hexsha
stringlengths 40
40
| size
int64 2
1.02M
| ext
stringclasses 10
values | lang
stringclasses 1
value | max_stars_repo_path
stringlengths 4
245
| max_stars_repo_name
stringlengths 6
130
| max_stars_repo_head_hexsha
stringlengths 40
40
| max_stars_repo_licenses
listlengths 1
10
| max_stars_count
int64 1
191k
⌀ | max_stars_repo_stars_event_min_datetime
stringlengths 24
24
⌀ | max_stars_repo_stars_event_max_datetime
stringlengths 24
24
⌀ | max_issues_repo_path
stringlengths 4
245
| max_issues_repo_name
stringlengths 6
130
| max_issues_repo_head_hexsha
stringlengths 40
40
| max_issues_repo_licenses
listlengths 1
10
| max_issues_count
int64 1
67k
⌀ | max_issues_repo_issues_event_min_datetime
stringlengths 24
24
⌀ | max_issues_repo_issues_event_max_datetime
stringlengths 24
24
⌀ | max_forks_repo_path
stringlengths 4
245
| max_forks_repo_name
stringlengths 6
130
| max_forks_repo_head_hexsha
stringlengths 40
40
| max_forks_repo_licenses
listlengths 1
10
| max_forks_count
int64 1
105k
⌀ | max_forks_repo_forks_event_min_datetime
stringlengths 24
24
⌀ | max_forks_repo_forks_event_max_datetime
stringlengths 24
24
⌀ | content
stringlengths 2
1.02M
| avg_line_length
float64 1
417k
| max_line_length
int64 1
987k
| alphanum_fraction
float64 0
1
| content_no_comment
stringlengths 0
1.01M
| is_comment_constant_removed
bool 1
class | is_sharp_comment_removed
bool 1
class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1c3ebb2ffa9845ad6f59777ce682acd6917daa3e
| 808
|
py
|
Python
|
wazimap_ng/points/services/locations.py
|
Faysalali534/wazimap-ng
|
64cff12c042167ce633658a3c45f7c142f3fd5ce
|
[
"Apache-2.0"
] | null | null | null |
wazimap_ng/points/services/locations.py
|
Faysalali534/wazimap-ng
|
64cff12c042167ce633658a3c45f7c142f3fd5ce
|
[
"Apache-2.0"
] | null | null | null |
wazimap_ng/points/services/locations.py
|
Faysalali534/wazimap-ng
|
64cff12c042167ce633658a3c45f7c142f3fd5ce
|
[
"Apache-2.0"
] | null | null | null |
import logging
from wazimap_ng.points.models import Location, Category, ProfileCategory
from wazimap_ng.profile.models import Profile
from wazimap_ng.datasets.models import Geography
from wazimap_ng.boundaries.models import GeographyBoundary
logger = logging.getLogger(__name__)
def get_locations(queryset, profile, category=None, geography_code=None):
geography = None
if category is not None:
queryset = queryset.filter(category=category)
if geography_code is not None:
version = profile.geography_hierarchy.root_geography.version
geography = Geography.objects.get(code=geography_code, version=version)
boundary = GeographyBoundary.objects.get(geography=geography)
queryset = queryset.filter(coordinates__within=boundary.geom)
return queryset
| 35.130435
| 80
| 0.782178
|
import logging
from wazimap_ng.points.models import Location, Category, ProfileCategory
from wazimap_ng.profile.models import Profile
from wazimap_ng.datasets.models import Geography
from wazimap_ng.boundaries.models import GeographyBoundary
logger = logging.getLogger(__name__)
def get_locations(queryset, profile, category=None, geography_code=None):
geography = None
if category is not None:
queryset = queryset.filter(category=category)
if geography_code is not None:
version = profile.geography_hierarchy.root_geography.version
geography = Geography.objects.get(code=geography_code, version=version)
boundary = GeographyBoundary.objects.get(geography=geography)
queryset = queryset.filter(coordinates__within=boundary.geom)
return queryset
| true
| true
|
1c3ebb6f9ba90fb1b3461a4f89c26e5e1da1a7b6
| 19,196
|
py
|
Python
|
conda/activate.py
|
richmoore1962/conda
|
ef36713bfeca5b9a8505ff8ae6d7899c2d7c6306
|
[
"BSD-3-Clause"
] | null | null | null |
conda/activate.py
|
richmoore1962/conda
|
ef36713bfeca5b9a8505ff8ae6d7899c2d7c6306
|
[
"BSD-3-Clause"
] | null | null | null |
conda/activate.py
|
richmoore1962/conda
|
ef36713bfeca5b9a8505ff8ae6d7899c2d7c6306
|
[
"BSD-3-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
from glob import glob
import os
from os.path import abspath, basename, dirname, expanduser, expandvars, isdir, join, normpath
import re
import sys
from tempfile import NamedTemporaryFile
from .base.context import ROOT_ENV_NAME, context
try:
from cytoolz.itertoolz import concatv, drop
except ImportError: # pragma: no cover
from ._vendor.toolz.itertoolz import concatv, drop # NOQA
class Activator(object):
# Activate and deactivate have three tasks
# 1. Set and unset environment variables
# 2. Execute/source activate.d/deactivate.d scripts
# 3. Update the command prompt
#
# Shells should also use 'reactivate' following conda's install, update, and
# remove/uninstall commands.
#
# All core logic is in build_activate() or build_deactivate(), and is independent of
# shell type. Each returns a map containing the keys:
# set_vars
# unset_var
# activate_scripts
# deactivate_scripts
#
# The value of the CONDA_PROMPT_MODIFIER environment variable holds conda's contribution
# to the command prompt.
#
# To implement support for a new shell, ideally one would only need to add shell-specific
# information to the __init__ method of this class.
def __init__(self, shell, arguments=None):
self.shell = shell
self._raw_arguments = arguments
if shell == 'posix':
self.pathsep_join = ':'.join
self.path_conversion = native_path_to_unix
self.script_extension = '.sh'
self.tempfile_extension = None # write instructions to stdout rather than a temp file
self.shift_args = 0
self.unset_var_tmpl = 'unset %s'
self.set_var_tmpl = 'export %s="%s"'
self.run_script_tmpl = '. "%s"'
elif shell == 'csh':
self.pathsep_join = ':'.join
self.path_conversion = native_path_to_unix
self.script_extension = '.csh'
self.tempfile_extension = None # write instructions to stdout rather than a temp file
self.shift_args = 0
self.unset_var_tmpl = 'unset %s'
self.set_var_tmpl = 'setenv %s "%s"'
self.run_script_tmpl = 'source "%s"'
elif shell == 'xonsh':
self.pathsep_join = ':'.join
self.path_conversion = native_path_to_unix
self.script_extension = '.xsh'
self.tempfile_extension = '.xsh'
self.shift_args = 0
self.unset_var_tmpl = 'del $%s'
self.set_var_tmpl = '$%s = "%s"'
self.run_script_tmpl = 'source "%s"'
elif shell == 'cmd.exe':
self.pathsep_join = ';'.join
self.path_conversion = path_identity
self.script_extension = '.bat'
self.tempfile_extension = '.bat'
self.shift_args = 1
self.unset_var_tmpl = '@SET %s='
self.set_var_tmpl = '@SET "%s=%s"'
self.run_script_tmpl = '@CALL "%s"'
elif shell == 'fish':
self.pathsep_join = ' '.join
self.path_conversion = native_path_to_unix
self.script_extension = '.fish'
self.tempfile_extension = None # write instructions to stdout rather than a temp file
self.shift_args = 0
self.unset_var_tmpl = 'set -e %s'
self.set_var_tmpl = 'set -gx %s "%s"'
self.run_script_tmpl = 'source "%s"'
elif shell == 'powershell':
self.pathsep_join = ';'.join
self.path_conversion = path_identity
self.script_extension = '.ps1'
self.tempfile_extension = None # write instructions to stdout rather than a temp file
self.shift_args = 0
self.unset_var_tmpl = 'Remove-Variable %s'
self.set_var_tmpl = '$env:%s = "%s"'
self.run_script_tmpl = '. "%s"'
else:
raise NotImplementedError()
def _finalize(self, commands, ext):
commands = concatv(commands, ('',)) # add terminating newline
if ext is None:
return '\n'.join(commands)
elif ext:
with NamedTemporaryFile(suffix=ext, delete=False) as tf:
tf.write(ensure_binary('\n'.join(commands)))
return tf.name
else:
raise NotImplementedError()
def activate(self):
return self._finalize(self._yield_commands(self.build_activate(self.env_name_or_prefix)),
self.tempfile_extension)
def deactivate(self):
return self._finalize(self._yield_commands(self.build_deactivate()),
self.tempfile_extension)
def reactivate(self):
return self._finalize(self._yield_commands(self.build_reactivate()),
self.tempfile_extension)
def execute(self):
# return value meant to be written to stdout
self._parse_and_set_args(self._raw_arguments)
return getattr(self, self.command)()
def _parse_and_set_args(self, arguments):
# the first index of arguments MUST be either activate, deactivate, or reactivate
if arguments is None:
from .exceptions import ArgumentError
raise ArgumentError("'activate', 'deactivate', or 'reactivate' command must be given")
command = arguments[0]
arguments = tuple(drop(self.shift_args + 1, arguments))
help_flags = ('-h', '--help', '/?')
non_help_args = tuple(arg for arg in arguments if arg not in help_flags)
help_requested = len(arguments) != len(non_help_args)
remainder_args = tuple(arg for arg in non_help_args if arg and arg != command)
if not command:
from .exceptions import ArgumentError
raise ArgumentError("'activate', 'deactivate', or 'reactivate' command must be given")
elif help_requested:
from . import CondaError
class Help(CondaError): # NOQA
pass
raise Help("help requested for %s" % command)
elif command not in ('activate', 'deactivate', 'reactivate'):
from .exceptions import ArgumentError
raise ArgumentError("invalid command '%s'" % command)
elif command == 'activate' and len(remainder_args) > 1:
from .exceptions import ArgumentError
raise ArgumentError('activate does not accept more than one argument')
elif command != 'activate' and remainder_args:
from .exceptions import ArgumentError
raise ArgumentError('%s does not accept arguments\nremainder_args: %s'
% (command, remainder_args))
if command == 'activate':
self.env_name_or_prefix = remainder_args and remainder_args[0] or 'root'
self.command = command
def _yield_commands(self, cmds_dict):
for key in sorted(cmds_dict.get('unset_vars', ())):
yield self.unset_var_tmpl % key
for key, value in sorted(iteritems(cmds_dict.get('set_vars', {}))):
yield self.set_var_tmpl % (key, value)
for script in cmds_dict.get('deactivate_scripts', ()):
yield self.run_script_tmpl % script
for script in cmds_dict.get('activate_scripts', ()):
yield self.run_script_tmpl % script
def build_activate(self, env_name_or_prefix):
test_path = expand(env_name_or_prefix)
if isdir(test_path):
prefix = test_path
if not isdir(join(prefix, 'conda-meta')):
from .exceptions import EnvironmentLocationNotFound
raise EnvironmentLocationNotFound(prefix)
elif re.search(r'\\|/', env_name_or_prefix):
prefix = env_name_or_prefix
if not isdir(join(prefix, 'conda-meta')):
from .exceptions import EnvironmentLocationNotFound
raise EnvironmentLocationNotFound(prefix)
elif env_name_or_prefix in (ROOT_ENV_NAME, 'root'):
prefix = context.root_prefix
else:
from .core.envs_manager import EnvsDirectory
prefix = EnvsDirectory.locate_prefix_by_name(env_name_or_prefix)
prefix = normpath(prefix)
# query environment
old_conda_shlvl = int(os.getenv('CONDA_SHLVL', 0))
old_conda_prefix = os.getenv('CONDA_PREFIX')
max_shlvl = context.max_shlvl
if old_conda_prefix == prefix:
return self.build_reactivate()
if os.getenv('CONDA_PREFIX_%s' % (old_conda_shlvl-1)) == prefix:
# in this case, user is attempting to activate the previous environment,
# i.e. step back down
return self.build_deactivate()
activate_scripts = self._get_activate_scripts(prefix)
conda_default_env = self._default_env(prefix)
conda_prompt_modifier = self._prompt_modifier(conda_default_env)
assert 0 <= old_conda_shlvl <= max_shlvl
if old_conda_shlvl == 0:
new_path = self.pathsep_join(self._add_prefix_to_path(prefix))
set_vars = {
'CONDA_PYTHON_EXE': self.path_conversion(sys.executable),
'PATH': new_path,
'CONDA_PREFIX': prefix,
'CONDA_SHLVL': old_conda_shlvl + 1,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
deactivate_scripts = ()
elif old_conda_shlvl == max_shlvl:
new_path = self.pathsep_join(self._replace_prefix_in_path(old_conda_prefix, prefix))
set_vars = {
'PATH': new_path,
'CONDA_PREFIX': prefix,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
deactivate_scripts = self._get_deactivate_scripts(old_conda_prefix)
else:
new_path = self.pathsep_join(self._add_prefix_to_path(prefix))
set_vars = {
'PATH': new_path,
'CONDA_PREFIX': prefix,
'CONDA_PREFIX_%d' % old_conda_shlvl: old_conda_prefix,
'CONDA_SHLVL': old_conda_shlvl + 1,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
deactivate_scripts = ()
return {
'unset_vars': (),
'set_vars': set_vars,
'deactivate_scripts': deactivate_scripts,
'activate_scripts': activate_scripts,
}
def build_deactivate(self):
# query environment
old_conda_shlvl = int(os.getenv('CONDA_SHLVL', 0))
if old_conda_shlvl <= 0:
return {
'unset_vars': (),
'set_vars': {},
'deactivate_scripts': (),
'activate_scripts': (),
}
old_conda_prefix = os.environ['CONDA_PREFIX']
deactivate_scripts = self._get_deactivate_scripts(old_conda_prefix)
new_conda_shlvl = old_conda_shlvl - 1
new_path = self.pathsep_join(self._remove_prefix_from_path(old_conda_prefix))
assert old_conda_shlvl > 0
if old_conda_shlvl == 1:
# TODO: warn conda floor
unset_vars = (
'CONDA_PREFIX',
'CONDA_DEFAULT_ENV',
'CONDA_PYTHON_EXE',
'CONDA_PROMPT_MODIFIER',
)
set_vars = {
'PATH': new_path,
'CONDA_SHLVL': new_conda_shlvl,
}
activate_scripts = ()
else:
new_prefix = os.getenv('CONDA_PREFIX_%d' % new_conda_shlvl)
conda_default_env = self._default_env(new_prefix)
conda_prompt_modifier = self._prompt_modifier(conda_default_env)
unset_vars = (
'CONDA_PREFIX_%d' % new_conda_shlvl,
)
set_vars = {
'PATH': new_path,
'CONDA_SHLVL': new_conda_shlvl,
'CONDA_PREFIX': new_prefix,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
activate_scripts = self._get_activate_scripts(new_prefix)
return {
'unset_vars': unset_vars,
'set_vars': set_vars,
'deactivate_scripts': deactivate_scripts,
'activate_scripts': activate_scripts,
}
def build_reactivate(self):
conda_prefix = os.environ.get('CONDA_PREFIX')
conda_shlvl = int(os.environ.get('CONDA_SHLVL', -1))
if not conda_prefix or conda_shlvl < 1:
# no active environment, so cannot reactivate; do nothing
return {
'unset_vars': (),
'set_vars': {},
'deactivate_scripts': (),
'activate_scripts': (),
}
conda_default_env = os.environ.get('CONDA_DEFAULT_ENV', self._default_env(conda_prefix))
# environment variables are set only to aid transition from conda 4.3 to conda 4.4
return {
'unset_vars': (),
'set_vars': {
'CONDA_SHLVL': conda_shlvl,
'CONDA_PROMPT_MODIFIER': self._prompt_modifier(conda_default_env),
},
'deactivate_scripts': self._get_deactivate_scripts(conda_prefix),
'activate_scripts': self._get_activate_scripts(conda_prefix),
}
def _get_starting_path_list(self):
path = os.environ['PATH']
if on_win:
# on Windows, the python interpreter prepends sys.prefix\Library\bin on startup WTF
return path.split(os.pathsep)[1:]
else:
return path.split(os.pathsep)
def _get_path_dirs(self, prefix):
if on_win: # pragma: unix no cover
yield prefix.rstrip("\\")
yield join(prefix, 'Library', 'mingw-w64', 'bin')
yield join(prefix, 'Library', 'usr', 'bin')
yield join(prefix, 'Library', 'bin')
yield join(prefix, 'Scripts')
yield join(prefix, 'bin')
else:
yield join(prefix, 'bin')
def _add_prefix_to_path(self, prefix, starting_path_dirs=None):
if starting_path_dirs is None:
starting_path_dirs = self._get_starting_path_list()
return self.path_conversion(concatv(
self._get_path_dirs(prefix),
starting_path_dirs,
))
def _remove_prefix_from_path(self, prefix, starting_path_dirs=None):
return self._replace_prefix_in_path(prefix, None, starting_path_dirs)
def _replace_prefix_in_path(self, old_prefix, new_prefix, starting_path_dirs=None):
if starting_path_dirs is None:
path_list = self._get_starting_path_list()
else:
path_list = list(starting_path_dirs)
if on_win: # pragma: unix no cover
# windows has a nasty habit of adding extra Library\bin directories
prefix_dirs = tuple(self._get_path_dirs(old_prefix))
try:
first_idx = path_list.index(prefix_dirs[0])
except ValueError:
first_idx = 0
else:
last_idx = path_list.index(prefix_dirs[-1])
del path_list[first_idx:last_idx+1]
if new_prefix is not None:
path_list[first_idx:first_idx] = list(self._get_path_dirs(new_prefix))
else:
try:
idx = path_list.index(join(old_prefix, 'bin'))
except ValueError:
idx = 0
else:
del path_list[idx]
if new_prefix is not None:
path_list.insert(idx, join(new_prefix, 'bin'))
return self.path_conversion(path_list)
def _default_env(self, prefix):
if prefix == context.root_prefix:
return 'base'
return basename(prefix) if basename(dirname(prefix)) == 'envs' else prefix
def _prompt_modifier(self, conda_default_env):
return "(%s) " % conda_default_env if context.changeps1 else ""
def _get_activate_scripts(self, prefix):
return self.path_conversion(glob(join(
prefix, 'etc', 'conda', 'activate.d', '*' + self.script_extension
)))
def _get_deactivate_scripts(self, prefix):
return self.path_conversion(glob(join(
prefix, 'etc', 'conda', 'deactivate.d', '*' + self.script_extension
)))
def expand(path):
return abspath(expanduser(expandvars(path)))
def ensure_binary(value):
try:
return value.encode('utf-8')
except AttributeError: # pragma: no cover
# AttributeError: '<>' object has no attribute 'encode'
# In this case assume already binary type and do nothing
return value
def native_path_to_unix(paths): # pragma: unix no cover
# on windows, uses cygpath to convert windows native paths to posix paths
if not on_win:
return path_identity(paths)
from subprocess import PIPE, Popen
from shlex import split
command = 'cygpath --path -f -'
p = Popen(split(command), stdin=PIPE, stdout=PIPE, stderr=PIPE)
single_path = isinstance(paths, string_types)
joined = paths if single_path else ("%s" % os.pathsep).join(paths)
if hasattr(joined, 'encode'):
joined = joined.encode('utf-8')
stdout, stderr = p.communicate(input=joined)
rc = p.returncode
if rc != 0 or stderr:
from subprocess import CalledProcessError
message = "\n stdout: %s\n stderr: %s\n rc: %s\n" % (stdout, stderr, rc)
print(message, file=sys.stderr)
raise CalledProcessError(rc, command, message)
if hasattr(stdout, 'decode'):
stdout = stdout.decode('utf-8')
stdout = stdout.strip()
final = stdout and stdout.split(':') or ()
return final[0] if single_path else tuple(final)
def path_identity(paths):
return paths if isinstance(paths, string_types) else tuple(paths)
on_win = bool(sys.platform == "win32")
PY2 = sys.version_info[0] == 2
if PY2: # pragma: py3 no cover
string_types = basestring, # NOQA
text_type = unicode # NOQA
def iteritems(d, **kw):
return d.iteritems(**kw)
else: # pragma: py2 no cover
string_types = str,
text_type = str
def iteritems(d, **kw):
return iter(d.items(**kw))
def main(argv=None):
argv = argv or sys.argv
assert len(argv) >= 3
assert argv[1].startswith('shell.')
shell = argv[1].replace('shell.', '', 1)
activator_args = argv[2:]
activator = Activator(shell, activator_args)
try:
sys.stdout.write(activator.execute())
return 0
except Exception as e:
from . import CondaError
if isinstance(e, CondaError):
sys.stderr.write(text_type(e))
return e.return_code
else:
raise
if __name__ == '__main__':
sys.exit(main())
| 38.163022
| 98
| 0.603824
|
from __future__ import absolute_import, division, print_function, unicode_literals
from glob import glob
import os
from os.path import abspath, basename, dirname, expanduser, expandvars, isdir, join, normpath
import re
import sys
from tempfile import NamedTemporaryFile
from .base.context import ROOT_ENV_NAME, context
try:
from cytoolz.itertoolz import concatv, drop
except ImportError:
from ._vendor.toolz.itertoolz import concatv, drop
class Activator(object):
# remove/uninstall commands.
#
# All core logic is in build_activate() or build_deactivate(), and is independent of
# shell type. Each returns a map containing the keys:
# set_vars
# unset_var
# activate_scripts
# deactivate_scripts
#
# The value of the CONDA_PROMPT_MODIFIER environment variable holds conda's contribution
def __init__(self, shell, arguments=None):
self.shell = shell
self._raw_arguments = arguments
if shell == 'posix':
self.pathsep_join = ':'.join
self.path_conversion = native_path_to_unix
self.script_extension = '.sh'
self.tempfile_extension = None
self.shift_args = 0
self.unset_var_tmpl = 'unset %s'
self.set_var_tmpl = 'export %s="%s"'
self.run_script_tmpl = '. "%s"'
elif shell == 'csh':
self.pathsep_join = ':'.join
self.path_conversion = native_path_to_unix
self.script_extension = '.csh'
self.tempfile_extension = None
self.shift_args = 0
self.unset_var_tmpl = 'unset %s'
self.set_var_tmpl = 'setenv %s "%s"'
self.run_script_tmpl = 'source "%s"'
elif shell == 'xonsh':
self.pathsep_join = ':'.join
self.path_conversion = native_path_to_unix
self.script_extension = '.xsh'
self.tempfile_extension = '.xsh'
self.shift_args = 0
self.unset_var_tmpl = 'del $%s'
self.set_var_tmpl = '$%s = "%s"'
self.run_script_tmpl = 'source "%s"'
elif shell == 'cmd.exe':
self.pathsep_join = ';'.join
self.path_conversion = path_identity
self.script_extension = '.bat'
self.tempfile_extension = '.bat'
self.shift_args = 1
self.unset_var_tmpl = '@SET %s='
self.set_var_tmpl = '@SET "%s=%s"'
self.run_script_tmpl = '@CALL "%s"'
elif shell == 'fish':
self.pathsep_join = ' '.join
self.path_conversion = native_path_to_unix
self.script_extension = '.fish'
self.tempfile_extension = None
self.shift_args = 0
self.unset_var_tmpl = 'set -e %s'
self.set_var_tmpl = 'set -gx %s "%s"'
self.run_script_tmpl = 'source "%s"'
elif shell == 'powershell':
self.pathsep_join = ';'.join
self.path_conversion = path_identity
self.script_extension = '.ps1'
self.tempfile_extension = None
self.shift_args = 0
self.unset_var_tmpl = 'Remove-Variable %s'
self.set_var_tmpl = '$env:%s = "%s"'
self.run_script_tmpl = '. "%s"'
else:
raise NotImplementedError()
def _finalize(self, commands, ext):
commands = concatv(commands, ('',))
if ext is None:
return '\n'.join(commands)
elif ext:
with NamedTemporaryFile(suffix=ext, delete=False) as tf:
tf.write(ensure_binary('\n'.join(commands)))
return tf.name
else:
raise NotImplementedError()
def activate(self):
return self._finalize(self._yield_commands(self.build_activate(self.env_name_or_prefix)),
self.tempfile_extension)
def deactivate(self):
return self._finalize(self._yield_commands(self.build_deactivate()),
self.tempfile_extension)
def reactivate(self):
return self._finalize(self._yield_commands(self.build_reactivate()),
self.tempfile_extension)
def execute(self):
self._parse_and_set_args(self._raw_arguments)
return getattr(self, self.command)()
def _parse_and_set_args(self, arguments):
if arguments is None:
from .exceptions import ArgumentError
raise ArgumentError("'activate', 'deactivate', or 'reactivate' command must be given")
command = arguments[0]
arguments = tuple(drop(self.shift_args + 1, arguments))
help_flags = ('-h', '--help', '/?')
non_help_args = tuple(arg for arg in arguments if arg not in help_flags)
help_requested = len(arguments) != len(non_help_args)
remainder_args = tuple(arg for arg in non_help_args if arg and arg != command)
if not command:
from .exceptions import ArgumentError
raise ArgumentError("'activate', 'deactivate', or 'reactivate' command must be given")
elif help_requested:
from . import CondaError
class Help(CondaError):
pass
raise Help("help requested for %s" % command)
elif command not in ('activate', 'deactivate', 'reactivate'):
from .exceptions import ArgumentError
raise ArgumentError("invalid command '%s'" % command)
elif command == 'activate' and len(remainder_args) > 1:
from .exceptions import ArgumentError
raise ArgumentError('activate does not accept more than one argument')
elif command != 'activate' and remainder_args:
from .exceptions import ArgumentError
raise ArgumentError('%s does not accept arguments\nremainder_args: %s'
% (command, remainder_args))
if command == 'activate':
self.env_name_or_prefix = remainder_args and remainder_args[0] or 'root'
self.command = command
def _yield_commands(self, cmds_dict):
for key in sorted(cmds_dict.get('unset_vars', ())):
yield self.unset_var_tmpl % key
for key, value in sorted(iteritems(cmds_dict.get('set_vars', {}))):
yield self.set_var_tmpl % (key, value)
for script in cmds_dict.get('deactivate_scripts', ()):
yield self.run_script_tmpl % script
for script in cmds_dict.get('activate_scripts', ()):
yield self.run_script_tmpl % script
def build_activate(self, env_name_or_prefix):
test_path = expand(env_name_or_prefix)
if isdir(test_path):
prefix = test_path
if not isdir(join(prefix, 'conda-meta')):
from .exceptions import EnvironmentLocationNotFound
raise EnvironmentLocationNotFound(prefix)
elif re.search(r'\\|/', env_name_or_prefix):
prefix = env_name_or_prefix
if not isdir(join(prefix, 'conda-meta')):
from .exceptions import EnvironmentLocationNotFound
raise EnvironmentLocationNotFound(prefix)
elif env_name_or_prefix in (ROOT_ENV_NAME, 'root'):
prefix = context.root_prefix
else:
from .core.envs_manager import EnvsDirectory
prefix = EnvsDirectory.locate_prefix_by_name(env_name_or_prefix)
prefix = normpath(prefix)
old_conda_shlvl = int(os.getenv('CONDA_SHLVL', 0))
old_conda_prefix = os.getenv('CONDA_PREFIX')
max_shlvl = context.max_shlvl
if old_conda_prefix == prefix:
return self.build_reactivate()
if os.getenv('CONDA_PREFIX_%s' % (old_conda_shlvl-1)) == prefix:
return self.build_deactivate()
activate_scripts = self._get_activate_scripts(prefix)
conda_default_env = self._default_env(prefix)
conda_prompt_modifier = self._prompt_modifier(conda_default_env)
assert 0 <= old_conda_shlvl <= max_shlvl
if old_conda_shlvl == 0:
new_path = self.pathsep_join(self._add_prefix_to_path(prefix))
set_vars = {
'CONDA_PYTHON_EXE': self.path_conversion(sys.executable),
'PATH': new_path,
'CONDA_PREFIX': prefix,
'CONDA_SHLVL': old_conda_shlvl + 1,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
deactivate_scripts = ()
elif old_conda_shlvl == max_shlvl:
new_path = self.pathsep_join(self._replace_prefix_in_path(old_conda_prefix, prefix))
set_vars = {
'PATH': new_path,
'CONDA_PREFIX': prefix,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
deactivate_scripts = self._get_deactivate_scripts(old_conda_prefix)
else:
new_path = self.pathsep_join(self._add_prefix_to_path(prefix))
set_vars = {
'PATH': new_path,
'CONDA_PREFIX': prefix,
'CONDA_PREFIX_%d' % old_conda_shlvl: old_conda_prefix,
'CONDA_SHLVL': old_conda_shlvl + 1,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
deactivate_scripts = ()
return {
'unset_vars': (),
'set_vars': set_vars,
'deactivate_scripts': deactivate_scripts,
'activate_scripts': activate_scripts,
}
def build_deactivate(self):
old_conda_shlvl = int(os.getenv('CONDA_SHLVL', 0))
if old_conda_shlvl <= 0:
return {
'unset_vars': (),
'set_vars': {},
'deactivate_scripts': (),
'activate_scripts': (),
}
old_conda_prefix = os.environ['CONDA_PREFIX']
deactivate_scripts = self._get_deactivate_scripts(old_conda_prefix)
new_conda_shlvl = old_conda_shlvl - 1
new_path = self.pathsep_join(self._remove_prefix_from_path(old_conda_prefix))
assert old_conda_shlvl > 0
if old_conda_shlvl == 1:
unset_vars = (
'CONDA_PREFIX',
'CONDA_DEFAULT_ENV',
'CONDA_PYTHON_EXE',
'CONDA_PROMPT_MODIFIER',
)
set_vars = {
'PATH': new_path,
'CONDA_SHLVL': new_conda_shlvl,
}
activate_scripts = ()
else:
new_prefix = os.getenv('CONDA_PREFIX_%d' % new_conda_shlvl)
conda_default_env = self._default_env(new_prefix)
conda_prompt_modifier = self._prompt_modifier(conda_default_env)
unset_vars = (
'CONDA_PREFIX_%d' % new_conda_shlvl,
)
set_vars = {
'PATH': new_path,
'CONDA_SHLVL': new_conda_shlvl,
'CONDA_PREFIX': new_prefix,
'CONDA_DEFAULT_ENV': conda_default_env,
'CONDA_PROMPT_MODIFIER': conda_prompt_modifier,
}
activate_scripts = self._get_activate_scripts(new_prefix)
return {
'unset_vars': unset_vars,
'set_vars': set_vars,
'deactivate_scripts': deactivate_scripts,
'activate_scripts': activate_scripts,
}
def build_reactivate(self):
conda_prefix = os.environ.get('CONDA_PREFIX')
conda_shlvl = int(os.environ.get('CONDA_SHLVL', -1))
if not conda_prefix or conda_shlvl < 1:
return {
'unset_vars': (),
'set_vars': {},
'deactivate_scripts': (),
'activate_scripts': (),
}
conda_default_env = os.environ.get('CONDA_DEFAULT_ENV', self._default_env(conda_prefix))
return {
'unset_vars': (),
'set_vars': {
'CONDA_SHLVL': conda_shlvl,
'CONDA_PROMPT_MODIFIER': self._prompt_modifier(conda_default_env),
},
'deactivate_scripts': self._get_deactivate_scripts(conda_prefix),
'activate_scripts': self._get_activate_scripts(conda_prefix),
}
def _get_starting_path_list(self):
path = os.environ['PATH']
if on_win:
return path.split(os.pathsep)[1:]
else:
return path.split(os.pathsep)
def _get_path_dirs(self, prefix):
if on_win:
yield prefix.rstrip("\\")
yield join(prefix, 'Library', 'mingw-w64', 'bin')
yield join(prefix, 'Library', 'usr', 'bin')
yield join(prefix, 'Library', 'bin')
yield join(prefix, 'Scripts')
yield join(prefix, 'bin')
else:
yield join(prefix, 'bin')
def _add_prefix_to_path(self, prefix, starting_path_dirs=None):
if starting_path_dirs is None:
starting_path_dirs = self._get_starting_path_list()
return self.path_conversion(concatv(
self._get_path_dirs(prefix),
starting_path_dirs,
))
def _remove_prefix_from_path(self, prefix, starting_path_dirs=None):
return self._replace_prefix_in_path(prefix, None, starting_path_dirs)
def _replace_prefix_in_path(self, old_prefix, new_prefix, starting_path_dirs=None):
if starting_path_dirs is None:
path_list = self._get_starting_path_list()
else:
path_list = list(starting_path_dirs)
if on_win:
prefix_dirs = tuple(self._get_path_dirs(old_prefix))
try:
first_idx = path_list.index(prefix_dirs[0])
except ValueError:
first_idx = 0
else:
last_idx = path_list.index(prefix_dirs[-1])
del path_list[first_idx:last_idx+1]
if new_prefix is not None:
path_list[first_idx:first_idx] = list(self._get_path_dirs(new_prefix))
else:
try:
idx = path_list.index(join(old_prefix, 'bin'))
except ValueError:
idx = 0
else:
del path_list[idx]
if new_prefix is not None:
path_list.insert(idx, join(new_prefix, 'bin'))
return self.path_conversion(path_list)
def _default_env(self, prefix):
if prefix == context.root_prefix:
return 'base'
return basename(prefix) if basename(dirname(prefix)) == 'envs' else prefix
def _prompt_modifier(self, conda_default_env):
return "(%s) " % conda_default_env if context.changeps1 else ""
def _get_activate_scripts(self, prefix):
return self.path_conversion(glob(join(
prefix, 'etc', 'conda', 'activate.d', '*' + self.script_extension
)))
def _get_deactivate_scripts(self, prefix):
return self.path_conversion(glob(join(
prefix, 'etc', 'conda', 'deactivate.d', '*' + self.script_extension
)))
def expand(path):
return abspath(expanduser(expandvars(path)))
def ensure_binary(value):
try:
return value.encode('utf-8')
except AttributeError:
return value
def native_path_to_unix(paths):
if not on_win:
return path_identity(paths)
from subprocess import PIPE, Popen
from shlex import split
command = 'cygpath --path -f -'
p = Popen(split(command), stdin=PIPE, stdout=PIPE, stderr=PIPE)
single_path = isinstance(paths, string_types)
joined = paths if single_path else ("%s" % os.pathsep).join(paths)
if hasattr(joined, 'encode'):
joined = joined.encode('utf-8')
stdout, stderr = p.communicate(input=joined)
rc = p.returncode
if rc != 0 or stderr:
from subprocess import CalledProcessError
message = "\n stdout: %s\n stderr: %s\n rc: %s\n" % (stdout, stderr, rc)
print(message, file=sys.stderr)
raise CalledProcessError(rc, command, message)
if hasattr(stdout, 'decode'):
stdout = stdout.decode('utf-8')
stdout = stdout.strip()
final = stdout and stdout.split(':') or ()
return final[0] if single_path else tuple(final)
def path_identity(paths):
return paths if isinstance(paths, string_types) else tuple(paths)
on_win = bool(sys.platform == "win32")
PY2 = sys.version_info[0] == 2
if PY2:
string_types = basestring,
text_type = unicode
def iteritems(d, **kw):
return d.iteritems(**kw)
else:
string_types = str,
text_type = str
def iteritems(d, **kw):
return iter(d.items(**kw))
def main(argv=None):
argv = argv or sys.argv
assert len(argv) >= 3
assert argv[1].startswith('shell.')
shell = argv[1].replace('shell.', '', 1)
activator_args = argv[2:]
activator = Activator(shell, activator_args)
try:
sys.stdout.write(activator.execute())
return 0
except Exception as e:
from . import CondaError
if isinstance(e, CondaError):
sys.stderr.write(text_type(e))
return e.return_code
else:
raise
if __name__ == '__main__':
sys.exit(main())
| true
| true
|
1c3ebd21eb0eee42e9366167ff296002780b5492
| 4,688
|
py
|
Python
|
python/galB_models.py
|
bsafdi/galacticB
|
cf90459799b0917340f7b6faceab6134dc3c35b0
|
[
"MIT"
] | null | null | null |
python/galB_models.py
|
bsafdi/galacticB
|
cf90459799b0917340f7b6faceab6134dc3c35b0
|
[
"MIT"
] | null | null | null |
python/galB_models.py
|
bsafdi/galacticB
|
cf90459799b0917340f7b6faceab6134dc3c35b0
|
[
"MIT"
] | null | null | null |
import numpy as np
import numpy.linalg as LA
#B-field model from https://arxiv.org/pdf/1204.3662.pdf
iopen=11.5 #degrees
rmx_array = np.array([5.1,6.3,7.1,8.3,9.8,11.4,12.7,15.5]) #kpc
def return_B(x,y):
'''
x,y in Galactic coords in kpc
Earth at (x,y) = (-8.5,0)
'''
r = np.sqrt(x,y)
phi = np.arctanM(y,x)
r_hat = np.array([np.cos(phi),np.sin(phi)])
phi_hat = np.array([-np.sin(phi),np.cos(phi)])
if r<5.0:
B = b_ring*phi_hat
bv_hat = np.sin(iopen)*r_hat+np.cos(iopen)*phi_hat
rs = rmx_array*np.exp(phi*np.tan(np.pi/2.-i_open))
entry = np.searchsorted(rs,r)
# Use paper https://academic.oup.com/mnras/article/431/1/683/1050400
B0 = 1. # \muG
Rscale = 20.0 # kpc
hg = 6.0 #kpc
Rmax = 20.0 # kpc
Rmol = 5.0 #kpc
theta_p = -11.5*np.pi/180. #radians
def Br(r):
return B0*np.exp(-r**2/Rscale**2)
def Bcoh(z):
return 1/np.cosh(z/hg)**2
def Bhat(x,y):
r = np.sqrt(x**2+y**2)
phi = arctanM(y,x)
if r < Rmol:
return np.array([np.cos(phi+np.pi/2.),np.sin(phi+np.pi/2.)])
else:
r_hat = np.array([np.cos(phi),np.sin(phi)])
phi_hat = np.array([-np.sin(phi),np.cos(phi)])
#print r_hat, phi_hat
return np.sin(theta_p)*r_hat+np.cos(theta_p)*phi_hat
def B_ASS(x,y,z):
r = np.sqrt(x**2+y**2)
return Br(r)*Bcoh(z)*Bhat(x,y)
ai = np.array([3, .5, -4, 1.2, -.8]) # spiral arm amplitudes
phi_0i = np.deg2rad(10+90*np.arange(1, 6)) # aximuthal orientation of the rotation of the spiral
Rcomp = 7.1 # kpc scale radius of compressed spiral arms
C0 = 2.5 # compression arm amplitude
rcc = 12 #kpc, region of constant compression
d0 = .3 # kpc, base width of arm enhancement
hc = 2. # kpc, scaleheight of the spiral compression
ThetaP = np.deg2rad(-11.5)
Rscale = 20.
def spiral_arm(phi, phi0):
beta = 1 /np.tan(ThetaP)
radius = Rcomp * np.exp((phi-phi0) / beta)
x = np.cos(phi)*radius
y = np.sin(phi)*radius
return np.array([x, y])
def min_distance(x, y, phi0):
phi = np.arctan(float(y)/float(x)) + np.arange(-10, 11) * np.pi
dists = np.zeros_like(phi)
best_phi = -10*np.pi
min_dist = 1e10
point_loc = np.array([x, y])
for i in range(len(phi)):
spiral_arm_loc = spiral_arm(phi[i], phi0)
dist = LA.norm(spiral_arm_loc - point_loc)
if dist < min_dist:
best_phi = phi[i]
min_dist = dist
return min_dist
def Barm(x,y,z):
r = np.sqrt(x**2 + y**2)
beta = 1 /np.tan(ThetaP)
phi = np.arctan2(y, x)
ri = np.zeros_like(ai)
for i in range(len(ri)):
ri[i] = min_distance(x, y, phi_0i[i])
Br = B0 * np.exp(-r**2 / Rscale**2)
cr = C0 * np.minimum(1, (r / rcc)**(-3))
d0_r = d0 / cr / Br
Bcomp = 1./np.cosh(z / hc)**2
rhoC = cr * Bcomp * np.exp(-ri**2 / d0_r**2)
if r < Rmol:
BVec = np.array([np.cos(phi+np.pi/2), np.sin(phi+np.pi/2), 0])
else:
r_hat = np.array([np.cos(phi),np.sin(phi), 0])
phi_hat = np.array([-np.sin(phi),np.cos(phi), 0])
BVec = np.sin(ThetaP)*r_hat+np.cos(ThetaP)*phi_hat
return np.sum((Br*ai*rhoC)[:, None]*BVec[None, :], axis = 0)
def arctanM(x,y):
tmp = np.arctan2(x,y)
if tmp<0:
res= 2*np.pi+tmp
else:
res = tmp
return res
######## B_ASS
# ais = np.array([3.0,0.5,-4.0,1.2,-0.8])
# phi0_is = np.array([10+90*1,10+90*2,10+90*3,10+90*4,10+90*5])*np.pi/180.
# Rcomp = 7.1 #kpc
# C0 = 2.5
# rcc =12.0 #kpc
# d0 = 0.3 #kpc
# hc = 2.0 #kpc
# def arctanM(x,y):
# tmp = np.arctan2(x,y)
# if tmp<0:
# res= 2*np.pi+tmp
# else:
# res = tmp
# return res
# def Bcomp(z):
# return 1./np.cosh(z/hc)**2
# def c(r):
# if r<rcc:
# return C0
# else:
# return C0*(r/rcc)**(-3.)
# def d0f(r):
# return d0/(c(r)*Br(r))
# def ri(phi,i):
# return 7.1*np.exp((phi-phi0_is[i-1])*np.tan(theta_p))
# def di(r,phi,i):
# riA = ri(phi,i)
# return np.abs(r-riA)
# def rhoc(x,y,z,d,i):
# r = np.sqrt(x**2+y**2)
# phi = arctanM(y,x)
# rI = ri(phi,i)
# return c(rI)*Bcomp(z)*np.exp(-d**2/d0f(rI)**2)
# def Barmi(x,y,z,i):
# r = np.sqrt(x**2+y**2)
# phi = arctanM(y,x)
# Bi = Br(r)
# ai = ais[i-1]
# #print ai
# d = di(r,phi,i)
# #print d, d0f(ri(phi,i))
# rhoci = rhoc(x,y,z,d,i)
# #print d, ri(phi,i), rhoci #, d0f(r)
# Bh = Bhat(x,y)
# return Bi*ai*rhoci*Bh
# def Barm(x,y,z):
# res = np.zeros(2)
# for i in [1,2,3,4,5]:
# res += Barmi(x,y,z,i)
# return res
| 23.323383
| 96
| 0.53221
|
import numpy as np
import numpy.linalg as LA
iopen=11.5
rmx_array = np.array([5.1,6.3,7.1,8.3,9.8,11.4,12.7,15.5])
def return_B(x,y):
r = np.sqrt(x,y)
phi = np.arctanM(y,x)
r_hat = np.array([np.cos(phi),np.sin(phi)])
phi_hat = np.array([-np.sin(phi),np.cos(phi)])
if r<5.0:
B = b_ring*phi_hat
bv_hat = np.sin(iopen)*r_hat+np.cos(iopen)*phi_hat
rs = rmx_array*np.exp(phi*np.tan(np.pi/2.-i_open))
entry = np.searchsorted(rs,r)
B0 = 1.
Rscale = 20.0
hg = 6.0
Rmax = 20.0
Rmol = 5.0
theta_p = -11.5*np.pi/180.
def Br(r):
return B0*np.exp(-r**2/Rscale**2)
def Bcoh(z):
return 1/np.cosh(z/hg)**2
def Bhat(x,y):
r = np.sqrt(x**2+y**2)
phi = arctanM(y,x)
if r < Rmol:
return np.array([np.cos(phi+np.pi/2.),np.sin(phi+np.pi/2.)])
else:
r_hat = np.array([np.cos(phi),np.sin(phi)])
phi_hat = np.array([-np.sin(phi),np.cos(phi)])
return np.sin(theta_p)*r_hat+np.cos(theta_p)*phi_hat
def B_ASS(x,y,z):
r = np.sqrt(x**2+y**2)
return Br(r)*Bcoh(z)*Bhat(x,y)
ai = np.array([3, .5, -4, 1.2, -.8])
phi_0i = np.deg2rad(10+90*np.arange(1, 6))
Rcomp = 7.1
C0 = 2.5
rcc = 12
d0 = .3
hc = 2.
ThetaP = np.deg2rad(-11.5)
Rscale = 20.
def spiral_arm(phi, phi0):
beta = 1 /np.tan(ThetaP)
radius = Rcomp * np.exp((phi-phi0) / beta)
x = np.cos(phi)*radius
y = np.sin(phi)*radius
return np.array([x, y])
def min_distance(x, y, phi0):
phi = np.arctan(float(y)/float(x)) + np.arange(-10, 11) * np.pi
dists = np.zeros_like(phi)
best_phi = -10*np.pi
min_dist = 1e10
point_loc = np.array([x, y])
for i in range(len(phi)):
spiral_arm_loc = spiral_arm(phi[i], phi0)
dist = LA.norm(spiral_arm_loc - point_loc)
if dist < min_dist:
best_phi = phi[i]
min_dist = dist
return min_dist
def Barm(x,y,z):
r = np.sqrt(x**2 + y**2)
beta = 1 /np.tan(ThetaP)
phi = np.arctan2(y, x)
ri = np.zeros_like(ai)
for i in range(len(ri)):
ri[i] = min_distance(x, y, phi_0i[i])
Br = B0 * np.exp(-r**2 / Rscale**2)
cr = C0 * np.minimum(1, (r / rcc)**(-3))
d0_r = d0 / cr / Br
Bcomp = 1./np.cosh(z / hc)**2
rhoC = cr * Bcomp * np.exp(-ri**2 / d0_r**2)
if r < Rmol:
BVec = np.array([np.cos(phi+np.pi/2), np.sin(phi+np.pi/2), 0])
else:
r_hat = np.array([np.cos(phi),np.sin(phi), 0])
phi_hat = np.array([-np.sin(phi),np.cos(phi), 0])
BVec = np.sin(ThetaP)*r_hat+np.cos(ThetaP)*phi_hat
return np.sum((Br*ai*rhoC)[:, None]*BVec[None, :], axis = 0)
def arctanM(x,y):
tmp = np.arctan2(x,y)
if tmp<0:
res= 2*np.pi+tmp
else:
res = tmp
return res
| true
| true
|
1c3ebe5799f9f096dadc724348ff63069ef11359
| 1,884
|
py
|
Python
|
alipay/aop/api/response/AlipayFundTransPayResponse.py
|
antopen/alipay-sdk-python-all
|
8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c
|
[
"Apache-2.0"
] | 213
|
2018-08-27T16:49:32.000Z
|
2021-12-29T04:34:12.000Z
|
alipay/aop/api/response/AlipayFundTransPayResponse.py
|
antopen/alipay-sdk-python-all
|
8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c
|
[
"Apache-2.0"
] | 29
|
2018-09-29T06:43:00.000Z
|
2021-09-02T03:27:32.000Z
|
alipay/aop/api/response/AlipayFundTransPayResponse.py
|
antopen/alipay-sdk-python-all
|
8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c
|
[
"Apache-2.0"
] | 59
|
2018-08-27T16:59:26.000Z
|
2022-03-25T10:08:15.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
class AlipayFundTransPayResponse(AlipayResponse):
def __init__(self):
super(AlipayFundTransPayResponse, self).__init__()
self._order_id = None
self._out_biz_no = None
self._pay_fund_order_id = None
self._status = None
self._trans_pay_time = None
@property
def order_id(self):
return self._order_id
@order_id.setter
def order_id(self, value):
self._order_id = value
@property
def out_biz_no(self):
return self._out_biz_no
@out_biz_no.setter
def out_biz_no(self, value):
self._out_biz_no = value
@property
def pay_fund_order_id(self):
return self._pay_fund_order_id
@pay_fund_order_id.setter
def pay_fund_order_id(self, value):
self._pay_fund_order_id = value
@property
def status(self):
return self._status
@status.setter
def status(self, value):
self._status = value
@property
def trans_pay_time(self):
return self._trans_pay_time
@trans_pay_time.setter
def trans_pay_time(self, value):
self._trans_pay_time = value
def parse_response_content(self, response_content):
response = super(AlipayFundTransPayResponse, self).parse_response_content(response_content)
if 'order_id' in response:
self.order_id = response['order_id']
if 'out_biz_no' in response:
self.out_biz_no = response['out_biz_no']
if 'pay_fund_order_id' in response:
self.pay_fund_order_id = response['pay_fund_order_id']
if 'status' in response:
self.status = response['status']
if 'trans_pay_time' in response:
self.trans_pay_time = response['trans_pay_time']
| 28.545455
| 99
| 0.664544
|
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
class AlipayFundTransPayResponse(AlipayResponse):
def __init__(self):
super(AlipayFundTransPayResponse, self).__init__()
self._order_id = None
self._out_biz_no = None
self._pay_fund_order_id = None
self._status = None
self._trans_pay_time = None
@property
def order_id(self):
return self._order_id
@order_id.setter
def order_id(self, value):
self._order_id = value
@property
def out_biz_no(self):
return self._out_biz_no
@out_biz_no.setter
def out_biz_no(self, value):
self._out_biz_no = value
@property
def pay_fund_order_id(self):
return self._pay_fund_order_id
@pay_fund_order_id.setter
def pay_fund_order_id(self, value):
self._pay_fund_order_id = value
@property
def status(self):
return self._status
@status.setter
def status(self, value):
self._status = value
@property
def trans_pay_time(self):
return self._trans_pay_time
@trans_pay_time.setter
def trans_pay_time(self, value):
self._trans_pay_time = value
def parse_response_content(self, response_content):
response = super(AlipayFundTransPayResponse, self).parse_response_content(response_content)
if 'order_id' in response:
self.order_id = response['order_id']
if 'out_biz_no' in response:
self.out_biz_no = response['out_biz_no']
if 'pay_fund_order_id' in response:
self.pay_fund_order_id = response['pay_fund_order_id']
if 'status' in response:
self.status = response['status']
if 'trans_pay_time' in response:
self.trans_pay_time = response['trans_pay_time']
| true
| true
|
1c3ebf1f7628958d5b11ec3a7ac39fd0b4c0e80d
| 13,347
|
py
|
Python
|
pytorch_lightning/accelerators/tpu_accelerator.py
|
aribornstein/pytorch-lightning
|
ca68cac57ad8eefc9b477ee126eb42a483f27a39
|
[
"Apache-2.0"
] | null | null | null |
pytorch_lightning/accelerators/tpu_accelerator.py
|
aribornstein/pytorch-lightning
|
ca68cac57ad8eefc9b477ee126eb42a483f27a39
|
[
"Apache-2.0"
] | null | null | null |
pytorch_lightning/accelerators/tpu_accelerator.py
|
aribornstein/pytorch-lightning
|
ca68cac57ad8eefc9b477ee126eb42a483f27a39
|
[
"Apache-2.0"
] | null | null | null |
# Copyright The PyTorch Lightning team.
#
# 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 io
import logging
import os
import re
from typing import Any, Callable, Optional, Union
import torch
import torch.multiprocessing as mp
from torch.optim import Optimizer
from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.cluster_environments import ClusterEnvironment
from pytorch_lightning.core import LightningModule
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.utilities import (
move_data_to_device,
rank_zero_info,
rank_zero_only,
rank_zero_warn,
TPU_AVAILABLE,
)
from pytorch_lightning.utilities.cloud_io import atomic_save
from pytorch_lightning.utilities.exceptions import MisconfigurationException
log = logging.getLogger(__name__)
if TPU_AVAILABLE:
import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as xla_pl
import torch_xla.distributed.xla_multiprocessing as xmp
class TPUAccelerator(Accelerator):
def __init__(self, trainer, cluster_environment: Optional[ClusterEnvironment] = None):
"""
Runs training using TPUs (colab, single machine or pod)
Example::
# default
trainer = Trainer(accelerator=TPUAccelerator())
"""
super().__init__(trainer, cluster_environment)
self.start_method = None
self.mp_queue = None
self.nickname = None
def setup(self, model):
rank_zero_info(f'training on {self.trainer.tpu_cores} TPU cores')
# TODO: Move this check to Trainer __init__ or device parser
if not TPU_AVAILABLE:
raise MisconfigurationException('PyTorch XLA not installed.')
# see: https://discuss.pytorch.org/t/segfault-with-multiprocessing-queue/81292/2
self.start_method = 'fork'
# pass in a state q
smp = mp.get_context(self.start_method)
self.mp_queue = smp.SimpleQueue()
self.trainer.model = model
def teardown(self):
model = self.trainer.model
# restore main state with best weights
best_path = self.mp_queue.get()
results = self.mp_queue.get()
last_path = self.mp_queue.get()
# transfer back the best path to the trainer
if self.trainer.checkpoint_callback is not None:
self.trainer.checkpoint_callback.best_model_path = best_path
# todo, pass also bets score
# load last weights
if last_path and not self.trainer.testing:
ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt)
self.trainer.model = model
# when training completes, load the weights back in main process
self.__load_weights_on_main_process()
return results
def train(self):
model = self.trainer.model
# train
if self.trainer.tpu_id is not None:
self.tpu_train_in_process(self.trainer.tpu_id, model, self.trainer, self.mp_queue)
else:
xmp.spawn(
self.tpu_train_in_process,
args=(model, self.trainer, self.mp_queue),
nprocs=self.trainer.tpu_cores,
start_method=self.start_method
)
def __load_weights_on_main_process(self):
model = self.trainer.model
# load weights if not interrupted
if self.trainer.on_colab_kaggle and not self.trainer.testing:
self.load_spawn_weights(model)
self.trainer.model = model
def tpu_train_in_process(self, tpu_core_idx: int, model: LightningModule, trainer=None, mp_queue=None):
"""
Here we are inside each individual process
"""
if not trainer:
trainer = self.trainer
trainer.call_setup_hook(model)
# setup TPU training
self.__setup_tpu_training(model, trainer)
self.trainer.setup_trainer(model)
# train or test
results = self.train_or_test()
# save weights at the end of training
self.__save_end_of_training_weights(model, trainer)
# persist info in spawn
self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
def _step(self, model_step: Callable, args):
args[0] = self.to_device(args[0])
return model_step(*args)
def training_step(self, args):
return self._step(self.trainer.model.training_step, args)
def validation_step(self, args):
return self._step(self.trainer.model.validation_step, args)
def test_step(self, args):
return self._step(self.trainer.model.test_step, args)
def process_dataloader(self, dataloader):
device = xm.xla_device(self.trainer.tpu_id)
dataloader = xla_pl.ParallelLoader(dataloader, [device])
dataloader = dataloader.per_device_loader(device)
return dataloader
def to_device(self, batch):
"""
Transfers the data to the TPU.
Args:
batch: A tensor or collection of tensors.
tpu_id: The id of the TPU core. If omitted, the first available core is chosen.
Return:
the tensor on the TPU device.
See Also:
- :func:`~pytorch_lightning.utilities.apply_func.move_data_to_device`
"""
if not TPU_AVAILABLE:
raise MisconfigurationException(
'Requested to transfer batch to TPU but XLA is not available.'
' Are you sure this machine has TPUs?'
)
device = xm.xla_device(self.trainer.tpu_id)
return self.batch_to_device(batch, device)
def __save_end_of_training_weights(self, model: LightningModule, trainer):
# when training ends on these platforms dump weights to get out of the main process
if trainer.on_colab_kaggle:
rank_zero_warn('cleaning up... please do not interrupt')
self.save_spawn_weights(model)
def __setup_tpu_training(self, model: LightningModule, trainer):
# use the default device from the process
# tpu_device = xm.xla_device()
# if given an ordinal device, use this as the device
if trainer.tpu_id is not None:
tpu_device = xm.xla_device(trainer.tpu_id)
else:
tpu_device = xm.xla_device()
# track the device and move model to it
trainer._device = tpu_device
model.to(trainer._device)
# get the appropriate tpu ranks
trainer.tpu_local_core_rank = xm.get_local_ordinal()
trainer.tpu_global_core_rank = xm.get_ordinal()
# avoid duplicating progress bar
if trainer.tpu_global_core_rank != 0 and trainer.progress_bar_callback is not None:
trainer.progress_bar_callback.disable()
trainer.global_rank = trainer.tpu_local_core_rank
rank_zero_only.rank = trainer.global_rank
# CHOOSE OPTIMIZER
# allow for lr schedulers as well
self.setup_optimizers(model)
# init 16 bit for TPU
if trainer.precision == 16:
os.environ['XLA_USE_BF16'] = str(1)
log.info(f'INIT TPU local core: {trainer.tpu_local_core_rank},'
f' global rank: {trainer.tpu_global_core_rank}'
f' with XLA_USE_BF16={os.environ.get("XLA_USE_BF16")}')
def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
# do backward pass
if self.trainer.train_loop.automatic_optimization:
model = self.trainer.get_model()
model.backward(closure_loss, optimizer, opt_idx)
else:
closure_loss.backward(*args, **kwargs)
# detach after backward
closure_loss = closure_loss.detach()
return closure_loss
def _clip_gradients(self, optimizer: Optimizer, grad_clip_val: Union[float, int], norm_type: float = 2.0):
# this code is a modification of torch.nn.utils.clip_grad_norm_
# with TPU support based on https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md
model = self.trainer.get_model()
parameters = model.parameters()
max_norm = grad_clip_val
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
device = parameters[0].device
out = torch.empty(len(parameters), device=device)
for i, p in enumerate(parameters):
torch.norm(p.grad.data.to(device), norm_type, out=out[i])
total_norm = torch.norm(out, norm_type)
clip_coef = torch.tensor(max_norm, device=device) / (total_norm + self.norm_clipping_epsilon)
clip_coef = torch.min(clip_coef, torch.ones_like(clip_coef))
for p in parameters:
p.grad.data.mul_(clip_coef.to(p.grad.data.device))
def barrier(self, name: Optional[str] = None):
torch_xla.core.xla_model.rendezvous(f"pl.Trainer.{name}")
def early_stopping_should_stop(self, pl_module):
stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device, dtype=torch.int32)
stop = xm.mesh_reduce("stop_signal", stop, sum)
torch_xla.core.xla_model.rendezvous("pl.EarlyStoppingCallback.stop_distributed_training_check")
should_stop = int(stop.item()) == self.trainer.world_size
return should_stop
def save_spawn_weights(self, model):
"""
Dump a temporary checkpoint after ddp ends to get weights out of the process
"""
if self.trainer.is_global_zero:
path = os.path.join(self.trainer.default_root_dir, '__temp_weight_distributed_end.ckpt')
self.trainer.save_checkpoint(path)
return path
def load_spawn_weights(self, original_model):
"""
Load the temp weights saved in the process
To recover the trained model from the ddp process we load the saved weights
"""
loaded_model = original_model
if self.trainer.is_global_zero:
# load weights saved in ddp
path = os.path.join(self.trainer.default_root_dir, '__temp_weight_distributed_end.ckpt')
loaded_model = original_model.__class__.load_from_checkpoint(path)
# copy loaded weights to old model
original_model.load_state_dict(loaded_model.state_dict())
# remove ddp weights
os.remove(path)
return loaded_model
def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
if self.trainer.distributed_backend not in ("ddp_spawn", "ddp_cpu", "tpu"):
return
# track the best model path
best_model_path = None
if self.trainer.checkpoint_callback is not None:
best_model_path = self.trainer.checkpoint_callback.best_model_path
if self.trainer.global_rank == 0 and mp_queue is not None:
rank_zero_warn('cleaning up ddp environment...')
# todo, pass complete checkpoint as state dictionary
mp_queue.put(best_model_path)
mp_queue.put(results)
# save the last weights
last_path = None
if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
state_dict = move_data_to_device(model.state_dict(), torch.device("cpu"))
atomic_save(state_dict, last_path)
mp_queue.put(last_path)
def broadcast(self, obj, src=0):
buffer = io.BytesIO()
torch.save(obj, buffer)
data = bytearray(buffer.getbuffer())
data_tensor = torch.tensor(data).to(xm.xla_device(), dtype=torch.float)
data = xm.all_gather(data_tensor)
buffer = io.BytesIO(data.cpu().byte().numpy())
obj = torch.load(buffer)
return obj
def sync_tensor(self,
tensor: Union[torch.Tensor],
group: Optional[Any] = None,
reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
return tensor
@property
def norm_clipping_epsilon(self):
return 1e-6
def on_save(self, checkpoint):
"""
Move XLA tensors to CPU before saving
Recommended on XLA Guide:
https://github.com/pytorch/xla/blob/master/API_GUIDE.md#saving-and-loading-xla-tensors
"""
return move_data_to_device(checkpoint, torch.device("cpu"))
@property
def distributed_sampler_kwargs(self):
return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
@property
def require_distributed_sampler(self):
return True
| 36.467213
| 110
| 0.66352
|
import io
import logging
import os
import re
from typing import Any, Callable, Optional, Union
import torch
import torch.multiprocessing as mp
from torch.optim import Optimizer
from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.cluster_environments import ClusterEnvironment
from pytorch_lightning.core import LightningModule
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.utilities import (
move_data_to_device,
rank_zero_info,
rank_zero_only,
rank_zero_warn,
TPU_AVAILABLE,
)
from pytorch_lightning.utilities.cloud_io import atomic_save
from pytorch_lightning.utilities.exceptions import MisconfigurationException
log = logging.getLogger(__name__)
if TPU_AVAILABLE:
import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as xla_pl
import torch_xla.distributed.xla_multiprocessing as xmp
class TPUAccelerator(Accelerator):
def __init__(self, trainer, cluster_environment: Optional[ClusterEnvironment] = None):
super().__init__(trainer, cluster_environment)
self.start_method = None
self.mp_queue = None
self.nickname = None
def setup(self, model):
rank_zero_info(f'training on {self.trainer.tpu_cores} TPU cores')
if not TPU_AVAILABLE:
raise MisconfigurationException('PyTorch XLA not installed.')
self.start_method = 'fork'
smp = mp.get_context(self.start_method)
self.mp_queue = smp.SimpleQueue()
self.trainer.model = model
def teardown(self):
model = self.trainer.model
best_path = self.mp_queue.get()
results = self.mp_queue.get()
last_path = self.mp_queue.get()
if self.trainer.checkpoint_callback is not None:
self.trainer.checkpoint_callback.best_model_path = best_path
if last_path and not self.trainer.testing:
ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt)
self.trainer.model = model
self.__load_weights_on_main_process()
return results
def train(self):
model = self.trainer.model
if self.trainer.tpu_id is not None:
self.tpu_train_in_process(self.trainer.tpu_id, model, self.trainer, self.mp_queue)
else:
xmp.spawn(
self.tpu_train_in_process,
args=(model, self.trainer, self.mp_queue),
nprocs=self.trainer.tpu_cores,
start_method=self.start_method
)
def __load_weights_on_main_process(self):
model = self.trainer.model
if self.trainer.on_colab_kaggle and not self.trainer.testing:
self.load_spawn_weights(model)
self.trainer.model = model
def tpu_train_in_process(self, tpu_core_idx: int, model: LightningModule, trainer=None, mp_queue=None):
if not trainer:
trainer = self.trainer
trainer.call_setup_hook(model)
self.__setup_tpu_training(model, trainer)
self.trainer.setup_trainer(model)
results = self.train_or_test()
self.__save_end_of_training_weights(model, trainer)
self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
def _step(self, model_step: Callable, args):
args[0] = self.to_device(args[0])
return model_step(*args)
def training_step(self, args):
return self._step(self.trainer.model.training_step, args)
def validation_step(self, args):
return self._step(self.trainer.model.validation_step, args)
def test_step(self, args):
return self._step(self.trainer.model.test_step, args)
def process_dataloader(self, dataloader):
device = xm.xla_device(self.trainer.tpu_id)
dataloader = xla_pl.ParallelLoader(dataloader, [device])
dataloader = dataloader.per_device_loader(device)
return dataloader
def to_device(self, batch):
if not TPU_AVAILABLE:
raise MisconfigurationException(
'Requested to transfer batch to TPU but XLA is not available.'
' Are you sure this machine has TPUs?'
)
device = xm.xla_device(self.trainer.tpu_id)
return self.batch_to_device(batch, device)
def __save_end_of_training_weights(self, model: LightningModule, trainer):
if trainer.on_colab_kaggle:
rank_zero_warn('cleaning up... please do not interrupt')
self.save_spawn_weights(model)
def __setup_tpu_training(self, model: LightningModule, trainer):
if trainer.tpu_id is not None:
tpu_device = xm.xla_device(trainer.tpu_id)
else:
tpu_device = xm.xla_device()
trainer._device = tpu_device
model.to(trainer._device)
trainer.tpu_local_core_rank = xm.get_local_ordinal()
trainer.tpu_global_core_rank = xm.get_ordinal()
if trainer.tpu_global_core_rank != 0 and trainer.progress_bar_callback is not None:
trainer.progress_bar_callback.disable()
trainer.global_rank = trainer.tpu_local_core_rank
rank_zero_only.rank = trainer.global_rank
self.setup_optimizers(model)
if trainer.precision == 16:
os.environ['XLA_USE_BF16'] = str(1)
log.info(f'INIT TPU local core: {trainer.tpu_local_core_rank},'
f' global rank: {trainer.tpu_global_core_rank}'
f' with XLA_USE_BF16={os.environ.get("XLA_USE_BF16")}')
def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
if self.trainer.train_loop.automatic_optimization:
model = self.trainer.get_model()
model.backward(closure_loss, optimizer, opt_idx)
else:
closure_loss.backward(*args, **kwargs)
closure_loss = closure_loss.detach()
return closure_loss
def _clip_gradients(self, optimizer: Optimizer, grad_clip_val: Union[float, int], norm_type: float = 2.0):
model = self.trainer.get_model()
parameters = model.parameters()
max_norm = grad_clip_val
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
device = parameters[0].device
out = torch.empty(len(parameters), device=device)
for i, p in enumerate(parameters):
torch.norm(p.grad.data.to(device), norm_type, out=out[i])
total_norm = torch.norm(out, norm_type)
clip_coef = torch.tensor(max_norm, device=device) / (total_norm + self.norm_clipping_epsilon)
clip_coef = torch.min(clip_coef, torch.ones_like(clip_coef))
for p in parameters:
p.grad.data.mul_(clip_coef.to(p.grad.data.device))
def barrier(self, name: Optional[str] = None):
torch_xla.core.xla_model.rendezvous(f"pl.Trainer.{name}")
def early_stopping_should_stop(self, pl_module):
stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device, dtype=torch.int32)
stop = xm.mesh_reduce("stop_signal", stop, sum)
torch_xla.core.xla_model.rendezvous("pl.EarlyStoppingCallback.stop_distributed_training_check")
should_stop = int(stop.item()) == self.trainer.world_size
return should_stop
def save_spawn_weights(self, model):
if self.trainer.is_global_zero:
path = os.path.join(self.trainer.default_root_dir, '__temp_weight_distributed_end.ckpt')
self.trainer.save_checkpoint(path)
return path
def load_spawn_weights(self, original_model):
loaded_model = original_model
if self.trainer.is_global_zero:
path = os.path.join(self.trainer.default_root_dir, '__temp_weight_distributed_end.ckpt')
loaded_model = original_model.__class__.load_from_checkpoint(path)
original_model.load_state_dict(loaded_model.state_dict())
os.remove(path)
return loaded_model
def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
if self.trainer.distributed_backend not in ("ddp_spawn", "ddp_cpu", "tpu"):
return
best_model_path = None
if self.trainer.checkpoint_callback is not None:
best_model_path = self.trainer.checkpoint_callback.best_model_path
if self.trainer.global_rank == 0 and mp_queue is not None:
rank_zero_warn('cleaning up ddp environment...')
mp_queue.put(best_model_path)
mp_queue.put(results)
last_path = None
if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
state_dict = move_data_to_device(model.state_dict(), torch.device("cpu"))
atomic_save(state_dict, last_path)
mp_queue.put(last_path)
def broadcast(self, obj, src=0):
buffer = io.BytesIO()
torch.save(obj, buffer)
data = bytearray(buffer.getbuffer())
data_tensor = torch.tensor(data).to(xm.xla_device(), dtype=torch.float)
data = xm.all_gather(data_tensor)
buffer = io.BytesIO(data.cpu().byte().numpy())
obj = torch.load(buffer)
return obj
def sync_tensor(self,
tensor: Union[torch.Tensor],
group: Optional[Any] = None,
reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
return tensor
@property
def norm_clipping_epsilon(self):
return 1e-6
def on_save(self, checkpoint):
return move_data_to_device(checkpoint, torch.device("cpu"))
@property
def distributed_sampler_kwargs(self):
return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
@property
def require_distributed_sampler(self):
return True
| true
| true
|
1c3ebf5598e69feb2c336c6da8f14570efb46fc9
| 2,548
|
py
|
Python
|
open_spiel/python/bots/is_mcts_test.py
|
texasmichelle/open_spiel
|
d9a9b8f9f1f44143867217fc3f6ff2db71b174b0
|
[
"Apache-2.0"
] | 3,167
|
2019-08-27T06:50:30.000Z
|
2022-03-31T22:33:48.000Z
|
open_spiel/python/bots/is_mcts_test.py
|
texasmichelle/open_spiel
|
d9a9b8f9f1f44143867217fc3f6ff2db71b174b0
|
[
"Apache-2.0"
] | 650
|
2019-08-27T16:24:09.000Z
|
2022-03-30T19:41:09.000Z
|
open_spiel/python/bots/is_mcts_test.py
|
texasmichelle/open_spiel
|
d9a9b8f9f1f44143867217fc3f6ff2db71b174b0
|
[
"Apache-2.0"
] | 774
|
2019-08-27T10:36:04.000Z
|
2022-03-29T15:44:42.000Z
|
# 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.
"""Unit test for Information Set MCTS bot.
This test mimics the basic C++ tests in algorithms/is_mcts_test.cc.
"""
# pylint: disable=g-unreachable-test-method
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
import numpy as np
from open_spiel.python.algorithms import evaluate_bots
import pyspiel
SEED = 12983641
class ISMCTSBotTest(absltest.TestCase):
def ismcts_play_game(self, game):
evaluator = pyspiel.RandomRolloutEvaluator(1, SEED)
for final_policy_type in [
pyspiel.ISMCTSFinalPolicyType.NORMALIZED_VISIT_COUNT,
pyspiel.ISMCTSFinalPolicyType.MAX_VISIT_COUNT,
pyspiel.ISMCTSFinalPolicyType.MAX_VALUE
]:
bot = pyspiel.ISMCTSBot(SEED, evaluator, 5.0, 1000, -1, final_policy_type,
False, False)
bots = [bot] * game.num_players()
evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
bot = pyspiel.ISMCTSBot(SEED, evaluator, 5.0, 1000, 10, final_policy_type,
False, False)
bots = [bot] * game.num_players()
evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
bot = pyspiel.ISMCTSBot(SEED, evaluator, 5.0, 1000, 10, final_policy_type,
True, True)
bots = [bot] * game.num_players()
evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
def test_basic_sim_kuhn(self):
game = pyspiel.load_game("kuhn_poker")
self.ismcts_play_game(game)
game = pyspiel.load_game("kuhn_poker(players=3)")
self.ismcts_play_game(game)
def test_basic_sim_leduc(self):
game = pyspiel.load_game("leduc_poker")
self.ismcts_play_game(game)
game = pyspiel.load_game("leduc_poker(players=3)")
self.ismcts_play_game(game)
if __name__ == "__main__":
np.random.seed(SEED)
absltest.main()
| 35.887324
| 80
| 0.720173
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
import numpy as np
from open_spiel.python.algorithms import evaluate_bots
import pyspiel
SEED = 12983641
class ISMCTSBotTest(absltest.TestCase):
def ismcts_play_game(self, game):
evaluator = pyspiel.RandomRolloutEvaluator(1, SEED)
for final_policy_type in [
pyspiel.ISMCTSFinalPolicyType.NORMALIZED_VISIT_COUNT,
pyspiel.ISMCTSFinalPolicyType.MAX_VISIT_COUNT,
pyspiel.ISMCTSFinalPolicyType.MAX_VALUE
]:
bot = pyspiel.ISMCTSBot(SEED, evaluator, 5.0, 1000, -1, final_policy_type,
False, False)
bots = [bot] * game.num_players()
evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
bot = pyspiel.ISMCTSBot(SEED, evaluator, 5.0, 1000, 10, final_policy_type,
False, False)
bots = [bot] * game.num_players()
evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
bot = pyspiel.ISMCTSBot(SEED, evaluator, 5.0, 1000, 10, final_policy_type,
True, True)
bots = [bot] * game.num_players()
evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
def test_basic_sim_kuhn(self):
game = pyspiel.load_game("kuhn_poker")
self.ismcts_play_game(game)
game = pyspiel.load_game("kuhn_poker(players=3)")
self.ismcts_play_game(game)
def test_basic_sim_leduc(self):
game = pyspiel.load_game("leduc_poker")
self.ismcts_play_game(game)
game = pyspiel.load_game("leduc_poker(players=3)")
self.ismcts_play_game(game)
if __name__ == "__main__":
np.random.seed(SEED)
absltest.main()
| true
| true
|
1c3ebf8534de15eccfa8e05d6772024a0cb19ba3
| 1,621
|
py
|
Python
|
src/izi/apps/catalogue/reviews/forms.py
|
izi-core/izi-core
|
21176be2d41f0cf54ca954f294209c585f643dba
|
[
"BSD-3-Clause"
] | null | null | null |
src/izi/apps/catalogue/reviews/forms.py
|
izi-core/izi-core
|
21176be2d41f0cf54ca954f294209c585f643dba
|
[
"BSD-3-Clause"
] | null | null | null |
src/izi/apps/catalogue/reviews/forms.py
|
izi-core/izi-core
|
21176be2d41f0cf54ca954f294209c585f643dba
|
[
"BSD-3-Clause"
] | null | null | null |
from django import forms
from django.utils.translation import gettext_lazy as _
from izi.core.loading import get_model
Vote = get_model('reviews', 'vote')
ProductReview = get_model('reviews', 'productreview')
class ProductReviewForm(forms.ModelForm):
name = forms.CharField(label=_('Name'), required=True)
email = forms.EmailField(label=_('Email'), required=True)
def __init__(self, product, user=None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.instance.product = product
if user and user.is_authenticated:
self.instance.user = user
del self.fields['name']
del self.fields['email']
class Meta:
model = ProductReview
fields = ('title', 'score', 'body', 'name', 'email')
class VoteForm(forms.ModelForm):
class Meta:
model = Vote
fields = ('delta',)
def __init__(self, review, user, *args, **kwargs):
super().__init__(*args, **kwargs)
self.instance.review = review
self.instance.user = user
@property
def is_up_vote(self):
return self.cleaned_data['delta'] == Vote.UP
@property
def is_down_vote(self):
return self.cleaned_data['delta'] == Vote.DOWN
class SortReviewsForm(forms.Form):
SORT_BY_SCORE = 'score'
SORT_BY_RECENCY = 'recency'
SORT_REVIEWS_BY_CHOICES = (
(SORT_BY_SCORE, _('Score')),
(SORT_BY_RECENCY, _('Recency')),
)
sort_by = forms.ChoiceField(
choices=SORT_REVIEWS_BY_CHOICES,
label=_('Sort by'),
initial=SORT_BY_SCORE,
required=False
)
| 26.57377
| 61
| 0.632326
|
from django import forms
from django.utils.translation import gettext_lazy as _
from izi.core.loading import get_model
Vote = get_model('reviews', 'vote')
ProductReview = get_model('reviews', 'productreview')
class ProductReviewForm(forms.ModelForm):
name = forms.CharField(label=_('Name'), required=True)
email = forms.EmailField(label=_('Email'), required=True)
def __init__(self, product, user=None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.instance.product = product
if user and user.is_authenticated:
self.instance.user = user
del self.fields['name']
del self.fields['email']
class Meta:
model = ProductReview
fields = ('title', 'score', 'body', 'name', 'email')
class VoteForm(forms.ModelForm):
class Meta:
model = Vote
fields = ('delta',)
def __init__(self, review, user, *args, **kwargs):
super().__init__(*args, **kwargs)
self.instance.review = review
self.instance.user = user
@property
def is_up_vote(self):
return self.cleaned_data['delta'] == Vote.UP
@property
def is_down_vote(self):
return self.cleaned_data['delta'] == Vote.DOWN
class SortReviewsForm(forms.Form):
SORT_BY_SCORE = 'score'
SORT_BY_RECENCY = 'recency'
SORT_REVIEWS_BY_CHOICES = (
(SORT_BY_SCORE, _('Score')),
(SORT_BY_RECENCY, _('Recency')),
)
sort_by = forms.ChoiceField(
choices=SORT_REVIEWS_BY_CHOICES,
label=_('Sort by'),
initial=SORT_BY_SCORE,
required=False
)
| true
| true
|
1c3ec00dd8b2fd9f7864c69b14adc250d65def9a
| 1,730
|
py
|
Python
|
setup.py
|
chiangtw/CircMiMi
|
f9700ab3afb79e5b730b95666482ba64f3e6ef75
|
[
"MIT"
] | 2
|
2021-09-13T13:12:46.000Z
|
2022-01-03T05:04:32.000Z
|
setup.py
|
chiangtw/CircMiMi
|
f9700ab3afb79e5b730b95666482ba64f3e6ef75
|
[
"MIT"
] | null | null | null |
setup.py
|
chiangtw/CircMiMi
|
f9700ab3afb79e5b730b95666482ba64f3e6ef75
|
[
"MIT"
] | 1
|
2020-05-16T13:24:13.000Z
|
2020-05-16T13:24:13.000Z
|
import codecs
import os
import re
from setuptools import setup, find_packages
here = os.path.abspath(os.path.dirname(__file__))
def read(*parts):
with codecs.open(os.path.join(here, *parts), 'r') as fp:
return fp.read()
def find_version(*file_paths):
version_file = read(*file_paths)
version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find version string.")
long_description = read('README.md')
setup(
name='circmimi',
version=find_version("circmimi", "__init__.py"),
url='https://github.com/TreesLab/CircMiMi',
packages=find_packages(),
install_requires=[
'click>=7.0',
'sqlalchemy>=1.3.8',
'numpy>=1.17.2',
'pandas>=0.25.1',
'openpyxl',
'networkx>=2.4',
'lxml>=4.5.0'
],
entry_points={
'console_scripts': [
'circmimi_tools = circmimi.scripts.circmimi_tools:cli',
'mp_blat.py = circmimi.scripts.mp_blat:cli',
'get_RCS.py = circmimi.scripts.get_RCS.get_RCS:cli',
'get_RCS_summary.py = circmimi.scripts.get_RCS.get_RCS_summary:cli',
'RCS_filter.py = circmimi.scripts.get_RCS.RCS_filter:cli',
'get_flanking_seq.py = circmimi.scripts.checkAA.get_flanking_seq:cli',
'checkAA_reads.py = circmimi.scripts.checkAA.checkAA_reads:cli'
]
},
description=("A package for constructing CLIP-seq data-supported "
"\"circRNA - miRNA - mRNA\" interactions"),
long_description=long_description,
long_description_content_type="text/markdown"
)
| 30.350877
| 82
| 0.625434
|
import codecs
import os
import re
from setuptools import setup, find_packages
here = os.path.abspath(os.path.dirname(__file__))
def read(*parts):
with codecs.open(os.path.join(here, *parts), 'r') as fp:
return fp.read()
def find_version(*file_paths):
version_file = read(*file_paths)
version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find version string.")
long_description = read('README.md')
setup(
name='circmimi',
version=find_version("circmimi", "__init__.py"),
url='https://github.com/TreesLab/CircMiMi',
packages=find_packages(),
install_requires=[
'click>=7.0',
'sqlalchemy>=1.3.8',
'numpy>=1.17.2',
'pandas>=0.25.1',
'openpyxl',
'networkx>=2.4',
'lxml>=4.5.0'
],
entry_points={
'console_scripts': [
'circmimi_tools = circmimi.scripts.circmimi_tools:cli',
'mp_blat.py = circmimi.scripts.mp_blat:cli',
'get_RCS.py = circmimi.scripts.get_RCS.get_RCS:cli',
'get_RCS_summary.py = circmimi.scripts.get_RCS.get_RCS_summary:cli',
'RCS_filter.py = circmimi.scripts.get_RCS.RCS_filter:cli',
'get_flanking_seq.py = circmimi.scripts.checkAA.get_flanking_seq:cli',
'checkAA_reads.py = circmimi.scripts.checkAA.checkAA_reads:cli'
]
},
description=("A package for constructing CLIP-seq data-supported "
"\"circRNA - miRNA - mRNA\" interactions"),
long_description=long_description,
long_description_content_type="text/markdown"
)
| true
| true
|
1c3ec0239a3b51d83462da9640c1889bef35013b
| 3,540
|
py
|
Python
|
decloud/models/monthly_synthesis_6_s2s1_images_david.py
|
CNES/decloud
|
6b06ae98bfe68821b4ebd0e7ba06723809cb9b42
|
[
"Apache-2.0"
] | 8
|
2022-02-25T13:15:07.000Z
|
2022-03-20T18:29:49.000Z
|
decloud/models/monthly_synthesis_6_s2s1_images_david.py
|
CNES/decloud
|
6b06ae98bfe68821b4ebd0e7ba06723809cb9b42
|
[
"Apache-2.0"
] | 1
|
2022-02-25T13:21:33.000Z
|
2022-02-25T13:21:33.000Z
|
decloud/models/monthly_synthesis_6_s2s1_images_david.py
|
CNES/decloud
|
6b06ae98bfe68821b4ebd0e7ba06723809cb9b42
|
[
"Apache-2.0"
] | 1
|
2022-03-31T23:43:12.000Z
|
2022-03-31T23:43:12.000Z
|
# -*- coding: utf-8 -*-
"""
Copyright (c) 2020-2022 INRAE
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.
"""
"""David model implementation (monthly synthesis of 6 optical & SAR couples of images)"""
from tensorflow.keras import layers
from decloud.models.model import Model
from decloud.preprocessing import constants
class monthly_synthesis_6_s2s1_images_david(Model):
def __init__(self, dataset_shapes,
dataset_input_keys=["s2_t0", "s2_t1", "s2_t2", "s2_t3", "s2_t4", "s2_t5",
"s1_t0", "s1_t1", "s1_t2", "s1_t3", "s1_t4", "s1_t5", constants.DEM_KEY],
model_output_keys=["s2_target"]):
super().__init__(dataset_input_keys=dataset_input_keys, model_output_keys=model_output_keys,
dataset_shapes=dataset_shapes)
def get_outputs(self, normalized_inputs):
# The network
features = []
conv1_s2 = layers.Conv2D(64, 5, 1, activation='relu', name="conv1_s2_relu", padding="same")
conv1_s1 = layers.Conv2D(64, 5, 1, activation='relu', name="conv1_s1_relu", padding="same")
conv1_dem = layers.Conv2D(64, 3, 1, activation='relu', name="conv1_dem_relu", padding="same")
conv2 = layers.Conv2D(128, 3, 2, activation='relu', name="conv2_bn_relu", padding="same")
conv3 = layers.Conv2D(256, 3, 2, activation='relu', name="conv3_bn_relu", padding="same")
deconv1 = layers.Conv2DTranspose(128, 3, 2, activation='relu', name="deconv1_bn_relu", padding="same")
deconv2 = layers.Conv2DTranspose(64, 3, 2, activation='relu', name="deconv2_bn_relu", padding="same")
conv4 = layers.Conv2D(4, 5, 1, activation='relu', name="s2_estim", padding="same")
for key, input_image in normalized_inputs.items():
if key != constants.DEM_KEY:
if key.startswith('s1'):
net = conv1_s1(input_image) # 256
elif key.startswith('s2'):
net = conv1_s2(input_image) # 256
net = conv2(net) # 128
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
net = layers.concatenate([net, net_dem], axis=-1)
net = conv3(net) # 64
features.append(net)
net = layers.concatenate(features, axis=-1)
net = deconv1(net) # 128
net = deconv2(net) # 256
s2_out = conv4(net) # 256
return {"s2_target": s2_out} # key must correspond to the key from the dataset
| 51.304348
| 110
| 0.666384
|
from tensorflow.keras import layers
from decloud.models.model import Model
from decloud.preprocessing import constants
class monthly_synthesis_6_s2s1_images_david(Model):
def __init__(self, dataset_shapes,
dataset_input_keys=["s2_t0", "s2_t1", "s2_t2", "s2_t3", "s2_t4", "s2_t5",
"s1_t0", "s1_t1", "s1_t2", "s1_t3", "s1_t4", "s1_t5", constants.DEM_KEY],
model_output_keys=["s2_target"]):
super().__init__(dataset_input_keys=dataset_input_keys, model_output_keys=model_output_keys,
dataset_shapes=dataset_shapes)
def get_outputs(self, normalized_inputs):
features = []
conv1_s2 = layers.Conv2D(64, 5, 1, activation='relu', name="conv1_s2_relu", padding="same")
conv1_s1 = layers.Conv2D(64, 5, 1, activation='relu', name="conv1_s1_relu", padding="same")
conv1_dem = layers.Conv2D(64, 3, 1, activation='relu', name="conv1_dem_relu", padding="same")
conv2 = layers.Conv2D(128, 3, 2, activation='relu', name="conv2_bn_relu", padding="same")
conv3 = layers.Conv2D(256, 3, 2, activation='relu', name="conv3_bn_relu", padding="same")
deconv1 = layers.Conv2DTranspose(128, 3, 2, activation='relu', name="deconv1_bn_relu", padding="same")
deconv2 = layers.Conv2DTranspose(64, 3, 2, activation='relu', name="deconv2_bn_relu", padding="same")
conv4 = layers.Conv2D(4, 5, 1, activation='relu', name="s2_estim", padding="same")
for key, input_image in normalized_inputs.items():
if key != constants.DEM_KEY:
if key.startswith('s1'):
net = conv1_s1(input_image)
elif key.startswith('s2'):
net = conv1_s2(input_image)
net = conv2(net)
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
net = layers.concatenate([net, net_dem], axis=-1)
net = conv3(net)
features.append(net)
net = layers.concatenate(features, axis=-1)
net = deconv1(net)
net = deconv2(net)
s2_out = conv4(net)
return {"s2_target": s2_out}
| true
| true
|
1c3ec0608c6faed4e34cec115e9524d65fc136d9
| 667
|
py
|
Python
|
utils/batch_helper.py
|
liushengzhong1023/multihead-siamese-nets
|
ccb544d5a8adefa41743ef7948aeb5f20a74507e
|
[
"MIT"
] | 175
|
2018-01-26T19:22:05.000Z
|
2022-02-24T01:20:21.000Z
|
utils/batch_helper.py
|
liushengzhong1023/multihead-siamese-nets
|
ccb544d5a8adefa41743ef7948aeb5f20a74507e
|
[
"MIT"
] | 19
|
2018-02-26T09:45:59.000Z
|
2022-02-09T23:57:22.000Z
|
utils/batch_helper.py
|
warisqr007/ConvSANN
|
0cede14601ce1a62bd58abf92a04ad3d7cc3be99
|
[
"MIT"
] | 41
|
2018-02-27T11:20:28.000Z
|
2021-04-13T04:57:57.000Z
|
class BatchHelper:
def __init__(self, x1, x2, labels, batch_size):
self.x1 = x1
# self.x1 = self.x1.reshape(-1, 1)
self.x2 = x2
# self.x2 = self.x2.reshape(-1, 1)
self.labels = labels
self.labels = self.labels.reshape(-1, 1)
self.batch_size = batch_size
def next(self, batch_id):
x1_batch = self.x1[batch_id * self.batch_size:(batch_id + 1) * self.batch_size]
x2_batch = self.x2[batch_id * self.batch_size:(batch_id + 1) * self.batch_size]
labels_batch = self.labels[batch_id * self.batch_size:(batch_id + 1) * self.batch_size]
return x1_batch, x2_batch, labels_batch
| 39.235294
| 95
| 0.623688
|
class BatchHelper:
def __init__(self, x1, x2, labels, batch_size):
self.x1 = x1
self.x2 = x2
self.labels = labels
self.labels = self.labels.reshape(-1, 1)
self.batch_size = batch_size
def next(self, batch_id):
x1_batch = self.x1[batch_id * self.batch_size:(batch_id + 1) * self.batch_size]
x2_batch = self.x2[batch_id * self.batch_size:(batch_id + 1) * self.batch_size]
labels_batch = self.labels[batch_id * self.batch_size:(batch_id + 1) * self.batch_size]
return x1_batch, x2_batch, labels_batch
| true
| true
|
1c3ec089f6634e8a94af71b4270bf1a322b29773
| 76,413
|
py
|
Python
|
openpype/tools/project_manager/project_manager/model.py
|
jonclothcat/OpenPype
|
d1208cbebc0a7f378de0062ccd653295c6399195
|
[
"MIT"
] | null | null | null |
openpype/tools/project_manager/project_manager/model.py
|
jonclothcat/OpenPype
|
d1208cbebc0a7f378de0062ccd653295c6399195
|
[
"MIT"
] | null | null | null |
openpype/tools/project_manager/project_manager/model.py
|
jonclothcat/OpenPype
|
d1208cbebc0a7f378de0062ccd653295c6399195
|
[
"MIT"
] | null | null | null |
import collections
import copy
import json
from uuid import uuid4
from pymongo import UpdateOne, DeleteOne
from Qt import QtCore, QtGui
from .constants import (
IDENTIFIER_ROLE,
ITEM_TYPE_ROLE,
DUPLICATED_ROLE,
HIERARCHY_CHANGE_ABLE_ROLE,
REMOVED_ROLE,
EDITOR_OPENED_ROLE,
PROJECT_NAME_ROLE
)
from .style import ResourceCache
from openpype.lib import CURRENT_DOC_SCHEMAS
class ProjectModel(QtGui.QStandardItemModel):
"""Load possible projects to modify from MongoDB.
Mongo collection must contain project document with "type" "project" and
matching "name" value with name of collection.
"""
def __init__(self, dbcon, *args, **kwargs):
self.dbcon = dbcon
self._items_by_name = {}
super(ProjectModel, self).__init__(*args, **kwargs)
def refresh(self):
"""Reload projects."""
self.dbcon.Session["AVALON_PROJECT"] = None
new_project_items = []
if None not in self._items_by_name:
none_project = QtGui.QStandardItem("< Select Project >")
self._items_by_name[None] = none_project
new_project_items.append(none_project)
project_docs = self.dbcon.projects(
projection={"name": 1},
only_active=True
)
project_names = set()
for project_doc in project_docs:
project_name = project_doc.get("name")
if not project_name:
continue
project_names.add(project_name)
if project_name not in self._items_by_name:
project_item = QtGui.QStandardItem(project_name)
project_item.setData(project_name, PROJECT_NAME_ROLE)
self._items_by_name[project_name] = project_item
new_project_items.append(project_item)
root_item = self.invisibleRootItem()
for project_name in tuple(self._items_by_name.keys()):
if project_name is None or project_name in project_names:
continue
project_item = self._items_by_name.pop(project_name)
root_item.removeRow(project_item.row())
if new_project_items:
root_item.appendRows(new_project_items)
class ProjectProxyFilter(QtCore.QSortFilterProxyModel):
"""Filters default project item."""
def __init__(self, *args, **kwargs):
super(ProjectProxyFilter, self).__init__(*args, **kwargs)
self._filter_default = False
def set_filter_default(self, enabled=True):
"""Set if filtering of default item is enabled."""
if enabled == self._filter_default:
return
self._filter_default = enabled
self.invalidateFilter()
def filterAcceptsRow(self, row, parent):
if not self._filter_default:
return True
model = self.sourceModel()
source_index = model.index(row, self.filterKeyColumn(), parent)
return source_index.data(PROJECT_NAME_ROLE) is not None
class HierarchySelectionModel(QtCore.QItemSelectionModel):
"""Selection model with defined allowed multiselection columns.
This model allows to select multiple rows and enter one of their
editors to edit value of all selected rows.
"""
def __init__(self, multiselection_columns, *args, **kwargs):
super(HierarchySelectionModel, self).__init__(*args, **kwargs)
self.multiselection_columns = multiselection_columns
def setCurrentIndex(self, index, command):
if index.column() in self.multiselection_columns:
if (
command & QtCore.QItemSelectionModel.Clear
and command & QtCore.QItemSelectionModel.Select
):
command = QtCore.QItemSelectionModel.NoUpdate
super(HierarchySelectionModel, self).setCurrentIndex(index, command)
class HierarchyModel(QtCore.QAbstractItemModel):
"""Main model for hierarchy modification and value changes.
Main part of ProjectManager.
Model should be able to load existing entities, create new, handle their
validations like name duplication and validate if is possible to save its
data.
Args:
dbcon (AvalonMongoDB): Connection to MongoDB with set AVALON_PROJECT in
its Session to current project.
"""
# Definition of all possible columns with their labels in default order
# - order is important as column names are used as keys for column indexes
_columns_def = [
("name", "Name"),
("type", "Type"),
("fps", "FPS"),
("frameStart", "Frame start"),
("frameEnd", "Frame end"),
("handleStart", "Handle start"),
("handleEnd", "Handle end"),
("resolutionWidth", "Width"),
("resolutionHeight", "Height"),
("clipIn", "Clip in"),
("clipOut", "Clip out"),
("pixelAspect", "Pixel aspect"),
("tools_env", "Tools")
]
# Columns allowing multiselection in edit mode
# - gives ability to set all of keys below on multiple items at once
multiselection_columns = {
"frameStart",
"frameEnd",
"fps",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env"
}
columns = [
item[0]
for item in _columns_def
]
columns_len = len(columns)
column_labels = {
idx: item[1]
for idx, item in enumerate(_columns_def)
}
index_moved = QtCore.Signal(QtCore.QModelIndex)
project_changed = QtCore.Signal()
def __init__(self, dbcon, parent=None):
super(HierarchyModel, self).__init__(parent)
self.multiselection_column_indexes = {
self.columns.index(key)
for key in self.multiselection_columns
}
# TODO Reset them on project change
self._current_project = None
self._root_item = None
self._items_by_id = {}
self._asset_items_by_name = collections.defaultdict(set)
self.dbcon = dbcon
self._reset_root_item()
@property
def items_by_id(self):
return self._items_by_id
def _reset_root_item(self):
"""Removes all previous content related to model."""
self._root_item = RootItem(self)
def refresh_project(self):
"""Reload project data and discard unsaved changes."""
self.set_project(self._current_project, True)
@property
def project_item(self):
"""Access to current project item.
Model can have 0-1 ProjectItems at once.
"""
output = None
for row in range(self._root_item.rowCount()):
item = self._root_item.child(row)
if isinstance(item, ProjectItem):
output = item
break
return output
def set_project(self, project_name, force=False):
"""Change project and discard unsaved changes.
Args:
project_name(str): New project name. Or None if just clearing
content.
force(bool): Force to change project even if project name is same
as current project.
"""
if self._current_project == project_name and not force:
return
# Reset attributes
self._items_by_id.clear()
self._asset_items_by_name.clear()
self.clear()
self._current_project = project_name
# Skip if project is None
if not project_name:
return
# Find project'd document
project_doc = self.dbcon.database[project_name].find_one(
{"type": "project"},
ProjectItem.query_projection
)
# Skip if project document does not exist
# - this shouldn't happen using only UI elements
if not project_doc:
return
# Create project item
project_item = ProjectItem(project_doc)
self.add_item(project_item)
# Query all assets of the project
asset_docs = self.dbcon.database[project_name].find(
{"type": "asset"},
AssetItem.query_projection
)
asset_docs_by_id = {
asset_doc["_id"]: asset_doc
for asset_doc in asset_docs
}
# Check if asset have published content and prepare booleans
# if asset item can be modified (name and hierarchy change)
# - the same must be applied to all it's parents
asset_ids = list(asset_docs_by_id.keys())
result = []
if asset_ids:
result = self.dbcon.database[project_name].aggregate([
{
"$match": {
"type": "subset",
"parent": {"$in": asset_ids}
}
},
{
"$group": {
"_id": "$parent",
"count": {"$sum": 1}
}
}
])
asset_modifiable = {
asset_id: True
for asset_id in asset_docs_by_id.keys()
}
for item in result:
asset_id = item["_id"]
count = item["count"]
asset_modifiable[asset_id] = count < 1
# Store assets by their visual parent to be able create their hierarchy
asset_docs_by_parent_id = collections.defaultdict(list)
for asset_doc in asset_docs_by_id.values():
parent_id = asset_doc["data"].get("visualParent")
asset_docs_by_parent_id[parent_id].append(asset_doc)
appending_queue = collections.deque()
appending_queue.append((None, project_item))
asset_items_by_id = {}
non_modifiable_items = set()
while appending_queue:
parent_id, parent_item = appending_queue.popleft()
asset_docs = asset_docs_by_parent_id.get(parent_id) or []
new_items = []
for asset_doc in sorted(asset_docs, key=lambda item: item["name"]):
# Create new Item
new_item = AssetItem(asset_doc)
# Store item to be added under parent in bulk
new_items.append(new_item)
# Store item by id for task processing
asset_id = asset_doc["_id"]
if not asset_modifiable[asset_id]:
non_modifiable_items.add(new_item.id)
asset_items_by_id[asset_id] = new_item
# Add item to appending queue
appending_queue.append((asset_id, new_item))
if new_items:
self.add_items(new_items, parent_item)
# Handle Asset's that are not modifiable
# - pass the information to all it's parents
non_modifiable_queue = collections.deque()
for item_id in non_modifiable_items:
non_modifiable_queue.append(item_id)
while non_modifiable_queue:
item_id = non_modifiable_queue.popleft()
item = self._items_by_id[item_id]
item.setData(False, HIERARCHY_CHANGE_ABLE_ROLE)
parent = item.parent()
if (
isinstance(parent, AssetItem)
and parent.id not in non_modifiable_items
):
non_modifiable_items.add(parent.id)
non_modifiable_queue.append(parent.id)
# Add task items
for asset_id, asset_item in asset_items_by_id.items():
asset_doc = asset_docs_by_id[asset_id]
asset_tasks = asset_doc["data"]["tasks"]
if not asset_tasks:
continue
task_items = []
for task_name in sorted(asset_tasks.keys()):
_task_data = copy.deepcopy(asset_tasks[task_name])
_task_data["name"] = task_name
task_item = TaskItem(_task_data)
task_items.append(task_item)
self.add_items(task_items, asset_item)
# Emit that project was successfully changed
self.project_changed.emit()
def rowCount(self, parent=None):
"""Number of rows for passed parent."""
if parent is None or not parent.isValid():
parent_item = self._root_item
else:
parent_item = parent.internalPointer()
return parent_item.rowCount()
def columnCount(self, *args, **kwargs):
"""Number of columns is static for this model."""
return self.columns_len
def data(self, index, role):
"""Access data for passed index and it's role.
Model is using principles implemented in BaseItem so converts passed
index column into key and ask item to return value for passed role.
"""
if not index.isValid():
return None
column = index.column()
key = self.columns[column]
item = index.internalPointer()
return item.data(role, key)
def setData(self, index, value, role=QtCore.Qt.EditRole):
"""Store data to passed index under role.
Pass values to corresponding item and behave by it's result.
"""
if not index.isValid():
return False
item = index.internalPointer()
column = index.column()
key = self.columns[column]
# Capture asset name changes for duplcated asset names validation.
if (
key == "name"
and role in (QtCore.Qt.EditRole, QtCore.Qt.DisplayRole)
):
self._rename_asset(item, value)
# Pass values to item and by result emi dataChanged signal or not
result = item.setData(value, role, key)
if result:
self.dataChanged.emit(index, index, [role])
return result
def headerData(self, section, orientation, role):
"""Header labels."""
if role == QtCore.Qt.DisplayRole:
if section < self.columnCount():
return self.column_labels[section]
return super(HierarchyModel, self).headerData(
section, orientation, role
)
def flags(self, index):
"""Index flags are defined by corresponding item."""
item = index.internalPointer()
if item is None:
return QtCore.Qt.NoItemFlags
column = index.column()
key = self.columns[column]
return item.flags(key)
def parent(self, index=None):
"""Parent for passed index as QModelIndex.
Args:
index(QModelIndex): Parent index. Root item is used if not passed.
"""
if not index.isValid():
return QtCore.QModelIndex()
item = index.internalPointer()
parent_item = item.parent()
# If it has no parents we return invalid
if not parent_item or parent_item is self._root_item:
return QtCore.QModelIndex()
return self.createIndex(parent_item.row(), 0, parent_item)
def index(self, row, column, parent=None):
"""Return index for row/column under parent.
Args:
row(int): Row number.
column(int): Column number.
parent(QModelIndex): Parent index. Root item is used if not passed.
"""
parent_item = None
if parent is not None and parent.isValid():
parent_item = parent.internalPointer()
return self.index_from_item(row, column, parent_item)
def index_for_item(self, item, column=0):
"""Index for passed item.
This is for cases that index operations are required on specific item.
Args:
item(BaseItem): Item from model that will be converted to
corresponding QModelIndex.
column(int): Which column will be part of returned index. By
default is used column 0.
"""
return self.index_from_item(
item.row(), column, item.parent()
)
def index_from_item(self, row, column, parent=None):
"""Index for passed row, column and parent item.
Same implementation as `index` method but "parent" is one of
BaseItem objects instead of QModelIndex.
Args:
row(int): Row number.
column(int): Column number.
parent(BaseItem): Parent item. Root item is used if not passed.
"""
if parent is None:
parent = self._root_item
child_item = parent.child(row)
if child_item:
return self.createIndex(row, column, child_item)
return QtCore.QModelIndex()
def add_new_asset(self, source_index):
"""Create new asset item in hierarchy.
Args:
source_index(QModelIndex): Parent under which new asset will be
added.
"""
item_id = source_index.data(IDENTIFIER_ROLE)
item = self.items_by_id[item_id]
if isinstance(item, TaskItem):
item = item.parent()
if isinstance(item, (RootItem, ProjectItem)):
name = "ep"
new_row = None
elif isinstance(item, AssetItem):
name = None
new_row = item.rowCount()
else:
return
asset_data = {}
if name:
asset_data["name"] = name
new_child = AssetItem(asset_data)
result = self.add_item(new_child, item, new_row)
if result is not None:
# WARNING Expecting result is index for column 0 ("name")
new_name = result.data(QtCore.Qt.EditRole)
self._validate_asset_duplicity(new_name)
return result
def add_new_task(self, parent_index):
"""Create new TaskItem under passed parent index or it's parent.
Args:
parent_index(QModelIndex): Index of parent AssetItem under which
will be task added. If index represents TaskItem it's parent is
used as parent.
"""
item_id = parent_index.data(IDENTIFIER_ROLE)
item = self.items_by_id[item_id]
if isinstance(item, TaskItem):
parent = item.parent()
else:
parent = item
if not isinstance(parent, AssetItem):
return None
new_child = TaskItem()
return self.add_item(new_child, parent)
def add_items(self, items, parent=None, start_row=None):
"""Add new items with definition of QAbstractItemModel.
Trigger `beginInsertRows` and `endInsertRows` to trigger proper
callbacks in view or proxy model.
Args:
items(list[BaseItem]): List of item that will be inserted in model.
parent(RootItem, ProjectItem, AssetItem): Parent of items under
which will be items added. Root item is used if not passed.
start_row(int): Define to which row will be items added. Next
available row of parent is used if not passed.
"""
if parent is None:
parent = self._root_item
if parent.data(REMOVED_ROLE):
return []
if start_row is None:
start_row = parent.rowCount()
end_row = start_row + len(items) - 1
parent_index = self.index_from_item(parent.row(), 0, parent.parent())
self.beginInsertRows(parent_index, start_row, end_row)
for idx, item in enumerate(items):
row = start_row + idx
if item.parent() is not parent:
item.set_parent(parent)
parent.add_child(item, row)
if isinstance(item, AssetItem):
name = item.data(QtCore.Qt.EditRole, "name")
self._asset_items_by_name[name].add(item.id)
if item.id not in self._items_by_id:
self._items_by_id[item.id] = item
self.endInsertRows()
indexes = []
for row in range(start_row, end_row + 1):
indexes.append(
self.index_from_item(row, 0, parent)
)
return indexes
def add_item(self, item, parent=None, row=None):
"""Add single item into model."""
result = self.add_items([item], parent, row)
if result:
return result[0]
return None
def remove_delete_flag(self, item_ids, with_children=True):
"""Remove deletion flag from items with matching ids.
The flag is also removed from all parents of passed children as it
wouldn't make sense to not to do so.
Children of passed item ids are by default also unset for deletion.
Args:
list(uuid4): Ids of model items where remove flag should be unset.
with_children(bool): Unset remove flag also on all children of
passed items.
"""
items_by_id = {}
for item_id in item_ids:
if item_id in items_by_id:
continue
item = self.items_by_id[item_id]
if isinstance(item, (AssetItem, TaskItem)):
items_by_id[item_id] = item
for item in tuple(items_by_id.values()):
parent = item.parent()
while True:
if not isinstance(parent, (AssetItem, TaskItem)):
break
if parent.id not in items_by_id:
items_by_id[parent.id] = parent
parent = parent.parent()
if not with_children:
continue
def _children_recursion(_item):
if not isinstance(_item, AssetItem):
return
for row in range(_item.rowCount()):
_child_item = _item.child(row)
if _child_item.id in items_by_id:
continue
items_by_id[_child_item.id] = _child_item
_children_recursion(_child_item)
_children_recursion(item)
for item in items_by_id.values():
if item.data(REMOVED_ROLE):
item.setData(False, REMOVED_ROLE)
if isinstance(item, AssetItem):
name = item.data(QtCore.Qt.EditRole, "name")
self._asset_items_by_name[name].add(item.id)
self._validate_asset_duplicity(name)
def delete_index(self, index):
"""Delete item of the index from model."""
return self.delete_indexes([index])
def delete_indexes(self, indexes):
"""Delete items from model."""
items_by_id = {}
processed_ids = set()
for index in indexes:
if not index.isValid():
continue
item_id = index.data(IDENTIFIER_ROLE)
# There may be indexes for multiple columns
if item_id not in processed_ids:
processed_ids.add(item_id)
item = self._items_by_id[item_id]
if isinstance(item, (TaskItem, AssetItem)):
items_by_id[item_id] = item
if not items_by_id:
return
for item in items_by_id.values():
self._remove_item(item)
def _remove_item(self, item):
"""Remove item from model or mark item for deletion.
Deleted items are using definition of QAbstractItemModel which call
`beginRemoveRows` and `endRemoveRows` to trigger proper view and proxy
model callbacks.
Item is not just removed but is checked if can be removed from model or
just mark it for deletion for save.
First of all will find all related children and based on their
attributes define if can be removed.
"""
# Skip if item is already marked for deletion
is_removed = item.data(REMOVED_ROLE)
if is_removed:
return
parent = item.parent()
# Find all descendants and store them by parent id
all_descendants = collections.defaultdict(dict)
all_descendants[parent.id][item.id] = item
def _fill_children(_all_descendants, cur_item, parent_item=None):
if parent_item is not None:
_all_descendants[parent_item.id][cur_item.id] = cur_item
if isinstance(cur_item, TaskItem):
was_removed = cur_item.data(REMOVED_ROLE)
task_removed = True
if not was_removed and parent_item is not None:
task_removed = parent_item.data(REMOVED_ROLE)
if not was_removed:
cur_item.setData(task_removed, REMOVED_ROLE)
return task_removed
remove_item = True
task_children = []
for row in range(cur_item.rowCount()):
child_item = cur_item.child(row)
if isinstance(child_item, TaskItem):
task_children.append(child_item)
continue
if not _fill_children(_all_descendants, child_item, cur_item):
remove_item = False
if remove_item:
cur_item.setData(True, REMOVED_ROLE)
if isinstance(cur_item, AssetItem):
self._rename_asset(cur_item, None)
# Process tasks as last because their logic is based on parent
# - tasks may be processed before parent check all asset children
for task_item in task_children:
_fill_children(_all_descendants, task_item, cur_item)
return remove_item
_fill_children(all_descendants, item)
modified_children = []
while all_descendants:
for parent_id in tuple(all_descendants.keys()):
children = all_descendants[parent_id]
if not children:
all_descendants.pop(parent_id)
continue
parent_children = {}
all_without_children = True
for child_id in tuple(children.keys()):
if child_id in all_descendants:
all_without_children = False
break
parent_children[child_id] = children[child_id]
if not all_without_children:
continue
# Row ranges of items to remove
# - store tuples of row "start", "end" (can be the same)
row_ranges = []
# Predefine start, end variables
start_row = end_row = None
chilren_by_row = {}
parent_item = self._items_by_id[parent_id]
for row in range(parent_item.rowCount()):
child_item = parent_item.child(row)
child_id = child_item.id
# Not sure if this can happen
# TODO validate this line it seems dangerous as start/end
# row is not changed
if child_id not in children:
continue
chilren_by_row[row] = child_item
children.pop(child_item.id)
removed_mark = child_item.data(REMOVED_ROLE)
if not removed_mark or not child_item.is_new:
# Skip row sequence store child for later processing
# and store current start/end row range
modified_children.append(child_item)
if end_row is not None:
row_ranges.append((start_row, end_row))
start_row = end_row = None
continue
end_row = row
if start_row is None:
start_row = row
if end_row is not None:
row_ranges.append((start_row, end_row))
if not row_ranges:
continue
# Remove items from model
parent_index = self.index_for_item(parent_item)
for start, end in row_ranges:
self.beginRemoveRows(parent_index, start, end)
for idx in range(start, end + 1):
child_item = chilren_by_row[idx]
# Force name validation
if isinstance(child_item, AssetItem):
self._rename_asset(child_item, None)
child_item.set_parent(None)
self._items_by_id.pop(child_item.id)
self.endRemoveRows()
# Trigger data change to repaint items
# - `BackgroundRole` is random role without any specific reason
for item in modified_children:
s_index = self.index_for_item(item)
e_index = self.index_for_item(item, column=self.columns_len - 1)
self.dataChanged.emit(s_index, e_index, [QtCore.Qt.BackgroundRole])
def _rename_asset(self, asset_item, new_name):
if not isinstance(asset_item, AssetItem):
return
prev_name = asset_item.data(QtCore.Qt.EditRole, "name")
if prev_name == new_name:
return
if asset_item.id in self._asset_items_by_name[prev_name]:
self._asset_items_by_name[prev_name].remove(asset_item.id)
self._validate_asset_duplicity(prev_name)
if new_name is None:
return
self._asset_items_by_name[new_name].add(asset_item.id)
self._validate_asset_duplicity(new_name)
def _validate_asset_duplicity(self, name):
if name not in self._asset_items_by_name:
return
item_ids = self._asset_items_by_name[name]
if not item_ids:
self._asset_items_by_name.pop(name)
elif len(item_ids) == 1:
for item_id in item_ids:
item = self._items_by_id[item_id]
index = self.index_for_item(item)
self.setData(index, False, DUPLICATED_ROLE)
else:
for item_id in item_ids:
item = self._items_by_id[item_id]
index = self.index_for_item(item)
self.setData(index, True, DUPLICATED_ROLE)
def _move_horizontal_single(self, index, direction):
if not index.isValid():
return
item_id = index.data(IDENTIFIER_ROLE)
if item_id is None:
return
item = self._items_by_id[item_id]
if isinstance(item, (RootItem, ProjectItem)):
return
if item.data(REMOVED_ROLE):
return
if (
isinstance(item, AssetItem)
and not item.data(HIERARCHY_CHANGE_ABLE_ROLE)
):
return
if abs(direction) != 1:
return
# Move under parent of parent
src_row = item.row()
src_parent = item.parent()
src_parent_index = self.index_from_item(
src_parent.row(), 0, src_parent.parent()
)
dst_row = None
dst_parent = None
if direction == -1:
if isinstance(src_parent, (RootItem, ProjectItem)):
return
dst_parent = src_parent.parent()
dst_row = src_parent.row() + 1
# Move under parent before or after if before is None
elif direction == 1:
src_row_count = src_parent.rowCount()
if src_row_count == 1:
return
item_row = item.row()
dst_parent = None
for row in reversed(range(item_row)):
_item = src_parent.child(row)
if not isinstance(_item, AssetItem):
continue
if _item.data(REMOVED_ROLE):
continue
dst_parent = _item
break
_next_row = item_row + 1
if dst_parent is None and _next_row < src_row_count:
for row in range(_next_row, src_row_count):
_item = src_parent.child(row)
if not isinstance(_item, AssetItem):
continue
if _item.data(REMOVED_ROLE):
continue
dst_parent = _item
break
if dst_parent is None:
return
dst_row = dst_parent.rowCount()
if src_parent is dst_parent:
return
if (
isinstance(item, TaskItem)
and not isinstance(dst_parent, AssetItem)
):
return
dst_parent_index = self.index_from_item(
dst_parent.row(), 0, dst_parent.parent()
)
self.beginMoveRows(
src_parent_index,
src_row,
src_row,
dst_parent_index,
dst_row
)
src_parent.remove_child(item)
dst_parent.add_child(item)
item.set_parent(dst_parent)
dst_parent.move_to(item, dst_row)
self.endMoveRows()
new_index = self.index(dst_row, index.column(), dst_parent_index)
self.index_moved.emit(new_index)
def move_horizontal(self, indexes, direction):
if not indexes:
return
if isinstance(indexes, QtCore.QModelIndex):
indexes = [indexes]
if len(indexes) == 1:
self._move_horizontal_single(indexes[0], direction)
return
items_by_id = {}
for index in indexes:
item_id = index.data(IDENTIFIER_ROLE)
item = self._items_by_id[item_id]
if isinstance(item, (RootItem, ProjectItem)):
continue
if (
direction == -1
and isinstance(item.parent(), (RootItem, ProjectItem))
):
continue
items_by_id[item_id] = item
if not items_by_id:
return
parents_by_id = {}
items_ids_by_parent_id = collections.defaultdict(set)
skip_ids = set(items_by_id.keys())
for item_id, item in tuple(items_by_id.items()):
item_parent = item.parent()
parent_ids = set()
skip_item = False
parent = item_parent
while parent is not None:
if parent.id in skip_ids:
skip_item = True
skip_ids |= parent_ids
break
parent_ids.add(parent.id)
parent = parent.parent()
if skip_item:
items_by_id.pop(item_id)
else:
parents_by_id[item_parent.id] = item_parent
items_ids_by_parent_id[item_parent.id].add(item_id)
if direction == 1:
for parent_id, parent in parents_by_id.items():
items_ids = items_ids_by_parent_id[parent_id]
if len(items_ids) == parent.rowCount():
for item_id in items_ids:
items_by_id.pop(item_id)
items = tuple(items_by_id.values())
if direction == -1:
items = reversed(items)
for item in items:
index = self.index_for_item(item)
self._move_horizontal_single(index, direction)
def _move_vertical_single(self, index, direction):
if not index.isValid():
return
item_id = index.data(IDENTIFIER_ROLE)
item = self._items_by_id[item_id]
if isinstance(item, (RootItem, ProjectItem)):
return
if item.data(REMOVED_ROLE):
return
if (
isinstance(item, AssetItem)
and not item.data(HIERARCHY_CHANGE_ABLE_ROLE)
):
return
if abs(direction) != 1:
return
src_parent = item.parent()
if not isinstance(src_parent, AssetItem):
return
src_parent_index = self.index_from_item(
src_parent.row(), 0, src_parent.parent()
)
source_row = item.row()
parent_items = []
parent = src_parent
while True:
parent = parent.parent()
parent_items.insert(0, parent)
if isinstance(parent, ProjectItem):
break
dst_parent = None
# Down
if direction == 1:
current_idxs = []
current_max_idxs = []
for parent_item in parent_items:
current_max_idxs.append(parent_item.rowCount())
if not isinstance(parent_item, ProjectItem):
current_idxs.append(parent_item.row())
current_idxs.append(src_parent.row())
indexes_len = len(current_idxs)
while True:
def _update_parents(idx, top=True):
if idx < 0:
return False
if current_max_idxs[idx] == current_idxs[idx]:
if not _update_parents(idx - 1, False):
return False
parent = parent_items[idx]
row_count = 0
if parent is not None:
row_count = parent.rowCount()
current_max_idxs[idx] = row_count
current_idxs[idx] = 0
return True
if top:
return True
current_idxs[idx] += 1
parent_item = parent_items[idx]
new_item = parent_item.child(current_idxs[idx])
parent_items[idx + 1] = new_item
return True
updated = _update_parents(indexes_len - 1)
if not updated:
return
start = current_idxs[-1]
end = current_max_idxs[-1]
current_idxs[-1] = current_max_idxs[-1]
parent = parent_items[-1]
for row in range(start, end):
child_item = parent.child(row)
if (
child_item is src_parent
or child_item.data(REMOVED_ROLE)
or not isinstance(child_item, AssetItem)
):
continue
dst_parent = child_item
destination_row = 0
break
if dst_parent is not None:
break
# Up
elif direction == -1:
current_idxs = []
for parent_item in parent_items:
if not isinstance(parent_item, ProjectItem):
current_idxs.append(parent_item.row())
current_idxs.append(src_parent.row())
max_idxs = [0 for _ in current_idxs]
indexes_len = len(current_idxs)
while True:
if current_idxs == max_idxs:
return
def _update_parents(_current_idx, top=True):
if _current_idx < 0:
return False
if current_idxs[_current_idx] == 0:
if not _update_parents(_current_idx - 1, False):
return False
parent = parent_items[_current_idx]
row_count = 0
if parent is not None:
row_count = parent.rowCount()
current_idxs[_current_idx] = row_count
return True
if top:
return True
current_idxs[_current_idx] -= 1
parent_item = parent_items[_current_idx]
new_item = parent_item.child(current_idxs[_current_idx])
parent_items[_current_idx + 1] = new_item
return True
updated = _update_parents(indexes_len - 1)
if not updated:
return
parent_item = parent_items[-1]
row_count = current_idxs[-1]
current_idxs[-1] = 0
for row in reversed(range(row_count)):
child_item = parent_item.child(row)
if (
child_item is src_parent
or child_item.data(REMOVED_ROLE)
or not isinstance(child_item, AssetItem)
):
continue
dst_parent = child_item
destination_row = dst_parent.rowCount()
break
if dst_parent is not None:
break
if dst_parent is None:
return
dst_parent_index = self.index_from_item(
dst_parent.row(), 0, dst_parent.parent()
)
self.beginMoveRows(
src_parent_index,
source_row,
source_row,
dst_parent_index,
destination_row
)
if src_parent is dst_parent:
dst_parent.move_to(item, destination_row)
else:
src_parent.remove_child(item)
dst_parent.add_child(item)
item.set_parent(dst_parent)
dst_parent.move_to(item, destination_row)
self.endMoveRows()
new_index = self.index(
destination_row, index.column(), dst_parent_index
)
self.index_moved.emit(new_index)
def move_vertical(self, indexes, direction):
"""Move item vertically in model to matching parent if possible.
If passed indexes contain items that has parent<->child relation at any
hierarchy level only the top parent is actually moved.
Example (items marked with star are passed in `indexes`):
- shots*
- ep01
- ep01_sh0010*
- ep01_sh0020*
In this case only `shots` item will be moved vertically and
both "ep01_sh0010" "ep01_sh0020" will stay as children of "ep01".
Args:
indexes(list[QModelIndex]): Indexes that should be moved
vertically.
direction(int): Which way will be moved -1 or 1 to determine.
"""
if not indexes:
return
# Convert single index to list of indexes
if isinstance(indexes, QtCore.QModelIndex):
indexes = [indexes]
# Just process single index
if len(indexes) == 1:
self._move_vertical_single(indexes[0], direction)
return
items_by_id = {}
for index in indexes:
item_id = index.data(IDENTIFIER_ROLE)
items_by_id[item_id] = self._items_by_id[item_id]
skip_ids = set(items_by_id.keys())
for item_id, item in tuple(items_by_id.items()):
parent = item.parent()
parent_ids = set()
skip_item = False
while parent is not None:
if parent.id in skip_ids:
skip_item = True
skip_ids |= parent_ids
break
parent_ids.add(parent.id)
parent = parent.parent()
if skip_item:
items_by_id.pop(item_id)
items = tuple(items_by_id.values())
if direction == 1:
items = reversed(items)
for item in items:
index = self.index_for_item(item)
self._move_vertical_single(index, direction)
def child_removed(self, child):
"""Callback for removed child."""
self._items_by_id.pop(child.id, None)
def column_name(self, column):
"""Return column key by index"""
if column < len(self.columns):
return self.columns[column]
return None
def clear(self):
"""Reset model."""
self.beginResetModel()
self._reset_root_item()
self.endResetModel()
def save(self):
"""Save all changes from current project manager session.
Will create new asset documents, update existing and asset documents
marked for deletion are removed from mongo if has published content or
their type is changed to `archived_asset` to not loose their data.
"""
# Check if all items are valid before save
all_valid = True
for item in self._items_by_id.values():
if not item.is_valid:
all_valid = False
break
if not all_valid:
return
# Check project item and do not save without it
project_item = None
for _project_item in self._root_item.children():
project_item = _project_item
if not project_item:
return
project_name = project_item.name
project_col = self.dbcon.database[project_name]
# Process asset items per one hierarchical level.
# - new assets are inserted per one parent
# - update and delete data are stored and processed at once at the end
to_process = collections.deque()
to_process.append(project_item)
bulk_writes = []
while to_process:
parent = to_process.popleft()
insert_list = []
for item in parent.children():
if not isinstance(item, AssetItem):
continue
to_process.append(item)
if item.is_new:
insert_list.append(item)
elif item.data(REMOVED_ROLE):
if item.data(HIERARCHY_CHANGE_ABLE_ROLE):
bulk_writes.append(DeleteOne(
{"_id": item.asset_id}
))
else:
bulk_writes.append(UpdateOne(
{"_id": item.asset_id},
{"$set": {"type": "archived_asset"}}
))
else:
update_data = item.update_data()
if update_data:
bulk_writes.append(UpdateOne(
{"_id": item.asset_id},
update_data
))
if insert_list:
new_docs = []
for item in insert_list:
new_docs.append(item.to_doc())
result = project_col.insert_many(new_docs)
for idx, mongo_id in enumerate(result.inserted_ids):
insert_list[idx].mongo_id = mongo_id
if bulk_writes:
project_col.bulk_write(bulk_writes)
self.refresh_project()
def copy_mime_data(self, indexes):
items = []
processed_ids = set()
for index in indexes:
if not index.isValid():
continue
item_id = index.data(IDENTIFIER_ROLE)
if item_id in processed_ids:
continue
processed_ids.add(item_id)
item = self._items_by_id[item_id]
items.append(item)
parent_item = None
for item in items:
if not isinstance(item, TaskItem):
raise ValueError("Can copy only tasks")
if parent_item is None:
parent_item = item.parent()
elif item.parent() is not parent_item:
raise ValueError("Can copy only tasks from same parent")
data = []
for task_item in items:
data.append(task_item.to_json_data())
encoded_data = QtCore.QByteArray()
stream = QtCore.QDataStream(encoded_data, QtCore.QIODevice.WriteOnly)
stream.writeQString(json.dumps(data))
mimedata = QtCore.QMimeData()
mimedata.setData("application/copy_task", encoded_data)
return mimedata
def paste_mime_data(self, index, mime_data):
if not index.isValid():
return
item_id = index.data(IDENTIFIER_ROLE)
item = self._items_by_id[item_id]
if not isinstance(item, (AssetItem, TaskItem)):
return
raw_data = mime_data.data("application/copy_task")
if isinstance(raw_data, QtCore.QByteArray):
# Raw data are already QByteArrat and we don't have to load them
encoded_data = raw_data
else:
encoded_data = QtCore.QByteArray.fromRawData(raw_data)
stream = QtCore.QDataStream(encoded_data, QtCore.QIODevice.ReadOnly)
text = stream.readQString()
try:
data = json.loads(text)
except Exception:
data = []
if not data:
return
if isinstance(item, TaskItem):
parent = item.parent()
else:
parent = item
for task_item_data in data:
task_data = {}
for name, data in task_item_data.items():
task_data = data
task_data["name"] = name
task_item = TaskItem(task_data, True)
self.add_item(task_item, parent)
class BaseItem:
"""Base item for HierarchyModel.
Is not meant to be used as real item but as superclass for all items used
in HierarchyModel.
TODO cleanup some attributes and methods related only to AssetItem and
TaskItem.
"""
columns = []
# Use `set` for faster result
editable_columns = set()
_name_icons = None
_is_duplicated = False
item_type = "base"
_None = object()
def __init__(self, data=None):
self._id = uuid4()
self._children = list()
self._parent = None
self._data = {
key: None
for key in self.columns
}
self._global_data = {}
self._source_data = data
if data:
for key, value in data.items():
if key in self.columns:
self._data[key] = value
def name_icon(self):
"""Icon shown next to name.
Item must imlpement this method to change it.
"""
return None
@property
def is_valid(self):
return not self._is_duplicated
def model(self):
return self._parent.model()
def move_to(self, item, row):
idx = self._children.index(item)
if idx == row:
return
self._children.pop(idx)
self._children.insert(row, item)
def _get_global_data(self, role):
"""Global data getter without column specification."""
if role == ITEM_TYPE_ROLE:
return self.item_type
if role == IDENTIFIER_ROLE:
return self._id
if role == DUPLICATED_ROLE:
return self._is_duplicated
if role == REMOVED_ROLE:
return False
return self._global_data.get(role, self._None)
def _set_global_data(self, value, role):
self._global_data[role] = value
return True
def data(self, role, key=None):
value = self._get_global_data(role)
if value is not self._None:
return value
if key not in self.columns:
return None
if role == QtCore.Qt.ForegroundRole:
if key == "name" and not self.is_valid:
return ResourceCache.colors["warning"]
return None
if role in (QtCore.Qt.DisplayRole, QtCore.Qt.EditRole):
value = self._data[key]
if value is None:
value = self.parent().data(role, key)
return value
if role == QtCore.Qt.DecorationRole and key == "name":
return self.name_icon()
return None
def setData(self, value, role, key=None):
if role == DUPLICATED_ROLE:
if value == self._is_duplicated:
return False
self._is_duplicated = value
return True
if role == QtCore.Qt.EditRole:
if key in self.editable_columns:
self._data[key] = value
# must return true if successful
return True
return self._set_global_data(value, role)
@property
def id(self):
return self._id
@property
def is_new(self):
return False
def rowCount(self):
return len(self._children)
def child(self, row):
if -1 < row < self.rowCount():
return self._children[row]
return None
def children(self):
return self._children
def child_row(self, child):
if child not in self._children:
return -1
return self._children.index(child)
def parent(self):
return self._parent
def set_parent(self, parent):
if parent is self._parent:
return
if self._parent:
self._parent.remove_child(self)
self._parent = parent
def row(self):
if self._parent is not None:
return self._parent.child_row(self)
return -1
def add_child(self, item, row=None):
if item in self._children:
return
row_count = self.rowCount()
if row is None or row == row_count:
self._children.append(item)
return
if row > row_count or row < 0:
raise ValueError(
"Invalid row number {} expected range 0 - {}".format(
row, row_count
)
)
self._children.insert(row, item)
def remove_child(self, item):
if item in self._children:
self._children.remove(item)
def flags(self, key):
flags = QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable
if key in self.editable_columns:
flags |= QtCore.Qt.ItemIsEditable
return flags
class RootItem(BaseItem):
"""Invisible root item used as base item for model."""
item_type = "root"
def __init__(self, model):
super(RootItem, self).__init__()
self._model = model
def model(self):
return self._model
def flags(self, *args, **kwargs):
return QtCore.Qt.NoItemFlags
class ProjectItem(BaseItem):
"""Item representing project document in Mongo.
Item is used only to read it's data. It is not possible to modify them.
"""
item_type = "project"
columns = {
"name",
"type",
"frameStart",
"frameEnd",
"fps",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env",
}
query_projection = {
"_id": 1,
"name": 1,
"type": 1,
"data.frameStart": 1,
"data.frameEnd": 1,
"data.fps": 1,
"data.resolutionWidth": 1,
"data.resolutionHeight": 1,
"data.handleStart": 1,
"data.handleEnd": 1,
"data.clipIn": 1,
"data.clipOut": 1,
"data.pixelAspect": 1,
"data.tools_env": 1
}
def __init__(self, project_doc):
self._mongo_id = project_doc["_id"]
data = self.data_from_doc(project_doc)
super(ProjectItem, self).__init__(data)
@property
def project_id(self):
"""Project Mongo ID."""
return self._mongo_id
@property
def asset_id(self):
"""Should not be implemented.
TODO Remove this method from ProjectItem.
"""
return None
@property
def name(self):
"""Project name"""
return self._data["name"]
def child_parents(self):
"""Used by children AssetItems for filling `data.parents` key."""
return []
@classmethod
def data_from_doc(cls, project_doc):
"""Convert document data into item data.
Project data are used as default value for it's children.
"""
data = {
"name": project_doc["name"],
"type": project_doc["type"]
}
doc_data = project_doc.get("data") or {}
for key in cls.columns:
if key in data:
continue
data[key] = doc_data.get(key)
return data
def flags(self, *args, **kwargs):
"""Project is enabled and selectable."""
return QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable
class AssetItem(BaseItem):
"""Item represent asset document.
Item have ability to set all required and optional data for OpenPype
workflow. Some of them are not modifiable in specific cases e.g. when asset
has published content it is not possible to change it's name or parent.
"""
item_type = "asset"
columns = {
"name",
"type",
"fps",
"frameStart",
"frameEnd",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env"
}
editable_columns = {
"name",
"frameStart",
"frameEnd",
"fps",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env"
}
query_projection = {
"_id": 1,
"data.tasks": 1,
"data.visualParent": 1,
"schema": 1,
"name": 1,
"type": 1,
"data.frameStart": 1,
"data.frameEnd": 1,
"data.fps": 1,
"data.resolutionWidth": 1,
"data.resolutionHeight": 1,
"data.handleStart": 1,
"data.handleEnd": 1,
"data.clipIn": 1,
"data.clipOut": 1,
"data.pixelAspect": 1,
"data.tools_env": 1
}
def __init__(self, asset_doc):
if not asset_doc:
asset_doc = {}
self.mongo_id = asset_doc.get("_id")
self._project_id = None
self._edited_columns = {
column_name: False
for column_name in self.editable_columns
}
# Item data
self._hierarchy_changes_enabled = True
self._removed = False
# Task children duplication variables
self._task_items_by_name = collections.defaultdict(list)
self._task_name_by_item_id = {}
self._duplicated_task_names = set()
# Copy of original document
self._origin_asset_doc = copy.deepcopy(asset_doc)
data = self.data_from_doc(asset_doc)
self._origin_data = copy.deepcopy(data)
super(AssetItem, self).__init__(data)
@property
def project_id(self):
"""Access to project "parent" id which is always set."""
if self._project_id is None:
self._project_id = self.parent().project_id
return self._project_id
@property
def asset_id(self):
"""Property access to mongo id."""
return self.mongo_id
@property
def is_new(self):
"""Item was created during current project manager session."""
return self.asset_id is None
@property
def is_valid(self):
"""Item is invalid for saving."""
if self._is_duplicated or not self._data["name"]:
return False
return True
@property
def name(self):
"""Asset name.
Returns:
str: If name is set.
None: If name is not yet set in that case is AssetItem marked as
invalid.
"""
return self._data["name"]
def child_parents(self):
"""Children AssetItem can use this method to get it's parent names.
This is used for `data.parents` key on document.
"""
parents = self.parent().child_parents()
parents.append(self.name)
return parents
def to_doc(self):
"""Convert item to Mongo document matching asset schema.
Method does no validate if item is valid or children are valid.
Returns:
dict: Document with all related data about asset item also
contains task children.
"""
tasks = {}
for item in self.children():
if isinstance(item, TaskItem):
tasks.update(item.to_doc_data())
doc_data = {
"parents": self.parent().child_parents(),
"visualParent": self.parent().asset_id,
"tasks": tasks
}
schema_name = (
self._origin_asset_doc.get("schema")
or CURRENT_DOC_SCHEMAS["asset"]
)
doc = {
"name": self.data(QtCore.Qt.EditRole, "name"),
"type": self.data(QtCore.Qt.EditRole, "type"),
"schema": schema_name,
"data": doc_data,
"parent": self.project_id
}
if self.mongo_id:
doc["_id"] = self.mongo_id
for key in self._data.keys():
if key in doc:
continue
# Use `data` method to get inherited values
doc_data[key] = self.data(QtCore.Qt.EditRole, key)
return doc
def update_data(self):
"""Changes dictionary ready for Mongo's update.
Method should be used on save. There is not other usage of this method.
# Example
```python
{
"$set": {
"name": "new_name"
}
}
```
Returns:
dict: May be empty if item was not changed.
"""
if not self.mongo_id:
return {}
document = self.to_doc()
changes = {}
for key, value in document.items():
if key in ("data", "_id"):
continue
if (
key in self._origin_asset_doc
and self._origin_asset_doc[key] == value
):
continue
changes[key] = value
if "data" not in self._origin_asset_doc:
changes["data"] = document["data"]
else:
origin_data = self._origin_asset_doc["data"]
for key, value in document["data"].items():
if key in origin_data and origin_data[key] == value:
continue
_key = "data.{}".format(key)
changes[_key] = value
if changes:
return {"$set": changes}
return {}
@classmethod
def data_from_doc(cls, asset_doc):
"""Convert asset document from Mongo to item data."""
# Minimum required data for cases that it is new AssetItem without doc
data = {
"name": None,
"type": "asset"
}
if asset_doc:
for key in data.keys():
if key in asset_doc:
data[key] = asset_doc[key]
doc_data = asset_doc.get("data") or {}
for key in cls.columns:
if key in data:
continue
data[key] = doc_data.get(key)
return data
def name_icon(self):
"""Icon shown next to name."""
if self.__class__._name_icons is None:
self.__class__._name_icons = ResourceCache.get_icons()["asset"]
if self._removed:
icon_type = "removed"
elif not self.is_valid:
icon_type = "invalid"
elif self.is_new:
icon_type = "new"
else:
icon_type = "default"
return self.__class__._name_icons[icon_type]
def _get_global_data(self, role):
"""Global data getter without column specification."""
if role == HIERARCHY_CHANGE_ABLE_ROLE:
return self._hierarchy_changes_enabled
if role == REMOVED_ROLE:
return self._removed
if role == QtCore.Qt.ToolTipRole:
name = self.data(QtCore.Qt.EditRole, "name")
if not name:
return "Name is not set"
elif self._is_duplicated:
return "Duplicated asset name \"{}\"".format(name)
return None
return super(AssetItem, self)._get_global_data(role)
def data(self, role, key=None):
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
return self._edited_columns[key]
if role == QtCore.Qt.DisplayRole and self._edited_columns.get(key):
return ""
return super(AssetItem, self).data(role, key)
def setData(self, value, role, key=None):
# Store information that column has opened editor
# - DisplayRole for the column will return empty string
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
self._edited_columns[key] = value
return True
if role == REMOVED_ROLE:
self._removed = value
return True
# This can be set only on project load (or save)
if role == HIERARCHY_CHANGE_ABLE_ROLE:
if self._hierarchy_changes_enabled == value:
return False
self._hierarchy_changes_enabled = value
return True
# Do not allow to change name if item is marked to not be able do any
# hierarchical changes.
if (
role == QtCore.Qt.EditRole
and key == "name"
and not self._hierarchy_changes_enabled
):
return False
return super(AssetItem, self).setData(value, role, key)
def flags(self, key):
if key == "name":
flags = QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable
if self._hierarchy_changes_enabled:
flags |= QtCore.Qt.ItemIsEditable
return flags
return super(AssetItem, self).flags(key)
def _add_task(self, item):
name = item.data(QtCore.Qt.EditRole, "name").lower()
item_id = item.data(IDENTIFIER_ROLE)
self._task_name_by_item_id[item_id] = name
self._task_items_by_name[name].append(item)
if len(self._task_items_by_name[name]) > 1:
self._duplicated_task_names.add(name)
for _item in self._task_items_by_name[name]:
_item.setData(True, DUPLICATED_ROLE)
elif item.data(DUPLICATED_ROLE):
item.setData(False, DUPLICATED_ROLE)
def _remove_task(self, item):
# This method is probably obsolete with changed logic and added
# `on_task_remove_state_change` method.
item_id = item.data(IDENTIFIER_ROLE)
if item_id not in self._task_name_by_item_id:
return
name = self._task_name_by_item_id.pop(item_id)
self._task_items_by_name[name].remove(item)
if not self._task_items_by_name[name]:
self._task_items_by_name.pop(name)
elif len(self._task_items_by_name[name]) == 1:
self._duplicated_task_names.remove(name)
for _item in self._task_items_by_name[name]:
_item.setData(False, DUPLICATED_ROLE)
def _rename_task(self, item):
# Skip processing if item is marked for removing
# - item is not in any of attributes below
if item.data(REMOVED_ROLE):
return
new_name = item.data(QtCore.Qt.EditRole, "name").lower()
item_id = item.data(IDENTIFIER_ROLE)
prev_name = self._task_name_by_item_id[item_id]
if new_name == prev_name:
return
# Remove from previous name mapping
self._task_items_by_name[prev_name].remove(item)
if not self._task_items_by_name[prev_name]:
self._task_items_by_name.pop(prev_name)
elif len(self._task_items_by_name[prev_name]) == 1:
self._duplicated_task_names.remove(prev_name)
for _item in self._task_items_by_name[prev_name]:
_item.setData(False, DUPLICATED_ROLE)
# Add to new name mapping
self._task_items_by_name[new_name].append(item)
if len(self._task_items_by_name[new_name]) > 1:
self._duplicated_task_names.add(new_name)
for _item in self._task_items_by_name[new_name]:
_item.setData(True, DUPLICATED_ROLE)
else:
item.setData(False, DUPLICATED_ROLE)
self._task_name_by_item_id[item_id] = new_name
def on_task_name_change(self, task_item):
"""Method called from TaskItem children on name change.
Helps to handle duplicated task name validations.
"""
self._rename_task(task_item)
def on_task_remove_state_change(self, task_item):
"""Method called from children TaskItem to handle name duplications.
Method is called when TaskItem children is marked for deletion or
deletion was reversed.
Name is removed/added to task item mapping attribute and removed/added
to `_task_items_by_name` used for determination of duplicated tasks.
"""
is_removed = task_item.data(REMOVED_ROLE)
item_id = task_item.data(IDENTIFIER_ROLE)
if is_removed:
name = self._task_name_by_item_id.pop(item_id)
self._task_items_by_name[name].remove(task_item)
else:
name = task_item.data(QtCore.Qt.EditRole, "name").lower()
self._task_name_by_item_id[item_id] = name
self._task_items_by_name[name].append(task_item)
# Remove from previous name mapping
if not self._task_items_by_name[name]:
self._task_items_by_name.pop(name)
elif len(self._task_items_by_name[name]) == 1:
if name in self._duplicated_task_names:
self._duplicated_task_names.remove(name)
task_item.setData(False, DUPLICATED_ROLE)
else:
self._duplicated_task_names.add(name)
for _item in self._task_items_by_name[name]:
_item.setData(True, DUPLICATED_ROLE)
def add_child(self, item, row=None):
"""Add new children.
Args:
item(AssetItem, TaskItem): New added item.
row(int): Optionally can be passed on which row (index) should be
children added.
"""
if item in self._children:
return
super(AssetItem, self).add_child(item, row)
# Call inner method for checking task name duplications
if isinstance(item, TaskItem):
self._add_task(item)
def remove_child(self, item):
"""Remove one of children from AssetItem children.
Skipped if item is not children of item.
Args:
item(AssetItem, TaskItem): Child item.
"""
if item not in self._children:
return
# Call inner method to remove task from registered task name
# validations.
if isinstance(item, TaskItem):
self._remove_task(item)
super(AssetItem, self).remove_child(item)
class TaskItem(BaseItem):
"""Item representing Task item on Asset document.
Always should be AssetItem children and never should have any other
children.
It's name value should be validated with it's parent which only knows if
has same name as other sibling under same parent.
"""
# String representation of item
item_type = "task"
columns = {
"name",
"type"
}
editable_columns = {
"name",
"type"
}
def __init__(self, data=None, is_new=None):
self._removed = False
if is_new is None:
is_new = data is None
self._is_new = is_new
if data is None:
data = {}
self._edited_columns = {
column_name: False
for column_name in self.editable_columns
}
self._origin_data = copy.deepcopy(data)
super(TaskItem, self).__init__(data)
@property
def is_new(self):
"""Task was created during current project manager session."""
return self._is_new
@property
def is_valid(self):
"""Task valid for saving."""
if self._is_duplicated or not self._data["type"]:
return False
if not self.data(QtCore.Qt.EditRole, "name"):
return False
return True
def name_icon(self):
"""Icon shown next to name."""
if self.__class__._name_icons is None:
self.__class__._name_icons = ResourceCache.get_icons()["task"]
if self._removed:
icon_type = "removed"
elif not self.is_valid:
icon_type = "invalid"
elif self.is_new:
icon_type = "new"
else:
icon_type = "default"
return self.__class__._name_icons[icon_type]
def add_child(self, item, row=None):
"""Reimplement `add_child` to avoid adding items under task."""
raise AssertionError("BUG: Can't add children to Task")
def _get_global_data(self, role):
"""Global data getter without column specification."""
if role == REMOVED_ROLE:
return self._removed
if role == QtCore.Qt.ToolTipRole:
if not self._data["type"]:
return "Type is not set"
name = self.data(QtCore.Qt.EditRole, "name")
if not name:
return "Name is not set"
elif self._is_duplicated:
return "Duplicated task name \"{}".format(name)
return None
return super(TaskItem, self)._get_global_data(role)
def to_doc_data(self):
"""Data for Asset document.
Returns:
dict: May be empty if task is marked as removed or with single key
dict with name as key and task data as value.
"""
if self._removed:
return {}
data = copy.deepcopy(self._data)
data.pop("name")
name = self.data(QtCore.Qt.EditRole, "name")
return {
name: data
}
def data(self, role, key=None):
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
return self._edited_columns[key]
# Return empty string if column is edited
if role == QtCore.Qt.DisplayRole and self._edited_columns.get(key):
return ""
if role in (QtCore.Qt.DisplayRole, QtCore.Qt.EditRole):
if key == "type":
return self._data["type"]
# Always require task type filled
if key == "name":
if not self._data["type"]:
if role == QtCore.Qt.DisplayRole:
return "< Select Type >"
if role == QtCore.Qt.EditRole:
return ""
else:
return self._data[key] or self._data["type"]
return super(TaskItem, self).data(role, key)
def setData(self, value, role, key=None):
# Store information that item on a column is edited
# - DisplayRole will return empty string in that case
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
self._edited_columns[key] = value
return True
if role == REMOVED_ROLE:
# Skip value change if is same as already set value
if value == self._removed:
return False
self._removed = value
self.parent().on_task_remove_state_change(self)
return True
# Convert empty string to None on EditRole
if (
role == QtCore.Qt.EditRole
and key == "name"
and not value
):
value = None
result = super(TaskItem, self).setData(value, role, key)
if role == QtCore.Qt.EditRole:
# Trigger task name change of parent AssetItem
if (
key == "name"
or (key == "type" and not self._data["name"])
):
self.parent().on_task_name_change(self)
return result
def to_json_data(self):
"""Convert json data without parent reference.
Method used for mime data on copy/paste
"""
return self.to_doc_data()
| 31.497527
| 79
| 0.561776
|
import collections
import copy
import json
from uuid import uuid4
from pymongo import UpdateOne, DeleteOne
from Qt import QtCore, QtGui
from .constants import (
IDENTIFIER_ROLE,
ITEM_TYPE_ROLE,
DUPLICATED_ROLE,
HIERARCHY_CHANGE_ABLE_ROLE,
REMOVED_ROLE,
EDITOR_OPENED_ROLE,
PROJECT_NAME_ROLE
)
from .style import ResourceCache
from openpype.lib import CURRENT_DOC_SCHEMAS
class ProjectModel(QtGui.QStandardItemModel):
def __init__(self, dbcon, *args, **kwargs):
self.dbcon = dbcon
self._items_by_name = {}
super(ProjectModel, self).__init__(*args, **kwargs)
def refresh(self):
self.dbcon.Session["AVALON_PROJECT"] = None
new_project_items = []
if None not in self._items_by_name:
none_project = QtGui.QStandardItem("< Select Project >")
self._items_by_name[None] = none_project
new_project_items.append(none_project)
project_docs = self.dbcon.projects(
projection={"name": 1},
only_active=True
)
project_names = set()
for project_doc in project_docs:
project_name = project_doc.get("name")
if not project_name:
continue
project_names.add(project_name)
if project_name not in self._items_by_name:
project_item = QtGui.QStandardItem(project_name)
project_item.setData(project_name, PROJECT_NAME_ROLE)
self._items_by_name[project_name] = project_item
new_project_items.append(project_item)
root_item = self.invisibleRootItem()
for project_name in tuple(self._items_by_name.keys()):
if project_name is None or project_name in project_names:
continue
project_item = self._items_by_name.pop(project_name)
root_item.removeRow(project_item.row())
if new_project_items:
root_item.appendRows(new_project_items)
class ProjectProxyFilter(QtCore.QSortFilterProxyModel):
def __init__(self, *args, **kwargs):
super(ProjectProxyFilter, self).__init__(*args, **kwargs)
self._filter_default = False
def set_filter_default(self, enabled=True):
if enabled == self._filter_default:
return
self._filter_default = enabled
self.invalidateFilter()
def filterAcceptsRow(self, row, parent):
if not self._filter_default:
return True
model = self.sourceModel()
source_index = model.index(row, self.filterKeyColumn(), parent)
return source_index.data(PROJECT_NAME_ROLE) is not None
class HierarchySelectionModel(QtCore.QItemSelectionModel):
def __init__(self, multiselection_columns, *args, **kwargs):
super(HierarchySelectionModel, self).__init__(*args, **kwargs)
self.multiselection_columns = multiselection_columns
def setCurrentIndex(self, index, command):
if index.column() in self.multiselection_columns:
if (
command & QtCore.QItemSelectionModel.Clear
and command & QtCore.QItemSelectionModel.Select
):
command = QtCore.QItemSelectionModel.NoUpdate
super(HierarchySelectionModel, self).setCurrentIndex(index, command)
class HierarchyModel(QtCore.QAbstractItemModel):
_columns_def = [
("name", "Name"),
("type", "Type"),
("fps", "FPS"),
("frameStart", "Frame start"),
("frameEnd", "Frame end"),
("handleStart", "Handle start"),
("handleEnd", "Handle end"),
("resolutionWidth", "Width"),
("resolutionHeight", "Height"),
("clipIn", "Clip in"),
("clipOut", "Clip out"),
("pixelAspect", "Pixel aspect"),
("tools_env", "Tools")
]
multiselection_columns = {
"frameStart",
"frameEnd",
"fps",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env"
}
columns = [
item[0]
for item in _columns_def
]
columns_len = len(columns)
column_labels = {
idx: item[1]
for idx, item in enumerate(_columns_def)
}
index_moved = QtCore.Signal(QtCore.QModelIndex)
project_changed = QtCore.Signal()
def __init__(self, dbcon, parent=None):
super(HierarchyModel, self).__init__(parent)
self.multiselection_column_indexes = {
self.columns.index(key)
for key in self.multiselection_columns
}
self._current_project = None
self._root_item = None
self._items_by_id = {}
self._asset_items_by_name = collections.defaultdict(set)
self.dbcon = dbcon
self._reset_root_item()
@property
def items_by_id(self):
return self._items_by_id
def _reset_root_item(self):
self._root_item = RootItem(self)
def refresh_project(self):
self.set_project(self._current_project, True)
@property
def project_item(self):
output = None
for row in range(self._root_item.rowCount()):
item = self._root_item.child(row)
if isinstance(item, ProjectItem):
output = item
break
return output
def set_project(self, project_name, force=False):
if self._current_project == project_name and not force:
return
self._items_by_id.clear()
self._asset_items_by_name.clear()
self.clear()
self._current_project = project_name
if not project_name:
return
project_doc = self.dbcon.database[project_name].find_one(
{"type": "project"},
ProjectItem.query_projection
)
# Skip if project document does not exist
# - this shouldn't happen using only UI elements
if not project_doc:
return
project_item = ProjectItem(project_doc)
self.add_item(project_item)
asset_docs = self.dbcon.database[project_name].find(
{"type": "asset"},
AssetItem.query_projection
)
asset_docs_by_id = {
asset_doc["_id"]: asset_doc
for asset_doc in asset_docs
}
asset_ids = list(asset_docs_by_id.keys())
result = []
if asset_ids:
result = self.dbcon.database[project_name].aggregate([
{
"$match": {
"type": "subset",
"parent": {"$in": asset_ids}
}
},
{
"$group": {
"_id": "$parent",
"count": {"$sum": 1}
}
}
])
asset_modifiable = {
asset_id: True
for asset_id in asset_docs_by_id.keys()
}
for item in result:
asset_id = item["_id"]
count = item["count"]
asset_modifiable[asset_id] = count < 1
# Store assets by their visual parent to be able create their hierarchy
asset_docs_by_parent_id = collections.defaultdict(list)
for asset_doc in asset_docs_by_id.values():
parent_id = asset_doc["data"].get("visualParent")
asset_docs_by_parent_id[parent_id].append(asset_doc)
appending_queue = collections.deque()
appending_queue.append((None, project_item))
asset_items_by_id = {}
non_modifiable_items = set()
while appending_queue:
parent_id, parent_item = appending_queue.popleft()
asset_docs = asset_docs_by_parent_id.get(parent_id) or []
new_items = []
for asset_doc in sorted(asset_docs, key=lambda item: item["name"]):
# Create new Item
new_item = AssetItem(asset_doc)
# Store item to be added under parent in bulk
new_items.append(new_item)
# Store item by id for task processing
asset_id = asset_doc["_id"]
if not asset_modifiable[asset_id]:
non_modifiable_items.add(new_item.id)
asset_items_by_id[asset_id] = new_item
# Add item to appending queue
appending_queue.append((asset_id, new_item))
if new_items:
self.add_items(new_items, parent_item)
# Handle Asset's that are not modifiable
non_modifiable_queue = collections.deque()
for item_id in non_modifiable_items:
non_modifiable_queue.append(item_id)
while non_modifiable_queue:
item_id = non_modifiable_queue.popleft()
item = self._items_by_id[item_id]
item.setData(False, HIERARCHY_CHANGE_ABLE_ROLE)
parent = item.parent()
if (
isinstance(parent, AssetItem)
and parent.id not in non_modifiable_items
):
non_modifiable_items.add(parent.id)
non_modifiable_queue.append(parent.id)
# Add task items
for asset_id, asset_item in asset_items_by_id.items():
asset_doc = asset_docs_by_id[asset_id]
asset_tasks = asset_doc["data"]["tasks"]
if not asset_tasks:
continue
task_items = []
for task_name in sorted(asset_tasks.keys()):
_task_data = copy.deepcopy(asset_tasks[task_name])
_task_data["name"] = task_name
task_item = TaskItem(_task_data)
task_items.append(task_item)
self.add_items(task_items, asset_item)
# Emit that project was successfully changed
self.project_changed.emit()
def rowCount(self, parent=None):
if parent is None or not parent.isValid():
parent_item = self._root_item
else:
parent_item = parent.internalPointer()
return parent_item.rowCount()
def columnCount(self, *args, **kwargs):
return self.columns_len
def data(self, index, role):
if not index.isValid():
return None
column = index.column()
key = self.columns[column]
item = index.internalPointer()
return item.data(role, key)
def setData(self, index, value, role=QtCore.Qt.EditRole):
if not index.isValid():
return False
item = index.internalPointer()
column = index.column()
key = self.columns[column]
# Capture asset name changes for duplcated asset names validation.
if (
key == "name"
and role in (QtCore.Qt.EditRole, QtCore.Qt.DisplayRole)
):
self._rename_asset(item, value)
# Pass values to item and by result emi dataChanged signal or not
result = item.setData(value, role, key)
if result:
self.dataChanged.emit(index, index, [role])
return result
def headerData(self, section, orientation, role):
if role == QtCore.Qt.DisplayRole:
if section < self.columnCount():
return self.column_labels[section]
return super(HierarchyModel, self).headerData(
section, orientation, role
)
def flags(self, index):
item = index.internalPointer()
if item is None:
return QtCore.Qt.NoItemFlags
column = index.column()
key = self.columns[column]
return item.flags(key)
def parent(self, index=None):
if not index.isValid():
return QtCore.QModelIndex()
item = index.internalPointer()
parent_item = item.parent()
# If it has no parents we return invalid
if not parent_item or parent_item is self._root_item:
return QtCore.QModelIndex()
return self.createIndex(parent_item.row(), 0, parent_item)
def index(self, row, column, parent=None):
parent_item = None
if parent is not None and parent.isValid():
parent_item = parent.internalPointer()
return self.index_from_item(row, column, parent_item)
def index_for_item(self, item, column=0):
return self.index_from_item(
item.row(), column, item.parent()
)
def index_from_item(self, row, column, parent=None):
if parent is None:
parent = self._root_item
child_item = parent.child(row)
if child_item:
return self.createIndex(row, column, child_item)
return QtCore.QModelIndex()
def add_new_asset(self, source_index):
item_id = source_index.data(IDENTIFIER_ROLE)
item = self.items_by_id[item_id]
if isinstance(item, TaskItem):
item = item.parent()
if isinstance(item, (RootItem, ProjectItem)):
name = "ep"
new_row = None
elif isinstance(item, AssetItem):
name = None
new_row = item.rowCount()
else:
return
asset_data = {}
if name:
asset_data["name"] = name
new_child = AssetItem(asset_data)
result = self.add_item(new_child, item, new_row)
if result is not None:
# WARNING Expecting result is index for column 0 ("name")
new_name = result.data(QtCore.Qt.EditRole)
self._validate_asset_duplicity(new_name)
return result
def add_new_task(self, parent_index):
item_id = parent_index.data(IDENTIFIER_ROLE)
item = self.items_by_id[item_id]
if isinstance(item, TaskItem):
parent = item.parent()
else:
parent = item
if not isinstance(parent, AssetItem):
return None
new_child = TaskItem()
return self.add_item(new_child, parent)
def add_items(self, items, parent=None, start_row=None):
if parent is None:
parent = self._root_item
if parent.data(REMOVED_ROLE):
return []
if start_row is None:
start_row = parent.rowCount()
end_row = start_row + len(items) - 1
parent_index = self.index_from_item(parent.row(), 0, parent.parent())
self.beginInsertRows(parent_index, start_row, end_row)
for idx, item in enumerate(items):
row = start_row + idx
if item.parent() is not parent:
item.set_parent(parent)
parent.add_child(item, row)
if isinstance(item, AssetItem):
name = item.data(QtCore.Qt.EditRole, "name")
self._asset_items_by_name[name].add(item.id)
if item.id not in self._items_by_id:
self._items_by_id[item.id] = item
self.endInsertRows()
indexes = []
for row in range(start_row, end_row + 1):
indexes.append(
self.index_from_item(row, 0, parent)
)
return indexes
def add_item(self, item, parent=None, row=None):
result = self.add_items([item], parent, row)
if result:
return result[0]
return None
def remove_delete_flag(self, item_ids, with_children=True):
items_by_id = {}
for item_id in item_ids:
if item_id in items_by_id:
continue
item = self.items_by_id[item_id]
if isinstance(item, (AssetItem, TaskItem)):
items_by_id[item_id] = item
for item in tuple(items_by_id.values()):
parent = item.parent()
while True:
if not isinstance(parent, (AssetItem, TaskItem)):
break
if parent.id not in items_by_id:
items_by_id[parent.id] = parent
parent = parent.parent()
if not with_children:
continue
def _children_recursion(_item):
if not isinstance(_item, AssetItem):
return
for row in range(_item.rowCount()):
_child_item = _item.child(row)
if _child_item.id in items_by_id:
continue
items_by_id[_child_item.id] = _child_item
_children_recursion(_child_item)
_children_recursion(item)
for item in items_by_id.values():
if item.data(REMOVED_ROLE):
item.setData(False, REMOVED_ROLE)
if isinstance(item, AssetItem):
name = item.data(QtCore.Qt.EditRole, "name")
self._asset_items_by_name[name].add(item.id)
self._validate_asset_duplicity(name)
def delete_index(self, index):
return self.delete_indexes([index])
def delete_indexes(self, indexes):
items_by_id = {}
processed_ids = set()
for index in indexes:
if not index.isValid():
continue
item_id = index.data(IDENTIFIER_ROLE)
# There may be indexes for multiple columns
if item_id not in processed_ids:
processed_ids.add(item_id)
item = self._items_by_id[item_id]
if isinstance(item, (TaskItem, AssetItem)):
items_by_id[item_id] = item
if not items_by_id:
return
for item in items_by_id.values():
self._remove_item(item)
def _remove_item(self, item):
# Skip if item is already marked for deletion
is_removed = item.data(REMOVED_ROLE)
if is_removed:
return
parent = item.parent()
# Find all descendants and store them by parent id
all_descendants = collections.defaultdict(dict)
all_descendants[parent.id][item.id] = item
def _fill_children(_all_descendants, cur_item, parent_item=None):
if parent_item is not None:
_all_descendants[parent_item.id][cur_item.id] = cur_item
if isinstance(cur_item, TaskItem):
was_removed = cur_item.data(REMOVED_ROLE)
task_removed = True
if not was_removed and parent_item is not None:
task_removed = parent_item.data(REMOVED_ROLE)
if not was_removed:
cur_item.setData(task_removed, REMOVED_ROLE)
return task_removed
remove_item = True
task_children = []
for row in range(cur_item.rowCount()):
child_item = cur_item.child(row)
if isinstance(child_item, TaskItem):
task_children.append(child_item)
continue
if not _fill_children(_all_descendants, child_item, cur_item):
remove_item = False
if remove_item:
cur_item.setData(True, REMOVED_ROLE)
if isinstance(cur_item, AssetItem):
self._rename_asset(cur_item, None)
# Process tasks as last because their logic is based on parent
# - tasks may be processed before parent check all asset children
for task_item in task_children:
_fill_children(_all_descendants, task_item, cur_item)
return remove_item
_fill_children(all_descendants, item)
modified_children = []
while all_descendants:
for parent_id in tuple(all_descendants.keys()):
children = all_descendants[parent_id]
if not children:
all_descendants.pop(parent_id)
continue
parent_children = {}
all_without_children = True
for child_id in tuple(children.keys()):
if child_id in all_descendants:
all_without_children = False
break
parent_children[child_id] = children[child_id]
if not all_without_children:
continue
# Row ranges of items to remove
# - store tuples of row "start", "end" (can be the same)
row_ranges = []
# Predefine start, end variables
start_row = end_row = None
chilren_by_row = {}
parent_item = self._items_by_id[parent_id]
for row in range(parent_item.rowCount()):
child_item = parent_item.child(row)
child_id = child_item.id
# Not sure if this can happen
# TODO validate this line it seems dangerous as start/end
# row is not changed
if child_id not in children:
continue
chilren_by_row[row] = child_item
children.pop(child_item.id)
removed_mark = child_item.data(REMOVED_ROLE)
if not removed_mark or not child_item.is_new:
# Skip row sequence store child for later processing
# and store current start/end row range
modified_children.append(child_item)
if end_row is not None:
row_ranges.append((start_row, end_row))
start_row = end_row = None
continue
end_row = row
if start_row is None:
start_row = row
if end_row is not None:
row_ranges.append((start_row, end_row))
if not row_ranges:
continue
# Remove items from model
parent_index = self.index_for_item(parent_item)
for start, end in row_ranges:
self.beginRemoveRows(parent_index, start, end)
for idx in range(start, end + 1):
child_item = chilren_by_row[idx]
# Force name validation
if isinstance(child_item, AssetItem):
self._rename_asset(child_item, None)
child_item.set_parent(None)
self._items_by_id.pop(child_item.id)
self.endRemoveRows()
# Trigger data change to repaint items
# - `BackgroundRole` is random role without any specific reason
for item in modified_children:
s_index = self.index_for_item(item)
e_index = self.index_for_item(item, column=self.columns_len - 1)
self.dataChanged.emit(s_index, e_index, [QtCore.Qt.BackgroundRole])
def _rename_asset(self, asset_item, new_name):
if not isinstance(asset_item, AssetItem):
return
prev_name = asset_item.data(QtCore.Qt.EditRole, "name")
if prev_name == new_name:
return
if asset_item.id in self._asset_items_by_name[prev_name]:
self._asset_items_by_name[prev_name].remove(asset_item.id)
self._validate_asset_duplicity(prev_name)
if new_name is None:
return
self._asset_items_by_name[new_name].add(asset_item.id)
self._validate_asset_duplicity(new_name)
def _validate_asset_duplicity(self, name):
if name not in self._asset_items_by_name:
return
item_ids = self._asset_items_by_name[name]
if not item_ids:
self._asset_items_by_name.pop(name)
elif len(item_ids) == 1:
for item_id in item_ids:
item = self._items_by_id[item_id]
index = self.index_for_item(item)
self.setData(index, False, DUPLICATED_ROLE)
else:
for item_id in item_ids:
item = self._items_by_id[item_id]
index = self.index_for_item(item)
self.setData(index, True, DUPLICATED_ROLE)
def _move_horizontal_single(self, index, direction):
if not index.isValid():
return
item_id = index.data(IDENTIFIER_ROLE)
if item_id is None:
return
item = self._items_by_id[item_id]
if isinstance(item, (RootItem, ProjectItem)):
return
if item.data(REMOVED_ROLE):
return
if (
isinstance(item, AssetItem)
and not item.data(HIERARCHY_CHANGE_ABLE_ROLE)
):
return
if abs(direction) != 1:
return
# Move under parent of parent
src_row = item.row()
src_parent = item.parent()
src_parent_index = self.index_from_item(
src_parent.row(), 0, src_parent.parent()
)
dst_row = None
dst_parent = None
if direction == -1:
if isinstance(src_parent, (RootItem, ProjectItem)):
return
dst_parent = src_parent.parent()
dst_row = src_parent.row() + 1
# Move under parent before or after if before is None
elif direction == 1:
src_row_count = src_parent.rowCount()
if src_row_count == 1:
return
item_row = item.row()
dst_parent = None
for row in reversed(range(item_row)):
_item = src_parent.child(row)
if not isinstance(_item, AssetItem):
continue
if _item.data(REMOVED_ROLE):
continue
dst_parent = _item
break
_next_row = item_row + 1
if dst_parent is None and _next_row < src_row_count:
for row in range(_next_row, src_row_count):
_item = src_parent.child(row)
if not isinstance(_item, AssetItem):
continue
if _item.data(REMOVED_ROLE):
continue
dst_parent = _item
break
if dst_parent is None:
return
dst_row = dst_parent.rowCount()
if src_parent is dst_parent:
return
if (
isinstance(item, TaskItem)
and not isinstance(dst_parent, AssetItem)
):
return
dst_parent_index = self.index_from_item(
dst_parent.row(), 0, dst_parent.parent()
)
self.beginMoveRows(
src_parent_index,
src_row,
src_row,
dst_parent_index,
dst_row
)
src_parent.remove_child(item)
dst_parent.add_child(item)
item.set_parent(dst_parent)
dst_parent.move_to(item, dst_row)
self.endMoveRows()
new_index = self.index(dst_row, index.column(), dst_parent_index)
self.index_moved.emit(new_index)
def move_horizontal(self, indexes, direction):
if not indexes:
return
if isinstance(indexes, QtCore.QModelIndex):
indexes = [indexes]
if len(indexes) == 1:
self._move_horizontal_single(indexes[0], direction)
return
items_by_id = {}
for index in indexes:
item_id = index.data(IDENTIFIER_ROLE)
item = self._items_by_id[item_id]
if isinstance(item, (RootItem, ProjectItem)):
continue
if (
direction == -1
and isinstance(item.parent(), (RootItem, ProjectItem))
):
continue
items_by_id[item_id] = item
if not items_by_id:
return
parents_by_id = {}
items_ids_by_parent_id = collections.defaultdict(set)
skip_ids = set(items_by_id.keys())
for item_id, item in tuple(items_by_id.items()):
item_parent = item.parent()
parent_ids = set()
skip_item = False
parent = item_parent
while parent is not None:
if parent.id in skip_ids:
skip_item = True
skip_ids |= parent_ids
break
parent_ids.add(parent.id)
parent = parent.parent()
if skip_item:
items_by_id.pop(item_id)
else:
parents_by_id[item_parent.id] = item_parent
items_ids_by_parent_id[item_parent.id].add(item_id)
if direction == 1:
for parent_id, parent in parents_by_id.items():
items_ids = items_ids_by_parent_id[parent_id]
if len(items_ids) == parent.rowCount():
for item_id in items_ids:
items_by_id.pop(item_id)
items = tuple(items_by_id.values())
if direction == -1:
items = reversed(items)
for item in items:
index = self.index_for_item(item)
self._move_horizontal_single(index, direction)
def _move_vertical_single(self, index, direction):
if not index.isValid():
return
item_id = index.data(IDENTIFIER_ROLE)
item = self._items_by_id[item_id]
if isinstance(item, (RootItem, ProjectItem)):
return
if item.data(REMOVED_ROLE):
return
if (
isinstance(item, AssetItem)
and not item.data(HIERARCHY_CHANGE_ABLE_ROLE)
):
return
if abs(direction) != 1:
return
src_parent = item.parent()
if not isinstance(src_parent, AssetItem):
return
src_parent_index = self.index_from_item(
src_parent.row(), 0, src_parent.parent()
)
source_row = item.row()
parent_items = []
parent = src_parent
while True:
parent = parent.parent()
parent_items.insert(0, parent)
if isinstance(parent, ProjectItem):
break
dst_parent = None
# Down
if direction == 1:
current_idxs = []
current_max_idxs = []
for parent_item in parent_items:
current_max_idxs.append(parent_item.rowCount())
if not isinstance(parent_item, ProjectItem):
current_idxs.append(parent_item.row())
current_idxs.append(src_parent.row())
indexes_len = len(current_idxs)
while True:
def _update_parents(idx, top=True):
if idx < 0:
return False
if current_max_idxs[idx] == current_idxs[idx]:
if not _update_parents(idx - 1, False):
return False
parent = parent_items[idx]
row_count = 0
if parent is not None:
row_count = parent.rowCount()
current_max_idxs[idx] = row_count
current_idxs[idx] = 0
return True
if top:
return True
current_idxs[idx] += 1
parent_item = parent_items[idx]
new_item = parent_item.child(current_idxs[idx])
parent_items[idx + 1] = new_item
return True
updated = _update_parents(indexes_len - 1)
if not updated:
return
start = current_idxs[-1]
end = current_max_idxs[-1]
current_idxs[-1] = current_max_idxs[-1]
parent = parent_items[-1]
for row in range(start, end):
child_item = parent.child(row)
if (
child_item is src_parent
or child_item.data(REMOVED_ROLE)
or not isinstance(child_item, AssetItem)
):
continue
dst_parent = child_item
destination_row = 0
break
if dst_parent is not None:
break
# Up
elif direction == -1:
current_idxs = []
for parent_item in parent_items:
if not isinstance(parent_item, ProjectItem):
current_idxs.append(parent_item.row())
current_idxs.append(src_parent.row())
max_idxs = [0 for _ in current_idxs]
indexes_len = len(current_idxs)
while True:
if current_idxs == max_idxs:
return
def _update_parents(_current_idx, top=True):
if _current_idx < 0:
return False
if current_idxs[_current_idx] == 0:
if not _update_parents(_current_idx - 1, False):
return False
parent = parent_items[_current_idx]
row_count = 0
if parent is not None:
row_count = parent.rowCount()
current_idxs[_current_idx] = row_count
return True
if top:
return True
current_idxs[_current_idx] -= 1
parent_item = parent_items[_current_idx]
new_item = parent_item.child(current_idxs[_current_idx])
parent_items[_current_idx + 1] = new_item
return True
updated = _update_parents(indexes_len - 1)
if not updated:
return
parent_item = parent_items[-1]
row_count = current_idxs[-1]
current_idxs[-1] = 0
for row in reversed(range(row_count)):
child_item = parent_item.child(row)
if (
child_item is src_parent
or child_item.data(REMOVED_ROLE)
or not isinstance(child_item, AssetItem)
):
continue
dst_parent = child_item
destination_row = dst_parent.rowCount()
break
if dst_parent is not None:
break
if dst_parent is None:
return
dst_parent_index = self.index_from_item(
dst_parent.row(), 0, dst_parent.parent()
)
self.beginMoveRows(
src_parent_index,
source_row,
source_row,
dst_parent_index,
destination_row
)
if src_parent is dst_parent:
dst_parent.move_to(item, destination_row)
else:
src_parent.remove_child(item)
dst_parent.add_child(item)
item.set_parent(dst_parent)
dst_parent.move_to(item, destination_row)
self.endMoveRows()
new_index = self.index(
destination_row, index.column(), dst_parent_index
)
self.index_moved.emit(new_index)
def move_vertical(self, indexes, direction):
if not indexes:
return
# Convert single index to list of indexes
if isinstance(indexes, QtCore.QModelIndex):
indexes = [indexes]
# Just process single index
if len(indexes) == 1:
self._move_vertical_single(indexes[0], direction)
return
items_by_id = {}
for index in indexes:
item_id = index.data(IDENTIFIER_ROLE)
items_by_id[item_id] = self._items_by_id[item_id]
skip_ids = set(items_by_id.keys())
for item_id, item in tuple(items_by_id.items()):
parent = item.parent()
parent_ids = set()
skip_item = False
while parent is not None:
if parent.id in skip_ids:
skip_item = True
skip_ids |= parent_ids
break
parent_ids.add(parent.id)
parent = parent.parent()
if skip_item:
items_by_id.pop(item_id)
items = tuple(items_by_id.values())
if direction == 1:
items = reversed(items)
for item in items:
index = self.index_for_item(item)
self._move_vertical_single(index, direction)
def child_removed(self, child):
self._items_by_id.pop(child.id, None)
def column_name(self, column):
if column < len(self.columns):
return self.columns[column]
return None
def clear(self):
self.beginResetModel()
self._reset_root_item()
self.endResetModel()
def save(self):
# Check if all items are valid before save
all_valid = True
for item in self._items_by_id.values():
if not item.is_valid:
all_valid = False
break
if not all_valid:
return
# Check project item and do not save without it
project_item = None
for _project_item in self._root_item.children():
project_item = _project_item
if not project_item:
return
project_name = project_item.name
project_col = self.dbcon.database[project_name]
# Process asset items per one hierarchical level.
# - new assets are inserted per one parent
# - update and delete data are stored and processed at once at the end
to_process = collections.deque()
to_process.append(project_item)
bulk_writes = []
while to_process:
parent = to_process.popleft()
insert_list = []
for item in parent.children():
if not isinstance(item, AssetItem):
continue
to_process.append(item)
if item.is_new:
insert_list.append(item)
elif item.data(REMOVED_ROLE):
if item.data(HIERARCHY_CHANGE_ABLE_ROLE):
bulk_writes.append(DeleteOne(
{"_id": item.asset_id}
))
else:
bulk_writes.append(UpdateOne(
{"_id": item.asset_id},
{"$set": {"type": "archived_asset"}}
))
else:
update_data = item.update_data()
if update_data:
bulk_writes.append(UpdateOne(
{"_id": item.asset_id},
update_data
))
if insert_list:
new_docs = []
for item in insert_list:
new_docs.append(item.to_doc())
result = project_col.insert_many(new_docs)
for idx, mongo_id in enumerate(result.inserted_ids):
insert_list[idx].mongo_id = mongo_id
if bulk_writes:
project_col.bulk_write(bulk_writes)
self.refresh_project()
def copy_mime_data(self, indexes):
items = []
processed_ids = set()
for index in indexes:
if not index.isValid():
continue
item_id = index.data(IDENTIFIER_ROLE)
if item_id in processed_ids:
continue
processed_ids.add(item_id)
item = self._items_by_id[item_id]
items.append(item)
parent_item = None
for item in items:
if not isinstance(item, TaskItem):
raise ValueError("Can copy only tasks")
if parent_item is None:
parent_item = item.parent()
elif item.parent() is not parent_item:
raise ValueError("Can copy only tasks from same parent")
data = []
for task_item in items:
data.append(task_item.to_json_data())
encoded_data = QtCore.QByteArray()
stream = QtCore.QDataStream(encoded_data, QtCore.QIODevice.WriteOnly)
stream.writeQString(json.dumps(data))
mimedata = QtCore.QMimeData()
mimedata.setData("application/copy_task", encoded_data)
return mimedata
def paste_mime_data(self, index, mime_data):
if not index.isValid():
return
item_id = index.data(IDENTIFIER_ROLE)
item = self._items_by_id[item_id]
if not isinstance(item, (AssetItem, TaskItem)):
return
raw_data = mime_data.data("application/copy_task")
if isinstance(raw_data, QtCore.QByteArray):
# Raw data are already QByteArrat and we don't have to load them
encoded_data = raw_data
else:
encoded_data = QtCore.QByteArray.fromRawData(raw_data)
stream = QtCore.QDataStream(encoded_data, QtCore.QIODevice.ReadOnly)
text = stream.readQString()
try:
data = json.loads(text)
except Exception:
data = []
if not data:
return
if isinstance(item, TaskItem):
parent = item.parent()
else:
parent = item
for task_item_data in data:
task_data = {}
for name, data in task_item_data.items():
task_data = data
task_data["name"] = name
task_item = TaskItem(task_data, True)
self.add_item(task_item, parent)
class BaseItem:
columns = []
editable_columns = set()
_name_icons = None
_is_duplicated = False
item_type = "base"
_None = object()
def __init__(self, data=None):
self._id = uuid4()
self._children = list()
self._parent = None
self._data = {
key: None
for key in self.columns
}
self._global_data = {}
self._source_data = data
if data:
for key, value in data.items():
if key in self.columns:
self._data[key] = value
def name_icon(self):
return None
@property
def is_valid(self):
return not self._is_duplicated
def model(self):
return self._parent.model()
def move_to(self, item, row):
idx = self._children.index(item)
if idx == row:
return
self._children.pop(idx)
self._children.insert(row, item)
def _get_global_data(self, role):
if role == ITEM_TYPE_ROLE:
return self.item_type
if role == IDENTIFIER_ROLE:
return self._id
if role == DUPLICATED_ROLE:
return self._is_duplicated
if role == REMOVED_ROLE:
return False
return self._global_data.get(role, self._None)
def _set_global_data(self, value, role):
self._global_data[role] = value
return True
def data(self, role, key=None):
value = self._get_global_data(role)
if value is not self._None:
return value
if key not in self.columns:
return None
if role == QtCore.Qt.ForegroundRole:
if key == "name" and not self.is_valid:
return ResourceCache.colors["warning"]
return None
if role in (QtCore.Qt.DisplayRole, QtCore.Qt.EditRole):
value = self._data[key]
if value is None:
value = self.parent().data(role, key)
return value
if role == QtCore.Qt.DecorationRole and key == "name":
return self.name_icon()
return None
def setData(self, value, role, key=None):
if role == DUPLICATED_ROLE:
if value == self._is_duplicated:
return False
self._is_duplicated = value
return True
if role == QtCore.Qt.EditRole:
if key in self.editable_columns:
self._data[key] = value
return True
return self._set_global_data(value, role)
@property
def id(self):
return self._id
@property
def is_new(self):
return False
def rowCount(self):
return len(self._children)
def child(self, row):
if -1 < row < self.rowCount():
return self._children[row]
return None
def children(self):
return self._children
def child_row(self, child):
if child not in self._children:
return -1
return self._children.index(child)
def parent(self):
return self._parent
def set_parent(self, parent):
if parent is self._parent:
return
if self._parent:
self._parent.remove_child(self)
self._parent = parent
def row(self):
if self._parent is not None:
return self._parent.child_row(self)
return -1
def add_child(self, item, row=None):
if item in self._children:
return
row_count = self.rowCount()
if row is None or row == row_count:
self._children.append(item)
return
if row > row_count or row < 0:
raise ValueError(
"Invalid row number {} expected range 0 - {}".format(
row, row_count
)
)
self._children.insert(row, item)
def remove_child(self, item):
if item in self._children:
self._children.remove(item)
def flags(self, key):
flags = QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable
if key in self.editable_columns:
flags |= QtCore.Qt.ItemIsEditable
return flags
class RootItem(BaseItem):
item_type = "root"
def __init__(self, model):
super(RootItem, self).__init__()
self._model = model
def model(self):
return self._model
def flags(self, *args, **kwargs):
return QtCore.Qt.NoItemFlags
class ProjectItem(BaseItem):
item_type = "project"
columns = {
"name",
"type",
"frameStart",
"frameEnd",
"fps",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env",
}
query_projection = {
"_id": 1,
"name": 1,
"type": 1,
"data.frameStart": 1,
"data.frameEnd": 1,
"data.fps": 1,
"data.resolutionWidth": 1,
"data.resolutionHeight": 1,
"data.handleStart": 1,
"data.handleEnd": 1,
"data.clipIn": 1,
"data.clipOut": 1,
"data.pixelAspect": 1,
"data.tools_env": 1
}
def __init__(self, project_doc):
self._mongo_id = project_doc["_id"]
data = self.data_from_doc(project_doc)
super(ProjectItem, self).__init__(data)
@property
def project_id(self):
return self._mongo_id
@property
def asset_id(self):
return None
@property
def name(self):
return self._data["name"]
def child_parents(self):
return []
@classmethod
def data_from_doc(cls, project_doc):
data = {
"name": project_doc["name"],
"type": project_doc["type"]
}
doc_data = project_doc.get("data") or {}
for key in cls.columns:
if key in data:
continue
data[key] = doc_data.get(key)
return data
def flags(self, *args, **kwargs):
return QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable
class AssetItem(BaseItem):
item_type = "asset"
columns = {
"name",
"type",
"fps",
"frameStart",
"frameEnd",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env"
}
editable_columns = {
"name",
"frameStart",
"frameEnd",
"fps",
"resolutionWidth",
"resolutionHeight",
"handleStart",
"handleEnd",
"clipIn",
"clipOut",
"pixelAspect",
"tools_env"
}
query_projection = {
"_id": 1,
"data.tasks": 1,
"data.visualParent": 1,
"schema": 1,
"name": 1,
"type": 1,
"data.frameStart": 1,
"data.frameEnd": 1,
"data.fps": 1,
"data.resolutionWidth": 1,
"data.resolutionHeight": 1,
"data.handleStart": 1,
"data.handleEnd": 1,
"data.clipIn": 1,
"data.clipOut": 1,
"data.pixelAspect": 1,
"data.tools_env": 1
}
def __init__(self, asset_doc):
if not asset_doc:
asset_doc = {}
self.mongo_id = asset_doc.get("_id")
self._project_id = None
self._edited_columns = {
column_name: False
for column_name in self.editable_columns
}
self._hierarchy_changes_enabled = True
self._removed = False
self._task_items_by_name = collections.defaultdict(list)
self._task_name_by_item_id = {}
self._duplicated_task_names = set()
self._origin_asset_doc = copy.deepcopy(asset_doc)
data = self.data_from_doc(asset_doc)
self._origin_data = copy.deepcopy(data)
super(AssetItem, self).__init__(data)
@property
def project_id(self):
if self._project_id is None:
self._project_id = self.parent().project_id
return self._project_id
@property
def asset_id(self):
return self.mongo_id
@property
def is_new(self):
return self.asset_id is None
@property
def is_valid(self):
if self._is_duplicated or not self._data["name"]:
return False
return True
@property
def name(self):
return self._data["name"]
def child_parents(self):
parents = self.parent().child_parents()
parents.append(self.name)
return parents
def to_doc(self):
tasks = {}
for item in self.children():
if isinstance(item, TaskItem):
tasks.update(item.to_doc_data())
doc_data = {
"parents": self.parent().child_parents(),
"visualParent": self.parent().asset_id,
"tasks": tasks
}
schema_name = (
self._origin_asset_doc.get("schema")
or CURRENT_DOC_SCHEMAS["asset"]
)
doc = {
"name": self.data(QtCore.Qt.EditRole, "name"),
"type": self.data(QtCore.Qt.EditRole, "type"),
"schema": schema_name,
"data": doc_data,
"parent": self.project_id
}
if self.mongo_id:
doc["_id"] = self.mongo_id
for key in self._data.keys():
if key in doc:
continue
doc_data[key] = self.data(QtCore.Qt.EditRole, key)
return doc
def update_data(self):
if not self.mongo_id:
return {}
document = self.to_doc()
changes = {}
for key, value in document.items():
if key in ("data", "_id"):
continue
if (
key in self._origin_asset_doc
and self._origin_asset_doc[key] == value
):
continue
changes[key] = value
if "data" not in self._origin_asset_doc:
changes["data"] = document["data"]
else:
origin_data = self._origin_asset_doc["data"]
for key, value in document["data"].items():
if key in origin_data and origin_data[key] == value:
continue
_key = "data.{}".format(key)
changes[_key] = value
if changes:
return {"$set": changes}
return {}
@classmethod
def data_from_doc(cls, asset_doc):
data = {
"name": None,
"type": "asset"
}
if asset_doc:
for key in data.keys():
if key in asset_doc:
data[key] = asset_doc[key]
doc_data = asset_doc.get("data") or {}
for key in cls.columns:
if key in data:
continue
data[key] = doc_data.get(key)
return data
def name_icon(self):
if self.__class__._name_icons is None:
self.__class__._name_icons = ResourceCache.get_icons()["asset"]
if self._removed:
icon_type = "removed"
elif not self.is_valid:
icon_type = "invalid"
elif self.is_new:
icon_type = "new"
else:
icon_type = "default"
return self.__class__._name_icons[icon_type]
def _get_global_data(self, role):
if role == HIERARCHY_CHANGE_ABLE_ROLE:
return self._hierarchy_changes_enabled
if role == REMOVED_ROLE:
return self._removed
if role == QtCore.Qt.ToolTipRole:
name = self.data(QtCore.Qt.EditRole, "name")
if not name:
return "Name is not set"
elif self._is_duplicated:
return "Duplicated asset name \"{}\"".format(name)
return None
return super(AssetItem, self)._get_global_data(role)
def data(self, role, key=None):
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
return self._edited_columns[key]
if role == QtCore.Qt.DisplayRole and self._edited_columns.get(key):
return ""
return super(AssetItem, self).data(role, key)
def setData(self, value, role, key=None):
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
self._edited_columns[key] = value
return True
if role == REMOVED_ROLE:
self._removed = value
return True
if role == HIERARCHY_CHANGE_ABLE_ROLE:
if self._hierarchy_changes_enabled == value:
return False
self._hierarchy_changes_enabled = value
return True
if (
role == QtCore.Qt.EditRole
and key == "name"
and not self._hierarchy_changes_enabled
):
return False
return super(AssetItem, self).setData(value, role, key)
def flags(self, key):
if key == "name":
flags = QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable
if self._hierarchy_changes_enabled:
flags |= QtCore.Qt.ItemIsEditable
return flags
return super(AssetItem, self).flags(key)
def _add_task(self, item):
name = item.data(QtCore.Qt.EditRole, "name").lower()
item_id = item.data(IDENTIFIER_ROLE)
self._task_name_by_item_id[item_id] = name
self._task_items_by_name[name].append(item)
if len(self._task_items_by_name[name]) > 1:
self._duplicated_task_names.add(name)
for _item in self._task_items_by_name[name]:
_item.setData(True, DUPLICATED_ROLE)
elif item.data(DUPLICATED_ROLE):
item.setData(False, DUPLICATED_ROLE)
def _remove_task(self, item):
item_id = item.data(IDENTIFIER_ROLE)
if item_id not in self._task_name_by_item_id:
return
name = self._task_name_by_item_id.pop(item_id)
self._task_items_by_name[name].remove(item)
if not self._task_items_by_name[name]:
self._task_items_by_name.pop(name)
elif len(self._task_items_by_name[name]) == 1:
self._duplicated_task_names.remove(name)
for _item in self._task_items_by_name[name]:
_item.setData(False, DUPLICATED_ROLE)
def _rename_task(self, item):
if item.data(REMOVED_ROLE):
return
new_name = item.data(QtCore.Qt.EditRole, "name").lower()
item_id = item.data(IDENTIFIER_ROLE)
prev_name = self._task_name_by_item_id[item_id]
if new_name == prev_name:
return
self._task_items_by_name[prev_name].remove(item)
if not self._task_items_by_name[prev_name]:
self._task_items_by_name.pop(prev_name)
elif len(self._task_items_by_name[prev_name]) == 1:
self._duplicated_task_names.remove(prev_name)
for _item in self._task_items_by_name[prev_name]:
_item.setData(False, DUPLICATED_ROLE)
self._task_items_by_name[new_name].append(item)
if len(self._task_items_by_name[new_name]) > 1:
self._duplicated_task_names.add(new_name)
for _item in self._task_items_by_name[new_name]:
_item.setData(True, DUPLICATED_ROLE)
else:
item.setData(False, DUPLICATED_ROLE)
self._task_name_by_item_id[item_id] = new_name
def on_task_name_change(self, task_item):
self._rename_task(task_item)
def on_task_remove_state_change(self, task_item):
is_removed = task_item.data(REMOVED_ROLE)
item_id = task_item.data(IDENTIFIER_ROLE)
if is_removed:
name = self._task_name_by_item_id.pop(item_id)
self._task_items_by_name[name].remove(task_item)
else:
name = task_item.data(QtCore.Qt.EditRole, "name").lower()
self._task_name_by_item_id[item_id] = name
self._task_items_by_name[name].append(task_item)
if not self._task_items_by_name[name]:
self._task_items_by_name.pop(name)
elif len(self._task_items_by_name[name]) == 1:
if name in self._duplicated_task_names:
self._duplicated_task_names.remove(name)
task_item.setData(False, DUPLICATED_ROLE)
else:
self._duplicated_task_names.add(name)
for _item in self._task_items_by_name[name]:
_item.setData(True, DUPLICATED_ROLE)
def add_child(self, item, row=None):
if item in self._children:
return
super(AssetItem, self).add_child(item, row)
if isinstance(item, TaskItem):
self._add_task(item)
def remove_child(self, item):
if item not in self._children:
return
if isinstance(item, TaskItem):
self._remove_task(item)
super(AssetItem, self).remove_child(item)
class TaskItem(BaseItem):
item_type = "task"
columns = {
"name",
"type"
}
editable_columns = {
"name",
"type"
}
def __init__(self, data=None, is_new=None):
self._removed = False
if is_new is None:
is_new = data is None
self._is_new = is_new
if data is None:
data = {}
self._edited_columns = {
column_name: False
for column_name in self.editable_columns
}
self._origin_data = copy.deepcopy(data)
super(TaskItem, self).__init__(data)
@property
def is_new(self):
return self._is_new
@property
def is_valid(self):
if self._is_duplicated or not self._data["type"]:
return False
if not self.data(QtCore.Qt.EditRole, "name"):
return False
return True
def name_icon(self):
if self.__class__._name_icons is None:
self.__class__._name_icons = ResourceCache.get_icons()["task"]
if self._removed:
icon_type = "removed"
elif not self.is_valid:
icon_type = "invalid"
elif self.is_new:
icon_type = "new"
else:
icon_type = "default"
return self.__class__._name_icons[icon_type]
def add_child(self, item, row=None):
raise AssertionError("BUG: Can't add children to Task")
def _get_global_data(self, role):
if role == REMOVED_ROLE:
return self._removed
if role == QtCore.Qt.ToolTipRole:
if not self._data["type"]:
return "Type is not set"
name = self.data(QtCore.Qt.EditRole, "name")
if not name:
return "Name is not set"
elif self._is_duplicated:
return "Duplicated task name \"{}".format(name)
return None
return super(TaskItem, self)._get_global_data(role)
def to_doc_data(self):
if self._removed:
return {}
data = copy.deepcopy(self._data)
data.pop("name")
name = self.data(QtCore.Qt.EditRole, "name")
return {
name: data
}
def data(self, role, key=None):
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
return self._edited_columns[key]
# Return empty string if column is edited
if role == QtCore.Qt.DisplayRole and self._edited_columns.get(key):
return ""
if role in (QtCore.Qt.DisplayRole, QtCore.Qt.EditRole):
if key == "type":
return self._data["type"]
# Always require task type filled
if key == "name":
if not self._data["type"]:
if role == QtCore.Qt.DisplayRole:
return "< Select Type >"
if role == QtCore.Qt.EditRole:
return ""
else:
return self._data[key] or self._data["type"]
return super(TaskItem, self).data(role, key)
def setData(self, value, role, key=None):
# Store information that item on a column is edited
# - DisplayRole will return empty string in that case
if role == EDITOR_OPENED_ROLE:
if key not in self._edited_columns:
return False
self._edited_columns[key] = value
return True
if role == REMOVED_ROLE:
# Skip value change if is same as already set value
if value == self._removed:
return False
self._removed = value
self.parent().on_task_remove_state_change(self)
return True
# Convert empty string to None on EditRole
if (
role == QtCore.Qt.EditRole
and key == "name"
and not value
):
value = None
result = super(TaskItem, self).setData(value, role, key)
if role == QtCore.Qt.EditRole:
# Trigger task name change of parent AssetItem
if (
key == "name"
or (key == "type" and not self._data["name"])
):
self.parent().on_task_name_change(self)
return result
def to_json_data(self):
return self.to_doc_data()
| true
| true
|
1c3ec10c0be0c50347ae5364aa263e5e25c717b6
| 4,954
|
py
|
Python
|
pdbtools/pdb_chain.py
|
andrewsb8/pdb-tools
|
2fecdebd7520505b68ab515c54fa7c128e7a8090
|
[
"Apache-2.0"
] | 192
|
2015-07-25T02:31:09.000Z
|
2022-03-29T11:09:45.000Z
|
pdbtools/pdb_chain.py
|
andrewsb8/pdb-tools
|
2fecdebd7520505b68ab515c54fa7c128e7a8090
|
[
"Apache-2.0"
] | 112
|
2016-08-16T20:00:16.000Z
|
2022-03-25T00:44:16.000Z
|
pdbtools/pdb_chain.py
|
andrewsb8/pdb-tools
|
2fecdebd7520505b68ab515c54fa7c128e7a8090
|
[
"Apache-2.0"
] | 55
|
2015-07-24T17:33:30.000Z
|
2022-03-17T17:36:33.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2018 João Pedro Rodrigues
#
# 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.
"""
Modifies the chain identifier column of a PDB file (default is an empty chain).
Usage:
python pdb_chain.py -<chain id> <pdb file>
Example:
python pdb_chain.py -C 1CTF.pdb
This program is part of the `pdb-tools` suite of utilities and should not be
distributed isolatedly. The `pdb-tools` were created to quickly manipulate PDB
files using the terminal, and can be used sequentially, with one tool streaming
data to another. They are based on old FORTRAN77 code that was taking too much
effort to maintain and compile. RIP.
"""
import os
import sys
__author__ = "Joao Rodrigues"
__email__ = "j.p.g.l.m.rodrigues@gmail.com"
def check_input(args):
"""Checks whether to read from stdin/file and validates user input/options.
"""
# Defaults
option = ' '
fh = sys.stdin # file handle
if not len(args):
# Reading from pipe with default option
if sys.stdin.isatty():
sys.stderr.write(__doc__)
sys.exit(1)
elif len(args) == 1:
# One of two options: option & Pipe OR file & default option
if args[0].startswith('-'):
option = args[0][1:]
if sys.stdin.isatty(): # ensure the PDB data is streamed in
emsg = 'ERROR!! No data to process!\n'
sys.stderr.write(emsg)
sys.stderr.write(__doc__)
sys.exit(1)
else:
if not os.path.isfile(args[0]):
emsg = 'ERROR!! File not found or not readable: \'{}\'\n'
sys.stderr.write(emsg.format(args[0]))
sys.stderr.write(__doc__)
sys.exit(1)
fh = open(args[0], 'r')
elif len(args) == 2:
# Two options: option & File
if not args[0].startswith('-'):
emsg = 'ERROR! First argument is not an option: \'{}\'\n'
sys.stderr.write(emsg.format(args[0]))
sys.stderr.write(__doc__)
sys.exit(1)
if not os.path.isfile(args[1]):
emsg = 'ERROR!! File not found or not readable: \'{}\'\n'
sys.stderr.write(emsg.format(args[1]))
sys.stderr.write(__doc__)
sys.exit(1)
option = args[0][1:]
fh = open(args[1], 'r')
else: # Whatever ...
sys.stderr.write(__doc__)
sys.exit(1)
# Validate option
if len(option) != 1:
emsg = 'ERROR!! Chain identifiers must be a single character: \'{}\'\n'
sys.stderr.write(emsg.format(option))
sys.exit(1)
return (fh, option)
def pad_line(line):
"""Helper function to pad line to 80 characters in case it is shorter"""
size_of_line = len(line)
if size_of_line < 80:
padding = 80 - size_of_line + 1
line = line.strip('\n') + ' ' * padding + '\n'
return line[:81] # 80 + newline character
def run(fhandle, chain_id):
"""
Set the chain identifier column in all ATOM/HETATM records to a value.
This function is a generator.
Parameters
----------
fhandle : a line-by-line iterator of the original PDB file.
chain_id : str
The new chain ID.
Yields
------
str (line-by-line)
The modified (or not) PDB line.
"""
_pad_line = pad_line
records = ('ATOM', 'HETATM', 'TER', 'ANISOU')
for line in fhandle:
if line.startswith(records):
line = _pad_line(line)
yield line[:21] + chain_id + line[22:]
else:
yield line
alter_chain = run
def main():
# Check Input
pdbfh, chain = check_input(sys.argv[1:])
# Do the job
new_pdb = run(pdbfh, chain)
try:
_buffer = []
_buffer_size = 5000 # write N lines at a time
for lineno, line in enumerate(new_pdb):
if not (lineno % _buffer_size):
sys.stdout.write(''.join(_buffer))
_buffer = []
_buffer.append(line)
sys.stdout.write(''.join(_buffer))
sys.stdout.flush()
except IOError:
# This is here to catch Broken Pipes
# for example to use 'head' or 'tail' without
# the error message showing up
pass
# last line of the script
# We can close it even if it is sys.stdin
pdbfh.close()
sys.exit(0)
if __name__ == '__main__':
main()
| 27.988701
| 79
| 0.594267
|
import os
import sys
__author__ = "Joao Rodrigues"
__email__ = "j.p.g.l.m.rodrigues@gmail.com"
def check_input(args):
option = ' '
fh = sys.stdin
if not len(args):
if sys.stdin.isatty():
sys.stderr.write(__doc__)
sys.exit(1)
elif len(args) == 1:
if args[0].startswith('-'):
option = args[0][1:]
if sys.stdin.isatty():
emsg = 'ERROR!! No data to process!\n'
sys.stderr.write(emsg)
sys.stderr.write(__doc__)
sys.exit(1)
else:
if not os.path.isfile(args[0]):
emsg = 'ERROR!! File not found or not readable: \'{}\'\n'
sys.stderr.write(emsg.format(args[0]))
sys.stderr.write(__doc__)
sys.exit(1)
fh = open(args[0], 'r')
elif len(args) == 2:
if not args[0].startswith('-'):
emsg = 'ERROR! First argument is not an option: \'{}\'\n'
sys.stderr.write(emsg.format(args[0]))
sys.stderr.write(__doc__)
sys.exit(1)
if not os.path.isfile(args[1]):
emsg = 'ERROR!! File not found or not readable: \'{}\'\n'
sys.stderr.write(emsg.format(args[1]))
sys.stderr.write(__doc__)
sys.exit(1)
option = args[0][1:]
fh = open(args[1], 'r')
else:
sys.stderr.write(__doc__)
sys.exit(1)
if len(option) != 1:
emsg = 'ERROR!! Chain identifiers must be a single character: \'{}\'\n'
sys.stderr.write(emsg.format(option))
sys.exit(1)
return (fh, option)
def pad_line(line):
size_of_line = len(line)
if size_of_line < 80:
padding = 80 - size_of_line + 1
line = line.strip('\n') + ' ' * padding + '\n'
return line[:81]
def run(fhandle, chain_id):
_pad_line = pad_line
records = ('ATOM', 'HETATM', 'TER', 'ANISOU')
for line in fhandle:
if line.startswith(records):
line = _pad_line(line)
yield line[:21] + chain_id + line[22:]
else:
yield line
alter_chain = run
def main():
pdbfh, chain = check_input(sys.argv[1:])
new_pdb = run(pdbfh, chain)
try:
_buffer = []
_buffer_size = 5000
for lineno, line in enumerate(new_pdb):
if not (lineno % _buffer_size):
sys.stdout.write(''.join(_buffer))
_buffer = []
_buffer.append(line)
sys.stdout.write(''.join(_buffer))
sys.stdout.flush()
except IOError:
pass
pdbfh.close()
sys.exit(0)
if __name__ == '__main__':
main()
| true
| true
|
1c3ec22b6437fb74df647b14f9882233d4fc1589
| 18,074
|
py
|
Python
|
napalm_yang/models/openconfig/network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/__init__.py
|
ckishimo/napalm-yang
|
8f2bd907bd3afcde3c2f8e985192de74748baf6c
|
[
"Apache-2.0"
] | 64
|
2016-10-20T15:47:18.000Z
|
2021-11-11T11:57:32.000Z
|
napalm_yang/models/openconfig/network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/__init__.py
|
ckishimo/napalm-yang
|
8f2bd907bd3afcde3c2f8e985192de74748baf6c
|
[
"Apache-2.0"
] | 126
|
2016-10-05T10:36:14.000Z
|
2019-05-15T08:43:23.000Z
|
napalm_yang/models/openconfig/network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/__init__.py
|
ckishimo/napalm-yang
|
8f2bd907bd3afcde3c2f8e985192de74748baf6c
|
[
"Apache-2.0"
] | 63
|
2016-11-07T15:23:08.000Z
|
2021-09-22T14:41:16.000Z
|
# -*- coding: utf-8 -*-
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from collections import OrderedDict
from decimal import Decimal
from bitarray import bitarray
import six
# PY3 support of some PY2 keywords (needs improved)
if six.PY3:
import builtins as __builtin__
long = int
elif six.PY2:
import __builtin__
from . import config
from . import state
class interface_ref(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/mpls/te-interface-attributes/interface/interface-ref. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Reference to an interface or subinterface
"""
__slots__ = ("_path_helper", "_extmethods", "__config", "__state")
_yang_name = "interface-ref"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"mpls",
"te-interface-attributes",
"interface",
"interface-ref",
]
def _get_config(self):
"""
Getter method for config, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/config (container)
YANG Description: Configured reference to interface / subinterface
"""
return self.__config
def _set_config(self, v, load=False):
"""
Setter method for config, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/config (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_config is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_config() directly.
YANG Description: Configured reference to interface / subinterface
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """config must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=config.config, is_container='container', yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__config = t
if hasattr(self, "_set"):
self._set()
def _unset_config(self):
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
def _get_state(self):
"""
Getter method for state, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/state (container)
YANG Description: Operational state for interface-ref
"""
return self.__state
def _set_state(self, v, load=False):
"""
Setter method for state, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/state (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_state() directly.
YANG Description: Operational state for interface-ref
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """state must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__state = t
if hasattr(self, "_set"):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
config = __builtin__.property(_get_config, _set_config)
state = __builtin__.property(_get_state, _set_state)
_pyangbind_elements = OrderedDict([("config", config), ("state", state)])
from . import config
from . import state
class interface_ref(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/mpls/te-interface-attributes/interface/interface-ref. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Reference to an interface or subinterface
"""
__slots__ = ("_path_helper", "_extmethods", "__config", "__state")
_yang_name = "interface-ref"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"mpls",
"te-interface-attributes",
"interface",
"interface-ref",
]
def _get_config(self):
"""
Getter method for config, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/config (container)
YANG Description: Configured reference to interface / subinterface
"""
return self.__config
def _set_config(self, v, load=False):
"""
Setter method for config, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/config (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_config is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_config() directly.
YANG Description: Configured reference to interface / subinterface
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """config must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=config.config, is_container='container', yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__config = t
if hasattr(self, "_set"):
self._set()
def _unset_config(self):
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
def _get_state(self):
"""
Getter method for state, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/state (container)
YANG Description: Operational state for interface-ref
"""
return self.__state
def _set_state(self, v, load=False):
"""
Setter method for state, mapped from YANG variable /network_instances/network_instance/mpls/te_interface_attributes/interface/interface_ref/state (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_state() directly.
YANG Description: Operational state for interface-ref
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """state must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__state = t
if hasattr(self, "_set"):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
config = __builtin__.property(_get_config, _set_config)
state = __builtin__.property(_get_state, _set_state)
_pyangbind_elements = OrderedDict([("config", config), ("state", state)])
| 38.619658
| 377
| 0.602302
|
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from collections import OrderedDict
from decimal import Decimal
from bitarray import bitarray
import six
if six.PY3:
import builtins as __builtin__
long = int
elif six.PY2:
import __builtin__
from . import config
from . import state
class interface_ref(PybindBase):
__slots__ = ("_path_helper", "_extmethods", "__config", "__state")
_yang_name = "interface-ref"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"mpls",
"te-interface-attributes",
"interface",
"interface-ref",
]
def _get_config(self):
return self.__config
def _set_config(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """config must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=config.config, is_container='container', yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__config = t
if hasattr(self, "_set"):
self._set()
def _unset_config(self):
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
def _get_state(self):
return self.__state
def _set_state(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """state must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__state = t
if hasattr(self, "_set"):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
config = __builtin__.property(_get_config, _set_config)
state = __builtin__.property(_get_state, _set_state)
_pyangbind_elements = OrderedDict([("config", config), ("state", state)])
from . import config
from . import state
class interface_ref(PybindBase):
__slots__ = ("_path_helper", "_extmethods", "__config", "__state")
_yang_name = "interface-ref"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"mpls",
"te-interface-attributes",
"interface",
"interface-ref",
]
def _get_config(self):
return self.__config
def _set_config(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """config must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=config.config, is_container='container', yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__config = t
if hasattr(self, "_set"):
self._set()
def _unset_config(self):
self.__config = YANGDynClass(
base=config.config,
is_container="container",
yang_name="config",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
def _get_state(self):
return self.__state
def _set_state(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """state must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""",
}
)
self.__state = t
if hasattr(self, "_set"):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=True,
)
config = __builtin__.property(_get_config, _set_config)
state = __builtin__.property(_get_state, _set_state)
_pyangbind_elements = OrderedDict([("config", config), ("state", state)])
| true
| true
|
1c3ec271455fb4a4203f0942fbcf2dc4f068a07e
| 5,204
|
py
|
Python
|
owlbot.py
|
LaudateCorpus1/python-gke-hub
|
09e7c033fea2e07a6c865ac633ec14b3d07edf7a
|
[
"Apache-2.0"
] | null | null | null |
owlbot.py
|
LaudateCorpus1/python-gke-hub
|
09e7c033fea2e07a6c865ac633ec14b3d07edf7a
|
[
"Apache-2.0"
] | null | null | null |
owlbot.py
|
LaudateCorpus1/python-gke-hub
|
09e7c033fea2e07a6c865ac633ec14b3d07edf7a
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2021 Google LLC
#
# 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 script is used to synthesize generated parts of this library."""
import os
import synthtool as s
import synthtool.gcp as gcp
from synthtool.languages import python
common = gcp.CommonTemplates()
default_version = "v1"
for library in s.get_staging_dirs(default_version):
submodules = [
"configmanagement",
"multiclusteringress",
]
for submodule in submodules:
# Move v1 submodule namespace from google.cloud.gkehub.{submodule}_v1 to google.cloud.gkehub_vX.{submodule}_v1
s.move(library / f"google/cloud/gkehub/{submodule}_v1", library / f"google/cloud/gkehub_{library.name}/{submodule}_v1")
# Adjust docs based on new submodule namespace google.cloud.gkehub_vX.{submodule}_v1.types"
s.replace(
library / f"docs/{submodule}_v1/types.rst",
f"google.cloud.gkehub.{submodule}_v1.types",
f"google.cloud.gkehub_{library.name}.{submodule}_v1.types",
)
# Move docs to correct location /docs/gkehub_vX/{submodule}_v1
s.move(library / f"docs/{submodule}_v1", library / f"docs/gkehub_{library.name}/{submodule}_v1")
# Rename v1 submodule imports from google.cloud.gkehub.submodule.v1 to google.cloud.gkehub_vX.submodule_v1
s.replace(
[
library / f"google/cloud/gkehub_{library.name}/**/*.py",
library / f"tests/unit/gapic/gkehub_{library.name}/**/*.py",
],
f"from google.cloud.gkehub.{submodule}.v1 import {submodule}_pb2",
f"from google.cloud.gkehub_{library.name} import {submodule}_v1"
)
s.replace(
library / f"google/cloud/gkehub_{library.name}/types/feature.py",
f"google.cloud.gkehub.{submodule}.v1.{submodule}_pb2",
f"google.cloud.gkehub_v1.{submodule}_v1"
)
s.replace(
library / f"google/cloud/gkehub_{library.name}/types/feature.py",
f"{submodule}_pb2",
f"{submodule}_v1"
)
# Work around docs issue. Fix proposed upstream in cl/382492769
s.replace(library / f"google/cloud/gkehub_{library.name}/types/feature.py",
" projects/{p}/locations/{l}/memberships/{m}",
"`projects/{p}/locations/{l}/memberships/{m}`"
)
# Work around docs issue. Fix proposed upstream in cl/382492769
s.replace(library / f"google/cloud/gkehub_{library.name}/types/membership.py",
"""the GKE cluster. For example:
//container.googleapis.com/projects/my-""",
"""the GKE cluster. For example:
//container.googleapis.com/projects/my-"""
)
excludes=[
"setup.py",
"README.rst",
"docs/index.rst",
"docs/configmanagement_v1/**",
"docs/multiclusteringress_v1/**",
"google/cloud/gkehub/configmanagement/**",
"google/cloud/gkehub/configmanagement_v1/**",
"google/cloud/gkehub/multiclusteringress/**",
"google/cloud/gkehub/multiclusteringress_v1/**"
]
s.move(library, excludes=excludes)
s.remove_staging_dirs()
# Work around gapic generator bug https://github.com/googleapis/gapic-generator-python/pull/1071
s.replace(
"google/cloud/**/types/*.py",
"""\.
This field is a member of `oneof`_""",
""".
This field is a member of `oneof`_"""
)
# Work around gapic generator bug
s.replace(
"google/cloud/**/types/configmanagement.py",
"""Configuration for Policy Controller\n
Attributes""",
"""Configuration for Policy Controller\n
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields\n
Attributes"""
)
# Work around issue with docstring
s.replace("google/cloud/gkehub_v1beta1/types/membership.py",
"""For example:
//gkeonprem.googleapis.com/projects/my-""",
"""For example:
//gkeonprem.googleapis.com/projects/my-""",
)
# Work around issue with docstring
s.replace("google/cloud/gkehub_v1beta1/types/membership.py",
"""For example:
//gkemulticloud.googleapis.com/projects/my-""",
"""For example:
//gkemulticloud.googleapis.com/projects/my-""",
)
# ----------------------------------------------------------------------------
# Add templated files
# ----------------------------------------------------------------------------
templated_files = common.py_library(cov_level=100, microgenerator=True)
python.py_samples(skip_readmes=True)
# the microgenerator has a good coveragerc file
excludes = [".coveragerc"]
s.move(
templated_files, excludes=excludes
)
s.shell.run(["nox", "-s", "blacken"], hide_output=False)
| 34.013072
| 127
| 0.64854
|
import os
import synthtool as s
import synthtool.gcp as gcp
from synthtool.languages import python
common = gcp.CommonTemplates()
default_version = "v1"
for library in s.get_staging_dirs(default_version):
submodules = [
"configmanagement",
"multiclusteringress",
]
for submodule in submodules:
s.move(library / f"google/cloud/gkehub/{submodule}_v1", library / f"google/cloud/gkehub_{library.name}/{submodule}_v1")
s.replace(
library / f"docs/{submodule}_v1/types.rst",
f"google.cloud.gkehub.{submodule}_v1.types",
f"google.cloud.gkehub_{library.name}.{submodule}_v1.types",
)
# Move docs to correct location /docs/gkehub_vX/{submodule}_v1
s.move(library / f"docs/{submodule}_v1", library / f"docs/gkehub_{library.name}/{submodule}_v1")
# Rename v1 submodule imports from google.cloud.gkehub.submodule.v1 to google.cloud.gkehub_vX.submodule_v1
s.replace(
[
library / f"google/cloud/gkehub_{library.name}/**/*.py",
library / f"tests/unit/gapic/gkehub_{library.name}/**/*.py",
],
f"from google.cloud.gkehub.{submodule}.v1 import {submodule}_pb2",
f"from google.cloud.gkehub_{library.name} import {submodule}_v1"
)
s.replace(
library / f"google/cloud/gkehub_{library.name}/types/feature.py",
f"google.cloud.gkehub.{submodule}.v1.{submodule}_pb2",
f"google.cloud.gkehub_v1.{submodule}_v1"
)
s.replace(
library / f"google/cloud/gkehub_{library.name}/types/feature.py",
f"{submodule}_pb2",
f"{submodule}_v1"
)
# Work around docs issue. Fix proposed upstream in cl/382492769
s.replace(library / f"google/cloud/gkehub_{library.name}/types/feature.py",
" projects/{p}/locations/{l}/memberships/{m}",
"`projects/{p}/locations/{l}/memberships/{m}`"
)
# Work around docs issue. Fix proposed upstream in cl/382492769
s.replace(library / f"google/cloud/gkehub_{library.name}/types/membership.py",
"""the GKE cluster. For example:
//container.googleapis.com/projects/my-""",
"""the GKE cluster. For example:
//container.googleapis.com/projects/my-"""
)
excludes=[
"setup.py",
"README.rst",
"docs/index.rst",
"docs/configmanagement_v1/**",
"docs/multiclusteringress_v1/**",
"google/cloud/gkehub/configmanagement/**",
"google/cloud/gkehub/configmanagement_v1/**",
"google/cloud/gkehub/multiclusteringress/**",
"google/cloud/gkehub/multiclusteringress_v1/**"
]
s.move(library, excludes=excludes)
s.remove_staging_dirs()
# Work around gapic generator bug https://github.com/googleapis/gapic-generator-python/pull/1071
s.replace(
"google/cloud/**/types/*.py",
"""\.
This field is a member of `oneof`_""",
""".
This field is a member of `oneof`_"""
)
# Work around gapic generator bug
s.replace(
"google/cloud/**/types/configmanagement.py",
"""Configuration for Policy Controller\n
Attributes""",
"""Configuration for Policy Controller\n
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields\n
Attributes"""
)
# Work around issue with docstring
s.replace("google/cloud/gkehub_v1beta1/types/membership.py",
"""For example:
//gkeonprem.googleapis.com/projects/my-""",
"""For example:
//gkeonprem.googleapis.com/projects/my-""",
)
# Work around issue with docstring
s.replace("google/cloud/gkehub_v1beta1/types/membership.py",
"""For example:
//gkemulticloud.googleapis.com/projects/my-""",
"""For example:
//gkemulticloud.googleapis.com/projects/my-""",
)
# ----------------------------------------------------------------------------
# Add templated files
# ----------------------------------------------------------------------------
templated_files = common.py_library(cov_level=100, microgenerator=True)
python.py_samples(skip_readmes=True)
# the microgenerator has a good coveragerc file
excludes = [".coveragerc"]
s.move(
templated_files, excludes=excludes
)
s.shell.run(["nox", "-s", "blacken"], hide_output=False)
| true
| true
|
1c3ec2f91b2d99d51d3ba428e99bf769634ff906
| 1,435
|
py
|
Python
|
pipeline/migrations/0012_templatecurrentversion.py
|
sdgdsffdsfff/bk-sops-tencent
|
e8aff91f822e79031e12b0f66943830f44ced506
|
[
"Apache-2.0"
] | 1
|
2020-09-24T07:39:16.000Z
|
2020-09-24T07:39:16.000Z
|
pipeline/migrations/0012_templatecurrentversion.py
|
sdgdsffdsfff/bk-sops-tencent
|
e8aff91f822e79031e12b0f66943830f44ced506
|
[
"Apache-2.0"
] | 5
|
2021-02-08T20:46:54.000Z
|
2021-06-10T22:54:45.000Z
|
pipeline/migrations/0012_templatecurrentversion.py
|
sdgdsffdsfff/bk-sops-tencent
|
e8aff91f822e79031e12b0f66943830f44ced506
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2020 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
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 unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pipeline', '0011_auto_20180906_1045'),
]
operations = [
migrations.CreateModel(
name='TemplateCurrentVersion',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('template_id', models.CharField(max_length=32, verbose_name='\u6a21\u677fID', db_index=True)),
('current_version', models.CharField(max_length=32, verbose_name='\u5feb\u7167\u5b57\u7b26\u4e32\u7684md5')),
],
),
]
| 39.861111
| 125
| 0.712892
|
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pipeline', '0011_auto_20180906_1045'),
]
operations = [
migrations.CreateModel(
name='TemplateCurrentVersion',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('template_id', models.CharField(max_length=32, verbose_name='\u6a21\u677fID', db_index=True)),
('current_version', models.CharField(max_length=32, verbose_name='\u5feb\u7167\u5b57\u7b26\u4e32\u7684md5')),
],
),
]
| true
| true
|
1c3ec314a0794bcb54b14f7270db1ec2ee447f38
| 4,129
|
py
|
Python
|
sdk/python/pulumi_aws/storagegateway/upload_buffer.py
|
michael-golden/pulumi-aws
|
165e876e166ecab1870e857822247585d78aef64
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_aws/storagegateway/upload_buffer.py
|
michael-golden/pulumi-aws
|
165e876e166ecab1870e857822247585d78aef64
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_aws/storagegateway/upload_buffer.py
|
michael-golden/pulumi-aws
|
165e876e166ecab1870e857822247585d78aef64
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class UploadBuffer(pulumi.CustomResource):
disk_id: pulumi.Output[str]
"""
Local disk identifier. For example, `pci-0000:03:00.0-scsi-0:0:0:0`.
"""
gateway_arn: pulumi.Output[str]
"""
The Amazon Resource Name (ARN) of the gateway.
"""
def __init__(__self__, resource_name, opts=None, disk_id=None, gateway_arn=None, __props__=None, __name__=None, __opts__=None):
"""
Manages an AWS Storage Gateway upload buffer.
> **NOTE:** The Storage Gateway API provides no method to remove an upload buffer disk. Destroying this resource does not perform any Storage Gateway actions.
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.storagegateway.UploadBuffer("example",
disk_id=data["aws_storagegateway_local_disk"]["example"]["id"],
gateway_arn=aws_storagegateway_gateway["example"]["arn"])
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] disk_id: Local disk identifier. For example, `pci-0000:03:00.0-scsi-0:0:0:0`.
:param pulumi.Input[str] gateway_arn: The Amazon Resource Name (ARN) of the gateway.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if disk_id is None:
raise TypeError("Missing required property 'disk_id'")
__props__['disk_id'] = disk_id
if gateway_arn is None:
raise TypeError("Missing required property 'gateway_arn'")
__props__['gateway_arn'] = gateway_arn
super(UploadBuffer, __self__).__init__(
'aws:storagegateway/uploadBuffer:UploadBuffer',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name, id, opts=None, disk_id=None, gateway_arn=None):
"""
Get an existing UploadBuffer resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] disk_id: Local disk identifier. For example, `pci-0000:03:00.0-scsi-0:0:0:0`.
:param pulumi.Input[str] gateway_arn: The Amazon Resource Name (ARN) of the gateway.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["disk_id"] = disk_id
__props__["gateway_arn"] = gateway_arn
return UploadBuffer(resource_name, opts=opts, __props__=__props__)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 42.56701
| 166
| 0.661177
|
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class UploadBuffer(pulumi.CustomResource):
disk_id: pulumi.Output[str]
gateway_arn: pulumi.Output[str]
def __init__(__self__, resource_name, opts=None, disk_id=None, gateway_arn=None, __props__=None, __name__=None, __opts__=None):
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if disk_id is None:
raise TypeError("Missing required property 'disk_id'")
__props__['disk_id'] = disk_id
if gateway_arn is None:
raise TypeError("Missing required property 'gateway_arn'")
__props__['gateway_arn'] = gateway_arn
super(UploadBuffer, __self__).__init__(
'aws:storagegateway/uploadBuffer:UploadBuffer',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name, id, opts=None, disk_id=None, gateway_arn=None):
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["disk_id"] = disk_id
__props__["gateway_arn"] = gateway_arn
return UploadBuffer(resource_name, opts=opts, __props__=__props__)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| true
| true
|
1c3ec347f88de693871c19dc660539788c4b5eca
| 540
|
py
|
Python
|
PythonSolutions/763_partition_labels.py
|
ChuckChen2020/Leetcode
|
08f98365f0fac6e4a151b557b632000a34734585
|
[
"MIT"
] | null | null | null |
PythonSolutions/763_partition_labels.py
|
ChuckChen2020/Leetcode
|
08f98365f0fac6e4a151b557b632000a34734585
|
[
"MIT"
] | null | null | null |
PythonSolutions/763_partition_labels.py
|
ChuckChen2020/Leetcode
|
08f98365f0fac6e4a151b557b632000a34734585
|
[
"MIT"
] | null | null | null |
#16:39 - 17:03
def partitionLabels(S: str):
dic = dict()
for i, c in enumerate(S):
dic[c] = i
end_pos = 0
ret = []
i = 0
while i <= len(S) - 1:
start = i
end_pos = dic[S[i]]
while i != end_pos:
end_pos = max(end_pos, dic[S[i]])
i += 1
ret.append(end_pos - start + 1)
i = end_pos + 1
return ret
if __name__ == "__main__":
print(partitionLabels("ababcbacadefegdehijhklij"))
| 23.478261
| 54
| 0.444444
|
def partitionLabels(S: str):
dic = dict()
for i, c in enumerate(S):
dic[c] = i
end_pos = 0
ret = []
i = 0
while i <= len(S) - 1:
start = i
end_pos = dic[S[i]]
while i != end_pos:
end_pos = max(end_pos, dic[S[i]])
i += 1
ret.append(end_pos - start + 1)
i = end_pos + 1
return ret
if __name__ == "__main__":
print(partitionLabels("ababcbacadefegdehijhklij"))
| true
| true
|
1c3ec3d0cac39cf9941d9ca1c590fc595be6ee34
| 493
|
py
|
Python
|
sdk/python/pulumi_azure_native/dbforpostgresql/v20210410privatepreview/__init__.py
|
sebtelko/pulumi-azure-native
|
711ec021b5c73da05611c56c8a35adb0ce3244e4
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/dbforpostgresql/v20210410privatepreview/__init__.py
|
sebtelko/pulumi-azure-native
|
711ec021b5c73da05611c56c8a35adb0ce3244e4
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/dbforpostgresql/v20210410privatepreview/__init__.py
|
sebtelko/pulumi-azure-native
|
711ec021b5c73da05611c56c8a35adb0ce3244e4
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
from ... import _utilities
import typing
# Export this package's modules as members:
from ._enums import *
from .configuration import *
from .firewall_rule import *
from .get_configuration import *
from .get_firewall_rule import *
from .get_server import *
from .server import *
from ._inputs import *
from . import outputs
| 29
| 80
| 0.744422
|
from ... import _utilities
import typing
# Export this package's modules as members:
from ._enums import *
from .configuration import *
from .firewall_rule import *
from .get_configuration import *
from .get_firewall_rule import *
from .get_server import *
from .server import *
from ._inputs import *
from . import outputs
| true
| true
|
1c3ec44eeefd7593b2bd53357219e6fbeeb19a01
| 887
|
py
|
Python
|
azcam_scripts/__init__.py
|
mplesser/azcam-scripts
|
116106f98352ccfc6c97b748e361fe9dadd16888
|
[
"MIT"
] | null | null | null |
azcam_scripts/__init__.py
|
mplesser/azcam-scripts
|
116106f98352ccfc6c97b748e361fe9dadd16888
|
[
"MIT"
] | null | null | null |
azcam_scripts/__init__.py
|
mplesser/azcam-scripts
|
116106f98352ccfc6c97b748e361fe9dadd16888
|
[
"MIT"
] | null | null | null |
import importlib
import os
import azcam
def load(scripts="all") -> None:
"""
Load all scripts from folder into azcam.db.cli_tools
"""
rootpackage = "azcam_scripts"
folder = importlib.util.find_spec(rootpackage).submodule_search_locations[0]
# bring all .py modules with same function name into namespace
_, _, filenames = next(os.walk(folder))
pyfiles = []
for files in filenames:
if files.endswith(".py"):
pyfiles.append(files[:-3])
if "__init__" in pyfiles:
pyfiles.remove("__init__")
for pfile in pyfiles:
try:
mod = importlib.import_module(f"{rootpackage}.{pfile}")
func = getattr(mod, pfile)
azcam.db.cli_tools[pfile] = func
except Exception as e:
azcam.log(e)
azcam.AzcamWarning(f"Could not import script {pfile}")
return
| 26.088235
| 80
| 0.620068
|
import importlib
import os
import azcam
def load(scripts="all") -> None:
rootpackage = "azcam_scripts"
folder = importlib.util.find_spec(rootpackage).submodule_search_locations[0]
_, _, filenames = next(os.walk(folder))
pyfiles = []
for files in filenames:
if files.endswith(".py"):
pyfiles.append(files[:-3])
if "__init__" in pyfiles:
pyfiles.remove("__init__")
for pfile in pyfiles:
try:
mod = importlib.import_module(f"{rootpackage}.{pfile}")
func = getattr(mod, pfile)
azcam.db.cli_tools[pfile] = func
except Exception as e:
azcam.log(e)
azcam.AzcamWarning(f"Could not import script {pfile}")
return
| true
| true
|
1c3ec5820cfff6b1650df4f4de61fd0b17c04a7e
| 9,946
|
py
|
Python
|
api/src/opentrons/protocol_api/legacy_wrapper/containers_wrapper.py
|
theosanderson/opentrons
|
df9f41d3dc56bd03ff0ef4e804d1224221272ddf
|
[
"Apache-2.0"
] | null | null | null |
api/src/opentrons/protocol_api/legacy_wrapper/containers_wrapper.py
|
theosanderson/opentrons
|
df9f41d3dc56bd03ff0ef4e804d1224221272ddf
|
[
"Apache-2.0"
] | null | null | null |
api/src/opentrons/protocol_api/legacy_wrapper/containers_wrapper.py
|
theosanderson/opentrons
|
df9f41d3dc56bd03ff0ef4e804d1224221272ddf
|
[
"Apache-2.0"
] | null | null | null |
import logging
from typing import Any, Dict
import jsonschema # type: ignore
from opentrons.data_storage import database as db_cmds
from opentrons.protocol_api.labware import save_definition
from opentrons.config import CONFIG
from opentrons.legacy_api.containers.placeable import Container, Well
from .util import log_call
log = logging.getLogger(__name__)
MODULE_BLACKLIST = ['tempdeck', 'magdeck', 'temperature-plate']
LW_TRANSLATION = {
'6-well-plate': 'corning_6_wellplate_16.8ml_flat',
'12-well-plate': 'corning_12_wellplate_6.9_ml',
'24-well-plate': 'corning_24_wellplate_3.4_ml',
'48-well-plate': 'corning_48_wellplate_1.6ml_flat',
'384-plate': 'corning_384_wellplate_112ul_flat',
'96-deep-well': 'usascientific_96_wellplate_2.4ml_deep',
'96-flat': 'corning_96_wellplate_360ul_flat',
'96-PCR-flat': 'biorad_96_wellplate_200ul_pcr',
'96-PCR-tall': 'biorad_96_wellplate_200ul_pcr',
'biorad-hardshell-96-PCR': 'biorad_96_wellplate_200ul_pcr',
'alum-block-pcr-strips': 'opentrons_40_aluminumblock_eppendorf_24x2ml_safelock_snapcap_generic_16x0.2ml_pcr_strip', # noqa(E501)
'opentrons-aluminum-block-2ml-eppendorf': 'opentrons_24_aluminumblock_generic_2ml_screwcap', # noqa(E501)
'opentrons-aluminum-block-2ml-screwcap': 'opentrons_24_aluminumblock_generic_2ml_screwcap', # noqa(E501)
'opentrons-aluminum-block-96-PCR-plate': 'opentrons_96_aluminum_biorad_plate_200_ul', # noqa(E501)
'opentrons-aluminum-block-PCR-strips-200ul': 'opentrons_96_aluminumblock_generic_pcr_strip_200ul', # noqa(E501)
'opentrons-tiprack-300ul': 'opentrons_96_tiprack_300ul',
'opentrons-tuberack-1.5ml-eppendorf': 'opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap', # noqa(E501)
'opentrons-tuberack-15_50ml': 'opentrons_10_tuberack_falcon_4x50ml_6x15ml_conical', # noqa(E501)
'opentrons-tuberack-15ml': 'opentrons_15_tuberack_15_ml_falcon',
'opentrons-tuberack-2ml-eppendorf': 'opentrons_24_tuberack_eppendorf_2ml_safelock_snapcap', # noqa(E501)
'opentrons-tuberack-2ml-screwcap': 'opentrons_24_tuberack_generic_2ml_screwcap', # noqa(E501)
'opentrons-tuberack-50ml': 'opentrons_6_tuberack_falcon_50ml_conical',
'PCR-strip-tall': 'opentrons_96_aluminumblock_generic_pcr_strip_200ul',
'tiprack-10ul': 'opentrons_96_tiprack_10ul',
'tiprack-200ul': 'tipone_96_tiprack_200ul',
'tiprack-1000ul': 'opentrons_96_tiprack_1000ul',
'trash-box': 'agilent_1_reservoir_290ml',
'trough-12row': 'usascientific_12_reservoir_22ml',
'tube-rack-.75ml': 'opentrons_24_tuberack_generic_0.75ml_snapcap_acrylic', # noqa(E501)
'tube-rack-2ml': 'opentrons_24_tuberack_eppendorf_2ml_safelock_snapcap_acrylic', # noqa(E501)
'tube-rack-15_50ml': 'opentrons_10_tuberack_falcon_4x50ml_6x15ml_conical_acrylic', # noqa(E501)
}
""" A table mapping old labware names to new labware names"""
def _determine_well_names(labware: Container):
# In the instance that the labware only contains one well, we must
# not index labware.wells() as it is not contained inside a WellSeries
if isinstance(labware.wells(), Well):
wells = [labware.wells().get_name()]
first_well = labware.wells()
return wells, first_well
return [well.get_name() for well in labware.wells()], labware.wells()[0]
def _add_metadata_from_v1(
labware: Container, lw_dict: Dict[str, Any], is_tiprack: bool):
wells, first_well = _determine_well_names(labware)
# Labware Information
lw_dict['groups'] = [{
'wells': wells,
'metadata': {}}]
if is_tiprack:
lw_dict['parameters']['tipLength'] = labware._coordinates['z']
lw_dict['parameters']['tipOverlap'] = 0
if (len(wells) > 1) and (len(labware.rows()[0]) > 1)\
and (wells[0][0] == labware.rows()[0][1]):
# Very ugly logic to check for row ordering instead of column ordering
# as some old labwares do not follow our current convention.
lw_dict['ordering'] = [
[well.get_name() for well in row] for row in labware.rows()]
else:
lw_dict['ordering'] = [
[well.get_name() for well in col] for col in labware.columns()]
lw_dict['cornerOffsetFromSlot'] = {
'x': labware._coordinates['x'],
'y': labware._coordinates['y'],
'z': 0
}
height_val = first_well.properties.get(
'depth', first_well.properties.get('height', 0))
height = height_val + first_well._coordinates['z']
lw_dict['dimensions'] = {
'xDimension': 127.76,
'yDimension': 85.48,
'zDimension': height
}
def _convert_labware_name(labware_name: str) -> str:
return labware_name.replace("-", "_").lower()
def _format_labware_definition(labware_name: str, labware: Container = None):
lw_dict: Dict[str, Any] = {}
lw_dict['wells'] = {}
converted_labware_name = _convert_labware_name(labware_name)
is_tiprack = True if 'tip' in converted_labware_name else False
# Definition Metadata
lw_dict['brand'] = {'brand': 'opentrons'}
lw_dict['schemaVersion'] = 2
lw_dict['version'] = 1
lw_dict['namespace'] = 'legacy_api'
lw_dict['metadata'] = {
'displayName': converted_labware_name,
'displayCategory': 'tipRack' if is_tiprack else 'other',
'displayVolumeUnits': 'µL'}
lw_dict['parameters'] = {
'format': 'irregular',
'isMagneticModuleCompatible': False,
'loadName': converted_labware_name,
'isTiprack': is_tiprack}
# If this method is being called with a placeable labware,
# format metadata based off that labware info.
if labware:
_add_metadata_from_v1(labware, lw_dict, is_tiprack)
return lw_dict
def _add_well(
lw_dict: Dict[str, Any],
well_name: str,
well_props: Dict[str, Any],
well_coordinates):
# Format one API v2 well entry
lw_dict['wells'][well_name] = {
'x': well_coordinates['x'],
'y': well_coordinates['y'],
'z': well_coordinates['z'],
'totalLiquidVolume': well_props.get('total-liquid-volume', 0),
'depth': well_props.get('depth', 0)}
if well_props.get('diameter'):
lw_dict['wells'][well_name]['diameter'] = well_props.get('diameter')
lw_dict['wells'][well_name]['shape'] = 'circular'
else:
lw_dict['wells'][well_name]['xDimension'] = well_props.get('length')
lw_dict['wells'][well_name]['yDimension'] = well_props.get('width')
lw_dict['wells'][well_name]['shape'] = 'rectangular'
def create_new_labware_definition(labware: Container, labware_name: str):
# shape metadata/parameter keys for labwares in v2 schema format
lw_dict = _format_labware_definition(labware_name, labware)
# Well Information
for well in labware.wells():
well_props = well.properties
well_coords = well._coordinates
well_name = well.get_name()
_add_well(lw_dict, well_name, well_props, well_coords)
return lw_dict
def perform_migration():
path_to_save_defs = CONFIG['labware_user_definitions_dir_v2']
all_containers = filter(
lambda lw: lw not in MODULE_BLACKLIST,
db_cmds.list_all_containers())
# filter out all module and standard labwares from the database
labware_to_create = filter(
lambda x: x not in LW_TRANSLATION.keys(),
all_containers)
validation_failure = []
for lw_name in labware_to_create:
labware = db_cmds.load_container(lw_name)
if labware.wells():
log.info(f"Migrating {lw_name} to API v2 format")
labware_def = create_new_labware_definition(labware, lw_name)
try:
save_definition(labware_def, location=path_to_save_defs)
except jsonschema.exceptions.ValidationError:
validation_failure.append(lw_name)
print(f"validation failure on {lw_name}")
else:
log.info(f"Skipping migration of {lw_name} because there are no",
"wells associated with this labware.")
log.info("Migration of API V1 labware complete.")
return True, validation_failure
@log_call(log)
def load(robot, container_name, slot, label=None, share=False):
"""
Examples
--------
>>> from opentrons import containers
>>> containers.load('96-flat', '1')
<Deck>/<Slot 1>/<Container 96-flat>
>>> containers.load('96-flat', '4', 'plate')
<Deck>/<Slot 4>/<Container plate>
>>> containers.load('non-existent-type', '4') # doctest: +ELLIPSIS
Exception: Container type "non-existent-type" not found in file ...
"""
return None
@log_call(log)
def list():
return []
@log_call(log)
def create(name, grid, spacing, diameter, depth, volume=0):
"""
Creates a labware definition based on a rectangular gird, depth, diameter,
and spacing. Note that this function can only create labware with regularly
spaced wells in a rectangular format, of equal height, depth, and radius.
Irregular labware defintions will have to be made in other ways or modified
using a regular definition as a starting point. Also, upon creation a
definition always has its lower-left well at (0, 0, 0), such that this
labware _must_ be calibrated before use.
:param name: the name of the labware to be used with `labware.load`
:param grid: a 2-tuple of integers representing (<n_columns>, <n_rows>)
:param spacing: a 2-tuple of floats representing
(<col_spacing, <row_spacing)
:param diameter: a float representing the internal diameter of each well
:param depth: a float representing the distance from the top of each well
to the internal bottom of the same well
:param volume: [optional] the maximum volume of each well
:return: the labware object created by this function
"""
return None
| 41.789916
| 133
| 0.688619
|
import logging
from typing import Any, Dict
import jsonschema
from opentrons.data_storage import database as db_cmds
from opentrons.protocol_api.labware import save_definition
from opentrons.config import CONFIG
from opentrons.legacy_api.containers.placeable import Container, Well
from .util import log_call
log = logging.getLogger(__name__)
MODULE_BLACKLIST = ['tempdeck', 'magdeck', 'temperature-plate']
LW_TRANSLATION = {
'6-well-plate': 'corning_6_wellplate_16.8ml_flat',
'12-well-plate': 'corning_12_wellplate_6.9_ml',
'24-well-plate': 'corning_24_wellplate_3.4_ml',
'48-well-plate': 'corning_48_wellplate_1.6ml_flat',
'384-plate': 'corning_384_wellplate_112ul_flat',
'96-deep-well': 'usascientific_96_wellplate_2.4ml_deep',
'96-flat': 'corning_96_wellplate_360ul_flat',
'96-PCR-flat': 'biorad_96_wellplate_200ul_pcr',
'96-PCR-tall': 'biorad_96_wellplate_200ul_pcr',
'biorad-hardshell-96-PCR': 'biorad_96_wellplate_200ul_pcr',
'alum-block-pcr-strips': 'opentrons_40_aluminumblock_eppendorf_24x2ml_safelock_snapcap_generic_16x0.2ml_pcr_strip',
'opentrons-aluminum-block-2ml-eppendorf': 'opentrons_24_aluminumblock_generic_2ml_screwcap',
'opentrons-aluminum-block-2ml-screwcap': 'opentrons_24_aluminumblock_generic_2ml_screwcap',
'opentrons-aluminum-block-96-PCR-plate': 'opentrons_96_aluminum_biorad_plate_200_ul',
'opentrons-aluminum-block-PCR-strips-200ul': 'opentrons_96_aluminumblock_generic_pcr_strip_200ul',
'opentrons-tiprack-300ul': 'opentrons_96_tiprack_300ul',
'opentrons-tuberack-1.5ml-eppendorf': 'opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap',
'opentrons-tuberack-15_50ml': 'opentrons_10_tuberack_falcon_4x50ml_6x15ml_conical',
'opentrons-tuberack-15ml': 'opentrons_15_tuberack_15_ml_falcon',
'opentrons-tuberack-2ml-eppendorf': 'opentrons_24_tuberack_eppendorf_2ml_safelock_snapcap',
'opentrons-tuberack-2ml-screwcap': 'opentrons_24_tuberack_generic_2ml_screwcap',
'opentrons-tuberack-50ml': 'opentrons_6_tuberack_falcon_50ml_conical',
'PCR-strip-tall': 'opentrons_96_aluminumblock_generic_pcr_strip_200ul',
'tiprack-10ul': 'opentrons_96_tiprack_10ul',
'tiprack-200ul': 'tipone_96_tiprack_200ul',
'tiprack-1000ul': 'opentrons_96_tiprack_1000ul',
'trash-box': 'agilent_1_reservoir_290ml',
'trough-12row': 'usascientific_12_reservoir_22ml',
'tube-rack-.75ml': 'opentrons_24_tuberack_generic_0.75ml_snapcap_acrylic',
'tube-rack-2ml': 'opentrons_24_tuberack_eppendorf_2ml_safelock_snapcap_acrylic',
'tube-rack-15_50ml': 'opentrons_10_tuberack_falcon_4x50ml_6x15ml_conical_acrylic',
}
def _determine_well_names(labware: Container):
if isinstance(labware.wells(), Well):
wells = [labware.wells().get_name()]
first_well = labware.wells()
return wells, first_well
return [well.get_name() for well in labware.wells()], labware.wells()[0]
def _add_metadata_from_v1(
labware: Container, lw_dict: Dict[str, Any], is_tiprack: bool):
wells, first_well = _determine_well_names(labware)
lw_dict['groups'] = [{
'wells': wells,
'metadata': {}}]
if is_tiprack:
lw_dict['parameters']['tipLength'] = labware._coordinates['z']
lw_dict['parameters']['tipOverlap'] = 0
if (len(wells) > 1) and (len(labware.rows()[0]) > 1)\
and (wells[0][0] == labware.rows()[0][1]):
lw_dict['ordering'] = [
[well.get_name() for well in row] for row in labware.rows()]
else:
lw_dict['ordering'] = [
[well.get_name() for well in col] for col in labware.columns()]
lw_dict['cornerOffsetFromSlot'] = {
'x': labware._coordinates['x'],
'y': labware._coordinates['y'],
'z': 0
}
height_val = first_well.properties.get(
'depth', first_well.properties.get('height', 0))
height = height_val + first_well._coordinates['z']
lw_dict['dimensions'] = {
'xDimension': 127.76,
'yDimension': 85.48,
'zDimension': height
}
def _convert_labware_name(labware_name: str) -> str:
return labware_name.replace("-", "_").lower()
def _format_labware_definition(labware_name: str, labware: Container = None):
lw_dict: Dict[str, Any] = {}
lw_dict['wells'] = {}
converted_labware_name = _convert_labware_name(labware_name)
is_tiprack = True if 'tip' in converted_labware_name else False
lw_dict['brand'] = {'brand': 'opentrons'}
lw_dict['schemaVersion'] = 2
lw_dict['version'] = 1
lw_dict['namespace'] = 'legacy_api'
lw_dict['metadata'] = {
'displayName': converted_labware_name,
'displayCategory': 'tipRack' if is_tiprack else 'other',
'displayVolumeUnits': 'µL'}
lw_dict['parameters'] = {
'format': 'irregular',
'isMagneticModuleCompatible': False,
'loadName': converted_labware_name,
'isTiprack': is_tiprack}
if labware:
_add_metadata_from_v1(labware, lw_dict, is_tiprack)
return lw_dict
def _add_well(
lw_dict: Dict[str, Any],
well_name: str,
well_props: Dict[str, Any],
well_coordinates):
lw_dict['wells'][well_name] = {
'x': well_coordinates['x'],
'y': well_coordinates['y'],
'z': well_coordinates['z'],
'totalLiquidVolume': well_props.get('total-liquid-volume', 0),
'depth': well_props.get('depth', 0)}
if well_props.get('diameter'):
lw_dict['wells'][well_name]['diameter'] = well_props.get('diameter')
lw_dict['wells'][well_name]['shape'] = 'circular'
else:
lw_dict['wells'][well_name]['xDimension'] = well_props.get('length')
lw_dict['wells'][well_name]['yDimension'] = well_props.get('width')
lw_dict['wells'][well_name]['shape'] = 'rectangular'
def create_new_labware_definition(labware: Container, labware_name: str):
lw_dict = _format_labware_definition(labware_name, labware)
for well in labware.wells():
well_props = well.properties
well_coords = well._coordinates
well_name = well.get_name()
_add_well(lw_dict, well_name, well_props, well_coords)
return lw_dict
def perform_migration():
path_to_save_defs = CONFIG['labware_user_definitions_dir_v2']
all_containers = filter(
lambda lw: lw not in MODULE_BLACKLIST,
db_cmds.list_all_containers())
labware_to_create = filter(
lambda x: x not in LW_TRANSLATION.keys(),
all_containers)
validation_failure = []
for lw_name in labware_to_create:
labware = db_cmds.load_container(lw_name)
if labware.wells():
log.info(f"Migrating {lw_name} to API v2 format")
labware_def = create_new_labware_definition(labware, lw_name)
try:
save_definition(labware_def, location=path_to_save_defs)
except jsonschema.exceptions.ValidationError:
validation_failure.append(lw_name)
print(f"validation failure on {lw_name}")
else:
log.info(f"Skipping migration of {lw_name} because there are no",
"wells associated with this labware.")
log.info("Migration of API V1 labware complete.")
return True, validation_failure
@log_call(log)
def load(robot, container_name, slot, label=None, share=False):
return None
@log_call(log)
def list():
return []
@log_call(log)
def create(name, grid, spacing, diameter, depth, volume=0):
return None
| true
| true
|
1c3ec6385ed9ff148175b1e4f2ec924d2e0a25ce
| 55,048
|
py
|
Python
|
kafka/consumer/group.py
|
fchartier/kafka-python
|
8e1741edcf368e4eba6af6a7218f788d4aafcee8
|
[
"Apache-2.0"
] | null | null | null |
kafka/consumer/group.py
|
fchartier/kafka-python
|
8e1741edcf368e4eba6af6a7218f788d4aafcee8
|
[
"Apache-2.0"
] | null | null | null |
kafka/consumer/group.py
|
fchartier/kafka-python
|
8e1741edcf368e4eba6af6a7218f788d4aafcee8
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import absolute_import, division
import copy
import logging
import socket
import time
from kafka.errors import KafkaConfigurationError, UnsupportedVersionError
from kafka.vendor import six
from kafka.client_async import KafkaClient, selectors
from kafka.consumer.fetcher import Fetcher
from kafka.consumer.subscription_state import SubscriptionState
from kafka.coordinator.consumer import ConsumerCoordinator
from kafka.coordinator.assignors.range import RangePartitionAssignor
from kafka.coordinator.assignors.roundrobin import RoundRobinPartitionAssignor
from kafka.metrics import MetricConfig, Metrics
from kafka.protocol.offset import OffsetResetStrategy
from kafka.structs import TopicPartition
from kafka.version import __version__
log = logging.getLogger(__name__)
class KafkaConsumer(six.Iterator):
"""Consume records from a Kafka cluster.
The consumer will transparently handle the failure of servers in the Kafka
cluster, and adapt as topic-partitions are created or migrate between
brokers. It also interacts with the assigned kafka Group Coordinator node
to allow multiple consumers to load balance consumption of topics (requires
kafka >= 0.9.0.0).
The consumer is not thread safe and should not be shared across threads.
Arguments:
*topics (str): optional list of topics to subscribe to. If not set,
call :meth:`~kafka.KafkaConsumer.subscribe` or
:meth:`~kafka.KafkaConsumer.assign` before consuming records.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): A name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
group_id (str or None): The name of the consumer group to join for dynamic
partition assignment (if enabled), and to use for fetching and
committing offsets. If None, auto-partition assignment (via
group coordinator) and offset commits are disabled.
Default: None
key_deserializer (callable): Any callable that takes a
raw message key and returns a deserialized key.
value_deserializer (callable): Any callable that takes a
raw message value and returns a deserialized value.
fetch_min_bytes (int): Minimum amount of data the server should
return for a fetch request, otherwise wait up to
fetch_max_wait_ms for more data to accumulate. Default: 1.
fetch_max_wait_ms (int): The maximum amount of time in milliseconds
the server will block before answering the fetch request if
there isn't sufficient data to immediately satisfy the
requirement given by fetch_min_bytes. Default: 500.
fetch_max_bytes (int): The maximum amount of data the server should
return for a fetch request. This is not an absolute maximum, if the
first message in the first non-empty partition of the fetch is
larger than this value, the message will still be returned to
ensure that the consumer can make progress. NOTE: consumer performs
fetches to multiple brokers in parallel so memory usage will depend
on the number of brokers containing partitions for the topic.
Supported Kafka version >= 0.10.1.0. Default: 52428800 (50 MB).
max_partition_fetch_bytes (int): The maximum amount of data
per-partition the server will return. The maximum total memory
used for a request = #partitions * max_partition_fetch_bytes.
This size must be at least as large as the maximum message size
the server allows or else it is possible for the producer to
send messages larger than the consumer can fetch. If that
happens, the consumer can get stuck trying to fetch a large
message on a certain partition. Default: 1048576.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 305000.
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
reconnect_backoff_max_ms (int): The maximum amount of time in
milliseconds to wait when reconnecting to a broker that has
repeatedly failed to connect. If provided, the backoff per host
will increase exponentially for each consecutive connection
failure, up to this maximum. To avoid connection storms, a
randomization factor of 0.2 will be applied to the backoff
resulting in a random range between 20% below and 20% above
the computed value. Default: 1000.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
auto_offset_reset (str): A policy for resetting offsets on
OffsetOutOfRange errors: 'earliest' will move to the oldest
available message, 'latest' will move to the most recent. Any
other value will raise the exception. Default: 'latest'.
enable_auto_commit (bool): If True , the consumer's offset will be
periodically committed in the background. Default: True.
auto_commit_interval_ms (int): Number of milliseconds between automatic
offset commits, if enable_auto_commit is True. Default: 5000.
default_offset_commit_callback (callable): Called as
callback(offsets, response) response will be either an Exception
or an OffsetCommitResponse struct. This callback can be used to
trigger custom actions when a commit request completes.
check_crcs (bool): Automatically check the CRC32 of the records
consumed. This ensures no on-the-wire or on-disk corruption to
the messages occurred. This check adds some overhead, so it may
be disabled in cases seeking extreme performance. Default: True
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata, even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
partition_assignment_strategy (list): List of objects to use to
distribute partition ownership amongst consumer instances when
group management is used.
Default: [RangePartitionAssignor, RoundRobinPartitionAssignor]
max_poll_records (int): The maximum number of records returned in a
single call to :meth:`~kafka.KafkaConsumer.poll`. Default: 500
max_poll_interval_ms (int): The maximum delay between invocations of
:meth:`~kafka.KafkaConsumer.poll` when using consumer group
management. This places an upper bound on the amount of time that
the consumer can be idle before fetching more records. If
:meth:`~kafka.KafkaConsumer.poll` is not called before expiration
of this timeout, then the consumer is considered failed and the
group will rebalance in order to reassign the partitions to another
member. Default 300000
session_timeout_ms (int): The timeout used to detect failures when
using Kafka's group management facilities. The consumer sends
periodic heartbeats to indicate its liveness to the broker. If
no heartbeats are received by the broker before the expiration of
this session timeout, then the broker will remove this consumer
from the group and initiate a rebalance. Note that the value must
be in the allowable range as configured in the broker configuration
by group.min.session.timeout.ms and group.max.session.timeout.ms.
Default: 10000
heartbeat_interval_ms (int): The expected time in milliseconds
between heartbeats to the consumer coordinator when using
Kafka's group management facilities. Heartbeats are used to ensure
that the consumer's session stays active and to facilitate
rebalancing when new consumers join or leave the group. The
value must be set lower than session_timeout_ms, but typically
should be set no higher than 1/3 of that value. It can be
adjusted even lower to control the expected time for normal
rebalances. Default: 3000
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: None (relies on
system defaults). The java client defaults to 32768.
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: None (relies on
system defaults). The java client defaults to 131072.
socket_options (list): List of tuple-arguments to socket.setsockopt
to apply to broker connection sockets. Default:
[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
consumer_timeout_ms (int): number of milliseconds to block during
message iteration before raising StopIteration (i.e., ending the
iterator). Default block forever [float('inf')].
skip_double_compressed_messages (bool): A bug in KafkaProducer <= 1.2.4
caused some messages to be corrupted via double-compression.
By default, the fetcher will return these messages as a compressed
blob of bytes with a single offset, i.e. how the message was
actually published to the cluster. If you prefer to have the
fetcher automatically detect corrupt messages and skip them,
set this option to True. Default: False.
security_protocol (str): Protocol used to communicate with brokers.
Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT.
ssl_context (ssl.SSLContext): Pre-configured SSLContext for wrapping
socket connections. If provided, all other ssl_* configurations
will be ignored. Default: None.
ssl_check_hostname (bool): Flag to configure whether ssl handshake
should verify that the certificate matches the brokers hostname.
Default: True.
ssl_cafile (str): Optional filename of ca file to use in certificate
verification. Default: None.
ssl_certfile (str): Optional filename of file in pem format containing
the client certificate, as well as any ca certificates needed to
establish the certificate's authenticity. Default: None.
ssl_keyfile (str): Optional filename containing the client private key.
Default: None.
ssl_password (str): Optional password to be used when loading the
certificate chain. Default: None.
ssl_crlfile (str): Optional filename containing the CRL to check for
certificate expiration. By default, no CRL check is done. When
providing a file, only the leaf certificate will be checked against
this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
Default: None.
api_version (tuple): Specify which Kafka API version to use. If set to
None, the client will attempt to infer the broker version by probing
various APIs. Different versions enable different functionality.
Examples:
(0, 9) enables full group coordination features with automatic
partition assignment and rebalancing,
(0, 8, 2) enables kafka-storage offset commits with manual
partition assignment only,
(0, 8, 1) enables zookeeper-storage offset commits with manual
partition assignment only,
(0, 8, 0) enables basic functionality but requires manual
partition assignment and offset management.
Default: None
api_version_auto_timeout_ms (int): number of milliseconds to throw a
timeout exception from the constructor when checking the broker
api version. Only applies if api_version set to 'auto'
connections_max_idle_ms: Close idle connections after the number of
milliseconds specified by this config. The broker closes idle
connections after connections.max.idle.ms, so this avoids hitting
unexpected socket disconnected errors on the client.
Default: 540000
metric_reporters (list): A list of classes to use as metrics reporters.
Implementing the AbstractMetricsReporter interface allows plugging
in classes that will be notified of new metric creation. Default: []
metrics_num_samples (int): The number of samples maintained to compute
metrics. Default: 2
metrics_sample_window_ms (int): The maximum age in milliseconds of
samples used to compute metrics. Default: 30000
selector (selectors.BaseSelector): Provide a specific selector
implementation to use for I/O multiplexing.
Default: selectors.DefaultSelector
exclude_internal_topics (bool): Whether records from internal topics
(such as offsets) should be exposed to the consumer. If set to True
the only way to receive records from an internal topic is
subscribing to it. Requires 0.10+ Default: True
sasl_mechanism (str): String picking sasl mechanism when security_protocol
is SASL_PLAINTEXT or SASL_SSL. Currently only PLAIN is supported.
Default: None
sasl_plain_username (str): Username for sasl PLAIN authentication.
Default: None
sasl_plain_password (str): Password for sasl PLAIN authentication.
Default: None
sasl_kerberos_service_name (str): Service name to include in GSSAPI
sasl mechanism handshake. Default: 'kafka'
Note:
Configuration parameters are described in more detail at
https://kafka.apache.org/documentation/#newconsumerconfigs
"""
DEFAULT_CONFIG = {
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'group_id': None,
'key_deserializer': None,
'value_deserializer': None,
'fetch_max_wait_ms': 500,
'fetch_min_bytes': 1,
'fetch_max_bytes': 52428800,
'max_partition_fetch_bytes': 1 * 1024 * 1024,
'request_timeout_ms': 305000, # chosen to be higher than the default of max_poll_interval_ms
'retry_backoff_ms': 100,
'reconnect_backoff_ms': 50,
'reconnect_backoff_max_ms': 1000,
'max_in_flight_requests_per_connection': 5,
'auto_offset_reset': 'latest',
'enable_auto_commit': True,
'auto_commit_interval_ms': 5000,
'default_offset_commit_callback': lambda offsets, response: True,
'check_crcs': True,
'metadata_max_age_ms': 5 * 60 * 1000,
'partition_assignment_strategy': (RangePartitionAssignor, RoundRobinPartitionAssignor),
'max_poll_records': 500,
'max_poll_interval_ms': 300000,
'session_timeout_ms': 10000,
'heartbeat_interval_ms': 3000,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'sock_chunk_bytes': 4096, # undocumented experimental option
'sock_chunk_buffer_count': 1000, # undocumented experimental option
'consumer_timeout_ms': float('inf'),
'skip_double_compressed_messages': False,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_crlfile': None,
'ssl_password': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'connections_max_idle_ms': 9 * 60 * 1000,
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
'metric_group_prefix': 'consumer',
'selector': selectors.DefaultSelector,
'exclude_internal_topics': True,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
'sasl_kerberos_service_name': 'kafka'
}
DEFAULT_SESSION_TIMEOUT_MS_0_9 = 30000
def __init__(self, *topics, **configs):
# Only check for extra config keys in top-level class
extra_configs = set(configs).difference(self.DEFAULT_CONFIG)
if extra_configs:
raise KafkaConfigurationError("Unrecognized configs: %s" % extra_configs)
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
deprecated = {'smallest': 'earliest', 'largest': 'latest'}
if self.config['auto_offset_reset'] in deprecated:
new_config = deprecated[self.config['auto_offset_reset']]
log.warning('use auto_offset_reset=%s (%s is deprecated)',
new_config, self.config['auto_offset_reset'])
self.config['auto_offset_reset'] = new_config
request_timeout_ms = self.config['request_timeout_ms']
fetch_max_wait_ms = self.config['fetch_max_wait_ms']
if request_timeout_ms <= fetch_max_wait_ms:
raise KafkaConfigurationError("Request timeout (%s) must be larger than fetch-max-wait-ms (%s)" %
(request_timeout_ms, fetch_max_wait_ms))
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [reporter() for reporter in self.config['metric_reporters']]
self._metrics = Metrics(metric_config, reporters)
# TODO _metrics likely needs to be passed to KafkaClient, etc.
# api_version was previously a str. Accept old format for now
if isinstance(self.config['api_version'], str):
str_version = self.config['api_version']
if str_version == 'auto':
self.config['api_version'] = None
else:
self.config['api_version'] = tuple(map(int, str_version.split('.')))
log.warning('use api_version=%s [tuple] -- "%s" as str is deprecated',
str(self.config['api_version']), str_version)
self._client = KafkaClient(metrics=self._metrics, **self.config)
# Get auto-discovered version from client if necessary
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
# Coordinator configurations are different for older brokers
# max_poll_interval_ms is not supported directly -- it must the be
# the same as session_timeout_ms. If the user provides one of them,
# use it for both. Otherwise use the old default of 30secs
if self.config['api_version'] < (0, 10, 1):
if 'session_timeout_ms' not in configs:
if 'max_poll_interval_ms' in configs:
self.config['session_timeout_ms'] = configs['max_poll_interval_ms']
else:
self.config['session_timeout_ms'] = self.DEFAULT_SESSION_TIMEOUT_MS_0_9
if 'max_poll_interval_ms' not in configs:
self.config['max_poll_interval_ms'] = self.config['session_timeout_ms']
if self.config['group_id'] is not None:
if self.config['request_timeout_ms'] <= self.config['session_timeout_ms']:
raise KafkaConfigurationError(
"Request timeout (%s) must be larger than session timeout (%s)" %
(self.config['request_timeout_ms'], self.config['session_timeout_ms']))
self._subscription = SubscriptionState(self.config['auto_offset_reset'])
self._fetcher = Fetcher(
self._client, self._subscription, self._metrics, **self.config)
self._coordinator = ConsumerCoordinator(
self._client, self._subscription, self._metrics,
assignors=self.config['partition_assignment_strategy'],
**self.config)
self._closed = False
self._iterator = None
self._consumer_timeout = float('inf')
if topics:
self._subscription.subscribe(topics=topics)
self._client.set_topics(topics)
def assign(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
Arguments:
partitions (list of TopicPartition): Assignment for this instance.
Raises:
IllegalStateError: If consumer has already called
:meth:`~kafka.KafkaConsumer.subscribe`.
Warning:
It is not possible to use both manual partition assignment with
:meth:`~kafka.KafkaConsumer.assign` and group assignment with
:meth:`~kafka.KafkaConsumer.subscribe`.
Note:
This interface does not support incremental assignment and will
replace the previous assignment (if there was one).
Note:
Manual topic assignment through this method does not use the
consumer's group management functionality. As such, there will be
no rebalance operation triggered when group membership or cluster
and topic metadata change.
"""
self._subscription.assign_from_user(partitions)
self._client.set_topics([tp.topic for tp in partitions])
def assignment(self):
"""Get the TopicPartitions currently assigned to this consumer.
If partitions were directly assigned using
:meth:`~kafka.KafkaConsumer.assign`, then this will simply return the
same partitions that were previously assigned. If topics were
subscribed using :meth:`~kafka.KafkaConsumer.subscribe`, then this will
give the set of topic partitions currently assigned to the consumer
(which may be None if the assignment hasn't happened yet, or if the
partitions are in the process of being reassigned).
Returns:
set: {TopicPartition, ...}
"""
return self._subscription.assigned_partitions()
def close(self, autocommit=True):
"""Close the consumer, waiting indefinitely for any needed cleanup.
Keyword Arguments:
autocommit (bool): If auto-commit is configured for this consumer,
this optional flag causes the consumer to attempt to commit any
pending consumed offsets prior to close. Default: True
"""
if self._closed:
return
log.debug("Closing the KafkaConsumer.")
self._closed = True
self._coordinator.close(autocommit=autocommit)
self._metrics.close()
self._client.close()
try:
self.config['key_deserializer'].close()
except AttributeError:
pass
try:
self.config['value_deserializer'].close()
except AttributeError:
pass
log.debug("The KafkaConsumer has closed.")
def commit_async(self, offsets=None, callback=None):
"""Commit offsets to kafka asynchronously, optionally firing callback.
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used. To avoid re-processing the last
message read if a consumer is restarted, the committed offset should be
the next message your application should consume, i.e.: last_offset + 1.
This is an asynchronous call and will not block. Any errors encountered
are either passed to the callback (if provided) or discarded.
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to currently
consumed offsets for all subscribed partitions.
callback (callable, optional): Called as callback(offsets, response)
with response as either an Exception or an OffsetCommitResponse
struct. This callback can be used to trigger custom actions when
a commit request completes.
Returns:
kafka.future.Future
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
log.debug("Committing offsets: %s", offsets)
future = self._coordinator.commit_offsets_async(
offsets, callback=callback)
return future
def commit(self, offsets=None):
"""Commit offsets to kafka, blocking until success or error.
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used. To avoid re-processing the last
message read if a consumer is restarted, the committed offset should be
the next message your application should consume, i.e.: last_offset + 1.
Blocks until either the commit succeeds or an unrecoverable error is
encountered (in which case it is thrown to the caller).
Currently only supports kafka-topic offset storage (not zookeeper).
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to currently
consumed offsets for all subscribed partitions.
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
self._coordinator.commit_offsets_sync(offsets)
def committed(self, partition):
"""Get the last committed offset for the given partition.
This offset will be used as the position for the consumer
in the event of a failure.
This call may block to do a remote call if the partition in question
isn't assigned to this consumer or if the consumer hasn't yet
initialized its cache of committed offsets.
Arguments:
partition (TopicPartition): The partition to check.
Returns:
The last committed offset, or None if there was no prior commit.
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
if self._subscription.is_assigned(partition):
committed = self._subscription.assignment[partition].committed
if committed is None:
self._coordinator.refresh_committed_offsets_if_needed()
committed = self._subscription.assignment[partition].committed
else:
commit_map = self._coordinator.fetch_committed_offsets([partition])
if partition in commit_map:
committed = commit_map[partition].offset
else:
committed = None
return committed
def topics(self):
"""Get all topics the user is authorized to view.
Returns:
set: topics
"""
cluster = self._client.cluster
if self._client._metadata_refresh_in_progress and self._client._topics:
future = cluster.request_update()
self._client.poll(future=future)
stash = cluster.need_all_topic_metadata
cluster.need_all_topic_metadata = True
future = cluster.request_update()
self._client.poll(future=future)
cluster.need_all_topic_metadata = stash
return cluster.topics()
def partitions_for_topic(self, topic):
"""Get metadata about the partitions for a given topic.
Arguments:
topic (str): Topic to check.
Returns:
set: Partition ids
"""
return self._client.cluster.partitions_for_topic(topic)
def poll(self, timeout_ms=0, max_records=None):
"""Fetch data from assigned topics / partitions.
Records are fetched and returned in batches by topic-partition.
On each poll, consumer will try to use the last consumed offset as the
starting offset and fetch sequentially. The last consumed offset can be
manually set through :meth:`~kafka.KafkaConsumer.seek` or automatically
set as the last committed offset for the subscribed list of partitions.
Incompatible with iterator interface -- use one or the other, not both.
Arguments:
timeout_ms (int, optional): Milliseconds spent waiting in poll if
data is not available in the buffer. If 0, returns immediately
with any records that are available currently in the buffer,
else returns empty. Must not be negative. Default: 0
max_records (int, optional): The maximum number of records returned
in a single call to :meth:`~kafka.KafkaConsumer.poll`.
Default: Inherit value from max_poll_records.
Returns:
dict: Topic to list of records since the last fetch for the
subscribed list of topics and partitions.
"""
assert timeout_ms >= 0, 'Timeout must not be negative'
if max_records is None:
max_records = self.config['max_poll_records']
assert isinstance(max_records, int), 'max_records must be an integer'
assert max_records > 0, 'max_records must be positive'
# Poll for new data until the timeout expires
start = time.time()
remaining = timeout_ms
while True:
records = self._poll_once(remaining, max_records)
if records:
return records
elapsed_ms = (time.time() - start) * 1000
remaining = timeout_ms - elapsed_ms
if remaining <= 0:
return {}
def _poll_once(self, timeout_ms, max_records):
"""Do one round of polling. In addition to checking for new data, this does
any needed heart-beating, auto-commits, and offset updates.
Arguments:
timeout_ms (int): The maximum time in milliseconds to block.
Returns:
dict: Map of topic to list of records (may be empty).
"""
self._coordinator.poll()
# Fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
self._update_fetch_positions(self._subscription.missing_fetch_positions())
# If data is available already, e.g. from a previous network client
# poll() call to commit, then just return it immediately
records, partial = self._fetcher.fetched_records(max_records)
if records:
# Before returning the fetched records, we can send off the
# next round of fetches and avoid block waiting for their
# responses to enable pipelining while the user is handling the
# fetched records.
if not partial:
self._fetcher.send_fetches()
return records
# Send any new fetches (won't resend pending fetches)
self._fetcher.send_fetches()
timeout_ms = min(timeout_ms, self._coordinator.time_to_next_poll() * 1000)
self._client.poll(timeout_ms=timeout_ms)
# after the long poll, we should check whether the group needs to rebalance
# prior to returning data so that the group can stabilize faster
if self._coordinator.need_rejoin():
return {}
records, _ = self._fetcher.fetched_records(max_records)
return records
def position(self, partition):
"""Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): Partition to check
Returns:
int: Offset
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
offset = self._subscription.assignment[partition].position
if offset is None:
self._update_fetch_positions([partition])
offset = self._subscription.assignment[partition].position
return offset
def highwater(self, partition):
"""Last known highwater offset for a partition.
A highwater offset is the offset that will be assigned to the next
message that is produced. It may be useful for calculating lag, by
comparing with the reported position. Note that both position and
highwater refer to the *next* offset -- i.e., highwater offset is
one greater than the newest available message.
Highwater offsets are returned in FetchResponse messages, so will
not be available if no FetchRequests have been sent for this partition
yet.
Arguments:
partition (TopicPartition): Partition to check
Returns:
int or None: Offset if available
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
return self._subscription.assignment[partition].highwater
def pause(self, *partitions):
"""Suspend fetching from the requested partitions.
Future calls to :meth:`~kafka.KafkaConsumer.poll` will not return any
records from these partitions until they have been resumed using
:meth:`~kafka.KafkaConsumer.resume`.
Note: This method does not affect partition subscription. In particular,
it does not cause a group rebalance when automatic assignment is used.
Arguments:
*partitions (TopicPartition): Partitions to pause.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Pausing partition %s", partition)
self._subscription.pause(partition)
def paused(self):
"""Get the partitions that were previously paused using
:meth:`~kafka.KafkaConsumer.pause`.
Returns:
set: {partition (TopicPartition), ...}
"""
return self._subscription.paused_partitions()
def resume(self, *partitions):
"""Resume fetching from the specified (paused) partitions.
Arguments:
*partitions (TopicPartition): Partitions to resume.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Resuming partition %s", partition)
self._subscription.resume(partition)
def seek(self, partition, offset):
"""Manually specify the fetch offset for a TopicPartition.
Overrides the fetch offsets that the consumer will use on the next
:meth:`~kafka.KafkaConsumer.poll`. If this API is invoked for the same
partition more than once, the latest offset will be used on the next
:meth:`~kafka.KafkaConsumer.poll`.
Note: You may lose data if this API is arbitrarily used in the middle of
consumption to reset the fetch offsets.
Arguments:
partition (TopicPartition): Partition for seek operation
offset (int): Message offset in partition
Raises:
AssertionError: If offset is not an int >= 0; or if partition is not
currently assigned.
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert isinstance(offset, int) and offset >= 0, 'Offset must be >= 0'
assert partition in self._subscription.assigned_partitions(), 'Unassigned partition'
log.debug("Seeking to offset %s for partition %s", offset, partition)
self._subscription.assignment[partition].seek(offset)
def seek_to_beginning(self, *partitions):
"""Seek to the oldest available offset for partitions.
Arguments:
*partitions: Optionally provide specific TopicPartitions, otherwise
default to all assigned partitions.
Raises:
AssertionError: If any partition is not currently assigned, or if
no partitions are assigned.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to beginning of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.EARLIEST)
def seek_to_end(self, *partitions):
"""Seek to the most recent available offset for partitions.
Arguments:
*partitions: Optionally provide specific TopicPartitions, otherwise
default to all assigned partitions.
Raises:
AssertionError: If any partition is not currently assigned, or if
no partitions are assigned.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to end of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.LATEST)
def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern.
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is one).
This method is incompatible with :meth:`~kafka.KafkaConsumer.assign`.
Arguments:
topics (list): List of topics for subscription.
pattern (str): Pattern to match available topics. You must provide
either topics or pattern, but not both.
listener (ConsumerRebalanceListener): Optionally include listener
callback, which will be called before and after each rebalance
operation.
As part of group management, the consumer will keep track of the
list of consumers that belong to a particular group and will
trigger a rebalance operation if one of the following events
trigger:
* Number of partitions change for any of the subscribed topics
* Topic is created or deleted
* An existing member of the consumer group dies
* A new member is added to the consumer group
When any of these events are triggered, the provided listener
will be invoked first to indicate that the consumer's assignment
has been revoked, and then again when the new assignment has
been received. Note that this listener will immediately override
any listener set in a previous call to subscribe. It is
guaranteed, however, that the partitions revoked/assigned
through this interface are from topics subscribed in this call.
Raises:
IllegalStateError: If called after previously calling
:meth:`~kafka.KafkaConsumer.assign`.
AssertionError: If neither topics or pattern is provided.
TypeError: If listener is not a ConsumerRebalanceListener.
"""
# SubscriptionState handles error checking
self._subscription.subscribe(topics=topics,
pattern=pattern,
listener=listener)
# Regex will need all topic metadata
if pattern is not None:
self._client.cluster.need_all_topic_metadata = True
self._client.set_topics([])
self._client.cluster.request_update()
log.debug("Subscribed to topic pattern: %s", pattern)
else:
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics(self._subscription.group_subscription())
log.debug("Subscribed to topic(s): %s", topics)
def subscription(self):
"""Get the current topic subscription.
Returns:
set: {topic, ...}
"""
if self._subscription.subscription is None:
return None
return self._subscription.subscription.copy()
def unsubscribe(self):
"""Unsubscribe from all topics and clear all assigned partitions."""
self._subscription.unsubscribe()
self._coordinator.close()
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics([])
log.debug("Unsubscribed all topics or patterns and assigned partitions")
def metrics(self, raw=False):
"""Get metrics on consumer performance.
This is ported from the Java Consumer, for details see:
https://kafka.apache.org/documentation/#new_consumer_monitoring
Warning:
This is an unstable interface. It may change in future
releases without warning.
"""
if raw:
return self._metrics.metrics
metrics = {}
for k, v in six.iteritems(self._metrics.metrics):
if k.group not in metrics:
metrics[k.group] = {}
if k.name not in metrics[k.group]:
metrics[k.group][k.name] = {}
metrics[k.group][k.name] = v.value()
return metrics
def offsets_for_times(self, timestamps):
"""Look up the offsets for the given partitions by timestamp. The
returned offset for each partition is the earliest offset whose
timestamp is greater than or equal to the given timestamp in the
corresponding partition.
This is a blocking call. The consumer does not have to be assigned the
partitions.
If the message format version in a partition is before 0.10.0, i.e.
the messages do not have timestamps, ``None`` will be returned for that
partition. ``None`` will also be returned for the partition if there
are no messages in it.
Note:
This method may block indefinitely if the partition does not exist.
Arguments:
timestamps (dict): ``{TopicPartition: int}`` mapping from partition
to the timestamp to look up. Unit should be milliseconds since
beginning of the epoch (midnight Jan 1, 1970 (UTC))
Returns:
``{TopicPartition: OffsetAndTimestamp}``: mapping from partition
to the timestamp and offset of the first message with timestamp
greater than or equal to the target timestamp.
Raises:
ValueError: If the target timestamp is negative
UnsupportedVersionError: If the broker does not support looking
up the offsets by timestamp.
KafkaTimeoutError: If fetch failed in request_timeout_ms
"""
if self.config['api_version'] <= (0, 10, 0):
raise UnsupportedVersionError(
"offsets_for_times API not supported for cluster version {}"
.format(self.config['api_version']))
for tp, ts in six.iteritems(timestamps):
timestamps[tp] = int(ts)
if ts < 0:
raise ValueError(
"The target time for partition {} is {}. The target time "
"cannot be negative.".format(tp, ts))
return self._fetcher.get_offsets_by_times(
timestamps, self.config['request_timeout_ms'])
def beginning_offsets(self, partitions):
"""Get the first offset for the given partitions.
This method does not change the current consumer position of the
partitions.
Note:
This method may block indefinitely if the partition does not exist.
Arguments:
partitions (list): List of TopicPartition instances to fetch
offsets for.
Returns:
``{TopicPartition: int}``: The earliest available offsets for the
given partitions.
Raises:
UnsupportedVersionError: If the broker does not support looking
up the offsets by timestamp.
KafkaTimeoutError: If fetch failed in request_timeout_ms.
"""
offsets = self._fetcher.beginning_offsets(
partitions, self.config['request_timeout_ms'])
return offsets
def end_offsets(self, partitions):
"""Get the last offset for the given partitions. The last offset of a
partition is the offset of the upcoming message, i.e. the offset of the
last available message + 1.
This method does not change the current consumer position of the
partitions.
Note:
This method may block indefinitely if the partition does not exist.
Arguments:
partitions (list): List of TopicPartition instances to fetch
offsets for.
Returns:
``{TopicPartition: int}``: The end offsets for the given partitions.
Raises:
UnsupportedVersionError: If the broker does not support looking
up the offsets by timestamp.
KafkaTimeoutError: If fetch failed in request_timeout_ms
"""
offsets = self._fetcher.end_offsets(
partitions, self.config['request_timeout_ms'])
return offsets
def _use_consumer_group(self):
"""Return True iff this consumer can/should join a broker-coordinated group."""
if self.config['api_version'] < (0, 9):
return False
elif self.config['group_id'] is None:
return False
elif not self._subscription.partitions_auto_assigned():
return False
return True
def _update_fetch_positions(self, partitions):
"""Set the fetch position to the committed position (if there is one)
or reset it using the offset reset policy the user has configured.
Arguments:
partitions (List[TopicPartition]): The partitions that need
updating fetch positions.
Raises:
NoOffsetForPartitionError: If no offset is stored for a given
partition and no offset reset policy is defined.
"""
# Lookup any positions for partitions which are awaiting reset (which may be the
# case if the user called :meth:`seek_to_beginning` or :meth:`seek_to_end`. We do
# this check first to avoid an unnecessary lookup of committed offsets (which
# typically occurs when the user is manually assigning partitions and managing
# their own offsets).
self._fetcher.reset_offsets_if_needed(partitions)
if not self._subscription.has_all_fetch_positions():
# if we still don't have offsets for all partitions, then we should either seek
# to the last committed position or reset using the auto reset policy
if (self.config['api_version'] >= (0, 8, 1) and
self.config['group_id'] is not None):
# first refresh commits for all assigned partitions
self._coordinator.refresh_committed_offsets_if_needed()
# Then, do any offset lookups in case some positions are not known
self._fetcher.update_fetch_positions(partitions)
def _message_generator(self):
assert self.assignment() or self.subscription() is not None, 'No topic subscription or manual partition assignment'
while time.time() < self._consumer_timeout:
self._coordinator.poll()
# Fetch offsets for any subscribed partitions that we arent tracking yet
if not self._subscription.has_all_fetch_positions():
partitions = self._subscription.missing_fetch_positions()
self._update_fetch_positions(partitions)
poll_ms = 1000 * (self._consumer_timeout - time.time())
if not self._fetcher.in_flight_fetches():
poll_ms = min(poll_ms, self.config['reconnect_backoff_ms'])
self._client.poll(timeout_ms=poll_ms)
# after the long poll, we should check whether the group needs to rebalance
# prior to returning data so that the group can stabilize faster
if self._coordinator.need_rejoin():
continue
# We need to make sure we at least keep up with scheduled tasks,
# like heartbeats, auto-commits, and metadata refreshes
timeout_at = self._next_timeout()
# Because the consumer client poll does not sleep unless blocking on
# network IO, we need to explicitly sleep when we know we are idle
# because we haven't been assigned any partitions to fetch / consume
if self._use_consumer_group() and not self.assignment():
sleep_time = max(timeout_at - time.time(), 0)
if sleep_time > 0 and not self._client.in_flight_request_count():
log.debug('No partitions assigned; sleeping for %s', sleep_time)
time.sleep(sleep_time)
continue
# Short-circuit the fetch iterator if we are already timed out
# to avoid any unintentional interaction with fetcher setup
if time.time() > timeout_at:
continue
for msg in self._fetcher:
yield msg
if time.time() > timeout_at:
log.debug("internal iterator timeout - breaking for poll")
break
if self._client.in_flight_request_count():
self._client.poll(timeout_ms=0)
# An else block on a for loop only executes if there was no break
# so this should only be called on a StopIteration from the fetcher
# We assume that it is safe to init_fetches when fetcher is done
# i.e., there are no more records stored internally
else:
self._fetcher.send_fetches()
def _next_timeout(self):
timeout = min(self._consumer_timeout,
self._client.cluster.ttl() / 1000.0 + time.time(),
self._coordinator.time_to_next_poll() + time.time())
return timeout
def __iter__(self): # pylint: disable=non-iterator-returned
return self
def __next__(self):
if not self._iterator:
self._iterator = self._message_generator()
self._set_consumer_timeout()
try:
return next(self._iterator)
except StopIteration:
self._iterator = None
raise
def _set_consumer_timeout(self):
# consumer_timeout_ms can be used to stop iteration early
if self.config['consumer_timeout_ms'] >= 0:
self._consumer_timeout = time.time() + (
self.config['consumer_timeout_ms'] / 1000.0)
# Old KafkaConsumer methods are deprecated
def configure(self, **configs):
raise NotImplementedError(
'deprecated -- initialize a new consumer')
def set_topic_partitions(self, *topics):
raise NotImplementedError(
'deprecated -- use subscribe() or assign()')
def fetch_messages(self):
raise NotImplementedError(
'deprecated -- use poll() or iterator interface')
def get_partition_offsets(self, topic, partition,
request_time_ms, max_num_offsets):
raise NotImplementedError(
'deprecated -- send an OffsetRequest with KafkaClient')
def offsets(self, group=None):
raise NotImplementedError('deprecated -- use committed(partition)')
def task_done(self, message):
raise NotImplementedError(
'deprecated -- commit offsets manually if needed')
| 47.537133
| 123
| 0.652067
|
from __future__ import absolute_import, division
import copy
import logging
import socket
import time
from kafka.errors import KafkaConfigurationError, UnsupportedVersionError
from kafka.vendor import six
from kafka.client_async import KafkaClient, selectors
from kafka.consumer.fetcher import Fetcher
from kafka.consumer.subscription_state import SubscriptionState
from kafka.coordinator.consumer import ConsumerCoordinator
from kafka.coordinator.assignors.range import RangePartitionAssignor
from kafka.coordinator.assignors.roundrobin import RoundRobinPartitionAssignor
from kafka.metrics import MetricConfig, Metrics
from kafka.protocol.offset import OffsetResetStrategy
from kafka.structs import TopicPartition
from kafka.version import __version__
log = logging.getLogger(__name__)
class KafkaConsumer(six.Iterator):
DEFAULT_CONFIG = {
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'group_id': None,
'key_deserializer': None,
'value_deserializer': None,
'fetch_max_wait_ms': 500,
'fetch_min_bytes': 1,
'fetch_max_bytes': 52428800,
'max_partition_fetch_bytes': 1 * 1024 * 1024,
'request_timeout_ms': 305000,
'retry_backoff_ms': 100,
'reconnect_backoff_ms': 50,
'reconnect_backoff_max_ms': 1000,
'max_in_flight_requests_per_connection': 5,
'auto_offset_reset': 'latest',
'enable_auto_commit': True,
'auto_commit_interval_ms': 5000,
'default_offset_commit_callback': lambda offsets, response: True,
'check_crcs': True,
'metadata_max_age_ms': 5 * 60 * 1000,
'partition_assignment_strategy': (RangePartitionAssignor, RoundRobinPartitionAssignor),
'max_poll_records': 500,
'max_poll_interval_ms': 300000,
'session_timeout_ms': 10000,
'heartbeat_interval_ms': 3000,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'sock_chunk_bytes': 4096,
'sock_chunk_buffer_count': 1000,
'consumer_timeout_ms': float('inf'),
'skip_double_compressed_messages': False,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_crlfile': None,
'ssl_password': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'connections_max_idle_ms': 9 * 60 * 1000,
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
'metric_group_prefix': 'consumer',
'selector': selectors.DefaultSelector,
'exclude_internal_topics': True,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
'sasl_kerberos_service_name': 'kafka'
}
DEFAULT_SESSION_TIMEOUT_MS_0_9 = 30000
def __init__(self, *topics, **configs):
extra_configs = set(configs).difference(self.DEFAULT_CONFIG)
if extra_configs:
raise KafkaConfigurationError("Unrecognized configs: %s" % extra_configs)
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
deprecated = {'smallest': 'earliest', 'largest': 'latest'}
if self.config['auto_offset_reset'] in deprecated:
new_config = deprecated[self.config['auto_offset_reset']]
log.warning('use auto_offset_reset=%s (%s is deprecated)',
new_config, self.config['auto_offset_reset'])
self.config['auto_offset_reset'] = new_config
request_timeout_ms = self.config['request_timeout_ms']
fetch_max_wait_ms = self.config['fetch_max_wait_ms']
if request_timeout_ms <= fetch_max_wait_ms:
raise KafkaConfigurationError("Request timeout (%s) must be larger than fetch-max-wait-ms (%s)" %
(request_timeout_ms, fetch_max_wait_ms))
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [reporter() for reporter in self.config['metric_reporters']]
self._metrics = Metrics(metric_config, reporters)
if isinstance(self.config['api_version'], str):
str_version = self.config['api_version']
if str_version == 'auto':
self.config['api_version'] = None
else:
self.config['api_version'] = tuple(map(int, str_version.split('.')))
log.warning('use api_version=%s [tuple] -- "%s" as str is deprecated',
str(self.config['api_version']), str_version)
self._client = KafkaClient(metrics=self._metrics, **self.config)
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
if self.config['api_version'] < (0, 10, 1):
if 'session_timeout_ms' not in configs:
if 'max_poll_interval_ms' in configs:
self.config['session_timeout_ms'] = configs['max_poll_interval_ms']
else:
self.config['session_timeout_ms'] = self.DEFAULT_SESSION_TIMEOUT_MS_0_9
if 'max_poll_interval_ms' not in configs:
self.config['max_poll_interval_ms'] = self.config['session_timeout_ms']
if self.config['group_id'] is not None:
if self.config['request_timeout_ms'] <= self.config['session_timeout_ms']:
raise KafkaConfigurationError(
"Request timeout (%s) must be larger than session timeout (%s)" %
(self.config['request_timeout_ms'], self.config['session_timeout_ms']))
self._subscription = SubscriptionState(self.config['auto_offset_reset'])
self._fetcher = Fetcher(
self._client, self._subscription, self._metrics, **self.config)
self._coordinator = ConsumerCoordinator(
self._client, self._subscription, self._metrics,
assignors=self.config['partition_assignment_strategy'],
**self.config)
self._closed = False
self._iterator = None
self._consumer_timeout = float('inf')
if topics:
self._subscription.subscribe(topics=topics)
self._client.set_topics(topics)
def assign(self, partitions):
self._subscription.assign_from_user(partitions)
self._client.set_topics([tp.topic for tp in partitions])
def assignment(self):
return self._subscription.assigned_partitions()
def close(self, autocommit=True):
if self._closed:
return
log.debug("Closing the KafkaConsumer.")
self._closed = True
self._coordinator.close(autocommit=autocommit)
self._metrics.close()
self._client.close()
try:
self.config['key_deserializer'].close()
except AttributeError:
pass
try:
self.config['value_deserializer'].close()
except AttributeError:
pass
log.debug("The KafkaConsumer has closed.")
def commit_async(self, offsets=None, callback=None):
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
log.debug("Committing offsets: %s", offsets)
future = self._coordinator.commit_offsets_async(
offsets, callback=callback)
return future
def commit(self, offsets=None):
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
self._coordinator.commit_offsets_sync(offsets)
def committed(self, partition):
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
if self._subscription.is_assigned(partition):
committed = self._subscription.assignment[partition].committed
if committed is None:
self._coordinator.refresh_committed_offsets_if_needed()
committed = self._subscription.assignment[partition].committed
else:
commit_map = self._coordinator.fetch_committed_offsets([partition])
if partition in commit_map:
committed = commit_map[partition].offset
else:
committed = None
return committed
def topics(self):
cluster = self._client.cluster
if self._client._metadata_refresh_in_progress and self._client._topics:
future = cluster.request_update()
self._client.poll(future=future)
stash = cluster.need_all_topic_metadata
cluster.need_all_topic_metadata = True
future = cluster.request_update()
self._client.poll(future=future)
cluster.need_all_topic_metadata = stash
return cluster.topics()
def partitions_for_topic(self, topic):
return self._client.cluster.partitions_for_topic(topic)
def poll(self, timeout_ms=0, max_records=None):
assert timeout_ms >= 0, 'Timeout must not be negative'
if max_records is None:
max_records = self.config['max_poll_records']
assert isinstance(max_records, int), 'max_records must be an integer'
assert max_records > 0, 'max_records must be positive'
start = time.time()
remaining = timeout_ms
while True:
records = self._poll_once(remaining, max_records)
if records:
return records
elapsed_ms = (time.time() - start) * 1000
remaining = timeout_ms - elapsed_ms
if remaining <= 0:
return {}
def _poll_once(self, timeout_ms, max_records):
self._coordinator.poll()
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
self._update_fetch_positions(self._subscription.missing_fetch_positions())
records, partial = self._fetcher.fetched_records(max_records)
if records:
if not partial:
self._fetcher.send_fetches()
return records
self._fetcher.send_fetches()
timeout_ms = min(timeout_ms, self._coordinator.time_to_next_poll() * 1000)
self._client.poll(timeout_ms=timeout_ms)
# after the long poll, we should check whether the group needs to rebalance
# prior to returning data so that the group can stabilize faster
if self._coordinator.need_rejoin():
return {}
records, _ = self._fetcher.fetched_records(max_records)
return records
def position(self, partition):
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
offset = self._subscription.assignment[partition].position
if offset is None:
self._update_fetch_positions([partition])
offset = self._subscription.assignment[partition].position
return offset
def highwater(self, partition):
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
return self._subscription.assignment[partition].highwater
def pause(self, *partitions):
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Pausing partition %s", partition)
self._subscription.pause(partition)
def paused(self):
return self._subscription.paused_partitions()
def resume(self, *partitions):
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Resuming partition %s", partition)
self._subscription.resume(partition)
def seek(self, partition, offset):
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert isinstance(offset, int) and offset >= 0, 'Offset must be >= 0'
assert partition in self._subscription.assigned_partitions(), 'Unassigned partition'
log.debug("Seeking to offset %s for partition %s", offset, partition)
self._subscription.assignment[partition].seek(offset)
def seek_to_beginning(self, *partitions):
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to beginning of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.EARLIEST)
def seek_to_end(self, *partitions):
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to end of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.LATEST)
def subscribe(self, topics=(), pattern=None, listener=None):
# SubscriptionState handles error checking
self._subscription.subscribe(topics=topics,
pattern=pattern,
listener=listener)
# Regex will need all topic metadata
if pattern is not None:
self._client.cluster.need_all_topic_metadata = True
self._client.set_topics([])
self._client.cluster.request_update()
log.debug("Subscribed to topic pattern: %s", pattern)
else:
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics(self._subscription.group_subscription())
log.debug("Subscribed to topic(s): %s", topics)
def subscription(self):
if self._subscription.subscription is None:
return None
return self._subscription.subscription.copy()
def unsubscribe(self):
self._subscription.unsubscribe()
self._coordinator.close()
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics([])
log.debug("Unsubscribed all topics or patterns and assigned partitions")
def metrics(self, raw=False):
if raw:
return self._metrics.metrics
metrics = {}
for k, v in six.iteritems(self._metrics.metrics):
if k.group not in metrics:
metrics[k.group] = {}
if k.name not in metrics[k.group]:
metrics[k.group][k.name] = {}
metrics[k.group][k.name] = v.value()
return metrics
def offsets_for_times(self, timestamps):
if self.config['api_version'] <= (0, 10, 0):
raise UnsupportedVersionError(
"offsets_for_times API not supported for cluster version {}"
.format(self.config['api_version']))
for tp, ts in six.iteritems(timestamps):
timestamps[tp] = int(ts)
if ts < 0:
raise ValueError(
"The target time for partition {} is {}. The target time "
"cannot be negative.".format(tp, ts))
return self._fetcher.get_offsets_by_times(
timestamps, self.config['request_timeout_ms'])
def beginning_offsets(self, partitions):
offsets = self._fetcher.beginning_offsets(
partitions, self.config['request_timeout_ms'])
return offsets
def end_offsets(self, partitions):
offsets = self._fetcher.end_offsets(
partitions, self.config['request_timeout_ms'])
return offsets
def _use_consumer_group(self):
if self.config['api_version'] < (0, 9):
return False
elif self.config['group_id'] is None:
return False
elif not self._subscription.partitions_auto_assigned():
return False
return True
def _update_fetch_positions(self, partitions):
# Lookup any positions for partitions which are awaiting reset (which may be the
# case if the user called :meth:`seek_to_beginning` or :meth:`seek_to_end`. We do
# this check first to avoid an unnecessary lookup of committed offsets (which
# typically occurs when the user is manually assigning partitions and managing
# their own offsets).
self._fetcher.reset_offsets_if_needed(partitions)
if not self._subscription.has_all_fetch_positions():
# if we still don't have offsets for all partitions, then we should either seek
if (self.config['api_version'] >= (0, 8, 1) and
self.config['group_id'] is not None):
self._coordinator.refresh_committed_offsets_if_needed()
self._fetcher.update_fetch_positions(partitions)
def _message_generator(self):
assert self.assignment() or self.subscription() is not None, 'No topic subscription or manual partition assignment'
while time.time() < self._consumer_timeout:
self._coordinator.poll()
if not self._subscription.has_all_fetch_positions():
partitions = self._subscription.missing_fetch_positions()
self._update_fetch_positions(partitions)
poll_ms = 1000 * (self._consumer_timeout - time.time())
if not self._fetcher.in_flight_fetches():
poll_ms = min(poll_ms, self.config['reconnect_backoff_ms'])
self._client.poll(timeout_ms=poll_ms)
if self._coordinator.need_rejoin():
continue
timeout_at = self._next_timeout()
if self._use_consumer_group() and not self.assignment():
sleep_time = max(timeout_at - time.time(), 0)
if sleep_time > 0 and not self._client.in_flight_request_count():
log.debug('No partitions assigned; sleeping for %s', sleep_time)
time.sleep(sleep_time)
continue
# Short-circuit the fetch iterator if we are already timed out
# to avoid any unintentional interaction with fetcher setup
if time.time() > timeout_at:
continue
for msg in self._fetcher:
yield msg
if time.time() > timeout_at:
log.debug("internal iterator timeout - breaking for poll")
break
if self._client.in_flight_request_count():
self._client.poll(timeout_ms=0)
# An else block on a for loop only executes if there was no break
# so this should only be called on a StopIteration from the fetcher
# We assume that it is safe to init_fetches when fetcher is done
# i.e., there are no more records stored internally
else:
self._fetcher.send_fetches()
def _next_timeout(self):
timeout = min(self._consumer_timeout,
self._client.cluster.ttl() / 1000.0 + time.time(),
self._coordinator.time_to_next_poll() + time.time())
return timeout
def __iter__(self): # pylint: disable=non-iterator-returned
return self
def __next__(self):
if not self._iterator:
self._iterator = self._message_generator()
self._set_consumer_timeout()
try:
return next(self._iterator)
except StopIteration:
self._iterator = None
raise
def _set_consumer_timeout(self):
# consumer_timeout_ms can be used to stop iteration early
if self.config['consumer_timeout_ms'] >= 0:
self._consumer_timeout = time.time() + (
self.config['consumer_timeout_ms'] / 1000.0)
# Old KafkaConsumer methods are deprecated
def configure(self, **configs):
raise NotImplementedError(
'deprecated -- initialize a new consumer')
def set_topic_partitions(self, *topics):
raise NotImplementedError(
'deprecated -- use subscribe() or assign()')
def fetch_messages(self):
raise NotImplementedError(
'deprecated -- use poll() or iterator interface')
def get_partition_offsets(self, topic, partition,
request_time_ms, max_num_offsets):
raise NotImplementedError(
'deprecated -- send an OffsetRequest with KafkaClient')
def offsets(self, group=None):
raise NotImplementedError('deprecated -- use committed(partition)')
def task_done(self, message):
raise NotImplementedError(
'deprecated -- commit offsets manually if needed')
| true
| true
|
1c3ec6d3f7be58abd038f8964534a0cb398cb936
| 2,732
|
py
|
Python
|
blog/migrations/0011_auto_20200728_0547.py
|
tbrlpld/wagtail-gatsby-blog-backend
|
f68f1d9e2577d5271960f142bf37dcbcdac6767a
|
[
"MIT"
] | null | null | null |
blog/migrations/0011_auto_20200728_0547.py
|
tbrlpld/wagtail-gatsby-blog-backend
|
f68f1d9e2577d5271960f142bf37dcbcdac6767a
|
[
"MIT"
] | null | null | null |
blog/migrations/0011_auto_20200728_0547.py
|
tbrlpld/wagtail-gatsby-blog-backend
|
f68f1d9e2577d5271960f142bf37dcbcdac6767a
|
[
"MIT"
] | null | null | null |
# Generated by Django 2.2.13 on 2020-07-28 05:47
from django.db import migrations
import wagtail.core.blocks
import wagtail.core.blocks.static_block
import wagtail.core.fields
import wagtail.documents.blocks
import wagtail.embeds.blocks
import wagtail.images.blocks
class Migration(migrations.Migration):
dependencies = [
('blog', '0010_auto_20200728_0540'),
]
operations = [
migrations.AlterField(
model_name='blogpage',
name='freeformbody',
field=wagtail.core.fields.StreamField([('heading', wagtail.core.blocks.CharBlock(classname='full title')), ('paragraph', wagtail.core.blocks.RichTextBlock()), ('image', wagtail.images.blocks.ImageChooserBlock()), ('text', wagtail.core.blocks.TextBlock()), ('email', wagtail.core.blocks.EmailBlock(help_text='Your email goes here.')), ('integer', wagtail.core.blocks.IntegerBlock(help_text='Just a number.')), ('float', wagtail.core.blocks.FloatBlock(help_text='A floating point number.')), ('decimal', wagtail.core.blocks.DecimalBlock(decimal_places=2, help_text='A decimal number.')), ('regex', wagtail.core.blocks.RegexBlock(error_messages={'invalid': 'You need to have " stuff " in the string.'}, help_text='A string with stuff in the middle.', regex='^.*stuff.*$')), ('url', wagtail.core.blocks.URLBlock()), ('bool', wagtail.core.blocks.BooleanBlock(required=False)), ('date', wagtail.core.blocks.DateBlock()), ('time', wagtail.core.blocks.TimeBlock()), ('datetime', wagtail.core.blocks.DateTimeBlock()), ('rawhtml', wagtail.core.blocks.RawHTMLBlock(help_text='Here you can show off your HTML skills.')), ('blockquote', wagtail.core.blocks.BlockQuoteBlock()), ('choice', wagtail.core.blocks.ChoiceBlock(choices=[('yes', 'Yes'), ('no', 'No'), ('maybe', 'Maybe')])), ('page', wagtail.core.blocks.PageChooserBlock()), ('doc', wagtail.documents.blocks.DocumentChooserBlock()), ('embed', wagtail.embeds.blocks.EmbedBlock()), ('static', wagtail.core.blocks.static_block.StaticBlock(admin_text='Latest Posts (no configuration needed)', help_text='If you include this block, the latest posts will be displayed here.')), ('person', wagtail.core.blocks.StructBlock([('first_name', wagtail.core.blocks.CharBlock()), ('last_name', wagtail.core.blocks.CharBlock()), ('biography', wagtail.core.blocks.TextBlock()), ('pic', wagtail.images.blocks.ImageChooserBlock(required=False))], icon='user')), ('list', wagtail.core.blocks.ListBlock(wagtail.core.blocks.CharBlock(label='List Item'))), ('substream', wagtail.core.blocks.StreamBlock([('image', wagtail.images.blocks.ImageChooserBlock()), ('quote', wagtail.core.blocks.BlockQuoteBlock()), ('author', wagtail.core.blocks.CharBlock(min_length=5))]))], blank=True),
),
]
| 109.28
| 2,214
| 0.725842
|
from django.db import migrations
import wagtail.core.blocks
import wagtail.core.blocks.static_block
import wagtail.core.fields
import wagtail.documents.blocks
import wagtail.embeds.blocks
import wagtail.images.blocks
class Migration(migrations.Migration):
dependencies = [
('blog', '0010_auto_20200728_0540'),
]
operations = [
migrations.AlterField(
model_name='blogpage',
name='freeformbody',
field=wagtail.core.fields.StreamField([('heading', wagtail.core.blocks.CharBlock(classname='full title')), ('paragraph', wagtail.core.blocks.RichTextBlock()), ('image', wagtail.images.blocks.ImageChooserBlock()), ('text', wagtail.core.blocks.TextBlock()), ('email', wagtail.core.blocks.EmailBlock(help_text='Your email goes here.')), ('integer', wagtail.core.blocks.IntegerBlock(help_text='Just a number.')), ('float', wagtail.core.blocks.FloatBlock(help_text='A floating point number.')), ('decimal', wagtail.core.blocks.DecimalBlock(decimal_places=2, help_text='A decimal number.')), ('regex', wagtail.core.blocks.RegexBlock(error_messages={'invalid': 'You need to have " stuff " in the string.'}, help_text='A string with stuff in the middle.', regex='^.*stuff.*$')), ('url', wagtail.core.blocks.URLBlock()), ('bool', wagtail.core.blocks.BooleanBlock(required=False)), ('date', wagtail.core.blocks.DateBlock()), ('time', wagtail.core.blocks.TimeBlock()), ('datetime', wagtail.core.blocks.DateTimeBlock()), ('rawhtml', wagtail.core.blocks.RawHTMLBlock(help_text='Here you can show off your HTML skills.')), ('blockquote', wagtail.core.blocks.BlockQuoteBlock()), ('choice', wagtail.core.blocks.ChoiceBlock(choices=[('yes', 'Yes'), ('no', 'No'), ('maybe', 'Maybe')])), ('page', wagtail.core.blocks.PageChooserBlock()), ('doc', wagtail.documents.blocks.DocumentChooserBlock()), ('embed', wagtail.embeds.blocks.EmbedBlock()), ('static', wagtail.core.blocks.static_block.StaticBlock(admin_text='Latest Posts (no configuration needed)', help_text='If you include this block, the latest posts will be displayed here.')), ('person', wagtail.core.blocks.StructBlock([('first_name', wagtail.core.blocks.CharBlock()), ('last_name', wagtail.core.blocks.CharBlock()), ('biography', wagtail.core.blocks.TextBlock()), ('pic', wagtail.images.blocks.ImageChooserBlock(required=False))], icon='user')), ('list', wagtail.core.blocks.ListBlock(wagtail.core.blocks.CharBlock(label='List Item'))), ('substream', wagtail.core.blocks.StreamBlock([('image', wagtail.images.blocks.ImageChooserBlock()), ('quote', wagtail.core.blocks.BlockQuoteBlock()), ('author', wagtail.core.blocks.CharBlock(min_length=5))]))], blank=True),
),
]
| true
| true
|
1c3ec6ff7238f3a67bc7e7cbadd77b8f9696718c
| 1,716
|
py
|
Python
|
src/third_party/red_tamarin_stable/tamarin-cental/halfmoon/templates/sexp.py
|
michaelpdu/flash_feature_extraction
|
29226a4c0e81240fd5c53fd9c80b0f6b0f5a8f95
|
[
"Apache-2.0"
] | 1
|
2018-11-25T01:05:03.000Z
|
2018-11-25T01:05:03.000Z
|
avmplus/halfmoon/templates/sexp.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | null | null | null |
avmplus/halfmoon/templates/sexp.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | 1
|
2019-04-12T04:18:27.000Z
|
2019-04-12T04:18:27.000Z
|
#!/usr/bin/env python
# -*- Mode: Python; indent-tabs-mode: nil -*-
# vi: set ts=4 sw=4 expandtab:
# borrowed from http://gist.github.com/240957
# no license information found
# Support for comments ; added by Adobe.
from string import whitespace
atom_end = set('()"\'') | set(whitespace)
def parse(sexp):
stack, i, length = [[]], 0, len(sexp)
while i < length:
c = sexp[i]
#print c, stack
if c == ';':
while i + 1 < length and sexp[i + 1] != '\n':
i += 1
else:
reading = type(stack[-1])
if reading == list:
if c == '(': stack.append([])
elif c == ')':
stack[-2].append(stack.pop())
if stack[-1][0] == ('quote',): stack[-2].append(stack.pop())
elif c == '"': stack.append('')
elif c == "'": stack.append([('quote',)])
elif c in whitespace: pass
else: stack.append((c,))
elif reading == str:
if c == '"':
stack[-2].append(stack.pop())
if stack[-1][0] == ('quote',): stack[-2].append(stack.pop())
elif c == '\\':
i += 1
stack[-1] += sexp[i]
else: stack[-1] += c
elif reading == tuple:
if c in atom_end:
atom = stack.pop()
if atom[0][0].isdigit(): stack[-1].append(eval(atom[0]))
else: stack[-1].append(atom[0])
if stack[-1][0] == ('quote',): stack[-2].append(stack.pop())
continue
else: stack[-1] = ((stack[-1][0] + c),)
i += 1
return stack.pop()
| 33.647059
| 78
| 0.434732
|
from string import whitespace
atom_end = set('()"\'') | set(whitespace)
def parse(sexp):
stack, i, length = [[]], 0, len(sexp)
while i < length:
c = sexp[i]
#print c, stack
if c == ';':
while i + 1 < length and sexp[i + 1] != '\n':
i += 1
else:
reading = type(stack[-1])
if reading == list:
if c == '(': stack.append([])
elif c == ')':
stack[-2].append(stack.pop())
if stack[-1][0] == ('quote',): stack[-2].append(stack.pop())
elif c == '"': stack.append('')
elif c == "'": stack.append([('quote',)])
elif c in whitespace: pass
else: stack.append((c,))
elif reading == str:
if c == '"':
stack[-2].append(stack.pop())
if stack[-1][0] == ('quote',): stack[-2].append(stack.pop())
elif c == '\\':
i += 1
stack[-1] += sexp[i]
else: stack[-1] += c
elif reading == tuple:
if c in atom_end:
atom = stack.pop()
if atom[0][0].isdigit(): stack[-1].append(eval(atom[0]))
else: stack[-1].append(atom[0])
if stack[-1][0] == ('quote',): stack[-2].append(stack.pop())
continue
else: stack[-1] = ((stack[-1][0] + c),)
i += 1
return stack.pop()
| true
| true
|
1c3ec72e9ec972f9c7d148b66f212bf3e366a340
| 2,457
|
py
|
Python
|
ctgan/data.py
|
JonathanDZiegler/CTGAN
|
7b1c110455bf776cf89a661e5ff8425d6519daf5
|
[
"MIT"
] | null | null | null |
ctgan/data.py
|
JonathanDZiegler/CTGAN
|
7b1c110455bf776cf89a661e5ff8425d6519daf5
|
[
"MIT"
] | null | null | null |
ctgan/data.py
|
JonathanDZiegler/CTGAN
|
7b1c110455bf776cf89a661e5ff8425d6519daf5
|
[
"MIT"
] | 1
|
2021-11-18T14:23:24.000Z
|
2021-11-18T14:23:24.000Z
|
"""Data loading."""
import json
import numpy as np
import pandas as pd
def read_csv(csv_filename, meta_filename=None, header=True, discrete=None):
"""Read a csv file."""
data = pd.read_csv(csv_filename, header='infer' if header else None)
if meta_filename:
with open(meta_filename) as meta_file:
metadata = json.load(meta_file)
discrete_columns = [
column['name']
for column in metadata['columns']
if column['type'] != 'continuous'
]
elif discrete:
discrete_columns = discrete.split(',')
if not header:
discrete_columns = [int(i) for i in discrete_columns]
else:
discrete_columns = []
return data, discrete_columns
def read_tsv(data_filename, meta_filename):
"""Read a tsv file."""
with open(meta_filename) as f:
column_info = f.readlines()
column_info_raw = [
x.replace('{', ' ').replace('}', ' ').split()
for x in column_info
]
discrete = []
continuous = []
column_info = []
for idx, item in enumerate(column_info_raw):
if item[0] == 'C':
continuous.append(idx)
column_info.append((float(item[1]), float(item[2])))
else:
assert item[0] == 'D'
discrete.append(idx)
column_info.append(item[1:])
meta = {
'continuous_columns': continuous,
'discrete_columns': discrete,
'column_info': column_info
}
with open(data_filename) as f:
lines = f.readlines()
data = []
for row in lines:
row_raw = row.split()
row = []
for idx, col in enumerate(row_raw):
if idx in continuous:
row.append(col)
else:
assert idx in discrete
row.append(column_info[idx].index(col))
data.append(row)
return np.asarray(data, dtype='float32'), meta['discrete_columns']
def write_tsv(data, meta, output_filename):
"""Write to a tsv file."""
with open(output_filename, 'w') as f:
for row in data:
for idx, col in enumerate(row):
if idx in meta['continuous_columns']:
print(col, end=' ', file=f)
else:
assert idx in meta['discrete_columns']
print(meta['column_info'][idx][int(col)], end=' ', file=f)
print(file=f)
| 25.863158
| 78
| 0.553521
|
import json
import numpy as np
import pandas as pd
def read_csv(csv_filename, meta_filename=None, header=True, discrete=None):
data = pd.read_csv(csv_filename, header='infer' if header else None)
if meta_filename:
with open(meta_filename) as meta_file:
metadata = json.load(meta_file)
discrete_columns = [
column['name']
for column in metadata['columns']
if column['type'] != 'continuous'
]
elif discrete:
discrete_columns = discrete.split(',')
if not header:
discrete_columns = [int(i) for i in discrete_columns]
else:
discrete_columns = []
return data, discrete_columns
def read_tsv(data_filename, meta_filename):
with open(meta_filename) as f:
column_info = f.readlines()
column_info_raw = [
x.replace('{', ' ').replace('}', ' ').split()
for x in column_info
]
discrete = []
continuous = []
column_info = []
for idx, item in enumerate(column_info_raw):
if item[0] == 'C':
continuous.append(idx)
column_info.append((float(item[1]), float(item[2])))
else:
assert item[0] == 'D'
discrete.append(idx)
column_info.append(item[1:])
meta = {
'continuous_columns': continuous,
'discrete_columns': discrete,
'column_info': column_info
}
with open(data_filename) as f:
lines = f.readlines()
data = []
for row in lines:
row_raw = row.split()
row = []
for idx, col in enumerate(row_raw):
if idx in continuous:
row.append(col)
else:
assert idx in discrete
row.append(column_info[idx].index(col))
data.append(row)
return np.asarray(data, dtype='float32'), meta['discrete_columns']
def write_tsv(data, meta, output_filename):
with open(output_filename, 'w') as f:
for row in data:
for idx, col in enumerate(row):
if idx in meta['continuous_columns']:
print(col, end=' ', file=f)
else:
assert idx in meta['discrete_columns']
print(meta['column_info'][idx][int(col)], end=' ', file=f)
print(file=f)
| true
| true
|
1c3ec7572013460e6d2b8a5b43c1333c84548a2f
| 5,023
|
py
|
Python
|
tests/test_dependency_loop.py
|
TinkerBoard-Android/external-google-fruit
|
57123c8a2477a4d99cb68c53d195e9fb428dd535
|
[
"Apache-2.0"
] | 1,666
|
2015-01-04T08:52:43.000Z
|
2022-03-28T11:06:19.000Z
|
tests/test_dependency_loop.py
|
TinkerBoard-Android/external-google-fruit
|
57123c8a2477a4d99cb68c53d195e9fb428dd535
|
[
"Apache-2.0"
] | 135
|
2015-02-19T11:35:07.000Z
|
2022-03-29T05:00:42.000Z
|
tests/test_dependency_loop.py
|
TinkerBoard-Android/external-google-fruit
|
57123c8a2477a4d99cb68c53d195e9fb428dd535
|
[
"Apache-2.0"
] | 253
|
2015-01-14T08:15:10.000Z
|
2022-03-24T07:49:53.000Z
|
#!/usr/bin/env python3
# Copyright 2016 Google Inc. 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.
from absl.testing import parameterized
from fruit_test_common import *
COMMON_DEFINITIONS = '''
#include "test_common.h"
struct X;
struct Annotation1 {};
using XAnnot1 = fruit::Annotated<Annotation1, X>;
struct Annotation2 {};
using XAnnot2 = fruit::Annotated<Annotation2, X>;
struct Annotation3 {};
using XAnnot3 = fruit::Annotated<Annotation3, X>;
'''
class TestDependencyLoop(parameterized.TestCase):
@parameterized.parameters([
('X', 'const X&', 'Y', 'const Y&'),
('fruit::Annotated<Annotation1, X>', 'ANNOTATED(Annotation1, const X&)',
'fruit::Annotated<Annotation2, Y>', 'ANNOTATED(Annotation2, const Y&)')
])
def test_loop_in_autoinject(self, XAnnot, XConstRefAnnot, YAnnot, YConstRefAnnot):
source = '''
struct Y;
struct X {
INJECT(X(YConstRefAnnot)) {};
};
struct Y {
INJECT(Y(XConstRefAnnot)) {};
};
fruit::Component<XAnnot> mutuallyConstructibleComponent() {
return fruit::createComponent();
}
'''
expect_compile_error(
'SelfLoopError<XAnnot,YAnnot>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
@parameterized.parameters([
('X', 'const X', 'const X&', 'Y', 'const Y&'),
('fruit::Annotated<Annotation1, X>', 'ANNOTATED(Annotation1, const X)', 'ANNOTATED(Annotation1, const X&)',
'fruit::Annotated<Annotation2, Y>', 'ANNOTATED(Annotation2, const Y&)')
])
def test_loop_in_autoinject_const(self, XAnnot, ConstXAnnot, XConstRefAnnot, YAnnot, YConstRefAnnot):
source = '''
struct Y;
struct X {
INJECT(X(YConstRefAnnot)) {};
};
struct Y {
INJECT(Y(XConstRefAnnot)) {};
};
fruit::Component<ConstXAnnot> mutuallyConstructibleComponent() {
return fruit::createComponent();
}
'''
expect_compile_error(
'SelfLoopError<XAnnot,YAnnot>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
def test_loop_in_register_provider(self):
source = '''
struct X {};
struct Y {};
fruit::Component<X> mutuallyConstructibleComponent() {
return fruit::createComponent()
.registerProvider<X(Y)>([](Y) {return X();})
.registerProvider<Y(X)>([](X) {return Y();});
}
'''
expect_compile_error(
'SelfLoopError<X,Y>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
def test_loop_in_register_provider_with_annotations(self):
source = '''
struct X {};
fruit::Component<fruit::Annotated<Annotation1, X>> mutuallyConstructibleComponent() {
return fruit::createComponent()
.registerProvider<fruit::Annotated<Annotation1, X>(fruit::Annotated<Annotation2, X>)>([](X x) {return x;})
.registerProvider<fruit::Annotated<Annotation2, X>(fruit::Annotated<Annotation1, X>)>([](X x) {return x;});
}
'''
expect_compile_error(
'SelfLoopError<fruit::Annotated<Annotation1, X>, fruit::Annotated<Annotation2, X>>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
def test_with_different_annotations_ok(self):
source = '''
struct X {};
fruit::Component<XAnnot3> getComponent() {
return fruit::createComponent()
.registerProvider<XAnnot1()>([](){return X();})
.registerProvider<XAnnot2(XAnnot1)>([](X x){return x;})
.registerProvider<XAnnot3(XAnnot2)>([](X x){return x;});
}
int main() {
fruit::Injector<XAnnot3> injector(getComponent);
injector.get<XAnnot3>();
}
'''
expect_success(
COMMON_DEFINITIONS,
source)
if __name__ == '__main__':
absltest.main()
| 33.939189
| 125
| 0.565399
|
from absl.testing import parameterized
from fruit_test_common import *
COMMON_DEFINITIONS = '''
#include "test_common.h"
struct X;
struct Annotation1 {};
using XAnnot1 = fruit::Annotated<Annotation1, X>;
struct Annotation2 {};
using XAnnot2 = fruit::Annotated<Annotation2, X>;
struct Annotation3 {};
using XAnnot3 = fruit::Annotated<Annotation3, X>;
'''
class TestDependencyLoop(parameterized.TestCase):
@parameterized.parameters([
('X', 'const X&', 'Y', 'const Y&'),
('fruit::Annotated<Annotation1, X>', 'ANNOTATED(Annotation1, const X&)',
'fruit::Annotated<Annotation2, Y>', 'ANNOTATED(Annotation2, const Y&)')
])
def test_loop_in_autoinject(self, XAnnot, XConstRefAnnot, YAnnot, YConstRefAnnot):
source = '''
struct Y;
struct X {
INJECT(X(YConstRefAnnot)) {};
};
struct Y {
INJECT(Y(XConstRefAnnot)) {};
};
fruit::Component<XAnnot> mutuallyConstructibleComponent() {
return fruit::createComponent();
}
'''
expect_compile_error(
'SelfLoopError<XAnnot,YAnnot>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
@parameterized.parameters([
('X', 'const X', 'const X&', 'Y', 'const Y&'),
('fruit::Annotated<Annotation1, X>', 'ANNOTATED(Annotation1, const X)', 'ANNOTATED(Annotation1, const X&)',
'fruit::Annotated<Annotation2, Y>', 'ANNOTATED(Annotation2, const Y&)')
])
def test_loop_in_autoinject_const(self, XAnnot, ConstXAnnot, XConstRefAnnot, YAnnot, YConstRefAnnot):
source = '''
struct Y;
struct X {
INJECT(X(YConstRefAnnot)) {};
};
struct Y {
INJECT(Y(XConstRefAnnot)) {};
};
fruit::Component<ConstXAnnot> mutuallyConstructibleComponent() {
return fruit::createComponent();
}
'''
expect_compile_error(
'SelfLoopError<XAnnot,YAnnot>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
def test_loop_in_register_provider(self):
source = '''
struct X {};
struct Y {};
fruit::Component<X> mutuallyConstructibleComponent() {
return fruit::createComponent()
.registerProvider<X(Y)>([](Y) {return X();})
.registerProvider<Y(X)>([](X) {return Y();});
}
'''
expect_compile_error(
'SelfLoopError<X,Y>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
def test_loop_in_register_provider_with_annotations(self):
source = '''
struct X {};
fruit::Component<fruit::Annotated<Annotation1, X>> mutuallyConstructibleComponent() {
return fruit::createComponent()
.registerProvider<fruit::Annotated<Annotation1, X>(fruit::Annotated<Annotation2, X>)>([](X x) {return x;})
.registerProvider<fruit::Annotated<Annotation2, X>(fruit::Annotated<Annotation1, X>)>([](X x) {return x;});
}
'''
expect_compile_error(
'SelfLoopError<fruit::Annotated<Annotation1, X>, fruit::Annotated<Annotation2, X>>',
'Found a loop in the dependencies',
COMMON_DEFINITIONS,
source,
locals())
def test_with_different_annotations_ok(self):
source = '''
struct X {};
fruit::Component<XAnnot3> getComponent() {
return fruit::createComponent()
.registerProvider<XAnnot1()>([](){return X();})
.registerProvider<XAnnot2(XAnnot1)>([](X x){return x;})
.registerProvider<XAnnot3(XAnnot2)>([](X x){return x;});
}
int main() {
fruit::Injector<XAnnot3> injector(getComponent);
injector.get<XAnnot3>();
}
'''
expect_success(
COMMON_DEFINITIONS,
source)
if __name__ == '__main__':
absltest.main()
| true
| true
|
1c3ec7b6b14e02b69b8c1732d3ec1b97b07bc9d9
| 3,069
|
py
|
Python
|
docs/conf.py
|
mollymwieringa/sit_assimilation_osses
|
56eb13227cf3cf481991bd162889c49880020635
|
[
"MIT"
] | null | null | null |
docs/conf.py
|
mollymwieringa/sit_assimilation_osses
|
56eb13227cf3cf481991bd162889c49880020635
|
[
"MIT"
] | null | null | null |
docs/conf.py
|
mollymwieringa/sit_assimilation_osses
|
56eb13227cf3cf481991bd162889c49880020635
|
[
"MIT"
] | null | null | null |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
import os
import pathlib
import sys
print("python exec:", sys.executable)
print("sys.path:", sys.path)
root = pathlib.Path(__file__).parent.parent.absolute()
os.environ["PYTHONPATH"] = str(root)
sys.path.insert(0, str(root))
import sit_assimilation_osses # isort:skip
# -- Project information -----------------------------------------------------
project = "sit_assimilation_osses"
copyright = "2021, Molly Wieringa"
author = "Molly Wieringa"
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
# see https://pypi.org/project/setuptools-scm/ for details
from pkg_resources import get_distribution
release = get_distribution('sit_assimilation_osses').version
# for example take major/minor
version = '.'.join(release.split('.')[:2])
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.viewcode",
"sphinx.ext.napoleon",
"nbsphinx",
"recommonmark",
"sphinx.ext.mathjax",
"sphinx.ext.autosummary",
"sphinx.ext.extlinks",
"sphinx.ext.intersphinx",
"numpydoc",
"nbsphinx",
"IPython.sphinxext.ipython_directive",
"IPython.sphinxext.ipython_console_highlighting",
"sphinxcontrib.srclinks",
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ["_build", "**.ipynb_checkpoints", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "pangeo"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# -- nbsphinx specific options ----------------------------------------------
# this allows notebooks to be run even if they produce errors.
nbsphinx_allow_errors = True
| 35.275862
| 79
| 0.681981
|
import os
import pathlib
import sys
print("python exec:", sys.executable)
print("sys.path:", sys.path)
root = pathlib.Path(__file__).parent.parent.absolute()
os.environ["PYTHONPATH"] = str(root)
sys.path.insert(0, str(root))
import sit_assimilation_osses
project = "sit_assimilation_osses"
copyright = "2021, Molly Wieringa"
author = "Molly Wieringa"
# |version| and |release|, also used in various other places throughout the
# built documents.
# see https://pypi.org/project/setuptools-scm/ for details
from pkg_resources import get_distribution
release = get_distribution('sit_assimilation_osses').version
# for example take major/minor
version = '.'.join(release.split('.')[:2])
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.viewcode",
"sphinx.ext.napoleon",
"nbsphinx",
"recommonmark",
"sphinx.ext.mathjax",
"sphinx.ext.autosummary",
"sphinx.ext.extlinks",
"sphinx.ext.intersphinx",
"numpydoc",
"nbsphinx",
"IPython.sphinxext.ipython_directive",
"IPython.sphinxext.ipython_console_highlighting",
"sphinxcontrib.srclinks",
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ["_build", "**.ipynb_checkpoints", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "pangeo"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# -- nbsphinx specific options ----------------------------------------------
# this allows notebooks to be run even if they produce errors.
nbsphinx_allow_errors = True
| true
| true
|
1c3ec87ee1e156d9bd81c49a7f5849c9a0477607
| 466
|
py
|
Python
|
data/scripts/templates/object/tangible/wearables/ithorian/shared_ith_jacket_s10.py
|
obi-two/GameServer
|
7d37024e2291a97d49522610cd8f1dbe5666afc2
|
[
"MIT"
] | 20
|
2015-02-23T15:11:56.000Z
|
2022-03-18T20:56:48.000Z
|
data/scripts/templates/object/tangible/wearables/ithorian/shared_ith_jacket_s10.py
|
apathyboy/swganh
|
665128efe9154611dec4cb5efc61d246dd095984
|
[
"MIT"
] | null | null | null |
data/scripts/templates/object/tangible/wearables/ithorian/shared_ith_jacket_s10.py
|
apathyboy/swganh
|
665128efe9154611dec4cb5efc61d246dd095984
|
[
"MIT"
] | 20
|
2015-04-04T16:35:59.000Z
|
2022-03-24T14:54:37.000Z
|
#### NOTICE: THIS FILE IS AUTOGENERATED
#### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY
#### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES
from swgpy.object import *
def create(kernel):
result = Tangible()
result.template = "object/tangible/wearables/ithorian/shared_ith_jacket_s10.iff"
result.attribute_template_id = 11
result.stfName("wearables_name","ith_jacket_s10")
#### BEGIN MODIFICATIONS ####
#### END MODIFICATIONS ####
return result
| 27.411765
| 81
| 0.738197
| true
| true
|
|
1c3ec8d51199ed160a9b1ec4dfce1a483ae737af
| 816
|
py
|
Python
|
profiles_project/urls.py
|
kienyiep/profile-rest-api
|
705c3bf9c6c45f7b860cd358520a81694c2a26cb
|
[
"MIT"
] | null | null | null |
profiles_project/urls.py
|
kienyiep/profile-rest-api
|
705c3bf9c6c45f7b860cd358520a81694c2a26cb
|
[
"MIT"
] | null | null | null |
profiles_project/urls.py
|
kienyiep/profile-rest-api
|
705c3bf9c6c45f7b860cd358520a81694c2a26cb
|
[
"MIT"
] | null | null | null |
"""profiles_project URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('api/', include('profiles_api.urls'))
]
| 32.64
| 77
| 0.707108
|
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('api/', include('profiles_api.urls'))
]
| true
| true
|
1c3ec94e2ccb841eb99eae10987aea3e9f60f21d
| 11,253
|
py
|
Python
|
mars/tensor/linalg/norm.py
|
ueshin/mars
|
0b542974243be4e0ff239eaf49ab0fb2935f3361
|
[
"Apache-2.0"
] | null | null | null |
mars/tensor/linalg/norm.py
|
ueshin/mars
|
0b542974243be4e0ff239eaf49ab0fb2935f3361
|
[
"Apache-2.0"
] | null | null | null |
mars/tensor/linalg/norm.py
|
ueshin/mars
|
0b542974243be4e0ff239eaf49ab0fb2935f3361
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
from collections.abc import Iterable
import numpy as np
from ... import opcodes as OperandDef
from ...serialize import ValueType, KeyField, AnyField, TupleField, BoolField
from ..utils import recursive_tile
from ..array_utils import device, as_same_device
from ..operands import TensorHasInput, TensorOperandMixin
from ..arithmetic import sqrt
from ..datasource import tensor as astensor
from .svd import svd
class TensorNorm(TensorHasInput, TensorOperandMixin):
_op_type_ = OperandDef.NORM
_input = KeyField('input')
_ord = AnyField('ord')
_axis = TupleField('axis', ValueType.int32)
_keepdims = BoolField('keepdims')
def __init__(self, ord=None, axis=None, keepdims=None, dtype=None, sparse=False, **kw):
super().__init__(_ord=ord, _axis=axis, _keepdims=keepdims, _dtype=dtype,
_sparse=sparse, **kw)
@property
def ord(self):
return getattr(self, '_ord', None)
@property
def axis(self):
return self._axis
@property
def keepdims(self):
return self._keepdims
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._input = self._inputs[0]
def __call__(self, x):
r = x.astype(self.dtype)
shape = self._norm(r, self._ord, self._axis, self._keepdims).shape
return self.new_tensor([x], shape)
@classmethod
def tile(cls, op):
x = op.input
axis = op.axis
ord = op.ord
keepdims = op.keepdims
axis_chunk_shapes = tuple(x.chunk_shape[i] for i in axis)
can_apply_norm = all(s == 1 for s in axis_chunk_shapes)
if can_apply_norm:
axis_set = set(axis)
get_shape = lambda shape: tuple(s if i not in axis_set else 1 for i, s in enumerate(shape)
if i not in axis_set or keepdims)
out_chunk_shape = get_shape(x.chunk_shape)
out_chunks = []
for idx in itertools.product(*[range(s) for s in out_chunk_shape]):
idx_iter = iter(idx)
in_idx = tuple(0 if i in axis_set and not keepdims else next(idx_iter)
for i in range(x.ndim))
c = x.cix[in_idx]
chunk_op = op.copy().reset_key()
out_chunk = chunk_op.new_chunk([c], shape=get_shape(c.shape), index=idx)
out_chunks.append(out_chunk)
nsplits = [tuple(c.shape[i] for c in out_chunks
if all(idx == 0 for j, idx in enumerate(c.index) if j != i))
for i in range(len(out_chunks[0].shape))]
new_op = op.copy()
return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=out_chunks, nsplits=nsplits)
r = cls._norm(x.astype(op.outputs[0].dtype), ord, axis, keepdims)
recursive_tile(r)
new_op = op.copy()
return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=r.chunks, nsplits=r.nsplits)
@staticmethod
def _norm(r, ord, axis, keepdims):
if ord is None:
return sqrt((abs(r) ** 2).sum(axis=axis, keepdims=keepdims))
elif ord == 'nuc':
if len(axis) == 1:
raise ValueError('Invalid norm order for vectors.')
return svd(r)[1][np.newaxis].sum(keepdims=keepdims)
elif ord == np.inf:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
r = abs(r)
if len(axis) == 1:
return r.max(axis=axis, keepdims=keepdims)
else:
return r.sum(axis=axis[1], keepdims=keepdims).max(keepdims=keepdims)
elif ord == -np.inf:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
r = abs(r)
if len(axis) == 1:
return r.min(axis=axis, keepdims=keepdims)
else:
return r.sum(axis=axis[1], keepdims=keepdims).min(keepdims=keepdims)
elif ord == 0:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
if len(axis) == 2:
raise ValueError('Invalid norm order for matrices.')
return (r != 0).astype(r.dtype).sum(axis=axis, keepdims=keepdims)
elif ord == 1:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
r = abs(r)
if len(axis) == 1:
return r.sum(axis=axis, keepdims=keepdims)
else:
return r.sum(axis=axis[0], keepdims=keepdims).max(keepdims=keepdims)
elif ord == -1 and len(axis) == 2:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
return abs(r).sum(axis=axis[0], keepdims=keepdims).min(keepdims=keepdims)
elif ord == 2 and len(axis) == 2:
return svd(r)[1][np.newaxis].max(keepdims=keepdims)
elif ord == -2 and len(axis) == 2:
return svd(r)[1][np.newaxis].min(keepdims=keepdims)
else:
if len(axis) == 2:
raise ValueError('Invalid norm order for matrices.')
return (abs(r) ** ord).sum(axis=axis, keepdims=keepdims) ** (1.0 / ord)
@classmethod
def execute(cls, ctx, op):
(x,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True)
with device(device_id):
ctx[op.outputs[0].key] = xp.linalg.norm(x, ord=op.ord, axis=op.axis,
keepdims=op.keepdims)
def norm(x, ord=None, axis=None, keepdims=False):
r"""
Matrix or vector norm.
This function is able to return one of eight different matrix norms,
or one of an infinite number of vector norms (described below), depending
on the value of the ``ord`` parameter.
Parameters
----------
x : array_like
Input tensor. If `axis` is None, `x` must be 1-D or 2-D.
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
Order of the norm (see table under ``Notes``). inf means mars tensor's
`inf` object.
axis : {int, 2-tuple of ints, None}, optional
If `axis` is an integer, it specifies the axis of `x` along which to
compute the vector norms. If `axis` is a 2-tuple, it specifies the
axes that hold 2-D matrices, and the matrix norms of these matrices
are computed. If `axis` is None then either a vector norm (when `x`
is 1-D) or a matrix norm (when `x` is 2-D) is returned.
keepdims : bool, optional
If this is set to True, the axes which are normed over are left in the
result as dimensions with size one. With this option the result will
broadcast correctly against the original `x`.
Returns
-------
n : float or Tensor
Norm of the matrix or vector(s).
Notes
-----
For values of ``ord <= 0``, the result is, strictly speaking, not a
mathematical 'norm', but it may still be useful for various numerical
purposes.
The following norms can be calculated:
===== ============================ ==========================
ord norm for matrices norm for vectors
===== ============================ ==========================
None Frobenius norm 2-norm
'fro' Frobenius norm --
'nuc' nuclear norm --
inf max(sum(abs(x), axis=1)) max(abs(x))
-inf min(sum(abs(x), axis=1)) min(abs(x))
0 -- sum(x != 0)
1 max(sum(abs(x), axis=0)) as below
-1 min(sum(abs(x), axis=0)) as below
2 2-norm (largest sing. value) as below
-2 smallest singular value as below
other -- sum(abs(x)**ord)**(1./ord)
===== ============================ ==========================
The Frobenius norm is given by [1]_:
:math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
The nuclear norm is the sum of the singular values.
References
----------
.. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
Examples
--------
>>> from mars.tensor import linalg as LA
>>> import mars.tensor as mt
>>> a = mt.arange(9) - 4
>>> a.execute()
array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
>>> b = a.reshape((3, 3))
>>> b.execute()
array([[-4, -3, -2],
[-1, 0, 1],
[ 2, 3, 4]])
>>> LA.norm(a).execute()
7.745966692414834
>>> LA.norm(b).execute()
7.745966692414834
>>> LA.norm(b, 'fro').execute()
7.745966692414834
>>> LA.norm(a, mt.inf).execute()
4.0
>>> LA.norm(b, mt.inf).execute()
9.0
>>> LA.norm(a, -mt.inf).execute()
0.0
>>> LA.norm(b, -mt.inf).execute()
2.0
>>> LA.norm(a, 1).execute()
20.0
>>> LA.norm(b, 1).execute()
7.0
>>> LA.norm(a, -1).execute()
0.0
>>> LA.norm(b, -1).execute()
6.0
>>> LA.norm(a, 2).execute()
7.745966692414834
>>> LA.norm(b, 2).execute()
7.3484692283495345
>>> LA.norm(a, -2).execute()
0.0
>>> LA.norm(b, -2).execute()
4.351066026358965e-18
>>> LA.norm(a, 3).execute()
5.8480354764257312
>>> LA.norm(a, -3).execute()
0.0
Using the `axis` argument to compute vector norms:
>>> c = mt.array([[ 1, 2, 3],
... [-1, 1, 4]])
>>> LA.norm(c, axis=0).execute()
array([ 1.41421356, 2.23606798, 5. ])
>>> LA.norm(c, axis=1).execute()
array([ 3.74165739, 4.24264069])
>>> LA.norm(c, ord=1, axis=1).execute()
array([ 6., 6.])
Using the `axis` argument to compute matrix norms:
>>> m = mt.arange(8).reshape(2,2,2)
>>> LA.norm(m, axis=(1,2)).execute()
array([ 3.74165739, 11.22497216])
>>> LA.norm(m[0, :, :]).execute(), LA.norm(m[1, :, :]).execute()
(3.7416573867739413, 11.224972160321824)
"""
x = astensor(x)
if ord == 'fro':
ord = None
if axis is not None:
if isinstance(axis, Iterable):
axis = tuple(axis)
else:
axis = (axis,)
else:
axis = tuple(range(x.ndim))
op = TensorNorm(ord=ord, axis=axis, keepdims=keepdims,
dtype=np.result_type(x.dtype, np.float_), sparse=x.issparse())
return op(x)
| 35.610759
| 105
| 0.555052
|
import itertools
from collections.abc import Iterable
import numpy as np
from ... import opcodes as OperandDef
from ...serialize import ValueType, KeyField, AnyField, TupleField, BoolField
from ..utils import recursive_tile
from ..array_utils import device, as_same_device
from ..operands import TensorHasInput, TensorOperandMixin
from ..arithmetic import sqrt
from ..datasource import tensor as astensor
from .svd import svd
class TensorNorm(TensorHasInput, TensorOperandMixin):
_op_type_ = OperandDef.NORM
_input = KeyField('input')
_ord = AnyField('ord')
_axis = TupleField('axis', ValueType.int32)
_keepdims = BoolField('keepdims')
def __init__(self, ord=None, axis=None, keepdims=None, dtype=None, sparse=False, **kw):
super().__init__(_ord=ord, _axis=axis, _keepdims=keepdims, _dtype=dtype,
_sparse=sparse, **kw)
@property
def ord(self):
return getattr(self, '_ord', None)
@property
def axis(self):
return self._axis
@property
def keepdims(self):
return self._keepdims
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._input = self._inputs[0]
def __call__(self, x):
r = x.astype(self.dtype)
shape = self._norm(r, self._ord, self._axis, self._keepdims).shape
return self.new_tensor([x], shape)
@classmethod
def tile(cls, op):
x = op.input
axis = op.axis
ord = op.ord
keepdims = op.keepdims
axis_chunk_shapes = tuple(x.chunk_shape[i] for i in axis)
can_apply_norm = all(s == 1 for s in axis_chunk_shapes)
if can_apply_norm:
axis_set = set(axis)
get_shape = lambda shape: tuple(s if i not in axis_set else 1 for i, s in enumerate(shape)
if i not in axis_set or keepdims)
out_chunk_shape = get_shape(x.chunk_shape)
out_chunks = []
for idx in itertools.product(*[range(s) for s in out_chunk_shape]):
idx_iter = iter(idx)
in_idx = tuple(0 if i in axis_set and not keepdims else next(idx_iter)
for i in range(x.ndim))
c = x.cix[in_idx]
chunk_op = op.copy().reset_key()
out_chunk = chunk_op.new_chunk([c], shape=get_shape(c.shape), index=idx)
out_chunks.append(out_chunk)
nsplits = [tuple(c.shape[i] for c in out_chunks
if all(idx == 0 for j, idx in enumerate(c.index) if j != i))
for i in range(len(out_chunks[0].shape))]
new_op = op.copy()
return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=out_chunks, nsplits=nsplits)
r = cls._norm(x.astype(op.outputs[0].dtype), ord, axis, keepdims)
recursive_tile(r)
new_op = op.copy()
return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=r.chunks, nsplits=r.nsplits)
@staticmethod
def _norm(r, ord, axis, keepdims):
if ord is None:
return sqrt((abs(r) ** 2).sum(axis=axis, keepdims=keepdims))
elif ord == 'nuc':
if len(axis) == 1:
raise ValueError('Invalid norm order for vectors.')
return svd(r)[1][np.newaxis].sum(keepdims=keepdims)
elif ord == np.inf:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
r = abs(r)
if len(axis) == 1:
return r.max(axis=axis, keepdims=keepdims)
else:
return r.sum(axis=axis[1], keepdims=keepdims).max(keepdims=keepdims)
elif ord == -np.inf:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
r = abs(r)
if len(axis) == 1:
return r.min(axis=axis, keepdims=keepdims)
else:
return r.sum(axis=axis[1], keepdims=keepdims).min(keepdims=keepdims)
elif ord == 0:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
if len(axis) == 2:
raise ValueError('Invalid norm order for matrices.')
return (r != 0).astype(r.dtype).sum(axis=axis, keepdims=keepdims)
elif ord == 1:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
r = abs(r)
if len(axis) == 1:
return r.sum(axis=axis, keepdims=keepdims)
else:
return r.sum(axis=axis[0], keepdims=keepdims).max(keepdims=keepdims)
elif ord == -1 and len(axis) == 2:
if r.ndim > 2:
raise ValueError('Improper number of dimensions to norm.')
return abs(r).sum(axis=axis[0], keepdims=keepdims).min(keepdims=keepdims)
elif ord == 2 and len(axis) == 2:
return svd(r)[1][np.newaxis].max(keepdims=keepdims)
elif ord == -2 and len(axis) == 2:
return svd(r)[1][np.newaxis].min(keepdims=keepdims)
else:
if len(axis) == 2:
raise ValueError('Invalid norm order for matrices.')
return (abs(r) ** ord).sum(axis=axis, keepdims=keepdims) ** (1.0 / ord)
@classmethod
def execute(cls, ctx, op):
(x,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True)
with device(device_id):
ctx[op.outputs[0].key] = xp.linalg.norm(x, ord=op.ord, axis=op.axis,
keepdims=op.keepdims)
def norm(x, ord=None, axis=None, keepdims=False):
x = astensor(x)
if ord == 'fro':
ord = None
if axis is not None:
if isinstance(axis, Iterable):
axis = tuple(axis)
else:
axis = (axis,)
else:
axis = tuple(range(x.ndim))
op = TensorNorm(ord=ord, axis=axis, keepdims=keepdims,
dtype=np.result_type(x.dtype, np.float_), sparse=x.issparse())
return op(x)
| true
| true
|
1c3ecac895f332fe1a4efb3869906afedc532dea
| 953
|
py
|
Python
|
examples/active_learning/plot_perf.py
|
sudodoki/trunklucator
|
7d5a96d650a50e62b3ad479f72de8d60790e93a8
|
[
"Apache-2.0"
] | null | null | null |
examples/active_learning/plot_perf.py
|
sudodoki/trunklucator
|
7d5a96d650a50e62b3ad479f72de8d60790e93a8
|
[
"Apache-2.0"
] | null | null | null |
examples/active_learning/plot_perf.py
|
sudodoki/trunklucator
|
7d5a96d650a50e62b3ad479f72de8d60790e93a8
|
[
"Apache-2.0"
] | null | null | null |
import matplotlib as mpl
import matplotlib.pyplot as plt
from io import BytesIO
import base64
mpl.use('Agg')
def plot_performance(performance_history):
fig, ax = plt.subplots(figsize=(8.5, 6), dpi=130)
ax.plot(performance_history)
ax.scatter(range(len(performance_history)), performance_history, s=13)
ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=5, integer=True))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=10))
ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))
ax.set_ylim(bottom=0, top=1)
ax.grid(True)
ax.set_title('Incremental classification accuracy')
ax.set_xlabel('Query iteration')
ax.set_ylabel('Classification Accuracy')
image = BytesIO()
plt.plot()
plt.savefig(image, format='png')
plt.cla()
plt.close(fig)
return ''' <img src="data:image/png;base64,{}" border="0" /> '''.format(base64.encodebytes(image.getvalue()).decode())
| 30.741935
| 122
| 0.716684
|
import matplotlib as mpl
import matplotlib.pyplot as plt
from io import BytesIO
import base64
mpl.use('Agg')
def plot_performance(performance_history):
fig, ax = plt.subplots(figsize=(8.5, 6), dpi=130)
ax.plot(performance_history)
ax.scatter(range(len(performance_history)), performance_history, s=13)
ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=5, integer=True))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=10))
ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))
ax.set_ylim(bottom=0, top=1)
ax.grid(True)
ax.set_title('Incremental classification accuracy')
ax.set_xlabel('Query iteration')
ax.set_ylabel('Classification Accuracy')
image = BytesIO()
plt.plot()
plt.savefig(image, format='png')
plt.cla()
plt.close(fig)
return ''' <img src="data:image/png;base64,{}" border="0" /> '''.format(base64.encodebytes(image.getvalue()).decode())
| true
| true
|
1c3ecaf3fc8f8dbaf8239f589fe967fdd4055863
| 1,757
|
py
|
Python
|
kirppuauth/migrations/0002_auto_20160127_2212.py
|
tracon/kirppu
|
65c926daa6138b14268693eeac6e6e517a3cd96b
|
[
"MIT"
] | null | null | null |
kirppuauth/migrations/0002_auto_20160127_2212.py
|
tracon/kirppu
|
65c926daa6138b14268693eeac6e6e517a3cd96b
|
[
"MIT"
] | 6
|
2017-02-03T07:42:57.000Z
|
2019-12-23T14:25:15.000Z
|
kirppuauth/migrations/0002_auto_20160127_2212.py
|
tracon/kirppu
|
65c926daa6138b14268693eeac6e6e517a3cd96b
|
[
"MIT"
] | 6
|
2015-01-26T22:27:26.000Z
|
2019-01-20T21:57:51.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
import django.core.validators
import django.contrib.auth.models
class Migration(migrations.Migration):
dependencies = [
('kirppuauth', '0001_initial'),
]
operations = [
migrations.AlterModelManagers(
name='user',
managers=[
('objects', django.contrib.auth.models.UserManager()),
],
),
migrations.AlterField(
model_name='user',
name='email',
field=models.EmailField(max_length=254, verbose_name='email address', blank=True),
),
migrations.AlterField(
model_name='user',
name='groups',
field=models.ManyToManyField(related_query_name='user', related_name='user_set', to='auth.Group', blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', verbose_name='groups'),
),
migrations.AlterField(
model_name='user',
name='last_login',
field=models.DateTimeField(null=True, verbose_name='last login', blank=True),
),
migrations.AlterField(
model_name='user',
name='username',
field=models.CharField(error_messages={'unique': 'A user with that username already exists.'}, max_length=30, validators=[django.core.validators.RegexValidator('^[\\w.@+-]+$', 'Enter a valid username. This value may contain only letters, numbers and @/./+/-/_ characters.', 'invalid')], help_text='Required. 30 characters or fewer. Letters, digits and @/./+/-/_ only.', unique=True, verbose_name='username'),
),
]
| 40.860465
| 420
| 0.623221
|
from __future__ import unicode_literals
from django.db import migrations, models
import django.core.validators
import django.contrib.auth.models
class Migration(migrations.Migration):
dependencies = [
('kirppuauth', '0001_initial'),
]
operations = [
migrations.AlterModelManagers(
name='user',
managers=[
('objects', django.contrib.auth.models.UserManager()),
],
),
migrations.AlterField(
model_name='user',
name='email',
field=models.EmailField(max_length=254, verbose_name='email address', blank=True),
),
migrations.AlterField(
model_name='user',
name='groups',
field=models.ManyToManyField(related_query_name='user', related_name='user_set', to='auth.Group', blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', verbose_name='groups'),
),
migrations.AlterField(
model_name='user',
name='last_login',
field=models.DateTimeField(null=True, verbose_name='last login', blank=True),
),
migrations.AlterField(
model_name='user',
name='username',
field=models.CharField(error_messages={'unique': 'A user with that username already exists.'}, max_length=30, validators=[django.core.validators.RegexValidator('^[\\w.@+-]+$', 'Enter a valid username. This value may contain only letters, numbers and @/./+/-/_ characters.', 'invalid')], help_text='Required. 30 characters or fewer. Letters, digits and @/./+/-/_ only.', unique=True, verbose_name='username'),
),
]
| true
| true
|
1c3ecb6add03b7112c2dee8c497792a4df9841d8
| 512
|
py
|
Python
|
gaphor/UML/interactions/__init__.py
|
bertob/gaphor
|
a1d6f8dd8c878f299980bba6c055436148573274
|
[
"Apache-2.0"
] | 867
|
2018-01-09T00:19:09.000Z
|
2022-03-31T02:49:23.000Z
|
gaphor/UML/interactions/__init__.py
|
burakozturk16/gaphor
|
86267a5200ac4439626d35d306dbb376c3800107
|
[
"Apache-2.0"
] | 790
|
2018-01-13T23:47:07.000Z
|
2022-03-31T16:04:27.000Z
|
gaphor/UML/interactions/__init__.py
|
burakozturk16/gaphor
|
86267a5200ac4439626d35d306dbb376c3800107
|
[
"Apache-2.0"
] | 117
|
2018-01-09T02:24:49.000Z
|
2022-03-23T08:07:42.000Z
|
from gaphor.UML.interactions import (
copypaste,
interactionsconnect,
interactionsgrouping,
interactionspropertypages,
)
from gaphor.UML.interactions.executionspecification import ExecutionSpecificationItem
from gaphor.UML.interactions.interaction import InteractionItem
from gaphor.UML.interactions.lifeline import LifelineItem
from gaphor.UML.interactions.message import MessageItem
__all__ = [
"ExecutionSpecificationItem",
"InteractionItem",
"LifelineItem",
"MessageItem",
]
| 28.444444
| 85
| 0.802734
|
from gaphor.UML.interactions import (
copypaste,
interactionsconnect,
interactionsgrouping,
interactionspropertypages,
)
from gaphor.UML.interactions.executionspecification import ExecutionSpecificationItem
from gaphor.UML.interactions.interaction import InteractionItem
from gaphor.UML.interactions.lifeline import LifelineItem
from gaphor.UML.interactions.message import MessageItem
__all__ = [
"ExecutionSpecificationItem",
"InteractionItem",
"LifelineItem",
"MessageItem",
]
| true
| true
|
1c3ecced193917d21de696859842864904d72d87
| 3,649
|
py
|
Python
|
benchmarks/kvbench/novelsm/novelsm.py
|
huangvincent170/cyclone
|
737af617ab1472dfb16e6c20a079e88dccf85850
|
[
"Apache-2.0"
] | 2
|
2019-04-16T01:33:36.000Z
|
2021-02-23T08:34:38.000Z
|
benchmarks/kvbench/novelsm/novelsm.py
|
huangvincent170/cyclone
|
737af617ab1472dfb16e6c20a079e88dccf85850
|
[
"Apache-2.0"
] | null | null | null |
benchmarks/kvbench/novelsm/novelsm.py
|
huangvincent170/cyclone
|
737af617ab1472dfb16e6c20a079e88dccf85850
|
[
"Apache-2.0"
] | 4
|
2020-03-27T18:06:33.000Z
|
2021-03-24T09:56:17.000Z
|
def launch_cmds_startup():
print("Configuring for rocksdb application")
def launch_cmds_server_gen(f, q, r, m, quorums, replicas, clients, ports):
passwd=''
if os.environ.has_key('CYCLONE_PASS'):
passwd=os.environ.get('CYCLONE_PASS')
cmd= ' echo ' + passwd + ' | sudo -S '
cmd=cmd + 'rm -rf /mnt/pmem0/rocksdata\n'
f.write(cmd)
cmd=' echo ' + passwd + ' | sudo -S '
cmd= cmd + 'rm -f /mnt/pmem0/rockswal/*\n'
f.write(cmd)
cmd=' echo ' + passwd + ' | sudo -S '
cmd=cmd + ' LD_LIBRARY_PATH=/usr/lib:/usr/local/lib '
cmd=cmd + '/home/cyclone/cyclone/cyclone.git/benchmarks/kvbench/novelsm/novelsm_loader\n'
f.write(cmd)
cmd= ' echo ' + passwd + ' | sudo -S '
cmd=cmd + 'cp -r /mnt/pmem0/preloaded /mnt/pmem0/rocksdata\n'
f.write(cmd)
cmd=''
if os.environ.has_key('RBT_SLEEP_USEC'):
cmd=cmd + 'RBT_SLEEP_USEC=' + os.environ.get('RBT_SLEEP_USEC') + ' '
cmd=cmd + ' echo ' + passwd + ' | sudo -S '
cmd=cmd + ' PMEM_IS_PMEM_FORCE=1 '
cmd=cmd + ' LD_LIBRARY_PATH=/usr/lib:/usr/local/lib '
cmd=cmd + '/home/cyclone/cyclone/cyclone.git/benchmarks/kvbench/novelsm/novelsm_server '
cmd=cmd + str(r) + ' '
cmd=cmd + str(m) + ' '
cmd=cmd + str(clients) + ' '
cmd=cmd + 'config_cluster.ini config_quorum.ini ' +str(ports) + ' &> server_log &\n'
f.write(cmd)
def launch_cmds_preload_gen(f, m, c, quorums, replicas, clients, machines, ports):
cmd=''
def launch_cmds_client_gen(f, m, c, quorums, replicas, clients, machines, ports, bufsize):
passwd=''
if m >= replicas:
client_machines=machines-replicas
if client_machines > clients:
client_machines = clients
clients_per_machine=clients/client_machines
c_start = clients_per_machine*(m - replicas)
c_stop = c_start + clients_per_machine
if m == replicas + client_machines - 1:
c_stop = clients
if c == 0 and m < replicas + client_machines:
cmd=''
if os.environ.has_key('KV_FRAC_READ'):
cmd=cmd + 'KV_FRAC_READ=' + os.environ.get('KV_FRAC_READ') + ' '
if os.environ.has_key('KV_KEYS'):
cmd=cmd + 'KV_KEYS=' + os.environ.get('KV_KEYS') + ' '
if os.environ.has_key('ACTIVE'):
cmd=cmd + 'ACTIVE=' + os.environ.get('ACTIVE') + ' '
if os.environ.has_key('CYCLONE_PASS'):
passwd=os.environ.get('CYCLONE_PASS')
cmd=cmd + ' echo ' + passwd + ' | sudo -S '
cmd=cmd + ' LD_LIBRARY_PATH=/usr/lib:/usr/local/lib '
#cmd=cmd + '/home/cyclone/cyclone/cyclone.git/test/rocksdb_client '
cmd=cmd + '/home/cyclone/cyclone/cyclone.git/benchmarks/kvbench/novelsm/novelsm_async_client '
cmd=cmd + str(c_start) + ' '
cmd=cmd + str(c_stop) + ' '
cmd=cmd + str(m) + ' '
cmd=cmd + str(replicas) + ' '
cmd=cmd + str(clients) + ' '
cmd=cmd + str(quorums) + ' '
#cmd=cmd + 'config_cluster.ini config_quorum ' + str(ports) + ' ' + ' &> client_log' + str(0) + ' &\n'
cmd=cmd + 'config_cluster.ini config_quorum ' + str(ports) + ' ' + str(bufsize) + ' &> client_log' + str(0) + ' &\n'
f.write(cmd)
def killall_cmds_gen(f):
passwd=''
if os.environ.has_key('CYCLONE_PASS'):
passwd=os.environ.get('CYCLONE_PASS')
f.write('echo ' + passwd + ' | sudo -S pkill novelsm_server\n')
f.write('echo ' + passwd + ' | sudo -S pkill rocksdb_client\n')
f.write('echo ' + passwd + ' | sudo -S pkill novelsm_async\n')
| 45.049383
| 128
| 0.580433
|
def launch_cmds_startup():
print("Configuring for rocksdb application")
def launch_cmds_server_gen(f, q, r, m, quorums, replicas, clients, ports):
passwd=''
if os.environ.has_key('CYCLONE_PASS'):
passwd=os.environ.get('CYCLONE_PASS')
cmd= ' echo ' + passwd + ' | sudo -S '
cmd=cmd + 'rm -rf /mnt/pmem0/rocksdata\n'
f.write(cmd)
cmd=' echo ' + passwd + ' | sudo -S '
cmd= cmd + 'rm -f /mnt/pmem0/rockswal/*\n'
f.write(cmd)
cmd=' echo ' + passwd + ' | sudo -S '
cmd=cmd + ' LD_LIBRARY_PATH=/usr/lib:/usr/local/lib '
cmd=cmd + '/home/cyclone/cyclone/cyclone.git/benchmarks/kvbench/novelsm/novelsm_loader\n'
f.write(cmd)
cmd= ' echo ' + passwd + ' | sudo -S '
cmd=cmd + 'cp -r /mnt/pmem0/preloaded /mnt/pmem0/rocksdata\n'
f.write(cmd)
cmd=''
if os.environ.has_key('RBT_SLEEP_USEC'):
cmd=cmd + 'RBT_SLEEP_USEC=' + os.environ.get('RBT_SLEEP_USEC') + ' '
cmd=cmd + ' echo ' + passwd + ' | sudo -S '
cmd=cmd + ' PMEM_IS_PMEM_FORCE=1 '
cmd=cmd + ' LD_LIBRARY_PATH=/usr/lib:/usr/local/lib '
cmd=cmd + '/home/cyclone/cyclone/cyclone.git/benchmarks/kvbench/novelsm/novelsm_server '
cmd=cmd + str(r) + ' '
cmd=cmd + str(m) + ' '
cmd=cmd + str(clients) + ' '
cmd=cmd + 'config_cluster.ini config_quorum.ini ' +str(ports) + ' &> server_log &\n'
f.write(cmd)
def launch_cmds_preload_gen(f, m, c, quorums, replicas, clients, machines, ports):
cmd=''
def launch_cmds_client_gen(f, m, c, quorums, replicas, clients, machines, ports, bufsize):
passwd=''
if m >= replicas:
client_machines=machines-replicas
if client_machines > clients:
client_machines = clients
clients_per_machine=clients/client_machines
c_start = clients_per_machine*(m - replicas)
c_stop = c_start + clients_per_machine
if m == replicas + client_machines - 1:
c_stop = clients
if c == 0 and m < replicas + client_machines:
cmd=''
if os.environ.has_key('KV_FRAC_READ'):
cmd=cmd + 'KV_FRAC_READ=' + os.environ.get('KV_FRAC_READ') + ' '
if os.environ.has_key('KV_KEYS'):
cmd=cmd + 'KV_KEYS=' + os.environ.get('KV_KEYS') + ' '
if os.environ.has_key('ACTIVE'):
cmd=cmd + 'ACTIVE=' + os.environ.get('ACTIVE') + ' '
if os.environ.has_key('CYCLONE_PASS'):
passwd=os.environ.get('CYCLONE_PASS')
cmd=cmd + ' echo ' + passwd + ' | sudo -S '
cmd=cmd + ' LD_LIBRARY_PATH=/usr/lib:/usr/local/lib '
cmd=cmd + '/home/cyclone/cyclone/cyclone.git/benchmarks/kvbench/novelsm/novelsm_async_client '
cmd=cmd + str(c_start) + ' '
cmd=cmd + str(c_stop) + ' '
cmd=cmd + str(m) + ' '
cmd=cmd + str(replicas) + ' '
cmd=cmd + str(clients) + ' '
cmd=cmd + str(quorums) + ' '
cmd=cmd + 'config_cluster.ini config_quorum ' + str(ports) + ' ' + str(bufsize) + ' &> client_log' + str(0) + ' &\n'
f.write(cmd)
def killall_cmds_gen(f):
passwd=''
if os.environ.has_key('CYCLONE_PASS'):
passwd=os.environ.get('CYCLONE_PASS')
f.write('echo ' + passwd + ' | sudo -S pkill novelsm_server\n')
f.write('echo ' + passwd + ' | sudo -S pkill rocksdb_client\n')
f.write('echo ' + passwd + ' | sudo -S pkill novelsm_async\n')
| true
| true
|
1c3eccfe5a2a05938061228955cc2ec0f6627de9
| 73,105
|
py
|
Python
|
packages/python/plotly/plotly/graph_objs/cone/_colorbar.py
|
eisenlohr/plotly.py
|
3b0e3df45036cf48f772b13bcc10ce347964aefc
|
[
"MIT"
] | 1
|
2021-12-11T07:01:40.000Z
|
2021-12-11T07:01:40.000Z
|
packages/python/plotly/plotly/graph_objs/cone/_colorbar.py
|
jiangrongbo/plotly.py
|
df19fc702b309586cc24e25373b87e8bdbb3ff60
|
[
"MIT"
] | null | null | null |
packages/python/plotly/plotly/graph_objs/cone/_colorbar.py
|
jiangrongbo/plotly.py
|
df19fc702b309586cc24e25373b87e8bdbb3ff60
|
[
"MIT"
] | 1
|
2021-11-29T22:55:05.000Z
|
2021-11-29T22:55:05.000Z
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ColorBar(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "cone"
_path_str = "cone.colorbar"
_valid_props = {
"bgcolor",
"bordercolor",
"borderwidth",
"dtick",
"exponentformat",
"len",
"lenmode",
"minexponent",
"nticks",
"outlinecolor",
"outlinewidth",
"separatethousands",
"showexponent",
"showticklabels",
"showtickprefix",
"showticksuffix",
"thickness",
"thicknessmode",
"tick0",
"tickangle",
"tickcolor",
"tickfont",
"tickformat",
"tickformatstopdefaults",
"tickformatstops",
"ticklabeloverflow",
"ticklabelposition",
"ticklen",
"tickmode",
"tickprefix",
"ticks",
"ticksuffix",
"ticktext",
"ticktextsrc",
"tickvals",
"tickvalssrc",
"tickwidth",
"title",
"titlefont",
"titleside",
"x",
"xanchor",
"xpad",
"y",
"yanchor",
"ypad",
}
# bgcolor
# -------
@property
def bgcolor(self):
"""
Sets the color of padded area.
The 'bgcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["bgcolor"]
@bgcolor.setter
def bgcolor(self, val):
self["bgcolor"] = val
# bordercolor
# -----------
@property
def bordercolor(self):
"""
Sets the axis line color.
The 'bordercolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["bordercolor"]
@bordercolor.setter
def bordercolor(self, val):
self["bordercolor"] = val
# borderwidth
# -----------
@property
def borderwidth(self):
"""
Sets the width (in px) or the border enclosing this color bar.
The 'borderwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["borderwidth"]
@borderwidth.setter
def borderwidth(self, val):
self["borderwidth"] = val
# dtick
# -----
@property
def dtick(self):
"""
Sets the step in-between ticks on this axis. Use with `tick0`.
Must be a positive number, or special strings available to
"log" and "date" axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick number. For
example, to set a tick mark at 1, 10, 100, 1000, ... set dtick
to 1. To set tick marks at 1, 100, 10000, ... set dtick to 2.
To set tick marks at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special values;
"L<f>", where `f` is a positive number, gives ticks linearly
spaced in value (but not position). For example `tick0` = 0.1,
`dtick` = "L0.5" will put ticks at 0.1, 0.6, 1.1, 1.6 etc. To
show powers of 10 plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is ignored for "D1" and
"D2". If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval between
ticks to one day, set `dtick` to 86400000.0. "date" also has
special values "M<n>" gives ticks spaced by a number of months.
`n` must be a positive integer. To set ticks on the 15th of
every third month, set `tick0` to "2000-01-15" and `dtick` to
"M3". To set ticks every 4 years, set `dtick` to "M48"
The 'dtick' property accepts values of any type
Returns
-------
Any
"""
return self["dtick"]
@dtick.setter
def dtick(self, val):
self["dtick"] = val
# exponentformat
# --------------
@property
def exponentformat(self):
"""
Determines a formatting rule for the tick exponents. For
example, consider the number 1,000,000,000. If "none", it
appears as 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If "SI", 1G. If
"B", 1B.
The 'exponentformat' property is an enumeration that may be specified as:
- One of the following enumeration values:
['none', 'e', 'E', 'power', 'SI', 'B']
Returns
-------
Any
"""
return self["exponentformat"]
@exponentformat.setter
def exponentformat(self, val):
self["exponentformat"] = val
# len
# ---
@property
def len(self):
"""
Sets the length of the color bar This measure excludes the
padding of both ends. That is, the color bar length is this
length minus the padding on both ends.
The 'len' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["len"]
@len.setter
def len(self, val):
self["len"] = val
# lenmode
# -------
@property
def lenmode(self):
"""
Determines whether this color bar's length (i.e. the measure in
the color variation direction) is set in units of plot
"fraction" or in *pixels. Use `len` to set the value.
The 'lenmode' property is an enumeration that may be specified as:
- One of the following enumeration values:
['fraction', 'pixels']
Returns
-------
Any
"""
return self["lenmode"]
@lenmode.setter
def lenmode(self, val):
self["lenmode"] = val
# minexponent
# -----------
@property
def minexponent(self):
"""
Hide SI prefix for 10^n if |n| is below this number. This only
has an effect when `tickformat` is "SI" or "B".
The 'minexponent' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["minexponent"]
@minexponent.setter
def minexponent(self, val):
self["minexponent"] = val
# nticks
# ------
@property
def nticks(self):
"""
Specifies the maximum number of ticks for the particular axis.
The actual number of ticks will be chosen automatically to be
less than or equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
The 'nticks' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int
"""
return self["nticks"]
@nticks.setter
def nticks(self, val):
self["nticks"] = val
# outlinecolor
# ------------
@property
def outlinecolor(self):
"""
Sets the axis line color.
The 'outlinecolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["outlinecolor"]
@outlinecolor.setter
def outlinecolor(self, val):
self["outlinecolor"] = val
# outlinewidth
# ------------
@property
def outlinewidth(self):
"""
Sets the width (in px) of the axis line.
The 'outlinewidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["outlinewidth"]
@outlinewidth.setter
def outlinewidth(self, val):
self["outlinewidth"] = val
# separatethousands
# -----------------
@property
def separatethousands(self):
"""
If "true", even 4-digit integers are separated
The 'separatethousands' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["separatethousands"]
@separatethousands.setter
def separatethousands(self, val):
self["separatethousands"] = val
# showexponent
# ------------
@property
def showexponent(self):
"""
If "all", all exponents are shown besides their significands.
If "first", only the exponent of the first tick is shown. If
"last", only the exponent of the last tick is shown. If "none",
no exponents appear.
The 'showexponent' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'first', 'last', 'none']
Returns
-------
Any
"""
return self["showexponent"]
@showexponent.setter
def showexponent(self, val):
self["showexponent"] = val
# showticklabels
# --------------
@property
def showticklabels(self):
"""
Determines whether or not the tick labels are drawn.
The 'showticklabels' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showticklabels"]
@showticklabels.setter
def showticklabels(self, val):
self["showticklabels"] = val
# showtickprefix
# --------------
@property
def showtickprefix(self):
"""
If "all", all tick labels are displayed with a prefix. If
"first", only the first tick is displayed with a prefix. If
"last", only the last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
The 'showtickprefix' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'first', 'last', 'none']
Returns
-------
Any
"""
return self["showtickprefix"]
@showtickprefix.setter
def showtickprefix(self, val):
self["showtickprefix"] = val
# showticksuffix
# --------------
@property
def showticksuffix(self):
"""
Same as `showtickprefix` but for tick suffixes.
The 'showticksuffix' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'first', 'last', 'none']
Returns
-------
Any
"""
return self["showticksuffix"]
@showticksuffix.setter
def showticksuffix(self, val):
self["showticksuffix"] = val
# thickness
# ---------
@property
def thickness(self):
"""
Sets the thickness of the color bar This measure excludes the
size of the padding, ticks and labels.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["thickness"]
@thickness.setter
def thickness(self, val):
self["thickness"] = val
# thicknessmode
# -------------
@property
def thicknessmode(self):
"""
Determines whether this color bar's thickness (i.e. the measure
in the constant color direction) is set in units of plot
"fraction" or in "pixels". Use `thickness` to set the value.
The 'thicknessmode' property is an enumeration that may be specified as:
- One of the following enumeration values:
['fraction', 'pixels']
Returns
-------
Any
"""
return self["thicknessmode"]
@thicknessmode.setter
def thicknessmode(self, val):
self["thicknessmode"] = val
# tick0
# -----
@property
def tick0(self):
"""
Sets the placement of the first tick on this axis. Use with
`dtick`. If the axis `type` is "log", then you must take the
log of your starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when `dtick`=*L<f>* (see
`dtick` for more info). If the axis `type` is "date", it should
be a date string, like date data. If the axis `type` is
"category", it should be a number, using the scale where each
category is assigned a serial number from zero in the order it
appears.
The 'tick0' property accepts values of any type
Returns
-------
Any
"""
return self["tick0"]
@tick0.setter
def tick0(self, val):
self["tick0"] = val
# tickangle
# ---------
@property
def tickangle(self):
"""
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the tick
labels vertically.
The 'tickangle' property is a angle (in degrees) that may be
specified as a number between -180 and 180. Numeric values outside this
range are converted to the equivalent value
(e.g. 270 is converted to -90).
Returns
-------
int|float
"""
return self["tickangle"]
@tickangle.setter
def tickangle(self, val):
self["tickangle"] = val
# tickcolor
# ---------
@property
def tickcolor(self):
"""
Sets the tick color.
The 'tickcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["tickcolor"]
@tickcolor.setter
def tickcolor(self, val):
self["tickcolor"] = val
# tickfont
# --------
@property
def tickfont(self):
"""
Sets the color bar's tick label font
The 'tickfont' property is an instance of Tickfont
that may be specified as:
- An instance of :class:`plotly.graph_objs.cone.colorbar.Tickfont`
- A dict of string/value properties that will be passed
to the Tickfont constructor
Supported dict properties:
color
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans",, "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
size
Returns
-------
plotly.graph_objs.cone.colorbar.Tickfont
"""
return self["tickfont"]
@tickfont.setter
def tickfont(self, val):
self["tickfont"] = val
# tickformat
# ----------
@property
def tickformat(self):
"""
Sets the tick label formatting rule using d3 formatting mini-
languages which are very similar to those in Python. For
numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format. And for
dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to d3's date
formatter: "%h" for half of the year as a decimal number as
well as "%{n}f" for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with tickformat
"%H~%M~%S.%2f" would display "09~15~23.46"
The 'tickformat' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["tickformat"]
@tickformat.setter
def tickformat(self, val):
self["tickformat"] = val
# tickformatstops
# ---------------
@property
def tickformatstops(self):
"""
The 'tickformatstops' property is a tuple of instances of
Tickformatstop that may be specified as:
- A list or tuple of instances of plotly.graph_objs.cone.colorbar.Tickformatstop
- A list or tuple of dicts of string/value properties that
will be passed to the Tickformatstop constructor
Supported dict properties:
dtickrange
range [*min*, *max*], where "min", "max" -
dtick values which describe some zoom level, it
is possible to omit "min" or "max" value by
passing "null"
enabled
Determines whether or not this stop is used. If
`false`, this stop is ignored even within its
`dtickrange`.
name
When used in a template, named items are
created in the output figure in addition to any
items the figure already has in this array. You
can modify these items in the output figure by
making your own item with `templateitemname`
matching this `name` alongside your
modifications (including `visible: false` or
`enabled: false` to hide it). Has no effect
outside of a template.
templateitemname
Used to refer to a named item in this array in
the template. Named items from the template
will be created even without a matching item in
the input figure, but you can modify one by
making an item with `templateitemname` matching
its `name`, alongside your modifications
(including `visible: false` or `enabled: false`
to hide it). If there is no template or no
matching item, this item will be hidden unless
you explicitly show it with `visible: true`.
value
string - dtickformat for described zoom level,
the same as "tickformat"
Returns
-------
tuple[plotly.graph_objs.cone.colorbar.Tickformatstop]
"""
return self["tickformatstops"]
@tickformatstops.setter
def tickformatstops(self, val):
self["tickformatstops"] = val
# tickformatstopdefaults
# ----------------------
@property
def tickformatstopdefaults(self):
"""
When used in a template (as
layout.template.data.cone.colorbar.tickformatstopdefaults),
sets the default property values to use for elements of
cone.colorbar.tickformatstops
The 'tickformatstopdefaults' property is an instance of Tickformatstop
that may be specified as:
- An instance of :class:`plotly.graph_objs.cone.colorbar.Tickformatstop`
- A dict of string/value properties that will be passed
to the Tickformatstop constructor
Supported dict properties:
Returns
-------
plotly.graph_objs.cone.colorbar.Tickformatstop
"""
return self["tickformatstopdefaults"]
@tickformatstopdefaults.setter
def tickformatstopdefaults(self, val):
self["tickformatstopdefaults"] = val
# ticklabeloverflow
# -----------------
@property
def ticklabeloverflow(self):
"""
Determines how we handle tick labels that would overflow either
the graph div or the domain of the axis. The default value for
inside tick labels is *hide past domain*. In other cases the
default is *hide past div*.
The 'ticklabeloverflow' property is an enumeration that may be specified as:
- One of the following enumeration values:
['allow', 'hide past div', 'hide past domain']
Returns
-------
Any
"""
return self["ticklabeloverflow"]
@ticklabeloverflow.setter
def ticklabeloverflow(self, val):
self["ticklabeloverflow"] = val
# ticklabelposition
# -----------------
@property
def ticklabelposition(self):
"""
Determines where tick labels are drawn.
The 'ticklabelposition' property is an enumeration that may be specified as:
- One of the following enumeration values:
['outside', 'inside', 'outside top', 'inside top',
'outside bottom', 'inside bottom']
Returns
-------
Any
"""
return self["ticklabelposition"]
@ticklabelposition.setter
def ticklabelposition(self, val):
self["ticklabelposition"] = val
# ticklen
# -------
@property
def ticklen(self):
"""
Sets the tick length (in px).
The 'ticklen' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["ticklen"]
@ticklen.setter
def ticklen(self, val):
self["ticklen"] = val
# tickmode
# --------
@property
def tickmode(self):
"""
Sets the tick mode for this axis. If "auto", the number of
ticks is set via `nticks`. If "linear", the placement of the
ticks is determined by a starting position `tick0` and a tick
step `dtick` ("linear" is the default value if `tick0` and
`dtick` are provided). If "array", the placement of the ticks
is set via `tickvals` and the tick text is `ticktext`. ("array"
is the default value if `tickvals` is provided).
The 'tickmode' property is an enumeration that may be specified as:
- One of the following enumeration values:
['auto', 'linear', 'array']
Returns
-------
Any
"""
return self["tickmode"]
@tickmode.setter
def tickmode(self, val):
self["tickmode"] = val
# tickprefix
# ----------
@property
def tickprefix(self):
"""
Sets a tick label prefix.
The 'tickprefix' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["tickprefix"]
@tickprefix.setter
def tickprefix(self, val):
self["tickprefix"] = val
# ticks
# -----
@property
def ticks(self):
"""
Determines whether ticks are drawn or not. If "", this axis'
ticks are not drawn. If "outside" ("inside"), this axis' are
drawn outside (inside) the axis lines.
The 'ticks' property is an enumeration that may be specified as:
- One of the following enumeration values:
['outside', 'inside', '']
Returns
-------
Any
"""
return self["ticks"]
@ticks.setter
def ticks(self, val):
self["ticks"] = val
# ticksuffix
# ----------
@property
def ticksuffix(self):
"""
Sets a tick label suffix.
The 'ticksuffix' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["ticksuffix"]
@ticksuffix.setter
def ticksuffix(self, val):
self["ticksuffix"] = val
# ticktext
# --------
@property
def ticktext(self):
"""
Sets the text displayed at the ticks position via `tickvals`.
Only has an effect if `tickmode` is set to "array". Used with
`tickvals`.
The 'ticktext' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["ticktext"]
@ticktext.setter
def ticktext(self, val):
self["ticktext"] = val
# ticktextsrc
# -----------
@property
def ticktextsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `ticktext`.
The 'ticktextsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["ticktextsrc"]
@ticktextsrc.setter
def ticktextsrc(self, val):
self["ticktextsrc"] = val
# tickvals
# --------
@property
def tickvals(self):
"""
Sets the values at which ticks on this axis appear. Only has an
effect if `tickmode` is set to "array". Used with `ticktext`.
The 'tickvals' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["tickvals"]
@tickvals.setter
def tickvals(self, val):
self["tickvals"] = val
# tickvalssrc
# -----------
@property
def tickvalssrc(self):
"""
Sets the source reference on Chart Studio Cloud for `tickvals`.
The 'tickvalssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["tickvalssrc"]
@tickvalssrc.setter
def tickvalssrc(self, val):
self["tickvalssrc"] = val
# tickwidth
# ---------
@property
def tickwidth(self):
"""
Sets the tick width (in px).
The 'tickwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["tickwidth"]
@tickwidth.setter
def tickwidth(self, val):
self["tickwidth"] = val
# title
# -----
@property
def title(self):
"""
The 'title' property is an instance of Title
that may be specified as:
- An instance of :class:`plotly.graph_objs.cone.colorbar.Title`
- A dict of string/value properties that will be passed
to the Title constructor
Supported dict properties:
font
Sets this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
side
Determines the location of color bar's title
with respect to the color bar. Note that the
title's location used to be set by the now
deprecated `titleside` attribute.
text
Sets the title of the color bar. Note that
before the existence of `title.text`, the
title's contents used to be defined as the
`title` attribute itself. This behavior has
been deprecated.
Returns
-------
plotly.graph_objs.cone.colorbar.Title
"""
return self["title"]
@title.setter
def title(self, val):
self["title"] = val
# titlefont
# ---------
@property
def titlefont(self):
"""
Deprecated: Please use cone.colorbar.title.font instead. Sets
this color bar's title font. Note that the title's font used to
be set by the now deprecated `titlefont` attribute.
The 'font' property is an instance of Font
that may be specified as:
- An instance of :class:`plotly.graph_objs.cone.colorbar.title.Font`
- A dict of string/value properties that will be passed
to the Font constructor
Supported dict properties:
color
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans",, "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
size
Returns
-------
"""
return self["titlefont"]
@titlefont.setter
def titlefont(self, val):
self["titlefont"] = val
# titleside
# ---------
@property
def titleside(self):
"""
Deprecated: Please use cone.colorbar.title.side instead.
Determines the location of color bar's title with respect to
the color bar. Note that the title's location used to be set by
the now deprecated `titleside` attribute.
The 'side' property is an enumeration that may be specified as:
- One of the following enumeration values:
['right', 'top', 'bottom']
Returns
-------
"""
return self["titleside"]
@titleside.setter
def titleside(self, val):
self["titleside"] = val
# x
# -
@property
def x(self):
"""
Sets the x position of the color bar (in plot fraction).
The 'x' property is a number and may be specified as:
- An int or float in the interval [-2, 3]
Returns
-------
int|float
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# xanchor
# -------
@property
def xanchor(self):
"""
Sets this color bar's horizontal position anchor. This anchor
binds the `x` position to the "left", "center" or "right" of
the color bar.
The 'xanchor' property is an enumeration that may be specified as:
- One of the following enumeration values:
['left', 'center', 'right']
Returns
-------
Any
"""
return self["xanchor"]
@xanchor.setter
def xanchor(self, val):
self["xanchor"] = val
# xpad
# ----
@property
def xpad(self):
"""
Sets the amount of padding (in px) along the x direction.
The 'xpad' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["xpad"]
@xpad.setter
def xpad(self, val):
self["xpad"] = val
# y
# -
@property
def y(self):
"""
Sets the y position of the color bar (in plot fraction).
The 'y' property is a number and may be specified as:
- An int or float in the interval [-2, 3]
Returns
-------
int|float
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# yanchor
# -------
@property
def yanchor(self):
"""
Sets this color bar's vertical position anchor This anchor
binds the `y` position to the "top", "middle" or "bottom" of
the color bar.
The 'yanchor' property is an enumeration that may be specified as:
- One of the following enumeration values:
['top', 'middle', 'bottom']
Returns
-------
Any
"""
return self["yanchor"]
@yanchor.setter
def yanchor(self, val):
self["yanchor"] = val
# ypad
# ----
@property
def ypad(self):
"""
Sets the amount of padding (in px) along the y direction.
The 'ypad' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["ypad"]
@ypad.setter
def ypad(self, val):
self["ypad"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing this
color bar.
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure excludes
the padding of both ends. That is, the color bar length
is this length minus the padding on both ends.
lenmode
Determines whether this color bar's length (i.e. the
measure in the color variation direction) is set in
units of plot "fraction" or in *pixels. Use `len` to
set the value.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This measure
excludes the size of the padding, ticks and labels.
thicknessmode
Determines whether this color bar's thickness (i.e. the
measure in the constant color direction) is set in
units of plot "fraction" or in "pixels". Use
`thickness` to set the value.
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display "09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.cone.colorbar.T
ickformatstop` instances or dicts with compatible
properties
tickformatstopdefaults
When used in a template (as layout.template.data.cone.c
olorbar.tickformatstopdefaults), sets the default
property values to use for elements of
cone.colorbar.tickformatstops
ticklabeloverflow
Determines how we handle tick labels that would
overflow either the graph div or the domain of the
axis. The default value for inside tick labels is *hide
past domain*. In other cases the default is *hide past
div*.
ticklabelposition
Determines where tick labels are drawn.
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
`ticktext`.
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
`tickvals`.
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.cone.colorbar.Title`
instance or dict with compatible properties
titlefont
Deprecated: Please use cone.colorbar.title.font
instead. Sets this color bar's title font. Note that
the title's font used to be set by the now deprecated
`titlefont` attribute.
titleside
Deprecated: Please use cone.colorbar.title.side
instead. Determines the location of color bar's title
with respect to the color bar. Note that the title's
location used to be set by the now deprecated
`titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position anchor. This
anchor binds the `x` position to the "left", "center"
or "right" of the color bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor This
anchor binds the `y` position to the "top", "middle" or
"bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
"""
_mapped_properties = {
"titlefont": ("title", "font"),
"titleside": ("title", "side"),
}
def __init__(
self,
arg=None,
bgcolor=None,
bordercolor=None,
borderwidth=None,
dtick=None,
exponentformat=None,
len=None,
lenmode=None,
minexponent=None,
nticks=None,
outlinecolor=None,
outlinewidth=None,
separatethousands=None,
showexponent=None,
showticklabels=None,
showtickprefix=None,
showticksuffix=None,
thickness=None,
thicknessmode=None,
tick0=None,
tickangle=None,
tickcolor=None,
tickfont=None,
tickformat=None,
tickformatstops=None,
tickformatstopdefaults=None,
ticklabeloverflow=None,
ticklabelposition=None,
ticklen=None,
tickmode=None,
tickprefix=None,
ticks=None,
ticksuffix=None,
ticktext=None,
ticktextsrc=None,
tickvals=None,
tickvalssrc=None,
tickwidth=None,
title=None,
titlefont=None,
titleside=None,
x=None,
xanchor=None,
xpad=None,
y=None,
yanchor=None,
ypad=None,
**kwargs
):
"""
Construct a new ColorBar object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.cone.ColorBar`
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing this
color bar.
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure excludes
the padding of both ends. That is, the color bar length
is this length minus the padding on both ends.
lenmode
Determines whether this color bar's length (i.e. the
measure in the color variation direction) is set in
units of plot "fraction" or in *pixels. Use `len` to
set the value.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This measure
excludes the size of the padding, ticks and labels.
thicknessmode
Determines whether this color bar's thickness (i.e. the
measure in the constant color direction) is set in
units of plot "fraction" or in "pixels". Use
`thickness` to set the value.
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display "09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.cone.colorbar.T
ickformatstop` instances or dicts with compatible
properties
tickformatstopdefaults
When used in a template (as layout.template.data.cone.c
olorbar.tickformatstopdefaults), sets the default
property values to use for elements of
cone.colorbar.tickformatstops
ticklabeloverflow
Determines how we handle tick labels that would
overflow either the graph div or the domain of the
axis. The default value for inside tick labels is *hide
past domain*. In other cases the default is *hide past
div*.
ticklabelposition
Determines where tick labels are drawn.
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
`ticktext`.
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
`tickvals`.
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.cone.colorbar.Title`
instance or dict with compatible properties
titlefont
Deprecated: Please use cone.colorbar.title.font
instead. Sets this color bar's title font. Note that
the title's font used to be set by the now deprecated
`titlefont` attribute.
titleside
Deprecated: Please use cone.colorbar.title.side
instead. Determines the location of color bar's title
with respect to the color bar. Note that the title's
location used to be set by the now deprecated
`titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position anchor. This
anchor binds the `x` position to the "left", "center"
or "right" of the color bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor This
anchor binds the `y` position to the "top", "middle" or
"bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
Returns
-------
ColorBar
"""
super(ColorBar, self).__init__("colorbar")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.cone.ColorBar
constructor must be a dict or
an instance of :class:`plotly.graph_objs.cone.ColorBar`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("bgcolor", None)
_v = bgcolor if bgcolor is not None else _v
if _v is not None:
self["bgcolor"] = _v
_v = arg.pop("bordercolor", None)
_v = bordercolor if bordercolor is not None else _v
if _v is not None:
self["bordercolor"] = _v
_v = arg.pop("borderwidth", None)
_v = borderwidth if borderwidth is not None else _v
if _v is not None:
self["borderwidth"] = _v
_v = arg.pop("dtick", None)
_v = dtick if dtick is not None else _v
if _v is not None:
self["dtick"] = _v
_v = arg.pop("exponentformat", None)
_v = exponentformat if exponentformat is not None else _v
if _v is not None:
self["exponentformat"] = _v
_v = arg.pop("len", None)
_v = len if len is not None else _v
if _v is not None:
self["len"] = _v
_v = arg.pop("lenmode", None)
_v = lenmode if lenmode is not None else _v
if _v is not None:
self["lenmode"] = _v
_v = arg.pop("minexponent", None)
_v = minexponent if minexponent is not None else _v
if _v is not None:
self["minexponent"] = _v
_v = arg.pop("nticks", None)
_v = nticks if nticks is not None else _v
if _v is not None:
self["nticks"] = _v
_v = arg.pop("outlinecolor", None)
_v = outlinecolor if outlinecolor is not None else _v
if _v is not None:
self["outlinecolor"] = _v
_v = arg.pop("outlinewidth", None)
_v = outlinewidth if outlinewidth is not None else _v
if _v is not None:
self["outlinewidth"] = _v
_v = arg.pop("separatethousands", None)
_v = separatethousands if separatethousands is not None else _v
if _v is not None:
self["separatethousands"] = _v
_v = arg.pop("showexponent", None)
_v = showexponent if showexponent is not None else _v
if _v is not None:
self["showexponent"] = _v
_v = arg.pop("showticklabels", None)
_v = showticklabels if showticklabels is not None else _v
if _v is not None:
self["showticklabels"] = _v
_v = arg.pop("showtickprefix", None)
_v = showtickprefix if showtickprefix is not None else _v
if _v is not None:
self["showtickprefix"] = _v
_v = arg.pop("showticksuffix", None)
_v = showticksuffix if showticksuffix is not None else _v
if _v is not None:
self["showticksuffix"] = _v
_v = arg.pop("thickness", None)
_v = thickness if thickness is not None else _v
if _v is not None:
self["thickness"] = _v
_v = arg.pop("thicknessmode", None)
_v = thicknessmode if thicknessmode is not None else _v
if _v is not None:
self["thicknessmode"] = _v
_v = arg.pop("tick0", None)
_v = tick0 if tick0 is not None else _v
if _v is not None:
self["tick0"] = _v
_v = arg.pop("tickangle", None)
_v = tickangle if tickangle is not None else _v
if _v is not None:
self["tickangle"] = _v
_v = arg.pop("tickcolor", None)
_v = tickcolor if tickcolor is not None else _v
if _v is not None:
self["tickcolor"] = _v
_v = arg.pop("tickfont", None)
_v = tickfont if tickfont is not None else _v
if _v is not None:
self["tickfont"] = _v
_v = arg.pop("tickformat", None)
_v = tickformat if tickformat is not None else _v
if _v is not None:
self["tickformat"] = _v
_v = arg.pop("tickformatstops", None)
_v = tickformatstops if tickformatstops is not None else _v
if _v is not None:
self["tickformatstops"] = _v
_v = arg.pop("tickformatstopdefaults", None)
_v = tickformatstopdefaults if tickformatstopdefaults is not None else _v
if _v is not None:
self["tickformatstopdefaults"] = _v
_v = arg.pop("ticklabeloverflow", None)
_v = ticklabeloverflow if ticklabeloverflow is not None else _v
if _v is not None:
self["ticklabeloverflow"] = _v
_v = arg.pop("ticklabelposition", None)
_v = ticklabelposition if ticklabelposition is not None else _v
if _v is not None:
self["ticklabelposition"] = _v
_v = arg.pop("ticklen", None)
_v = ticklen if ticklen is not None else _v
if _v is not None:
self["ticklen"] = _v
_v = arg.pop("tickmode", None)
_v = tickmode if tickmode is not None else _v
if _v is not None:
self["tickmode"] = _v
_v = arg.pop("tickprefix", None)
_v = tickprefix if tickprefix is not None else _v
if _v is not None:
self["tickprefix"] = _v
_v = arg.pop("ticks", None)
_v = ticks if ticks is not None else _v
if _v is not None:
self["ticks"] = _v
_v = arg.pop("ticksuffix", None)
_v = ticksuffix if ticksuffix is not None else _v
if _v is not None:
self["ticksuffix"] = _v
_v = arg.pop("ticktext", None)
_v = ticktext if ticktext is not None else _v
if _v is not None:
self["ticktext"] = _v
_v = arg.pop("ticktextsrc", None)
_v = ticktextsrc if ticktextsrc is not None else _v
if _v is not None:
self["ticktextsrc"] = _v
_v = arg.pop("tickvals", None)
_v = tickvals if tickvals is not None else _v
if _v is not None:
self["tickvals"] = _v
_v = arg.pop("tickvalssrc", None)
_v = tickvalssrc if tickvalssrc is not None else _v
if _v is not None:
self["tickvalssrc"] = _v
_v = arg.pop("tickwidth", None)
_v = tickwidth if tickwidth is not None else _v
if _v is not None:
self["tickwidth"] = _v
_v = arg.pop("title", None)
_v = title if title is not None else _v
if _v is not None:
self["title"] = _v
_v = arg.pop("titlefont", None)
_v = titlefont if titlefont is not None else _v
if _v is not None:
self["titlefont"] = _v
_v = arg.pop("titleside", None)
_v = titleside if titleside is not None else _v
if _v is not None:
self["titleside"] = _v
_v = arg.pop("x", None)
_v = x if x is not None else _v
if _v is not None:
self["x"] = _v
_v = arg.pop("xanchor", None)
_v = xanchor if xanchor is not None else _v
if _v is not None:
self["xanchor"] = _v
_v = arg.pop("xpad", None)
_v = xpad if xpad is not None else _v
if _v is not None:
self["xpad"] = _v
_v = arg.pop("y", None)
_v = y if y is not None else _v
if _v is not None:
self["y"] = _v
_v = arg.pop("yanchor", None)
_v = yanchor if yanchor is not None else _v
if _v is not None:
self["yanchor"] = _v
_v = arg.pop("ypad", None)
_v = ypad if ypad is not None else _v
if _v is not None:
self["ypad"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
| 35.643588
| 90
| 0.557582
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ColorBar(_BaseTraceHierarchyType):
_parent_path_str = "cone"
_path_str = "cone.colorbar"
_valid_props = {
"bgcolor",
"bordercolor",
"borderwidth",
"dtick",
"exponentformat",
"len",
"lenmode",
"minexponent",
"nticks",
"outlinecolor",
"outlinewidth",
"separatethousands",
"showexponent",
"showticklabels",
"showtickprefix",
"showticksuffix",
"thickness",
"thicknessmode",
"tick0",
"tickangle",
"tickcolor",
"tickfont",
"tickformat",
"tickformatstopdefaults",
"tickformatstops",
"ticklabeloverflow",
"ticklabelposition",
"ticklen",
"tickmode",
"tickprefix",
"ticks",
"ticksuffix",
"ticktext",
"ticktextsrc",
"tickvals",
"tickvalssrc",
"tickwidth",
"title",
"titlefont",
"titleside",
"x",
"xanchor",
"xpad",
"y",
"yanchor",
"ypad",
}
@property
def bgcolor(self):
return self["bgcolor"]
@bgcolor.setter
def bgcolor(self, val):
self["bgcolor"] = val
@property
def bordercolor(self):
return self["bordercolor"]
@bordercolor.setter
def bordercolor(self, val):
self["bordercolor"] = val
@property
def borderwidth(self):
return self["borderwidth"]
@borderwidth.setter
def borderwidth(self, val):
self["borderwidth"] = val
@property
def dtick(self):
return self["dtick"]
@dtick.setter
def dtick(self, val):
self["dtick"] = val
@property
def exponentformat(self):
return self["exponentformat"]
@exponentformat.setter
def exponentformat(self, val):
self["exponentformat"] = val
@property
def len(self):
return self["len"]
@len.setter
def len(self, val):
self["len"] = val
@property
def lenmode(self):
return self["lenmode"]
@lenmode.setter
def lenmode(self, val):
self["lenmode"] = val
@property
def minexponent(self):
return self["minexponent"]
@minexponent.setter
def minexponent(self, val):
self["minexponent"] = val
@property
def nticks(self):
return self["nticks"]
@nticks.setter
def nticks(self, val):
self["nticks"] = val
@property
def outlinecolor(self):
return self["outlinecolor"]
@outlinecolor.setter
def outlinecolor(self, val):
self["outlinecolor"] = val
@property
def outlinewidth(self):
return self["outlinewidth"]
@outlinewidth.setter
def outlinewidth(self, val):
self["outlinewidth"] = val
@property
def separatethousands(self):
return self["separatethousands"]
@separatethousands.setter
def separatethousands(self, val):
self["separatethousands"] = val
@property
def showexponent(self):
return self["showexponent"]
@showexponent.setter
def showexponent(self, val):
self["showexponent"] = val
@property
def showticklabels(self):
return self["showticklabels"]
@showticklabels.setter
def showticklabels(self, val):
self["showticklabels"] = val
@property
def showtickprefix(self):
return self["showtickprefix"]
@showtickprefix.setter
def showtickprefix(self, val):
self["showtickprefix"] = val
@property
def showticksuffix(self):
return self["showticksuffix"]
@showticksuffix.setter
def showticksuffix(self, val):
self["showticksuffix"] = val
@property
def thickness(self):
return self["thickness"]
@thickness.setter
def thickness(self, val):
self["thickness"] = val
@property
def thicknessmode(self):
return self["thicknessmode"]
@thicknessmode.setter
def thicknessmode(self, val):
self["thicknessmode"] = val
@property
def tick0(self):
return self["tick0"]
@tick0.setter
def tick0(self, val):
self["tick0"] = val
@property
def tickangle(self):
return self["tickangle"]
@tickangle.setter
def tickangle(self, val):
self["tickangle"] = val
@property
def tickcolor(self):
return self["tickcolor"]
@tickcolor.setter
def tickcolor(self, val):
self["tickcolor"] = val
@property
def tickfont(self):
return self["tickfont"]
@tickfont.setter
def tickfont(self, val):
self["tickfont"] = val
@property
def tickformat(self):
return self["tickformat"]
@tickformat.setter
def tickformat(self, val):
self["tickformat"] = val
@property
def tickformatstops(self):
return self["tickformatstops"]
@tickformatstops.setter
def tickformatstops(self, val):
self["tickformatstops"] = val
@property
def tickformatstopdefaults(self):
return self["tickformatstopdefaults"]
@tickformatstopdefaults.setter
def tickformatstopdefaults(self, val):
self["tickformatstopdefaults"] = val
@property
def ticklabeloverflow(self):
return self["ticklabeloverflow"]
@ticklabeloverflow.setter
def ticklabeloverflow(self, val):
self["ticklabeloverflow"] = val
@property
def ticklabelposition(self):
return self["ticklabelposition"]
@ticklabelposition.setter
def ticklabelposition(self, val):
self["ticklabelposition"] = val
@property
def ticklen(self):
return self["ticklen"]
@ticklen.setter
def ticklen(self, val):
self["ticklen"] = val
@property
def tickmode(self):
return self["tickmode"]
@tickmode.setter
def tickmode(self, val):
self["tickmode"] = val
@property
def tickprefix(self):
return self["tickprefix"]
@tickprefix.setter
def tickprefix(self, val):
self["tickprefix"] = val
@property
def ticks(self):
return self["ticks"]
@ticks.setter
def ticks(self, val):
self["ticks"] = val
@property
def ticksuffix(self):
return self["ticksuffix"]
@ticksuffix.setter
def ticksuffix(self, val):
self["ticksuffix"] = val
@property
def ticktext(self):
return self["ticktext"]
@ticktext.setter
def ticktext(self, val):
self["ticktext"] = val
@property
def ticktextsrc(self):
return self["ticktextsrc"]
@ticktextsrc.setter
def ticktextsrc(self, val):
self["ticktextsrc"] = val
@property
def tickvals(self):
return self["tickvals"]
@tickvals.setter
def tickvals(self, val):
self["tickvals"] = val
@property
def tickvalssrc(self):
return self["tickvalssrc"]
@tickvalssrc.setter
def tickvalssrc(self, val):
self["tickvalssrc"] = val
@property
def tickwidth(self):
return self["tickwidth"]
@tickwidth.setter
def tickwidth(self, val):
self["tickwidth"] = val
@property
def title(self):
return self["title"]
@title.setter
def title(self, val):
self["title"] = val
@property
def titlefont(self):
return self["titlefont"]
@titlefont.setter
def titlefont(self, val):
self["titlefont"] = val
@property
def titleside(self):
return self["titleside"]
@titleside.setter
def titleside(self, val):
self["titleside"] = val
@property
def x(self):
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
@property
def xanchor(self):
return self["xanchor"]
@xanchor.setter
def xanchor(self, val):
self["xanchor"] = val
@property
def xpad(self):
return self["xpad"]
@xpad.setter
def xpad(self, val):
self["xpad"] = val
@property
def y(self):
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
@property
def yanchor(self):
return self["yanchor"]
@yanchor.setter
def yanchor(self, val):
self["yanchor"] = val
@property
def ypad(self):
return self["ypad"]
@ypad.setter
def ypad(self, val):
self["ypad"] = val
@property
def _prop_descriptions(self):
return """\
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing this
color bar.
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure excludes
the padding of both ends. That is, the color bar length
is this length minus the padding on both ends.
lenmode
Determines whether this color bar's length (i.e. the
measure in the color variation direction) is set in
units of plot "fraction" or in *pixels. Use `len` to
set the value.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This measure
excludes the size of the padding, ticks and labels.
thicknessmode
Determines whether this color bar's thickness (i.e. the
measure in the constant color direction) is set in
units of plot "fraction" or in "pixels". Use
`thickness` to set the value.
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display "09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.cone.colorbar.T
ickformatstop` instances or dicts with compatible
properties
tickformatstopdefaults
When used in a template (as layout.template.data.cone.c
olorbar.tickformatstopdefaults), sets the default
property values to use for elements of
cone.colorbar.tickformatstops
ticklabeloverflow
Determines how we handle tick labels that would
overflow either the graph div or the domain of the
axis. The default value for inside tick labels is *hide
past domain*. In other cases the default is *hide past
div*.
ticklabelposition
Determines where tick labels are drawn.
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
`ticktext`.
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
`tickvals`.
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.cone.colorbar.Title`
instance or dict with compatible properties
titlefont
Deprecated: Please use cone.colorbar.title.font
instead. Sets this color bar's title font. Note that
the title's font used to be set by the now deprecated
`titlefont` attribute.
titleside
Deprecated: Please use cone.colorbar.title.side
instead. Determines the location of color bar's title
with respect to the color bar. Note that the title's
location used to be set by the now deprecated
`titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position anchor. This
anchor binds the `x` position to the "left", "center"
or "right" of the color bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor This
anchor binds the `y` position to the "top", "middle" or
"bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
"""
_mapped_properties = {
"titlefont": ("title", "font"),
"titleside": ("title", "side"),
}
def __init__(
self,
arg=None,
bgcolor=None,
bordercolor=None,
borderwidth=None,
dtick=None,
exponentformat=None,
len=None,
lenmode=None,
minexponent=None,
nticks=None,
outlinecolor=None,
outlinewidth=None,
separatethousands=None,
showexponent=None,
showticklabels=None,
showtickprefix=None,
showticksuffix=None,
thickness=None,
thicknessmode=None,
tick0=None,
tickangle=None,
tickcolor=None,
tickfont=None,
tickformat=None,
tickformatstops=None,
tickformatstopdefaults=None,
ticklabeloverflow=None,
ticklabelposition=None,
ticklen=None,
tickmode=None,
tickprefix=None,
ticks=None,
ticksuffix=None,
ticktext=None,
ticktextsrc=None,
tickvals=None,
tickvalssrc=None,
tickwidth=None,
title=None,
titlefont=None,
titleside=None,
x=None,
xanchor=None,
xpad=None,
y=None,
yanchor=None,
ypad=None,
**kwargs
):
super(ColorBar, self).__init__("colorbar")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.cone.ColorBar
constructor must be a dict or
an instance of :class:`plotly.graph_objs.cone.ColorBar`"""
)
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
_v = arg.pop("bgcolor", None)
_v = bgcolor if bgcolor is not None else _v
if _v is not None:
self["bgcolor"] = _v
_v = arg.pop("bordercolor", None)
_v = bordercolor if bordercolor is not None else _v
if _v is not None:
self["bordercolor"] = _v
_v = arg.pop("borderwidth", None)
_v = borderwidth if borderwidth is not None else _v
if _v is not None:
self["borderwidth"] = _v
_v = arg.pop("dtick", None)
_v = dtick if dtick is not None else _v
if _v is not None:
self["dtick"] = _v
_v = arg.pop("exponentformat", None)
_v = exponentformat if exponentformat is not None else _v
if _v is not None:
self["exponentformat"] = _v
_v = arg.pop("len", None)
_v = len if len is not None else _v
if _v is not None:
self["len"] = _v
_v = arg.pop("lenmode", None)
_v = lenmode if lenmode is not None else _v
if _v is not None:
self["lenmode"] = _v
_v = arg.pop("minexponent", None)
_v = minexponent if minexponent is not None else _v
if _v is not None:
self["minexponent"] = _v
_v = arg.pop("nticks", None)
_v = nticks if nticks is not None else _v
if _v is not None:
self["nticks"] = _v
_v = arg.pop("outlinecolor", None)
_v = outlinecolor if outlinecolor is not None else _v
if _v is not None:
self["outlinecolor"] = _v
_v = arg.pop("outlinewidth", None)
_v = outlinewidth if outlinewidth is not None else _v
if _v is not None:
self["outlinewidth"] = _v
_v = arg.pop("separatethousands", None)
_v = separatethousands if separatethousands is not None else _v
if _v is not None:
self["separatethousands"] = _v
_v = arg.pop("showexponent", None)
_v = showexponent if showexponent is not None else _v
if _v is not None:
self["showexponent"] = _v
_v = arg.pop("showticklabels", None)
_v = showticklabels if showticklabels is not None else _v
if _v is not None:
self["showticklabels"] = _v
_v = arg.pop("showtickprefix", None)
_v = showtickprefix if showtickprefix is not None else _v
if _v is not None:
self["showtickprefix"] = _v
_v = arg.pop("showticksuffix", None)
_v = showticksuffix if showticksuffix is not None else _v
if _v is not None:
self["showticksuffix"] = _v
_v = arg.pop("thickness", None)
_v = thickness if thickness is not None else _v
if _v is not None:
self["thickness"] = _v
_v = arg.pop("thicknessmode", None)
_v = thicknessmode if thicknessmode is not None else _v
if _v is not None:
self["thicknessmode"] = _v
_v = arg.pop("tick0", None)
_v = tick0 if tick0 is not None else _v
if _v is not None:
self["tick0"] = _v
_v = arg.pop("tickangle", None)
_v = tickangle if tickangle is not None else _v
if _v is not None:
self["tickangle"] = _v
_v = arg.pop("tickcolor", None)
_v = tickcolor if tickcolor is not None else _v
if _v is not None:
self["tickcolor"] = _v
_v = arg.pop("tickfont", None)
_v = tickfont if tickfont is not None else _v
if _v is not None:
self["tickfont"] = _v
_v = arg.pop("tickformat", None)
_v = tickformat if tickformat is not None else _v
if _v is not None:
self["tickformat"] = _v
_v = arg.pop("tickformatstops", None)
_v = tickformatstops if tickformatstops is not None else _v
if _v is not None:
self["tickformatstops"] = _v
_v = arg.pop("tickformatstopdefaults", None)
_v = tickformatstopdefaults if tickformatstopdefaults is not None else _v
if _v is not None:
self["tickformatstopdefaults"] = _v
_v = arg.pop("ticklabeloverflow", None)
_v = ticklabeloverflow if ticklabeloverflow is not None else _v
if _v is not None:
self["ticklabeloverflow"] = _v
_v = arg.pop("ticklabelposition", None)
_v = ticklabelposition if ticklabelposition is not None else _v
if _v is not None:
self["ticklabelposition"] = _v
_v = arg.pop("ticklen", None)
_v = ticklen if ticklen is not None else _v
if _v is not None:
self["ticklen"] = _v
_v = arg.pop("tickmode", None)
_v = tickmode if tickmode is not None else _v
if _v is not None:
self["tickmode"] = _v
_v = arg.pop("tickprefix", None)
_v = tickprefix if tickprefix is not None else _v
if _v is not None:
self["tickprefix"] = _v
_v = arg.pop("ticks", None)
_v = ticks if ticks is not None else _v
if _v is not None:
self["ticks"] = _v
_v = arg.pop("ticksuffix", None)
_v = ticksuffix if ticksuffix is not None else _v
if _v is not None:
self["ticksuffix"] = _v
_v = arg.pop("ticktext", None)
_v = ticktext if ticktext is not None else _v
if _v is not None:
self["ticktext"] = _v
_v = arg.pop("ticktextsrc", None)
_v = ticktextsrc if ticktextsrc is not None else _v
if _v is not None:
self["ticktextsrc"] = _v
_v = arg.pop("tickvals", None)
_v = tickvals if tickvals is not None else _v
if _v is not None:
self["tickvals"] = _v
_v = arg.pop("tickvalssrc", None)
_v = tickvalssrc if tickvalssrc is not None else _v
if _v is not None:
self["tickvalssrc"] = _v
_v = arg.pop("tickwidth", None)
_v = tickwidth if tickwidth is not None else _v
if _v is not None:
self["tickwidth"] = _v
_v = arg.pop("title", None)
_v = title if title is not None else _v
if _v is not None:
self["title"] = _v
_v = arg.pop("titlefont", None)
_v = titlefont if titlefont is not None else _v
if _v is not None:
self["titlefont"] = _v
_v = arg.pop("titleside", None)
_v = titleside if titleside is not None else _v
if _v is not None:
self["titleside"] = _v
_v = arg.pop("x", None)
_v = x if x is not None else _v
if _v is not None:
self["x"] = _v
_v = arg.pop("xanchor", None)
_v = xanchor if xanchor is not None else _v
if _v is not None:
self["xanchor"] = _v
_v = arg.pop("xpad", None)
_v = xpad if xpad is not None else _v
if _v is not None:
self["xpad"] = _v
_v = arg.pop("y", None)
_v = y if y is not None else _v
if _v is not None:
self["y"] = _v
_v = arg.pop("yanchor", None)
_v = yanchor if yanchor is not None else _v
if _v is not None:
self["yanchor"] = _v
_v = arg.pop("ypad", None)
_v = ypad if ypad is not None else _v
if _v is not None:
self["ypad"] = _v
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False
| true
| true
|
1c3ecd48137353b90db4a7570b4ac12b3113cd43
| 218
|
py
|
Python
|
catalyst/contrib/criterion/__init__.py
|
ferrine/catalyst
|
b5bc4fb5f692e1fde2d95ef4a534296dccd0f717
|
[
"MIT"
] | null | null | null |
catalyst/contrib/criterion/__init__.py
|
ferrine/catalyst
|
b5bc4fb5f692e1fde2d95ef4a534296dccd0f717
|
[
"MIT"
] | null | null | null |
catalyst/contrib/criterion/__init__.py
|
ferrine/catalyst
|
b5bc4fb5f692e1fde2d95ef4a534296dccd0f717
|
[
"MIT"
] | null | null | null |
# flake8: noqa
from torch.nn import *
from .bcece import *
from .ce import *
from .center_loss import *
from .contrastive import *
from .dice import *
from .focal_loss import *
from .huber import *
from .unet import *
| 19.818182
| 26
| 0.729358
|
from torch.nn import *
from .bcece import *
from .ce import *
from .center_loss import *
from .contrastive import *
from .dice import *
from .focal_loss import *
from .huber import *
from .unet import *
| true
| true
|
1c3ecd98fcb15ba4a6f5bccae63e6e80e435759b
| 9,412
|
py
|
Python
|
vulnerabilities/importers/rust.py
|
anshsrtv/vulnerablecode
|
b79117f3ea50470547aa2a173265aa4e89403b50
|
[
"Apache-2.0"
] | null | null | null |
vulnerabilities/importers/rust.py
|
anshsrtv/vulnerablecode
|
b79117f3ea50470547aa2a173265aa4e89403b50
|
[
"Apache-2.0"
] | null | null | null |
vulnerabilities/importers/rust.py
|
anshsrtv/vulnerablecode
|
b79117f3ea50470547aa2a173265aa4e89403b50
|
[
"Apache-2.0"
] | null | null | null |
# Copyright (c) 2017 nexB Inc. and others. All rights reserved.
# http://nexb.com and https://github.com/nexB/vulnerablecode/
# The VulnerableCode software is licensed under the Apache License version 2.0.
# Data generated with VulnerableCode require an acknowledgment.
#
# You may not use this software except in compliance with the License.
# You may obtain a copy of the License at: http://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.
#
# When you publish or redistribute any data created with VulnerableCode or any VulnerableCode
# derivative work, you must accompany this data with the following acknowledgment:
#
# Generated with VulnerableCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES
# OR CONDITIONS OF ANY KIND, either express or implied. No content created from
# VulnerableCode should be considered or used as legal advice. Consult an Attorney
# for any legal advice.
# VulnerableCode is a free software code scanning tool from nexB Inc. and others.
# Visit https://github.com/nexB/vulnerablecode/ for support and download.
import asyncio
import re
from itertools import chain
from typing import Optional
from typing import Mapping
from typing import List
from typing import Set
from typing import Tuple
from urllib.error import HTTPError
from urllib.request import urlopen
import toml
from univers.version_specifier import VersionSpecifier
from univers.versions import SemverVersion
from packageurl import PackageURL
from vulnerabilities.data_source import Advisory
from vulnerabilities.data_source import GitDataSource
from vulnerabilities.data_source import Reference
from vulnerabilities.package_managers import CratesVersionAPI
from vulnerabilities.helpers import load_toml
class RustDataSource(GitDataSource):
def __enter__(self):
super(RustDataSource, self).__enter__()
if not getattr(self, "_added_files", None):
self._added_files, self._updated_files = self.file_changes(
subdir="crates", # TODO Consider importing the advisories for cargo, etc as well.
recursive=True,
file_ext="md",
)
@property
def crates_api(self):
if not hasattr(self, "_crates_api"):
setattr(self, "_crates_api", CratesVersionAPI())
return self._crates_api
def set_api(self, packages):
asyncio.run(self.crates_api.load_api(packages))
def added_advisories(self) -> Set[Advisory]:
return self._load_advisories(self._added_files)
def updated_advisories(self) -> Set[Advisory]:
return self._load_advisories(self._updated_files)
def _load_advisories(self, files) -> Set[Advisory]:
# per @tarcieri It will always be named RUSTSEC-0000-0000.md
# https://github.com/nexB/vulnerablecode/pull/281/files#r528899864
files = [f for f in files if not f.endswith("-0000.md")] # skip temporary files
packages = self.collect_packages(files)
self.set_api(packages)
while files:
batch, files = files[: self.batch_size], files[self.batch_size :]
advisories = []
for path in batch:
advisory = self._load_advisory(path)
if advisory:
advisories.append(advisory)
yield advisories
def collect_packages(self, paths):
packages = set()
for path in paths:
record = get_advisory_data(path)
packages.add(record["advisory"]["package"])
return packages
def _load_advisory(self, path: str) -> Optional[Advisory]:
record = get_advisory_data(path)
advisory = record.get("advisory", {})
crate_name = advisory["package"]
references = []
if advisory.get("url"):
references.append(Reference(url=advisory["url"]))
all_versions = self.crates_api.get(crate_name)
# FIXME: Avoid wildcard version ranges for now.
# See https://github.com/RustSec/advisory-db/discussions/831
affected_ranges = [
VersionSpecifier.from_scheme_version_spec_string("semver", r)
for r in chain.from_iterable(record.get("affected", {}).get("functions", {}).values())
if r != "*"
]
unaffected_ranges = [
VersionSpecifier.from_scheme_version_spec_string("semver", r)
for r in record.get("versions", {}).get("unaffected", [])
if r != "*"
]
resolved_ranges = [
VersionSpecifier.from_scheme_version_spec_string("semver", r)
for r in record.get("versions", {}).get("patched", [])
if r != "*"
]
unaffected, affected = categorize_versions(
all_versions, unaffected_ranges, affected_ranges, resolved_ranges
)
impacted_purls = {PackageURL(type="cargo", name=crate_name, version=v) for v in affected}
resolved_purls = {PackageURL(type="cargo", name=crate_name, version=v) for v in unaffected}
cve_id = None
if "aliases" in advisory:
for alias in advisory["aliases"]:
if alias.startswith("CVE-"):
cve_id = alias
break
references.append(
Reference(
reference_id=advisory["id"],
url="https://rustsec.org/advisories/{}.html".format(advisory["id"]),
)
)
return Advisory(
summary=advisory.get("description", ""),
impacted_package_urls=impacted_purls,
resolved_package_urls=resolved_purls,
vulnerability_id=cve_id,
references=references,
)
def categorize_versions(
all_versions: Set[str],
unaffected_version_ranges: List[VersionSpecifier],
affected_version_ranges: List[VersionSpecifier],
resolved_version_ranges: List[VersionSpecifier],
) -> Tuple[Set[str], Set[str]]:
"""
Categorize all versions of a crate according to the given version ranges.
:return: unaffected versions, affected versions
"""
unaffected, affected = set(), set()
if (
not unaffected_version_ranges
and not affected_version_ranges
and not resolved_version_ranges
):
return unaffected, affected
# TODO: This is probably wrong
for version in all_versions:
version_obj = SemverVersion(version)
if affected_version_ranges and all([version_obj in av for av in affected_version_ranges]):
affected.add(version)
elif unaffected_version_ranges and all(
[version_obj in av for av in unaffected_version_ranges]
):
unaffected.add(version)
elif resolved_version_ranges and all([version_obj in av for av in resolved_version_ranges]):
unaffected.add(version)
# If some versions were not classified above, one or more of the given ranges might be empty, so
# the remaining versions default to either affected or unaffected.
uncategorized_versions = all_versions - unaffected.union(affected)
if uncategorized_versions:
if not affected_version_ranges:
affected.update(uncategorized_versions)
else:
unaffected.update(uncategorized_versions)
return unaffected, affected
def get_toml_lines(lines):
"""
Yield lines of TOML extracted from an iterable of text ``lines``.
The lines are expected to be from a RustSec Markdown advisory file with
embedded TOML metadata.
For example::
>>> text = '''
... ```toml
... [advisory]
... id = "RUST-001"
...
... [versions]
... patch = [">= 1.2.1"]
... ```
... # Use-after-free with objects returned by `Stream`'s `get_format_info`
...
... Affected versions contained a pair of use-after-free issues with the objects.
... '''
>>> list(get_toml_lines(text.splitlines()))
['', '[advisory]', 'id = "RUST-001"', '', '[versions]', 'patch = [">= 1.2.1"]']
"""
for line in lines:
line = line.strip()
if line.startswith("```toml"):
continue
elif line.endswith("```"):
break
else:
yield line
def data_from_toml_lines(lines):
"""
Return a mapping of data from an iterable of TOML text ``lines``.
For example::
>>> lines = ['[advisory]', 'id = "RUST1"', '', '[versions]', 'patch = [">= 1"]']
>>> data_from_toml_lines(lines)
{'advisory': {'id': 'RUST1'}, 'versions': {'patch': ['>= 1']}}
"""
return toml.loads("\n".join(lines))
def get_advisory_data(location):
"""
Return a mapping of vulnerability data from a RustSec advisory file at
``location``.
RustSec advisories documents are .md files starting with a block of TOML
identified as the text inside a tripple-backtick TOML block. Per
https://github.com/RustSec/advisory-db#advisory-format:
Advisories are formatted in Markdown with TOML "front matter".
"""
with open(location) as lines:
toml_lines = get_toml_lines(lines)
return data_from_toml_lines(toml_lines)
| 36.061303
| 100
| 0.657777
|
import asyncio
import re
from itertools import chain
from typing import Optional
from typing import Mapping
from typing import List
from typing import Set
from typing import Tuple
from urllib.error import HTTPError
from urllib.request import urlopen
import toml
from univers.version_specifier import VersionSpecifier
from univers.versions import SemverVersion
from packageurl import PackageURL
from vulnerabilities.data_source import Advisory
from vulnerabilities.data_source import GitDataSource
from vulnerabilities.data_source import Reference
from vulnerabilities.package_managers import CratesVersionAPI
from vulnerabilities.helpers import load_toml
class RustDataSource(GitDataSource):
def __enter__(self):
super(RustDataSource, self).__enter__()
if not getattr(self, "_added_files", None):
self._added_files, self._updated_files = self.file_changes(
subdir="crates",
recursive=True,
file_ext="md",
)
@property
def crates_api(self):
if not hasattr(self, "_crates_api"):
setattr(self, "_crates_api", CratesVersionAPI())
return self._crates_api
def set_api(self, packages):
asyncio.run(self.crates_api.load_api(packages))
def added_advisories(self) -> Set[Advisory]:
return self._load_advisories(self._added_files)
def updated_advisories(self) -> Set[Advisory]:
return self._load_advisories(self._updated_files)
def _load_advisories(self, files) -> Set[Advisory]:
les = [f for f in files if not f.endswith("-0000.md")]
packages = self.collect_packages(files)
self.set_api(packages)
while files:
batch, files = files[: self.batch_size], files[self.batch_size :]
advisories = []
for path in batch:
advisory = self._load_advisory(path)
if advisory:
advisories.append(advisory)
yield advisories
def collect_packages(self, paths):
packages = set()
for path in paths:
record = get_advisory_data(path)
packages.add(record["advisory"]["package"])
return packages
def _load_advisory(self, path: str) -> Optional[Advisory]:
record = get_advisory_data(path)
advisory = record.get("advisory", {})
crate_name = advisory["package"]
references = []
if advisory.get("url"):
references.append(Reference(url=advisory["url"]))
all_versions = self.crates_api.get(crate_name)
affected_ranges = [
VersionSpecifier.from_scheme_version_spec_string("semver", r)
for r in chain.from_iterable(record.get("affected", {}).get("functions", {}).values())
if r != "*"
]
unaffected_ranges = [
VersionSpecifier.from_scheme_version_spec_string("semver", r)
for r in record.get("versions", {}).get("unaffected", [])
if r != "*"
]
resolved_ranges = [
VersionSpecifier.from_scheme_version_spec_string("semver", r)
for r in record.get("versions", {}).get("patched", [])
if r != "*"
]
unaffected, affected = categorize_versions(
all_versions, unaffected_ranges, affected_ranges, resolved_ranges
)
impacted_purls = {PackageURL(type="cargo", name=crate_name, version=v) for v in affected}
resolved_purls = {PackageURL(type="cargo", name=crate_name, version=v) for v in unaffected}
cve_id = None
if "aliases" in advisory:
for alias in advisory["aliases"]:
if alias.startswith("CVE-"):
cve_id = alias
break
references.append(
Reference(
reference_id=advisory["id"],
url="https://rustsec.org/advisories/{}.html".format(advisory["id"]),
)
)
return Advisory(
summary=advisory.get("description", ""),
impacted_package_urls=impacted_purls,
resolved_package_urls=resolved_purls,
vulnerability_id=cve_id,
references=references,
)
def categorize_versions(
all_versions: Set[str],
unaffected_version_ranges: List[VersionSpecifier],
affected_version_ranges: List[VersionSpecifier],
resolved_version_ranges: List[VersionSpecifier],
) -> Tuple[Set[str], Set[str]]:
unaffected, affected = set(), set()
if (
not unaffected_version_ranges
and not affected_version_ranges
and not resolved_version_ranges
):
return unaffected, affected
for version in all_versions:
version_obj = SemverVersion(version)
if affected_version_ranges and all([version_obj in av for av in affected_version_ranges]):
affected.add(version)
elif unaffected_version_ranges and all(
[version_obj in av for av in unaffected_version_ranges]
):
unaffected.add(version)
elif resolved_version_ranges and all([version_obj in av for av in resolved_version_ranges]):
unaffected.add(version)
uncategorized_versions = all_versions - unaffected.union(affected)
if uncategorized_versions:
if not affected_version_ranges:
affected.update(uncategorized_versions)
else:
unaffected.update(uncategorized_versions)
return unaffected, affected
def get_toml_lines(lines):
for line in lines:
line = line.strip()
if line.startswith("```toml"):
continue
elif line.endswith("```"):
break
else:
yield line
def data_from_toml_lines(lines):
return toml.loads("\n".join(lines))
def get_advisory_data(location):
with open(location) as lines:
toml_lines = get_toml_lines(lines)
return data_from_toml_lines(toml_lines)
| true
| true
|
1c3ecdaaf358b3e1ac6e7c89f30b3b9afcab7ca7
| 1,820
|
py
|
Python
|
regulations/tests/generator_subterp_tests.py
|
navigo/regulations-site
|
910c24e46f4e921210a40da452dff69feae692d4
|
[
"CC0-1.0"
] | 18
|
2015-01-14T15:58:45.000Z
|
2019-08-17T06:15:59.000Z
|
regulations/tests/generator_subterp_tests.py
|
navigo/regulations-site
|
910c24e46f4e921210a40da452dff69feae692d4
|
[
"CC0-1.0"
] | 260
|
2016-04-05T22:06:10.000Z
|
2021-01-07T22:08:15.000Z
|
regulations/tests/generator_subterp_tests.py
|
navigo/regulations-site
|
910c24e46f4e921210a40da452dff69feae692d4
|
[
"CC0-1.0"
] | 45
|
2015-01-26T16:24:46.000Z
|
2021-02-20T10:50:59.000Z
|
from unittest import TestCase
from mock import patch
from regulations.generator import subterp
class SubterpTest(TestCase):
@patch('regulations.generator.subterp.fetch_toc')
def test_filter_by_subterp(self, fetch_toc):
nodes = [{'label': ['1005', 'h1', 'Interp']},
{'label': ['1005', '2', 'Interp']},
{'label': ['1005', '3', 'Interp']},
{'label': ['1005', '4', 'Interp']},
{'label': ['1005', 'A', 'Interp']},
{'label': ['1005', 'A_B', 'Interp']},
{'label': ['1005', 'B', 'Interp']}]
self.assertEqual(nodes[1:4], subterp.filter_by_subterp(
nodes, ['1005', 'Subpart', 'Interp'], 'vvvv'))
self.assertFalse(fetch_toc.called)
self.assertEqual(nodes[4:], subterp.filter_by_subterp(
nodes, ['1005', 'Appendices', 'Interp'], 'vvvv'))
self.assertFalse(fetch_toc.called)
fetch_toc.return_value = [
{'index': ['1005', 'Subpart', 'A'],
'sub_toc': [{'index': ['1005', '1']}, {'index': ['1005', '2']}]},
{'index': ['1005', 'Subpart', 'B'],
'sub_toc': [{'index': ['1005', '3']}, {'index': ['1005', '4']}]},
{'index': ['1005', 'A']},
{'index': ['1005', 'B']},
{'index': ['1005', 'Interp'],
'sub_toc': [{'index': ['1005', 'Subpart', 'A', 'Interp']},
{'index': ['1005', 'Subpart', 'B', 'Interp']},
{'index': ['1005', 'Appendices', 'Interp']}]}]
self.assertEqual(nodes[1:2], subterp.filter_by_subterp(
nodes, ['1005', 'Subpart', 'A', 'Interp'], 'vvvv'))
self.assertEqual(nodes[2:4], subterp.filter_by_subterp(
nodes, ['1005', 'Subpart', 'B', 'Interp'], 'vvvv'))
| 44.390244
| 78
| 0.482967
|
from unittest import TestCase
from mock import patch
from regulations.generator import subterp
class SubterpTest(TestCase):
@patch('regulations.generator.subterp.fetch_toc')
def test_filter_by_subterp(self, fetch_toc):
nodes = [{'label': ['1005', 'h1', 'Interp']},
{'label': ['1005', '2', 'Interp']},
{'label': ['1005', '3', 'Interp']},
{'label': ['1005', '4', 'Interp']},
{'label': ['1005', 'A', 'Interp']},
{'label': ['1005', 'A_B', 'Interp']},
{'label': ['1005', 'B', 'Interp']}]
self.assertEqual(nodes[1:4], subterp.filter_by_subterp(
nodes, ['1005', 'Subpart', 'Interp'], 'vvvv'))
self.assertFalse(fetch_toc.called)
self.assertEqual(nodes[4:], subterp.filter_by_subterp(
nodes, ['1005', 'Appendices', 'Interp'], 'vvvv'))
self.assertFalse(fetch_toc.called)
fetch_toc.return_value = [
{'index': ['1005', 'Subpart', 'A'],
'sub_toc': [{'index': ['1005', '1']}, {'index': ['1005', '2']}]},
{'index': ['1005', 'Subpart', 'B'],
'sub_toc': [{'index': ['1005', '3']}, {'index': ['1005', '4']}]},
{'index': ['1005', 'A']},
{'index': ['1005', 'B']},
{'index': ['1005', 'Interp'],
'sub_toc': [{'index': ['1005', 'Subpart', 'A', 'Interp']},
{'index': ['1005', 'Subpart', 'B', 'Interp']},
{'index': ['1005', 'Appendices', 'Interp']}]}]
self.assertEqual(nodes[1:2], subterp.filter_by_subterp(
nodes, ['1005', 'Subpart', 'A', 'Interp'], 'vvvv'))
self.assertEqual(nodes[2:4], subterp.filter_by_subterp(
nodes, ['1005', 'Subpart', 'B', 'Interp'], 'vvvv'))
| true
| true
|
1c3ece1119d3406c43d3f2ded5d89016c2782b23
| 7,500
|
py
|
Python
|
moses/contrib/arrow-pipelines/python/manager.py
|
anshsarkar/TailBench
|
25845756aee9a892229c25b681051591c94daafd
|
[
"MIT"
] | 3
|
2018-01-25T00:51:56.000Z
|
2022-01-07T15:09:38.000Z
|
moses/contrib/arrow-pipelines/python/manager.py
|
anshsarkar/TailBench
|
25845756aee9a892229c25b681051591c94daafd
|
[
"MIT"
] | 1
|
2021-11-25T18:08:22.000Z
|
2021-11-25T18:08:22.000Z
|
moses/contrib/arrow-pipelines/python/manager.py
|
anshsarkar/TailBench
|
25845756aee9a892229c25b681051591c94daafd
|
[
"MIT"
] | 3
|
2018-06-08T08:36:27.000Z
|
2021-12-26T20:36:16.000Z
|
import logging
import os
from concurrent.futures import Future, ThreadPoolExecutor
from functools import partial
from pypeline.helpers.parallel_helpers import eval_pipeline, \
cons_function_component, \
cons_wire, \
cons_split_wire, \
cons_unsplit_wire, \
cons_dictionary_wire
#
# Some logging please
#
FORMAT = '%(asctime)-15s : %(threadName)s : %(levelname)s - %(message)s'
logging.basicConfig(format = FORMAT, level = logging.DEBUG)
logger = logging.getLogger("manager")
# Build the pipeline components
def build_components(components, configuration, executor):
pipeline_components = dict()
pipeline_configuration = dict()
for component_id, module_name in components.items():
logger.info("Loading [%s] component from [%s]..." % (component_id, module_name))
module = __import__(module_name, fromlist = ['configure', 'initialise'])
# Component builds its own configuration object
config_func = getattr(module, 'configure')
component_config = config_func(configuration)
pipeline_configuration.update(component_config)
# Now build the component
init_func = getattr(module, 'initialise')
component_function = init_func(component_config)
# A wrapper for the component's function that submits to the executor
def get_component_function_wrapper(inner_function, comp_id, mod_name):
def component_function_wrapper(a, s):
logger.info("Running component [%s], from module [%s], with value [%s] and state [%s]..." % \
(comp_id, mod_name, a, s))
return inner_function(a, s)
return component_function_wrapper
# Arrowize the component
component = cons_function_component(get_component_function_wrapper(component_function, component_id, module_name))
# And store
pipeline_components[component_id] = component
return pipeline_components, pipeline_configuration
# Go!
def main(src_lang, trg_lang, src_filename, trg_filename):
# Global configuration
# One day, this configuration shall be constructed from
# command line options, or a properties file.
configuration = {
'moses_installation_dir': os.environ['MOSES_HOME'],
'irstlm_installation_dir': os.environ['IRSTLM'],
'giza_installation_dir': os.environ['GIZA_HOME'],
'src_lang': src_lang,
'src_tokenisation_dir': './tokenisation',
'trg_lang': trg_lang,
'trg_tokenisation_dir': './tokenisation',
'segment_length_limit': 60,
'irstlm_smoothing_method': 'improved-kneser-ney',
'language_model_directory': './language-model',
'translation_model_directory': './translation-model',
'mert_working_directory': './mert',
'evaluation_data_size': 100,
'development_data_size': 100
}
# The modules to load
# In the future, the components shall be specified in some kind
# pipeline description file.
component_modules = {
'src_tokenizer': 'training.components.tokenizer.src_tokenizer',
'trg_tokenizer': 'training.components.tokenizer.trg_tokenizer',
'cleanup': 'training.components.cleanup.cleanup',
'data_split': 'training.components.data_split.data_split',
'irstlm_build': 'training.components.irstlm_build.irstlm_build',
'model_training': 'training.components.model_training.model_training',
'mert': 'training.components.mert.mert'
}
# The thread pool
executor = ThreadPoolExecutor(max_workers = 3)
# Phew, build the required components
components, component_config = build_components(component_modules, configuration, executor)
#
# Wire up components
# Description of wiring should be, in the future, alongside the component
# specification in some kind of confuguration file. Components shall be
# declared then used, i.e., bind a component instance to a unique component
# identifier, then wire component instances together by identifier.
#
#
# Tokenisation of source and target...
#
# IRSTLM Build components
irstlm_build_component = cons_split_wire() >> \
(cons_wire(lambda a, s: {'input_filename': a['tokenised_trg_filename']}) >> \
components['irstlm_build']).second() >> \
cons_unsplit_wire(lambda t, b: {'tokenised_trg_filename': t['tokenised_trg_filename'],
'trg_language_model_filename': b['compiled_lm_filename']})
# The complete tokenisation component
tokenisation_component = (components['src_tokenizer'] & components['trg_tokenizer']) >> \
irstlm_build_component.second() >> \
cons_unsplit_wire(lambda t, b: {'src_filename': t['tokenised_src_filename'],
'trg_filename': b['tokenised_trg_filename'],
'trg_language_model_filename': b['trg_language_model_filename']})
#
# Cleanup and Data Spliting...
#
#
# A function that clips off the last '.' delimited string
#
def clip_last_bit(filename):
bn = os.path.basename(filename)
directory = os.path.dirname(filename)
bits = bn.split(".")
bits.pop()
return os.path.join(directory, ".".join(bits))
cleanup_datasplit_component = components['cleanup'] >> \
cons_wire(lambda a, s: {'src_filename': a['cleaned_src_filename'],
'trg_filename': a['cleaned_trg_filename']}) >> \
components['data_split'] >> \
cons_wire(lambda a, s: {'training_data_filename': clip_last_bit(a['train_src_filename']),
'eval_src_filename': a['eval_src_filename'],
'eval_trg_filename': a['eval_trg_filename']})
#
# Translation model training
#
translation_model_component = cons_split_wire() >> \
components['model_training'].first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': b['eval_src_filename']})
#
# The whole pipeline
#
pipeline = tokenisation_component >> \
cons_split_wire() >> \
(cleanup_datasplit_component >> translation_model_component).first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': clip_last_bit(t['development_data_filename']),
'trg_language_model_filename': b['trg_language_model_filename'],
'trg_language_model_order': 3,
'trg_language_model_type': 9}) >> \
components['mert']
#
# The input to the pipeline
#
value = {'src_filename': src_filename,
'trg_filename': trg_filename}
#
# Evaluate the pipeline
#
logger.info("Evaluating pipeline with input [%s]..." % value)
new_value = eval_pipeline(executor, pipeline, value, component_config)
#
# Wait for all components to finish
#
executor.shutdown(True)
logger.info("Pipeline evaluated to %s" % new_value)
if __name__ == '__main__':
import sys
main(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
| 38.860104
| 124
| 0.637333
|
import logging
import os
from concurrent.futures import Future, ThreadPoolExecutor
from functools import partial
from pypeline.helpers.parallel_helpers import eval_pipeline, \
cons_function_component, \
cons_wire, \
cons_split_wire, \
cons_unsplit_wire, \
cons_dictionary_wire
FORMAT = '%(asctime)-15s : %(threadName)s : %(levelname)s - %(message)s'
logging.basicConfig(format = FORMAT, level = logging.DEBUG)
logger = logging.getLogger("manager")
def build_components(components, configuration, executor):
pipeline_components = dict()
pipeline_configuration = dict()
for component_id, module_name in components.items():
logger.info("Loading [%s] component from [%s]..." % (component_id, module_name))
module = __import__(module_name, fromlist = ['configure', 'initialise'])
config_func = getattr(module, 'configure')
component_config = config_func(configuration)
pipeline_configuration.update(component_config)
init_func = getattr(module, 'initialise')
component_function = init_func(component_config)
def get_component_function_wrapper(inner_function, comp_id, mod_name):
def component_function_wrapper(a, s):
logger.info("Running component [%s], from module [%s], with value [%s] and state [%s]..." % \
(comp_id, mod_name, a, s))
return inner_function(a, s)
return component_function_wrapper
# Arrowize the component
component = cons_function_component(get_component_function_wrapper(component_function, component_id, module_name))
# And store
pipeline_components[component_id] = component
return pipeline_components, pipeline_configuration
# Go!
def main(src_lang, trg_lang, src_filename, trg_filename):
# Global configuration
# One day, this configuration shall be constructed from
# command line options, or a properties file.
configuration = {
'moses_installation_dir': os.environ['MOSES_HOME'],
'irstlm_installation_dir': os.environ['IRSTLM'],
'giza_installation_dir': os.environ['GIZA_HOME'],
'src_lang': src_lang,
'src_tokenisation_dir': './tokenisation',
'trg_lang': trg_lang,
'trg_tokenisation_dir': './tokenisation',
'segment_length_limit': 60,
'irstlm_smoothing_method': 'improved-kneser-ney',
'language_model_directory': './language-model',
'translation_model_directory': './translation-model',
'mert_working_directory': './mert',
'evaluation_data_size': 100,
'development_data_size': 100
}
# The modules to load
# In the future, the components shall be specified in some kind
# pipeline description file.
component_modules = {
'src_tokenizer': 'training.components.tokenizer.src_tokenizer',
'trg_tokenizer': 'training.components.tokenizer.trg_tokenizer',
'cleanup': 'training.components.cleanup.cleanup',
'data_split': 'training.components.data_split.data_split',
'irstlm_build': 'training.components.irstlm_build.irstlm_build',
'model_training': 'training.components.model_training.model_training',
'mert': 'training.components.mert.mert'
}
# The thread pool
executor = ThreadPoolExecutor(max_workers = 3)
# Phew, build the required components
components, component_config = build_components(component_modules, configuration, executor)
#
# Wire up components
# Description of wiring should be, in the future, alongside the component
# specification in some kind of confuguration file. Components shall be
# declared then used, i.e., bind a component instance to a unique component
# identifier, then wire component instances together by identifier.
#
#
# Tokenisation of source and target...
#
# IRSTLM Build components
irstlm_build_component = cons_split_wire() >> \
(cons_wire(lambda a, s: {'input_filename': a['tokenised_trg_filename']}) >> \
components['irstlm_build']).second() >> \
cons_unsplit_wire(lambda t, b: {'tokenised_trg_filename': t['tokenised_trg_filename'],
'trg_language_model_filename': b['compiled_lm_filename']})
# The complete tokenisation component
tokenisation_component = (components['src_tokenizer'] & components['trg_tokenizer']) >> \
irstlm_build_component.second() >> \
cons_unsplit_wire(lambda t, b: {'src_filename': t['tokenised_src_filename'],
'trg_filename': b['tokenised_trg_filename'],
'trg_language_model_filename': b['trg_language_model_filename']})
#
# Cleanup and Data Spliting...
#
#
# A function that clips off the last '.' delimited string
#
def clip_last_bit(filename):
bn = os.path.basename(filename)
directory = os.path.dirname(filename)
bits = bn.split(".")
bits.pop()
return os.path.join(directory, ".".join(bits))
cleanup_datasplit_component = components['cleanup'] >> \
cons_wire(lambda a, s: {'src_filename': a['cleaned_src_filename'],
'trg_filename': a['cleaned_trg_filename']}) >> \
components['data_split'] >> \
cons_wire(lambda a, s: {'training_data_filename': clip_last_bit(a['train_src_filename']),
'eval_src_filename': a['eval_src_filename'],
'eval_trg_filename': a['eval_trg_filename']})
#
# Translation model training
#
translation_model_component = cons_split_wire() >> \
components['model_training'].first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': b['eval_src_filename']})
#
# The whole pipeline
#
pipeline = tokenisation_component >> \
cons_split_wire() >> \
(cleanup_datasplit_component >> translation_model_component).first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': clip_last_bit(t['development_data_filename']),
'trg_language_model_filename': b['trg_language_model_filename'],
'trg_language_model_order': 3,
'trg_language_model_type': 9}) >> \
components['mert']
#
# The input to the pipeline
#
value = {'src_filename': src_filename,
'trg_filename': trg_filename}
#
# Evaluate the pipeline
#
logger.info("Evaluating pipeline with input [%s]..." % value)
new_value = eval_pipeline(executor, pipeline, value, component_config)
#
# Wait for all components to finish
#
executor.shutdown(True)
logger.info("Pipeline evaluated to %s" % new_value)
if __name__ == '__main__':
import sys
main(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
| true
| true
|
1c3ece2c42a04a5c61f6788d458259f9c5c0c5cc
| 5,260
|
py
|
Python
|
tests/python/unittest/test_codegen_device.py
|
zhanghaohit/incubator-tvm
|
ee0af843f3c5a3429e888079afb5f30789bd9bee
|
[
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 2
|
2019-01-07T06:00:27.000Z
|
2019-02-28T15:07:16.000Z
|
tests/python/unittest/test_codegen_device.py
|
zhanghaohit/incubator-tvm
|
ee0af843f3c5a3429e888079afb5f30789bd9bee
|
[
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 4
|
2021-03-30T11:59:59.000Z
|
2022-03-12T00:40:23.000Z
|
tests/python/unittest/test_codegen_device.py
|
zhanghaohit/incubator-tvm
|
ee0af843f3c5a3429e888079afb5f30789bd9bee
|
[
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 3
|
2021-07-20T07:40:15.000Z
|
2021-08-03T08:39:17.000Z
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import tvm
from tvm.contrib import util
import numpy as np
def test_large_uint_imm():
value = (1 << 63) + 123
other = tvm.const(3, "uint64")
n = 12
num_thread = 2
A = tvm.compute((n,), lambda *i: tvm.const(value, "uint64") + other, name='A')
s = tvm.create_schedule(A.op)
xo, xi = s[A].split(A.op.axis[0], factor=num_thread)
s[A].bind(xi, tvm.thread_axis("threadIdx.x"))
s[A].bind(xo, tvm.thread_axis("blockIdx.x"))
def check_target(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
f = tvm.build(s, [A], device)
# launch the kernel.
a = tvm.nd.empty((n, ), dtype=A.dtype, ctx=ctx)
f(a)
assert a.asnumpy()[0] == value + 3
check_target("cuda")
check_target("vulkan")
def test_add_pipeline():
n = tvm.size_var('n')
A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) + B(), name='C')
D = tvm.compute(A.shape, lambda *i: C(*i) + 1, name='D')
s = tvm.create_schedule(D.op)
# GPU schedule have to split by gridIdx and threadIdx
num_thread = 256
xo, xi = s[C].split(C.op.axis[0], factor=num_thread)
s[C].bind(xi, tvm.thread_axis("threadIdx.x"))
s[C].bind(xo, tvm.thread_axis("blockIdx.x"))
xo, xi = s[D].split(D.op.axis[0], factor=num_thread)
s[D].bind(xi, tvm.thread_axis("threadIdx.x"))
s[D].bind(xo, tvm.thread_axis("blockIdx.x"))
# compile to IR
s = s.normalize()
bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
Bb = tvm.decl_buffer(B.shape, B.dtype, name='B')
Db = tvm.decl_buffer(D.shape, D.dtype, name='D')
stmt = tvm.ir_pass.LoopPartition(stmt, False)
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B:Bb, D:Db}, 64)
stmt = tvm.ir_pass.Simplify(stmt)
fapi = tvm.ir_pass.MakeAPI(stmt, "myadd", [Ab, Bb, Db], 0, True)
fsplits = [x for x in tvm.ir_pass.SplitHostDevice(fapi)]
# lower the floordiv(use stackvm rules so it works for all targets)
fsplits = [tvm.ir_pass.LowerIntrin(x, "stackvm") for x in fsplits]
fsplits[0] = tvm.ir_pass.LowerTVMBuiltin(fsplits[0])
def check_target(device, host="stackvm"):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
if not tvm.module.enabled(host):
return
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
mhost.import_module(mdev)
code = mdev.get_source()
f = mhost.entry_func
# launch the kernel.
n = 1027
a = tvm.nd.array(np.random.uniform(size=n).astype(Ab.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx)
f(a, b, d)
tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
def check_module_save(device, host="stackvm"):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
if not tvm.module.enabled(host):
return
if device == "cuda":
fmt = "ptx"
elif device == "rocm":
fmt = "hsaco"
else:
fmt = device
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
temp = util.tempdir()
mpath = temp.relpath("test.%s" % fmt)
mdev.save(mpath)
mdev2 = tvm.module.load(mpath)
mhost.import_module(mdev2)
f = mhost.entry_func
# launch the kernel.
n = 1027
a = tvm.nd.array(np.random.uniform(size=n).astype(Ab.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx)
f(a, b, d)
tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
check_target("cuda", host="stackvm")
check_target("cuda", host="llvm")
check_module_save("cuda", host="stackvm")
check_target("nvptx", host="llvm")
check_target("vulkan", host="llvm")
check_module_save("vulkan", host="stackvm")
check_target("rocm", host="llvm")
check_module_save("rocm", host="llvm")
if __name__ == "__main__":
test_large_uint_imm()
test_add_pipeline()
| 36.783217
| 82
| 0.619962
|
import tvm
from tvm.contrib import util
import numpy as np
def test_large_uint_imm():
value = (1 << 63) + 123
other = tvm.const(3, "uint64")
n = 12
num_thread = 2
A = tvm.compute((n,), lambda *i: tvm.const(value, "uint64") + other, name='A')
s = tvm.create_schedule(A.op)
xo, xi = s[A].split(A.op.axis[0], factor=num_thread)
s[A].bind(xi, tvm.thread_axis("threadIdx.x"))
s[A].bind(xo, tvm.thread_axis("blockIdx.x"))
def check_target(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
f = tvm.build(s, [A], device)
a = tvm.nd.empty((n, ), dtype=A.dtype, ctx=ctx)
f(a)
assert a.asnumpy()[0] == value + 3
check_target("cuda")
check_target("vulkan")
def test_add_pipeline():
n = tvm.size_var('n')
A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) + B(), name='C')
D = tvm.compute(A.shape, lambda *i: C(*i) + 1, name='D')
s = tvm.create_schedule(D.op)
num_thread = 256
xo, xi = s[C].split(C.op.axis[0], factor=num_thread)
s[C].bind(xi, tvm.thread_axis("threadIdx.x"))
s[C].bind(xo, tvm.thread_axis("blockIdx.x"))
xo, xi = s[D].split(D.op.axis[0], factor=num_thread)
s[D].bind(xi, tvm.thread_axis("threadIdx.x"))
s[D].bind(xo, tvm.thread_axis("blockIdx.x"))
s = s.normalize()
bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
Bb = tvm.decl_buffer(B.shape, B.dtype, name='B')
Db = tvm.decl_buffer(D.shape, D.dtype, name='D')
stmt = tvm.ir_pass.LoopPartition(stmt, False)
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B:Bb, D:Db}, 64)
stmt = tvm.ir_pass.Simplify(stmt)
fapi = tvm.ir_pass.MakeAPI(stmt, "myadd", [Ab, Bb, Db], 0, True)
fsplits = [x for x in tvm.ir_pass.SplitHostDevice(fapi)]
fsplits = [tvm.ir_pass.LowerIntrin(x, "stackvm") for x in fsplits]
fsplits[0] = tvm.ir_pass.LowerTVMBuiltin(fsplits[0])
def check_target(device, host="stackvm"):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
if not tvm.module.enabled(host):
return
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
mhost.import_module(mdev)
code = mdev.get_source()
f = mhost.entry_func
n = 1027
a = tvm.nd.array(np.random.uniform(size=n).astype(Ab.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx)
f(a, b, d)
tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
def check_module_save(device, host="stackvm"):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
if not tvm.module.enabled(host):
return
if device == "cuda":
fmt = "ptx"
elif device == "rocm":
fmt = "hsaco"
else:
fmt = device
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
temp = util.tempdir()
mpath = temp.relpath("test.%s" % fmt)
mdev.save(mpath)
mdev2 = tvm.module.load(mpath)
mhost.import_module(mdev2)
f = mhost.entry_func
n = 1027
a = tvm.nd.array(np.random.uniform(size=n).astype(Ab.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx)
f(a, b, d)
tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
check_target("cuda", host="stackvm")
check_target("cuda", host="llvm")
check_module_save("cuda", host="stackvm")
check_target("nvptx", host="llvm")
check_target("vulkan", host="llvm")
check_module_save("vulkan", host="stackvm")
check_target("rocm", host="llvm")
check_module_save("rocm", host="llvm")
if __name__ == "__main__":
test_large_uint_imm()
test_add_pipeline()
| true
| true
|
1c3ecf27e6ed0d2f16c459636707fbe1dbed8674
| 4,553
|
py
|
Python
|
notebooks/kf-1.0-workshop.py
|
zaxcie/flower_workshop
|
c879b9e1687e786a1510a640e1b1680375dff172
|
[
"FTL"
] | null | null | null |
notebooks/kf-1.0-workshop.py
|
zaxcie/flower_workshop
|
c879b9e1687e786a1510a640e1b1680375dff172
|
[
"FTL"
] | null | null | null |
notebooks/kf-1.0-workshop.py
|
zaxcie/flower_workshop
|
c879b9e1687e786a1510a640e1b1680375dff172
|
[
"FTL"
] | null | null | null |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 0.8.1
# kernelspec:
# display_name: Python (dl)
# language: python
# name: dl
# language_info:
# codemirror_mode:
# name: ipython
# version: 3
# file_extension: .py
# mimetype: text/x-python
# name: python
# nbconvert_exporter: python
# pygments_lexer: ipython3
# version: 3.6.6
# ---
# ## Flowers image classification workshop
# ### Basic task in computer vision
# #### Image classification
# #### Image localization
# #### Image segmentation
# ### Image classification
# #### How do human recognize car?
# - Rectangular-box shape
# - 4 wheels
# - Pair of headlights
# - Pair of Tail lights
# - etc...
#
# #### How do human differenciate between car make/model?
# - Hard to tell...
# - Rond vs carré
# - Numbre de porte
# - Type de grillage
# #### Concolutional Neural Network (CNN)
# ##### Conceptually
# Instagram filter
# Edge Detection
# ##### Learning the kernel - CNN
# Conv
# ##### Inception v3
# Network
# ### Flowers dataset
# 4 242 images of flowers. Data is based on Flicr, Google Images and Yandex Image.
# Images are split into 5 categories
# - Chamomile
# - Tulip
# - Rose
# - Sunflower
# - Dandelion
#
# Every classes has about 800 images. Dimension of image isn't fixed.
# +
# %load_ext autoreload
# %autoreload 2\
import os
import random
from skimage.io import imread, imshow
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# %matplotlib inline
# -
def show_images_horizontal(path, n=5):
files = random.sample(os.listdir(path), 5)
images = list()
for file in files:
images.append(mpimg.imread(path + file))
plt.figure(figsize=(20, 10))
columns = 5
for i, image in enumerate(images):
plt.subplot(len(images) / columns + 1, columns, i + 1)
plt.imshow(image)
# #### Daisy
path = "data/raw/daisy/"
show_images_horizontal(path)
path = "data/raw/dandelion/"
show_images_horizontal(path)
path = "data/raw/roses/"
show_images_horizontal(path)
path = "data/raw/sunflowers/"
show_images_horizontal(path)
path = "data/raw/tulips/"
show_images_horizontal(path)
os.listdir("data/raw")
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
from keras.metrics import top_k_categorical_accuracy, categorical_accuracy
train_datagen = image.ImageDataGenerator()
val_datagen = image.ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(
directory=r"data/processed/train",
target_size=(299, 299),
color_mode="rgb",
batch_size=32,
class_mode="categorical",
shuffle=True,
seed=966
)
val_generator = val_datagen.flow_from_directory(
directory=r"data/processed/val",
target_size=(299, 299),
color_mode="rgb",
batch_size=32,
class_mode="categorical",
shuffle=True,
seed=966
)
inception = InceptionV3(weights='imagenet', include_top=False)
x = inception.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(2048, activation='relu')(x)
out = Dense(5, activation='softmax')(x)
model = Model(inputs=inception.input, outputs=out)
for layer in inception.layers:
layer.trainable = False
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=["categorical_accuracy"])
model.fit_generator(generator=train_generator,
steps_per_epoch=train_generator.n//train_generator.batch_size,
validation_data=val_generator,
validation_steps=val_generator.n//val_generator.batch_size,
epochs=1,
verbose=1
)
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=["categorical_accuracy"])
model.fit_generator(generator=train_generator,
steps_per_epoch=train_generator.n//train_generator.batch_size,
validation_data=val_generator,
validation_steps=val_generator.n//val_generator.batch_size,
epochs=10,
verbose=1
)
| 23.963158
| 120
| 0.682627
|
tory(
directory=r"data/processed/train",
target_size=(299, 299),
color_mode="rgb",
batch_size=32,
class_mode="categorical",
shuffle=True,
seed=966
)
val_generator = val_datagen.flow_from_directory(
directory=r"data/processed/val",
target_size=(299, 299),
color_mode="rgb",
batch_size=32,
class_mode="categorical",
shuffle=True,
seed=966
)
inception = InceptionV3(weights='imagenet', include_top=False)
x = inception.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(2048, activation='relu')(x)
out = Dense(5, activation='softmax')(x)
model = Model(inputs=inception.input, outputs=out)
for layer in inception.layers:
layer.trainable = False
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=["categorical_accuracy"])
model.fit_generator(generator=train_generator,
steps_per_epoch=train_generator.n//train_generator.batch_size,
validation_data=val_generator,
validation_steps=val_generator.n//val_generator.batch_size,
epochs=1,
verbose=1
)
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=["categorical_accuracy"])
model.fit_generator(generator=train_generator,
steps_per_epoch=train_generator.n//train_generator.batch_size,
validation_data=val_generator,
validation_steps=val_generator.n//val_generator.batch_size,
epochs=10,
verbose=1
)
| true
| true
|
1c3ed05ee52038ba5c0bd1bdc2585a78b8cf946e
| 492
|
py
|
Python
|
src/coop_assembly/__version__.py
|
createchaos/coop_assembly
|
71108b0639323cf3d996d63b0f702d45f4d60d67
|
[
"MIT"
] | 3
|
2019-09-21T09:20:15.000Z
|
2020-02-12T21:53:07.000Z
|
src/coop_assembly/__version__.py
|
createchaos/coop_assembly
|
71108b0639323cf3d996d63b0f702d45f4d60d67
|
[
"MIT"
] | null | null | null |
src/coop_assembly/__version__.py
|
createchaos/coop_assembly
|
71108b0639323cf3d996d63b0f702d45f4d60d67
|
[
"MIT"
] | 1
|
2019-12-18T12:51:08.000Z
|
2019-12-18T12:51:08.000Z
|
__title__ = 'coop_assembly'
__description__ = 'Geometry generation of robotically assembled spatial structures'
__url__ = 'https://github.com/createchaos/coop_assembly'
__version__ = '0.0.1'
__author__ = 'Stefana Parascho'
__author_email__ = 'parascho@princeton.edu'
__license__ = 'MIT license'
__copyright__ = 'Copyright 2019 CREATE Princeton'
__all__ = ['__author__', '__author_email__', '__copyright__', '__description__',
'__license__', '__title__', '__url__', '__version__']
| 41
| 83
| 0.762195
|
__title__ = 'coop_assembly'
__description__ = 'Geometry generation of robotically assembled spatial structures'
__url__ = 'https://github.com/createchaos/coop_assembly'
__version__ = '0.0.1'
__author__ = 'Stefana Parascho'
__author_email__ = 'parascho@princeton.edu'
__license__ = 'MIT license'
__copyright__ = 'Copyright 2019 CREATE Princeton'
__all__ = ['__author__', '__author_email__', '__copyright__', '__description__',
'__license__', '__title__', '__url__', '__version__']
| true
| true
|
1c3ed1847f6aa6d5eb851011932ff553610206dd
| 12,107
|
py
|
Python
|
tracer/android_tracer.py
|
XTechnologyTR/appmon
|
974418b80e2a3d49278b3acb2c86651894260dda
|
[
"Apache-2.0"
] | 1,392
|
2016-04-29T13:31:53.000Z
|
2022-03-31T07:40:50.000Z
|
tracer/android_tracer.py
|
XTechnologyTR/appmon
|
974418b80e2a3d49278b3acb2c86651894260dda
|
[
"Apache-2.0"
] | 105
|
2016-04-29T12:13:18.000Z
|
2022-03-24T17:27:58.000Z
|
tracer/android_tracer.py
|
XTechnologyTR/appmon
|
974418b80e2a3d49278b3acb2c86651894260dda
|
[
"Apache-2.0"
] | 298
|
2016-04-29T12:03:15.000Z
|
2022-03-25T13:45:01.000Z
|
#!/usr/bin/python
###
# Copyright (c) 2016 Nishant Das Patnaik.
#
# 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, sys, frida, re, argparse, codecs, json
from termcolor import colored
print("""
___ .______ .______ .___ ___. ______ .__ __.
/ \ | _ \ | _ \ | \/ | / __ \ | \ | |
/ ^ \ | |_) | | |_) | | \ / | | | | | | \| |
/ /_\ \ | ___/ | ___/ | |\/| | | | | | | . ` |
/ _____ \ | | | | | | | | | `--" | | |\ |
/__/ \__\ | _| | _| |__| |__| \______/ |__| \__|
github.com/dpnishant
""")
parser = argparse.ArgumentParser()
parser.add_argument("-a", action="store", dest="app_name", default="",
help='''Process Name;
Accepts "com.twitter.android"''')
parser.add_argument("-c", action="store", dest="class_name", default="",
help='''Class Name;
Example: "OpenSSL*SHA*"''')
parser.add_argument("-m", action="store", dest="method_name", default="",
help='''Method Name;
Example: "*digest*";''')
parser.add_argument("-v", action="version", version="AppMon Android Method Tracer v0.2, Copyright 2016 Nishant Das Patnaik")
if len(sys.argv) < 2:
parser.print_help()
sys.exit(1)
results = parser.parse_args()
appName = results.app_name
className = results.class_name
classCandidates = []
method = results.method_name
if len(className) >= 1 and len(className) < 3:
print(colored("[ERROR] Class Name should be at least 3 characters", "red"))
sys.exit(1)
def on_message(message, data):
if message["type"] == "send":
payload = json.loads(message["payload"])
if payload["type"] == "classEnum":
if "overloads" in payload and "className" in payload and "methodName" in payload and "argCount" in payload:
classCandidates.append([ payload["className"], payload["overloads"], payload["methodName"], payload["argCount"] ])
print('[FOUND] "%s" in "%s"' % (colored(payload['methodName'], "yellow", attrs=["bold"]), colored(payload['className'], "magenta", attrs=["bold"])))
elif "className" in payload and not "overloads" in payload and not "methodName" in payload:
print('[FOUND] "%s"' % colored(payload['className'], "magenta", attrs=["bold"]))
elif payload['type'] == "methodTrace":
payload['overloadIndex']
print("%(methodName)s \n\tCalled by: %(caller)s \n\tDefined at: %(className)s [%(overloadIndex)s]\n" % { "methodName": colored(payload['methodName'], "green", attrs=["bold"]), "caller": colored(payload['caller'].split("class ")[1], "blue", attrs=["bold"]), "className": colored(payload['className'], "magenta", attrs=["bold"]), "overloadIndex": colored(payload['overloadIndex'], "red", attrs=["bold"]) })
def build_search_script(className, method):
if className and className != "" and not method or method == "":
script = """Java.perform(function (){
function wildcard_search(string, search) {
var prevIndex = -1,
array = search.split('*'),
result = true;
for (var i = 0; i < array.length && result; i++) {
var index = string.indexOf(array[i]);
if (index == -1 || index < prevIndex) {
return false;
}
}
return result;
}
var classes = Java.enumerateLoadedClassesSync();
classes = classes.sort();
for(var i=0; i < classes.length; i++ ) {
if(wildcard_search(classes[i], '%(className)s')) {
var payload = {
"type": "classEnum",
"className": classes[i].replace(/\//gi, '.').replace(/\[/gi, '').replace(/^L/, '').replace(/;$/, '')
};
send(JSON.stringify(payload));
}
}
});
""" % { "className": className }
else:
script = """Java.perform(function() {
function wildcard_search(string, search) {
var prevIndex = -1,
array = search.split('*'),
result = true;
for (var i = 0; i < array.length && result; i++) {
var index = string.indexOf(array[i]);
if (index == -1 || index < prevIndex) {
return false;
}
}
return result;
}
Java.enumerateLoadedClasses({
onMatch: function(name) {
name = name.replace(/\//gi, '.').replace(/\[/gi, '').replace(/^L/, '').replace(/;$/, '');
if (wildcard_search(name, '%(className)s')) {
try {
var handle = Java.use(name);
var currentMethods = handle.class.getMethods();
for (var i = 0; i < currentMethods.length; i++) {
var argsCount = currentMethods[i].toString().split('(')[1].split(')')[0].split(',').length;
var items = currentMethods[i].toString().split('(')[0].split(' ');
var currentMethodName = items[items.length - 1];
currentMethodName = currentMethodName.replace(name.toString(), '');
if (currentMethodName.split('.').length-1 > 1) {
continue
} else {
currentMethodName = currentMethodName.replace('.', '');
}
if (wildcard_search(currentMethodName, '%(methodName)s')) {
if (currentMethodName in handle) {
var overload_count = handle[currentMethodName].overloads.length;
var payload = {
"type": "classEnum",
"className": name,
"overloads": overload_count,
"methodName": currentMethodName,
"argCount": argsCount
};
send(JSON.stringify(payload));
} else {
console.log(currentMethodName + ' not found in ' + name);
}
}
}
} catch (e) { console.log(e.stack); }
}
},
onComplete: function() {}
});
});
""" % { "className": className, "methodName": method }
return script
def begin_instrumentation(appName, script_source):
device = frida.get_usb_device()
try:
session = device.attach(appName)
except Exception as e:
print(colored('[ERROR]: ' + str(e), "red"))
sys.exit()
try:
script = session.create_script(script_source)
script.on('message', on_message)
script.load()
except Exception as e:
print(colored('[ERROR]: ' + str(e), "red"))
sys.exit()
def enumerate_overloads(overloadIndx, currentClassName, overload_count, methodName):
generated_overloads = []
template ="""
var class_%(overloadIndx)s = "%(currentClassName)s";
var c_%(overloadIndx)s = Java.use(class_%(overloadIndx)s);
c_%(overloadIndx)s.%(methodName)s.overloads[i].implementation = function(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15) {
var methodName = c_%(overloadIndx)s.%(methodName)s.overloads[i].toString().split("function")[1].split("{")[0].trim().split("(")[0];
var argTypes = getType(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15);
var args = "";
for (var i = 0; i < argTypes.length; i++) {
if (i != argTypes.length - 1) {
args += argTypes[i] + " arg" + i + ", ";
} else {
args += argTypes[i] + " arg" + i;
}
}
var methodName = methodName + "(" + args + ")";
var payload = {
"type": "methodTrace",
"methodName": methodName,
"className": class_%(overloadIndx)s,
"overloadIndex": ovrldindexplaceholder,
"caller": this.getClass().toString()
};
send(JSON.stringify(payload));
return this.%(methodName)s.overloads[i].apply(this, arguments);
};""" % { "overloadIndx": overloadIndx, "currentClassName": currentClassName, "methodName": methodName }
for index in range(0, overload_count):
argString = ""
current_template = ""
current_overload = ""
current_template = template
current_template = current_template.replace("overloads[i]", "overloads[" + str(index) +"]")
current_template = current_template.replace("ovrldindexplaceholder", str(index))
generated_overloads.append(current_template)
return generated_overloads
def build_trace_script(candidates, methodName):
all_overloads = ""
generated_trace_scripts = []
for candidate in candidates:
overloadIndx = str(candidates.index(candidate))
for overload_variant in enumerate_overloads(overloadIndx, candidate[0], candidate[1], candidate[2]):
if overload_variant == "":
continue
all_overloads += overload_variant
tracer_template = """'use strict';
var checkType = function(arg) {
var type = "";
if (arg.getClass) {
type = arg.getClass().toString().split("class ")[1];
} else if (typeof arg === "string") {
type = "String";
} else if (typeof arg === "number") {
type = "Number";
} else if (typeof arg === "boolean") {
type = "Boolean";
} else if (arg.length) {
type = "Array";
} else if (typeof arg === "object") {
type = "Object";
} else {
type = typeof arg;
}
return type;
}
var getType = function(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15) {
var type = [];
if (a1) {
type.push(checkType(a1));
}
if (a2) {
type.push(checkType(a2));
}
if (a3) {
type.push(checkType(a3));
}
if (a4) {
type.push(checkType(a4));
}
if (a5) {
type.push(checkType(a5));
}
if (a6) {
type.push(checkType(a6));
}
if (a7) {
type.push(checkType(a7));
}
if (a8) {
type.push(checkType(a8));
}
if (a9) {
type.push(checkType(a9));
}
if (a10) {
type.push(checkType(a10));
}
if (a11) {
type.push(checkType(a11));
}
if (a12) {
type.push(checkType(a12));
}
if (a13) {
type.push(checkType(a13));
}
if (a14) {
type.push(checkType(a14));
}
if (a15) {
type.push(checkType(a15));
}
return type;
}
Java.perform(function () {
%s
});
""" % (all_overloads)
generated_trace_scripts.append(tracer_template)
return generated_trace_scripts
def generate_tracer_js(scriptName, txtScript):
script_dir = "__handlers__"
if not os.path.exists(script_dir):
os.makedirs(script_dir)
tracer_file_path = os.path.join(script_dir, scriptName + ".js")
with codecs.open(tracer_file_path, 'w', 'utf-8') as f:
f.write(txtScript)
return tracer_file_path
if not method or method == "" and not className or className == "":
print(colored('Enumerating loaded classes...', "green", attrs=["bold"]))
else:
print('Searching method "%s" in loaded classes...' % colored(method, "green", attrs=["bold"]))
begin_instrumentation(appName, build_search_script(className, method))
if len(classCandidates) > 0:
tracer_script_source = ""
for script in build_trace_script(classCandidates, method):
tracer_script_source += script
begin_instrumentation(appName, tracer_script_source)
print(colored("\nTracing methods...\n", "blue", attrs=["bold"]))
try:
sys.stdin.readlines()
except KeyboardInterrupt:
sys.exit()
else:
print(colored('Didn\'t find anything...quitting!', "red"))
sys.exit()
| 37.252308
| 416
| 0.563971
|
mport os, sys, frida, re, argparse, codecs, json
from termcolor import colored
print("""
___ .______ .______ .___ ___. ______ .__ __.
/ \ | _ \ | _ \ | \/ | / __ \ | \ | |
/ ^ \ | |_) | | |_) | | \ / | | | | | | \| |
/ /_\ \ | ___/ | ___/ | |\/| | | | | | | . ` |
/ _____ \ | | | | | | | | | `--" | | |\ |
/__/ \__\ | _| | _| |__| |__| \______/ |__| \__|
github.com/dpnishant
""")
parser = argparse.ArgumentParser()
parser.add_argument("-a", action="store", dest="app_name", default="",
help='''Process Name;
Accepts "com.twitter.android"''')
parser.add_argument("-c", action="store", dest="class_name", default="",
help='''Class Name;
Example: "OpenSSL*SHA*"''')
parser.add_argument("-m", action="store", dest="method_name", default="",
help='''Method Name;
Example: "*digest*";''')
parser.add_argument("-v", action="version", version="AppMon Android Method Tracer v0.2, Copyright 2016 Nishant Das Patnaik")
if len(sys.argv) < 2:
parser.print_help()
sys.exit(1)
results = parser.parse_args()
appName = results.app_name
className = results.class_name
classCandidates = []
method = results.method_name
if len(className) >= 1 and len(className) < 3:
print(colored("[ERROR] Class Name should be at least 3 characters", "red"))
sys.exit(1)
def on_message(message, data):
if message["type"] == "send":
payload = json.loads(message["payload"])
if payload["type"] == "classEnum":
if "overloads" in payload and "className" in payload and "methodName" in payload and "argCount" in payload:
classCandidates.append([ payload["className"], payload["overloads"], payload["methodName"], payload["argCount"] ])
print('[FOUND] "%s" in "%s"' % (colored(payload['methodName'], "yellow", attrs=["bold"]), colored(payload['className'], "magenta", attrs=["bold"])))
elif "className" in payload and not "overloads" in payload and not "methodName" in payload:
print('[FOUND] "%s"' % colored(payload['className'], "magenta", attrs=["bold"]))
elif payload['type'] == "methodTrace":
payload['overloadIndex']
print("%(methodName)s \n\tCalled by: %(caller)s \n\tDefined at: %(className)s [%(overloadIndex)s]\n" % { "methodName": colored(payload['methodName'], "green", attrs=["bold"]), "caller": colored(payload['caller'].split("class ")[1], "blue", attrs=["bold"]), "className": colored(payload['className'], "magenta", attrs=["bold"]), "overloadIndex": colored(payload['overloadIndex'], "red", attrs=["bold"]) })
def build_search_script(className, method):
if className and className != "" and not method or method == "":
script = """Java.perform(function (){
function wildcard_search(string, search) {
var prevIndex = -1,
array = search.split('*'),
result = true;
for (var i = 0; i < array.length && result; i++) {
var index = string.indexOf(array[i]);
if (index == -1 || index < prevIndex) {
return false;
}
}
return result;
}
var classes = Java.enumerateLoadedClassesSync();
classes = classes.sort();
for(var i=0; i < classes.length; i++ ) {
if(wildcard_search(classes[i], '%(className)s')) {
var payload = {
"type": "classEnum",
"className": classes[i].replace(/\//gi, '.').replace(/\[/gi, '').replace(/^L/, '').replace(/;$/, '')
};
send(JSON.stringify(payload));
}
}
});
""" % { "className": className }
else:
script = """Java.perform(function() {
function wildcard_search(string, search) {
var prevIndex = -1,
array = search.split('*'),
result = true;
for (var i = 0; i < array.length && result; i++) {
var index = string.indexOf(array[i]);
if (index == -1 || index < prevIndex) {
return false;
}
}
return result;
}
Java.enumerateLoadedClasses({
onMatch: function(name) {
name = name.replace(/\//gi, '.').replace(/\[/gi, '').replace(/^L/, '').replace(/;$/, '');
if (wildcard_search(name, '%(className)s')) {
try {
var handle = Java.use(name);
var currentMethods = handle.class.getMethods();
for (var i = 0; i < currentMethods.length; i++) {
var argsCount = currentMethods[i].toString().split('(')[1].split(')')[0].split(',').length;
var items = currentMethods[i].toString().split('(')[0].split(' ');
var currentMethodName = items[items.length - 1];
currentMethodName = currentMethodName.replace(name.toString(), '');
if (currentMethodName.split('.').length-1 > 1) {
continue
} else {
currentMethodName = currentMethodName.replace('.', '');
}
if (wildcard_search(currentMethodName, '%(methodName)s')) {
if (currentMethodName in handle) {
var overload_count = handle[currentMethodName].overloads.length;
var payload = {
"type": "classEnum",
"className": name,
"overloads": overload_count,
"methodName": currentMethodName,
"argCount": argsCount
};
send(JSON.stringify(payload));
} else {
console.log(currentMethodName + ' not found in ' + name);
}
}
}
} catch (e) { console.log(e.stack); }
}
},
onComplete: function() {}
});
});
""" % { "className": className, "methodName": method }
return script
def begin_instrumentation(appName, script_source):
device = frida.get_usb_device()
try:
session = device.attach(appName)
except Exception as e:
print(colored('[ERROR]: ' + str(e), "red"))
sys.exit()
try:
script = session.create_script(script_source)
script.on('message', on_message)
script.load()
except Exception as e:
print(colored('[ERROR]: ' + str(e), "red"))
sys.exit()
def enumerate_overloads(overloadIndx, currentClassName, overload_count, methodName):
generated_overloads = []
template ="""
var class_%(overloadIndx)s = "%(currentClassName)s";
var c_%(overloadIndx)s = Java.use(class_%(overloadIndx)s);
c_%(overloadIndx)s.%(methodName)s.overloads[i].implementation = function(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15) {
var methodName = c_%(overloadIndx)s.%(methodName)s.overloads[i].toString().split("function")[1].split("{")[0].trim().split("(")[0];
var argTypes = getType(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15);
var args = "";
for (var i = 0; i < argTypes.length; i++) {
if (i != argTypes.length - 1) {
args += argTypes[i] + " arg" + i + ", ";
} else {
args += argTypes[i] + " arg" + i;
}
}
var methodName = methodName + "(" + args + ")";
var payload = {
"type": "methodTrace",
"methodName": methodName,
"className": class_%(overloadIndx)s,
"overloadIndex": ovrldindexplaceholder,
"caller": this.getClass().toString()
};
send(JSON.stringify(payload));
return this.%(methodName)s.overloads[i].apply(this, arguments);
};""" % { "overloadIndx": overloadIndx, "currentClassName": currentClassName, "methodName": methodName }
for index in range(0, overload_count):
argString = ""
current_template = ""
current_overload = ""
current_template = template
current_template = current_template.replace("overloads[i]", "overloads[" + str(index) +"]")
current_template = current_template.replace("ovrldindexplaceholder", str(index))
generated_overloads.append(current_template)
return generated_overloads
def build_trace_script(candidates, methodName):
all_overloads = ""
generated_trace_scripts = []
for candidate in candidates:
overloadIndx = str(candidates.index(candidate))
for overload_variant in enumerate_overloads(overloadIndx, candidate[0], candidate[1], candidate[2]):
if overload_variant == "":
continue
all_overloads += overload_variant
tracer_template = """'use strict';
var checkType = function(arg) {
var type = "";
if (arg.getClass) {
type = arg.getClass().toString().split("class ")[1];
} else if (typeof arg === "string") {
type = "String";
} else if (typeof arg === "number") {
type = "Number";
} else if (typeof arg === "boolean") {
type = "Boolean";
} else if (arg.length) {
type = "Array";
} else if (typeof arg === "object") {
type = "Object";
} else {
type = typeof arg;
}
return type;
}
var getType = function(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15) {
var type = [];
if (a1) {
type.push(checkType(a1));
}
if (a2) {
type.push(checkType(a2));
}
if (a3) {
type.push(checkType(a3));
}
if (a4) {
type.push(checkType(a4));
}
if (a5) {
type.push(checkType(a5));
}
if (a6) {
type.push(checkType(a6));
}
if (a7) {
type.push(checkType(a7));
}
if (a8) {
type.push(checkType(a8));
}
if (a9) {
type.push(checkType(a9));
}
if (a10) {
type.push(checkType(a10));
}
if (a11) {
type.push(checkType(a11));
}
if (a12) {
type.push(checkType(a12));
}
if (a13) {
type.push(checkType(a13));
}
if (a14) {
type.push(checkType(a14));
}
if (a15) {
type.push(checkType(a15));
}
return type;
}
Java.perform(function () {
%s
});
""" % (all_overloads)
generated_trace_scripts.append(tracer_template)
return generated_trace_scripts
def generate_tracer_js(scriptName, txtScript):
script_dir = "__handlers__"
if not os.path.exists(script_dir):
os.makedirs(script_dir)
tracer_file_path = os.path.join(script_dir, scriptName + ".js")
with codecs.open(tracer_file_path, 'w', 'utf-8') as f:
f.write(txtScript)
return tracer_file_path
if not method or method == "" and not className or className == "":
print(colored('Enumerating loaded classes...', "green", attrs=["bold"]))
else:
print('Searching method "%s" in loaded classes...' % colored(method, "green", attrs=["bold"]))
begin_instrumentation(appName, build_search_script(className, method))
if len(classCandidates) > 0:
tracer_script_source = ""
for script in build_trace_script(classCandidates, method):
tracer_script_source += script
begin_instrumentation(appName, tracer_script_source)
print(colored("\nTracing methods...\n", "blue", attrs=["bold"]))
try:
sys.stdin.readlines()
except KeyboardInterrupt:
sys.exit()
else:
print(colored('Didn\'t find anything...quitting!', "red"))
sys.exit()
| true
| true
|
1c3ed2bf9c387aab43e5622dc881396402695b4f
| 8,064
|
py
|
Python
|
pollbot/telegram/job.py
|
annihilatorrrr/ultimate-poll-bot
|
7ecd1c0ac09a01bd4224c654bf951196dfb0207c
|
[
"MIT"
] | 112
|
2019-06-11T17:52:57.000Z
|
2022-03-18T00:05:21.000Z
|
pollbot/telegram/job.py
|
annihilatorrrr/ultimate-poll-bot
|
7ecd1c0ac09a01bd4224c654bf951196dfb0207c
|
[
"MIT"
] | 91
|
2019-05-28T11:33:40.000Z
|
2022-02-27T12:12:07.000Z
|
pollbot/telegram/job.py
|
annihilatorrrr/ultimate-poll-bot
|
7ecd1c0ac09a01bd4224c654bf951196dfb0207c
|
[
"MIT"
] | 69
|
2019-07-10T16:58:06.000Z
|
2022-03-30T22:09:44.000Z
|
"""Handle messages."""
from datetime import date, datetime, timedelta
from sqlalchemy import or_
from sqlalchemy.orm import joinedload
from sqlalchemy.orm.exc import ObjectDeletedError, StaleDataError
from sqlalchemy.orm.scoping import scoped_session
from telegram.error import BadRequest, RetryAfter, Unauthorized
from telegram.ext.callbackcontext import CallbackContext
from pollbot.config import config
from pollbot.enums import PollDeletionMode
from pollbot.i18n import i18n
from pollbot.models import DailyStatistic, Poll, Update, UserStatistic
from pollbot.models.poll import Poll
from pollbot.poll.delete import delete_poll
from pollbot.poll.update import send_updates, update_poll_messages
from pollbot.sentry import sentry
from pollbot.telegram.session import job_wrapper
@job_wrapper
def message_update_job(context: CallbackContext, session: scoped_session) -> None:
"""Update all polls that are scheduled for an update."""
try:
context.job.enabled = False
now = datetime.now()
update_count = session.query(Update).filter(Update.next_update <= now).count()
while update_count > 0:
updates = (
session.query(Update)
.filter(Update.next_update <= now)
.options(joinedload(Update.poll))
.order_by(Update.next_update.asc())
.limit(50)
.all()
)
for update in updates:
try:
send_updates(session, context.bot, update.poll)
session.delete(update)
session.commit()
except ObjectDeletedError:
# The update has already been handled somewhere else.
# This could be either a job or a person that voted in this very moment
session.rollback()
except RetryAfter as e:
# Schedule an update after the RetryAfter timeout + 1 second buffer
update.next_update = now + timedelta(seconds=int(e.retry_after) + 1)
try:
session.commit()
except StaleDataError:
# The update has already been handled somewhere else
session.rollback()
# Update the count again.
# Updates can be removed by normal operation as well
update_count = (
session.query(Update).filter(Update.next_update <= now).count()
)
except Exception as e:
sentry.capture_job_exception(e)
finally:
context.job.enabled = True
@job_wrapper
def delete_polls(context: CallbackContext, session: scoped_session) -> None:
"""Delete polls from the database and their messages if requested."""
try:
context.job.enabled = False
# Only delete a few polls at a time to prevent RAM usage spikes
polls_to_delete = (
session.query(Poll)
.filter(Poll.delete.isnot(None))
.order_by(Poll.updated_at.asc())
.limit(20)
.all()
)
for poll in polls_to_delete:
if poll.delete == PollDeletionMode.DB_ONLY.name:
delete_poll(session, context, poll)
elif poll.delete == PollDeletionMode.WITH_MESSAGES.name:
delete_poll(session, context, poll, True)
session.commit()
except Exception as e:
sentry.capture_job_exception(e)
finally:
context.job.enabled = True
@job_wrapper
def send_notifications(context: CallbackContext, session: scoped_session) -> None:
"""Notify the users about the poll being closed soon."""
polls = (
session.query(Poll)
.filter(
or_(
Poll.next_notification <= datetime.now(),
Poll.due_date <= datetime.now(),
)
)
.filter(Poll.closed.is_(False))
.all()
)
for poll in polls:
time_step = poll.due_date - poll.next_notification
if time_step == timedelta(days=7):
send_notifications_for_poll(context, session, poll, "notification.one_week")
poll.next_notification = poll.due_date - timedelta(days=1)
# One day remaining reminder
elif time_step == timedelta(days=1):
send_notifications_for_poll(context, session, poll, "notification.one_day")
poll.next_notification = poll.due_date - timedelta(hours=6)
# Six hours remaining reminder
elif time_step == timedelta(hours=6):
send_notifications_for_poll(
context, session, poll, "notification.six_hours"
)
poll.next_notification = poll.due_date
# Send the closed notification, remove all notifications and close the poll
elif poll.due_date <= datetime.now():
poll.closed = True
update_poll_messages(session, context.bot, poll)
send_notifications_for_poll(context, session, poll, "notification.closed")
for notification in poll.notifications:
session.delete(notification)
session.commit()
def send_notifications_for_poll(
context: CallbackContext, session: scoped_session, poll: Poll, message_key: str
) -> None:
"""Send the notifications for a single poll depending on the remaining time."""
locale = poll.locale
for notification in poll.notifications:
try:
# Get the chat and send the notification
tg_chat = context.bot.get_chat(notification.chat_id)
tg_chat.send_message(
i18n.t(message_key, locale=locale, name=poll.name),
parse_mode="markdown",
reply_to_message_id=notification.poll_message_id,
)
except BadRequest as e:
if e.message == "Chat not found":
session.delete(notification)
# Bot was removed from group
except Unauthorized:
session.delete(notification)
except Exception as e:
sentry.capture_job_exception(e)
@job_wrapper
def create_daily_stats(context: CallbackContext, session: scoped_session) -> None:
"""Create the daily stats entity for today and tomorrow."""
try:
today = date.today()
tomorrow = today + timedelta(days=1)
for stat_date in [today, tomorrow]:
statistic = session.query(DailyStatistic).get(stat_date)
if statistic is None:
statistic = DailyStatistic(stat_date)
session.add(statistic)
session.commit()
except Exception as e:
sentry.capture_job_exception(e)
@job_wrapper
def perma_ban_checker(context: CallbackContext, session: scoped_session) -> None:
"""Perma-ban people that send more than 250 votes for at least 3 days in the last week."""
vote_limit = config["telegram"]["max_user_votes_per_day"]
stats = (
session.query(UserStatistic)
.filter(UserStatistic.votes >= vote_limit)
.filter(UserStatistic.date == date.today())
.all()
)
for stat in stats:
# Check how often the user reached the limit in the last week
days_above_limit = (
session.query(UserStatistic)
.filter(UserStatistic.votes >= vote_limit)
.filter(UserStatistic.date >= date.today() - timedelta(days=6))
.filter(UserStatistic.date <= date.today() - timedelta(days=1))
.filter(UserStatistic.user == stat.user)
.all()
)
# If the user reached the limit on two other days in the last week (three days in total)
if len(days_above_limit) >= 2:
stat.user.banned = True
@job_wrapper
def cleanup(context: CallbackContext, session: scoped_session) -> None:
"""Remove all user statistics after 7 days."""
threshold = date.today() - timedelta(days=7)
session.query(UserStatistic).filter(UserStatistic.date < threshold).delete()
| 36.324324
| 96
| 0.625868
|
from datetime import date, datetime, timedelta
from sqlalchemy import or_
from sqlalchemy.orm import joinedload
from sqlalchemy.orm.exc import ObjectDeletedError, StaleDataError
from sqlalchemy.orm.scoping import scoped_session
from telegram.error import BadRequest, RetryAfter, Unauthorized
from telegram.ext.callbackcontext import CallbackContext
from pollbot.config import config
from pollbot.enums import PollDeletionMode
from pollbot.i18n import i18n
from pollbot.models import DailyStatistic, Poll, Update, UserStatistic
from pollbot.models.poll import Poll
from pollbot.poll.delete import delete_poll
from pollbot.poll.update import send_updates, update_poll_messages
from pollbot.sentry import sentry
from pollbot.telegram.session import job_wrapper
@job_wrapper
def message_update_job(context: CallbackContext, session: scoped_session) -> None:
try:
context.job.enabled = False
now = datetime.now()
update_count = session.query(Update).filter(Update.next_update <= now).count()
while update_count > 0:
updates = (
session.query(Update)
.filter(Update.next_update <= now)
.options(joinedload(Update.poll))
.order_by(Update.next_update.asc())
.limit(50)
.all()
)
for update in updates:
try:
send_updates(session, context.bot, update.poll)
session.delete(update)
session.commit()
except ObjectDeletedError:
session.rollback()
except RetryAfter as e:
update.next_update = now + timedelta(seconds=int(e.retry_after) + 1)
try:
session.commit()
except StaleDataError:
session.rollback()
update_count = (
session.query(Update).filter(Update.next_update <= now).count()
)
except Exception as e:
sentry.capture_job_exception(e)
finally:
context.job.enabled = True
@job_wrapper
def delete_polls(context: CallbackContext, session: scoped_session) -> None:
try:
context.job.enabled = False
polls_to_delete = (
session.query(Poll)
.filter(Poll.delete.isnot(None))
.order_by(Poll.updated_at.asc())
.limit(20)
.all()
)
for poll in polls_to_delete:
if poll.delete == PollDeletionMode.DB_ONLY.name:
delete_poll(session, context, poll)
elif poll.delete == PollDeletionMode.WITH_MESSAGES.name:
delete_poll(session, context, poll, True)
session.commit()
except Exception as e:
sentry.capture_job_exception(e)
finally:
context.job.enabled = True
@job_wrapper
def send_notifications(context: CallbackContext, session: scoped_session) -> None:
polls = (
session.query(Poll)
.filter(
or_(
Poll.next_notification <= datetime.now(),
Poll.due_date <= datetime.now(),
)
)
.filter(Poll.closed.is_(False))
.all()
)
for poll in polls:
time_step = poll.due_date - poll.next_notification
if time_step == timedelta(days=7):
send_notifications_for_poll(context, session, poll, "notification.one_week")
poll.next_notification = poll.due_date - timedelta(days=1)
elif time_step == timedelta(days=1):
send_notifications_for_poll(context, session, poll, "notification.one_day")
poll.next_notification = poll.due_date - timedelta(hours=6)
elif time_step == timedelta(hours=6):
send_notifications_for_poll(
context, session, poll, "notification.six_hours"
)
poll.next_notification = poll.due_date
elif poll.due_date <= datetime.now():
poll.closed = True
update_poll_messages(session, context.bot, poll)
send_notifications_for_poll(context, session, poll, "notification.closed")
for notification in poll.notifications:
session.delete(notification)
session.commit()
def send_notifications_for_poll(
context: CallbackContext, session: scoped_session, poll: Poll, message_key: str
) -> None:
locale = poll.locale
for notification in poll.notifications:
try:
tg_chat = context.bot.get_chat(notification.chat_id)
tg_chat.send_message(
i18n.t(message_key, locale=locale, name=poll.name),
parse_mode="markdown",
reply_to_message_id=notification.poll_message_id,
)
except BadRequest as e:
if e.message == "Chat not found":
session.delete(notification)
except Unauthorized:
session.delete(notification)
except Exception as e:
sentry.capture_job_exception(e)
@job_wrapper
def create_daily_stats(context: CallbackContext, session: scoped_session) -> None:
try:
today = date.today()
tomorrow = today + timedelta(days=1)
for stat_date in [today, tomorrow]:
statistic = session.query(DailyStatistic).get(stat_date)
if statistic is None:
statistic = DailyStatistic(stat_date)
session.add(statistic)
session.commit()
except Exception as e:
sentry.capture_job_exception(e)
@job_wrapper
def perma_ban_checker(context: CallbackContext, session: scoped_session) -> None:
vote_limit = config["telegram"]["max_user_votes_per_day"]
stats = (
session.query(UserStatistic)
.filter(UserStatistic.votes >= vote_limit)
.filter(UserStatistic.date == date.today())
.all()
)
for stat in stats:
days_above_limit = (
session.query(UserStatistic)
.filter(UserStatistic.votes >= vote_limit)
.filter(UserStatistic.date >= date.today() - timedelta(days=6))
.filter(UserStatistic.date <= date.today() - timedelta(days=1))
.filter(UserStatistic.user == stat.user)
.all()
)
if len(days_above_limit) >= 2:
stat.user.banned = True
@job_wrapper
def cleanup(context: CallbackContext, session: scoped_session) -> None:
threshold = date.today() - timedelta(days=7)
session.query(UserStatistic).filter(UserStatistic.date < threshold).delete()
| true
| true
|
1c3ed32e53fa0dfa1d1ba7876f61cbf18edade25
| 5,090
|
py
|
Python
|
utils.py
|
smitkiri/nypd-misconduct-dashboard
|
2b16d24f33bab7f3b09e8a068a2bb7233d978928
|
[
"MIT"
] | 2
|
2020-09-12T00:13:03.000Z
|
2020-12-05T07:01:04.000Z
|
utils.py
|
smitkiri/nypd-misconduct-dashboard
|
2b16d24f33bab7f3b09e8a068a2bb7233d978928
|
[
"MIT"
] | null | null | null |
utils.py
|
smitkiri/nypd-misconduct-dashboard
|
2b16d24f33bab7f3b09e8a068a2bb7233d978928
|
[
"MIT"
] | 1
|
2020-08-24T16:05:25.000Z
|
2020-08-24T16:05:25.000Z
|
import pandas as pd
import pickle
import plotly.graph_objs as go
def get_command(x, command_key):
try:
command = command_key[x]
except:
command = float('nan')
return command
def get_command_key():
#Get command abbreviations
command_df = pd.read_excel('NYPD-Misconduct-Complaint-Database-Updated/CCRB Filespecs 04.20.2021.xlsx',
sheet_name = 'Tab3_Command Key')
command_df['Command Abrev.'] = command_df['Command Abrev.'].apply(lambda x: ''.join(x.split(' ')).lower())
return command_df.set_index(command_df['Command Abrev.'])['Command Desc.'].to_dict()
def get_rank_key():
# Get rank abbreviations
return pd.read_excel('data/CCRB Data Layout Table.xlsx', sheet_name = 'Rank Abbrevs').set_index('Abbreviation')['Rank'].to_dict()
def get_sustained_list(outcomes):
return outcomes[outcomes['Disposition'].str.contains('Substantiated')]['Disposition'].apply(
lambda x: ' '.join(x.replace('(', '').replace(')', '').split(' ')[1:]))
def get_unsustained_list(outcomes, sustained_list):
return outcomes[~outcomes['Disposition'].str.contains('|'.join(list(sustained_list)))]['Disposition']
def get_sustained_count(outcomes_df, sustained_list):
return outcomes_df[outcomes_df['Disposition'].str.contains('|'.join(list(sustained_list)))]['count'].sum()
def get_unsustained_count(outcomes_df, sustained_list):
return outcomes_df[~outcomes_df['Disposition'].str.contains('|'.join(list(sustained_list)))]['count'].sum()
def add_newlines(outcomes_df):
outcomes_df['Disposition'] = outcomes_df['Disposition'].apply(
lambda x: 'Complainant <br> Uncooperative' if x == 'Complainant Uncooperative' else x)
outcomes_df['Disposition'] = outcomes_df['Disposition'].apply(
lambda x: 'Complaint <br> Withdrawn' if x == 'Complaint Withdrawn' else x)
outcomes_df['Disposition'] = outcomes_df['Disposition'].apply(
lambda x: 'Complainant <br> Unavailable' if x == 'Complainant Unavailable' else x)
return outcomes_df
def open_pickle(file):
with open(file, 'rb') as f:
return pickle.load(f)
def save_pickle(file, variable):
with open(file, 'wb') as f:
pickle.dump(variable, f)
def get_timeseries_plot(df, date_col, count_col, freq = "M", return_trace = False, filename = None):
counts = df.set_index(date_col).groupby(pd.Grouper(freq = freq)).count()[count_col]
counts = counts[counts.index.year > 1985]
total_trace = go.Scatter(x = counts.index, y = counts, hovertemplate = '%{x}: %{y}<extra></extra>', name = "Total allegations")
if return_trace:
return total_trace
fig = go.Figure(data = total_trace)
for typ in list(set(df['FADO Type'])):
counts = df[df['FADO Type'] == typ].set_index(date_col).groupby(pd.Grouper(freq = freq)).count()[count_col]
counts = counts[counts.index.year > 1985]
trace = go.Scatter(x = counts.index, y = counts, hovertemplate = '%{x}: %{y}<extra></extra>', name = typ)
fig.add_trace(trace)
fig.update_layout(template = 'plotly_white',
margin = dict(t = 1, b = 0, r = 0, l = 0))
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
def get_pie_counts(df, group_col, count_col, hole = None, return_trace = False, filename = None):
counts = df.groupby(group_col).count()[count_col]
trace = go.Pie(labels = counts.index, values = counts, hole = hole)
fig = go.Figure(data = [trace])
if return_trace:
return trace
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
def get_hbar_plot(df, group_col, count_col, desc_key = None, top_n = 5, return_trace = False, filename = None):
counts = df.groupby(group_col).count()[count_col].reset_index()
if desc_key is not None:
counts[group_col] = counts[group_col].apply(lambda x: desc_key[x] if x in desc_key.keys() else x)
counts = counts.groupby(group_col).sum()[count_col]
top = counts.sort_values().iloc[-top_n:]
trace = go.Bar(x = top, y = top.index, orientation = 'h', showlegend = False,
hovertemplate = '%{x}<extra></extra>', marker_color='rgb(55, 83, 109)')
fig = go.Figure(trace)
if return_trace:
return trace
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
def get_suburst_plot(labels, parents, values, return_trace = False, filename = None):
trace = go.Sunburst(labels = labels, parents = parents, values = values, branchvalues = "total",
marker = dict(colorscale='Emrld'))
if return_trace:
return trace
fig = go.Figure(trace)
fig.update_layout(margin = dict(t = 0, b = 0, r = 0, l = 0))
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
| 38.560606
| 133
| 0.644401
|
import pandas as pd
import pickle
import plotly.graph_objs as go
def get_command(x, command_key):
try:
command = command_key[x]
except:
command = float('nan')
return command
def get_command_key():
command_df = pd.read_excel('NYPD-Misconduct-Complaint-Database-Updated/CCRB Filespecs 04.20.2021.xlsx',
sheet_name = 'Tab3_Command Key')
command_df['Command Abrev.'] = command_df['Command Abrev.'].apply(lambda x: ''.join(x.split(' ')).lower())
return command_df.set_index(command_df['Command Abrev.'])['Command Desc.'].to_dict()
def get_rank_key():
return pd.read_excel('data/CCRB Data Layout Table.xlsx', sheet_name = 'Rank Abbrevs').set_index('Abbreviation')['Rank'].to_dict()
def get_sustained_list(outcomes):
return outcomes[outcomes['Disposition'].str.contains('Substantiated')]['Disposition'].apply(
lambda x: ' '.join(x.replace('(', '').replace(')', '').split(' ')[1:]))
def get_unsustained_list(outcomes, sustained_list):
return outcomes[~outcomes['Disposition'].str.contains('|'.join(list(sustained_list)))]['Disposition']
def get_sustained_count(outcomes_df, sustained_list):
return outcomes_df[outcomes_df['Disposition'].str.contains('|'.join(list(sustained_list)))]['count'].sum()
def get_unsustained_count(outcomes_df, sustained_list):
return outcomes_df[~outcomes_df['Disposition'].str.contains('|'.join(list(sustained_list)))]['count'].sum()
def add_newlines(outcomes_df):
outcomes_df['Disposition'] = outcomes_df['Disposition'].apply(
lambda x: 'Complainant <br> Uncooperative' if x == 'Complainant Uncooperative' else x)
outcomes_df['Disposition'] = outcomes_df['Disposition'].apply(
lambda x: 'Complaint <br> Withdrawn' if x == 'Complaint Withdrawn' else x)
outcomes_df['Disposition'] = outcomes_df['Disposition'].apply(
lambda x: 'Complainant <br> Unavailable' if x == 'Complainant Unavailable' else x)
return outcomes_df
def open_pickle(file):
with open(file, 'rb') as f:
return pickle.load(f)
def save_pickle(file, variable):
with open(file, 'wb') as f:
pickle.dump(variable, f)
def get_timeseries_plot(df, date_col, count_col, freq = "M", return_trace = False, filename = None):
counts = df.set_index(date_col).groupby(pd.Grouper(freq = freq)).count()[count_col]
counts = counts[counts.index.year > 1985]
total_trace = go.Scatter(x = counts.index, y = counts, hovertemplate = '%{x}: %{y}<extra></extra>', name = "Total allegations")
if return_trace:
return total_trace
fig = go.Figure(data = total_trace)
for typ in list(set(df['FADO Type'])):
counts = df[df['FADO Type'] == typ].set_index(date_col).groupby(pd.Grouper(freq = freq)).count()[count_col]
counts = counts[counts.index.year > 1985]
trace = go.Scatter(x = counts.index, y = counts, hovertemplate = '%{x}: %{y}<extra></extra>', name = typ)
fig.add_trace(trace)
fig.update_layout(template = 'plotly_white',
margin = dict(t = 1, b = 0, r = 0, l = 0))
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
def get_pie_counts(df, group_col, count_col, hole = None, return_trace = False, filename = None):
counts = df.groupby(group_col).count()[count_col]
trace = go.Pie(labels = counts.index, values = counts, hole = hole)
fig = go.Figure(data = [trace])
if return_trace:
return trace
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
def get_hbar_plot(df, group_col, count_col, desc_key = None, top_n = 5, return_trace = False, filename = None):
counts = df.groupby(group_col).count()[count_col].reset_index()
if desc_key is not None:
counts[group_col] = counts[group_col].apply(lambda x: desc_key[x] if x in desc_key.keys() else x)
counts = counts.groupby(group_col).sum()[count_col]
top = counts.sort_values().iloc[-top_n:]
trace = go.Bar(x = top, y = top.index, orientation = 'h', showlegend = False,
hovertemplate = '%{x}<extra></extra>', marker_color='rgb(55, 83, 109)')
fig = go.Figure(trace)
if return_trace:
return trace
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
def get_suburst_plot(labels, parents, values, return_trace = False, filename = None):
trace = go.Sunburst(labels = labels, parents = parents, values = values, branchvalues = "total",
marker = dict(colorscale='Emrld'))
if return_trace:
return trace
fig = go.Figure(trace)
fig.update_layout(margin = dict(t = 0, b = 0, r = 0, l = 0))
if filename is not None:
fig.write_html(filename, include_plotlyjs = 'cdn')
else:
fig.show()
| true
| true
|
1c3ed46d063c4579b35ae4dbdad3ad13ad8a9ac0
| 3,072
|
py
|
Python
|
indico/core/signals/event/registration.py
|
tobiashuste/indico
|
c1e6ec0c8c84745988e38c9b1768142a6feb9e0e
|
[
"MIT"
] | null | null | null |
indico/core/signals/event/registration.py
|
tobiashuste/indico
|
c1e6ec0c8c84745988e38c9b1768142a6feb9e0e
|
[
"MIT"
] | null | null | null |
indico/core/signals/event/registration.py
|
tobiashuste/indico
|
c1e6ec0c8c84745988e38c9b1768142a6feb9e0e
|
[
"MIT"
] | null | null | null |
# This file is part of Indico.
# Copyright (C) 2002 - 2020 CERN
#
# Indico is free software; you can redistribute it and/or
# modify it under the terms of the MIT License; see the
# LICENSE file for more details.
from __future__ import unicode_literals
from blinker import Namespace
_signals = Namespace()
registration_personal_data_modified = _signals.signal('registration-personal-data-modified', """
Called when the registration personal data is modified. The `sender` is the
`Registration` object; the change is passed in the `change` kwarg.
""")
registration_state_updated = _signals.signal('registration-state-updated', """
Called when the state of a registration changes. The `sender` is the
`Registration` object; the previous state is passed in the `previous_state`
kwarg.
""")
registration_checkin_updated = _signals.signal('registration-checkin-updated', """
Called when the checkin state of a registration changes. The `sender` is the
`Registration` object.
""")
registration_created = _signals.signal('registration-created', """
Called when a new registration has been created. The `sender` is the `Registration` object.
The `management` kwarg is set to `True` if the registration was created from the event management area.
""")
registration_updated = _signals.signal('registration-updated', """
Called when a registration has been updated. The `sender` is the `Registration` object.
The `management` kwarg is set to `True` if the registration was updated from the event management area.
""")
registration_deleted = _signals.signal('registration-deleted', """
Called when a registration is removed. The `sender` is the `Registration` object.
""")
registration_form_created = _signals.signal('registration-form-created', """
Called when a new registration form is created. The `sender` is the
`RegistrationForm` object.
""")
generate_ticket_qr_code = _signals.signal('generate-ticket-qr-code', """
Called when generating the QR code for a ticket. The data included in the QR code is passed
in the `ticket_data` kwarg and may be modified.
""")
registration_form_deleted = _signals.signal('registration-form-deleted', """
Called when a registration form is removed. The `sender` is the
`RegistrationForm` object.
""")
is_ticketing_handled = _signals.signal('is-ticketing-handled', """
Called when resolving whether Indico should send tickets with e-mails
or it will be handled by other module. The `sender` is the
`RegistrationForm` object.
If this signal returns ``True``, no ticket will be emailed on registration.
""")
is_ticket_blocked = _signals.signal('is-ticket-blocked', """
Called when resolving whether Indico should let a registrant download
their ticket. The `sender` is the registrant's `Registration` object.
If this signal returns ``True``, the user will not be able to download
their ticket. Any badge containing a ticket-specific placeholder such as
the ticket qr code is considered a ticket, and the restriction applies to
both users trying to get their own ticket and managers trying to get a
ticket for a registrant.
""")
| 39.384615
| 103
| 0.77181
|
from __future__ import unicode_literals
from blinker import Namespace
_signals = Namespace()
registration_personal_data_modified = _signals.signal('registration-personal-data-modified', """
Called when the registration personal data is modified. The `sender` is the
`Registration` object; the change is passed in the `change` kwarg.
""")
registration_state_updated = _signals.signal('registration-state-updated', """
Called when the state of a registration changes. The `sender` is the
`Registration` object; the previous state is passed in the `previous_state`
kwarg.
""")
registration_checkin_updated = _signals.signal('registration-checkin-updated', """
Called when the checkin state of a registration changes. The `sender` is the
`Registration` object.
""")
registration_created = _signals.signal('registration-created', """
Called when a new registration has been created. The `sender` is the `Registration` object.
The `management` kwarg is set to `True` if the registration was created from the event management area.
""")
registration_updated = _signals.signal('registration-updated', """
Called when a registration has been updated. The `sender` is the `Registration` object.
The `management` kwarg is set to `True` if the registration was updated from the event management area.
""")
registration_deleted = _signals.signal('registration-deleted', """
Called when a registration is removed. The `sender` is the `Registration` object.
""")
registration_form_created = _signals.signal('registration-form-created', """
Called when a new registration form is created. The `sender` is the
`RegistrationForm` object.
""")
generate_ticket_qr_code = _signals.signal('generate-ticket-qr-code', """
Called when generating the QR code for a ticket. The data included in the QR code is passed
in the `ticket_data` kwarg and may be modified.
""")
registration_form_deleted = _signals.signal('registration-form-deleted', """
Called when a registration form is removed. The `sender` is the
`RegistrationForm` object.
""")
is_ticketing_handled = _signals.signal('is-ticketing-handled', """
Called when resolving whether Indico should send tickets with e-mails
or it will be handled by other module. The `sender` is the
`RegistrationForm` object.
If this signal returns ``True``, no ticket will be emailed on registration.
""")
is_ticket_blocked = _signals.signal('is-ticket-blocked', """
Called when resolving whether Indico should let a registrant download
their ticket. The `sender` is the registrant's `Registration` object.
If this signal returns ``True``, the user will not be able to download
their ticket. Any badge containing a ticket-specific placeholder such as
the ticket qr code is considered a ticket, and the restriction applies to
both users trying to get their own ticket and managers trying to get a
ticket for a registrant.
""")
| true
| true
|
1c3ed4b0cc5ab9bf06582c115f4991fc26fb456c
| 260
|
py
|
Python
|
examples/optimizers/create_hs.py
|
macoldibelli/opytimizer
|
ca0574d520ecc17b1ac875bc6271d466c88d18ac
|
[
"MIT"
] | null | null | null |
examples/optimizers/create_hs.py
|
macoldibelli/opytimizer
|
ca0574d520ecc17b1ac875bc6271d466c88d18ac
|
[
"MIT"
] | null | null | null |
examples/optimizers/create_hs.py
|
macoldibelli/opytimizer
|
ca0574d520ecc17b1ac875bc6271d466c88d18ac
|
[
"MIT"
] | null | null | null |
from opytimizer.optimizers.hs import HS
# One should declare a hyperparameters object based
# on the desired algorithm that will be used
hyperparams = {
'HMCR': 0.7,
'PAR': 0.7,
'bw': 1
}
# Creating a HS optimizer
o = HS(hyperparams=hyperparams)
| 20
| 51
| 0.7
|
from opytimizer.optimizers.hs import HS
hyperparams = {
'HMCR': 0.7,
'PAR': 0.7,
'bw': 1
}
o = HS(hyperparams=hyperparams)
| true
| true
|
1c3ed5c4ddde80567b53059419d01c45b6cac254
| 634
|
py
|
Python
|
Amelie/pack_feature_vector_cdll.py
|
HuMingqi/Amelie_S
|
1044441d0a302b4833fe6aab0f177fdf89443623
|
[
"MIT"
] | null | null | null |
Amelie/pack_feature_vector_cdll.py
|
HuMingqi/Amelie_S
|
1044441d0a302b4833fe6aab0f177fdf89443623
|
[
"MIT"
] | null | null | null |
Amelie/pack_feature_vector_cdll.py
|
HuMingqi/Amelie_S
|
1044441d0a302b4833fe6aab0f177fdf89443623
|
[
"MIT"
] | null | null | null |
import ctypes
#locate .dll file. using absolute path in django. relative path just in python intertive mode
#!!! feature_vector depend on opencv_world310d.dll , you can include it in system32
dll_path="G:/ResearchTraining/Amelie_Server/DLLS/feature_vector.dll"
#dll_path="D:/Clothes Search System/AmelieServer/DLLS/feature_vector.dll"
dll=ctypes.CDLL(dll_path)
#void get_feature_vector(double[24],string)
get_feature_vector=dll.get_feature_vector
get_feature_vector.argtypes=(ctypes.c_double*24,ctypes.c_char_p)
#using method
'''
hsv=(c_float*24)()
get_feature_vector(hsv,bytes(image_path,encoding='utf-8'))
'''
| 33.368421
| 94
| 0.782334
|
import ctypes
dll_path="G:/ResearchTraining/Amelie_Server/DLLS/feature_vector.dll"
dll=ctypes.CDLL(dll_path)
get_feature_vector=dll.get_feature_vector
get_feature_vector.argtypes=(ctypes.c_double*24,ctypes.c_char_p)
| true
| true
|
1c3ed60041bd004a3090a6f1457dc9479b8f6e65
| 2,196
|
py
|
Python
|
tests/unit/test_integration.py
|
shogo82148/acme-cert-updater
|
4693ef2e96654c2eba199dd84635d1b87264ec48
|
[
"MIT"
] | 9
|
2019-03-10T11:02:11.000Z
|
2021-07-02T12:39:59.000Z
|
tests/unit/test_integration.py
|
shogo82148/acme-cert-updater
|
4693ef2e96654c2eba199dd84635d1b87264ec48
|
[
"MIT"
] | 58
|
2019-08-12T05:37:34.000Z
|
2022-03-25T10:35:55.000Z
|
tests/unit/test_integration.py
|
shogo82148/acme-cert-updater
|
4693ef2e96654c2eba199dd84635d1b87264ec48
|
[
"MIT"
] | 1
|
2021-09-16T00:29:26.000Z
|
2021-09-16T00:29:26.000Z
|
"""tests of acme-cert-updater"""
import unittest
import secrets
import traceback
import boto3
from typing import List
from updater import app
# pylint: disable=missing-docstring
AUTHOR_ACCOUNT = '445285296882'
class DummyConfig:
def __init__(self):
self._prefix = secrets.token_hex(16)
@property
def domains(self) -> List[str]:
return ['shogo82148.com', '*.shogo82148.com', '*.acme.shogo82148.com']
@property
def cert_name(self) -> str:
return 'example.com'
@property
def email(self) -> str:
return "shogo82148@gmail.com"
@property
def bucket_name(self) -> str:
return "shogo82148-acme-cert-updater-test"
@property
def prefix(self) -> str:
return self._prefix
@property
def environment(self) -> str:
return 'staging'
@property
def acme_server(self) -> str:
return 'https://acme-v02.api.letsencrypt.org/directory'
@property
def notification(self) -> str:
# pylint: disable=line-too-long
return 'arn:aws:sns:ap-northeast-1:445285296882:acme-cert-updater-test-UpdateTopic-141WK4DP5P40E'
class TestIntegration(unittest.TestCase):
def setUp(self):
identity = {}
try:
sts = boto3.client('sts')
identity = sts.get_caller_identity()
except:
self.skipTest("external resource not available")
if identity.get('Account') != AUTHOR_ACCOUNT:
self.skipTest("external resource not available")
self.__config = DummyConfig()
def tearDown(self):
cfg = self.__config
s3 = boto3.resource('s3') # pylint: disable=invalid-name
s3.Bucket(cfg.bucket_name).objects.filter(
Prefix=cfg.prefix+'/',
).delete()
def test_certonly(self):
cfg = self.__config
assert app.needs_init(cfg)
app.certonly(cfg)
assert not app.needs_init(cfg)
app.renew(cfg)
def test_notify_failed(self):
try:
raise Exception("some error")
except:
app.notify_failed(self.__config, traceback.format_exc())
if __name__ == '__main__':
unittest.main()
| 24.4
| 105
| 0.624317
|
import unittest
import secrets
import traceback
import boto3
from typing import List
from updater import app
AUTHOR_ACCOUNT = '445285296882'
class DummyConfig:
def __init__(self):
self._prefix = secrets.token_hex(16)
@property
def domains(self) -> List[str]:
return ['shogo82148.com', '*.shogo82148.com', '*.acme.shogo82148.com']
@property
def cert_name(self) -> str:
return 'example.com'
@property
def email(self) -> str:
return "shogo82148@gmail.com"
@property
def bucket_name(self) -> str:
return "shogo82148-acme-cert-updater-test"
@property
def prefix(self) -> str:
return self._prefix
@property
def environment(self) -> str:
return 'staging'
@property
def acme_server(self) -> str:
return 'https://acme-v02.api.letsencrypt.org/directory'
@property
def notification(self) -> str:
return 'arn:aws:sns:ap-northeast-1:445285296882:acme-cert-updater-test-UpdateTopic-141WK4DP5P40E'
class TestIntegration(unittest.TestCase):
def setUp(self):
identity = {}
try:
sts = boto3.client('sts')
identity = sts.get_caller_identity()
except:
self.skipTest("external resource not available")
if identity.get('Account') != AUTHOR_ACCOUNT:
self.skipTest("external resource not available")
self.__config = DummyConfig()
def tearDown(self):
cfg = self.__config
s3 = boto3.resource('s3')
s3.Bucket(cfg.bucket_name).objects.filter(
Prefix=cfg.prefix+'/',
).delete()
def test_certonly(self):
cfg = self.__config
assert app.needs_init(cfg)
app.certonly(cfg)
assert not app.needs_init(cfg)
app.renew(cfg)
def test_notify_failed(self):
try:
raise Exception("some error")
except:
app.notify_failed(self.__config, traceback.format_exc())
if __name__ == '__main__':
unittest.main()
| true
| true
|
1c3ed6c03b8fe64c7476a5230fcf005cc7c91298
| 31,311
|
py
|
Python
|
language/mentionmemory/modules/mention_losses_test.py
|
urikz/language
|
503aca178c98fed4c606cf83e58ae0f84012a4d9
|
[
"Apache-2.0"
] | 1,199
|
2018-10-16T01:30:18.000Z
|
2022-03-31T21:05:24.000Z
|
language/mentionmemory/modules/mention_losses_test.py
|
urikz/language
|
503aca178c98fed4c606cf83e58ae0f84012a4d9
|
[
"Apache-2.0"
] | 116
|
2018-10-18T03:31:46.000Z
|
2022-03-24T13:40:50.000Z
|
language/mentionmemory/modules/mention_losses_test.py
|
urikz/language
|
503aca178c98fed4c606cf83e58ae0f84012a4d9
|
[
"Apache-2.0"
] | 303
|
2018-10-22T12:35:12.000Z
|
2022-03-27T17:38:17.000Z
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for losses for mention encodings."""
import functools
from absl.testing import absltest
from absl.testing import parameterized
import jax
import jax.numpy as jnp
import language.mentionmemory.modules.mention_losses as mention_losses
from language.mentionmemory.utils import mention_utils
from language.mentionmemory.utils import test_utils
import numpy as np
import scipy.spatial
class MentionLossesTest(test_utils.TestCase):
"""Mention encoding losses tests."""
entity_vocab_size = 100
hidden_size = 3
n_mentions = 37
n_devices = 3
batch_size = 5
metrics_prefix = 'test_'
def _gen_array(self, gen_fn):
array = [gen_fn() for _ in range(len(self.devices))]
array_sharded = jax.device_put_sharded(array, self.devices)
array_stacked = np.stack(array)
array_stacked = array_stacked.reshape([-1] + list(array_stacked.shape[2:]))
return array_stacked, array_sharded
def setUp(self):
super().setUp()
test_utils.force_multi_devices(self.n_devices)
self.devices = jax.local_devices()
# pylint: disable=g-long-lambda
(self.mention_encodings_stacked,
self.mention_encodings_sharded) = self._gen_array(
lambda: 10.0 * np.random.random((self.n_mentions, self.hidden_size)))
(self.mention_target_ids_stacked, self.mention_target_ids_sharded
) = self._gen_array(lambda: np.random.randint(
self.entity_vocab_size, size=(self.n_mentions)))
(self.mention_batch_positions_stacked,
self.mention_batch_positions_sharded) = self._gen_array(
lambda: np.random.randint(self.batch_size, size=(self.n_mentions)))
(self.mention_target_is_masked_stacked,
self.mention_target_is_masked_sharded
) = self._gen_array(lambda: np.random.randint(2, size=(self.n_mentions)))
def test_build_coref_positive_negative_mask(self):
all_mention_target_ids = jax.device_put_replicated(
self.mention_target_ids_stacked, self.devices)
get_batch_positions = functools.partial(
mention_utils.get_globally_consistent_batch_positions,
batch_size=self.batch_size)
get_batch_positions = jax.pmap(get_batch_positions, axis_name='batch')
(local_mention_batch_positions,
global_mention_batch_positions) = get_batch_positions(
self.mention_batch_positions_sharded)
(positive_mask, negative_mask) = jax.pmap(
mention_losses.build_coref_positive_negative_mask,
axis_name='batch')(local_mention_batch_positions,
global_mention_batch_positions,
self.mention_target_ids_sharded,
all_mention_target_ids)
n_all_mentions = self.n_mentions * self.n_devices
self.assertSequenceEqual(positive_mask.shape, negative_mask.shape)
self.assertSequenceEqual(positive_mask.shape,
(self.n_devices, self.n_mentions, n_all_mentions))
positive_mask = positive_mask.reshape(-1, n_all_mentions)
negative_mask = negative_mask.reshape(-1, n_all_mentions)
for i in range(n_all_mentions):
for j in range(n_all_mentions):
is_same_device = i // self.n_mentions == j // self.n_mentions
is_same_passage = (
self.mention_batch_positions_stacked[i] ==
self.mention_batch_positions_stacked[j])
is_same_passage = is_same_passage and is_same_device
if (self.mention_target_ids_stacked[i] == 0 or
self.mention_target_ids_stacked[j] == 0 or is_same_passage):
self.assertEqual(positive_mask[i, j], 0)
self.assertEqual(negative_mask[i, j], 0)
continue
self.assertEqual(
positive_mask[i, j], self.mention_target_ids_stacked[i] ==
self.mention_target_ids_stacked[j])
self.assertEqual(
negative_mask[i, j], self.mention_target_ids_stacked[i] !=
self.mention_target_ids_stacked[j])
@parameterized.parameters(('dot',), ('cos'), ('dot_sqrt'))
def test_coref_resolution_loss_multiple_devices(self, mode):
"""Testing coreference resolution loss."""
def compute_loss(mention_encodings, mention_batch_positions,
mention_target_is_masked, mention_ids):
return mention_losses.coreference_resolution_loss(
mention_encodings, mention_batch_positions, mention_ids,
self.batch_size, mode, mention_target_is_masked, self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
compute_loss, axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_is_masked_sharded,
self.mention_target_ids_sharded)
num_total_mentions, hidden_dim = self.mention_encodings_stacked.shape
scores = np.zeros((num_total_mentions, num_total_mentions))
total_avg_scores, total_unnorm_avg_scores = [], []
for i in range(num_total_mentions):
current_avg_scores = []
current_unnorm_avg_scores = []
for j in range(num_total_mentions):
if mode == 'dot':
scores[i, j] = np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
elif mode == 'dot_sqrt':
scores[i, j] = np.dot(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]) / np.sqrt(hidden_dim)
elif mode == 'cos':
scores[i, j] = 1 - scipy.spatial.distance.cosine(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
else:
raise ValueError('Unknown coreference resolution mode: ' + mode)
if self.mention_target_ids_stacked[j] != 0:
current_avg_scores.append(scores[i, j])
current_unnorm_avg_scores.append(
np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]))
# pylint: disable=g-explicit-length-test
if len(current_avg_scores) > 0:
current_avg_scores = np.array(current_avg_scores)
total_avg_scores.append(current_avg_scores.mean())
current_unnorm_avg_scores = np.array(current_unnorm_avg_scores)
total_unnorm_avg_scores.append(current_unnorm_avg_scores.mean())
else:
total_avg_scores.append(0)
total_unnorm_avg_scores.append(0)
self.assertLen(total_avg_scores, len(self.mention_target_ids_stacked))
expected_loss, expected_acc, expected_denom = 0, 0, 0
expected_denom_masked, expected_denom_non_masked = 0, 0
expected_acc_masked, expected_acc_non_masked = 0, 0
expected_n_positives, expected_n_negatives = 0, 0
expected_avg_scores, expected_unnorm_avg_scores = 0, 0
expected_avg_norms = 0
for i in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[i] == 0:
continue
positive_scores, negative_scores = [], []
for j in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[j] == 0:
continue
is_same_device = i // self.n_mentions == j // self.n_mentions
is_same_passage = (
self.mention_batch_positions_stacked[i] ==
self.mention_batch_positions_stacked[j])
is_same_passage = is_same_passage and is_same_device
if is_same_passage:
continue
if (self.mention_target_ids_stacked[i] ==
self.mention_target_ids_stacked[j]):
positive_scores.append(scores[i, j])
else:
negative_scores.append(scores[i, j])
n_pos = len(positive_scores)
n_neg = len(negative_scores)
max_negative_scores = max(negative_scores)
if n_pos == 0 or n_neg == 0:
continue
current_loss, current_acc = 0, 0
for pos_index in range(n_pos):
current_scores = np.array([positive_scores[pos_index]] +
negative_scores)
current_scores = jax.nn.log_softmax(current_scores)
current_loss += -current_scores[0]
current_acc += int(positive_scores[pos_index] > max_negative_scores)
expected_loss += current_loss / n_pos
expected_acc += current_acc / n_pos
expected_denom += 1
if self.mention_target_is_masked_stacked[i] > 0:
expected_denom_masked += 1
expected_acc_masked += current_acc / n_pos
else:
expected_denom_non_masked += 1
expected_acc_non_masked += current_acc / n_pos
expected_n_positives += n_pos
expected_n_negatives += n_neg
expected_avg_scores += total_avg_scores[i]
expected_unnorm_avg_scores += total_unnorm_avg_scores[i]
expected_avg_norms += np.linalg.norm(self.mention_encodings_stacked[i])
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
metrics_sharded_masked = metrics_sharded[self.metrics_prefix +
'coref_resolution_masked']
metrics_sharded_non_masked = metrics_sharded[self.metrics_prefix +
'coref_resolution_non_masked']
metrics_sharded = metrics_sharded[self.metrics_prefix + 'coref_resolution']
loss_sharded = jnp.sum(loss_sharded)
self.assertAlmostEqual(loss_sharded, expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['loss'], expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['acc'], expected_acc, places=3)
self.assertEqual(metrics_sharded['denominator'], expected_denom)
self.assertEqual(metrics_sharded['n_positive'], expected_n_positives)
self.assertEqual(metrics_sharded['n_negative'], expected_n_negatives)
self.assertAlmostEqual(
metrics_sharded['avg_score'], expected_avg_scores, places=2)
self.assertAlmostEqual(
metrics_sharded['avg_unnorm_score'],
expected_unnorm_avg_scores,
places=2)
self.assertAlmostEqual(
metrics_sharded['avg_norm'], expected_avg_norms, places=2)
self.assertAlmostEqual(
metrics_sharded_masked['acc'], expected_acc_masked, places=3)
self.assertEqual(metrics_sharded_masked['denominator'],
expected_denom_masked)
self.assertAlmostEqual(
metrics_sharded_non_masked['acc'], expected_acc_non_masked, places=3)
self.assertEqual(metrics_sharded_non_masked['denominator'],
expected_denom_non_masked)
def test_coref_resolution_loss_multiple_vs_single_devices(self):
"""Comparing coreference resolution loss on multiple vs single devices."""
def compute_loss(mention_encodings, mention_batch_positions, mention_ids,
mention_target_is_masked):
return mention_losses.coreference_resolution_loss(
mention_encodings, mention_batch_positions, mention_ids,
self.batch_size, 'dot', mention_target_is_masked, self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
compute_loss, axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_ids_sharded,
self.mention_target_is_masked_sharded)
mention_batch_positions_stacked = (
self.mention_batch_positions_stacked.reshape(self.n_devices, -1))
mention_batch_positions_stacked = mention_batch_positions_stacked.copy()
mention_batch_positions_stacked += (
np.expand_dims(np.arange(self.n_devices), 1) * self.batch_size)
mention_batch_positions_stacked = mention_batch_positions_stacked.reshape(
-1)
loss_stacked, metrics_stacked = compute_loss(
self.mention_encodings_stacked, mention_batch_positions_stacked,
self.mention_target_ids_stacked, self.mention_target_is_masked_stacked)
loss_sharded = jnp.sum(loss_sharded)
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
self.assertAlmostEqual(loss_sharded, loss_stacked, places=2)
for metric_group_name in metrics_stacked:
for metric_name in metrics_stacked[metric_group_name]:
self.assertAlmostEqual(
metrics_sharded[metric_group_name][metric_name],
metrics_stacked[metric_group_name][metric_name],
places=2)
@parameterized.parameters(('dot',), ('cos'), ('dot_sqrt'))
def test_mtb_loss_multiple_devices(self, mode):
"""Testing MTB loss."""
def compute_loss(mention_encodings, mention_batch_positions, mention_ids,
mention_target_is_masked):
return mention_losses.mtb_loss(mention_encodings, mention_batch_positions,
mention_ids, self.batch_size, mode,
mention_target_is_masked,
self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
compute_loss, axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_ids_sharded,
self.mention_target_is_masked_sharded)
batches = []
for i in range(self.n_devices):
batches.append([])
for j in range(self.batch_size):
batches[i].append(set())
for j in range(self.n_mentions):
if self.mention_target_ids_sharded[i, j] > 0:
batches[i][self.mention_batch_positions_sharded[i, j]].add(
self.mention_target_ids_sharded[i, j].item())
num_total_mentions, hidden_dim = self.mention_encodings_stacked.shape
# Compute the scores between mentions.
scores = np.zeros((num_total_mentions, num_total_mentions))
total_avg_scores, total_unnorm_avg_scores = [], []
for i in range(num_total_mentions):
current_avg_scores = []
current_unnorm_avg_scores = []
for j in range(num_total_mentions):
if mode == 'dot':
scores[i, j] = np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
elif mode == 'dot_sqrt':
scores[i, j] = np.dot(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]) / np.sqrt(hidden_dim)
elif mode == 'cos':
scores[i, j] = 1 - scipy.spatial.distance.cosine(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
else:
raise ValueError('Unknown coreference resolution mode: ' + mode)
if self.mention_target_ids_stacked[j] != 0:
current_avg_scores.append(scores[i, j])
current_unnorm_avg_scores.append(
np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]))
# pylint: disable=g-explicit-length-test
if len(current_avg_scores) > 0:
current_avg_scores = np.array(current_avg_scores)
total_avg_scores.append(current_avg_scores.mean())
current_unnorm_avg_scores = np.array(current_unnorm_avg_scores)
total_unnorm_avg_scores.append(current_unnorm_avg_scores.mean())
else:
total_avg_scores.append(0)
total_unnorm_avg_scores.append(0)
self.assertLen(total_avg_scores, len(self.mention_target_ids_stacked))
# Compute the loss and metrics.
expected_loss, expected_acc, expected_denom = 0, 0, 0
expected_n_positives, expected_n_negatives = 0, 0
expected_n_hard_negatives = 0
expected_avg_scores, expected_unnorm_avg_scores = 0, 0
expected_denom_masked, expected_denom_non_masked = 0, 0
expected_acc_masked, expected_acc_non_masked = 0, 0
expected_avg_norms = 0
for i in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[i] == 0:
continue
device_i = i // self.n_mentions
unique_entities_i = (
batches[device_i][self.mention_batch_positions_sharded[
device_i, i % self.n_mentions]])
positive_scores, hard_negative_scores, negative_scores = [], [], []
for j in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[j] == 0:
continue
device_j = j // self.n_mentions
is_same_device = device_i == device_j
is_same_passage = (
self.mention_batch_positions_stacked[i] ==
self.mention_batch_positions_stacked[j])
is_same_passage = is_same_passage and is_same_device
if is_same_passage:
continue
if (self.mention_target_ids_stacked[i] ==
self.mention_target_ids_stacked[j]):
unique_entities_j = (
batches[device_j][self.mention_batch_positions_sharded[
device_j, j % self.n_mentions]])
num_common_entities = len(
unique_entities_i.intersection(unique_entities_j))
if num_common_entities >= 2:
positive_scores.append(scores[i, j])
else:
hard_negative_scores.append(scores[i, j])
else:
negative_scores.append(scores[i, j])
negative_scores = negative_scores + hard_negative_scores
n_pos = len(positive_scores)
n_neg = len(negative_scores)
n_hard_neg = len(hard_negative_scores)
max_negative_scores = max(negative_scores)
if n_pos == 0 or n_hard_neg == 0:
continue
current_loss, current_acc = 0, 0
for pos_index in range(n_pos):
current_scores = np.array([positive_scores[pos_index]] +
negative_scores)
current_scores = jax.nn.log_softmax(current_scores)
current_loss += -current_scores[0]
current_acc += int(positive_scores[pos_index] > max_negative_scores)
expected_loss += current_loss / n_pos
expected_acc += current_acc / n_pos
expected_denom += 1
if self.mention_target_is_masked_stacked[i] > 0:
expected_denom_masked += 1
expected_acc_masked += current_acc / n_pos
else:
expected_denom_non_masked += 1
expected_acc_non_masked += current_acc / n_pos
expected_n_positives += n_pos
expected_n_negatives += n_neg
expected_n_hard_negatives += n_hard_neg
expected_avg_scores += total_avg_scores[i]
expected_unnorm_avg_scores += total_unnorm_avg_scores[i]
expected_avg_norms += np.linalg.norm(self.mention_encodings_stacked[i])
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
metrics_sharded_masked = metrics_sharded[self.metrics_prefix + 'mtb_masked']
metrics_sharded_non_masked = metrics_sharded[self.metrics_prefix +
'mtb_non_masked']
metrics_sharded = metrics_sharded[self.metrics_prefix + 'mtb']
loss_sharded = jnp.sum(loss_sharded)
self.assertAlmostEqual(loss_sharded, expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['loss'], expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['acc'], expected_acc, places=3)
self.assertEqual(metrics_sharded['denominator'], expected_denom)
self.assertEqual(metrics_sharded['n_positive'], expected_n_positives)
self.assertEqual(metrics_sharded['n_negative'], expected_n_negatives)
self.assertEqual(metrics_sharded['n_hard_negative'],
expected_n_hard_negatives)
self.assertAlmostEqual(
metrics_sharded['avg_score'], expected_avg_scores, places=2)
self.assertAlmostEqual(
metrics_sharded['avg_unnorm_score'],
expected_unnorm_avg_scores,
places=2)
self.assertAlmostEqual(
metrics_sharded['avg_norm'], expected_avg_norms, places=2)
self.assertAlmostEqual(
metrics_sharded_masked['acc'], expected_acc_masked, places=3)
self.assertEqual(metrics_sharded_masked['denominator'],
expected_denom_masked)
self.assertAlmostEqual(
metrics_sharded_non_masked['acc'], expected_acc_non_masked, places=3)
self.assertEqual(metrics_sharded_non_masked['denominator'],
expected_denom_non_masked)
def test_mtb_loss_multiple_vs_single_devices(self):
"""Comparing MTB loss on multiple vs single devices."""
def loss_fn_multi_device(mention_encodings, mention_batch_positions,
mention_ids, mention_target_is_masked):
return mention_losses.mtb_loss(mention_encodings, mention_batch_positions,
mention_ids, self.batch_size, 'dot',
mention_target_is_masked,
self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
loss_fn_multi_device,
axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_ids_sharded,
self.mention_target_is_masked_sharded)
mention_batch_positions_stacked = (
self.mention_batch_positions_stacked.reshape(self.n_devices, -1))
mention_batch_positions_stacked = mention_batch_positions_stacked.copy()
mention_batch_positions_stacked += (
np.expand_dims(np.arange(self.n_devices), 1) * self.batch_size)
mention_batch_positions_stacked = mention_batch_positions_stacked.reshape(
-1)
loss_stacked, metrics_stacked = mention_losses.mtb_loss(
self.mention_encodings_stacked, mention_batch_positions_stacked,
self.mention_target_ids_stacked, self.batch_size * self.n_devices,
'dot', self.mention_target_is_masked_stacked, self.metrics_prefix)
loss_sharded = jnp.sum(loss_sharded)
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
self.assertAlmostEqual(loss_sharded, loss_stacked, places=2)
for metric_group_name in metrics_stacked:
for metric_name in metrics_stacked[metric_group_name]:
self.assertAlmostEqual(
metrics_sharded[metric_group_name][metric_name],
metrics_stacked[metric_group_name][metric_name],
places=2)
@parameterized.parameters(('dot',), ('cos'), ('dot_sqrt'))
def test_entity_linking_loss(self, mode):
n_mentions = 5
n_entities = 10
hidden_size = 3
mention_encodings = np.random.random((n_mentions, hidden_size))
entity_embeddings = np.random.random((n_entities, hidden_size))
mention_target_ids = np.random.randint(n_entities, size=(n_mentions))
mention_target_weights = np.random.randint(2, size=(n_mentions))
(actual_loss, actual_metrics,
(actual_acc_per_mention,
actual_weight_per_mention)) = mention_losses.entity_linking_loss(
mention_encodings, entity_embeddings, mention_target_ids,
mention_target_weights, mode)
# Simple consistency checks
self.assertArrayEqual(mention_target_weights, actual_weight_per_mention)
self.assertEqual(actual_metrics['loss'], actual_loss)
self.assertAlmostEqual(
actual_metrics['acc'], actual_acc_per_mention.sum(), places=6)
self.assertAlmostEqual(
actual_metrics['denominator'], mention_target_weights.sum(), places=8)
scores = np.matmul(mention_encodings, np.transpose(entity_embeddings))
if mode == 'dot_sqrt':
scores /= np.sqrt(hidden_size)
if mode == 'cos':
scores /= np.expand_dims(np.linalg.norm(mention_encodings, axis=-1), 1)
scores /= np.expand_dims(np.linalg.norm(entity_embeddings, axis=-1), 0)
log_probs = np.log(scipy.special.softmax(scores, axis=-1))
expected_loss = 0
expected_acc_per_mention = []
expected_cos_sim_per_mention = []
for i in range(n_mentions):
if mention_target_weights[i] == 1:
expected_loss += -log_probs[i, mention_target_ids[i]]
is_correct = int(np.argmax(log_probs[i]) == mention_target_ids[i])
expected_acc_per_mention.append(is_correct)
expected_cos_sim_per_mention.append(1 - scipy.spatial.distance.cosine(
mention_encodings[i], entity_embeddings[mention_target_ids[i]]))
else:
expected_acc_per_mention.append(0)
expected_cos_sim_per_mention.append(0)
expected_acc_per_mention = np.array(expected_acc_per_mention)
expected_cos_sim_per_mention = np.array(expected_cos_sim_per_mention)
self.assertAlmostEqual(actual_loss, expected_loss, places=4)
self.assertAlmostEqual(
actual_metrics['denominator'], mention_target_weights.sum(), places=8)
self.assertArrayAlmostEqual(
actual_acc_per_mention, expected_acc_per_mention, places=8)
self.assertAlmostEqual(
actual_metrics['cos_sim'], expected_cos_sim_per_mention.sum(), places=2)
@parameterized.parameters([
{
'batch_size': 1,
},
{
'batch_size': 11,
},
{
'batch_size': 11,
'entity_vocab_size': 1000000,
},
{
'batch_size': 2,
'n_target_mentions': 19,
},
{
'batch_size': 2,
'n_target_mentions': 19,
'entity_vocab_size': 2,
},
{
'batch_size': 2,
'k_top': 1,
},
{
'batch_size': 2,
'n_mentions_per_memory_passage': 21,
},
{
'batch_size': 10,
'n_mentions_per_memory_passage': 41,
},
{
'batch_size': 10,
'n_mentions': 100,
},
{
'batch_size': 2,
'n_mentions': 1,
},
{
'batch_size': 11,
'p_memory_mask': 0,
},
{
'batch_size': 11,
'p_memory_mask': 1,
},
])
def test_same_entity_set_retrieval_loss(self,
batch_size,
n_target_mentions=11,
n_mentions=21,
entity_vocab_size=10,
k_top=10,
n_mentions_per_memory_passage=4,
p_memory_mask=0.5):
np.random.seed(0)
mention_target_batch_positions = np.random.randint(
batch_size, size=(n_target_mentions))
mention_target_ids = np.random.randint(
entity_vocab_size, size=(n_target_mentions))
mention_target_weights = np.random.randint(2, size=(n_target_mentions))
mention_batch_positions = np.random.randint(batch_size, size=(n_mentions))
mention_mask = np.random.randint(2, size=(n_mentions))
memory_mask = np.random.random((n_mentions, k_top)) < p_memory_mask
memory_mask = memory_mask.astype(np.int32)
# `memory_text_entities` are assumed to contain unique IDs in the last dim.
memory_text_entities = np.zeros(
(n_mentions, k_top, n_mentions_per_memory_passage), np.int32)
for m_index in range(n_mentions):
for r_index in range(k_top):
current_text_entities = np.random.choice(
entity_vocab_size,
size=(min(n_mentions_per_memory_passage, entity_vocab_size)),
replace=False)
memory_text_entities[
m_index,
r_index, :len(current_text_entities)] = current_text_entities
memory_attention_weights = np.random.random((n_mentions, k_top))
memory_attention_weights /= memory_attention_weights.sum(
axis=-1, keepdims=True)
actual_entity_overlap = mention_losses.get_batch_and_retrievals_entity_overlap(
mention_target_batch_positions=mention_target_batch_positions,
mention_target_ids=mention_target_ids,
mention_target_weights=mention_target_weights,
memory_text_entities=memory_text_entities.reshape(
[n_mentions * k_top, -1]),
batch_size=batch_size,
)
actual_entity_overlap = actual_entity_overlap.reshape(
[batch_size, n_mentions, k_top])
expected_entity_overlap = np.zeros((batch_size, n_mentions, k_top))
for batch_index in range(batch_size):
sample_ids = mention_target_ids[mention_target_batch_positions ==
batch_index]
sample_weights = mention_target_weights[mention_target_batch_positions ==
batch_index]
sample_ids = sample_ids[sample_weights > 0]
sample_ids = set([x for x in sample_ids if x != 0])
for m_index in range(n_mentions):
for r_index in range(k_top):
common_ids = set(
memory_text_entities[m_index, r_index]).intersection(sample_ids)
expected_entity_overlap[batch_index, m_index,
r_index] = len(common_ids)
self.assertArrayEqual(expected_entity_overlap, actual_entity_overlap)
for same_entity_set_target_threshold in [1, 2, 3]:
(actual_loss, actual_avg_probs,
actual_denom) = mention_losses.same_entity_set_retrieval_loss(
mention_target_batch_positions=mention_target_batch_positions,
mention_target_ids=mention_target_ids,
mention_target_weights=mention_target_weights,
mention_batch_positions=mention_batch_positions,
mention_mask=mention_mask,
memory_text_entities=memory_text_entities,
memory_attention_weights=memory_attention_weights,
memory_mask=memory_mask,
batch_size=batch_size,
same_entity_set_target_threshold=same_entity_set_target_threshold,
)
expected_loss, expected_avg_probs, expected_denom = 0, 0, 0
for batch_index in range(batch_size):
for m_index in range(n_mentions):
if mention_batch_positions[m_index] != batch_index:
continue
if mention_mask[m_index] == 0:
continue
correct_prob, n_positive, n_negative = 0, 0, 0
for r_index in range(k_top):
if memory_mask[m_index, r_index] == 0:
continue
if (expected_entity_overlap[batch_index, m_index, r_index] >=
same_entity_set_target_threshold):
correct_prob += memory_attention_weights[m_index, r_index]
n_positive += 1
else:
n_negative += 1
if n_positive > 0 and n_negative > 0:
expected_loss -= np.log(correct_prob + 1e-5)
expected_avg_probs += correct_prob
expected_denom += 1
self.assertEqual(actual_denom, expected_denom)
self.assertAlmostEqual(actual_loss, expected_loss, places=4)
self.assertAlmostEqual(actual_avg_probs, expected_avg_probs, places=4)
if __name__ == '__main__':
absltest.main()
| 43.976124
| 83
| 0.67414
|
import functools
from absl.testing import absltest
from absl.testing import parameterized
import jax
import jax.numpy as jnp
import language.mentionmemory.modules.mention_losses as mention_losses
from language.mentionmemory.utils import mention_utils
from language.mentionmemory.utils import test_utils
import numpy as np
import scipy.spatial
class MentionLossesTest(test_utils.TestCase):
entity_vocab_size = 100
hidden_size = 3
n_mentions = 37
n_devices = 3
batch_size = 5
metrics_prefix = 'test_'
def _gen_array(self, gen_fn):
array = [gen_fn() for _ in range(len(self.devices))]
array_sharded = jax.device_put_sharded(array, self.devices)
array_stacked = np.stack(array)
array_stacked = array_stacked.reshape([-1] + list(array_stacked.shape[2:]))
return array_stacked, array_sharded
def setUp(self):
super().setUp()
test_utils.force_multi_devices(self.n_devices)
self.devices = jax.local_devices()
(self.mention_encodings_stacked,
self.mention_encodings_sharded) = self._gen_array(
lambda: 10.0 * np.random.random((self.n_mentions, self.hidden_size)))
(self.mention_target_ids_stacked, self.mention_target_ids_sharded
) = self._gen_array(lambda: np.random.randint(
self.entity_vocab_size, size=(self.n_mentions)))
(self.mention_batch_positions_stacked,
self.mention_batch_positions_sharded) = self._gen_array(
lambda: np.random.randint(self.batch_size, size=(self.n_mentions)))
(self.mention_target_is_masked_stacked,
self.mention_target_is_masked_sharded
) = self._gen_array(lambda: np.random.randint(2, size=(self.n_mentions)))
def test_build_coref_positive_negative_mask(self):
all_mention_target_ids = jax.device_put_replicated(
self.mention_target_ids_stacked, self.devices)
get_batch_positions = functools.partial(
mention_utils.get_globally_consistent_batch_positions,
batch_size=self.batch_size)
get_batch_positions = jax.pmap(get_batch_positions, axis_name='batch')
(local_mention_batch_positions,
global_mention_batch_positions) = get_batch_positions(
self.mention_batch_positions_sharded)
(positive_mask, negative_mask) = jax.pmap(
mention_losses.build_coref_positive_negative_mask,
axis_name='batch')(local_mention_batch_positions,
global_mention_batch_positions,
self.mention_target_ids_sharded,
all_mention_target_ids)
n_all_mentions = self.n_mentions * self.n_devices
self.assertSequenceEqual(positive_mask.shape, negative_mask.shape)
self.assertSequenceEqual(positive_mask.shape,
(self.n_devices, self.n_mentions, n_all_mentions))
positive_mask = positive_mask.reshape(-1, n_all_mentions)
negative_mask = negative_mask.reshape(-1, n_all_mentions)
for i in range(n_all_mentions):
for j in range(n_all_mentions):
is_same_device = i // self.n_mentions == j // self.n_mentions
is_same_passage = (
self.mention_batch_positions_stacked[i] ==
self.mention_batch_positions_stacked[j])
is_same_passage = is_same_passage and is_same_device
if (self.mention_target_ids_stacked[i] == 0 or
self.mention_target_ids_stacked[j] == 0 or is_same_passage):
self.assertEqual(positive_mask[i, j], 0)
self.assertEqual(negative_mask[i, j], 0)
continue
self.assertEqual(
positive_mask[i, j], self.mention_target_ids_stacked[i] ==
self.mention_target_ids_stacked[j])
self.assertEqual(
negative_mask[i, j], self.mention_target_ids_stacked[i] !=
self.mention_target_ids_stacked[j])
@parameterized.parameters(('dot',), ('cos'), ('dot_sqrt'))
def test_coref_resolution_loss_multiple_devices(self, mode):
def compute_loss(mention_encodings, mention_batch_positions,
mention_target_is_masked, mention_ids):
return mention_losses.coreference_resolution_loss(
mention_encodings, mention_batch_positions, mention_ids,
self.batch_size, mode, mention_target_is_masked, self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
compute_loss, axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_is_masked_sharded,
self.mention_target_ids_sharded)
num_total_mentions, hidden_dim = self.mention_encodings_stacked.shape
scores = np.zeros((num_total_mentions, num_total_mentions))
total_avg_scores, total_unnorm_avg_scores = [], []
for i in range(num_total_mentions):
current_avg_scores = []
current_unnorm_avg_scores = []
for j in range(num_total_mentions):
if mode == 'dot':
scores[i, j] = np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
elif mode == 'dot_sqrt':
scores[i, j] = np.dot(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]) / np.sqrt(hidden_dim)
elif mode == 'cos':
scores[i, j] = 1 - scipy.spatial.distance.cosine(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
else:
raise ValueError('Unknown coreference resolution mode: ' + mode)
if self.mention_target_ids_stacked[j] != 0:
current_avg_scores.append(scores[i, j])
current_unnorm_avg_scores.append(
np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]))
if len(current_avg_scores) > 0:
current_avg_scores = np.array(current_avg_scores)
total_avg_scores.append(current_avg_scores.mean())
current_unnorm_avg_scores = np.array(current_unnorm_avg_scores)
total_unnorm_avg_scores.append(current_unnorm_avg_scores.mean())
else:
total_avg_scores.append(0)
total_unnorm_avg_scores.append(0)
self.assertLen(total_avg_scores, len(self.mention_target_ids_stacked))
expected_loss, expected_acc, expected_denom = 0, 0, 0
expected_denom_masked, expected_denom_non_masked = 0, 0
expected_acc_masked, expected_acc_non_masked = 0, 0
expected_n_positives, expected_n_negatives = 0, 0
expected_avg_scores, expected_unnorm_avg_scores = 0, 0
expected_avg_norms = 0
for i in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[i] == 0:
continue
positive_scores, negative_scores = [], []
for j in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[j] == 0:
continue
is_same_device = i // self.n_mentions == j // self.n_mentions
is_same_passage = (
self.mention_batch_positions_stacked[i] ==
self.mention_batch_positions_stacked[j])
is_same_passage = is_same_passage and is_same_device
if is_same_passage:
continue
if (self.mention_target_ids_stacked[i] ==
self.mention_target_ids_stacked[j]):
positive_scores.append(scores[i, j])
else:
negative_scores.append(scores[i, j])
n_pos = len(positive_scores)
n_neg = len(negative_scores)
max_negative_scores = max(negative_scores)
if n_pos == 0 or n_neg == 0:
continue
current_loss, current_acc = 0, 0
for pos_index in range(n_pos):
current_scores = np.array([positive_scores[pos_index]] +
negative_scores)
current_scores = jax.nn.log_softmax(current_scores)
current_loss += -current_scores[0]
current_acc += int(positive_scores[pos_index] > max_negative_scores)
expected_loss += current_loss / n_pos
expected_acc += current_acc / n_pos
expected_denom += 1
if self.mention_target_is_masked_stacked[i] > 0:
expected_denom_masked += 1
expected_acc_masked += current_acc / n_pos
else:
expected_denom_non_masked += 1
expected_acc_non_masked += current_acc / n_pos
expected_n_positives += n_pos
expected_n_negatives += n_neg
expected_avg_scores += total_avg_scores[i]
expected_unnorm_avg_scores += total_unnorm_avg_scores[i]
expected_avg_norms += np.linalg.norm(self.mention_encodings_stacked[i])
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
metrics_sharded_masked = metrics_sharded[self.metrics_prefix +
'coref_resolution_masked']
metrics_sharded_non_masked = metrics_sharded[self.metrics_prefix +
'coref_resolution_non_masked']
metrics_sharded = metrics_sharded[self.metrics_prefix + 'coref_resolution']
loss_sharded = jnp.sum(loss_sharded)
self.assertAlmostEqual(loss_sharded, expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['loss'], expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['acc'], expected_acc, places=3)
self.assertEqual(metrics_sharded['denominator'], expected_denom)
self.assertEqual(metrics_sharded['n_positive'], expected_n_positives)
self.assertEqual(metrics_sharded['n_negative'], expected_n_negatives)
self.assertAlmostEqual(
metrics_sharded['avg_score'], expected_avg_scores, places=2)
self.assertAlmostEqual(
metrics_sharded['avg_unnorm_score'],
expected_unnorm_avg_scores,
places=2)
self.assertAlmostEqual(
metrics_sharded['avg_norm'], expected_avg_norms, places=2)
self.assertAlmostEqual(
metrics_sharded_masked['acc'], expected_acc_masked, places=3)
self.assertEqual(metrics_sharded_masked['denominator'],
expected_denom_masked)
self.assertAlmostEqual(
metrics_sharded_non_masked['acc'], expected_acc_non_masked, places=3)
self.assertEqual(metrics_sharded_non_masked['denominator'],
expected_denom_non_masked)
def test_coref_resolution_loss_multiple_vs_single_devices(self):
def compute_loss(mention_encodings, mention_batch_positions, mention_ids,
mention_target_is_masked):
return mention_losses.coreference_resolution_loss(
mention_encodings, mention_batch_positions, mention_ids,
self.batch_size, 'dot', mention_target_is_masked, self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
compute_loss, axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_ids_sharded,
self.mention_target_is_masked_sharded)
mention_batch_positions_stacked = (
self.mention_batch_positions_stacked.reshape(self.n_devices, -1))
mention_batch_positions_stacked = mention_batch_positions_stacked.copy()
mention_batch_positions_stacked += (
np.expand_dims(np.arange(self.n_devices), 1) * self.batch_size)
mention_batch_positions_stacked = mention_batch_positions_stacked.reshape(
-1)
loss_stacked, metrics_stacked = compute_loss(
self.mention_encodings_stacked, mention_batch_positions_stacked,
self.mention_target_ids_stacked, self.mention_target_is_masked_stacked)
loss_sharded = jnp.sum(loss_sharded)
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
self.assertAlmostEqual(loss_sharded, loss_stacked, places=2)
for metric_group_name in metrics_stacked:
for metric_name in metrics_stacked[metric_group_name]:
self.assertAlmostEqual(
metrics_sharded[metric_group_name][metric_name],
metrics_stacked[metric_group_name][metric_name],
places=2)
@parameterized.parameters(('dot',), ('cos'), ('dot_sqrt'))
def test_mtb_loss_multiple_devices(self, mode):
def compute_loss(mention_encodings, mention_batch_positions, mention_ids,
mention_target_is_masked):
return mention_losses.mtb_loss(mention_encodings, mention_batch_positions,
mention_ids, self.batch_size, mode,
mention_target_is_masked,
self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
compute_loss, axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_ids_sharded,
self.mention_target_is_masked_sharded)
batches = []
for i in range(self.n_devices):
batches.append([])
for j in range(self.batch_size):
batches[i].append(set())
for j in range(self.n_mentions):
if self.mention_target_ids_sharded[i, j] > 0:
batches[i][self.mention_batch_positions_sharded[i, j]].add(
self.mention_target_ids_sharded[i, j].item())
num_total_mentions, hidden_dim = self.mention_encodings_stacked.shape
scores = np.zeros((num_total_mentions, num_total_mentions))
total_avg_scores, total_unnorm_avg_scores = [], []
for i in range(num_total_mentions):
current_avg_scores = []
current_unnorm_avg_scores = []
for j in range(num_total_mentions):
if mode == 'dot':
scores[i, j] = np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
elif mode == 'dot_sqrt':
scores[i, j] = np.dot(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]) / np.sqrt(hidden_dim)
elif mode == 'cos':
scores[i, j] = 1 - scipy.spatial.distance.cosine(
self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j])
else:
raise ValueError('Unknown coreference resolution mode: ' + mode)
if self.mention_target_ids_stacked[j] != 0:
current_avg_scores.append(scores[i, j])
current_unnorm_avg_scores.append(
np.dot(self.mention_encodings_stacked[i],
self.mention_encodings_stacked[j]))
if len(current_avg_scores) > 0:
current_avg_scores = np.array(current_avg_scores)
total_avg_scores.append(current_avg_scores.mean())
current_unnorm_avg_scores = np.array(current_unnorm_avg_scores)
total_unnorm_avg_scores.append(current_unnorm_avg_scores.mean())
else:
total_avg_scores.append(0)
total_unnorm_avg_scores.append(0)
self.assertLen(total_avg_scores, len(self.mention_target_ids_stacked))
expected_loss, expected_acc, expected_denom = 0, 0, 0
expected_n_positives, expected_n_negatives = 0, 0
expected_n_hard_negatives = 0
expected_avg_scores, expected_unnorm_avg_scores = 0, 0
expected_denom_masked, expected_denom_non_masked = 0, 0
expected_acc_masked, expected_acc_non_masked = 0, 0
expected_avg_norms = 0
for i in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[i] == 0:
continue
device_i = i // self.n_mentions
unique_entities_i = (
batches[device_i][self.mention_batch_positions_sharded[
device_i, i % self.n_mentions]])
positive_scores, hard_negative_scores, negative_scores = [], [], []
for j in range(len(self.mention_target_ids_stacked)):
if self.mention_target_ids_stacked[j] == 0:
continue
device_j = j // self.n_mentions
is_same_device = device_i == device_j
is_same_passage = (
self.mention_batch_positions_stacked[i] ==
self.mention_batch_positions_stacked[j])
is_same_passage = is_same_passage and is_same_device
if is_same_passage:
continue
if (self.mention_target_ids_stacked[i] ==
self.mention_target_ids_stacked[j]):
unique_entities_j = (
batches[device_j][self.mention_batch_positions_sharded[
device_j, j % self.n_mentions]])
num_common_entities = len(
unique_entities_i.intersection(unique_entities_j))
if num_common_entities >= 2:
positive_scores.append(scores[i, j])
else:
hard_negative_scores.append(scores[i, j])
else:
negative_scores.append(scores[i, j])
negative_scores = negative_scores + hard_negative_scores
n_pos = len(positive_scores)
n_neg = len(negative_scores)
n_hard_neg = len(hard_negative_scores)
max_negative_scores = max(negative_scores)
if n_pos == 0 or n_hard_neg == 0:
continue
current_loss, current_acc = 0, 0
for pos_index in range(n_pos):
current_scores = np.array([positive_scores[pos_index]] +
negative_scores)
current_scores = jax.nn.log_softmax(current_scores)
current_loss += -current_scores[0]
current_acc += int(positive_scores[pos_index] > max_negative_scores)
expected_loss += current_loss / n_pos
expected_acc += current_acc / n_pos
expected_denom += 1
if self.mention_target_is_masked_stacked[i] > 0:
expected_denom_masked += 1
expected_acc_masked += current_acc / n_pos
else:
expected_denom_non_masked += 1
expected_acc_non_masked += current_acc / n_pos
expected_n_positives += n_pos
expected_n_negatives += n_neg
expected_n_hard_negatives += n_hard_neg
expected_avg_scores += total_avg_scores[i]
expected_unnorm_avg_scores += total_unnorm_avg_scores[i]
expected_avg_norms += np.linalg.norm(self.mention_encodings_stacked[i])
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
metrics_sharded_masked = metrics_sharded[self.metrics_prefix + 'mtb_masked']
metrics_sharded_non_masked = metrics_sharded[self.metrics_prefix +
'mtb_non_masked']
metrics_sharded = metrics_sharded[self.metrics_prefix + 'mtb']
loss_sharded = jnp.sum(loss_sharded)
self.assertAlmostEqual(loss_sharded, expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['loss'], expected_loss, places=2)
self.assertAlmostEqual(metrics_sharded['acc'], expected_acc, places=3)
self.assertEqual(metrics_sharded['denominator'], expected_denom)
self.assertEqual(metrics_sharded['n_positive'], expected_n_positives)
self.assertEqual(metrics_sharded['n_negative'], expected_n_negatives)
self.assertEqual(metrics_sharded['n_hard_negative'],
expected_n_hard_negatives)
self.assertAlmostEqual(
metrics_sharded['avg_score'], expected_avg_scores, places=2)
self.assertAlmostEqual(
metrics_sharded['avg_unnorm_score'],
expected_unnorm_avg_scores,
places=2)
self.assertAlmostEqual(
metrics_sharded['avg_norm'], expected_avg_norms, places=2)
self.assertAlmostEqual(
metrics_sharded_masked['acc'], expected_acc_masked, places=3)
self.assertEqual(metrics_sharded_masked['denominator'],
expected_denom_masked)
self.assertAlmostEqual(
metrics_sharded_non_masked['acc'], expected_acc_non_masked, places=3)
self.assertEqual(metrics_sharded_non_masked['denominator'],
expected_denom_non_masked)
def test_mtb_loss_multiple_vs_single_devices(self):
def loss_fn_multi_device(mention_encodings, mention_batch_positions,
mention_ids, mention_target_is_masked):
return mention_losses.mtb_loss(mention_encodings, mention_batch_positions,
mention_ids, self.batch_size, 'dot',
mention_target_is_masked,
self.metrics_prefix)
loss_sharded, metrics_sharded = jax.pmap(
loss_fn_multi_device,
axis_name='batch')(self.mention_encodings_sharded,
self.mention_batch_positions_sharded,
self.mention_target_ids_sharded,
self.mention_target_is_masked_sharded)
mention_batch_positions_stacked = (
self.mention_batch_positions_stacked.reshape(self.n_devices, -1))
mention_batch_positions_stacked = mention_batch_positions_stacked.copy()
mention_batch_positions_stacked += (
np.expand_dims(np.arange(self.n_devices), 1) * self.batch_size)
mention_batch_positions_stacked = mention_batch_positions_stacked.reshape(
-1)
loss_stacked, metrics_stacked = mention_losses.mtb_loss(
self.mention_encodings_stacked, mention_batch_positions_stacked,
self.mention_target_ids_stacked, self.batch_size * self.n_devices,
'dot', self.mention_target_is_masked_stacked, self.metrics_prefix)
loss_sharded = jnp.sum(loss_sharded)
metrics_sharded = jax.tree_map(jnp.sum, metrics_sharded)
self.assertAlmostEqual(loss_sharded, loss_stacked, places=2)
for metric_group_name in metrics_stacked:
for metric_name in metrics_stacked[metric_group_name]:
self.assertAlmostEqual(
metrics_sharded[metric_group_name][metric_name],
metrics_stacked[metric_group_name][metric_name],
places=2)
@parameterized.parameters(('dot',), ('cos'), ('dot_sqrt'))
def test_entity_linking_loss(self, mode):
n_mentions = 5
n_entities = 10
hidden_size = 3
mention_encodings = np.random.random((n_mentions, hidden_size))
entity_embeddings = np.random.random((n_entities, hidden_size))
mention_target_ids = np.random.randint(n_entities, size=(n_mentions))
mention_target_weights = np.random.randint(2, size=(n_mentions))
(actual_loss, actual_metrics,
(actual_acc_per_mention,
actual_weight_per_mention)) = mention_losses.entity_linking_loss(
mention_encodings, entity_embeddings, mention_target_ids,
mention_target_weights, mode)
self.assertArrayEqual(mention_target_weights, actual_weight_per_mention)
self.assertEqual(actual_metrics['loss'], actual_loss)
self.assertAlmostEqual(
actual_metrics['acc'], actual_acc_per_mention.sum(), places=6)
self.assertAlmostEqual(
actual_metrics['denominator'], mention_target_weights.sum(), places=8)
scores = np.matmul(mention_encodings, np.transpose(entity_embeddings))
if mode == 'dot_sqrt':
scores /= np.sqrt(hidden_size)
if mode == 'cos':
scores /= np.expand_dims(np.linalg.norm(mention_encodings, axis=-1), 1)
scores /= np.expand_dims(np.linalg.norm(entity_embeddings, axis=-1), 0)
log_probs = np.log(scipy.special.softmax(scores, axis=-1))
expected_loss = 0
expected_acc_per_mention = []
expected_cos_sim_per_mention = []
for i in range(n_mentions):
if mention_target_weights[i] == 1:
expected_loss += -log_probs[i, mention_target_ids[i]]
is_correct = int(np.argmax(log_probs[i]) == mention_target_ids[i])
expected_acc_per_mention.append(is_correct)
expected_cos_sim_per_mention.append(1 - scipy.spatial.distance.cosine(
mention_encodings[i], entity_embeddings[mention_target_ids[i]]))
else:
expected_acc_per_mention.append(0)
expected_cos_sim_per_mention.append(0)
expected_acc_per_mention = np.array(expected_acc_per_mention)
expected_cos_sim_per_mention = np.array(expected_cos_sim_per_mention)
self.assertAlmostEqual(actual_loss, expected_loss, places=4)
self.assertAlmostEqual(
actual_metrics['denominator'], mention_target_weights.sum(), places=8)
self.assertArrayAlmostEqual(
actual_acc_per_mention, expected_acc_per_mention, places=8)
self.assertAlmostEqual(
actual_metrics['cos_sim'], expected_cos_sim_per_mention.sum(), places=2)
@parameterized.parameters([
{
'batch_size': 1,
},
{
'batch_size': 11,
},
{
'batch_size': 11,
'entity_vocab_size': 1000000,
},
{
'batch_size': 2,
'n_target_mentions': 19,
},
{
'batch_size': 2,
'n_target_mentions': 19,
'entity_vocab_size': 2,
},
{
'batch_size': 2,
'k_top': 1,
},
{
'batch_size': 2,
'n_mentions_per_memory_passage': 21,
},
{
'batch_size': 10,
'n_mentions_per_memory_passage': 41,
},
{
'batch_size': 10,
'n_mentions': 100,
},
{
'batch_size': 2,
'n_mentions': 1,
},
{
'batch_size': 11,
'p_memory_mask': 0,
},
{
'batch_size': 11,
'p_memory_mask': 1,
},
])
def test_same_entity_set_retrieval_loss(self,
batch_size,
n_target_mentions=11,
n_mentions=21,
entity_vocab_size=10,
k_top=10,
n_mentions_per_memory_passage=4,
p_memory_mask=0.5):
np.random.seed(0)
mention_target_batch_positions = np.random.randint(
batch_size, size=(n_target_mentions))
mention_target_ids = np.random.randint(
entity_vocab_size, size=(n_target_mentions))
mention_target_weights = np.random.randint(2, size=(n_target_mentions))
mention_batch_positions = np.random.randint(batch_size, size=(n_mentions))
mention_mask = np.random.randint(2, size=(n_mentions))
memory_mask = np.random.random((n_mentions, k_top)) < p_memory_mask
memory_mask = memory_mask.astype(np.int32)
memory_text_entities = np.zeros(
(n_mentions, k_top, n_mentions_per_memory_passage), np.int32)
for m_index in range(n_mentions):
for r_index in range(k_top):
current_text_entities = np.random.choice(
entity_vocab_size,
size=(min(n_mentions_per_memory_passage, entity_vocab_size)),
replace=False)
memory_text_entities[
m_index,
r_index, :len(current_text_entities)] = current_text_entities
memory_attention_weights = np.random.random((n_mentions, k_top))
memory_attention_weights /= memory_attention_weights.sum(
axis=-1, keepdims=True)
actual_entity_overlap = mention_losses.get_batch_and_retrievals_entity_overlap(
mention_target_batch_positions=mention_target_batch_positions,
mention_target_ids=mention_target_ids,
mention_target_weights=mention_target_weights,
memory_text_entities=memory_text_entities.reshape(
[n_mentions * k_top, -1]),
batch_size=batch_size,
)
actual_entity_overlap = actual_entity_overlap.reshape(
[batch_size, n_mentions, k_top])
expected_entity_overlap = np.zeros((batch_size, n_mentions, k_top))
for batch_index in range(batch_size):
sample_ids = mention_target_ids[mention_target_batch_positions ==
batch_index]
sample_weights = mention_target_weights[mention_target_batch_positions ==
batch_index]
sample_ids = sample_ids[sample_weights > 0]
sample_ids = set([x for x in sample_ids if x != 0])
for m_index in range(n_mentions):
for r_index in range(k_top):
common_ids = set(
memory_text_entities[m_index, r_index]).intersection(sample_ids)
expected_entity_overlap[batch_index, m_index,
r_index] = len(common_ids)
self.assertArrayEqual(expected_entity_overlap, actual_entity_overlap)
for same_entity_set_target_threshold in [1, 2, 3]:
(actual_loss, actual_avg_probs,
actual_denom) = mention_losses.same_entity_set_retrieval_loss(
mention_target_batch_positions=mention_target_batch_positions,
mention_target_ids=mention_target_ids,
mention_target_weights=mention_target_weights,
mention_batch_positions=mention_batch_positions,
mention_mask=mention_mask,
memory_text_entities=memory_text_entities,
memory_attention_weights=memory_attention_weights,
memory_mask=memory_mask,
batch_size=batch_size,
same_entity_set_target_threshold=same_entity_set_target_threshold,
)
expected_loss, expected_avg_probs, expected_denom = 0, 0, 0
for batch_index in range(batch_size):
for m_index in range(n_mentions):
if mention_batch_positions[m_index] != batch_index:
continue
if mention_mask[m_index] == 0:
continue
correct_prob, n_positive, n_negative = 0, 0, 0
for r_index in range(k_top):
if memory_mask[m_index, r_index] == 0:
continue
if (expected_entity_overlap[batch_index, m_index, r_index] >=
same_entity_set_target_threshold):
correct_prob += memory_attention_weights[m_index, r_index]
n_positive += 1
else:
n_negative += 1
if n_positive > 0 and n_negative > 0:
expected_loss -= np.log(correct_prob + 1e-5)
expected_avg_probs += correct_prob
expected_denom += 1
self.assertEqual(actual_denom, expected_denom)
self.assertAlmostEqual(actual_loss, expected_loss, places=4)
self.assertAlmostEqual(actual_avg_probs, expected_avg_probs, places=4)
if __name__ == '__main__':
absltest.main()
| true
| true
|
1c3ed72bc6c644886efad8191a3375ad2c5fc776
| 17,558
|
py
|
Python
|
src/pip/_internal/resolution/legacy/resolver.py
|
lhchavez/pip
|
bd5ac261c0bf4c9a37322b2cfe1b2564479f56ad
|
[
"MIT"
] | 2
|
2021-11-11T10:44:41.000Z
|
2022-03-29T17:18:30.000Z
|
src/pip/_internal/resolution/legacy/resolver.py
|
illia-v/pip
|
76cd70ac42cbbe6a51d83bd100f500ac4c21f26b
|
[
"MIT"
] | 3
|
2021-12-16T17:05:16.000Z
|
2022-03-14T21:51:36.000Z
|
src/pip/_internal/resolution/legacy/resolver.py
|
illia-v/pip
|
76cd70ac42cbbe6a51d83bd100f500ac4c21f26b
|
[
"MIT"
] | null | null | null |
"""Dependency Resolution
The dependency resolution in pip is performed as follows:
for top-level requirements:
a. only one spec allowed per project, regardless of conflicts or not.
otherwise a "double requirement" exception is raised
b. they override sub-dependency requirements.
for sub-dependencies
a. "first found, wins" (where the order is breadth first)
"""
# The following comment should be removed at some point in the future.
# mypy: strict-optional=False
import logging
import sys
from collections import defaultdict
from itertools import chain
from typing import DefaultDict, Iterable, List, Optional, Set, Tuple
from pip._vendor.packaging import specifiers
from pip._vendor.pkg_resources import Distribution
from pip._internal.cache import WheelCache
from pip._internal.exceptions import (
BestVersionAlreadyInstalled,
DistributionNotFound,
HashError,
HashErrors,
UnsupportedPythonVersion,
)
from pip._internal.index.package_finder import PackageFinder
from pip._internal.models.link import Link
from pip._internal.operations.prepare import RequirementPreparer
from pip._internal.req.req_install import (
InstallRequirement,
check_invalid_constraint_type,
)
from pip._internal.req.req_set import RequirementSet
from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider
from pip._internal.utils.compatibility_tags import get_supported
from pip._internal.utils.logging import indent_log
from pip._internal.utils.misc import dist_in_usersite, normalize_version_info
from pip._internal.utils.packaging import check_requires_python, get_requires_python
logger = logging.getLogger(__name__)
DiscoveredDependencies = DefaultDict[str, List[InstallRequirement]]
def _check_dist_requires_python(
dist: Distribution,
version_info: Tuple[int, int, int],
ignore_requires_python: bool = False,
) -> None:
"""
Check whether the given Python version is compatible with a distribution's
"Requires-Python" value.
:param version_info: A 3-tuple of ints representing the Python
major-minor-micro version to check.
:param ignore_requires_python: Whether to ignore the "Requires-Python"
value if the given Python version isn't compatible.
:raises UnsupportedPythonVersion: When the given Python version isn't
compatible.
"""
requires_python = get_requires_python(dist)
try:
is_compatible = check_requires_python(
requires_python, version_info=version_info
)
except specifiers.InvalidSpecifier as exc:
logger.warning(
"Package %r has an invalid Requires-Python: %s", dist.project_name, exc
)
return
if is_compatible:
return
version = ".".join(map(str, version_info))
if ignore_requires_python:
logger.debug(
"Ignoring failed Requires-Python check for package %r: " "%s not in %r",
dist.project_name,
version,
requires_python,
)
return
raise UnsupportedPythonVersion(
"Package {!r} requires a different Python: {} not in {!r}".format(
dist.project_name, version, requires_python
)
)
class Resolver(BaseResolver):
"""Resolves which packages need to be installed/uninstalled to perform \
the requested operation without breaking the requirements of any package.
"""
_allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"}
def __init__(
self,
preparer: RequirementPreparer,
finder: PackageFinder,
wheel_cache: Optional[WheelCache],
make_install_req: InstallRequirementProvider,
use_user_site: bool,
ignore_dependencies: bool,
ignore_installed: bool,
ignore_requires_python: bool,
force_reinstall: bool,
upgrade_strategy: str,
py_version_info: Optional[Tuple[int, ...]] = None,
) -> None:
super().__init__()
assert upgrade_strategy in self._allowed_strategies
if py_version_info is None:
py_version_info = sys.version_info[:3]
else:
py_version_info = normalize_version_info(py_version_info)
self._py_version_info = py_version_info
self.preparer = preparer
self.finder = finder
self.wheel_cache = wheel_cache
self.upgrade_strategy = upgrade_strategy
self.force_reinstall = force_reinstall
self.ignore_dependencies = ignore_dependencies
self.ignore_installed = ignore_installed
self.ignore_requires_python = ignore_requires_python
self.use_user_site = use_user_site
self._make_install_req = make_install_req
self._discovered_dependencies: DiscoveredDependencies = defaultdict(list)
def resolve(
self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
) -> RequirementSet:
"""Resolve what operations need to be done
As a side-effect of this method, the packages (and their dependencies)
are downloaded, unpacked and prepared for installation. This
preparation is done by ``pip.operations.prepare``.
Once PyPI has static dependency metadata available, it would be
possible to move the preparation to become a step separated from
dependency resolution.
"""
requirement_set = RequirementSet(check_supported_wheels=check_supported_wheels)
for req in root_reqs:
if req.constraint:
check_invalid_constraint_type(req)
requirement_set.add_requirement(req)
# Actually prepare the files, and collect any exceptions. Most hash
# exceptions cannot be checked ahead of time, because
# _populate_link() needs to be called before we can make decisions
# based on link type.
discovered_reqs: List[InstallRequirement] = []
hash_errors = HashErrors()
for req in chain(requirement_set.all_requirements, discovered_reqs):
try:
discovered_reqs.extend(self._resolve_one(requirement_set, req))
except HashError as exc:
exc.req = req
hash_errors.append(exc)
if hash_errors:
raise hash_errors
return requirement_set
def _is_upgrade_allowed(self, req: InstallRequirement) -> bool:
if self.upgrade_strategy == "to-satisfy-only":
return False
elif self.upgrade_strategy == "eager":
return True
else:
assert self.upgrade_strategy == "only-if-needed"
return req.user_supplied or req.constraint
def _set_req_to_reinstall(self, req: InstallRequirement) -> None:
"""
Set a requirement to be installed.
"""
# Don't uninstall the conflict if doing a user install and the
# conflict is not a user install.
if not self.use_user_site or dist_in_usersite(req.satisfied_by):
req.should_reinstall = True
req.satisfied_by = None
def _check_skip_installed(
self, req_to_install: InstallRequirement
) -> Optional[str]:
"""Check if req_to_install should be skipped.
This will check if the req is installed, and whether we should upgrade
or reinstall it, taking into account all the relevant user options.
After calling this req_to_install will only have satisfied_by set to
None if the req_to_install is to be upgraded/reinstalled etc. Any
other value will be a dist recording the current thing installed that
satisfies the requirement.
Note that for vcs urls and the like we can't assess skipping in this
routine - we simply identify that we need to pull the thing down,
then later on it is pulled down and introspected to assess upgrade/
reinstalls etc.
:return: A text reason for why it was skipped, or None.
"""
if self.ignore_installed:
return None
req_to_install.check_if_exists(self.use_user_site)
if not req_to_install.satisfied_by:
return None
if self.force_reinstall:
self._set_req_to_reinstall(req_to_install)
return None
if not self._is_upgrade_allowed(req_to_install):
if self.upgrade_strategy == "only-if-needed":
return "already satisfied, skipping upgrade"
return "already satisfied"
# Check for the possibility of an upgrade. For link-based
# requirements we have to pull the tree down and inspect to assess
# the version #, so it's handled way down.
if not req_to_install.link:
try:
self.finder.find_requirement(req_to_install, upgrade=True)
except BestVersionAlreadyInstalled:
# Then the best version is installed.
return "already up-to-date"
except DistributionNotFound:
# No distribution found, so we squash the error. It will
# be raised later when we re-try later to do the install.
# Why don't we just raise here?
pass
self._set_req_to_reinstall(req_to_install)
return None
def _find_requirement_link(self, req: InstallRequirement) -> Optional[Link]:
upgrade = self._is_upgrade_allowed(req)
best_candidate = self.finder.find_requirement(req, upgrade)
if not best_candidate:
return None
# Log a warning per PEP 592 if necessary before returning.
link = best_candidate.link
if link.is_yanked:
reason = link.yanked_reason or "<none given>"
msg = (
# Mark this as a unicode string to prevent
# "UnicodeEncodeError: 'ascii' codec can't encode character"
# in Python 2 when the reason contains non-ascii characters.
"The candidate selected for download or install is a "
"yanked version: {candidate}\n"
"Reason for being yanked: {reason}"
).format(candidate=best_candidate, reason=reason)
logger.warning(msg)
return link
def _populate_link(self, req: InstallRequirement) -> None:
"""Ensure that if a link can be found for this, that it is found.
Note that req.link may still be None - if the requirement is already
installed and not needed to be upgraded based on the return value of
_is_upgrade_allowed().
If preparer.require_hashes is True, don't use the wheel cache, because
cached wheels, always built locally, have different hashes than the
files downloaded from the index server and thus throw false hash
mismatches. Furthermore, cached wheels at present have undeterministic
contents due to file modification times.
"""
if req.link is None:
req.link = self._find_requirement_link(req)
if self.wheel_cache is None or self.preparer.require_hashes:
return
cache_entry = self.wheel_cache.get_cache_entry(
link=req.link,
package_name=req.name,
supported_tags=get_supported(),
)
if cache_entry is not None:
logger.debug("Using cached wheel link: %s", cache_entry.link)
if req.link is req.original_link and cache_entry.persistent:
req.original_link_is_in_wheel_cache = True
req.link = cache_entry.link
def _get_dist_for(self, req: InstallRequirement) -> Distribution:
"""Takes a InstallRequirement and returns a single AbstractDist \
representing a prepared variant of the same.
"""
if req.editable:
return self.preparer.prepare_editable_requirement(req)
# satisfied_by is only evaluated by calling _check_skip_installed,
# so it must be None here.
assert req.satisfied_by is None
skip_reason = self._check_skip_installed(req)
if req.satisfied_by:
return self.preparer.prepare_installed_requirement(req, skip_reason)
# We eagerly populate the link, since that's our "legacy" behavior.
self._populate_link(req)
dist = self.preparer.prepare_linked_requirement(req)
# NOTE
# The following portion is for determining if a certain package is
# going to be re-installed/upgraded or not and reporting to the user.
# This should probably get cleaned up in a future refactor.
# req.req is only avail after unpack for URL
# pkgs repeat check_if_exists to uninstall-on-upgrade
# (#14)
if not self.ignore_installed:
req.check_if_exists(self.use_user_site)
if req.satisfied_by:
should_modify = (
self.upgrade_strategy != "to-satisfy-only"
or self.force_reinstall
or self.ignore_installed
or req.link.scheme == "file"
)
if should_modify:
self._set_req_to_reinstall(req)
else:
logger.info(
"Requirement already satisfied (use --upgrade to upgrade):" " %s",
req,
)
return dist
def _resolve_one(
self,
requirement_set: RequirementSet,
req_to_install: InstallRequirement,
) -> List[InstallRequirement]:
"""Prepare a single requirements file.
:return: A list of additional InstallRequirements to also install.
"""
# Tell user what we are doing for this requirement:
# obtain (editable), skipping, processing (local url), collecting
# (remote url or package name)
if req_to_install.constraint or req_to_install.prepared:
return []
req_to_install.prepared = True
# Parse and return dependencies
dist = self._get_dist_for(req_to_install)
# This will raise UnsupportedPythonVersion if the given Python
# version isn't compatible with the distribution's Requires-Python.
_check_dist_requires_python(
dist,
version_info=self._py_version_info,
ignore_requires_python=self.ignore_requires_python,
)
more_reqs: List[InstallRequirement] = []
def add_req(subreq: Distribution, extras_requested: Iterable[str]) -> None:
sub_install_req = self._make_install_req(
str(subreq),
req_to_install,
)
parent_req_name = req_to_install.name
to_scan_again, add_to_parent = requirement_set.add_requirement(
sub_install_req,
parent_req_name=parent_req_name,
extras_requested=extras_requested,
)
if parent_req_name and add_to_parent:
self._discovered_dependencies[parent_req_name].append(add_to_parent)
more_reqs.extend(to_scan_again)
with indent_log():
# We add req_to_install before its dependencies, so that we
# can refer to it when adding dependencies.
if not requirement_set.has_requirement(req_to_install.name):
# 'unnamed' requirements will get added here
# 'unnamed' requirements can only come from being directly
# provided by the user.
assert req_to_install.user_supplied
requirement_set.add_requirement(req_to_install, parent_req_name=None)
if not self.ignore_dependencies:
if req_to_install.extras:
logger.debug(
"Installing extra requirements: %r",
",".join(req_to_install.extras),
)
missing_requested = sorted(
set(req_to_install.extras) - set(dist.extras)
)
for missing in missing_requested:
logger.warning("%s does not provide the extra '%s'", dist, missing)
available_requested = sorted(
set(dist.extras) & set(req_to_install.extras)
)
for subreq in dist.requires(available_requested):
add_req(subreq, extras_requested=available_requested)
return more_reqs
def get_installation_order(
self, req_set: RequirementSet
) -> List[InstallRequirement]:
"""Create the installation order.
The installation order is topological - requirements are installed
before the requiring thing. We break cycles at an arbitrary point,
and make no other guarantees.
"""
# The current implementation, which we may change at any point
# installs the user specified things in the order given, except when
# dependencies must come earlier to achieve topological order.
order = []
ordered_reqs: Set[InstallRequirement] = set()
def schedule(req: InstallRequirement) -> None:
if req.satisfied_by or req in ordered_reqs:
return
if req.constraint:
return
ordered_reqs.add(req)
for dep in self._discovered_dependencies[req.name]:
schedule(dep)
order.append(req)
for install_req in req_set.requirements.values():
schedule(install_req)
return order
| 38.674009
| 87
| 0.651498
|
import logging
import sys
from collections import defaultdict
from itertools import chain
from typing import DefaultDict, Iterable, List, Optional, Set, Tuple
from pip._vendor.packaging import specifiers
from pip._vendor.pkg_resources import Distribution
from pip._internal.cache import WheelCache
from pip._internal.exceptions import (
BestVersionAlreadyInstalled,
DistributionNotFound,
HashError,
HashErrors,
UnsupportedPythonVersion,
)
from pip._internal.index.package_finder import PackageFinder
from pip._internal.models.link import Link
from pip._internal.operations.prepare import RequirementPreparer
from pip._internal.req.req_install import (
InstallRequirement,
check_invalid_constraint_type,
)
from pip._internal.req.req_set import RequirementSet
from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider
from pip._internal.utils.compatibility_tags import get_supported
from pip._internal.utils.logging import indent_log
from pip._internal.utils.misc import dist_in_usersite, normalize_version_info
from pip._internal.utils.packaging import check_requires_python, get_requires_python
logger = logging.getLogger(__name__)
DiscoveredDependencies = DefaultDict[str, List[InstallRequirement]]
def _check_dist_requires_python(
dist: Distribution,
version_info: Tuple[int, int, int],
ignore_requires_python: bool = False,
) -> None:
requires_python = get_requires_python(dist)
try:
is_compatible = check_requires_python(
requires_python, version_info=version_info
)
except specifiers.InvalidSpecifier as exc:
logger.warning(
"Package %r has an invalid Requires-Python: %s", dist.project_name, exc
)
return
if is_compatible:
return
version = ".".join(map(str, version_info))
if ignore_requires_python:
logger.debug(
"Ignoring failed Requires-Python check for package %r: " "%s not in %r",
dist.project_name,
version,
requires_python,
)
return
raise UnsupportedPythonVersion(
"Package {!r} requires a different Python: {} not in {!r}".format(
dist.project_name, version, requires_python
)
)
class Resolver(BaseResolver):
_allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"}
def __init__(
self,
preparer: RequirementPreparer,
finder: PackageFinder,
wheel_cache: Optional[WheelCache],
make_install_req: InstallRequirementProvider,
use_user_site: bool,
ignore_dependencies: bool,
ignore_installed: bool,
ignore_requires_python: bool,
force_reinstall: bool,
upgrade_strategy: str,
py_version_info: Optional[Tuple[int, ...]] = None,
) -> None:
super().__init__()
assert upgrade_strategy in self._allowed_strategies
if py_version_info is None:
py_version_info = sys.version_info[:3]
else:
py_version_info = normalize_version_info(py_version_info)
self._py_version_info = py_version_info
self.preparer = preparer
self.finder = finder
self.wheel_cache = wheel_cache
self.upgrade_strategy = upgrade_strategy
self.force_reinstall = force_reinstall
self.ignore_dependencies = ignore_dependencies
self.ignore_installed = ignore_installed
self.ignore_requires_python = ignore_requires_python
self.use_user_site = use_user_site
self._make_install_req = make_install_req
self._discovered_dependencies: DiscoveredDependencies = defaultdict(list)
def resolve(
self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
) -> RequirementSet:
requirement_set = RequirementSet(check_supported_wheels=check_supported_wheels)
for req in root_reqs:
if req.constraint:
check_invalid_constraint_type(req)
requirement_set.add_requirement(req)
discovered_reqs: List[InstallRequirement] = []
hash_errors = HashErrors()
for req in chain(requirement_set.all_requirements, discovered_reqs):
try:
discovered_reqs.extend(self._resolve_one(requirement_set, req))
except HashError as exc:
exc.req = req
hash_errors.append(exc)
if hash_errors:
raise hash_errors
return requirement_set
def _is_upgrade_allowed(self, req: InstallRequirement) -> bool:
if self.upgrade_strategy == "to-satisfy-only":
return False
elif self.upgrade_strategy == "eager":
return True
else:
assert self.upgrade_strategy == "only-if-needed"
return req.user_supplied or req.constraint
def _set_req_to_reinstall(self, req: InstallRequirement) -> None:
# conflict is not a user install.
if not self.use_user_site or dist_in_usersite(req.satisfied_by):
req.should_reinstall = True
req.satisfied_by = None
def _check_skip_installed(
self, req_to_install: InstallRequirement
) -> Optional[str]:
if self.ignore_installed:
return None
req_to_install.check_if_exists(self.use_user_site)
if not req_to_install.satisfied_by:
return None
if self.force_reinstall:
self._set_req_to_reinstall(req_to_install)
return None
if not self._is_upgrade_allowed(req_to_install):
if self.upgrade_strategy == "only-if-needed":
return "already satisfied, skipping upgrade"
return "already satisfied"
# Check for the possibility of an upgrade. For link-based
# requirements we have to pull the tree down and inspect to assess
# the version #, so it's handled way down.
if not req_to_install.link:
try:
self.finder.find_requirement(req_to_install, upgrade=True)
except BestVersionAlreadyInstalled:
return "already up-to-date"
except DistributionNotFound:
pass
self._set_req_to_reinstall(req_to_install)
return None
def _find_requirement_link(self, req: InstallRequirement) -> Optional[Link]:
upgrade = self._is_upgrade_allowed(req)
best_candidate = self.finder.find_requirement(req, upgrade)
if not best_candidate:
return None
# Log a warning per PEP 592 if necessary before returning.
link = best_candidate.link
if link.is_yanked:
reason = link.yanked_reason or "<none given>"
msg = (
# Mark this as a unicode string to prevent
# "UnicodeEncodeError: 'ascii' codec can't encode character"
"The candidate selected for download or install is a "
"yanked version: {candidate}\n"
"Reason for being yanked: {reason}"
).format(candidate=best_candidate, reason=reason)
logger.warning(msg)
return link
def _populate_link(self, req: InstallRequirement) -> None:
if req.link is None:
req.link = self._find_requirement_link(req)
if self.wheel_cache is None or self.preparer.require_hashes:
return
cache_entry = self.wheel_cache.get_cache_entry(
link=req.link,
package_name=req.name,
supported_tags=get_supported(),
)
if cache_entry is not None:
logger.debug("Using cached wheel link: %s", cache_entry.link)
if req.link is req.original_link and cache_entry.persistent:
req.original_link_is_in_wheel_cache = True
req.link = cache_entry.link
def _get_dist_for(self, req: InstallRequirement) -> Distribution:
if req.editable:
return self.preparer.prepare_editable_requirement(req)
assert req.satisfied_by is None
skip_reason = self._check_skip_installed(req)
if req.satisfied_by:
return self.preparer.prepare_installed_requirement(req, skip_reason)
self._populate_link(req)
dist = self.preparer.prepare_linked_requirement(req)
# NOTE
# The following portion is for determining if a certain package is
# going to be re-installed/upgraded or not and reporting to the user.
# This should probably get cleaned up in a future refactor.
# req.req is only avail after unpack for URL
# pkgs repeat check_if_exists to uninstall-on-upgrade
# (#14)
if not self.ignore_installed:
req.check_if_exists(self.use_user_site)
if req.satisfied_by:
should_modify = (
self.upgrade_strategy != "to-satisfy-only"
or self.force_reinstall
or self.ignore_installed
or req.link.scheme == "file"
)
if should_modify:
self._set_req_to_reinstall(req)
else:
logger.info(
"Requirement already satisfied (use --upgrade to upgrade):" " %s",
req,
)
return dist
def _resolve_one(
self,
requirement_set: RequirementSet,
req_to_install: InstallRequirement,
) -> List[InstallRequirement]:
# Tell user what we are doing for this requirement:
# obtain (editable), skipping, processing (local url), collecting
# (remote url or package name)
if req_to_install.constraint or req_to_install.prepared:
return []
req_to_install.prepared = True
# Parse and return dependencies
dist = self._get_dist_for(req_to_install)
# This will raise UnsupportedPythonVersion if the given Python
# version isn't compatible with the distribution's Requires-Python.
_check_dist_requires_python(
dist,
version_info=self._py_version_info,
ignore_requires_python=self.ignore_requires_python,
)
more_reqs: List[InstallRequirement] = []
def add_req(subreq: Distribution, extras_requested: Iterable[str]) -> None:
sub_install_req = self._make_install_req(
str(subreq),
req_to_install,
)
parent_req_name = req_to_install.name
to_scan_again, add_to_parent = requirement_set.add_requirement(
sub_install_req,
parent_req_name=parent_req_name,
extras_requested=extras_requested,
)
if parent_req_name and add_to_parent:
self._discovered_dependencies[parent_req_name].append(add_to_parent)
more_reqs.extend(to_scan_again)
with indent_log():
# We add req_to_install before its dependencies, so that we
# can refer to it when adding dependencies.
if not requirement_set.has_requirement(req_to_install.name):
# 'unnamed' requirements will get added here
# 'unnamed' requirements can only come from being directly
# provided by the user.
assert req_to_install.user_supplied
requirement_set.add_requirement(req_to_install, parent_req_name=None)
if not self.ignore_dependencies:
if req_to_install.extras:
logger.debug(
"Installing extra requirements: %r",
",".join(req_to_install.extras),
)
missing_requested = sorted(
set(req_to_install.extras) - set(dist.extras)
)
for missing in missing_requested:
logger.warning("%s does not provide the extra '%s'", dist, missing)
available_requested = sorted(
set(dist.extras) & set(req_to_install.extras)
)
for subreq in dist.requires(available_requested):
add_req(subreq, extras_requested=available_requested)
return more_reqs
def get_installation_order(
self, req_set: RequirementSet
) -> List[InstallRequirement]:
# The current implementation, which we may change at any point
# installs the user specified things in the order given, except when
# dependencies must come earlier to achieve topological order.
order = []
ordered_reqs: Set[InstallRequirement] = set()
def schedule(req: InstallRequirement) -> None:
if req.satisfied_by or req in ordered_reqs:
return
if req.constraint:
return
ordered_reqs.add(req)
for dep in self._discovered_dependencies[req.name]:
schedule(dep)
order.append(req)
for install_req in req_set.requirements.values():
schedule(install_req)
return order
| true
| true
|
1c3edbaee88f4ef3b685314892ef219d9fb001ea
| 4,083
|
py
|
Python
|
dolo/tests/test_splines.py
|
christophe-gouel/dolo
|
d9aef6d78d19899e2669e49ee6b7ad9aacf0e35d
|
[
"BSD-2-Clause"
] | null | null | null |
dolo/tests/test_splines.py
|
christophe-gouel/dolo
|
d9aef6d78d19899e2669e49ee6b7ad9aacf0e35d
|
[
"BSD-2-Clause"
] | null | null | null |
dolo/tests/test_splines.py
|
christophe-gouel/dolo
|
d9aef6d78d19899e2669e49ee6b7ad9aacf0e35d
|
[
"BSD-2-Clause"
] | null | null | null |
#
# if __name__ == '__main__':
#
# from dolo.numeric.interpolation.splines import MultivariateSplines
# from dolo.numeric.interpolation.multilinear import MultilinearInterpolator
#
#
# import numpy as np
#
# N_grid = 20
# N_fine_grid = 50
# d = 3
#
# smin = np.array( [0.0]*d )
# smax = np.array( [1.0]*d )
# orders = [N_grid]*d
# orders = orders[:d]
# from itertools import product
# grid = np.row_stack( product( *[np.linspace(smin[i],smax[i], orders[i]) for i in range(d)] )).T
# points = np.row_stack( product( *[np.linspace(smin[i]-0.1,smax[i]+0.1, N_fine_grid) for i in range(d)] )).T
#
#
# if d == 1:
# f = lambda x: np.row_stack( [np.cos(x)] )
# # f = lambda x: np.row_stack( [np.power(0.1+x,1.0/4.0)] )
# #f = lambda x: np.row_stack( [x*x] )
# v = f(grid[0,:])
# elif d == 2:
# # f = lambda x,y: np.row_stack( [np.sin(x)*y, np.sin(y)*x] )
# f = lambda x,y: np.row_stack( [2*x + 3*y] )
# v = f(grid[0,:], grid[1,:])
# elif d == 3:
# f = lambda x,y,z: np.row_stack( [np.sin(x)*y/np.sqrt(z)] )
# f = lambda x,y,z: np.row_stack( [2*x + 3*y + 4*z] )
# v = f(grid[0,:], grid[1,:], grid[2,:])
#
# elif d == 4:
# f = lambda x,y,z,t: np.row_stack( [np.sin(x)*y/np.sqrt(z)*t] )
# v = f(grid[0,:], grid[1,:], grid[2,:], grid[3,:])
# #
# points = np.row_stack( product( *[np.linspace(smin[i],smax[i], N_fine_grid) for i in range(d)] )).T
#
# points = np.ascontiguousarray(points) # TODO : this should be checked in the interpolators
#
# mvs = MultivariateSplines(smin, smax, orders)
# mvs.set_values( f( *[s for i,s in enumerate(mvs.grid) if i <= d] ) )
#
# mls = MultilinearInterpolator(smin, smax, orders)
# mls.set_values( f( *[s for i,s in enumerate(mls.grid) if i <= d] ) )
#
# # print(mls.grid - mvs.grid)
# # exit()
#
# import time
# t = time.time()
# for i in range(10):
# out = mvs(points)
# s = time.time()
# print('Splines : {}'.format(s-t))
#
#
# import time
# t = time.time()
# for i in range(10):
# out2 = mls(points)
# s = time.time()
# print('Linear : {}'.format(s-t))
#
#
# print(out2)
#
# print( abs(out2 - out).max() )# print(mls.grid - mvs.grid)
# # exit()
#
# mvs(points)
# #
# # import time
# # t = time.time()
# # for i in range(10):
# # #out = mvs(points)
# # [out2, dout2] = spline_c(smin,smax,orders,mvs2.values, points, derivatives=True)
# # s = time.time()
# # print('Splines (diff) : {}'.format(s-t))
# #
# # print(dout2[0,:])
# #
# # import time
# # t = time.time()
# # for i in range(10):
# # v_sg = sg(np.atleast_2d( points) )[0,:]
# # v_sg = sg(np.atleast_2d( points) )[0,:]
# # s = time.time()
# # print('Chebychev : {}'.format(s-t))
# # print( abs(out-true_vals).mean(axis=1) )
# # print( abs(v_sg-true_vals).mean(axis=1) )
# # print( abs(out-true_vals).max(axis=1) )
# # print( abs(v_sg-true_vals).max(axis=1) )
# #
# #
# #
# # out = spline_c(smin,smax,orders,v, points)
# # out_e = mvs(points)
# # print(out)
# #
# # from matplotlib import pyplot
# # points = points.flatten()
# #
# #
# # pyplot.plot(points, out_e.flatten(), label='splines (with extrap)')
# #
# # pyplot.plot(points, out.flatten(), label='splines')
# #
# # pyplot.plot(points, f(points)[0,:], label='true')
# # pyplot.plot(grid[0,:],v.flatten(),'o',label='data')
# # pyplot.plot(points, v_sg,label='cheb')
# # pyplot.legend()
# #
# # pyplot.figure()
# # pyplot.plot(points, (out-f(points))[0,:], label='splines')
# #
# # from dolo.numeric.interpolation.smolyak import SmolyakGrid
# #
# # print(grid.shape)
# # sg = SmolyakGrid( np.array([0.0]), [1.0], 4 )
# # sg.set_values( f(sg.grid) )
# # v_sg = sg(np.atleast_2d( points) )[0,:]
# # pyplot.plot(points, v_sg-f(points)[0,:],label='cheb')
# # pyplot.legend()
# # pyplot.show()
# #
# # exit()
# ## print(points)
# #
| 30.470149
| 113
| 0.532697
| true
| true
|
|
1c3edc1a9535df18cdc85a59a9b87070c0014d91
| 3,535
|
py
|
Python
|
scripts/mixexpDemo.py
|
karalleyna/pyprobml
|
72195e46fdffc4418910e76d02e3d6469f4ce272
|
[
"MIT"
] | 2
|
2021-06-22T05:43:25.000Z
|
2021-06-22T08:40:16.000Z
|
scripts/mixexpDemo.py
|
Rebeca98/pyprobml
|
2a4b9a267f64720cbba35dfa41af3e995ea006ca
|
[
"MIT"
] | null | null | null |
scripts/mixexpDemo.py
|
Rebeca98/pyprobml
|
2a4b9a267f64720cbba35dfa41af3e995ea006ca
|
[
"MIT"
] | 1
|
2021-06-21T01:18:07.000Z
|
2021-06-21T01:18:07.000Z
|
import pyprobml_utils as pml
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.special import logsumexp
from sklearn.linear_model import LinearRegression
from scipy.stats import multivariate_normal
n = 200
np.random.seed(1)
y = np.random.rand(n, 1)
eta = np.random.randn(n,1)*0.05
x = y + 0.3*np.sin(2*3.1415*y) + eta
data = np.concatenate((x, y), axis=1)
K = 3
X = x.reshape(-1, 1)
y = y.reshape(-1, 1)
xtest = (x)
ytest = (y)
plt.figure()
plt.scatter(x, y, edgecolors='blue', color="none")
plt.title('Inverse problem')
plt.savefig('Inverse_problem')
plt.show()
def normalizelogspace(x):
L = logsumexp(x, axis=1).reshape(-1, 1)
Lnew = np.repeat(L, 3, axis=1)
y = x - Lnew
return y, Lnew
def is_pos_def(x):
return np.all(np.linalg.eigvals(x) > 0)
K = 3 #nmix
D = np.size(X, axis=1)
N = np.size(X, axis=0)
norm = 50
max_iter = 39
iteration = 0
r = np.zeros((N, K))
while iteration < max_iter:
#E-step :
np.random.seed(iteration)
Wy = 0.1*np.random.randn(D, K)
bias = 0.3*np.random.randn(D, K)
mixweights = np.random.rand(1, K)
normmw = np.linalg.norm(mixweights)
mixweights = mixweights/normmw
sigma2 = 0.1*np.random.randn(1, K)
q = np.log(mixweights)
logprior = np.repeat(q, N, axis=0)
loglik = np.zeros((N, K))
for k in range(K):
vecM = X*Wy[:, k] + bias[:, k]
vecM = vecM.reshape(200, )
cov = sigma2[0, k]
cov = np.abs(cov)
vecX = y
x = multivariate_normal.logpdf(vecX, mean=vecM, cov=cov)
x = x /norm
loglik[:, k] = x
logpost = loglik + logprior
logpost, logZ = normalizelogspace(logpost)
ll = np.sum(logZ)
post = np.exp(logpost)
#M-step:
r = post
mixweights = np.sum(r, axis=0)/N
mixweights = mixweights.reshape(1, -1)
for k in range(K):
reg = LinearRegression()
model = reg.fit(X, y, r[:, k])
Wy[:, k] = model.coef_
bias[:, k] = model.intercept_
yhat_ = np.multiply(X, Wy[:, k]) + bias[:, k]
sigma2[:, k] = np.sum(np.multiply(r[:, k], np.square(y-yhat_))) / sum(r[:, k])
iteration = iteration + 1
N = np.size(X, axis=0)
D = np.size(X, axis=1)
K = 3
weights = np.repeat(mixweights, N, axis=0)
muk = np.zeros((N, K))
vk = np.zeros((N, K))
mu = np.zeros((N, ))
v = np.zeros((N, 1))
b = 0.3*np.random.randn(D, K)
for k in range(K):
w = X*Wy[:, k] + bias[:, k]
w = w.reshape(-1, )
muk[:, k] = w
q = np.multiply(weights[:, k], muk[:, k])
mu = mu + q
vk[:, k] = sigma2[:, k]
v = v + np.multiply(weights[:, k], (vk[:, k] + np.square(muk[:, k]))).reshape(-1, 1)
v = v - np.square(mu).reshape(-1, 1)
plt.figure()
plt.scatter(xtest, y, edgecolors='blue', color="none")
plt.plot(xtest, muk[:, 0])
plt.plot(xtest, muk[:, 1])
plt.plot(xtest, muk[:, 2])
plt.title('Expert-predictions')
pml.save_fig('mixexp_expert_predictions.pdf')
plt.show()
plt.figure()
for i in range(K):
plt.scatter(y, post[:, i])
plt.title('Gating functions')
pml.save_fig('mixexp_gating_functions.pdf')
plt.show()
map = np.empty((K, 1))
map = np.argmax(post, axis=1)
map = map.reshape(-1, 1)
yhat = np.empty((N, 1))
for i in range(N):
yhat[i, 0] = muk[i, map[i, 0]]
plt.figure()
plt.scatter(xtest, yhat, marker=6, color='black')
plt.scatter(xtest, mu, marker='X', color='red')
plt.scatter(xtest, y, edgecolors='blue', color="none")
plt.title('prediction')
plt.legend(['mode', 'mean'])
pml.save_fig('mixexp_predictions.pdf')
plt.show()
| 25.80292
| 88
| 0.596322
|
import pyprobml_utils as pml
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.special import logsumexp
from sklearn.linear_model import LinearRegression
from scipy.stats import multivariate_normal
n = 200
np.random.seed(1)
y = np.random.rand(n, 1)
eta = np.random.randn(n,1)*0.05
x = y + 0.3*np.sin(2*3.1415*y) + eta
data = np.concatenate((x, y), axis=1)
K = 3
X = x.reshape(-1, 1)
y = y.reshape(-1, 1)
xtest = (x)
ytest = (y)
plt.figure()
plt.scatter(x, y, edgecolors='blue', color="none")
plt.title('Inverse problem')
plt.savefig('Inverse_problem')
plt.show()
def normalizelogspace(x):
L = logsumexp(x, axis=1).reshape(-1, 1)
Lnew = np.repeat(L, 3, axis=1)
y = x - Lnew
return y, Lnew
def is_pos_def(x):
return np.all(np.linalg.eigvals(x) > 0)
K = 3
D = np.size(X, axis=1)
N = np.size(X, axis=0)
norm = 50
max_iter = 39
iteration = 0
r = np.zeros((N, K))
while iteration < max_iter:
np.random.seed(iteration)
Wy = 0.1*np.random.randn(D, K)
bias = 0.3*np.random.randn(D, K)
mixweights = np.random.rand(1, K)
normmw = np.linalg.norm(mixweights)
mixweights = mixweights/normmw
sigma2 = 0.1*np.random.randn(1, K)
q = np.log(mixweights)
logprior = np.repeat(q, N, axis=0)
loglik = np.zeros((N, K))
for k in range(K):
vecM = X*Wy[:, k] + bias[:, k]
vecM = vecM.reshape(200, )
cov = sigma2[0, k]
cov = np.abs(cov)
vecX = y
x = multivariate_normal.logpdf(vecX, mean=vecM, cov=cov)
x = x /norm
loglik[:, k] = x
logpost = loglik + logprior
logpost, logZ = normalizelogspace(logpost)
ll = np.sum(logZ)
post = np.exp(logpost)
r = post
mixweights = np.sum(r, axis=0)/N
mixweights = mixweights.reshape(1, -1)
for k in range(K):
reg = LinearRegression()
model = reg.fit(X, y, r[:, k])
Wy[:, k] = model.coef_
bias[:, k] = model.intercept_
yhat_ = np.multiply(X, Wy[:, k]) + bias[:, k]
sigma2[:, k] = np.sum(np.multiply(r[:, k], np.square(y-yhat_))) / sum(r[:, k])
iteration = iteration + 1
N = np.size(X, axis=0)
D = np.size(X, axis=1)
K = 3
weights = np.repeat(mixweights, N, axis=0)
muk = np.zeros((N, K))
vk = np.zeros((N, K))
mu = np.zeros((N, ))
v = np.zeros((N, 1))
b = 0.3*np.random.randn(D, K)
for k in range(K):
w = X*Wy[:, k] + bias[:, k]
w = w.reshape(-1, )
muk[:, k] = w
q = np.multiply(weights[:, k], muk[:, k])
mu = mu + q
vk[:, k] = sigma2[:, k]
v = v + np.multiply(weights[:, k], (vk[:, k] + np.square(muk[:, k]))).reshape(-1, 1)
v = v - np.square(mu).reshape(-1, 1)
plt.figure()
plt.scatter(xtest, y, edgecolors='blue', color="none")
plt.plot(xtest, muk[:, 0])
plt.plot(xtest, muk[:, 1])
plt.plot(xtest, muk[:, 2])
plt.title('Expert-predictions')
pml.save_fig('mixexp_expert_predictions.pdf')
plt.show()
plt.figure()
for i in range(K):
plt.scatter(y, post[:, i])
plt.title('Gating functions')
pml.save_fig('mixexp_gating_functions.pdf')
plt.show()
map = np.empty((K, 1))
map = np.argmax(post, axis=1)
map = map.reshape(-1, 1)
yhat = np.empty((N, 1))
for i in range(N):
yhat[i, 0] = muk[i, map[i, 0]]
plt.figure()
plt.scatter(xtest, yhat, marker=6, color='black')
plt.scatter(xtest, mu, marker='X', color='red')
plt.scatter(xtest, y, edgecolors='blue', color="none")
plt.title('prediction')
plt.legend(['mode', 'mean'])
pml.save_fig('mixexp_predictions.pdf')
plt.show()
| true
| true
|
1c3edd0047b74206ceda57dd243e48dfb9db1917
| 2,637
|
py
|
Python
|
fairseq/optim/lr_scheduler/fixed_schedule.py
|
kayoyin/DialogueMT
|
aa426ebcdbdfe0366ed06081a842945f2108e85f
|
[
"MIT"
] | 112
|
2021-01-04T13:19:24.000Z
|
2022-03-23T21:49:00.000Z
|
fairseq/optim/lr_scheduler/fixed_schedule.py
|
kayoyin/DialogueMT
|
aa426ebcdbdfe0366ed06081a842945f2108e85f
|
[
"MIT"
] | 21
|
2021-03-18T09:39:00.000Z
|
2022-03-22T09:41:48.000Z
|
fairseq/optim/lr_scheduler/fixed_schedule.py
|
kayoyin/DialogueMT
|
aa426ebcdbdfe0366ed06081a842945f2108e85f
|
[
"MIT"
] | 19
|
2021-01-28T08:07:03.000Z
|
2022-03-31T08:31:36.000Z
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('fixed')
class FixedSchedule(FairseqLRScheduler):
"""Decay the LR on a fixed schedule."""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
# set defaults
args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0
self.lr = args.lr[0]
if args.warmup_updates > 0:
self.warmup_factor = 1. / args.warmup_updates
else:
self.warmup_factor = 1
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--force-anneal', '--fa', type=int, metavar='N',
help='force annealing at specified epoch')
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing, lr_new = (lr * lr_shrink)')
parser.add_argument('--warmup-updates', default=0, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
# fmt: on
def state_dict(self):
return {'lr': self.lr}
def load_state_dict(self, state_dict):
if 'lr' in state_dict:
self.lr = state_dict['lr']
def get_next_lr(self, epoch):
lrs = self.args.lr
if self.args.force_anneal is None or epoch < self.args.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(epoch, len(lrs) - 1)]
else:
# annneal based on lr_shrink
next_lr = lrs[-1] * self.args.lr_shrink ** (epoch + 1 - self.args.force_anneal)
return next_lr
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if self.args.warmup_updates > 0 and num_updates < self.args.warmup_updates:
self.warmup_factor = (num_updates + 1) / float(self.args.warmup_updates)
self.optimizer.set_lr(self.warmup_factor * self.lr)
else:
self.optimizer.set_lr(self.lr)
return self.optimizer.get_lr()
| 38.217391
| 93
| 0.618127
|
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('fixed')
class FixedSchedule(FairseqLRScheduler):
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0
self.lr = args.lr[0]
if args.warmup_updates > 0:
self.warmup_factor = 1. / args.warmup_updates
else:
self.warmup_factor = 1
@staticmethod
def add_args(parser):
parser.add_argument('--force-anneal', '--fa', type=int, metavar='N',
help='force annealing at specified epoch')
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing, lr_new = (lr * lr_shrink)')
parser.add_argument('--warmup-updates', default=0, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
def state_dict(self):
return {'lr': self.lr}
def load_state_dict(self, state_dict):
if 'lr' in state_dict:
self.lr = state_dict['lr']
def get_next_lr(self, epoch):
lrs = self.args.lr
if self.args.force_anneal is None or epoch < self.args.force_anneal:
next_lr = lrs[min(epoch, len(lrs) - 1)]
else:
next_lr = lrs[-1] * self.args.lr_shrink ** (epoch + 1 - self.args.force_anneal)
return next_lr
def step(self, epoch, val_loss=None):
super().step(epoch, val_loss)
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
if self.args.warmup_updates > 0 and num_updates < self.args.warmup_updates:
self.warmup_factor = (num_updates + 1) / float(self.args.warmup_updates)
self.optimizer.set_lr(self.warmup_factor * self.lr)
else:
self.optimizer.set_lr(self.lr)
return self.optimizer.get_lr()
| true
| true
|
1c3edd7dc690e18f8c678b526484905b94fc01d9
| 1,144
|
py
|
Python
|
py/leetcode/RedundantConnection.py
|
danyfang/SourceCode
|
8168f6058648f2a330a7354daf3a73a4d8a4e730
|
[
"MIT"
] | null | null | null |
py/leetcode/RedundantConnection.py
|
danyfang/SourceCode
|
8168f6058648f2a330a7354daf3a73a4d8a4e730
|
[
"MIT"
] | null | null | null |
py/leetcode/RedundantConnection.py
|
danyfang/SourceCode
|
8168f6058648f2a330a7354daf3a73a4d8a4e730
|
[
"MIT"
] | null | null | null |
'''
Leetcode problem No 684 Redundant Connection
Solution written by Xuqiang Fang on 12 June, 2018
'''
class Solution(object):
'''
union find
'''
def findRedundantConnection(self, edge):
n = len(edge)
p = [0 for x in range(n+1)]
s = [1 for x in range(n+1)]
for e in edge:
u = e[0]; v = e[1]
if p[u] == 0:
p[u] = u
if p[v] == 0:
p[v] = v
pu = self.find(u, p)
pv = self.find(v, p)
if pu == pv:
return e
if s[pv] > s[pu]:
t = pu
pu = pv
pv = t
p[pv] = pu
s[pu] += s[pv]
return [None, None]
def find(self, child, parent):
while(child != parent[child]):
parent[child] = parent[parent[child]]
child = parent[child]
return child
def main():
s = Solution()
edge = [[1,2],[1,3],[2,3]]
edg = [[1,2],[2,3],[3,4],[1,4],[1,5]]
print(s.findRedundantConnection(edge))
print(s.findRedundantConnection(edg))
main()
| 24.869565
| 49
| 0.434441
|
class Solution(object):
def findRedundantConnection(self, edge):
n = len(edge)
p = [0 for x in range(n+1)]
s = [1 for x in range(n+1)]
for e in edge:
u = e[0]; v = e[1]
if p[u] == 0:
p[u] = u
if p[v] == 0:
p[v] = v
pu = self.find(u, p)
pv = self.find(v, p)
if pu == pv:
return e
if s[pv] > s[pu]:
t = pu
pu = pv
pv = t
p[pv] = pu
s[pu] += s[pv]
return [None, None]
def find(self, child, parent):
while(child != parent[child]):
parent[child] = parent[parent[child]]
child = parent[child]
return child
def main():
s = Solution()
edge = [[1,2],[1,3],[2,3]]
edg = [[1,2],[2,3],[3,4],[1,4],[1,5]]
print(s.findRedundantConnection(edge))
print(s.findRedundantConnection(edg))
main()
| true
| true
|
1c3edeaa50330434fd48e38aa8cbe9af373be385
| 35
|
py
|
Python
|
OOP/OOP-Practice/underscore/main.py
|
siddhantdixit/OOP-ClassWork
|
ce414a3836d03aa7dee0eb1d7a69e849fb6707c0
|
[
"MIT"
] | null | null | null |
OOP/OOP-Practice/underscore/main.py
|
siddhantdixit/OOP-ClassWork
|
ce414a3836d03aa7dee0eb1d7a69e849fb6707c0
|
[
"MIT"
] | null | null | null |
OOP/OOP-Practice/underscore/main.py
|
siddhantdixit/OOP-ClassWork
|
ce414a3836d03aa7dee0eb1d7a69e849fb6707c0
|
[
"MIT"
] | null | null | null |
from prob import *
Pref()._myfunc()
| 17.5
| 18
| 0.714286
|
from prob import *
Pref()._myfunc()
| true
| true
|
1c3edfd0a7ee08c38a2c867ee1df04078c23142d
| 448
|
py
|
Python
|
data/scripts/templates/object/mobile/shared_nightspider_aggressor.py
|
obi-two/GameServer
|
7d37024e2291a97d49522610cd8f1dbe5666afc2
|
[
"MIT"
] | 20
|
2015-02-23T15:11:56.000Z
|
2022-03-18T20:56:48.000Z
|
data/scripts/templates/object/mobile/shared_nightspider_aggressor.py
|
apathyboy/swganh
|
665128efe9154611dec4cb5efc61d246dd095984
|
[
"MIT"
] | null | null | null |
data/scripts/templates/object/mobile/shared_nightspider_aggressor.py
|
apathyboy/swganh
|
665128efe9154611dec4cb5efc61d246dd095984
|
[
"MIT"
] | 20
|
2015-04-04T16:35:59.000Z
|
2022-03-24T14:54:37.000Z
|
#### NOTICE: THIS FILE IS AUTOGENERATED
#### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY
#### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES
from swgpy.object import *
def create(kernel):
result = Creature()
result.template = "object/mobile/shared_nightspider_aggressor.iff"
result.attribute_template_id = 9
result.stfName("monster_name","hermit_spider")
#### BEGIN MODIFICATIONS ####
#### END MODIFICATIONS ####
return result
| 26.352941
| 67
| 0.732143
| true
| true
|
|
1c3ee1adcd0fc78c7b46e0181a9583404d382497
| 570
|
py
|
Python
|
analytics/migrations/0006_auto_20201019_1554.py
|
12remember/qrl-analytics
|
17d728225026354a9b2af3bd81cf11cef06279df
|
[
"MIT"
] | null | null | null |
analytics/migrations/0006_auto_20201019_1554.py
|
12remember/qrl-analytics
|
17d728225026354a9b2af3bd81cf11cef06279df
|
[
"MIT"
] | 1
|
2022-03-03T21:55:24.000Z
|
2022-03-03T21:55:24.000Z
|
analytics/migrations/0006_auto_20201019_1554.py
|
12remember/quantascan-backend
|
17d728225026354a9b2af3bd81cf11cef06279df
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.1.1 on 2020-10-19 15:54
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('analytics', '0005_auto_20201019_1549'),
]
operations = [
migrations.RenameField(
model_name='qrlaggregatedtransactiondata',
old_name='type_of_transaction',
new_name='transaction_type',
),
migrations.AlterUniqueTogether(
name='qrlaggregatedtransactiondata',
unique_together={('date', 'transaction_type')},
),
]
| 24.782609
| 59
| 0.622807
|
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('analytics', '0005_auto_20201019_1549'),
]
operations = [
migrations.RenameField(
model_name='qrlaggregatedtransactiondata',
old_name='type_of_transaction',
new_name='transaction_type',
),
migrations.AlterUniqueTogether(
name='qrlaggregatedtransactiondata',
unique_together={('date', 'transaction_type')},
),
]
| true
| true
|
1c3ee1dfba8b2f8904b4d92a37b045666bf735f3
| 1,175
|
py
|
Python
|
src/servo.py
|
macbury/pussificator
|
1a8a3e0d029ec6f22161dfc41349f82b5df55a9f
|
[
"MIT"
] | 3
|
2019-01-09T09:54:25.000Z
|
2019-06-30T07:10:01.000Z
|
src/servo.py
|
macbury/pussificator
|
1a8a3e0d029ec6f22161dfc41349f82b5df55a9f
|
[
"MIT"
] | null | null | null |
src/servo.py
|
macbury/pussificator
|
1a8a3e0d029ec6f22161dfc41349f82b5df55a9f
|
[
"MIT"
] | null | null | null |
import time
import json
from boot import CONFIG, LOGGER
from mqtt import MqttClient
import RPi.GPIO as GPIO
mqtt = MqttClient(CONFIG['mqtt'])
SERVO_PIN = 12
GPIO.setmode(GPIO.BOARD)
GPIO.setup(SERVO_PIN, GPIO.OUT)
pwm = GPIO.PWM(SERVO_PIN, 50)
pwm.start(0)
def turnOff():
GPIO.output(SERVO_PIN, False)
pwm.ChangeDutyCycle(0)
def setAngle(angle):
LOGGER.info("Set angle to: {}".format(angle))
duty = angle / 18 + 2
GPIO.output(SERVO_PIN, True)
pwm.ChangeDutyCycle(duty)
time.sleep(1)
def feed():
LOGGER.info("Moving fan to fill state...")
setAngle(0)
time.sleep(1)
LOGGER.info("Moving fan to extract state...")
setAngle(180)
time.sleep(1)
def neutral():
LOGGER.info("Moving fan back to neutral state...")
setAngle(90)
time.sleep(1)
LOGGER.info("Feed Done...")
def onCommandEvent(topic, body):
LOGGER.info("Received: {}".format(body))
feed()
neutral()
turnOff()
def onConnect(mqtt):
LOGGER.info("Connected!")
mqtt.onConnect = onConnect
mqtt.subscribe(CONFIG['servo']['command_topic'], onCommandEvent)
mqtt.start()
try:
LOGGER.info("Started!")
neutral()
while True:
time.sleep(1)
except KeyboardInterrupt:
pass
| 19.262295
| 64
| 0.700426
|
import time
import json
from boot import CONFIG, LOGGER
from mqtt import MqttClient
import RPi.GPIO as GPIO
mqtt = MqttClient(CONFIG['mqtt'])
SERVO_PIN = 12
GPIO.setmode(GPIO.BOARD)
GPIO.setup(SERVO_PIN, GPIO.OUT)
pwm = GPIO.PWM(SERVO_PIN, 50)
pwm.start(0)
def turnOff():
GPIO.output(SERVO_PIN, False)
pwm.ChangeDutyCycle(0)
def setAngle(angle):
LOGGER.info("Set angle to: {}".format(angle))
duty = angle / 18 + 2
GPIO.output(SERVO_PIN, True)
pwm.ChangeDutyCycle(duty)
time.sleep(1)
def feed():
LOGGER.info("Moving fan to fill state...")
setAngle(0)
time.sleep(1)
LOGGER.info("Moving fan to extract state...")
setAngle(180)
time.sleep(1)
def neutral():
LOGGER.info("Moving fan back to neutral state...")
setAngle(90)
time.sleep(1)
LOGGER.info("Feed Done...")
def onCommandEvent(topic, body):
LOGGER.info("Received: {}".format(body))
feed()
neutral()
turnOff()
def onConnect(mqtt):
LOGGER.info("Connected!")
mqtt.onConnect = onConnect
mqtt.subscribe(CONFIG['servo']['command_topic'], onCommandEvent)
mqtt.start()
try:
LOGGER.info("Started!")
neutral()
while True:
time.sleep(1)
except KeyboardInterrupt:
pass
| true
| true
|
1c3ee281afe8d6eb06955116a1e1fc292afe6064
| 5,186
|
py
|
Python
|
backend/main.py
|
waltercassiano/micropython-spa-react
|
110fcd88c170251adad2b7e2e14dbdad94657370
|
[
"MIT"
] | 6
|
2020-05-29T01:39:21.000Z
|
2022-03-08T02:29:29.000Z
|
backend/main.py
|
waltercassiano/micropython-spa-react
|
110fcd88c170251adad2b7e2e14dbdad94657370
|
[
"MIT"
] | 2
|
2022-02-13T11:59:34.000Z
|
2022-02-27T03:49:49.000Z
|
backend/main.py
|
waltercassiano/micropython-spa-react
|
110fcd88c170251adad2b7e2e14dbdad94657370
|
[
"MIT"
] | null | null | null |
import network
import ure as re
import ubinascii
import uhashlib
from mimes import mime_content_type
import utils
if __name__ == "__main__":
picoweb = utils.import_or_install("picoweb", "picoweb")
logging = utils.import_or_install("micropython-ulogging", "ulogging")
from app_esp import config_service, user_service
logging.basicConfig(level=logging.DEBUG)
CORS_ENABLED = True
user_service.add_user("admin", "123456")
# Networks WIFI and acess point
sta_if = network.WLAN(network.STA_IF)
ap = network.WLAN(network.AP_IF)
##
_client_id = "897DD9C9-F2CDB-CB4226245EC958AF"
_client_secret = "BBC344BA-28463-6F838E84419B6B81"
# WEBAPP IP
webapp_ip = ""
# Access point Setup
def getIpEspServerEsp(ifconfig):
ifconfiglist = list(ifconfig)
return str(ifconfiglist[0])
def getWifi(wifiListDetected):
return list(map(lambda wifi: utils.decode(wifi[0]), wifiListDetected))
app = picoweb.WebApp(__name__, routes=None, serve_static=None)
def generate_access_token(data_to_token):
return utils.decode(ubinascii.hexlify((uhashlib.sha1(data_to_token).digest())))
def require_access_token(func):
def access_token_validade(req, resp):
db_access_token = config_service.get_active_user("access_token")
header_access_token = req.headers.get(b"access_token")
if not db_access_token or not header_access_token:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "invalid access token1"}')
return
db_access_token = db_access_token.get("access_token")
if header_access_token.decode() != db_access_token:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "invalid access token2"}')
return
yield from func(req, resp)
return access_token_validade
def require_auth(func):
def auth(req, resp):
auth = req.headers.get(b"Authorization")
if not auth:
yield from resp.awrite('{ "msg" : "wrong user or password"}')
yield from start_response(req, resp, status="401")
return
auth = ubinascii.a2b_base64(auth).decode()
client_id, client_secrect = auth.split(":", 1)
username = req.headers.get(b"username")
pwd = req.headers.get(b"pwd")
if not username or not pwd:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "wrong user or password"}')
return
username = username.decode('utf-8')
pwd = pwd.decode('utf-8')
if client_id != _client_id or client_secrect != _client_secret:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "wrong user or password"}')
return
if not user_service.auth_user(username, pwd):
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "wrong user or password"}')
return
access_token = generate_access_token(username + ":" + pwd)
config_service.save_active_user("access_token", access_token)
yield from func(req, resp)
yield from resp.awrite('{"access_token": "' + access_token + '"}')
return auth
def cors(func):
def _cors(req, resp):
if CORS_ENABLED is not True:
yield from func(req, resp)
header = b"Access-Control-Allow-Origin: *\r\n"
header += b"Access-Control-Allow-Method: POST, DELETE, PUT, GET\r\n"
header += b"Access-Control-Allow-Headers: *\r\n"
if req and req.method == "OPTIONS":
yield from picoweb.start_response(resp, content_type="text/html; charset=utf-8", status="200", headers=header)
return
yield from func(req, resp)
return _cors
def start_response(req, resp, **kwargs):
if CORS_ENABLED is not True:
yield from picoweb.start_response(resp, **kwargs)
return
if not kwargs.get("headers"):
kwargs["headers"] = b"Access-Control-Allow-Origin: *\r\n"
yield from picoweb.start_response(resp, **kwargs)
@app.route(re.compile("/api/access-token"))
@cors
@require_auth
def handle_api_request(req, resp):
yield from start_response(req, resp)
@app.route("/api/config/wifi")
@cors
@require_access_token
def route_api(req, resp):
yield from start_response(req, resp)
@app.route(re.compile('\/(.+\.js|.+\.css|.+\.png|.+\.jpeg|.+\.svg)$'))
def static_files_gzip(req, resp):
file_path = "build/" + req.url_match.group(1)
file_mime_type = mime_content_type(file_path)
headers = b"Cache-Control: max-age=100\r\n"
if b"gzip" in req.headers.get(b"Accept-Encoding"):
file_path += ".gz"
headers += b"Content-Encoding: gzip\r\n"
yield from app.sendfile(resp, file_path, file_mime_type, headers)
@app.route("/")
def frontend(req, resp):
yield from app.sendfile(resp, "/build/index.html.gz", "text/html", b"Content-Encoding: gzip\r\n")
if sta_if.isconnected() is True:
webapp_ip = getIpEspServerEsp(sta_if.ifconfig())
if __name__ == "__main__":
app.run(host=webapp_ip, port=3000, debug=1)
| 32.822785
| 122
| 0.66371
|
import network
import ure as re
import ubinascii
import uhashlib
from mimes import mime_content_type
import utils
if __name__ == "__main__":
picoweb = utils.import_or_install("picoweb", "picoweb")
logging = utils.import_or_install("micropython-ulogging", "ulogging")
from app_esp import config_service, user_service
logging.basicConfig(level=logging.DEBUG)
CORS_ENABLED = True
user_service.add_user("admin", "123456")
sta_if = network.WLAN(network.STA_IF)
ap = network.WLAN(network.AP_IF)
_client_id = "897DD9C9-F2CDB-CB4226245EC958AF"
_client_secret = "BBC344BA-28463-6F838E84419B6B81"
webapp_ip = ""
def getIpEspServerEsp(ifconfig):
ifconfiglist = list(ifconfig)
return str(ifconfiglist[0])
def getWifi(wifiListDetected):
return list(map(lambda wifi: utils.decode(wifi[0]), wifiListDetected))
app = picoweb.WebApp(__name__, routes=None, serve_static=None)
def generate_access_token(data_to_token):
return utils.decode(ubinascii.hexlify((uhashlib.sha1(data_to_token).digest())))
def require_access_token(func):
def access_token_validade(req, resp):
db_access_token = config_service.get_active_user("access_token")
header_access_token = req.headers.get(b"access_token")
if not db_access_token or not header_access_token:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "invalid access token1"}')
return
db_access_token = db_access_token.get("access_token")
if header_access_token.decode() != db_access_token:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "invalid access token2"}')
return
yield from func(req, resp)
return access_token_validade
def require_auth(func):
def auth(req, resp):
auth = req.headers.get(b"Authorization")
if not auth:
yield from resp.awrite('{ "msg" : "wrong user or password"}')
yield from start_response(req, resp, status="401")
return
auth = ubinascii.a2b_base64(auth).decode()
client_id, client_secrect = auth.split(":", 1)
username = req.headers.get(b"username")
pwd = req.headers.get(b"pwd")
if not username or not pwd:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "wrong user or password"}')
return
username = username.decode('utf-8')
pwd = pwd.decode('utf-8')
if client_id != _client_id or client_secrect != _client_secret:
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "wrong user or password"}')
return
if not user_service.auth_user(username, pwd):
yield from start_response(req, resp, status="401")
yield from resp.awrite('{ "msg" : "wrong user or password"}')
return
access_token = generate_access_token(username + ":" + pwd)
config_service.save_active_user("access_token", access_token)
yield from func(req, resp)
yield from resp.awrite('{"access_token": "' + access_token + '"}')
return auth
def cors(func):
def _cors(req, resp):
if CORS_ENABLED is not True:
yield from func(req, resp)
header = b"Access-Control-Allow-Origin: *\r\n"
header += b"Access-Control-Allow-Method: POST, DELETE, PUT, GET\r\n"
header += b"Access-Control-Allow-Headers: *\r\n"
if req and req.method == "OPTIONS":
yield from picoweb.start_response(resp, content_type="text/html; charset=utf-8", status="200", headers=header)
return
yield from func(req, resp)
return _cors
def start_response(req, resp, **kwargs):
if CORS_ENABLED is not True:
yield from picoweb.start_response(resp, **kwargs)
return
if not kwargs.get("headers"):
kwargs["headers"] = b"Access-Control-Allow-Origin: *\r\n"
yield from picoweb.start_response(resp, **kwargs)
@app.route(re.compile("/api/access-token"))
@cors
@require_auth
def handle_api_request(req, resp):
yield from start_response(req, resp)
@app.route("/api/config/wifi")
@cors
@require_access_token
def route_api(req, resp):
yield from start_response(req, resp)
@app.route(re.compile('\/(.+\.js|.+\.css|.+\.png|.+\.jpeg|.+\.svg)$'))
def static_files_gzip(req, resp):
file_path = "build/" + req.url_match.group(1)
file_mime_type = mime_content_type(file_path)
headers = b"Cache-Control: max-age=100\r\n"
if b"gzip" in req.headers.get(b"Accept-Encoding"):
file_path += ".gz"
headers += b"Content-Encoding: gzip\r\n"
yield from app.sendfile(resp, file_path, file_mime_type, headers)
@app.route("/")
def frontend(req, resp):
yield from app.sendfile(resp, "/build/index.html.gz", "text/html", b"Content-Encoding: gzip\r\n")
if sta_if.isconnected() is True:
webapp_ip = getIpEspServerEsp(sta_if.ifconfig())
if __name__ == "__main__":
app.run(host=webapp_ip, port=3000, debug=1)
| true
| true
|
1c3ee31a3897e2679ba3ab6df576be1484820e70
| 121
|
py
|
Python
|
products/admin.py
|
anilchoudhary/ecommerce
|
f16a6e56ac1eb1050027c1f7c9a1d3e795dd3ffe
|
[
"MIT"
] | null | null | null |
products/admin.py
|
anilchoudhary/ecommerce
|
f16a6e56ac1eb1050027c1f7c9a1d3e795dd3ffe
|
[
"MIT"
] | null | null | null |
products/admin.py
|
anilchoudhary/ecommerce
|
f16a6e56ac1eb1050027c1f7c9a1d3e795dd3ffe
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Product
# Register your models here.
admin.site.register(Product)
| 17.285714
| 32
| 0.801653
|
from django.contrib import admin
from .models import Product
admin.site.register(Product)
| true
| true
|
1c3ee34bf04c7cd14bd2cad070e9fb1c573743f3
| 4,115
|
py
|
Python
|
drive.py
|
eagleanurag/End-to-End-Learning-for-Self-Driving-Cars
|
0a32d90a6714515b6f0f0366b298b9c6d06119ab
|
[
"MIT"
] | null | null | null |
drive.py
|
eagleanurag/End-to-End-Learning-for-Self-Driving-Cars
|
0a32d90a6714515b6f0f0366b298b9c6d06119ab
|
[
"MIT"
] | null | null | null |
drive.py
|
eagleanurag/End-to-End-Learning-for-Self-Driving-Cars
|
0a32d90a6714515b6f0f0366b298b9c6d06119ab
|
[
"MIT"
] | 1
|
2018-11-05T05:11:02.000Z
|
2018-11-05T05:11:02.000Z
|
import argparse
import base64
from io import BytesIO
import cv2
import eventlet.wsgi
import numpy as np
import socketio
from PIL import Image
from flask import Flask
from keras.models import model_from_json
# Fix error with Keras and TensorFlow
# tf.python.control_flow_ops = tf
sio = socketio.Server()
app = Flask(__name__)
model = None
prev_image_array = None
@sio.on('telemetry')
def telemetry(sid, data):
# The current steering angle of the car
steering_angle = data["steering_angle"]
# The current throttle of the car
throttle = data["throttle"]
# The current speed of the car
speed = data["speed"]
"""For actual road"""
# The current image from the center camera of the car
# imgString = data["image"]
# image = Image.open(BytesIO(base64.b64decode(imgString)))
# open_cv_image = np.array(image)
# # Convert RGB to BGR
# open_cv_image = open_cv_image[:, :, ::-1].copy()
# img = cv2.resize(open_cv_image, (320, 160))
#
# #transformed_image_array = image_array[None, :, :, :]
#
# #resize the image
# #transformed_image_array = ( cv2.resize((cv2.cvtColor(transformed_image_array[0], cv2.COLOR_RGB2HSV))[:,:,1],(32,16))).reshape(1,16,32,1)
#
# # This model currently assumes that the features of the model are just the images. Feel free to change this.
# steering_angle = model.predict(img[None, :, :, :].transpose(0, 3, 1, 2))[0][0]
# # The driving model currently just outputs a constant throttle. Feel free to edit this.
# throttle = 0.2
"""For sim"""
imgString = data["image"]
image = Image.open(BytesIO(base64.b64decode(imgString)))
image_array = np.asarray(image)
transformed_image_array = image_array[None, :, :, :]
# resize the image
transformed_image_array = (
cv2.resize((cv2.cvtColor(transformed_image_array[0], cv2.COLOR_RGB2HSV))[:, :, 1], (32, 16))).reshape(1, 16, 32, 1)
# This model currently assumes that the features of the model are just the images. Feel free to change this.
steering_angle = float(model.predict(transformed_image_array, batch_size=1))
# The driving model currently just outputs a constant throttle. Feel free to edit this.
throttle = 0.2
#adaptive speed
if (float(speed) < 10):
throttle = 0.4
elif (float(speed)<2):
throttle = 0.7
else:
# When speed is below 20 then increase throttle by speed_factor
if ((float(speed)) < 25):
speed_factor = 1.35
else:
speed_factor = 1.0
if (abs(steering_angle) < 0.1):
throttle = 0.3 * speed_factor
elif (abs(steering_angle) < 0.5):
throttle = 0.2 * speed_factor
else:
throttle = 0.15 * speed_factor
print('Steering angle =', '%5.2f'%(float(steering_angle)), 'Throttle =', '%.2f'%(float(throttle)), 'Speed =', '%.2f'%(float(speed)))
send_control(steering_angle, throttle)
@sio.on('connect')
def connect(sid, environ):
print("connect ", sid)
send_control(0, 0)
def send_control(steering_angle, throttle):
sio.emit("steer", data={
'steering_angle': steering_angle.__str__(),
'throttle': throttle.__str__()
}, skip_sid=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Remote Driving')
parser.add_argument('model', type=str,
help='Path to model definition json. Model weights should be on the same path.')
args = parser.parse_args()
with open(args.model, 'r') as jfile:
# NOTE: if you saved the file by calling json.dump(model.to_json(), ...)
# then you will have to call:
#
#model = model_from_json(json.loads(jfile.read()))
#
# instead.
model = model_from_json(jfile.read())
model.compile("adam", "mse")
weights_file = args.model.replace('json', 'h5')
model.load_weights(weights_file)
# wrap Flask application with engineio's middleware
app = socketio.Middleware(sio, app)
# deploy as an eventlet WSGI server
eventlet.wsgi.server(eventlet.listen(('', 4567)), app)
| 31.899225
| 143
| 0.652734
|
import argparse
import base64
from io import BytesIO
import cv2
import eventlet.wsgi
import numpy as np
import socketio
from PIL import Image
from flask import Flask
from keras.models import model_from_json
sio = socketio.Server()
app = Flask(__name__)
model = None
prev_image_array = None
@sio.on('telemetry')
def telemetry(sid, data):
steering_angle = data["steering_angle"]
throttle = data["throttle"]
speed = data["speed"]
_angle = float(model.predict(transformed_image_array, batch_size=1))
throttle = 0.2
if (float(speed) < 10):
throttle = 0.4
elif (float(speed)<2):
throttle = 0.7
else:
if ((float(speed)) < 25):
speed_factor = 1.35
else:
speed_factor = 1.0
if (abs(steering_angle) < 0.1):
throttle = 0.3 * speed_factor
elif (abs(steering_angle) < 0.5):
throttle = 0.2 * speed_factor
else:
throttle = 0.15 * speed_factor
print('Steering angle =', '%5.2f'%(float(steering_angle)), 'Throttle =', '%.2f'%(float(throttle)), 'Speed =', '%.2f'%(float(speed)))
send_control(steering_angle, throttle)
@sio.on('connect')
def connect(sid, environ):
print("connect ", sid)
send_control(0, 0)
def send_control(steering_angle, throttle):
sio.emit("steer", data={
'steering_angle': steering_angle.__str__(),
'throttle': throttle.__str__()
}, skip_sid=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Remote Driving')
parser.add_argument('model', type=str,
help='Path to model definition json. Model weights should be on the same path.')
args = parser.parse_args()
with open(args.model, 'r') as jfile:
model = model_from_json(jfile.read())
model.compile("adam", "mse")
weights_file = args.model.replace('json', 'h5')
model.load_weights(weights_file)
app = socketio.Middleware(sio, app)
# deploy as an eventlet WSGI server
eventlet.wsgi.server(eventlet.listen(('', 4567)), app)
| true
| true
|
1c3ee39784619349a5434c775720649d03c9987e
| 76,938
|
py
|
Python
|
graph_objs/layout/polar/_angularaxis.py
|
wwwidonja/changed_plotly
|
1bda35a438539a97c84a3ab3952e95e8848467bd
|
[
"MIT"
] | null | null | null |
graph_objs/layout/polar/_angularaxis.py
|
wwwidonja/changed_plotly
|
1bda35a438539a97c84a3ab3952e95e8848467bd
|
[
"MIT"
] | null | null | null |
graph_objs/layout/polar/_angularaxis.py
|
wwwidonja/changed_plotly
|
1bda35a438539a97c84a3ab3952e95e8848467bd
|
[
"MIT"
] | null | null | null |
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class AngularAxis(_BaseLayoutHierarchyType):
# class properties
# --------------------
_parent_path_str = "layout.polar"
_path_str = "layout.polar.angularaxis"
_valid_props = {
"autotypenumbers",
"categoryarray",
"categoryarraysrc",
"categoryorder",
"color",
"direction",
"dtick",
"exponentformat",
"gridcolor",
"gridwidth",
"hoverformat",
"layer",
"linecolor",
"linewidth",
"minexponent",
"nticks",
"period",
"rotation",
"separatethousands",
"showexponent",
"showgrid",
"showline",
"showticklabels",
"showtickprefix",
"showticksuffix",
"thetaunit",
"tick0",
"tickangle",
"tickcolor",
"tickfont",
"tickformat",
"tickformatstopdefaults",
"tickformatstops",
"ticklen",
"tickmode",
"tickprefix",
"ticks",
"ticksuffix",
"ticktext",
"ticktextsrc",
"tickvals",
"tickvalssrc",
"tickwidth",
"type",
"uirevision",
"visible",
}
# autotypenumbers
# ---------------
@property
def autotypenumbers(self):
"""
Using "strict" a numeric string in trace data is not converted
to a number. Using *convert types* a numeric string in trace
data may be treated as a number during automatic axis `type`
detection. Defaults to layout.autotypenumbers.
The 'autotypenumbers' property is an enumeration that may be specified as:
- One of the following enumeration values:
['convert types', 'strict']
Returns
-------
Any
"""
return self["autotypenumbers"]
@autotypenumbers.setter
def autotypenumbers(self, val):
self["autotypenumbers"] = val
# categoryarray
# -------------
@property
def categoryarray(self):
"""
Sets the order in which categories on this axis appear. Only
has an effect if `categoryorder` is set to "array". Used with
`categoryorder`.
The 'categoryarray' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["categoryarray"]
@categoryarray.setter
def categoryarray(self, val):
self["categoryarray"] = val
# categoryarraysrc
# ----------------
@property
def categoryarraysrc(self):
"""
Sets the source reference on Chart Studio Cloud for
categoryarray .
The 'categoryarraysrc' property must be specified as a string or
as a new_plotly.grid_objs.Column object
Returns
-------
str
"""
return self["categoryarraysrc"]
@categoryarraysrc.setter
def categoryarraysrc(self, val):
self["categoryarraysrc"] = val
# categoryorder
# -------------
@property
def categoryorder(self):
"""
Specifies the ordering logic for the case of categorical
variables. By default, new_plotly uses "trace", which specifies the
order that is present in the data supplied. Set `categoryorder`
to *category ascending* or *category descending* if order
should be determined by the alphanumerical order of the
category names. Set `categoryorder` to "array" to derive the
ordering from the attribute `categoryarray`. If a category is
not found in the `categoryarray` array, the sorting behavior
for that attribute will be identical to the "trace" mode. The
unspecified categories will follow the categories in
`categoryarray`. Set `categoryorder` to *total ascending* or
*total descending* if order should be determined by the
numerical order of the values. Similarly, the order can be
determined by the min, max, sum, mean or median of all the
values.
The 'categoryorder' property is an enumeration that may be specified as:
- One of the following enumeration values:
['trace', 'category ascending', 'category descending',
'array', 'total ascending', 'total descending', 'min
ascending', 'min descending', 'max ascending', 'max
descending', 'sum ascending', 'sum descending', 'mean
ascending', 'mean descending', 'median ascending', 'median
descending']
Returns
-------
Any
"""
return self["categoryorder"]
@categoryorder.setter
def categoryorder(self, val):
self["categoryorder"] = val
# color
# -----
@property
def color(self):
"""
Sets default for all colors associated with this axis all at
once: line, font, tick, and grid colors. Grid color is
lightened by blending this with the plot background Individual
pieces can override this.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# direction
# ---------
@property
def direction(self):
"""
Sets the direction corresponding to positive angles.
The 'direction' property is an enumeration that may be specified as:
- One of the following enumeration values:
['counterclockwise', 'clockwise']
Returns
-------
Any
"""
return self["direction"]
@direction.setter
def direction(self, val):
self["direction"] = val
# dtick
# -----
@property
def dtick(self):
"""
Sets the step in-between ticks on this axis. Use with `tick0`.
Must be a positive number, or special strings available to
"log" and "date" axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick number. For
example, to set a tick mark at 1, 10, 100, 1000, ... set dtick
to 1. To set tick marks at 1, 100, 10000, ... set dtick to 2.
To set tick marks at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special values;
"L<f>", where `f` is a positive number, gives ticks linearly
spaced in value (but not position). For example `tick0` = 0.1,
`dtick` = "L0.5" will put ticks at 0.1, 0.6, 1.1, 1.6 etc. To
show powers of 10 plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is ignored for "D1" and
"D2". If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval between
ticks to one day, set `dtick` to 86400000.0. "date" also has
special values "M<n>" gives ticks spaced by a number of months.
`n` must be a positive integer. To set ticks on the 15th of
every third month, set `tick0` to "2000-01-15" and `dtick` to
"M3". To set ticks every 4 years, set `dtick` to "M48"
The 'dtick' property accepts values of any type
Returns
-------
Any
"""
return self["dtick"]
@dtick.setter
def dtick(self, val):
self["dtick"] = val
# exponentformat
# --------------
@property
def exponentformat(self):
"""
Determines a formatting rule for the tick exponents. For
example, consider the number 1,000,000,000. If "none", it
appears as 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If "SI", 1G. If
"B", 1B.
The 'exponentformat' property is an enumeration that may be specified as:
- One of the following enumeration values:
['none', 'e', 'E', 'power', 'SI', 'B']
Returns
-------
Any
"""
return self["exponentformat"]
@exponentformat.setter
def exponentformat(self, val):
self["exponentformat"] = val
# gridcolor
# ---------
@property
def gridcolor(self):
"""
Sets the color of the grid lines.
The 'gridcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["gridcolor"]
@gridcolor.setter
def gridcolor(self, val):
self["gridcolor"] = val
# gridwidth
# ---------
@property
def gridwidth(self):
"""
Sets the width (in px) of the grid lines.
The 'gridwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["gridwidth"]
@gridwidth.setter
def gridwidth(self, val):
self["gridwidth"] = val
# hoverformat
# -----------
@property
def hoverformat(self):
"""
Sets the hover text formatting rule using d3 formatting mini-
languages which are very similar to those in Python. For
numbers, see: https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for dates
see: https://github.com/d3/d3-time-format#locale_format We add
one item to d3's date formatter: "%{n}f" for fractional seconds
with n digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
The 'hoverformat' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["hoverformat"]
@hoverformat.setter
def hoverformat(self, val):
self["hoverformat"] = val
# layer
# -----
@property
def layer(self):
"""
Sets the layer on which this axis is displayed. If *above
traces*, this axis is displayed above all the subplot's traces
If *below traces*, this axis is displayed below all the
subplot's traces, but above the grid lines. Useful when used
together with scatter-like traces with `cliponaxis` set to
False to show markers and/or text nodes above this axis.
The 'layer' property is an enumeration that may be specified as:
- One of the following enumeration values:
['above traces', 'below traces']
Returns
-------
Any
"""
return self["layer"]
@layer.setter
def layer(self, val):
self["layer"] = val
# linecolor
# ---------
@property
def linecolor(self):
"""
Sets the axis line color.
The 'linecolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["linecolor"]
@linecolor.setter
def linecolor(self, val):
self["linecolor"] = val
# linewidth
# ---------
@property
def linewidth(self):
"""
Sets the width (in px) of the axis line.
The 'linewidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["linewidth"]
@linewidth.setter
def linewidth(self, val):
self["linewidth"] = val
# minexponent
# -----------
@property
def minexponent(self):
"""
Hide SI prefix for 10^n if |n| is below this number. This only
has an effect when `tickformat` is "SI" or "B".
The 'minexponent' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["minexponent"]
@minexponent.setter
def minexponent(self, val):
self["minexponent"] = val
# nticks
# ------
@property
def nticks(self):
"""
Specifies the maximum number of ticks for the particular axis.
The actual number of ticks will be chosen automatically to be
less than or equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
The 'nticks' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int
"""
return self["nticks"]
@nticks.setter
def nticks(self, val):
self["nticks"] = val
# period
# ------
@property
def period(self):
"""
Set the angular period. Has an effect only when
`angularaxis.type` is "category".
The 'period' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["period"]
@period.setter
def period(self, val):
self["period"] = val
# rotation
# --------
@property
def rotation(self):
"""
Sets that start position (in degrees) of the angular axis By
default, polar subplots with `direction` set to
"counterclockwise" get a `rotation` of 0 which corresponds to
due East (like what mathematicians prefer). In turn, polar with
`direction` set to "clockwise" get a rotation of 90 which
corresponds to due North (like on a compass),
The 'rotation' property is a angle (in degrees) that may be
specified as a number between -180 and 180. Numeric values outside this
range are converted to the equivalent value
(e.g. 270 is converted to -90).
Returns
-------
int|float
"""
return self["rotation"]
@rotation.setter
def rotation(self, val):
self["rotation"] = val
# separatethousands
# -----------------
@property
def separatethousands(self):
"""
If "true", even 4-digit integers are separated
The 'separatethousands' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["separatethousands"]
@separatethousands.setter
def separatethousands(self, val):
self["separatethousands"] = val
# showexponent
# ------------
@property
def showexponent(self):
"""
If "all", all exponents are shown besides their significands.
If "first", only the exponent of the first tick is shown. If
"last", only the exponent of the last tick is shown. If "none",
no exponents appear.
The 'showexponent' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'first', 'last', 'none']
Returns
-------
Any
"""
return self["showexponent"]
@showexponent.setter
def showexponent(self, val):
self["showexponent"] = val
# showgrid
# --------
@property
def showgrid(self):
"""
Determines whether or not grid lines are drawn. If True, the
grid lines are drawn at every tick mark.
The 'showgrid' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showgrid"]
@showgrid.setter
def showgrid(self, val):
self["showgrid"] = val
# showline
# --------
@property
def showline(self):
"""
Determines whether or not a line bounding this axis is drawn.
The 'showline' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showline"]
@showline.setter
def showline(self, val):
self["showline"] = val
# showticklabels
# --------------
@property
def showticklabels(self):
"""
Determines whether or not the tick labels are drawn.
The 'showticklabels' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showticklabels"]
@showticklabels.setter
def showticklabels(self, val):
self["showticklabels"] = val
# showtickprefix
# --------------
@property
def showtickprefix(self):
"""
If "all", all tick labels are displayed with a prefix. If
"first", only the first tick is displayed with a prefix. If
"last", only the last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
The 'showtickprefix' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'first', 'last', 'none']
Returns
-------
Any
"""
return self["showtickprefix"]
@showtickprefix.setter
def showtickprefix(self, val):
self["showtickprefix"] = val
# showticksuffix
# --------------
@property
def showticksuffix(self):
"""
Same as `showtickprefix` but for tick suffixes.
The 'showticksuffix' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'first', 'last', 'none']
Returns
-------
Any
"""
return self["showticksuffix"]
@showticksuffix.setter
def showticksuffix(self, val):
self["showticksuffix"] = val
# thetaunit
# ---------
@property
def thetaunit(self):
"""
Sets the format unit of the formatted "theta" values. Has an
effect only when `angularaxis.type` is "linear".
The 'thetaunit' property is an enumeration that may be specified as:
- One of the following enumeration values:
['radians', 'degrees']
Returns
-------
Any
"""
return self["thetaunit"]
@thetaunit.setter
def thetaunit(self, val):
self["thetaunit"] = val
# tick0
# -----
@property
def tick0(self):
"""
Sets the placement of the first tick on this axis. Use with
`dtick`. If the axis `type` is "log", then you must take the
log of your starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when `dtick`=*L<f>* (see
`dtick` for more info). If the axis `type` is "date", it should
be a date string, like date data. If the axis `type` is
"category", it should be a number, using the scale where each
category is assigned a serial number from zero in the order it
appears.
The 'tick0' property accepts values of any type
Returns
-------
Any
"""
return self["tick0"]
@tick0.setter
def tick0(self, val):
self["tick0"] = val
# tickangle
# ---------
@property
def tickangle(self):
"""
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the tick
labels vertically.
The 'tickangle' property is a angle (in degrees) that may be
specified as a number between -180 and 180. Numeric values outside this
range are converted to the equivalent value
(e.g. 270 is converted to -90).
Returns
-------
int|float
"""
return self["tickangle"]
@tickangle.setter
def tickangle(self, val):
self["tickangle"] = val
# tickcolor
# ---------
@property
def tickcolor(self):
"""
Sets the tick color.
The 'tickcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["tickcolor"]
@tickcolor.setter
def tickcolor(self, val):
self["tickcolor"] = val
# tickfont
# --------
@property
def tickfont(self):
"""
Sets the tick font.
The 'tickfont' property is an instance of Tickfont
that may be specified as:
- An instance of :class:`new_plotly.graph_objs.layout.polar.angularaxis.Tickfont`
- A dict of string/value properties that will be passed
to the Tickfont constructor
Supported dict properties:
color
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans",, "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
size
Returns
-------
new_plotly.graph_objs.layout.polar.angularaxis.Tickfont
"""
return self["tickfont"]
@tickfont.setter
def tickfont(self, val):
self["tickfont"] = val
# tickformat
# ----------
@property
def tickformat(self):
"""
Sets the tick label formatting rule using d3 formatting mini-
languages which are very similar to those in Python. For
numbers, see: https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for dates
see: https://github.com/d3/d3-time-format#locale_format We add
one item to d3's date formatter: "%{n}f" for fractional seconds
with n digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
The 'tickformat' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["tickformat"]
@tickformat.setter
def tickformat(self, val):
self["tickformat"] = val
# tickformatstops
# ---------------
@property
def tickformatstops(self):
"""
The 'tickformatstops' property is a tuple of instances of
Tickformatstop that may be specified as:
- A list or tuple of instances of new_plotly.graph_objs.layout.polar.angularaxis.Tickformatstop
- A list or tuple of dicts of string/value properties that
will be passed to the Tickformatstop constructor
Supported dict properties:
dtickrange
range [*min*, *max*], where "min", "max" -
dtick values which describe some zoom level, it
is possible to omit "min" or "max" value by
passing "null"
enabled
Determines whether or not this stop is used. If
`false`, this stop is ignored even within its
`dtickrange`.
name
When used in a template, named items are
created in the output figure in addition to any
items the figure already has in this array. You
can modify these items in the output figure by
making your own item with `templateitemname`
matching this `name` alongside your
modifications (including `visible: false` or
`enabled: false` to hide it). Has no effect
outside of a template.
templateitemname
Used to refer to a named item in this array in
the template. Named items from the template
will be created even without a matching item in
the input figure, but you can modify one by
making an item with `templateitemname` matching
its `name`, alongside your modifications
(including `visible: false` or `enabled: false`
to hide it). If there is no template or no
matching item, this item will be hidden unless
you explicitly show it with `visible: true`.
value
string - dtickformat for described zoom level,
the same as "tickformat"
Returns
-------
tuple[new_plotly.graph_objs.layout.polar.angularaxis.Tickformatstop]
"""
return self["tickformatstops"]
@tickformatstops.setter
def tickformatstops(self, val):
self["tickformatstops"] = val
# tickformatstopdefaults
# ----------------------
@property
def tickformatstopdefaults(self):
"""
When used in a template (as layout.template.layout.polar.angula
raxis.tickformatstopdefaults), sets the default property values
to use for elements of layout.polar.angularaxis.tickformatstops
The 'tickformatstopdefaults' property is an instance of Tickformatstop
that may be specified as:
- An instance of :class:`new_plotly.graph_objs.layout.polar.angularaxis.Tickformatstop`
- A dict of string/value properties that will be passed
to the Tickformatstop constructor
Supported dict properties:
Returns
-------
new_plotly.graph_objs.layout.polar.angularaxis.Tickformatstop
"""
return self["tickformatstopdefaults"]
@tickformatstopdefaults.setter
def tickformatstopdefaults(self, val):
self["tickformatstopdefaults"] = val
# ticklen
# -------
@property
def ticklen(self):
"""
Sets the tick length (in px).
The 'ticklen' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["ticklen"]
@ticklen.setter
def ticklen(self, val):
self["ticklen"] = val
# tickmode
# --------
@property
def tickmode(self):
"""
Sets the tick mode for this axis. If "auto", the number of
ticks is set via `nticks`. If "linear", the placement of the
ticks is determined by a starting position `tick0` and a tick
step `dtick` ("linear" is the default value if `tick0` and
`dtick` are provided). If "array", the placement of the ticks
is set via `tickvals` and the tick text is `ticktext`. ("array"
is the default value if `tickvals` is provided).
The 'tickmode' property is an enumeration that may be specified as:
- One of the following enumeration values:
['auto', 'linear', 'array']
Returns
-------
Any
"""
return self["tickmode"]
@tickmode.setter
def tickmode(self, val):
self["tickmode"] = val
# tickprefix
# ----------
@property
def tickprefix(self):
"""
Sets a tick label prefix.
The 'tickprefix' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["tickprefix"]
@tickprefix.setter
def tickprefix(self, val):
self["tickprefix"] = val
# ticks
# -----
@property
def ticks(self):
"""
Determines whether ticks are drawn or not. If "", this axis'
ticks are not drawn. If "outside" ("inside"), this axis' are
drawn outside (inside) the axis lines.
The 'ticks' property is an enumeration that may be specified as:
- One of the following enumeration values:
['outside', 'inside', '']
Returns
-------
Any
"""
return self["ticks"]
@ticks.setter
def ticks(self, val):
self["ticks"] = val
# ticksuffix
# ----------
@property
def ticksuffix(self):
"""
Sets a tick label suffix.
The 'ticksuffix' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["ticksuffix"]
@ticksuffix.setter
def ticksuffix(self, val):
self["ticksuffix"] = val
# ticktext
# --------
@property
def ticktext(self):
"""
Sets the text displayed at the ticks position via `tickvals`.
Only has an effect if `tickmode` is set to "array". Used with
`tickvals`.
The 'ticktext' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["ticktext"]
@ticktext.setter
def ticktext(self, val):
self["ticktext"] = val
# ticktextsrc
# -----------
@property
def ticktextsrc(self):
"""
Sets the source reference on Chart Studio Cloud for ticktext .
The 'ticktextsrc' property must be specified as a string or
as a new_plotly.grid_objs.Column object
Returns
-------
str
"""
return self["ticktextsrc"]
@ticktextsrc.setter
def ticktextsrc(self, val):
self["ticktextsrc"] = val
# tickvals
# --------
@property
def tickvals(self):
"""
Sets the values at which ticks on this axis appear. Only has an
effect if `tickmode` is set to "array". Used with `ticktext`.
The 'tickvals' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["tickvals"]
@tickvals.setter
def tickvals(self, val):
self["tickvals"] = val
# tickvalssrc
# -----------
@property
def tickvalssrc(self):
"""
Sets the source reference on Chart Studio Cloud for tickvals .
The 'tickvalssrc' property must be specified as a string or
as a new_plotly.grid_objs.Column object
Returns
-------
str
"""
return self["tickvalssrc"]
@tickvalssrc.setter
def tickvalssrc(self, val):
self["tickvalssrc"] = val
# tickwidth
# ---------
@property
def tickwidth(self):
"""
Sets the tick width (in px).
The 'tickwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["tickwidth"]
@tickwidth.setter
def tickwidth(self, val):
self["tickwidth"] = val
# type
# ----
@property
def type(self):
"""
Sets the angular axis type. If "linear", set `thetaunit` to
determine the unit in which axis value are shown. If *category,
use `period` to set the number of integer coordinates around
polar axis.
The 'type' property is an enumeration that may be specified as:
- One of the following enumeration values:
['-', 'linear', 'category']
Returns
-------
Any
"""
return self["type"]
@type.setter
def type(self, val):
self["type"] = val
# uirevision
# ----------
@property
def uirevision(self):
"""
Controls persistence of user-driven changes in axis `rotation`.
Defaults to `polar<N>.uirevision`.
The 'uirevision' property accepts values of any type
Returns
-------
Any
"""
return self["uirevision"]
@uirevision.setter
def uirevision(self, val):
self["uirevision"] = val
# visible
# -------
@property
def visible(self):
"""
A single toggle to hide the axis while preserving interaction
like dragging. Default is true when a cheater plot is present
on the axis, otherwise false
The 'visible' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["visible"]
@visible.setter
def visible(self, val):
self["visible"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
autotypenumbers
Using "strict" a numeric string in trace data is not
converted to a number. Using *convert types* a numeric
string in trace data may be treated as a number during
automatic axis `type` detection. Defaults to
layout.autotypenumbers.
categoryarray
Sets the order in which categories on this axis appear.
Only has an effect if `categoryorder` is set to
"array". Used with `categoryorder`.
categoryarraysrc
Sets the source reference on Chart Studio Cloud for
categoryarray .
categoryorder
Specifies the ordering logic for the case of
categorical variables. By default, new_plotly uses "trace",
which specifies the order that is present in the data
supplied. Set `categoryorder` to *category ascending*
or *category descending* if order should be determined
by the alphanumerical order of the category names. Set
`categoryorder` to "array" to derive the ordering from
the attribute `categoryarray`. If a category is not
found in the `categoryarray` array, the sorting
behavior for that attribute will be identical to the
"trace" mode. The unspecified categories will follow
the categories in `categoryarray`. Set `categoryorder`
to *total ascending* or *total descending* if order
should be determined by the numerical order of the
values. Similarly, the order can be determined by the
min, max, sum, mean or median of all the values.
color
Sets default for all colors associated with this axis
all at once: line, font, tick, and grid colors. Grid
color is lightened by blending this with the plot
background Individual pieces can override this.
direction
Sets the direction corresponding to positive angles.
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
gridcolor
Sets the color of the grid lines.
gridwidth
Sets the width (in px) of the grid lines.
hoverformat
Sets the hover text formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for
dates see: https://github.com/d3/d3-time-
format#locale_format We add one item to d3's date
formatter: "%{n}f" for fractional seconds with n
digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
layer
Sets the layer on which this axis is displayed. If
*above traces*, this axis is displayed above all the
subplot's traces If *below traces*, this axis is
displayed below all the subplot's traces, but above the
grid lines. Useful when used together with scatter-like
traces with `cliponaxis` set to False to show markers
and/or text nodes above this axis.
linecolor
Sets the axis line color.
linewidth
Sets the width (in px) of the axis line.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
period
Set the angular period. Has an effect only when
`angularaxis.type` is "category".
rotation
Sets that start position (in degrees) of the angular
axis By default, polar subplots with `direction` set to
"counterclockwise" get a `rotation` of 0 which
corresponds to due East (like what mathematicians
prefer). In turn, polar with `direction` set to
"clockwise" get a rotation of 90 which corresponds to
due North (like on a compass),
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showgrid
Determines whether or not grid lines are drawn. If
True, the grid lines are drawn at every tick mark.
showline
Determines whether or not a line bounding this axis is
drawn.
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thetaunit
Sets the format unit of the formatted "theta" values.
Has an effect only when `angularaxis.type` is "linear".
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the tick font.
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for
dates see: https://github.com/d3/d3-time-
format#locale_format We add one item to d3's date
formatter: "%{n}f" for fractional seconds with n
digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
tickformatstops
A tuple of :class:`new_plotly.graph_objects.layout.polar.an
gularaxis.Tickformatstop` instances or dicts with
compatible properties
tickformatstopdefaults
When used in a template (as layout.template.layout.pola
r.angularaxis.tickformatstopdefaults), sets the default
property values to use for elements of
layout.polar.angularaxis.tickformatstops
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
ticktext .
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
tickvals .
tickwidth
Sets the tick width (in px).
type
Sets the angular axis type. If "linear", set
`thetaunit` to determine the unit in which axis value
are shown. If *category, use `period` to set the number
of integer coordinates around polar axis.
uirevision
Controls persistence of user-driven changes in axis
`rotation`. Defaults to `polar<N>.uirevision`.
visible
A single toggle to hide the axis while preserving
interaction like dragging. Default is true when a
cheater plot is present on the axis, otherwise false
"""
def __init__(
self,
arg=None,
autotypenumbers=None,
categoryarray=None,
categoryarraysrc=None,
categoryorder=None,
color=None,
direction=None,
dtick=None,
exponentformat=None,
gridcolor=None,
gridwidth=None,
hoverformat=None,
layer=None,
linecolor=None,
linewidth=None,
minexponent=None,
nticks=None,
period=None,
rotation=None,
separatethousands=None,
showexponent=None,
showgrid=None,
showline=None,
showticklabels=None,
showtickprefix=None,
showticksuffix=None,
thetaunit=None,
tick0=None,
tickangle=None,
tickcolor=None,
tickfont=None,
tickformat=None,
tickformatstops=None,
tickformatstopdefaults=None,
ticklen=None,
tickmode=None,
tickprefix=None,
ticks=None,
ticksuffix=None,
ticktext=None,
ticktextsrc=None,
tickvals=None,
tickvalssrc=None,
tickwidth=None,
type=None,
uirevision=None,
visible=None,
**kwargs
):
"""
Construct a new AngularAxis object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`new_plotly.graph_objs.layout.polar.AngularAxis`
autotypenumbers
Using "strict" a numeric string in trace data is not
converted to a number. Using *convert types* a numeric
string in trace data may be treated as a number during
automatic axis `type` detection. Defaults to
layout.autotypenumbers.
categoryarray
Sets the order in which categories on this axis appear.
Only has an effect if `categoryorder` is set to
"array". Used with `categoryorder`.
categoryarraysrc
Sets the source reference on Chart Studio Cloud for
categoryarray .
categoryorder
Specifies the ordering logic for the case of
categorical variables. By default, new_plotly uses "trace",
which specifies the order that is present in the data
supplied. Set `categoryorder` to *category ascending*
or *category descending* if order should be determined
by the alphanumerical order of the category names. Set
`categoryorder` to "array" to derive the ordering from
the attribute `categoryarray`. If a category is not
found in the `categoryarray` array, the sorting
behavior for that attribute will be identical to the
"trace" mode. The unspecified categories will follow
the categories in `categoryarray`. Set `categoryorder`
to *total ascending* or *total descending* if order
should be determined by the numerical order of the
values. Similarly, the order can be determined by the
min, max, sum, mean or median of all the values.
color
Sets default for all colors associated with this axis
all at once: line, font, tick, and grid colors. Grid
color is lightened by blending this with the plot
background Individual pieces can override this.
direction
Sets the direction corresponding to positive angles.
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
gridcolor
Sets the color of the grid lines.
gridwidth
Sets the width (in px) of the grid lines.
hoverformat
Sets the hover text formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for
dates see: https://github.com/d3/d3-time-
format#locale_format We add one item to d3's date
formatter: "%{n}f" for fractional seconds with n
digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
layer
Sets the layer on which this axis is displayed. If
*above traces*, this axis is displayed above all the
subplot's traces If *below traces*, this axis is
displayed below all the subplot's traces, but above the
grid lines. Useful when used together with scatter-like
traces with `cliponaxis` set to False to show markers
and/or text nodes above this axis.
linecolor
Sets the axis line color.
linewidth
Sets the width (in px) of the axis line.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
period
Set the angular period. Has an effect only when
`angularaxis.type` is "category".
rotation
Sets that start position (in degrees) of the angular
axis By default, polar subplots with `direction` set to
"counterclockwise" get a `rotation` of 0 which
corresponds to due East (like what mathematicians
prefer). In turn, polar with `direction` set to
"clockwise" get a rotation of 90 which corresponds to
due North (like on a compass),
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showgrid
Determines whether or not grid lines are drawn. If
True, the grid lines are drawn at every tick mark.
showline
Determines whether or not a line bounding this axis is
drawn.
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thetaunit
Sets the format unit of the formatted "theta" values.
Has an effect only when `angularaxis.type` is "linear".
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the tick font.
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for
dates see: https://github.com/d3/d3-time-
format#locale_format We add one item to d3's date
formatter: "%{n}f" for fractional seconds with n
digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
tickformatstops
A tuple of :class:`new_plotly.graph_objects.layout.polar.an
gularaxis.Tickformatstop` instances or dicts with
compatible properties
tickformatstopdefaults
When used in a template (as layout.template.layout.pola
r.angularaxis.tickformatstopdefaults), sets the default
property values to use for elements of
layout.polar.angularaxis.tickformatstops
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
ticktext .
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
tickvals .
tickwidth
Sets the tick width (in px).
type
Sets the angular axis type. If "linear", set
`thetaunit` to determine the unit in which axis value
are shown. If *category, use `period` to set the number
of integer coordinates around polar axis.
uirevision
Controls persistence of user-driven changes in axis
`rotation`. Defaults to `polar<N>.uirevision`.
visible
A single toggle to hide the axis while preserving
interaction like dragging. Default is true when a
cheater plot is present on the axis, otherwise false
Returns
-------
AngularAxis
"""
super(AngularAxis, self).__init__("angularaxis")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the new_plotly.graph_objs.layout.polar.AngularAxis
constructor must be a dict or
an instance of :class:`new_plotly.graph_objs.layout.polar.AngularAxis`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("autotypenumbers", None)
_v = autotypenumbers if autotypenumbers is not None else _v
if _v is not None:
self["autotypenumbers"] = _v
_v = arg.pop("categoryarray", None)
_v = categoryarray if categoryarray is not None else _v
if _v is not None:
self["categoryarray"] = _v
_v = arg.pop("categoryarraysrc", None)
_v = categoryarraysrc if categoryarraysrc is not None else _v
if _v is not None:
self["categoryarraysrc"] = _v
_v = arg.pop("categoryorder", None)
_v = categoryorder if categoryorder is not None else _v
if _v is not None:
self["categoryorder"] = _v
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("direction", None)
_v = direction if direction is not None else _v
if _v is not None:
self["direction"] = _v
_v = arg.pop("dtick", None)
_v = dtick if dtick is not None else _v
if _v is not None:
self["dtick"] = _v
_v = arg.pop("exponentformat", None)
_v = exponentformat if exponentformat is not None else _v
if _v is not None:
self["exponentformat"] = _v
_v = arg.pop("gridcolor", None)
_v = gridcolor if gridcolor is not None else _v
if _v is not None:
self["gridcolor"] = _v
_v = arg.pop("gridwidth", None)
_v = gridwidth if gridwidth is not None else _v
if _v is not None:
self["gridwidth"] = _v
_v = arg.pop("hoverformat", None)
_v = hoverformat if hoverformat is not None else _v
if _v is not None:
self["hoverformat"] = _v
_v = arg.pop("layer", None)
_v = layer if layer is not None else _v
if _v is not None:
self["layer"] = _v
_v = arg.pop("linecolor", None)
_v = linecolor if linecolor is not None else _v
if _v is not None:
self["linecolor"] = _v
_v = arg.pop("linewidth", None)
_v = linewidth if linewidth is not None else _v
if _v is not None:
self["linewidth"] = _v
_v = arg.pop("minexponent", None)
_v = minexponent if minexponent is not None else _v
if _v is not None:
self["minexponent"] = _v
_v = arg.pop("nticks", None)
_v = nticks if nticks is not None else _v
if _v is not None:
self["nticks"] = _v
_v = arg.pop("period", None)
_v = period if period is not None else _v
if _v is not None:
self["period"] = _v
_v = arg.pop("rotation", None)
_v = rotation if rotation is not None else _v
if _v is not None:
self["rotation"] = _v
_v = arg.pop("separatethousands", None)
_v = separatethousands if separatethousands is not None else _v
if _v is not None:
self["separatethousands"] = _v
_v = arg.pop("showexponent", None)
_v = showexponent if showexponent is not None else _v
if _v is not None:
self["showexponent"] = _v
_v = arg.pop("showgrid", None)
_v = showgrid if showgrid is not None else _v
if _v is not None:
self["showgrid"] = _v
_v = arg.pop("showline", None)
_v = showline if showline is not None else _v
if _v is not None:
self["showline"] = _v
_v = arg.pop("showticklabels", None)
_v = showticklabels if showticklabels is not None else _v
if _v is not None:
self["showticklabels"] = _v
_v = arg.pop("showtickprefix", None)
_v = showtickprefix if showtickprefix is not None else _v
if _v is not None:
self["showtickprefix"] = _v
_v = arg.pop("showticksuffix", None)
_v = showticksuffix if showticksuffix is not None else _v
if _v is not None:
self["showticksuffix"] = _v
_v = arg.pop("thetaunit", None)
_v = thetaunit if thetaunit is not None else _v
if _v is not None:
self["thetaunit"] = _v
_v = arg.pop("tick0", None)
_v = tick0 if tick0 is not None else _v
if _v is not None:
self["tick0"] = _v
_v = arg.pop("tickangle", None)
_v = tickangle if tickangle is not None else _v
if _v is not None:
self["tickangle"] = _v
_v = arg.pop("tickcolor", None)
_v = tickcolor if tickcolor is not None else _v
if _v is not None:
self["tickcolor"] = _v
_v = arg.pop("tickfont", None)
_v = tickfont if tickfont is not None else _v
if _v is not None:
self["tickfont"] = _v
_v = arg.pop("tickformat", None)
_v = tickformat if tickformat is not None else _v
if _v is not None:
self["tickformat"] = _v
_v = arg.pop("tickformatstops", None)
_v = tickformatstops if tickformatstops is not None else _v
if _v is not None:
self["tickformatstops"] = _v
_v = arg.pop("tickformatstopdefaults", None)
_v = tickformatstopdefaults if tickformatstopdefaults is not None else _v
if _v is not None:
self["tickformatstopdefaults"] = _v
_v = arg.pop("ticklen", None)
_v = ticklen if ticklen is not None else _v
if _v is not None:
self["ticklen"] = _v
_v = arg.pop("tickmode", None)
_v = tickmode if tickmode is not None else _v
if _v is not None:
self["tickmode"] = _v
_v = arg.pop("tickprefix", None)
_v = tickprefix if tickprefix is not None else _v
if _v is not None:
self["tickprefix"] = _v
_v = arg.pop("ticks", None)
_v = ticks if ticks is not None else _v
if _v is not None:
self["ticks"] = _v
_v = arg.pop("ticksuffix", None)
_v = ticksuffix if ticksuffix is not None else _v
if _v is not None:
self["ticksuffix"] = _v
_v = arg.pop("ticktext", None)
_v = ticktext if ticktext is not None else _v
if _v is not None:
self["ticktext"] = _v
_v = arg.pop("ticktextsrc", None)
_v = ticktextsrc if ticktextsrc is not None else _v
if _v is not None:
self["ticktextsrc"] = _v
_v = arg.pop("tickvals", None)
_v = tickvals if tickvals is not None else _v
if _v is not None:
self["tickvals"] = _v
_v = arg.pop("tickvalssrc", None)
_v = tickvalssrc if tickvalssrc is not None else _v
if _v is not None:
self["tickvalssrc"] = _v
_v = arg.pop("tickwidth", None)
_v = tickwidth if tickwidth is not None else _v
if _v is not None:
self["tickwidth"] = _v
_v = arg.pop("type", None)
_v = type if type is not None else _v
if _v is not None:
self["type"] = _v
_v = arg.pop("uirevision", None)
_v = uirevision if uirevision is not None else _v
if _v is not None:
self["uirevision"] = _v
_v = arg.pop("visible", None)
_v = visible if visible is not None else _v
if _v is not None:
self["visible"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
| 36.918426
| 105
| 0.571044
|
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class AngularAxis(_BaseLayoutHierarchyType):
_parent_path_str = "layout.polar"
_path_str = "layout.polar.angularaxis"
_valid_props = {
"autotypenumbers",
"categoryarray",
"categoryarraysrc",
"categoryorder",
"color",
"direction",
"dtick",
"exponentformat",
"gridcolor",
"gridwidth",
"hoverformat",
"layer",
"linecolor",
"linewidth",
"minexponent",
"nticks",
"period",
"rotation",
"separatethousands",
"showexponent",
"showgrid",
"showline",
"showticklabels",
"showtickprefix",
"showticksuffix",
"thetaunit",
"tick0",
"tickangle",
"tickcolor",
"tickfont",
"tickformat",
"tickformatstopdefaults",
"tickformatstops",
"ticklen",
"tickmode",
"tickprefix",
"ticks",
"ticksuffix",
"ticktext",
"ticktextsrc",
"tickvals",
"tickvalssrc",
"tickwidth",
"type",
"uirevision",
"visible",
}
@property
def autotypenumbers(self):
return self["autotypenumbers"]
@autotypenumbers.setter
def autotypenumbers(self, val):
self["autotypenumbers"] = val
@property
def categoryarray(self):
return self["categoryarray"]
@categoryarray.setter
def categoryarray(self, val):
self["categoryarray"] = val
@property
def categoryarraysrc(self):
return self["categoryarraysrc"]
@categoryarraysrc.setter
def categoryarraysrc(self, val):
self["categoryarraysrc"] = val
@property
def categoryorder(self):
return self["categoryorder"]
@categoryorder.setter
def categoryorder(self, val):
self["categoryorder"] = val
@property
def color(self):
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
@property
def direction(self):
return self["direction"]
@direction.setter
def direction(self, val):
self["direction"] = val
@property
def dtick(self):
return self["dtick"]
@dtick.setter
def dtick(self, val):
self["dtick"] = val
@property
def exponentformat(self):
return self["exponentformat"]
@exponentformat.setter
def exponentformat(self, val):
self["exponentformat"] = val
@property
def gridcolor(self):
return self["gridcolor"]
@gridcolor.setter
def gridcolor(self, val):
self["gridcolor"] = val
@property
def gridwidth(self):
return self["gridwidth"]
@gridwidth.setter
def gridwidth(self, val):
self["gridwidth"] = val
@property
def hoverformat(self):
return self["hoverformat"]
@hoverformat.setter
def hoverformat(self, val):
self["hoverformat"] = val
@property
def layer(self):
return self["layer"]
@layer.setter
def layer(self, val):
self["layer"] = val
@property
def linecolor(self):
return self["linecolor"]
@linecolor.setter
def linecolor(self, val):
self["linecolor"] = val
@property
def linewidth(self):
return self["linewidth"]
@linewidth.setter
def linewidth(self, val):
self["linewidth"] = val
@property
def minexponent(self):
return self["minexponent"]
@minexponent.setter
def minexponent(self, val):
self["minexponent"] = val
@property
def nticks(self):
return self["nticks"]
@nticks.setter
def nticks(self, val):
self["nticks"] = val
@property
def period(self):
return self["period"]
@period.setter
def period(self, val):
self["period"] = val
@property
def rotation(self):
return self["rotation"]
@rotation.setter
def rotation(self, val):
self["rotation"] = val
@property
def separatethousands(self):
return self["separatethousands"]
@separatethousands.setter
def separatethousands(self, val):
self["separatethousands"] = val
@property
def showexponent(self):
return self["showexponent"]
@showexponent.setter
def showexponent(self, val):
self["showexponent"] = val
@property
def showgrid(self):
return self["showgrid"]
@showgrid.setter
def showgrid(self, val):
self["showgrid"] = val
@property
def showline(self):
return self["showline"]
@showline.setter
def showline(self, val):
self["showline"] = val
@property
def showticklabels(self):
return self["showticklabels"]
@showticklabels.setter
def showticklabels(self, val):
self["showticklabels"] = val
@property
def showtickprefix(self):
return self["showtickprefix"]
@showtickprefix.setter
def showtickprefix(self, val):
self["showtickprefix"] = val
@property
def showticksuffix(self):
return self["showticksuffix"]
@showticksuffix.setter
def showticksuffix(self, val):
self["showticksuffix"] = val
@property
def thetaunit(self):
return self["thetaunit"]
@thetaunit.setter
def thetaunit(self, val):
self["thetaunit"] = val
@property
def tick0(self):
return self["tick0"]
@tick0.setter
def tick0(self, val):
self["tick0"] = val
@property
def tickangle(self):
return self["tickangle"]
@tickangle.setter
def tickangle(self, val):
self["tickangle"] = val
@property
def tickcolor(self):
return self["tickcolor"]
@tickcolor.setter
def tickcolor(self, val):
self["tickcolor"] = val
@property
def tickfont(self):
return self["tickfont"]
@tickfont.setter
def tickfont(self, val):
self["tickfont"] = val
@property
def tickformat(self):
return self["tickformat"]
@tickformat.setter
def tickformat(self, val):
self["tickformat"] = val
@property
def tickformatstops(self):
return self["tickformatstops"]
@tickformatstops.setter
def tickformatstops(self, val):
self["tickformatstops"] = val
@property
def tickformatstopdefaults(self):
return self["tickformatstopdefaults"]
@tickformatstopdefaults.setter
def tickformatstopdefaults(self, val):
self["tickformatstopdefaults"] = val
@property
def ticklen(self):
return self["ticklen"]
@ticklen.setter
def ticklen(self, val):
self["ticklen"] = val
@property
def tickmode(self):
return self["tickmode"]
@tickmode.setter
def tickmode(self, val):
self["tickmode"] = val
@property
def tickprefix(self):
return self["tickprefix"]
@tickprefix.setter
def tickprefix(self, val):
self["tickprefix"] = val
@property
def ticks(self):
return self["ticks"]
@ticks.setter
def ticks(self, val):
self["ticks"] = val
@property
def ticksuffix(self):
return self["ticksuffix"]
@ticksuffix.setter
def ticksuffix(self, val):
self["ticksuffix"] = val
@property
def ticktext(self):
return self["ticktext"]
@ticktext.setter
def ticktext(self, val):
self["ticktext"] = val
@property
def ticktextsrc(self):
return self["ticktextsrc"]
@ticktextsrc.setter
def ticktextsrc(self, val):
self["ticktextsrc"] = val
@property
def tickvals(self):
return self["tickvals"]
@tickvals.setter
def tickvals(self, val):
self["tickvals"] = val
@property
def tickvalssrc(self):
return self["tickvalssrc"]
@tickvalssrc.setter
def tickvalssrc(self, val):
self["tickvalssrc"] = val
@property
def tickwidth(self):
return self["tickwidth"]
@tickwidth.setter
def tickwidth(self, val):
self["tickwidth"] = val
@property
def type(self):
return self["type"]
@type.setter
def type(self, val):
self["type"] = val
@property
def uirevision(self):
return self["uirevision"]
@uirevision.setter
def uirevision(self, val):
self["uirevision"] = val
@property
def visible(self):
return self["visible"]
@visible.setter
def visible(self, val):
self["visible"] = val
@property
def _prop_descriptions(self):
return """\
autotypenumbers
Using "strict" a numeric string in trace data is not
converted to a number. Using *convert types* a numeric
string in trace data may be treated as a number during
automatic axis `type` detection. Defaults to
layout.autotypenumbers.
categoryarray
Sets the order in which categories on this axis appear.
Only has an effect if `categoryorder` is set to
"array". Used with `categoryorder`.
categoryarraysrc
Sets the source reference on Chart Studio Cloud for
categoryarray .
categoryorder
Specifies the ordering logic for the case of
categorical variables. By default, new_plotly uses "trace",
which specifies the order that is present in the data
supplied. Set `categoryorder` to *category ascending*
or *category descending* if order should be determined
by the alphanumerical order of the category names. Set
`categoryorder` to "array" to derive the ordering from
the attribute `categoryarray`. If a category is not
found in the `categoryarray` array, the sorting
behavior for that attribute will be identical to the
"trace" mode. The unspecified categories will follow
the categories in `categoryarray`. Set `categoryorder`
to *total ascending* or *total descending* if order
should be determined by the numerical order of the
values. Similarly, the order can be determined by the
min, max, sum, mean or median of all the values.
color
Sets default for all colors associated with this axis
all at once: line, font, tick, and grid colors. Grid
color is lightened by blending this with the plot
background Individual pieces can override this.
direction
Sets the direction corresponding to positive angles.
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
gridcolor
Sets the color of the grid lines.
gridwidth
Sets the width (in px) of the grid lines.
hoverformat
Sets the hover text formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for
dates see: https://github.com/d3/d3-time-
format#locale_format We add one item to d3's date
formatter: "%{n}f" for fractional seconds with n
digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
layer
Sets the layer on which this axis is displayed. If
*above traces*, this axis is displayed above all the
subplot's traces If *below traces*, this axis is
displayed below all the subplot's traces, but above the
grid lines. Useful when used together with scatter-like
traces with `cliponaxis` set to False to show markers
and/or text nodes above this axis.
linecolor
Sets the axis line color.
linewidth
Sets the width (in px) of the axis line.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
period
Set the angular period. Has an effect only when
`angularaxis.type` is "category".
rotation
Sets that start position (in degrees) of the angular
axis By default, polar subplots with `direction` set to
"counterclockwise" get a `rotation` of 0 which
corresponds to due East (like what mathematicians
prefer). In turn, polar with `direction` set to
"clockwise" get a rotation of 90 which corresponds to
due North (like on a compass),
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showgrid
Determines whether or not grid lines are drawn. If
True, the grid lines are drawn at every tick mark.
showline
Determines whether or not a line bounding this axis is
drawn.
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thetaunit
Sets the format unit of the formatted "theta" values.
Has an effect only when `angularaxis.type` is "linear".
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the tick font.
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format And for
dates see: https://github.com/d3/d3-time-
format#locale_format We add one item to d3's date
formatter: "%{n}f" for fractional seconds with n
digits. For example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display "09~15~23.46"
tickformatstops
A tuple of :class:`new_plotly.graph_objects.layout.polar.an
gularaxis.Tickformatstop` instances or dicts with
compatible properties
tickformatstopdefaults
When used in a template (as layout.template.layout.pola
r.angularaxis.tickformatstopdefaults), sets the default
property values to use for elements of
layout.polar.angularaxis.tickformatstops
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
ticktext .
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
tickvals .
tickwidth
Sets the tick width (in px).
type
Sets the angular axis type. If "linear", set
`thetaunit` to determine the unit in which axis value
are shown. If *category, use `period` to set the number
of integer coordinates around polar axis.
uirevision
Controls persistence of user-driven changes in axis
`rotation`. Defaults to `polar<N>.uirevision`.
visible
A single toggle to hide the axis while preserving
interaction like dragging. Default is true when a
cheater plot is present on the axis, otherwise false
"""
def __init__(
self,
arg=None,
autotypenumbers=None,
categoryarray=None,
categoryarraysrc=None,
categoryorder=None,
color=None,
direction=None,
dtick=None,
exponentformat=None,
gridcolor=None,
gridwidth=None,
hoverformat=None,
layer=None,
linecolor=None,
linewidth=None,
minexponent=None,
nticks=None,
period=None,
rotation=None,
separatethousands=None,
showexponent=None,
showgrid=None,
showline=None,
showticklabels=None,
showtickprefix=None,
showticksuffix=None,
thetaunit=None,
tick0=None,
tickangle=None,
tickcolor=None,
tickfont=None,
tickformat=None,
tickformatstops=None,
tickformatstopdefaults=None,
ticklen=None,
tickmode=None,
tickprefix=None,
ticks=None,
ticksuffix=None,
ticktext=None,
ticktextsrc=None,
tickvals=None,
tickvalssrc=None,
tickwidth=None,
type=None,
uirevision=None,
visible=None,
**kwargs
):
super(AngularAxis, self).__init__("angularaxis")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the new_plotly.graph_objs.layout.polar.AngularAxis
constructor must be a dict or
an instance of :class:`new_plotly.graph_objs.layout.polar.AngularAxis`"""
)
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
_v = arg.pop("autotypenumbers", None)
_v = autotypenumbers if autotypenumbers is not None else _v
if _v is not None:
self["autotypenumbers"] = _v
_v = arg.pop("categoryarray", None)
_v = categoryarray if categoryarray is not None else _v
if _v is not None:
self["categoryarray"] = _v
_v = arg.pop("categoryarraysrc", None)
_v = categoryarraysrc if categoryarraysrc is not None else _v
if _v is not None:
self["categoryarraysrc"] = _v
_v = arg.pop("categoryorder", None)
_v = categoryorder if categoryorder is not None else _v
if _v is not None:
self["categoryorder"] = _v
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("direction", None)
_v = direction if direction is not None else _v
if _v is not None:
self["direction"] = _v
_v = arg.pop("dtick", None)
_v = dtick if dtick is not None else _v
if _v is not None:
self["dtick"] = _v
_v = arg.pop("exponentformat", None)
_v = exponentformat if exponentformat is not None else _v
if _v is not None:
self["exponentformat"] = _v
_v = arg.pop("gridcolor", None)
_v = gridcolor if gridcolor is not None else _v
if _v is not None:
self["gridcolor"] = _v
_v = arg.pop("gridwidth", None)
_v = gridwidth if gridwidth is not None else _v
if _v is not None:
self["gridwidth"] = _v
_v = arg.pop("hoverformat", None)
_v = hoverformat if hoverformat is not None else _v
if _v is not None:
self["hoverformat"] = _v
_v = arg.pop("layer", None)
_v = layer if layer is not None else _v
if _v is not None:
self["layer"] = _v
_v = arg.pop("linecolor", None)
_v = linecolor if linecolor is not None else _v
if _v is not None:
self["linecolor"] = _v
_v = arg.pop("linewidth", None)
_v = linewidth if linewidth is not None else _v
if _v is not None:
self["linewidth"] = _v
_v = arg.pop("minexponent", None)
_v = minexponent if minexponent is not None else _v
if _v is not None:
self["minexponent"] = _v
_v = arg.pop("nticks", None)
_v = nticks if nticks is not None else _v
if _v is not None:
self["nticks"] = _v
_v = arg.pop("period", None)
_v = period if period is not None else _v
if _v is not None:
self["period"] = _v
_v = arg.pop("rotation", None)
_v = rotation if rotation is not None else _v
if _v is not None:
self["rotation"] = _v
_v = arg.pop("separatethousands", None)
_v = separatethousands if separatethousands is not None else _v
if _v is not None:
self["separatethousands"] = _v
_v = arg.pop("showexponent", None)
_v = showexponent if showexponent is not None else _v
if _v is not None:
self["showexponent"] = _v
_v = arg.pop("showgrid", None)
_v = showgrid if showgrid is not None else _v
if _v is not None:
self["showgrid"] = _v
_v = arg.pop("showline", None)
_v = showline if showline is not None else _v
if _v is not None:
self["showline"] = _v
_v = arg.pop("showticklabels", None)
_v = showticklabels if showticklabels is not None else _v
if _v is not None:
self["showticklabels"] = _v
_v = arg.pop("showtickprefix", None)
_v = showtickprefix if showtickprefix is not None else _v
if _v is not None:
self["showtickprefix"] = _v
_v = arg.pop("showticksuffix", None)
_v = showticksuffix if showticksuffix is not None else _v
if _v is not None:
self["showticksuffix"] = _v
_v = arg.pop("thetaunit", None)
_v = thetaunit if thetaunit is not None else _v
if _v is not None:
self["thetaunit"] = _v
_v = arg.pop("tick0", None)
_v = tick0 if tick0 is not None else _v
if _v is not None:
self["tick0"] = _v
_v = arg.pop("tickangle", None)
_v = tickangle if tickangle is not None else _v
if _v is not None:
self["tickangle"] = _v
_v = arg.pop("tickcolor", None)
_v = tickcolor if tickcolor is not None else _v
if _v is not None:
self["tickcolor"] = _v
_v = arg.pop("tickfont", None)
_v = tickfont if tickfont is not None else _v
if _v is not None:
self["tickfont"] = _v
_v = arg.pop("tickformat", None)
_v = tickformat if tickformat is not None else _v
if _v is not None:
self["tickformat"] = _v
_v = arg.pop("tickformatstops", None)
_v = tickformatstops if tickformatstops is not None else _v
if _v is not None:
self["tickformatstops"] = _v
_v = arg.pop("tickformatstopdefaults", None)
_v = tickformatstopdefaults if tickformatstopdefaults is not None else _v
if _v is not None:
self["tickformatstopdefaults"] = _v
_v = arg.pop("ticklen", None)
_v = ticklen if ticklen is not None else _v
if _v is not None:
self["ticklen"] = _v
_v = arg.pop("tickmode", None)
_v = tickmode if tickmode is not None else _v
if _v is not None:
self["tickmode"] = _v
_v = arg.pop("tickprefix", None)
_v = tickprefix if tickprefix is not None else _v
if _v is not None:
self["tickprefix"] = _v
_v = arg.pop("ticks", None)
_v = ticks if ticks is not None else _v
if _v is not None:
self["ticks"] = _v
_v = arg.pop("ticksuffix", None)
_v = ticksuffix if ticksuffix is not None else _v
if _v is not None:
self["ticksuffix"] = _v
_v = arg.pop("ticktext", None)
_v = ticktext if ticktext is not None else _v
if _v is not None:
self["ticktext"] = _v
_v = arg.pop("ticktextsrc", None)
_v = ticktextsrc if ticktextsrc is not None else _v
if _v is not None:
self["ticktextsrc"] = _v
_v = arg.pop("tickvals", None)
_v = tickvals if tickvals is not None else _v
if _v is not None:
self["tickvals"] = _v
_v = arg.pop("tickvalssrc", None)
_v = tickvalssrc if tickvalssrc is not None else _v
if _v is not None:
self["tickvalssrc"] = _v
_v = arg.pop("tickwidth", None)
_v = tickwidth if tickwidth is not None else _v
if _v is not None:
self["tickwidth"] = _v
_v = arg.pop("type", None)
_v = type if type is not None else _v
if _v is not None:
self["type"] = _v
_v = arg.pop("uirevision", None)
_v = uirevision if uirevision is not None else _v
if _v is not None:
self["uirevision"] = _v
_v = arg.pop("visible", None)
_v = visible if visible is not None else _v
if _v is not None:
self["visible"] = _v
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False
| true
| true
|
1c3ee3ab8eb676d6083f1638cf4a2fa7730a9183
| 2,353
|
py
|
Python
|
inception/inception/imagenet_distributed_train.py
|
robrkerr/tensorflow-models
|
3656a07e89be134c2bc333c60a6c709e475024a6
|
[
"Apache-2.0"
] | 308
|
2018-09-06T18:46:57.000Z
|
2022-03-28T08:22:45.000Z
|
inception/inception/imagenet_distributed_train.py
|
robrkerr/tensorflow-models
|
3656a07e89be134c2bc333c60a6c709e475024a6
|
[
"Apache-2.0"
] | 64
|
2018-06-20T10:14:17.000Z
|
2021-09-08T05:58:25.000Z
|
inception/inception/imagenet_distributed_train.py
|
robrkerr/tensorflow-models
|
3656a07e89be134c2bc333c60a6c709e475024a6
|
[
"Apache-2.0"
] | 69
|
2018-09-18T12:06:56.000Z
|
2022-03-14T11:49:16.000Z
|
# Copyright 2016 Google Inc. 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.
# ==============================================================================
# pylint: disable=line-too-long
"""A binary to train Inception in a distributed manner using multiple systems.
Please see accompanying README.md for details and instructions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from inception import inception_distributed_train
from inception.imagenet_data import ImagenetData
FLAGS = tf.app.flags.FLAGS
def main(unused_args):
assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'
# Extract all the hostnames for the ps and worker jobs to construct the
# cluster spec.
ps_hosts = FLAGS.ps_hosts.split(',')
worker_hosts = FLAGS.worker_hosts.split(',')
tf.logging.info('PS hosts are: %s' % ps_hosts)
tf.logging.info('Worker hosts are: %s' % worker_hosts)
cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
'worker': worker_hosts})
server = tf.train.Server(
{'ps': ps_hosts,
'worker': worker_hosts},
job_name=FLAGS.job_name,
task_index=FLAGS.task_id)
if FLAGS.job_name == 'ps':
# `ps` jobs wait for incoming connections from the workers.
server.join()
else:
# `worker` jobs will actually do the work.
dataset = ImagenetData(subset=FLAGS.subset)
assert dataset.data_files()
# Only the chief checks for or creates train_dir.
if FLAGS.task_id == 0:
if not tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.MakeDirs(FLAGS.train_dir)
inception_distributed_train.train(server.target, dataset, cluster_spec)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
| 35.651515
| 80
| 0.700382
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from inception import inception_distributed_train
from inception.imagenet_data import ImagenetData
FLAGS = tf.app.flags.FLAGS
def main(unused_args):
assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'
ps_hosts = FLAGS.ps_hosts.split(',')
worker_hosts = FLAGS.worker_hosts.split(',')
tf.logging.info('PS hosts are: %s' % ps_hosts)
tf.logging.info('Worker hosts are: %s' % worker_hosts)
cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
'worker': worker_hosts})
server = tf.train.Server(
{'ps': ps_hosts,
'worker': worker_hosts},
job_name=FLAGS.job_name,
task_index=FLAGS.task_id)
if FLAGS.job_name == 'ps':
server.join()
else:
dataset = ImagenetData(subset=FLAGS.subset)
assert dataset.data_files()
if FLAGS.task_id == 0:
if not tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.MakeDirs(FLAGS.train_dir)
inception_distributed_train.train(server.target, dataset, cluster_spec)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
| true
| true
|
1c3ee3f422b44ece53bc045044eb6ac24646c86c
| 1,354
|
py
|
Python
|
app.py
|
tamercuba/Hole-Checker
|
4aac2c12023bd967a9a967d09145ce96c0f7fc27
|
[
"MIT"
] | null | null | null |
app.py
|
tamercuba/Hole-Checker
|
4aac2c12023bd967a9a967d09145ce96c0f7fc27
|
[
"MIT"
] | null | null | null |
app.py
|
tamercuba/Hole-Checker
|
4aac2c12023bd967a9a967d09145ce96c0f7fc27
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
from unicodedata import normalize
import re
template = {
'A': 1, 'B': 2, 'C': 0, 'D': 1, 'E': 0, 'F': 0, 'G': 0, 'H': 0, 'I':0,
'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 1, 'P': 1, 'Q': 1, 'R': 1,
'S': 0, 'T': 0, 'U': 0, 'V': 0, 'X': 0, 'Z': 0, 'Y': 0, 'W': 0,
'a': 1, 'b': 1, 'c': 0, 'd': 1, 'e': 1, 'f': 0, 'g': 0, 'h': 0, 'i': 0,
'j': 0, 'k': 0, 'l': 0, 'm': 0, 'n': 0, 'o': 1, 'p': 1, 'q': 1, 'r': 0,
's': 0, 't': 0, 'u': 0, 'v': 0, 'x': 0, 'z': 0, 'y': 0, 'w': 0
}
def remove_acentos(word):
'''
Função que remove acentos e cedilhas de uma string
'''
return normalize('NFKD', word).encode('ASCII', 'ignore').decode('ASCII')
def checa_buracos(inputword):
'''
Função que checa quantos buracos tem uma string
'''
word = str(inputword)
if len(word) == 0:
return 0
else:
try:
palavra = remove_acentos(word)
buracos = 0
for letra in palavra:
buracos += template[letra]
return buracos
except KeyError:
raise KeyError('A palavra inserida deve conter apenas letras')
return buracos
if __name__ == "__main__":
palavra = str(input("Digite uma palavra (apenas letras): "))
buracos = checa_buracos(palavra)
print(palavra,"tem ", buracos, "buracos.")
| 32.238095
| 76
| 0.488922
|
from unicodedata import normalize
import re
template = {
'A': 1, 'B': 2, 'C': 0, 'D': 1, 'E': 0, 'F': 0, 'G': 0, 'H': 0, 'I':0,
'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 1, 'P': 1, 'Q': 1, 'R': 1,
'S': 0, 'T': 0, 'U': 0, 'V': 0, 'X': 0, 'Z': 0, 'Y': 0, 'W': 0,
'a': 1, 'b': 1, 'c': 0, 'd': 1, 'e': 1, 'f': 0, 'g': 0, 'h': 0, 'i': 0,
'j': 0, 'k': 0, 'l': 0, 'm': 0, 'n': 0, 'o': 1, 'p': 1, 'q': 1, 'r': 0,
's': 0, 't': 0, 'u': 0, 'v': 0, 'x': 0, 'z': 0, 'y': 0, 'w': 0
}
def remove_acentos(word):
return normalize('NFKD', word).encode('ASCII', 'ignore').decode('ASCII')
def checa_buracos(inputword):
word = str(inputword)
if len(word) == 0:
return 0
else:
try:
palavra = remove_acentos(word)
buracos = 0
for letra in palavra:
buracos += template[letra]
return buracos
except KeyError:
raise KeyError('A palavra inserida deve conter apenas letras')
return buracos
if __name__ == "__main__":
palavra = str(input("Digite uma palavra (apenas letras): "))
buracos = checa_buracos(palavra)
print(palavra,"tem ", buracos, "buracos.")
| true
| true
|
1c3ee498ef484f6bcd6ee5707607fd898847237d
| 10,678
|
py
|
Python
|
jorldy/core/agent/rnd_ppo.py
|
ramanuzan/JORLDY
|
be371ad0607e5dba5d5082101c38c6a9f2c96767
|
[
"Apache-2.0"
] | null | null | null |
jorldy/core/agent/rnd_ppo.py
|
ramanuzan/JORLDY
|
be371ad0607e5dba5d5082101c38c6a9f2c96767
|
[
"Apache-2.0"
] | null | null | null |
jorldy/core/agent/rnd_ppo.py
|
ramanuzan/JORLDY
|
be371ad0607e5dba5d5082101c38c6a9f2c96767
|
[
"Apache-2.0"
] | null | null | null |
import torch
torch.backends.cudnn.benchmark = True
import torch.nn.functional as F
from torch.distributions import Normal, Categorical
import os
import numpy as np
from .ppo import PPO
from core.network import Network
class RND_PPO(PPO):
"""Random Network Distillation (RND) with PPO agent.
Args:
state_size (int): dimension of state.
action_size (int): dimension of action.
hidden_size (int): dimension of hidden unit.
rnd_network (str): key of network class in _network_dict.txt.
gamma_i (float): discount factor of intrinsic reward.
extrinsic_coeff (float): coefficient of extrinsic reward.
intrinsic_coeff (float): coefficient of intrinsic reward.
obs_normalize (bool): parameter that determine whether to normalize observation.
ri_normalize (bool): parameter that determine whether to normalize intrinsic reward.
batch_norm (bool): parameter that determine whether to use batch normalization.
non_episodic (bool): parameter that determine whether to use non episodic return(only intrinsic).
non_extrinsic (bool): parameter that determine whether to use intrinsic reward only.
"""
def __init__(
self,
state_size,
action_size,
hidden_size=512,
# Parameters for Random Network Distillation
rnd_network="rnd_mlp",
gamma_i=0.99,
extrinsic_coeff=2.0,
intrinsic_coeff=1.0,
obs_normalize=True,
ri_normalize=True,
batch_norm=True,
non_episodic=True,
non_extrinsic=False,
**kwargs,
):
super(RND_PPO, self).__init__(
state_size=state_size,
action_size=action_size,
hidden_size=hidden_size,
**kwargs,
)
self.rnd_network = rnd_network
self.gamma_i = gamma_i
self.extrinsic_coeff = extrinsic_coeff
self.intrinsic_coeff = intrinsic_coeff
self.obs_normalize = obs_normalize
self.ri_normalize = ri_normalize
self.batch_norm = batch_norm
self.non_episodic = non_episodic
self.non_extrinsic = non_extrinsic
self.rnd = Network(
rnd_network,
state_size,
action_size,
self.num_workers,
gamma_i,
ri_normalize,
obs_normalize,
batch_norm,
D_hidden=hidden_size,
).to(self.device)
self.optimizer.add_param_group({"params": self.rnd.parameters()})
@torch.no_grad()
def act(self, state, training=True):
self.network.train(training)
if self.action_type == "continuous":
mu, std, _ = self.network(self.as_tensor(state))
z = torch.normal(mu, std) if training else mu
action = torch.tanh(z)
else:
pi, _ = self.network(self.as_tensor(state))
action = (
torch.multinomial(pi, 1)
if training
else torch.argmax(pi, dim=-1, keepdim=True)
)
return {"action": action.cpu().numpy()}
def learn(self):
transitions = self.memory.sample()
for key in transitions.keys():
transitions[key] = self.as_tensor(transitions[key])
state = transitions["state"]
action = transitions["action"]
reward = transitions["reward"]
next_state = transitions["next_state"]
done = transitions["done"]
# use extrinsic check
if self.non_extrinsic:
reward *= 0.0
# set pi_old and advantage
with torch.no_grad():
# RND: calculate exploration reward, update moments of obs and r_i
self.rnd.update_rms_obs(next_state)
r_i = self.rnd(next_state, update_ri=True)
if self.action_type == "continuous":
mu, std, value = self.network(state)
m = Normal(mu, std)
z = torch.atanh(torch.clamp(action, -1 + 1e-7, 1 - 1e-7))
log_prob = m.log_prob(z)
else:
pi, value = self.network(state)
log_prob = pi.gather(1, action.long()).log()
log_prob_old = log_prob
v_i = self.network.get_v_i(state)
next_value = self.network(next_state)[-1]
delta = reward + (1 - done) * self.gamma * next_value - value
next_v_i = self.network.get_v_i(next_state)
episodic_factor = 1.0 if self.non_episodic else (1 - done)
delta_i = r_i + episodic_factor * self.gamma_i * next_v_i - v_i
adv, adv_i = delta.clone(), delta_i.clone()
adv, adv_i = adv.view(-1, self.n_step), adv_i.view(-1, self.n_step)
done = done.view(-1, self.n_step)
for t in reversed(range(self.n_step - 1)):
adv[:, t] += (
(1 - done[:, t]) * self.gamma * self._lambda * adv[:, t + 1]
)
episodic_factor = 1.0 if self.non_episodic else (1 - done[:, t])
adv_i[:, t] += (
episodic_factor * self.gamma_i * self._lambda * adv_i[:, t + 1]
)
ret = adv.view(-1, 1) + value
ret_i = adv_i.view(-1, 1) + v_i
adv = self.extrinsic_coeff * adv + self.intrinsic_coeff * adv_i
if self.use_standardization:
adv = (adv - adv.mean(dim=1, keepdim=True)) / (
adv.std(dim=1, keepdim=True) + 1e-7
)
adv, done = adv.view(-1, 1), done.view(-1, 1)
mean_ret = ret.mean().item()
mean_ret_i = ret_i.mean().item()
# start train iteration
actor_losses, critic_e_losses, critic_i_losses = [], [], []
entropy_losses, rnd_losses, ratios, probs = [], [], [], []
idxs = np.arange(len(reward))
for idx_epoch in range(self.n_epoch):
np.random.shuffle(idxs)
for offset in range(0, len(reward), self.batch_size):
idx = idxs[offset : offset + self.batch_size]
(
_state,
_action,
_value,
_ret,
_ret_i,
_next_state,
_adv,
_log_prob_old,
) = map(
lambda x: [_x[idx] for _x in x] if isinstance(x, list) else x[idx],
[
state,
action,
value,
ret,
ret_i,
next_state,
adv,
log_prob_old,
],
)
if self.action_type == "continuous":
mu, std, value_pred = self.network(_state)
m = Normal(mu, std)
z = torch.atanh(torch.clamp(_action, -1 + 1e-7, 1 - 1e-7))
log_prob = m.log_prob(z)
else:
pi, value_pred = self.network(_state)
m = Categorical(pi)
log_prob = m.log_prob(_action.squeeze(-1)).unsqueeze(-1)
v_i = self.network.get_v_i(_state)
ratio = (log_prob - _log_prob_old).sum(1, keepdim=True).exp()
surr1 = ratio * _adv
surr2 = (
torch.clamp(
ratio, min=1 - self.epsilon_clip, max=1 + self.epsilon_clip
)
* _adv
)
actor_loss = -torch.min(surr1, surr2).mean()
critic_e_loss = F.mse_loss(value_pred, _ret).mean()
critic_i_loss = F.mse_loss(v_i, _ret_i).mean()
critic_loss = critic_e_loss + critic_i_loss
entropy_loss = -m.entropy().mean()
ppo_loss = (
actor_loss
+ self.vf_coef * critic_loss
+ self.ent_coef * entropy_loss
)
rnd_loss = self.rnd.forward(_next_state).mean()
loss = ppo_loss + rnd_loss
self.optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.network.parameters(), self.clip_grad_norm
)
torch.nn.utils.clip_grad_norm_(
self.rnd.parameters(), self.clip_grad_norm
)
self.optimizer.step()
probs.append(log_prob.exp().min().item())
ratios.append(ratio.max().item())
actor_losses.append(actor_loss.item())
critic_e_losses.append(critic_e_loss.item())
critic_i_losses.append(critic_i_loss.item())
entropy_losses.append(entropy_loss.item())
rnd_losses.append(rnd_loss.item())
result = {
"actor_loss": np.mean(actor_losses),
"critic_e_loss": np.mean(critic_e_losses),
"critic_i_loss": np.mean(critic_i_losses),
"entropy_loss": np.mean(entropy_losses),
"r_i": np.mean(rnd_losses),
"max_ratio": max(ratios),
"min_prob": min(probs),
"mean_ret": mean_ret,
"mean_ret_i": mean_ret_i,
}
return result
def process(self, transitions, step):
result = {}
# Process per step
self.memory.store(transitions)
delta_t = step - self.time_t
self.time_t = step
self.learn_stamp += delta_t
if len(transitions) > 0 and transitions[0]["done"]:
self.state_seq = None
# Process per epi
if self.learn_stamp >= self.n_step:
result = self.learn()
self.learning_rate_decay(step)
self.learn_stamp -= self.n_step
return result
def save(self, path):
print(f"...Save model to {path}...")
torch.save(
{
"network": self.network.state_dict(),
"rnd": self.rnd.state_dict(),
"optimizer": self.optimizer.state_dict(),
},
os.path.join(path, "ckpt"),
)
def load(self, path):
print(f"...Load model from {path}...")
checkpoint = torch.load(os.path.join(path, "ckpt"), map_location=self.device)
self.network.load_state_dict(checkpoint["network"])
self.rnd.load_state_dict(checkpoint["rnd"])
self.optimizer.load_state_dict(checkpoint["optimizer"])
| 35.712375
| 105
| 0.52847
|
import torch
torch.backends.cudnn.benchmark = True
import torch.nn.functional as F
from torch.distributions import Normal, Categorical
import os
import numpy as np
from .ppo import PPO
from core.network import Network
class RND_PPO(PPO):
def __init__(
self,
state_size,
action_size,
hidden_size=512,
rnd_network="rnd_mlp",
gamma_i=0.99,
extrinsic_coeff=2.0,
intrinsic_coeff=1.0,
obs_normalize=True,
ri_normalize=True,
batch_norm=True,
non_episodic=True,
non_extrinsic=False,
**kwargs,
):
super(RND_PPO, self).__init__(
state_size=state_size,
action_size=action_size,
hidden_size=hidden_size,
**kwargs,
)
self.rnd_network = rnd_network
self.gamma_i = gamma_i
self.extrinsic_coeff = extrinsic_coeff
self.intrinsic_coeff = intrinsic_coeff
self.obs_normalize = obs_normalize
self.ri_normalize = ri_normalize
self.batch_norm = batch_norm
self.non_episodic = non_episodic
self.non_extrinsic = non_extrinsic
self.rnd = Network(
rnd_network,
state_size,
action_size,
self.num_workers,
gamma_i,
ri_normalize,
obs_normalize,
batch_norm,
D_hidden=hidden_size,
).to(self.device)
self.optimizer.add_param_group({"params": self.rnd.parameters()})
@torch.no_grad()
def act(self, state, training=True):
self.network.train(training)
if self.action_type == "continuous":
mu, std, _ = self.network(self.as_tensor(state))
z = torch.normal(mu, std) if training else mu
action = torch.tanh(z)
else:
pi, _ = self.network(self.as_tensor(state))
action = (
torch.multinomial(pi, 1)
if training
else torch.argmax(pi, dim=-1, keepdim=True)
)
return {"action": action.cpu().numpy()}
def learn(self):
transitions = self.memory.sample()
for key in transitions.keys():
transitions[key] = self.as_tensor(transitions[key])
state = transitions["state"]
action = transitions["action"]
reward = transitions["reward"]
next_state = transitions["next_state"]
done = transitions["done"]
if self.non_extrinsic:
reward *= 0.0
with torch.no_grad():
self.rnd.update_rms_obs(next_state)
r_i = self.rnd(next_state, update_ri=True)
if self.action_type == "continuous":
mu, std, value = self.network(state)
m = Normal(mu, std)
z = torch.atanh(torch.clamp(action, -1 + 1e-7, 1 - 1e-7))
log_prob = m.log_prob(z)
else:
pi, value = self.network(state)
log_prob = pi.gather(1, action.long()).log()
log_prob_old = log_prob
v_i = self.network.get_v_i(state)
next_value = self.network(next_state)[-1]
delta = reward + (1 - done) * self.gamma * next_value - value
next_v_i = self.network.get_v_i(next_state)
episodic_factor = 1.0 if self.non_episodic else (1 - done)
delta_i = r_i + episodic_factor * self.gamma_i * next_v_i - v_i
adv, adv_i = delta.clone(), delta_i.clone()
adv, adv_i = adv.view(-1, self.n_step), adv_i.view(-1, self.n_step)
done = done.view(-1, self.n_step)
for t in reversed(range(self.n_step - 1)):
adv[:, t] += (
(1 - done[:, t]) * self.gamma * self._lambda * adv[:, t + 1]
)
episodic_factor = 1.0 if self.non_episodic else (1 - done[:, t])
adv_i[:, t] += (
episodic_factor * self.gamma_i * self._lambda * adv_i[:, t + 1]
)
ret = adv.view(-1, 1) + value
ret_i = adv_i.view(-1, 1) + v_i
adv = self.extrinsic_coeff * adv + self.intrinsic_coeff * adv_i
if self.use_standardization:
adv = (adv - adv.mean(dim=1, keepdim=True)) / (
adv.std(dim=1, keepdim=True) + 1e-7
)
adv, done = adv.view(-1, 1), done.view(-1, 1)
mean_ret = ret.mean().item()
mean_ret_i = ret_i.mean().item()
actor_losses, critic_e_losses, critic_i_losses = [], [], []
entropy_losses, rnd_losses, ratios, probs = [], [], [], []
idxs = np.arange(len(reward))
for idx_epoch in range(self.n_epoch):
np.random.shuffle(idxs)
for offset in range(0, len(reward), self.batch_size):
idx = idxs[offset : offset + self.batch_size]
(
_state,
_action,
_value,
_ret,
_ret_i,
_next_state,
_adv,
_log_prob_old,
) = map(
lambda x: [_x[idx] for _x in x] if isinstance(x, list) else x[idx],
[
state,
action,
value,
ret,
ret_i,
next_state,
adv,
log_prob_old,
],
)
if self.action_type == "continuous":
mu, std, value_pred = self.network(_state)
m = Normal(mu, std)
z = torch.atanh(torch.clamp(_action, -1 + 1e-7, 1 - 1e-7))
log_prob = m.log_prob(z)
else:
pi, value_pred = self.network(_state)
m = Categorical(pi)
log_prob = m.log_prob(_action.squeeze(-1)).unsqueeze(-1)
v_i = self.network.get_v_i(_state)
ratio = (log_prob - _log_prob_old).sum(1, keepdim=True).exp()
surr1 = ratio * _adv
surr2 = (
torch.clamp(
ratio, min=1 - self.epsilon_clip, max=1 + self.epsilon_clip
)
* _adv
)
actor_loss = -torch.min(surr1, surr2).mean()
critic_e_loss = F.mse_loss(value_pred, _ret).mean()
critic_i_loss = F.mse_loss(v_i, _ret_i).mean()
critic_loss = critic_e_loss + critic_i_loss
entropy_loss = -m.entropy().mean()
ppo_loss = (
actor_loss
+ self.vf_coef * critic_loss
+ self.ent_coef * entropy_loss
)
rnd_loss = self.rnd.forward(_next_state).mean()
loss = ppo_loss + rnd_loss
self.optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.network.parameters(), self.clip_grad_norm
)
torch.nn.utils.clip_grad_norm_(
self.rnd.parameters(), self.clip_grad_norm
)
self.optimizer.step()
probs.append(log_prob.exp().min().item())
ratios.append(ratio.max().item())
actor_losses.append(actor_loss.item())
critic_e_losses.append(critic_e_loss.item())
critic_i_losses.append(critic_i_loss.item())
entropy_losses.append(entropy_loss.item())
rnd_losses.append(rnd_loss.item())
result = {
"actor_loss": np.mean(actor_losses),
"critic_e_loss": np.mean(critic_e_losses),
"critic_i_loss": np.mean(critic_i_losses),
"entropy_loss": np.mean(entropy_losses),
"r_i": np.mean(rnd_losses),
"max_ratio": max(ratios),
"min_prob": min(probs),
"mean_ret": mean_ret,
"mean_ret_i": mean_ret_i,
}
return result
def process(self, transitions, step):
result = {}
self.memory.store(transitions)
delta_t = step - self.time_t
self.time_t = step
self.learn_stamp += delta_t
if len(transitions) > 0 and transitions[0]["done"]:
self.state_seq = None
if self.learn_stamp >= self.n_step:
result = self.learn()
self.learning_rate_decay(step)
self.learn_stamp -= self.n_step
return result
def save(self, path):
print(f"...Save model to {path}...")
torch.save(
{
"network": self.network.state_dict(),
"rnd": self.rnd.state_dict(),
"optimizer": self.optimizer.state_dict(),
},
os.path.join(path, "ckpt"),
)
def load(self, path):
print(f"...Load model from {path}...")
checkpoint = torch.load(os.path.join(path, "ckpt"), map_location=self.device)
self.network.load_state_dict(checkpoint["network"])
self.rnd.load_state_dict(checkpoint["rnd"])
self.optimizer.load_state_dict(checkpoint["optimizer"])
| true
| true
|
1c3ee539cc4950ecc94f13b442b74342ad083484
| 4,764
|
py
|
Python
|
mfr/extensions/tabular/render.py
|
yacchin1205/RDM-modular-file-renderer
|
5bd18175a681d21e7be7fe0238132335a1cd8ded
|
[
"Apache-2.0"
] | 36
|
2015-08-31T20:24:22.000Z
|
2021-12-17T17:02:44.000Z
|
mfr/extensions/tabular/render.py
|
yacchin1205/RDM-modular-file-renderer
|
5bd18175a681d21e7be7fe0238132335a1cd8ded
|
[
"Apache-2.0"
] | 190
|
2015-01-02T06:22:01.000Z
|
2022-01-19T11:27:03.000Z
|
mfr/extensions/tabular/render.py
|
yacchin1205/RDM-modular-file-renderer
|
5bd18175a681d21e7be7fe0238132335a1cd8ded
|
[
"Apache-2.0"
] | 47
|
2015-01-27T15:45:22.000Z
|
2021-01-27T22:43:03.000Z
|
import json
import gc
import logging
import os
from humanfriendly import format_size
from mako.lookup import TemplateLookup
from mfr.core import extension
from mfr.extensions.tabular import settings, exceptions
logger = logging.getLogger(__name__)
class TabularRenderer(extension.BaseRenderer):
TEMPLATE = TemplateLookup(
directories=[
os.path.join(os.path.dirname(__file__), 'templates')
]).get_template('viewer.mako')
def render(self):
file_size = os.path.getsize(self.file_path)
if file_size > settings.MAX_FILE_SIZE:
raise exceptions.FileTooLargeError(
'Tabular files larger than {} are not rendered. Please download '
'the file to view.'.format(format_size(settings.MAX_FILE_SIZE, binary=True)),
file_size=file_size,
max_size=settings.MAX_FILE_SIZE,
extension=self.metadata.ext,
)
with open(self.file_path, errors='replace') as fp:
sheets, size, nbr_rows, nbr_cols = self._render_grid(fp, self.metadata.ext)
# Force GC
gc.collect()
if sheets and size:
return self.TEMPLATE.render(
base=self.assets_url,
width=settings.TABLE_WIDTH,
height=settings.TABLE_HEIGHT,
sheets=json.dumps(sheets),
options=json.dumps(size),
)
assert nbr_rows and nbr_cols
raise exceptions.TableTooBigError(
'Table is too large to render.',
extension=self.metadata.ext,
nbr_cols=nbr_cols,
nbr_rows=nbr_rows
)
@property
def file_required(self):
return True
@property
def cache_result(self):
return True
def _render_grid(self, fp, ext, *args, **kwargs):
"""Render a tabular file to html
:param fp: file pointer object
:return: RenderResult object containing html and assets
"""
self._renderer_tabular_metrics = {}
sheets = self._populate_data(fp, ext)
size = settings.SMALL_TABLE
self._renderer_tabular_metrics['size'] = 'small'
self._renderer_tabular_metrics['nbr_sheets'] = len(sheets)
table_too_big = False
nbr_cols = 0
nbr_rows = 0
for sheet_title in sheets:
sheet = sheets[sheet_title]
# sheet is a two-element list. sheet[0] is a list of dicts containing metadata about
# the column headers. Each dict contains four keys: `field`, `name`, `sortable`, `id`.
# sheet[1] is a list of dicts where each dict contains the row data. The keys are the
# fields the data belongs to and the values are the data values.
nbr_cols = len(sheet[0])
if nbr_cols > 9:
size = settings.BIG_TABLE
self._renderer_tabular_metrics['size'] = 'big'
nbr_rows = len(sheet[1])
if nbr_cols > settings.MAX_SIZE or nbr_rows > settings.MAX_SIZE:
table_too_big = True
break
if table_too_big:
del sheets
return None, None, nbr_rows, nbr_cols
return sheets, size, None, None
def _populate_data(self, fp, ext):
"""Determine the appropriate library and use it to populate rows and columns
:param fp: file pointer
:param ext: file extension
:return: a dict mapping sheet titles to tuples of column headers and row data
"""
function_preference = settings.LIBS.get(ext.lower())
for populate_func in function_preference:
try:
imported = populate_func()
except ImportError:
pass
else:
self._renderer_tabular_metrics['importer'] = populate_func.__name__
try:
return imported(fp)
except (KeyError, ValueError) as err:
logger.error('WB has encountered an unexpected error '
'when trying to render a tabular file: {}'.format(err))
raise exceptions.UnexpectedFormattingError(
'Unexpected formatting error.',
extension=self.metadata.ext,
formatting_function=str(populate_func),
)
# this will only occur if function_preference is an empty set
# or all functions in the set raise an import error
raise exceptions.MissingRequirementsError(
'Renderer requirements are not met',
extension=self.metadata.ext,
function_preference=function_preference,
)
| 34.521739
| 99
| 0.594039
|
import json
import gc
import logging
import os
from humanfriendly import format_size
from mako.lookup import TemplateLookup
from mfr.core import extension
from mfr.extensions.tabular import settings, exceptions
logger = logging.getLogger(__name__)
class TabularRenderer(extension.BaseRenderer):
TEMPLATE = TemplateLookup(
directories=[
os.path.join(os.path.dirname(__file__), 'templates')
]).get_template('viewer.mako')
def render(self):
file_size = os.path.getsize(self.file_path)
if file_size > settings.MAX_FILE_SIZE:
raise exceptions.FileTooLargeError(
'Tabular files larger than {} are not rendered. Please download '
'the file to view.'.format(format_size(settings.MAX_FILE_SIZE, binary=True)),
file_size=file_size,
max_size=settings.MAX_FILE_SIZE,
extension=self.metadata.ext,
)
with open(self.file_path, errors='replace') as fp:
sheets, size, nbr_rows, nbr_cols = self._render_grid(fp, self.metadata.ext)
gc.collect()
if sheets and size:
return self.TEMPLATE.render(
base=self.assets_url,
width=settings.TABLE_WIDTH,
height=settings.TABLE_HEIGHT,
sheets=json.dumps(sheets),
options=json.dumps(size),
)
assert nbr_rows and nbr_cols
raise exceptions.TableTooBigError(
'Table is too large to render.',
extension=self.metadata.ext,
nbr_cols=nbr_cols,
nbr_rows=nbr_rows
)
@property
def file_required(self):
return True
@property
def cache_result(self):
return True
def _render_grid(self, fp, ext, *args, **kwargs):
self._renderer_tabular_metrics = {}
sheets = self._populate_data(fp, ext)
size = settings.SMALL_TABLE
self._renderer_tabular_metrics['size'] = 'small'
self._renderer_tabular_metrics['nbr_sheets'] = len(sheets)
table_too_big = False
nbr_cols = 0
nbr_rows = 0
for sheet_title in sheets:
sheet = sheets[sheet_title]
nbr_cols = len(sheet[0])
if nbr_cols > 9:
size = settings.BIG_TABLE
self._renderer_tabular_metrics['size'] = 'big'
nbr_rows = len(sheet[1])
if nbr_cols > settings.MAX_SIZE or nbr_rows > settings.MAX_SIZE:
table_too_big = True
break
if table_too_big:
del sheets
return None, None, nbr_rows, nbr_cols
return sheets, size, None, None
def _populate_data(self, fp, ext):
function_preference = settings.LIBS.get(ext.lower())
for populate_func in function_preference:
try:
imported = populate_func()
except ImportError:
pass
else:
self._renderer_tabular_metrics['importer'] = populate_func.__name__
try:
return imported(fp)
except (KeyError, ValueError) as err:
logger.error('WB has encountered an unexpected error '
'when trying to render a tabular file: {}'.format(err))
raise exceptions.UnexpectedFormattingError(
'Unexpected formatting error.',
extension=self.metadata.ext,
formatting_function=str(populate_func),
)
raise exceptions.MissingRequirementsError(
'Renderer requirements are not met',
extension=self.metadata.ext,
function_preference=function_preference,
)
| true
| true
|
1c3ee72bbc28c79942db067dc586c55460ee4caf
| 2,589
|
py
|
Python
|
tools/mo/unit_tests/mo/front/onnx/activation_ext_test.py
|
ryanloney/openvino-1
|
4e0a740eb3ee31062ba0df88fcf438564f67edb7
|
[
"Apache-2.0"
] | 1,127
|
2018-10-15T14:36:58.000Z
|
2020-04-20T09:29:44.000Z
|
tools/mo/unit_tests/mo/front/onnx/activation_ext_test.py
|
ryanloney/openvino-1
|
4e0a740eb3ee31062ba0df88fcf438564f67edb7
|
[
"Apache-2.0"
] | 439
|
2018-10-20T04:40:35.000Z
|
2020-04-19T05:56:25.000Z
|
tools/mo/unit_tests/mo/front/onnx/activation_ext_test.py
|
ryanloney/openvino-1
|
4e0a740eb3ee31062ba0df88fcf438564f67edb7
|
[
"Apache-2.0"
] | 414
|
2018-10-17T05:53:46.000Z
|
2020-04-16T17:29:53.000Z
|
# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import unittest
import numpy as np
import onnx
from generator import generator, generate
import openvino.tools.mo.front.onnx.activation_ext as extractors
from openvino.tools.mo.ops.activation_ops import Elu
from openvino.tools.mo.graph.graph import Node
from openvino.tools.mo.ops.op import Op
from unit_tests.utils.extractors import PB
from unit_tests.utils.graph import build_graph
@generator
class ActivationOpsONNXExtractorTest(unittest.TestCase):
@staticmethod
def _create_node(op_name: str):
pb = onnx.helper.make_node(op_name, ["X"], ["Y"])
graph = build_graph({'node_0': {'pb': pb}}, [])
return Node(graph, 'node_0')
@staticmethod
def _base_attrs(op_name: str):
# reference output Node attributes
return (
dict(
op=op_name,
)
)
def _match(self, out, ref):
for key in ref.keys():
status = out[key] == ref[key]
if type(status) in [list, np.ndarray]:
status = np.all(status)
self.assertTrue(status, 'Mismatch for field {}, observed: {}, expected: {}'.format(key, out[key], ref[key]))
@staticmethod
def _extract(op_name):
node = __class__._create_node(op_name)
getattr(extractors, op_name + 'Extractor').extract(node)
return node.graph.node[node.id]
@generate(*['Abs', 'Acos', 'Asin', 'Atan', 'Acosh', 'Asinh', 'Atanh', 'Cos', 'Cosh', 'Erf', 'Exp', 'Floor', 'Log', 'Not', 'Sigmoid', 'Sin',
'Sinh', 'Tan', 'Tanh'])
def test_default(self, op_name):
ref = self._base_attrs(op_name)
if ref['op'] == 'Not':
ref['op'] = 'LogicalNot'
out = self._extract(op_name)
self._match(out, ref)
@generator
class TestEluONNXExt(unittest.TestCase):
@staticmethod
def _create_elu_node(alpha=1.0):
pb = onnx.helper.make_node(
'Elu',
inputs=['x'],
outputs=['y'],
alpha=alpha
)
node = PB({'pb': pb})
return node
@classmethod
def setUpClass(cls):
Op.registered_ops['Elu'] = Elu
@generate(*[1.0, 2.0, 3.0])
def test_elu_ext(self, alpha):
node = self._create_elu_node(alpha)
extractors.EluExtractor.extract(node)
exp_res = {
'type': 'Elu',
'alpha': alpha,
'infer': Elu.infer
}
for key in exp_res.keys():
self.assertEqual(node[key], exp_res[key])
| 29.420455
| 143
| 0.591348
|
import unittest
import numpy as np
import onnx
from generator import generator, generate
import openvino.tools.mo.front.onnx.activation_ext as extractors
from openvino.tools.mo.ops.activation_ops import Elu
from openvino.tools.mo.graph.graph import Node
from openvino.tools.mo.ops.op import Op
from unit_tests.utils.extractors import PB
from unit_tests.utils.graph import build_graph
@generator
class ActivationOpsONNXExtractorTest(unittest.TestCase):
@staticmethod
def _create_node(op_name: str):
pb = onnx.helper.make_node(op_name, ["X"], ["Y"])
graph = build_graph({'node_0': {'pb': pb}}, [])
return Node(graph, 'node_0')
@staticmethod
def _base_attrs(op_name: str):
return (
dict(
op=op_name,
)
)
def _match(self, out, ref):
for key in ref.keys():
status = out[key] == ref[key]
if type(status) in [list, np.ndarray]:
status = np.all(status)
self.assertTrue(status, 'Mismatch for field {}, observed: {}, expected: {}'.format(key, out[key], ref[key]))
@staticmethod
def _extract(op_name):
node = __class__._create_node(op_name)
getattr(extractors, op_name + 'Extractor').extract(node)
return node.graph.node[node.id]
@generate(*['Abs', 'Acos', 'Asin', 'Atan', 'Acosh', 'Asinh', 'Atanh', 'Cos', 'Cosh', 'Erf', 'Exp', 'Floor', 'Log', 'Not', 'Sigmoid', 'Sin',
'Sinh', 'Tan', 'Tanh'])
def test_default(self, op_name):
ref = self._base_attrs(op_name)
if ref['op'] == 'Not':
ref['op'] = 'LogicalNot'
out = self._extract(op_name)
self._match(out, ref)
@generator
class TestEluONNXExt(unittest.TestCase):
@staticmethod
def _create_elu_node(alpha=1.0):
pb = onnx.helper.make_node(
'Elu',
inputs=['x'],
outputs=['y'],
alpha=alpha
)
node = PB({'pb': pb})
return node
@classmethod
def setUpClass(cls):
Op.registered_ops['Elu'] = Elu
@generate(*[1.0, 2.0, 3.0])
def test_elu_ext(self, alpha):
node = self._create_elu_node(alpha)
extractors.EluExtractor.extract(node)
exp_res = {
'type': 'Elu',
'alpha': alpha,
'infer': Elu.infer
}
for key in exp_res.keys():
self.assertEqual(node[key], exp_res[key])
| true
| true
|
1c3ee7b266d51215d446e12b11014bfad3376df9
| 1,438
|
py
|
Python
|
docs/conf.py
|
qlixed/pymemwiper
|
216bc05f9b67e37d0c98e3f3a70f08e3362f07d9
|
[
"MIT"
] | 2
|
2017-12-08T22:52:20.000Z
|
2018-10-28T20:40:42.000Z
|
docs/conf.py
|
qlixed/pymemwiper
|
216bc05f9b67e37d0c98e3f3a70f08e3362f07d9
|
[
"MIT"
] | 20
|
2017-06-04T22:13:06.000Z
|
2020-01-05T22:32:24.000Z
|
docs/conf.py
|
qlixed/python-memwiper
|
216bc05f9b67e37d0c98e3f3a70f08e3362f07d9
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.coverage',
'sphinx.ext.doctest',
'sphinx.ext.extlinks',
'sphinx.ext.ifconfig',
'sphinx.ext.napoleon',
'sphinx.ext.todo',
'sphinx.ext.viewcode',
]
if os.getenv('SPELLCHECK'):
extensions += 'sphinxcontrib.spelling',
spelling_show_suggestions = True
spelling_lang = 'en_US'
source_suffix = '.rst'
master_doc = 'index'
project = u'python-memwiper'
year = u'2017'
author = u'Ezequiel Hector Brizuela - qlixed'
copyright = '{0}, {1}'.format(year, author)
version = release = u'0.9.0.alpha'
pygments_style = 'trac'
templates_path = ['.']
extlinks = {
'issue': ('https://github.com/qlixed/python-memwiper/issues/%s', '#'),
'pr': ('https://github.com/qlixed/python-memwiper/pull/%s', 'PR #'),
}
import sphinx_py3doc_enhanced_theme
html_theme = "sphinx_py3doc_enhanced_theme"
html_theme_path = [sphinx_py3doc_enhanced_theme.get_html_theme_path()]
html_theme_options = {
'githuburl': 'https://github.com/qlixed/python-memwiper/'
}
html_use_smartypants = True
html_last_updated_fmt = '%b %d, %Y'
html_split_index = False
html_sidebars = {
'**': ['searchbox.html', 'globaltoc.html', 'sourcelink.html'],
}
html_short_title = '%s-%s' % (project, version)
napoleon_use_ivar = True
napoleon_use_rtype = False
napoleon_use_param = False
| 26.145455
| 74
| 0.698192
|
from __future__ import unicode_literals
import os
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.coverage',
'sphinx.ext.doctest',
'sphinx.ext.extlinks',
'sphinx.ext.ifconfig',
'sphinx.ext.napoleon',
'sphinx.ext.todo',
'sphinx.ext.viewcode',
]
if os.getenv('SPELLCHECK'):
extensions += 'sphinxcontrib.spelling',
spelling_show_suggestions = True
spelling_lang = 'en_US'
source_suffix = '.rst'
master_doc = 'index'
project = u'python-memwiper'
year = u'2017'
author = u'Ezequiel Hector Brizuela - qlixed'
copyright = '{0}, {1}'.format(year, author)
version = release = u'0.9.0.alpha'
pygments_style = 'trac'
templates_path = ['.']
extlinks = {
'issue': ('https://github.com/qlixed/python-memwiper/issues/%s', '#'),
'pr': ('https://github.com/qlixed/python-memwiper/pull/%s', 'PR #'),
}
import sphinx_py3doc_enhanced_theme
html_theme = "sphinx_py3doc_enhanced_theme"
html_theme_path = [sphinx_py3doc_enhanced_theme.get_html_theme_path()]
html_theme_options = {
'githuburl': 'https://github.com/qlixed/python-memwiper/'
}
html_use_smartypants = True
html_last_updated_fmt = '%b %d, %Y'
html_split_index = False
html_sidebars = {
'**': ['searchbox.html', 'globaltoc.html', 'sourcelink.html'],
}
html_short_title = '%s-%s' % (project, version)
napoleon_use_ivar = True
napoleon_use_rtype = False
napoleon_use_param = False
| true
| true
|
1c3ee841d66b0cc7cfd54b1431b94a4ac454cbf5
| 1,596
|
py
|
Python
|
aliyun-python-sdk-imm/aliyunsdkimm/request/v20170906/DeleteTagSetRequest.py
|
sdk-team/aliyun-openapi-python-sdk
|
384730d707e6720d1676ccb8f552e6a7b330ec86
|
[
"Apache-2.0"
] | 1
|
2019-12-23T12:36:43.000Z
|
2019-12-23T12:36:43.000Z
|
aliyun-python-sdk-imm/aliyunsdkimm/request/v20170906/DeleteTagSetRequest.py
|
sdk-team/aliyun-openapi-python-sdk
|
384730d707e6720d1676ccb8f552e6a7b330ec86
|
[
"Apache-2.0"
] | null | null | null |
aliyun-python-sdk-imm/aliyunsdkimm/request/v20170906/DeleteTagSetRequest.py
|
sdk-team/aliyun-openapi-python-sdk
|
384730d707e6720d1676ccb8f552e6a7b330ec86
|
[
"Apache-2.0"
] | 1
|
2021-02-23T11:27:54.000Z
|
2021-02-23T11:27:54.000Z
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
class DeleteTagSetRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'imm', '2017-09-06', 'DeleteTagSet','imm')
def get_LazyMode(self):
return self.get_query_params().get('LazyMode')
def set_LazyMode(self,LazyMode):
self.add_query_param('LazyMode',LazyMode)
def get_Project(self):
return self.get_query_params().get('Project')
def set_Project(self,Project):
self.add_query_param('Project',Project)
def get_SetId(self):
return self.get_query_params().get('SetId')
def set_SetId(self,SetId):
self.add_query_param('SetId',SetId)
def get_CheckEmpty(self):
return self.get_query_params().get('CheckEmpty')
def set_CheckEmpty(self,CheckEmpty):
self.add_query_param('CheckEmpty',CheckEmpty)
| 33.25
| 71
| 0.754386
|
from aliyunsdkcore.request import RpcRequest
class DeleteTagSetRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'imm', '2017-09-06', 'DeleteTagSet','imm')
def get_LazyMode(self):
return self.get_query_params().get('LazyMode')
def set_LazyMode(self,LazyMode):
self.add_query_param('LazyMode',LazyMode)
def get_Project(self):
return self.get_query_params().get('Project')
def set_Project(self,Project):
self.add_query_param('Project',Project)
def get_SetId(self):
return self.get_query_params().get('SetId')
def set_SetId(self,SetId):
self.add_query_param('SetId',SetId)
def get_CheckEmpty(self):
return self.get_query_params().get('CheckEmpty')
def set_CheckEmpty(self,CheckEmpty):
self.add_query_param('CheckEmpty',CheckEmpty)
| true
| true
|
1c3eea5aece6d683e89d4098eb1688877e50e47b
| 1,110
|
py
|
Python
|
tscore-test.py
|
newsgac/fasttext-runs
|
50cc6bf4d8441f7208efb6b71eb45e7641d1af09
|
[
"Apache-2.0"
] | null | null | null |
tscore-test.py
|
newsgac/fasttext-runs
|
50cc6bf4d8441f7208efb6b71eb45e7641d1af09
|
[
"Apache-2.0"
] | null | null | null |
tscore-test.py
|
newsgac/fasttext-runs
|
50cc6bf4d8441f7208efb6b71eb45e7641d1af09
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python3 -W all
"""
tscore-test.py: tests for tscore.py
usage: tscore-test.py
20171124 erikt(at)xs4all.nl
"""
import io
import re
import sys
import unittest
from contextlib import redirect_stdout
from tscore import computeTscore
from tscore import readData
from tscore import tscore
from tscore import writeData
DATA1 = { "totalFreq":3, "nbrOfWords":2, "wordFreqs":{"a":1, "b":2 } }
DATA2 = { "totalFreq":3, "nbrOfWords":2, "wordFreqs":{"b":1, "c":2 } }
RESULTS = {'c': -1.1547005383792517, 'a': 0.7071067811865475, 'b': 0.5}
def compareDicts(dict1,dict2):
for key in dict1:
if not key in dict2: return(False)
if dict2[key] != dict1[key]: return(False)
for key in dict2:
if not key in dict1: return(False)
return(True)
class myTest(unittest.TestCase):
def testComputeTscore(self):
results = computeTscore(DATA1,DATA2)
self.assertTrue(compareDicts(results,RESULTS))
def testReadData(self): pass
def testTscore(self): pass
def testWriteData(self): pass
if __name__ == '__main__':
unittest.main()
| 25.813953
| 71
| 0.67027
|
import io
import re
import sys
import unittest
from contextlib import redirect_stdout
from tscore import computeTscore
from tscore import readData
from tscore import tscore
from tscore import writeData
DATA1 = { "totalFreq":3, "nbrOfWords":2, "wordFreqs":{"a":1, "b":2 } }
DATA2 = { "totalFreq":3, "nbrOfWords":2, "wordFreqs":{"b":1, "c":2 } }
RESULTS = {'c': -1.1547005383792517, 'a': 0.7071067811865475, 'b': 0.5}
def compareDicts(dict1,dict2):
for key in dict1:
if not key in dict2: return(False)
if dict2[key] != dict1[key]: return(False)
for key in dict2:
if not key in dict1: return(False)
return(True)
class myTest(unittest.TestCase):
def testComputeTscore(self):
results = computeTscore(DATA1,DATA2)
self.assertTrue(compareDicts(results,RESULTS))
def testReadData(self): pass
def testTscore(self): pass
def testWriteData(self): pass
if __name__ == '__main__':
unittest.main()
| true
| true
|
1c3eea8396057943c4ed0c68011b18c7115b86f1
| 8,057
|
py
|
Python
|
Main_.py
|
snehasharma0707/License-Plate-Recognition
|
433251795c5fdef06ab07497d5d13537a89c1a41
|
[
"CNRI-Python"
] | null | null | null |
Main_.py
|
snehasharma0707/License-Plate-Recognition
|
433251795c5fdef06ab07497d5d13537a89c1a41
|
[
"CNRI-Python"
] | null | null | null |
Main_.py
|
snehasharma0707/License-Plate-Recognition
|
433251795c5fdef06ab07497d5d13537a89c1a41
|
[
"CNRI-Python"
] | null | null | null |
import cv2
import numpy as np
import os
from skimage import io
import requests
import random
import numpy as np # linear algebra
import pandas as pd # data processing
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import urllib3
import json
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from urllib.request import urlopen
import scipy.misc
import Detect_Char
import Detect_Plate
import PossiblePlates
# module level variables ##########################################################################
SCALAR_BLACK = (0.0, 0.0, 0.0)
SCALAR_WHITE = (255.0, 255.0, 255.0)
SCALAR_YELLOW = (0.0, 255.0, 255.0)
SCALAR_GREEN = (0.0, 255.0, 0.0)
SCALAR_RED = (0.0, 0.0, 255.0)
showSteps = False
###################################################################################################
def main():
blnKNNTrainingSuccessful = Detect_Char.loadKNNDataAndTrainKNN() # attempt KNN training
if blnKNNTrainingSuccessful == False: # if KNN training was not successful
print("\nerror: KNN traning was not successful\n") # show error message
return # and exit program
# end if
# Reading data from the dataset
data = pd.read_json(r'C:\Users\Sneha Sharma\Desktop\vehicle-number-plate-detection Datasets (2)\Indian_Number_plates.json',lines=True)
pd.set_option('display.max_colwidth', -1) #to set the list as it was
# Delete the empty column
del data['extras']
#Delete the annotation info
del data['annotation']
URL = [] #list to store the array of URLs
def downloadTraining(df): #function to download the training data
for index, row in df.head(n=10).iterrows(): #for loop to extract the dataset head(n=5) constricts the for loop to only 10 data entries
url=row[0]
URL.append(url)
print("done")
downloadTraining(data) #calling the dataset extraction function
print("complete")
url=URL[6] #############IMPORTANT::::: EXTRACTING THE 8th ELEMENT OF THE DATASET
imgOriginalScene = io.imread(url) # open image
if imgOriginalScene is None: # if image was not read successfully
print("\nerror: image not read from file \n\n") # print error message to std out
os.system("pause") # pause so user can see error message
return # and exit program
# end if
listOfPossiblePlates = Detect_Plate.detectPlatesInScene(imgOriginalScene) # detect plates
listOfPossiblePlates = Detect_Char.detectCharsInPlates(listOfPossiblePlates) # detect chars in plates
cv2.imshow("imgOriginalScene", imgOriginalScene) # show scene image
if len(listOfPossiblePlates) == 0: # if no plates were found
print("\nno license plates were detected\n")
else: # else
listOfPossiblePlates.sort(key = lambda possiblePlate: len(possiblePlate.strChars), reverse = True) # sort the list of possible plates in descending order
licPlate = listOfPossiblePlates[0] # suppose the plate with the most recognized chars (the first plate in sorted by string length descending order) is the actual plate
cv2.imshow("imgPlate", licPlate.imgPlate) # show crop of plate and threshold of plate
cv2.imshow("imgThresh", licPlate.imgThresh)
if len(licPlate.strChars) == 0: # if no chars were found in the plate
print("\nno characters were detected\n\n") # show message
return # and exit program
# end if
drawRedRectangleAroundPlate(imgOriginalScene, licPlate) # draw red rectangle around plate
print("\nlicense plate read from image = " + licPlate.strChars + "\n") # write license plate text to std out
print("----------------------------------------")
writeLicensePlateCharsOnImage(imgOriginalScene, licPlate) # write license plate text on the image
cv2.imshow("imgOriginalScene", imgOriginalScene) # re-show scene image
cv2.imwrite("imgOriginalScene.png", imgOriginalScene) # write image out to file
# end if else
cv2.waitKey(0) # hold windows open until user presses a key
return
# end main
###################################################################################################
def drawRedRectangleAroundPlate(imgOriginalScene, licPlate):
p2fRectPoints = cv2.boxPoints(licPlate.rrLocationOfPlateInScene) # get 4 vertices of rotated rect
cv2.line(imgOriginalScene, tuple(p2fRectPoints[0]), tuple(p2fRectPoints[1]), SCALAR_RED, 2) # draw 4 red lines
cv2.line(imgOriginalScene, tuple(p2fRectPoints[1]), tuple(p2fRectPoints[2]), SCALAR_RED, 2)
cv2.line(imgOriginalScene, tuple(p2fRectPoints[2]), tuple(p2fRectPoints[3]), SCALAR_RED, 2)
cv2.line(imgOriginalScene, tuple(p2fRectPoints[3]), tuple(p2fRectPoints[0]), SCALAR_RED, 2)
# end function
###################################################################################################
def writeLicensePlateCharsOnImage(imgOriginalScene, licPlate):
ptCenterOfTextAreaX = 0 # this will be the center of the area the text will be written to
ptCenterOfTextAreaY = 0
ptLowerLeftTextOriginX = 0 # this will be the bottom left of the area that the text will be written to
ptLowerLeftTextOriginY = 0
sceneHeight, sceneWidth, sceneNumChannels = imgOriginalScene.shape
plateHeight, plateWidth, plateNumChannels = licPlate.imgPlate.shape
intFontFace = cv2.FONT_HERSHEY_SIMPLEX # choose a plain jane font
fltFontScale = float(plateHeight) / 30.0 # base font scale on height of plate area
intFontThickness = int(round(fltFontScale * 1.5)) # base font thickness on font scale
textSize, baseline = cv2.getTextSize(licPlate.strChars, intFontFace, fltFontScale, intFontThickness) # call getTextSize
# unpack roatated rect into center point, width and height, and angle
( (intPlateCenterX, intPlateCenterY), (intPlateWidth, intPlateHeight), fltCorrectionAngleInDeg ) = licPlate.rrLocationOfPlateInScene
intPlateCenterX = int(intPlateCenterX) # make sure center is an integer
intPlateCenterY = int(intPlateCenterY)
ptCenterOfTextAreaX = int(intPlateCenterX) # the horizontal location of the text area is the same as the plate
if intPlateCenterY < (sceneHeight * 0.75): # if the license plate is in the upper 3/4 of the image
ptCenterOfTextAreaY = int(round(intPlateCenterY)) + int(round(plateHeight * 1.6)) # write the chars in below the plate
else: # else if the license plate is in the lower 1/4 of the image
ptCenterOfTextAreaY = int(round(intPlateCenterY)) - int(round(plateHeight * 1.6)) # write the chars in above the plate
# end if
textSizeWidth, textSizeHeight = textSize # unpack text size width and height
ptLowerLeftTextOriginX = int(ptCenterOfTextAreaX - (textSizeWidth / 2)) # calculate the lower left origin of the text area
ptLowerLeftTextOriginY = int(ptCenterOfTextAreaY + (textSizeHeight / 2)) # based on the text area center, width, and height
# write the text on the image
cv2.putText(imgOriginalScene, licPlate.strChars, (ptLowerLeftTextOriginX, ptLowerLeftTextOriginY), intFontFace, fltFontScale, SCALAR_YELLOW, intFontThickness)
if __name__ == "__main__":
main()
| 42.856383
| 185
| 0.616358
|
import cv2
import numpy as np
import os
from skimage import io
import requests
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import urllib3
import json
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from urllib.request import urlopen
import scipy.misc
import Detect_Char
import Detect_Plate
import PossiblePlates
| true
| true
|
1c3eec849a00dba654771c573292ef1c74284641
| 796
|
py
|
Python
|
celeba/networks/ConvNetworkImgClfCelebA.py
|
thomassutter/MoPoE
|
477a441ecb6c735a0b8af4d643fe3ac04c58171f
|
[
"MIT"
] | 3
|
2021-05-06T18:29:09.000Z
|
2022-01-13T03:23:25.000Z
|
celeba/networks/ConvNetworkImgClfCelebA.py
|
thomassutter/MoPoE
|
477a441ecb6c735a0b8af4d643fe3ac04c58171f
|
[
"MIT"
] | 1
|
2022-02-02T07:49:59.000Z
|
2022-02-16T08:16:20.000Z
|
celeba/networks/ConvNetworkImgClfCelebA.py
|
thomassutter/MoPoE
|
477a441ecb6c735a0b8af4d643fe3ac04c58171f
|
[
"MIT"
] | 2
|
2021-05-13T02:20:42.000Z
|
2022-03-30T04:05:43.000Z
|
import torch
import torch.nn as nn
from celeba.networks.FeatureExtractorImg import FeatureExtractorImg
class ClfImg(nn.Module):
def __init__(self, flags):
super(ClfImg, self).__init__();
self.feature_extractor = FeatureExtractorImg(flags, a=2.0, b=0.3);
self.dropout = nn.Dropout(p=0.5, inplace=False);
self.linear = nn.Linear(in_features=flags.num_layers_img*flags.DIM_img, out_features=40, bias=True);
self.sigmoid = nn.Sigmoid();
def forward(self, x_img):
h = self.feature_extractor(x_img);
h = self.dropout(h);
h = h.view(h.size(0), -1);
h = self.linear(h);
out = self.sigmoid(h)
return out;
def get_activations(self, x_img):
h = self.feature_extractor(x_img);
return h;
| 30.615385
| 108
| 0.639447
|
import torch
import torch.nn as nn
from celeba.networks.FeatureExtractorImg import FeatureExtractorImg
class ClfImg(nn.Module):
def __init__(self, flags):
super(ClfImg, self).__init__();
self.feature_extractor = FeatureExtractorImg(flags, a=2.0, b=0.3);
self.dropout = nn.Dropout(p=0.5, inplace=False);
self.linear = nn.Linear(in_features=flags.num_layers_img*flags.DIM_img, out_features=40, bias=True);
self.sigmoid = nn.Sigmoid();
def forward(self, x_img):
h = self.feature_extractor(x_img);
h = self.dropout(h);
h = h.view(h.size(0), -1);
h = self.linear(h);
out = self.sigmoid(h)
return out;
def get_activations(self, x_img):
h = self.feature_extractor(x_img);
return h;
| true
| true
|
1c3eef416f2f4b2e50f755eea0af040fb2ac3e74
| 305
|
py
|
Python
|
2015/12/death-states-20151124/graphic_config.py
|
nprapps/graphics-archive
|
97b0ef326b46a959df930f5522d325e537f7a655
|
[
"FSFAP"
] | 14
|
2015-05-08T13:41:51.000Z
|
2021-02-24T12:34:55.000Z
|
2015/12/death-states-20151124/graphic_config.py
|
nprapps/graphics-archive
|
97b0ef326b46a959df930f5522d325e537f7a655
|
[
"FSFAP"
] | null | null | null |
2015/12/death-states-20151124/graphic_config.py
|
nprapps/graphics-archive
|
97b0ef326b46a959df930f5522d325e537f7a655
|
[
"FSFAP"
] | 7
|
2015-04-04T04:45:54.000Z
|
2021-02-18T11:12:48.000Z
|
#!/usr/bin/env python
import base_filters
COPY_GOOGLE_DOC_KEY = '1s9AJPa3Tyim5sl5xA1U0Rn3imyJ8Ycy0pjM9zNYje2A'
USE_ASSETS = False
# Use these variables to override the default cache timeouts for this graphic
# DEFAULT_MAX_AGE = 20
# ASSETS_MAX_AGE = 300
JINJA_FILTER_FUNCTIONS = base_filters.FILTERS
| 21.785714
| 77
| 0.819672
|
import base_filters
COPY_GOOGLE_DOC_KEY = '1s9AJPa3Tyim5sl5xA1U0Rn3imyJ8Ycy0pjM9zNYje2A'
USE_ASSETS = False
JINJA_FILTER_FUNCTIONS = base_filters.FILTERS
| true
| true
|
1c3eef61636dfc6d89773110c3f2c0042d21da47
| 5,720
|
py
|
Python
|
prompty/vcs.py
|
tnhung2011/prompty
|
e2a08b235675d469153b233b86e535551b54fba6
|
[
"MIT"
] | 5
|
2016-01-07T15:15:48.000Z
|
2018-04-04T11:32:06.000Z
|
prompty/vcs.py
|
tnhung2011/prompty
|
e2a08b235675d469153b233b86e535551b54fba6
|
[
"MIT"
] | 21
|
2015-05-20T11:30:43.000Z
|
2020-08-01T14:09:19.000Z
|
prompty/vcs.py
|
tnhung2011/prompty
|
e2a08b235675d469153b233b86e535551b54fba6
|
[
"MIT"
] | 1
|
2021-12-18T13:25:25.000Z
|
2021-12-18T13:25:25.000Z
|
#!/usr/bin/env python
# vim:set softtabstop=4 shiftwidth=4 tabstop=4 expandtab:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import abc
ABC = abc.ABCMeta(str('ABC'), (object,), {'__slots__': ()}) # noqa, compatible with Python 2 *and* 3
from builtins import str
import subprocess
from prompty import functionBase
class VCS(object):
"""
A container class for Version Control System
sub classes.
"""
def __init__(self, status):
self.status = status
self.vcsObjs = []
self.ranStatus = False
self.cwd = None
self.populateVCS()
self.currentVcsObj = self.vcsObjs[0]
def populateVCS(self):
# The order here defines the order in which repository
# types are tested. The first one found to be a valid repo
# will halt all further searching, so put them in priority
# order.
from . import git
self.vcsObjs.append(git.Git(self.status))
from . import svn
self.vcsObjs.append(svn.Subversion(self.status))
def __getattribute__(self, name):
"""
If we have not yet run a status call then run one before
attempting to get the attribute. _runStatus() is also called
again if the working directory has changed.
"""
if name in ["populateVCS", "vcsObjs", "ranStatus", "cwd", "currentVcsObj", "status"]:
return object.__getattribute__(self, name)
if not self.ranStatus or self.cwd != self.status.getWorkingDir():
self.cwd = self.status.getWorkingDir()
self.ranStatus = True
for vcs in self.vcsObjs:
if vcs.isRepo:
self.currentVcsObj = vcs
break
return getattr(object.__getattribute__(self, "currentVcsObj"), name)
class VCSBase(ABC):
"""
An abstract base class for VCS sub classes
"""
@abc.abstractmethod
def __init__(self, status, cmd):
self.status = status
self.ranStatus = False
self.command = cmd
self.cwd = None
self.branch = ""
self.remoteBranch = ""
self.staged = 0
self.changed = 0
self.untracked = 0
self.unmerged = 0
self.ahead = 0
self.behind = 0
self.installed = None
self.isRepo = None
self.commit = ""
self.last_fetched = 0
self.relative_root = ""
@abc.abstractmethod
def _runStatus(self):
"""
Method is abstract and must be implemented. This method
sets appropriately all of the member variables defined
in __init__()
"""
pass
def __getattribute__(self, name):
"""
If we have not yet run a status call then run one before
attempting to get the attribute. _runStatus() is also called
again if the working directory has changed.
"""
if name in ["ranStatus", "cwd", "status"]:
return object.__getattribute__(self, name)
if not self.ranStatus or self.cwd != self.status.getWorkingDir():
self.cwd = self.status.getWorkingDir()
self.ranStatus = True
self._runStatus()
return object.__getattribute__(self, name)
def runCommand(self, cmdList):
# Raises OSError if command doesn't exist
proc = subprocess.Popen(cmdList,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=self.status.getWorkingDir())
stdout, stderr = proc.communicate()
return stdout.decode('utf-8'), stderr.decode('utf-8'), proc.returncode
# --------------------------
# Prompty functions
# --------------------------
class VCSFunctions(functionBase.PromptyFunctions):
def isrepo(self):
"""
Return ``True`` if the current working directory is a version control repository.
:rtype: bool
"""
return self.status.vcs.isRepo
def repobranch(self):
"""
The repository branch name.
"""
return self.status.vcs.branch
def isrepodirty(self):
"""
Return ``True`` if the repository has uncommited modifications.
:rtype: bool
"""
if self.status.vcs.changed + self.status.vcs.staged + self.status.vcs.unmerged > 0:
return True
else:
return False
def ahead(self):
"""
Get the number of commits ahead of the remote repository.
"""
return self.status.vcs.ahead
def behind(self):
"""
Get the number of commits behind the remote repository.
"""
return self.status.vcs.behind
def commit(self):
return self.status.vcs.commit
def staged(self):
"""
Get the number of files that are currently staged.
"""
return self.status.vcs.staged
def changed(self):
"""
Get the number of files that are modified and not staged.
"""
return self.status.vcs.changed
def untracked(self):
"""
Get the number of untracked files that are in the repository (excluding those ignored).
"""
return self.status.vcs.untracked
def last_fetched(self):
"""
Get the time, in seconds, since the remote was last fetched.
"""
return self.status.vcs.last_fetched
def last_fetched_min(self):
"""
Get the time, in minutes, since the remote was last fetched.
"""
return self.status.vcs.last_fetched // 60
| 29.637306
| 101
| 0.589685
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import abc
ABC = abc.ABCMeta(str('ABC'), (object,), {'__slots__': ()})
from builtins import str
import subprocess
from prompty import functionBase
class VCS(object):
def __init__(self, status):
self.status = status
self.vcsObjs = []
self.ranStatus = False
self.cwd = None
self.populateVCS()
self.currentVcsObj = self.vcsObjs[0]
def populateVCS(self):
from . import git
self.vcsObjs.append(git.Git(self.status))
from . import svn
self.vcsObjs.append(svn.Subversion(self.status))
def __getattribute__(self, name):
if name in ["populateVCS", "vcsObjs", "ranStatus", "cwd", "currentVcsObj", "status"]:
return object.__getattribute__(self, name)
if not self.ranStatus or self.cwd != self.status.getWorkingDir():
self.cwd = self.status.getWorkingDir()
self.ranStatus = True
for vcs in self.vcsObjs:
if vcs.isRepo:
self.currentVcsObj = vcs
break
return getattr(object.__getattribute__(self, "currentVcsObj"), name)
class VCSBase(ABC):
@abc.abstractmethod
def __init__(self, status, cmd):
self.status = status
self.ranStatus = False
self.command = cmd
self.cwd = None
self.branch = ""
self.remoteBranch = ""
self.staged = 0
self.changed = 0
self.untracked = 0
self.unmerged = 0
self.ahead = 0
self.behind = 0
self.installed = None
self.isRepo = None
self.commit = ""
self.last_fetched = 0
self.relative_root = ""
@abc.abstractmethod
def _runStatus(self):
pass
def __getattribute__(self, name):
if name in ["ranStatus", "cwd", "status"]:
return object.__getattribute__(self, name)
if not self.ranStatus or self.cwd != self.status.getWorkingDir():
self.cwd = self.status.getWorkingDir()
self.ranStatus = True
self._runStatus()
return object.__getattribute__(self, name)
def runCommand(self, cmdList):
proc = subprocess.Popen(cmdList,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=self.status.getWorkingDir())
stdout, stderr = proc.communicate()
return stdout.decode('utf-8'), stderr.decode('utf-8'), proc.returncode
# --------------------------
# Prompty functions
# --------------------------
class VCSFunctions(functionBase.PromptyFunctions):
def isrepo(self):
return self.status.vcs.isRepo
def repobranch(self):
return self.status.vcs.branch
def isrepodirty(self):
if self.status.vcs.changed + self.status.vcs.staged + self.status.vcs.unmerged > 0:
return True
else:
return False
def ahead(self):
return self.status.vcs.ahead
def behind(self):
return self.status.vcs.behind
def commit(self):
return self.status.vcs.commit
def staged(self):
return self.status.vcs.staged
def changed(self):
return self.status.vcs.changed
def untracked(self):
return self.status.vcs.untracked
def last_fetched(self):
return self.status.vcs.last_fetched
def last_fetched_min(self):
return self.status.vcs.last_fetched // 60
| true
| true
|
1c3eef73558629c63b8748c6c61ec75dac6338d5
| 338
|
py
|
Python
|
votepredictapp/urls.py
|
davidhammaker/votepredictbackend
|
766467f85faf8a1d11da8c798b30904af9268504
|
[
"MIT"
] | null | null | null |
votepredictapp/urls.py
|
davidhammaker/votepredictbackend
|
766467f85faf8a1d11da8c798b30904af9268504
|
[
"MIT"
] | null | null | null |
votepredictapp/urls.py
|
davidhammaker/votepredictbackend
|
766467f85faf8a1d11da8c798b30904af9268504
|
[
"MIT"
] | null | null | null |
from django.urls import include, path
from rest_framework.routers import DefaultRouter
from . import views
router = DefaultRouter()
router.register(r"questions", views.QuestionViewSet)
urlpatterns = [
path("", include(router.urls)),
path("reply/", views.ReplyView.as_view()),
path("totals/", views.TotalsView.as_view()),
]
| 24.142857
| 52
| 0.730769
|
from django.urls import include, path
from rest_framework.routers import DefaultRouter
from . import views
router = DefaultRouter()
router.register(r"questions", views.QuestionViewSet)
urlpatterns = [
path("", include(router.urls)),
path("reply/", views.ReplyView.as_view()),
path("totals/", views.TotalsView.as_view()),
]
| true
| true
|
1c3eef7ba4a7c4fdc2c91473e4bd1ee6d01d74ee
| 672
|
py
|
Python
|
WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/fileinput/fileinput_change_subnet_noisy.py
|
webdevhub42/Lambda
|
b04b84fb5b82fe7c8b12680149e25ae0d27a0960
|
[
"MIT"
] | null | null | null |
WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/fileinput/fileinput_change_subnet_noisy.py
|
webdevhub42/Lambda
|
b04b84fb5b82fe7c8b12680149e25ae0d27a0960
|
[
"MIT"
] | null | null | null |
WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/fileinput/fileinput_change_subnet_noisy.py
|
webdevhub42/Lambda
|
b04b84fb5b82fe7c8b12680149e25ae0d27a0960
|
[
"MIT"
] | null | null | null |
# Copyright (c) 2010 Doug Hellmann. All rights reserved.
#
"""Simplistic sed implementation
"""
# end_pymotw_header
import fileinput
import glob
import sys
from_base = sys.argv[1]
to_base = sys.argv[2]
files = sys.argv[3:]
for line in fileinput.input(files, inplace=True):
if fileinput.isfirstline():
sys.stderr.write("Started processing {}\n".format(fileinput.filename()))
sys.stderr.write("Directory contains: {}\n".format(glob.glob("etc_hosts.txt*")))
line = line.rstrip().replace(from_base, to_base)
print(line)
sys.stderr.write("Finished processing\n")
sys.stderr.write("Directory contains: {}\n".format(glob.glob("etc_hosts.txt*")))
| 28
| 88
| 0.709821
|
import fileinput
import glob
import sys
from_base = sys.argv[1]
to_base = sys.argv[2]
files = sys.argv[3:]
for line in fileinput.input(files, inplace=True):
if fileinput.isfirstline():
sys.stderr.write("Started processing {}\n".format(fileinput.filename()))
sys.stderr.write("Directory contains: {}\n".format(glob.glob("etc_hosts.txt*")))
line = line.rstrip().replace(from_base, to_base)
print(line)
sys.stderr.write("Finished processing\n")
sys.stderr.write("Directory contains: {}\n".format(glob.glob("etc_hosts.txt*")))
| true
| true
|
1c3eefbea593be09972f9ea725f0661fe763ffae
| 723
|
py
|
Python
|
generators/simple/templates/src/platform/extensionRunner/util.py
|
jfallaire/generator-ps-boilerplate-project
|
36f544a54442c191430451715425da98ea76a63e
|
[
"MIT"
] | 2
|
2019-07-24T16:00:51.000Z
|
2019-10-03T18:36:20.000Z
|
generators/simple/templates/src/platform/extensionRunner/util.py
|
jfallaire/generator-ps-boilerplate-project
|
36f544a54442c191430451715425da98ea76a63e
|
[
"MIT"
] | 19
|
2019-06-20T21:58:44.000Z
|
2020-11-05T13:48:42.000Z
|
generators/simple/templates/src/platform/extensionRunner/util.py
|
jfallaire/generator-ps-boilerplate-project
|
36f544a54442c191430451715425da98ea76a63e
|
[
"MIT"
] | 1
|
2019-06-22T17:30:42.000Z
|
2019-06-22T17:30:42.000Z
|
import os
import json
import tempfile
import zlib
import base64
import urllib.request
def zlib_compress_str(content):
outdata = zlib.compress(content, zlib.Z_BEST_COMPRESSION)
encodedData = base64.encodebytes(outdata)
#print('zlib compressed >>> {}'.format(encodedData))
return encodedData
def get_html_content(url):
hdr = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36'}
req = urllib.request.Request(url, headers=hdr)
try:
with urllib.request.urlopen(req) as response:
return response.read()
except Exception as e:
print(e)
return ''
| 27.807692
| 149
| 0.672199
|
import os
import json
import tempfile
import zlib
import base64
import urllib.request
def zlib_compress_str(content):
outdata = zlib.compress(content, zlib.Z_BEST_COMPRESSION)
encodedData = base64.encodebytes(outdata)
return encodedData
def get_html_content(url):
hdr = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36'}
req = urllib.request.Request(url, headers=hdr)
try:
with urllib.request.urlopen(req) as response:
return response.read()
except Exception as e:
print(e)
return ''
| true
| true
|
1c3ef042bcfda68952527fd044dab09d320c8203
| 13,289
|
py
|
Python
|
configs/example/memtest.py
|
hyu-iot/gem5
|
aeccc8bd8e9a86f96fc7a6f40d978f8494337fc5
|
[
"BSD-3-Clause"
] | 765
|
2015-01-14T16:17:04.000Z
|
2022-03-28T07:46:28.000Z
|
configs/example/memtest.py
|
hyu-iot/gem5
|
aeccc8bd8e9a86f96fc7a6f40d978f8494337fc5
|
[
"BSD-3-Clause"
] | 30
|
2015-01-01T21:49:38.000Z
|
2021-04-20T19:01:54.000Z
|
configs/example/memtest.py
|
hyu-iot/gem5
|
aeccc8bd8e9a86f96fc7a6f40d978f8494337fc5
|
[
"BSD-3-Clause"
] | 807
|
2015-01-06T09:55:38.000Z
|
2022-03-30T10:23:36.000Z
|
# Copyright (c) 2015, 2018 ARM Limited
# All rights reserved.
#
# The license below extends only to copyright in the software and shall
# not be construed as granting a license to any other intellectual
# property including but not limited to intellectual property relating
# to a hardware implementation of the functionality of the software
# licensed hereunder. You may use the software subject to the license
# terms below provided that you ensure that this notice is replicated
# unmodified and in its entirety in all distributions of the software,
# modified or unmodified, in source code or in binary form.
#
# Copyright (c) 2006-2007 The Regents of The University of Michigan
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer;
# redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution;
# neither the name of the copyright holders nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
import random
import sys
import m5
from m5.objects import *
# This example script stress tests the memory system by creating false
# sharing in a tree topology. At the bottom of the tree is a shared
# memory, and then at each level a number of testers are attached,
# along with a number of caches that them selves fan out to subtrees
# of testers and caches. Thus, it is possible to create a system with
# arbitrarily deep cache hierarchies, sharing or no sharing of caches,
# and testers not only at the L1s, but also at the L2s, L3s etc.
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-a", "--atomic", action="store_true",
help="Use atomic (non-timing) mode")
parser.add_argument("-b", "--blocking", action="store_true",
help="Use blocking caches")
parser.add_argument("-l", "--maxloads", metavar="N", default=0,
help="Stop after N loads")
parser.add_argument("-m", "--maxtick", type=int, default=m5.MaxTick,
metavar="T",
help="Stop after T ticks")
# The tree specification consists of two colon-separated lists of one
# or more integers, one for the caches, and one for the testers. The
# first integer is the number of caches/testers closest to main
# memory. Each cache then fans out to a subtree. The last integer in
# the list is the number of caches/testers associated with the
# uppermost level of memory. The other integers (if any) specify the
# number of caches/testers connected at each level of the crossbar
# hierarchy. The tester string should have one element more than the
# cache string as there should always be testers attached to the
# uppermost caches.
parser.add_argument("-c", "--caches", type=str, default="2:2:1",
help="Colon-separated cache hierarchy specification, "
"see script comments for details ")
parser.add_argument("--noncoherent-cache", action="store_true",
help="Adds a non-coherent, last-level cache")
parser.add_argument("-t", "--testers", type=str, default="1:1:0:2",
help="Colon-separated tester hierarchy specification, "
"see script comments for details ")
parser.add_argument("-f", "--functional", type=int, default=10,
metavar="PCT",
help="Target percentage of functional accesses ")
parser.add_argument("-u", "--uncacheable", type=int, default=10,
metavar="PCT",
help="Target percentage of uncacheable accesses ")
parser.add_argument("-r", "--random", action="store_true",
help="Generate a random tree topology")
parser.add_argument("--progress", type=int, default=100000,
metavar="NLOADS",
help="Progress message interval ")
parser.add_argument("--sys-clock", action="store", type=str,
default='1GHz',
help="""Top-level clock for blocks running at system
speed""")
args = parser.parse_args()
# Get the total number of testers
def numtesters(cachespec, testerspec):
# Determine the tester multiplier for each level as the
# elements are per subsystem and it fans out
multiplier = [1]
for c in cachespec:
multiplier.append(multiplier[-1] * c)
total = 0
for t, m in zip(testerspec, multiplier):
total += t * m
return total
block_size = 64
# Start by parsing the command line args and do some basic sanity
# checking
if args.random:
# Generate a tree with a valid number of testers
while True:
tree_depth = random.randint(1, 4)
cachespec = [random.randint(1, 3) for i in range(tree_depth)]
testerspec = [random.randint(1, 3) for i in range(tree_depth + 1)]
if numtesters(cachespec, testerspec) < block_size:
break
print("Generated random tree -c", ':'.join(map(str, cachespec)),
"-t", ':'.join(map(str, testerspec)))
else:
try:
cachespec = [int(x) for x in args.caches.split(':')]
testerspec = [int(x) for x in args.testers.split(':')]
except:
print("Error: Unable to parse caches or testers option")
sys.exit(1)
if len(cachespec) < 1:
print("Error: Must have at least one level of caches")
sys.exit(1)
if len(cachespec) != len(testerspec) - 1:
print("Error: Testers must have one element more than caches")
sys.exit(1)
if testerspec[-1] == 0:
print("Error: Must have testers at the uppermost level")
sys.exit(1)
for t in testerspec:
if t < 0:
print("Error: Cannot have a negative number of testers")
sys.exit(1)
for c in cachespec:
if c < 1:
print("Error: Must have 1 or more caches at each level")
sys.exit(1)
if numtesters(cachespec, testerspec) > block_size:
print("Error: Limited to %s testers because of false sharing"
% (block_size))
sys.exit(1)
# Define a prototype L1 cache that we scale for all successive levels
proto_l1 = Cache(size = '32kB', assoc = 4,
tag_latency = 1, data_latency = 1, response_latency = 1,
tgts_per_mshr = 8, clusivity = 'mostly_incl',
writeback_clean = True)
if args.blocking:
proto_l1.mshrs = 1
else:
proto_l1.mshrs = 4
cache_proto = [proto_l1]
# Now add additional cache levels (if any) by scaling L1 params, the
# first element is Ln, and the last element L1
for scale in cachespec[:-1]:
# Clone previous level and update params
prev = cache_proto[0]
next = prev()
next.size = prev.size * scale
next.tag_latency = prev.tag_latency * 10
next.data_latency = prev.data_latency * 10
next.response_latency = prev.response_latency * 10
next.assoc = prev.assoc * scale
next.mshrs = prev.mshrs * scale
# Swap the inclusivity/exclusivity at each level. L2 is mostly
# exclusive with respect to L1, L3 mostly inclusive, L4 mostly
# exclusive etc.
next.writeback_clean = not prev.writeback_clean
if (prev.clusivity.value == 'mostly_incl'):
next.clusivity = 'mostly_excl'
else:
next.clusivity = 'mostly_incl'
cache_proto.insert(0, next)
# Make a prototype for the tester to be used throughout
proto_tester = MemTest(max_loads = args.maxloads,
percent_functional = args.functional,
percent_uncacheable = args.uncacheable,
progress_interval = args.progress)
# Set up the system along with a simple memory and reference memory
system = System(physmem = SimpleMemory(),
cache_line_size = block_size)
system.voltage_domain = VoltageDomain(voltage = '1V')
system.clk_domain = SrcClockDomain(clock = args.sys_clock,
voltage_domain = system.voltage_domain)
# For each level, track the next subsys index to use
next_subsys_index = [0] * (len(cachespec) + 1)
# Recursive function to create a sub-tree of the cache and tester
# hierarchy
def make_cache_level(ncaches, prototypes, level, next_cache):
global next_subsys_index, proto_l1, testerspec, proto_tester
index = next_subsys_index[level]
next_subsys_index[level] += 1
# Create a subsystem to contain the crossbar and caches, and
# any testers
subsys = SubSystem()
setattr(system, 'l%dsubsys%d' % (level, index), subsys)
# The levels are indexing backwards through the list
ntesters = testerspec[len(cachespec) - level]
# Scale the progress threshold as testers higher up in the tree
# (smaller level) get a smaller portion of the overall bandwidth,
# and also make the interval of packet injection longer for the
# testers closer to the memory (larger level) to prevent them
# hogging all the bandwidth
limit = (len(cachespec) - level + 1) * 100000000
testers = [proto_tester(interval = 10 * (level * level + 1),
progress_check = limit) \
for i in range(ntesters)]
if ntesters:
subsys.tester = testers
if level != 0:
# Create a crossbar and add it to the subsystem, note that
# we do this even with a single element on this level
xbar = L2XBar()
subsys.xbar = xbar
if next_cache:
xbar.mem_side_ports = next_cache.cpu_side
# Create and connect the caches, both the ones fanning out
# to create the tree, and the ones used to connect testers
# on this level
tree_caches = [prototypes[0]() for i in range(ncaches[0])]
tester_caches = [proto_l1() for i in range(ntesters)]
subsys.cache = tester_caches + tree_caches
for cache in tree_caches:
cache.mem_side = xbar.cpu_side_ports
make_cache_level(ncaches[1:], prototypes[1:], level - 1, cache)
for tester, cache in zip(testers, tester_caches):
tester.port = cache.cpu_side
cache.mem_side = xbar.cpu_side_ports
else:
if not next_cache:
print("Error: No next-level cache at top level")
sys.exit(1)
if ntesters > 1:
# Create a crossbar and add it to the subsystem
xbar = L2XBar()
subsys.xbar = xbar
xbar.mem_side_ports = next_cache.cpu_side
for tester in testers:
tester.port = xbar.cpu_side_ports
else:
# Single tester
testers[0].port = next_cache.cpu_side
# Top level call to create the cache hierarchy, bottom up
make_cache_level(cachespec, cache_proto, len(cachespec), None)
# Connect the lowest level crossbar to the last-level cache and memory
# controller
last_subsys = getattr(system, 'l%dsubsys0' % len(cachespec))
last_subsys.xbar.point_of_coherency = True
if args.noncoherent_cache:
system.llc = NoncoherentCache(size = '16MB', assoc = 16, tag_latency = 10,
data_latency = 10, sequential_access = True,
response_latency = 20, tgts_per_mshr = 8,
mshrs = 64)
last_subsys.xbar.mem_side_ports = system.llc.cpu_side
system.llc.mem_side = system.physmem.port
else:
last_subsys.xbar.mem_side_ports = system.physmem.port
root = Root(full_system = False, system = system)
if args.atomic:
root.system.mem_mode = 'atomic'
else:
root.system.mem_mode = 'timing'
# The system port is never used in the tester so merely connect it
# to avoid problems
root.system.system_port = last_subsys.xbar.cpu_side_ports
# Instantiate configuration
m5.instantiate()
# Simulate until program terminates
exit_event = m5.simulate(args.maxtick)
print('Exiting @ tick', m5.curTick(), 'because', exit_event.getCause())
| 41.270186
| 79
| 0.662503
|
import argparse
import random
import sys
import m5
from m5.objects import *
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-a", "--atomic", action="store_true",
help="Use atomic (non-timing) mode")
parser.add_argument("-b", "--blocking", action="store_true",
help="Use blocking caches")
parser.add_argument("-l", "--maxloads", metavar="N", default=0,
help="Stop after N loads")
parser.add_argument("-m", "--maxtick", type=int, default=m5.MaxTick,
metavar="T",
help="Stop after T ticks")
parser.add_argument("-c", "--caches", type=str, default="2:2:1",
help="Colon-separated cache hierarchy specification, "
"see script comments for details ")
parser.add_argument("--noncoherent-cache", action="store_true",
help="Adds a non-coherent, last-level cache")
parser.add_argument("-t", "--testers", type=str, default="1:1:0:2",
help="Colon-separated tester hierarchy specification, "
"see script comments for details ")
parser.add_argument("-f", "--functional", type=int, default=10,
metavar="PCT",
help="Target percentage of functional accesses ")
parser.add_argument("-u", "--uncacheable", type=int, default=10,
metavar="PCT",
help="Target percentage of uncacheable accesses ")
parser.add_argument("-r", "--random", action="store_true",
help="Generate a random tree topology")
parser.add_argument("--progress", type=int, default=100000,
metavar="NLOADS",
help="Progress message interval ")
parser.add_argument("--sys-clock", action="store", type=str,
default='1GHz',
help="""Top-level clock for blocks running at system
speed""")
args = parser.parse_args()
def numtesters(cachespec, testerspec):
multiplier = [1]
for c in cachespec:
multiplier.append(multiplier[-1] * c)
total = 0
for t, m in zip(testerspec, multiplier):
total += t * m
return total
block_size = 64
if args.random:
while True:
tree_depth = random.randint(1, 4)
cachespec = [random.randint(1, 3) for i in range(tree_depth)]
testerspec = [random.randint(1, 3) for i in range(tree_depth + 1)]
if numtesters(cachespec, testerspec) < block_size:
break
print("Generated random tree -c", ':'.join(map(str, cachespec)),
"-t", ':'.join(map(str, testerspec)))
else:
try:
cachespec = [int(x) for x in args.caches.split(':')]
testerspec = [int(x) for x in args.testers.split(':')]
except:
print("Error: Unable to parse caches or testers option")
sys.exit(1)
if len(cachespec) < 1:
print("Error: Must have at least one level of caches")
sys.exit(1)
if len(cachespec) != len(testerspec) - 1:
print("Error: Testers must have one element more than caches")
sys.exit(1)
if testerspec[-1] == 0:
print("Error: Must have testers at the uppermost level")
sys.exit(1)
for t in testerspec:
if t < 0:
print("Error: Cannot have a negative number of testers")
sys.exit(1)
for c in cachespec:
if c < 1:
print("Error: Must have 1 or more caches at each level")
sys.exit(1)
if numtesters(cachespec, testerspec) > block_size:
print("Error: Limited to %s testers because of false sharing"
% (block_size))
sys.exit(1)
proto_l1 = Cache(size = '32kB', assoc = 4,
tag_latency = 1, data_latency = 1, response_latency = 1,
tgts_per_mshr = 8, clusivity = 'mostly_incl',
writeback_clean = True)
if args.blocking:
proto_l1.mshrs = 1
else:
proto_l1.mshrs = 4
cache_proto = [proto_l1]
for scale in cachespec[:-1]:
prev = cache_proto[0]
next = prev()
next.size = prev.size * scale
next.tag_latency = prev.tag_latency * 10
next.data_latency = prev.data_latency * 10
next.response_latency = prev.response_latency * 10
next.assoc = prev.assoc * scale
next.mshrs = prev.mshrs * scale
next.writeback_clean = not prev.writeback_clean
if (prev.clusivity.value == 'mostly_incl'):
next.clusivity = 'mostly_excl'
else:
next.clusivity = 'mostly_incl'
cache_proto.insert(0, next)
proto_tester = MemTest(max_loads = args.maxloads,
percent_functional = args.functional,
percent_uncacheable = args.uncacheable,
progress_interval = args.progress)
system = System(physmem = SimpleMemory(),
cache_line_size = block_size)
system.voltage_domain = VoltageDomain(voltage = '1V')
system.clk_domain = SrcClockDomain(clock = args.sys_clock,
voltage_domain = system.voltage_domain)
next_subsys_index = [0] * (len(cachespec) + 1)
def make_cache_level(ncaches, prototypes, level, next_cache):
global next_subsys_index, proto_l1, testerspec, proto_tester
index = next_subsys_index[level]
next_subsys_index[level] += 1
subsys = SubSystem()
setattr(system, 'l%dsubsys%d' % (level, index), subsys)
ntesters = testerspec[len(cachespec) - level]
limit = (len(cachespec) - level + 1) * 100000000
testers = [proto_tester(interval = 10 * (level * level + 1),
progress_check = limit) \
for i in range(ntesters)]
if ntesters:
subsys.tester = testers
if level != 0:
xbar = L2XBar()
subsys.xbar = xbar
if next_cache:
xbar.mem_side_ports = next_cache.cpu_side
tree_caches = [prototypes[0]() for i in range(ncaches[0])]
tester_caches = [proto_l1() for i in range(ntesters)]
subsys.cache = tester_caches + tree_caches
for cache in tree_caches:
cache.mem_side = xbar.cpu_side_ports
make_cache_level(ncaches[1:], prototypes[1:], level - 1, cache)
for tester, cache in zip(testers, tester_caches):
tester.port = cache.cpu_side
cache.mem_side = xbar.cpu_side_ports
else:
if not next_cache:
print("Error: No next-level cache at top level")
sys.exit(1)
if ntesters > 1:
xbar = L2XBar()
subsys.xbar = xbar
xbar.mem_side_ports = next_cache.cpu_side
for tester in testers:
tester.port = xbar.cpu_side_ports
else:
testers[0].port = next_cache.cpu_side
make_cache_level(cachespec, cache_proto, len(cachespec), None)
last_subsys = getattr(system, 'l%dsubsys0' % len(cachespec))
last_subsys.xbar.point_of_coherency = True
if args.noncoherent_cache:
system.llc = NoncoherentCache(size = '16MB', assoc = 16, tag_latency = 10,
data_latency = 10, sequential_access = True,
response_latency = 20, tgts_per_mshr = 8,
mshrs = 64)
last_subsys.xbar.mem_side_ports = system.llc.cpu_side
system.llc.mem_side = system.physmem.port
else:
last_subsys.xbar.mem_side_ports = system.physmem.port
root = Root(full_system = False, system = system)
if args.atomic:
root.system.mem_mode = 'atomic'
else:
root.system.mem_mode = 'timing'
root.system.system_port = last_subsys.xbar.cpu_side_ports
m5.instantiate()
exit_event = m5.simulate(args.maxtick)
print('Exiting @ tick', m5.curTick(), 'because', exit_event.getCause())
| true
| true
|
1c3ef05626d3001d6615345a37559166642dbf90
| 2,329
|
py
|
Python
|
src/tt_xsolla/tt_xsolla/service.py
|
Alacrate/the-tale
|
43b211f3a99e93964e95abc20a8ed649a205ffcf
|
[
"BSD-3-Clause"
] | 85
|
2017-11-21T12:22:02.000Z
|
2022-03-27T23:07:17.000Z
|
src/tt_xsolla/tt_xsolla/service.py
|
Alacrate/the-tale
|
43b211f3a99e93964e95abc20a8ed649a205ffcf
|
[
"BSD-3-Clause"
] | 545
|
2017-11-04T14:15:04.000Z
|
2022-03-27T14:19:27.000Z
|
src/tt_xsolla/tt_xsolla/service.py
|
Alacrate/the-tale
|
43b211f3a99e93964e95abc20a8ed649a205ffcf
|
[
"BSD-3-Clause"
] | 45
|
2017-11-11T12:36:30.000Z
|
2022-02-25T06:10:44.000Z
|
import logging
import asyncio
from aiohttp import web
from tt_web import log
from tt_web import postgresql
from tt_web.common import event
from . import conf
from . import logic
PAYMENT_PROCESSING_TASK = None
async def start_payment_processing(config):
global PAYMENT_PROCESSING_TASK
async def process_payments():
event.get(conf.PROCESS_INVOICE_EVENT_NAME).set()
while True:
await logic.process_invoices(logic.make_payment, config)
await asyncio.sleep(config['custom']['sleep_if_no_payments_interval'])
logging.info('start payment processing background task')
PAYMENT_PROCESSING_TASK = asyncio.ensure_future(process_payments())
async def stop_payment_processing():
global PAYMENT_PROCESSING_TASK
if PAYMENT_PROCESSING_TASK:
PAYMENT_PROCESSING_TASK.cancel()
PAYMENT_PROCESSING_TASK = None
async def initialize(config, allow_callbacks):
await postgresql.initialize(config['database'])
if allow_callbacks and config['custom']['run_callbacks']:
await start_payment_processing(config)
async def deinitialize(config):
await stop_payment_processing()
await postgresql.deinitialize()
async def on_startup(app):
await initialize(app['config'], allow_callbacks=True)
async def on_cleanup(app):
await deinitialize(app['config'])
def register_routers(app):
from . import handlers
app.router.add_post('/xsolla-hook', handlers.xsolla_hook)
app.router.add_post('/get-token', handlers.get_token)
app.router.add_post('/data-protection-collect-data', handlers.data_protection_collect_data)
app.router.add_post('/data-protection-delete-data', handlers.data_protection_delete_data)
app.router.add_post('/debug-clear-service', handlers.debug_clear_service)
def create_application(config, loop=None):
app = web.Application()
app['config'] = config
log.initilize(config['log'])
app.on_startup.append(on_startup)
app.on_cleanup.append(on_cleanup)
register_routers(app)
return app
def run_utility(config, utility):
async def runner():
await initialize(config, allow_callbacks=False)
log.initilize(config['log'])
await utility()
await deinitialize(config)
asyncio.get_event_loop().run_until_complete(runner())
| 22.833333
| 95
| 0.738085
|
import logging
import asyncio
from aiohttp import web
from tt_web import log
from tt_web import postgresql
from tt_web.common import event
from . import conf
from . import logic
PAYMENT_PROCESSING_TASK = None
async def start_payment_processing(config):
global PAYMENT_PROCESSING_TASK
async def process_payments():
event.get(conf.PROCESS_INVOICE_EVENT_NAME).set()
while True:
await logic.process_invoices(logic.make_payment, config)
await asyncio.sleep(config['custom']['sleep_if_no_payments_interval'])
logging.info('start payment processing background task')
PAYMENT_PROCESSING_TASK = asyncio.ensure_future(process_payments())
async def stop_payment_processing():
global PAYMENT_PROCESSING_TASK
if PAYMENT_PROCESSING_TASK:
PAYMENT_PROCESSING_TASK.cancel()
PAYMENT_PROCESSING_TASK = None
async def initialize(config, allow_callbacks):
await postgresql.initialize(config['database'])
if allow_callbacks and config['custom']['run_callbacks']:
await start_payment_processing(config)
async def deinitialize(config):
await stop_payment_processing()
await postgresql.deinitialize()
async def on_startup(app):
await initialize(app['config'], allow_callbacks=True)
async def on_cleanup(app):
await deinitialize(app['config'])
def register_routers(app):
from . import handlers
app.router.add_post('/xsolla-hook', handlers.xsolla_hook)
app.router.add_post('/get-token', handlers.get_token)
app.router.add_post('/data-protection-collect-data', handlers.data_protection_collect_data)
app.router.add_post('/data-protection-delete-data', handlers.data_protection_delete_data)
app.router.add_post('/debug-clear-service', handlers.debug_clear_service)
def create_application(config, loop=None):
app = web.Application()
app['config'] = config
log.initilize(config['log'])
app.on_startup.append(on_startup)
app.on_cleanup.append(on_cleanup)
register_routers(app)
return app
def run_utility(config, utility):
async def runner():
await initialize(config, allow_callbacks=False)
log.initilize(config['log'])
await utility()
await deinitialize(config)
asyncio.get_event_loop().run_until_complete(runner())
| true
| true
|
1c3ef07816a957f2cca01e78a60ccc6f7ff6c908
| 3,936
|
py
|
Python
|
tests/rbac/common/addresser/task_admin_tests.py
|
fthornton67/sawtooth-next-directory
|
79479afb8d234911c56379bb1d8abf11f28ef86d
|
[
"Apache-2.0"
] | 75
|
2018-04-06T09:13:34.000Z
|
2020-05-18T18:59:47.000Z
|
tests/rbac/common/addresser/task_admin_tests.py
|
fthornton67/sawtooth-next-directory
|
79479afb8d234911c56379bb1d8abf11f28ef86d
|
[
"Apache-2.0"
] | 989
|
2018-04-18T21:01:56.000Z
|
2019-10-23T15:37:09.000Z
|
tests/rbac/common/addresser/task_admin_tests.py
|
fthornton67/sawtooth-next-directory
|
79479afb8d234911c56379bb1d8abf11f28ef86d
|
[
"Apache-2.0"
] | 72
|
2018-04-13T18:29:12.000Z
|
2020-05-29T06:00:33.000Z
|
# Copyright 2019 Contributors to Hyperledger Sawtooth
#
# 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.
# -----------------------------------------------------------------------------
"""Test Task Admin Addresser"""
import pytest
from rbac.common import addresser
from rbac.common.logs import get_default_logger
from tests.rbac.common.assertions import TestAssertions
LOGGER = get_default_logger(__name__)
@pytest.mark.addressing
@pytest.mark.library
class TestTaskAdminAddresser(TestAssertions):
"""Test Task Admin Addresser"""
def test_address(self):
"""Tests address makes an address that identifies as the correct AddressSpace"""
task_id = addresser.task.admin.unique_id()
next_id = addresser.user.unique_id()
rel_address = addresser.task.admin.address(
object_id=task_id, related_id=next_id
)
self.assertIsAddress(rel_address)
self.assertEqual(
addresser.get_address_type(rel_address), addresser.AddressSpace.TASKS_ADMINS
)
def test_address_deterministic(self):
"""Tests address makes an address that identifies as the correct AddressSpace"""
task_id = addresser.task.admin.unique_id()
next_id = addresser.user.unique_id()
rel_address1 = addresser.task.admin.address(
object_id=task_id, related_id=next_id
)
rel_address2 = addresser.task.admin.address(
object_id=task_id, related_id=next_id
)
self.assertIsAddress(rel_address1)
self.assertIsAddress(rel_address2)
self.assertEqual(rel_address1, rel_address2)
self.assertEqual(
addresser.get_address_type(rel_address1),
addresser.AddressSpace.TASKS_ADMINS,
)
def test_address_random(self):
"""Tests address makes a unique address given different inputs"""
task_id1 = addresser.task.admin.unique_id()
user_id1 = addresser.user.unique_id()
task_id2 = addresser.task.admin.unique_id()
user_id2 = addresser.user.unique_id()
rel_address1 = addresser.task.admin.address(
object_id=task_id1, related_id=user_id1
)
rel_address2 = addresser.task.admin.address(
object_id=task_id2, related_id=user_id2
)
self.assertIsAddress(rel_address1)
self.assertIsAddress(rel_address2)
self.assertNotEqual(rel_address1, rel_address2)
self.assertEqual(
addresser.get_address_type(rel_address1),
addresser.AddressSpace.TASKS_ADMINS,
)
self.assertEqual(
addresser.get_address_type(rel_address2),
addresser.AddressSpace.TASKS_ADMINS,
)
def test_addresser_parse(self):
"""Test addresser.parse returns a parsed address"""
task_id = addresser.task.unique_id()
next_id = addresser.user.unique_id()
rel_address = addresser.task.admin.address(task_id, next_id)
parsed = addresser.parse(rel_address)
self.assertEqual(parsed.object_type, addresser.ObjectType.TASK)
self.assertEqual(parsed.related_type, addresser.ObjectType.USER)
self.assertEqual(parsed.relationship_type, addresser.RelationshipType.ADMIN)
self.assertEqual(parsed.address_type, addresser.AddressSpace.TASKS_ADMINS)
self.assertEqual(parsed.object_id, task_id)
self.assertEqual(parsed.related_id, next_id)
| 40.163265
| 88
| 0.689278
|
import pytest
from rbac.common import addresser
from rbac.common.logs import get_default_logger
from tests.rbac.common.assertions import TestAssertions
LOGGER = get_default_logger(__name__)
@pytest.mark.addressing
@pytest.mark.library
class TestTaskAdminAddresser(TestAssertions):
def test_address(self):
task_id = addresser.task.admin.unique_id()
next_id = addresser.user.unique_id()
rel_address = addresser.task.admin.address(
object_id=task_id, related_id=next_id
)
self.assertIsAddress(rel_address)
self.assertEqual(
addresser.get_address_type(rel_address), addresser.AddressSpace.TASKS_ADMINS
)
def test_address_deterministic(self):
task_id = addresser.task.admin.unique_id()
next_id = addresser.user.unique_id()
rel_address1 = addresser.task.admin.address(
object_id=task_id, related_id=next_id
)
rel_address2 = addresser.task.admin.address(
object_id=task_id, related_id=next_id
)
self.assertIsAddress(rel_address1)
self.assertIsAddress(rel_address2)
self.assertEqual(rel_address1, rel_address2)
self.assertEqual(
addresser.get_address_type(rel_address1),
addresser.AddressSpace.TASKS_ADMINS,
)
def test_address_random(self):
task_id1 = addresser.task.admin.unique_id()
user_id1 = addresser.user.unique_id()
task_id2 = addresser.task.admin.unique_id()
user_id2 = addresser.user.unique_id()
rel_address1 = addresser.task.admin.address(
object_id=task_id1, related_id=user_id1
)
rel_address2 = addresser.task.admin.address(
object_id=task_id2, related_id=user_id2
)
self.assertIsAddress(rel_address1)
self.assertIsAddress(rel_address2)
self.assertNotEqual(rel_address1, rel_address2)
self.assertEqual(
addresser.get_address_type(rel_address1),
addresser.AddressSpace.TASKS_ADMINS,
)
self.assertEqual(
addresser.get_address_type(rel_address2),
addresser.AddressSpace.TASKS_ADMINS,
)
def test_addresser_parse(self):
task_id = addresser.task.unique_id()
next_id = addresser.user.unique_id()
rel_address = addresser.task.admin.address(task_id, next_id)
parsed = addresser.parse(rel_address)
self.assertEqual(parsed.object_type, addresser.ObjectType.TASK)
self.assertEqual(parsed.related_type, addresser.ObjectType.USER)
self.assertEqual(parsed.relationship_type, addresser.RelationshipType.ADMIN)
self.assertEqual(parsed.address_type, addresser.AddressSpace.TASKS_ADMINS)
self.assertEqual(parsed.object_id, task_id)
self.assertEqual(parsed.related_id, next_id)
| true
| true
|
1c3ef0d84790c32f8626dfe621ea95c9af18d260
| 1,646
|
py
|
Python
|
logging/logging.py
|
badgeteam/micropython-lib
|
fca0235c166ebbada489d88c42fc549267832797
|
[
"PSF-2.0"
] | 6
|
2017-12-31T16:19:16.000Z
|
2021-04-26T02:03:30.000Z
|
logging/logging.py
|
badgeteam/micropython-lib
|
fca0235c166ebbada489d88c42fc549267832797
|
[
"PSF-2.0"
] | 1
|
2020-06-24T02:37:58.000Z
|
2020-06-24T02:37:58.000Z
|
logging/logging.py
|
badgeteam/micropython-lib
|
fca0235c166ebbada489d88c42fc549267832797
|
[
"PSF-2.0"
] | 3
|
2019-07-01T18:29:54.000Z
|
2020-06-24T20:15:10.000Z
|
import sys
CRITICAL = 50
ERROR = 40
WARNING = 30
INFO = 20
DEBUG = 10
NOTSET = 0
_level_dict = {
CRITICAL: "CRIT",
ERROR: "ERROR",
WARNING: "WARN",
INFO: "INFO",
DEBUG: "DEBUG",
}
_stream = sys.stderr
class Logger:
def __init__(self, name):
self.level = NOTSET
self.name = name
def _level_str(self, level):
if level in _level_dict:
return _level_dict[level]
return "LVL" + str(level)
def log(self, level, msg, *args):
if level >= (self.level or _level):
print(("%s:%s:" + msg) % ((self._level_str(level), self.name) + args), file=_stream)
def debug(self, msg, *args):
self.log(DEBUG, msg, *args)
def info(self, msg, *args):
self.log(INFO, msg, *args)
def warning(self, msg, *args):
self.log(WARNING, msg, *args)
def error(self, msg, *args):
self.log(ERROR, msg, *args)
def critical(self, msg, *args):
self.log(CRITICAL, msg, *args)
_level = INFO
_loggers = {}
def getLogger(name):
if name in _loggers:
return _loggers[name]
l = Logger(name)
_loggers[name] = l
return l
def info(msg, *args):
getLogger(None).info(msg, *args)
def debug(msg, *args):
getLogger(None).debug(msg, *args)
def basicConfig(level=INFO, filename=None, stream=None, format=None):
global _level, _stream
_level = level
if stream:
_stream = stream
if filename is not None:
print("logging.basicConfig: filename arg is not supported")
if format is not None:
print("logging.basicConfig: format arg is not supported")
| 21.657895
| 96
| 0.595383
|
import sys
CRITICAL = 50
ERROR = 40
WARNING = 30
INFO = 20
DEBUG = 10
NOTSET = 0
_level_dict = {
CRITICAL: "CRIT",
ERROR: "ERROR",
WARNING: "WARN",
INFO: "INFO",
DEBUG: "DEBUG",
}
_stream = sys.stderr
class Logger:
def __init__(self, name):
self.level = NOTSET
self.name = name
def _level_str(self, level):
if level in _level_dict:
return _level_dict[level]
return "LVL" + str(level)
def log(self, level, msg, *args):
if level >= (self.level or _level):
print(("%s:%s:" + msg) % ((self._level_str(level), self.name) + args), file=_stream)
def debug(self, msg, *args):
self.log(DEBUG, msg, *args)
def info(self, msg, *args):
self.log(INFO, msg, *args)
def warning(self, msg, *args):
self.log(WARNING, msg, *args)
def error(self, msg, *args):
self.log(ERROR, msg, *args)
def critical(self, msg, *args):
self.log(CRITICAL, msg, *args)
_level = INFO
_loggers = {}
def getLogger(name):
if name in _loggers:
return _loggers[name]
l = Logger(name)
_loggers[name] = l
return l
def info(msg, *args):
getLogger(None).info(msg, *args)
def debug(msg, *args):
getLogger(None).debug(msg, *args)
def basicConfig(level=INFO, filename=None, stream=None, format=None):
global _level, _stream
_level = level
if stream:
_stream = stream
if filename is not None:
print("logging.basicConfig: filename arg is not supported")
if format is not None:
print("logging.basicConfig: format arg is not supported")
| true
| true
|
1c3ef0d87119236f2d3e758926f3d1b3c3e375d9
| 625
|
py
|
Python
|
lab/refactoring/decompose_conditional.py
|
ikejs/SPD-2.31-Testing-and-Architecture
|
0f1cc3dc726d748cbd3ae75b336c42697a4b9d82
|
[
"MIT"
] | null | null | null |
lab/refactoring/decompose_conditional.py
|
ikejs/SPD-2.31-Testing-and-Architecture
|
0f1cc3dc726d748cbd3ae75b336c42697a4b9d82
|
[
"MIT"
] | null | null | null |
lab/refactoring/decompose_conditional.py
|
ikejs/SPD-2.31-Testing-and-Architecture
|
0f1cc3dc726d748cbd3ae75b336c42697a4b9d82
|
[
"MIT"
] | null | null | null |
# By Kami Bigdely
# Decompose conditional: You have a complicated conditional(if-then-else) statement. Extract
# methods from the condition, then part, and else part(s).
def make_alert_sound():
print('made alert sound.')
def make_accept_sound():
print('made acceptance sound')
ingredients = [
'sodium benzoate'
]
toxins = [
'sodium nitrate',
'sodium benzoate',
'sodium oxide'
]
if toxins in ingredients:
print('!!!')
print('there is a toxin in the food!')
print('!!!')
make_alert_sound()
else:
print('***')
print('Toxin Free')
print('***')
make_accept_sound()
| 21.551724
| 92
| 0.6432
|
def make_alert_sound():
print('made alert sound.')
def make_accept_sound():
print('made acceptance sound')
ingredients = [
'sodium benzoate'
]
toxins = [
'sodium nitrate',
'sodium benzoate',
'sodium oxide'
]
if toxins in ingredients:
print('!!!')
print('there is a toxin in the food!')
print('!!!')
make_alert_sound()
else:
print('***')
print('Toxin Free')
print('***')
make_accept_sound()
| true
| true
|
1c3ef25388e77a1795dd23955ad7fc87a0bb0ddd
| 12,693
|
py
|
Python
|
torch/tensor.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
torch/tensor.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
torch/tensor.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
import torch
from . import _tensor_str
from ._utils import _type, _cuda, _range, _rebuild_tensor
import sys
class _TensorBase(object):
#: bool: True if this is a CUDA tensor
is_cuda = False
is_sparse = False
def new(self, *args, **kwargs):
"""Constructs a new tensor of the same data type."""
return self.__class__(*args, **kwargs)
def type_as(self, tensor):
"""Returns this tensor cast to the type of the given tensor.
This is a no-op if the tensor is already of the correct type. This is
equivalent to::
self.type(tensor.type())
Params:
tensor (Tensor): the tensor which has the desired type
"""
return self.type(tensor.type())
def cpu(self):
"""Returns a CPU copy of this tensor if it's not already on the CPU"""
return self.type(getattr(torch, self.__class__.__name__))
def double(self):
"""Casts this tensor to double type"""
return self.type(type(self).__module__ + '.DoubleTensor')
def float(self):
"""Casts this tensor to float type"""
return self.type(type(self).__module__ + '.FloatTensor')
def half(self):
"""Casts this tensor to half-precision float type"""
return self.type(type(self).__module__ + '.HalfTensor')
def long(self):
"""Casts this tensor to long type"""
return self.type(type(self).__module__ + '.LongTensor')
def int(self):
"""Casts this tensor to int type"""
return self.type(type(self).__module__ + '.IntTensor')
def short(self):
"""Casts this tensor to short type"""
return self.type(type(self).__module__ + '.ShortTensor')
def char(self):
"""Casts this tensor to char type"""
return self.type(type(self).__module__ + '.CharTensor')
def byte(self):
"""Casts this tensor to byte type"""
return self.type(type(self).__module__ + '.ByteTensor')
def is_pinned(self):
"""Returns true if this tensor resides in pinned memory"""
storage = self.storage()
return storage.is_pinned() if storage else False
def pin_memory(self):
"""Copies the tensor to pinned memory, if it's not already pinned."""
if self.is_cuda:
raise TypeError("cannot pin '{0}' only CPU memory can be pinned"
.format(self.type()))
storage = self.storage()
if storage is None:
storage = (self.storage_type())()
return type(self)().set_(storage.pin_memory()).view_as(self)
def share_memory_(self):
"""Moves the underlying storage to shared memory.
This is a no-op if the underlying storage is already in shared memory
and for CUDA tensors. Tensors in shared memory cannot be resized.
"""
self.storage().share_memory_()
return self
def is_shared(self):
"""Checks if tensor is in shared memory.
This is always ``True`` for CUDA tensors.
"""
return self.storage().is_shared()
def __deepcopy__(self, _memo):
memo = _memo.setdefault('torch', {})
if self._cdata in memo:
return memo[self._cdata]
new_storage = self.storage().__deepcopy__(_memo)
new_tensor = self.new()
new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride())
memo[self._cdata] = new_tensor
return new_tensor
def __reduce__(self):
# NOTE: _rebuild_tensor does not call __setstate__
args = self.__getstate__()
return (_rebuild_tensor, args)
def __getstate__(self):
return (self.storage(),
self.storage_offset(),
tuple(self.size()),
self.stride())
def __setstate__(self, state):
self.set_(*state)
def __repr__(self):
return str(self)
def __str__(self):
# All strings are unicode in Python 3, while we have to encode unicode
# strings in Python2. If we can't, let python decide the best
# characters to replace unicode characters with.
if sys.version_info > (3,):
return _tensor_str._str(self)
else:
if hasattr(sys.stdout, 'encoding'):
return _tensor_str._str(self).encode(
sys.stdout.encoding or 'UTF-8', 'replace')
else:
return _tensor_str._str(self).encode('UTF-8', 'replace')
def __bool__(self):
if self.numel() == 0:
return False
raise RuntimeError("bool value of non-empty " + torch.typename(self) +
" objects is ambiguous")
__nonzero__ = __bool__
def __iter__(self):
return iter(map(lambda i: self.select(0, i), _range(self.size(0))))
def split(self, split_size, dim=0):
"""Splits this tensor into a tuple of tensors.
See :func:`torch.split`.
"""
return torch.split(self, split_size, dim)
def chunk(self, n_chunks, dim=0):
"""Splits this tensor into a tuple of tensors.
See :func:`torch.chunk`.
"""
return torch.chunk(self, n_chunks, dim)
def tolist(self):
"""Returns a nested list represenation of this tensor."""
dim = self.dim()
if dim == 1:
return [v for v in self]
elif dim > 0:
return [subt.tolist() for subt in self]
return []
def view_as(self, tensor):
"""Returns this tensor viewed as the size as the specified tensor.
This is equivalent to::
self.view(tensor.size())
"""
return self.view(tensor.size())
def permute(self, *dims):
"""Permute the dimensions of this tensor.
Args:
*dims (int...): The desired ordering of dimensions
Example:
>>> x = torch.randn(2, 3, 5)
>>> x.size()
torch.Size([2, 3, 5])
>>> x.permute(2, 0, 1).size()
torch.Size([5, 2, 3])
"""
perm = list(dims)
tensor = self
n_dims = tensor.dim()
assert len(perm) == n_dims, 'Invalid permutation'
for i, p in enumerate(perm):
if p != i and p != -1:
j = i
while True:
assert 0 <= perm[j] and perm[j] < n_dims, 'Invalid permutation'
tensor = tensor.transpose(j, perm[j])
perm[j], j = -1, perm[j]
if perm[j] == i:
break
perm[j] = -1
return tensor
def expand_as(self, tensor):
"""Expands this tensor to the size of the specified tensor.
This is equivalent to::
self.expand(tensor.size())
"""
return self.expand(tensor.size())
def repeat(self, *sizes):
"""Repeats this tensor along the specified dimensions.
Unlike :meth:`expand`, this function copies the tensor's data.
Args:
*sizes (torch.Size or int...): The number of times to repeat this tensor along each dimension
Example:
>>> x = torch.Tensor([1, 2, 3])
>>> x.repeat(4, 2)
1 2 3 1 2 3
1 2 3 1 2 3
1 2 3 1 2 3
1 2 3 1 2 3
[torch.FloatTensor of size 4x6]
>>> x.repeat(4, 2, 1).size()
torch.Size([4, 2, 3])
"""
# If args == (torch.Size,), then we need to unpack the tuple
if len(sizes) == 1 and isinstance(sizes[0], torch.Size):
sizes = sizes[0]
repeats = list(sizes)
result = self.new()
src = self.contiguous()
if len(repeats) < src.dim():
raise ValueError('Number of dimensions of repeat dims can not be '
'smaller than number of dimensions of tensor')
xtensor = src.new().set_(src)
xsize = list(xtensor.size())
for i in _range(len(repeats) - src.dim()):
xsize = [1] + xsize
size = torch.Size([a * b for a, b in zip(xsize, repeats)])
xtensor.resize_(torch.Size(xsize))
result.resize_(size)
urtensor = result.new(result)
for i in _range(xtensor.dim()):
urtensor = urtensor.unfold(i, xtensor.size(i), xtensor.size(i))
for i in _range(urtensor.dim() - xtensor.dim()):
xsize = [1] + xsize
xtensor.resize_(torch.Size(xsize))
xxtensor = xtensor.expand_as(urtensor)
urtensor.copy_(xxtensor)
return result
# TODO: add tests for operators
def __add__(self, other):
return self.add(other)
__radd__ = __add__
def __iadd__(self, other):
return self.add_(other)
def __sub__(self, other):
return self.sub(other)
def __rsub__(self, other):
return self.new().resize_as_(self).fill_(other).add_(-1, self)
def __isub__(self, other):
return self.sub_(other)
def __mul__(self, other):
return self.mul(other)
__rmul__ = __mul__
def __imul__(self, other):
return self.mul_(other)
def __matmul__(self, other):
dim_self = self.dim()
try:
dim_other = other.dim()
except AttributeError: # not a tensor
return NotImplemented
if dim_self == 1 and dim_other == 1:
return self.dot(other)
if dim_self == 2 and dim_other == 1:
return self.mv(other)
if dim_self == 1 and dim_other == 2:
return self.unsqueeze(0).mm(other).squeeze(0)
elif dim_self == 2 and dim_other == 2:
return self.mm(other)
raise ValueError("both arguments to __matmul__ need to be 1D or 2D, "
"but they are {}D and {}D".format(dim_self, dim_other))
def __pow__(self, other):
return self.pow(other)
def __ipow__(self, other):
return self.pow_(other)
def __div__(self, other):
return self.div(other)
__truediv__ = __div__
def __rdiv__(self, other):
return self.new().resize_as_(self).fill_(other).div_(self)
__rtruediv__ = __rdiv__
def __idiv__(self, other):
return self.div_(other)
def __mod__(self, other):
return self.remainder(other)
def __neg__(self):
return self.neg()
def __eq__(self, other):
return self.eq(other)
def __ne__(self, other):
return self.ne(other)
def __lt__(self, other):
return self.lt(other)
def __le__(self, other):
return self.le(other)
def __gt__(self, other):
return self.gt(other)
def __ge__(self, other):
return self.ge(other)
# TODO: add native add or and xor in the libs
def __and__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return (self + other).eq(2)
def __or__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return (self + other).gt(0)
def __xor__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return (self + other).eq(1)
def __iand__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return self.mul_(other)
def __ior__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return self.copy_((self + other).gt(0))
def __ixor__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return self.copy_((self + other).eq(1))
def __invert__(self):
if type(self).__name__ != 'ByteTensor':
raise RuntimeError('logical operations are supported on ByteTensors only')
return (1 - self)
def __hash__(self):
return id(self)
_TensorBase.type = _type
_TensorBase.cuda = _cuda
| 32.134177
| 105
| 0.573308
|
import torch
from . import _tensor_str
from ._utils import _type, _cuda, _range, _rebuild_tensor
import sys
class _TensorBase(object):
is_cuda = False
is_sparse = False
def new(self, *args, **kwargs):
return self.__class__(*args, **kwargs)
def type_as(self, tensor):
return self.type(tensor.type())
def cpu(self):
return self.type(getattr(torch, self.__class__.__name__))
def double(self):
return self.type(type(self).__module__ + '.DoubleTensor')
def float(self):
return self.type(type(self).__module__ + '.FloatTensor')
def half(self):
return self.type(type(self).__module__ + '.HalfTensor')
def long(self):
return self.type(type(self).__module__ + '.LongTensor')
def int(self):
return self.type(type(self).__module__ + '.IntTensor')
def short(self):
return self.type(type(self).__module__ + '.ShortTensor')
def char(self):
return self.type(type(self).__module__ + '.CharTensor')
def byte(self):
return self.type(type(self).__module__ + '.ByteTensor')
def is_pinned(self):
storage = self.storage()
return storage.is_pinned() if storage else False
def pin_memory(self):
if self.is_cuda:
raise TypeError("cannot pin '{0}' only CPU memory can be pinned"
.format(self.type()))
storage = self.storage()
if storage is None:
storage = (self.storage_type())()
return type(self)().set_(storage.pin_memory()).view_as(self)
def share_memory_(self):
self.storage().share_memory_()
return self
def is_shared(self):
return self.storage().is_shared()
def __deepcopy__(self, _memo):
memo = _memo.setdefault('torch', {})
if self._cdata in memo:
return memo[self._cdata]
new_storage = self.storage().__deepcopy__(_memo)
new_tensor = self.new()
new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride())
memo[self._cdata] = new_tensor
return new_tensor
def __reduce__(self):
args = self.__getstate__()
return (_rebuild_tensor, args)
def __getstate__(self):
return (self.storage(),
self.storage_offset(),
tuple(self.size()),
self.stride())
def __setstate__(self, state):
self.set_(*state)
def __repr__(self):
return str(self)
def __str__(self):
# characters to replace unicode characters with.
if sys.version_info > (3,):
return _tensor_str._str(self)
else:
if hasattr(sys.stdout, 'encoding'):
return _tensor_str._str(self).encode(
sys.stdout.encoding or 'UTF-8', 'replace')
else:
return _tensor_str._str(self).encode('UTF-8', 'replace')
def __bool__(self):
if self.numel() == 0:
return False
raise RuntimeError("bool value of non-empty " + torch.typename(self) +
" objects is ambiguous")
__nonzero__ = __bool__
def __iter__(self):
return iter(map(lambda i: self.select(0, i), _range(self.size(0))))
def split(self, split_size, dim=0):
return torch.split(self, split_size, dim)
def chunk(self, n_chunks, dim=0):
return torch.chunk(self, n_chunks, dim)
def tolist(self):
dim = self.dim()
if dim == 1:
return [v for v in self]
elif dim > 0:
return [subt.tolist() for subt in self]
return []
def view_as(self, tensor):
return self.view(tensor.size())
def permute(self, *dims):
perm = list(dims)
tensor = self
n_dims = tensor.dim()
assert len(perm) == n_dims, 'Invalid permutation'
for i, p in enumerate(perm):
if p != i and p != -1:
j = i
while True:
assert 0 <= perm[j] and perm[j] < n_dims, 'Invalid permutation'
tensor = tensor.transpose(j, perm[j])
perm[j], j = -1, perm[j]
if perm[j] == i:
break
perm[j] = -1
return tensor
def expand_as(self, tensor):
return self.expand(tensor.size())
def repeat(self, *sizes):
# If args == (torch.Size,), then we need to unpack the tuple
if len(sizes) == 1 and isinstance(sizes[0], torch.Size):
sizes = sizes[0]
repeats = list(sizes)
result = self.new()
src = self.contiguous()
if len(repeats) < src.dim():
raise ValueError('Number of dimensions of repeat dims can not be '
'smaller than number of dimensions of tensor')
xtensor = src.new().set_(src)
xsize = list(xtensor.size())
for i in _range(len(repeats) - src.dim()):
xsize = [1] + xsize
size = torch.Size([a * b for a, b in zip(xsize, repeats)])
xtensor.resize_(torch.Size(xsize))
result.resize_(size)
urtensor = result.new(result)
for i in _range(xtensor.dim()):
urtensor = urtensor.unfold(i, xtensor.size(i), xtensor.size(i))
for i in _range(urtensor.dim() - xtensor.dim()):
xsize = [1] + xsize
xtensor.resize_(torch.Size(xsize))
xxtensor = xtensor.expand_as(urtensor)
urtensor.copy_(xxtensor)
return result
# TODO: add tests for operators
def __add__(self, other):
return self.add(other)
__radd__ = __add__
def __iadd__(self, other):
return self.add_(other)
def __sub__(self, other):
return self.sub(other)
def __rsub__(self, other):
return self.new().resize_as_(self).fill_(other).add_(-1, self)
def __isub__(self, other):
return self.sub_(other)
def __mul__(self, other):
return self.mul(other)
__rmul__ = __mul__
def __imul__(self, other):
return self.mul_(other)
def __matmul__(self, other):
dim_self = self.dim()
try:
dim_other = other.dim()
except AttributeError: # not a tensor
return NotImplemented
if dim_self == 1 and dim_other == 1:
return self.dot(other)
if dim_self == 2 and dim_other == 1:
return self.mv(other)
if dim_self == 1 and dim_other == 2:
return self.unsqueeze(0).mm(other).squeeze(0)
elif dim_self == 2 and dim_other == 2:
return self.mm(other)
raise ValueError("both arguments to __matmul__ need to be 1D or 2D, "
"but they are {}D and {}D".format(dim_self, dim_other))
def __pow__(self, other):
return self.pow(other)
def __ipow__(self, other):
return self.pow_(other)
def __div__(self, other):
return self.div(other)
__truediv__ = __div__
def __rdiv__(self, other):
return self.new().resize_as_(self).fill_(other).div_(self)
__rtruediv__ = __rdiv__
def __idiv__(self, other):
return self.div_(other)
def __mod__(self, other):
return self.remainder(other)
def __neg__(self):
return self.neg()
def __eq__(self, other):
return self.eq(other)
def __ne__(self, other):
return self.ne(other)
def __lt__(self, other):
return self.lt(other)
def __le__(self, other):
return self.le(other)
def __gt__(self, other):
return self.gt(other)
def __ge__(self, other):
return self.ge(other)
# TODO: add native add or and xor in the libs
def __and__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return (self + other).eq(2)
def __or__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return (self + other).gt(0)
def __xor__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return (self + other).eq(1)
def __iand__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return self.mul_(other)
def __ior__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return self.copy_((self + other).gt(0))
def __ixor__(self, other):
if (type(self).__name__ != 'ByteTensor' or
type(other).__name__ != 'ByteTensor'):
raise RuntimeError('logical operations are supported on ByteTensors only')
return self.copy_((self + other).eq(1))
def __invert__(self):
if type(self).__name__ != 'ByteTensor':
raise RuntimeError('logical operations are supported on ByteTensors only')
return (1 - self)
def __hash__(self):
return id(self)
_TensorBase.type = _type
_TensorBase.cuda = _cuda
| true
| true
|
1c3ef48bc5e3786c556eee182690af45b40856ac
| 1,854
|
py
|
Python
|
secao04_factory/abstract_factory.py
|
ravellys/design_patterns_python
|
6c29d1d77d4522255e10eca237f8f95b69d7210c
|
[
"MIT"
] | null | null | null |
secao04_factory/abstract_factory.py
|
ravellys/design_patterns_python
|
6c29d1d77d4522255e10eca237f8f95b69d7210c
|
[
"MIT"
] | null | null | null |
secao04_factory/abstract_factory.py
|
ravellys/design_patterns_python
|
6c29d1d77d4522255e10eca237f8f95b69d7210c
|
[
"MIT"
] | null | null | null |
from abc import ABC, abstractmethod
# Abstract factory
class PizzaFactory(ABC):
@abstractmethod
def criar_pizza(self):
pass
@abstractmethod
def criar_pizza_vegana(self):
pass
# Concrect Factory
class PizzaBrasileiraFactory(PizzaFactory):
def criar_pizza(self):
return PizzaCamarao()
def criar_pizza_vegana(self):
return PizzaMandioca()
class PizzaItalianaFactory(PizzaFactory):
def criar_pizza(self):
return PizzaBolonha()
def criar_pizza_vegana(self):
return PizzaBrocolis()
# Abstract Product
class PizzaVegana(ABC):
@abstractmethod
def preparar(self):
pass
class Pizza(ABC):
@abstractmethod
def servir(self, PizzaVeg):
pass
# Concret Product
class PizzaMandioca(PizzaVegana):
def preparar(self):
print(f'Preparando {type(self).__name__}')
class PizzaBrocolis(PizzaVegana):
def preparar(self):
print(f'Preparando {type(self).__name__}')
class PizzaCamarao(Pizza):
def servir(self, pizza_vegana: PizzaVegana):
print(f'Pizza {type(self).__name__} é servida com camarão na pizza {type(pizza_vegana).__name__}')
class PizzaBolonha(Pizza):
def servir(self, pizza_vegana: PizzaVegana):
print(f'Pizza {type(self).__name__} é servida com bolonha na pizza {type(pizza_vegana).__name__}')
# Cliente
class Pizzaria:
def fazer_pizzas(self):
for factory in [PizzaBrasileiraFactory(), PizzaItalianaFactory()]:
self.factory: PizzaFactory = factory
self.pizza: Pizza = self.factory.criar_pizza()
self.pizza_vegana: PizzaVegana = self.factory.criar_pizza_vegana()
self.pizza_vegana.preparar()
self.pizza.servir(self.pizza_vegana)
if __name__ == '__main__':
pizzaria = Pizzaria()
pizzaria.fazer_pizzas()
| 22.888889
| 106
| 0.686084
|
from abc import ABC, abstractmethod
class PizzaFactory(ABC):
@abstractmethod
def criar_pizza(self):
pass
@abstractmethod
def criar_pizza_vegana(self):
pass
class PizzaBrasileiraFactory(PizzaFactory):
def criar_pizza(self):
return PizzaCamarao()
def criar_pizza_vegana(self):
return PizzaMandioca()
class PizzaItalianaFactory(PizzaFactory):
def criar_pizza(self):
return PizzaBolonha()
def criar_pizza_vegana(self):
return PizzaBrocolis()
class PizzaVegana(ABC):
@abstractmethod
def preparar(self):
pass
class Pizza(ABC):
@abstractmethod
def servir(self, PizzaVeg):
pass
class PizzaMandioca(PizzaVegana):
def preparar(self):
print(f'Preparando {type(self).__name__}')
class PizzaBrocolis(PizzaVegana):
def preparar(self):
print(f'Preparando {type(self).__name__}')
class PizzaCamarao(Pizza):
def servir(self, pizza_vegana: PizzaVegana):
print(f'Pizza {type(self).__name__} é servida com camarão na pizza {type(pizza_vegana).__name__}')
class PizzaBolonha(Pizza):
def servir(self, pizza_vegana: PizzaVegana):
print(f'Pizza {type(self).__name__} é servida com bolonha na pizza {type(pizza_vegana).__name__}')
class Pizzaria:
def fazer_pizzas(self):
for factory in [PizzaBrasileiraFactory(), PizzaItalianaFactory()]:
self.factory: PizzaFactory = factory
self.pizza: Pizza = self.factory.criar_pizza()
self.pizza_vegana: PizzaVegana = self.factory.criar_pizza_vegana()
self.pizza_vegana.preparar()
self.pizza.servir(self.pizza_vegana)
if __name__ == '__main__':
pizzaria = Pizzaria()
pizzaria.fazer_pizzas()
| true
| true
|
1c3ef5954090ff2844946ed9928596b498fc65ba
| 3,200
|
py
|
Python
|
sqlalchemy_collectd/client/tests/test_plugin.py
|
jvanasco/sqlalchemy-collectd
|
8fb2aa04aa66b3b4edfdf04dce1eca70b4a1ab5e
|
[
"MIT"
] | null | null | null |
sqlalchemy_collectd/client/tests/test_plugin.py
|
jvanasco/sqlalchemy-collectd
|
8fb2aa04aa66b3b4edfdf04dce1eca70b4a1ab5e
|
[
"MIT"
] | null | null | null |
sqlalchemy_collectd/client/tests/test_plugin.py
|
jvanasco/sqlalchemy-collectd
|
8fb2aa04aa66b3b4edfdf04dce1eca70b4a1ab5e
|
[
"MIT"
] | null | null | null |
import unittest
import mock
from sqlalchemy.engine import url as sqla_url
from .. import plugin
class PluginTest(unittest.TestCase):
def test_start_no_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.URL("mysql+pymysql://scott:tiger@localhost/")
p = plugin.Plugin(url, {})
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual([mock.call(engine)], start_plugin.mock_calls)
def test_start_engine_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.URL("mysql+pymysql://scott:tiger@localhost/")
p = plugin.Plugin(
url, {"collectd_host": "127.0.0.1", "collectd_port": 5678}
)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1", collectd_port=5678)],
start_plugin.mock_calls,
)
def test_start_url_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.make_url(
"mysql+pymysql://scott:tiger@localhost/"
"?collectd_host=127.0.0.1&somekey=somevalue&collectd_port=1234"
)
kwargs = {"unrelated": "bar"}
p = plugin.Plugin(url, kwargs)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1", collectd_port=1234)],
start_plugin.mock_calls,
)
self.assertEqual({"somekey": "somevalue"}, url.query)
self.assertEqual({"unrelated": "bar"}, kwargs)
def test_start_url_args_no_port(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.make_url(
"mysql+pymysql://scott:tiger@localhost/"
"?collectd_host=127.0.0.1&somekey=somevalue"
)
kwargs = {"unrelated": "bar"}
p = plugin.Plugin(url, kwargs)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1")],
start_plugin.mock_calls,
)
self.assertEqual({"somekey": "somevalue"}, url.query)
self.assertEqual({"unrelated": "bar"}, kwargs)
def test_start_both_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.make_url(
"mysql+pymysql://scott:tiger@localhost/"
"?collectd_host=127.0.0.1&collectd_port=1234"
)
kwargs = {"collectd_host": "172.18.0.2", "collectd_port": 5678}
p = plugin.Plugin(url, kwargs)
engine = mock.Mock()
p.engine_created(engine)
# argument is popped from both but favors url argument
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1", collectd_port=1234)],
start_plugin.mock_calls,
)
self.assertEqual({}, url.query)
self.assertEqual({}, kwargs)
| 36.781609
| 79
| 0.584688
|
import unittest
import mock
from sqlalchemy.engine import url as sqla_url
from .. import plugin
class PluginTest(unittest.TestCase):
def test_start_no_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.URL("mysql+pymysql://scott:tiger@localhost/")
p = plugin.Plugin(url, {})
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual([mock.call(engine)], start_plugin.mock_calls)
def test_start_engine_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.URL("mysql+pymysql://scott:tiger@localhost/")
p = plugin.Plugin(
url, {"collectd_host": "127.0.0.1", "collectd_port": 5678}
)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1", collectd_port=5678)],
start_plugin.mock_calls,
)
def test_start_url_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.make_url(
"mysql+pymysql://scott:tiger@localhost/"
"?collectd_host=127.0.0.1&somekey=somevalue&collectd_port=1234"
)
kwargs = {"unrelated": "bar"}
p = plugin.Plugin(url, kwargs)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1", collectd_port=1234)],
start_plugin.mock_calls,
)
self.assertEqual({"somekey": "somevalue"}, url.query)
self.assertEqual({"unrelated": "bar"}, kwargs)
def test_start_url_args_no_port(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.make_url(
"mysql+pymysql://scott:tiger@localhost/"
"?collectd_host=127.0.0.1&somekey=somevalue"
)
kwargs = {"unrelated": "bar"}
p = plugin.Plugin(url, kwargs)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1")],
start_plugin.mock_calls,
)
self.assertEqual({"somekey": "somevalue"}, url.query)
self.assertEqual({"unrelated": "bar"}, kwargs)
def test_start_both_args(self):
with mock.patch.object(plugin, "start_plugin") as start_plugin:
url = sqla_url.make_url(
"mysql+pymysql://scott:tiger@localhost/"
"?collectd_host=127.0.0.1&collectd_port=1234"
)
kwargs = {"collectd_host": "172.18.0.2", "collectd_port": 5678}
p = plugin.Plugin(url, kwargs)
engine = mock.Mock()
p.engine_created(engine)
self.assertEqual(
[mock.call(engine, collectd_host="127.0.0.1", collectd_port=1234)],
start_plugin.mock_calls,
)
self.assertEqual({}, url.query)
self.assertEqual({}, kwargs)
| true
| true
|
1c3ef6016330ed5b1789e5f911abf858f0b1bf46
| 8,016
|
py
|
Python
|
homeassistant/components/tibber/sensor.py
|
kdschlosser/home-assistant
|
a94a24f6f83508642e220fadf2799789dc32a25b
|
[
"Apache-2.0"
] | 2
|
2020-06-17T01:23:01.000Z
|
2020-06-18T22:17:14.000Z
|
homeassistant/components/tibber/sensor.py
|
kdschlosser/home-assistant
|
a94a24f6f83508642e220fadf2799789dc32a25b
|
[
"Apache-2.0"
] | 1
|
2019-02-09T15:44:11.000Z
|
2019-02-09T15:44:11.000Z
|
homeassistant/components/tibber/sensor.py
|
kdschlosser/home-assistant
|
a94a24f6f83508642e220fadf2799789dc32a25b
|
[
"Apache-2.0"
] | 1
|
2019-08-13T11:54:30.000Z
|
2019-08-13T11:54:30.000Z
|
"""
Support for Tibber.
For more details about this platform, please refer to the documentation at
https://home-assistant.io/components/sensor.tibber/
"""
import asyncio
import logging
from datetime import timedelta
import aiohttp
from homeassistant.components.tibber import DOMAIN as TIBBER_DOMAIN
from homeassistant.exceptions import PlatformNotReady
from homeassistant.helpers.entity import Entity
from homeassistant.util import dt as dt_util
from homeassistant.util import Throttle
_LOGGER = logging.getLogger(__name__)
ICON = 'mdi:currency-usd'
ICON_RT = 'mdi:power-plug'
SCAN_INTERVAL = timedelta(minutes=1)
MIN_TIME_BETWEEN_UPDATES = timedelta(minutes=5)
async def async_setup_platform(hass, config, async_add_entities,
discovery_info=None):
"""Set up the Tibber sensor."""
if discovery_info is None:
return
tibber_connection = hass.data.get(TIBBER_DOMAIN)
dev = []
for home in tibber_connection.get_homes(only_active=False):
try:
await home.update_info()
except asyncio.TimeoutError as err:
_LOGGER.error("Timeout connecting to Tibber home: %s ", err)
raise PlatformNotReady()
except aiohttp.ClientError as err:
_LOGGER.error("Error connecting to Tibber home: %s ", err)
raise PlatformNotReady()
if home.has_active_subscription:
dev.append(TibberSensorElPrice(home))
if home.has_real_time_consumption:
dev.append(TibberSensorRT(home))
async_add_entities(dev, True)
class TibberSensorElPrice(Entity):
"""Representation of an Tibber sensor for el price."""
def __init__(self, tibber_home):
"""Initialize the sensor."""
self._tibber_home = tibber_home
self._last_updated = None
self._last_data_timestamp = None
self._state = None
self._is_available = False
self._device_state_attributes = {}
self._unit_of_measurement = self._tibber_home.price_unit
self._name = 'Electricity price {}'.format(tibber_home.info['viewer']
['home']['appNickname'])
async def async_update(self):
"""Get the latest data and updates the states."""
now = dt_util.now()
if self._tibber_home.current_price_total and self._last_updated and \
self._last_updated.hour == now.hour and self._last_data_timestamp:
return
if (not self._last_data_timestamp or
(self._last_data_timestamp - now).total_seconds()/3600 < 12
or not self._is_available):
_LOGGER.debug("Asking for new data.")
await self._fetch_data()
self._is_available = self._update_current_price()
@property
def device_state_attributes(self):
"""Return the state attributes."""
return self._device_state_attributes
@property
def available(self):
"""Return True if entity is available."""
return self._is_available
@property
def name(self):
"""Return the name of the sensor."""
return self._name
@property
def state(self):
"""Return the state of the device."""
return self._state
@property
def icon(self):
"""Return the icon to use in the frontend."""
return ICON
@property
def unit_of_measurement(self):
"""Return the unit of measurement of this entity."""
return self._unit_of_measurement
@property
def unique_id(self):
"""Return a unique ID."""
home = self._tibber_home.info['viewer']['home']
return home['meteringPointData']['consumptionEan']
@Throttle(MIN_TIME_BETWEEN_UPDATES)
async def _fetch_data(self):
try:
await self._tibber_home.update_info()
await self._tibber_home.update_price_info()
except (asyncio.TimeoutError, aiohttp.ClientError):
return
data = self._tibber_home.info['viewer']['home']
self._device_state_attributes['app_nickname'] = data['appNickname']
self._device_state_attributes['grid_company'] = \
data['meteringPointData']['gridCompany']
self._device_state_attributes['estimated_annual_consumption'] = \
data['meteringPointData']['estimatedAnnualConsumption']
def _update_current_price(self):
state = None
max_price = 0
min_price = 10000
sum_price = 0
num = 0
now = dt_util.now()
for key, price_total in self._tibber_home.price_total.items():
price_time = dt_util.as_local(dt_util.parse_datetime(key))
price_total = round(price_total, 3)
time_diff = (now - price_time).total_seconds()/60
if (not self._last_data_timestamp or
price_time > self._last_data_timestamp):
self._last_data_timestamp = price_time
if 0 <= time_diff < 60:
state = price_total
self._last_updated = price_time
if now.date() == price_time.date():
max_price = max(max_price, price_total)
min_price = min(min_price, price_total)
num += 1
sum_price += price_total
self._state = state
self._device_state_attributes['max_price'] = max_price
self._device_state_attributes['avg_price'] = round(sum_price / num, 3)
self._device_state_attributes['min_price'] = min_price
return state is not None
class TibberSensorRT(Entity):
"""Representation of an Tibber sensor for real time consumption."""
def __init__(self, tibber_home):
"""Initialize the sensor."""
self._tibber_home = tibber_home
self._state = None
self._device_state_attributes = {}
self._unit_of_measurement = 'W'
nickname = tibber_home.info['viewer']['home']['appNickname']
self._name = 'Real time consumption {}'.format(nickname)
async def async_added_to_hass(self):
"""Start unavailability tracking."""
await self._tibber_home.rt_subscribe(self.hass.loop,
self._async_callback)
async def _async_callback(self, payload):
"""Handle received data."""
errors = payload.get('errors')
if errors:
_LOGGER.error(errors[0])
return
data = payload.get('data')
if data is None:
return
live_measurement = data.get('liveMeasurement')
if live_measurement is None:
return
self._state = live_measurement.pop('power', None)
for key, value in live_measurement.items():
if value is None:
continue
self._device_state_attributes[key] = value
self.async_schedule_update_ha_state()
@property
def device_state_attributes(self):
"""Return the state attributes."""
return self._device_state_attributes
@property
def available(self):
"""Return True if entity is available."""
return self._tibber_home.rt_subscription_running
@property
def name(self):
"""Return the name of the sensor."""
return self._name
@property
def should_poll(self):
"""Return the polling state."""
return False
@property
def state(self):
"""Return the state of the device."""
return self._state
@property
def icon(self):
"""Return the icon to use in the frontend."""
return ICON_RT
@property
def unit_of_measurement(self):
"""Return the unit of measurement of this entity."""
return self._unit_of_measurement
@property
def unique_id(self):
"""Return a unique ID."""
home = self._tibber_home.info['viewer']['home']
_id = home['meteringPointData']['consumptionEan']
return'{}_rt_consumption'.format(_id)
| 33.261411
| 78
| 0.631362
|
import asyncio
import logging
from datetime import timedelta
import aiohttp
from homeassistant.components.tibber import DOMAIN as TIBBER_DOMAIN
from homeassistant.exceptions import PlatformNotReady
from homeassistant.helpers.entity import Entity
from homeassistant.util import dt as dt_util
from homeassistant.util import Throttle
_LOGGER = logging.getLogger(__name__)
ICON = 'mdi:currency-usd'
ICON_RT = 'mdi:power-plug'
SCAN_INTERVAL = timedelta(minutes=1)
MIN_TIME_BETWEEN_UPDATES = timedelta(minutes=5)
async def async_setup_platform(hass, config, async_add_entities,
discovery_info=None):
if discovery_info is None:
return
tibber_connection = hass.data.get(TIBBER_DOMAIN)
dev = []
for home in tibber_connection.get_homes(only_active=False):
try:
await home.update_info()
except asyncio.TimeoutError as err:
_LOGGER.error("Timeout connecting to Tibber home: %s ", err)
raise PlatformNotReady()
except aiohttp.ClientError as err:
_LOGGER.error("Error connecting to Tibber home: %s ", err)
raise PlatformNotReady()
if home.has_active_subscription:
dev.append(TibberSensorElPrice(home))
if home.has_real_time_consumption:
dev.append(TibberSensorRT(home))
async_add_entities(dev, True)
class TibberSensorElPrice(Entity):
def __init__(self, tibber_home):
self._tibber_home = tibber_home
self._last_updated = None
self._last_data_timestamp = None
self._state = None
self._is_available = False
self._device_state_attributes = {}
self._unit_of_measurement = self._tibber_home.price_unit
self._name = 'Electricity price {}'.format(tibber_home.info['viewer']
['home']['appNickname'])
async def async_update(self):
now = dt_util.now()
if self._tibber_home.current_price_total and self._last_updated and \
self._last_updated.hour == now.hour and self._last_data_timestamp:
return
if (not self._last_data_timestamp or
(self._last_data_timestamp - now).total_seconds()/3600 < 12
or not self._is_available):
_LOGGER.debug("Asking for new data.")
await self._fetch_data()
self._is_available = self._update_current_price()
@property
def device_state_attributes(self):
return self._device_state_attributes
@property
def available(self):
return self._is_available
@property
def name(self):
return self._name
@property
def state(self):
return self._state
@property
def icon(self):
return ICON
@property
def unit_of_measurement(self):
return self._unit_of_measurement
@property
def unique_id(self):
home = self._tibber_home.info['viewer']['home']
return home['meteringPointData']['consumptionEan']
@Throttle(MIN_TIME_BETWEEN_UPDATES)
async def _fetch_data(self):
try:
await self._tibber_home.update_info()
await self._tibber_home.update_price_info()
except (asyncio.TimeoutError, aiohttp.ClientError):
return
data = self._tibber_home.info['viewer']['home']
self._device_state_attributes['app_nickname'] = data['appNickname']
self._device_state_attributes['grid_company'] = \
data['meteringPointData']['gridCompany']
self._device_state_attributes['estimated_annual_consumption'] = \
data['meteringPointData']['estimatedAnnualConsumption']
def _update_current_price(self):
state = None
max_price = 0
min_price = 10000
sum_price = 0
num = 0
now = dt_util.now()
for key, price_total in self._tibber_home.price_total.items():
price_time = dt_util.as_local(dt_util.parse_datetime(key))
price_total = round(price_total, 3)
time_diff = (now - price_time).total_seconds()/60
if (not self._last_data_timestamp or
price_time > self._last_data_timestamp):
self._last_data_timestamp = price_time
if 0 <= time_diff < 60:
state = price_total
self._last_updated = price_time
if now.date() == price_time.date():
max_price = max(max_price, price_total)
min_price = min(min_price, price_total)
num += 1
sum_price += price_total
self._state = state
self._device_state_attributes['max_price'] = max_price
self._device_state_attributes['avg_price'] = round(sum_price / num, 3)
self._device_state_attributes['min_price'] = min_price
return state is not None
class TibberSensorRT(Entity):
def __init__(self, tibber_home):
self._tibber_home = tibber_home
self._state = None
self._device_state_attributes = {}
self._unit_of_measurement = 'W'
nickname = tibber_home.info['viewer']['home']['appNickname']
self._name = 'Real time consumption {}'.format(nickname)
async def async_added_to_hass(self):
await self._tibber_home.rt_subscribe(self.hass.loop,
self._async_callback)
async def _async_callback(self, payload):
errors = payload.get('errors')
if errors:
_LOGGER.error(errors[0])
return
data = payload.get('data')
if data is None:
return
live_measurement = data.get('liveMeasurement')
if live_measurement is None:
return
self._state = live_measurement.pop('power', None)
for key, value in live_measurement.items():
if value is None:
continue
self._device_state_attributes[key] = value
self.async_schedule_update_ha_state()
@property
def device_state_attributes(self):
return self._device_state_attributes
@property
def available(self):
return self._tibber_home.rt_subscription_running
@property
def name(self):
return self._name
@property
def should_poll(self):
return False
@property
def state(self):
return self._state
@property
def icon(self):
return ICON_RT
@property
def unit_of_measurement(self):
return self._unit_of_measurement
@property
def unique_id(self):
home = self._tibber_home.info['viewer']['home']
_id = home['meteringPointData']['consumptionEan']
return'{}_rt_consumption'.format(_id)
| true
| true
|
1c3ef6e63a51101f65832c61279954bc32fb9cee
| 432
|
py
|
Python
|
src/0973_K_closest_points_to_origin.py
|
hariharanragothaman/leetcode-solutions
|
44e759f80d3c9df382fdf8d694d6378881e3649d
|
[
"Apache-2.0"
] | 2
|
2021-04-21T07:59:42.000Z
|
2021-06-17T17:14:26.000Z
|
src/0973_K_closest_points_to_origin.py
|
hariharanragothaman/pyrevise-leetcode
|
44e759f80d3c9df382fdf8d694d6378881e3649d
|
[
"Apache-2.0"
] | null | null | null |
src/0973_K_closest_points_to_origin.py
|
hariharanragothaman/pyrevise-leetcode
|
44e759f80d3c9df382fdf8d694d6378881e3649d
|
[
"Apache-2.0"
] | null | null | null |
"""
Given an array of points, find the number of closest points to origin
"""
from typing import List
import heapq
def KClosest_using_sort(points: List[List[int]], k: int) -> List[List[int]]:
points = sorted(points, lambda P: P[0] ** 2 + P[1] ** 2)
return points[:k]
def KClosest_using_heap(points: List[List[int]], k: int) -> List[List[int]]:
return heapq.nsmallest(k, points, key=lambda P: P[0] ** 2 + P[1] ** 2)
| 27
| 76
| 0.652778
|
from typing import List
import heapq
def KClosest_using_sort(points: List[List[int]], k: int) -> List[List[int]]:
points = sorted(points, lambda P: P[0] ** 2 + P[1] ** 2)
return points[:k]
def KClosest_using_heap(points: List[List[int]], k: int) -> List[List[int]]:
return heapq.nsmallest(k, points, key=lambda P: P[0] ** 2 + P[1] ** 2)
| true
| true
|
1c3ef75effc0c8895dcb921e1215e239572fd9cc
| 4,592
|
py
|
Python
|
python/oneflow/compatible/single_client/nn/optimizer/sgd.py
|
mosout/oneflow
|
afbb221d900f1a340568ae2462b2022f8fcc4b3d
|
[
"Apache-2.0"
] | 1
|
2022-01-19T07:50:28.000Z
|
2022-01-19T07:50:28.000Z
|
python/oneflow/compatible/single_client/nn/optimizer/sgd.py
|
mosout/oneflow
|
afbb221d900f1a340568ae2462b2022f8fcc4b3d
|
[
"Apache-2.0"
] | null | null | null |
python/oneflow/compatible/single_client/nn/optimizer/sgd.py
|
mosout/oneflow
|
afbb221d900f1a340568ae2462b2022f8fcc4b3d
|
[
"Apache-2.0"
] | null | null | null |
"""
Copyright 2020 The OneFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import collections
from typing import Callable, Dict, Iterator, List, Union
from oneflow.compatible import single_client as flow
from oneflow.compatible.single_client.nn.parameter import Parameter
from .optimizer import Optimizer, ParamGroup
class SGD(Optimizer):
"""Implements SGD algorithm.
This algorithm takes a random sample’s gradient as an approximate estimate of the overall gradient in small batch gradient descent.
When the momentum = 0, the equation of parameters updating is:
.. math::
param_{new} = param_{old} - learning\\_rate * grad
With momentum, the equation of parameters updating is:
.. math::
& V_t = \\beta * V_{t-1} + learning\\_rate * g_t
& param_{new} = param_{old} - V_t
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
momentum (float, optional): Momentum factor (default: 0.0)
scale (float, optional): the scale factor of loss (default: 1.0)
"""
def __init__(
self,
parameters: Union[Iterator[Parameter], List[Dict]],
lr: float = 0.001,
momentum: float = 0.0,
scale: float = 1.0,
):
super().__init__()
assert lr >= 0.0, f"Invalid learning rate: {lr}"
assert momentum >= 0.0, f"Invalid momentum: {momentum}"
assert scale >= 0.0, f"Invalid scale factor: {scale}"
self._default_options["lr"] = lr
self._default_options["scale"] = scale
self._default_options["momentum"] = momentum
if isinstance(parameters, collections.abc.Iterator):
self.param_groups.append(ParamGroup(parameters, self._default_options))
else:
for param in parameters:
self.param_groups.append(ParamGroup(param, self._default_options))
for param_group in self.param_groups:
for param in param_group.parameters:
assert param.is_leaf, "parameters must be leaf tensor"
self._state[param] = dict()
if param_group["momentum"] != 0.0:
self._state[param]["momentum_buf"] = flow.experimental.zeros_like(
param
)
self._momentum_sgd = (
flow.stateful_op("momentum_update")
.Input("model")
.Input("model_diff")
.Input("momentum")
.Build()
)
self._sgd = (
flow.stateful_op("sgd_update").Input("model").Input("model_diff").Build()
)
def step(self, closure: Callable = None):
with flow.no_grad():
loss = None
if closure is not None:
loss = closure()
for param_group in self.param_groups:
lr = param_group["lr"]
for param in param_group.parameters:
if param.grad is None:
continue
if param_group["momentum"] == 0.0:
scale = param_group["scale"]
flow._C.dispatch_sgd_update(
self._sgd,
(param, param.grad),
learning_rate=lr,
scale=scale,
)
else:
momentum_buf = self._state[param]["momentum_buf"]
scale = param_group["scale"]
beta = param_group["momentum"]
flow._C.dispatch_momentum_update(
self._momentum_sgd,
(param, param.grad, momentum_buf),
learning_rate=lr,
scale=scale,
beta=beta,
)
self._state["step"] = self._state["step"] + 1
return loss
| 37.639344
| 135
| 0.564895
|
import collections
from typing import Callable, Dict, Iterator, List, Union
from oneflow.compatible import single_client as flow
from oneflow.compatible.single_client.nn.parameter import Parameter
from .optimizer import Optimizer, ParamGroup
class SGD(Optimizer):
def __init__(
self,
parameters: Union[Iterator[Parameter], List[Dict]],
lr: float = 0.001,
momentum: float = 0.0,
scale: float = 1.0,
):
super().__init__()
assert lr >= 0.0, f"Invalid learning rate: {lr}"
assert momentum >= 0.0, f"Invalid momentum: {momentum}"
assert scale >= 0.0, f"Invalid scale factor: {scale}"
self._default_options["lr"] = lr
self._default_options["scale"] = scale
self._default_options["momentum"] = momentum
if isinstance(parameters, collections.abc.Iterator):
self.param_groups.append(ParamGroup(parameters, self._default_options))
else:
for param in parameters:
self.param_groups.append(ParamGroup(param, self._default_options))
for param_group in self.param_groups:
for param in param_group.parameters:
assert param.is_leaf, "parameters must be leaf tensor"
self._state[param] = dict()
if param_group["momentum"] != 0.0:
self._state[param]["momentum_buf"] = flow.experimental.zeros_like(
param
)
self._momentum_sgd = (
flow.stateful_op("momentum_update")
.Input("model")
.Input("model_diff")
.Input("momentum")
.Build()
)
self._sgd = (
flow.stateful_op("sgd_update").Input("model").Input("model_diff").Build()
)
def step(self, closure: Callable = None):
with flow.no_grad():
loss = None
if closure is not None:
loss = closure()
for param_group in self.param_groups:
lr = param_group["lr"]
for param in param_group.parameters:
if param.grad is None:
continue
if param_group["momentum"] == 0.0:
scale = param_group["scale"]
flow._C.dispatch_sgd_update(
self._sgd,
(param, param.grad),
learning_rate=lr,
scale=scale,
)
else:
momentum_buf = self._state[param]["momentum_buf"]
scale = param_group["scale"]
beta = param_group["momentum"]
flow._C.dispatch_momentum_update(
self._momentum_sgd,
(param, param.grad, momentum_buf),
learning_rate=lr,
scale=scale,
beta=beta,
)
self._state["step"] = self._state["step"] + 1
return loss
| true
| true
|
1c3ef780c4c334aeb6fe7c61688c81c9940bc419
| 14,109
|
py
|
Python
|
test/search_api/filters/test_search_api_where_filter.py
|
antolinos/datagateway-api
|
c6aa15ea01545f7a8e58e656c569523c60f7e4ef
|
[
"Apache-2.0"
] | null | null | null |
test/search_api/filters/test_search_api_where_filter.py
|
antolinos/datagateway-api
|
c6aa15ea01545f7a8e58e656c569523c60f7e4ef
|
[
"Apache-2.0"
] | null | null | null |
test/search_api/filters/test_search_api_where_filter.py
|
antolinos/datagateway-api
|
c6aa15ea01545f7a8e58e656c569523c60f7e4ef
|
[
"Apache-2.0"
] | null | null | null |
import pytest
from datagateway_api.src.common.date_handler import DateHandler
from datagateway_api.src.common.filter_order_handler import FilterOrderHandler
from datagateway_api.src.search_api.filters import SearchAPIWhereFilter
from datagateway_api.src.search_api.nested_where_filters import NestedWhereFilters
from datagateway_api.src.search_api.query import SearchAPIQuery
class TestSearchAPIWhereFilter:
@pytest.mark.parametrize(
"filter_input, entity_name, expected_query",
[
pytest.param(
SearchAPIWhereFilter("name", "WISH", "eq"),
"Instrument",
"SELECT o FROM Instrument o WHERE o.name = 'WISH'",
id="Regular WHERE filter",
),
pytest.param(
SearchAPIWhereFilter("pid", "1", "eq"),
"Instrument",
"SELECT o FROM Instrument o WHERE o.pid = '1'",
id="Pid instrument value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("pid", "pid:1", "eq"),
"Instrument",
"SELECT o FROM Instrument o WHERE o.id = '1'",
id="Id instrument value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("title", "My Dataset 1", "ne"),
"Dataset",
"SELECT o FROM Dataset o WHERE o.name != 'My Dataset 1'",
id="WHERE filter with non-default operator",
),
pytest.param(
SearchAPIWhereFilter("pid", "1", "eq"),
"Dataset",
"SELECT o FROM Dataset o WHERE o.doi = '1'",
id="Doi dataset value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("pid", "pid:1", "eq"),
"Dataset",
"SELECT o FROM Dataset o WHERE o.id = '1'",
id="Id dataset value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
# DataGateway API date format: "2018-05-05 15:00:00"
SearchAPIWhereFilter("startDate", "2018-05-05T15:00:00.000Z", "gt"),
"Document",
"SELECT o FROM Investigation o WHERE o.startDate >"
" '2018 05 05 15:00:00 00:00'",
id="WHERE filter with date value",
),
pytest.param(
SearchAPIWhereFilter("pid", "1", "eq"),
"Document",
"SELECT o FROM Investigation o WHERE o.doi = '1'",
id="Doi document value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("pid", "pid:1", "eq"),
"Document",
"SELECT o FROM Investigation o WHERE o.id = '1'",
id="Id document value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("facility", "ISIS", "like"),
"Instrument",
"SELECT o FROM Instrument o JOIN o.facility AS f WHERE f.name like"
" '%ISIS%'",
id="WHERE filter on ICAT related entity",
),
pytest.param(
SearchAPIWhereFilter("keywords", "Keyword", "like"),
"Document",
"SELECT o FROM Investigation o JOIN o.keywords AS s1 WHERE s1.name like"
" '%Keyword%'",
id="WHERE filter on ICAT related entity with 0-many relationship",
),
pytest.param(
SearchAPIWhereFilter("samples.description", "Test description", "like"),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 JOIN s1.parameters AS s2"
" JOIN s2.type AS s3 WHERE s3.description like '%Test description%'",
id="WHERE filter on ICAT related entity with a PaNOSC hop",
),
pytest.param(
SearchAPIWhereFilter("datasets.files.name", "Test filename", "like"),
"Document",
"SELECT o FROM Investigation o JOIN o.datasets AS s1 JOIN s1.datafiles"
" AS s2 WHERE s2.name like '%Test filename%'",
id="WHERE filter on ICAT related entity with two PaNOSC hops",
),
pytest.param(
SearchAPIWhereFilter(
"documents.parameters.document.pid", "Test DOI", "eq",
),
"Dataset",
"SELECT o FROM Dataset o JOIN o.investigation AS i JOIN i.parameters AS"
" s1 JOIN s1.investigation AS s2 WHERE s2.doi = 'Test DOI'",
id="WHERE filter on ICAT related entity with three PaNOSC hops",
),
pytest.param(
SearchAPIWhereFilter("techniques.pid", "1", "eq"),
"Dataset",
"",
id="Pid technique value (mapping that maps to multiple ICAT fields)",
# Skipped because ICAT 5 mapping on techniques
marks=pytest.mark.skip,
),
pytest.param(
SearchAPIWhereFilter("techniques.pid", "pid:1", "eq"),
"Dataset",
"",
id="Id technique value (mapping that maps to multiple ICAT fields)",
# Skipped because ICAT 5 mapping on techniques
marks=pytest.mark.skip,
),
pytest.param(
SearchAPIWhereFilter("samples.pid", "1", "eq"),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 WHERE s1.pid = '1'",
id="Pid sample value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("samples.pid", "pid:1", "eq"),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 WHERE s1.id = '1'",
id="Id sample value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", "My Parameter", "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.stringValue = 'My Parameter'",
id="String parameter value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter(
"parameters.value", "2018-05-05T15:00:00.000Z", "eq",
),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.dateTimeValue = '2018-05-05T15:00:00.000Z'",
id="Datetime (of type string) parameter value (mapping that maps to"
" multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter(
"parameters.value",
DateHandler.str_to_datetime_object("2018-05-05T15:00:00.000Z"),
"eq",
),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.dateTimeValue = '2018-05-05 15:00:00+00:00'",
id="Datetime (of type date) parameter value (mapping that maps to"
" multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", 20, "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.numericValue = '20'",
id="Numeric (int) parameter value (mapping that maps to multiple ICAT"
" fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", 20.0, "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.numericValue = '20.0'",
id="Numeric (float) parameter value (mapping that maps to multiple ICAT"
"fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", [20, 30], "between"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.numericValue between '20' and '30'",
id="Numeric (int) parameter value with between operator (mapping that"
" maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", ["test"], "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.stringValue = '['test']'",
id="Other type parameter value (mapping that maps to multiple ICAT"
"fields)",
),
],
)
def test_valid_apply_where_filter(self, filter_input, entity_name, expected_query):
filter_handler = FilterOrderHandler()
filter_handler.add_filter(filter_input)
test_query = SearchAPIQuery(entity_name)
filter_handler.apply_filters(test_query)
assert str(test_query.icat_query.query) == expected_query
@pytest.mark.parametrize(
"filter_input, entity_name, expected_query",
[
pytest.param(
NestedWhereFilters(
[],
[SearchAPIWhereFilter("name", "SANS2D", "like")],
"and",
SearchAPIQuery("Instrument"),
),
"Instrument",
"SELECT o FROM Instrument o WHERE (o.name like '%SANS2D%')",
id="Nested input with single filter",
),
pytest.param(
NestedWhereFilters(
[],
[SearchAPIWhereFilter("facility", "ISIS", "like")],
"or",
SearchAPIQuery("Instrument"),
),
"Instrument",
"SELECT o FROM Instrument o JOIN o.facility AS f WHERE (f.name like"
" '%ISIS%')",
id="Nested input with single filter on ICAT related entity",
),
pytest.param(
NestedWhereFilters(
[SearchAPIWhereFilter("summary", "My Test Summary", "eq")],
[SearchAPIWhereFilter("title", "Test title", "eq")],
"or",
SearchAPIQuery("Document"),
),
"Document",
"SELECT o FROM Investigation o WHERE (o.summary = 'My Test Summary' or"
" o.name = 'Test title')",
id="Nested input with LHS and RHS present",
),
pytest.param(
NestedWhereFilters(
[SearchAPIWhereFilter("summary", "My Test Summary", "eq")],
[SearchAPIWhereFilter("keywords", "Test keyword", "eq")],
"and",
SearchAPIQuery("Document"),
),
"Document",
"SELECT o FROM Investigation o JOIN o.keywords AS s1 WHERE (o.summary ="
" 'My Test Summary' and s1.name = 'Test keyword')",
id="Nested input with filter on ICAT related entity with 0-many"
" relationship",
),
pytest.param(
NestedWhereFilters(
[SearchAPIWhereFilter("title", "Test title", "eq")],
[
SearchAPIWhereFilter(
"samples.description", "Test description", "like",
),
],
"and",
SearchAPIQuery("Dataset"),
),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 JOIN s1.parameters AS s2"
" JOIN s2.type AS s3 WHERE (o.name = 'Test title' and"
" s3.description like '%Test description%')",
id="Nested input with filter on ICAT related entity with multiple hops",
),
pytest.param(
NestedWhereFilters(
[
NestedWhereFilters(
[SearchAPIWhereFilter("summary", "My Test Summary", "eq")],
[SearchAPIWhereFilter("title", "Test title", "like")],
"or",
),
],
[
NestedWhereFilters(
[SearchAPIWhereFilter("pid", "Test pid", "eq")],
[SearchAPIWhereFilter("doi", "Test doi", "eq")],
"or",
),
],
"and",
SearchAPIQuery("Document"),
),
"Document",
"SELECT o FROM Investigation o WHERE ((o.summary = 'My Test Summary' or"
" o.name like '%Test title%') and (o.doi = 'Test pid' or o.doi ="
" 'Test doi'))",
id="Nested input - (A or B) and (C or D)",
),
],
)
def test_valid_apply_nested_filters(
self, filter_input, entity_name, expected_query,
):
test_query = SearchAPIQuery(entity_name)
filter_handler = FilterOrderHandler()
filter_handler.add_filter(filter_input)
filter_handler.apply_filters(test_query)
assert str(test_query.icat_query.query) == expected_query
def test_str_where_filter(self):
test_filter = SearchAPIWhereFilter("name", "WISH", "eq")
assert str(test_filter) == repr(test_filter)
| 44.507886
| 88
| 0.495995
|
import pytest
from datagateway_api.src.common.date_handler import DateHandler
from datagateway_api.src.common.filter_order_handler import FilterOrderHandler
from datagateway_api.src.search_api.filters import SearchAPIWhereFilter
from datagateway_api.src.search_api.nested_where_filters import NestedWhereFilters
from datagateway_api.src.search_api.query import SearchAPIQuery
class TestSearchAPIWhereFilter:
@pytest.mark.parametrize(
"filter_input, entity_name, expected_query",
[
pytest.param(
SearchAPIWhereFilter("name", "WISH", "eq"),
"Instrument",
"SELECT o FROM Instrument o WHERE o.name = 'WISH'",
id="Regular WHERE filter",
),
pytest.param(
SearchAPIWhereFilter("pid", "1", "eq"),
"Instrument",
"SELECT o FROM Instrument o WHERE o.pid = '1'",
id="Pid instrument value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("pid", "pid:1", "eq"),
"Instrument",
"SELECT o FROM Instrument o WHERE o.id = '1'",
id="Id instrument value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("title", "My Dataset 1", "ne"),
"Dataset",
"SELECT o FROM Dataset o WHERE o.name != 'My Dataset 1'",
id="WHERE filter with non-default operator",
),
pytest.param(
SearchAPIWhereFilter("pid", "1", "eq"),
"Dataset",
"SELECT o FROM Dataset o WHERE o.doi = '1'",
id="Doi dataset value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("pid", "pid:1", "eq"),
"Dataset",
"SELECT o FROM Dataset o WHERE o.id = '1'",
id="Id dataset value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("startDate", "2018-05-05T15:00:00.000Z", "gt"),
"Document",
"SELECT o FROM Investigation o WHERE o.startDate >"
" '2018 05 05 15:00:00 00:00'",
id="WHERE filter with date value",
),
pytest.param(
SearchAPIWhereFilter("pid", "1", "eq"),
"Document",
"SELECT o FROM Investigation o WHERE o.doi = '1'",
id="Doi document value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("pid", "pid:1", "eq"),
"Document",
"SELECT o FROM Investigation o WHERE o.id = '1'",
id="Id document value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("facility", "ISIS", "like"),
"Instrument",
"SELECT o FROM Instrument o JOIN o.facility AS f WHERE f.name like"
" '%ISIS%'",
id="WHERE filter on ICAT related entity",
),
pytest.param(
SearchAPIWhereFilter("keywords", "Keyword", "like"),
"Document",
"SELECT o FROM Investigation o JOIN o.keywords AS s1 WHERE s1.name like"
" '%Keyword%'",
id="WHERE filter on ICAT related entity with 0-many relationship",
),
pytest.param(
SearchAPIWhereFilter("samples.description", "Test description", "like"),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 JOIN s1.parameters AS s2"
" JOIN s2.type AS s3 WHERE s3.description like '%Test description%'",
id="WHERE filter on ICAT related entity with a PaNOSC hop",
),
pytest.param(
SearchAPIWhereFilter("datasets.files.name", "Test filename", "like"),
"Document",
"SELECT o FROM Investigation o JOIN o.datasets AS s1 JOIN s1.datafiles"
" AS s2 WHERE s2.name like '%Test filename%'",
id="WHERE filter on ICAT related entity with two PaNOSC hops",
),
pytest.param(
SearchAPIWhereFilter(
"documents.parameters.document.pid", "Test DOI", "eq",
),
"Dataset",
"SELECT o FROM Dataset o JOIN o.investigation AS i JOIN i.parameters AS"
" s1 JOIN s1.investigation AS s2 WHERE s2.doi = 'Test DOI'",
id="WHERE filter on ICAT related entity with three PaNOSC hops",
),
pytest.param(
SearchAPIWhereFilter("techniques.pid", "1", "eq"),
"Dataset",
"",
id="Pid technique value (mapping that maps to multiple ICAT fields)",
marks=pytest.mark.skip,
),
pytest.param(
SearchAPIWhereFilter("techniques.pid", "pid:1", "eq"),
"Dataset",
"",
id="Id technique value (mapping that maps to multiple ICAT fields)",
marks=pytest.mark.skip,
),
pytest.param(
SearchAPIWhereFilter("samples.pid", "1", "eq"),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 WHERE s1.pid = '1'",
id="Pid sample value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("samples.pid", "pid:1", "eq"),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 WHERE s1.id = '1'",
id="Id sample value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", "My Parameter", "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.stringValue = 'My Parameter'",
id="String parameter value (mapping that maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter(
"parameters.value", "2018-05-05T15:00:00.000Z", "eq",
),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.dateTimeValue = '2018-05-05T15:00:00.000Z'",
id="Datetime (of type string) parameter value (mapping that maps to"
" multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter(
"parameters.value",
DateHandler.str_to_datetime_object("2018-05-05T15:00:00.000Z"),
"eq",
),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.dateTimeValue = '2018-05-05 15:00:00+00:00'",
id="Datetime (of type date) parameter value (mapping that maps to"
" multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", 20, "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.numericValue = '20'",
id="Numeric (int) parameter value (mapping that maps to multiple ICAT"
" fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", 20.0, "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.numericValue = '20.0'",
id="Numeric (float) parameter value (mapping that maps to multiple ICAT"
"fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", [20, 30], "between"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.numericValue between '20' and '30'",
id="Numeric (int) parameter value with between operator (mapping that"
" maps to multiple ICAT fields)",
),
pytest.param(
SearchAPIWhereFilter("parameters.value", ["test"], "eq"),
"Document",
"SELECT o FROM Investigation o JOIN o.parameters AS p WHERE"
" p.stringValue = '['test']'",
id="Other type parameter value (mapping that maps to multiple ICAT"
"fields)",
),
],
)
def test_valid_apply_where_filter(self, filter_input, entity_name, expected_query):
filter_handler = FilterOrderHandler()
filter_handler.add_filter(filter_input)
test_query = SearchAPIQuery(entity_name)
filter_handler.apply_filters(test_query)
assert str(test_query.icat_query.query) == expected_query
@pytest.mark.parametrize(
"filter_input, entity_name, expected_query",
[
pytest.param(
NestedWhereFilters(
[],
[SearchAPIWhereFilter("name", "SANS2D", "like")],
"and",
SearchAPIQuery("Instrument"),
),
"Instrument",
"SELECT o FROM Instrument o WHERE (o.name like '%SANS2D%')",
id="Nested input with single filter",
),
pytest.param(
NestedWhereFilters(
[],
[SearchAPIWhereFilter("facility", "ISIS", "like")],
"or",
SearchAPIQuery("Instrument"),
),
"Instrument",
"SELECT o FROM Instrument o JOIN o.facility AS f WHERE (f.name like"
" '%ISIS%')",
id="Nested input with single filter on ICAT related entity",
),
pytest.param(
NestedWhereFilters(
[SearchAPIWhereFilter("summary", "My Test Summary", "eq")],
[SearchAPIWhereFilter("title", "Test title", "eq")],
"or",
SearchAPIQuery("Document"),
),
"Document",
"SELECT o FROM Investigation o WHERE (o.summary = 'My Test Summary' or"
" o.name = 'Test title')",
id="Nested input with LHS and RHS present",
),
pytest.param(
NestedWhereFilters(
[SearchAPIWhereFilter("summary", "My Test Summary", "eq")],
[SearchAPIWhereFilter("keywords", "Test keyword", "eq")],
"and",
SearchAPIQuery("Document"),
),
"Document",
"SELECT o FROM Investigation o JOIN o.keywords AS s1 WHERE (o.summary ="
" 'My Test Summary' and s1.name = 'Test keyword')",
id="Nested input with filter on ICAT related entity with 0-many"
" relationship",
),
pytest.param(
NestedWhereFilters(
[SearchAPIWhereFilter("title", "Test title", "eq")],
[
SearchAPIWhereFilter(
"samples.description", "Test description", "like",
),
],
"and",
SearchAPIQuery("Dataset"),
),
"Dataset",
"SELECT o FROM Dataset o JOIN o.sample AS s1 JOIN s1.parameters AS s2"
" JOIN s2.type AS s3 WHERE (o.name = 'Test title' and"
" s3.description like '%Test description%')",
id="Nested input with filter on ICAT related entity with multiple hops",
),
pytest.param(
NestedWhereFilters(
[
NestedWhereFilters(
[SearchAPIWhereFilter("summary", "My Test Summary", "eq")],
[SearchAPIWhereFilter("title", "Test title", "like")],
"or",
),
],
[
NestedWhereFilters(
[SearchAPIWhereFilter("pid", "Test pid", "eq")],
[SearchAPIWhereFilter("doi", "Test doi", "eq")],
"or",
),
],
"and",
SearchAPIQuery("Document"),
),
"Document",
"SELECT o FROM Investigation o WHERE ((o.summary = 'My Test Summary' or"
" o.name like '%Test title%') and (o.doi = 'Test pid' or o.doi ="
" 'Test doi'))",
id="Nested input - (A or B) and (C or D)",
),
],
)
def test_valid_apply_nested_filters(
self, filter_input, entity_name, expected_query,
):
test_query = SearchAPIQuery(entity_name)
filter_handler = FilterOrderHandler()
filter_handler.add_filter(filter_input)
filter_handler.apply_filters(test_query)
assert str(test_query.icat_query.query) == expected_query
def test_str_where_filter(self):
test_filter = SearchAPIWhereFilter("name", "WISH", "eq")
assert str(test_filter) == repr(test_filter)
| true
| true
|
1c3ef815b3a802937a031342ebb24aaf30ca0421
| 406,377
|
py
|
Python
|
Data/scigrid-de/pypower/scigrid_2011_01_06_19.py
|
thanever/SOC
|
9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4
|
[
"MIT"
] | null | null | null |
Data/scigrid-de/pypower/scigrid_2011_01_06_19.py
|
thanever/SOC
|
9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4
|
[
"MIT"
] | null | null | null |
Data/scigrid-de/pypower/scigrid_2011_01_06_19.py
|
thanever/SOC
|
9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4
|
[
"MIT"
] | null | null | null |
from numpy import array
def scigrid_2011_01_06_19():
ppc = {"version": '2'}
ppc["baseMVA"] = 100.0
ppc["bus"] = array([
[586, 3, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[589, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[590, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[593, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[594, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[595, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[598, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[599, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[601, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[602, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[603, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[607, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[608, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[609, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[612, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[613, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[614, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[616, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[617, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[618, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[619, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[621, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[624, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[628, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[629, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[631, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[632, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[637, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[638, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[640, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[641, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[642, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[643, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[647, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[650, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[652, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[655, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[663, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[666, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[670, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[672, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[676, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[681, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[683, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[687, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[689, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[691, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[694, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[695, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[696, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[697, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[698, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[702, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[705, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[707, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[713, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[714, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[716, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[717, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[719, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[722, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[723, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[724, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[727, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[728, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[730, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[732, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[735, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[738, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[741, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[742, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[743, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[746, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[747, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[748, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[749, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[750, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[753, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[758, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[760, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[761, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[762, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[763, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[765, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[767, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[769, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[771, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[772, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[774, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[777, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[778, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[781, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[784, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[785, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[787, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[788, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[789, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[791, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[792, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[795, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[800, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[801, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[802, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[805, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[806, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[808, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[809, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[811, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[814, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[816, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[817, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[821, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[822, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[826, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[830, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[834, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[835, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[836, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[837, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[839, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[841, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[843, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[844, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[845, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[849, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[850, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[851, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[853, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[855, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[856, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[857, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[858, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[859, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[860, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[864, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[865, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[867, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[869, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[870, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[872, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[873, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[874, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[875, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[877, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[881, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[882, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[883, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[885, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[886, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[889, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[890, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[893, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[894, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[895, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[896, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[898, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[900, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[902, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[903, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[905, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[906, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[907, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[909, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[915, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[917, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[918, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[920, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[921, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[922, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[923, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[925, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[931, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[935, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[936, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[937, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[939, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[940, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[944, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[950, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[952, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[957, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[958, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[959, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[960, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[963, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[965, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[966, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[967, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[968, 2, 0, 0, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[969, 2, 0, 0, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[971, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[973, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[976, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[978, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[981, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[982, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[983, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[984, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[985, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[986, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[987, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[988, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[993, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[994, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[995, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[997, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[999, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1000, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1002, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1003, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1007, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1008, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1010, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1011, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1012, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1014, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1026, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1027, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1028, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1029, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1030, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1031, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1032, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1033, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1034, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1035, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1036, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1037, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1038, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1039, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1040, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1041, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1042, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1043, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1044, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1045, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1046, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1047, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1048, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1049, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1050, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1051, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1052, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1053, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1054, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1055, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1056, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1057, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1058, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1059, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1060, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1061, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1062, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1063, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1064, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1065, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1066, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1067, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1068, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1069, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1070, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1071, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1072, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1073, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1074, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1075, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1076, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1077, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1078, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1079, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1080, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1081, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1082, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1083, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1084, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1085, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1086, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1087, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1088, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1089, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1090, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1091, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1092, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1093, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1094, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1095, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1096, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1097, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1098, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1099, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1100, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1101, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1102, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1103, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1104, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1105, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1106, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1107, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1108, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1109, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1110, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1111, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1112, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1113, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1114, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1115, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1116, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1117, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1118, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1119, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1120, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1121, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1122, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1123, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1124, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1125, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1126, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1127, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1128, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1129, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1130, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1131, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1132, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1133, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1134, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1135, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1136, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1137, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1138, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1139, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1140, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1141, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1142, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1143, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1144, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1145, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1146, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1147, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1148, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1149, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1150, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1151, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1152, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1153, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1154, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1155, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1156, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1157, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1158, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1159, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1160, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1161, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1162, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1163, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1164, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1165, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1166, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1167, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1168, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1169, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1170, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1171, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1172, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1173, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1174, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1175, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1176, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1177, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1178, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1179, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1180, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1181, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1182, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1183, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1184, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1185, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1186, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1187, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1188, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1189, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1190, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1191, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1192, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1193, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1194, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1195, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1196, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1197, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1198, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1201, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1202, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1203, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1204, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1205, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1206, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1207, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1208, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1209, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1210, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1211, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1212, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1213, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1214, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1215, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1216, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1217, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1218, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1219, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1220, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1221, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1222, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1223, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1224, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1225, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1226, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1227, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1228, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1229, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1230, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1231, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1232, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1235, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1236, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1237, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1238, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1239, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1240, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1241, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1242, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1243, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1244, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1245, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1246, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1247, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1248, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1249, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1250, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1251, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1252, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1253, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1254, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1255, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1256, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1257, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1258, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1259, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1260, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1261, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1262, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1263, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1264, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1265, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1266, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1267, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1268, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1269, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1270, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1271, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1272, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1273, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1274, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1275, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1276, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1277, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1278, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1279, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1280, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1281, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1282, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1283, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1284, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1285, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1286, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1287, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1288, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1289, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1290, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1291, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1292, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1293, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1294, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1295, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1296, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1297, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1298, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1299, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1300, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1301, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1302, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1303, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1304, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1305, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1306, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1307, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1308, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1309, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1310, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1311, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1312, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1313, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1314, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1315, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1316, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1317, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1318, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1319, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1320, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1321, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1322, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1323, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1324, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1325, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1326, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1327, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1328, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1329, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1330, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1331, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1332, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1333, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1334, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1335, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1336, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1337, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1338, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1339, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1340, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1341, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1342, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1343, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1344, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1345, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1346, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1347, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1348, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1349, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1350, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1351, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1352, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1354, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1355, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1356, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1357, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1358, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1359, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1360, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1361, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1362, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1363, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1364, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1365, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1366, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1367, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1368, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1369, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1370, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1371, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1372, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1373, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1376, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1377, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1378, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1379, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1380, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1381, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1382, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1383, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1384, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1385, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1386, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1387, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1388, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1389, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1390, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1391, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1392, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1393, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1394, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1395, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1396, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1397, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1398, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1399, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1400, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1401, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1402, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1403, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1404, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1405, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1406, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1407, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1408, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1409, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1410, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1411, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1412, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1413, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1414, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1415, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1416, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1417, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1418, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1419, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1420, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1421, 2, 0, 0, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[1422, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1423, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1424, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1425, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1426, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1427, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1428, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1429, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1430, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1431, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1432, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1433, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1434, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1435, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1436, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1437, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1438, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1439, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1440, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1441, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1442, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1443, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1444, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1445, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1446, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1447, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1448, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1449, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1450, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1451, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1452, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1453, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1454, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1455, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1456, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1457, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1458, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1459, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1460, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1461, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1462, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1463, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1464, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1465, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1466, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1467, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1468, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1469, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1470, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1471, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1472, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1473, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1474, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1475, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1476, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1477, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1478, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1479, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1480, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1481, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1482, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1483, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1484, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1485, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1486, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1487, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1488, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1489, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1490, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1491, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1492, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1493, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1494, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1495, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1496, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1497, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1498, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1499, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1500, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1501, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1502, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1503, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1504, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1505, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1506, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1507, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1508, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1509, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1510, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1511, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1512, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1513, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1514, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1515, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1516, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1517, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1518, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1519, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1, 1, 299.357139, 59.871428, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[2, 1, 0, 0, 0, 0, 0, 1.000011, 0, 380.0, 0, 1.1, 0.9 ],
[3, 1, 52.469255, 10.493851, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[4, 1, 86.287429, 17.257486, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[5, 1, 0, 0, 0, 0, 0, 0.999623, 0, 380.0, 0, 1.1, 0.9 ],
[6, 1, 253.37566, 50.675132, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[7, 1, 190.950068, 38.190014, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[8, 1, 159.773374, 31.954675, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[9, 1, 108.052236, 21.610447, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[10, 1, 0, 0, 0, 0, 0, 1.000968, 0, 380.0, 0, 1.1, 0.9 ],
[11, 1, 94.672178, 18.934436, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[12, 1, 0, 0, 0, 0, 0, 1.000956, 0, 380.0, 0, 1.1, 0.9 ],
[13, 1, 0, 0, 0, 0, 0, 1.00017, 0, 380.0, 0, 1.1, 0.9 ],
[14, 1, 226.42112, 45.284224, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[15, 1, 0, 0, 0, 0, 0, 1.00024, 0, 380.0, 0, 1.1, 0.9 ],
[16, 1, 386.152959, 77.230592, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[17, 1, 90.949166, 18.189833, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[18, 1, 0, 0, 0, 0, 0, 1.002568, 0, 380.0, 0, 1.1, 0.9 ],
[19, 1, 224.701104, 44.940221, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[20, 1, 0, 0, 0, 0, 0, 0.999285, 0, 380.0, 0, 1.1, 0.9 ],
[21, 1, 966.249214, 193.249843, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[22, 1, 0, 0, 0, 0, 0, 1.000582, 0, 380.0, 0, 1.1, 0.9 ],
[23, 1, 126.514784, 25.302957, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[24, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[25, 1, 60.512903, 12.102581, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[26, 1, 0, 0, 0, 0, 0, 1.000747, 0, 380.0, 0, 1.1, 0.9 ],
[27, 1, 74.281264, 14.856253, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[28, 1, 219.47888, 43.895776, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[29, 1, 80.619162, 16.123832, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[30, 1, 0, 0, 0, 0, 0, 1.000284, 0, 380.0, 0, 1.1, 0.9 ],
[31, 1, 158.656429, 31.731286, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[32, 1, 0, 0, 0, 0, 0, 0.996654, 0, 380.0, 0, 1.1, 0.9 ],
[33, 1, 198.925347, 39.785069, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[34, 1, 39.465856, 7.893171, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[35, 1, 2.612848, 0.52257, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[36, 1, 8.650765, 1.730153, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[37, 1, 0, 0, 0, 0, 0, 1.002852, 0, 380.0, 0, 1.1, 0.9 ],
[38, 1, 208.416239, 41.683248, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[39, 1, 68.245581, 13.649116, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[40, 1, 71.284643, 14.256929, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[41, 1, 76.614835, 15.322967, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[42, 1, 0, 0, 0, 0, 0, 1.001121, 0, 380.0, 0, 1.1, 0.9 ],
[43, 1, 117.492302, 23.49846, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[44, 1, 150.313981, 30.062796, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[45, 1, 79.790022, 15.958004, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[46, 1, 0, 0, 0, 0, 0, 1.000273, 0, 380.0, 0, 1.1, 0.9 ],
[47, 1, 346.933422, 69.386684, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[48, 1, 238.470527, 47.694105, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[49, 1, 60.321012, 12.064202, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[50, 1, 87.835536, 17.567107, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[51, 1, 113.829121, 22.765824, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[52, 1, 0, 0, 0, 0, 0, 1.000133, 0, 380.0, 0, 1.1, 0.9 ],
[53, 1, 172.71746, 34.543492, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[54, 1, 87.750526, 17.550105, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[55, 1, 86.057622, 17.211524, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[56, 1, 0, 0, 0, 0, 0, 0.999733, 0, 380.0, 0, 1.1, 0.9 ],
[57, 1, 102.725918, 20.545184, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[58, 1, 235.309339, 47.061868, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[59, 1, 67.205785, 13.441157, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[60, 1, 35.432755, 7.086551, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[61, 1, 0, 0, 0, 0, 0, 0.999718, 0, 380.0, 0, 1.1, 0.9 ],
[62, 1, 270.131503, 54.026301, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[63, 1, 159.456313, 31.891263, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[64, 1, 1692.15463, 338.430926, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[65, 1, 5.638288, 1.127658, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[66, 1, 178.896436, 35.779287, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[67, 1, 383.762992, 76.752598, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[68, 1, 0, 0, 0, 0, 0, 0.998457, 0, 380.0, 0, 1.1, 0.9 ],
[69, 1, 0, 0, 0, 0, 0, 1.00038, 0, 380.0, 0, 1.1, 0.9 ],
[70, 1, 725.992049, 145.19841, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[71, 1, 168.711201, 33.74224, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[72, 1, 276.325796, 55.265159, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[73, 1, 88.4623, 17.69246, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[74, 1, 0, 0, 0, 0, 0, 1.003234, 0, 380.0, 0, 1.1, 0.9 ],
[75, 1, 110.255161, 22.051032, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[76, 1, 106.420421, 21.284084, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[77, 1, 103.07545, 20.61509, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[78, 1, 0, 0, 0, 0, 0, 0.995361, 0, 380.0, 0, 1.1, 0.9 ],
[79, 1, 106.433346, 21.286669, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[80, 1, 113.048636, 22.609727, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[81, 1, 127.616514, 25.523303, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[82, 1, 4.247153, 0.849431, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[83, 1, 284.165823, 56.833165, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[84, 1, 27.974372, 5.594874, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[85, 1, 97.009691, 19.401938, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[86, 1, 0, 0, 0, 0, 0, 1.000053, 0, 380.0, 0, 1.1, 0.9 ],
[87, 1, 0, 0, 0, 0, 0, 1.000327, 0, 380.0, 0, 1.1, 0.9 ],
[88, 1, 78.299678, 15.659936, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[89, 1, 97.142735, 19.428547, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[90, 1, 112.19557, 22.439114, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[91, 1, 38.971155, 7.794231, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[92, 1, 42.531201, 8.50624, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[93, 1, 41.714588, 8.342918, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[94, 1, 0, 0, 0, 0, 0, 1.00087, 0, 380.0, 0, 1.1, 0.9 ],
[95, 1, 0, 0, 0, 0, 0, 1.001187, 0, 380.0, 0, 1.1, 0.9 ],
[96, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[97, 1, 5.866845, 1.173369, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[98, 1, 107.867686, 21.573537, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[99, 1, 0, 0, 0, 0, 0, 1.000716, 0, 380.0, 0, 1.1, 0.9 ],
[100, 1, 0, 0, 0, 0, 0, 1.001847, 0, 380.0, 0, 1.1, 0.9 ],
[101, 1, 76.381322, 15.276264, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[102, 1, 147.839608, 29.567922, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[103, 1, 172.85311, 34.570622, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[104, 1, 0, 0, 0, 0, 0, 0.999953, 0, 380.0, 0, 1.1, 0.9 ],
[105, 1, 0, 0, 0, 0, 0, 1.000152, 0, 380.0, 0, 1.1, 0.9 ],
[106, 1, 0, 0, 0, 0, 0, 0.99996, 0, 380.0, 0, 1.1, 0.9 ],
[107, 1, 0, 0, 0, 0, 0, 1.000002, 0, 380.0, 0, 1.1, 0.9 ],
[108, 1, 121.926795, 24.385359, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[109, 1, 49.366079, 9.873216, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[110, 1, 64.079149, 12.81583, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[111, 1, 112.924775, 22.584955, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[112, 1, 57.154171, 11.430834, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[113, 1, 90.095678, 18.019136, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[114, 1, 132.688902, 26.53778, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[115, 1, 85.536735, 17.107347, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[116, 1, 143.133962, 28.626792, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[117, 1, 0, 0, 0, 0, 0, 1.000294, 0, 380.0, 0, 1.1, 0.9 ],
[118, 1, 221.622459, 44.324492, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[119, 1, 42.959533, 8.591907, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[120, 1, 0, 0, 0, 0, 0, 1.001148, 0, 380.0, 0, 1.1, 0.9 ],
[121, 1, 58.339066, 11.667813, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[122, 1, 51.07526, 10.215052, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[123, 1, 0, 0, 0, 0, 0, 1.000162, 0, 380.0, 0, 1.1, 0.9 ],
[124, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[125, 1, 0, 0, 0, 0, 0, 0.999713, 0, 380.0, 0, 1.1, 0.9 ],
[126, 1, 267.788855, 53.557771, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[127, 1, 207.028957, 41.405791, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[128, 1, 0, 0, 0, 0, 0, 1.001581, 0, 380.0, 0, 1.1, 0.9 ],
[129, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[130, 1, 285.45527, 57.091054, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[131, 1, 63.028277, 12.605655, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[132, 1, 164.116103, 32.823221, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[133, 1, 54.972465, 10.994493, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[134, 1, 54.747354, 10.949471, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[135, 1, 54.81994, 10.963988, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[136, 1, 53.105693, 10.621139, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[137, 1, 42.479666, 8.495933, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[138, 1, 0, 0, 0, 0, 0, 1.000183, 0, 380.0, 0, 1.1, 0.9 ],
[139, 1, 83.213361, 16.642672, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[140, 1, 57.545602, 11.50912, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[141, 1, 68.181381, 13.636276, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[142, 1, 75.023859, 15.004772, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[143, 1, 0, 0, 0, 0, 0, 0.999983, 0, 380.0, 0, 1.1, 0.9 ],
[144, 1, 68.338979, 13.667796, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[145, 1, 198.799897, 39.759979, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[146, 1, 256.290464, 51.258093, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[147, 1, 157.090963, 31.418193, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[148, 1, 221.684188, 44.336838, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[149, 1, 142.918192, 28.583638, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[150, 1, 186.594538, 37.318908, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[151, 1, 43.970718, 8.794144, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[152, 1, 91.278651, 18.25573, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[153, 1, 162.855958, 32.571192, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[154, 1, 167.285387, 33.457077, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[155, 1, 174.242515, 34.848503, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[156, 1, 0, 0, 0, 0, 0, 0.99999, 0, 380.0, 0, 1.1, 0.9 ],
[157, 1, 0, 0, 0, 0, 0, 1.001193, 0, 380.0, 0, 1.1, 0.9 ],
[158, 1, 45.9071, 9.18142, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[159, 1, 0, 0, 0, 0, 0, 0.999774, 0, 380.0, 0, 1.1, 0.9 ],
[160, 1, 0, 0, 0, 0, 0, 0.999991, 0, 380.0, 0, 1.1, 0.9 ],
[161, 1, 142.515256, 28.503051, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[162, 1, 213.018072, 42.603614, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[163, 1, 42.601603, 8.520321, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[164, 1, 42.772929, 8.554586, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[165, 1, 0, 0, 0, 0, 0, 0.999992, 0, 380.0, 0, 1.1, 0.9 ],
[166, 1, 50.008474, 10.001695, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[167, 1, 70.349335, 14.069867, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[168, 1, 48.012523, 9.602505, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[169, 1, 164.360711, 32.872142, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[170, 1, 123.503215, 24.700643, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[171, 1, 105.409947, 21.081989, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[172, 1, 51.73233, 10.346466, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[173, 1, 49.419687, 9.883937, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[174, 1, 74.161243, 14.832249, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[175, 1, 49.387297, 9.877459, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[176, 1, 172.096394, 34.419279, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[177, 1, 28.062824, 5.612565, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[178, 1, 148.627602, 29.72552, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[179, 1, 54.764142, 10.952828, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[180, 1, 48.139082, 9.627816, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[181, 1, 36.333993, 7.266799, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[182, 1, 1.645947, 0.329189, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[183, 1, 492.683663, 98.536733, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[184, 1, 0, 0, 0, 0, 0, 1.000023, 0, 380.0, 0, 1.1, 0.9 ],
[185, 1, 105.357552, 21.07151, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[186, 1, 56.734494, 11.346899, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[187, 1, 33.183902, 6.63678, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[188, 1, 49.387297, 9.877459, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[189, 1, 181.220426, 36.244085, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[190, 1, 239.697919, 47.939584, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[191, 1, 0, 0, 0, 0, 0, 0.999998, 0, 380.0, 0, 1.1, 0.9 ],
[192, 1, 57.72652, 11.545304, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[193, 1, 49.307632, 9.861526, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[194, 1, 34.037848, 6.80757, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[195, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[196, 1, 47.753116, 9.550623, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[197, 1, 75.658474, 15.131695, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[198, 1, 44.770634, 8.954127, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[199, 1, 57.640701, 11.52814, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[200, 1, 49.388443, 9.877689, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[201, 1, 0, 0, 0, 0, 0, 1.00096, 0, 380.0, 0, 1.1, 0.9 ],
[202, 1, 50.609135, 10.121827, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[203, 1, 6.668207, 1.333641, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[204, 1, 195.44382, 39.088764, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[205, 1, 97.730708, 19.546142, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[206, 1, 46.90391, 9.380782, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[207, 1, 139.472027, 27.894405, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[208, 1, 41.069012, 8.213802, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[209, 1, 57.071575, 11.414315, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[210, 1, 65.564558, 13.112912, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[211, 1, 230.408553, 46.081711, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[212, 1, 57.748654, 11.549731, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[213, 1, 270.71278, 54.142556, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[214, 1, 182.155387, 36.431077, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[215, 1, 385.176656, 77.035331, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[216, 1, 129.876458, 25.975292, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[217, 1, 41.617029, 8.323406, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[218, 1, 126.787729, 25.357546, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[219, 1, 203.763334, 40.752667, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[220, 1, 0, 0, 0, 0, 0, 1.000027, 0, 380.0, 0, 1.1, 0.9 ],
[221, 1, 116.237427, 23.247485, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[222, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[223, 1, 115.198486, 23.039697, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[224, 1, 133.959969, 26.791994, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[225, 1, 240.532809, 48.106562, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[226, 1, 84.025543, 16.805109, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[227, 1, 104.678785, 20.935757, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[228, 1, 102.634351, 20.52687, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[229, 1, 227.112315, 45.422463, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[230, 1, 54.474503, 10.894901, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[231, 1, 0, 0, 0, 0, 0, 1.000717, 0, 380.0, 0, 1.1, 0.9 ],
[232, 1, 0, 0, 0, 0, 0, 0.999969, 0, 380.0, 0, 1.1, 0.9 ],
[233, 1, 0, 0, 0, 0, 0, 0.999805, 0, 380.0, 0, 1.1, 0.9 ],
[234, 1, 194.044238, 38.808848, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[235, 1, 63.1006, 12.62012, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[236, 1, 0, 0, 0, 0, 0, 0.999975, 0, 380.0, 0, 1.1, 0.9 ],
[237, 1, 0.522229, 0.104446, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[238, 1, 71.399477, 14.279895, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[239, 1, 98.64733, 19.729466, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[240, 1, 622.248439, 124.449688, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[241, 1, 460.442229, 92.088446, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[242, 1, 167.655348, 33.53107, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[243, 1, 135.264433, 27.052887, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[244, 1, 161.157525, 32.231505, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[245, 1, 0, 0, 0, 0, 0, 1.001372, 0, 380.0, 0, 1.1, 0.9 ],
[246, 1, 0, 0, 0, 0, 0, 0.999902, 0, 380.0, 0, 1.1, 0.9 ],
[247, 1, 31.9808, 6.39616, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[248, 1, 0, 0, 0, 0, 0, 0.999997, 0, 380.0, 0, 1.1, 0.9 ],
[249, 1, 0, 0, 0, 0, 0, 0.999996, 0, 380.0, 0, 1.1, 0.9 ],
[250, 1, 0, 0, 0, 0, 0, 0.999994, 0, 380.0, 0, 1.1, 0.9 ],
[251, 1, 79.369092, 15.873818, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[252, 1, 203.545412, 40.709082, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[253, 1, 89.364203, 17.872841, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[254, 1, 28.532507, 5.706501, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[255, 1, 140.320492, 28.064098, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[256, 1, 160.923187, 32.184637, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[257, 1, 77.66509, 15.533018, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[258, 1, 253.101148, 50.62023, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[259, 1, 0, 0, 0, 0, 0, 0.999295, 0, 380.0, 0, 1.1, 0.9 ],
[260, 1, 157.520213, 31.504043, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[261, 1, 0, 0, 0, 0, 0, 1.002014, 0, 380.0, 0, 1.1, 0.9 ],
[262, 1, 0, 0, 0, 0, 0, 0.999674, 0, 380.0, 0, 1.1, 0.9 ],
[263, 1, 225.962539, 45.192508, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[264, 1, 292.520695, 58.504139, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[265, 1, 0, 0, 0, 0, 0, 1.000009, 0, 380.0, 0, 1.1, 0.9 ],
[266, 1, 140.975489, 28.195098, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[267, 1, 178.303407, 35.660681, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[268, 1, 62.00365, 12.40073, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[269, 1, 49.791256, 9.958251, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[270, 1, 0, 0, 0, 0, 0, 0.99999, 0, 380.0, 0, 1.1, 0.9 ],
[271, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[272, 1, 1.015925, 0.203185, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[273, 1, 138.928224, 27.785645, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[274, 1, 270.058165, 54.011633, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[275, 1, 50.556363, 10.111273, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[276, 1, 197.081554, 39.416311, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[277, 1, 0, 0, 0, 0, 0, 0.998827, 0, 380.0, 0, 1.1, 0.9 ],
[278, 1, 153.854372, 30.770874, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[279, 1, 0, 0, 0, 0, 0, 0.998808, 0, 380.0, 0, 1.1, 0.9 ],
[280, 1, 0, 0, 0, 0, 0, 0.999709, 0, 380.0, 0, 1.1, 0.9 ],
[281, 1, 203.2232, 40.64464, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[282, 1, 287.389078, 57.477816, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[283, 1, 115.198021, 23.039604, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[284, 1, 174.760733, 34.952147, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[285, 1, 77.937156, 15.587431, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[286, 1, 163.343691, 32.668738, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[287, 1, 100.394621, 20.078924, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[288, 1, 64.573121, 12.914624, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[289, 1, 101.554791, 20.310958, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[290, 1, 0, 0, 0, 0, 0, 1.004653, 0, 380.0, 0, 1.1, 0.9 ],
[291, 1, 66.832008, 13.366402, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[292, 1, 131.756242, 26.351248, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[293, 1, 116.121759, 23.224352, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[294, 1, 30.944694, 6.188939, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[295, 1, 64.747078, 12.949416, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[296, 1, 183.817107, 36.763421, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[297, 1, 193.193842, 38.638768, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[298, 1, 102.010189, 20.402038, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[299, 1, 98.79619, 19.759238, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[300, 1, 269.147572, 53.829514, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[301, 1, 0, 0, 0, 0, 0, 1.000038, 0, 380.0, 0, 1.1, 0.9 ],
[302, 1, 226.723905, 45.344781, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[303, 1, 116.451973, 23.290395, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[304, 1, 99.99739, 19.999478, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[305, 1, 0, 0, 0, 0, 0, 0.99962, 0, 380.0, 0, 1.1, 0.9 ],
[306, 1, 0, 0, 0, 0, 0, 1.001477, 0, 380.0, 0, 1.1, 0.9 ],
[307, 1, 118.606198, 23.72124, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[308, 1, 146.22564, 29.245128, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[309, 1, 239.24571, 47.849142, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[310, 1, 0, 0, 0, 0, 0, 1.000141, 0, 380.0, 0, 1.1, 0.9 ],
[311, 1, 203.217454, 40.643491, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[312, 1, 91.392543, 18.278509, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[313, 1, 0, 0, 0, 0, 0, 1.000343, 0, 380.0, 0, 1.1, 0.9 ],
[314, 1, 283.075925, 56.615185, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[315, 1, 0, 0, 0, 0, 0, 1.001462, 0, 380.0, 0, 1.1, 0.9 ],
[316, 1, 110.912827, 22.182565, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[317, 1, 149.340059, 29.868012, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[318, 1, 245.420849, 49.08417, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[319, 1, 8.791956, 1.758391, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[320, 1, 0, 0, 0, 0, 0, 0.999996, 0, 380.0, 0, 1.1, 0.9 ],
[321, 1, 207.977107, 41.595421, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[322, 1, 26.476825, 5.295365, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[323, 1, 2.754688, 0.550938, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[324, 1, 486.962231, 97.392446, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[325, 1, 158.630045, 31.726009, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[326, 1, 12.861232, 2.572246, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[327, 1, 110.679681, 22.135936, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[328, 1, 188.615186, 37.723037, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[329, 1, 283.694054, 56.738811, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[330, 1, 0, 0, 0, 0, 0, 1.001153, 0, 380.0, 0, 1.1, 0.9 ],
[331, 1, 22.524343, 4.504869, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[332, 1, 0, 0, 0, 0, 0, 0.994596, 0, 380.0, 0, 1.1, 0.9 ],
[333, 1, 236.669238, 47.333848, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[334, 1, 0, 0, 0, 0, 0, 0.999169, 0, 380.0, 0, 1.1, 0.9 ],
[335, 1, 241.538812, 48.307762, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[336, 1, 0, 0, 0, 0, 0, 0.996999, 0, 380.0, 0, 1.1, 0.9 ],
[337, 1, 96.077057, 19.215411, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[338, 1, 260.766786, 52.153357, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[339, 1, 161.28065, 32.25613, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[340, 1, 136.35953, 27.271906, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[341, 1, 123.27174, 24.654348, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[342, 1, 213.835906, 42.767181, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[343, 1, 117.313496, 23.462699, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[344, 1, 294.133032, 58.826606, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[345, 1, 321.622889, 64.324578, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[346, 1, 319.289533, 63.857907, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[347, 1, 111.661092, 22.332218, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[348, 1, 291.889448, 58.37789, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[349, 1, 0, 0, 0, 0, 0, 0.99991, 0, 380.0, 0, 1.1, 0.9 ],
[350, 1, 153.129465, 30.625893, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[351, 1, 0, 0, 0, 0, 0, 0.999667, 0, 380.0, 0, 1.1, 0.9 ],
[352, 1, 1013.60899, 202.721798, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[353, 1, 3.047247, 0.609449, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[354, 1, 20.702734, 4.140547, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[355, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[356, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[357, 1, 0.051895, 0.010379, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[358, 1, 0, 0, 0, 0, 0, 1.00123, 0, 380.0, 0, 1.1, 0.9 ],
[359, 1, 3.029978, 0.605996, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[360, 1, 0, 0, 0, 0, 0, 1.000731, 0, 380.0, 0, 1.1, 0.9 ],
[361, 1, 77.549559, 15.509912, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[362, 1, 221.056378, 44.211276, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[363, 1, 325.466629, 65.093326, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[364, 1, 76.78937, 15.357874, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[365, 1, 68.922537, 13.784507, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[366, 1, 136.604249, 27.32085, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[367, 1, 66.02882, 13.205764, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[368, 1, 32.513676, 6.502735, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[369, 1, 26.717579, 5.343516, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[370, 1, 78.657315, 15.731463, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[371, 1, 395.768976, 79.153795, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[372, 1, 229.512116, 45.902423, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[373, 1, 154.874942, 30.974988, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[374, 1, 79.417248, 15.88345, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[375, 1, 260.516148, 52.10323, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[376, 1, 285.737149, 57.14743, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[377, 1, 204.46909, 40.893818, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[378, 1, 204.07553, 40.815106, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[379, 1, 70.336094, 14.067219, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[380, 1, 0, 0, 0, 0, 0, 1.001431, 0, 380.0, 0, 1.1, 0.9 ],
[381, 1, 235.208187, 47.041637, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[382, 1, 0, 0, 0, 0, 0, 0.99931, 0, 380.0, 0, 1.1, 0.9 ],
[383, 1, 0, 0, 0, 0, 0, 0.999355, 0, 380.0, 0, 1.1, 0.9 ],
[384, 1, 82.999126, 16.599825, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[385, 1, 104.761187, 20.952237, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[386, 1, 84.172473, 16.834495, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[387, 1, 171.420679, 34.284136, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[388, 1, 920.52654, 184.105308, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[389, 1, 0, 0, 0, 0, 0, 0.999927, 0, 380.0, 0, 1.1, 0.9 ],
[390, 1, 76.005723, 15.201145, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[391, 1, 86.577001, 17.3154, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[392, 1, 166.140394, 33.228079, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[393, 1, 207.478268, 41.495654, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[394, 1, 74.623968, 14.924794, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[395, 1, 103.424209, 20.684842, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[396, 1, 73.254309, 14.650862, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[397, 1, 587.418866, 117.483773, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[398, 1, 254.423799, 50.88476, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[399, 1, 108.403069, 21.680614, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[400, 1, 57.755339, 11.551068, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[401, 1, 0, 0, 0, 0, 0, 1.000607, 0, 380.0, 0, 1.1, 0.9 ],
[402, 1, 0, 0, 0, 0, 0, 1.000402, 0, 380.0, 0, 1.1, 0.9 ],
[403, 1, 28.676869, 5.735374, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[404, 1, 101.030383, 20.206077, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[405, 1, 761.669209, 152.333842, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[406, 1, 57.709613, 11.541923, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[407, 1, 114.237444, 22.847489, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[408, 1, 330.310625, 66.062125, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[409, 1, 0, 0, 0, 0, 0, 0.999945, 0, 380.0, 0, 1.1, 0.9 ],
[410, 1, 42.765284, 8.553057, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[411, 1, 40.436328, 8.087266, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[412, 1, 2.840209, 0.568042, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[413, 1, 141.788379, 28.357676, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[414, 1, 12.039367, 2.407873, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[415, 1, 0, 0, 0, 0, 0, 1.000216, 0, 380.0, 0, 1.1, 0.9 ],
[416, 1, 171.453258, 34.290652, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[417, 1, 6.708639, 1.341728, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[418, 1, 139.803992, 27.960798, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[419, 1, 74.72424, 14.944848, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[420, 1, 75.232124, 15.046425, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[421, 1, 108.369957, 21.673991, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[422, 1, 79.395462, 15.879092, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[423, 1, 166.748016, 33.349603, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[424, 1, 12.022102, 2.40442, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[425, 1, 98.731794, 19.746359, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[426, 1, 8.180233, 1.636047, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[427, 1, 68.746812, 13.749362, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[428, 1, 30.823937, 6.164787, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[429, 1, 347.840816, 69.568163, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[430, 1, 185.282839, 37.056568, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[431, 1, 123.901479, 24.780296, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[432, 1, 144.833229, 28.966646, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[433, 1, 74.034885, 14.806977, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[434, 1, 38.53136, 7.706272, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[435, 1, 154.101184, 30.820237, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[436, 1, 82.272037, 16.454407, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[437, 1, 18.736593, 3.747319, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[438, 1, 50.283885, 10.056777, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[439, 1, 93.622094, 18.724419, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[440, 1, 79.120237, 15.824047, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[441, 1, 60.656262, 12.131252, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[442, 1, 80.268768, 16.053754, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[443, 1, 174.030244, 34.806049, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[444, 1, 0, 0, 0, 0, 0, 0.999997, 0, 380.0, 0, 1.1, 0.9 ],
[445, 1, 79.077332, 15.815466, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[446, 1, 36.667449, 7.33349, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[447, 1, 69.711986, 13.942397, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[448, 1, 51.231231, 10.246246, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[449, 1, 258.32521, 51.665042, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[450, 1, 158.082702, 31.61654, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[451, 1, 67.549518, 13.509904, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[452, 1, 0, 0, 0, 0, 0, 0.999998, 0, 380.0, 0, 1.1, 0.9 ],
[453, 1, 45.271283, 9.054257, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[454, 1, 31.584233, 6.316847, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[455, 1, 51.495434, 10.299087, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[456, 1, 51.495434, 10.299087, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[457, 1, 157.923583, 31.584717, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[458, 1, 150.20524, 30.041048, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[459, 1, 182.805366, 36.561073, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[460, 1, 240.243914, 48.048783, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[461, 1, 249.905766, 49.981153, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[462, 1, 76.447469, 15.289494, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[463, 1, 39.172162, 7.834432, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[464, 1, 39.219512, 7.843902, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[465, 1, 63.350231, 12.670046, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[466, 1, 51.432373, 10.286475, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[467, 1, 47.463564, 9.492713, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[468, 1, 77.821485, 15.564297, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[469, 1, 48.224415, 9.644883, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[470, 1, 122.809076, 24.561815, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[471, 1, 120.916609, 24.183322, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[472, 1, 42.292985, 8.458597, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[473, 1, 77.660006, 15.532001, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[474, 1, 40.110581, 8.022116, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[475, 1, 39.362455, 7.872491, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[476, 1, 44.48605, 8.89721, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[477, 1, 71.790863, 14.358173, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[478, 1, 90.182408, 18.036482, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[479, 1, 163.430553, 32.686111, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[480, 1, 71.634573, 14.326915, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[481, 1, 62.210779, 12.442156, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[482, 1, 70.637663, 14.127533, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[483, 1, 60.072155, 12.014431, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[484, 1, 47.093648, 9.41873, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[485, 1, 70.345446, 14.069089, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[486, 1, 647.143737, 129.428747, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[487, 1, 163.983232, 32.796646, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[488, 1, 472.509932, 94.501986, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[489, 1, 124.36327, 24.872654, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[490, 1, 38.697212, 7.739442, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[491, 1, 53.209163, 10.641833, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[492, 1, 82.974923, 16.594985, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[493, 1, 106.944743, 21.388949, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[494, 1, 146.164126, 29.232825, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[495, 1, 115.05729, 23.011458, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[496, 1, 8.1497, 1.62994, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[497, 1, 1019.11742, 203.823484, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[498, 1, 47.795688, 9.559138, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[499, 1, 66.71495, 13.34299, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[500, 1, 36.52565, 7.30513, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[501, 1, 61.795103, 12.359021, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[502, 1, 243.892472, 48.778494, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[503, 1, 74.69475, 14.93895, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[504, 1, 48.913631, 9.782726, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[505, 1, 346.933422, 69.386684, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[506, 1, 108.898132, 21.779626, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[507, 1, 103.585169, 20.717034, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[508, 1, 150.590165, 30.118033, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[509, 1, 198.447968, 39.689594, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[510, 1, 125.371437, 25.074287, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[511, 1, 109.362196, 21.872439, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[512, 1, 72.240483, 14.448097, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[513, 1, 39.796797, 7.959359, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[514, 1, 99.050376, 19.810075, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[515, 1, 88.35882, 17.671764, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[516, 1, 98.852728, 19.770546, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[517, 1, 46.433493, 9.286699, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[518, 1, 261.516372, 52.303274, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[519, 1, 25.737999, 5.1476, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[520, 1, 103.914264, 20.782853, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[521, 1, 93.869868, 18.773974, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[522, 1, 80.372141, 16.074428, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[523, 1, 43.26341, 8.652682, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[524, 1, 125.571667, 25.114333, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[525, 1, 149.598388, 29.919678, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[526, 1, 45.355417, 9.071083, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[527, 1, 49.797061, 9.959412, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[528, 1, 108.686742, 21.737348, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[529, 1, 139.32031, 27.864062, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[530, 1, 59.038257, 11.807651, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[531, 1, 60.026309, 12.005262, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[532, 1, 57.614792, 11.522958, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[533, 1, 51.629806, 10.325961, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[534, 1, 142.423899, 28.48478, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[535, 1, 178.305581, 35.661116, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[536, 1, 140.543223, 28.108645, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[537, 1, 46.752939, 9.350588, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[538, 1, 34.949307, 6.989861, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[539, 1, 37.083364, 7.416673, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[540, 1, 33.39194, 6.678388, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[541, 1, 86.254263, 17.250853, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[542, 1, 118.486697, 23.697339, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[543, 1, 64.716851, 12.94337, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[544, 1, 120.536044, 24.107209, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[545, 1, 259.53387, 51.906774, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[546, 1, 130.082295, 26.016459, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[547, 1, 168.139914, 33.627983, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[548, 1, 54.427586, 10.885517, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[549, 1, 46.540239, 9.308048, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[550, 1, 38.403612, 7.680722, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[551, 1, 37.020156, 7.404031, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[552, 1, 183.837924, 36.767585, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[553, 1, 1.271874, 0.254375, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[554, 1, 186.247009, 37.249402, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[555, 1, 70.962172, 14.192434, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[556, 1, 109.780842, 21.956168, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[557, 1, 233.244794, 46.648959, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[558, 1, 137.534821, 27.506964, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[559, 1, 73.607312, 14.721462, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[560, 1, 114.992034, 22.998407, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[561, 1, 63.058275, 12.611655, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[562, 1, 172.270483, 34.454097, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[563, 1, 121.120189, 24.224038, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[564, 1, 239.15215, 47.83043, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[565, 1, 180.452148, 36.09043, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[566, 1, 0.289845, 0.057969, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[567, 1, 293.333059, 58.666612, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[568, 1, 271.262107, 54.252421, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[569, 1, 190.861924, 38.172385, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[570, 1, 297.969832, 59.593966, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[571, 1, 219.388065, 43.877613, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[572, 1, 386.963917, 77.392783, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[573, 1, 112.640123, 22.528025, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[574, 1, 214.622449, 42.92449, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[575, 1, 4.033141, 0.806628, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[576, 1, 260.979458, 52.195892, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[577, 1, 287.702645, 57.540529, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[578, 1, 274.688852, 54.93777, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[579, 1, 100.21399, 20.042798, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[580, 1, 20.863062, 4.172612, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[581, 1, 0.119881, 0.023976, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[582, 1, 75.482663, 15.096533, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[583, 1, 86.575841, 17.315168, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[584, 1, 49.673071, 9.934614, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[585, 1, 86.238563, 17.247713, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ]
])
ppc["gen"] = array([
[586, 272.0, 0, 9999, -9999, 1.0, 100, 1, 272.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[589, 63.1, 0, 9999, -9999, 1.0, 100, 1, 63.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[590, 38.0, 0, 9999, -9999, 1.0, 100, 1, 38.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[593, 11.1, 0, 9999, -9999, 1.0, 100, 1, 11.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[594, 19.0, 0, 9999, -9999, 1.0, 100, 1, 19.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[595, 1510.82619, 0, 9999, -9999, 1.0, 100, 1, 4730.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[598, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[599, 9.3, 0, 9999, -9999, 1.0, 100, 1, 9.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[601, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[602, 24.6, 0, 9999, -9999, 1.0, 100, 1, 24.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[603, 1382.09855, 0, 9999, -9999, 1.0, 100, 1, 3455.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[607, 1800.0, 0, 9999, -9999, 1.0, 100, 1, 1800.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[608, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[609, 36.4, 0, 9999, -9999, 1.0, 100, 1, 36.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[612, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[613, 85.0, 0, 9999, -9999, 1.0, 100, 1, 85.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[614, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[616, 29.0, 0, 9999, -9999, 1.0, 100, 1, 29.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[617, 137.0, 0, 9999, -9999, 1.0, 100, 1, 137.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[618, 33.4, 0, 9999, -9999, 1.0, 100, 1, 33.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[619, 118.0, 0, 9999, -9999, 1.0, 100, 1, 118.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[621, 765.0, 0, 9999, -9999, 1.0, 100, 1, 765.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[624, 27.0, 0, 9999, -9999, 1.0, 100, 1, 27.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[628, 449.0, 0, 9999, -9999, 1.0, 100, 1, 449.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[629, 75.3, 0, 9999, -9999, 1.0, 100, 1, 75.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[631, 79.8, 0, 9999, -9999, 1.0, 100, 1, 79.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[632, 45.1, 0, 9999, -9999, 1.0, 100, 1, 45.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[637, 53.7, 0, 9999, -9999, 1.0, 100, 1, 53.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[638, 128.7, 0, 9999, -9999, 1.0, 100, 1, 128.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[640, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[641, 12.6, 0, 9999, -9999, 1.0, 100, 1, 12.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[642, 28.9, 0, 9999, -9999, 1.0, 100, 1, 28.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[643, 857.0, 0, 9999, -9999, 1.0, 100, 1, 857.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[647, 14.0, 0, 9999, -9999, 1.0, 100, 1, 14.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[650, 650.644627, 0, 9999, -9999, 1.0, 100, 1, 1324.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[652, 46.9, 0, 9999, -9999, 1.0, 100, 1, 46.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[655, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[663, 15.0, 0, 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[666, 28.9, 0, 9999, -9999, 1.0, 100, 1, 28.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[670, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[672, 33.1, 0, 9999, -9999, 1.0, 100, 1, 33.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[676, 370.0, 0, 9999, -9999, 1.0, 100, 1, 370.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[681, 40.1, 0, 9999, -9999, 1.0, 100, 1, 40.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[683, 27.5, 0, 9999, -9999, 1.0, 100, 1, 27.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[687, 1329.0, 0, 9999, -9999, 1.0, 100, 1, 1329.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[689, 310.0, 0, 9999, -9999, 1.0, 100, 1, 310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[691, 26.0, 0, 9999, -9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[694, 16.4, 0, 9999, -9999, 1.0, 100, 1, 16.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[695, 14.7, 0, 9999, -9999, 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[696, 721.0, 0, 9999, -9999, 1.0, 100, 1, 721.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[697, 11.6, 0, 9999, -9999, 1.0, 100, 1, 11.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[698, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[702, 73.4, 0, 9999, -9999, 1.0, 100, 1, 73.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[705, 17.0, 0, 9999, -9999, 1.0, 100, 1, 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[707, 34.0, 0, 9999, -9999, 1.0, 100, 1, 34.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[713, 13.4, 0, 9999, -9999, 1.0, 100, 1, 13.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[714, 15.0, 0, 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[716, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[717, 11.0, 0, 9999, -9999, 1.0, 100, 1, 11.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[719, 1254.748674, 0, 9999, -9999, 1.0, 100, 1, 1958.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[722, 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[723, 19.7, 0, 9999, -9999, 1.0, 100, 1, 19.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[724, 12.1, 0, 9999, -9999, 1.0, 100, 1, 12.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[727, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[728, 510.0, 0, 9999, -9999, 1.0, 100, 1, 510.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[730, 633.2, 0, 9999, -9999, 1.0, 100, 1, 633.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[732, 14.6, 0, 9999, -9999, 1.0, 100, 1, 14.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[735, 84.8, 0, 9999, -9999, 1.0, 100, 1, 84.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[738, 138.5, 0, 9999, -9999, 1.0, 100, 1, 138.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[741, 214.0, 0, 9999, -9999, 1.0, 100, 1, 214.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[742, 9.0, 0, 9999, -9999, 1.0, 100, 1, 9.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[743, 1410.0, 0, 9999, -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[746, 100.0, 0, 9999, -9999, 1.0, 100, 1, 100.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[747, 12.5, 0, 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[748, 110.0, 0, 9999, -9999, 1.0, 100, 1, 110.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[749, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[750, 90.8, 0, 9999, -9999, 1.0, 100, 1, 90.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[753, 311.8, 0, 9999, -9999, 1.0, 100, 1, 311.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[758, 18.5, 0, 9999, -9999, 1.0, 100, 1, 18.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[760, 294.128123, 0, 9999, -9999, 1.0, 100, 1, 794.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[761, 15.7, 0, 9999, -9999, 1.0, 100, 1, 15.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[762, 1105.0, 0, 9999, -9999, 1.0, 100, 1, 1105.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[763, 20.3, 0, 9999, -9999, 1.0, 100, 1, 20.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[765, 59.0, 0, 9999, -9999, 1.0, 100, 1, 59.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[767, 11.2, 0, 9999, -9999, 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[769, 43.3, 0, 9999, -9999, 1.0, 100, 1, 43.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[771, 684.364258, 0, 9999, -9999, 1.0, 100, 1, 690.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[772, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[774, 33.5, 0, 9999, -9999, 1.0, 100, 1, 33.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[777, 79.0, 0, 9999, -9999, 1.0, 100, 1, 79.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[778, 14.7, 0, 9999, -9999, 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[781, 971.759122, 0, 9999, -9999, 1.0, 100, 1, 1310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[784, 890.776074, 0, 9999, -9999, 1.0, 100, 1, 1275.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[785, 3.0, 0, 9999, -9999, 1.0, 100, 1, 3.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[787, 778.0, 0, 9999, -9999, 1.0, 100, 1, 778.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[788, 875.0, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[789, 77.4, 0, 9999, -9999, 1.0, 100, 1, 77.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[791, 10.0, 0, 9999, -9999, 1.0, 100, 1, 10.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[792, 62.7, 0, 9999, -9999, 1.0, 100, 1, 62.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[795, 13.6, 0, 9999, -9999, 1.0, 100, 1, 13.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[800, 36.5, 0, 9999, -9999, 1.0, 100, 1, 36.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[801, 21.82418, 0, 9999, -9999, 1.0, 100, 1, 50.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[802, 500.0, 0, 9999, -9999, 1.0, 100, 1, 500.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[805, 848.970643, 0, 9999, -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[806, 35.8, 0, 9999, -9999, 1.0, 100, 1, 35.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[808, 217.5, 0, 9999, -9999, 1.0, 100, 1, 217.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[809, 12.5, 0, 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[811, 25.2, 0, 9999, -9999, 1.0, 100, 1, 25.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[814, 89.0, 0, 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[816, 80.1, 0, 9999, -9999, 1.0, 100, 1, 80.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[817, 54.0, 0, 9999, -9999, 1.0, 100, 1, 54.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[821, 82.5, 0, 9999, -9999, 1.0, 100, 1, 82.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[822, 134.0, 0, 9999, -9999, 1.0, 100, 1, 134.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[826, 58.0, 0, 9999, -9999, 1.0, 100, 1, 58.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[830, 55.516834, 0, 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[834, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[835, 63.7, 0, 9999, -9999, 1.0, 100, 1, 63.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[836, 25.5, 0, 9999, -9999, 1.0, 100, 1, 25.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[837, 472.0, 0, 9999, -9999, 1.0, 100, 1, 472.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[839, 73.3, 0, 9999, -9999, 1.0, 100, 1, 73.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[841, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[843, 333.0, 0, 9999, -9999, 1.0, 100, 1, 333.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[844, 40.0, 0, 9999, -9999, 1.0, 100, 1, 40.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[845, 318.0, 0, 9999, -9999, 1.0, 100, 1, 318.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[849, 779.0, 0, 9999, -9999, 1.0, 100, 1, 779.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[850, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[851, 79.5, 0, 9999, -9999, 1.0, 100, 1, 79.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[853, 11.6, 0, 9999, -9999, 1.0, 100, 1, 11.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[855, 688.0, 0, 9999, -9999, 1.0, 100, 1, 688.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[856, 36.0, 0, 9999, -9999, 1.0, 100, 1, 36.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[857, 1402.0, 0, 9999, -9999, 1.0, 100, 1, 1402.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[858, 56.8, 0, 9999, -9999, 1.0, 100, 1, 56.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[859, 3.480201, 0, 9999, -9999, 1.0, 100, 1, 85.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[860, 25.0, 0, 9999, -9999, 1.0, 100, 1, 25.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[864, 875.0, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[865, 11.0, 0, 9999, -9999, 1.0, 100, 1, 11.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[867, 769.0, 0, 9999, -9999, 1.0, 100, 1, 769.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[869, 1360.0, 0, 9999, -9999, 1.0, 100, 1, 1360.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[870, 58.4, 0, 9999, -9999, 1.0, 100, 1, 58.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[872, 22.5, 0, 9999, -9999, 1.0, 100, 1, 22.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[873, 122.0, 0, 9999, -9999, 1.0, 100, 1, 122.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[874, 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[875, 24.4, 0, 9999, -9999, 1.0, 100, 1, 24.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[877, 24.8, 0, 9999, -9999, 1.0, 100, 1, 24.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[881, 337.281055, 0, 9999, -9999, 1.0, 100, 1, 1001.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[882, 17.4, 0, 9999, -9999, 1.0, 100, 1, 17.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[883, 18.0, 0, 9999, -9999, 1.0, 100, 1, 18.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[885, 117.63457, 0, 9999, -9999, 1.0, 100, 1, 490.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[886, 2572.0, 0, 9999, -9999, 1.0, 100, 1, 2572.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[889, 9.5, 0, 9999, -9999, 1.0, 100, 1, 9.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[890, 48.0, 0, 9999, -9999, 1.0, 100, 1, 48.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[893, 60.0, 0, 9999, -9999, 1.0, 100, 1, 60.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[894, 158.0, 0, 9999, -9999, 1.0, 100, 1, 158.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[895, 19.0, 0, 9999, -9999, 1.0, 100, 1, 19.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[896, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[898, 84.6, 0, 9999, -9999, 1.0, 100, 1, 84.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[900, 112.6, 0, 9999, -9999, 1.0, 100, 1, 112.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[902, 19.5, 0, 9999, -9999, 1.0, 100, 1, 19.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[903, 20.1, 0, 9999, -9999, 1.0, 100, 1, 20.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[905, 137.3, 0, 9999, -9999, 1.0, 100, 1, 137.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[906, 66.0, 0, 9999, -9999, 1.0, 100, 1, 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[907, 67.3, 0, 9999, -9999, 1.0, 100, 1, 67.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[909, 36.8, 0, 9999, -9999, 1.0, 100, 1, 36.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[915, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[917, 17.0, 0, 9999, -9999, 1.0, 100, 1, 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[918, 38.5, 0, 9999, -9999, 1.0, 100, 1, 38.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[920, 12.8, 0, 9999, -9999, 1.0, 100, 1, 12.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[921, 124.0, 0, 9999, -9999, 1.0, 100, 1, 124.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[922, 164.0, 0, 9999, -9999, 1.0, 100, 1, 164.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[923, 146.0, 0, 9999, -9999, 1.0, 100, 1, 146.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[925, 26.0, 0, 9999, -9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[931, 217.1, 0, 9999, -9999, 1.0, 100, 1, 217.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[935, 23.1, 0, 9999, -9999, 1.0, 100, 1, 23.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[936, 104.4, 0, 9999, -9999, 1.0, 100, 1, 104.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[937, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[939, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[940, 29.6, 0, 9999, -9999, 1.0, 100, 1, 29.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[944, 25.4, 0, 9999, -9999, 1.0, 100, 1, 25.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[950, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[952, 31.7, 0, 9999, -9999, 1.0, 100, 1, 31.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[957, 6.0, 0, 9999, -9999, 1.0, 100, 1, 6.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[958, 66.7, 0, 9999, -9999, 1.0, 100, 1, 66.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[959, 45.5, 0, 9999, -9999, 1.0, 100, 1, 45.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[960, 26.5, 0, 9999, -9999, 1.0, 100, 1, 26.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[963, 757.748298, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[965, 352.0, 0, 9999, -9999, 1.0, 100, 1, 352.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[966, 66.0, 0, 9999, -9999, 1.0, 100, 1, 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[967, 37.5, 0, 9999, -9999, 1.0, 100, 1, 37.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[968, 54.0, 0, 9999, -9999, 0.999554, 100, 1, 54.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[969, 56.9, 0, 9999, -9999, 0.999554, 100, 1, 56.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[971, 20.0, 0, 9999, -9999, 1.0, 100, 1, 20.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[973, 1347.0, 0, 9999, -9999, 1.0, 100, 1, 1347.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[976, 26.9, 0, 9999, -9999, 1.0, 100, 1, 26.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[978, 4.6, 0, 9999, -9999, 1.0, 100, 1, 4.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[981, 99.016829, 0, 9999, -9999, 1.0, 100, 1, 119.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[982, 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[983, 44.0, 0, 9999, -9999, 1.0, 100, 1, 44.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[984, 465.0, 0, 9999, -9999, 1.0, 100, 1, 465.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[985, 22.0, 0, 9999, -9999, 1.0, 100, 1, 22.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[986, 11.2, 0, 9999, -9999, 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[987, 164.5, 0, 9999, -9999, 1.0, 100, 1, 164.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[988, 5.1, 0, 9999, -9999, 1.0, 100, 1, 5.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[993, 392.0, 0, 9999, -9999, 1.0, 100, 1, 392.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[994, 33.0, 0, 9999, -9999, 1.0, 100, 1, 33.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[995, 4.2, 0, 9999, -9999, 1.0, 100, 1, 4.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[997, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[999, 15.6, 0, 9999, -9999, 1.0, 100, 1, 15.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1000, 49.0, 0, 9999, -9999, 1.0, 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1002, 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1003, 900.0, 0, 9999, -9999, 1.0, 100, 1, 900.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1007, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1008, 49.0, 0, 9999, -9999, 1.0, 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1010, 358.638683, 0, 9999, -9999, 1.0, 100, 1, 750.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1011, 18.7, 0, 9999, -9999, 1.0, 100, 1, 18.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1012, 1598.86858, 0, 9999, -9999, 1.0, 100, 1, 2835.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1014, 750.0, 0, 9999, -9999, 1.0, 100, 1, 750.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1026, 655.6, 0, 9999, -9999, 1.0, 100, 1, 655.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1027, 6.608085, 0, 9999, -9999, 1.0, 100, 1, 48.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1028, 104.030085, 0, 9999, -9999, 1.0, 100, 1, 400.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1029, 0.659424, 0, 9999, -9999, 1.0, 100, 1, 60.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1030, 236.668442, 0, 9999, -9999, 1.0, 100, 1, 1018.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1031, 178.027662, 0, 9999, -9999, 1.0, 100, 1, 1447.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1032, 77.598072, 0, 9999, -9999, 1.0, 100, 1, 153.510391, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1033, 36.059975, 0, 9999, -9999, 1.0, 100, 1, 50.164506, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1034, 26.058195, 0, 9999, -9999, 1.0, 100, 1, 84.262779, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1035, 33.33615, 0, 9999, -9999, 1.0, 100, 1, 49.886469, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1036, 50.715498, 0, 9999, -9999, 1.0, 100, 1, 67.223077, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1037, 13.306083, 0, 9999, -9999, 1.0, 100, 1, 94.684044, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1038, 12.955878, 0, 9999, -9999, 1.0, 100, 1, 85.798525, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1039, 82.927654, 0, 9999, -9999, 1.0, 100, 1, 132.724114, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1040, 0.006512, 0, 9999, -9999, 1.0, 100, 1, 0.064179, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1041, 105.224635, 0, 9999, -9999, 1.0, 100, 1, 204.187624, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1042, 33.31008, 0, 9999, -9999, 1.0, 100, 1, 52.70053, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1043, 0.89438, 0, 9999, -9999, 1.0, 100, 1, 6.035538, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1044, 1.604991, 0, 9999, -9999, 1.0, 100, 1, 36.163532, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1045, 2.84692, 0, 9999, -9999, 1.0, 100, 1, 61.836204, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1046, 3.159564, 0, 9999, -9999, 1.0, 100, 1, 106.787063, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1047, 0.11702, 0, 9999, -9999, 1.0, 100, 1, 13.029581, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1048, 1.874472, 0, 9999, -9999, 1.0, 100, 1, 71.656883, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1049, 135.960004, 0, 9999, -9999, 1.0, 100, 1, 293.755375, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1050, 50.376748, 0, 9999, -9999, 1.0, 100, 1, 52.781606, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1051, 296.97528, 0, 9999, -9999, 1.0, 100, 1, 304.42978, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1052, 0.007977, 0, 9999, -9999, 1.0, 100, 1, 20.66869, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1053, 0.005683, 0, 9999, -9999, 1.0, 100, 1, 16.368087, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1054, 0.018487, 0, 9999, -9999, 1.0, 100, 1, 273.855776, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1055, 1.869334, 0, 9999, -9999, 1.0, 100, 1, 2.856069, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1056, 328.102405, 0, 9999, -9999, 1.0, 100, 1, 603.943953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1057, 154.862136, 0, 9999, -9999, 1.0, 100, 1, 426.979979, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1058, 590.783379, 0, 9999, -9999, 1.0, 100, 1, 1055.735174, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1059, 295.545938, 0, 9999, -9999, 1.0, 100, 1, 414.871332, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1060, 6.855672, 0, 9999, -9999, 1.0, 100, 1, 10.351632, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1061, 124.441219, 0, 9999, -9999, 1.0, 100, 1, 161.862597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1062, 1.821353, 0, 9999, -9999, 1.0, 100, 1, 2.878561, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1063, 5.38714, 0, 9999, -9999, 1.0, 100, 1, 8.670916, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1064, 123.682419, 0, 9999, -9999, 1.0, 100, 1, 209.786524, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1065, 237.798684, 0, 9999, -9999, 1.0, 100, 1, 339.421643, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1066, 128.229023, 0, 9999, -9999, 1.0, 100, 1, 134.399019, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1067, 12.727235, 0, 9999, -9999, 1.0, 100, 1, 32.653526, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1068, 3.22426, 0, 9999, -9999, 1.0, 100, 1, 5.009022, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1069, 1.824218, 0, 9999, -9999, 1.0, 100, 1, 3.190759, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1070, 0.473903, 0, 9999, -9999, 1.0, 100, 1, 0.788599, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1071, 2.819964, 0, 9999, -9999, 1.0, 100, 1, 4.328696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1072, 4.927999, 0, 9999, -9999, 1.0, 100, 1, 112.606433, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1073, 0.016514, 0, 9999, -9999, 1.0, 100, 1, 77.81765, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1074, 6.014763, 0, 9999, -9999, 1.0, 100, 1, 153.592986, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1075, 10.918523, 0, 9999, -9999, 1.0, 100, 1, 15.783448, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1076, 0.300183, 0, 9999, -9999, 1.0, 100, 1, 2.29551, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1077, 14.615588, 0, 9999, -9999, 1.0, 100, 1, 26.120041, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1078, 7.065166, 0, 9999, -9999, 1.0, 100, 1, 34.413246, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1079, 2.798967, 0, 9999, -9999, 1.0, 100, 1, 72.327992, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1080, 49.695174, 0, 9999, -9999, 1.0, 100, 1, 132.149983, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1081, 347.16283, 0, 9999, -9999, 1.0, 100, 1, 405.642115, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1082, 476.885634, 0, 9999, -9999, 1.0, 100, 1, 510.054159, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1083, 495.180028, 0, 9999, -9999, 1.0, 100, 1, 633.681488, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1084, 549.155419, 0, 9999, -9999, 1.0, 100, 1, 602.719371, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1085, 109.258361, 0, 9999, -9999, 1.0, 100, 1, 113.714399, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1086, 209.987466, 0, 9999, -9999, 1.0, 100, 1, 225.59917, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1087, 104.908763, 0, 9999, -9999, 1.0, 100, 1, 116.66597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1088, 35.596807, 0, 9999, -9999, 1.0, 100, 1, 36.782492, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1089, 270.799357, 0, 9999, -9999, 1.0, 100, 1, 384.449592, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1090, 0.028127, 0, 9999, -9999, 1.0, 100, 1, 89.140897, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1091, 1.57761, 0, 9999, -9999, 1.0, 100, 1, 45.7939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1092, 0.778957, 0, 9999, -9999, 1.0, 100, 1, 54.002032, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1093, 79.950854, 0, 9999, -9999, 1.0, 100, 1, 155.605298, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1094, 3.677528, 0, 9999, -9999, 1.0, 100, 1, 3.759038, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1095, 0.200242, 0, 9999, -9999, 1.0, 100, 1, 0.204951, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1096, 75.731488, 0, 9999, -9999, 1.0, 100, 1, 84.50612, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1097, 1.18816, 0, 9999, -9999, 1.0, 100, 1, 4.601122, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1098, 2.597672, 0, 9999, -9999, 1.0, 100, 1, 71.025499, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1099, 0.015673, 0, 9999, -9999, 1.0, 100, 1, 290.937198, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1100, 0.003921, 0, 9999, -9999, 1.0, 100, 1, 0.026696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1101, 18.961359, 0, 9999, -9999, 1.0, 100, 1, 83.930665, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1102, 140.103409, 0, 9999, -9999, 1.0, 100, 1, 350.979988, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1103, 67.236383, 0, 9999, -9999, 1.0, 100, 1, 245.381701, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1104, 0.201805, 0, 9999, -9999, 1.0, 100, 1, 0.206918, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1105, 2.1618, 0, 9999, -9999, 1.0, 100, 1, 2.178593, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1106, 2.253376, 0, 9999, -9999, 1.0, 100, 1, 2.289793, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1107, 74.033819, 0, 9999, -9999, 1.0, 100, 1, 76.221615, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1108, 294.151901, 0, 9999, -9999, 1.0, 100, 1, 320.422751, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1109, 0.773974, 0, 9999, -9999, 1.0, 100, 1, 0.77821, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1110, 1.644838, 0, 9999, -9999, 1.0, 100, 1, 1.654557, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1111, 72.444777, 0, 9999, -9999, 1.0, 100, 1, 89.637993, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1112, 69.501815, 0, 9999, -9999, 1.0, 100, 1, 69.53429, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1113, 3.517851, 0, 9999, -9999, 1.0, 100, 1, 3.536361, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1114, 12.959909, 0, 9999, -9999, 1.0, 100, 1, 13.446889, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1115, 50.529108, 0, 9999, -9999, 1.0, 100, 1, 50.575278, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1116, 32.590705, 0, 9999, -9999, 1.0, 100, 1, 32.601142, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1117, 90.740798, 0, 9999, -9999, 1.0, 100, 1, 90.792541, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1118, 7.238119, 0, 9999, -9999, 1.0, 100, 1, 8.725012, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1119, 43.247173, 0, 9999, -9999, 1.0, 100, 1, 43.254023, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1120, 2.249546, 0, 9999, -9999, 1.0, 100, 1, 2.416001, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1121, 0.52564, 0, 9999, -9999, 1.0, 100, 1, 0.540589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1122, 1.415755, 0, 9999, -9999, 1.0, 100, 1, 1.462883, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1123, 1.368314, 0, 9999, -9999, 1.0, 100, 1, 1.464336, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1124, 1.254099, 0, 9999, -9999, 1.0, 100, 1, 1.288283, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1125, 25.510688, 0, 9999, -9999, 1.0, 100, 1, 25.818899, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1126, 29.011219, 0, 9999, -9999, 1.0, 100, 1, 29.154893, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1127, 75.065189, 0, 9999, -9999, 1.0, 100, 1, 105.296621, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1128, 3.04291, 0, 9999, -9999, 1.0, 100, 1, 3.06139, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1129, 4.711862, 0, 9999, -9999, 1.0, 100, 1, 4.738747, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1130, 1.019131, 0, 9999, -9999, 1.0, 100, 1, 1.025754, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1131, 2.880583, 0, 9999, -9999, 1.0, 100, 1, 2.897078, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1132, 0.357118, 0, 9999, -9999, 1.0, 100, 1, 0.359497, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1133, 0.699698, 0, 9999, -9999, 1.0, 100, 1, 0.719597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1134, 0.494393, 0, 9999, -9999, 1.0, 100, 1, 0.508453, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1135, 7.75526, 0, 9999, -9999, 1.0, 100, 1, 8.117819, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1136, 0.390993, 0, 9999, -9999, 1.0, 100, 1, 0.4027, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1137, 2.447861, 0, 9999, -9999, 1.0, 100, 1, 3.669012, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1138, 1.152243, 0, 9999, -9999, 1.0, 100, 1, 1.254278, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1139, 19.805849, 0, 9999, -9999, 1.0, 100, 1, 19.822769, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1140, 27.354023, 0, 9999, -9999, 1.0, 100, 1, 28.389457, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1141, 117.761296, 0, 9999, -9999, 1.0, 100, 1, 119.46456, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1142, 1.119845, 0, 9999, -9999, 1.0, 100, 1, 1.215733, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1143, 23.95844, 0, 9999, -9999, 1.0, 100, 1, 25.239356, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1144, 52.472607, 0, 9999, -9999, 1.0, 100, 1, 52.527382, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1145, 0.009343, 0, 9999, -9999, 1.0, 100, 1, 175.889627, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1146, 0.837499, 0, 9999, -9999, 1.0, 100, 1, 0.861317, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1147, 45.670633, 0, 9999, -9999, 1.0, 100, 1, 45.703707, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1148, 15.193514, 0, 9999, -9999, 1.0, 100, 1, 17.645529, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1149, 8.516184, 0, 9999, -9999, 1.0, 100, 1, 8.556784, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1150, 3.565438, 0, 9999, -9999, 1.0, 100, 1, 3.62256, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1151, 12.933916, 0, 9999, -9999, 1.0, 100, 1, 13.036113, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1152, 0.114832, 0, 9999, -9999, 1.0, 100, 1, 0.116518, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1153, 0.066338, 0, 9999, -9999, 1.0, 100, 1, 0.068788, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1154, 0.154903, 0, 9999, -9999, 1.0, 100, 1, 0.160625, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1155, 0.603868, 0, 9999, -9999, 1.0, 100, 1, 0.609451, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1156, 15.943274, 0, 9999, -9999, 1.0, 100, 1, 16.022334, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1157, 4.319464, 0, 9999, -9999, 1.0, 100, 1, 4.354147, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1158, 1.020134, 0, 9999, -9999, 1.0, 100, 1, 1.04304, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1159, 13.341573, 0, 9999, -9999, 1.0, 100, 1, 13.498087, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1160, 61.348753, 0, 9999, -9999, 1.0, 100, 1, 238.377761, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1161, 13.37679, 0, 9999, -9999, 1.0, 100, 1, 25.263391, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1162, 295.52795, 0, 9999, -9999, 1.0, 100, 1, 502.409178, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1163, 204.813364, 0, 9999, -9999, 1.0, 100, 1, 330.03194, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1164, 169.080818, 0, 9999, -9999, 1.0, 100, 1, 285.625412, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1165, 32.736142, 0, 9999, -9999, 1.0, 100, 1, 57.188579, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1166, 10.324392, 0, 9999, -9999, 1.0, 100, 1, 83.277163, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1167, 4.944229, 0, 9999, -9999, 1.0, 100, 1, 5.05378, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1168, 1.260694, 0, 9999, -9999, 1.0, 100, 1, 1.345774, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1169, 2.587141, 0, 9999, -9999, 1.0, 100, 1, 2.721845, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1170, 0.259966, 0, 9999, -9999, 1.0, 100, 1, 0.26599, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1171, 3.699796, 0, 9999, -9999, 1.0, 100, 1, 9.029885, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1172, 1.223448, 0, 9999, -9999, 1.0, 100, 1, 3.584043, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1173, 106.980744, 0, 9999, -9999, 1.0, 100, 1, 254.253327, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1174, 1.232698, 0, 9999, -9999, 1.0, 100, 1, 1.260082, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1175, 0.771596, 0, 9999, -9999, 1.0, 100, 1, 0.855454, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1176, 0.229984, 0, 9999, -9999, 1.0, 100, 1, 0.23222, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1177, 22.538602, 0, 9999, -9999, 1.0, 100, 1, 27.87401, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1178, 3.150843, 0, 9999, -9999, 1.0, 100, 1, 3.167999, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1179, 1.299251, 0, 9999, -9999, 1.0, 100, 1, 1.306293, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1180, 0.673136, 0, 9999, -9999, 1.0, 100, 1, 0.688545, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1181, 16.564349, 0, 9999, -9999, 1.0, 100, 1, 85.739557, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1182, 20.777501, 0, 9999, -9999, 1.0, 100, 1, 99.319579, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1183, 29.900467, 0, 9999, -9999, 1.0, 100, 1, 38.222575, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1184, 4.094736, 0, 9999, -9999, 1.0, 100, 1, 4.219005, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1185, 11.338664, 0, 9999, -9999, 1.0, 100, 1, 11.343971, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1186, 37.012936, 0, 9999, -9999, 1.0, 100, 1, 38.916368, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1187, 9.432298, 0, 9999, -9999, 1.0, 100, 1, 9.814574, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1188, 35.700043, 0, 9999, -9999, 1.0, 100, 1, 179.712741, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1189, 13.755058, 0, 9999, -9999, 1.0, 100, 1, 20.261805, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1190, 219.178004, 0, 9999, -9999, 1.0, 100, 1, 220.533673, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1191, 72.800116, 0, 9999, -9999, 1.0, 100, 1, 73.079413, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1192, 8.977024, 0, 9999, -9999, 1.0, 100, 1, 21.454569, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1193, 1.137177, 0, 9999, -9999, 1.0, 100, 1, 2.399953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1194, 4.479308, 0, 9999, -9999, 1.0, 100, 1, 8.986036, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1195, 0.083395, 0, 9999, -9999, 1.0, 100, 1, 0.202359, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1196, 30.791933, 0, 9999, -9999, 1.0, 100, 1, 160.697956, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1197, 18.077938, 0, 9999, -9999, 1.0, 100, 1, 90.592266, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1198, 23.334118, 0, 9999, -9999, 1.0, 100, 1, 39.819157, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1201, 19.641372, 0, 9999, -9999, 1.0, 100, 1, 25.166667, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1202, 27.702181, 0, 9999, -9999, 1.0, 100, 1, 49.89238, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1203, 166.58394, 0, 9999, -9999, 1.0, 100, 1, 182.623256, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1204, 35.170656, 0, 9999, -9999, 1.0, 100, 1, 47.541821, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1205, 0.204675, 0, 9999, -9999, 1.0, 100, 1, 0.548843, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1206, 1.775686, 0, 9999, -9999, 1.0, 100, 1, 3.806894, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1207, 1.694167, 0, 9999, -9999, 1.0, 100, 1, 3.575453, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1208, 1.819097, 0, 9999, -9999, 1.0, 100, 1, 2.242031, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1209, 0.108058, 0, 9999, -9999, 1.0, 100, 1, 1.268261, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1210, 1.703029, 0, 9999, -9999, 1.0, 100, 1, 9.02599, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1211, 17.394643, 0, 9999, -9999, 1.0, 100, 1, 18.005229, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1212, 89.204411, 0, 9999, -9999, 1.0, 100, 1, 91.171888, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1213, 56.865564, 0, 9999, -9999, 1.0, 100, 1, 57.342704, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1214, 2.103582, 0, 9999, -9999, 1.0, 100, 1, 4.505907, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1215, 0.750611, 0, 9999, -9999, 1.0, 100, 1, 2.252965, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1216, 24.438918, 0, 9999, -9999, 1.0, 100, 1, 67.754469, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1217, 24.249023, 0, 9999, -9999, 1.0, 100, 1, 35.871617, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1218, 0.566199, 0, 9999, -9999, 1.0, 100, 1, 0.980482, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1219, 12.313625, 0, 9999, -9999, 1.0, 100, 1, 12.33953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1220, 28.947375, 0, 9999, -9999, 1.0, 100, 1, 30.597849, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1221, 362.770634, 0, 9999, -9999, 1.0, 100, 1, 593.230436, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1222, 209.632122, 0, 9999, -9999, 1.0, 100, 1, 211.057769, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1223, 3.779223, 0, 9999, -9999, 1.0, 100, 1, 3.806101, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1224, 62.035215, 0, 9999, -9999, 1.0, 100, 1, 160.523778, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1225, 31.599486, 0, 9999, -9999, 1.0, 100, 1, 34.931481, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1226, 3.430153, 0, 9999, -9999, 1.0, 100, 1, 3.982858, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1227, 12.458532, 0, 9999, -9999, 1.0, 100, 1, 17.482807, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1228, 0.787282, 0, 9999, -9999, 1.0, 100, 1, 3.021367, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1229, 34.545517, 0, 9999, -9999, 1.0, 100, 1, 51.244222, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1230, 0.110943, 0, 9999, -9999, 1.0, 100, 1, 1.681276, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1231, 16.90505, 0, 9999, -9999, 1.0, 100, 1, 33.55478, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1232, 44.228643, 0, 9999, -9999, 1.0, 100, 1, 75.075088, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1235, 0.457018, 0, 9999, -9999, 1.0, 100, 1, 9.03734, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1236, 3.498451, 0, 9999, -9999, 1.0, 100, 1, 82.225035, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1237, 13.898558, 0, 9999, -9999, 1.0, 100, 1, 14.605409, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1238, 124.493668, 0, 9999, -9999, 1.0, 100, 1, 188.691049, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1239, 0.000875, 0, 9999, -9999, 1.0, 100, 1, 2.267706, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1240, 234.917427, 0, 9999, -9999, 1.0, 100, 1, 339.51051, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1241, 329.977461, 0, 9999, -9999, 1.0, 100, 1, 385.361595, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1242, 16.492805, 0, 9999, -9999, 1.0, 100, 1, 27.074038, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1243, 73.44164, 0, 9999, -9999, 1.0, 100, 1, 83.079842, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1244, 2.21417, 0, 9999, -9999, 1.0, 100, 1, 323.472536, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1245, 7.597448, 0, 9999, -9999, 1.0, 100, 1, 8.080896, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1246, 10.509823, 0, 9999, -9999, 1.0, 100, 1, 57.127825, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1247, 9.134905, 0, 9999, -9999, 1.0, 100, 1, 21.833396, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1248, 53.949763, 0, 9999, -9999, 1.0, 100, 1, 91.958275, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1249, 71.087635, 0, 9999, -9999, 1.0, 100, 1, 76.135177, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1250, 26.948774, 0, 9999, -9999, 1.0, 100, 1, 30.830519, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1251, 21.826987, 0, 9999, -9999, 1.0, 100, 1, 23.404345, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1252, 13.904591, 0, 9999, -9999, 1.0, 100, 1, 14.887727, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1253, 40.004932, 0, 9999, -9999, 1.0, 100, 1, 64.502694, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1254, 15.914068, 0, 9999, -9999, 1.0, 100, 1, 82.278695, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1255, 2.91361, 0, 9999, -9999, 1.0, 100, 1, 3.818419, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1256, 11.486854, 0, 9999, -9999, 1.0, 100, 1, 15.091842, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1257, 64.291554, 0, 9999, -9999, 1.0, 100, 1, 88.95288, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1258, 30.398898, 0, 9999, -9999, 1.0, 100, 1, 235.487329, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1259, 77.360255, 0, 9999, -9999, 1.0, 100, 1, 109.288719, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1260, 19.068998, 0, 9999, -9999, 1.0, 100, 1, 20.168717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1261, 141.137179, 0, 9999, -9999, 1.0, 100, 1, 201.699555, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1262, 0.325949, 0, 9999, -9999, 1.0, 100, 1, 0.524108, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1263, 0.251304, 0, 9999, -9999, 1.0, 100, 1, 0.352421, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1264, 55.338841, 0, 9999, -9999, 1.0, 100, 1, 82.035361, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1265, 4.605899, 0, 9999, -9999, 1.0, 100, 1, 6.654727, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1266, 95.273884, 0, 9999, -9999, 1.0, 100, 1, 119.710849, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1267, 37.425318, 0, 9999, -9999, 1.0, 100, 1, 39.469006, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1268, 1.088852, 0, 9999, -9999, 1.0, 100, 1, 3.4295, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1269, 1.14527, 0, 9999, -9999, 1.0, 100, 1, 5.105829, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1270, 18.624163, 0, 9999, -9999, 1.0, 100, 1, 38.950511, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1271, 34.874339, 0, 9999, -9999, 1.0, 100, 1, 47.371792, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1272, 0.767634, 0, 9999, -9999, 1.0, 100, 1, 1.23166, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1273, 0.573609, 0, 9999, -9999, 1.0, 100, 1, 2.169201, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1274, 50.404078, 0, 9999, -9999, 1.0, 100, 1, 53.095629, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1275, 91.873338, 0, 9999, -9999, 1.0, 100, 1, 99.0753, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1276, 22.042664, 0, 9999, -9999, 1.0, 100, 1, 25.655641, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1277, 54.415572, 0, 9999, -9999, 1.0, 100, 1, 65.611252, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1278, 137.849688, 0, 9999, -9999, 1.0, 100, 1, 170.437781, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1279, 4.5e-05, 0, 9999, -9999, 1.0, 100, 1, 0.004344, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1280, 0.044687, 0, 9999, -9999, 1.0, 100, 1, 0.626494, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1281, 0.502718, 0, 9999, -9999, 1.0, 100, 1, 2.51246, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1282, 0.295983, 0, 9999, -9999, 1.0, 100, 1, 4.363037, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1283, 69.836828, 0, 9999, -9999, 1.0, 100, 1, 1297.764428, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1284, 11.051678, 0, 9999, -9999, 1.0, 100, 1, 28.426322, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1285, 0.459963, 0, 9999, -9999, 1.0, 100, 1, 2.937048, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1286, 9.739874, 0, 9999, -9999, 1.0, 100, 1, 17.872201, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1287, 79.244769, 0, 9999, -9999, 1.0, 100, 1, 93.199628, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1288, 141.096543, 0, 9999, -9999, 1.0, 100, 1, 148.402692, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1289, 176.093898, 0, 9999, -9999, 1.0, 100, 1, 184.149235, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1290, 2.87747, 0, 9999, -9999, 1.0, 100, 1, 4.901974, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1291, 85.365953, 0, 9999, -9999, 1.0, 100, 1, 98.293351, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1292, 34.357008, 0, 9999, -9999, 1.0, 100, 1, 41.682074, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1293, 1.672049, 0, 9999, -9999, 1.0, 100, 1, 2.402107, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1294, 3.454541, 0, 9999, -9999, 1.0, 100, 1, 5.39743, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1295, 3.810908, 0, 9999, -9999, 1.0, 100, 1, 5.873666, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1296, 5.426327, 0, 9999, -9999, 1.0, 100, 1, 27.356489, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1297, 39.649397, 0, 9999, -9999, 1.0, 100, 1, 177.778742, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1298, 0.614727, 0, 9999, -9999, 1.0, 100, 1, 4.014603, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1299, 0.232699, 0, 9999, -9999, 1.0, 100, 1, 2.158207, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1300, 23.336616, 0, 9999, -9999, 1.0, 100, 1, 23.74405, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1301, 59.759643, 0, 9999, -9999, 1.0, 100, 1, 60.863304, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1302, 4.74882, 0, 9999, -9999, 1.0, 100, 1, 4.877299, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1303, 4.215176, 0, 9999, -9999, 1.0, 100, 1, 4.335516, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1304, 9.191642, 0, 9999, -9999, 1.0, 100, 1, 9.594319, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1305, 0.004544, 0, 9999, -9999, 1.0, 100, 1, 0.004567, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1306, 1.805996, 0, 9999, -9999, 1.0, 100, 1, 1.827014, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1307, 0.290083, 0, 9999, -9999, 1.0, 100, 1, 0.29894, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1308, 2.287693, 0, 9999, -9999, 1.0, 100, 1, 3.278321, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1309, 2.080815, 0, 9999, -9999, 1.0, 100, 1, 3.34909, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1310, 1.024479, 0, 9999, -9999, 1.0, 100, 1, 1.64589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1311, 2.457558, 0, 9999, -9999, 1.0, 100, 1, 11.854004, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1312, 3.294717, 0, 9999, -9999, 1.0, 100, 1, 262.264924, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1313, 29.677875, 0, 9999, -9999, 1.0, 100, 1, 30.836748, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1314, 11.648153, 0, 9999, -9999, 1.0, 100, 1, 12.003987, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1315, 7.823385, 0, 9999, -9999, 1.0, 100, 1, 7.879027, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1316, 0.440241, 0, 9999, -9999, 1.0, 100, 1, 2.757497, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1317, 22.594495, 0, 9999, -9999, 1.0, 100, 1, 23.958574, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1318, 1.214, 0, 9999, -9999, 1.0, 100, 1, 1.956332, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1319, 7.726802, 0, 9999, -9999, 1.0, 100, 1, 17.708276, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1320, 15.658252, 0, 9999, -9999, 1.0, 100, 1, 20.75859, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1321, 0.105733, 0, 9999, -9999, 1.0, 100, 1, 0.161123, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1322, 0.673281, 0, 9999, -9999, 1.0, 100, 1, 0.929763, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1323, 88.070537, 0, 9999, -9999, 1.0, 100, 1, 199.111909, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1324, 7.462697, 0, 9999, -9999, 1.0, 100, 1, 13.063258, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1325, 35.850661, 0, 9999, -9999, 1.0, 100, 1, 90.497559, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1326, 43.155673, 0, 9999, -9999, 1.0, 100, 1, 56.928865, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1327, 32.057922, 0, 9999, -9999, 1.0, 100, 1, 50.796895, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1328, 7.62562, 0, 9999, -9999, 1.0, 100, 1, 16.063343, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1329, 143.638084, 0, 9999, -9999, 1.0, 100, 1, 218.675424, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1330, 13.942682, 0, 9999, -9999, 1.0, 100, 1, 30.131028, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1331, 0.287325, 0, 9999, -9999, 1.0, 100, 1, 0.289238, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1332, 18.971857, 0, 9999, -9999, 1.0, 100, 1, 26.293088, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1333, 39.377002, 0, 9999, -9999, 1.0, 100, 1, 45.650254, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1334, 0.165238, 0, 9999, -9999, 1.0, 100, 1, 1.215341, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1335, 1.675624, 0, 9999, -9999, 1.0, 100, 1, 3.306939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1336, 22.179569, 0, 9999, -9999, 1.0, 100, 1, 29.773035, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1337, 1.895167, 0, 9999, -9999, 1.0, 100, 1, 121.31241, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1338, 0.343096, 0, 9999, -9999, 1.0, 100, 1, 0.832524, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1339, 8.094061, 0, 9999, -9999, 1.0, 100, 1, 10.086482, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1340, 1.719329, 0, 9999, -9999, 1.0, 100, 1, 70.098327, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1341, 5.747566, 0, 9999, -9999, 1.0, 100, 1, 205.513321, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1342, 0.061568, 0, 9999, -9999, 1.0, 100, 1, 0.734589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1343, 0.082546, 0, 9999, -9999, 1.0, 100, 1, 1.102108, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1344, 0.093284, 0, 9999, -9999, 1.0, 100, 1, 0.226057, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1345, 2.160254, 0, 9999, -9999, 1.0, 100, 1, 3.971188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1346, 207.05021, 0, 9999, -9999, 1.0, 100, 1, 214.719215, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1347, 30.730149, 0, 9999, -9999, 1.0, 100, 1, 414.115976, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1348, 0.076903, 0, 9999, -9999, 1.0, 100, 1, 22.707927, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1349, 1.054008, 0, 9999, -9999, 1.0, 100, 1, 42.352342, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1350, 0.023105, 0, 9999, -9999, 1.0, 100, 1, 0.094971, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1351, 0.000591, 0, 9999, -9999, 1.0, 100, 1, 0.015958, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1352, 0.04782, 0, 9999, -9999, 1.0, 100, 1, 0.83726, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1354, 0.004264, 0, 9999, -9999, 1.0, 100, 1, 0.147716, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1355, 1.046975, 0, 9999, -9999, 1.0, 100, 1, 1.688324, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1356, 52.744967, 0, 9999, -9999, 1.0, 100, 1, 73.486231, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1357, 42.848835, 0, 9999, -9999, 1.0, 100, 1, 56.459913, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1358, 0.153292, 0, 9999, -9999, 1.0, 100, 1, 0.247293, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1359, 60.088697, 0, 9999, -9999, 1.0, 100, 1, 70.633589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1360, 17.130838, 0, 9999, -9999, 1.0, 100, 1, 17.135983, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1361, 63.030411, 0, 9999, -9999, 1.0, 100, 1, 63.207173, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1362, 79.073054, 0, 9999, -9999, 1.0, 100, 1, 79.107216, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1363, 0.009694, 0, 9999, -9999, 1.0, 100, 1, 0.036158, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1364, 0.014171, 0, 9999, -9999, 1.0, 100, 1, 0.061068, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1365, 0.00016, 0, 9999, -9999, 1.0, 100, 1, 0.000456, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1366, 0.790668, 0, 9999, -9999, 1.0, 100, 1, 1.229992, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1367, 16.844243, 0, 9999, -9999, 1.0, 100, 1, 43.863891, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1368, 0.533701, 0, 9999, -9999, 1.0, 100, 1, 3.298243, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1369, 5.730756, 0, 9999, -9999, 1.0, 100, 1, 7.968859, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1370, 0.206567, 0, 9999, -9999, 1.0, 100, 1, 0.343308, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1371, 2.629506, 0, 9999, -9999, 1.0, 100, 1, 81.767208, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1372, 186.488603, 0, 9999, -9999, 1.0, 100, 1, 192.966588, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1373, 34.443544, 0, 9999, -9999, 1.0, 100, 1, 35.200257, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1376, 105.317936, 0, 9999, -9999, 1.0, 100, 1, 176.213655, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1377, 112.728506, 0, 9999, -9999, 1.0, 100, 1, 234.376272, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1378, 128.013354, 0, 9999, -9999, 1.0, 100, 1, 246.029906, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1379, 0.782569, 0, 9999, -9999, 1.0, 100, 1, 0.805984, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1380, 1.205328, 0, 9999, -9999, 1.0, 100, 1, 1.213356, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1381, 0.986293, 0, 9999, -9999, 1.0, 100, 1, 1.01257, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1382, 138.007244, 0, 9999, -9999, 1.0, 100, 1, 138.839906, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1383, 109.413691, 0, 9999, -9999, 1.0, 100, 1, 109.821439, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1384, 4.66695, 0, 9999, -9999, 1.0, 100, 1, 4.669135, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1385, 0.120034, 0, 9999, -9999, 1.0, 100, 1, 0.124455, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1386, 0.658834, 0, 9999, -9999, 1.0, 100, 1, 0.673858, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1387, 3.47374, 0, 9999, -9999, 1.0, 100, 1, 3.493561, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1388, 0.922047, 0, 9999, -9999, 1.0, 100, 1, 0.928188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1389, 0.212123, 0, 9999, -9999, 1.0, 100, 1, 0.213536, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1390, 3.711638, 0, 9999, -9999, 1.0, 100, 1, 3.732816, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1391, 0.518853, 0, 9999, -9999, 1.0, 100, 1, 0.521719, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1392, 18.867476, 0, 9999, -9999, 1.0, 100, 1, 19.306386, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1393, 1.277892, 0, 9999, -9999, 1.0, 100, 1, 1.376509, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1394, 1.009398, 0, 9999, -9999, 1.0, 100, 1, 1.077886, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1395, 0.063961, 0, 9999, -9999, 1.0, 100, 1, 0.073776, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1396, 0.021924, 0, 9999, -9999, 1.0, 100, 1, 0.026112, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1397, 22.814948, 0, 9999, -9999, 1.0, 100, 1, 25.084545, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1398, 2.522882, 0, 9999, -9999, 1.0, 100, 1, 2.779641, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1399, 17.766171, 0, 9999, -9999, 1.0, 100, 1, 17.868157, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1400, 1.20352, 0, 9999, -9999, 1.0, 100, 1, 1.297197, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1401, 84.971313, 0, 9999, -9999, 1.0, 100, 1, 89.339497, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1402, 25.706285, 0, 9999, -9999, 1.0, 100, 1, 26.328902, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1403, 25.589062, 0, 9999, -9999, 1.0, 100, 1, 119.651672, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1404, 20.034146, 0, 9999, -9999, 1.0, 100, 1, 134.800518, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1405, 26.655143, 0, 9999, -9999, 1.0, 100, 1, 29.550802, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1406, 7.405844, 0, 9999, -9999, 1.0, 100, 1, 10.763987, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1407, 0.206231, 0, 9999, -9999, 1.0, 100, 1, 0.211614, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1408, 33.729648, 0, 9999, -9999, 1.0, 100, 1, 41.078698, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1409, 9.208431, 0, 9999, -9999, 1.0, 100, 1, 12.019786, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1410, 28.273097, 0, 9999, -9999, 1.0, 100, 1, 37.466518, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1411, 30.074024, 0, 9999, -9999, 1.0, 100, 1, 39.395367, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1412, 1.056231, 0, 9999, -9999, 1.0, 100, 1, 5.987601, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1413, 0.921657, 0, 9999, -9999, 1.0, 100, 1, 5.679791, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1414, 5.84373, 0, 9999, -9999, 1.0, 100, 1, 25.992489, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1415, 1.354626, 0, 9999, -9999, 1.0, 100, 1, 7.454501, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1416, 1.195782, 0, 9999, -9999, 1.0, 100, 1, 7.958002, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1417, 0.000204, 0, 9999, -9999, 1.0, 100, 1, 0.001311, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1418, 73.547107, 0, 9999, -9999, 1.0, 100, 1, 88.264613, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1419, 29.245381, 0, 9999, -9999, 1.0, 100, 1, 33.260903, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1420, 1.062106, 0, 9999, -9999, 1.0, 100, 1, 1.399757, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1421, 3.729871, 0, 9999, -9999, 0.999554, 100, 1, 6.972369, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1422, 2.701524, 0, 9999, -9999, 1.0, 100, 1, 4.730495, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1423, 1.036575, 0, 9999, -9999, 1.0, 100, 1, 1.931017, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1424, 6.870415, 0, 9999, -9999, 1.0, 100, 1, 219.092115, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1425, 4.240743, 0, 9999, -9999, 1.0, 100, 1, 21.366402, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1426, 48.393481, 0, 9999, -9999, 1.0, 100, 1, 68.762602, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1427, 320.481892, 0, 9999, -9999, 1.0, 100, 1, 480.698671, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1428, 206.447115, 0, 9999, -9999, 1.0, 100, 1, 334.885743, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1429, 1.934629, 0, 9999, -9999, 1.0, 100, 1, 13.279826, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1430, 0.000918, 0, 9999, -9999, 1.0, 100, 1, 0.034248, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1431, 107.667744, 0, 9999, -9999, 1.0, 100, 1, 227.662022, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1432, 0.512128, 0, 9999, -9999, 1.0, 100, 1, 12.058931, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1433, 32.437405, 0, 9999, -9999, 1.0, 100, 1, 1289.241188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1434, 0.724661, 0, 9999, -9999, 1.0, 100, 1, 99.440014, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1435, 2.719421, 0, 9999, -9999, 1.0, 100, 1, 86.713217, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1436, 0.810927, 0, 9999, -9999, 1.0, 100, 1, 98.434116, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1437, 233.547643, 0, 9999, -9999, 1.0, 100, 1, 238.321958, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1438, 262.517934, 0, 9999, -9999, 1.0, 100, 1, 392.815158, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1439, 25.144625, 0, 9999, -9999, 1.0, 100, 1, 99.103164, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1440, 0.527204, 0, 9999, -9999, 1.0, 100, 1, 0.833609, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1441, 0.10379, 0, 9999, -9999, 1.0, 100, 1, 0.171578, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1442, 0.358625, 0, 9999, -9999, 1.0, 100, 1, 0.715522, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1443, 0.005191, 0, 9999, -9999, 1.0, 100, 1, 103.005076, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1444, 4.836207, 0, 9999, -9999, 1.0, 100, 1, 8.981696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1445, 10.804131, 0, 9999, -9999, 1.0, 100, 1, 25.036799, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1446, 603.424909, 0, 9999, -9999, 1.0, 100, 1, 758.547933, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1447, 77.589286, 0, 9999, -9999, 1.0, 100, 1, 89.477411, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1448, 1.990229, 0, 9999, -9999, 1.0, 100, 1, 7.523578, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1449, 75.799398, 0, 9999, -9999, 1.0, 100, 1, 95.437673, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1450, 29.696998, 0, 9999, -9999, 1.0, 100, 1, 59.256809, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1451, 38.222514, 0, 9999, -9999, 1.0, 100, 1, 68.198838, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1452, 8.496963, 0, 9999, -9999, 1.0, 100, 1, 24.068921, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1453, 64.705767, 0, 9999, -9999, 1.0, 100, 1, 64.93775, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1454, 153.672244, 0, 9999, -9999, 1.0, 100, 1, 155.126607, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1455, 0.648449, 0, 9999, -9999, 1.0, 100, 1, 0.654438, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1456, 36.643758, 0, 9999, -9999, 1.0, 100, 1, 50.054822, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1457, 1.989422, 0, 9999, -9999, 1.0, 100, 1, 2.002672, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1458, 0.24457, 0, 9999, -9999, 1.0, 100, 1, 0.246199, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1459, 4.925247, 0, 9999, -9999, 1.0, 100, 1, 5.309059, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1460, 39.822684, 0, 9999, -9999, 1.0, 100, 1, 101.498473, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1461, 17.933929, 0, 9999, -9999, 1.0, 100, 1, 17.951737, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1462, 2.401562, 0, 9999, -9999, 1.0, 100, 1, 2.402686, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1463, 0.689129, 0, 9999, -9999, 1.0, 100, 1, 0.711207, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1464, 176.333456, 0, 9999, -9999, 1.0, 100, 1, 218.884211, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1465, 5.213592, 0, 9999, -9999, 1.0, 100, 1, 5.299939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1466, 5.640449, 0, 9999, -9999, 1.0, 100, 1, 5.685017, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1467, 2.081537, 0, 9999, -9999, 1.0, 100, 1, 2.096155, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1468, 23.073463, 0, 9999, -9999, 1.0, 100, 1, 23.789171, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1469, 61.686727, 0, 9999, -9999, 1.0, 100, 1, 65.007467, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1470, 0.001122, 0, 9999, -9999, 1.0, 100, 1, 78.965265, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1471, 0.038276, 0, 9999, -9999, 1.0, 100, 1, 159.165074, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1472, 11.952646, 0, 9999, -9999, 1.0, 100, 1, 11.980182, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1473, 8.177237, 0, 9999, -9999, 1.0, 100, 1, 8.362608, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1474, 1.370149, 0, 9999, -9999, 1.0, 100, 1, 1.398948, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1475, 0.352563, 0, 9999, -9999, 1.0, 100, 1, 0.39088, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1476, 35.385904, 0, 9999, -9999, 1.0, 100, 1, 250.480113, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1477, 9.087521, 0, 9999, -9999, 1.0, 100, 1, 12.122974, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1478, 0.001027, 0, 9999, -9999, 1.0, 100, 1, 0.035833, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1479, 3.883098, 0, 9999, -9999, 1.0, 100, 1, 5.592606, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1480, 12.250601, 0, 9999, -9999, 1.0, 100, 1, 18.681964, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1481, 0.021901, 0, 9999, -9999, 1.0, 100, 1, 0.053146, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1482, 4.135907, 0, 9999, -9999, 1.0, 100, 1, 17.51083, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1483, 3.580244, 0, 9999, -9999, 1.0, 100, 1, 3.599649, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1484, 0.029847, 0, 9999, -9999, 1.0, 100, 1, 0.02991, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1485, 0.562364, 0, 9999, -9999, 1.0, 100, 1, 0.563547, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1486, 2.893254, 0, 9999, -9999, 1.0, 100, 1, 2.89934, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1487, 0.458304, 0, 9999, -9999, 1.0, 100, 1, 1.142917, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1488, 0.911802, 0, 9999, -9999, 1.0, 100, 1, 5.569856, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1489, 0.115649, 0, 9999, -9999, 1.0, 100, 1, 0.118938, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1490, 5.673013, 0, 9999, -9999, 1.0, 100, 1, 782.463701, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1491, 76.534474, 0, 9999, -9999, 1.0, 100, 1, 84.622838, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1492, 222.990441, 0, 9999, -9999, 1.0, 100, 1, 229.927503, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1493, 78.147874, 0, 9999, -9999, 1.0, 100, 1, 83.557175, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1494, 326.80152, 0, 9999, -9999, 1.0, 100, 1, 404.486733, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1495, 60.458248, 0, 9999, -9999, 1.0, 100, 1, 66.920717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1496, 4.6e-05, 0, 9999, -9999, 1.0, 100, 1, 0.000282, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1497, 57.039458, 0, 9999, -9999, 1.0, 100, 1, 89.070006, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1498, 97.421461, 0, 9999, -9999, 1.0, 100, 1, 105.800802, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1499, 0.45968, 0, 9999, -9999, 1.0, 100, 1, 2.286676, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1500, 0.068834, 0, 9999, -9999, 1.0, 100, 1, 0.154817, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1501, 2.367818, 0, 9999, -9999, 1.0, 100, 1, 8.165333, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1502, 0.111192, 0, 9999, -9999, 1.0, 100, 1, 0.938928, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1503, 24.40196, 0, 9999, -9999, 1.0, 100, 1, 45.972187, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1504, 126.871686, 0, 9999, -9999, 1.0, 100, 1, 188.822836, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1505, 4.377864, 0, 9999, -9999, 1.0, 100, 1, 26.765913, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1506, 14.359595, 0, 9999, -9999, 1.0, 100, 1, 56.406717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1507, 3.264294, 0, 9999, -9999, 1.0, 100, 1, 15.438042, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1508, 0.064798, 0, 9999, -9999, 1.0, 100, 1, 0.065259, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1509, 4.7e-05, 0, 9999, -9999, 1.0, 100, 1, 0.005193, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1510, 56.12069, 0, 9999, -9999, 1.0, 100, 1, 107.008141, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1511, 95.027293, 0, 9999, -9999, 1.0, 100, 1, 155.22192, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1512, 39.855633, 0, 9999, -9999, 1.0, 100, 1, 64.130052, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1513, 16.938458, 0, 9999, -9999, 1.0, 100, 1, 23.051786, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1514, 0.003798, 0, 9999, -9999, 1.0, 100, 1, 0.027711, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1515, 0.0001, 0, 9999, -9999, 1.0, 100, 1, 0.00633, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1516, 0.011036, 0, 9999, -9999, 1.0, 100, 1, 0.02881, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1517, 0.816063, 0, 9999, -9999, 1.0, 100, 1, 1.286804, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1518, 0.650791, 0, 9999, -9999, 1.0, 100, 1, 0.670542, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1519, 0.045169, 0, 9999, -9999, 1.0, 100, 1, 0.04654, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
ppc["branch"] = array([
[586, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[589, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[590, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[593, 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[594, 114, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[595, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[598, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[599, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[601, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[602, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[603, 526, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[607, 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[608, 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[609, 529, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[612, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[613, 130, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[614, 130, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[616, 132, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[617, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[618, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[619, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[621, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[624, 14, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[628, 142, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[629, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[631, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[632, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[637, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[638, 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[640, 153, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[641, 155, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[642, 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[643, 534, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[647, 536, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[650, 166, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[652, 167, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[655, 170, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[663, 178, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[666, 180, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[670, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[672, 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[676, 19, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[681, 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[683, 200, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[687, 202, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[689, 204, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[691, 209, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[694, 21, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[695, 210, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[696, 211, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[697, 211, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[698, 212, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[702, 215, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[705, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[707, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[713, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[714, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[716, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[717, 227, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[719, 229, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[722, 545, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[723, 235, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[724, 238, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[727, 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[728, 244, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[730, 547, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[732, 247, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[735, 253, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[738, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[741, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[742, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[743, 500, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[746, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[747, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[748, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[749, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[750, 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[753, 28, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[758, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[760, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[761, 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[762, 289, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[763, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[765, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[767, 292, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[769, 293, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[771, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[772, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[774, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[777, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[778, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[781, 303, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[784, 563, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[785, 501, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[787, 308, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[788, 311, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[789, 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[791, 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[792, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[795, 319, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[800, 326, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[801, 327, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[802, 327, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[805, 328, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[806, 328, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[808, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[809, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[811, 568, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[814, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[816, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[817, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[821, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[822, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[826, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[830, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[834, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[835, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[836, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[837, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[839, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[841, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[843, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[844, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[845, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[849, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[850, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[851, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[853, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[855, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[856, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[857, 365, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[858, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[859, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[860, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[864, 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[865, 375, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[867, 376, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[869, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[870, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[872, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[873, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[874, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[875, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[877, 578, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[881, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[882, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[883, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[885, 393, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[886, 394, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[889, 397, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[890, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[893, 400, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[894, 400, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[895, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[896, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[898, 403, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[900, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[902, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[903, 406, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[905, 413, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[906, 414, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[907, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[909, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[915, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[917, 43, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[918, 424, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[920, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[921, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[922, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[923, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[925, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[931, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[935, 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[936, 445, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[937, 447, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[939, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[940, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[944, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[950, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[952, 47, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[957, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[958, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[959, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[960, 479, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[963, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[965, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[966, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[967, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[968, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[969, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[971, 51, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[973, 506, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[976, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[978, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[981, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[982, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[983, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[984, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[985, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[986, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[987, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[988, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[993, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[994, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[995, 509, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[997, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[999, 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1000, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1002, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1003, 72, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1007, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1008, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1010, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1011, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1012, 81, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1014, 83, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1026, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1027, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1028, 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1029, 268, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1030, 269, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1031, 498, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1032, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1033, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1034, 4, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1035, 6, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1036, 7, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1037, 8, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1038, 9, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1039, 11, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1040, 14, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1041, 16, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1042, 17, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1043, 19, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1044, 21, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1045, 23, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1046, 25, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1047, 27, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1048, 28, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1049, 29, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1050, 31, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1051, 33, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1052, 34, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1053, 35, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1054, 36, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1055, 38, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1056, 39, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1057, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1058, 41, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1059, 43, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1060, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1061, 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1062, 47, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1063, 48, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1064, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1065, 50, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1066, 51, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1067, 53, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1068, 54, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1069, 55, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1070, 57, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1071, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1072, 59, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1073, 60, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1074, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1075, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1076, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1077, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1078, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1079, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1080, 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1081, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1082, 72, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1083, 73, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1084, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1085, 76, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1086, 77, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1087, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1088, 80, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1089, 81, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1090, 82, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1091, 83, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1092, 84, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1093, 85, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1094, 88, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1095, 89, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1096, 90, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1097, 91, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1098, 92, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1099, 93, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1100, 97, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1101, 98, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1102, 101, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1103, 102, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1104, 103, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1105, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1106, 109, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1107, 110, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1108, 111, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1109, 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1110, 113, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1111, 114, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1112, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1113, 116, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1114, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1115, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1116, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1117, 122, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1118, 126, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1119, 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1120, 130, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1121, 131, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1122, 132, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1123, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1124, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1125, 135, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1126, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1127, 137, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1128, 139, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1129, 140, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1130, 141, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1131, 142, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1132, 144, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1133, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1134, 146, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1135, 147, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1136, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1137, 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1138, 150, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1139, 151, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1140, 152, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1141, 153, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1142, 154, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1143, 155, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1144, 158, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1145, 161, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1146, 162, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1147, 163, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1148, 164, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1149, 166, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1150, 167, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1151, 168, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1152, 169, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1153, 170, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1154, 171, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1155, 172, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1156, 173, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1157, 174, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1158, 175, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1159, 176, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1160, 177, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1161, 178, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1162, 179, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1163, 180, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1164, 181, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1165, 182, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1166, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1167, 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1168, 186, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1169, 187, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1170, 188, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1171, 189, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1172, 190, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1173, 192, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1174, 193, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1175, 194, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1176, 196, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1177, 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1178, 198, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1179, 199, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1180, 200, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1181, 202, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1182, 203, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1183, 204, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1184, 205, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1185, 206, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1186, 207, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1187, 208, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1188, 209, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1189, 210, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1190, 211, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1191, 212, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1192, 213, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1193, 214, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1194, 215, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1195, 216, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1196, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1197, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1198, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1201, 223, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1202, 224, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1203, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1204, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1205, 227, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1206, 228, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1207, 229, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1208, 230, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1209, 234, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1210, 235, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1211, 237, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1212, 238, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1213, 239, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1214, 240, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1215, 241, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1216, 242, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1217, 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1218, 244, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1219, 247, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1220, 251, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1221, 252, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1222, 253, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1223, 254, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1224, 255, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1225, 256, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1226, 257, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1227, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1228, 260, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1229, 263, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1230, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1231, 266, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1232, 267, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1235, 271, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1236, 272, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1237, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1238, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1239, 275, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1240, 276, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1241, 278, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1242, 281, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1243, 282, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1244, 283, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1245, 284, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1246, 285, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1247, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1248, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1249, 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1250, 289, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1251, 291, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1252, 292, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1253, 293, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1254, 294, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1255, 295, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1256, 296, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1257, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1258, 298, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1259, 299, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1260, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1261, 302, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1262, 303, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1263, 304, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1264, 307, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1265, 308, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1266, 309, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1267, 311, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1268, 312, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1269, 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1270, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1271, 317, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1272, 318, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1273, 319, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1274, 321, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1275, 322, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1276, 323, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1277, 324, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1278, 325, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1279, 326, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1280, 327, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1281, 328, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1282, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1283, 331, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1284, 333, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1285, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1286, 337, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1287, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1288, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1289, 340, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1290, 341, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1291, 342, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1292, 343, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1293, 344, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1294, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1295, 346, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1296, 347, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1297, 348, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1298, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1299, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1300, 353, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1301, 354, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1302, 355, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1303, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1304, 357, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1305, 359, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1306, 361, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1307, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1308, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1309, 364, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1310, 365, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1311, 366, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1312, 367, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1313, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1314, 369, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1315, 370, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1316, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1317, 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1318, 373, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1319, 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1320, 375, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1321, 376, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1322, 377, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1323, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1324, 379, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1325, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1326, 384, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1327, 385, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1328, 386, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1329, 387, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1330, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1331, 390, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1332, 391, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1333, 392, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1334, 393, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1335, 394, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1336, 395, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1337, 396, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1338, 397, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1339, 398, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1340, 399, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1341, 400, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1342, 403, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1343, 404, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1344, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1345, 406, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1346, 407, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1347, 408, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1348, 410, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1349, 411, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1350, 412, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1351, 413, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1352, 414, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1354, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1355, 418, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1356, 419, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1357, 420, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1358, 421, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1359, 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1360, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1361, 424, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1362, 425, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1363, 426, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1364, 427, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1365, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1366, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1367, 430, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1368, 431, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1369, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1370, 433, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1371, 434, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1372, 435, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1373, 436, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1376, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1377, 440, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1378, 441, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1379, 442, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1380, 443, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1381, 445, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1382, 446, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1383, 447, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1384, 448, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1385, 449, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1386, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1387, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1388, 453, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1389, 454, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1390, 455, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1391, 456, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1392, 457, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1393, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1394, 459, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1395, 460, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1396, 461, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1397, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1398, 463, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1399, 464, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1400, 465, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1401, 466, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1402, 467, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1403, 468, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1404, 469, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1405, 470, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1406, 471, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1407, 472, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1408, 473, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1409, 474, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1410, 475, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1411, 476, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1412, 477, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1413, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1414, 479, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1415, 480, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1416, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1417, 482, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1418, 483, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1419, 484, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1420, 485, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1421, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1422, 487, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1423, 488, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1424, 489, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1425, 490, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1426, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1427, 492, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1428, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1429, 494, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1430, 495, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1431, 496, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1432, 497, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1433, 498, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1434, 499, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1435, 500, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1436, 501, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1437, 502, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1438, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1439, 504, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1440, 505, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1441, 506, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1442, 507, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1443, 508, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1444, 509, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1445, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1446, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1447, 512, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1448, 513, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1449, 514, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1450, 515, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1451, 516, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1452, 517, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1453, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1454, 519, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1455, 520, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1456, 521, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1457, 522, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1458, 523, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1459, 524, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1460, 525, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1461, 526, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1462, 527, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1463, 528, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1464, 529, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1465, 530, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1466, 531, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1467, 532, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1468, 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1469, 534, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1470, 535, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1471, 536, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1472, 537, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1473, 538, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1474, 539, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1475, 540, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1476, 541, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1477, 542, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1478, 543, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1479, 544, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1480, 545, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1481, 546, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1482, 547, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1483, 548, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1484, 549, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1485, 550, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1486, 551, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1487, 552, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1488, 554, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1489, 555, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1490, 556, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1491, 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1492, 558, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1493, 559, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1494, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1495, 561, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1496, 562, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1497, 563, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1498, 564, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1499, 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1500, 566, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1501, 567, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1502, 568, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1503, 569, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1504, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1505, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1506, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1507, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1508, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1509, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1510, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1511, 577, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1512, 578, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1513, 579, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1514, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1515, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1516, 582, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1517, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1518, 584, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1519, 585, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1, 490, 0, 0.01433884297520661, 0.151691958358336, 991.0, 991.0, 991.0, 0, 2, 1, -360, 43.375 ],
[3, 4, 0, 0.006291637811634348, 0.903417549506624, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 72.681 ],
[491, 6, 0, 0.011200661157024791, 0.118492839955776, 991.0, 991.0, 991.0, 0, 2, 1, -360, 33.882 ],
[7, 5, 0, 0.005794840720221606, 0.20802058859584005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 33.471 ],
[8, 9, 0, 0.0024379328254847646, 0.350063268897336, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 28.163 ],
[492, 11, 0, 0.018224793388429753, 0.0482004476327704, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.565 ],
[11, 493, 0, 0.030286942148760328, 0.08010209706571599, 495.0, 495.0, 495.0, 0, 1, 1, -360, 45.809 ],
[492, 493, 0, 0.04521652892561983, 0.11958747011094399, 495.0, 495.0, 495.0, 0, 1, 1, -360, 68.39 ],
[494, 14, 0, 0.012990743801652892, 0.137430291356512, 991.0, 991.0, 991.0, 0, 2, 1, -360, 39.297 ],
[13, 15, 0, 0.007681959833795014, 0.27576354266704156, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 44.371 ],
[16, 5, 0, 0.006275623268698061, 0.22527950450957998, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 36.248000000000005 ],
[17, 18, 0, 0.04623522622347646, 0.9335989000302801, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 200.291 ],
[17, 12, 0, 0.0056020313942728535, 0.113118303398186, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.268 ],
[14, 495, 0, 0.0017957024793388433, 0.018996904156819597, 991.0, 991.0, 991.0, 0, 1, 1, -360, 5.432 ],
[494, 19, 0, 0.010246611570247935, 0.10839986031771602, 991.0, 991.0, 991.0, 0, 1, 1, -360, 30.996 ],
[20, 21, 0, 0.005415685595567867, 0.19440984828307922, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 31.281 ],
[20, 22, 0, 0.0049706544321329645, 0.713737278110032, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 57.42100000000001 ],
[497, 23, 0, 0.002190413223140496, 0.005793146490362, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.313 ],
[23, 499, 0, 0.020799669421487598, 0.22004164444829602, 991.0, 991.0, 991.0, 0, 1, 1, -360, 62.919 ],
[25, 26, 0, 0.00141845567867036, 0.050919084651523595, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 8.193 ],
[25, 22, 0, 0.0035578254847645433, 0.0319293051869808, 856.0, 856.0, 856.0, 0, 1, 1, -360, 10.275 ],
[23, 27, 0, 0.027738181818181818, 0.073361203699828, 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.95399999999999 ],
[28, 23, 0, 0.012841652892561981, 0.0339632611780132, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.423 ],
[8, 21, 0, 0.004948753462603878, 0.17764812836304802, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 28.584 ],
[9, 29, 0, 0.002212863573407202, 0.31774552934092004, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 25.563000000000002 ],
[30, 25, 0, 0.019958795013850415, 0.17911796401827998, 856.0, 856.0, 856.0, 0, 1, 1, -360, 57.641000000000005 ],
[31, 32, 0, 0.0299776084949446, 0.605319030583196, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 129.863 ],
[32, 33, 0, 0.016762234533725762, 0.33846927983213604, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 72.61399999999999 ],
[34, 35, 0, 0.001931900826446281, 0.020437759184893597, 991.0, 991.0, 991.0, 0, 2, 1, -360, 5.843999999999999 ],
[35, 36, 0, 0.0008730578512396695, 0.0092361605077588, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.641 ],
[490, 6, 0, 0.049352066115702475, 0.130525028606764, 495.0, 495.0, 495.0, 0, 1, 1, -360, 74.645 ],
[37, 10, 0, 0.02404639889196676, 0.485553838251812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 104.169 ],
[10, 38, 0, 0.006848799630657894, 0.13829351176534158, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 29.669 ],
[37, 38, 0, 0.01437834718372576, 1.1613317560186958, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 124.574 ],
[39, 40, 0, 0.04521629732222991, 0.913024308337812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 195.877 ],
[39, 41, 0, 0.017466989843005543, 0.35269996139852006, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 75.667 ],
[42, 41, 0, 0.031145429362880884, 0.6289001042979919, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 134.922 ],
[18, 42, 0, 0.03439750692520776, 0.6945672650962679, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 149.01 ],
[492, 43, 0, 0.01819173553719008, 0.192452068436848, 991.0, 991.0, 991.0, 0, 2, 1, -360, 55.03 ],
[44, 45, 0, 0.02562314049586777, 0.067767398802972, 495.0, 495.0, 495.0, 0, 1, 1, -360, 38.755 ],
[44, 505, 0, 0.006061487603305785, 0.0160312607980052, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.168 ],
[46, 12, 0, 0.0014741170360110802, 0.2116687641962416, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 17.029 ],
[47, 48, 0, 0.005344182825484765, 0.01199019212302604, 428.0, 428.0, 428.0, 0, 1, 1, -360, 7.7170000000000005 ],
[49, 50, 0, 0.0019151662049861494, 0.0171874439892256, 856.0, 856.0, 856.0, 0, 1, 1, -360, 5.531000000000001 ],
[31, 33, 0, 0.013475992613088641, 0.27211225959163604, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 58.378 ],
[31, 51, 0, 0.003518611495844875, 0.5052381383693519, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 40.647 ],
[52, 53, 0, 0.010464421745152355, 1.5025884408875438, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 120.885 ],
[52, 54, 0, 0.0076126500461911354, 0.1537174637168, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 32.978 ],
[506, 55, 0, 0.012634380165289257, 0.133660287181212, 991.0, 991.0, 991.0, 0, 1, 1, -360, 38.219 ],
[506, 507, 0, 0.044157355371900825, 0.11678619613628, 495.0, 495.0, 495.0, 0, 1, 1, -360, 66.788 ],
[57, 506, 0, 0.004687272727272727, 0.049587095736244, 991.0, 991.0, 991.0, 0, 1, 1, -360, 14.179 ],
[57, 58, 0, 0.014436363636363634, 0.0381809096340232, 495.0, 495.0, 495.0, 0, 1, 1, -360, 21.835 ],
[58, 506, 0, 0.019797685950413223, 0.052360391943288, 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.944000000000003 ],
[59, 60, 0, 0.019407548476454296, 0.174170863885556, 856.0, 856.0, 856.0, 0, 1, 1, -360, 56.049 ],
[508, 62, 0, 0.051111404958677685, 0.03379452026753001, 248.0, 248.0, 248.0, 0, 1, 1, -360, 38.653 ],
[30, 61, 0, 0.03143698060941828, 0.28212765137935203, 856.0, 856.0, 856.0, 0, 1, 1, -360, 90.79 ],
[63, 506, 0, 0.027457190082644623, 0.072618044249872, 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.528999999999996 ],
[13, 64, 0, 0.0014816481994459833, 0.2127501654814608, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 17.116 ],
[65, 66, 0, 0.03778185595567867, 0.7629053006222161, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 163.671 ],
[59, 67, 0, 0.0051880193905817175, 0.046559297286324804, 856.0, 856.0, 856.0, 0, 1, 1, -360, 14.982999999999999 ],
[61, 67, 0, 0.012931440443213295, 0.1160517597580644, 856.0, 856.0, 856.0, 0, 1, 1, -360, 37.346 ],
[68, 69, 0, 0.011149584487534626, 0.4002427745096039, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 64.4 ],
[70, 69, 0, 0.009625346260387812, 0.345526355460808, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.596000000000004 ],
[71, 72, 0, 0.008878635734072021, 0.318721276477736, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.283 ],
[73, 74, 0, 0.012529547553116345, 0.253001288604392, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 54.278 ],
[37, 75, 0, 0.027459141274238225, 0.5544652029066119, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 118.95299999999999 ],
[72, 75, 0, 0.006688711911357341, 0.240108375006292, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 38.634 ],
[37, 72, 0, 0.036222068328739615, 0.7314094881920841, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 156.914 ],
[76, 77, 0, 0.004683777700831025, 0.6725445900750401, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 54.107 ],
[77, 51, 0, 0.00363183864265928, 0.5214964473447999, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 41.955 ],
[73, 72, 0, 0.025475069252077563, 0.514402082018968, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 110.35799999999999 ],
[18, 40, 0, 0.01302770083102493, 0.26306018504072, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 56.43600000000001 ],
[492, 45, 0, 0.0308703030303719, 0.18370114733484796, 743.0, 743.0, 743.0, 0, 1, 1, -360, 70.03699999999999 ],
[10, 74, 0, 0.030167359187465374, 0.609150547206812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 130.685 ],
[45, 511, 0, 0.08203371900826446, 0.05424014819960001, 248.0, 248.0, 248.0, 0, 1, 1, -360, 62.038000000000004 ],
[78, 32, 0, 0.013458795013850415, 0.48313777647302397, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 77.738 ],
[79, 80, 0, 0.0038086911357340715, 0.1367226831743568, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 21.999000000000002 ],
[81, 79, 0, 0.010767832409972299, 0.3865388099484561, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 62.195 ],
[34, 82, 0, 0.0015497520661157025, 0.00409874294399768, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.344 ],
[83, 84, 0, 0.00902611570247934, 0.0238720301499152, 495.0, 495.0, 495.0, 0, 1, 1, -360, 13.652000000000001 ],
[83, 499, 0, 0.04179570247933885, 0.0276350398834796, 248.0, 248.0, 248.0, 0, 1, 1, -360, 31.608 ],
[85, 86, 0, 0.00802354570637119, 0.28802563884886, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 46.343999999999994 ],
[87, 86, 0, 0.01904968836565097, 0.683837154069184, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 110.031 ],
[88, 89, 0, 0.00380297520661157, 0.010058007429140002, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.752000000000001 ],
[90, 86, 0, 0.012097818559556786, 0.434282055192244, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 69.877 ],
[91, 86, 0, 9.26246537396122e-05, 0.013299992817559201, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 1.07 ],
[86, 92, 0, 0.0001852493074792244, 0.0066499964087796005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.07 ],
[86, 93, 0, 0.008152181440443215, 0.292643346635492, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 47.086999999999996 ],
[94, 86, 0, 0.012883829639889197, 0.46249792780547194, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 74.417 ],
[86, 95, 0, 0.010421052631578947, 0.37409026526870803, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 60.192 ],
[513, 517, 0, 0.0008733884297520661, 0.0023099144321748, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.321 ],
[97, 66, 0, 0.03812777008310249, 0.34217338998058805, 856.0, 856.0, 856.0, 0, 1, 1, -360, 110.113 ],
[42, 98, 0, 0.003091759002770083, 0.44394630230884, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 35.716 ],
[99, 100, 0, 0.016371537396121884, 0.587698093837988, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 94.56200000000001 ],
[42, 101, 0, 0.008165339335180054, 0.29311568282888, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 47.163000000000004 ],
[102, 42, 0, 0.012403047091412742, 0.44523901189173193, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 71.64 ],
[103, 87, 0, 0.007073060941828254, 0.25390556381756, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 40.854 ],
[104, 103, 0, 0.0028852146814404432, 0.1035721403291428, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.665 ],
[105, 87, 0, 0.006406682825484765, 0.22998422159488002, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 37.005 ],
[106, 107, 0, 0.005714219759923823, 0.11538365264216799, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.754 ],
[108, 107, 0, 0.0025427631578947367, 0.09127896939786201, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.687000000000001 ],
[109, 106, 0, 0.003030470914127424, 0.10878648330773438, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 17.504 ],
[110, 111, 0, 0.019821849030470913, 0.7115558306889919, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 114.491 ],
[87, 112, 0, 0.006135907202216068, 0.220264039928212, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 35.441 ],
[113, 87, 0, 0.003981648199445983, 0.14293141813921081, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 22.998 ],
[87, 85, 0, 0.011046225761772853, 0.3965324494097, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 63.803000000000004 ],
[110, 114, 0, 0.011665339335180056, 0.418757110306188, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 67.37899999999999 ],
[115, 116, 0, 0.007048925619834712, 0.07457124214588401, 991.0, 991.0, 991.0, 0, 1, 1, -360, 21.323 ],
[117, 118, 0, 0.005987534626038782, 0.21493782785077598, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 34.584 ],
[117, 119, 0, 0.0038738746537396117, 0.5562504472696961, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 44.751000000000005 ],
[117, 120, 0, 0.005886686288088643, 0.8452704781039522, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 68.003 ],
[121, 122, 0, 0.0021170360110803325, 0.0759964075574972, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 12.228 ],
[123, 124, 0, 0.0018386426592797783, 0.0660027680945204, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 10.62 ],
[125, 126, 0, 0.004941135734072022, 0.17737467056702802, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 28.54 ],
[127, 119, 0, 0.0029027008310249305, 0.1041998502705648, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.766 ],
[118, 128, 0, 0.007397160664819945, 0.265539950057812, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 42.726000000000006 ],
[121, 119, 0, 0.002552458448753463, 0.0916270065931116, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.743 ],
[530, 527, 0, 0.022726611570247933, 0.060106736329903994, 495.0, 495.0, 495.0, 0, 1, 1, -360, 34.374 ],
[125, 130, 0, 0.002931440443213297, 0.105231531956442, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.932000000000002 ],
[125, 123, 0, 0.0019078081717451524, 0.2739425623421336, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 22.039 ],
[131, 132, 0, 0.0035744459833795014, 0.12831385593973843, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.646 ],
[133, 123, 0, 0.003864439058171745, 0.13872389704704202, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 22.320999999999998 ],
[524, 134, 0, 0.008092231404958678, 0.08560847143881999, 991.0, 991.0, 991.0, 0, 1, 1, -360, 24.479 ],
[135, 136, 0, 0.005242901662049862, 0.1882073282678, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.283 ],
[123, 131, 0, 0.003138331024930748, 0.1126583971045252, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 18.127 ],
[117, 128, 0, 0.010800034626038782, 0.38769479063117196, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 62.381 ],
[137, 521, 0, 0.013832396694214875, 0.14633421587532003, 991.0, 991.0, 991.0, 0, 2, 1, -360, 41.843 ],
[531, 514, 0, 0.0059504132231404955, 0.035409362037522, 743.0, 743.0, 743.0, 0, 1, 1, -360, 13.5 ],
[139, 521, 0, 0.021257520661157023, 0.05622132386323199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.152 ],
[140, 514, 0, 0.018527603305785127, 0.04900131122836401, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.023000000000003 ],
[522, 141, 0, 0.012168595041322314, 0.032183175718526795, 495.0, 495.0, 495.0, 0, 1, 1, -360, 18.405 ],
[142, 523, 0, 0.007060165289256198, 0.0746901476577608, 991.0, 991.0, 991.0, 0, 2, 1, -360, 21.357 ],
[530, 526, 0, 0.020281652892561983, 0.053640374808152, 495.0, 495.0, 495.0, 0, 1, 1, -360, 30.676 ],
[140, 532, 0, 0.004669090909090909, 0.0123486871461184, 495.0, 495.0, 495.0, 0, 1, 1, -360, 7.062 ],
[142, 144, 0, 0.006678126721756199, 0.0397397958689204, 743.0, 743.0, 743.0, 0, 1, 1, -360, 15.151 ],
[140, 522, 0, 0.020450247933884298, 0.05408627047793199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 30.930999999999997 ],
[145, 146, 0, 0.028527603305785125, 0.07544904460236, 495.0, 495.0, 495.0, 0, 1, 1, -360, 43.148 ],
[147, 523, 0, 0.02461289256198347, 0.0650955220034416, 495.0, 495.0, 495.0, 0, 2, 1, -360, 37.227 ],
[144, 523, 0, 0.008479338842975206, 0.0224259292904064, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.825 ],
[139, 523, 0, 0.029245619834710742, 0.0193370088934308, 248.0, 248.0, 248.0, 0, 1, 1, -360, 22.116999999999997 ],
[140, 141, 0, 0.008362975206611572, 0.022118173847506, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.649000000000001 ],
[528, 526, 0, 0.015389090909090908, 0.0407006573227188, 495.0, 495.0, 495.0, 0, 1, 1, -360, 23.276 ],
[528, 148, 0, 0.014306115702479338, 0.0378364333712244, 495.0, 495.0, 495.0, 0, 1, 1, -360, 21.638 ],
[149, 150, 0, 0.013604628099173552, 0.035981157661543604, 495.0, 495.0, 495.0, 0, 1, 1, -360, 20.576999999999998 ],
[145, 528, 0, 0.00320595041322314, 0.0084790121737992, 495.0, 495.0, 495.0, 0, 1, 1, -360, 4.849 ],
[530, 151, 0, 0.013144462809917355, 0.0347641247737036, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.881 ],
[524, 152, 0, 0.014598347107438016, 0.03860931919944, 495.0, 495.0, 495.0, 0, 1, 1, -360, 22.08 ],
[149, 525, 0, 0.016897190082644627, 0.17875695122823998, 991.0, 991.0, 991.0, 0, 2, 1, -360, 51.114 ],
[139, 514, 0, 0.007824132231404959, 0.020693056313687997, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.834000000000001 ],
[126, 120, 0, 0.012780297783933518, 0.458781387757004, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 73.819 ],
[530, 153, 0, 0.02254545454545455, 0.059627617060924, 495.0, 495.0, 495.0, 0, 1, 1, -360, 34.1 ],
[528, 147, 0, 0.15786710743801652, 0.104380679149868, 248.0, 248.0, 248.0, 0, 1, 1, -360, 119.387 ],
[528, 154, 0, 0.006528264462809917, 0.017265779790547203, 495.0, 495.0, 495.0, 0, 2, 1, -360, 9.874 ],
[130, 120, 0, 0.01450502077562327, 0.5206947188067639, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 83.781 ],
[528, 155, 0, 0.16064132231404957, 0.1062149715341, 248.0, 248.0, 248.0, 0, 1, 1, -360, 121.485 ],
[524, 533, 0, 0.004432727272727273, 0.0468942356109744, 991.0, 991.0, 991.0, 0, 1, 1, -360, 13.409 ],
[524, 149, 0, 0.0056413223140495865, 0.05968007537478799, 991.0, 991.0, 991.0, 0, 2, 1, -360, 17.065 ],
[154, 150, 0, 0.007539173553719007, 0.0199394052006688, 495.0, 495.0, 495.0, 0, 2, 1, -360, 11.402999999999999 ],
[157, 110, 0, 0.009962084487534625, 0.357614433044424, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 57.541000000000004 ],
[119, 158, 0, 0.0002490189289012004, 0.08045252664623159, 5134.0, 5134.0, 5134.0, 0, 3, 1, -360, 4.315 ],
[159, 60, 0, 0.010967451523545706, 0.0984261617997728, 856.0, 856.0, 856.0, 0, 1, 1, -360, 31.674 ],
[536, 161, 0, 0.021314380165289255, 0.056371704363524, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.238 ],
[115, 151, 0, 0.00379404958677686, 0.0401376047510724, 991.0, 991.0, 991.0, 0, 1, 1, -360, 11.477 ],
[162, 134, 0, 0.0015910743801652895, 0.016832124393744, 991.0, 991.0, 991.0, 0, 2, 1, -360, 4.813 ],
[115, 526, 0, 0.0037884297520661154, 0.010019537998747198, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.73 ],
[138, 87, 0, 0.0011838642659279777, 0.16999131006813442, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 13.675999999999998 ],
[123, 163, 0, 0.0022778739612188364, 0.08177009602828919, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.157 ],
[112, 164, 0, 0.0008672957063711912, 0.12453516639176802, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 10.019 ],
[112, 165, 0, 0.005989439058171744, 0.21500619230086396, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 34.595 ],
[166, 165, 0, 0.002632790858725762, 0.09451074335350361, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 15.207 ],
[167, 537, 0, 0.00832595041322314, 0.08808100664460242, 991.0, 991.0, 991.0, 0, 2, 1, -360, 25.186 ],
[168, 104, 0, 0.002552458448753463, 0.0916270065931116, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.743 ],
[531, 520, 0, 0.016156694214876033, 0.042730794079516396, 495.0, 495.0, 495.0, 0, 1, 1, -360, 24.436999999999998 ],
[139, 520, 0, 0.010682314049586776, 0.0282522993797748, 495.0, 495.0, 495.0, 0, 1, 1, -360, 16.157 ],
[520, 169, 0, 0.0011328925619834712, 0.0119849761681232, 991.0, 991.0, 991.0, 0, 2, 1, -360, 3.427 ],
[168, 105, 0, 0.007340893351800554, 0.26352009133553606, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 42.401 ],
[520, 170, 0, 0.005842644628099174, 0.015452470732151198, 495.0, 495.0, 495.0, 0, 2, 1, -360, 8.837 ],
[171, 89, 0, 0.005505454545454546, 0.058242717567848004, 991.0, 991.0, 991.0, 0, 1, 1, -360, 16.654 ],
[521, 172, 0, 0.006304793388429752, 0.06669899780522001, 991.0, 991.0, 991.0, 0, 1, 1, -360, 19.072 ],
[123, 173, 0, 0.005247403047091413, 0.18836891696656402, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.309 ],
[521, 174, 0, 0.013300495867768597, 0.035176796844864404, 495.0, 495.0, 495.0, 0, 1, 1, -360, 20.117 ],
[37, 39, 0, 0.004338873499549862, 0.35044859579205606, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 37.592 ],
[530, 175, 0, 0.013128595041322313, 0.0347221581224188, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.857 ],
[530, 176, 0, 0.005685289256198347, 0.01503630144005, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.599 ],
[88, 530, 0, 0.006015867768595041, 0.0159106066755372, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.099 ],
[177, 496, 0, 0.018632066115702478, 0.19711036673178398, 991.0, 991.0, 991.0, 0, 2, 1, -360, 56.361999999999995 ],
[178, 525, 0, 0.03106842975206612, 0.08216895464241199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 46.99100000000001 ],
[179, 493, 0, 0.057079669421487594, 0.15096278779194802, 495.0, 495.0, 495.0, 0, 1, 1, -360, 86.333 ],
[180, 181, 0, 0.041027438016528923, 0.10850827416682, 495.0, 495.0, 495.0, 0, 1, 1, -360, 62.053999999999995 ],
[182, 180, 0, 0.00866314049586777, 0.09164817200545601, 991.0, 991.0, 991.0, 0, 2, 1, -360, 26.206 ],
[179, 181, 0, 0.01957223140495868, 0.051764115772731996, 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.603 ],
[180, 493, 0, 0.06676561983471074, 0.17657993119175203, 495.0, 495.0, 495.0, 0, 1, 1, -360, 100.98299999999999 ],
[183, 30, 0, 0.0024804362880886427, 0.356166349712776, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 28.654 ],
[183, 21, 0, 0.0025647506925207757, 0.36827307214930394, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 29.628 ],
[538, 185, 0, 0.018631404958677687, 0.0123189607681008, 248.0, 248.0, 248.0, 0, 1, 1, -360, 14.09 ],
[538, 89, 0, 0.014509752066115702, 0.038375005396288, 495.0, 495.0, 495.0, 0, 1, 1, -360, 21.945999999999998 ],
[184, 186, 0, 0.0016554709141274237, 0.059427351084826, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 9.562000000000001 ],
[184, 187, 0, 0.002698753462603878, 0.09687863927102919, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 15.588 ],
[520, 172, 0, 0.0034188429752066113, 0.0361682589818792, 991.0, 991.0, 991.0, 0, 2, 1, -360, 10.342 ],
[89, 175, 0, 0.0037309090909090903, 0.0098674088877672, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.643 ],
[185, 89, 0, 0.005812892561983471, 0.0153737832609196, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.792 ],
[89, 188, 0, 0.003108760330578513, 0.008221966434607202, 495.0, 495.0, 495.0, 0, 1, 1, -360, 4.702 ],
[189, 190, 0, 0.008599492151454294, 0.17364414688031998, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 37.253 ],
[539, 172, 0, 0.0021570247933884296, 0.022819366646419197, 991.0, 991.0, 991.0, 0, 2, 1, -360, 6.525 ],
[504, 192, 0, 0.0003084297520661157, 0.00326290713886456, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.9329999999999999 ],
[105, 186, 0, 0.003273372576177285, 0.1175060580379876, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 18.907 ],
[105, 187, 0, 0.0021712257617728533, 0.0779416868808324, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 12.540999999999999 ],
[539, 193, 0, 0.005608595041322314, 0.01483346262541, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.482999999999999 ],
[187, 194, 0, 4.8649584487534626e-05, 0.0069856037041576, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 0.562 ],
[539, 540, 0, 0.004394710743801653, 0.0116230138006708, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.647 ],
[539, 196, 0, 0.00332297520661157, 0.008788516227194, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.026 ],
[197, 540, 0, 0.004737190082644629, 0.012528794024621601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 7.165 ],
[110, 198, 0, 0.00018724030470914128, 0.02688587333118328, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 2.1630000000000003 ],
[197, 539, 0, 0.009172231404958677, 0.024258473063998802, 495.0, 495.0, 495.0, 0, 1, 1, -360, 13.873 ],
[199, 537, 0, 0.03612826446280991, 0.0238877676441712, 248.0, 248.0, 248.0, 0, 1, 1, -360, 27.322 ],
[134, 526, 0, 0.007771239669421488, 0.020553167475975197, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.754000000000001 ],
[200, 193, 0, 0.0009322314049586776, 0.009862163056380801, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.82 ],
[4, 201, 0, 0.013726108033240996, 0.49273365914097605, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 79.282 ],
[202, 86, 0, 0.00013365650969529087, 0.00479794133417816, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.772 ],
[85, 203, 0, 0.0019011426592797783, 0.2729854600553416, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 21.962 ],
[147, 204, 0, 0.0073874380165289254, 0.0781523963903056, 991.0, 991.0, 991.0, 0, 2, 1, -360, 22.346999999999998 ],
[147, 205, 0, 0.005959669421487603, 0.00394049369636956, 248.0, 248.0, 248.0, 0, 1, 1, -360, 4.507 ],
[123, 206, 0, 0.0005753116343490305, 0.0826091142668064, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 6.646 ],
[537, 207, 0, 0.018456198347107437, 0.048812461297776, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.915 ],
[165, 208, 0, 0.00414612188365651, 0.14883562055771601, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 23.948 ],
[4, 94, 0, 0.013687673130193905, 0.49135394025941603, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 79.06 ],
[4, 2, 0, 5.2054478301015697e-05, 0.016817654469309, 5134.0, 5134.0, 5134.0, 0, 3, 1, -360, 0.902 ],
[209, 4, 0, 0.0022369286703601107, 0.32120104149338397, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 25.840999999999998 ],
[119, 163, 0, 0.003535145429362881, 0.12690306230914922, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.419 ],
[210, 3, 0, 0.0003150969529085873, 0.011311208844832242, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.82 ],
[99, 211, 0, 0.0035045013850415513, 0.1258030161741948, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.242 ],
[99, 69, 0, 0.021717970914127423, 0.7796219621557, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 125.443 ],
[212, 99, 0, 0.008453774238227147, 0.30346978938770003, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 48.82899999999999 ],
[213, 214, 0, 0.01490115702479339, 0.15764073118032798, 991.0, 991.0, 991.0, 0, 2, 1, -360, 45.076 ],
[510, 215, 0, 0.002174710743801653, 0.09202587186721281, 1981.0, 1981.0, 1981.0, 0, 4, 1, -360, 13.157 ],
[128, 69, 0, 0.010711651662049862, 1.538088234801848, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 123.741 ],
[216, 69, 0, 0.009628462603878117, 1.3825528982351443, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 111.228 ],
[217, 98, 0, 0.0012787396121883656, 0.045903620070299994, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 7.386 ],
[504, 218, 0, 0.027480991735537193, 0.072680994226412, 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.565 ],
[177, 504, 0, 0.07054809917355372, 0.18658373169634002, 495.0, 495.0, 495.0, 0, 1, 1, -360, 106.704 ],
[219, 209, 0, 0.003938798476454294, 0.5655728721401839, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 45.501000000000005 ],
[219, 220, 0, 0.0013026315789473684, 0.1870451326342096, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 15.048 ],
[94, 95, 0, 0.01070740997229917, 0.38436979242743197, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 61.846000000000004 ],
[159, 221, 0, 0.009937153739612188, 0.356719480257712, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 57.397 ],
[34, 161, 0, 0.010965289256198347, 0.116002818645824, 991.0, 991.0, 991.0, 0, 2, 1, -360, 33.17 ],
[222, 221, 0, 0.0046457756232686975, 0.16677196601221997, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 26.834 ],
[211, 52, 0, 0.05267313019390582, 0.472709090515552, 856.0, 856.0, 856.0, 0, 1, 1, -360, 152.12 ],
[215, 223, 0, 0.04873190082644628, 0.128884831985184, 495.0, 495.0, 495.0, 0, 1, 1, -360, 73.707 ],
[224, 215, 0, 0.019086280991735535, 0.050478887076288004, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.868000000000002 ],
[225, 224, 0, 0.04200925619834711, 0.11110496071615601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 63.538999999999994 ],
[224, 223, 0, 0.031061818181818183, 0.082151468537468, 495.0, 495.0, 495.0, 0, 1, 1, -360, 46.981 ],
[226, 6, 0, 0.06420099173553719, 0.0424492677936932, 248.0, 248.0, 248.0, 0, 1, 1, -360, 48.552 ],
[7, 3, 0, 0.009332929362880887, 0.335029305054692, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 53.907 ],
[216, 227, 0, 0.01989941135734072, 0.7143401282507, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 114.939 ],
[228, 229, 0, 0.010545454545454545, 0.027890337012274, 495.0, 495.0, 495.0, 0, 1, 1, -360, 15.95 ],
[227, 230, 0, 0.003993074792243767, 0.573366419334696, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 46.128 ],
[231, 53, 0, 0.007193213296398893, 1.0328749562310842, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 83.096 ],
[544, 545, 0, 0.013061818181818181, 0.034545548464856, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.756 ],
[234, 235, 0, 0.04608859504132231, 0.121893887321888, 495.0, 495.0, 495.0, 0, 1, 1, -360, 69.709 ],
[546, 214, 0, 0.057025454545454546, 0.15081940173295602, 495.0, 495.0, 495.0, 0, 1, 1, -360, 86.251 ],
[233, 227, 0, 0.0029001038781163438, 0.1041066260218888, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.750999999999998 ],
[237, 238, 0, 0.026324628099173554, 0.06962267451304, 495.0, 495.0, 495.0, 0, 1, 1, -360, 39.816 ],
[212, 100, 0, 0.007955505540166205, 0.285583163531816, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 45.951 ],
[519, 239, 0, 0.01740429752066116, 0.046030422038308406, 495.0, 495.0, 495.0, 0, 1, 1, -360, 26.324 ],
[238, 519, 0, 0.015166280991735538, 0.040111375593995205, 495.0, 495.0, 495.0, 0, 1, 1, -360, 22.939 ],
[213, 240, 0, 0.01665388429752066, 0.04404574915373599, 1200.0, 1200.0, 1200.0, 0, 1, 1, -360, 25.189 ],
[241, 242, 0, 0.009862015235457064, 0.3540221919932281, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 56.963 ],
[70, 241, 0, 0.003819858033240997, 0.5484941897752321, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 44.126999999999995 ],
[509, 213, 0, 0.011363636363636364, 0.120216969880216, 991.0, 991.0, 991.0, 0, 2, 1, -360, 34.375 ],
[68, 243, 0, 0.003611668975069252, 0.1296500701715312, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.861 ],
[243, 244, 0, 0.0007699099722991691, 0.027637882270859202, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 4.447 ],
[68, 244, 0, 0.004104051246537396, 0.147325387728876, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 23.705 ],
[544, 547, 0, 0.02418776859504132, 0.255884661882476, 991.0, 991.0, 991.0, 0, 1, 1, -360, 73.168 ],
[245, 227, 0, 0.012676419667590028, 0.45505241780707606, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 73.219 ],
[246, 208, 0, 0.0010155817174515235, 0.0364568961999408, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 5.8660000000000005 ],
[112, 208, 0, 0.0017927631578947367, 0.0643558063672372, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 10.355 ],
[165, 247, 0, 0.0002113919667590028, 0.0075884538459086, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.2209999999999999 ],
[537, 549, 0, 0.00032066115702479337, 0.00084807607842936, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.485 ],
[537, 550, 0, 0.00032198347107438016, 0.0008515732993697601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.48700000000000004 ],
[537, 551, 0, 0.0002651239669421488, 0.0007011927988648, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.401 ],
[110, 251, 0, 0.00023857340720221602, 0.008564200982522441, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.3780000000000001 ],
[510, 252, 0, 0.08467702479338843, 0.055987884365424005, 248.0, 248.0, 248.0, 0, 1, 1, -360, 64.03699999999999 ],
[529, 253, 0, 0.04859504132231405, 0.12852286961777998, 495.0, 495.0, 495.0, 0, 1, 1, -360, 73.5 ],
[237, 239, 0, 0.03309421487603306, 0.08752669712542799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 50.055 ],
[254, 238, 0, 0.07815008264462811, 0.05167231372274401, 248.0, 248.0, 248.0, 0, 1, 1, -360, 59.101000000000006 ],
[69, 255, 0, 0.0009369806094182826, 0.134541235754472, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 10.824000000000002 ],
[510, 225, 0, 0.021953719008264466, 0.232250442756508, 991.0, 991.0, 991.0, 0, 1, 1, -360, 66.41 ],
[256, 257, 0, 0.010125619834710746, 0.0267799693631888, 495.0, 495.0, 495.0, 0, 1, 1, -360, 15.315 ],
[258, 190, 0, 0.011717451523545707, 0.10515695255750121, 856.0, 856.0, 856.0, 0, 1, 1, -360, 33.84 ],
[258, 259, 0, 0.015782548476454293, 0.1416387085570408, 856.0, 856.0, 856.0, 0, 1, 1, -360, 45.58 ],
[260, 261, 0, 0.006791031855955679, 0.9751256416231477, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 78.45 ],
[554, 553, 0, 0.17583338842975205, 0.11625986438453201, 248.0, 248.0, 248.0, 0, 1, 1, -360, 132.974 ],
[515, 263, 0, 0.006987107438016529, 0.0739172618295936, 991.0, 991.0, 991.0, 0, 2, 1, -360, 21.136 ],
[14, 264, 0, 0.01700694214876033, 0.17991802858084, 991.0, 991.0, 991.0, 0, 1, 1, -360, 51.446000000000005 ],
[116, 555, 0, 0.0009768595041322315, 0.0103342878835768, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.955 ],
[151, 116, 0, 0.007244958677685951, 0.0191612735410668, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.958 ],
[111, 114, 0, 0.008806613573407202, 0.3161358573133961, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.867 ],
[77, 111, 0, 0.00288452216066482, 0.41418912211817605, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 33.321999999999996 ],
[266, 525, 0, 0.01042909090909091, 0.027582581569373602, 495.0, 495.0, 495.0, 0, 1, 1, -360, 15.774000000000001 ],
[267, 120, 0, 0.013136945983379503, 0.471584184581432, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 75.87899999999999 ],
[268, 269, 0, 0.0010327272727272726, 0.0027313295556817604, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.5619999999999998 ],
[556, 271, 0, 0.052289586776859506, 0.0345735262323792, 248.0, 248.0, 248.0, 0, 1, 1, -360, 39.544000000000004 ],
[556, 272, 0, 0.04685355371900827, 0.030979257409249603, 248.0, 248.0, 248.0, 0, 1, 1, -360, 35.433 ],
[529, 273, 0, 0.0034604958677685953, 0.009152227205140799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.234 ],
[128, 274, 0, 0.0029350761772853184, 0.1053620459045884, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.953 ],
[34, 275, 0, 0.0008290909090909092, 0.00054818938265696, 248.0, 248.0, 248.0, 0, 1, 1, -360, 0.627 ],
[503, 276, 0, 0.006707438016528925, 0.07095861291266, 991.0, 991.0, 991.0, 0, 2, 1, -360, 20.29 ],
[503, 504, 0, 0.06432727272727272, 0.680524223098808, 991.0, 991.0, 991.0, 0, 2, 1, -360, 194.59 ],
[177, 218, 0, 0.04330380165289256, 0.114528740018308, 495.0, 495.0, 495.0, 0, 1, 1, -360, 65.497 ],
[277, 278, 0, 0.007191135734072023, 1.032576638635032, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 83.072 ],
[557, 558, 0, 0.04341289256198347, 0.258338836678648, 743.0, 743.0, 743.0, 0, 1, 1, -360, 98.493 ],
[557, 559, 0, 0.03415867768595042, 0.09034195998366001, 495.0, 495.0, 495.0, 0, 1, 1, -360, 51.665 ],
[559, 558, 0, 0.04474314049586777, 0.11833546501370001, 495.0, 495.0, 495.0, 0, 1, 1, -360, 67.67399999999999 ],
[277, 78, 0, 0.03585768698060942, 0.32180078416049196, 856.0, 856.0, 856.0, 0, 1, 1, -360, 103.557 ],
[277, 279, 0, 0.021390927977839334, 0.191970480441328, 856.0, 856.0, 856.0, 0, 1, 1, -360, 61.777 ],
[78, 279, 0, 0.015811980609418283, 0.1419028439283376, 856.0, 856.0, 856.0, 0, 1, 1, -360, 45.665 ],
[281, 282, 0, 0.0023178670360110803, 0.08320574945862161, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.388 ],
[283, 161, 0, 0.036741157024793386, 0.09717203248350399, 495.0, 495.0, 495.0, 0, 2, 1, -360, 55.571000000000005 ],
[268, 161, 0, 0.018883636363636366, 0.199771751868832, 991.0, 991.0, 991.0, 0, 2, 1, -360, 57.123000000000005 ],
[256, 284, 0, 0.010755371900826446, 0.113782083346976, 991.0, 991.0, 991.0, 0, 2, 1, -360, 32.535 ],
[515, 516, 0, 0.04071140495867769, 0.107672438361532, 495.0, 495.0, 495.0, 0, 1, 1, -360, 61.576 ],
[263, 516, 0, 0.0030355371900826445, 0.128452925198488, 1981.0, 1981.0, 1981.0, 0, 2, 1, -360, 18.365 ],
[516, 285, 0, 0.006908429752066116, 0.018271230811372, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.449000000000002 ],
[63, 286, 0, 0.019088925619834708, 0.050485881518556, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.872 ],
[287, 516, 0, 0.01732892561983471, 0.011457770111127998, 248.0, 248.0, 248.0, 0, 1, 1, -360, 13.105 ],
[8, 102, 0, 0.015100069252077563, 0.542055501663692, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 87.21799999999999 ],
[8, 101, 0, 0.019246883656509697, 0.69091598202144, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 111.17 ],
[80, 288, 0, 0.007984072022160666, 0.2866086302684072, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 46.11600000000001 ],
[80, 289, 0, 0.0003782317636201524, 0.122198345223416, 5134.0, 5134.0, 5134.0, 0, 4, 1, -360, 6.553999999999999 ],
[276, 560, 0, 0.01778314049586777, 0.047032375838192794, 495.0, 495.0, 495.0, 0, 2, 1, -360, 26.897 ],
[37, 290, 0, 0.005629501385041551, 0.4546919507138321, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 48.773999999999994 ],
[290, 74, 0, 0.02071595106187673, 1.673216783321968, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 179.483 ],
[512, 291, 0, 0.0053299173553719, 0.056385693247479204, 991.0, 991.0, 991.0, 0, 2, 1, -360, 16.123 ],
[78, 292, 0, 0.0058149815327908595, 0.469673087481408, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 50.381 ],
[199, 548, 0, 0.0015530578512396695, 0.00410748599634868, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.349 ],
[491, 293, 0, 0.014176528925619833, 0.009373426429729999, 248.0, 248.0, 248.0, 0, 1, 1, -360, 10.720999999999998 ],
[4, 294, 0, 9.669321329639889e-05, 0.013884198109531681, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 1.117 ],
[490, 541, 0, 0.050580495867768596, 0.133773946861896, 495.0, 495.0, 495.0, 0, 1, 1, -360, 76.503 ],
[491, 295, 0, 0.010613553719008264, 0.028070443890777202, 495.0, 495.0, 495.0, 0, 1, 1, -360, 16.053 ],
[491, 296, 0, 0.004400661157024794, 0.0116387512948784, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.656000000000001 ],
[295, 297, 0, 0.020297520661157024, 0.053682341459340005, 495.0, 495.0, 495.0, 0, 1, 1, -360, 30.7 ],
[508, 161, 0, 0.023239669421487603, 0.061463658055360006, 495.0, 495.0, 495.0, 0, 1, 1, -360, 35.15 ],
[117, 123, 0, 0.005876211911357341, 0.21094161505628, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 33.941 ],
[133, 117, 0, 0.004469182825484764, 0.0401081792747688, 856.0, 856.0, 856.0, 0, 1, 1, -360, 12.907 ],
[71, 74, 0, 0.03904524469065097, 0.7884161162841721, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 169.144 ],
[74, 278, 0, 0.0077122576177285325, 1.10740463560792, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 89.09200000000001 ],
[298, 515, 0, 0.021701157024793388, 0.05739464148919599, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.823 ],
[5, 299, 0, 0.0016232686980609415, 0.058271370400665996, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 9.376 ],
[32, 292, 0, 0.009679362880886427, 0.34746541983297996, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.908 ],
[5, 29, 0, 0.00743395083102493, 1.0674425076571843, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 85.87700000000001 ],
[503, 560, 0, 0.015140495867768593, 0.160172719142436, 991.0, 991.0, 991.0, 0, 1, 1, -360, 45.8 ],
[300, 301, 0, 0.004892053324099723, 0.7024509290644521, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 56.513000000000005 ],
[51, 300, 0, 0.002573493767313019, 0.3695284920307039, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 29.729 ],
[244, 302, 0, 0.007714508310249307, 1.107727813004004, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 89.118 ],
[31, 302, 0, 0.004369113573407203, 0.6273619041941161, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 50.472 ],
[51, 282, 0, 0.006288434903047093, 0.9029576432132521, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 72.64399999999999 ],
[303, 304, 0, 8.795013850415512e-05, 0.000789298639172312, 856.0, 856.0, 856.0, 0, 1, 1, -360, 0.254 ],
[305, 304, 0, 0.003881117266849031, 0.0783689646873844, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 16.813 ],
[305, 259, 0, 0.0025625, 0.36794989475177603, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 29.601999999999997 ],
[306, 307, 0, 0.03223268698060942, 0.289268628831688, 856.0, 856.0, 856.0, 0, 1, 1, -360, 93.088 ],
[305, 308, 0, 0.0024272853185595567, 0.0217833994511184, 856.0, 856.0, 856.0, 0, 1, 1, -360, 7.01 ],
[305, 309, 0, 0.011014773776523545, 0.22241441259921202, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 47.716 ],
[310, 309, 0, 0.009565962603878117, 0.343394627639832, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.253 ],
[306, 309, 0, 0.035333795013850415, 0.31709917455019604, 856.0, 856.0, 856.0, 0, 1, 1, -360, 102.044 ],
[311, 280, 0, 0.003433691135734072, 0.1232611016590444, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 19.833 ],
[280, 278, 0, 0.009749769159764544, 0.7874838737974121, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 84.47200000000001 ],
[311, 32, 0, 0.01205909510619806, 0.9740069506375919, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 104.48 ],
[13, 312, 0, 0.0043324965373961214, 0.622104056565324, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 50.049 ],
[313, 314, 0, 0.006092624653739613, 0.218710302449316, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 35.191 ],
[312, 313, 0, 0.00893957756232687, 0.32090893884734, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.635 ],
[547, 566, 0, 0.027035702479338848, 0.286013220297816, 991.0, 991.0, 991.0, 0, 1, 1, -360, 81.783 ],
[245, 315, 0, 0.014162569252077564, 0.508401547875772, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 81.803 ],
[312, 316, 0, 8.803670360110802e-05, 0.01264120812658816, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 1.0170000000000001 ],
[312, 314, 0, 0.005339854570637119, 0.191687700220296, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.843000000000004 ],
[554, 546, 0, 0.08174743801652892, 0.21620344446439202, 495.0, 495.0, 495.0, 0, 1, 1, -360, 123.64299999999999 ],
[262, 216, 0, 0.042641966759002774, 0.38268554099981195, 856.0, 856.0, 856.0, 0, 1, 1, -360, 123.15 ],
[317, 233, 0, 0.005647276084951523, 0.114031901035644, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.464000000000002 ],
[318, 317, 0, 0.008311634349030471, 0.16783161497270002, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 36.006 ],
[231, 52, 0, 0.035263677285318554, 1.2658796434850879, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 203.683 ],
[319, 567, 0, 0.006089586776859504, 0.0644223069721, 991.0, 991.0, 991.0, 0, 1, 1, -360, 18.421 ],
[557, 321, 0, 0.010004628099173555, 0.10583989458750401, 991.0, 991.0, 991.0, 0, 2, 1, -360, 30.264 ],
[277, 65, 0, 0.009430170821779778, 0.7616700793261759, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 81.703 ],
[322, 288, 0, 0.006545013850415513, 0.528637424797136, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 56.706 ],
[322, 323, 0, 0.0018503000923372577, 0.14944779312484, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 16.031 ],
[277, 324, 0, 0.019719529085872576, 0.39818407235049996, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 85.425 ],
[324, 325, 0, 0.01103508771932133, 0.22282459929396403, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 47.803999999999995 ],
[277, 325, 0, 0.008665743305609418, 0.174981914850048, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 37.54 ],
[326, 327, 0, 0.007654214876033058, 0.0202436634226288, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.577 ],
[328, 326, 0, 0.10300958677685952, 0.068109252150368, 248.0, 248.0, 248.0, 0, 1, 1, -360, 77.90100000000001 ],
[328, 327, 0, 0.09827173553719008, 0.064976616491468, 248.0, 248.0, 248.0, 0, 1, 1, -360, 74.318 ],
[326, 329, 0, 0.028062148760330575, 0.07421802283046801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 42.443999999999996 ],
[568, 329, 0, 0.05699900826446282, 0.15074945731414802, 495.0, 495.0, 495.0, 0, 1, 1, -360, 86.211 ],
[568, 326, 0, 0.03218644628099173, 0.08512585494846397, 495.0, 495.0, 495.0, 0, 1, 1, -360, 48.681999999999995 ],
[332, 78, 0, 0.006471029547541551, 0.522661750455416, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 56.065 ],
[333, 306, 0, 0.008580159279778392, 0.308006702824228, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 49.559 ],
[332, 333, 0, 0.007504674515235457, 0.26939943395502003, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 43.347 ],
[332, 334, 0, 0.017124653739612188, 0.15368328149175597, 856.0, 856.0, 856.0, 0, 1, 1, -360, 49.456 ],
[66, 334, 0, 0.030625, 0.27484062260471603, 856.0, 856.0, 856.0, 0, 1, 1, -360, 88.445 ],
[330, 335, 0, 0.00550536703601108, 0.790516769355108, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 63.598 ],
[336, 66, 0, 0.015054362880886425, 0.1351036887216764, 856.0, 856.0, 856.0, 0, 1, 1, -360, 43.477 ],
[330, 336, 0, 0.039036357340720224, 0.350327404269788, 856.0, 856.0, 856.0, 0, 1, 1, -360, 112.73700000000001 ],
[68, 70, 0, 0.016314058171745152, 0.14640868261713597, 856.0, 856.0, 856.0, 0, 1, 1, -360, 47.115 ],
[509, 337, 0, 0.03494082644628099, 0.09241056617056001, 495.0, 495.0, 495.0, 0, 1, 1, -360, 52.848 ],
[324, 288, 0, 0.012627423822714683, 0.11332339674541761, 856.0, 856.0, 856.0, 0, 1, 1, -360, 36.468 ],
[338, 559, 0, 0.009228099173553718, 0.097624922595552, 991.0, 991.0, 991.0, 0, 2, 1, -360, 27.915 ],
[339, 559, 0, 0.03560595041322315, 0.023542417076125203, 248.0, 248.0, 248.0, 0, 1, 1, -360, 26.927 ],
[339, 340, 0, 0.08711537190082644, 0.23040041287850396, 495.0, 495.0, 495.0, 0, 1, 1, -360, 131.762 ],
[559, 340, 0, 0.20983272727272728, 0.138740000599684, 248.0, 248.0, 248.0, 0, 1, 1, -360, 158.686 ],
[341, 292, 0, 0.0009329409048961218, 0.07535316024134399, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 8.083 ],
[557, 342, 0, 0.006019834710743802, 0.0636843933534336, 991.0, 991.0, 991.0, 0, 2, 1, -360, 18.21 ],
[558, 343, 0, 0.010650247933884296, 0.11266996708783199, 991.0, 991.0, 991.0, 0, 1, 1, -360, 32.217 ],
[502, 340, 0, 0.021737520661157025, 0.22996326026071198, 991.0, 991.0, 991.0, 0, 2, 1, -360, 65.756 ],
[72, 32, 0, 0.00675502077562327, 0.969954803293024, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 78.03399999999999 ],
[344, 345, 0, 0.0005762927054480609, 0.04654686738645321, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 4.993 ],
[346, 47, 0, 0.0011340027700831024, 0.04070792194158799, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 6.55 ],
[46, 47, 0, 0.0008975069252077563, 0.0322183003580208, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 5.184 ],
[346, 345, 0, 0.0007217797783933517, 0.025910126194627202, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 4.169 ],
[347, 328, 0, 0.029905454545454544, 0.07909314882361201, 495.0, 495.0, 495.0, 0, 1, 1, -360, 45.232 ],
[347, 348, 0, 0.04883438016528925, 0.129155866607944, 495.0, 495.0, 495.0, 0, 1, 1, -360, 73.862 ],
[571, 348, 0, 0.041548429752066116, 0.10988617921762801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 62.842 ],
[347, 572, 0, 0.016052231404958678, 0.04245451362512801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 24.279 ],
[571, 570, 0, 0.17379041322314048, 0.11490906279551602, 248.0, 248.0, 248.0, 0, 1, 1, -360, 131.429 ],
[14, 350, 0, 0.02166743801652892, 0.05730546235524, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.772 ],
[350, 573, 0, 0.026277685950413226, 0.06949852316919598, 495.0, 495.0, 495.0, 0, 1, 1, -360, 39.745 ],
[15, 351, 0, 0.02639265927977839, 0.236857956201204, 856.0, 856.0, 856.0, 0, 1, 1, -360, 76.222 ],
[352, 15, 0, 0.0015260560941828254, 0.219126704094076, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 17.629 ],
[15, 335, 0, 0.0035338758079432133, 1.1417173740880242, 5134.0, 5134.0, 5134.0, 0, 1, 1, -360, 61.235 ],
[232, 227, 0, 5.5747922437673134e-05, 0.000500303468136644, 1200.0, 1200.0, 1200.0, 0, 1, 1, -360, 0.161 ],
[565, 544, 0, 0.0394803305785124, 0.10441652566461601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 59.714 ],
[235, 567, 0, 0.02391404958677686, 0.25298896294275997, 991.0, 991.0, 991.0, 0, 1, 1, -360, 72.34 ],
[567, 286, 0, 0.008068760330578512, 0.34144067500694797, 1981.0, 1981.0, 1981.0, 0, 1, 1, -360, 48.816 ],
[353, 519, 0, 0.007621818181818182, 0.080631926038356, 991.0, 991.0, 991.0, 0, 1, 1, -360, 23.055999999999997 ],
[354, 353, 0, 0.0008436363636363636, 0.00892490784392768, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.552 ],
[355, 354, 0, 0.0068502479338842966, 0.0181173530898976, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.360999999999999 ],
[354, 356, 0, 0.01855404958677686, 0.049071255647172, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.063000000000002 ],
[357, 358, 0, 0.0034823407202216067, 0.5000300103406239, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 40.228 ],
[574, 359, 0, 0.013352066115702478, 0.0353131884615884, 495.0, 495.0, 495.0, 0, 1, 1, -360, 20.195 ],
[235, 575, 0, 0.007459504132231404, 0.0789147905557, 991.0, 991.0, 991.0, 0, 1, 1, -360, 22.565 ],
[167, 361, 0, 0.000616198347107438, 0.0065188198358579995, 991.0, 991.0, 991.0, 0, 1, 1, -360, 1.864 ],
[528, 362, 0, 0.0011960330578512398, 0.012652945368078402, 991.0, 991.0, 991.0, 0, 1, 1, -360, 3.6180000000000003 ],
[363, 344, 0, 0.0002662742382271468, 0.009558592968871479, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.538 ],
[259, 364, 0, 0.013069713758102496, 0.26390852570525997, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 56.618 ],
[54, 56, 0, 0.007723337950138504, 0.0693122289241068, 856.0, 856.0, 856.0, 0, 1, 1, -360, 22.305 ],
[365, 364, 0, 0.0049974607571537395, 0.10091058802821559, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 21.649 ],
[231, 366, 0, 0.0013273891966759002, 0.0476500209962672, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 7.667000000000001 ],
[30, 367, 0, 0.01126108033240997, 0.1010613005635992, 856.0, 856.0, 856.0, 0, 1, 1, -360, 32.522 ],
[61, 367, 0, 0.020337603878116343, 0.18251754162067196, 856.0, 856.0, 856.0, 0, 1, 1, -360, 58.735 ],
[254, 368, 0, 0.0004297520661157025, 0.00454638722456732, 991.0, 991.0, 991.0, 0, 1, 1, -360, 1.3 ],
[254, 369, 0, 0.00015999999999999999, 0.00169265493591832, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.484 ],
[254, 370, 0, 0.0003669421487603306, 0.0038819152455960805, 991.0, 991.0, 991.0, 0, 2, 1, -360, 1.11 ],
[99, 358, 0, 0.0020184383656509696, 0.28982797432374396, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 23.316999999999997 ],
[354, 519, 0, 0.006762644628099174, 0.07154264880985199, 991.0, 991.0, 991.0, 0, 1, 1, -360, 20.457 ],
[571, 371, 0, 0.023726942148760328, 0.06275238397221199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 35.887 ],
[207, 372, 0, 0.002329256198347108, 0.006160354689297601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.523 ],
[57, 373, 0, 0.0017725619834710745, 0.0046880246727212796, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.681 ],
[209, 374, 0, 0.0010122922437673131, 0.0363388121515216, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 5.847 ],
[375, 376, 0, 0.0045364727608518006, 0.0916021467933684, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 19.652 ],
[376, 377, 0, 0.0030886426592797783, 0.062367022394423606, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 13.38 ],
[16, 49, 0, 0.002266101108033241, 0.32538991773524, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 26.178 ],
[318, 377, 0, 0.004755078485685596, 0.0960163149704152, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 20.599 ],
[378, 297, 0, 0.01753917355371901, 0.046387138574374404, 495.0, 495.0, 495.0, 0, 1, 1, -360, 26.528000000000002 ],
[562, 379, 0, 0.01802314049586777, 0.047667121439141605, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.26 ],
[576, 563, 0, 0.001808264462809917, 0.004782449638150801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.735 ],
[576, 381, 0, 0.0034320661157024794, 0.009077036954898, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.191 ],
[577, 576, 0, 0.06004495867768594, 0.15880530575430396, 495.0, 495.0, 495.0, 0, 1, 1, -360, 90.818 ],
[244, 383, 0, 0.006845567867036011, 0.1382282547912684, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 29.655 ],
[244, 306, 0, 0.02679108956599723, 0.5409756541164079, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 116.059 ],
[383, 306, 0, 0.0300685595567867, 0.269846910348376, 856.0, 856.0, 856.0, 0, 1, 1, -360, 86.838 ],
[380, 306, 0, 0.00025605955678670365, 0.03676764369572, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 2.958 ],
[252, 225, 0, 0.062094545454545444, 0.041056499553586, 248.0, 248.0, 248.0, 0, 1, 1, -360, 46.958999999999996 ],
[220, 76, 0, 0.002772074099722992, 0.398042682239984, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 32.023 ],
[542, 384, 0, 0.007939834710743802, 0.020999063146094, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.009 ],
[385, 384, 0, 0.053734876033057856, 0.035529141854791196, 248.0, 248.0, 248.0, 0, 1, 1, -360, 40.637 ],
[542, 385, 0, 0.011306115702479337, 0.119608453436296, 991.0, 991.0, 991.0, 0, 2, 1, -360, 34.201 ],
[386, 385, 0, 0.003668760330578512, 0.0388121580140316, 991.0, 991.0, 991.0, 0, 1, 1, -360, 11.097999999999999 ],
[387, 578, 0, 0.015444628099173553, 0.16339016240905604, 991.0, 991.0, 991.0, 0, 1, 1, -360, 46.72 ],
[332, 388, 0, 0.014036184210526315, 0.5038646344377999, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 81.07300000000001 ],
[382, 332, 0, 0.017764369806094183, 0.637697365901468, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 102.60700000000001 ],
[382, 388, 0, 0.00476159972299169, 0.17092976750548, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 27.503 ],
[579, 578, 0, 0.01911074380165289, 0.050543585664, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.905 ],
[577, 387, 0, 0.07597818181818182, 0.20094506949431204, 495.0, 495.0, 495.0, 0, 1, 1, -360, 114.917 ],
[144, 390, 0, 0.0004277685950413223, 0.0011313509747276, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.647 ],
[37, 49, 0, 0.008441481994459835, 0.303028527944352, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 48.758 ],
[391, 233, 0, 0.014211218836565096, 0.1275369872004348, 856.0, 856.0, 856.0, 0, 1, 1, -360, 41.042 ],
[392, 310, 0, 0.007035318559556785, 0.06313767618386361, 856.0, 856.0, 856.0, 0, 1, 1, -360, 20.317999999999998 ],
[260, 393, 0, 0.006341412742382271, 0.0569102963692744, 856.0, 856.0, 856.0, 0, 1, 1, -360, 18.314 ],
[394, 230, 0, 0.0007590027700831025, 0.00681158510656168, 856.0, 856.0, 856.0, 0, 1, 1, -360, 2.1919999999999997 ],
[395, 282, 0, 0.008762984764542936, 0.314569689934484, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.615 ],
[395, 244, 0, 0.0034046052631578946, 0.12221699007344, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 19.665 ],
[25, 396, 0, 0.008809037396121884, 0.316222866612064, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.881 ],
[81, 74, 0, 0.0075207756232686974, 0.26997742429652244, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 43.44 ],
[278, 80, 0, 0.016286011080332407, 0.5846279085788, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 94.068 ],
[81, 278, 0, 0.021054016620498613, 0.755787629231688, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 121.60799999999999 ],
[569, 570, 0, 0.03253950413223141, 0.08605961294018, 495.0, 495.0, 495.0, 0, 1, 1, -360, 49.216 ],
[397, 552, 0, 0.006289586776859504, 0.0166345314104904, 1200.0, 1200.0, 1200.0, 0, 1, 1, -360, 9.513 ],
[542, 398, 0, 0.0005580165289256199, 0.0059033089500572, 991.0, 991.0, 991.0, 0, 1, 1, -360, 1.6880000000000002 ],
[398, 385, 0, 0.021893553719008262, 0.05790348713648401, 495.0, 495.0, 495.0, 0, 1, 1, -360, 33.114000000000004 ],
[399, 499, 0, 0.03266380165289256, 0.021597087927192803, 248.0, 248.0, 248.0, 0, 1, 1, -360, 24.701999999999998 ],
[83, 399, 0, 0.025700495867768593, 0.016992996557050798, 248.0, 248.0, 248.0, 0, 1, 1, -360, 19.436 ],
[498, 400, 0, 0.012134214876033058, 0.032092247974028, 495.0, 495.0, 495.0, 0, 1, 1, -360, 18.352999999999998 ],
[518, 239, 0, 0.04685289256198347, 0.123915281026504, 495.0, 495.0, 495.0, 0, 1, 1, -360, 70.865 ],
[575, 543, 0, 0.0030307438016528923, 0.032062521596058796, 991.0, 991.0, 991.0, 0, 1, 1, -360, 9.168 ],
[401, 360, 0, 0.007957063711911357, 0.071409774520472, 856.0, 856.0, 856.0, 0, 1, 1, -360, 22.98 ],
[580, 581, 0, 0.007134545454545454, 0.018869255592422397, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.790999999999999 ],
[401, 402, 0, 0.0033434903047091418, 0.030005778188384805, 856.0, 856.0, 856.0, 0, 1, 1, -360, 9.656 ],
[403, 231, 0, 0.009592105263157893, 0.08608327126915, 856.0, 856.0, 856.0, 0, 1, 1, -360, 27.701999999999998 ],
[189, 360, 0, 0.028456024930747923, 0.255375399471348, 856.0, 856.0, 856.0, 0, 1, 1, -360, 82.181 ],
[234, 404, 0, 0.008092561983471074, 0.0214029921648796, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.24 ],
[235, 404, 0, 0.05107504132231405, 0.13508190749437998, 495.0, 495.0, 495.0, 0, 1, 1, -360, 77.251 ],
[235, 580, 0, 0.000580495867768595, 0.00153527999352772, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.878 ],
[216, 259, 0, 0.0022115650969529088, 0.079389770210892, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 12.774000000000001 ],
[405, 259, 0, 0.0052832409972299165, 0.1896554115982928, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 30.516 ],
[405, 318, 0, 0.0066348684210526315, 0.23817552558268398, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 38.323 ],
[406, 230, 0, 8.098164819944598e-05, 0.046512685161986804, 6845.0, 6845.0, 6845.0, 0, 1, 1, -360, 1.871 ],
[542, 407, 0, 0.025569586776859506, 0.067625761355152, 495.0, 495.0, 495.0, 0, 1, 1, -360, 38.674 ],
[23, 408, 0, 0.03224528925619835, 0.08528148128033601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 48.771 ],
[577, 348, 0, 0.012999008264462809, 0.13751772188026398, 991.0, 991.0, 991.0, 0, 2, 1, -360, 39.321999999999996 ],
[562, 564, 0, 0.06921520661157024, 0.18305853298686803, 495.0, 495.0, 495.0, 0, 1, 1, -360, 104.68799999999999 ],
[582, 507, 0, 0.006357685950413223, 0.016814638289042002, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.616 ],
[27, 410, 0, 0.0030042975206611565, 0.007945685980170399, 495.0, 495.0, 495.0, 0, 1, 1, -360, 4.544 ],
[501, 27, 0, 0.003811570247933884, 0.040322957460962, 991.0, 991.0, 991.0, 0, 1, 1, -360, 11.53 ],
[27, 411, 0, 0.004648595041322314, 0.012294480221518, 495.0, 495.0, 495.0, 0, 1, 1, -360, 7.031000000000001 ],
[411, 410, 0, 0.002054214876033058, 0.0054329327333556, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.1069999999999998 ],
[403, 360, 0, 0.008191481994459833, 0.07351353506655639, 856.0, 856.0, 856.0, 0, 1, 1, -360, 23.656999999999996 ],
[412, 360, 0, 0.016761772853185596, 0.15042664773666, 856.0, 856.0, 856.0, 0, 1, 1, -360, 48.408 ],
[326, 413, 0, 0.012077024793388432, 0.12776397267356798, 991.0, 991.0, 991.0, 0, 2, 1, -360, 36.533 ],
[414, 413, 0, 0.008093223140495867, 0.08561896310149601, 991.0, 991.0, 991.0, 0, 2, 1, -360, 24.482 ],
[6, 297, 0, 0.019472396694214876, 0.0128750188978664, 248.0, 248.0, 248.0, 0, 1, 1, -360, 14.725999999999999 ],
[554, 580, 0, 0.07435371900826447, 0.196648733567264, 495.0, 495.0, 495.0, 0, 1, 1, -360, 112.46 ],
[262, 401, 0, 0.03931232686980609, 0.35280406181043206, 856.0, 856.0, 856.0, 0, 1, 1, -360, 113.53399999999999 ],
[499, 556, 0, 0.04185586776859504, 0.11069928308639199, 495.0, 495.0, 495.0, 0, 2, 1, -360, 63.306999999999995 ],
[224, 229, 0, 0.004135206611570248, 0.0437467367631624, 991.0, 991.0, 991.0, 0, 1, 1, -360, 12.509 ],
[583, 507, 0, 0.024632727272727268, 0.065147980317596, 495.0, 495.0, 495.0, 0, 1, 1, -360, 37.257 ],
[415, 307, 0, 0.015675554016620498, 0.1406784987952448, 856.0, 856.0, 856.0, 0, 1, 1, -360, 45.271 ],
[416, 507, 0, 0.0010555371900826446, 0.011166626467730801, 991.0, 991.0, 991.0, 0, 1, 1, -360, 3.193 ],
[284, 561, 0, 0.015221487603305786, 0.16102953827307598, 991.0, 991.0, 991.0, 0, 1, 1, -360, 46.045 ],
[543, 417, 0, 0.0006614876033057851, 0.027991756419545603, 1981.0, 1981.0, 1981.0, 0, 4, 1, -360, 4.002 ],
[418, 506, 0, 0.0009395041322314049, 0.009939101917118, 991.0, 991.0, 991.0, 0, 1, 1, -360, 2.842 ],
[220, 157, 0, 0.004599549861495845, 0.165112574384632, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 26.566999999999997 ],
[295, 419, 0, 0.0012023140495867769, 0.012719392565946, 991.0, 991.0, 991.0, 0, 1, 1, -360, 3.637 ],
[295, 420, 0, 0.0008003305785123967, 0.008466771900532, 991.0, 991.0, 991.0, 0, 1, 1, -360, 2.421 ],
[541, 62, 0, 0.05133355371900827, 0.0339414035471236, 248.0, 248.0, 248.0, 0, 1, 1, -360, 38.821 ],
[52, 421, 0, 0.00013885041551246538, 0.004984389831631239, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.802 ],
[60, 160, 0, 6.128808864265928e-05, 0.000550023067454096, 856.0, 856.0, 856.0, 0, 2, 1, -360, 0.177 ],
[535, 161, 0, 3.735537190082645e-05, 0.00039518596644331203, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.113 ],
[267, 282, 0, 0.0065652700831024926, 0.235677115717012, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 37.921 ],
[52, 365, 0, 0.007655586334279779, 0.15458444922992, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 33.164 ],
[28, 27, 0, 0.015726942148760328, 0.041594197273402404, 495.0, 495.0, 495.0, 0, 1, 1, -360, 23.787 ],
[30, 201, 0, 0.009128289473684211, 0.327683234253536, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 52.725 ],
[422, 81, 0, 0.0004226685133887349, 0.13655487952674, 5134.0, 5134.0, 5134.0, 0, 6, 1, -360, 7.324 ],
[119, 425, 0, 0.003579120498614958, 0.1284816595874996, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.673000000000002 ],
[423, 425, 0, 0.0006518351800554017, 0.0233992864289392, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 3.765 ],
[424, 425, 0, 0.005922957063711911, 0.21261965153389198, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 34.211 ],
[426, 428, 0, 0.013948429752066116, 0.14756174042535197, 991.0, 991.0, 991.0, 0, 2, 1, -360, 42.193999999999996 ],
[427, 428, 0, 0.0002664462809917355, 0.0028187600792304794, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.8059999999999999 ],
[19, 428, 0, 0.023607603305785128, 0.24974703912892798, 991.0, 991.0, 991.0, 0, 2, 1, -360, 71.413 ],
[45, 429, 0, 0.02562314049586777, 0.067767398802972, 495.0, 495.0, 495.0, 0, 1, 1, -360, 38.755 ],
[44, 429, 0, 5.289256198347107e-05, 0.00013988883767892, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.08 ],
[505, 429, 0, 0.006012561983471073, 0.015901863623161996, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.094 ],
[231, 431, 0, 0.011677285318559558, 0.4191859418495199, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 67.44800000000001 ],
[190, 431, 0, 0.009600761772853185, 0.34464383257266795, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.45399999999999 ],
[430, 431, 0, 0.0028100761772853187, 0.1008748520662472, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.230999999999998 ],
[286, 433, 0, 0.01568694214876033, 0.16595362535967603, 991.0, 991.0, 991.0, 0, 1, 1, -360, 47.453 ],
[432, 433, 0, 0.00010049586776859504, 0.00106315516636076, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.304 ],
[506, 433, 0, 0.0065904132231404955, 0.06972059669946801, 991.0, 991.0, 991.0, 0, 1, 1, -360, 19.936 ],
[23, 434, 0, 0.02613685950413223, 0.069126069139116, 495.0, 495.0, 495.0, 0, 2, 1, -360, 39.532 ],
[400, 434, 0, 0.008155371900826446, 0.021569110159669603, 495.0, 495.0, 495.0, 0, 2, 1, -360, 12.335 ],
[500, 434, 0, 0.006338512396694216, 0.0167639285853336, 495.0, 495.0, 495.0, 0, 2, 1, -360, 9.587 ],
[32, 436, 0, 0.0044813019390581715, 0.16086776359270402, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 25.884 ],
[435, 436, 0, 0.0006634349030470914, 0.023815688073266, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 3.832 ],
[78, 436, 0, 0.00897680055401662, 0.32224515307884394, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.85 ],
[86, 438, 0, 0.014693213296398892, 0.52745036936438, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 84.868 ],
[437, 438, 0, 1.0387811634349031e-05, 0.0003728969948845, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.06 ],
[221, 438, 0, 0.002280124653739612, 0.081850890377238, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.17 ],
[207, 439, 0, 0.055703801652892564, 0.0368309823503996, 248.0, 248.0, 248.0, 0, 1, 1, -360, 42.126000000000005 ],
[516, 439, 0, 0.05448462809917355, 0.03602487292327441, 248.0, 248.0, 248.0, 0, 1, 1, -360, 41.20399999999999 ],
[513, 439, 0, 0.046726611570247926, 0.0308953241066316, 248.0, 248.0, 248.0, 0, 1, 1, -360, 35.336999999999996 ],
[181, 441, 0, 0.040805289256198356, 0.10792074104825197, 495.0, 495.0, 495.0, 0, 1, 1, -360, 61.718 ],
[440, 441, 0, 0.0001322314049586777, 0.000349722094197784, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.2 ],
[504, 441, 0, 0.05916099173553719, 0.156467413554364, 495.0, 495.0, 495.0, 0, 1, 1, -360, 89.48100000000001 ],
[135, 442, 0, 0.004956890581717451, 0.177940231009092, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 28.631 ],
[109, 442, 0, 0.0015380886426592797, 0.055213615042649204, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 8.884 ],
[112, 442, 0, 0.0027304362880886425, 0.09801597510545401, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 15.770999999999999 ],
[113, 443, 0, 0.0019885734072022164, 0.07138491472072879, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 11.485999999999999 ],
[132, 443, 0, 0.006788434903047091, 0.24368818615747198, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 39.21 ],
[107, 443, 0, 2.2333795013850418e-05, 0.000801728539002036, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.129 ],
[444, 445, 0, 7.877423822714682e-05, 0.00282780221121528, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.455 ],
[112, 445, 0, 0.002816135734072022, 0.101092375313206, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.266 ],
[109, 445, 0, 0.0014354224376731304, 0.0515281497432104, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 8.291 ],
[119, 447, 0, 0.005212690443213296, 0.74849127803204, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 60.217 ],
[100, 447, 0, 0.0050695117728531865, 0.7279322237145921, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 58.563 ],
[446, 447, 0, 2.9518698060941832e-05, 0.00423859584186224, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 0.341 ],
[124, 448, 0, 6.509695290858726e-05, 0.00233682116794768, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.376 ],
[125, 448, 0, 0.00615148891966759, 0.22082338542026803, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 35.531 ],
[131, 448, 0, 3.912742382271468e-05, 0.0014045786807313759, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.226 ],
[449, 450, 0, 0.0023614958448753462, 0.08477191683710039, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.64 ],
[173, 450, 0, 0.002862361495844876, 0.10275176694050518, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.533 ],
[184, 450, 0, 0.004022853185595568, 0.14441057621844403, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 23.236 ],
[144, 451, 0, 0.007672727272727273, 0.020292624515794402, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.605 ],
[140, 451, 0, 0.006991074380165291, 0.018489807120219602, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.574000000000002 ],
[514, 451, 0, 0.01149289256198347, 0.030396095817207994, 495.0, 495.0, 495.0, 0, 1, 1, -360, 17.383 ],
[537, 585, 0, 0.05072595041322314, 0.134158641165824, 495.0, 495.0, 495.0, 0, 1, 1, -360, 76.723 ],
[141, 585, 0, 0.007994710743801653, 0.0211441978151932, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.092 ],
[584, 585, 0, 9.256198347107438e-05, 0.000244805465938352, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.14 ],
[522, 454, 0, 0.0035008264462809916, 0.0092588924438956, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.295 ],
[144, 454, 0, 0.00452892561983471, 0.011977981726290799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.85 ],
[453, 454, 0, 0.001114710743801653, 0.0029481572540882, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.686 ],
[199, 456, 0, 0.013063140495867768, 0.0086372614214612, 248.0, 248.0, 248.0, 0, 1, 1, -360, 9.879 ],
[140, 456, 0, 0.005061818181818182, 0.013387361765852802, 495.0, 495.0, 495.0, 0, 2, 1, -360, 7.656000000000001 ],
[455, 456, 0, 0.0011365289256198346, 0.00300586139962416, 495.0, 495.0, 495.0, 0, 2, 1, -360, 1.719 ],
[537, 456, 0, 0.039058512396694216, 0.025825228046024003, 248.0, 248.0, 248.0, 0, 1, 1, -360, 29.538 ],
[538, 457, 0, 0.027927272727272728, 0.0184653265736368, 248.0, 248.0, 248.0, 0, 1, 1, -360, 21.12 ],
[153, 457, 0, 0.030093223140495867, 0.019897438549384, 248.0, 248.0, 248.0, 0, 1, 1, -360, 22.758000000000003 ],
[176, 457, 0, 0.004579173553719009, 0.0030277190305137603, 248.0, 248.0, 248.0, 0, 1, 1, -360, 3.463 ],
[524, 459, 0, 0.004318677685950414, 0.011421923596476799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.532 ],
[458, 459, 0, 0.001993388429752066, 0.0052720605700488, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.015 ],
[134, 459, 0, 0.011813553719008265, 0.031244171895617998, 495.0, 495.0, 495.0, 0, 1, 1, -360, 17.868 ],
[460, 461, 0, 6.611570247933885e-05, 0.000174861047098892, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.1 ],
[150, 461, 0, 0.008018512396694214, 0.021207147792120403, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.128 ],
[149, 461, 0, 0.005586115702479339, 0.0147740098693748, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.449 ],
[521, 463, 0, 0.014348429752066114, 0.009487086110365599, 248.0, 248.0, 248.0, 0, 1, 1, -360, 10.850999999999999 ],
[462, 463, 0, 0.007197355371900825, 0.0047588433967958406, 248.0, 248.0, 248.0, 0, 1, 1, -360, 5.443 ],
[538, 463, 0, 0.012211570247933883, 0.0080742088497664, 248.0, 248.0, 248.0, 0, 1, 1, -360, 9.235 ],
[110, 464, 0, 0.0025753116343490306, 0.0924473799817492, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.875 ],
[90, 464, 0, 0.007328947368421053, 0.26309125979076, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 42.332 ],
[165, 464, 0, 0.002152527700831025, 0.0772704722900764, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 12.433 ],
[458, 465, 0, 0.002003305785123967, 0.0052982897270776, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.03 ],
[134, 465, 0, 0.011838677685950413, 0.031310619093534, 495.0, 495.0, 495.0, 0, 1, 1, -360, 17.906 ],
[524, 465, 0, 0.004293553719008264, 0.0113554763986092, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.494 ],
[466, 467, 0, 0.0023509349030470914, 0.084392804892244, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.579 ],
[110, 467, 0, 0.0025337603878116343, 0.09095579200221118, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.635 ],
[165, 467, 0, 0.0022891274238227145, 0.08217406777274441, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.222000000000001 ],
[468, 469, 0, 0.0005269421487603305, 0.0013936425453786, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.797 ],
[541, 469, 0, 0.022390743801652895, 0.05921844221026801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 33.866 ],
[490, 469, 0, 0.028243305785123966, 0.07469714209944801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 42.718 ],
[263, 471, 0, 0.0371900826446281, 0.0245898347482832, 248.0, 248.0, 248.0, 0, 1, 1, -360, 28.125 ],
[470, 471, 0, 0.001570909090909091, 0.0010386746197682802, 248.0, 248.0, 248.0, 0, 1, 1, -360, 1.188 ],
[534, 471, 0, 0.024497190082644622, 0.0161973787927468, 248.0, 248.0, 248.0, 0, 1, 1, -360, 18.526 ],
[136, 472, 0, 0.0007079293628808865, 0.025412930201351602, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 4.0889999999999995 ],
[110, 472, 0, 0.00019511772853185596, 0.0070042485539216805, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.127 ],
[251, 472, 0, 4.207063711911357e-05, 0.00151023282928764, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.243 ],
[226, 474, 0, 0.017639669421487602, 0.011663231841509601, 248.0, 248.0, 248.0, 0, 1, 1, -360, 13.34 ],
[473, 474, 0, 0.003467107438016529, 0.00916971330986216, 495.0, 495.0, 495.0, 0, 2, 1, -360, 5.244 ],
[257, 474, 0, 0.020264462809917356, 0.053594910935781594, 495.0, 495.0, 495.0, 0, 2, 1, -360, 30.65 ],
[6, 474, 0, 0.08066247933884299, 0.05333349367016, 248.0, 248.0, 248.0, 0, 1, 1, -360, 61.001000000000005 ],
[299, 475, 0, 0.013238227146814403, 0.47521993028123993, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 76.464 ],
[3, 475, 0, 0.0002794321329639889, 0.010030929162389441, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.614 ],
[210, 475, 0, 0.0001481994459833795, 0.00531999712702368, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.856 ],
[297, 476, 0, 0.0193500826446281, 0.05117658265464801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.267 ],
[296, 476, 0, 0.005596694214876033, 0.014801987636898, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.465 ],
[295, 476, 0, 0.0009474380165289256, 0.00250575880492432, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.433 ],
[313, 478, 0, 0.008696849030470914, 0.31219557906752804, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.233000000000004 ],
[477, 478, 0, 1.5235457063711912e-05, 0.0005469155924977479, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.08800000000000001 ],
[245, 478, 0, 0.005264542936288089, 0.188984197007248, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.408 ],
[479, 481, 0, 0.028420495867768597, 0.07516576970575199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 42.986000000000004 ],
[565, 481, 0, 0.024842314049586776, 0.065702289836964, 495.0, 495.0, 495.0, 0, 1, 1, -360, 37.574 ],
[480, 481, 0, 7.735537190082645e-05, 0.000204587425105844, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.11699999999999999 ],
[415, 482, 0, 0.011021814404432133, 0.0989140353680364, 856.0, 856.0, 856.0, 0, 1, 1, -360, 31.831 ],
[56, 482, 0, 0.002630886426592798, 0.0236105947261788, 856.0, 856.0, 856.0, 0, 1, 1, -360, 7.598 ],
[409, 482, 0, 0.0007635041551246537, 0.0068519822810072005, 856.0, 856.0, 856.0, 0, 1, 1, -360, 2.205 ],
[483, 484, 0, 9.037396121883656e-05, 0.000811050963873968, 856.0, 856.0, 856.0, 0, 1, 1, -360, 0.261 ],
[3, 484, 0, 0.010022160664819944, 0.08994275516621358, 856.0, 856.0, 856.0, 0, 1, 1, -360, 28.944000000000003 ],
[301, 484, 0, 0.00966516620498615, 0.08673894848517479, 856.0, 856.0, 856.0, 0, 1, 1, -360, 27.913 ],
[233, 485, 0, 0.01410180055401662, 0.1265550251138996, 856.0, 856.0, 856.0, 0, 1, 1, -360, 40.726 ],
[392, 485, 0, 0.00914819944598338, 0.0820994883738036, 856.0, 856.0, 856.0, 0, 1, 1, -360, 26.42 ],
[391, 485, 0, 8.518005540166207e-05, 0.000764438839512864, 856.0, 856.0, 856.0, 0, 1, 1, -360, 0.24600000000000002 ],
[579, 488, 0, 0.004636473829194215, 0.11036180126571601, 1486.0, 1486.0, 1486.0, 0, 1, 1, -360, 21.038 ],
[486, 488, 0, 0.00016969696969690082, 0.00403929018798184, 1486.0, 1486.0, 1486.0, 0, 1, 1, -360, 0.77 ],
[487, 488, 0, 0.00014567493112954544, 0.00346749456396992, 1486.0, 1486.0, 1486.0, 0, 1, 1, -360, 0.6609999999999999 ],
[270, 489, 0, 0.0001745152354570637, 0.0062646695140596, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.008 ],
[331, 489, 0, 0.003002943213296399, 0.10779830627119119, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 17.345 ],
[396, 489, 0, 0.01124792243767313, 0.40377286606072005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 64.968 ],
[519, 253, 0, 0.013353485337561985, 0.141267767926912, 991.0, 991.0, 991.0, 0, 1, 1, -360, 40.394293146100004 ],
[382, 349, 0, 0.009091647380263157, 1.30547149138788, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 105.02671053600001 ],
[349, 351, 0, 0.0005858117819605263, 0.0841168325920224, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 6.76729770521 ],
[459, 465, 0, 1.578788789911157e-05, 0.00016702153987596, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.047758360894800005 ],
[549, 550, 0, 3.680432518409091e-05, 0.000389356391787088, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.111333083682 ],
[550, 551, 0, 5.755645674710744e-05, 0.0006088951287918401, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.17410828165999997 ],
[194, 195, 0, 1.7560672583171745e-05, 0.00252154053805592, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.202860889681 ],
[247, 248, 0, 2.1755213937811637e-05, 0.0031238355819477198, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.25131623141 ],
[2, 294, 0, 2.3531392658518004e-05, 0.003378877444715, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.271834647991 ],
[549, 551, 0, 9.265809538429751e-05, 0.0009802386406577602, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.28029073853799996 ],
[54, 365, 0, 2.573045189134349e-05, 0.00369464080598484, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.297238180249 ],
[131, 265, 0, 2.7616389041343487e-05, 0.00396544290388756, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.319024526206 ],
[91, 92, 0, 2.8945628197853184e-05, 0.0041563086239824396, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.33437989694200004 ],
[247, 249, 0, 3.098840072160664e-05, 0.00444963074500788, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.357978005136 ],
[186, 191, 0, 3.1591661821191135e-05, 0.00453625312865552, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.36494687735799997 ],
[129, 173, 0, 3.202671277479225e-05, 0.00459872218332188, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.369972585975 ],
[96, 202, 0, 3.5971247867797784e-05, 0.00516511877739804, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.415539855369 ],
[53, 320, 0, 3.784209581142659e-05, 0.00543375421308236, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.437151890814 ],
[24, 396, 0, 4.144748602818559e-05, 0.005951452925597279, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.47880135859800005 ],
[133, 156, 0, 4.431754564044322e-05, 0.0063635653674415605, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.511956287238 ],
[442, 452, 0, 4.483572190450138e-05, 0.006437970402313801, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.517942259441 ],
[445, 452, 0, 4.490753296371191e-05, 0.0064482817668697215, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.518771820797 ],
[247, 250, 0, 4.594910768732687e-05, 0.00659784169268824, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.530804092004 ],
[187, 195, 0, 4.755760376239612e-05, 0.006828805970367921, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.549385438663 ],
[216, 236, 0, 5.03353075283241e-05, 0.00722765701751724, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.581473472567 ],
[244, 389, 0, 5.1633313019736845e-05, 0.007414037889302401, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.596468032004 ],
[394, 406, 0, 5.6346419007686985e-05, 0.008090793734075721, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.650913832377 ],
[442, 445, 0, 6.388070648310249e-05, 0.00917264360085512, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.737949921293 ],
[442, 444, 0, 6.584378362735456e-05, 0.00945452224616264, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.760627388463 ],
[198, 472, 0, 8.37554210498615e-05, 0.0120264578966664, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.967542623967 ],
[464, 467, 0, 8.460287496468144e-05, 0.01214814397621276, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.977332411594 ],
[198, 251, 0, 8.83613182396122e-05, 0.012687819608389479, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 1.0207499483 ],
[112, 143, 0, 9.049653833033241e-05, 0.012994416294241841, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 1.04541601079 ],
[2, 490, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[5, 491, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[10, 492, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[12, 493, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[13, 494, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[15, 495, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[18, 496, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[20, 497, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[22, 498, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[24, 499, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[26, 500, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[30, 501, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[32, 502, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[37, 503, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[42, 504, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[46, 505, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[52, 506, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[56, 507, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[61, 508, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[68, 509, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[69, 510, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[74, 511, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[78, 512, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[86, 513, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[87, 514, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[94, 515, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[95, 516, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[96, 517, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[99, 518, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[100, 519, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[104, 520, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[105, 521, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[106, 522, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[107, 523, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[117, 524, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[120, 525, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[123, 526, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[124, 527, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[125, 528, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[128, 529, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[129, 530, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[138, 531, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[143, 532, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[156, 533, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[157, 534, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[159, 535, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[160, 536, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[165, 537, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[184, 538, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[191, 539, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[195, 540, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[201, 541, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[220, 542, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[231, 543, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[232, 544, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[233, 545, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[236, 546, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[245, 547, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[246, 548, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[248, 549, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[249, 550, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[250, 551, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[259, 552, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[261, 553, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[262, 554, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[265, 555, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[270, 556, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[277, 557, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[279, 558, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[280, 559, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[290, 560, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[301, 561, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[305, 562, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[306, 563, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[310, 564, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[313, 565, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[315, 566, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[320, 567, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[330, 568, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[332, 569, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[334, 570, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[336, 571, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[349, 572, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[351, 573, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[358, 574, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[360, 575, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[380, 576, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[382, 577, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[383, 578, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[389, 579, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[401, 580, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[402, 581, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[409, 582, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[415, 583, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[444, 584, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[452, 585, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ]
])
ppc["gen_control"] = array([
[586, 1, 0.08658028904199107, 4.329014452099554, 0, 0, 0],
[589, 1, 0.010042676909098597, 0.5021338454549299, 0, 0, 0],
[590, 1, 0.012095775674984046, 0.6047887837492023, 0, 0, 0],
[593, 1, 0.0017666198683200384, 0.08833099341600192, 0, 0, 0],
[594, 1, 0.006047887837492023, 0.30239439187460115, 0, 0, 0],
[595, 1, 1.50560576164933, 75.2802880824665, 0, 0, 0],
[598, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[599, 1, 0.0029602819415092537, 0.1480140970754627, 0, 0, 0],
[601, 1, 0.019576058000303126, 0.9788029000151565, 0, 0, 0],
[602, 1, 0.007830423200121252, 0.39152116000606263, 0, 0, 0],
[603, 1, 1.0997606567649967, 54.98803283824984, 0, 0, 0],
[607, 1, 0.5729577951308232, 28.64788975654116, 0, 0, 0],
[608, 1, 0.0076394372684109755, 0.3819718634205488, 0, 0, 0],
[609, 1, 0.0057932399285449895, 0.2896619964272495, 0, 0, 0],
[612, 1, 0.00954929658551372, 0.477464829275686, 0, 0, 0],
[613, 1, 0.027056340325622208, 1.3528170162811104, 0, 0, 0],
[614, 1, 0.00954929658551372, 0.477464829275686, 0, 0, 0],
[616, 1, 0.0046154933496649645, 0.23077466748324824, 0, 0, 0],
[617, 1, 0.04360845440717932, 2.1804227203589663, 0, 0, 0],
[618, 1, 0.010631550198538607, 0.5315775099269304, 0, 0, 0],
[619, 1, 0.037560566569687294, 1.8780283284843649, 0, 0, 0],
[621, 1, 0.24350706293059987, 12.175353146529993, 0, 0, 0],
[624, 1, 0.004297183463481174, 0.21485917317405873, 0, 0, 0],
[628, 1, 0.14292113889652203, 7.1460569448261015, 0, 0, 0],
[629, 1, 0.023968734429639437, 1.198436721481972, 0, 0, 0],
[631, 1, 0.025401128917466494, 1.2700564458733248, 0, 0, 0],
[632, 1, 0.01435577586688896, 0.717788793344448, 0, 0, 0],
[637, 1, 0.017093240888069558, 0.854662044403478, 0, 0, 0],
[638, 1, 0.02048324117592693, 1.0241620587963465, 0, 0, 0],
[640, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[641, 1, 0.0040107045659157625, 0.20053522829578813, 0, 0, 0],
[642, 1, 0.00919915571071155, 0.4599577855355775, 0, 0, 0],
[643, 1, 0.27279157245950864, 13.639578622975431, 0, 0, 0],
[647, 1, 0.00445633840657307, 0.2228169203286535, 0, 0, 0],
[650, 1, 0.4216014442504307, 21.080072212521536, 0, 0, 0],
[652, 1, 0.00746436683100989, 0.37321834155049455, 0, 0, 0],
[655, 1, 0.019576058000303126, 0.9788029000151565, 0, 0, 0],
[663, 1, 0.00238732414637843, 0.1193662073189215, 0, 0, 0],
[666, 1, 0.00919915571071155, 0.4599577855355775, 0, 0, 0],
[670, 1, 0.0076394372684109755, 0.3819718634205488, 0, 0, 0],
[672, 1, 0.010536057232683471, 0.5268028616341736, 0, 0, 0],
[676, 1, 0.11777465788800255, 5.888732894400127, 0, 0, 0],
[681, 1, 0.0063821132179850025, 0.31910566089925013, 0, 0, 0],
[683, 1, 0.008753521870054244, 0.4376760935027122, 0, 0, 0],
[687, 1, 0.42303383873825773, 21.151691936912886, 0, 0, 0],
[689, 1, 0.09867606471697511, 4.933803235848756, 0, 0, 0],
[691, 1, 0.008276057040778557, 0.4138028520389279, 0, 0, 0],
[694, 1, 0.005220282133414166, 0.2610141066707083, 0, 0, 0],
[695, 1, 0.004679155326901723, 0.23395776634508614, 0, 0, 0],
[696, 1, 0.22950142793851305, 11.475071396925653, 0, 0, 0],
[697, 1, 0.0036923946797319715, 0.1846197339865986, 0, 0, 0],
[698, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[702, 1, 0.023363945645890238, 1.168197282294512, 0, 0, 0],
[705, 1, 0.005411268065124442, 0.27056340325622213, 0, 0, 0],
[707, 1, 0.010822536130248884, 0.5411268065124443, 0, 0, 0],
[713, 1, 0.004265352474862795, 0.21326762374313976, 0, 0, 0],
[714, 1, 0.00477464829275686, 0.238732414637843, 0, 0, 0],
[716, 1, 1.5915494309189534e-05, 0.0007957747154594768, 0, 0, 0],
[717, 1, 0.0017507043740108488, 0.08753521870054244, 0, 0, 0],
[719, 1, 0.623250757147862, 31.162537857393104, 0, 0, 0],
[722, 1, 0.006589014644004467, 0.3294507322002233, 0, 0, 0],
[723, 1, 0.006270704757820675, 0.31353523789103377, 0, 0, 0],
[724, 1, 0.0019257748114119334, 0.09628874057059668, 0, 0, 0],
[727, 1, 0.019576058000303126, 0.9788029000151565, 0, 0, 0],
[728, 1, 0.16233804195373325, 8.116902097686662, 0, 0, 0],
[730, 1, 0.10077690996578814, 5.038845498289407, 0, 0, 0],
[732, 1, 0.004647324338283344, 0.2323662169141672, 0, 0, 0],
[735, 1, 0.013496339174192726, 0.6748169587096363, 0, 0, 0],
[738, 1, 0.04408591923645501, 2.2042959618227504, 0, 0, 0],
[741, 1, 0.0340591578216656, 1.7029578910832803, 0, 0, 0],
[742, 1, 0.0028647889756541157, 0.14323944878270578, 0, 0, 0],
[743, 1, 0.44881693951914486, 22.440846975957243, 0, 0, 0],
[746, 1, 0.03183098861837907, 1.5915494309189535, 0, 0, 0],
[747, 1, 0.0039788735772973835, 0.1989436788648692, 0, 0, 0],
[748, 1, 0.03501408748021698, 1.7507043740108488, 0, 0, 0],
[749, 1, 0.0025464790894703256, 0.12732395447351627, 0, 0, 0],
[750, 1, 0.028902537665488188, 1.4451268832744095, 0, 0, 0],
[753, 1, 0.049624511256052974, 2.4812255628026487, 0, 0, 0],
[758, 1, 0.0058887328944001276, 0.2944366447200064, 0, 0, 0],
[760, 1, 0.2527380496299298, 12.636902481496492, 0, 0, 0],
[761, 1, 0.004997465213085514, 0.2498732606542757, 0, 0, 0],
[762, 1, 0.3517324242330887, 17.586621211654435, 0, 0, 0],
[763, 1, 0.006461690689530951, 0.32308453447654756, 0, 0, 0],
[765, 1, 0.018780283284843647, 0.9390141642421824, 0, 0, 0],
[767, 1, 0.0035650707252584553, 0.17825353626292276, 0, 0, 0],
[769, 1, 0.013782818071758136, 0.6891409035879068, 0, 0, 0],
[771, 1, 0.21963382146681557, 10.981691073340778, 0, 0, 0],
[772, 1, 0.002992112930127632, 0.1496056465063816, 0, 0, 0],
[774, 1, 0.010663381187156987, 0.5331690593578494, 0, 0, 0],
[777, 1, 0.012573240504259732, 0.6286620252129866, 0, 0, 0],
[778, 1, 0.004679155326901723, 0.23395776634508614, 0, 0, 0],
[781, 1, 0.4169859509007658, 20.84929754503829, 0, 0, 0],
[784, 1, 0.4058451048843331, 20.292255244216655, 0, 0, 0],
[785, 1, 0.00047746482927568597, 0.0238732414637843, 0, 0, 0],
[787, 1, 0.24764509145098912, 12.382254572549456, 0, 0, 0],
[788, 1, 0.2785211504108168, 13.926057520540843, 0, 0, 0],
[789, 1, 0.0123185925953127, 0.615929629765635, 0, 0, 0],
[791, 1, 0.0031830988618379067, 0.15915494309189535, 0, 0, 0],
[792, 1, 0.009979014931861837, 0.49895074659309185, 0, 0, 0],
[795, 1, 0.004329014452099553, 0.2164507226049777, 0, 0, 0],
[800, 1, 0.0058091554228541795, 0.290457771142709, 0, 0, 0],
[801, 1, 0.007957747154594767, 0.3978873577297384, 0, 0, 0],
[802, 1, 0.07957747154594767, 3.9788735772973833, 0, 0, 0],
[805, 1, 0.44881693951914486, 22.440846975957243, 0, 0, 0],
[806, 1, 0.005697746962689853, 0.2848873481344927, 0, 0, 0],
[808, 1, 0.034616200122487235, 1.7308100061243619, 0, 0, 0],
[809, 1, 0.0039788735772973835, 0.1989436788648692, 0, 0, 0],
[811, 1, 0.0040107045659157625, 0.20053522829578813, 0, 0, 0],
[814, 1, 0.014164789935178685, 0.7082394967589343, 0, 0, 0],
[816, 1, 0.012748310941660816, 0.6374155470830408, 0, 0, 0],
[817, 1, 0.017188733853924696, 0.8594366926962349, 0, 0, 0],
[821, 1, 0.013130282805081364, 0.6565141402540683, 0, 0, 0],
[822, 1, 0.04265352474862795, 2.1326762374313977, 0, 0, 0],
[826, 1, 0.018461973398659858, 0.9230986699329929, 0, 0, 0],
[830, 1, 0.02832957987035737, 1.4164789935178685, 0, 0, 0],
[834, 1, 0.007416620348082323, 0.37083101740411617, 0, 0, 0],
[835, 1, 0.010138169874953733, 0.5069084937476867, 0, 0, 0],
[836, 1, 0.008116902097686661, 0.4058451048843331, 0, 0, 0],
[837, 1, 0.15024226627874918, 7.512113313937459, 0, 0, 0],
[839, 1, 0.011666057328635928, 0.5833028664317964, 0, 0, 0],
[841, 1, 0.0037083101740411615, 0.18541550870205808, 0, 0, 0],
[843, 1, 0.10599719209920229, 5.2998596049601145, 0, 0, 0],
[844, 1, 0.012732395447351627, 0.6366197723675814, 0, 0, 0],
[845, 1, 0.10122254380644544, 5.061127190322272, 0, 0, 0],
[849, 1, 0.24796340133717296, 12.398170066858649, 0, 0, 0],
[850, 1, 0.005092958178940651, 0.25464790894703254, 0, 0, 0],
[851, 1, 0.01265281797580568, 0.632640898790284, 0, 0, 0],
[853, 1, 0.0036923946797319715, 0.1846197339865986, 0, 0, 0],
[855, 1, 0.21899720169444797, 10.949860084722399, 0, 0, 0],
[856, 1, 0.011459155902616463, 0.5729577951308231, 0, 0, 0],
[857, 1, 0.4462704604296745, 22.313523021483725, 0, 0, 0],
[858, 1, 0.01808000153523931, 0.9040000767619655, 0, 0, 0],
[859, 1, 0.027056340325622208, 1.3528170162811104, 0, 0, 0],
[860, 1, 0.0039788735772973835, 0.1989436788648692, 0, 0, 0],
[864, 1, 0.2785211504108168, 13.926057520540843, 0, 0, 0],
[865, 1, 0.0035014087480216977, 0.17507043740108488, 0, 0, 0],
[867, 1, 0.24478030247533505, 12.239015123766753, 0, 0, 0],
[869, 1, 0.4329014452099553, 21.645072260497766, 0, 0, 0],
[870, 1, 0.018589297353133374, 0.9294648676566688, 0, 0, 0],
[872, 1, 0.00716197243913529, 0.3580986219567645, 0, 0, 0],
[873, 1, 0.038833806114422456, 1.941690305721123, 0, 0, 0],
[874, 1, 0.006589014644004467, 0.3294507322002233, 0, 0, 0],
[875, 1, 0.007766761222884492, 0.38833806114422464, 0, 0, 0],
[877, 1, 0.007894085177358009, 0.39470425886790045, 0, 0, 0],
[881, 1, 0.3187236890358296, 15.93618445179148, 0, 0, 0],
[882, 1, 0.005538592019597957, 0.2769296009798979, 0, 0, 0],
[883, 1, 0.005729577951308231, 0.28647889756541156, 0, 0, 0],
[885, 1, 0.15597184423005742, 7.798592211502871, 0, 0, 0],
[886, 1, 0.8186930272647096, 40.93465136323548, 0, 0, 0],
[889, 1, 0.0030239439187460114, 0.15119719593730058, 0, 0, 0],
[890, 1, 0.0076394372684109755, 0.3819718634205488, 0, 0, 0],
[893, 1, 0.00954929658551372, 0.477464829275686, 0, 0, 0],
[894, 1, 0.025146481008519465, 1.2573240504259733, 0, 0, 0],
[895, 1, 0.0030239439187460114, 0.15119719593730058, 0, 0, 0],
[896, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[898, 1, 0.013464508185574344, 0.6732254092787172, 0, 0, 0],
[900, 1, 0.03584169318429482, 1.7920846592147412, 0, 0, 0],
[902, 1, 0.006207042780583919, 0.31035213902919595, 0, 0, 0],
[903, 1, 0.0031990143561470966, 0.15995071780735484, 0, 0, 0],
[905, 1, 0.021851973686517232, 1.0925986843258617, 0, 0, 0],
[906, 1, 0.010504226244065093, 0.5252113122032547, 0, 0, 0],
[907, 1, 0.02142225534016911, 1.0711127670084555, 0, 0, 0],
[909, 1, 0.005856901905781748, 0.2928450952890874, 0, 0, 0],
[915, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[917, 1, 0.005411268065124442, 0.27056340325622213, 0, 0, 0],
[918, 1, 0.012254930618075942, 0.612746530903797, 0, 0, 0],
[920, 1, 0.0020371832715762603, 0.10185916357881303, 0, 0, 0],
[921, 1, 0.019735212943395024, 0.9867606471697512, 0, 0, 0],
[922, 1, 0.05220282133414166, 2.6101410667070835, 0, 0, 0],
[923, 1, 0.023236621691416718, 1.161831084570836, 0, 0, 0],
[925, 1, 0.008276057040778557, 0.4138028520389279, 0, 0, 0],
[931, 1, 0.03455253814525047, 1.7276269072625237, 0, 0, 0],
[935, 1, 0.007352958370845565, 0.36764791854227824, 0, 0, 0],
[936, 1, 0.016615776058793875, 0.8307888029396938, 0, 0, 0],
[937, 1, 0.00477464829275686, 0.238732414637843, 0, 0, 0],
[939, 1, 1.5915494309189534e-05, 0.0007957747154594768, 0, 0, 0],
[940, 1, 0.009421972631040205, 0.47109863155201026, 0, 0, 0],
[944, 1, 0.004042535554534142, 0.2021267777267071, 0, 0, 0],
[950, 1, 0.005092958178940651, 0.25464790894703254, 0, 0, 0],
[952, 1, 0.005045211696013082, 0.2522605848006541, 0, 0, 0],
[957, 1, 0.0019098593171027439, 0.0954929658551372, 0, 0, 0],
[958, 1, 0.010615634704229418, 0.530781735211471, 0, 0, 0],
[959, 1, 0.007241549910681238, 0.3620774955340619, 0, 0, 0],
[960, 1, 0.004217605991935227, 0.21088029959676136, 0, 0, 0],
[963, 1, 0.2785211504108168, 13.926057520540843, 0, 0, 0],
[965, 1, 0.11204507993669433, 5.602253996834716, 0, 0, 0],
[966, 1, 0.021008452488130186, 1.0504226244065094, 0, 0, 0],
[967, 1, 0.01193662073189215, 0.5968310365946076, 0, 0, 0],
[968, 1, 0.017188733853924696, 0.8594366926962349, 0, 0, 0],
[969, 1, 0.018111832523857688, 0.9055916261928845, 0, 0, 0],
[971, 1, 0.0031830988618379067, 0.15915494309189535, 0, 0, 0],
[973, 1, 0.4287634166895661, 21.438170834478306, 0, 0, 0],
[976, 1, 0.008562535938343968, 0.4281267969171984, 0, 0, 0],
[978, 1, 0.0007321127382227185, 0.03660563691113593, 0, 0, 0],
[981, 1, 0.03787887645587108, 1.8939438227935543, 0, 0, 0],
[982, 1, 0.0015756339366097638, 0.07878169683048819, 0, 0, 0],
[983, 1, 0.01400563499208679, 0.7002817496043395, 0, 0, 0],
[984, 1, 0.14801409707546268, 7.400704853773133, 0, 0, 0],
[985, 1, 0.0035014087480216977, 0.17507043740108488, 0, 0, 0],
[986, 1, 0.0017825353626292277, 0.08912676813146138, 0, 0, 0],
[987, 1, 0.02618098813861678, 1.3090494069308392, 0, 0, 0],
[988, 1, 0.0008116902097686662, 0.04058451048843331, 0, 0, 0],
[993, 1, 0.06238873769202297, 3.119436884601149, 0, 0, 0],
[994, 1, 0.010504226244065093, 0.5252113122032547, 0, 0, 0],
[995, 1, 0.0006684507609859605, 0.033422538049298026, 0, 0, 0],
[997, 1, 0.005984225860255264, 0.2992112930127632, 0, 0, 0],
[999, 1, 0.004965634224467135, 0.24828171122335674, 0, 0, 0],
[1000, 1, 0.015597184423005743, 0.7798592211502873, 0, 0, 0],
[1002, 1, 0.0031512678732195276, 0.15756339366097638, 0, 0, 0],
[1003, 1, 0.2864788975654116, 14.32394487827058, 0, 0, 0],
[1007, 1, 0.007416620348082323, 0.37083101740411617, 0, 0, 0],
[1008, 1, 0.015597184423005743, 0.7798592211502873, 0, 0, 0],
[1010, 1, 0.238732414637843, 11.93662073189215, 0, 0, 0],
[1011, 1, 0.005952394871636886, 0.2976197435818443, 0, 0, 0],
[1012, 1, 0.9024085273310466, 45.12042636655233, 0, 0, 0],
[1014, 1, 0.238732414637843, 11.93662073189215, 0, 0, 0],
[1026, 1, 0.20868396138209316, 10.434198069104658, 0, 0, 0],
[1027, 3, 0.002298550022578703, 0.11492750112893517, 2.22, 61.69, 0.004502],
[1028, 2, 0.025464790894703257, 1.273239544735163, 0, 0, 0],
[1029, 2, 0.0015996029245410612, 0.07998014622705306, 0, 0, 0],
[1030, 2, 0.06480789282701978, 3.2403946413509894, 0, 0, 0],
[1031, 2, 0.06463074564767912, 3.2315372823839565, 0, 0, 0],
[1032, 2, 0.009772775025341927, 0.4886387512670964, 0, 0, 0],
[1033, 2, 0.0031935716694765437, 0.15967858347382718, 0, 0, 0],
[1034, 2, 0.005364335122251813, 0.26821675611259066, 0, 0, 0],
[1035, 3, 0.00317587127473044, 0.158793563736522, 2.22, 61.69, 0.004502],
[1036, 2, 0.0042795539826391196, 0.21397769913195597, 0, 0, 0],
[1037, 2, 0.004583737816416693, 0.22918689082083465, 0, 0, 0],
[1038, 2, 0.004358800228219271, 0.21794001141096359, 0, 0, 0],
[1039, 2, 0.008449479506347874, 0.42247397531739384, 0, 0, 0],
[1040, 3, 2.5955064969193202e-06, 0.00012977532484596601, 2.22, 61.69, 0.004502],
[1041, 2, 0.012998987840239671, 0.6499493920119837, 0, 0, 0],
[1042, 2, 0.00335501991632689, 0.1677509958163445, 0, 0, 0],
[1043, 3, 0.0003026685105316776, 0.015133425526583881, 2.22, 61.69, 0.004502],
[1044, 3, 0.0011243820116265814, 0.05621910058132907, 2.22, 61.69, 0.004502],
[1045, 2, 0.0019373243262327522, 0.09686621631163762, 0, 0, 0],
[1046, 2, 0.0031015144255394987, 0.15507572127697494, 0, 0, 0],
[1047, 3, 0.00034416981541931054, 0.017208490770965527, 2.22, 61.69, 0.004502],
[1048, 2, 0.0020485945786587064, 0.10242972893293534, 0, 0, 0],
[1049, 2, 0.01870104799381521, 0.9350523996907605, 0, 0, 0],
[1050, 2, 0.0033601814151550304, 0.1680090707577515, 0, 0, 0],
[1051, 2, 0.019380601737792977, 0.969030086889649, 0, 0, 0],
[1052, 3, 0.0005247651571922151, 0.026238257859610755, 2.22, 61.69, 0.004502],
[1053, 3, 0.00041550140953476974, 0.02077507047673849, 2.22, 61.69, 0.004502],
[1054, 2, 0.0069428381079974354, 0.3471419053998717, 0, 0, 0],
[1055, 3, 0.0001818229987415119, 0.009091149937075596, 2.22, 61.69, 0.004502],
[1056, 2, 0.0384482661909012, 1.9224133095450602, 0, 0, 0],
[1057, 2, 0.02718238967557453, 1.3591194837787268, 0, 0, 0],
[1058, 2, 0.06721018861714274, 3.3605094308571375, 0, 0, 0],
[1059, 2, 0.02641152929543176, 1.320576464771588, 0, 0, 0],
[1060, 3, 0.0006590053340983933, 0.03295026670491967, 2.22, 61.69, 0.004502],
[1061, 2, 0.010304492946979937, 0.5152246473489969, 0, 0, 0],
[1062, 3, 0.00018325491392786168, 0.009162745696393085, 2.22, 61.69, 0.004502],
[1063, 3, 0.0005520076745724519, 0.0276003837286226, 2.22, 61.69, 0.004502],
[1064, 2, 0.013355424896304362, 0.667771244815218, 0, 0, 0],
[1065, 2, 0.021608252882636087, 1.0804126441318045, 0, 0, 0],
[1066, 2, 0.008556107291276397, 0.4278053645638199, 0, 0, 0],
[1067, 3, 0.002078788013715776, 0.1039394006857888, 2.22, 61.69, 0.004502],
[1068, 3, 0.0003188842576981683, 0.015944212884908417, 2.22, 61.69, 0.004502],
[1069, 3, 0.00020313001706596343, 0.010156500853298172, 2.22, 61.69, 0.004502],
[1070, 3, 5.020379247175116e-05, 0.0025101896235875582, 2.22, 61.69, 0.004502],
[1071, 3, 0.0002755733400308117, 0.013778667001540588, 2.22, 61.69, 0.004502],
[1072, 2, 0.0034911570519954678, 0.1745578525997734, 0, 0, 0],
[1073, 2, 0.001974161472118056, 0.09870807360590281, 0, 0, 0],
[1074, 2, 0.0046620003597127105, 0.23310001798563554, 0, 0, 0],
[1075, 3, 0.0010048055180333312, 0.05024027590166657, 2.22, 61.69, 0.004502],
[1076, 3, 0.00010624248611578546, 0.005312124305789274, 2.22, 61.69, 0.004502],
[1077, 3, 0.0016628534246063698, 0.08314267123031849, 2.22, 61.69, 0.004502],
[1078, 3, 0.0021908153060440304, 0.10954076530220153, 2.22, 61.69, 0.004502],
[1079, 2, 0.002190700708933187, 0.10953503544665937, 0, 0, 0],
[1080, 2, 0.008412929217414397, 0.4206464608707199, 0, 0, 0],
[1081, 2, 0.025823979083824652, 1.2911989541912325, 0, 0, 0],
[1082, 2, 0.03247105626963941, 1.623552813481971, 0, 0, 0],
[1083, 2, 0.04034141649573272, 2.017070824786636, 0, 0, 0],
[1084, 2, 0.0383703068502718, 1.9185153425135901, 0, 0, 0],
[1085, 2, 0.007239283505967098, 0.3619641752983549, 0, 0, 0],
[1086, 2, 0.01436208920263519, 0.7181044601317595, 0, 0, 0],
[1087, 2, 0.007427186304799236, 0.3713593152399618, 0, 0, 0],
[1088, 3, 0.0023416461987310717, 0.11708230993655358, 2.22, 61.69, 0.004502],
[1089, 2, 0.024474821190373128, 1.2237410595186564, 0, 0, 0],
[1090, 2, 0.0022624979772680404, 0.11312489886340203, 0, 0, 0],
[1091, 3, 0.0013601543234855855, 0.06800771617427928, 2.22, 61.69, 0.004502],
[1092, 2, 0.0014626466159500494, 0.07313233079750248, 0, 0, 0],
[1093, 2, 0.009906140914748767, 0.49530704573743833, 0, 0, 0],
[1094, 3, 0.00023930778294026586, 0.011965389147013294, 2.22, 61.69, 0.004502],
[1095, 3, 1.3047613994501091e-05, 0.0006523806997250545, 2.22, 61.69, 0.004502],
[1096, 2, 0.005379826679377905, 0.2689913339688953, 0, 0, 0],
[1097, 3, 0.0002929164939619051, 0.014645824698095257, 2.22, 61.69, 0.004502],
[1098, 2, 0.0021303060183860277, 0.10651530091930138, 0, 0, 0],
[1099, 2, 0.0073754261124176915, 0.3687713056208846, 0, 0, 0],
[1100, 3, 1.3306005265883919e-06, 6.653002632941959e-05, 2.22, 61.69, 0.004502],
[1101, 2, 0.005343192104787693, 0.2671596052393847, 0, 0, 0],
[1102, 2, 0.02234407998394998, 1.1172039991974991, 0, 0, 0],
[1103, 2, 0.01562148424141561, 0.7810742120707805, 0, 0, 0],
[1104, 3, 1.3172819714966009e-05, 0.0006586409857483004, 2.22, 61.69, 0.004502],
[1105, 3, 0.0001386935566767763, 0.006934677833838815, 2.22, 61.69, 0.004502],
[1106, 3, 0.00014577275883068604, 0.0072886379415343025, 2.22, 61.69, 0.004502],
[1107, 2, 0.004852418696402547, 0.24262093482012728, 0, 0, 0],
[1108, 2, 0.02039874588539438, 1.019937294269719, 0, 0, 0],
[1109, 3, 4.9542410867097304e-05, 0.002477120543354865, 2.22, 61.69, 0.004502],
[1110, 3, 0.00010533237807450261, 0.00526661890372513, 2.22, 61.69, 0.004502],
[1111, 2, 0.005706531882583417, 0.2853265941291709, 0, 0, 0],
[1112, 2, 0.004426690383932842, 0.2213345191966421, 0, 0, 0],
[1113, 3, 0.00022513170529279912, 0.011256585264639957, 2.22, 61.69, 0.004502],
[1114, 3, 0.0008560555102861403, 0.042802775514307015, 2.22, 61.69, 0.004502],
[1115, 2, 0.0032197222090973076, 0.16098611045486538, 0, 0, 0],
[1116, 3, 0.002075453185310181, 0.10377265926550905, 2.22, 61.69, 0.004502],
[1117, 2, 0.005780032679669937, 0.2890016339834969, 0, 0, 0],
[1118, 3, 0.0005554515385863421, 0.027772576929317106, 2.22, 61.69, 0.004502],
[1119, 3, 0.0027536366373517632, 0.13768183186758817, 2.22, 61.69, 0.004502],
[1120, 3, 0.0001538074296570127, 0.007690371482850636, 2.22, 61.69, 0.004502],
[1121, 3, 3.4414977793908876e-05, 0.0017207488896954439, 2.22, 61.69, 0.004502],
[1122, 3, 9.313004041299959e-05, 0.00465650202064998, 2.22, 61.69, 0.004502],
[1123, 3, 9.32225252447514e-05, 0.00466112626223757, 2.22, 61.69, 0.004502],
[1124, 3, 8.201464578534214e-05, 0.004100732289267108, 2.22, 61.69, 0.004502],
[1125, 3, 0.0016436821796102436, 0.08218410898051219, 2.22, 61.69, 0.004502],
[1126, 3, 0.0018560581327172175, 0.09280290663586088, 2.22, 61.69, 0.004502],
[1127, 2, 0.006703391093283916, 0.3351695546641958, 0, 0, 0],
[1128, 3, 0.0001948941120002845, 0.009744705600014225, 2.22, 61.69, 0.004502],
[1129, 3, 0.0003016780123772693, 0.015083900618863466, 2.22, 61.69, 0.004502],
[1130, 3, 6.530151955301432e-05, 0.003265075977650716, 2.22, 61.69, 0.004502],
[1131, 3, 0.00018443373362804407, 0.009221686681402204, 2.22, 61.69, 0.004502],
[1132, 3, 2.2886271300209156e-05, 0.0011443135650104578, 2.22, 61.69, 0.004502],
[1133, 3, 4.5810964480308454e-05, 0.002290548224015423, 2.22, 61.69, 0.004502],
[1134, 3, 3.236913111220881e-05, 0.0016184565556104404, 2.22, 61.69, 0.004502],
[1135, 3, 0.0005167964323996007, 0.025839821619980042, 2.22, 61.69, 0.004502],
[1136, 3, 2.5636662405410735e-05, 0.0012818331202705368, 2.22, 61.69, 0.004502],
[1137, 3, 0.00023357652984116472, 0.011678826492058236, 2.22, 61.69, 0.004502],
[1138, 3, 7.98498118498449e-05, 0.003992490592492246, 2.22, 61.69, 0.004502],
[1139, 3, 0.0012619566606414858, 0.0630978330320743, 2.22, 61.69, 0.004502],
[1140, 3, 0.0018073289497007397, 0.09036644748503699, 2.22, 61.69, 0.004502],
[1141, 2, 0.0076053500901520025, 0.38026750450760016, 0, 0, 0],
[1142, 3, 7.73959943559724e-05, 0.00386979971779862, 2.22, 61.69, 0.004502],
[1143, 3, 0.0016067873237582107, 0.08033936618791054, 2.22, 61.69, 0.004502],
[1144, 2, 0.00334399697192306, 0.16719984859615303, 0, 0, 0],
[1145, 2, 0.004458888300690503, 0.2229444150345252, 0, 0, 0],
[1146, 3, 5.4833151376821656e-05, 0.002741657568841083, 2.22, 61.69, 0.004502],
[1147, 3, 0.002909588342312674, 0.14547941711563372, 2.22, 61.69, 0.004502],
[1148, 3, 0.0011233492673683868, 0.05616746336841934, 2.22, 61.69, 0.004502],
[1149, 3, 0.0005447417794635118, 0.02723708897317559, 2.22, 61.69, 0.004502],
[1150, 3, 0.0002306193019977063, 0.011530965099885314, 2.22, 61.69, 0.004502],
[1151, 3, 0.0008299047575760064, 0.04149523787880033, 2.22, 61.69, 0.004502],
[1152, 3, 7.417749437366368e-06, 0.0003708874718683184, 2.22, 61.69, 0.004502],
[1153, 3, 4.37920348658174e-06, 0.000218960174329087, 2.22, 61.69, 0.004502],
[1154, 3, 1.0225677287248534e-05, 0.0005112838643624266, 2.22, 61.69, 0.004502],
[1155, 3, 3.879887736397654e-05, 0.001939943868198827, 2.22, 61.69, 0.004502],
[1156, 3, 0.0010200134924871187, 0.05100067462435595, 2.22, 61.69, 0.004502],
[1157, 3, 0.00027719360593007886, 0.013859680296503944, 2.22, 61.69, 0.004502],
[1158, 3, 6.640198284893194e-05, 0.003320099142446597, 2.22, 61.69, 0.004502],
[1159, 3, 0.0008593149079194712, 0.04296574539597356, 2.22, 61.69, 0.004502],
[1160, 2, 0.015175599618213626, 0.7587799809106813, 0, 0, 0],
[1161, 3, 0.001608317428775011, 0.08041587143875056, 2.22, 61.69, 0.004502],
[1162, 2, 0.031984361657767045, 1.5992180828883522, 0, 0, 0],
[1163, 2, 0.021010485834812704, 1.0505242917406352, 0, 0, 0],
[1164, 2, 0.018183478445661972, 0.9091739222830987, 0, 0, 0],
[1165, 2, 0.003640738012495192, 0.18203690062475963, 0, 0, 0],
[1166, 2, 0.0037355845995397383, 0.18677922997698693, 0, 0, 0],
[1167, 3, 0.00032173361521807824, 0.016086680760903912, 2.22, 61.69, 0.004502],
[1168, 3, 8.56746647323757e-05, 0.004283733236618785, 2.22, 61.69, 0.004502],
[1169, 3, 0.00017327803824915608, 0.008663901912457804, 2.22, 61.69, 0.004502],
[1170, 3, 1.6933420442211857e-05, 0.000846671022110593, 2.22, 61.69, 0.004502],
[1171, 3, 0.0005748603194505088, 0.02874301597252544, 2.22, 61.69, 0.004502],
[1172, 3, 0.0002281672447033917, 0.011408362235169585, 2.22, 61.69, 0.004502],
[1173, 2, 0.01618626952698487, 0.8093134763492436, 0, 0, 0],
[1174, 3, 8.021928882473966e-05, 0.004010964441236983, 2.22, 61.69, 0.004502],
[1175, 3, 5.445989361520192e-05, 0.002722994680760096, 2.22, 61.69, 0.004502],
[1176, 3, 1.4783581244732665e-05, 0.0007391790622366333, 2.22, 61.69, 0.004502],
[1177, 3, 0.0017745146198091144, 0.08872573099045572, 2.22, 61.69, 0.004502],
[1178, 3, 0.00020168108435446162, 0.010084054217723081, 2.22, 61.69, 0.004502],
[1179, 3, 8.316119408334767e-05, 0.004158059704167384, 2.22, 61.69, 0.004502],
[1180, 3, 4.3834108298364086e-05, 0.002191705414918204, 2.22, 61.69, 0.004502],
[1181, 2, 0.005289917788662048, 0.2644958894331024, 0, 0, 0],
[1182, 2, 0.006322880792722177, 0.3161440396361089, 0, 0, 0],
[1183, 3, 0.0024333246840658566, 0.12166623420329284, 2.22, 61.69, 0.004502],
[1184, 3, 0.00026859021396164037, 0.013429510698082018, 2.22, 61.69, 0.004502],
[1185, 3, 0.0007221796423758263, 0.036108982118791315, 2.22, 61.69, 0.004502],
[1186, 3, 0.0024774929167619207, 0.12387464583809603, 2.22, 61.69, 0.004502],
[1187, 3, 0.0006248151564821885, 0.031240757824109424, 2.22, 61.69, 0.004502],
[1188, 2, 0.011369992521217407, 0.5684996260608703, 0, 0, 0],
[1189, 3, 0.001289906586581014, 0.06449532932905071, 2.22, 61.69, 0.004502],
[1190, 2, 0.01403960969000889, 0.7019804845004446, 0, 0, 0],
[1191, 2, 0.004652379906159672, 0.23261899530798363, 0, 0, 0],
[1192, 3, 0.0013658402687938922, 0.06829201343969461, 2.22, 61.69, 0.004502],
[1193, 3, 0.00015278576957249078, 0.007639288478624539, 2.22, 61.69, 0.004502],
[1194, 3, 0.0005720688022791215, 0.028603440113956075, 2.22, 61.69, 0.004502],
[1195, 3, 1.2882573563174789e-05, 0.0006441286781587394, 2.22, 61.69, 0.004502],
[1196, 2, 0.009842783066129698, 0.4921391533064849, 0, 0, 0],
[1197, 2, 0.00575541689021183, 0.2877708445105915, 0, 0, 0],
[1198, 3, 0.002534966273924786, 0.12674831369623932, 2.22, 61.69, 0.004502],
[1201, 3, 0.0016021597716395785, 0.08010798858197893, 2.22, 61.69, 0.004502],
[1202, 3, 0.0031762475555186724, 0.15881237777593363, 2.22, 61.69, 0.004502],
[1203, 2, 0.011626157559117188, 0.5813078779558594, 0, 0, 0],
[1204, 3, 0.0030266063343556363, 0.15133031671778183, 2.22, 61.69, 0.004502],
[1205, 3, 3.4940417699210975e-05, 0.0017470208849605492, 2.22, 61.69, 0.004502],
[1206, 3, 0.00024235441128435216, 0.012117720564217609, 2.22, 61.69, 0.004502],
[1207, 3, 0.00022762038155293296, 0.011381019077646649, 2.22, 61.69, 0.004502],
[1208, 3, 0.0001427321512302434, 0.007136607561512171, 2.22, 61.69, 0.004502],
[1209, 3, 4.75873361221428e-05, 0.00237936680610714, 2.22, 61.69, 0.004502],
[1210, 3, 0.0005454262850371943, 0.027271314251859715, 2.22, 61.69, 0.004502],
[1211, 3, 0.0011462484513341364, 0.057312422566706815, 2.22, 61.69, 0.004502],
[1212, 2, 0.005804182676892941, 0.290209133844647, 0, 0, 0],
[1213, 2, 0.0036505499187602444, 0.18252749593801224, 0, 0, 0],
[1214, 3, 0.0002868549194435664, 0.014342745972178321, 2.22, 61.69, 0.004502],
[1215, 3, 0.00014342822681200328, 0.0071714113406001635, 2.22, 61.69, 0.004502],
[1216, 2, 0.00431338348440427, 0.21566917422021353, 0, 0, 0],
[1217, 3, 0.0022836580531031417, 0.11418290265515707, 2.22, 61.69, 0.004502],
[1218, 3, 6.241945072080783e-05, 0.003120972536040392, 2.22, 61.69, 0.004502],
[1219, 3, 0.0007855588922898729, 0.03927794461449365, 2.22, 61.69, 0.004502],
[1220, 3, 0.001947919590347708, 0.0973959795173854, 2.22, 61.69, 0.004502],
[1221, 2, 0.0377662225422596, 1.88831112711298, 0, 0, 0],
[1222, 2, 0.013436354905899806, 0.6718177452949904, 0, 0, 0],
[1223, 3, 0.00024230393037435297, 0.01211519651871765, 2.22, 61.69, 0.004502],
[1224, 2, 0.010219261097938644, 0.5109630548969322, 0, 0, 0],
[1225, 3, 0.0022238071565315737, 0.1111903578265787, 2.22, 61.69, 0.004502],
[1226, 3, 0.0002535566380389208, 0.012677831901946041, 2.22, 61.69, 0.004502],
[1227, 3, 0.0011129900410750567, 0.05564950205375283, 2.22, 61.69, 0.004502],
[1228, 3, 0.00019234621639044032, 0.009617310819522017, 2.22, 61.69, 0.004502],
[1229, 2, 0.00326230849376, 0.16311542468800003, 0, 0, 0],
[1230, 3, 5.774224065377648e-05, 0.0028871120326888237, 2.22, 61.69, 0.004502],
[1231, 3, 0.0021361636602669084, 0.10680818301334541, 2.22, 61.69, 0.004502],
[1232, 2, 0.004779428513216963, 0.23897142566084817, 0, 0, 0],
[1235, 3, 0.00028910830796175294, 0.014455415398087644, 2.22, 61.69, 0.004502],
[1236, 2, 0.002535004450133525, 0.12675022250667625, 0, 0, 0],
[1237, 3, 0.0009298092078685558, 0.04649046039342779, 2.22, 61.69, 0.004502],
[1238, 2, 0.012012445276594919, 0.600622263829746, 0, 0, 0],
[1239, 3, 5.75756369436291e-05, 0.0028787818471814556, 2.22, 61.69, 0.004502],
[1240, 2, 0.021613910382114798, 1.08069551910574, 0, 0, 0],
[1241, 2, 0.024532881090784327, 1.2266440545392163, 0, 0, 0],
[1242, 3, 0.0017235867616422773, 0.08617933808211387, 2.22, 61.69, 0.004502],
[1243, 2, 0.005289026999236673, 0.26445134996183367, 0, 0, 0],
[1244, 2, 0.00846072422785893, 0.4230362113929465, 0, 0, 0],
[1245, 3, 0.0005144458090049472, 0.025722290450247362, 2.22, 61.69, 0.004502],
[1246, 2, 0.00337806806675036, 0.16890340333751802, 0, 0, 0],
[1247, 3, 0.0013899571448864774, 0.06949785724432388, 2.22, 61.69, 0.004502],
[1248, 2, 0.005854245631350222, 0.2927122815675111, 0, 0, 0],
[1249, 2, 0.004846915908139961, 0.24234579540699805, 0, 0, 0],
[1250, 3, 0.0019627317861894665, 0.09813658930947333, 2.22, 61.69, 0.004502],
[1251, 3, 0.0014899668826355728, 0.07449834413177864, 2.22, 61.69, 0.004502],
[1252, 3, 0.0009477821555247328, 0.047389107776236644, 2.22, 61.69, 0.004502],
[1253, 2, 0.004106369053307717, 0.20531845266538587, 0, 0, 0],
[1254, 2, 0.005081603543623868, 0.2540801771811934, 0, 0, 0],
[1255, 3, 0.0002430881191708174, 0.01215440595854087, 2.22, 61.69, 0.004502],
[1256, 3, 0.0009607764830526361, 0.048038824152631804, 2.22, 61.69, 0.004502],
[1257, 2, 0.005662916214121937, 0.28314581070609685, 0, 0, 0],
[1258, 2, 0.010814994241697335, 0.5407497120848668, 0, 0, 0],
[1259, 2, 0.00695753592752513, 0.34787679637625657, 0, 0, 0],
[1260, 3, 0.0012839803779623614, 0.06419901889811806, 2.22, 61.69, 0.004502],
[1261, 2, 0.012840592447306919, 0.6420296223653459, 0, 0, 0],
[1262, 3, 3.3365758929065435e-05, 0.0016682879464532717, 2.22, 61.69, 0.004502],
[1263, 3, 2.243579925674327e-05, 0.0011217899628371635, 2.22, 61.69, 0.004502],
[1264, 2, 0.005222533303161435, 0.2611266651580718, 0, 0, 0],
[1265, 3, 0.0004236530619172327, 0.021182653095861634, 2.22, 61.69, 0.004502],
[1266, 2, 0.007621029313600565, 0.38105146568002835, 0, 0, 0],
[1267, 3, 0.002512674942558201, 0.12563374712791006, 2.22, 61.69, 0.004502],
[1268, 3, 0.0002183287451274897, 0.010916437256374485, 2.22, 61.69, 0.004502],
[1269, 3, 0.0003250471975980552, 0.01625235987990276, 2.22, 61.69, 0.004502],
[1270, 3, 0.0024796665722395645, 0.12398332861197821, 2.22, 61.69, 0.004502],
[1271, 3, 0.0030157819134425234, 0.15078909567212617, 2.22, 61.69, 0.004502],
[1272, 3, 7.840992648188318e-05, 0.003920496324094159, 2.22, 61.69, 0.004502],
[1273, 3, 0.00013809561181086458, 0.006904780590543229, 2.22, 61.69, 0.004502],
[1274, 2, 0.0033801727100761705, 0.1690086355038085, 0, 0, 0],
[1275, 2, 0.006307329492962109, 0.3153664746481055, 0, 0, 0],
[1276, 3, 0.001633288835647369, 0.08166444178236844, 2.22, 61.69, 0.004502],
[1277, 2, 0.004176942042758357, 0.20884710213791788, 0, 0, 0],
[1278, 2, 0.010850406134369231, 0.5425203067184615, 0, 0, 0],
[1279, 3, 1.1547461499241629e-07, 5.773730749620814e-06, 2.22, 61.69, 0.004502],
[1280, 3, 2.2052402508424647e-05, 0.0011026201254212323, 2.22, 61.69, 0.004502],
[1281, 3, 0.0001599481510691144, 0.007997407553455719, 2.22, 61.69, 0.004502],
[1282, 3, 0.00015112854883249187, 0.007556427441624595, 2.22, 61.69, 0.004502],
[1283, 2, 0.04214075813046536, 2.1070379065232685, 0, 0, 0],
[1284, 3, 0.0018096758437742202, 0.09048379218871101, 2.22, 61.69, 0.004502],
[1285, 3, 0.0001531107626377273, 0.0076555381318863655, 2.22, 61.69, 0.004502],
[1286, 3, 0.0011377796471657795, 0.05688898235828898, 2.22, 61.69, 0.004502],
[1287, 2, 0.005933272587501368, 0.29666362937506835, 0, 0, 0],
[1288, 2, 0.00944760882155904, 0.472380441077952, 0, 0, 0],
[1289, 2, 0.011723304434111076, 0.5861652217055537, 0, 0, 0],
[1290, 3, 0.0003120693634598793, 0.015603468172993969, 2.22, 61.69, 0.004502],
[1291, 2, 0.0062575490505418305, 0.31287745252709154, 0, 0, 0],
[1292, 3, 0.002653563231501149, 0.13267816157505744, 2.22, 61.69, 0.004502],
[1293, 3, 0.00015292290721046804, 0.007646145360523402, 2.22, 61.69, 0.004502],
[1294, 3, 0.0003436110439431119, 0.017180552197155596, 2.22, 61.69, 0.004502],
[1295, 3, 0.00037392918854889465, 0.01869645942744473, 2.22, 61.69, 0.004502],
[1296, 3, 0.0017284338192132009, 0.08642169096066006, 2.22, 61.69, 0.004502],
[1297, 2, 0.011317746197608284, 0.5658873098804141, 0, 0, 0],
[1298, 3, 0.00020595303360804683, 0.010297651680402344, 2.22, 61.69, 0.004502],
[1299, 3, 8.9869986756113e-05, 0.00449349933780565, 2.22, 61.69, 0.004502],
[1300, 3, 0.001511593201166196, 0.07557966005830981, 2.22, 61.69, 0.004502],
[1301, 2, 0.0038746782543149596, 0.193733912715748, 0, 0, 0],
[1302, 3, 0.0003104985267932093, 0.015524926339660468, 2.22, 61.69, 0.004502],
[1303, 3, 0.00027600750632746427, 0.013800375316373212, 2.22, 61.69, 0.004502],
[1304, 3, 0.000610793340517708, 0.030539667025885397, 2.22, 61.69, 0.004502],
[1305, 3, 2.9075695387122924e-07, 1.4537847693561463e-05, 2.22, 61.69, 0.004502],
[1306, 3, 0.00011631130798083146, 0.005815565399041573, 2.22, 61.69, 0.004502],
[1307, 3, 1.9031130574577255e-05, 0.0009515565287288628, 2.22, 61.69, 0.004502],
[1308, 3, 0.00020870441847665842, 0.010435220923832922, 2.22, 61.69, 0.004502],
[1309, 3, 0.0002132096944766602, 0.01066048472383301, 2.22, 61.69, 0.004502],
[1310, 3, 0.00010478060392325507, 0.005239030196162754, 2.22, 61.69, 0.004502],
[1311, 3, 0.0007546493032032542, 0.037732465160162716, 2.22, 61.69, 0.004502],
[1312, 2, 0.0070428013304282035, 0.3521400665214102, 0, 0, 0],
[1313, 3, 0.0019631283227609974, 0.09815641613804986, 2.22, 61.69, 0.004502],
[1314, 3, 0.0007641975650906521, 0.038209878254532606, 2.22, 61.69, 0.004502],
[1315, 3, 0.0005015944131679134, 0.02507972065839567, 2.22, 61.69, 0.004502],
[1316, 3, 0.000145780634856578, 0.007289031742828901, 2.22, 61.69, 0.004502],
[1317, 3, 0.0015252502049763412, 0.07626251024881707, 2.22, 61.69, 0.004502],
[1318, 3, 0.00012454395408676328, 0.0062271977043381645, 2.22, 61.69, 0.004502],
[1319, 3, 0.001127343871228203, 0.05636719356141015, 2.22, 61.69, 0.004502],
[1320, 3, 0.0013215329138219017, 0.06607664569109509, 2.22, 61.69, 0.004502],
[1321, 3, 1.025741798764967e-05, 0.0005128708993824835, 2.22, 61.69, 0.004502],
[1322, 3, 5.919056262068799e-05, 0.0029595281310344, 2.22, 61.69, 0.004502],
[1323, 2, 0.012675857799799822, 0.6337928899899912, 0, 0, 0],
[1324, 3, 0.0008316328586631403, 0.04158164293315702, 2.22, 61.69, 0.004502],
[1325, 2, 0.0057612535388438385, 0.2880626769421919, 0, 0, 0],
[1326, 2, 0.0036242041289439157, 0.1812102064471958, 0, 0, 0],
[1327, 2, 0.0032338308031027566, 0.16169154015513784, 0, 0, 0],
[1328, 3, 0.0010226241895011407, 0.05113120947505704, 2.22, 61.69, 0.004502],
[1329, 2, 0.013921309839652627, 0.6960654919826315, 0, 0, 0],
[1330, 3, 0.0019182008434651947, 0.09591004217325974, 2.22, 61.69, 0.004502],
[1331, 3, 1.841349064624893e-05, 0.0009206745323124464, 2.22, 61.69, 0.004502],
[1332, 3, 0.0016738699394560756, 0.08369349697280379, 2.22, 61.69, 0.004502],
[1333, 3, 0.0029061854047842247, 0.14530927023921122, 2.22, 61.69, 0.004502],
[1334, 3, 5.761014482450118e-05, 0.0028805072412250595, 2.22, 61.69, 0.004502],
[1335, 3, 0.00021052629514022267, 0.010526314757011134, 2.22, 61.69, 0.004502],
[1336, 3, 0.0018954102795459078, 0.0947705139772954, 2.22, 61.69, 0.004502],
[1337, 2, 0.003303921795797683, 0.16519608978988415, 0, 0, 0],
[1338, 3, 5.300015004820578e-05, 0.0026500075024102894, 2.22, 61.69, 0.004502],
[1339, 3, 0.0006421253879349708, 0.032106269396748544, 2.22, 61.69, 0.004502],
[1340, 2, 0.0019890355643717287, 0.09945177821858646, 0, 0, 0],
[1341, 2, 0.005924529413907861, 0.2962264706953931, 0, 0, 0],
[1342, 3, 2.7387437160360416e-05, 0.0013693718580180209, 2.22, 61.69, 0.004502],
[1343, 3, 3.943679326899658e-05, 0.001971839663449829, 2.22, 61.69, 0.004502],
[1344, 3, 1.4391232894862565e-05, 0.0007195616447431282, 2.22, 61.69, 0.004502],
[1345, 3, 0.00025281368060892654, 0.012640684030446329, 2.22, 61.69, 0.004502],
[1346, 2, 0.013669449762218379, 0.6834724881109189, 0, 0, 0],
[1347, 2, 0.01477118570778878, 0.7385592853894392, 0, 0, 0],
[1348, 3, 0.000584562357708931, 0.02922811788544655, 2.22, 61.69, 0.004502],
[1349, 3, 0.0012037349571321803, 0.06018674785660902, 2.22, 61.69, 0.004502],
[1350, 3, 6.046050411995944e-06, 0.0003023025205997972, 2.22, 61.69, 0.004502],
[1351, 3, 4.796502941013963e-07, 2.3982514705069816e-05, 2.22, 61.69, 0.004502],
[1352, 3, 2.760384018212869e-05, 0.0013801920091064345, 2.22, 61.69, 0.004502],
[1354, 3, 4.276029671133181e-06, 0.00021380148355665902, 2.22, 61.69, 0.004502],
[1355, 3, 0.0001074820707981226, 0.005374103539906131, 2.22, 61.69, 0.004502],
[1356, 2, 0.004678278776831856, 0.23391393884159278, 0, 0, 0],
[1357, 2, 0.003594349677217709, 0.17971748386088549, 0, 0, 0],
[1358, 3, 1.57431431082847e-05, 0.0007871571554142351, 2.22, 61.69, 0.004502],
[1359, 2, 0.004496673943395517, 0.22483369716977586, 0, 0, 0],
[1360, 3, 0.0010909105792324338, 0.054545528961621695, 2.22, 61.69, 0.004502],
[1361, 2, 0.0040238936307783425, 0.20119468153891715, 0, 0, 0],
[1362, 2, 0.005036121783141224, 0.2518060891570612, 0, 0, 0],
[1363, 3, 2.301886324440155e-06, 0.00011509431622200775, 2.22, 61.69, 0.004502],
[1364, 3, 3.887723536233725e-06, 0.00019438617681168623, 2.22, 61.69, 0.004502],
[1365, 3, 2.8999446623259055e-08, 1.449972331162953e-06, 2.22, 61.69, 0.004502],
[1366, 3, 7.830373844390861e-05, 0.003915186922195431, 2.22, 61.69, 0.004502],
[1367, 3, 0.0027924620350495274, 0.13962310175247636, 2.22, 61.69, 0.004502],
[1368, 3, 0.00017611255606875446, 0.008805627803437724, 2.22, 61.69, 0.004502],
[1369, 3, 0.0005073133310147165, 0.025365666550735824, 2.22, 61.69, 0.004502],
[1370, 3, 2.185563890765493e-05, 0.0010927819453827466, 2.22, 61.69, 0.004502],
[1371, 2, 0.0024031239337826537, 0.12015619668913267, 0, 0, 0],
[1372, 2, 0.012284634505654547, 0.6142317252827274, 0, 0, 0],
[1373, 3, 0.0022409179594482334, 0.11204589797241167, 2.22, 61.69, 0.004502],
[1376, 2, 0.011218109707548912, 0.5609054853774457, 0, 0, 0],
[1377, 2, 0.01492085689824784, 0.7460428449123921, 0, 0, 0],
[1378, 2, 0.01566275025445262, 0.783137512722631, 0, 0, 0],
[1379, 3, 5.1310566028095876e-05, 0.002565528301404794, 2.22, 61.69, 0.004502],
[1380, 3, 7.724465320438908e-05, 0.003862232660219454, 2.22, 61.69, 0.004502],
[1381, 3, 6.446222679588771e-05, 0.003223111339794386, 2.22, 61.69, 0.004502],
[1382, 2, 0.008838822964419164, 0.4419411482209583, 0, 0, 0],
[1383, 2, 0.006991449967869686, 0.34957249839348425, 0, 0, 0],
[1384, 3, 0.0002972463393517766, 0.014862316967588829, 2.22, 61.69, 0.004502],
[1385, 3, 7.92302201959824e-06, 0.0003961511009799121, 2.22, 61.69, 0.004502],
[1386, 3, 4.2899112828393286e-05, 0.002144955641419664, 2.22, 61.69, 0.004502],
[1387, 3, 0.00022240699424911273, 0.011120349712455638, 2.22, 61.69, 0.004502],
[1388, 3, 5.909025672850305e-05, 0.0029545128364251525, 2.22, 61.69, 0.004502],
[1389, 3, 1.3594135764164036e-05, 0.0006797067882082019, 2.22, 61.69, 0.004502],
[1390, 3, 0.00023763846235409512, 0.011881923117704758, 2.22, 61.69, 0.004502],
[1391, 3, 3.321367742134543e-05, 0.0016606838710672715, 2.22, 61.69, 0.004502],
[1392, 3, 0.0012290826914265437, 0.06145413457132718, 2.22, 61.69, 0.004502],
[1393, 3, 8.763130962106806e-05, 0.004381565481053403, 2.22, 61.69, 0.004502],
[1394, 3, 6.862035771367977e-05, 0.003431017885683988, 2.22, 61.69, 0.004502],
[1395, 3, 4.696755105006889e-06, 0.00023483775525034447, 2.22, 61.69, 0.004502],
[1396, 3, 1.6623117797696163e-06, 8.311558898848081e-05, 2.22, 61.69, 0.004502],
[1397, 3, 0.0015969317375463513, 0.07984658687731756, 2.22, 61.69, 0.004502],
[1398, 3, 0.00017695743260373348, 0.008847871630186674, 2.22, 61.69, 0.004502],
[1399, 3, 0.0011375222056992432, 0.05687611028496216, 2.22, 61.69, 0.004502],
[1400, 3, 8.258214886247176e-05, 0.004129107443123589, 2.22, 61.69, 0.004502],
[1401, 2, 0.005687529053514607, 0.28437645267573036, 0, 0, 0],
[1402, 3, 0.001676149990745289, 0.08380749953726446, 2.22, 61.69, 0.004502],
[1403, 2, 0.007617262031172502, 0.38086310155862513, 0, 0, 0],
[1404, 2, 0.0067734988181819555, 0.33867494090909783, 0, 0, 0],
[1405, 3, 0.0018812625008740895, 0.09406312504370447, 2.22, 61.69, 0.004502],
[1406, 3, 0.0006852566793279422, 0.03426283396639711, 2.22, 61.69, 0.004502],
[1407, 3, 1.3471796788943673e-05, 0.0006735898394471837, 2.22, 61.69, 0.004502],
[1408, 3, 0.002615151153581973, 0.13075755767909866, 2.22, 61.69, 0.004502],
[1409, 3, 0.0007652033584917757, 0.038260167924588785, 2.22, 61.69, 0.004502],
[1410, 3, 0.002385192626051519, 0.11925963130257596, 2.22, 61.69, 0.004502],
[1411, 3, 0.0025079869254713357, 0.1253993462735668, 2.22, 61.69, 0.004502],
[1412, 3, 0.00034193149839380297, 0.01709657491969015, 2.22, 61.69, 0.004502],
[1413, 3, 0.0003039144901162519, 0.015195724505812597, 2.22, 61.69, 0.004502],
[1414, 3, 0.001654733253695335, 0.08273666268476676, 2.22, 61.69, 0.004502],
[1415, 3, 0.0004362516227410405, 0.021812581137052027, 2.22, 61.69, 0.004502],
[1416, 3, 0.0004029092265882156, 0.020145461329410783, 2.22, 61.69, 0.004502],
[1417, 3, 6.808952303623334e-08, 3.404476151811667e-06, 2.22, 61.69, 0.004502],
[1418, 2, 0.005619099755523237, 0.28095498777616185, 0, 0, 0],
[1419, 3, 0.00211745485704481, 0.10587274285224049, 2.22, 61.69, 0.004502],
[1420, 3, 8.91112970779674e-05, 0.00445556485389837, 2.22, 61.69, 0.004502],
[1421, 3, 0.00044387476697737416, 0.02219373834886871, 2.22, 61.69, 0.004502],
[1422, 3, 0.00030115264331514286, 0.015057632165757144, 2.22, 61.69, 0.004502],
[1423, 3, 0.00012293234040278847, 0.006146617020139425, 2.22, 61.69, 0.004502],
[1424, 2, 0.00641540397482647, 0.3207701987413235, 0, 0, 0],
[1425, 3, 0.001350721738292593, 0.06753608691462964, 2.22, 61.69, 0.004502],
[1426, 2, 0.004377563184547638, 0.2188781592273819, 0, 0, 0],
[1427, 2, 0.03060222784928668, 1.5301113924643341, 0, 0, 0],
[1428, 2, 0.021319488529000553, 1.0659744264500277, 0, 0, 0],
[1429, 3, 0.000658318690093667, 0.03291593450468335, 2.22, 61.69, 0.004502],
[1430, 3, 9.820641622425884e-07, 4.9103208112129425e-05, 2.22, 61.69, 0.004502],
[1431, 2, 0.014493414492796078, 0.724670724639804, 0, 0, 0],
[1432, 3, 0.0003716433863367817, 0.01858216931683909, 2.22, 61.69, 0.004502],
[1433, 2, 0.036688879163843384, 1.8344439581921694, 0, 0, 0],
[1434, 2, 0.0026062503484175956, 0.13031251742087976, 0, 0, 0],
[1435, 2, 0.002539145570389532, 0.1269572785194766, 0, 0, 0],
[1436, 2, 0.002591208267120717, 0.12956041335603585, 0, 0, 0],
[1437, 2, 0.015172047044780135, 0.7586023522390068, 0, 0, 0],
[1438, 2, 0.025007389641183632, 1.2503694820591817, 0, 0, 0],
[1439, 2, 0.0063091033600462575, 0.3154551680023129, 0, 0, 0],
[1440, 3, 5.306917668409132e-05, 0.0026534588342045657, 2.22, 61.69, 0.004502],
[1441, 3, 1.0923020560921105e-05, 0.0005461510280460552, 2.22, 61.69, 0.004502],
[1442, 3, 4.555157486056611e-05, 0.0022775787430283057, 2.22, 61.69, 0.004502],
[1443, 2, 0.0026111964035441713, 0.13055982017720855, 0, 0, 0],
[1444, 3, 0.0005717925297728792, 0.028589626488643962, 2.22, 61.69, 0.004502],
[1445, 3, 0.0015938921576921367, 0.07969460788460683, 2.22, 61.69, 0.004502],
[1446, 2, 0.04829066125331256, 2.414533062665628, 0, 0, 0],
[1447, 2, 0.005696308888305882, 0.2848154444152941, 0, 0, 0],
[1448, 3, 0.00047896583949883246, 0.023948291974941624, 2.22, 61.69, 0.004502],
[1449, 2, 0.006075750962706547, 0.3037875481353274, 0, 0, 0],
[1450, 2, 0.0037724056227270084, 0.18862028113635043, 0, 0, 0],
[1451, 2, 0.0043416728967246255, 0.21708364483623127, 0, 0, 0],
[1452, 3, 0.0015322750739690742, 0.0766137536984537, 2.22, 61.69, 0.004502],
[1453, 2, 0.004134065549943135, 0.20670327749715672, 0, 0, 0],
[1454, 2, 0.009875666531734596, 0.49378332658672985, 0, 0, 0],
[1455, 3, 4.166284213856912e-05, 0.0020831421069284557, 2.22, 61.69, 0.004502],
[1456, 2, 0.0031865889687578697, 0.15932944843789354, 0, 0, 0],
[1457, 3, 0.00012749408723576006, 0.006374704361788003, 2.22, 61.69, 0.004502],
[1458, 3, 1.5673534819523866e-05, 0.0007836767409761935, 2.22, 61.69, 0.004502],
[1459, 3, 0.00033798517072819835, 0.01689925853640992, 2.22, 61.69, 0.004502],
[1460, 2, 0.006461593448980158, 0.3230796724490079, 0, 0, 0],
[1461, 3, 0.001142843079861875, 0.05714215399309376, 2.22, 61.69, 0.004502],
[1462, 3, 0.00015295973435731913, 0.007647986717865956, 2.22, 61.69, 0.004502],
[1463, 3, 4.5276834778775515e-05, 0.002263841738938776, 2.22, 61.69, 0.004502],
[1464, 2, 0.013934601684842136, 0.6967300842421068, 0, 0, 0],
[1465, 3, 0.0003374045759652472, 0.01687022879826236, 2.22, 61.69, 0.004502],
[1466, 3, 0.0003619193984034768, 0.01809596992017384, 2.22, 61.69, 0.004502],
[1467, 3, 0.00013344536897072216, 0.006672268448536108, 2.22, 61.69, 0.004502],
[1468, 3, 0.0015144656821575462, 0.0757232841078773, 2.22, 61.69, 0.004502],
[1469, 2, 0.004138503876498319, 0.20692519382491598, 0, 0, 0],
[1470, 2, 0.0020014495173752657, 0.10007247586876329, 0, 0, 0],
[1471, 2, 0.004038395628360613, 0.20191978141803063, 0, 0, 0],
[1472, 3, 0.0007626820845032627, 0.03813410422516314, 2.22, 61.69, 0.004502],
[1473, 3, 0.0005323801851315335, 0.026619009256576683, 2.22, 61.69, 0.004502],
[1474, 3, 8.905977123682595e-05, 0.004452988561841298, 2.22, 61.69, 0.004502],
[1475, 3, 2.4884191103347185e-05, 0.0012442095551673594, 2.22, 61.69, 0.004502],
[1476, 2, 0.01216740582073879, 0.6083702910369395, 0, 0, 0],
[1477, 3, 0.0007717725169969112, 0.03858862584984556, 2.22, 61.69, 0.004502],
[1478, 3, 1.03629245449834e-06, 5.181462272491701e-05, 2.22, 61.69, 0.004502],
[1479, 3, 0.00035603636123413484, 0.01780181806170674, 2.22, 61.69, 0.004502],
[1480, 3, 0.0011893307912248102, 0.05946653956124052, 2.22, 61.69, 0.004502],
[1481, 3, 3.3833873695351113e-06, 0.00016916936847675558, 2.22, 61.69, 0.004502],
[1482, 3, 0.0011147740798471094, 0.055738703992355476, 2.22, 61.69, 0.004502],
[1483, 3, 0.0002291607516312977, 0.011458037581564884, 2.22, 61.69, 0.004502],
[1484, 3, 1.9041073525508303e-06, 9.520536762754152e-05, 2.22, 61.69, 0.004502],
[1485, 3, 3.5876538426778735e-05, 0.0017938269213389369, 2.22, 61.69, 0.004502],
[1486, 3, 0.00018457774197472868, 0.009228887098736434, 2.22, 61.69, 0.004502],
[1487, 3, 7.276038526853737e-05, 0.0036380192634268686, 2.22, 61.69, 0.004502],
[1488, 3, 0.0003000059684869966, 0.01500029842434983, 2.22, 61.69, 0.004502],
[1489, 3, 7.571817467557017e-06, 0.00037859087337785094, 2.22, 61.69, 0.004502],
[1490, 2, 0.020504288751418347, 1.0252144375709173, 0, 0, 0],
[1491, 2, 0.005387257187745477, 0.26936285938727383, 0, 0, 0],
[1492, 2, 0.014637639488319377, 0.7318819744159688, 0, 0, 0],
[1493, 2, 0.005319414988695112, 0.26597074943475557, 0, 0, 0],
[1494, 2, 0.0257504251653254, 1.28752125826627, 0, 0, 0],
[1495, 2, 0.004260305180484296, 0.2130152590242148, 0, 0, 0],
[1496, 3, 1.5185873075624022e-08, 7.592936537812012e-07, 2.22, 61.69, 0.004502],
[1497, 2, 0.005670372667342641, 0.28351863336713207, 0, 0, 0],
[1498, 2, 0.006735488235440387, 0.3367744117720194, 0, 0, 0],
[1499, 3, 0.00014557430965896176, 0.0072787154829480885, 2.22, 61.69, 0.004502],
[1500, 3, 9.85597782087346e-06, 0.000492798891043673, 2.22, 61.69, 0.004502],
[1501, 3, 0.0005198212383651805, 0.02599106191825903, 2.22, 61.69, 0.004502],
[1502, 3, 4.105448673151168e-05, 0.002052724336575584, 2.22, 61.69, 0.004502],
[1503, 3, 0.0029266803181735935, 0.14633401590867967, 2.22, 61.69, 0.004502],
[1504, 2, 0.012020835078490423, 0.6010417539245212, 0, 0, 0],
[1505, 3, 0.0014407364034016888, 0.07203682017008443, 2.22, 61.69, 0.004502],
[1506, 2, 0.0035909631390018642, 0.17954815695009319, 0, 0, 0],
[1507, 3, 0.000982816273068341, 0.04914081365341705, 2.22, 61.69, 0.004502],
[1508, 3, 4.154538017488063e-06, 0.00020772690087440316, 2.22, 61.69, 0.004502],
[1509, 3, 1.37186634032331e-07, 6.85933170161655e-06, 2.22, 61.69, 0.004502],
[1510, 2, 0.00681234986437375, 0.34061749321868756, 0, 0, 0],
[1511, 2, 0.00988173435818505, 0.4940867179092525, 0, 0, 0],
[1512, 2, 0.004082645917281524, 0.20413229586407625, 0, 0, 0],
[1513, 3, 0.001467522271804366, 0.07337611359021831, 2.22, 61.69, 0.004502],
[1514, 3, 1.3202056818036577e-06, 6.601028409018288e-05, 2.22, 61.69, 0.004502],
[1515, 3, 1.7255068668904044e-07, 8.627534334452021e-06, 2.22, 61.69, 0.004502],
[1516, 3, 1.8340973111507537e-06, 9.170486555753769e-05, 2.22, 61.69, 0.004502],
[1517, 3, 8.192048507877762e-05, 0.0040960242539388805, 2.22, 61.69, 0.004502],
[1518, 3, 4.268803271333055e-05, 0.0021344016356665274, 2.22, 61.69, 0.004502],
[1519, 3, 2.9627970642356104e-06, 0.00014813985321178054, 2.22, 61.69, 0.004502]
])
ppc["branch_switch"] = array([
[586, 1, 0 ],
[589, 108, 0 ],
[590, 108, 0 ],
[593, 112, 0 ],
[594, 114, 0 ],
[595, 115, 0 ],
[598, 118, 0 ],
[599, 119, 0 ],
[601, 119, 0 ],
[602, 121, 0 ],
[603, 526, 0 ],
[607, 127, 0 ],
[608, 127, 0 ],
[609, 529, 0 ],
[612, 493, 0 ],
[613, 130, 0 ],
[614, 130, 0 ],
[616, 132, 0 ],
[617, 133, 0 ],
[618, 133, 0 ],
[619, 134, 0 ],
[621, 136, 0 ],
[624, 14, 0 ],
[628, 142, 0 ],
[629, 145, 0 ],
[631, 145, 0 ],
[632, 145, 0 ],
[637, 148, 0 ],
[638, 149, 0 ],
[640, 153, 0 ],
[641, 155, 0 ],
[642, 533, 0 ],
[643, 534, 0 ],
[647, 536, 0 ],
[650, 166, 0 ],
[652, 167, 0 ],
[655, 170, 0 ],
[663, 178, 0 ],
[666, 180, 0 ],
[670, 183, 0 ],
[672, 185, 0 ],
[676, 19, 0 ],
[681, 197, 0 ],
[683, 200, 0 ],
[687, 202, 0 ],
[689, 204, 0 ],
[691, 209, 0 ],
[694, 21, 0 ],
[695, 210, 0 ],
[696, 211, 0 ],
[697, 211, 0 ],
[698, 212, 0 ],
[702, 215, 0 ],
[705, 217, 0 ],
[707, 219, 0 ],
[713, 225, 0 ],
[714, 225, 0 ],
[716, 226, 0 ],
[717, 227, 0 ],
[719, 229, 0 ],
[722, 545, 0 ],
[723, 235, 0 ],
[724, 238, 0 ],
[727, 243, 0 ],
[728, 244, 0 ],
[730, 547, 0 ],
[732, 247, 0 ],
[735, 253, 0 ],
[738, 258, 0 ],
[741, 264, 0 ],
[742, 264, 0 ],
[743, 500, 0 ],
[746, 273, 0 ],
[747, 273, 0 ],
[748, 274, 0 ],
[749, 274, 0 ],
[750, 557, 0 ],
[753, 28, 0 ],
[758, 286, 0 ],
[760, 287, 0 ],
[761, 288, 0 ],
[762, 289, 0 ],
[763, 560, 0 ],
[765, 560, 0 ],
[767, 292, 0 ],
[769, 293, 0 ],
[771, 297, 0 ],
[772, 3, 0 ],
[774, 300, 0 ],
[777, 300, 0 ],
[778, 300, 0 ],
[781, 303, 0 ],
[784, 563, 0 ],
[785, 501, 0 ],
[787, 308, 0 ],
[788, 311, 0 ],
[789, 565, 0 ],
[791, 314, 0 ],
[792, 316, 0 ],
[795, 319, 0 ],
[800, 326, 0 ],
[801, 327, 0 ],
[802, 327, 0 ],
[805, 328, 0 ],
[806, 328, 0 ],
[808, 329, 0 ],
[809, 329, 0 ],
[811, 568, 0 ],
[814, 570, 0 ],
[816, 335, 0 ],
[817, 571, 0 ],
[821, 338, 0 ],
[822, 339, 0 ],
[826, 339, 0 ],
[830, 345, 0 ],
[834, 572, 0 ],
[835, 572, 0 ],
[836, 572, 0 ],
[837, 350, 0 ],
[839, 350, 0 ],
[841, 573, 0 ],
[843, 352, 0 ],
[844, 352, 0 ],
[845, 356, 0 ],
[849, 574, 0 ],
[850, 574, 0 ],
[851, 575, 0 ],
[853, 362, 0 ],
[855, 363, 0 ],
[856, 363, 0 ],
[857, 365, 0 ],
[858, 368, 0 ],
[859, 368, 0 ],
[860, 371, 0 ],
[864, 374, 0 ],
[865, 375, 0 ],
[867, 376, 0 ],
[869, 503, 0 ],
[870, 503, 0 ],
[872, 378, 0 ],
[873, 576, 0 ],
[874, 576, 0 ],
[875, 381, 0 ],
[877, 578, 0 ],
[881, 388, 0 ],
[882, 388, 0 ],
[883, 388, 0 ],
[885, 393, 0 ],
[886, 394, 0 ],
[889, 397, 0 ],
[890, 40, 0 ],
[893, 400, 0 ],
[894, 400, 0 ],
[895, 580, 0 ],
[896, 581, 0 ],
[898, 403, 0 ],
[900, 405, 0 ],
[902, 405, 0 ],
[903, 406, 0 ],
[905, 413, 0 ],
[906, 414, 0 ],
[907, 583, 0 ],
[909, 417, 0 ],
[915, 423, 0 ],
[917, 43, 0 ],
[918, 424, 0 ],
[920, 428, 0 ],
[921, 428, 0 ],
[922, 429, 0 ],
[923, 432, 0 ],
[925, 44, 0 ],
[931, 439, 0 ],
[935, 45, 0 ],
[936, 445, 0 ],
[937, 447, 0 ],
[939, 450, 0 ],
[940, 451, 0 ],
[944, 458, 0 ],
[950, 462, 0 ],
[952, 47, 0 ],
[957, 478, 0 ],
[958, 478, 0 ],
[959, 478, 0 ],
[960, 479, 0 ],
[963, 481, 0 ],
[965, 49, 0 ],
[966, 49, 0 ],
[967, 49, 0 ],
[968, 486, 0 ],
[969, 486, 0 ],
[971, 51, 0 ],
[973, 506, 0 ],
[976, 58, 0 ],
[978, 491, 0 ],
[981, 62, 0 ],
[982, 62, 0 ],
[983, 62, 0 ],
[984, 63, 0 ],
[985, 63, 0 ],
[986, 64, 0 ],
[987, 65, 0 ],
[988, 66, 0 ],
[993, 67, 0 ],
[994, 67, 0 ],
[995, 509, 0 ],
[997, 510, 0 ],
[999, 70, 0 ],
[1000, 71, 0 ],
[1002, 71, 0 ],
[1003, 72, 0 ],
[1007, 511, 0 ],
[1008, 75, 0 ],
[1010, 79, 0 ],
[1011, 79, 0 ],
[1012, 81, 0 ],
[1014, 83, 0 ],
[1026, 518, 0 ],
[1027, 218, 0 ],
[1028, 221, 0 ],
[1029, 268, 0 ],
[1030, 269, 0 ],
[1031, 498, 0 ],
[1032, 1, 0 ],
[1033, 3, 0 ],
[1034, 4, 0 ],
[1035, 6, 0 ],
[1036, 7, 0 ],
[1037, 8, 0 ],
[1038, 9, 0 ],
[1039, 11, 0 ],
[1040, 14, 0 ],
[1041, 16, 0 ],
[1042, 17, 0 ],
[1043, 19, 0 ],
[1044, 21, 0 ],
[1045, 23, 0 ],
[1046, 25, 0 ],
[1047, 27, 0 ],
[1048, 28, 0 ],
[1049, 29, 0 ],
[1050, 31, 0 ],
[1051, 33, 0 ],
[1052, 34, 0 ],
[1053, 35, 0 ],
[1054, 36, 0 ],
[1055, 38, 0 ],
[1056, 39, 0 ],
[1057, 40, 0 ],
[1058, 41, 0 ],
[1059, 43, 0 ],
[1060, 44, 0 ],
[1061, 45, 0 ],
[1062, 47, 0 ],
[1063, 48, 0 ],
[1064, 49, 0 ],
[1065, 50, 0 ],
[1066, 51, 0 ],
[1067, 53, 0 ],
[1068, 54, 0 ],
[1069, 55, 0 ],
[1070, 57, 0 ],
[1071, 58, 0 ],
[1072, 59, 0 ],
[1073, 60, 0 ],
[1074, 62, 0 ],
[1075, 63, 0 ],
[1076, 64, 0 ],
[1077, 65, 0 ],
[1078, 66, 0 ],
[1079, 67, 0 ],
[1080, 70, 0 ],
[1081, 71, 0 ],
[1082, 72, 0 ],
[1083, 73, 0 ],
[1084, 75, 0 ],
[1085, 76, 0 ],
[1086, 77, 0 ],
[1087, 79, 0 ],
[1088, 80, 0 ],
[1089, 81, 0 ],
[1090, 82, 0 ],
[1091, 83, 0 ],
[1092, 84, 0 ],
[1093, 85, 0 ],
[1094, 88, 0 ],
[1095, 89, 0 ],
[1096, 90, 0 ],
[1097, 91, 0 ],
[1098, 92, 0 ],
[1099, 93, 0 ],
[1100, 97, 0 ],
[1101, 98, 0 ],
[1102, 101, 0 ],
[1103, 102, 0 ],
[1104, 103, 0 ],
[1105, 108, 0 ],
[1106, 109, 0 ],
[1107, 110, 0 ],
[1108, 111, 0 ],
[1109, 112, 0 ],
[1110, 113, 0 ],
[1111, 114, 0 ],
[1112, 115, 0 ],
[1113, 116, 0 ],
[1114, 118, 0 ],
[1115, 119, 0 ],
[1116, 121, 0 ],
[1117, 122, 0 ],
[1118, 126, 0 ],
[1119, 127, 0 ],
[1120, 130, 0 ],
[1121, 131, 0 ],
[1122, 132, 0 ],
[1123, 133, 0 ],
[1124, 134, 0 ],
[1125, 135, 0 ],
[1126, 136, 0 ],
[1127, 137, 0 ],
[1128, 139, 0 ],
[1129, 140, 0 ],
[1130, 141, 0 ],
[1131, 142, 0 ],
[1132, 144, 0 ],
[1133, 145, 0 ],
[1134, 146, 0 ],
[1135, 147, 0 ],
[1136, 148, 0 ],
[1137, 149, 0 ],
[1138, 150, 0 ],
[1139, 151, 0 ],
[1140, 152, 0 ],
[1141, 153, 0 ],
[1142, 154, 0 ],
[1143, 155, 0 ],
[1144, 158, 0 ],
[1145, 161, 0 ],
[1146, 162, 0 ],
[1147, 163, 0 ],
[1148, 164, 0 ],
[1149, 166, 0 ],
[1150, 167, 0 ],
[1151, 168, 0 ],
[1152, 169, 0 ],
[1153, 170, 0 ],
[1154, 171, 0 ],
[1155, 172, 0 ],
[1156, 173, 0 ],
[1157, 174, 0 ],
[1158, 175, 0 ],
[1159, 176, 0 ],
[1160, 177, 0 ],
[1161, 178, 0 ],
[1162, 179, 0 ],
[1163, 180, 0 ],
[1164, 181, 0 ],
[1165, 182, 0 ],
[1166, 183, 0 ],
[1167, 185, 0 ],
[1168, 186, 0 ],
[1169, 187, 0 ],
[1170, 188, 0 ],
[1171, 189, 0 ],
[1172, 190, 0 ],
[1173, 192, 0 ],
[1174, 193, 0 ],
[1175, 194, 0 ],
[1176, 196, 0 ],
[1177, 197, 0 ],
[1178, 198, 0 ],
[1179, 199, 0 ],
[1180, 200, 0 ],
[1181, 202, 0 ],
[1182, 203, 0 ],
[1183, 204, 0 ],
[1184, 205, 0 ],
[1185, 206, 0 ],
[1186, 207, 0 ],
[1187, 208, 0 ],
[1188, 209, 0 ],
[1189, 210, 0 ],
[1190, 211, 0 ],
[1191, 212, 0 ],
[1192, 213, 0 ],
[1193, 214, 0 ],
[1194, 215, 0 ],
[1195, 216, 0 ],
[1196, 217, 0 ],
[1197, 218, 0 ],
[1198, 219, 0 ],
[1201, 223, 0 ],
[1202, 224, 0 ],
[1203, 225, 0 ],
[1204, 226, 0 ],
[1205, 227, 0 ],
[1206, 228, 0 ],
[1207, 229, 0 ],
[1208, 230, 0 ],
[1209, 234, 0 ],
[1210, 235, 0 ],
[1211, 237, 0 ],
[1212, 238, 0 ],
[1213, 239, 0 ],
[1214, 240, 0 ],
[1215, 241, 0 ],
[1216, 242, 0 ],
[1217, 243, 0 ],
[1218, 244, 0 ],
[1219, 247, 0 ],
[1220, 251, 0 ],
[1221, 252, 0 ],
[1222, 253, 0 ],
[1223, 254, 0 ],
[1224, 255, 0 ],
[1225, 256, 0 ],
[1226, 257, 0 ],
[1227, 258, 0 ],
[1228, 260, 0 ],
[1229, 263, 0 ],
[1230, 264, 0 ],
[1231, 266, 0 ],
[1232, 267, 0 ],
[1235, 271, 0 ],
[1236, 272, 0 ],
[1237, 273, 0 ],
[1238, 274, 0 ],
[1239, 275, 0 ],
[1240, 276, 0 ],
[1241, 278, 0 ],
[1242, 281, 0 ],
[1243, 282, 0 ],
[1244, 283, 0 ],
[1245, 284, 0 ],
[1246, 285, 0 ],
[1247, 286, 0 ],
[1248, 287, 0 ],
[1249, 288, 0 ],
[1250, 289, 0 ],
[1251, 291, 0 ],
[1252, 292, 0 ],
[1253, 293, 0 ],
[1254, 294, 0 ],
[1255, 295, 0 ],
[1256, 296, 0 ],
[1257, 297, 0 ],
[1258, 298, 0 ],
[1259, 299, 0 ],
[1260, 300, 0 ],
[1261, 302, 0 ],
[1262, 303, 0 ],
[1263, 304, 0 ],
[1264, 307, 0 ],
[1265, 308, 0 ],
[1266, 309, 0 ],
[1267, 311, 0 ],
[1268, 312, 0 ],
[1269, 314, 0 ],
[1270, 316, 0 ],
[1271, 317, 0 ],
[1272, 318, 0 ],
[1273, 319, 0 ],
[1274, 321, 0 ],
[1275, 322, 0 ],
[1276, 323, 0 ],
[1277, 324, 0 ],
[1278, 325, 0 ],
[1279, 326, 0 ],
[1280, 327, 0 ],
[1281, 328, 0 ],
[1282, 329, 0 ],
[1283, 331, 0 ],
[1284, 333, 0 ],
[1285, 335, 0 ],
[1286, 337, 0 ],
[1287, 338, 0 ],
[1288, 339, 0 ],
[1289, 340, 0 ],
[1290, 341, 0 ],
[1291, 342, 0 ],
[1292, 343, 0 ],
[1293, 344, 0 ],
[1294, 345, 0 ],
[1295, 346, 0 ],
[1296, 347, 0 ],
[1297, 348, 0 ],
[1298, 350, 0 ],
[1299, 352, 0 ],
[1300, 353, 0 ],
[1301, 354, 0 ],
[1302, 355, 0 ],
[1303, 356, 0 ],
[1304, 357, 0 ],
[1305, 359, 0 ],
[1306, 361, 0 ],
[1307, 362, 0 ],
[1308, 363, 0 ],
[1309, 364, 0 ],
[1310, 365, 0 ],
[1311, 366, 0 ],
[1312, 367, 0 ],
[1313, 368, 0 ],
[1314, 369, 0 ],
[1315, 370, 0 ],
[1316, 371, 0 ],
[1317, 372, 0 ],
[1318, 373, 0 ],
[1319, 374, 0 ],
[1320, 375, 0 ],
[1321, 376, 0 ],
[1322, 377, 0 ],
[1323, 378, 0 ],
[1324, 379, 0 ],
[1325, 381, 0 ],
[1326, 384, 0 ],
[1327, 385, 0 ],
[1328, 386, 0 ],
[1329, 387, 0 ],
[1330, 388, 0 ],
[1331, 390, 0 ],
[1332, 391, 0 ],
[1333, 392, 0 ],
[1334, 393, 0 ],
[1335, 394, 0 ],
[1336, 395, 0 ],
[1337, 396, 0 ],
[1338, 397, 0 ],
[1339, 398, 0 ],
[1340, 399, 0 ],
[1341, 400, 0 ],
[1342, 403, 0 ],
[1343, 404, 0 ],
[1344, 405, 0 ],
[1345, 406, 0 ],
[1346, 407, 0 ],
[1347, 408, 0 ],
[1348, 410, 0 ],
[1349, 411, 0 ],
[1350, 412, 0 ],
[1351, 413, 0 ],
[1352, 414, 0 ],
[1354, 417, 0 ],
[1355, 418, 0 ],
[1356, 419, 0 ],
[1357, 420, 0 ],
[1358, 421, 0 ],
[1359, 422, 0 ],
[1360, 423, 0 ],
[1361, 424, 0 ],
[1362, 425, 0 ],
[1363, 426, 0 ],
[1364, 427, 0 ],
[1365, 428, 0 ],
[1366, 429, 0 ],
[1367, 430, 0 ],
[1368, 431, 0 ],
[1369, 432, 0 ],
[1370, 433, 0 ],
[1371, 434, 0 ],
[1372, 435, 0 ],
[1373, 436, 0 ],
[1376, 439, 0 ],
[1377, 440, 0 ],
[1378, 441, 0 ],
[1379, 442, 0 ],
[1380, 443, 0 ],
[1381, 445, 0 ],
[1382, 446, 0 ],
[1383, 447, 0 ],
[1384, 448, 0 ],
[1385, 449, 0 ],
[1386, 450, 0 ],
[1387, 451, 0 ],
[1388, 453, 0 ],
[1389, 454, 0 ],
[1390, 455, 0 ],
[1391, 456, 0 ],
[1392, 457, 0 ],
[1393, 458, 0 ],
[1394, 459, 0 ],
[1395, 460, 0 ],
[1396, 461, 0 ],
[1397, 462, 0 ],
[1398, 463, 0 ],
[1399, 464, 0 ],
[1400, 465, 0 ],
[1401, 466, 0 ],
[1402, 467, 0 ],
[1403, 468, 0 ],
[1404, 469, 0 ],
[1405, 470, 0 ],
[1406, 471, 0 ],
[1407, 472, 0 ],
[1408, 473, 0 ],
[1409, 474, 0 ],
[1410, 475, 0 ],
[1411, 476, 0 ],
[1412, 477, 0 ],
[1413, 478, 0 ],
[1414, 479, 0 ],
[1415, 480, 0 ],
[1416, 481, 0 ],
[1417, 482, 0 ],
[1418, 483, 0 ],
[1419, 484, 0 ],
[1420, 485, 0 ],
[1421, 486, 0 ],
[1422, 487, 0 ],
[1423, 488, 0 ],
[1424, 489, 0 ],
[1425, 490, 0 ],
[1426, 491, 0 ],
[1427, 492, 0 ],
[1428, 493, 0 ],
[1429, 494, 0 ],
[1430, 495, 0 ],
[1431, 496, 0 ],
[1432, 497, 0 ],
[1433, 498, 0 ],
[1434, 499, 0 ],
[1435, 500, 0 ],
[1436, 501, 0 ],
[1437, 502, 0 ],
[1438, 503, 0 ],
[1439, 504, 0 ],
[1440, 505, 0 ],
[1441, 506, 0 ],
[1442, 507, 0 ],
[1443, 508, 0 ],
[1444, 509, 0 ],
[1445, 510, 0 ],
[1446, 511, 0 ],
[1447, 512, 0 ],
[1448, 513, 0 ],
[1449, 514, 0 ],
[1450, 515, 0 ],
[1451, 516, 0 ],
[1452, 517, 0 ],
[1453, 518, 0 ],
[1454, 519, 0 ],
[1455, 520, 0 ],
[1456, 521, 0 ],
[1457, 522, 0 ],
[1458, 523, 0 ],
[1459, 524, 0 ],
[1460, 525, 0 ],
[1461, 526, 0 ],
[1462, 527, 0 ],
[1463, 528, 0 ],
[1464, 529, 0 ],
[1465, 530, 0 ],
[1466, 531, 0 ],
[1467, 532, 0 ],
[1468, 533, 0 ],
[1469, 534, 0 ],
[1470, 535, 0 ],
[1471, 536, 0 ],
[1472, 537, 0 ],
[1473, 538, 0 ],
[1474, 539, 0 ],
[1475, 540, 0 ],
[1476, 541, 0 ],
[1477, 542, 0 ],
[1478, 543, 0 ],
[1479, 544, 0 ],
[1480, 545, 0 ],
[1481, 546, 0 ],
[1482, 547, 0 ],
[1483, 548, 0 ],
[1484, 549, 0 ],
[1485, 550, 0 ],
[1486, 551, 0 ],
[1487, 552, 0 ],
[1488, 554, 0 ],
[1489, 555, 0 ],
[1490, 556, 0 ],
[1491, 557, 0 ],
[1492, 558, 0 ],
[1493, 559, 0 ],
[1494, 560, 0 ],
[1495, 561, 0 ],
[1496, 562, 0 ],
[1497, 563, 0 ],
[1498, 564, 0 ],
[1499, 565, 0 ],
[1500, 566, 0 ],
[1501, 567, 0 ],
[1502, 568, 0 ],
[1503, 569, 0 ],
[1504, 570, 0 ],
[1505, 571, 0 ],
[1506, 572, 0 ],
[1507, 573, 0 ],
[1508, 574, 0 ],
[1509, 575, 0 ],
[1510, 576, 0 ],
[1511, 577, 0 ],
[1512, 578, 0 ],
[1513, 579, 0 ],
[1514, 580, 0 ],
[1515, 581, 0 ],
[1516, 582, 0 ],
[1517, 583, 0 ],
[1518, 584, 0 ],
[1519, 585, 0 ],
[1, 490, 0 ],
[3, 4, 1 ],
[491, 6, 0 ],
[7, 5, 0 ],
[8, 9, 0 ],
[492, 11, 0 ],
[11, 493, 0 ],
[492, 493, 1 ],
[494, 14, 0 ],
[13, 15, 0 ],
[16, 5, 0 ],
[17, 18, 1 ],
[17, 12, 0 ],
[14, 495, 0 ],
[494, 19, 0 ],
[20, 21, 0 ],
[20, 22, 1 ],
[497, 23, 0 ],
[23, 499, 1 ],
[25, 26, 0 ],
[25, 22, 0 ],
[23, 27, 0 ],
[28, 23, 0 ],
[8, 21, 0 ],
[9, 29, 0 ],
[30, 25, 1 ],
[31, 32, 1 ],
[32, 33, 1 ],
[34, 35, 0 ],
[35, 36, 0 ],
[490, 6, 1 ],
[37, 10, 1 ],
[10, 38, 0 ],
[37, 38, 1 ],
[39, 40, 1 ],
[39, 41, 1 ],
[42, 41, 1 ],
[18, 42, 1 ],
[492, 43, 1 ],
[44, 45, 0 ],
[44, 505, 0 ],
[46, 12, 0 ],
[47, 48, 0 ],
[49, 50, 0 ],
[31, 33, 1 ],
[31, 51, 0 ],
[52, 53, 1 ],
[52, 54, 0 ],
[506, 55, 0 ],
[506, 507, 1 ],
[57, 506, 0 ],
[57, 58, 0 ],
[58, 506, 0 ],
[59, 60, 1 ],
[508, 62, 0 ],
[30, 61, 1 ],
[63, 506, 0 ],
[13, 64, 0 ],
[65, 66, 1 ],
[59, 67, 0 ],
[61, 67, 0 ],
[68, 69, 1 ],
[70, 69, 1 ],
[71, 72, 1 ],
[73, 74, 1 ],
[37, 75, 1 ],
[72, 75, 0 ],
[37, 72, 1 ],
[76, 77, 1 ],
[77, 51, 0 ],
[73, 72, 1 ],
[18, 40, 1 ],
[492, 45, 1 ],
[10, 74, 1 ],
[45, 511, 1 ],
[78, 32, 1 ],
[79, 80, 0 ],
[81, 79, 1 ],
[34, 82, 0 ],
[83, 84, 0 ],
[83, 499, 0 ],
[85, 86, 0 ],
[87, 86, 1 ],
[88, 89, 0 ],
[90, 86, 1 ],
[91, 86, 0 ],
[86, 92, 0 ],
[86, 93, 0 ],
[94, 86, 1 ],
[86, 95, 1 ],
[513, 517, 0 ],
[97, 66, 1 ],
[42, 98, 0 ],
[99, 100, 1 ],
[42, 101, 0 ],
[102, 42, 1 ],
[103, 87, 0 ],
[104, 103, 0 ],
[105, 87, 0 ],
[106, 107, 0 ],
[108, 107, 0 ],
[109, 106, 0 ],
[110, 111, 1 ],
[87, 112, 0 ],
[113, 87, 0 ],
[87, 85, 1 ],
[110, 114, 1 ],
[115, 116, 0 ],
[117, 118, 0 ],
[117, 119, 0 ],
[117, 120, 1 ],
[121, 122, 0 ],
[123, 124, 0 ],
[125, 126, 0 ],
[127, 119, 0 ],
[118, 128, 0 ],
[121, 119, 0 ],
[530, 527, 0 ],
[125, 130, 0 ],
[125, 123, 0 ],
[131, 132, 0 ],
[133, 123, 0 ],
[524, 134, 0 ],
[135, 136, 0 ],
[123, 131, 0 ],
[117, 128, 1 ],
[137, 521, 0 ],
[531, 514, 0 ],
[139, 521, 0 ],
[140, 514, 0 ],
[522, 141, 0 ],
[142, 523, 0 ],
[530, 526, 0 ],
[140, 532, 0 ],
[142, 144, 0 ],
[140, 522, 0 ],
[145, 146, 0 ],
[147, 523, 0 ],
[144, 523, 0 ],
[139, 523, 0 ],
[140, 141, 0 ],
[528, 526, 0 ],
[528, 148, 0 ],
[149, 150, 0 ],
[145, 528, 0 ],
[530, 151, 0 ],
[524, 152, 0 ],
[149, 525, 1 ],
[139, 514, 0 ],
[126, 120, 1 ],
[530, 153, 0 ],
[528, 147, 1 ],
[528, 154, 0 ],
[130, 120, 1 ],
[528, 155, 1 ],
[524, 533, 0 ],
[524, 149, 0 ],
[154, 150, 0 ],
[157, 110, 1 ],
[119, 158, 0 ],
[159, 60, 0 ],
[536, 161, 0 ],
[115, 151, 0 ],
[162, 134, 0 ],
[115, 526, 0 ],
[138, 87, 0 ],
[123, 163, 0 ],
[112, 164, 0 ],
[112, 165, 0 ],
[166, 165, 0 ],
[167, 537, 0 ],
[168, 104, 0 ],
[531, 520, 0 ],
[139, 520, 0 ],
[520, 169, 0 ],
[168, 105, 0 ],
[520, 170, 0 ],
[171, 89, 0 ],
[521, 172, 0 ],
[123, 173, 0 ],
[521, 174, 0 ],
[37, 39, 0 ],
[530, 175, 0 ],
[530, 176, 0 ],
[88, 530, 0 ],
[177, 496, 1 ],
[178, 525, 0 ],
[179, 493, 1 ],
[180, 181, 1 ],
[182, 180, 0 ],
[179, 181, 0 ],
[180, 493, 1 ],
[183, 30, 0 ],
[183, 21, 0 ],
[538, 185, 0 ],
[538, 89, 0 ],
[184, 186, 0 ],
[184, 187, 0 ],
[520, 172, 0 ],
[89, 175, 0 ],
[185, 89, 0 ],
[89, 188, 0 ],
[189, 190, 0 ],
[539, 172, 0 ],
[504, 192, 0 ],
[105, 186, 0 ],
[105, 187, 0 ],
[539, 193, 0 ],
[187, 194, 0 ],
[539, 540, 0 ],
[539, 196, 0 ],
[197, 540, 0 ],
[110, 198, 0 ],
[197, 539, 0 ],
[199, 537, 0 ],
[134, 526, 0 ],
[200, 193, 0 ],
[4, 201, 1 ],
[202, 86, 0 ],
[85, 203, 0 ],
[147, 204, 0 ],
[147, 205, 0 ],
[123, 206, 0 ],
[537, 207, 0 ],
[165, 208, 0 ],
[4, 94, 1 ],
[4, 2, 0 ],
[209, 4, 0 ],
[119, 163, 0 ],
[210, 3, 0 ],
[99, 211, 0 ],
[99, 69, 1 ],
[212, 99, 0 ],
[213, 214, 0 ],
[510, 215, 0 ],
[128, 69, 1 ],
[216, 69, 1 ],
[217, 98, 0 ],
[504, 218, 0 ],
[177, 504, 1 ],
[219, 209, 0 ],
[219, 220, 0 ],
[94, 95, 1 ],
[159, 221, 1 ],
[34, 161, 0 ],
[222, 221, 0 ],
[211, 52, 1 ],
[215, 223, 1 ],
[224, 215, 0 ],
[225, 224, 1 ],
[224, 223, 0 ],
[226, 6, 0 ],
[7, 3, 1 ],
[216, 227, 1 ],
[228, 229, 0 ],
[227, 230, 0 ],
[231, 53, 1 ],
[544, 545, 0 ],
[234, 235, 1 ],
[546, 214, 1 ],
[233, 227, 0 ],
[237, 238, 0 ],
[212, 100, 0 ],
[519, 239, 0 ],
[238, 519, 0 ],
[213, 240, 0 ],
[241, 242, 1 ],
[70, 241, 0 ],
[509, 213, 0 ],
[68, 243, 0 ],
[243, 244, 0 ],
[68, 244, 0 ],
[544, 547, 1 ],
[245, 227, 1 ],
[246, 208, 0 ],
[112, 208, 0 ],
[165, 247, 0 ],
[537, 549, 0 ],
[537, 550, 0 ],
[537, 551, 0 ],
[110, 251, 0 ],
[510, 252, 1 ],
[529, 253, 1 ],
[237, 239, 1 ],
[254, 238, 1 ],
[69, 255, 0 ],
[510, 225, 1 ],
[256, 257, 0 ],
[258, 190, 0 ],
[258, 259, 0 ],
[260, 261, 1 ],
[554, 553, 1 ],
[515, 263, 0 ],
[14, 264, 1 ],
[116, 555, 0 ],
[151, 116, 0 ],
[111, 114, 1 ],
[77, 111, 0 ],
[266, 525, 0 ],
[267, 120, 1 ],
[268, 269, 0 ],
[556, 271, 0 ],
[556, 272, 0 ],
[529, 273, 0 ],
[128, 274, 0 ],
[34, 275, 0 ],
[503, 276, 0 ],
[503, 504, 1 ],
[177, 218, 1 ],
[277, 278, 1 ],
[557, 558, 1 ],
[557, 559, 1 ],
[559, 558, 1 ],
[277, 78, 1 ],
[277, 279, 1 ],
[78, 279, 0 ],
[281, 282, 0 ],
[283, 161, 1 ],
[268, 161, 1 ],
[256, 284, 0 ],
[515, 516, 1 ],
[263, 516, 0 ],
[516, 285, 0 ],
[63, 286, 0 ],
[287, 516, 0 ],
[8, 102, 1 ],
[8, 101, 1 ],
[80, 288, 0 ],
[80, 289, 0 ],
[276, 560, 0 ],
[37, 290, 0 ],
[290, 74, 1 ],
[512, 291, 0 ],
[78, 292, 1 ],
[199, 548, 0 ],
[491, 293, 0 ],
[4, 294, 0 ],
[490, 541, 1 ],
[491, 295, 0 ],
[491, 296, 0 ],
[295, 297, 0 ],
[508, 161, 0 ],
[117, 123, 0 ],
[133, 117, 0 ],
[71, 74, 1 ],
[74, 278, 1 ],
[298, 515, 0 ],
[5, 299, 0 ],
[32, 292, 1 ],
[5, 29, 1 ],
[503, 560, 0 ],
[300, 301, 1 ],
[51, 300, 0 ],
[244, 302, 1 ],
[31, 302, 1 ],
[51, 282, 1 ],
[303, 304, 0 ],
[305, 304, 0 ],
[305, 259, 0 ],
[306, 307, 1 ],
[305, 308, 0 ],
[305, 309, 0 ],
[310, 309, 1 ],
[306, 309, 1 ],
[311, 280, 0 ],
[280, 278, 1 ],
[311, 32, 1 ],
[13, 312, 1 ],
[313, 314, 0 ],
[312, 313, 1 ],
[547, 566, 1 ],
[245, 315, 1 ],
[312, 316, 0 ],
[312, 314, 0 ],
[554, 546, 1 ],
[262, 216, 1 ],
[317, 233, 0 ],
[318, 317, 0 ],
[231, 52, 1 ],
[319, 567, 0 ],
[557, 321, 0 ],
[277, 65, 1 ],
[322, 288, 1 ],
[322, 323, 0 ],
[277, 324, 1 ],
[324, 325, 0 ],
[277, 325, 0 ],
[326, 327, 0 ],
[328, 326, 1 ],
[328, 327, 1 ],
[326, 329, 0 ],
[568, 329, 1 ],
[568, 326, 0 ],
[332, 78, 1 ],
[333, 306, 0 ],
[332, 333, 0 ],
[332, 334, 0 ],
[66, 334, 1 ],
[330, 335, 1 ],
[336, 66, 0 ],
[330, 336, 1 ],
[68, 70, 0 ],
[509, 337, 1 ],
[324, 288, 0 ],
[338, 559, 0 ],
[339, 559, 0 ],
[339, 340, 1 ],
[559, 340, 1 ],
[341, 292, 0 ],
[557, 342, 0 ],
[558, 343, 0 ],
[502, 340, 1 ],
[72, 32, 1 ],
[344, 345, 0 ],
[346, 47, 0 ],
[46, 47, 0 ],
[346, 345, 0 ],
[347, 328, 0 ],
[347, 348, 1 ],
[571, 348, 1 ],
[347, 572, 0 ],
[571, 570, 1 ],
[14, 350, 0 ],
[350, 573, 0 ],
[15, 351, 1 ],
[352, 15, 0 ],
[15, 335, 1 ],
[232, 227, 0 ],
[565, 544, 1 ],
[235, 567, 1 ],
[567, 286, 0 ],
[353, 519, 0 ],
[354, 353, 0 ],
[355, 354, 0 ],
[354, 356, 0 ],
[357, 358, 0 ],
[574, 359, 0 ],
[235, 575, 0 ],
[167, 361, 0 ],
[528, 362, 0 ],
[363, 344, 0 ],
[259, 364, 1 ],
[54, 56, 0 ],
[365, 364, 0 ],
[231, 366, 0 ],
[30, 367, 0 ],
[61, 367, 1 ],
[254, 368, 0 ],
[254, 369, 0 ],
[254, 370, 0 ],
[99, 358, 0 ],
[354, 519, 0 ],
[571, 371, 0 ],
[207, 372, 0 ],
[57, 373, 0 ],
[209, 374, 0 ],
[375, 376, 0 ],
[376, 377, 0 ],
[16, 49, 0 ],
[318, 377, 0 ],
[378, 297, 0 ],
[562, 379, 0 ],
[576, 563, 0 ],
[576, 381, 0 ],
[577, 576, 1 ],
[244, 383, 0 ],
[244, 306, 1 ],
[383, 306, 1 ],
[380, 306, 0 ],
[252, 225, 0 ],
[220, 76, 0 ],
[542, 384, 0 ],
[385, 384, 0 ],
[542, 385, 0 ],
[386, 385, 0 ],
[387, 578, 0 ],
[332, 388, 1 ],
[382, 332, 1 ],
[382, 388, 0 ],
[579, 578, 0 ],
[577, 387, 1 ],
[144, 390, 0 ],
[37, 49, 0 ],
[391, 233, 0 ],
[392, 310, 0 ],
[260, 393, 0 ],
[394, 230, 0 ],
[395, 282, 1 ],
[395, 244, 0 ],
[25, 396, 1 ],
[81, 74, 0 ],
[278, 80, 1 ],
[81, 278, 1 ],
[569, 570, 0 ],
[397, 552, 0 ],
[542, 398, 0 ],
[398, 385, 0 ],
[399, 499, 0 ],
[83, 399, 0 ],
[498, 400, 0 ],
[518, 239, 1 ],
[575, 543, 0 ],
[401, 360, 0 ],
[580, 581, 0 ],
[401, 402, 0 ],
[403, 231, 0 ],
[189, 360, 1 ],
[234, 404, 0 ],
[235, 404, 1 ],
[235, 580, 0 ],
[216, 259, 0 ],
[405, 259, 0 ],
[405, 318, 0 ],
[406, 230, 0 ],
[542, 407, 0 ],
[23, 408, 0 ],
[577, 348, 0 ],
[562, 564, 1 ],
[582, 507, 0 ],
[27, 410, 0 ],
[501, 27, 0 ],
[27, 411, 0 ],
[411, 410, 0 ],
[403, 360, 0 ],
[412, 360, 0 ],
[326, 413, 0 ],
[414, 413, 0 ],
[6, 297, 0 ],
[554, 580, 1 ],
[262, 401, 1 ],
[499, 556, 1 ],
[224, 229, 0 ],
[583, 507, 0 ],
[415, 307, 0 ],
[416, 507, 0 ],
[284, 561, 0 ],
[543, 417, 0 ],
[418, 506, 0 ],
[220, 157, 0 ],
[295, 419, 0 ],
[295, 420, 0 ],
[541, 62, 0 ],
[52, 421, 0 ],
[60, 160, 0 ],
[535, 161, 0 ],
[267, 282, 0 ],
[52, 365, 0 ],
[28, 27, 0 ],
[30, 201, 1 ],
[422, 81, 0 ],
[119, 425, 0 ],
[423, 425, 0 ],
[424, 425, 0 ],
[426, 428, 0 ],
[427, 428, 0 ],
[19, 428, 1 ],
[45, 429, 0 ],
[44, 429, 0 ],
[505, 429, 0 ],
[231, 431, 1 ],
[190, 431, 1 ],
[430, 431, 0 ],
[286, 433, 0 ],
[432, 433, 0 ],
[506, 433, 0 ],
[23, 434, 0 ],
[400, 434, 0 ],
[500, 434, 0 ],
[32, 436, 0 ],
[435, 436, 0 ],
[78, 436, 1 ],
[86, 438, 1 ],
[437, 438, 0 ],
[221, 438, 0 ],
[207, 439, 0 ],
[516, 439, 0 ],
[513, 439, 0 ],
[181, 441, 1 ],
[440, 441, 0 ],
[504, 441, 1 ],
[135, 442, 0 ],
[109, 442, 0 ],
[112, 442, 0 ],
[113, 443, 0 ],
[132, 443, 0 ],
[107, 443, 0 ],
[444, 445, 0 ],
[112, 445, 0 ],
[109, 445, 0 ],
[119, 447, 1 ],
[100, 447, 1 ],
[446, 447, 0 ],
[124, 448, 0 ],
[125, 448, 0 ],
[131, 448, 0 ],
[449, 450, 0 ],
[173, 450, 0 ],
[184, 450, 0 ],
[144, 451, 0 ],
[140, 451, 0 ],
[514, 451, 0 ],
[537, 585, 1 ],
[141, 585, 0 ],
[584, 585, 0 ],
[522, 454, 0 ],
[144, 454, 0 ],
[453, 454, 0 ],
[199, 456, 0 ],
[140, 456, 0 ],
[455, 456, 0 ],
[537, 456, 0 ],
[538, 457, 0 ],
[153, 457, 0 ],
[176, 457, 0 ],
[524, 459, 0 ],
[458, 459, 0 ],
[134, 459, 0 ],
[460, 461, 0 ],
[150, 461, 0 ],
[149, 461, 0 ],
[521, 463, 0 ],
[462, 463, 0 ],
[538, 463, 0 ],
[110, 464, 0 ],
[90, 464, 0 ],
[165, 464, 0 ],
[458, 465, 0 ],
[134, 465, 0 ],
[524, 465, 0 ],
[466, 467, 0 ],
[110, 467, 0 ],
[165, 467, 0 ],
[468, 469, 0 ],
[541, 469, 0 ],
[490, 469, 0 ],
[263, 471, 0 ],
[470, 471, 0 ],
[534, 471, 0 ],
[136, 472, 0 ],
[110, 472, 0 ],
[251, 472, 0 ],
[226, 474, 0 ],
[473, 474, 0 ],
[257, 474, 0 ],
[6, 474, 1 ],
[299, 475, 1 ],
[3, 475, 0 ],
[210, 475, 0 ],
[297, 476, 0 ],
[296, 476, 0 ],
[295, 476, 0 ],
[313, 478, 1 ],
[477, 478, 0 ],
[245, 478, 0 ],
[479, 481, 0 ],
[565, 481, 0 ],
[480, 481, 0 ],
[415, 482, 0 ],
[56, 482, 0 ],
[409, 482, 0 ],
[483, 484, 0 ],
[3, 484, 0 ],
[301, 484, 0 ],
[233, 485, 0 ],
[392, 485, 0 ],
[391, 485, 0 ],
[579, 488, 0 ],
[486, 488, 0 ],
[487, 488, 0 ],
[270, 489, 0 ],
[331, 489, 0 ],
[396, 489, 1 ],
[519, 253, 0 ],
[382, 349, 1 ],
[349, 351, 0 ],
[459, 465, 0 ],
[549, 550, 0 ],
[550, 551, 0 ],
[194, 195, 0 ],
[247, 248, 0 ],
[2, 294, 0 ],
[549, 551, 0 ],
[54, 365, 0 ],
[131, 265, 0 ],
[91, 92, 0 ],
[247, 249, 0 ],
[186, 191, 0 ],
[129, 173, 0 ],
[96, 202, 0 ],
[53, 320, 0 ],
[24, 396, 0 ],
[133, 156, 0 ],
[442, 452, 0 ],
[445, 452, 0 ],
[247, 250, 0 ],
[187, 195, 0 ],
[216, 236, 0 ],
[244, 389, 0 ],
[394, 406, 0 ],
[442, 445, 0 ],
[442, 444, 0 ],
[198, 472, 0 ],
[464, 467, 0 ],
[198, 251, 0 ],
[112, 143, 0 ],
[2, 490, 0 ],
[5, 491, 0 ],
[10, 492, 0 ],
[12, 493, 0 ],
[13, 494, 0 ],
[15, 495, 0 ],
[18, 496, 0 ],
[20, 497, 0 ],
[22, 498, 0 ],
[24, 499, 0 ],
[26, 500, 0 ],
[30, 501, 0 ],
[32, 502, 0 ],
[37, 503, 0 ],
[42, 504, 0 ],
[46, 505, 0 ],
[52, 506, 0 ],
[56, 507, 0 ],
[61, 508, 0 ],
[68, 509, 0 ],
[69, 510, 0 ],
[74, 511, 0 ],
[78, 512, 0 ],
[86, 513, 0 ],
[87, 514, 0 ],
[94, 515, 0 ],
[95, 516, 0 ],
[96, 517, 0 ],
[99, 518, 0 ],
[100, 519, 0 ],
[104, 520, 0 ],
[105, 521, 0 ],
[106, 522, 0 ],
[107, 523, 0 ],
[117, 524, 0 ],
[120, 525, 0 ],
[123, 526, 0 ],
[124, 527, 0 ],
[125, 528, 0 ],
[128, 529, 0 ],
[129, 530, 0 ],
[138, 531, 0 ],
[143, 532, 0 ],
[156, 533, 0 ],
[157, 534, 0 ],
[159, 535, 0 ],
[160, 536, 0 ],
[165, 537, 0 ],
[184, 538, 0 ],
[191, 539, 0 ],
[195, 540, 0 ],
[201, 541, 0 ],
[220, 542, 0 ],
[231, 543, 0 ],
[232, 544, 0 ],
[233, 545, 0 ],
[236, 546, 0 ],
[245, 547, 0 ],
[246, 548, 0 ],
[248, 549, 0 ],
[249, 550, 0 ],
[250, 551, 0 ],
[259, 552, 0 ],
[261, 553, 0 ],
[262, 554, 0 ],
[265, 555, 0 ],
[270, 556, 0 ],
[277, 557, 0 ],
[279, 558, 0 ],
[280, 559, 0 ],
[290, 560, 0 ],
[301, 561, 0 ],
[305, 562, 0 ],
[306, 563, 0 ],
[310, 564, 0 ],
[313, 565, 0 ],
[315, 566, 0 ],
[320, 567, 0 ],
[330, 568, 0 ],
[332, 569, 0 ],
[334, 570, 0 ],
[336, 571, 0 ],
[349, 572, 0 ],
[351, 573, 0 ],
[358, 574, 0 ],
[360, 575, 0 ],
[380, 576, 0 ],
[382, 577, 0 ],
[383, 578, 0 ],
[389, 579, 0 ],
[401, 580, 0 ],
[402, 581, 0 ],
[409, 582, 0 ],
[415, 583, 0 ],
[444, 584, 0 ],
[452, 585, 0 ]
])
ppc["parameters"] = {
"x_trans_sg": 0.003,
"x_trans_fm": 0.001,
"x_trans_fl": 0.001,
"d_l": 1e-3,
"d_l_perturb": 1e-5,
"w_1_ij": 1,
"w_2_ij": 1,
"w_3_ij": 1,
"w_4_ij": 1,
"b_r": 238,
"b_c": 248 }
return ppc
| 70.933322
| 137
| 0.463252
|
from numpy import array
def scigrid_2011_01_06_19():
ppc = {"version": '2'}
ppc["baseMVA"] = 100.0
ppc["bus"] = array([
[586, 3, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[589, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[590, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[593, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[594, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[595, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[598, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[599, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[601, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[602, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[603, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[607, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[608, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[609, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[612, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[613, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[614, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[616, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[617, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[618, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[619, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[621, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[624, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[628, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[629, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[631, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[632, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[637, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[638, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[640, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[641, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[642, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[643, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[647, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[650, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[652, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[655, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[663, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[666, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[670, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[672, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[676, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[681, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[683, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[687, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[689, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[691, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[694, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[695, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[696, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[697, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[698, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[702, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[705, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[707, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[713, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[714, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[716, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[717, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[719, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[722, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[723, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[724, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[727, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[728, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[730, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[732, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[735, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[738, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[741, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[742, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[743, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[746, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[747, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[748, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[749, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[750, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[753, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[758, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[760, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[761, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[762, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[763, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[765, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[767, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[769, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[771, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[772, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[774, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[777, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[778, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[781, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[784, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[785, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[787, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[788, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[789, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[791, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[792, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[795, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[800, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[801, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[802, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[805, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[806, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[808, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[809, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[811, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[814, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[816, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[817, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[821, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[822, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[826, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[830, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[834, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[835, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[836, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[837, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[839, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[841, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[843, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[844, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[845, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[849, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[850, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[851, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[853, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[855, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[856, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[857, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[858, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[859, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[860, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[864, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[865, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[867, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[869, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[870, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[872, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[873, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[874, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[875, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[877, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[881, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[882, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[883, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[885, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[886, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[889, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[890, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[893, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[894, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[895, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[896, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[898, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[900, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[902, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[903, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[905, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[906, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[907, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[909, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[915, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[917, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[918, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[920, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[921, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[922, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[923, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[925, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[931, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[935, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[936, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[937, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[939, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[940, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[944, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[950, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[952, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[957, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[958, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[959, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[960, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[963, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[965, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[966, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[967, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[968, 2, 0, 0, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[969, 2, 0, 0, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[971, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[973, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[976, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[978, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[981, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[982, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[983, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[984, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[985, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[986, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[987, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[988, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[993, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[994, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[995, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[997, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[999, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1000, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1002, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1003, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1007, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1008, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1010, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1011, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1012, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1014, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1026, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1027, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1028, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1029, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1030, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1031, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1032, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1033, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1034, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1035, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1036, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1037, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1038, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1039, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1040, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1041, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1042, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1043, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1044, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1045, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1046, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1047, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1048, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1049, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1050, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1051, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1052, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1053, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1054, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1055, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1056, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1057, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1058, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1059, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1060, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1061, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1062, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1063, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1064, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1065, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1066, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1067, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1068, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1069, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1070, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1071, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1072, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1073, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1074, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1075, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1076, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1077, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1078, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1079, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1080, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1081, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1082, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1083, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1084, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1085, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1086, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1087, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1088, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1089, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1090, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1091, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1092, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1093, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1094, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1095, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1096, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1097, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1098, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1099, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1100, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1101, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1102, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1103, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1104, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1105, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1106, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1107, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1108, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1109, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1110, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1111, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1112, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1113, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1114, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1115, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1116, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1117, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1118, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1119, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1120, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1121, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1122, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1123, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1124, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1125, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1126, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1127, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1128, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1129, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1130, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1131, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1132, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1133, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1134, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1135, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1136, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1137, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1138, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1139, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1140, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1141, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1142, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1143, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1144, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1145, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1146, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1147, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1148, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1149, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1150, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1151, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1152, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1153, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1154, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1155, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1156, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1157, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1158, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1159, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1160, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1161, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1162, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1163, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1164, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1165, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1166, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1167, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1168, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1169, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1170, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1171, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1172, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1173, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1174, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1175, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1176, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1177, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1178, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1179, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1180, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1181, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1182, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1183, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1184, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1185, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1186, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1187, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1188, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1189, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1190, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1191, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1192, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1193, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1194, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1195, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1196, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1197, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1198, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1201, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1202, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1203, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1204, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1205, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1206, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1207, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1208, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1209, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1210, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1211, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1212, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1213, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1214, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1215, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1216, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1217, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1218, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1219, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1220, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1221, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1222, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1223, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1224, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1225, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1226, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1227, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1228, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1229, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1230, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1231, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1232, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1235, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1236, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1237, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1238, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1239, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1240, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1241, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1242, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1243, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1244, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1245, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1246, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1247, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1248, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1249, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1250, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1251, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1252, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1253, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1254, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1255, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1256, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1257, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1258, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1259, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1260, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1261, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1262, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1263, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1264, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1265, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1266, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1267, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1268, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1269, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1270, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1271, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1272, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1273, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1274, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1275, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1276, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1277, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1278, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1279, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1280, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1281, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1282, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1283, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1284, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1285, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1286, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1287, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1288, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1289, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1290, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1291, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1292, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1293, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1294, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1295, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1296, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1297, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1298, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1299, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1300, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1301, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1302, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1303, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1304, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1305, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1306, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1307, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1308, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1309, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1310, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1311, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1312, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1313, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1314, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1315, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1316, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1317, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1318, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1319, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1320, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1321, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1322, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1323, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1324, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1325, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1326, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1327, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1328, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1329, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1330, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1331, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1332, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1333, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1334, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1335, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1336, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1337, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1338, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1339, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1340, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1341, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1342, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1343, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1344, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1345, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1346, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1347, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1348, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1349, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1350, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1351, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1352, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1354, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1355, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1356, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1357, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1358, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1359, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1360, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1361, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1362, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1363, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1364, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1365, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1366, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1367, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1368, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1369, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1370, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1371, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1372, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1373, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1376, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1377, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1378, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1379, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1380, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1381, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1382, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1383, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1384, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1385, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1386, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1387, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1388, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1389, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1390, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1391, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1392, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1393, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1394, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1395, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1396, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1397, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1398, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1399, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1400, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1401, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1402, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1403, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1404, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1405, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1406, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1407, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1408, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1409, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1410, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1411, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1412, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1413, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1414, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1415, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1416, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1417, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1418, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1419, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1420, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1421, 2, 0, 0, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[1422, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1423, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1424, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[1425, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1426, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1427, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1428, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1429, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1430, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1431, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1432, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1433, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1434, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1435, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1436, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1437, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1438, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1439, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1440, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1441, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1442, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1443, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1444, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1445, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1446, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1447, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1448, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1449, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1450, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1451, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1452, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1453, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1454, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1455, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1456, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1457, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1458, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1459, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1460, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1461, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1462, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1463, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1464, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1465, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1466, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1467, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1468, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1469, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1470, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1471, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1472, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1473, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1474, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1475, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1476, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1477, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1478, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1479, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1480, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1481, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1482, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1483, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1484, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1485, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1486, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1487, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1488, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1489, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1490, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1491, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1492, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1493, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1494, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1495, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1496, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1497, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1498, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1499, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1500, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1501, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1502, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1503, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1504, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1505, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1506, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1507, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1508, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1509, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1510, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1511, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1512, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1513, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1514, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1515, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1516, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1517, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1518, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1519, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[1, 1, 299.357139, 59.871428, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[2, 1, 0, 0, 0, 0, 0, 1.000011, 0, 380.0, 0, 1.1, 0.9 ],
[3, 1, 52.469255, 10.493851, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[4, 1, 86.287429, 17.257486, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[5, 1, 0, 0, 0, 0, 0, 0.999623, 0, 380.0, 0, 1.1, 0.9 ],
[6, 1, 253.37566, 50.675132, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[7, 1, 190.950068, 38.190014, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[8, 1, 159.773374, 31.954675, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[9, 1, 108.052236, 21.610447, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[10, 1, 0, 0, 0, 0, 0, 1.000968, 0, 380.0, 0, 1.1, 0.9 ],
[11, 1, 94.672178, 18.934436, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[12, 1, 0, 0, 0, 0, 0, 1.000956, 0, 380.0, 0, 1.1, 0.9 ],
[13, 1, 0, 0, 0, 0, 0, 1.00017, 0, 380.0, 0, 1.1, 0.9 ],
[14, 1, 226.42112, 45.284224, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[15, 1, 0, 0, 0, 0, 0, 1.00024, 0, 380.0, 0, 1.1, 0.9 ],
[16, 1, 386.152959, 77.230592, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[17, 1, 90.949166, 18.189833, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[18, 1, 0, 0, 0, 0, 0, 1.002568, 0, 380.0, 0, 1.1, 0.9 ],
[19, 1, 224.701104, 44.940221, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[20, 1, 0, 0, 0, 0, 0, 0.999285, 0, 380.0, 0, 1.1, 0.9 ],
[21, 1, 966.249214, 193.249843, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[22, 1, 0, 0, 0, 0, 0, 1.000582, 0, 380.0, 0, 1.1, 0.9 ],
[23, 1, 126.514784, 25.302957, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[24, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[25, 1, 60.512903, 12.102581, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[26, 1, 0, 0, 0, 0, 0, 1.000747, 0, 380.0, 0, 1.1, 0.9 ],
[27, 1, 74.281264, 14.856253, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[28, 1, 219.47888, 43.895776, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[29, 1, 80.619162, 16.123832, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[30, 1, 0, 0, 0, 0, 0, 1.000284, 0, 380.0, 0, 1.1, 0.9 ],
[31, 1, 158.656429, 31.731286, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[32, 1, 0, 0, 0, 0, 0, 0.996654, 0, 380.0, 0, 1.1, 0.9 ],
[33, 1, 198.925347, 39.785069, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[34, 1, 39.465856, 7.893171, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[35, 1, 2.612848, 0.52257, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[36, 1, 8.650765, 1.730153, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[37, 1, 0, 0, 0, 0, 0, 1.002852, 0, 380.0, 0, 1.1, 0.9 ],
[38, 1, 208.416239, 41.683248, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[39, 1, 68.245581, 13.649116, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[40, 1, 71.284643, 14.256929, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[41, 1, 76.614835, 15.322967, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[42, 1, 0, 0, 0, 0, 0, 1.001121, 0, 380.0, 0, 1.1, 0.9 ],
[43, 1, 117.492302, 23.49846, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[44, 1, 150.313981, 30.062796, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[45, 1, 79.790022, 15.958004, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[46, 1, 0, 0, 0, 0, 0, 1.000273, 0, 380.0, 0, 1.1, 0.9 ],
[47, 1, 346.933422, 69.386684, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[48, 1, 238.470527, 47.694105, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[49, 1, 60.321012, 12.064202, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[50, 1, 87.835536, 17.567107, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[51, 1, 113.829121, 22.765824, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[52, 1, 0, 0, 0, 0, 0, 1.000133, 0, 380.0, 0, 1.1, 0.9 ],
[53, 1, 172.71746, 34.543492, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[54, 1, 87.750526, 17.550105, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[55, 1, 86.057622, 17.211524, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[56, 1, 0, 0, 0, 0, 0, 0.999733, 0, 380.0, 0, 1.1, 0.9 ],
[57, 1, 102.725918, 20.545184, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[58, 1, 235.309339, 47.061868, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[59, 1, 67.205785, 13.441157, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[60, 1, 35.432755, 7.086551, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[61, 1, 0, 0, 0, 0, 0, 0.999718, 0, 380.0, 0, 1.1, 0.9 ],
[62, 1, 270.131503, 54.026301, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[63, 1, 159.456313, 31.891263, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[64, 1, 1692.15463, 338.430926, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[65, 1, 5.638288, 1.127658, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[66, 1, 178.896436, 35.779287, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[67, 1, 383.762992, 76.752598, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[68, 1, 0, 0, 0, 0, 0, 0.998457, 0, 380.0, 0, 1.1, 0.9 ],
[69, 1, 0, 0, 0, 0, 0, 1.00038, 0, 380.0, 0, 1.1, 0.9 ],
[70, 1, 725.992049, 145.19841, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[71, 1, 168.711201, 33.74224, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[72, 1, 276.325796, 55.265159, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[73, 1, 88.4623, 17.69246, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[74, 1, 0, 0, 0, 0, 0, 1.003234, 0, 380.0, 0, 1.1, 0.9 ],
[75, 1, 110.255161, 22.051032, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[76, 1, 106.420421, 21.284084, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[77, 1, 103.07545, 20.61509, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[78, 1, 0, 0, 0, 0, 0, 0.995361, 0, 380.0, 0, 1.1, 0.9 ],
[79, 1, 106.433346, 21.286669, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[80, 1, 113.048636, 22.609727, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[81, 1, 127.616514, 25.523303, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[82, 1, 4.247153, 0.849431, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[83, 1, 284.165823, 56.833165, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[84, 1, 27.974372, 5.594874, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[85, 1, 97.009691, 19.401938, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[86, 1, 0, 0, 0, 0, 0, 1.000053, 0, 380.0, 0, 1.1, 0.9 ],
[87, 1, 0, 0, 0, 0, 0, 1.000327, 0, 380.0, 0, 1.1, 0.9 ],
[88, 1, 78.299678, 15.659936, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[89, 1, 97.142735, 19.428547, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[90, 1, 112.19557, 22.439114, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[91, 1, 38.971155, 7.794231, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[92, 1, 42.531201, 8.50624, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[93, 1, 41.714588, 8.342918, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[94, 1, 0, 0, 0, 0, 0, 1.00087, 0, 380.0, 0, 1.1, 0.9 ],
[95, 1, 0, 0, 0, 0, 0, 1.001187, 0, 380.0, 0, 1.1, 0.9 ],
[96, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[97, 1, 5.866845, 1.173369, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[98, 1, 107.867686, 21.573537, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[99, 1, 0, 0, 0, 0, 0, 1.000716, 0, 380.0, 0, 1.1, 0.9 ],
[100, 1, 0, 0, 0, 0, 0, 1.001847, 0, 380.0, 0, 1.1, 0.9 ],
[101, 1, 76.381322, 15.276264, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[102, 1, 147.839608, 29.567922, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[103, 1, 172.85311, 34.570622, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[104, 1, 0, 0, 0, 0, 0, 0.999953, 0, 380.0, 0, 1.1, 0.9 ],
[105, 1, 0, 0, 0, 0, 0, 1.000152, 0, 380.0, 0, 1.1, 0.9 ],
[106, 1, 0, 0, 0, 0, 0, 0.99996, 0, 380.0, 0, 1.1, 0.9 ],
[107, 1, 0, 0, 0, 0, 0, 1.000002, 0, 380.0, 0, 1.1, 0.9 ],
[108, 1, 121.926795, 24.385359, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[109, 1, 49.366079, 9.873216, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[110, 1, 64.079149, 12.81583, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[111, 1, 112.924775, 22.584955, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[112, 1, 57.154171, 11.430834, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[113, 1, 90.095678, 18.019136, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[114, 1, 132.688902, 26.53778, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[115, 1, 85.536735, 17.107347, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[116, 1, 143.133962, 28.626792, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[117, 1, 0, 0, 0, 0, 0, 1.000294, 0, 380.0, 0, 1.1, 0.9 ],
[118, 1, 221.622459, 44.324492, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[119, 1, 42.959533, 8.591907, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[120, 1, 0, 0, 0, 0, 0, 1.001148, 0, 380.0, 0, 1.1, 0.9 ],
[121, 1, 58.339066, 11.667813, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[122, 1, 51.07526, 10.215052, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[123, 1, 0, 0, 0, 0, 0, 1.000162, 0, 380.0, 0, 1.1, 0.9 ],
[124, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[125, 1, 0, 0, 0, 0, 0, 0.999713, 0, 380.0, 0, 1.1, 0.9 ],
[126, 1, 267.788855, 53.557771, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[127, 1, 207.028957, 41.405791, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[128, 1, 0, 0, 0, 0, 0, 1.001581, 0, 380.0, 0, 1.1, 0.9 ],
[129, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[130, 1, 285.45527, 57.091054, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[131, 1, 63.028277, 12.605655, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[132, 1, 164.116103, 32.823221, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[133, 1, 54.972465, 10.994493, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[134, 1, 54.747354, 10.949471, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[135, 1, 54.81994, 10.963988, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[136, 1, 53.105693, 10.621139, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[137, 1, 42.479666, 8.495933, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[138, 1, 0, 0, 0, 0, 0, 1.000183, 0, 380.0, 0, 1.1, 0.9 ],
[139, 1, 83.213361, 16.642672, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[140, 1, 57.545602, 11.50912, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[141, 1, 68.181381, 13.636276, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[142, 1, 75.023859, 15.004772, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[143, 1, 0, 0, 0, 0, 0, 0.999983, 0, 380.0, 0, 1.1, 0.9 ],
[144, 1, 68.338979, 13.667796, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[145, 1, 198.799897, 39.759979, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[146, 1, 256.290464, 51.258093, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[147, 1, 157.090963, 31.418193, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[148, 1, 221.684188, 44.336838, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[149, 1, 142.918192, 28.583638, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[150, 1, 186.594538, 37.318908, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[151, 1, 43.970718, 8.794144, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[152, 1, 91.278651, 18.25573, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[153, 1, 162.855958, 32.571192, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[154, 1, 167.285387, 33.457077, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[155, 1, 174.242515, 34.848503, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[156, 1, 0, 0, 0, 0, 0, 0.99999, 0, 380.0, 0, 1.1, 0.9 ],
[157, 1, 0, 0, 0, 0, 0, 1.001193, 0, 380.0, 0, 1.1, 0.9 ],
[158, 1, 45.9071, 9.18142, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[159, 1, 0, 0, 0, 0, 0, 0.999774, 0, 380.0, 0, 1.1, 0.9 ],
[160, 1, 0, 0, 0, 0, 0, 0.999991, 0, 380.0, 0, 1.1, 0.9 ],
[161, 1, 142.515256, 28.503051, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[162, 1, 213.018072, 42.603614, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[163, 1, 42.601603, 8.520321, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[164, 1, 42.772929, 8.554586, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[165, 1, 0, 0, 0, 0, 0, 0.999992, 0, 380.0, 0, 1.1, 0.9 ],
[166, 1, 50.008474, 10.001695, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[167, 1, 70.349335, 14.069867, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[168, 1, 48.012523, 9.602505, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[169, 1, 164.360711, 32.872142, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[170, 1, 123.503215, 24.700643, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[171, 1, 105.409947, 21.081989, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[172, 1, 51.73233, 10.346466, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[173, 1, 49.419687, 9.883937, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[174, 1, 74.161243, 14.832249, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[175, 1, 49.387297, 9.877459, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[176, 1, 172.096394, 34.419279, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[177, 1, 28.062824, 5.612565, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[178, 1, 148.627602, 29.72552, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[179, 1, 54.764142, 10.952828, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[180, 1, 48.139082, 9.627816, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[181, 1, 36.333993, 7.266799, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[182, 1, 1.645947, 0.329189, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[183, 1, 492.683663, 98.536733, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[184, 1, 0, 0, 0, 0, 0, 1.000023, 0, 380.0, 0, 1.1, 0.9 ],
[185, 1, 105.357552, 21.07151, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[186, 1, 56.734494, 11.346899, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[187, 1, 33.183902, 6.63678, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[188, 1, 49.387297, 9.877459, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[189, 1, 181.220426, 36.244085, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[190, 1, 239.697919, 47.939584, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[191, 1, 0, 0, 0, 0, 0, 0.999998, 0, 380.0, 0, 1.1, 0.9 ],
[192, 1, 57.72652, 11.545304, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[193, 1, 49.307632, 9.861526, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[194, 1, 34.037848, 6.80757, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[195, 1, 0, 0, 0, 0, 0, 0.999999, 0, 380.0, 0, 1.1, 0.9 ],
[196, 1, 47.753116, 9.550623, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[197, 1, 75.658474, 15.131695, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[198, 1, 44.770634, 8.954127, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[199, 1, 57.640701, 11.52814, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[200, 1, 49.388443, 9.877689, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[201, 1, 0, 0, 0, 0, 0, 1.00096, 0, 380.0, 0, 1.1, 0.9 ],
[202, 1, 50.609135, 10.121827, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[203, 1, 6.668207, 1.333641, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[204, 1, 195.44382, 39.088764, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[205, 1, 97.730708, 19.546142, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[206, 1, 46.90391, 9.380782, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[207, 1, 139.472027, 27.894405, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[208, 1, 41.069012, 8.213802, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[209, 1, 57.071575, 11.414315, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[210, 1, 65.564558, 13.112912, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[211, 1, 230.408553, 46.081711, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[212, 1, 57.748654, 11.549731, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[213, 1, 270.71278, 54.142556, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[214, 1, 182.155387, 36.431077, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[215, 1, 385.176656, 77.035331, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[216, 1, 129.876458, 25.975292, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[217, 1, 41.617029, 8.323406, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[218, 1, 126.787729, 25.357546, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[219, 1, 203.763334, 40.752667, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[220, 1, 0, 0, 0, 0, 0, 1.000027, 0, 380.0, 0, 1.1, 0.9 ],
[221, 1, 116.237427, 23.247485, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[222, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[223, 1, 115.198486, 23.039697, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[224, 1, 133.959969, 26.791994, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[225, 1, 240.532809, 48.106562, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[226, 1, 84.025543, 16.805109, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[227, 1, 104.678785, 20.935757, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[228, 1, 102.634351, 20.52687, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[229, 1, 227.112315, 45.422463, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[230, 1, 54.474503, 10.894901, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[231, 1, 0, 0, 0, 0, 0, 1.000717, 0, 380.0, 0, 1.1, 0.9 ],
[232, 1, 0, 0, 0, 0, 0, 0.999969, 0, 380.0, 0, 1.1, 0.9 ],
[233, 1, 0, 0, 0, 0, 0, 0.999805, 0, 380.0, 0, 1.1, 0.9 ],
[234, 1, 194.044238, 38.808848, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[235, 1, 63.1006, 12.62012, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[236, 1, 0, 0, 0, 0, 0, 0.999975, 0, 380.0, 0, 1.1, 0.9 ],
[237, 1, 0.522229, 0.104446, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[238, 1, 71.399477, 14.279895, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[239, 1, 98.64733, 19.729466, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[240, 1, 622.248439, 124.449688, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[241, 1, 460.442229, 92.088446, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[242, 1, 167.655348, 33.53107, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[243, 1, 135.264433, 27.052887, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[244, 1, 161.157525, 32.231505, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[245, 1, 0, 0, 0, 0, 0, 1.001372, 0, 380.0, 0, 1.1, 0.9 ],
[246, 1, 0, 0, 0, 0, 0, 0.999902, 0, 380.0, 0, 1.1, 0.9 ],
[247, 1, 31.9808, 6.39616, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[248, 1, 0, 0, 0, 0, 0, 0.999997, 0, 380.0, 0, 1.1, 0.9 ],
[249, 1, 0, 0, 0, 0, 0, 0.999996, 0, 380.0, 0, 1.1, 0.9 ],
[250, 1, 0, 0, 0, 0, 0, 0.999994, 0, 380.0, 0, 1.1, 0.9 ],
[251, 1, 79.369092, 15.873818, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[252, 1, 203.545412, 40.709082, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[253, 1, 89.364203, 17.872841, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[254, 1, 28.532507, 5.706501, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[255, 1, 140.320492, 28.064098, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[256, 1, 160.923187, 32.184637, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[257, 1, 77.66509, 15.533018, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[258, 1, 253.101148, 50.62023, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[259, 1, 0, 0, 0, 0, 0, 0.999295, 0, 380.0, 0, 1.1, 0.9 ],
[260, 1, 157.520213, 31.504043, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[261, 1, 0, 0, 0, 0, 0, 1.002014, 0, 380.0, 0, 1.1, 0.9 ],
[262, 1, 0, 0, 0, 0, 0, 0.999674, 0, 380.0, 0, 1.1, 0.9 ],
[263, 1, 225.962539, 45.192508, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[264, 1, 292.520695, 58.504139, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[265, 1, 0, 0, 0, 0, 0, 1.000009, 0, 380.0, 0, 1.1, 0.9 ],
[266, 1, 140.975489, 28.195098, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[267, 1, 178.303407, 35.660681, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[268, 1, 62.00365, 12.40073, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[269, 1, 49.791256, 9.958251, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[270, 1, 0, 0, 0, 0, 0, 0.99999, 0, 380.0, 0, 1.1, 0.9 ],
[271, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[272, 1, 1.015925, 0.203185, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[273, 1, 138.928224, 27.785645, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[274, 1, 270.058165, 54.011633, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[275, 1, 50.556363, 10.111273, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[276, 1, 197.081554, 39.416311, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[277, 1, 0, 0, 0, 0, 0, 0.998827, 0, 380.0, 0, 1.1, 0.9 ],
[278, 1, 153.854372, 30.770874, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[279, 1, 0, 0, 0, 0, 0, 0.998808, 0, 380.0, 0, 1.1, 0.9 ],
[280, 1, 0, 0, 0, 0, 0, 0.999709, 0, 380.0, 0, 1.1, 0.9 ],
[281, 1, 203.2232, 40.64464, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[282, 1, 287.389078, 57.477816, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[283, 1, 115.198021, 23.039604, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[284, 1, 174.760733, 34.952147, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[285, 1, 77.937156, 15.587431, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[286, 1, 163.343691, 32.668738, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[287, 1, 100.394621, 20.078924, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[288, 1, 64.573121, 12.914624, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[289, 1, 101.554791, 20.310958, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[290, 1, 0, 0, 0, 0, 0, 1.004653, 0, 380.0, 0, 1.1, 0.9 ],
[291, 1, 66.832008, 13.366402, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[292, 1, 131.756242, 26.351248, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[293, 1, 116.121759, 23.224352, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[294, 1, 30.944694, 6.188939, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[295, 1, 64.747078, 12.949416, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[296, 1, 183.817107, 36.763421, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[297, 1, 193.193842, 38.638768, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[298, 1, 102.010189, 20.402038, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[299, 1, 98.79619, 19.759238, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[300, 1, 269.147572, 53.829514, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[301, 1, 0, 0, 0, 0, 0, 1.000038, 0, 380.0, 0, 1.1, 0.9 ],
[302, 1, 226.723905, 45.344781, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[303, 1, 116.451973, 23.290395, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[304, 1, 99.99739, 19.999478, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[305, 1, 0, 0, 0, 0, 0, 0.99962, 0, 380.0, 0, 1.1, 0.9 ],
[306, 1, 0, 0, 0, 0, 0, 1.001477, 0, 380.0, 0, 1.1, 0.9 ],
[307, 1, 118.606198, 23.72124, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[308, 1, 146.22564, 29.245128, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[309, 1, 239.24571, 47.849142, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[310, 1, 0, 0, 0, 0, 0, 1.000141, 0, 380.0, 0, 1.1, 0.9 ],
[311, 1, 203.217454, 40.643491, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[312, 1, 91.392543, 18.278509, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[313, 1, 0, 0, 0, 0, 0, 1.000343, 0, 380.0, 0, 1.1, 0.9 ],
[314, 1, 283.075925, 56.615185, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[315, 1, 0, 0, 0, 0, 0, 1.001462, 0, 380.0, 0, 1.1, 0.9 ],
[316, 1, 110.912827, 22.182565, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[317, 1, 149.340059, 29.868012, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[318, 1, 245.420849, 49.08417, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[319, 1, 8.791956, 1.758391, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[320, 1, 0, 0, 0, 0, 0, 0.999996, 0, 380.0, 0, 1.1, 0.9 ],
[321, 1, 207.977107, 41.595421, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[322, 1, 26.476825, 5.295365, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[323, 1, 2.754688, 0.550938, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[324, 1, 486.962231, 97.392446, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[325, 1, 158.630045, 31.726009, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[326, 1, 12.861232, 2.572246, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[327, 1, 110.679681, 22.135936, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[328, 1, 188.615186, 37.723037, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[329, 1, 283.694054, 56.738811, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[330, 1, 0, 0, 0, 0, 0, 1.001153, 0, 380.0, 0, 1.1, 0.9 ],
[331, 1, 22.524343, 4.504869, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[332, 1, 0, 0, 0, 0, 0, 0.994596, 0, 380.0, 0, 1.1, 0.9 ],
[333, 1, 236.669238, 47.333848, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[334, 1, 0, 0, 0, 0, 0, 0.999169, 0, 380.0, 0, 1.1, 0.9 ],
[335, 1, 241.538812, 48.307762, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[336, 1, 0, 0, 0, 0, 0, 0.996999, 0, 380.0, 0, 1.1, 0.9 ],
[337, 1, 96.077057, 19.215411, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[338, 1, 260.766786, 52.153357, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[339, 1, 161.28065, 32.25613, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[340, 1, 136.35953, 27.271906, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[341, 1, 123.27174, 24.654348, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[342, 1, 213.835906, 42.767181, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[343, 1, 117.313496, 23.462699, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[344, 1, 294.133032, 58.826606, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[345, 1, 321.622889, 64.324578, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[346, 1, 319.289533, 63.857907, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[347, 1, 111.661092, 22.332218, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[348, 1, 291.889448, 58.37789, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[349, 1, 0, 0, 0, 0, 0, 0.99991, 0, 380.0, 0, 1.1, 0.9 ],
[350, 1, 153.129465, 30.625893, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[351, 1, 0, 0, 0, 0, 0, 0.999667, 0, 380.0, 0, 1.1, 0.9 ],
[352, 1, 1013.60899, 202.721798, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[353, 1, 3.047247, 0.609449, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[354, 1, 20.702734, 4.140547, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[355, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[356, 1, 0.0, 0.0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[357, 1, 0.051895, 0.010379, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[358, 1, 0, 0, 0, 0, 0, 1.00123, 0, 380.0, 0, 1.1, 0.9 ],
[359, 1, 3.029978, 0.605996, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[360, 1, 0, 0, 0, 0, 0, 1.000731, 0, 380.0, 0, 1.1, 0.9 ],
[361, 1, 77.549559, 15.509912, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[362, 1, 221.056378, 44.211276, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[363, 1, 325.466629, 65.093326, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[364, 1, 76.78937, 15.357874, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[365, 1, 68.922537, 13.784507, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[366, 1, 136.604249, 27.32085, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[367, 1, 66.02882, 13.205764, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[368, 1, 32.513676, 6.502735, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[369, 1, 26.717579, 5.343516, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[370, 1, 78.657315, 15.731463, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[371, 1, 395.768976, 79.153795, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[372, 1, 229.512116, 45.902423, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[373, 1, 154.874942, 30.974988, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[374, 1, 79.417248, 15.88345, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[375, 1, 260.516148, 52.10323, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[376, 1, 285.737149, 57.14743, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[377, 1, 204.46909, 40.893818, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[378, 1, 204.07553, 40.815106, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[379, 1, 70.336094, 14.067219, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[380, 1, 0, 0, 0, 0, 0, 1.001431, 0, 380.0, 0, 1.1, 0.9 ],
[381, 1, 235.208187, 47.041637, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[382, 1, 0, 0, 0, 0, 0, 0.99931, 0, 380.0, 0, 1.1, 0.9 ],
[383, 1, 0, 0, 0, 0, 0, 0.999355, 0, 380.0, 0, 1.1, 0.9 ],
[384, 1, 82.999126, 16.599825, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[385, 1, 104.761187, 20.952237, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[386, 1, 84.172473, 16.834495, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[387, 1, 171.420679, 34.284136, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[388, 1, 920.52654, 184.105308, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[389, 1, 0, 0, 0, 0, 0, 0.999927, 0, 380.0, 0, 1.1, 0.9 ],
[390, 1, 76.005723, 15.201145, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[391, 1, 86.577001, 17.3154, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[392, 1, 166.140394, 33.228079, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[393, 1, 207.478268, 41.495654, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[394, 1, 74.623968, 14.924794, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[395, 1, 103.424209, 20.684842, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[396, 1, 73.254309, 14.650862, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[397, 1, 587.418866, 117.483773, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[398, 1, 254.423799, 50.88476, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[399, 1, 108.403069, 21.680614, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[400, 1, 57.755339, 11.551068, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[401, 1, 0, 0, 0, 0, 0, 1.000607, 0, 380.0, 0, 1.1, 0.9 ],
[402, 1, 0, 0, 0, 0, 0, 1.000402, 0, 380.0, 0, 1.1, 0.9 ],
[403, 1, 28.676869, 5.735374, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[404, 1, 101.030383, 20.206077, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[405, 1, 761.669209, 152.333842, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[406, 1, 57.709613, 11.541923, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[407, 1, 114.237444, 22.847489, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[408, 1, 330.310625, 66.062125, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[409, 1, 0, 0, 0, 0, 0, 0.999945, 0, 380.0, 0, 1.1, 0.9 ],
[410, 1, 42.765284, 8.553057, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[411, 1, 40.436328, 8.087266, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[412, 1, 2.840209, 0.568042, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[413, 1, 141.788379, 28.357676, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[414, 1, 12.039367, 2.407873, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[415, 1, 0, 0, 0, 0, 0, 1.000216, 0, 380.0, 0, 1.1, 0.9 ],
[416, 1, 171.453258, 34.290652, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[417, 1, 6.708639, 1.341728, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[418, 1, 139.803992, 27.960798, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[419, 1, 74.72424, 14.944848, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[420, 1, 75.232124, 15.046425, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[421, 1, 108.369957, 21.673991, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[422, 1, 79.395462, 15.879092, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[423, 1, 166.748016, 33.349603, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[424, 1, 12.022102, 2.40442, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[425, 1, 98.731794, 19.746359, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[426, 1, 8.180233, 1.636047, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[427, 1, 68.746812, 13.749362, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[428, 1, 30.823937, 6.164787, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[429, 1, 347.840816, 69.568163, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[430, 1, 185.282839, 37.056568, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[431, 1, 123.901479, 24.780296, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[432, 1, 144.833229, 28.966646, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[433, 1, 74.034885, 14.806977, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[434, 1, 38.53136, 7.706272, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[435, 1, 154.101184, 30.820237, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[436, 1, 82.272037, 16.454407, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[437, 1, 18.736593, 3.747319, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[438, 1, 50.283885, 10.056777, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[439, 1, 93.622094, 18.724419, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[440, 1, 79.120237, 15.824047, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[441, 1, 60.656262, 12.131252, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[442, 1, 80.268768, 16.053754, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[443, 1, 174.030244, 34.806049, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[444, 1, 0, 0, 0, 0, 0, 0.999997, 0, 380.0, 0, 1.1, 0.9 ],
[445, 1, 79.077332, 15.815466, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[446, 1, 36.667449, 7.33349, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[447, 1, 69.711986, 13.942397, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[448, 1, 51.231231, 10.246246, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[449, 1, 258.32521, 51.665042, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[450, 1, 158.082702, 31.61654, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[451, 1, 67.549518, 13.509904, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[452, 1, 0, 0, 0, 0, 0, 0.999998, 0, 380.0, 0, 1.1, 0.9 ],
[453, 1, 45.271283, 9.054257, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[454, 1, 31.584233, 6.316847, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[455, 1, 51.495434, 10.299087, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[456, 1, 51.495434, 10.299087, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[457, 1, 157.923583, 31.584717, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[458, 1, 150.20524, 30.041048, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[459, 1, 182.805366, 36.561073, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[460, 1, 240.243914, 48.048783, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[461, 1, 249.905766, 49.981153, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[462, 1, 76.447469, 15.289494, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[463, 1, 39.172162, 7.834432, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[464, 1, 39.219512, 7.843902, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[465, 1, 63.350231, 12.670046, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[466, 1, 51.432373, 10.286475, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[467, 1, 47.463564, 9.492713, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[468, 1, 77.821485, 15.564297, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[469, 1, 48.224415, 9.644883, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[470, 1, 122.809076, 24.561815, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[471, 1, 120.916609, 24.183322, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[472, 1, 42.292985, 8.458597, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[473, 1, 77.660006, 15.532001, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[474, 1, 40.110581, 8.022116, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[475, 1, 39.362455, 7.872491, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[476, 1, 44.48605, 8.89721, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[477, 1, 71.790863, 14.358173, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[478, 1, 90.182408, 18.036482, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[479, 1, 163.430553, 32.686111, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[480, 1, 71.634573, 14.326915, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[481, 1, 62.210779, 12.442156, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[482, 1, 70.637663, 14.127533, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[483, 1, 60.072155, 12.014431, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[484, 1, 47.093648, 9.41873, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[485, 1, 70.345446, 14.069089, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[486, 1, 647.143737, 129.428747, 0, 0, 0, 0.999554, 0, 220.0, 0, 1.1, 0.9 ],
[487, 1, 163.983232, 32.796646, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[488, 1, 472.509932, 94.501986, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[489, 1, 124.36327, 24.872654, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[490, 1, 38.697212, 7.739442, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[491, 1, 53.209163, 10.641833, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[492, 1, 82.974923, 16.594985, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[493, 1, 106.944743, 21.388949, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[494, 1, 146.164126, 29.232825, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[495, 1, 115.05729, 23.011458, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[496, 1, 8.1497, 1.62994, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[497, 1, 1019.11742, 203.823484, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[498, 1, 47.795688, 9.559138, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[499, 1, 66.71495, 13.34299, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[500, 1, 36.52565, 7.30513, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[501, 1, 61.795103, 12.359021, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[502, 1, 243.892472, 48.778494, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[503, 1, 74.69475, 14.93895, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[504, 1, 48.913631, 9.782726, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[505, 1, 346.933422, 69.386684, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[506, 1, 108.898132, 21.779626, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[507, 1, 103.585169, 20.717034, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[508, 1, 150.590165, 30.118033, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[509, 1, 198.447968, 39.689594, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[510, 1, 125.371437, 25.074287, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[511, 1, 109.362196, 21.872439, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[512, 1, 72.240483, 14.448097, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[513, 1, 39.796797, 7.959359, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[514, 1, 99.050376, 19.810075, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[515, 1, 88.35882, 17.671764, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[516, 1, 98.852728, 19.770546, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[517, 1, 46.433493, 9.286699, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[518, 1, 261.516372, 52.303274, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[519, 1, 25.737999, 5.1476, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[520, 1, 103.914264, 20.782853, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[521, 1, 93.869868, 18.773974, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[522, 1, 80.372141, 16.074428, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[523, 1, 43.26341, 8.652682, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[524, 1, 125.571667, 25.114333, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[525, 1, 149.598388, 29.919678, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[526, 1, 45.355417, 9.071083, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[527, 1, 49.797061, 9.959412, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[528, 1, 108.686742, 21.737348, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[529, 1, 139.32031, 27.864062, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[530, 1, 59.038257, 11.807651, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[531, 1, 60.026309, 12.005262, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[532, 1, 57.614792, 11.522958, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[533, 1, 51.629806, 10.325961, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[534, 1, 142.423899, 28.48478, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[535, 1, 178.305581, 35.661116, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[536, 1, 140.543223, 28.108645, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[537, 1, 46.752939, 9.350588, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[538, 1, 34.949307, 6.989861, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[539, 1, 37.083364, 7.416673, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[540, 1, 33.39194, 6.678388, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[541, 1, 86.254263, 17.250853, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[542, 1, 118.486697, 23.697339, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[543, 1, 64.716851, 12.94337, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[544, 1, 120.536044, 24.107209, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[545, 1, 259.53387, 51.906774, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[546, 1, 130.082295, 26.016459, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[547, 1, 168.139914, 33.627983, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[548, 1, 54.427586, 10.885517, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[549, 1, 46.540239, 9.308048, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[550, 1, 38.403612, 7.680722, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[551, 1, 37.020156, 7.404031, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[552, 1, 183.837924, 36.767585, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[553, 1, 1.271874, 0.254375, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[554, 1, 186.247009, 37.249402, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[555, 1, 70.962172, 14.192434, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[556, 1, 109.780842, 21.956168, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[557, 1, 233.244794, 46.648959, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[558, 1, 137.534821, 27.506964, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[559, 1, 73.607312, 14.721462, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[560, 1, 114.992034, 22.998407, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[561, 1, 63.058275, 12.611655, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[562, 1, 172.270483, 34.454097, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[563, 1, 121.120189, 24.224038, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[564, 1, 239.15215, 47.83043, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[565, 1, 180.452148, 36.09043, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[566, 1, 0.289845, 0.057969, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[567, 1, 293.333059, 58.666612, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[568, 1, 271.262107, 54.252421, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[569, 1, 190.861924, 38.172385, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[570, 1, 297.969832, 59.593966, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[571, 1, 219.388065, 43.877613, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[572, 1, 386.963917, 77.392783, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[573, 1, 112.640123, 22.528025, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[574, 1, 214.622449, 42.92449, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[575, 1, 4.033141, 0.806628, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[576, 1, 260.979458, 52.195892, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[577, 1, 287.702645, 57.540529, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[578, 1, 274.688852, 54.93777, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[579, 1, 100.21399, 20.042798, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[580, 1, 20.863062, 4.172612, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[581, 1, 0.119881, 0.023976, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[582, 1, 75.482663, 15.096533, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[583, 1, 86.575841, 17.315168, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[584, 1, 49.673071, 9.934614, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[585, 1, 86.238563, 17.247713, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ]
])
ppc["gen"] = array([
[586, 272.0, 0, 9999, -9999, 1.0, 100, 1, 272.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[589, 63.1, 0, 9999, -9999, 1.0, 100, 1, 63.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[590, 38.0, 0, 9999, -9999, 1.0, 100, 1, 38.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[593, 11.1, 0, 9999, -9999, 1.0, 100, 1, 11.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[594, 19.0, 0, 9999, -9999, 1.0, 100, 1, 19.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[595, 1510.82619, 0, 9999, -9999, 1.0, 100, 1, 4730.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[598, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[599, 9.3, 0, 9999, -9999, 1.0, 100, 1, 9.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[601, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[602, 24.6, 0, 9999, -9999, 1.0, 100, 1, 24.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[603, 1382.09855, 0, 9999, -9999, 1.0, 100, 1, 3455.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[607, 1800.0, 0, 9999, -9999, 1.0, 100, 1, 1800.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[608, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[609, 36.4, 0, 9999, -9999, 1.0, 100, 1, 36.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[612, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[613, 85.0, 0, 9999, -9999, 1.0, 100, 1, 85.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[614, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[616, 29.0, 0, 9999, -9999, 1.0, 100, 1, 29.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[617, 137.0, 0, 9999, -9999, 1.0, 100, 1, 137.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[618, 33.4, 0, 9999, -9999, 1.0, 100, 1, 33.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[619, 118.0, 0, 9999, -9999, 1.0, 100, 1, 118.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[621, 765.0, 0, 9999, -9999, 1.0, 100, 1, 765.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[624, 27.0, 0, 9999, -9999, 1.0, 100, 1, 27.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[628, 449.0, 0, 9999, -9999, 1.0, 100, 1, 449.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[629, 75.3, 0, 9999, -9999, 1.0, 100, 1, 75.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[631, 79.8, 0, 9999, -9999, 1.0, 100, 1, 79.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[632, 45.1, 0, 9999, -9999, 1.0, 100, 1, 45.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[637, 53.7, 0, 9999, -9999, 1.0, 100, 1, 53.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[638, 128.7, 0, 9999, -9999, 1.0, 100, 1, 128.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[640, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[641, 12.6, 0, 9999, -9999, 1.0, 100, 1, 12.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[642, 28.9, 0, 9999, -9999, 1.0, 100, 1, 28.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[643, 857.0, 0, 9999, -9999, 1.0, 100, 1, 857.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[647, 14.0, 0, 9999, -9999, 1.0, 100, 1, 14.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[650, 650.644627, 0, 9999, -9999, 1.0, 100, 1, 1324.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[652, 46.9, 0, 9999, -9999, 1.0, 100, 1, 46.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[655, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[663, 15.0, 0, 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[666, 28.9, 0, 9999, -9999, 1.0, 100, 1, 28.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[670, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[672, 33.1, 0, 9999, -9999, 1.0, 100, 1, 33.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[676, 370.0, 0, 9999, -9999, 1.0, 100, 1, 370.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[681, 40.1, 0, 9999, -9999, 1.0, 100, 1, 40.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[683, 27.5, 0, 9999, -9999, 1.0, 100, 1, 27.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[687, 1329.0, 0, 9999, -9999, 1.0, 100, 1, 1329.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[689, 310.0, 0, 9999, -9999, 1.0, 100, 1, 310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[691, 26.0, 0, 9999, -9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[694, 16.4, 0, 9999, -9999, 1.0, 100, 1, 16.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[695, 14.7, 0, 9999, -9999, 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[696, 721.0, 0, 9999, -9999, 1.0, 100, 1, 721.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[697, 11.6, 0, 9999, -9999, 1.0, 100, 1, 11.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[698, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[702, 73.4, 0, 9999, -9999, 1.0, 100, 1, 73.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[705, 17.0, 0, 9999, -9999, 1.0, 100, 1, 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[707, 34.0, 0, 9999, -9999, 1.0, 100, 1, 34.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[713, 13.4, 0, 9999, -9999, 1.0, 100, 1, 13.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[714, 15.0, 0, 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[716, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[717, 11.0, 0, 9999, -9999, 1.0, 100, 1, 11.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[719, 1254.748674, 0, 9999, -9999, 1.0, 100, 1, 1958.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[722, 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[723, 19.7, 0, 9999, -9999, 1.0, 100, 1, 19.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[724, 12.1, 0, 9999, -9999, 1.0, 100, 1, 12.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[727, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[728, 510.0, 0, 9999, -9999, 1.0, 100, 1, 510.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[730, 633.2, 0, 9999, -9999, 1.0, 100, 1, 633.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[732, 14.6, 0, 9999, -9999, 1.0, 100, 1, 14.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[735, 84.8, 0, 9999, -9999, 1.0, 100, 1, 84.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[738, 138.5, 0, 9999, -9999, 1.0, 100, 1, 138.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[741, 214.0, 0, 9999, -9999, 1.0, 100, 1, 214.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[742, 9.0, 0, 9999, -9999, 1.0, 100, 1, 9.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[743, 1410.0, 0, 9999, -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[746, 100.0, 0, 9999, -9999, 1.0, 100, 1, 100.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[747, 12.5, 0, 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[748, 110.0, 0, 9999, -9999, 1.0, 100, 1, 110.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[749, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[750, 90.8, 0, 9999, -9999, 1.0, 100, 1, 90.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[753, 311.8, 0, 9999, -9999, 1.0, 100, 1, 311.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[758, 18.5, 0, 9999, -9999, 1.0, 100, 1, 18.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[760, 294.128123, 0, 9999, -9999, 1.0, 100, 1, 794.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[761, 15.7, 0, 9999, -9999, 1.0, 100, 1, 15.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[762, 1105.0, 0, 9999, -9999, 1.0, 100, 1, 1105.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[763, 20.3, 0, 9999, -9999, 1.0, 100, 1, 20.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[765, 59.0, 0, 9999, -9999, 1.0, 100, 1, 59.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[767, 11.2, 0, 9999, -9999, 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[769, 43.3, 0, 9999, -9999, 1.0, 100, 1, 43.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[771, 684.364258, 0, 9999, -9999, 1.0, 100, 1, 690.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[772, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[774, 33.5, 0, 9999, -9999, 1.0, 100, 1, 33.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[777, 79.0, 0, 9999, -9999, 1.0, 100, 1, 79.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[778, 14.7, 0, 9999, -9999, 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[781, 971.759122, 0, 9999, -9999, 1.0, 100, 1, 1310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[784, 890.776074, 0, 9999, -9999, 1.0, 100, 1, 1275.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[785, 3.0, 0, 9999, -9999, 1.0, 100, 1, 3.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[787, 778.0, 0, 9999, -9999, 1.0, 100, 1, 778.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[788, 875.0, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[789, 77.4, 0, 9999, -9999, 1.0, 100, 1, 77.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[791, 10.0, 0, 9999, -9999, 1.0, 100, 1, 10.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[792, 62.7, 0, 9999, -9999, 1.0, 100, 1, 62.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[795, 13.6, 0, 9999, -9999, 1.0, 100, 1, 13.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[800, 36.5, 0, 9999, -9999, 1.0, 100, 1, 36.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[801, 21.82418, 0, 9999, -9999, 1.0, 100, 1, 50.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[802, 500.0, 0, 9999, -9999, 1.0, 100, 1, 500.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[805, 848.970643, 0, 9999, -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[806, 35.8, 0, 9999, -9999, 1.0, 100, 1, 35.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[808, 217.5, 0, 9999, -9999, 1.0, 100, 1, 217.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[809, 12.5, 0, 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[811, 25.2, 0, 9999, -9999, 1.0, 100, 1, 25.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[814, 89.0, 0, 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[816, 80.1, 0, 9999, -9999, 1.0, 100, 1, 80.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[817, 54.0, 0, 9999, -9999, 1.0, 100, 1, 54.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[821, 82.5, 0, 9999, -9999, 1.0, 100, 1, 82.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[822, 134.0, 0, 9999, -9999, 1.0, 100, 1, 134.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[826, 58.0, 0, 9999, -9999, 1.0, 100, 1, 58.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[830, 55.516834, 0, 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[834, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[835, 63.7, 0, 9999, -9999, 1.0, 100, 1, 63.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[836, 25.5, 0, 9999, -9999, 1.0, 100, 1, 25.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[837, 472.0, 0, 9999, -9999, 1.0, 100, 1, 472.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[839, 73.3, 0, 9999, -9999, 1.0, 100, 1, 73.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[841, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[843, 333.0, 0, 9999, -9999, 1.0, 100, 1, 333.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[844, 40.0, 0, 9999, -9999, 1.0, 100, 1, 40.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[845, 318.0, 0, 9999, -9999, 1.0, 100, 1, 318.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[849, 779.0, 0, 9999, -9999, 1.0, 100, 1, 779.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[850, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[851, 79.5, 0, 9999, -9999, 1.0, 100, 1, 79.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[853, 11.6, 0, 9999, -9999, 1.0, 100, 1, 11.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[855, 688.0, 0, 9999, -9999, 1.0, 100, 1, 688.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[856, 36.0, 0, 9999, -9999, 1.0, 100, 1, 36.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[857, 1402.0, 0, 9999, -9999, 1.0, 100, 1, 1402.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[858, 56.8, 0, 9999, -9999, 1.0, 100, 1, 56.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[859, 3.480201, 0, 9999, -9999, 1.0, 100, 1, 85.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[860, 25.0, 0, 9999, -9999, 1.0, 100, 1, 25.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[864, 875.0, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[865, 11.0, 0, 9999, -9999, 1.0, 100, 1, 11.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[867, 769.0, 0, 9999, -9999, 1.0, 100, 1, 769.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[869, 1360.0, 0, 9999, -9999, 1.0, 100, 1, 1360.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[870, 58.4, 0, 9999, -9999, 1.0, 100, 1, 58.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[872, 22.5, 0, 9999, -9999, 1.0, 100, 1, 22.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[873, 122.0, 0, 9999, -9999, 1.0, 100, 1, 122.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[874, 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[875, 24.4, 0, 9999, -9999, 1.0, 100, 1, 24.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[877, 24.8, 0, 9999, -9999, 1.0, 100, 1, 24.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[881, 337.281055, 0, 9999, -9999, 1.0, 100, 1, 1001.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[882, 17.4, 0, 9999, -9999, 1.0, 100, 1, 17.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[883, 18.0, 0, 9999, -9999, 1.0, 100, 1, 18.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[885, 117.63457, 0, 9999, -9999, 1.0, 100, 1, 490.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[886, 2572.0, 0, 9999, -9999, 1.0, 100, 1, 2572.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[889, 9.5, 0, 9999, -9999, 1.0, 100, 1, 9.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[890, 48.0, 0, 9999, -9999, 1.0, 100, 1, 48.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[893, 60.0, 0, 9999, -9999, 1.0, 100, 1, 60.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[894, 158.0, 0, 9999, -9999, 1.0, 100, 1, 158.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[895, 19.0, 0, 9999, -9999, 1.0, 100, 1, 19.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[896, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[898, 84.6, 0, 9999, -9999, 1.0, 100, 1, 84.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[900, 112.6, 0, 9999, -9999, 1.0, 100, 1, 112.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[902, 19.5, 0, 9999, -9999, 1.0, 100, 1, 19.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[903, 20.1, 0, 9999, -9999, 1.0, 100, 1, 20.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[905, 137.3, 0, 9999, -9999, 1.0, 100, 1, 137.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[906, 66.0, 0, 9999, -9999, 1.0, 100, 1, 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[907, 67.3, 0, 9999, -9999, 1.0, 100, 1, 67.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[909, 36.8, 0, 9999, -9999, 1.0, 100, 1, 36.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[915, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[917, 17.0, 0, 9999, -9999, 1.0, 100, 1, 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[918, 38.5, 0, 9999, -9999, 1.0, 100, 1, 38.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[920, 12.8, 0, 9999, -9999, 1.0, 100, 1, 12.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[921, 124.0, 0, 9999, -9999, 1.0, 100, 1, 124.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[922, 164.0, 0, 9999, -9999, 1.0, 100, 1, 164.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[923, 146.0, 0, 9999, -9999, 1.0, 100, 1, 146.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[925, 26.0, 0, 9999, -9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[931, 217.1, 0, 9999, -9999, 1.0, 100, 1, 217.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[935, 23.1, 0, 9999, -9999, 1.0, 100, 1, 23.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[936, 104.4, 0, 9999, -9999, 1.0, 100, 1, 104.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[937, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[939, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[940, 29.6, 0, 9999, -9999, 1.0, 100, 1, 29.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[944, 25.4, 0, 9999, -9999, 1.0, 100, 1, 25.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[950, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[952, 31.7, 0, 9999, -9999, 1.0, 100, 1, 31.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[957, 6.0, 0, 9999, -9999, 1.0, 100, 1, 6.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[958, 66.7, 0, 9999, -9999, 1.0, 100, 1, 66.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[959, 45.5, 0, 9999, -9999, 1.0, 100, 1, 45.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[960, 26.5, 0, 9999, -9999, 1.0, 100, 1, 26.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[963, 757.748298, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[965, 352.0, 0, 9999, -9999, 1.0, 100, 1, 352.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[966, 66.0, 0, 9999, -9999, 1.0, 100, 1, 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[967, 37.5, 0, 9999, -9999, 1.0, 100, 1, 37.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[968, 54.0, 0, 9999, -9999, 0.999554, 100, 1, 54.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[969, 56.9, 0, 9999, -9999, 0.999554, 100, 1, 56.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[971, 20.0, 0, 9999, -9999, 1.0, 100, 1, 20.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[973, 1347.0, 0, 9999, -9999, 1.0, 100, 1, 1347.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[976, 26.9, 0, 9999, -9999, 1.0, 100, 1, 26.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[978, 4.6, 0, 9999, -9999, 1.0, 100, 1, 4.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[981, 99.016829, 0, 9999, -9999, 1.0, 100, 1, 119.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[982, 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[983, 44.0, 0, 9999, -9999, 1.0, 100, 1, 44.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[984, 465.0, 0, 9999, -9999, 1.0, 100, 1, 465.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[985, 22.0, 0, 9999, -9999, 1.0, 100, 1, 22.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[986, 11.2, 0, 9999, -9999, 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[987, 164.5, 0, 9999, -9999, 1.0, 100, 1, 164.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[988, 5.1, 0, 9999, -9999, 1.0, 100, 1, 5.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[993, 392.0, 0, 9999, -9999, 1.0, 100, 1, 392.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[994, 33.0, 0, 9999, -9999, 1.0, 100, 1, 33.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[995, 4.2, 0, 9999, -9999, 1.0, 100, 1, 4.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[997, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[999, 15.6, 0, 9999, -9999, 1.0, 100, 1, 15.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1000, 49.0, 0, 9999, -9999, 1.0, 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1002, 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1003, 900.0, 0, 9999, -9999, 1.0, 100, 1, 900.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1007, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1008, 49.0, 0, 9999, -9999, 1.0, 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1010, 358.638683, 0, 9999, -9999, 1.0, 100, 1, 750.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1011, 18.7, 0, 9999, -9999, 1.0, 100, 1, 18.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1012, 1598.86858, 0, 9999, -9999, 1.0, 100, 1, 2835.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1014, 750.0, 0, 9999, -9999, 1.0, 100, 1, 750.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1026, 655.6, 0, 9999, -9999, 1.0, 100, 1, 655.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1027, 6.608085, 0, 9999, -9999, 1.0, 100, 1, 48.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1028, 104.030085, 0, 9999, -9999, 1.0, 100, 1, 400.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1029, 0.659424, 0, 9999, -9999, 1.0, 100, 1, 60.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1030, 236.668442, 0, 9999, -9999, 1.0, 100, 1, 1018.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1031, 178.027662, 0, 9999, -9999, 1.0, 100, 1, 1447.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1032, 77.598072, 0, 9999, -9999, 1.0, 100, 1, 153.510391, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1033, 36.059975, 0, 9999, -9999, 1.0, 100, 1, 50.164506, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1034, 26.058195, 0, 9999, -9999, 1.0, 100, 1, 84.262779, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1035, 33.33615, 0, 9999, -9999, 1.0, 100, 1, 49.886469, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1036, 50.715498, 0, 9999, -9999, 1.0, 100, 1, 67.223077, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1037, 13.306083, 0, 9999, -9999, 1.0, 100, 1, 94.684044, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1038, 12.955878, 0, 9999, -9999, 1.0, 100, 1, 85.798525, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1039, 82.927654, 0, 9999, -9999, 1.0, 100, 1, 132.724114, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1040, 0.006512, 0, 9999, -9999, 1.0, 100, 1, 0.064179, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1041, 105.224635, 0, 9999, -9999, 1.0, 100, 1, 204.187624, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1042, 33.31008, 0, 9999, -9999, 1.0, 100, 1, 52.70053, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1043, 0.89438, 0, 9999, -9999, 1.0, 100, 1, 6.035538, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1044, 1.604991, 0, 9999, -9999, 1.0, 100, 1, 36.163532, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1045, 2.84692, 0, 9999, -9999, 1.0, 100, 1, 61.836204, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1046, 3.159564, 0, 9999, -9999, 1.0, 100, 1, 106.787063, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1047, 0.11702, 0, 9999, -9999, 1.0, 100, 1, 13.029581, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1048, 1.874472, 0, 9999, -9999, 1.0, 100, 1, 71.656883, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1049, 135.960004, 0, 9999, -9999, 1.0, 100, 1, 293.755375, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1050, 50.376748, 0, 9999, -9999, 1.0, 100, 1, 52.781606, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1051, 296.97528, 0, 9999, -9999, 1.0, 100, 1, 304.42978, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1052, 0.007977, 0, 9999, -9999, 1.0, 100, 1, 20.66869, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1053, 0.005683, 0, 9999, -9999, 1.0, 100, 1, 16.368087, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1054, 0.018487, 0, 9999, -9999, 1.0, 100, 1, 273.855776, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1055, 1.869334, 0, 9999, -9999, 1.0, 100, 1, 2.856069, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1056, 328.102405, 0, 9999, -9999, 1.0, 100, 1, 603.943953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1057, 154.862136, 0, 9999, -9999, 1.0, 100, 1, 426.979979, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1058, 590.783379, 0, 9999, -9999, 1.0, 100, 1, 1055.735174, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1059, 295.545938, 0, 9999, -9999, 1.0, 100, 1, 414.871332, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1060, 6.855672, 0, 9999, -9999, 1.0, 100, 1, 10.351632, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1061, 124.441219, 0, 9999, -9999, 1.0, 100, 1, 161.862597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1062, 1.821353, 0, 9999, -9999, 1.0, 100, 1, 2.878561, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1063, 5.38714, 0, 9999, -9999, 1.0, 100, 1, 8.670916, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1064, 123.682419, 0, 9999, -9999, 1.0, 100, 1, 209.786524, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1065, 237.798684, 0, 9999, -9999, 1.0, 100, 1, 339.421643, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1066, 128.229023, 0, 9999, -9999, 1.0, 100, 1, 134.399019, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1067, 12.727235, 0, 9999, -9999, 1.0, 100, 1, 32.653526, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1068, 3.22426, 0, 9999, -9999, 1.0, 100, 1, 5.009022, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1069, 1.824218, 0, 9999, -9999, 1.0, 100, 1, 3.190759, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1070, 0.473903, 0, 9999, -9999, 1.0, 100, 1, 0.788599, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1071, 2.819964, 0, 9999, -9999, 1.0, 100, 1, 4.328696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1072, 4.927999, 0, 9999, -9999, 1.0, 100, 1, 112.606433, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1073, 0.016514, 0, 9999, -9999, 1.0, 100, 1, 77.81765, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1074, 6.014763, 0, 9999, -9999, 1.0, 100, 1, 153.592986, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1075, 10.918523, 0, 9999, -9999, 1.0, 100, 1, 15.783448, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1076, 0.300183, 0, 9999, -9999, 1.0, 100, 1, 2.29551, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1077, 14.615588, 0, 9999, -9999, 1.0, 100, 1, 26.120041, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1078, 7.065166, 0, 9999, -9999, 1.0, 100, 1, 34.413246, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1079, 2.798967, 0, 9999, -9999, 1.0, 100, 1, 72.327992, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1080, 49.695174, 0, 9999, -9999, 1.0, 100, 1, 132.149983, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1081, 347.16283, 0, 9999, -9999, 1.0, 100, 1, 405.642115, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1082, 476.885634, 0, 9999, -9999, 1.0, 100, 1, 510.054159, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1083, 495.180028, 0, 9999, -9999, 1.0, 100, 1, 633.681488, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1084, 549.155419, 0, 9999, -9999, 1.0, 100, 1, 602.719371, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1085, 109.258361, 0, 9999, -9999, 1.0, 100, 1, 113.714399, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1086, 209.987466, 0, 9999, -9999, 1.0, 100, 1, 225.59917, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1087, 104.908763, 0, 9999, -9999, 1.0, 100, 1, 116.66597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1088, 35.596807, 0, 9999, -9999, 1.0, 100, 1, 36.782492, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1089, 270.799357, 0, 9999, -9999, 1.0, 100, 1, 384.449592, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1090, 0.028127, 0, 9999, -9999, 1.0, 100, 1, 89.140897, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1091, 1.57761, 0, 9999, -9999, 1.0, 100, 1, 45.7939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1092, 0.778957, 0, 9999, -9999, 1.0, 100, 1, 54.002032, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1093, 79.950854, 0, 9999, -9999, 1.0, 100, 1, 155.605298, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1094, 3.677528, 0, 9999, -9999, 1.0, 100, 1, 3.759038, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1095, 0.200242, 0, 9999, -9999, 1.0, 100, 1, 0.204951, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1096, 75.731488, 0, 9999, -9999, 1.0, 100, 1, 84.50612, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1097, 1.18816, 0, 9999, -9999, 1.0, 100, 1, 4.601122, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1098, 2.597672, 0, 9999, -9999, 1.0, 100, 1, 71.025499, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1099, 0.015673, 0, 9999, -9999, 1.0, 100, 1, 290.937198, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1100, 0.003921, 0, 9999, -9999, 1.0, 100, 1, 0.026696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1101, 18.961359, 0, 9999, -9999, 1.0, 100, 1, 83.930665, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1102, 140.103409, 0, 9999, -9999, 1.0, 100, 1, 350.979988, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1103, 67.236383, 0, 9999, -9999, 1.0, 100, 1, 245.381701, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1104, 0.201805, 0, 9999, -9999, 1.0, 100, 1, 0.206918, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1105, 2.1618, 0, 9999, -9999, 1.0, 100, 1, 2.178593, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1106, 2.253376, 0, 9999, -9999, 1.0, 100, 1, 2.289793, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1107, 74.033819, 0, 9999, -9999, 1.0, 100, 1, 76.221615, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1108, 294.151901, 0, 9999, -9999, 1.0, 100, 1, 320.422751, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1109, 0.773974, 0, 9999, -9999, 1.0, 100, 1, 0.77821, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1110, 1.644838, 0, 9999, -9999, 1.0, 100, 1, 1.654557, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1111, 72.444777, 0, 9999, -9999, 1.0, 100, 1, 89.637993, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1112, 69.501815, 0, 9999, -9999, 1.0, 100, 1, 69.53429, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1113, 3.517851, 0, 9999, -9999, 1.0, 100, 1, 3.536361, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1114, 12.959909, 0, 9999, -9999, 1.0, 100, 1, 13.446889, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1115, 50.529108, 0, 9999, -9999, 1.0, 100, 1, 50.575278, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1116, 32.590705, 0, 9999, -9999, 1.0, 100, 1, 32.601142, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1117, 90.740798, 0, 9999, -9999, 1.0, 100, 1, 90.792541, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1118, 7.238119, 0, 9999, -9999, 1.0, 100, 1, 8.725012, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1119, 43.247173, 0, 9999, -9999, 1.0, 100, 1, 43.254023, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1120, 2.249546, 0, 9999, -9999, 1.0, 100, 1, 2.416001, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1121, 0.52564, 0, 9999, -9999, 1.0, 100, 1, 0.540589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1122, 1.415755, 0, 9999, -9999, 1.0, 100, 1, 1.462883, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1123, 1.368314, 0, 9999, -9999, 1.0, 100, 1, 1.464336, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1124, 1.254099, 0, 9999, -9999, 1.0, 100, 1, 1.288283, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1125, 25.510688, 0, 9999, -9999, 1.0, 100, 1, 25.818899, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1126, 29.011219, 0, 9999, -9999, 1.0, 100, 1, 29.154893, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1127, 75.065189, 0, 9999, -9999, 1.0, 100, 1, 105.296621, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1128, 3.04291, 0, 9999, -9999, 1.0, 100, 1, 3.06139, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1129, 4.711862, 0, 9999, -9999, 1.0, 100, 1, 4.738747, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1130, 1.019131, 0, 9999, -9999, 1.0, 100, 1, 1.025754, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1131, 2.880583, 0, 9999, -9999, 1.0, 100, 1, 2.897078, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1132, 0.357118, 0, 9999, -9999, 1.0, 100, 1, 0.359497, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1133, 0.699698, 0, 9999, -9999, 1.0, 100, 1, 0.719597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1134, 0.494393, 0, 9999, -9999, 1.0, 100, 1, 0.508453, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1135, 7.75526, 0, 9999, -9999, 1.0, 100, 1, 8.117819, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1136, 0.390993, 0, 9999, -9999, 1.0, 100, 1, 0.4027, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1137, 2.447861, 0, 9999, -9999, 1.0, 100, 1, 3.669012, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1138, 1.152243, 0, 9999, -9999, 1.0, 100, 1, 1.254278, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1139, 19.805849, 0, 9999, -9999, 1.0, 100, 1, 19.822769, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1140, 27.354023, 0, 9999, -9999, 1.0, 100, 1, 28.389457, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1141, 117.761296, 0, 9999, -9999, 1.0, 100, 1, 119.46456, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1142, 1.119845, 0, 9999, -9999, 1.0, 100, 1, 1.215733, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1143, 23.95844, 0, 9999, -9999, 1.0, 100, 1, 25.239356, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1144, 52.472607, 0, 9999, -9999, 1.0, 100, 1, 52.527382, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1145, 0.009343, 0, 9999, -9999, 1.0, 100, 1, 175.889627, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1146, 0.837499, 0, 9999, -9999, 1.0, 100, 1, 0.861317, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1147, 45.670633, 0, 9999, -9999, 1.0, 100, 1, 45.703707, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1148, 15.193514, 0, 9999, -9999, 1.0, 100, 1, 17.645529, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1149, 8.516184, 0, 9999, -9999, 1.0, 100, 1, 8.556784, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1150, 3.565438, 0, 9999, -9999, 1.0, 100, 1, 3.62256, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1151, 12.933916, 0, 9999, -9999, 1.0, 100, 1, 13.036113, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1152, 0.114832, 0, 9999, -9999, 1.0, 100, 1, 0.116518, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1153, 0.066338, 0, 9999, -9999, 1.0, 100, 1, 0.068788, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1154, 0.154903, 0, 9999, -9999, 1.0, 100, 1, 0.160625, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1155, 0.603868, 0, 9999, -9999, 1.0, 100, 1, 0.609451, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1156, 15.943274, 0, 9999, -9999, 1.0, 100, 1, 16.022334, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1157, 4.319464, 0, 9999, -9999, 1.0, 100, 1, 4.354147, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1158, 1.020134, 0, 9999, -9999, 1.0, 100, 1, 1.04304, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1159, 13.341573, 0, 9999, -9999, 1.0, 100, 1, 13.498087, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1160, 61.348753, 0, 9999, -9999, 1.0, 100, 1, 238.377761, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1161, 13.37679, 0, 9999, -9999, 1.0, 100, 1, 25.263391, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1162, 295.52795, 0, 9999, -9999, 1.0, 100, 1, 502.409178, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1163, 204.813364, 0, 9999, -9999, 1.0, 100, 1, 330.03194, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1164, 169.080818, 0, 9999, -9999, 1.0, 100, 1, 285.625412, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1165, 32.736142, 0, 9999, -9999, 1.0, 100, 1, 57.188579, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1166, 10.324392, 0, 9999, -9999, 1.0, 100, 1, 83.277163, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1167, 4.944229, 0, 9999, -9999, 1.0, 100, 1, 5.05378, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1168, 1.260694, 0, 9999, -9999, 1.0, 100, 1, 1.345774, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1169, 2.587141, 0, 9999, -9999, 1.0, 100, 1, 2.721845, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1170, 0.259966, 0, 9999, -9999, 1.0, 100, 1, 0.26599, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1171, 3.699796, 0, 9999, -9999, 1.0, 100, 1, 9.029885, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1172, 1.223448, 0, 9999, -9999, 1.0, 100, 1, 3.584043, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1173, 106.980744, 0, 9999, -9999, 1.0, 100, 1, 254.253327, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1174, 1.232698, 0, 9999, -9999, 1.0, 100, 1, 1.260082, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1175, 0.771596, 0, 9999, -9999, 1.0, 100, 1, 0.855454, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1176, 0.229984, 0, 9999, -9999, 1.0, 100, 1, 0.23222, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1177, 22.538602, 0, 9999, -9999, 1.0, 100, 1, 27.87401, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1178, 3.150843, 0, 9999, -9999, 1.0, 100, 1, 3.167999, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1179, 1.299251, 0, 9999, -9999, 1.0, 100, 1, 1.306293, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1180, 0.673136, 0, 9999, -9999, 1.0, 100, 1, 0.688545, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1181, 16.564349, 0, 9999, -9999, 1.0, 100, 1, 85.739557, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1182, 20.777501, 0, 9999, -9999, 1.0, 100, 1, 99.319579, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1183, 29.900467, 0, 9999, -9999, 1.0, 100, 1, 38.222575, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1184, 4.094736, 0, 9999, -9999, 1.0, 100, 1, 4.219005, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1185, 11.338664, 0, 9999, -9999, 1.0, 100, 1, 11.343971, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1186, 37.012936, 0, 9999, -9999, 1.0, 100, 1, 38.916368, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1187, 9.432298, 0, 9999, -9999, 1.0, 100, 1, 9.814574, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1188, 35.700043, 0, 9999, -9999, 1.0, 100, 1, 179.712741, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1189, 13.755058, 0, 9999, -9999, 1.0, 100, 1, 20.261805, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1190, 219.178004, 0, 9999, -9999, 1.0, 100, 1, 220.533673, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1191, 72.800116, 0, 9999, -9999, 1.0, 100, 1, 73.079413, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1192, 8.977024, 0, 9999, -9999, 1.0, 100, 1, 21.454569, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1193, 1.137177, 0, 9999, -9999, 1.0, 100, 1, 2.399953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1194, 4.479308, 0, 9999, -9999, 1.0, 100, 1, 8.986036, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1195, 0.083395, 0, 9999, -9999, 1.0, 100, 1, 0.202359, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1196, 30.791933, 0, 9999, -9999, 1.0, 100, 1, 160.697956, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1197, 18.077938, 0, 9999, -9999, 1.0, 100, 1, 90.592266, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1198, 23.334118, 0, 9999, -9999, 1.0, 100, 1, 39.819157, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1201, 19.641372, 0, 9999, -9999, 1.0, 100, 1, 25.166667, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1202, 27.702181, 0, 9999, -9999, 1.0, 100, 1, 49.89238, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1203, 166.58394, 0, 9999, -9999, 1.0, 100, 1, 182.623256, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1204, 35.170656, 0, 9999, -9999, 1.0, 100, 1, 47.541821, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1205, 0.204675, 0, 9999, -9999, 1.0, 100, 1, 0.548843, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1206, 1.775686, 0, 9999, -9999, 1.0, 100, 1, 3.806894, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1207, 1.694167, 0, 9999, -9999, 1.0, 100, 1, 3.575453, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1208, 1.819097, 0, 9999, -9999, 1.0, 100, 1, 2.242031, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1209, 0.108058, 0, 9999, -9999, 1.0, 100, 1, 1.268261, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1210, 1.703029, 0, 9999, -9999, 1.0, 100, 1, 9.02599, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1211, 17.394643, 0, 9999, -9999, 1.0, 100, 1, 18.005229, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1212, 89.204411, 0, 9999, -9999, 1.0, 100, 1, 91.171888, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1213, 56.865564, 0, 9999, -9999, 1.0, 100, 1, 57.342704, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1214, 2.103582, 0, 9999, -9999, 1.0, 100, 1, 4.505907, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1215, 0.750611, 0, 9999, -9999, 1.0, 100, 1, 2.252965, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1216, 24.438918, 0, 9999, -9999, 1.0, 100, 1, 67.754469, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1217, 24.249023, 0, 9999, -9999, 1.0, 100, 1, 35.871617, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1218, 0.566199, 0, 9999, -9999, 1.0, 100, 1, 0.980482, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1219, 12.313625, 0, 9999, -9999, 1.0, 100, 1, 12.33953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1220, 28.947375, 0, 9999, -9999, 1.0, 100, 1, 30.597849, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1221, 362.770634, 0, 9999, -9999, 1.0, 100, 1, 593.230436, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1222, 209.632122, 0, 9999, -9999, 1.0, 100, 1, 211.057769, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1223, 3.779223, 0, 9999, -9999, 1.0, 100, 1, 3.806101, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1224, 62.035215, 0, 9999, -9999, 1.0, 100, 1, 160.523778, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1225, 31.599486, 0, 9999, -9999, 1.0, 100, 1, 34.931481, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1226, 3.430153, 0, 9999, -9999, 1.0, 100, 1, 3.982858, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1227, 12.458532, 0, 9999, -9999, 1.0, 100, 1, 17.482807, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1228, 0.787282, 0, 9999, -9999, 1.0, 100, 1, 3.021367, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1229, 34.545517, 0, 9999, -9999, 1.0, 100, 1, 51.244222, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1230, 0.110943, 0, 9999, -9999, 1.0, 100, 1, 1.681276, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1231, 16.90505, 0, 9999, -9999, 1.0, 100, 1, 33.55478, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1232, 44.228643, 0, 9999, -9999, 1.0, 100, 1, 75.075088, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1235, 0.457018, 0, 9999, -9999, 1.0, 100, 1, 9.03734, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1236, 3.498451, 0, 9999, -9999, 1.0, 100, 1, 82.225035, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1237, 13.898558, 0, 9999, -9999, 1.0, 100, 1, 14.605409, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1238, 124.493668, 0, 9999, -9999, 1.0, 100, 1, 188.691049, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1239, 0.000875, 0, 9999, -9999, 1.0, 100, 1, 2.267706, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1240, 234.917427, 0, 9999, -9999, 1.0, 100, 1, 339.51051, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1241, 329.977461, 0, 9999, -9999, 1.0, 100, 1, 385.361595, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1242, 16.492805, 0, 9999, -9999, 1.0, 100, 1, 27.074038, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1243, 73.44164, 0, 9999, -9999, 1.0, 100, 1, 83.079842, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1244, 2.21417, 0, 9999, -9999, 1.0, 100, 1, 323.472536, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1245, 7.597448, 0, 9999, -9999, 1.0, 100, 1, 8.080896, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1246, 10.509823, 0, 9999, -9999, 1.0, 100, 1, 57.127825, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1247, 9.134905, 0, 9999, -9999, 1.0, 100, 1, 21.833396, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1248, 53.949763, 0, 9999, -9999, 1.0, 100, 1, 91.958275, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1249, 71.087635, 0, 9999, -9999, 1.0, 100, 1, 76.135177, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1250, 26.948774, 0, 9999, -9999, 1.0, 100, 1, 30.830519, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1251, 21.826987, 0, 9999, -9999, 1.0, 100, 1, 23.404345, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1252, 13.904591, 0, 9999, -9999, 1.0, 100, 1, 14.887727, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1253, 40.004932, 0, 9999, -9999, 1.0, 100, 1, 64.502694, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1254, 15.914068, 0, 9999, -9999, 1.0, 100, 1, 82.278695, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1255, 2.91361, 0, 9999, -9999, 1.0, 100, 1, 3.818419, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1256, 11.486854, 0, 9999, -9999, 1.0, 100, 1, 15.091842, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1257, 64.291554, 0, 9999, -9999, 1.0, 100, 1, 88.95288, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1258, 30.398898, 0, 9999, -9999, 1.0, 100, 1, 235.487329, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1259, 77.360255, 0, 9999, -9999, 1.0, 100, 1, 109.288719, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1260, 19.068998, 0, 9999, -9999, 1.0, 100, 1, 20.168717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1261, 141.137179, 0, 9999, -9999, 1.0, 100, 1, 201.699555, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1262, 0.325949, 0, 9999, -9999, 1.0, 100, 1, 0.524108, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1263, 0.251304, 0, 9999, -9999, 1.0, 100, 1, 0.352421, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1264, 55.338841, 0, 9999, -9999, 1.0, 100, 1, 82.035361, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1265, 4.605899, 0, 9999, -9999, 1.0, 100, 1, 6.654727, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1266, 95.273884, 0, 9999, -9999, 1.0, 100, 1, 119.710849, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1267, 37.425318, 0, 9999, -9999, 1.0, 100, 1, 39.469006, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1268, 1.088852, 0, 9999, -9999, 1.0, 100, 1, 3.4295, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1269, 1.14527, 0, 9999, -9999, 1.0, 100, 1, 5.105829, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1270, 18.624163, 0, 9999, -9999, 1.0, 100, 1, 38.950511, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1271, 34.874339, 0, 9999, -9999, 1.0, 100, 1, 47.371792, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1272, 0.767634, 0, 9999, -9999, 1.0, 100, 1, 1.23166, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1273, 0.573609, 0, 9999, -9999, 1.0, 100, 1, 2.169201, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1274, 50.404078, 0, 9999, -9999, 1.0, 100, 1, 53.095629, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1275, 91.873338, 0, 9999, -9999, 1.0, 100, 1, 99.0753, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1276, 22.042664, 0, 9999, -9999, 1.0, 100, 1, 25.655641, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1277, 54.415572, 0, 9999, -9999, 1.0, 100, 1, 65.611252, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1278, 137.849688, 0, 9999, -9999, 1.0, 100, 1, 170.437781, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1279, 4.5e-05, 0, 9999, -9999, 1.0, 100, 1, 0.004344, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1280, 0.044687, 0, 9999, -9999, 1.0, 100, 1, 0.626494, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1281, 0.502718, 0, 9999, -9999, 1.0, 100, 1, 2.51246, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1282, 0.295983, 0, 9999, -9999, 1.0, 100, 1, 4.363037, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1283, 69.836828, 0, 9999, -9999, 1.0, 100, 1, 1297.764428, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1284, 11.051678, 0, 9999, -9999, 1.0, 100, 1, 28.426322, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1285, 0.459963, 0, 9999, -9999, 1.0, 100, 1, 2.937048, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1286, 9.739874, 0, 9999, -9999, 1.0, 100, 1, 17.872201, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1287, 79.244769, 0, 9999, -9999, 1.0, 100, 1, 93.199628, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1288, 141.096543, 0, 9999, -9999, 1.0, 100, 1, 148.402692, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1289, 176.093898, 0, 9999, -9999, 1.0, 100, 1, 184.149235, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1290, 2.87747, 0, 9999, -9999, 1.0, 100, 1, 4.901974, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1291, 85.365953, 0, 9999, -9999, 1.0, 100, 1, 98.293351, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1292, 34.357008, 0, 9999, -9999, 1.0, 100, 1, 41.682074, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1293, 1.672049, 0, 9999, -9999, 1.0, 100, 1, 2.402107, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1294, 3.454541, 0, 9999, -9999, 1.0, 100, 1, 5.39743, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1295, 3.810908, 0, 9999, -9999, 1.0, 100, 1, 5.873666, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1296, 5.426327, 0, 9999, -9999, 1.0, 100, 1, 27.356489, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1297, 39.649397, 0, 9999, -9999, 1.0, 100, 1, 177.778742, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1298, 0.614727, 0, 9999, -9999, 1.0, 100, 1, 4.014603, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1299, 0.232699, 0, 9999, -9999, 1.0, 100, 1, 2.158207, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1300, 23.336616, 0, 9999, -9999, 1.0, 100, 1, 23.74405, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1301, 59.759643, 0, 9999, -9999, 1.0, 100, 1, 60.863304, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1302, 4.74882, 0, 9999, -9999, 1.0, 100, 1, 4.877299, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1303, 4.215176, 0, 9999, -9999, 1.0, 100, 1, 4.335516, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1304, 9.191642, 0, 9999, -9999, 1.0, 100, 1, 9.594319, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1305, 0.004544, 0, 9999, -9999, 1.0, 100, 1, 0.004567, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1306, 1.805996, 0, 9999, -9999, 1.0, 100, 1, 1.827014, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1307, 0.290083, 0, 9999, -9999, 1.0, 100, 1, 0.29894, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1308, 2.287693, 0, 9999, -9999, 1.0, 100, 1, 3.278321, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1309, 2.080815, 0, 9999, -9999, 1.0, 100, 1, 3.34909, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1310, 1.024479, 0, 9999, -9999, 1.0, 100, 1, 1.64589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1311, 2.457558, 0, 9999, -9999, 1.0, 100, 1, 11.854004, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1312, 3.294717, 0, 9999, -9999, 1.0, 100, 1, 262.264924, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1313, 29.677875, 0, 9999, -9999, 1.0, 100, 1, 30.836748, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1314, 11.648153, 0, 9999, -9999, 1.0, 100, 1, 12.003987, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1315, 7.823385, 0, 9999, -9999, 1.0, 100, 1, 7.879027, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1316, 0.440241, 0, 9999, -9999, 1.0, 100, 1, 2.757497, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1317, 22.594495, 0, 9999, -9999, 1.0, 100, 1, 23.958574, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1318, 1.214, 0, 9999, -9999, 1.0, 100, 1, 1.956332, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1319, 7.726802, 0, 9999, -9999, 1.0, 100, 1, 17.708276, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1320, 15.658252, 0, 9999, -9999, 1.0, 100, 1, 20.75859, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1321, 0.105733, 0, 9999, -9999, 1.0, 100, 1, 0.161123, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1322, 0.673281, 0, 9999, -9999, 1.0, 100, 1, 0.929763, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1323, 88.070537, 0, 9999, -9999, 1.0, 100, 1, 199.111909, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1324, 7.462697, 0, 9999, -9999, 1.0, 100, 1, 13.063258, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1325, 35.850661, 0, 9999, -9999, 1.0, 100, 1, 90.497559, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1326, 43.155673, 0, 9999, -9999, 1.0, 100, 1, 56.928865, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1327, 32.057922, 0, 9999, -9999, 1.0, 100, 1, 50.796895, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1328, 7.62562, 0, 9999, -9999, 1.0, 100, 1, 16.063343, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1329, 143.638084, 0, 9999, -9999, 1.0, 100, 1, 218.675424, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1330, 13.942682, 0, 9999, -9999, 1.0, 100, 1, 30.131028, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1331, 0.287325, 0, 9999, -9999, 1.0, 100, 1, 0.289238, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1332, 18.971857, 0, 9999, -9999, 1.0, 100, 1, 26.293088, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1333, 39.377002, 0, 9999, -9999, 1.0, 100, 1, 45.650254, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1334, 0.165238, 0, 9999, -9999, 1.0, 100, 1, 1.215341, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1335, 1.675624, 0, 9999, -9999, 1.0, 100, 1, 3.306939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1336, 22.179569, 0, 9999, -9999, 1.0, 100, 1, 29.773035, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1337, 1.895167, 0, 9999, -9999, 1.0, 100, 1, 121.31241, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1338, 0.343096, 0, 9999, -9999, 1.0, 100, 1, 0.832524, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1339, 8.094061, 0, 9999, -9999, 1.0, 100, 1, 10.086482, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1340, 1.719329, 0, 9999, -9999, 1.0, 100, 1, 70.098327, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1341, 5.747566, 0, 9999, -9999, 1.0, 100, 1, 205.513321, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1342, 0.061568, 0, 9999, -9999, 1.0, 100, 1, 0.734589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1343, 0.082546, 0, 9999, -9999, 1.0, 100, 1, 1.102108, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1344, 0.093284, 0, 9999, -9999, 1.0, 100, 1, 0.226057, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1345, 2.160254, 0, 9999, -9999, 1.0, 100, 1, 3.971188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1346, 207.05021, 0, 9999, -9999, 1.0, 100, 1, 214.719215, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1347, 30.730149, 0, 9999, -9999, 1.0, 100, 1, 414.115976, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1348, 0.076903, 0, 9999, -9999, 1.0, 100, 1, 22.707927, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1349, 1.054008, 0, 9999, -9999, 1.0, 100, 1, 42.352342, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1350, 0.023105, 0, 9999, -9999, 1.0, 100, 1, 0.094971, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1351, 0.000591, 0, 9999, -9999, 1.0, 100, 1, 0.015958, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1352, 0.04782, 0, 9999, -9999, 1.0, 100, 1, 0.83726, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1354, 0.004264, 0, 9999, -9999, 1.0, 100, 1, 0.147716, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1355, 1.046975, 0, 9999, -9999, 1.0, 100, 1, 1.688324, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1356, 52.744967, 0, 9999, -9999, 1.0, 100, 1, 73.486231, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1357, 42.848835, 0, 9999, -9999, 1.0, 100, 1, 56.459913, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1358, 0.153292, 0, 9999, -9999, 1.0, 100, 1, 0.247293, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1359, 60.088697, 0, 9999, -9999, 1.0, 100, 1, 70.633589, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1360, 17.130838, 0, 9999, -9999, 1.0, 100, 1, 17.135983, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1361, 63.030411, 0, 9999, -9999, 1.0, 100, 1, 63.207173, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1362, 79.073054, 0, 9999, -9999, 1.0, 100, 1, 79.107216, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1363, 0.009694, 0, 9999, -9999, 1.0, 100, 1, 0.036158, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1364, 0.014171, 0, 9999, -9999, 1.0, 100, 1, 0.061068, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1365, 0.00016, 0, 9999, -9999, 1.0, 100, 1, 0.000456, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1366, 0.790668, 0, 9999, -9999, 1.0, 100, 1, 1.229992, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1367, 16.844243, 0, 9999, -9999, 1.0, 100, 1, 43.863891, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1368, 0.533701, 0, 9999, -9999, 1.0, 100, 1, 3.298243, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1369, 5.730756, 0, 9999, -9999, 1.0, 100, 1, 7.968859, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1370, 0.206567, 0, 9999, -9999, 1.0, 100, 1, 0.343308, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1371, 2.629506, 0, 9999, -9999, 1.0, 100, 1, 81.767208, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1372, 186.488603, 0, 9999, -9999, 1.0, 100, 1, 192.966588, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1373, 34.443544, 0, 9999, -9999, 1.0, 100, 1, 35.200257, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1376, 105.317936, 0, 9999, -9999, 1.0, 100, 1, 176.213655, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1377, 112.728506, 0, 9999, -9999, 1.0, 100, 1, 234.376272, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1378, 128.013354, 0, 9999, -9999, 1.0, 100, 1, 246.029906, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1379, 0.782569, 0, 9999, -9999, 1.0, 100, 1, 0.805984, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1380, 1.205328, 0, 9999, -9999, 1.0, 100, 1, 1.213356, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1381, 0.986293, 0, 9999, -9999, 1.0, 100, 1, 1.01257, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1382, 138.007244, 0, 9999, -9999, 1.0, 100, 1, 138.839906, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1383, 109.413691, 0, 9999, -9999, 1.0, 100, 1, 109.821439, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1384, 4.66695, 0, 9999, -9999, 1.0, 100, 1, 4.669135, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1385, 0.120034, 0, 9999, -9999, 1.0, 100, 1, 0.124455, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1386, 0.658834, 0, 9999, -9999, 1.0, 100, 1, 0.673858, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1387, 3.47374, 0, 9999, -9999, 1.0, 100, 1, 3.493561, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1388, 0.922047, 0, 9999, -9999, 1.0, 100, 1, 0.928188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1389, 0.212123, 0, 9999, -9999, 1.0, 100, 1, 0.213536, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1390, 3.711638, 0, 9999, -9999, 1.0, 100, 1, 3.732816, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1391, 0.518853, 0, 9999, -9999, 1.0, 100, 1, 0.521719, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1392, 18.867476, 0, 9999, -9999, 1.0, 100, 1, 19.306386, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1393, 1.277892, 0, 9999, -9999, 1.0, 100, 1, 1.376509, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1394, 1.009398, 0, 9999, -9999, 1.0, 100, 1, 1.077886, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1395, 0.063961, 0, 9999, -9999, 1.0, 100, 1, 0.073776, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1396, 0.021924, 0, 9999, -9999, 1.0, 100, 1, 0.026112, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1397, 22.814948, 0, 9999, -9999, 1.0, 100, 1, 25.084545, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1398, 2.522882, 0, 9999, -9999, 1.0, 100, 1, 2.779641, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1399, 17.766171, 0, 9999, -9999, 1.0, 100, 1, 17.868157, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1400, 1.20352, 0, 9999, -9999, 1.0, 100, 1, 1.297197, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1401, 84.971313, 0, 9999, -9999, 1.0, 100, 1, 89.339497, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1402, 25.706285, 0, 9999, -9999, 1.0, 100, 1, 26.328902, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1403, 25.589062, 0, 9999, -9999, 1.0, 100, 1, 119.651672, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1404, 20.034146, 0, 9999, -9999, 1.0, 100, 1, 134.800518, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1405, 26.655143, 0, 9999, -9999, 1.0, 100, 1, 29.550802, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1406, 7.405844, 0, 9999, -9999, 1.0, 100, 1, 10.763987, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1407, 0.206231, 0, 9999, -9999, 1.0, 100, 1, 0.211614, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1408, 33.729648, 0, 9999, -9999, 1.0, 100, 1, 41.078698, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1409, 9.208431, 0, 9999, -9999, 1.0, 100, 1, 12.019786, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1410, 28.273097, 0, 9999, -9999, 1.0, 100, 1, 37.466518, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1411, 30.074024, 0, 9999, -9999, 1.0, 100, 1, 39.395367, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1412, 1.056231, 0, 9999, -9999, 1.0, 100, 1, 5.987601, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1413, 0.921657, 0, 9999, -9999, 1.0, 100, 1, 5.679791, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1414, 5.84373, 0, 9999, -9999, 1.0, 100, 1, 25.992489, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1415, 1.354626, 0, 9999, -9999, 1.0, 100, 1, 7.454501, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1416, 1.195782, 0, 9999, -9999, 1.0, 100, 1, 7.958002, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1417, 0.000204, 0, 9999, -9999, 1.0, 100, 1, 0.001311, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1418, 73.547107, 0, 9999, -9999, 1.0, 100, 1, 88.264613, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1419, 29.245381, 0, 9999, -9999, 1.0, 100, 1, 33.260903, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1420, 1.062106, 0, 9999, -9999, 1.0, 100, 1, 1.399757, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1421, 3.729871, 0, 9999, -9999, 0.999554, 100, 1, 6.972369, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1422, 2.701524, 0, 9999, -9999, 1.0, 100, 1, 4.730495, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1423, 1.036575, 0, 9999, -9999, 1.0, 100, 1, 1.931017, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1424, 6.870415, 0, 9999, -9999, 1.0, 100, 1, 219.092115, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1425, 4.240743, 0, 9999, -9999, 1.0, 100, 1, 21.366402, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1426, 48.393481, 0, 9999, -9999, 1.0, 100, 1, 68.762602, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1427, 320.481892, 0, 9999, -9999, 1.0, 100, 1, 480.698671, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1428, 206.447115, 0, 9999, -9999, 1.0, 100, 1, 334.885743, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1429, 1.934629, 0, 9999, -9999, 1.0, 100, 1, 13.279826, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1430, 0.000918, 0, 9999, -9999, 1.0, 100, 1, 0.034248, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1431, 107.667744, 0, 9999, -9999, 1.0, 100, 1, 227.662022, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1432, 0.512128, 0, 9999, -9999, 1.0, 100, 1, 12.058931, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1433, 32.437405, 0, 9999, -9999, 1.0, 100, 1, 1289.241188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1434, 0.724661, 0, 9999, -9999, 1.0, 100, 1, 99.440014, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1435, 2.719421, 0, 9999, -9999, 1.0, 100, 1, 86.713217, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1436, 0.810927, 0, 9999, -9999, 1.0, 100, 1, 98.434116, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1437, 233.547643, 0, 9999, -9999, 1.0, 100, 1, 238.321958, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1438, 262.517934, 0, 9999, -9999, 1.0, 100, 1, 392.815158, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1439, 25.144625, 0, 9999, -9999, 1.0, 100, 1, 99.103164, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1440, 0.527204, 0, 9999, -9999, 1.0, 100, 1, 0.833609, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1441, 0.10379, 0, 9999, -9999, 1.0, 100, 1, 0.171578, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1442, 0.358625, 0, 9999, -9999, 1.0, 100, 1, 0.715522, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1443, 0.005191, 0, 9999, -9999, 1.0, 100, 1, 103.005076, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1444, 4.836207, 0, 9999, -9999, 1.0, 100, 1, 8.981696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1445, 10.804131, 0, 9999, -9999, 1.0, 100, 1, 25.036799, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1446, 603.424909, 0, 9999, -9999, 1.0, 100, 1, 758.547933, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1447, 77.589286, 0, 9999, -9999, 1.0, 100, 1, 89.477411, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1448, 1.990229, 0, 9999, -9999, 1.0, 100, 1, 7.523578, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1449, 75.799398, 0, 9999, -9999, 1.0, 100, 1, 95.437673, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1450, 29.696998, 0, 9999, -9999, 1.0, 100, 1, 59.256809, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1451, 38.222514, 0, 9999, -9999, 1.0, 100, 1, 68.198838, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1452, 8.496963, 0, 9999, -9999, 1.0, 100, 1, 24.068921, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1453, 64.705767, 0, 9999, -9999, 1.0, 100, 1, 64.93775, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1454, 153.672244, 0, 9999, -9999, 1.0, 100, 1, 155.126607, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1455, 0.648449, 0, 9999, -9999, 1.0, 100, 1, 0.654438, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1456, 36.643758, 0, 9999, -9999, 1.0, 100, 1, 50.054822, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1457, 1.989422, 0, 9999, -9999, 1.0, 100, 1, 2.002672, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1458, 0.24457, 0, 9999, -9999, 1.0, 100, 1, 0.246199, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1459, 4.925247, 0, 9999, -9999, 1.0, 100, 1, 5.309059, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1460, 39.822684, 0, 9999, -9999, 1.0, 100, 1, 101.498473, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1461, 17.933929, 0, 9999, -9999, 1.0, 100, 1, 17.951737, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1462, 2.401562, 0, 9999, -9999, 1.0, 100, 1, 2.402686, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1463, 0.689129, 0, 9999, -9999, 1.0, 100, 1, 0.711207, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1464, 176.333456, 0, 9999, -9999, 1.0, 100, 1, 218.884211, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1465, 5.213592, 0, 9999, -9999, 1.0, 100, 1, 5.299939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1466, 5.640449, 0, 9999, -9999, 1.0, 100, 1, 5.685017, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1467, 2.081537, 0, 9999, -9999, 1.0, 100, 1, 2.096155, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1468, 23.073463, 0, 9999, -9999, 1.0, 100, 1, 23.789171, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1469, 61.686727, 0, 9999, -9999, 1.0, 100, 1, 65.007467, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1470, 0.001122, 0, 9999, -9999, 1.0, 100, 1, 78.965265, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1471, 0.038276, 0, 9999, -9999, 1.0, 100, 1, 159.165074, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1472, 11.952646, 0, 9999, -9999, 1.0, 100, 1, 11.980182, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1473, 8.177237, 0, 9999, -9999, 1.0, 100, 1, 8.362608, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1474, 1.370149, 0, 9999, -9999, 1.0, 100, 1, 1.398948, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1475, 0.352563, 0, 9999, -9999, 1.0, 100, 1, 0.39088, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1476, 35.385904, 0, 9999, -9999, 1.0, 100, 1, 250.480113, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1477, 9.087521, 0, 9999, -9999, 1.0, 100, 1, 12.122974, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1478, 0.001027, 0, 9999, -9999, 1.0, 100, 1, 0.035833, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1479, 3.883098, 0, 9999, -9999, 1.0, 100, 1, 5.592606, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1480, 12.250601, 0, 9999, -9999, 1.0, 100, 1, 18.681964, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1481, 0.021901, 0, 9999, -9999, 1.0, 100, 1, 0.053146, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1482, 4.135907, 0, 9999, -9999, 1.0, 100, 1, 17.51083, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1483, 3.580244, 0, 9999, -9999, 1.0, 100, 1, 3.599649, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1484, 0.029847, 0, 9999, -9999, 1.0, 100, 1, 0.02991, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1485, 0.562364, 0, 9999, -9999, 1.0, 100, 1, 0.563547, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1486, 2.893254, 0, 9999, -9999, 1.0, 100, 1, 2.89934, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1487, 0.458304, 0, 9999, -9999, 1.0, 100, 1, 1.142917, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1488, 0.911802, 0, 9999, -9999, 1.0, 100, 1, 5.569856, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1489, 0.115649, 0, 9999, -9999, 1.0, 100, 1, 0.118938, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1490, 5.673013, 0, 9999, -9999, 1.0, 100, 1, 782.463701, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1491, 76.534474, 0, 9999, -9999, 1.0, 100, 1, 84.622838, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1492, 222.990441, 0, 9999, -9999, 1.0, 100, 1, 229.927503, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1493, 78.147874, 0, 9999, -9999, 1.0, 100, 1, 83.557175, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1494, 326.80152, 0, 9999, -9999, 1.0, 100, 1, 404.486733, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1495, 60.458248, 0, 9999, -9999, 1.0, 100, 1, 66.920717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1496, 4.6e-05, 0, 9999, -9999, 1.0, 100, 1, 0.000282, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1497, 57.039458, 0, 9999, -9999, 1.0, 100, 1, 89.070006, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1498, 97.421461, 0, 9999, -9999, 1.0, 100, 1, 105.800802, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1499, 0.45968, 0, 9999, -9999, 1.0, 100, 1, 2.286676, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1500, 0.068834, 0, 9999, -9999, 1.0, 100, 1, 0.154817, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1501, 2.367818, 0, 9999, -9999, 1.0, 100, 1, 8.165333, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1502, 0.111192, 0, 9999, -9999, 1.0, 100, 1, 0.938928, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1503, 24.40196, 0, 9999, -9999, 1.0, 100, 1, 45.972187, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1504, 126.871686, 0, 9999, -9999, 1.0, 100, 1, 188.822836, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1505, 4.377864, 0, 9999, -9999, 1.0, 100, 1, 26.765913, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1506, 14.359595, 0, 9999, -9999, 1.0, 100, 1, 56.406717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1507, 3.264294, 0, 9999, -9999, 1.0, 100, 1, 15.438042, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1508, 0.064798, 0, 9999, -9999, 1.0, 100, 1, 0.065259, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1509, 4.7e-05, 0, 9999, -9999, 1.0, 100, 1, 0.005193, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1510, 56.12069, 0, 9999, -9999, 1.0, 100, 1, 107.008141, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1511, 95.027293, 0, 9999, -9999, 1.0, 100, 1, 155.22192, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1512, 39.855633, 0, 9999, -9999, 1.0, 100, 1, 64.130052, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1513, 16.938458, 0, 9999, -9999, 1.0, 100, 1, 23.051786, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1514, 0.003798, 0, 9999, -9999, 1.0, 100, 1, 0.027711, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1515, 0.0001, 0, 9999, -9999, 1.0, 100, 1, 0.00633, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1516, 0.011036, 0, 9999, -9999, 1.0, 100, 1, 0.02881, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1517, 0.816063, 0, 9999, -9999, 1.0, 100, 1, 1.286804, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1518, 0.650791, 0, 9999, -9999, 1.0, 100, 1, 0.670542, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1519, 0.045169, 0, 9999, -9999, 1.0, 100, 1, 0.04654, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
ppc["branch"] = array([
[586, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[589, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[590, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[593, 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[594, 114, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[595, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[598, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[599, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[601, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[602, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[603, 526, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[607, 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[608, 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[609, 529, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[612, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[613, 130, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[614, 130, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[616, 132, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[617, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[618, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[619, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[621, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[624, 14, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[628, 142, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[629, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[631, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[632, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[637, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[638, 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[640, 153, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[641, 155, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[642, 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[643, 534, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[647, 536, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[650, 166, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[652, 167, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[655, 170, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[663, 178, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[666, 180, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[670, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[672, 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[676, 19, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[681, 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[683, 200, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[687, 202, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[689, 204, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[691, 209, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[694, 21, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[695, 210, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[696, 211, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[697, 211, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[698, 212, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[702, 215, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[705, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[707, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[713, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[714, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[716, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[717, 227, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[719, 229, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[722, 545, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[723, 235, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[724, 238, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[727, 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[728, 244, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[730, 547, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[732, 247, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[735, 253, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[738, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[741, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[742, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[743, 500, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[746, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[747, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[748, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[749, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[750, 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[753, 28, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[758, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[760, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[761, 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[762, 289, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[763, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[765, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[767, 292, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[769, 293, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[771, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[772, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[774, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[777, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[778, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[781, 303, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[784, 563, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[785, 501, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[787, 308, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[788, 311, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[789, 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[791, 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[792, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[795, 319, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[800, 326, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[801, 327, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[802, 327, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[805, 328, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[806, 328, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[808, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[809, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[811, 568, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[814, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[816, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[817, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[821, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[822, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[826, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[830, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[834, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[835, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[836, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[837, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[839, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[841, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[843, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[844, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[845, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[849, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[850, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[851, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[853, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[855, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[856, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[857, 365, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[858, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[859, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[860, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[864, 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[865, 375, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[867, 376, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[869, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[870, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[872, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[873, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[874, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[875, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[877, 578, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[881, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[882, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[883, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[885, 393, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[886, 394, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[889, 397, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[890, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[893, 400, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[894, 400, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[895, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[896, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[898, 403, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[900, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[902, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[903, 406, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[905, 413, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[906, 414, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[907, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[909, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[915, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[917, 43, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[918, 424, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[920, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[921, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[922, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[923, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[925, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[931, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[935, 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[936, 445, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[937, 447, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[939, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[940, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[944, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[950, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[952, 47, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[957, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[958, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[959, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[960, 479, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[963, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[965, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[966, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[967, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[968, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[969, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[971, 51, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[973, 506, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[976, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[978, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[981, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[982, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[983, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[984, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[985, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[986, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[987, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[988, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[993, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[994, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[995, 509, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[997, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[999, 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1000, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1002, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1003, 72, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1007, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1008, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1010, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1011, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1012, 81, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1014, 83, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1026, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1027, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1028, 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1029, 268, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1030, 269, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1031, 498, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1032, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1033, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1034, 4, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1035, 6, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1036, 7, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1037, 8, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1038, 9, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1039, 11, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1040, 14, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1041, 16, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1042, 17, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1043, 19, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1044, 21, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1045, 23, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1046, 25, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1047, 27, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1048, 28, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1049, 29, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1050, 31, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1051, 33, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1052, 34, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1053, 35, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1054, 36, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1055, 38, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1056, 39, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1057, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1058, 41, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1059, 43, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1060, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1061, 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1062, 47, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1063, 48, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1064, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1065, 50, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1066, 51, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1067, 53, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1068, 54, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1069, 55, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1070, 57, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1071, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1072, 59, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1073, 60, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1074, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1075, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1076, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1077, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1078, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1079, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1080, 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1081, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1082, 72, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1083, 73, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1084, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1085, 76, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1086, 77, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1087, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1088, 80, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1089, 81, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1090, 82, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1091, 83, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1092, 84, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1093, 85, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1094, 88, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1095, 89, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1096, 90, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1097, 91, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1098, 92, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1099, 93, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1100, 97, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1101, 98, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1102, 101, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1103, 102, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1104, 103, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1105, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1106, 109, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1107, 110, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1108, 111, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1109, 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1110, 113, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1111, 114, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1112, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1113, 116, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1114, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1115, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1116, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1117, 122, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1118, 126, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1119, 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1120, 130, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1121, 131, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1122, 132, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1123, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1124, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1125, 135, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1126, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1127, 137, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1128, 139, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1129, 140, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1130, 141, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1131, 142, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1132, 144, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1133, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1134, 146, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1135, 147, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1136, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1137, 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1138, 150, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1139, 151, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1140, 152, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1141, 153, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1142, 154, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1143, 155, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1144, 158, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1145, 161, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1146, 162, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1147, 163, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1148, 164, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1149, 166, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1150, 167, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1151, 168, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1152, 169, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1153, 170, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1154, 171, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1155, 172, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1156, 173, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1157, 174, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1158, 175, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1159, 176, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1160, 177, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1161, 178, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1162, 179, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1163, 180, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1164, 181, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1165, 182, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1166, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1167, 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1168, 186, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1169, 187, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1170, 188, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1171, 189, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1172, 190, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1173, 192, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1174, 193, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1175, 194, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1176, 196, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1177, 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1178, 198, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1179, 199, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1180, 200, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1181, 202, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1182, 203, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1183, 204, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1184, 205, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1185, 206, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1186, 207, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1187, 208, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1188, 209, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1189, 210, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1190, 211, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1191, 212, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1192, 213, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1193, 214, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1194, 215, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1195, 216, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1196, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1197, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1198, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1201, 223, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1202, 224, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1203, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1204, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1205, 227, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1206, 228, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1207, 229, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1208, 230, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1209, 234, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1210, 235, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1211, 237, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1212, 238, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1213, 239, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1214, 240, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1215, 241, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1216, 242, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1217, 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1218, 244, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1219, 247, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1220, 251, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1221, 252, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1222, 253, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1223, 254, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1224, 255, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1225, 256, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1226, 257, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1227, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1228, 260, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1229, 263, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1230, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1231, 266, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1232, 267, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1235, 271, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1236, 272, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1237, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1238, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1239, 275, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1240, 276, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1241, 278, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1242, 281, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1243, 282, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1244, 283, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1245, 284, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1246, 285, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1247, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1248, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1249, 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1250, 289, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1251, 291, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1252, 292, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1253, 293, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1254, 294, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1255, 295, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1256, 296, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1257, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1258, 298, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1259, 299, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1260, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1261, 302, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1262, 303, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1263, 304, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1264, 307, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1265, 308, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1266, 309, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1267, 311, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1268, 312, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1269, 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1270, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1271, 317, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1272, 318, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1273, 319, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1274, 321, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1275, 322, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1276, 323, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1277, 324, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1278, 325, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1279, 326, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1280, 327, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1281, 328, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1282, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1283, 331, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1284, 333, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1285, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1286, 337, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1287, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1288, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1289, 340, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1290, 341, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1291, 342, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1292, 343, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1293, 344, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1294, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1295, 346, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1296, 347, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1297, 348, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1298, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1299, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1300, 353, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1301, 354, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1302, 355, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1303, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1304, 357, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1305, 359, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1306, 361, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1307, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1308, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1309, 364, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1310, 365, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1311, 366, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1312, 367, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1313, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1314, 369, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1315, 370, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1316, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1317, 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1318, 373, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1319, 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1320, 375, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1321, 376, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1322, 377, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1323, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1324, 379, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1325, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1326, 384, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1327, 385, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1328, 386, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1329, 387, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1330, 388, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1331, 390, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1332, 391, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1333, 392, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1334, 393, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1335, 394, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1336, 395, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1337, 396, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1338, 397, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1339, 398, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1340, 399, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1341, 400, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1342, 403, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1343, 404, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1344, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1345, 406, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1346, 407, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1347, 408, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1348, 410, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1349, 411, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1350, 412, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1351, 413, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1352, 414, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1354, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1355, 418, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1356, 419, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1357, 420, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1358, 421, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1359, 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1360, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1361, 424, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1362, 425, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1363, 426, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1364, 427, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1365, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1366, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1367, 430, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1368, 431, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1369, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1370, 433, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1371, 434, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1372, 435, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1373, 436, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1376, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1377, 440, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1378, 441, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1379, 442, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1380, 443, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1381, 445, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1382, 446, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1383, 447, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1384, 448, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1385, 449, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1386, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1387, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1388, 453, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1389, 454, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1390, 455, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1391, 456, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1392, 457, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1393, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1394, 459, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1395, 460, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1396, 461, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1397, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1398, 463, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1399, 464, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1400, 465, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1401, 466, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1402, 467, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1403, 468, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1404, 469, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1405, 470, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1406, 471, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1407, 472, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1408, 473, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1409, 474, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1410, 475, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1411, 476, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1412, 477, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1413, 478, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1414, 479, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1415, 480, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1416, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1417, 482, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1418, 483, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1419, 484, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1420, 485, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1421, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1422, 487, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1423, 488, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1424, 489, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1425, 490, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1426, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1427, 492, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1428, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1429, 494, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1430, 495, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1431, 496, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1432, 497, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1433, 498, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1434, 499, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1435, 500, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1436, 501, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1437, 502, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1438, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1439, 504, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1440, 505, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1441, 506, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1442, 507, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1443, 508, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1444, 509, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1445, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1446, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1447, 512, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1448, 513, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1449, 514, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1450, 515, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1451, 516, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1452, 517, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1453, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1454, 519, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1455, 520, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1456, 521, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1457, 522, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1458, 523, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1459, 524, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1460, 525, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1461, 526, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1462, 527, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1463, 528, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1464, 529, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1465, 530, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1466, 531, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1467, 532, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1468, 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1469, 534, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1470, 535, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1471, 536, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1472, 537, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1473, 538, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1474, 539, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1475, 540, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1476, 541, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1477, 542, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1478, 543, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1479, 544, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1480, 545, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1481, 546, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1482, 547, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1483, 548, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1484, 549, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1485, 550, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1486, 551, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1487, 552, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1488, 554, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1489, 555, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1490, 556, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1491, 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1492, 558, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1493, 559, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1494, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1495, 561, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1496, 562, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1497, 563, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1498, 564, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1499, 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1500, 566, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1501, 567, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1502, 568, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1503, 569, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1504, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1505, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1506, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1507, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1508, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1509, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1510, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1511, 577, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1512, 578, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1513, 579, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1514, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1515, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1516, 582, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1517, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1518, 584, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1519, 585, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ],
[1, 490, 0, 0.01433884297520661, 0.151691958358336, 991.0, 991.0, 991.0, 0, 2, 1, -360, 43.375 ],
[3, 4, 0, 0.006291637811634348, 0.903417549506624, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 72.681 ],
[491, 6, 0, 0.011200661157024791, 0.118492839955776, 991.0, 991.0, 991.0, 0, 2, 1, -360, 33.882 ],
[7, 5, 0, 0.005794840720221606, 0.20802058859584005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 33.471 ],
[8, 9, 0, 0.0024379328254847646, 0.350063268897336, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 28.163 ],
[492, 11, 0, 0.018224793388429753, 0.0482004476327704, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.565 ],
[11, 493, 0, 0.030286942148760328, 0.08010209706571599, 495.0, 495.0, 495.0, 0, 1, 1, -360, 45.809 ],
[492, 493, 0, 0.04521652892561983, 0.11958747011094399, 495.0, 495.0, 495.0, 0, 1, 1, -360, 68.39 ],
[494, 14, 0, 0.012990743801652892, 0.137430291356512, 991.0, 991.0, 991.0, 0, 2, 1, -360, 39.297 ],
[13, 15, 0, 0.007681959833795014, 0.27576354266704156, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 44.371 ],
[16, 5, 0, 0.006275623268698061, 0.22527950450957998, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 36.248000000000005 ],
[17, 18, 0, 0.04623522622347646, 0.9335989000302801, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 200.291 ],
[17, 12, 0, 0.0056020313942728535, 0.113118303398186, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.268 ],
[14, 495, 0, 0.0017957024793388433, 0.018996904156819597, 991.0, 991.0, 991.0, 0, 1, 1, -360, 5.432 ],
[494, 19, 0, 0.010246611570247935, 0.10839986031771602, 991.0, 991.0, 991.0, 0, 1, 1, -360, 30.996 ],
[20, 21, 0, 0.005415685595567867, 0.19440984828307922, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 31.281 ],
[20, 22, 0, 0.0049706544321329645, 0.713737278110032, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 57.42100000000001 ],
[497, 23, 0, 0.002190413223140496, 0.005793146490362, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.313 ],
[23, 499, 0, 0.020799669421487598, 0.22004164444829602, 991.0, 991.0, 991.0, 0, 1, 1, -360, 62.919 ],
[25, 26, 0, 0.00141845567867036, 0.050919084651523595, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 8.193 ],
[25, 22, 0, 0.0035578254847645433, 0.0319293051869808, 856.0, 856.0, 856.0, 0, 1, 1, -360, 10.275 ],
[23, 27, 0, 0.027738181818181818, 0.073361203699828, 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.95399999999999 ],
[28, 23, 0, 0.012841652892561981, 0.0339632611780132, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.423 ],
[8, 21, 0, 0.004948753462603878, 0.17764812836304802, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 28.584 ],
[9, 29, 0, 0.002212863573407202, 0.31774552934092004, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 25.563000000000002 ],
[30, 25, 0, 0.019958795013850415, 0.17911796401827998, 856.0, 856.0, 856.0, 0, 1, 1, -360, 57.641000000000005 ],
[31, 32, 0, 0.0299776084949446, 0.605319030583196, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 129.863 ],
[32, 33, 0, 0.016762234533725762, 0.33846927983213604, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 72.61399999999999 ],
[34, 35, 0, 0.001931900826446281, 0.020437759184893597, 991.0, 991.0, 991.0, 0, 2, 1, -360, 5.843999999999999 ],
[35, 36, 0, 0.0008730578512396695, 0.0092361605077588, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.641 ],
[490, 6, 0, 0.049352066115702475, 0.130525028606764, 495.0, 495.0, 495.0, 0, 1, 1, -360, 74.645 ],
[37, 10, 0, 0.02404639889196676, 0.485553838251812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 104.169 ],
[10, 38, 0, 0.006848799630657894, 0.13829351176534158, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 29.669 ],
[37, 38, 0, 0.01437834718372576, 1.1613317560186958, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 124.574 ],
[39, 40, 0, 0.04521629732222991, 0.913024308337812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 195.877 ],
[39, 41, 0, 0.017466989843005543, 0.35269996139852006, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 75.667 ],
[42, 41, 0, 0.031145429362880884, 0.6289001042979919, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 134.922 ],
[18, 42, 0, 0.03439750692520776, 0.6945672650962679, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 149.01 ],
[492, 43, 0, 0.01819173553719008, 0.192452068436848, 991.0, 991.0, 991.0, 0, 2, 1, -360, 55.03 ],
[44, 45, 0, 0.02562314049586777, 0.067767398802972, 495.0, 495.0, 495.0, 0, 1, 1, -360, 38.755 ],
[44, 505, 0, 0.006061487603305785, 0.0160312607980052, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.168 ],
[46, 12, 0, 0.0014741170360110802, 0.2116687641962416, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 17.029 ],
[47, 48, 0, 0.005344182825484765, 0.01199019212302604, 428.0, 428.0, 428.0, 0, 1, 1, -360, 7.7170000000000005 ],
[49, 50, 0, 0.0019151662049861494, 0.0171874439892256, 856.0, 856.0, 856.0, 0, 1, 1, -360, 5.531000000000001 ],
[31, 33, 0, 0.013475992613088641, 0.27211225959163604, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 58.378 ],
[31, 51, 0, 0.003518611495844875, 0.5052381383693519, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 40.647 ],
[52, 53, 0, 0.010464421745152355, 1.5025884408875438, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 120.885 ],
[52, 54, 0, 0.0076126500461911354, 0.1537174637168, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 32.978 ],
[506, 55, 0, 0.012634380165289257, 0.133660287181212, 991.0, 991.0, 991.0, 0, 1, 1, -360, 38.219 ],
[506, 507, 0, 0.044157355371900825, 0.11678619613628, 495.0, 495.0, 495.0, 0, 1, 1, -360, 66.788 ],
[57, 506, 0, 0.004687272727272727, 0.049587095736244, 991.0, 991.0, 991.0, 0, 1, 1, -360, 14.179 ],
[57, 58, 0, 0.014436363636363634, 0.0381809096340232, 495.0, 495.0, 495.0, 0, 1, 1, -360, 21.835 ],
[58, 506, 0, 0.019797685950413223, 0.052360391943288, 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.944000000000003 ],
[59, 60, 0, 0.019407548476454296, 0.174170863885556, 856.0, 856.0, 856.0, 0, 1, 1, -360, 56.049 ],
[508, 62, 0, 0.051111404958677685, 0.03379452026753001, 248.0, 248.0, 248.0, 0, 1, 1, -360, 38.653 ],
[30, 61, 0, 0.03143698060941828, 0.28212765137935203, 856.0, 856.0, 856.0, 0, 1, 1, -360, 90.79 ],
[63, 506, 0, 0.027457190082644623, 0.072618044249872, 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.528999999999996 ],
[13, 64, 0, 0.0014816481994459833, 0.2127501654814608, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 17.116 ],
[65, 66, 0, 0.03778185595567867, 0.7629053006222161, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 163.671 ],
[59, 67, 0, 0.0051880193905817175, 0.046559297286324804, 856.0, 856.0, 856.0, 0, 1, 1, -360, 14.982999999999999 ],
[61, 67, 0, 0.012931440443213295, 0.1160517597580644, 856.0, 856.0, 856.0, 0, 1, 1, -360, 37.346 ],
[68, 69, 0, 0.011149584487534626, 0.4002427745096039, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 64.4 ],
[70, 69, 0, 0.009625346260387812, 0.345526355460808, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.596000000000004 ],
[71, 72, 0, 0.008878635734072021, 0.318721276477736, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.283 ],
[73, 74, 0, 0.012529547553116345, 0.253001288604392, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 54.278 ],
[37, 75, 0, 0.027459141274238225, 0.5544652029066119, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 118.95299999999999 ],
[72, 75, 0, 0.006688711911357341, 0.240108375006292, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 38.634 ],
[37, 72, 0, 0.036222068328739615, 0.7314094881920841, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 156.914 ],
[76, 77, 0, 0.004683777700831025, 0.6725445900750401, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 54.107 ],
[77, 51, 0, 0.00363183864265928, 0.5214964473447999, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 41.955 ],
[73, 72, 0, 0.025475069252077563, 0.514402082018968, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 110.35799999999999 ],
[18, 40, 0, 0.01302770083102493, 0.26306018504072, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 56.43600000000001 ],
[492, 45, 0, 0.0308703030303719, 0.18370114733484796, 743.0, 743.0, 743.0, 0, 1, 1, -360, 70.03699999999999 ],
[10, 74, 0, 0.030167359187465374, 0.609150547206812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 130.685 ],
[45, 511, 0, 0.08203371900826446, 0.05424014819960001, 248.0, 248.0, 248.0, 0, 1, 1, -360, 62.038000000000004 ],
[78, 32, 0, 0.013458795013850415, 0.48313777647302397, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 77.738 ],
[79, 80, 0, 0.0038086911357340715, 0.1367226831743568, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 21.999000000000002 ],
[81, 79, 0, 0.010767832409972299, 0.3865388099484561, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 62.195 ],
[34, 82, 0, 0.0015497520661157025, 0.00409874294399768, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.344 ],
[83, 84, 0, 0.00902611570247934, 0.0238720301499152, 495.0, 495.0, 495.0, 0, 1, 1, -360, 13.652000000000001 ],
[83, 499, 0, 0.04179570247933885, 0.0276350398834796, 248.0, 248.0, 248.0, 0, 1, 1, -360, 31.608 ],
[85, 86, 0, 0.00802354570637119, 0.28802563884886, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 46.343999999999994 ],
[87, 86, 0, 0.01904968836565097, 0.683837154069184, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 110.031 ],
[88, 89, 0, 0.00380297520661157, 0.010058007429140002, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.752000000000001 ],
[90, 86, 0, 0.012097818559556786, 0.434282055192244, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 69.877 ],
[91, 86, 0, 9.26246537396122e-05, 0.013299992817559201, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 1.07 ],
[86, 92, 0, 0.0001852493074792244, 0.0066499964087796005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.07 ],
[86, 93, 0, 0.008152181440443215, 0.292643346635492, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 47.086999999999996 ],
[94, 86, 0, 0.012883829639889197, 0.46249792780547194, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 74.417 ],
[86, 95, 0, 0.010421052631578947, 0.37409026526870803, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 60.192 ],
[513, 517, 0, 0.0008733884297520661, 0.0023099144321748, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.321 ],
[97, 66, 0, 0.03812777008310249, 0.34217338998058805, 856.0, 856.0, 856.0, 0, 1, 1, -360, 110.113 ],
[42, 98, 0, 0.003091759002770083, 0.44394630230884, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 35.716 ],
[99, 100, 0, 0.016371537396121884, 0.587698093837988, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 94.56200000000001 ],
[42, 101, 0, 0.008165339335180054, 0.29311568282888, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 47.163000000000004 ],
[102, 42, 0, 0.012403047091412742, 0.44523901189173193, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 71.64 ],
[103, 87, 0, 0.007073060941828254, 0.25390556381756, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 40.854 ],
[104, 103, 0, 0.0028852146814404432, 0.1035721403291428, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.665 ],
[105, 87, 0, 0.006406682825484765, 0.22998422159488002, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 37.005 ],
[106, 107, 0, 0.005714219759923823, 0.11538365264216799, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.754 ],
[108, 107, 0, 0.0025427631578947367, 0.09127896939786201, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.687000000000001 ],
[109, 106, 0, 0.003030470914127424, 0.10878648330773438, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 17.504 ],
[110, 111, 0, 0.019821849030470913, 0.7115558306889919, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 114.491 ],
[87, 112, 0, 0.006135907202216068, 0.220264039928212, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 35.441 ],
[113, 87, 0, 0.003981648199445983, 0.14293141813921081, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 22.998 ],
[87, 85, 0, 0.011046225761772853, 0.3965324494097, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 63.803000000000004 ],
[110, 114, 0, 0.011665339335180056, 0.418757110306188, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 67.37899999999999 ],
[115, 116, 0, 0.007048925619834712, 0.07457124214588401, 991.0, 991.0, 991.0, 0, 1, 1, -360, 21.323 ],
[117, 118, 0, 0.005987534626038782, 0.21493782785077598, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 34.584 ],
[117, 119, 0, 0.0038738746537396117, 0.5562504472696961, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 44.751000000000005 ],
[117, 120, 0, 0.005886686288088643, 0.8452704781039522, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 68.003 ],
[121, 122, 0, 0.0021170360110803325, 0.0759964075574972, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 12.228 ],
[123, 124, 0, 0.0018386426592797783, 0.0660027680945204, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 10.62 ],
[125, 126, 0, 0.004941135734072022, 0.17737467056702802, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 28.54 ],
[127, 119, 0, 0.0029027008310249305, 0.1041998502705648, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.766 ],
[118, 128, 0, 0.007397160664819945, 0.265539950057812, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 42.726000000000006 ],
[121, 119, 0, 0.002552458448753463, 0.0916270065931116, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.743 ],
[530, 527, 0, 0.022726611570247933, 0.060106736329903994, 495.0, 495.0, 495.0, 0, 1, 1, -360, 34.374 ],
[125, 130, 0, 0.002931440443213297, 0.105231531956442, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.932000000000002 ],
[125, 123, 0, 0.0019078081717451524, 0.2739425623421336, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 22.039 ],
[131, 132, 0, 0.0035744459833795014, 0.12831385593973843, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.646 ],
[133, 123, 0, 0.003864439058171745, 0.13872389704704202, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 22.320999999999998 ],
[524, 134, 0, 0.008092231404958678, 0.08560847143881999, 991.0, 991.0, 991.0, 0, 1, 1, -360, 24.479 ],
[135, 136, 0, 0.005242901662049862, 0.1882073282678, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.283 ],
[123, 131, 0, 0.003138331024930748, 0.1126583971045252, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 18.127 ],
[117, 128, 0, 0.010800034626038782, 0.38769479063117196, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 62.381 ],
[137, 521, 0, 0.013832396694214875, 0.14633421587532003, 991.0, 991.0, 991.0, 0, 2, 1, -360, 41.843 ],
[531, 514, 0, 0.0059504132231404955, 0.035409362037522, 743.0, 743.0, 743.0, 0, 1, 1, -360, 13.5 ],
[139, 521, 0, 0.021257520661157023, 0.05622132386323199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.152 ],
[140, 514, 0, 0.018527603305785127, 0.04900131122836401, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.023000000000003 ],
[522, 141, 0, 0.012168595041322314, 0.032183175718526795, 495.0, 495.0, 495.0, 0, 1, 1, -360, 18.405 ],
[142, 523, 0, 0.007060165289256198, 0.0746901476577608, 991.0, 991.0, 991.0, 0, 2, 1, -360, 21.357 ],
[530, 526, 0, 0.020281652892561983, 0.053640374808152, 495.0, 495.0, 495.0, 0, 1, 1, -360, 30.676 ],
[140, 532, 0, 0.004669090909090909, 0.0123486871461184, 495.0, 495.0, 495.0, 0, 1, 1, -360, 7.062 ],
[142, 144, 0, 0.006678126721756199, 0.0397397958689204, 743.0, 743.0, 743.0, 0, 1, 1, -360, 15.151 ],
[140, 522, 0, 0.020450247933884298, 0.05408627047793199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 30.930999999999997 ],
[145, 146, 0, 0.028527603305785125, 0.07544904460236, 495.0, 495.0, 495.0, 0, 1, 1, -360, 43.148 ],
[147, 523, 0, 0.02461289256198347, 0.0650955220034416, 495.0, 495.0, 495.0, 0, 2, 1, -360, 37.227 ],
[144, 523, 0, 0.008479338842975206, 0.0224259292904064, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.825 ],
[139, 523, 0, 0.029245619834710742, 0.0193370088934308, 248.0, 248.0, 248.0, 0, 1, 1, -360, 22.116999999999997 ],
[140, 141, 0, 0.008362975206611572, 0.022118173847506, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.649000000000001 ],
[528, 526, 0, 0.015389090909090908, 0.0407006573227188, 495.0, 495.0, 495.0, 0, 1, 1, -360, 23.276 ],
[528, 148, 0, 0.014306115702479338, 0.0378364333712244, 495.0, 495.0, 495.0, 0, 1, 1, -360, 21.638 ],
[149, 150, 0, 0.013604628099173552, 0.035981157661543604, 495.0, 495.0, 495.0, 0, 1, 1, -360, 20.576999999999998 ],
[145, 528, 0, 0.00320595041322314, 0.0084790121737992, 495.0, 495.0, 495.0, 0, 1, 1, -360, 4.849 ],
[530, 151, 0, 0.013144462809917355, 0.0347641247737036, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.881 ],
[524, 152, 0, 0.014598347107438016, 0.03860931919944, 495.0, 495.0, 495.0, 0, 1, 1, -360, 22.08 ],
[149, 525, 0, 0.016897190082644627, 0.17875695122823998, 991.0, 991.0, 991.0, 0, 2, 1, -360, 51.114 ],
[139, 514, 0, 0.007824132231404959, 0.020693056313687997, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.834000000000001 ],
[126, 120, 0, 0.012780297783933518, 0.458781387757004, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 73.819 ],
[530, 153, 0, 0.02254545454545455, 0.059627617060924, 495.0, 495.0, 495.0, 0, 1, 1, -360, 34.1 ],
[528, 147, 0, 0.15786710743801652, 0.104380679149868, 248.0, 248.0, 248.0, 0, 1, 1, -360, 119.387 ],
[528, 154, 0, 0.006528264462809917, 0.017265779790547203, 495.0, 495.0, 495.0, 0, 2, 1, -360, 9.874 ],
[130, 120, 0, 0.01450502077562327, 0.5206947188067639, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 83.781 ],
[528, 155, 0, 0.16064132231404957, 0.1062149715341, 248.0, 248.0, 248.0, 0, 1, 1, -360, 121.485 ],
[524, 533, 0, 0.004432727272727273, 0.0468942356109744, 991.0, 991.0, 991.0, 0, 1, 1, -360, 13.409 ],
[524, 149, 0, 0.0056413223140495865, 0.05968007537478799, 991.0, 991.0, 991.0, 0, 2, 1, -360, 17.065 ],
[154, 150, 0, 0.007539173553719007, 0.0199394052006688, 495.0, 495.0, 495.0, 0, 2, 1, -360, 11.402999999999999 ],
[157, 110, 0, 0.009962084487534625, 0.357614433044424, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 57.541000000000004 ],
[119, 158, 0, 0.0002490189289012004, 0.08045252664623159, 5134.0, 5134.0, 5134.0, 0, 3, 1, -360, 4.315 ],
[159, 60, 0, 0.010967451523545706, 0.0984261617997728, 856.0, 856.0, 856.0, 0, 1, 1, -360, 31.674 ],
[536, 161, 0, 0.021314380165289255, 0.056371704363524, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.238 ],
[115, 151, 0, 0.00379404958677686, 0.0401376047510724, 991.0, 991.0, 991.0, 0, 1, 1, -360, 11.477 ],
[162, 134, 0, 0.0015910743801652895, 0.016832124393744, 991.0, 991.0, 991.0, 0, 2, 1, -360, 4.813 ],
[115, 526, 0, 0.0037884297520661154, 0.010019537998747198, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.73 ],
[138, 87, 0, 0.0011838642659279777, 0.16999131006813442, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 13.675999999999998 ],
[123, 163, 0, 0.0022778739612188364, 0.08177009602828919, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.157 ],
[112, 164, 0, 0.0008672957063711912, 0.12453516639176802, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 10.019 ],
[112, 165, 0, 0.005989439058171744, 0.21500619230086396, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 34.595 ],
[166, 165, 0, 0.002632790858725762, 0.09451074335350361, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 15.207 ],
[167, 537, 0, 0.00832595041322314, 0.08808100664460242, 991.0, 991.0, 991.0, 0, 2, 1, -360, 25.186 ],
[168, 104, 0, 0.002552458448753463, 0.0916270065931116, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.743 ],
[531, 520, 0, 0.016156694214876033, 0.042730794079516396, 495.0, 495.0, 495.0, 0, 1, 1, -360, 24.436999999999998 ],
[139, 520, 0, 0.010682314049586776, 0.0282522993797748, 495.0, 495.0, 495.0, 0, 1, 1, -360, 16.157 ],
[520, 169, 0, 0.0011328925619834712, 0.0119849761681232, 991.0, 991.0, 991.0, 0, 2, 1, -360, 3.427 ],
[168, 105, 0, 0.007340893351800554, 0.26352009133553606, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 42.401 ],
[520, 170, 0, 0.005842644628099174, 0.015452470732151198, 495.0, 495.0, 495.0, 0, 2, 1, -360, 8.837 ],
[171, 89, 0, 0.005505454545454546, 0.058242717567848004, 991.0, 991.0, 991.0, 0, 1, 1, -360, 16.654 ],
[521, 172, 0, 0.006304793388429752, 0.06669899780522001, 991.0, 991.0, 991.0, 0, 1, 1, -360, 19.072 ],
[123, 173, 0, 0.005247403047091413, 0.18836891696656402, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.309 ],
[521, 174, 0, 0.013300495867768597, 0.035176796844864404, 495.0, 495.0, 495.0, 0, 1, 1, -360, 20.117 ],
[37, 39, 0, 0.004338873499549862, 0.35044859579205606, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 37.592 ],
[530, 175, 0, 0.013128595041322313, 0.0347221581224188, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.857 ],
[530, 176, 0, 0.005685289256198347, 0.01503630144005, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.599 ],
[88, 530, 0, 0.006015867768595041, 0.0159106066755372, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.099 ],
[177, 496, 0, 0.018632066115702478, 0.19711036673178398, 991.0, 991.0, 991.0, 0, 2, 1, -360, 56.361999999999995 ],
[178, 525, 0, 0.03106842975206612, 0.08216895464241199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 46.99100000000001 ],
[179, 493, 0, 0.057079669421487594, 0.15096278779194802, 495.0, 495.0, 495.0, 0, 1, 1, -360, 86.333 ],
[180, 181, 0, 0.041027438016528923, 0.10850827416682, 495.0, 495.0, 495.0, 0, 1, 1, -360, 62.053999999999995 ],
[182, 180, 0, 0.00866314049586777, 0.09164817200545601, 991.0, 991.0, 991.0, 0, 2, 1, -360, 26.206 ],
[179, 181, 0, 0.01957223140495868, 0.051764115772731996, 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.603 ],
[180, 493, 0, 0.06676561983471074, 0.17657993119175203, 495.0, 495.0, 495.0, 0, 1, 1, -360, 100.98299999999999 ],
[183, 30, 0, 0.0024804362880886427, 0.356166349712776, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 28.654 ],
[183, 21, 0, 0.0025647506925207757, 0.36827307214930394, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 29.628 ],
[538, 185, 0, 0.018631404958677687, 0.0123189607681008, 248.0, 248.0, 248.0, 0, 1, 1, -360, 14.09 ],
[538, 89, 0, 0.014509752066115702, 0.038375005396288, 495.0, 495.0, 495.0, 0, 1, 1, -360, 21.945999999999998 ],
[184, 186, 0, 0.0016554709141274237, 0.059427351084826, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 9.562000000000001 ],
[184, 187, 0, 0.002698753462603878, 0.09687863927102919, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 15.588 ],
[520, 172, 0, 0.0034188429752066113, 0.0361682589818792, 991.0, 991.0, 991.0, 0, 2, 1, -360, 10.342 ],
[89, 175, 0, 0.0037309090909090903, 0.0098674088877672, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.643 ],
[185, 89, 0, 0.005812892561983471, 0.0153737832609196, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.792 ],
[89, 188, 0, 0.003108760330578513, 0.008221966434607202, 495.0, 495.0, 495.0, 0, 1, 1, -360, 4.702 ],
[189, 190, 0, 0.008599492151454294, 0.17364414688031998, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 37.253 ],
[539, 172, 0, 0.0021570247933884296, 0.022819366646419197, 991.0, 991.0, 991.0, 0, 2, 1, -360, 6.525 ],
[504, 192, 0, 0.0003084297520661157, 0.00326290713886456, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.9329999999999999 ],
[105, 186, 0, 0.003273372576177285, 0.1175060580379876, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 18.907 ],
[105, 187, 0, 0.0021712257617728533, 0.0779416868808324, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 12.540999999999999 ],
[539, 193, 0, 0.005608595041322314, 0.01483346262541, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.482999999999999 ],
[187, 194, 0, 4.8649584487534626e-05, 0.0069856037041576, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 0.562 ],
[539, 540, 0, 0.004394710743801653, 0.0116230138006708, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.647 ],
[539, 196, 0, 0.00332297520661157, 0.008788516227194, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.026 ],
[197, 540, 0, 0.004737190082644629, 0.012528794024621601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 7.165 ],
[110, 198, 0, 0.00018724030470914128, 0.02688587333118328, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 2.1630000000000003 ],
[197, 539, 0, 0.009172231404958677, 0.024258473063998802, 495.0, 495.0, 495.0, 0, 1, 1, -360, 13.873 ],
[199, 537, 0, 0.03612826446280991, 0.0238877676441712, 248.0, 248.0, 248.0, 0, 1, 1, -360, 27.322 ],
[134, 526, 0, 0.007771239669421488, 0.020553167475975197, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.754000000000001 ],
[200, 193, 0, 0.0009322314049586776, 0.009862163056380801, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.82 ],
[4, 201, 0, 0.013726108033240996, 0.49273365914097605, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 79.282 ],
[202, 86, 0, 0.00013365650969529087, 0.00479794133417816, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.772 ],
[85, 203, 0, 0.0019011426592797783, 0.2729854600553416, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 21.962 ],
[147, 204, 0, 0.0073874380165289254, 0.0781523963903056, 991.0, 991.0, 991.0, 0, 2, 1, -360, 22.346999999999998 ],
[147, 205, 0, 0.005959669421487603, 0.00394049369636956, 248.0, 248.0, 248.0, 0, 1, 1, -360, 4.507 ],
[123, 206, 0, 0.0005753116343490305, 0.0826091142668064, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 6.646 ],
[537, 207, 0, 0.018456198347107437, 0.048812461297776, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.915 ],
[165, 208, 0, 0.00414612188365651, 0.14883562055771601, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 23.948 ],
[4, 94, 0, 0.013687673130193905, 0.49135394025941603, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 79.06 ],
[4, 2, 0, 5.2054478301015697e-05, 0.016817654469309, 5134.0, 5134.0, 5134.0, 0, 3, 1, -360, 0.902 ],
[209, 4, 0, 0.0022369286703601107, 0.32120104149338397, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 25.840999999999998 ],
[119, 163, 0, 0.003535145429362881, 0.12690306230914922, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.419 ],
[210, 3, 0, 0.0003150969529085873, 0.011311208844832242, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.82 ],
[99, 211, 0, 0.0035045013850415513, 0.1258030161741948, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.242 ],
[99, 69, 0, 0.021717970914127423, 0.7796219621557, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 125.443 ],
[212, 99, 0, 0.008453774238227147, 0.30346978938770003, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 48.82899999999999 ],
[213, 214, 0, 0.01490115702479339, 0.15764073118032798, 991.0, 991.0, 991.0, 0, 2, 1, -360, 45.076 ],
[510, 215, 0, 0.002174710743801653, 0.09202587186721281, 1981.0, 1981.0, 1981.0, 0, 4, 1, -360, 13.157 ],
[128, 69, 0, 0.010711651662049862, 1.538088234801848, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 123.741 ],
[216, 69, 0, 0.009628462603878117, 1.3825528982351443, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 111.228 ],
[217, 98, 0, 0.0012787396121883656, 0.045903620070299994, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 7.386 ],
[504, 218, 0, 0.027480991735537193, 0.072680994226412, 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.565 ],
[177, 504, 0, 0.07054809917355372, 0.18658373169634002, 495.0, 495.0, 495.0, 0, 1, 1, -360, 106.704 ],
[219, 209, 0, 0.003938798476454294, 0.5655728721401839, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 45.501000000000005 ],
[219, 220, 0, 0.0013026315789473684, 0.1870451326342096, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 15.048 ],
[94, 95, 0, 0.01070740997229917, 0.38436979242743197, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 61.846000000000004 ],
[159, 221, 0, 0.009937153739612188, 0.356719480257712, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 57.397 ],
[34, 161, 0, 0.010965289256198347, 0.116002818645824, 991.0, 991.0, 991.0, 0, 2, 1, -360, 33.17 ],
[222, 221, 0, 0.0046457756232686975, 0.16677196601221997, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 26.834 ],
[211, 52, 0, 0.05267313019390582, 0.472709090515552, 856.0, 856.0, 856.0, 0, 1, 1, -360, 152.12 ],
[215, 223, 0, 0.04873190082644628, 0.128884831985184, 495.0, 495.0, 495.0, 0, 1, 1, -360, 73.707 ],
[224, 215, 0, 0.019086280991735535, 0.050478887076288004, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.868000000000002 ],
[225, 224, 0, 0.04200925619834711, 0.11110496071615601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 63.538999999999994 ],
[224, 223, 0, 0.031061818181818183, 0.082151468537468, 495.0, 495.0, 495.0, 0, 1, 1, -360, 46.981 ],
[226, 6, 0, 0.06420099173553719, 0.0424492677936932, 248.0, 248.0, 248.0, 0, 1, 1, -360, 48.552 ],
[7, 3, 0, 0.009332929362880887, 0.335029305054692, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 53.907 ],
[216, 227, 0, 0.01989941135734072, 0.7143401282507, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 114.939 ],
[228, 229, 0, 0.010545454545454545, 0.027890337012274, 495.0, 495.0, 495.0, 0, 1, 1, -360, 15.95 ],
[227, 230, 0, 0.003993074792243767, 0.573366419334696, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 46.128 ],
[231, 53, 0, 0.007193213296398893, 1.0328749562310842, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 83.096 ],
[544, 545, 0, 0.013061818181818181, 0.034545548464856, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.756 ],
[234, 235, 0, 0.04608859504132231, 0.121893887321888, 495.0, 495.0, 495.0, 0, 1, 1, -360, 69.709 ],
[546, 214, 0, 0.057025454545454546, 0.15081940173295602, 495.0, 495.0, 495.0, 0, 1, 1, -360, 86.251 ],
[233, 227, 0, 0.0029001038781163438, 0.1041066260218888, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.750999999999998 ],
[237, 238, 0, 0.026324628099173554, 0.06962267451304, 495.0, 495.0, 495.0, 0, 1, 1, -360, 39.816 ],
[212, 100, 0, 0.007955505540166205, 0.285583163531816, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 45.951 ],
[519, 239, 0, 0.01740429752066116, 0.046030422038308406, 495.0, 495.0, 495.0, 0, 1, 1, -360, 26.324 ],
[238, 519, 0, 0.015166280991735538, 0.040111375593995205, 495.0, 495.0, 495.0, 0, 1, 1, -360, 22.939 ],
[213, 240, 0, 0.01665388429752066, 0.04404574915373599, 1200.0, 1200.0, 1200.0, 0, 1, 1, -360, 25.189 ],
[241, 242, 0, 0.009862015235457064, 0.3540221919932281, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 56.963 ],
[70, 241, 0, 0.003819858033240997, 0.5484941897752321, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 44.126999999999995 ],
[509, 213, 0, 0.011363636363636364, 0.120216969880216, 991.0, 991.0, 991.0, 0, 2, 1, -360, 34.375 ],
[68, 243, 0, 0.003611668975069252, 0.1296500701715312, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.861 ],
[243, 244, 0, 0.0007699099722991691, 0.027637882270859202, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 4.447 ],
[68, 244, 0, 0.004104051246537396, 0.147325387728876, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 23.705 ],
[544, 547, 0, 0.02418776859504132, 0.255884661882476, 991.0, 991.0, 991.0, 0, 1, 1, -360, 73.168 ],
[245, 227, 0, 0.012676419667590028, 0.45505241780707606, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 73.219 ],
[246, 208, 0, 0.0010155817174515235, 0.0364568961999408, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 5.8660000000000005 ],
[112, 208, 0, 0.0017927631578947367, 0.0643558063672372, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 10.355 ],
[165, 247, 0, 0.0002113919667590028, 0.0075884538459086, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.2209999999999999 ],
[537, 549, 0, 0.00032066115702479337, 0.00084807607842936, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.485 ],
[537, 550, 0, 0.00032198347107438016, 0.0008515732993697601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.48700000000000004 ],
[537, 551, 0, 0.0002651239669421488, 0.0007011927988648, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.401 ],
[110, 251, 0, 0.00023857340720221602, 0.008564200982522441, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.3780000000000001 ],
[510, 252, 0, 0.08467702479338843, 0.055987884365424005, 248.0, 248.0, 248.0, 0, 1, 1, -360, 64.03699999999999 ],
[529, 253, 0, 0.04859504132231405, 0.12852286961777998, 495.0, 495.0, 495.0, 0, 1, 1, -360, 73.5 ],
[237, 239, 0, 0.03309421487603306, 0.08752669712542799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 50.055 ],
[254, 238, 0, 0.07815008264462811, 0.05167231372274401, 248.0, 248.0, 248.0, 0, 1, 1, -360, 59.101000000000006 ],
[69, 255, 0, 0.0009369806094182826, 0.134541235754472, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 10.824000000000002 ],
[510, 225, 0, 0.021953719008264466, 0.232250442756508, 991.0, 991.0, 991.0, 0, 1, 1, -360, 66.41 ],
[256, 257, 0, 0.010125619834710746, 0.0267799693631888, 495.0, 495.0, 495.0, 0, 1, 1, -360, 15.315 ],
[258, 190, 0, 0.011717451523545707, 0.10515695255750121, 856.0, 856.0, 856.0, 0, 1, 1, -360, 33.84 ],
[258, 259, 0, 0.015782548476454293, 0.1416387085570408, 856.0, 856.0, 856.0, 0, 1, 1, -360, 45.58 ],
[260, 261, 0, 0.006791031855955679, 0.9751256416231477, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 78.45 ],
[554, 553, 0, 0.17583338842975205, 0.11625986438453201, 248.0, 248.0, 248.0, 0, 1, 1, -360, 132.974 ],
[515, 263, 0, 0.006987107438016529, 0.0739172618295936, 991.0, 991.0, 991.0, 0, 2, 1, -360, 21.136 ],
[14, 264, 0, 0.01700694214876033, 0.17991802858084, 991.0, 991.0, 991.0, 0, 1, 1, -360, 51.446000000000005 ],
[116, 555, 0, 0.0009768595041322315, 0.0103342878835768, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.955 ],
[151, 116, 0, 0.007244958677685951, 0.0191612735410668, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.958 ],
[111, 114, 0, 0.008806613573407202, 0.3161358573133961, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.867 ],
[77, 111, 0, 0.00288452216066482, 0.41418912211817605, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 33.321999999999996 ],
[266, 525, 0, 0.01042909090909091, 0.027582581569373602, 495.0, 495.0, 495.0, 0, 1, 1, -360, 15.774000000000001 ],
[267, 120, 0, 0.013136945983379503, 0.471584184581432, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 75.87899999999999 ],
[268, 269, 0, 0.0010327272727272726, 0.0027313295556817604, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.5619999999999998 ],
[556, 271, 0, 0.052289586776859506, 0.0345735262323792, 248.0, 248.0, 248.0, 0, 1, 1, -360, 39.544000000000004 ],
[556, 272, 0, 0.04685355371900827, 0.030979257409249603, 248.0, 248.0, 248.0, 0, 1, 1, -360, 35.433 ],
[529, 273, 0, 0.0034604958677685953, 0.009152227205140799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.234 ],
[128, 274, 0, 0.0029350761772853184, 0.1053620459045884, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.953 ],
[34, 275, 0, 0.0008290909090909092, 0.00054818938265696, 248.0, 248.0, 248.0, 0, 1, 1, -360, 0.627 ],
[503, 276, 0, 0.006707438016528925, 0.07095861291266, 991.0, 991.0, 991.0, 0, 2, 1, -360, 20.29 ],
[503, 504, 0, 0.06432727272727272, 0.680524223098808, 991.0, 991.0, 991.0, 0, 2, 1, -360, 194.59 ],
[177, 218, 0, 0.04330380165289256, 0.114528740018308, 495.0, 495.0, 495.0, 0, 1, 1, -360, 65.497 ],
[277, 278, 0, 0.007191135734072023, 1.032576638635032, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 83.072 ],
[557, 558, 0, 0.04341289256198347, 0.258338836678648, 743.0, 743.0, 743.0, 0, 1, 1, -360, 98.493 ],
[557, 559, 0, 0.03415867768595042, 0.09034195998366001, 495.0, 495.0, 495.0, 0, 1, 1, -360, 51.665 ],
[559, 558, 0, 0.04474314049586777, 0.11833546501370001, 495.0, 495.0, 495.0, 0, 1, 1, -360, 67.67399999999999 ],
[277, 78, 0, 0.03585768698060942, 0.32180078416049196, 856.0, 856.0, 856.0, 0, 1, 1, -360, 103.557 ],
[277, 279, 0, 0.021390927977839334, 0.191970480441328, 856.0, 856.0, 856.0, 0, 1, 1, -360, 61.777 ],
[78, 279, 0, 0.015811980609418283, 0.1419028439283376, 856.0, 856.0, 856.0, 0, 1, 1, -360, 45.665 ],
[281, 282, 0, 0.0023178670360110803, 0.08320574945862161, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.388 ],
[283, 161, 0, 0.036741157024793386, 0.09717203248350399, 495.0, 495.0, 495.0, 0, 2, 1, -360, 55.571000000000005 ],
[268, 161, 0, 0.018883636363636366, 0.199771751868832, 991.0, 991.0, 991.0, 0, 2, 1, -360, 57.123000000000005 ],
[256, 284, 0, 0.010755371900826446, 0.113782083346976, 991.0, 991.0, 991.0, 0, 2, 1, -360, 32.535 ],
[515, 516, 0, 0.04071140495867769, 0.107672438361532, 495.0, 495.0, 495.0, 0, 1, 1, -360, 61.576 ],
[263, 516, 0, 0.0030355371900826445, 0.128452925198488, 1981.0, 1981.0, 1981.0, 0, 2, 1, -360, 18.365 ],
[516, 285, 0, 0.006908429752066116, 0.018271230811372, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.449000000000002 ],
[63, 286, 0, 0.019088925619834708, 0.050485881518556, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.872 ],
[287, 516, 0, 0.01732892561983471, 0.011457770111127998, 248.0, 248.0, 248.0, 0, 1, 1, -360, 13.105 ],
[8, 102, 0, 0.015100069252077563, 0.542055501663692, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 87.21799999999999 ],
[8, 101, 0, 0.019246883656509697, 0.69091598202144, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 111.17 ],
[80, 288, 0, 0.007984072022160666, 0.2866086302684072, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 46.11600000000001 ],
[80, 289, 0, 0.0003782317636201524, 0.122198345223416, 5134.0, 5134.0, 5134.0, 0, 4, 1, -360, 6.553999999999999 ],
[276, 560, 0, 0.01778314049586777, 0.047032375838192794, 495.0, 495.0, 495.0, 0, 2, 1, -360, 26.897 ],
[37, 290, 0, 0.005629501385041551, 0.4546919507138321, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 48.773999999999994 ],
[290, 74, 0, 0.02071595106187673, 1.673216783321968, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 179.483 ],
[512, 291, 0, 0.0053299173553719, 0.056385693247479204, 991.0, 991.0, 991.0, 0, 2, 1, -360, 16.123 ],
[78, 292, 0, 0.0058149815327908595, 0.469673087481408, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 50.381 ],
[199, 548, 0, 0.0015530578512396695, 0.00410748599634868, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.349 ],
[491, 293, 0, 0.014176528925619833, 0.009373426429729999, 248.0, 248.0, 248.0, 0, 1, 1, -360, 10.720999999999998 ],
[4, 294, 0, 9.669321329639889e-05, 0.013884198109531681, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 1.117 ],
[490, 541, 0, 0.050580495867768596, 0.133773946861896, 495.0, 495.0, 495.0, 0, 1, 1, -360, 76.503 ],
[491, 295, 0, 0.010613553719008264, 0.028070443890777202, 495.0, 495.0, 495.0, 0, 1, 1, -360, 16.053 ],
[491, 296, 0, 0.004400661157024794, 0.0116387512948784, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.656000000000001 ],
[295, 297, 0, 0.020297520661157024, 0.053682341459340005, 495.0, 495.0, 495.0, 0, 1, 1, -360, 30.7 ],
[508, 161, 0, 0.023239669421487603, 0.061463658055360006, 495.0, 495.0, 495.0, 0, 1, 1, -360, 35.15 ],
[117, 123, 0, 0.005876211911357341, 0.21094161505628, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 33.941 ],
[133, 117, 0, 0.004469182825484764, 0.0401081792747688, 856.0, 856.0, 856.0, 0, 1, 1, -360, 12.907 ],
[71, 74, 0, 0.03904524469065097, 0.7884161162841721, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 169.144 ],
[74, 278, 0, 0.0077122576177285325, 1.10740463560792, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 89.09200000000001 ],
[298, 515, 0, 0.021701157024793388, 0.05739464148919599, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.823 ],
[5, 299, 0, 0.0016232686980609415, 0.058271370400665996, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 9.376 ],
[32, 292, 0, 0.009679362880886427, 0.34746541983297996, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.908 ],
[5, 29, 0, 0.00743395083102493, 1.0674425076571843, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 85.87700000000001 ],
[503, 560, 0, 0.015140495867768593, 0.160172719142436, 991.0, 991.0, 991.0, 0, 1, 1, -360, 45.8 ],
[300, 301, 0, 0.004892053324099723, 0.7024509290644521, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 56.513000000000005 ],
[51, 300, 0, 0.002573493767313019, 0.3695284920307039, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 29.729 ],
[244, 302, 0, 0.007714508310249307, 1.107727813004004, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 89.118 ],
[31, 302, 0, 0.004369113573407203, 0.6273619041941161, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 50.472 ],
[51, 282, 0, 0.006288434903047093, 0.9029576432132521, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 72.64399999999999 ],
[303, 304, 0, 8.795013850415512e-05, 0.000789298639172312, 856.0, 856.0, 856.0, 0, 1, 1, -360, 0.254 ],
[305, 304, 0, 0.003881117266849031, 0.0783689646873844, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 16.813 ],
[305, 259, 0, 0.0025625, 0.36794989475177603, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 29.601999999999997 ],
[306, 307, 0, 0.03223268698060942, 0.289268628831688, 856.0, 856.0, 856.0, 0, 1, 1, -360, 93.088 ],
[305, 308, 0, 0.0024272853185595567, 0.0217833994511184, 856.0, 856.0, 856.0, 0, 1, 1, -360, 7.01 ],
[305, 309, 0, 0.011014773776523545, 0.22241441259921202, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 47.716 ],
[310, 309, 0, 0.009565962603878117, 0.343394627639832, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.253 ],
[306, 309, 0, 0.035333795013850415, 0.31709917455019604, 856.0, 856.0, 856.0, 0, 1, 1, -360, 102.044 ],
[311, 280, 0, 0.003433691135734072, 0.1232611016590444, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 19.833 ],
[280, 278, 0, 0.009749769159764544, 0.7874838737974121, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 84.47200000000001 ],
[311, 32, 0, 0.01205909510619806, 0.9740069506375919, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 104.48 ],
[13, 312, 0, 0.0043324965373961214, 0.622104056565324, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 50.049 ],
[313, 314, 0, 0.006092624653739613, 0.218710302449316, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 35.191 ],
[312, 313, 0, 0.00893957756232687, 0.32090893884734, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.635 ],
[547, 566, 0, 0.027035702479338848, 0.286013220297816, 991.0, 991.0, 991.0, 0, 1, 1, -360, 81.783 ],
[245, 315, 0, 0.014162569252077564, 0.508401547875772, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 81.803 ],
[312, 316, 0, 8.803670360110802e-05, 0.01264120812658816, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 1.0170000000000001 ],
[312, 314, 0, 0.005339854570637119, 0.191687700220296, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.843000000000004 ],
[554, 546, 0, 0.08174743801652892, 0.21620344446439202, 495.0, 495.0, 495.0, 0, 1, 1, -360, 123.64299999999999 ],
[262, 216, 0, 0.042641966759002774, 0.38268554099981195, 856.0, 856.0, 856.0, 0, 1, 1, -360, 123.15 ],
[317, 233, 0, 0.005647276084951523, 0.114031901035644, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.464000000000002 ],
[318, 317, 0, 0.008311634349030471, 0.16783161497270002, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 36.006 ],
[231, 52, 0, 0.035263677285318554, 1.2658796434850879, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 203.683 ],
[319, 567, 0, 0.006089586776859504, 0.0644223069721, 991.0, 991.0, 991.0, 0, 1, 1, -360, 18.421 ],
[557, 321, 0, 0.010004628099173555, 0.10583989458750401, 991.0, 991.0, 991.0, 0, 2, 1, -360, 30.264 ],
[277, 65, 0, 0.009430170821779778, 0.7616700793261759, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 81.703 ],
[322, 288, 0, 0.006545013850415513, 0.528637424797136, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 56.706 ],
[322, 323, 0, 0.0018503000923372577, 0.14944779312484, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 16.031 ],
[277, 324, 0, 0.019719529085872576, 0.39818407235049996, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 85.425 ],
[324, 325, 0, 0.01103508771932133, 0.22282459929396403, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 47.803999999999995 ],
[277, 325, 0, 0.008665743305609418, 0.174981914850048, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 37.54 ],
[326, 327, 0, 0.007654214876033058, 0.0202436634226288, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.577 ],
[328, 326, 0, 0.10300958677685952, 0.068109252150368, 248.0, 248.0, 248.0, 0, 1, 1, -360, 77.90100000000001 ],
[328, 327, 0, 0.09827173553719008, 0.064976616491468, 248.0, 248.0, 248.0, 0, 1, 1, -360, 74.318 ],
[326, 329, 0, 0.028062148760330575, 0.07421802283046801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 42.443999999999996 ],
[568, 329, 0, 0.05699900826446282, 0.15074945731414802, 495.0, 495.0, 495.0, 0, 1, 1, -360, 86.211 ],
[568, 326, 0, 0.03218644628099173, 0.08512585494846397, 495.0, 495.0, 495.0, 0, 1, 1, -360, 48.681999999999995 ],
[332, 78, 0, 0.006471029547541551, 0.522661750455416, 2567.0, 2567.0, 2567.0, 0, 2, 1, -360, 56.065 ],
[333, 306, 0, 0.008580159279778392, 0.308006702824228, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 49.559 ],
[332, 333, 0, 0.007504674515235457, 0.26939943395502003, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 43.347 ],
[332, 334, 0, 0.017124653739612188, 0.15368328149175597, 856.0, 856.0, 856.0, 0, 1, 1, -360, 49.456 ],
[66, 334, 0, 0.030625, 0.27484062260471603, 856.0, 856.0, 856.0, 0, 1, 1, -360, 88.445 ],
[330, 335, 0, 0.00550536703601108, 0.790516769355108, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 63.598 ],
[336, 66, 0, 0.015054362880886425, 0.1351036887216764, 856.0, 856.0, 856.0, 0, 1, 1, -360, 43.477 ],
[330, 336, 0, 0.039036357340720224, 0.350327404269788, 856.0, 856.0, 856.0, 0, 1, 1, -360, 112.73700000000001 ],
[68, 70, 0, 0.016314058171745152, 0.14640868261713597, 856.0, 856.0, 856.0, 0, 1, 1, -360, 47.115 ],
[509, 337, 0, 0.03494082644628099, 0.09241056617056001, 495.0, 495.0, 495.0, 0, 1, 1, -360, 52.848 ],
[324, 288, 0, 0.012627423822714683, 0.11332339674541761, 856.0, 856.0, 856.0, 0, 1, 1, -360, 36.468 ],
[338, 559, 0, 0.009228099173553718, 0.097624922595552, 991.0, 991.0, 991.0, 0, 2, 1, -360, 27.915 ],
[339, 559, 0, 0.03560595041322315, 0.023542417076125203, 248.0, 248.0, 248.0, 0, 1, 1, -360, 26.927 ],
[339, 340, 0, 0.08711537190082644, 0.23040041287850396, 495.0, 495.0, 495.0, 0, 1, 1, -360, 131.762 ],
[559, 340, 0, 0.20983272727272728, 0.138740000599684, 248.0, 248.0, 248.0, 0, 1, 1, -360, 158.686 ],
[341, 292, 0, 0.0009329409048961218, 0.07535316024134399, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 8.083 ],
[557, 342, 0, 0.006019834710743802, 0.0636843933534336, 991.0, 991.0, 991.0, 0, 2, 1, -360, 18.21 ],
[558, 343, 0, 0.010650247933884296, 0.11266996708783199, 991.0, 991.0, 991.0, 0, 1, 1, -360, 32.217 ],
[502, 340, 0, 0.021737520661157025, 0.22996326026071198, 991.0, 991.0, 991.0, 0, 2, 1, -360, 65.756 ],
[72, 32, 0, 0.00675502077562327, 0.969954803293024, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 78.03399999999999 ],
[344, 345, 0, 0.0005762927054480609, 0.04654686738645321, 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 4.993 ],
[346, 47, 0, 0.0011340027700831024, 0.04070792194158799, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 6.55 ],
[46, 47, 0, 0.0008975069252077563, 0.0322183003580208, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 5.184 ],
[346, 345, 0, 0.0007217797783933517, 0.025910126194627202, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 4.169 ],
[347, 328, 0, 0.029905454545454544, 0.07909314882361201, 495.0, 495.0, 495.0, 0, 1, 1, -360, 45.232 ],
[347, 348, 0, 0.04883438016528925, 0.129155866607944, 495.0, 495.0, 495.0, 0, 1, 1, -360, 73.862 ],
[571, 348, 0, 0.041548429752066116, 0.10988617921762801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 62.842 ],
[347, 572, 0, 0.016052231404958678, 0.04245451362512801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 24.279 ],
[571, 570, 0, 0.17379041322314048, 0.11490906279551602, 248.0, 248.0, 248.0, 0, 1, 1, -360, 131.429 ],
[14, 350, 0, 0.02166743801652892, 0.05730546235524, 495.0, 495.0, 495.0, 0, 1, 1, -360, 32.772 ],
[350, 573, 0, 0.026277685950413226, 0.06949852316919598, 495.0, 495.0, 495.0, 0, 1, 1, -360, 39.745 ],
[15, 351, 0, 0.02639265927977839, 0.236857956201204, 856.0, 856.0, 856.0, 0, 1, 1, -360, 76.222 ],
[352, 15, 0, 0.0015260560941828254, 0.219126704094076, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 17.629 ],
[15, 335, 0, 0.0035338758079432133, 1.1417173740880242, 5134.0, 5134.0, 5134.0, 0, 1, 1, -360, 61.235 ],
[232, 227, 0, 5.5747922437673134e-05, 0.000500303468136644, 1200.0, 1200.0, 1200.0, 0, 1, 1, -360, 0.161 ],
[565, 544, 0, 0.0394803305785124, 0.10441652566461601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 59.714 ],
[235, 567, 0, 0.02391404958677686, 0.25298896294275997, 991.0, 991.0, 991.0, 0, 1, 1, -360, 72.34 ],
[567, 286, 0, 0.008068760330578512, 0.34144067500694797, 1981.0, 1981.0, 1981.0, 0, 1, 1, -360, 48.816 ],
[353, 519, 0, 0.007621818181818182, 0.080631926038356, 991.0, 991.0, 991.0, 0, 1, 1, -360, 23.055999999999997 ],
[354, 353, 0, 0.0008436363636363636, 0.00892490784392768, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.552 ],
[355, 354, 0, 0.0068502479338842966, 0.0181173530898976, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.360999999999999 ],
[354, 356, 0, 0.01855404958677686, 0.049071255647172, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.063000000000002 ],
[357, 358, 0, 0.0034823407202216067, 0.5000300103406239, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 40.228 ],
[574, 359, 0, 0.013352066115702478, 0.0353131884615884, 495.0, 495.0, 495.0, 0, 1, 1, -360, 20.195 ],
[235, 575, 0, 0.007459504132231404, 0.0789147905557, 991.0, 991.0, 991.0, 0, 1, 1, -360, 22.565 ],
[167, 361, 0, 0.000616198347107438, 0.0065188198358579995, 991.0, 991.0, 991.0, 0, 1, 1, -360, 1.864 ],
[528, 362, 0, 0.0011960330578512398, 0.012652945368078402, 991.0, 991.0, 991.0, 0, 1, 1, -360, 3.6180000000000003 ],
[363, 344, 0, 0.0002662742382271468, 0.009558592968871479, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.538 ],
[259, 364, 0, 0.013069713758102496, 0.26390852570525997, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 56.618 ],
[54, 56, 0, 0.007723337950138504, 0.0693122289241068, 856.0, 856.0, 856.0, 0, 1, 1, -360, 22.305 ],
[365, 364, 0, 0.0049974607571537395, 0.10091058802821559, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 21.649 ],
[231, 366, 0, 0.0013273891966759002, 0.0476500209962672, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 7.667000000000001 ],
[30, 367, 0, 0.01126108033240997, 0.1010613005635992, 856.0, 856.0, 856.0, 0, 1, 1, -360, 32.522 ],
[61, 367, 0, 0.020337603878116343, 0.18251754162067196, 856.0, 856.0, 856.0, 0, 1, 1, -360, 58.735 ],
[254, 368, 0, 0.0004297520661157025, 0.00454638722456732, 991.0, 991.0, 991.0, 0, 1, 1, -360, 1.3 ],
[254, 369, 0, 0.00015999999999999999, 0.00169265493591832, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.484 ],
[254, 370, 0, 0.0003669421487603306, 0.0038819152455960805, 991.0, 991.0, 991.0, 0, 2, 1, -360, 1.11 ],
[99, 358, 0, 0.0020184383656509696, 0.28982797432374396, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 23.316999999999997 ],
[354, 519, 0, 0.006762644628099174, 0.07154264880985199, 991.0, 991.0, 991.0, 0, 1, 1, -360, 20.457 ],
[571, 371, 0, 0.023726942148760328, 0.06275238397221199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 35.887 ],
[207, 372, 0, 0.002329256198347108, 0.006160354689297601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.523 ],
[57, 373, 0, 0.0017725619834710745, 0.0046880246727212796, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.681 ],
[209, 374, 0, 0.0010122922437673131, 0.0363388121515216, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 5.847 ],
[375, 376, 0, 0.0045364727608518006, 0.0916021467933684, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 19.652 ],
[376, 377, 0, 0.0030886426592797783, 0.062367022394423606, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 13.38 ],
[16, 49, 0, 0.002266101108033241, 0.32538991773524, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 26.178 ],
[318, 377, 0, 0.004755078485685596, 0.0960163149704152, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 20.599 ],
[378, 297, 0, 0.01753917355371901, 0.046387138574374404, 495.0, 495.0, 495.0, 0, 1, 1, -360, 26.528000000000002 ],
[562, 379, 0, 0.01802314049586777, 0.047667121439141605, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.26 ],
[576, 563, 0, 0.001808264462809917, 0.004782449638150801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.735 ],
[576, 381, 0, 0.0034320661157024794, 0.009077036954898, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.191 ],
[577, 576, 0, 0.06004495867768594, 0.15880530575430396, 495.0, 495.0, 495.0, 0, 1, 1, -360, 90.818 ],
[244, 383, 0, 0.006845567867036011, 0.1382282547912684, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 29.655 ],
[244, 306, 0, 0.02679108956599723, 0.5409756541164079, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 116.059 ],
[383, 306, 0, 0.0300685595567867, 0.269846910348376, 856.0, 856.0, 856.0, 0, 1, 1, -360, 86.838 ],
[380, 306, 0, 0.00025605955678670365, 0.03676764369572, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 2.958 ],
[252, 225, 0, 0.062094545454545444, 0.041056499553586, 248.0, 248.0, 248.0, 0, 1, 1, -360, 46.958999999999996 ],
[220, 76, 0, 0.002772074099722992, 0.398042682239984, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 32.023 ],
[542, 384, 0, 0.007939834710743802, 0.020999063146094, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.009 ],
[385, 384, 0, 0.053734876033057856, 0.035529141854791196, 248.0, 248.0, 248.0, 0, 1, 1, -360, 40.637 ],
[542, 385, 0, 0.011306115702479337, 0.119608453436296, 991.0, 991.0, 991.0, 0, 2, 1, -360, 34.201 ],
[386, 385, 0, 0.003668760330578512, 0.0388121580140316, 991.0, 991.0, 991.0, 0, 1, 1, -360, 11.097999999999999 ],
[387, 578, 0, 0.015444628099173553, 0.16339016240905604, 991.0, 991.0, 991.0, 0, 1, 1, -360, 46.72 ],
[332, 388, 0, 0.014036184210526315, 0.5038646344377999, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 81.07300000000001 ],
[382, 332, 0, 0.017764369806094183, 0.637697365901468, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 102.60700000000001 ],
[382, 388, 0, 0.00476159972299169, 0.17092976750548, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 27.503 ],
[579, 578, 0, 0.01911074380165289, 0.050543585664, 495.0, 495.0, 495.0, 0, 1, 1, -360, 28.905 ],
[577, 387, 0, 0.07597818181818182, 0.20094506949431204, 495.0, 495.0, 495.0, 0, 1, 1, -360, 114.917 ],
[144, 390, 0, 0.0004277685950413223, 0.0011313509747276, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.647 ],
[37, 49, 0, 0.008441481994459835, 0.303028527944352, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 48.758 ],
[391, 233, 0, 0.014211218836565096, 0.1275369872004348, 856.0, 856.0, 856.0, 0, 1, 1, -360, 41.042 ],
[392, 310, 0, 0.007035318559556785, 0.06313767618386361, 856.0, 856.0, 856.0, 0, 1, 1, -360, 20.317999999999998 ],
[260, 393, 0, 0.006341412742382271, 0.0569102963692744, 856.0, 856.0, 856.0, 0, 1, 1, -360, 18.314 ],
[394, 230, 0, 0.0007590027700831025, 0.00681158510656168, 856.0, 856.0, 856.0, 0, 1, 1, -360, 2.1919999999999997 ],
[395, 282, 0, 0.008762984764542936, 0.314569689934484, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.615 ],
[395, 244, 0, 0.0034046052631578946, 0.12221699007344, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 19.665 ],
[25, 396, 0, 0.008809037396121884, 0.316222866612064, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.881 ],
[81, 74, 0, 0.0075207756232686974, 0.26997742429652244, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 43.44 ],
[278, 80, 0, 0.016286011080332407, 0.5846279085788, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 94.068 ],
[81, 278, 0, 0.021054016620498613, 0.755787629231688, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 121.60799999999999 ],
[569, 570, 0, 0.03253950413223141, 0.08605961294018, 495.0, 495.0, 495.0, 0, 1, 1, -360, 49.216 ],
[397, 552, 0, 0.006289586776859504, 0.0166345314104904, 1200.0, 1200.0, 1200.0, 0, 1, 1, -360, 9.513 ],
[542, 398, 0, 0.0005580165289256199, 0.0059033089500572, 991.0, 991.0, 991.0, 0, 1, 1, -360, 1.6880000000000002 ],
[398, 385, 0, 0.021893553719008262, 0.05790348713648401, 495.0, 495.0, 495.0, 0, 1, 1, -360, 33.114000000000004 ],
[399, 499, 0, 0.03266380165289256, 0.021597087927192803, 248.0, 248.0, 248.0, 0, 1, 1, -360, 24.701999999999998 ],
[83, 399, 0, 0.025700495867768593, 0.016992996557050798, 248.0, 248.0, 248.0, 0, 1, 1, -360, 19.436 ],
[498, 400, 0, 0.012134214876033058, 0.032092247974028, 495.0, 495.0, 495.0, 0, 1, 1, -360, 18.352999999999998 ],
[518, 239, 0, 0.04685289256198347, 0.123915281026504, 495.0, 495.0, 495.0, 0, 1, 1, -360, 70.865 ],
[575, 543, 0, 0.0030307438016528923, 0.032062521596058796, 991.0, 991.0, 991.0, 0, 1, 1, -360, 9.168 ],
[401, 360, 0, 0.007957063711911357, 0.071409774520472, 856.0, 856.0, 856.0, 0, 1, 1, -360, 22.98 ],
[580, 581, 0, 0.007134545454545454, 0.018869255592422397, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.790999999999999 ],
[401, 402, 0, 0.0033434903047091418, 0.030005778188384805, 856.0, 856.0, 856.0, 0, 1, 1, -360, 9.656 ],
[403, 231, 0, 0.009592105263157893, 0.08608327126915, 856.0, 856.0, 856.0, 0, 1, 1, -360, 27.701999999999998 ],
[189, 360, 0, 0.028456024930747923, 0.255375399471348, 856.0, 856.0, 856.0, 0, 1, 1, -360, 82.181 ],
[234, 404, 0, 0.008092561983471074, 0.0214029921648796, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.24 ],
[235, 404, 0, 0.05107504132231405, 0.13508190749437998, 495.0, 495.0, 495.0, 0, 1, 1, -360, 77.251 ],
[235, 580, 0, 0.000580495867768595, 0.00153527999352772, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.878 ],
[216, 259, 0, 0.0022115650969529088, 0.079389770210892, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 12.774000000000001 ],
[405, 259, 0, 0.0052832409972299165, 0.1896554115982928, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 30.516 ],
[405, 318, 0, 0.0066348684210526315, 0.23817552558268398, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 38.323 ],
[406, 230, 0, 8.098164819944598e-05, 0.046512685161986804, 6845.0, 6845.0, 6845.0, 0, 1, 1, -360, 1.871 ],
[542, 407, 0, 0.025569586776859506, 0.067625761355152, 495.0, 495.0, 495.0, 0, 1, 1, -360, 38.674 ],
[23, 408, 0, 0.03224528925619835, 0.08528148128033601, 495.0, 495.0, 495.0, 0, 1, 1, -360, 48.771 ],
[577, 348, 0, 0.012999008264462809, 0.13751772188026398, 991.0, 991.0, 991.0, 0, 2, 1, -360, 39.321999999999996 ],
[562, 564, 0, 0.06921520661157024, 0.18305853298686803, 495.0, 495.0, 495.0, 0, 1, 1, -360, 104.68799999999999 ],
[582, 507, 0, 0.006357685950413223, 0.016814638289042002, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.616 ],
[27, 410, 0, 0.0030042975206611565, 0.007945685980170399, 495.0, 495.0, 495.0, 0, 1, 1, -360, 4.544 ],
[501, 27, 0, 0.003811570247933884, 0.040322957460962, 991.0, 991.0, 991.0, 0, 1, 1, -360, 11.53 ],
[27, 411, 0, 0.004648595041322314, 0.012294480221518, 495.0, 495.0, 495.0, 0, 1, 1, -360, 7.031000000000001 ],
[411, 410, 0, 0.002054214876033058, 0.0054329327333556, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.1069999999999998 ],
[403, 360, 0, 0.008191481994459833, 0.07351353506655639, 856.0, 856.0, 856.0, 0, 1, 1, -360, 23.656999999999996 ],
[412, 360, 0, 0.016761772853185596, 0.15042664773666, 856.0, 856.0, 856.0, 0, 1, 1, -360, 48.408 ],
[326, 413, 0, 0.012077024793388432, 0.12776397267356798, 991.0, 991.0, 991.0, 0, 2, 1, -360, 36.533 ],
[414, 413, 0, 0.008093223140495867, 0.08561896310149601, 991.0, 991.0, 991.0, 0, 2, 1, -360, 24.482 ],
[6, 297, 0, 0.019472396694214876, 0.0128750188978664, 248.0, 248.0, 248.0, 0, 1, 1, -360, 14.725999999999999 ],
[554, 580, 0, 0.07435371900826447, 0.196648733567264, 495.0, 495.0, 495.0, 0, 1, 1, -360, 112.46 ],
[262, 401, 0, 0.03931232686980609, 0.35280406181043206, 856.0, 856.0, 856.0, 0, 1, 1, -360, 113.53399999999999 ],
[499, 556, 0, 0.04185586776859504, 0.11069928308639199, 495.0, 495.0, 495.0, 0, 2, 1, -360, 63.306999999999995 ],
[224, 229, 0, 0.004135206611570248, 0.0437467367631624, 991.0, 991.0, 991.0, 0, 1, 1, -360, 12.509 ],
[583, 507, 0, 0.024632727272727268, 0.065147980317596, 495.0, 495.0, 495.0, 0, 1, 1, -360, 37.257 ],
[415, 307, 0, 0.015675554016620498, 0.1406784987952448, 856.0, 856.0, 856.0, 0, 1, 1, -360, 45.271 ],
[416, 507, 0, 0.0010555371900826446, 0.011166626467730801, 991.0, 991.0, 991.0, 0, 1, 1, -360, 3.193 ],
[284, 561, 0, 0.015221487603305786, 0.16102953827307598, 991.0, 991.0, 991.0, 0, 1, 1, -360, 46.045 ],
[543, 417, 0, 0.0006614876033057851, 0.027991756419545603, 1981.0, 1981.0, 1981.0, 0, 4, 1, -360, 4.002 ],
[418, 506, 0, 0.0009395041322314049, 0.009939101917118, 991.0, 991.0, 991.0, 0, 1, 1, -360, 2.842 ],
[220, 157, 0, 0.004599549861495845, 0.165112574384632, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 26.566999999999997 ],
[295, 419, 0, 0.0012023140495867769, 0.012719392565946, 991.0, 991.0, 991.0, 0, 1, 1, -360, 3.637 ],
[295, 420, 0, 0.0008003305785123967, 0.008466771900532, 991.0, 991.0, 991.0, 0, 1, 1, -360, 2.421 ],
[541, 62, 0, 0.05133355371900827, 0.0339414035471236, 248.0, 248.0, 248.0, 0, 1, 1, -360, 38.821 ],
[52, 421, 0, 0.00013885041551246538, 0.004984389831631239, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.802 ],
[60, 160, 0, 6.128808864265928e-05, 0.000550023067454096, 856.0, 856.0, 856.0, 0, 2, 1, -360, 0.177 ],
[535, 161, 0, 3.735537190082645e-05, 0.00039518596644331203, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.113 ],
[267, 282, 0, 0.0065652700831024926, 0.235677115717012, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 37.921 ],
[52, 365, 0, 0.007655586334279779, 0.15458444922992, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 33.164 ],
[28, 27, 0, 0.015726942148760328, 0.041594197273402404, 495.0, 495.0, 495.0, 0, 1, 1, -360, 23.787 ],
[30, 201, 0, 0.009128289473684211, 0.327683234253536, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 52.725 ],
[422, 81, 0, 0.0004226685133887349, 0.13655487952674, 5134.0, 5134.0, 5134.0, 0, 6, 1, -360, 7.324 ],
[119, 425, 0, 0.003579120498614958, 0.1284816595874996, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 20.673000000000002 ],
[423, 425, 0, 0.0006518351800554017, 0.0233992864289392, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 3.765 ],
[424, 425, 0, 0.005922957063711911, 0.21261965153389198, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 34.211 ],
[426, 428, 0, 0.013948429752066116, 0.14756174042535197, 991.0, 991.0, 991.0, 0, 2, 1, -360, 42.193999999999996 ],
[427, 428, 0, 0.0002664462809917355, 0.0028187600792304794, 991.0, 991.0, 991.0, 0, 2, 1, -360, 0.8059999999999999 ],
[19, 428, 0, 0.023607603305785128, 0.24974703912892798, 991.0, 991.0, 991.0, 0, 2, 1, -360, 71.413 ],
[45, 429, 0, 0.02562314049586777, 0.067767398802972, 495.0, 495.0, 495.0, 0, 1, 1, -360, 38.755 ],
[44, 429, 0, 5.289256198347107e-05, 0.00013988883767892, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.08 ],
[505, 429, 0, 0.006012561983471073, 0.015901863623161996, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.094 ],
[231, 431, 0, 0.011677285318559558, 0.4191859418495199, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 67.44800000000001 ],
[190, 431, 0, 0.009600761772853185, 0.34464383257266795, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 55.45399999999999 ],
[430, 431, 0, 0.0028100761772853187, 0.1008748520662472, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.230999999999998 ],
[286, 433, 0, 0.01568694214876033, 0.16595362535967603, 991.0, 991.0, 991.0, 0, 1, 1, -360, 47.453 ],
[432, 433, 0, 0.00010049586776859504, 0.00106315516636076, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.304 ],
[506, 433, 0, 0.0065904132231404955, 0.06972059669946801, 991.0, 991.0, 991.0, 0, 1, 1, -360, 19.936 ],
[23, 434, 0, 0.02613685950413223, 0.069126069139116, 495.0, 495.0, 495.0, 0, 2, 1, -360, 39.532 ],
[400, 434, 0, 0.008155371900826446, 0.021569110159669603, 495.0, 495.0, 495.0, 0, 2, 1, -360, 12.335 ],
[500, 434, 0, 0.006338512396694216, 0.0167639285853336, 495.0, 495.0, 495.0, 0, 2, 1, -360, 9.587 ],
[32, 436, 0, 0.0044813019390581715, 0.16086776359270402, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 25.884 ],
[435, 436, 0, 0.0006634349030470914, 0.023815688073266, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 3.832 ],
[78, 436, 0, 0.00897680055401662, 0.32224515307884394, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.85 ],
[86, 438, 0, 0.014693213296398892, 0.52745036936438, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 84.868 ],
[437, 438, 0, 1.0387811634349031e-05, 0.0003728969948845, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.06 ],
[221, 438, 0, 0.002280124653739612, 0.081850890377238, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.17 ],
[207, 439, 0, 0.055703801652892564, 0.0368309823503996, 248.0, 248.0, 248.0, 0, 1, 1, -360, 42.126000000000005 ],
[516, 439, 0, 0.05448462809917355, 0.03602487292327441, 248.0, 248.0, 248.0, 0, 1, 1, -360, 41.20399999999999 ],
[513, 439, 0, 0.046726611570247926, 0.0308953241066316, 248.0, 248.0, 248.0, 0, 1, 1, -360, 35.336999999999996 ],
[181, 441, 0, 0.040805289256198356, 0.10792074104825197, 495.0, 495.0, 495.0, 0, 1, 1, -360, 61.718 ],
[440, 441, 0, 0.0001322314049586777, 0.000349722094197784, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.2 ],
[504, 441, 0, 0.05916099173553719, 0.156467413554364, 495.0, 495.0, 495.0, 0, 1, 1, -360, 89.48100000000001 ],
[135, 442, 0, 0.004956890581717451, 0.177940231009092, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 28.631 ],
[109, 442, 0, 0.0015380886426592797, 0.055213615042649204, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 8.884 ],
[112, 442, 0, 0.0027304362880886425, 0.09801597510545401, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 15.770999999999999 ],
[113, 443, 0, 0.0019885734072022164, 0.07138491472072879, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 11.485999999999999 ],
[132, 443, 0, 0.006788434903047091, 0.24368818615747198, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 39.21 ],
[107, 443, 0, 2.2333795013850418e-05, 0.000801728539002036, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.129 ],
[444, 445, 0, 7.877423822714682e-05, 0.00282780221121528, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.455 ],
[112, 445, 0, 0.002816135734072022, 0.101092375313206, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.266 ],
[109, 445, 0, 0.0014354224376731304, 0.0515281497432104, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 8.291 ],
[119, 447, 0, 0.005212690443213296, 0.74849127803204, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 60.217 ],
[100, 447, 0, 0.0050695117728531865, 0.7279322237145921, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 58.563 ],
[446, 447, 0, 2.9518698060941832e-05, 0.00423859584186224, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 0.341 ],
[124, 448, 0, 6.509695290858726e-05, 0.00233682116794768, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.376 ],
[125, 448, 0, 0.00615148891966759, 0.22082338542026803, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 35.531 ],
[131, 448, 0, 3.912742382271468e-05, 0.0014045786807313759, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.226 ],
[449, 450, 0, 0.0023614958448753462, 0.08477191683710039, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.64 ],
[173, 450, 0, 0.002862361495844876, 0.10275176694050518, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.533 ],
[184, 450, 0, 0.004022853185595568, 0.14441057621844403, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 23.236 ],
[144, 451, 0, 0.007672727272727273, 0.020292624515794402, 495.0, 495.0, 495.0, 0, 1, 1, -360, 11.605 ],
[140, 451, 0, 0.006991074380165291, 0.018489807120219602, 495.0, 495.0, 495.0, 0, 1, 1, -360, 10.574000000000002 ],
[514, 451, 0, 0.01149289256198347, 0.030396095817207994, 495.0, 495.0, 495.0, 0, 1, 1, -360, 17.383 ],
[537, 585, 0, 0.05072595041322314, 0.134158641165824, 495.0, 495.0, 495.0, 0, 1, 1, -360, 76.723 ],
[141, 585, 0, 0.007994710743801653, 0.0211441978151932, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.092 ],
[584, 585, 0, 9.256198347107438e-05, 0.000244805465938352, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.14 ],
[522, 454, 0, 0.0035008264462809916, 0.0092588924438956, 495.0, 495.0, 495.0, 0, 1, 1, -360, 5.295 ],
[144, 454, 0, 0.00452892561983471, 0.011977981726290799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.85 ],
[453, 454, 0, 0.001114710743801653, 0.0029481572540882, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.686 ],
[199, 456, 0, 0.013063140495867768, 0.0086372614214612, 248.0, 248.0, 248.0, 0, 1, 1, -360, 9.879 ],
[140, 456, 0, 0.005061818181818182, 0.013387361765852802, 495.0, 495.0, 495.0, 0, 2, 1, -360, 7.656000000000001 ],
[455, 456, 0, 0.0011365289256198346, 0.00300586139962416, 495.0, 495.0, 495.0, 0, 2, 1, -360, 1.719 ],
[537, 456, 0, 0.039058512396694216, 0.025825228046024003, 248.0, 248.0, 248.0, 0, 1, 1, -360, 29.538 ],
[538, 457, 0, 0.027927272727272728, 0.0184653265736368, 248.0, 248.0, 248.0, 0, 1, 1, -360, 21.12 ],
[153, 457, 0, 0.030093223140495867, 0.019897438549384, 248.0, 248.0, 248.0, 0, 1, 1, -360, 22.758000000000003 ],
[176, 457, 0, 0.004579173553719009, 0.0030277190305137603, 248.0, 248.0, 248.0, 0, 1, 1, -360, 3.463 ],
[524, 459, 0, 0.004318677685950414, 0.011421923596476799, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.532 ],
[458, 459, 0, 0.001993388429752066, 0.0052720605700488, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.015 ],
[134, 459, 0, 0.011813553719008265, 0.031244171895617998, 495.0, 495.0, 495.0, 0, 1, 1, -360, 17.868 ],
[460, 461, 0, 6.611570247933885e-05, 0.000174861047098892, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.1 ],
[150, 461, 0, 0.008018512396694214, 0.021207147792120403, 495.0, 495.0, 495.0, 0, 1, 1, -360, 12.128 ],
[149, 461, 0, 0.005586115702479339, 0.0147740098693748, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.449 ],
[521, 463, 0, 0.014348429752066114, 0.009487086110365599, 248.0, 248.0, 248.0, 0, 1, 1, -360, 10.850999999999999 ],
[462, 463, 0, 0.007197355371900825, 0.0047588433967958406, 248.0, 248.0, 248.0, 0, 1, 1, -360, 5.443 ],
[538, 463, 0, 0.012211570247933883, 0.0080742088497664, 248.0, 248.0, 248.0, 0, 1, 1, -360, 9.235 ],
[110, 464, 0, 0.0025753116343490306, 0.0924473799817492, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.875 ],
[90, 464, 0, 0.007328947368421053, 0.26309125979076, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 42.332 ],
[165, 464, 0, 0.002152527700831025, 0.0772704722900764, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 12.433 ],
[458, 465, 0, 0.002003305785123967, 0.0052982897270776, 495.0, 495.0, 495.0, 0, 1, 1, -360, 3.03 ],
[134, 465, 0, 0.011838677685950413, 0.031310619093534, 495.0, 495.0, 495.0, 0, 1, 1, -360, 17.906 ],
[524, 465, 0, 0.004293553719008264, 0.0113554763986092, 495.0, 495.0, 495.0, 0, 1, 1, -360, 6.494 ],
[466, 467, 0, 0.0023509349030470914, 0.084392804892244, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.579 ],
[110, 467, 0, 0.0025337603878116343, 0.09095579200221118, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 14.635 ],
[165, 467, 0, 0.0022891274238227145, 0.08217406777274441, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 13.222000000000001 ],
[468, 469, 0, 0.0005269421487603305, 0.0013936425453786, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.797 ],
[541, 469, 0, 0.022390743801652895, 0.05921844221026801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 33.866 ],
[490, 469, 0, 0.028243305785123966, 0.07469714209944801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 42.718 ],
[263, 471, 0, 0.0371900826446281, 0.0245898347482832, 248.0, 248.0, 248.0, 0, 1, 1, -360, 28.125 ],
[470, 471, 0, 0.001570909090909091, 0.0010386746197682802, 248.0, 248.0, 248.0, 0, 1, 1, -360, 1.188 ],
[534, 471, 0, 0.024497190082644622, 0.0161973787927468, 248.0, 248.0, 248.0, 0, 1, 1, -360, 18.526 ],
[136, 472, 0, 0.0007079293628808865, 0.025412930201351602, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 4.0889999999999995 ],
[110, 472, 0, 0.00019511772853185596, 0.0070042485539216805, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.127 ],
[251, 472, 0, 4.207063711911357e-05, 0.00151023282928764, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.243 ],
[226, 474, 0, 0.017639669421487602, 0.011663231841509601, 248.0, 248.0, 248.0, 0, 1, 1, -360, 13.34 ],
[473, 474, 0, 0.003467107438016529, 0.00916971330986216, 495.0, 495.0, 495.0, 0, 2, 1, -360, 5.244 ],
[257, 474, 0, 0.020264462809917356, 0.053594910935781594, 495.0, 495.0, 495.0, 0, 2, 1, -360, 30.65 ],
[6, 474, 0, 0.08066247933884299, 0.05333349367016, 248.0, 248.0, 248.0, 0, 1, 1, -360, 61.001000000000005 ],
[299, 475, 0, 0.013238227146814403, 0.47521993028123993, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 76.464 ],
[3, 475, 0, 0.0002794321329639889, 0.010030929162389441, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.614 ],
[210, 475, 0, 0.0001481994459833795, 0.00531999712702368, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.856 ],
[297, 476, 0, 0.0193500826446281, 0.05117658265464801, 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.267 ],
[296, 476, 0, 0.005596694214876033, 0.014801987636898, 495.0, 495.0, 495.0, 0, 1, 1, -360, 8.465 ],
[295, 476, 0, 0.0009474380165289256, 0.00250575880492432, 495.0, 495.0, 495.0, 0, 1, 1, -360, 1.433 ],
[313, 478, 0, 0.008696849030470914, 0.31219557906752804, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 50.233000000000004 ],
[477, 478, 0, 1.5235457063711912e-05, 0.0005469155924977479, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 0.08800000000000001 ],
[245, 478, 0, 0.005264542936288089, 0.188984197007248, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 30.408 ],
[479, 481, 0, 0.028420495867768597, 0.07516576970575199, 495.0, 495.0, 495.0, 0, 1, 1, -360, 42.986000000000004 ],
[565, 481, 0, 0.024842314049586776, 0.065702289836964, 495.0, 495.0, 495.0, 0, 1, 1, -360, 37.574 ],
[480, 481, 0, 7.735537190082645e-05, 0.000204587425105844, 495.0, 495.0, 495.0, 0, 1, 1, -360, 0.11699999999999999 ],
[415, 482, 0, 0.011021814404432133, 0.0989140353680364, 856.0, 856.0, 856.0, 0, 1, 1, -360, 31.831 ],
[56, 482, 0, 0.002630886426592798, 0.0236105947261788, 856.0, 856.0, 856.0, 0, 1, 1, -360, 7.598 ],
[409, 482, 0, 0.0007635041551246537, 0.0068519822810072005, 856.0, 856.0, 856.0, 0, 1, 1, -360, 2.205 ],
[483, 484, 0, 9.037396121883656e-05, 0.000811050963873968, 856.0, 856.0, 856.0, 0, 1, 1, -360, 0.261 ],
[3, 484, 0, 0.010022160664819944, 0.08994275516621358, 856.0, 856.0, 856.0, 0, 1, 1, -360, 28.944000000000003 ],
[301, 484, 0, 0.00966516620498615, 0.08673894848517479, 856.0, 856.0, 856.0, 0, 1, 1, -360, 27.913 ],
[233, 485, 0, 0.01410180055401662, 0.1265550251138996, 856.0, 856.0, 856.0, 0, 1, 1, -360, 40.726 ],
[392, 485, 0, 0.00914819944598338, 0.0820994883738036, 856.0, 856.0, 856.0, 0, 1, 1, -360, 26.42 ],
[391, 485, 0, 8.518005540166207e-05, 0.000764438839512864, 856.0, 856.0, 856.0, 0, 1, 1, -360, 0.24600000000000002 ],
[579, 488, 0, 0.004636473829194215, 0.11036180126571601, 1486.0, 1486.0, 1486.0, 0, 1, 1, -360, 21.038 ],
[486, 488, 0, 0.00016969696969690082, 0.00403929018798184, 1486.0, 1486.0, 1486.0, 0, 1, 1, -360, 0.77 ],
[487, 488, 0, 0.00014567493112954544, 0.00346749456396992, 1486.0, 1486.0, 1486.0, 0, 1, 1, -360, 0.6609999999999999 ],
[270, 489, 0, 0.0001745152354570637, 0.0062646695140596, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.008 ],
[331, 489, 0, 0.003002943213296399, 0.10779830627119119, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 17.345 ],
[396, 489, 0, 0.01124792243767313, 0.40377286606072005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 64.968 ],
[519, 253, 0, 0.013353485337561985, 0.141267767926912, 991.0, 991.0, 991.0, 0, 1, 1, -360, 40.394293146100004 ],
[382, 349, 0, 0.009091647380263157, 1.30547149138788, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 105.02671053600001 ],
[349, 351, 0, 0.0005858117819605263, 0.0841168325920224, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 6.76729770521 ],
[459, 465, 0, 1.578788789911157e-05, 0.00016702153987596, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.047758360894800005 ],
[549, 550, 0, 3.680432518409091e-05, 0.000389356391787088, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.111333083682 ],
[550, 551, 0, 5.755645674710744e-05, 0.0006088951287918401, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.17410828165999997 ],
[194, 195, 0, 1.7560672583171745e-05, 0.00252154053805592, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.202860889681 ],
[247, 248, 0, 2.1755213937811637e-05, 0.0031238355819477198, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.25131623141 ],
[2, 294, 0, 2.3531392658518004e-05, 0.003378877444715, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.271834647991 ],
[549, 551, 0, 9.265809538429751e-05, 0.0009802386406577602, 991.0, 991.0, 991.0, 0, 1, 1, -360, 0.28029073853799996 ],
[54, 365, 0, 2.573045189134349e-05, 0.00369464080598484, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.297238180249 ],
[131, 265, 0, 2.7616389041343487e-05, 0.00396544290388756, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.319024526206 ],
[91, 92, 0, 2.8945628197853184e-05, 0.0041563086239824396, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.33437989694200004 ],
[247, 249, 0, 3.098840072160664e-05, 0.00444963074500788, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.357978005136 ],
[186, 191, 0, 3.1591661821191135e-05, 0.00453625312865552, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.36494687735799997 ],
[129, 173, 0, 3.202671277479225e-05, 0.00459872218332188, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.369972585975 ],
[96, 202, 0, 3.5971247867797784e-05, 0.00516511877739804, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.415539855369 ],
[53, 320, 0, 3.784209581142659e-05, 0.00543375421308236, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.437151890814 ],
[24, 396, 0, 4.144748602818559e-05, 0.005951452925597279, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.47880135859800005 ],
[133, 156, 0, 4.431754564044322e-05, 0.0063635653674415605, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.511956287238 ],
[442, 452, 0, 4.483572190450138e-05, 0.006437970402313801, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.517942259441 ],
[445, 452, 0, 4.490753296371191e-05, 0.0064482817668697215, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.518771820797 ],
[247, 250, 0, 4.594910768732687e-05, 0.00659784169268824, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.530804092004 ],
[187, 195, 0, 4.755760376239612e-05, 0.006828805970367921, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.549385438663 ],
[216, 236, 0, 5.03353075283241e-05, 0.00722765701751724, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.581473472567 ],
[244, 389, 0, 5.1633313019736845e-05, 0.007414037889302401, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.596468032004 ],
[394, 406, 0, 5.6346419007686985e-05, 0.008090793734075721, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.650913832377 ],
[442, 445, 0, 6.388070648310249e-05, 0.00917264360085512, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.737949921293 ],
[442, 444, 0, 6.584378362735456e-05, 0.00945452224616264, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.760627388463 ],
[198, 472, 0, 8.37554210498615e-05, 0.0120264578966664, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.967542623967 ],
[464, 467, 0, 8.460287496468144e-05, 0.01214814397621276, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 0.977332411594 ],
[198, 251, 0, 8.83613182396122e-05, 0.012687819608389479, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 1.0207499483 ],
[112, 143, 0, 9.049653833033241e-05, 0.012994416294241841, 3423.0, 3423.0, 3423.0, 0, 1, 1, -360, 1.04541601079 ],
[2, 490, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[5, 491, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[10, 492, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[12, 493, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[13, 494, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[15, 495, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[18, 496, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[20, 497, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[22, 498, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[24, 499, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[26, 500, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[30, 501, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[32, 502, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[37, 503, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[42, 504, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[46, 505, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[52, 506, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[56, 507, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[61, 508, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[68, 509, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[69, 510, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[74, 511, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[78, 512, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[86, 513, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[87, 514, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[94, 515, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[95, 516, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[96, 517, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[99, 518, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[100, 519, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[104, 520, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[105, 521, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[106, 522, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[107, 523, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[117, 524, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[120, 525, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[123, 526, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[124, 527, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[125, 528, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[128, 529, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[129, 530, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[138, 531, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[143, 532, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[156, 533, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[157, 534, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[159, 535, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[160, 536, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[165, 537, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[184, 538, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[191, 539, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[195, 540, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[201, 541, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[220, 542, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[231, 543, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[232, 544, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[233, 545, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[236, 546, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[245, 547, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[246, 548, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[248, 549, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[249, 550, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[250, 551, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[259, 552, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[261, 553, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[262, 554, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[265, 555, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[270, 556, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[277, 557, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[279, 558, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[280, 559, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[290, 560, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[301, 561, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[305, 562, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[306, 563, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[310, 564, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[313, 565, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[315, 566, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[320, 567, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[330, 568, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[332, 569, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[334, 570, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[336, 571, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[349, 572, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[351, 573, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[358, 574, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[360, 575, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[380, 576, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[382, 577, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[383, 578, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[389, 579, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[401, 580, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[402, 581, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[409, 582, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[415, 583, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[444, 584, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ],
[452, 585, 0, 0.005, 0.0, 2000.0, 2000.0, 2000.0, 1.0, 0, 1, -360, 360 ]
])
ppc["gen_control"] = array([
[586, 1, 0.08658028904199107, 4.329014452099554, 0, 0, 0],
[589, 1, 0.010042676909098597, 0.5021338454549299, 0, 0, 0],
[590, 1, 0.012095775674984046, 0.6047887837492023, 0, 0, 0],
[593, 1, 0.0017666198683200384, 0.08833099341600192, 0, 0, 0],
[594, 1, 0.006047887837492023, 0.30239439187460115, 0, 0, 0],
[595, 1, 1.50560576164933, 75.2802880824665, 0, 0, 0],
[598, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[599, 1, 0.0029602819415092537, 0.1480140970754627, 0, 0, 0],
[601, 1, 0.019576058000303126, 0.9788029000151565, 0, 0, 0],
[602, 1, 0.007830423200121252, 0.39152116000606263, 0, 0, 0],
[603, 1, 1.0997606567649967, 54.98803283824984, 0, 0, 0],
[607, 1, 0.5729577951308232, 28.64788975654116, 0, 0, 0],
[608, 1, 0.0076394372684109755, 0.3819718634205488, 0, 0, 0],
[609, 1, 0.0057932399285449895, 0.2896619964272495, 0, 0, 0],
[612, 1, 0.00954929658551372, 0.477464829275686, 0, 0, 0],
[613, 1, 0.027056340325622208, 1.3528170162811104, 0, 0, 0],
[614, 1, 0.00954929658551372, 0.477464829275686, 0, 0, 0],
[616, 1, 0.0046154933496649645, 0.23077466748324824, 0, 0, 0],
[617, 1, 0.04360845440717932, 2.1804227203589663, 0, 0, 0],
[618, 1, 0.010631550198538607, 0.5315775099269304, 0, 0, 0],
[619, 1, 0.037560566569687294, 1.8780283284843649, 0, 0, 0],
[621, 1, 0.24350706293059987, 12.175353146529993, 0, 0, 0],
[624, 1, 0.004297183463481174, 0.21485917317405873, 0, 0, 0],
[628, 1, 0.14292113889652203, 7.1460569448261015, 0, 0, 0],
[629, 1, 0.023968734429639437, 1.198436721481972, 0, 0, 0],
[631, 1, 0.025401128917466494, 1.2700564458733248, 0, 0, 0],
[632, 1, 0.01435577586688896, 0.717788793344448, 0, 0, 0],
[637, 1, 0.017093240888069558, 0.854662044403478, 0, 0, 0],
[638, 1, 0.02048324117592693, 1.0241620587963465, 0, 0, 0],
[640, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[641, 1, 0.0040107045659157625, 0.20053522829578813, 0, 0, 0],
[642, 1, 0.00919915571071155, 0.4599577855355775, 0, 0, 0],
[643, 1, 0.27279157245950864, 13.639578622975431, 0, 0, 0],
[647, 1, 0.00445633840657307, 0.2228169203286535, 0, 0, 0],
[650, 1, 0.4216014442504307, 21.080072212521536, 0, 0, 0],
[652, 1, 0.00746436683100989, 0.37321834155049455, 0, 0, 0],
[655, 1, 0.019576058000303126, 0.9788029000151565, 0, 0, 0],
[663, 1, 0.00238732414637843, 0.1193662073189215, 0, 0, 0],
[666, 1, 0.00919915571071155, 0.4599577855355775, 0, 0, 0],
[670, 1, 0.0076394372684109755, 0.3819718634205488, 0, 0, 0],
[672, 1, 0.010536057232683471, 0.5268028616341736, 0, 0, 0],
[676, 1, 0.11777465788800255, 5.888732894400127, 0, 0, 0],
[681, 1, 0.0063821132179850025, 0.31910566089925013, 0, 0, 0],
[683, 1, 0.008753521870054244, 0.4376760935027122, 0, 0, 0],
[687, 1, 0.42303383873825773, 21.151691936912886, 0, 0, 0],
[689, 1, 0.09867606471697511, 4.933803235848756, 0, 0, 0],
[691, 1, 0.008276057040778557, 0.4138028520389279, 0, 0, 0],
[694, 1, 0.005220282133414166, 0.2610141066707083, 0, 0, 0],
[695, 1, 0.004679155326901723, 0.23395776634508614, 0, 0, 0],
[696, 1, 0.22950142793851305, 11.475071396925653, 0, 0, 0],
[697, 1, 0.0036923946797319715, 0.1846197339865986, 0, 0, 0],
[698, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[702, 1, 0.023363945645890238, 1.168197282294512, 0, 0, 0],
[705, 1, 0.005411268065124442, 0.27056340325622213, 0, 0, 0],
[707, 1, 0.010822536130248884, 0.5411268065124443, 0, 0, 0],
[713, 1, 0.004265352474862795, 0.21326762374313976, 0, 0, 0],
[714, 1, 0.00477464829275686, 0.238732414637843, 0, 0, 0],
[716, 1, 1.5915494309189534e-05, 0.0007957747154594768, 0, 0, 0],
[717, 1, 0.0017507043740108488, 0.08753521870054244, 0, 0, 0],
[719, 1, 0.623250757147862, 31.162537857393104, 0, 0, 0],
[722, 1, 0.006589014644004467, 0.3294507322002233, 0, 0, 0],
[723, 1, 0.006270704757820675, 0.31353523789103377, 0, 0, 0],
[724, 1, 0.0019257748114119334, 0.09628874057059668, 0, 0, 0],
[727, 1, 0.019576058000303126, 0.9788029000151565, 0, 0, 0],
[728, 1, 0.16233804195373325, 8.116902097686662, 0, 0, 0],
[730, 1, 0.10077690996578814, 5.038845498289407, 0, 0, 0],
[732, 1, 0.004647324338283344, 0.2323662169141672, 0, 0, 0],
[735, 1, 0.013496339174192726, 0.6748169587096363, 0, 0, 0],
[738, 1, 0.04408591923645501, 2.2042959618227504, 0, 0, 0],
[741, 1, 0.0340591578216656, 1.7029578910832803, 0, 0, 0],
[742, 1, 0.0028647889756541157, 0.14323944878270578, 0, 0, 0],
[743, 1, 0.44881693951914486, 22.440846975957243, 0, 0, 0],
[746, 1, 0.03183098861837907, 1.5915494309189535, 0, 0, 0],
[747, 1, 0.0039788735772973835, 0.1989436788648692, 0, 0, 0],
[748, 1, 0.03501408748021698, 1.7507043740108488, 0, 0, 0],
[749, 1, 0.0025464790894703256, 0.12732395447351627, 0, 0, 0],
[750, 1, 0.028902537665488188, 1.4451268832744095, 0, 0, 0],
[753, 1, 0.049624511256052974, 2.4812255628026487, 0, 0, 0],
[758, 1, 0.0058887328944001276, 0.2944366447200064, 0, 0, 0],
[760, 1, 0.2527380496299298, 12.636902481496492, 0, 0, 0],
[761, 1, 0.004997465213085514, 0.2498732606542757, 0, 0, 0],
[762, 1, 0.3517324242330887, 17.586621211654435, 0, 0, 0],
[763, 1, 0.006461690689530951, 0.32308453447654756, 0, 0, 0],
[765, 1, 0.018780283284843647, 0.9390141642421824, 0, 0, 0],
[767, 1, 0.0035650707252584553, 0.17825353626292276, 0, 0, 0],
[769, 1, 0.013782818071758136, 0.6891409035879068, 0, 0, 0],
[771, 1, 0.21963382146681557, 10.981691073340778, 0, 0, 0],
[772, 1, 0.002992112930127632, 0.1496056465063816, 0, 0, 0],
[774, 1, 0.010663381187156987, 0.5331690593578494, 0, 0, 0],
[777, 1, 0.012573240504259732, 0.6286620252129866, 0, 0, 0],
[778, 1, 0.004679155326901723, 0.23395776634508614, 0, 0, 0],
[781, 1, 0.4169859509007658, 20.84929754503829, 0, 0, 0],
[784, 1, 0.4058451048843331, 20.292255244216655, 0, 0, 0],
[785, 1, 0.00047746482927568597, 0.0238732414637843, 0, 0, 0],
[787, 1, 0.24764509145098912, 12.382254572549456, 0, 0, 0],
[788, 1, 0.2785211504108168, 13.926057520540843, 0, 0, 0],
[789, 1, 0.0123185925953127, 0.615929629765635, 0, 0, 0],
[791, 1, 0.0031830988618379067, 0.15915494309189535, 0, 0, 0],
[792, 1, 0.009979014931861837, 0.49895074659309185, 0, 0, 0],
[795, 1, 0.004329014452099553, 0.2164507226049777, 0, 0, 0],
[800, 1, 0.0058091554228541795, 0.290457771142709, 0, 0, 0],
[801, 1, 0.007957747154594767, 0.3978873577297384, 0, 0, 0],
[802, 1, 0.07957747154594767, 3.9788735772973833, 0, 0, 0],
[805, 1, 0.44881693951914486, 22.440846975957243, 0, 0, 0],
[806, 1, 0.005697746962689853, 0.2848873481344927, 0, 0, 0],
[808, 1, 0.034616200122487235, 1.7308100061243619, 0, 0, 0],
[809, 1, 0.0039788735772973835, 0.1989436788648692, 0, 0, 0],
[811, 1, 0.0040107045659157625, 0.20053522829578813, 0, 0, 0],
[814, 1, 0.014164789935178685, 0.7082394967589343, 0, 0, 0],
[816, 1, 0.012748310941660816, 0.6374155470830408, 0, 0, 0],
[817, 1, 0.017188733853924696, 0.8594366926962349, 0, 0, 0],
[821, 1, 0.013130282805081364, 0.6565141402540683, 0, 0, 0],
[822, 1, 0.04265352474862795, 2.1326762374313977, 0, 0, 0],
[826, 1, 0.018461973398659858, 0.9230986699329929, 0, 0, 0],
[830, 1, 0.02832957987035737, 1.4164789935178685, 0, 0, 0],
[834, 1, 0.007416620348082323, 0.37083101740411617, 0, 0, 0],
[835, 1, 0.010138169874953733, 0.5069084937476867, 0, 0, 0],
[836, 1, 0.008116902097686661, 0.4058451048843331, 0, 0, 0],
[837, 1, 0.15024226627874918, 7.512113313937459, 0, 0, 0],
[839, 1, 0.011666057328635928, 0.5833028664317964, 0, 0, 0],
[841, 1, 0.0037083101740411615, 0.18541550870205808, 0, 0, 0],
[843, 1, 0.10599719209920229, 5.2998596049601145, 0, 0, 0],
[844, 1, 0.012732395447351627, 0.6366197723675814, 0, 0, 0],
[845, 1, 0.10122254380644544, 5.061127190322272, 0, 0, 0],
[849, 1, 0.24796340133717296, 12.398170066858649, 0, 0, 0],
[850, 1, 0.005092958178940651, 0.25464790894703254, 0, 0, 0],
[851, 1, 0.01265281797580568, 0.632640898790284, 0, 0, 0],
[853, 1, 0.0036923946797319715, 0.1846197339865986, 0, 0, 0],
[855, 1, 0.21899720169444797, 10.949860084722399, 0, 0, 0],
[856, 1, 0.011459155902616463, 0.5729577951308231, 0, 0, 0],
[857, 1, 0.4462704604296745, 22.313523021483725, 0, 0, 0],
[858, 1, 0.01808000153523931, 0.9040000767619655, 0, 0, 0],
[859, 1, 0.027056340325622208, 1.3528170162811104, 0, 0, 0],
[860, 1, 0.0039788735772973835, 0.1989436788648692, 0, 0, 0],
[864, 1, 0.2785211504108168, 13.926057520540843, 0, 0, 0],
[865, 1, 0.0035014087480216977, 0.17507043740108488, 0, 0, 0],
[867, 1, 0.24478030247533505, 12.239015123766753, 0, 0, 0],
[869, 1, 0.4329014452099553, 21.645072260497766, 0, 0, 0],
[870, 1, 0.018589297353133374, 0.9294648676566688, 0, 0, 0],
[872, 1, 0.00716197243913529, 0.3580986219567645, 0, 0, 0],
[873, 1, 0.038833806114422456, 1.941690305721123, 0, 0, 0],
[874, 1, 0.006589014644004467, 0.3294507322002233, 0, 0, 0],
[875, 1, 0.007766761222884492, 0.38833806114422464, 0, 0, 0],
[877, 1, 0.007894085177358009, 0.39470425886790045, 0, 0, 0],
[881, 1, 0.3187236890358296, 15.93618445179148, 0, 0, 0],
[882, 1, 0.005538592019597957, 0.2769296009798979, 0, 0, 0],
[883, 1, 0.005729577951308231, 0.28647889756541156, 0, 0, 0],
[885, 1, 0.15597184423005742, 7.798592211502871, 0, 0, 0],
[886, 1, 0.8186930272647096, 40.93465136323548, 0, 0, 0],
[889, 1, 0.0030239439187460114, 0.15119719593730058, 0, 0, 0],
[890, 1, 0.0076394372684109755, 0.3819718634205488, 0, 0, 0],
[893, 1, 0.00954929658551372, 0.477464829275686, 0, 0, 0],
[894, 1, 0.025146481008519465, 1.2573240504259733, 0, 0, 0],
[895, 1, 0.0030239439187460114, 0.15119719593730058, 0, 0, 0],
[896, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[898, 1, 0.013464508185574344, 0.6732254092787172, 0, 0, 0],
[900, 1, 0.03584169318429482, 1.7920846592147412, 0, 0, 0],
[902, 1, 0.006207042780583919, 0.31035213902919595, 0, 0, 0],
[903, 1, 0.0031990143561470966, 0.15995071780735484, 0, 0, 0],
[905, 1, 0.021851973686517232, 1.0925986843258617, 0, 0, 0],
[906, 1, 0.010504226244065093, 0.5252113122032547, 0, 0, 0],
[907, 1, 0.02142225534016911, 1.0711127670084555, 0, 0, 0],
[909, 1, 0.005856901905781748, 0.2928450952890874, 0, 0, 0],
[915, 1, 0.0038197186342054878, 0.1909859317102744, 0, 0, 0],
[917, 1, 0.005411268065124442, 0.27056340325622213, 0, 0, 0],
[918, 1, 0.012254930618075942, 0.612746530903797, 0, 0, 0],
[920, 1, 0.0020371832715762603, 0.10185916357881303, 0, 0, 0],
[921, 1, 0.019735212943395024, 0.9867606471697512, 0, 0, 0],
[922, 1, 0.05220282133414166, 2.6101410667070835, 0, 0, 0],
[923, 1, 0.023236621691416718, 1.161831084570836, 0, 0, 0],
[925, 1, 0.008276057040778557, 0.4138028520389279, 0, 0, 0],
[931, 1, 0.03455253814525047, 1.7276269072625237, 0, 0, 0],
[935, 1, 0.007352958370845565, 0.36764791854227824, 0, 0, 0],
[936, 1, 0.016615776058793875, 0.8307888029396938, 0, 0, 0],
[937, 1, 0.00477464829275686, 0.238732414637843, 0, 0, 0],
[939, 1, 1.5915494309189534e-05, 0.0007957747154594768, 0, 0, 0],
[940, 1, 0.009421972631040205, 0.47109863155201026, 0, 0, 0],
[944, 1, 0.004042535554534142, 0.2021267777267071, 0, 0, 0],
[950, 1, 0.005092958178940651, 0.25464790894703254, 0, 0, 0],
[952, 1, 0.005045211696013082, 0.2522605848006541, 0, 0, 0],
[957, 1, 0.0019098593171027439, 0.0954929658551372, 0, 0, 0],
[958, 1, 0.010615634704229418, 0.530781735211471, 0, 0, 0],
[959, 1, 0.007241549910681238, 0.3620774955340619, 0, 0, 0],
[960, 1, 0.004217605991935227, 0.21088029959676136, 0, 0, 0],
[963, 1, 0.2785211504108168, 13.926057520540843, 0, 0, 0],
[965, 1, 0.11204507993669433, 5.602253996834716, 0, 0, 0],
[966, 1, 0.021008452488130186, 1.0504226244065094, 0, 0, 0],
[967, 1, 0.01193662073189215, 0.5968310365946076, 0, 0, 0],
[968, 1, 0.017188733853924696, 0.8594366926962349, 0, 0, 0],
[969, 1, 0.018111832523857688, 0.9055916261928845, 0, 0, 0],
[971, 1, 0.0031830988618379067, 0.15915494309189535, 0, 0, 0],
[973, 1, 0.4287634166895661, 21.438170834478306, 0, 0, 0],
[976, 1, 0.008562535938343968, 0.4281267969171984, 0, 0, 0],
[978, 1, 0.0007321127382227185, 0.03660563691113593, 0, 0, 0],
[981, 1, 0.03787887645587108, 1.8939438227935543, 0, 0, 0],
[982, 1, 0.0015756339366097638, 0.07878169683048819, 0, 0, 0],
[983, 1, 0.01400563499208679, 0.7002817496043395, 0, 0, 0],
[984, 1, 0.14801409707546268, 7.400704853773133, 0, 0, 0],
[985, 1, 0.0035014087480216977, 0.17507043740108488, 0, 0, 0],
[986, 1, 0.0017825353626292277, 0.08912676813146138, 0, 0, 0],
[987, 1, 0.02618098813861678, 1.3090494069308392, 0, 0, 0],
[988, 1, 0.0008116902097686662, 0.04058451048843331, 0, 0, 0],
[993, 1, 0.06238873769202297, 3.119436884601149, 0, 0, 0],
[994, 1, 0.010504226244065093, 0.5252113122032547, 0, 0, 0],
[995, 1, 0.0006684507609859605, 0.033422538049298026, 0, 0, 0],
[997, 1, 0.005984225860255264, 0.2992112930127632, 0, 0, 0],
[999, 1, 0.004965634224467135, 0.24828171122335674, 0, 0, 0],
[1000, 1, 0.015597184423005743, 0.7798592211502873, 0, 0, 0],
[1002, 1, 0.0031512678732195276, 0.15756339366097638, 0, 0, 0],
[1003, 1, 0.2864788975654116, 14.32394487827058, 0, 0, 0],
[1007, 1, 0.007416620348082323, 0.37083101740411617, 0, 0, 0],
[1008, 1, 0.015597184423005743, 0.7798592211502873, 0, 0, 0],
[1010, 1, 0.238732414637843, 11.93662073189215, 0, 0, 0],
[1011, 1, 0.005952394871636886, 0.2976197435818443, 0, 0, 0],
[1012, 1, 0.9024085273310466, 45.12042636655233, 0, 0, 0],
[1014, 1, 0.238732414637843, 11.93662073189215, 0, 0, 0],
[1026, 1, 0.20868396138209316, 10.434198069104658, 0, 0, 0],
[1027, 3, 0.002298550022578703, 0.11492750112893517, 2.22, 61.69, 0.004502],
[1028, 2, 0.025464790894703257, 1.273239544735163, 0, 0, 0],
[1029, 2, 0.0015996029245410612, 0.07998014622705306, 0, 0, 0],
[1030, 2, 0.06480789282701978, 3.2403946413509894, 0, 0, 0],
[1031, 2, 0.06463074564767912, 3.2315372823839565, 0, 0, 0],
[1032, 2, 0.009772775025341927, 0.4886387512670964, 0, 0, 0],
[1033, 2, 0.0031935716694765437, 0.15967858347382718, 0, 0, 0],
[1034, 2, 0.005364335122251813, 0.26821675611259066, 0, 0, 0],
[1035, 3, 0.00317587127473044, 0.158793563736522, 2.22, 61.69, 0.004502],
[1036, 2, 0.0042795539826391196, 0.21397769913195597, 0, 0, 0],
[1037, 2, 0.004583737816416693, 0.22918689082083465, 0, 0, 0],
[1038, 2, 0.004358800228219271, 0.21794001141096359, 0, 0, 0],
[1039, 2, 0.008449479506347874, 0.42247397531739384, 0, 0, 0],
[1040, 3, 2.5955064969193202e-06, 0.00012977532484596601, 2.22, 61.69, 0.004502],
[1041, 2, 0.012998987840239671, 0.6499493920119837, 0, 0, 0],
[1042, 2, 0.00335501991632689, 0.1677509958163445, 0, 0, 0],
[1043, 3, 0.0003026685105316776, 0.015133425526583881, 2.22, 61.69, 0.004502],
[1044, 3, 0.0011243820116265814, 0.05621910058132907, 2.22, 61.69, 0.004502],
[1045, 2, 0.0019373243262327522, 0.09686621631163762, 0, 0, 0],
[1046, 2, 0.0031015144255394987, 0.15507572127697494, 0, 0, 0],
[1047, 3, 0.00034416981541931054, 0.017208490770965527, 2.22, 61.69, 0.004502],
[1048, 2, 0.0020485945786587064, 0.10242972893293534, 0, 0, 0],
[1049, 2, 0.01870104799381521, 0.9350523996907605, 0, 0, 0],
[1050, 2, 0.0033601814151550304, 0.1680090707577515, 0, 0, 0],
[1051, 2, 0.019380601737792977, 0.969030086889649, 0, 0, 0],
[1052, 3, 0.0005247651571922151, 0.026238257859610755, 2.22, 61.69, 0.004502],
[1053, 3, 0.00041550140953476974, 0.02077507047673849, 2.22, 61.69, 0.004502],
[1054, 2, 0.0069428381079974354, 0.3471419053998717, 0, 0, 0],
[1055, 3, 0.0001818229987415119, 0.009091149937075596, 2.22, 61.69, 0.004502],
[1056, 2, 0.0384482661909012, 1.9224133095450602, 0, 0, 0],
[1057, 2, 0.02718238967557453, 1.3591194837787268, 0, 0, 0],
[1058, 2, 0.06721018861714274, 3.3605094308571375, 0, 0, 0],
[1059, 2, 0.02641152929543176, 1.320576464771588, 0, 0, 0],
[1060, 3, 0.0006590053340983933, 0.03295026670491967, 2.22, 61.69, 0.004502],
[1061, 2, 0.010304492946979937, 0.5152246473489969, 0, 0, 0],
[1062, 3, 0.00018325491392786168, 0.009162745696393085, 2.22, 61.69, 0.004502],
[1063, 3, 0.0005520076745724519, 0.0276003837286226, 2.22, 61.69, 0.004502],
[1064, 2, 0.013355424896304362, 0.667771244815218, 0, 0, 0],
[1065, 2, 0.021608252882636087, 1.0804126441318045, 0, 0, 0],
[1066, 2, 0.008556107291276397, 0.4278053645638199, 0, 0, 0],
[1067, 3, 0.002078788013715776, 0.1039394006857888, 2.22, 61.69, 0.004502],
[1068, 3, 0.0003188842576981683, 0.015944212884908417, 2.22, 61.69, 0.004502],
[1069, 3, 0.00020313001706596343, 0.010156500853298172, 2.22, 61.69, 0.004502],
[1070, 3, 5.020379247175116e-05, 0.0025101896235875582, 2.22, 61.69, 0.004502],
[1071, 3, 0.0002755733400308117, 0.013778667001540588, 2.22, 61.69, 0.004502],
[1072, 2, 0.0034911570519954678, 0.1745578525997734, 0, 0, 0],
[1073, 2, 0.001974161472118056, 0.09870807360590281, 0, 0, 0],
[1074, 2, 0.0046620003597127105, 0.23310001798563554, 0, 0, 0],
[1075, 3, 0.0010048055180333312, 0.05024027590166657, 2.22, 61.69, 0.004502],
[1076, 3, 0.00010624248611578546, 0.005312124305789274, 2.22, 61.69, 0.004502],
[1077, 3, 0.0016628534246063698, 0.08314267123031849, 2.22, 61.69, 0.004502],
[1078, 3, 0.0021908153060440304, 0.10954076530220153, 2.22, 61.69, 0.004502],
[1079, 2, 0.002190700708933187, 0.10953503544665937, 0, 0, 0],
[1080, 2, 0.008412929217414397, 0.4206464608707199, 0, 0, 0],
[1081, 2, 0.025823979083824652, 1.2911989541912325, 0, 0, 0],
[1082, 2, 0.03247105626963941, 1.623552813481971, 0, 0, 0],
[1083, 2, 0.04034141649573272, 2.017070824786636, 0, 0, 0],
[1084, 2, 0.0383703068502718, 1.9185153425135901, 0, 0, 0],
[1085, 2, 0.007239283505967098, 0.3619641752983549, 0, 0, 0],
[1086, 2, 0.01436208920263519, 0.7181044601317595, 0, 0, 0],
[1087, 2, 0.007427186304799236, 0.3713593152399618, 0, 0, 0],
[1088, 3, 0.0023416461987310717, 0.11708230993655358, 2.22, 61.69, 0.004502],
[1089, 2, 0.024474821190373128, 1.2237410595186564, 0, 0, 0],
[1090, 2, 0.0022624979772680404, 0.11312489886340203, 0, 0, 0],
[1091, 3, 0.0013601543234855855, 0.06800771617427928, 2.22, 61.69, 0.004502],
[1092, 2, 0.0014626466159500494, 0.07313233079750248, 0, 0, 0],
[1093, 2, 0.009906140914748767, 0.49530704573743833, 0, 0, 0],
[1094, 3, 0.00023930778294026586, 0.011965389147013294, 2.22, 61.69, 0.004502],
[1095, 3, 1.3047613994501091e-05, 0.0006523806997250545, 2.22, 61.69, 0.004502],
[1096, 2, 0.005379826679377905, 0.2689913339688953, 0, 0, 0],
[1097, 3, 0.0002929164939619051, 0.014645824698095257, 2.22, 61.69, 0.004502],
[1098, 2, 0.0021303060183860277, 0.10651530091930138, 0, 0, 0],
[1099, 2, 0.0073754261124176915, 0.3687713056208846, 0, 0, 0],
[1100, 3, 1.3306005265883919e-06, 6.653002632941959e-05, 2.22, 61.69, 0.004502],
[1101, 2, 0.005343192104787693, 0.2671596052393847, 0, 0, 0],
[1102, 2, 0.02234407998394998, 1.1172039991974991, 0, 0, 0],
[1103, 2, 0.01562148424141561, 0.7810742120707805, 0, 0, 0],
[1104, 3, 1.3172819714966009e-05, 0.0006586409857483004, 2.22, 61.69, 0.004502],
[1105, 3, 0.0001386935566767763, 0.006934677833838815, 2.22, 61.69, 0.004502],
[1106, 3, 0.00014577275883068604, 0.0072886379415343025, 2.22, 61.69, 0.004502],
[1107, 2, 0.004852418696402547, 0.24262093482012728, 0, 0, 0],
[1108, 2, 0.02039874588539438, 1.019937294269719, 0, 0, 0],
[1109, 3, 4.9542410867097304e-05, 0.002477120543354865, 2.22, 61.69, 0.004502],
[1110, 3, 0.00010533237807450261, 0.00526661890372513, 2.22, 61.69, 0.004502],
[1111, 2, 0.005706531882583417, 0.2853265941291709, 0, 0, 0],
[1112, 2, 0.004426690383932842, 0.2213345191966421, 0, 0, 0],
[1113, 3, 0.00022513170529279912, 0.011256585264639957, 2.22, 61.69, 0.004502],
[1114, 3, 0.0008560555102861403, 0.042802775514307015, 2.22, 61.69, 0.004502],
[1115, 2, 0.0032197222090973076, 0.16098611045486538, 0, 0, 0],
[1116, 3, 0.002075453185310181, 0.10377265926550905, 2.22, 61.69, 0.004502],
[1117, 2, 0.005780032679669937, 0.2890016339834969, 0, 0, 0],
[1118, 3, 0.0005554515385863421, 0.027772576929317106, 2.22, 61.69, 0.004502],
[1119, 3, 0.0027536366373517632, 0.13768183186758817, 2.22, 61.69, 0.004502],
[1120, 3, 0.0001538074296570127, 0.007690371482850636, 2.22, 61.69, 0.004502],
[1121, 3, 3.4414977793908876e-05, 0.0017207488896954439, 2.22, 61.69, 0.004502],
[1122, 3, 9.313004041299959e-05, 0.00465650202064998, 2.22, 61.69, 0.004502],
[1123, 3, 9.32225252447514e-05, 0.00466112626223757, 2.22, 61.69, 0.004502],
[1124, 3, 8.201464578534214e-05, 0.004100732289267108, 2.22, 61.69, 0.004502],
[1125, 3, 0.0016436821796102436, 0.08218410898051219, 2.22, 61.69, 0.004502],
[1126, 3, 0.0018560581327172175, 0.09280290663586088, 2.22, 61.69, 0.004502],
[1127, 2, 0.006703391093283916, 0.3351695546641958, 0, 0, 0],
[1128, 3, 0.0001948941120002845, 0.009744705600014225, 2.22, 61.69, 0.004502],
[1129, 3, 0.0003016780123772693, 0.015083900618863466, 2.22, 61.69, 0.004502],
[1130, 3, 6.530151955301432e-05, 0.003265075977650716, 2.22, 61.69, 0.004502],
[1131, 3, 0.00018443373362804407, 0.009221686681402204, 2.22, 61.69, 0.004502],
[1132, 3, 2.2886271300209156e-05, 0.0011443135650104578, 2.22, 61.69, 0.004502],
[1133, 3, 4.5810964480308454e-05, 0.002290548224015423, 2.22, 61.69, 0.004502],
[1134, 3, 3.236913111220881e-05, 0.0016184565556104404, 2.22, 61.69, 0.004502],
[1135, 3, 0.0005167964323996007, 0.025839821619980042, 2.22, 61.69, 0.004502],
[1136, 3, 2.5636662405410735e-05, 0.0012818331202705368, 2.22, 61.69, 0.004502],
[1137, 3, 0.00023357652984116472, 0.011678826492058236, 2.22, 61.69, 0.004502],
[1138, 3, 7.98498118498449e-05, 0.003992490592492246, 2.22, 61.69, 0.004502],
[1139, 3, 0.0012619566606414858, 0.0630978330320743, 2.22, 61.69, 0.004502],
[1140, 3, 0.0018073289497007397, 0.09036644748503699, 2.22, 61.69, 0.004502],
[1141, 2, 0.0076053500901520025, 0.38026750450760016, 0, 0, 0],
[1142, 3, 7.73959943559724e-05, 0.00386979971779862, 2.22, 61.69, 0.004502],
[1143, 3, 0.0016067873237582107, 0.08033936618791054, 2.22, 61.69, 0.004502],
[1144, 2, 0.00334399697192306, 0.16719984859615303, 0, 0, 0],
[1145, 2, 0.004458888300690503, 0.2229444150345252, 0, 0, 0],
[1146, 3, 5.4833151376821656e-05, 0.002741657568841083, 2.22, 61.69, 0.004502],
[1147, 3, 0.002909588342312674, 0.14547941711563372, 2.22, 61.69, 0.004502],
[1148, 3, 0.0011233492673683868, 0.05616746336841934, 2.22, 61.69, 0.004502],
[1149, 3, 0.0005447417794635118, 0.02723708897317559, 2.22, 61.69, 0.004502],
[1150, 3, 0.0002306193019977063, 0.011530965099885314, 2.22, 61.69, 0.004502],
[1151, 3, 0.0008299047575760064, 0.04149523787880033, 2.22, 61.69, 0.004502],
[1152, 3, 7.417749437366368e-06, 0.0003708874718683184, 2.22, 61.69, 0.004502],
[1153, 3, 4.37920348658174e-06, 0.000218960174329087, 2.22, 61.69, 0.004502],
[1154, 3, 1.0225677287248534e-05, 0.0005112838643624266, 2.22, 61.69, 0.004502],
[1155, 3, 3.879887736397654e-05, 0.001939943868198827, 2.22, 61.69, 0.004502],
[1156, 3, 0.0010200134924871187, 0.05100067462435595, 2.22, 61.69, 0.004502],
[1157, 3, 0.00027719360593007886, 0.013859680296503944, 2.22, 61.69, 0.004502],
[1158, 3, 6.640198284893194e-05, 0.003320099142446597, 2.22, 61.69, 0.004502],
[1159, 3, 0.0008593149079194712, 0.04296574539597356, 2.22, 61.69, 0.004502],
[1160, 2, 0.015175599618213626, 0.7587799809106813, 0, 0, 0],
[1161, 3, 0.001608317428775011, 0.08041587143875056, 2.22, 61.69, 0.004502],
[1162, 2, 0.031984361657767045, 1.5992180828883522, 0, 0, 0],
[1163, 2, 0.021010485834812704, 1.0505242917406352, 0, 0, 0],
[1164, 2, 0.018183478445661972, 0.9091739222830987, 0, 0, 0],
[1165, 2, 0.003640738012495192, 0.18203690062475963, 0, 0, 0],
[1166, 2, 0.0037355845995397383, 0.18677922997698693, 0, 0, 0],
[1167, 3, 0.00032173361521807824, 0.016086680760903912, 2.22, 61.69, 0.004502],
[1168, 3, 8.56746647323757e-05, 0.004283733236618785, 2.22, 61.69, 0.004502],
[1169, 3, 0.00017327803824915608, 0.008663901912457804, 2.22, 61.69, 0.004502],
[1170, 3, 1.6933420442211857e-05, 0.000846671022110593, 2.22, 61.69, 0.004502],
[1171, 3, 0.0005748603194505088, 0.02874301597252544, 2.22, 61.69, 0.004502],
[1172, 3, 0.0002281672447033917, 0.011408362235169585, 2.22, 61.69, 0.004502],
[1173, 2, 0.01618626952698487, 0.8093134763492436, 0, 0, 0],
[1174, 3, 8.021928882473966e-05, 0.004010964441236983, 2.22, 61.69, 0.004502],
[1175, 3, 5.445989361520192e-05, 0.002722994680760096, 2.22, 61.69, 0.004502],
[1176, 3, 1.4783581244732665e-05, 0.0007391790622366333, 2.22, 61.69, 0.004502],
[1177, 3, 0.0017745146198091144, 0.08872573099045572, 2.22, 61.69, 0.004502],
[1178, 3, 0.00020168108435446162, 0.010084054217723081, 2.22, 61.69, 0.004502],
[1179, 3, 8.316119408334767e-05, 0.004158059704167384, 2.22, 61.69, 0.004502],
[1180, 3, 4.3834108298364086e-05, 0.002191705414918204, 2.22, 61.69, 0.004502],
[1181, 2, 0.005289917788662048, 0.2644958894331024, 0, 0, 0],
[1182, 2, 0.006322880792722177, 0.3161440396361089, 0, 0, 0],
[1183, 3, 0.0024333246840658566, 0.12166623420329284, 2.22, 61.69, 0.004502],
[1184, 3, 0.00026859021396164037, 0.013429510698082018, 2.22, 61.69, 0.004502],
[1185, 3, 0.0007221796423758263, 0.036108982118791315, 2.22, 61.69, 0.004502],
[1186, 3, 0.0024774929167619207, 0.12387464583809603, 2.22, 61.69, 0.004502],
[1187, 3, 0.0006248151564821885, 0.031240757824109424, 2.22, 61.69, 0.004502],
[1188, 2, 0.011369992521217407, 0.5684996260608703, 0, 0, 0],
[1189, 3, 0.001289906586581014, 0.06449532932905071, 2.22, 61.69, 0.004502],
[1190, 2, 0.01403960969000889, 0.7019804845004446, 0, 0, 0],
[1191, 2, 0.004652379906159672, 0.23261899530798363, 0, 0, 0],
[1192, 3, 0.0013658402687938922, 0.06829201343969461, 2.22, 61.69, 0.004502],
[1193, 3, 0.00015278576957249078, 0.007639288478624539, 2.22, 61.69, 0.004502],
[1194, 3, 0.0005720688022791215, 0.028603440113956075, 2.22, 61.69, 0.004502],
[1195, 3, 1.2882573563174789e-05, 0.0006441286781587394, 2.22, 61.69, 0.004502],
[1196, 2, 0.009842783066129698, 0.4921391533064849, 0, 0, 0],
[1197, 2, 0.00575541689021183, 0.2877708445105915, 0, 0, 0],
[1198, 3, 0.002534966273924786, 0.12674831369623932, 2.22, 61.69, 0.004502],
[1201, 3, 0.0016021597716395785, 0.08010798858197893, 2.22, 61.69, 0.004502],
[1202, 3, 0.0031762475555186724, 0.15881237777593363, 2.22, 61.69, 0.004502],
[1203, 2, 0.011626157559117188, 0.5813078779558594, 0, 0, 0],
[1204, 3, 0.0030266063343556363, 0.15133031671778183, 2.22, 61.69, 0.004502],
[1205, 3, 3.4940417699210975e-05, 0.0017470208849605492, 2.22, 61.69, 0.004502],
[1206, 3, 0.00024235441128435216, 0.012117720564217609, 2.22, 61.69, 0.004502],
[1207, 3, 0.00022762038155293296, 0.011381019077646649, 2.22, 61.69, 0.004502],
[1208, 3, 0.0001427321512302434, 0.007136607561512171, 2.22, 61.69, 0.004502],
[1209, 3, 4.75873361221428e-05, 0.00237936680610714, 2.22, 61.69, 0.004502],
[1210, 3, 0.0005454262850371943, 0.027271314251859715, 2.22, 61.69, 0.004502],
[1211, 3, 0.0011462484513341364, 0.057312422566706815, 2.22, 61.69, 0.004502],
[1212, 2, 0.005804182676892941, 0.290209133844647, 0, 0, 0],
[1213, 2, 0.0036505499187602444, 0.18252749593801224, 0, 0, 0],
[1214, 3, 0.0002868549194435664, 0.014342745972178321, 2.22, 61.69, 0.004502],
[1215, 3, 0.00014342822681200328, 0.0071714113406001635, 2.22, 61.69, 0.004502],
[1216, 2, 0.00431338348440427, 0.21566917422021353, 0, 0, 0],
[1217, 3, 0.0022836580531031417, 0.11418290265515707, 2.22, 61.69, 0.004502],
[1218, 3, 6.241945072080783e-05, 0.003120972536040392, 2.22, 61.69, 0.004502],
[1219, 3, 0.0007855588922898729, 0.03927794461449365, 2.22, 61.69, 0.004502],
[1220, 3, 0.001947919590347708, 0.0973959795173854, 2.22, 61.69, 0.004502],
[1221, 2, 0.0377662225422596, 1.88831112711298, 0, 0, 0],
[1222, 2, 0.013436354905899806, 0.6718177452949904, 0, 0, 0],
[1223, 3, 0.00024230393037435297, 0.01211519651871765, 2.22, 61.69, 0.004502],
[1224, 2, 0.010219261097938644, 0.5109630548969322, 0, 0, 0],
[1225, 3, 0.0022238071565315737, 0.1111903578265787, 2.22, 61.69, 0.004502],
[1226, 3, 0.0002535566380389208, 0.012677831901946041, 2.22, 61.69, 0.004502],
[1227, 3, 0.0011129900410750567, 0.05564950205375283, 2.22, 61.69, 0.004502],
[1228, 3, 0.00019234621639044032, 0.009617310819522017, 2.22, 61.69, 0.004502],
[1229, 2, 0.00326230849376, 0.16311542468800003, 0, 0, 0],
[1230, 3, 5.774224065377648e-05, 0.0028871120326888237, 2.22, 61.69, 0.004502],
[1231, 3, 0.0021361636602669084, 0.10680818301334541, 2.22, 61.69, 0.004502],
[1232, 2, 0.004779428513216963, 0.23897142566084817, 0, 0, 0],
[1235, 3, 0.00028910830796175294, 0.014455415398087644, 2.22, 61.69, 0.004502],
[1236, 2, 0.002535004450133525, 0.12675022250667625, 0, 0, 0],
[1237, 3, 0.0009298092078685558, 0.04649046039342779, 2.22, 61.69, 0.004502],
[1238, 2, 0.012012445276594919, 0.600622263829746, 0, 0, 0],
[1239, 3, 5.75756369436291e-05, 0.0028787818471814556, 2.22, 61.69, 0.004502],
[1240, 2, 0.021613910382114798, 1.08069551910574, 0, 0, 0],
[1241, 2, 0.024532881090784327, 1.2266440545392163, 0, 0, 0],
[1242, 3, 0.0017235867616422773, 0.08617933808211387, 2.22, 61.69, 0.004502],
[1243, 2, 0.005289026999236673, 0.26445134996183367, 0, 0, 0],
[1244, 2, 0.00846072422785893, 0.4230362113929465, 0, 0, 0],
[1245, 3, 0.0005144458090049472, 0.025722290450247362, 2.22, 61.69, 0.004502],
[1246, 2, 0.00337806806675036, 0.16890340333751802, 0, 0, 0],
[1247, 3, 0.0013899571448864774, 0.06949785724432388, 2.22, 61.69, 0.004502],
[1248, 2, 0.005854245631350222, 0.2927122815675111, 0, 0, 0],
[1249, 2, 0.004846915908139961, 0.24234579540699805, 0, 0, 0],
[1250, 3, 0.0019627317861894665, 0.09813658930947333, 2.22, 61.69, 0.004502],
[1251, 3, 0.0014899668826355728, 0.07449834413177864, 2.22, 61.69, 0.004502],
[1252, 3, 0.0009477821555247328, 0.047389107776236644, 2.22, 61.69, 0.004502],
[1253, 2, 0.004106369053307717, 0.20531845266538587, 0, 0, 0],
[1254, 2, 0.005081603543623868, 0.2540801771811934, 0, 0, 0],
[1255, 3, 0.0002430881191708174, 0.01215440595854087, 2.22, 61.69, 0.004502],
[1256, 3, 0.0009607764830526361, 0.048038824152631804, 2.22, 61.69, 0.004502],
[1257, 2, 0.005662916214121937, 0.28314581070609685, 0, 0, 0],
[1258, 2, 0.010814994241697335, 0.5407497120848668, 0, 0, 0],
[1259, 2, 0.00695753592752513, 0.34787679637625657, 0, 0, 0],
[1260, 3, 0.0012839803779623614, 0.06419901889811806, 2.22, 61.69, 0.004502],
[1261, 2, 0.012840592447306919, 0.6420296223653459, 0, 0, 0],
[1262, 3, 3.3365758929065435e-05, 0.0016682879464532717, 2.22, 61.69, 0.004502],
[1263, 3, 2.243579925674327e-05, 0.0011217899628371635, 2.22, 61.69, 0.004502],
[1264, 2, 0.005222533303161435, 0.2611266651580718, 0, 0, 0],
[1265, 3, 0.0004236530619172327, 0.021182653095861634, 2.22, 61.69, 0.004502],
[1266, 2, 0.007621029313600565, 0.38105146568002835, 0, 0, 0],
[1267, 3, 0.002512674942558201, 0.12563374712791006, 2.22, 61.69, 0.004502],
[1268, 3, 0.0002183287451274897, 0.010916437256374485, 2.22, 61.69, 0.004502],
[1269, 3, 0.0003250471975980552, 0.01625235987990276, 2.22, 61.69, 0.004502],
[1270, 3, 0.0024796665722395645, 0.12398332861197821, 2.22, 61.69, 0.004502],
[1271, 3, 0.0030157819134425234, 0.15078909567212617, 2.22, 61.69, 0.004502],
[1272, 3, 7.840992648188318e-05, 0.003920496324094159, 2.22, 61.69, 0.004502],
[1273, 3, 0.00013809561181086458, 0.006904780590543229, 2.22, 61.69, 0.004502],
[1274, 2, 0.0033801727100761705, 0.1690086355038085, 0, 0, 0],
[1275, 2, 0.006307329492962109, 0.3153664746481055, 0, 0, 0],
[1276, 3, 0.001633288835647369, 0.08166444178236844, 2.22, 61.69, 0.004502],
[1277, 2, 0.004176942042758357, 0.20884710213791788, 0, 0, 0],
[1278, 2, 0.010850406134369231, 0.5425203067184615, 0, 0, 0],
[1279, 3, 1.1547461499241629e-07, 5.773730749620814e-06, 2.22, 61.69, 0.004502],
[1280, 3, 2.2052402508424647e-05, 0.0011026201254212323, 2.22, 61.69, 0.004502],
[1281, 3, 0.0001599481510691144, 0.007997407553455719, 2.22, 61.69, 0.004502],
[1282, 3, 0.00015112854883249187, 0.007556427441624595, 2.22, 61.69, 0.004502],
[1283, 2, 0.04214075813046536, 2.1070379065232685, 0, 0, 0],
[1284, 3, 0.0018096758437742202, 0.09048379218871101, 2.22, 61.69, 0.004502],
[1285, 3, 0.0001531107626377273, 0.0076555381318863655, 2.22, 61.69, 0.004502],
[1286, 3, 0.0011377796471657795, 0.05688898235828898, 2.22, 61.69, 0.004502],
[1287, 2, 0.005933272587501368, 0.29666362937506835, 0, 0, 0],
[1288, 2, 0.00944760882155904, 0.472380441077952, 0, 0, 0],
[1289, 2, 0.011723304434111076, 0.5861652217055537, 0, 0, 0],
[1290, 3, 0.0003120693634598793, 0.015603468172993969, 2.22, 61.69, 0.004502],
[1291, 2, 0.0062575490505418305, 0.31287745252709154, 0, 0, 0],
[1292, 3, 0.002653563231501149, 0.13267816157505744, 2.22, 61.69, 0.004502],
[1293, 3, 0.00015292290721046804, 0.007646145360523402, 2.22, 61.69, 0.004502],
[1294, 3, 0.0003436110439431119, 0.017180552197155596, 2.22, 61.69, 0.004502],
[1295, 3, 0.00037392918854889465, 0.01869645942744473, 2.22, 61.69, 0.004502],
[1296, 3, 0.0017284338192132009, 0.08642169096066006, 2.22, 61.69, 0.004502],
[1297, 2, 0.011317746197608284, 0.5658873098804141, 0, 0, 0],
[1298, 3, 0.00020595303360804683, 0.010297651680402344, 2.22, 61.69, 0.004502],
[1299, 3, 8.9869986756113e-05, 0.00449349933780565, 2.22, 61.69, 0.004502],
[1300, 3, 0.001511593201166196, 0.07557966005830981, 2.22, 61.69, 0.004502],
[1301, 2, 0.0038746782543149596, 0.193733912715748, 0, 0, 0],
[1302, 3, 0.0003104985267932093, 0.015524926339660468, 2.22, 61.69, 0.004502],
[1303, 3, 0.00027600750632746427, 0.013800375316373212, 2.22, 61.69, 0.004502],
[1304, 3, 0.000610793340517708, 0.030539667025885397, 2.22, 61.69, 0.004502],
[1305, 3, 2.9075695387122924e-07, 1.4537847693561463e-05, 2.22, 61.69, 0.004502],
[1306, 3, 0.00011631130798083146, 0.005815565399041573, 2.22, 61.69, 0.004502],
[1307, 3, 1.9031130574577255e-05, 0.0009515565287288628, 2.22, 61.69, 0.004502],
[1308, 3, 0.00020870441847665842, 0.010435220923832922, 2.22, 61.69, 0.004502],
[1309, 3, 0.0002132096944766602, 0.01066048472383301, 2.22, 61.69, 0.004502],
[1310, 3, 0.00010478060392325507, 0.005239030196162754, 2.22, 61.69, 0.004502],
[1311, 3, 0.0007546493032032542, 0.037732465160162716, 2.22, 61.69, 0.004502],
[1312, 2, 0.0070428013304282035, 0.3521400665214102, 0, 0, 0],
[1313, 3, 0.0019631283227609974, 0.09815641613804986, 2.22, 61.69, 0.004502],
[1314, 3, 0.0007641975650906521, 0.038209878254532606, 2.22, 61.69, 0.004502],
[1315, 3, 0.0005015944131679134, 0.02507972065839567, 2.22, 61.69, 0.004502],
[1316, 3, 0.000145780634856578, 0.007289031742828901, 2.22, 61.69, 0.004502],
[1317, 3, 0.0015252502049763412, 0.07626251024881707, 2.22, 61.69, 0.004502],
[1318, 3, 0.00012454395408676328, 0.0062271977043381645, 2.22, 61.69, 0.004502],
[1319, 3, 0.001127343871228203, 0.05636719356141015, 2.22, 61.69, 0.004502],
[1320, 3, 0.0013215329138219017, 0.06607664569109509, 2.22, 61.69, 0.004502],
[1321, 3, 1.025741798764967e-05, 0.0005128708993824835, 2.22, 61.69, 0.004502],
[1322, 3, 5.919056262068799e-05, 0.0029595281310344, 2.22, 61.69, 0.004502],
[1323, 2, 0.012675857799799822, 0.6337928899899912, 0, 0, 0],
[1324, 3, 0.0008316328586631403, 0.04158164293315702, 2.22, 61.69, 0.004502],
[1325, 2, 0.0057612535388438385, 0.2880626769421919, 0, 0, 0],
[1326, 2, 0.0036242041289439157, 0.1812102064471958, 0, 0, 0],
[1327, 2, 0.0032338308031027566, 0.16169154015513784, 0, 0, 0],
[1328, 3, 0.0010226241895011407, 0.05113120947505704, 2.22, 61.69, 0.004502],
[1329, 2, 0.013921309839652627, 0.6960654919826315, 0, 0, 0],
[1330, 3, 0.0019182008434651947, 0.09591004217325974, 2.22, 61.69, 0.004502],
[1331, 3, 1.841349064624893e-05, 0.0009206745323124464, 2.22, 61.69, 0.004502],
[1332, 3, 0.0016738699394560756, 0.08369349697280379, 2.22, 61.69, 0.004502],
[1333, 3, 0.0029061854047842247, 0.14530927023921122, 2.22, 61.69, 0.004502],
[1334, 3, 5.761014482450118e-05, 0.0028805072412250595, 2.22, 61.69, 0.004502],
[1335, 3, 0.00021052629514022267, 0.010526314757011134, 2.22, 61.69, 0.004502],
[1336, 3, 0.0018954102795459078, 0.0947705139772954, 2.22, 61.69, 0.004502],
[1337, 2, 0.003303921795797683, 0.16519608978988415, 0, 0, 0],
[1338, 3, 5.300015004820578e-05, 0.0026500075024102894, 2.22, 61.69, 0.004502],
[1339, 3, 0.0006421253879349708, 0.032106269396748544, 2.22, 61.69, 0.004502],
[1340, 2, 0.0019890355643717287, 0.09945177821858646, 0, 0, 0],
[1341, 2, 0.005924529413907861, 0.2962264706953931, 0, 0, 0],
[1342, 3, 2.7387437160360416e-05, 0.0013693718580180209, 2.22, 61.69, 0.004502],
[1343, 3, 3.943679326899658e-05, 0.001971839663449829, 2.22, 61.69, 0.004502],
[1344, 3, 1.4391232894862565e-05, 0.0007195616447431282, 2.22, 61.69, 0.004502],
[1345, 3, 0.00025281368060892654, 0.012640684030446329, 2.22, 61.69, 0.004502],
[1346, 2, 0.013669449762218379, 0.6834724881109189, 0, 0, 0],
[1347, 2, 0.01477118570778878, 0.7385592853894392, 0, 0, 0],
[1348, 3, 0.000584562357708931, 0.02922811788544655, 2.22, 61.69, 0.004502],
[1349, 3, 0.0012037349571321803, 0.06018674785660902, 2.22, 61.69, 0.004502],
[1350, 3, 6.046050411995944e-06, 0.0003023025205997972, 2.22, 61.69, 0.004502],
[1351, 3, 4.796502941013963e-07, 2.3982514705069816e-05, 2.22, 61.69, 0.004502],
[1352, 3, 2.760384018212869e-05, 0.0013801920091064345, 2.22, 61.69, 0.004502],
[1354, 3, 4.276029671133181e-06, 0.00021380148355665902, 2.22, 61.69, 0.004502],
[1355, 3, 0.0001074820707981226, 0.005374103539906131, 2.22, 61.69, 0.004502],
[1356, 2, 0.004678278776831856, 0.23391393884159278, 0, 0, 0],
[1357, 2, 0.003594349677217709, 0.17971748386088549, 0, 0, 0],
[1358, 3, 1.57431431082847e-05, 0.0007871571554142351, 2.22, 61.69, 0.004502],
[1359, 2, 0.004496673943395517, 0.22483369716977586, 0, 0, 0],
[1360, 3, 0.0010909105792324338, 0.054545528961621695, 2.22, 61.69, 0.004502],
[1361, 2, 0.0040238936307783425, 0.20119468153891715, 0, 0, 0],
[1362, 2, 0.005036121783141224, 0.2518060891570612, 0, 0, 0],
[1363, 3, 2.301886324440155e-06, 0.00011509431622200775, 2.22, 61.69, 0.004502],
[1364, 3, 3.887723536233725e-06, 0.00019438617681168623, 2.22, 61.69, 0.004502],
[1365, 3, 2.8999446623259055e-08, 1.449972331162953e-06, 2.22, 61.69, 0.004502],
[1366, 3, 7.830373844390861e-05, 0.003915186922195431, 2.22, 61.69, 0.004502],
[1367, 3, 0.0027924620350495274, 0.13962310175247636, 2.22, 61.69, 0.004502],
[1368, 3, 0.00017611255606875446, 0.008805627803437724, 2.22, 61.69, 0.004502],
[1369, 3, 0.0005073133310147165, 0.025365666550735824, 2.22, 61.69, 0.004502],
[1370, 3, 2.185563890765493e-05, 0.0010927819453827466, 2.22, 61.69, 0.004502],
[1371, 2, 0.0024031239337826537, 0.12015619668913267, 0, 0, 0],
[1372, 2, 0.012284634505654547, 0.6142317252827274, 0, 0, 0],
[1373, 3, 0.0022409179594482334, 0.11204589797241167, 2.22, 61.69, 0.004502],
[1376, 2, 0.011218109707548912, 0.5609054853774457, 0, 0, 0],
[1377, 2, 0.01492085689824784, 0.7460428449123921, 0, 0, 0],
[1378, 2, 0.01566275025445262, 0.783137512722631, 0, 0, 0],
[1379, 3, 5.1310566028095876e-05, 0.002565528301404794, 2.22, 61.69, 0.004502],
[1380, 3, 7.724465320438908e-05, 0.003862232660219454, 2.22, 61.69, 0.004502],
[1381, 3, 6.446222679588771e-05, 0.003223111339794386, 2.22, 61.69, 0.004502],
[1382, 2, 0.008838822964419164, 0.4419411482209583, 0, 0, 0],
[1383, 2, 0.006991449967869686, 0.34957249839348425, 0, 0, 0],
[1384, 3, 0.0002972463393517766, 0.014862316967588829, 2.22, 61.69, 0.004502],
[1385, 3, 7.92302201959824e-06, 0.0003961511009799121, 2.22, 61.69, 0.004502],
[1386, 3, 4.2899112828393286e-05, 0.002144955641419664, 2.22, 61.69, 0.004502],
[1387, 3, 0.00022240699424911273, 0.011120349712455638, 2.22, 61.69, 0.004502],
[1388, 3, 5.909025672850305e-05, 0.0029545128364251525, 2.22, 61.69, 0.004502],
[1389, 3, 1.3594135764164036e-05, 0.0006797067882082019, 2.22, 61.69, 0.004502],
[1390, 3, 0.00023763846235409512, 0.011881923117704758, 2.22, 61.69, 0.004502],
[1391, 3, 3.321367742134543e-05, 0.0016606838710672715, 2.22, 61.69, 0.004502],
[1392, 3, 0.0012290826914265437, 0.06145413457132718, 2.22, 61.69, 0.004502],
[1393, 3, 8.763130962106806e-05, 0.004381565481053403, 2.22, 61.69, 0.004502],
[1394, 3, 6.862035771367977e-05, 0.003431017885683988, 2.22, 61.69, 0.004502],
[1395, 3, 4.696755105006889e-06, 0.00023483775525034447, 2.22, 61.69, 0.004502],
[1396, 3, 1.6623117797696163e-06, 8.311558898848081e-05, 2.22, 61.69, 0.004502],
[1397, 3, 0.0015969317375463513, 0.07984658687731756, 2.22, 61.69, 0.004502],
[1398, 3, 0.00017695743260373348, 0.008847871630186674, 2.22, 61.69, 0.004502],
[1399, 3, 0.0011375222056992432, 0.05687611028496216, 2.22, 61.69, 0.004502],
[1400, 3, 8.258214886247176e-05, 0.004129107443123589, 2.22, 61.69, 0.004502],
[1401, 2, 0.005687529053514607, 0.28437645267573036, 0, 0, 0],
[1402, 3, 0.001676149990745289, 0.08380749953726446, 2.22, 61.69, 0.004502],
[1403, 2, 0.007617262031172502, 0.38086310155862513, 0, 0, 0],
[1404, 2, 0.0067734988181819555, 0.33867494090909783, 0, 0, 0],
[1405, 3, 0.0018812625008740895, 0.09406312504370447, 2.22, 61.69, 0.004502],
[1406, 3, 0.0006852566793279422, 0.03426283396639711, 2.22, 61.69, 0.004502],
[1407, 3, 1.3471796788943673e-05, 0.0006735898394471837, 2.22, 61.69, 0.004502],
[1408, 3, 0.002615151153581973, 0.13075755767909866, 2.22, 61.69, 0.004502],
[1409, 3, 0.0007652033584917757, 0.038260167924588785, 2.22, 61.69, 0.004502],
[1410, 3, 0.002385192626051519, 0.11925963130257596, 2.22, 61.69, 0.004502],
[1411, 3, 0.0025079869254713357, 0.1253993462735668, 2.22, 61.69, 0.004502],
[1412, 3, 0.00034193149839380297, 0.01709657491969015, 2.22, 61.69, 0.004502],
[1413, 3, 0.0003039144901162519, 0.015195724505812597, 2.22, 61.69, 0.004502],
[1414, 3, 0.001654733253695335, 0.08273666268476676, 2.22, 61.69, 0.004502],
[1415, 3, 0.0004362516227410405, 0.021812581137052027, 2.22, 61.69, 0.004502],
[1416, 3, 0.0004029092265882156, 0.020145461329410783, 2.22, 61.69, 0.004502],
[1417, 3, 6.808952303623334e-08, 3.404476151811667e-06, 2.22, 61.69, 0.004502],
[1418, 2, 0.005619099755523237, 0.28095498777616185, 0, 0, 0],
[1419, 3, 0.00211745485704481, 0.10587274285224049, 2.22, 61.69, 0.004502],
[1420, 3, 8.91112970779674e-05, 0.00445556485389837, 2.22, 61.69, 0.004502],
[1421, 3, 0.00044387476697737416, 0.02219373834886871, 2.22, 61.69, 0.004502],
[1422, 3, 0.00030115264331514286, 0.015057632165757144, 2.22, 61.69, 0.004502],
[1423, 3, 0.00012293234040278847, 0.006146617020139425, 2.22, 61.69, 0.004502],
[1424, 2, 0.00641540397482647, 0.3207701987413235, 0, 0, 0],
[1425, 3, 0.001350721738292593, 0.06753608691462964, 2.22, 61.69, 0.004502],
[1426, 2, 0.004377563184547638, 0.2188781592273819, 0, 0, 0],
[1427, 2, 0.03060222784928668, 1.5301113924643341, 0, 0, 0],
[1428, 2, 0.021319488529000553, 1.0659744264500277, 0, 0, 0],
[1429, 3, 0.000658318690093667, 0.03291593450468335, 2.22, 61.69, 0.004502],
[1430, 3, 9.820641622425884e-07, 4.9103208112129425e-05, 2.22, 61.69, 0.004502],
[1431, 2, 0.014493414492796078, 0.724670724639804, 0, 0, 0],
[1432, 3, 0.0003716433863367817, 0.01858216931683909, 2.22, 61.69, 0.004502],
[1433, 2, 0.036688879163843384, 1.8344439581921694, 0, 0, 0],
[1434, 2, 0.0026062503484175956, 0.13031251742087976, 0, 0, 0],
[1435, 2, 0.002539145570389532, 0.1269572785194766, 0, 0, 0],
[1436, 2, 0.002591208267120717, 0.12956041335603585, 0, 0, 0],
[1437, 2, 0.015172047044780135, 0.7586023522390068, 0, 0, 0],
[1438, 2, 0.025007389641183632, 1.2503694820591817, 0, 0, 0],
[1439, 2, 0.0063091033600462575, 0.3154551680023129, 0, 0, 0],
[1440, 3, 5.306917668409132e-05, 0.0026534588342045657, 2.22, 61.69, 0.004502],
[1441, 3, 1.0923020560921105e-05, 0.0005461510280460552, 2.22, 61.69, 0.004502],
[1442, 3, 4.555157486056611e-05, 0.0022775787430283057, 2.22, 61.69, 0.004502],
[1443, 2, 0.0026111964035441713, 0.13055982017720855, 0, 0, 0],
[1444, 3, 0.0005717925297728792, 0.028589626488643962, 2.22, 61.69, 0.004502],
[1445, 3, 0.0015938921576921367, 0.07969460788460683, 2.22, 61.69, 0.004502],
[1446, 2, 0.04829066125331256, 2.414533062665628, 0, 0, 0],
[1447, 2, 0.005696308888305882, 0.2848154444152941, 0, 0, 0],
[1448, 3, 0.00047896583949883246, 0.023948291974941624, 2.22, 61.69, 0.004502],
[1449, 2, 0.006075750962706547, 0.3037875481353274, 0, 0, 0],
[1450, 2, 0.0037724056227270084, 0.18862028113635043, 0, 0, 0],
[1451, 2, 0.0043416728967246255, 0.21708364483623127, 0, 0, 0],
[1452, 3, 0.0015322750739690742, 0.0766137536984537, 2.22, 61.69, 0.004502],
[1453, 2, 0.004134065549943135, 0.20670327749715672, 0, 0, 0],
[1454, 2, 0.009875666531734596, 0.49378332658672985, 0, 0, 0],
[1455, 3, 4.166284213856912e-05, 0.0020831421069284557, 2.22, 61.69, 0.004502],
[1456, 2, 0.0031865889687578697, 0.15932944843789354, 0, 0, 0],
[1457, 3, 0.00012749408723576006, 0.006374704361788003, 2.22, 61.69, 0.004502],
[1458, 3, 1.5673534819523866e-05, 0.0007836767409761935, 2.22, 61.69, 0.004502],
[1459, 3, 0.00033798517072819835, 0.01689925853640992, 2.22, 61.69, 0.004502],
[1460, 2, 0.006461593448980158, 0.3230796724490079, 0, 0, 0],
[1461, 3, 0.001142843079861875, 0.05714215399309376, 2.22, 61.69, 0.004502],
[1462, 3, 0.00015295973435731913, 0.007647986717865956, 2.22, 61.69, 0.004502],
[1463, 3, 4.5276834778775515e-05, 0.002263841738938776, 2.22, 61.69, 0.004502],
[1464, 2, 0.013934601684842136, 0.6967300842421068, 0, 0, 0],
[1465, 3, 0.0003374045759652472, 0.01687022879826236, 2.22, 61.69, 0.004502],
[1466, 3, 0.0003619193984034768, 0.01809596992017384, 2.22, 61.69, 0.004502],
[1467, 3, 0.00013344536897072216, 0.006672268448536108, 2.22, 61.69, 0.004502],
[1468, 3, 0.0015144656821575462, 0.0757232841078773, 2.22, 61.69, 0.004502],
[1469, 2, 0.004138503876498319, 0.20692519382491598, 0, 0, 0],
[1470, 2, 0.0020014495173752657, 0.10007247586876329, 0, 0, 0],
[1471, 2, 0.004038395628360613, 0.20191978141803063, 0, 0, 0],
[1472, 3, 0.0007626820845032627, 0.03813410422516314, 2.22, 61.69, 0.004502],
[1473, 3, 0.0005323801851315335, 0.026619009256576683, 2.22, 61.69, 0.004502],
[1474, 3, 8.905977123682595e-05, 0.004452988561841298, 2.22, 61.69, 0.004502],
[1475, 3, 2.4884191103347185e-05, 0.0012442095551673594, 2.22, 61.69, 0.004502],
[1476, 2, 0.01216740582073879, 0.6083702910369395, 0, 0, 0],
[1477, 3, 0.0007717725169969112, 0.03858862584984556, 2.22, 61.69, 0.004502],
[1478, 3, 1.03629245449834e-06, 5.181462272491701e-05, 2.22, 61.69, 0.004502],
[1479, 3, 0.00035603636123413484, 0.01780181806170674, 2.22, 61.69, 0.004502],
[1480, 3, 0.0011893307912248102, 0.05946653956124052, 2.22, 61.69, 0.004502],
[1481, 3, 3.3833873695351113e-06, 0.00016916936847675558, 2.22, 61.69, 0.004502],
[1482, 3, 0.0011147740798471094, 0.055738703992355476, 2.22, 61.69, 0.004502],
[1483, 3, 0.0002291607516312977, 0.011458037581564884, 2.22, 61.69, 0.004502],
[1484, 3, 1.9041073525508303e-06, 9.520536762754152e-05, 2.22, 61.69, 0.004502],
[1485, 3, 3.5876538426778735e-05, 0.0017938269213389369, 2.22, 61.69, 0.004502],
[1486, 3, 0.00018457774197472868, 0.009228887098736434, 2.22, 61.69, 0.004502],
[1487, 3, 7.276038526853737e-05, 0.0036380192634268686, 2.22, 61.69, 0.004502],
[1488, 3, 0.0003000059684869966, 0.01500029842434983, 2.22, 61.69, 0.004502],
[1489, 3, 7.571817467557017e-06, 0.00037859087337785094, 2.22, 61.69, 0.004502],
[1490, 2, 0.020504288751418347, 1.0252144375709173, 0, 0, 0],
[1491, 2, 0.005387257187745477, 0.26936285938727383, 0, 0, 0],
[1492, 2, 0.014637639488319377, 0.7318819744159688, 0, 0, 0],
[1493, 2, 0.005319414988695112, 0.26597074943475557, 0, 0, 0],
[1494, 2, 0.0257504251653254, 1.28752125826627, 0, 0, 0],
[1495, 2, 0.004260305180484296, 0.2130152590242148, 0, 0, 0],
[1496, 3, 1.5185873075624022e-08, 7.592936537812012e-07, 2.22, 61.69, 0.004502],
[1497, 2, 0.005670372667342641, 0.28351863336713207, 0, 0, 0],
[1498, 2, 0.006735488235440387, 0.3367744117720194, 0, 0, 0],
[1499, 3, 0.00014557430965896176, 0.0072787154829480885, 2.22, 61.69, 0.004502],
[1500, 3, 9.85597782087346e-06, 0.000492798891043673, 2.22, 61.69, 0.004502],
[1501, 3, 0.0005198212383651805, 0.02599106191825903, 2.22, 61.69, 0.004502],
[1502, 3, 4.105448673151168e-05, 0.002052724336575584, 2.22, 61.69, 0.004502],
[1503, 3, 0.0029266803181735935, 0.14633401590867967, 2.22, 61.69, 0.004502],
[1504, 2, 0.012020835078490423, 0.6010417539245212, 0, 0, 0],
[1505, 3, 0.0014407364034016888, 0.07203682017008443, 2.22, 61.69, 0.004502],
[1506, 2, 0.0035909631390018642, 0.17954815695009319, 0, 0, 0],
[1507, 3, 0.000982816273068341, 0.04914081365341705, 2.22, 61.69, 0.004502],
[1508, 3, 4.154538017488063e-06, 0.00020772690087440316, 2.22, 61.69, 0.004502],
[1509, 3, 1.37186634032331e-07, 6.85933170161655e-06, 2.22, 61.69, 0.004502],
[1510, 2, 0.00681234986437375, 0.34061749321868756, 0, 0, 0],
[1511, 2, 0.00988173435818505, 0.4940867179092525, 0, 0, 0],
[1512, 2, 0.004082645917281524, 0.20413229586407625, 0, 0, 0],
[1513, 3, 0.001467522271804366, 0.07337611359021831, 2.22, 61.69, 0.004502],
[1514, 3, 1.3202056818036577e-06, 6.601028409018288e-05, 2.22, 61.69, 0.004502],
[1515, 3, 1.7255068668904044e-07, 8.627534334452021e-06, 2.22, 61.69, 0.004502],
[1516, 3, 1.8340973111507537e-06, 9.170486555753769e-05, 2.22, 61.69, 0.004502],
[1517, 3, 8.192048507877762e-05, 0.0040960242539388805, 2.22, 61.69, 0.004502],
[1518, 3, 4.268803271333055e-05, 0.0021344016356665274, 2.22, 61.69, 0.004502],
[1519, 3, 2.9627970642356104e-06, 0.00014813985321178054, 2.22, 61.69, 0.004502]
])
ppc["branch_switch"] = array([
[586, 1, 0 ],
[589, 108, 0 ],
[590, 108, 0 ],
[593, 112, 0 ],
[594, 114, 0 ],
[595, 115, 0 ],
[598, 118, 0 ],
[599, 119, 0 ],
[601, 119, 0 ],
[602, 121, 0 ],
[603, 526, 0 ],
[607, 127, 0 ],
[608, 127, 0 ],
[609, 529, 0 ],
[612, 493, 0 ],
[613, 130, 0 ],
[614, 130, 0 ],
[616, 132, 0 ],
[617, 133, 0 ],
[618, 133, 0 ],
[619, 134, 0 ],
[621, 136, 0 ],
[624, 14, 0 ],
[628, 142, 0 ],
[629, 145, 0 ],
[631, 145, 0 ],
[632, 145, 0 ],
[637, 148, 0 ],
[638, 149, 0 ],
[640, 153, 0 ],
[641, 155, 0 ],
[642, 533, 0 ],
[643, 534, 0 ],
[647, 536, 0 ],
[650, 166, 0 ],
[652, 167, 0 ],
[655, 170, 0 ],
[663, 178, 0 ],
[666, 180, 0 ],
[670, 183, 0 ],
[672, 185, 0 ],
[676, 19, 0 ],
[681, 197, 0 ],
[683, 200, 0 ],
[687, 202, 0 ],
[689, 204, 0 ],
[691, 209, 0 ],
[694, 21, 0 ],
[695, 210, 0 ],
[696, 211, 0 ],
[697, 211, 0 ],
[698, 212, 0 ],
[702, 215, 0 ],
[705, 217, 0 ],
[707, 219, 0 ],
[713, 225, 0 ],
[714, 225, 0 ],
[716, 226, 0 ],
[717, 227, 0 ],
[719, 229, 0 ],
[722, 545, 0 ],
[723, 235, 0 ],
[724, 238, 0 ],
[727, 243, 0 ],
[728, 244, 0 ],
[730, 547, 0 ],
[732, 247, 0 ],
[735, 253, 0 ],
[738, 258, 0 ],
[741, 264, 0 ],
[742, 264, 0 ],
[743, 500, 0 ],
[746, 273, 0 ],
[747, 273, 0 ],
[748, 274, 0 ],
[749, 274, 0 ],
[750, 557, 0 ],
[753, 28, 0 ],
[758, 286, 0 ],
[760, 287, 0 ],
[761, 288, 0 ],
[762, 289, 0 ],
[763, 560, 0 ],
[765, 560, 0 ],
[767, 292, 0 ],
[769, 293, 0 ],
[771, 297, 0 ],
[772, 3, 0 ],
[774, 300, 0 ],
[777, 300, 0 ],
[778, 300, 0 ],
[781, 303, 0 ],
[784, 563, 0 ],
[785, 501, 0 ],
[787, 308, 0 ],
[788, 311, 0 ],
[789, 565, 0 ],
[791, 314, 0 ],
[792, 316, 0 ],
[795, 319, 0 ],
[800, 326, 0 ],
[801, 327, 0 ],
[802, 327, 0 ],
[805, 328, 0 ],
[806, 328, 0 ],
[808, 329, 0 ],
[809, 329, 0 ],
[811, 568, 0 ],
[814, 570, 0 ],
[816, 335, 0 ],
[817, 571, 0 ],
[821, 338, 0 ],
[822, 339, 0 ],
[826, 339, 0 ],
[830, 345, 0 ],
[834, 572, 0 ],
[835, 572, 0 ],
[836, 572, 0 ],
[837, 350, 0 ],
[839, 350, 0 ],
[841, 573, 0 ],
[843, 352, 0 ],
[844, 352, 0 ],
[845, 356, 0 ],
[849, 574, 0 ],
[850, 574, 0 ],
[851, 575, 0 ],
[853, 362, 0 ],
[855, 363, 0 ],
[856, 363, 0 ],
[857, 365, 0 ],
[858, 368, 0 ],
[859, 368, 0 ],
[860, 371, 0 ],
[864, 374, 0 ],
[865, 375, 0 ],
[867, 376, 0 ],
[869, 503, 0 ],
[870, 503, 0 ],
[872, 378, 0 ],
[873, 576, 0 ],
[874, 576, 0 ],
[875, 381, 0 ],
[877, 578, 0 ],
[881, 388, 0 ],
[882, 388, 0 ],
[883, 388, 0 ],
[885, 393, 0 ],
[886, 394, 0 ],
[889, 397, 0 ],
[890, 40, 0 ],
[893, 400, 0 ],
[894, 400, 0 ],
[895, 580, 0 ],
[896, 581, 0 ],
[898, 403, 0 ],
[900, 405, 0 ],
[902, 405, 0 ],
[903, 406, 0 ],
[905, 413, 0 ],
[906, 414, 0 ],
[907, 583, 0 ],
[909, 417, 0 ],
[915, 423, 0 ],
[917, 43, 0 ],
[918, 424, 0 ],
[920, 428, 0 ],
[921, 428, 0 ],
[922, 429, 0 ],
[923, 432, 0 ],
[925, 44, 0 ],
[931, 439, 0 ],
[935, 45, 0 ],
[936, 445, 0 ],
[937, 447, 0 ],
[939, 450, 0 ],
[940, 451, 0 ],
[944, 458, 0 ],
[950, 462, 0 ],
[952, 47, 0 ],
[957, 478, 0 ],
[958, 478, 0 ],
[959, 478, 0 ],
[960, 479, 0 ],
[963, 481, 0 ],
[965, 49, 0 ],
[966, 49, 0 ],
[967, 49, 0 ],
[968, 486, 0 ],
[969, 486, 0 ],
[971, 51, 0 ],
[973, 506, 0 ],
[976, 58, 0 ],
[978, 491, 0 ],
[981, 62, 0 ],
[982, 62, 0 ],
[983, 62, 0 ],
[984, 63, 0 ],
[985, 63, 0 ],
[986, 64, 0 ],
[987, 65, 0 ],
[988, 66, 0 ],
[993, 67, 0 ],
[994, 67, 0 ],
[995, 509, 0 ],
[997, 510, 0 ],
[999, 70, 0 ],
[1000, 71, 0 ],
[1002, 71, 0 ],
[1003, 72, 0 ],
[1007, 511, 0 ],
[1008, 75, 0 ],
[1010, 79, 0 ],
[1011, 79, 0 ],
[1012, 81, 0 ],
[1014, 83, 0 ],
[1026, 518, 0 ],
[1027, 218, 0 ],
[1028, 221, 0 ],
[1029, 268, 0 ],
[1030, 269, 0 ],
[1031, 498, 0 ],
[1032, 1, 0 ],
[1033, 3, 0 ],
[1034, 4, 0 ],
[1035, 6, 0 ],
[1036, 7, 0 ],
[1037, 8, 0 ],
[1038, 9, 0 ],
[1039, 11, 0 ],
[1040, 14, 0 ],
[1041, 16, 0 ],
[1042, 17, 0 ],
[1043, 19, 0 ],
[1044, 21, 0 ],
[1045, 23, 0 ],
[1046, 25, 0 ],
[1047, 27, 0 ],
[1048, 28, 0 ],
[1049, 29, 0 ],
[1050, 31, 0 ],
[1051, 33, 0 ],
[1052, 34, 0 ],
[1053, 35, 0 ],
[1054, 36, 0 ],
[1055, 38, 0 ],
[1056, 39, 0 ],
[1057, 40, 0 ],
[1058, 41, 0 ],
[1059, 43, 0 ],
[1060, 44, 0 ],
[1061, 45, 0 ],
[1062, 47, 0 ],
[1063, 48, 0 ],
[1064, 49, 0 ],
[1065, 50, 0 ],
[1066, 51, 0 ],
[1067, 53, 0 ],
[1068, 54, 0 ],
[1069, 55, 0 ],
[1070, 57, 0 ],
[1071, 58, 0 ],
[1072, 59, 0 ],
[1073, 60, 0 ],
[1074, 62, 0 ],
[1075, 63, 0 ],
[1076, 64, 0 ],
[1077, 65, 0 ],
[1078, 66, 0 ],
[1079, 67, 0 ],
[1080, 70, 0 ],
[1081, 71, 0 ],
[1082, 72, 0 ],
[1083, 73, 0 ],
[1084, 75, 0 ],
[1085, 76, 0 ],
[1086, 77, 0 ],
[1087, 79, 0 ],
[1088, 80, 0 ],
[1089, 81, 0 ],
[1090, 82, 0 ],
[1091, 83, 0 ],
[1092, 84, 0 ],
[1093, 85, 0 ],
[1094, 88, 0 ],
[1095, 89, 0 ],
[1096, 90, 0 ],
[1097, 91, 0 ],
[1098, 92, 0 ],
[1099, 93, 0 ],
[1100, 97, 0 ],
[1101, 98, 0 ],
[1102, 101, 0 ],
[1103, 102, 0 ],
[1104, 103, 0 ],
[1105, 108, 0 ],
[1106, 109, 0 ],
[1107, 110, 0 ],
[1108, 111, 0 ],
[1109, 112, 0 ],
[1110, 113, 0 ],
[1111, 114, 0 ],
[1112, 115, 0 ],
[1113, 116, 0 ],
[1114, 118, 0 ],
[1115, 119, 0 ],
[1116, 121, 0 ],
[1117, 122, 0 ],
[1118, 126, 0 ],
[1119, 127, 0 ],
[1120, 130, 0 ],
[1121, 131, 0 ],
[1122, 132, 0 ],
[1123, 133, 0 ],
[1124, 134, 0 ],
[1125, 135, 0 ],
[1126, 136, 0 ],
[1127, 137, 0 ],
[1128, 139, 0 ],
[1129, 140, 0 ],
[1130, 141, 0 ],
[1131, 142, 0 ],
[1132, 144, 0 ],
[1133, 145, 0 ],
[1134, 146, 0 ],
[1135, 147, 0 ],
[1136, 148, 0 ],
[1137, 149, 0 ],
[1138, 150, 0 ],
[1139, 151, 0 ],
[1140, 152, 0 ],
[1141, 153, 0 ],
[1142, 154, 0 ],
[1143, 155, 0 ],
[1144, 158, 0 ],
[1145, 161, 0 ],
[1146, 162, 0 ],
[1147, 163, 0 ],
[1148, 164, 0 ],
[1149, 166, 0 ],
[1150, 167, 0 ],
[1151, 168, 0 ],
[1152, 169, 0 ],
[1153, 170, 0 ],
[1154, 171, 0 ],
[1155, 172, 0 ],
[1156, 173, 0 ],
[1157, 174, 0 ],
[1158, 175, 0 ],
[1159, 176, 0 ],
[1160, 177, 0 ],
[1161, 178, 0 ],
[1162, 179, 0 ],
[1163, 180, 0 ],
[1164, 181, 0 ],
[1165, 182, 0 ],
[1166, 183, 0 ],
[1167, 185, 0 ],
[1168, 186, 0 ],
[1169, 187, 0 ],
[1170, 188, 0 ],
[1171, 189, 0 ],
[1172, 190, 0 ],
[1173, 192, 0 ],
[1174, 193, 0 ],
[1175, 194, 0 ],
[1176, 196, 0 ],
[1177, 197, 0 ],
[1178, 198, 0 ],
[1179, 199, 0 ],
[1180, 200, 0 ],
[1181, 202, 0 ],
[1182, 203, 0 ],
[1183, 204, 0 ],
[1184, 205, 0 ],
[1185, 206, 0 ],
[1186, 207, 0 ],
[1187, 208, 0 ],
[1188, 209, 0 ],
[1189, 210, 0 ],
[1190, 211, 0 ],
[1191, 212, 0 ],
[1192, 213, 0 ],
[1193, 214, 0 ],
[1194, 215, 0 ],
[1195, 216, 0 ],
[1196, 217, 0 ],
[1197, 218, 0 ],
[1198, 219, 0 ],
[1201, 223, 0 ],
[1202, 224, 0 ],
[1203, 225, 0 ],
[1204, 226, 0 ],
[1205, 227, 0 ],
[1206, 228, 0 ],
[1207, 229, 0 ],
[1208, 230, 0 ],
[1209, 234, 0 ],
[1210, 235, 0 ],
[1211, 237, 0 ],
[1212, 238, 0 ],
[1213, 239, 0 ],
[1214, 240, 0 ],
[1215, 241, 0 ],
[1216, 242, 0 ],
[1217, 243, 0 ],
[1218, 244, 0 ],
[1219, 247, 0 ],
[1220, 251, 0 ],
[1221, 252, 0 ],
[1222, 253, 0 ],
[1223, 254, 0 ],
[1224, 255, 0 ],
[1225, 256, 0 ],
[1226, 257, 0 ],
[1227, 258, 0 ],
[1228, 260, 0 ],
[1229, 263, 0 ],
[1230, 264, 0 ],
[1231, 266, 0 ],
[1232, 267, 0 ],
[1235, 271, 0 ],
[1236, 272, 0 ],
[1237, 273, 0 ],
[1238, 274, 0 ],
[1239, 275, 0 ],
[1240, 276, 0 ],
[1241, 278, 0 ],
[1242, 281, 0 ],
[1243, 282, 0 ],
[1244, 283, 0 ],
[1245, 284, 0 ],
[1246, 285, 0 ],
[1247, 286, 0 ],
[1248, 287, 0 ],
[1249, 288, 0 ],
[1250, 289, 0 ],
[1251, 291, 0 ],
[1252, 292, 0 ],
[1253, 293, 0 ],
[1254, 294, 0 ],
[1255, 295, 0 ],
[1256, 296, 0 ],
[1257, 297, 0 ],
[1258, 298, 0 ],
[1259, 299, 0 ],
[1260, 300, 0 ],
[1261, 302, 0 ],
[1262, 303, 0 ],
[1263, 304, 0 ],
[1264, 307, 0 ],
[1265, 308, 0 ],
[1266, 309, 0 ],
[1267, 311, 0 ],
[1268, 312, 0 ],
[1269, 314, 0 ],
[1270, 316, 0 ],
[1271, 317, 0 ],
[1272, 318, 0 ],
[1273, 319, 0 ],
[1274, 321, 0 ],
[1275, 322, 0 ],
[1276, 323, 0 ],
[1277, 324, 0 ],
[1278, 325, 0 ],
[1279, 326, 0 ],
[1280, 327, 0 ],
[1281, 328, 0 ],
[1282, 329, 0 ],
[1283, 331, 0 ],
[1284, 333, 0 ],
[1285, 335, 0 ],
[1286, 337, 0 ],
[1287, 338, 0 ],
[1288, 339, 0 ],
[1289, 340, 0 ],
[1290, 341, 0 ],
[1291, 342, 0 ],
[1292, 343, 0 ],
[1293, 344, 0 ],
[1294, 345, 0 ],
[1295, 346, 0 ],
[1296, 347, 0 ],
[1297, 348, 0 ],
[1298, 350, 0 ],
[1299, 352, 0 ],
[1300, 353, 0 ],
[1301, 354, 0 ],
[1302, 355, 0 ],
[1303, 356, 0 ],
[1304, 357, 0 ],
[1305, 359, 0 ],
[1306, 361, 0 ],
[1307, 362, 0 ],
[1308, 363, 0 ],
[1309, 364, 0 ],
[1310, 365, 0 ],
[1311, 366, 0 ],
[1312, 367, 0 ],
[1313, 368, 0 ],
[1314, 369, 0 ],
[1315, 370, 0 ],
[1316, 371, 0 ],
[1317, 372, 0 ],
[1318, 373, 0 ],
[1319, 374, 0 ],
[1320, 375, 0 ],
[1321, 376, 0 ],
[1322, 377, 0 ],
[1323, 378, 0 ],
[1324, 379, 0 ],
[1325, 381, 0 ],
[1326, 384, 0 ],
[1327, 385, 0 ],
[1328, 386, 0 ],
[1329, 387, 0 ],
[1330, 388, 0 ],
[1331, 390, 0 ],
[1332, 391, 0 ],
[1333, 392, 0 ],
[1334, 393, 0 ],
[1335, 394, 0 ],
[1336, 395, 0 ],
[1337, 396, 0 ],
[1338, 397, 0 ],
[1339, 398, 0 ],
[1340, 399, 0 ],
[1341, 400, 0 ],
[1342, 403, 0 ],
[1343, 404, 0 ],
[1344, 405, 0 ],
[1345, 406, 0 ],
[1346, 407, 0 ],
[1347, 408, 0 ],
[1348, 410, 0 ],
[1349, 411, 0 ],
[1350, 412, 0 ],
[1351, 413, 0 ],
[1352, 414, 0 ],
[1354, 417, 0 ],
[1355, 418, 0 ],
[1356, 419, 0 ],
[1357, 420, 0 ],
[1358, 421, 0 ],
[1359, 422, 0 ],
[1360, 423, 0 ],
[1361, 424, 0 ],
[1362, 425, 0 ],
[1363, 426, 0 ],
[1364, 427, 0 ],
[1365, 428, 0 ],
[1366, 429, 0 ],
[1367, 430, 0 ],
[1368, 431, 0 ],
[1369, 432, 0 ],
[1370, 433, 0 ],
[1371, 434, 0 ],
[1372, 435, 0 ],
[1373, 436, 0 ],
[1376, 439, 0 ],
[1377, 440, 0 ],
[1378, 441, 0 ],
[1379, 442, 0 ],
[1380, 443, 0 ],
[1381, 445, 0 ],
[1382, 446, 0 ],
[1383, 447, 0 ],
[1384, 448, 0 ],
[1385, 449, 0 ],
[1386, 450, 0 ],
[1387, 451, 0 ],
[1388, 453, 0 ],
[1389, 454, 0 ],
[1390, 455, 0 ],
[1391, 456, 0 ],
[1392, 457, 0 ],
[1393, 458, 0 ],
[1394, 459, 0 ],
[1395, 460, 0 ],
[1396, 461, 0 ],
[1397, 462, 0 ],
[1398, 463, 0 ],
[1399, 464, 0 ],
[1400, 465, 0 ],
[1401, 466, 0 ],
[1402, 467, 0 ],
[1403, 468, 0 ],
[1404, 469, 0 ],
[1405, 470, 0 ],
[1406, 471, 0 ],
[1407, 472, 0 ],
[1408, 473, 0 ],
[1409, 474, 0 ],
[1410, 475, 0 ],
[1411, 476, 0 ],
[1412, 477, 0 ],
[1413, 478, 0 ],
[1414, 479, 0 ],
[1415, 480, 0 ],
[1416, 481, 0 ],
[1417, 482, 0 ],
[1418, 483, 0 ],
[1419, 484, 0 ],
[1420, 485, 0 ],
[1421, 486, 0 ],
[1422, 487, 0 ],
[1423, 488, 0 ],
[1424, 489, 0 ],
[1425, 490, 0 ],
[1426, 491, 0 ],
[1427, 492, 0 ],
[1428, 493, 0 ],
[1429, 494, 0 ],
[1430, 495, 0 ],
[1431, 496, 0 ],
[1432, 497, 0 ],
[1433, 498, 0 ],
[1434, 499, 0 ],
[1435, 500, 0 ],
[1436, 501, 0 ],
[1437, 502, 0 ],
[1438, 503, 0 ],
[1439, 504, 0 ],
[1440, 505, 0 ],
[1441, 506, 0 ],
[1442, 507, 0 ],
[1443, 508, 0 ],
[1444, 509, 0 ],
[1445, 510, 0 ],
[1446, 511, 0 ],
[1447, 512, 0 ],
[1448, 513, 0 ],
[1449, 514, 0 ],
[1450, 515, 0 ],
[1451, 516, 0 ],
[1452, 517, 0 ],
[1453, 518, 0 ],
[1454, 519, 0 ],
[1455, 520, 0 ],
[1456, 521, 0 ],
[1457, 522, 0 ],
[1458, 523, 0 ],
[1459, 524, 0 ],
[1460, 525, 0 ],
[1461, 526, 0 ],
[1462, 527, 0 ],
[1463, 528, 0 ],
[1464, 529, 0 ],
[1465, 530, 0 ],
[1466, 531, 0 ],
[1467, 532, 0 ],
[1468, 533, 0 ],
[1469, 534, 0 ],
[1470, 535, 0 ],
[1471, 536, 0 ],
[1472, 537, 0 ],
[1473, 538, 0 ],
[1474, 539, 0 ],
[1475, 540, 0 ],
[1476, 541, 0 ],
[1477, 542, 0 ],
[1478, 543, 0 ],
[1479, 544, 0 ],
[1480, 545, 0 ],
[1481, 546, 0 ],
[1482, 547, 0 ],
[1483, 548, 0 ],
[1484, 549, 0 ],
[1485, 550, 0 ],
[1486, 551, 0 ],
[1487, 552, 0 ],
[1488, 554, 0 ],
[1489, 555, 0 ],
[1490, 556, 0 ],
[1491, 557, 0 ],
[1492, 558, 0 ],
[1493, 559, 0 ],
[1494, 560, 0 ],
[1495, 561, 0 ],
[1496, 562, 0 ],
[1497, 563, 0 ],
[1498, 564, 0 ],
[1499, 565, 0 ],
[1500, 566, 0 ],
[1501, 567, 0 ],
[1502, 568, 0 ],
[1503, 569, 0 ],
[1504, 570, 0 ],
[1505, 571, 0 ],
[1506, 572, 0 ],
[1507, 573, 0 ],
[1508, 574, 0 ],
[1509, 575, 0 ],
[1510, 576, 0 ],
[1511, 577, 0 ],
[1512, 578, 0 ],
[1513, 579, 0 ],
[1514, 580, 0 ],
[1515, 581, 0 ],
[1516, 582, 0 ],
[1517, 583, 0 ],
[1518, 584, 0 ],
[1519, 585, 0 ],
[1, 490, 0 ],
[3, 4, 1 ],
[491, 6, 0 ],
[7, 5, 0 ],
[8, 9, 0 ],
[492, 11, 0 ],
[11, 493, 0 ],
[492, 493, 1 ],
[494, 14, 0 ],
[13, 15, 0 ],
[16, 5, 0 ],
[17, 18, 1 ],
[17, 12, 0 ],
[14, 495, 0 ],
[494, 19, 0 ],
[20, 21, 0 ],
[20, 22, 1 ],
[497, 23, 0 ],
[23, 499, 1 ],
[25, 26, 0 ],
[25, 22, 0 ],
[23, 27, 0 ],
[28, 23, 0 ],
[8, 21, 0 ],
[9, 29, 0 ],
[30, 25, 1 ],
[31, 32, 1 ],
[32, 33, 1 ],
[34, 35, 0 ],
[35, 36, 0 ],
[490, 6, 1 ],
[37, 10, 1 ],
[10, 38, 0 ],
[37, 38, 1 ],
[39, 40, 1 ],
[39, 41, 1 ],
[42, 41, 1 ],
[18, 42, 1 ],
[492, 43, 1 ],
[44, 45, 0 ],
[44, 505, 0 ],
[46, 12, 0 ],
[47, 48, 0 ],
[49, 50, 0 ],
[31, 33, 1 ],
[31, 51, 0 ],
[52, 53, 1 ],
[52, 54, 0 ],
[506, 55, 0 ],
[506, 507, 1 ],
[57, 506, 0 ],
[57, 58, 0 ],
[58, 506, 0 ],
[59, 60, 1 ],
[508, 62, 0 ],
[30, 61, 1 ],
[63, 506, 0 ],
[13, 64, 0 ],
[65, 66, 1 ],
[59, 67, 0 ],
[61, 67, 0 ],
[68, 69, 1 ],
[70, 69, 1 ],
[71, 72, 1 ],
[73, 74, 1 ],
[37, 75, 1 ],
[72, 75, 0 ],
[37, 72, 1 ],
[76, 77, 1 ],
[77, 51, 0 ],
[73, 72, 1 ],
[18, 40, 1 ],
[492, 45, 1 ],
[10, 74, 1 ],
[45, 511, 1 ],
[78, 32, 1 ],
[79, 80, 0 ],
[81, 79, 1 ],
[34, 82, 0 ],
[83, 84, 0 ],
[83, 499, 0 ],
[85, 86, 0 ],
[87, 86, 1 ],
[88, 89, 0 ],
[90, 86, 1 ],
[91, 86, 0 ],
[86, 92, 0 ],
[86, 93, 0 ],
[94, 86, 1 ],
[86, 95, 1 ],
[513, 517, 0 ],
[97, 66, 1 ],
[42, 98, 0 ],
[99, 100, 1 ],
[42, 101, 0 ],
[102, 42, 1 ],
[103, 87, 0 ],
[104, 103, 0 ],
[105, 87, 0 ],
[106, 107, 0 ],
[108, 107, 0 ],
[109, 106, 0 ],
[110, 111, 1 ],
[87, 112, 0 ],
[113, 87, 0 ],
[87, 85, 1 ],
[110, 114, 1 ],
[115, 116, 0 ],
[117, 118, 0 ],
[117, 119, 0 ],
[117, 120, 1 ],
[121, 122, 0 ],
[123, 124, 0 ],
[125, 126, 0 ],
[127, 119, 0 ],
[118, 128, 0 ],
[121, 119, 0 ],
[530, 527, 0 ],
[125, 130, 0 ],
[125, 123, 0 ],
[131, 132, 0 ],
[133, 123, 0 ],
[524, 134, 0 ],
[135, 136, 0 ],
[123, 131, 0 ],
[117, 128, 1 ],
[137, 521, 0 ],
[531, 514, 0 ],
[139, 521, 0 ],
[140, 514, 0 ],
[522, 141, 0 ],
[142, 523, 0 ],
[530, 526, 0 ],
[140, 532, 0 ],
[142, 144, 0 ],
[140, 522, 0 ],
[145, 146, 0 ],
[147, 523, 0 ],
[144, 523, 0 ],
[139, 523, 0 ],
[140, 141, 0 ],
[528, 526, 0 ],
[528, 148, 0 ],
[149, 150, 0 ],
[145, 528, 0 ],
[530, 151, 0 ],
[524, 152, 0 ],
[149, 525, 1 ],
[139, 514, 0 ],
[126, 120, 1 ],
[530, 153, 0 ],
[528, 147, 1 ],
[528, 154, 0 ],
[130, 120, 1 ],
[528, 155, 1 ],
[524, 533, 0 ],
[524, 149, 0 ],
[154, 150, 0 ],
[157, 110, 1 ],
[119, 158, 0 ],
[159, 60, 0 ],
[536, 161, 0 ],
[115, 151, 0 ],
[162, 134, 0 ],
[115, 526, 0 ],
[138, 87, 0 ],
[123, 163, 0 ],
[112, 164, 0 ],
[112, 165, 0 ],
[166, 165, 0 ],
[167, 537, 0 ],
[168, 104, 0 ],
[531, 520, 0 ],
[139, 520, 0 ],
[520, 169, 0 ],
[168, 105, 0 ],
[520, 170, 0 ],
[171, 89, 0 ],
[521, 172, 0 ],
[123, 173, 0 ],
[521, 174, 0 ],
[37, 39, 0 ],
[530, 175, 0 ],
[530, 176, 0 ],
[88, 530, 0 ],
[177, 496, 1 ],
[178, 525, 0 ],
[179, 493, 1 ],
[180, 181, 1 ],
[182, 180, 0 ],
[179, 181, 0 ],
[180, 493, 1 ],
[183, 30, 0 ],
[183, 21, 0 ],
[538, 185, 0 ],
[538, 89, 0 ],
[184, 186, 0 ],
[184, 187, 0 ],
[520, 172, 0 ],
[89, 175, 0 ],
[185, 89, 0 ],
[89, 188, 0 ],
[189, 190, 0 ],
[539, 172, 0 ],
[504, 192, 0 ],
[105, 186, 0 ],
[105, 187, 0 ],
[539, 193, 0 ],
[187, 194, 0 ],
[539, 540, 0 ],
[539, 196, 0 ],
[197, 540, 0 ],
[110, 198, 0 ],
[197, 539, 0 ],
[199, 537, 0 ],
[134, 526, 0 ],
[200, 193, 0 ],
[4, 201, 1 ],
[202, 86, 0 ],
[85, 203, 0 ],
[147, 204, 0 ],
[147, 205, 0 ],
[123, 206, 0 ],
[537, 207, 0 ],
[165, 208, 0 ],
[4, 94, 1 ],
[4, 2, 0 ],
[209, 4, 0 ],
[119, 163, 0 ],
[210, 3, 0 ],
[99, 211, 0 ],
[99, 69, 1 ],
[212, 99, 0 ],
[213, 214, 0 ],
[510, 215, 0 ],
[128, 69, 1 ],
[216, 69, 1 ],
[217, 98, 0 ],
[504, 218, 0 ],
[177, 504, 1 ],
[219, 209, 0 ],
[219, 220, 0 ],
[94, 95, 1 ],
[159, 221, 1 ],
[34, 161, 0 ],
[222, 221, 0 ],
[211, 52, 1 ],
[215, 223, 1 ],
[224, 215, 0 ],
[225, 224, 1 ],
[224, 223, 0 ],
[226, 6, 0 ],
[7, 3, 1 ],
[216, 227, 1 ],
[228, 229, 0 ],
[227, 230, 0 ],
[231, 53, 1 ],
[544, 545, 0 ],
[234, 235, 1 ],
[546, 214, 1 ],
[233, 227, 0 ],
[237, 238, 0 ],
[212, 100, 0 ],
[519, 239, 0 ],
[238, 519, 0 ],
[213, 240, 0 ],
[241, 242, 1 ],
[70, 241, 0 ],
[509, 213, 0 ],
[68, 243, 0 ],
[243, 244, 0 ],
[68, 244, 0 ],
[544, 547, 1 ],
[245, 227, 1 ],
[246, 208, 0 ],
[112, 208, 0 ],
[165, 247, 0 ],
[537, 549, 0 ],
[537, 550, 0 ],
[537, 551, 0 ],
[110, 251, 0 ],
[510, 252, 1 ],
[529, 253, 1 ],
[237, 239, 1 ],
[254, 238, 1 ],
[69, 255, 0 ],
[510, 225, 1 ],
[256, 257, 0 ],
[258, 190, 0 ],
[258, 259, 0 ],
[260, 261, 1 ],
[554, 553, 1 ],
[515, 263, 0 ],
[14, 264, 1 ],
[116, 555, 0 ],
[151, 116, 0 ],
[111, 114, 1 ],
[77, 111, 0 ],
[266, 525, 0 ],
[267, 120, 1 ],
[268, 269, 0 ],
[556, 271, 0 ],
[556, 272, 0 ],
[529, 273, 0 ],
[128, 274, 0 ],
[34, 275, 0 ],
[503, 276, 0 ],
[503, 504, 1 ],
[177, 218, 1 ],
[277, 278, 1 ],
[557, 558, 1 ],
[557, 559, 1 ],
[559, 558, 1 ],
[277, 78, 1 ],
[277, 279, 1 ],
[78, 279, 0 ],
[281, 282, 0 ],
[283, 161, 1 ],
[268, 161, 1 ],
[256, 284, 0 ],
[515, 516, 1 ],
[263, 516, 0 ],
[516, 285, 0 ],
[63, 286, 0 ],
[287, 516, 0 ],
[8, 102, 1 ],
[8, 101, 1 ],
[80, 288, 0 ],
[80, 289, 0 ],
[276, 560, 0 ],
[37, 290, 0 ],
[290, 74, 1 ],
[512, 291, 0 ],
[78, 292, 1 ],
[199, 548, 0 ],
[491, 293, 0 ],
[4, 294, 0 ],
[490, 541, 1 ],
[491, 295, 0 ],
[491, 296, 0 ],
[295, 297, 0 ],
[508, 161, 0 ],
[117, 123, 0 ],
[133, 117, 0 ],
[71, 74, 1 ],
[74, 278, 1 ],
[298, 515, 0 ],
[5, 299, 0 ],
[32, 292, 1 ],
[5, 29, 1 ],
[503, 560, 0 ],
[300, 301, 1 ],
[51, 300, 0 ],
[244, 302, 1 ],
[31, 302, 1 ],
[51, 282, 1 ],
[303, 304, 0 ],
[305, 304, 0 ],
[305, 259, 0 ],
[306, 307, 1 ],
[305, 308, 0 ],
[305, 309, 0 ],
[310, 309, 1 ],
[306, 309, 1 ],
[311, 280, 0 ],
[280, 278, 1 ],
[311, 32, 1 ],
[13, 312, 1 ],
[313, 314, 0 ],
[312, 313, 1 ],
[547, 566, 1 ],
[245, 315, 1 ],
[312, 316, 0 ],
[312, 314, 0 ],
[554, 546, 1 ],
[262, 216, 1 ],
[317, 233, 0 ],
[318, 317, 0 ],
[231, 52, 1 ],
[319, 567, 0 ],
[557, 321, 0 ],
[277, 65, 1 ],
[322, 288, 1 ],
[322, 323, 0 ],
[277, 324, 1 ],
[324, 325, 0 ],
[277, 325, 0 ],
[326, 327, 0 ],
[328, 326, 1 ],
[328, 327, 1 ],
[326, 329, 0 ],
[568, 329, 1 ],
[568, 326, 0 ],
[332, 78, 1 ],
[333, 306, 0 ],
[332, 333, 0 ],
[332, 334, 0 ],
[66, 334, 1 ],
[330, 335, 1 ],
[336, 66, 0 ],
[330, 336, 1 ],
[68, 70, 0 ],
[509, 337, 1 ],
[324, 288, 0 ],
[338, 559, 0 ],
[339, 559, 0 ],
[339, 340, 1 ],
[559, 340, 1 ],
[341, 292, 0 ],
[557, 342, 0 ],
[558, 343, 0 ],
[502, 340, 1 ],
[72, 32, 1 ],
[344, 345, 0 ],
[346, 47, 0 ],
[46, 47, 0 ],
[346, 345, 0 ],
[347, 328, 0 ],
[347, 348, 1 ],
[571, 348, 1 ],
[347, 572, 0 ],
[571, 570, 1 ],
[14, 350, 0 ],
[350, 573, 0 ],
[15, 351, 1 ],
[352, 15, 0 ],
[15, 335, 1 ],
[232, 227, 0 ],
[565, 544, 1 ],
[235, 567, 1 ],
[567, 286, 0 ],
[353, 519, 0 ],
[354, 353, 0 ],
[355, 354, 0 ],
[354, 356, 0 ],
[357, 358, 0 ],
[574, 359, 0 ],
[235, 575, 0 ],
[167, 361, 0 ],
[528, 362, 0 ],
[363, 344, 0 ],
[259, 364, 1 ],
[54, 56, 0 ],
[365, 364, 0 ],
[231, 366, 0 ],
[30, 367, 0 ],
[61, 367, 1 ],
[254, 368, 0 ],
[254, 369, 0 ],
[254, 370, 0 ],
[99, 358, 0 ],
[354, 519, 0 ],
[571, 371, 0 ],
[207, 372, 0 ],
[57, 373, 0 ],
[209, 374, 0 ],
[375, 376, 0 ],
[376, 377, 0 ],
[16, 49, 0 ],
[318, 377, 0 ],
[378, 297, 0 ],
[562, 379, 0 ],
[576, 563, 0 ],
[576, 381, 0 ],
[577, 576, 1 ],
[244, 383, 0 ],
[244, 306, 1 ],
[383, 306, 1 ],
[380, 306, 0 ],
[252, 225, 0 ],
[220, 76, 0 ],
[542, 384, 0 ],
[385, 384, 0 ],
[542, 385, 0 ],
[386, 385, 0 ],
[387, 578, 0 ],
[332, 388, 1 ],
[382, 332, 1 ],
[382, 388, 0 ],
[579, 578, 0 ],
[577, 387, 1 ],
[144, 390, 0 ],
[37, 49, 0 ],
[391, 233, 0 ],
[392, 310, 0 ],
[260, 393, 0 ],
[394, 230, 0 ],
[395, 282, 1 ],
[395, 244, 0 ],
[25, 396, 1 ],
[81, 74, 0 ],
[278, 80, 1 ],
[81, 278, 1 ],
[569, 570, 0 ],
[397, 552, 0 ],
[542, 398, 0 ],
[398, 385, 0 ],
[399, 499, 0 ],
[83, 399, 0 ],
[498, 400, 0 ],
[518, 239, 1 ],
[575, 543, 0 ],
[401, 360, 0 ],
[580, 581, 0 ],
[401, 402, 0 ],
[403, 231, 0 ],
[189, 360, 1 ],
[234, 404, 0 ],
[235, 404, 1 ],
[235, 580, 0 ],
[216, 259, 0 ],
[405, 259, 0 ],
[405, 318, 0 ],
[406, 230, 0 ],
[542, 407, 0 ],
[23, 408, 0 ],
[577, 348, 0 ],
[562, 564, 1 ],
[582, 507, 0 ],
[27, 410, 0 ],
[501, 27, 0 ],
[27, 411, 0 ],
[411, 410, 0 ],
[403, 360, 0 ],
[412, 360, 0 ],
[326, 413, 0 ],
[414, 413, 0 ],
[6, 297, 0 ],
[554, 580, 1 ],
[262, 401, 1 ],
[499, 556, 1 ],
[224, 229, 0 ],
[583, 507, 0 ],
[415, 307, 0 ],
[416, 507, 0 ],
[284, 561, 0 ],
[543, 417, 0 ],
[418, 506, 0 ],
[220, 157, 0 ],
[295, 419, 0 ],
[295, 420, 0 ],
[541, 62, 0 ],
[52, 421, 0 ],
[60, 160, 0 ],
[535, 161, 0 ],
[267, 282, 0 ],
[52, 365, 0 ],
[28, 27, 0 ],
[30, 201, 1 ],
[422, 81, 0 ],
[119, 425, 0 ],
[423, 425, 0 ],
[424, 425, 0 ],
[426, 428, 0 ],
[427, 428, 0 ],
[19, 428, 1 ],
[45, 429, 0 ],
[44, 429, 0 ],
[505, 429, 0 ],
[231, 431, 1 ],
[190, 431, 1 ],
[430, 431, 0 ],
[286, 433, 0 ],
[432, 433, 0 ],
[506, 433, 0 ],
[23, 434, 0 ],
[400, 434, 0 ],
[500, 434, 0 ],
[32, 436, 0 ],
[435, 436, 0 ],
[78, 436, 1 ],
[86, 438, 1 ],
[437, 438, 0 ],
[221, 438, 0 ],
[207, 439, 0 ],
[516, 439, 0 ],
[513, 439, 0 ],
[181, 441, 1 ],
[440, 441, 0 ],
[504, 441, 1 ],
[135, 442, 0 ],
[109, 442, 0 ],
[112, 442, 0 ],
[113, 443, 0 ],
[132, 443, 0 ],
[107, 443, 0 ],
[444, 445, 0 ],
[112, 445, 0 ],
[109, 445, 0 ],
[119, 447, 1 ],
[100, 447, 1 ],
[446, 447, 0 ],
[124, 448, 0 ],
[125, 448, 0 ],
[131, 448, 0 ],
[449, 450, 0 ],
[173, 450, 0 ],
[184, 450, 0 ],
[144, 451, 0 ],
[140, 451, 0 ],
[514, 451, 0 ],
[537, 585, 1 ],
[141, 585, 0 ],
[584, 585, 0 ],
[522, 454, 0 ],
[144, 454, 0 ],
[453, 454, 0 ],
[199, 456, 0 ],
[140, 456, 0 ],
[455, 456, 0 ],
[537, 456, 0 ],
[538, 457, 0 ],
[153, 457, 0 ],
[176, 457, 0 ],
[524, 459, 0 ],
[458, 459, 0 ],
[134, 459, 0 ],
[460, 461, 0 ],
[150, 461, 0 ],
[149, 461, 0 ],
[521, 463, 0 ],
[462, 463, 0 ],
[538, 463, 0 ],
[110, 464, 0 ],
[90, 464, 0 ],
[165, 464, 0 ],
[458, 465, 0 ],
[134, 465, 0 ],
[524, 465, 0 ],
[466, 467, 0 ],
[110, 467, 0 ],
[165, 467, 0 ],
[468, 469, 0 ],
[541, 469, 0 ],
[490, 469, 0 ],
[263, 471, 0 ],
[470, 471, 0 ],
[534, 471, 0 ],
[136, 472, 0 ],
[110, 472, 0 ],
[251, 472, 0 ],
[226, 474, 0 ],
[473, 474, 0 ],
[257, 474, 0 ],
[6, 474, 1 ],
[299, 475, 1 ],
[3, 475, 0 ],
[210, 475, 0 ],
[297, 476, 0 ],
[296, 476, 0 ],
[295, 476, 0 ],
[313, 478, 1 ],
[477, 478, 0 ],
[245, 478, 0 ],
[479, 481, 0 ],
[565, 481, 0 ],
[480, 481, 0 ],
[415, 482, 0 ],
[56, 482, 0 ],
[409, 482, 0 ],
[483, 484, 0 ],
[3, 484, 0 ],
[301, 484, 0 ],
[233, 485, 0 ],
[392, 485, 0 ],
[391, 485, 0 ],
[579, 488, 0 ],
[486, 488, 0 ],
[487, 488, 0 ],
[270, 489, 0 ],
[331, 489, 0 ],
[396, 489, 1 ],
[519, 253, 0 ],
[382, 349, 1 ],
[349, 351, 0 ],
[459, 465, 0 ],
[549, 550, 0 ],
[550, 551, 0 ],
[194, 195, 0 ],
[247, 248, 0 ],
[2, 294, 0 ],
[549, 551, 0 ],
[54, 365, 0 ],
[131, 265, 0 ],
[91, 92, 0 ],
[247, 249, 0 ],
[186, 191, 0 ],
[129, 173, 0 ],
[96, 202, 0 ],
[53, 320, 0 ],
[24, 396, 0 ],
[133, 156, 0 ],
[442, 452, 0 ],
[445, 452, 0 ],
[247, 250, 0 ],
[187, 195, 0 ],
[216, 236, 0 ],
[244, 389, 0 ],
[394, 406, 0 ],
[442, 445, 0 ],
[442, 444, 0 ],
[198, 472, 0 ],
[464, 467, 0 ],
[198, 251, 0 ],
[112, 143, 0 ],
[2, 490, 0 ],
[5, 491, 0 ],
[10, 492, 0 ],
[12, 493, 0 ],
[13, 494, 0 ],
[15, 495, 0 ],
[18, 496, 0 ],
[20, 497, 0 ],
[22, 498, 0 ],
[24, 499, 0 ],
[26, 500, 0 ],
[30, 501, 0 ],
[32, 502, 0 ],
[37, 503, 0 ],
[42, 504, 0 ],
[46, 505, 0 ],
[52, 506, 0 ],
[56, 507, 0 ],
[61, 508, 0 ],
[68, 509, 0 ],
[69, 510, 0 ],
[74, 511, 0 ],
[78, 512, 0 ],
[86, 513, 0 ],
[87, 514, 0 ],
[94, 515, 0 ],
[95, 516, 0 ],
[96, 517, 0 ],
[99, 518, 0 ],
[100, 519, 0 ],
[104, 520, 0 ],
[105, 521, 0 ],
[106, 522, 0 ],
[107, 523, 0 ],
[117, 524, 0 ],
[120, 525, 0 ],
[123, 526, 0 ],
[124, 527, 0 ],
[125, 528, 0 ],
[128, 529, 0 ],
[129, 530, 0 ],
[138, 531, 0 ],
[143, 532, 0 ],
[156, 533, 0 ],
[157, 534, 0 ],
[159, 535, 0 ],
[160, 536, 0 ],
[165, 537, 0 ],
[184, 538, 0 ],
[191, 539, 0 ],
[195, 540, 0 ],
[201, 541, 0 ],
[220, 542, 0 ],
[231, 543, 0 ],
[232, 544, 0 ],
[233, 545, 0 ],
[236, 546, 0 ],
[245, 547, 0 ],
[246, 548, 0 ],
[248, 549, 0 ],
[249, 550, 0 ],
[250, 551, 0 ],
[259, 552, 0 ],
[261, 553, 0 ],
[262, 554, 0 ],
[265, 555, 0 ],
[270, 556, 0 ],
[277, 557, 0 ],
[279, 558, 0 ],
[280, 559, 0 ],
[290, 560, 0 ],
[301, 561, 0 ],
[305, 562, 0 ],
[306, 563, 0 ],
[310, 564, 0 ],
[313, 565, 0 ],
[315, 566, 0 ],
[320, 567, 0 ],
[330, 568, 0 ],
[332, 569, 0 ],
[334, 570, 0 ],
[336, 571, 0 ],
[349, 572, 0 ],
[351, 573, 0 ],
[358, 574, 0 ],
[360, 575, 0 ],
[380, 576, 0 ],
[382, 577, 0 ],
[383, 578, 0 ],
[389, 579, 0 ],
[401, 580, 0 ],
[402, 581, 0 ],
[409, 582, 0 ],
[415, 583, 0 ],
[444, 584, 0 ],
[452, 585, 0 ]
])
ppc["parameters"] = {
"x_trans_sg": 0.003,
"x_trans_fm": 0.001,
"x_trans_fl": 0.001,
"d_l": 1e-3,
"d_l_perturb": 1e-5,
"w_1_ij": 1,
"w_2_ij": 1,
"w_3_ij": 1,
"w_4_ij": 1,
"b_r": 238,
"b_c": 248 }
return ppc
| true
| true
|
1c3ef8d5d2ecd45e965099d0b652f213cd2a3600
| 1,333
|
py
|
Python
|
var/spack/repos/builtin/packages/gnat/package.py
|
HaochengLIU/spack
|
26e51ff1705a4d6234e2a0cf734f93f7f95df5cb
|
[
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 2
|
2018-11-27T03:39:44.000Z
|
2021-09-06T15:50:35.000Z
|
var/spack/repos/builtin/packages/gnat/package.py
|
HaochengLIU/spack
|
26e51ff1705a4d6234e2a0cf734f93f7f95df5cb
|
[
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 1
|
2019-01-11T20:11:52.000Z
|
2019-01-11T20:11:52.000Z
|
var/spack/repos/builtin/packages/gnat/package.py
|
HaochengLIU/spack
|
26e51ff1705a4d6234e2a0cf734f93f7f95df5cb
|
[
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 1
|
2020-10-14T14:20:17.000Z
|
2020-10-14T14:20:17.000Z
|
# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Gnat(MakefilePackage):
"""The GNAT Ada compiler. Ada is a modern programming language designed
for large, long-lived applications - and embedded systems in particular
- where reliability and efficiency are essential."""
homepage = "https://libre.adacore.com/tools/gnat-gpl-edition/"
# NOTE: This is a binary installer intended to bootstrap GCC's Ada compiler
# There may actually be a way to install GNAT from source. If you go to
# the GNAT Download page: https://libre.adacore.com/download/
# select "Free Software or Academic Development", select your platform,
# expand GNAT Ada, and expand Sources, you'll see links to download the
# source code for GNAT and all of its dependencies. Most of these
# dependencies are already in Spack.
# This is the GPL release for Linux x86-64
version('2016', '9741107cca1a6a4ddb0d5e8de824a90c', extension='tar.gz',
url="http://mirrors.cdn.adacore.com/art/5739cefdc7a447658e0b016b")
phases = ['install']
def install(self, spec, prefix):
make('ins-all', 'prefix={0}'.format(prefix))
| 40.393939
| 79
| 0.71943
|
from spack import *
class Gnat(MakefilePackage):
homepage = "https://libre.adacore.com/tools/gnat-gpl-edition/"
# There may actually be a way to install GNAT from source. If you go to
# the GNAT Download page: https://libre.adacore.com/download/
# select "Free Software or Academic Development", select your platform,
# expand GNAT Ada, and expand Sources, you'll see links to download the
version('2016', '9741107cca1a6a4ddb0d5e8de824a90c', extension='tar.gz',
url="http://mirrors.cdn.adacore.com/art/5739cefdc7a447658e0b016b")
phases = ['install']
def install(self, spec, prefix):
make('ins-all', 'prefix={0}'.format(prefix))
| true
| true
|
1c3ef8da034054faf6b90a5fb4f4bab244f16ddb
| 8,709
|
py
|
Python
|
nncf/torch/tensor_statistics/collectors.py
|
MaximProshin/nncf
|
2290d2f4cebcf6749e419dc76850e7bd8b7d8da1
|
[
"Apache-2.0"
] | null | null | null |
nncf/torch/tensor_statistics/collectors.py
|
MaximProshin/nncf
|
2290d2f4cebcf6749e419dc76850e7bd8b7d8da1
|
[
"Apache-2.0"
] | null | null | null |
nncf/torch/tensor_statistics/collectors.py
|
MaximProshin/nncf
|
2290d2f4cebcf6749e419dc76850e7bd8b7d8da1
|
[
"Apache-2.0"
] | null | null | null |
"""
Copyright (c) 2022 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from typing import Union, List, Deque
import torch
from nncf.common.tensor import NNCFTensor
from nncf.common.tensor import TensorElementsType
from nncf.common.tensor_statistics.collectors import MinMaxStatisticCollector
from nncf.common.tensor_statistics.collectors import NNCFCollectorTensorProcessor
from nncf.common.tensor_statistics.collectors import MedianMADStatisticCollector
from nncf.common.tensor_statistics.collectors import PercentileStatisticCollector
from nncf.common.tensor_statistics.collectors import MeanPercentileStatisticCollector
from nncf.common.tensor_statistics.collectors import MixedMinMaxStatisticCollector
from nncf.common.tensor_statistics.collectors import MeanMinMaxStatisticCollector
from nncf.common.tensor_statistics.collectors import ReductionShape
from nncf.common.tensor_statistics.reduction import np_percentile_reduce_like
from nncf.torch.tensor_statistics.reduction import expand_like
from nncf.torch.tensor_statistics.statistics import PTMinMaxTensorStatistic
from nncf.torch.tensor_statistics.statistics import PTMedianMADTensorStatistic
from nncf.torch.tensor_statistics.statistics import PTPercentileTensorStatistic
from nncf.torch.dynamic_graph.context import no_nncf_trace
from nncf.torch.tensor import PTNNCFTensor
class PTNNCFCollectorTensorProcessor(NNCFCollectorTensorProcessor):
"""
A realization of the processing methods for PTNNCFTensors.
"""
@staticmethod
def reduce_min(x: NNCFTensor, axis: Union[int, tuple]) -> NNCFTensor:
return PTNNCFTensor(torch.amin(x.tensor, dim=axis))
@staticmethod
def reduce_max(x: NNCFTensor, axis: Union[int, tuple]) -> NNCFTensor:
return PTNNCFTensor(torch.amax(x.tensor, dim=axis))
@staticmethod
def abs(x: NNCFTensor) -> NNCFTensor:
return PTNNCFTensor(torch.abs(x.tensor))
@staticmethod
def min(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
return PTNNCFTensor(torch.min(x1.tensor, x2.tensor))
@staticmethod
def max(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
return PTNNCFTensor(torch.max(x1.tensor, x2.tensor))
@staticmethod
def mean(x: NNCFTensor, axis: Union[int, tuple]) -> NNCFTensor:
return PTNNCFTensor(x.tensor.mean(dim=axis))
@staticmethod
def stack(x: Union[List[NNCFTensor], Deque[NNCFTensor]], axis: int = 0) -> NNCFTensor:
x = [t.tensor for t in x]
return PTNNCFTensor(torch.stack(x, dim=axis))
@staticmethod
def unstack(x: NNCFTensor, axis: int = 0) -> List[NNCFTensor]:
tensor = x.tensor
if list(tensor.shape) == []:
tensor = tensor.unsqueeze(0)
tensor_list = torch.unbind(tensor, dim=axis)
return [PTNNCFTensor(t) for t in tensor_list]
@staticmethod
def sum(tensor: NNCFTensor) -> TensorElementsType:
return torch.sum(tensor.tensor).item()
class PTMinMaxStatisticCollector(MinMaxStatisticCollector):
def __init__(self, use_abs_max: bool, reduction_shape: ReductionShape, output_shape: ReductionShape,
num_samples: int = None):
super().__init__(use_abs_max, reduction_shape, num_samples)
self._output_shape = output_shape
@staticmethod
def _get_processor() -> NNCFCollectorTensorProcessor:
return PTNNCFCollectorTensorProcessor()
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._register_input_common(PTNNCFTensor(x))
def _get_statistics(self) -> PTMinMaxTensorStatistic:
min_values = self._min_values.tensor.view(self._output_shape)
max_values = self._max_values.tensor.view(self._output_shape)
return PTMinMaxTensorStatistic(min_values, max_values)
class PTMixedMinMaxStatisticCollector(MixedMinMaxStatisticCollector):
def __init__(self,
use_per_sample_stats: bool,
use_abs_max: bool,
use_means_of_mins: bool,
use_means_of_maxs: bool,
reduction_shape: ReductionShape,
output_shape: ReductionShape,
num_samples: int = None,
window_size: int = None):
super().__init__(use_per_sample_stats, use_abs_max, use_means_of_mins,
use_means_of_maxs, reduction_shape, num_samples, window_size)
self._output_shape = output_shape
@staticmethod
def _get_processor() -> NNCFCollectorTensorProcessor:
return PTNNCFCollectorTensorProcessor()
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._register_input_common(PTNNCFTensor(x))
def _get_statistics(self) -> PTMinMaxTensorStatistic:
min_values = self._min_aggregate().tensor.view(self._output_shape)
max_values = self._max_aggregate().tensor.view(self._output_shape)
return PTMinMaxTensorStatistic(min_values, max_values)
class PTMeanMinMaxStatisticCollector(MeanMinMaxStatisticCollector):
def __init__(self,
use_per_sample_stats: bool,
use_abs_max: bool,
reduction_shape: ReductionShape,
output_shape: ReductionShape,
num_samples: int = None,
window_size: int = None):
super().__init__(use_per_sample_stats, use_abs_max, reduction_shape,
num_samples, window_size)
self._output_shape = output_shape
@staticmethod
def _get_processor() -> NNCFCollectorTensorProcessor:
return PTNNCFCollectorTensorProcessor()
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._register_input_common(PTNNCFTensor(x))
def _get_statistics(self) -> PTMinMaxTensorStatistic:
min_values = self._min_aggregate().tensor.view(self._output_shape)
max_values = self._max_aggregate().tensor.view(self._output_shape)
return PTMinMaxTensorStatistic(min_values, max_values)
class PTMedianMADStatisticCollector(MedianMADStatisticCollector):
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._samples.append(x.detach().cpu().numpy())
def _get_statistics(self) -> PTMedianMADTensorStatistic:
numpy_median, numpy_mad = self._prepare_statistics()
median_tensor = torch.from_numpy(numpy_median).to(dtype=torch.float)
mad_tensor = torch.from_numpy(numpy_mad).to(dtype=torch.float)
median_tensor = expand_like(median_tensor, list(self._reduction_shape))
mad_tensor = expand_like(mad_tensor, list(self._reduction_shape))
return PTMedianMADTensorStatistic(median_tensor, mad_tensor)
class PTPercentileStatisticCollector(PercentileStatisticCollector):
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._samples.append(x.detach().cpu().numpy())
def _get_statistics(self) -> PTPercentileTensorStatistic:
percentile_vs_values_dict = self._prepare_statistics()
for key, val in percentile_vs_values_dict.items():
torch_percentiles = torch.from_numpy(val).to(dtype=torch.float)
percentile_vs_values_dict[key] = expand_like(torch_percentiles, list(self._reduction_shape))
return PTPercentileTensorStatistic(percentile_vs_values_dict)
class PTMeanPercentileStatisticCollector(MeanPercentileStatisticCollector):
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
for pct, val in self._all_pct_values.items():
np_vals = np_percentile_reduce_like(x.cpu().numpy(), self._reduction_shape, pct)
torch_vals = torch.from_numpy(np_vals).to(dtype=torch.float)
val.append(torch_vals)
def _get_statistics(self) -> PTPercentileTensorStatistic:
mean_percentile_values = {}
for pct, val in self._all_pct_values.items():
stacked_pct_vals = torch.stack(list(val))
mean_percentile_values[pct] = stacked_pct_vals.mean(dim=0).view(self._reduction_shape)
return PTPercentileTensorStatistic(mean_percentile_values)
| 43.328358
| 104
| 0.722701
|
from typing import Union, List, Deque
import torch
from nncf.common.tensor import NNCFTensor
from nncf.common.tensor import TensorElementsType
from nncf.common.tensor_statistics.collectors import MinMaxStatisticCollector
from nncf.common.tensor_statistics.collectors import NNCFCollectorTensorProcessor
from nncf.common.tensor_statistics.collectors import MedianMADStatisticCollector
from nncf.common.tensor_statistics.collectors import PercentileStatisticCollector
from nncf.common.tensor_statistics.collectors import MeanPercentileStatisticCollector
from nncf.common.tensor_statistics.collectors import MixedMinMaxStatisticCollector
from nncf.common.tensor_statistics.collectors import MeanMinMaxStatisticCollector
from nncf.common.tensor_statistics.collectors import ReductionShape
from nncf.common.tensor_statistics.reduction import np_percentile_reduce_like
from nncf.torch.tensor_statistics.reduction import expand_like
from nncf.torch.tensor_statistics.statistics import PTMinMaxTensorStatistic
from nncf.torch.tensor_statistics.statistics import PTMedianMADTensorStatistic
from nncf.torch.tensor_statistics.statistics import PTPercentileTensorStatistic
from nncf.torch.dynamic_graph.context import no_nncf_trace
from nncf.torch.tensor import PTNNCFTensor
class PTNNCFCollectorTensorProcessor(NNCFCollectorTensorProcessor):
@staticmethod
def reduce_min(x: NNCFTensor, axis: Union[int, tuple]) -> NNCFTensor:
return PTNNCFTensor(torch.amin(x.tensor, dim=axis))
@staticmethod
def reduce_max(x: NNCFTensor, axis: Union[int, tuple]) -> NNCFTensor:
return PTNNCFTensor(torch.amax(x.tensor, dim=axis))
@staticmethod
def abs(x: NNCFTensor) -> NNCFTensor:
return PTNNCFTensor(torch.abs(x.tensor))
@staticmethod
def min(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
return PTNNCFTensor(torch.min(x1.tensor, x2.tensor))
@staticmethod
def max(x1: NNCFTensor, x2: NNCFTensor) -> NNCFTensor:
return PTNNCFTensor(torch.max(x1.tensor, x2.tensor))
@staticmethod
def mean(x: NNCFTensor, axis: Union[int, tuple]) -> NNCFTensor:
return PTNNCFTensor(x.tensor.mean(dim=axis))
@staticmethod
def stack(x: Union[List[NNCFTensor], Deque[NNCFTensor]], axis: int = 0) -> NNCFTensor:
x = [t.tensor for t in x]
return PTNNCFTensor(torch.stack(x, dim=axis))
@staticmethod
def unstack(x: NNCFTensor, axis: int = 0) -> List[NNCFTensor]:
tensor = x.tensor
if list(tensor.shape) == []:
tensor = tensor.unsqueeze(0)
tensor_list = torch.unbind(tensor, dim=axis)
return [PTNNCFTensor(t) for t in tensor_list]
@staticmethod
def sum(tensor: NNCFTensor) -> TensorElementsType:
return torch.sum(tensor.tensor).item()
class PTMinMaxStatisticCollector(MinMaxStatisticCollector):
def __init__(self, use_abs_max: bool, reduction_shape: ReductionShape, output_shape: ReductionShape,
num_samples: int = None):
super().__init__(use_abs_max, reduction_shape, num_samples)
self._output_shape = output_shape
@staticmethod
def _get_processor() -> NNCFCollectorTensorProcessor:
return PTNNCFCollectorTensorProcessor()
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._register_input_common(PTNNCFTensor(x))
def _get_statistics(self) -> PTMinMaxTensorStatistic:
min_values = self._min_values.tensor.view(self._output_shape)
max_values = self._max_values.tensor.view(self._output_shape)
return PTMinMaxTensorStatistic(min_values, max_values)
class PTMixedMinMaxStatisticCollector(MixedMinMaxStatisticCollector):
def __init__(self,
use_per_sample_stats: bool,
use_abs_max: bool,
use_means_of_mins: bool,
use_means_of_maxs: bool,
reduction_shape: ReductionShape,
output_shape: ReductionShape,
num_samples: int = None,
window_size: int = None):
super().__init__(use_per_sample_stats, use_abs_max, use_means_of_mins,
use_means_of_maxs, reduction_shape, num_samples, window_size)
self._output_shape = output_shape
@staticmethod
def _get_processor() -> NNCFCollectorTensorProcessor:
return PTNNCFCollectorTensorProcessor()
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._register_input_common(PTNNCFTensor(x))
def _get_statistics(self) -> PTMinMaxTensorStatistic:
min_values = self._min_aggregate().tensor.view(self._output_shape)
max_values = self._max_aggregate().tensor.view(self._output_shape)
return PTMinMaxTensorStatistic(min_values, max_values)
class PTMeanMinMaxStatisticCollector(MeanMinMaxStatisticCollector):
def __init__(self,
use_per_sample_stats: bool,
use_abs_max: bool,
reduction_shape: ReductionShape,
output_shape: ReductionShape,
num_samples: int = None,
window_size: int = None):
super().__init__(use_per_sample_stats, use_abs_max, reduction_shape,
num_samples, window_size)
self._output_shape = output_shape
@staticmethod
def _get_processor() -> NNCFCollectorTensorProcessor:
return PTNNCFCollectorTensorProcessor()
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._register_input_common(PTNNCFTensor(x))
def _get_statistics(self) -> PTMinMaxTensorStatistic:
min_values = self._min_aggregate().tensor.view(self._output_shape)
max_values = self._max_aggregate().tensor.view(self._output_shape)
return PTMinMaxTensorStatistic(min_values, max_values)
class PTMedianMADStatisticCollector(MedianMADStatisticCollector):
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._samples.append(x.detach().cpu().numpy())
def _get_statistics(self) -> PTMedianMADTensorStatistic:
numpy_median, numpy_mad = self._prepare_statistics()
median_tensor = torch.from_numpy(numpy_median).to(dtype=torch.float)
mad_tensor = torch.from_numpy(numpy_mad).to(dtype=torch.float)
median_tensor = expand_like(median_tensor, list(self._reduction_shape))
mad_tensor = expand_like(mad_tensor, list(self._reduction_shape))
return PTMedianMADTensorStatistic(median_tensor, mad_tensor)
class PTPercentileStatisticCollector(PercentileStatisticCollector):
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
self._samples.append(x.detach().cpu().numpy())
def _get_statistics(self) -> PTPercentileTensorStatistic:
percentile_vs_values_dict = self._prepare_statistics()
for key, val in percentile_vs_values_dict.items():
torch_percentiles = torch.from_numpy(val).to(dtype=torch.float)
percentile_vs_values_dict[key] = expand_like(torch_percentiles, list(self._reduction_shape))
return PTPercentileTensorStatistic(percentile_vs_values_dict)
class PTMeanPercentileStatisticCollector(MeanPercentileStatisticCollector):
def _register_input(self, x: torch.Tensor):
with no_nncf_trace():
for pct, val in self._all_pct_values.items():
np_vals = np_percentile_reduce_like(x.cpu().numpy(), self._reduction_shape, pct)
torch_vals = torch.from_numpy(np_vals).to(dtype=torch.float)
val.append(torch_vals)
def _get_statistics(self) -> PTPercentileTensorStatistic:
mean_percentile_values = {}
for pct, val in self._all_pct_values.items():
stacked_pct_vals = torch.stack(list(val))
mean_percentile_values[pct] = stacked_pct_vals.mean(dim=0).view(self._reduction_shape)
return PTPercentileTensorStatistic(mean_percentile_values)
| true
| true
|
1c3efa4ec23c095d461e41407238996c900843aa
| 548
|
py
|
Python
|
logic-programming/11-lesson/homework.py
|
BrendanGlancy/akron
|
3e067ce2e622f4c6d18aecb62402eb888b0bdb7a
|
[
"MIT"
] | 3
|
2021-12-07T22:24:09.000Z
|
2022-02-25T01:11:40.000Z
|
logic-programming/11-lesson/homework.py
|
BrendanGlancy/akron
|
3e067ce2e622f4c6d18aecb62402eb888b0bdb7a
|
[
"MIT"
] | null | null | null |
logic-programming/11-lesson/homework.py
|
BrendanGlancy/akron
|
3e067ce2e622f4c6d18aecb62402eb888b0bdb7a
|
[
"MIT"
] | null | null | null |
"""
Complete Programming Review Questions 1-10 page 464 Programming Exercises 1 on page 467.
1. B
2. C
3. D
4. A
5. B
6. C
7. A
8. B
9. A
10. B
"""
# Design a program that asks the user to enter 10 golf scores. The scores should be stored in an Integer array. Sort the array in ascending order and display its contents.
def main():
scores = []
for i in range(10):
print('Enter golf score ',i)
score = int(input(" "))
scores.append(score)
scores.sort()
print(scores)
if __name__ == "__main__":
main()
| 18.896552
| 171
| 0.638686
|
def main():
scores = []
for i in range(10):
print('Enter golf score ',i)
score = int(input(" "))
scores.append(score)
scores.sort()
print(scores)
if __name__ == "__main__":
main()
| true
| true
|
1c3efabc4da2175b4ec67c8a48bd257fa8e46bd9
| 5,107
|
py
|
Python
|
action_prediction_dataset.py
|
yukw777/GATA-public
|
e8c424093377874b395abaf9662f6fb2c553e0f5
|
[
"MIT"
] | 26
|
2020-02-24T01:35:32.000Z
|
2022-02-17T03:57:06.000Z
|
action_prediction_dataset.py
|
yukw777/GATA-public
|
e8c424093377874b395abaf9662f6fb2c553e0f5
|
[
"MIT"
] | 21
|
2020-03-11T20:07:01.000Z
|
2021-11-14T03:12:17.000Z
|
action_prediction_dataset.py
|
yukw777/GATA-public
|
e8c424093377874b395abaf9662f6fb2c553e0f5
|
[
"MIT"
] | 12
|
2020-03-02T22:50:35.000Z
|
2022-03-08T19:10:51.000Z
|
import os
import json
from os.path import join as pjoin
from tqdm import tqdm
import numpy as np
import gym
from graph_dataset import GraphDataset
class APData(gym.Env):
FILENAMES_MAP = {
"full": {
"train": "train.full.json",
"valid": "valid.full.json",
"test": "test.full.json"
},
"seen": {
"train": "train.seen.json",
"valid": "valid.seen.json",
"test": "test.seen.json"
}
}
def __init__(self, config):
self.rng = None
self.config = config
self.read_config()
self.seed(self.random_seed)
# Load dataset splits.
self.dataset = {}
for split in ["train", "valid", "test"]:
self.dataset[split] = {
"current_graph": [],
"previous_graph": [],
"target_action": [],
"action_choices": []
}
self.load_dataset_for_ap(split)
print("loaded dataset from {} ...".format(self.data_path))
self.train_size = len(self.dataset["train"]["current_graph"])
self.valid_size = len(self.dataset["valid"]["current_graph"])
self.test_size = len(self.dataset["test"]["current_graph"])
self.batch_pointer = None
self.data_size, self.batch_size, self.data = None, None, None
self.split = "train"
def load_dataset_for_ap(self, split):
file_path = pjoin(self.data_path, self.FILENAMES_MAP[self.graph_type][split])
with open(file_path) as f:
data = json.load(f)
graph_dataset = GraphDataset.loads(data["graph_index"])
self.dataset[split]["graph_dataset"] = graph_dataset
desc = "Loading {}".format(os.path.basename(file_path))
for example in tqdm(data["examples"], desc=desc):
target_action = example["target_action"]
curr_graph = example["current_graph"]
prev_graph = example["previous_graph"]
candidates = example["action_choices"]
self.dataset[split]["current_graph"].append(curr_graph)
self.dataset[split]["previous_graph"].append(prev_graph)
self.dataset[split]["target_action"].append(target_action)
self.dataset[split]["action_choices"].append(candidates)
def read_config(self):
self.data_path = self.config["ap"]["data_path"]
self.graph_type = self.config["ap"]["graph_type"]
self.random_seed = self.config["general"]["random_seed"]
self.use_this_many_data = self.config["general"]["use_this_many_data"]
self.training_batch_size = self.config["general"]["training"]["batch_size"]
self.evaluate_batch_size = self.config["general"]["evaluate"]["batch_size"]
def split_reset(self, split):
if split == "train":
self.data_size = self.train_size
self.batch_size = self.training_batch_size
elif split == "valid":
self.data_size = self.valid_size
self.batch_size = self.evaluate_batch_size
else:
self.data_size = self.test_size
self.batch_size = self.evaluate_batch_size
if split == "train" and self.use_this_many_data > 0:
self.data = {"current_graph": self.dataset[split]["current_graph"][: self.use_this_many_data],
"previous_graph": self.dataset[split]["previous_graph"][: self.use_this_many_data],
"target_action": self.dataset[split]["target_action"][: self.use_this_many_data],
"action_choices": self.dataset[split]["action_choices"][: self.use_this_many_data]}
self.data_size = self.use_this_many_data
else:
self.data = self.dataset[split]
self.split = split
self.batch_pointer = 0
def get_batch(self):
if self.split == "train":
indices = self.rng.choice(self.data_size, self.training_batch_size)
else:
start = self.batch_pointer
end = min(start + self.training_batch_size, self.data_size)
indices = np.arange(start, end)
self.batch_pointer += self.training_batch_size
if self.batch_pointer >= self.data_size:
self.batch_pointer = 0
current_graph, previous_graph, target_action, action_choices = [], [], [], []
decompress = self.dataset[self.split]["graph_dataset"].decompress
for idx in indices:
target_action.append(self.data["target_action"][idx])
action_choices.append(self.data["action_choices"][idx])
# Perform just-in-time decompression.
current_graph.append(decompress(self.data["current_graph"][idx]))
previous_graph.append(decompress(self.data["previous_graph"][idx]))
return current_graph, previous_graph, target_action, action_choices
def render(self, mode='human'):
return
def close(self):
return
def seed(self, seed):
self.rng = np.random.RandomState(seed)
| 37.277372
| 108
| 0.60329
|
import os
import json
from os.path import join as pjoin
from tqdm import tqdm
import numpy as np
import gym
from graph_dataset import GraphDataset
class APData(gym.Env):
FILENAMES_MAP = {
"full": {
"train": "train.full.json",
"valid": "valid.full.json",
"test": "test.full.json"
},
"seen": {
"train": "train.seen.json",
"valid": "valid.seen.json",
"test": "test.seen.json"
}
}
def __init__(self, config):
self.rng = None
self.config = config
self.read_config()
self.seed(self.random_seed)
self.dataset = {}
for split in ["train", "valid", "test"]:
self.dataset[split] = {
"current_graph": [],
"previous_graph": [],
"target_action": [],
"action_choices": []
}
self.load_dataset_for_ap(split)
print("loaded dataset from {} ...".format(self.data_path))
self.train_size = len(self.dataset["train"]["current_graph"])
self.valid_size = len(self.dataset["valid"]["current_graph"])
self.test_size = len(self.dataset["test"]["current_graph"])
self.batch_pointer = None
self.data_size, self.batch_size, self.data = None, None, None
self.split = "train"
def load_dataset_for_ap(self, split):
file_path = pjoin(self.data_path, self.FILENAMES_MAP[self.graph_type][split])
with open(file_path) as f:
data = json.load(f)
graph_dataset = GraphDataset.loads(data["graph_index"])
self.dataset[split]["graph_dataset"] = graph_dataset
desc = "Loading {}".format(os.path.basename(file_path))
for example in tqdm(data["examples"], desc=desc):
target_action = example["target_action"]
curr_graph = example["current_graph"]
prev_graph = example["previous_graph"]
candidates = example["action_choices"]
self.dataset[split]["current_graph"].append(curr_graph)
self.dataset[split]["previous_graph"].append(prev_graph)
self.dataset[split]["target_action"].append(target_action)
self.dataset[split]["action_choices"].append(candidates)
def read_config(self):
self.data_path = self.config["ap"]["data_path"]
self.graph_type = self.config["ap"]["graph_type"]
self.random_seed = self.config["general"]["random_seed"]
self.use_this_many_data = self.config["general"]["use_this_many_data"]
self.training_batch_size = self.config["general"]["training"]["batch_size"]
self.evaluate_batch_size = self.config["general"]["evaluate"]["batch_size"]
def split_reset(self, split):
if split == "train":
self.data_size = self.train_size
self.batch_size = self.training_batch_size
elif split == "valid":
self.data_size = self.valid_size
self.batch_size = self.evaluate_batch_size
else:
self.data_size = self.test_size
self.batch_size = self.evaluate_batch_size
if split == "train" and self.use_this_many_data > 0:
self.data = {"current_graph": self.dataset[split]["current_graph"][: self.use_this_many_data],
"previous_graph": self.dataset[split]["previous_graph"][: self.use_this_many_data],
"target_action": self.dataset[split]["target_action"][: self.use_this_many_data],
"action_choices": self.dataset[split]["action_choices"][: self.use_this_many_data]}
self.data_size = self.use_this_many_data
else:
self.data = self.dataset[split]
self.split = split
self.batch_pointer = 0
def get_batch(self):
if self.split == "train":
indices = self.rng.choice(self.data_size, self.training_batch_size)
else:
start = self.batch_pointer
end = min(start + self.training_batch_size, self.data_size)
indices = np.arange(start, end)
self.batch_pointer += self.training_batch_size
if self.batch_pointer >= self.data_size:
self.batch_pointer = 0
current_graph, previous_graph, target_action, action_choices = [], [], [], []
decompress = self.dataset[self.split]["graph_dataset"].decompress
for idx in indices:
target_action.append(self.data["target_action"][idx])
action_choices.append(self.data["action_choices"][idx])
current_graph.append(decompress(self.data["current_graph"][idx]))
previous_graph.append(decompress(self.data["previous_graph"][idx]))
return current_graph, previous_graph, target_action, action_choices
def render(self, mode='human'):
return
def close(self):
return
def seed(self, seed):
self.rng = np.random.RandomState(seed)
| true
| true
|
1c3efb94a5faeb625776595e939cf45335985bee
| 161
|
py
|
Python
|
tests/model_control/detailed/transf_Integration/model_control_one_enabled_Integration_LinearTrend_BestCycle_MLP.py
|
jmabry/pyaf
|
afbc15a851a2445a7824bf255af612dc429265af
|
[
"BSD-3-Clause"
] | null | null | null |
tests/model_control/detailed/transf_Integration/model_control_one_enabled_Integration_LinearTrend_BestCycle_MLP.py
|
jmabry/pyaf
|
afbc15a851a2445a7824bf255af612dc429265af
|
[
"BSD-3-Clause"
] | 1
|
2019-11-30T23:39:38.000Z
|
2019-12-01T04:34:35.000Z
|
tests/model_control/detailed/transf_Integration/model_control_one_enabled_Integration_LinearTrend_BestCycle_MLP.py
|
jmabry/pyaf
|
afbc15a851a2445a7824bf255af612dc429265af
|
[
"BSD-3-Clause"
] | null | null | null |
import pyaf.tests.model_control.test_ozone_custom_models_enabled as testmod
testmod.build_model( ['Integration'] , ['LinearTrend'] , ['BestCycle'] , ['MLP'] );
| 40.25
| 83
| 0.751553
|
import pyaf.tests.model_control.test_ozone_custom_models_enabled as testmod
testmod.build_model( ['Integration'] , ['LinearTrend'] , ['BestCycle'] , ['MLP'] );
| true
| true
|
1c3efbd6ca771f960b74e6c39b6eb04038e73e32
| 2,519
|
py
|
Python
|
experiments/06_preprocessing/plot_histograms.py
|
fsschneider/cockpit-experiments
|
a9eaf3dc5da5a58356ac0eef25a11235bf0891c4
|
[
"MIT"
] | 7
|
2021-11-02T11:23:49.000Z
|
2022-02-16T13:25:47.000Z
|
experiments/06_preprocessing/plot_histograms.py
|
fsschneider/cockpit-experiments
|
a9eaf3dc5da5a58356ac0eef25a11235bf0891c4
|
[
"MIT"
] | null | null | null |
experiments/06_preprocessing/plot_histograms.py
|
fsschneider/cockpit-experiments
|
a9eaf3dc5da5a58356ac0eef25a11235bf0891c4
|
[
"MIT"
] | 2
|
2021-11-02T11:23:54.000Z
|
2022-02-02T15:56:03.000Z
|
"""Plot the histograms for different pre-processing strategies."""
import os
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt
import run_histograms_cifar10
import run_histograms_imagenet
import seaborn as sns
from cockpit import CockpitPlotter
from cockpit.instruments.histogram_2d_gauge import _get_xmargin_histogram_data
sys.path.append(os.getcwd())
from experiments.utils.plotting import TikzExport # noqa
mpl.use("Agg")
HERE = os.path.abspath(__file__)
HEREDIR = os.path.dirname(HERE)
SAVEDIR = os.path.join(HEREDIR, "output/fig_histogram")
os.makedirs(SAVEDIR, exist_ok=True)
def get_cockpit_plotter(filepath, global_step=0):
"""Use a cockpit plotter to read in the tracked data."""
cp = CockpitPlotter()
cp._read_tracking_results(filepath)
# drop all data except first step
clean_tracking_data = cp.tracking_data.loc[
cp.tracking_data["iteration"] == global_step
]
cp.tracking_data = clean_tracking_data
return cp
def get_out_file(tproblem, suffix=".tex"):
"""Get savefile path."""
suffix = "" if suffix is None else suffix
filename = f"{tproblem}{suffix}"
return os.path.join(SAVEDIR, filename)
def plot_net_paper(problem, color, global_step=0):
"""Create TikZ plots for the paper."""
filepath = run_histograms_cifar10.get_out_file(problem)
filepath = os.path.splitext(filepath)[0]
cp = get_cockpit_plotter(filepath, global_step=global_step)
vals, mid_points, bin_size = _get_xmargin_histogram_data(cp.tracking_data)
start_points = [x - bin_size / 2 for x in mid_points]
plot_histogram(start_points, vals, bin_size, color)
TikzExport().save_fig(
get_out_file(problem, suffix=None), png_preview=True, tex_preview=False
)
plt.close()
def plot_histogram(start_points, vals, bin_size, color):
"""Plot the histogram."""
fig, ax = plt.subplots()
ax.set_xscale("log")
ax.set_facecolor("white")
ax.barh(
start_points,
vals,
height=bin_size,
color=color,
linewidth=0.1,
log=True,
left=0.9,
align="edge",
)
ax.set_ylim([min(start_points), max(start_points) + bin_size])
return fig, ax
if __name__ == "__main__":
COLORS = sns.color_palette("tab10")[:2]
for problem, color in zip(run_histograms_cifar10.PROBLEMS, COLORS):
plot_net_paper(problem, color)
for problem, color in zip(run_histograms_imagenet.PROBLEMS, COLORS):
plot_net_paper(problem, color)
| 27.681319
| 79
| 0.709408
|
import os
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt
import run_histograms_cifar10
import run_histograms_imagenet
import seaborn as sns
from cockpit import CockpitPlotter
from cockpit.instruments.histogram_2d_gauge import _get_xmargin_histogram_data
sys.path.append(os.getcwd())
from experiments.utils.plotting import TikzExport
mpl.use("Agg")
HERE = os.path.abspath(__file__)
HEREDIR = os.path.dirname(HERE)
SAVEDIR = os.path.join(HEREDIR, "output/fig_histogram")
os.makedirs(SAVEDIR, exist_ok=True)
def get_cockpit_plotter(filepath, global_step=0):
cp = CockpitPlotter()
cp._read_tracking_results(filepath)
clean_tracking_data = cp.tracking_data.loc[
cp.tracking_data["iteration"] == global_step
]
cp.tracking_data = clean_tracking_data
return cp
def get_out_file(tproblem, suffix=".tex"):
suffix = "" if suffix is None else suffix
filename = f"{tproblem}{suffix}"
return os.path.join(SAVEDIR, filename)
def plot_net_paper(problem, color, global_step=0):
filepath = run_histograms_cifar10.get_out_file(problem)
filepath = os.path.splitext(filepath)[0]
cp = get_cockpit_plotter(filepath, global_step=global_step)
vals, mid_points, bin_size = _get_xmargin_histogram_data(cp.tracking_data)
start_points = [x - bin_size / 2 for x in mid_points]
plot_histogram(start_points, vals, bin_size, color)
TikzExport().save_fig(
get_out_file(problem, suffix=None), png_preview=True, tex_preview=False
)
plt.close()
def plot_histogram(start_points, vals, bin_size, color):
fig, ax = plt.subplots()
ax.set_xscale("log")
ax.set_facecolor("white")
ax.barh(
start_points,
vals,
height=bin_size,
color=color,
linewidth=0.1,
log=True,
left=0.9,
align="edge",
)
ax.set_ylim([min(start_points), max(start_points) + bin_size])
return fig, ax
if __name__ == "__main__":
COLORS = sns.color_palette("tab10")[:2]
for problem, color in zip(run_histograms_cifar10.PROBLEMS, COLORS):
plot_net_paper(problem, color)
for problem, color in zip(run_histograms_imagenet.PROBLEMS, COLORS):
plot_net_paper(problem, color)
| true
| true
|
1c3efc920c9907cf51d01d38531a5b3c94401441
| 1,139
|
py
|
Python
|
plot.py
|
s-gv/pymotoplus
|
873b967747d98d9c9e066496547aa09ce164c8a1
|
[
"BSD-2-Clause"
] | 2
|
2021-08-16T07:07:43.000Z
|
2022-01-24T16:05:35.000Z
|
plot.py
|
s-gv/pymotoplus
|
873b967747d98d9c9e066496547aa09ce164c8a1
|
[
"BSD-2-Clause"
] | null | null | null |
plot.py
|
s-gv/pymotoplus
|
873b967747d98d9c9e066496547aa09ce164c8a1
|
[
"BSD-2-Clause"
] | 1
|
2021-07-19T02:28:10.000Z
|
2021-07-19T02:28:10.000Z
|
# Copyright (c) 2019 Sagar Gubbi. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import numpy as np
import matplotlib
import json
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def main():
rows = []
with open('log.txt') as f:
for line in f:
if line.startswith(':u_fx:'):
txt = '{"' + line[1:].strip().replace(': ', '": ').replace(', ', ', "') + '}'
row = json.loads(txt)
if row['rx'] > 0: row['rx'] -= 360*10000
rows.append(row)
prop_names = ['u_fx', 'u_fy', 'u_fz', 'u_frx', 'u_fry', 'u_frz', 'pz', 'rx', 'ry', 'fx', 'fy', 'fz', 'frx', 'fry']
fig, axes = plt.subplots(len(prop_names))
for i, prop_name in enumerate(prop_names):
axes[i].plot(range(len(rows)), np.array([row[prop_name] for row in rows]))
axes[i].yaxis.set_label_position("right")
axes[i].set_ylabel(prop_name, rotation=0, labelpad=20)
fig.set_size_inches(7.5, 10)
fig.savefig('fig.png')
if __name__ == '__main__':
main()
| 30.783784
| 118
| 0.565408
|
import numpy as np
import matplotlib
import json
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def main():
rows = []
with open('log.txt') as f:
for line in f:
if line.startswith(':u_fx:'):
txt = '{"' + line[1:].strip().replace(': ', '": ').replace(', ', ', "') + '}'
row = json.loads(txt)
if row['rx'] > 0: row['rx'] -= 360*10000
rows.append(row)
prop_names = ['u_fx', 'u_fy', 'u_fz', 'u_frx', 'u_fry', 'u_frz', 'pz', 'rx', 'ry', 'fx', 'fy', 'fz', 'frx', 'fry']
fig, axes = plt.subplots(len(prop_names))
for i, prop_name in enumerate(prop_names):
axes[i].plot(range(len(rows)), np.array([row[prop_name] for row in rows]))
axes[i].yaxis.set_label_position("right")
axes[i].set_ylabel(prop_name, rotation=0, labelpad=20)
fig.set_size_inches(7.5, 10)
fig.savefig('fig.png')
if __name__ == '__main__':
main()
| true
| true
|
1c3efd1348ec046804b606eac521ae4e8ed3cb77
| 7,645
|
py
|
Python
|
src/json_utils.py
|
moreymat/demi-ou-moitie
|
72e3cf4a4a4db082969b5c33152ecca389214eab
|
[
"MIT"
] | null | null | null |
src/json_utils.py
|
moreymat/demi-ou-moitie
|
72e3cf4a4a4db082969b5c33152ecca389214eab
|
[
"MIT"
] | 4
|
2021-01-12T18:24:20.000Z
|
2021-01-20T16:03:07.000Z
|
src/json_utils.py
|
moreymat/demi-ou-moitie
|
72e3cf4a4a4db082969b5c33152ecca389214eab
|
[
"MIT"
] | null | null | null |
# functions to manipulate the datas
import json
import io
import os
from pathlib import Path
import json
# Parameter: json_path: path to json file
#
# Output: a string representing the data
def extract_text_from_json(json_path):
with open(json_path) as json_file:
data = json.load(json_file)
result = ""
print("Nombre d'arrété dans ", json_path, ": ", len(data["arretes"]))
for arrete in data["arretes"]:
for line in arrete["title"]:
result += line["text"]
result += "\n"
for intro in arrete["intros"]:
for line in intro:
result += line["text"]
result += "\n"
for article in arrete["articles"]:
for line in article:
result += line["text"]
result += "\n"
result += "\n"
result += "\n"
return result
# Fuse all the json into one
def fuse_json(repertory_path):
data_path = Path(repertory_path)
reps = os.listdir(data_path)
all_datas = {}
json_list = []
for json_file in reps:
datas = {}
json_path = data_path / json_file
with open(json_path) as json_file:
data = json.load(json_file)
json_name = os.path.splitext(json_path)[0]
print("Working on", json_name, "...")
datas["pdf"] = json_name
datas["content"] = data
json_list.append(datas)
all_datas["data"] = json_list
with open(r"data/out/all_data.json", "w", encoding="utf-8") as outfile:
json.dump(all_datas, outfile)
# extract text from the fused json
def extract_text_from_all_json(json_path):
with open(json_path) as json_file:
data = json.load(json_file)
result = ""
nb_arr = 0
for js in data["data"]:
print("currently on", js["pdf"])
for arrete in js["content"]["arretes"]:
nb_arr += 1
for line in arrete["title"]:
result += line["text"]
result += "\n"
for intro in arrete["intros"]:
for line in intro:
result += line["text"]
result += "\n"
for article in arrete["articles"]:
for line in article:
result += line["text"]
result += "\n"
result += "\n"
result += "\n"
print("Nombre d'arrété dans ", json_path, ": ", nb_arr)
return result
# entrée: un mot avec une apostrophe | ie. "l'article"
# sortie: deux mots séparés par l'apostrope | ie. "l'" et "article"
def split_apostrophe(word):
for i, char in enumerate(word):
if char == "'":
return word[: i + 1], word[i + 1 :]
print("oops")
return 1
# Entrée: arrete sous format json
# Sortie: arrete sous format .txt séparé en entité nommé pour AllenNLP
def prepare_tokens(arrete):
f = open(
r"data/test/" + arrete["title"][0]["text"][0:17] + ".txt",
"w",
encoding="utf-8",
)
flag_line = False
result = ""
for line in arrete["title"]:
words = line["text"].split(" ")
for word in words:
todo = []
if "'" in word:
word1, word2 = split_apostrophe(word)
todo.append(word1)
todo.append(word2)
else:
todo.append(word)
for word in todo:
if len(word) > 0:
if (
word[len(word) - 1] == ","
or (word[len(word) - 1] == "." and word[len(word) - 2] != "L")
or word[len(word) - 1] == "?"
or word[len(word) - 1] == "!"
):
result += word[: len(word) - 1] + " " + "O" + "\n"
result += word[len(word) - 1 :] + " " + "O" + "\n"
flag_line = False
if word[len(word) - 1] == ".":
result += "\n"
flag_line = True
else:
flag_line = False
result += word + " " + "O" + "\n"
if not flag_line:
result += "\n"
flag_line = False
for intro in arrete["intros"]:
for line in intro:
words = line["text"].split(" ")
for word in words:
todo = []
if "'" in word:
word1, word2 = split_apostrophe(word)
todo.append(word1)
todo.append(word2)
else:
todo.append(word)
for word in todo:
if len(word) > 0:
if (
word[len(word) - 1] == ","
or (
word[len(word) - 1] == "."
and word[len(word) - 2] != "L"
)
or word[len(word) - 1] == "?"
or word[len(word) - 1] == "!"
):
result += word[: len(word) - 1] + " " + "O" + "\n"
result += word[len(word) - 1 :] + " " + "O" + "\n"
flag_line = False
if word[len(word) - 1] == ".":
result += "\n"
flag_line = True
else:
result += word + " " + "O" + "\n"
flag_line = False
if not flag_line:
result += "\n"
flag_line = False
for article in arrete["articles"]:
for line in article:
words = line["text"].split(" ")
for word in words:
todo = []
if "'" in word:
word1, word2 = split_apostrophe(word)
todo.append(word1)
todo.append(word2)
else:
todo.append(word)
for word in todo:
if len(word) > 0:
if (
word[len(word) - 1] == ","
or (
word[len(word) - 1] == "."
and word[len(word) - 2] != "L"
)
or word[len(word) - 1] == "?"
or word[len(word) - 1] == "!"
):
result += word[: len(word) - 1] + " " + "O" + "\n"
result += word[len(word) - 1 :] + " " + "O" + "\n"
flag_line = False
if word[len(word) - 1] == ".":
result += "\n"
flag_line = True
else:
result += word + " " + "O" + "\n"
flag_line = False
if not flag_line:
result += "\n"
f.write(result)
f.close()
"""
result = extract_text_from_json(r"data/out/raa_ndeg590.json")
f = open("text.txt", "w", encoding="utf-8")
f.write(result)
f.close()"""
"""fuse_json("data/out/")"""
"""extract_text_from_all_json(r"data/out/all_data.json")"""
| 31.590909
| 87
| 0.398169
|
import json
import io
import os
from pathlib import Path
import json
def extract_text_from_json(json_path):
with open(json_path) as json_file:
data = json.load(json_file)
result = ""
print("Nombre d'arrété dans ", json_path, ": ", len(data["arretes"]))
for arrete in data["arretes"]:
for line in arrete["title"]:
result += line["text"]
result += "\n"
for intro in arrete["intros"]:
for line in intro:
result += line["text"]
result += "\n"
for article in arrete["articles"]:
for line in article:
result += line["text"]
result += "\n"
result += "\n"
result += "\n"
return result
# Fuse all the json into one
def fuse_json(repertory_path):
data_path = Path(repertory_path)
reps = os.listdir(data_path)
all_datas = {}
json_list = []
for json_file in reps:
datas = {}
json_path = data_path / json_file
with open(json_path) as json_file:
data = json.load(json_file)
json_name = os.path.splitext(json_path)[0]
print("Working on", json_name, "...")
datas["pdf"] = json_name
datas["content"] = data
json_list.append(datas)
all_datas["data"] = json_list
with open(r"data/out/all_data.json", "w", encoding="utf-8") as outfile:
json.dump(all_datas, outfile)
# extract text from the fused json
def extract_text_from_all_json(json_path):
with open(json_path) as json_file:
data = json.load(json_file)
result = ""
nb_arr = 0
for js in data["data"]:
print("currently on", js["pdf"])
for arrete in js["content"]["arretes"]:
nb_arr += 1
for line in arrete["title"]:
result += line["text"]
result += "\n"
for intro in arrete["intros"]:
for line in intro:
result += line["text"]
result += "\n"
for article in arrete["articles"]:
for line in article:
result += line["text"]
result += "\n"
result += "\n"
result += "\n"
print("Nombre d'arrété dans ", json_path, ": ", nb_arr)
return result
# sortie: deux mots séparés par l'apostrope | ie. "l'" et "article"
def split_apostrophe(word):
for i, char in enumerate(word):
if char == "'":
return word[: i + 1], word[i + 1 :]
print("oops")
return 1
def prepare_tokens(arrete):
f = open(
r"data/test/" + arrete["title"][0]["text"][0:17] + ".txt",
"w",
encoding="utf-8",
)
flag_line = False
result = ""
for line in arrete["title"]:
words = line["text"].split(" ")
for word in words:
todo = []
if "'" in word:
word1, word2 = split_apostrophe(word)
todo.append(word1)
todo.append(word2)
else:
todo.append(word)
for word in todo:
if len(word) > 0:
if (
word[len(word) - 1] == ","
or (word[len(word) - 1] == "." and word[len(word) - 2] != "L")
or word[len(word) - 1] == "?"
or word[len(word) - 1] == "!"
):
result += word[: len(word) - 1] + " " + "O" + "\n"
result += word[len(word) - 1 :] + " " + "O" + "\n"
flag_line = False
if word[len(word) - 1] == ".":
result += "\n"
flag_line = True
else:
flag_line = False
result += word + " " + "O" + "\n"
if not flag_line:
result += "\n"
flag_line = False
for intro in arrete["intros"]:
for line in intro:
words = line["text"].split(" ")
for word in words:
todo = []
if "'" in word:
word1, word2 = split_apostrophe(word)
todo.append(word1)
todo.append(word2)
else:
todo.append(word)
for word in todo:
if len(word) > 0:
if (
word[len(word) - 1] == ","
or (
word[len(word) - 1] == "."
and word[len(word) - 2] != "L"
)
or word[len(word) - 1] == "?"
or word[len(word) - 1] == "!"
):
result += word[: len(word) - 1] + " " + "O" + "\n"
result += word[len(word) - 1 :] + " " + "O" + "\n"
flag_line = False
if word[len(word) - 1] == ".":
result += "\n"
flag_line = True
else:
result += word + " " + "O" + "\n"
flag_line = False
if not flag_line:
result += "\n"
flag_line = False
for article in arrete["articles"]:
for line in article:
words = line["text"].split(" ")
for word in words:
todo = []
if "'" in word:
word1, word2 = split_apostrophe(word)
todo.append(word1)
todo.append(word2)
else:
todo.append(word)
for word in todo:
if len(word) > 0:
if (
word[len(word) - 1] == ","
or (
word[len(word) - 1] == "."
and word[len(word) - 2] != "L"
)
or word[len(word) - 1] == "?"
or word[len(word) - 1] == "!"
):
result += word[: len(word) - 1] + " " + "O" + "\n"
result += word[len(word) - 1 :] + " " + "O" + "\n"
flag_line = False
if word[len(word) - 1] == ".":
result += "\n"
flag_line = True
else:
result += word + " " + "O" + "\n"
flag_line = False
if not flag_line:
result += "\n"
f.write(result)
f.close()
| true
| true
|
1c3efe1206769bebd4f2f227ff1d560751c18fc7
| 3,245
|
py
|
Python
|
training/word_vectors.py
|
howl-anderson/spacy-dev-resources
|
4ba4bea947c4bb6779066f4ef6a30decaad304e4
|
[
"MIT"
] | 2
|
2018-03-14T08:49:07.000Z
|
2019-05-20T02:31:38.000Z
|
training/word_vectors.py
|
howl-anderson/spacy-dev-resources
|
4ba4bea947c4bb6779066f4ef6a30decaad304e4
|
[
"MIT"
] | null | null | null |
training/word_vectors.py
|
howl-anderson/spacy-dev-resources
|
4ba4bea947c4bb6779066f4ef6a30decaad304e4
|
[
"MIT"
] | 1
|
2020-11-06T06:02:20.000Z
|
2020-11-06T06:02:20.000Z
|
#!/usr/bin/env python
from __future__ import print_function, unicode_literals, division
import io
import bz2
import logging
from os import path
import os
import random
from collections import defaultdict
import plac
try:
import ujson as json
except ImportError:
import json
from gensim.models import Word2Vec
from preshed.counter import PreshCounter
from spacy.strings import hash_string
import spacy
logger = logging.getLogger(__name__)
class Corpus(object):
def __init__(self, directory, min_freq=10):
self.directory = directory
self.counts = PreshCounter()
self.strings = {}
self.min_freq = min_freq
def count_doc(self, doc):
# Get counts for this document
for word in doc:
self.counts.inc(word.orth, 1)
return len(doc)
def __iter__(self):
for text_loc in iter_dir(self.directory):
with io.open(text_loc, 'r', encoding='utf8') as file_:
text = file_.read()
yield self._line_segment(text)
def _line_segment(line_text):
doc = nlp.make_doc(text)
for token in doc:
yield token.orth_
def iter_dir(loc):
for fn in os.listdir(loc):
if path.isdir(path.join(loc, fn)):
for sub in os.listdir(path.join(loc, fn)):
yield path.join(loc, fn, sub)
else:
yield path.join(loc, fn)
@plac.annotations(
lang=("ISO language code"),
in_dir=("Location of input directory"),
out_loc=("Location of output file"),
n_workers=("Number of workers", "option", "n", int),
size=("Dimension of the word vectors", "option", "d", int),
window=("Context window size", "option", "w", int),
min_count=("Min count", "option", "m", int),
negative=("Number of negative samples", "option", "g", int),
nr_iter=("Number of iterations", "option", "i", int),
)
def main(lang, in_dir, out_loc, negative=5, n_workers=4, window=5, size=128, min_count=10, nr_iter=2):
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
model = Word2Vec(
size=size,
window=window,
min_count=min_count,
workers=n_workers,
sample=1e-5,
negative=negative
)
nlp = spacy.blank(lang, parser=False, tagger=False, entity=False)
corpus = Corpus(in_dir)
total_words = 0
total_sents = 0
for text_no, text_loc in enumerate(iter_dir(corpus.directory)):
with io.open(text_loc, 'r', encoding='utf8') as file_:
text = file_.read()
total_sents += text.count('\n')
doc = nlp.make_doc(text)
total_words += corpus.count_doc(doc)
logger.info("PROGRESS: at batch #%i, processed %i words, keeping %i word types",
text_no, total_words, len(corpus.strings))
model.corpus_count = total_sents
model.raw_vocab = defaultdict(int)
for orth, freq in corpus.counts:
if freq >= min_count:
model.raw_vocab[nlp.vocab.strings[orth]] = freq
model.scale_vocab()
model.finalize_vocab()
model.iter = nr_iter
model.train(corpus)
model.wv.save_word2vec_format(out_loc, binary=False)
if __name__ == '__main__':
plac.call(main)
| 31.504854
| 102
| 0.639137
|
from __future__ import print_function, unicode_literals, division
import io
import bz2
import logging
from os import path
import os
import random
from collections import defaultdict
import plac
try:
import ujson as json
except ImportError:
import json
from gensim.models import Word2Vec
from preshed.counter import PreshCounter
from spacy.strings import hash_string
import spacy
logger = logging.getLogger(__name__)
class Corpus(object):
def __init__(self, directory, min_freq=10):
self.directory = directory
self.counts = PreshCounter()
self.strings = {}
self.min_freq = min_freq
def count_doc(self, doc):
for word in doc:
self.counts.inc(word.orth, 1)
return len(doc)
def __iter__(self):
for text_loc in iter_dir(self.directory):
with io.open(text_loc, 'r', encoding='utf8') as file_:
text = file_.read()
yield self._line_segment(text)
def _line_segment(line_text):
doc = nlp.make_doc(text)
for token in doc:
yield token.orth_
def iter_dir(loc):
for fn in os.listdir(loc):
if path.isdir(path.join(loc, fn)):
for sub in os.listdir(path.join(loc, fn)):
yield path.join(loc, fn, sub)
else:
yield path.join(loc, fn)
@plac.annotations(
lang=("ISO language code"),
in_dir=("Location of input directory"),
out_loc=("Location of output file"),
n_workers=("Number of workers", "option", "n", int),
size=("Dimension of the word vectors", "option", "d", int),
window=("Context window size", "option", "w", int),
min_count=("Min count", "option", "m", int),
negative=("Number of negative samples", "option", "g", int),
nr_iter=("Number of iterations", "option", "i", int),
)
def main(lang, in_dir, out_loc, negative=5, n_workers=4, window=5, size=128, min_count=10, nr_iter=2):
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
model = Word2Vec(
size=size,
window=window,
min_count=min_count,
workers=n_workers,
sample=1e-5,
negative=negative
)
nlp = spacy.blank(lang, parser=False, tagger=False, entity=False)
corpus = Corpus(in_dir)
total_words = 0
total_sents = 0
for text_no, text_loc in enumerate(iter_dir(corpus.directory)):
with io.open(text_loc, 'r', encoding='utf8') as file_:
text = file_.read()
total_sents += text.count('\n')
doc = nlp.make_doc(text)
total_words += corpus.count_doc(doc)
logger.info("PROGRESS: at batch #%i, processed %i words, keeping %i word types",
text_no, total_words, len(corpus.strings))
model.corpus_count = total_sents
model.raw_vocab = defaultdict(int)
for orth, freq in corpus.counts:
if freq >= min_count:
model.raw_vocab[nlp.vocab.strings[orth]] = freq
model.scale_vocab()
model.finalize_vocab()
model.iter = nr_iter
model.train(corpus)
model.wv.save_word2vec_format(out_loc, binary=False)
if __name__ == '__main__':
plac.call(main)
| true
| true
|
1c3efe168a0e06acb0d1c01f34daba0d3e09ab59
| 3,817
|
py
|
Python
|
fastreid/data/build.py
|
tenghehan/reid_without_id
|
d1d0ff273b1ef19fc6da8cbbf210527779b37455
|
[
"MIT"
] | null | null | null |
fastreid/data/build.py
|
tenghehan/reid_without_id
|
d1d0ff273b1ef19fc6da8cbbf210527779b37455
|
[
"MIT"
] | null | null | null |
fastreid/data/build.py
|
tenghehan/reid_without_id
|
d1d0ff273b1ef19fc6da8cbbf210527779b37455
|
[
"MIT"
] | null | null | null |
# encoding: utf-8
"""
@author: l1aoxingyu
@contact: sherlockliao01@gmail.com
"""
import os
import torch
from torch._six import container_abcs, string_classes, int_classes
from torch.utils.data import DataLoader
from fastreid.utils import comm
from . import samplers
from .common import CommDataset
from .datasets import DATASET_REGISTRY
from .transforms import build_transforms
_root = os.getenv("FASTREID_DATASETS", "datasets")
def build_reid_train_loader(cfg):
cfg = cfg.clone()
cfg.defrost()
train_items = list()
for d in cfg.DATASETS.NAMES:
dataset = DATASET_REGISTRY.get(d)(root=_root, dataset_name=cfg.SPECIFIC_DATASET, combineall=cfg.DATASETS.COMBINEALL)
if comm.is_main_process():
dataset.show_train()
train_items.extend(dataset.train)
iters_per_epoch = len(train_items) // cfg.SOLVER.IMS_PER_BATCH
cfg.SOLVER.MAX_ITER *= iters_per_epoch
train_transforms = build_transforms(cfg, is_train=True)
train_set = CommDataset(train_items, train_transforms, relabel=True)
num_workers = cfg.DATALOADER.NUM_WORKERS
num_instance = cfg.DATALOADER.NUM_INSTANCE
mini_batch_size = cfg.SOLVER.IMS_PER_BATCH // comm.get_world_size()
if cfg.DATALOADER.PK_SAMPLER:
if cfg.DATALOADER.NAIVE_WAY:
data_sampler = samplers.NaiveIdentitySampler(train_set.img_items,
cfg.SOLVER.IMS_PER_BATCH, num_instance)
else:
data_sampler = samplers.BalancedIdentitySampler(train_set.img_items,
cfg.SOLVER.IMS_PER_BATCH, num_instance)
else:
data_sampler = samplers.TrainingSampler(len(train_set))
batch_sampler = torch.utils.data.sampler.BatchSampler(data_sampler, mini_batch_size, True)
train_loader = torch.utils.data.DataLoader(
train_set,
num_workers=num_workers,
batch_sampler=batch_sampler,
collate_fn=fast_batch_collator,
pin_memory=True,
)
return train_loader
def build_reid_test_loader(cfg, dataset_name):
cfg = cfg.clone()
cfg.defrost()
dataset = DATASET_REGISTRY.get(dataset_name)(root=_root, dataset_name=cfg.SPECIFIC_DATASET)
if comm.is_main_process():
dataset.show_test()
test_items = dataset.query + dataset.gallery
test_transforms = build_transforms(cfg, is_train=False)
test_set = CommDataset(test_items, test_transforms, relabel=False)
mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = torch.utils.data.BatchSampler(data_sampler, mini_batch_size, False)
test_loader = DataLoader(
test_set,
batch_sampler=batch_sampler,
num_workers=0, # save some memory
collate_fn=fast_batch_collator,
pin_memory=True,
)
return test_loader, len(dataset.query)
def trivial_batch_collator(batch):
"""
A batch collator that does nothing.
"""
return batch
def fast_batch_collator(batched_inputs):
"""
A simple batch collator for most common reid tasks
"""
elem = batched_inputs[0]
if isinstance(elem, torch.Tensor):
out = torch.zeros((len(batched_inputs), *elem.size()), dtype=elem.dtype)
for i, tensor in enumerate(batched_inputs):
out[i] += tensor
return out
elif isinstance(elem, container_abcs.Mapping):
return {key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem}
elif isinstance(elem, float):
return torch.tensor(batched_inputs, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batched_inputs)
elif isinstance(elem, string_classes):
return batched_inputs
| 33.482456
| 124
| 0.698192
|
import os
import torch
from torch._six import container_abcs, string_classes, int_classes
from torch.utils.data import DataLoader
from fastreid.utils import comm
from . import samplers
from .common import CommDataset
from .datasets import DATASET_REGISTRY
from .transforms import build_transforms
_root = os.getenv("FASTREID_DATASETS", "datasets")
def build_reid_train_loader(cfg):
cfg = cfg.clone()
cfg.defrost()
train_items = list()
for d in cfg.DATASETS.NAMES:
dataset = DATASET_REGISTRY.get(d)(root=_root, dataset_name=cfg.SPECIFIC_DATASET, combineall=cfg.DATASETS.COMBINEALL)
if comm.is_main_process():
dataset.show_train()
train_items.extend(dataset.train)
iters_per_epoch = len(train_items) // cfg.SOLVER.IMS_PER_BATCH
cfg.SOLVER.MAX_ITER *= iters_per_epoch
train_transforms = build_transforms(cfg, is_train=True)
train_set = CommDataset(train_items, train_transforms, relabel=True)
num_workers = cfg.DATALOADER.NUM_WORKERS
num_instance = cfg.DATALOADER.NUM_INSTANCE
mini_batch_size = cfg.SOLVER.IMS_PER_BATCH // comm.get_world_size()
if cfg.DATALOADER.PK_SAMPLER:
if cfg.DATALOADER.NAIVE_WAY:
data_sampler = samplers.NaiveIdentitySampler(train_set.img_items,
cfg.SOLVER.IMS_PER_BATCH, num_instance)
else:
data_sampler = samplers.BalancedIdentitySampler(train_set.img_items,
cfg.SOLVER.IMS_PER_BATCH, num_instance)
else:
data_sampler = samplers.TrainingSampler(len(train_set))
batch_sampler = torch.utils.data.sampler.BatchSampler(data_sampler, mini_batch_size, True)
train_loader = torch.utils.data.DataLoader(
train_set,
num_workers=num_workers,
batch_sampler=batch_sampler,
collate_fn=fast_batch_collator,
pin_memory=True,
)
return train_loader
def build_reid_test_loader(cfg, dataset_name):
cfg = cfg.clone()
cfg.defrost()
dataset = DATASET_REGISTRY.get(dataset_name)(root=_root, dataset_name=cfg.SPECIFIC_DATASET)
if comm.is_main_process():
dataset.show_test()
test_items = dataset.query + dataset.gallery
test_transforms = build_transforms(cfg, is_train=False)
test_set = CommDataset(test_items, test_transforms, relabel=False)
mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = torch.utils.data.BatchSampler(data_sampler, mini_batch_size, False)
test_loader = DataLoader(
test_set,
batch_sampler=batch_sampler,
num_workers=0,
collate_fn=fast_batch_collator,
pin_memory=True,
)
return test_loader, len(dataset.query)
def trivial_batch_collator(batch):
return batch
def fast_batch_collator(batched_inputs):
elem = batched_inputs[0]
if isinstance(elem, torch.Tensor):
out = torch.zeros((len(batched_inputs), *elem.size()), dtype=elem.dtype)
for i, tensor in enumerate(batched_inputs):
out[i] += tensor
return out
elif isinstance(elem, container_abcs.Mapping):
return {key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem}
elif isinstance(elem, float):
return torch.tensor(batched_inputs, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batched_inputs)
elif isinstance(elem, string_classes):
return batched_inputs
| true
| true
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.