068, 3.4837632475739206, 3.4460244817460834, 4.959761961254883, 1.8012880563072504, 1.418959306116109, 0.8503567079717379, 0.0, 12.113279258337407, 9.353923787689116, 7.0947965305805445, 5.40386416892175, 9.919523922509766, 4.824434274444517, 3.4837632475739206, 3.2067955397250487, 4.537959380867034, 3.3899015538447745, 1.9916666721121876, 0.71379218945247, 0.0), # 163
(10.817137549112616, 7.669031150199204, 9.761517671566903, 9.961132807929381, 8.894968179696201, 4.404184469388787, 3.405802012379573, 3.3811765263260463, 4.867850988105186, 1.762946461692788, 1.3891219630557858, 0.8327755851795738, 0.0, 11.870544305830926, 9.160531436975312, 6.945609815278928, 5.288839385078362, 9.735701976210372, 4.733647136856465, 3.405802012379573, 3.1458460495634197, 4.447484089848101, 3.320377602643128, 1.9523035343133808, 0.6971846500181095, 0.0), # 164
(10.559953118288028, 7.475035541474793, 9.546789011637559, 9.735043487169904, 8.697853742973145, 4.310432393036548, 3.3225078115566578, 3.3097966567096977, 4.766394246041056, 1.7216453062356458, 1.35691456809043, 0.8137444105488828, 0.0, 11.606365628342832, 8.951188516037709, 6.7845728404521495, 5.164935918706936, 9.532788492082112, 4.633715319393577, 3.3225078115566578, 3.078880280740391, 4.348926871486572, 3.245014495723302, 1.909357802327512, 0.6795486855886177, 0.0), # 165
(10.286273093496636, 7.270568421181199, 9.315354846571905, 9.492638939949002, 8.485669375528229, 4.208815492947715, 3.234266061173029, 3.2323159465447184, 4.656017749665372, 1.6775919113707654, 1.322501970043808, 0.7933631346262003, 0.0, 11.322198105046873, 8.726994480888202, 6.612509850219039, 5.0327757341122945, 9.312035499330744, 4.525242325162606, 3.234266061173029, 3.0062967806769394, 4.242834687764114, 3.1642129799830014, 1.8630709693143812, 0.6609607655619273, 0.0), # 166
(9.997353673783238, 7.056470952695688, 9.06842264216894, 9.235121406959813, 8.259509001324778, 4.099891735511655, 3.14146217729654, 3.1491654694787847, 4.537347513581013, 1.6309935985330861, 1.2860490177396875, 0.7717317079580612, 0.0, 11.019496615116793, 8.489048787538673, 6.430245088698436, 4.892980795599257, 9.074695027162026, 4.408831657270299, 3.14146217729654, 2.928494096794039, 4.129754500662389, 3.0783738023199385, 1.8136845284337881, 0.6414973593359717, 0.0), # 167
(9.694451058192634, 6.833584299395522, 8.807199864227664, 8.963693128895455, 8.020466544326124, 3.9842190871177325, 3.0444815759950434, 3.0607762991595733, 4.411009552390856, 1.5820576891575493, 1.247720560001835, 0.7489500810910016, 0.0, 10.69971603772634, 8.238450892001017, 6.2386028000091756, 4.746173067472647, 8.822019104781711, 4.285086818823403, 3.0444815759950434, 2.8458707765126663, 4.010233272163062, 2.987897709631819, 1.7614399728455332, 0.6212349363086839, 0.0), # 168
(9.378821445769624, 6.602749624657969, 8.53289397854708, 8.67955634644906, 7.769635928495594, 3.8623555141553156, 2.9437096733363934, 2.9675795092347634, 4.277629880697781, 1.5309915046790952, 1.2076814456540184, 0.7251182045715564, 0.0, 10.364311252049257, 7.976300250287119, 6.038407228270092, 4.592974514037284, 8.555259761395561, 4.154611312928669, 2.9437096733363934, 2.7588253672537966, 3.884817964247797, 2.8931854488163538, 1.706578795709416, 0.6002499658779973, 0.0), # 169
(9.051721035559014, 6.3648080918602945, 8.24671245092618, 8.383913300313743, 7.508111077796515, 3.7348589830137664, 2.8395318853884426, 2.870006173352032, 4.137834513104661, 1.4780023665326634, 1.1660965235200045, 0.7003360289462612, 0.0, 10.014737137259289, 7.7036963184088725, 5.830482617600023, 4.43400709959799, 8.275669026209322, 4.018008642692845, 2.8395318853884426, 2.6677564164384044, 3.7540555388982577, 2.7946377667712485, 1.649342490185236, 0.5786189174418451, 0.0), # 170
(8.7144060266056, 6.12060086437976, 7.949862747163971, 8.077966231182643, 7.23698591619222, 3.602287460082452, 2.7323336282190445, 2.7684873651590554, 3.992249464214377, 1.4232975961531957, 1.1231306424235596, 0.6747035047616515, 0.0, 9.652448572530185, 7.421738552378166, 5.615653212117798, 4.269892788459586, 7.984498928428754, 3.8758823112226777, 2.7323336282190445, 2.5730624714874657, 3.61849295809611, 2.692655410394215, 1.5899725494327943, 0.5564182603981601, 0.0), # 171
(8.368132617954185, 5.870969105593635, 7.643552333059449, 7.762917379748876, 6.9573543676460305, 3.4651989117507385, 2.6225003178960526, 2.663454158303514, 3.8415007486298056, 1.3670845149756323, 1.0789486511884518, 0.648320582564263, 0.0, 9.278900437035686, 7.1315264082068905, 5.3947432559422595, 4.101253544926896, 7.683001497259611, 3.7288358216249198, 2.6225003178960526, 2.475142079821956, 3.4786771838230153, 2.587639126582959, 1.52871046661189, 0.5337244641448761, 0.0), # 172
(8.014157008649567, 5.616753978879182, 7.328988674411616, 7.439968986705571, 6.6703103561212815, 3.3241513044079904, 2.51041737048732, 2.5553376264330825, 3.6862143809538255, 1.309570444434913, 1.0337153986384477, 0.62128721290063, 0.0, 8.89554760994954, 6.83415934190693, 5.168576993192238, 3.9287113333047383, 7.372428761907651, 3.5774726770063157, 2.51041737048732, 2.37439378886285, 3.3351551780606408, 2.479989662235191, 1.4657977348823235, 0.5106139980799257, 0.0), # 173
(7.6537353977365505, 5.358796647613667, 7.00737923701947, 7.110323292745849, 6.376947805581297, 3.179702604443573, 2.3964702020607005, 2.4445688431954404, 3.527016375789314, 1.250962705965979, 0.9875957335973142, 0.5937033463172892, 0.0, 8.503844970445494, 6.53073680949018, 4.93797866798657, 3.7528881178979363, 7.054032751578628, 3.4223963804736166, 2.3964702020607005, 2.2712161460311235, 3.1884739027906486, 2.370107764248617, 1.401475847403894, 0.4871633316012425, 0.0), # 174
(7.288123984259929, 5.097938275174352, 6.679931486682011, 6.7751825385628415, 6.078360639989406, 3.0324107782468537, 2.2810442286840464, 2.331578882238264, 3.36453274773915, 1.19146862100377, 0.9407545048888186, 0.5656689333607753, 0.0, 8.105247397697292, 6.222358266968527, 4.703772524444093, 3.574405863011309, 6.7290654954783, 3.26421043513357, 2.2810442286840464, 2.1660076987477526, 3.039180319994703, 2.2583941795209475, 1.3359862973364023, 0.46344893410675936, 0.0), # 175
(6.91857896726451, 4.835020024938507, 6.347852889198238, 6.435748964849671, 5.775642783308939, 2.882833792207196, 2.164524866425212, 2.216798817209233, 3.199389511406209, 1.131295510983227, 0.8933565613367281, 0.537283924577624, 0.0, 7.701209770878679, 5.910123170353863, 4.46678280668364, 3.39388653294968, 6.398779022812418, 3.103518344092926, 2.164524866425212, 2.0591669944337117, 2.8878213916544695, 2.1452496549498905, 1.2695705778396478, 0.4395472749944098, 0.0), # 176
(6.546356545795092, 4.570883060283395, 6.012350910367152, 6.093224812299459, 5.469888159503225, 2.731529612713966, 2.0472975313520503, 2.100659721756022, 3.0322126813933705, 1.07065069733929, 0.8455667517648098, 0.5086482705143706, 0.0, 7.2931869691634, 5.595130975658075, 4.227833758824048, 3.211952092017869, 6.064425362786741, 2.9409236104584306, 2.0472975313520503, 1.9510925805099755, 2.7349440797516125, 2.0310749374331536, 1.2024701820734305, 0.4155348236621269, 0.0), # 177
(6.172712918896475, 4.306368544586282, 5.6746330159877525, 5.74881232160534, 5.162190692535588, 2.5790562061565305, 1.929747639532414, 1.9835926695263104, 2.863628272303512, 1.0097415015069002, 0.7975499249968301, 0.4798619217175504, 0.0, 6.882633871725203, 5.278481138893053, 3.98774962498415, 3.0292245045207, 5.727256544607024, 2.7770297373368344, 1.929747639532414, 1.8421830043975218, 2.581095346267794, 1.916270773868447, 1.1349266031975505, 0.3914880495078438, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 133
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 135
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
37, # 1
)
| 279.186096 | 491 | 0.771973 | 32,987 | 261,039 | 6.108588 | 0.230849 | 0.35374 | 0.339447 | 0.643163 | 0.365387 | 0.360494 | 0.359492 | 0.359179 | 0.359179 | 0.359179 | 0 | 0.851548 | 0.094752 | 261,039 | 934 | 492 | 279.485011 | 0.001181 | 0.015366 | 0 | 0.200873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.005459 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e35d57f58aaa04623b4713a0caad47753f69b469 | 153 | py | Python | xcparse/Helpers/__init__.py | samdmarshall/xcparser | 4f78af149127325e60e3785b6e09d6dbfeedc799 | [
"BSD-3-Clause"
] | 59 | 2015-02-27T21:45:37.000Z | 2021-03-16T04:37:40.000Z | xcparse/Helpers/__init__.py | samdmarshall/xcparser | 4f78af149127325e60e3785b6e09d6dbfeedc799 | [
"BSD-3-Clause"
] | 14 | 2015-03-02T18:53:51.000Z | 2016-07-19T23:20:23.000Z | xcparse/Helpers/__init__.py | samdmarshall/xcparser | 4f78af149127325e60e3785b6e09d6dbfeedc799 | [
"BSD-3-Clause"
] | 8 | 2015-03-02T02:32:09.000Z | 2017-07-31T21:14:51.000Z | from path_helper import path_helper
from plist_helper import plist_helper
from xcrun_helper import xcrun_helper
from logging_helper import logging_helper | 38.25 | 41 | 0.901961 | 24 | 153 | 5.416667 | 0.291667 | 0.369231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.098039 | 153 | 4 | 41 | 38.25 | 0.942029 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8b8e79e18ecea72a4356137064178e55af562cbe | 33 | py | Python | fontcrunch/__init__.py | moyogo/fontcrunch | f849691ce7afb2202f9d19cac77afb621e48c5c6 | [
"Apache-2.0"
] | null | null | null | fontcrunch/__init__.py | moyogo/fontcrunch | f849691ce7afb2202f9d19cac77afb621e48c5c6 | [
"Apache-2.0"
] | null | null | null | fontcrunch/__init__.py | moyogo/fontcrunch | f849691ce7afb2202f9d19cac77afb621e48c5c6 | [
"Apache-2.0"
] | null | null | null | from .fontcrunch import optimize
| 16.5 | 32 | 0.848485 | 4 | 33 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8b9ab9221fc6f176dbf4a2ca3877b1d89499e28f | 25 | py | Python | asapy/load/frame/__init__.py | CLRafaelR/python_asa | c37374556f5ad5fc85a04e23f4f5d9584f195904 | [
"MIT"
] | 21 | 2019-08-03T04:27:17.000Z | 2021-05-17T16:11:00.000Z | asapy/load/frame/__init__.py | CLRafaelR/python_asa | c37374556f5ad5fc85a04e23f4f5d9584f195904 | [
"MIT"
] | 6 | 2020-01-11T23:02:26.000Z | 2021-07-13T02:35:52.000Z | asapy/load/frame/__init__.py | CLRafaelR/python_asa | c37374556f5ad5fc85a04e23f4f5d9584f195904 | [
"MIT"
] | 8 | 2019-09-27T16:22:38.000Z | 2021-05-25T05:26:49.000Z | from .Dict2 import Dict2
| 12.5 | 24 | 0.8 | 4 | 25 | 5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.095238 | 0.16 | 25 | 1 | 25 | 25 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
478ced591a2972d1e2eb552214c3d9856144f8cc | 24 | py | Python | bert_pytorch/__init__.py | mortonjt/BERT-pytorch | d10dc4f9d5a6f2ca74380f62039526eb7277c671 | [
"Apache-2.0"
] | 5,013 | 2018-10-16T06:02:03.000Z | 2022-03-31T11:36:18.000Z | bert_pytorch/__init__.py | mortonjt/BERT-pytorch | d10dc4f9d5a6f2ca74380f62039526eb7277c671 | [
"Apache-2.0"
] | 81 | 2018-10-15T14:28:32.000Z | 2022-02-07T14:21:53.000Z | bert_pytorch/__init__.py | mortonjt/BERT-pytorch | d10dc4f9d5a6f2ca74380f62039526eb7277c671 | [
"Apache-2.0"
] | 1,129 | 2018-10-17T04:01:40.000Z | 2022-03-31T15:41:14.000Z | from .model import BERT
| 12 | 23 | 0.791667 | 4 | 24 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
47b80f8960e225d716ed6cd0a1159a19d0a3ab72 | 31,055 | py | Python | energystoragetechnologies/charts.py | xiaoshir/EnergyStorageTechnologies | 0298073d7bbd267919c6f08af4f24ca85168d629 | [
"BSD-3-Clause"
] | null | null | null | energystoragetechnologies/charts.py | xiaoshir/EnergyStorageTechnologies | 0298073d7bbd267919c6f08af4f24ca85168d629 | [
"BSD-3-Clause"
] | null | null | null | energystoragetechnologies/charts.py | xiaoshir/EnergyStorageTechnologies | 0298073d7bbd267919c6f08af4f24ca85168d629 | [
"BSD-3-Clause"
] | null | null | null | import pygal
from pygal import Config
from pygal.style import Style
import math
from energystoragetechnologies import db
from energystoragetechnologies.models import Technology, Source, Parameter
def drawfigure(techlist, par):
config = Config()
config.show_legend = False
config.xrange = (0, len(techlist)+1)
#labels, dots and stroke depending on number of technologies compared
config.dots_size = 7
config.stroke_style = {'width': 50}
labelsize = 12
if len(techlist) > 3:
config.truncate_label = 20
config.x_label_rotation = 20
config.dots_size = 6
config.stroke_style = {'width': 40}
if len(techlist) > 5:
config.dots_size = 5
config.stroke_style = {'width': 30}
if len(techlist) > 10:
config.stroke_style = {'width': 27}
if len(techlist) > 13:
config.dots_size = 4
config.stroke_style = {'width': 25}
if len(techlist) > 15:
config.dots_size = 4
config.stroke_style = {'width': 22}
labelsize=11
if len(techlist) > 17:
config.dots_size = 3
config.stroke_style = {'width': 20}
if len(techlist) > 20:
labelsize=10
config.stroke_style = {'width': 18}
if len(techlist) > 23:
labelsize=9
config.stroke_style = {'width': 15}
if len(techlist) > 27:
labelsize=8
config.stroke_style = {'width': 14}
if len(techlist) > 31:
labelsize = 7
config.stroke_style = {'width': 12}
config.human_readable = True
unit =Parameter.query.filter_by(name=par+'_min').first().unit
config.y_title = par.replace('_', ' ') + ' [' + unit + ']'
#config.show_dots = False
config.style = pygal.style.styles['default'](stroke_opacity=1, label_font_size=labelsize,
stroke_opacity_hover=1, transition='100000000000s ease-in')
if par == 'efficiency':
config.range = (0, 100)
xy_chart = pygal.XY(config)
if par == 'discharge_time':
xy_chart.y_labels = [
{'label': 'milliseconds', 'value': 1},
{'label': 'seconds', 'value': 2},
{'label': 'minutes', 'value': 3},
{'label': 'hours', 'value': 4},
{'label': 'days', 'value': 5},
{'label': 'weeks', 'value': 6},
{'label': 'months', 'value': 7}]
if par == 'response_time':
xy_chart.y_labels = [
{'label': 'milliseconds', 'value': 1},
{'label': 'seconds', 'value': 2},
{'label': 'minutes', 'value': 3}]
dictlist = []
ymin=100000000000
ymax=0
logscale=False
for tech in techlist:
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_min").first().value != None:
ymin = min(Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_min").first().value, ymin)
ymax = max(Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_max").first().value, ymax)
if (ymin*100) < ymax:
xy_chart.logarithmic = True
xy_chart.xrange = (0, 10**(len(techlist)+1))
if ymax<100000:
xy_chart.range = (10 ** int(math.floor(math.log10(ymin))), 10 ** (int(math.floor(math.log10(ymax))) + 1) + 1)
else:
xy_chart.range = (10 ** int(math.floor(math.log10(ymin))), 10 ** (int(math.floor(math.log10(ymax)))) + 1)
logscale=True
if logscale:
i = 10
else:
i = 1
for tech in techlist:
minxlink=''
maxxlink=''
minlabel = ''
maxlabel = ''
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=par + "_min").first().source_id).first() is not None:
minxlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=par + "_min").first().source_id).first().link
minlabel = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=par + "_min").first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_min").first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=par + "_max").first().source_id).first() is not None:
maxxlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=par + "_max").first().source_id).first().link
maxlabel = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=par + "_max").first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_max").first().source_id).first().releaseyear)
xy_chart.add(f"{tech.name}", [
{'value': (i, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_min").first().value),
'label': minlabel,
'xlink': {'href': minxlink, 'target': '_blank'}},
{'value': (i, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=par + "_max").first().value),
'label': maxlabel,
'xlink': {'href': maxxlink, 'target': '_blank'}}])
dictlist.append({
'label': f"{tech.name}",
'value': i})
if logscale:
i=i*10
else:
i=i+1
xy_chart.x_labels = (dictlist)
xy_chart.x_value_formatter = lambda x: ""
xy_chart.render()
return xy_chart.render_data_uri()
def drawappplicationsfigure(techlist, applicationslist):
config = Config()
config.show_legend = False
config.xrange = (0, len(techlist)+1)
#labels, dots depending on number of technologies compared
config.dots_size = 7
labelsize = 10
if len(techlist) > 3:
config.truncate_label = 20
config.x_label_rotation = 20
config.dots_size = 6
if len(techlist) > 5:
config.dots_size = 6
if len(techlist) > 13:
config.dots_size = 5
if len(techlist) > 17:
config.dots_size = 4
labelsize = 9
if len(techlist) > 23:
labelsize = 8
if len(techlist) > 28:
labelsize = 7
config.human_readable = True
#config.show_dots = False
config.style = pygal.style.styles['default'](label_font_size=labelsize)
xy_chart = pygal.XY(config)
dictlist = []
applicationsconverter={
'frequency containment reserve (primary control)': 1,
'frequency restoration reserve (secondary control)': 2,
'replacement reserve (tertiary control)': 3,
'black start': 4,
'energy arbitrage': 5,
'grid investment deferral': 6,
'increase of self-consumption': 7,
'island operation': 8,
'load levelling': 9,
'mobility': 10,
'off grid applications': 11,
'peak shaving': 12,
'portable electronic applications': 13,
'power reliability': 14,
'renewable energy integration': 15,
'uninterrupted power supply': 16,
'voltage support': 17}
xy_chart.y_labels = [
{'label': 'frequency containment reserve', 'value': 1},
{'label': 'frequency restoration reserve', 'value': 2},
{'label': 'replacement reserve', 'value': 3},
{'label': 'black start', 'value': 4},
{'label': 'energy arbitrage', 'value': 5},
{'label': 'grid investment deferral', 'value': 6},
{'label': 'increase of self-consumption', 'value': 7},
{'label': 'island operation', 'value': 8},
{'label': 'load levelling', 'value': 9},
{'label': 'mobility', 'value': 10},
{'label': 'off-grid applications', 'value': 11},
{'label': 'peak shaving', 'value': 12},
{'label': 'portable electronic applications', 'value': 13},
{'label': 'power reliability', 'value': 14},
{'label': 'renewable energy integration', 'value': 15},
{'label': 'uninterrupted power supply', 'value': 16},
{'label': 'voltage support', 'value': 17}]
i = 1
for tech in techlist:
for application in applicationslist:
if application in tech.applications:
xy_chart.add(f"{tech.name}", [
{'value': (i, applicationsconverter[application]),
'label': "",
'color':'DodgerBlue'}])
dictlist.append({
'label': f"{tech.name}",
'value': i})
i=i+1
xy_chart.x_labels = (dictlist)
xy_chart.x_value_formatter = lambda x: ""
xy_chart.render()
return xy_chart.render_data_uri()
def drawdensityfigure(techlist, par):
config = Config()
config.show_legend = True
config.human_readable = True
config.dots_size = 3
config.x_label_rotation=270
config.legend_at_bottom=True
if par == "gravimetric":
power_unit="[W/kg]"
energy_unit="[Wh/kg]"
else:
power_unit="[kW/m^3]"
energy_unit="[kWh/m^3]"
config.x_title = par + " power density " + power_unit
config.y_title = par + " energy density " + energy_unit
#config.show_dots = False
config.logarithmic = True
config.fill = True
#config.show_minor_x_labels = False
config.stroke_style = {'width': 1}
config.style = pygal.style.styles['default'](label_font_size=12, stroke_opacity=0,
stroke_opacity_hover=0, transition='100000000000s ease-in')
xy_chart = pygal.XY(config)
xmin=10000
ymin=10000
xmax=0.001
ymax=0.001
for tech in techlist:
power_minstring = par+"_power_density_min"
power_maxstring = par+"_power_density_max"
energy_minstring = par+"_energy_density_min"
energy_maxstring = par+"_energy_density_max"
minpowerlink=''
maxpowerlink=''
minenergylink=''
maxenergylink=''
minpowerlabel = ''
maxpowerlabel = ''
minenergylabel = ''
maxenergylabel = ''
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first() is not None:
minpowerlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first().link
minpowerlabel = 'min. power density: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first() is not None:
maxpowerlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first().link
maxpowerlabel = 'max. power density: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first() is not None:
minenergylink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first().link
minenergylabel = 'min. energy density: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first() is not None:
maxenergylink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first().link
maxenergylabel = 'max. energy density: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().source_id).first().releaseyear)
xy_chart.add(f"{tech.name}", [
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': minenergylabel,
'xlink': {'href': minenergylink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value),
'label': minpowerlabel,
'xlink': {'href': minpowerlink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value),
'label': maxenergylabel,
'xlink': {'href': maxenergylink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': maxpowerlabel,
'xlink': {'href': maxpowerlink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': minenergylabel,
'xlink': {'href': minenergylink, 'target': '_blank'}}])
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value is not None:
xmin = min(xmin, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value)
xmax = max(xmax, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value)
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value is not None:
ymin = min(ymin, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value)
ymax = max(ymax, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value)
xy_chart.xrange = (10**int(math.floor(math.log10(xmin))), 10**(int(math.floor(math.log10(xmax)))+1)+1)
xy_chart.range = (10**int(math.floor(math.log10(ymin))), 10**(int(math.floor(math.log10(ymax)))+1)+1)
xy_chart.render()
return xy_chart.render_data_uri()
def drawcapitalcostfigure(techlist):
config = Config()
config.show_legend = True
config.human_readable = True
config.dots_size = 3
config.x_label_rotation=270
config.legend_at_bottom=True
config.x_title = "power specific capital cost [$/kW]"
config.y_title = "energy specific capital cost [$/kWh]"
#config.show_dots = False
config.fill = True
#config.show_minor_x_labels = False
config.stroke_style = {'width': 1}
config.style = pygal.style.styles['default'](label_font_size=12, stroke_opacity=0,
stroke_opacity_hover=0, transition='100000000000s ease-in')
xy_chart = pygal.XY(config)
xmin=10000
ymin=10000
xmax=0.001
ymax=0.001
for tech in techlist:
power_minstring = "capital_cost_powerspecific_min"
power_maxstring = "capital_cost_powerspecific_max"
energy_minstring = "capital_cost_energyspecific_min"
energy_maxstring = "capital_cost_energyspecific_max"
minpowerlink=''
maxpowerlink=''
minenergylink=''
maxenergylink=''
minpowerlabel=''
maxpowerlabel=''
minenergylabel=''
maxenergylabel=''
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first() is not None:
minpowerlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first().link
minpowerlabel = 'min. power specific capital cost: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first() is not None:
maxpowerlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first().link
maxpowerlabel = 'max. power specific capital cost: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first() is not None:
minenergylink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first().link
minenergylabel = 'min. energy specific capital cost: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first() is not None:
maxenergylink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first().link
maxenergylabel = 'max. energy specific capital cost: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().source_id).first().releaseyear)
xy_chart.add(f"{tech.name}", [
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': minenergylabel,
'xlink': {'href': minenergylink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value),
'label': minpowerlabel,
'xlink': {'href': minpowerlink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value),
'label': maxenergylabel,
'xlink': {'href': maxenergylink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': maxpowerlabel,
'xlink': {'href': maxpowerlink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': minenergylabel,
'xlink': {'href': minenergylink, 'target': '_blank'}}])
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value is not None:
xmin = min(xmin, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value)
xmax = max(xmax, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value)
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value is not None:
ymin = min(ymin, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value)
ymax = max(ymax, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value)
xup = 10**int(math.floor(math.log10(xmax)))
yup = 10**int(math.floor(math.log10(ymax)))
while xup < xmax:
xup=xup+10**int(math.floor(math.log10(xmax)))
while yup < ymax:
yup=yup+10**int(math.floor(math.log10(ymax)))
xy_chart.xrange = (10**int(math.floor(math.log10(xmin))), xup)
xy_chart.range = (10**int(math.floor(math.log10(ymin))), yup)
xy_chart.render()
return xy_chart.render_data_uri()
def drawcapitalcostcomponentsfigure(techlist):
config = Config()
config.show_legend = True
config.human_readable = True
config.dots_size = 3
config.x_label_rotation=270
config.legend_at_bottom=True
config.x_title = "capital cost of power based components [$/kW]"
config.y_title = "capital cost of energy based components [$/kWh]"
#config.show_dots = False
config.fill = True
#config.show_minor_x_labels = False
config.stroke_style = {'width': 1}
config.style = pygal.style.styles['default'](label_font_size=12, stroke_opacity=0,
stroke_opacity_hover=0, transition='100000000000s ease-in')
xy_chart = pygal.XY(config)
xmin=10000
ymin=10000
xmax=0.001
ymax=0.001
for tech in techlist:
power_minstring = "capital_cost_of_power_based_components_min"
power_maxstring = "capital_cost_of_power_based_components_max"
energy_minstring = "capital_cost_of_energy_based_components_min"
energy_maxstring = "capital_cost_of_energy_based_components_max"
minpowerlink=''
maxpowerlink=''
minenergylink=''
maxenergylink=''
minpowerlabel=''
maxpowerlabel=''
minenergylabel=''
maxenergylabel=''
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first() is not None:
minpowerlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first().link
minpowerlabel = 'min. cost of power based components: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_minstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first() is not None:
maxpowerlink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first().link
maxpowerlabel = 'max. cost of power based components: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=power_maxstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first() is not None:
minenergylink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first().link
minenergylabel = 'min. cost of energy based components: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_minstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().source_id).first().releaseyear)
if Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first() is not None:
maxenergylink = Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first().link
maxenergylabel = 'max. cost of energy based components: ' + Source.query.filter_by(id=Parameter.query.filter_by(technology_name=tech.name).filter_by(
name=energy_maxstring).first().source_id).first().author + ', ' + str(Source.query.filter_by(
id=Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().source_id).first().releaseyear)
xy_chart.add(f"{tech.name}", [
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': minenergylabel,
'xlink': {'href': minenergylink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value),
'label': minpowerlabel,
'xlink': {'href': minpowerlink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value),
'label': maxenergylabel,
'xlink': {'href': maxenergylink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': maxpowerlabel,
'xlink': {'href': maxpowerlink, 'target': '_blank'}},
{'value': (Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value,
Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value),
'label': minenergylabel,
'xlink': {'href': minenergylink, 'target': '_blank'}}])
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value is not None:
xmin = min(xmin, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_minstring).first().value)
xmax = max(xmax, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=power_maxstring).first().value)
if Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value is not None:
ymin = min(ymin, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_minstring).first().value)
ymax = max(ymax, Parameter.query.filter_by(technology_name=tech.name).filter_by(name=energy_maxstring).first().value)
xup = 10**int(math.floor(math.log10(xmax)))
yup = 10**int(math.floor(math.log10(ymax)))
while xup < xmax:
xup=xup+10**int(math.floor(math.log10(xmax)))
while yup < ymax:
yup=yup+10**int(math.floor(math.log10(ymax)))
xy_chart.xrange = (10**int(math.floor(math.log10(xmin))), xup)
xy_chart.range = (10**int(math.floor(math.log10(ymin))), yup)
xy_chart.render()
return xy_chart.render_data_uri() | 59.492337 | 161 | 0.652842 | 3,841 | 31,055 | 5.072377 | 0.05988 | 0.112919 | 0.110763 | 0.124211 | 0.863522 | 0.842221 | 0.827645 | 0.816045 | 0.807473 | 0.806806 | 0 | 0.016413 | 0.20541 | 31,055 | 522 | 162 | 59.492337 | 0.77314 | 0.011174 | 0 | 0.677551 | 0 | 0 | 0.098472 | 0.009512 | 0 | 0 | 0 | 0 | 0 | 1 | 0.010204 | false | 0 | 0.012245 | 0 | 0.032653 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
47f38391056b295ee6813ed88be61d470a7cf272 | 63 | py | Python | code_03.py | emelianovss/ypb-good-develop-01 | 5350e3e2ce258a641e1dc3afa8cde9b7de4d3139 | [
"MIT"
] | null | null | null | code_03.py | emelianovss/ypb-good-develop-01 | 5350e3e2ce258a641e1dc3afa8cde9b7de4d3139 | [
"MIT"
] | null | null | null | code_03.py | emelianovss/ypb-good-develop-01 | 5350e3e2ce258a641e1dc3afa8cde9b7de4d3139 | [
"MIT"
] | null | null | null | class ImportClass:
pass
print('Ooops, code was executed') | 12.6 | 33 | 0.714286 | 8 | 63 | 5.625 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.190476 | 63 | 5 | 33 | 12.6 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0.375 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0.333333 | 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 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
47f3ba636531e16d1d0974f2f8603e4957005b9c | 18,416 | py | Python | apis/nb/clients/inventory_manager_client/GlobalcredentialApi.py | CiscoDevNet/APIC-EM-Generic-Scripts- | 74211d9488f1e77cf56ef86dba20ec8e8eb49cc1 | [
"ECL-2.0",
"Apache-2.0"
] | 45 | 2016-06-09T15:41:25.000Z | 2019-08-06T17:13:11.000Z | apis/nb/clients/inventory_manager_client/GlobalcredentialApi.py | CiscoDevNet/APIC-EM-Generic-Scripts | 74211d9488f1e77cf56ef86dba20ec8e8eb49cc1 | [
"ECL-2.0",
"Apache-2.0"
] | 36 | 2016-06-12T03:03:56.000Z | 2017-03-13T18:20:11.000Z | apis/nb/clients/inventory_manager_client/GlobalcredentialApi.py | CiscoDevNet/APIC-EM-Generic-Scripts | 74211d9488f1e77cf56ef86dba20ec8e8eb49cc1 | [
"ECL-2.0",
"Apache-2.0"
] | 15 | 2016-06-22T03:51:37.000Z | 2019-07-10T10:06:02.000Z | #!/usr/bin/env python
#pylint: skip-file
# This source code is licensed under the Apache license found in the
# LICENSE file in the root directory of this project.
import sys
import os
import urllib.request, urllib.parse, urllib.error
from .models import *
class GlobalcredentialApi(object):
def __init__(self, apiClient):
self.apiClient = apiClient
def getGlobalCredential(self, **kwargs):
"""Retrieves global credential for the given credential sub type
Args:
credentialSubType, str: Credential type as CLI / SNMPV2_READ_COMMUNITY / SNMPV2_WRITE_COMMUNITY / SNMPV3 (required)
Returns: GlobalCredentialListResult
"""
allParams = ['credentialSubType']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method getGlobalCredential" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'GET'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('credentialSubType' in params):
queryParams['credentialSubType'] = self.apiClient.toPathValue(params['credentialSubType'])
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'GlobalCredentialListResult')
return responseObject
def updateCliCredential(self, **kwargs):
"""Updates global CLI credential
Args:
globalCredentialNio, CLICredentialDTO: CLI credentials (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNio']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method updateCliCredential" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/cli'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'PUT'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNio' in params):
bodyParam = params['globalCredentialNio']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def addCliCredential(self, **kwargs):
"""Creates global CLI credential
Args:
globalCredentialNioList, list[CLICredentialDTO]: List of CLI credentials (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNioList']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method addCliCredential" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/cli'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'POST'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNioList' in params):
bodyParam = params['globalCredentialNioList']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def updateSnmpReadCommunity(self, **kwargs):
"""Updates global SNMP read community
Args:
globalCredentialNio, SNMPv2ReadCommunityDTO: SNMP read community details (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNio']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method updateSnmpReadCommunity" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/snmpv2-read-community'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'PUT'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNio' in params):
bodyParam = params['globalCredentialNio']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def addSnmpReadCommunity(self, **kwargs):
"""Creates global SNMP read community
Args:
globalCredentialNioList, List[SNMPv2ReadCommunityDTO]: List of SNMP read communities (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNioList']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method addSnmpReadCommunity" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/snmpv2-read-community'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'POST'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNioList' in params):
bodyParam = params['globalCredentialNioList']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def updateSnmpWriteCommunity(self, **kwargs):
"""Updates global SNMP write community
Args:
globalCredentialNio, SNMPv2WriteCommunityDTO: SNMP write community details (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNio']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method updateSnmpWriteCommunity" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/snmpv2-write-community'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'PUT'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNio' in params):
bodyParam = params['globalCredentialNio']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def addSnmpWriteCommunity(self, **kwargs):
"""Creates global SNMP write community
Args:
globalCredentialNioList, List[SNMPv2WriteCommunityDTO]: List of SNMP write communities (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNioList']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method addSnmpWriteCommunity" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/snmpv2-write-community'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'POST'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNioList' in params):
bodyParam = params['globalCredentialNioList']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def updateSnmpv3Credential(self, **kwargs):
"""Updates global SNMPv3 credential
Args:
globalCredentialNio, SNMPv3CredentialDTO: SNMPv3 credential details (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNio']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method updateSnmpv3Credential" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/snmpv3'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'PUT'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNio' in params):
bodyParam = params['globalCredentialNio']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def addSnmpv3Credential(self, **kwargs):
"""Creates global SNMPv3 credential
Args:
globalCredentialNioList, List[SNMPv3CredentialDTO]: List of SNMPv3 credentials (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialNioList']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method addSnmpv3Credential" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/snmpv3'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'POST'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialNioList' in params):
bodyParam = params['globalCredentialNioList']
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def deleteGlobalCredential(self, **kwargs):
"""Retrieves global credential by ID
Args:
globalCredentialId, str: ID of global-credential (required)
Returns: TaskIdResult
"""
allParams = ['globalCredentialId']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method deleteGlobalCredential" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/{globalCredentialId}'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'DELETE'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('globalCredentialId' in params):
replacement = str(self.apiClient.toPathValue(params['globalCredentialId']))
replacement = urllib.parse.quote(replacement)
resourcePath = resourcePath.replace('{' + 'globalCredentialId' + '}',
replacement)
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'TaskIdResult')
return responseObject
def getGlobalCredentialSubTypeByID(self, **kwargs):
"""Retrieves credential sub type for the given credential Id
Args:
id, str: Global Credential ID (required)
Returns: GlobalCredentialSubTypeResult
"""
allParams = ['id']
params = locals()
for (key, val) in list(params['kwargs'].items()):
if key not in allParams:
raise TypeError("Got an unexpected keyword argument '%s' to method getGlobalCredentialSubTypeByID" % key)
params[key] = val
del params['kwargs']
resourcePath = '/global-credential/{id}'
resourcePath = resourcePath.replace('{format}', 'json')
method = 'GET'
queryParams = {}
headerParams = {}
formParams = {}
files = {}
bodyParam = None
headerParams['Accept'] = 'application/json'
headerParams['Content-Type'] = 'application/json'
if ('id' in params):
replacement = str(self.apiClient.toPathValue(params['id']))
replacement = urllib.parse.quote(replacement)
resourcePath = resourcePath.replace('{' + 'id' + '}',
replacement)
postData = (formParams if formParams else bodyParam)
response = self.apiClient.callAPI(resourcePath, method, queryParams,
postData, headerParams, files=files)
if not response:
return None
responseObject = self.apiClient.deserialize(response, 'GlobalCredentialSubTypeResult')
return responseObject
| 25.901547 | 127 | 0.546861 | 1,391 | 18,416 | 7.234364 | 0.096334 | 0.03488 | 0.040048 | 0.019676 | 0.796681 | 0.772036 | 0.765676 | 0.765676 | 0.741628 | 0.741628 | 0 | 0.001966 | 0.364737 | 18,416 | 710 | 128 | 25.938028 | 0.858193 | 0.111208 | 0 | 0.851613 | 0 | 0 | 0.179194 | 0.050731 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03871 | false | 0 | 0.012903 | 0 | 0.125806 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
47fce51805fba6fdfe73f36228e3d2e9f8b0bc51 | 207 | py | Python | lms/views/__init__.py | yankai14/event-management-telegram-bot-backend | c0b4b2294ab7d06100b221d9b41a8f52d500075d | [
"MIT"
] | null | null | null | lms/views/__init__.py | yankai14/event-management-telegram-bot-backend | c0b4b2294ab7d06100b221d9b41a8f52d500075d | [
"MIT"
] | 6 | 2021-06-28T07:23:15.000Z | 2021-07-22T12:59:33.000Z | lms/views/__init__.py | yankai14/event-management-telegram-bot-backend | c0b4b2294ab7d06100b221d9b41a8f52d500075d | [
"MIT"
] | null | null | null | from lms.views.event_views import EventViewSet
from lms.views.user_views import UserViewSet
from lms.views.enrollment_views import EnrollmentViewSet
from lms.views.feedback_views import EventInstanceFeedback | 51.75 | 58 | 0.888889 | 28 | 207 | 6.428571 | 0.428571 | 0.155556 | 0.266667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.072464 | 207 | 4 | 58 | 51.75 | 0.9375 | 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 | 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 | 6 |
9a19edc943ecce02f923eec3bc51eb9ce86ad92d | 37 | py | Python | dark_emulator/darkemu/__init__.py | DarkQuestCosmology/dark_emulator_public | f0f2eb2fcf3bf95d0e93b3e7239928cc7107a3c2 | [
"MIT"
] | 13 | 2021-03-22T11:47:50.000Z | 2021-05-19T12:27:32.000Z | dark_emulator/darkemu/__init__.py | DarkQuestCosmology/dark_emulator_public | f0f2eb2fcf3bf95d0e93b3e7239928cc7107a3c2 | [
"MIT"
] | 12 | 2021-05-05T14:24:47.000Z | 2021-11-10T17:57:42.000Z | dark_emulator/darkemu/__init__.py | DarkQuestCosmology/dark_emulator_public | f0f2eb2fcf3bf95d0e93b3e7239928cc7107a3c2 | [
"MIT"
] | 2 | 2021-03-28T09:05:41.000Z | 2022-02-16T23:55:51.000Z | from .de_interface import base_class
| 18.5 | 36 | 0.864865 | 6 | 37 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 37 | 1 | 37 | 37 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7bdb00ea74f701f4b06125b5215bc331d736c0b2 | 164 | py | Python | pyrelational/__init__.py | RelationRx/pyrelational | 41ededeff84158bd88b76d39006764de3388c821 | [
"Apache-2.0"
] | 42 | 2022-02-09T16:36:37.000Z | 2022-03-25T00:25:34.000Z | pyrelational/__init__.py | RelationRx/pyrelational | 41ededeff84158bd88b76d39006764de3388c821 | [
"Apache-2.0"
] | 4 | 2022-03-22T13:22:38.000Z | 2022-03-25T16:14:40.000Z | pyrelational/__init__.py | RelationRx/pyrelational | 41ededeff84158bd88b76d39006764de3388c821 | [
"Apache-2.0"
] | 3 | 2022-02-15T17:50:30.000Z | 2022-03-10T18:14:16.000Z | import pyrelational.data
import pyrelational.informativeness
import pyrelational.models
import pyrelational.strategies
from pyrelational.version import __version__
| 27.333333 | 44 | 0.896341 | 17 | 164 | 8.411765 | 0.470588 | 0.503497 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.073171 | 164 | 5 | 45 | 32.8 | 0.940789 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d05c06cd1d0845bd4d56edc583f94db91bda7a8b | 95 | py | Python | TermTk/libbpytop/__init__.py | ceccopierangiolieugenio/py-ttk | 117d61844bb7344bbe22a7797b7e3763d5fe4de5 | [
"MIT"
] | null | null | null | TermTk/libbpytop/__init__.py | ceccopierangiolieugenio/py-ttk | 117d61844bb7344bbe22a7797b7e3763d5fe4de5 | [
"MIT"
] | null | null | null | TermTk/libbpytop/__init__.py | ceccopierangiolieugenio/py-ttk | 117d61844bb7344bbe22a7797b7e3763d5fe4de5 | [
"MIT"
] | null | null | null | from .input import *
from .term import *
from .colors import *
from .inputkey import * | 23.75 | 23 | 0.663158 | 12 | 95 | 5.25 | 0.5 | 0.47619 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.252632 | 95 | 4 | 24 | 23.75 | 0.887324 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d06cf5ed7bc57f0b3ead7f0ad3e9ab195d9bd0b7 | 161 | py | Python | tests/test_cli.py | Git-fighters/cs107-FinalProject | 88400db735e409363642af96613418026edb6d9b | [
"MIT"
] | null | null | null | tests/test_cli.py | Git-fighters/cs107-FinalProject | 88400db735e409363642af96613418026edb6d9b | [
"MIT"
] | null | null | null | tests/test_cli.py | Git-fighters/cs107-FinalProject | 88400db735e409363642af96613418026edb6d9b | [
"MIT"
] | 1 | 2021-11-17T01:56:50.000Z | 2021-11-17T01:56:50.000Z | from gitfighters.git_fighters import *
from gitfighters.vector import *
from cli import main
def test_main():
pass
# not sure how to test input/output
| 17.888889 | 39 | 0.745342 | 24 | 161 | 4.916667 | 0.708333 | 0.254237 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.198758 | 161 | 8 | 40 | 20.125 | 0.914729 | 0.204969 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0.2 | 0.6 | 0 | 0.8 | 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 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 6 |
d0d6de3adfff997e226990da0a37a5b48c5d010f | 38 | py | Python | tests/gis/tests/__init__.py | pavanv/django-tastypie | b4ffc642aa56d25d3c577ccae0a03c820b71c4bc | [
"BSD-3-Clause"
] | 1,570 | 2015-02-03T10:19:33.000Z | 2022-03-29T10:34:18.000Z | tests/gis/tests/__init__.py | pavanv/django-tastypie | b4ffc642aa56d25d3c577ccae0a03c820b71c4bc | [
"BSD-3-Clause"
] | 587 | 2015-02-06T13:59:23.000Z | 2022-03-09T22:56:30.000Z | tests/gis/tests/__init__.py | pavanv/django-tastypie | b4ffc642aa56d25d3c577ccae0a03c820b71c4bc | [
"BSD-3-Clause"
] | 492 | 2015-02-07T06:18:36.000Z | 2022-03-29T19:06:44.000Z | from gis.tests.views import * # noqa
| 19 | 37 | 0.710526 | 6 | 38 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.184211 | 38 | 1 | 38 | 38 | 0.870968 | 0.105263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d0f322c887e8b59aa4a8b234b4a91cb5f220ba02 | 2,512 | py | Python | SevenSeg/Seg.py | medtrab/SevenSeg | de3eace776b170998450c2b52b7d3b4a29b58939 | [
"MIT"
] | null | null | null | SevenSeg/Seg.py | medtrab/SevenSeg | de3eace776b170998450c2b52b7d3b4a29b58939 | [
"MIT"
] | null | null | null | SevenSeg/Seg.py | medtrab/SevenSeg | de3eace776b170998450c2b52b7d3b4a29b58939 | [
"MIT"
] | null | null | null | from machine import Pin
# this class is for single 7 segment display
class Seg(machine):
def __init__(self, common, a, b, c, d, e, f, g, dp):
self.common = common
self.a = a
self.b = b
self.c = c
self.d = d
self.e = e
self.f = f
self.g = g
self.dp = dp
def init(self):
if common.lower() == "k":
a=Pin(a, Pin.OUT)
b=Pin(b, Pin.OUT)
c=Pin(c, Pin.OUT)
d=Pin(d, Pin.OUT)
e=Pin(e, Pin.OUT)
f=Pin(f, Pin.OUT)
g=Pin(g, Pin.OUT)
dp=Pin(dp, Pin.OUT)
action = 1
elif common.lower() == "a":
a=Pin(a, Pin.OUT)
b=Pin(b, Pin.OUT)
c=Pin(c, Pin.OUT)
d=Pin(d, Pin.OUT)
e=Pin(e, Pin.OUT)
f=Pin(f, Pin.OUT)
g=Pin(g, Pin.OUT)
dp=Pin(dp, Pin.OUT)
action = 0
def count(self,x):
if x==0:
zero = {a.value(action),b.value(action),c.value(action),d.value(action),e.value(action),f.value(action),g.value(0),dp.value(0)}
elif x==action:
one = {a.value(0),b.value(action),c.value(action),d.value(0),e.value(0),f.value(0),g.value(0),dp.value(0)}
elif x==2:
two = {a.value(action),b.value(action),c.value(0),d.value(action),e.value(action),f.value(0),g.value(action),dp.value(0)}
elif x==3:
three = {a.value(action),b.value(action),c.value(action),d.value(action),e.value(0),f.value(0),g.value(action),dp.value(0)}
elif x==4:
four = {a.value(0),b.value(action),c.value(action),d.value(0),e.value(0),f.value(action),g.value(action),dp.value(0)}
elif x==5:
five = {a.value(action),b.value(0),c.value(action),d.value(action),e.value(0),f.value(action),g.value(action),dp.value(0)}
elif x==6:
six = {a.value(action),b.value(0),c.value(action),d.value(action),e.value(action),f.value(action),g.value(action),dp.value(0)}
elif x==7:
seven = {a.value(action),b.value(action),c.value(action),d.value(0),e.value(0),f.value(0),g.value(0),dp.value(0)}
elif x==8:
eight = {a.value(action),b.value(action),c.value(action),d.value(action),e.value(action),f.value(action),g.value(action),dp.value(0)}
elif x==9:
nine = {a.value(action),b.value(action),c.value(action),d.value(action),e.value(0),f.value(action),g.value(action),dp.value(0)}
| 43.310345 | 145 | 0.530255 | 433 | 2,512 | 3.066975 | 0.122402 | 0.405873 | 0.060241 | 0.088102 | 0.791416 | 0.791416 | 0.791416 | 0.791416 | 0.740211 | 0.740211 | 0 | 0.023357 | 0.267118 | 2,512 | 57 | 146 | 44.070175 | 0.69799 | 0.01672 | 0 | 0.296296 | 0 | 0 | 0.00081 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.055556 | false | 0 | 0.018519 | 0 | 0.092593 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 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 | 6 |
efe9c672fa17ff4f552e2b509f813412f13750af | 192 | py | Python | theano/tensor/nnet/__init__.py | ganguli-lab/Theano | d61c929b6d1a5bae314545cba79c879de687ea18 | [
"BSD-3-Clause"
] | 1 | 2015-11-05T13:58:11.000Z | 2015-11-05T13:58:11.000Z | theano/tensor/nnet/__init__.py | ganguli-lab/Theano | d61c929b6d1a5bae314545cba79c879de687ea18 | [
"BSD-3-Clause"
] | null | null | null | theano/tensor/nnet/__init__.py | ganguli-lab/Theano | d61c929b6d1a5bae314545cba79c879de687ea18 | [
"BSD-3-Clause"
] | null | null | null | from nnet import *
from conv import conv2d, ConvOp
from Conv3D import *
from ConvGrad3D import *
from ConvTransp3D import *
from sigm import softplus, sigmoid, sigmoid_inplace, scalar_sigmoid
| 27.428571 | 67 | 0.8125 | 26 | 192 | 5.923077 | 0.538462 | 0.25974 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02439 | 0.145833 | 192 | 6 | 68 | 32 | 0.914634 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ef133656535a6710711cd9f2cdac463e28ded309 | 153 | py | Python | rec_to_nwb/processing/builder/__init__.py | LorenFrankLab/rec_to_nwb | d0630f414662963ebbe23aedf8f3ce07628636bc | [
"Apache-2.0"
] | 1 | 2021-01-20T00:26:30.000Z | 2021-01-20T00:26:30.000Z | rec_to_nwb/processing/builder/__init__.py | LorenFrankLab/rec_to_nwb | d0630f414662963ebbe23aedf8f3ce07628636bc | [
"Apache-2.0"
] | 12 | 2020-11-13T01:36:32.000Z | 2022-01-23T20:35:55.000Z | rec_to_nwb/processing/builder/__init__.py | LorenFrankLab/rec_to_nwb | d0630f414662963ebbe23aedf8f3ce07628636bc | [
"Apache-2.0"
] | 3 | 2020-10-20T06:52:45.000Z | 2021-07-06T23:00:53.000Z | from rec_to_nwb.processing.builder.raw_to_nwb_builder import RawToNWBBuilder
from rec_to_nwb.processing.metadata.metadata_manager import MetadataManager
| 51 | 76 | 0.908497 | 22 | 153 | 5.954545 | 0.545455 | 0.114504 | 0.137405 | 0.183206 | 0.335878 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.052288 | 153 | 2 | 77 | 76.5 | 0.903448 | 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 | 0 | 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 | 6 |
ef1c19655e6e3713cd53edb38e1913bf6d57956f | 22 | py | Python | iiotsapps/farms/models/__init__.py | CorpofloTechCommunity/Intelligent-IOT-system- | 99af2efe1e8c7623ed76df246e29cfb5654474af | [
"MIT"
] | null | null | null | iiotsapps/farms/models/__init__.py | CorpofloTechCommunity/Intelligent-IOT-system- | 99af2efe1e8c7623ed76df246e29cfb5654474af | [
"MIT"
] | 1 | 2020-08-02T11:58:22.000Z | 2020-08-02T11:58:22.000Z | iiotsapps/farms/models/__init__.py | CorpofloTechCommunity/Intelligent-IOT-system- | 99af2efe1e8c7623ed76df246e29cfb5654474af | [
"MIT"
] | 2 | 2020-07-31T11:08:14.000Z | 2020-08-19T10:46:43.000Z | from .farm import Farm | 22 | 22 | 0.818182 | 4 | 22 | 4.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 22 | 1 | 22 | 22 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
322e6f2b5f211e83c280c322646bbc3a30279e6c | 100 | py | Python | erpnext_support/patches/reset_activations_last_sync_on.py | manuthu/test-package | 906a2a3ce7878d81a01149a60fb06e07400e6f47 | [
"MIT"
] | null | null | null | erpnext_support/patches/reset_activations_last_sync_on.py | manuthu/test-package | 906a2a3ce7878d81a01149a60fb06e07400e6f47 | [
"MIT"
] | null | null | null | erpnext_support/patches/reset_activations_last_sync_on.py | manuthu/test-package | 906a2a3ce7878d81a01149a60fb06e07400e6f47 | [
"MIT"
] | null | null | null | import frappe
def execute():
frappe.installer.update_site_config("activations_last_sync_on", 0) | 25 | 70 | 0.8 | 14 | 100 | 5.357143 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011111 | 0.1 | 100 | 4 | 70 | 25 | 0.822222 | 0 | 0 | 0 | 0 | 0 | 0.237624 | 0.237624 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 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 | 0 | 1 | 0 | 0 | 6 |
32441894d7aaaf925aec330a29d45426e1596a31 | 3,646 | py | Python | fileheaderPy/fileheaderPy.py | batestin1/scriptHeaderPy | f04b94920b4e093f6ddee35e16cbc980cbf4b4a9 | [
"MIT"
] | null | null | null | fileheaderPy/fileheaderPy.py | batestin1/scriptHeaderPy | f04b94920b4e093f6ddee35e16cbc980cbf4b4a9 | [
"MIT"
] | null | null | null | fileheaderPy/fileheaderPy.py | batestin1/scriptHeaderPy | f04b94920b4e093f6ddee35e16cbc980cbf4b4a9 | [
"MIT"
] | null | null | null | #pip install -r requirements.txt
from art import *
from datetime import date
import getpass
import platform
import subprocess
import os
import sys
def fileheaderPy(file):
try:
users=getpass.getuser()
res = subprocess.run(["git", "config", "user.name"], stdout=subprocess.PIPE)
git_username = res.stdout.strip().decode()
fileName = file.replace(' ', '_')
with open(f"{fileName}.py", "w", encoding='UTF-8') as output:
title = text2art(f"Hello,{users}!", font='fancy1',chr_ignore=False)
file_name = f"filename: {fileName}.py"
so = platform.system()
system = f"system: {so}"
ma = platform.architecture()
bit = f"version: {ma[0]}"
by = f"by: {users} <https://github.com/{git_username}>"
data_atual = date.today()
data = f"""created: {data_atual.strftime('%Y-%m-%d')}"""
ast = '*'
spac = ' '
final = "import your librarys below"
output.write(f"""
#{ast.ljust(80,ast)}#
#{spac.ljust(80,spac)}#
#{title.center(80)}#
#{spac.ljust(80,spac)}#
# {file_name[:50].ljust(77)}#
# {data.ljust(77)}#
# {system.ljust(77)}#
# {bit.ljust(77)}#
# {by.rjust(76)} #
#{ast.ljust(80,ast)}#
#{final.center(80)}#
#{ast.ljust(80,ast)}#
""")
print(f"the {fileName}.py was built successfully!")
print(f"the {fileName}.py is locate here: {os.path.abspath(os.getcwd())}\{fileName}.py")
except:
users=getpass.getuser()
res = subprocess.run(["git", "config", "user.name"], stdout=subprocess.PIPE)
git_username = res.stdout.strip().decode()
fileName = file.replace(' ', '_')
with open(f"{fileName}.py","w", encoding='UTF-8') as output:
title = text2art(f"Hello, coder !", font='fancy1',chr_ignore=False)
file_name = f"filename: {fileName}.py"
so = platform.system()
system = "system: coder!"
ma = platform.architecture()
bit = "version: coder!"
by = f"by: coder <https://github.com/coder>"
data_atual = date.today()
data = f"""created: {data_atual.strftime('%Y-%m-%d')}"""
ast = '*'
spac = ' '
final = "import your librarys below"
output.write(f"""
#{ast.ljust(80,ast)}#
#{spac.ljust(80,spac)}#
#{title.center(80)}#
#{spac.ljust(80,spac)}#
# {file_name[:50].ljust(77)}#
# {data.ljust(77)}#
# {system.ljust(77)}#
# {bit.ljust(77)}#
# {by.rjust(76)} #
#{ast.ljust(80,ast)}#
#{final.center(80)}#
#{ast.ljust(80,ast)}#
""")
print(f"the {fileName}.py was built successfully!") | 41.908046 | 108 | 0.406747 | 332 | 3,646 | 4.421687 | 0.295181 | 0.047684 | 0.040872 | 0.053134 | 0.75545 | 0.742507 | 0.742507 | 0.742507 | 0.742507 | 0.742507 | 0 | 0.029251 | 0.446791 | 3,646 | 87 | 109 | 41.908046 | 0.698562 | 0.008502 | 0 | 0.746667 | 0 | 0.013333 | 0.494053 | 0.105671 | 0 | 0 | 0 | 0 | 0 | 1 | 0.013333 | false | 0.04 | 0.12 | 0 | 0.133333 | 0.04 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
325a7cc7d542ce9b004b97003206a963f7824a7f | 159 | py | Python | ortholang_jupyter_kernel/__main__.py | jefdaj/ortholang-jupyter-kernel | 5f78b5ea33b8d6512e7d58b8e9b4181eea5c87f5 | [
"BSD-3-Clause"
] | null | null | null | ortholang_jupyter_kernel/__main__.py | jefdaj/ortholang-jupyter-kernel | 5f78b5ea33b8d6512e7d58b8e9b4181eea5c87f5 | [
"BSD-3-Clause"
] | null | null | null | ortholang_jupyter_kernel/__main__.py | jefdaj/ortholang-jupyter-kernel | 5f78b5ea33b8d6512e7d58b8e9b4181eea5c87f5 | [
"BSD-3-Clause"
] | null | null | null | from ipykernel.kernelapp import IPKernelApp
from . import OrthoLangKernel
def main():
IPKernelApp.launch_instance(kernel_class=OrthoLangKernel)
# main()
| 19.875 | 61 | 0.805031 | 17 | 159 | 7.411765 | 0.705882 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119497 | 159 | 7 | 62 | 22.714286 | 0.9 | 0.037736 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 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 | 0 | 1 | 0 | 0 | 6 |
086d4c0b98be4b1a4f00b2efb34ce6c0880e0b3e | 33 | py | Python | miette/__init__.py | kxrob/Miette | 9a635474effa2108dd57aaf15b99f2c8fb1af2bf | [
"BSD-2-Clause"
] | 4 | 2015-02-23T14:07:35.000Z | 2018-06-10T16:54:01.000Z | miette/__init__.py | kxrob/Miette | 9a635474effa2108dd57aaf15b99f2c8fb1af2bf | [
"BSD-2-Clause"
] | null | null | null | miette/__init__.py | kxrob/Miette | 9a635474effa2108dd57aaf15b99f2c8fb1af2bf | [
"BSD-2-Clause"
] | 3 | 2015-12-15T23:19:39.000Z | 2022-03-06T22:33:34.000Z | from miette.doc import DocReader
| 16.5 | 32 | 0.848485 | 5 | 33 | 5.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
089f49e506fa666d9917071e0f13512b15aa55a6 | 13,033 | py | Python | tests/treepath/test_3d_list_match.py | monkeydevtools/treepath-python | 56f6cbf662f8a4c13f0c9e753a839fc9f6323dba | [
"Apache-2.0"
] | 2 | 2021-05-26T08:26:25.000Z | 2021-09-24T21:26:01.000Z | tests/treepath/test_3d_list_match.py | monkeydevtools/treepath-python | 56f6cbf662f8a4c13f0c9e753a839fc9f6323dba | [
"Apache-2.0"
] | null | null | null | tests/treepath/test_3d_list_match.py | monkeydevtools/treepath-python | 56f6cbf662f8a4c13f0c9e753a839fc9f6323dba | [
"Apache-2.0"
] | null | null | null | import pytest
from tests.utils.traverser_utils import assert_done_iterating, gen_test_data, naia, yaia, nyiy, yyia
from treepath import get, path, find, find_matches, get_match, wildcard, PopError
from treepath.path.exceptions.match_not_found_error import MatchNotFoundError
def test_empty_list_index_MatchNotFoundError():
empty_list = []
with pytest.raises(MatchNotFoundError):
get(path[0], empty_list)
def test_empty_list_wildcard_MatchNotFoundError():
empty_list = []
with pytest.raises(MatchNotFoundError):
get(path[wildcard], empty_list)
def test_3d_10_MatchNotFoundError(three_dimensional_list):
with pytest.raises(MatchNotFoundError):
get(path[10], three_dimensional_list)
def test_3d_0_10_MatchNotFoundError(three_dimensional_list):
with pytest.raises(MatchNotFoundError):
get(path[0][10], three_dimensional_list)
def test_3d_0_0_10_MatchNotFoundError(three_dimensional_list):
with pytest.raises(MatchNotFoundError):
get(path[0][0][10], three_dimensional_list)
def test_3d_0_10_0_MatchNotFoundError(three_dimensional_list):
with pytest.raises(MatchNotFoundError):
get(path[0][10][0], three_dimensional_list)
def test_list_on_key_MatchNotFoundError(k_a_a_k_a_a_a_k):
with pytest.raises(MatchNotFoundError):
get(path[0], k_a_a_k_a_a_a_k)
def test_list_wildcard_on_key_MatchNotFoundError(k_a_a_k_a_a_a_k):
with pytest.raises(MatchNotFoundError):
get(path[wildcard], k_a_a_k_a_a_a_k)
def test_3d_root(three_dimensional_list):
expected = three_dimensional_list
actual = get(path, three_dimensional_list)
assert actual == expected
def test_3d_0(three_dimensional_list):
expected = three_dimensional_list[0]
actual = get(path[0], three_dimensional_list)
assert actual == expected
def test_3d_0_path(three_dimensional_list):
expected = three_dimensional_list[0]
actual = get_match(path[0], three_dimensional_list)
assert str(actual) == f"$[0]={expected}"
def test_3d_0_0(three_dimensional_list):
expected = three_dimensional_list[0][0]
actual = get(path[0][0], three_dimensional_list)
assert actual == expected
def test_3d_0_0_path(three_dimensional_list):
expected = three_dimensional_list[0][0]
actual = get_match(path[0][0], three_dimensional_list)
assert str(actual) == f"$[0][0]={expected}"
def test_3d_0_0_0(three_dimensional_list):
expected = three_dimensional_list[0][0][0]
actual = get(path[0][0][0], three_dimensional_list)
assert actual == expected
def test_3d_0_0_0_path(three_dimensional_list):
expected = three_dimensional_list[0][0][0]
actual = get_match(path[0][0][0], three_dimensional_list)
assert str(actual) == f"$[0][0][0]={expected}"
def test_3d_find_all_slice(three_dimensional_list):
result = find(path[:][:][:], three_dimensional_list)
for expected in range(1, 28):
actual = next(result)
assert actual == expected
assert_done_iterating(result)
def test_3d_find_all_slice_path(three_dimensional_list):
match_iter = find_matches(path[:][:][:], three_dimensional_list)
for expected_path, expected_value in gen_test_data(three_dimensional_list, naia, naia, yaia):
actual = next(match_iter)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(match_iter)
def test_3d_find_all_slice_variety(three_dimensional_list):
result = find(path[::-1][:1:][0::2], three_dimensional_list)
for x in range(*slice(None, None, -1).indices(3)):
for y in range(*slice(None, 1, None).indices(3)):
for z in range(*slice(0, None, 2).indices(3)):
actual = next(result)
assert actual == three_dimensional_list[x][y][z]
assert_done_iterating(result)
def test_3d_find_all_slice_variety_path(three_dimensional_list):
test_data = [(expected_path, expected_value) for expected_path, expected_value in
gen_test_data(three_dimensional_list, naia, naia, yaia)]
for actual in find_matches(path[::-1][:1:][0::2], three_dimensional_list):
expected_path, expected_value = test_data[actual.data - 1]
assert str(actual) == f"{expected_path}={expected_value}"
def test_3d_find_all_comma_delimited(three_dimensional_list):
result = find(path[2, 1, 0][0, 1][0, 2, 1], three_dimensional_list)
for x in [2, 1, 0]:
for y in [0, 1]:
for z in [0, 2, 1]:
actual = next(result)
assert actual == three_dimensional_list[x][y][z]
assert_done_iterating(result)
def test_3d_find_all__comma_delimited_path(three_dimensional_list):
test_data = [(expected_path, expected_value) for expected_path, expected_value in
gen_test_data(three_dimensional_list, naia, naia, yaia)]
for actual in find_matches(path[2, 1, 0][0, 1][0, 2, 1], three_dimensional_list):
expected_path, expected_value = test_data[actual.data - 1]
assert str(actual) == f"{expected_path}={expected_value}"
def test_a_k_k_a_k_k_k_a_find_all_slice(a_k_k_a_k_k_k_a):
result = find(path[:].y.y[:].y.y.y[:], a_k_k_a_k_k_k_a)
for expected_path, expected_value in gen_test_data(a_k_k_a_k_k_k_a, naia, nyiy, nyiy, naia, nyiy, nyiy, nyiy, yaia):
actual = next(result)
assert actual == expected_value
assert_done_iterating(result)
def test_a_k_k_a_k_k_k_a_find_all_slice_path(a_k_k_a_k_k_k_a):
result = find_matches(path[:].y.y[:].y.y.y[:], a_k_k_a_k_k_k_a)
for expected_path, expected_value in gen_test_data(a_k_k_a_k_k_k_a, naia, nyiy, nyiy, naia, nyiy, nyiy, nyiy, yaia):
actual = next(result)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(result)
def test_k_a_a_k_a_a_a_k_find_all_slice(k_a_a_k_a_a_a_k):
result = find(path.y[:][:].y[:][:][:].y, k_a_a_k_a_a_a_k)
for expected_path, expected_value in gen_test_data(k_a_a_k_a_a_a_k, nyiy, naia, naia, nyiy, naia, naia, naia, yyia):
actual = next(result)
assert actual == expected_value
assert_done_iterating(result)
def test_k_a_a_k_a_a_a_k_find_all_slice_path(k_a_a_k_a_a_a_k):
result = find_matches(path.y[:][:].y[:][:][:].y, k_a_a_k_a_a_a_k)
for expected_path, expected_value in gen_test_data(k_a_a_k_a_a_a_k, nyiy, naia, naia, nyiy, naia, naia, naia, yyia):
actual = next(result)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(result)
def test_3d_find_all_wildcard(three_dimensional_list):
result = find(path[wildcard][wildcard][wildcard], three_dimensional_list)
for expected in range(1, 28):
actual = next(result)
assert actual == expected
assert_done_iterating(result)
def test_3d_find_all_wildcard_path(three_dimensional_list):
result = find_matches(path[wildcard][wildcard][wildcard], three_dimensional_list)
for l1 in range(0, 3):
for l2 in range(0, 3):
for l3 in range(0, 3):
actual = next(result)
assert str(actual) == f"$[{l1}][{l2}][{l3}]={actual.data}"
assert_done_iterating(result)
def test_a_k_k_a_k_k_k_a_find_all_wildcard(a_k_k_a_k_k_k_a):
result = find(path[wildcard].y.y[wildcard].y.y.y[wildcard], a_k_k_a_k_k_k_a)
for expected_path, expected_value in gen_test_data(a_k_k_a_k_k_k_a, naia, nyiy, nyiy, naia, nyiy, nyiy, nyiy, yaia):
actual = next(result)
assert actual == expected_value
assert_done_iterating(result)
def test_a_k_k_a_k_k_k_a_find_all_wildcard_path(a_k_k_a_k_k_k_a):
result = find_matches(path[wildcard].y.y[wildcard].y.y.y[wildcard], a_k_k_a_k_k_k_a)
for expected_path, expected_value in gen_test_data(a_k_k_a_k_k_k_a, naia, nyiy, nyiy, naia, nyiy, nyiy, nyiy, yaia):
actual = next(result)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(result)
def test_k_a_a_k_a_a_a_k_find_all_wildcard(k_a_a_k_a_a_a_k):
result = find(path.y[wildcard][wildcard].y[wildcard][wildcard][wildcard].y, k_a_a_k_a_a_a_k)
for expected_path, expected_value in gen_test_data(k_a_a_k_a_a_a_k, nyiy, naia, naia, nyiy, naia, naia, naia, yyia):
actual = next(result)
assert actual == expected_value
assert_done_iterating(result)
def test_k_a_a_k_a_a_a_k_find_all_wildcard_path(k_a_a_k_a_a_a_k):
result = find_matches(path.y[wildcard][wildcard].y[wildcard][wildcard][wildcard].y, k_a_a_k_a_a_a_k)
for expected_path, expected_value in gen_test_data(k_a_a_k_a_a_a_k, nyiy, naia, naia, nyiy, naia, naia, naia, yyia):
actual = next(result)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(result)
def test_3d_find_specific_tuple(three_dimensional_list):
result = find(path[2, 3, "a", 1], three_dimensional_list)
actual = next(result)
expected = three_dimensional_list[2]
assert actual == expected
actual = next(result)
expected = three_dimensional_list[1]
assert actual == expected
assert_done_iterating(result)
def test_3d_find_all_tuple(three_dimensional_list):
result = find(path[0, 1, 2][0, 1, 2][0, 1, 2], three_dimensional_list)
for expected in range(1, 28):
actual = next(result)
assert actual == expected
assert_done_iterating(result)
def test_3d_find_all_tuple_path(three_dimensional_list):
match_iter = find_matches(path[0, 1, 2][0, 1, 2][0, 1, 2], three_dimensional_list)
for expected_path, expected_value in gen_test_data(three_dimensional_list, naia, naia, yaia):
actual = next(match_iter)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(match_iter)
def test_a_k_k_a_k_k_k_a_find_all_tuple(a_k_k_a_k_k_k_a):
result = find(path[0, 1, 2].y.y[0, 1, 2].y.y.y[0, 1, 2], a_k_k_a_k_k_k_a)
for expected_path, expected_value in gen_test_data(a_k_k_a_k_k_k_a, naia, nyiy, nyiy, naia, nyiy, nyiy, nyiy, yaia):
actual = next(result)
assert actual == expected_value
assert_done_iterating(result)
def test_a_k_k_a_k_k_k_a_find_all_tuple_path(a_k_k_a_k_k_k_a):
result = find_matches(path[0, 1, 2].y.y[0, 1, 2].y.y.y[0, 1, 2], a_k_k_a_k_k_k_a)
for expected_path, expected_value in gen_test_data(a_k_k_a_k_k_k_a, naia, nyiy, nyiy, naia, nyiy, nyiy, nyiy, yaia):
actual = next(result)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(result)
def test_k_a_a_k_a_a_a_k_find_all_tuple(k_a_a_k_a_a_a_k):
result = find(path.y[0, 1, 2][0, 1, 2].y[0, 1, 2][0, 1, 2][0, 1, 2].y, k_a_a_k_a_a_a_k)
for expected_path, expected_value in gen_test_data(k_a_a_k_a_a_a_k, nyiy, naia, naia, nyiy, naia, naia, naia, yyia):
actual = next(result)
assert actual == expected_value
assert_done_iterating(result)
def test_k_a_a_k_a_a_a_k_find_all_tuple_path(k_a_a_k_a_a_a_k):
result = find_matches(path.y[0, 1, 2][0, 1, 2].y[0, 1, 2][0, 1, 2][0, 1, 2].y, k_a_a_k_a_a_a_k)
for expected_path, expected_value in gen_test_data(k_a_a_k_a_a_a_k, nyiy, naia, naia, nyiy, naia, naia, naia, yyia):
actual = next(result)
assert str(actual) == f"{expected_path}={expected_value}"
assert_done_iterating(result)
def test_match_assign_set_0_0_0_to_2():
actual = [[[1]]]
expected = [[[2]]]
match = get_match(path[0][0][0], actual)
assert match.data == 1
match.data = 2
assert match.data == 2
assert actual == expected
def test_match_del_0_0_0():
actual = [[[1]]]
expected = [[[]]]
match = get_match(path[0][0][0], actual)
assert match.data == 1
del match.data
assert actual == expected
def test_match_pop_0_0_0():
actual = [[[1]]]
expected = [[[]]]
match = get_match(path[0][0][0], actual)
assert match.data == 1
actual_return = match.pop()
assert actual_return == 1
assert actual == expected
def test_match_pop_0_0_0_default():
actual = [[[1]]]
expected = [[[]]]
match = get_match(path[0][0][0], actual)
assert match.data == 1
actual_return = match.pop(2)
assert actual_return == 1
actual_return = match.pop(2)
assert actual_return == 2
assert actual == expected
def test_match_pop_0_0_0_lookup_error():
actual = [[[1]]]
match = get_match(path[0][0][0], actual)
assert match.data == 1
actual_return = match.pop()
assert actual_return == 1
with pytest.raises(PopError) as exc_info:
match.pop()
actual = repr(exc_info.value)
assert actual == "PopError(The reference data[0] does not exist. Unable to del\n path: $[0][0][0])"
def test_match_assign_set_0_0_to_0_2():
actual = [[[2, 2]]]
expected = [[[0]]]
match = get_match(path[0][0], actual)
assert match.data == [2, 2]
match.data = [0]
assert match.data == [0]
assert actual == expected
new_match = get_match(path[0], match)
assert repr(new_match) == '$[0][0][0]=0'
| 36.202778 | 120 | 0.704519 | 2,185 | 13,033 | 3.798169 | 0.043021 | 0.025063 | 0.142186 | 0.090372 | 0.898301 | 0.885287 | 0.862514 | 0.807808 | 0.791421 | 0.739728 | 0 | 0.027628 | 0.175171 | 13,033 | 359 | 121 | 36.303621 | 0.744372 | 0 | 0 | 0.53876 | 0 | 0.003876 | 0.038518 | 0.028696 | 0 | 0 | 0 | 0 | 0.27907 | 1 | 0.170543 | false | 0 | 0.015504 | 0 | 0.186047 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f5ba5636ffe03816af56069ee340962bb3649810 | 64 | py | Python | media/code_files/ac9cd4bb94e15e6693c059f4007d86d21c394fa9.py | TolimanStaR/Course-Work | 79dbfcbaef0ae79209295fe8d36b6fd9610e99b8 | [
"MIT"
] | 1 | 2020-03-31T22:09:34.000Z | 2020-03-31T22:09:34.000Z | media/code_files/ac9cd4bb94e15e6693c059f4007d86d21c394fa9.py | TolimanStaR/Course-Work | 79dbfcbaef0ae79209295fe8d36b6fd9610e99b8 | [
"MIT"
] | 8 | 2021-03-30T14:20:10.000Z | 2022-03-12T00:52:03.000Z | media/code_files/ac9cd4bb94e15e6693c059f4007d86d21c394fa9.py | TolimanStaR/Course-Work | 79dbfcbaef0ae79209295fe8d36b6fd9610e99b8 | [
"MIT"
] | null | null | null | import os
os.system("shutdown /s /t 1")
os.system("shutdown /n") | 21.333333 | 29 | 0.6875 | 12 | 64 | 3.666667 | 0.666667 | 0.363636 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017544 | 0.109375 | 64 | 3 | 30 | 21.333333 | 0.754386 | 0 | 0 | 0 | 0 | 0 | 0.415385 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 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 | 0 | 0 | 0 | 6 |
f5f59b0c121eddfbbe7d3cda741580d7d87f6e08 | 45 | py | Python | python/tak/model/__init__.py | Innoviox/taktician | c2997342264a8c9dba33b401984a2cba45d91386 | [
"MIT"
] | 55 | 2016-05-03T04:24:12.000Z | 2022-03-11T02:36:22.000Z | python/tak/model/__init__.py | Innoviox/taktician | c2997342264a8c9dba33b401984a2cba45d91386 | [
"MIT"
] | 19 | 2016-05-22T02:13:21.000Z | 2021-11-10T21:46:58.000Z | python/tak/model/__init__.py | Innoviox/taktician | c2997342264a8c9dba33b401984a2cba45d91386 | [
"MIT"
] | 13 | 2016-05-22T00:42:20.000Z | 2021-06-21T12:36:50.000Z | from .model import *
from .evaluate import *
| 15 | 23 | 0.733333 | 6 | 45 | 5.5 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.177778 | 45 | 2 | 24 | 22.5 | 0.891892 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
eb11251c537ac990ad9f6620efcb524493a30739 | 124 | py | Python | simio/app/__init__.py | RB387/simio | f799a08b0dc8871d6fc5eebe4e8635881721b511 | [
"Apache-2.0"
] | null | null | null | simio/app/__init__.py | RB387/simio | f799a08b0dc8871d6fc5eebe4e8635881721b511 | [
"Apache-2.0"
] | null | null | null | simio/app/__init__.py | RB387/simio | f799a08b0dc8871d6fc5eebe4e8635881721b511 | [
"Apache-2.0"
] | null | null | null | from simio.app.app import Application
from simio.app.builder import AppBuilder
from simio.app.config_names import AppConfig
| 31 | 44 | 0.854839 | 19 | 124 | 5.526316 | 0.526316 | 0.257143 | 0.342857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096774 | 124 | 3 | 45 | 41.333333 | 0.9375 | 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 | 0 | 0 | 0 | 6 |
eb163d7c635b7efb7cc0348c67303a4fcc07f89d | 869 | py | Python | tests/test3.py | CrafterKolyan/rubymine-is2018 | 9ac3049135c3235ecc875fc6253aba89bdeed32d | [
"Apache-2.0"
] | 1 | 2018-04-30T17:57:39.000Z | 2018-04-30T17:57:39.000Z | tests/test3.py | CrafterKolyan/rubymine-is2018 | 9ac3049135c3235ecc875fc6253aba89bdeed32d | [
"Apache-2.0"
] | null | null | null | tests/test3.py | CrafterKolyan/rubymine-is2018 | 9ac3049135c3235ecc875fc6253aba89bdeed32d | [
"Apache-2.0"
] | null | null | null | if 5 * 5 < 25:
# false
pass
if 5 * 5 < 13 + 13:
# true
pass
if -1 * -1 > -0 * (10 - 20):
# true
pass
if 10 - 20:
# true
pass
if (2 + 5) / 3 > 2:
# true
pass
if (1 + 2) * 3 == 9:
# true
pass
if (2 + 5) // 3 > 2:
# false
pass
if -7 % 3 == 2:
# true
pass
if 7 % 3 == 1:
# true
pass
if (True ^ 0) == 1:
# true
pass
if (-1 ^ True) == -2:
# true
pass
if ~True == -2:
# true
pass
if ~False == -1:
# true
pass
if ~13847628374638628973456893465 == -13847628374638628973456893466:
# true
pass
if (((1 << 2) | (-312893612846 >> 15)) ^ 12897 + (128 - 912397891724987128487) ** 2) % 1000000007 == 341987264:
# true
pass
if not(1 + 2 > 2 * 2) or 5 > 2 * 5:
# true
pass
if 1 << 0 == 1 and 1 >> 0 == 1 and -1 >> 32 == -1:
# true
pass | 12.970149 | 111 | 0.443038 | 125 | 869 | 3.08 | 0.224 | 0.249351 | 0.363636 | 0.142857 | 0.324675 | 0.072727 | 0.072727 | 0 | 0 | 0 | 0 | 0.351044 | 0.393556 | 869 | 67 | 112 | 12.970149 | 0.379507 | 0.098964 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
de78b05c9387a25925d89218bb315f38b0d6c96f | 25 | py | Python | clubmate.py | boerniee/project-mate | 072b0e871525d527d438f2ec0238fa94c4547f85 | [
"MIT"
] | 2 | 2019-12-18T09:42:18.000Z | 2019-12-20T13:16:52.000Z | clubmate.py | boerniee/project-mate | 072b0e871525d527d438f2ec0238fa94c4547f85 | [
"MIT"
] | 17 | 2019-12-18T12:45:30.000Z | 2021-02-06T14:44:36.000Z | clubmate.py | boerniee/project-mate | 072b0e871525d527d438f2ec0238fa94c4547f85 | [
"MIT"
] | null | null | null | from app import app, cli
| 12.5 | 24 | 0.76 | 5 | 25 | 3.8 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 25 | 1 | 25 | 25 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
de8d7a6c05cfe2b1f41f5ea45880fa34e16461ee | 10,337 | py | Python | deuce/tests/test_openstack_swift_hook.py | BenjamenMeyer/deuce | fbca31cb5248a808a85bfc24af10119453359276 | [
"Apache-2.0"
] | null | null | null | deuce/tests/test_openstack_swift_hook.py | BenjamenMeyer/deuce | fbca31cb5248a808a85bfc24af10119453359276 | [
"Apache-2.0"
] | null | null | null | deuce/tests/test_openstack_swift_hook.py | BenjamenMeyer/deuce | fbca31cb5248a808a85bfc24af10119453359276 | [
"Apache-2.0"
] | null | null | null | import falcon
import mock
import binascii
import base64
import json
import deuce
from deuce.transport.wsgi import hooks
from deuce.drivers import swift
from deuce.tests import HookTest
def before_hooks_swift(req, resp, params):
return [
hooks.OpenstackSwiftHook(req, resp, params)
]
class DummyClassObject(object):
pass
class TestOpenstackSwiftHook(HookTest):
def setUp(self):
super(TestOpenstackSwiftHook, self).setUp()
self.datacenter = 'test'
self.headers = {}
deuce.context = DummyClassObject()
deuce.context.datacenter = self.datacenter
deuce.context.project_id = self.create_project_id()
deuce.context.transaction = DummyClassObject()
deuce.context.transaction.request_id = 'openstack-hook-test'
deuce.context.openstack = DummyClassObject()
def test_is_not_swift_driver(self):
with mock.patch('deuce.storage_driver', object):
self.app_setup(before_hooks_swift)
self.simulate_get('/v1.0')
def test_is_swift_driver(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('deuce.transport.wsgi.hooks.'
'openstackswifthook.check_storage_url'
) as hook_check_storage_url:
self.app_setup(before_hooks_swift)
hook_check_storage_url.return_value = True
self.simulate_get('/v1.0')
def test_missing_service_catalog(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver):
self.app_setup(before_hooks_swift)
response = self.simulate_get('/v1.0', headers=self.headers)
self.assertEqual(self.srmock.status, falcon.HTTP_400)
def test_has_service_catalog(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('deuce.transport.wsgi.hooks.'
'openstackswifthook.decode_service_catalog'
) as decode_catalog:
decode_catalog.return_value = True
with mock.patch('deuce.transport.wsgi.hooks.'
'openstackswifthook.find_storage_url'
) as find_storage:
find_storage.return_value = 'test_url'
self.assertFalse(hasattr(deuce.context.openstack, 'swift'))
self.app_setup(before_hooks_swift)
self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertTrue(hasattr(deuce.context.openstack, 'swift'))
self.assertTrue(hasattr(deuce.context.openstack.swift,
'storage_url'))
self.assertEqual(deuce.context.openstack.swift.storage_url,
'test_url')
def test_failed_base64_decode_service_catalog(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.side_effect = binascii.Error('mock')
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_failed_json_decode_service_catalog(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.return_value = str('test-data').encode(
encoding='utf-8', errors='strict')
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_json_decode_service_catalog(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.return_value = json.dumps(
{'hello': 'test'}).encode(encoding='utf-8',
errors='strict')
with mock.patch('deuce.transport.wsgi.hooks.'
'openstackswifthook.find_storage_url'
) as find_storage:
find_storage.return_value = 'test_url'
self.assertFalse(hasattr(deuce.context.openstack, 'swift'))
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertTrue(hasattr(deuce.context.openstack, 'swift'))
self.assertTrue(hasattr(deuce.context.openstack.swift,
'storage_url'))
self.assertEqual(deuce.context.openstack.swift.storage_url,
'test_url')
def test_find_storage_url_invalid_service_catalog(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
json_data = json.dumps({'hello': 'test'})
byte_data = json_data.encode(encoding='utf-8', errors='strict')
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.return_value = byte_data
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_find_storage_url_invalid_service_catalog_with_access(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('base64.b64decode') as b64_decoder:
test_dict = {'access': {'hello': 'test'}}
b64_decoder.return_value = json.dumps(test_dict).encode(
encoding='utf-8', errors='strict')
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_find_storage_url_no_object_store(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.return_value = json.dumps(
self.create_service_catalog(
objectStoreType='mock')).encode(
encoding='utf-8', errors='strict')
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_find_storage_url_no_endpoints(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
catalog = self.create_service_catalog(endpoints=False)
json_data = json.dumps(catalog)
byte_data = json_data.encode(encoding='utf-8', errors='strict')
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.return_value = byte_data
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_find_storage_url_no_region(self):
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
with mock.patch('base64.b64decode') as b64_decoder:
b64_decoder.return_value = json.dumps(
self.create_service_catalog(region='other')).encode(
encoding='utf-8', errors='strict')
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': 'mock'})
self.assertEqual(self.srmock.status, falcon.HTTP_412)
def test_find_storage_url_final(self):
catalog = self.create_service_catalog(region=self.datacenter,
url='test_url')
json_data = json.dumps(catalog)
utf8_data = json_data.encode(encoding='utf-8', errors='strict')
b64_data = base64.b64encode(utf8_data)
with mock.patch('deuce.storage_driver',
spec=swift.SwiftStorageDriver) as swift_driver:
self.assertFalse(hasattr(deuce.context.openstack, 'swift'))
self.app_setup(before_hooks_swift)
response = self.simulate_get(
'/v1.0',
headers={
'x-service-catalog': b64_data})
self.assertTrue(hasattr(deuce.context.openstack, 'swift'))
self.assertTrue(hasattr(deuce.context.openstack.swift,
'storage_url'))
self.assertEqual(deuce.context.openstack.swift.storage_url,
'test_url')
| 43.432773 | 79 | 0.557996 | 1,027 | 10,337 | 5.387537 | 0.104187 | 0.036147 | 0.058738 | 0.055305 | 0.809687 | 0.787638 | 0.777155 | 0.763239 | 0.755648 | 0.732333 | 0 | 0.020133 | 0.346522 | 10,337 | 237 | 80 | 43.616034 | 0.798964 | 0 | 0 | 0.666667 | 0 | 0 | 0.115217 | 0.024669 | 0 | 0 | 0 | 0 | 0.10101 | 1 | 0.075758 | false | 0.005051 | 0.045455 | 0.005051 | 0.136364 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
deb0c58b17ff78cb07d678f180a42ce1a4a4cf3a | 5,637 | py | Python | esmm/mmoe.py | neoyinyao/Recommender | 7904a9d3aad6711ab9cf0dc5c245dfc98710f7a7 | [
"Apache-2.0"
] | null | null | null | esmm/mmoe.py | neoyinyao/Recommender | 7904a9d3aad6711ab9cf0dc5c245dfc98710f7a7 | [
"Apache-2.0"
] | null | null | null | esmm/mmoe.py | neoyinyao/Recommender | 7904a9d3aad6711ab9cf0dc5c245dfc98710f7a7 | [
"Apache-2.0"
] | null | null | null | from layers import MLP
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
class MMOE(keras.Model):
def __init__(self, num_tasks, num_experts, expert_hidden_units, task_hidden_units, feat_vocab,
embedding_size):
super().__init__()
self.embedding_layer = {feat: keras.layers.Embedding(vocab_size, embedding_size) for feat, vocab_size in
feat_vocab.items()}
self.num_tasks = num_tasks
self.experts = [MLP(units=expert_hidden_units, last_activation='relu') for _ in range(num_experts)]
self.gates = [layers.Dense(num_experts, activation='softmax', use_bias=True) for _ in range(num_tasks)]
self.task_towers = [MLP(units=task_hidden_units, last_activation='sigmoid') for _ in range(num_tasks)]
def compute_embedding(self, inputs):
embedding = [self.embedding_layer[feat](inputs[feat]) for feat in inputs]
embedding = tf.concat(embedding, axis=-1)
embedding = tf.squeeze(embedding, axis=1)
return embedding
def call(self, inputs, training=None, mask=None):
inputs = self.compute_embedding(inputs)
experts_outputs = []
for expert in self.experts:
expert_output = expert(inputs)
expert_output = tf.expand_dims(expert_output, axis=0)
experts_outputs.append(expert_output)
experts_outputs = tf.concat(experts_outputs, axis=0) # num_experts,None,expert_hidden_units[-1]
experts_outputs = tf.transpose(experts_outputs, perm=(1, 0, 2)) # None,num_experts,expert_hidden_units[-1]
outputs = []
for i in range(self.num_tasks):
gate = self.gates[i]
task_tower = self.task_towers[i]
gate_weights = gate(inputs) # None,num_experts
gate_weights = tf.expand_dims(gate_weights, axis=1) # None,1,num_experts
weighted_outputs = tf.matmul(gate_weights, experts_outputs) # None,1,expert_hidden_units[-1]
weighted_outputs = tf.squeeze(weighted_outputs, axis=1)
task_output = task_tower(weighted_outputs)
outputs.append(task_output)
outputs[1] = outputs[0] * outputs[1]
outputs = tf.concat(outputs, axis=1)
return outputs
def compute_cvr(self, inputs):
inputs = self.compute_embedding(inputs)
experts_outputs = []
for expert in self.experts:
expert_output = expert(inputs)
expert_output = tf.expand_dims(expert_output, axis=0)
experts_outputs.append(expert_output)
experts_outputs = tf.concat(experts_outputs, axis=0) # num_experts,None,expert_hidden_units[-1]
experts_outputs = tf.transpose(experts_outputs, perm=(1, 0, 2)) # None,num_experts,expert_hidden_units[-1]
outputs = []
for i in range(self.num_tasks):
gate = self.gates[i]
task_tower = self.task_towers[i]
gate_weights = gate(inputs) # None,num_experts
gate_weights = tf.expand_dims(gate_weights, axis=1) # None,1,num_experts
weighted_outputs = tf.matmul(gate_weights, experts_outputs) # None,1,expert_hidden_units[-1]
weighted_outputs = tf.squeeze(weighted_outputs, axis=1)
task_output = task_tower(weighted_outputs)
outputs.append(task_output)
return outputs[1]
def compute_ctr(self, inputs):
inputs = self.compute_embedding(inputs)
experts_outputs = []
for expert in self.experts:
expert_output = expert(inputs)
expert_output = tf.expand_dims(expert_output, axis=0)
experts_outputs.append(expert_output)
experts_outputs = tf.concat(experts_outputs, axis=0) # num_experts,None,expert_hidden_units[-1]
experts_outputs = tf.transpose(experts_outputs, perm=(1, 0, 2)) # None,num_experts,expert_hidden_units[-1]
outputs = []
for i in range(self.num_tasks):
gate = self.gates[i]
task_tower = self.task_towers[i]
gate_weights = gate(inputs) # None,num_experts
gate_weights = tf.expand_dims(gate_weights, axis=1) # None,1,num_experts
weighted_outputs = tf.matmul(gate_weights, experts_outputs) # None,1,expert_hidden_units[-1]
weighted_outputs = tf.squeeze(weighted_outputs, axis=1)
task_output = task_tower(weighted_outputs)
outputs.append(task_output)
return outputs[0]
def compute_ctcvr(self, inputs):
inputs = self.compute_embedding(inputs)
experts_outputs = []
for expert in self.experts:
expert_output = expert(inputs)
expert_output = tf.expand_dims(expert_output, axis=0)
experts_outputs.append(expert_output)
experts_outputs = tf.concat(experts_outputs, axis=0) # num_experts,None,expert_hidden_units[-1]
experts_outputs = tf.transpose(experts_outputs, perm=(1, 0, 2)) # None,num_experts,expert_hidden_units[-1]
outputs = []
for i in range(self.num_tasks):
gate = self.gates[i]
task_tower = self.task_towers[i]
gate_weights = gate(inputs) # None,num_experts
gate_weights = tf.expand_dims(gate_weights, axis=1) # None,1,num_experts
weighted_outputs = tf.matmul(gate_weights, experts_outputs) # None,1,expert_hidden_units[-1]
weighted_outputs = tf.squeeze(weighted_outputs, axis=1)
task_output = task_tower(weighted_outputs)
outputs.append(task_output)
return outputs[0] * outputs[1]
| 51.245455 | 115 | 0.657797 | 717 | 5,637 | 4.899582 | 0.099024 | 0.111586 | 0.067748 | 0.061486 | 0.772559 | 0.754626 | 0.754626 | 0.754626 | 0.754626 | 0.754626 | 0 | 0.013621 | 0.244634 | 5,637 | 109 | 116 | 51.715596 | 0.811414 | 0.105553 | 0 | 0.712871 | 0 | 0 | 0.003585 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.059406 | false | 0 | 0.039604 | 0 | 0.158416 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
deeb59e54cdad1bd7594e519a75fbceb35be60ae | 1,513 | py | Python | LeetCode_Solutions/1576. Replace All ?'s to Avoid Consecutive Repeating Characters.py | foxfromworld/Coding-Interview-Preparation-with-LeetCode-and-An-Algorithm-Book | e0c704d196fe0d8452ea639a92f2a75c3b46a9b0 | [
"BSD-2-Clause"
] | null | null | null | LeetCode_Solutions/1576. Replace All ?'s to Avoid Consecutive Repeating Characters.py | foxfromworld/Coding-Interview-Preparation-with-LeetCode-and-An-Algorithm-Book | e0c704d196fe0d8452ea639a92f2a75c3b46a9b0 | [
"BSD-2-Clause"
] | null | null | null | LeetCode_Solutions/1576. Replace All ?'s to Avoid Consecutive Repeating Characters.py | foxfromworld/Coding-Interview-Preparation-with-LeetCode-and-An-Algorithm-Book | e0c704d196fe0d8452ea639a92f2a75c3b46a9b0 | [
"BSD-2-Clause"
] | null | null | null | # Source : https://leetcode.com/problems/replace-all-s-to-avoid-consecutive-repeating-characters/
# Author : foxfromworld
# Date : 16/02/2021
# Second attempt
class Solution:
def modifyString(self, s: str) -> str:
if s =='?': return 'a'
elif len(s)==1: return s
s = list(s)
alphabet = "abcdefghijklmnopqrstuvwxyz"
for i in range(len(s)):
if s[i] == '?':
if i == 0:
for ch in alphabet:
if ch != s[i+1]:
s[i] = ch
break
elif i == len(s)-1:
for ch in alphabet:
if ch != s[i-1]:
s[i] = ch
break
else:
for ch in alphabet:
if ch != s[i+1] and ch != s[i-1]:
s[i] = ch
break
return "".join(s)
# Date : 16/02/2021
# First attempt
class Solution:
def modifyString(self, s: str) -> str:
if len(s)==1 and s=="?": return 'a'
if len(s)==1: return s
s = list(s)
alphabet = "abcdefghijklmnopqrstuvwxyz"
for i in range(len(s)):
if s[i] == '?':
if i == 0:
for ch in alphabet:
if ch != s[i+1]:
s[i] = ch
break
elif i == len(s)-1:
for ch in alphabet:
if ch != s[i-1]:
s[i] = ch
break
else:
for ch in alphabet:
if ch != s[i+1] and ch != s[i-1]:
s[i] = ch
break
return "".join(s)
| 24.803279 | 97 | 0.435558 | 207 | 1,513 | 3.183575 | 0.227053 | 0.048558 | 0.048558 | 0.060698 | 0.75569 | 0.75569 | 0.75569 | 0.75569 | 0.75569 | 0.75569 | 0 | 0.035591 | 0.424323 | 1,513 | 60 | 98 | 25.216667 | 0.72101 | 0.122274 | 0 | 0.916667 | 0 | 0 | 0.043939 | 0.039394 | 0 | 0 | 0 | 0 | 0 | 1 | 0.041667 | false | 0 | 0 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
7200e04721a5e957ad0fd6104acbeb4ccdd308d5 | 38 | py | Python | __init__.py | lupeboy2/luparnet | 31e585a2ba82a16fbf97b965e926502afe24c933 | [
"MIT"
] | null | null | null | __init__.py | lupeboy2/luparnet | 31e585a2ba82a16fbf97b965e926502afe24c933 | [
"MIT"
] | null | null | null | __init__.py | lupeboy2/luparnet | 31e585a2ba82a16fbf97b965e926502afe24c933 | [
"MIT"
] | null | null | null | from net import *
from fit import *
| 12.666667 | 18 | 0.684211 | 6 | 38 | 4.333333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.263158 | 38 | 2 | 19 | 19 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7218fae83d071c6188669460f2b99084442f630e | 242 | py | Python | pritha/default_storages.py | ashish2020kashyap/cessini | 9713fd76d2e31a95266ec69da2abc98424a46e52 | [
"MIT"
] | null | null | null | pritha/default_storages.py | ashish2020kashyap/cessini | 9713fd76d2e31a95266ec69da2abc98424a46e52 | [
"MIT"
] | null | null | null | pritha/default_storages.py | ashish2020kashyap/cessini | 9713fd76d2e31a95266ec69da2abc98424a46e52 | [
"MIT"
] | null | null | null | from django.conf import settings
from storages.backends.s3boto3 import S3Boto3Storage
class StaticStorage(S3Boto3Storage):
location = settings.STATIC_FOLDER
class MediaStorage(S3Boto3Storage):
location = settings.MEDIA_FOLDER | 30.25 | 53 | 0.805785 | 25 | 242 | 7.72 | 0.64 | 0.227979 | 0.310881 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.038462 | 0.140496 | 242 | 8 | 54 | 30.25 | 0.889423 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 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 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
721bbdda49378e13dcac969cc4d838049e5156f9 | 40 | py | Python | cluster_funk/core/notebooks/sagemaker_notebook_booter.py | johnnyiller/cluster_funk | 132b376b600b606d54861c8edef0e8cdc6dfe740 | [
"MIT"
] | null | null | null | cluster_funk/core/notebooks/sagemaker_notebook_booter.py | johnnyiller/cluster_funk | 132b376b600b606d54861c8edef0e8cdc6dfe740 | [
"MIT"
] | null | null | null | cluster_funk/core/notebooks/sagemaker_notebook_booter.py | johnnyiller/cluster_funk | 132b376b600b606d54861c8edef0e8cdc6dfe740 | [
"MIT"
] | null | null | null | class SagemakerNotebookBooter:
pass
| 13.333333 | 30 | 0.8 | 3 | 40 | 10.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.175 | 40 | 2 | 31 | 20 | 0.969697 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 1 | 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 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
72495dea6e97cc00403fb83be034d48ed6a5990f | 53 | py | Python | 7.py | karttrak/project-euler | 921432dd01b83fa81578c3d65655de30e7505516 | [
"MIT"
] | null | null | null | 7.py | karttrak/project-euler | 921432dd01b83fa81578c3d65655de30e7505516 | [
"MIT"
] | null | null | null | 7.py | karttrak/project-euler | 921432dd01b83fa81578c3d65655de30e7505516 | [
"MIT"
] | null | null | null | from primes import nth_prime
print(nth_prime(10001)) | 17.666667 | 28 | 0.830189 | 9 | 53 | 4.666667 | 0.777778 | 0.380952 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104167 | 0.09434 | 53 | 3 | 29 | 17.666667 | 0.770833 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 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 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
a0d32bab5d25ef60197b7e75be294f8f342be514 | 36 | py | Python | chess/board/__init__.py | theovoss/Chess | ec423a2b3300debd50439ac8ec35ea7100583957 | [
"MIT"
] | 5 | 2017-07-30T16:20:08.000Z | 2021-05-19T23:58:44.000Z | chess/board/__init__.py | theovoss/Chess | ec423a2b3300debd50439ac8ec35ea7100583957 | [
"MIT"
] | 15 | 2015-08-23T00:54:10.000Z | 2020-08-08T16:05:13.000Z | chess/board/__init__.py | theovoss/Chess | ec423a2b3300debd50439ac8ec35ea7100583957 | [
"MIT"
] | 2 | 2017-12-12T13:27:31.000Z | 2020-09-05T10:15:48.000Z | from .chess_board import ChessBoard
| 18 | 35 | 0.861111 | 5 | 36 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 36 | 1 | 36 | 36 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a0ebd9d5255a091d0d9bbc87e8f026192d434bc2 | 4,840 | py | Python | users/access/requires.py | donvvo/questr-master | 6363ffb4c11ef61f3b6976e75c86a5cbc7f38590 | [
"MIT"
] | null | null | null | users/access/requires.py | donvvo/questr-master | 6363ffb4c11ef61f3b6976e75c86a5cbc7f38590 | [
"MIT"
] | null | null | null | users/access/requires.py | donvvo/questr-master | 6363ffb4c11ef61f3b6976e75c86a5cbc7f38590 | [
"MIT"
] | null | null | null |
import logging
from django.shortcuts import render, redirect
from django.core.exceptions import PermissionDenied
from users.models import QuestrUserProfile, UserToken
from quests.models import QuestToken
def verified(a_view):
"""
email verification decorator; redirects with to the home page with verfication message.
"""
def _wrapped_function(request, *args, **kwargs):
if request.user.is_authenticated():
email = request.user
try:
user = QuestrUserProfile.objects.get(email=email)
if user :
if user.email_status:
pagetype="emailnotverified"
return a_view(request, *args, **kwargs)
else:
pagetype="emailnotverified"
request.session['alert_message'] = dict(type="warning",message="Please check your inbox and verify your email !")
alert_message = request.session['alert_message']
return render(request, 'thankyou.html', locals())
success = False
message = "User not Found"
return render(request, 'thankyou.html', locals())
except QuestrUserProfile.DoesNotExist:
return render(request,'error_pages/something_broke.html', locals())
return redirect('signin')
return _wrapped_function
def is_alive(a_view):
"""
checks whether the token is alive or dead
"""
def _wrapped_function(request, *args, **kwargs):
questr_token = request.GET['questr_token']
if questr_token:
try:
token = UserToken.objects.get(token = questr_token)
# check whether the token is alive and take dedcision
if token:
if token.is_alive():
return a_view(request, *args, **kwargs)
else:
success = False
request.session['alert_message'] = dict(type="warning",message="The link has expired!")
alert_message = request.session['alert_message']
return render(request, 'verification.html', locals())
except UserToken.DoesNotExist:
success = False
request.session['alert_message'] = dict(type="danger",message="Invalid Request !")
alert_message = request.session['alert_message']
return render(request, 'verification.html', locals())
else:
return render(request,'error_pages/something_broke.html', locals())
return _wrapped_function
def is_quest_alive(a_view):
"""
checks whether the token is alive or dead
"""
def _wrapped_function(request, *args, **kwargs):
quest_token = request.GET['quest_token']
if quest_token:
try:
token = QuestToken.objects.get(token_id = quest_token)
# check whether the token is alive and take dedcision
if token:
if token.is_alive():
return a_view(request, *args, **kwargs)
else:
success = False
request.session['alert_message'] = dict(type="warning",message="The link has expired, and perhaps some other courier has been selected!")
alert_message = request.session['alert_message']
return redirect('home')
except QuestToken.DoesNotExist:
success = False
request.session['alert_message'] = dict(type="danger",message="Invalid Request !")
alert_message = request.session['alert_message']
return render(request, 'thankyou.html', locals())
else:
return render(request,'error_pages/something_broke.html', locals())
return _wrapped_function
def is_superuser(a_view):
"""
Checks if a user is super user
"""
def _wrapped_function(request, *args, **kwargs):
if request.user.is_authenticated():
email = request.user
try:
user = QuestrUserProfile.objects.get(email=email)
if user :
if user.is_superuser:
logging.warn("is superuser")
return a_view(request, *args, **kwargs)
else:
logging.warn("is not superuser")
return redirect('home')
success = False
return redirect('home')
except QuestrUserProfile.DoesNotExist:
return render(request,'error_pages/something_broke.html', locals())
return redirect('signin')
return _wrapped_function
def is_signup_token_alive(a_view):
"""
checks whether the token is alive or dead
"""
def _wrapped_function(request, *args, **kwargs):
questr_token = request.GET['questr_token']
if questr_token:
try:
token = UserToken.objects.get(token = questr_token)
# check whether the token is alive and take dedcision
if token:
if token.is_alive():
return a_view(request, *args, **kwargs)
else:
success = False
request.session['alert_message'] = dict(type="warning",message="The link has expired please contact us to request again!")
alert_message = request.session['alert_message']
return redirect('index')
except UserToken.DoesNotExist:
success = False
request.session['alert_message'] = dict(type="danger",message="Invalid Request !")
alert_message = request.session['alert_message']
return redirect('index')
else:
return render(request,'error_pages/something_broke.html', locals())
return _wrapped_function
| 34.820144 | 143 | 0.711157 | 599 | 4,840 | 5.599332 | 0.171953 | 0.075134 | 0.079308 | 0.108527 | 0.783244 | 0.783244 | 0.772212 | 0.753131 | 0.723315 | 0.702445 | 0 | 0 | 0.176033 | 4,840 | 138 | 144 | 35.072464 | 0.841023 | 0.082851 | 0 | 0.794643 | 0 | 0 | 0.193754 | 0.036471 | 0 | 0 | 0 | 0 | 0 | 1 | 0.089286 | false | 0 | 0.044643 | 0 | 0.375 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
9d36e9fd3fb9dd56856854b775ea8be0e33a9ab2 | 41 | py | Python | PythPower/__init__.py | TIPDYT/PythPower | a67e34bf9adc470bededcaf3c4473c9cec4211d4 | [
"MIT"
] | 1 | 2022-03-06T15:03:36.000Z | 2022-03-06T15:03:36.000Z | PythPower/__init__.py | TIPDYT/PythPower | a67e34bf9adc470bededcaf3c4473c9cec4211d4 | [
"MIT"
] | null | null | null | PythPower/__init__.py | TIPDYT/PythPower | a67e34bf9adc470bededcaf3c4473c9cec4211d4 | [
"MIT"
] | null | null | null | from PythPower.pythpower import PowerData | 41 | 41 | 0.902439 | 5 | 41 | 7.4 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.073171 | 41 | 1 | 41 | 41 | 0.973684 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9d3f0f33e318bf2946f8e40b5822f4b6431cfb90 | 8,492 | py | Python | tests/docker_push_latest_if_changed_test.py | dsteinkopf/docker-push-latest-if-changed | bd2c1b0e2f7e3d18df0c298e92789ce35fe30321 | [
"MIT"
] | 23 | 2017-10-26T21:04:31.000Z | 2022-02-08T00:44:51.000Z | tests/docker_push_latest_if_changed_test.py | dsteinkopf/docker-push-latest-if-changed | bd2c1b0e2f7e3d18df0c298e92789ce35fe30321 | [
"MIT"
] | 4 | 2021-09-30T00:53:26.000Z | 2022-02-03T01:16:54.000Z | tests/docker_push_latest_if_changed_test.py | dsteinkopf/docker-push-latest-if-changed | bd2c1b0e2f7e3d18df0c298e92789ce35fe30321 | [
"MIT"
] | 10 | 2017-10-26T20:56:28.000Z | 2022-02-02T23:17:49.000Z | import re
import pytest
from docker_push_latest_if_changed import _get_image
from docker_push_latest_if_changed import _push_image
from docker_push_latest_if_changed import _tag_image
from docker_push_latest_if_changed import ImageKey
from docker_push_latest_if_changed import ImageNotFoundError
from docker_push_latest_if_changed import main
from testing.helpers import are_two_images_on_registry_the_same
from testing.helpers import is_image_on_registry
from testing.helpers import is_local_image_the_same_on_registry
IMAGE_KEY_RE_SUFFIX = (
r"ImageKey\(commands_hash='(?P<commands_hash>\w+)', "
r"packages_hash='(?P<packages_hash>\w+)'"
)
def test_push_new_image(
capsys,
fake_docker_registry,
fake_image_foo_name,
fake_image_bar_name,
):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:foo')
_tag_image(source.name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_bar_name}:latest')
_tag_image(target.name, target.uri, is_dry_run=False)
assert not is_image_on_registry(source)
assert not is_image_on_registry(target)
_push_image(target.uri, is_dry_run=False)
out, _ = capsys.readouterr()
assert f'Pushing image {target.uri}' in out
assert not is_image_on_registry(source)
assert is_image_on_registry(target)
main(('--source', source.uri, '--target', target.uri))
assert are_two_images_on_registry_the_same(source, target)
assert is_local_image_the_same_on_registry(source, target)
def test_push_new_image_dry_run(
capsys,
fake_docker_registry,
fake_image_foo_name,
fake_image_bar_name,
):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:foo')
_tag_image(source.name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_bar_name}:latest')
_tag_image(target.name, target.uri, is_dry_run=False)
assert not is_image_on_registry(source)
assert not is_image_on_registry(target)
_push_image(target.uri, is_dry_run=False)
out, _ = capsys.readouterr()
assert f'Pushing image {target.uri}' in out
assert not is_image_on_registry(source)
assert is_image_on_registry(target)
main(('--source', source.uri, '--target', target.uri, '--dry-run'))
out, _ = capsys.readouterr()
assert 'Image was not actually tagged since this is a dry run' in out
assert 'Image was not actually pushed since this is a dry run' in out
assert not is_image_on_registry(source)
assert is_image_on_registry(target)
def test_two_same_images(capsys, fake_docker_registry, fake_image_foo_name):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:foo')
_tag_image(source.name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:latest')
_tag_image(target.name, target.uri, is_dry_run=False)
assert not is_image_on_registry(source)
assert not is_image_on_registry(target)
_push_image(target.uri, is_dry_run=False)
out, _ = capsys.readouterr()
assert f'Pushing image {target.uri}' in out
assert not is_image_on_registry(source)
assert is_image_on_registry(target)
main(('--source', source.uri, '--target', target.uri))
out, _ = capsys.readouterr()
assert f'Pushing image {target.uri}' not in out
assert 'Image has NOT changed' in out
assert are_two_images_on_registry_the_same(source, target)
assert is_local_image_the_same_on_registry(source, target)
def test_two_same_images_with_different_packages(
capsys,
fake_docker_registry,
fake_baz_dummy_deb_images,
):
baz_dummy_deb_name, baz_no_dummy_deb_name = fake_baz_dummy_deb_images
source = _get_image(f'{fake_docker_registry}/{baz_dummy_deb_name}:baz')
_tag_image(source.name, source.uri, is_dry_run=False)
target = _get_image(
f'{fake_docker_registry}/{baz_no_dummy_deb_name}:latest'
)
_tag_image(target.name, target.uri, is_dry_run=False)
assert not is_image_on_registry(source)
assert not is_image_on_registry(target)
_push_image(target.uri, is_dry_run=False)
out, _ = capsys.readouterr()
assert f'Pushing image {target.uri}' in out
assert not is_image_on_registry(source)
assert is_image_on_registry(target)
main(('--source', source.uri, '--target', target.uri))
out, _ = capsys.readouterr()
source_key = ImageKey(
**re.search(
f'Source key: {IMAGE_KEY_RE_SUFFIX}',
out,
).groupdict()
)
target_key = ImageKey(
**re.search(
f'Target key: {IMAGE_KEY_RE_SUFFIX}',
out,
).groupdict()
)
assert source_key.packages_hash != target_key.packages_hash
assert source_key.commands_hash == target_key.commands_hash
assert are_two_images_on_registry_the_same(source, target)
assert is_local_image_the_same_on_registry(source, target)
def test_no_target(fake_docker_registry, fake_image_foo_name):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:foo')
_tag_image(fake_image_foo_name, source.uri, is_dry_run=False)
expected_target = _get_image(
f'{fake_docker_registry}/{fake_image_foo_name}:latest'
)
assert not is_image_on_registry(source)
assert not is_image_on_registry(expected_target)
main(('--source', source.uri))
assert is_local_image_the_same_on_registry(source, expected_target)
def test_no_previous_image(fake_docker_registry, fake_image_foo_name):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:foo')
_tag_image(fake_image_foo_name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:latest')
_tag_image(target.name, target.uri, is_dry_run=False)
assert not is_image_on_registry(source)
assert not is_image_on_registry(target)
main(('--source', source.uri, '--target', target.uri))
assert are_two_images_on_registry_the_same(source, target)
assert is_local_image_the_same_on_registry(source, target)
def test_omit_target_tag(fake_docker_registry, fake_image_foo_name):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:foo')
_tag_image(fake_image_foo_name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}')
_tag_image(target.name, target.uri, is_dry_run=False)
assert not is_image_on_registry(source)
assert not is_image_on_registry(target)
main(('--source', source.uri, '--target', target.uri))
expected_target = _get_image(
f'{fake_docker_registry}/{fake_image_foo_name}:latest'
)
assert are_two_images_on_registry_the_same(source, expected_target)
assert is_local_image_the_same_on_registry(source, expected_target)
def test_source_has_no_tag(fake_docker_registry, fake_image_foo_name):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}')
_tag_image(fake_image_foo_name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:latest')
_tag_image(target.name, target.uri, is_dry_run=False)
with pytest.raises(ValueError) as excinfo:
main(('--source', source.uri, '--target', target.uri))
assert f'{source.uri} does not have a tag!' in str(excinfo.value)
def test_source_and_target_have_the_same_tag(
fake_docker_registry,
fake_image_foo_name,
fake_image_bar_name,
):
source = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:latest')
_tag_image(fake_image_foo_name, source.uri, is_dry_run=False)
target = _get_image(f'{fake_docker_registry}/{fake_image_foo_name}:latest')
_tag_image(target.name, target.uri, is_dry_run=False)
with pytest.raises(ValueError) as excinfo:
main(('--source', source.uri, '--target', target.uri))
assert 'repo:tags cannot be the same' in str(excinfo.value)
def test_image_doesnt_exist():
source = _get_image('woowoo.spoopy.com/woo:latest')
with pytest.raises(ImageNotFoundError) as excinfo:
main(('--source', source.uri))
assert f'The image {source.uri} was not found' in str(excinfo.value)
def test_invalid_image_name():
fake_invalid_image_name = 'lol'
with pytest.raises(ValueError) as excinfo:
main(('--source', fake_invalid_image_name))
msg = str(excinfo.value)
assert f'Image uri {fake_invalid_image_name} is malformed' in msg
| 36.446352 | 79 | 0.750824 | 1,285 | 8,492 | 4.51751 | 0.076265 | 0.065461 | 0.086822 | 0.077175 | 0.86615 | 0.811197 | 0.788286 | 0.742808 | 0.712834 | 0.693885 | 0 | 0 | 0.149081 | 8,492 | 232 | 80 | 36.603448 | 0.803349 | 0 | 0 | 0.655367 | 0 | 0 | 0.198187 | 0.131536 | 0 | 0 | 0 | 0 | 0.276836 | 1 | 0.062147 | false | 0 | 0.062147 | 0 | 0.124294 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c24325479cf2eefd54c74dd86a74948bfdd9476b | 25 | py | Python | pysocrata/__init__.py | ResidentMario/pysocrata | 78d31ed24f9966284043eee45acebd62aa67e5b1 | [
"MIT"
] | null | null | null | pysocrata/__init__.py | ResidentMario/pysocrata | 78d31ed24f9966284043eee45acebd62aa67e5b1 | [
"MIT"
] | 1 | 2017-08-12T17:44:56.000Z | 2017-08-12T17:44:56.000Z | pysocrata/__init__.py | ResidentMario/pysocrata | 78d31ed24f9966284043eee45acebd62aa67e5b1 | [
"MIT"
] | null | null | null | from .pysocrata import *
| 12.5 | 24 | 0.76 | 3 | 25 | 6.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 25 | 1 | 25 | 25 | 0.904762 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
dfc10a8b4becd81efae966d9b432a6b032fe803e | 77 | py | Python | acq4/util/metaarray.py | aleonlein/acq4 | 4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555 | [
"MIT"
] | 1 | 2020-06-04T17:04:53.000Z | 2020-06-04T17:04:53.000Z | acq4/util/metaarray.py | aleonlein/acq4 | 4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555 | [
"MIT"
] | 24 | 2016-09-27T17:25:24.000Z | 2017-03-02T21:00:11.000Z | acq4/util/metaarray.py | sensapex/acq4 | 9561ba73caff42c609bd02270527858433862ad8 | [
"MIT"
] | 4 | 2016-10-19T06:39:36.000Z | 2019-09-30T21:06:45.000Z | from __future__ import print_function
from acq4.pyqtgraph.metaarray import *
| 25.666667 | 38 | 0.857143 | 10 | 77 | 6.1 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014493 | 0.103896 | 77 | 2 | 39 | 38.5 | 0.869565 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0.5 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
dfcf2a64ea26cfeed3142011f434ee5707682937 | 26 | py | Python | terrascript/github/__init__.py | vfoucault/python-terrascript | fe82b3d7e79ffa72b7871538f999828be0a115d0 | [
"BSD-2-Clause"
] | null | null | null | terrascript/github/__init__.py | vfoucault/python-terrascript | fe82b3d7e79ffa72b7871538f999828be0a115d0 | [
"BSD-2-Clause"
] | null | null | null | terrascript/github/__init__.py | vfoucault/python-terrascript | fe82b3d7e79ffa72b7871538f999828be0a115d0 | [
"BSD-2-Clause"
] | null | null | null | """2017-11-28 18:07:36"""
| 13 | 25 | 0.538462 | 6 | 26 | 2.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.583333 | 0.076923 | 26 | 1 | 26 | 26 | 0 | 0.730769 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
dfe0c137a1bc05272781535c2b59fa8bb4abd9b0 | 109 | py | Python | nuqql.py | modk/nuqql | c0142b207115a9a225970fb0e1d38092ba85ae1d | [
"MIT"
] | 3 | 2019-04-15T18:33:36.000Z | 2019-04-21T19:18:10.000Z | nuqql.py | modk/nuqql | c0142b207115a9a225970fb0e1d38092ba85ae1d | [
"MIT"
] | 15 | 2019-04-15T18:35:56.000Z | 2019-09-14T08:24:32.000Z | nuqql.py | modk/nuqql | c0142b207115a9a225970fb0e1d38092ba85ae1d | [
"MIT"
] | 1 | 2019-06-16T12:00:30.000Z | 2019-06-16T12:00:30.000Z | #!/usr/bin/env python3
import sys
import nuqql.main
import nuqql
# start nuqql
sys.exit(nuqql.main.run())
| 10.9 | 26 | 0.733945 | 18 | 109 | 4.444444 | 0.611111 | 0.275 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010638 | 0.137615 | 109 | 9 | 27 | 12.111111 | 0.840426 | 0.302752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a08bdb667f32a73f29825d8832f7eeb13585d7b2 | 28 | py | Python | dmiapi/__init__.py | niklascp/py-dmiapi | b475877dbafd714db0d75a4c602f8a67ce2897dd | [
"MIT"
] | null | null | null | dmiapi/__init__.py | niklascp/py-dmiapi | b475877dbafd714db0d75a4c602f8a67ce2897dd | [
"MIT"
] | null | null | null | dmiapi/__init__.py | niklascp/py-dmiapi | b475877dbafd714db0d75a4c602f8a67ce2897dd | [
"MIT"
] | 1 | 2022-02-16T11:07:21.000Z | 2022-02-16T11:07:21.000Z | from .DmiApiClient import *
| 14 | 27 | 0.785714 | 3 | 28 | 7.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 28 | 1 | 28 | 28 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2674189ccb154717bc06fe2912f36a490fc60fd9 | 2,015 | py | Python | Pacote Download/Ex042_12_Analisando_Triangulos_V2.py | BrunoCruzIglesias/Python | 01465632a8471271e994eb4565a14a547db6578d | [
"MIT"
] | null | null | null | Pacote Download/Ex042_12_Analisando_Triangulos_V2.py | BrunoCruzIglesias/Python | 01465632a8471271e994eb4565a14a547db6578d | [
"MIT"
] | null | null | null | Pacote Download/Ex042_12_Analisando_Triangulos_V2.py | BrunoCruzIglesias/Python | 01465632a8471271e994eb4565a14a547db6578d | [
"MIT"
] | null | null | null | # Refaça o desafio dos triangulos da aula 35 e acrescente
# o recurso pra mostrar o tipo de triangulo que ele é:
# Equilátero = todos os lados iguais
# Isósceles = dois lados iguais
# Escaleno = todos os lados diferentes
# r1 = float(input('Digite o primeiro segmento de reta: '))
# r2 = float(input('Digite o segundo segmento de reta: '))
# r3 = float(input('Digite o terceiro segmento de reta: '))
#
# if r1 < r2 + r3 and r2 < r1 + r3 and r3 < r1 + r2: #aqui faz o teste se o triangulo pode existir, se ele n puder ele nem faz o bloco abaixo
# print('Os segmentos acima podem formar um triangulo')
# if r1 == r2 and r1 == r3: #esse IF só acontece se o triangulo existir, aqui ele diz qual a categoria do triangulo
# print('Este é um triângulo Equilátero, pois possui todos os lados iguais')
# elif (r1 == r2 and r1 and r2 != r3) or (r1 == r3 and r1 and r3 != r2) or (r2 == r3 and r2 and r3 != r1):
# print('Este é um triângulo Isósceles, pois possui dois lados iguais e um diferente')
# elif r1 != r2 and r1 != r3 and r2 != r3:
# print('Este é um triângulo Escaleno, pois possui todos os lados diferentes.')
# else:
# print('Os segmentos acima NÃO podem formar triangulo')
#jeito do professor
r1 = float(input('Digite o primeiro segmento de reta: '))
r2 = float(input('Digite o segundo segmento de reta: '))
r3 = float(input('Digite o terceiro segmento de reta: '))
if r1 < r2 + r3 and r2 < r1 + r3 and r3 < r1 + r2: #aqui faz o teste se o triangulo pode existir, se ele n puder ele nem faz o bloco abaixo
print('Os segmentos acima podem formar um triangulo')
if r1 == r2 == r3:
print('Este é um triângulo Equilátero, pois possui todos os lados iguais')
elif r1 != r2 != r3 != r1:
print('Este é um triângulo Escaleno, pois possui todos os lados diferentes.')
else:
print('Este é um triângulo Isósceles, pois possui dois lados iguais e um diferente')
else:
print('Os segmentos acima NÃO podem formar triangulo')
| 53.026316 | 142 | 0.673945 | 329 | 2,015 | 4.12766 | 0.234043 | 0.02651 | 0.053019 | 0.07511 | 0.781296 | 0.761414 | 0.761414 | 0.751105 | 0.751105 | 0.68704 | 0 | 0.037565 | 0.233747 | 2,015 | 37 | 143 | 54.459459 | 0.841969 | 0.641191 | 0 | 0.153846 | 0 | 0 | 0.578797 | 0 | 0 | 0 | 0 | 0.027027 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.384615 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cd3fd58a56bedbd682f950424aa540fb02a005d3 | 29 | py | Python | api/models.py | outloudvi/lyricize | b8575d149b74869b1b0d2b3d9ea1a4509dc48a82 | [
"MIT"
] | 2 | 2019-05-20T01:34:59.000Z | 2019-05-23T09:44:48.000Z | api/models.py | outloudvi/lyricize | b8575d149b74869b1b0d2b3d9ea1a4509dc48a82 | [
"MIT"
] | null | null | null | api/models.py | outloudvi/lyricize | b8575d149b74869b1b0d2b3d9ea1a4509dc48a82 | [
"MIT"
] | null | null | null | from frontend.models import * | 29 | 29 | 0.827586 | 4 | 29 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103448 | 29 | 1 | 29 | 29 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
cd4cc6c230ea085ed21bd3c091692401774dd07e | 21,880 | py | Python | asrp/preprocessing.py | voidful/asrp | aa300670b838f2a6c2bc01c1131353f316ea795c | [
"Apache-2.0"
] | 5 | 2021-11-02T03:55:13.000Z | 2022-03-24T07:23:15.000Z | asrp/preprocessing.py | voidful/asrp | aa300670b838f2a6c2bc01c1131353f316ea795c | [
"Apache-2.0"
] | null | null | null | asrp/preprocessing.py | voidful/asrp | aa300670b838f2a6c2bc01c1131353f316ea795c | [
"Apache-2.0"
] | 1 | 2021-12-22T14:18:42.000Z | 2021-12-22T14:18:42.000Z | import re
import string
import unicodedata
import unidecode
langs = ['ab', 'ar', 'as', 'br', 'ca', 'cnh', 'cs', 'cv', 'cy', 'de', 'dv', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa',
'fi', 'fr', 'fy-NL', 'ga-IE', 'hi', 'hsb', 'hu', 'ia', 'id', 'it', 'ja', 'ka', 'kab', 'ky', 'lg', 'lt', 'lv',
'mn', 'mt', 'nl', 'or', 'pa-IN', 'pl', 'pt', 'rm-sursilv', 'rm-vallader', 'ro', 'ru', 'rw', 'sah', 'sl',
'sv-SE', 'ta', 'th', 'tr', 'tt', 'uk', 'vi', 'vot', 'zh-CN', 'zh-HK', 'zh-TW']
def fun_ar(batch):
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\؛\\\\\\\\\\\\\\\\—\\\\\\\\\\\\\\\\_get\\\\\\\\\\\\\\\\«\\\\\\\\\\\\\\\\»\\\\\\\\\\\\\\\\ـ\\\\\\\\\\\\\\\\ـ\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�\\\\\\\\\\\\\\\\#\\\\\\\\\\\\\\\\،\\\\\\\\\\\\\\\\☭,\\\\\\\\\\\\\\\\؟]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_as(batch):
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\”\\়\\।]'
batch["sentence"] = re.sub('’ ', ' ', batch["sentence"])
batch["sentence"] = re.sub(' ‘', ' ', batch["sentence"])
batch["sentence"] = re.sub('’|‘', '\'', batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_br(batch):
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
batch["sentence"] = re.sub("ʼ", "'", batch["sentence"])
batch["sentence"] = re.sub("’", "'", batch["sentence"])
batch["sentence"] = re.sub('‘', "'", batch["sentence"])
return batch
def fun_ca(batch):
chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_cnh(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\/]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_cs(batch):
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", '«', '»', '—', '…', '(', ')', '*',
'”', '“']
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().strip()
batch["sentence"] = re.sub(re.compile('[äá]'), 'a', batch['sentence'])
batch["sentence"] = re.sub(re.compile('[öó]'), 'o', batch['sentence'])
batch["sentence"] = re.sub(re.compile('[èé]'), 'e', batch['sentence'])
batch["sentence"] = re.sub(re.compile("[ïí]"), 'i', batch['sentence'])
batch["sentence"] = re.sub(re.compile("[üů]"), 'u', batch['sentence'])
batch['sentence'] = re.sub(' ', ' ', batch['sentence'])
return batch
def fun_cv(batch):
sent = batch["sentence"].lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_cy(batch):
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\u2013\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\u2014\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_de(batch):
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_dv(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\،\.\؟\!\'\"\–\’]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_el(batch):
chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_en(batch):
sent = batch["sentence"].lower()
# normalize apostrophes
sent = sent.replace("’", "'")
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() or ch == "'" else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_eo(batch):
chars_to_ignore_regex = """[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�\\\\\\\\„\\\\\\\\«\\\\\\\\(\\\\\\\\»\\\\\\\\)\\\\\\\\’\\\\\\\\']"""
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace('—', ' ').replace('–', ' ')
return batch
def fun_es(batch):
# remove_special_characters
chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]'
chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)
batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
# replace_diacritics
sentence = batch["sentence"]
sentence = re.sub('ì', 'í', sentence)
sentence = re.sub('ù', 'ú', sentence)
sentence = re.sub('ò', 'ó', sentence)
sentence = re.sub('à', 'á', sentence)
batch["sentence"] = sentence
# replace_additional
sentence = batch["sentence"]
sentence = re.sub('ã', 'a', sentence) # Portuguese, as in São Paulo
sentence = re.sub('ō', 'o', sentence) # Japanese
sentence = re.sub('ê', 'e', sentence) # Português
batch["sentence"] = sentence
return batch
def fun_et(batch):
sent = batch["sentence"].lower()
# normalize apostrophes
sent = sent.replace("’", "'")
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() or ch == "'" else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_eu(batch):
chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]'
chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)
batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
return batch
def fun_fa(batch):
import hazm
_normalizer = hazm.Normalizer()
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "؟", "?", "«", "»", "،", "(", ")", "؛", "'ٔ", "٬", 'ٔ', ",", "?",
".", "!", "-", ";", ":", '"', "“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„',
'ā', 'š',
# "ء",
]
chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
chars_to_mapping = {
'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی',
'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی",
"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع",
"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", 'ﺍ': "ا", 'ة': "ه",
'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
# "ها": " ها", "ئ": "ی",
"a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ",
"g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ",
"m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ",
"s": " اس ", "t": " تی ", "u": " یو ", "v": " وی ", "w": " دبلیو ", "x": " اکس ",
"y": " وای ", "z": " زد ",
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = _normalizer.normalize(text)
# multiple_replace
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
text = re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
# remove_special_characters
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
text = re.sub(" +", " ", text)
text = text.strip() + " "
batch["sentence"] = text
return batch
def fun_fi(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\...\…\–\é]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_fr(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_fy_NL(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\'\“\%\‘\”]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_ga_IE(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_hi(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\।]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"])
return batch
def fun_hsb(batch):
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�\\\\\\\\\\\\\\\\–\\\\\\\\\\\\\\\\—\\\\\\\\\\\\\\\\¬\\\\\\\\\\\\\\\\⅛]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_hu(batch):
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
def fun_ia(batch):
chars_to_ignore_regex = '[\.\,\!\?\-\"\:\;\'\“\”]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_id(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_it(batch):
chars_to_remove = [",", "?", ".", "!", "-", ";", ":", '""', "%", '"', "�", 'ʿ', '“', '”', '(', '=', '`', '_', '+',
'«', '<', '>', '~', '…', '«', '»', '–', '\[', '\]', '°', '̇', '´', 'ʾ', '„', '̇', '̇', '̇',
'¡'] # All extra characters
chars_to_remove_regex = f'[{"".join(chars_to_remove)}]'
batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower().replace('‘', "'").replace('ʻ',
"'").replace(
'ʼ', "'").replace('’', "'").replace('ʹ', "''").replace('̇', '')
allowed_characters = [
" ",
"'",
'a',
'b',
'c',
'd',
'e',
'f',
'g',
'h',
'i',
'j',
'k',
'l',
'm',
'n',
'o',
'p',
'q',
'r',
's',
't',
'u',
'v',
'w',
'x',
'y',
'z',
'à',
'á',
'è',
'é',
'ì',
'í',
'ò',
'ó',
'ù',
'ú',
]
def remove_accents(input_str):
if input_str in allowed_characters:
return input_str
if input_str == 'ø':
return 'o'
elif input_str == 'ß' or input_str == 'ß':
return 'b'
elif input_str == 'ё':
return 'e'
elif input_str == 'đ':
return 'd'
nfkd_form = unicodedata.normalize('NFKD', input_str)
only_ascii = nfkd_form.encode('ASCII', 'ignore').decode()
if only_ascii is None or only_ascii == '':
return input_str
else:
return only_ascii
def fix_accents(sentence):
new_sentence = ''
for char in sentence:
new_sentence += remove_accents(char)
return new_sentence
batch["sentence"] = fix_accents(batch["sentence"])
return batch
def fun_ja(batch):
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper().strip()
return batch
def fun_ka(batch):
chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_ky(batch):
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", "—", "–", "”"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_lg(batch):
chars_to_ignore_regex = '[\[\],?.!;:%"“”(){}‟ˮʺ″«»/…‽�–]'
batch["sentence"] = re.sub(r'(\w)[‘’´`](\w)', r"\1'\2", batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower().strip()
batch["sentence"] = re.sub(r"(-|' | '| +)", " ", batch["sentence"])
batch["sentence"] = unidecode.unidecode(batch["sentence"]).strip()
return batch
def fun_lt(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_lv(batch):
sent = batch["sentence"].lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_mn(batch):
sent = batch["sentence"].lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_mt(batch):
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
def fun_nl(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\'\“\%\‘\”]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_or(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…\'\_\’\।\|]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_pa_IN(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_pl(batch):
chars_to_ignore_regex = '[\—\…\,\?\.\!\-\;\:\"\“\„\%\‘\”\�\«\»\'\’]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_pt(batch):
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
def fun_rm_sursilv(batch):
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\…\\«\\»\\–]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_rm_vallader(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\„\–\…\«\»]'
batch["sentence"] = re.sub('’ ', ' ', batch["sentence"])
batch["sentence"] = re.sub(' ‘', ' ', batch["sentence"])
batch["sentence"] = re.sub('’|‘', '\'', batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_ro(batch):
sent = batch["sentence"].lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_ru(batch):
sent = batch["sentence"].lower()
# these letters are considered equivalent in written Russian
sent = sent.replace('ё', 'е')
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_sah(batch):
sent = batch["sentence"].lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_sl(batch):
sent = batch["sentence"].lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
batch["sentence"] = sent
return batch
def fun_sv_SE(batch):
chars_to_ignore_regex = '[,?.!\\-;:"“]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_ta(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_th(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_tr(batch):
chars_to_ignore_regex = '[\,\?\.\!\-\;\'\:\"\“\%\‘\”\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_tt(batch):
sent = batch["sentence"].lower()
# 'ё' is equivalent to 'е'
sent = sent.replace('ё', 'е')
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
batch["sentence"] = " ".join(sent.split())
return batch
def fun_uk(batch):
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", '«', '»', '—', '…', '(', ')', '*',
'”', '“']
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
batch["sentence"] = re.sub(re.compile("['`]"), '’', batch['sentence'])
batch["sentence"] = re.sub(re.compile(chars_to_ignore_regex), '', batch["sentence"]).lower().strip()
batch["sentence"] = re.sub(re.compile('i'), 'і', batch['sentence'])
batch["sentence"] = re.sub(re.compile('o'), 'о', batch['sentence'])
batch["sentence"] = re.sub(re.compile('a'), 'а', batch['sentence'])
batch["sentence"] = re.sub(re.compile('ы'), 'и', batch['sentence'])
batch["sentence"] = re.sub(re.compile("–"), '', batch['sentence'])
batch['sentence'] = re.sub(' ', ' ', batch['sentence'])
return batch
def fun_vi(batch):
chars_to_ignore_regex = '[\\\+\@\ǀ\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_zh_CN(batch):
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]"
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_zh_HK(batch):
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]"
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
def fun_zh_TW(batch):
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]"
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
| 38.794326 | 531 | 0.44808 | 2,505 | 21,880 | 3.945709 | 0.135329 | 0.223594 | 0.145994 | 0.165722 | 0.787333 | 0.744132 | 0.716815 | 0.704573 | 0.668151 | 0.664508 | 0.001371 | 0.001249 | 0.194927 | 21,880 | 563 | 532 | 38.863233 | 0.53006 | 0.043921 | 0 | 0.453202 | 0 | 0.009852 | 0.268113 | 0.151223 | 0.007389 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0.012315 | 0 | 0.312808 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 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 | 6 |
26f52cd9483db4c1e6c060730d766e86f8ccd939 | 12,791 | py | Python | tests/unit/resources/storage/test_storage_systems.py | doziya/hpeOneView | ef9bee2a0e1529e93bd6e8d84eff07fb8533049d | [
"MIT"
] | 107 | 2015-02-16T12:40:36.000Z | 2022-03-09T05:27:58.000Z | tests/unit/resources/storage/test_storage_systems.py | doziya/hpeOneView | ef9bee2a0e1529e93bd6e8d84eff07fb8533049d | [
"MIT"
] | 148 | 2015-03-17T16:09:39.000Z | 2020-02-09T16:28:06.000Z | tests/unit/resources/storage/test_storage_systems.py | doziya/hpeOneView | ef9bee2a0e1529e93bd6e8d84eff07fb8533049d | [
"MIT"
] | 80 | 2015-01-03T22:58:53.000Z | 2021-04-16T11:37:03.000Z | # -*- coding: utf-8 -*-
###
# (C) Copyright (2012-2017) Hewlett Packard Enterprise Development LP
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
###
import unittest
import mock
from hpOneView.connection import connection
from hpOneView.resources.storage.storage_systems import StorageSystems
from hpOneView.resources.resource import ResourceClient
class StorageSystemsTest(unittest.TestCase):
def setUp(self):
self.host = '127.0.0.1'
self.connection = connection(self.host)
self._storage_systems = StorageSystems(self.connection)
@mock.patch.object(ResourceClient, 'get_all')
def test_get_all_called_once(self, mock_get_all):
filter = 'name=TestName'
sort = 'name:ascending'
self._storage_systems.get_all(2, 500, filter, sort)
mock_get_all.assert_called_once_with(2, 500, filter=filter, sort=sort)
@mock.patch.object(ResourceClient, 'get_all')
def test_get_all_called_once_with_default(self, mock_get_all):
self._storage_systems.get_all()
mock_get_all.assert_called_once_with(0, -1, filter='', sort='')
@mock.patch.object(ResourceClient, 'get')
def test_get_by_id_called_once(self, mock_get):
storage_systems_id = "TXQ1010306"
self._storage_systems.get(storage_systems_id)
mock_get.assert_called_once_with(storage_systems_id)
@mock.patch.object(ResourceClient, 'get')
def test_get_by_uri_called_once(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
self._storage_systems.get(storage_systems_uri)
mock_get.assert_called_once_with(storage_systems_uri)
@mock.patch.object(ResourceClient, 'get')
def test_get_host_types_called_once(self, mock_get):
storage_systems_host_types_uri = "/rest/storage-systems/host-types"
self._storage_systems.get_host_types()
mock_get.assert_called_once_with(storage_systems_host_types_uri)
@mock.patch.object(ResourceClient, 'get_collection')
def test_get_managed_ports_called_once_with_uri(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
storage_systems_managed_ports_uri = "/rest/storage-systems/TXQ1010306/managedPorts"
self._storage_systems.get_managed_ports(storage_systems_uri)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'get_collection')
def test_get_managed_ports_called_once_with_id(self, mock_get):
storage_systems_id = "TXQ1010306"
storage_systems_managed_ports_uri = "/rest/storage-systems/TXQ1010306/managedPorts"
self._storage_systems.get_managed_ports(storage_systems_id)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'get_collection')
def test_get_managed_ports_called_once_with_uri_and_port_id(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
port_id = "C862833E-907C-4124-8841-BDC75444CF76"
storage_systems_managed_ports_uri = \
"/rest/storage-systems/TXQ1010306/managedPorts/C862833E-907C-4124-8841-BDC75444CF76"
self._storage_systems.get_managed_ports(storage_systems_uri, port_id)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'get_collection')
def test_get_managed_ports_called_once_with_id_and_port_id(self, mock_get):
storage_systems_id = "TXQ1010306"
port_id = "C862833E-907C-4124-8841-BDC75444CF76"
storage_systems_managed_ports_uri = \
"/rest/storage-systems/TXQ1010306/managedPorts/C862833E-907C-4124-8841-BDC75444CF76"
self._storage_systems.get_managed_ports(storage_systems_id, port_id)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'get_collection')
def test_get_managed_ports_called_once_with_uri_and_port_uri(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
port_uri = \
"/rest/storage-systems/TXQ1010306/managedPorts/C862833E-907C-4124-8841-BDC75444CF76"
storage_systems_managed_ports_uri = \
"/rest/storage-systems/TXQ1010306/managedPorts/C862833E-907C-4124-8841-BDC75444CF76"
self._storage_systems.get_managed_ports(storage_systems_uri, port_uri)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'get_collection')
def test_get_managed_ports_called_once_with_id_and_port_uri(self, mock_get):
storage_systems_id = "TXQ1010306"
port_uri = \
"/rest/storage-systems/TXQ1010306/managedPorts/C862833E-907C-4124-8841-BDC75444CF76"
storage_systems_managed_ports_uri = \
"/rest/storage-systems/TXQ1010306/managedPorts/C862833E-907C-4124-8841-BDC75444CF76"
self._storage_systems.get_managed_ports(storage_systems_id, port_uri)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'create')
def test_add_called_once_with_defaults(self, mock_create):
storage_system = {
"ip_hostname": "example.com",
"username": "username",
"password": "password"
}
self._storage_systems.add(storage_system)
mock_create.assert_called_once_with(storage_system, timeout=-1)
@mock.patch.object(ResourceClient, 'create')
def test_add_called_once(self, mock_create):
storage_system = {
"ip_hostname": "example.com",
"username": "username",
"password": "password"
}
self._storage_systems.add(storage_system, 70)
mock_create.assert_called_once_with(storage_system, timeout=70)
@mock.patch.object(ResourceClient, 'get')
def test_get_storage_pools_called_once_with_uri(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
storage_systems_managed_ports_uri = "/rest/storage-systems/TXQ1010306/storage-pools"
self._storage_systems.get_storage_pools(storage_systems_uri)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'get')
def test_get_storage_pools_called_once_with_id(self, mock_get):
storage_systems_id = "TXQ1010306"
storage_systems_managed_ports_uri = "/rest/storage-systems/TXQ1010306/storage-pools"
self._storage_systems.get_storage_pools(storage_systems_id)
mock_get.assert_called_once_with(storage_systems_managed_ports_uri)
@mock.patch.object(ResourceClient, 'update')
def test_update_called_once_with_defaults(self, update):
storage_system = {
"type": "StorageSystemV3",
"credentials": {
"ip_hostname": "example.com",
"username": "username"
},
"name": "StoreServ1",
}
self._storage_systems.update(storage_system)
update.assert_called_once_with(storage_system, timeout=-1)
@mock.patch.object(ResourceClient, 'update')
def test_update_called_once(self, mock_update):
storage_system = {
"type": "StorageSystemV3",
"credentials": {
"ip_hostname": "example.com",
"username": "username"
},
"name": "StoreServ1",
}
self._storage_systems.update(storage_system, 70)
mock_update.assert_called_once_with(storage_system, timeout=70)
@mock.patch.object(ResourceClient, 'delete')
def test_remove_called_once(self, mock_delete):
id = 'ad28cf21-8b15-4f92-bdcf-51cb2042db32'
if_match_header = {'If-Match': '*'}
self._storage_systems.remove(id, force=True, timeout=50)
mock_delete.assert_called_once_with(id, force=True, timeout=50, custom_headers=if_match_header)
@mock.patch.object(ResourceClient, 'delete')
def test_remove_called_once_with_defaults(self, mock_delete):
id = 'ad28cf21-8b15-4f92-bdcf-51cb2042db32'
if_match_header = {'If-Match': '*'}
self._storage_systems.remove(id)
mock_delete.assert_called_once_with(id, force=False, timeout=-1, custom_headers=if_match_header)
@mock.patch.object(ResourceClient, 'get_by')
def test_get_by_called_once(self, mock_get_by):
self._storage_systems.get_by("name", "test name")
mock_get_by.assert_called_once_with("name", "test name")
@mock.patch.object(ResourceClient, 'get_by_name')
def test_get_by_name_called_once(self, mock_get_by):
self._storage_systems.get_by_name("test name")
mock_get_by.assert_called_once_with(name="test name")
@mock.patch.object(ResourceClient, 'get_all')
def test_get_by_ip_hostname_find_value(self, get_all):
get_all.return_value = [
{"credentials": {
"ip_hostname": "10.0.0.0",
"username": "username"}},
{"credentials": {
"ip_hostname": "20.0.0.0",
"username": "username"}},
]
result = self._storage_systems.get_by_ip_hostname("20.0.0.0")
get_all.assert_called_once()
self.assertEqual(
{"credentials": {
"ip_hostname": "20.0.0.0",
"username": "username"}}, result)
@mock.patch.object(ResourceClient, 'get_all')
def test_get_by_ip_hostname_value_not_found(self, get_all):
get_all.return_value = [
{"credentials": {
"ip_hostname": "10.0.0.0",
"username": "username"}},
{"credentials": {
"ip_hostname": "20.0.0.0",
"username": "username"}},
]
result = self._storage_systems.get_by_ip_hostname("30.0.0.0")
get_all.assert_called_once()
self.assertIsNone(result)
@mock.patch.object(ResourceClient, 'get_all')
def test_get_by_hostname(self, get_all):
get_all.return_value = [
{"hostname": "10.0.0.0",
"username": "username"},
{"hostname": "20.0.0.0",
"username": "username"}
]
result = self._storage_systems.get_by_hostname("20.0.0.0")
get_all.assert_called_once()
self.assertEqual(
{"hostname": "20.0.0.0",
"username": "username"}, result)
@mock.patch.object(ResourceClient, 'get')
def test_get_reachable_ports_called_once(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
reachable_ports_uri = "/rest/storage-systems/TXQ1010306/reachable-ports?start=0&count=-1"
self._storage_systems.get_reachable_ports(storage_systems_uri)
mock_get.assert_called_once_with(reachable_ports_uri)
@mock.patch.object(ResourceClient, 'get')
def test_get_reachable_ports_called_once_with_networks(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
networks = ['rest/net1', 'rest/net2']
reachable_ports_uri = "/rest/storage-systems/TXQ1010306/reachable-ports" \
"?networks='rest/net1,rest/net2'&start=0&count=-1"
self._storage_systems.get_reachable_ports(storage_systems_uri, networks=networks)
mock_get.assert_called_once_with(reachable_ports_uri)
@mock.patch.object(ResourceClient, 'get')
def test_get_templates_called_once(self, mock_get):
storage_systems_uri = "/rest/storage-systems/TXQ1010306"
templates_uri = "/rest/storage-systems/TXQ1010306/templates?start=0&count=-1"
self._storage_systems.get_templates(storage_systems_uri)
mock_get.assert_called_once_with(templates_uri)
| 45.519573 | 104 | 0.706356 | 1,607 | 12,791 | 5.257623 | 0.126945 | 0.160729 | 0.061309 | 0.092674 | 0.816428 | 0.795715 | 0.7853 | 0.756894 | 0.730737 | 0.680436 | 0 | 0.050198 | 0.191697 | 12,791 | 280 | 105 | 45.682143 | 0.766999 | 0.08678 | 0 | 0.525346 | 0 | 0 | 0.198215 | 0.113781 | 0 | 0 | 0 | 0 | 0.138249 | 1 | 0.129032 | false | 0.009217 | 0.023041 | 0 | 0.156682 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f83043b5af29c42bcb48308f8dfca908ea4e0d23 | 63 | py | Python | nano_profiler/__init__.py | EvgeniyMakhmudov/nano_profiler | 71f137594855881051d8d60dc2cccf3459e30f43 | [
"MIT"
] | null | null | null | nano_profiler/__init__.py | EvgeniyMakhmudov/nano_profiler | 71f137594855881051d8d60dc2cccf3459e30f43 | [
"MIT"
] | null | null | null | nano_profiler/__init__.py | EvgeniyMakhmudov/nano_profiler | 71f137594855881051d8d60dc2cccf3459e30f43 | [
"MIT"
] | null | null | null | from .nano_profiler import NanoProfiler, nano_profiler # noqa
| 31.5 | 62 | 0.825397 | 8 | 63 | 6.25 | 0.75 | 0.48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126984 | 63 | 1 | 63 | 63 | 0.909091 | 0.063492 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f84e57ffae2e04cb69875c768458dfabee88c2b2 | 19 | py | Python | src/pe/pe/__init__.py | zbelateche/ee272_cgra | 4cf2e3cf4a4bdf585d87a9209a5bf252666bc6a2 | [
"BSD-3-Clause"
] | 1 | 2020-07-23T02:57:12.000Z | 2020-07-23T02:57:12.000Z | src/pe/pe/__init__.py | zbelateche/ee272_cgra | 4cf2e3cf4a4bdf585d87a9209a5bf252666bc6a2 | [
"BSD-3-Clause"
] | null | null | null | src/pe/pe/__init__.py | zbelateche/ee272_cgra | 4cf2e3cf4a4bdf585d87a9209a5bf252666bc6a2 | [
"BSD-3-Clause"
] | 1 | 2021-04-27T23:13:43.000Z | 2021-04-27T23:13:43.000Z | from .isa import *
| 9.5 | 18 | 0.684211 | 3 | 19 | 4.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.210526 | 19 | 1 | 19 | 19 | 0.866667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f85b194ce3f737451d569bd3113b8de3e489dea3 | 182 | py | Python | pysindy/feature_library/__init__.py | billtubbs/pysindy | f9ac15b5d073c71bb210b77ff6a5579beb8ed94b | [
"MIT"
] | 7 | 2020-04-02T00:19:29.000Z | 2021-11-02T07:22:28.000Z | pysindy/feature_library/__init__.py | billtubbs/pysindy | f9ac15b5d073c71bb210b77ff6a5579beb8ed94b | [
"MIT"
] | null | null | null | pysindy/feature_library/__init__.py | billtubbs/pysindy | f9ac15b5d073c71bb210b77ff6a5579beb8ed94b | [
"MIT"
] | null | null | null | from .custom_library import CustomLibrary
from .fourier_library import FourierLibrary
from .identity_library import IdentityLibrary
from .polynomial_library import PolynomialLibrary
| 36.4 | 49 | 0.89011 | 20 | 182 | 7.9 | 0.55 | 0.329114 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.087912 | 182 | 4 | 50 | 45.5 | 0.951807 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f87ae7baed56c9c8640dbf6e8b0e65192de42774 | 6,407 | py | Python | loldib/getratings/models/NA/na_garen/na_garen_mid.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_garen/na_garen_mid.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_garen/na_garen_mid.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | from getratings.models.ratings import Ratings
class NA_Garen_Mid_Aatrox(Ratings):
pass
class NA_Garen_Mid_Ahri(Ratings):
pass
class NA_Garen_Mid_Akali(Ratings):
pass
class NA_Garen_Mid_Alistar(Ratings):
pass
class NA_Garen_Mid_Amumu(Ratings):
pass
class NA_Garen_Mid_Anivia(Ratings):
pass
class NA_Garen_Mid_Annie(Ratings):
pass
class NA_Garen_Mid_Ashe(Ratings):
pass
class NA_Garen_Mid_AurelionSol(Ratings):
pass
class NA_Garen_Mid_Azir(Ratings):
pass
class NA_Garen_Mid_Bard(Ratings):
pass
class NA_Garen_Mid_Blitzcrank(Ratings):
pass
class NA_Garen_Mid_Brand(Ratings):
pass
class NA_Garen_Mid_Braum(Ratings):
pass
class NA_Garen_Mid_Caitlyn(Ratings):
pass
class NA_Garen_Mid_Camille(Ratings):
pass
class NA_Garen_Mid_Cassiopeia(Ratings):
pass
class NA_Garen_Mid_Chogath(Ratings):
pass
class NA_Garen_Mid_Corki(Ratings):
pass
class NA_Garen_Mid_Darius(Ratings):
pass
class NA_Garen_Mid_Diana(Ratings):
pass
class NA_Garen_Mid_Draven(Ratings):
pass
class NA_Garen_Mid_DrMundo(Ratings):
pass
class NA_Garen_Mid_Ekko(Ratings):
pass
class NA_Garen_Mid_Elise(Ratings):
pass
class NA_Garen_Mid_Evelynn(Ratings):
pass
class NA_Garen_Mid_Ezreal(Ratings):
pass
class NA_Garen_Mid_Fiddlesticks(Ratings):
pass
class NA_Garen_Mid_Fiora(Ratings):
pass
class NA_Garen_Mid_Fizz(Ratings):
pass
class NA_Garen_Mid_Galio(Ratings):
pass
class NA_Garen_Mid_Gangplank(Ratings):
pass
class NA_Garen_Mid_Garen(Ratings):
pass
class NA_Garen_Mid_Gnar(Ratings):
pass
class NA_Garen_Mid_Gragas(Ratings):
pass
class NA_Garen_Mid_Graves(Ratings):
pass
class NA_Garen_Mid_Hecarim(Ratings):
pass
class NA_Garen_Mid_Heimerdinger(Ratings):
pass
class NA_Garen_Mid_Illaoi(Ratings):
pass
class NA_Garen_Mid_Irelia(Ratings):
pass
class NA_Garen_Mid_Ivern(Ratings):
pass
class NA_Garen_Mid_Janna(Ratings):
pass
class NA_Garen_Mid_JarvanIV(Ratings):
pass
class NA_Garen_Mid_Jax(Ratings):
pass
class NA_Garen_Mid_Jayce(Ratings):
pass
class NA_Garen_Mid_Jhin(Ratings):
pass
class NA_Garen_Mid_Jinx(Ratings):
pass
class NA_Garen_Mid_Kalista(Ratings):
pass
class NA_Garen_Mid_Karma(Ratings):
pass
class NA_Garen_Mid_Karthus(Ratings):
pass
class NA_Garen_Mid_Kassadin(Ratings):
pass
class NA_Garen_Mid_Katarina(Ratings):
pass
class NA_Garen_Mid_Kayle(Ratings):
pass
class NA_Garen_Mid_Kayn(Ratings):
pass
class NA_Garen_Mid_Kennen(Ratings):
pass
class NA_Garen_Mid_Khazix(Ratings):
pass
class NA_Garen_Mid_Kindred(Ratings):
pass
class NA_Garen_Mid_Kled(Ratings):
pass
class NA_Garen_Mid_KogMaw(Ratings):
pass
class NA_Garen_Mid_Leblanc(Ratings):
pass
class NA_Garen_Mid_LeeSin(Ratings):
pass
class NA_Garen_Mid_Leona(Ratings):
pass
class NA_Garen_Mid_Lissandra(Ratings):
pass
class NA_Garen_Mid_Lucian(Ratings):
pass
class NA_Garen_Mid_Lulu(Ratings):
pass
class NA_Garen_Mid_Lux(Ratings):
pass
class NA_Garen_Mid_Malphite(Ratings):
pass
class NA_Garen_Mid_Malzahar(Ratings):
pass
class NA_Garen_Mid_Maokai(Ratings):
pass
class NA_Garen_Mid_MasterYi(Ratings):
pass
class NA_Garen_Mid_MissFortune(Ratings):
pass
class NA_Garen_Mid_MonkeyKing(Ratings):
pass
class NA_Garen_Mid_Mordekaiser(Ratings):
pass
class NA_Garen_Mid_Morgana(Ratings):
pass
class NA_Garen_Mid_Nami(Ratings):
pass
class NA_Garen_Mid_Nasus(Ratings):
pass
class NA_Garen_Mid_Nautilus(Ratings):
pass
class NA_Garen_Mid_Nidalee(Ratings):
pass
class NA_Garen_Mid_Nocturne(Ratings):
pass
class NA_Garen_Mid_Nunu(Ratings):
pass
class NA_Garen_Mid_Olaf(Ratings):
pass
class NA_Garen_Mid_Orianna(Ratings):
pass
class NA_Garen_Mid_Ornn(Ratings):
pass
class NA_Garen_Mid_Pantheon(Ratings):
pass
class NA_Garen_Mid_Poppy(Ratings):
pass
class NA_Garen_Mid_Quinn(Ratings):
pass
class NA_Garen_Mid_Rakan(Ratings):
pass
class NA_Garen_Mid_Rammus(Ratings):
pass
class NA_Garen_Mid_RekSai(Ratings):
pass
class NA_Garen_Mid_Renekton(Ratings):
pass
class NA_Garen_Mid_Rengar(Ratings):
pass
class NA_Garen_Mid_Riven(Ratings):
pass
class NA_Garen_Mid_Rumble(Ratings):
pass
class NA_Garen_Mid_Ryze(Ratings):
pass
class NA_Garen_Mid_Sejuani(Ratings):
pass
class NA_Garen_Mid_Shaco(Ratings):
pass
class NA_Garen_Mid_Shen(Ratings):
pass
class NA_Garen_Mid_Shyvana(Ratings):
pass
class NA_Garen_Mid_Singed(Ratings):
pass
class NA_Garen_Mid_Sion(Ratings):
pass
class NA_Garen_Mid_Sivir(Ratings):
pass
class NA_Garen_Mid_Skarner(Ratings):
pass
class NA_Garen_Mid_Sona(Ratings):
pass
class NA_Garen_Mid_Soraka(Ratings):
pass
class NA_Garen_Mid_Swain(Ratings):
pass
class NA_Garen_Mid_Syndra(Ratings):
pass
class NA_Garen_Mid_TahmKench(Ratings):
pass
class NA_Garen_Mid_Taliyah(Ratings):
pass
class NA_Garen_Mid_Talon(Ratings):
pass
class NA_Garen_Mid_Taric(Ratings):
pass
class NA_Garen_Mid_Teemo(Ratings):
pass
class NA_Garen_Mid_Thresh(Ratings):
pass
class NA_Garen_Mid_Tristana(Ratings):
pass
class NA_Garen_Mid_Trundle(Ratings):
pass
class NA_Garen_Mid_Tryndamere(Ratings):
pass
class NA_Garen_Mid_TwistedFate(Ratings):
pass
class NA_Garen_Mid_Twitch(Ratings):
pass
class NA_Garen_Mid_Udyr(Ratings):
pass
class NA_Garen_Mid_Urgot(Ratings):
pass
class NA_Garen_Mid_Varus(Ratings):
pass
class NA_Garen_Mid_Vayne(Ratings):
pass
class NA_Garen_Mid_Veigar(Ratings):
pass
class NA_Garen_Mid_Velkoz(Ratings):
pass
class NA_Garen_Mid_Vi(Ratings):
pass
class NA_Garen_Mid_Viktor(Ratings):
pass
class NA_Garen_Mid_Vladimir(Ratings):
pass
class NA_Garen_Mid_Volibear(Ratings):
pass
class NA_Garen_Mid_Warwick(Ratings):
pass
class NA_Garen_Mid_Xayah(Ratings):
pass
class NA_Garen_Mid_Xerath(Ratings):
pass
class NA_Garen_Mid_XinZhao(Ratings):
pass
class NA_Garen_Mid_Yasuo(Ratings):
pass
class NA_Garen_Mid_Yorick(Ratings):
pass
class NA_Garen_Mid_Zac(Ratings):
pass
class NA_Garen_Mid_Zed(Ratings):
pass
class NA_Garen_Mid_Ziggs(Ratings):
pass
class NA_Garen_Mid_Zilean(Ratings):
pass
class NA_Garen_Mid_Zyra(Ratings):
pass
| 15.364508 | 46 | 0.761667 | 972 | 6,407 | 4.59465 | 0.151235 | 0.216301 | 0.370802 | 0.463502 | 0.797582 | 0.797582 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173404 | 6,407 | 416 | 47 | 15.401442 | 0.843278 | 0 | 0 | 0.498195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.498195 | 0.00361 | 0 | 0.501805 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
f88e5ea2e9d97b9fbf42779b95d2b983e9fb189d | 230 | py | Python | demo/q0w_demo_analyzer/core/__init__.py | YourNorth/rezak-summarizator | 3ab2f4bf1044ea9654b4084a39030987e4b8bfe8 | [
"MIT"
] | 3 | 2020-03-28T16:48:10.000Z | 2020-12-01T17:18:55.000Z | demo/q0w_demo_analyzer/core/__init__.py | YourNorth/rezak-summarizator | 3ab2f4bf1044ea9654b4084a39030987e4b8bfe8 | [
"MIT"
] | 31 | 2020-03-20T17:53:08.000Z | 2021-03-10T11:48:11.000Z | demo/q0w_demo_analyzer/core/__init__.py | YourNorth/rezak-summarizator | 3ab2f4bf1044ea9654b4084a39030987e4b8bfe8 | [
"MIT"
] | 1 | 2020-03-20T05:01:16.000Z | 2020-03-20T05:01:16.000Z | from .selector import create_selection, create_total_selection
from .summarization import impls
# FIXME: modify structure?
#from ._tokenizer import tokenize_sentences
from nltk.tokenize import sent_tokenize as tokenize_sentences | 46 | 62 | 0.856522 | 29 | 230 | 6.551724 | 0.586207 | 0.178947 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104348 | 230 | 5 | 63 | 46 | 0.92233 | 0.286957 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 | 1 | 0 | true | 0 | 1 | 0 | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f8b2807afda313f1dfe986eae9d00b9c00649367 | 49 | py | Python | channel/__init__.py | thomaswhiteway/channel | 3e96910d9ee058ae6b02620a9cf8c2466d07c78d | [
"Apache-2.0"
] | null | null | null | channel/__init__.py | thomaswhiteway/channel | 3e96910d9ee058ae6b02620a9cf8c2466d07c78d | [
"Apache-2.0"
] | null | null | null | channel/__init__.py | thomaswhiteway/channel | 3e96910d9ee058ae6b02620a9cf8c2466d07c78d | [
"Apache-2.0"
] | null | null | null | from .channel import Channel, Full, Empty, Closed | 49 | 49 | 0.795918 | 7 | 49 | 5.571429 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122449 | 49 | 1 | 49 | 49 | 0.906977 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f8c79b0b72ac3eb8acffc689a9570f28bc82c43d | 211 | py | Python | Dmzj_backup/middlewares.py | eesxy/Dmzj_backup | a7e67d9df16099a495d16acc5366c5e9bc284713 | [
"MIT"
] | null | null | null | Dmzj_backup/middlewares.py | eesxy/Dmzj_backup | a7e67d9df16099a495d16acc5366c5e9bc284713 | [
"MIT"
] | null | null | null | Dmzj_backup/middlewares.py | eesxy/Dmzj_backup | a7e67d9df16099a495d16acc5366c5e9bc284713 | [
"MIT"
] | 1 | 2021-03-11T12:02:11.000Z | 2021-03-11T12:02:11.000Z | class DmzjBackupProxyMiddleware:
def process_request(self, request, spider):
if spider.mysettings.MY_PROXY_ENABLED:
request.meta['proxy'] = spider.mysettings.MY_PROXY
return None
| 35.166667 | 62 | 0.706161 | 23 | 211 | 6.304348 | 0.652174 | 0.22069 | 0.248276 | 0.317241 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.21327 | 211 | 5 | 63 | 42.2 | 0.873494 | 0 | 0 | 0 | 0 | 0 | 0.023697 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0 | 0 | 0.6 | 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 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
6ef73b96d18c53c7ec89ff6f151992903760f57c | 131 | py | Python | adapters/pox/ext/debugger/component_launcher/__init__.py | ARCCN/elt | 3bf4e6cc0c7abbe442d6513ed294e956143c3bea | [
"BSD-3-Clause"
] | 1 | 2016-07-14T14:45:56.000Z | 2016-07-14T14:45:56.000Z | adapters/pox/ext/debugger/component_launcher/__init__.py | ARCCN/elt | 3bf4e6cc0c7abbe442d6513ed294e956143c3bea | [
"BSD-3-Clause"
] | null | null | null | adapters/pox/ext/debugger/component_launcher/__init__.py | ARCCN/elt | 3bf4e6cc0c7abbe442d6513ed294e956143c3bea | [
"BSD-3-Clause"
] | null | null | null | from .component_launcher import ComponentLauncher
from pox.core import core
def launch():
core.registerNew(ComponentLauncher)
| 21.833333 | 49 | 0.816794 | 15 | 131 | 7.066667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122137 | 131 | 5 | 50 | 26.2 | 0.921739 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 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 | 0 | 1 | 0 | 0 | 6 |
3e447652ea4704287fa6dbfdd900f8a4150ffd44 | 48 | py | Python | new/models/__init__.py | byeongjokim/LateTemporalModeling3DCNN_for_sign | e3a802fcf91dc3930aea782464ee34d9b747d3ab | [
"MIT"
] | null | null | null | new/models/__init__.py | byeongjokim/LateTemporalModeling3DCNN_for_sign | e3a802fcf91dc3930aea782464ee34d9b747d3ab | [
"MIT"
] | null | null | null | new/models/__init__.py | byeongjokim/LateTemporalModeling3DCNN_for_sign | e3a802fcf91dc3930aea782464ee34d9b747d3ab | [
"MIT"
] | null | null | null | from .rgb_I3D import *
from .rgb_depth import *
| 16 | 24 | 0.75 | 8 | 48 | 4.25 | 0.625 | 0.411765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025 | 0.166667 | 48 | 2 | 25 | 24 | 0.825 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
3e4843ed11898dbf7326118fbed2db2875127cfe | 27 | py | Python | pi/stream_processor/producer/__init__.py | DebasishMaji/PI | e293982cae8f8755d28d7b3de22966dc74759b90 | [
"Apache-2.0"
] | null | null | null | pi/stream_processor/producer/__init__.py | DebasishMaji/PI | e293982cae8f8755d28d7b3de22966dc74759b90 | [
"Apache-2.0"
] | null | null | null | pi/stream_processor/producer/__init__.py | DebasishMaji/PI | e293982cae8f8755d28d7b3de22966dc74759b90 | [
"Apache-2.0"
] | null | null | null | from .log_producer import * | 27 | 27 | 0.814815 | 4 | 27 | 5.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 27 | 1 | 27 | 27 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3e84b9f1982d112abf815b7a65804b8f1abe97d7 | 35 | py | Python | tkcomponents/extensions/__init__.py | immijimmi/tkcomponents | c9f5d08ddf6d78a80927fa89727e71eb3e09715f | [
"MIT"
] | null | null | null | tkcomponents/extensions/__init__.py | immijimmi/tkcomponents | c9f5d08ddf6d78a80927fa89727e71eb3e09715f | [
"MIT"
] | null | null | null | tkcomponents/extensions/__init__.py | immijimmi/tkcomponents | c9f5d08ddf6d78a80927fa89727e71eb3e09715f | [
"MIT"
] | null | null | null | from .gridhelper import GridHelper
| 17.5 | 34 | 0.857143 | 4 | 35 | 7.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 1 | 35 | 35 | 0.967742 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e4dcdde3d9180559b21276ef1ab5073e2aadc1fa | 29 | py | Python | bangla_stemmer/__init__.py | smafjal/Bangla-stemmer | ae4a31841bfe4a13e7f14f5a4800961a9e53d732 | [
"Apache-2.0"
] | null | null | null | bangla_stemmer/__init__.py | smafjal/Bangla-stemmer | ae4a31841bfe4a13e7f14f5a4800961a9e53d732 | [
"Apache-2.0"
] | null | null | null | bangla_stemmer/__init__.py | smafjal/Bangla-stemmer | ae4a31841bfe4a13e7f14f5a4800961a9e53d732 | [
"Apache-2.0"
] | null | null | null | from .stemmer import stemmer
| 14.5 | 28 | 0.827586 | 4 | 29 | 6 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 1 | 29 | 29 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9000fd59974296fbbb9b1f583ffe5b5397ec289d | 134 | py | Python | imix/engine/hooks/momentum_scheduler.py | linxi1158/iMIX | af87a17275f02c94932bb2e29f132a84db812002 | [
"Apache-2.0"
] | 23 | 2021-06-26T08:45:19.000Z | 2022-03-02T02:13:33.000Z | imix/engine/hooks/momentum_scheduler.py | XChuanLee/iMIX | 99898de97ef8b45462ca1d6bf2542e423a73d769 | [
"Apache-2.0"
] | null | null | null | imix/engine/hooks/momentum_scheduler.py | XChuanLee/iMIX | 99898de97ef8b45462ca1d6bf2542e423a73d769 | [
"Apache-2.0"
] | 9 | 2021-06-10T02:36:20.000Z | 2021-11-09T02:18:16.000Z | from .base_hook import HookBase
from .builder import HOOKS
@HOOKS.register_module()
class MomentumSchedulerHook(HookBase):
pass
| 16.75 | 38 | 0.798507 | 16 | 134 | 6.5625 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.134328 | 134 | 7 | 39 | 19.142857 | 0.905172 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.4 | 0 | 0.6 | 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 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
900aa1c1f18059b3b04bc867d144350b61f4ba79 | 44,830 | py | Python | polygon/base_client.py | webclinic017/polygon | bddbb6ab7a250d3a40f88c8973484c399b72d7d5 | [
"MIT"
] | null | null | null | polygon/base_client.py | webclinic017/polygon | bddbb6ab7a250d3a40f88c8973484c399b72d7d5 | [
"MIT"
] | null | null | null | polygon/base_client.py | webclinic017/polygon | bddbb6ab7a250d3a40f88c8973484c399b72d7d5 | [
"MIT"
] | null | null | null | # ========================================================= #
import requests
import httpx
from typing import Union
from requests.models import Response
from httpx import Response as HttpxResponse
from enum import Enum
import os
import datetime
# ========================================================= #
TIME_FRAME_CHUNKS = {'minute': datetime.timedelta(days=45),
'min': datetime.timedelta(days=45),
'hour': datetime.timedelta(days=60),
'day': datetime.timedelta(days=3500),
'week': datetime.timedelta(days=3500),
'month': datetime.timedelta(days=3500),
'quarter': datetime.timedelta(days=3500),
'year': datetime.timedelta(days=3500)
}
# ========================================================= #
# Just a very basic method to house methods which are common to both sync and async clients
class Base:
def split_date_range(self, start, end, timespan: str, high_volatility: bool = False, reverse: bool = True) -> list:
"""
Internal helper function to split a BIGGER date range into smaller chunks to be able to easily fetch
aggregate bars data. The chunks duration is supposed to be different for time spans.
For 1 minute bars, multiplier would be 1, timespan would be 'minute'
:param start: start of the time frame. accepts date, datetime objects or a string ``YYYY-MM-DD``
:param end: end of the time frame. accepts date, datetime objects or a string ``YYYY-MM-DD``
:param timespan: The frequency type. like day or minute. see :class:`polygon.enums.Timespan` for choices
:param high_volatility: Specifies whether the symbol/security in question is highly volatile. If set to True,
the lib will use a smaller chunk of time to ensure we don't miss any data due to 50k
candle limit. Defaults to False.
:param reverse: If True (the default), will reverse the order of chunks (chronologically)
:return: a list of tuples. each tuple is in format ``(start, end)`` and represents one chunk of time frame
"""
# The Time Travel begins
if timespan == 'min':
timespan = 'minute'
try:
delta, temp = TIME_FRAME_CHUNKS[timespan], (start, end)
except KeyError:
raise ValueError('Invalid timespan. Use a correct enum or a correct value. See '
'https://polygon.readthedocs.io/en/latest/Library-Interface-Documentation.html#polygon'
'.enums.Timespan')
if high_volatility:
if timespan in ['minute', 'hour']:
delta = datetime.timedelta(days=delta.days - 20)
else:
delta = datetime.timedelta(days=delta.days - 1500)
start, end = self.normalize_datetime(start), self.normalize_datetime(end, _dir='end')
start, end = self.normalize_datetime(start, 'datetime'), self.normalize_datetime(end, 'datetime')
if (end - start).days < delta.days:
return [(self.normalize_datetime(temp[0], 'nts'), self.normalize_datetime(temp[1], 'nts'))]
final_time_chunks, timespan, current = [], self._change_enum(timespan), start
while 1:
probable_next_date = current + delta
if probable_next_date >= end:
if current == probable_next_date:
break
final_time_chunks.append((current, end))
break
final_time_chunks.append((current, probable_next_date))
current = probable_next_date
if reverse:
final_time_chunks.reverse()
return final_time_chunks
# def snap_and_stretch(self, start, end, multiplier: int = 1, timespan: str = 'day'):
# """
# A method to manage the `snap and stretch behavior logic <https://polygon.io/blog/aggs-api-updates/>`__ on
# the aggregates' endpoints
#
# :param start: input start time of the aggregate window
# :param end: input end time of the aggregate window
# :param multiplier: size of the aggregate window
# :param timespan: bars type to return in the aggregate window
# :return: Corrected tuple (start, end) with snap and stretch logic applied.
# """
#
# # Snap First # TODO: will pick up some other time
@staticmethod
def normalize_datetime(dt, output_type: str = 'ts', _dir: str = 'start', _format: str = '%Y-%m-%d',
unit: str = 'ms'):
"""
a core method to perform some specific datetime operations before/after interaction with the API
:param dt: The datetime input
:param output_type: what to return. defaults to timestamp (utc if unaware obj)
:param _dir: whether the input is meant for start of a range or end of it
:param _format: The format string to use IFF expected to return as string
:param unit: the timestamp units to work with. defaults to ms (milliseconds)
:return: The output timestamp or formatted string
"""
if unit == 'ms':
factor = 1000
elif unit == 'ns':
factor = 1000000000
else:
factor = 1
if isinstance(dt, datetime.datetime):
if output_type == 'date':
return dt.date()
dt = dt.replace(tzinfo=datetime.timezone.utc) if (dt.tzinfo is None) or (dt.tzinfo.utcoffset(dt) is None) \
else dt
if output_type == 'datetime':
return dt
elif output_type in ['ts', 'nts']:
return int(dt.timestamp() * factor)
# elif output_type == 'str':
return dt.strftime(_format)
if isinstance(dt, str):
dt = datetime.datetime.strptime(dt, _format).date()
if isinstance(dt, datetime.date):
if output_type == 'ts' and _dir == 'start':
return int(datetime.datetime(dt.year, dt.month, dt.day).replace(
tzinfo=datetime.timezone.utc).timestamp() * factor)
elif output_type == 'ts' and _dir == 'end':
return int(datetime.datetime(dt.year, dt.month, dt.day, 23, 59).replace(
tzinfo=datetime.timezone.utc).timestamp() * factor)
elif output_type in ['str', 'nts']:
return dt.strftime(_format)
elif output_type == 'datetime':
return datetime.datetime(dt.year, dt.month, dt.day).replace(tzinfo=datetime.timezone.utc)
# elif output_type == 'date':
return dt
elif isinstance(dt, (int, float)):
if output_type in ['ts', 'nts']:
return dt
dt = datetime.datetime.utcfromtimestamp(dt / factor).replace(tzinfo=datetime.timezone.utc)
if output_type == 'str':
return dt.strftime(_format)
elif output_type == 'datetime':
return dt
# elif output_type == 'date':
return dt.date()
@staticmethod
def _change_enum(val: Union[str, Enum, float, int], allowed_type=str):
if isinstance(val, Enum):
try:
return val.value
except AttributeError:
raise ValueError(f'The value supplied: ({val}) does not match the required type: ({allowed_type}). '
f'Please consider using the specified enum in the docs for this function or recheck '
f'the value supplied.')
if isinstance(allowed_type, list):
if type(val) in allowed_type:
return val
raise ValueError(f'The value supplied: ({val}) does not match the required type: ({allowed_type}). '
f'Please consider using the specified enum in the docs for this function or recheck '
f'the value supplied.')
if isinstance(val, allowed_type) or val is None:
return val
# ========================================================= #
class BaseClient(Base):
"""
These docs are not meant for general users. These are library API references. The actual docs will be
available on the index page when they are prepared.
This is the **base client class** for all other REST clients which inherit from this class and implement their own
endpoints on top of it.
"""
def __init__(self, api_key: str, connect_timeout: int = 10, read_timeout: int = 10):
"""
Initiates a Client to be used to access all the endpoints.
:param api_key: Your API Key. Visit your dashboard to get yours.
:param connect_timeout: The connection timeout in seconds. Defaults to 10. basically the number of seconds to
wait for a connection to be established. Raises a ``ConnectTimeout`` if unable to
connect within specified time limit.
:param read_timeout: The read timeout in seconds. Defaults to 10. basically the number of seconds to wait for
date to be received. Raises a ``ReadTimeout`` if unable to connect within the specified
time limit.
"""
self.KEY = api_key
self.BASE = 'https://api.polygon.io'
self.time_out_conf = (connect_timeout, read_timeout)
self.session = requests.session()
self.session.headers.update({'Authorization': f'Bearer {self.KEY}'})
# Context Managers
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.session.close()
def close(self):
"""
Closes the ``requests.Session`` and frees up resources. It is recommended to call this method in your
exit handlers
"""
self.session.close()
# Internal Functions
def _get_response(self, path: str, params: dict = None,
raw_response: bool = True) -> Union[Response, dict]:
"""
Get response on a path. Meant to be used internally but can be used if you know what you're doing
:param path: RESTful path for the endpoint. Available on the docs for the endpoint right above its name.
:param params: Query Parameters to be supplied with the request. These are mapped 1:1 with the endpoint.
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to check the
status code or inspect the headers. Defaults to True which returns the ``Response`` object.
:return: A Response object by default. Make ``raw_response=False`` to get JSON decoded Dictionary
"""
_res = self.session.request('GET', self.BASE + path, params=params, timeout=self.time_out_conf)
if raw_response:
return _res
return _res.json()
def get_page_by_url(self, url: str, raw_response: bool = False) -> Union[Response, dict]:
"""
Get the next page of a response. The URl is returned within ``next_url`` attribute on endpoints which support
pagination (eg the tickers endpoint). If the response doesn't contain this attribute, either all pages were
received or the endpoint doesn't have pagination. Meant for internal use primarily.
:param url: The next URL. As contained in ``next_url`` of the response.
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to say check the
status code or inspect the headers. Defaults to False which returns the json decoded
dictionary.
:return: Either a Dictionary or a Response object depending on value of raw_response. Defaults to Dict.
"""
_res = self.session.request('GET', url)
if raw_response:
return _res
return _res.json()
def get_next_page(self, old_response: Union[Response, dict],
raw_response: bool = False) -> Union[Response, dict, bool]:
"""
Get the next page using the most recent old response. This function simply parses the next_url attribute
from the existing response and uses it to get the next page. Returns False if there is no next page
remaining (which implies that you have reached the end of all pages or the endpoint doesn't support pagination).
:param old_response: The most recent existing response. Can be either ``Response`` Object or Dictionaries
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to say check the
status code or inspect the headers. Defaults to False which returns the json decoded
dictionary.
:return: A JSON decoded Dictionary by default. Make ``raw_response=True`` to get underlying response object
"""
try:
if not isinstance(old_response, (dict, list)):
old_response = old_response.json()
_next_url = old_response['next_url']
return self.get_page_by_url(_next_url, raw_response=raw_response)
except KeyError:
return False
def get_previous_page(self, old_response: Union[Response, dict],
raw_response: bool = False) -> Union[Response, dict, bool]:
"""
Get the previous page using the most recent old response. This function simply parses the previous_url attribute
from the existing response and uses it to get the previous page. Returns False if there is no previous page
remaining (which implies that you have reached the start of all pages or the endpoint doesn't support
pagination).
:param old_response: The most recent existing response. Can be either ``Response`` Object or Dictionaries
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to say check the
status code or inspect the headers. Defaults to False which returns the json decoded
dictionary.
:return: A JSON decoded Dictionary by default. Make ``raw_response=True`` to get underlying response object
"""
try:
if not isinstance(old_response, (dict, list)):
old_response = old_response.json()
_prev_url = old_response['previous_url']
return self.get_page_by_url(_prev_url, raw_response=raw_response)
except KeyError:
return False
def get_all_pages(self, old_response, max_pages: int = None, direction: str = 'next',
raw_responses: bool = False):
"""
A helper function for endpoints which implement pagination using ``next_url`` and ``previous_url`` attributes.
Can be used externally too to get all responses in a list.
:param old_response: The last response you had. In most cases, this would be simply the very first response.
:param max_pages: If you want to limit the number of pages to retrieve. Defaults to None which fetches ALL
available pages
:param direction: The direction to paginate in. Defaults to next which grabs all next_pages. see
:class:`polygon.enums.PaginationDirection` for choices
:param raw_responses: If set to True, the elements in container list, you will get underlying Response object
instead of the json formatted dict/list. Only use if you need to check status codes or
headers. Defaults to False, which makes it return decoded data in list.
:return: A list of responses. By default, responses are actual json decoded dict/list. Depending on value of
``raw_response``
"""
direction, container, _res = self._change_enum(direction, str), [], old_response
if not max_pages:
max_pages = float('inf')
if direction in ['prev', 'previous']:
fn = self.get_previous_page
else:
fn = self.get_next_page
# Start paginating
while 1:
if len(container) >= max_pages:
break
_res = fn(_res, raw_response=True)
if not _res:
break
if raw_responses:
container.append(_res)
continue
container.append(_res.json())
return container
def _paginate(self, _res, merge_all_pages: bool = True, max_pages: int = None, raw_page_responses: bool = False):
"""
Internal function to call the core pagination methods to build the response object to be parsed by individual
methods.
:param merge_all_pages: whether to merge all the pages into one response. defaults to True
:param max_pages: number of pages to fetch. defaults to all available pages.
:param raw_page_responses: whether to keep raw response objects or decode them. Only considered if
merge_all_pages is set to False. Defaults to False.
:return:
"""
if isinstance(max_pages, int):
max_pages -= 1
# How many pages do you want?? YES!!!
if merge_all_pages: # prepare for a merge
pages = [_res.json()] + self.get_all_pages(_res, max_pages=max_pages)
elif raw_page_responses: # we don't need your help, adventurer (no merge, no decoding)
return [_res] + self.get_all_pages(_res, raw_responses=True, max_pages=max_pages)
else: # okay a little bit of help is fine (no merge, only decoding)
return [_res.json()] + self.get_all_pages(_res, max_pages=max_pages)
# We need your help adventurer (decode and merge)
container = []
try:
for page in pages:
container += page['results']
except KeyError:
return pages
return container
def get_full_range_aggregates(self, fn, symbol: str, time_chunks: list, run_parallel: bool = True,
max_concurrent_workers: int = os.cpu_count() * 5, warnings: bool = True,
adjusted: bool = True, sort='asc', limit: int = 5000,
multiplier: int = 1, timespan='day') -> list:
"""
Internal helper function to fetch aggregate bars for BIGGER time ranges. Should only be used internally.
Users should prefer the relevant aggregate function with additional parameters.
:param fn: The method to call in each chunked timeframe
:param symbol: The ticker symbol to get data for
:param time_chunks: The list of time chunks as returned by method ``split_datetime_range``
:param run_parallel: If true (the default), it will use an internal ``ThreadPool`` to get the responses in
parallel. **Note That** since python has the GIL restrictions, it would mean that if you
have a ThreadPool of your own, only one ThreadPool will be running at a time and the
other pool will wait. set to False to get all responses in sequence (will take time)
:param warnings: Defaults to True which prints warnings. Set to False to disable warnings.
:param max_concurrent_workers: This is only used if run_parallel is set to true. Controls how many worker
threads are spawned in the internal thread pool. Defaults to ``your cpu core
count * 5``
:param adjusted: Whether or not the results are adjusted for splits. By default, results are adjusted. Set this
to false to get results that are NOT adjusted for splits.
:param sort: Sort the results by timestamp. See :class:`polygon.enums.SortOrder` for choices. ``asc`` default.
:param limit: Limits the number of base aggregates queried to create the aggregate results. Max 50000 and
Default 5000.
:param multiplier: The size of the timespan multiplier. Must be a positive whole number.
:param timespan: The size of the time window. See :class:`polygon.enums.Timespan` for choices. defaults to
``day``
:return: A single merged list of ALL candles/bars
"""
if run_parallel and warnings:
print(f'WARNING: Running with threading will spawn an internal ThreadPool to get responses in parallel. '
f'It is fine if you are not running a ThreadPool of your own. But If you are, know that only one '
f'pool will run at a time due to python GIL restriction. Other pool will wait. You can pass '
f'warnings=False to disable this warning OR pass run_parallel=False to disable running internal '
f'thread pool')
if (not run_parallel) and warnings:
print(f'WARNING: Running sequentially can take a lot of time especially if you are pulling minute/hour '
f'aggs on a BIG time frame. If you have more than one symbol to run, it is suggested to run both '
f'of them in their own thread. You can pass warnings=False to disable this warning OR '
f'pass run_parallel=True to run an internal thread pool if you are not running a thread pool of '
f'your own')
# The aggregation begins
dupe_handler, final_results = 0, []
if run_parallel:
from concurrent.futures import ThreadPoolExecutor
sort_order = self._change_enum(sort)
futures = []
with ThreadPoolExecutor(max_workers=max_concurrent_workers) as pool:
for chunk in time_chunks:
chunk = (self.normalize_datetime(chunk[0], 'nts'),
self.normalize_datetime(chunk[1], 'nts', _dir='end'))
futures.append(pool.submit(fn, symbol, chunk[0], chunk[1], adjusted=adjusted, sort='asc',
limit=500000, multiplier=multiplier, timespan=timespan))
for future in reversed(futures):
try:
data = future.result()['results']
except KeyError:
if warnings:
print(f'No data returned. response: {future.result()}')
continue
if len(data) < 1:
if warnings:
print(f'No data returned. response: {future.result()}')
continue
final_results += [candle for candle in data if (candle['t'] > dupe_handler)]
dupe_handler = final_results[-1]['t']
if sort_order in ['desc', 'descending']:
final_results.reverse()
return final_results
# Sequential
current_dt = self.normalize_datetime(time_chunks[0])
end_dt = self.normalize_datetime(time_chunks[1], _dir='end')
first_entry = self.normalize_datetime(time_chunks[0])
try:
delta = TIME_FRAME_CHUNKS[timespan]
except KeyError:
raise ValueError('Invalid timespan. Use a correct enum or a correct value. See '
'https://polygon.readthedocs.io/en/latest/Library-Interface-Documentation.html#polygon'
'.enums.Timespan')
if (self.normalize_datetime(end_dt, 'datetime', 'end') - self.normalize_datetime(
first_entry, 'datetime')).days <= delta.days:
res = fn(symbol, current_dt, end_dt, adjusted=adjusted, sort=sort, limit=500000,
multiplier=multiplier, timespan=timespan, full_range=False)
try:
return res['results']
except KeyError:
if warnings:
print(f'no data returned for {symbol} for range {first_entry} to {end_dt}. Response: '
f'{res}')
return []
dupe_handler = current_dt
while 1:
if current_dt >= end_dt:
break
res = fn(symbol, current_dt, end_dt, adjusted=adjusted, sort=sort, limit=500000, multiplier=multiplier,
timespan=timespan, full_range=False)
try:
data = res['results']
except KeyError:
if warnings:
print(f'No data found for {symbol} between {current_dt} and {end_dt} with '
f'response: {res}. Terminating loop...')
break
if len(data) < 1:
if warnings:
print(f'No data found for {symbol} between {current_dt} and {end_dt} with '
f'response: {res}. Terminating loop...')
break
temp_len = len(final_results)
final_results += [candle for candle in data if (candle['t'] > dupe_handler)]
if len(final_results) == temp_len:
if data[-1]['t'] <= dupe_handler:
break
current_dt = final_results[-1]['t']
dupe_handler = current_dt
return final_results
# ========================================================= #
class BaseAsyncClient(Base):
"""
These docs are not meant for general users. These are library API references. The actual docs will be
available on the index page when they are prepared.
This is the **base async client class** for all other REST clients which inherit from this class and implement
their own endpoints on top of it.
"""
def __init__(self, api_key: str, connect_timeout: int = 10, read_timeout: int = 10, pool_timeout: int = 10,
max_connections: int = None, max_keepalive: int = None, write_timeout: int = 10):
"""
Initiates a Client to be used to access all the endpoints.
:param api_key: Your API Key. Visit your dashboard to get yours.
:param connect_timeout: The connection timeout in seconds. Defaults to 10. basically the number of seconds to
wait for a connection to be established. Raises a ``ConnectTimeout`` if unable to
connect within specified time limit.
:param read_timeout: The read timeout in seconds. Defaults to 10. basically the number of seconds to wait for
data to be received. Raises a ``ReadTimeout`` if unable to connect within the specified
time limit.
:param pool_timeout: The pool timeout in seconds. Defaults to 10. Basically the number of seconds to wait while
trying to get a connection from connection pool. Do NOT change if you're unsure of what it
implies
:param max_connections: Max number of connections in the pool. Defaults to NO LIMITS. Do NOT change if you're
unsure of application
:param max_keepalive: max number of allowable keep alive connections in the pool. Defaults to no limit.
Do NOT change if you're unsure of the applications.
:param write_timeout: The write timeout in seconds. Defaults to 10. basically the number of seconds to wait for
data to be written/posted. Raises a ``WriteTimeout`` if unable to connect within the
specified time limit.
"""
self.KEY = api_key
self.BASE = 'https://api.polygon.io'
self.time_out_conf = httpx.Timeout(connect=connect_timeout, read=read_timeout, pool=pool_timeout,
write=write_timeout)
self._conn_pool_limits = httpx.Limits(max_connections=max_connections,
max_keepalive_connections=max_keepalive)
self.session = httpx.AsyncClient(timeout=self.time_out_conf, limits=self._conn_pool_limits)
self.session.headers.update({'Authorization': f'Bearer {self.KEY}'})
@staticmethod
async def aw_task(aw, semaphore):
async with semaphore:
return await aw
# Context Managers
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.session.aclose()
async def close(self):
"""
Closes the ``httpx.AsyncClient`` and frees up resources. It is recommended to call this method in your
exit handlers. This method should be awaited as this is a coroutine.
"""
await self.session.aclose()
# Internal Functions
async def _get_response(self, path: str, params: dict = None,
raw_response: bool = True) -> Union[HttpxResponse, dict]:
"""
Get response on a path - meant to be used internally but can be used if you know what you're doing
:param path: RESTful path for the endpoint. Available on the docs for the endpoint right above its name.
:param params: Query Parameters to be supplied with the request. These are mapped 1:1 with the endpoint.
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to check the
status code or inspect the headers. Defaults to True which returns the ``Response`` object.
:return: A Response object by default. Make ``raw_response=False`` to get JSON decoded Dictionary
"""
_res = await self.session.request('GET', self.BASE + path, params=params)
if raw_response:
return _res
return _res.json()
async def get_page_by_url(self, url: str, raw_response: bool = False) -> Union[HttpxResponse, dict]:
"""
Get the next page of a response. The URl is returned within ``next_url`` attribute on endpoints which support
pagination (eg the tickers endpoint). If the response doesn't contain this attribute, either all pages were
received or the endpoint doesn't have pagination. Meant for internal use primarily.
:param url: The next URL. As contained in ``next_url`` of the response.
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to say check the
status code or inspect the headers. Defaults to False which returns the json decoded
dictionary.
:return: Either a Dictionary or a Response object depending on value of raw_response. Defaults to Dict.
"""
_res = await self.session.request('GET', url)
if raw_response:
return _res
return _res.json()
async def get_next_page(self, old_response: Union[HttpxResponse, dict],
raw_response: bool = False) -> Union[HttpxResponse, dict, bool]:
"""
Get the next page using the most recent old response. This function simply parses the next_url attribute
from the existing response and uses it to get the next page. Returns False if there is no next page
remaining (which implies that you have reached the end of all pages or the endpoint doesn't support
pagination) - Async method
:param old_response: The most recent existing response. Can be either ``Response`` Object or Dictionaries
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to say check the
status code or inspect the headers. Defaults to False which returns the json decoded
dictionary.
:return: A JSON decoded Dictionary by default. Make ``raw_response=True`` to get underlying response object
"""
try:
if not isinstance(old_response, dict):
old_response = old_response.json()
_next_url = old_response['next_url']
return await self.get_page_by_url(_next_url, raw_response=raw_response)
except KeyError:
return False
async def get_previous_page(self, old_response: Union[HttpxResponse, dict],
raw_response: bool = False) -> Union[HttpxResponse, dict, bool]:
"""
Get the previous page using the most recent old response. This function simply parses the previous_url attribute
from the existing response and uses it to get the previous page. Returns False if there is no previous page
remaining (which implies that you have reached the start of all pages or the endpoint doesn't support
pagination) - Async method
:param old_response: The most recent existing response. Can be either ``Response`` Object or Dictionaries
:param raw_response: Whether or not to return the ``Response`` Object. Useful for when you need to say check the
status code or inspect the headers. Defaults to False which returns the json decoded
dictionary.
:return: A JSON decoded Dictionary by default. Make ``raw_response=True`` to get underlying response object
"""
try:
if not isinstance(old_response, dict):
old_response = old_response.json()
_prev_url = old_response['previous_url']
return await self.get_page_by_url(_prev_url, raw_response=raw_response)
except KeyError:
return False
async def get_all_pages(self, old_response, max_pages: int = None, direction: str = 'next',
raw_responses: bool = False):
"""
A helper function for endpoints which implement pagination using ``next_url`` and ``previous_url`` attributes.
Can be used externally too to get all responses in a list.
:param old_response: The last response you had. In most cases, this would be simply the very first response.
:param max_pages: If you want to limit the number of pages to retrieve. Defaults to None which fetches ALL
available pages
:param direction: The direction to paginate in. Defaults to next which grabs all next_pages. see
:class:`polygon.enums.PaginationDirection` for choices
:param raw_responses: If set to True, the elements in container list, you will get underlying Response object
instead of the json formatted dict/list. Only use if you need to check status codes or
headers. Defaults to False, which makes it return decoded data in list.
:return: A list of responses. By default, responses are actual json decoded dict/list. Depending on value of
``raw_response``
"""
direction, container, _res = self._change_enum(direction, str), [], old_response
if not max_pages:
max_pages = float('inf')
if direction in ['prev', 'previous']:
fn = self.get_previous_page
else:
fn = self.get_next_page
# Start paginating
while 1:
if len(container) >= max_pages:
break
_res = await fn(_res, raw_response=True)
if not _res:
break
if raw_responses:
container.append(_res)
continue
container.append(_res.json())
return container
async def _paginate(self, _res, merge_all_pages: bool = True, max_pages: int = None,
raw_page_responses: bool = False):
"""
Internal function to call the core pagination methods to build the response object to be parsed by individual
methods.
:param merge_all_pages: whether to merge all the pages into one response. defaults to True
:param max_pages: number of pages to fetch. defaults to all available pages.
:param raw_page_responses: whether to keep raw response objects or decode them. Only considered if
merge_all_pages is set to False. Defaults to False.
:return:
"""
if isinstance(max_pages, int):
max_pages -= 1
# How many pages do you want?? YES!!!
if merge_all_pages: # prepare for a merge
pages = [_res.json()] + await self.get_all_pages(_res, max_pages=max_pages)
elif raw_page_responses: # we don't need your help, adventurer (no merge, no decoding)
return [_res] + await self.get_all_pages(_res, raw_responses=True, max_pages=max_pages)
else: # okay a little bit of help is fine (no merge, only decoding)
return [_res.json()] + await self.get_all_pages(_res, max_pages=max_pages)
# We need your help adventurer (decode and merge)
container = []
try:
for page in pages:
container += page['results']
except KeyError:
return pages
return container
async def get_full_range_aggregates(self, fn, symbol: str, time_chunks: list, run_parallel: bool = True,
max_concurrent_workers: int = os.cpu_count() * 5, warnings: bool = True,
adjusted: bool = True, sort='asc', limit: int = 5000,
multiplier: int = 1, timespan='day') -> list:
"""
Internal helper function to fetch aggregate bars for BIGGER time ranges. Should only be used internally.
Users should prefer the relevant aggregate function with additional parameters.
:param fn: The method to call in each chunked timeframe
:param symbol: The ticker symbol to get data for
:param time_chunks: The list of time chunks as returned by method ``split_datetime_range``
:param run_parallel: If true (the default), it will use an internal ``ThreadPool`` to get the responses in
parallel. **Note That** since python has the GIL restrictions, it would mean that if you
have a ThreadPool of your own, only one ThreadPool will be running at a time and the
other pool will wait. set to False to get all responses in sequence (will take time)
:param warnings: Defaults to True which prints warnings. Set to False to disable warnings.
:param max_concurrent_workers: This is only used if run_parallel is set to true. Controls how many worker
coroutines are spawned internally. Defaults to ``your cpu core count * 5``.
An ``asyncio.Semaphore()`` is used behind the scenes.
:param adjusted: Whether or not the results are adjusted for splits. By default, results are adjusted. Set this
to false to get results that are NOT adjusted for splits.
:param sort: Sort the results by timestamp. See :class:`polygon.enums.SortOrder` for choices. ``asc`` default.
:param limit: Limits the number of base aggregates queried to create the aggregate results. Max 50000 and
Default 5000.
:param multiplier: The size of the timespan multiplier. Must be a positive whole number.
:param timespan: The size of the time window. See :class:`polygon.enums.Timespan` for choices. defaults to
``day``
:return: A single merged list of ALL candles/bars
"""
if (not run_parallel) and warnings:
print(f'WARNING: Running sequentially can take a lot of time especially if you are pulling minute/hour '
f'aggs on a BIG time frame. If you have more than one symbols to run, it is suggested to run one '
f'coroutine for each ticker. You can pass warnings=False to disable this warning OR '
f'pass run_parallel=True to spawn internal coroutines to get data in parallel')
# The aggregation begins
dupe_handler, final_results = 0, []
if run_parallel:
import asyncio
sort_order = self._change_enum(sort)
futures, semaphore = [], asyncio.Semaphore(max_concurrent_workers)
for chunk in time_chunks:
chunk = (self.normalize_datetime(chunk[0], 'nts'), self.normalize_datetime(chunk[1], 'nts', _dir='end'))
futures.append(self.aw_task(fn(symbol, chunk[0], chunk[1], adjusted=adjusted, sort='asc',
limit=500000, multiplier=multiplier, timespan=timespan,
full_range=False), semaphore))
futures = await asyncio.gather(*futures)
for future in reversed(futures):
try:
data = future['results']
except KeyError:
if warnings:
print(f'No data returned. Response: {future}')
continue
if len(data) < 1:
if warnings:
print(f'No data returned. Response: {future}')
continue
# final_results += [candle for candle in data if (candle['t'] > dupe_handler) and (
# candle['t'] <= last_entry) and (candle['t'] >= first_entry)]
final_results += [candle for candle in data if (candle['t'] > dupe_handler)]
dupe_handler = final_results[-1]['t']
if sort_order in ['desc', 'descending']:
final_results.reverse()
return final_results
# Sequential
current_dt = self.normalize_datetime(time_chunks[0])
end_dt = self.normalize_datetime(time_chunks[1], _dir='end')
first_entry = self.normalize_datetime(time_chunks[0])
try:
delta = TIME_FRAME_CHUNKS[timespan]
except KeyError:
raise ValueError('Invalid timespan. Use a correct enum or a correct value. See '
'https://polygon.readthedocs.io/en/latest/Library-Interface-Documentation.html#polygon'
'.enums.Timespan')
if (self.normalize_datetime(end_dt, 'datetime', 'end') - self.normalize_datetime(
first_entry, 'datetime')).days <= delta.days:
res = await fn(symbol, current_dt, end_dt, adjusted=adjusted, sort=sort, limit=500000,
multiplier=multiplier, timespan=timespan, full_range=False)
try:
return res['results']
except KeyError:
if warnings:
print(f'no data returned for {symbol} for range {first_entry} to {end_dt}. Response: '
f'{res}')
return []
dupe_handler = current_dt
while 1:
if current_dt >= end_dt:
break
res = await fn(symbol, current_dt, end_dt, adjusted=adjusted, sort=sort, limit=500000,
multiplier=multiplier, timespan=timespan, full_range=False)
try:
data = res['results']
except KeyError:
if warnings:
print(f'No data found for {symbol} between {current_dt} and {end_dt} with '
f'response: {res}. Terminating loop...')
break
if len(data) < 1:
if warnings:
print(f'No data found for {symbol} between {current_dt} and {end_dt} with '
f'response: {res}. Terminating loop...')
break
temp_len = len(final_results)
final_results += [candle for candle in data if (candle['t'] > dupe_handler)]
if len(final_results) == temp_len:
if data[-1]['t'] <= dupe_handler:
break
current_dt = final_results[-1]['t']
dupe_handler = current_dt
return final_results
# ========================================================= #
if __name__ == '__main__': # Tests
print('Don\'t You Dare Running Lib Files Directly')
# ========================================================= #
| 48.30819 | 120 | 0.599197 | 5,540 | 44,830 | 4.740614 | 0.101805 | 0.017591 | 0.015992 | 0.006092 | 0.819366 | 0.809199 | 0.790656 | 0.77474 | 0.763774 | 0.752123 | 0 | 0.006134 | 0.319987 | 44,830 | 927 | 121 | 48.360302 | 0.855371 | 0.283114 | 0 | 0.636771 | 0 | 0.013453 | 0.136783 | 0 | 0 | 0 | 0 | 0.001079 | 0 | 1 | 0.033632 | false | 0.013453 | 0.022422 | 0.002242 | 0.186099 | 0.03139 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
9039a7a4cc5181b56209dfb87f41bb3a0fc92a17 | 40 | py | Python | distributions/__init__.py | fcooper8472/streamlit-workshop | 69ccd95ce97e0cbd393a14270394024a8633ff39 | [
"MIT"
] | null | null | null | distributions/__init__.py | fcooper8472/streamlit-workshop | 69ccd95ce97e0cbd393a14270394024a8633ff39 | [
"MIT"
] | null | null | null | distributions/__init__.py | fcooper8472/streamlit-workshop | 69ccd95ce97e0cbd393a14270394024a8633ff39 | [
"MIT"
] | null | null | null | from .normal import normal_distribution
| 20 | 39 | 0.875 | 5 | 40 | 6.8 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 40 | 1 | 40 | 40 | 0.944444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
904a7408e14e4481e326192bbaa6f9952cc57901 | 44 | py | Python | differentiable_sorting/torch/__init__.py | asaran/differentiable_sorting | 4b7438f9617397f9b32e796c46275d9f8da8c4fe | [
"MIT"
] | 134 | 2019-06-17T12:50:54.000Z | 2022-03-25T10:25:31.000Z | differentiable_sorting/torch/__init__.py | asaran/differentiable_sorting | 4b7438f9617397f9b32e796c46275d9f8da8c4fe | [
"MIT"
] | 3 | 2020-06-04T04:54:48.000Z | 2022-03-22T12:38:19.000Z | differentiable_sorting/torch/__init__.py | asaran/differentiable_sorting | 4b7438f9617397f9b32e796c46275d9f8da8c4fe | [
"MIT"
] | 9 | 2019-06-18T20:09:52.000Z | 2021-01-04T02:45:09.000Z | from .differentiable_sorting_torch import *
| 22 | 43 | 0.863636 | 5 | 44 | 7.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 44 | 1 | 44 | 44 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5f3b808b2073aca1fd0436d52ac3e5ede4007c3a | 27 | py | Python | src/start/__init__.py | superserver/MinecraftServerAutoDeploy | d2af2b4528572924c83e80b05ceee69166803083 | [
"Apache-2.0"
] | 1 | 2019-01-31T14:08:24.000Z | 2019-01-31T14:08:24.000Z | src/start/__init__.py | superserver/MinecraftServerAutoDeploy | d2af2b4528572924c83e80b05ceee69166803083 | [
"Apache-2.0"
] | null | null | null | src/start/__init__.py | superserver/MinecraftServerAutoDeploy | d2af2b4528572924c83e80b05ceee69166803083 | [
"Apache-2.0"
] | null | null | null | from src.start import start | 27 | 27 | 0.851852 | 5 | 27 | 4.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 27 | 1 | 27 | 27 | 0.958333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5f44905245af950f32b885baab416dabc5f9c90e | 34 | py | Python | nets/resnet/__init__.py | SpatialPerceptionNeuralNetwork/SOA_DORN_TF | 33814467e9135036abf28f2da19c5984c8744089 | [
"Unlicense"
] | 17 | 2019-02-17T07:39:39.000Z | 2021-08-17T05:20:19.000Z | nets/resnet/__init__.py | SpatialPerceptionNeuralNetwork/SOA_DORN_TF | 33814467e9135036abf28f2da19c5984c8744089 | [
"Unlicense"
] | 6 | 2019-03-04T14:17:22.000Z | 2019-11-07T15:06:55.000Z | nets/resnet/__init__.py | SpatialPerceptionNeuralNetwork/SOA_DORN_TF | 33814467e9135036abf28f2da19c5984c8744089 | [
"Unlicense"
] | 4 | 2019-02-17T07:39:47.000Z | 2019-08-13T17:13:23.000Z | from . import resnet_v2, resnet_v1 | 34 | 34 | 0.823529 | 6 | 34 | 4.333333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066667 | 0.117647 | 34 | 1 | 34 | 34 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5f83e859c152013a92c111b7471d3aa2e89dc9d5 | 62 | py | Python | src/aircraft/deploys/ubuntu/__init__.py | relaxdiego/aircraft | ce9a6724fe33be38777991fbb1cd731e197fa468 | [
"Apache-2.0"
] | 9 | 2021-01-15T18:26:44.000Z | 2021-07-29T07:40:15.000Z | src/aircraft/deploys/ubuntu/__init__.py | relaxdiego/aircraft | ce9a6724fe33be38777991fbb1cd731e197fa468 | [
"Apache-2.0"
] | null | null | null | src/aircraft/deploys/ubuntu/__init__.py | relaxdiego/aircraft | ce9a6724fe33be38777991fbb1cd731e197fa468 | [
"Apache-2.0"
] | 1 | 2021-04-26T01:39:26.000Z | 2021-04-26T01:39:26.000Z | from . import apache2
from . import dnsmasq
from . import pxe
| 15.5 | 21 | 0.758065 | 9 | 62 | 5.222222 | 0.555556 | 0.638298 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02 | 0.193548 | 62 | 3 | 22 | 20.666667 | 0.92 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5f98e846cbf500d072e12f80e984f540da600cca | 40 | py | Python | scripts/skinning/gui/proxies/__init__.py | robertjoosten/skinning-tools | 1f1ec6c092fdc1e39aa82a711a13a0041f9d5730 | [
"MIT"
] | 31 | 2018-09-08T16:42:01.000Z | 2022-03-31T12:31:21.000Z | scripts/skinning/gui/proxies/__init__.py | robertjoosten/skinning-tools | 1f1ec6c092fdc1e39aa82a711a13a0041f9d5730 | [
"MIT"
] | null | null | null | scripts/skinning/gui/proxies/__init__.py | robertjoosten/skinning-tools | 1f1ec6c092fdc1e39aa82a711a13a0041f9d5730 | [
"MIT"
] | 11 | 2018-10-01T09:57:53.000Z | 2022-03-19T06:53:02.000Z | from skinning.gui.proxies.tree import *
| 20 | 39 | 0.8 | 6 | 40 | 5.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 40 | 1 | 40 | 40 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5f9d94b9bfbccfe0ec6919e07fa5ce7674ccc442 | 158 | py | Python | backend/core/clientes/exceptions.py | jklemm/menu-fullstack-challenge | 519871e5889a827fbf39afd5dfb8944be8c21f3f | [
"Unlicense"
] | null | null | null | backend/core/clientes/exceptions.py | jklemm/menu-fullstack-challenge | 519871e5889a827fbf39afd5dfb8944be8c21f3f | [
"Unlicense"
] | null | null | null | backend/core/clientes/exceptions.py | jklemm/menu-fullstack-challenge | 519871e5889a827fbf39afd5dfb8944be8c21f3f | [
"Unlicense"
] | null | null | null | class ClienteNotFoundException(Exception):
pass
class RequiredDataException(Exception):
pass
class DuplicatedEntityException(Exception):
pass
| 14.363636 | 43 | 0.78481 | 12 | 158 | 10.333333 | 0.5 | 0.314516 | 0.290323 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.158228 | 158 | 10 | 44 | 15.8 | 0.932331 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
5fbacb8c426deb59155c5751183f037d4fb278d5 | 28 | py | Python | rife/utils/__init__.py | xhlulu/RIFE | 256f601b5516e258b1d4bfd8d6050a33307ec50d | [
"MIT"
] | 5 | 2021-06-21T22:49:23.000Z | 2021-12-18T10:19:23.000Z | rife/utils/__init__.py | xhlulu/rife | 256f601b5516e258b1d4bfd8d6050a33307ec50d | [
"MIT"
] | null | null | null | rife/utils/__init__.py | xhlulu/rife | 256f601b5516e258b1d4bfd8d6050a33307ec50d | [
"MIT"
] | null | null | null | from . import pytorch_msssim | 28 | 28 | 0.857143 | 4 | 28 | 5.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107143 | 28 | 1 | 28 | 28 | 0.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
396a98a4b23f11f0551841507d545a4de2ce0b33 | 154 | py | Python | nlpipe/Tools/__init__.py | ccs-amsterdam/nlpipe | 3aeb7885a883bd9d513e5e5c64749e0be7e486f1 | [
"MIT"
] | null | null | null | nlpipe/Tools/__init__.py | ccs-amsterdam/nlpipe | 3aeb7885a883bd9d513e5e5c64749e0be7e486f1 | [
"MIT"
] | null | null | null | nlpipe/Tools/__init__.py | ccs-amsterdam/nlpipe | 3aeb7885a883bd9d513e5e5c64749e0be7e486f1 | [
"MIT"
] | null | null | null | from . import test_upper, alpino, alpinonaf, coreNLP, frog, newsreader, parzu, udpipe, gensim, spacy, portulan # forces the tools to register themselves
| 77 | 153 | 0.779221 | 20 | 154 | 5.95 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 154 | 1 | 154 | 154 | 0.901515 | 0.253247 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
39b4f20f3b100a2294dbc56801818d49832b1afe | 63 | py | Python | HardwareTests/KnownValues/__init__.py | JetStarBlues/Nand-2-Tetris | c27b5c2ac659f1edb63d36d89bf87e226bc5672c | [
"MIT"
] | null | null | null | HardwareTests/KnownValues/__init__.py | JetStarBlues/Nand-2-Tetris | c27b5c2ac659f1edb63d36d89bf87e226bc5672c | [
"MIT"
] | null | null | null | HardwareTests/KnownValues/__init__.py | JetStarBlues/Nand-2-Tetris | c27b5c2ac659f1edb63d36d89bf87e226bc5672c | [
"MIT"
] | null | null | null | from .kv_1__elementary import *
from .kv_2__arithmetic import * | 31.5 | 31 | 0.825397 | 10 | 63 | 4.6 | 0.7 | 0.26087 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.035714 | 0.111111 | 63 | 2 | 32 | 31.5 | 0.785714 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
39c4651f136b900a31571313e1ba9fecdfe24c5a | 33 | py | Python | fleet_adapter_mir/__init__.py | JunHaoChng/fleet_adapter_rnabot | 5482f7c1a6cb709c499fb08d15288bcd96d8d709 | [
"Apache-2.0"
] | 4 | 2020-08-28T21:36:25.000Z | 2021-08-25T09:25:45.000Z | fleet_adapter_mir/__init__.py | JunHaoChng/fleet_adapter_rnabot | 5482f7c1a6cb709c499fb08d15288bcd96d8d709 | [
"Apache-2.0"
] | 10 | 2020-12-08T08:25:37.000Z | 2021-08-04T05:28:58.000Z | fleet_adapter_mir/__init__.py | JunHaoChng/fleet_adapter_rnabot | 5482f7c1a6cb709c499fb08d15288bcd96d8d709 | [
"Apache-2.0"
] | 7 | 2020-08-27T09:36:10.000Z | 2022-03-23T07:48:46.000Z | from .fleet_adapter_mir import *
| 16.5 | 32 | 0.818182 | 5 | 33 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.862069 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
39c5a00392e63fb2624044ef76b1082b90eb123b | 9,071 | py | Python | test/unittests/parrot_blame/test_parrot_blame.py | Malrig/slack_party_parrot_provider | 611e1cc8d0c00c81c251a246732e6fb099341441 | [
"MIT"
] | 3 | 2020-06-11T09:19:35.000Z | 2020-06-28T15:46:25.000Z | test/unittests/parrot_blame/test_parrot_blame.py | Malrig/slack_party_parrot_provider | 611e1cc8d0c00c81c251a246732e6fb099341441 | [
"MIT"
] | 2 | 2018-12-07T11:51:54.000Z | 2019-10-21T20:40:30.000Z | test/unittests/parrot_blame/test_parrot_blame.py | Malrig/slack_party_parrot_provider | 611e1cc8d0c00c81c251a246732e6fb099341441 | [
"MIT"
] | null | null | null | import unittest
import os
import shutil
import json
from datetime import datetime
from freezegun import freeze_time
from unittest.mock import MagicMock, patch
from src.parrot_blame.parrot_blame import ParrotBlame, ParrotBlameInfo
mockdate = datetime(2000, 1, 1, 0, 0, 0)
parrot_blame_data = [
{
"parrot_name": "joy_parrot",
"username": "TestUser",
"created_date": "2018-07-24 20:35:43.687369",
"team_id": "T001"
},
{
"parrot_name": "sweat_smile_parrot",
"username": "AnotherUser",
"created_date": "2018-07-10 20:35:43.687369",
"team_id": "T003"
}
]
class TestParrotBlameInitialisation(unittest.TestCase):
def setUp(self):
self.data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"test_data_dir")
def tearDown(self):
shutil.rmtree(self.data_dir)
def test_initiation_creates_file(self):
parrot_blame = ParrotBlame(self.data_dir)
assert os.path.exists(self.data_dir) == 1
assert parrot_blame._get_parrot_blame_information() == []
def test_initiation_doesnt_override_existing_file(self):
parrot_file_path = os.path.join(self.data_dir, "parrot_blame.json")
os.makedirs(os.path.dirname(parrot_file_path), exist_ok=True)
if os.path.isfile(parrot_file_path):
return
with open(parrot_file_path, 'w+') as new_json_file:
json.dump(parrot_blame_data, new_json_file)
ParrotBlame(self.data_dir)
with open(parrot_file_path, 'r') as blame_data:
data = json.load(blame_data)
self.assertListEqual(parrot_blame_data, data)
class TestParrotBlameWithFile(unittest.TestCase):
def setUp(self):
self.data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"test_data_dir")
self.parrot_file_path = os.path.join(self.data_dir, "parrot_blame.json")
os.makedirs(os.path.dirname(self.parrot_file_path), exist_ok=True)
if os.path.isfile(self.parrot_file_path):
return
with open(self.parrot_file_path, 'w+') as new_json_file:
json.dump(parrot_blame_data, new_json_file)
self.parrot_blame = ParrotBlame(self.data_dir)
def tearDown(self):
shutil.rmtree(self.data_dir)
def test_blame_parrot(self):
blame_parrot = self.parrot_blame.blame_parrot(":joy_parrot:",
"T001")
self.assertEqual(ParrotBlameInfo("joy_parrot",
"TestUser",
datetime.strptime("2018-07-24 20:35:43.687369",
"%Y-%m-%d %H:%M:%S.%f"),
"T001"),
blame_parrot)
def test_blame_parrot_with_colons(self):
blame_parrot = self.parrot_blame.blame_parrot("sweat_smile_parrot",
"T003")
self.assertEqual(ParrotBlameInfo("sweat_smile_parrot",
"AnotherUser",
datetime.strptime("2018-07-10 20:35:43.687369",
"%Y-%m-%d %H:%M:%S.%f"),
"T003"),
blame_parrot)
def test_blame_parrot_without_entry(self):
with self.assertRaises(ValueError) as val_err:
self.parrot_blame.blame_parrot(":not_like_this_parrot:", "T001")
self.assertEqual("('No blame information for :%s: was found.', "
"'not_like_this_parrot')",
str(val_err.exception))
def test_blame_parrot_without_team(self):
with self.assertRaises(ValueError) as val_err:
self.parrot_blame.blame_parrot(":joy_parrot:", "T003")
self.assertEqual("('No blame information for :%s: was found.', 'joy_parrot')",
str(val_err.exception))
@freeze_time("2018-08-03")
def test_create_blame_entry(self):
self.parrot_blame.add_parrot_blame_information(":tick_parrot:",
"YetAnotherUser",
"T005")
final_data = [
{
"parrot_name": "joy_parrot",
"username": "TestUser",
"created_date": "2018-07-24 20:35:43.687369",
"team_id": "T001"
},
{
"parrot_name": "sweat_smile_parrot",
"username": "AnotherUser",
"created_date": "2018-07-10 20:35:43.687369",
"team_id": "T003"
},
{
"parrot_name": "tick_parrot",
"username": "YetAnotherUser",
"created_date": str(datetime(2018, 8, 3)),
"team_id": "T005"
}
]
with open(self.parrot_file_path, 'r') as blame_data:
data = json.load(blame_data)
self.assertListEqual(final_data, data)
# def test_override_blame_entry(self):
# print("test_override_blame_entry")
class TestParrotBlameWithoutFile(unittest.TestCase):
def setUp(self):
self.patcher = patch('src.parrot_blame.parrot_blame.ParrotBlame._prepare_blame_file')
self.mock_prepare_blame_file = self.patcher.start()
self.parrot_blame = ParrotBlame("file_path")
self.parrot_blame._get_parrot_blame_information = MagicMock(return_value=parrot_blame_data)
self.parrot_blame._save_parrot_blame_information = MagicMock()
def tearDown(self):
self.patcher.stop()
def test_blame_parrot(self):
blame_parrot = self.parrot_blame.blame_parrot(":joy_parrot:",
"T001")
self.parrot_blame._get_parrot_blame_information.assert_called()
self.assertEqual(ParrotBlameInfo("joy_parrot",
"TestUser",
datetime.strptime("2018-07-24 20:35:43.687369",
"%Y-%m-%d %H:%M:%S.%f"),
"T001"),
blame_parrot)
def test_blame_parrot_with_colons(self):
blame_parrot = self.parrot_blame.blame_parrot("sweat_smile_parrot",
"T003")
self.parrot_blame._get_parrot_blame_information.assert_called()
self.assertEqual(ParrotBlameInfo("sweat_smile_parrot",
"AnotherUser",
datetime.strptime("2018-07-10 20:35:43.687369",
"%Y-%m-%d %H:%M:%S.%f"),
"T003"),
blame_parrot)
def test_blame_parrot_without_entry(self):
with self.assertRaises(ValueError) as val_err:
self.parrot_blame.blame_parrot(":not_like_this_parrot:", "T001")
self.parrot_blame._get_parrot_blame_information.assert_called()
self.assertEqual("('No blame information for :%s: was found.', "
"'not_like_this_parrot')",
str(val_err.exception))
def test_blame_parrot_without_team(self):
with self.assertRaises(ValueError) as val_err:
self.parrot_blame.blame_parrot(":joy_parrot:", "T003")
self.parrot_blame._get_parrot_blame_information.assert_called()
self.assertEqual("('No blame information for :%s: was found.', 'joy_parrot')",
str(val_err.exception))
@freeze_time("2018-08-03")
def test_create_blame_entry(self):
self.parrot_blame.add_parrot_blame_information(":tick_parrot:",
"YetAnotherUser",
"T005")
self.parrot_blame._get_parrot_blame_information.assert_called()
self.parrot_blame._save_parrot_blame_information.assert_called_with([
{
"parrot_name": "joy_parrot",
"username": "TestUser",
"created_date": "2018-07-24 20:35:43.687369",
"team_id": "T001"
},
{
"parrot_name": "sweat_smile_parrot",
"username": "AnotherUser",
"created_date": "2018-07-10 20:35:43.687369",
"team_id": "T003"
},
{
"parrot_name": "tick_parrot",
"username": "YetAnotherUser",
"created_date": str(datetime(2018, 8, 3)),
"team_id": "T005"
}
])
# def test_override_blame_entry(self):
# print("test_override_blame_entry")
| 39.785088 | 99 | 0.539301 | 943 | 9,071 | 4.870626 | 0.135737 | 0.105378 | 0.068583 | 0.026127 | 0.857609 | 0.842369 | 0.811888 | 0.786196 | 0.770738 | 0.770738 | 0 | 0.051806 | 0.353103 | 9,071 | 227 | 100 | 39.960352 | 0.730913 | 0.016646 | 0 | 0.675824 | 0 | 0 | 0.180258 | 0.016938 | 0 | 0 | 0 | 0 | 0.120879 | 1 | 0.098901 | false | 0 | 0.043956 | 0 | 0.17033 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
39ce6accf280fb98683ee7e0d5ab925799e008d0 | 187 | py | Python | model.py | Paulinakhew/rpg_game | 2dc0e23a1769fd9d0bc74f1d93c2dc156c64522b | [
"MIT"
] | null | null | null | model.py | Paulinakhew/rpg_game | 2dc0e23a1769fd9d0bc74f1d93c2dc156c64522b | [
"MIT"
] | 1 | 2020-01-08T00:12:19.000Z | 2020-01-08T00:12:19.000Z | model.py | Paulinakhew/rpg_game | 2dc0e23a1769fd9d0bc74f1d93c2dc156c64522b | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
def fight_monster(health_points):
health_points -= 25
return health_points
def add_health(health_points):
health_points += 25
return health_points | 20.777778 | 33 | 0.743316 | 26 | 187 | 5.038462 | 0.461538 | 0.549618 | 0.274809 | 0.366412 | 0.671756 | 0.671756 | 0.671756 | 0.671756 | 0 | 0 | 0 | 0.032468 | 0.176471 | 187 | 9 | 34 | 20.777778 | 0.818182 | 0.112299 | 0 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.666667 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
39f3763bcd8c4ab1b2b28ba37030d9545cec2d24 | 23,072 | py | Python | examples/AdvancedSizeStatistics/AdvancedSizeStatistics.py | SystemsBiologyUniandes/PyEcoLib | 3c46a34af51e29a2d5cca1f894606bbc9738f7a0 | [
"MIT"
] | 1 | 2020-12-31T06:37:14.000Z | 2020-12-31T06:37:14.000Z | examples/AdvancedSizeStatistics/AdvancedSizeStatistics.py | SystemsBiologyUniandes/PyEcoLib | 3c46a34af51e29a2d5cca1f894606bbc9738f7a0 | [
"MIT"
] | null | null | null | examples/AdvancedSizeStatistics/AdvancedSizeStatistics.py | SystemsBiologyUniandes/PyEcoLib | 3c46a34af51e29a2d5cca1f894606bbc9738f7a0 | [
"MIT"
] | 1 | 2021-12-19T19:48:20.000Z | 2021-12-19T19:48:20.000Z | import sys
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.stats import bayes_mvs as bayesest
import time
from PyEcoLib.simulator import Simulator
mean_size = 1 # femto liter
doubling_time = 18 # min
tmax: int = 180 # min
sample_time = 2 # min
div_steps = 10
ncells: int = 5000
gr = np.log(2)/doubling_time
if not os.path.exists('./data'):
os.makedirs('./data') # data path
if not os.path.exists('./figures'):
os.makedirs('./figures') # Figures path
start = time.time()
sim = Simulator(ncells=ncells, gr=gr, sb=mean_size, steps=div_steps)
sim.divstrat(tmax=tmax, sample_time=0.1*doubling_time, nameDSM="./data/dataDSMadder.csv")
print('It took', np.int(time.time()-start), 'seconds.')
start = time.time()
sim = Simulator(ncells=ncells, gr=gr, sb=mean_size, steps=div_steps, lamb=2)
sim.divstrat(tmax=tmax, sample_time=0.1*doubling_time, nameDSM="./data/dataDSMsizer.csv")
print('It took', np.int(time.time()-start), 'seconds.')
start = time.time()
sim = Simulator(ncells=ncells, gr=gr, sb=mean_size, steps=div_steps, lamb=0.5)
sim.divstrat(tmax=tmax, sample_time=0.1*doubling_time, nameDSM="./data/dataDSMtimer.csv")
print('It took', np.int(time.time()-start), 'seconds.')
start = time.time()
sim = Simulator(ncells=ncells, gr=gr, sb=mean_size, steps=div_steps)
sim.szdyn(tmax=tmax, sample_time=0.1*doubling_time, nameCRM="./data/dataCRM1.csv")
print('It took', np.int(time.time()-start), 'seconds.')
CV2sz = 0.02
v0 = mean_size*np.random.gamma(shape=1/CV2sz, scale=CV2sz, size=ncells)
start = time.time()
sim = Simulator(ncells=ncells, gr=gr, sb=mean_size, steps=div_steps, V0array=v0)
sim.szdyn(tmax=tmax, sample_time=0.1*doubling_time, nameCRM="./data/dataCRM2.csv")
print('It took', np.int(time.time()-start), 'seconds.')
CV2div = 0.002
CV2gr = 0.02
start = time.time()
sim = Simulator(ncells=ncells, gr=gr, sb=mean_size, steps=div_steps, CV2div=CV2div, CV2gr=CV2gr)
sim.szdyn(tmax=tmax, sample_time=0.1*doubling_time, nameCRM="./data/dataCRM3.csv")
print('It took', np.int(time.time()-start), 'seconds.')
data1 = pd.read_csv("./data/dataCRM1.csv")
timearray1 = data1.time.unique()
mnszarray1 = []
cvszarray1 = []
errcv2szarray1 = []
errmnszarray1 = []
df = data1
del df['time']
for m in range(len(df)):
szs = df.loc[m, :].values.tolist()
mean_cntr, var_cntr, std_cntr = bayesest(szs, alpha=0.95)
mnszarray1.append(np.mean(szs))
errmnszarray1.append(mean_cntr[1][1]-mean_cntr[0])
cvszarray1.append(np.var(szs)/np.mean(szs)**2)
errv = (var_cntr[1][1]-var_cntr[0])/mean_cntr[0]**2+2*(mean_cntr[1][1]-mean_cntr[0])*var_cntr[0]/mean_cntr[0]**3
errcv2szarray1.append(errv)
data1 = pd.read_csv("./data/dataCRM2.csv")
timearray2 = data1.time.unique()
mnszarray2 = []
cvszarray2 = []
errcv2szarray2 = []
errmnszarray2 = []
df = data1
del df['time']
for m in range(len(df)):
szs = df.loc[m, :].values.tolist()
mean_cntr, var_cntr, std_cntr = bayesest(szs, alpha=0.95)
mnszarray2.append(np.mean(szs))
errmnszarray2.append(mean_cntr[1][1]-mean_cntr[0])
cvszarray2.append(np.var(szs)/np.mean(szs)**2)
errv = (var_cntr[1][1]-var_cntr[0])/mean_cntr[0]**2+2*(mean_cntr[1][1]-mean_cntr[0])*var_cntr[0]/mean_cntr[0]**3
errcv2szarray2.append(errv)
data1 = pd.read_csv("./data/dataCRM3.csv")
timearray3 = data1.time.unique()
mnszarray3 = []
cvszarray3 = []
errcv2szarray3 = []
errmnszarray3 = []
df = data1
del df['time']
for m in range(len(df)):
szs = df.loc[m, :].values.tolist()
mean_cntr, var_cntr, std_cntr = bayesest(szs, alpha=0.95)
mnszarray3.append(np.mean(szs))
errmnszarray3.append(mean_cntr[1][1]-mean_cntr[0])
cvszarray3.append(np.var(szs)/np.mean(szs)**2)
errv = (var_cntr[1][1]-var_cntr[0])/mean_cntr[0]**2+2*(mean_cntr[1][1]-mean_cntr[0])*var_cntr[0]/mean_cntr[0]**3
errcv2szarray3.append(errv)
start = time.time()
sim = Simulator(ncells=1, gr=gr, sb=mean_size, steps=div_steps)
sim.szdynFSP(tmax=tmax, nameFSP="./data/dataFSP0.csv")
print('It took', np.int(time.time()-start), 'seconds.')
start = time.time()
CV2sz = 0.02
sim = Simulator(ncells=1, gr=gr, sb=mean_size, steps=div_steps)
sim.szdynFSP(tmax=tmax, nameFSP="./data/dataFSP.csv", CV2sz=CV2sz)
print('It took', np.int(time.time()-start), 'seconds.')
fig, ax = plt.subplots(2, 3, figsize=(16, 6), sharex=True)
data = pd.read_csv("./data/dataCRM1.csv")
tt = data.time
del data['time']
mmar = data.columns
for column in df.columns[0:10]:
ax[0, 0].plot(tt/doubling_time, data[column], c="#B9B9B9", label='_nolegend_')
data = pd.read_csv("./data/dataCRM2.csv")
tt = data.time
del data['time']
mmar = data.columns
for column in df.columns[0:10]:
ax[0,1].plot(tt/doubling_time,data[column],c="#B9B9B9",label='_nolegend_')
data = pd.read_csv("./data/dataCRM3.csv")
tt = data.time
del data['time']
mmar = data.columns
for column in df.columns[0:10]:
ax[0, 2].plot(tt/doubling_time, data[column], c="#B9B9B9")
ax[0, 0].plot(np.array(timearray1)/doubling_time, mnszarray1, lw=2)
ax[0, 0].fill_between(np.array(timearray1)/doubling_time, np.array(mnszarray1)-np.array(errmnszarray1),
np.array(mnszarray1) + np.array(errmnszarray1), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0, label="SSA")
ax[1, 0].plot(np.array(timearray1)/doubling_time, cvszarray1, lw=2)
ax[1, 0].fill_between(np.array(timearray1)/doubling_time, np.array(cvszarray1)-np.array(errcv2szarray1),
np.array(cvszarray1)+np.array(errcv2szarray1), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0)
ax[0, 1].plot(np.array(timearray2)/doubling_time, mnszarray2, lw=2)
ax[0, 1].fill_between(np.array(timearray2)/doubling_time, np.array(mnszarray2)-np.array(errmnszarray2),
np.array(mnszarray2)+np.array(errmnszarray2), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0, label="SSA")
ax[1, 1].plot(np.array(timearray2)/doubling_time, cvszarray2, lw=2)
ax[1, 1].fill_between(np.array(timearray2)/doubling_time, np.array(cvszarray2)-np.array(errcv2szarray2),
np.array(cvszarray2)+np.array(errcv2szarray2), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0)
ax[0, 2].plot(np.array(timearray3)/doubling_time, mnszarray3, lw=2)
ax[0, 2].fill_between(np.array(timearray3)/doubling_time, np.array(mnszarray3)-np.array(errmnszarray3),
np.array(mnszarray3)+np.array(errmnszarray3), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0, label="SSA")
ax[1, 2].plot(np.array(timearray3)/doubling_time, cvszarray3, lw=2)
ax[1, 2].fill_between(np.array(timearray3)/doubling_time, np.array(cvszarray3)-np.array(errcv2szarray3),
np.array(cvszarray3)+np.array(errcv2szarray3), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0)
ax[0, 0].set_title("Stochastic division", fontsize=15)
ax[0, 1].set_title("Finite Initial Distribution", fontsize=15)
ax[0, 2].set_title("Noisy Splitting", fontsize=15)
data = pd.read_csv("./data/dataFSP.csv")
ax[0, 1].plot(data.time/doubling_time, data.Meansize, ls='--', c='g', label="Numeric")
ax[1, 1].plot(data.time/doubling_time, data.VarSize/data.Meansize**2, ls='--', c='g')
data = pd.read_csv("./data/dataFSP0.csv")
ax[0, 0].plot(data.time/doubling_time, data.Meansize, ls='--', c='g', label="Numeric")
ax[1, 0].plot(data.time/doubling_time, data.VarSize/data.Meansize**2, ls='--', c='g')
ax[0, 0].legend(fontsize=15)
ax[0, 1].legend(fontsize=15)
ax[0, 0].set_ylabel(r"$\langle s\rangle$ $(\mu m)$", size=15)
ax[1, 0].set_ylabel("$C_V^2(s)$", size=15)
ax[1, 0].set_xlabel(r"$t/\tau$", size=15)
ax[1, 1].set_xlabel(r"$t/\tau$", size=15)
ax[1, 2].set_xlabel(r"$t/\tau$", size=15)
for l in [0, 1]:
for m in [0, 1, 2]:
ax[l, m].set_xlim([0, 6])
taqui = np.arange(0, 7, step=1)
ax[l, m].set_xticks(np.array(taqui))
ax[l, m].grid()
ax[l, m].tick_params(axis='x', labelsize=12)
ax[l, m].tick_params(axis='y', labelsize=12)
for axis in ['bottom', 'left']:
ax[l, m].spines[axis].set_linewidth(2)
ax[l, m].tick_params(axis='both', width=2, length=6)
for axis in ['top', 'right']:
ax[l, m].spines[axis].set_linewidth(0)
ax[l, m].tick_params(axis='both', width=0, length=6)
taqui = np.arange(0, 0.13, step=0.02)
ax[1, m].set_yticks(np.array(taqui))
taqui = np.arange(0.5, 3, step=.5)
ax[0, m].set_yticks(np.array(taqui))
ax[1, m].set_ylim([0, 0.13])
ax[0, m].set_ylim([0.5, 3])
plt.subplots_adjust(hspace=0.15, wspace=0.2)
plt.savefig('./figures/size_statistics_comp1.eps',bbox_inches='tight')
plt.savefig('./figures/size_statistics_comp1.svg',bbox_inches='tight')
plt.savefig('./figures/size_statistics_comp1.png',bbox_inches='tight')
data2 = pd.read_csv("./data/dataDSMadder.csv")
data2 = data2[data2.time > 5*doubling_time]
quantnumber = 5
pvadd2 = data2
CV2darr1 = []
deltarr1 = []
sbarr1 = []
errcv2darr1 = []
errdeltarr1 = []
errsbarr1 = []
for i in range(quantnumber):
lperv0 = np.percentile(pvadd2.S_b, i*100/quantnumber)
hperv0 = np.percentile(pvadd2.S_b, (i+1)*100/quantnumber)
quanta1 = pvadd2[pvadd2.S_b > lperv0]
quanta2 = quanta1[quanta1.S_b < hperv0]
mean_cntr, var_cntr, std_cntr = bayesest((quanta2.S_d-quanta2.S_b)/np.mean(pvadd2.S_d-pvadd2.S_b), alpha=0.95)
meanv0_cntr, varv0_cntr, stdv0_cntr = bayesest(quanta2.S_b/np.mean(pvadd2.S_b), alpha=0.95)
CV2darr1.append(var_cntr[0]/mean_cntr[0]**2)
deltarr1.append(mean_cntr[0])
sbarr1.append(meanv0_cntr[0])
errv = (var_cntr[1][1]-var_cntr[0])/mean_cntr[0]**2+2*(mean_cntr[1][1]-mean_cntr[0])*var_cntr[0]/mean_cntr[0]**3
errcv2darr1.append(errv)
errdeltarr1.append(mean_cntr[1][1]-mean_cntr[0])
errsbarr1.append(meanv0_cntr[1][1]-meanv0_cntr[0])
data3 = pd.read_csv("./data/dataDSMsizer.csv")
data3 = data3[data3.time>5*doubling_time]
quantnumber = 5
pvadd2 = data3
CV2darr2 = []
deltarr2 = []
sbarr2 = []
errcv2darr2 = []
errdeltarr2 = []
errsbarr2 = []
for i in range(quantnumber):
lperv0 = np.percentile(pvadd2.S_b, i*100/quantnumber)
hperv0 = np.percentile(pvadd2.S_b, (i+1)*100/quantnumber)
quanta1 = pvadd2[pvadd2.S_b > lperv0]
quanta2 = quanta1[quanta1.S_b < hperv0]
mean_cntr, var_cntr, std_cntr = bayesest((quanta2.S_d-quanta2.S_b)/np.mean(pvadd2.S_d-pvadd2.S_b), alpha=0.95)
meanv0_cntr, varv0_cntr, stdv0_cntr = bayesest(quanta2.S_b/np.mean(pvadd2.S_b), alpha=0.95)
CV2darr2.append(var_cntr[0]/mean_cntr[0]**2)
deltarr2.append(mean_cntr[0])
sbarr2.append(meanv0_cntr[0])
errv = (var_cntr[1][1]-var_cntr[0])/mean_cntr[0]**2+2*(mean_cntr[1][1]-mean_cntr[0])*var_cntr[0]/mean_cntr[0]**3
errcv2darr2.append(errv)
errdeltarr2.append(mean_cntr[1][1]-mean_cntr[0])
errsbarr2.append(meanv0_cntr[1][1]-meanv0_cntr[0])
data4 = pd.read_csv("./data/dataDSMtimer.csv")
data4 = data4[data4.time>5*doubling_time]
quantnumber = 5
pvadd2 = data4
CV2darr3 = []
deltarr3 = []
sbarr3 = []
errcv2darr3 = []
errdeltarr3 = []
errsbarr3 = []
for i in range(quantnumber):
lperv0 = np.percentile(pvadd2.S_b,i*100/quantnumber)
hperv0 = np.percentile(pvadd2.S_b,(i+1)*100/quantnumber)
quanta1 = pvadd2[pvadd2.S_b>lperv0]
quanta2 = quanta1[quanta1.S_b<hperv0]
mean_cntr, var_cntr, std_cntr = bayesest((quanta2.S_d-quanta2.S_b)/np.mean(pvadd2.S_d-pvadd2.S_b), alpha=0.95)
meanv0_cntr, varv0_cntr, stdv0_cntr = bayesest(quanta2.S_b/np.mean(pvadd2.S_b), alpha=0.95)
CV2darr3.append(var_cntr[0]/mean_cntr[0]**2)
deltarr3.append(mean_cntr[0])
sbarr3.append(meanv0_cntr[0])
errv = (var_cntr[1][1]-var_cntr[0])/mean_cntr[0]**2+2*(mean_cntr[1][1]-mean_cntr[0])*var_cntr[0]/mean_cntr[0]**3
errcv2darr3.append(errv)
errdeltarr3.append(mean_cntr[1][1]-mean_cntr[0])
errsbarr3.append(meanv0_cntr[1][1]-meanv0_cntr[0])
print(np.mean(pvadd2.S_b))
print(np.mean(pvadd2.S_d-pvadd2.S_b))
sim = Simulator(ncells=1, gr=gr, sb=mean_size, steps=div_steps, lamb=0.5)
sbar = np.linspace(0.5, 1.5, 100)*mean_size
cv2tim = []
delttim = []
for i in sbar:
sd, cv2 = sim.SdStat(i)
cv2tim.append(cv2)
delttim.append(sd-i)
sim = Simulator(ncells=1, gr=gr, sb=mean_size, steps=div_steps)
sbar = np.linspace(0.5, 1.5, 100)*mean_size
cv2ad = []
deltad = []
for i in sbar:
sd, cv2 = sim.SdStat(i)
cv2ad.append(cv2)
deltad.append(sd-i)
sim = Simulator(ncells=1, gr = gr, sb=mean_size, steps = div_steps,lamb=2)
sbar = np.linspace(0.5,1.5,100)*mean_size
cv2sz = []
deltsz = []
for i in sbar:
sd, cv2 = sim.SdStat(i)
cv2sz.append(cv2)
deltsz.append(sd-i)
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
ax[0].errorbar(np.array(sbarr1), np.array(deltarr1), xerr=errsbarr1, yerr=errdeltarr1, fmt='o', mec='k', capsize=5,
markersize='8', elinewidth=3, c='k')
ax[1].errorbar(np.array(sbarr1), CV2darr1, xerr=errsbarr1, yerr=errcv2darr1, fmt='o', mec='k', capsize=5,
markersize='8', elinewidth=3, c='k')
ax[0].errorbar(np.array(sbarr2), np.array(deltarr2), xerr=errsbarr2, yerr=errdeltarr2, fmt='o', mec='k', capsize=5,
markersize='8', elinewidth=3, c='r')
ax[1].errorbar(np.array(sbarr2), CV2darr2, xerr=errsbarr2, yerr=errcv2darr2, fmt='o', mec='k', capsize=5,
markersize='8', elinewidth=3, c='r')
ax[0].errorbar(np.array(sbarr3), np.array(deltarr3), xerr=errsbarr3, yerr=errdeltarr3, fmt='o', mec='k', capsize=5,
markersize='8', elinewidth=3, c='g')
ax[1].errorbar(np.array(sbarr3), CV2darr3, xerr=errsbarr3, yerr=errcv2darr3, fmt='o', mec='k', capsize=5,
markersize='8', elinewidth=3, c='g')
ax[1].set_ylim([0, 0.3])
ax[0].set_xlabel("$s_b/\overline{s_b}$", size=20)
ax[1].set_xlabel("$s_b/\overline{s_b}$", size=20)
ax[0].set_ylabel("$\Delta/\overline{s_b}$", size=15)
ax[1].set_ylabel("$C_V^2(\Delta)$", size=15)
for l in [0, 1]:
ax[l].grid()
ax[l].tick_params(axis='x', labelsize=15)
ax[l].tick_params(axis='y', labelsize=15)
for axis in ['bottom', 'left']:
ax[l].spines[axis].set_linewidth(2)
ax[l].tick_params(axis='both', width=2, length=6)
for axis in ['top', 'right']:
ax[l].spines[axis].set_linewidth(0)
ax[l].tick_params(axis='both', width=0, length=6)
ax[0].plot(np.array(sbar)/mean_size, np.array(delttim)/mean_size, lw=2,c='g', label="$\lambda=0.5$")
ax[1].plot(np.array(sbar)/mean_size, cv2tim, lw=2, c='g')
ax[0].plot(np.array(sbar)/mean_size, np.array(deltad)/mean_size, lw=2, c='k', label="$\lambda=1$")
ax[1].plot(np.array(sbar)/mean_size, cv2ad, lw=2, c='k')
ax[0].plot(np.array(sbar)/mean_size, np.array(deltsz)/mean_size, lw=2, c='r', label="$\lambda=2$")
ax[1].plot(np.array(sbar)/mean_size, cv2sz, lw=2, c='r')
ax[0].set_ylim(0.75,1.35)
ax[1].set_ylim(0.03,0.17)
ax[0].text(0.55, 1.27, "$\lambda = 2$", rotation=-35, fontsize=10)
ax[0].text(0.55, 1.01, "$\lambda = 1$", fontsize=10)
ax[0].text(0.55, 0.87, "$\lambda = 0.5$", rotation=35, fontsize=10)
ax[1].text(0.5, 0.05, "$\lambda = 2$", rotation=15, fontsize=10)
ax[1].text(0.5, 0.11, "$\lambda = 1$", fontsize=10)
ax[1].text(0.5, 0.155, "$\lambda = 0.5$", rotation=-10, fontsize=10)
plt.savefig('./figures/full_div_strategy.eps',bbox_inches='tight')
plt.savefig('./figures/full_div_strategy.svg',bbox_inches='tight')
plt.savefig('./figures/full_div_strategy.png',bbox_inches='tight')
fig, ax = plt.subplots(2, 4, figsize=(16, 5))
data = pd.read_csv("./data/dataCRM1.csv")
tt = data.time
del data['time']
for column in data.columns[0:10]:
ax[0, 0].plot(tt/doubling_time, data[column], c="#B9B9B9", label='_nolegend_')
data = pd.read_csv("./data/dataCRM2.csv")
tt = data.time
del data['time']
for column in data.columns[0:10]:
ax[0, 1].plot(tt/doubling_time, data[column], c="#B9B9B9", label='_nolegend_')
data = pd.read_csv("./data/dataCRM3.csv")
tt = data.time
del data['time']
for column in data.columns[0:10]:
ax[0, 2].plot(tt/doubling_time, data[column], c="#B9B9B9", label='_nolegend_')
ax[0, 0].plot(np.array(timearray1)/doubling_time, mnszarray1, lw=2)
ax[0, 0].fill_between(np.array(timearray1)/doubling_time, np.array(mnszarray1)-np.array(errmnszarray1),
np.array(mnszarray1)+np.array(errmnszarray1), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0, label="SSA")
ax[1, 0].plot(np.array(timearray1)/doubling_time, cvszarray1, lw=2)
ax[1, 0].fill_between(np.array(timearray1)/doubling_time, np.array(cvszarray1)-np.array(errcv2szarray1),
np.array(cvszarray1)+np.array(errcv2szarray1), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0)
ax[0, 1].plot(np.array(timearray2)/doubling_time, mnszarray2, lw=2)
ax[0, 1].fill_between(np.array(timearray2)/doubling_time, np.array(mnszarray2)-np.array(errmnszarray2),
np.array(mnszarray2)+np.array(errmnszarray2), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0, label="SSA")
ax[1, 1].plot(np.array(timearray2)/doubling_time, cvszarray2, lw=2)
ax[1, 1].fill_between(np.array(timearray2)/doubling_time, np.array(cvszarray2)-np.array(errcv2szarray2),
np.array(cvszarray2)+np.array(errcv2szarray2), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0)
ax[0, 2].plot(np.array(timearray3)/doubling_time, mnszarray3, lw=2)
ax[0, 2].fill_between(np.array(timearray3)/doubling_time, np.array(mnszarray3)-np.array(errmnszarray3),
np.array(mnszarray3)+np.array(errmnszarray3), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0, label="SSA")
ax[1, 2].plot(np.array(timearray3)/doubling_time, cvszarray3, lw=2)
ax[1, 2].fill_between(np.array(timearray3)/doubling_time, np.array(cvszarray3)-np.array(errcv2szarray3),
np.array(cvszarray3)+np.array(errcv2szarray3), alpha=1, edgecolor='#4db8ff', facecolor='#4db8ff',
linewidth=0)
ax[0, 0].set_title("Stochastic division", fontsize=15)
ax[0, 1].set_title("Finite Initial Distribution", fontsize=15)
ax[0, 2].set_title("Noisy Splitting", fontsize=15)
data = pd.read_csv("./data/dataFSP.csv")
ax[0, 1].plot(data.time/doubling_time, data.Meansize, ls='--', c='g', label="Numeric")
ax[1, 1].plot(data.time/doubling_time, data.VarSize/data.Meansize**2, ls='--', c='g')
data = pd.read_csv("./data/dataFSP0.csv")
ax[0, 0].plot(data.time/doubling_time, data.Meansize, ls='--', c='g', label="Numeric")
ax[1, 0].plot(data.time/doubling_time, data.VarSize/data.Meansize**2, ls='--', c='g')
ax[0, 0].legend(fontsize=10)
ax[0, 1].legend(fontsize=10)
ax[0, 2].legend(fontsize=10)
ax[0, 3].errorbar(np.array(sbarr1), np.array(deltarr1), xerr=errsbarr1, yerr=errdeltarr1, fmt='o', mec='k', capsize=3,
markersize='6', elinewidth=3, c='k')
ax[1, 3].errorbar(np.array(sbarr1), CV2darr1, xerr=errsbarr1, yerr=errcv2darr1, fmt='o', mec='k', capsize=3,
markersize='6', elinewidth=3, c='k')
ax[0, 3].errorbar(np.array(sbarr2), np.array(deltarr2), xerr=errsbarr2, yerr=errdeltarr2, fmt='o', mec='k', capsize=3,
markersize='6', elinewidth=3, c='r')
ax[1, 3].errorbar(np.array(sbarr2), CV2darr2, xerr=errsbarr2, yerr=errcv2darr2, fmt='o', mec='k', capsize=3,
markersize='6', elinewidth=3, c='r')
ax[0, 3].errorbar(np.array(sbarr3), np.array(deltarr3), xerr=errsbarr3, yerr=errdeltarr3, fmt='o', mec='k', capsize=3,
markersize='6', elinewidth=3, c='g')
ax[1, 3].errorbar(np.array(sbarr3), CV2darr3, xerr=errsbarr3, yerr=errcv2darr3, fmt='o', mec='k', capsize=3,
markersize='6', elinewidth=3, c='g')
ax[0, 3].plot(np.array(sbar)/mean_size, np.array(delttim)/mean_size, lw=2, c='g', label="$\lambda=0.5$")
ax[1, 3].plot(np.array(sbar)/mean_size, cv2tim, lw=2, c='g')
ax[0, 3].plot(np.array(sbar)/mean_size, np.array(deltad)/mean_size, lw=2, c='k', label="$\lambda=1$")
ax[1, 3].plot(np.array(sbar)/mean_size, cv2ad, lw=2, c='k')
ax[0, 3].plot(np.array(sbar)/mean_size, np.array(deltsz)/mean_size, lw=2, c='r', label="$\lambda=2$")
ax[1, 3].plot(np.array(sbar)/mean_size, cv2sz, lw=2, c='r')
ax[0, 0].set_ylabel(r"$\langle s\rangle$ $(fl)$", size=15)
ax[1, 0].set_ylabel("$C_V^2(s)$", size=15)
ax[1, 0].set_xlabel(r"$t/\tau$", size=15)
ax[1, 1].set_xlabel(r"$t/\tau$", size=15)
ax[1, 2].set_xlabel(r"$t/\tau$", size=15)
ax[1, 3].set_xlabel(r"$s_b/\overline{s_b}$", size=15)
for l in [0, 1]:
for m in [0, 1, 2, 3]:
ax[l, m].grid()
ax[l, m].tick_params(axis='x', labelsize=12)
ax[l, m].tick_params(axis='y', labelsize=12)
for axis in ['bottom', 'left']:
ax[l, m].spines[axis].set_linewidth(2)
ax[l, m].tick_params(axis='both', width=2, length=6)
for axis in ['top', 'right']:
ax[l, m].spines[axis].set_linewidth(0)
ax[l, m].tick_params(axis='both', width=0, length=6)
if m != 3:
ax[l, m].set_xlim([0, 6])
taqui = np.arange(0, 7, step=1)
ax[l, m].set_xticks(np.array(taqui))
taqui = np.arange(0, 0.13, step=0.02)
ax[1, m].set_yticks(np.array(taqui))
taqui = np.arange(0.5, 3.5, step=0.5)
ax[0, m].set_yticks(np.array(taqui))
ax[1, m].set_ylim([0, 0.13])
ax[0, m].set_ylim([0.5, 2.9])
plt.subplots_adjust(hspace=0.3, wspace=0.35)
if not os.path.exists('./figures'):
os.makedirs('./figures')
ax[0, 0].set_title("Stochastic division", fontsize=15)
ax[0, 1].set_title("Finite Initial Distribution", fontsize=15)
ax[0, 2].set_title("Noisy Splitting", fontsize=15)
ax[0, 3].set_title("Division Strategy", fontsize=15)
ax[0, 3].set_ylim(0.75, 1.35)
ax[1, 3].set_ylim(0.03, 0.17)
ax[0, 3].text(0.5, 1.31, "$\lambda = 2$", rotation=-35, fontsize=10)
ax[0, 3].text(0.5, 1.01, "$\lambda = 1$", fontsize=10)
ax[0, 3].text(0.5, 0.9, "$\lambda = 0.5$", rotation=35, fontsize=10)
ax[1, 3].text(0.5, 0.055, "$\lambda = 2$", rotation=12, fontsize=10)
ax[1, 3].text(0.5, 0.11, "$\lambda = 1$", fontsize=10)
ax[1, 3].text(0.5, 0.16, "$\lambda = 0.5$", rotation=-10, fontsize=10)
ax[0, 3].set_ylabel(r"$\Delta/\overline{s_b}$", size=15)
ax[1, 3].set_ylabel(r"$C_v^2(\Delta)$", size=15)
ax[0, 0].text(-1, 3, "a)", fontsize=15)
ax[0, 1].text(-1, 3., "b)", fontsize=15)
ax[0, 2].text(-1, 3., "c)", fontsize=15)
ax[1, 0].text(-1, 0.13, "e)", fontsize=15)
ax[1, 1].text(-1, 0.13, "f)", fontsize=15)
ax[1, 2].text(-1, 0.13, "g)", fontsize=15)
ax[0, 3].text(0.25, 1.35, "d)", fontsize=15)
ax[1, 3].text(0.25, 0.17, "h)", fontsize=15)
plt.savefig('./figures/full_size_statistics_comparison.svg', bbox_inches='tight')
plt.savefig('./figures/full_size_statistics_comparison.png', bbox_inches='tight')
plt.savefig('./figures/full_size_statistics_comparison.eps', bbox_inches='tight')
| 40.907801 | 119 | 0.656987 | 3,837 | 23,072 | 3.851707 | 0.077404 | 0.053996 | 0.018269 | 0.014074 | 0.865011 | 0.842953 | 0.828338 | 0.802558 | 0.760133 | 0.72373 | 0 | 0.071066 | 0.138219 | 23,072 | 563 | 120 | 40.980462 | 0.672233 | 0.001994 | 0 | 0.523077 | 0 | 0 | 0.097311 | 0.02246 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.017582 | 0 | 0.017582 | 0.021978 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f2fa4e4973f96962abb9e344b92100d19a26a324 | 4,848 | py | Python | example_h1.py | rot256/lelele | 23b03e5d786f934a0ce2acea6630a485f35f50c3 | [
"MIT"
] | 4 | 2021-11-01T14:54:30.000Z | 2021-11-10T01:49:37.000Z | example_h1.py | rot256/lelele | 23b03e5d786f934a0ce2acea6630a485f35f50c3 | [
"MIT"
] | null | null | null | example_h1.py | rot256/lelele | 23b03e5d786f934a0ce2acea6630a485f35f50c3 | [
"MIT"
] | null | null | null | from lelele import *
n = 8948962207650232551656602815159153422162609644098354511344597187200057010413418528378981730643524959857451398370029280583094215613882043973354392115544169
B = 32
L = 512
inv = 809037182693296649508274663574311665602072311159314871104234612434053535801385254074066516094470888810590831727492223370598442029694376099092663685681553
t = 5159249867668349911243806627181242575833458202543206475047697474526358261869186924011382790189208655648910161810615828513665526818684388075299493416861603
u = 6993593402624441730478397271530050151544407547792883514757931020250156884687820739065010112505728782340758650089770258982037764124192083288488753903779687
ti = [4294967296, 18446744073709551616, 79228162514264337593543950336, 340282366920938463463374607431768211456, 1461501637330902918203684832716283019655932542976, 6277101735386680763835789423207666416102355444464034512896, 26959946667150639794667015087019630673637144422540572481103610249216, 115792089237316195423570985008687907853269984665640564039457584007913129639936, 497323236409786642155382248146820840100456150797347717440463976893159497012533375533056, 2135987035920910082395021706169552114602704522356652769947041607822219725780640550022962086936576, 9173994463960286046443283581208347763186259956673124494950355357547691504353939232280074212440502746218496, 39402006196394479212279040100143613805079739270465446667948293404245721771497210611414266254884915640806627990306816, 169230328010303641331690318856389386196071598838855992136870091590247882556495704531248437872567112920983350278405979725889536, 726838724295606890549323807888004534353641360687318060281490199180639288113397923326191050713763565560762521606266177933534601628614656, 3121748550315992231381597229793166305748598142664971150859156959625371738819765620120306103063491971159826931121406622895447975679288285306290176, 5159249867668349911243806627181242575833458202543206475047697474526358261869186924011382790189208655648910161810615828513665526818684388075299493416861603, 1615056927036222818536540436109511519897880109009208407005747059412815424582253519683500743111683921432735379004679626245981298691864745311539294454869738, 165033952520132305845221975095335876275310054958068643604221901334921373713378731448193168912109735395014614900730614330295001823627033790824622720574798, 8538993764909927512725465125072948411611389093098376500017490333638579940195855321816766152050399699417663249783916519027558890177789891145279765673646186, 7437844578275358797920905804207733541334697030084163577642854216151366443130584275911579241324264849909853017288917439589749179986446785278284519597153548, 671961560180748200175764921083712656678424119063417676317140427682003478526673548807501634025370008436823855766736660119782480731755259340313925289100308, 4090498791478852095796373200800494142856281205280292783724202877291646007887551223487729744254706027507400213499428971261412228401440093576471682819293047, 1309129359049556234897378024648248702416927627124507615866715695323143581577221641746061500406639027631560634996165695404506230854702778948093571179423237, 7872694592936430487407260201144407041197521404075260551797350619721896669850754337069964658720737745810244292385162908059379521538887504875334828092395573, 4227537719683061951200547278578466784178344867945907007156646279435530638234534167791463591546519431255322194844837519233856146008122260876804939528407469, 6295805285142874717046908583316057841361155719229253575489532306657145726724216717016350010309628218130567815868098313194386117116767265341631593139695818, 6789891312924109772207020745457489661673756461148293944786807980125263681920809006865701942100237692522027476906559843914728343837358455258810492286648726, 68014054937275377984618706023347270571161826637680939845006761384208345950039868325812735313880442026491007547740460351261192519926976103078575735154674, 5100638363729968122847966899845984045067415870781405759299587418132822364387310366297232822090588331870734853397859494587628588710081739832263074650376077, 3859739349047082709153133922872005952332653211271251798099273208830273538376012742302101850398304078087861545008913026711753858017524849463175791327909591, 1142479709472781717609648600178318148593919213242417191766100639042258601494469986554560718079956978800535690595313107166646008506058297217318781007756673]
le = LeLeLe()
q = le.var()
V = [le.var().short() for _ in range(len(ti))]
ti[0] * V[0] + ti[1] * V[1]
# define short linear combination mod n
w = sum([t*v for (v, t) in zip(V, ti)])
w += inv * u * q
w %= n
w.short()
# q should be taken at most once: require that q * <<large number>> is small
(q * 0x100).short()
# print a description of the constraint system
print(le)
# find a solution
le.solve()
# print values assigned in solution
print(-int(w), [int(v) for v in V])
| 124.307692 | 3,690 | 0.941419 | 139 | 4,848 | 32.827338 | 0.647482 | 0.002192 | 0.002192 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.906783 | 0.033003 | 4,848 | 38 | 3,691 | 127.578947 | 0.066553 | 0.042698 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.001079 | 0 | 0 | 1 | 0 | false | 0 | 0.05 | 0 | 0.05 | 0.1 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
840bc7318b5440a5af3d7ce8a187f0ad0b60e663 | 137 | py | Python | pyflow/__init__.py | ajenkins-cargometrics/pyflow | 277dfd091e96a51672ace462a2368f80e0f42b1e | [
"MIT"
] | 5 | 2017-04-19T13:20:53.000Z | 2021-09-10T11:43:57.000Z | pyflow/__init__.py | ajenkins-cargometrics/pyflow | 277dfd091e96a51672ace462a2368f80e0f42b1e | [
"MIT"
] | 6 | 2017-02-22T23:11:22.000Z | 2017-09-08T21:42:00.000Z | pyflow/__init__.py | ajenkins-cargometrics/pyflow | 277dfd091e96a51672ace462a2368f80e0f42b1e | [
"MIT"
] | 1 | 2017-05-29T00:38:51.000Z | 2017-05-29T00:38:51.000Z | from .workflow import *
from .decider import *
from .exceptions import *
from .invocation_descriptors import *
from .utils import logger
| 22.833333 | 37 | 0.788321 | 17 | 137 | 6.294118 | 0.529412 | 0.373832 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145985 | 137 | 5 | 38 | 27.4 | 0.91453 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
84204d6830e05687c2fca7f8ce21fc1a7ddf1e62 | 22 | py | Python | torch_ssge/__init__.py | nonconvexopt/pytorch_ssge | 3e07e1748d97bd227c66f76ee76c04dc3e8b4ec4 | [
"MIT"
] | null | null | null | torch_ssge/__init__.py | nonconvexopt/pytorch_ssge | 3e07e1748d97bd227c66f76ee76c04dc3e8b4ec4 | [
"MIT"
] | 1 | 2022-01-11T10:37:49.000Z | 2022-01-11T10:37:49.000Z | torch_ssge/__init__.py | nonconvexopt/pytorch_ssge | 3e07e1748d97bd227c66f76ee76c04dc3e8b4ec4 | [
"MIT"
] | null | null | null | from .core import SSGE | 22 | 22 | 0.818182 | 4 | 22 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 22 | 1 | 22 | 22 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8423bfa55d1fa843003eee836ed1438323c25dfd | 116 | py | Python | bulq/templates/transform/{{cookiecutter.project_slug}}/{{cookiecutter.plugin_package}}/__init__.py | koji-m/bulq | 78f97d2e57d6bcb0ec3fa2b0c7539db3ebaa104a | [
"Apache-2.0"
] | null | null | null | bulq/templates/transform/{{cookiecutter.project_slug}}/{{cookiecutter.plugin_package}}/__init__.py | koji-m/bulq | 78f97d2e57d6bcb0ec3fa2b0c7539db3ebaa104a | [
"Apache-2.0"
] | null | null | null | bulq/templates/transform/{{cookiecutter.project_slug}}/{{cookiecutter.plugin_package}}/__init__.py | koji-m/bulq | 78f97d2e57d6bcb0ec3fa2b0c7539db3ebaa104a | [
"Apache-2.0"
] | null | null | null | from . import {{cookiecutter.plugin_module}}
plugin = {{cookiecutter.plugin_module}}.{{cookiecutter.plugin_class}}
| 29 | 69 | 0.775862 | 12 | 116 | 7.25 | 0.5 | 0.62069 | 0.551724 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.068966 | 116 | 3 | 70 | 38.666667 | 0.805556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.5 | null | null | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
8439fc32b81273a719f98b4f2b893f579523d19d | 28 | py | Python | TrainingRestnet18withTinyImagenetDataset/src/visualization/weights/__init__.py | csharpshooter/DeepLearning | c1d20660c32076468970f7376931e1fcd0d2644e | [
"MIT"
] | null | null | null | TrainingRestnet18withTinyImagenetDataset/src/visualization/weights/__init__.py | csharpshooter/DeepLearning | c1d20660c32076468970f7376931e1fcd0d2644e | [
"MIT"
] | null | null | null | TrainingRestnet18withTinyImagenetDataset/src/visualization/weights/__init__.py | csharpshooter/DeepLearning | c1d20660c32076468970f7376931e1fcd0d2644e | [
"MIT"
] | null | null | null | from .weights import Weights | 28 | 28 | 0.857143 | 4 | 28 | 6 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107143 | 28 | 1 | 28 | 28 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
844d72de799b74ae16be83794355b268ae1c4a5a | 3,916 | py | Python | utils.py | atishdixit16/ressim | 48721ab6c2da11dd9ef0540c95598cf04e6bb8d9 | [
"MIT"
] | 3 | 2018-11-28T16:45:23.000Z | 2020-10-21T02:08:31.000Z | utils.py | atishdixit16/ressim | 48721ab6c2da11dd9ef0540c95598cf04e6bb8d9 | [
"MIT"
] | null | null | null | utils.py | atishdixit16/ressim | 48721ab6c2da11dd9ef0540c95598cf04e6bb8d9 | [
"MIT"
] | 5 | 2018-11-28T16:46:51.000Z | 2021-08-19T13:58:56.000Z | """ Useful functions for reservoir simulation tasks """
import numpy
def linear_mobility(s, mu_w, mu_o, s_wir, s_oir, deriv=False):
""" Linear mobility model
Parameters
----------
s : ndarray, shape (ny, nx) | (ny*nx,)
Saturation
mu_w : float
Viscosity of water
mu_o : float
Viscosity of oil
s_wir : float
Irreducible water saturation
s_oir : float
Irreducible oil saturation
deriv : bool
If True, also return derivatives
Returns
-------
if deriv=False,
lamb_w, lamb_o : (2x) ndarray, shape (ny, nx) | (ny*nx,)
lamb_w : water mobility
lamb_o : oil mobility
if deriv=True,
lamb_w, lamb_o, dlamb_w, dlamb_o : (4x) ndarray, shape (ny, nx) | (ny*nx,)
lamb_w : water mobility
lamb_o : oil mobility
dlamb_w : derivative of water mobility
dlamb_o : derivative of oil mobility
"""
mu_w, mu_o, s_wir, s_oir = float(mu_w), float(mu_o), float(s_wir), float(s_oir)
_s = (s-s_wir)/(1.0-s_wir-s_oir)
lamb_w = _s/mu_w
lamb_o = (1.0-_s)/mu_o
if deriv:
dlamb_w = 1.0/(mu_w*(1.0-s_wir-s_oir))
dlamb_o = -1.0/(mu_o*(1.0-s_wir-s_oir))
return lamb_w, lamb_o, dlamb_w, dlamb_o
return lamb_w, lamb_o
def quadratic_mobility(s, mu_w, mu_o, s_wir, s_oir, deriv=False):
""" Quadratic mobility model
Parameters
----------
s : ndarray, shape (ny, nx) | (ny*nx,)
Saturation
mu_w : float
Viscosity of water
mu_o : float
Viscosity of oil
s_wir : float
Irreducible water saturation
s_oir : float
Irreducible oil saturation
deriv : bool
If True, also return derivatives
Returns
-------
if deriv=False,
lamb_w, lamb_o : (2x) ndarray, shape (ny, nx) | (ny*nx,)
lamb_w : water mobility
lamb_o : oil mobility
if deriv=True,
lamb_w, lamb_o, dlamb_w, dlamb_o : (4x) ndarray, shape (ny, nx) | (ny*nx,)
lamb_w : water mobility
lamb_o : oil mobility
dlamb_w : derivative of water mobility
dlamb_o : derivative of oil mobility
"""
mu_w, mu_o, s_wir, s_oir = float(mu_w), float(mu_o), float(s_wir), float(s_oir)
_s = (s-s_wir)/(1.0-s_wir-s_oir)
lamb_w = _s**2/mu_w
lamb_o = (1.0-_s)**2/mu_o
if deriv:
dlamb_w = 2.0*_s/(mu_w*(1.0-s_wir-s_oir))
dlamb_o = -2.0*(1.0-_s)/(mu_o*(1.0-s_wir-s_oir))
return lamb_w, lamb_o, dlamb_w, dlamb_o
return lamb_w, lamb_o
def f_fn(s, mobi_fn):
""" Water fractional flow
Parameters
----------
s : ndarray, shape (ny, nx) | (ny*nx,)
Saturation
mobi_fn : callable
Mobility function lamb_w, lamb_o = mobi_fn(s) where:
lamb_w : water mobility
lamb_o : oil mobility
"""
lamb_w, lamb_o = mobi_fn(s)
return lamb_w / (lamb_w + lamb_o)
def df_fn(s, mobi_fn):
""" Derivative (element-wise) of water fractional flow
Parameters
----------
s : ndarray, shape (ny, nx) | (ny*nx,)
Saturation
mobi_fn : callable
Mobility function lamb_w, lamb_o, dlamb_w, dlamb_o = mobi_fn(s, deriv=True) where:
lamb_w : water mobility
lamb_o : oil mobility
dlamb_w : derivative of water mobility
dlamb_o : derivative of oil mobility
"""
lamb_w, lamb_o, dlamb_w, dlamb_o = mobi_fn(s, deriv=True)
return dlamb_w / (lamb_w + lamb_o) - lamb_w * (dlamb_w + dlamb_o) / (lamb_w + lamb_o)**2
def lamb_fn(s, mobi_fn):
""" Total mobility
Parameters
----------
s : ndarray, shape (ny, nx) | (ny*nx,)
Saturation
mobi_fn : callable
Mobility function lamb_w, lamb_o = mobi_fn(s) where:
lamb_w : water mobility
lamb_o : oil mobility
"""
lamb_w, lamb_o = mobi_fn(s)
return lamb_w + lamb_o
| 25.594771 | 92 | 0.584525 | 609 | 3,916 | 3.495895 | 0.098522 | 0.068107 | 0.056364 | 0.084547 | 0.901832 | 0.890559 | 0.875528 | 0.865195 | 0.865195 | 0.862846 | 0 | 0.011965 | 0.29571 | 3,916 | 152 | 93 | 25.763158 | 0.759971 | 0.583759 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0.033333 | 0 | 0.433333 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ffdd1f4da3f6d6491fce24afe0f71faddd73f04b | 99 | py | Python | example/test/test_hello_alice.py | FoundryAI/spark-pytest | 6ece4c72a811da570029dadd913214e0ecc4b584 | [
"MIT"
] | 3 | 2020-04-13T22:05:45.000Z | 2020-08-04T21:09:12.000Z | example/test/test_hello_alice.py | FoundryAI/spark-pytest | 6ece4c72a811da570029dadd913214e0ecc4b584 | [
"MIT"
] | null | null | null | example/test/test_hello_alice.py | FoundryAI/spark-pytest | 6ece4c72a811da570029dadd913214e0ecc4b584 | [
"MIT"
] | null | null | null | from src.hello_alice import hello_alice
def test_hello_alice():
assert hello_alice() == 3600
| 16.5 | 39 | 0.757576 | 15 | 99 | 4.666667 | 0.6 | 0.571429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.048193 | 0.161616 | 99 | 5 | 40 | 19.8 | 0.795181 | 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 | 0 | 0 | 0 | 6 |
08053f2a44659d41e9d7fccf186e42a702cd8755 | 1,389 | py | Python | ado1/plot_graph.py | ViniciusLimaFernandes/systems-and-signals | 372f309bdbce8eecc2b3e70cf009bf83f1a74e04 | [
"MIT"
] | null | null | null | ado1/plot_graph.py | ViniciusLimaFernandes/systems-and-signals | 372f309bdbce8eecc2b3e70cf009bf83f1a74e04 | [
"MIT"
] | null | null | null | ado1/plot_graph.py | ViniciusLimaFernandes/systems-and-signals | 372f309bdbce8eecc2b3e70cf009bf83f1a74e04 | [
"MIT"
] | null | null | null | import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
def plotWithYAxis(yPoints, yLabel, graphTitle, fileName):
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
ax.plot(yPoints)
plt.ylabel(yLabel)
plt.title(graphTitle)
fig.savefig('./graphs/graph{}.png'.format(fileName))
print("Your graph are done! File: graphs/graph{}.png".format(fileName))
def plotGraph(xPoints,x2Points,xLabel,yPoints, y2Points,yLabel,graphTitle, fileName):
print("\nCreating graph {} ...".format(fileName))
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
ax.plot(xPoints, yPoints)
ax.plot(x2Points, y2Points)
plt.xlabel(xLabel)
plt.ylabel(yLabel)
plt.title(graphTitle)
fig.savefig('./graphs/graph{}.png'.format(fileName))
print("Your graph are done! File: graphs/graph{}.png".format(fileName))
def plotGraphThree(xPoints,x2Points,x3Points,xLabel,yPoints, y2Points,y3Points,yLabel,graphTitle, fileName):
print("\nCreating graph {} ...".format(fileName))
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
ax.plot(xPoints, yPoints)
ax.plot(x2Points, y2Points)
ax.plot(x3Points, y3Points)
plt.xlabel(xLabel)
plt.xticks(rotation=35)
plt.ylabel(yLabel)
plt.title(graphTitle)
fig.savefig('./graphs/graph{}.png'.format(fileName))
print("Your graph are done! File: graphs/graph{}.png".format(fileName))
| 29.553191 | 108 | 0.718503 | 188 | 1,389 | 5.292553 | 0.25 | 0.112563 | 0.084422 | 0.120603 | 0.713568 | 0.713568 | 0.713568 | 0.713568 | 0.713568 | 0.713568 | 0 | 0.026101 | 0.117351 | 1,389 | 46 | 109 | 30.195652 | 0.785481 | 0 | 0 | 0.742857 | 0 | 0 | 0.175666 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.085714 | false | 0 | 0.057143 | 0 | 0.142857 | 0.142857 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
083c7b21886681043f120c7425535bdf229b73db | 40 | py | Python | pyNeuralEMPC/objective/__init__.py | Enderdead/pyNeuralEMPC | 032a3675b10389c10bf3e687633462b489b5f26f | [
"MIT"
] | 2 | 2021-08-23T19:05:35.000Z | 2022-02-24T20:32:04.000Z | pyNeuralEMPC/objective/__init__.py | Enderdead/pyNeuralEMPC | 032a3675b10389c10bf3e687633462b489b5f26f | [
"MIT"
] | null | null | null | pyNeuralEMPC/objective/__init__.py | Enderdead/pyNeuralEMPC | 032a3675b10389c10bf3e687633462b489b5f26f | [
"MIT"
] | null | null | null |
from pyNeuralEMPC.objective import jax | 13.333333 | 38 | 0.85 | 5 | 40 | 6.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 40 | 3 | 38 | 13.333333 | 0.971429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f2a3f6d83cec9a013eb5015aea864ee51bf9b939 | 5,249 | py | Python | python/helper/load_data.py | stefanseibert/interactive-ml | c9af95678264c9da9b6041b96be0a474d724aae0 | [
"Apache-2.0"
] | 1 | 2018-02-19T22:15:05.000Z | 2018-02-19T22:15:05.000Z | python/helper/load_data.py | stefanseibert/interactive-ml | c9af95678264c9da9b6041b96be0a474d724aae0 | [
"Apache-2.0"
] | null | null | null | python/helper/load_data.py | stefanseibert/interactive-ml | c9af95678264c9da9b6041b96be0a474d724aae0 | [
"Apache-2.0"
] | null | null | null | import OpenEXR
import Imath
import array
import numpy as np
from helper.constants import get_width, get_height, get_channels
IMG_WIDTH = get_width()
IMG_HEIGHT = get_height()
IMG_CHANNELS = get_channels()
def read_input_data(near, far, r_channel, g_channel, b_channel, d_channel):
data = np.zeros([1,IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS], dtype=np.float32)
value_range = far - near
for y in range(IMG_HEIGHT):
for x in range(IMG_WIDTH):
pos = y * IMG_WIDTH + x
normalized_d = (d_channel[pos] - near) / value_range
data[0, x, y, 0] = r_channel[pos]
data[0, x, y, 1] = g_channel[pos]
data[0, x, y, 2] = b_channel[pos]
data[0, x, y, 3] = normalized_d
return data
def read_truth_data(t_channel):
data = np.zeros([1, IMG_WIDTH, IMG_HEIGHT, 1], dtype=np.float32)
for y in range(IMG_HEIGHT):
for x in range(IMG_WIDTH):
pos = y * IMG_WIDTH + x
data[0, x, y, 0] = t_channel[pos]
return data
def read_input_set(modellocation, count, path):
near_range = 0.1
far_range = 1000.0
models = []
with open(modellocation) as f:
for line in f:
if line.endswith("\n"):
line = line[:-1]
models.append(line)
model_count = 0
data = np.zeros([len(models), IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS], dtype=np.float32)
for model in models:
input_filename = path + model + "/input_" + model + "_" + str(count) + ".exr"
img_file = OpenEXR.InputFile(input_filename)
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
R,G,B = [ array.array('f', img_file.channel(Chan, FLOAT)).tolist() for Chan in ("R", "G", "B") ]
D = array.array('f', img_file.channel("depth.V", FLOAT)).tolist()
value_range = far_range - near_range
for y in range(IMG_HEIGHT):
for x in range(IMG_WIDTH):
pos = y * IMG_WIDTH + x
normalized_d = (D[pos] - near_range) / value_range
data[model_count, x, y, 0] = R[pos]
data[model_count, x, y, 1] = G[pos]
data[model_count, x, y, 2] = B[pos]
data[model_count, x, y, 3] = normalized_d
model_count += 1
return data
def read_input_set_slim(modellocation, count, path):
near_range = 0.1
far_range = 1000.0
models = []
with open(modellocation) as f:
for line in f:
if line.endswith("\n"):
line = line[:-1]
models.append(line)
model_count = 0
data = np.zeros([len(models), IMG_WIDTH, IMG_HEIGHT, 1], dtype=np.float32)
for model in models:
input_filename = path + model + "/input_" + model + "_" + str(count) + ".exr"
img_file = OpenEXR.InputFile(input_filename)
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
D = array.array('f', img_file.channel("depth.V", FLOAT)).tolist()
value_range = far_range - near_range
for y in range(IMG_HEIGHT):
for x in range(IMG_WIDTH):
pos = y * IMG_WIDTH + x
normalized_d = (D[pos] - near_range) / value_range
data[model_count, x, y, 0] = normalized_d
model_count += 1
return data
def read_truth_set(modellocation, count, path):
models = []
with open(modellocation) as f:
for line in f:
if line.endswith("\n"):
line = line[:-1]
models.append(line)
model_count = 0
data = np.zeros([len(models), IMG_WIDTH, IMG_HEIGHT, 1], dtype=np.float32)
for model in models:
truth_filename = path + model + "/groundtruth_" + model + "_" + str(count) + ".exr"
img_file = OpenEXR.InputFile(truth_filename)
T = array.array('f', img_file.channel("R", Imath.PixelType(Imath.PixelType.FLOAT))).tolist()
for y in range(IMG_HEIGHT):
for x in range(IMG_WIDTH):
pos = y * IMG_WIDTH + x
data[model_count, x, y, 0] = T[pos]
model_count += 1
return data
def read_input_file(input_filename):
img_file = OpenEXR.InputFile(input_filename)
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
R,G,B = [ array.array('f', img_file.channel(Chan, FLOAT)).tolist() for Chan in ("R", "G", "B") ]
D = array.array('f', img_file.channel("depth.V", FLOAT)).tolist()
return R,G,B,D
def read_truth_file(truth_filename):
img_file = OpenEXR.InputFile(truth_filename)
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
T = array.array('f', img_file.channel("R", FLOAT)).tolist()
return T
def read_input_files(input_filenames):
for filename in input_filenames:
img_file = OpenEXR.InputFile(filename)
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
R,G,B = [ array.array('f', img_file.channel(Chan, FLOAT)).tolist() for Chan in ("R", "G", "B") ]
D = array.array('f', img_file.channel("depth.V", FLOAT)).tolist()
return R,G,B,D
def read_truth_files(truth_filenames):
for filename in truth_filenames:
img_file = OpenEXR.InputFile(filename)
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
T = array.array('f', img_file.channel("R", FLOAT)).tolist()
return T
| 37.76259 | 104 | 0.603353 | 750 | 5,249 | 4.041333 | 0.1 | 0.039261 | 0.032992 | 0.046189 | 0.851534 | 0.818212 | 0.755526 | 0.755526 | 0.721214 | 0.654899 | 0 | 0.014022 | 0.266336 | 5,249 | 138 | 105 | 38.036232 | 0.773046 | 0 | 0 | 0.696721 | 0 | 0 | 0.01867 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.07377 | false | 0 | 0.040984 | 0 | 0.188525 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4b9f4853ad49660bfff88c85a674c9958fe37cc6 | 20,832 | py | Python | eeauditor/auditors/aws/Amazon_SQS_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 442 | 2020-03-15T20:56:36.000Z | 2022-03-31T22:13:07.000Z | eeauditor/auditors/aws/Amazon_SQS_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 57 | 2020-03-15T22:09:56.000Z | 2022-03-31T13:17:06.000Z | eeauditor/auditors/aws/Amazon_SQS_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 59 | 2020-03-15T21:19:10.000Z | 2022-03-31T15:01:31.000Z | #This file is part of ElectricEye.
#SPDX-License-Identifier: Apache-2.0
#Licensed to the Apache Software Foundation (ASF) under one
#or more contributor license agreements. See the NOTICE file
#distributed with this work for additional information
#regarding copyright ownership. The ASF licenses this file
#to you 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 datetime
from dateutil import parser
import boto3
import json
from check_register import CheckRegister
registry = CheckRegister()
sqs = boto3.client("sqs")
cloudwatch = boto3.client("cloudwatch")
def list_queues(cache):
response = cache.get("list_queues")
if response:
return response
cache["list_queues"] = sqs.list_queues()
return cache["list_queues"]
@registry.register_check("sqs")
def sqs_old_message_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SQS.1] SQS messages should not be older than 80 percent of message retention"""
response = list_queues(cache)
iso8601Time = datetime.datetime.now(datetime.timezone.utc).isoformat()
if 'QueueUrls' in response:
for queueUrl in response["QueueUrls"]:
queueName = queueUrl.rsplit("/", 1)[-1]
attributes = sqs.get_queue_attributes(
QueueUrl=queueUrl, AttributeNames=["MessageRetentionPeriod", "QueueArn"]
)
messageRetention = attributes["Attributes"]["MessageRetentionPeriod"]
queueArn = attributes["Attributes"]["QueueArn"]
metricResponse = cloudwatch.get_metric_data(
MetricDataQueries=[
{
"Id": "m1",
"MetricStat": {
"Metric": {
"Namespace": "AWS/SQS",
"MetricName": "ApproximateAgeOfOldestMessage",
"Dimensions": [{"Name": "QueueName", "Value": queueName}],
},
"Period": 3600,
"Stat": "Maximum",
"Unit": "Seconds",
},
},
],
StartTime=datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(days=1),
EndTime=datetime.datetime.now(datetime.timezone.utc),
)
metrics = metricResponse["MetricDataResults"]
counter = 0
fail = False
for metric in metrics:
for value in metric["Values"]:
if value > int(messageRetention) * 0.8:
counter += 1
if counter > 2:
fail = True
break
if not fail:
finding = {
"SchemaVersion": "2018-10-08",
"Id": queueArn + "/sqs-old-message-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": queueArn,
"AwsAccountId": awsAccountId,
"Types": ["Software and Configuration Checks/AWS Security Best Practices"],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SQS.1] SQS messages should not be older than 80 percent of message retention",
"Description": "SQS queue "
+ queueName
+ " has not had at least 3 messages waiting for longer than 80 percent of the message retention.",
"Remediation": {
"Recommendation": {
"Text": "For more information on best practices for SQS queue messages refer to the Quotas related to messages section of the Amazon SQS Developer Guide",
"Url": "https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-quotas.html#quotas-messages",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsSqsQueue",
"Id": queueArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {"AwsSqsQueue": {"QueueName": queueName}}
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF ID.AM-2",
"NIST SP 800-53 CM-8",
"NIST SP 800-53 PM-5",
"AICPA TSC CC3.2",
"AICPA TSC CC6.1",
"ISO 27001:2013 A.8.1.1",
"ISO 27001:2013 A.8.1.2",
"ISO 27001:2013 A.12.5.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
finding = {
"SchemaVersion": "2018-10-08",
"Id": queueArn + "/sqs-old-message-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": queueArn,
"AwsAccountId": awsAccountId,
"Types": ["Software and Configuration Checks/AWS Security Best Practices"],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SQS.1] SQS messages should not be older than 80 percent of message retention",
"Description": "SQS queue "
+ queueName
+ " has had at least 3 messages waiting for longer than 80 percent of the message retention.",
"Remediation": {
"Recommendation": {
"Text": "For more information on best practices for SQS queue messages refer to the Quotas related to messages section of the Amazon SQS Developer Guide",
"Url": "https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-quotas.html#quotas-messages",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsSqsQueue",
"Id": queueArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {"AwsSqsQueue": {"QueueName": queueName}}
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF ID.AM-2",
"NIST SP 800-53 CM-8",
"NIST SP 800-53 PM-5",
"AICPA TSC CC3.2",
"AICPA TSC CC6.1",
"ISO 27001:2013 A.8.1.1",
"ISO 27001:2013 A.8.1.2",
"ISO 27001:2013 A.12.5.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
else:
# No queues listed
pass
@registry.register_check("sqs")
def sqs_queue_encryption_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SQS.2] SQS queues should use Server Side encryption"""
response = list_queues(cache)
iso8601Time = datetime.datetime.now(datetime.timezone.utc).isoformat()
if 'QueueUrls' in response:
for queueUrl in response["QueueUrls"]:
queueName = queueUrl.rsplit("/", 1)[-1]
attributes = sqs.get_queue_attributes(
QueueUrl=queueUrl, AttributeNames=["QueueArn", "KmsMasterKeyId"]
)
queueArn=attributes["Attributes"]["QueueArn"]
queueEncryption=attributes["Attributes"].get('KmsMasterKeyId')
if queueEncryption != None:
finding = {
"SchemaVersion": "2018-10-08",
"Id": queueArn + "/sqs_queue_encryption_check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": queueArn,
"AwsAccountId": awsAccountId,
"Types": ["Software and Configuration Checks/AWS Security Best Practices"],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SQS.2] SQS queues should use Server Side encryption",
"Description": f"SQS queue {queueName} has Server Side encryption enabled.",
"Remediation": {
"Recommendation": {
"Text": "For more information on best practices for encryption of SQS queues, refer to the Data Encryption section of the Amazon SQS Developer Guide",
"Url": "https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-server-side-encryption.html",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsSqsQueue",
"Id": queueArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsSqsQueue": {
"QueueName": queueName,
"KmsMasterKeyId": str(queueEncryption)
}
}
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.DS-1",
"NIST CSF PR.DS-5",
"NIST CSF PR.PT-3",
"AICPA TSC CC6.1",
"ISO 27001:2013 A.8.2.3"
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
finding = {
"SchemaVersion": "2018-10-08",
"Id": queueArn + "/sqs_queue_encryption_check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": queueArn,
"AwsAccountId": awsAccountId,
"Types": ["Software and Configuration Checks/AWS Security Best Practices"],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "HIGH"},
"Confidence": 99,
"Title": "[SQS.2] SQS queues should use server side encryption",
"Description": f"SQS queue {queueName} has not enabled Server side encryption. Refer to the recommendations to remediate.",
"Remediation": {
"Recommendation": {
"Text": "For more information on best practices for encryption of SQS queues, refer to the Data Encryption section of the Amazon SQS Developer Guide",
"Url": "https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-server-side-encryption.html",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsSqsQueue",
"Id": queueArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {"AwsSqsQueue": {"QueueName": queueName}}
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.DS-1",
"NIST CSF PR.DS-5",
"NIST CSF PR.PT-3",
"AICPA TSC CC6.1",
"ISO 27001:2013 A.8.2.3"
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
else:
# No queues listed
pass
@registry.register_check("sqs")
def sqs_queue_public_accessibility_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SQS.3] SQS queues should not be unconditionally open to the public"""
response = list_queues(cache)
iso8601Time = datetime.datetime.now(datetime.timezone.utc).isoformat()
if 'QueueUrls' in response:
for queueUrl in response["QueueUrls"]:
queueName = queueUrl.rsplit("/", 1)[-1]
attributes = sqs.get_queue_attributes(
QueueUrl=queueUrl, AttributeNames=["QueueArn", "Policy"]
)
queueArn=attributes["Attributes"]["QueueArn"]
queuePolicy=json.loads(attributes["Attributes"]["Policy"])
accessibility = "not_public"
for statement in queuePolicy["Statement"]:
if statement["Effect"] == 'Allow':
if statement.get("Principal") == '*':
if statement.get('Condition') == None:
accessibility = "public"
if accessibility == "not_public":
finding = {
"SchemaVersion": "2018-10-08",
"Id": queueArn + "/sqs_queue_public_accessibility_check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": queueArn,
"AwsAccountId": awsAccountId,
"Types": ["Software and Configuration Checks/AWS Security Best Practices"],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SQS.3] SQS queues should not be unconditionally open to the public",
"Description": f"SQS queue {queueName} is not unconditionally open to the public.",
"Remediation": {
"Recommendation": {
"Text": "For more information on best practices for SQS Policies, refer to the Identity and Access Management section of the Amazon SQS Developer Guide",
"Url": "https://docs.amazonaws.cn/en_us/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-authentication-and-access-control.html",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsSqsQueue",
"Id": queueArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {"AwsSqsQueue": {"QueueName": queueName}}
},
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-4",
"NIST CSF PR.DS-5",
"NIST CSF PR.PT-3",
"NIST SP 800-53 AC-1"
"NIST SP 800-53 AC-3"
"NIST SP 800-53 AC-17"
"NIST SP 800-53 AC-22"
"ISO 27001:2013 A.13.1.2"
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
finding = {
"SchemaVersion": "2018-10-08",
"Id": queueArn + "/sqs_queue_public_accessibility_check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": queueArn,
"AwsAccountId": awsAccountId,
"Types": ["Software and Configuration Checks/AWS Security Best Practices"],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "HIGH"},
"Confidence": 99,
"Title": "[SQS.3] SQS queues should not be unconditionally open to the public",
"Description": f"SQS queue {queueName} is unconditionally open to the public.",
"Remediation": {
"Recommendation": {
"Text": "For more information on best practices for SQS Policies, refer to the Identity and Access Management section of the Amazon SQS Developer Guide",
"Url": "https://docs.amazonaws.cn/en_us/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-authentication-and-access-control.html",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsSqsQueue",
"Id": queueArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {"AwsSqsQueue": {"QueueName": queueName}}
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-4",
"NIST CSF PR.DS-5",
"NIST CSF PR.PT-3",
"NIST SP 800-53 AC-1"
"NIST SP 800-53 AC-3"
"NIST SP 800-53 AC-17"
"NIST SP 800-53 AC-22"
"ISO 27001:2013 A.13.1.2"
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
else:
# No queues listed
pass | 49.718377 | 182 | 0.460541 | 1,622 | 20,832 | 5.885327 | 0.181874 | 0.011733 | 0.011314 | 0.013828 | 0.790593 | 0.787974 | 0.77687 | 0.774565 | 0.774565 | 0.77048 | 0 | 0.035595 | 0.437644 | 20,832 | 419 | 183 | 49.718377 | 0.779257 | 0.050643 | 0 | 0.701571 | 0 | 0.031414 | 0.350334 | 0.039151 | 0 | 0 | 0 | 0 | 0 | 1 | 0.010471 | false | 0.015707 | 0.013089 | 0 | 0.028796 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 6 |
4bbcfedb3e0f64a7d2f194dd2cdb22d735ef9e23 | 23 | py | Python | array/array.py | ThePyProgrammer/dstructs | 21705c2f9b59d544bfc1a04d77d12af136ec30d2 | [
"MIT"
] | null | null | null | array/array.py | ThePyProgrammer/dstructs | 21705c2f9b59d544bfc1a04d77d12af136ec30d2 | [
"MIT"
] | null | null | null | array/array.py | ThePyProgrammer/dstructs | 21705c2f9b59d544bfc1a04d77d12af136ec30d2 | [
"MIT"
] | null | null | null | class Array(list): pass | 23 | 23 | 0.782609 | 4 | 23 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.086957 | 23 | 1 | 23 | 23 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
29aa5cb6f0abd14b53a64635f8516d72f83c56e1 | 155 | py | Python | _1_BasicIO/hello_world.py | xanthium-enterprises/Python3-Tutorial | cb0a7b6a1af819b31592c377a084c4db2e5de330 | [
"MIT"
] | null | null | null | _1_BasicIO/hello_world.py | xanthium-enterprises/Python3-Tutorial | cb0a7b6a1af819b31592c377a084c4db2e5de330 | [
"MIT"
] | null | null | null | _1_BasicIO/hello_world.py | xanthium-enterprises/Python3-Tutorial | cb0a7b6a1af819b31592c377a084c4db2e5de330 | [
"MIT"
] | null | null | null | # Hello World in Python 3.x
print("Hello World in Python 3.0")
print("=========================")
input_data = input("Enter something -")
print(input_data) | 31 | 39 | 0.606452 | 22 | 155 | 4.181818 | 0.545455 | 0.217391 | 0.26087 | 0.391304 | 0.413043 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021739 | 0.109677 | 155 | 5 | 40 | 31 | 0.644928 | 0.16129 | 0 | 0 | 0 | 0 | 0.51938 | 0.193798 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.75 | 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 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
29edaeb0161e846313d68a9d0eab08016e0dc371 | 264 | py | Python | ctreport_selenium/ctreport_html/scripts/tooltip.py | naveens33/ctreport-selenium | 9553b5c4b8deb52e46cf0fb3e1ea7092028cf090 | [
"MIT"
] | 2 | 2020-08-30T13:12:52.000Z | 2020-09-03T05:38:28.000Z | ctreport_selenium/ctreport_html/scripts/tooltip.py | naveens33/ctreport-selenium | 9553b5c4b8deb52e46cf0fb3e1ea7092028cf090 | [
"MIT"
] | 5 | 2020-01-10T07:01:24.000Z | 2020-06-25T10:49:43.000Z | ctreport_selenium/ctreport_html/scripts/tooltip.py | naveens33/ctreport-selenium | 9553b5c4b8deb52e46cf0fb3e1ea7092028cf090 | [
"MIT"
] | 1 | 2020-10-13T02:27:04.000Z | 2020-10-13T02:27:04.000Z | content = '''
<script>
$(document).ready(function(){
$('#testdetails').tooltip().mouseover();
setTimeout(function(){ $('#testdetails').tooltip('hide'); }, 5000);
$('[data-toggle="tooltip"]').tooltip();
});
</script>
'''
| 26.4 | 70 | 0.511364 | 19 | 264 | 7.105263 | 0.684211 | 0.281481 | 0.385185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.019417 | 0.219697 | 264 | 9 | 71 | 29.333333 | 0.635922 | 0 | 0 | 0 | 0 | 0 | 0.935606 | 0.621212 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 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 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d9c0f4e8e2b80a02c6d304c2cfce02a3fd686212 | 51 | py | Python | test/fixtures/python/corpus/exec-statement.A.py | matsubara0507/semantic | 67899f701abc0f1f0cb4374d8d3c249afc33a272 | [
"MIT"
] | 8,844 | 2019-05-31T15:47:12.000Z | 2022-03-31T18:33:51.000Z | test/fixtures/python/corpus/exec-statement.A.py | matsubara0507/semantic | 67899f701abc0f1f0cb4374d8d3c249afc33a272 | [
"MIT"
] | 401 | 2019-05-31T18:30:26.000Z | 2022-03-31T16:32:29.000Z | test/fixtures/python/corpus/exec-statement.A.py | matsubara0507/semantic | 67899f701abc0f1f0cb4374d8d3c249afc33a272 | [
"MIT"
] | 504 | 2019-05-31T17:55:03.000Z | 2022-03-30T04:15:04.000Z | exec '1+1'
exec 'x+=1' in None
exec 'x+=1' in a, b
| 12.75 | 19 | 0.54902 | 14 | 51 | 2 | 0.5 | 0.357143 | 0.428571 | 0.571429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 0.215686 | 51 | 3 | 20 | 17 | 0.6 | 0 | 0 | 0 | 0 | 0 | 0.215686 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d9cb8d06424551d933343ecf2faacbd9848b8532 | 40 | py | Python | rts/engine/__init__.py | awesome-archive/ELF | ba956fbfc74d28d6df26472f5b464d2d038c040c | [
"BSD-3-Clause"
] | 1 | 2021-09-29T07:34:27.000Z | 2021-09-29T07:34:27.000Z | rts/engine/__init__.py | awesome-archive/ELF | ba956fbfc74d28d6df26472f5b464d2d038c040c | [
"BSD-3-Clause"
] | null | null | null | rts/engine/__init__.py | awesome-archive/ELF | ba956fbfc74d28d6df26472f5b464d2d038c040c | [
"BSD-3-Clause"
] | 1 | 2021-09-29T07:33:29.000Z | 2021-09-29T07:33:29.000Z | from .common_loader import CommonLoader
| 20 | 39 | 0.875 | 5 | 40 | 6.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 40 | 1 | 40 | 40 | 0.944444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d9e7e68fd132b7f3a6a893fd977a2afc926fb312 | 42 | py | Python | src/brain_service_backend/__init__.py | Zakhar-the-Robot/brain_service | 7d4ecc9c7467f9ce7fa46343bdbaeb705bb4fc4a | [
"MIT"
] | null | null | null | src/brain_service_backend/__init__.py | Zakhar-the-Robot/brain_service | 7d4ecc9c7467f9ce7fa46343bdbaeb705bb4fc4a | [
"MIT"
] | 1 | 2022-02-23T18:22:13.000Z | 2022-02-23T18:22:13.000Z | src/brain_service_backend/__init__.py | Zakhar-the-Robot/brain_service | 7d4ecc9c7467f9ce7fa46343bdbaeb705bb4fc4a | [
"MIT"
] | null | null | null | from .backend import ZakharServiceBackend
| 21 | 41 | 0.880952 | 4 | 42 | 9.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.095238 | 42 | 1 | 42 | 42 | 0.973684 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8a266a8b5e0f154d14091017efefdcdf30eb8cc9 | 109 | py | Python | python/lsst/charge_transfer_analysis/__init__.py | jchiang87/charge_transfer_analysis | e2c78e18804b3ed896621e665bcc198867e6c95e | [
"BSD-3-Clause"
] | null | null | null | python/lsst/charge_transfer_analysis/__init__.py | jchiang87/charge_transfer_analysis | e2c78e18804b3ed896621e665bcc198867e6c95e | [
"BSD-3-Clause"
] | null | null | null | python/lsst/charge_transfer_analysis/__init__.py | jchiang87/charge_transfer_analysis | e2c78e18804b3ed896621e665bcc198867e6c95e | [
"BSD-3-Clause"
] | null | null | null | from __future__ import absolute_import
from .charge_transfer_analysis import *
from .MultiPanelPlot import *
| 27.25 | 39 | 0.853211 | 13 | 109 | 6.615385 | 0.615385 | 0.232558 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.110092 | 109 | 3 | 40 | 36.333333 | 0.886598 | 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 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
76dae353bb25ad77a123fd11cfeaa85ce355b9a8 | 53 | py | Python | main/mpkgl/test_mpkgl.py | chaosannals/trial-python | 740b91fa4b1b1b9839b7524515995a6d417612ca | [
"MIT"
] | null | null | null | main/mpkgl/test_mpkgl.py | chaosannals/trial-python | 740b91fa4b1b1b9839b7524515995a6d417612ca | [
"MIT"
] | 8 | 2020-12-26T07:48:15.000Z | 2022-03-12T00:25:14.000Z | main/mpkgl/test_mpkgl.py | chaosannals/trial-python | 740b91fa4b1b1b9839b7524515995a6d417612ca | [
"MIT"
] | null | null | null | def my_test_func():
print('fftt38989893555')
| 17.666667 | 28 | 0.660377 | 6 | 53 | 5.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.261905 | 0.207547 | 53 | 3 | 29 | 17.666667 | 0.52381 | 0 | 0 | 0 | 0 | 0 | 0.277778 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
0a0c4181a866dbfcbde86c8226ff405fa2d08699 | 41 | py | Python | model/__init__.py | jairotunior/gym_production | 5ff5d7902dd604a1a10231c2949f2c2f7ba8f025 | [
"MIT"
] | 1 | 2019-04-12T20:30:05.000Z | 2019-04-12T20:30:05.000Z | model/__init__.py | jairotunior/gym_production | 5ff5d7902dd604a1a10231c2949f2c2f7ba8f025 | [
"MIT"
] | null | null | null | model/__init__.py | jairotunior/gym_production | 5ff5d7902dd604a1a10231c2949f2c2f7ba8f025 | [
"MIT"
] | null | null | null | from gym_factory.model.model import Model | 41 | 41 | 0.878049 | 7 | 41 | 5 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.073171 | 41 | 1 | 41 | 41 | 0.921053 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
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