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import io
import os
import sys
import toml
import click
import pprint
import importlib
import webbrowser
from pathlib import Path
from dynaconf import default_settings
from dynaconf import constants
from dynaconf.validator import Validator
from dynaconf.utils.parse_conf import parse_conf_data
from dotenv import cli as dotenv_cli
from contextlib import suppress
flask_app = None
django_app = None
if 'FLASK_APP' in os.environ: # pragma: no cover
with suppress(ImportError, click.UsageError):
from flask.cli import ScriptInfo
flask_app = ScriptInfo().load_app()
settings = flask_app.config
click.echo(click.style('Flask app detected', fg='white', bg='black'))
if 'DJANGO_SETTINGS_MODULE' in os.environ: # pragma: no cover
sys.path.insert(0, os.path.abspath('.'))
with suppress(Exception):
import dynaconf.contrib.django_dynaconf # noqa
from django.conf import settings as django_settings
django_settings.configure()
settings = django_settings
django_app = True
click.echo(click.style('Django app detected', fg='white', bg='black'))
if not django_app and not flask_app:
from dynaconf import settings
CWD = Path.cwd()
ENVS = ['default', 'development', 'staging', 'testing', 'production', 'global']
EXTS = ['ini', 'toml', 'yaml', 'json', 'py', 'env']
WRITERS = ['ini', 'toml', 'yaml', 'json', 'py', 'redis', 'vault', 'env']
ENC = default_settings.ENCODING_FOR_DYNACONF
def split_vars(_vars):
"""Splits values like foo=bar=zaz in {'foo': 'bar=zaz'}"""
return {
k.upper().strip(): parse_conf_data(v.strip(), tomlfy=True)
for k, _, v
in [item.partition('=') for item in _vars]
} if _vars else {}
def read_file_in_root_directory(*names, **kwargs):
"""Read a file."""
return io.open(
os.path.join(os.path.dirname(__file__), *names),
encoding=kwargs.get('encoding', 'utf-8')
).read().strip()
def show_banner(ctx, param, value):
"""Shows dynaconf awesome banner"""
if not value or ctx.resilient_parsing:
return
click.echo(settings.dynaconf_banner)
click.echo('Learn more at: http://github.com/rochacbruno/dynaconf')
ctx.exit()
@click.group()
@click.option('--version', is_flag=True, callback=print_version,
expose_value=False, is_eager=True, help="Show dynaconf version")
@click.option('--docs', is_flag=True, callback=open_docs, expose_value=False,
is_eager=True, help="Open documentation in browser")
@click.option('--banner', is_flag=True, callback=show_banner,
expose_value=False, is_eager=True, help="Show awesome banner")
def main():
"""Dynaconf - Command Line Interface\n
Documentation: http://dynaconf.readthedocs.io/
"""
@main.command()
@click.option('--format', 'fileformat', '-f', default='toml',
type=click.Choice(EXTS))
@click.option('--path', '-p', default=CWD,
help='defaults to current directory')
@click.option('--env', '-e', default=None,
help='Sets the working env in `.env` file')
@click.option('--vars', '_vars', '-v', multiple=True, default=None,
help=(
'extra values to write to settings file '
'file e.g: `dynaconf init -v NAME=foo -v X=2'
))
@click.option('--secrets', '_secrets', '-s', multiple=True, default=None,
help=(
'secret key values to be written in .secrets '
'e.g: `dynaconf init -s TOKEN=kdslmflds'
))
@click.option('--wg/--no-wg', default=True)
@click.option('-y', default=False, is_flag=True)
def init(fileformat, path, env, _vars, _secrets, wg, y):
"""Inits a dynaconf project
By default it creates a settings.toml and a .secrets.toml
for [default|development|staging|testing|production|global] envs.
The format of the files can be changed passing
--format=yaml|json|ini|py.
This command must run on the project's root folder or you must pass
--path=/myproject/root/folder.
If you want to have a .env created with the ENV defined there e.g:
`ENV_FOR_DYNACONF=production` just pass --env=production and then .env
will also be created and the env defined to production.
"""
click.echo('Cofiguring your Dynaconf environment')
env = env or settings.current_env.lower()
loader = importlib.import_module(
"dynaconf.loaders.{}_loader".format(fileformat)
)
# Turn foo=bar=zaz in {'foo': 'bar=zaz'}
env_data = split_vars(_vars)
_secrets = split_vars(_secrets)
# create placeholder data for every env
settings_data = {k: {'value': 'value for {}'.format(k)} for k in ENVS}
secrets_data = {k: {'secret': 'secret for {}'.format(k)} for k in ENVS}
if env_data:
settings_data[env] = env_data
settings_data['default'] = {k: 'default' for k in env_data}
if _secrets:
secrets_data[env] = _secrets
secrets_data['default'] = {k: 'default' for k in _secrets}
path = Path(path)
if str(path).endswith(constants.ALL_EXTENSIONS + ('py',)):
settings_path = path
secrets_path = path.parent / '.secrets.{}'.format(fileformat)
dotenv_path = path.parent / '.env'
gitignore_path = path.parent / '.gitignore'
else:
if fileformat == 'env':
if str(path) in ('.env', './.env'): # pragma: no cover
settings_path = path
elif str(path).endswith('/.env'):
settings_path = path
elif str(path).endswith('.env'): # pragma: no cover
settings_path = path.parent / '.env'
else:
settings_path = path / '.env'
Path.touch(settings_path)
secrets_path = None
else:
settings_path = path / 'settings.{}'.format(fileformat)
secrets_path = path / '.secrets.{}'.format(fileformat)
dotenv_path = path / '.env'
gitignore_path = path / '.gitignore'
if fileformat in ['py', 'env']:
# for Python and .env files writes a single env
settings_data = settings_data[env]
secrets_data = secrets_data[env]
if not y and settings_path and settings_path.exists(): # pragma: no cover
click.confirm(
'{} exists do you want to overwrite it?'.format(settings_path),
abort=True
)
if not y and secrets_path and secrets_path.exists(): # pragma: no cover
click.confirm(
'{} exists do you want to overwrite it?'.format(secrets_path),
abort=True
)
if settings_path and settings_data:
loader.write(settings_path, settings_data, merge=True)
if secrets_path and secrets_data:
loader.write(secrets_path, secrets_data, merge=True)
# write .env file
# if env not in ['default', 'development']: # pragma: no cover
if not dotenv_path.exists(): # pragma: no cover
Path.touch(dotenv_path)
dotenv_cli.set_key(str(dotenv_path), 'ENV_FOR_DYNACONF', env.upper())
else: # pragma: no cover
click.echo(
'.env already exists please set ENV_FOR_DYNACONF={}'.format(
env.upper()
)
)
if wg:
# write .gitignore
ignore_line = ".secrets.*"
comment = "\n# Ignore dynaconf secret files\n"
if not gitignore_path.exists():
with io.open(str(gitignore_path), 'w', encoding=ENC) as f:
f.writelines([comment, ignore_line, '\n'])
else:
existing = ignore_line in io.open(
str(gitignore_path), encoding=ENC
).read()
if not existing: # pragma: no cover
with io.open(str(gitignore_path), 'a+', encoding=ENC) as f:
f.writelines(
[comment, ignore_line, '\n']
)
@main.command(name='list')
@click.option('--env', '-e', default=None,
help='Filters the env to get the values')
@click.option('--key', '-k', default=None, help='Filters a single key')
@click.option('--more', '-m', default=None,
help='Pagination more|less style', is_flag=True)
@click.option('--loader', '-l', default=None,
help='a loader identifier to filter e.g: toml|yaml')
def _list(env, key, more, loader):
"""Lists all defined config values"""
if env:
env = env.strip()
if key:
key = key.strip()
if loader:
loader = loader.strip()
if env:
settings.setenv(env)
cur_env = settings.current_env.lower()
click.echo(
click.style(
'Working in %s environment ' % cur_env,
bold=True, bg='blue', fg='white'
)
)
if not loader:
data = settings.store
else:
identifier = '{}_{}'.format(loader, cur_env)
data = settings._loaded_by_loaders.get(identifier, {})
data = data or settings._loaded_by_loaders.get(loader, {})
# remove to avoid displaying twice
data.pop('SETTINGS_MODULE', None)
if not key:
datalines = '\n'.join(
'%s: %s' % (click.style(k, bg=color(k), fg='white'),
pprint.pformat(v))
for k, v in data.items()
)
(click.echo_via_pager if more else click.echo)(datalines)
else:
key = key.upper()
value = data.get(key)
if not value:
click.echo(click.style('Key not found', bg='red', fg='white'))
return
click.echo(
'%s: %s' % (
click.style(key.upper(), bg=color(key), fg='white'),
pprint.pformat(value)
)
)
if env:
settings.setenv()
@main.command()
@click.argument('to', required=True, type=click.Choice(WRITERS))
@click.option('--vars', '_vars', '-v', multiple=True, default=None,
help=(
'key values to be written '
'e.g: `dynaconf write toml -e NAME=foo -e X=2'
))
@click.option('--secrets', '_secrets', '-s', multiple=True, default=None,
help=(
'secret key values to be written in .secrets '
'e.g: `dynaconf write toml -s TOKEN=kdslmflds -s X=2'
))
@click.option('--path', '-p', default=CWD,
help='defaults to current directory/settings.{ext}')
@click.option(
'--env', '-e', default='default',
help=(
'env to write to defaults to DEVELOPMENT for files '
'for external sources like Redis and Vault '
'it will be DYNACONF or the value set in '
'$GLOBAL_ENV_FOR_DYNACONF'
)
)
@click.option('-y', default=False, is_flag=True)
def write(to, _vars, _secrets, path, env, y):
"""Writes data to specific source"""
_vars = split_vars(_vars)
_secrets = split_vars(_secrets)
loader = importlib.import_module("dynaconf.loaders.{}_loader".format(to))
if to in EXTS:
# Lets write to a file
path = Path(path)
if str(path).endswith(constants.ALL_EXTENSIONS + ('py',)):
settings_path = path
secrets_path = path.parent / '.secrets.{}'.format(to)
else:
if to == 'env':
if str(path) in ('.env', './.env'): # pragma: no cover
settings_path = path
elif str(path).endswith('/.env'):
settings_path = path
elif str(path).endswith('.env'):
settings_path = path.parent / '.env'
else:
settings_path = path / '.env'
Path.touch(settings_path)
secrets_path = None
_vars.update(_secrets)
else:
settings_path = path / 'settings.{}'.format(to)
secrets_path = path / '.secrets.{}'.format(to)
if _vars and not y and settings_path and settings_path.exists(): # pragma: no cover # noqa
click.confirm(
'{} exists do you want to overwrite it?'.format(settings_path),
abort=True
)
if _secrets and not y and secrets_path and secrets_path.exists(): # pragma: no cover # noqa
click.confirm(
'{} exists do you want to overwrite it?'.format(secrets_path),
abort=True
)
if to not in ['py', 'env']:
if _vars:
_vars = {env: _vars}
if _secrets:
_secrets = {env: _secrets}
if _vars and settings_path:
loader.write(settings_path, _vars, merge=True)
click.echo('Data successful written to {}'.format(settings_path))
if _secrets and secrets_path:
loader.write(secrets_path, _secrets, merge=True)
click.echo('Data successful written to {}'.format(secrets_path))
else: # pragma: no cover
# lets write to external source
loader.write(settings, _vars, **_secrets)
click.echo('Data successful written to {}'.format(to))
@main.command()
@click.option('--path', '-p', default=CWD,
help='defaults to current directory')
def validate(path): # pragma: no cover
"""Validates Dynaconf settings based on rules defined in
dynaconf_validators.toml"""
# reads the 'dynaconf_validators.toml' from path
# for each section register the validator for specific env
# call validate
if not str(path).endswith('.toml'):
path = path / "dynaconf_validators.toml"
if not Path(path).exists(): # pragma: no cover # noqa
click.echo(click.style(
"{} not found".format(path), fg="white", bg="red"
))
sys.exit(1)
validation_data = toml.load(open(str(path)))
for env, name_data in validation_data.items():
for name, data in name_data.items():
if not isinstance(data, dict): # pragma: no cover
click.echo(click.style(
"Invalid rule for parameter '{}'".format(name),
fg="white", bg="yellow"
))
else: # pragma: no cover
data.setdefault('env', env)
click.echo(click.style(
"Validating '{}' with '{}'".format(name, data),
fg="white", bg="blue"
))
Validator(name, **data).validate(settings)
# pragma: no cover
click.echo(click.style(
"Validation success!", fg="white", bg="green"
))
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1069,
1023,
33529,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
3904,
13,
10414,
2533,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
90,
92,
7160,
466,
345,
765,
284,
49312,
340,
30,
4458,
18982,
7,
2363,
8004,
62,
6978,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15614,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
611,
6460,
62,
6978,
290,
6460,
62,
7890,
25,
198,
220,
220,
220,
220,
220,
220,
220,
40213,
13,
13564,
7,
33692,
62,
6978,
11,
6460,
62,
7890,
11,
20121,
28,
17821,
8,
198,
220,
220,
220,
611,
13141,
62,
6978,
290,
13141,
62,
7890,
25,
198,
220,
220,
220,
220,
220,
220,
220,
40213,
13,
13564,
7,
2363,
8004,
62,
6978,
11,
13141,
62,
7890,
11,
20121,
28,
17821,
8,
628,
220,
220,
220,
1303,
3551,
764,
24330,
2393,
198,
220,
220,
220,
1303,
611,
17365,
407,
287,
37250,
12286,
3256,
705,
31267,
6,
5974,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
611,
407,
16605,
24330,
62,
6978,
13,
1069,
1023,
33529,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
10644,
13,
29332,
7,
26518,
24330,
62,
6978,
8,
198,
220,
220,
220,
220,
220,
220,
220,
16605,
24330,
62,
44506,
13,
2617,
62,
2539,
7,
2536,
7,
26518,
24330,
62,
6978,
828,
705,
1677,
53,
62,
13775,
62,
35,
40760,
2246,
1340,
37,
3256,
17365,
13,
45828,
28955,
198,
220,
220,
220,
2073,
25,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
3904,
13,
30328,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45302,
24330,
1541,
7160,
3387,
900,
12964,
53,
62,
13775,
62,
35,
40760,
2246,
1340,
37,
34758,
92,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17365,
13,
45828,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
611,
266,
70,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3551,
764,
18300,
46430,
198,
220,
220,
220,
220,
220,
220,
220,
8856,
62,
1370,
796,
27071,
2363,
8004,
15885,
1,
198,
220,
220,
220,
220,
220,
220,
220,
2912,
796,
37082,
77,
2,
41032,
37860,
7807,
69,
3200,
3696,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
17606,
46430,
62,
6978,
13,
1069,
1023,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
33245,
13,
9654,
7,
2536,
7,
18300,
46430,
62,
6978,
828,
705,
86,
3256,
21004,
28,
24181,
8,
355,
277,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
13,
8933,
20655,
26933,
23893,
11,
8856,
62,
1370,
11,
705,
59,
77,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4683,
796,
8856,
62,
1370,
287,
33245,
13,
9654,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
965,
7,
18300,
46430,
62,
6978,
828,
21004,
28,
24181,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6739,
961,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
4683,
25,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
33245,
13,
9654,
7,
2536,
7,
18300,
46430,
62,
6978,
828,
705,
64,
10,
3256,
21004,
28,
24181,
8,
355,
277,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
13,
8933,
20655,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
23893,
11,
8856,
62,
1370,
11,
705,
59,
77,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
198,
31,
12417,
13,
21812,
7,
3672,
11639,
4868,
11537,
198,
31,
12976,
13,
18076,
10786,
438,
24330,
3256,
705,
12,
68,
3256,
4277,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1037,
11639,
11928,
1010,
262,
17365,
284,
651,
262,
3815,
11537,
198,
31,
12976,
13,
18076,
10786,
438,
2539,
3256,
705,
12,
74,
3256,
4277,
28,
14202,
11,
1037,
11639,
11928,
1010,
257,
2060,
1994,
11537,
198,
31,
12976,
13,
18076,
10786,
438,
3549,
3256,
705,
12,
76,
3256,
4277,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1037,
11639,
47,
363,
1883,
517,
91,
1203,
3918,
3256,
318,
62,
32109,
28,
17821,
8,
198,
31,
12976,
13,
18076,
10786,
438,
29356,
3256,
705,
12,
75,
3256,
4277,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1037,
11639,
64,
40213,
27421,
284,
8106,
304,
13,
70,
25,
284,
4029,
91,
88,
43695,
11537,
198,
4299,
4808,
4868,
7,
24330,
11,
1994,
11,
517,
11,
40213,
2599,
198,
220,
220,
220,
37227,
43,
1023,
477,
5447,
4566,
3815,
37811,
198,
220,
220,
220,
611,
17365,
25,
198,
220,
220,
220,
220,
220,
220,
220,
17365,
796,
17365,
13,
36311,
3419,
198,
220,
220,
220,
611,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
1994,
13,
36311,
3419,
198,
220,
220,
220,
611,
40213,
25,
198,
220,
220,
220,
220,
220,
220,
220,
40213,
796,
40213,
13,
36311,
3419,
628,
220,
220,
220,
611,
17365,
25,
198,
220,
220,
220,
220,
220,
220,
220,
6460,
13,
2617,
24330,
7,
24330,
8,
628,
220,
220,
220,
1090,
62,
24330,
796,
6460,
13,
14421,
62,
24330,
13,
21037,
3419,
628,
220,
220,
220,
3904,
13,
30328,
7,
198,
220,
220,
220,
220,
220,
220,
220,
3904,
13,
7635,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
28516,
287,
4064,
82,
2858,
705,
4064,
1090,
62,
24330,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10758,
28,
17821,
11,
275,
70,
11639,
17585,
3256,
277,
70,
11639,
11186,
6,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
611,
407,
40213,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
6460,
13,
8095,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
27421,
796,
705,
90,
92,
23330,
92,
4458,
18982,
7,
29356,
11,
1090,
62,
24330,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
6460,
13557,
14578,
62,
1525,
62,
2220,
364,
13,
1136,
7,
738,
7483,
11,
23884,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
1366,
393,
6460,
13557,
14578,
62,
1525,
62,
2220,
364,
13,
1136,
7,
29356,
11,
23884,
8,
628,
220,
220,
220,
1303,
4781,
284,
3368,
19407,
5403,
198,
220,
220,
220,
1366,
13,
12924,
10786,
28480,
51,
20754,
62,
33365,
24212,
3256,
6045,
8,
628,
220,
220,
220,
611,
407,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4818,
282,
1127,
796,
705,
59,
77,
4458,
22179,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4,
82,
25,
4064,
82,
6,
4064,
357,
12976,
13,
7635,
7,
74,
11,
275,
70,
28,
8043,
7,
74,
828,
277,
70,
11639,
11186,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
4798,
13,
79,
18982,
7,
85,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
479,
11,
410,
287,
1366,
13,
23814,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
357,
12976,
13,
30328,
62,
8869,
62,
79,
3536,
611,
517,
2073,
3904,
13,
30328,
5769,
67,
10254,
1127,
8,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
1994,
13,
45828,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
1366,
13,
1136,
7,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3904,
13,
30328,
7,
12976,
13,
7635,
10786,
9218,
407,
1043,
3256,
275,
70,
11639,
445,
3256,
277,
70,
11639,
11186,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
198,
220,
220,
220,
220,
220,
220,
220,
3904,
13,
30328,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4,
82,
25,
4064,
82,
6,
4064,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3904,
13,
7635,
7,
2539,
13,
45828,
22784,
275,
70,
28,
8043,
7,
2539,
828,
277,
70,
11639,
11186,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
4798,
13,
79,
18982,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
611,
17365,
25,
198,
220,
220,
220,
220,
220,
220,
220,
6460,
13,
2617,
24330,
3419,
628,
198,
31,
12417,
13,
21812,
3419,
198,
31,
12976,
13,
49140,
10786,
1462,
3256,
2672,
28,
17821,
11,
2099,
28,
12976,
13,
46770,
7,
18564,
2043,
4877,
4008,
198,
31,
12976,
13,
18076,
10786,
438,
85,
945,
3256,
705,
62,
85,
945,
3256,
705,
12,
85,
3256,
3294,
28,
17821,
11,
4277,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1037,
16193,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
2539,
3815,
284,
307,
3194,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
68,
13,
70,
25,
4600,
67,
2047,
7807,
69,
3551,
284,
4029,
532,
68,
36751,
28,
21943,
532,
68,
1395,
28,
17,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15306,
198,
31,
12976,
13,
18076,
10786,
438,
2363,
8004,
3256,
705,
62,
2363,
8004,
3256,
705,
12,
82,
3256,
3294,
28,
17821,
11,
4277,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1037,
16193,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
21078,
1994,
3815,
284,
307,
3194,
287,
764,
2363,
8004,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
68,
13,
70,
25,
4600,
67,
2047,
7807,
69,
3551,
284,
4029,
532,
82,
5390,
43959,
28,
74,
67,
6649,
76,
69,
335,
82,
532,
82,
1395,
28,
17,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15306,
198,
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"""Define a data buffer for contextual bandit algorithms."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
class ContextualDataset(object):
"""The buffer is able to append new data, and sample random minibatches."""
def __init__(self, context_dim, num_actions, buffer_s=-1, memory_size=-1, intercept=False):
"""Creates a ContextualDataset object.
The data is stored in attributes: contexts and rewards.
The sequence of taken actions are stored in attribute actions.
Args:
context_dim: Dimension of the contexts.
num_actions: Number of arms for the multi-armed bandit.
buffer_s: Size of buffer for training. Only last buffer_s will be
returned as minibatch. If buffer_s = -1, all data will be used.
memory_size: Specify the number of examples to store in memory.
if buffer_s = -1, all data will be stored.
intercept: If True, it adds a constant (1.0) dimension to each context X,
at the end.
"""
self._context_dim = context_dim
self._num_actions = num_actions
self._contexts = None
self._rewards = None
self.actions = []
self.buffer_s = buffer_s
self.memory_size = memory_size
self.intercept = intercept
def add(self, context, action, reward):
"""Adds a new triplet (context, action, reward) to the dataset.
The reward for the actions that weren't played is assumed to be zero.
Args:
context: A d-dimensional vector with the context.
action: Integer between 0 and k-1 representing the chosen arm.
reward: Real number representing the reward for the (context, action).
"""
if self.intercept:
c = np.array(context[:])
c = np.append(c, 1.0).reshape((1, self.context_dim + 1))
else:
c = np.array(context[:]).reshape((1, self.context_dim))
if self.contexts is None:
self.contexts = c
else:
self.contexts = np.vstack((self.contexts, c))
r = np.zeros((1, self.num_actions))
r[0, action] = reward
if self.rewards is None:
self.rewards = r
else:
self.rewards = np.vstack((self.rewards, r))
self.actions.append(action)
#Drop oldest example if memory constraint
if self.memory_size != -1:
if self.contexts.shape[0] > self.memory_size:
self.contexts = self.contexts[1:, :]
self.rewards = self.rewards[1:, :]
self.actions = self.actions[1:]
#Assert lengths match
assert len(self.actions) == len(self.rewards)
assert len(self.actions) == len(self.contexts)
def get_batch(self, batch_size):
"""Returns a random minibatch of (contexts, rewards) with batch_size."""
n, _ = self.contexts.shape
if self.buffer_s == -1:
# use all the data
ind = np.random.choice(range(n), batch_size)
else:
# use only buffer (last buffer_s observations)
ind = np.random.choice(range(max(0, n - self.buffer_s), n), batch_size)
return self.contexts[ind, :], self.rewards[ind, :]
def get_data(self, action):
"""Returns all (context, reward) where the action was played."""
n, _ = self.contexts.shape
ind = np.array([i for i in range(n) if self.actions[i] == action])
return self.contexts[ind, :], self.rewards[ind, action]
def get_data_with_weights(self):
"""Returns all observations with one-hot weights for actions."""
weights = np.zeros((self.contexts.shape[0], self.num_actions))
a_ind = np.array([(i, val) for i, val in enumerate(self.actions)])
weights[a_ind[:, 0], a_ind[:, 1]] = 1.0
return self.contexts, self.rewards, weights
def get_batch_with_weights(self, batch_size):
"""Returns a random mini-batch with one-hot weights for actions."""
n, _ = self.contexts.shape
if self.buffer_s == -1:
# use all the data
ind = np.random.choice(range(n), batch_size)
else:
# use only buffer (last buffer_s obs)
ind = np.random.choice(range(max(0, n - self.buffer_s), n), batch_size)
weights = np.zeros((batch_size, self.num_actions))
sampled_actions = np.array(self.actions)[ind]
a_ind = np.array([(i, val) for i, val in enumerate(sampled_actions)])
weights[a_ind[:, 0], a_ind[:, 1]] = 1.0
return self.contexts[ind, :], self.rewards[ind, :], weights
def num_points(self, f=None):
"""Returns number of points in the buffer (after applying function f)."""
if f is not None:
return f(self.contexts.shape[0])
return self.contexts.shape[0]
@property
@property
@property
@contexts.setter
@property
@actions.setter
@property
@rewards.setter | [
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] | 2.331965 | 2,193 |
"""Add quiz_game_setups Table
Revision ID: 822caa87a652
Revises: 3b809ceaf543
Create Date: 2021-06-26 11:48:17.948434
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '822caa87a652'
down_revision = '3b809ceaf543'
branch_labels = None
depends_on = None
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] | 2.496 | 125 |
# -*- coding: utf-8 -*-
from ..routes import Routes
from .base import *
__all__ = (
'EmojiWrapper',
)
class EmojiWrapper(EndpointsWrapper):
"""A higher-level wrapper around Emoji endpoints.
.. seealso:: Emoji endpoints https://discordapp.com/developers/docs/resources/emoji
"""
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import re
import functools
from modularodm import Q
from rest_framework.filters import OrderingFilter
from rest_framework import serializers as ser
class ODMOrderingFilter(OrderingFilter):
"""Adaptation of rest_framework.filters.OrderingFilter to work with modular-odm."""
# override
query_pattern = re.compile(r'filter\[\s*(?P<field>\S*)\s*\]\s*')
# Used to make intersection "reduce-able"
class FilterMixin(object):
""" View mixin with helper functions for filtering. """
TRUTHY = set(['true', 'True', 1, '1'])
FALSY = set(['false', 'False', 0, '0'])
DEFAULT_OPERATOR = 'eq'
# Used so that that queries by _id will work
# Used to convert string values from query params to Python booleans when necessary
class ODMFilterMixin(FilterMixin):
"""View mixin that adds a get_query_from_request method which converts query params
of the form `filter[field_name]=value` into an ODM Query object.
Subclasses must define `get_default_odm_query()`.
Serializers that want to restrict which fields are used for filtering need to have a variable called
filterable_fields which is a frozenset of strings representing the field names as they appear in the serialization.
"""
# TODO Handle simple and complex non-standard fields
field_comparison_operators = {
ser.CharField: 'icontains',
ser.ListField: 'in',
}
def query_params_to_odm_query(self, query_params):
"""Convert query params to a modularodm Query object."""
fields_dict = query_params_to_fields(query_params)
if fields_dict:
query_parts = [
Q(self.convert_key(key=key), self.get_comparison_operator(key=key), self.convert_value(value=value, field=key))
for key, value in fields_dict.items() if self.is_filterable_field(key=key)
]
# TODO Ensure that if you try to filter on an invalid field, it returns a useful error. Fix related test.
try:
query = functools.reduce(intersect, query_parts)
except TypeError:
query = None
else:
query = None
return query
class ListFilterMixin(FilterMixin):
"""View mixin that adds a get_queryset_from_request method which uses query params
of the form `filter[field_name]=value` to filter a list of objects.
Subclasses must define `get_default_queryset()`.
Serializers that want to restrict which fields are used for filtering need to have a variable called
filterable_fields which is a frozenset of strings representing the field names as they appear in the serialization.
"""
def param_queryset(self, query_params, default_queryset):
"""filters default queryset based on query parameters"""
fields_dict = query_params_to_fields(query_params)
queryset = set(default_queryset)
if fields_dict:
for field_name, value in fields_dict.items():
if self.is_filterable_field(key=field_name):
queryset = queryset.intersection(set(self.get_filtered_queryset(field_name, value, default_queryset)))
return list(queryset)
def get_filtered_queryset(self, field_name, value, default_queryset):
"""filters default queryset based on the serializer field type"""
field = self.serializer_class._declared_fields[field_name]
if isinstance(field, ser.SerializerMethodField):
return_val = [item for item in default_queryset if self.get_serializer_method(field_name)(item) == self.convert_value(value, field_name)]
elif isinstance(field, ser.BooleanField):
return_val = [item for item in default_queryset if getattr(item, field_name, None) == self.convert_value(value, field_name)]
elif isinstance(field, ser.CharField):
return_val = [item for item in default_queryset if value.lower() in getattr(item, field_name, None).lower()]
else:
# TODO Ensure that if you try to filter on an invalid field, it returns a useful error.
return_val = [item for item in default_queryset if value in getattr(item, field_name, None)]
return return_val
def get_serializer_method(self, field_name):
"""
:param field_name: The name of a SerializerMethodField
:return: The function attached to the SerializerMethodField to get its value
"""
serializer = self.get_serializer()
serializer_method_name = 'get_' + field_name
return getattr(serializer, serializer_method_name)
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] | 2.688707 | 1,709 |
import pygame
import pygame_gui
import engine.app
from engine.math import Vector
import engine.math
############################
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] | 3.911765 | 34 |
from scripta import cast_recorder
from scripta import scripta
from unittest import IsolatedAsyncioTestCase
from unittest.mock import patch
import asyncio
import tdir
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# All rights reserved by forest fairy.
# You cannot modify or share anything without sacrifice.
# If you don't agree, keep calm and don't look at code bellow!
__author__ = "VirtualV <https://github.com/virtualvfix>"
__date__ = "12/18/2017 4:43 PM"
from config import CONFIG
from libs.core.tools.utility import Utility
from libs.core.template import CASE, NAME, PARAMS
from libs.core.unittest.config import RESULT_NAMES
from libs.core.logger import getLogger, getSysLogger
from .config import CONSOLE_RESULT_TABLE_SIZES as SIZES, EMPTY_RECORD
class Console:
"""
Print TestCases results to console
"""
@staticmethod
def print_results(logger=None, case=None, cycle=None, suite=None):
"""
Print current TestSuite results to console
Args:
logger (logging): Logger to print
case (dict): TestCase dict
suite (dict): TestSuite dict
cycle (int): Current global cycle
"""
logger = logger or getLogger(__file__)
syslogger = getSysLogger()
try:
for _cycle in range(CONFIG.SYSTEM.TOTAL_CYCLES_GLOBAL):
if cycle is not None and _cycle != cycle-1:
continue
# TestCases
for _case in CONFIG.UNITTEST.SELECTED_TEST_CASES:
if case is not None and _case != case:
continue
logger.newline()
logger.table('_*', border_delimiter=' ')
# logger.table(' ')
logger.table(('Results of %s TestCase. Cycle %d/%d'
% (CASE.safe_substitute(case=_case['name'], index=_case['index']),
_cycle+1, CONFIG.SYSTEM.TOTAL_CYCLES_GLOBAL), 'C'))
# TestSuites
for _suite in _case['suites']:
if suite is not None and _suite != suite:
continue
logger.table(('%s TestSuite %s' % (NAME.safe_substitute(name=_suite['name']),
'with parameters: %s'
% PARAMS.safe_substitute(name=_suite['params'])
if _suite['params'] is not None
else 'without parameters'), 'C'))
logger.table('-*')
logger.table(('#', SIZES['number'], 'C'), # number
('Test id'.upper(), SIZES['test_id'], 'C'), # id
('Test name'.upper(), SIZES['test_name'], 'C'), # name
('Description'.upper(), SIZES['description'], 'C'), # description
('Cycles'.upper(), SIZES['cycles'], 'C'), # cycles
('Time'.upper(), SIZES['time'], 'C'), # time
('Result'.upper(), SIZES['result'], 'C'), # result
('Pass Rate'.upper(), SIZES['rate'], 'C')) # pass rate
logger.table('-*')
# Tests
for t, _test in enumerate(_suite['tests']):
_res = _test['results'][_cycle] if _test['results'] is not None and \
len(_test['results']) > _cycle else None
_res_cycle = _res['cycle'] if _res is not None else 0
_res_cycles = _res['cycles'] if _res is not None else 0
_time = _res['time'] if _res is not None else EMPTY_RECORD
if _time != EMPTY_RECORD and _time > 60:
_time = Utility.seconds_to_time_format(_time)
_result = _res['result'] if _res is not None else RESULT_NAMES['not run']
_rate = _res['rate'] if _res is not None else 0
logger.table(('%d' % (t+1), SIZES['number'], 'C'), # number
('%s' % _test['id'], SIZES['test_id'], 'C'), # id
('%s' % (_test['name'] or EMPTY_RECORD), SIZES['test_name'], 'C'), # name
('%s' % (_test['desc'] or EMPTY_RECORD), SIZES['description'], 'C'), # description
(('%d/%d' % (_res_cycle, _res_cycles)) if _res is not None else EMPTY_RECORD,
SIZES['cycles'], 'C'), # cycles
('%s' % _time, SIZES['time'], 'C'), # time
('%s' % _result, SIZES['result'], 'C'), # result
('%.1f %%' % _rate, SIZES['rate'], 'C')) # pass rate
logger.table('-*')
logger.newline()
except Exception as e:
syslogger.exception(e)
if CONFIG.SYSTEM.DEBUG:
raise
logger.error(e)
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] | 1.706671 | 3,133 |
from scraper import *
s = Scraper(start=57024, end=58805, max_iter=30, scraper_instance=32)
s.scrape_letterboxd() | [
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] | 2.5 | 46 |
# encoding: utf-8
from Storm.Localized import Strings as LocalizedStrings
from Storm.GameData import Catalog
from Storm.DepotIndex import DepotIndex
from Storm.DepotCataFile import DepotCataFile, TYPE_NONE, TYPE_PRODUCTS, TYPE_LICENSES
from sys import argv
from os.path import exists
if __name__ != '__main__':
print_utf8('catalog.py is a CLI file, not a module')
exit(-1)
if len(argv) < 2:
print_utf8('Usage: python %s path_to_mods_dir [path_to_program_data [locale [region]]]' % (argv[0]))
exit(1)
MissingProducts = []
MissingLicenses = []
RootDir = argv[1]
RootDirLength = len(RootDir)
RootDepotDir = 'C:/ProgramData'
if len(argv) > 2:
RootDepotDir = argv[2]
RootDepotDir = '%s/Blizzard Entertainment/Battle.net/' % (argv[2])
RootDepotDirLength = len(RootDepotDir)
RootLocale = 'enus'
if len(argv) > 3:
RootLocale = argv[3]
# 1 = us, 2 = eu, 3 = ko, 5? = cn, 98 = xx (ww ptr), ?? = cxx (cn ptr), ?? = xx-02 (tournament)
RootRegion = 1
if len(argv) > 4:
RootRegion = int(argv[4])
print_utf8('Loading economy data')
Depot = DepotIndex(RootDepotDir)
EconomyCatalogs = list(map(lambda x: DepotCataFile(x.path), Depot.cata))
GameDataList = ['%s/heroesdata.stormmod' % RootDir]
GameDataList += list(map(lambda x: '%s/%s/' % (RootDir, x.get('value').lower()[5:]), Catalog('%s/heroesdata.stormmod/base.stormdata/Includes.xml' % RootDir)))
CRewardById = {}
CCatalogs = []
CCombinedLocale = {}
print_utf8('Loading reward data')
for gameDataDir in GameDataList:
gameDataPath = '%s/base.stormdata/GameData.xml' % gameDataDir
if not exists(gameDataPath):
print_utf8('Catalog stormmod %s does not exist!' % gameDataPath[RootDirLength:])
continue
CCombinedLocale = LocalizedStrings(CCombinedLocale).Load('%s/%s.stormdata/LocalizedData/GameStrings.txt' % (gameDataDir, RootLocale)).data
GameDataCatalog = Catalog(gameDataPath)
for CatalogEntry in GameDataCatalog:
catalogPath = '%s/base.stormdata/%s' % (gameDataDir, CatalogEntry)
if not exists(catalogPath):
print_utf8('Catalog file %s does not exist!' % catalogPath[RootDirLength:])
continue
CatalogFile = Catalog(catalogPath)
CCatalogs.append(CatalogFile)
for CRewardType in ['Banner', 'VoiceLine', 'Spray', 'Hero', 'Skin', 'Mount', 'AnnouncerPack', 'Icon']:
CRewards = CatalogFile.findall('CReward%s' % CRewardType)
for CReward in CRewards: CRewardById[CReward.get('id')] = CReward
CCombinedLocale = LocalizedStrings(CCombinedLocale)
print_utf8('Parsing reward data')
for CatalogFile in CCatalogs:
CItems = []
for CRewardType in ['Banner', 'VoiceLine', 'Spray', 'Hero', 'Skin', 'Mount', 'AnnouncerPack', 'Icon']:
CItems += CatalogFile.findall('C%s' % CRewardType)
parseRewards(CItems, RootRegion, EconomyCatalogs, CCombinedLocale, CatalogFile, CRewardById)
print_utf8("Missing Products: \n\t%s" % '\n\t'.join(MissingProducts))
print_utf8("Missing Licenses: \n\t%s" % '\n\t'.join(MissingLicenses))
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] | 2.433725 | 1,275 |
"""
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
"""
OPTIONS_DEFAULT_DIRECTED = {
"nodes": {
"borderWidthSelected": 0,
"borderWidth": 0,
"color": {
"background": "rgba(210, 229, 255, 1)",
"border": "transparent",
"highlight": {
"background": "rgba(9, 104, 178, 1)",
"border": "rgba(8, 62, 100, 1)"
}
},
"shadow": {
"enabled": False
},
"shape": "circle",
"widthConstraint": {
"minimum": 70,
"maximum": 70
},
"font": {
"face": "courier new",
"color": "black",
"size": 12
},
},
"edges": {
"color": {
"inherit": False
},
"smooth": {
"enabled": True,
"type": "straightCross"
},
"arrows": {
"to": {
"enabled": True,
"type": "arrow"
}
},
"font": {
"face": "courier new"
}
},
"interaction": {
"hover": True,
"hoverConnectedEdges": True,
"selectConnectedEdges": False
},
"physics": {
"minVelocity": 0.75,
"barnesHut": {
"centralGravity": 0.1,
"gravitationalConstant": -50450,
"springLength": 95,
"springConstant": 0.04,
"damping": 0.09,
"avoidOverlap": 0.1
},
"solver": "barnesHut",
"enabled": True,
"adaptiveTimestep": True,
"stabilization": {
"enabled": True,
"iterations": 1
}
}
}
def vis_options_merge(original, target):
"""Merge the target dict with the original dict, without modifying the input dicts.
:param original: the original dict.
:param target: the target dict that takes precedence when there are type conflicts or value conflicts.
:return: a new dict containing references to objects in both inputs.
"""
resultdict = {}
common_keys = original.keys() & target.keys()
for key in common_keys:
obj1 = original[key]
obj2 = target[key]
if type(obj1) is dict and type(obj2) is dict:
resultdict[key] = vis_options_merge(obj1, obj2)
else:
resultdict[key] = obj2
for key in (original.keys() - target.keys()):
resultdict[key] = original[key]
for key in (target.keys() - original.keys()):
resultdict[key] = target[key]
return resultdict
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1222,
2496,
13,
13083,
3419,
628,
220,
220,
220,
329,
1994,
287,
2219,
62,
13083,
25,
198,
220,
220,
220,
220,
220,
220,
220,
26181,
16,
796,
2656,
58,
2539,
60,
198,
220,
220,
220,
220,
220,
220,
220,
26181,
17,
796,
2496,
58,
2539,
60,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2099,
7,
26801,
16,
8,
318,
8633,
290,
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7,
26801,
17,
8,
318,
8633,
25,
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220,
220,
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220,
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220,
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220,
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11,
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17,
8,
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220,
220,
220,
220,
220,
220,
2073,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
1255,
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2539,
60,
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17,
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220,
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220,
329,
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287,
357,
14986,
13,
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532,
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13,
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3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
11600,
58,
2539,
60,
796,
2656,
58,
2539,
60,
628,
220,
220,
220,
329,
1994,
287,
357,
16793,
13,
13083,
3419,
532,
2656,
13,
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3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
11600,
58,
2539,
60,
796,
2496,
58,
2539,
60,
628,
220,
220,
220,
1441,
1255,
11600,
198
] | 1.949778 | 1,354 |
# shell game
import sys, os, time, random, math
relFold = '../../../'
sys.path.append(relFold+'module')
import sabr
sabrUnroll() | [
2,
7582,
983,
198,
198,
11748,
25064,
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640,
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] | 2.54902 | 51 |
class ExternalResourceType(GuidEnum):
"""
A type class used to distinguish between different kinds of external resource.
ExternalResourceType(guid: Guid)
"""
@staticmethod
def __new__(self, guid):
""" __new__(cls: type,guid: Guid) """
pass
| [
4871,
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220,
220,
220,
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220,
220,
1208,
201,
198
] | 2.557522 | 113 |
import streamlit as st
import glob
import json
from podcasts import pipeline
from threading import Thread
st.title("Podcast Summaries")
json_files = glob.glob('*.json')
episode_id = st.sidebar.text_input("Episode ID")
button = st.sidebar.button("Download Episode summary")
if button and episode_id:
st.sidebar.write("Get auto chapters...")
#pipeline(episode_id)
t = Thread(target=pipeline, args=(episode_id,))
t.start()
for file in json_files:
with open(file, 'r') as f:
data = json.load(f)
chapter = data['chapters']
episode_title = data['episode_title']
thumbnail = data['thumbnail']
podcast_title = data['podcast_title']
audio = data['audio_url']
with st.expander(f"{podcast_title} - {episode_title}"):
st.image(thumbnail, width=200)
st.markdown(f'#### {episode_title}')
st.write(get_clean_summary(chapter))
| [
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1,
90,
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532,
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2599,
198,
220,
220,
220,
220,
220,
220,
220,
336,
13,
9060,
7,
400,
20566,
11,
9647,
28,
2167,
8,
198,
220,
220,
220,
220,
220,
220,
220,
336,
13,
4102,
2902,
7,
69,
6,
4242,
1391,
38668,
62,
7839,
92,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
336,
13,
13564,
7,
1136,
62,
27773,
62,
49736,
7,
43582,
4008,
198
] | 2.674627 | 335 |
"""
Reproduce the standard glob package behaviour but use TSystem to be able to
query remote file systems such as xrootd
"""
from __future__ import print_function
from rootpy.ROOT import gSystem
import glob as gl
import os.path
import fnmatch
__all__ = ["glob", "iglob"]
if __name__ == "__main__":
test_paths = [
"*.*",
"*/*.txt",
"data/L1Ntuple_test_3.root",
"""root://eoscms.cern.ch//eos/cms/store/group/dpg_trigger/"""
"""comm_trigger/L1Trigger/L1Menu2016/Stage2/"""
"""l1t-integration-v88p1-CMSSW-8021/SingleMuon/"""
"""crab_l1t-integration-v88p1-CMSSW-8021__SingleMuon_2016H_v2/"""
"""161031_120512/0000/L1Ntuple_999.root""",
"""root://eoscms.cern.ch//eos/cms/store/group/dpg_trigger/"""
"""comm_trigger/L1Trigger/L1Menu2016/Stage2/"""
"""l1t-integration-v88p1-CMSSW-8021/SingleMuon/"""
"""crab_l1t-integration-v88p1-CMSSW-8021__SingleMuon_2016H_v2/"""
"""161031_120512/0000/L1Ntuple_99*.root""",
"""root://eoscms.cern.ch//eos/cms/store/group/dpg_trigger/"""
"""comm_trigger/L1Trigger/L1Menu2016/Stage2/"""
"""l1t-integration-v88p1-CMSSW-8021/SingleMuon/"""
"""crab_l1t-integration-v88p1-CMSSW-8021__SingleMuon_2016H_v*/"""
"""161031_120*/0000/L1Ntuple_99*.root""",
"""root://eoscms.cern.ch//eos/cms/store/group/dpg_trigger/"""
"""comm_trigger/L1Trigger/L1Menu2016/Stage2/"""
"""l1t-integration-v88p1-CMSSW-8021/SingleMuon/"""
"""crab_l1t-integration-v88p1-CMSSW-8021__SingleMuon_2016H_v*/"""
"""161031_120*""",
]
import pprint
for i, path in enumerate(test_paths):
print(path, "=>")
expanded = glob(path)
print(len(expanded), "files:", pprint.pformat(expanded))
| [
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62,
85,
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37811,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
1433,
940,
3132,
62,
10232,
9,
15931,
1600,
198,
220,
220,
220,
2361,
198,
220,
220,
220,
1330,
279,
4798,
198,
220,
220,
220,
329,
1312,
11,
3108,
287,
27056,
378,
7,
9288,
62,
6978,
82,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
6978,
11,
366,
14804,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
9902,
796,
15095,
7,
6978,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
11925,
7,
11201,
12249,
828,
366,
16624,
25,
1600,
279,
4798,
13,
79,
18982,
7,
11201,
12249,
4008,
198
] | 2.045455 | 880 |
#!/usr/bin/env python
from distutils.core import setup
from glob import glob
from setuptools import find_packages
setup(name='Fibonacci',
version='1.0',
description='Python Distribution Utilities',
author='Kevin Chen',
packages=find_packages('src'),
package_dir={'': 'src'},
py_modules=[splitext(basename(path))[0] for path in glob('src/*.py')],
) | [
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"""
This file is part of the tagup Python module which is released under MIT.
See file LICENSE for full license details.
"""
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import torch
import torch.nn as nn
import torch.nn.functional as F
from models.pct_utils import TDLayer, TULayer, PTBlock, TRBlock
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# Copyright 2019 The Kubeflow 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.
import argparse
import importlib
import json
import os
from pathlib import Path
from typing import Text
from nbformat import NotebookNode
from nbformat.v4 import new_notebook, new_code_cell
import tornado.ioloop
import tornado.web
exporter = importlib.import_module("exporter")
parser = argparse.ArgumentParser(description="Server Arguments")
parser.add_argument(
"--timeout",
type=int,
default=os.getenv('KERNEL_TIMEOUT', 100),
help="Amount of time in seconds that a visualization can run for before " +
"being stopped."
)
args = parser.parse_args()
_exporter = exporter.Exporter(args.timeout)
class VisualizationHandler(tornado.web.RequestHandler):
"""Custom RequestHandler that generates visualizations via post requests.
"""
def validate_and_get_arguments_from_body(self) -> dict:
"""Validates and converts arguments from post request to dict.
Returns:
Arguments provided from post request as a dict.
"""
try:
arguments = {
"arguments": "{}",
"type": self.get_body_argument("type")
}
except tornado.web.MissingArgumentError:
raise Exception("No type provided.")
try:
arguments["arguments"] = self.get_body_argument("arguments")
except tornado.web.MissingArgumentError:
# If no arguments are provided, ignore error as arguments has been
# set to a stringified JSON object by default.
pass
try:
arguments["arguments"] = json.loads(arguments.get("arguments"))
except json.decoder.JSONDecodeError as e:
raise Exception("Invalid JSON provided as arguments: {}".format(str(e)))
# If invalid JSON is provided that is incorretly escaped
# arguments.get("arguments") can be a string. This Ensure that
# json.loads properly converts stringified JSON to dict.
if type(arguments.get("arguments")) != dict:
raise Exception("Invalid JSON provided as arguments!")
try:
arguments["source"] = self.get_body_argument("source")
except tornado.web.MissingArgumentError:
arguments["source"] = ""
if arguments.get("type") != "custom":
if len(arguments.get("source")) == 0:
raise Exception("No source provided.")
return arguments
def generate_notebook_from_arguments(
self,
arguments: dict,
source: Text,
visualization_type: Text
) -> NotebookNode:
"""Generates a NotebookNode from provided arguments.
Args:
arguments: JSON object containing provided arguments.
source: Path or path pattern to be used as data reference for
visualization.
visualization_type: Name of visualization to be generated.
Returns:
NotebookNode that contains all parameters from a post request.
"""
nb = new_notebook()
nb.cells.append(exporter.create_cell_from_args(arguments))
nb.cells.append(new_code_cell('source = "{}"'.format(source)))
if visualization_type == "custom":
code = arguments.get("code", [])
nb.cells.append(exporter.create_cell_from_custom_code(code))
else:
visualization_file = str(Path.cwd() / "types/{}.py".format(visualization_type))
nb.cells.append(exporter.create_cell_from_file(visualization_file))
return nb
def get(self):
"""Health check.
"""
self.write("alive")
def post(self):
"""Generates visualization based on provided arguments.
"""
# Validate arguments from request and return them as a dictionary.
try:
request_arguments = self.validate_and_get_arguments_from_body()
except Exception as e:
return self.send_error(400, reason=str(e))
# Create notebook with arguments from request.
nb = self.generate_notebook_from_arguments(
request_arguments.get("arguments"),
request_arguments.get("source"),
request_arguments.get("type")
)
# Generate visualization (output for notebook).
html = _exporter.generate_html_from_notebook(nb)
self.write(html)
if __name__ == "__main__":
application = tornado.web.Application([
(r"/", VisualizationHandler),
])
application.listen(8888)
tornado.ioloop.IOLoop.current().start()
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198
] | 2.600202 | 1,976 |
from unittest import TestCase
from ..nbbase import (
NotebookNode,
new_code_cell, new_text_cell, new_worksheet, new_notebook, new_output,
new_author, new_metadata, new_heading_cell, nbformat
)
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# -*- coding: utf-8 -*-
# 新規注文後、一定時間経ってもオーダーが残っている場合に
# 処理を続行するか否か
ORDER_IGNORE_TIMEOUT = True
# 注文完了待ち確認間隔
ORDER_EXECUTED_RETRY_INTERVAL = 2
# 注文完了待ち確認回数
ORDER_EXECUTED_RETRY = 40
# クローズ済みトレード取得時の許容時間誤差 [s]
TIME_ERROR_ALLOW = 3
# API呼び出しリトライ数
RETRY_API_CALL = 10
API_URI = 'https://api.liquid.com'
API_USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.10240'
# Path Info
API_PATH_BOARD = '/products/5/price_levels'
API_PATH_TICK = '/products/5'
API_PATH_BALANCE = '/accounts/balance'
API_PATH_ACCOUNT = '/trading_accounts'
API_PATH_LIST_ORDERS = '/orders?currency_pair_code=BTCJPY&status=live&product_code=CASH'
API_PATH_EXECUTIONS = '/executions/me?product_id=5'
API_PATH_ORDERS = '/orders/'
API_PATH_TRADES = '/trades'
API_PATH_TRADE_CLOSE = '/trades/{id}/close'
PRICE_TICK_SIZE = 2.5
BOARD_SIDE_ASK = 'sell_price_levels'
BOARD_SIDE_BID = 'buy_price_levels'
BALANCE_CURRENCY = 'currency'
BALANCE_VALUE = 'balance'
BALANCE_CURRENCY_0 = 'JPY'
BALANCE_CURRENCY_1 = 'BTC'
ACCOUNT_PRODUCT_ID = 'product_id'
ACCOUNT_EQUITY = 'equity'
ACCOUNT_FREE_MARGIN = 'free_margin'
ACCOUNT_MARGIN = 'margin'
ACCOUNT_KEEPRATE = 'keep_rate'
# 成行買い
ORDER_TYPE = 'market'
ORDER_PRODUCT_ID = 5
ORDER_FUNDING_CURRENCY = 'JPY'
ORDER_SIDE_BUY = 'buy'
ORDER_SIDE_SELL = 'sell'
ORDER_LEVELAGE_LEVEL = 10
ORDER_MODELS = 'models'
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] | 1.859603 | 755 |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------------------------------------------------------
# ROS-MAGNA
# ----------------------------------------------------------------------------------------------------------------------
# The MIT License (MIT)
# Copyright (c) 2016 GRVC University of Seville
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS
# OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# ----------------------------------------------------------------------------------------------------------------------
"""
Created on Mon Feb 21 2018
@author: josmilrom
"""
import sys
import rospy
import std_msgs.msg
import time
import math
import numpy as np
import tf, tf2_ros
import json
import copy
import random
import rospkg
from std_msgs.msg import Header, ColorRGBA
from geometry_msgs.msg import *
from sensor_msgs.msg import *
# from xml.dom import minidom
# from gazebo_msgs.srv import DeleteModel,SpawnModel
from visualization_msgs.msg import Marker
from jsk_recognition_msgs.msg import BoundingBox, BoundingBoxArray, TorusArray, PolygonArray
from jsk_recognition_msgs.msg import Torus as jsk_Torus
# from sympy import Point3D, Line3D, Segment3D
# from sympy import Point as Point2D
# from sympy import Polygon as Polygon2D
# import xml.etree.ElementTree
from magna.srv import *
from TFElements import *
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85,
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198,
6738,
309,
15112,
3639,
1330,
1635,
628,
628,
628
] | 3.847267 | 622 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
| [
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"""Class defintion for BaseFeaturizeViaLambda abstract class."""
from typing import Callable
import pandas as pd
from .base_featurizer import BaseFeaturizer
class BaseFeaturizerViaLambda(BaseFeaturizer):
"""
Abstract class to create secondary featurization via a custom function.
Attributes:
_callable {Callable[[pd.DataFrame], pd.Series]}
-- User-defined function to extract metafeatures from dataframe.
sec_feature_names {List[str]}
-- Names for secondary metafeatures
Refer to superclass for additional attributes.
"""
def __init__(
self,
method: str,
callable_: Callable[[pd.DataFrame], pd.Series],
normalizable: bool,
):
"""
Init function.
Extends superclass method.
Arguments:
method {str}
-- Description of secondary metafeatures. Used in naming
`sec_feature_names`.
callable_ {Callable[[pd.DataFrame], pd.Series]}
-- User-defined function to extract metafeatures from dataframe.
normalizable {bool}
-- Whether the generated feature should be normalized.
"""
super().__init__(method=method, normalizable=normalizable)
self._callable = callable_
self.sec_feature_names = super()._mark_nonnormalizable(
[self.method], normalizable=self.normalizable
)
| [
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8,
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13,
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62,
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62,
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62,
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62,
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11265,
13821,
7,
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220,
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220,
685,
944,
13,
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28,
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13,
11265,
13821,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198
] | 2.436455 | 598 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
if __name__ == '__main__':
A = tuple(map(int, input().split()))
print('максимальный элемент имеет номер ', A.index(max(A)))
zero_1 = zero_2 = -1
for i, item in enumerate(A):
if (item == 0) and (zero_1 != -1) and (zero_2 == -1):
zero_2 = i
if (item == 0) and (zero_1 == -1):
zero_1 = i
print('первый нулевой элемент в позиции ', zero_1, ' второй нулевой элемент в позиции ', zero_2)
mult = 1
for item in A[zero_1 + 1:zero_2]:
mult *= item
print('произведение элементов между нулевыми элементами ', mult)
| [
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201,
198
] | 1.534562 | 434 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 14 08:29:23 2017
@author: davidpvilaca
"""
import matplotlib.pyplot as plt
import cv2
img1 = cv2.imread('vermelho3.jpg')
img1_hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
i = img1_hsv[:,:, 0] < 30
img1_hsv[i, 0] += 30
i = img1_hsv[:,:, 0] > 150
img1_hsv[i, 0] -= 150
img_saida = cv2.cvtColor(img1_hsv, cv2.COLOR_HSV2BGR)
plt.subplot(121), plt.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
plt.title('Original')
plt.subplot(122), plt.imshow(cv2.cvtColor(img_saida, cv2.COLOR_BGR2RGB))
plt.title('Saída')
| [
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83,
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10786,
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] | 1.942953 | 298 |
from itertools import chain
from collections import OrderedDict
import os.path
from typing import Tuple
from argparse import Namespace
from tabulate import tabulate
from termcolor import colored as clr
import numpy as np
import torch
from torch import Tensor
from torch import nn
import torch.nn.functional as F
from torch import optim
from torch import autograd
from torchvision.utils import save_image
from models import Student
from models import get_model
from models import generative, classifiers
from models.classifiers import sample_classifier
from professors.professor import Professor, PostTrainProfessor
from utils import get_optimizer, nparams, grad_info
from loss_utils import cos, mse, l2
def grad_of(outputs, inputs, grad_outputs=None):
"""Call autograd.grad with create & retain graph, and ignore other
leaf variables.
"""
return autograd.grad(outputs, inputs,
grad_outputs=grad_outputs,
create_graph=True,
retain_graph=True,
only_inputs=True)
| [
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62,
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] | 2.890957 | 376 |
import numpy as np
import h5py
import scisoftpy as dnp
from time import sleep
from math import cos, sin
print("Starting")
data = h5py.File('/dls/tmp/ssg37927/31_processed_160905_141219.nxs','r')['entry/result/data']
angles = h5py.File('/dls/tmp/ssg37927/31_processed_160905_141219.nxs','r')['entry/result/Angle']
frame = 300
dnp.plot.image(data[:,frame,:])
cor = 140
pad = 50
xs, ys = np.meshgrid(np.arange(data.shape[0]+(2*pad))-(cor+pad), np.arange(data.shape[0]+(2*pad))-(cor+pad))
dnp.plot.image(xs, name='xs')
dnp.plot.image(ys, name='ys')
result = np.zeros([data.shape[1]]+list(xs.shape))
#dnp.plot.image(result, name='result')
angles = np.deg2rad(angles)
for f in range(100,data.shape[1]):
print("F is ", f)
dnp.plot.image(data[:,f,:])
for i in range(angles.shape[0]):
angle = angles[i]
#print("Angle : ", angle)
xx = xs*cos(angle) - ys*sin(angle)
xx = xx.astype(np.int16) + (cor+pad)
xx[xx>data.shape[0]-1] = data.shape[0]-1
#yy = ys*cos(angle) + xs*sin(angle)
#dnp.plot.image(xx, name='xx')
#dnp.plot.image(yy, name='yy')
stripe = data[i,f,:][xx]
#dnp.plot.image(stripe, name='stripe')
result[f,:,:] = result[f,:,:] + stripe
dnp.plot.image(result[f,:,:], name='result')
print("Opening file")
output = h5py.File('/dls/tmp/ssg37927/mb1.h5','w')
output.create_dataset("data", data=result)
output.close()
print("Done")
| [
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] | 2.119883 | 684 |
# -*- coding: utf-8 -*-
import csv
import torch
import torch.utils.data as tud
from torch.nn.utils.rnn import pad_sequence
TRAIN_DATA_PATH = './data/data_with_slots_intent_train.csv'
DEV_DATA_PATH = './data/data_with_slots_intent_dev.csv'
SLOT_PATH = './data/slot_maping.csv'
INTENT_PATH = './data/intent_maping.csv'
BATCH_SIZE = 64
MIN_FREQ = 1
#Make char dict
char2id = {'<pad>':0, '<unk>':1}
char2freq = {}
with open(TRAIN_DATA_PATH, 'r', encoding='utf8') as rf:
r = csv.reader(rf)
for row in r:
data = row[0].split()[:-2]
for each in data:
char = each.split(':')[0]
char2freq[char] = char2freq.get(char, 0) + 1
filtered_chars = [char for char, freq in char2freq.items() if freq >= MIN_FREQ]
for ind, char in enumerate(filtered_chars, 2):
char2id[char] = ind
#Make slot dict
slot2id = {'<pad>':0}
with open(SLOT_PATH, 'r', encoding='utf8') as rf:
r = csv.reader(rf)
for ind, row in enumerate(r, 1):
slot2id[row[1]] = ind
print(slot2id)
id2slot = {}
for k, v in slot2id.items():
id2slot[v] = k
#Make intent dict
intent2id = {}
with open(INTENT_PATH, 'r', encoding='utf8') as rf:
r = csv.reader(rf)
for ind, row in enumerate(r, 0):
intent2id[row[1]] = ind
id2intent = {}
for k, v in intent2id.items():
id2intent[v] = k
def collate_fn(batch_data):
"""
DataLoader所需的collate_fun函数，将数据处理成tensor形式
Args:
batch_data: batch数据
Returns:
"""
input_ids_list, slot_ids_list, intent_id_list, mask_list = [], [], [], []
for instance in batch_data:
# 按照batch中的最大数据长度,对数据进行padding填充
input_ids_temp = instance["input_ids"]
slot_ids_temp = instance["slot_ids"]
mask_temp = instance["mask"]
# 将input_ids_temp和slot_ids_temp添加到对应的list中
input_ids_list.append(torch.tensor(input_ids_temp, dtype=torch.long))
slot_ids_list.append(torch.tensor(slot_ids_temp, dtype=torch.long))
mask_list.append(torch.tensor(mask_temp, dtype=torch.long))
intent_id_list.append(instance["intent_id"])
# 使用pad_sequence函数，会将list中所有的tensor进行长度补全，补全到一个batch数据中的最大长度，补全元素为padding_value
return {"input_ids": pad_sequence(input_ids_list, batch_first=True, padding_value=0),
"slot_ids": pad_sequence(slot_ids_list, batch_first=True, padding_value=0),
"intent_ids": torch.tensor(intent_id_list, dtype=torch.long),
"mask": pad_sequence(mask_list, batch_first=True, padding_value=0)}
traindataset = IntentDataset(TRAIN_DATA_PATH)
traindataloader = tud.DataLoader(traindataset, BATCH_SIZE, shuffle=True, collate_fn=collate_fn)
valdataset = IntentDataset(DEV_DATA_PATH)
valdataloader = tud.DataLoader(valdataset, BATCH_SIZE, shuffle=False, collate_fn=collate_fn)
| [
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] | 2.022694 | 1,366 |
from __future__ import print_function
from glob2 import glob
import pandas as pd
import os
from pdb import set_trace
head = """
[home](http://tiny.cc/sbse) |
[models](xx) |
[data](xx) |
[discuss](https://github.com/ai-se/ResourcesDataDrivenSBSE/issues) |
[citation](https://github.com/ai-se/ResourcesDataDrivenSBSE/blob/master/CITATION.md) |
[copyright](https://github.com/ai-se/ResourcesDataDrivenSBSE/blob/master/LICENSE.md) ©2018
<br>
[<img width=900 src="https://github.com/ai-se/ResourcesDataDrivenSBSE/raw/master/img/banner.png">](http://tiny.cc/sbse)<br>
[](https://zenodo.org/badge/latestdoi/116411075)
"""
print(head)
file = os.path.abspath("../var/data.csv")
csv = pd.read_csv(file)
columns = csv.columns
row_sep = pd.DataFrame([["---" for col in columns]], columns=columns)
csv = pd.concat([row_sep, csv])
print(csv.to_csv(path_or_buf=None, sep="|", index=False))
tail = """
## License
[](https://creativecommons.org/publicdomain/zero/1.0/)
To the extent possible under law, we waive all copyright and related or neighboring rights to this work.
"""
print(tail)
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] | 2.524793 | 484 |
import feedparser
import csv
import json
#import pandas as pd
from urllib.request import urlopen
from bs4 import BeautifulSoup
query="engineer"
extract(query) | [
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] | 2.847458 | 59 |
import logging
import asyncio
import pathlib
import uuid
from functools import wraps
from typing import Callable, Any, Union, List
from bleak.backends.device import BLEDevice
from bleak.backends.dotnet.utils import BleakDataReader
from bleak.exc import BleakError, BleakDotNetTaskError
from bleak.backends.scanner import BaseBleakScanner
# Import of Bleak CLR->UWP Bridge. It is not needed here, but it enables loading of Windows.Devices
from BleakBridge import Bridge
from Windows.Devices.Bluetooth.Advertisement import (
BluetoothLEAdvertisementWatcher,
BluetoothLEScanningMode,
BluetoothLEAdvertisementType,
)
from Windows.Foundation import TypedEventHandler
logger = logging.getLogger(__name__)
_here = pathlib.Path(__file__).parent
class BleakScannerDotNet(BaseBleakScanner):
"""The native Windows Bleak BLE Scanner.
Implemented using `pythonnet <https://pythonnet.github.io/>`_, a package that provides an integration to
the .NET Common Language Runtime (CLR). Therefore, much of the code below has a distinct C# feel.
Keyword Args:
scanning mode (str): Set to ``Passive`` to avoid the ``Active`` scanning mode.
SignalStrengthFilter (``Windows.Devices.Bluetooth.BluetoothSignalStrengthFilter``): A
BluetoothSignalStrengthFilter object used for configuration of Bluetooth LE advertisement
filtering that uses signal strength-based filtering.
AdvertisementFilter (``Windows.Devices.Bluetooth.Advertisement.BluetoothLEAdvertisementFilter``): A
BluetoothLEAdvertisementFilter object used for configuration of Bluetooth LE advertisement
filtering that uses payload section-based filtering.
"""
async def set_scanning_filter(self, **kwargs):
"""Set a scanning filter for the BleakScanner.
Keyword Args:
SignalStrengthFilter (``Windows.Devices.Bluetooth.BluetoothSignalStrengthFilter``): A
BluetoothSignalStrengthFilter object used for configuration of Bluetooth
LE advertisement filtering that uses signal strength-based filtering.
AdvertisementFilter (Windows.Devices.Bluetooth.Advertisement.BluetoothLEAdvertisementFilter): A
BluetoothLEAdvertisementFilter object used for configuration of Bluetooth LE
advertisement filtering that uses payload section-based filtering.
"""
if "SignalStrengthFilter" in kwargs:
# TODO: Handle SignalStrengthFilter parameters
self._signal_strength_filter = kwargs["SignalStrengthFilter"]
if "AdvertisementFilter" in kwargs:
# TODO: Handle AdvertisementFilter parameters
self._advertisement_filter = kwargs["AdvertisementFilter"]
@staticmethod
def register_detection_callback(self, callback: Callable):
"""Set a function to act as Received Event Handler.
Documentation for the Event Handler:
https://docs.microsoft.com/en-us/uwp/api/windows.devices.bluetooth.advertisement.bluetoothleadvertisementwatcher.received
Args:
callback: Function accepting two arguments:
sender (``Windows.Devices.Bluetooth.AdvertisementBluetoothLEAdvertisementWatcher``) and
eventargs (``Windows.Devices.Bluetooth.Advertisement.BluetoothLEAdvertisementReceivedEventArgs``)
"""
self._callback = callback
# Windows specific
@property
def status(self) -> int:
"""Get status of the Watcher.
Returns:
Aborted 4
An error occurred during transition or scanning that stopped the watcher due to an error.
Created 0
The initial status of the watcher.
Started 1
The watcher is started.
Stopped 3
The watcher is stopped.
Stopping 2
The watcher stop command was issued.
"""
return self.watcher.Status if self.watcher else None
@classmethod
async def find_device_by_address(
cls, device_identifier: str, timeout: float = 10.0, **kwargs
) -> Union[BLEDevice, None]:
"""A convenience method for obtaining a ``BLEDevice`` object specified by Bluetooth address.
Args:
device_identifier (str): The Bluetooth address of the Bluetooth peripheral.
timeout (float): Optional timeout to wait for detection of specified peripheral
before giving up. Defaults to 10.0 seconds.
Keyword Args:
scanning mode (str): Set to ``Passive`` to avoid the ``Active`` scanning mode.
SignalStrengthFilter (``Windows.Devices.Bluetooth.BluetoothSignalStrengthFilter``): A
BluetoothSignalStrengthFilter object used for configuration of Bluetooth LE advertisement
filtering that uses signal strength-based filtering.
AdvertisementFilter (``Windows.Devices.Bluetooth.Advertisement.BluetoothLEAdvertisementFilter``): A
BluetoothLEAdvertisementFilter object used for configuration of Bluetooth LE
advertisement filtering that uses payload section-based filtering.
Returns:
The ``BLEDevice`` sought or ``None`` if not detected.
"""
ulong_id = int(device_identifier.replace(":", ""), 16)
loop = asyncio.get_event_loop()
stop_scanning_event = asyncio.Event()
scanner = cls(timeout=timeout)
return await scanner._find_device_by_address(
device_identifier, stop_scanning_event, stop_if_detected, timeout
)
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11,
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198,
220,
220,
220,
220,
220,
220,
220,
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198
] | 2.934141 | 1,898 |
from super_gradients.training.datasets.detection_datasets.detection_dataset import DetectionDataSet
from super_gradients.training.datasets.datasets_conf import COCO_DETECTION_CLASSES_LIST
class COCODetectionDataSet(DetectionDataSet):
"""
COCODetectionDataSet - Detection Data Set Class COCO Data Set
"""
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] | 2.944444 | 108 |
import unittest
import uuid
import requests
SERVICE_URL = 'http://localhost:8080'
if __name__ == '__main__':
unittest.main()
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] | 2.627451 | 51 |
import json
from src.user.use_cases import UserUseCase
from src.user.schemas import UserSchema
from abc import ABC, abstractmethod
from src.containers import Services
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from ..dao import axis_dao
from .controllers import controllers_service
motion_service = MotionService()
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import unittest
from py_CLI_menus.int_return_menu import IntReturnMenu
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# Generated by Django 3.2.4 on 2021-09-19 22:33
from django.db import migrations, models
import django.db.models.deletion
import smartMoney_app.models
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] | 2.942308 | 52 |
#
# databases.py
# Start of dataabases.py
#
# Created by FOSS-X UDAPI Desgin Team on 7/05/20.
# Copyright © 2020 FOSS-X. All rights reserved.
#
from flask import Flask, jsonify, request,Blueprint
from ..util_mongodb import *
from ..util import *
from ..util_mysql import *
import pymongo
mod = Blueprint('databasesMongodb', __name__)
client = pymongo.MongoClient()
@mod.route('/databases', methods=['GET'])
@token_required
def get_mysql_db(username):
""" List all the databases of databaseType = mongodb """
databaseType = 'mongodb'
try:
cnx = connectSQLServerDB('root', 'password', 'udapiDB')
mycursor = cnx.cursor()
sql = "SELECT * FROM udapiDB.configs WHERE (username='" + username + "') AND (databaseType='" + databaseType + "');"
mycursor.execute(sql)
entities = mycursor.fetchall()
attributes = [desc[0] for desc in mycursor.description]
fieldType = [FieldType.get_info(desc[1]) for desc in mycursor.description] # Debug code
results = []
for entity in entities:
results.append(entity[1])
cnx.close()
return jsonify(success=1, mongodb=results)
except mysql.connector.Error as err:
return jsonify(success=0, error_code=err.errno, message=err.msg)
@mod.route('/databases', methods=['POST'])
@token_required
@mod.route('/databases/<databaseName>', methods=['DELETE'])
@token_required
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31,
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14,
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6,
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31,
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62,
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31,
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14,
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14,
27,
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28,
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7206,
2538,
9328,
6,
12962,
198,
31,
30001,
62,
35827,
628,
198
] | 2.511384 | 571 |
from __future__ import print_function
import os
import tensorflow
import saver
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from nose.tools import assert_raises
from syn.tagmathon.b import Frame, Env, eval
#-------------------------------------------------------------------------------
# Frame
#-------------------------------------------------------------------------------
# Env
#-------------------------------------------------------------------------------
# eval
#-------------------------------------------------------------------------------
if __name__ == '__main__': # pragma: no cover
from syn.base_utils import run_all_tests
run_all_tests(globals(), verbose=True, print_errors=False)
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'''
@author: kaicai.hu
'''
import unittest
import tempfile
from kvmagent import kvmagent
from kvmagent.plugins import vm_plugin
from zstacklib.utils import bash
if __name__ == "__main__":
unittest.main() | [
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] | 2.763158 | 76 |
import os
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.ticker import FuncFormatter, MultipleLocator
from scipy import integrate
# total time
base_time = 4.0
pause_time = 1.5
total_time = base_time + pause_time
# fig
fig, ax = plt.subplots()
# plots
plot_0 = None
plot_25 = None
plot_50 = None
plot_75 = None
plot_100 = None
plot_125 = None
plot_150 = None
plot_175 = None
plot_200 = None
# delta time
play_speed = 0.5
dt = 0.02 * play_speed
# figure size (pixels->inches)
# https://matplotlib.org/devdocs/gallery/subplots_axes_and_figures/figure_size_units.html
px = 1/plt.rcParams["figure.dpi"]
fig_width = float(960) * px
fig_height = float(960) * px
# clear
fig, ax = plt.subplots(figsize=(fig_width, fig_height))
# show grid
ax.grid()
# animation
anim = animation.FuncAnimation(fig, init_func=init_figure, func=animation_frame, frames=np.arange(0, total_time, dt), interval=dt * 1000 / play_speed)
# save to gif
anim.save("contrast.gif", writer='pillow')
# save last frame to png
fig.savefig("contrast.png") | [
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import spidev
import os
spi = spidev.SpiDev()
spi.open(0,0)
print("Yay")
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#!/usr/bin/env python3
#I mainly followed this post https://ruslanspivak.com/lsbasi-part7/
#Lexer
#Tokenize the inputs
#Token type PLUS MUL MINUS INTEGER
import sys
sys.tracebacklimit=0
#String representation for debugging just in case
#lexer
#advance to the next character
#skipping white spaces
#multiple digits
#return lexical token one at a time
#Parser
#Parse the tokens into an AST
#some value from term
#some value form expression
#some value from term
#some value form expression
#some factor
#some value from term
#A node for all the integers
#Only evaluate integers and create num node
#Only evaluate multiplication and create mul node
#Evaluate plus and minus and create nodes
#Interpreter
#Evaluate the programing with AST
#for tree checking
'''
text = "45-3-7-2"
lex = Lexer(text)
par = Parser(lex)
tree = par.parse()
inter = Interpreter(tree)
#inter.load_tree()
print(inter.visit())
'''
if __name__ == '__main__':
main() | [
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# -*- coding: utf-8 -*-
import logging as log
import os
from OpenGL.GL import *
import cyglfw3 as glfw
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###############################################################################
# dice box
###############################################################################
import cv
import cv2
from datetime import datetime
import json
import requests
import os
import numpy
import math
from lib import dicebox_config as config # import our high level configuration
# from PIL import Image
# import sys
import os
import errno
# https://stackoverflow.com/questions/273192/how-can-i-create-a-directory-if-it-does-not-exist
###############################################################################
# configure our camera, and begin our capture and prediction loop
###############################################################################
# Camera 0 is the integrated web cam on my netbook
camera_port = 0
# Number of frames to throw away while the camera adjusts to light levels
ramp_frames = 3
# Now we can initialize the camera capture object with the cv2.VideoCapture class.
# All it needs is the index to a camera port.
camera = cv2.VideoCapture(camera_port)
camera.set(cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
font = cv.CV_FONT_HERSHEY_SIMPLEX
# Ramp the camera - these frames will be discarded and are only used to allow v4l2
# to adjust light levels, if necessary
for i in xrange(ramp_frames):
temp = get_image()
# Get our classification categories
server_category_map = get_category_map()
# Setup our default state
global CURRENT_EXPECTED_CATEGORY_INDEX
CURRENT_EXPECTED_CATEGORY_INDEX = 11
MAX_EXPECTED_CATEGORY_INDEX = len(server_category_map)
global MISCLASSIFIED_CATEGORY_INDEX
MISCLASSIFIED_CATEGORY_INDEX = True
global KEEP_INPUT
KEEP_INPUT = False
global ONLY_KEEP_MISCLASSIFIED_INPUT
ONLY_KEEP_MISCLASSIFIED_INPUT = True
global SERVER_ERROR
SERVER_ERROR = False
###############################################################################
# main loop
###############################################################################
while True:
# Take the actual image we want to keep
# camera_capture, resized_image = get_image()
camera_capture = get_image()
cropped_images, marked_capture = crop_image(camera_capture)
left_filename = datetime.now().strftime('capture_left_%Y-%m-%d_%H_%M_%S_%f.png')
middle_filename = datetime.now().strftime('capture_middle_%Y-%m-%d_%H_%M_%S_%f.png')
right_filename = datetime.now().strftime('capture_right_%Y-%m-%d_%H_%M_%S_%f.png')
left_tmp_file_path = "%s/%s" % (config.TMP_DIR, left_filename)
middle_tmp_file_path = "%s/%s" % (config.TMP_DIR, middle_filename)
right_tmp_file_path = "%s/%s" % (config.TMP_DIR, right_filename)
# A nice feature of the imwrite method is that it will automatically choose the
# correct format based on the file extension you provide. Convenient!
left_cropped_image = cropped_images[0]
cropped_image = cropped_images[1]
right_cropped_image = cropped_images[2]
cv2.imwrite(left_tmp_file_path, left_cropped_image)
with open(left_tmp_file_path, 'rb') as tmp_file:
left_content = tmp_file.read()
cv2.imwrite(middle_tmp_file_path, cropped_image)
with open(middle_tmp_file_path, 'rb') as tmp_file:
middle_content = tmp_file.read()
cv2.imwrite(right_tmp_file_path, right_cropped_image)
with open(right_tmp_file_path, 'rb') as tmp_file:
right_content = tmp_file.read()
if KEEP_INPUT:
if not MISCLASSIFIED_CATEGORY_INDEX and ONLY_KEEP_MISCLASSIFIED_INPUT:
os.remove(left_tmp_file_path)
os.remove(middle_tmp_file_path)
os.remove(right_tmp_file_path)
else:
new_path = "%s/%s" % (config.TMP_DIR, server_category_map[str(CURRENT_EXPECTED_CATEGORY_INDEX-1)])
make_sure_path_exists(new_path)
new_full_path = "%s/%s" % (new_path, middle_filename)
os.rename(middle_tmp_file_path, new_full_path)
os.remove(left_tmp_file_path)
os.remove(right_tmp_file_path)
else:
os.remove(left_tmp_file_path)
os.remove(middle_tmp_file_path)
os.remove(right_tmp_file_path)
base64_encoded_left_content = left_content.encode('base64')
base64_encoded_middle_content = middle_content.encode('base64')
base64_encoded_right_content = right_content.encode('base64')
outbound_content = [base64_encoded_left_content, base64_encoded_middle_content, base64_encoded_right_content]
categories = []
category_result = []
for content in outbound_content:
outjson = {}
outjson['data'] = content
json_data = json.dumps(outjson)
prediction = {}
category = {}
SERVER_ERROR = False
response = make_api_call('api/classify', json_data, 'POST')
if 'classification' in response:
prediction = response['classification']
if prediction != -1:
category = server_category_map[str(prediction)]
categories.append(category)
else:
SERVER_ERROR = True
if category == server_category_map[str(CURRENT_EXPECTED_CATEGORY_INDEX-1)]:
# MISCLASSIFIED_CATEGORY_INDEX = False
category_result.append(False)
else:
# MISCLASSIFIED_CATEGORY_INDEX = True
category_result.append(True)
MISCLASSIFIED_CATEGORY_INDEX = category_result[1]
cv2.namedWindow('dice box', cv2.WINDOW_NORMAL)
output_display = camera_capture
#resized_display = cv2.resize(output_display, (config.IMAGE_WIDTH, config.IMAGE_HEIGHT))
resized_display = cropped_image
height, width = output_display.shape[:2]
output_display[height - config.IMAGE_HEIGHT:height, 0:config.IMAGE_WIDTH] = resized_display # cv2.cvtColor(resized_display, cv2.COLOR_BGR2GRAY)
output_display = cv2.cvtColor(output_display, cv2.COLOR_GRAY2RGB)
output_label_1 = "[expecting %s]" % server_category_map[str(CURRENT_EXPECTED_CATEGORY_INDEX - 1)]
cv2.putText(output_display, output_label_1, (5, 20), font, 0.7, (255, 255, 255), 2)
if len(categories) == 3:
output_label_2 = "[left][classified %s][match? %r]" % (categories[0], not category_result[0])
output_label_3 = "[middle][classified %s][match? %r]" % (categories[1], not category_result[1])
output_label_4 = "[right][classified %s][match? %r]" % (categories[2], not category_result[2])
cv2.putText(output_display, output_label_2, (5, 50), font, 0.7, (255, 255, 255), 2)
cv2.putText(output_display, output_label_3, (5, 80), font, 0.7, (255, 255, 255), 2)
cv2.putText(output_display, output_label_4, (5, 110), font, 0.7, (255, 255, 255), 2)
output_label_5 = "[record? %r][only keep misclassified? %r]" % (KEEP_INPUT, ONLY_KEEP_MISCLASSIFIED_INPUT)
output_label_6 = "[server error? %r]" % SERVER_ERROR
cv2.putText(output_display, output_label_5, (5, 140), font, 0.5, (255, 0, 0), 2)
cv2.putText(output_display, output_label_6, (5, 170), font, 0.5, (0, 255, 255), 0)
try:
cv2.imshow('dice box', output_display)
except:
print("Unable to display output!")
input_key = cv2.waitKey(1)
if input_key & 0xFF == ord('q'):
break
if input_key & 0xFF == ord('c'):
KEEP_INPUT = False
if CURRENT_EXPECTED_CATEGORY_INDEX >= MAX_EXPECTED_CATEGORY_INDEX:
CURRENT_EXPECTED_CATEGORY_INDEX = 1
else:
CURRENT_EXPECTED_CATEGORY_INDEX += 1
if input_key & 0xFF == ord('z'):
if KEEP_INPUT is True:
KEEP_INPUT = False
else:
KEEP_INPUT = True
if input_key & 0xFF == ord('b'):
if ONLY_KEEP_MISCLASSIFIED_INPUT is True:
ONLY_KEEP_MISCLASSIFIED_INPUT = False
else:
ONLY_KEEP_MISCLASSIFIED_INPUT = True
###############################################################################
# cleanup
###############################################################################
# You'll want to release the camera, otherwise you won't be able to create a new
# capture object until your script exits
camera.release()
cv2.destroyAllWindows()
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] | 2.525424 | 3,245 |
# Licensed to Elasticsearch B.V under one or more agreements.
# Elasticsearch B.V licenses this file to you under the Apache 2.0 License.
# See the LICENSE file in the project root for more information
from eland.operations import Operations
| [
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import os,sys, math, numpy as np, itertools
from matplotlib.patches import Patch
import matplotlib.pyplot as plt
from pylab import *
import src.utilities as utils
config = utils.read_config()
mpl.rcParams.update(mpl.rcParamsDefault) # VS Code plots not black
plt.style.use(config['viz'])
infile='dist.dat' #First input file
outname='dist' #Name output files will take
xlbl='Amino Acid Number'
ylbl='Amino Acid Number'
ttl=''
maxc=16
mi=[]
mj=[]
ol=[]
i=-1
#############################################################################
# Read arguments from terminal, and assign input files and a name that all output files will contain.
#############################################################################
for x in range(1,len(sys.argv)):
if sys.argv[x] == '-i':
infile = sys.argv[x+1]
if sys.argv[x] == '-out':
outname = sys.argv[x+1]
if sys.argv[x]=='-xlabel':
xlbl = sys.argv[x+1]
if sys.argv[x]=='-ylabel':
ylbl = sys.argv[x+1]
if sys.argv[x]=='-title':
ttl = sys.argv[x+1]
if sys.argv[x]=='-val':
maxc = sys.argv[x+1]
if sys.argv[x]=='-help':
print('\n\nProgram to plot overlap data...\n\nOPTIONS:\n'\
'-i = Name of input file (Default=overlap.dat)\n'\
'-xlabel = Label for x axis (Default=mode i)\n'\
'-ylabel = Label for y axis (Default=mode j)\n'\
'-title = Title for plot\n')
exit()
inlines=open(infile,'r').readlines()
if inlines[-1]=='\n':
inlines[-1:]=[]
i=i+1
mi.append([])
mj.append([])
ol.append([])
for line in inlines:
if line=='\n':
i=i+1
mi.append([])
mj.append([])
ol.append([])
else:
mi[i].append(int(line.split()[0]))
mj[i].append(int(line.split()[1]))
ol[i].append(float(line.split()[2]))
mi=np.array(mi)
mj=np.array(mj)
ol=np.array(ol)
maxv = mi.max()
for x in range(1,len(sys.argv)):
if sys.argv[x] == '-max':
maxv = float(sys.argv[x+1])
fig=plt.figure(1, figsize=(11,8))
ax=fig.add_subplot(111)
cmain=ax.pcolor(mi,mj,ol,vmin=0, vmax=maxc,cmap=plt.cm.gist_yarg_r)
ax.set_title(ttl)
ax.set_xlabel(xlbl)
ax.set_xlim(mi.min(), maxv)
ax.set_ylabel(ylbl)
ax.set_ylim(maxv, mj.min())
cbar=fig.colorbar(cmain,aspect=10,ticks=[0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30])
fig.text(.85, .95, 'Distance / $\AA{}$', horizontalalignment='center')
# plt.rcParams.update({'font.size': 22})
plt.savefig(outname+'.png',format='png')
plt.show()
print('DONE')
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3,
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6,
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7,
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489,
83,
13,
12860,
3419,
198,
198,
4798,
10786,
35,
11651,
11537,
198
] | 2.178995 | 1,095 |
import numpy as np
import xarray as xr
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] | 2.866667 | 15 |
import pygame
from pygame.locals import *
import math
from . import *
#from .functions import *
#from .constants import *
| [
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from profit.dataset.preprocessing import mol_feats
from profit.dataset.preprocessing import mutator
from profit.dataset.preprocessing import seq_feats
from profit.dataset.preprocessing.mol_feats import construct_adj_matrix
from profit.dataset.preprocessing.mol_feats import construct_mol_features
from profit.dataset.preprocessing.mol_feats import check_num_atoms
from profit.dataset.preprocessing.mol_feats import construct_pos_matrix
from profit.dataset.preprocessing.mol_feats import MolFeatureExtractionError
from profit.dataset.preprocessing.mutator import PDBMutator
from profit.dataset.preprocessing.seq_feats import check_num_residues
from profit.dataset.preprocessing.seq_feats import construct_embedding
from profit.dataset.preprocessing.seq_feats import SequenceFeatureExtractionError
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] | 3.385593 | 236 |
# Copyright 2020 Neal Lathia
#
# 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 os
import json
import numpy as np
import pytest
import tensorflow as tf
from modelstore.models.tensorflow import (
MODEL_DIRECTORY,
TensorflowManager,
_save_model,
_save_weights,
save_json,
)
# pylint: disable=protected-access
# pylint: disable=redefined-outer-name
tf.config.threading.set_inter_op_parallelism_threads(1)
@pytest.fixture()
@pytest.fixture
@pytest.mark.parametrize(
"ml_library,should_match",
[
("tensorflow", True),
("keras", True),
("xgboost", False),
],
)
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220,
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828,
198,
220,
220,
220,
16589,
198,
8,
628,
628,
628,
628,
628
] | 2.786571 | 417 |
from game_hero.main_game import HeroGame
def test_start_game():
"""
Tests output of the game as string
"""
game_instance = HeroGame()
game_output = game_instance.start_game()
assert isinstance(game_output, str), "Output of the game is not string!"
def test_game_instance():
"""
Tests singleton implementation for the HeroGame instances.
"""
first_instance = HeroGame()
second_instance = HeroGame()
assert first_instance is second_instance, "Different instances for game!"
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329,
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2474,
198
] | 3.156627 | 166 |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 4 23:15:21 2019
@author: erikh
"""
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 4 21:15:45 2019
@author: erikh
"""
# Data Preprocessing Template
"""
"""
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle
# Importing the dataset
X = pd.read_csv('train_samples.csv', header=None)#, nrows = 5000)
y = pd.read_csv('train_labels.csv', header=None)#, nrows = 5000)
"""
X = dataset.iloc[:, [2,3]].values
y = dataset.iloc[:, 4].values
"""
print('Dataset loaded')
for i in range(len(y)):
if y.iloc[i][0] == 5 or y.iloc[i][0] == 7:
y = y.append(y.iloc[i])
y = y.append(y.iloc[i])
X = X.append(X.iloc[i])
X = X.append(X.iloc[i])
print ('Classes 5 and 7 doubled')
X = X.append(X)
y = y.append(y)
X, y = shuffle(X, y)
print('Dataset doubled and shuffled')
mu, sigma = 0, 0.15
# creating a noise with the same dimension as the dataset (2,2)
noise = np.random.normal(mu, sigma, [X.shape[0],X.shape[1]])
X = X + noise
print('Noise generated')
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
#X_train, X_test2, y_train, y_test2 = train_test_split(X_train2, y_train2, test_size = 0.2)
# Feature Scaling
"""
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
"""
# Fitting Random forrest to the Training set
# Create classifier
"""
# Random Forest Classifier
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 99, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
"""
# Classic SVM
"""
from sklearn.svm import SVC
classifier = SVC(C = 0.9, kernel = 'linear')
#classifier.fit(X_train, y_train)
"""
"""
from sklearn.model_selection import GridSearchCV
parameters =[ {'C': [0.01, 0.1, 1, 10], #so called `eta` value
'kernel': ['linear'],
'gamma': [0.001, 0.01, 0.1, 1],
'random_state': [0]
},
{'C': [0.01, 0.1, 1, 10], #so called `eta` value
'kernel': ['sigmoid'],
'coef0': [0.0, 0.1, 0.3, 0.4],
'gamma': [0.001, 0.01, 0.1, 1],
'random_state': [0]
}
]
grid_search = GridSearchCV(estimator = classifier,
param_grid = parameters,
scoring = 'accuracy',
cv = 5,
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
"""
# Fine Tuned XGBoost
"""
from xgboost import XGBClassifier
classifier = XGBClassifier(n_estimators = 100, learning_rate = 0.05, max_depth = 2, min_child_weight = 2, gamma = 0.05, subsample = 0.7, colsample_bytree = 0.9, n_jobs = -1)
"""
# Perceptron neural network
from sklearn.neural_network import MLPClassifier # importul clasei
from sklearn.linear_model import Perceptron
classifier = MLPClassifier(hidden_layer_sizes=((100)),
activation='relu', solver='adam', batch_size='auto',
learning_rate='adaptive', learning_rate_init=0.001, power_t=0.5,
max_iter=100, shuffle=True, random_state=None, tol=0.0001,
momentum=0.9, early_stopping=True, validation_fraction=0.25, verbose = True)
#perceptron_model.fit(X, y)
classifier.fit(X_train, y_train)
# Predicting the test set results
y_pred = classifier.predict(X_test)
x_pred = classifier.predict(X_train)
"""
y_pred2 = classifier.predict(X_test2)
x_pred2 = classifier.predict(X_train2)
"""
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm2 = confusion_matrix(y_train, x_pred)
# PERCEPTRON GRID SEARCH
"""
from sklearn.model_selection import GridSearchCV
parameters = {'hidden_layer_sizes': [(200, 200), (150, 150)], #so called `eta` value
'learning_rate': ['adaptive'],
'max_iter': [100],
'early_stopping': [True]
}
grid_search = GridSearchCV(estimator = classifier,
param_grid = parameters,
scoring = 'accuracy',
cv = 5,
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
"""
# Applying 10-Fold Cross Validation
"""
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, n_jobs = -1, cv = 5)
avg_accuracy = accuracies.mean()
accuracies.std()
"""
# Applying Grid Search to find the best model and the best parameters
"""
from sklearn.model_selection import GridSearchCV
parameters = {'learning_rate': [0.05], #so called `eta` value
'max_depth': [2],
'min_child_weight': [2],
'gamma': [0.05],
'subsample': [0.7],
'colsample_bytree': [0.9],
'n_estimators': [100]
}
grid_search = GridSearchCV(estimator = classifier,
param_grid = parameters,
scoring = 'accuracy',
cv = 3,
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
"""
y_pred.shape = (len(y_pred), y_test.shape[1])
x_pred.shape = (len(x_pred), y_train.shape[1])
y_pred2.shape = (len(y_pred2), y_test2.shape[1])
x_pred2.shape = (len(x_pred2), y_train2.shape[1])
print('Accuracy TRAIN: ', get_accuracy(x_pred, y_train))
print('Accuracy TEST: ', get_accuracy(y_pred, y_test))
print('Accuracy TRAIN 2: ', get_accuracy(x_pred2, y_train2))
print('Accuracy TEST 2: ', get_accuracy(y_pred2, y_test2))
print('----- CREATING KAGGLE SUBMISSION FORMAT ----')
to_predict = pd.read_csv('test_samples.csv', header=None)
results = pd.DataFrame(columns = ['Id', 'Prediction'])
sample_predictions = classifier.predict(to_predict)
for i in range(len(to_predict)):
results = results.append({'Id': i+1, 'Prediction':sample_predictions[i]}, ignore_index=True)
results.to_csv('PERCEPTRON-NN-15k--NOISE-0.15-variable-1-layerss-doubled-100-neurons.csv', encoding='utf-8', index=False)
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] | 2.091684 | 3,283 |
import os
import re
import shutil
import sys
import stat
import time
import subprocess
import glob
from appionlib import apDisplay
from appionlib import apParam
from pyami import imagic2mrc
#======================
#======================
#======================
def executeImagicBatchFile(filename, showcmd=True, verbose=False, logfile=None):
"""
executes an IMAGIC batch file in a controlled fashion
"""
proc = subprocess.Popen("chmod 755 "+filename, shell=True)
proc.wait()
path = os.path.dirname(filename)
os.chdir(path)
waited = False
t0 = time.time()
try:
if logfile is not None:
logf = open(logfile, 'a')
process = subprocess.Popen(filename, shell=True, stdout=logf, stderr=logf)
elif verbose is False:
devnull = open('/dev/null', 'w')
process = subprocess.Popen(filename, shell=True, stdout=devnull, stderr=devnull)
else:
process = subprocess.Popen(filename, shell=True)
if verbose is True:
out, err = process.communicate()
if out is not None and err is not None:
print "IMAGIC error", out, err
else:
out, err = process.communicate()
### continuous check
waittime = 0.01
while process.poll() is None:
if waittime > 0.05:
waited = True
sys.stderr.write(".")
waittime *= 1.02
time.sleep(waittime)
except:
apDisplay.printWarning("could not run IMAGIC batchfile: "+filename)
raise
tdiff = time.time() - t0
if tdiff > 20:
apDisplay.printMsg("completed in "+apDisplay.timeString(tdiff))
elif waited is True:
print ""
#======================
#======================
#======================
#======================
def checkLogFileForErrors(logfile):
"""
checks for any errors arising in IMAGIC log file, provided as a full path & filename
"""
logf = open(logfile)
loglines = logf.readlines()
for line in loglines:
if re.search("ERROR in program", line):
apDisplay.printError("ERROR IN IMAGIC SUBROUTINE, please check the logfile: "+logfile)
elif re.search("ERROR: all pixels", line):
apDisplay.printError("ERROR IN IMAGIC SUBROUTINE, please check the logfile: "+logfile)
logf.close()
#======================
def mask2D(boxsz, mask, infile=False, maskfile="mask2Dimgfile", path=os.path.abspath('.'), keepfiles=False):
"""
creates a 2d circular mask
if infile is specified, mask is applied to stack & then mask is deleted
boxsz is the box size in pixels
mask is the size of the mask to apply as a fraction
"""
imagicroot = checkImagicExecutablePath()
batchfile = os.path.join(path, 'maskimg.batch')
logf = os.path.join(path, 'maskimg.log')
### generate a 2D mask
f=open(batchfile,"w")
f.write("#!/bin/csh -f\n")
f.write("setenv IMAGIC_BATCH 1\n")
f.write("%s/stand/testim.e <<EOF\n"%imagicroot)
f.write("%s\n"%maskfile)
f.write("%i,%i\n"%(boxsz,boxsz))
f.write("real\n")
f.write("disc\n")
f.write("%.3f\n"%mask)
f.write("EOF\n")
if not infile:
f.close()
apDisplay.printMsg("creating 2D mask")
executeImagicBatchFile(batchfile, logfile=logf)
# check proper execution
if not os.path.exists(maskfile+".hed"):
apDisplay.printError("mask generation did not execute properly")
checkLogFileForErrors(logf)
if keepfiles is not True:
os.remove(batchfile)
os.remove(logf)
return maskfile+".hed"
### if infile is specified, apply mask to images
fname,ext=os.path.splitext(infile)
if not os.path.exists(fname+".hed"):
apDisplay.printError("input file: '%s' is not in imagic format"%infile)
file_ma=fname+"_ma"
f.write("%s/stand/twoimag.e <<EOF\n"%imagicroot)
f.write("mul\n")
f.write("%s\n"%fname)
f.write("%s\n"%maskfile)
f.write("%s\n"%file_ma)
f.write("EOF\n")
f.close()
apDisplay.printMsg("applying 2D mask")
executeImagicBatchFile(batchfile, logfile=logf)
# check proper execution
if not os.path.exists(file_ma+".hed"):
apDisplay.printError("masking did not execute properly")
checkLogFileForErrors(logf)
if keepfiles is not True:
os.remove(batchfile)
os.remove(logf)
return file_ma
#======================
def rotateStack(infile, ang, path=os.path.abspath('.'), keepfiles=False):
"""
creates a rotated copy of a stack
"""
imagicroot = checkImagicExecutablePath()
imagicv = getImagicVersion(imagicroot)
batchfile = os.path.join(path, 'rotate.batch')
logf = os.path.join(path, 'rotate.log')
fname,ext=os.path.splitext(infile)
if not os.path.exists(fname+".hed"):
apDisplay.printError("input file: '%s' is not in imagic format"%infile)
file_rot=fname+"_rot"
### rotate batch
f=open(batchfile,'w')
f.write("#!/bin/csh -f\n")
f.write("setenv IMAGIC_BATCH 1\n")
f.write("%s/stand/rotate.e MODE ROTATE << EOF\n"%imagicroot)
f.write("NO\n")
f.write("%s\n"%fname)
f.write("%s\n"%file_rot)
if imagicv < 100312:
f.write("NO\n")
f.write("%.3f\n"%ang)
f.write("NO\n")
f.write("EOF\n")
f.close()
apDisplay.printMsg("rotating particles by %.3f degrees"%ang)
executeImagicBatchFile(batchfile, logfile=logf)
# check proper execution
if not os.path.exists(file_rot+".hed"):
apDisplay.printError("rotate.e did not execute properly")
checkLogFileForErrors(logf)
if keepfiles is not True:
os.remove(batchfile)
os.remove(logf)
return file_rot
#======================
def runMSA(infile, maskf="none.hed", iter=50, numeig=69, overcor=0.8, nproc=1, path=os.path.abspath('.'), keepfiles=False):
"""
performs multivariate statistical analysis
"""
imagicroot = checkImagicExecutablePath()
imagicv = getImagicVersion(imagicroot)
batchfile = os.path.join(path, 'msa.batch')
logf = os.path.join(path, 'msa.log')
fname,ext=os.path.splitext(infile)
if not os.path.exists(fname+".hed"):
apDisplay.printError("input file: '%s' is not in imagic format"%infile)
if maskf is not False:
mname,ext=os.path.splitext(maskf)
if not os.path.exists(mname+".hed"):
apDisplay.printError("input mask file: '%s' is not in imagic format"%infile)
outbase = os.path.join(path,"my_msa")
### msa batch
f=open(batchfile,'w')
f.write("#!/bin/csh -f\n")
f.write("setenv IMAGIC_BATCH 1\n")
if nproc > 1:
f.write("%s/openmpi/bin/mpirun -np %i"%(imagicroot,nproc)+\
" -x IMAGIC_BATCH %s/msa/msa.e_mpi <<EOF\n"%imagicroot)
f.write("YES\n")
f.write("%i\n"%nproc)
else:
f.write("%s/msa/msa.e << EOF\n"%imagicroot)
f.write("NO\n")
f.write("FRESH\n")
f.write("MODULATION\n")
f.write("%s\n"%fname)
if nproc > 1:
f.write("NO\n")
f.write("%s\n"%mname)
f.write("%s\n"%os.path.join(path,"eigenim"))
f.write("%s\n"%os.path.join(path,"pixcoos"))
f.write("%s\n"%os.path.join(path,"eigenpix"))
f.write("%i\n"%iter)
f.write("%i\n"%numeig)
f.write("%.2f\n"%overcor)
f.write("%s\n"%outbase)
f.write("EOF\n")
f.close()
apDisplay.printMsg("running IMAGIC MSA")
executeImagicBatchFile(batchfile, logfile=logf)
# check proper execution
if not os.path.exists(outbase+".plt"):
apDisplay.printError("msa.e did not execute properly")
checkLogFileForErrors(logf)
if keepfiles is not True:
os.remove(batchfile)
os.remove(logf)
return outbase
#======================
def classifyAndAvg(infile, numcls, path=os.path.abspath('.'), keepfiles=False):
"""
classify particles using eigenvectors
and create class averages
"""
imagicroot = checkImagicExecutablePath()
imagicv = getImagicVersion(imagicroot)
batchfile = os.path.join(path, 'classify.batch')
logf = os.path.join(path, 'classify.log')
fname,ext=os.path.splitext(infile)
if not os.path.exists(fname+".hed"):
apDisplay.printError("input file: '%s' is not in imagic format"%infile)
classlist=os.path.join(path,"classlist")
classavgs=os.path.join(path,"classes")
### classify batch
f=open(batchfile,'w')
f.write("#!/bin/csh -f\n")
f.write("setenv IMAGIC_BATCH 1\n")
f.write("%s/msa/classify.e <<EOF\n"%imagicroot)
f.write("IMAGES/VOLUMES\n")
f.write("%s\n"%fname)
f.write("0\n")
f.write("69\n")
f.write("YES\n")
f.write("%i\n"%numcls)
f.write("%s\n"%classlist)
f.write("EOF\n")
f.write("%s/msa/classum.e << EOF\n"%imagicroot)
f.write("%s\n"%fname)
f.write("%s\n"%classlist)
f.write("%s\n"%classavgs)
f.write("YES\n")
f.write("NONE\n")
f.write("0\n")
f.write("EOF\n")
f.close()
apDisplay.printMsg("running IMAGIC classification")
executeImagicBatchFile(batchfile, logfile=logf)
# check proper execution
if not os.path.exists(classavgs+".hed"):
apDisplay.printError("classification did not execute properly")
checkLogFileForErrors(logf)
if keepfiles is not True:
os.remove(batchfile)
os.remove(logf)
return classavgs
#======================
#======================
def prealignClassAverages(rundir, avgs):
'''function to iteratively align class averages to each other prior to
input into angular reconstitution (optional) '''
imagicroot = checkImagicExecutablePath()
batchfile = os.path.join(rundir, "prealignClassAverages.batch")
f = open(batchfile, 'w')
f.write("#!/bin/csh -f\n")
f.write("setenv IMAGIC_BATCH 1\n")
f.write("cd "+rundir+"/\n")
### this is the actual alignment
f.write(str(imagicroot)+"/align/alirefs.e <<EOF >> prealignClassAverages.log\n")
f.write("ALL\n")
f.write("CCF\n")
f.write(str(os.path.basename(avgs)[:-4])+"\n")
f.write("NO\n")
f.write("0.99\n")
f.write(str(os.path.basename(avgs)[:-4])+"_aligned\n")
f.write("-999.\n")
f.write("0.2\n")
f.write("-180,180\n")
f.write("NO\n")
f.write("5\n")
f.write("NO\n")
f.write("EOF\n")
f.close()
avgs = avgs[:-4]+"_aligned.img"
proc = subprocess.Popen('chmod 755 '+batchfile, shell=True)
proc.wait()
apParam.runCmd(batchfile, "IMAGIC")
return avgs
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] | 2.380165 | 3,993 |
# Generated by Django 3.1.4 on 2020-12-21 11:36
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
def set_edited_by(apps, schema_editor):
"""将修改人设置为录入人"""
Item = apps.get_model("storage", "Item")
for item in Item.objects.all():
item.edited_by = item.created_by
item.save()
def reverse_set_edited_by(apps, schema_editor):
"""删除 storage_id 为空的物品"""
Item = apps.get_model("storage", "Item")
for item in Item.objects.filter(storage_id__isnull=True).all():
item.delete()
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220,
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13,
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] | 2.308 | 250 |
# coding=utf-8
"""
@desc:
国际象棋中的皇后比中国象棋里的大车还厉害，皇后能横向，纵向和斜向移动，在这三条线上的其他棋子都可以被吃掉。
所谓八皇后问题就是：将八位皇后放在一张8x8的棋盘上，使得每位皇后都无法吃掉别的皇后，（即任意两个皇后都不在同一条横线，
竖线和斜线上），问一共有多少种摆法。此问题是在1848年由棋手马克思·贝瑟尔提出的，后面陆续有包括高斯等大数学家们给出
自己的思考和解法，所以此问题不只是有年头了，简直比82年的拉菲还有年头，我们今天不妨尝尝这老酒。
@author: huijz
@version 0.1
@date 2020-08-31
@email [email protected]
"""
BOARD_SIZE = 8 # 棋盘大小 8*8=64
for answer in solve(BOARD_SIZE):
print answer
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84,
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89,
23,
17657,
31,
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13,
785,
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198,
8202,
9795,
62,
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796,
807,
220,
1303,
10545,
96,
233,
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246,
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807,
9,
23,
28,
2414,
628,
628,
198,
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287,
8494,
7,
8202,
9795,
62,
33489,
2599,
198,
220,
220,
220,
3601,
3280,
198
] | 0.74141 | 553 |
N = int(input())
ans = 0
K = 100
for i in range(N):
for j in range(N):
a, b = i * 2 * K, (i + 1) * 2 * K
c, d = j * 2 * K, (j + 1) * 2 * K
if in_(a, c) and in_(a, d) and in_(b, c) and in_(b, d):
ans += 1
print(ans)
| [
198,
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15853,
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7,
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8,
198
] | 1.765517 | 145 |
from flask import Blueprint
gee_gateway = Blueprint('gee_gateway', __name__, template_folder='templates', static_folder='static', static_url_path='/static/gee_gateway')
from . import gee, web
| [
6738,
42903,
1330,
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] | 3.288136 | 59 |
# -*- coding: utf-8 -*-
import random
import numpy as np
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import os
import csv
"""
NOT USED.
leaky_relu사용.
Pred 값이 - ~ +가 나옴
real target value is only 0 or 1 value
"""
cx = Variable(torch.zeros(3,1, 512))
hx = Variable(torch.zeros(3,1, 512))
f = open('data.csv','r',encoding='utf-8')
rdr = csv.reader(f)
data = []
for line in rdr:
data.append(line[-7:])
# print(line[-7:1])
f.close()
data = data[3:]
np_data = np.array(data, dtype=np.long)
torch_data = torch.from_numpy(np_data).type(torch.LongTensor)
main_num = torch_data[:,:6]
bonus_num = torch_data[:,6].unsqueeze(1)
#flip data seq
inv_idx = torch.arange(main_num.size(0)-1, -1, -1).long()
main_num = main_num.index_select(0, inv_idx)
bonus_num = bonus_num.index_select(0, inv_idx)
main_data = Variable(torch.zeros(main_num.size(0),46).scatter_(1,main_num,1)[:,1:].unsqueeze(0))
bonus_data = Variable(torch.zeros(bonus_num.size(0),46).scatter_(1,bonus_num,1)[:,1:].unsqueeze(0))
net = network()
#loss = nn.CrossEntropyLoss()
#crit = nn.KLDivLoss()
crit = nn.MSELoss()
#crit = nn.BCELoss(size_average = True)
opti = optim.Adam(net.parameters(),lr=0.0001)
for i in range(main_data.size(1)):
out,hx,cx = net(main_data[0,i,:].view(1,1,-1),hx,cx)
out = out.view(-1)
if i == main_data.size(1)-1 :
print(out.data.numpy())
break;
target = main_data[0,i+1,:]
loss = crit(out, target)
print(out.data.numpy())
print('i : ', i ,' loss :',loss.data[0])
net.zero_grad()
loss.backward(retain_graph=True)
nn.utils.clip_grad_norm(net.parameters(), 10) # Clip gradients (normalising by max value of gradient L2 norm)
opti.step()
#out ,hx,cx = net(main_data,hx,cx)
#last_out = out[:,-1,:]
#
#
#
#
#
#
#loss = nn.CrossEntropyLoss()
#
#target = Variable(torch.LongTensor(batch_size).random_(0, classes_no-1))
#
#err = loss(last_output, target)
#err.backward()
#
#
#
#
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2,
198,
2,
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2,
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2,
198,
2,
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198,
2,
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2,
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7,
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354,
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51,
22854,
7,
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62,
7857,
737,
25120,
41052,
15,
11,
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62,
3919,
12,
16,
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198,
2,
198,
2,
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796,
2994,
7,
12957,
62,
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11,
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8,
198,
2,
8056,
13,
1891,
904,
3419,
198,
2,
198,
2,
198,
2,
198,
2,
198
] | 2.132231 | 968 |
from discord.ext import commands
from Bot.utils.staff.staff_checks import *
from main import main_db
from pathlib import Path
from config import prefixes
users = main_db["users"]
blacklisted_files = ["shutdown", "start", "reload"]
| [
6738,
36446,
13,
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] | 3.530303 | 66 |
# pyright: reportUnusedClass=false
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Iterator, Optional, TypeVar
import pytest
from antidote import implements, inject, injectable, interface, world
from antidote.lib.injectable import register_injectable_provider
from antidote.lib.interface import NeutralWeight, predicate, Predicate, register_interface_provider
T = TypeVar("T")
@dataclass
@pytest.fixture(autouse=True)
@predicate
@predicate
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] | 3.482759 | 145 |
# -*- coding: utf-8 -*-
# $Id: webservergluecgi.py $
"""
Test Manager Core - Web Server Abstraction Base Class.
"""
__copyright__ = \
"""
Copyright (C) 2012-2015 Oracle Corporation
This file is part of VirtualBox Open Source Edition (OSE), as
available from http://www.virtualbox.org. This file is free software;
you can redistribute it and/or modify it under the terms of the GNU
General Public License (GPL) as published by the Free Software
Foundation, in version 2 as it comes in the "COPYING" file of the
VirtualBox OSE distribution. VirtualBox OSE is distributed in the
hope that it will be useful, but WITHOUT ANY WARRANTY of any kind.
The contents of this file may alternatively be used under the terms
of the Common Development and Distribution License Version 1.0
(CDDL) only, as it comes in the "COPYING.CDDL" file of the
VirtualBox OSE distribution, in which case the provisions of the
CDDL are applicable instead of those of the GPL.
You may elect to license modified versions of this file under the
terms and conditions of either the GPL or the CDDL or both.
"""
__version__ = "$Revision: 100880 $"
# Standard python imports.
import cgi;
import cgitb;
import os;
import sys;
# Validation Kit imports.
from testmanager.core.webservergluebase import WebServerGlueBase;
from testmanager import config;
class WebServerGlueCgi(WebServerGlueBase):
"""
CGI glue.
"""
| [
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] | 3.389021 | 419 |
# Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Tests for the ``vjp`` method of LightningQubit.
"""
from cmath import exp
import pytest
import pennylane as qml
from pennylane import numpy as np
try:
from pennylane_lightning.lightning_qubit_ops import (
VectorJacobianProductC64,
VectorJacobianProductC128,
)
except (ImportError, ModuleNotFoundError):
pytest.skip("No binary module found. Skipping.", allow_module_level=True)
class TestComputeVJP:
"""Tests for the numeric computation of VJPs"""
@pytest.fixture
@pytest.mark.skipif(
not hasattr(np, "complex256"), reason="Numpy only defines complex256 in Linux-like system"
)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_computation(self, tol, dev, C):
"""Test that the correct VJP is returned"""
dev._state = dev._asarray(dev._state, C)
dy = np.array([[1.0, 2.0], [3.0, 4.0]])
jac = np.array([[[1.0, 0.1, 0.2], [0.2, 0.6, 0.1]], [[0.4, -0.7, 1.2], [-0.5, -0.6, 0.7]]])
vjp = dev.compute_vjp(dy, jac)
expected = np.tensordot(dy, jac, axes=[[0, 1], [0, 1]])
assert vjp.shape == (3,)
assert np.allclose(vjp, expected, atol=tol, rtol=0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_computation_num(self, tol, dev, C):
"""Test that the correct VJP is returned"""
dev._state = dev._asarray(dev._state, C)
dy = np.array([[1.0, 2.0], [3.0, 4.0]])
jac = np.array([[[1.0, 0.1, 0.2], [0.2, 0.6, 0.1]], [[0.4, -0.7, 1.2], [-0.5, -0.6, 0.7]]])
vjp = dev.compute_vjp(dy, jac, num=4)
expected = np.tensordot(dy, jac, axes=[[0, 1], [0, 1]])
assert vjp.shape == (3,)
assert np.allclose(vjp, expected, atol=tol, rtol=0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_computation_num_error(self, dev, C):
"""Test that the correct VJP is returned"""
dev._state = dev._asarray(dev._state, C)
dy = np.array([[1.0, 2.0], [3.0, 4.0]])
jac = np.array([[[1.0, 0.1, 0.2], [0.2, 0.6, 0.1]], [[0.4, -0.7, 1.2], [-0.5, -0.6, 0.7]]])
with pytest.raises(ValueError, match="Invalid size for the gradient-output vector"):
dev.compute_vjp(dy, jac, num=3)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_jacobian_is_none(self, dev, C):
"""A None Jacobian returns a None VJP"""
dev._state = dev._asarray(dev._state, C)
dy = np.array([[1.0, 2.0], [3.0, 4.0]])
jac = None
vjp = dev.compute_vjp(dy, jac)
assert vjp is None
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_zero_dy(self, dev, C):
"""A zero dy vector will return a zero matrix"""
dev._state = dev._asarray(dev._state, C)
dy = np.zeros([2, 2])
jac = np.array([[[1.0, 0.1, 0.2], [0.2, 0.6, 0.1]], [[0.4, -0.7, 1.2], [-0.5, -0.6, 0.7]]])
vjp = dev.compute_vjp(dy, jac)
assert np.all(vjp == np.zeros([3]))
def test_array_dy(self, dev):
"""Test vjp_compute using Python array"""
dy = [1.0, 1.0, 1.0, 1.0]
jac = [dy, dy, dy, dy]
expected = [4.0, 4.0, 4.0, 4.0]
vjp = dev.compute_vjp(dy, jac)
assert np.all(vjp == expected)
def test_torch_tensor_dy(self, dev):
"""Test vjp_compute using the Torch interface"""
torch = pytest.importorskip("torch")
dtype = getattr(torch, "float32")
dy = torch.ones(4, dtype=dtype)
jac = torch.ones((4, 4), dtype=dtype)
expected = torch.tensor([4.0, 4.0, 4.0, 4.0], dtype=dtype)
vjp = dev.compute_vjp(dy, jac)
assert torch.all(vjp == expected)
def test_tf_tensor_dy(self, dev):
"""Test vjp_compute using the Tensorflow interface"""
tf = pytest.importorskip("tensorflow")
dy = tf.ones(4, dtype=tf.float32)
jac = tf.ones((4, 4), dtype=tf.float32)
expected = tf.constant([4.0, 4.0, 4.0, 4.0], dtype=tf.float32)
vjp = dev.compute_vjp(dy, jac)
assert tf.reduce_all(vjp == expected)
class TestVectorJacobianProduct:
"""Tests for the `vjp` function"""
@pytest.fixture
@pytest.mark.skipif(
not hasattr(np, "complex256"), reason="Numpy only defines complex256 in Linux-like system"
)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_use_device_state(self, tol, dev, C):
"""Tests that when using the device state, the correct answer is still returned."""
dev._state = dev._asarray(dev._state, C)
x, y, z = [0.5, 0.3, -0.7]
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
qml.Rot(x, y, z, wires=[0])
qml.RY(-0.2, wires=[0])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {1, 2, 3}
dy = np.array([1.0])
fn1 = dev.vjp(tape, dy)
vjp1 = fn1(tape)
qml.execute([tape], dev, None)
fn2 = dev.vjp(tape, dy, use_device_state=True)
vjp2 = fn2(tape)
assert np.allclose(vjp1, vjp2, atol=tol, rtol=0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_provide_starting_state(self, tol, dev, C):
"""Tests provides correct answer when provided starting state."""
dev._state = dev._asarray(dev._state, C)
x, y, z = [0.5, 0.3, -0.7]
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
qml.Rot(x, y, z, wires=[0])
qml.RY(-0.2, wires=[0])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {1, 2, 3}
dy = np.array([1.0])
fn1 = dev.vjp(tape, dy)
vjp1 = fn1(tape)
qml.execute([tape], dev, None)
fn2 = dev.vjp(tape, dy, starting_state=dev._pre_rotated_state)
vjp2 = fn2(tape)
assert np.allclose(vjp1, vjp2, atol=tol, rtol=0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_not_expval(self, dev, C):
"""Test if a QuantumFunctionError is raised for a tape with measurements that are not
expectation values"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape:
qml.RX(0.1, wires=0)
qml.var(qml.PauliZ(0))
dy = np.array([1.0])
with pytest.raises(qml.QuantumFunctionError, match="Adjoint differentiation method does"):
dev.vjp(tape, dy)(tape)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_finite_shots_warns(self, C):
"""Tests warning raised when finite shots specified"""
dev = qml.device("lightning.qubit", wires=1, shots=1)
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape:
qml.expval(qml.PauliZ(0))
dy = np.array([1.0])
with pytest.warns(
UserWarning, match="Requested adjoint differentiation to be computed with finite shots."
):
dev.vjp(tape, dy)(tape)
from pennylane_lightning import LightningQubit as lq
@pytest.mark.skipif(not lq._CPP_BINARY_AVAILABLE, reason="Lightning binary required")
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_unsupported_op(self, dev, C):
"""Test if a QuantumFunctionError is raised for an unsupported operation, i.e.,
multi-parameter operations that are not qml.Rot"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape:
qml.CRot(0.1, 0.2, 0.3, wires=[0, 1])
qml.expval(qml.PauliZ(0))
dy = np.array([1.0])
with pytest.raises(
qml.QuantumFunctionError, match="The CRot operation is not supported using the"
):
dev.vjp(tape, dy)(tape)
with qml.tape.QuantumTape() as tape:
qml.SingleExcitation(0.1, wires=[0, 1])
qml.expval(qml.PauliZ(0))
with pytest.raises(
qml.QuantumFunctionError,
match="The SingleExcitation operation is not supported using the",
):
dev.vjp(tape, dy)(tape)
@pytest.mark.skipif(not lq._CPP_BINARY_AVAILABLE, reason="Lightning binary required")
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_proj_unsupported(self, dev, C):
"""Test if a QuantumFunctionError is raised for a Projector observable"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape:
qml.CRX(0.1, wires=[0, 1])
qml.expval(qml.Projector([0, 1], wires=[0, 1]))
dy = np.array([1.0])
with pytest.raises(
qml.QuantumFunctionError, match="differentiation method does not support the Projector"
):
dev.vjp(tape, dy)(tape)
with qml.tape.QuantumTape() as tape:
qml.CRX(0.1, wires=[0, 1])
qml.expval(qml.Projector([0], wires=[0]) @ qml.PauliZ(0))
with pytest.raises(
qml.QuantumFunctionError, match="differentiation method does not support the Projector"
):
dev.vjp(tape, dy)(tape)
@pytest.mark.skipif(not lq._CPP_BINARY_AVAILABLE, reason="Lightning binary required")
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_no_trainable_parameters(self, dev, C):
"""A tape with no trainable parameters will simply return None"""
dev._state = dev._asarray(dev._state, C)
x = 0.4
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {}
dy = np.array([1.0])
fn = dev.vjp(tape, dy)
vjp = fn(tape)
assert vjp is None
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_no_trainable_parameters_NEW(self, dev, C):
"""A tape with no trainable parameters will simply return None"""
dev._state = dev._asarray(dev._state, C)
x = 0.4
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {}
dy = np.array([1.0])
fn = dev.vjp(tape, dy)
vjp = fn(tape)
assert vjp is None
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_no_trainable_parameters_(self, dev, C):
"""A tape with no trainable parameters will simply return None"""
dev._state = dev._asarray(dev._state, C)
x = 0.4
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {}
dy = np.array([1.0])
fn = dev.vjp(tape, dy)
vjp = fn(tape)
assert vjp is None
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_zero_dy(self, dev, C):
"""A zero dy vector will return no tapes and a zero matrix"""
dev._state = dev._asarray(dev._state, C)
x = 0.4
y = 0.6
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=0)
qml.RX(y, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {0, 1}
dy = np.array([0.0])
fn = dev.vjp(tape, dy)
vjp = fn(tape)
assert np.all(vjp == np.zeros([len(tape.trainable_params)]))
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_single_expectation_value(self, tol, dev, C):
"""Tests correct output shape and evaluation for a tape
with a single expval output"""
dev._state = dev._asarray(dev._state, C)
x = 0.543
y = -0.654
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=[0])
qml.RY(y, wires=[1])
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0) @ qml.PauliX(1))
tape.trainable_params = {0, 1}
dy = np.array([1.0])
fn = dev.vjp(tape, dy)
vjp = fn(tape)
expected = np.array([-np.sin(y) * np.sin(x), np.cos(y) * np.cos(x)])
assert np.allclose(vjp, expected, atol=tol, rtol=0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_multiple_expectation_values(self, tol, dev, C):
"""Tests correct output shape and evaluation for a tape
with multiple expval outputs"""
dev._state = dev._asarray(dev._state, C)
x = 0.543
y = -0.654
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=[0])
qml.RY(y, wires=[1])
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
qml.expval(qml.PauliX(1))
tape.trainable_params = {0, 1}
dy = np.array([1.0, 2.0])
fn = dev.vjp(tape, dy)
vjp = fn(tape)
expected = np.array([-np.sin(x), 2 * np.cos(y)])
assert np.allclose(vjp, expected, atol=tol, rtol=0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_prob_expectation_values(self, dev, C):
"""Tests correct output shape and evaluation for a tape
with prob and expval outputs"""
dev._state = dev._asarray(dev._state, C)
x = 0.543
y = -0.654
with qml.tape.QuantumTape() as tape:
qml.RX(x, wires=[0])
qml.RY(y, wires=[1])
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
qml.probs(wires=[0, 1])
tape.trainable_params = {0, 1}
dy = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
with pytest.raises(qml.QuantumFunctionError, match="Adjoint differentiation method does"):
dev.vjp(tape, dy)(tape)
class TestBatchVectorJacobianProduct:
"""Tests for the batch_vjp function"""
@pytest.fixture
@pytest.mark.skipif(
not hasattr(np, "complex256"), reason="Numpy only defines complex256 in Linux-like system"
)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_one_tape_no_trainable_parameters(self, dev, C):
"""A tape with no trainable parameters will simply return None"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = {}
tape2.trainable_params = {0, 1}
tapes = [tape1, tape2]
dys = [np.array([1.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys)
vjps = fn(tapes)
assert vjps[0] is None
assert vjps[1] is not None
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_all_tapes_no_trainable_parameters(self, dev, C):
"""If all tapes have no trainable parameters all outputs will be None"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = set()
tape2.trainable_params = set()
tapes = [tape1, tape2]
dys = [np.array([1.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys)
vjps = fn(tapes)
assert vjps[0] is None
assert vjps[1] is None
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_zero_dy(self, dev, C):
"""A zero dy vector will return no tapes and a zero matrix"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = {0}
tape2.trainable_params = {0, 1}
tapes = [tape1, tape2]
dys = [np.array([0.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys)
vjps = fn(tapes)
assert np.allclose(vjps[0], 0)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_reduction_append(self, dev, C):
"""Test the 'append' reduction strategy"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = {0}
tape2.trainable_params = {0, 1}
tapes = [tape1, tape2]
dys = [np.array([1.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys, reduction="append")
vjps = fn(tapes)
assert len(vjps) == 2
assert all(isinstance(v, np.ndarray) for v in vjps)
assert all(len(v) == len(t.trainable_params) for t, v in zip(tapes, vjps))
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_reduction_append_callable(self, dev, C):
"""Test the 'append' reduction strategy"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = {0}
tape2.trainable_params = {0, 1}
tapes = [tape1, tape2]
dys = [np.array([1.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys, reduction="append")
vjps = fn(tapes)
assert len(vjps) == 2
assert all(isinstance(v, np.ndarray) for v in vjps)
assert all(len(v) == len(t.trainable_params) for t, v in zip(tapes, vjps))
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_reduction_extend(self, dev, C):
"""Test the 'extend' reduction strategy"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = {0}
tape2.trainable_params = {0, 1}
tapes = [tape1, tape2]
dys = [np.array([1.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys, reduction="extend")
vjps = fn(tapes)
assert len(vjps) == sum(len(t.trainable_params) for t in tapes)
@pytest.mark.parametrize("C", [np.complex64, np.complex128])
def test_reduction_extend_callable(self, dev, C):
"""Test the 'extend' reduction strategy"""
dev._state = dev._asarray(dev._state, C)
with qml.tape.QuantumTape() as tape1:
qml.RX(0.4, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
with qml.tape.QuantumTape() as tape2:
qml.RX(0.4, wires=0)
qml.RX(0.6, wires=0)
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0))
tape1.trainable_params = {0}
tape2.trainable_params = {0, 1}
tapes = [tape1, tape2]
dys = [np.array([1.0]), np.array([1.0])]
fn = dev.batch_vjp(tapes, dys, reduction=list.extend)
vjps = fn(tapes)
assert len(vjps) == sum(len(t.trainable_params) for t in tapes)
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] | 1.964622 | 10,741 |
#!/usr/bin/env python
#
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This test simulates the first time a database has to be split.
- we start with a keyspace with a single shard and a single table
- we add and populate the sharding key
- we set the sharding key in the topology
- we clone into 2 instances
- we enable filtered replication
- we move all serving types
- we remove the source tablets
- we remove the original shard
"""
import logging
import unittest
from vtdb import keyrange_constants
import base_sharding
import environment
import tablet
import utils
# use_l2vtgate is set if we want to use l2vtgate processes.
# We'll set them up to have:
# l2vtgate1: covers the initial shard, and -80
# l2vtgate2: covers 80-
use_l2vtgate = False
# the l2vtgate processes, if applicable
l2vtgate1 = None
l2vtgate2 = None
# initial shard, covers everything
shard_master = tablet.Tablet()
shard_replica = tablet.Tablet()
shard_rdonly1 = tablet.Tablet()
# split shards
# range '' - 80
shard_0_master = tablet.Tablet()
shard_0_replica = tablet.Tablet()
shard_0_rdonly1 = tablet.Tablet()
# range 80 - ''
shard_1_master = tablet.Tablet()
shard_1_replica = tablet.Tablet()
shard_1_rdonly1 = tablet.Tablet()
all_tablets = [shard_master, shard_replica, shard_rdonly1,
shard_0_master, shard_0_replica, shard_0_rdonly1,
shard_1_master, shard_1_replica, shard_1_rdonly1]
# create_schema will create the same schema on the keyspace
# _insert_startup_value inserts a value in the MySQL database before it
# is sharded
# _check_lots returns how many of the values we have, in percents.
# _check_lots_not_present makes sure no data is in the wrong shard
if __name__ == '__main__':
utils.main()
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12417,
834,
10354,
198,
220,
3384,
4487,
13,
12417,
3419,
198
] | 3.052349 | 745 |
from .settings import *
# DATABASES = {
# 'default': {
# 'ENGINE': 'django.db.backends.postgresql_psycopg2',
# 'NAME': 'djadyen',
# 'USERNAME': 'djadyen',
# 'PASSWORD': 'djadyen',
# }
# }
| [
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220,
220,
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198,
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] | 1.832 | 125 |
import random
import torch
from torch import nn
import torch.nn.functional as F
| [
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] | 3.727273 | 22 |
# Zaimplementować funkcję remove_duplicates(txt: str) -> str, która zwróci wartość parametru txt pozbawioną sąsiadujących duplikujących się znaków. Przykład: XXYZZZ -> XYZ
print(remove_duplicates("XXYZZ")) | [
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] | 2.06 | 100 |
#!/usr/bin/python
import cv2
import numpy as np
p = Process('/root/Desktop/b.jpg')
img_disp = ImageDisplay('result')
img_disp.spin(p)
| [
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] | 2.413793 | 58 |
# This file is part of Moksha.
# Copyright (C) 2008-2014 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import logging
import signal
import sys
import os
try:
from twisted.internet.error import ReactorNotRunning
except ImportError: # Twisted 8.2.0 on RHEL5
from moksha.common.lib.helpers import appconfig
from moksha.common.lib.helpers import get_moksha_config_path
log = logging.getLogger('moksha.hub')
NO_CONFIG_MESSAGE = """
Cannot find Moksha configuration! Place a development.ini or production.ini
in /etc/moksha or in the current directory.
"""
from moksha.hub.hub import CentralMokshaHub
def main(options=None, consumers=None, producers=None, framework=True):
""" The main MokshaHub method """
# If we're running as a framework, then we're strictly calling other
# people's code. So, as the outermost piece of software in the stack, we're
# responsible for setting up logging.
# If we're not running as a framework, but as a library, then someone else
# is calling us. Therefore, we'll let them set up the logging themselves.
if framework:
setup_logger('-v' in sys.argv or '--verbose' in sys.argv)
config = {}
if not options:
if sys.argv[-1].endswith('.ini'):
config_path = os.path.abspath(sys.argv[-1])
else:
config_path = get_moksha_config_path()
if not config_path:
print(NO_CONFIG_MESSAGE)
return
cfg = appconfig('config:' + config_path)
config.update(cfg)
else:
config.update(options)
hub = CentralMokshaHub(config, consumers=consumers, producers=producers)
global _hub
_hub = hub
signal.signal(signal.SIGHUP, handle_signal)
signal.signal(signal.SIGINT, handle_signal)
log.info("Running the MokshaHub reactor")
from moksha.hub.reactor import reactor
threadcount = config.get('moksha.threadpool_size', None)
if not threadcount:
N = int(config.get('moksha.workers_per_consumer', 1))
threadcount = 1 + hub.num_producers + hub.num_consumers * N
threadcount = int(threadcount)
log.info("Suggesting threadpool size at %i" % threadcount)
reactor.suggestThreadPoolSize(threadcount)
reactor.run(installSignalHandlers=False)
log.info("MokshaHub reactor stopped")
| [
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7203,
44,
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26270,
16066,
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198
] | 2.837624 | 1,010 |
import sys
a = 10/0 | [
11748,
25064,
198,
198,
64,
796,
838,
14,
15
] | 2.222222 | 9 |
from django.contrib import admin
from .models import Income, Expense, Budget, Extract
@admin.register(Budget)
@admin.register(Income)
@admin.register(Expense)
@admin.register(Extract)
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# 410000001
if sm.hasQuest(38002):
sm.removeEscapeButton()
sm.flipDialoguePlayerAsSpeaker()
sm.sendNext("What happened? A house and a new name... But what happened to my friends? Are they alive? If I am, then maybe we failed to seal the Black Mage...")
sm.sendSay("No. They wouldn't give up that easily. They're probably hiding out somewhere, waiting to get back together. I need to look after myself for now, and get my strength back.")
sm.sendSay("Level 10... It's better than nothing, but it's not the best feeling. I'll hang around and get stronger. That's the only thing I can do now.")
sm.setQRValue(38002, "clear", False)
elif sm.hasQuest(38018):
sm.removeEscapeButton()
sm.flipDialoguePlayerAsSpeaker()
sm.sendNext("W-what is that thing? It looks so fuzzy. I don't think I should touch it...")
sm.setQRValue(38018, "clear", False) | [
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] | 3.135714 | 280 |
import collections
import typing
import copy
import inspect
import ast
import astunparse
import dace
import dace.sdfg.nodes as nd
import dace.data as dt
from dace.frontend.python.parser import DaceProgram
from daceml.autodiff.base_abc import BackwardContext, BackwardResult
import daceml.util.utils as utils
def forward_in_desc_with_name(forward_node: nd.Node, context: BackwardContext,
name) -> dt.Data:
""" Find the descriptor of the data that connects to input connector `name`.
:param forward_node: the node.
:param context: the backward context.
:param name: the input connector name.
:return: the descriptor of the data that connects to connector `name`.
"""
return utils.in_desc_with_name(forward_node, context.forward_state,
context.forward_sdfg, name)
def forward_out_desc_with_name(forward_node: nd.Node, context: BackwardContext,
name) -> dt.Data:
""" Find the descriptor of the data that connects to output connector `name`.
:param forward_node: the node.
:param context: the backward context.
:param name: the output connector name.
:return: the descriptor of the data that connects to connector `name`.
"""
return utils.out_desc_with_name(forward_node, context.forward_state,
context.forward_sdfg, name)
def add_backward_desc_for_connector(backward_sdfg: dace.SDFG,
forward_node: nd.Node,
context: BackwardContext, connector: str,
input: bool) -> str:
""" Adds the backward array for the connector of ``forward_node``.
:param backward_sdfg: the sdfg to add to.
:param forward_node: the forward node with the connector that we want to add a descriptor for
:param connector: the connector on the forward node that we want to add the descriptor for
:param input: ``True`` if the connector is an input, ``False`` otherwise
:return: the name of the newly added array in ``backward_sdfg``.
"""
if input:
edge = utils.in_edge_with_name(forward_node, context.forward_state,
connector)
else:
edge = utils.out_edge_with_name(forward_node, context.forward_state,
connector)
arr_name = edge.data.data
forward_desc = context.forward_sdfg.arrays[arr_name]
new_desc = copy.deepcopy(forward_desc)
new_desc.transient = False
return backward_sdfg.add_datadesc(arr_name + "_grad",
new_desc,
find_new_name=True)
def add_backward_desc(backward_sdfg: dace.SDFG, forward_sdfg: dace.SDFG,
forward_desc: dt.Data, forward_name: str) -> str:
""" Adds the backward array for the given descriptor.
:param backward_sdfg: the sdfg to add to.
:param forward_sdfg: the forward sdfg.
:param forward_desc: the data descriptor of the forward array from ``forward_sdfg``.
:param forward_name: a name for the forward array (does not have to match it's actual name).
:return: the name of the newly added array in ``backward_sdfg``.
"""
backward_name = utils.find_str_not_in_set(forward_sdfg.arrays,
forward_name + "_grad")
new_desc = copy.deepcopy(forward_desc)
new_desc.transient = False
return backward_sdfg.add_datadesc(backward_name, new_desc)
def backward_program_for_node(
program, context: BackwardContext,
forward_node: nd.Node) -> typing.Tuple[nd.Node, BackwardResult]:
""" Expand a function to the backward function for a node.
The dtypes for the arguments will be extracted by matching the parameter names to edges.
Gradient parameters should be the name of the forward parameter, appended with _grad. For these arguments the
data descriptors will match the data descriptors of the inputs/outputs they correspond to.
"""
input_names = set(inp.name for inp in forward_node.schema.inputs)
output_names = set(outp.name for outp in forward_node.schema.outputs)
if input_names.intersection(output_names):
# this is currently the case for only one onnx op
raise ValueError(
"program_for_node cannot be applied on nodes of this type;"
" '{}' is both an input and an output".format(
next(input_names.intersection(output_names))))
params = inspect.signature(program).parameters
backward_result = BackwardResult.empty()
inputs = {}
outputs = {}
for name, param in params.items():
if name in input_names:
inputs[name] = forward_in_desc_with_name(forward_node, context,
name)
elif name_without_grad_in(name, input_names):
outputs[name] = forward_in_desc_with_name(forward_node, context,
name[:-5])
backward_result.required_grad_names[name[:-5]] = name
elif name in output_names:
inputs[name] = forward_out_desc_with_name(forward_node, context,
name)
elif name_without_grad_in(name, output_names):
inputs[name] = forward_out_desc_with_name(forward_node, context,
name[:-5])
backward_result.given_grad_names[name[:-5]] = name
else:
raise ValueError(
"'{}' was not found as an input or output for {}".format(
name, forward_node.schema.name))
program.__annotations__ = {**inputs, **outputs}
sdfg = DaceProgram(program, (), {}, False, dace.DeviceType.CPU).to_sdfg()
result_node = context.backward_state.add_nested_sdfg(
sdfg, None, set(inputs), set(outputs))
return result_node, backward_result
def connect_output_from_forward(forward_node: nd.Node, backward_node: nd.Node,
context: BackwardContext,
output_connector_name: str):
""" Connect an output of the forward node as an input to the backward node. This is done by forwarding the array
from the forward pass.
Conceptually, this is similar to pytorch's ctx.save_for_backward.
:param forward_node: the node in the forward pass.
:param backward_node: the node in the backward pass.
:param context: the backward context.
:param output_connector_name: the name of the connector on the backward pass. The output of that connector will
be forwarded to the connector of the same name on the backward node.
"""
output_edge = utils.out_edge_with_name(forward_node, context.forward_state,
output_connector_name)
# add the array of the output to backward_input_arrays that it will be forwarded by the autodiff engine
output_arr_name = output_edge.data.data
if output_arr_name not in context.backward_generator.backward_input_arrays:
data_desc = context.forward_sdfg.arrays[output_arr_name]
context.backward_generator.backward_input_arrays[
output_arr_name] = copy.deepcopy(data_desc)
if context.backward_generator.separate_sdfgs:
data_desc.transient = False
context.backward_sdfg.add_datadesc(output_arr_name, data_desc)
read = context.backward_state.add_read(output_arr_name)
else:
cand = [
n for n, _ in context.backward_state.all_nodes_recursive()
if isinstance(n, nd.AccessNode) and n.data == output_arr_name
]
assert len(cand) == 1
read = cand[0]
context.backward_state.add_edge(read, None, backward_node,
output_connector_name,
copy.deepcopy(output_edge.data))
def cast_consts_to_type(code: str, dtype: dace.typeclass) -> str:
""" Convert a piece of code so that constants are wrapped in casts to ``dtype``.
For example:
x * (3 / 2)
becomes:
x * (dace.float32(3) / dace.float32(2))
This is only done when it is required due to a Div operator.
:param code: the code string to convert.
:param dtype: the dace typeclass to wrap cast to
:return: a string of the converted code.
"""
return astunparse.unparse(CastConsts().visit(ast.parse(code)))
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] | 2.284224 | 3,835 |
import click
from flask.cli import with_appcontext
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy.ext.compiler import compiles
from sqlalchemy.types import DateTime
# Force mysql to compile fraction of seconds
@compiles(DateTime, "mysql")
db = SQLAlchemy()
@click.command("init-db")
@with_appcontext
@click.command("clear-db")
@with_appcontext
def init_app(app):
"""Register database functions with the Flask app. This is called by
the application factory.
"""
app.cli.add_command(init_db_command)
app.cli.add_command(clear_db_command)
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] | 3.074468 | 188 |
import numpy as np
import subprocess
import json
import matplotlib
import matplotlib.pyplot as plt
import matplotlib as mpl
plt.rcParams['text.usetex'] = True
plt.rcParams['text.latex.preamble'] = [r'\usepackage{lmodern}']
font = {'family':'serif'}
plt.rc('font',**font)
NM = range(2,150,4)
# NM = range(2,20,2)
NREP_small = 10000
NREP_medium = 100
NREP_large = 10
AVG_CPU_TIME = []
res_file = 'riccati_benchmark_prometeo.json'
RUN = False
UPDATE_res = False
UPDATE_FIGURE = True
figname = 'riccati_benchmark'
blasfeo_res_file = 'riccati_benchmark_blasfeo_api.json'
LOAD_BLASFEO_RES = True
numpy_res_file = 'riccati_benchmark_numpy.json'
LOAD_NUMPY_RES = True
numpy_blasfeo_res_file = 'riccati_benchmark_numpy_blasfeo.json'
LOAD_NUMPY_BLASFEO_RES = True
julia_res_file = 'riccati_benchmark_julia.json'
LOAD_JULIA_RES = True
if not UPDATE_res:
print('Warning: not updating result file! This will just '
'plot the results at the end of the benchmark.')
if RUN:
for i in range(len(NM)):
print('running Riccati benchmark for case NM = {}'.format(NM[i]))
code = ""
if NM[i] < 30:
NREP = NREP_small
elif NM[i] < 100:
NREP = NREP_medium
else:
NREP = NREP_large
with open('riccati_mass_spring.py.in') as template:
code = template.read()
code = code.replace('NM', str(NM[i]))
code = code.replace('NREP', str(NREP))
with open('riccati_mass_spring.py', 'w+') as bench_file:
bench_file.write(code)
cmd = 'pmt riccati_mass_spring.py --cgen=True'
proc = subprocess.Popen([cmd], shell=True, stdout=subprocess.PIPE)
try:
outs, errs = proc.communicate()
except TimeOutExpired:
proc.kill()
print('Exception raised at NM = {}'.format(NM[i]))
outs, errs = proc.communicate()
AVG_CPU_TIME.append([float(outs.decode())/NREP, NM[i]])
if UPDATE_res:
with open(res_file, 'w+') as res:
json.dump(AVG_CPU_TIME, res)
else:
with open(res_file) as res:
AVG_CPU_TIME = json.load(res)
AVG_CPU_TIME = np.array(AVG_CPU_TIME)
plt.figure()
plt.semilogy(2*AVG_CPU_TIME[:,1], AVG_CPU_TIME[:,0])
legend = [r'\texttt{prometeo}']
if LOAD_BLASFEO_RES:
with open(blasfeo_res_file) as res:
AVG_CPU_TIME_BLASFEO = json.load(res)
AVG_CPU_TIME_BLASFEO = np.array(AVG_CPU_TIME_BLASFEO)
plt.semilogy(2*AVG_CPU_TIME_BLASFEO[:,1], AVG_CPU_TIME_BLASFEO[:,0], 'o')
legend.append(r'\texttt{BLASFEO}')
if LOAD_NUMPY_RES:
with open(numpy_res_file) as res:
AVG_CPU_TIME_BLASFEO = json.load(res)
AVG_CPU_TIME_BLASFEO = np.array(AVG_CPU_TIME_BLASFEO)
plt.semilogy(2*AVG_CPU_TIME_BLASFEO[:,1], AVG_CPU_TIME_BLASFEO[:,0], '--', alpha=0.7)
legend.append(r'\texttt{NumPy}')
if LOAD_JULIA_RES:
with open(julia_res_file) as res:
AVG_CPU_TIME_BLASFEO = json.load(res)
AVG_CPU_TIME_BLASFEO = np.array(AVG_CPU_TIME_BLASFEO)
plt.semilogy(2*AVG_CPU_TIME_BLASFEO[:,1], AVG_CPU_TIME_BLASFEO[:,0], '--',alpha=0.7)
legend.append(r'\texttt{Julia}')
if LOAD_NUMPY_BLASFEO_RES:
with open(numpy_blasfeo_res_file) as res:
AVG_CPU_TIME_BLASFEO = json.load(res)
AVG_CPU_TIME_BLASFEO = np.array(AVG_CPU_TIME_BLASFEO)
plt.semilogy(2*AVG_CPU_TIME_BLASFEO[:,1], AVG_CPU_TIME_BLASFEO[:,0])
legend.append(r'\texttt{NumPy + BLASFEO}')
plt.legend(legend)
plt.grid()
plt.xlabel(r'matrix size ($n_x$)')
plt.ylabel(r'CPU time [s]')
plt.title(r'Riccati factorization')
if UPDATE_FIGURE:
plt.savefig(figname + '.png', dpi=300, bbox_inches="tight")
plt.show()
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] | 2.036071 | 1,802 |
del_items(0x8012F26C)
SetType(0x8012F26C, "void GameOnlyTestRoutine__Fv()")
del_items(0x8012F274)
SetType(0x8012F274, "int vecleny__Fii(int a, int b)")
del_items(0x8012F298)
SetType(0x8012F298, "int veclenx__Fii(int a, int b)")
del_items(0x8012F2C4)
SetType(0x8012F2C4, "void GetDamageAmt__FiPiT1(int i, int *mind, int *maxd)")
del_items(0x8012F8BC)
SetType(0x8012F8BC, "int CheckBlock__Fiiii(int fx, int fy, int tx, int ty)")
del_items(0x8012F9A4)
SetType(0x8012F9A4, "int FindClosest__Fiii(int sx, int sy, int rad)")
del_items(0x8012FB40)
SetType(0x8012FB40, "int GetSpellLevel__Fii(int id, int sn)")
del_items(0x8012FBB4)
SetType(0x8012FBB4, "int GetDirection8__Fiiii(int x1, int y1, int x2, int y2)")
del_items(0x8012FDD0)
SetType(0x8012FDD0, "int GetDirection16__Fiiii(int x1, int y1, int x2, int y2)")
del_items(0x8012FFEC)
SetType(0x8012FFEC, "void DeleteMissile__Fii(int mi, int i)")
del_items(0x80130044)
SetType(0x80130044, "void GetMissileVel__Fiiiiii(int i, int sx, int sy, int dx, int dy, int v)")
del_items(0x801301F8)
SetType(0x801301F8, "void PutMissile__Fi(int i)")
del_items(0x801302FC)
SetType(0x801302FC, "void GetMissilePos__Fi(int i)")
del_items(0x80130424)
SetType(0x80130424, "void MoveMissilePos__Fi(int i)")
del_items(0x8013058C)
SetType(0x8013058C, "unsigned char MonsterTrapHit__FiiiiiUc(int m, int mindam, int maxdam, int dist, int t, int shift)")
del_items(0x80130900)
SetType(0x80130900, "unsigned char MonsterMHit__FiiiiiiUc(int pnum, int m, int mindam, int maxdam, int dist, int t, int shift)")
del_items(0x80131060)
SetType(0x80131060, "unsigned char PlayerMHit__FiiiiiiUcUc(int pnum, int m, int dist, int mind, int maxd, int mtype, int shift, int earflag)")
del_items(0x80131ACC)
SetType(0x80131ACC, "unsigned char Plr2PlrMHit__FiiiiiiUc(int pnum, int p, int mindam, int maxdam, int dist, int mtype, int shift)")
del_items(0x801322A8)
SetType(0x801322A8, "void CheckMissileCol__FiiiUciiUc(int i, int mindam, int maxdam, unsigned char shift, int mx, int my, int nodel)")
del_items(0x80132724)
SetType(0x80132724, "unsigned char GetTableValue__FUci(unsigned char code, int dir)")
del_items(0x801327B8)
SetType(0x801327B8, "void SetMissAnim__Fii(int mi, int animtype)")
del_items(0x80132888)
SetType(0x80132888, "void SetMissDir__Fii(int mi, int dir)")
del_items(0x801328CC)
SetType(0x801328CC, "void AddLArrow__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80132A8C)
SetType(0x80132A8C, "void AddArrow__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80132C48)
SetType(0x80132C48, "void GetVileMissPos__Fiii(int mi, int dx, int dy)")
del_items(0x80132D6C)
SetType(0x80132D6C, "void AddRndTeleport__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801330DC)
SetType(0x801330DC, "void AddFirebolt__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int micaster, int id, int dam)")
del_items(0x80133348)
SetType(0x80133348, "void AddMagmaball__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x8013345C)
SetType(0x8013345C, "void AddTeleport__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80133654)
SetType(0x80133654, "void AddLightball__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801337A8)
SetType(0x801337A8, "void AddFirewall__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80133990)
SetType(0x80133990, "void AddFireball__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80133BEC)
SetType(0x80133BEC, "void AddLightctrl__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80133CD4)
SetType(0x80133CD4, "void AddLightning__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80133E9C)
SetType(0x80133E9C, "void AddMisexp__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801340A8)
SetType(0x801340A8, "void AddWeapexp__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80134190)
SetType(0x80134190, "unsigned char CheckIfTrig__Fii(int x, int y)")
del_items(0x80134274)
SetType(0x80134274, "void AddTown__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80134698)
SetType(0x80134698, "void AddFlash__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801348A8)
SetType(0x801348A8, "void AddFlash2__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80134A88)
SetType(0x80134A88, "void AddManashield__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80134B50)
SetType(0x80134B50, "void AddFiremove__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80134CAC)
SetType(0x80134CAC, "void AddGuardian__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135118)
SetType(0x80135118, "void AddChain__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135174)
SetType(0x80135174, "void AddRhino__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135330)
SetType(0x80135330, "void AddFlare__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135614)
SetType(0x80135614, "void AddAcid__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135718)
SetType(0x80135718, "void AddAcidpud__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801357F0)
SetType(0x801357F0, "void AddStone__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135AE8)
SetType(0x80135AE8, "void AddGolem__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135CA0)
SetType(0x80135CA0, "void AddBoom__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135D34)
SetType(0x80135D34, "void AddHeal__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135F5C)
SetType(0x80135F5C, "void AddHealOther__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80135FC4)
SetType(0x80135FC4, "void AddElement__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801361F0)
SetType(0x801361F0, "void AddIdentify__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801362A0)
SetType(0x801362A0, "void AddFirewallC__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80136550)
SetType(0x80136550, "void AddInfra__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x8013664C)
SetType(0x8013664C, "void AddWave__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801366D0)
SetType(0x801366D0, "void AddNova__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801368E8)
SetType(0x801368E8, "void AddRepair__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80136998)
SetType(0x80136998, "void AddRecharge__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80136A48)
SetType(0x80136A48, "void AddDisarm__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80136AB0)
SetType(0x80136AB0, "void AddApoca__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80136CEC)
SetType(0x80136CEC, "void AddFlame__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int seqno)")
del_items(0x80136F08)
SetType(0x80136F08, "void AddFlamec__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80136FF8)
SetType(0x80136FF8, "void AddCbolt__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int micaster, int id, int dam)")
del_items(0x801371EC)
SetType(0x801371EC, "void AddHbolt__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int micaster, int id, int dam)")
del_items(0x801373AC)
SetType(0x801373AC, "void AddResurrect__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80137420)
SetType(0x80137420, "void AddResurrectBeam__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801374A8)
SetType(0x801374A8, "void AddTelekinesis__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x80137510)
SetType(0x80137510, "void AddBoneSpirit__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x8013770C)
SetType(0x8013770C, "void AddRportal__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801377AC)
SetType(0x801377AC, "void AddDiabApoca__Fiiiiiicii(int mi, int sx, int sy, int dx, int dy, int midir, int mienemy, int id, int dam)")
del_items(0x801378E8)
SetType(0x801378E8, "int AddMissile__Fiiiiiiciii(int sx, int sy, int v1, int v2, int midir, int mitype, int micaster, int id, int v3, int spllvl)")
del_items(0x80137D38)
SetType(0x80137D38, "int Sentfire__Fiii(int i, int sx, int sy)")
del_items(0x80137F1C)
SetType(0x80137F1C, "void MI_Dummy__Fi(int i)")
del_items(0x80137F24)
SetType(0x80137F24, "void MI_Golem__Fi(int i)")
del_items(0x80138180)
SetType(0x80138180, "void MI_SetManashield__Fi(int i)")
del_items(0x801381BC)
SetType(0x801381BC, "void MI_LArrow__Fi(int i)")
del_items(0x80138924)
SetType(0x80138924, "void MI_Arrow__Fi(int i)")
del_items(0x80138B40)
SetType(0x80138B40, "void MI_Firebolt__Fi(int i)")
del_items(0x80139200)
SetType(0x80139200, "void MI_Lightball__Fi(int i)")
del_items(0x80139488)
SetType(0x80139488, "void MI_Acidpud__Fi(int i)")
del_items(0x80139598)
SetType(0x80139598, "void MI_Firewall__Fi(int i)")
del_items(0x8013985C)
SetType(0x8013985C, "void MI_Fireball__Fi(int i)")
del_items(0x8013A220)
SetType(0x8013A220, "void MI_Lightctrl__Fi(int i)")
del_items(0x8013A59C)
SetType(0x8013A59C, "void MI_Lightning__Fi(int i)")
del_items(0x8013A688)
SetType(0x8013A688, "void MI_Town__Fi(int i)")
del_items(0x8013A8C0)
SetType(0x8013A8C0, "void MI_Flash__Fi(int i)")
del_items(0x8013AC14)
SetType(0x8013AC14, "void MI_Flash2__Fi(int i)")
del_items(0x8013ADDC)
SetType(0x8013ADDC, "void MI_Manashield__Fi(int i)")
del_items(0x8013B100)
SetType(0x8013B100, "void MI_Firemove__Fi(int i)")
del_items(0x8013B38C)
SetType(0x8013B38C, "void MI_Guardian__Fi(int i)")
del_items(0x8013B63C)
SetType(0x8013B63C, "void MI_Chain__Fi(int i)")
del_items(0x8013B8A8)
SetType(0x8013B8A8, "void MI_Weapexp__Fi(int i)")
del_items(0x8013BB60)
SetType(0x8013BB60, "void MI_Misexp__Fi(int i)")
del_items(0x8013BE1C)
SetType(0x8013BE1C, "void MI_Acidsplat__Fi(int i)")
del_items(0x8013BFB8)
SetType(0x8013BFB8, "void MI_Teleport__Fi(int i)")
del_items(0x8013C380)
SetType(0x8013C380, "void MI_Stone__Fi(int i)")
del_items(0x8013C52C)
SetType(0x8013C52C, "void MI_Boom__Fi(int i)")
del_items(0x8013C624)
SetType(0x8013C624, "void MI_Rhino__Fi(int i)")
del_items(0x8013C9D0)
SetType(0x8013C9D0, "void MI_FirewallC__Fi(int i)")
del_items(0x8013CC58)
SetType(0x8013CC58, "void MI_Infra__Fi(int i)")
del_items(0x8013CD10)
SetType(0x8013CD10, "void MI_Apoca__Fi(int i)")
del_items(0x8013CFA4)
SetType(0x8013CFA4, "void MI_Wave__Fi(int i)")
del_items(0x8013D4A0)
SetType(0x8013D4A0, "void MI_Nova__Fi(int i)")
del_items(0x8013D760)
SetType(0x8013D760, "void MI_Flame__Fi(int i)")
del_items(0x8013D958)
SetType(0x8013D958, "void MI_Flamec__Fi(int i)")
del_items(0x8013DBE0)
SetType(0x8013DBE0, "void MI_Cbolt__Fi(int i)")
del_items(0x8013DEE4)
SetType(0x8013DEE4, "void MI_Hbolt__Fi(int i)")
del_items(0x8013E1F0)
SetType(0x8013E1F0, "void MI_Element__Fi(int i)")
del_items(0x8013E8A8)
SetType(0x8013E8A8, "void MI_Bonespirit__Fi(int i)")
del_items(0x8013ECB0)
SetType(0x8013ECB0, "void MI_ResurrectBeam__Fi(int i)")
del_items(0x8013ED20)
SetType(0x8013ED20, "void MI_Rportal__Fi(int i)")
del_items(0x8013EF44)
SetType(0x8013EF44, "void ProcessMissiles__Fv()")
del_items(0x8013F338)
SetType(0x8013F338, "void ClearMissileSpot__Fi(int mi)")
del_items(0x8013F3F0)
SetType(0x8013F3F0, "void MoveToScrollTarget__7CBlocks(struct CBlocks *this)")
del_items(0x8013F404)
SetType(0x8013F404, "void MonstPartJump__Fi(int m)")
del_items(0x8013F598)
SetType(0x8013F598, "void DeleteMonster__Fi(int i)")
del_items(0x8013F5D0)
SetType(0x8013F5D0, "int M_GetDir__Fi(int i)")
del_items(0x8013F62C)
SetType(0x8013F62C, "void M_StartDelay__Fii(int i, int len)")
del_items(0x8013F674)
SetType(0x8013F674, "void M_StartRAttack__Fiii(int i, int missile_type, int dam)")
del_items(0x8013F78C)
SetType(0x8013F78C, "void M_StartRSpAttack__Fiii(int i, int missile_type, int dam)")
del_items(0x8013F8B0)
SetType(0x8013F8B0, "void M_StartSpAttack__Fi(int i)")
del_items(0x8013F998)
SetType(0x8013F998, "void M_StartEat__Fi(int i)")
del_items(0x8013FA68)
SetType(0x8013FA68, "void M_GetKnockback__Fi(int i)")
del_items(0x8013FC40)
SetType(0x8013FC40, "void M_StartHit__Fiii(int i, int pnum, int dam)")
del_items(0x8013FF38)
SetType(0x8013FF38, "void M_DiabloDeath__FiUc(int i, unsigned char sendmsg)")
del_items(0x8014024C)
SetType(0x8014024C, "void M2MStartHit__Fiii(int mid, int i, int dam)")
del_items(0x801404F8)
SetType(0x801404F8, "void MonstStartKill__FiiUc(int i, int pnum, unsigned char sendmsg)")
del_items(0x801407E4)
SetType(0x801407E4, "void M2MStartKill__Fii(int i, int mid)")
del_items(0x80140BAC)
SetType(0x80140BAC, "void M_StartKill__Fii(int i, int pnum)")
del_items(0x80140C9C)
SetType(0x80140C9C, "void M_StartFadein__FiiUc(int i, int md, unsigned char backwards)")
del_items(0x80140DF0)
SetType(0x80140DF0, "void M_StartFadeout__FiiUc(int i, int md, unsigned char backwards)")
del_items(0x80140F38)
SetType(0x80140F38, "void M_StartHeal__Fi(int i)")
del_items(0x80140FB8)
SetType(0x80140FB8, "void M_ChangeLightOffset__Fi(int monst)")
del_items(0x80141120)
SetType(0x80141120, "int M_DoStand__Fi(int i)")
del_items(0x80141188)
SetType(0x80141188, "int M_DoWalk__Fi(int i)")
del_items(0x8014140C)
SetType(0x8014140C, "int M_DoWalk2__Fi(int i)")
del_items(0x801415F8)
SetType(0x801415F8, "int M_DoWalk3__Fi(int i)")
del_items(0x801418BC)
SetType(0x801418BC, "void M_TryM2MHit__Fiiiii(int i, int mid, int hper, int mind, int maxd)")
del_items(0x80141A84)
SetType(0x80141A84, "void M_TryH2HHit__Fiiiii(int i, int pnum, int Hit, int MinDam, int MaxDam)")
del_items(0x80142098)
SetType(0x80142098, "int M_DoAttack__Fi(int i)")
del_items(0x8014223C)
SetType(0x8014223C, "int M_DoRAttack__Fi(int i)")
del_items(0x801423B4)
SetType(0x801423B4, "int M_DoRSpAttack__Fi(int i)")
del_items(0x801425A4)
SetType(0x801425A4, "int M_DoSAttack__Fi(int i)")
del_items(0x80142678)
SetType(0x80142678, "int M_DoFadein__Fi(int i)")
del_items(0x80142748)
SetType(0x80142748, "int M_DoFadeout__Fi(int i)")
del_items(0x8014285C)
SetType(0x8014285C, "int M_DoHeal__Fi(int i)")
del_items(0x80142908)
SetType(0x80142908, "int M_DoTalk__Fi(int i)")
del_items(0x80142E74)
SetType(0x80142E74, "void M_Teleport__Fi(int i)")
del_items(0x801430A8)
SetType(0x801430A8, "int M_DoGotHit__Fi(int i)")
del_items(0x80143108)
SetType(0x80143108, "void DoEnding__Fv()")
del_items(0x801431C8)
SetType(0x801431C8, "void PrepDoEnding__Fv()")
del_items(0x801432E0)
SetType(0x801432E0, "int M_DoDeath__Fi(int i)")
del_items(0x801434B0)
SetType(0x801434B0, "int M_DoSpStand__Fi(int i)")
del_items(0x80143554)
SetType(0x80143554, "int M_DoDelay__Fi(int i)")
del_items(0x80143644)
SetType(0x80143644, "int M_DoStone__Fi(int i)")
del_items(0x801436C8)
SetType(0x801436C8, "void M_WalkDir__Fii(int i, int md)")
del_items(0x801438F0)
SetType(0x801438F0, "void GroupUnity__Fi(int i)")
del_items(0x80143CDC)
SetType(0x80143CDC, "unsigned char M_CallWalk__Fii(int i, int md)")
del_items(0x80143EC8)
SetType(0x80143EC8, "unsigned char M_PathWalk__Fi(int i, char plr2monst[9], unsigned char (*Check)())")
del_items(0x80143F8C)
SetType(0x80143F8C, "unsigned char M_CallWalk2__Fii(int i, int md)")
del_items(0x801440A0)
SetType(0x801440A0, "unsigned char M_DumbWalk__Fii(int i, int md)")
del_items(0x801440F4)
SetType(0x801440F4, "unsigned char M_RoundWalk__FiiRi(int i, int md, int *dir)")
del_items(0x80144294)
SetType(0x80144294, "void MAI_Zombie__Fi(int i)")
del_items(0x8014448C)
SetType(0x8014448C, "void MAI_SkelSd__Fi(int i)")
del_items(0x80144624)
SetType(0x80144624, "void MAI_Snake__Fi(int i)")
del_items(0x80144A08)
SetType(0x80144A08, "void MAI_Bat__Fi(int i)")
del_items(0x80144DC0)
SetType(0x80144DC0, "void MAI_SkelBow__Fi(int i)")
del_items(0x80144FA4)
SetType(0x80144FA4, "void MAI_Fat__Fi(int i)")
del_items(0x80145154)
SetType(0x80145154, "void MAI_Sneak__Fi(int i)")
del_items(0x80145540)
SetType(0x80145540, "void MAI_Fireman__Fi(int i)")
del_items(0x80145838)
SetType(0x80145838, "void MAI_Fallen__Fi(int i)")
del_items(0x80145B54)
SetType(0x80145B54, "void MAI_Cleaver__Fi(int i)")
del_items(0x80145C3C)
SetType(0x80145C3C, "void MAI_Round__FiUc(int i, unsigned char special)")
del_items(0x801460A8)
SetType(0x801460A8, "void MAI_GoatMc__Fi(int i)")
del_items(0x801460C8)
SetType(0x801460C8, "void MAI_Ranged__FiiUc(int i, int missile_type, unsigned char special)")
del_items(0x801462E8)
SetType(0x801462E8, "void MAI_GoatBow__Fi(int i)")
del_items(0x8014630C)
SetType(0x8014630C, "void MAI_Succ__Fi(int i)")
del_items(0x80146330)
SetType(0x80146330, "void MAI_AcidUniq__Fi(int i)")
del_items(0x80146354)
SetType(0x80146354, "void MAI_Scav__Fi(int i)")
del_items(0x8014676C)
SetType(0x8014676C, "void MAI_Garg__Fi(int i)")
del_items(0x8014694C)
SetType(0x8014694C, "void MAI_RoundRanged__FiiUciUc(int i, int missile_type, unsigned char checkdoors, int dam, int lessmissiles)")
del_items(0x80146E60)
SetType(0x80146E60, "void MAI_Magma__Fi(int i)")
del_items(0x80146E8C)
SetType(0x80146E8C, "void MAI_Storm__Fi(int i)")
del_items(0x80146EB8)
SetType(0x80146EB8, "void MAI_Acid__Fi(int i)")
del_items(0x80146EE8)
SetType(0x80146EE8, "void MAI_Diablo__Fi(int i)")
del_items(0x80146F14)
SetType(0x80146F14, "void MAI_RR2__Fiii(int i, int mistype, int dam)")
del_items(0x80147414)
SetType(0x80147414, "void MAI_Mega__Fi(int i)")
del_items(0x80147438)
SetType(0x80147438, "void MAI_SkelKing__Fi(int i)")
del_items(0x80147974)
SetType(0x80147974, "void MAI_Rhino__Fi(int i)")
del_items(0x80147E1C)
SetType(0x80147E1C, "void MAI_Counselor__Fi(int i, unsigned char counsmiss[4], int _mx, int _my)")
del_items(0x801482E8)
SetType(0x801482E8, "void MAI_Garbud__Fi(int i)")
del_items(0x801484F0)
SetType(0x801484F0, "void MAI_Zhar__Fi(int i)")
del_items(0x801486E8)
SetType(0x801486E8, "void MAI_SnotSpil__Fi(int i)")
del_items(0x80148934)
SetType(0x80148934, "void MAI_Lazurus__Fi(int i)")
del_items(0x80148BA8)
SetType(0x80148BA8, "void MAI_Lazhelp__Fi(int i)")
del_items(0x80148CC8)
SetType(0x80148CC8, "void MAI_Lachdanan__Fi(int i)")
del_items(0x80148E74)
SetType(0x80148E74, "void MAI_Warlord__Fi(int i)")
del_items(0x80148FC0)
SetType(0x80148FC0, "void DeleteMonsterList__Fv()")
del_items(0x801490DC)
SetType(0x801490DC, "void ProcessMonsters__Fv()")
del_items(0x8014966C)
SetType(0x8014966C, "unsigned char DirOK__Fii(int i, int mdir)")
del_items(0x80149A54)
SetType(0x80149A54, "unsigned char PosOkMissile__Fii(int x, int y)")
del_items(0x80149ABC)
SetType(0x80149ABC, "unsigned char CheckNoSolid__Fii(int x, int y)")
del_items(0x80149B00)
SetType(0x80149B00, "unsigned char LineClearF__FPFii_Uciiii(unsigned char (*Clear)(), int x1, int y1, int x2, int y2)")
del_items(0x80149D88)
SetType(0x80149D88, "unsigned char LineClear__Fiiii(int x1, int y1, int x2, int y2)")
del_items(0x80149DC8)
SetType(0x80149DC8, "unsigned char LineClearF1__FPFiii_Uciiiii(unsigned char (*Clear)(), int monst, int x1, int y1, int x2, int y2)")
del_items(0x8014A05C)
SetType(0x8014A05C, "void M_FallenFear__Fii(int x, int y)")
del_items(0x8014A22C)
SetType(0x8014A22C, "void PrintMonstHistory__Fi(int mt)")
del_items(0x8014A4E0)
SetType(0x8014A4E0, "void PrintUniqueHistory__Fv()")
del_items(0x8014A604)
SetType(0x8014A604, "void MissToMonst__Fiii(int i, int x, int y)")
del_items(0x8014AA80)
SetType(0x8014AA80, "unsigned char PosOkMonst2__Fiii(int i, int x, int y)")
del_items(0x8014AC9C)
SetType(0x8014AC9C, "unsigned char PosOkMonst3__Fiii(int i, int x, int y)")
del_items(0x8014AF90)
SetType(0x8014AF90, "int M_SpawnSkel__Fiii(int x, int y, int dir)")
del_items(0x8014B0E8)
SetType(0x8014B0E8, "void TalktoMonster__Fi(int i)")
del_items(0x8014B214)
SetType(0x8014B214, "void SpawnGolum__Fiiii(int i, int x, int y, int mi)")
del_items(0x8014B46C)
SetType(0x8014B46C, "unsigned char CanTalkToMonst__Fi(int m)")
del_items(0x8014B4A4)
SetType(0x8014B4A4, "unsigned char CheckMonsterHit__FiRUc(int m, unsigned char *ret)")
del_items(0x8014B570)
SetType(0x8014B570, "void MAI_Golum__Fi(int i)")
del_items(0x8014B8E4)
SetType(0x8014B8E4, "unsigned char MAI_Path__Fi(int i)")
del_items(0x8014BA48)
SetType(0x8014BA48, "void M_StartAttack__Fi(int i)")
del_items(0x8014BB30)
SetType(0x8014BB30, "void M_StartWalk__Fiiiiii(int i, int xvel, int yvel, int xadd, int yadd, int EndDir)")
del_items(0x8014BC90)
SetType(0x8014BC90, "void FreeInvGFX__Fv()")
del_items(0x8014BC98)
SetType(0x8014BC98, "void InvDrawSlot__Fiii(int X, int Y, int Frame)")
del_items(0x8014BD1C)
SetType(0x8014BD1C, "void InvDrawSlotBack__FiiiiUc(int X, int Y, int W, int H, int Flag)")
del_items(0x8014BF70)
SetType(0x8014BF70, "void InvDrawItem__FiiiUci(int ItemX, int ItemY, int ItemNo, unsigned char StatFlag, int TransFlag)")
del_items(0x8014C040)
SetType(0x8014C040, "void InvDrawSlots__Fv()")
del_items(0x8014C318)
SetType(0x8014C318, "void PrintStat__FiiPcUc(int Y, int Txt0, char *Txt1, unsigned char Col)")
del_items(0x8014C3E4)
SetType(0x8014C3E4, "void DrawInvStats__Fv()")
del_items(0x8014CF00)
SetType(0x8014CF00, "void DrawInvBack__Fv()")
del_items(0x8014CF88)
SetType(0x8014CF88, "void DrawInvCursor__Fv()")
del_items(0x8014D464)
SetType(0x8014D464, "void DrawInvMsg__Fv()")
del_items(0x8014D62C)
SetType(0x8014D62C, "void DrawInvUnique__Fv()")
del_items(0x8014D750)
SetType(0x8014D750, "void DrawInv__Fv()")
del_items(0x8014D790)
SetType(0x8014D790, "void DrawInvTSK__FP4TASK(struct TASK *T)")
del_items(0x8014DAD4)
SetType(0x8014DAD4, "void DoThatDrawInv__Fv()")
del_items(0x8014E29C)
SetType(0x8014E29C, "unsigned char AutoPlace__FiiiiUc(int pnum, int ii, int sx, int sy, int saveflag)")
del_items(0x8014E5BC)
SetType(0x8014E5BC, "unsigned char SpecialAutoPlace__FiiiiUc(int pnum, int ii, int sx, int sy, int saveflag)")
del_items(0x8014E958)
SetType(0x8014E958, "unsigned char GoldAutoPlace__Fi(int pnum)")
del_items(0x8014EE28)
SetType(0x8014EE28, "unsigned char WeaponAutoPlace__Fi(int pnum)")
del_items(0x8014F0B4)
SetType(0x8014F0B4, "int SwapItem__FP10ItemStructT0(struct ItemStruct *a, struct ItemStruct *b)")
del_items(0x8014F1B0)
SetType(0x8014F1B0, "void CheckInvPaste__Fiii(int pnum, int mx, int my)")
del_items(0x80150E9C)
SetType(0x80150E9C, "void CheckInvCut__Fiii(int pnum, int mx, int my)")
del_items(0x8015194C)
SetType(0x8015194C, "void RemoveInvItem__Fii(int pnum, int iv)")
del_items(0x80151BF4)
SetType(0x80151BF4, "void RemoveSpdBarItem__Fii(int pnum, int iv)")
del_items(0x80151CE8)
SetType(0x80151CE8, "void CheckInvScrn__Fv()")
del_items(0x80151D60)
SetType(0x80151D60, "void CheckItemStats__Fi(int pnum)")
del_items(0x80151DE4)
SetType(0x80151DE4, "void CheckBookLevel__Fi(int pnum)")
del_items(0x80151F18)
SetType(0x80151F18, "void CheckQuestItem__Fi(int pnum)")
del_items(0x80152394)
SetType(0x80152394, "void InvGetItem__Fii(int pnum, int ii)")
del_items(0x80152690)
SetType(0x80152690, "void AutoGetItem__Fii(int pnum, int ii)")
del_items(0x80153100)
SetType(0x80153100, "void SyncGetItem__FiiiUsi(int x, int y, int idx, unsigned short ci, int iseed)")
del_items(0x8015328C)
SetType(0x8015328C, "unsigned char TryInvPut__Fv()")
del_items(0x80153454)
SetType(0x80153454, "int InvPutItem__Fiii(int pnum, int x, int y)")
del_items(0x801538FC)
SetType(0x801538FC, "int SyncPutItem__FiiiiUsiUciiiiiUl(int pnum, int x, int y, int idx, int icreateinfo, int iseed, int Id, int dur, int mdur, int ch, int mch, int ivalue, unsigned long ibuff)")
del_items(0x80153E58)
SetType(0x80153E58, "char CheckInvHLight__Fv()")
del_items(0x801541A0)
SetType(0x801541A0, "void RemoveScroll__Fi(int pnum)")
del_items(0x80154384)
SetType(0x80154384, "unsigned char UseScroll__Fv()")
del_items(0x801545EC)
SetType(0x801545EC, "void UseStaffCharge__FP12PlayerStruct(struct PlayerStruct *ptrplr)")
del_items(0x80154654)
SetType(0x80154654, "unsigned char UseStaff__Fv()")
del_items(0x80154714)
SetType(0x80154714, "void StartGoldDrop__Fv()")
del_items(0x80154810)
SetType(0x80154810, "unsigned char UseInvItem__Fii(int pnum, int cii)")
del_items(0x80154D34)
SetType(0x80154D34, "void DoTelekinesis__Fv()")
del_items(0x80154E5C)
SetType(0x80154E5C, "long CalculateGold__Fi(int pnum)")
del_items(0x80154F94)
SetType(0x80154F94, "unsigned char DropItemBeforeTrig__Fv()")
del_items(0x80154FEC)
SetType(0x80154FEC, "void ControlInv__Fv()")
del_items(0x801552F8)
SetType(0x801552F8, "void InvGetItemWH__Fi(int Pos)")
del_items(0x801553EC)
SetType(0x801553EC, "void InvAlignObject__Fv()")
del_items(0x801555A0)
SetType(0x801555A0, "void InvSetItemCurs__Fv()")
del_items(0x80155730)
SetType(0x80155730, "void InvMoveCursLeft__Fv()")
del_items(0x801558D8)
SetType(0x801558D8, "void InvMoveCursRight__Fv()")
del_items(0x80155B8C)
SetType(0x80155B8C, "void InvMoveCursUp__Fv()")
del_items(0x80155D84)
SetType(0x80155D84, "void InvMoveCursDown__Fv()")
del_items(0x8015608C)
SetType(0x8015608C, "void DumpMonsters__7CBlocks(struct CBlocks *this)")
del_items(0x801560B4)
SetType(0x801560B4, "void Flush__4CPad(struct CPad *this)")
del_items(0x801560D8)
SetType(0x801560D8, "void SetRGB__6DialogUcUcUc(struct Dialog *this, unsigned char R, unsigned char G, unsigned char B)")
del_items(0x801560F8)
SetType(0x801560F8, "void SetBack__6Dialogi(struct Dialog *this, int Type)")
del_items(0x80156100)
SetType(0x80156100, "void SetBorder__6Dialogi(struct Dialog *this, int Type)")
del_items(0x80156108)
SetType(0x80156108, "int SetOTpos__6Dialogi(struct Dialog *this, int OT)")
del_items(0x80156114)
SetType(0x80156114, "void ___6Dialog(struct Dialog *this, int __in_chrg)")
del_items(0x8015613C)
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del_items(0x80156198)
SetType(0x80156198, "void StartAutomap__Fv()")
del_items(0x801561A8)
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del_items(0x801561E8)
SetType(0x801561E8, "void AutomapLeft__Fv()")
del_items(0x80156208)
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SetType(0x80156228, "struct LINE_F2 *AMGetLine__FUcUcUc(unsigned char R, unsigned char G, unsigned char B)")
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SetType(0x801562D4, "void AmDrawLine__Fiiii(int x0, int y0, int x1, int y1)")
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SetType(0x8015633C, "void AmDrawPlayer__Fiiiii(int x0, int y0, int x1, int y1, int PNum)")
del_items(0x801563C4)
SetType(0x801563C4, "void DrawAutomapPlr__Fv()")
del_items(0x80156714)
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SetType(0x801568FC, "void DrawAutoMapVertDoor__Fii(int X, int Y)")
del_items(0x80156AD0)
SetType(0x80156AD0, "void DrawAutoMapHorzDoor__Fii(int X, int Y)")
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SetType(0x80156CA8, "void DrawAutoMapVertGrate__Fii(int X, int Y)")
del_items(0x80156D5C)
SetType(0x80156D5C, "void DrawAutoMapHorzGrate__Fii(int X, int Y)")
del_items(0x80156E10)
SetType(0x80156E10, "void DrawAutoMapSquare__Fii(int X, int Y)")
del_items(0x80156F58)
SetType(0x80156F58, "void DrawAutoMapStairs__Fii(int X, int Y)")
del_items(0x80157158)
SetType(0x80157158, "void DrawAutomap__Fv()")
del_items(0x801575FC)
SetType(0x801575FC, "void PRIM_GetPrim__FPP7LINE_F2(struct LINE_F2 **Prim)")
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] | 2.145856 | 13,575 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright: (c) 2020, CTERA Networks Ltd.
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
ANSIBLE_METADATA = {
'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'
}
DOCUMENTATION = '''
---
module: ctera_portal_plan
short_description: CTERA-Networks Portal Plan configuration and management
description:
- Create, modify and delete plans.
extends_documentation_fragment:
- ctera.ctera.vportal
author:
- Saimon Michelson (@saimonation)
- Ygal Blum (@ygalblum)
options:
state:
description:
- Whether the specified plan should exist or not.
type: str
choices: ['present', 'absent']
default: 'present'
name:
description: The name of the plan
required: True
type: str
retention:
description: The data retention policy
type: list
elements: dict
suboptions:
policy_name:
description: The name of the policy
type: str
required: True
choices:
- retainAll
- hourly
- daily
- weekly
- monthly
- quarterly
- yearly
- retainDeleted
duration:
description: The duration for the policy
type: int
required: True
quotas:
description: The items included in the plan and their respective quota
type: list
elements: dict
suboptions:
item_name:
description: The name of the plan item
type: str
required: True
choices:
- EV4
- EV8
- EV16
- EV32
- EV64
- EV128
- WA
- SA
- Share
- Connect
amount:
description: The quota's amount
type: int
required: True
'''
EXAMPLES = '''
- name: Portal Plan
ctera_portal_plan:
name: 'example'
retention:
- policy_name: retainAll
duration: 24
quotas:
- item_name: EV16
amount: 100
ctera_host: "{{ ctera_portal_hostname }}"
ctera_user: "{{ ctera_portal_user }}"
ctera_password: "{{ ctera_portal_password }}"
'''
RETURN = '''
name:
description: Name of the Plan
returned: when state is present
type: str
sample: example
'''
import ansible_collections.ctera.ctera.plugins.module_utils.ctera_common as ctera_common
from ansible_collections.ctera.ctera.plugins.module_utils.ctera_portal_base import CteraPortalBase
try:
from cterasdk import CTERAException
except ImportError: # pragma: no cover
pass # caught by ctera_common
if __name__ == '__main__': # pragma: no cover
main()
| [
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] | 2.392208 | 1,155 |
import adafruit_mcp3xxx.mcp3008 as MCP
from multiprocessing import Process
from moistureSensor import MoistureSensor
moisture_one = MoistureSensor(MCP.P0)
moisture_two = MoistureSensor(MCP.P1)
moisture_three = MoistureSensor(MCP.P3)
# Calibrates the sensors in parallel.
p1 = Process(target=moisture_one.calibrate())
p1.start()
p2 = Process(target=moisture_two.calibrate())
p2.start()
p3 = Process(target=moisture_three.calibrate())
p3.start()
p1.join()
p2.join()
p3.join()
with open("/home/pi/CompostMonitoringSystem/calibrationValues.csv", "w") as ofile:
ofile.write("Sensor, AirVal, WaterVal\n")
sensors = [moisture_one, moisture_two, moisture_three]
for s in sensors:
ofile.write(f"{s.pinNum},{s.airVal},{s.waterVal}\n") | [
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] | 2.47138 | 297 |
#!/usr/bin/env python
from flask import Flask, render_template, Response
# emulated camera
#from camera import Camera
from camera_pi import Camera
# Raspberry Pi camera module (requires picamera package)
# from camera_pi import Camera
from motion_tracker import get_frame
app = Flask(__name__)
@app.route('/')
def index():
"""Video streaming home page."""
return render_template('index.html')
def gen(camera):
"""Video streaming generator function."""
#motion_track()
while True:
frame = camera.get_frame()
# print type(frame),frame
# yield (frame)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
from StringIO import StringIO
@app.route('/video_feed')
def video_feed():
"""Video streaming route. Put this in the src attribute of an img tag."""
return Response(gen(Camera()),
mimetype='multipart/x-mixed-replace; boundary=frame')
@app.route('/tracking')
if __name__ == '__main__':
app.run(host='0.0.0.0', debug=True, threaded=True)
| [
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7,
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828,
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198,
2,
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88,
1164,
357,
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8,
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220,
220,
220,
220,
220,
220,
220,
7800,
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65,
6,
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59,
81,
59,
77,
6,
198,
220,
220,
220,
220,
220,
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220,
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220,
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6,
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62,
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2008,
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286,
281,
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7621,
526,
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198,
220,
220,
220,
1441,
18261,
7,
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7,
35632,
3419,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17007,
2963,
431,
11639,
16680,
541,
433,
14,
87,
12,
76,
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12,
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26,
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28,
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198,
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31,
1324,
13,
38629,
10786,
14,
36280,
11537,
198,
198,
361,
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3672,
834,
6624,
705,
834,
12417,
834,
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198,
220,
220,
220,
598,
13,
5143,
7,
4774,
11639,
15,
13,
15,
13,
15,
13,
15,
3256,
14257,
28,
17821,
11,
40945,
28,
17821,
8,
198,
197,
198
] | 2.665829 | 398 |
from jsub.error import JsubError
| [
6738,
474,
7266,
13,
18224,
1330,
449,
7266,
12331,
198
] | 3.3 | 10 |
import re
from collections import defaultdict
from typing import List, DefaultDict, Generator
from adventofcode.util.exceptions import SolutionNotFoundException
from adventofcode.util.helpers import solution_timer
from adventofcode.util.input_helpers import get_input_for_day
Coord = tuple[int, int]
GridType = DefaultDict[Coord, int]
LinePositions = tuple[Coord, Coord]
Line = List[Coord]
line_pattern = re.compile(r'(\d+)')
@solution_timer(2021, 5, 1)
@solution_timer(2021, 5, 2)
if __name__ == '__main__':
data = get_input_for_day(2021, 5)
part_one(data)
part_two(data)
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8,
198,
220,
220,
220,
636,
62,
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7,
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8,
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] | 2.790698 | 215 |
from typing import Any, List
from .base_set import BaseSet
from .base_var import VarSet
from .predicates import Predicate
| [
6738,
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] | 3.571429 | 35 |
import json
from django_mysql.models.fields import JSONField as MySQLJSONField
from wicked_historian.encoder import JSONEncoder
__all__ = (
'JSONField',
)
| [
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import numpy as np
import random
if __name__ == '__main__':
X = np.empty((10,5))
Y = np.linspace(0,9,10)
d = AssembleDataset(X,Y,5,seed = 0)
a,b = d.get_fold_data()
print(a,b)
a,b = d.get_res_data()
print(a,b)
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] | 1.904762 | 126 |
# Copyright 2019 DeepMind Technologies Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Compute the value of action given a policy vs a best responder."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from open_spiel.python import policy
from open_spiel.python.algorithms import action_value
from open_spiel.python.algorithms import get_all_states
from open_spiel.python.algorithms import policy_utils
import pyspiel
def _transitions(state, policies):
"""Returns a list of (action, prob) pairs from the specified state."""
if state.is_chance_node():
return state.chance_outcomes()
else:
pl = state.current_player()
return list(policies[pl].action_probabilities(state).items())
_CalculatorReturn = collections.namedtuple(
"_CalculatorReturn",
[
# The exploitability of the opponent strategy, i.e. the value of the
# best-responder player BR.
"exploitability",
# An array of shape `[len(info_states), game.num_distinct_actions()]`
# giving the value of each action vs the best response.
# Will be zero for invalid actions.
"values_vs_br",
# The player's counterfactual reach probability of this infostate when
# playing against the BR, as a list of shape [num_info_states].
"counterfactual_reach_probs_vs_br",
# The reach probability of the current player at the infostates when
# playing against the BR, as list shape [num_info_states].
# This is the product of the current player probs along *one* trajectory
# leading to this info-state (this number should be the same along
# any trajectory leading to this info-state because of perfect recall).
"player_reach_probs_vs_br",
])
class Calculator(object):
"""Class to orchestrate the calculation."""
def __call__(self, player, player_policy, info_states):
"""Computes action values per state for the player.
Args:
player: The id of the player (0 <= player < game.num_players()). This
player will play `player_policy`, while the opponent will play a best
response.
player_policy: A `policy.Policy` object.
info_states: A list of info state strings.
Returns:
A `_CalculatorReturn` nametuple. See its docstring for the documentation.
"""
self.player = player
opponent = 1 - player
# If the policy is a TabularPolicy, we can directly copy the infostate
# strings & values from the class. This is significantly faster than having
# to create the infostate strings.
if isinstance(player_policy, policy.TabularPolicy):
tabular_policy = {
key: _tuples_from_policy(player_policy.policy_for_key(key))
for key in player_policy.state_lookup
}
# Otherwise, we have to calculate all the infostate strings everytime. This
# is ~2x slower.
else:
# We cache these as they are expensive to compute & do not change.
if self._all_states is None:
self._all_states = get_all_states.get_all_states(
self.game,
depth_limit=-1,
include_terminals=False,
include_chance_states=False)
self._state_to_information_state = {
state: self._all_states[state].information_state_string()
for state in self._all_states
}
tabular_policy = policy_utils.policy_to_dict(
player_policy, self.game, self._all_states,
self._state_to_information_state)
# When constructed, TabularBestResponse does a lot of work; we can save that
# work by caching it.
if self._best_responder[player] is None:
self._best_responder[player] = pyspiel.TabularBestResponse(
self.game, opponent, tabular_policy)
else:
self._best_responder[player].set_policy(tabular_policy)
# Computing the value at the root calculates best responses everywhere.
history = str(self.game.new_initial_state())
best_response_value = self._best_responder[player].value(history)
best_response_actions = self._best_responder[
player].get_best_response_actions()
# Compute action values
self._action_value_calculator.compute_all_states_action_values({
player:
player_policy,
opponent:
policy.tabular_policy_from_callable(
self.game, best_response_policy, [opponent]),
})
obj = self._action_value_calculator._get_tabular_statistics( # pylint: disable=protected-access
((player, s) for s in info_states))
# Return values
return _CalculatorReturn(
exploitability=best_response_value,
values_vs_br=obj.action_values,
counterfactual_reach_probs_vs_br=obj.counterfactual_reach_probs,
player_reach_probs_vs_br=obj.player_reach_probs)
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62,
16250,
62,
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198
] | 2.814449 | 1,924 |
import torch
from fline.losses.segmentation.dice import BCEDiceLoss
| [
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28034,
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198,
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274,
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5154,
341,
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1330,
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1961,
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43,
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628
] | 2.916667 | 24 |
# Cycles - For
import random
n = 10000
m = 0
l = 1000
for i in range(n):
count = 0
x = -1
while x != l:
x = random.randint(1, l)
count=count+1
m=m+count
print("Done! average:", m/n) | [
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] | 2.009259 | 108 |
#-*- coding:utf-8 -*-
import sys
import time
from .torrentstatus import TorrentStatus | [
2,
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] | 3 | 29 |
from pydotplus import Dot, Node, Edge
import os
# 该图配置
graph = {'A': ['B', 'C', 'F'],
'B': ['C', 'D'],
'C': ['D'],
'D': ['C'],
'E': ['F', 'D'],
'F': ['C']
}
def find_path(graph, start, end, path=[]):
"""
在图graph中找路径：
从顶点start到顶点end
走过的路径为path
"""
path = path + [start]
# 3.0 若当找到路径尾部，则返回该路径
if start == end:
return path
# 1.0 判断当前顶点是否在图内
if start not in graph.keys():
return None
for node in graph[start]:
if node not in path:
# 2.0 以当前顶点为起点，继续找路径
newpath = find_path(graph, node, end, path)
# 4.0 返回该路径
if newpath:
return newpath
# 这个没有什么用吗 ？
# return path
if __name__ == '__main__':
result = find_path(graph, 'A', 'D')
print("1. 路径查找结果：", result)
print('---------------------------------')
result = find_all_paths(graph, 'A', 'D')
print("2. 全路径查找结果：", result)
print("路径个数：", len(result))
i = 1
for path in result:
print('路径{0:2d}为：{1}'.format(i, path))
i += 1
print('---------------------------------')
result = find_short_path(graph, 'A', 'D')
print("3. 查找最短路径：", result)
print('---------------------------------')
# 生成图表
dotgraph(graph)
# 广度优先遍历
result = breadth_first_search(graph, 'A')
print(result)
# 深度优先遍历
result = depth_first_search(graph, 'A')
print(result)
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] | 1.552192 | 958 |
# Auth
ALLOW_HOST = []
# Bot Address
SEND_AS = ""
# Google
GOOGLE_WORKSPACE_USER = ""
GOOGLE_WORKSPACE_SERVICE_ACCOUNT_CREDENTIALS = '''{
"type": "service_account",
"project_id": "",
"private_key_id": "",
"private_key": "",
"client_email": "",
"client_id": "",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": ""
}'''
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] | 2.152893 | 242 |
"""
Test code for adult.py
=======
Testing class that simply checks to see if the adult dataset
is loadable
"""
import numpy
from pylearn2.datasets.adult import adult
from pylearn2.testing.skip import skip_if_no_data
def test_adult():
"""
Tests if it will work correctly for train and test set.
"""
skip_if_no_data()
adult_train = adult(which_set='train')
assert (adult_train.X >= 0.).all()
assert adult_train.y.dtype == bool
assert adult_train.X.shape == (30162, 104)
assert adult_train.y.shape == (30162, 1)
adult_test = adult(which_set='test')
assert (adult_test.X >= 0.).all()
assert adult_test.y.dtype == bool
assert adult_test.X.shape == (15060, 103)
assert adult_test.y.shape == (15060, 1)
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] | 2.690391 | 281 |
from django import forms
from multichoice.models import MCQuestion
from quiz.forms import QuestionForm
class CreationMultiChoiceForm(QuestionForm):
"""
Form dedicated to the creation of a MultiChoice Question.
It inherits from QuestionForm and adds the fields
answerN and and answerN_correct.
"""
answer1 = forms.CharField(max_length=1000, label="Réponse 1")
answer1_correct = forms.BooleanField(required=False, label="Correcte")
answer2 = forms.CharField(max_length=1000, label="Réponse 2")
answer2_correct = forms.BooleanField(required=False, label="Correcte")
answer3 = forms.CharField(max_length=1000, label="Réponse 3")
answer3_correct = forms.BooleanField(required=False, label="Correcte")
class MultiChoiceForm(forms.Form):
"""
Form used for the taking of a quiz.
It is used for getting the student's answer to a multichoice question.
This answer will be compared to the one decided by the creator of
the quiz in order to decided if it is right or wrong.
"""
CHOICES = ((None, ""), (True, "Vrai"), (False, "Faux"))
answer1 = forms.ChoiceField(choices=CHOICES, widget=forms.Select(), required=True)
answer2 = forms.ChoiceField(choices=CHOICES, widget=forms.Select(), required=True)
answer3 = forms.ChoiceField(choices=CHOICES, widget=forms.Select(), required=True)
qid = forms.IntegerField(widget=forms.HiddenInput()) | [
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] | 3.109649 | 456 |
#!/usr/bin/env python3
from argparse_dataclass import dataclass
@dataclass
if __name__ == "__main__":
args = Options.parse_args()
if args.v:
print("args.v is true")
if args.n == 0:
args.n = 5
print(fib(args.n))
| [
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] | 2.146552 | 116 |
import logging
import colorlog
from coloured_log import ColoredFormatter
DEBUG_TRACE_NUM = 9
logging.Logger.trace = trace
logging.addLevelName(9, 'TRACE')
class bcolours:
"""The ANSI colour codes
"""
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
ERROR = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def dprint(data, parent='data', level=0):
"""Prints a dictionary with formatting
Args:
data (dict): The dictionary to be printed
parent (str, optional): The key from the parent for nested dictionaries
level (int, optional): How many nested dictionaries in the recursion is
"""
tabs = '\t' * level
cprint('{}' + tabs + parent + '{}: ', bcolours.OKBLUE)
tabs = '\t' * (level + 1)
for key, value in data.items():
if isinstance(value, dict):
dprint(value, parent=key, level=level + 1)
elif isinstance(value, list):
value = [str(x) for x in value]
cprint('{}' + tabs + key + '{}: {}{}{}', bcolours.ERROR, bcolours.WARNING, str(value), bcolours.ENDC)
elif isinstance(value, int):
cprint('{}' + tabs + key + '{}: {}{}{}', bcolours.ERROR, bcolours.OKGREEN, str(value), bcolours.ENDC)
elif isinstance(value, str):
cprint('{}' + tabs + key + '{}: {}', bcolours.ERROR, str(value))
def cprint(text, colour, *args):
"""Prints a message with colour
Args:
text (str): The text to be coloured
colour (bcolours.COLOR): The colour of the text
*args: Any extra strings to be printed
"""
print(text.format(colour, bcolours.ENDC, *args))
return text.format(colour, bcolours.ENDC, *args) + '\n' | [
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] | 2.221681 | 821 |
import pandas as pd
import numpy as np
import torch
import optuna
from dataset import HotelDataSet
from model import DeepNeuralNetwork
import config
from engine import Engine
from sklearn.metrics import roc_auc_score
import torch.optim as optim
def train(fold, params, save_model=False):
"""Finding the optimal DNN parameters (Architecture) based on the best ROC-AUC performance
Args:
fold ([int]): [Stratified 5-Fold (avoids overfitting) ]
params ([dict]): [define a combination of hyperparameters]
save_model (bool, optional): [save optimal model's parameters]. Defaults to False.
Returns:
[float]: [optimal ROC-AUC metric]
"""
df = pd.read_csv(config.TRAINING_FOLDS)
train_df = df[df.kfold != fold].reset_index(drop=True)
valid_df = df[df.kfold == fold].reset_index(drop=True)
# split the data into training and testing set (define features, target) values
y_train = train_df[["is_canceled"]].values
x_train = train_df.drop("is_canceled", axis=1).values
y_test = valid_df[["is_canceled"]].values
x_test = valid_df.drop("is_canceled", axis=1).values
# feed the data into custom Dataset
train_dataset = HotelDataSet(x_train, y_train)
test_dataset = HotelDataSet(x_test, y_test)
# initiate custom dataset and feed to dataloader
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=config.TRAIN_BATCH_SIZE
)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=config.TEST_BATCH_SIZE
)
# inititate DNN with params
model = DeepNeuralNetwork(
n_features=x_train.shape[1],
n_targets=y_train.shape[1],
n_layers=params["num_layers"],
hidden_size=params["hidden_size"],
dropout=params["dropout"],
)
optimizer = params["optimizer"](model.parameters(), lr=params["learning_rate"])
eng = Engine(model, optimizer)
best_metric = 0
for epochs in range(config.EPOCHS):
# initiating training and evaluation function
train_targets, train_outputs = eng.train_fn(train_loader)
eval_targets, eval_outputs = eng.eval_fn(test_loader)
train_outputs = np.array(eval_outputs) >= 0.5
eval_outputs = np.array(eval_outputs) >= 0.5
# calculating roc-auc score for train&eval
train_metric = roc_auc_score(train_targets, train_outputs)
eval_metric = roc_auc_score(eval_targets, eval_outputs)
print(
f"Epoch:{epochs+1}/{config.EPOCHS}, Train ROC-AUC: {train_metric:.4f}, Eval ROC-AUC: {eval_metric:.4f}"
)
# save optimal metrics to model.bin
if eval_metric > best_metric:
best_metric = eval_metric
if save_model:
torch.save(model.state_dict(), f"../models/model{fold}.bin")
return best_metric
if __name__ == "__main__":
def objective(trial):
"""[define a combination of hyperparameters]
Args:
trial ([type]): [trial object is used to construct a model inside the objective function]
Raises:
optuna.exceptions.TrialPruned: [If pruned, we go to next n_trials]
Returns:
[type]: [the value that Optuna will optimize]
"""
params = {
"optimizer": trial.suggest_categorical(
"optimizer", [optim.SGD, optim.Adam, optim.AdamW]
),
"num_layers": trial.suggest_int("num_layers", 1, 10),
"hidden_size": trial.suggest_int("hidden_size", 2, 112),
"dropout": trial.suggest_uniform("dropout", 0.1, 0.4),
"learning_rate": trial.suggest_loguniform("learning_rate", 0.0001, 0.01),
}
all_metrics = []
for i in range(5):
temp_metric = train(i, params, save_model=False)
all_metrics.append(temp_metric)
if trial.should_prune():
raise optuna.exceptions.TrialPruned()
return np.mean(all_metrics)
# study object contains information about the required parameter space
# increase the return value of our optimization function
study = optuna.create_study(
sampler=optuna.samplers.TPESampler(), direction="maximize"
)
# initiate optimize with 10 trials
study.optimize(objective, n_trials=10)
# define number of pruned&completed trials (saves time and computing power)
pruned_trials = [
t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED
]
complete_trials = [
t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE
]
# print metric and optimal combiniation of hyperparameters
n_trial = study.best_trial
print(f"Best Trial: {n_trial}, Value: {n_trial.values}")
print(f"Best Parameters: {n_trial.params}")
scores = 0
for j in range(1):
scr = train(j, n_trial.params, save_model=True)
scores += scr
# plot param importance and contour
fig = optuna.visualization.plot_param_importances(study)
fig2 = optuna.visualization.plot_contour(
study, params=["learning_rate", "optimizer"]
)
fig.show()
fig2.show()
df = study.trials_dataframe().drop(
["state", "datetime_start", "datetime_complete"], axis=1
)
print(f"SCORE: {scores}")
print(f"Number of Finished Trials {len(study.trials)}")
print(f"Number of Pruned Trials {len(pruned_trials)}")
print(f"Number of Completed Trials {len(complete_trials)}")
print(df)
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] | 2.395561 | 2,298 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import os
import copy
import time
import pickle
import numpy as np
from tqdm import tqdm
import torch
from tensorboardX import SummaryWriter
import torchvision.utils as vutils
from options import args_parser
from update import LocalUpdate, test_inference, AdversaryGanUpdateMnist, AdversaryGanUpdateCifar, AdversaryUpdate, AdversaryGanUpdateSVHN
from models import MLP, CNNMnist, CNNFashion_Mnist, CNNCifar, DCGANDiscriminator_mnist, DCGANGenerator_mnist, DCGANDiscriminator_cifar10, DCGANGenerator_cifar10, DCGANDiscriminator_SVHN, DCGANGenerator_SVHN
from utils import get_dataset, average_weights, exp_details, get_dataset_ganattack, get_dataset_split_by_label, \
get_dataset_idxgroup_ganattack, get_experiment_result_location, save_grid, generate_gif_from_file, \
generate_gif_from_list,plot_loss_acc, compute_avgpsnr, plot_avg_psnr
if __name__ == '__main__':
start_time = time.time()
# define paths
path_project = os.path.abspath('..')
logger = SummaryWriter('./logs')
args = args_parser()
exp_details(args)
if args.gpu:
torch.cuda.set_device('cuda:{}'.format(args.gpu))
device = 'cuda' if args.gpu else 'cpu'
# load dataset and user groups
if args.model == 'dcgan':
train_dataset, test_dataset, user_groups = get_dataset(args)
# _, _, user_groups = get_dataset(args)
# train_dataset, test_dataset, label_indexs = get_dataset_split_by_label(args)
else:
train_dataset, test_dataset, user_groups = get_dataset(args)
global_model = None
# BUILD MODEL
if args.model == 'cnn':
# Convolutional neural netork
if args.dataset == 'mnist':
global_model = CNNMnist(args=args)
elif args.dataset == 'fmnist':
global_model = CNNFashion_Mnist(args=args)
elif args.dataset == 'cifar':
# global_model = DCGANDiscriminator_cifar10(args=args)
global_model = CNNCifar(args=args)
elif args.model == 'mlp':
# Multi-layer preceptron
img_size = train_dataset[0][0].shape
len_in = 1
for x in img_size:
len_in *= x
global_model = MLP(dim_in=len_in, dim_hidden=64,
dim_out=args.num_classes)
elif args.model == 'dcgan':
# deep convolutional generative adversarial networks
if args.dataset == 'mnist':
global_model = DCGANDiscriminator_mnist(args=args)
elif args.dataset == 'cifar':
global_model = DCGANDiscriminator_cifar10(args=args)
elif args.dataset == 'svhn':
global_model = DCGANDiscriminator_SVHN(args=args)
else:
# TODO add datasets support
exit('Error: unrecognized dataset')
else:
exit('Error: unrecognized model')
# Set the model to train and send it to device.
global_model.to(device)
global_model.train()
# copy weights
global_weights = global_model.state_dict()
# Training
train_loss, train_accuracy = [], []
fake_images = []
val_acc_list, net_list = [], []
cv_loss, cv_acc = [], []
avg_psnrs = []
print_every = 2
val_loss_pre, counter = 0, 0
save_location = get_experiment_result_location(args.model, args.dataset,
args.wanted_label_index,
{'ganlr': args.local_gan_lr,
'ganepoch': args.local_gan_epoch,
'optimizer': args.optimizer,
'localepoch':args.local_ep},
args.mode,
args.experiment_name)
# adversary model
if args.model == 'dcgan' and args.dataset == 'mnist':
generator_model = DCGANGenerator_mnist(args=args)
adversary_gan_update = AdversaryGanUpdateMnist(copy.deepcopy(global_model), generator_model,
args, logger, args.wanted_label_index, false_label_index=10)
elif args.model == 'dcgan' and args.dataset == 'cifar':
generator_model = DCGANGenerator_cifar10(args=args)
adversary_gan_update = AdversaryGanUpdateCifar(copy.deepcopy(global_model), generator_model,
args, logger, args.wanted_label_index, false_label_index=10)
elif args.model == 'dcgan' and args.dataset == 'svhn':
generator_model = DCGANGenerator_SVHN(args=args)
adversary_gan_update = AdversaryGanUpdateCifar(copy.deepcopy(global_model), generator_model,
args, logger, args.wanted_label_index, false_label_index=10)
for epoch in tqdm(range(args.epochs)):
local_weights, local_losses = [], []
# label_split = [[i] for i in range(10)]
# idx_group = get_dataset_idxgroup_ganattack(args, label_split, label_indexs)
print(f'\n | Global Training Round : {epoch+1} |\n')
global_model.train()
m = max(int(args.frac * args.num_users), 1)
# 从总用户中随机抽取需要的用户
idxs_users = np.random.choice(range(args.num_users), m, replace=False)
# if (len(idxs_users) != len(idx_group)):
# raise ValueError('len(idx_users)!=len(idx_group)')
data_idx = 0
for idx in idxs_users:
# TODO 不应该每一轮都新建一个Update类
global_model_copy = copy.deepcopy(global_model)
local_model = LocalUpdate(args=args, dataset=train_dataset,
idxs=user_groups[idx], logger=logger)
w, loss = local_model.update_weights(
model=global_model_copy, global_round=epoch)
local_weights.append(copy.deepcopy(w))
local_losses.append(copy.deepcopy(loss))
data_idx += 1
# 服务器进行攻击
if args.model == 'dcgan':
global_model_copy = copy.deepcopy(global_model)
server_adversary = AdversaryUpdate(args=args, dataset=train_dataset,
idxs=[], logger=logger,
adversary_gan_update=adversary_gan_update,
discriminator_model=global_model_copy)
server_adversary.train_generator()
w = server_adversary.update_weights(
model=global_model_copy, global_round=epoch)
local_weights.append(copy.deepcopy(w))
# update global weights
global_weights = average_weights(local_weights)
# update global weights
global_model.load_state_dict(global_weights)
loss_avg = sum(local_losses) / len(local_losses)
train_loss.append(loss_avg)
# Calculate avg training accuracy over all users at every epoch
list_acc, list_loss = [], []
global_model.eval()
# print('test idx:{}'.format(idx))
for c in range(args.num_users):
# FIXME
# 这里的user_groups[idx]是否应该是user_groups[c]?
local_model = LocalUpdate(args=args, dataset=train_dataset,
idxs=user_groups[c], logger=logger)
acc, loss = local_model.inference(model=global_model)
list_acc.append(acc)
list_loss.append(loss)
train_accuracy.append(sum(list_acc)/len(list_acc))
# print global training loss after every 'i' rounds
if (epoch+1) % print_every == 0:
print(f' \nAvg Training Stats after {epoch+1} global rounds:')
print(f'Training Loss : {np.mean(np.array(train_loss))}')
print('Train Accuracy: {:.2f}% \n'.format(100*train_accuracy[-1]))
# save generated fake images each epoch
if args.model == 'dcgan':
randz = torch.randn(1, 100, 1, 1, device=device)
generated_fake_image = generator_model(randz).to('cpu').detach()
vutils.save_image(
generated_fake_image, os.path.join(save_location, os.path.join('fake_images', 'epoch_{}.png'.format(epoch))))
fake_images.append(generated_fake_image[0])
want_targets = (train_dataset.targets == args.wanted_label_index)
want_targets = [i for i in range(len(want_targets)) if want_targets[i]==True]
# 随机抽取图片计算 AVG PSNR
random_image_idxs = np.random.choice(want_targets, 10, replace=False)
batch_images = []
for idx in random_image_idxs:
batch_images.append(train_dataset.data[idx])
avg_psnr = compute_avgpsnr(generated_fake_image, batch_images)
avg_psnrs.append(avg_psnr)
# Test inference after completion of training
test_acc, test_loss = test_inference(args, global_model, test_dataset)
plot_loss_acc(train_loss, train_accuracy, save_location)
plot_avg_psnr(avg_psnrs, save_location)
generate_gif_from_file(os.path.join(save_location, 'fake_images'), os.path.join(save_location, 'training.gif'))
print('fake images shape:{}'.format(fake_images[0].shape))
save_grid(fake_images, save_location)
print(f' \n Results after {args.epochs} global rounds of training:')
print("|---- Avg Train Accuracy: {:.2f}%".format(100*train_accuracy[-1]))
print("|---- Test Accuracy: {:.2f}%".format(100*test_acc))
print('\n Total Run Time: {0:0.4f}'.format(time.time()-start_time))
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] | 2.02394 | 4,762 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 19 07:23:35 2020
Illustrate the use of Runge-Kutta methods to solve ODEs
@author: zettergm
"""
# Imports
import numpy as np
import matplotlib.pyplot as plt
# RHS of ODE for use with RK4
# Time grid
N=15
tmin=0
tmax=6
t=np.linspace(tmin,tmax,num=N)
dt=t[1]-t[0]
# Analytical solution for comparison
y0=1
alpha=2
ybar=y0*np.exp(-alpha*t)
# RK2
yRK2=np.zeros((N))
yRK2[0]=y0
for n in range(1,N):
yhalf=yRK2[n-1]+dt/2*(-alpha*yRK2[n-1])
yRK2[n]=yRK2[n-1]+dt*(-alpha*yhalf)
# RK4
yRK4=np.zeros((N))
yRK4[0]=y0
for n in range(1,N):
dy1=dt*fRK(t[n-1],yRK4[n-1],alpha)
dy2=dt*fRK(t[n-1]+dt/2,yRK4[n-1]+dy1/2,alpha)
dy3=dt*fRK(t[n-1]+dt/2,yRK4[n-1]+dy2/2,alpha)
dy4=dt*fRK(t[n-1]+dt,yRK4[n-1]+dy3,alpha)
yRK4[n]=yRK4[n-1]+1/6*(dy1+2*dy2+2*dy3+dy4)
# Plot results
plt.figure()
plt.plot(t,ybar,"o-")
plt.xlabel("t")
plt.ylabel("y(t)")
plt.plot(t,yRK2,"--")
plt.plot(t,yRK4,"-.")
plt.legend(("exact","RK2","RK4"))
plt.show()
# RK2 stability plot
adt=np.linspace(0.01,3,20)
ladt=adt.size
G=np.zeros((ladt))
for igain in range(0,ladt):
G[igain]=(1-adt[igain]+1/2*adt[igain]**2)
plt.figure()
plt.plot(adt,G,"o")
plt.xlabel("a*dt")
plt.ylabel("gain factor")
plt.show() | [
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] | 1.72027 | 740 |
# Generated by Django 3.1.2 on 2020-11-04 19:12
from django.db import migrations, models
| [
2,
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515,
416,
37770,
513,
13,
16,
13,
17,
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12,
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1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
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